From 70e5420b7e4de5e5abf346bcaa042589b295820f Mon Sep 17 00:00:00 2001 From: statwangz <48282751+statwangz@users.noreply.github.com> Date: Sun, 15 Oct 2023 18:29:50 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20YangLabH?= =?UTF-8?q?KUST/mfair@2b4506f5eeb83f2b30af7533c47aa050777d9f62=20?= =?UTF-8?q?=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- articles/ml100k.html | 18 +++++++++--------- .../figure-html/unnamed-chunk-4-1.png | Bin 78484 -> 77309 bytes .../figure-html/unnamed-chunk-9-1.png | Bin 137934 -> 136565 bytes pkgdown.yml | 2 +- reference/fitGreedy.html | 5 +++++ reference/fitSFFully.html | 2 +- reference/fitSFMissing.html | 2 +- reference/fitSFSparse.html | 2 +- reference/index.html | 4 ++-- reference/softImputeCrossVal.html | 6 +++--- reference/softImputeCrossValSparse.html | 6 +++--- search.json | 2 +- 12 files changed, 27 insertions(+), 22 deletions(-) diff --git a/articles/ml100k.html b/articles/ml100k.html index a401501..f17c3dc 100644 --- a/articles/ml100k.html +++ b/articles/ml100k.html @@ -124,17 +124,17 @@

Fitting the MFAI model#> Set K_max = 3! #> Initialize the parameters of factor 1...... #> After 2 iterations Stage 1 ends! -#> After 53 iterations Stage 2 ends! +#> After 95 iterations Stage 2 ends! #> Factor 1 retained! #> Save the initializaiton information...... #> Initialize the parameters of factor 2...... #> After 2 iterations Stage 1 ends! -#> After 54 iterations Stage 2 ends! +#> After 181 iterations Stage 2 ends! #> Factor 2 retained! #> Save the initializaiton information...... #> Initialize the parameters of factor 3...... #> After 2 iterations Stage 1 ends! -#> After 28 iterations Stage 2 ends! +#> After 35 iterations Stage 2 ends! #> Factor 3 retained! #> Save the initializaiton information...... @@ -217,15 +217,15 @@

Negative control#> Set K_max = 3! #> Use the user-specific initialization for factor 1...... #> After 2 iterations Stage 1 ends! -#> After 52 iterations Stage 2 ends! +#> After 96 iterations Stage 2 ends! #> Factor 1 retained! #> Use the user-specific initialization for factor 2...... #> After 2 iterations Stage 1 ends! -#> After 55 iterations Stage 2 ends! +#> After 176 iterations Stage 2 ends! #> Factor 2 retained! #> Use the user-specific initialization for factor 3...... #> After 2 iterations Stage 1 ends! -#> After 27 iterations Stage 2 ends! +#> After 34 iterations Stage 2 ends! #> Factor 3 retained! 
 # Get importance score
diff --git a/articles/ml100k_files/figure-html/unnamed-chunk-4-1.png b/articles/ml100k_files/figure-html/unnamed-chunk-4-1.png
index 0f9880a4778953d06513805c401db4125076da36..fea85ff16d396a0b75a62749d31ba7b7d505d262 100644
GIT binary patch
literal 77309
zcmce81yqz<`|sGIgo+A+fJzDiD%~m)gLF!(NO!|fiV7+MI&`NrjI^|hf^>JOlrV(E
z&~cwvIN$gE*Ijqr`@d`5%R1-C!C~fo_q+GAfA#EFitWB*p
zb?D*oqwo{mgB^PC$I(akr0&9>@U<>p<^_Kc*+^^Jqfj)y$iD{y#}^Dys7omH-P@`z
z(KAD??&k;l
z`+l3&vhp>OmnW59w4=^=pJ4HSnetL;h5fRT*yh&Gcahq!(a*0j3vkup*Im}GM@OtT
z8yhvdPr8XN#mx6UKH&2(n%PvNVD#?=qEM|y(boBWm2aF*F&hLr?f($d&gs%HLp|VSmXB_QS4QeI
z>pBm;zsFg@eem=F)N})$Fx@OPZ(%3EH{=jh^X3Bi)0Z$sYD33~U-VfNy$lwoAw{*FPR9(+xYSYYkJSI45&n-y*KBeO
zg);Fp@5$-nY7w??UoUl<&EOk$Njh@n#Qb=ee)sEhY!34ktJ9_U96zm#BueV)Df5+^
z@^M}}C3*SzAyH9m=+OS7s0m4$Vd46QhC=fm4v&>S^+XQ
z-dqF6e$!M5Az9RUDR}k4r%zU92T~Qpwp9m;t$XH%%G9*9GJ=E867W3=^ilzxFHh5t
z+3+XQu(n=-CwYtd)bYb(tD6df`W_Re@}5=t@Xym*mojWSd*B&6R+I2Ok1-0dL_P5A
z{P~XJ_+f6V!H*w0Dt9)_J+=uwTBQ!@IaNED_o=B3M)`#{&rw<@r~gdkI?ZZ{Y%h24
zh>BsB(*kHkGRVZX6t7;rikW&J)(LmRa`$+_H5jl~ABVX?&4w_}Kqty2m(t>(hQLg`dW6t_k1b;5MlJt88ayn$I(PR<^$oh3Sv
zg|FoGEiEv)hTey;X|xAXWYfZGG!dV1^3e-SR#6a9<1GJkr?92B78QAtaPRSuLx)@`
zpIqW7+D*s+-ru#PngF<-iS&DM;&Gbk)oS?O)YNr|OgO&1z1=Ksa(w(MC#S-_FpjPawann?
z=xf#2&E5z(<=KAUSqSv{-Dez^z0wEsv(TuCDqAyOGD*_EeRtDqm(wCJ^9QAY+v=Pe
z22%>N6mQl+Qk`Z$gnD2E^EbUZb7jVVr8gYaPdfeQJKTtT^zm(vZcSr}WZ6%RNSQU0
zmX>Zsro+!_-%PlG*Z%$?4=Gt$S(S~9QuN#xB#?Pcb^XDuUcNVU!Y+zaQ&ZUF1BZ_O
z9(J4Wx?miA*|+*~U~ST~V`OG9-4epOm!~?UsCg|~k=cRF(bYlwKqmR<`{wQf0s;w=
zhFV&YRuu$wjNN#u{r&5}z@E`Su^f1t`?NHe{poSxVpF+W9
zsX*GU-)%|eF_jpC8LTGr?{6O@N%u6P)8&w0ed+278)
zf)v>+`tpr6sO_(DaL60Sx~9xktPWE(9HDk}^j9*X^r3Z|xq)t9TNup@A*Y~7ZA+5M
zG^h>OfM=YEyb%;XgBYc$VLe_|OywbmS_^Ej*-!-dU{qeOu>306Ka7)*qN$8hi>k!1Le+X1n3-(qMj@w#16CvaqDxVCshZH>8v-tWEbGHF2CDuF$0Q
z-I$27Wg3M#)Gct496D9nXVUyMF(f9J`b%jAbzp#diFH!W>=+SXpqqZ(j
z=*rY+_-SME^UI5_nqNnr)FhJPIIxauZu1q)>3P1kur5V+x9pKg87byfDZ2GX9LkFQ
zX$7Z4M&f`r`rmHwT997kkB23G4=O2h%xW!3gSam#+N?6{!bK5`Dt0+2ZY#NY-P`);
zaGA4p_R@7)3KUxcDK1A~`p-Ew_1|3@`XSXnTiI~sXI>vdMZOj+FP%uMw&aaELr%`)
z@LRD0R#_ysDRyUj-pjlrMXvPMYql$uqMu}u<&?3oxm~(BSEipnn2q
z*OyNn?-XQTYxnWkTuIN$%IaS4&emp5w-Z9eXUCITe}qNwY>We?5)YfhgTyl9me{{<
zjR3u_0_zbyY=Qk`^9Pdh#YMY{t;L2yj*$wtRmD+jDC8s<)hu{w*yPdLz#c&bm}ue#
zPVAa_3^T$)&U3^SkG*SiQ3Z^hKS`E)s&)$d!^;?)58Ne(Ei;
z6f1C6ZRN?_yO+sMG2{5B#cL_JHZl-+Q=mLy60{xwAt-EF1Q1v+??nu9VuE8Z!Wl
zkwVT^*@nwAeJZeZd(*oAxotU6BnL&vym#FSpn%yfEw{X0LZ~w)>SsF@R*Eo?iScBcU^M$e1w8qTNrgo$(QeIB#(K4
zQB!1V)vS}7zg`PcNhg|P*iwT!DYV2Y#{mh{1khxPdTf~Vmx8A
zh*=@@A*CblHO6$W-5sqGyZ10YUZ&MPBpZtjoa0OD2wvD;P8I8dnjaD!tz#Jx9!^=Y
zP(!U%WD$XE`oCKf6u&Ij!Z0JKp<=07sJ^+m`}4D7j`delOqwPt)jj?CkD
zYBJ*ZR<->PTzV;3{?p8l>!lz}p#)Mffz*z4WYA$5_U9S;+1cBN#Kqy()?5fM=7)|F
zx5Aon1msq4l+R;+?TP07Ur|<+HLh4E2_e!?87nh=Nmo@;OuDl)mqiaeIqw7&UuJ@C0$+
zM4F?hf~!z}PhXvcZAQb?)bwsW5Tn46`9Nubl9H0VK8w>=PofmPMB_O7FBcp?Q+|v3
z0i_Rv=}@VoQovzUf6fEl!QSz_yu9F$kaKl!Q7A%Zub{9AzB{|W)Y1HTB0|zs@HT7@
zULK7ndFk)(?{pm1I>E@mz-=aw8nn59I%WCl)vGqr1E^MM%LLL%ibJhFgErM9fU=#R
zg)U$!08L_(KYrx@_Vp{b_~QwvqnFEuaz1|iXyV(RHE2~_u7^vmPY$a7`O@xgQ)X6{
z?2Q{YvUiMp8^%wFcn61vvy@d3hHH6<#SIM&0j%(O94sg(Q1#mAhy#kp)1#9FlMQ#f
z;r@HlRTtsKb@voY_hVJoSl}X5F
zLBJpc?*dz$1#cAW97!}X_eJ9rIKu8J8~VPqnLP}2sKkDf*HssRfj>S!qb`vB6P?2*
z%5{~EO~t|@%d~~MPLSaGwqYFmkTNAOKQZwNcQjgA8Jq4)PgLZebtd)k9B=e;wX0z_
zz6};^BF!DY>#LksP^TT&$HVYIvrJoW!qp{6Dwa9T$`0HL44$vr)ss){=oAyAPbeth
zUVfGW)j7rL`rSLXZ$CYA=8SBV@ay1TzaF^?)1sASWL{sTzW?qKrO$~ICjiY_Y*62<
z4WRLp_I8E^xdF9sINnf)L&IcnI60Z2W9Qa2HnyOFS#@{~a<5Ns-2$BYmI0nj8hnaU
z0E;lLClhW$C2se{ix&wDpP+nFjhmQ-#l=}qI|Zuci~_}g8D%z7=}{m~)G+ImnVfuA
zdECDZ1}m_Z{N3SsKtc|{?YK=Y09b|VM2EcEN={B@s%?u#t}#5^q~F|sS$y+$hWnj~
zU={gSc6S}wI5?pEq*TluhOu~ICFnTQ+g8AL;XPfyWQ?HWO-Jt@nX2k%v&q0eqZTDQ
zp+F`f139k=Wccq_8u|Z2rGqmAjUpvvFIa8k9h+uDcN+Qdx;)KZE
zHYqyb#F0>T2d*s~K6qe#+w;i5r$$%l_O8;TLHbY8%{?Y88i&(cI!6A%v#KhB{?Ojv
zpECX5Dl>MHLa6W_KrV$AeK-B7`Q)7kZGCk(!qjnk*^P~|z>_i+;zX&qjNVB4Q!$oR
z*sh*%7mbT`UqpLsOr;6}m?L~8S6~qkDA9ZbV7_OAfDeg@$u2J!?z7-Uv>6#WIW;r0
zj9)OGhTbJUo*fa2|OsE3fB7A%rP?zc(8#{q^4F6zw?CM(L
z3BQX+80HZQo%r4LxLqX+3yb`(AM7XP5!3+uv`e(5u1-l_KHxm3UY3CUL>k;@R|*-B
zPhd9n_4OI=emIusfTVG9dlS%F+xldjzGZBS*scx?I85&B)7tj)`g;Ri;K|@(s|+(d
zQoue12I@UJh$$VQ6%>z+wb4KVa^F2Uy2vGlXy|Fq-wdP?N*UQ%DF(LI4kX8Mb;!9V
z;**68G0=ccaWWAGG!VBZDGG5pIl5)_*NJCGs`LS?sOag%8a0L|eDkAVV`OA3m~owq
zVMh1o8u)H`R$U~OGU*4#3I%DPSi{yfx5#opo&IxFNC+dEir3}ZX@k%W#3~}nMVG&>0B=2>H^!NRpv&byP0$M^5<$V
zu|;!XOK_u~M=iUz?-vGSWyhS_y4LUbQLX=mk*GBTzH{=n&%^sn&6jXJ=F`pS&r9`q
z0&K<-0+Ici?@5?7lXR`Ros<~f{rHA9G&aTqZ0`2w8>Za22Gb)<*C`@^PAmsOF#w1O
z@_*)rE3~V2x90(0IN-C3&DxXE%-Q#&&Q#mCh%lq4dvI~I9?Na5rBIwPgz5LjaUSZx
z0y;qbC@^We(iF+VR$u{@EF?HMIWO-fGA{x27@71#nP5aCuMAQUO(&t|{SCs7nn`KP
zqzX)*WV$oo-VGOJ`~^6ly^lOh1s0`?RRw&Nn3yQokbV!g19;9<73FGc+dEVh725f*
zIhy#kUV(K6wX~qx*4BD}P=pv%oFOhK6r5H`EMA|E%tH3RAl-hepuKvw_*rfmthMV)oA`dLl1X
zjaa{$PkHQb&+?tMCq6@=C6cBmDdh%nQroNz+l_h4bKyQiRz1n+pfB>&Hy%AtuBR;_
zl)N=IG@Np-+Khpvj-XPxSm6>q=O!v@@rMr|rlrmnn09>0X_#_g6%o}B;^&uv2X!xk{xjG*;!8%C
z!+U<7F-#|sOmyuQHJ?@ISX~gK+VE}mYu8k(Jhwr5&1~Q(lZCBN*~%)1qh#z1I;2`q
zx*dc~J>xS%XErr5W)j@@l*9bK;CJs5dV6~X8;++=8=MEw(B+eE;tzmg#;FX1+(a6g
z{6$PfLRmPd8Y!M{SkJ)oiFl
zjxK-723lv$ulhWEj%n_jU7?yMzV3JH(xt#!gXyJ_Stno2`zV6Ty$PItmK_b5=xM`l
z`xG-8O>LpeAFZQG17qZkpH(e=wlB?DYy5g*mOT>|jggqIw=k{3@6#?^`p=T-n%&?J
z27JL!_?C^U*0dY{m?5HgLL#Ws`Ayn}gUURxyX=mP*mB1Z0u5^a%uuO=-rc66fjypud
zuvN$?{~m#fPe*<8@k<}#3um1Ahb!DdU<{u>KMcZYPP3h77Hs_v4`2EDac#f&kPfp6
zhU?jCP*-qhs86K|2%OoX2_Vm%gXi@1>sJK60nHPy<+*XAt$-v;`Z}>VKqrtz6Aou+
z9`Su=GP7`8uL^J#wGLm2PL3iCE&Ss1=g%d-wGKkX9&;*_(_TG#@TuaRPd{zKdF63E
zY=6hN%Ir~l)}~U1uyf!YAB29Tl3_1V08-bfKjD{=L8k7$4{35Tr_a290xxkw55hP+LE
zgND|E8PII_qS5mvBQDD_7Hp$icY=~PMaM<}Gx2G&W?7@lM5nq!2DKaVbQ
z^Z59v4vNVK6r1y|l%ELZz=iEjL+M~75czpuYxMMLTftVkT~3p`t9|wX}v@v=BWckOa+!>
zevS%O(0T3}S`f2r60?~!y;cOcDC5TVYqp%X43gTNnzm-ZK1bD}Y`g*|4og=UmKdTz5k=n(N!+zzsX
zHK3tmemp(KZPZ8^5*B6-B6xq5msfr^P9;?y5$+kLU`r+{6N4S}Ji8V{`ptLLRv%yg
z2rvKk?HghmFqi_n>zWYQ-C6BZyU&dCFWNpp5kR}Y0fr}Q>xpfd9E-7qxb
z)e47$8ul*cmc0!?y1T<5{-i~48Bdo?#&*E!FLcCre^`PiIpnpwO~q}Zcv9+q#8?H8
zQ9#gXRiJQ3-Z1ci8&IJ)r6K}lva^~@EDLm0JnVUGaQ(^#v9PX1R(cH{){c;rg6JbI
zE`Ufua2Y%kC7`4&gRYFuxVZA=jrH5Ze=p`1{x-}ED%e+!l29chh5>*-#88ltk-1ZS
zgP59EmR|CWBGgvZ(de6oud!I{@}$_VCdf4Hbh~S{tuDpykeSP_TdE55u>(pEBFBR&
zLjW-=Jw2UDz%Cie0sfl7k55k%s-AJ`ZssEE>P$KOeX
z(%v$_qrIAQsq{e1-SS%c>g&lLw@iGMQ(cO+bDut8s5`$~@fc85V!wcgD5Gmd9^+XF
zp^Z`QfJh^l#iVH`|&mAg8V&+_zjx{XeXqFl
z(e?%b&;{OJAr_okyY{%xep45yBNpI>d5UzHL%yZ6FN
zVAs854QqJc*x0Ceg@>L#sjsI5JOt!-<_1feDmnIE7cU@?M1$%Flw5?y3OY_FdwUsKta8^XE#2w}_K92s`gnQe?2*Il9zM
zAawlwYPj^-FHOK{CLB;BGnz`!AuXq5f(qRZie2r;Vt7dp9ovO}vHd9Zk#0GjH0MjCv)}W^?f1}Vh`Sj!fGdumMg?^8Q@FChYyMX^Lqp)qmvpq
zt8@@^4Av0?8bDDu;Ilh*qgh$kEc^1_Yq>(9Lu58aG`9xQBC&?Je=c0j+)
z*00j*>+1ue6VznXp=liRzOz#mZi|(JBd5DJj8i`c1V+=rkG7?w0YYPTB?T~);I*+r
zee&2?ZVSI*z-`va(lL$uYQ`J_j5LvF{j6x7HT03?R>d1;$2CFK%5y=th&5
z1Iq4z6)r^s!_z;xzP=9DriPPK5tD3q{D4)NTsxV;{0@IW#=)nz3aju`na-2VF_^J;
zQez7)86L4vA)})&vg?)Kx1VTKiWBwt!a;XU?9}lz&YV0MRr{
z?2QqEQ-8r3dUJn28AE0&vw4f3uzqm&KI=u7G-(^E|0gHpo`kF#=}Xsf@ZH8x(*&4(H8x
zP;1jky~6e<0h2j2Xp-IkWwm(PcG*QZd!y#M*Z9O2~^73o1FK3`5fu^
zCN@*i
z$KLVDmN-sWqOkYlVW(1roy{b&`lll3PdOidz5dW}w8uEVkBVmZzc4pN#6_>b5Zd!w
zL^Dl0QV`1ne2{mbQmdJ3!Xz#QpT%+1pU+3s4-`bhFx?a|A@GDAeGN&;8kly#>_IiZ
z-D?U`VwkEx@?A6<{j8`EiUng>L)Y)|>@V}H+E+}^BKjx7$6SPCsYK@f#n-YFIgeQ9
z@i@A>!mE=72WOq4cgd38x$Np8
zt768LL#S{i;DUqxS>n|qO+!=>OWmScg@}OwfC~!%C7h~*St0fsS}XUFMDMUXOu>Y4
zkneWP>V`rU@83`RYZ#`;MV;qWnsE>eiH+qzZ~RUQoZhm1P&~k6+MfKO17OzWbUXeN
zY+IlHRwLA{hQwo>S~{?{7_KrhN`O@)=|2VZtOG^uy^TS;!^Fw8P%d0@&
zhO%;f@~uZITSs81GaM`75x`%7$!t#J=M4L-onUp`Yc*ddc$g~g?(W%mYDB7#kD+pk
z9GUL{?|}k@94KSC-8a81th*=Z1p;3xtm)VKsKWv5=X{lIYdgYSa;rdy?+~r0Mn?Pf
zt5-x74!MP(?Ix6UWN&|@@=198`oz6XBnE-)92-4NLD6G|w+XDJuNtB2*DxaAszis>
z=H}pSxsTA@;9FXsw=205h~{119K=E`lk;l6|5I?zt^}OBY4-&iZnbQBb93gysTv;B
zA8tUUq}qzHK3VN^;FibNZ{OSWLkPar8k^nl8PK*OR-TwYwOtrA;_JW3@qN2HXv39Xl
zVW?8!W!F5Z6Ho#a@qSYfh{;6k0e=}%F#A;Rk%kkbER$V?X@Tad?EZf9{MMyY8xJYr
zPN&J^K)LkacxcE2GP|k<1E}NcwP_JY$gO~+q{v-7%)|4Uy1e^qO%3mw(2QM41|w%<
z^evLZ2UA3(4n4ia@)>W#FH3D~6K%v5x2qPUW;7JoPDZ4cU*rZHa}`qonrn(kw^ebO
z7R?2Y<_FlS^eQ1L6<;@DTF7|B*9&qN5%rwOFY>$c>M*BP=hH*S?hv)Z%2o5D;3O5&
zzkWLx;tWHco3jtyRCo=EhonuuZ{RVx^4cGR_MqRBiQ7g*?9<3-ZPQ%*EjZ|Vf%UXyRH&vi{?3o^}v=0zn!YHbiyErMBwki?Hq2f*LqnsG;pu4HFCE<1a0{+S%nDC8NED#_X(5juSA{
zXQkjL$@LU*Jd$v2chD0WdajHXoO%!hknlSXrJan{qW=cj-p2R9k^$b^n^S{Y)ZL{*
z?_~#ATu4>|Tkx>j$E;(nY+m$RP0cj;^T@`6Fl5EVp6kT0;~=m&fwo!=N&R4vo#C(V
z`|!Nk(x*dYz`V6uBDkbrlZ5TV=OO6u;$Y2%f>jO$IR$nWzK`yEYdv0+GVuU=B0vMs
zL$S%Z2DRPh7vMFp_nOyX84(+fNiOm%CRY=98j|tZ{z>N53M-q(ygTcIp=|i|gc@TQ
zF7-fK5eFE>AqZ7qS@j)^^82_Qr#ms1?A)&i@c(kUcj20?=iL{$SE
zqY9q5pu_KY&+UyV#L;|vS==nx6h7X1Sa^GsZnt%!DXP$G*ArrZXcFo$XYc_Ljs^rL
z6`>bkHUnjy9w^2stElkUjY&f#>M0Z6ADsm0KZz2_@84f#XV3oJ0GfwHrh0aUV<|)i
z;B&q(+2PN?_Wp{T+3eTXSB1@7z>WQBMM|6PfR}JT{PO&R$O{Fd|6mR)g2np`!;;$I
z|1S6V*Ly=Q@5vuwkAbT-?2rTi+|CST&bRvED}Kg0(AAijnCja)n_!E*PTWyDMZiFH
zVt}7Y@)&Qh=_ybt!f0f(0H&);zgyQdOtt-
zNO&%%W)-JZ^;8)^SV2QtC&_|IUJ|3j5fU1j0U$F){;nGM@@b%krzk`h8%>Dd_Z|ld<@g`5&~K+m{{A=|RFFYC7xzU0HqhH*ehpdm#fYw7
zG;AZ4z&;ieyS;LGg0}l0>dK4#P%6{8{0`26RlW5jn(t^;CJNcJ#)&b9IiK)8yZle4
z^1mHQRaI6_+~XK^<8~547x%B1aM!!%4)6~;nXiy6ypd?Qx$U{Wz4J1ZXzvB_uJ3*n
z9{-1T@Y2)M_g~hm2fY%8Ck0$T$$t@)N78?^z5?%HK!fUnW!NVjuZ*NaA5k8y2;M6v
ztk&%|xN*^ek2>jtgw<`cftIBmFScXlpBHEBn!YOsb#UNoY
zB<}|ZTpj!lp2y!$A)ZodEXFL`kW;@>2MS%sN83?{S{mo?<94P{@W2y6a!;U#B;7Em
zG3iWG${WZW$>(=p`+y)FndhKIo5HVkO+%I`&I=kaxoy3L^W03}KwbcX!chC~Ar
z8UimW#%->oD+Nq5Jek+pGbG43y99${4p~v8_#%EzW=<)qMIRWYz>wg-fbnD$6u_;q
zHNgN~K@tiP5$UybUgcU?KK>OeHL)G%OQ+I!*+1r#c6Qm9nw1}cwW^>H24;4dh05yQ
zszLSho+)Qqg<@JiZddoyMiqZ$e&M#5Cf7)AvOz!^t)01ixerl
z5$TXubJ5Y!A36rDoPX~ES|aHu#r05c42&*(zy9VZyEPBo8*rk}n{{UHqWZtBiwAUP
zN3!2Nqy!N^d*`Ei#ZzFjX2M;qk%#eD0&DHtqwLzwm)G_5@@HC^t4#mN@QU(pnG6CS
zSwt#RZxf_E(^2B?1OGO+xQhRRsm<*@Fr~JtZ8<9jl{!2~!*sLu
zh-MEIp|htpK4TJs2pKD7Jr>@xeU*(BpXtPCJ7}FsQDmeOv
z3*&DGx(1eHRMO(!&BIA+SZLB1Jw+XSQ@Kmt>>&{A4fwiDK7XM*@KB&3Id
z6nEj{I;<93-^`4~ko!_oDa3Qc5Bx|L^Zg?gP#%-X+<
zF5G`4R=@-yoH@Z2*Gs29G~URSiEbyGrOnTmi9Q3y*NN4~&gPGswKrjV;|Iwllfkqx
z{02{oLaeYRw&2mX*B_kiPnq?`U#T$!a9H>rF!pyI+6%yWM<9;g8?+#G#*k=KEMlp5ySiF>v41lXEK+{d*y*j
z|K-Ykv1H&bnI8E`CV3TQWrvyz#+~7!pm($Dd{l&G-e2a7!>rEfT5f=4%YcR&fsl-B
z`~@&vLOFD`@XK*K%c*~Zc6dOQ)1cOxL_VYh_V|Kf%7@x2t!NMc9L0u^&em-p&Pd(~
zflsh#&Bir&Y<}Dli1}HS2l{P7RYQIK^hKw{_sdY}y7{kESSQzy-XHiw4))8*^Vbj)
z2uYz;k*ta7;rKd*I3QUI{uqhYuo*a;kBbMuA)%O6j0_o=B_&7$9!w)JiJK$60^#A|
zRGbnPmf&-yt-6AEX-d}?aJc&V9fF5t&(ZwjD=COg78qz<==iaA^c`5Lz|9~D!nWoS
z#2R-(Mp_y|JU=1#pr)!CzOlv0$w@{qvi`kV!U!cjaw8ht9O1R&1sD1!ey+J!Do(xy
z1MkD^^9Q(+l(|v({;1bvW{s~8$N@@?fh#3y(@U3t>{nlxr85TmCe8OTxHeP1qBdni
zp|4bKI#bJU{4BqTCqR*jxS1o(waS^u+jyOr9kwoILxmxma|+-kIRk?--_f#h>)5||NbWpFY(!Atqjf$&EN
zAhW;@_nE{329W|(ZORVbR(eMM2}ko0`M4Ge;E&YQ=F>;T8Nf}gn{p`H7}X-QWkWhj
zedsL_FN49ON3vexOqBWt
zlfJQ(?tT4XT*QEgdPzXpkd+Fv%aD~b%Tq0FnhRNFQJ9Y88zTT%#l4G(0Ch%-^-rpi
zmmBR^9X(R3$Kw`lRV=hz#>2tU3;~>H;CBEll#BUM*gpT~flyr|jalPP%$viodpUx%
zpy-X{JG($et8S*qy4#bbnFKqK$V}bYSDcGTG7F&z+p8lzIInHIqIEh9t~3@pEEcKG
z$#>-*#S4i((mmu`JVvCcagjhXSf2A-Jo#T+BollH0fM5>cXC>uw1U9}kFX2V<^;_d
z*aAo?3WjY1Vo;z`fGiS360;y;;sHDm%#yd@Tn>BfdZ61KdvrAnB7S^0RaaMs5Rdfv
zfFnoT#5zM9*nw1NXgXTr&i(U&jlPF*M6MOXioccIbkrb~#%yWj
zcbmAfVcsHWvxNcGWuQX`)qq^V?Yj850-Oj0F(AGP?A$Q_0sP4W)XUD5#pzWrpaD0q
zg2@fHpimk+;grW0e$4mj8ssC)VkSp+cbtPpuaEB6m?wmSo!czeSeTiagI`|cxA}pd
ztOC1+0S)w6{r2tKznA1=1YhX=AoQE#Ug$!7mPLX$_;VUIA`sX`7}>DEqCQPrrGsF5v6Y%m0eAX+QReNp_m9lvfFh5^Vu=4V%TNP_72roQ`KC?TvNN{9z*bSFcI-T+I>*G+5eW@S+
z=;hI%t1!+0%@|B)dUFX{HDKO>rmdCt_!ZKuf)sa5Cx9D`)e)~MD6gr|y<`sdMOUbt
zoLm>D=fcTs-LfJ*d?{ql2_V+73JGarVL6MvPfqR>^a6bWYPcBEZ_*87C=UqQm~7^D
zDErwkM86TS06-gqT4g~Kd@y9v6Fg|Z^oB>gih<+>vY`*zHL!QpBh4+2O*}m&(1#)y
zPA%z}oK?^f2a
z^5d`rfur0ZwzEKHIb5y@IyhN{^lYt&0Ysj%!t^j`q$LUl5`ogUpBVM|41Vm#n7=Ke(n1s1^(++|JXr$od7X|qP
zg5jC4zDe|){*$~1S&8fDL_;e_8ZiD?hZv-Mo4EMVA*6R&N5kV+cE;6z2GNpQ_u$@!
zjr#Ag$^Y>0hEK-4Ug#Znh}@hkgN#ncR4@coomj98ZgCJ?MWNVh1~v8!<^JodUAcM8
z3@oBouL7k{Pm8|=f!Npgq~Ya|lm)wJp9B7vZb2Utl#QKdl(2P8dhgYcev#Kg4LM_M
zX`=AjCleEz=a{Q%b58g&v*H!XzbAbCJVB{JfjUKdfeOV;@ph{$W^s2!Y=)?a
z1N66bYkdhJW$-912S4V1>!$wO+y@bQZHt(;&hDapZ?#+8yXO&jHQ?ijzp8!n=8cK(
zV*zLqP~VG1Qvb^xWU>X@o4fHJR;>)-R_L2Z1JfZ5l-Q=qxqb7usWNuLh*GiwGf@;e
zyKf5NCCK#rh*o+l#zVD(BO_VST4hd_+~WJ4tfv?mT-e8;NJGjXj=>c4x6+A7a|SeY
zMbeIb-G7ngDRJ)m-@)c!Km%=72cXj|x@iHFs$tx23PPQ&FqO@8oz@q}k$`)RKXt0m
z+(#tcg4_%AYr*c{1^qt$7r2lPG3*en=saxun2JANPL?jW-$UwxU;`%`TL;3CkdBAV
z`Kq43*~SgrZo7i#Vo>W%z`eUbfQ?Mvf7dGaIy&?S;!q~f!R1_2Vd>T(UW
zHB#O&pt>TPwJ9_x2yV{g6UKrR5dCd&a~mF8P^{OED^g*hsjUlUh+>jEbV1;8ReJDT
zHCE?_#L+sCuEAC9XdzuEi;IigUOVn0Yd@ZKg^ECvW`S83E7ESERqm1tZjzFD%V3az
z%b$2!%TIJq&7y5knT(gyUTPgfP?I;FwgvTEF{Y
zbq-)1yKnXBjdFM(K%Jrh04IV$tqW?AV`va;{4g6ek=DE4`2cBko%##~J-0sUd2Kry
zK7PzC7!eLHL5yL@WE}Shefu^ZHgny9BFhd$y|$l_g?LN07N;)UsZx^UR3@&n9Z*6c
zq&z{JEBph?bDH4|z+9}O`$_w?3}-6GsW5Fs*XHHrt=am9|bEg9u{)|p~CYH?lxt-JqZ0r8Bs!hP9rM*?%zWUAwLU79CMZD0A0NKO5#+p4DI5tUbHELv@54_s>@P=_SWua67
zwV^Pud7w8lwQbVUJ|CFT$K6IumY0r{j~~}sfu2g$N&ocfCOePJLB80a78ZMb^W?DOqxm2ct!!9Uo7Hn^%6Cb$SClI=gAf#flWHfMM2KCK*!4sCovBT)4&#nM
zfX@s@9GeW99dK4atO#dCc5ql;_$*ln^&w7!Q$O@@FM~>eWWGRhWYAIZ~+$F<4}YQ>n@80zEH;
z#f@%9Qm9tG9aE~%Kf5#OwIfC6v8)uc$)#M&z|1U#D_@k^_j%W!;cWe%O%L2(AMI9a
zDDS3_T`W4M=Br|gs1BxHC_djJI~|}j1>*eaDNHWUsO*DsSOaTS4DMgfzp+VnzmAIN
zK>n@?$xZNfzX;H4CRP58-C8pr7Be@q`&7>tNsawcSnmpdBTANs2o3QQ4?>pevu5pp
zXvSO$JbR}6{M(oqb~IT1ZP0nEWU%q!VKrjFoI88g1bQx!)|PBTl8gL`^2Te`dXAJ&
zp*%@4x`2joKik(E2yDyW`u6)4f)L={PVsbVLJ2N?(1$X##O7cOMR~MEjt#)
z+DNl1Y_LfB8ZPvF9sm5du-&!F1c${j^vLA$@^bu=uI;yTl`W9Dxn28fcFJRaM;P`y
zjH`pLIgtO*W^?J(X~iZ$z>ysRvVq9K4nTSgTNjZysY9gKqoPdbv;kHzusX2ZHa|Ya
zSG?IKk9j)x0X&9jXa!dWJE<&pS(U62jKsfg+@O)Q>F#3-m@ch{tgoaqsHVWu
z|EvI^Bn;3b(l6BU(`Ic)PfkYWCS2w4((sHeT>S>-Yh;NCwo(uW)ccu}-brS^Z;$$&
zZU#3NkGFk+o!y=2+sM`gA0(l)KdqOCOd${hI#iBXs=94Fs>)uh2r-tLAo_Zp80lXR
z3Hcl7Q_s4eu)OR5Iad}|*80@}D30qGk=5j8?xU9e;^#6v>_A^UDt85F;Uu>6{o%0M
zQKe^EOfK2#nU4&b20l5!!rhwOAk5v3-!*vI*4CC%`QZ2q7;-ooL$6AIo6Zjs0aINR
zH-}HwNlOp94UFaNoc_73tI^$6wS8kr;0NT@U__|ncI74^hSO0qmEF_izOyNI#=Lu$
zg@@Ut$-VimjL;knS%21ml9?+xhhj9%I&}(tshzOqweuXpDo9WM_KMpS{{E6J|
z4UWXk{!R4~2!zV$yRx!xOs#Lq=<8C0XB#Q_8NcA--m_vPhAZzuBUfAT*NEak@
zUAIbp_7X1W?ChlV-#3vwpz(;@+{~;Uh#2Ev99o7RK=v!7qgBsyeOxc%2RkQcSf{YO
z^@p(>h|It=^sUEFUmAc=$C8Jr<8-hO86B-nC3qT}qHg+j`-aDJ<@x!fi=QElI&<$%
z05xCE&^oScP7vMnO2YbP0pCJ^kORPpc;qM~O!-Pjke`mUi5chCB6t-Y2_6i!Y?cKPGY@~sh!C=Cn
z5kQXNAPj*EK9{Sn!vanL!}d2w(Ntod?q%cB%8;r?PE05e5Vg7vpg6QM`gs+!=BlZy
zPxy_)I@ZcJ@&}=U(eGO#$o|c^Yy0r)0CC#`Wtf!+sAE7N5zU7Gw?}PN+XGdm;Mrit
zLo|>|t3pOKjKqh;*6S1yn~H|t<`$ghVSPgcqyz)f0QM@}7q8*rHJyb7sM8LT^}
z>E`fiWlzGa2&f!LQVvlGR^~@=fbxnXnMFnQ;J}0jTe^GKvP_Bj6WMu;&izulz=8{W
zu+y3|X!c0{ZTJy!@9?89&xtV5Hiev3f~5NLw6g2Je{Y3ehh+#g^z&`q(Y|)xkJfYT
zp~q_3h?=CN53p%CTmypbLOdMv&xy#9h#HKWEcBNyRP-$(jrsKMrICN8-m_GFX&M(hHP9Y1nnBomj$eRJMvyH0qhjh)FHV&kHa0re?cfVWY;^G>(
zrjNwcTIYm8o9SAGUYht04V>OA^_|P%P}11%f4Bv5@Nq!}Z9E<^6X7TW)8TSg%Vr8`
zmX-Y(yupk4#|yvp{NE1m>3<(1bobw92b}^>e*cpH%ZuyJ&!JG^ai+zWRs5noRhq(|NKOY{
zf6IEcPiT8Ar?}pX#F@d;zC`)#xpQ5!RTP*|2|2l|O>x)G_sPbdwk;!PyqdKsI(5ag
z(f_Bm=#zjOu8v1P5fh8sMH4mMqoLYmWcY@ByodKvmYlz1N^|6nN^&yAm!-VYoEx{*
zM~*wAP=Z&^XzhK5u>{d<@0{OdTSC&&MNIMbE0M8l#;YZ-E+g6C4yh(pO6=)FUp
zd=#JcTxUT*Ms{`=aBUvI_+33c&L#V9c08Y7aF5O}VIVA^#Ara(LQbTpua|+;`&Gzk
zLb$dA02ZXLe1^e=
z{tX;90n9TC38r=xz?KV9Picsqv;#v2W%@lNcvyfe5WoAP4@d&>rod~m@!iJ>de0AA
zA2zkHK!i7>^B)b%2w@t?;T|v%0YSj@MSOGcU7~?I8y0FIJ#@app?eeBw|sCI4Cbj<
z%&~77kRxXv2JF|t3L>K6<9W8Dl3-b|v9ih_2c7^p)s`*3e+`j(Axrokf|(BH#5&KL
zL4#D)&`1UGJigj!f8yxg_U5~DQPllDfU~y@KvRok=3+SkqJRq31y(cXT^AE^7*B?&
zL_ZGfw?{pNBoH~91E}vkETFSQu(4&3j<|CgI&HItdp8*GeEBo=
zf$8aKhpjbRbUXZ-c>l?Qq9S!Ta!6H9&VRlPDCraoHU^{&>RPQ
zaeVx(C|*mmg&|00A@L)`x`d-1%$HV&$03-8bZ@}v2^l!ts@!TI($@_PWQkF(0+a(J
zvH@H|0;0Q+jZ%+ln3^uqDz?%cpcYq#bODGM?bDkrqMNdC7DfajXK(j-ZHEDN?PuRl
zhIt%=d0{ircwuA`B4Jk^kE^SzvqEPna&`kmCA&beEz#jXh8lMBc!a*fR-k~KhVni1
zDi1SzMcoi2Qcd9~1n77Tfetn#)r6o<*q>=wGeHrDO|2VwqL3VeB<6dGgJ6cR0aKAr
zRY6)AA(X}qi6v;8g*0FV?0m>hfn=AFgKr=uLHsc~c2M1^Y$$9{7sThv^(O0$?&@*7
zba?AH_r+Jx$}Omi)I9jY@SQ0?{R5WYvIi~nEqt$MXJKj0E*{RX_Szh%oKH+j;sFv5
zQ$!wFKYGyI^Oo5KjAm9eB5ffFZ~Z&nW`$ERGn7>a)@^aTz*>3~l}
z!*xPFN(-JDw#$qlcMoD<&}sy`e*tl;U{#+dL};#C
zt8f)dwQKaCq32%1I1)-^O2tu9MtB~dcD>ml%Jq0$QbNKMPRTitiYN-m1Asb{xv{Z9
zx|(LGU}*UMZ^45+j@zf#Vgdr15V=IMHG--W;ENsdoVVc*j*H_&mx4y4&EUK(7bBPyRGMT>eL*LN3s&w(1&k)vC7P@Wp1_Q?|`Rd*Av3351)H~`V6
zF!*Rw%C#lUaMOT(kQ`Ljbj~+-nzFoRe6>_mf~LsJhdtVcFqLHlV+ISh{9-a?`!dTF
zetIP3?DXcjz2xgRPJ=U>S|>bXeux|`jk17#>C?G1PnO_mr5l9tvMgVDFLKj9+T*9s
z1#64fs2%i>V(c~8S%R9*^ViOgsPG3n7swRb7xzde+S6x;RD}687%hY3_s#
z5GmJ111WD@F}ZNw?;9U?HPlX8n^;%{5KyT!w1(6)97E?Cm*&1V>svjRfB2)seTWx4
zaxyZML7WoTn_J&cG_U#US3+py@H5?>fdW
zGLi~t1}krOd95A`|3%Uf|4(CMV^26eXV{@38%ifE?z1#BG;=onVqdxj*d=hpzFK<$@8Et
zGt1T9!hzXJ+CN5C&ysOD{?P4jJe|NmMMdRa@uq2z7Ok(#98o9&Bv!YQG>~CpV9|t;
zoxN#w9g4ms6ORymbcj`2=ukIZ#!kfpp^b3q54FZ_80~F*)lMe(u<3!K8`8)*91d%L
z9wLDkL~cW}{-E9Cp-DqC%4-b{Hv4v){PS>Z9dA^2$E&xBV(nIiTIXmihwHl@0>|6cOxQvVp@$te!nsV`5@Z`*4y{3#9flvU)-Kay@1fD+}XMk84u=n8M)G~wz
zAxD^X;Pwxz;D{cM`!dG}i4fLwfg2cTJS5$&xlT+Y>i&_rdb$9@#~`&h+92$12?^{D
z>8XQamOLSKfQ^lR{PER@m
zf)ADf5zJtjRsN(DrVWH$B>`G*OyMY{%bMMA{@z(wYrtHPTp>aSt=-T~qw|ooPlHG_
z0KMb)4iX@d-ijO+3h)m)*o#~iv`tOZamTE+w6uKnMfZmEN#)ZUF7(o157N4hokpwx
zNPV;e7=-S_*!U#{I2jRkx3VXvon(Qd%~ft%A)#pKIxz2Q;D;6$h+kRS4+*kD@kW?s
zH%=c1Y@CgcuXw=FN6XUkf3WuFaXIhn{y5%R%dn7GW*Q9LnN^0AQX$kWGDOj6Dh(=W
zo?C`Oljc>cihI0PN>#CkOkaY#mR7N~&4Tr1=s#3JDEVR4m;xoiU&`p~Q*f$@V<
zF@oi))a+%;G9I8;$L>JUidP+D~tT}1uV&A=FYoR&*tw}wh
z&(U%5#fG7pk;CF>pLnDeeb!oJta5L|%%yWW
zb?_k^-|a%At@F`Rl}z4U$)|H9suyxxIcDa6OfZ2Nlgvt)bywuYD;{c)X8#EX0scSz
zL2vn2+V8bP|6?QCyc4Ylb)V$SL=khrnf-)oyhRUxQsIBmU|w)u{0Bswi82%KyF&>5
z?FOJtlq_eBPD%BE{`vEeB|4in7c8fQG#t_-asw;&YWA9mU*edP^6mf7FK)Xy@#_zb
zfK)tyj3G2J@i+pdDxaOYOc7tFldwFIKm@{PeeS)DGT0xv6oc4r$(WP!BLt)xiSJlJ
z)z#HX?`wLndUUX_;pzEw_4irqZ-hR6`M9Y>fJyyu@P6;2fV$)B7f_zyr!CHz0qEJW
zvpvC^qsqY2KLX|z-3es}$5sO@*MZVLx`Len!}al
zw&}V+4#`(ZjO*GpYYM^l_C*-k$s^t_V*LVn-?sX0OYDb1ct6@xL_|dBsYGgq$hkZ}
zbIp~?z~!WW*3TlUTTfpfPvvn4yd|3By9ImDiYcnA``hDu)`J3OXOmfS4F$}W78E=L
z8?C9VX1vF@n|(dImSxPq*3*Imixot5zVB>04uVaMliLv
z$T1y>TN6+NiHf0=I;yGJ<*Nbj8~l;udT(j_6sXJ)=&Il^X;UVePbVvEmQS(6qru`z
zQUE7_&d*=rfmF@sVQ9GEuW5$EpOSvi+t2?tK+4KkT}78JYG@Hbuo{Y-gc@_zmvvAC
zlD=r%aS29Y>)aIl7Z^&2wt>&}Qr=(3gYzUI)A3fZv!81OGWuAg2s0^m6-^wdv6TQg
z(U4MfC*YJJm~5WlTj~Pw+nF629$tbxgfqmKk$V;OVryGl->VryI^bq26O99jYb3uZ
zsm&qWc>?PUIv_d>aqy8Q6it2v7|x_)gV>kR@j`-~)E=Ep6g8?^#OFZv*Geim${d80
zTn#1hVQp<~mpTVXrP9810@$H&6_|WKG;}xPr^fSa3FT<>0?&#>g}o?46!Dl9+^
zkDTAc)wVs3Zm%^StT~mE((!4bti3wbr+S2-w_oKebvg%%ESqK_o
z|5~omCiydG&bW;ELfno9R>#=Z=T^djyFLj~C)^GQb;WP7@f&TD#j~qhwX7xVcxw4M
z?XT$v#*0;dWp+F58Z442yrR=5aUEPzZ>BApJGc1j#kK3!*}Z=Ur3(Ma$A&Q#u?SBd
zX+I}jj71fWz5ZdU8t|hn7M;~fbwIAw9Y*@l@+YIEdkQ{&vKk;1Pn4a3uU@@UD#rN<
zpiY2kQEPS1Z6^;$u)M5
ze)+bk=gqFwfN&JYGXBuhK4|T#Tvwp)D<>N0^itzht{^Pj6XY?vut2c7+;l^U{S*4*=}R0$Qj>9PeuTIGcwb^i46{3NjbNyASi`nPjF4_$3rWvzwP>aXPkUAZ@eRhSjn)*opX#Y
z8TI8(&G^Qp<%22A79em_)ID?iOY7}MYnF@|I~&36!_@2x|DM-2l0Kkd1>sPaXsuc5
z7nXdbTDdEy)pOI9BP4|ZDg
z=wF+TH5?idX5_GU?lbg_Que7hc!1~Ly?uN3qbE6NMt^iht`ZQ46VtQ~Y;Dy;-4?%1
z(A)@?XFJhW>Od=H&1Eu#IgcqT!_pR@dhmhT@3kfyJ|4*Kx2*C8o2yW6jFd_2=i)Kb)6-*!XlgP91g=hW$bc^Q+l`i&l>CT{ORCZy2nr2dt5`1NUgT=C
zv{YgLeooM`gXQk%w10`3n^N^6xk^k*Dy=xn7HA2&>!3zgfey*Sg@#zuz;KY~cs@~P
z+6SAFWg-dLSkF{8V#jtmbuYfA8%S4=S?DGcZiby%ZAehiJ?quiot&m%r(td~v%!D#
z$4=GE%~!M^l#p)K`$MfG`q#_{2OK5Q*Z^^_i1;q9$c*nx)Kk56$zn0D_o~@rSBzwr
z=KelUfAx|`LMTvO;`k9aX}l@)yaNoKMOu$P*WJw!{zGXX{Ao+qo6u6d4%0WZOP*+&J_g5o-o@rT8qG_F@e-z#|yLBaN
z@TW-BnZ!Vy+_9vL90$ga2Me_&$42shi!qcH+MK|R%Z5!s@xkU%8NNwXb+tX|$!3`
zTX+;wS%frD4D)!7GhkjXu2C1kB)KC!E*YGezpL!E8&OW(30y0AfvKpK^D#N3;}CF`
znpSJJVa*y{lQi_&=Gl<+z$g;{)Kh>oXr#vjulN|H0+tq8KmBj8T~&6@Rz_i;E|JhX
zsPW2Ifcp}?+?m3JPGGZf{zDA?RY4tcm8u6ShQCM6$d%Z80o@&TO^FekuB5G7wlpJW
zYiH9i1X|)pYw>phCUjTUF3&9%3aaT;@`+76oxh_SzDH7@%jGlCG2gdtUBjfPh*w)(k5ZpKS%>=Ukh=5tsTbc?^)J0R
z?^+|s>p``q;8!UgiL@5Tmj-3zf8#F%9H;t^aP?#*Wb)hwd*f!`%P%?lqd}bZuQ$I!
z^Z&szPrfl3JccJtCEVsJkZo$^V6`osneUq#CDp7soyP(90t!
zET6fbwCxAk0ZgylHd@xtg?Cq$&uTX`>m>j@DJTfV!>Z<=ioyFSPEN&E^uOV7htzFt
zQ}FJy38R{tb2V?9##@b6EEfuPhoN~-~a0oM#+IJcIF
z?4vMxRC_N)|I9FG&a<}ywEfmsd=Z;)<4>_S7NJ`JOnxKx4
zn4Cu5>T}Rh=R-dC{(ILiG4C?5jpG%i`ay!(Sf|t(o`s+Qglh%>=`|
z@-`wJ+ai+{;7T~WfB*h}yzUje?v+AfMJ_;sO@ADJf4eAU;;(4Gn=@uSR6o2xX7j32
z7_60`{HW(z^<=f-iC=c?C_nHldjap6%2STOKOsihqO;?4;&__t3NCcc9XD>UB2!{D
zn9Qw61{6iS^Y7<#-dPK*RGGhYX{X$;F;(iQ)efca9`|JLI9FK5-gW+$jW_!<{_nbU
zyCX;rr4F4O8h`naA7-Dg=i>O*bU_UE7kfYo<1Ao!_)!2~*l968MT{7V%Ts2y_Fu$#
ziLC?W@9&S;OzNx4lZ5tXbeOl1haVE>k9sO+&ICeC8AK2qw3&$HD?$r*2P0jaeZpa>NJ59HNx0TFE
z>Hs(pYUD}PsU5%yf%+w5
zeQ2=)m-lH{7`ZbAnCM{*ME|JdgC`ZrQ;@x+-Bx>Xgd|QaWUEXWt(&|S0OKwwH+2pZ
z69;vr87L_oH2Bn3f@3o4Z$DADoh&snvm}GihGYL0Z%zV7O^M&);1ONAy^Mh
zRjh^Pr2OuS$NGnqCM1E&*CqT{DpdY`1K{3~A6e36pjrV%@PM@l1tCtwq7I$({&3-PT)aWBId%`GaxR)3eNlS!#~ISR5SpEQOaVFt%G-zN
zWiZVJP%O=RfP5Tz%}24CXf}z9MiPoHqKxq=Nu^4O_P@+cIVd$4gm*{gaRiQfkcS~2T$BPjKYL5*;X%z
zpEL7zNlIkR)6BNEwo+ImA1|_2VU(t#fsHk`cAHQ9M^?~@gg`IvC>s(bzEET3Bu9<7
zW#O$I%cs>^7bgpD}CNaamwibXy6wxBqrTvgjZKghGH1er+vAA2_qZ~OAq;e1(1
z$-Q_5NDsRpX?w-PtXk31b)m~jE1?R%pUHDTAGL7WC?`Pg-?_)8uLV0Aq$LFCxJqj`
zpcsU;MWjKNLDnfUI{9z6B8qLsM@KqyVOIAf<&V54zXSo-y
z*YEUz5FM&scjWV(6@dz@75ct=KUirsis6T3FFm<1)iEtw`YHE^4@jhwK?_kMcn%2y
zJLg7GxmDuVen4J+hM3!c4dgYU?ojpTtK6M`?ak@#nYrH(J#8Vzp3~Ml6S+7aITvqw
zJ@6tlbRW45m@lAP<793d?e@?2_2Uz}aIzrl;Q9C{g{Ypc_+d9`Shq#xhgw}(78+e+
zsTMD>FIV2Q00W&=@5KG90?8B|AfOI~gM0~o+4rj-iBYhx9qBnV3)xNnLv}$R5ZpoX
z&(tqe1^%chG=zkfI57>IKs>l48IqEcf=c!Yv5PZfu;V`zeQNz1P?8fTo+7OMw)EsV
zdbPf`>NQJi%~!5j^R(E^68z|R&s6WQ%lNn0RbV|uwr+K5S6NnqtqvO`LnNUKh1d*}
z-G8FDEyYS2`WY1&*{@z@+HaY58#v2;yPCNGMJy6;<1H>Y&pCwS2Q3^!WY(Jn&Egpn
z)jPmnBGV+qZ4gayov<{nIt<0lN}Q6!)&j^6#+r?aT*@#hZ_+?Dg!&b2T0Pg}0{}+T
z+WcW0TetQ@iruu-^1X0R3-=wGwrL`Vg@+$VHO&Xts`TDch
zD-IpAH4xcX>-3PnXVS${OHJOwTK>r}aqaYA)oLAI@2MafKiXD!w@wxe<$Wx?_YUb$
z1@yJ{^_5+Bf2Nt*Z8qR`4bYH9kh+MAT!pLlt-=QtXKr4x>lis-CYGABL{m1N-^{Y<
zo@8FnSnwv3@X;K;a}4#%LkV|5EnFOR_VgYw0|B|`BRECM-J|^*9*{_6YhL{lHW3TSQE-Az
z%5iRR_y5EVO0I;!lIDRhBoS6-_xYCV#CH3;B%XSTLDECq12EsALO1&$LMs7l^rjZP
zl$dRg%^fLRk=WdwwtX%*{@=6^F(p4Q#z6`;A7K;FzeZ@3u!|GnDMGKvrA{6mVwMR*
z$BqFV)4_tlkp13|079z2{M}|Rx=ISaAZ;h2kd+M$4HMbMuN_w~ghuh1N(A+PQi74YXC!|#_=ERfXoBKXFhY~R+ViMKcavx0cT)M<8~}T9Sc4tYGaam
z5k1k#u2dbRlP3LlGRpsm9s7@dm@UvO=D#I&^6reZ_cK<|#QjK|)0p(a|#q_<%Eon=^UkUCfPt(XZXK79gc1ylPuD2rRD-p#Y
zbmUk#Ga+Ed5h^v(t4JUuF{n^722*SfWs|`fkjb+#DN~peS~kaN_7fjw*^$1Ajg}i0
z$h+h#g&iXb6(f(#D6?az<)8x|GHkv1_Whd|JN*%Ph+QEsdno1m$siw{y{+5#rO0eX
zDv$9!e)jEk^c-rRJ(Jg%vX0S2i`(>vlL6Prwt@SXCKE9pfCY-EQvg3{fz_w=sig&-
zpggv51Hmky{bQqDW4)A&0}Fi^kO~sEsY6#z*|#FbpI<8p+1kKNeH4@nd;p5Lf}F<<
z(RhtENRP^}U_}%fbPD>0lG%$MD{#fl@Y#AHLmr@d@Xw#N$BrFqX$^}iV+qLj#zooX
z4)|j49f7dBgbEvgEHjr%snrdYC79wX#5d39Jum6N_TZ&;>Rf#yqWL`&~--~=v#01D9e_3yH9?o)V7U(E!!BQ(m
zoE)V~wLk-+@BXFFHMOd2>oy1OQ4G#g#I_kGMxPwM%T^lKC79g43C7;TIC2f%s>0HZ
zF--9vBXQlpt`eGm->Pvjloc_#lZ*fd-~!PoG(=!N>|wVuUCT47xW=xiz{}$@sf%>0
zI4kzCAW}ea9|+$tL?T#Dq`W}fR2jL)h)SeQ@~}Cc)HDo&2tysSP~Fts{@dlH%_!`Z
zF=A%|_$3Svfbl@HI7o`tEa}nb3k21kQ0_MJchMhFN&vJAhV&hv8x5zFZW23U$iPvI
zGeIj1G6SL7N;D+`U%CwKTurDgT0F!x0K_E+;|G`nkx+{9abI9Zr)RDuI}I9Y6F9v;gV7QBWanOc&u3)=+4WQUl$N4b{GO=`VhKc8SBMA>J1@9bZbu61Hl~
z*N(xwP{SfvFKE1-=A49pfSte&pOQWZ0+%*-S-wJ$jSUaKqH)xRDux~#nstHjfmk`m
za}ci~%Bc`{7K>~_?9?8#@vE6hut8UL2u#2cj%imX8A(jx;%OcRpA!tyc6fBmMf2yb
zBPMfxAXw;y(4hA7rQb?38c$$y2UH8x!1)a4k8y>i4c}ordY*5_WOurHv7*mm^ZH(66n%|^bvzP
z3zTj&f(FUyAqbUF5K8Sm0$-Y!g*Pwrw)WYxs}X5bI)If+?Ihjf8U%u2TH+xyvpB5M
zUp;%ERMJPN(NL#FI`;)@P`Ht){VH}q2#GU{HEk(U>aoQR~T_N>*}^S?)sR=3y+f8+H(II1^2h^EsVP
z?cQC}MjokCmHaV-j=sO^JwzI0o#2
z(E@bOP(-TpDrktuBpMj1J^j2Hy18=bpv>ScM3|J|Cu{AA?k}yc8_?DN4=t<;qiCc-
z4VhRk>kj@?X687o;&Ef<&NLER7vK2R*`S*jkpgmatF+5YgA{|hEw{IqYpkAA
zR;=1Jr1`{Rk44)zIkBlL>OV`&8I`+srDxZsu~FA?{p^TAzukt(b#=8JRW;((4c)y%
z;%Ybi#2&wUlApRlKw-_r)3@&6e{GA(spd0F;BuS9u&k|NeHXqJeFal9J6`
zBbwJRC90L%Wni2l#31A5+pjB+0VJ8>V@Y9eL5*ig-}cffrOsyTTWm#|c(@cgOeL+!
zr}5}QO@<1)6~8;{-X~sBLp7J^k-=FGrxL1?{XaqDK|(N0+9FXuK%Y1rj+FUcW0tR*
zvfsVa5p_i=rhf6g0EG#XJjZUNO>^J?8Q9Qm2r?;c*+PjeR7{0g#T@FBA5z0^?$)q^
zjfWCe*)l#zXDDlEbjp9bzs+PXi{h2yEY}d#ego7SP_LD@1M&JrAKzFJTyqf($@j(5#i<8HS;f
znb9U^lw4V3;75AlGilHZL$Ss3aW2?AY=-*jDjJysRb_IT>e6z#9T%U~_vbu{p`MNR=Y-I`LktXM
zm&wB^FE-EL^-!?BCgMx%nUaTJFiYmxa&$DkjG1)Xr_Y=MSa@t_<^r_b%S{p;zwO`wP*Nj
z|E3hZz~ymqmfC9W)X4SHJSzn94z;Xnt5Pm24vPvmVuYW2^100rX;%hc$M}BREG{h=
zlcn2e*OA}8ac_}1L=qTc#orWvqV?OMw?!T+>MeU0u6e$9pC4<`?N2$~b^KqKDBSQf
zsH}4TWN`V^qO^^Ngp%8%7g+2Uf=Qj3E0nQ`lfQXqyY%dMhf5zN!ZT2N!n-H#pVC`v
zr?XDCHIqvCyag5BZ6Azp`{%?K0v5(e@rX
z(j#Ax)_uz;_8yUu@I=@qkP>aVxx*doM{~qF|RjWcuyObkT9wVv(39kF<
zkj6v>n`U$wKv?K+1Kp&^z+2jyF!o(&>=AxU1YGc+Q~9Wb2x<)aj?dY5dFjs`sI;el
zUD+B96*C|-4f4k!ZUXe##=7k3L5=s|P@_%^Az86i+41XABr!|Lr4J7B5dtKPX~i{kRu|EXX1YK^4Xbl*7_PI
zz4wakOM73gdGzR=(UH0_b&v)#hvHLF=uwPS5^7KiT*gk8Ub-FiS>>}+GA|d#qJShx
z)niItMuI(@;VVB~1V~cenv8Edp3!2DxltsIjL>*(hJmLoNG~Zt#MN(}R?Ep<0c&0X
z)Ej0vR$!vj__l~_gxHT;P|d0U>R}9O`1GfC+2xDwz63oOB5vjqNP{E00KmKI9T@8$
zt-5dds(iD=9SFb!5j6F~Df$PFXWsAsFr>A_K$oxa2vrCAf08AbTK^rD58IKuQfT5lX($9`0c0py1$Eyi+N(dcBCF
zYsLvh>Nl9ADeVpMCA!cc!0^@HJEKV)ri5n7217gwozkH}L9YicCH2^F9t2-}Pd(@zJ=ASg@!Cs8aAo|zQ5!^)^5N^{Dn{La_iMO
zfY?tAr1-Tzw&QmY))3%NJ@6&|D@KVH;vk)WK?SR(O05pzH^WYoe_y$Ib4-oFR>TH#
z87QO4Me9JpKplBi6=KkWRo+?-nv`r}mnP4sRBuF9c^|eI>E>Q&1%{oPoA;$;ptxhQ
zFcjC*G4m38=NkRbxZmnCnL8$a^PxqHqRbW!ZIdd4gh7qal_Ik&jIq|PZd@_(@4M&B
zNpYM8bgd<+EwUJgyLj(K>4|^Z0mbi?(*FnbyKzBF?m9{6?z_rn&&g~^44DwU&)x?;
zbZoYs`^r^|p}jbw##1_hJ35{y@MlY|K;x=+bHrxfKHBpAN#WPS=RG8<>wBN*dC6|I
zDk;b=FYf&Qr1q;jo}@O;mE?dNK@wXUjM%?_O6C2PD=aw^Z*&3s{9TWoE2$F?Ut+Q9c>|B`cx
z9;zaOZm|5U^^!HTFuAb6!%+6=px?$Gvx6x9@lOyI`Rsqj>k)xR;YN-)4~Jv_?N5^n
zBCXv5vd63yie6lBzQmVd-{C*>!9Mju+u-|hq2iz~KW4YC@?*GsJ#leSoHvo0yU
zk)Wbnc-dgSi*nG7i=jGhACKG>IPJCW*ALJ4OwRZquuuKTo}6FyTw1<-;KfT^ZE3K
zP`=V?J0E3&(2kfk(yvNVYa*;obr5kHx)#|=R1$02_1#7HvBmP+Nq@`F*KzigK<>yfjmvcG#ECWIgg37@AD8p|=~w4*Ia2}*$}uFI?g>t?3#dvT
z?GqwVkL>wCY~tw0ajIQb5UZa=6a{jMWOK^RDdeUELX&DkRXSgBw)J5e0}?Em}5zp0QoEFI}emh
zwEDtj!lecU5!*&uPeNa?3g|k+t6)?vU*2L$fo4MpuSUcZc$K)ddY2MHK5rpCA4nG6
z5?vtucNyq#TJVhVMmAkY>E+_!g0G__!%?*JE5Hx|X2!{9s#PM{^^J|%A1-h-1EpBW
zYZ@Cr9BHr^qYMDJh8|Tl74>Nl#wh1@Uil0$Y6W9`alpbrj{X-td}|CyD)nmMV2g$Wy!^9P#Qobt`y7EvuB$I2Q}_evLe}=uSOH!2$of
z0f2N;*J#^&HP4LMo&D{2_laBTpYOEK5A$zzNPey7B_=TJ7pCM|9BSMLYs#~m@ywd|
z_qFk0ok|~f>&OJ$Cc=WEI$tyd^`jE8Mmm*_T*nu_t%qy(0Y&5+?}#8m#n4Eli4ujC`##A5(s%_O=Y$={XtrZ4Kb=
z=sbDwjV_7VOp#E!$3Grg3m}Gim9OPe(z!I@?>c?NLp0
z+w_?G@AL9plQ{;~2rv`{IIFIG!%5V9D=j
z4-qBCT@y!y=R#iRVGjgHpq;ak!3`k})zqgJ;e^DUA`P$9*iRGScxh7|4v*cMJMrPm
zpx)RhVUR0&J>f2%Lx-^ehjNSq^Ze#J(;x03fcZqHP&g!brH_U*dVvTD7^xTm
z9gGix#uIeg%St-RuV&W2Bk2v9w7@eXkeKG4qoCHoCmZF)JC<(cFKG2P|LPA~7`>>*(A`(BlXCWYJSgc&O+0iMn
z`J6&Rf(Yh}C^%?pXlR7=F#ftZFaIG={Lc!Re}=}K|KQ!u!Es#EDKp@}DlSq{?cKXK
zIj)R)Lc7BO;CR{pjfe0HI-i3e4wkYf`{u}h>XL@
zChg#L8zLOW3KiST))wDgX(25gd2467iXtcT$bE^-=xO)w<5AH|Rrq=~Sas_!X9NN_
z`F;4XUs*d?WZ;;~A9w2hD4Xq~5s2Dss-3e>E})tZaXNl~?zpi;#ose+wC$(D)m>c7
zB4L+KS!QVW@#LB_Ue+ngKY5@BTsC8=J_pCpZ;0q+|MA68Hb;f4T2G3)%4ex9Ub0+Y
zs+LO5UfE>Tk&EI%`(>MYk`xuUtl*Csxcn=}@#zqh`hVwk%BzV}PiS&0C|)(Xx}u(5
z?6GXW|_c}T6>{O06
z%rc;`-sCM^=n|m`P1JB@%dQUph24ocBT4W;rA^{E_EipT%#he
z$i98U!}F*j2EnyLW;4#4ofJ4Dnoo2Dq1ciOq8JnmMo~VLE(<#~lk#IIBbO$^uJSiM
z!YIR*MOxzA++51$-U6WIUfay8yNE49AJ_LSb5>iBheGSg{0Tz%sUbuAiA;f_QcXDfZqDG76`(P9yiO?Om2bg7hwKXxZcb9p9@zrEa$^mSaFdl-#@&w
zz%xA_dXDU&5pexQFj-a#X6VzNSQ57|Q(#UPj0FrPrNp62sod}Y0oIT_5Z5akE&-Lr
zLbVHjlEjqU6_ljodantp2bb_M*gM<5iQ-Mxk=o>uLUw+>7X$Yf
zEpfy_{EYVZ1Ceh_R0xT4Rc}fE2TJMwX_8Df}uNxmiO&4%{Py(*;3Z`x`
z08s(n)(&59?~3}QOBoO8cXYL*i-d0)zNt`plwFmvJ)0M#3Tq`LQ{{V{c8%N3!TFbfD?X;ESR6UP+9qfSEl7@;pLwP3Me)D~!4N38
z=cAFGw#qV@JF23`0y;BNI6zQ=mh~FH#^{0rDA>j|2mqyzfpS5Qp7SmileNHPDY76w
z>QyMmHF^x(SiChBbp|#m%7~ITjzjW5ufx|^WX0Q9B~NqC#6vP-FLpEj`lC_%{Q|D~
z4q9Ffm5N!9B(yG6nY{Wi{NZNthbDt6-)f~>9wQ%3!Zp%9K28yJOE`9>ecxhT^bRiy
zYRpY#dC#9_mW{>_nekVIXlyw#vSd@mPsixm8hRMWS9{q3~*x6SyU*6212K
zElaISUbUFMO--pODKAHA2WBWpOcWuFa&BD^ToC-S?NKeY`55}l|Mpd<5fnD1m6dCm
z$V!v^{M2#uwXL5t6n01sde0Riqn-Lko0HbLoFq}aROhCT00x|%VF--B)J)cT+)G4h
zdq$jjRe#~j;XK%L?#ajts4km*jZ;9Yv@Ja{Qn1J{rnTEM2rU!zReh~HasPb0V^WMphv8yFC>)I+^q`S2o|WP5>FF7(9~K{9R%CXo#9r*-
zzqb#^<7+ok_rUk(QXo2yJwmjrRtpK`7cr$V;cBsG#)m%lUOS03;32Yke;sCJ*2^Vw
zCR`pJn^D+=piKm?LD~|6@kI*6ltR)^Y$vobYj!Wi9e||ed8S$4o;L+^yYD0TofH>a
z4Jv68hYq>|8W=_!I21RB$=yWCBm{2qdrE>bhCqOD2`JfQS(T01rO-W8Y7BjyIJKWi
zpt3c_(j5=Vr#KfktEmYNUv0w
zahNr~2JQoK31r^@63t*z;|;aeqdbTA0$y?~xQBDi&vfBFsQiP$Vl2fGC}z?giY@is
z$CJ*gKqR8FPH{OG+`XT0r0Y$U`TD5i?;D}rArNT#)m?;KQLrA9MS%_-9TPpn!!iC0
z7d~YGgrLTO-NJ@TjwW}}9ZhIJh2AO>{&w-=eOgR)13J5IKQEh
zQ>Hl^C|_t2a=h<-ob=@AhxMtYRE+o!bc{-fWf>n%!n~2PrGuiC&K|$AyTJgwJ;XS;
zGtu{+Q?wNZ*R+1y;E&usjODmLe%8QHrlpq7##P75h8swzkpxU-J5i5iC9$Rs#iOgGMAggp}!!(}IkJ
zLZs3?Qb5>|3+IX1u&V3}F-EAo4ph73F^hWRiw==oCB4ms{%@rJUN$P`6q*jO22lt-umR3oBu44
zF|>li|KheIy;09Y^ySRWqsP5xQ|?$e{204afAM{XxrMlq&t)k!4&m9rwhPuc2Q7c9
zpcQiDEWN(n8llps`_K7A7g;J#
z;n<|KoVb%ya%P9fpQ(|t-bhX?&ZDI}qo&{WZWrd@Sg;oEIN(gX1brQ}
zPV>%1|F)y+-`f>2<`FR-I%-zW6F;5i8hj$RK79;@Y9LJM3rt37aytf)7f?<;zcU+-t|0YY$z-~9e~EqGtNCroC>=J*n*a!srU(D=$Q~fZ|&8%VPba^
zk}Vk^Hy9}cv-oVzABnHi99TE;nnQe&mQxowXS%n~X0-x>zs;asmFYTxqj1RaOw-_8
zEj>1vFTf-nA9(z1hC$-Rn}2BmWG7k*4u;btp?|p$o~j7MAqgj?Ah3c^mE<9+Y7aGXm>`Mx-BUgg2q9Utd6!PQwg@EpjYT-hRZ!1
zqzM{ZwFMV)Im2nUlZFX?AGCFE`QHQUA+pY%iGcg4wS(DIm97qjhP_F31q*s~*7k3X
zWO%Ymhfj!rC`53TBJVUDK+NbhEs3fqg8@y5LMb2BaIE{g=)~=~NAM0|jhOmCE?P`6
zCjxcQR*J43H7%&fLg^$VDKH{nNP?h;2{73GRnqNK$Ms?`;UZV?x&p$n`CPEe$fG2n
zGZj}b9EpWek(0hdB_5`U*(|IeGch5
zT$1efo099{=!4t}(VD}7=_@iMUgr31+pNv{h9nhS9$hyBLjA|3*NA#}XW{MbOBRAHVRTnU$V1wz1r$pho1wWM`8G$D^St=Nmhh?
z_~VaSK^2Ez{d8E-cJQq9$H%;9oRi+73pOw|+}EGzwEu6tQjTEXVZ;(d%Ut^)e3L-l
zlbp4`1b2Mna2_i!wooA)x1lB;F});xW3r$w(62EqK5*D04;bPdQyn?y`;8Z0m?AY?
zjs=*~TX2@mAryEtL}8pre_|}*uRw!%ivk!1RrT{fgC8pEV5?pV#tJW6IG(^Txp7=h
zHN@ZC2YP@&1t{hl7`*%XA==sAy1j8A;E-I#NerOKzaL4Kd8pBY5J)keBqb==Y3hyS
z0`c!Jo;?$8v?UzmiwW*|$#YAI&2Smuj2Fz8hM^Jzj8?YBq6CJ*bsrc>J{`{;{<8f~453}m9dc?Bo;}Yr!Aeg>rAUbg4I^rABn}_%^JzhUqj1o4DZ(^f
z+vWL|o62^dxLz8?{d7bs?xK}p^pMEeX!#03JBJ!oNd*awF{Mbi@FmIzqx%=%;w$%B
zWrn4jqpQubLfr1gGkU6@d}dC>D~~|n?vhkZ$}~^UGF@9NZrY(~>P{=1dF7ZWQ_yme
zplJp#tOvZE+40vaDl01u1uS6$T3r4U(ygbIZuO*?#LH02AlO(R#f@=9d0SuT;}>Jn
zdAF~j%Sas1j}{RXm9_SQFx#TV=Xney`0&2LQF7L~C=4A9zmNkDd_-Tz7Rg-!+!_(_
zPw@7()&3-#?KwklJIu4x%P?^064M2!^M(PK3Rn002u&(&<^|t8~gL8|VwAy$T
zp-%qt#}BNI6XJ_?<8O)EWF3g6O7|HjZU%mXV!rLl0)+X@+i#chmZi3ywBeoKs=c%F
zuGORKB00O2omGU@FJw$cjpvcyM94sLirV)1J1v~ko>{+QSAy@$#}q#{dtr5R)qu{L
z7KQMbJ>=Hlv;DSn1SjISq1$ilNuVo>0dmp;x;^bj;K7o?K6WUbS4#6`1DyK6FQ=qn
zT(KfD!)VLLa1!Spbg|Ff^HD(G+Excxfd9da>C@-Tnlo{tT)Q+$Xv%hUAL#VeivqfB
zuu>e}`dQ%2aDQ%$xZ1%dnL<}~EvLvf7EOFTZq3Pdw0{I2vZH8Ch~FbP9K~Es
z{)o3?jL$KEx*P}uf2J_hY7`5JGg%L#V^ED!2%C*0+aJoQh7wWE{cO9C!(g^T6Ul$c
zy9R2w4QJU6pV@giIVF%Ae(dPBn5g(1Bf&ZSe1IfzXPPyIY|7de3O<9&#;X@3oDMXY
z1t)&E4T@wZZOg5-FR
z?H<~y7(hsDZpZkQ{h7$H=7@Pe&tq@HlEO)YGwp>4AZZhktRN^0o_A
znWJQZ;A9R{r@z&!lnJ4@lRrFN#BRBAn0!l1i(P!p^~+g0&x&i#RZSFV&$uBs5uY!-
zE6IG|!H{ioRbGnbo^4m{(%6$Cl22Y^3phCb1#$lmzpGs^^c6G;bj_N>%Vsy5j*bmJ
z7`vXs#&kc)0l;_sQ()e7Gb@cu;m
zF%;@t_<@l;P)1Fi@#o!WpkAw)zqv7=Djh1Dpd@61T&KPrX)sYn=_4ePCRbVVc1_K<
z>;f0ud4wHu&1qg|3HHd&VfZs>v^se#Y48QF!_V%+UbCDt@wiQQ&Ll7xu{j_RR}|-Z
zrI?werQKvtN5G=38iMz%52G_wSQJ)4HVq0+00aWuBM@`BXf6mq&L9F_e_bH1;qqt0Ckp}tL?FV8x6TYWyYKYadHE4*=wvWy1
zGb$a?iOzh!Ra#XD`fn$DS
zc^~wGWJx9KkVLiixgG)jl@z?%_b-)F3Cz98u~O@FNL;a_E{mIU3doCL+vN8}A3p-%
z9lBE9cDKCkfcYZ3;DCVT70Y|C9~X@ef1UEatY(#P&Jy
z!i!>U{mfsCMx-rORZ%G^Dq^(@i)%SsSGQ)V+^A9bebTQiF7@H--z>LJ>zMi=zr6FQ
zR9Li(`k81M9MAF-3JlQ2^5(y*YkLjB7qg~f`EE#;NOkdzCBnG6jdQ2$m{|~pFCmbZ
z#h}TjhdtilK!yX$({%NT$`hBL;1rU7p5-Ity>ViDJ9!dZ8|ySC2Vh2M*M<8y3FP7E
zI$xp-Ovvnu$*EJP0Opz&G2Qb~&}_^~<;5UG>=n-4rdu_mPj5mlXETPG-Vo32Fo)`X
zAKne6u)0P_uY;TFmEVjv6e}jpHF|_vhD>aVio>SKcDX!FC&XhPMEtZoauDAdArJM5
z{hLv`qaxbJeBM8z7sDgpYY8Q&c^Idtk&lX%rgxK}rq^J%uuV6DXSey6o!{jvc{JR1
zWa{M%xhT`HrA88Jn^|$$HZfUujzxJ#-f?dn^l6q-0Hls1xT-COt7B1^rsxqUEWUoP
zKHI1Nt|b0^!bNWel{+uESnb759%<>3;XEOC^WWb;-ErjC5hZ28NZ093NWhe1x#G0p
zDlgr-6HOHNW^cG#-zhY-oKHLZ<}8soRF_VOEXb1vob3o?KWc4#)~R}2j3(n}7`pMb
z@%2I@in0Vmc-P;*CZwuE9}Jm0-xr+eo<^S-pgNSmA7dOeLAP(;zI``?4}%rgk2#-Q
zfskuFn2%LqJp94cm#jjZVZxx~CaQ%Q?4dt@Dy83W)bV
zwaC*g22aIBep8f2o(H}&>k@83y?eGL=mD&%oY^P84#^Fr`QtT8b!F4eKFiGYyMMAK
zjH~&Q<@7JEQ%XDaE(`N=Og#Ii_ga*dbRlIdIvj`kv`6*Ux$D#z3Be%9`Dtkop
z=6Z$SoA}9b#nuMi|E2_)qQ-u)<~n*YmZG)aclfZM~}G*dSII
z!)3^Hi;Bg~_1-GzzkoNKbUstlKfwOj1r89JMNq^LG|ENn8K%0>&VgGirC4bkDI;H2
zNxNr%UGk0n;68Lfn2nSo(rPJWJfDcrHsHCPrfF!=DVPX#iHigZj7_*xeIIS@7PCLy
z|5YJDQcsTd<@qfL{N!`XO(rnWeDF-Xa_e(Oy~HklsVsXJ@$#PKiujF7pfj(?Q!CU+gM
zl$0rUf&_Q`J)3tT){6b-e>$cA5zzeaMFbN#r6Tr*1?!LBZa-A{Tu*J{m7YxjNamOd
z6PC{C&p~$w@GYvyX6U@&T~?-q%lIDCGK&7f!u0&QaRk#re0A4*uE)G#Z`le`s*68s
zujGNGOalWv}C5of$A8x!5}xjDndP@?hPnOJb>goI(KT-#O$lX#h-{W>-e
z5S_{XexG+iNl=AZGDt~oi%2+~hoIpIpy0IkhDeX-YF=emOPs$mEDH^q{7hW2B*~Gn
zW7%x8`-l{kWtjpD_|OuR$u{s8c)UvtsDfKq1j8vYf?J7Zq!90BUW|`jYuPZ9S@fdm
zei)#c6y!E>8KBao!UNA^~M<=QN@JxyorXs7PZ2+Yh4ydQ^F)L@&XT)M%hyw
zMZ7O`vJ$qxsDY2gXHMgPJ3A7VRA?tCeHcS+TAa8Ibwo!1U@4be!DxUuWLL(W%ADeV
z;ng(Kntr|b;`FHC#OGSJWKN2l9B4i4KcVEI2YZOR-(|lI$N>Q+j1g83UgHbTVRq(W
z-T;7x!_Z#gX5#!r?k7P>Xm=CKs!B?N$aW8vw=A#O9ct!CJWRd}z_|!|p$L5h67hSQ
ziTjg<8jT7?Xzykv+NWz$oG-y|SVAoS_k#S_!(kL>Gcz7MDQQ<&2YRZ5`|D
z`JyZn>dJ8jiSfkS`d!zvQIiQMaBSWqHQP1p)FSu%fJ0Ae(ucWw63jx>+Vr(;uk*R>
z&c3it^EYULplcMD^0?cu#ptN&;E)lw*Rg@X_pRkO-+$;!v`UFcT3Th^dCe@vCX}-)
zZ?NRctGhP#PL?PU2|l#?tc!_aG~&FX<$32tVBBZLV<|j5O!MClSJba$9(>km
zF#qFQ9E3tlYT&=P6~Ol;&%%B@+#Y$)s{F~St{ema>w-071tFr`>u}c316N>GgN#>z
ziA_;5$#7zE_c|bZ=@kTV6roO!DLmf9P+=J6a^^-&&ob_u`_VT2?Z{Jfp#bnd`1dk;
zE#F~q#EJ*WRdcuD6b8jbSC+Cfpz<-daLeISZxo(hBZARSkST3zjRk8+ftP42vDmNa
zrvjF1&BzPyHcXF#;%>Y+97(nW80>B$gxM-ZZioA>VDmhT9
zJnKt!T?uan^=o&kbs>?$u54M5`1#vk^!)FWJ7=H*sg|6|zg;4vgX
z7uy5{Mka5xvQY*5PYO-ilZop=54sAE0DM&^&iR{p(p+_3NQ#UjOaKkDf&yK|Lscj5
z0+36g@OZS%#El-gaqjmM>%Qid3>DDhl2$
zb%N9V`>nUT;pBr3M6z>ZVn;BBM}3!-hq&=E_oeF)pkk9jVA}VYragyCJNahyxG}u4
z=UXgpMWgCwbnfg~SUf`B7eMHnUT0Jxz5?LqoD@uiiZ%wM3}zpfI4Ug6r9_&_)D>S{
zj%i-fhg>iqAfu>5I=wCml33=Ait)lU>M5q)Cl1KKn90cSf;aYGVK$i#XP|^k=Q&bJ
zlA;HIcyHp&{BDfsS>#G0PRLUs*xMiw^|}12N2wY{3|Xp7#Y+^()Q!V&gVo$>>|*ii
zB_5zcWc_TvkB_T?FOPqIBh7Y;Ff-fode^y3A;15XQedgYVO
z;>W@e&yVa6*yY^Kr20Re!835Cx{Xbv(L0kV4t|$%psY0G605m2wphVMpI1Bova1GP
z!CfyoOoV{-H!Lwx?4q+do+AqGrRRu%FbLGt_f-PPRx1jqpNm6J&0+!Gl<>8vCAS{zwmA{oM+}L1VW+?$S^#P-Ic3^LoHkzm8MDqWgCZftgp`
zy=`cY-Zf#zeG_L8v#JM7&Y6`Kl6B-Ucff9NFQf6ObWzm9sAu
z55`=sZ;+K9atD}CV#9a*Zo923AXFkHg+;ZovtZhf&-b#^(WYUudklTS*FJ0hhCciR
zzW*NZl|cI=z0yZMFH9#tB!Yay=$wVe=o-Ts^J@`hO=1vOvSU>vao4a9VY!o|4=#Mb
zA~+^d8IU8H2E$;ijhntSyZT-EmYfkGA#KuQJ<^0zD3HJ!wMmuX5XJeU_78=&sr5S!
zVcfhE&Z)LBF)@9}m3-4z6kA=bHa?V(>=7jUge>HicpdP;%8@F}Ga!)>T8&tW(73cbuoDFk>7
z#UZUHpp3MQ&`ft8XV-0*@B8{OCKhYYs?0-9-V;RE6%USU!%)H86gL1burcAmKNShM
zgD&gh5sZ(uZy|ON*)w@~&nMTbUFZE5!FU5(wf^$WKjG8=*zCp;p1<5#;oWKqmBzM}
zQ10QiHF0Vn8(bg9NFSeV4jfBR!uA5M)O#-W+8lHK8%X+JeRVG%ZU58ftTPA4GcmRm
z@Ay&0HVcyfo;3asaZmQOJpVVu^3LXpUtU_XeR}WG!D2-K9SCm#k*6?%m3#(nJH`73qjH0TDIEO3|$Xf{2A8y-1NB1w{pvUPM4ZdXp~w
zf98hd5uEq9|3
z+}u59@^u*)0!Z7uUUV2lBlI)gy@$Z~1%!$z0(nS%I{bL*iJ8VXQ{Sc;TkCDiSdfpg
z@R#XNK7i{2_gFAe_igo?(>C>zeT!nPnhzL;M%;?{iwj^FL{W59{^MvYz|&A+;V8JU
z5H#CWOli<@`T>sa)l-pLUWyriFbMlqfEIY<&!5XR$8JD19$0Qsm;X@g!4Pgf3VP@
z@Jxk|%0^{P715f*G(aQy>l)
zKq8wvpcL_9ko%ZlB*JAi*_jFS|BH*W+J6`U(*pNDxph5Ih_{0nI+N6eVT^XyXY-KtXMW=lTM-6m$JBj)L)j9FA?YU1u#0Ka_!y!$d+BF&#QK<=s
zD*O%2b{)~G_Fa`9{SCJpJEU%LKdejs!9AFNJwA9KyA~6tCA}xl;z(x&9NENe{mf1C
zlfYXj=sxFEx?v`JHFIp-J|Qh;{+thRj{XM)A^t
z?>2kw&xh%~Ki#Vv4~X1@pftClZDl|@6RW&X4GFMu&Ni#wvpTl&2^-fOk6)16#nQV{
zi2-{<+n!hz2Mbg<5>`GxUfllmy0*~fsIcuT^J{C$-WhsHJUHQDm!w{~TlP90KwKqa
zxX`2xH2p|u^o5xqk*k7#3o~;fk13bk0qK_7a8j^VpC2*Tv(6LWo7;;gg}^CR0C@A!
zRbss((E6I-`ga6@X}S(>vj}X}DmD+sN;$81iUNCZH8J|$;~_hsc|wZ`vL~&1+^YB^
zFMlMI9?oqyldJzJt+bW7-szD^!{s6*@bKDdqd^PU`K}F3EoWKLd^YR5vZJA6^}<;p
z@$ur+N};c#Rw|coV%lytpP+dGpU2vYRW_jzow3}{J_!iR-Sa%Vp80G}Vy5`5w_R;Q
zFOTH+Sbb_3M)Y@_VoEk=|BDij_=PF1Vs{o@7btX=GuScmvTHDht&cQ_1{ROqFhNR^
zA3d-@O7U-oBe|T9^&@%AyQJi2vWw;}?a@WCa%4
zBJ4@~lk{dS2f9i#ub$h~^@8Czn7Rg^vnK`U%7ERwss!rQ8@_&h1_aeGN{fjjuI;qb
z_4cW;)df8BERcH9;D(TV7`;3hCu8eSU_td!+|a*p@#5B><-cDf%`rsAaZbh{=jMY_
zxQ=;S|CKqJ;Iqj8f1`Gs0-0%x9^2(1Hy(qL?b{}>Vuc&eVVDIbOA*OpWt|3$a4;OK
zM|vr*g~;tOs}~DYwjCq^+14>^?rPw61&i`Vb{t;}++Xxg_1GUZU59Zv0>!{gJZjeT
zA8-54LYd@w-ZgaX_j7W1wzm3>rF=4V(%rIo^M(TDX{
zZr=9mFM>E=2kw<{Uzy*smGq*3nHYN)>H*=vsTCOIl2NlFNoz&d&}T0;kDE`@|5|LT
zcu|6@Yl-rvLGvdg<)B6X&2`}Yq+_@4qmo9(mHptq+~(BMi2OaE>%VQlbb4N$`F9Oi
zN*(Av1tcp3T9c%=!y1h-Rt0f#;Na$zbq0MD*@95*X#~DE^=JTal;ws_&}J_ULYr-_
zXf~s78Pr!RhR)GBIGmpStycD0-X2>5U~FOD8LL>?-Z?Jy8JJZB$|{-GINXS4y55F{
zK@>Ky(Cb}W|DN@p$Y01geEB+ChU#b4wKl~VQ@RJ4K@;y1q1-z1nw+zzgH=17m~dU7XRR%IE6!?k+ONSw#>`@(fp1|
zGzLvufuYHoN;m_kVc-o|LIMpOed<#2!rL?T!yftuA*Fcfm7_yac$L*IBBxHC)RMk$
znBa!l_y)gg2}Q^{k)hAqV|Y2UHrg!Z*;;fEUVi`8jA5%6Y8hb|ZKLKF>kEgO37TKX
zs=fNgaUkyyNw6}E))7-hoM!fdc+wsZ5<fk4{)yP$ty7fZ`*sIo}J>|b417sMT8
zWQ9@i8A7qyj`=uoVIyMSX7L${b?gw9wV=kVdo1IqrzcVH_nDsq@##?}Y#R@L?Zwfb
zBAZ^w@1?t%>4-kE(oGzbMV`ZD4*y+qmYe9B(D8T~sVxF_T8@V!O#007>#ZSwzcic=
z>cAI24VnTaa!Xd(s3n$dA=(nm-~hC+1K1tgCQfx#KYbe7H8zo9xcPoAh%eW~i?m_MMiJ8*boUs$|soJp8M*d=Xak9{Gh9&1G0{NqsN
znfcddoe?#;ySicVO72UdjQcuoS@-=?PxHuP5bJ9K?K
zH()S{k*1U_l{YM1vLLDP)?Dm^8za|%ISft)D*#<1KkxZfCDQe0C-2##5%A6901&z}
zP+Jb)VHo#(eb)*Bf8mfM8aeWff(3~ly}HRn~Mlap}j_N~}rk3}SnJfNG`
zD?w9#+tzP_Q;(_*pe75Pk}f8tQD!zUn2;QQ-Dm@ZQI1_W~2mniVQzoYUnxCjSMtDD@t1>XSUzz}z$}OLj=<+q5~xu60?J!*ZwA~PkX7G+;X
zB%CLxsKVfdPA>r$UidEw25%APt*vz2HhIO7HECoBj~*oPDy4W%v3|pvY9PL2v{crN
z+dbD$G=SwNW!=Hmq5Sj=^V_I1Fh|gaw81v4(H>JsMDe`wSWy$yfzCKsubXKuGnGDp
zpwrdn?knUYd1dV0)}AJsdJE^;4ymdCZ6pMM{U-I^#Cc_quMK<-nAW>mUQcB&Zg<+W
zYg%$K8C$*4UoAj&Jkc`M{MIr#bc)k{@x^>CCyPSpMZ*=7CMG3g^%j
zMZA{+PyhXEQ}pu-yH6UPE#m!=~@k43;N*|RB+6&Qu>2I#yf
zof2(Wn`Fk2n+0MFdAFWnR6U;RrJFe%zu6s!`zQ;OZw5whkU*1(0iuO~{E&nUVvwc2
zQ{BXcwEW)_mjSg@l5^U|N3a4r4GBF(3(8d`HJho$DSczQ9hx)3t08d6sXhG+5I>pbl4k{|%>25Yua_8*
za08go2BL05=3PcZF-Yj!U@cZg?j_4CUi@Y-^#1U%95FbZA%gVk`1oK3ZL);fvZG8e
zyR|4>u=OF^3o)M}noP@tVHY`}>g=3`RsIw)jYc5lkf&W?!-Cg5zf7lGO|mcnbj2&$5M>MLTT6qXZT?VQfym`
z*CF}r0QONFU!DLt%j)bVFHOGn?Jpe{fo;L*n$e6EGaG^=hAZVL(
z_7Uh5b-rK7&v~FC&(m$s(;K^1-`MkX7(zF4tvPUU`$`E6&O2zDe!lyrff0srXtUXV
zFAHARHWU
zXP}oLXJN!609mka!8JJu^6aY)z#MIB<%h0QKq+=+=+pM1pn|+$_KXW2%2(&t(47r0e&)#aVZo4YqbsK
zDEY$OS?s~YRFcT}=t7KecF|IRj+*j4+5XV*xhAVQNWa8%pt#k5G|2bIix>+AGn$EL`m|wE;bvTEaUSDJ>tLCXqa0%B|g{cm%9&RGpAbz1Oq5qjC}1G
z#$c}cz$xze5CO9aEW{3k(U2@=-$xuyu!$k&9w7{f!P8Zd?Z1mS^v=tbc?6H~MqIRUe6pkQTxd1f_P;K83SZ@d%vZ@7mo_veZ+Ab!&eAH*WQjwmj+YisI(sFioPTtwf
zW%tZ0_o`X<%=-gigYkxDIU)0@TA9yjlS}3d;_4nG(A9gR%V}w{KTpf4|7ajg+^msLO7;O;+
zPgx37USk%|)uZ3*(y?W|sY?Nv>C3R!se>s49J1KquGQ_^yy=k!;E(5w><=Tfr}Q|E
zoOB|-OMWzud((ER&R&s!n4d}JF$o&jxqz^B@+v3l6Q_N=$m6=>$=Zw7*VS})IBnWE_9}WO}XFn
z-(Mnzcfp4+4Cc`^7CO19L#4uO{WkRW{rlDfyw!J*KP0gt%oET++r2s)={xQn{uVg%
z@Y4EGWwG)ybEp3pBVn@s91;zrY6lZ0H++ul+fixpr>mYshLQJnb^9y+?yV~+e~SN#op5`LR8%|=EItL
z^U+K8*cX{zlJ&!dUg>vjWV#n=J~;vSFrHLfA@C(`vD<%&CnGdavtH<#h+UgJ0v8z}
zE)(PCp__6WChJ-o$Im?026+>{m0||2Ui-n+EVOc*hk!W@coOv9{g6>p^YzWK{yhiw
z!s-k{iJY2mGI$W*yx)y$ny7Nl$3QB1#9{8$%oyp+wtnr{i}%)FIhw__Nn@h0s9<`O
zgTI+d;pSg|x%EZqqLCPn;x8vooX|`VHbzyt@>k@2#F!0vsAy>QOYI9@kg8!mSl%Ev
zDe*p9Va+>dhA-png4f39*R~j)c_iDfs#sqYbhE5Y_V}f;)Gn=pCHR#z38hJjh}?(o
zF@L7%M+M$7HmrQxdfiCId7dO&?cmuVc(TOq;ChrFZ|a1EAmGK$NOH6SGCZa9YgBoZ
z8SpT=$6Rr@m)Wl!p6hL`$0F{pt&jk$X#u6TX?gTfcE~s$QXyd9n9L`%??hM|i^bZ~
zxpS}OcJnmRthM1fl}687k4%ZR6;|N9cg5HN%H5}3P+n1@3H3lYLbHPKR2#7R!1#b*
z2~XMg7*d8;CjGYd07mw})|o6&1#1ITuEU^KTAb6G`#PvLyPpPojJLaF~RTO_^Yq}LQUSU_Jh=m+nh>6&Fzdn8VoE*UVRRe>lDBiF<
z$$n%3=j>mv@1iUMCJUpI2cDvyxuVyAHv0AnxDV!T`Do*~oB|l&$j6{FMji5$FwYz$
zs8B#9p%!y8CX4C|cJwZA5`zLUlEt6|IVJra38!m!FjXNNJl`K?Kzb(tPn+hTZQGAQ
zOCka~R|BMg@FJLh9Crr3#N=WHG#U@fwbz>J>LmAT#jVBeyFgmjlR9vC7h^w6?ntPl
zKnBG=o-2WfM^Ml=Lqzz!wCBMhX$~d0dcg#5wtqWtScQ
zHgJT>Cych#u}4rH4cPiVqXlf{LL#ZbjE2oeNif%WeiEy*Gh<>r&NV|wJUwrKRv>;R
z9Ezy2ra&?{i~te%gO5`B@a{79Z|2a>6&1a
z?Z-Ezd`(`0_2yl1bCA#@NNS!^WX+i*9+B(lky>fUS~D?jPv4Z>0|1p$c#R&2$HRjz
z6ZQ_<9>%?q$2c`YGiHZK#%OFcyz5EXogl`mWp(Q1A~1odz!7hAa1@x*nQK1?j*kB3YSq}(I6
zTKj2PSW$~viD({ZY{)@*waY>g$SH7uD7XhE8gHFiZNe9)db$6Z!&k#TwsN^y_xItv
zyP5OhnXo5$CsUB?x*(4mGN-oix3%5dvW>s(2M(cTFL}KJBRBrm(_TVrt5$Jva6wK1
z%hWu*Z)D1d_PT}Jv6-EYylT0?r=i#Yz=_cvzXzhdm>j`61(KWsTuC-RjG
zEqika_min^mE)h|-GAdy^{;c|h-k19F()?#RZjL;%H(H@{`qk7G>%VCP|YL6Jcv9H
zsn6{i6vApnXIMm{3d@NBOW-|-Jyve9ds^^p_3dk`Z!4sf#h^u{v1x>lJ-{iH`TO7f
z)iFBX{_=p;*l8oj*znFtgea566x+!F
z{5R?4J52*k|Go$CqfEuTJX_8Z89gu(v>Z&vM*P2RT2DBN@u)MnT6(+!{`G~nO>#O&
z=1rsvU12ZsnukG3lu}y=1Vcyy4uZE1+S4}5qX9z_Jp4dl0pyzx162%k{o>a8wZZkC
zP{Revvms0gI3T!iQA&-#_C0DF43EA>*6hoz2_Cmm65>B+c=*y`^4%Gvf_9%aiqD
z9#8M(xQxccwOWa3CBYtUP$mGupKcJ_(^u}#jFo7;ugBvySF}w6eQ#@lHUpVd~
zj;rh-j5jD-lceh@M=S%-S4bF#Q{)2d$=W5NtS4DmTCa@Rxdxb~0EUTos0uo=srF0+uu2y|n`(14pFe#3kG(ALarkXhk1qXXda^Tb_3g2?_0$zv
zeF_}a8I$_-I9Fldg@?|VuHw7OnTEd6PnmsatU|IM<5_;JFzoSM@4edzkKxa7ehw^x
zd+T$>2Q26J_td-g8m^7(1Xapx70Mwxkq9Qj!dN-)!B#aaBA
zNVw$hl^qrXKPGp+!C)3pp8*bwNzB}J>gp@X#
zrf4kgR`v5rZ%eM*~9dh&~OckV}<0($dm<>zyK{QS&Y9?#27xDBt`@Tx0!
zl)Z$)sy%S*@z?JYH+5O_BkOHWP7z^z{kf`qh^U*4JpvN9T`s_|6NCbM|TG|Z0#-l(``lTzjBvhsMT
zZ%5jM_uw4%S1zwToz;3;$m@sRz$Y5+A310KVC||xn9PZL>K;#Rqz|H)_t5LC$1I8S
zYG!Pc^!TZd?>@O0YPB}oUmGZXY3|^11-DSc$KyFoYZbrwbn!$xKIlGv5twd{+=;u&
ziEgOz0vDvRoYSxRb|A*kyn<6)BV9l(aHMOM<7G6>WP}RQ)?Eg9RLpErwEc9j-dYLQ~5=c{J*G`mjLH-!j_9PqDC4z)hU?g@@#quw8^g692Q)hz)xXp>j()EfP
z&WN`}U}AHvya|<(t?d+ZZF?|T(}rPxMP%Hude?Zd8micsh447_*`Je_SMv4U9eyTd
z(}OoIy>{x?9?ix%*#J2ad0E5GJFy=$&o*Rs#k_n2x*?!nD&;7aF^2KY9!H{Kn^yFZ
zi$KIF;hw08z@TGxMX}}XB?#3Bgw2?3)rYAfffr(*XgBQk@>mO$pzUqL2Qy~>vG!9q
z35}i`|97>jt*&i}`nsD-C%<{~eg@7v)T^Zw9!OdhyyGzDQIrt}4)A-}RujgD05BZd
ze@dV=`<*iXjZ1Y82rK9SxG1V^$m!u$;}_Xn4B@=jge^LUfl_%O*|F3q-i
z`O^}cvwH9%17FQVZMFT>S${P_hDuzm=Rrz20e{sm&FL^7ntYM}VMV0dGSv$(JSD&C
zsK#jsAMT0jTDr7Ck9$7HpC*4-VLLyJ1!B>mnl0p=#qsBpe^Y@?ReMJc##f#X7(VS5
zx@B{+neWz9d56x|n(HOVa%|6@(k}h^gvZ@dHDKJx%VJArWt3v#&m7I%|HDy?<|}eE
zrz+;-#HfmwnI%d!0<^&R{IqRsf6mklMep^!PL3l6s8N7FSq%2jOpDSJ%ac*UJt;5>
zePwz(DGlS8#+Fmsi>Ge*da(Fb@iNb_R!;Hyq+zG?DG&DFHdm)sp3N`E!S0gs3l@n0L0fFk)CaOL_(x5OE9nW;?caoE1;>2$t*HA7NcicKafb%1N4t%VFQ-i#mmfU
zR7VPoC-^tiU^Hofocx-Shf9d>R?AbV%@JsB!T5**8=&-S7XVnY#sNhjoNNVRbA%ON
zocq_e+xMU1@eI9eDw@$rvr|3cnF;6>bpHbW?y|BnQ;5_sJ)!(|8b3f!@wVX%Nu2P&
zX~Xb{l6&X!h$(<$=lOYRSX@30Ul)p7a*PQ>Qjy$1B92MY7*GZ>>7XHeiSz#AJkpV9
z3!PP)Q_YC}l3kyR0fNY&grSs2@>#vJB$s?TfChucP0=AFA}7y!#l!+ceLlbo0~=)W
zn1fqaZP(z)9ZusSUhXTaLb`|Jheeiw-^hqh85bKAhiv~DC;T_AMdX;|mWONasrS%f
zaK)HCFw}}ZGvp!fsUhnwAjC%gJa(<7Q(CsnAbib&*Gp&p!HCb`^e|A-@z^QdW!Ygo
zZ`qZ{(8*LdxfRE80?6|OGKE5`aBy$YlFCOKo!%!ro%}zZx+%qPTH_HA)Y0?(MMPF>
z%@Wm%=rmsAxxh?iW-w_k$?OUvD%6Qk^Az^e@cQ|V{a{v3%4gtuk;XhDSqMF+pSHV&P!qRH~g7i-*pN8xSy)$WubD>3-miXQ`HFcpq&
zU-IQczS@T{-qt{OfwM{-4hl;?a%R78H8{fW#F*Z$Iv#O;dB*`mhSLMfFn`l^4l*xC
zb@twwX6KauxtepHorz_|V>!tRe&rS$IF`0^1)uOAIk<@%^XZvXM^ASM)$A>ha}3Ay
z(m?odT@xo}J!%k}YRoUuDZuo;dXKoc4+F8rKZW{TS}D1lV@u?cqj?E~-nA?In+yY1)a7nki|
ze-NXk#LA5TnmZ3j-$TLHFZc1?yO?&BIJK9^o+>G+c%r}AsmwO+0U#5JvRY`KV>*a%
zlrYCzUbG7-Jb(n=rd9{M>Gj4-HeTb}a@7ONDJm+;BmU+mM_Kzo08WZ!B6dk0zVa6b
z9Mf7j`5%`S-!_a&!e|L~q=Z2)YC-d~IY1ODmq$?<11K3YF|znq_W!tK#Jv(#wcR=l1y2L(auk5m&RT7p@?Cf$V$67lV!c|t
ztvt!tF#Z&HU^uW4W+i65j!z4W%v>PJ|Kx6Y7$7mto~uvU~#
z(rETXZh2_-L6iO8w3Bjy@$&sLolW&x9vC#Qmg}`~=}X?RhlLN@-qoHC*!Z&Yp&i7j
zet|h|#bPf_`%TqM%qrR3u4xyyZ+O{em$Gr>)#n$T=7kF
z$Y=i|@G9&I)tSC&r=XPUhi$yA6sJ&)+2qM|WNwxGA9HPP11?lFY41LWH)=GRDj*i%
za8+geg9a2YH9B?X+?oZaz=ga)L5!-
z>;4qGV(O5tXqKC@;rX{FCI43bJoyrT2K`qB)VQ=?Z|hU)nA&5_9e-YITam!Q!Mpd;
zWMy^pz1*NMD7+x02FNVrxUgGv*VI+w_!n<<)G((FZ{K=i>JmQeI%mkQY9xu|sO7Z>
zcJA2|*7$Mxuw`q%+eGENgFkG~<~E&(I(K7zZ|ua{vM<&}Y`e${3s=9K{UvwKPU-QB
zt68gC+yiHGvz|meD69R@nR|d^CBJL^iuGJG*YSia+LJpChibT!{p+dEa>2rNy2#{L
z``3^4@1l*K?AV^pcLdgPT_=soYX?co>Tr|@_y?4VwSFQHGlI(igoMKULU&FHv_D+Kuez>N
z3sN9~<)Z7mxITcX{1kgie`l2rD-QIvfHoOW@Y;KU%cyd%oBTfc(kRJ}yP|a)7y=$Q
zv#A0gFrx*z+WElrD0+10T>9nSbU@s!0Bo84Ek$cz7fdbxQ&^|6dq8D_5ShV5@s3}I
zi7g`p@+Ck%_sG(HWo;^$U-uYvhH+k9Q&Ria?|ubny8SSG=!cO}7)rxFm>U*=AyI9{
zx5b!|595zr_Wj&goT
zVD0_u`TwNMdvj>{l#5h3yomY)$%PHYAR&soSI;M^X%O0xP>{|kCMUbT08<_mQ!2zU~9C
zJ`+%CNMa9
zvWrt^pq?o%?~G}cRgmU2jr9uA*?mrS#he)%o#a`{UZjTj>J+5TjXI~^i#naBSss}w
z=9Lnpfr~N5Qn<|^_EJm+B^pGf_T0dOdVFRzj0(^eD40l(TbRO;mRtnL3Yx5iU~v#9
zH31i0?X*t(Ofw8q6{JY1#^SM@b3P-Z|A%^4v|Ak>#_SqP?+LCEYp(kTF8Me`d&81|
zJ==P9vs^;VJb>QS4hwM4d4IH4I)}60Sd1qghC%BlH00gwo*6KlKEqfsGVp81^j=F|
zC2gQFHqBCqXUdV!zuR6?9lO(+Y=XE3AALCDb>?KlNYK3%+X4}d(I$~=mxBb+hd)U?LLCeh4Ay#53;rXvFKW8|De=uC}@q7?e4%ik`lqf!8R=zJ!mXhWj1iJLMI2
z+`vzkPsKON;rV_=7RdMKn&yRhEqf;ijtU6)Sohgl1_mqI-r_o$wyffr!~jqDo*{pr
z+T_?RK&f)7*Q&j9+{@Q|qPzavc~HOzwJ{s5+O<<)zn`=^1#zHNfZDP=puP*!P;fAH
zQ04MSn0cT7p1HyV1SIe;Jd0Xf^F|;f1?lapt5t&dd~uMv=OQkOjRe9O)H|X7P5y#6
zp)J2a``+SfzPVf8-zrJhQ{|TJT?e(q`XrN^-*@Tb0w!9#_0Hst3)XKlF&t`b_PRFE
z-nPWW-LRw{c{PttOPReZ4+zRGc;BDy;HHJ*(Q8^5-`+dwIXBT_yx7VYr|xllG=J+U
zleIQ+oLxpTS@30&iA${em~<~nNe#3DvJNVOdr`V1-b{O7)l-|{hn=Vz{l2(O@L4pB
z+_o9^*50ZtWN;^>aBT3`?>}M}W`0UA_hlHvT!f`O@7=GK;2yIA(2uL!R@jAg!SeCQ
z(D}z){wUQ`7;K|XHab)P?AbFT+Xs_d>vl!bT9b;5VT>^p_@5deJc1FmU|h@wlq(jG
z!K=mc(%(FDKZ5V0G2rZhIwwTZr;G
z2y!(k_Cu`9azxxSOx-tXfLz^|_ZV2p{(W^*WLD&pH9yX^I?LaJSd=h^F$_*-Hh~eI
z{O#3j-&`3G1q^#B1PYq*D@Tnm&XcyPyUdQ_1(Bza!Vn?#Tf4kz>H#$(y7KM1>{@Z>
z1|b8c^aPGIozaY9sYl;&Vy_9YYQ9STbg(WDBwZzCKS9Xl`0eG-Xo#W~wogGi+fhe&
zZ{>_CmioE_p-Y7h8B*022ts)^L}6_ZAW_DkI`Uwq#?$mVE?9+J?%h)w
z+eIUpZ5@8=Se1<0w3XrygtrQu)9KVtLxxBpuTtMGF
zgICIYJK6R~0sm$Yj8b=Mz5I{;iza{X{}<2MQ4423NGGFEy;5Z0#t$i&^-p>Nuq%a|
zz11M35=saK*X{SGr*3N&$QzkxGhpn)#1jMNAbt~UG!#|%2X4|bE$0B(gF>N(F)S$m
zs=Q}>NeT-N9dUK1z9!5d@8T;F^O|)CF$D??)@j6B^H5C&TOC#a#Fz}d8uV_*{j*({
z-g%`^nZ3zX^BB+MmuXg3__hPM)lFwPXvbeUfKRiQnOt{62&p!kh&&1EU~MXKni=ab
zwZbF7w2GU$en6-nZR5W+fmsumenB2ShI5Hel^Y+>e4Tue5UT
z(rjUc7ioCH|MwK?W2uCz(L@c*mn_HS^MJ>}ZzmGXVi4S}-?i8fM+B~Wre;Q0aEY2x
zubIR~t{DCDGo^FikL5*v^EuIES(1c
zWN}yrf=R4H&blbFR|VfWB|3Sjj4<_Mh`VQ|fJsa~;ke&`*<*~s)}(}F5cwLq&r*^o
z(@jLr7h~@`{Qw-ZW55a!*nSh*11+TL{rA!3(b)sBeO}tYl;2
z=O<`OUI%r7-9>IO_(Bvddh6?C`?Y-wEKyqk_(
zz}Nu9RqV^hdwuG=s7zSqlK4NPD<`Dh|D0BPWtk1Wp%737C?ZjB6Nv#wY#3TBX5p<>
zJL3%Vc+y~sL8(Q=9fhLu+3WiJ0&XSxNwVk$CBGHAoa?XeEBvRJZyfiOWxpdISL!X0
zu&DNgG&Y)f$sPq6D*&*Z{6Hc(Pk}8~0Ma?h8M4MLI4|BZ8m+1s>$E3O7{)t%h+Ggz
zy9^fz(k9@3k;@$sk|9DWIl*iA!~x1q@+!I)D*87vw@<^tbZY-M3z$1VB(~?3F$RV#284bvG2QZJp-6g}
z72AMtr>|$CuL5Ir=B7`C`!46qmVnD#xz_j@m5oNCs*`EB0xA3+dp
z;e5KAbk#l+Hv-~`P4FU$Hw4_@BX>-*_JPf6-iqsERw0#VAV3AjgyZ|r?1B>w>^Y+$
z?+LUG273nr+QI8v$-&(9+o3Bv9$)T{bMR;}TDBUNY(r96US0w{n&!L(wYUqH3|^!$_umB7Tut4`Ho+28_8G9SG0
zozz3a!uTojv)GQBN?VqRbxSrJ+PB-a8xydi&<<&~jk!!Mlv_chF-9$Zh)u<0CO@Qx
z51YTrRO7Mi3UA11z=?s&%tE-X?{dtV`{#*BmuN@ZuxO;Dx0T9rt
zbk4*&uS+7E7jSDBdi7ZL*Bf2kyr$wQU)*q#C(GZm_q&}~5)Vw!Y?ikdy2mIc-RAii
z!W*gdj_>5#ELp++cGbx6bMmpn(_#-rH$K%5c#V-KLwulITx1-;1~D)}sEBzyk70~#
zNY+BdYx~%31{p*9gpd3UB=h`EK(Z47$pS@4WnDBfLh%CVC<5bd1+9MbT~lY@n>RAZ
z7J<9q`BIj@|B4Y^td!9H$%E`s6MXlMp~{Is93FPEc$bOu%;4-AaS#*NgQkIjkz4>V*50q^LU$*A@|OlrK-g6++)G8LP1A9?0d{v5_iz&h#iLYb&QfCC=-
zPiYe&*9uOgUyBrnr{!H6?Q|$UxbgToCN#ibp5eeHU>Zyu
zr}?2d4XSvL!sQ2#VGF~f$(~s3=7L(`_37Wlg#A
zCJ*bq9x>>Xdc|w<^M7so3ZY)f*Q&J|80D_<$UcsViB0m@O7UthptVKI59WXvf!v0Z
zT{1zi8P%MpCRFo#Kv6?_;NVxyi+(h*4?t5@{_U0q*yF^Y4(!ahY83z7>a6^5^BOje
zGTwwFH}s!PeBs`t{<8Ewh7*DmsMrV4k*SV9R*2
z=||h6du3}=-;|}{RP0a|{B%)k;uD^f`Zji_V>wKwm_@%f_*|k-GGyo1z0Wmr{dRg{Zo~Tu#Sw-QA(JIs$lD-st%u@Df@m-Gt
zk!p9O#s)zNqQHW2((;iCrPM8MWtCc@Q&qYjkWwGKEQDZ=XzKaJR{#}&b_9(*R8po;
z$FuI*xRz0J?4m_X7Hl$0YbCF*<&rlvIk&lNzw%+Y7rZ!kIBN>&4gdvG?5KOD5Yq+1
zIDWvdO8cauP`d$fDL+x&QWU&aZxDG_U;7>5`yx!n5@b4(7PsAG`Ls4vL=>N7J25sW
z*=ChuTqHmN?gYF+BXVcSLkx$rU;qUL9F4mgavvs9Ha(74?fswa#P&=UP>+N}XNdS*
zTXD6l?_Sh7{+3Z(;8G%22u!F5{IC38^BWP+Q-x_n6o#sYu|5=G@+150&uL0;{v_x5
z^w9y7}Hio>8>T&l^X>G
zjjXEvGqSFPQ*R?=J7TD;iTjJ@PTHpV79Q4;V3ifHg)~C(M2;K~L-=lJIxW=)?*cL#
zpqMPO=sL1Va9#I{RNm|Jr;2sxpbB!KdkhB);e~qd0EQhj&L|qjSNJc!F6jD~F8xTa
zk1oz!TLPr3!;~h8s2ycDE9&grBgA{9{XR&nFqnMdzU4OZQ{S7z_y2W+GzH)P%L}tf
z!O0&$-3x+*J41=|nK0h?jfH^ChBLNZCTo8Ef2DexDX8Dn?B7)%midD+r~89267YIDPVd3p-`yD(8xYW5zY&7+f{>*u@Shzo^B2
zFz4Q)HRNr0>XgpFZ^zeb^)|a)Jj&gquwK!A`F0hGJVM{JpVdba&zj-V#KPtCRcjH=
zRtN^`DP|$f?+}G6w)gTOO+08wfnqQrtT3=a0%PFT)d7{jANq0(##LE1*}V46FlMvx
zSrZ$KiCdfec)dJ>N#{Ow%#GLDVXvo&IvQ*O@)|ATb%%#iSw<xddR^kIo0OS_U}!|19~;K!9MOSc9iPjn6|>Le^xyU8r7&PfEKr
z!x-fxwSD;A4D{a+q|y^t@WQGdKQ;Ib6P0IBZnKa)d=_f1AY8he6MS+Qs*SmHR5{oz
zR3Dz`R*^ojCOq~h(eT(S>YL_YT4VL=4AG}Q|9nep`_dnl?mE5j*KGz?hd%G#%{~2x
zpS+G9v{`+l?#CN%H*?N1dL?n~7Kgy^jO{-%gHz7Cd{b0P&}Z2M7q$$xJ$Rs2g4VpF
zDe3gPz}8ZnjfPe?FKO#4C@6%MxP80)Hwq;vv{K-#*56G29~Ul2oxB4azdrJwGPvfr
z!14O;dFJ$I(i)N++w%^=GXeSzRYLROb=Pi}0^v&p>Y7%V5FfzsC&J=b1iX@mB$`k+
zx8UMSLpTP%7`QzZr#e{MwwE77xo`lp>_vQh`n9ItrK9@pgg&qApbS&nfp)Fl=K%7a
z0h!P*`%V8K&(;mdSAU3e!M3mYAg{Cy!9Yb&W8~G7F9Z(Y^}3ePSu($7hxoj&JuSFq
z%}%P=aOe6!`^TV54I7@CjZGYVOgZOa!Bwk%i;0O@6e+J+*UVJU)(opt7(d6p+aUjb
zwH>mkN;a-ebvSQ!Ft0I6RCQLTTu07}!r)rvYbA5PT{+5K#TYwcD|6vUH`2=}&jGZ<
z(^`gTW68d7$BrNCqHbS^#x#d~jrH%jUAvZ#W<<;=e`QqLpsjw(+VmLEl%RxqoOKFZ
zoxWi&m7W`gZ;uX9q$kwfpUv;%>mT~=m3!od{UbLu@e0cK1by34JpsmH$eqU+&E%{4
z(2*NY*Qzf)9&uyMP_G|RNMQl_E%_Y$LDI%$A``mHxYzw;?>}Ix+fw9tefG?m%=qN_
z52JTYyTC=RObDZ@?BLkIq;zVmbdk(Wp&=&)h0&x?kQxZ2sq4GiQVUhi)uTFS6nO~7
z{USO4)F)zRG}Epnnre8c#lHjKMc&=uSAICIRrg(f>eK8r-|0Uibe^IZ$0ibFL5MCo
z2qIP(TE-9v6JsmPs6fP9#oq-(J0jm&{JA=~rAwYC6eQIwNcv)s09nGXjW8GWk#$PQk^WuO9c(Mt`1S5zKW6Bkt^Ems
z!Lm;U+H){odbM}O<*URMyzKao}9W&6nP@m`Kw
zHZ96m>pTvA@D|N%5cw6Tf!LBu^45*6oV?P9HkxPgBXf%2&ZG<+Bwi<-{SFUbw}gb#
z+x}!8Xlbt|wy&X2sUcF=@$g>#A#;4A6?D5tB0Y^)x=*Ox6DgE?d`ex>5AOkL-M6xep<<-Ju;~(L~X4{gD<-HI^E1oCVAS-
zi$`$N8wXGax5Yl7TOHzqfdNVESbabdG`>ev?|w^byyWItC&Lmc^<{8?0!PM(T!N6v
z7oce0&AvNZvkD}ShpSsLT*ecv>i77`Icvi!jTqA?4@&U9Pzi4bHCOfQ*%u{B;p)vr
z<9q%07}J#q;Idqt-X5E?%~M472rNwnSFe86)UiVg)FMYX`ZM&jbzRVt3Fi#cM*e=w
zoSj7cXS4+QT7(wrSh62Me7uxX4N{A)k#h#6HL`M-a4jl}H$9x^ExHqw4_mFh(b)xu
z=_Ae=7*)Rtjeuu4DUQi!hb)ff&!10IF({&>1r|A9mg{ioGrs$VYc2Q*D4QvV+@%+@`q1)@&M?nAT>PRhF^AUfJO)WYSNrKf-*a~N0NCSop^F`)pedf%MHi5
z>$5h5&BUcbF^gw_6`*+hN|nW7m~_aE^{J)dC`W}#T_sL_avF~OEPYAbJ)IBd)$!=Y
zk2PEi7Tojp)@-WPUF)#h_-nM|{4SPBq12-09Xoa`RHTf#78Es!ro4TvXfUqL(*OS0U4NqmEDIZV#ImP~RxFZ~0~9
z>QQcbNnD@FDiWYb+PHx^`>KkPuvwn08iB0daCPltrhQ!$cssLocj`oN5cUowoS78o
zX6=Hpa)amWH&VmL*`LKb!-uhY^$wzqCAqYhi_X?jlgST`*zM1$yrF;tnAx=DRVWtYKd
zcRZX|GB?QJgvXpdV3*ck_uZ>F%PLf&wK6dZ)ax}&^zjFhPU5&2=cb#xsWB?ZxFqym
zl3pC=hV>qD=CZ>!-)cGO`b6rb_*7z+^hb6Nl+Augdezd~pYfzrMPB7|d^;LQxsHp*
z1GRdbQ6=4LvXRb94DcGP4-G%cee&<;Q_nh^w(guOx@l(x%j=uN=!}@*G;VUK&Lg@k
zLZ|GNXEA4=aa%#~@AGJ->kY3+e`4#1@)3Ufow0bWM#V#n5V(h(w=~%F4n|Q}E|V_g
zC{=O3-rYB*lE7X&ODA_$q4IjYJ9twg%;OvFU_|kL`wDCM{(MB_+
zj_v*li=CyV=&ULX1RoFH!j0oVecF25g301^h+7GSo9qSMPST2u*r)QdrN*}BIEJ8KO`H{*RACcYg
zOTR`;IJ8>t&Mn^rE(&>b>pOLb_dW<6RlUjryDJ#RM(a^qv&3M=LP5gn?osU8!=tWy
z=G?jcnhz%rzDuIj7dI=H*kJJ%!S54lPBAhPjgHROUH|8%IeMvQ55j}^DLKr*5znVd
z&!OR-aMzc6mqgqb9lSkx;Mb5_94R4D2GXR}E+s9?|2gc&i4s9+ak+tpj20XPJ;VIB
zPkYF9==2$%<@(j-%$&?-ftH`cDu0;%JBfZ!#p~aLRwRRrf@d6E;Q)V>Jg;X8tUSYB
z=>*HPeor*zx_pyWxgm8+G)%&dR9?T8p2wYt9ii;$_viFS-JkS~SNlm?K14;P
zVd3|3ao2X9V3SxJE-6_?y2S=@Le>}GA
zje5z&^!a3*69Mqrjl><*&VS;QS#~^Nm%r!eg0i~iB0<&c))^en
z96(+QL{Vvq7K?5MtR#NMT7QOkOXb*6Oqr
z;FymvE!QWLD!dnQv5-w0Z&sm-O<5hAGC_ahX64a4`hQkjK9)zHuZA0j
z0q|U;9eOBVYT^9(kAVe-;TfOA`s*KV{M`)yasY+S;UaBPZl%)#*=G{u7d7cZ^O^7elA?c3=;
zu3l}!o}(eTRuEjkH`_!-3tAiTDgNAH8_qU1_)!P0UTClb6c>0rE@6ZsC&%g`-z9y9k5m@=fX-@WJo-XY)V0K?x`GM5qy?hRA
zM>0%~g3HS_DM%Cmbkw)_xtaMm+{kw!-neK_K|ulRD?K0;%mDIO)Ydf>luguKsv*&A
zqwEi*+KIx%PJ^%B;jZ;OSR
zS3ekz$ifW-1B0q=P^rYd
z0T>Wxw^#x7+7-r5mK9i;XL68R;CqsF=g!=eDhn|2UyX+Msh|mSgDHV;6AIcK;hcS&
z;nL9_>l_%Rci_O0IlR)v=FT|Ad%mYVkBxOj@0}8{mn917LuQMOW0f3U=<~VWyoS<#
zwDV4>`UM|p*U}Z*y0s$A+NrPkC>pP`Rfg~)b6p`{ZSIF%>qWNrrw+S%q)`rpx{;Eu
z(h+RjwitJyIXq}sddh_@x4}t8zezv_0qvsEPb?q8(JRaTS?Bj@9ADN}E#zp`tfJ>;
zx)%m0lqy}aohdQk?H*G7eolRUIEG2h7
zSu(Cq-sp>&4k_hvQ!R4PUGAmP&|JyS2Rgek03Zn(TH7d_6n^5IQ|Sa)xizL8E;N>K
z9b{gew?YH#mi5kgK+mWmyC;`%P2|`S+nTu5u-=_6<4G!y?eSzIco#m<@Zo93bv}${
z6HTVD?u$p{9F8T*5|(`;MZ;(i0+&>LcpFr_&LHEN)TF1e$xmc$nl0fn
zP;p8-#V*1}(lYYy-Gy6%c3TNPd*xGk05ckVnTsuIp+
zXt)k0_2>GC=RD9gD}HtxyGsxj$$N`L64Zn3g1dD%;l`)tJtF{F|<&5
zxJ?V$4x1|ViW7^9itgcNpvfNII74F~4?G>kSOMIxl^opc{r|vmb(`xmn0wH9yOJ5F
z&H$!Gg1v%+n6wyJRLou?%#C|ym;}trDb)_=;_ZhI^(~ySAm0FwG4e!}f9(Ed)G}K<
zu3ov4Jo*6%K`(H_@?u3rMXzMmek)J+_U2CX__m}(?~ok&Ec1#RFdvn49B4N#_4oB{
zi^&6}@nlkrR0&>_Nohg4v(uWjYqL*kUClhW
zx(tgutXNz`_r;M?QJd!ceI-Y^)30AFaW1++&)umXVs=eq9{`U&5i1L=aRXlp3}o|C
zeh5_b7|4G8`koG45LC(Jh}|K0l~ao@*v@Ugc(iBHa;${C*QxHwMM@aW6>t$SgfO(
zq`3aB1d7qbY>|}q&E)*ZBBlhkSFY>{*bbbnN&uQjbHB!M6f0Gpi4|sOR?p}JN!zp(
zzWywSu)xY#>q7@Ow53UDJ7eCGpQ*JI
zpdgE!HvlqmVjlNXKZ=xb%+tvS(pS>56bHh+`}ei#ezL$C{X7njn)(De%O0(5jZ1`e
z1+Cga1;arf&Ikbvd+&UO29adpIK;%^C($J+JS_Y=L#?|@TWo0xp16H_hgo>@K(xFq
zfL!+}D|_Y{7Eyx));0x`v=%(Q3gn@!=bS;-pRMV}Cr5wa_{EN0MLp7X*z(fk`B>L)
z&!ufYPuwXf`g+Xjb=Q7d;tVzpKunz1fm;54rCy3%e`w^B>@R^mUHTh^B_vA2q=kgg
zW6KPh?N<#JXIJQS0zfNkX>27rkvO5?0
z-`a3K@o`4$dew8XhYe(KkzfXL@bSBJLOU@EWisux)@uR%fb6>YKTIx5aeg|+*07(eU&Sy`
z3&PaXqXFmG33~iKe0Fztm*{%CUqwZvTQgP2z`6fkQWX%T8)9Wb)zx#g`?#VoZg$3H
zrG%12%a(;THyc%$C2-mQf300@Op{j>Cc*|z6LcePql(#z`_RA!Slw_0bTh0&JBLEs
zX<<_7SfG5YYF9^qu%!a(G&O*QKp7vgP!Y94eM@m!HXzz`SfF&l){QxEv?+v-IUJqr
zIoy?>YCJ^PKZMvNgK{_{UL{l9giMR6lsu442xL{tYS{aGaILtm^nL
zl}ZNH=cXH8M!+Qf;Av?T{Qj
z&%_l!^uA}80aSh0xOuV%%xoJC=4u>V9lT1C>LEB1s~EBE*{Dm#ypdnwT=&%eyLZvV
z+T@)Z7!Q-h)`fx#T)~Iy5nH+=PlRa7s8}t!=?pz5fkGrwMaJ2%_jU~aywyiem>MLC>noWIhEj#?T*ooH@G%L_2L-vODs!MHaoWc5%L85x7s
zu^t3hqyR**tlWr^5X6R*%R%7Lj?&uA;UO%5#{CCdrib+(zweX{#6e}4+V+K0G=BlY#4Pdq1Z
zZf!5a$qHJfJa{+;roM;W+uzSpt>pIM70eN5HJZFCE{}I#aF0$`)qvS7+kYQ0eUv%Z
zan0)K41`p4_=XMY#i=4Fsr@!vgBI%7k@`-j#HmhFvyF0Sa-N%0VS=w2Poo?rXvK;c
z;5aFbUYMgPcN+2>2v+K1J8w#PNl7v0*Kj9hu7#JscZ8);OsGF~U%u_>18AMy;ePc`
zE-u*@U6~kEy1K)@9FKy9P8jGtblYIK3&O2W_)QTkn{Z1D(}EZ{TlBdHp)S!CqC22K
zVZz`heL4DvnHsVZR8WK&&I9S!ke>KlpysycDEuR+^Qsn1@bW~2)PHPma|^A81^<3s?fx`zbPpMRz0k2?-5tBYYK?GOXs68Lep(8kT)jXN{iZrNpT9A)@vG18k?GEXpNYqNOA!wlomBEX7Hys0Y#DQ6clH!Vi=SftSU~Q<_iSp
z5HH!95`!s~#y3mGLLW48iu)Q~nzy(ra6->4%Lw^v$?*0PFlC(6Ri7c|MycZkF7Dh^
zXxQ{dpfHh8QuYYI%@2GuU>xu`wFmr#-?Dw{bS(O@uopYD^1Dk$;@K%l|34o6-)#hs
cz3Sm5|L?_t58C7H8MJurPTo5gcYIX(H&;yab^rhX

literal 78484
zcmcG$1yq!Mv@bj=h(TBg0)og02qL0%D+r8~G?F6HjdUs~U?4h_(p^J$Bch~8cMFJg
z4jtcq)c5_qd+vAFIp?l>XXz4|dj7Td{>6S?-j|akIYD&-g+h^Fq{J0bC_*O`>WJR4
zqwpK;!yP*C&(TNHlH%|$JhsHjJmH_?)>03ipimcmk-vumCYKFRs7ok}_-$pUh`AwG
zw{!T4gXN6{#&GIGM{Jc&Wv8StzdCpN#)-Sv;_je=1?{k%vK<+i+ug74v*
z%K!d&Dqd>o@SmSW-(0Rf`S;fxul}#SnC1Hn3dLFZ`STt3-Q_w}-$N+2{!h=2*mplL
z>T>2Q8pzlfcFj6{`7o-p+aOds>B)D#WSyq{c`MJ?)|{xPrN4&Cid;4pEbJ-$;Tl^H
z(+D`ou$M1+b?Ko*Uvul%zHzzYBrPp%Qgi6jBc&vnR7bCkuN1mLCSj-F9z)&NbR2eO
zI6M$R&yQqlABc{gm>q7scOKJURzg_vi6{*{YZJ;-mbW}R718s
zgUbpE42&Nf)WW<Q-lgnVPo8)+80j
zqvzjw$BA(@1gFN~)xIN(J;N2Qc$j|UrVzIN0<+M2_wEISge2)yxR}(xqo)#d%BCUp
z?TS4t>jm2_ALUV{>!TN8TiO4u;{$QcJ3Y#koxPA+1kKbtkbFxlFh5{b)m1d&fxG+q{B%;dW&4+xXAScD!g%kXnh7nO#?=c~=@
z_-w@`-_o=(MZ3l2=W|xT2xoe8;t5Y5mVJ$U`=$ATq8{uO>2%WgzU)5p8Q5n>NvIND
zvsbDQl{xn8ZH#1*5~0n?N%l*Y&zxYi{-0t
zL#}J5-TKZe7B3l4$ghmzc*`xAd*Ez((=q%V>YA{*xOzdGoa7bIddgqBNXfesr9;G@k$L}^$^ilpV*01DRR^U~KZ0auF
zlae~-TxBs(7$)ePE7Q@LYtXdT$eQ)bhuZQIA1dayaSekRGS@dBO9gKzDJpgrS?M;1
z^T?0a1*qxi#X8LQC$6@LjCe6WOA>=!c>je-*!KR;Vu8)LghgNO-4V}&3PIX{QoHH6
z{k@&pT57BGt_(Ta0Lf^#m6zS=58|yVwsx`e2$XRq?lz@sA`wmy+0EA;-MBwbx6`SO
zAFtVG#sfsU83F|3=*4`D=lcs98ynkubUiaxR%|Qw)&~X~2`?G8`9znJ6b;OmPDQzz
zMob~A52J@285AAevUvC@<85l%kK;>?2It7hxrzlZ^&=-vImctqMQFWOUuAft!nMd@
zaX6!DAD8
z(@7@X#g-^P!~C)LCN-=Za|JHuGAK483#Z5ZOJ|wn2y~+%)}6k3*3S?L5-R8`SEh$u
z7rSC^dw0f)5%ERJeN0M)sA*teFdeL>)ApoAC|THj`?>q)&1Xl5IBkB2V-%x>-Pb-H
zA^Vk{_Z?d^_O5Qc4W7z>^!3%Kn3>Dkuh;^!o*Cr50*mGGJBwAi5H3@>D`E2xlCj*n
zdDE`$q9d{vD^7xq-@kWv$ctofnRWll?~v<&?XPRW_xe1WB5eF9mzk^D4t)l;{E>oE
zM55o~Ndn$zmpKsXI{s>8bmR2GhZ?f5b?JG*>~@J>3~WOsAfbfD0}yNNLFA_O?&
z+iz+3;`C~KR0Q0=z9PRTBcptgedGOAJJ6zVp#Dt2+GUCUNADl+tX)0hBl`dB9
zqy?S*Wq-qZRJ8d62gBoUInSRHM7po{xr{y|O1nnEn}RcWbF+gJKM^Q@4=dqMMXo4!
z=Z=>T33XDT#lTEa)n3N1`-b+AZ4(<;)Q75-MppivxjbAXDXPtjyF9Jdw1%Xnb+F{g
z6%0&@f)-7(EQ-npo{%aSLw*4dsg`kA@B0w^av=c?jjlXX|HM3kYW+-L`TE30R^d@H
z+H08pY(24H6C}(O?65t(c?QaRHW?~9%elow7d
zOou2LI7oO($++eqo?~DsRLA8iZ_>o`<7cT1E}}GO`^mztIZYq9A|=fRvB=WFBaTvE9`!zu(BGwhbpTj8yV
zvlW{^UZ0kCIMU|xJG>SMW%pHiRv|eyI5;@cc9@P%omSB4zT&vhx6%6d4A_L|-3cbH
z?-WiqmmsmEMfxrmqrRN!U%Kz#=EDwI%BpW-Q0d^dg8Cx3H%vv>Iuejj5)Vv$C
zeU=Wjc0ycDwx8{$5^~8OcAmQEzSbs*c50hLLd^5cg|bCub@dcTGtCivN~FH=je?Vt
zlU>*8oWiU|&u2=_LgGPB!wJl?sf4W|g)N1TVzxnva>kKlMSuGy(;O`lJ#if+4Veb&
zqR1;4ez(o+L>rXsv)fWA6#d~M9c{PeuX2XxpVK|tg
ze%%?G(%3?)5!!WBR(iTjR>itTL6hUcAfM9`kJ0xC{vnp`Bw1=qI|RM7$Yisg%xaFZ
z^VhW~9ikwA64BkPo>xa{z^avQHRx?~~at6TYcHAjWkTE(yl)Rx8c
zPOKMD`2_i61shcdJ5?Il51+wa+Fwf+c|S<_kgtEKe|gKzWxdY`mRL76Apu?No@A1i
zknsCB!O<|g;1i1^bTJ=dlOW!w#-AyXAv82JAc+GC3_do^LK#ThCqFLO@pBn8o@)-{
z!u8)Qb(l{J)%AFQ41wEfI9*_&m|@PXXxPQ9e#CueT?MC_afg=nbg>ZRT7&8+!o6*L
z#}P8Yn35+`rprIRYLq+K1m|XzuiO*b9Q7_3S#sH)WcR#4V-wub2LclXy*pLc`jqm)It%
zW@=s79k47?o5s7rxXmD$zk1DpfAKTG3hIVerx_$tBm-#S3UVqpf1E7Qx{jagQ`+0v
z=qa`te;j7$PZWafEOWF%#(l6mB~supuQ5MZq9hzG5Ba}M-g8^YpOWKFYHBKd)0eus
zS=e*kKfb;WhK%n`M0WISl1Rw=_py+kW+1hw$;rJXrQwa~kQ0!FY3cIk8SZ_|^utF_
znOc8xgL)^m7IjLLTF0^L>jhD2(wNj%pJ)v&t%wjd%_R7KBFeBal8v2wzMJwD%!_$3O%+vqG#3zta@5J
z_g9Ze5}|m5A+0LG+Ts{x!<0uZU(fF|7ht%cr2mlk|7c17-vSH2>;JItFjIC*qqlK2
zsHf)>)6=hAo^*qa&T{qYOQ{E_r~Mqn43{S-e*SDU%)6U#4JA6i>y8e6H;KD{hQHOUzWoy{l=62DeP&27l^2Z`LP;B*3*{4Io>M^;H
z)x%mez=na6_P(2&8-w_Hp~j{rgQ9`pckdqBGy6BrJ2cJW@1;rc^U;vLW-!%COiX+g
z5MZ@VA)s|#ZRvc%#Khyi`Q{D&Op`DOdn%6}J?dldVPwAEHC(+4raRUqnA=%uBEuv9bGs6zGdq5
z_V(+5fPhB*vjKGsSv|O*pmR8})X$3>M85SeU%ys@FRN&T7!t;1rs_R@f!{7=8$YX-
zY!GKam+K<(GQ_=lX3;gFvV(-2x6rsNE%?eT8O@PRV!c7F#yw
zP&T=f96Ut}qq`}q#2wByx6kLgZhkV()TPZ`g5XkIX6XzI0=Vook^qrx9_de0VQnHs
zhcz|?GLchI81)^JR=Rri>TyOkSd_EDm}n_}kKJOx8*wWH!q``@UQH}Jp)pWoHS^n8
zg(bbE6UprC>`akyQFd7S)#itGA|j@YiH1XMuTeW`OGvg4up#c|OE_`YYHrx|nT(?zHP#7nRg17jF)^ZwcRyD_{tWqYnzxX5Z>@vvf#)dY{WPh~B9o3<
zEbrmEWAN<-*K$5aN3*A1ZnMDeJ0pYWp8m19JYG#;RlN6PxZL@?c-LB(RS)f7^lkj}
z|0XdKr@;0gSNy_-3oZb8(nC56yj9>DV=~s2tOU^
zkZWxap1Eu%q=q04+LmZ(RFc^=)m|B}kP&KN_t8vj6q%
zR!b)W6krEQyo`m40TPJ8Ka(~e|^4<9IR?k$bgAgJlb_&6TQb~bcvQi|@M@o-w9ofI-Y9qHZm
zsCxDKH5XJn-lovDd%r(&u;U4E
z#s5qVR1)~j@qniB<>%#qo4H|c$euiZt@Yc-TU`Lx-%TGmN_^kJp%8)`4(+6UJrqXb
z<>|RGBTgpK7viTvgNit
zm)EkkyV_y~GzJ1=xq7seW^V@23Z;$guNSdgyVjYlS7SHgzR~sJlerWz5h+b;e1I?u
zW~L)KfKfgw{q048Mow9%uH^HKT2uj!0$CMDc=9|m8yj2k93h3)B}{**Jq`$ynz3VY
z0|08#sqoD#EG+Fmo5NuYd3kwPFi>Wi2U+0eMytL1A#L}X{61@K`u(%`ZuRd05c5__
zGn?wDIyfgccEa}@w)gf&&7xb3^w6@&SKKm_OpfaAxK+MuDJeN_ZgwqS`k8zB%9Iiv
zzOP%|xJI@E2sVWanv>hFySdTX(USV+!JfJ)Ri*L!RE&x{RuB1b1JbKbWC(g3>-27!3T!saL;IjuXX&#&x7{j_76rb)uPwwmwHk*y
zQ_haAOO-N%Okg-*Yhq=k6hanW%w7M_gC
zy$zGfUbz{EPF2g&aryC*$!iu0euRS2aT+6(4dcZ4lF@bVZmn{;Z?BE!!hTS>ckdO{
zPzA6p5rkQ7qFHGE9*w-RMR4^^ccwN@cW*|mdKw}YfZD*dXmOi_qKsUq1ERwO94&iI
zA_W{n(7_=gY1!G?wYH{;5aeJNxvfozA$vkc)68OK;G!2>x{GDYN#^wu~=}
z<8v_iJ<&b08IEtBJ-#i*?pLOjd9+K3b55*niBGbo@)QYvbam@mK+OzZx=n#Bc=2<=
ztC7W`a#|>%uW45)+kB(Rq?PGN->`AIHWpCMrvk)sBBF5wkzNdez<1F!)=
zm!JJr)SoZ3)u13_l#5`-Y_E05i!}m;c28bD6z#q>
z8A{)jp|8}
zTmZ6A)wy2%DG(VD9SWS5A3@HU?lX$+;+u--_{E`2n;WB9WZ4zsxxY0rE+|tNDRYjReShdceLs>=Rs#(~*7>gp?Dl$8dS3WM;D<1^zlW~VP
zuk3X+K51->9825L;oex)eo0`Jr!tTuZ`vVSp_DAwC5Lm1Q&)O$0AR{1%jnw&h2#lK
zu}8cPb05&Dz-V!Cmj+Wm1H#Z3?bIT)p@kuD_yIiJ6(Fca4)%)@UQJ{ua};KwEe4A@
zK8$J?Sw^A>58kd7?y3JJb+S9w=lbFS*1}s-0rPN$tm*?Et`5w5{``50;L`wfbW8G|
zp`{~*i%0VBVkLnC3p+hRE*Tpbm_eGTfP{LHo{5Qx6q%oQUzvnKk^<)FP2(3;gJ*i^
znH^^Wk17BFvxD3)=?|#^hy@1)DQ8hqDcRWM5ZCxMI`qst_;%`Y7Sh9oz|tu^@T3i3
znm8;5=l$zx6%`E)mZE`+!T2e>i#H}?b8|E0O%4>xrW?A^EfiRq468T6XJ3=NfA{YG
z6eSCpYg2c)27~WTo+A+
z1ICBQwc$I!$?y7N+?+n8S_(Thw&gI52z{M!AgDeM`8Qa(ZAUeYa=d%Ynwg1-Y112W
z1uf2h
z-FM(~^}{$#uqRHOpt6s|c2>H(V%py`NSdYxG-_`T0KPKV29(T(f$}mCC}-fBBWRhLs
zf;V03Mez8NuZS@`gCs>D^6jAY608CNiHzzqv3RAoCl
zp|ZT1`skQf>D!l@x(;R9$Z3G`17YQ%m^K@!boVc;g!(kqbz=dW00gvbX#w!XeT?0#S?
ziIlXolBsDL!s*a>Tz;p8NSpAW^5zOC7K<+hmX6D!pA%v0koDxY=s)Km40MVwt&0?3
zaf=_vSh3|m${?o@57e3p0N^$#mT+iUhFIW#T;_7>IQ46Z-5~^TLz#p778n$i04mGP
zxy`aeYTEWH*37;TiXr7?GzzNex)%uVE{S2tYjk)czaO*sTl(H7hZQn~rJorn%Hp;h
zOhJl7SZ|i=*GWn15Z0H3R!|-ImXKOLk6ow4goIx}F$4mB&lK4mBdcDvWJm>8RU_Z{
zy=sGktl8&x+Vlm;qCb!Sn{w-qbkaXx@;tQs=Z7B^+*gH%
zv3P+B9X?{QdxClm+ZR2m6bCIaijb%c@TV5>jXzg07$&v{RYRP?KWs5rIDiv^h8P+f
z;b_HjovW#>So{DwF(_0T3z#(MofSOe6MHT|vhYhFw%HIv{0s?PA&b%L&kr{Y`?*W8
zFJ7{jwkTf-KlE2#7Ek@sB46*(Zq~Owey|v=GKa-p)AD@Ug@34I*C`f}E|0z|CdB*L
zoklfF*YxvVo*CQXDi3lAyjPU+L|4vJxv9dPJH~&(|#AwPI@2V*qZ`@i%mFmY
zs?s!o%Nd$Q-Of{y$+*nYd!gD69|31w!T6G1Y)`-hdNqvC&6y*KJfZyfSw@5k!zLga
z&}67?H@TWttVu`G3vEx(ff4Nw3UWx}4TgEs5l_A=xVV(UK?APlzQx>s8BRK6e*H7%
z7?v~T+0XST5m^w8iI?!lqM^+4r{cN?E8zm@Oa9|1oU#-oMf?H~L%%pRHN^^=3LhUI
zLc%YUugYN#F1T*g*(m?q*b&p|;Z$AxPq1Eg6H(*F&K|^=Zpb!3LZ6F>g0c`aZ$xBE
zO^&+U@Mp50vPdm`J|p0`5Y#jYl+Aru7VpJ_IU^AzL&Ky(%b~O%KOQ9}CHZJq78k3-
zwIX~HBFqrcI*Cq2I!2v2b7o>=2fiQy&_!x?_Ko6i7mOleK~tOs_Or)}@Fb_x(x|EH
z&!|RVL=ohF54*iK9S=DO2YNlwKtO|Z%=P63hlVC!RZ23h_96s;f-6k-@;Z!U6GV4}
ztld>=Zw56wU=|}62*(Bbb)?8h!pt&LIg%Eu4s^;K=1&M@0IiLkz`H;NGMk?v9L+vq
zuN@k|_3Y%KW_JNr-Qp#e3VI5jFkHyeUdAq;i(r(YyIwu8faD`1#uUY;DE&$@S**;S
znAhxiRLOL_Y3yb3tct@IRo{4%l2O$-A~KV;z;d7}X|_iwJZ}6Z!**|LZ{MYk5;Q@V
zbmeI7;uT>66x&CDdkV7DRJ^L0(;MYiA{McuFRmP}aLg)O0DAB3=uRZRy<)Olq}}F{
zo`I3kZ0T&~$+F!Dz9#^fI!m4yRqU=b0idroA@vmlDytOa{F>|3>e0=HILF=p5$+lE
znR5dmmPKqLK+PsV)nK;2yA>wv?owbdIWh68aKN&It!VI!8@sTuj+%~hBH)80AabQ)
z>m|!985$XxEew|EB_oQF;@5X)0EF20cQ?W!1MgT78bI!&_eH#&SrO2EI%zBhh$|MM
zh44JEQXnL1PV05T>$zNZZqA`iT`}B)^A3^*XBcDX2fELtRQbGI=jH_XR*rMa__=Ls!A0IQLnxW<)8m^H{AwLZ$UXe_VHUUmXB`JWKM-H10
zMO=18Byb|$O(LLq&^HVv-3QDA&IM^KjQJe50u2R4^2@VK_pri1kx>PbLA3*0i$uTz
zk}lMP@qX(Be=CD9S|O*l30qs+-y|XbY~j^46q5wkPgj2)t&*#vVKj-bsbb|
za98+Knejq}L{hl`!L?vD65v>M4}_F7hik*q*Gt50Y{neFHi`)Vo@3$Q$mz!;1pQe?
znPURYH;DJPT0B)jRfhTk$HqVdrS_c7xE(*vzo5jrL@Re-Ue;qQCe>LmYSh9L^t$G4
zRgcuTP+MS#mttQx2M^4X>O@*DMB{+;WoHj~=>P}?C-Tm|;`sHDjVQU
zfHvJJmsj1fpo*kkL8I$o*J^CN?X;dxZ#FdRp1OOe%`qszu$fa9q4X5*$`4sr=xP?2
zCQ7qcC?nvsj?R;2?l`qIY-9{#6=Cbc1Y8tg#6ZO@=3gVIEUX{|6-~kMVYx_t6-|lB
zBMnMJiml1=(WZ1VMNq{f^OLD&8v|S~n(vf9)98CBumNdO1T(R`hp^&(eE2tvntq#r
zkPMOPU#VaY%~=6MQMuiuYCeoSPXJf0eo@Y!Dr}Tww;L)ieotTR>uOS7H#Gv)ee}d9
z_op4o#Aev3^oif-(fH@UbiX4o;H)(cO9D8mT&wClIB%R$m79BW%dIlyP4m<$;n{K!
zHitQmUVp>2Rvs^G;WX)f45e{1x#Ox4mxVD1!ZIB}a2g9EExyAffjFA5zBocB^R9Ba
zmKyZ8+r3+dkG~3LRh!8yTR7qRwSr#%m&6_IZru)VdyUV*=s#(Jlk}#vu_B;(nC(mfzjvcbH>NeWazq8uh&QUw|Srs7iaQ2uL7*+P{V>4O*9_xCW;R9YmnT
zCP>n`D}x^A?7w;dLRwpUqI3bAoOjNw0S#rafn5S>G)E?^L$7OlWo;~uWMyiX`TT_)
zkUTScO8;Ec)38==3Ry$oeyS5@pq{jC+{&ulHpP&?Y=`qfG$Jo6t5W5$hsLm1tYOP_
ztRjC%$;t*RFOTMJ8$(TdqhS})x$pc}GfE#2-q|rTJz2WSZ=H|*{`9B4l3oXPK+I^@U3X**M>^bu1;KAho)aXp-}K>VA54pvQD6O6<<
zY!GA$i1?MYdgs$_A$WltGyCzD5esC@Bop8hFM{;mku2W>$O^nmoX#r~{Goq2<<6sG
zoH3Yl=gu($r;6o#^u*&JM^m-elZn9ZCL3PyiNSgWvAgJE+EK
z3*WTGOR70%!EDa7C%%Ry&my98yRZ1w`#Ufyj4~hY<2Dghw2szQ=9b~N)~RUE%odS@
z@npLg{s%4g9QnMkFLGhsfYEyHYw*t_ea45Ud
zWuY|;Kbp41-PNdYDL|kH1kI)~>lCAwC}6O9-qDK%efXe0yYhGKOD|WyW_%CY<(oHe
z_}Kxq5wYo3miJFX9)j;73$M?>fbXRC&4qXl{aROe*y&3m({nqYiwzuMw!*3StC
zFOS!mfkA`oyMEDt*`UXtkIpxL^S^1p{OzekK19?hDk|gLVhEJ-S7v#L#i06IUS1AK
zA6eXJnp(vmh5U~n=@@-VANrab6L$Pi{F{UU6M{|8@clHUry?YU1+@ze3A3px6y6C$
zX~>t>xEXr3*yTn`#=nxtsl1w)gJsR^>=sB+YVLzApVEd&lh_C-K&q6$aI$
z`88fXOJ24|lZT8;{vGa*BI?}8wZ^cuw8w!H?;h9dD-g7M($Y-^(idIf=v96%$}!=u
z^>HWyj9cOaVw`_3>c4NOg+t5=a0B!n;I>qK|KLYdOo-G{?K{3FKMBs}0Dtk?xX(wg
z{>B@>kiT#t#^(K~|7p-OLI0>0H;0n6i~eiS{|jAm`Op2iqDzn9gZ#k#CyfO+CU}6X
zN1u|?37Z>8aeU>Jb-DWI`(pa&tEm|d3ps%8$00E}SsJUFp_GhNsOkG<`ye0&eUq?ZZo+@x*E
z8aOm?fRdB1F+en0tk{SW-kG}vW5+oz4kIp7L^++m346C{^D8@|5JC#oqR@4-DOjcQ
zi5;*klLhq=f(KHEaDAK_Z!E8(e61
zqn{2LJbwHOJOTkyLw_bF#(<%kdXl)N6=LEg3>BAY5V`?)h0YX(#&6#`%N8qL8wspH
zJ&S@8{@q5Qd0!mjZ|Kd|`|bO1-~(O;h`6IYN=jaiQ{N*IHxmS#?;;1g?@SO+C72Sk
zz+T}~US%~Rj7UaEu!f7YpD*a~VH
ziuQLpIU+lKEkPeYrUJ-J11tpg553TXF<&~bhM{V2qS@~Pi%8^#ss#_ocLvr6D!)es
zvsFLC%#gPs=DJ#ZIat84EZA03UxrN&6O&X#1%pZ$(P9uwMycasW^pkuU?DSbhPuGe
z5XWl@aCcmcxRWPNZ~~o0a*>*&O
zjDCCZc8JehkTgl?gtZ{4D#4UY6xpQ$s86@b_yEG`WEJ&!W(Kog0(P3kk&Bb*Sz-P7a>Pnp
zMAu=8tr=>gNec@pYMm{6&f{9={A>5}YzHVasz?Bzna*a}hM#N38-*D{C04SJaM_d<
zI#!+CBLM<7ojei&bxMil+bJzJ$7+_jJnc=Y81RvnDW8_bIp;Q1_xB0ko(7;&?_5cq
zZtijHmQ%RCsB$c@nr}guTlQ;{SC$_eB0~SToA)1!Q
zD=8o!AW0lCfoob-B!2t$?Hz%Y=)(%xz9kQEO;g=6e1kZ#UqsS2l)g`Z6*T$(fVBX5
z0EZPt4dcrMWVYdXqS&Iul);*EzExoV!$(bt+pJrM&J9{RE?rj5htm|;xpYLjXkaLP
zV9Cc^Qz-`I%l?6fpt{9X?89nS1k4}C54S$X)s4U0cDd8B3giKT1AOF^f
znAG7710rdjdY&Ld4AbM=itg`7=Ir>6Bx#L2xh2Ow@h#nb-J~u7>^Ysa_FKK(A59
z(ccF&K9oC>BdTdOSt1!ji+-`E({EmA=vm1Msa+$<#1!18pry?CnBCC$AE9MBV0s@J
z5k#w-2K694r#dx)hJ=ZOLjf_s!TJ<{HGK%o;?*WQ8;dybx
z)aqRW^WZvj|L$!#bBJ^YW&@jOYC!3QWw9?`Z|9E%Z_snf1F7*{!-Ji;<`|>XyhB;y
zP9(%S^F44tqq#F)F@VnstYprQIt=q3ge|edK7#I@^p3l5p`d{0(1ty!3k9oz0&t9rBar8O2tP&6fB-3{Xej`}~
zj%ckz)=qwtjDQe8lTZgE_GV9zg*);O&SD2)`C1NP_
zWidUCLkDJx9qYA^{+1n=wqfG$=w$JH;6<(&N9?|&?+FH7pp6uf5cGKXx^}5lnv^Oy
zee(NloOrB;-5NgtUaR$|PUabLQ-U%=3B<4h@XLS^7V5!NxG+<1+PHKk_FgWi8cWiu|G%O7u{97>rEz{0x=7*
zJ0R^Nwojgj(H;h$kX!_BEaar=2Bv611G0hb6Kfxuo0>W{Mm%*b?%)BlhIC9rLjK)s
z@%i)ThY<*{MWjCHq~X5Iaaa1EC3N5r7Yn+|%zPh;HM9v3^935UhIuAXI%*VK$0Al(
zaJw9J?F6Y^iA-qioncE9(!F5}Mr8V?q~zpTimJ^QuyQIm`ysmyS;PnfV}#7og$r$O
zZ%>O9f%X=}jE(Qr4?va!oJ<-S-Dgbz4yyqplJ9xofw+*dEVTWGzy|1je*REfAW}|f
zrJe!#uzZ82^Ftmx^YoL0@bRU{0dd<}Ia+s)&HfLrc-ni{i>EPNvq(`O
zJa#v!rg8@6lLvi(qVvCSO9FaL_$K+mX7p`z>AZ*o_!PT}GyA`2J`kJvx8~M9{8x$k
zZ}t!(nC$oCSc|Jo#1A2duU{WL*iW9z~pL$d|FJtbw?4
zt*c<|%=JK}9RFW{VtOi6M3`RSR3riUSIiZMfHU87Sn;M|2oV*N;~c_NztpR;u%r&W
zLUz-lBa29--mLt>)cuvjx5w@hqEg-vlRQ;gm9EX?y>PJME7V|UBMR>nZ5llgmXmld
ziyetOW{#y<;j-TQZaFAQ3=J*k&;OnQ6zad$XjxEb*i40hIIz*@@dq)swHN+nr*eD=
ztca0T7myo+fDHoDjjPWE6p`PJi?W4stOfb=f5dIkIRMo9P}cy)@_JbZJ{X+XD?V1$
zbVQ9bYz{N9XZ~|hqTXL%iDnF;7{ew&9Gr$d@)x7013ApKUFr+%OwcyPwe3O`{##ib
z(E8_snkj^w^kx26lH$|52#4^$kQC4Ul@#+uiQ7(zfqu@0P?FI=$lEWJOaWuB0OW$M
z=UxG5QcCl=|8+dQ5;%Gjx^92B
z6M!WI!LPvOBHaKfh*$|$XmjEu6Qm;DUTI>-gC;oxkXZ>nP)=Z;6Nw&(MRj|99@=<7
z5iQVX#Z;l?pw5KS<
zqcKorfIz7TL<_W(4N;vf2gnCS%HjqNQX!yZW#|^Oc7ZqzSpZ^4VsexVi}@Xn`x3>&
zpM#r3pw(!9r|LixN>pC=Ki2s`T41=fU8WO3!uZ{43M+MVst>%Jus;$JOaqWiFEAg7
z)~XstO~gD3uV4`n&@_s6FN|{jc>&A~KHtt#x)$Zq9n5d+m=#oD^WAqGg!VVbp`DL@
z(F$y?fLl8dtr1)!_+M=db1UyRW&RAZH5Z|f{!@XoTV3w6_
z*yj=c25B!wk|AtzyJoJQPS)>$YJR%~1su{P-qNIkV;*P%q-t=z6*YuiHxo&*h_M&MV%+>kSC;>du{NWY-|X8zJD`~YcEIUh6|O_}
zJq=^l1S5+8j{%G!LLw_H0fL+TK}Y-#LSm~W`O|;A0RKT$#LF@MgqH&w(g}>P_acy>
z)1ZPNeqDP=T(I7b`Gp|W>n>Kl2)c>07c=sTUzpNX7voriS#r&(tz+#0
z=MpNV{UHf_R+`l9KCTtR4=yX$DhsE#Sz1cLMgq#zESsreTSIj#csDO(6mP4VuYf8+Ra;_h>!wht9VYNa-^m
zD!BLZc64+gn00i$TLrpYu2-xnA}!R;&d#N4?b5RF&BO1{`peg*X3<
zI(6yxGjZ8dza$X5jt;MQ>}WK#A;~}MGxxKQ+K}5>h>nalAp6$p#|<~b
zpLuzUTi=ZO`|oRNoAqE!HvLnoiCD4L-m4%kSB!*>(zWIO*wA+mmKqYTQBLb_-x<
z%+h<&DNZ7CZYOS?`#FA!x}YZ-925A02B}PutztYEUrcwncxd2|r7!1mf-C{9h?Azh
z0rwc44DMyo-Io-q?{U7TSmSS7u)DsbOol~oxm~0^_F!05)pcxrm*vue3wN48zI&A0>$S`)iJ5vq<*J4{uZzN6GrDJ}tU$628
z?3OUUvgW0s)hEAu8Wam=7M44*vLs>Q;jV5CO%%Ed<2&0OKuBnNGNI8wloY0FWKv&Evvz$=I~dk
zbA9BH;+V3-qMOxo1O>X#ctL26Yg|Xi#gM{6Zb7h98=INU@`Q|)=DCi2CPaOLq`x!A
zHXrrI!o*}bY!u%k`~bQYdvf1MFd#wmxsT7ffZS@LIX89boX}$atX=8t(6h|n3~zB#
zHT%gYX=bRZ1q8LW-p}GOlz8?1yBq-_Aw4gz`t*ID;IEHt3f%Sd^cY
zzI^SP%=CR18(x~sC|Q>ZonVV>K^X-nr{eb5wI9Q`*qE86f_oU_WA`VR=-e!7yuH1N
zhJ4DR#TcIHWJ#)j867xO5OMMz-J)FbS;oNQq8p1j@7|rd`{P{|b?~l`F(=g_yXeq+
z+9LJ%Q?#_1y{AZQkzanzJTkGW)G!icD}-8d(mLUF+sVVR{g9WgO)*4RyeZAXFP
z*x-$h*xdmmVvgkyf0l@-&rY3(?qrkyu;|ZaaV*-rKmWLeg~bO!=bPPGx&csj!`SY`
zw-tvBLGr@&tXfmaFbwy6@ZIlc0`p>U4|)Y;Q`AGLkS<=EcgF*u5P{x1p+Uz96QNQm`+s
z@Eqta1M>y_0#nW~`a0fsBR7t41abH7(NG;Poqf1;_1K^3jAJf6Q(j5aQHjlB3(^sZ
zF&9D&^&Ers$&n+seWY}X;Ooq7VG+DbMGsS;ucna^6B;vK^zSv46Nar&RfR!99}Jk2K%FSIcA
zV0zsUH~{m`;TVA&W+pMoGtTCbNlA=ZR>o`G&tC{Q%Y?+Pulf7?+gH>t6!hq<^6q*u
zzjB%kRzsnXb{cax^yR+~s=g1SDjZvrrci!Uw>q&vAtw%&Fw`h8*HqoTK|md$Ds;#&
z*lZ9yw2G|{VbQm-Xigp;eQ=tlq@{g+bK%xD8xaZyvJ~LC8{e{QQ_;}eOxPOv+!J`}
zw!y)dK85e&FOHDiJT%nQldyXU)|jw6uqVPtkB1iZ8=m*+YuiZw*+-liOYAFzz-k%
zfEbL6jXgxoV@ZJOk?x1;I+{48l1Vn~IfyeqfR6yr8+Q}{XcT(IRk?90O!N>=K9$+aP{?^Ix$Q@M5GUG=<3kj
z*5kb0uk%1%of7&y-hceqH{}+!sULmn?>=k$RZCm`?`4P|dUagqpPh0!4)v9Q{h$4K
zTH)y6?}fjh^aJhM^SJ4=BKuT9EvdsWI$l`UVxpqu^GQ7Oe6b|(zLpGoA)Z~_(6
zW_%7ul-z+zRtoTBRwbhf?)0XOqkPfPNPL_`^yyHhkIOMHW(j)c`VEi3oF0@~;x;`q
zRToT1oCoI2L*d0}E9-%(%|uFM3~>t8?Frr)N)5hNU$57{*7)#qk`+Q@dd7knC
z{?wWI&_~8WO55rG9@xuq^#ApVmsxsy2l~8fS0t7(DL#CyqIT4@ap
zDxm##brz(cc>{?XhftQqV@F@a(<93PkGS{Gj}N1A0Yr9ob={6{Ln6cn-~?b8JUl(`xwHwuu&|M}G5xU68$QzWzI=;=n_ayQOR3{jnj)+512_XuFf;zYYTNc_r
zi|;LWz6F#YyQEX6FGzx@Pr%|`I_CAc?cY&J!Kj`(!QGNjC^an`LJ))mVhjjwe*y!~
zPGpN|!4J4SP%X%jFMs|t)UxkVQO|vR{5-o3vA@K)%}2nU<-=rfOY+r}I!cD#OX!z}pf#JdlZX@%MJA4@==^wLMRkgrA`hq`q
z98#5i^P(NUf{xBx_+VufmDu?BBcSdWD^kSSs#WsrA_5mDN=RMMW>T
z(aIqds8u#KBs>pccNBwi;HE?LPsH%QCbM+(DU@c=G>bw3b^EhNCq^RvG<0nI`t{4&
zT~%A#e`dzi4Y*_{=w}sVB>%Iqm|ux$K)11cyPpqXCNf
z+O;o0al5*EY_FL_2)k3k-JOo_&sOc91Ob09*Wr{z3jHV$af`;<9P$|Ub=AO~@*t&w;sj4dD>@PWu#)cv2oY4o--p}9v
zMaT3Pu*ffgX!QxI&Xn|!kn?a0f=8fq!A-l!12Hv5nZW^2n
zH?N&P3B9^r7X=(dab2ZJFMND1GcX(hatLA$6Wn>qADVKV=IDhY>{Sq8CM6^D1=x63
z8SpUNadQ)djnQqz2kul}mo?+)RrPD}unD(&
zlwxuQJbQ`Gzb4ey);=L|YZs02YBu!lm{#Tw(wp?C3~TLRiw>zA)<6SfM6y9J^=4!H+nnME2^dSXLV7r1j(n|IR19%F$5iB=v
zALzOVb`SbGv~z+em@`4N^|tgKDrQ-*mLAfBO~g1cZ%YN0fo$BGt0PV4aS+drjaYL8&e
z12tQ3W}bUuDsZ5HtZ|LthWq}YhA2`N#2yew9AL#A%G~`pw1g{(Mb7w5&|PPUgb9EV
z^1o>^OkkQffT81$>p1If_J7~$_eiVA?A#xU`4`sqXUR&~?lsIOWi_b6t&z4}E?+kA
zi~FUta|wg}G5=b_L&*i_+h$$!#>)HzY5}9|xql;E*qz^wzlG0ZBkHql3xg-G>-sg+
zC0<@i@QrTpk3{f2IjXFqsEF-(J_CtG+fzPDu+Qv**QH_~L*6Gp-_$w#*FrQ6Fd#9~h66teauFedHz7^N#KfSyE{%d|
zwGOTjIRIm?STlbWMSz7s%XPkhHo2gnKo2y+V<%6FDJyrz{Ft4Y@ha)?6gnh!=gui|
z^0}gw-Hg1vvlFMe3`QA*wcK-G3bu)GK4?WbB40k0Zi6B{>rb!>xZ=w5Um^Ul&
zP+a`^qX3LHDIgJux3RIY$4;D3iYQjkiz}n`ph93pith`F^x|?
z0a!hE_qnexD}YspvWu`sP_tIdgR|_syuDWP&$j{vzd>#G9D%9Uyjqb&Yc3TmS+b?2
zrLA3EFY4>162+$Pp1a!H4|@
zYDk`j6)(hCq%<}*CRW*vSAk)`p}Ta9wkm+&_Wf&xO(u^I;^^q%K4w~;YHDg5A3JG?
zuXt|mm<_q_wc0Kc3baZFR|p9U4BF$ffCA^W`*jLhkEwU3i
zK+jz@q`YCHc=xa5YEH5QO+tb8{6jHk>GwS%6meabnWZZHahHy@8gr(}Q*JfxMNl@{
zoc5Eb@vyJ5N$QxESbOniP{*<8>dbJKt>UL1j`zN~84EnLT^ect=)Eqb+~nRcJ6=#o
z53%FaU4`JB5?!^_g8hk!NGkm&$pA;KSknF<*1iOs>UQsXr)V;kB2$DWWhhkUBGI6f
zqB5pIWh`^X21Fv_)Ad9wHZ
z@BeV$zu#Kxx0aaXs3g^Y;;yfuV4b<1`%k{m+z*Y)46^M{+`88XsYQ6IDSqiP5U_E$
zvq5jNCJs+5bQw=yzg`Ktngx?V^M4lXfaqmDrG`o!-O*Iw#kNpGS8s%uX&%;OSE$xr
zW|!|$RJ`-xfgA2C6~F7)5HK+C4d$RPA|vyFI%888dEq2I*Q0d&ST(+(hvgI)PD=Xn
zFu*zUPbm8dXlQ8ApVPV~33CWaW7dI3j~-=~+rXPK1WPs?YeYG`0k{mMWtOTkFSx}m
zy%HQGS&v=%2EE$$h|aya2L-juJIp!ih|pZYCTxyQMXuduJJ;vri)@&T72abzez)H%;}~$i)e5+*mU5>NQ|Nx*mSDzLpz`QJM*q~wlPH2odpT>%13kj)sXXY-!VVmJ7I_CP#GM}dQ(0md$3o7n@^)?K)G
zF#-0Bo51;tbl0U?@bB8S>(H@dZs>3{G&M82mUi$wj!#Zrft}+s;M_HfYrqwJ14Jo{
z))afGedbt9LPF)oYuki{O>;#;_-EFSHKCQRh68lWfP}zq^FRps1_rR^)6rp
z_b4d?!WWq;q1c70IQJmxzSi#ZlA|Ne-Xi;F&}JmoC^kG=A=e@Z;@CAS`*
zcUx(X&3X4Hv&33n&owyrtjBZKdtzIr5Tvl6E6+3~-!z3^TwLSM2rCA#f<2L0@%+i%e2l|-rL)F^T4&8yKGapjOa_&8zg8kb4KmI
zs>v#tlx#)422AmEI9?R>(Y0J{={ujJ8}pVXC0SAx4;^9-4ZRoX9xKeE97b#S?z%$Q
z;O4x2txQ~@u798N|JtvQMN6=Zl=3YZYh5P*7og0O!T`Vg7
zowJmbT;0&XbKLe)rj6~OQ(YZyxd3li^Cj|zqK1Jro6F?c6vgMdBNi6g^=obC?nVy}8rJ3iPoSbwbPI>K)(nKg81?bxKQ*9l
zI?&tJ6#wVmw#tBRfqH9{-rT1e*^1`vll2JofK-JvD3u?VW@uZ>A|pY(JoJZr}FuNgq)bc^ZyUCOnR
zj99Gu8X^yc&#k#i3vj5v!O6O!W1=YN>gvX|_B|W6mxWKjEwsR;EIfao5WCz3=!3u5
zcWEDjS0duDX_%hZmd3H^{X}b#DP~H1z>0>#>&Azdb-<(2R_X1c;ws<^<`D
zVe^|C%nP1Y^qNZr5u{<~&Ur6izWh*G=?c9w_M(MYsHU>_$EKyNavsZHZT@S_g56tr
z|6@r8&{rcTw*8Rm&*+y!m+&p2kS8B_WM;XL=NfSMJeF2g&1h}kSy6SU=VazijkrBd
zO-E0U5;zcwJ}d~Q)+$X7`RDwiJt#A{0A!AD1$MJTICU6pX(6vas;SdvVHZ)oSe+Gk
zUS(Y!xytz$lGC4KAT2C4m7TosY+1lOuY#+n%%4O?7E2s7G1&}3`*F;y@uP9-mXrWy
zifVB?Bs;zp-QC^ldV0R-npR_!7ofsus>A=i#|-&SGK6X3&{bm#RX4{>oGrP_7uDCm)LnfdDg;!9J7M}tx=$e4)i@3N#bbRi(
zn7=T~OKw}1<;z==qX{oF(GdohL_u`;%qv&YMSb*y$uuW?UGuPAV}Q-mF`qd)=|Z{EDi0bBbR;cy^-&>eF4JvI6w
zCWe;M0QX+qQW_W7AUj~bnI{6}_&f^Q>|4r)A#K`wSqpZ)j}ySoBW^vR;kMrM_oj2^
zBdkVHt9N5N13DC@FP?wUPvEohYZs|t_cx9?kM=)Tw>VOCA^E5MDJ2sr>5TekPkEgE
z1m<;o`E;kjZGL0#oyr%%0gc@UV@y0|y5qJ{NcJqJOcHDDf9Y+;dtb;IZR{>JO^Mo~
zETOk8jqsi9mu~&AxfR!rvY-0I@KDRr!sMHCT7Vsw(;mJ*8=%+|`&-CPc)eq0DRsV!
zSz%IK>*bvc?OE$RJq!3kLV%`u>RlGH)0*_nwNE+{=R8TM$7)%M2
zy|NPG{J~UM|7oZMaB6TBW|txgolL!
zi)op!7E}1EqLPv~nCp;zz(9eO=^H$k-ngSE&Lr&xo&p%J
z{_eJ@gARvcsrMwkzu$Ffpg`q^*KzCAS@#sMxs;ivjLKIp`f9%2UO-`wRPs96DM4l*
zJv+VB-=dsLG8+5*CDq=ir>F-_YV?|T)cGP@=ZWe7lC;!3$7c
zUvZ#DHESQrce0cU@+^EhJTcFz<{C$AnxN__*EyV2U0t$GC-63srJ9%ZtTb|78
z|J1S%-!4fbt-3yj10Aj3+w>>YS5s)mPd9
z?gBJpPtV<#iTmX|&S-J@a^l5Rb|+yWSR$8V#jB_reLRs4k7F*BGTy7FI`+g?czJp8
zd9H!pPt@YuTc^@A=y5F2(c1-ns;RlYhsSOK3fpU6a?l8hTA!ETR!J)6B8L1
zU$kCScruwu&%~rDz?jB!FY|l0?cf@hzKD?f>5R8?qENuQImC2Fq4QVqTd49fD%1;ZWlP6A`X;vQ1MS(Yl~C>vX+$-g1akax)TTkrsPM;LwZsgXU3PB_y$
zBdVXq#xi1&w-J2Z<{#Av^nd+MD(U02xkb*GQ&SAVlgSZ}x|s&1CN8Fqzw&Ks`An5`SU+hlE_1jcq_2CGRgxe`
z`MxIAy{}LHNWO8n+s2IZe)K{+XY5!7(nr@HXS$HFc68;nsulq~#uBRB24VDi7z4%H
zT!6d#`ta~DANMcPzYv70_Q?>BpLy)aeI$(^hH4h6^~om6MW_8SL%sPG$h+si|qT
z%EmLz_#zVoD-A^s`#g_ff2?-eC8g2axlVXZ6RHP_3yA-8-L*e47S~)8dBm~2-+X&K
zWXeFqQsulrDKqpBK$L99RRz!qPn;0N1=0~A$(1KHH12iN8etlWd-(Bte0?2}b5~&W
zDN}B^<>5d^)0C+~8^peJ7-4i-?@5ZpL2&QjUov`M?=1KS+P-voX6wHJ3v@T}sxHIA
ze+bnjq^Hdo$j&M$0^3Xh{P7i)n)2n#TLDH1sG`c^0__Bq1071%z`(%LbVvXHjvBmP
z(V?cC^7=Kr_t-)7bW6~E!bB%M1qzur(3@%Ji$R}I^XbzmTwc`Jl`bvCB_%YJOzbBB
zU%u{T&^|aMt$<_FOya2zF47_2wIie}j#bg1x;IzyrcLx9`tWtQhF^gRk8NI(i37~TeCR~rxgVIMWGPl@`79ZU)Im6g_%KV2{9+o7CAaU-Ti)Qi;pzx
zt+_|ogJv<>)s0a0_?$FNqeZFU2FI#HVo_JPCq3WI4ZJWwScg>#k)~&TQ
zNxx{~E^+d(d77TT+=syg7_dBMFeh%0Wz*qh@1TU&aQ8e@wt
z-1MMXGMO=V;{LaN``_37Zv+PW2dZ9Z96ICz?$Qv?+Ftive{Z4FM0{}RoaAF{{EH#e
zB=tM+Mv%5Dd-uK>?yMGbo^}8t`l)>c&$>tGB6Zd&W{J6@xU&Vs{ZZ!KED-SUUr
z3FOvx=>0Zy%8SqrpawZz)V+U3(2*ng7pYNU*+1a-?LRjEh426BEhg5jwN)jyk?S{X
zm`6eTGP1W%cW)fk5D+0|1+%!C=e`_EBvjntFW~vJlwuBqT^}Q)0F#*)lm*LXP}_-c
z05aU$ckZ~t(uslsCB$iB1BSpZI-s*8&EaSxP81}b%1Sm0yVQQ#BL+l
z8gixj)zvI4#4uem_;&e0eFziLrDD>2J^*v=A*}S@uqW-tZ$QMJWqK0Z3CLo?Y>$j(
zW@qzY`?f_}Mp|03?NgV|ol0+QT&7c4Jo+alA)g#NnMRJ`{rduBE5##*3VHyB4R#G6
zj!`nO64n?kBAI+jJ~4<<`4&Whz|ckdi){;o%y{eFfGua1kZb<=vlh@MbSIDiEW|8u
zK4x?*u&4gq*aWN=%rJ;g2$Cc~7vByDSOi@S5D2)kEWy7U3B-;n8*Y7LVfJ$ikOP4O4{~<`j)XZ$PhAbXGYSRppd+mhWhoY>Mg(nZXzRMa-
zFFySvZDCfOs^5aBkLZYEE!XFlIOcG8zhlYKo0n@WIci(7%Nt5jimk(i)4T$zG#|2J
zvXh!o9!owpQ4ym@r^SaJvR-F&0vz4pGn}E{`!|nHAh8Up*d@v>$9X@7a2RR~jSHm&
zDU6z5Q1n(e-R3Ckw0ULe6GQVRH^93E<$TX7md_{-r=^}d3idNe1dfKT->A#P&CUI%
zX>}?oglAX8sS)17uf4zS5$2Z{%&*9Xce3g)sNGX1tdAXgP;mye>86vfqp9s67a_DR
zfcKgC&z|GBhYJi9A7Ebx@c__h^wC+#$7FG!;PzDXZH>*q+&T7kn%>@CBO{|+_||2<
ztA|L}LWd$cL-0P&{(&9U&;tsX*8rkF;;`k5pChfbF(LJ4SydDaj
zN|RKMyAgd_&e;Z2dhmnJCDdlHnXFNJS%H?S3q@eJJD47wCrS{
zHeA@wQ*(&PWwfP=Dk;Nk1Q1nO1f8TjXBcJx+3D#*N*|&}q`vLO6ekpPS6aS(lPTwS
z7WO<9_pT2sZC1$-CFTXx5A=vsS!LeycZrsYUnsI+t(@GKkx>9FEZp3^{+E_7Up{r%
z=Zb^B-`#4AhYe1!UsW5;@3iXF**!YwPOw*se1p1Q`~BY{qt@q5P5ZWffvXbHQSd|%
z<)-}aA$gWnt8T)MX83sdGwg}LF;a
z3YGvma6wS!K2uzEOgXI5Dt{BGt?mOa??4fZqMA=erlJ3!>)2z5yJjyY6{mw2Gcm2U
zk!aaI8%tuTqUqpyuIg>#QGEJpezO9KW$>}5
zQtEXS@8Q%DsWF$j8P-@h7n@O`t<`nLe*H32GC^w-Xb2~6)AxE%nMg@V0Y%7gcXwxs
z4&v*3J(IUy*ui#B?n|RPKiFezgxA+t6j4`RlMSBQolUpk
zUd+OA4np&AcyDoBck<-Pd6s&H8?Bs5Ok(_UHq_VEp~POWbHLW=uhiJ@YQvT6W15tXr)XT@~#RSv}gzUh>1$NZfecXm?R-
zww?sqxG%bC2k5U)Z7gi>PCd<^#h__u3{~~~w6*F>x+}Kzi6uh^)HrLK#05ARn}S^zZwRQ+DQaubU{WIFBWq-lN&
zM@ku&KB?`t1NsVY@k=v5U11LPLCVRfm65&y(DlR#sV8RyRKYYCn7c{z01=>A%ByXb
z*ZoJupoOJ86jIMl`Lxv^B?gTs^TFc)*{Z&`KW^
z=yMzGe6!t(_l(#Pb}uXpZrm<_UCJIwEX6<$0}Sa|J^cQD
zvXQ>>QKWwR`)JVX1G|5r2#lI{oS3Wm?+qzWM
zkJmG_aDn{X_fvnuk!(1y2w{XGG<>lL{o16R$_(4vu8}t>
z=Ys9%h?W4gAvBgf2@QnKlAmsor%;F=^+lB>#7)8igKMWxpN5Q*$EEo68`yx5YE3)S
zmIVS9ehig*!*%Cf@+}~ttACH}foP765HQ%h2f&1}FJ%+%sT8WKw|;=e1bQQsRM0{y
zXlrwQFMD_mlp1MFS}ys&JC8aM(O@l@`%^+uO2Tk99e=d|$K
z<%p{_eh0fJPe|W6U}eA!^|=q6YZjv1Uo9mqJ%|~31C-3V-3M5A6WL%#SJxYWSKd$$
z##Y#`{reoioKX%PG~icm%%<#tLKmAa_Ngmn|Cwgvh8)fo?%L*PMtDNDZQB+G-Vb9?
z1i=7Ec4fbRAw)nyj;4xwhUHR0YMQHJVq&y%oMegm)Bb;4+pm><{P=OgnbHTk^~XWP
z0e8#%Jv~`eT)flX-af>F!&~|ExpQG*VfhkSV4X0VFoptm5TMwI%8wGm{+~zJ`G0Cg
zl*u;IV0nf`MlQy2k5z|EUnheTaBXnU)9={&1!^9=&*%$Hb=IfQ{h<@cghv20PlrMl
ze*q8?@ct}X0|Ss$y6tl~TquB>FHWiJYOo&L3)tpN_DLIm$y`N2TGCaR55Fxazyt>5
zL@fjl!nt+&nHWNZ(;hc(cB;RI(pQm57KO4WQXrZmQ5%d
z7cb@`a)p>t5`iW*anu>Mn2NK2jlFSGHaDUN=?G3m$Sr)KJ;uhHfB+a6@WE{18byA-@Q8@y$AX7gwWi-ALBsR3Qpybhm4F&zyeee>P}=PWxjjp?@yRC
z!?u$99=C2?$Ic39DghXy#<64HaEHH*N%_)s;ycuKxxjTuFL?@I51N$h!qyn5FWeC*
zHy_<>J~$O^)dp1OWJ!Y2Ee)AM0fE+1xo7pr2{ou_u&E}TE?yRWB6$h6Y}sXBfg;(0
zX2VYF4c%XyY(Nv7H7L1&gBs05&d=V#YdCZQzV;Rdh#6`-;|??gXcznYcb8@k&4-``
z(|_2+?8X0%P3%(t2?&r07XTJ&U*Yf6A$I-hv8de`7%@$2dtlIzZi?GkE5mv-CNOkD
zZeR+fG2Lpr-46i>b?)HQAcBD>%hF1U8|%(3pmiXsS|Ee_8Ss_(Y(NJhiljYA(dWac
zLSnPHI4k&Z4^PiGkc%fEB@>FHT*Ebq(9J`r!~_ktF!dZzL#=rIdBWPME<|yP1gtQZu-)W=Ad>Q-KVg8ZRG3%ii2xBwL&3^nfmlDe(lia{thWpC
zP|auS?NU2jniV(G;H|rI?p(V^jvXq`=wlb-A~q{?j84^6R*HQa75X5%->=}5y3{SX
z;)6lMBN_+vTG_H5y+0dLXO7z~Ha3>8Kr|3=mw@WwGa22w&KFD+YqXvFL^kw6jEMm<
zQH~0lXVWls*=R_AZvr!%iazRc{oIW12M4t_J=j_J6DP`YYWFhfNoDWWa(8Gczdk?J)Db;4vW!B-};9$4IuSrD%r+1
zZCXHqjpoxmm#B@Ic0&LLUBSeN1HDrT&nQeIGUC#T5c$=}c36u&(7tBK9As4iG!Ta%
z$cMsH&Rr91WmjS}k~5l#!k<8~Kx0N@fjVh54W36~Vik2gyx_C;8hW3qhNpI{VQ{vNsWCqR29Z!BPHTx7|Hq%bN
zgIF@>9OfT`)XwFmG++2!5$$ezK}BmrmXpf}?-51W7j2IkPRfqexhK79j8A{~7z@nj&-~M->L)~!A
zr&@MrZ+B4GKh4S7e97+fgVzD0kt6hmd2o6e0BAI-GO&`34NV3-pY<`(Huc~#HtLM7
zvBQ9=Ffb#5Wu1kSQ~F8jX`m_()P|ls7+j0SFTo(#Z0(EPATUu4zRAzO&v-@)JutQO
zH(qe1A(!JOre`pMWuLYFa<~|WckCWjJhNF}GC3Ecak_X>2(Mw`;)=H1?&z1}pV=7R
zX~on#?Bv{
zetl9(SyivGY+)U3R@^%kAG103Gu8Raw`?x&=K`u^_z|>~sNIj#(Rw*Num!_YdP6zP
z!lWD564I)gbI0c&*iI~jFz4|iDk>jkyUtBxjAcS;*6&hM*=ecEsj)*{WYL=^zB`r^RaV#;=`&$xuFN-(o>~zSXOddiz1KTIhvV)eLygPEU#8>eqUD=z
zxlJ6|$Jtk*MYpeWs?5G4&B^R{PJ_jZwoFYtkxzxSn9K{rPKf2zFI4r@HhiM%IPy`p
zFTe-Wt_q|3;#;L@gNtNaU4j>%veUYTK~q?1?_4L+mcisGc%Y4E-Cm@M%22?C&@5!)
z(U>a!gqr@(G!X1u+L9yGKnCWVVX!=jJq-b@H!)?zMxbP(xB)?jQidO>IO=YWF}gYK
zqw+;)&u7X;O~}s3&O$LqO80QU_vs=B%so@LfE&|xE)_|qv6st+6^4mut8Oq!l+P+3
zg1Xr5L)*k-8M%Z?Bd!m(7v{(0%o}gp!v$e#=Gxhz+g}`1K&kU6P-(0CCfLBnY)9zo
zgt-cqFrnSpq-lvQ*ON&>BW#+NeLE8p5@H2mVizC=@tOHS!y3pKgrBHBnzgrxKzyvcd^e@Yr2^I=VTMFf8Ut`qg$4lD=Z%%aE35`~n
zUbxUzIa${THP#d-S9;<0U@=2F%*c_30%79x6mwWZjyvkibwJ{mMDr#O0_w&?LlW2$
z(CQGu7)9NUHeW!%?e{@Sf_0S4x+7iya7r@C%u0fnL`)c{bipq
zwRAXHWLgU97AV8%vvixxc0jfL7E&a%m3m*{4zUxW!+LOrp3
z0jTr9@Do|N509*8Ny%O?1G_*jkH}E4W41PWT>BkFVM1yp*AzRz7?Y=ESV`SC5e0=&YU%?Heolj#6
z7QMD
z+n~*#(HyqK^UghoN`2euMDFYjly~NVoelGXdGOvjG(+o7{I1Oy9b4aZ<#$W}0ie2T
z`4(t+thn~zvHhlH{VOV~JjN6cF6QINyYTCj5j|R21bmRy0tO5399BPs2rdF%f2a3Uu#ER
zyv%%ujMomA1NR=@&V2an=T?`A+Xn1X?DRs+^rd$y#O|2EHlD?DEDau~;)a-|U(5hqBpKtT?%hsDyDCvr>Yzirv28u26
za=e``wNIZMjMrKCJnxmxq?v3=pi`z=X+>vB+q0^-2fNxNqO%XyJmOY(RBvWYIp23N
zalXoBtFsF~#8B6+yf`r+lb8=fxP$aPCtuI_RZ;XQVY#!eWt8)tG$OPAIeY`o?aa^Z
z=j;D*JwpHS%O$4P6!w6ZQBld}&2QA4E0D;F2JNV}b~M}$x&do^w8@#&fs*|pv*7GI
zeOW&cT5)~X(*WoC9OoPoX+XTCz}0LvMi?0Tn;`
z{6pKhj_up$MO?B-n8zpVg-Mt*C#ePY7{*2v<;6>HXFf~8{t1)r@D7BQH($o{Op;^)
zE9o%F47k7?7~x$eCNWM^-FooxPTVzPFGs0bLaQ6lmsG7*4Nq-h)w;CH5h#^p@6O)F
zl&bkU;P8N?_DWm<4=doL~3(bY^<7-kLZ&W=ZaU(lA#A+})Fd6A^y1*%kzUXUWiif-mro+V7h-8ymuZ&>
zX%1VW%Hw}Nu6F=hLPIz?IHXu@>wh~UH<|?g60c1l&uRrnK*?eS3A$Xq1O6nQEB83g
zn4`gDRRcyQBrDGnlFfJC5eW(EL^y+N1PO%awIO@Gd!&n4ltbD!46D&Du&S|AGpCMDWJzZHEjR@3mZwsw#8*D(
zogH>DaS#(am)}cRv_o^o4(-|V>L!frsmYC>CaE=;vA=;q17e!Ff|d1c;K8Fu?-Q)-
zJtj;iQh>A(7w|evl4JDD3gssPrtfg~Rlz5STzoks9TGP`Oow?N|9aqPAPcc^2V%-|
z`7@+nf~pVzNP&y;9~zg`wO>eahyK4?Klko{C6;BsaDCCwQEkd~&1f*{%qbnu02Fd3
zjV})_@0XW3U{*h{D5XBgobyXCwflZW`@W5CU$)RIx)}(paDG+lizMnwlT*rEXTLwq
zQsC8}_!+;8=W5=QPCJjl_JCYfF7S*xYL;Rh^&YE-f|Et1;2*F)@QI
zRJO}kl6A>>FkrYwRlv56>ghE#dmad{`0{h=yHov!h`>;|q6g&r$o|w)b@$5RSH9)3
zL#?&{=^+)2Wm#Wo&)>YlLK%G^hPDIp*Ni9C8fQ72ug^83pZ7$X9O1~TvM-yQMFY!d
z6b?SDbsM$T?X9=is+Vod?smR3WiO+^y`S7UI`Ihjwe-l
z@?@v)_HP~mEw^}N)9c4k1$Ci#If70nrN!Fn@iYV-EsS2Qhv8khGG*R*y${}2D>fXp
zP}W9-eLAWvL&o~VbH3z3I9+tx$c90-ZP2#W^!x!M4hp*m=554fuyGfI>k?L}aixU8PgZVY6a1e|EA5oU6JHI|C
zGDYG_m)cor`mV)P?9^%u67DLZoO%F=wf#vb6EJC?q|>SQk)QWVBP?v<0`RvC>i
zBO^OUwjAcu`8H#G>5{De^+qS8H?iovwEV=e4mqN&>>tToV>A8f3+HG8!q#N{JQ`$W=lB`06EDY=o3w7%oMeI&vCVutY-iyq
zAC86)F^sU42J^64H&YAV6F2b$-g_g#k^1-8JFc-g#=@fEM3Q%=YGxLm&8b4?U+%D!
z_X}?_HcF{SyI^x3^*f0|hm~SFV?JCIhM{aSLfNjmdEN2OWwUE!v^R~&g!XnWAE(##
zy||7VFxOW0<%t2oeH2*!{rqN?+8@}^NE}8d$vuP*Lrp_NG`1Ht^2|fNfmG-tucJ@@
z<68^kTQ5i1dm{1dfs*eCqK^c<#N(@n#6R>RJNgE&NSw!Jk)=zL1~YVWAb2t@R9!oJ
zU0vjC=PZ2>sDGW^)?LJRy8=x6^Har2t2_Hgl_RYG*Iz!f4dw19f0M+{#=(4YGX({i
zpk{pe?AdPIpD-{y>s}RsJZ1Pg3T!~`iuGjfw3N|>3$U!eSKTQteNXOVUENCsUc{1Y
zIqiYM=77(Oeq`MO;~=|Jv-xo87O$Y=Q?CNE2fn8oT>9Rs^@AOR;Sm%!Q1;e7wfr_Z
ziLzj=YDt38G(HIHup9lVQ`YN)7SPN~e*Jnc$^C&mYroHnt%$(RX^MlNN}^&X^fKfi
z{m20qUtW$h>^N!^)OL@!_pZYVhIZXg0QL%liZT9R91ow-{~fyU_Oexy=Z#$TUT$LL#1;D8Y4GlTY>VP9MeN$2(7^cHN}&)!9+LDUEK-+JteUwL(-t
z^+s&ZJiV_b1E24Qw6G7b1s^up|5_()2V+~SmWbwNz8q|{ZYKuDhR^KXoR`m*uf5Vl
z@kIVSS{ip>yQslQlM9So(`k2F$_~fU8X9lC`lI#@$JxBkG_LgAKUSBAR6jg}uaUCr
z2m4Nj6n$f?zz4GP$4kr059_XFild>>31_#>UQ6Bs77ImOOmCcd@vgnvCa{)6uv<2m
z#Fo-)WK7A`njH4ooHovN=l-+i<5Sf!ER-kCxH~><4l(HH(W$p8EmF64_%&L_xDc;k
z7eCuKt)xfmpjvURxnpqR%wPbk(gI36N4nAMhy5?_r+>Qn?ltyM^c_+8!K>TA_ni*x
z2w`yjxsYY{roEkdjZKzy$qoDAko(a{dPnh0>e@2K**m#oc|)Gp^=RF!6POV05w(9S
zO22Pq@N5sJ(afkT@b+xt!?{Mv#q7{OJpybD9m}x-zkYoTpAWe46A28%(nbPafHPAe
z%s+~8^iiL$|D}}0vqn;@X9y=?(OCZP)Slrq9I1~RI9L$Syid7JO87#SgQH@^3Vjgd;=YzgCGn(
zf;5X!y}Py`$ZS1p6$VH75NHk+q29Vc^IdEY$@QU7a`kt)vN?VW`TZ-R50Yp#=eVe-
zBRV=k#1o1vI09kx0f5_j?rY4#
z`x}2|Phlz{u*!BD3PT_hHWQOD@<|5y1&BpW{`Yu=1Sm4q!^CMCMJlL#Wk@c2Ugb{a
zEnHpHYaN9^SOhV8Nb}6xC3LIFN(SE(kfX#n9n%D?n>{BS5g&CArNvYC+EDnUo>0lQ{=-3jvYZr`+K49fV+^5uj%@ZeHl0yen8i^g(>MhHj
zx3_vhDBc#(K@~CVO8@8RT^QF7tl`6vTX@~Vr#!zu54rb-AgXdAZK`o_
z{(S>>*f`@KvWnN4QL!p7=kJsU;g8!-Li(04lzw~tuIA~-J@+-He){z3di~d*lh+Yu
zQs3Kml-_Z}val67dUfp;B_<=nJP)$?2CBz<$Rq;8?g;cz1U)y8Ps#bEoEMwhKvGvG$)-AG3sHV1<
z)ZUR?^JVZnyKvO7q9)Sa~`xVY`a99xXH$ho9COgI%L!=n>n28aRZFN
zm1`&3H6(h7WvHp#kNxJ|DsX4SH$e8vYjOhrw9D|E3EkaE##~gT98wUErRG0`8^pqS
z`);+X^!Xh}1A7If9f5One}=bkQ{Jm!mTPAms|{q2tT23KlKS!TZiiyN>{e#IOLUhU
zBDJ>(wH9wo<8^C&NNVnNZa!r``8{oWv;-Nq9{Rd_7qLHg&#w%#P<(oujG};c?rlgr
z0wRgKPuFwU>!hY8n+;Dy;3dzVpPw_%Lo6_S8Fz3M?Uyg_IMaq~FiLph)#;nq{}Gt@
zLQTZk>ls|Zo5ss+hDk?_78)S5y`>f}^9Q}@A8e{s01DHCG~pKRBh~dHAb-iyrEr6a
zLb@PN!N*_&_Nr|r;qTAxvz@S9&ghP)Mmh%@_THtt=Q}>$YfN?BsPp>b%h$Wj>d4hT
zdU?goO3%qnpb7`p_m0@lIWI=4EQ#+4Wg2
zz|rA^EYq=Hj0Yr^WU8nfWbz~rwROFTtiATrs6EH{b(X%Znl`1HH7#GA!+>i35L9j5CDxrtA<1xL(Fsp54vT;xDK#K`;MpX!$1pc)(@B>E*rZ!;BTq#
z0fy<`Q5Xp5Yb?L=vqv3Kq3}(U0K042{xKK<1R2S@tD3Vtmj}r;ic3f!APseTMbDuL
zjf)pA4*T>&6-?k6F7+m?5r=R)L}bS=e1tv^$!6I1ckYacprP|C;@5%(zofieQaVt0
zclNe#I`KVftr^>3N!={@8F{-I@rajglw&yMhK}>bEV6mjDE9cH=6p^HyY}B1@hD50F%C*b#%-M&rfDa^sV4ibQ5S)
z`-#Ql8d3+|pY=-9t236YKGl72zB5B*|NpbicslX7&A?pZLr>cZsqRfNuooqgF0v)T
zeg7hK_QcYi4DWj*W|)9ChQV5Jg?flsUNj7qAZfyO_ZA`F5y>}lCoa!yBH_}wlhW$y
zP7nuwzhSfig;^)Dg};{1_Y^uhn652+|G@)v0!gP$j_hZwsM23!t^Y}jZY@5>3(uKqP
z(8ZD;6-HX!aP4qsXa>@aR);jbMvRG^FMk#Hu7iD<_a}tWbdMAWNiuOt?!UYCz+r6o
zh$johF&^*}2hXl6GF!CIUrpWz%>_opaVao0hD1Quj>jwDc#sUKnIkr%N8;xXhZORu
z0vnphffTRfvE~u(eiCyA^pF5w=u6WB53LUeuGYbwiDs?iRm%{#2GuGAajjFWIv){q
zwwSGa=E!rU&?ap-_aB-eM~*`5Tvlabg+`F&HGkM5;j0kpbi<@;x4}H}a<)gig_tQxm}uBwgWn;@OeonNSXKz{3;H@AW2SjpIPz
z{^Z@+`P)p+qWa5#?>`_iPm{*fU%$et9K+U=dZWpR3%|Zk^VW-*aFAQ5U
z@^gE-BXpi<0#TJj&A(*ls+R0C$c5~Ge0&`s%G{$6$`zgbk8
zi@XVi622Im!_EK1In-?#u2>v#K}$Ed_9=PQwUWn8Q`^o+2cZL0eP4M0;logT@UcJO
zys@9#nY#P_s&~nS9FBOiKjTu*@sju7U?b}C8OKasj~{RmE%Q2#6^Ux}#%TJSXd7Va
zkzRPBF$R&98fvL&h1K+l(h_+R(M(}DQ@Q#dnSXS$w*_$1QF7nl>vTNOGnmSGM19Yg
z|Ek1?Jxo5t@@B08-d3MtA7vF1TgG|8nw>&f8s;?{a6|cb9qvDMB>&nDvru7J0L9#M
z(0qd8^g8@De~z1?vLd5)-jn{Lnwl4(j+N!G0X{&|c?5tU6C9a!i4{j&l)VCk9BL;r~p5|MnG##PdUb4*F`Ko^`qDA*
zKL_Let6~L(bADYSM+0K~6AF#+e#ibRiz`|n->d?=0aT%>xgQcuDn`DLKXr+uBub&(
zk~&bYqg&q|H^UqB-j>&$p1Ivz7NOzU3Kf4kj8u*yYZmJe$-Tx*Dmhi8VS!NW5R?be
zBL-vG2hH(IlzUQ7X?|cM
zdqym~{lYQ(*hQjD5H|q`jI4sPm_jGpDfpXM!g$2T$7@w9#5^|W_U1ii@l?p^LUD-R)t1^zcHujNA2XM2e`ijqPfEE
z8+whJ3Gq4?uc9AM+{+nUqP6VIPwTZz-2SWko2LHY&!hd*KKUElHCuO9Zl@Dkl6h@E
zq#q4yn(u4sM#4(>-e&Ykd&zE$nUGaeClU1ts7;E5TtM$g_!sd@twoGBop7^9fXPvU
z}VhkJRFLjBbd-_
zR2u>zrz8)br$X-j>&w+VoMK~QyldIaY#@Z^kEpI~86@#kctQ%eBu^JH0}I@^b?eq<
z8Q>&0ot!g}pnEvm`DgM^HY;?}N9rn)R{$(PxYl}pLw!9e*R6U9?oV5*;IZ0;
zWpZlK_ZDs)9sOw+Uu*o_go(5|-I43J4l&pAv^Jabl*z0!`+ZM4v)5pxX=$W>mf5M{
z$mxbi7(1=9(F!k;K@*ZHmMkyIzwQ`I&SYc%Fg0gd;=3ixZ4SfWFJ-W{*LtdToqQ$M
z8}eYEK&P*pQcbX;Kg*`o!_uEOYROHw!WN!`K_A9YZYBuhat7o>4;2QTJG2w$J3c&J
z{GQq}_#(#Yt9<$GThYyQpEpYrb!+8^vqrA-Vr>F6lBym1L$}bS!M_lUX2u(8XfCh$_*+cFJ>~x?{ICn`Y
z2#Y&7(tY1W*y)usgm7;ic5-rx`@Z~q{@u;r;3q8*xGsuIafW1hZgTE#LsQ4CfI&}!
z0`KLzb9rgvj}Oyg4xmT@{2uw{fsSU?mbXb>jOeZX81=?PC&$VvDz?RZZv60pH(wO@
z%c<{t+-5~x4Ba|(?_4U6^0SR6)ZMi>b(8vD>0rhQkL_4@k$1Encrn0%5!-a7C2d)~
z6K}d`XQcrFW?%i|#dsU-yJON}RB<=>AaF3#^v<=!b7cZ!CokZ#YqMh-dgKkN6U0|b
z)8o|nrv5%G?^jmT>)T$R*}y2p04eG1alzA3R08QtLi(|Ap)5Mt&=5@Xa@u5Ku2)%}
zZN&<~)dDlGVV+Nmm!M80TVi1ASd4114827+cAa=_{7|41JAdM7
zXZM+xva_RZ@>7%Q6d;j+e#3C7w%O4K)w6&q+1g0_IJlBkZhZNoNq{D?H0qHsc2MRA
zwl&tYC(Ou6^anzjbQL^T8aiVt!b-Xi(Pc?sV>WB29)gb0sqCsb)^E(Mf6*PynLS^_
z3utw}auOR;3Am@4G-YSiTkHc9LgJYri2neTGz!k%8}RrNSPd2x!`D{-Qc#OXTpc>I
zo#yH*yV@|5W&q3dvfx+*cw+5X%oG(Jt%2p=e+cOwr&7dF1OMFEXSVP36q~U2=azBY
z-WM?E^JvHAv%3Z@?@LFX8=v_S6oHAqU~tmEEdXaLRo;I?o)G++q7_p8ATs@K>|yA6
z@Lbh44@Uw-!}E*>@*y_?-Jcw%EhH#(?zcZ>YfR&B6CLmEl+WVZKP|^!ce)N8rYP9_
zb6sqM^7hz=Mcr|mLrOYr2uD(XDT1Cm;;FS}&Snx~h}ympoxz6~7nZ7{8NWaDmo{2Y
z#2xk1Yv?bX0moI?Ea%F(|D}-SUsW$1+klUgJa|Og(YPw4wEQN*Jxhl_2SK3()xKw>
zT-&#vwQDdH4A|EMNWA6*
zw1s@Cq{Jr=xi`DmE;oY?jM3DP$uD|4zE4f`FzTno4#9!!Dqxc
zM+YKoe-q_&ujq?X##9xD;@1(yubYp(KPsRsRJd~7xQ#uA>tt)rO2FZywxN
z5`H!EbMdXB#pi*^9X3gw0h&jK9p85~9Xw{H7#%E+M7ZXg0#OO}NKOxVR&fCTX%=60
z3D0kLFE-C;RBbC>Qh0mC>%ke0Ll7C-*GRp3w{U^q!R^|+1`B7Q{VGM
zZ-v(nSmy5gr0=P+ZGp}d^*)L>L<#KkvxJ`L-KLfGz1Hu8q&2IWPo1|}r-TAr)>HP2
z?b&y;baviNX`IQK&dhR0Ikd~qVNkc_dpwB|n@EGfW}l4O_U7vy?Z8@6r@kP~OBoAW
zsckxEMRdR#0w%m$b?z#pKTjlE5uG;ayAj`
zW1^maebbsLnSJgp9BFlV)_`
zqc&hjEbDl7BA&I1?CA8%LDSUV&M8|d=X`96G4axLT~osS{nKj>steS+ox`YRV1!1D
z;;S;#bJ3SJ1!U!!I$^^(EmQoTq_cDd!eeuXsPRT
zh6)sMcZXZQc6IH6zcy^k_i$Tj_zzvs<25ld@;JGlTh`t(Wzce4@Nu!-+U9w*>0hrd
zJ@rSZLCjH6F1!S9@^su@h9{Y>%#Ldl2CHeuZx%d`{=24*bYGf0NdQ~M9}r*fn-<;o
z3<`H-@*)HhtnU%FF$(vPPc2YI41KEl
zAv-lmuHWW^=j_6)uOF7{l_UYoOtlJKv=v-lqVPQsa4Jeq09}Fu0LTCpfgz1;40Qps
zTZuWtFKUrNE{DX0dysiNfrPyat2mXIshr2zo3SZh1AifrBZQCOWh^9Rbm6Ndile){*9d(JMT@XE5*>G%mim1$O5
zmSn*|ss+>wLQrB@3a#VcCw)*G!GAKixfAnecDLMZsj~mj^8S-}1H1i`lRNoe_%p_;
zTVH&Qv{dMDdTtr(=xPMNDVq6Ix@|-uyh8M9y49!-_qI0s=4N7bbGr#n|{T`&G
z{0CN41g0bYg7(Z?#nS;Nznj_-Th__Z6WR}G(BP`QoYZ?brc;X_w(6W?zRkmC5{^r^mQp_Aq3F}##X@H
z|IA;vOEi1M3@K34BRX?bF4s|;*!KcTy)QtdvKEDrAj~1p!%{q)zj``8XV>ie($NCL
zNlq-6+J|x*^8S}7fXXb$9i>_Y#A
zi6_
zwiXdNpaD)2JPi&{~yl21gyrjZQHhOwvCXX
zq`}Z2q|l_%khF>-luD%{&7pa=O-Z9=CrL$vG|xpNq9kdaRYG&CG;3J(pLZ?${oe0?
z-|v6_&*9hycCFTWp8L7)>pG_^Au&-M+!OR;WPq?X=6-P8n#GrUX$U3!OC`gnQAT+-
z_qGt?Vfy2H8__TjLOX-1j|flC
z?ovNEjDeUH@bm9Pzqi#_N)>5cv6u&!i2n6vfgV&6rm$8^;Oayf0}O$}&HCfMPDDd)
z(|j{J;XH|}pJv^y3Ua6m>DX0J&<0jda!J`ANBgoK6*muF|FUc7Rg(6Kg4?c4HxF{mNSDji#*5K3-8|m}^o;?B$n4p@
z3M4J-Egl0?x3++39-nC*KJ6hsjp6A8ml1N|kM4
zMm9F-K<1#%d+zFSp)Nt28UY!*DQuq{x6p6>lqE2S3_xngiwg%09mL3KhcZ{83IUr_
z4b3r^RU^WIjIeNmXZruGN}}z@7TDBckx(Jkk%X6_sG6F_602>l*ZB>%^m>Mzwy*u8
zcwjt;Lp~n}g9u8+wo%9SQO8oI@K%gT4B|MLkD3pi0YTp0WpvRzEWh2TrrGk<+(I4U
zAQhsc(pnccLq9hv`le@Mc)}T2grWQD0N`)qQvxm)d;Er39O@KO-mdX2x_2rh?$UA%
zJxQ&$Gtm4l{PSuz5vO*4zie#Ft|s0Iq0!UMC$OJZw<`_yu29SM3o^+$zk(tO1w0Q0
zp@Tj%zc)X>9L^n`latfhzufCAn{opPEF(xw(2`Z5^QJnUCE?%|
zWx2U)_Wj>t-kuZRd=eDI{>T-q?J(;+5a5Mi+mf|&7mCF__
zL=
zqqa`xFI*UnS>fy42|ch9=dXCgZs}P&o`_x-p)~Wl?b3U}gHZ<|j%=Yn2n;kX9#9(!
zc=S$k=i)0fTg>fJT19NLrQ1Y5ZJ)Z@P~>rX=-k+h1*`l>e>()3Re54tTfGfu5J
zahM|4m00zq5ux9hq<0;g=cb_2r)yNCPLRpoAEkzmk`rWUsTGnCs1eu*Qq)%*#0HA=
zR8gp4Y$YvHe3v7sA+oCE>lOmK;WBQA6d2h+^Ptm;Xq9~SS|=7=`)wk@DS;~O3jGL|_nD^PNk{o$O~wbfk>B0?>#b-yp#8Hh%ROW-~7
zi~j}gmiKit=37xadFNEgMbrt?*{+Ocivb(2y8|`n^G~K?j*`!-?L|m^3YvD!H*RX!
zUUaPi`-sIiYD3h$bq63=otN}{Cu#t)PGxnkT9|&aW{IP6{}ex_UZALFZw9_f%eLEPRuJ0
zwzvJncu?pvV>gxW#Jd+NtGITkKZZjt*T7$yxrs{Yvyao`ja1oC~0e
zK~g^f^k#w~dr+qt``*a_NA73+eCh^NapnI1O@sicOh!i0FoP~`-1sB-_~Ta6-c*#q
z7oqTDY#J#Wic~TH{rOjClm<&ns~~Qu-0Aj1T$l-@x43C)QLv0ymjDvhG1>V;`+`?hW51+3xi
z?D5+Bu}fGR^%$9KlnYw}sagoA^67GsF`^(JA`cMi&euQRVQ&{=jykF>Mraa3X7ss#(y
zANl`LzyBL(*f#aotE2y6U&Ck=S^J=7-hK1nlP)*Z`1)y=HkLt#x_0?u$)gu}pba<;
zD3c7oNav26wI<7sW{RLB&9Li&NAR@E^@0Y2IJ8%|3l||9x9oDUAZHwluBSv
z>+K$)vPLb6`c#34Os94T&tI~0<=8iN1>4kW;E*R6DozZ2pJ7m;3e;WMeR~>c*1gN;
zzjTBp=5m)RP<#xNdWs&T7oyv0WPi8l+0UzzwooLd`cg+p@bn`UarRnBP~riRkb<5l
zk3L;MRiSB?OmrQ
z=I_(X&xIU0U22em5yg^g%bVY>tW^X9>@F*MlN
zM1;*7g-b)ECD}}4h6c^iWO+{Q$syFaDuj+rHS$ulW#1rQZ0oM_)?@v{z2H|@=_cL!
z-xpu!m9KPLTwp5#74tsU<%Ky{MUKnWif}YWzS~{vIyEA1N&Z5p-U6YW8?>gTHPTmj
zuy?9?#nZ=^O(?NXC@IJIw7#=8%xp94|JBfPj#%t`LU#xbbhzCYU?`AJPd4h90LJ<1
zTvADEH~rNIDLJ3Ab3zZSB?JE$91`+#V>yh+E)$q2+QBr)2y!Pnf{1{CoA9FO`B3>)
zVfrwxJ_NYLBAbhB14qPS*#HZ$W$Q#x5Skv4?64iUy6fG|_r)c{_3swi38^M|GH|3Y
zTQ#73sj%t(2sNxV)FHIVaucjpuwy2dCBvN|4`
z>hI!faCUCeot;$w2R1KpHb(}nEJWdV=JX5Qqa)p0{?T8Vn(ie~{EM${Nf`ONoAg??
z*?{Y{h+U?-+k+Z%=h03~Oo?c`6wCE%ah#SLgddQ${
zca+zkZwLD!wfc{0;_Ku5+|LOaIkFoH>ZZtZZs~L|#JxpVm7ZRMuFAbE(4c*D?Y-t-
zS8EGDKJrm|{4Avs1Fphlh2cfmMuC8HU~pY5w0cU^8It`e^%7PJzV7eM@vZ$so3Ff$
z6ioc%MRR|bBk|sH+{vcR3H-{+>frtP(ju~h0uWDomtf#e3JV?*&f>B%b)2#297f@f
zHAd8eX1a+#w=q)Vq(fBjfaMJb4|Ea231yReKnB2IIzr0sMs#Vvrp=M5X&1{liDU;y
zC?Ks({;oS6N@at)Wf!X~)
zX$((Nh56Ce+L_BKfNGu}9{BTh=P*CcM`ZC6kv=e}kuc=jDs*2ye;At$o-<;(YBbH)
zMhSn+?QC2k-0A!fKHrQw*N?XdHsf_+McwjH^l!4rKSoMeVM
zU1aoscy7I2%B_gOF|RM+gbF*^+h^kBOlWQ43-ABBQAN}*t+O-Z@3HETnHTkC52&&F
zVhvQQ0xLgPfe9>u=Av0S@G0uaj1`@oolQd#yMqBnKo=Ct*4)qqw^8yy`s2qh?-%D-
zd&q}_=R_TzarDERy(J3Pqffyy}L!qjiqU#YRMg1i*)c@~dT6{}k-LwDQSJ3Q%-rJHXEuj@^x&1__H*Wx*n
z&1T@Nzk3}HKuXanU7wflU7o#oq4DZmdCEY;uBmG^P1$;^$%_*GJ9l&>-#pzb=KD+J
zeR^e#_y>au5!`e4^Wmtu=N0=WZ1Oe;
zMyG`G9k{m}ZX@)0L=b~v>vfo+!Q(O`l#EFakU+Xcm%5*7WYht<8<*->YZBoZj#^S?kr%*U
zk
zpwz)wlPvX@a3Q%42`+s@
ztEOWUvJ|N!M8O?zQu^_s$d2eI!%Xgnz{l3X4fX_%eFZ8Lm&~XBHQZm1bn)(S{&9&S
zkm*@lS7#^Tb1E`cdcII7&-VL#SatCVwg~e40T{``5c8~@M}jWhN~mLCb}1*kC8-yw
z5m23{l$3ZSdXB%9OVCJxQS)iR1;UJ=bDk>IO$Zk7{nZi`$?eZmspj+ghXq&Ja*{qE_2#suCfkmi
zWO%fVxEtUaB2`5-mM*Ds;>2C-D^Em6EsuDyK0)7CB5~#0ZOjB0jBcmg!(}Aq8GRn+
zi2D_JD4xBGVeD;go5o$L##;ao
zwCl8*S`S(`;<>`)c>L|Dan)0Det;=F_B;VWyhoAdm__=_o*olCVHA(vp|RdN6>u3R
zu~LaO0+t|D?!-OkHakNAK7`l`yH4Bh3+~?Vd#IVy{LNgzZC_Ccw=XcRhr4n!!@>>%
zz6}m>0}M!wuyp>q(OkaS^)AM(qoIJLO22(M6`7Y&*hH1#W!8@Bz_4L9F^flq-Wvm|
z9Z9%Wt
z58Chad8e7iKoGL7F|X)4F*{~CyWZ*c18y{vBpV}1Xp8CJukjmRBMj|{?o?wP5z<2#
zsm0;bZp65;*7*M;&swKD#MIo+?fCh+ss~ogJC)70m(>*Yppg?jf&^yrD@Y>T=vvL@
z1q;l47X1{RoZrU$H!t_Z{v8{Ly8BbeoZ=5C*&I;f>h21B5?c=*U+EMX6&=}NG1~*a
zamyA|>b3IuDlvs(-xJVq+qM?|a$>!SlV$eUw(55UDtyM@Y_kPCw$8yBzV>9%J(kCiRmNpOuu%ixlBDebd5#f7aqLjxnG+O-ThmswEQsRBQIF6XYJDY
z>#<O2-=4|Zt1*hyYvhXMofJCX%`ol>|r=@&Fhm606eL}J_1`5k?Je$
zdLb8Z_KF9{3+6n+0Fv5K1jG`tp5f`NB^w>2;k{%LW7&ziX{N$?Po>Uyz{
z;;pKyM*+VuZ^}4Ex?mi%O@@f7R{$H3W
zOi-N?z8}?4`}ovo>nyz$;S1g*{(=;!j>sVw2A3i|{k?#k12{QRri=nMAVe*cXtCCq
zVU(_sSJGZd;Ay14nIQ8cncD)s%vQv|q7FEVINEaX4o@Il$Se?zPE4#Lfjm&S5y}Eq
zuM>b&NT-0+0XOk!&3xBfAT%kW1Li23LG*40A@j?xi@Qi!3=1;Ev{6`7;BBK*Qd9w8
z$G6l$vW}}%41yL0;)i0EA@W|V*!XxLYk}j=1ihjohHMEVJqMI;UO^eg=N*6uL$ECk
zgcL<6I&|c
zRy1jFrG3mfV^a7i?nl`}MJ}K99oH{PZgrhX>9xF$j2xGzA#3yL6|Tjev+)_GQ(%j|
zjPez@E8AI1Wu$5hA`{r%%iY7px)pV)@AHDU53_D&U2bb%cS$8Fg-dj*5qUl_tu#tU
z@|7brKU!xt^U@X_9@gbO*eJ@u)@-4$NmxJQJ^G;BFfVj=x|13Kw6%xC$?FFbBpUkI
zbkMZ0b+lGtNsOX#FscYUytFP8I4u!)VZoXAS%S?WlHO6k#xVeN5FcNSVpGez(gutj
z(yU}=ei-E9*wf6mHGjQm02Cat0<<2
zfr@tRlokMirz)WdXa|jukVQ=3Yk2Jm+6>p`iVb_@AX5xzqWK(yn&1TX>%6h_#B_&N
z0$T%+C&jF<)X;65L;6)D#0EQ?KWa0e`egS-@l(^bvIt8O-iDMLRUrSjL`U|?BQz(w
zM4SUUITK;qj5~5$Hry3fWJ#_b0
zm%2fqoBE&nOH)1t-JC9UBzM3?Q14(x`;~48J_ESPi9`!IgMe}pkYGO3e@R^5qY~Ps
zUL*r4Jr5z9zp4_&Jpt1RZu2CqWEU@Q%TbOxiQrR3aZIZ-rfR+yavn?-6%{S~k_}am
z7LT3emr+M#Eg4cOvC%bjI0+Gl35W@j69CpmBHe8M&>P{z@u*K*aezzw(qoI<(b^j}
z#w)pG*qtz*`@3xb$uTI>se`aH?yVE-_?bb^*+tDBMBG0=@c6)W=8pz5%|go|CwOtz
zTudGMYLtv*mjmd9tOo6%reAo{V+MlWfi(}kG(pw$e{5u7VNqiNZRDL?PEbPby|?Wk
zsjN3m6=1%M0#4R7ht!gQ@Vl{+&For;#sex&+t
zW^l^Q)&XLfv`&#ZiKeeU@5`Wl%A}er1@H}o|4X^79~=X|5ZBtfqBRylNIE(?+4&;2
zcSA!3Dg16+sFwIg+l(-vTe8__kQRV^xe^(~tZZlYNSn&}WyjlqbxC-x4{pYzUGHkb
zH%RNe^1{r)Ouuu?Q*KN0s}0dWo!pkyU~}eD
z%^)fprCGXZl}QbIEoNe`?~%srN55+7qW)+1?hHs_+H|1MD@ptBpKV($3r8abU80*u
z-|#W7;faNFb`(ZEUEs2`gd*##$ZFsgrqIAjE^oz)jl6DSmHEUb(yEkA~eD*buiLFfL-n#e_6Oz`!#x+q`aU*fAGyT80-U
zNEb2Z;G$~#R7mkrMD~^Y%SgF$ElknADERVW(beLdD9HzrLFl;zRLpp-%yq>eMw$m))$xR?1E~a0)t5_lFXSKCHkSFBxIgM!m;K|0
z&Bc|CS(BF-J;r%KW&|-|#|E1aTgWX$2U0z=Og&IM!ZIgFw@_17h
zMypHI9bX*09|A5EF1!>+FeXj80uYQo)%q$_LdH#DePZaHna`tLyLQzA`#v{uQf$69
z^$SeN|NXbduBOaHvK>(>NoA~dVM*R}yI_%y-N^AoG=y(;R&Ll6u@Py>D8G2pxz98y
z^*iRaRLPtbI89=%az(k6>oyp=d@U?2RQ6}c78fvYe$}KDXrr5}lL)E7swt;@SGqo>
zL>C^%r6mg?z#_pTR1yGpX6X2DsR@d(xx`ky8N^_K7Z$#ln|+4m?4>`siB(h`^7`s2
zh(ZW2L8(l+P_iw5ep9--i~N*zEG)ek80*h14OS+e^I!(-7zpdX5J~1%L?gHm6*_*S
zjjkeYaU|@5-9-&tM*V>agu9@@M`cUW|Ik$?vtoW5(KC^9RYJ5q*yqdd7aDI=ijrV
zn927seUeV5<3tm|k|(4PNebj9J6Sb)1JbV;VP|a2b8!k9hm^M$*2uJonS|;g;U=;K
zi7p*lRGZ%T%#4RRs;Xhc3n^OpToOlN7uIAoO2$N#>cqU-=4^PdnHc|wt%`gj0b~%-
zY2mwW{{1xETx2O|=<~&emR>jKNI_AXbvEJtF?}SyJGR|q8A0B)2I7(LlY~wPPQBSg
z6iJIQGZ8wg!RcF6`2+jRGce9`+z4T}QHpiPrGBg=v@n5Fj(puHWuHVe1#r0-LmvlU
zrf+q5`D1v>3HORF+t>g6ZLDNjP|inD^O2lMTi8InD9`Mqt$jg?=mrQ$nm`N0>52!rKlau#z}E2#g-qss}NB%
z*)53X=8u~PNrR2{{h{zJJyu8t0BTZ4n<6>3+`SEXQDf7Sq)TTs1D1@4&Nag+pKi{M
zUxeZczK^UB>OBXJ#b}d=jos}&?jvd5J*2y2SZ0RD%>Dj
zAVpL9^4&2PXCtZfd3y1Na6nMGaYJNWCeJXtw+^VEJb7<&e894J@)vG-k9mQ{e{0B3
zF#PU8F%w-;su9Tr4`VN|$ZS^+5#d>jV70BU<~&kYkGa^6xUC)Gpj`^NyT)yMfWr5M
z`z;Uu>zDjb0D(W1F<0q2VW-X)Q+1LVozjZ!>7RdU0nRTDsQG0A^p3%>|X_Kw~yJqI7_+%w9es(QJu4~pMGMi={!CE2KK8D`ia!H
zpjkttm*5<%C3)0m{o1ugDCMQsF;Iwe{PN#lkD)&5vw=2l=h&c#uO?{Z;gxXmpN@5ri)=hi;KHsFF{pTBV#D<43
zR{MYe64OHx!z6G6V)N^2YRDE&P>%FEo!A#vof!`~J>+m3d}X;y|M`Vrs5urQr9ql_
zLh%NaL?Vd17XlTE$v$F5hx6&%_>{=GmkWsAg`gIIN!EJES@%@)1G0?4c!wYv3PyOM
zt;b!zQ3W{{H)@zd9XmAu^tn){YOfSJgi2+@t-IEe7asobTnvg-{jA*Y?gbjNx$;}?
z*7qXnFKVyTj@^gDPf!Ht)QQG~50WRolsa9qkrjmt5u0^(90@^f|<%@Z2a#xDkM6UF0bywyCgVoa)GVrR$wQ!DV
zaGDln^^n`?Ss;~>RN!iPLGnSsk8BnWD|Ay7fmV3F*=!{^6_C
zj>B74mo&ZF)ye76_=^3FKTYBLmaUjH-vwa377*}gb}L;Oq6?sEHAr4~0@L>)D$>~^
z+-Aq!)KT#VeKQ7vW7}81nUXc%F~yfxQ1Y6OF!r^VGI&J;n024!5}7Z3)hh+DJ%NOdlBQT}L3Cf)}3cZ^RN
zs7DPC52xnIU7u@ZLeB1T;`;btE1N1^2H&5OvU263g=mG19sEqPNXPQ*qGV>M9xK#W
zNjRLoSVm@;a+$TLDo)uP6JkBJZLMOKWzW*P3f#5<)ROj|q2%BO_ot3=*c$IyM4f-X
z8W;6laI19d^_nRU?XlltGsl0JOF$or3V{mxYuS!*C#&i>f7&@ta{bS!g}pLc5>;i~
zaQUwnZki7Lt7Y#W35>J_NR^Rf{-kx4RwdZ>$R
zu?aat@9U@k?or}{Ka#ARd{nPXqX>&d3;Er)Xyr?MTRiYi$+k4~i^f>bps9LQi?~AB
zlX{kBMS>}JCf>*el+>7{^&@ISKXH7ZwY5r*@deG-773aq
zB83}ppl3~2uTh>X;ACn946SJo)lh~m+x2`umB2(uim_2iVjMs#x4c@`H`CedJ%Kwtz3E0PFIYs`ei4}ITlZ31+Ha8{@uZBR77
zr`cv^$6;^=(WMac6-gK;Ni!&~h+-0OuLy=p;kbISM3#ebQ*5~H@2*ClV~rQ+fd%WR
zg9e`Lhf)q#R6RZ+$CXIcwTb`v0_lw3ZJpBVU9*y4Z5{Gf_rT;N-{!1I4xumPkC
zyzK`s#e
zH>KI%)teQd+s+6xO8#2xraTm8Y&${jvo)EFT7Ic!#8
zYR@kVY9GrnF8G3fBC!7L$1rOHWuOW$jc8(HkJM&@FF^1PfdeTjvdvD6&ld6pRM7bZ
zfW!!;vYDVGeTCe}jav6{4$E!n{v+{F$K>(?1;@$mmYp@xeE5xh-frz%ZahttJx@Ahr+UoINgP)zWWYt_7l}KY!L0
z+V!}a*4WrsqIMyXq(m?auKwqLp}dyonvT@@sWS5x|20vuJK!B0Uwb$_Qu80=^!@T<^x&F|6K
z5eJb6J%7k0O>y@7+`y0_Op1`XH=#p8zlC*2$(5}FeuuL+qKUuijawpUM7aN68)fMV
zvz^|y_C1as*Bkj|ou-))$3$<-hf_5(bhBFZTl#d{E?|mQrw2C;O2Tw~4!v@WatxYqG5=Ja656``oQ}YrS<6CQb@jUbAu64n
z1S+E!U88J@(vc+oBG?5W7hn1(h*C+EI5>HttLUKz7WR(f@G*3`(mXI;Bp-pwDoyf>
zlorYNoQqu2;!w;)Pn&!~;O5Pn8oFUmAgS6q$zvsVC}t|K=qn{_B@CL$qn+*T4Oh63
z#))OgC;<0F(m{~w={>1=l7_hp23vy=K90h1r_VEi)T7XC-6j7+c8>u3JNJutJA!{{
z_~hCVyfS*M4P`TBylrsmxE+WHzw9-;vG#%emrQP$uV_$2ik26*UqN1aD{8~m!tWf>`?&=v!!1!Q|(=p%V8i)
zsyk8lg4IgO4e9g`O}R8yEoaKI?2OQTMz4I5-`1HBaidQ~-}RHyD&2})?D;Eg+&}8r
z1MuFnyx^atVwT&RtYc6f$JPmIG%=}`?7r4S%P>2BGq4^u`}+f-7Ma=2*~!^jKw82o
zjNEB{rN8PPMV(+1m*r^a6CyAV>H1w^S7R}mUv*dM7u^2X*tHp`b3pZ#b@6$5Enkcn
z8~sMV&%ae-l)4;@wD-A*zoTG?apLWlApg$@z7Xj0h-`w)7@#$0i;bOI935#~to>{-Cg4#QslVf|vm7j3+W&sDH?|UbA`r?A_#v!>$rD30;Z965BdKvH|cB
zwK#<2P;w#FKM->X2v0qJhi~6}!SZ+timgo6d6!RB_ryhnY(w&DC+-XS=2Bp=#w{2O
zNxP-}&^2pZkqWpRa${oGRDQcgFtChq@V9ukU7!Et3;dq^+bV+b75}c}xT3+R&lEoF
z?a^)b5=Ic6SbRxo@jz&8`!5UPpjVlDg`n-?7?t-2(j{8rkM%){g`1D)zhCdcW93J`MzQ^nFmOpu8=gbs-h7Hnd8S0`1DXjlb
z;mkj)Y)1Y7b}rUzeGg#TNRjMJa)E<7kX+zMB;pXeQ{SZ)VD?8)h!9S_a>FjhTH}XM
zYX{7DWC9FkYXHIF_?Qi*aXw%Pcht6X+2ZSs82tnl0i<4)9DC-!^O7dM(-c<2!&Hd4
z_zQ%XKUM)O2HsADBGfc!@N5I@V0y0ag}*jpx9a@2J+?xHRwoV)z#!3=L-PKm5UegG
zD(M{|oIOgq#Jb*N;sZ!d5Tsy}JC3DV#V)}3nV5rJVY-}EXg|saDjEv^OCd18DN>0O
z8Wj=TYsinhF>Ha5QYsPdqX4mXXFNKO?w9l|xEcb)7)neQ;)^16j5Vb08G%Jd8{r8%
zh`I^x*jh*)qL9mzS>p`0NwjesMZOnZn$4SV?Zh@g)Jo{aw9$7$LzsvJxNs8S3M8I`
z_l7Q}`4QI{k=>bRHWS-1^0~TU-L8f?84BtrP%80o9GicHt}cf_EEg83lf)-RYe+qE
zOK%$=AD?JEx-)bk1P}zqK!g#|?N3Zm5*nvJ!g%ZYUc6Y5&g^KS8yS?;6qku10YFHI
z@ru~+$=DLa=jwUkBjFX9J-Sd)W`UW8Uc{iPDVA&AMRT>qeUYH!x>P1hVYV7P+aj2P
zR4;5L*N+W=JH5IVQr5>nMi3u@G-s}$y10C;GiT~!W)l8m<;WS8|GO+yJiu5B>zH%8
zPJfWd0U68%?Mm#TfxqnreU{{Wg_u2tSnMFUQtlf7y`g62=v-+7WjfKX2aP&Pae@$c
za0Cs6Gs`lk6Vhhd6#uYZuy*dRTZxopA`ZtW
zUTbp@Z3#X?g;DkWsf*KghIKMG#*~ghwQN3hn1GEaaY`T`DfCH>jHQ1th&VM6hG&XW
zbS}Wy0YiJ^cb
zbH6AXkt_G5_qzqWd!VHbn>E6xh4_YG8?`=5Xrju=l><5opoE>t6oMwIW?d`I3nj|2
z2SNvcVppR#LhR%Xrp{2*eo+Crb^|&@I{dSwyYjMg*hc2uBQ#$dTA%*8|7FiAN4afl
zP5kRW+73o)r)cn=eI%6c`D0CL0=zE#<6<1Gj|K-^UC*XBIaiQegfSPW3QaPV8NKx;NVx^q>bhs*$p-3FgNwrLqT^Oas5r#e
zlGY9P7uX$_^Y=d^GDhuqqVw`}AgUWbt*h(SD6VYR-r)RB>mqtezN6wwm59%HN+1hx
zHm3lyQ#CDwSpUi|=2ZXM_YMZ$ZN;ML*MFoeN0k@PDgMA)IaneRY&%X!4N?I*<|gOC
z#P}|yDZ=p}nhQXhV7>`hXd}+C=ZLVijqF~_sgkm?0ZlGWv6P6Uq~p7SMm5k!AwdY=
z*K@q_HrM5gbuw>0(jbpC;88>|C?I%titY+<7PTqr%#DHNuDDfy?%&j%7tQykH7htM
zsHw++tysu6;A}#*a|Qi3g;ZJKfB8@Yj2eRN!2v
zQIB7xrq4lzcxkn;HVFO+|7AQeW8A*)0^U7{Gbk!ww_3Ru+#MxiD4mgmgEscE2tGsL
zys(;^*oopBM16+aMPhMD0JR0Z8SDt$#^g$am8c>U2@&3N7Q-jF%QRxP2eNHpK?^Y93h9wK*DiyIM0kWR^k$aiof{qFbki{e
z=wGy6Enapev*GGJT(jih`mn5ol-7h
z%wx2^fgL^)$esd~*rOrj1bi{4CcHF*1R9Eo!|z(o^ky6Y>jM3HPq5)a_1;;B{xMyoqLl
zNaq0ZJHpj&KJl07{PA<17n^80jvrN};+t?I+ji3RbDI%LbYu9~Bk>bE)Z)qs!h?MP
zHcR4$A_;tC{v==AAfu+R_D4;zAoeV%v@9Umk|LJKWgwb&Awi3gL6=I(cfv>sC706kXXjLABr+z4!s6lWx*TFsrY
z$|BY+)iFYGMva&Yg1UPt1ZGnp$uYpes1az)(z0{Vz!mBEZrID~eb@GwFq=2eQ$|~3
zcJLYtaUlOM>f8lu@N=Odg4~S?!$r)E;XXJWsKjPLgnv|GN8x^ChZ2?=0YzeQ+ocG}
zv*gy`*K8=313dF}uRJugqQ7UCvY5V5(YM=*mMkI_$4MtB8-?0qfLPZ`4
zO?W*DCXQ601gC^iHVy!garxr|K(|v6E*1@qhaKwxu;^-y^_CbOn#l4+@P9A8sh6!>c>-ra&!SN#hWH)|)6t6FzgLMwoS3Xsw>O<4-P}m`8RS~1r<_kuG6>)eR7n{iBTA0bkr-D
zbNogeIEZoy1HMd=m7Hwcar+alEyW}{Uh>lC{lt6&ucfT4tZ1T!TPD)dkI|dqhXZb>
zf=Yn6a*1nM|IP29%OJ)ji6&$#Bb*yyIv@lzZ?WXHBSjM#Qv;p@gBH-+)I;;>q7>h0opL}dJ0^^1mgs^`(?Zcrp6)+?7jp6ee?+Eb)`kt<-OSH9uI2L^4e^q5l}^iB0T*
zr?_HY=c6@K*%}KspP)BmYES@>GB#<)G~^K6RSUnd>|g4OO*R;YIbf}4!SbvnK*my$
zWr_%Z=8n|B-tME)pHCSG6Qynz;&w*jEnRxkdOtx9O~e=QiAUFT1|qXU`H}b&u4>oZj-NDH^@Ll!*?unj%mq@!9qM@hLc2J
zVRM`Bo=4)9a0ZBY>s4c8Cm?sQDL
z*Q{mg$g60X3GC|cHWCl@B0@v6hYgqC%qB&il+gDQgVIYmzV3g(
zqtV5y5Ex_kyiUPnpV0C}3xn_MxJz28Z~L=0+^X2&y5ML-NSfGa#z0L!rOdkc2CZ&-
zmnxkO6_!S`d(=>a<3d^hTlV;?G+h%S0&fjGbuSiVYUDo}kkt@o_!jhxDvcQ}kz}_D
zx~>s--rd%(v2_>C{_ei!6sLFVsTe!I7G49}Qty1U
z*i-1Uf%7@`0ut4P#vCDPq7{CI?EAt>xM-_Q8X>^fcXLCNYJ&$ltv{ms_HLP4A8PU-
znXUfftVd${U-$19>Hx8_GjW*T%eth&{XY01kpnj(0Rf0Tv3qzj`zf-7h+hr5gyg%C
zb8X3+Q9EBQSxzSx5(QhkHJu~SROfDdY^-VOM^X#TLID`fFeSqdO>$j`M3V$e5*bN=
zteb?saNx?YUN3A9nUVsoYX`d%PL(~{msb9n$;w9z!97z8!Qeq!`{mv@%Z`%cibW
z89hTb&~BMeyQcj9RzfInGdpdwgY$uyj6Djb%@G);%reY++@wJed7?W#3e1-5@O>eR
zN#+y*yWg~O-PW_P*iD=fI}OYMZ8*Yb0umXYk2jk!XqM-A6JbUltYXO@=vf$*ec>O?n5Pq)-Qk&#hDE{-#)I#sa3D%l$65p
zVE`joHDsQ>W}1r9_9<@ieAOdSEd6S-*4^AWnQ4HGQNa`>#mOA*Lpg*dNAu?hQxh@9
z!CXo)?;TLA1hK-C?)BOQ9J^}c3s?kBVLA!Ob?CK%r5seVZE2MVoFf77kk2Z>jiTbn
z(e0HiX3fxNC+Uo}WP9w|-TS?NetsP+RVR&&jbE=$Hazx;RBkc(l8z%?qQh_|pRMc^
zajAMc@7yEf5g|uprc|OIF~~G~BdML+(tl?)0j+|-cAk$Y6a0N0_$z<
zp#1e8+M3NrWl#$LYiILQ=B@L0wfr7HX>a6OoC?r!G>+<^wdUs&1wr)?2a`MWXHm#B
z;Te?y0)_cP=p&6$T~cx(K~fc!r?CmvJh6@ii&=n)!Kj**FOKOHdBa0I;k=g;Gq>r3Kn&tu)w}C*!UM;lz5P&BtW}2E!G$~F+7D_4aoL8|y9gS@|
zVz#a&_{fpdt+QNir8l^5u&&qO;obR%B4Ty#E!=f{UCiyVbHDX1RNQg<_g8BByC?3P
zt_WKva8q?>Sh>k>+cQqYs;*nR`pNnd{nxx(vMKzOO@E%>W98=dK`>K;HQFW{9OC?7rQO{xU%veH;6eRk*O-o)!bwye*3cLD={7NmoEz7`I;)F)=!=uXW{V2L(k!XeDvq!jhnnKTQuCx7+!~NGS|y1e~6|
zFWK5KwQ=>;Bv^+`8d4%5?)d6AF0%a4ExiZyGa?Qm{yhVu&K-mHA85l4tCJMtyi7;S
zO!40|FJo*P8*^4^kBUtb|1U7b^_6)Q-^D(C3@z)KxYsxFVvC!%LFY7OEHp8k$IY8Q
zRR_0)yukLS5fMg6K}9F4t}xZmzW2QQ<&1)huaq5%q=C{i&v
z`57>*x-YlGN=P^s!futQ4Wr&A$rMS(yk_n7d
zYY2jkv9YW_vNnsTv}C6m%D5@A5XJf5yJv7&t`>SNcl?ZP{28*#blvFbBo&iVDN=Vf
z9Kw_{>~}>lYTgDfLZMEM(PE*%)?&YF0~gjdilT}NQTGV=6iSds8efMEZbvS1!S337
z9L1T=;^9+J>^i%Gkv_KI_dk~X^zN^UJoC5tecRiGKV@5T{OkXAyelTZWFSThPQ2H%
zS@23|`PM^~ENVr+ya_`iCJ)!kJ?_Y;s90bX>yIG5(fLQx4B~~6dWO)n$cTtD?5kh5
zVVDDvuyB*09S;wW^+y)PlEV;k1}e8r|0fNgfoZs2S0I!o1qQV`+29ENy`k}H*in2R
z8MZ}Hg@T)?kI0Qd3+#m}&POo;LQ+{(CE&9y>yfk*883F9JKdsL6ODALcvv~H?%+VC
zMxdJACL&Vv<#yZ&P0bim{NJIKy@uKw?2sS@XM%s0-GBvd#Iv}#YIt9GD8@YD#!<_zKuCmA9f#J_~Y+rBx;%~Ltzq_D!
z{{DvBs-+}X|JPI;aCDu*j4LS!jWYfCteJR|ZgxDH5c
zQzeQa@(SO+ohM5Hay8`dt6>hdf=kiCnQ7y`Y2(i6hJ6_dQ+Sw0V51c68bibpjMF-c
zw*Dd8!JT-iV>4Utt$npSR2^;?J~Z0tUh~VM0wJ&E6x}l%OIGb5gS7|}iVr!Ofe6nu
z7?C%*Z{0rQC3h2|Ao6G(NI!P;Xf>)-!UR5^S~>n)m9K3OQ*3)dV8#t*!$7o%{+s{h
z!{%Ok$H+u#jB;L@Z+>4;mve|E_{=*i?#{?F??SnWhML3y9ydp}%9YZxz}bSFD&^$(P{g5NVs-|Rh)Xn(5jAy?;x;n
zIACYEdKa?eZ^1HpcBS-)Uu*GxD#>HU-l%pY0Lm0y3w
zn(YXvdj9;m-}uF|CpH??3%|3{e)2)-TUlmvww)<}Jqee|v{u`1iI<0?f`>15)}Pa@
zE}ihj1*{%;nS4u0c!3bXH$${
z*$#jvReqZf3B$ht-uD=a+mlzWq)fbRhsBA^lO+N)plTE<8D_a%Qc|nCyL)tOzg
zpxFjR{X!B7Nz$ebpqj_?@4PDrsG6bSQ>0zigdbU{?L7F_1OtjV?LYs5#U}J^MxVdz
z?t=h*em810x^}b#D#W61ViJQQE%L_?`#Gl_`Whl%jMq$8q>9OsI8y|qd`rzgQxYoZ
z9VknCBr92D=#RhwWn7+239lr5<)J_9IAc8TXX*Kj-l?|x+!Geb%jSva?I{_I25AVj
zI#P*6dicZMj(}AZld7lQL?i+_pqB{0=W<-JhBKzCeTENUNCncqdR_9s?MxzsC!Rc6
zL|_K>68FGt-p2>K!&~UDr4y&F64aa%Ee8IKNPlL&AV(^V3v}vW`>41^e49QB&y=T!C
zLw#CF!^{)0x*!MqA3ZXY8M6soJXH65qUz%W+$2eYJ-nj$@i&J!#MN+60DbGL-#siL
z4Y$*{Q`U9JX3*`I79lS{b}J{B2^qEifmT^r>DYAB>1_19!eM)&gQ`zV;L!B*dGpF4+C+h%NGubB
zF&n{h!!2gv2DSBSkENrrt`*a1X=y)y7_cwmbtTITPL!tyT!a#ydG9u3`}V^16&|fb
zR|H2KC#O_2#3Lr~*anLxhvXN247L3j8fnTg2=;O(T1=J%0w<&H5xcG{NNmwb2FREmQ(C7uiJZkGHXhu_)FB)oneCR&(e
z?AJN@+g`!D`46F#z{#Bs(hM>0r>NuGS_1c8oHXMqnOIiDxa~U#=qCZY7noIo_v1)=
zhBwLv>m#t`s2evH#U4sqYxA`Uef@`IomZ|)5@^nGOLgZikx0LcGdj}6X1Y_p=OAci
zUbCivlcfh)0p*DCSVC!8R_M#VFQp};jZ?iRq78}}l8HzmAmm0HKJ~(N>AP6@lAX;w3R>_PsR1y5u1g^fRs
zgb2Vjs;;je_Eb4I2I}|PF;CUI2b+J`=GBlbb6e}5rjo5DVKQgKe!0rsoj*%Ye4$O}
zpG>j&Pz220Q{n70&?3^9{Lo?ihy*kO9?7K@kSDKzjzMkZI1Fo2E+{o4dd9oQ
z^XGE1xw%L1t>X-)Z8y=zUOaaK+(fpYk?H_gXAryjiNh0kNVH;
z1O;d>jp5#-Mvx?yil|?9|6FYe$2)k2)bokT13X9-jw}L2PnoUpoLJV$I4Pgr|Ld;^
zsE@WGu9D2W)=V?n41XqklMp8pf|U%i013}S!H~Sn>U=jh^q3m#4pre6Go8-y8#~S<
z9?$<(snhb%^YJZ`Ys7m_1ENEzM1lgL8@>xu$!cZJ
zikZDEy8I9r?30FuhT&7^f1W_co_tio6QNgd%NRf*HOK0DNxLfcKxk^E$*2z$21J`j
z27N%gp8s(s`F{STviZly)P#6qalG_Q%k0{vjI@Xf(B5dT)rkeW4q|C;3b79(|8&D>
zl)C$!tclR!WODJ@hB(eh$eM0h<>a&JDz7^Iub0?BuF_T%l%ohQEwatiQAxwZ2eckl
zSXR8nk{TrR^ch8bZdFHI
z4X+WD?Hw!_h^F2H5X5H8BYpa&6Js@Vz=w8}9N{k>W2gu}O4biwo;~EWS@X~+nq2Qf
z=Os*PY1MK`a}QirSD5DqT1?g2X~;uGIGfB5itECajx?X`fb^->_~0Z+^ni*PdE&T*
z0do*@H#hl56XfZfnY{4xvVWahF3=a5VAMm$S2Ps0#%q)&Jl`-jhHOO?J_M~89z+J`
zPOm~XEZjaFvI%%&;r@|bC%5f6YEeoh*Z?z&f#z&=jB9djVp+fbVN-N;^a&@n>tt4>
zG{UkQa~x8+2cm=VZ2it(H~-eKr>ev4&Q0P&hNos?Llxm}v#VOwJ(XRy?!O2~F@rC!37^Vc@r
za*F*Gj*#kDbwR5s2>+JX&p@w|`GVHlC~Cxx>Xnc!kLnS|+w#~$fZSc-N0<;h5Ypcp
zm6vf#uezJ@z~63{ayQVq(?0!M_~Z%;=jJ|MwPBV7#+hZSMnBbqP}ei89wN_zZY3Ku
zlLM?*^3t=uGiz47A`eT;YvSUiIFF*rZAQ{dRS`0lzpyzY&eY_7#U5L
zGRi6{nL=!)$Hktc&cBag$xGYF`ykC2r6rrvf>^DNuOt?4w8iEBw07N5QQlb;%~7*!
zL`^gbBDfS0EP#N3;wT$X$3~MPJzxN-QHn~BxG7RYL`0-W5b5Y59T^OX8U+nF)PWfk
zr3|3KVL)L<_P){e?4I2{XZM`-PxZje_x;Ly_uhA}0N9A0>98ld6*4SZ`<>RO=K$$I
z#YZYfWf~hGPB>b%Lb8u90+Lb#GM|@;gCcbX)<``fF-EHR6MuhUt{II68o#){IZeKR
zVkq)-6`bKK9n}4^#T!Mf$Zmtk;P~cvz|mxIjMfBAT~*&{*{im9{>Wde6I?{g{>#V(
z(&txuXKP{EyU?ge)4~*ZxX{!B8$_dT##&qODL|)~8aF4Bs!c6LkK>j@Hqnj_JaA&j
zoY{)rHwb#hEn8WV^|rZSP!&!RE}?HP9i{Ux<-R+mcK&7MJCG2!y$zK8U~-!8&&QTR
zsWePI$dg}UutPTluz-fK2bb1a_&n3Fz1;Nf_)!NL)Iuj7UK_Pa&yKgfYP!S-x1&}7
zPVn-AWqwSutWrEKxZ=69*Vv!dg^A+g7CTf`%QL+iH8(4Ve;y>ab3-STir`dN9O6K4
zLb1|@rEk7+xdK+VtFHR2vO)TTtbmz&>q`-*na>@RTu!fOZdfu$NIugz{Qgk2xc_YT
z%ElVpv?!5AMD^3=8|&Y2AAk!*XT+F^O*Y)^<#aY#M9B@5_mj@sCMTWQVUv=B-jH-A
zdzsAqN}lbn&e#5&{&sV>OAd|7Z_3~d91ti(%3zifIHOh@F
z|IW}jYwhOM(z~6^j1I30Ny(jKY>X0DK6LbY)b5=JW^X-18^mr0
z(W;QME>!fAqc(5wa)oE1WZ9rR+1Tx+cu3OsiYHL_6F*0pODIZ*Vl?W>#Hc24I2+0~
zFNkqr{}|o&xuP+xk-zV=MK62wuP3r_2o;`q!R;>dNC<}F4tyyerVSV-wh9ZYqk!)q
zbU+`>|HuqgETz3<&BXsjWRfK5n-qOPauB^sHeWy!sRPD~BGou*q~wf@5^_DlV3P%!
zm?!=H#nN3{@>cP4Yivsvs0DlfmMDpO9laWbnPW3WQ&5s(JUgLf)
zk|vq|r(W*R=156>>j%)7#2j@C6FgDT4B
z?=hN7{9n*;s|leqfVyd5iXsh%reM*7SaNNE!Fd=4*n9AxHJBEPnB-To#KoBK>gwwu
zNyiS1izcV0=I7)nt0*hyK`{V`=%0rmcrH{Se2|NZonJgi(X~Z*;1&rS^ns
zT-<1C1_p-#9<`%55TbyD>_}(b0PvAvWc}K}6AwPFIq(&vQ7^1UQ_W!=8jI7Y`~5UY
zt?_+;A{Br>BmeLT8UgQy-~(Exh6tKLBg-FfXjW(+KrZ9}G;^RpGSCTg>mQmSe3zP%
zY2cWJz-QKLX_@07F~^$Lr4aUE)5uo9meKse>js|uZupQCmql6*Vj#Ek0+g_1p5dd0
zh?>MBdo}~Q87Vser-v?gh{L|V|7`QF1bN>#C6lITtz*$br_Dep$||iaR(8WlBfM=h
zQ@&nZJr-#xug3ggt2T#s0(GAj=6_k@J)0pBb2(|4k<}ki?Qk3OvHY@9K!a=hMy$lK
z^4n`3c*D{~JuiG_V%SY?dx*}^gMU++is
zMjD`8afJ*F31N;8?Ma;GoGLj^FnD
zH<)6IE7G(*;sMd-t@5k7H7LNPLu~jKe{N4-l?qu2l0WVZ@H0`qB!;J^ldf@;OuW7h
z2bdy4jMvsDE@*PN{+*za=YUpr+0U|ei%8^h3U|xSk
zBl>LoB+-^w*=pOiaeTJnWrY*2GYu+)H^ZH3ns)=qtJJDuTJs#7?$HRqRSf2*Upz0v
zHVavC+v^TJsXU-_@bW9h!+bh^@@)VOmiHGccg0x>&~JM(oW+A=yzwZq(yw;{&KPzi
zeufo^GwO}I{o}Cf9YKdA>2hv{o?u##?Qq!dx1zLIfVfEwhUmB~7_DkwwK|o@*mZ&z
ze&VJWehF;bPSLH!5n1TWVeZoudmWZf^rIm$V%G$DXNsFLwsRaDe0pojaiQiyvjkUg
zY-j+$`(cvaPCPCX=MoKo&#gC&1ZU7R1~Dqlyt?;Tm4{=j5L2Bbj7qSTE-Gq1cWxQ8
z{VDdF@tkPjZ`gO%c`2TWNAPDQdRIZ$B#qhys86Ai532xjTJ${-m5Ob!7;Nb9BGslR0ZI+Ahr}BRD|i{Qhseg
zIH(O?#bwy{>wzKgo^*z9v*rMmLLSc-HEQAvj-@KlH~d)yzJTsDzz|=v(FKd;b!LXR
zH*3uu931@ZD>;sCMJIr}XLT6j^L5=pwJ6K}=JPYE@0Z|D*-t)RggPA~-D{_yb5hsR
zY8ecmyrEM%Sx>i@@6gtM$gD4J`od}r
zn6Dor0vRoYP+@vrki^~=8z?=?$LusZc;#8P`6+>~Ojf==3Gs_LUQ!4N@HwNu6-Xxq7(NhaJ
zxoQrus~wocMIowyq52mM-U^FXYBuyR;4hv&Sg5i80UFp&&5=AiW$eIWo@`+&_z>7&
zA54M~j)LxxZa=;-Og@-o+7n9Q7`USUH1$dR`!De254u=^I@jT23SZw>zK<$=?E`92
zDo1dvcVz$GQymb&aRL@g(8}D~aNW}*F+)-gOtkG}WVmGlyql(uJqHd5L(jM-dzVz-
z?%@Ig=2aju1GnYkP7gPf-spjc$PWSd6Lk>C;$~NUm4=?Ik_E}N402l0!XyVwlk^0A7f_&{#Bc-HVS4%1(Pm5a{u5C@9-PTfcSbKK%
z$f6rnwvIw2OEJ^bRId_xBR@X~db)d_BUIWf+$w#(#%o4Mc@2QVtZARTA9pA4p%5hw
zD4L6er=}NzTd`MJS_O8U=W>AiDh^e7t;cp8*Ej1aZ*w{+Z33j1?inKLaeKnMr1hQA
zOQVlAUMSHPxF1T&{^^rS%EX;bdG%k8nhNLr%HO?eS3#+ITj`*zAD$8)j%9mdYFm`e
z{FI961O+m%ci+BpSm2OLE=@xvCnm08(x5&CkYX9O>tEFUxG3*HpFv<)Tvl#f3QOqGf(NN5JI>06)T5{WZKR>&y$`OY0OX(soYa^#eg$N;d`
zK+qMNQm10r-myDX>6JfBmta+3D@FH~4FvsG-2=8OZUiRNJ`m0E$4&Rp`bK6xn^~_eUm^
zIWX75$jB&qMamC9{E(cKq{xK!(=ysG@Y6qFo7cR&x1LA@foug#68FSyd3^V)w(_GL
zR7EUA^9m31{rNSg?7|7ZqGypbb!ZCSpzM=*>Na5aDin!8fSp^T-j&?ky)T`D6@N3YLmouk*L?6AVtL2cP^gZ8nwGY|
zbn60i(beLgKQU$NpWeC*RZwR2b_`WzmFSHI*Oq&=snY
zxL23K2kpuR7jzTPX+Uip8Vz!6CdU#uECjGHSfMqP<~+|0#?J1e`_69|4^=J&lK`ki
z<&=L#FtAd}yBo;P;f%GT^%LGzN*J=|OE|+tF5K!8mTNKzu7QO&s{X1XD)1
z*71ob^sxN+vwH{+9%}gvC|Basa8nqrv>})Y=r5d)j+gZU(Jv=`mP3^p%J)`G{>p|e
z>V%M9{8V*^If@7&XNaj0%qmPv|50XFSp>Ez^BA%kJS2F9JZRUQ=@>(>i7leitf>6(Q9#pFyk
zxLjTIJnoFisWONjLYxJ7NV@=eo1y5AKme}{1|>IC=fA{(^(w3wWSJ6!!Ck5CzCUgf
z2@#98a?x(}^ki)DwfoWS0Z0;#1~O#1hDgcK9Fo-9nc^ykP5m_^B@1gZ3o=C_(VD+H
zbwEt{Sx7p5T0aolrT4K(y4X~G{G
zEzpzFhyaDy#PP4fxPy*3INq;ho9BaWlvtRO0tBj3ZBBVeJ(5oa6
zhj`#BiaIh;Cf}e#h&aT`_(s_JkrjAUGTgz3?ghnxGJIb{N2>m@Y~Y32G>3s4Or8GK
z7%!(tb+|=R12h`batl{y2c5YnY{)OZW;i#EicutxgN)xr2zgVgHaXs*k7aW(jLrs1
zMk9UbE{1718Y+bnRNgA8s@`~*6p4j~qKKpbmD~{TXkHmM3K^q|L}4f$4j@l03w6sf
z1%*;P2GX{Z$tCQI$P`1c%lrGLuU31N<-f%Ac23>}liAtFzY*b%SDbD7mL*bQV(icY
zs;Xa>VhOL=dt|FzykTMVhiCrF_8&h=YUh7~D=xkreV5dAFfNc7+eCfa_u)IoL8)&o
z{%?>6eGVz}e?cecZ~x=RlUxe)KcfCo9Fsr0VEM!3p2NN2=Jan&lY_=3e|9d328w<>FyFxKnbNKq*NNDkuCv21f;tp
zAG(_}rtk9o&bQCr``In-z#oK`cOKgy2%|6hU##G%RRaV$i^xjddgK_p
zFzn(;IaPtbvb?~*eTuAF7YX&0NPKj&;H+_pA5{$-#gzyg?MrD7uaCU$@^Y3_V(V^J
z4paQ#rTfm$P5F&i?e;;Z6MqXzF;PWS(V`2UKNSc1)73uf)aVI%y?EK#!
zvHs#?u`V`WWhcpVhfkgAXz&A(jZjjy1lHai1fAT`&^+k17TjA0lpH)*oe!r
zD1*4WNc$5q^1{c5``Z@wUV=An(C=?AZ`(*oNp-3D`}?=bub2;)o{4u|^Z)0y=lgQc
zoi3N^avrj3WQ^j`mr1BQ%W7uw6wJDui=x>UA}zz)TsM@mQKVsTyF9-ud@VK-`{&d;_a8(+A-&P%;qhhqR%KV
zMTs1x);Xbnp>eJlv1pmN-Sh8_`2Y7#{P+7#^n%3G&u^gUf%qY%$00_CJ~kWfi(0Yy
zFtJ}#zQd>+b$@?<)O8#7h9E}zFGA7jd3h8@KVxe;l5gi)jEYfGQX-zy&9|iH>xIjw
z)Us6IrZD)DGB@PvH(G8jJoZcSD=85ga+-;AT&ck)r>Cc%z3P9bPR+NNA*IvbX6EO3
zj74QlqNK0o+HYAkPAaL}wKUr0;;
z^K;n{wp$@=LUBJm8#*W>xpf+LRwqS+Iu)<`-Kl^VCxRD;Pw2w1eYib3xxZZ1I8bQv
zE+XPpO1wLr*V&;l0{>KgUB(e>vBM3er-p_-wC<}D(r~xQ%N&+vY;E}>xOIZ$H{nTlD
z#<|XMa1aEGZqY1NY-`uo*WV<=)vRzn^Z4=Oz7nf*dHMNpCEoo0{TsfV6kUVx(6N`7
z*UD^iu-?j8&1_E=J*@V`&N^>V|~5`4zGKg=?9wVHm>oK5^5@aw^Q%4Dz8G6!k-S>>Zc$1@(4h@-ROLPE=vb`?iT9`3_Gjh4IPYC&p5wv@dB~!ag*`w8cOBmhZff5Y61&91ZVN0%7|Fg3#}{k0Y!W2l!pL`i<0zOlf(q
zUF*ov5f9d(=Du>Jq4R+_F*4hm!)*97)*o)FL{4v=d2w<{3C&=UnT^|*=?D$;TW}#G
zxHMh`(hE+!H~MhP*f_m+XKj65i#q(`#fwqA1}9n1oqJL|m}}I=^dnxJIo{*wdQwu-
z%8zSpj#@uqUy)N%`UC_J5z$;l*hBr9J6MubY9gIu?RFl>e-N45E7NZba@P_deg3tcz9%|%tZB@%DB3Uz}J0jBsP_l
zT`AW5^l?M{@qrOiT}LXuP{femA3%OiCPAyr-f{dT^~$&}vtF$a;pRvfw?+}s>guX?
z<7x6VX?=_(BGx^bS!Z%5#OK=^Y8*sSN$JVem7*ZO=wLfXt)t93B_$;VSV28|J4WLB
zw6Fuc#N76F3>c*rWMlmN9!WZJHFtUh+QIU~LAI8LHH?Pfb*$#QE+W;ZxXZ`3N#!W1
zsgpl`#6?zqhdywQ{p@jkR5BHzfgn(Wwn{VM)vkWMD-}HvEX=T?9h^^c{Jr+&DXl{}
z`51w$Z#|MyX84$u_?3!5bU4YFRyaDHUQhF5|T)5Uy<&?>E%k*a!s$#eAInF7FELoC^86$`ZdjccZQK
z2Zmsg3B8Sr3k<)k3M*Ux+v_vgYJPsMHNrz$ce}^E&Yops!*v+8qdwYg^O$&do2lmA
zx!Z3eBYpRm%5tQ8WX?b9q1nHo{
z0PebJZOtoSKI9iHeiTKhy+-@byQMscZzweBC1Yd^tU8$DZpUoeeNIbHUlXocJFhc!
z=86ZVEA2FrkeZr0GWYt;8!QAavB^6jKfkVKcr&ivac0*QtP!UfMs^2(js!K|ebwG<
zEi6R*Xh+}kTw;%BoGTgmJ422f?Zbl^@omRjgb4KGEnyEmJyy@?Q)PL1d4}J{Y=tX=
zl*9%smxiuCQc-b^Gl$nejG>QrMeHaB7k3N0!n@vEqIh=+>Jaz&enx6)>ajb}Gf~jU
z2S5!Qv0Uim38r>b$vCrIxu4y;Yf(DK-urD7?s4=+y4usHY{(c~673ZKH*ah<`wI4G~MZrK0Wd`DRMT3X61kbBcnBeQN4kX*5t!~3E0NjLQvm&6SW3}8XUT+&PId4b~9
zUqn;MMDUM}`-{b}-$)WZi+?$NL4M5t!iUste^KP1?f8HcIm^vW%3ZNR;l4ADe<$R(
z`p>U6Gb>AF%e7FhTT=>z`@Y(w^s3+s$C02259!(nFVXvUve#Nlp?;*oWp&7=4GU=q
zpq+FM5AaLPS}A0*xNEmF-<^?b+>(hobx&T$ej4b-nOSkqtBIJ0@1
z^N8Mj^C~YF5jonKe9K0do12Tm+FZ5NMP24U>x+ejC5qpK3Q9BDzfA`UzMGYQe_9Jo
zGPvq+(Ly_toSNGA{N0GLPm$WSy*eI&7nMFc#x8Z-X13dRI6gWgr=ptP8Yh_QE`Ub%
zRh^26L5S>{(a(C=!tcVvUuI@z3b!x`+j1Q3R~?tFp4hJGN>kEu3&gV2(!HWyKmZ%s
zjyPV-O&Cg;#hrW5TXxlhi^GM>0m>c28o}!78hjwS`KY3^YO_aUcCgsuO@ova;JH=%
zg}xVzH|$G}vO4GH3!H<3gBwSWwghR2?zO&K-nk59;=U8WI;$40;qAtB#w#c7>fo*hoKkv%&uhP
zdeVGG`D!uN^h@SEnk_$=nFYO%bi12Vw=mVJL30zDL4vw1u
zrT}n}!BuxZ++(?Tk+8yf6M#Gy6KNrFwoV9V%XQ=x{#7bgPy_$-cxU*h4+4KdA0HL&tx){^5&3gU
zw0!)1(4pkx@8g}!|NBo1V`;}CBgd+Sp0zrc5|8(XvZX+`g7vWRoEA1@W}Uwub^_cE
z;W&%MqDz-9srjBJKT-Yun$bZKCjiDfXMTPVebb$xivQ=i-+OVF;l&=KzjQ}WFX_R$
zPMLk6oe;T#=Z-UDwk|cX6s4tM{`GbiV06!K0o-~IrN-5W_kFaeg{9>eX5hR@pLuBx
zI=3RY&{ucfTJQ%5&E9&hh4<3s%UFnWMP;{6f-nO~pQ(V)%aFcgg`X6vV|B$Y
zsJpB+nHNh^O8fZu;KEPN%TlDLrz2fMetXu}(ob}EcfU1we0uX&Lm<7Iwv47G1N;5x
zuHU7qyB>zohQ
zSNfmv;R&&}nF`Ry%BL7(sIy`lr4^*$ITDhRECdn>b*rhc^#C$z+%_MrWX$8-FZi&v
zI5-L5)^yDk3MvpCXkL7LT`4r`(JI-+1&Z-F;#z}gqViK}_ytzPP+T6lkzf}%FlR4#
z@P7yvJ3g?{EU~QNZb3ofBbF;yNG2PDEq;mQaV8fOP~L1MmU{H)(OsXy+-a9^FY)+Dtob2{G@e@F%%c|LDSy?3t1z%20
zO???$L{RwF&7RcK+^j1QqwI13sNk!)T3v1JBd2o&T|)wj0$C8igN*@~OIn0NcS$OiWCdk2YQu)BK7LEtLmK<)2F_2r)wk(LY}{VDcUX#(L&+-24crhCEleLUiJqc0^n9~YL#}G(E>uNg9YDF
z=3oJLwIooZ()GIi(vZI$rsr$K%<|&DSKtVcngSPm72`s^@!FS7t*v&%;x1d9fcai~
zd13#Ey`En)6)WaOmv7W2(-_3Kx;1EVPDEsAW|QH%HEvG%+UvnDK=z*isA&56vjH%I
zrtH&OWR?JqCqo~Iz34W!yL&VZd^m&+AOaEaO7>8~oA(uk1O=&~pPF~>>z6^>&8gf=
zA6bRnVP7_k;rq~(x3J})G}B9`K#7&m9hq-0KG3?W^(>H0)@*+JR9D#7!pPn^TjS|%
zj7j_k?6L!Tm35v3)A)g^CxvMhq>LoK#^OimuK2gI_Di?yPfIHlS_#L=D>GkHzaHFM
znf*&b@`S~p!mcP^BzINH07e<(uJUZk8MC*i**Y4i5X8bC?|_(Td#8)aXAS#aZhS{K
z`DmWq10d3@@b2#J>_FjVDsJrn&_4*gJZozupj+TyzkVHMRKTCgf5rn61*(XHlAOET
z6}ZVAv+K?PLV<_9=#9j;*g5p}S-QTA{
zfk^=7fOwkpW+%dCPnHR+1(oADJfE-j@44!XW?pO&57kl!YTv5Ps+
z#s)hS8@8u*%5y(9S+Ulf^Jbr7Z?>LDt{W$?r4f|U7|5|N(+a0nAr
zLjwM6p0^cNuFA?(F-eeoM)~F&#VpzUr|`)tco(QIFG!NLjl~TSv)IPVmo8y5N_fv_
zoO8R6eTg}KYigUVC6C=b*i#^gUK)A``uxIp2jl#H(=Yv|@XTB4LiiU8Y?-qggcj|~
zMk&e{yd#^uf!idzY%c*+ZlANgbMIa~030ktT3VWE=A8kpNl#`Cy+!$p+e}Wl9tV@m
z73>Z%r5{9HwyNiB^?rSnr4e=JgLZ$G0K2xXj>l|(Z)16s8McX#)r5};(|N1?sYspD
zR~IKQz}A2g?|Qh|n{9PFnCS!pl(sr&@a5mFt4^Y^qL|Uy-WmL=VSAxJUq13A@-`}p
zAmQ_wh0W&X=C|+Oy#S~(@se7*x?+1|P&fAe
zC`1L3cd!WNI#VRca2E!PNnx$~1q9TKZV!34i61k71cQexj8xD=H@I@;=Zs^H5g*+u
z=tRzp$BV_IzB^HZ(C)4%3y;qr>1#3^-)=B)uO+)U6m
z#Vj%+^oj?yavGys4QFlVP3si2w1_LTa|R}LuD42PG9N=T-}!mt-iq+(;&Tac7_O|Y8K}!|f9>4KDzGP3S62Qs
zZ}V2-?)G@|k?Q1blkU}>#T|uh?pLXVVctoJs#0OEn>1d|WyN4k{hq&g%N2A2$}?wJ
zKzOOz`NmCm#zLt_Nks(@AvkqvUFZR{F9ZSU%8p*lwC^fNIC!8C%G|q01WndgHk2I;
zao=sxnE-L*8%o4L`Z_ppC8FbN!nB&$6uqf66`0(eiQg7>A1HVZeVu@qcvCp=LLO&X
zoLaPSRnH?;)w*%Zf<5ckNwcLqrazY@OaOn3+X`#AT{o$UFyo`saB~V5b1Zx)U!lF+
zneoKAf(fw6V}ou9#w!3jGBQ9h`PfX4lfW)>o`sBpB56RXpw&=mb5%!Kc}i{tE^{}#
z7a$KQg-46_UCmBIhWRz-#pl@Aqzb=ZJpbYnfmiS1v#T_&a@dzFrumJ;`%R1RCY9ba
zH_J8UW$p>Z$){w9k&=+8*o8d!T67Bjvgm}rGpB}cOySz=VIgA%z(<$&{iDNnDHFKh
zJf9kb0%ej%f8LsRW02~SYL+hfrk7Tn!~N;N4?s;n}&lxn567a2(KKR<{Mf_k!V
z){g*xC9ZZ^Q|W90fs88Mmt8{@-58^#)oa
zKklvS>K8azUFB=b{;h^})H<$M2&#P?lpbBtD%IUxn}U@X1(XHlb5&er7NeEO*y}@Q
zjUrR56{TM)9fc;UP*hQ=5!4C96J&m@R43=|Pj{&ohRdjZNf@ya)oqf$Z~Bq=9&Fqs
zTk*H=-}}Q3E!F-8%bWU&W;Gymkhb21g>|v!7#3KJGFem|3IL2*1$8SSA)%VPg%OAq
zd|W{8xhB0=RKLQ-`vP2+LC6w$_wL>7a9Q!7E8Jj*AuD_k&2pXA4S}AuFK)gcuk+O(
zt#kwCd7-wp76mj_^`P4R^ALRHe8ZN-_!X0=^H|jprfLNYjqx^*qx{C_sGJNHp;(Ow
zNRe8(BM-Frz{mN9PnL$hmP`iSa8)-{Q=`##-!*`qt@}OXyjtE<|3SwJ?O><%X{Uw#
zS)jcQAi1ti28;LXwm$~WlM5dh)X%X*UnWJfWBtD$;x{c?&rpH1c+{PqRCI$(PhXz}
z%4BWr)o1Nz%MaFC)C!&{yz!Xg0WA)72T*D8M~omT?65Vb_YNaY7gVw}QKkJHJ3Af}
zDFFeh)9m-EqxntRD_k6c*SnYOZ_Ul$O0?}w;EexUMhjkYIi@L>Lh*xQT)^I{f0ahl
zt=~E1?c0Y&u5Ks`gtutp#wge>^pS(sdFR1{ZQ=p|FD9_dBd{nvJ81em>BtV^4@dQ^
z?1QjnNT0no5X1sV$Mq3F{hx{r0z1w*nShc+Oe7nje9HQ(?9e~oeSkz#_Qbu>I8s|0
z;$LzTb;x3UI!=3@PSGl9R()E#T&BAJX+&M*Ft+_Yp>|S_Y1;?Uw@2$6c87bLG-9qo
zxZ)!HVFt8mX=$+P1bKOR%jFy0t^WFZ>f&29ce}7b?F2sU2^;s`d)^a~SF~z(*Nvaj+W|B$Xs;QT9BGZ-?vcBD7u_uAK7w96JM{J1D-y=nJo*hF+SQw>D^Q|#
z4Jspm+~3sFQU|qL;n5?XpK&+z;rh~CxBjS88V?BO&m-wGn4nEWZe{(2!>D3V>Zb_(y2z)qw3V!&m;@
zSRTOKcl%%9;3LTQ@87eZfW9CBn^wTIZ;YqLqSKoXfPR#34VWOFAd?a?i!m7H(^9=Tfodfn
zzfS*n@BNPmTGkp9A0G@-zuxM^H@`P;yg|U^CxHd;IcjHd1;J^{X4
zgu(pjVgChD9Z2Nz8#}uC%|q9W)4=)P#FjBXtpmmc~AsgU*B(fH978^mr>Kl
ze7z7V`D8<&4ZX_#@qN^|fTnmanvXE~cCbA$DJf}V!ShZ_(3ly-Txl-I$D)XA{)f27
zk*kldZ`n~C2v})KDSFU`fOORY(puUZ8$a_m3t&|N3f0=$zZQ$TDjy?3V3a_|W%m$fFrET7E^7wMU8J
zlRBsNH5MC*5&{piUM{NXFkWoQd*H8Ppv`59&&tZbHYMB>KXb?ZX<$<@^j1$W0`(7e
zR_)9qLPJ6jF3+Y9@!_;)rd4-ffG
zjD@n?e-`LR8Pl4(zi;2LUqFydwOnjq>$dbMB8;Oz96|T^-E%?$Br@bIFK?doB-~pZ
z1eS>t!)UhF4Wt?r9ZbM_&!LyR>UwNyWdsf&*}h7*vffpoCojG~wa%)_4ZFgLRO?=a
z?U^LIZW0J(0>FM~acGZU(yX)Aucy3DhZRLspmd-_j;BXmh=BwI3IqX|KJVI|TbQfH
z372!yo2QV6E>NuSh?j=wDqUb}dQ8}Qh<1*}s*SGA#hfd?zP@!JwFol=gJPh`bsKyt
zjBWl$D!?l*zdu!6T-5=fn$dj{0R;E5-;~5s>VvY1ibM!oxAyk4DG~GH!6&(DfMc=-
z%?D0WP$sGh0+M}7mL!F{HB>9b({|7tg*dr1
zxkj=1iHD(%8(Zq}0Aj!^N9Dw?uh7K@l8d^frRAG%Gyszl*jLBqrsclgS$9a1h20J2
zqITmcFXR?kv}UeW{M!}PZ>cKHt+&yAw#Gl>kjQUlP}NiEWPW*H`Rhg18#<@ykM@W&
z>@+UU(D5Ien=DHL#2XO8S~R`QRiD8x@CL1wE(%l*kM#uRdKz}CV(~}fRu}B*zCsh~1^XMG(cIEGfZg#)7;b`_;Jm#w+%@s{ofVKTC%E4+gO@e&`+KNg
zaIk}&_gE`*9;=CZLMWo8V(EqgF{^u+oJsf!^`Gy@d;woac>QoKvdZ?*?f7te618_x
z9@({_C8SqR08J%%#LgGWA<#h?85um((F0MwB(N8=86{}(!FYcOe2;$~4(R?t$vol-
zxC$?Y@v1IVhZ{Ervq<}whRY^_{nY{d3fkFm1or?o586PZ#62oA;Z^GTLo%-4vvLkqd-tC$0%0yd|Vv^2=6&d@JFTSd^@L4>#Y*%a;)
z>^$gRpXRR|f!qn4#WNaD843bcP^vq$b-)a_U>hVzuF6FigoTG=r9LO4f+)?H-@K}V
z0u};Bqs99(1YG%jpA{|4ZvE#2rJujUi(7X3%j*{;pR^^yacfwx*u(2DB7Q{a*VF{&
zS#BJMa`Mw6XD&EmU3i*Y$u19nW4X(3h0UKE-Bo^nwNpUZCN^(j;B(%soFz4^?ZeU6
zg-gH4olxl=F!R_0kG<1-TZ{Cwmj56fz6*{XJQDVCgn-KwwPNn)W2x~9v@4J)T&@ky
zeD@g)veLa&Wiitpu=xe|lAr#)Zc@ge!ar{Yso^ZD0M>%&&(dO@AX)kZk`i)qCy#a-
z#qoqG|1KIuLUu>lYyUHVHiN_1LkxU#gvt2>jy`cuu}
z;47{OWqs4Tg?_+|6mcglZP405^!x&7tauzaKUxU10Uvz)^y$fbSDGUxtN5Xsnwk-9
z5(g`2p3sDRIa^Re*|0VGWkCU-$oh|Kz;cY@vM&GwdKVIcjiAy>2J=K%7$qmT>WJm$
zyfSE~W3VmOY4!mYy$uQRLixmILnxpUl-z(>)K~CaDZly7->WP53bi2kU;TqIm%h$0
z`S^6X;@H7&3faz*kuVDT0L$rpZv+I#5i}dE<0ADe^h%%>Q#B50*LyKeP9iiv0>1{e
zCkQT>aOc7D@ew!NE4S_889EhB&48JB4<==0W%JeP>1kGw8y|?Q;)aBV4o94mrTAZi
zz2Rw)3eDGD_RF?x3okN9tan-DvdMz9j@sNHjn8wIT{kEf+uEb30nIF8irqn6p^UTj
zy^cxPVTVdHVL{*_h-iaS*G-mHQ#$+STQWW!UV`55HgI`;Ur~kM;__5^`m}k7@v^wZ
zj59xY6Wxs-izXa}jcL>RGM8so2YQvg&f*G1zj7K3GES{aa}qII$6##ul_r$do$%YI
z`BMiby9V4!tld@hV@K8mTWesc)}n~Cn#;d0LzNpXN}vhaQIv;=hpTys
zjJ@9U{d-N=MI{so{|T&!?aiKJyKv!87|hV{GpR;rKIBMHW4;zr5FJ?>f>2oFThv?z
z@5c&tU`kI@P>@0I$xzJ^xZy4v;u@`UcR=nsR<(I?@1R^2|MR>1-Jqfv=ARG_pqDtc
z-#u=*6E;L9O`gQD_ecc{o;@?=$B$`e$>=@-GiYb&x_h~?s@wQww=uCT&8Ff$56-hm
z5qD@#H+|a^_U7-D4KFEq-K3Ph-&K%1vXt33$_{sax`}XRU9(vzCNG$c|
z-MK>m!3FIc^g8o6ENLQ^CZ9A04|dl(fRn>j11QSI&aMYr8}S5{xcGT^%yNY36I8u~
zex+(ny;2eIcN2D5_jyG=T82w5&}kr)Tt$bIt_<
zZ1>F!9h6itHA0+-1nx!yNEu)V6C_L%Vjw<823a8pwv>Dpcip*)i~&EiBUbuQ+2yV^
z4;0F-ryBq(VDMhr?96qUVs^j4KxyD{^F-U90#1U$0V~#t(F1@xe2u8qY4*mf0&EIg
zEcVc}@9QT1YtEK7MWGRZ^`T`U{8fzJiFWbnVG-A|4z~xXwlg?e07ODhxdo18+rFJJ
zqph%3Mpm7{<5$Dxz8%jtoHPn4ee1@B1uv5pcB5_~YW(fw3={j-=vumrWw9IQ=oBc9
zUbaf#EluaK`v}y2E=qOU;-~#X7mR7)dO>R;YSb}G0*`;_5cd%z?Q}7avn`2J9&T09
zwZ$9_wap(HoW=s2f{*AzTt)4x^`}9h^IuD#_5wfoZfA)$Nd$aC(JT2VOzagz6-@04
zIut`eYzRr1);9w+(_RA~Ie?0e&{dYKc6o&SI;`Cci)o}E4puNV&1@X63{xOyV9>NE
zrU20AU8B1awUQmo*^+(w4Z!EM19c@p#&5p;ns0f$ue>~caHZfgh!+AeS>2JH=EY@K
zvhT5n_U?Txgk}MjUst_5pa!uxn+<2Te2nfV5^z=0hgHB10##B-NUc!tD%c3w>$cCH
zr5anD1ds2L+aH6Eq8y+J!O4>+0eJ&<@B@zzEnZeJt$4n5FTQ-a+RdH<7~<*US7!;d
zi<5Im9PxR#c4bOSsAM|+MtSCG|ZhVCdVyq{}Qi6S)^z!>xh8;#xV*SU8MEDO=
zoSb>p6%{A!;yOAyAoaj)zdRBMs})|6hS}qQ2hj!CZ1?$uClH>pBNED~+O9zX<5cGB
z7U8d>|Eoggui@Dxn=5JXY~
zs2q>Xpzb0f+n+L^pWV#Su6P?CzdT>O1*#t&QVz+czABHZ3}q8QY*bw8J`)oY&b<)n
z(19>$rcM=;j@#xLfFJ+}F!uy)XCI>F1j<@db8{^S2%0%xUnluN(bfg_ECjWq^OOhU
zEi$l+@lkhAHCr?7niyz-aW@=fAOH!ScDT75f;5o&Eq{!uc=ul)^)pCEAJtKp#c7Rg
zVsesxfle2qkN^yD;aTrM*jQPyEMKWT<%q^Q0jHPUr>CIs>gsyrx-;&p9huULL4S!<
zYzKi1*ei%!01ekR5{CHRk01BScP8GzN0v}giD;a1iY~ssRCVlLetfhaC2W^|`6mSY
zbRo@yc%pG%Fbyw_IAgfVA>5L^3o-(a`H)bt%FjT%@l0@TkdYf1Md5B#Ws`SkEj>%
zhYLoq{=I5+Z$hL10xNHTt)GUJVc;bj$zQ*It@P;!CjyNGXX`E7_pG+zL5nKJoT?*X
z=n(kcHK=JWaVt18eBNbQv`EC*}zlaaPtWNGLRU12Ei}?ZA2e{yn@_zq%wX
zU5o4QOZSeA@PB;b$6@lv-;mJt{^xzC0!B4-g`Z=2<>0X+`Am)_F1kJUr9QIWTM3y4
zWUjaN`#a(Zs-kYaAGy1ZFPp5TaGbl&QeYQdbGzDS>{CBRdrDL9eSP`Q>_ZZj;X&fb
z8mIFwPMsZULNhdgpNR+Zje;?CdYImvMzH8u2#)Sg^A{}--vpE3|E#vAO!ET9`15$@
zF^kQJC@3}M{aKO3(5alWB)2%|5V+ef74xj`?KqAeP5}TzBVrQin{@r(W(|Idd=Pv1ne+0sYo{R?VM8o#zd!@E^cAF#
zQTHI4zs0O_VZjuQ5&-*24J9~){9Z7#`%aBnABY)N2Rl}11PidbkNmch{{Dc~MK^Mt
zV+S__A?x1++D=nvil0E#q7E|UXuMSV4OJq9=$KlD!_5Az7+k@G+P?w-Zy^ZK1+6;g
z&H0zm(M7MdSmEQEqkgU$qL0D6
zkdd9(H@T6fyoi4~iKA$5?R65t;|SFKTvja`CV3ozLvz2S^Ot)#+xL`1mOtT`v`qOF
zrNTkEegQOrG`NTr)Q~?w>XfCDVLX}+9zKb$WFZ>DC;-TGIP#)y{Gr`pQuumFUKJ`o
zGWa{l9V(3;QAu~`CkHhs(!Jy}5>Fe~yaLHDdh#-rpDl%$xb*Z(-94zQTneEdPnThk
z_uPcF%_+-wj-*x(R3pGYYDoV3BD*2$$Gs{u3nr
zC=*S9tSf`?ae*cAf)T24q2>m}4vIZad3geD9fOp84VpUbH;VsCQQo{WaBT7Vzr_^_
zOdK5@g+R1=1+5rO4WMlkf=5b5N7Lq#;IIyxID%b<$c8-B(i;BFi^7|;cjqgbR>G@_
zLa8|XlZ!m}3+;Z@{<#J8*HStOQ#$9A9veJXIV@5%vEFtDNggFRl+xYo@+LMy43Kjw$=NBHoM61>|~Huxe0Ua8T(yd`C64zS%xH>y23jq#7=UXwU;$*&47{
z6(G%Up$60TUz_si56gv8fFPGuGOIyP8*aGx;D&(3NC5c$-E-=QXKyx6SfyFaT7CUs
zUf3qyi4)zlDQur|bb{-~Q5Q!+Q**)2?a0+y_1`=}CCmySp1@^#XlGgkh)75p#E(`<
zb0(>|)FHFu*VJ?;r_+(Og}1q-1w$0<=l3jJix1Qn$Yk(4-FSX~vDY;~Mn8`9xKNOSP??eq2E6aOpY4ne^qBWfsgNU;F&`QZx0hv
z-96k8X1c*Z0#AW1le)DDW}S6Yo~8xkb3QsZ;b~$#9AH8D^DB^@GtBALp`&+!pdtfA
zqaaQ
z`~rm6LwgeF^N$`-N}Iv}-p%{c6IqWd{!1etZd!58gFyha5y6N;l!z0gV_89W(8Aw=
zaIi@gyJK>4GAOg6lXH3+vI_!5;u8(OFq@l8)Kj&2X@WmTC@7#^DYH9SrzIdV>4CXhVtnxDeF6fn
zg!##;Et4|m3hhFap#yj9Zb*T`^NZAI(Zqs(y@=6HIPSsfeliJZK|_6ZI3{brUvUa6
zUHxLOy|LIbvUKQ`@zp%%{-RY+Wp?i`D+p7#W!0G|fttC&3|8U`HrXfvGdd>cS(f0=
zu*gV#z$+*%_)GzCb-M}+c_6m?G%n>k_#>z?x95|4-5*i|`+CbG6##O%K^)i-o(y-l
zwy4D#Y6DmgZa+H31S|Zj7>yG6qWmU3W1t{H3P}n+fg8t}
zzfF6X12pe+6?OI3K>At5#74R+JOx3L`37@dZ)0N6X*PPwL<^Z91~fj%0<)MP9+^)<
zZ$$k!RS%xRVZh0bpuF?g%|Al;b1Y%f2m6}gk5mV@Cr_R@?oLHQp5g^4?r5gLd~(xS
z6h?Yd=2cDqe&yK<=qHE>4aGt3$jUyq9KgbXXVYmrEZa`E6vXisgMQ!r0Ja3!R$q`*f{MXV?okDZn
z(W<+;7y6c~=2A$zX}gZkKmCJ?vob0(7#G~mk%QkFh3v@ll9MA~^`xG$l%ZpxCA#_O
zPJ-m0Az|Zdu`JF_iFgU3IcQ+pAL2Ksr4*#p)#=b-7x9B}()-bZAnU@I*-b-3!{k@J
zGq5h2nwqNPk2WrV4OA>^_157Dv*wH4!Ang-GlO2Gc#@8vd_BDWgU=O%tFloc-IQPsaLP)0j=^<<&{EH2IV9!t@_tPU
z8MrPU3#urkkaqDNe)>8nYm&{aW5S)2R2Uh$U%0tmm}xvbP4wf{-zM5J>?a02Wwxj4k@^sApp53YiDcCl%Dy
z2Xq;@WGSUJPgxQLlFu&Mp6fRi=rb+4>1!of@aYgTol{$1()l}U-3td7#VTGvBz4SE
z$n{bNn{4{13Y4uEB#8>yAZ`99Jq&|sBfg!+30wMj?F~-l5P5EA;cJ?+bsioSh-uAI
zxTAEPfduq+sS(XGdtyDy$$66x-Uk3NTDzEy1M=bS%Mzc&Ozw)BvYxpFV+#mKTYCku
z|I6&K7?%rlIH342`$QQM5(1G(hzO(l5Cn>azxQGwUw2katWrL2BO|9$!pZ6S^v}3A
z;GnRArR{b0W=?N1M6RQ5I9x$W3V&@nyk{f`fRxE->jOf#}aHILyfrl7D;1
zQ60!kw2
zNfKr;BE)gCTNM(PDI$eB*FfdD7w2@SJdMxfh^9gQOoV~81~c`vpouH2gU7V7G(3oL
zRG>uX$*=NN$Wch=eqVPyM<4-ZQ~|;(Frt#Kzkv^dA1(!3;kaJ0M2%BRy*;uQO>D|2
zOW)z%T1h*R!&}8|SF+Zw7#KF1t(~K4rn4?5ihZ-&>e7r$A=7bTaq_@qn8D*sG{~Z_
z(K<8Q(Dkm8X}S5JwV$N2fPsCDx*vaWtA!&NTFly6Xo|WtPj!{qn`brh65FE_@h4O+`iQgz4CiJ^C=j09@t-Oqv9`e{o6h=Tun=*DLP~!a=8uMygM?;?PHm$$O;45vIJZg7F_EA-BK*uM
z*J4`j8ZHaweduy{c)3NZ!nqK$d=mm?b!gKxig%-e^cgzK4|M|_LPSSdXFF3KRO|f>
zNf^KB8lH|xjewX#A_$8mFsuS^LOp9KG7n-qG6nQ_z-3K%vYGJqd6h4PYwdFHE}b-1;x7zNy2D(k$c%rYQ<8&K
zUDSHcn*3#w?vI-KXSG?YEjb~$A+6d*_U<0-j~)6?eK!}l5mWvl7@t(vQ0&_B`~KY@
zMkCkO-*1n4h$rm^(O$ztU@Cd3$r~dD5mtWwGv{sx)uUN5gkaz2%3u8Z4aRoDu7Xei
zNtVQpXQ12(pvm&h&CQWu53ERSEHQ^A2J~1G*S)zEEvCu)g2T9fHtXtYBeP4{y=P`-
z-(boEiqb2;=^G#bg1e~?Y8gye-L|pG-?oFu<0@#}dSLJi!M>i!yh8^0oO-D0a1KO8
zi~U#4o&C{crhJ2c1z<-|3n}D)E#y9_!$Us|Oi48YrjnH3Z#oxj`{Zfl$IqW9V2t$J
zvitT8k>i7P_sJiI*QVRzg8|k#I9AFelLPNi%hfvp+!ZWxCfiaFGW;gYgQKJAz~8&}
zdj|{!Zo?M#{~(d1v8b_6KB#ApLMkwhK|4x?krGfr@I$}*!0|4q;s;t+$b2m@$d
z4`NAidYUZ@U7
zUNj5}E{4zAmVS88;6#H=7iJ$hcS$jVBuSJ|SSw6feYBc%^3QTmuvE0*8tQ$Y(2JR5lv4w(a&k3Q
zOdiHAR^gN(NZ)v!t-qesF2Y1Ar4Zl|%f2NdhYJW$YS{B|`cX;B+}jTyn3f;@K}XI+
zMlWcydA_52r(BY-rN0L+vgKt!K!Bq!MMQETItqxOzkZ0r$;0+l^O@Q6TaZoSQ#UKG
zW0_y=($FANh}?(MB%mL^Mh_u@wHx3dwD>veOrlB1+4F_G>o6cuSj@k`VhFl7r5(jtc;GWYRPPSuoC$Wk&XK~
zaInx~HR!G?j`r*MU+zZc0O}z>KstujjQ_5CS5(gy;)#k@XlX`G7pgq8U&YwSz
zX6JO?!x;!DNH5<$h8G2>g6?>(;-Lw_EZA-k;X_3N^uj)f7sDD%0vZ&;mK&`1x9kYP
zP@VHfi^^R*1nkby@jQ4D0AD8%G+qQs$IFgqKHbLBfYHzjjQZtJI*INufr!TnCj-Fo
z3^(QFNkC!05bx>*r>da-Auu;buo~ew5&}Td4Gl7xT4hxH{QRjI8Fg^dMa8;x*|6h6
zZXwZMuYx3GWf60iuV5o%=rI%E$w6tb
zf}Fy^+yf5@FlegxM&QV)zI-DvG{wfemnSM%6hyChQ}p?
z;6RTNTt|m#>#t+Eb_dU`VA&$=i^ZXhldDH%7Y?pSDfn=<47-cM=ghp}>3c^IQX$*uAA(FMl
z?yfLFV4I7J%Ms#WS6z23mo2kY-hg+Gj!vUfb2z{Y!L7kU(D)E|a{wntm;DF97Xn*@
zq9uZ>4$g%*m}vd^Q^wt046QU}OQolThqt@YsR;yuxcAAjR?REaXfp04gE&~e$~r4{
zR3M-ibCpV+O!#?PDHAfxy&RaIB|2p(QhhlgY|?g5*kwb~Lxtj`;Ycb9T}DQd+vSo3
zEsOSbi}sB-0xnyB)wKMoxk_>_Q%Y!>BC+y;&mzqgp0uHd0gsG^-`?|CBw&+|_1pt_
zJ=!8fGiWB^XCwik_Dj0Sg&$AF&~o(Y^Jnam4Jnh`ZlH@Jo{&w)W7L6R2;kC*pFgvo
zt%1xxGhdnr0AKX-!J@>P49jMUj)^&Z#SA7Gf|l9kt5ZuAoGl}A6KLu+_d(QE7TxP!
zXU7`Fk9f2_4mUlmP%lFzM>`ODh+_-n0MMzR6DLl9O-97=FNMi%gxY-vomoYlB_QZ@
zpwuiq?1#^R+?11(YhMK%kB7iGI*YERv$JzMube7LP3iTAAJR(?vV+z>E`~2H>{b?~
z)ice1p8{qjJi7w6B`X&fF=Sk}*d4{;NUjpiY)^A~F9?4_)h51P3H1N~dyJz6_u~i{
z=)duZ{R3c$dug)dkq%|UU|uke$on^J%igTeW-i(fUV_Qb+C_WcOr_FO3X~NO9BGK~
zXaXCAZc}QI_~I}HUl>b{G5)=8D8p8u&IP4V%1+6|#N<)SlH9v&eO0ihMpD=SQoWfc}NQy@{~aY~Z;yj{eNlc(yMlqIw
zGNELRXArjY((YVLm+{~fu%EYJb}e|roB05NBQ$EumMh=D^e~CrGp=*zaH>Dy-T(5c
zFr<$-{Kq1v90UBu+EH1BfvJE-`{_ZTzTOe1aj_sk(E!;i%e{@~DBOq3ECay_onrDt
zh*81pHo&~9qs^T5XMBue{p{*qs*p-PE$uBKyOvR*K*2S-B+R<2#YQ)^*}YWx!RxFe
zy{dXv(O^!F^*R@Mk5Y1n)TVKiOtSxu+Wn8XInkJN=Pwd!mOk|RjgzO9n3Z+EyRix?
zm-*d4q4n_I>)muZtZ|LNmIGvx(@&RIf3kU&x|rH+ocvlRxx**UL&(g`jIdMej5K)&
zTbh`BWXsh3mHExRREye%kY8vD=yoETW4C7UWpxb=W#*YFLT&!Wldhxkiu$n~J>!*K
zE-^1TUf~hZcmh{X`udd?8r$5%epqAJ2n-QX+a7>3g@r)ELF*BZ_IL2E|D3pF?m&Bf
z3&eOf-B`{RRHl}UsDKeQ5XjLo0Rq(oAjA^z!IcNk{)NNygaC(`WXW*RjzbrS2hhC^
zX5Sv_=q&e+m$ppRqNmV6M;eDw(@m0i01Ut%fH38P_pz*`4&~XRd=)Q<;kp;ZXraMy
ze#Tt=zcK@oQjXUb;IhK<`jHe*4@h-jR%4;l)Bkwlt8Y8SKmr``fMY(!fVA8O
z-q{1j7+4JZ_cOYUSHV|qIN(16-4a?T8V?0f0`!GcSPL>C+oKSbaDg
zuo_H0!5Ky6PU{nlLCo81I3nVF5W+q`lIU|@Q{T^l0FD8iWXkzJIC~Sg8r!vPd^JcJ
znvtPFN>NlqDVkJ-hLwnDP=*wxG-;O7AVpG0r4`ClM1y9H2#E$7)k3T^N%Qm_SLAu0
z_uK#X`@Vla`?vQ_R%_k&bzj$c9_KLxZhUp4eyX6iNiXtDSE5ff;lvE2NXePO4_lh9
z@yYWF%|W&)PnU=B%!n8{D)PZiTx_
zPJLqaqiuo|9xa$^utO&FBkjx89m~P6n2phrb@Zy=!baT5B040HriJw0PoS2eD;)Y+
zA>%MHb_zT1!6QekKoTJ`G_Z?B*fnz6SLpC=6w;rQn12w%rURp2rY%cpLtwZ`mSHpU
zIq>@0I#gOci@O}38tmJ5uD;%ckX^zz{$z)TMPt|Sn(xzcCQURPr3U}GHLzx-VEZK|
zfdpQIKK^>*-(RLlTrEOTCcr2I9R;9Rx`gVmj@p7baG!Yk^eJpE&i~VaL_Wt&3Aspi
z0^n=v7@**^Q>rdV`FHgXOpyp!7#$r%G}!1pN{l;-@WBnRM$;NGv>={yZLkk`URhZl
z>)ISVrMz6UXc1AxEZOAFkN%M;bxd8GX5^_185d!vB-BKhoYFD517Rbp_3UCm
za*WEyU+yw8x`FSakkwFIlbw*jMAY>bnGJcx016@Qz#n{OS^MT(&eUboz=&2L#Hs?=
zDgo}}f#Io4#7jVzNTie4h2M5*I>4ZXEPJ37=q%Kt4nbukL2T8|$>g&G=>kDX0GI&|
zLhED4O2C_;?%a6>6l%f*QkKbVIA1R0F$a|jNYO=jkqdB;4KF`)_ADE5iy}`e5d=bd
zHRC+kEtZ(vEv3K4(+>BwMT-|JXPoxL>AP!K^WDx`zN}wzMP2#%Y0@m;_Kw!rWkSeb
zq8xf<+s$!?<5Q(yQ7^urxNhhSvoRIdF&xjZIez@SD|_d=#>OQz6QSaJqxilKWtu#&
zwzWOyDxfZIINs)@)av-OO;CG}uC8t`=Tfml<`rp69DS5y^}1O^E^=$hwJBvy)zV88
zwq2*d;ILFt`<*uk6L@wz4?q4mCS+nL1wJh9NVjagI8RD(jprDYmp8b&wBRB`69C_f
zi2|%>%er_hQ&>r98@+M;sLcc4uv-#Qmi18Qw=;ZFx{F6fcures^Gq-zI!#XYYz?p`>~`i{)Y~{X50;f_Wt_
zC_Y-g?Rto2kOGh8ugj9Xcjex^3B|0DCZMCMJA+&0(J66^gL%YV`}+FMERP~eV7&EE
zVO152{n+4rx9d*~!Ov2FVu8oL`M^uT6f5mu6E*S5q8ulOOrTC
z2knf-zVHR7<$1hxXO8w(w(;V9@c38c_$*JK+yVTT$kN)i^#Yg27wQV847B){!?7te
z*wEfl*@ZK`PA2$?V2$@gXNZ#FReM+aK#8IWwk%z%rBsGk070iUM;|aVHrl%!-ZAw+
z!G)&>7+Nb3t1_s{mtEW9;^LzD1zh+uoW|L1I5<#jhkW^|ycLu&7GJ4+vjtr>4?(&5
z0Rx!NL+HgZjP}=Mo=G27x}ujf`)PAiKyj;YjZJ5d%QktMq`P@P5~vjmNY&dzXqQ(;F(2`tD<`PlP7MK@1ddR=^t;IckS9Wa4cZHgr>3k{4k!y8=d0o
zz%=8A^-X6SdO<8()DqB9Zoa%=yzo|C@S(k&qYwS7tV0=BK>B`blg?Aqik04cFtWZu
z4s{y(n!B6oBvFaM|AL7EowJ`_Qgh__cNws5+DUvTCIC!h#tODR%I{{p-2>}m;J3iv
zZJ@F3NymvVi*7=$=@2G%;l)A56V|8hwzFxhga57C4bpSB$>O^$?*|{Zn2LR31|c6I~#FQr06^T!AF5jP&7-@zwuPQUynt{iKTkOV^A
z0JoB;_QHf!!(hV|czpnv!1to(^_AB0wEOqxfSIHQqymuZ89ieIgSVv@r#}!6&QLiJ
z9R9D+RY9ArIHL%C8YN?qfLM_+l`dJeUtc
zX79ie4{@v^05NlvbszQ`uQW#CgE{mKI@Wb@6j(CgD=deN>caJ_r?FLr63lp}Pn#e~
z^Gq#hozcX2nLA2sa~QZ%f$H0Qoc$56B2lUu4bRB379hP|Cq?
zYq1MlV8C)BI(iV?w*VI1eSgns)cIvAUdCxf9U^A6xD}hRg90p_{uJ{7QWVt#heKuT
zyPZQDj>UuyAmcMU8)yv1#`>Rd`(gL1etlWzLNOgb$&g;Sd;j?|^6ReKxhLOLmfep6
zDmqALbg=kJp(W*_486AZW{H;inXmsBdz4(`=@%^u>SZRe9wfCSyfQ$<_4hrBqpA0g
zyDCmSO$@zA=}-77Fv0ug8LKA&!uJ{QOFyAq2S0r3&<8&gkRl6(h`|X{W?~G(q{)#~
z-%~_?K!)IiT@1-c_X#ThBa3Iu#4(qU@%|sn4l`kSyZ;H3^nPz9RF3wlC$nIDu7rNv
z)Y}J;wIg0d&`*Ry!IdPQBNR1^*FY@G?Yl-eP@X;N-IMTlQ-M{fCpraOj{q?pV(x&t
zhAhwDo6;tE;&=l^;UZ
z@ICr1KCXlPyWXe=Ka2xv@@+V6*_Ta1`bHghzrrfa$8@#vbC)f-!)35p+%?Lxe{a+-
zud&hEoi8^SpO0Ms&?J-o<@oOOvO7QQ1B-dyW*E=DWyfi}440f;`(#Y@Ubim`aNK`KPVbf-V-6ZGBR0+@
z>=dk={>65WKR$HnG@%rx9jcH8rwr6@upGg$o04A>^HvACb~oRx)zPzw6+47Rgfy?*
zmBoSs5p#bW7id&sUD}fG-`B+O3+^1gm|&qF01Uti;F}nR9Dr~LV1DAmhuoN!VG6J(
z$7Pj`jSWUsfnX_lw108Q>4k~|N=9Nm0sgN5FnXY{Z{NQ41re^bzs|fI+~TTK^8#PR
zk-_e8kh~XS;|A3Xv%@`k?(4`ML$AA-*m}dNjF_%MgwYCkc?-(u=gy1qP49`mczV5U
zDgnBsE39p7im;F$x^|n91q)5dNi`S~t;F6>2;c&;7QU_p1ZMrOfOG1Oe*kBD^ViSN
z77^v{(sHgv^6WIpBAfKH*O%8e+9}-MeWWp}g{QYs&vaFD!MT)bQa3y0-q~>K-BsQL
z1tBzn8ZG~y2WqWrYcTfm-gl>$CL%!USMH*1k@OZoZ{LkvbJSpKaY3NHHzOm%E%1a^
znsi`e7)?^!xliZm0q)n{vlhhHi*1`iJ+){h9Q$8x5YrfpAeE%RaYaC%PB~4nQJ;2o
zAD_RCeVbwT&$Csy+Q7F_u!@rQ6CK*`VJ(RH#NF`ihYvw;7_*-mGyshQMK|05D9$pV
zWFTuKR^7m4f!HTOpW3Z=E)Pz5xCtmHQPX4rdOiUXQzF2k$>I3PVB)_^fAhPXrck`)
zSPHSxSG{}Zhw%X~q0FI%0Wbvcklax6P0Co@yZ(OI2p>R0ihI*Tgq$_-H>g<(mb`lg(R4%oqr!j+II150t{GHjv45VTp#fx3}6W
z3i<9o*l?HFKiF`t*JtLIf@{lx|J%Jn$?aDLZsH=@{iigBU@+XdS5OuH*cgZqPhJLt
zG1g_M6X8EHGIHY!sF!(7=KH&d;S?S>2h7IojhDbw6k}|1fIfGe1>hr4$Q`p+fWk1g
znb9O+{BEzlDR4(IiF-NQ__cWSlGf5XBe!l83eU{pi`AQ%SS8RNWV!v+_-dnq&rJc{
zw%O>1+0HC|!&4gd{yP@`ffhT1b109)Z|3g4cm6JCowYptmP1yC?Lh&5=>>m(M#wy<
zIK9SYSFF%UPkjkiFBGpeLX!{#jFv>=0n$r)<8u04`sMoYg=$Uh4!2BiZM>eKy-o0r
z=MN=;Y4{dKt(wkRuMeNWZ=~;>oi40ENZMY>+OPXJ^msMsbvmppSHfa_n~||zV9wbK
zEfdspn%2cDBF9t9`YxKKc0W0JL22KrNLdIr=2Du&<5!^krchvnq+YQ?zs(GGt;#~T?PtDYacRPJ5#^EOeC*1K_HC*6<=n3%UCLK=GiB&l^d?WVnh92jmuDp
zNQGmPxEo}z5fRm<;_%7E4p0-XmM9)V8!VEIXwjnex^3iec*e783UW15YR(=US(*B3
zd6BzKdVR9l-1pYSA
zckOpgGOcLW%P-rJxusFer+(cVXY0eZ`K>pss&0k#i^qfkJT)c9OYd#M@H`JdZi_W@
zzJ0@;;arv>*QcP#?wVYTW_zdC_58Zl;0vF2If~eX?o4`*VP-Fwb=WGIC`6Oql7$fu
zC3-}W!$rYZ=f}u$#mii7=@}Vjy@fkDeJyl$-mo2IGOTq5uF~vNUV(`buAna-a|<-&
z-)infw+JSn`n|x>e8SaqhI%0uKH*dl%_A`o_|0+oQYS1oU8y^->Q~vKe|Hd24lN^N
zJ~#w`^ZFMnIDXzrDMSz87u5t}CFN6gM@k>(&WI9Nq<77Wez4Ib)oF&qQ_OnJ9fV@F
zf^+UgE$k{5Bb)}xC;Z4}47}#Bol$t&P+jd`TU#3xO;Rsl4oRRdod2aj5l^5)xjPUH
zf1s%^TAVK^y$XAyz^@}XnK{PlusENfP{{C-d1TGif5XllcK;jf{A%A}u$loZkqP%h
zSd?Q$x{W@}bL$mt_8K&`pUlK=OVs`-$v~DG)M>7*-AyK&MW6{}TV8UjTeo4uLNZoB
zTZ4@yFLh-wEeK9}^9dl1w@fn$`}rsndLmzM-+5q;tfN?f^W1El09#~T-MR3PTUhh+
zy!J9wZnH7LGd4Rl1HTcZu5&hV(_0_Epi7n7fmQpnxUro_9qJA&=KljYvt%EktnSYn
z1r|=+j){s7^2zZs+jJA++wJYukII0sUkK4<0;GdBqA@7+1op1oUEnkFKHd_b!w`{(
zwSbe~+zNvda#9d+UQk`mGVIT&v>rm{irOL(;PE#nOvR`&w&3v+bBx^ak2dKhJ7U()
z#c)@ILL7pL4F#N0P-(UryENyXQiP1l$gB1Z9AII(M5d>X9^_x3089N1T%>OnVURasWEZtj>&v-0@$`(Y&`wi-5+Hxa1ISkm>|obqWG9RVdj;&~VQ_
zU7+v8iqzTJ8TJVHEkrs%cs$Y33;G?wyM{J}f}7-niUgX~RN*$vE{HQ3xfFz?1MNNW
zCj;k43dCY~t|TN!#lv?IXIgb_ttBp6RSM_ybEE}~XqF8g%h)yXq;-ZC4k;#kb1LxG
zNx}?{HRJ0p(1;K*9Lw2oVb$h_%2XL_(oR#nU%>3@<+qZO0$16}3dn*sg>hD2h=cix
zFj&XU5X2Zx4z6D41XT}yL_IHUp1%bCm0SXY2<~}W{AdWH+2qvJav=PE-Wz4ioD{MW
z3U-IBT<*1gl=lxBPb(qP*7S4Od5OVGh$wc01>Gmctl74`{mv?u^tgik&!foLOJMtl
zAohM4l<)a3aC@52`r=b%zGZf1iKjNQZ8co9nn4%TIrbuLKJ5w{1+~JEu|j3T_6Ux&
zW11%wCdXf}wZSI&kv)%goV9@2BQg
zFRpylnjON_>sQ~=v%SF7xh~H{vS5RA`JI97T(1izlVj^E7Y7@*+)FaOwLQd@RZ}za
zTXpkGQNsrnswdbe3uMeRf0IuhdX2ge&r2|#;s1B=IxVyrQk90LEq|@>omTew_EnQJ5x|Y%u(D+e8_5ysd&_qPzM!{ZPh3Zb{Z}
zrOK!@QI%5)Nm^0Xej7KY&}kGPn@{E0cK*$oO)iT`8WKhm1hK>_*-XDe>q^*Z)@v(T5`@
zTj+ny5L2Lnqs{?`7~{t_bm*%{PLjRff~@L5JP=y&Ie{kSv%>z~>8>)S9UJ)x>f1HK_Me+tX8#J60zsW6fA?pC^^|_V4Pm0Br>8T~!4|pzCLemB
z1BJd!O!`G2J}pw69IkZeOs^}lme+_nB)@Lm0Zh$4=^bkgE8w}7tQ-C9GHFK9`gQs
zsFi%D)#Hz#xMLcSBc`#~U_(l^KvHpQk8)Qcxgnt|ge|}_n5~qLLkh}w_3~9PVeg(q
z8}p|TAfy$b9|{L8f!p$fJ>ERo;`CmZNHkE_<74r{)3a20HTm(4Jrp6IF$c^^XNtKc
z2D4-tCDhi`{HCltPg}B!9vPG@aqc!}e_l`UlZ_oGL}|}W?{Lml$Y0AiL7}3Ua^&0^
zsd&01IQ?z(F*^yy8!ox!dh@|-qC;7#=fbw0doze{oAt5Rx2wjAKZY;
zP9(Jn2}^&-KZ6>1reKN_Q^LkwKenHG@D*r!RY?LA9E7pO>o`{FJ+KxGj)yP0Ea0sL
zMttB)ZXV|?s1jfy;Oyan3OqSZh!})-^b4jGGkqfJU76^h{8d&DCK;;XA*iA6kBG#=SZD_;Y13a?4;18%x
zrIznj-h8A4Z6{nh#4m^==h
zN1-T0mJ*PGWJ~>&_>J-!BI3g@<2nQWZFYc^E6q)Z=sN;7!;oV!m)C%z82uHL#8>S?#zv;kHo}IoYQ-&-tU}M-_fkO
zAp>MCtI*hG7rigp1j$^)QzmLSGj3auWb3WUC!!~#2+#y#&uzR{Sc$=o7;NDkD2<`&
zrcfXXE-W9#Y~ArfT9)ooz;^7rzhV8-c>`3zw!J$SI&ZKDv(*f6WF>F5_e{`|@h
zH5e3vY`ZN&3#62&U!(xgW`-`~{Z&{C|wji2JW{^gNv!TQt
zl%hAZr;6#QPc~KU0pTeSG8s7cc0U9m2N{NimXAJxJ42!}nO>1%=L|Pd^iC*?CXHt{;<7f8_C;w~VlNAknZO-rP?B7nS9?b7t
z*)mhQdS$hXkvG>xMyOKmGBSo2Je7NKTjW5L{rIX7OQX^CwUJg^JKJM5?=*NJ3a6#z
z*0stlv&7BTl`T9j*acsC8aD)m0#6=gqJHkZ&K;ZyJl(3Un7gA@z2QG~sO+CLDp0U@Bn>Q)gWcW#2@Zuo6|Ioz{@59Ypn}{g_;J&lh2Z>Y&7eeD+jYeXX1+q;E
z>wrSR!01=Z?x){4U3;-OBbANufSvt#Y#{18iXU=?!2_#TKJMbOo;d2G;_&6l^THLR
ze5!M1o$ef+1*f-yVwKA)b$I_NPTq8LLSu-wj>}}Vs;V$h0;XcvHO~=A%h@J)9w`HM
z&&W6o1SR6rk6k`@bMT$GEOU&~
z_hsJIdgxuRjH>}eT60(6Z1iYWKr?W4adg*s`_=$Uv)6zMttQ{D%Sj0wQX`MC(R|)}
zbsdQ+>n*i9+s|v>>E=r?t%*}IyjP>`{0g-UyB~LzOAzdOU|F1ar^CG6c+??rYa}6U
z?=kkcp6{HamxNg2F8M3Pty`}sGC60q^1s}mbH39tR2WE4yuQc0yELRe9gC;6u1|=)qZF5px!-Uw#0>ckiVXVe6Ci8u{hx-wA(VV%H7{
zsRv(fy!Xh8{i}`DGUq-czzYhArR6eMDwsC|{WQKF4HEA-Z?52~cxxPt*iSOe^@09L
zv~KlY5PTa%dMLdcQj;pa{IuOKE+J9Af%bu{)pEK{(D+@@MBUfzz=Fbuf0D9IFa=jA
z=K;pFz=q-Z{3U?t#P@k8#g~$lshBHHpeLwIK*N{$`XHDOF+-1klO4);0G7lc4$}@a
z3AWa~)%OJ&wXzRHI4Dw9H7sJkODmnnhgGyK+
z8pVFzSwCd~(IVVYcx!#~$75k9HCyrE;6nqwC{>QU;F(&y>^O^y_$?`+b!CV{xr&k`
z2|O+oy-c9&JOj*F1xv>Qmd9CHSrtk>Ve(N#y9^R0Q6_-sOcFhFVNrnnxSX`1Bpg-w
zfe1T^g&^Z9Y}Qt<|zc1W~#ALVrDi>vpz#9M)Vpi0X}rf?Z4n%pvel-1zQd50v|E
ztzuN9Sic9GosG>(;@LptrW9Yq5iPUGMjy5iR?M(g!pS2I-sTyP?mXjk{f*P^SN*Ig
zI*+0T+B{y|BnqG_yK(SBT87iU{#?I2C^;W;)MI~gbck);4l8!N;5;=+@xxo_XTq*eBR
z;5XtRMainJeEs(%-j0qC7KQk4|*s5)Bi^R
zOV6|!%4=9k5KR(siz13El6Fl-;keSzT2GK1L^2_Qh*-LN=d);#vNRzz
zu(7p8v_Ytz>6YnYg`o(jV)bLlfLwI{x4LxZocG_I4#Zy*l(#l`-V4qKq0yqHQ5K&M
z)&aq-Uq-H2O6MTm2(SgXXc*MgZF!&Ky!1}^9JN^UoGI*z5f<^d@tZ9Ma
z^2;7j9I40wh1Rdjl>n(x1C$2h64w0?=}SD=M|GDZ&T{%H#kbn?i0?D*?yZ*FBYtt0
zVlFEvHJEtRH$TRHG5Pci_x8znA>%{m2(Kc!+{!OHJN-B06u6{2ChM{o2WHI{*9?kt
zuB9Pch>38dL)FtfF)k(iVnVBw-k&AQI5d)ygBVTFBOamSPi}Accx_U3(;|k42HVcj
zZdJjxE{^S6rXtL$U8of(Rxi%N?QXi(UH;s!SGVzOhp_S7Nasflop7&LhF2oLESy|p
zuU*(Zx_+8d!1zuG-UpDACVMq|%jA8u?c`BBp4oI3psQQpUsZ&|&0yEHMjZOnTmXuu
zse0mf71e%Bzt}0j)Nd>Pek)|@^_EGv1y4L!CcKmHUv(!4W3ypWwv^;10xcyXFH$N}
zZeY8BNg&~lAo`0WSP|1CoH0D58*Hj20cKeMFxrFm3$E4;U1|THTD7@Q#>sj;JnlH0
zO?+nmuq=p8Bzy-q(fX&?CM^s%`fZtqe<|zU6+=2*1YM6NnOK
z;6!>x+#5*&2*@4BOor6ie~s4FJ~(260#ePa4f!~%aHLs-VN{oP$7+fNSJBIv*mQ7s
zbWalwwnP0C;>Si
zYA(eseAl~Go|rt~X;BMX_nT5tb||xf&`bq%tzXV{OBY`2n_WqNdFoDj`noq4{cj(u
zIMKEvQ;y%YhA*q5rS2*V9c8=_&=+%!W5s)rVxKXQdJXj1KHt=
zt*T*J`eSts4
z$VdH(WVw%M$sIUIYz^(p0ztK??xIk5xy+qr>b7C)%*~?5wurzcqyrhol
z{RMNs55|Bdh?U8MeVK4<$}a>XpW}BKf-!c0?
zsI7kd^gMLrNT!DgP6#Nsls^v<_fzbjz^XdqBu*S%>>~CxZYA_oWjEcnRC(v84g)p2
zp+L>%`?Bs68TkeB?zGEX4Dv+bt!z2?@pwn_kirMtQ^1dZxu@n++|!g|XT_fJ*Ln|j
zFWwcc^XO)^4ju#^ACYynEo*XBM)W&adF7VJ&|xh|xphz_XmZ_!!w+
zSMlD;tEsL1>h;p#zfa`9emzVIu+L2CZ`wQrQ@*ahuJM$O<1$yU#HAP2M~7G9;5D+u
zo-VlBYR%OXsQX)Sc9-G|ha7mZlhUnchto~a6|4=~L6fMmNjGqak@D^KKL6SEqIKlCmykx~D?D_6>8%^tAG-cg)!vS~1IKTnI$fOD
zt&(fhes8UwT2py}eS>!A=)Ttta@05UmlL%%>34N|MrNf8?hVWguMb}w&^=*vaucg(
zOUBO8Qxa5$i@8nsS}NmIz_rv&rM@w~nDRxz$rX)`6{|qk5#-X?wMW2HBk=4Z(W_M1E$JpD!3N$m)EL8n(|Il55{
ztqQ@QyntdJu$%LtZ__(6zc|LP)pLQo6YVpx27cR%JMIP>O(H$-@2IoFgxH&&Zn7%Q
zYhdrL{4}-cf0_WID9uidn-$ZyjX&g7N2RA8gq{W`q?=j5GAXSq7ynhu^pb;_U;o
z0!GW>JJa_=Kt=UYrZf4o7qAzyEOgJDbLNh1^wgW=$8?y5yG@XdhZ+uLBmlAT_*B=ba%@3;(vZTTq8N
zivr%#F-+3kGW`+wIRS6|{o)`gG}B)+*3$6%Ja=|L14EMG2^o}NIZ!4O4l}@p;$!Pa
zb-`UGzF7|RGGftArY>-tCh{X`q=W+?ovS7-{ht2xNp4rO&*Q)Nk!xd9-D8QaI?cWM;Zt(h%9a*lgW0^E-%DRMAIRX8Q#V;N*sEuugN9xP7Kyny(Fhu_-ma1T
z!R`*@jDLw*J_*W%SbpezwC8ZLbBwBw_Y{DmfkX|kA2#bHuAdq<)n-X6U(DF~1ae}u
z!@0%ZcsCs_Aheq)U_~5MI?SSye}}39%h?5t%wFo=Iu&Q{EtvlRuX)J(Whiqh85P=L
zX&n1%me1t;$M#Lg!ws4wi{#haf{g)%^CFt01(4laxjpQFE;zd$I7c;2Df;xy}sEo?Yj7p
z<);eb%F9n29)0z5-1~^i+CITgWsB{9IA-Hcf)?Kj=q~H)HCxfM^Z5BiR8>`NHddXL
zIC-@d?L7$7iJX&+d#SL4uz38@eOyri^@GKfZ}xQ$hkun
z3b;=IQujQlH42O{V)V{-s^(xBFT0SPqOItydwZyBFlNW?td0}v+)n%0s+C??J}Sw3
z5TWJ8%tgV;XlJg#6`%plNcO5p#%njVmi|b|5CeX#!9#3sU6t+p`|dVZUtE4^Mar5~
zJ>d!$_5h9gVk!6LFkgo^nV|?5SWt0++Q$0JH1nR{vl|ESzq~k;-u0b2B|7+cqBhK$
z@~w+mw*KLi1Rpbi=;A>5gx%<y$+*OUmPkC4Gf%5o6n58v
z|CLvC1V0}a7uOaPQSrqQmuB-1V`KswPnt8dwn}%@S68#%NIYKMkzr`Q2?kTyx)x6|
zGi?``pe)JGlRS3xXqEHu;IJnaAVukoDXrXMsbG^GuCgY
zX>6RMMTLN{di;302|auG;QAP=lr#HGOx#2DWq?iNnsm{D`LMGxPvzv9F28BScp4km
zAo$60?@UTgQ}g#_ZSL>EtwPI>$IvK{ch``!+x>Rmah*K2Dy!?T#XZ2q{jE=qm8LF<
zFc`I8DihUwU#?%ju2<}Smzy?M|LdA*a=+y8m4v15f}x#dp@0R@@}&opekKZJ3%1|>
z@#two%XyOzPY%$3qThz+*AwGaUMj)_zTr|GMLiAq?lHkM^PSYT^tDv-v
z!G@p|)Jl>U*b0Sjh=3hZt;YH)C7$>s8cF|EU-CkOCo1?9ESz4Dgjf
z4hMoL)lkjb6^swu^t>rL@1N(k1m~ONX()VK=!zJjnY3h>KK+qa@$lHkHITF3FF&9zB;=gfU}Y(5AuUybK>JBjue7rlUVq0v!xYMCBNfBM+pA&J-
zd_n|4=2?Uo6NMa!p4}cT9gevG-)!l(LYhZP{uba9RR!S-c^AGVd>uF-EO=(|@V%@S
zmF#B(f5I|d5k#_sroPzL;&t6mK+vI15_8puY?eHf2&Soq=eX
zIE%u#WI3X9h2)Nb_J%mq@?FNJ^PESBx%0D`!X>1<2f~*5zK1Lb!gChReIPk>IowNWTnTeM5
zyXV1?k*jB7w409T?=xaLM34%plN{($kY$OW+f>M>Bepa}#QzHP37S3(gQBa*k=jVXCMcPeszGeGa=9rDjq0vRo%iD{sMvDu!YO4h$c$ZdzBr0?5sUR
zWJ3)c^_+rHVYzauIr#J7XsgS)73UL_8RcV!d9e|z#t!2sX7TpYqWaS$S-dwD4}z2y
znss^|_rjwEWuplO{fRew?_)rg-L#wWh%q>t^CR4xt7s6QPG@h~$-9yveg={^8@vSb
z1zAp0G^z3JtO*8N>kgGkc4lQ4KzI?dELiV>PqwW-Y^6p-bnKJ4LQ2x&-TC6pk{e#I
zJKXhDiMQ)GiM_7wWDbjFZ6~^?l%Ma+nNO>|RQ=q%uQ!fmU}pOM?{aSpBqJYoh?1L)
z2p22VbcDRu6Wi@M6Ea))ah1Tt))_H+%RNse7_d`h!J)&lqWFri0|MNdIyyRz74-?q
zhfaH+#aOft2XaK%32O(Hm9p+*M5P81vvYLN8%Mq+l9A;j6y{fWbMb1>YK1F#qOQQx
z>0MAz*{65YLYBS2JbpoUD=y>6rE7|d=46@AT)ymwu}(x=wk)(aY0~BCDTv$l-uHdo
zQ;$1pA{Q9k+2AeP@g=otBwTP&xozw&v({IR;j!#kc^BjP_7>J3Qc&v}&Rl#znYk>O
z@2!tl${NTG9O^|9OE?7ROZ0A)E7k;R@Sa9*Vs=f{M}?pl7#NC5NX#VG|I5NRMnoPW
zfHU+0Wki!~W)>!ulEsXW9Q{56)me4XT!kRQgLLr&Mx_{Og=Q6FREp|nl%HjKdlUzv
zqM2s|(=5?sJk13yiX<`wXtEQ
zKtDfda}`&P2nRuZ$)uMQ!P#@LVA89)b#lOdk_{fG9eZ|#(6A3}x_u=(0$G1Qj%_LW
z8g2c|f?Fj%l^o$k=6#P$9v~cJMVRE%F>h6k1a9E;I|a8H2Gf%B*bqvK2~HF_#5?*m
zsQlo&Bq>D-?R`+26}JR38T^r}(S_oZLV?+M!y1u_HK=PL7Ci9iTVM6Zj((ftPRQx9
z8?Ato7bK;qZ%(VJd1RNjp7=HV)!nCe*b^TQo9P|STidmdmTykr&NY+4(86id{gj7?
zGQZaMj4+^H4wp}zD`f_af2SaX|M)Fv4}tr=lKV9?@w$CLT{|foCZ4Ce+a;*fN15V})6o?1SD94CqUbKkY^U3rlclsLdqt(T3>
zm?(|#W6HBRQB$<2vtK?v?P)b;SiOnDNB3f!09p}I>W1I@wK
zqu>dpGX}-BZ8|%W9UHnl_w>0hS+a}z)4NaS$F+WOmi=Lj(tB^z+(&V}aYnO9K4_as
zom-Or^7O7w>t(+dS`Dc@*(05;$Nuf>UDSOxy9$Wr!*9Q-}I)Z&wW{>RC-vo
z)%D)mR-V3X&-Vwh1q0me?8XS_sqy<{*Jki0=&J*}KR!-s_&!4&4$;er*q$Iu6iD&U
z3Pme}J3uJ}e%y3V5%gS6iW_2ZVA91E^ZNEKdF-c<2}cmj1zF%kV
zvkBB0qJsziV4Lc;BE%2Wt=#c}29R26%1~oLP>v(Fz9Sf_6f^-uIq07EmrlY^9r0?8
zx26m?59SAAG|gEs{Mx=Jre#cfbQ7vSoX;hAHbu)wz6e_7dCuNI{0zb?o-}k4Z5S~Q
zC&Ya0Xx;K}zz|6fv2Zj9O@j%M0%(i1
z9>GFE<@_WXCnsf35Ln${i=sU4-Q-;7>}~?}w*yp*VMm06P&&RL(ZU55kpaKpc
z;nqWbTXpmU=n>8Be~?{7!c51H`E^7c~z%+nLz2SXn@KEkW%^+{5d-tSMdaioNTnm-Tn9seh8wxgLi&
zY!_S?5uqj;okWyuGcHd57>{$Aid%3IP&7*rN{Dt_4g>U{14I-6nn_AFhdLMd$TBh+S0UxiuK
zzsaA6)q7@s|0*(if#?!-LWjH_h*XH-B;W4L1pk9D{|zF~#Y>Ra_*Wf?ky=ICdOf$v
zg6M%6jga{fe+!j({n1t2?ACqz(+=t+C0zg()X=GoSrlr+8{>9su3DO;ZpTeCkgV?P
z>6sj?y+t2%c25j;R$e%Fu%l33VDR2Ziv>@2Hp6*eKr}V4OhB*Ofw3XKCo7aYshNIm
zz&`Kos7tO<8huH!bJk1i(Zz3FH?ayNdC6~U62gxzx)o=<@X=T~vFO-Nq#qPQ^MS9Q
z0OiTecC-=#&e^zJQz;I8@spt*T~8$}_iW!jo7@)MuVt%N>5h6~w?NHFxV&J!%nU4M
z(xQ@7a*P%c=)*!;zT=87vSZny
zzEh0ZU?BF=Ejvc>gCrJbPsNsm;O
zw(iRoN_TKLHC{&#eTOsAe9rB0M&GcgoyOdQ+8
zcQ=2#jNS64lE2;XxcAWbgi&DCGSFed%P%oxvu_QqVG&3av_E=O$C-Uo#Gwt02~JjK
z2d}XW{Ig2;&Wu4m#nP}Xg#ur+0q93DxZSmIOerQ_KZ8^bx4dS&mXvljXPwn`m3W!9
z1Nm+bxp2`D^#Mb!nTW-h4|8Oi!c=0NlK~#XPrRYg3AbZef;Fu5lYZE
z6DHpQqYg9!Fsr~L0tW#2s1rX(cyH}kY@9dL)g?LxFwENCz8tBgB&q3m98Pj%rWN7;
zQz$r_O!W%f`)vT)9(~cYr|#VJlQ1$f9*s&7X*7r`7u^ix<$%eT2%Y+TQ78%f^k~nk
zwUwVp>;NReTsP=paOlhnKrGEsQh~gs~(mdn+OiLHkY6UP*|e5J%Zd{N!poRU5c(bPmapV>4QAUoHWNy#jr?j(V^Ofu&XdQ(@D
z;fqd}&HP$TQCE9h8V^7Io*~G^%8XhGM!U7g9q&Sl974lHJ$ba@@Z-{5UsuiF{;Ivo
zW^jH}Y0?7VZ>a1B9kTbYez5Nna3WKQH`OPVH)SdpmZou1Fpi$8^OHTm^?}eIf9`OK
z-|gw>*;C0hddYom1T+?HTa_L+baHI}$;iwQ*|t=j8#*x2+@CL{D87)$CmNrR5@9EF
zJ;IYCzEeb+4*L~V7HNrOzg%
znQKF=rg8Q?Bg?%q|3D31s(reV8bHSCTZn)?H7RDD?R@y-p%dDI7KqKZ6UjmbVh{vufk3_}$C`l{pn
z+oPm|>+_01`OnrBRsHpLGtXo`=IcGExIO`$nfbP=`v*SxL(gGHilEL1@Z~|V;KjY7
zj^@S6A2pP|2mbf;1*DOzD=IJSK*$G(_ZnDem;L>AGJU*lK|vw3@&pZn`%H27ATeJf
z>4b!bOiTtX$It{36AO$q;5R!tTPH%vOZGU@P7zGB49LhspGoiQ8&C0q9Yp@A{83&Q
z!2|=syP2|Jx{~SVLLL9MmztLEhM{4$pwJvPQX<6IW#4H?1O4VlEqzu^X9^@vg=>M?$3$T_h#7-C2=i
zt-+to)^g{!3+_TdVI#s&)QN9}@h}R{gmDiPXIX6_QicLl%Q@BLI(aF6UPHf9$$TV6
z+wo9FRjgq_S2H?}VlBQlxF#jd8
z8f^E(BAR5)6j|zwC72`ntRw%}`GYt9Uim6DNJZyvCA2sblk)fLX_7QFO+?p8Msu#s
zr)Gl5zSY5h`f1SYrH{^*7rDA)0oQTJHbJx+>j_k4r9e59v{L%-YVyYrN8TEnH8TZ3
zBfxA7(MiHH;Ui(FB?q%z67LAZ8Eh>)bhyJ3Vh7@GiGeii^X>4=`w%z}e%XHAp6$w{
zd}X2#LpRJ~NvVV$tPEfdWdGn6W|Bb|9T
zjh3CokP~kh&m`0VYNO+q^i`e_Giiha7b8+JSRyXyiXE!2q0g7}dnzZ#Dr0$5Rfk{b
zpMXF&s0Gru5OlI_`aUCkDC0!q^f<$=8=1iYxoe2<&dXJ
zr$T%sfg$lBEnw>bzreb)>(;MtgEwcmfGRgT@#;fGvM22z2aZ0aa~lF?jurwp
zOVW!i0(Qkjfg0Dg_tVE=YWhU}*3#k<_RgxFa{8RB_Vt92SViL(MUvQ^nA-`oq#U$VFR+6dkO
zNSVC?8MU{Vz5|uVyxV|Zyv@Izn2dw>xU3ALk^t!n(19H^e-g)1;)X&}MB$!Dawy2q
zQ)!iLKcfayhq{SzpNYyzLViE-%ReYgjfb8`6
zMno6$RA2%`VBuw7J$gQ=qa&sVEHEnsH1S{@Fr6Iui}y{^XOY(-Cy!fi+iI)XY-h;A
z%UQzav~ky}5K&l#Ry(+hRygi|zxaj4Ui}-1&t|KxSp1yrz4zPzjk8J)=L2~LK>2qN
zS3qAbrgL_W@uqLqsku5a1wt}rMK98h1^pCeXXR#RH5Am&3^n)~`ZmlX?}b!2P@!$r
zUK41KDN6$Oo
z>KfA@s2>P?9_u{`NiP!x{AsJ`KBNzA0a^vhSM7>t`;Z@Sv2mB_$=B=1145`ddd1Z#
zLMSbi=OWe&(=8-+tKz}o0EJKPetv#_g#ePZL?d!mt~=dWTYG!l1*&?=H|OYCq#N)>
zO{O=h+ms(dv%XA)<8SKMzYLLJDAlKrhHC?h-az+5h1dko)9sxtnZV*GzDSl>-M$X?
z0+d4Zr%H2o=4{G}tiQY&62+tgj5L|N?R>KWh}Qwk__a+_tz8!Zsg8MJW9FSnVGa*RD8SkUwvGW0>I4j(#N*O{(lofF_$eYbLB(Us)a5aA9ws=QEV
zsN?$lbGIrSt&Hg4E^gWh=S9#ZemH1Y+4-xni?X0X$s+Wm
zL1x2)j`i%a&CXfwaW~5q(Zm$hi;OoPUP^-Fq7WcTCuLO@_ac<`6btalP4zMnK1V48
zhIMIC3lJ?P3b9Cn4hX{TCw|TE-Z4j4T)yySN8HARYFiA`W$1vWC6-o5{5kq)C7@HX
z&4qUziNpeK3O!!6w+w9Az;t_Zln?hHQQYT$8^7;2q$4BZ4_GXyP33nW8zi)c9Q)po
zZXL}fdK*hH+yD*Kjl4T|dmV%}3CL
zf9YQ)US48L`*?7~C$#>hK4axR7I41Ef+Fbxk}v<$xc^ESF41s%y#9!-1S+3Wzyyyl
zmqw`SI?FxK@k3U?_pb7ClU+G@r$Nx=S^};Eo!ukJVQSv&jRPC*D(H;2;oZ4xXaU?%
zD!BM>5kEKn(=GK;;}|iMo+%5*)$AX+b$mtY@wEJ;E$hNdyryWn43
z1>gz~?3+^vaGC|sHfITJH2IuKTsbi+$K>pr{14p8?vqO(!w6PtY&~%UOm86A)FiGy
zJqaChz^B2eE6BRnH6!m4}T512ioB5#!zo`_t`bF9t@KciBU%ile^~gRxZhoGN
zdeYCiTCdGnh$i(8=M1ZNe0}2XqN2-)|2Do1zjl0IlOnhM`Rke^l?Nhj-l$nFDmDAz
z$6pJV@lfxEIy{$jS^2!eYJ96^aV%O
zu4}cX*NS8MHn~HFObvlTnj$#hw>(+CyIRXfffCdgS-vx(X
zT{#Uu4I0FSC~tqHbvNMR5gP-QcrK#jK{0d@&K6o!^p+tgdi-7Poj@9aI=8=G0bByj
zI%Kkl&=w^LdIQ$8B3Tha&_J75BpM5N+9}Ec7=*gbWZwQO&ci$*Stl
z4yM&T2MF?SD@J^)7MQ0MoDB+sTBh)pu3*WK3t}s7k5~6z*iu<1gwL>QRlZR9_T_n~
zUx&`D?djQ2YP3^b5L7MMg}#?wILaLc|TNiPr|
zV!3>o<}P^~W~FOOH=k9bMrK7T?1>W-BCz!m$
z5K76&?7SkW{UJ)
z-u0Djl22uD;wl5$^pK($p;*e=!b}TD!Q#!nA9Q%b*h1*!R9bEADoGbq3%5kUJ&x1
z+u1tD>%hiDa)VXa(yL-R8n(8?Faf8&p`c!LeM1A!95yBjuzlE+tq4oDMKKG;4p;`{
zL1niC*|EsZQQ}%d=UK@R)PEfI>K_q)p@9kL6(5B_W0HBVO8*Nx4n?>wDzMpPBq>r*D3^o%Re_oas|Z~C-_os
zc392YN|=GUn=;D>tDhtI;`E+mDCJ;o1!IL-lUL5Ux4R%IoZ?IHLvgw31N&k=0To3_
zU@}PqL2_DQ_n>WR`B{yn)pd1NV0BZJBC;7$GXEZpC+6jucKGH&VS$n|Bc*`#uh1&E
zboHVj!xGGcKtYGZcXPD^3C}_AgAEw-|fdpUT{p+_hHTGPw2F|}gj@pnLO`MH@
z1XF-C;F<#{7Qk731qL9ns4Z~<%aA~f#8i$1eBiYJO_Q{3yPVDprb(v1R@2!*WS@|`
zx&s*++71qK^JUB$la3+?0nH?cIL-7dum>0?kHPD$yFcsEtN5u)7%J0n|HfO+2iu>f
z%z2b{WUDCcAlU1FuI|W>gaflC92DVW7Rx~P9@{z`0Kfk(xl(TifA!3R2UkpZn|9@n
z$+M3`XD{l7e=He{^bMNUIa!GAHje*#&k^hTznFXTa4grofA~U%3aJ#CLX#vkN#>+z
z)J$bAsmz7ULoy|#(y&aGkc3p`IbxMEX2>iFnKRGt=hSL=o@f8|vw!dL{_(D3AN$yQ
zt(D=v@9R9j-_MAQyV&&adk|%kHzYm##vBr9xDe3y`mO-gHes1kR_D)luX&YV8Q!Xo
zIOWX>crOp0{|l}PJ+M0zH7o0X-GZogJO@)hz5>3?vRSE6N!1+Vjf+z6elGa%^HV`y
z3O=;j>uzbb#RxnhD1Sf21l$^1y7L5>*R1sVd6g6T(2NR>du@Uyia>B7$9RIo9l)bkM&
zMHF^^VL8vWLH^F_?J8P0+SS`!NWBC1*HNFO^}F*@zojrgsa?Z9Z|5s%+eMiRTyq|n
zG>RT(-&xfvpB}Pk=bG=J4j7){JC3$MN(Epi@y%Y%);gqwtejiUyO9KQ&_*)+aK7RIjIjtApq>JKb-;&|ae1}kgrG2+vxEP6d
zL#^$pBYy9j__!P35}|89;S#z`)&*G$pBlh*TOnXkDAd{ruB>+?QtG-%N5*?9|6BpA
zj9_^E9|;{ANy4A2e{y6w*M`^q>_x2?ht}qB%LN~5&yZ7Fw(3B(r13$1HhffHtlRZe
zevs2ZoO7T06}7CS6P^tBIJB&3NX%e2;Lz=3VoG~Xt3GJ
z?4aSm@j1?rtOh>@i<%6PyAeqm{MZZt2I4lL+8HdGEkm|67WQ38Jc}tV=f>y*QgVaw
zniT|2xbP0KDQ91XOFsHCkHHmUHdrYt9O_wvyyY?S8a9W(ECrPumbA8MIu}4wkzkB@kN*DR>3bW%A#WAh&Fju$gCB{Dv!l)-&+V01i9|Ggp3YOOt}i?th@HeN|)2H
z>~9tJoh~AUcmVM{SzXjmILSCU{xmL~OIP;8*-IL6?)Uda-0N)W`{JgR6tHqP8pVpe
zgcD-O@3mhE*u3Ulr_f>!|E7vgdZ=O1J^>I}sM{K!oTBK#znX@|><_Ys*GiSaI}xDa
z9m%Syzjd|4{=TOR;@XF-k4Dw=(eTdI%4j%8D;iSUky1}1ddD)Gv+aRa9EU{U{;I7j
zu3Mq@gCm9j4)d|bxNd*ZdlhK}v=qedaZ~lDY%}-%beL^c7vHgCv*&)`2}HsX!v5Q)
z9S@<_Ikt7Mr(yyp)F-xRaJ?7eI>3HFx4DRBQP7&9d#hSkw}%ZVDbbO%ryqZPG*}$Gdfy%nT#x?0uE)Ro947)88Pf$t?j#bo
z3o7nzu2i3?D(^fmq3y8E{p5*?#r}>{jc3KCKl0@TJrs02F1fj)c~-%-j5TiDi#ZQW
z0f~p!zpLp^Vi7cbUxM8v=m<$zB~i9kTs;G0ef7PL92oAfXKB9J(Zf+dEBLf#3i^}f
z^tSR5&sJ_=6?`-r%%oD?JFjV@a&PC410eP9+utb9CnMMnQ7_a7K`bwyYFngahL#v`
z3A)`kL1a#gwt<&i0NNIN|4QYbZ@bc4td0ITNDEy|A?E4rfm{f8X(+S_6E#)ZKH92^
z3K}Y6-~F3;LciWzdA$B-#L*VPK#}@_VD~A=pItwk&Aps8p-E+9=ace5$0Z$w!|j0H
zuj*Q-*n^rswE!QN!+GVO19*dm0(e7$3p{mkJ0}BgA-xR{^T1WW2_avJyIqD>ko09%
zNP>6n+T}jG4>|6+ua5D7%jS_Q)o+!!X!;p_s7)7QByQyW5i#fJyyh3%Fba=*K9*ieWaB+CX>F%l1X-A;4p
zWJAU2DikGm4n;`*75nzZv#I)q2EMB4eXDU`Yfk4TA*9-^u~!we4}hVahb&HLIsZK_
z$I1Lnt_b@yyYix7uG5z%q&b=Mhia+>^LsU?7iupoO5!-O
zXOUAH3Xuybm8NkuGd)Nad}V{6$<2BV2%02JkKDLWgVT%y?80#f#h^%aA=D1C`RfWd
z%3;EtJx&n2DR52CZ{Shp!kr5rJs6aEbRg<&G3*4qGj(y}iyWI5cMe{Al$Q}}Ysz^2
z`<-hTfA)-RRSdEf6?>}mk>zc~$f$Eq(rKuW`KP;eP;kn*2D3ptMy2V6Y~Sp`V-8aD;HtI7YnK@%m&riqgq3`68RRI5p!2`KQ3TRlumI))i8A3=e
zv^<&Ws|2~)Q5G(VAOK1SWNnS)Z>9Z5{hf9`FJNV<>W4SqP<6rORuB0}0W?YAys?#4
zrA}U6#IA|Zu_9^P~&S}5WcU1FZffzpJN8E%L*6%Q@&
z(;CKoH83#9)hji9w@Wsu9R3mwkxu6gMcV@d1EHo{!S}8#u&>h$<4om
zUb)TfgCD}wJD@OnWI_%B(t^?!>ckqIu)-yBI4k-ufRpIQlzjz@S6_23^}w={PbWwb)ZJk!L@om1AwA%|me>AjjHaC~fFA^HcSXh`C(q?+qn0y`;W(pz>y}MER
z%_>{c{OR>Y(T6@?Kc-ifr}VKGY5^vSL`p_ZUp{t0KE1$ixiXr`oxSU&pJdTxBjfBU
zfaPiU!k4qkjxQAHABV69r=j&p&iZ!ymAHiVq!$6x)#q|2~0=QKmnrvK|>9qgn
zFQjzl;17KM$7{cg9r_g^NT-p}ei~ZhLj@-??McTLh=zPo;@l$t0L$gudB0qfPY_F6
z_TYcJrXxWdXHPTZ-lID-fB{3inUF7GL
zyMCEO*@IVA(6EK?!4yC}z(sRJKJ`BvBhngs3t&PeVOU5H%Z04}bDkIK>s=Dl_u?!3
zTeqPZ8a`xTcA=b!oDDQZ%^=ZbqLAYBHB^uN`}xC)D-vKj2~0#lx*M*n(-0Gp5U@Gh
zJ|KOh3@(n86$Iq!0`#QB4D#f7OWM7@t2RI;BJI(=+8xB>2L^`w{lGh4b7V`IZOt
zbw>#8360#!Wf@BLZnkoB-OVpV7CikSyTu~)tz13Q&p>l1J?7)E8kC<6@0lB&E(AVR
zhOsSmQrosCtV(i73vQmw1@duahM!nDjHRCz?0B~5kO$qkynKaS$|1L^Mj-nMrnmNY
zOC2okX4MJ$jXl_Z+fZcP*=I&RPgc1sTS?yengc;3yPv|Vr)61jY_>XF50$tJck3j~
z)DsstB_(`~a1-$Tks>oBpd!Hz)3u_3BbFd!KEfvWZX%
zx>3^Y2pQ?C#5yx&U!I&Nhr&pI({-M0`0fvghzAdwNbrtal2$JW8pX}!8uH1=xJSW3yWQUuHCZV1PIX~_Lh@s
z7}K(1(zQ$hU@QT34Bw#s?eD!r(tfYgzoC#>=ekuoY;zR?gHO_=HbRgdpmF5ZrT(rp
znB}rUkL~hWOd+C>{OOD&>E`QY48H{K|A^9E6!>-LY7OV?bv>yzHCs@RlvfPx^b~1u
zp!z`H8e-7s^@Y}+GW(%{AxJHeWMXqcV)7@WeDYc#d6x3D9$%7d&3GPwL=#ry8N>
zMTo2jCBl4Sa=QJRt-%qtIn~|6a;l~)zeP*M8xeSA8ML<)kPc)X9^pB3(d!Y@?wbsg
z{nFL56Zs>d4OJaeZ;d1hhQ2r>ZS?IKl;tKr5`sqo>15*f2gi(*gxJfpwkLoRaHuCE
zkC6msm9|wXqwC_2M<>(5|s*bSl?3VN;#zG8&C+;v#+Csl%oP`!7V(A2U1y;lTQ(XUQYd|&cEuWwC
z2;8^2-eleGn^-q6a7WZ$J!xl`R+)(|(E8P@U32(JY(-%fJlvwRgi~3393fGUguW%d
zhwl)Yb!1c!ahdVOJ^LTbyTWEEeNyC3vWZ-j>DMs(pA{7d@Jt^}wA6)}w5QO75*y$SO!S0pYtkEGrI6n=pF(AJ#_(lR9~>(rYN!w8Yy^3RN)%cU
zC1@QTLD_x&%P_X1!v3?@_Bhp*KcACBBljvEZa2k!NIByHw*2O#7CxM7H#usKS&lZj
zI)lz^!>2TWME6?$r6Qf9yU@?e)8!{{f87o0(m}9q^rWwDC%LombHfOXEN@a^4%ZDL
zqz@^{Zvsa`QUM?$C)vQ5?7J=14zc$m?n@6C6C@;Jf?p>P>_8;KCP}Bb<^QojvPZ&o
zjTUK;&&VaMIdA=J!_Rxv-yFju*C6t35CYJLqn$#ppDFST(y{W+53AT>z+$u7-tJHc&F-#kQX7-e%ly+BC#6@pNo0F_p;(6a8F4d_$`*hjJNFHEYfsXWY)=FP
zkn)7kQya>d`99h!6b!9$bHhs&;C44ijC_;}jks}}2QYAYA6D~^5U
zc+TyvTJWI<-e(eP&F#mev^JQj=_M?&@Y|YKE8*Ggf13NCx#U@9{!11X>
zng9BFfcYw$B9p-Ef8||1L@y>lL3xbW_K(dQ`}_NO6BFrcPSVMPSs?=H)K}W~_qhYA
z(Ze&Yc~WcTB7s$_=#igQzsNN-R6FwgH>3L?h^$gsetylXY-O9CEYSM>e-)Yf4;{_v
zB*jQ*hCqt7chU}lywk{VAw}01T+(^x{rGY4GpS^i_pY0*`nq+C=ya@->wvx72nxDR
z#@zZBjl&L+i#|tlx<72(`0EdO-W{Bnr}e-vAjW9boi}-;l-^#%S{Z6@INf%=ANITVX~iS6&LpEpD^G93qQ#3zbgAbXy}&664BBY1
zQ9;{NS@_=WGPCJP8(4r9MenYVkXVYrn$VxExnrwIT{d0-f<{+q*~OH1
z6Q&K4a#C6AN_0a^tgOS<@)(!F0lR*9h3`VfL{j|(tM|KOg7fFlu3WjY30e#U5p2!zBK}`ILmCbDLe{j
zg!y#zyKumsbd<&;JF_{WW5`yBeIEXkj5~;wx$&8YmQr7BS^c5VVx!O!=3KM#XLpU42d?R0B(w%37Plr&MV3Fr
zC5;~qE6b}~&bhx{D3#){c?~9q5$gaSyDrXI&uiDNDDabVu8kWv658?RI{B|lE{-SI6>|s=RsqDMg0KXgC(xvXIa${Ew
z9c-t@E&p!42>-<+SbgTsol8-OSWhslg1DCO_MSdIZbyaP-srg;j#5IKIx)oK8)IjF
zZ6fdQ?+>+n4$zg9mI}F;($Z3LiIV3^oY|E$${QH8ps543-MPdoj%;BCL4Sqt!@59{
zNiizCxVXYvhs}xoiiCH*#~cVR1B`Kr^&5L4w4*Bo0nt!|`pqjS*deks45>rF^6}wm
z-<*jksx%IkN_-ZtT)9$GSJ$=cTP+%v{3=K2jr1L~RU?PI=ZBS6x0M%WzVGPlc;z@(
zUz}5lx9%f3F~pf$7LR>9-`L%ylMwPapty}XwYtMX3M7o}Hm-ta0|&!Olc(H{s=ibg
zNtc_6X}NIzyi1NwpWf!8{tWy6fKEv}O@yRt`Q~jKR=2v{xGStQ%q3ekWkJUtd;8{f
zBd01`Q;tWs8r!heNqdai*V7pFZbw><>h%-lK*PdH+q=b6^Eg6sKgqP64q|Z43ENo5
zb*#@S4EcA}uTps-t+^wn?MsXzSe!k*y{wu4=&d9O
z%NH~0#KV^U@SqTK-7X|8Ptox0>+54Z?O)i+#LWC<`hbdxg08LrscnYzJMbF*p&2n@
zT7#xGANqF8PuQBqEQ{M9Yx(7JCn)6b(IH|}fFgB+ICwExl8q)ALOPH5`yb8*9zwZZaPj)^T#g4tIODm+rG~Y6)x1Wahu0OhwBM#Re>lzQ?W7L-Pb?(iZ+m|I{ForxA)7#zT
z|BmL%@_751TN%h5q3@mP=z3KBY4YAZ&NR^_lv#k)<=%#bz$&Ox@N1LIB@&MX2u<3j
zJYed1KpY<6i^(x4de_LbC!o3pecZm)xge+g$jc93cFhoaS^d
zY{C0cYitTkHu!i?eqLVn(ViEi?i>Uon7_9se6_N(9Y?i2tUVE-Q@n2q3Tlmy@Uu1D
zHwejr)YDGz%f*SipB3KC#%HZMX(W
zIz?TE9fymH3vnic!{N0$(R3lbhisbCL6;|njgjw)TP-%=yn(4PPsa9ThzN>|m)yI~
z&WM(lw(Dzrmb|Lp4z=V!$KlIgo_yyz7rn3d`o7gW933;u$1bMuOixcUv9NSooT}t>
zZ+6TQ^k)(f5D<;&H5{}hfpLWeZ>!XuzP9xC_6{ahsXY&+S->ftUHfoQD6I5vO+z|b
zTK<4N89axI%PtTipH=(fcOP|;;dUulBN>tPm-KG+F#Vd)LbJqu0DUgjxZO!QznLqR
z{+No2XZgfN(=@vg(aEVP8@uVG#KemulN;|dFY?MOT(~WwyaQL9&8f`Mcb0WJ;tJ6f
zdccO#r_$4Lz%l3I^_9v{?Hd_s3yG;~#`k)7u>JTl$B`b*o^@=hsr8*K(5NBjK0TxI
zIhuk;((~KC^CVw3Ef|sfbcwgm$BvXzse~W)7-F<uDM1-
zEq?3$qQ5$kwR<9rGzC>w2@O?O(pqdW}L2*
zhl6bJ)Ozht0~v9V<*72AVq@Rdbqv1J3rj0|H*$Iv`#c-&FiXB`N`ejfbNePHlQJ6z
z+$O&o+Gk$mP0Eb>Y)Bus#h}1{y=#4i_B*r8(5)h!>0d5wn^TvT)G}dNp!Uwn(B7|H
z?VZii(Bhe%^yxv9m?aD5&Y>k3HPr{qPfBA~TvZD`ls^LF>QiTsQ%*vdoev7-p+*EF
zS&d2UFlIT***hZxd2G~(u<;38nyw1`+1Q&0Di{9yp{VxX=-p@k7Yk
zNhk>P_(lyDBxD!r7?Re1@F3gk*RNAoX
zbR@t4Rn!mT6d2Uziq@Ny-_74p7M$B7v
z%nCLpHar@l
zv{x=_hULJldY`O-01fHkL2S0M@NSpHI}ecKwTlR_?0UAvIhJikat019u%9(14S21H
zz^}*DOX&(S2}pntLP{Wf4aqgmfAeNYJw4VMA%=v&f%Reuw!qd14|jLshDEdMgL)~<
zX)8$+B+A9;&K0)gq;6LJg}KG=xWDj
ztQ8eyh5dnKz+p0A{UeOB)T$ngIp0wG2$9v=
z$)Rv}-L2AR{KoF3Wa87*KwGSm*XKx(ryqDvZ5!!0o6QEjwbId
zlmkPAfbGs^PtSFtnyGUzbwRe$7@KwWkD=w|lPh-(a4lkWr=>{HGS*`|%NcBq0FBi~
zI#_XYtBwl^)O+f32D;S!{%KTt0X>9lgRNWbgjG{E4asOcB{gNh$$$?9*@QGiFQ*1=
z72faa>KgSTk=tU6-|rt?rE$AM-*={5@lk*xXbjaMD;^H`9E3Zi8c)ok=ux$a%A+3N
z@BM;z9y{SX@}&R{I4Zn^lx2|Rjp{?go6R=i-@lR~llCDF7Ma&pN7$AxUjVp=fJm^h
zSa*cq1$@+NA&UPGNp_r)0wh4N1!8#Luwet4tpJkIP$-St2|y>wL{E>zfPR>&
zFXcA`>hVv|)5F72fcRXnkP`3Jjkjkc3jg{Kpp~@xsjEZ7ND}pi8;it)1DZfgl`_n!
z_>0FR3(pDeG?IB?QkP7FpI({IW)4shPha02ab7}z1LJra9qo@?XfXHrF*;bTN;cy(V{DluQ#=a7SNwMxT+lw58}%-p$#H0-DZHNXAKB<+{hB;wUG%^8-Z#N36+MZhq?%g=2AZ-&Yw3A
z8K^FZ4NH61?#qyVno$BPLttfqjMbVc%51pn1c0K8P8ic5xrcPA3HPcMXV?~GJ78LQ
z(_!zfn=O95kZ$rsWN5vrc78+>CNn9Ao=H*r{Z4PyItLQuzpN|dD0_P+C*;`z>yYD_
zeCs4c_lR>jIs4z1@`AU)QB3>sih
z4oSB}oG3GoBpY+?1623|nt|LIs9FKM4&KOd)kQQK>!~&?rO0vuLQu}fqK}gbksB&2
zNy!{;7{v5?K&;SJ9Zx6d|M%H?qRcCj
z&A~$q&Z@mp0a@g&(drislqM^Hk!*?xcjkOmMY()CHRx`Hi&0Mm!
z;+yX8!w$k1UST9LFs&X{alzE1WEB?TAuA@{?*RiqZ%}cAKf&5R6+~m-BGph`-QLY6
zr5~>z#HZ|RRUxn;|xz9`6~8VJ?fCZ#GmiVQ=9DsDXP`A
z@Dh{t7}!u$Sy^ckZ5<(JZSxt5K&*Ffa1y4Rr*)y6Mst@F@d3z~>X=LTIG@|hB{T4T
zlX}XuVA6y`1@TF%S==e;iI~CP*Wn~#$a%y+1~vhp!4Kr5_L%$P
zU$@AOh8^pzZ5{v^N;0V@nPXkTZNyJVl9d2D!@3!Ne+0#ybQF){;l^N;h_0cs5&Xte
zqzafG<0pZ5fz8&u_xAGIZf5qpUdqVum}=uN3!e-p9to@RUUb1>epiS(N;Id;PNwm|g)%#xr7FZdUa<6B^z7seD
zuEVO9ooSBs*ZT&^u?j(05n0QNFPuAv1?*L)ck(*)V{mwfYB!3Oq3rGb*_WpZ-rf~w
z#soBnsw=#idA-}$?R$c$Zuz(Qtv3(0f0}T_yW^rzVo+!{oK%?k0Q8PN2}Wi4}Wp4vv&mzF-_Ht{s}wm+>)cG#s5&8pxQKD-+QDgtgaaWx;d|%ZWhY5XXT;CD
zIeb(F#l@fJZa2+{N=dn&mWeH8V7NKq;X{|IHYZ%1RqI`k3R_zD<}(7pUwz9fsqK24
zM!}G+?8ggR6crV%Pi+f79F;8&UyMunw3B?gmX`VW$y{@?CnPH_
zYypPjoY-b6cU&dD+D=+d`?9lLn^ej0z6M8U>&&RQIAsL|>ha;Iq#fq(s(`c_+_-Ur
zO-xx&RnXt!)G9Hjz21(m&upC42KKN7wR&pB6@ac#MF@IG;L@c_86I>CmfTw7bncvx
zb7J6bWj6sky0+cgsXe#=IxYAn#PcIgW9X^NN_(2r7(xIJ#|1ru1?0sCyurJ2kypL_
zaC6YFQSa)YoMX~h4T`}I(dkxw$8tOoh
zWV4wg;8TjExm(!OhKz%`HQ~wiV+zMG;PM?&wTv+Jb+T-%7F(!
z6tcog4pGQ}=Rh0b*KsR-#v$A>7gMsTF#8aeptm6di``c_8uTsXAkLHA(#)LVRD67V
z?CPvjpezU?u-6VMN2151vgeDX>)i6ibwkWb7|Ec>{35q|%l&;%qN54DW{#;T?s4u(
zynR4AaiAlKB`4v_vFM7wKYVzD6|k$aL&UFszZ1Dxn@G(u2tH%IF-%wwj|!)4>Hg!_
z?0n+QAa)o>xK%gTqp&dPsWXl}btwd|!pgYy$zMu>+mSom;5cDSLWtgI&y1_34XsuU
zc|S!GF7u|RCgMqCnz2JTnKu!3!`mY*B*cV>4bd+bw~=fqdt_xw4D1wKD2FXMZZK`r
zkL};%JTV`FcF&($@2^6D8H^y@_-!#{TuT0c_lwWFY6+2xp$3N3dy&?tS;QMrRE@Da
zx0aamr3Fp{%_rI3#QW%f|Ni++s;h4idRN9LODWf1
zZr8PeQUH?$<~UjIq%trM(m9Nq^5+~Lb@UoZbxmmtX&LXJcLpes-PpJs6BBNSE1!(4
zY6=YP{N$$*EK}`*k^u%F9>ioOxMIZy*s8EvR2jOi0meu=L`i8nz$3i+#9;zTlgLe<
zbBhZI3(vC~?s8FlNE{3h&!GV3DzJjQH*a(isfHVGc+bfiZ*T7%coU#|Hfw#iWDXajxx1C%?iMvO!$uR?~}gCQ)^TGk%7H6bw(#ku@4GIa#}
z?JP}NwP>!Xa$}DumSEcqK2#`ww-S=Q#(RF*IlFBfoSf~gp0;Eyexuhs_2f0x_wSc^
z(FM*mr~c%Qqw}x`^
z1B-+uwsbIg-8XFii<%%cAT)IOy!rE^W;EA1Y>E55qRC91-yu0>sPPd}5zB%#a!b{Cx;=F#hR{_BE^Q*@FxH%7^}T
z-3#Lh-qELo(g0FFvy&Eb)%QsY6Icx
zY`PDO$D~};`u)n@Xefl24PiT6xvCCDyRTaZ!ACmJbWRP`Gh?FWdaA;kLgT$8_mPXgEU0=FD!y<{Fe
z{-Bn9|4tCo#b>3ln`0S)d=xntb%V%ACq2f*e1y2qvoC!Sc5|ZfQ&!B~m-(y@TAwt+
z@yLv~6R7X)+qbKYYlAnZUa5CFetaowEuujn@VUtlIv2d_iwl@VYc
zl+F>|Eoa|zf`=mqLF^T^f5W7kC5PU=J=<-9rinc4&_-VYQQ)N1n;h-u6xX5t^X>JM
z7Cu%Gl)4+&CX%xsI~lYr2z|)Lj8Dpc$ZAfBJ5E~LPjn|ijlqZPXdk>l3w$F^^9=4i{Oqjg$+{XWFyD(fFW>Em166VdKIb~S9)$vuDF97{zf1w*zq^sp80;?
zKB0*Fa@E$r4-SdU2HFvzKd=M+BPEC|-!AmUK+K3aw{LthiLXE*uFB5dWRM+DZRe$Y
zwb&M;>2DTNV#=R+PEA`_$m%*R=+Wa2NWP`2>E$P8-|ASH$^-d9^A9lr@>+Ntw-s_!
z>Zj!Bm{nW*m@+fCDz{mhejXU|eACu;F45>4EBPG!i{+R=RiN=1Nf$w~jX_ZmO|Cdu
zj|embLfojwV@|qZ-Kgz0K#vL-d{Qw@h>gJ4-5|E9nv<+ANg@yyb)!!&=t+Xs
zR?U@*bb)I?9VrXd0xQI9A3q*Vn=-~f#~E7JNHVUm3Zm9Po6yDP=H?O|3DZuhI8>Mx
zEJ(Wl^*om*xwi-f=A^8wd>WQK@?#NQ)v-QXu3KRiQYTl7i{F@#2K55F6?x`>_YsAk
ziI%msq?Y5DKFQN3i@NfXbcHsXZj*J%5Wu=lR(5w7q2P^^@C}4f1bu6RR-&<{dl&~Qe-&CnQZhme#r?S
z%8F2=jHEaqLYIcP`Cu?!0;5g?vNw0OggQ1F|C{cU*R7WV!kJo4_ZnCw9kSMpPI1+D)JZmqa@b
z1GM1Cpl7&!cEtvYXF6)d9*@ou-{+zKm?#T^Mg|644+>dCGaIy^94bA_@4T_G
z8+dk%Z53r@uM_H+nVBuBxA1E}|HZ;kINb0bE#D`n!4airGSFm>@D$0pK0ke_-zzK2
zDM{0H)aLFtOd(f+%hM%q6uJcjEQe7iAc-f2mz3NO3z(=
zd*<6f$E($gyvFTqKNSl*2m~uWED7WA0RRJ#uvlNxZvF7UK#TG9%Fu#qZ}b|DGeM=V
z6*^NnqG?}bnz&$hW`5hcD)Yp;&LWS_kS|@5i23mQJo1{B^t6hX}`244GFmS@i`C@5wRF;
z#3D>XL4@_C68Aq3h1=$%Ng(8bOA?Dj)So+_YSa-YTNc8RZr4Xn5l1YAsG902rPRMq
z%&a$Z?kzV_+3wVR`y?v`m1{-un$u0P@RU^q{4JcH=Be(d-(6iMTY%loqS^7rtU=~i2(
zLP9ux@2Zd6`m!>zyuoAXkB$c&PYcN6p!$n|!Ex_fW3V8eJ80oOYA?Ntl_p^)WOIb3
zyzTS8A8J<`>c21EwphiGjG4KAU0dw8n9aU;0QBPT?e$Io(?(QT8GJ`Bo3GK9dS`gK
zytm_zbw<(lKxlN6?7!Z;v3U_qJT?nr_(S&04qzHU7p|jgt_84u&nv-B0y994K<}F4
z45avCJ>4sgB}?3}tP^Sn>J#KR!<3?k4+mU&vtLbFIqjZp0itq@A$L}y*k6DPPX+xro|mHAe9eX`8|LSGRF>k>)ty}oweDpR5A7anT-UI
zfchHOw|i2q03XKN9YD?CsnR&F~Nlc2?736EsJh
z8QP7nmE)vj1jQ~UCb5N`FX${G2#Q-E(|H}H8Cd)XrnpTK-1XPUzii`WgN?~E{AI>bmScXFq-xjKwblx-j!w9@j
zIWiYdKJgNeYU@fl1U_F!m>?8kY3U}o?Epn%!Gav7uExm}UEboH%tWRId~<6_4Q4VM
z*}u8$ObgmNSW0^Xh>M>h@uOk3CmhpdDSjMZF{oL~n2|G8DA%NV*4;4+wd
zdQ3hiEAXq#89%o?{rB?(J^%89e~22$7A`cMI5M&wYJO`dV1Pb#cOR_#>a!VwfPY7%
zFNbwxz)JG>m!8^_71dO6A%RRLln%*y-nXx7*a4%IItn}+O-#QBY{rFXQ>Hw(V;6I7
zHS?EUzYi>1Rs3OD!lut9dpcp$Yo*KEF1f9M@_+a4?M6l-a152Vru(}C=F(a&tzB9n
zv-WT*cn5iw%vnYE$5c
zk`4=!zpnVODWWmioDO@4G5`<+$8SlqQ(8(_a|-==;o6){F-{6M^S(`sDyKhJZkxAAeY}IfJ35()`2%&k9E~5rgwMl%d$;
zHi{tn0Ic&nfSDqz;s}<>kGiRv+uURE?htPH-8)6(@ot^M`!$L$W=_I9PbC$7tzP@@
z-3G6UvxW=e(*}~mXnLz%K0t>WVY$k$ez!bD%)@|I>KXwm;Nb?SV2x{=H@{l$4{uJ{
z)VMu;F9=JT?1-`pWF^B{MdYop)5NrPqA2eWVZL0pQ}T3>vU
zl$4cQAbctS^Mq3q$1@@vd`S2OzA>?0A&Z9AONkbrru~5u4?)G@$|nQNhhfsB<1{nk
zQ~;yFDV3FrAS*--ERi%owow2Apf7K+s#zhTb$@R2-OnveltE6DLK8|o;lh%$sQtzl
z2M~fQnc!pu4j_BNN!H6F8!R5fe?fGK=r%~MjI4^&_JC*uFEp%W;A>6GSa}^@}VY#Uj4K_NNw#iSk6v~FJQ^0WH6``CV
zZRMAfHwx4JGu+g7$&@R(1oBJxmxukQb
zfi+BRFw))_l(bFh$Gc;SnOw{aeheX7xP1Pgz_AqANnaH`Z)7z14+S1pst{Ic{hI=h
zJsg$Js~9P=)VqALvUAAF$4Ub-ZdtNy_Iow1XyS$hA}Z=F*2cMwani)v0F`o5Zu@Ou
zx5uN0tT;74?=5b&zy2~j{LT4XIkllNIqL;Hl7#Ms*ZF4PYBnY`a7@wwbxFpq@6lFp?A
zZ{7Lrn;9@)m~7s~mKkD@u14Q4d|S`Yyj|qcS&)~#TIv<&5Qa`K9W|`_U_Yr>Cf;=PRLkaeF1zMnG@8saS^SNUV}g@!nvR
zlMEJHz=Ehz3a3R>S4F5fvO|YuT7&F60!(
z_U>L*G}3q#!T1M-2HPcvRwnY$WOquUWG8N*CN=2ufccjR%Urd0cG}5t-}Wr)=;*zz
znHAUQF))>`d*W`Jyy%BCUHl3}0AKHf}HgNe^BS{Aunc$w{bDOQUB^E)SLQ&YPd5V^A
zKF3BCgx;`d+hFkp1xLMR4G>acwdaQ=0=@w(wU4awO4l5KYpi4Qou2nCAx-~<7GHgf
zY&x^Fcn)|kjQ_zVg*)WagjQXx@rdIxF|Cl7knXMXfm4
z{4YS-sagkwSVT{`ccNPGQ$_JqOAYGR69}0Ue-DD`XtEcG+?xf&v=w|S23q&?24%UCc_h>07C?p+|8Ay872Swm(tP3=C+&A7W
zgy7I=dLR_eEqhb-!aTk#J$E+
zQ&Tf?pxR@D1TTER7sp;V)SB8>r&|oCSw=SUUoI~ejV|wiwzcc)K>Tv@;Wy+MedMGV$zeY+O^7cQKP(M
zRVT|7Ya0itAtJuGyGh+qMAfD%|L)KIdjr)3Hy%9&r{>6o>peQ_4u9%dSko!QQbgob
zFUPb`p1iOXTMhb07Kj@A{T^^ELgh~7*kR74d?#4+-L78M!DS8US5Y;gokP%s9aGr8PWwKSn=&`mS|a3rx*C$ae7CJW5W!H!}nC@?ODK89}dk6m7^Om{sr&@i`RvUA69b@DqDwa}z|
zhP%tEb5=zpj|+h610?YoW2i(t0T6htmi&74*%WP$hju;M9)7qP0}xSe4jviAzWN#7
zQ4B7C^0mZvw%-R4fBJ{H6Wp`Q2dEOiHUl-*n9;5LV-II6soZMxLxWo1JdNl1|0K5W
z+mj_t5=zxYtnLxbE7`>B8vn(jf8E==Gu~RWANBA$_yeTqD$%6=9l^BU06FDNl<5^B
ziUF*_4Yr-@?UAqoedw$EYkuPj+%~9esrubgpx5poLtP6byHL-}L`$Nn`t;^7@5!b|eww$8w+Sa%wCBN~2E2b(=TeP71rfb@OJJO#6l>LF6=N
zUV-HG-*5KDU*G`8U3gXTVfn;WAMUM>z);}Hvoc%``{OR0Hjo@^Y9)cAN1Qkyc?_#2
z-C0TJ5>CRK)}Upm4u}9ifkG|+SDdfLw+b3+`^Qr0>ZGe&62%VkvhWnI`QKLiE8Mm3
zSPJNAw+ohdOf>zW>bQC%(^uG98nQFOro$VWoNjXQ;)`6G;-LM#fBz=g@3!zijx6oH
z_3Jdsvm32#$YR8xtdVk&9^k=j`xp-S(TNF?*G8xVO#N5TWRm@LF|XQqRKi*C-iX$c
z$ymXho4jO)Y2mwfud!Xg)cLK<7QZ7&n=gAo!u4_X*3I=BWYZpV`2ENX?wpFaG6IZ|
zs1a`O3IAXLKnoF~xrqtCo|N3RF-q0$+PJc{z3E_FL^F!vy0Bfuu;R&%?Jb>vOf|w@dGUQ1D@C&8rP)H$pED;Y#$_SDr>4X>yyt-E?8e6Hz9g}
zwR;*XQ9*_s>hCif8+;}9{r=-B+8^82Srf!F&S`2sF7a$w!_KLt)EY&CJpIcII$<8?YXfh+|$pG)Hm>o8g9J*TqlPXRi+C`v
zCE&rMQ@u`pXh0~5k%{-c1X(gHT(1!kfk}&W@sUIV0fC(6MA>b8zP^_x^hulnoSFzK
z5yTiwn#-|EGe3w~=Z&N~jdbgU*r#|gTpTT9)Wb>7u?q5J<<8vag$g8dfJ``mT#6-K*4NC*+Xrzh#9r-1n
zVp_iGT=>_tzegwDet?cYjsH@6qmGicX{Eh;z4zPVi~Yg!o1{qv#}=OFgRZ+=bg4;W
zrVk|IK>kclLj068L;6QKVL~bvngi8J
z`hQ?>0=`W2&JZ{8AXNoHWVrws>l-qUNAHBpfFhw-_*fDB|Bn+C5tmIC^?*(Q)gph9
z5p+;eRG!3txMJI``~Y5$7V4Rn0Bk}-!D%nUm`3X$}Hw>j{df!Ya?rL!RbGw(p#cWWOX92&$xB+gvk
zR(^i!#oIF*hr9>J_hg=I^O43TUZ5`?>bK<7>SfsI{f?=|`x4@|L+w~YzBqGSu~S&-
zxdGEojt+ahjrXk`8e2dxbxsIh&idYUzRUD^*v1zQVGGo34!fV~y&E_7{(Hd0`p)kf
zaaFaq_uA*?zXQxmp@|O6Q?C;pWgXnx-9KiVr{@%HfBn|_z|KI8p14VyJ&B1|S8d11#;c5MNz5m%noxe62uvW1kW-R-`c2}N6
zcFO|1wn(XYJH^Y(>jPBpG`<~4$5qSe?C3YM3aS-5J#}iY%IHj2y`9ElwQ2cjH5Els
z&pqKwl&U@WO3vI?Zv7WZDS`R;u
zn7^i;7{*w|&`&9?zZQEuTt-M_X?jWD^YuwL*)HBcrUkC$KHpRPupQT!%R{l*^i?k0YW-^kBw^thm}WB{oOW
z^$V9K4G?WQ(5vC$VXfARB`9e-GJf!41jz?ES
z=CdHb6lJ2le$BhWHdG4?USl)W>T-KGjjA$S$X8JJcQvz^dT7qKm6JJ#^`WvzVQP$N
zY)<1T4Z3)-+FNlQ6NjJV&1IUgE}9!4=~A+dkpR{ozUrWI{Gl03!9u;DY5NCtz1-F*
z412VNH?Cd#_Tj@@xm*68f~Zj|j+@FH`^rrro9nM~8Gn_l#JqxcRZ_@n^@^^<++%kZ==7)MO?88a@L7S$$C&L=xEbpy5_+Ee&+M
zQXjCaD%ce!yuS)EHdJtgQnrCk+KH;Z5G&*D@3NZ4b}HbnBCeC|%vV(rKs<{n#t4J)
zEB9E0dTqoB9oy!%Xrg%AszqLf4w3RG0ny>iZxh>p)mN8unnfk*=)C*;mVWW1Ej+jR
ziv9^9_G>yGWh=atK9}35h%5!rJW_i)`NLUQSBR@hN$~w%1LOo`t%^D-Og|B2WM?N0
z)wj-wQ;*Di3#~K@-Z^SQ$+0oyQ}SBQ?^0VhMu8lw(rn%pI|cn~JLixuEGM?q8;+E(
z6&sKg$SYE4#T|N($sw!x>briPE+;fXo@%k};bb(eoRS29R_g&iVvH
zj+x8iNK+ww@YXnop#(KY2n>-iLtaY!pooj;97=_D)HbMqH``&YU#6U{xpZ<`=5+el
z?f12l6E7StA|MFIc72gNux4EP1&kVZ^mz}6D9Y$e1cJsN8GF?mS{m3b_|YshX=KFy
zuMcOZKkxZ_>*O?~66ilv6b!{pA-AQ^-ObI$fjQAMo$bO2bGoQ`V-Y3O&HoQ;UjbHS
zx~}~p3L?@fC}m(FA|@rEpoocMVURkQGziiS7GPtLV}RoTVl6rZVMY{CR8$&7y1W1T
zL1%F0>~r=$|NrmHYhQaiBf?tWdf(@{GYXK$moC|S`^B@q422r+OL5nM+;!Eq=v38N
z+1YvzCiSg0!-o+07YIHsN;^^JRv#?}^__N`@939XB{F%iPb0GHRRzbxX$~ld^foW&
z;o}o-^ju%w{WSPOo^o1~v-m{66Wf#Zm-H%VG^qw9ZWBBhglq=$nwb@;ocQiR7X;s)j2pygEuI~7@DYWu8S`s7s-+kTxkuYzB02?;
z7}>=UjGbl_g3KXX+f}?0t83dFjF(L(d=_4PkeK)tm~;2uJ%_1w=F}hqbGcc~2*kWS
z$A&;uVVp+9s$W6Ucg$dLM129DHd(0
z!_*G`ylBoGcBH|#DUG;)RviP6Pq#Fm=Fn#l$diGW9Cbmw;5BgV48nUr5?TB{E@!ff
zo|jNjTF|yNR8<^o7=&B166^&eoIhed)#qCO9TMRlP)Tx~dC?5^;;Hz*&f#ZO*
z_8|1}AX_`F&CDFpf^N}VtQdsi8{}!Z;=!junmoXTslz8@IBM(tfiY}el1%d3FyuIa
z@c`(*1_D?p`@~xuB>vO_RJ;q^_%J6!=>Glt$WU_IA4LVtBt4o>|krfaDs8Hiif*K!{!o%aQ8Mk52a=hw7{h6kw
zBtToeEr;jb!fEF`m1dgQ;cCHiF9*yto%a!(gIRnt1=0WbG2IM(T_AkMCyv6?eC8#+y2uLBjx~T}&ICc>*h(hiVJM>Ny$K
z!M*G5j_F@cClsQx2v@}DwNtq#vc%WAXC|J_$yQRi80NsB6*L95s3>3&TIQx4_V)n4
zx|m4!z_E>)UtRr*MOn}MS#(4|_<9PD5Ck-pmJT={kcq9oSQ$oHAb<{oqEZ%4QLbZa
zSEb7F@~%thX2X*~4*_Di$khcQ5O~rI%m;*Tou9FC)59UfKB1gPyzKT}AowabOU}&<
z@c3zDY(}^vw6fKK1nwjug`hUW2FJ^{&hc_Q3`FZ4&JCJ=XMx5Bs9RQk=x57$1i!x~
zEb6O9l6gLULQqx41wsK?eas!W5N75hXxfuu>aA
zO)o+)G-dq&B1MP9I9#HLnIK&>fcHR4d@_I=5_UlOfH1pDoGND{J(I+h`Gr0hFQ^zD0gzQ5BD$_1F9LI+VPc<>ch**BRkgp~$bOX3?
zsI~x!;^gzk3C^I*k75flm)wFS8vN?1tFDg8KR!)w?dW9$I_WKpB_dL-swe3?
zckT8)CU0JviNJ8m=}5w@h2zuuy7|-npmDatO8|95Yr!IEX?WIQwwM4RA|&9-cZyJH
zjSM@2|KI%%*Kr%=6yLt>8G0BW);CA-i{g%9@lm{^h%#veGxd(}aIjO7D-LU94`dqc
zgWL&;nU%e5Rd^GhqYfhIC0^W&<4^nFZHPr@G++j}24L0etwU7CsfH*PfK
zb}Pv`wcsglBj+^kbt?O!_8?F_(7uLa)uDaYLJy~!6n=F2L#=$@&=1Dj6b+sv33`su
z(6(`-M8ryw$;50i3}xXT&I{qWv4ta9pYQ2@zdW@OF`S|#UJsfPkdu>h@%r`CB<S
zL?-7KXb~aLAc-4ZqE_q)he+D~$ItXbz=Pm}#Rw?PK{T8WCV+(nW6ofRfVyldlt6fF
zkZ7EL{3)U1f9CiB&|&gROZ_RmC=J&ze2E-*p#kBN10LwM-OCmnA(n%^mQJ{u34<2<
z3ZDBmqJSuSMOU^bPNr%1u3aS7rAW6vmNpc~ywDOr6a_N@2X<8bnv!8VnBJ*h7{dKH
zlk%P;D)z@=FCZZcbYkRkMpUcf;wsbOfIxl>kmDAeOSn8AIuCf
zDW&z?-Py14W2I*ud;Dp9@<>tOAhqrvB6Cr)r5lv1gBmWMhN#osU5x&<%nZ)O}9^{cv(q%#QnFF7hfW;xJ!oB0iEDmr=Oc)&Z!m4MRs27#q#q
zyRT|wR1gjE;e$`=a90fFr}fnVmZQoYef#REB;bLU41XIZ17`=88wwGs@GAn&Ngi~1
z5D}nL&EGUK>$cx>SI|0YUyyuu_V9
z;jN^io%eSUFSKf!>-)>{akmuh3@gb^Dha-%
zUEckoH`uNIsp}+f2jhJoA~@DYt2+O2Vu6WJIRBiPg9_dflJlojPQF^jef4(BxPAV`
z4lP$2TDPAOni#xSWA3(@EBrUEsm=U@>#FIR)0X{@L+<0O!4SzPh1NRO!^XqLkV>n1
zkbGJ=F2R1;9xvazX_M8b$kn89*U>oyh5NRSxM+z4)r%=Mb%IRm(9?1L+WPT+4XA|P
z77N$3;*h_V)N@c-Nj>;N>PCA4*hp9Z{L!!RqUIHk?>rXKD(M%WihhA-10)&1L#7$M7r-_jpjK
zJS!$GSSM!9NKBFPG12BawnKL6#z=#drs_?`^{(nEvOPz(;4yQjoO|R?kh=>So|d2XH}7smz+haEDQr3L@Xf@G
zM>6o3F$|V1Q}Iq=&D#PF$)#0RlUBkVll>g3lHrQ|uHVMM%uvq$MUOwda^Harw-+Evt
z0};ikEEb5gw%!iMUAwO699rM$4f_fZ{q*J$;9P^}#7I{fzbJOin}#k!5zU8%D%~}f
z2nqez5r-Ui#Fy&XmCXJ9mP?qmn^|;hcF9SPE%Q2#g$8O#;X>0F&-iQV8}I7;858W|
zPck2^c{W58w3U3sH~V|V=Dpz+(M@n($b9VJwQw}xRLbVASBgu{$7luK99b*lm8Y=O
zX%Xd|u3%w!VUFcIz~vY<15a}G9G$i1p9_BN%&cH7xKeW1C)o*1sTKD;M`rY7)p*Ms
z;F$Q)BKnG7@6$oxA@scCY2gA>01GD@s4Y!o;^dVGSRg_Q8-B@o`^$FzskU*Ym9oo9
zx+1E#hFairThu>)Y#MSeWJT?nC5t~!YXxll=(?}^=`(C&3myp4~
zf@1>Ge^1TZmJ2$NsfJ>Ww+sEWq|{+)gQQ-syRPBC{>bY+eVgNJBoH0OLlMwUeamDG
zQW|YPA+@*N6#tvyY!37b*dnR68aFe
z$v!gXR#O364dO5hQpBLQ6i4OQ(~NNHMU0J^4wQQ2?^
zU^_H!zX`)-NFlW_+>-Fs3j5qrr=1u2S
z%vxA$$w3JICup_3ATp&8mV#^oO4x=p0RvX;g~Mb<2mc0hhb1iN5XNmm2^x%^I6sfG
zc$|=IOm1lFKu%HS
zJ8Y^(eeKm|iDsmZfjk9~8?8w8z`&{0S@mDuVg)?ab7S|O2S<0IrSJ)h-tE=jV$;t!I9EfHLgCU5eMbyEdx1E)0j67{bEn-CA
z_&IYEzBDqRFd=sxx7Thj-6`IAc)CITLPf>tF#6vs=mh#3zh(Zkb2R*8wP!VS-fL!S
zX$T$LFURvD#%&y0OUn_7$YjVRo4Nf+TX*s%;~!Vz20exEy>|p&qVvFJIhW2J+sM@W
zC6AXUXAnjWb_bP%KZOp4=*nh8eiCa{y@{2Bv!10VVzctwqT0V`btNa=KAY}7r!Z+8
zFNVGve}NKK8^SsC*^vJSYoM3r?Q~j$fO_KWC%WuUx*MiXMINaS;z0J%5S|vP>?g8x
zcG%FP?tA9CZ{_!1J1O$J-`Ee?DSo_sY9wiz@iD_?&RYN_9E|qc!Rpd{1OWBAqV?gJ
z!bO>yZ*bA9&6;|A@3r+ntnl6-OwB`_cWi~NV}F;n2)fIyr16Ccm%KapFp=&OVT}wJ
zh1TyYEPOj_{jW_t^ZxjR47qe(&mX=a0enLu*NpMCp;(}Q?Ck70s;a75
zKaElH{OVpH8ZS3PJeFkTa6gIfWzsQKG14XfLdx+tiZVKdGI2|c>vk)6R@@tXitOUrn|CL%VziysgKW|
zMO79Llq;hNey?7wJ9zMy!s?$TJ0P^CzX_A=S{Rb?qeK3T%yqkcE+LukZdviH>q8L3
zK~PSB0kV*#Rd)16Fwy;Ku-p@P%@Bo3_3r*2yk2MoKBsh{-
zmmvpV8>~ara*tqv;2Arg?q5twSsNRh)&k(wfJ*R-%(M7t1+>>nr-FEgxV$Q&@ZCF#
zg(mel9QU>Sf$~ecp6L$hxs>pn&gL=6dL*Ov+T~MpY~LkUM&VyCtj6cNMSSRWEzU);
z{Ae%D7M186{lY=Vo2u_DO3XWZoIaaBwo^~G8nANBi*W8)ie{R!8u=^r?+eq>Q`BW|
zFx6_GWi<@VV!6ud2Mv6oS_J~X_I#E~bN~9tlcugg2Qm0qS{0sF_Yau!h3akHavP+z
zIih%dn*A^8wQ#LAe*7s(m7{1RdV9kSA?X_9fuZb!u@fESPBdR%_~qMJshGDq(C9tUPJozcTGYTAAvnKAgPgFuHl
zy{Xk_Rd&%Ji#6KE!%(Rd($&{WUnMD@vTb7;5@HO8YOQpd;0>3A{jBYL+aGnyp*C+%}|
zbbP2$yC<>{R$~ET9*t}^*A7hPpHX9(tS{Nf?~wWEx1#>TB3TifCyUwH4W30bR|?(4
zPQ*Xl79=EXIWF9q7kQ)}Cg*MDPKmBi!1`&jY9Pv8CoW)L^LF5lj2i}=82a%q;O=V^
zP1F61WH`_a@I=B%Ax<)IjCeGpP{c2W6iku00qNjkv$5qiY=IgBwL}?$k@DKNw6(F9}La
z+}N{f1j~xyzG+CO&w^S;Ktcim0AAmg)6xhIhp}s;Qq;jy#}JS;+o_Q$SgMS$b9PIc
z?$~iD$ETW-#NdHIl9<&^VLVI>QUNWmyNqTo4|q>7OP!YYNM^)~E++jKFXOQ!VOD5Z
z;ar;^4no5nf>{(DV3}BWuk^|TwVWmVNv}ub%beI5;BD6X;R^k}2IiW%DgzLGuqVn&
zc?7Qpr3*-3RXK78*;EXJ9~LBKkAm|8_>N)Bdtfzb?y6gUt|vClz6e9*>fZ$>paTWg
zdmu9DymP}E)pAmV>aRM^SaSIf5AjU_N%k{~S0?Y3X=Y_xTrS|84$Iz~4w{Np!gKGN7lb#iP#69
zW#t$QJ+5>AB+tz)TNUZZ>fu+H-B508$EjXot`Kgun$xUB1Ln*8lAw>0MTp=s%v&pl
zex_P${`vF}6|0=)kIBLBmvu`TnJ={q`^SE1*^i)?y4yQJ|yt5xlJ
zyqz%fhURJ%3$I97(dtkXmoE$|pe$|CJ0I!jTay{Q^d6?Cp?uL-%WvYw63EMwq@HZ_
z^pgqgf7K$=HI~gnM`y`zv*d{^5uXFXSliDnMJ&Tl^H4w=*Lr+NwSoep_B_yFkGUY)
z7k5G#4XRk}^5bKQi{j+MmFVHpH|Je*M*Q$E-Xam}W`}c>4%h3X_D>}fJg6E&
zVfy+xr06vP2d*wWK)66BE+MX@v3xWFt^8;jb=&mtV4LCKB`^;=zsIS&7S
zK{KvB^M}rl%TYzym-lrx;2tdC
z9zkUnOz=<$B-Borfut3x735xWQrPj=>$7Toh)CBYX$@SR|Cz1?+QfXI>m!4Yu5=
z?0(5bxY40949JP!e=p(*b}aB0mWt-B<>@8&K+-;-H@4x?s@yvn#ovh3N15VIgwPj^Rnt4|kHA|oN}|C^N=g<`
z9}Ys1w}v8}Oh0=S8n}vs-*xY`f9w5yhscP+dUE`?E}`(km4E((DE*3Y>%3}MHo+6-
zFvK2FD+w+;A-sVc48@mXOyO74{_7>di(}{ImXux;3{yfCSSmDZQT{uM{k}fg0)Zi=
z;lb1?fhC7xoAGbNLdS|VAg}@9ehMyw3foY33MZ2Wyyuk5k1_n>l`G>JB5r@+nvRM8
zjce+w1L229kgT|rl?PG06ABJdKTi?U*JSm+MJ2*L4ocn^Sj8IYtC$bifk9B#ZXCu+)lQYnqXbkIATp7pH*+_&nOH6s^A^=ar2?AmYW)P(B7G
z{O7-{Ki7Nm`^R~3gu|1K#HJYPu@M?R-A_af{*)n*1Ym+CSyp4D8UY|9!!08Rp}jo?
z=epk4fgkVH%6vt
zrjFxfYF33Qmngdg?a8G^`@vGxRY6J>1;f*fp}WWAGdwFi-Y|lFg(z?oD`c@?vY9zQ+9_!=#EfSDwQlp-bZAc8eqO&(rEtHZ3F)cyGcIk=Ixsp&8FM@-JaSZeY@XV@ti
z=`RKaP4-r2yY-r=V2YjmZlQ%DFsL#Pd=cp>fFwv@=toWwMPlP6Y=fA78iu6~Xv$Ek(jngbl@e`T0?e_{9qs
z(h4`jr-m}d(TXdIoY1(5KSb(gm}Z;|UYGug6B4{EIfL5GM-#OH0U-U6G}f?mvi0uy
z+T4CDFSN)v!U1p4*HlP2uyHePKtX5YgMfP*$`TqGP@YkC$cbJi)q&rN`mj}zSRb!h
ze8}hd1D^w5HcaGv7jX&46$B^i;
zc#&BVXk1=CE$_@>qv&lB)_oH$^FNeXR6A5QY-A603$nsSsl^&Uq87VKP%r#NtGMdr
zqs0DIw_ckWOseDYF-l7DZ{F&pKC5%c=a8$@yM`_gl@NATgTe1L;%RLk%~s5SMAJiL
zYm10q)`hyaE2cDOcsuDql;Wi1Q^dWAeMwoa-1#I&2@`V+ySoLt0j7A*dJf+Or
zNA7`(gqnGd<^EsNk%i0bu4?P#^d1^$)zm)OWIBxUv-C$YC_QLu%{mF)*M}H`0P0#m
z*lGPLydOS5KC_wzX|2Kki@3Zv=g}<7N8P^4b3@nEJgbTk3d#Q|xm!E@f;ObX1%A_W
z9;r%tYE+2GzSI08+Dl?S{`FXcy-|B2mXvyFj;`z31KSs5134*P`Z4e%drF|jf-7_Q
zl`O|SR|~OL|IS?=4f0g@rUfn;Sbi^TMe7VdO~&HrMD{d!<~5j0il3O&KgdHBK+Kdww0R+r;x3DVBj5
zEBPHdc0E7-v=R@?bE+pgZ~#3eEpjo@(YeM6*x%w!I|rN0UCBj=YdoaotS7QJu=E2d
zZ;Fk5B2OA!-z>dcIClkvgxC?B0#WV#UG+O}*ad3)tFZ#sKqXx4Quk}B3Et7~f@SxK
zH^vr~&Y25%l291QFfOgjw?U-NK&s0oTTpVcP>)gK466hkHjbeLm>z}18LLk{%&9A`
z>5onjEiXM?*T3tkV^wTKVY8mfQ%hma!l~X7+7^JRD4g!-)6Zssmeeq5RsapAA^03p
zJ(ek)LRkb{0TE`RNw8?Nn$_SgLs-sqG?hdwYmp5Vb2XE6FE<^IA9}mHYGwYX>!m`_
zhJd(<3r>a95XV{I&`LYshE3xcDZ!1QVw}k0Sq3Gpqwcowtl^#J*vz|w@(o}GgS`lj
zp$QPREgQ|TU>I-p3fug5!|=Ri9DZzsey?=c-YP08RD*q?{DG=jC;J}GYbLm6lU>al
z!^iuhFbJ*3=naCvQ`~>EQn0^TDxbvakH$JX)bkNlLyY85DmH%{W@~Cr2E^!{nXm~e
z1`!{D#*^BkEB{+!DnI7sfRV)WPAN3{^YKH3cH2YTjF30zjPKs{9+~8os3R;5u;M#Y
z8B^F%lJOT)**$AksGVC;&-OJ(bv@B1%GED6-mBDE!H%!8e*GVYI~PoGY1s2DFJPKX
zP9+`&#*NHGMiO>!-!^o~+9^dF6%L2J3O^Mvr=CN$CV`4=j!EmO^}U&<#$s5
z)*)b-M4O!WAhWtHQwS{zGx3rb=wlr)6Frq!#E(2>%Ht%C3(w0M3`5jAhP5>`aZWjo
zwN8bA#OWP(UkyFY=-M;$*>3ZWyu~Nr{=WQ2&{u1|eF;L-rhRByo66EV)448ExMui6
zS;wc9u0txRLBk>1zZMS1qym}D`$^sJjsAknh3|SBtA4TS)Q=Qz(Fl)rPYXQcs^Vie
z6=2%)vQ-+@gCfVXZMQ+p@3rl>aKQ*x|Fid#(n#6`F&h|$s&HQktLuT6E{$Wbg5Ak^
z0<8uIGuR%O`X$2BFB4~9T^lPtYoVR9`!$fFalEb-mCX;z8l$zZ&>Vw$F(_9_I1AZj
zY6XtO4sGQmx-^jlBewb!Kk)ZB`;K|kz}07(-4CX8w4Q9Mxrxa-+@jywB-lEgZwx#S
zG<~n9=XlH2ELCRFBI}#kk7UptTmPniY=}QSGCs*Y(0CujW>77ZFLCYT9`0tPvM~?~
zm3OWp^%(?)lqHN!u{HE2&3v$|jvA4nf1$d=eI
zQ(SjK<%;-0_>b4#e$#5c0nwJEB|`h5yDYD7lIGtLfq=gw0tGD=o_A@vZ>qUEkU5N}
z%W33y5U@b?aFX@Z^*{!Uv6%QXybdVnI)qAX)u_jqkXFAdEbQo_HnqP5llh-QwE|m7&3!os`EU|m$7*60m^@8jSmyTOynEd0#=E{HD*MHgBGkMLC
zvc!LKsvR#5E~P!xcUTWiz7Uy_GIvQ8Wv4V
z?%v(qwlS`*pgT!Ug>ZJ#zomBPUB(l6f7i>J7k`$J4UeSAe0w>f>E&SMrF^G*uScMW
z1Y{p3y#AYde=QIEmELYET0hIz^MX`HH!Lz?rbsx{&cZ`rSCsbexK~-_^%6@DjwkMw
z+-a0W3;`WFgDL-&Le9f8dI7Tl3t1DjhbnNO+_}EWbOs_so4c)h*PH<%3+W*x#~@wc
z7xc*?`4Y?lcH8Ud_rmt(JRCfC^EJw-gSM&6bwmYq@)~8)&<8)qN6L;~LQJ=wZ;p=c
zwpg^L?7X)n9rM3a{}2Was4~N0_610IIQb2ix+7K5`YwY+v(vV6wpws;<^aiJbu8BtOl$plq
zoWJh}t+Y$|o^VLqsuEY7J9}2J@#t6NNUKf2pN;DaUQ+wDVu8+m{MKPLB+rR-mbrN#7r75oKHF>dPgkP`}!dE>`hQ&Zk9
zxN%MCA06DypSNKmF7Hm^wQ+I(b(gfK*iSt~^dr91TV0@Wd-0^F0~XB^KjV&F>!0}v
z0)MDLdzWS>S%8@^X4Z=Q>{kp^AU7!@ps)v^^donSO7q#p*=wcl=%KfV49+7Xz_MV4
zZJnvPZtUdL8Ugw#6$TXoB%6MSluFRkj3}5je}05c!!2Z4LA^ILRie!k>poP*WMy0f
zalnr<=J?y&+07*nK)6g^j1k;%c%v+^YPYk$@q?$?$SAzTF$?J}PIVf0HVB@P3Tv33
zoc-vxj5TIs$JCt``XU$>w)q*UjSM0WfCRGc_~_EJ17Szvp;Djo=g;5C+OPHxK
z@2nrW=<)ugte
z_)`d?1<0=rECMvHxJyif1H5W&kV2BG2ohTal`Vuln3Ey3E8L5+x^WCtt*{tSDH~?L
zg_0pz6MZa9lUtNfg5uOB+%Alw5&Q1vmM*~WLbQTT1%hL&I-aY{8djrnYa|WLJtszGEla#$FYwdz!VMD
z8I^FLxX{8u$QJ74Q}D#Koii952jB+kYgiglW|5BVz@oqN7=;-eOa6Z#uTwKC&fssI
z{NIz(;tZ0gu>o^n5KQW?e?Z8vdbk@Lh>&0aNQs3Y6{XHx5Rpk-wV(MiPl&3{pwvQl
zy*D0%+mVsKADa$#?SB$@w=7NZ_veK``1!Z>7q>9hkgnN1!MIJe>oop=PM1Ljp4}Fm
zm3BLlVWhsk#PjqqyGO9w`k3&~&u&1f131(Ows4YFj)miGsBXGxp
z3`^&|eL%hJh+DowzzB5fk
zAdnR^JjFEwfOL!LzZ875J%La7U=S+G%z2XDamK8%WPcc+cmp06hzM*xrJ+M(ks3-3{q%a04)+u*#;Ahmnikl+nD-dG(dCUs-NA00h
z%;^BALLIe&e5|lXFIcQHr67?Ak`}tp`4C8w}odOew=_MF46&
zU8IlbUV7%xvK!-_c`Lo1uN2$xdto)=!OaH(9H^H?_gt#{|RV9!qQ8L&xG3TIctjx<4>rdx)Ps5N<1(dvea~OogF7w{>wF&MAw4R>qdXjP_aGEGyv}#Kwth
zdUP(v#!n3qJMLd8GeimE;M<3vFQ=Tnjc~SX`{LJof!i^ZKR#HN{Jzk<(W3fG^dV>S
zeyU6z~cvheG#*J(Y8g~?5
zAKEuLBtPN5X&hr(F+X?YZ2d2eUhN&zebxd6;6(}~G;sYZRn18an3+jM
zf2b#Ry{v<-OK?gU4Qw`L@D%SkaG>Flwt;y^Xh;Yz^AQOun5`Q4bFdE=n#zlfeeY5a
zBhUHFL{o$d`$a7nAN#hwQ~d7G)w9jZzf5dbUWm#^*s-vYw#Y46w20flxoaxSJI-hI
zv@C(cfr&c^@_Spyye-$=AcAch$@zI1cp4+1aA2D8OSbR^0*0)0hg)AQ&!b+01J6v-
zF~9fL(P;=B0e-IXD~Hk9MIKrd)Z?~R3&J3WV#2J3LF=L_<(4Frx{UL+N9`=wQ!34l
z`Sb(#svpKtIy2V%ojAM*RH%q@%+;Ow8n{cl6E3%-rMaTTbwru0zgPs*kFKZ$|B-z=
zK=mv*_Ht;EhF^Njto%0wy8pjjRfhy`#bBI901E@Y4*73LU^$31GjvGC7e$Mrsu-n+
z7Pu^&MuvJG?vLUf>UFp^LIggj7!plm%A6UPv(&!*XXKui!+7&9ZLCLGP|B%6IOkC|
z+bdr5UUeeIkOY*NsPP;LH(Hzyz-~fZGnlu4x3MzkMp4JxD}XI3@KrYJ0ASZb`wx$r{*a3SiH1IzOlyqyxm{2Pc?ok)Fr#0tD?Gih@V*w7O%ig|<0a
z?*-RItt)KX|DMb(_=tSDm6x#;Qd=>2|B1E>#y(?qVlc%^9wgZ)Vjo!@8~kQ?BiBdu
z`~n-upYcw29{+@uX%0ZaGywr^=%DiR#y)7spRdRHxc;Tf_NOV>xe>}h4BypF
z?;}!6gb_!v#Q93+=Aq==A0!0JVeozIJkZT`cnt@X&^|`UG6?`pb3gWjw))*4|4mo?
zG%nMoX|CTiwgykE6YbmC%-5X*gBD3$&c#K?`
z*u*R`Fb{&6dXBdUYD`b~WeLp!C+Tz(3nwrFc7}i}loPnS$fU0ZL0LQbDqJq6J`gw!v)zTsyTR0*U=_9HYw-20)=&A^
zWQ(^R5H+OWXG2{eH&4QNrC`@AMmYA4KM*0>T6t~>T%m0RIJiPd%7om7>?#*LvD8o*
zSeH8j`JY)1On`{%odLHG=3qc?vPH7!X#lGR@TOS$c9U&lValy|_t!yr>$P*^)#^Ci
zATfbXvN8c@5U%6Dg4Ya&>UALA0W<@5
z5pJuQBus%)fs`X=4F-tPfyXKhL3$*O!Bha-2Jq|(AX2oBE(He9q`~fO0Y@%9`4lp`
zcyX7=3G+jWmX+i8UVA}0eRQ?pIRX>^?Aa=qaI@~dXn|qfvi-|O(u$FiSFgJW_Bd#L
z4&rWQ>(SB0^nv;aQe8VW6kzH=#0Kfk@W%i^Aj&&x4;e*~ZTDWsU_CrTz)Qt=If40+
zP7S%G2LWW#>RhA^p)dU&66G9BN}Rq0*poYro+$VuU>V1I(DwC~IU2HPJh;Bvei=|T
z1Ts*N8$q%#>4>N}JSoc#pI=j4)=3EsNVOPMU&C*Jsp6NYC?4$MYXL*B+D_Wq(%!CL
zgGwC$FF;Dwc9gv^V{txlkt&yN7;sbyHh@d}nM;
zaLs^O4aZF$sVH7wjfdH19d8^*y3DFu8Df4%a%&#mzMWB(968GEH
z9Gzm~!aHci!xdl5rrRO)_*3JhLhh@dO#H9Mo}68w7^Hj+QtgAWaVv_s$M0_C*k|W`
zV(nV@oHC%^m;tEUdNc$?>Ez2p6P7T87+@h2s~nL~aacj)!$@D~NIJEbcXrwYHYts$
ziNsoXzlJ!uPj&*#0|;Cw+3(B?XDZBF5OyvgQHqvS>fk#9=oyPT&6)i0mgC&}GO}&k
zHcjXuF>Mj7*giE0zMFO68R_(?N*QRb3KTx~g5YuLz5oIbK_RK`Ox5rqLfVPyp`RYS
z2cPwIBo}BJRB2RBiI4+4s-B_3l9B;vK9L|kB^nPDiYxMm6no@{fYh_+JlY$3(0lW_
z1`vIypeH8?G^N+_py(F`&Y6^(3KgdztBBpf;_$vb5BW;dZ6odo_1>+l-L|IYsq0y<
zj(Q7)aSn3U3St%tVneEY!pDuvnvi=Pr_s7_N3`p$*YkRPPlOvRU-NTNi_t?a6ns`g;i2FNB1Yni?O|1}>C&*DBB&&WSlt_5u?J
z3`?u}5(AFWRXuQ$J%;jgzd{CO>PW)rRi!Xw2ENqGh1P?TCjnF?yvE%p!h|8wuI8C%
zdReC{^k+<3sKya|kP&K|0i~*jz_PK0sg47W-lwLa8YLjirz*UK5r5(&Tg6p4LKbMGVYMXbWU+lA~OlhtF!o4ESx<4$vf&KfLv_sV^
z^LO7U702C-htVMz0nAA0`r0Mp04MQ?6;inzow(kHBI*<(8GXqJt*XHr0bAyIVs^7$
z7miUD7#aqLGz(wB9e4$yvSOHcU6j-bd_ArH0A#vM&OwL}DtPZ6*l~YFn3dM-iwKbI
zy(*7Gh^;mu6uPT6WuNU<3Svji%`qfDDGG~J2MCj1#(I&N*_$NE6G5U*02B`kQ$Lw}
z&0YvAfU4*+uge&#OU#5Dowe^=sVa7Uj}WH`1NMD*DkfhucK&*C*$tVn6Z-huqy+n}
z`odw-VcBpZ=diGldDB59r^v>8f&OqU+qw=gk<+LS*O8H$|B#z2o3`d#`
z2b5g#x)ROXlg>V=v1p#2c_3I*;g9O}Fg>+h=|#Sz#l{$k&mFtA+RzF}ykKSO6FrO0
zbJc;tyt2J02PQ`gb}0@>3cxcf;0l3_31V0|_~Tvn`53h#WPlEDiZdn44QamdlCarr
zNJGy-qpgIUwS=fBci029pPS26-WN`oW+##4tonTF_T&ol{&-_Q&D+`P_rTc|nPOZk
z#+IpHgU&USw!Xc(L0dsbtM{?XoeI^0x|TW{+xbB{7qx6Q+$o6XMxfTamV!lsXUZ)U
zX3q|G-(w;-S*X=~@9%|-&NO_1^sHJZUSxh5E*cLUqqD2Usb$d@g*l5|1&#+5KSky8
z_yg$;uZk?cv>Uokn8U~EKN(+(IX$1zYYdOm8|UMamJ
zzFEMmW)V+mZhUH;_U6s(4DQ}|LKI*>w2XPSVQl{P@RdF_z~8fQfRv4&K5>OTSg#Lg
z0j4z1ge7KaPY@y%FqGIYL<_wygrVq-Z#6dopf!vN*;9TRvVkO+#?a#0mt(+>r4SaL
z@i$isBjZaQI6_%~3I3ZhBbYR3+WV?0CKzNXz3*UIp{a9H!fA|Ik=XuX#KlFn_00kE
z1oDKO%zGYGmnqeEIgtJjkB#+#h(zmVJP2#+Nx*^8DK7I7^1?z(oW^897xD%52fny8
zVqndJ^pOl;U?T$Up$4HHqfA~)j+{tvWj((%cQa$-tVFW#_YZUirf$ZM4v}tha|?>B
zI~0b{JDr9Xh7#>yW?UT^YW~fxMb+JA9aLum5Y;O>E4$v94DUO@E
zV*`67=da~W8pvzyovTTE9022y4O5L{tqH
zHCP|zVBtU#oPAS5dZ8lcp!UdyJS;za`mh#xmYe3RBElU0B=*=mp5FX{YgANzaQqYx{F$HrIr$_12g
z;*{ph@3Pu<`u0bT%la|C85R74k(Ep~Z^%yf;q71-yVKT-u8d2C_h0d@SmHGrw
z;H5|D1H^3^e8ti$KxGU@Hoz+GA>McodHh!Kx;Wxt!b&wnu!Wo9hW+3+V8--FZov>(
zY_dutrf`m|?6!xVzab$90$v<=F+E&_Xkjj^v0Qy6glIe95*D}$^irHT76t
zMe%YBZ(FX76pg7#5?B{Qr|!v}%2JQ{`O8}*`BI=Y9_;wV8N2f*JWx+yWv@dK
z>ac?5xNFArX@)Q*@_oI<4-Rwner9)TKi)Z`bpMc|1DJDRxw*dlh`-^6M|0gD7d7EY
zsQ9~E`=>Z|{$&>Kuy%6OCAT{t@N3KkkG&qaj!#gQjsX4pZ6fU0l-%k
zK)^WU+TgWaJuTIg%h9Ell0gD!D}mP~}VC1V9%M
z4n_!+qF^?7CPaKJ53n9T76RAY3m&fxf)b7Ij*4%-_vGg61I6JAF+e@`J~NpP;IR$J
zZrokU-=U)gk%L$sQT2Ehf+&W;3gC}tsx9X*6y-RhUGA2BA}jF65x>1J28mht9k|ll_8$8r
zM1CFwGYli*QZy#Hdw;-(`0rU{fumVu-!C5sV+=`3daQF*YY-Up&55b(HkL(;7A{T@*fO{RGL0Y>{9^G2T#i;YTvu*Nh!Y_?}qz#}9QL
z(RH9TwxA}jW%k?Rg*ET{9FlkDp15-s
ztp`th>lv}Y>E8b`J+&?EE-QNpcAGTY#
zNuP4wA7>Gsef~IXMlkZUIVK{lay@)kWLtzz7?>-j6C|HcfoOUjXe-=4o)8pcw73n9
z953t)N=ZP^J2>cpr)3D7qdqAJz|^rJ<$buhMrW>sjzAJp9_aUB^uzFp*}`?8sEjhd
zDGClA8kMu*ct6SZ0FDAqhEG*pPNqu`godkTRcOfnJw#D^vbaJihLJ;<0Tte1$`Dz8
z_fw7Nt_2?W%|(DG5BnA#WD?!MagpE$pLZopA4mlSJGd4+F?2kGTF$}QW#{mjB$YG}fw$FC=y%~#pcyC=-zlCr9
z9EfTU5T>Qy@;JDY!DZIWJ4VF*#%DBj1XG>^rUDIKB&3J-q
z2ra1$;w62Ry(9!2;;T@^8Nb3-P#SH{h#_m1bC9|elHo0K1pmpG4jIs%|9BQ=X>89W
z!`vJ|@h}nKf(La+iu;g5q>
z-!TP(5HPUe5b-b)!8b#<3DA2E1~*u06>p*agfz7vxNUz#2qusS5vt~IVW|KL5_bFi
zEpBh|DQ9>L<$$Dnq?BMOM%rlNR!MYT;JK8utX#*k*Oi~hN;S&O$+12$p^X~TmLGoA
z8rK3?pJHR^4^vVEdXRHZ1bs^{M75DbP2MeeJ}lsxm^vvX`J{3#me;ery}c5Pi-drW
zu0_&CQD&`2Ul_^bbOX};4__Am;ykQrvdybylkGwAs-?6PVb&
zG9TFjy+cyhZE}(1>e;RG$DHXtT(viAK6KS60BaeZuKs(r0RQmG3`^B}>bZIDzb}wv
zD@@Y+-Ci&ot7*dI%5~3G#IoZ}gl`_D*@mg9auVI`2sfP#3*TM?U#WiyhJ-$zC4
zc~P>mB?Qtqh>*_${iuW=rrmqWzS=?`<~qfG){qm)UOeSh7?pI_MZQ?henU%&i~El#Y-7|W!1R6NoR<b`whYeL$QyPxIl`osb3q0iK+3
zN~Mf&fSNr>Q0;NH!IH^>z5LrC2(G=M3(6ZN0iR=C%dCmx!JLJ?SD7TwnyF$nI%*1GJOgybOO;X>(=ft+v5W`E
z#^}jk3|Y3eIqUU-7x(6;N3VdU96NoFT-MzqIQ<7x9e<4}3$c{HQJOHmEkaGgwE!~v
z(cJA4$n0yzZsa>HT#2vcxu^h21L8+S&3AoPL3sTPh8%+uHGzSF>comYhdsS&8p>#o_)1JqQW-iZJM*Rr854~AzrXU#a}^P
z((a>E^n)io9rK;t0z?{(^o$Hk;x?jMUdtPpbf8M2trernV1z3KrHF6IhB;=GMY?yd
z+ql+@?6Qa(1I>>APHKO-h;c(>8EAIoO9A2`n>x@CtOR}9J52w!^vcv(Ov7u6tBrK-
z58k&gwuc%PnjlYf-o!_J=huMIed5=^S9-<)+QnBUhvrd+bMSPjRof|?1Lbv
zdsFtA%Y*$)8f?$OBy*}J*sc|U*m19bxsrP{<8#?IysofIkeg|hIdp2*l}<{Ju;31d
zdx|v7V1V!|plFM>Hs8LaMy2P`79UUj|C(MMAoe-
zRKzv+9OUxQwp8RSC7f(%{cylM9R20Sl*NsG4WHy0N3F)zywj4A9zDEh%AhP9`j+(r
z`$}3|Ml;Ak|7MUF9=R>Epx7kswUG8|f$p_J;10;iOBJMecW_RUvK-VU)!(w%gs=g&
z0&dcH@V*icPh?b-H?|f$Bk=uEP&Jg5z6)1ICn^su%~(}iwTB7Z2+m@FJK~d8WaOc7hcqISXpi$RX=*ttU;1+oCepznm4R!EBlWI
zQr`RVX$A#_m@cvA?doSmk4>+2P!t9Z3H)(0yg?Nf(0Cg$qaw9y#-!;>{jfwvW*vgB
z@jMkYoQ!u@dVyYe&NWSoFz*6^!BAGgX*TsH`1zDYQTQ0A`yVdFGD#T}4P`$s3&#|*
zrZCV1ptF(x@5^EH0r35CAVh0nHWI9${Hd(0ES}*#aXlPZ(57ffwL64TB08n?K+chB
zFz|g(9h{Mrh{c!JmmMSoN=Racdahzd0oIy+ZKW6@YccGGoshFWvo6lG{qIX^1lt$y
zJ0K15F@kuag7?J(El1v(5L}Q4pNpMIM5Zl$Y7Xi4Rs6Z4jxB@}X9=7mEP<`BJ!c8o
z6~WQ@Do^1fq(jWDUChG?P_`>fv2ySvrrQ`SRu+UOOnSOs{Kq9#
zOEPSdzM!r2)w9wkJTuK$P!xFoBVhXPiQH$lzw=;);wGYIPzyxRAjFn{eEJxGmBAIF
zcMVQbviuSV0%;$4Z^m{pD($u&MhMpQPG_s4#)UOaQJ_m8-0cM-8;($NfU)oo%vru%
z2!&lyMSzDOE1_l`f@<;Q+tS$isPP!kPO|?%xAFgWsmyUH`uv%A8!8Hfk;QUz22M}p
zcTL9di2Ya>R3)$tHbxh+`Hruzs{)ggTfUaw`A-+Pp)?>Nt@RIL61F4)E=9^eX^_b(
z1Z|Y{4RclzpgpD_
zUCgLBLV=ph$3rleDLuO>e3xEkn#`XOE)wJdm8gv2df1ToSd#+9u#;$W?G=;4t3+$z2g$12X;v5>J*zJ
zYuL)zL2mnmm^HnQZOAX5h656*-KWs`(O36v8NyVP2>^g_==0*rA55L`nSts!rl@Cy
z+Pi_qCyS`NGrdF$T3V2D%K
z{rfJrI`;uwA&C21ojZW&3N{9+iB1JqP^z?GVYMrkK0uMhkDtFS8r6u|$<@
zBJ-{%z2>V{b^I4xs2;bNc4$?XR5KKH_CdKPYs&AB3q3(KM`wC(ximXD{+h&*AYG71
zqdcqBA*W%GIvu@tNv{X$%PO)$jv#J!x9F#@|kaw=!%05_IFSB^!O|42%C*SG|-J49cK+Uz$Bte21K*z?y~YjQ>8U;Uo^kC0uySw%UiPCf@*UdvaXiNKiM)HOOe>cBX}e6G5rpU
zBM}6SX@INU9afkaMyXFCT2JVsQfZX>v~F8Enf>afYP-qKvvxGI@a>dYEr;B{T$V@U
z%r~lT&?<_ZI+t{1`2c-CWwE#uCXbulq8Q}JAyDZYzhxfx{^?S$5_i2lgxQu!Q#)1c
z|KR7YU40+*<~5qSEa>@kz|YUGB)4jrrff4%*tU`8`zf*y_D1w239My$ZpJ6tn5!99z4kw%Wf#
z#s5Oi%p>Xn%io4%#1BZ#%A(GbJsey;E4k|~K6$c&q3jKn+$Bqp`A)p6K(e;E%V66E
z>Z&m-K#v8f>%b@>o`p$6jO#=O65+}Ay6!*Fk^S{;@kS-Mlkftk;Q>IKVcvV)OX+lUOB@rt7=t2L4n3Jb93@U5z&MFic-d0
z9864PzUGAnz&H{YwH!8gT0CHLW6M7&i^7A4$Q4fgUonZCWdE+f0-XVkNNb+!un=}<
z5I&ixOKi;)()PNOF<{YHAmpv?4((qh1kCD)#(_Lb6Oz(K^z($OmP9SN{O@T%XF*51
z|8%uVGh-!o&i~(SS74D24|F#**xo=IZ`=Q&?M=XH+}E)2w~!>tSfmn}sf?jf*@#qz
zt!P3L(wydzREEkNnj~aO6PjD4NTSR|vsBVN53AvSKe4yY-e;e4zVEyK`*NMjKAUQ-
z^}fINdG4|Avx5~9>GweCqm^K37*}{6{*Ji(Vhnpr`B7j1a2EMsHE&wLkNU~BaEsV~
zJ!|S;uToUJ=sZ|Q>~DUplow%DjEOY4zo*vp9a3k$S;zqpKmZCg+!XIOd)v4W;l^ON
zEd%pXgY?i%=WnB;%U5QYuNzNCo0&rXLzIXZkcAA#zwOFj=!oq^(gR{12t#Tp69xVokMt)MmgL_A
zu=--4kId}i-wU}uV8a{h6{g-f<-W5QWcqJqu(t4iUH5A_qP2zVN-@*|N&P6iGWc-!
zcM*RXEY1ZBo_cIu1I_aA37f>B9Dt(%$sOpWA3O9%xxoibuhX!N;hOM@-N_p8!3qi$qKDX$A#d0Q
z*EPD!Jf7d)5=J`;ZD*I49XywlOd}&0ON@-oi7wb~X<)%;M73O|t&$KyL23)tA
z=y=jV#|#4VILWF(nqeGiM4MAa5hCtb_aoPQ#9B?gS(pQ^!T1TVo=>)kq1*`U=J5*w
zR9PUTI8erREEzZ<&;o}c;?lK;2yY1Ova%w=fuT~~7c>yNVT-0%HU#bQzzqZSBDp#-
zUP;tcTHDxM2cu50O;n2YN3!u?S5ZuUAEh@Dy8-g;`}gk@j!2a`CRgC@qgp)#I>HIe
zLn(9YMwTc8NfptE)Te^;{5cSpZR&EUxA`7ZS`s`!l1vB?7&qh{7~q=gje*EfE5+fF
zevPe7Yt?4*2*CPSc5F~W>{kxo3nS#mQMeAxV=qI?X9DH2kU
z@7(XAU#`=bwa>;Jev07XdeE`B?{4Nigg6C=JANf&T0%GLfw)4m0|%&7Z20-?O!cMs
z3P@gpX8Rblo-H*TZVHh6NX!YNc&hPvuMO(g6|bXn;op$UJBv^QN*j
zWF1Bh8u^ru1?yeA$GQ9`dsW)=5Z5b4_}_p3otR6+3~(?Ir{N=q*I_WG3kU?nU62Rh
zy{a#aGiWSHxr_ij0E9&y=b_u6{|~gRh+ichQIPzfh9qwfNK2r~_@ek6o}&L`6fDeW
zW!l~HH5>FfVG-04L={|U2?8(UBI;#P@vI!7UQ{B5<~rHOFFK_AfK7oh0
zWN2C1zj+_>+Y@^f^Ax9AcRYm|@X#Z!3p;42F+kIYNCUsajYp4`7m0OEoR`l+yJq@s
zZd*b5QGHiAW`s?HGX$$dF$8#ed2v`P4m6qG`!Lm9EMvqkf#t*z!Q-$i2oTQy#)-9Np>@;%Nu32ySy6A8
zLy;foa}>bmowWSV1YZBjr+<)lA0m{l#d}$q@zwO-qO4-PTG}?1`Q(PfvIp@ABscH{
z#l0{$JG>$we}MCj7D7?R>*ebIQ77Lc@~CV~G~SOd5TVE0)K8@(dFay4M!p~Y7=S-Q
zO*koO8(vT;Bzr#J7o%r#OlaZ*>0xPjkOX2sMq~!HLT)l1nN;kXn3$;d^tI4`XUkaD
z#(3Baxa}HT&%hBST5l>>!AXklqvBhB!ZMSgfx%62y)YT9G{?7qxL~}*+DH}`^LI{d
zDFlad&+Ea6>3=HH&RsNF=}l?unW%*zGA*)hOkf7y-d3o1z>*OQvVCh``F854ntg(^
zuL+nG^|*g6R~p!iWjOWz_!$qxIGFUTDc>3wT?lNQ7b?15F}{1#jjx?&&GkQ=mtFF@
zbY7f`D~r$X0((()uk3i=JFg?frdzg%`@U{mweqZW-{^!Q-YWZBJSF@>fV_djn%5O9
zCWjS#t#)PZBb8zzL7J1`lFB7;I@3EQrtCQ#Vh^;6n8!`JkS4kLBYLq4!kpm(CU-uD
zTzUUd7oiMc2a+EZ@GNHfA_QG2=7---wd={u)~pmaLME0-xYZS1RNxfKHtcOU^-&$5
z4d7iyc8A`uZ(DMjAoD{4pJR|dTxwTTKh|EVhb<)JgNpe0$m(mXZ-e+YceVhdv^Vxs
zm-<`e-k_)N@=TrxV8UoNc5&6iSvhcmN_KvcX>tlyL;$(wInMcS#8m>geIK4QDzBQ{H#|d%(AyH{
zPF+UP)YQ~(i9*Bd3~kqqB|Sd4OrKyuiQfNeDtszPCL$pY1VaR@R_rl!#{#4VF*wcZ%-S50PV)h4PfhLs>}E9h_f&eNP=w
z$T^fqTQ!tOYeh~|Iiz8T5a;2A=G
zzP{C8*lFMYhdj16_C6kX5D9eHvD`^+PfO^^p*!eHNof=eKqF8Ai8ekX2q7x{{=SBG
zS@kEl7eYBHhgKf5o~886CO#F91D(B}q7A(A8deP%#13o`7{mGaKTvNmPMbITwFn<^Qrfkguuw@<;?6<1Ml
z!M1R<%7IHcmGu0l(QPnTwZb9*x=yiEancTs{T!qfB?mSP>awlDXs{44CaL2@YNzeo_hF3X4nTPw-F=@LA^
zNKW@n6<&y`1(d&_terWrh^99TuhmxSKoo>yb`jt+$Zl{zddgfd?Sq_qcuou?IpO-X
z0+T2Iv_#dkCl)CN_4}5U2>eaWifxZcOl=$Y{XiAGXP5|&rt~AXX`u3x8^?Y}T>T0{9;oP*;w#@F{{3zHmI
zL4mbCCl|g{d;(&=-fh&#N{PupyO9#RsHprG4p6k^_EBjD%gB06_RMaeN3b|~WL3KK
zcN;C#O}sF`2AS-!VXaU2FtM{6IBUW@hL8JqVp9%apH5FdQUV!u##UHj`WdS|0>KB*
z(Sir@
z9uG7Q^z%g~w?L!K(}cdQ|6xP_o5r)&=!eErf(@#CEzl|Qr;y(ra#Wgqzvr7jh-PcJ@W|XLc}a^E{NGMv&S7db}^Uq
zY29DX!{WJpds%FryfTapaA%RpxCnAt284hYxrJ+&{1#ul-x7mpQDNL=b~yH$mFlFid?2s_+i6
z!8~_gCt*DKbFs~X{sHsY3=6?tbe1rTS4O&IMG;?9y)$R+zi3cjnpsidSi~9l)WA7H
zu1%&_mqc*=3v^SS*0bQJ#520L4eo(5M3tpsyr-lge*@8%)+H{ou(va+W
z;_X2=SVxi4Omx8mr$@UY^l~cAt$cG1uNdthr7u=po7+86vx?{J>sJ55
zMwR1J=h&vo$jHda-y6l@FU?+E8mBO!ZL%g;msN0J=YsdGFUR#j*_O)pSZm?Y1{c3m
zzkjP#6E8WoZeyV0K3OlT%5#GOQ5==!xi}CufShLt8DFcXC&DyMawgI#vE;9A+Nwtn
zA5tpAWz@*i-d6(@l7XEOL1a|$i&Y*?E@)@P-&R~Wc^LC|3N=d*Tmk_N@LYiupRs)T
zBs>(syuimneS>i7ErxWF`g8AV<1+wqf4DSN7y$Fv&f}T9IXRlnD5@WqO34NfDW=5lFgcn&t2(?0!7Ua`*t*@lPj?=hG&39Np=vd#@4Nz4E1-Tz8aP?dSO81Q5$l_fK#@F^zTV2TJh5&qK$4yy{tViu+Q$C{%PUwoi`F)@9ZFeW*PG7_cu|
zOV(FgY}t>-q@q1t;%&xU-lr{-!@_13iCLdA+!>kmW`FF6ww4pd+s8k*Z5;>zcaXF?#E*FUrXHRJIKGuA05EU6ZI;-(c;9n=P~M4~S%0Bpr^Z-Y;#lTK4$lf_$Umr70?ShI
zVA>V)8lDiV!t^q;p276%vb@lxxIqYe0U{>fx#)a0Wm{&~_TqI2q7kX0xpr*+cPgX`
z)o63*+W0DL#JGS4(@ErW#sh5Lg6B){E|?x_7ou)kv5$3V`yo
zvFq1<-IS`Q(_!rlPS32>TtXdwNS-dO;~QZ_8n|;`DU`o1JO;9Tg!4t^D`@6&>iS3{
zT||EVC^=UWdII1=WRs7Ghj$KywapG8;7GBQf?yK#Y+k@8J=JIT&W}b;O5Eo6B%R<{$$OJvv`1te6vXbN=^|<00_z!yh-hC*#tBot^wh(fcpv_84D1vhRTt2
z(>Md9p%D0tsivs*=ni$r}5HDuDq;vI}ET37jeUp^fbV9lNP!o@@w*`
zo2B9J_Qy8!dG_~8%<#!5$Ee*1Wv%k70&!|185Z@=r<
zi@i4hFSKVcRcbg}1J9K17VYPg-poF)(v+`Y8zPQfzI!^ib>D206#={b49IOe=QYsN
zs7cAWlWv#z&eU8}8>!f04jkKGR7oyx+5?tdXclU~yLj}(SO+*=5`Nf0ZESItjTjID
zYRK;tNOD@|5t5lJfhGLj#53-1%l_(rG{@?(4N)@vpZ0&pY0~+}Gtr{xWHYDyM5OeQ
z{u}q=;HgXs%91;Me11@9nr>O}`~Z%Do>eU%U~#d%F&KRsY2kWVe-3^8N=KFB>%Yx>
z4+gNyRiWkiOgTL>gh=4#M-0_(7_c<4q-4{~?u<>ljn(192++LC|AS1lDxeMJLQwZHliO;!zFB#Fg5Mq&{=)
zOF}R2!BlUo5D5%}Y!<)xVV5M7rUQE*bPO-=Q$<_Xc?nk&BxJ#L>!c8a|Fx^UsL3$U
z(Ny`5He7vd(Z6_8wOjt;O~vu~w@n_Y7P=bm)PBuz4!(&8_|MDtGJ19OIGmBxm&7FY9Q>)J<*@$!$)$fan-;@$>3Z%%g?0DL
z!K#W2>Zf9XR~SIS+FKf`W)v0ipXlU@S{-(P3t|j$AD2_Jq=iLvjx7A~SUTZ9UiSLy
z5IR!^BDcGxwN)-xXxV`?c}KQw+tv)xQPSoR0M1$M4werb
z&$}2wTM0-B-tJOJ`sDf-{n{qalwFs%8$opoKU|l!7QY#ae~YXc;$mCNeS3u2@UQA0z6|9;a3B%0KI
z3|~4#93iwleYD*)Yt_hsfx2RQ^9h~B3Qck1n_~9y3I^;|=H0I06*7urn_6Lk$JJ@e
z?)X6mw~a@5!)@(zjGuev@LN;d>!7dUbN^y@^8O|1jxMR}4-K}Ru}P&LzQ83&c17$fWMdXvc)wFR
z9|#oNaNwr9gr<5A-w+dhJ)3i%?cJl7Wu}EY}w}5q@)a
zmP_sWG8(HP*QzJ|^Oh_*ckWTj4-M~#f7S4Qn`x}(@x&`&CW1rZ;32&|2ZK?bdxzF9
zHs6E)%Q(68LP}U2#*m`MuL5*+6Rc7dn5_-~#en!@Kh6U58-e#JGTimk-AQ?GNX)>w
zK;}lwMg14w%snX>lnD}$iG6IoiP;3AW!7AUJKN_7^(+>cf@rTzP7Q*p!hv_$Q+xMWPj;z5&vU
zz%~|1Vco?WjE{lJ4oRN>?BHNd@(V);&=iBEf!?K%Wl4Ro#*Roo;vW3NB1VAK|
z)3HnsLV&kqZk8y_@xNTqZhe1gwM)RTEs=wYkPL%QpZEe6^Ja{(3Vv*Gxc>qQsldfU
zM%rNZvyduc2emM@|4R;)?8YVs2-3`fBG9tPH09{`zcoxw-<^i`F@}ju?V0a_gky|$
zIH@7hD-RjyodZ!cu*Ket`ca=DMoVlJ-q#lgVg;_5e{VOk5dKXq9xwN24-B8~IeYR5{(5#^lzq
zC6ygGU?Ue{Y69BXdu)mm!)3PkIWAPF2pn;pPq48gVE74ccXYj|
z$46|svmJ59N7*vKIFJlZa(qg#LZGO51^gjYnnrUC{e|N3?j#TC)}GEk9mJR`21JYR
z@8{tPdTDS}fT3ayR2w!lVh`GjCaW%F44%ZgLKB1{dqNjYqF9!i=H;f6=B5j1ST&5rf(XFrovH^JQz}7QmMwKRc36^Jdxg
z`Ka94o+ukyr(*WeSUp<6szla*AxrQOO;aS1RfElJ#y{5G!QxT~3Jk=oRJ#KpCC*+iEKDMwm_4}|j1
zDXpD)t-e58X-{dDqmyEFVYIYwx!XXY*u=+!l~SP-jH#UF+2T>$J}aK@oO^hiTZl!E
zBd_hcQF?QNZLY(cJ#*&F43jQ|BaUXeP}Ej#u@hOT^rVHrrg(fZoMW=P^jFddUNf<_4$fZ)EFE4mjY{RK_7)2jejFx?W-u
zvaE)~5EA89E3DVCUPn_iKcdopsr5Gv|F06pkf#7?LA9SdlIgV;b}YCB&f4}$QGLBb
zy-Dh&kiLj;=(ZMZo6f%*!Y33s+4|Qu77#psrr$kM{nU@Kw&nJ<;gRQY`%`Q-Z$=tC
z*~Fk?-vC6ZEqaGim%(Rn+39>lP$*;PQt4ISacySDqXCLgwx~;#+20wlW5JFGI-`8n
zRXoz!aqmNgxtT@OiIvqymjBT|bwn<=U=HB25qYIDb0PND3a@A=P8x~AE0ic{ssc7{
zTym-8e!gPUqux5R;%zNc52)X=HAIk97i4kuM|#0o;>=4@y>6R{bMNX^hCvOsUhOp&
zv{)eQWwLjJ(OAWfO=5^a$ujki7G?QQx#zKZk(E_K#aSQvdppZJF{F_bE6jdM_
zkv0Hyc#+8*ZZN^o1v%rGjVrBq-7`}YWf6?ILHyiquL_iU3FW6E2Ex8><4Hc8xA-CK
zlLnBChgdwOzFHzUx427jFhjbzBYDpD&qSjmtN`a>Ny%+!=s5v$@f_F7n?SJ=>|{7#
z0U6f_mY|>+2T;m~Y$H?{PCj)!&ScD{dmk)yxk($dXHW0uPf^u2PNueQUhLIKZnYc?
zZ+)MDEfVJ*$h8qN6C;)CR|lqWO<8Ha`^-#>Ip?z->i4Yd7g==Ft@t+1Z44i{@8Vqt
zsp^HR12BXvSl+~GS$e0tCv4MKaDMSIByM;-%)re{oEIs`L_lbSeo&!2
zy}+U7@%4?Ww&fVlV2gW#sb1QDj_4eMfYDzXpVd89RGyciC281H>Utl^bdlQ;bw?01
zNUSP={oPnNRvi#3gR$Wwa7it9KJ0n;Mlz6KC*+$Y?QNpPG0}V1q&7wKU>Uu3=w%T>vM)!T~oZqC+h+i>OxD~Xz0-pB^D?o
z4{IGD>LdH^
z<#TYX^dK
z5X2xsX$%w{$+)4gZp5tFuE)K+W}>SHlOQnYqVvhfHbV0k#lIF+?NNB3xoulTw7NcC
zuFDNmMDjpeczV-9QRlWT;l;;>EKzCKC8j2p`?Gpy^>?}s2w+CY0Ip0?)d$dOT-l+e0KGMiVlf5oe8}N(1wU;Mpr*5Uil~k`XrUNB5@E=M$LS^r2~DU{rJye
z(b3~@IZ=SROZz?j_Uw@(NT5a8=44=|um)T!IKV3qk)@1r4K2LI#7bV(5N#Iad#4ty`_4G;MW6zr(4&gb%@z6C&ZsBQG-q+|qPvzJBTzBE0v0NMUBhY$noV=;%
z!cZIGT)ksBN~NBkjW0|*vH+flPGL#WX%o1IPod<31wy+FmfvejL_xvR4AU?MF-Y|RpbKy2gDeu;XSSjV(
zcjNlJaKC;8+|KYeLbig4^m1eV-ueI
zlHUTl-x8N2@vI+-Q0f1g2t|JzI~6Ab4yp+h+28>{+889$iJpGFQc27?$?w;@`DT?35Nrav2smT&m^dVb#B$?_}%Y@vvLm_bg4(};{)AO
z*vu)B5D!g?g2P^q40D0N3DVL$-&BSBvy
zf3frv;zID$F$bxHpclybcY{SQ{?|b43uR*#W78bLP^K}x;u~8J##NCG93H{l%cdsm
z!5)J>lT`f}-YC}1r?qqD0o@v(O4xjnj5$$20L63Xga-4Yamo_Hg0sB(+}95WmT5gY
zbTP*UVjMid@QbKid}^`}FryNtU^4|Wb!=TLHX{=X=@fWPD5FBae_2c&nyM%v3AqW2
zrWAi#u~*?R5)!0e+JE+q?%w9YkdhwnLOg2>*Dx9=Z~_{IIAf^J#UX~Z{#}h7gx{y0
zzO9T2T;(6K=dwti=%IoJ=^n>bL9Vf>y%I`ek|aAjEmzx$8pIW{o$ZCku7Cdi9;<$F
zhLnr{#k|6k#qv%KoY@IuG3S~oxV#8vH9dL|6o+%B!X(g1A=P7}OoNkNWlbo5YV%{^
zPo>8qd%NXUMfyj!zvQ{t8sVGOs;GCKeX7{2tEhnqwIBPUbEPt7w_?98L8Gi>Uxm5p
zLVJn9#$r(c#LlkfrvDj8&~Z6ZVwb#pi9u-`w0gi&P<)X&S~A(~r#Ko18>k|@n<<|o
zoChcm;z5(2P7+=&PidQ@H<3Yft>z-J#u%(}EBMY>+#pdO=K$9|1B*=-bm_#^3{X
zB3)uP3^*>S+z%ak0$hX?4v-8=*f*ZS{qcpABN!g=daIkW59sGa=GQ{0hba)8Irw5M
z1gF8#)_s078brG8Gt{3@|6*iC7LG4G5TMI?MLU0CJ$C+$A{Q+=>si_mu-8fTWQJ{z
z#3=1QN6Vd`ryc!Cy&FvdTjCe%1%iz2SP{Hj%2CSSCI_hr)bX#LG@p(1K@`wp9}oJ%
zmLKM^PUgXfL&t=b{8!7Y>j@aXPZYCIXQq_u2I%5n7no_)6(%`lb=qWIf$9Rvc^g4p
zp5rULCvC(kPf^otyN>#=-ZW-0hHuC~GAZukT}b~#T-*pZk|B>;a@*TYq?v3eNvU0u
z2T32iE0p<1HENi5__>E;SwPhDGo0Fs_dP$Ocj`^KIl0361m@y-{R~o9v57qR1^;Q3
zee3=~>M5ZHGYKV=lWztLkGqZpU>~<%TR7vkE21M_1fEe;=q^sEmE8=^+e1kV;%%H?
zi;>%fYIMR?5WhDT&r_UeWDv^Na~z}S7{}AUS28z6_Xc}cKM*zKSkVtlL3yBlhjVg3
zodE#6?9#syFMBJ$FZl
z$luLN)#G`nz)|!DRYJOnQBC4-Iil0hG$RBSCc+u<1T?LniZTW`SB>?jl=S=-SExG}
zH-uWn8S)J0e2G9ROivBg#aIjt^q<^E!YU%8*33SCMEA_tf`-{1N_*mx7LNKI*bf%n
z@cM}xk=kx+q>Dd;$ct6i)As5c*WRnxAGMNoX!7LARUbR;Tr*01_>Na>HIkMvi>#CV
zLko~3!_t`BRx2pM&uy2)C&fDq(-fr^J?25b$CF}J|BW}3`^qA4g>Df4Qs1BVI98FJ
zut`f$LGoh3oE6;;gTR!Df0vWqhaiI+*eYum@rG(}yDOHxv%ykaw0w9-9N>&3IBo%4>CUWR2w*Ay8CF%=3}`ozOkviyj*aVr8r5yps1=q
zeuj=e8yT__m}_XqJfM{dX3CXpbRaN^9XWSeY2>;>MHV
z;Xc5jN(>hQRp?k5m?GF_y(GQic}n{R7bB-du2fWQr>ZxsKD+G!l=WmY)XS38Pn||9
zeSY~EOdVr3saNwYhw*jCy+@_~Ah|6BJn5kJ8B8Gk)>&!ovIzWCC%a|!G~Y9K5Oe%h
zQzkR@(U(m-(ISrV5&SmViE?vB4@+yGwpqfzb;UT)+4d>4x=W-lz!o3C{T6{@pAIJ%
z`~H|2!Iw5ftdQS30o!w0tqt$!(XF*Tr&3g^H_2UjRV7hTfqHMKiR?t@uvaX69lWgC
z>YSfk-1+Q%$tHtq`s?5_tRHK9Z(9v&89ef(H`9h*9Nr}uuC?&iaLMM=&M#W9x#Ag!
z(-F5h*Xt721%;6m#NDy+bjlqBROVRJJ*8qMw~kb|Pp-C_`lYK3ZOzSewY%Q(U5l(V
z3STbBE7JHfTAKgYAi1HmqOr9sV5}{Bwt6rx13q_y#$KKq$Y`p72G?Au1mjF
zSiLIxFCiKe4?m1Zn6L0lR$?jMgx$>@nOHAL9&i`}ZY;Hxo02c6i@0g#m}RGopUX=jXofqmHMKv%RqJLi-$R$skoE~7KRiaL++aSKKI~-#tu%b2R;(-)2G)WRtx8O
zicN9U?sQH7I{4TKudUCp!>0trJH9Gy)FcK6l!zBI0mOAYvTJ4y2|hmRft6$3+=+W3
zWfDY^1iqrQ!aPmduBl9|dNU|ABn;NBaKht3qJ<)hMy
z!#UlvhJOX+5sf+(ngA~^G2F;Mqap<8|Hh9URh#Mrnc92#Ik`cgB`d7a8ZxrVYyiv`uA$`1XxVmKRUvh=a
zEND6^11||cjnvxIQLZu9VVbu3N=dW&&yVIVSdam}7-IlYN?@X)@BCmQG-5`B?>m&o
zl|w`f{!tE#94JpEk}Wfoh=7Ck3q~tIg=F?cgfcJS=a>@G1R}2u_Zf*@ie69>EnUjs
zRS>j>pr5ChRymmbNPSY>NSi>L3@L;2d{!)uCPdehqXszQ1{IZb#3(0i(f+m6i)+kH
z&oRKJ`GfSV@+<}~Oi}SP6+@7b_zEir;-?@1J&MshiPvbLR7iC5yTN^F(&=?!o05ST
z;TIIskW}yR+gm%&#vT|97`CsTrSTTSka;9Roi|XgB#7G&n-m8&6d}6-x3Be&*
zXrQ?2-rc)OXv@Sp;fZJhh%?&{fgG4bTa4oLH0qgy<`&y1kRd=_hYKH%A^Q((Tj1P<
zUKijeg@(}wgX0;;o`-1P1Kxp6V?v2_9yAy7)HU4wZL{-63K@>p&VXKybYiJ={NEwXk^X@71aJO$z|u9D%c
zKBMn__}mEo@#PQxG4@kYBYU)w*^}c+DLvW(hX)$_FI>!HGwi|-we1qp8@e;Q_dnQi
zuf2Y;Me%4Ov&zpBvp&}I@i8heFDi)=WZXUj$2^%?Ag;72f=nq(Y-N-qk*mD
zjS+xy7o#F}4tj(sN9>?ZwX$dvXmkOd7L`J4O$!T{!YH7|66u8k78ttlj5l`;zjf;i
z&TL5GDS=4}k#eRws;azt;5?}WAl{`fQGb*kqZ9T$!lM0AFO-iUQHU%gJAtVkh9fdf
zK-2vN8t$NHRp8?hOE9<~4nxklra=xiwq+kv%D2p$^SZyVCW6ba@wOg2T;sMT{&W8E
zD;ecYY{6+fS>{Q_4XxViolit&Z`*OZZXi^n)QLKEZ#QZb_y!3>Yigl#mmPCBZJ?f?
zr*Is1&`OSe(Y&3ZD6^TGB|d%G=jl7<24+NKi8t#PnTOl_4-${N&8Cxtyd0YvIMaZU
z0g-8)WY-mDEuW4~htYP20r($LX^8P0Bjky=L0tW)gYt$-Gb`xKnX+?5hH@Zw>HWdY
zQ1r})0^z|Iyse3GJQi6wfU${#!Hi*=Gm(4vWoa`JgkcJM4-HNdpfe{pQJ-P35HNa;
z?}3WQ?q1AM7W}d9`$Z0Q+dv1I$A1~#$J!N@d_Ps6D<&&jWHODNc|pOpI4LK;1t04$
zx2&(rUk9r;p45@ZV)eI6Mpob`b*K2oF3X>7rUkj{6K-5n&Fs{4a{loKE+x;`b%s{kA+>viV`5DH5SSxh0)8G
zEwf&wHsdT)rdg!BM}0|3%B6HYr#6p2#(YU@lCbJ{YRt`qAWueElAqflIY?&qGj?Kd
z&s28OHz*t=LL^zIG+J-Jzt{S-HEe5S-EXT(qoaFrN@H4%Xh#=%=CQP^`!>y;*s?-x
zy7gm&xtZDdT}n5FZA*NeHaD$qeJt#_prhY`*QnW%O{%&~v8YZ0ZY;~A6i
zYUU3Z1JkOXb5GAMmyVf7$W{;3MQ3j}JXg@NI6SKR^{BqVzI}@8tpOGQ%ptS@K;jEK
zQC1D|Q#nO){@w6jwXLX_d;bt&B&S^1glII8)991k^sNrLqLHG$}&!1Y{S6JBhxoo?ZRmr6{x7s`H>!j2lhv8f}PFiGq;L245wPxb_(?(+Oh$CBOu34cfb0JM%7S_Yf(|JO}YS74BHVmJw&
zuE-?v1`)d9HE5|wT|w(MFt!G;cX7bbmy%GwPP8XB`{@*O7>?xh+;s^p>2j6
zhKsr(%^wI=mA4Zehn0+R!(|_50lt`VBPa*Hadgt!22=rFW~jDexYWiklK*zfAKo}~
zFP3@cGka70HyUg=h*M(*yeN$<+kjhe2r?+MDqz;B{JN33Yes#)UOo;ZI>V5>1>Z&Y
z8H_np!bmlRV4uj;gWc1TUS2~f%MUj<^@J5g!NNsJtw??0g2NW;R#k4$DO`Ct_x+Dx
z&s>N$9~fye0H>182-AJ&QcX8Z_
zdpQF-fF`J0k)b?XDYSOj7c7r>)ZrMzwEQr~Tt6yjfrP|5hz+HCvB^+OsyS3&u&3wF
z%Dx9l9{_f=qnTa?t_^o0YGv>Nq7&*vMf{-O@mS$9Y!Wai9Nl`y16C^PQH(*h`W7hv
zFfhmMdx)I_Q7+FjAjw#1$oQ>>MU)WLC1
zUxiZ75lM?YpXQL3V!Sa0X&7NG*TzFh5L&1$){~VC=s4;k%|K5}wod
z$9Dg|V^DZmStB&U2`cBwKZ%$hDMi1Cn0~B~yS-lD%=JBsdb-HrC|YV|3%>lt#q5e$
zQ6OeLy0lQ(2r%*N^YaS!SZ5ztmMDASsZpr4aBkMhSNp9ZXW8H3D_QRy?qVmYm&}cc
z6~_)IgX9DYCJl1Lgo6eBwjh==#%M$R-(Nbwc7;NM*b|MpYq68`BOOC&{>3lgUGyrj
zNdxI}nPa>2|B;GWWeRf%9_if2m2k-6bKhGOZvoIV9gmH{btnK$h-HuR^@hDJi8#tC
zIAvufP?LIT0H-9_HiVgFP{0|a9=J@9qJVs)_*ogh#4a03L+99uicx{u>)M1H`c(vbTQnqS)t`epAA$6-+nPG3rYgXNMm$E$;K4RsJQlqCogh41Cd
zm-QCB!xTCW-C8T0{-Qjr3SPUPO%%-7#@<8QP)eh2F-Kf{`ivP*OuB|@Ad7IJG7K2Y
zAc-XDu3i0To|(*?K}zgnRlF2(KY~jLdLRQ`9`yF@t5YHApK+FZ>z#+SN3GN3M~L%t
z!}{kS>pFLjK3|8S%<=KybrI*uz(>kGy0&!lB=%QTSkdKAw!e@%tu0#X^gepiEd440
zp5uKTg1J(>uubC(FD;y}fg3IVR;={i?&omgVLY}Oux;>!P2qmN$hvPP#Oc0h4)!^p
zSN(hOs&$|{SsnH^F8Yr_bd*03>q-`Of4!=d=VHaGCUa4qiz_)?M9*u%dPu~|#N2YN
z!+ovM#dWV7syZbD+U7$a1*|Fz5C+0)!OJNjkMov#Nbcs0jtp(UW)Rj`_1(G^9&YuH
zYJp)-M-*mlVuA4OFbFz3w;uc~hc1&KU%(?Gwp(xr$T8nqmM>O!7Cthk0T`a+wA_X$
za5ttE1)fooJqueN^^$0_Y_a{AhjZQ~jTaH|1s-W9%!11b?d}%EN3Egs(aD%G{si93
z+kgk4pzKQx%#CzlLpi$~gUJ5a1kf&fqs>i@p|Qh3wMG)&C1^!9yf@a;x~yN1p`fK(
z@TEw`aV0UcxQjUc!2qewuBacO`a?0fc5PF~^{9k|EBS+3o$<-|`9$*m)JocfKvs(BLc`SA%<+W9G7ivuB-Y%>#gKsl18T^`){|#^G%ha0
z>EM9_-z=v&E=F0!2!?u$IvX3l*UqsiFsX?!aMthfXoY_DA=k0=ilFXi&z^}4wHG>!
zR483KlVj<@x8h71hUMa(Qp4!JCKm|oR@%3SyLlDML}hT4*|ONeP;KkzWg}p&;azT7
zBc>%hFw#xzaNS_iM7>?ste8*YM}U@xJgr`^;_M3>zt>xg44W?LbUy0E)XFgMiY59O
z2y*GWbQ}6z8d7)Zz&FcIbTM4+!qsJ)_qCqtYDLfJg>#^=K;mMvRWhGQchd`yUc
zXc)c;bvP0<4J74x1py!#P|*uK+)T|DqNTmk$f@6
z4^52#l8Fe5vk!LZIY`YY@JNEu6IJ^O4@eHAcWQy_(H>fD
zISiY?Q8=8Q&a#u~VC7te_Qt;~2*$Xacu=n8jjjVS#1&zvK=$(TMK@tl)$E2n{>2R#Q
zqh7H7ch`ttaW?#4gyFAK###s*`!^_0$sgzcYdJ(-C2k66lg+RExgWM|PGc|-AdEXC
zA~Jl&%$YSabyGYVj|>UdaS))d3WEwR`o;t6(c^`7Cz@dxrqp#HrEnRvVvHM{BwSJ(
zf04*a&%@k;%j(4t;la?chrHhJOCDfkCLt~&Q&2Qx-u&e^;PYpN%%lX(2l}0P(Xx()
zt1{03)!7Kt8|+|&ycTy8h3vvVXX-2rrvZV=l>SK;LejG1AWS25Gj`u;(4zc}#&P|5
zpPs~6`lrY*LfeZ*cqDgYcc#1=_yE7H*o=S%vOH1XVXI4STa2#P^`>?yuguS)PzdlX!nbhE!1$ya%SitiF0PK{T#2Y31hT?^e2U
z=X95*W+Hj;7l-Aj3;rn$w9hp_a^-PmrO$@={DP&erK1-Avrur1^-e71*eh`ZA{Q8s
zUm9;0CLzlfyA$>kn@!Xy;@@NS%%$LEw>1ClM6Q3_GB^A}C^-GbC(Bd38+w?qA^G9B
zqwN}J`Y?tp(}<_CQsIp4YwYi>Rv5|99QME1-xkL8khOc`S4)*==(bMIpKp`(#)1JS?Dln^A(s_@(H
zlM)17G%8|8t#VBv@<>@#d?OCWq_X_qb?-;7RBdM^T6nOs(mdIYGPvZDwC|Fbwn15+
zaA=Qf#Pg$}bMa+RV2(?n3Vm{T#YpNLsnv+gXZLz;zXns+APM#qR@(bJj0Z`u`gK_9
z4?EXdCr)4gMcA>o=Du9?t_YXpnmbg8^L5Dij1^o
zOxh1&8s^1(Gs(h%23X%21~!=AFy4|&4*GwqoPS4x`3vQVwCTfC+3>*yOAS`8csb{6
zkpn5h=FUFuH@DA+ZuVVG$7c{}{$II1vqkli?G_KF^Fb+Fw4&_;uvIKsS$z>dBz~l}
z!xU3JKG}K8j+ozT_E-Z>g%Gr5-?@q=*E(c+ZQI-U0&%hQ6r^8JyatSp;3{emtx!U
z%6QZ0SMIdA;|St}jtVPx43Sj$5sqELu_5wf`~nueHa>{e3{aKtlWRX?7>9_X(qK
z{HJx55s`nB(-FiYo16qgm}WMHdx=__SE=osZXtw?Q>h55H`EQAmvb>VwCwyx<-w&5
z>)ff$`=Lro3y1%r#lF?I;*wXM!4i`L&sT%Y2Y2KGXGI2R;Z!Fa}faANhaEe*6B!C2_
ziLLh3z)$XDO+3zQikDe9d1S=>!?t68EQUgyDMzwyEls_|f`x*SogL}kvClyhgJXZS
zYkyX47Zr%jINKJVG}d!Ze86x}VHK}D`mrn{Bev^9gvwlVmsjh-5J>osgvfEpR3D^_
z3Bq|6r95gbHgG88uB8YYn%Q({0(-*MQWl(dkhBa=tddkcfJDQjc;D45-a6xK*P}eIn|emFz}cG^yr!u<2UxQ^t#xzf0BHx>24!A4+SN3Y#u`PgNv>RW
z6k)p^yM)$`(i4RqITTC0v{}hlwGLo`JE4btN_ih6`9#Xdv6*%xpLxjoGL`|wSKeQ&
z3vmRgnwy^qnZYASf0rWfK`T*9E8dL>mCsozJ@M~W9>cz>2IWEQ`cS=36`Sxos{tdW
z(5O;>&FTCeFB|hq&%Y>{yC5WPQsHD2p_V-RrsFl^tatt>4sd(9#pX?`)E=jHPYsFH
zD2U5=^29En>`0Y~Oib2p{q`M>PxBr2AAixkBdcOnNQGfueOF1tO8srE-31M&8!bFP
zA6XevxH(j#z}Q5iX{oi?Aw(fCZd5PU&dS-#F7HFG9tH6+kkn!bam>ldarRY)^B5X8
z8qRv!!E!)?7hPzLh~HWt!J{?*#@DCADSzR`i`iR0HH=&R{XTDsZvNrDk7Hu~S)y1Q
z2LC%JLs%9>MDcB`oRhx3>#(4ZJqyo0Sgri$Os8-eZ*Ok$`GF9l?8+d1Xe`h4SI-M-N0k-_(np+U4a&s%H)^;O0
z0n$nQ63IuU+t(Ib*b+he*DZfs+Sgz!;i}Nr_KwtcD2d^{);Ebx5?Juq(}w9&uqxPZ
z>@ubmw^XkjMdVdOqsD^T|EOPdzg-K00!g=1DeE2X3!P^Ofd@5L2CH6sT>P(7FmvRE
zvi1zP1LKFAW#Og)ms2r58J~=`kSL64&;2
zt_^RTcr&gc?3r=uKk7L52kwS+i%~XnIY5LUnvLgLU$$ea>%|ZHgBaWtgt`y@$7rqB
zA3t(k>#Le3%Gz5PFo?yCY-G(J%S5vJR||TkLn-Hcm38eJ^CK-m)34f4;r%?|d%Ya*
zQ}_POP1kU*mUIw(1)D)z;0a~c=^FOtx${9s!s7bgCaQ%WgSX{}YywC=_6S%>lKY~I3?K8WscQ;wOl#rH{Y)>dw#Gq;~wjn
z^|#mQU(UVn1VL;Jx8Rz_w`Q4Hc;^geeJmTn{P-s;rjd1wc{mR)-#4(}yuV?_Ee@YRV#qQVD@GH+KwXyTq<%id3*0BmYPrQ%2+8yJ+
zo@NPU?>sX9(}3BAyL~0djHvW(+1O!vPmQf(*138!9LbyH@28K>S)6vNYW|`{zeDIN
zeh@+;2RKtA7V$s%f;prIML?1&iRGR`ZSS|K@6?&$k%0!9>(FFtoLdIqxELJ6WPffO
z2p49tN|CtYP=i#e=){%2%b~U~DNWTqcOf9)9vVJ&>C7risy?AW|RAUqi4V+~x_SQy{pyYD?>vFkY8
z;5B`nrT*e6u>VcPKKOK&WhxZz8_?zCedDeXODfpB27kf2_(;CWIrQ~ihg4Hr=lY6Q
zI>-E{BuD8$VgIVRQ~Bv-wMF_d{6TRe+YBL&D`kiE&TT>urpYg@5h_UeO)1(p0=(
z2S47Of^_JbB~(uO`Q_t{UnJpU1%SbI8{%NZ=Pg~j4KU?o|L0dA
z__M|!p|aJ%A#t^B!(odPfp_*R1P2D*mv`>b(w;uDFxJS=Nj?O)`7*nv-SZOF~7>WyVC#M4ahy)zrD%$GJci5q2Wyk
zJj9#jin!!`PoA8*U~$^$pKpPh!PuslwZne?+scWhm@~($2e(fN
za$(yGsmY!b!h3Z;Uizz^bDu7KFH-5dP}~RF_9I@8(nd$F=a3VY
zE41HG;lpQ)n(EL)Y)4hZYwPzF`E~DEpZk&(Z@j+s=5rJ-s`U`HzrzZ*KegdqxF6Q~
zu>Y^MD~)RMT(_JK4yPd1${z=#T>1zL2BwxPwd7p9b{lK_U
z|K#T6SSlMF&@o#b`100P5RY5YYx2)K)ggHB;DI-ENR><@%EaR%c@T>M73Jylk9Hm&
zg%A+-hQfhS^v64DLm)LL0c4+a^{R?a)2HV&O>4U2FTgqo4z#ke(R@7_vijgw_Xj%b
zlk2n4Q{A_JKhv`&3K+HIio+vJRV<2tDS6+1cOWkG^{-KNtH_^Xn(m7oJyte0j%c7E
zG-y)bv>hNEg9;}fh;#~*nGMmrA!v0hxa8?t@c0LO>)2BtwswKVnvNUUA^7oyQ`r=y
zE#xQDaEFYIt}*a8@o(^4PY1>*iL{B=1qjX0rmruffJ{g?jjS*V6=ZtOha!7o~$0`
zf@`S7mWjoYQ-{N>2<2mm<&w87v?dxauX`L(g$DGoq5HFzj~QL-
zl_VAb&MP7Q
zPXp^5j;XE3|3V`83|Sx)UoM(K!N2lV|K><%w}U6jv8-8M$EK{;%6PtPO?P0I5p5v#
zh=#sCi}Lsz=jU6cT~$K6)@$P9n0%#MNH9d>_6d%|+<#gw70&MNIM@)4BcoWUc!mLj
z=B088!Nq*PZ&Q%IsuLz_e|
zPR?+|Z<>PdW7i}Y1zt*$T(DmfyDY6qAt-199ggRu`_$7@xMgD-E1ESG_LhYtc8$xe
zEno#*=WO+n^Ja6eZ?ssj4z}sh82WBgRn{6)QxAYq&N_r@E~7pIg{~p{+(2L77w$Zb
zj)PgLdY*`W=#ObpXb}6+9x7S&*E^bZs7jGoh1gbhFawj*EHY-=&b}yYc}C2oU0!X)
z3SXcYA+cbWptA9Kp}TRpo7SWx3bnB}KqfTAg%Bu1Qd)ubD#E&}Rge;Vm1wN&Z=~zp
z#NB&L;n>K-2N4n{Iqz}H@AQoWlH*e3sQ2-R?~%e$649v7g}&MsRU<2ApVU?aEQ8L4
z^XD@f8XBzY63VV?{rKWt&e+}j*V$JzR<5)FwlMF<5jK7%R
z`9+R~cjq06a4oQ?oG3bM7U)W7eP>iQR&)EWDT5td58lF_Gk=}1G8kzVAoeAO+O8q+cFJ<@S2rl3Os{%r7M8nMDw
z6U1G6q)M1D=|cy~`q@99yluGEmNf^a0(Dt?UtcDO{0ziPx@Q9h_IVgZbB!4V+>FjC
z;&mL6c)f?xKuSy^HV*ROc~)yB-ELtNYz~iKO`MSj4xwR0;A(XIEv<^>!JcmszekZ;
z5t0i!pE`x-cIXVYyQ;QEm&^uyHok!mRDTmQ0h7>Nw(O6@F||K^-?ZOhK{k~{MsFN1
zUkHg#t~Fa;*fO7-dt4DNfUrVXxXx1*l6Lo7*Q@%sQHuwj?D>6;R0%K7S^Llw(#*PN
z&lb?D0UhqxVPn&OF=5`@$p{+%^3TMdQyfc!;1Ue`#0!qN`K+`#f)T%?lao~*y!_c0uXMR>gQ>|x+I%XioU#fgPaEnd`GorbwNmaPD1eeY3S
z0)+3TujE&QgLhTU2OUS)ywmxal%>QY4FB@Uu-j;?^+v7C7Xrikm341Kv5#NN)#WD<)=LzSjkgC>HbzLm_RGJw@@h4rAm3jP
zeQpQT3a*)_{ZF)ya8dMx;;mDVA)eQZ6u;l!$y`i6ogRQC9d(V3GD9oX)gPLWe3w6p
zB-vBb_LF1qs}Btrh+^uul?J8}71J@1qqDoCwvB7P#me_Z*L>sN-u@=7DH{pJ4%EVl
zAusnOb1JHq+E}ENEEw5v3k6WmyqOOPGv20O_v0Li%O#TUV@`L0a;<{yASn_X_tK|M
zEcgYN%XfVErBT?sOf?^nfnju&TDkGF_S^S7KLe`g={~>Ddds3U-12Y4t5yQSnL=1j
z{R0DeWo5fzR
zC3A0wD#hNUw2I^}ekpal4Zi?=z;Er^P;CgXx&Hb$3o;=0&=~RCDYbI9!n48&Xf!$F
zQs0f7O__9|`g{#JqIDwqTy*);hi1Q;%sT1KP`G7IOwre!`mAaINUGfchaS-BE^!*z
zF_b<08jF)#0U{SlOf@u^T@^PtMj?agNZfL;XH16Q&K0b6aGEH(_I=bJEa+KmoS7aI
zMX3&t9st|70z=E??s~4LwwS%-wuh`2UKl@+n}8KHkF0QWdcK3=A`L=L%Bh_}XtI}9
zEO-&G6z*-gS@!E(P2!Yad_J4cmN85sa<84#;c&RZjzR=2{pVdk;k
z@!15*}xOiRK3}6eYueE2!1>hpuheLSEbhmA`JNY(j}c0;bLEbn_TeW<>(L
zPD(S#(?Bvu_u->QOt7;BNc>4zh=7!fEO6OC=S9KV#C)d3D+N=iSM0(iVL|$Gq_ff)
zKg~f)sQ+-KoT_RO?wb3-hjL0Af-?cLAw8PVbFS)bAxE39j{Jfu=F>lSU3Oppvx1kp
z692CY`~O!L;6L`m#lCiu=Omfiw@Y|ET!cmZm;;d17H;dQC{9j#*+2{Qsh5zsVqTyosE74J;WbZ8;iwnlS&SSlE$i46qNcc5c$pVoPMQXgjUgQ!G0JK4S7t`
z<;%JFbP#)U)6;)Ia(U{^8GZH$P(~)z{3Za@1gZ5xkw80oGLHBLuxITNId0D$MOSzPbCkfLD};=$>@$+%=>+o){&|W*OO#D2?hXABscp
z95xtK3A(B(tH)AD2hBeAniT`Chk7MF44ro;;7xdIN;|HD8Kk*(?Jkg9vWPpt#PCE&
zstpzi=a33d_2VC^&u{;1ivX;-VS@`6O|gr8*za*asAOT+s-hsbXzP+jlvu~YibJHO
zq<&jpt7EVPC;Eft`J|+Mmbz+7uw^5swC4Kt`yFP0sMiWbN^mZ^(asau$ao$>II*FZ
zP^S$pIVFO1b!tw%(?o|#!ea=2MKn(XH?Q0pip_0Gz`;q=lhyVjgNW@OmCcRtDB_j%
zUv&M9KL#v|KCMuYhDwNI%mWl=v(aG`u#fN_Vm!h-gQp@)d$D|AK7I7Ot?5heR9cls
zFND)&a<2fwaUdpaHMKaWr581R0HhZfExIROfzUL6g8|@u3#+1Py(ddtAfz^GL^YZt
zCAH=53W@t^a`h||I|51rDn|_Z`TRLH>%g5(H43#tQQsUn(W#(Elz@DpZ>fYm(aj;q
zh@0_bB7E+bHil?7BK;7+p9kpJAY_XtMvKrhNk{5hBWbVk!P*uY^rALtbfZV17*w-I
z@yu1tZ_&WI0E@z#&cfx}24j^2=9P6LTx0s*Rvj6B
zyKW5yL5Z{?i&498V}Hb!vLe(xqb^mgpj|YVHKbQLSOjQ;(JA`y{nPzZuHGjS;HYU$(8O3K1a^J2b5{Y;p=*4RP9Z5!GE*zc&QkD}zf-FdAQ(03v?G
z!)=1^ZVZ-JPyl?)oqFf!O>{OKv=czZyYzV+?4uPqdkBajVO1XZ!1B%j(pbi$omIn?
zAADv!Dm`QrlT_IT$SVsG<#q$de+qVk1c@_J+O5(eEM@wa1a^0A_uGo==Dl`~wH{0{
zUCy^|kxb%gw8xZC|4Cosp}A}~YVaCm+V@^<(4Ab49uhQL@1NM#1Ujwd=qF@(~
zYz#YpVx*cz((Z`BWG#CRa|YdfUY)j~Vd>L-W(ba9
z(;zhk04!#Bi90aHITh;R;c>p{XGKT<8%7C0;23Jk*3N4ZDaVTZ*{2kxq`o%iFOorS
zfUoIGuuhncs0MX|Na5;d&;yJQoa_=E|4o~YN4?KbyU(7O&ov
z2|<)0Ldt635t7hmqX76o{;;qDLlYKul0sgkZTr*
zjQ85TVy$FrjC%|`Yk(QBF->#e>}QIAW^oC#{G&YTskp>v_9*9nYBopnu5F%+c$ITc
zf;tVQuC6CkO7y*>IHh6MzCn}|bc}Nsh0GXMzm-9-qa*&HbxeHKt8i
zZQmS$i^iYBu5i=pNAcZQr0y2hpTr->EMD2B8kB<^Rr^yH
zqJd8ROxA_;(LV-&euDAw-{}lcKKO?r;}XLt*pTrCV={C0xPW7w?0(oKW8WRT5Qqby
z^d=d(%(v^CI|gul#=xGLlX`9Uh;jF1e0dcBT65UwTxx(J)iSMhVvk}Q1_NEuGm#J`
zaKZ4y-rU^0;N(xZPlu)gqo-Um!eSS(=_1&bho2{gcCcGD5~Y2(wI%4rJM7%~9R-0p
ze?v-{nCk0c-{@DMa=H^r+UJ{G{NWMno;+y=YD^#D3jGC}OFHHx$3v7Uz+vYhYRRam
z#tW1415=zHk=zB11$^lbl{@F`b}uXnJRbM^{Er7KZvOI+?)z-J%nB@^=s;;3QVJ|v(DYV(|mvd)FT@e`TGyO34U0@h=CvtlmihW3f%{6z-)Bn$zVYgFZ)bO{Rt`-Eqi+`gr{KmGQ)YQm$yv&I8s5fBUXcu5{L
zQB_X)yLSmM1-aGy6xm*EO|p_NJ`K5)*B>6fge#?FQ;P3!s1g$_);I{2mv8IZURl3O
zSn?ru8Y*>qr6;AAEb=|Ld6D2iv*6@Wbv3m?AcmX$OFJ2W6W}jk3e2HDb=mlJhaJ+e
zM)POKk00MJqk{@I7hgUx6KRyb(ER6fn3WfxokIn;%z$@;ggw8k`JWD?l9*1A*R1K>
z_+4-^TR#&c0fNL;ckJYM6?xXsd|sFJE;tm9br>rVHa&S?luqS+HIu
zK&aG)X5gR%jZQM3p2nCQp!Gc|Z5XoGHyCI0%Rs(s~#F=F&I`%>f!4*UG8Wl*Sl>FMfcJCXQ!x-lLKPt
z(=NKGYw!?k!-f$As9W6Cf}N5<3p3r)Y?~DMR$48lRzhw88w9D82-94_y-@hgfRGXa
zY@?xH!A;A#>g3$m|4vcT)pcg4crUiNxLhtQd-eA!(s~G%!LLEsCsSa8+*vn*_S!<7
z1iYn3ZuTPhAd)_NL`y=)E*{0C7sE?G)cPb?hjnNc-C6Xmwn%9m8H)^SI_1wZI&Y|<
zVTGB(SqIS#7Fv?NaA*AV%%%Z0qLm1m0|M7>vD5B4o&);Vq>z}T!KYS0DwPG^%%PIJ
zJ&M7Voo$szipY=&yyg-o6^U5HMn^>5(VY$b{SfST^}}6Y=H%$GlaiB#;7O7`8tzZX
z^t5_Fc8r>chtrv3VvDsQP;&FbYRqyc
zF-Y>eWJ!cr6G*fjfyoDhmVs
zv+=eRN((YcK&s_~qCW-15>gI%{~6Q`0RGJ;-GA`FQjv*kWo2gm134|)D$kL^gre(7
zEv=i&MhrrAXb%me%OKw5!Wiv<`S8UT1#&cxw74+*#ojUV7Z>;E@Po^d(K{g@6+k`4
z8TO;R@iA^|22OSGoQ2A+{red%T-c}|Y9@`SIA0~%xLlM
z#wYRMC-+Wt8Ngq7CO73};BWZam9F%GzfRdH+;v0{xyy;&Da
ze6zdo%|m?sJk~Lj;w@s|C`RUge+ld>lLlxYNg5(&{{1uNwbN&-Wd8jn6>`ep-~TU#
zu@Q%d?C+OGq={c_(BCh1g)4P8|NSuX_piq;{QGePx0F}T{CyZ1?j<(2;J*(G@+1F$
z`EtKs68i?RU%ZHa;lc%_Kq;B-2hK}F=Nke^wjYgP;}a*9l?i=@*VMRuftA(h+v_Xh
z?H(I*8swCectY^TEHZ{9(gP9d?{}|A#q$c0+IwIcbnceBZc?%-0_l#aMcj5`}XA
ze_rnY`Q3f@6N{8gV{q$tTH-{W&!J^%7kXo8uGv^U4HEU3edika?p;{9w{=r0F0Jh>UmJsxLUQ&(WxY`3*=mnuw-m6dg@WYDHz
zWxTw{(fB>9qgKFw`*Il(3Zx9He+aZd>X`!D?uf$42eohr90N=izRQBhB<2TKYr@a%@6+x!-8*t6k!eBD4|x_pyHf>`6r51B1aW$T?-
z?v!U}e185aDLwrXU)8-zm)BPx60<7Smsk(xTl8=|r@0p1ip9Kk9<%b9M%+{A841IZ
zpeP3i0X81d=dZ6guQ<=&xuc~uvGA*CZn(1agvKp)W3+Vmg|I5Jb8ys;Hw4-*^j{&T
zps2Gbo|uWVZ5pfdzv6wc!&W_WzgV}x=h)lxXm?h~afawJCE=N~XRq9EA%(YtcWu=3
zDl%68lyIlr)_c-Z!1`CAnNGRGhqgj^XV0Jl#2Q0&=&$j*PE02fFfn2BrLoa??DG>U
z32#sTb9S~>t9atOQ)j;=N-#j-khihn7T;gE>IusvJtwEJDU4>9`WzWtbQxo_8o9>c7yG7~(4-ZLR
z&0gld=FiR
z8tPDtCFSJso)Oc9sJ;_nx_p@^nl-tw@JyoD)_JWQ-KW{vmp7J2M<@FlLqkJXVR>UC
zd)vzqu!U3fN^PJTRG7cGCDd`
z5-HL$Om&Iw@yWn3-*L0{5FOic|9QNpHqn#zK_eiBK#K6ce@-
zW>}k|OYF`#>_RF5ivX3?zUJU+XG$QV4t<*urm
zn~+=>^;4I{f$`nh#O6Q-@1RS!Kk~cXu7Sx`?QFk>G!RNwb4Zn+ARt;Ni_!RL=G-K$!)L}
zjMEb~sPQ~M+wsoy+;wtZ&u-}MnLi3l<*j}deo*Yvy-c|mxDZHW}u7u_|NnPi482oN-Pdmd8*I*0nO*?bNQ3*JITV
z(*(XT`;m1$S=w00rAwDit6uauzcbMoYLI4Zs4qjV{MONQ-V*iQ2A6&UqL3n&HE3w+b5;68A_-spCTLavaK4#4O5`VOk
z5>ThGqicyyne8kd3H|fCMGwZ|g24LoWjqO`+sw?&Z7ND$w1c6AXH@XhL6dm>kCzer
zEB+QT9ye@H!ouh>jRI#?s>D!&=+5MVlhpP*>5et!(l|8Mht*=jn%#Bog
zgnp5dd9QWUJ!xG|d_3k)vH~FFYWYo>5ED9jo#6^+Y~)~PGJ-(6oWJfyovI0um9Y+!
z*mTp&ZW#4g7K;^kMPKUzw8HqpLcWE8;+qr&>gGm&`p&Zhjc4$~s?A=#^vui?>U|9T
zZ>~OE`Epi`f9DH5aU%RU^yn6b
zEBh?7u*2m1C5~1jkoi3&{h|k9j>6Cu@JZ;u6nz&bM5wOW#II}eF{hN&kGfZJ^^7mq
z>~r_#=;7yRh1MP)E-NpciJH&}ycRZW(;R)_rX8$lfGo3uO3^r}ok3MqqM}DTlccg_
z!NI{);+}4yjW?|E_y62-GFXO>*JqeGISB_#tQ(-;={E&FeL7w~o6s!C#zcCPavbHVl-#jl+9ng
z4oY8KE2X#PbaqowIei4nSFVRhx5j@E2yY6qP5$8LPjCF
z+sN-6f+8k(^Gv`2%FD}l`f87G0UQGQJMTJVk5BKpBK76Vm$GlEeV79KNjdaG0kElu
z_Z{E*ljcKOnrTZU(+*q@ySX1sO#sfCaFo-X@zqxon<&FT==BLqRs?=v}>t
z(hnl~GhABdF?P%rT-gmYX@+q
z4gf63K7K4r$*oT{))EwyekDsguj%-3^w@ZPrd_<
z=m)xTckiOtml+pshji*{-msRlv%sGp=$F|s8MUE=^UgR_Y&U>te;%u{cCF51b6&F-
zh!KGzP&CV_s#jIpFVwh(9WWG#=^n^L3DEjr+Cz@1!(|!rlh96BK{yNOiTP)
znGJE4a7j)OE!54)A6yK2{``)}OUer&%odiGSlz=xdyZGBiM~C!%Bn~Z;{FJLkT(y@
zrAw!P>{)Jk%B7MaNuB0*9uv1TTi?I`el$%dLTi*-LV*qidb
zfx60g@VEv`QU){~o(#g?6657Vsze_a3_Bm0=y?OW(XfgnefYuim
zuUW$4R@c=fNa7}=Vqh3OlbrGFm({aQa~argFX4LqT6DQ{=MIu?IZSuire*Dj5{`nut(%u*GMO+s87er{&
zF~{f9JrrUiBgeFfxauAhp)hrIbv09&^i@oZ$k{53Z0V^zQEqP40-D8VFJHY9esOzo
zuvA-w_F4*iN>vqYK|w*LC%cw`!IJV!`s%=!al54KY@#byu4H=ZWv8UnzrnE>t*QRr
znh&HiH9fs0PiJCc!ZWj>$BwvlHaL3oIduoF0PXUtSFbt(3;#8Aj~
z?tY2+tE8k0z?NkCaH&;GF`6+Xr6`g0@BU9Q`hSvbGL{`b%McU{F23e}bOyyLyX&(o
zKwK(am*q4x$SRzE``J%68E?#WsvOl@LtUEro=5^bvd_TdG%&$U53vs)KA;pUoL1Nm
zKDN{EpSTUrPB)a&NMZK`wt#eJlCss7uZ@F
z4h^HZCK7vY&VlJ)Z#v~O;J9>p4RT|?H~9cjr)FkE0u8I&$T+ogWuVpq8Xuf<0B{)r
zZ#NZW7&lB#De2j>liTj9@j_2x4ZVMv^?$e-n-7)${=vCYy!U6V~Iwq%R#!$a>+Uc%gdVeBE+^Ed(54pt-KI3Fkf}awde-te
zabQE<`ptyVSg-VFrjp`nFS8$Zs}oxYjbLK^+aKKCs45y<3}JX7c~6*qSIXr=Ub*dM
z{0raJF66ahmVMM}XVCd$h?zVY$2pow9_
z-Z7Z{4(JsCLU^4&{$%4h!vKT4Z4WWPU_f0t5JnajY!>-&8Q?h2QuUL0YXQ{ax0hWFD82W`T3nd=?0>H_iiw(1C*a9dhcBd2??=-dvgNGHLN8r`t_WQ
zMGmJ*A1b3a2oWA!MK52He`#t;$;|wU;SVE|?abTtSJeBZnzUr{`9aYoI=L+KoBKT$
z#hHBPf1L3%uHEyvYF;`BMeA@tE3hGXzA!T*LqjA0FQfWEneD|-CVquz)|kY^Zhi0K
z;$mR&nj+8fG_|$&Me}M&lBOcm_KQehGiOYTYE;m6J5=pED&zY>vHE4D#gB&o6A#H9
zH7WRPQov4)opi995#vFkHVyY4J7&y`p-P7SvOyJK^HM=Q2R6iO*?Sqi
zV6c5a*?8_qFKoX*9cw5O*gBa8TLyZ7=iz2why6vh1RR8jR){xg`roC2E0D|W(iH#J
zMry%owfm+6<`x!m>-KG|?qa`c4QQf#SFhTG+9`T`uwJ%3;z1KuB#eoAkq0+&qbz%4
z!--bHn-&)rcW$th2VvynJA(=-Yinz~9$Qu?{d_@&W4dyM%--I$gEDbXo4y5uS6Z@TFr>Ub)p0sTR^?;2&xGtV+{q
zq~d&}nvB}12>@p)>FFn-hwL=+cz3>ciz`Y1Zh=TTIelt=3@j2KNl#C=J1=hX#b%uk
zvhNlpb^Ru#pqO^1Rs)i)xFYC!WMm5cyXe;q@9T>$l~?O9ZT4hV09pVtz>hGs)F1jV
z(%v4^;%@%N*Nb&?4D+5nD%OR2l~|&==v>zRz`xN%N=7EY>sW+>@d)@9=3`#DSF`A&6K
zdqT$h>i1YL_C#r=Ua8IRXFtrJ7lnl-(gi8wcZx^=30U_dt@FpzU=EY~~r87%O|
zGfNMO&bwESEbfcsG@A2^oQ+dTOjH`V-P_$=#_DE!Al8T3#JahXxvU`)6){CHWX>oM_((^;?I+V_VnpJCwHXj+f5wxjA_08}
z3nkjueJj%1plCKieMMzX#Li_|@^6o?dveNv;F7~OHlTSS_l@-Ro|B5vcGF)eF6)<(
zYGqM?ENL61O)*ev8&NvqPI&6nDO6BXS63&EoF=9bB=Tc4f@a%aYMba-3CObmC^4B<9$wzI}--}lSEy_+0q
zuXn*@yUCyt?&KZs@l-+m*nu@VVgHt1%-sm|M=nZfWo05zu)?ADnvPUG)&X$7R5nRm
z8*`qHE-co-{S>$xIt3=UTG=||dp=^!wZ8(0tLdgz5@53?ekVMo4d-VMKKF
z8PSa&(jm@>+TY7kV@zm3ApC+&r5;R_r-AhDCxHQjZqnJ=NhRvWYvBI-!ezaZ?8fT}
z*QkhgFowq=DCL4zL9fJUTh)5U^#Yeg-RE@IDe11e1TdadP*(o(^3v^mxITbn8YVqA
zEAK^Cx<|GS3$(Od{0ln9>M5(pqit$-AAEZ&kac%`&Pn8fw;#SM*5u^&OUHxlxX|$OT{)cr&StY~7Atpu#>aC2H
zR?N?2Qq&UDI7zVIM?s}}4%^r`B-@ZhF{nT{xd($4#<$p)lC7l>eP>&q&=q$sY$eg*
z2d55klpqZ)vHU$zV`JYNqpraPI#3l5lF_tBr^y6q>W^;|Q(tJSv&h+(i?}guls}k!
zP8Lx=IOsO#_szxaO1wAmqySsbW>@744vz+5Mn*>6#_ujH#Bt_ZUAji9uJd6EyNHj`
zKU?C2iuSFr{AF&=5^pSDzDqrc>@NmfK0JeWQRRKUEjj#W^9SO21)&lp;rC7e*4SUt
zL7$uONzkz#sX7mxCV(xXSO5&DID5qPBT=f==;8oIh68Mo6+0KXuo41|oxXzW!hGz`g=PB0)ZUHUt5MF}}fldns&g&K$JfMpM5&
zQ0f5~%2-=-!LtVjpS{QoS_zm0z^FiZ0fTaGv{piVZ;s{thY#amwypPzlrsGf5oAtV
z6aFM}@o4J0OjCMboosXcUwz&H!hq7A064Bij`1f!x~Z>v@K6r&H`O1%E`Y7u8X
z{GH_CSO(p;r8&W~!+ATQ1Em@eI%+XS#eL%zK8$s1>k&Xx$Yo2f6?Z?Io@MaCsQH}9
zS*x-b2?VpLw;OX(txx(%)x`<<-k=k6VEdJM?((szB`mlUiGquce1>j4JhKn>9#U4a
zXMO4qu6<4`{BzQ+Tx}HCL#aIqWfhmcqDVHqXd3TddqpGNl+Yjr87O*4f*7^G6#(Xu
z(_
z8NrKI99ROIw1gCbai@EFdJx3FZfr}cS>2SMnn^Lb8Q;_bw8le=!w+4*llm}TnQ>F2
z7jm=)Cu*P6L}F6=`*r4=<}0nZd3XX@6=Bn4W=nS)c!P`ELAd~pT`6!<&f8j?jSbJe
z+Tgd{5D5ArdQF-Cs1zwFC!qJ8_MXzYgvt!P`tShpT17HS%9-9`T}M!EP*}H>oaTWC
zV)Msj%-G&A&f`5XXpbH7A+_DEA7shjFSuNHSsFT(#JxJVsL31J=m%;vF@SnkV$0DQ
zF(j#?f*LM47iFsFx(PqE7@PJ~=qJfhRttV=r=_7^7&j?W#*;USI7?{Go_Uf<)uU9i
zLbAD-UF%gtd#>(d&O+bbwOnhn+ONUaiq}hRyCvxwEG}p54SsCenZIr7>7ZO^*O7Ha
zd{-oJdN$GMl7t~1U-Zu2l(>7ix7h#y3-Fqu5jgiYldcQ>SV$Qt
z{BhM_6#nyN;pM3;PtFKC6AvMx6Y0kAgCK;ew1AK9?Pb%6II%Ocu%Jk(3ZM#D0}kH2
zsKj*Lb;-#^7HFG@)0{7q_v?WIJy87N2ZC1pzFzxV;ANgb{xA4T&GA~|-wI3q8TvmK
zsAM5U55F@D3zv6_4>d(FL|lE?!FcUj->yb6XmV9s1D0;%PtT#kGC0ib)-{GlYq1?Z
zqdv!nf4pPI8D+%T>s&as00H#uM|Q)oC{Pi*>muxpb$rB@F(;
zsc!dvXwZm*g9C#6{{1^l^}5}dBqHhJQgV}_5a{e@H2u=d;6vNm+6sPdP>4kB^T!OgpVhpe!4-M!e>~PpHxM$L0HTZQr)@zrXlR+C2rJ001a*S&$kT!Aycp
z6h#HQKi>9suMehB0BYIG*jN@35o(ZwL%yhMYEB=Jxkk
za&uk0F>K=E1_etbZC}5Fs1K%JM$4Y1g$2+=EXmdN+j)H~92}X`TFjq@QsVwtet%Y&
zg`m_+Mjble)?8&I?j721qaq`~x_2x(@zj2Cv-*+SW=tcMGE-ZozZWKKDS(MQ_d9)h@OC;r&Z|Kk2
zGDYFTHJ5(yfb9ru$W+5xZ>2!#KX;qliJABui^C{xV{aaAlJekq;+IQR-bqW7N?f;y
z3FVOC2|bfm8FHh87HZsAp!q&zC|(O}_k?WN3kjuvCyL%{0pC>@wi`oaGy>Q|`l}v;
zkbO(!y%wnAphm8yNgOKvR{y)U6)wCU`;f$>ct(6@3>!_gTz&BEbFpQg-R_#%hXxBx
zAdxoO>4tw{HuEhyHn&UW`k>~4_VHT8nFG8jYy>Rd*m{kBEg3ShOOVSVpuX<1fS_BI
z9C{uVzd0$;4{RvH&Iu6oF>Z~%fIgbu=fECaiBOX1(*&r)U`;hA9v?i+YvW{Rmvna*
z0ob<0H)JJf3N9$Rz0d`X_D9>AT{Z$j!r>T@lcGRY1#k)C0%~i6(bDi<@x{uwtKW^5
zN2+b^sZGt!vH*BT>}I~5e<<+g&DqggH(=allN2$8OXSkhR1u_{;N|{
zQ$|3JUJKbXgF83VCHQYi&IJ#OOaEU|GB4&WbTw5jtvB
z*v^6g{sg2+(Us5GHeK@a@}>}Dfqu@OnVCsSLxT})KR#MMh5*x$>!??Uabshn+fwTH4#4iEAM{0ycXvG~jRVuJ@VI2R4lJ
z)t377CFY9nHaH~gaZ$>Wfnk9XRHwPf!9uTJO*E#q>||D@gRp9fDihOdxoW;KSWO*v
zYL%Qdr`hirxBCPmL7!hpi9sWZUADZ$P9`y4OGVdLbFgkzfv$8{)9tB_ZNz&
zpu~f61$7}GoPAJ+J@mf^oH}a^cP8Y{2loe6NChxY9LHD{Iq(1`|8nopHN5_xgnkxX`OWNm>#1)*wIp7idGW
zdIpR*57mFhY-Kf?JZUt6Fqsw^(SFZ^qFv`s>;Di+*S5AIbknO3p39~FBa>>z8iE-C
z-z&9G!)G?)d-IYwRSLWf@$Z(!x`#@n;7auLF};tUpSS4ee4Ch*otbIz#Qbe;NlEVq
z`{-LQx$%pB5f%N)HYf63>%+1rPutk=uqn_v5i@0W?ld{MpzX+W5dMKZ3k9C?^983U
z6>>U#x%V939D$P?$$CmnY?B>+(oYNJxDQm)=mqCzMu7
z-fJ`drETc-G&7R}RYt(9z5#m%M+$^k*;1R~eDFjOyGI}NM!_8HOqC}AGz1#r`;Q+@
zz_mtvfrrIDnvPqJ@mzZat#WGW{zHdzD?T)T$!Pj?J}ytj2yi2`X5GJkzwE*_aCO6-
zF}=pH|6hxHRXFaz2f*sy?DJreB3;QcJR|@rwk~7?yB2jL(Fg{-+=+C{Uj>ldpd!NC
zSRCvVK3tt_#zOL{)7gAbGfd8WygX^HW{sq?Kcl}{sF
zOynq^R!(QAwYIN?^gy)%7|UU6+Z0NW`U{gg8|_@?IrNnzq(=_+o#L5nEzS-1>Xf{B
zchh2BDBNw|<^R!87451=|%=h~vdHn^TB2t(y-WL~`2<#5kA)*1O6A#rpvX*HBa
z&1<`Lm}f{qd|a9%N#b6v78yg#7X4N(B-8&uFKt{f=nbx(t8OJgzx>K0r
zK$i<%?OiQE{jfPur*~R9c<+>wKuqGWO<<3OEI7Cv3@)hM7~us!1nELI2YREr*@=p(
zs-ee+X@4|R_@w?IuEGz&X#91~6!hWmii|#IFRsM})GSEvCWPM5hBf`;i)m_M;mZ?b;FDop*9HQhbSDB*CJ+|GI@pNfRRfk&v%hp5l6?$FWMm|gl>41a
z?VJ-N8V&m1L`M30AMP1LXbcrr0otsrHZx!&auM{4f?Nm+jyrrfWoy_swB*7z#b$DD
zY5Z4vi{jj@&bo2uJIDlJu03df`xGQNw*yR+IVW6^ggmGb_C>H
z1EtTQ!xo~R55LpHE~;=g=7op~@)@K_&(W3#(R!bU6;$uH4c{JW>7#npFZVAz&ul@>
z=BC&Kg@)*}qhUoMP^=9o{ZNBDye)y;*dU*;FIV}4h|1l-kjvjO0$fQhjfymylOra`(#=OaC@+AFr1o%ExOr3C%{Km
zAaybMO530gax%eS`*aB#0tRMNji3AZwLC-Dw2*xz!y+a3AKx%c7uP1s&Q`cepZVT53@VXMu4!u&NW*5Ax-jlkz5p#4}g&9p%(im
z9xgnDEc1`wPG~?0!Zxc-G`5UDh!h8hUMs#}8!u?4mxjbLgzuO_Qf?+zmX3N~H}F^#
zKv$~BJ7QD<-x&`qaNo25(XMTFvt;JKkX2U!l<{5=dU?&hzGPFeq}(~lN+1Pprdp?x(|DU>god`fS+A+pG4l})=htW|K5!*@4wNJ1FyXeGzdEM
zY}C2$9)0Yfj%eYG)jxOjskny;|2J2sC$O#WA4ap*nH1n`+}vn4E6|A#dk*cf+JGcS
zi>3@${wc1_U77Bbe-A~ZJSZ9&ATAjkX!uc6&5UskVtzh74Pe8+sf{Lh3%W%82bjC!>@j0?xvl5UTNedVLE
za+0jn{}2Ku)#GY1PrK?fP0Qwwm6VIX-%9LO@(X04K(e_Dk?%SdLYbd#H-%l2RNEB)zjpLAIngJ2WP
zIYb7>L~4-;9fwed=&fJJ6KwqWyw6tu5`!6W1mE;^*SNl7R6N6`{MuLCYY!5>T^J{e
z5xl>Hr?qOkg-?wA7tc&iUC5GJwV%+F)=B?d#D7h
zs8AiYrKQF9CDY&fQY?y+s{i5?_H0ZSRpWzzo{xj;OvQIUsCgI2Ty1p259sQ;I#eC
zfTM8|pU)%`2V;B-;35cl?eU8r?U+I)SL*h1X#VD8^geUV>gZ|f2IvGUYi&FbV0Zd`
zM$l#`2%ZM)8zNxrn`Oi8m!$tL0E@>!JAVi{Or1nfsv?|?0znWX00>sGxjm_f$pd?>
z0jgir%a=G(r0NiuM^!wiQNTDQJv}{NOtgFlDC59~=GGyPej}RIb9Xux5OzHz9Ec&d
z0#i8Ka4}BSH#DF|0$@rC`20dXN1~uQ-izt0_3=4aa;_MTz@`BH4>0_hR;euku9aSx4SF`@SwyWg2_M&HZ0fvd%PT3N
zqzsiEHy`SeSVB`Lo`8R}vNX{j8u*Px|*dpM0`l1pQKx4ab%#UPj@BP8ii4U;`_3+T4
zGgXjaci7b<#c`VZf%RQ!}O
zhSwM%-GL@{tY5Jz_LzNWn$q>UEODHFaF8~$R`km%Q+cI#R&958Ke7Yv))B4w=_ef6
zAKEmynk#>jZ0Zm@!`0^y@5p^NdPQK!!;J&KTtfj(??{dROGF15Y{=%u|Hem}cOmyU
z9sT-cYisYKya*m+TMoe$Uy`TSPE
zG9R4PSo^iEaER8K4t#oKbB_>N8F2PJs4#O(cCS0P^7G^
zAG3F2cd7v5b_gjQ-J-oG#ODwM)Nh-~gG&v-s9{p>ctyk;AleViCiKE}k+6&4F+niM
z+Re(!O2MTQayS}#dV>oN1cIA}o4uG#8-^o7fwe3VV8%*;KU3g3u2Xcwu8nXuW54o7
z*-nVo0HY_+iP`inrUk$Of5dxK)*7UM;jA*H{G6H;M2&*Yr$0eOVN{H^Xj9Amo|=-9
z)RnPZBF!xU`IEc&*J+9GXlt`T&S5gp{gu|edyAf%+F^Zc7CaKEopV+il%T$>$B{#b
zA&>jMlL#Gylj01`7DDH`?W+2^yM^>Zo4$Nu?=99aGehIKqTT`3
z0_hpFFRNy9b8?(ZbTrpQP!S$b4pjA!>`^Ph>v|3z9DyqsGE4^(E
z9{#u`KXgYor=;$X*-%Gt+vJPhjT?D=f%pBi#kYFgUYt?b^cnL+qisDFPZ>`#)xO*O
z-dB=uI32oc)UWEp!+|TcvQMS8FU)wcpa<1(81zKmsTQxcI=`TveBra*qwmcp&~)+Q
z5HvWB$;yHQ&zw-u<_tI880S~i$JEZJkL#;9ta_UYv;T6fj-FQcsdN^3XF?K$E2aL4
zcHUJjd20DJHIBymr5I)j^^hG8L9Zu_}M@
zL=Ml!+cJ%oUm$lPMsq)s4bpmDqh&;kTU(PG$jql(cqY-uh-z?X!51Q%g>QaLzIJQt!SOe3D49m60
z;%E*nDL3O7Y<$3P@pygb{_mq0nF|ji|GtAE6fx_nsuCjrJgPe%;S!ENGH-XW#RTmDlKVp~)duFJ$ZT98Dp-cs>Q
z1_TK*%(MXHh)qVEwS6l-Cosy)zZuC%Rx%PJ&Nzd;S>l8|VAjn#Wr}lN7
z{YECx{-ZWSQ-VNl0#+uOw$4(WJbs8xhI9mYmk#J!sD3Mp9za<;0Q4K}SZTq+pZYGOFvX@1Zi2Xb?Qml{2kSj8Yx$lKka`%#xC$4
zλk{JKsg39|2;0Oa~L$zTj5QM<|Ax|f5V#16LRzL_L2_itG0f`31k2(Td=Fg&B
zAhorDhFLLM?aHT;3pZnuVEhkNaUiE1hV9FONZfm(^T)K+&|`Yi_d`a8<1@-DU@43P
zf
zJO(%ia2DS_5NNT<$4*ZX|5e8Ho
z3v?=Jm_bm%0YlK
zOxO?XG5E&8MuLIUts|$0f3pBrVXEjHOeer)rKW#8MK=2hfE$Qq4UliL2fPkhlZkV+
zJGk`TJH{|sc&%*cqgByRTE5(5Irb(5sespFqZb0QaN(#Lg6Sh65#M37sovB|+&=Y}
zE9^c)y_HeV_0xzEOksn_D9+VjQegDg6F}!l|C=XZoiO9Aya*Wux5eVVc%L|BHuPm6
z`QbM91Rttj2s1>33kwU;L7Ld{LLfq-+e40!uXO&E0GK`ieO0KCcR3BAc>iaRfT#{LkJ>OSp@jOplps9Inouuu%#O2zt&?iCNFmUrZ
zLgdEj&HSI3SX1*>Ar!>eFAWJJlAykN8py$2u5iXY18#{by9p)*$w(g=n6l8_~v*;8(I9*s&RNdhL6Kg|GT`H&=0!45s`b)_#
zD>rbV!4E|}?K__H^d7&AQAy5)vq1+6$S_fta@+-Z^!WGonfwcx;8bp3c
z0|Ntp#cg2ZsGnodoc8`bBg|8ST^82V84W%@2Ss@DrI*}kz4t*MEYJbrUh~t3LtXHD
z*77-C;x?d+!MpkighL_daWJ5zL9TgE3)D-cz#oigYRMjphqVky3)A-%VP|o$YWI5b^5Hf`th2GPr9c>(zL^g4(#XnJ&OD*OR<+0T
z*VbVO%Wtsi$-oRgy&D3Bs1YTlP}9-rGx#_vZ?#i9lhhK$4VniRUBHQ$b863n`{q$J
zyDCSDlW1bj^@lS^_gumA0|_*SojR<^GVw>U-@X8U>)^a)?2Q~nQk+`dm9Ojjw*%Wq
zxO(KoLCVhI*njhIcQ@@wAb=WU6q9%iPuUWiSUbpu%JoN|XxUQ6AjZu2`>l3g%eD|g
zo)b8&?|*BX9&&x_0tsK4BJDHQV_&!U1Gk|Sbt8pfff&qkFL&`(ImlPD0-EV|pAQBC7w7-K^M35>UvGq0{
z&leO+@c&0JJCNOw%ImvU>G52$_EtuZ#o>HP>W4S&4`94evzh87XsWP{5Tx2*c@;~R
z9g>J(UY*)Kgps3nwk8k~-aiz4HUPo>hH*PanCJPej)s{J_YS-Mf$?+=(5{eq?-$)}
z#Nf}N!>?>?a<5KhL!VHH25sQZT^O~Xr=vTKIw3+ffCM4*bVm;Y&FCBuXuK-k=_7By
zwza+MOMp2Hh*ts6mytX2*;*1457`@GIJVqMP`PnNA&M2x+?U-o`NkE6s7lIm9NFoB
z)H9s@x{oD4D8ShOtI-4U8t)@?Ga}C7c1g3J&=qrdR2C$u!8>DQ!av@-Gp(&C7~5_z
zySC5ruinT6a_W_E2xM(e1+z4TE>
zAgnfu28;JH&OBXBWjH(u@&;;8!>JoNtuY*mii*fviEPUG8$VrHXp}537TcD?ZyeS3
zpo2wVfSEvs1k4A(ksx{699X!k{udMGRY-J!2Qf#!w)
zcD7zAF;F0Q+-CqoFd8;}ymRiy0l^B)^8mhIp6pVSSlpnpg`S3dMh90w$@_9VplfQ%
zV(@sH0@#}Try)?1nIHuMD^(UoUaSP;j-7Qi$X7vIFoCxR2aep>w^BF>$8XeuN@oP)
zB$zEX(H%gNoyM5IlT#_Z1)+HJw@^3)Qidst;sGzIf=22x09VSz{amq=s*3bbPIO?P1aXB<^FeA{U0GI=Cz5|s*1;kus$hV8xd3c%u0M>{5%q0hk
zSiyM>|9p{C174g)h5pSxLwQBTQz4OJ
zTt^<`C;A)-gR5gxKK&MCypqYRyc)l@=^OA%f1=O{VwF84Er0L5Ml;M%SFl1B8qQeR+y7Wi
z?O*r90@EeskN1>rw09gqPZa?!hI^XfnvMPsPwh~yNDv)iegOtvva?ia+64nmignR5
zLEyB8&9}3^FW(BmO1a9m#}mKZGjcBGkLvJAX+9@#8;=mW5eG(_9M>T49ISLd#`H7(
zFcRQu41V>U;5uz$JpbgL2v=8D4RWS7J|w+DLX@7Q4XeHRgKC;(Vni3F`xl-C4Pf^4
zHpdZ4l4U1s2r2dagDumW?IH^5uL@Fv!mnX=Sjd{(`9K<`_be1=*y7rc7hi!&t7)Qs
z|9lvTbrBJPzp_I2!bX$8?$%&_rpA-Qyhq%mQG%c#i6vZaLr+bCHM9zeh6!j=yl<4zN$ManmdIDH
zo&w5G-?ET>c#FL8c%z*2N&=;^DA$dE+l%VDW}Z9v=-lwVx|LdHW%+8-@mtqlMt(!?
zmpzFyeFNjg6@JM#{2t}uGY@4p8sQ~5wTa>t7(xP&NJI{AfvFDrAPBoUebx8&&`?|=RaOHg_3=Rw0NdhV;h_ZKydT^V)FFXj4u`Db-<{Ph!seDC
zUjfjzW_N}Ljs=3;-L;e>XGnGBdu-dp3)!Cl$TI5Pe3UI^prmo5;33
z5YN$JUK7CQ=t)xpw*vltDDn&FhuQ-gg`kn{r{aEjIo@y-2zn}!9#JKntAw6}laj)W
z`U?s-Z(3e4h-QWC3dETvK~*>!2S~{d**D0m1NMXQmSK|`_-$|+P-&GwNk&^+ds4f0
zk0QQfS%Q1UrBXv*|2Y7B!1N|Z2fJJ|Ahf8VM-1#8FWL#A5+UeMlTdGLujIhTMMo|`
zlY;XgP%tEA1|<;YTl(d%{Oh(y5kSqS{|%@K3jqRL^4I_xF_^Fj0+RnT2t3!p^pQ;|
znj1ky4zOSYx%5hKE=1h?=ky{}VdmmG9g;EzS_4S`&0s1M!BapMk`a6iD8Fz^Q8yPF
zJTmK?
z;~;RX&;@WwPr~qYMTN)#9B=^9YKU5I#zpxsjUxpE!RK7adVwnbz{6fFrqsoPaao$dT6*!F~;n8RBXHq+_sPur~Sp3
ztgXkTeV9eE!9mR}%dwi^fXi)>X5}}nEcsa#{j^O7)7e9gk99Bm7w^owU?
z$>;;6&(8W$hHrC5{8ku=w&tOaxR|-lO#Nhf*`fRn7rsDVIkr!!K6k7|A67_;B)M$Q
zS0=kk>BEHmCEP)5XjCi${QL+4I^&?|GE5c0MTF#O02ti+fz8d$XgUf#+RPqE5~L%H
zL6($SpPimA)>+Oe9SMO!7!apv!Az=yjML#{^otkhfhwp~0$B;ms4+3`10iZ^YH+rB
zj6ftf8VB-)W~Xkei9JPeH>BKjKADptW0MgQ&r;Y?TUhT?nF`p-aJ)c))h|B%3a1R&
zDgc))-@bi;f5wjSbeyOOrpp-O)JM~ax6C_VhqG)203@bmhzg7LF0
z7-9=ATTYCy+!1!GHh^xGLdL*Al2k5hsps`)01Y<6Q`$tB>VQFK7`hN;uZw&0<~(19
zD9jW>0>KnCFR4k-dt&`oC6o*Y%yQrM4_Cg5BTsRFflp)2NMevD%f
z-n=vdr)nhGLp(qN-BWM~(#|Z@I`niB6c<1&G3oJN;Uj$##u>zZCQ13DA&^Oc6-`h%
z2KjAa90UPu&ONmQ$$o5PetzDs=>i+uv-sw|SJLS=#GW~sxt^JmmXAD&FTQw^7qnwS
z(&w;SfypHl_5{B{K|$d&y!hd%5jBZJa#(rkgU{4qE*MO<`f#4hmjgP@H!hCky-HyR
zIBNY?UIpM!%+Bke*=_e~yK;SN?zCLC{0nf5f>rT;#M0;Od51EQ50#{{oueUj_xYf3
zWYKCgJCy53nhDRP#_5vpJdk4rV|K&cOUC)Osj2B5%#t!-3c0YJT3{ovyh(9XdYNkW
zhAfKZHroBeKW?tQFgvfJq5>XO@Sf=6N%;1t30JpDaP{6r-ODlg44hiGjdo!X${w&i
zg_C_$(?W%a!Sa8ndahG^L8)(?t`3f|0x9v&9&?3UW$pI!nb+Z3rlzTx(_>B|hdHwF
zK2k*_QXzl}okO#7hIk=HsvYC|@go~}u02bahHXkE9-f`w9=gw0JXoGm&54_$y7Hf>
ze)!{8HUJ%xj!)0eHx0-3pN{=z%~K;pKtOQdV~7z21OL9P&+giE;H5x$f0=V?c29-k`Az(ZsLmyV_9>$mn97ARoZdnE#p1?yo%Rg3sZ
z-Z+R;J!j%vD)03;oLr#4(T;)1ndJFud80nkWbf~(IStquj!k44R-b*bsD$kP7iaGQ
zj`iR60bf)kyMzWRii%K1*}ILarDT_qjL6=lkXb5%$;wLhdOu(N
zb&uzH-uF1(j{kArM@yIA_@3waS?3z)=TIO&KX%?3yqbZs9l>C42Voeap?o`6$V@GA
zk_3Ogi1M>Oy+5L3u*d_r*F76l8;Ecg%l2EQH_$1wJx3-LkszR&=i}Go%wq@VtFt^0
zIU{k|(GM%fRvBaRAQo}3O}chUeS=OUD=B++W|BmqOcx67kHFwc(w*?{l{f7v?Xqjd
zIavVu90Dyb<3b<%PALK-gYNp0X}=jG1@N7z1Z_3y8vthkkXbbYD4B64Xy3n0Mwe2P
zli49bT!I2um{<-SG-SyHjyju^Lk9v05=F;m&UtlmWzBO8J<0#q&@(X`91u_lTk|!<
zBkhlrGKQ^PC^8(*b1uW?L%q_T9VJwT7Mz<`<=R>{tPu
z3SeD6gjKRG2yOZEqjMAKX|$4RPvB{pQLs!pj36vhjUajW`Lt>!6P;&M_I(_&&@Bjl
z&j7!0q(k!dJ~+CL#Pf+~9f0ov$0C=6+qVpxgv3(x%}<{$fiUFfQbV7434M1TAu-Oj
z4!a~HAqk3-@u7}*9!Bkc_Zy3UEXgJhVg{Bh~3(S?s@ht|`rTPITSgonbezCl)Y73^w#DZ|Hv
zIX@s(3R+L6*o5oz7ge|9@nW?or7+VwJI>SL$Q?sVYy+t-gcn5Qi_U^*Jg%-)U9-4A
zQuJV`z}pw!E>NXcZ9iueKmBX|*}mq2%hru7ywN=3lm=3e&W+bln@YXl@4tl5s)!dM
zKH)^?SNk&O{}wxDH=a1?MMLC%EylYBYYg-=0W=Hf@{#O9LAyYNly~HumqKCDZZ^-F
z$HkKf6K^5^i>TPRMVKg>Pb*Fy)8=AACBriapMuWDKg1K*r|$RUv#sG0HFyJAo;SqM
z=&{qDc=Z^gba3mVT?wrw+m__1LF~_Y@R8;)wMxaO{EeH6z
z4grcpVFV@8A_``(Vxl7Y6EH?(UU)tD0*!MO{$FR5%z`;y4F413hu5%LndDJB~ArPAgXL{EUXDuw`*X5=^ofE)b39U1|0P4cYMzI)@Hj0zQ}h
z`_o?Kcb!m`Pj&0$U99T*&5EuS2a<-3jX2mSanG44DeJ$&Np%=OVpx8aNjFg_5QXp?
zXh8c46C;UsL4HPa#Ihn_fe=IG|0s*R78XXQPCW-cjZ8S0c@ZU)j}$ZvyNhr#0Z_O2
z@|>o&%S942nIHhg)PB`;1>7WGGHm$KDD5X2Ow6&2D~Ny=
z%vE7=WN7F`&?}g0QWCsq{bIOlru27fA+==u#H9vz*X8Oo6)#6n|1OobACL`Xsku24
zU($A_>i7m7blnC?(ocz0q{{TLp<$B4;U%IVGn@Oeg(gjLs8`yJOG-&S
z(OL`q9S9z(aa1{CV7>Puwnt$G=E5lVS*SMe*
zxkyizdTSj#xC}M+&@o4&9o028_!UuTR4QYx9XXxUw6k{z??^_2Rn6yq(p|LMUbe{K
zVnFLDuQrx%4Cu4L7E!T&Qwcd02~8zLkv5K7*yk??#isHdRi{qPs`J;4XZhcK
z5k*N)mF?MBU%xDE+W=jjeAokZ-QcpFrQ+s0r1(Ti;@ZEsrL2Asc4COSt+?-iZ+wmi
zUuv7S>$HM-$^DTD_Y^kz6BQ0jy(_rLdny2DvQp2LvUr4n1qr>H>ywlJqfdsvnX*vP
zQXcX)iV<$>jB&2uz6-m*lJPlcC)+|Oh-H5|MQPG2v!U}S&|Ie3T$Q-%NBFV)fDyC|A;@XqPKL4CC
z9arDExxHg^WTG2SqJ#W{N4DBSPORBJ{qNfI+wR{!($`hJ#W$XZ^`CFyWW_z@F@TDL
zKmbo5t$#6p(!1NLIxuh|^3vKYflk&f5+!?xQT2EM$HC%4|7V%?jGB(4n^4SiO5hVr~2
zvt?)pmJZi5>(+kxviMlowX0X}ISA>PO00ZfF8@b`zD~wR)G(Ln-uOjnu=B0lGXAwa
z>hA9Hh)rAMt?U|JhEJ-^(f*Y6Kuv3Wd0Sgsp~suU`|-ritWp#qsVz{75isrD$pf-?
z*1%HWIUQR>`fQ?zgZ>qkYy7&sy*-9u3ZnEl2KVQFwFvHX-x0x8>yK@&X-H8k&yNeOHE{BP2;meV4aBZDwXR&?GA;AfRUNq45C6_dFWc
zWR*Udb+QLV?`cOY4CP1~+tg!iZvM;hIw~jEoQI{ZSFf`1<|eB2e?jOx)S|@8pV@L(
z^}#Q-ztL^pkvM#^`*et3<`WbJBRI40U4BWkjI3)>vp^F-Ar{dmtN+rk`d7$_hRkQw
zd3yMc(YTQZ5k%Loyfj*gyccf&))yWQl*;~}VD@2y{{XWuzP~nKt%l`(#$1S7Fx*?u
z1)pm%Pz;-i-)HVX4)ikx)$Qk-w~0|-EeQEZG?IY#5sB_|#|lc@sMRax(Kt+e)vxiQ
z3?O&-!NZ3OQE@Sn7XsCyN5crMI-x@02+M$YyM{%QL<*5uSJG4hVgb^^K;^iGGZJ7o
zk^SLt)|TD(PbrDvB?r1D9Ma-o20T5}bU<5MpGBd9UgoA0Zu
zoxjlkTM#{05xlk4?eAu_=#zJ|o_@A^a4XY0+Q5w}vKs31UF0BXn6_xi8DBhP?zQZ$
zUpTvBk=L8?XG{Mn*Kuc!rMy+%mz7;EJ2f;59U%#&g4l&PSt!;?;=4bixS3n!xjrk8PFC~7VDH@6iY;CO~el**#t>ssP2?|c#a`JqNf0gS_>If-ZOYK+92i{
zo~>^=*Nlje*U*;+q7j5j1Ug=#J;#OdF-WqO41@WB7(odajo6?dVzw-X)QT8HFMaES
zFK{W5lZS25_lLuP|GUKeV^U2>Ek8_v0DW6XATS!Jm-iO-cSL&z$3}Z(7Ci^PYy|R*
z2wyiRW#ipS;cO#09mvmq-DNl7yCX`Id{aa~PAnTBB`9SL%iq3Oy;Vw`3Nby0I>Smh
zy5I$aiJheD%$_rxEkN985;doA)tOhH9wS*Xg
zlmntJkGI>2I2-8h>;O#eZK<^es<0FxmZOi4HzVP~G{50jonIu}Q5T9X0*jKT2$DCwCqQsvvcazcsLD1F1zcMAyiJ~$72RTK
z`x4pxJE1Sy5}uZoH{g;(xo5t^-e+@2e_w76bk5Wpyvb1)JRL`Sho|Z6FZbRs-SNHO
z^!Sf((;ZJOYs`<0ck_poJb1Sy)NOKqe0xWh!{GGCv-_KEl7@dVI+n$6NC=M>RYF=rtU3q9fhD3qgV
z7Aqd_kEDY+5_z;ZVabS3l+S4vUt6uI-GfdfX*j>j&IA}S2_%I3Z?QTRLJhP?->hz)
z0P6=?%yZ|VQ{VxWsT?HS6*Qs~lZI1`Rof^TOv$?
z<_?e7N_ehFR@2F28VFJ)Hy5MDG6eQ2<#|kBY$d5J2JXX$;aLXlN=6FE>&ac~%bWE;
z2%-s^54f_qkoXt^QxZEqgK@egesQc_)bk48%z!UwYi*82sNJY`s~}1ABJ?2wU>N7x
ze{=|>g$B^n+$7rMF)2b;A;2)ehUvsgTnqRiayy9I#M7|sKAKTt&^0b$MP?6ijHmlX
zJ$~SvWALT-nxUTK2FcPVFQ2&U**!+Xs5CVG_{U|((*%;`D2lTxaN|1_bn3Y2vCnqL
z;PX#aH3MoGu_Z5`zFGI-@UJS9U%&SV=XI(b1pTiuOv$e`+YrH>a*pN9qr23s)hV{w
z(vp%-GCk7kd1}0`kHm}&dzZ9TsyRlOM@P>Cvnd^_D@I=Lr4FtiPJEUhvU&&Yv9M&b
z%5`K`#W;sBc{H9dXAsdNH1*z46U@p*yZvH-SjS_cD!LU>@fvdxjKB$1qZQoGVE}so
ze?u>)E65j^>Y>{}O|(H)Vk;a`mRdQO#i8o`$~lX3kIG)_?pxJAK0(2W
z+yoQ*#WhMvwGU9OoVuUHQtqj5Zcrj!I32W{b9kgLigMcpdR_|Uj=bw<#i;%tljhPb
z7?y?mZ7pHO0Ab5j9!DG*9JMR*~lm*$qLP|5DuormUOJtH!Id$rk_6LgCf56ra
zyZ;4S&nVbl=r`1&f{v8KPh(ibPgHEJKvEMBEQa2dCCGSQQY6
z@^&0bp6Lcdy)NhjN}%dGhXfH%oz+NT$8lW*0IJ|)2f}m*zMKfT%;VXdPs7dRb9M5&
zd0kJ}bF_dK&(h-6s2nX0S^S$~6V*V-RPFbEO@X)BR*k20wA(Sm<6TxjzRm;GJj6`C
zXSWwluV+@L>bhqsZ%+IfDIYpM?BagNJ_Gl5-@bi^Z0r^`*{nj?ZQbUugu$E9{i+$d
zZSmzIoO`QFj_rgROq=6!JJ(udNay{iX-&NnBZObW%Ou8~;uutG&<=-O%+qL~)eDvV
zgy=->sOoOla%-o=Ic_m)aUwiY?SOmG-Rg?!>NmDZLpDfEEc8%~djOi$bAh;IxmIiy
z19UHS2=!Z50gnena)1eUA2d
z0R=LXI{A;A36#IOPxh-y!_`U6%*~PIh8Q*|i4)zbaSEpkidwREojrTdJ*hST0=fs8
znVB_bR4{Wf%a%g}g2{nk%M;78NwT3Fua4U@N
z-s+xVYR<~XK!!Cd=r#zY9zB8?U`zPkmkcra-~BmD%XOk7%TCo;i5+hP&R&wsS*iuz
zGfESmVZQ!+ogWc_iW4RUr~aXsxL6e
zr@mKaEO9kbjEAKrmHq*E0Myo}@+Uz=%-G_tJymeh9*Q^~D&Toh2ZME)>-+q^wJOth
zS25ASp6c!&V+0Qy#S40gH12RXm+rk{jnURR?|!8re_?F@0t1ZOI`ZRgf=U^|nX)gF
zRyCz@a&U~z9w{a58JZ!Ib_TBuU^<-uK^0KL#>=u3rYVg7C!Fv3=5PP;DrGcrh5ZUb
zTO;%U9)Bd3WpK7Ze((?6?L3M|4N69f&#I|Hx81EkjQXGUjL6^oZNJFo5#|3E3n`Pq|3aqN@iL$LdVA4gl${FolcGNy3L%DBVmzgZDa0QyZK#_b=K5WRglMPf7u#ut+2
zMv!wj#0~}wg4QgTAJq$(F$4Vt6_GkY&plyp8c5HZ7BPDT)HPtXpOBP#!7}TOm8$`Q5Hu(}jJ!z;(aw{btjWJ0Y!rq=VOlYl4-Jw%VZ+$}
z2l$TII0du2C@`(iVHM9#mCrV#01+~Z5D<5E|EXs#c6CxZ7v@;~-Y
z%i|(Jk9srjKOh+^_yPnj{8I?_0A2-FKq80Hv193nMs?3Igi<0qo7^U%ZpWC`>AM|n
zG_v%k(Yb?XH6)!|!Wq%9l<+uyy8sLIYIzrlr^5i{`BCy9RJpa|{Hc`4Ke0M^hN)1S
zsB97vG7E1^y<77$oA{M;-AL=rE76BlGkYUX
zFnkA4efaQY7}b2HobKu}Njn=)-CufmU-6-l_51#L$Mi|6pxu5nf$)JZgeJyw#x1q*
zjIy-$#n)@rNL|y{_~$-S%M0d_eG%OSO<*=MW+r+L2MXPq&RE{ZxS`r^XyyK}{Z}6@
ztX@?U{P4s^f8Ll|6bg93F-Ph0`n{ox{7rlkBKh+CO)QcPvs}3vLr-3g5z6?sdor~)
zD4*5i(~cf<>;199=iE;yC(xDCsEaFcEYm(+p<2-#z-wSFN-5(rsWIUJNYukbw;!Pi
zBfmVk(>1;G68vMToW_eLOSK|vH^?~UFg))Xt~fJL)f&S+I_-PgcX4M)V>xz2ELoY&
zA<(@NcrhWtW;qwq;N3C8rv?DJ)Tixl`}cLqv%(_o6&TY
zy1;AgT=F8beQt&xeFmF+5S`mVi|fsv`D?*xiuV7F0R%SYUs#azD&%h*Xty4eRqAJ;
z>xV>yLQ$uJ3?>un)NqogI=X+dyhm>6!#0@H1OJ8WP#vq}8+il|P>r~d`vL_tF`MzlTP>|lwb4HY1PrzF@3WBL8O*3IfVThP7c%q`G>F@e}uP&$e*
z6jLY!J4YfsR=P1jFGI#`FeKiU+~0JL9dDaoKp688?2tSn*-i&7BD
zT^obH8TL;mbt-a{$fl7N{3R>|6tl|eO}p1lg8y+zNX#S1NuWHa@0f!_kUdY9#ce+F
zObJ^(aH3-dhAE5=LJb6aECu6e6~5gKoky7Pv84{#=oqoq$3p7(*hPVLG0(v!SxD_v
z8-kzk9=)sOTK{!w)RR1#Y8-2mY8fNTcq_A^l;|uhC%+@G+dqy4w4KtB2SZR)KJEAg<}i#5@I0iB={jI?-F1j)U%!T4Y~
zXsyh4_0FBZ5?)ubencD(5}Q`mrYg1JlnjoP^5M8*y7SulTvVFzeQCDzlO3IYqwx9jQI%C{!d-&eEmm
zTolk1_C1CF%)`BpB$ih<^675zHR~Brn){?cx+x-|2@25t7+=p)8e%O#Q9C!#tPGVn
z#>#YV9mEOR=6?9_;cCw+3@BhVkUn?mo(;sc6ct7LUMTu?~Irgtsz*&Si*P~wsNx&>Z2;0WhJFE554H>V$<2c+y1DVBx$}c};R#UbZ=B^%ex#E{eeuv7l
zgmUWTj;E}=kWO?CPxyT{#KI4B#n>14yK9*ZQ{{lYD4K1yUneS~!266Jr`O2jd$7KA
zE5ZnghzJwR9wzihGm`}n>BNr&g%O0Dh<5%U7ap)c
zTmO7KGLFfDu*vTyA|oQE8ZN^&!MNf2lcKR(w{AJe>JY|K>EGI0x*+RbH3hp)HYg2<
zz!!ozZe)61BuX9=EL)Iy#CLndPnGS`k1s#MuzjgBD=h1aEX
zEq24B-IQ^GBS}Eg8!nS28!^2(^7hpiJyON*{xhcrk^rdUaDzKfr85
zKNl`HTSG_Xpc)tiqmNFvZq{G(_ckvn27pY>MA$umLI7$Ln@rr?jc(q}hUfF;3
zNg041+c4xn8|c4jB$4Cq(Xt5ISD`%@b~j-2AW8NoJLIqi1C8r*KiR`t&^wW67b4Y$
zI0&bSKW61vo8pbM4s`T
zJ9nsiSI0A0+cOMN3a(MfK1c9Zh
zR%RP0o>BS4!4(=BTFtWF?+J`M<*@xBe-c!Rz
zXEl#f4NX!xB)5Po#;Fn=$cVOOwn*j6~l>&oW3wQJVJ@M1@9%x19l@IhSWQN(Ua
zaL{TByo{|2wumEa*XwrI-b!I;@A
zMv)MVa_2Liua@h6Ard`C%uePm@B9~jXla3*;kA&v0()Laj$cf8K!kxEiXlsT-(5LG
z>*p5zhM2mg;4-(OhaC1xkuan-XmDh)NHKt9JOP6wAl_I_!S2!xCwGwK
z4JYi%O~(F~Z+OnB;hX=d!R-37o@03B$h1Wgr*VK;i)$H0=Yww;IAJxX)n2T$=U_TTD+!yK_2Z*Y;c%E(Q2(9Lum?%%jORb6b-_
z|HgWTH8E{5@q-#xG?=Fk%j(&b?+RR-0UC|Mb=`lJ4``8+C1z+H8kXNh&apMJEgAAeuR}bKMr`MjNy&&ev=wfZxDJQvH2inad=eXQw%%+zL@(~
zY*MEl^`L&T9iKycGoJeR`1o}UOWyFiAxiUTL?LiQuKK-mjKdD|b|-5Ki$nI)nz73?
zV-MDFQ;<;tpsC&V3HH0pjAK2K9B;@|amxKtfKTbiFNQ}2F1-vZ3D$N^l%IxvnYe9W
zs=?!pMh(F-#Hx9#>=dr=nx8KWomv)QGYcCAco>$5yL$nv9EGwSYg7WxxJx%AG`OWp
zayA`&0PPRz>O@Bu_<4e+L$>o#(bC*}69g^E5FXqUw1%^IEhVK>=Hh&5lVLqRu=Fx=
z@J5atf3zp`62jS=p;&=%#iBBEXQ%-1`pb7jF{
zx@a9_RgkO6V+ZD!c9J4Y?m7Y#Ea39YcW+&urf8WFA5|
z62cs}U(gcZzn!;vFy~WcL;67gqsxu}QhrbwM}X1VU&WBw^6c^LzA|~3dgDmXk%YxH
zTCT^jHvwBy;>ZRU31^nE;N#cdJ$x6E1WiQSS;+@AzYcONvPwTb)J|_UwDN-v*m?18
zmqh*)o$;>WiMYlBOqpy8*T!(y4O!mVb)urcIp*?tAaGd(%-jI@g~}6N2?ek)y+k+?
z^}Jr+)2u7+8J<}6_KCYkx1CH)pM2sG;tT@fiDRa~EP)mxA2Ls)Ejb>wfx~c=vE_5^
z=O5$O-fRVFLL#aN00b2|%kIztZ@wuC)_;|t>R()%hO4fIfHW*w?$#RcfU9D_Nlv*z
z>XM06Bry?O;>pA5hO|fcfb{K;UEokZbFJC3XG%Y)wEZG9{3G=n{bLl_xru64LQ)bB
znd4;dZzs8#Y4feO5!@P&%NP%N<_lrRE7MPFdmgw*?n|mwO{(3Ql8`H-M{40SO!IIS
zJv$O_F1;&(VV|}+!Ze1R5%j)+uNzA8+2kZBChVGtBXj_R
zo8hPw1^>`0sA*pt`E9akb$uWf>i*?!lH5++knxYpBzsnf++ozkYN?){=__R@+BHjE
z0F0b^c`Ws$tE#H^>^I_JS|8!gs({b1sRzLqGeau9LsdvHcd(L@lpN1uWEgg}l2{2>
z(1EJ|1+!eXO;9klN@g%vA`C(S5;=w8$O~F3=rM`)1`32$5_|_!(p1%w{{z)5YWj8S
z@2e3#(m1@fn>P2q;YgL#w6ru;nS-q1V!=|J2+ZW;3qkYWDxEjZOlgLgc1c9v|Bh4k
z-dr}{^aJM+T9+Gg6+VR0S(hhB%7Ir80=9a4eb79RJpmwuz@v95s?Kq;vJ}d!+4w8G
zA@qoLP%J3MK}?oB6C(^LRP5M!4aVj{gWUYr_oqb?p#OiPZ~xX+nx_ww4UqvSKir?!
z0uUK0uTwJ(rOe%QKh%5R&CS%3iS{2WIE`yZk+XMswSr@x9u}Q4LLCe1RLyt#&GQ$`
zWhtq*L`4jd>;m$|;Pqn(FTIS_+b|UMkx7*d+lf8L{~I*a2b00;vEhyhf90o|h3@~??94P9~P5_n8&CG>HCk}VHl4CsR<
zN#Tw^d~~LIqvLd*m0}Z5N0Kr9D2RQ
z%~ZL;al@^B<~u(n3%pxQ4fK5#B(mB=jz`R4=mlvQV?LgqOsC07x?W8dU6$ZVWTryi
z48rSq+k$23p#sWb?f1?UF!x*JS|YHBEU`0Uxz-MIegRISU;v&ih=;!75_O>-!Mb8t
zb^wKiKzDnZbp7mUHT%O_Z;iTV`}5LPNI#nX)1lhd4MSIgRn2q%R({B}%S&%%SSEPt
zi40fZTzcuBXCy8bf5YX{oF1i%clzZ9!MV6z0v-XruK8CG
z#-WD?WhpTq?^W4HgF7tYT|nXB;E4N|y6H&}7HQ6C9ang~!G9f=nEF9qegbP7$hsl2
zBZO$>p%)>0$w;mYMG4e2S*!x&w!lon1aoZ$d?@e+@cW-f3?2z+BuWw2zE||9g*iW*
z|4+jnN-W)ezNh;Vzm5ExUIXEc=W6vG)l>B68|P}opDdk@P8-qR{2#)Jfj=4^V$p;#
z1af36aYO9S06;YOX|Jd<+a*7fX&AqPp|5Kt#0A*?bNh$LYWn}nZpTi;+I-|Y9fo@jLdbt1n<3GfDH0Ntp@mUCmv
zUK5}?iSZ
z?%%)P67hMzWfeq$={i)|z_X8qno7EdU9zTa&_{Eqca7Aq7<>M?bQ?xznq0WFvc1MH}TI4&L
zg5Rvt-&ZbVoZ>;`SD@1lqLpP8o?L0C_JTE4oBquGe^h_d?@#EKxiq1bYa5JvVFmDf
zwjMp=5f=X#bdo#Wqqv^r52H3c`E2=Exb$h`LAo&SsxGDp`B5i1YE8=pa{ce;Xx`F>
z+lb_+&yN0oJJJHSzhFcfD{@X$o48M9RiLQX(g7pFdB&hy1G~{zDHXs5qZkvMdlR^QF#kfNO
zCni?-syI83OA0z2Ja{l^BUXw3^MeP1zixJ!ZhPcFy}4TG-mv`Ao=uawyYy(I|
z;#;@2*lBCrd-8-psg+OqDgPaXg>FD}*q
z1lALgCDy$U_#Y5gsP?Yg`z0xaK-BvBUQT`R#Dt3{^
z80yph;r%hm{7$dlw`-Sn?@c#Ep2XKI0lfEq0=xh69zBj|*VfTNAp5gA$@IdO0Y|Qw
zIrL7|<52Flg$|``P*
z?+cTm^}FkBjX#I&*%e!LIjBt9fbEi$;T?_ZS37q@m>_X0eDUUUc^a7CBy(3~_T@%h
zV14fL-lKO@{R+#bbG@kvy26{An6orvt4_LINa2o7QaC-X?%U3_!m#0S%`e7ht`JPT
zu@~)@`Iu?codao~Zq5E{g
zVV}aI+|fVP^gm5jr#j~!T#of*pBOXLR`;=DBLkiO+d=T2Wc4Y|jpwl5V5|6(BA7Nq
zfE{WM>%`ADTyd7jHH%w?QJcUO_7NoY3+o6N(Jck>O*PZwHvmqM8aVj*xV14vWKqk6
z=h2bWRZ!MgCEUB$91%M_tm-OK$V{jDrHh6Hor3*eTLNiDnvC^!o%%3rQ`NJ7g}PV-
zF7i3@XV2=M7O2;p{|2J)=B?}grE&7BxOogC-VAf!Pxpb=L6n3#TlIK6@CVD1xzFs9
z;<4ZxrLKST0Fbg3=oaHjuT$Pq-e~H@Dzn}SjLJ}XXw)9t=wi)YI>5w{@+_DA{KEGAAJOd%%1LVEPN2xKYbzZbbr>Xt5?^sEi&;lqAq#pcTrH~)uDuS42xXj
z9OW)1+|=D$lXg#j_`b`hy9d?eQxA{JFTTaUYtgfW_D@RdjkMp7CHmIK!tM+0iJn}4gyWG`TkHXyY}>po2g9<$q$?(^
zkJK(xQkhhLOdV~zkbBqLChOFDm#4`aWZhr%n7`3`GMOM;>Jun>hrR8@Oz4B+H&g42
zJ0}gDvd|_iRNm#Ha78ovon;1l@O8%YM@at8GrUDF?-{>hJ!?xxo{QK4-rp9!!*YWC
z8(E*oX&!A}zsDQf8zuX8>t<^-_6)ZY-!a7qPE>Mh@C4#H#6UiXz((HqBMEahd$>Nu
zdoi#P?eyGoq2j*V=dE%=w04KGEna9BMB_K@-ZwgV
z&|$5Y`@qsi7hFs?eD)kntN@0t9A-)c)6xxg%RmPrR37jtnlc}B43GggKL}Cq-!YBA
zRc+`z(koV;b;=C?n`Y5fBYv9%CsOc)?bc+kH_7s4@p>gBMxN6YWbtZkJ}*MWhl
zQGbrMk4URi*_K?U*tLH(S@Fg2Fd11)u@62+b@yib*+rRkmo9i1@6xZ`_r-Jnl?>k9u?lr7u$uema>W~(8+4+(O-yh36;sM
z?pk)`_2Nk{_PQ1P?aJlT`Z=psdnMNM<5oua4ooZe%XK<`k<
z^(A9@GJadN9fNeTjS~_QC|*B*{=8KPY50;gBm86LBc{tHs;0-lk>S^O{)G>l-@cOjlAlQr$nAr>+we+}Yvx
z2`0=nY>S!3dSTv7E%|0CwF-_WyI`Rl8Clu2rM4OtIsrwYhh`1CLwwafr@nf%LaEmA
z0sHyoODjmc7g+6B_jI~*tI14t;$FlEtYwVD
zGp$=o(fAN8Txw=%c^L+>2CG`xJ3>egsog^NIP3SaZe
z^{w2KYI!2_d9FfRI1vxprhlx}*Yu8GcJ|C>1nB(vv4Vy%TWx_7;+M8${oD%dmieh^
zJj+aBaGs-X%Q*q*=-cb_JF~0b%zvC}J{+L_BtgUM?AZmBIW>s9i`|F8y83z>G{0wO
z8zH$y)986A?wSDl*U_G>LC-B}X741oi0&VG*l|!dpLX6OW6Fuo)ry5Xp#n6NMwcED
zH;BuEcQis~mooUFR-0va60PjvOE
zp9E(aDj?(^T5ffAW
zCmOhJf|3tCpXwb5^GtX_1Q&sd8T3`0A~enP4DF8j>+K)1$RNgmM1xX@ONU4*DCis)
zP%=^G5Mq*58Vx-QEq3)LT3GBzjX|)ML%kl
zo>e}ixt^Sy$EziM`r<@~s${jgI6abKO{K}po%3(dvQLa9dOjnh%BWKj=GaK7&h4d%^2tr5dBPZ{RIONrk*7z9D@!7e?#a
zpu2h6LJ!YNR2I}5gp^z#a8siOy7KR~Zrhc@ETyvQVe!4w|D1%AmnZGCq?I2XT*Kxi
z)IF57#qAXebh;eaD`)3NsRsIw5qp0EAu@T$1Hwn{$%
z3FO?#QD*J245=xAatp(Af3F8G7VGA0&MyXO9qJ&(Yt3SCL}p2oleqGZP>>Ho#6{fI
z(IcTr%dAjW;E$vT@)o9mOT*7RHB>bs&;`c@sjBd24&9y7FlQFzQ73v)74NDqY
zek&*h{i^3b!do4wa^M1ax*|aTVac910v!ns23sCkBoiHG+JHa|A_*mVG4J2+(X)NG
z9w4}TgzvEqK(4Fy=J1x?p->oA#B$Fc0zx_Duy}++0B~V*kaT0d
zI+y-JxCs%o5Nmo2R&ggZB7-m-w$Uq&(SVnQXi5x-Ha1U)#x#9%iinJ)A(D!&nysq$
z*|1vt0vdm-lHHepYC<8_lTkQ3qzHN{KD9RaDTlf;&LI`?qR_dNvpX9j4WGiGI_8$E
z(6O=Vl47@Is&0$zD^kO&cXmO~^qXaUf*Q4&R~cIu&{4_kykUTr&OZj)jJ^HZE+FEn7P>!z+p|Q^^`}k4ndw_iKxj&!5%2(~_;PuDIOow0)H#*c$i-x_8GLn2UJBrn
zQc-hn__26BJVOwN5%}wy$7C2+0cf(i@V==e84@jD&;)s)$C``)^;rULo2h?0ht~C)YLO=tO(nsJjZ6W-gXMCF}S5p->>a-$zk>Nda`1RyV-p7%
zwnfbfUnlP5LK)QS7t9@a)$H=y9{8I>(E`m#wDX)p}dL}SOWpq7`Ov$ozJ%D%t
zXgKGTW{yc|$JVqXPI_42cpXvMIK7nh+MJNA8Tf>Kp}Y}!N5rf4~_Fq75uWSBM`
zpO@RjR2y9g7Yarc-S0C~zqB@VI2H-ZK02L;nRC|GoA?Sic?*Z^Y^N)NV3hQmkwgYgdzB3T<=Cy
zjMw~0Il+Kd@y6g1{jI{B49?CjJ5~N^3aj(>xzH>9^OM`Li8W6h7pti)Y<#^J`E&@N
zMlGjMh))=~%RJ|^M@Ae-H&BM?i@`by;->37iA?j`
zAnDr0tvnBKEW+GK^!A^UO8+e~mdK);1$he#Dsl^bQx=2hk1*9>dx*W;`|_
z+-Z170lT#z<|QQJBftZ&wuRw&Q>Ifrc{67}IM4Ra&WL*ax*lzWeIeu)rj|!WMiK~_
z#2OQT30qH**XKukix@(D*DNN=U1E)-$8Hjzpj76!Kuhg#4<>>h;t06X9hPZB*8
z5iB4GL;+)*27FbJj^eBqy%!gU7xQ!i5edcj}4xAGgh!dT;Yf4jt_>mxAJC!l#}em>Ksn-8u@MiZIDZCt#BRJxT4zM
zKB}`CbCbRi_P|iQu(tFBmhw{WEtl{3aC@0!CV|*MP!mC>U%hpnyvfnCXE(F(DFi~#
zg=A;5=zavrLtR96`~bhDz_*4;0tPiSoCPFYhom-vk|YXr=&E-hIm@IN0Kx!TStNnW
z&5X5?s7CNcu(+a)C1E5`u(d^;O?(YC!Mext;AV(S4cmx`&ZX{!B0Q!r17C)s0+|BK
z9mjA8UPF%%78aJ#_FIjk=;7mX0dG_J3yA^CE+{GWi%k?6)g>xKi?*(-RtjSvU2Ji-
z)7RtowHM#l7J6cTQ{Y|2E0p3|*-KXMURZQaVbt#AQAy`OV>SbLV3_6pP7Pp6y*~B5
zpJfBBAW+yKXSs%LS9g0kzQ8-FFBX0$-U{^wHad&FpK1L3`Ek!L*rK6IN))H$<1*Rn
z=Tbku;>#1_oYw*jD=4hrz4eXt`=d=A3}-7>$Tp3_quMq3++RN&>Qq5FT9{>B`~1Z}!Y%BD6u@-C1F3PMNXy%+IHl-=9p)W(cOA?iK9l_Z1R7R8@
z3oQ}Yy+=svwwk`yy|Z~@x9LVwcMYq2GWzOCW2N(eec{?CvQ9N!^d_qk`_dt|ezN)z
z<52jW0mfR5DJ#>NP(lAzuX4i|J&!e`cQLg%vh<{~a<^<3f1iABbOwFyi0gId3PsjX
zfiu!qyMT@*aNZvsso2KaGO7AZ?%~!gCp?xd>ms`?ND?=u)pM2VT*NkpU<`RpX>`j>
zE4D5K8uDq$eLlAVnv1WE4vo@Lh-Dus0lZ0hGcq0!C1J)S;Y1`SE!%m>Mfi?ECk_cp
zCU1JL=ChKF>RgW#T|F-PZ`*C}L!H+*U|Jf6EnVp0lS-T~GkUK6U?DZ?Jh88BbP`DTAu&?*7v!sNfFo6gJb%-OS5A2$Zwf-<(Uu5QU(OiiNWb}B~Kq~&zJOe;dq
z4Hn|Y+g|`%CE&)J;2?6pL;NAHpiA1eKTUCZdiq(dxF0`$bRW?sm=~JjG{)C<-JP8k
z9w8lo!W?MQ9z9wFIVlXH;q9<`qV!WLrpK_FhyWU<<3M?PHt>HQg@0D-YqCl9w!`Zd
z0HK0Sy_iun8;<^rbH+(4PbssJm78russvez*r^ZPpnn*MLJZD<4`+z-70TL4CnS2~
z?5+Ir#Sb$%S?HuVJ6>)B2zErqDwXjHnhLx6C^g{*$xTYPThwqk3zaj
zvCCPrrDmzV==__whYu|R$4;QjfKR@5bOC!O5#=Of1CJ1<9Y6}YA8ZU=y4E8b!x%sb
zZVpa^#dO|C1MsZQxHz{P1M38pjvqh%LDsIcv~&=a`2?t6fJ#LE3orrFd4!{pU<<^=
zaN)NKp-!zzGx&>WDWWoh60mx;h=e7RA`Br#ac?8pPuReD4GzvmKTprzvxbETAotJe
z%Vbp2$$Lp2c%rJj7r*#8su?=W7(CxrdFclq@7sMc70AJV{R1!!lIR&MUb!9zA&rD6knxLX
zjz~%>B5+A|J^ty}xN?YQ3H|)<=N$mIOoKh2xzJ*orx$v^UPIDrQeG*8>TMxe
z0a$+1S7TdtY8z3;VD55|y$h#51Vv5px!&-HCuPNS_PWeH6qWhmr>VbLF8KN7hd*O~
zq$k8t04F_`EFpOKzT@_d+q7!~=Gu;>+cxS{%Pw5EjT!L
zY@0ZL{K04`y)aX!oFm)om)|Ozc+)(ryQWls^rLCk@D&78ILRH^{&4|kfP(m#+o`x5
z`cwAr*%rF@A(c4cc|A?MWU4dftxN1v9vc>%xDZZv1NS>7Awi^d8Bx%?S@P0v+vJxK1EsPp)EvB~4q0;B4z{MD52vy-CJGgDee2*`Tk
z=b4wyPY$}Ix3EzBgT=oT%zTz*btyKQ&YPtul?SACy(_%gjz9UX;%M_Dn)RRemY^6z
zeH;RQ-l8FMIcbxH!>aoy5N(vxS1NV=&lQ?`WmYaGf9K2`x00Fo4IW|mjLAwd68}%&
zQ>fIGIu_4E-+rMb;m<0)PHuHiK6_l4lRSJ)lZ~Sk3Utx6n}z4!!k8PaJlPUvkOeNF
zV9et>@3eO>qr=ZH7f1mV5fLF-3B-y7Tq#2@(a#3FC6<|sL-Qp!^5q19US@vBDz@OJ
za!XsS&c*tKRw(GUQiyzk>@h&b0|IfxftXAI2iXSh^AHmt43gwHN)z`o
z)`hL8@f^5pL*5BAs|7$|#4&Gvh*so-9nX7>+URnQ#S2NGH~vHn6sm}sAU!eJi)qtV
ztRB=qI++aZ=Fg@)J_tV$;!*&hl{i=dsQxxz_1DH?j%mlc80RI0f-Kdbl_QFPX`H48
zOZXKgf$kkaz8GkjOx&Yg`*x5N73fa=ZvDU|
zByrwu3elihJ5(W9C#rb_LIU75FGNE_43#?r`4%rEd@^hUM9u@RAk}$(9*X&md5BK#
zSQrUZB>O5T=MY4s&V)pOF``ePkmeoRju&J#uz;&2QHO+_MJm();8pboW!JF*V_c}Y
zea}eF0DeAz>1GiCUy`lDfK&y7yufY=ne0wHCvy=ZI46l09(pwXAJS(&3Al}BH5HMV
zcKj>q)QY+~f3l{6#1vsl2H73+c@PdGZrGX$Xh(gZq0F`rfPzuY)59dquP=K@6Fzhr
z3IGIR2nRAvZa;>66(o`XK|6kY6{e1L{%_IjLU1aCT0~Tda5f{nMkUM?f@WigWQi#~
z@zsx&kP)r>Aw`Ntw)6RKmfuGw5#FFi^NL`Q<`etY`0HC=FWHW$$rrDcwD}C#+q$l5R8{NOR-k15}RQS&a{Qb$97$X%Z5)EtT~2
z60bLs^hC6BSDOS+;9W(@Ttw5^SQ&G)h>uUtTwB!<4-5wnyTfJhzU~00(;1~qgbl!1$Dw{@d&?`3Q8qP)
zb<_ioLso;Vmy_>8{aUU6VQ8_zfw=1C%&&x66*-HC6h-sX%(T|*Z!HpnfR&{_M6-wk
z|Fh3>D4a<$2~>k8+LV&MVWxo8*E_ueL;q9o#T
zfEZ;2weFH=QVNB<(csQ=XNHrAuQuT55oKi>;*b!h(|S8&(MSFxa`btVkBR
z%Hn+{%SUA`y1SghPOjU}_1Ev-(_P{#up5hM`9Di8LnGxR=f3&JMt_seDZzu;-K-xB
ze+Ayj!@W~lbI*~xVb2E7cbxRqjG-EhK580Q-;Tbl?b&}&B4-&B9jGPyAexF<*L`88
zs#7C=H+{8*-hgs(G|?~Gg0MMIJP#CR=Yrai;&o+55*o(p+S)d}1-N|V4B~J+3?ySulUIsqDJQ~nd#@G!)6_w;;NLx&
z20)JjvlP#H18j;~M7e`;2Op&O_2ufs)vH-{R*Ttxy}H&Wd|4l9=PTN7=JoONq}2DaFKT6+#wKFu$FF2DXFlM3jn!$@
zr2}*k;0u)2T-Cn2b@v#*1ZTkdA2t$K<3fasGLK4J^^1wqPfgQB`RygmyGI@$1d&~K
z-8_3jgu}jT(xdSJ%XPqVyF0?gV$z=+8WLQ5acw=@u`uirXt9%dkojsWZAi%ibt+Rd
zmLgsIZ4@w?kbXq$l%~yl2&tjA45@omd6>fuHMm?tDPe^a#m2byj^7z>*);9Ml_D5cIeoHP=&=6qF
zS~KvpF#ycSX^rr+T82O~8aqj^?|$oi1w
zz)&a@)Ogp!2xEB(zjbeaE~s*nYxb=La4cc>q8Xk2W7IgD=v~&bbxvI5?(A97sB>r?
z5ybFW^WkTp_i6i$gueNztbP094tviT=o4HdKAhaT{u|xP{8eSAUcPSWYzTQhK>LV&
zR1$$f!!Af4K<3^siy)FPYewApe`vX1OT4RCk+rNb`ab(mtV5G&b%4J|p5f+!y55mX
zp3RYEKOXuo*l>Mgi(kVa7N)g*7rQPC-fn?
zUK;Mv$A!agEO#1ybwlZ)Hx%@R7E*1OxeWX=R@o!FZ!Tl9=#CYjFNzlw*Rhhhh*0LH
zoI_h3gYY#he3q@i%Tf9D>+R5XwD~lho1TuCP64usJvsg#kNQ142ZUmVdIlAnr>^5a
z`TEwR@{U3hRY&?3VsX91uPXoZvv7=ewzJ*{Wmz{@WGkEf+6Cw1!DR_tOYz+x5E{9D2Zz$1q2;iU8zlC&WJ8a($!VW
zd5TJI46!x|)tx)#MdQh?7_#?iYBFzD7Vf(PoP6_hnpsmM!N~_7gz^ECT3oqk
zX6HIE)x=BLZ^Mh%vL{kH3@;LMdV|C9560-pZDK`)h2J1K8F6#|amGkEw2f}#mUXcB
z)&egbQ5HRW7AWh!9s8XLBQQ6x7SG3%$NWaSPcD<)`N;vcQ_P2PKqfc}QGpScv?(7s
zawIQjk*kT7dE>)wf)OcvQGJAjEH%^@*shd-Sm;;rAK-^}CfL_rJNi)~n{J7L%prQi0vH
z6b{brW!})?r24;oNAuIc-Nei~oNbByawlJwDg7P?=CY873jTozft!O#NW!oD%!bWtLNNY@!ric
z#;eVPadDk-@qsD5K1Z8mtjQ1aq*@=Z$%I=P@D>Er7i08;I1f
zP0ua}qv@X?t>hmSgyEEqa_G>ZX5`lea{sV>MV`)P|ILXN{1QP1JZ*m&kpBONwl@LG
zdENKMe+?>>2&pv56jDhl%_?L}Sfo@$hDrm?bDAWT48=-=LP(QRX`VwgNb_9MJZYrC
z`?*;b>zuv!Iq!S@|La<3Uu&PeG(6Amx$p1yGsb1_d9WDQOjwjcedauRS~8Ae1_xxq
zgo0RnqjqAm0bD6i*s-VT=XYTs@*4Alnws+{k~c^)7Sl4UOakPvmZ4EV7(u~&c%$72{B%>pOGy8a`r(CwlaTRqgICT?4=NQxv7W#Fc53s7C6
z0y~wame%5{iz&zjG5g|hpqT$0qfZ_GlJh*1P@G?89O-FkGV)ZT_SVG^B)NM#5f%pM
z*Ot8`$d!!Soewh@jg>oN6A}&$F4Q|Ha$vcf6Ks?C=XqJ#CZp3&)JiyaS$5a$}EhxxC#!B@w#$>G-_G!GCnC@Xk|@
z9UB#kHk)@H5WIS0^fN2=qXTY0_q
zClQ++zU8mtpIqaPDEDJkY@UAda@SdA?dC<@jSM?#8OMC{1gB(1%G_;4_jB8d@LkDJ
z00v7QoK;M;q}P>m^O@|yiFWi??o?K$BRlndL`ah(e8^jOnigacBSjj!sML(*Ns=U_
z#bWg!nb0wutdy^RuD|E4jMrkori-mmP+sTcu(XN4$KsE;tjE35Wz~yYQWqSS)nQy?
z!NX{j~5LiXVDVka#`>WryyD$dyR;6G^2Zfx55(0(o`DEwbu`(5@Tdyd769wygW3
zQ4Tp&2nAj91N}Xda6c?8Ol{RB77!!86yMcK35Fx
z;Oa0gYCeh_ey)%+niS~h3
zj`ypV%)`UPgQ>u!Krg-G(+?#0=Ihh1BvdYxUP4yW_fcs#SRf>$hq#GxK_*gF<$<#w
z!(v9B*Lx5k!D?KaoD8Z(P|uX%SiRjz8u?&2@5{!-wZauX_gl&k?V}mK|DAWA+>%b3
zq^&fS4i*$N9k-dyxc^^p;YRrm=yei%voi@3XK88q#`thg6d6KfoXLxUSrtbeCgj|X
zi`~!d5&7D9_z{#7z{PQ86@!ZL8=rjuq6IZN`bAfVOAdQ6QE)d@(^4crL6M(IYm5mN
zHMg|P?cO`RieN_TypW^upunI$Dqqd;_J?xo>eDpeo*`G9yI0t
zZMC`Ut}VW>J0G26Xxz@hl*VRt56Vqhs21w7@Y0cv17g6%kduevoA5_>{TZ%C;##5p04_s_AwUmAJqg5Rr^ODP|H!zn(7lL&tRH`M=g^Ay??)ON
z`Saj4FzSz<*Tl1hl@6gi<5B|v#HP6GiTF~^SYYJ)91;FjGXGgv&4TDXFEu{_~cT(n{rmpbk4l&L#exU{w^
zWG7}wiYPp9{~~kwX1aE+3%2^D$y`0Q_4}=@F3@!D=Fo?;E2z$c(#?!J^xI#GbVQnm
zsHo6!;@il<_k(=Hz6$Y(9X`3ZW+@7?=o=4i6|LUI2C(DnrJtDJo8$PFV@kU76T1bw
zbgqa<{=9|0tIYVBl-}y~;4+PaljJ*>33hFP|If*-{KVP!a9Zxw7rQGktM=FLt@5E6
zQ>VIu`uEsfVi+Zf9K^weW4#E>5WZ-BN~_3OJ2f|5
z{1ShuB=A^~s1VY8jdIw~Q$K&i@L9ZT>I%34h~EW3P+}>uv%%=HfK2xo4Z|Nnc?xw9
zP%E7Oius*A-wqAGPs9O4p0}Yd_KSwAeyAXk6$OfJd#GO80if835oseVF6!cFT@*w0
zjxO0gCF9?{*#~JYqAk^%?G?C~>!CCkyeigGeW=mNCB}lct9L{3#lpU<*N%Y?^t63!
zp)OY!<>(8X9b2rAtFU=w$6`m`lljw6w$67~S`^RaUOp082rZns@2aGsQvwT_R2qlM
zwR&t+#LQOJc|5q3)z|-0>389@^(?Xka;MDKV}-6yN)ctXl^>4k+-fyxbF--^(*LvR
z$+Wvh%Xxi?R>c(P9f4JdixPonU_O8^#2HksC(2X8YhuU_eXW6lU
zn}5pRWDX$1gnGoR=$;ojiQ1qVF6V?bogDSvTN49^!`RBbH&&_M5D%+2Ma7&IQ{%xU
z?qBv?E+1LuxTxtdhEv=CS9?doly750BfA!Mu
z4>|$6q0bAK7umRR4h532=w1vo5SZ-Fdq?5orbw9fxzHz86fiyQT?R%*d)K2^g4DVf
z@#p}%gOo0?xU#-%OHZZZ&DZqkK>?L>=bIsh2bvIuunCz+L;6!#iZ6A$6iw@<}U~T*y41VpW``;dOZqk
zj9+=@VBoqMF6uP*;0R;(_0BcGUOGqMtH{yY)Ksd27d+T%?M;P*+It@karBIO#q;^c-IZ2EXB+0zs$09fzw+f!{UwyH)w8}G
z06y60E!OD2YS)J2oWtQ7){FA4d_mg>UMDcjXlWZX4X1bA7M&fmchM`*yB0L-?@Gu9
z`(;lgWk>yXm3u1Pzmoe%XW5QQ+E|@szCcJmlLcg>QIAc@#^UEkYCcR&?j1ez{P2M#
ztHZPHS|?r>=#iC7RCOXy4?%(yS$yi6GxYOi?*6=-8I~V8cZDGyW*aIS+8+zwpHBEI
zY&iJJ`H$eD!|X#a2ILt+g0yMG?b@n}s8-}V+=r3g_(
z&xQ)n49F!<`AX`)x#j5lw-(+O>s9zRW9TbMG&)XIQV@F&-Lpt(f}@dS6@i=a!XlE9
z_c!dpL73Ar=!nVaY+MwtAq0f^Y(py{RJNu^c2oI0P1U6|&_}dFKU+lD#C|cm<;%Z+
zlvwN&F711jP=gAj+wZ^Zf1C8k9MC2m&u{nTP3<*%U$Y%Ja2-qfUsvg^;gBy4u5>z!
z*!QG0-G6W!6{M@?R%O}Q%DwxvLAX$KLy)b2Sj@NTwgNus%Xeh<=Br)x?EX=1pln_Aax$~}3(2kF-om*iSU&Hx%MNAJO~SSjhd!(Cflzr4@_d?VSwMW*r2o9
zy)ud2vkmMHepGl1ytn#+w?ml&CjJLoHGkg8yCtyiRz^{%Ut=e%
zTmlo4##t%oTnxA({(b{y;iXXbShryV9c4e1!>FD&`?Wd?abK-k&0pCmlE|*!4xWsb
z!hlo3@zeUaXp9k&@wZL_kfA65NYLn=&yT}udfZ{G611}vbW0?d6WGuBybK)?GEo9C
zLOF%jR3FJLC!7Gu_PVj^KFTXdOeTJ6#EYy9K>8!Qh|V<>gMLSi;Ao6DtQIEZ4)j-(
zg_4>6pO}AAaQ;{E$T1@a+YGBi1lU4II?!WBGqinp9N8Zbu#@!{*eaho{gE__l!4UV
z#8O9i+sRA0s2GaV-)p!f^0`d>_)fc)RM&ukuECx%pfg#*Z~#wv9RjSp0&
zV3UsHmk!D~DkgOv)N?+9v{5sIjvX6x%Rg=T-|$#^#A5%Oj7#0>s|OM?L9*F2w4%EY
zZa_Gs2DJnp?*temW#W@{;hHL#qrn4$;w=*-FY)_1Hq_6>M31?{{MwqC5A$_EXFx~}
zW23b#>cg+l58}-!3!(cU18fbL$(d^1{7y1+$4h7-~1RmabF5F$W
zMpAF~Fc?<(P_;qPaEuKzjZIAiO?46Ru
z{PJ-NQ#_N1B{b99
ze?v17YoUfl&8hU~LhZzCC}WTP0(GfvDuD-;mu$as`TfWhhKKA5jIKF}-!7$b?Xez~
zD@P&5E?aG%X}J@lYY!jv|7I>`J4>D7i-Jy&!FKjwxyRb&%IhDg1*j)&hRpz$gO1}j
zZMwoI91)~*a^(7(6r5#m6frAMsq>P)+ax9sC=;#Cxg7fi)?N|;2Y4E}4lO`OAw@$$
z+=ovXtl_%(c~%-H%jjt+D=WX{Al8~c8h)-X;iEY81?fW2K|9DT$R%hIEM^wCbZ}D-B5ZVuf^Oe_*eJ(cJdrse2VEV=
zg;CoODsflOUn?Mk?J~gBQ65FUDW8FgjyL?jKaiWPHC7%X35o-QUY|Rn-y5
zsQ$4$a}B0}+hut!guV_`FdfGA`PlCrG)_4ByLtBC!VX|)wUV>HW=`tBRRC(lX5D9*
z*3_zU9umcY0ldcNa{WZ=`^SybcK3Q!8o1XQ?LW7itCi!R-(tCX&XwHrchoe)$&lFW
z3)P_dpjkJ@u8l+vl-F9O{{<(9W-=4t;axnTuYWicucf?z&o5@_Z{g`8-ztiND3Ki%QH8~wJfq&pG9rB`M~^rg2g6cdhJRv2`Cc&$NL?Aqks
z>_Ffknwhy-q1V{!-LG-*jRbN-!g@DAA>-6u%P+PCCW-xTWwRHWb!FJEEVw`Mxsqel
z7b4cEO%FCNa1eg7Q2jknK<4i~u!p~;t$)GSL`W1I-Be~*)3zCIij)+AvBlAUo@GDW
zmodS)f1{eEJ6ojiB5=g2fO{{lhtc1a!
zoJ7D74r3xV!A14N_;n9c?4kKSwPU6;9k}Kd*CvaOMJ95tiLJh%w-N
zzY@$yQWkIpJ&+`uJg4mC$v?wM**))X&%t9YaJpe1kr|5?0iuOPteZNygZGEZLxqil
zCiBh+AQrud9_Ptuov=s8`Wex6O
zSfCW4F8no%2wW`l=nsFpyjwG!SyBG3uMX{a{DU(){JIq!8kK%b%L8R)&!%9Tbhy3KmIY?NOcA6zp
zgJ?*#yChJD$7VQ-NYF(qG(tq@L@;$UVj)tAKKbuLnbmk#xe_rG31?2~L0fVkln>%A
z$oS9%ODF`HBz_#I#mcMM2FVL>o&=8{gXo*raq=W_RVDzugusDxnBmQa&EU$FR?JJk
z>FCk(-0HsUz~RI6)OG!1%Xi**aK~va8|u`44Kr=RdA(zLP_;aM@}yi|0VRVSVRcU*7U9$r(aD9zts(|
z*O?Tb
zvsLvTp5S8`UJ(BbRu?J9X(@!A(zbZF^}}nsfJ*6k8U<*m>54!c5+ILeeqKsx>z?@E
zDSVW)W^rDD&mhGU4e$g4dj%YZ@oOh^R7g9gP9S`v2I=o0LrjqXi+yOlfi{~D4MlWY
z#I%DKmjv4}k@Gx6vD#5iqsl7o+1fIT)^!V+x_)Fn{JHleO_%ifW!pDFrcZbk
zU>zjzA)-^HDvkLX`hJwbgpBp@DER@RhroT6>;+grpE^Ha54%z8u|vw^+WV5OV8)2P
zLEL+(!p)r{+Z#q7EX(*S;Q@Q`6|BaF%!+-o^t3ME=4-(R8^nKYl|uwJn#BQUGEf}&
z98)b|(t;#>@oIwyr=h^f^GZug$Jve?X+bM=v*0l{7MJw@@k#=oU3|w}Ml*
zPJGSHxlop1m7jxF)fk_s*-77tm9pg=hfxQ-4T#;_wiw8LLX*b
zpVR7ZxAtyUsDNEH@vk|@T*mzJr0!dcrYb}*|8DA{A1dIM!zkgm-tr5|SZw((sRql;d
zER!=aONG*R>DK3l%fH8*$t(5~pkKQQ9MoXM~S?QXEMic3g@V}JoT
z1X3mi0ah=}J~&z%LQ#m$Z=B-Xl<1{TdoSF>B|$0rCC_+HY@2JobZ02)8VKe;nZxQx
z?YcqQZHRjVOI~k$7ng832TTsrmTPq;<{QveYMW)ZniAcfnWu$u7A-ycZTn}{{AAbzGZg{+lLhcI*9e!Z%3_>1l&~J-0HR#mX>GUTJB5AHk1fj
zZ0uSo(QsOfQ|u@deqcPIk5A@PZJaA|fG{w21Lo2!HtK6Rm<^^f7mc7~kk*CbUf{Vx
zgx&qyxh;CLpm08)&8N+*%V44KLrOjL?Vi)ipB7yYxp4hA-(^!%Q_NC{
zgjof<3Es*NcL!LfgWMFE=v7STX@1zYXG6pvnudQIh|=p%Tp!pYI3`iIGO1&4WYTO|
zlJ&8EFWQVNRr@2k1ADD~=0rxa-R4e*iKbec=Mz)5)^3
zd)C<_rGSL2D9jGCQdyRR6eTY&PdVcgYxZs6Np`j(!HrJ)JRAH?LGxpHGbuhD2xlOWyCucySssUeuN$0HqFA{_;D
z80iN8b1bX)^VpcXia-udF9c(P&F+s|#jWrTYiMl9B@KI#6U!AT6Rk
zgjVQXk{!y^8q#eRbNP5^WnvdWs&K~ktw>#%o}MP26-+GNU&aGme*H?!$3JYRP>Dfs_t0ll|E-jk<^rShDNWE#st;|oSA2bU
zAh@!5`)?1yMGyPSN24kQ@UJ3)Lo=`#nN~FA<>iymrP}Fd@l5SseL75!!^2nq*Gn0~
z`~`I^38yy%<%@Co%p6{?WRxe$93Bxdj0^d*_L5)!=6f|fW3Tx2L^%*C`9+xF*EjB7
zd#h#jU(YuA_pj5uaUY~*Hn+{-1zAdxDdUiC1uaht41(KHWjQ@FL&iyx1WW4@wzED8
zVrDEj_bUzjZ{2c(_GxQ6-wRD2ydeZOGflMQm)eFOM5b48lX941ETsn8FLO$-NI(R`i?HKA!`-6G@;yynZP$qP}{i
z>gMUG)#wQAHFhI2G~7VgqKLN)A58MLVsD6?s)ZVzL~LN?1!S>8LV{f+b27X@jC?J-
zaUtvr32hr@!mcN^PclpsnF{AI54lM2vFHTQ->$AzVSfNN4iNU3JUTAQI#;4
ztsuxRat27(0_Cjh_=N~I$iWc$VUHDlwU*vqZvYW@?7ofyqsFgJqC{bE&dr1763O$h
zf4C^}XTzjKo^mTIEA$|dhwag$HYCQA!Z&=VHjXb|xX>BT077^?qGEmH8RpDsg=wEu
zHf4)4gR;Di8YIH^Yeut^8w)(dTuDjE$CU=y^%vu(CcGYSv;G)QqR@iuZ^z-oOGqF-
z(qlD*PMJ&tLqn2014ZsUJdAjQfJP{z9SL!?sT*}KT}mimWqFjSxlvY5uDq=5#Lm%v
zW(HT~URHXe=Z%{(sUaQaU87Kw*KIREY)
zIG=X?X6azxspK6ItH8ccLN@vtBpi%@cnrEo?e@ffk7
z-ph2XU9@x%Eg~$u0K|HV&Bz+mS!{w$lxks1#^>kwxg$oyE&+sf+;)YEek1Y$zh&TdUP#LkFFxBwwOha1UPOcqd4}lWI16~=W
zCx-$zj(1$=L?;0K0upJuU6|0!uz`HR-qR
zdN+Qbe(+oOfO34=mz`q)Q6!0`tboAuXiAx)DACfuLgk*Kd=^qEBd
z9Z6ow!{Y;hE&Ra)w>?9PEGqomhslP7+)aSlnT1Vd3uQrD&4!~IEGu$1Ufo8!A-qGs77DFoTn(bM7m>(TSiFn>F4_g
z?|(V0t6M6UGoTuH(!o7iK~1f6f0{}AA>e#g&0W_f*6fJzpDM}qVeSJ{!(a+cuSKQv+`sq_)
z@$42ZmWc;zLdQsuua}D67S?h;oR!tuOrf1;@%`|F3l(1L5qZ=orZFWOsJbe{^4gZj
zwogB`08-7tM-)!6T8^*k>-+QsCywIO;)o3WT(`NzLytm8H`wf15IdWZ>M)*%M?*vI
zPHnzM5oSILa!D=mxiok4H-Fgy9><9Z=@wRJO%n
zwkP>qvGU5s-O&O23EYdGp5AnqU6NWWqA49WM#$Butgn|-+NoVoc1%Ix`@@g(oPC7D
zBL|l0vlZiiFM&#`gPfn|>QKig=z#O4SoU*G%#+CzC4?)^YND7#bjkSZQ;nIKnUipV
z(qh^r!N^J&m12#oFsxuTB*ZOd*Ow`{pv<6H0-T;#U%!0CiWO&iy;KBeR~j@86sDR&DgQ
z07&y=Wi)u9rfb)JHnePZdIUE4H%k+q_cf+1TYvlM(Nk-GZ4PphAJfiJ&ZIk#*x9hR
z4)J{(<~tII@)jW-xJ=|Xxb9t(hfG7VP$2931VoO+3CCUcC^B*_@xUS+f!JV4ujSiM
z-YNnlaq@ji2@b
zu!BDG)2!ux+gO8e#XB6)(IP44#P334FXZgTO)DJi?c)$0eNWcryOS(
ze+~1#x@&H>5+g40#e*eEu)%qVWbjFlDm*N|ZGAN^Z0X<5BK3xl_OSoSkYyZwJ>TZz
z*Kl)lLxK^ln!=2E=vuMWY;y^lN^+B|Nthz3#DdJv^jr}B1#yyM52N}#KoQCitI*QY
zl2TgO10Pi6=Rf7*RhD+~mW^{3JA-g^pG=6_oQfWq%s|quti&tC!y6UWR
z^|LrUwlbcO_tRhTpa{|Aq)QmC=~ZjieAeb}!+at&i6La`*4js793C#3(F|qsv~in#
zog0HRk;z(qE`??Rt#_0@-(#(V^&AK|aBQhYxGhg=G#zs;+9g@NqZ>
zqb9tIr*J={`^+{heWA(XaS~BH!JoNubxJcbGGusO-BE+F0F8I}!#eLJ^2M
zM9L}KT!VMvA1C--Ha7*~J_Ey{xaQ_D9>F^%IBZeWMAfDBB}|+QC(cBi`#8#z3}*-j
z6Bb6{PL+u5W$s*P7cyk?bHs)1!C3YVp7IfiSDJY9F^{kCSGBHn!ZlMZ_sGwW)x_2A
zNu@zbTH3@e$q2do<>$)Yzo(_rLqn0ehA|<%FAG8vqC!70He!#6p9AY}H*g%YloDCU
zBtZRFm_lgx5Zf=TKbs#WwCU#3@dOsf9*=Cs)E
zs`1MqNt^y&6PY+=AG7ML%@t+NoNSwq!HyOfJe)hMrZ8RW)T3ajWrx9P*TpY>{gGE?
zN^^D4*{e@rVSZe9=n)b7FfCX>5~luv5E%bBR9|
zuK3K?^^^rZF3_8Sx;K{S$G0SF6?R*qvj&0nMo$Z?GX^a3VHic-G6_c@+y2a0b=tGQ
z7}SKF6X#lmf^iWMzaYn{@e4#umSX=!46a0jfRBGXIcz=hPVyZngck%`#NR-GcOE``
zt8xY_HU{u&BikYk8R0{R54!XT1}cK>J4tni)s_|>nK;fo(T2p3
z#CW;?fPn1d^w}|uTQ|7mEC&N~UX5&a==teBM~2N3O*y=g=k3S2m(jgDaQ@~_VH_DJhOew
zV3%X?!{8^pb;H^W^1b)ltwx(T4!!n&F11H)$YwA8Q)9zU6YV@2d?bBHz_jR|n0h=(s;MRa*>b_qkkr(aVRy$POVW@zB#bz)hvPWJg#qafacvH*RAn1UNCkV1&42+X2M{JKq4dZD0H2uNI97I3B1v)<
z>kXbJI*QEpc;xXgKoGEE<3<)vPR_;vz#n?UZ3V$4pyRkPsv*qc6cCqD+YBHstCUyo
z51WyeJlw{E-Mim}4LJ9Z_BF>%3+w)ftk^o*fet!l{KJx{oD-%!sRrlHwWKj=0|h?p
zwBQ6s;gjB2Y%zlZ=EZjw33>zwbmOP^Mop>DQ4VUg%N6Qp0TZ&60fe2Luzlyi?ZV+Z<(I7MSC(YcrRRC7Lum-i~B
z2+wu*ooNzTG7ql~L&uhTa>m-nk6SwwJl}lS$(K`btVn)Us}cU7)wix|p(m9e&Vsd9
z?p&4@Bd2fbaVncLW^>;Q`j#x-N!g}-evFy*dB?C@8b<7oY#$4$Pb}V*>Bo3B#s>nrN6&1vg4HQ=Hb!NMCnk6-8FTF
z9KjY@*}VC_%jakg2c7TeQGi
zh;zLQ^E-rHzIt)IhSAv0@SF!2n2^HenyC%6GNpQLV?%><#v^8S7`!le(uHf|_2T}@
z&E}#{)JNdGx61zFc3%XrC{?h)lIz@
zKg-NOhesLjd3lF(vHg?i=#GKq!SS;-F?${yT-VUhFe&WoPU7i`6d~;l}W}@XX3RTzq6N0
zuFvp?W-zSPtCxT0ZwJIfE32U*OfeRFaPUb%=gXiVt=QlVC03Er`B+lPB9k3UclH?Y
zq^nM|BcXR>ZNwzsRaY-loC;L6vYL?J5P=!kd3^DQx&oJd`BLu#V={n%qy`s-C>n7C
z0co3UJTx=Yn%~r7VJUm=9|*`h+Nq9vzs;Kpopwj{udEXkbjC!F*B6zq(X&=qo2hKT
z=T!#kKJhi=P-xF!5L{RX%&c$Yk1R3Xu)R-k#!MfX^d%U;WBob906#Te_?a
zLWHQ^nV{!bE<|BLf}ruwv17~G*%_W3z6xHgwd}q!v~Mk64@QI8hS~3BrgrVN3~1Xg
z3=CTQ6I`ff`ETe#l1K9f@E!=leO;gfjR=W@qe$-2-2yxqzmvZv&nS|
zAtv(PIQCCLqD0kAy+yp5qP@L+;MDl&C<*AHdwJZK=<$&)=T&R(uj+;fjL^VLeWaAq
z<0H1k16Yb4w*H1@RE((Z+C{p1z|Vyp5PW95SzJ7^^o^l6DUKrn&5$^ebS>jlJZJ&J
z+6Kcal4Tb71+%amqc+DKax5mGIcG-j!NEp51m%`Am+-HnKAvv1wT<~e!&XWW(xv2L
zdE{JB&eyIntL)fu(IFA^Ci_lb?i@&*2^^2g66#CpBvsI^-_+!}Ue2TH22=5}RbrNX(*l
zS3pecMo`ng-$hR+v7n|6!i|$qK$HGAYt^uRZ>!t-dGCumqZD}ppjo#*oJ(U
z#H~BD?hH5q;evub7azs2gHK3oC_bVBva$`&PNn-rY7^W$@zF+qZ5w
z0Fb(PHa;EqX^8#jUB}Zb;TL8Z`uzF1#wW;%VQ0z4hdXcIp&#ohHe{nCIu8#L9vxZB
zE7z}oV8+{Pne_^p!>HJ?G!S@Ob7MCTVs-WJo9YK)0Gk#ZK4IA0g{}W~X
zY+x-E8Nf&LuzV6tN`ei3NkH~wTu9Hz_}_Ey?~;z0XV@)
zFC9jEG)h3Qsp~Ye1s0T{np1wLST1m>OC)wz9Kx#m_7wp0V=nC87aHtcZ946H7Wm)9;}4
z216P2Uqt*3rjdZRxc`#Vc*)~cXfkMX@+q(gNX7@v9g$TAtsthVzAKr*AZO3bxH#{t
zVkJcVGk}n;@;V?kJx($>+T=|i)>yzv$BDOH@aCnN;I5|z7SltqT5>7XLUuX;n|oh@b;115xI%8NbGH_
zFYg@-mS6IIai8+)@x=R^kGt+IU7Q)Sxx`dhDsR}>Rsl+(=*)ffx`1uDGqd6Y&(Gk-
zAJJsBZ@LszMHkw+HcvF&F;aYH+W5kS7n8DQv*wR`;BzB~sR)<7sBwdoFZut
zt0{aQIPMJCW4pgiT|O@C85$Z|E57qo;=d>PEfV)uwIwE9^Ymn#u)?&2c@sgkm!U5r
z+hqfK92C+&zuE9TQv`GOfy>LfGNb!_nwv&)Z|#qCRl470J`2|gCW5yiLP@uOywRs0
z=g-Y-|FyU8LU9P-gN%y{h8&osiY+__ZA}B(Goa6?o;f6!SkZ01f0%9srgwb9tAo5%
zJuVJ{4(j91PRud}B8`McPE7x2oG(lT)ESA%Bp8K6Bk7@NEjd_2O3c^O%G
z6Qxan6iJzPU9Y`)hdGa5PL7AoiymO_$AB#CxRj~GBigLl9tg#%Q@$^(M}N@m1p^70
znN7Pdj0S^T(%fe&%@(n7$2E?V0S)?cqh-oqvOIS`yo}dKL&);+-r$1s>!qY>K<)nt
z=LcDw;mz^3A)hr0beQGK{-zTq)8iq(UCa*#S?cHqaSu=wJRQhPaITe=@V7u!1ul$>
zvGvt(_BdCr;wcLe?|X|Z2Xtz!AbkWVXkd39-}VG1%4G=-R7;3u-@wt;hOtZH8b!?W
zGbL)Y?tTm5+IsOpWw&qsId4xs$CP55iNWTbNbt>eZmYYeQ4^pj#IDt!m%4#DFAQM3qMe-8k-{7V_Od{YBvDVv(dvszu5wIYz84Gi#5`D?qbd#CPP}huV2&u4;*Z%QEbQC3AT=c=8&Y4*SD^e|#VW{`S;i|->q_)s+(`$)QENtfX
zrLY;dL`ya}GObe8fK!pek!2z}`9vXtIn*8wN6LB5e0B|?`PBEl@!hg<#hz!rslBXE
z6TJU$D$w}jkIH`8@!SE`yI-fm`_83lKuG0#Z<)}tWy_*mxt`DVX3fr5x?lE*^<+hU
ziXwkYzjAt3oTRU--2RH{vwd5I8MtulX6Pt%<<$TU
zrJidYrv`)Wy_nrQ*va$dGLRLX-3z2HvRHXsx+14GnO-85_p*h#DDC{j65rXO7D4&m
z9K49}8gJ`a^U>f{ZD(A;SUf4O@Z}j}g|OH&o
zc`V^fPF@}zO5EI)?i2oQqBs&7V;o^HV|YPHS;uQnFiHrhpr(6KR~q&E!uw8d|DHX0
zh$rl;GjT#Y^_?pS$cf>r#eb64I?6dv$JQ%QZ0nm4%kCI*W|#kC`s3+ztbV%y_)M}%
z^9~49lVk6TpkEXpKoAnxZV2!C`)XMXMH7bE7WC8fncD%b1c(klpa`+qGPx_cd3b0F
zb$UmW_tgxDCnUERZ|q$Eklo&T8EtQfQ}PXM{3oV&WZ
zi~}9O+S5=#wXtbpcFG1dhJEA;MwJLqoH*QxGYoU>F!YQTL9c8{KP2XFlGX+fT6bco
zKa}{~TQAasQduT^9FDqmIJQ7}FT^Ub_0lJPLBUsrUC|)9I03HSKfK;-ut^SdO+uzN
zY{t+X)4MCV0EF1K=H^NKW>uiWk
zVYtZ6*=JTeYb%5sU%bG1Ype5XR==K^^M6lVH=X-`a!nz(!l!*vI9b^t^H
zjHWnsbD~7*xeXw8110Db3HWWL{`D~K0BHWG#BWlat*tqiyuKcR3Ikv`2G}pcyOW6l
z7lJ)-Af+*5i`m#na&BUxcu=)IW6u&Vm2_5FL!bST$}lp*&h+V)>od^8qRezSQf^f*
z9Jd{S#Tx(5$`2p1&xu1an_XQUIM}8EpcY1DccgnBTEKQhORHE;wdK25Y7t0o0L$q<
z0?oHcjz7PWuC8txV{I+;5}0*}P9?3X8DZyO{V_=ykEE8EE{N}(8(PhIR%+S3R_oZ<
z*v>*%B2qJPefG!Eqd+AWWA6`~krWp;-Glfn!BhHqY7
z7d!oZ`OtV_u#_U79=KdHGFc(ijwr9`t?yT(Tg`wQVCPrC6&Y7fawNDk{Q9v}zj2Dq
zZ6Xx*uE&2<*r%I0&+eBJV|KlBhXb@IzOPj(H{YgLr^O;;(pX3weVp>jDHH9R<8;P%aije&+D3g^;?
z0>6+4w}nN_Ucy{8)!*{|6^*?ypt^TV10OO{rG#S+*jqqBe*$)}2)idXK)h@&pw-Ffa!Vbw84dwL
zs2VqCvOtm!f{pkHkRR;=Aw~d+GzBn5Q8*Iek@)lpB?EZvGmAMi@M|H)UI|$-AcLTm
z-bA!8I_Vq-pClgbiHqEuoQ3>na45u4N(3;FfDw+Hm)F>0ia6v;N(%Ag;{c@rh6-w)
z(Boixv;9yO*~%!iC87g&-f?gl3JEcvhtn!*({@zM)?D~~b%8|T0#gAamHX7vX>lW;ne
zof8LH{V(_ZX9HlDNN1h6q~xUv=y33}<8Knsud;pnu|~C`@m>wTjK)H37VDw~Munox
zlF@4~7^zKDrVx(-+BI{+*mwHO8R(hYUUr5gXcqc*{ANMPFezJWJ%ViMY@zd80DoKC
z+6u_YWmLfGGW)Uje=q=yJ?2LA6k-1Wm0pD##pNUiLiDk!&*5{NT!9z62^~ggN?VMf
z66$^g^{mC~O`K6kWe&Pvg4UnO38!yX)2P^U#{>c57yTl5Dm@V1uo52#(oB)uhd^B)
zm>-B6|43*_|J5T=1z@FVC?ceu{xU*nVV@mzxRoF!VIUp_vP|5}%h2|$0v3`3Dud5y
zdJ`%@z#IcB@ed62gtQNy7CH)yT9Q#MlGjKB%t|O;fa!w%As;J143M*I!Xh&{HKo;H_
zB1ITrLNSCTF@AIUopxrtf{i}YyIjQMV48%aWYA2R_#GBEOR)Wy&b1uFI2*jb^T0C(
z*PJK4Ph(awzIt3|0DZ^x=pw1eXpUvKpb%?lH$
z_(ZU?_SYzZy|!nu^tkluSj=YoYS#qrLEJs*1F-qQj54IjK;ee!u=oZ@M>9I0T8x?w
z4F!ya4|93|P28+a6OfT^SBE67QHSr-A$)x#i%b%OatrEu>A+DSK+9FQx;ev6ve1`j
zzP~E{{R3CoWRbW;sB*m
zenCMi66~EdqmwlEhgtQO{rUePTRnPYdHvHgA~^VmxZfQ>IYgYmOaOzN@Mr!_t2uM_
z0AOGcRhXAaR5kmqSU@Jkj6#4=)L=Y|;p66JzPtC#reKHn?*nvK{A;FIn^$A1vgm|p
z3qZ^i(^gtgQ12Xr#WYiG?GqQ6Qs9au#}#%8hhPs_t)Dx#OCm
zqM~!bC~4CG%8O?l$^=G-kS7x?!seB7Iw5**s7Bn`85g&c92`D{D;BS%1{j~^
zyget5rNE$in}?4De?Cm<+_skkwVVA7QxbR=_n9FFkmqScrqWpF9T!v9lcF4tXSqd}
z^pqk2d5rP1gH&_>-mx;@jL12UdGA5moUR}m&4}fc(9h2T30U<{^7+URm?sFV49D_&
zpY>AX*>Mkb%S)092965&YrH+RJhj~spL!Bo2C{7bL}W#`?eV06xY(mO`!s_UpmU_E
zbangQNhf}S{AA!_6X@vjsw%8<*MR-eopW0HVndaV86bw!$@<;zBnIX~y_}Tw;^UUG
zkiCPxhu0T?%9IJ_Teghx=+UE)TbieBJOooiY~oAEFF!OEDH8dh&yG_>xo?{hk`|JZ
znyNYDe@^uL_hGd+%%mEYfq@sRyj0$IZ_$q(4kY!K
zJb8Ul&#(}$E2RnE?+FzNyaBLGelZTJG}yx7(c1LPOaeu>(V5jl-6gnX!}rxL6e
zytEz`z`Y1Y(esa}BRR#z^Y!BIq^(1djehJJ>sr=zcy-6xve)QMs>Z-KWFg}x=Taku
zc(KzXj3!P_Qt=mRYt0PR6r}Oa2}c1UZ4w%4EM4+zB0ccQe(>Vhjmwv5vH0LTS+RL@
zMUN9U**VU_p+e?6z9I6AqKF_7$5M*Cy34q(dRoun#*q9$47U`8y(&&xVe{t26lrIy
zOTVyl2FkJ{Q|fpTcVGd=ww0oL^?L&+7I(|6fO1|Vf!qDO_akETx}N#;`-EQjmYy!_
zZ3F%ZaSsXS?S9Rb4<-Nv4tn9Ur7%vT=@!v
z>h+KQ{jxKycwoZvMwkmzCuUZku&^*=-R-&vYsEc)p$C%6O10JIZ=u69?W@@YGco_l
zm3c-wFy0X>Hw0kbW2EkYSH&`IkL3tsxW`*S}M}r;l#j45a}SV^N!kV(I@;
z(H^B1VpUl}7L5i3XO3`X%F$7}|u}A)gug7l-RiCKf;&jMZ=_o?bruCsu
zcP<5Esp9F=4-Wap)%oKCKRbdJ?;=0GB4|C!!RIxj1!Zz((8&+zj{qc)J+}WIiGmpvHQaxaw{M}3BGE~KhX5U7PjE><;&Jy4PU>xE
zypY@I9biOz_cD{UC_i5%(_5Jv)*D8^iIDfzC-UZrVv?llc2b{Cg=Nit_c?d(?4x#%nMGe%2Xp{_w#KPGc(Wt4$^p
z7(^6ev_a6RiKFLuSBQ~1+@#ATdF^nlsBASf2c#6i))siLxVd0XHHh+1s1HXO8>pACMt+M8m
zwRQ$`t@!ZZ-F*Zw#ei={WXAX$)+C-^{HY|kcW9QoOW~B_!6%2Y^cCD#z2~dG6RuU|
z7~eE_`=K;h_}wU9VrBLVFHlwBV4I7(%m;N#1Pmut*zlch0+k&zGE6QixKvUcXQWMd
z4zQX#&GhQhP()U9LFee2o9fS#0&$}l$NiqsFv;?j9smTi{a_9W_deU-Jo`@1?Vy*+
zWk7Y%M}9v1#w8kYK-ji$v8MYR-u3I}d38V>n=ka^lcC?f@LAj0otpVN#{Rl7M2~|3
ziBXdP?|KRddmd|F(9wPHBAt79ZQ^b$lr)0g*+=yQr
z7uPVV9FJ*ZbSK9DA3Xn=ieor*v9GRyWb9p=`uf}WU}&H`4s^Fr5=Ig`?h%|d(*1g^
z`1&Ls7t`D$Kzx^`zM%l^Do$t1qjK7Xq4(}x!=PaN6;JmtiWyq)-a*h2b?ioU)7P$;
zkXq^9l>b2`&{(-Ce}T~%&DftA0pmQOzg_^)bYEc@o-X}HQP@lRn~1@J)G?q@Yz0nt
z;P+4
zElW=oUfZ|M(IaN=TE+kOoassx4)V=81+~lqpggEJVn#OhrmmLZwKP
zY(*;bu!yLHCP~YzLgvi#_x0A^Tl<{nJkR<4^E=nR_H}k=AH%)w`*XkFuhF>PX4Ns|
zPT-3{r?!5>27X97VJZsS5pm(#wX-ml7{m4p&av75iq?%)qdoVxAY^8Ifl8M+{zaVI
zv~m0Uo7lZ8UCzQO>V!P;lK0_5p{b6-N2^{9@eK+|Pmvq1y?6B*y(7AJcWf?7GSH5^
z+xd9!h3|VeCr34H;zXA+&GlmgCUcnc-kgimx05PWRrP!Lid7i9J2`d3z`8|eqjuj_
z-ZgowftmfWv8OikCvI8FR_o3?rXf1R|J2l$w%)M^k1q`cx;DG#_3M=+#@-!CK~=W=kWj?`4M!zXuF-E$RfkBZH7;z5*{x44vkAN34cvTI^3n)T?65NoU2)T+o
zXFrUX^iGG&Q69XHpPld6hitV0f0Nj~j?Oop8@rV
zF~;|cg7%9&l2!UrU*{C#z5mMeTWKDq>{ZRvr4e6LUSkvyH>pSgy-g^^`eIgMHO1lX
zXI4p1iE+$^s@zs8fTmyEPJ9g*4bedg1>?db55&WFDOE<(oa37W3O@zYb@`x3tBC4@$uXYq#5=*gyMOUK(5Y^3x0coNe6)uRpRaj+&QJc
z3}Y|87(7y0pg+35!W>A!{4`MGC@4L861E4(kK?}*j)`gr+V)BioY{XIszn%f$hP_H
zv1u-p6=X(?|KWCjS(k|!0N4H{JWH<%VruBJ55F&h0~d<3njwhPj9W!798R?U>L+n81+KpM-BsRa*pbQd$aIG=j1!_A4!8K_TkE0QVU1y9(@%nMR8Vu)E4Gthf$?c_B*zW;mJJz)X-cZc(%W3#uBm4
z$WmrkeU4Wt6Msp)!xfi_z8)jHu&%Rh?#bUo?ho|O{o47njoR4j?LwE&e*4APq7HiQ
zpF+j%SPE|ZsyIGb;?BGBtup@iIpMaoG6Bsc#!{g|UYerp`6{uRGZwF-gqxt7?KNwH
zFI}p-RQttJAQ6jbM)SB~aGPNSyF0TUvKL9Gmet3~(@IT>f%%=>x++A;PKePRqw6+g
z@Vkj_VDlQ>{ZMXOhZ>d9jtwE7K-Z3bC{rN){C}xy>_L-i)`r+>6
zht*A2rk%-If9hj-=-|ukP=?`iPmNNuZ<&EGCGcOxHI5wzmQUld>B_?^Ei_kH2EQyG
z2yLZtGWnTH!7Vx{+j@#NU7X_*y8PlQ&;8wzA3-oK2-x)HjC<}e1rJkQoT1(ln?mvR
z271LW<$lGmp!q{Q|Arr8Pg1wZER=~FII#5U>;h>tdm|(dQbLw+?j%8E^%?2z{?QnY
zRI6zVQbWyU{Jq(_>SxQhCM0k2a_EEjwD$xHUk}+j<0pDd^p(a#e8*CDLm1oc#EDk+
z99Rk0Out5S*4-V6TMwveU+1iT-7Ji+3x^q3YDWH){LFpc&Q7ns9)HVnY%dfVb9$yx
z&e?X=x={^N*xjS4(--+)`zxQFzSMtw>Vyg0HLayVWN
zDi3!r46R5VFWQBVMaauU`@^!?v$LXkEH-R7Q(^tc+I`1a^XKRF!*?y1b9Kh?ZBZLy
z2H>52dB7cjZljm21!4xvyY7BsmCQ~9iTqTmHq}ZpY0cTjoN)s)3(uWB_~feAm_K$c
z$WUwfdOXqar5m>Fj2e;o;+pw6-d6-ve1}iW{Jh9fHeH8fM1!%Ej5)3G)VWeQ{|`{K
zi}(tQi|2p0W8${Ry7wt3{0e(M%4^DnLPVRek8vT3qj~o!#^z0%h;0RW_6XtUF`2EP
z)WwIWHCA@`_?UdI+*eOmop{H
z<((s-5@uS@L3&-|KLDMTk#E)95etojWcrJw!^@{B5B0ZE?F>>>zdV+MeF+0MNF{bb
zbL5T)Z48Al&XJ4|;ESYTP)LZ}yrw~9sZu?4YSd6^GpnYkFZ}LZ3c-UNiCIquKBy*I
zhc<_)IJJJ*sYRw`yeghy8UFc_dulcwJ8I4DgH+)NtR03x-kW_FAL6uv1ZA7nfI$PE{IIO}t
zNn{Z!qu~{OeeJig;CVY%*<_*%oOrPJbuG9V+DsgepZch#GvGfWDCqVoV8zJx&2e>H
z<8%4~3+@kmGUUYt(Dhu%t9w2Q>@YJRMXWPIJohm|(RtsfD4v9QV97Io!6(JgZJohO
zJVsMsf#-gdNsqbNm+uG-*6}qNcp3%J9tOYoIf+f$x(|1whK0K}O>yq?srB3u$SlVR
z4C>R^>s>f<5dAQvASfd0<_{oJYoSDd1MZu(F7wv5L_N9hryi9d;}}6<(yLbOz2yG@
zdQdv+Po3KEL;Tr|g4?%l&0hI(FYHUmg2(d;{hhR}BD29(2~;K&2r@4>e#C!sndLT-
z9gIF+*cP#2Y&d=?7HcTb2?pYYPgQKRg?xbY!x`4WaJNv}A4DmB1*Uf`1+8!b)l8i)
zK6XcKGjyl@+E+07OfkO{3os4B?6V3HyD>dI*uBuTDcs`uwF3_m6Q|YIreLtRr=9dXTS`i7c|Dpb8rWxN8CE?sIPN6&a)
z(Uegu#f1PU;Q6kk;5;yLFvPAyA_-7Z+8h#)XEfUH?(!>-Wb2n+EK)}rL&qT>QBif{
z2A2_>;@x?CrGC71NRDL?ZKunO-Mjr~eY(G5D!r7{V;dyJTh|QNU&j`gdLWi2uha2hh7bvgF1wXaNQC{
zSid<(>R
zFGCqJSBzPzL2Qt1=aFUGJA0uFx>W`VU%{}n<}MDauS
z4`GCZc9%|T9B|D)K4Waehrow4yg9%NS?NJZ}Zev&tl$5sX=?O9@AR2cUB2wLO;*o?L9P5e5X4tyOBUS|q
zTc(Mh%D2Z+P>4|?{v#u<4@Y7P%Kwg1VkaCXIImR
zBn=Z7<8TN5xKbhfh4rHlu$3he3}N;%f%zG$yOrblH@#zvhg1+HO#X6%BH`%@f|y}o
z+8(dn#}v#QUdBaxS8P&oYkIlR;#BYB;Z^+gF@;Rym@^XVw8z~WIP#>pe?h?#A8g}Z
znbI%gs`A%ENj7tR)zm4WANuw>Iws$oTkd{7Sy8T|v%a?Wf=wUSyFsS$@@Xiro^LD0
zyv{-VHa|=1-1DmAnqJWuPD`0no(kW!5%fM8a`9cvjDs$AgF@w>KQGuMcpcr9SGLG(
zk83QEf+=8)H&<<$6uO^L4RXsobXI3;ouwi=yw`G6bIg_}JzNo-XJFCLHffH%bGmb)
z-jrPiPgP5^u54(2~TMwuf-lv!rNqj?aAdcOnfTAO@a-6*Xdpi!wssv&C
zrmW1DR(_N&PiO1N1)03QB5z-JH|P-}IS?3wjEad2ZQz)JrqoRxq{K8aHhyH=xL3I~
zYXZ>Lq|GT1n`^+*L1~_pixd_%)Odtk1B?X0SrvZRBJg>(4b7c97u^aXgdE^W?&gWU
zkfikfNGz!se+yJ^oGYC$;a>cN@SmS*jO;m_pl|CPATJ3t9Z-!K{XJ{m3HmIDWL`3P
zo*H`H%Xnm{8`x35c}2Y1Kt$w+zEdB%ZhzoteQ)J@SC;dEH5k@eFHxl6G2oc6t8-bb
z=87X;%nw5E&S*9?S}S*Wsb{anS&!j{A!4((0XPsB16ZMoWYJ=5%M@|4b?fmqoehrQ;y%L$I`@%DYzn^X|4negL)JY&e0Ut
zw1V9;4ucQ*6w_p(bOR*`IbqqM$4}LmNp&(oU*rxUp7hMQb6|VD@(#lGAhOFYA8BB{
zJ}WNt%@;fM(Xe)~IU+ha7WIoac|6(#wx6|&=efrAzLMe2iC&?h?>60nJxbH1yljJW
zLiAXl)Tx5b8n1HZ#)i58m(Xmik&1X)uC3^GE;TelVldg+_mkC`)PMdxTyfOY>A1@*
za~H+a(FOGup~OxLa#jAo3SDP4K&9&TtjidcKATKsUSVi?FN^ZWMMy6_TwK&xm+#kO
z8RM92eG-eMDA_fA@%NyiqVULx>2a&?!*7qU7j%xmX=+Io*$zz9BF4={taB$-l47sbdG6iL@;^4>yMlPy!S#^HECbp^<
z_IP+ve(ISeZ!#`&y-?{trhbQg=u#(h?rY>aQ%*}WxvgMSW#H~ZpmzLDwysPLwL
z$#rmdSdv%wX^+VfvAcc-$HcQ3!tFkO%ogsOh;SJ!soSf}AV`4QvOIJ3n8LEM{Go5Y
zzP_FH-RN~L{d`jRVT(`HRV%SOf|w}G@W)+3%u4lV!u!>%Gb&I5$Y=YDwm6r#W
zl0=B!CQ$~dXOVK#fb7-Q)>fAA`t-x_eOgP{F$5x%4&79{p0L=yEc?Ca(vSNS+~cF2r`9Vvh!CnZAGbj={hZ(~&R;Ar8Y8E`}P`^G`uAHcwz^@812f
zd8u9l;8|w9PT+I!mEU6kxj->m*Th}A+*h^8=HeI@a6@_
z%rwQz8w~LFJ$q`bwit$WbR8O+@b`Ruq2iYGG$$K``j~*U#*Cc~PKgwu(b4*Nj}O^_
zel~DwU5gj-a7r6;uJjBR2IaPXJ{mpR*=aJS2I!eco{M!F!%s(d5}2Psi}UL|$m>MD
zAnc2J22&&@eSri~;B~!%ltZnwxT59o;D?0iQG0W+Rv@Df%knGpQd|krwhp>TpP2G~
zrGu60*R_diV5!7jfY
ziAuPIXra3Omil|B0;wo$I)#eE>WUu*)1IVla;d5KzkkMIJkjb<%~J8JTGU~eQm3{&
z3V5bs()J!i$7!>U8E7Hk{$g-&y_G%|?r75hq}?SqtCm1Q>&!PWGzXbbYHH=?J*Z7B
ztdGO;2^>k@qAca+>xy|$Z{E0Zo+2bu%Yu@+VugF#rnz+PJaS|KQV)NN|BA$h4xMds
zK_ni<%5PA<^F?;>c%Fk`Zb6`5
z%a9i0z;Nv+%g_A#Mi$ZVl-$%(W<e#DC3`-?XT-%Vwkr3o@ZF`VH_h&gN)cXOMv}Rr82SO(4dw$)IVfS@b1XQ
ziC<~SBmbQV#m2br2@eLQP7e|k3^di3IsQww1vj!SL_44eyafcwA+^jjvGB-R$Pb|P
zLR1RtfkEmtP*rtZeVgRx|MAvr_S7OHyyRd52j7NN*@d=>BS+`~27W$wyZ~E~6$v5V
z7Nk-^_O1@QH}(KVx^YYeZgH}7(+lDF(>0rbP>6YsR}vh#WJ}fOWh|7J%|)6Mz5?mc
z*+T=(rJhC*a{&5XV{CkD!}ecu@$O)S7JdKbjVsU-A>b+4k=C+vicH0LDT?j#{GE>=
zf&TYZP|USv<41=;6_5@TONSCjB3!9^30GB!f!m!7Ih5RnodKX3i4HIaNLDWm&V!CJ
zGsYyxvg7;j&<0nd4pJSPCmyoV@&4AYsoxl&Pb(+_#6gil9m)1s6_c>m%+3v*nb9h3-yE-KPHd@QojF
z?YGGI?<+>+D)L8HM;4;J!U!22a$l*83V#YUP4b+b9S$l~=41nuKNnLA6p{FA_X8a_
z#Mgy>uZ>89EdaGq6+uNGKT`gdI=B@;1hs5;k
zbYY<5cs9=9?xC$R6JSiGbE~hJU2fu&k62e++LWmH;$xVF9_h$Brv*I;>FA2>H_9Bx
zmj6&+S8~YW)W9o=T+P&w{LGwBjh)hlwmy3wv?74z(p}O^Y=JMC!Ohr)0f37Ee1s<;8b&&t)F|li!Bi4@DGR$L6#UshVx-a8ig|G9s>2y|v+xLgD-i-d|Y@3#AUa+0JT2
zcLs`>7ubq%8!u_tpHGc50tTA^4)8=gr5@1xoS;{B%osl{
zY{cH>mqSzw5O|O-no^~=X@)`UpENNK+Y2Q7@#BvR>=@Rj6DCafS#048zTHPkO%gI!
zD)NHTXDqa*n4tdTWf@6cqb+UWGzN5AO-wN60Jv9#u5RHSp@&B?uy$s2d;w%?376|C!yeHZ1P4*r9chI&rg|0M-A0tV_0xcSTQ
zHyEa5KL_J4Lxgqr5bBSyeNNh?KwF;iKeoI%vu86HXa&gqf&6%+Hp|Jjl&_zp6Zkb+
z8#c_f+7GS=Gm```5i@g~3)%N+FsEOTd~
zIWEIcPv}r<(je9y_eVmHLR&sF@bBJ2F74
zWx>R=(uRgj7mG5qwG#E3ifTl)O
z$am1<^}#2#8?Tn3oKMIU_GTjZvD@iq)Tu@S1wr
zeiFx;QrfmL5Er7W9p8qvt0G6r#b#d_M2s-K5WchLEboGQ>J_39e{q!_USl7+pml?B
z^VY3r<`TJ&yW8JuIXkG0d?QgsyjjI?Iv{it)C)xyV>wZGc23>=F8@eu6~F=t$tL&R
z_}d*!VCYm=0|OGs=`mQi)kYyaVT7a1Yc~H{Q-c-JO=^7{7RBib#~8!OWw)}v{DX>Y
zwfkXfV?)8*_oJe07iMlP+5OkvRPf>jgMql0Nobm~@&0}({)i*t^=j!4VC
zc>Q`qkpj*h;;!kI!{`R=DhIb3g$@C@=9oYF?sjIr_P?$(Hx7OB{!0t+f37y6{|gPQSSOHzmC1uyc;vazT%%s4Z!PB!
z+;2KMaxts)wPkcUn9^YbaBaG?6K9y5VVVyW(_)0q0`J-KYGE{jOjN{z=Taf{62V!v
zVg{h&H+|$i^P%gv5h70D!A+ll3S}N-;cJ4UVkS2#q;!5h3ZKXv1{@P2yCZ+Y*BIG!
zPFA&9Hj*a)m3Cl0bjfh>4mhZBhF$5;;X9r_R3Bs
zX)&?hQF@7B$=sBh61as`+C?xjG8i!8>7+TWq@XP@jLE4KC5T0v2SPOvh^&>FOGx>P
zYEpN+U8;d~k{=mK>#kVdkweczq!|Nc6eh?Qr*1AJfpnKdW
zKbrrJrxrQ9Ob6BC7n^&|vdW6%@xg
z3!_u2V#>(ROc6m)s4BOlb4wb&pI*}#y)Ryfa?}9Y2!UY408PB%tXUow=hR~ptyRdk
zJ51^gWEQ1;&rR#RJD3ls-V>0-b#%^SIL7C}3pMULztmqMt=&!2C#2ve#_w!jeSSp?
zTrkOV?17`ZB1x^-v=~DeDPyQjp*f{Z2>g`ZN7&?z@~-_>`~7=W5Y8AXLZ{DJW;m`79tPa5Ip+E<
zR-DCXNQ3;8oe~!}++YP?Ik|>|Y^Qww7V^FLi3)eAnp0!sKyi`AcVPxzG;0thHpG5;
z;2G2@fFUf=lHllbafBM>DR-S~&t(J@m60^TDj%vRNL2Y5gzi{Ei3^>529{CKzDR@=
z0Nv1vSim;B%X=U0et=ILQy#Hz6w?RwIoNGT{NZd3yfX2R|6fSM#OI{Yr{oa84X1D(
z0K23gl4wHWM{qXF!vsWFA)HZSD5`~1Ipw1{RJZmJPqjV?9T3DDRW=tbTBE7-6?2|t
zf45bMb5|+zEBIXzDJegEwg3ja%NK(SqtAxwt}0`4B(3KIZ+6FH2H?Y0DpD6gEDIO%FrzM-{6%6^1%5dOC<95n2^q2H`e{_i#m?%199SrxhBv8;kWm9gCyc(l}2$I=qb)t0Sw)O0jIJ`fT(m
zdawyngY+dnkm@UnUplrhs(jk{b$(i-z0aT1`lCifE)Agm#<PHxFUbWek@aCSyIpkcig)$h_$+<%WVF2k2{tjR%JoPQe%xf?P{7QfY-bg
z(uJ}jqZpkv#c|e3h|Ah;T%jX78EXqjNhiHCC!JP_3+X)E{nmGDjcrT&NoI^r|0*w~
z33D=X=X8Mi)em&(DBObunNqDGC^Br#MOhHEA%t~fPpSmIas??=u1!i~!A(d)tjcmS
zyxF&{5|0TXx?Jb=(fv4cru&c@lH$R$uYu`2aIv8;RAoQvDHj{#9z_;rK`Ny$YX4tq
zh!sg>3@UGM2oZz(ktjNxs0uu=ZNeujzU_v5I}Hp%a_Kz_8%uYl)F8Q{fna5sxCb4b
z&?I!27&oe&1H&*}1z}d}lKdH>a9)O7CVw}vmY9J0hd{8CeF0=&49+u5^%lX7e}sn@
z@!WFZe_NC`dG8Ru?jzZAfCrLFtR#N!pLj?^((z_-JfBx-SplLEn+f(Gx6I0x`b;lV
z-M6YKyr0kS(7Ku2%=4XF>$@3SXsbt9h>%xi^x{(4++Js)z}}U5->1y}$>*uzHh|vT
z(P8_Meiau7hf#$=KL)c!&ioj>^vY_ct~pDVcistokV^6{mC^|e$g(@G)hO~J
zVa>*t<9jY&pRk|N&GSa3tE4)n1RD}1|DnmFMWAdL;Zj!JEolZ3FjJm89r!0{s{w%|
zJ*@Wh$E+fkOC^hp{)C8h7rO^-UhVizm9s0amwdGp(2(9EgF}*xZ+{Msa(k}f4*(&n
z1Wy0$$mOCtH56b6+J#=^{o3MiSdvrE!NDQ)<-7l^OIB!p7;;^`)6Ym`uS-)x`*MRg
z1PGzH@eGU?e6tRWj4$u_wu`1f6f>H^`mqcVMq?QeN4+yi1a`<>8{&HQ{f7@c3^L5v
zUsplthofG;N>uaV1g<9EDf?3KX`58#Ia6+*?8Q9<@#cO>W0IF0j?DoI5rAh+}?Jc7`rmdHHE$hmVb49o(D
z8>%Fyg23#i0}h4|4O@MAF+RG!=z%UOEWTQ$d@Hlm^k%4=nbo>p=gT?}qXT&*5uDjQ
z+b!@fbS&F{A3zpBHOzD4l`h`|5Qi`#1_Qd}lQ)*1xR1;PJdr3Qxbi8iePhI~-j0=b
zhQoe|;{o%W>zN6>2cXf8!s#*Y#neZrVsdFJ-n?Iy4UXIs4{&G(!|-(~zG)PCpZkBUOzvB<#K9>Afb
zd$eoPBa*W}BPVtbdOeGHEG#6p1Ej%QgW4VljHZBQXaOL_?Q&EcwavuDB(y$(`$edN
zRIPBhQTy*<9W@3=mtIw7Z8$zf*8#Cb?~eY6Wovr3uRUAcH#$>0`cOgdxQ^cAo(?L9
z*+R~mt>R7nJ+q&8U*9ONdPHqVyRRo_rOf>7nk{4c>>q_>-nG4crc}r{y8{1AG6nfF
z5?-G;HO3-JLNW0L&qZD<^7dbPh4vCR)Enb-p;bYy9zx=ugsM*G--K%aQn`&zwQs^y
zqd{*~jHVRO
zg1jb6z~dAVGEOIa5SIKFfa^(%s67GxV)Rh;EL_W7`fac!JlcU%q)}fW5%eo@`@Zj&
zl$dk@BoCPCUdcO1qkVzP;IctDxkoWT*wxssAfX!|yGNA&84%DK9}5k9C6%*cR)_ic
zGzRsw*MQMLbNE6e4dSdp9e1-@h#7LF;4aVdv-kr-`zce%iI3HFu;~SeAd)3vkjr2S
z1tlRJ%m^PPNw?6CBjtBMj2EKXyV(y)I~NH=xGk*gnFz9x8sGLks*Fwqb98qu}_)O8B67#MnzF6SX;
zh@!bMnq+WX&?rgLEIl;ufScm46{p(wiB>UuGUe%D)i=Wpce)xLG_rax&
zke?T(`q&Mzm^}h9<`Ve}8OX!G$-QW0#B^8@v850Lmrx&-42JCUQTOH`cK@6*mmz1T
z_P6W1yVyhLA(;ghIk!(hgpd5gpht6VDEB{E|x}UUBTT@
z<0j_+pcs9~hFkChNg!b_8?Q|xgcCpCl>0t*WvF*l|+-poIr_=dAr-c;V~g
zb_MgfUr75rJY5t&IYcs4`s!E~Ni2YZu`l1gRpICUwcdKXKcA!PrLBOl()2U&1o;;ZVC(Wg034E8paN=onZ8~RdoOeJ;d)2B{
z6_$z)H;i^HpiFE47Bv04zluhhEVl7aapu7VritFGV9KcV&v!6>QcTDNmXlMSLfs`<
z&d~4j<~|1QfAP!8Q%a%h*RJ(eaJ02R9xuKu*cv@1|HUzoUV3D>qE$9AFKN+eax7dV
zcMZtgpM7e|+a}7!{VBk4tg;NVzKl(qOqvwx%oDH}gB&g*h2MwfZmJZk@|_@Guxy9o
z8nl-htpHJ4v*8QUY;k5@p>IUd9o*fx9xEj
z%cu27s}H}gMnlK6wwMJ`Qc_Y9BfYw&2POje-MUxi@F>pof4P)vyo~Ow6_fua!e=Nc
z?Q5_cfrt2~?zm&OaMV;$z!~mH@Pic(Y2m2IeR^j6$dM|tv|Zahh$jGt5vDn^z&Sc^
zUB8|W6n|d_3o%aX5h8i>*(@%?Pf6N=KEMr|H&0L!|9fP*Fdc1Fe4nr(!!F-FWV&=J
zhI5JrXU$APw8a?(gRM95Bz+8(U(-7guY(U=a|d!BKgeCf96@dlRA{@~xfVCt#vjnZ
zVN7~kEaRA@XW@oIF#&Y2W~jHm{Dz^LWep7p$wW635QG1q<9k9zdvp
zXp!OO&;*3IKtl0?t?=`R7v1G@i24_iM@c>MP|raE%1x1#Ic2}Z>SdtjKmd9B6rMEQ
zBE=00!!@mzQKfzr)6cI%;t45u<#JcL^u7-Td{*%zstLj+=1*CjjS<
z3yMw|v1_fY3s4(3#}QJKfY{4D%mi$Mv>YOU7@$S8Q_li2OZ7HXmvK}~nS?XAV
zv+^)Cj=4=Dzpt>;Qm}^v#AdyVr$n;9LU6i^bGAO-WB=0a5%Hnn=bCoReBN2DDs^WS
zPf*)Q&9klJ;yNcd_-v>Y7dlhKcfIS0&R=E^71;Q^tDpBr)s+aTxI7VFF3$-&#L{@(
z%w^0fXebs^sm3_ZRX+>V$bHgXRfNB6Z-L{FJR0Bt0jp+%
z5~p2##c
z=xOL;dix^a8OPmpWTvj)xY3()^wdW-UL<(&AaXi#4?O8pF*=}u>pAQ?Q2UpuRwGV;
z48ti=?5~0Q@N$PKXu-|S(gxdP!>@e))9;2bpqjS05_W`nXW$6drx=|qAro~$&tY+Q
z@4LJuf?}Snkx{VQzB4a;`&QbPKlPGBAIR{ZWcei~8*>c)yZ>cQ!GWb22bOwEtcxZb
zCn$FBLj#L3b;6+8RlPP;id}kja$SS%g-tF9TbgpB?}Je3>x0X{0N$5pR7r)}ChLo`
zRg|4W_Q?ji&apo%Xy9EXW7O7pdK)kgGH2Y9hgO?n{}Brc7z~^?As*BuhEWbIa%+-4
zFli*O0As`&H46|4E|V9E53nYmC5WUMMKtHACRx4f5yQ_52jpeA!{H9j
zd=$gB@$++b6BrCL3yU1ec!S|@7CdtJpfEn-8fV}Sc^L3N7;v)p1uL;-v>R&{HsvJ7
zOVw%A8~&~JCgTHmPW<0~y1XCmTl!0-kf3;y$DUgWk+L8!nDylO0dgB{3WwxBFo4Z}
zVE~&qZfs3Rg+TolRFhDPo!A!y#Rdbg0@u0BDJx2CCSao|4fI2mIg(4E!FW`B4EF*l
z+Zin;g`DR#@L3r@{~j03NBW88MBOr_<^CqjmO
zvk3j_9q^Li9(Uezhp%bV^(`_UJ0jW>`;DHdypz6jgO?WC;5=Ri{w4l#5}&1h7tBm&
z#=l3ZMM9FpwA%(p`khZ*N={!jv7l0q{Z)>+>HG(dpQ$e1DFC3iOBHrxABn|;b41N8
zp_6;Y%Yr3Q1LPIH);CLMR3QuGH|s`d!VeAO3mQ@vo>|SjYb)}E4bEV&>+Y4%;K}Ds
zf95J}obk<$*|b84@z$>ZEBVVf4?Q_y9jU3G*-^YPFhAi8nAW)*xs}mhp;M-ypxTaJ
z1HGhTLkEMyZN~5))?%Pz$z}sSfTwq0>vbrpGir>63H2
z)B&ciBe9>Wo15KWbRZ%1nKL4b-X4RBU`NE?GHMsSGtN#6CXONJ-QxkA2S0VxZYlgT
zQZ4@Ak@xx+i>*(JgxMs^DE~!U+>@U=rV4X_6W(x&JgFwLZ
zXIFXxy6K@`SN4xzkEn0noa?$(bg8ygKUS4)@MEQc$o>dJxDtVt0W;-v4IrYsV9qxo
zXv0^}^|cZ4tL!&l|F1ZC=%}EyCkLL|P)|WOVo$~};1Ie|;J3%pFIP&@(p4%4*%rVJ
zN#Eu+PH(0aV7>o=dGD_$rtZuce$tOaJWPD238K{~*&okoCa9rN(?UgHcf})*lqWB=
z+%!Vuix;^VRGNbn^E%O7`ZVg7h>wh-vlGnD
z@Luc(u7LBEJNI$F;&0-AuG7^mg3gJMof2?ML=?G|bb}yhrS559{H*74cNKx8pd{C&)Al#dG)2?+Ye@kDz=N+7+KZ{ZQ?QBJVzlJu`-@%OR%5bpHrpE3A{q&LWlt
zs_(>rt1B!d(WG%6RX<#{k~GJQtNHDpe*c7d~!vvpz
z8PF0o=PEGH8b^-&QQ?;Gf9{yx>c6iRwE{edR8E8RFikIr>Ur@>#xOAxQgdj1<2go!
z|L2N(T-~^B&wuL#&Sb_X`&7fqP3tif1oWGug&RsH6KpON@dVG(yuU-O?~ZNyyCdL~
z$V^eyzUPk{f*4KXRqxe=!(qX*IJ5K
zFbr{clRk$U3a)6dGvFVZ?L6yEZo+A4k1o*xm!QX56X28VVszOiRY(hcv@K4yX3|6
zqsZpYoIQKZr5ceYC&@{Z=6r4_P6hq}c;6K;HQKAPgmTU!IRtNUfUG44O9Mv{$hu+1
z^JeI}xPiLC(yA)#xIzTsM#$4)7-gLq?|x`InhDZr34&yde}N%tdvH08z(A`81w51g
z*R!rILr_Z9DShsO8XP9l$b3E$yZ3Tqd_0||qiR{byKD0t2eDPJ1JnQndTnvqTeB}-
z^%MKLcg>rKF#Cz7eRu}2ca38Bh1NiWk9v_zkdddERl_D?Gr?aZ7aIIipVCL!nv0Ds
z#x*|~=bskhOVWq$H(UR`$%<6LjLa2>ZT>B9
zThmKsd>ePy=Bw*&haE6?6%2IVHnLog^2n|jwr!&>)LF-ZVi)%LF|r9UQ<31xEb~vEMTEDY@D3D`S0GoW5)f8ujsuXJLAUrahp`m24vR+NR|lJ@Aau_m9g+cNjvBcj074+w
zSqz>*4wb-mF@9E|FChV4P|%QIm?w`mT4mz)4Z%u-Byuj0;Rq^os|j(;H@^nchZ9<<
zNuLc-2dVn;?YW@4@1Xbn_DO}ycd=GK?
z?Sup|fW?^c$$qSB3mYBsJK>bC4)lrRG#d0Vzo{Pkma=HolRW47Y79Np`GRrpF9)Q7
zvR_QxXjlx)5gU;fZD`rZlo83YOe>dL?mj&uy#qCrdXky#4H
z1}KW0qrCEuA+QQP(>^drG1BoAQk@4h&)_Q{tK);VOCtM_LNJBCI!IW+?1@G9!-p@L
zbsRv;>~GBi2?v}l7KgWGjtS2DSf6_o8sNvrqVWf|kQ0;+w!Urp=pPr!c8<4}y4Df?
z0Nd{66VDsNW&B@OciecBz?W)VT9nWY^krVMyfsT(?pyCsga{rz8kzn!!CKYtT;d>J
zq_UIG(LXXzk;&4)x9~_u{fdC2yQ*>(CT6bOAXqb1UVNmbV`AsPQuEKDrcM8%`^aJbrht#v4-I!7H
z`f8@#e5jxy5v}?7-5SptTydGWH(Q~J)RgHfi0UldW60&YdBvzf!pM-2Ib?^-8Q85J
zIrSU|{DasWgT&*bNiRZ*fE-CjpHPDZ0md&bNR#i8ELRws0KUa{;10rK5I6lCU^Fwc
zc|&&4VuARfh$M_P1fldNVA&Y%GXu0@RsV4jg2&WzI3-0t3_**?=8`!@Zc!96oya^x
z=LIZnM_0f;;fXUax>^eOJblhg1D(K0U-Pt6D_A&69p6FrMl+ePW|#VHz$euoBO3kH`uRcHn@Ek!El)_0^?ZL~%0895--*!g#-a+ywF}lrbefEnfBX;*w
zvYo{ejghVpX^ERW7Ot^H4JD8g#}*lQWEh#*P18zfnv(J`I&!Lup)u9B04q6-C{!b~
zNk9xPw#0~hCghn*n4=Ct_f5E#6qHG>T0$&*vS8tvr3&3gkhtII3mQfe{vF6j%U?ac
z9c~s9(jX|V_$X5+#e7zj5}%g0OVsmSH$y%>%_;f;E6*s`9ORS-ZfQOPz@B1=d<*jO
zNGY(_tL%|%u38E5MJ1)&+hwq@g0ar+`0!ql{T)_m@I0e^=OuvE0JKFvPZ+XsusTzC
ztSd_UuwbW;AH4_0U`RZn%YPGQvjHBZ4yD=tFF}?#ty+P?0&cV95CLJ~u9cofF~!HA
z8Nk#jnS4leFM>yK%b5>Y)wBWk4T?|9x)l?1(IP`G+xih??BIEUW|XSFSjek4z*QFQ
zaXPCobnioE{PwfjAV#|Dn^c{9yZdG67pX5i1Rsa@nyU9WF66V!4@nm+Ggh3O^@t68
zI4#!9-af%7`U~R9$Lb#0vl<4q;*np1Kz-I;WLHA8OeiVMC!RtQX9&1yw(%8#kd)ko3h3lriogu{dB;#*!^TTD|!K
z0*2opDH(gO%wXFg;b*o18)gfyS$QLQ$)ydmQ`1sV5hVma>E-w3X_Wva9i=AM%cl?I
zBfxb#L;$Z_w-5AqCJRkGIa`UnP~Q4HX7`aLjR}L@Ld1Yq09|i-14YL|nZeWnfPnAao
zb`t+Lt}BUbKuWb&@iE>vA7@5mz{2DrT$_|2d=Jde$%gX6ARX0#_MM29ltAc_#arVT
zot7f@VBt}503*nJ#?aDTEj7dsejeJdo_)$w@S0wS(3x_-WB7ZSL;WH~I<)lNbp$@c
z1WPhQ0RPaOI;{kl2D-`;dYlq?cyG4$z_w4>1rXih>-PKCfmo)OGaz$jo6XZa=S6B=
z7n3kvR)a>Ai-8{Vb}3VJWVP5}>D{a!V9-E{FFfJYc7oM4$O`)))kc)7?VmxFEM&!p
zQjY8CZ+u#4=SKFP^Vz%j)iHw^6m>zo=qNr2Xub-X*n|`Hlc7bK{ASNYd`f6giNQ1<
zZw2`2Ih<2HY#4Yk2U6&adieX*;qOoC9mHdhUe%U^c8fW7nA;B%<8oXMhB3zLXd0Py{7BQ=^edlmos+7;ZOEy+qKX
zW5%b*&wMR=8zy0~o2aNK*dfq{}2oHBP%fQMi`0@&cS|52)`cn+#|Qs&{^46FgJ
zd_hD90t&#ZCJUMjv`DQrpEEI@8J0(ClOGsIRVPfRhcf)H^9iFYCh&s@^aDae@)x>!oes9mbiS{U!
zBiW;^qWSuc@~elTEk_ktos}LYWX_Eh@SH>Z5yEM~oa}f#`nI`{&BOkDz#TCs+YZK8
zvIje&l^)C*`FTafKk&h1jrRlTWE6rQz8BCL`Cm0Q)M$@25XC$^Np6wHT$tiP@4@b}cUhu5Wsm!^9b_O*jg1SEJzE?Aw6Xc)K1GVeQGy9yAV
zHikj51?j)2w?#BYo!;Bh8?jJoCwydAkfsKvje2CsAWl8Ypk+z91T}&&d~gf~WDOJx
zh8+RFG`ig~+6vT!VM*J0oq~3Nt|3s1Tm%%8bt5DmYzj=s>DYv6@jxmCm48y#*xrY>
z!DTydlo)n`-lnA;=NcY@bm-%Y?*33*Y=QhQ3WFe}3Z5~WT5XDcLUy!ZWo2275x4
z7ux9lx#nI)#J@23Ll|{huRLw=K{^CEYlfrlDJty7wHjP+`@Y6_eb3TV$0?*b#$rtI
zsF)`c1Kx7HYDn2%>^z`8B|Zh~8F9p!7O26%X(dIuW%&Q=f6qn#51b9_AyQO(fXp+5
zW8p94Y0#4#RE_IlZc$#OCIoJ8oLptQOS!s{HZU
zn5cv+$c~;@Z7q}CeSfe&^ZADf`E!hYqQeVAqr2>dArwAVGc~M!wQOJV8+m?R;jZ%A
zeGbR?7EF%$;Blevl+O2zo#Jk*ytpq{m=~eULs`;?0z)yF738L(i-?XD0OW$xmh%H=
zg?}D!2+3uD4G{oNvA}rkB06liwi)XWlIVaumV|e}@US0n4`Bd6csS+{1)k1nO<2O^6`;$q;5C0}3W68c2PL-~)F1=b>@t{@j#0RBwEkpr(g=N-GR@t8n!v&kE3b_?D!G@+d
z-fl%j!W|i=X%>`ODRN=}7fwJzBUvZ#Zf~3amHGm+)xlV%8p0Z#IfI
zHLuhW%(4^MB%8>LfmO!7Dvj83@KM#E48TRy-X46odb@;!?xdKZihYKgjkzh!=tIZ^mFL4M4YJm;bq`54$F>2q;ixuGQx>lv(jzlMRxavIwfJmL&@-bQKaEazz
z!@jDf`8t88^A2(|2KR5Y$UYJOj?-oMIjZVw^Lo=cq5@)`bpbV&Om+a=AZf;R;{XHT
zR4{o`++Dvu#vm9_7p)d7WhL#799T^H_*O(OmOry!N+i?j*^SMN{eGjvy6kEOR@XL4
z2Rhha=;Eu9n|r!M3UhJ!qpkcBg_<+s{ko$LteLIDM}VD*P>rpk)<#q2+yr*v!|>hD
zC4OrT0UYNmsd5$(NmUpS{fp`niZ){%Q1)#uik5PXN@dAI9lA}6GZkF|OgjwPU07o=
z$x^p1!#r9(HfQI;#{4Xa7md>d(*i>|!pe}!#~pB3j}Zzl-s%(mfQ#_&zeV@8P|#N&
z;Em`fH;b%^$}fM9XAf;>28P>$^Rh$tU=_z3{?BC?bc*8ufn)tgn=n+=ue=+Nm>>7d
z%G&Xq-(~!Jupb9(g67z1!O;^ZeshgGDkuLj076DA#Q5>oTxC3nh~Ub73_H!`j+`3>
zDBxX-KSNhvbntT>7mx#3A@Ev+LP5i;`X2fL+02P116KM$=%>MP7nW(Y)6lR$ty_?`
z?jEwNCt2WgQZ-x;FklQz2!g}E_hY!>T3$8*lFtx52hkLij{=)aQu9$i_N6%y9i}{+
zWl_`xv{eJ?mXBGt-@aI}Zenlu<=n3ZZF@65mXy4<^RKMy(u>%|+n_VPJYYfHB|MCF
zCnd2K!GkxI6r+&6!W3R@l>|1HVa|6i+<#P%Yir=A_StXd_FxSX%CX`T*%JctvURBG
zLDQgBm0U~82?67!SCdk<80HpiC84{4tmTTD5yJIO9~PcT?-m`*yEYm`tCng{*3m8o
zetL#0hI0q;(9lp}hCPs24^fjt{kZSDk>OJ6iE_5v&=z58@Asb>b&j@2=J~f%^kxRs
zOIETP12TFhNgo=%7XY!AWFrd@Y^?3!lOx}Fw&lExKG#c|?smHjH4j0vLlIGu;a8U*
z7RBxgV}Q1f&I|TI)V&RRdaz>E;)%yplDy1j#ecma_AiXUa)DKsR8<&R7H(L1J!01a
zKv-eO$wPsh#Y+wh@Ca(XdFM`+xnxtOITU)5{+(%oYQq&Al;)h~R6HjT{`_>KB|J|X
zMt+4mZTHW4@_hnij3eZESDpi#qL~p}f)kqp*vavl>=U%qA^zKRk8&~LA6<=AU
zP=Gzls`iaE_0p(w6gK_2h03wl-OfNDN(-d^GnJG$rq>ak1qv9?_je1c5nAAkFs?oW
z(avB)rcp&|^!xrImw_p?sgu|mv{X6B`}0`yXck5I=z|I=wJ`z$8K6BV9`qI*Rfq_|
z{HY09;=OG*zIVpBQ&Sh>TCjO5!d8QDYQ!#xNJyNAAd2*u5LAo`$R>L_1}gA7kg%i5
z&x52*2w*u{G!FEDmqr-kgL5cGy>q;(VXm6#tBF)C4w?!hhmz4
zSTViab?T^WjSb<*vH`_pg7A?~DJP%2sT5Irh!A01K@6F7@0>6jVdz%pOmR*Ak--jm
zfkaWWM6>2EQ<^|SflV7Sr3yWPQUkGPbLbV!0{#L!iZJEd5&16n;1DjCIEBMrj64jKAL%s!f(nisQVNV
z`wVp@;ecurWuJnF0J+UjhjF}g+C7_(@MW|{y9|s)@nYBEL3kf!K0@7
z_4vwX&s5^_4F1Xmc*dja0vr%md@a|uA4B%47P87w-6^10`TAn(8PORVx&)-zm6Ao
zmHYc)xwA-eX}-$?w-Fm}I}5A%$l@q{s$KhodwdYtkb$9)-w1Cus1e5a%WQoHLIl9C
z{ykXGepwmG6HgHtAk(nfk{_a}8&s-XMg5n+V%_{)4z$vgIgW%KOvE6rCITZP+ADJj
zv+de{=4WkJHuZ(i$1aE<*z<79Qe-uXBgB3?GLeCUs{%7tf^Fk8oIiw)6U8DIl>8tb
z-gD%_Q(ip92gWbeOLdq8Mv-PtKffGkKw|(wG19?F(+9-*+}Fo!+SGG{)-Ygj1A^oZLw4QX@8K*
zR9dDgR{#HKdk?58&vb40MHEC)5E~%P-YJTLQU!a%*cGW(lrBxm1_ddiU`GWhQG*5n
zvCylCs3@Q)MLLKmo8CMBb)yMn&Lnf@JL{j7b;emIF}U~pKF@t$wRVB=DU9||*HpM+
zV3~%vJZ19MW803Mb!r?JCpkr-CTmQkLgzuQmV!0z-n(bH!WfQ%E3dyS-vaG1)gFF2
zJ{@=)=o+8H)m2qN;8yWF8^1n=zYRo7L~W7VJE-K`R%qBc8unimXDBKGm{Y$A3WX48
zf;=GPdt@9uKXl~J>Lg(giohE4KTmSXnSk8)KGz_A41lVcjbaR2ew`6bRl9Wi25O$F+7CJd5PcneEi
zq3nNLw!*PVV>)SHS%O0pBEguL0=ExQRLJwLbfRhc5u!X-WR>>)Iu~4{?!L%8pPVke
zE1!HGP&yLV-7{eiPP@3`l7(62MP2vd5f||2v|U~))_TrTr?Mt%Fz$J@$OVOJ
z?kfF<=utw)01~uJW$XPM!_y8pLNfnr@7l8``@la0_+WPaoS;*?StQZlIn8vux$;(*
zm09lPd-+rQ>+O{wnvVY}7po51R9F0gb=UxS`IH){ui>KhDUGfFa)3`wJV33)PJVzD
z6&V@Z=YO)}%xU0>mm1sFbphglLXr>|N*&yVmImHgi*+}-Opb}^?yIL8RErMu)(dhz`pg;>4
z2yjH$pVP$eCo6k(Kmij85M~(Q(g51f8pJw4xu0-W(9{8>`MM5|Hq*=J7A4NFli>(b
z77kG=R)T`7w-Hn0L!BfsdQdUKf4=bU_%nUn724@4$ngVnCb`%%jvBC*IchWqUY+Pv
z+OuZx7suktCRV<)l5TaWJ04Zo7u!`mU1A?8(Nf$<_NeTc)3wNE363|K&VY
z%_RvlAt{IdEhw*P05$L2*i9Q78-J`%kce>{3pr{c(+;|R)R8AKKdHvWCedn2Pi^pPogqn
zoJCy}a@Amb?@SOWYfe^C;`REFuVXkZ<<{AovX#GQ#3(ByaE=pHIAmiJPKIBC{U809@+eblmJ
z!%gI7wN3l7Nkt1EvoB)#PxYE1A*9|^2p{o*^^b!84F?kAb
z#?6~(nE7I_V^J46q&=3TAmuNq!84*inyre1>P@Gg$@(-waqHq)x!ord{X43;bY@)Y
zye3liwd0=G3sc?|;&at*AAMoE$X(CTnl-AVl$)<#;IWur++kqKXOzn(HQy?X(Ytv-
za%yYmq-OsQF(LdKJ@6s*3{0<*lDzcB(%Uqt{Y_bD_;hTJMh!4pMwvld3C>Duzu3?S
z#DmUp7x3&Wnua?=j44%iHlv>t?%)FI&OrY8uDA9vW>g8_;7
zVtdvX<83oTWsA>VAjELG^JhOE7Nndxu_NxtU3DbkkcS2%!eA~D*e)v}kibo_=#FZA
z#!S!szUBY7Z7q@*0pbk*n{BOd?0;C$47aU}pQ?Y`pW&`qh6F;!pa6e7Q%KYvIpK;tnX{ru64}|
zoAxy;fbXmtIfM}JKLHTsJ#n4?YEMJXGjw^0n1?5j~d&J%OXP!+1|18YDV2QMS3a&LP
zcwV@W2GqLPmFmaGe~uisAk^Ja9#$cPB5k*SDcHf3zb2K_5cf}Km6W3g^2k=@6+_yE
zA*<&d(wxXNZEkUQVIs03$)-s`IqTQ+596gSa>J&{i8idmsO;cmu>5nXyYe>4YXPf5
z8q@QEK8PVzXM8W=`UG4G3IokNq5GEHPc(EsEJOUE{i_Q@ONfQ|#zw9i#m3cFv?y_$
z#O~fXx0UR6H{QLmSKuzfXJjK<4?)z7KR$c}80Eox4bd{O9BnDnX5mI1WQ`*B4x%$4
zxha_#Z^ek!>ueWdK!)VVLU3sCz3U4Wb^Vbn|8dt3H-R}VHY!pX=+eOWf^TSx)2pH1
zfAsW(QI_xdX8{^W#nw)lzX!q0N>-BC7Hc!Id!Dm<0oPLm4cOKuWOc<}NV08PK&Zf{
z<6s1`_`*^3&SEK3bQPt0qcdq8UPpi1n)!;|-|pxNx&}lhct1pDa0Tp<)Gh|AArMB2
zDN^jIeF7!f$(uOgsq>Pol4wL92bi5AOi_73%{_Ow8w9gMp8zMt^XyHbGk-&+72Jy#
z0pAS|svnvft?P@h0>4Cv7B;tY}X()d#o5i~H0p;;0DepT?6pGU=l
zq$e1%L1hk>G*Ou9<;#yu%b#yG{Fj{Lalax}je6eJ0TTt^&B5R>ChY>%(0jjLceKvI
z{d~_OWX$$H0@p$gTgm?^dli*TLb8-ed=nQo`A$8eVt-V)d1Dr8uHta|LwUPz?S>6J
za95D(n!GbroaT6d0TBG%4)^iN4?%4GcCvOSdSSW$n)~>crpAw^v@c7Fx}au#ZFCD;
z5%2)4=a89EEwf}sW=4iOmMEBIi`w17j2){D8ob{HG;@;59uner14a-)1Fyunn3`ZE~J9JpCAxQL;pthQK%g8B<$1&{l;{mvdo0
zz>@)cQwsKZX_FGg*1E!}fRB(Tk7)44`xHP=^?sF0N}-wtpvL&E5bZ(cRLG@M@SC7`
z=cAL))MJ|h(XR4;U-GIV{Q=4mQ-*5Dw!J=bCMEFpFCq~f$WSaY;65qAo;nVofW{xm
z8PJ5P~baauI@KPG&DU1M!sMwIb;*AW{_7
zgr|1GssRKc-b;MRq0s>Np_u+FwH8>%u^QV|ww{G2w?SOw)2Gpj$7(rPY
z3=anaw`94JTN+Mn+T@w%>Y%m8p^@tnwqd-7sI(^MD~>muSd~=H11J56M3nZ9VQwrv
zN@{qN1A!XDoR36CSH$c^ryeE+HlB8J@=^*Gb}ZHkB0u9#wPc6usQ8>BwoRw6W)BFi
z-lsq1vQQYCA?m`(=hHs;yO2^a-edI$6qe&WL7X24gZS@P8rDCyLwziR)H0y64%0C1
zj1;^u45iXn+JdDA-CMZjDxBEBNL6QC0x3uAKP~S73CYO>`ASRMB!qBKj;!}ph|6bnOQSsJ+e4}Ga*@pc;l
zXfAN0M3oO;WT1_qZ$@>^#hEQY=#1fUew#|pWceHZ(}67X=*SZ;mo
zq~}@P){aGU7G*pyH&v9b8kwvjTk|fuvSHP{YUig~F^}6_%$M+1<~c7nW;te->le?B
z?z`S|TVNVi-BywPOtQo*z5irR|NSVZ9M<#`i}XTl3*$>pyu&7@v0
z*bTUN1u4#w?k~|f^G*-MZ~p)~ZEGm{m;RK--qIEN>$>TM6Q1Lk-Q;IV{@P~8rB=ND
zUKkagUS7083yriFD#w4VKi2mJs>r+(pzJs-em`sjvK%Uh1TP)n8rZXZG$WrZ%1HWv
z6FJ`4x#amEPR>;|j*)=|wU?cZEL_YRW$tG}z<&aVNnphx%d`I(h`FjZzYm(&R8z`L
zExJ2)t~ZY<3NM`wc_~72a+Kd{S+AcZ@QWBHzGt-W;evPDJSQ>ZD_(JY&q@1cF2~w*
z=Y6!pw%xp7@4Key%H$$7Z5XL3f3%E{Z<^p%MiDl=gId&sq1#Wk2pq^qZykG}I#u
z?^WqZH#fJj*S~~5cyM~-J#>%^;@+1UrmXr_xdWaSm{~8j$;rvJXyu&hR@wvlT4DDK
z>wBLwqQ`b;kD
zE^ebMLbdRM0mer+GpdHhn*fqOeXLZx6X1W$3CjI%-@Xl;V}q`|l_Ke)szu&)uyw@t
zrWO{fY)v#CXz$$5GTsjqv`K%}H`Z@MbD7wo3Ok>WskcC|4K%=k{&+`=sfTQRTW8PY
zDN`a9R8pluBv1hh7#@ZJY0zMr02)S~DTDU3cBdcusFj#9P(KusSgNuxyM47>zG(cw
z-*AG4qMvRHc0TD#nElaVu_J~zCJ=E!MkowL(ZL~~>+6PovJn92iy`r&E<9|iVm+R1
ztB1=t?Kfo(mvK@bFX2GAhz5SYm!_YhgOPq~`YpII;DiReNh#Dsy!N;m9y^u{_VoJ)
zokbnC{m|caeWjjaNDJWpatO|2Q_8`e$B!?2KcR64EjSE?9QImZp;g1HFkVC+L7Q$M
z9}@llA~lWDtT6VLDh%i`)!eu5X1cNi{)~`jFuGV+p%{9+X%Rr~V^MbFhlG0q2R^QG
zb`RbEYz#$7ff#vYRJfdF162ftHsJ(|-mJ5K|7T;BU_>#PyRi|-22m$;q4B4s5<5re
z*f*UmltuPH*+3t(?PY)}(EO-Qv4|Ew)u{-xQ8*AD{$I#Uvn%S(VUS{OtT;J`gp_bC
zaUNq2pfhby<`vV6&5yb}aMzI-)5i?b4{TJB<=sz88@53y2aXJPpFL*|D934_1)#RY
zvqbbJ{-c-}OXo1PQqv4ca%OP;BpAXLM52_HZ8D1j>rzc=E8Ti~<=LNtHB4eD5Ot7q
zFEKznFtnvrrSH22wcb)hFh+9tJ$N;g+9u7}>gNs)W7I=sMex@DM}l(Ig_tL?v3(r6
z{k>ff29P)zEG|M~w7Q$xp8Xf1)5Zd+>xVTsZ%o%+{V_$>yuHV;;UWHk`K)Z=;Fx+f^D|7}P`Wx=*4Qod$COi!
z4+Hh~rask^;WQAPtLFa`>oPfZtSpH;2h9P-1KU;vL#d9f6w}k0he_aGfp@AF7J@Y2
zc~Ksp8ClB^>-4G{VWp)Pg0s_uu~fl9Au%Ypq5w&oUZv7VqiLCz
z1WMzMd^wG@Ni^BN-MU2q&mcNzXS|wlDXbG*C7MIX2ID&MO&2?C_kFKhy#VASeCII^Fd&=o>%n!AVcZo|0r~b)nBjs%hC@FB|PIjN^_k5
zUMS-+)x)~*OdQ#B9M@-cQuan$xzD*ne^x&DD7lg$kq33%kE-0b3q2sNJn&3`QRCqk
z8bbP}-<7)~DLg_KBMW4H^C*@LkU`4fA{?s|goV$+{ull3=)YJ{4-Y`vpNyg>>eVHy
zYK0r5BdDkn#{!&+8Y2_9K&VQaUSu(c)?aXY0w&bQp~E=goYVv6(%XwPQ?!*n%ZSBm
z0f3PLPoeJO#$kkMX%+Mb7=!3-Ezp(#;)$S(vrqZEK!uJ1Yu%|
z^!dX0(+iHSUCTw~K2Zi>VJQe3*8`=Qz=1j)2`Qr}?F7%QG@uD8JfV>cMdpBHCE5S1
zrj2_V8n>Nw6oEKrVa-M)Ic0ZJJSAA#+xh#4zLHsc=cP=O0LPe&{vU5(w7@qLeg4xbphAPj~7cT7dsV#P+x&2DgBf8O`UrqJi^vL2lwdaIl#$
zY5_s2x3q_e7W44iBPZQ5nqU6ls}LW=5t!%G(%;vyYQ6~=$CDj05YFfM_$aLFa4|w4yxp&{OBy$-qF)xpt0g&{qtfuo|IE6hDJtb
zSI;Q&Y*EubsgUQ4^XOv7)yzC^e^!>!a?NC~eR$hmLCC@|$o&D2$S}(9<)E$&Z^6C5
zhk<@Dm2s-JP0HZ|XdELu_==NXVQy$j)$o(YavFhtX)1Gv<{BD!oUwGL-rYW*(QWg*l~dgBYBq;jBw>CQlRYD#YjAd!;;e5
zkvGxI4*UK08uJ)?lpdYO(^UzBd4*;GmM>M*(LhxUAvcAVWxx4ei?<6EJZX3zq>p7w
z;IaW#K$JeRKb;V*4Hi$@tYS5g5lAD-(8jR}j2omu(m4AA*g;DRKj$@LjHb>xhRk1h
zBVZY)Onkfc+EvKEb8FhSXi9jGI74+~xKZGIQ@)xADys2BzyFt2^XD>29iD@~$hJ}z
zXnr22&<3Ist-!H7mC7p%eY48Z_W6zfbK2vF_t{xpDUSu=(b9O^HuvS1mOfZ6NfgY<
zg#F^7BI8~zD3D<`!$QgZupCsCq7gU9xvaHclFmr0!4QxZTSw6Y;{4GX`y$mM4Uo2Mw(m?Dk|w|F!QB
z&GbV_JL6IyQhwklNkuEmTaAq3N6)M)t^T_HCv6_$hN)Y;!HfmRJOBfqyTMXb&Aa{%
zM-cpIZhLDEFn<`=03aX^0V3*=Fuzfn=;i=rQBW)BMMU64czp91r%2<;y`SDkwm<8I
zYJOzKW2#-iKEMUQ-&K$16^d}1ojUBw=?Iz<&mns>Q^XIY;TZhB6-G(1y1&m=KAq?9
zqixGW5(u*RV>%D4+fa)LlZ!WQyt3{}zPaqg6tY1eFqU#Hp$&0`193(D4+XB;uq6A=F|Os8>d
zL_u{HH?XQLdm%>y)2NuMvo1O~6`va3G%f$6xe@zAsTS?i4vh*^`OaWpY?K@CZO7*8
zX-Ay)SkaB!RwN0}M(0v@aSac6@p9y6zBu(Dfg2e$It#9~zd0!%Rf=w?(cS0n$sOpE
z;hp*Bupkqbbnm!;k(cz?<;a6B}f-eG&5y@Cq)Lo15*U5>EfFg1tGfYr}$T
z=l~4R=TDWH2#q=WyiP%`c+KWEi7(5nabA|@
zYhY`|S8b(X5V5y&?JfTryky*l;uA`xwD{06Ax#1lD!@?ZQyPgnA1MAUzVeCqNA)OtkJYe6w_4TdLddTNW
zi=Hf6*!@LS0DlUevZ4}m4$!~>g~gJE*KT}-dF>(;vhSVs|8;HLWEX#ZF}3b
z%M`E!SZ}DaQS4lms$aiKg6SWpqGQUlU#IKj_^E%)u6`43)jr`qu&!q9-nHPGF5g--
zK_wNh>V9+lS#ujyL8G=9B8+R5_ZG>Ng^)dbNpL&a!H;VJiJh};(%kg~-=ZIe+8-FS
zvn1@`UJ8E%;(w@igJvDfDa4UNOYC
zd+>*&K=Q(p1x1TlN4*w?+PkTnmH!8DVC@^0F#aCcK{Lik+`30Ts_WhA4DI;2AR>xa!?`jcGjJ?zIC4;pRrFsQ(_
zZS8}dGr(@b$wMtSP>)C(7s-3);0*c`CD?fW2THIf${@!36xmYisx47YG5RmWvs50`
zOAs|9$1erXse+c6&YP-`p8!F_y2!sG1SyM-2L6MR*xxJiS$LHO#Z}anBuw*>(y{V5
zj2Dcu@)j!$P1WBOYkpm|+TWplA9-A&jEe_@60I7x!I}dJT6o4O^x|ms`~PJ@51#*H
zLFdZ8?gATj8ZZK&NZhCdF^fSeRN|zNoT}zEczdDt)0uMgPp_%7R;H@vv*VK^jf>A2
z6-MeoSv)8UdW+cue`+(|-~=NO0BD4OcGL~$D?g-wl9fQdv3i&xN7>PHSi-YCR8bQ=
z&Y!2?7Z7Y0&|zwOyB-b5n85cQiya!rxA|>WPW^-B~erN(80W+dFIY#e&=R*Ej*q<3TTwrvjsf3!JH|YTxpv8g{_CQSw$gb@|wi
z5dnECR$Pi>C6>lzR$dko)NiJapNm<#ad8d$sPp0E&-T*lh
z|H7{BC!yTiS?h2`z%VA6igb>g28UtF*Ul+zDGT{=bUPw=9tD0qx9^LLaXk?;w7ZU;
zFyeSh#uFg1hDFhrdhGB$-vHL=J)!HR>EfwomQbU?R}tUe%?3MLIXuF6!dZcMCO1sW
z6L|>1diZ0i^b}-)+*Cv3OLDL{*U5DVnwXl@ISOP}WnLR^WdGHWgoK22N#0mZaL8mT
z3Tbrbljg1b5WIBg)e;i9S~jYPW$`c}K@{)bOR$90&W!Z5si#jThKHepL9f8^K~l!D
zi^@*xke@`40wyZ_l!>WBzs+=KwCRo~b|~h+cm^6qQu`7rN0MYXe=)aJu6SNPf7UFQ
z%g>aNgpFyWYy7ZycwT|-SGSy<&|NcATACj@oSo<>8p)8+PDgK87_^$ZCd9;ikP0$R
zcp_8kI)`WypKMTkud?E}8A>?0L+{(pt51S-~c+l7mzLXTw&=;t~KR|j4
zpmu@`R5hk(<02mzh8esq1i49ET7N(vgESO1>-Oyv!kWQ@08Lm;001JbDec}sS-lyg
zp%p-}Q?M#PH-8$jMS4vYY5X3mCD}E#wJ)f|0JTYo4uXDN7j#zVr*PI;yk9=wVU;9G
z7#BBPUHC%bUR{kI!JP|sB}JZA@H&kA@M#d}QpFD}?|Mk%B0u$@Ka~RPAzZ-sg?y?CXu@&Ia5H59matis{RYwy
zeQ92KgtNTG=^uX@88PMlXrA;};WC>g_$N=C>UN^UAU+d3#)1~zNj
z*S^(H{=Gpw$V(viNWp@oHho+;Vy}=GffKFB~bqZ
z5Nt$S#6)tUB9N^7!;Sb{Q`ZodON5Yp+x`+Ciyo#2vfl8+;CE@g
z3#cX9O98?qQ;QAwq7e)LB_Q#xXqs508Oe*jp>;t0;Z=l%0pUkG1PVa^N905j%Q)L0mXv{#SIMz;Rk=m
z!vg9A0G13)Ej~sy-gPVaNryun_An#RHileSND(7-e+zEP53KT!Ps;zYG
zRLmOveQ()GM{7gVk3f|+@h-aa{@cg<2GT2X)sZkSCBtZ?-+O&X4FUU7Z0m>I%7ZRg
zgtkJ3U6Pw+ZmlGDbFknvtQPpJ|feBkcg*LO6_
zVKVsXxFHEY;vmX`+{DcH`QRJawvE37VqO8fzNgU~MhbY`q7^UCqNN-}e>w%L;2(&d
zH3D37Lu=IfS!!ss7z~SpE*_7Rxo_DhC(MI0alrT3K}iiLav3$BRA54YNrF53)0rJe
zl10V}1+3=8zVs3$(Y3{EdC{r>pMx~`mQwY}K3?{1Q6?#>NH
z9MR$2CRycP^HSK!T8Fa$Md417Oik+VKEWiD1|}-w4fOGJ+3wm^b-4_)o!Q0_kHu`i
zO{X&s0Va}_`C(#3N)w(I7?MqllHdSx!fX+229uHT!SnP_)IZK&eW!9q6o?%T)ugzc
zWW#4n`<{=;)UYYI9UU1NsguEe4J6I)a@<28(8%Q)fwK_<3S77$H((OWK5CwqSNJJU
zaj{BV_}n>jVi)b0C7_3I31_Kl0K1`N3uc##<*VEmkNp8>2yMWhi9j5Hxq!@&hD;_t
zoL-2CV}Dq8e8-N7jD);=e%)i()*C%tS3!Tl@SscQ;(I6$KougoP9@c)0#8X+5=o
zh)(mkZs*{tqaBvkuTKOnyjkK?R;JtAQ#w#@)(Zv)*hKf{U#)-C=5jEnPoGvYb}oN@
zXbIAc`J(+@g+o5Qxvg+`$<0FUUs63%bXD<=Sr-e2;iT)hC0w{T!!);~WIW6k4eU7q
zceg*-lNXentcriXcjlJbzO$GbL7$|{bb6-gQxGdyr3r%b;tRjxB*sbWtlmn^IUMbiw_QWKY}AmAEy<-}Ao6f3?hmK)Plg1C)45byv*f%D4Vt&$o5
zLHRS%R<(P*#jn3cEkbd6n-5Iqdl$7In>MCgP~Ur^a@JBc`6(`slHkBFk8uMS(U_YY
zj6UF$loVq}M?Tlc$mR8N*7!m9qzB8vnNFECI~SbVmjtBy^`Q|=iMNKw>JJC&GfNRf
znWyC*$BqHER1h&EUSXG5#I*$i9{Ag-?G`k5B~AdkhhYZooA7HMyI4%x(ENERPcViS
zMDpl?52kLP3&^!xHRF=0+-S_v%HL18BZ>r?54dkV0Z<`<_^jc}Y@WUJSr?=dFTs+evPgBTt<2SqgEGwPx`}a+S@HD~s
znZq%B)$YWq-2)t?5kJ@zq$6UVmyxqiIWr2EArT;$@S?w%psi%E$OpcBq?XHqi0Km+
z@3HNr@P}oNQJ9vYgC5b2M53_vLjwDLBM5{T!IW@+L-*zq?6vkpy#mp?F2g0zt!
zopGZTu>q8}2c-r6DE=jJK5)4?;pxDeZvSi)B_y=F}$WG4TH5^nlp)z@`$E
zl~E844s`3AZhg_(mX^;7l^v88tMpuog*zJ7-=g`~DMdN2e$~(dy&)Ny@{89My}
z2nHJPqy!frAFEAGrO!A!H$hLW9=f}7u=lFBT1DaA%qgCvKCSt@Y4AAP&^1I
zHrP?a^|vEw>H9sO!F*ptLIvl*xF8Ja1DLs>jPx;FGw0}dczdHVM+_ksG;9;UHh1pK
zg$qN{!=^!p#{qc|KywNmL~P>&aUG3}08~cOzZyN+s2x0eKr$V7HngM{aG(BiW>^RJ
z+ym{NdN-t)-ZSZYM}Lf!)(pR2)@XWO$u%TrygG`n`R>
zs3*ehf!l2r>%nKAd{FsXiyOEr^@IX&G@+4`B+H5FO$#Atz^R6Rruxj8Gd5nYl0Fv}
zLUtend1<2hcWjKUb8-G6$~fyz7hoc)|HCuxnB4MMyk?ST8mn(!NH8ce0MA6mCaab|
z^MnRwWUBP&(WCiM!lV??($!X5LV_&gDD_zaF|s@PMG-JZwpHx>
z>Qie+r=Q_(?|{0u-VWdJd7)OmYfqpt;1?0$g##+18?KpXNkxPr^!D$z*7)o3T^&22
zs;Ben`dOpLkN)}^(U9|D9{oVYt
zvXvxInWZDc_|7b9h%anN{Xw=_`ah7hlW?2Pvy||XgfT7
zaplW{cSqRE-dEqqwmiVX%RKvpgOi>h7GO%TJ^t)g!!Yr|D~Z674g5J!uFbY4iW@NM
zdEsSanC$oOPg5u{xy~V(~E5)+=557ao)rWGvc_Dr`ggIJIsQZV3noM;PVg4f0@!tdOnK
zWgU7q5$8E2QR762fc6Ose&v%aWh!_^>>EjHu_n2%p5b%qAMgWjc0ru4c$0K&lhAW-`hXJdS`?B8V8A6XlrcNCA%>9r0I$LqaMm9~y;G1WLSBM~=)P
z{MBcPJuWfGC7iJ}Kv4}s(-ol)3}ajreHtOM#-9;{jdR&%Cu<#%_y}46&WTeDe3m
zLhrz~w5b_U1%^-_q1p|_X`|$NL9voqiDcUVV-#kevRo;OX235y`S7XZ%EMnI*2WiC
z92Sg}Ff8FGh3h*_uyifi0(=61M9}UKh2!m*^sigGI0BK3f$O5E6uM@_k~!@TNkcq&`SP{v@ckixEWs2oZ*z&1PWb)*y$OcUu#=yse?%d(TwVetR
z3C>gigxN(!yrH|a=BwPffB$|KvZW{DvHCc;Hd2rsme-cRRxQKj
zzI$5uX3xCmr<%t)0se6O!ENX7)*WB&JL)EHqg&7;t%N=qoXvm$5*CCM8&9>+iZI#6
zGa1XIpz8#SJ`^=2qMYd>bS8IFADB7wKZ}a8Qw)#d3(Q|TyTni(7l+!4tVS(9wSv-w
zf?x-P!QihZX!vn$YUh;}6nFp{&_2j?CVBBxMY+Y<;^|Y~)VTfV1NHOxP=U=-@ON)(
zuu2+UG;~gBQ_mLZy^+S@f%`twz#p&{em9E7g0@H2F3=CL+xj
zFwjWEYI&m~YU4d~-y%!DIZ(87aByJArOqoIY%kF^Zh^p_eZvE#T(JXV7snMEyO7{ExZ
z3wc~4SsC#7cJ?(mddrpCPvlcNyQNy7{Rw?Lg%w02DD7#)v5swb-Li
z4^9~X5*9+rtX?QoSK|;((T~8XN$rbFavSoVJ3li$w)3+;jfjpz&!D`eUintQ&X`>e
zxT+x4kj8#^AP&6Tg2XunFHRBru|rBuvm
zwKub0vj|u1y@sZ8R|jF0Ntv4xl%i`Jdhr`*-%edKCI;o{(9$ebzopM3O+UQkIYua_^|PAiaZRt~thM^u
zqB!%eEumXQ!3Ar#*aNC{f-OW>Lgl;U^iP}t9YyGbVqbP@sN8{pEK+O)C#OTCH!2q0
zuVhq_D3&lF#AU#*a^Mrz>Zz@=E8?sTP_pGFC0Ww4mX4Y%{VtJPEicu|<`L6|Go)9HQL#*+m5|
zN!uTMIoyY(eUo9cgMD+C?+0CJ`PnvcI$20nn{DG^XSgr}!`jKkb8s?`4Ga+%G4Y=#
zreO7Uu1xoK2M33M%6v{B@Vu&fbdqb6dQLo&+FHv|vLkPbf2&@lL05sng|&AIeMhx(
zj2NPVgzTT&?Kx+Y7BSHz^4{w|_U58Pwx0Q7d;bq{refoj!-
z_Y)0gT&oiciYs}Oox}Y=aj_#PQzr1&U!NZqNtNz|f}VSLu5yLK)iP0iVECrJoo3)y
zR<^YrjM$4N<5Y)v7+Cfdg)m@ww=K9v_!LD+pwPz50Z9arWY2c~!2K`@KsOv}UJDWD
zBTD%YOfu4hqrX#N-d~pN1AP!gpd7Q3l8Ws*B&DiMQTs)7572TP>0z@?PzwuJf;XO^
zxVNdS=+M~M&vossPkZTNa~i%)BM>Gt`0(5z8nthomMpIfMtI6_!N$-h7R)ntY%0#x
z=p@J<5YkO82bB3EHrFgK96--GoCX7dn(y!p%zQ=
zSF(#P#|cTQCDIBm$vWt*qq@m(j$2Wcr)2sOoYO>NF{>8-))Sc@UpBd*gY^LO+@nIh5)A3JHO
zppyXEh*Nkq8ZO|t3*7#|X2FFgD3Ju5uSyh#xOV1$Zx9JNPo=|N;
zof_6BiJ3F+WEntyC{vaXXIg%Pfzm#f#tVHJ%$rD#EeZdQ*kp(=%qT*@Ve2$O!Th5S
z9kyJ-77fopR3#upyW4WuSYRi0&1gK;@-^nR#-b@R(WiyvcO}V_5>?g!A_-+(Vvi3_
zx!8Z`8M-@gpKn-}Gn$KjMFNj1CQ(FMLgyNl<#QWlG{`5iRr2G{1rS5sG{`?nR3G-<
zn%(#0QqdFqEidm&vB$Y{&j(2>Nr&L>bAv2chi$YJW|EHlv#YEDIH%#0?Gxq`5=3j{
z?y&wIL2%rYqV1L^&_`o^~vRH$4
z6$h8z+>A#s1)2@h>P%70Ftn}#OaU#&>18A#;uH0E8=B9`=5A?4KR_!!~2W)qb#Wg*LbU~eP(~WuvP870Q
zyZ}Xy@bZ;OId+YO>gf^n@SK8u2ayLjLrkF_4=%4AaAX`FW=W9d52XKf2
zWtEg1R3pYsii-yY2l1h4Dy1sX^n_@@{6vSi9hdWT%uoP{>?$7v>{hUNqg#3$ZfHD)
zD;LK>9}A5Uwm0hyYda!C^Z9&bcvHun^oz7JEP=wA`jdQo())~Lg{e;*uREFgj1LZL
zv+7^Rqs@Vu8etk0fcfwt{Q_emln1L|lTPfc7)zF8B-`H4u*biO4hcQI>A!8hIU-_j
zZB8_e9Y%#*;5_rR?ZzQ?2$v>MdbEY&#(M!78Tpto)^#q6#G68qu4D|(>wb!p4U$wU
zUO;6ht}d?G_5W`h2&vOsqQxj^92C`Pc!tpW5L-Cqv;Xx{U1>6UF^xbAX2{4)fGXi<
zTnM5fyU!z1S0Dedl2;zP4H(c`Sc$psH9%Gof6H;6?v>8e1V1l6$wgB#9!W{{NFR;%
z$uWzwva%ZA)Bn7{%~V}4&Ngmwet&7g{E%(l99Bu6*lot8V=AUi3)t4NY`L;?io~n#
zllJnGt@MATCaK@+ht3^)PuBZrb3>Q+AVHhV(FeO{oDJ09=^gLYTciWD3y_)TxzF5vSoa
zM^*wTjH-@&@_wTmqTU1o4wy#uP=rEpmp)~p=c7LzUxt}HI_U~p2=TBH1qYk%-lxYV
z$VsA4mTI}QSzU)Q@27^0lw#^#3jWs&BhFeo5f#gtomgeVC6#$o8?Bmz4F|Wx#LAGd
z{@L%XMEzai`$lh7u?4c;!n0D;RNTh)ogRtJ&_&x3t5&643D;QsuXrWLmCCEz4zWxQ
zHm3EK2)sGv+F;B7ra!ZzoqwZJV}i=)Du3UGKCqrCxz19Rzm5!dN_+7_ezx^K!x@i)
z=kmiB*QjYel@i=|nF9gg$=(Y0D#xugG72p{2zJ+Rhq=$Pe_3TZmFj^L1%HubgDY0s
zwQDlwf$Am36)!v(r>|eXYWviU^}jxV>Mkw@nJL5BLnoEt9W@T{zB$FPSsV=3NhUCT
z?z6!kW-}9=dkfJ_dEbkaWLhi@;V7)J9L&yL?u)&D9poCXn?TwD-#f0_!nX_{5M+g%
zVZlKtvNaE10A2D(ZtfP5OODvQZ@l{|mNh-~Ea*H;a!z3_Ksmmwm@)8y-KHz}vd(#b
z(D(iz4UMlp>t~7G_RT+We($1}l%;$wRXo4t{rz9=@DI{|fQSF2|M1n}jXf~79V7&}
zzLdn};y{br%xd_cdMI+Z^8-YNik1-H2Rq+b%S#g5lY0Q8nOM?>>DM+U)UCiVUxq05FO(B;uo*haKA8}kJCx`Sx
zY>ADJp_E9rypmFFNrCP}hJIbz`!1qzl64z)4+pD_p)nhehSarRJ
zS)%a_uCZ}CydCB%uV-E9)4wk?zwlF&8+(00m|?}1Z~1!@s*LuX-MhytJ($1pr^EI%fhIJ=
zx`8LudRxWockk9>`|@yEC4BF+x2x{2>&y?Q;r<3N%W;
zpz}3S1Kdg$>n>!W4$zocpR0l_6t<@pgMy){X8i#8wnNDPul_pO=cJ{l_b6TecU!>O
zN!E^3{YD9r81^556EL;`mP64C2ri6!+me6KyBv=RX*8U|l*jGZv4hI<%(~Ro)E=R3
zgR&?A@tzb82LB7WA0XoF!iu30kDqVb86TTj0Blh4at6pJIjpp_#)0p_35&Q;>M~q#
z?M;Ij)?e~s!OT31)TTM#Dd_g;Q&|MPWZzSn81$l89N{|zCj%&kYXa6AYWIfou`S;+
zl>)JmFby9eKA4mvPgb;5vj4kj-PsU@WLR`gh38<6#;G+?$tz%`SnU5zF+(h`?1v4>2pC>WA9DXyQh0O&C@s6%
z=Yes5kS|{Dyf1Kz0Gn=t32mmNq}OmpV=dZ7ycld+Aly4&Yp~8>*^Ie82Sy>h095n>
z@_@Z05g={I?oc&61#oMZHC*iZlBMkM8o=4V;af)FNTqQHmB^0pjqp#J^o3Mw6z1}S
zbmu9--izo9AZeY7i69U+=61vNN*@C#0Zgi;Ci>MLN4fRoZ=MLzdep}_TlB)9lC!M
z(}~qo>$mxf7ms2#`!0Xy{mZb2RE_IQ>@mUVh|OcD@eSC2XihY=nBBxdasVV8O_!IyF>SZDwig6}MF<+g_5rys5Vy5RKY5`ak+Tx$ImML#L!}CK
zdCQtf5NVRZ5cvdDmCU)oyRw<|a-=n-py%%vl%<3X774H;v4kuMho}oD)*tKdgPr=I
z$VeeuV<2H>9q=0>SOXmUSHOS7KXIZnSd$8n(Oe9Wa1o?VgJB~i4g57Vq=Q2WZbN>I
zE(R8*c8^|+J=2jMivblAEoIqKyc+`v5EoqL(FQ+X^f<0jePOAl}B5oRD
z*aGj+HA8DwFBHc!UDSe-sqQi7J8H#=EM`>Zm|%I|2RN6AYcK*^H|R!cy5Y4o&Ufs~
zb2RH~e@w(Ou#igRLau=fbi@e1Y3|qsfGwj`kI3$=sY)r5QkZZtMi({0{!{@*eKl0J
z5b4%m{qpI%MgCKRi7QvGjMn>C_J&&|YEWNlaq~CuGZ^eaOCpbu0N`P&*ww@ZB=k9iiB-)A+?e)G<6ZPoQbaTiBN^yk|t1X-n2*_80>+pCB6
zD=MvZF&)p{S%^Qu(mw&bxIFa^`(#~#jY<{+@09a9OeCJO_J6C=x`Fh5(RdKo582gP3pUiBcAj%^h0n_eSO*gM)@hA4l)MH0jj>4
zzvH*)r>&y*|9Qz_7^uenSNsf@NC);q05L={P?ZX5&=J=Q(WVU=zIA1Vo6t^22t3ST
zI7=xA2m2?LpzrFK`v0o*d}YZ|kqP!z2I4A+Ky9{lOJZ;i>2kY_uZ{r|g1_wDWah2?
zU{zOJyBf%KsUGh+kTQ#>c-Q!p`Qd+Szyy67nV8m?7I0ne4tE;tLF
zD6SmRd|w`3XyS}Q6E6wU@W%>7f@!Mk?|U`V+jK(;W_MNC7@#$v;~v@%R%bakW$ukR
zdq(s6yCbQ%CCs4v+PPtNY8n?}}sRQA?CS%UoQ@qo4@Qz7~Bh7$lsCd9Hh
zBg1AdDcrNT@!(0D^gX`FUr;)y0J|rOnk7@khl?&rY0cnIT5nm9SZ2^QDA>f|_-n?J
zH8Un4gYXP&eyy?f*elQ}5S`wHattt5Q0+NPy*n*(a^U(|XLc?S1=q{dv(asCo9tDr
zefS9&CrAc$%Ni4RMl3$++S9Kf5Va675e^T>UWy9e_Ly(4TjdJBVv_Gg_do1nrm;0hF{ZoM358maok7E9n_
zP_omfc)rihc3LNP%Ei~U&MoRkQDJrW79{MI{}!=#Q9g3~ELLiHv-KTcSj*`>A1QwA
zw~RkEn)Pa=)MUOrF-VMKwRcan2KG`|Udyr2CqwIH#N3{gZ$9Zc%?Kvta4JMe=(Wua
zo)8m3`D-VFyf9mgXy(OdTBm|%_tuQqt60|MCJZhrL0g5$mfxf1_M~ltlp?#JU`1_)
z!E)c26V5vq)HV4nIq(z}@F2}S>VyRazkRZF?`taCu&>~x4In}ZHWd=%k#Dxh
z#H-*~5LX7a2#D+}H&JjwMTt@`x=~t&LjoYsna!&3TK_@DctJOhM4?r40%5I)P84)@
zc2?Rt;*_sEx{SmEoC^-RXPxA?^J_fiKjC?VgiBw-vuD>f8FFE@yq?Xjt#vsdY2DLf
zCr#`_QVjABZ8kkxzI-`4p^(GW{FsH}ZPBUr;sF47Qc}z+Zpr(N5a2!b^!NNq*E9^%v_nJDBOS9|wEK+I$GV3~g;N
zvQ&&sra+X$B{FtsPHh@jo)DtedmJ+hkR{hsnZM1GjI*Y0CftQwNXa}Q=8
zg0Z!>tqXkcz+juoYV(192Qs@!rcM(U-iEL|>%C{wD2qOXxyBBHbAT5I)F8I1{?ZPg~jPF>p7V8PL
z?I;VeytuLX4)RIiA~EZGmHX*a`L#XT91Ma)&iNYuW$gnc`!?Y^q!knigWZXEMrUo;
z`hQyR^7*26?b#Domy?&%Ja_hN4h9N%HxTQ=)DT&$9an>}xIYw5{cE&j&r5@}hT$n1
z#bgZqDFi%L69IwXI;XG~fp`91HmNb&bP7HtK&ohS^D#rc!&>kh9Z{R-sAK4gG%P@I
z2tr{GCmH}gzOi{o%
z5^M%)H$YdgVV7KU|CZOKADT_=~|7y4H1+0D9#L@V`$7^TuIncfyr~brqskZUh>9^bQZM
zLNn>AojWH{wG-S1l%fes9z{w4r3MV}=QR9$BK9m*VE>rY*_O8eufzBAYkCuu#&jk^
z(Ee>nJ@lVRTCStGxY}bCO>lD}*=#0!$~rlCfr
zI?ED}Bk|D5!cz!*%}RO5xETHR^wm=2gQK4yETa)I7oA3hLrc!Zl3&D!Su$Ptm#ij1
zX2mruEEw-NF&*2x{qeBj|68$b>Nrk}U?kooNgj1L78*bYOYbAJniLBwS*fscCFSKp
zsaX!$P5<~)>yMe3G(?PbuD>p+bq;HK&x#pK_%ZvowG4egYFvEJ%0!ua+dYt8P=VN6
zK3*>83>%nmKz?9Z1Y7sHZV8wc)ZM_hR91Z~4zmIzV^k;Z{5Ub?dAl7uOe6o&@o3`{
zLqGC|rI2l?VtXXnFchz1=++*zK>XMtp%VeB2b{+I)}L!6-_~RXM8?D2?7&cyK(I*P
zW@;(VXf+}$!_PO*-gAHB_-x{)E!q@57h}Z&&_Go}Ofq!9>?K>kATheIv9Qix{_|zU
z=O?gjYzYTql?HpC^y@x9+ggdF&?XEoy1kuExI3u74|smc1v%jh(V)92%nHr~n7M(ID)~AoB=w6fiFfYF)3p|a!n|x149nF^-_>c7L!}@
zpQx)X-VQ4qhie2MmPS6iL$SQrU+`PtLt2jxTl}hLcn_v~5|(@??Bq0o;njx*6&c-3
zr_3oll38lA1aJy*Kokv|46>0p8NAmqdOv)AYLM5<5v}zXtKSY@q|Bwl?ZfS=7p(`u
zL6IW$HA!P;VVn^1^qW~P|MkiwB@X~gaE09Yp$KG~Q7rFIeys6d__2=3#e9amPz2&y
zDs!b6VmJ=wAf|Qr6~z;bgM`!xjc_Gp77J5}Ss
zW-H9(j1x|TGK~MUgAWu34D?nr43sDMT35{ypax%cATsz=p;yNDOR-R8$sFWl2QOOG
ztEYOvGN^!H_KF?r$@xBntmFH1e3v_Y8(F?R5q&
zZ^-iWVM;Bs6$PI%jur?o#%>Ub0~#RFDe`rZ$2W@A;#(?#zJy96N_nQk0r@uN`vY>}
zi4EP2Q+))Kn6R~e2)wrIzgO{n4l!2fjS2man!PJK&O|HVFam;sVbSi-{cyzceJVwm
zE+0KV{%m|tGpa4~>)IjrNhjr=oxRht0~p_>d6&+L42g1x0)|2Sj1pIV-^XLJw!OKR
zR3i9n&&GyFG=W7JIdXLPQTcT4Dz&gAsBtW;teQnGEKF~S&Ss;YrGug}mpBA`ZHYBla+8Fb`=TlxuBGUfg^GdF1}~OlCA8VA
zExDr!=_ED^%SG8K#cf4~%N2^fZJY(-#b{#!|BD)+SF`_Son+Y+4DOR-7z#uh&YCs<
zJJ;P5Ie(>J7HdYHw|k}oSJ>MdGLoTE1y?p`%$=-pQ1`*7eU8dCDj~;EAb5Be9DCxq82z3jczN5dU1yh_
z$fZ=tqG%*|QdpP`A|0>|j|=ZP+@2RHxo{z8sx+m%QvTT>oK5)*~#%qB0#DX8rvd2q+
z&a(7iM=({>Vj?4R4f^0A9!ZXG^earq?F}{z+hp7QMgA`t2LIf*rR74lEcwu^Ux*PjpzzKNXn2xc?X;~Xe+F`#}F0@zsvJ2
zsqLv)h-ez91S&O(LAH}=&ZAJV9^2n@`ZlDJ=m+=@<#hnvbH(Zo43d-%7-u_fx?6lP
z;ANs&EW4Z?9b*k!>`el8qC)RM9uiZEX=zb*#a-=-6NFXe*+F28RbbSC-YU6m%^Y6TsjV#W&G)A~rl|yLUy+Q9oT7
zQi36SoSMGo0
zEv5wT!2vvL*!@7zy{dAKS4++n&u(#$4gfm{Us{nv2WS{2))lOSwrw>h;VuF_O7>uR
z*ZjJ-f4D@fu_QjbC5nFQJ{a$i1ql2WL{V?{;Z4cVH
zyPawHzHG#u_wr?jZDF9>f9L&ZBr`2Z9Hb2K1)+GYZKd`sfpML88*W@|Jk%fLnxH3K
zUu7XH5V_EfC4^tq21rUs=;r(E!QXYWFR##dqt4Y`_bt=3j{=650CD&Q)zFHmCER14
z^^0VQs-5>vs2|;SD<=U!>(nRJx+WEkX)A7oublGS49Fai61xuDvZXo_S8eea$16*G
z1)mtDQko~D+_1FgcQ;^N0kdtova-khf^BjngDi-Ydjnci3i%qKYH`MLHKH@Zj=L=V
zkjaJ(9_ePd-CnSk}puwZ2k`w5%@|IS-DyUcC-zJk>Y3YFBX6RxG?
z72ewNmV&y^1MWfF%y*~ZX}|VJ`xUzd8SEf;ffS7MI|kw&;_}3XQi;L{xRzWXoIcJG
z17+RFQKQPyT!-`qsutRR4*iKkYQ?Y|^MNLBN75w2Em718WJ!nBb#Id&a6c?BNDQzl
z%nAvX8NC6b8Bh+t8W$zS3B0fwivEh%QcjBTi5E9x%wKpHbLq7FSwzU?O?Kfx}mh`uA(H6Ys!aBV_{o@9aU$UjHWL(*#
z+CI(PNd
z2M$Z3uBPXeV&rUiG+bSik-?M`zEQj_*mSsfTnw8}+axwcamL
zjMXY=afarzvAE^|{1h1#b&&p~f3=KERr_CiQl3093ADKQvid%tr4%SSALeu*p`9@)
z7?p^$jT~7mozXG4%b?{?66w{)j5hDZ9@P|9H4p4ojs5$Ni))F)UT>3Ba40K?9FsfD
z>bndH)+?7Y+v18q!t3`^rfoR2({QxGWddwEhyL&A$tXUeR?@38%VS72Q(fYVPTwav98@h{h^t645`>5;X!w?I!V4orRA6B1;4#YF@$22rF
z={LA|-A(b@ed76w}0>?gRSS=G4-nf0c9!(bN21SsrX_#{{{#9iqE6jh)id>5l
zWGaZ5rR{$$5+szwCA>c^Cf6d8fsOh%h
zVT7eUKCuDIKne52+S}WIpcrc;uJ!KLwv$&DNUClkHx;-(l&;>En}yj&)yl41bh8+u
zEWhdMy3;rTjo_{@kmeEXjz@#6Rul_wsJyvV9pUv2k}pztdv>R#)J)5l^NL0|DYQfc$yI$ecC$ZZA#Xji#OB-c
zIbceA9T1*A?lr#-Jh?Gv`e%WfRm;6*O#i&p(G8@Bdr~dq^x9Qu==^uC9
zyh5j1h?H8BcNmGx7x?8xuZIJguxN>R2!2mp8L^L$&`MTB)0YgM0l=~Kx%Qon2Y86fA_->ij8^fG_cQvL}g!bPCu^#Y`6Ad
z-TI)Cgw^VS$FQ$3bVw<0k?e5X@SXPN^Yx=*Vp3?dCFEL+PF?!@?^b8_5`CRj(eGK&VNn}nVBMyCE(pO)JVu5ui
z^pnU?Bgw(0ZNg`$$JL??B}G=D{?LKU(1oJlYA9@$lK-_Y`CEm(3N&W@+_8)6|K~mZ
zIGCg*u(-O%jRT>DdZ^*#_)q#3n8>*IHW`2x-AEiwl=Y0nErU;p1R{@jMHDROLZJ@YXklVPP?$q3Kpk^8mc403h2Y{do;6MC~Mp|CV-;*YgU`d7YDBwq_#$fP`W~~giGfU>qabySj=R2Dut#n!`J=(kgjeeMtOc%L
zPA0t4xW@fBeckeG1s3fiT)xV{J1f<-Gs#p;!ui~`;1V2qYVSAYpRo#X
z3Q$R}Cx3&y!@IFra$rBiJFPIRFBOqr*l%LE%9XZvL6PF=59g3g-iLXa!`?@oh~DO@
zIZzpA+Hqn0$WCc7{Nv|4*SyQum~}1#lf&;4Sdvd0=xUX*I}JgojVk%5V_}g1xY~AN
zPZ1hjp-W&)xI}~1c?xizD$Vl!MNHS|Tym
zN>tG$vCyNJlT&RTw$XiwvU48$$n
zbe3>)_HD&Qegj!?J%~zX3<{II7reYp(m1*RHh05
zpArgBCy95yE8i0tyy02uT-?Qrmms~c%Q^2jtv>Dej-qeR=XXl3rbR?Y(?JGG!Ydy^
zC*WqXae>SIeOFi74WAU3eu?6_oXe+IR5fi)&067elXC?V_mv>6cOSzQlqY4PY4MKQKEP%hPlN`
zq=;O@qA2cVX(@-El3uGa*nDrstU0#Ok)tJ|T!ZQ(>lNAkdW71kf8O>ZT`PYrwoyhG
zX}z+;+NIvVYN#c=Sb5ec$>oUDO}_c$o0*HgH#>WVmaoL*HcFr5a&+B3^e!1iz>TS~
z3?s`ICsBBb-szb>><9SGj6Hkm$|!pGCwiAQKs|Bh$jg8;8crb=yj9h$!#mfKKV_&w
zC0ozM<*5VCy%|L)g7Nraen0NbVJ{Suukaa%`@KZ^bLi;!gJIJ`x9?4V
zzU$D8{bj$0M=p@o1eE0*0AJ2HRh@rVzBI%8TGz<%_NnI#S}557f*Og8!49ZI(HcFj
z-)26j`gzLC{p+<(m|rcbXfwGuZ;*IekPUWFFdygPGkj+rBQwoHM0#L?;*kRYgvS@!
z(fnchLG215-o9=JoG)_rnsE4p-xpbD>IJ1vpz{5b8cw(H5Ci_P+!s3Pm0=sJxHIs(xjX>#YR
z({yedlZms&z}i{t{iM##$6OztXd8PkCzIm)^huaP(9sXV+%sG(62#%A7b~}+t!k=$EufX`Q-7q
zNaky+OSv=^ptq0ASIz6UnC6u~U+i^wMO7C+1~K5D7uA
z0W&I;8C8UEg$u3#hwoJa#MzdEEgT23KePX)0R!
z_em?9IrX~Z(DrcvAw(_VxuBpxWosBuTu_`k;WJuyUlkBokxt&bTi>+f(H@XcP@cJq
zf|;rJ=SZ+=X(b=8m36$uL50kl1QF_?72*l02lD9k(4~pEe%<2Cr-pbB8HdX`B`^)~
z1-}|h9|KEEnaQMn59`Z2SZfyr1q3gPE&(+AqiSd00UD+^+(*PS@+5*p+!k3a)}(@1
zU;>`%{=43g}G`e%lmn&s3;bW}}sZTnqBA
z8`J~?Uw$5Vwy)Quq1HY~ao|xYEDYbgT5!fLIo9UiHmzT8mr-OPvOz{hzKuO>rXr2r
z2jeX3P@+~%j{Lm=(T>>di&I{;1N~m6AZ+SkBjG?{Yk$j&z*I0UU9xjx+s+7R%DX`Eo`wjZ>p+pkcfvi&M-eu
z`Th8m^DqIpgevO>GVvi&%;@H|gcmL8+4TZ!<>QR4cYt_M}*C5k^z4t@J*%=9ZFtw-~yL(GBKly#jyV?mFo8#kiB+BTW4^9QJfoSQ9l$iedGW7q&(M3i?(KGkWli(>6j4-x8E-h&u{qK
zh(>#3@f1gx7Nr>u;3xAi}jr
z+aJ~?1MIrlhsub(g)CMv(Rn+4wtnc2=jKc^}E{
zZNPA~Rmi@`h3!{1+M8I4lapefzMGENJ!Hn--lUqJ7v6Pj2%2DLc@I7lE)=06zmYk?
zdU7ZuDd_;CXdv3`Y_FVajLu|DC?P}>4Tikd9p&}pXfX1>77e!k`B{Nj_C+oJO5hu#
zj^)$la7Ch-{s5~(i!cI+flXu#L;G%lr0ol}1uFWhE6ekwdZ0WeI1&)`
z{I{In?@XTw*TrwBTmIN4XqA;1Bf4z0fNTKre|tzI`sDzUk3c=@?Q(wkc~+}ehY)YZ
zM%t&-3S(7$NN1Bl5@Ezm1y(gT6=S3@-tD=le7Din0&^phf5LIX2l|qehQ0
zxI{=~%TtHA>$EPNN_g2%-vC>^g)!v#sBGBqhC6N{m5k7Uh)t1?`V5HwhG&fb=NU0q
zX?YPOhTMz4a?e_$FNfNl5VS|!xaOjz7VSC1<3eoE=~N;bz7QL{sJ_Go(MAWU>ci|s
z`w;0S<7Pj4wtbcJ<6imlwHFT5)C6d7&b~U}M5K@}B&K6n;a35-d{p9-H4{4(k0E+3
zP>j$&<#f{8+8$w@5aCQ?ndTV|$dsa3PkgJv5w}I31o5tCFP1y}Pr#ZaeS6BFn|IzV
zG_OZj_1Rwpf`wLW+l%>fH&EK6W0N^RLc{BN^r7S>i)k&Pl4@o!LK#YjepyGHlXtWJ
zv5e{WyC4JbT~y(8JeseQFv#U7hKaC1gVRS6g#4cHnVi<5$3$*}A;!tzsLtRcKNYs0
zx{dY=p!sBg#Ft3Bd)t^4;G`>JOznsc3WUh~~H^!_S=fp*U&3)fx+Js+Cd+Qh$(
z#5p1^j)BH0ZY}j1X`YSN5ujbBnHuQ)6J9tOmx#9|26eR8o=w
z2_8<6+E~IAksH`URI_tlwJH(GBS^G`4m0On<~<-!ER3zIysfB*e0Y$nFzbkpPCS%i
zxqTB~``iGRtyfhw136Dr-tp!G?YMOC>IRj3N3totvpa@jvgA=ye=_j4*-AiQ*Q*tQ
zneT}gW_%NIqdIN%Yk-|;Jm^*x3=e~~D;QON5`fArO3L_V0NBucJy$S(%72tiWovFj
zKZwyO$DF}lH>FUB*9?8>!ZmUSi^VFE@r#5i0<)cvW#{%<
zA?X!zR>Rn%4!m@s(obPi=w;@5h>-Mr7u&~cvs3@tVJ8f5Lc|;~
zm6A>z8DO&C-aCWHAiCA^qI2=EH6w0ktX8P_&ZESvOZ{dV?p6ZY$BrO+bI9nO9IKv8
z(vDMAa_hc^Xris#7iqu|j5V|UF<@}v%ovTIWA{=-Kwy#mv|$;FfjHzLJS3+fErysb
z7a0-ps^fb%#EM2RL@o))E8;fd8%@z49$xaXhT%W%fXjk-Jmv1)Jpk^AeR$u8ETQ>B
zaUL)gZPnqu$gZgK`i{RVl_u)MGfJ1k|N}?AW@nvOh|;0Jl`~__D5Ki@iy-
z8q7J5$C*T&-m5f@BDgvsf=2}YzF-KuD8-_9;qthJNN5PP5u#E(rE;
zTvvvypz_j7hE8BQ0Jb_tQ6BqNV$FuA-wIqJ8=qJu_kQo+{0*1}z1z2K^T`#`$F0WvAoHwARA8uV
zgRoIdL>P$n$8SHZvvcFrzjowGPvS9B-%_9j@HA6Wg<9VX#mGDR5Cs%DLFYa+vlSyQ
zpN?)1c_W(2u|lL6$^p5-bu7@r*Jaq(|G|zE}-uUBIYWu}=Zo-=6p_2L?}Ihn=H@WyAx!H>DY6mHLbmxE)#t
z%trC9YQfoi2nTh5h6cHNz&PT*BWkN+u-9DFgzK8-bXo`#cud`+7?cta}+4u#t-A<
zNIAXxxW%Cy^?6m_4>mMDSVmY*S46XT#-SMej@^f+=lOmT%a+}T5#^1e9^^A%=zc3N
zk$Ngwrv;9~*KZ;~a%>k&9*u;O1!TOGoF;f`lXZPVmX?;;VBzz|kxp`)xTh5&Zr$q1
zVs;W^&v|^*Bg7or&{Q+0&tDlfkH<$M#q}}swQxapF>%poKWmL0425sQu}C-%IuKb}
zA_|d@j*NzchO(g8C`D$D_nzV-Y6VI-yIFh7Rm!7c`7{yK9-?P0G$Kk66V}MX5H9z>
ze*juwCpRU%@Qn?dW3v?AdDxL%;A%raD?+7S<@993lMBFa
z(kVmRzcicLiVi3Jjw~Q6J-C0gL^xhKj>T~l9Ntt{N0AWjRVl~hbB@s^_4rn4#vv_P
zHrh`|Q~QyCr@)*+L8J~I3&FK4*Z@Q*h?;HM93fHgjuNSP3rnZoT_o}DlBVlcW1(vE
zQx}wd4*pomG8^e;;BUom>FajYFyV`R{yMnEw9Ek(5F~vwpt;`$)te
zLoA9FQRT>Mb_oWe*EJ}QDwa_0cbh}Lp?a+L2P7fPyJ0@HU%z1k{d;R%eo9JF_sYNU
zi`_U3RCtvfpJ51n$rMdaj8b9GD&>jl9)Kb_>W^+E_S}vkDXv4^8^VLt6G)eUfcY$z
z@KxXBViBY9LnS(chU0$e&pcp)Q2JUI|FY$D`$k6PYB@Or?4AJt0^rW~FfNV@#6}-i
zf`n$T!vu2wKr7a`<4}W6=3V(T34HIK%Tr~$r---|J{UgcEOa2CiqL9$TpF~v{D`M^
zG@UnYRLJ*6qpJJFm^WD$!S)XXDZ7c%7aS$3N{Dx0yJ7x;3q{N2a`KjZbB__A(8J0B
zZJJ92Gbe9r*@TCw)1R#E`|vD)|8&+uTR_DqE3?2{t{Bxk^)6%gM#J>f%)`^ivv(V;
z<;9s(G3+@Z^t2v=B)3p5JKHXNxDa|QjLFgmO(hyxyHuN&+p#yo8jT%ku=&G
zaud+#dlGKT=b`Vc5VhYNq)I%AT(~N)Q2A#Hey>NGvblt-6`YQ_<&W^cWsiX5%E7@8
zNPJV?3~4#jhn1$DEhmf3fNNAwAb#^3E~iBIVY9?`Ww#noLik<9_hFxv^>{eAr@q}O
z07clkZl>c!f^Of*MN_vtJq+bvZQajz2T?7%u*5I}d~3qS&=Ltn?G)Es`=8sSOgNR<
z)7x~I@IkQE{E=G#{XjN4b!XX5D5vsL%Q^JTrI?erp8zU6{ZZ9y0HZ>R3?rvUrBd~t
z@4)FL)1UP2Rk!($A)h=6Iv|IBZ|l$CHQl3(wF~dQymb-Ec(N4CAFGqAx;xOQjj16#
zKDY?ug>BHpX@J&~JFLjL!L46qR)e_owjo`K=ft^#dPZ9%R
z8vvz{3?%A^bBvk
z5ZlvC=Gf=WIUpd44puqQG56R21?zO4J5tW5^80XAq7+GGuM9}?Ma~(mtg3SU_D)?2
z$SlQ|9|?e`rdyS8t9EsN0za!@Y}dtcrUSatMojKhKY^ko9YOtYjxSOGBJhKGc**UW
zzTu8U-Vm>dY6$lJ9fq>qCa?yziSJ>B_y$(8-dZBM1qH!4+x9xZDWeWuZwkUzq8K_=
zI7ZJ%J$yI*)4x*VNOMx%Wz*A2pqc+
zwG^NhT6ql8l<}|y^!WH)VENADSW@Egn)qmS;2`gzLc!e)(G~F=uxbRm1oNt8p@&bL
zDpXMUO+g6lLEJ>do`|9-H*TUQt{xtfSQoNjf&g$~_Iv`y6$f_-+qG4fJRbvW1K~+g{j`NAQ99FpaX3ClM}m36$w{+vCt-!=y51OJ+Z9dm
zJYXV^+}3)CNx4#FEM6D&w3mxUsage
   minbucket = round(minsplit/3),
   maxdepth = 2,
   other_tree_para = list(),
+  null_check = TRUE,
   tol_snr = 0.002,
   verbose_greedy = TRUE,
   save_init = FALSE,
@@ -100,6 +101,10 @@ 
ArgumentsUsage
   stage1 = TRUE,
   iter_max = 5000,
   tol_stage1 = 0.1,
-  tol_stage2 = 1e-05,
+  tol_stage2 = 1e-06,
   verbose_sf = TRUE,
   verbose_loop = TRUE,
   save_tree_list = TRUE
diff --git a/reference/fitSFMissing.html b/reference/fitSFMissing.html
index 233a3f4..0854732 100644
--- a/reference/fitSFMissing.html
+++ b/reference/fitSFMissing.html
@@ -65,7 +65,7 @@ 
Usage
   stage1 = TRUE,
   iter_max = 5000,
   tol_stage1 = 0.1,
-  tol_stage2 = 1e-05,
+  tol_stage2 = 1e-06,
   verbose_sf = TRUE,
   verbose_loop = TRUE,
   save_tree_list = TRUE
diff --git a/reference/fitSFSparse.html b/reference/fitSFSparse.html
index b0a819a..db45a9d 100644
--- a/reference/fitSFSparse.html
+++ b/reference/fitSFSparse.html
@@ -65,7 +65,7 @@ 
Usage
   stage1 = TRUE,
   iter_max = 5000,
   tol_stage1 = 0.1,
-  tol_stage2 = 1e-05,
+  tol_stage2 = 1e-06,
   verbose_sf = TRUE,
   verbose_loop = TRUE,
   save_tree_list = TRUE
diff --git a/reference/index.html b/reference/index.html
index fb8f730..606391f 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -167,12 +167,12 @@ 
All functionssoftImputeCrossVal() 
         
-        
Cross-validation for softImpute

+        
Cross-validation for softImpute.

       

           
           softImputeCrossValSparse() 
         

-        
Sparse version of cross-validation for softImpute

+        
Sparse version of cross-validation for softImpute.

       

           
           updateMFAIR() 
diff --git a/reference/softImputeCrossVal.html b/reference/softImputeCrossVal.html
index d08dd12..b44da2f 100644
--- a/reference/softImputeCrossVal.html
+++ b/reference/softImputeCrossVal.html
@@ -1,5 +1,5 @@
 
-Cross-validation for softImpute — softImputeCrossVal • mfairCross-validation for softImpute. — softImputeCrossVal • mfair
@@ -44,13 +44,13 @@
 

 

   

-      
Cross-validation for softImpute

+      
Cross-validation for softImpute.

       Source: R/softImputeCV.R
       
softImputeCrossVal.Rd

     

 
     

-    
Cross-validation for softImpute

+    
Cross-validation for softImpute.

     

 
     

diff --git a/reference/softImputeCrossValSparse.html b/reference/softImputeCrossValSparse.html
index 81c2c60..f420436 100644
--- a/reference/softImputeCrossValSparse.html
+++ b/reference/softImputeCrossValSparse.html
@@ -1,5 +1,5 @@
 
-Sparse version of cross-validation for softImpute — softImputeCrossValSparse • mfairSparse version of cross-validation for softImpute. — softImputeCrossValSparse • mfair
@@ -44,13 +44,13 @@
 

 

   

-      
Sparse version of cross-validation for softImpute

+      
Sparse version of cross-validation for softImpute.

       Source: R/softImputeCV.R
       
softImputeCrossValSparse.Rd

     

 
     

-    
Sparse version of cross-validation for softImpute

+    
Sparse version of cross-validation for softImpute.

     

 
     

diff --git a/search.json b/search.json
index be71000..130d30e 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://yanglabhkust.github.io/mfair/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2023 mfair authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://yanglabhkust.github.io/mfair/articles/ml100k.html","id":"ml100k-data","dir":"Articles","previous_headings":"","what":"ml100k data","title":"Enrichment of movie genre information","text":"row represents user column represents movie rating matrix, row represents movie column represents genre genre matrix (use help(ml100k) details data).","code":""},{"path":"https://yanglabhkust.github.io/mfair/articles/ml100k.html","id":"fitting-the-mfai-model","dir":"Articles","previous_headings":"","what":"Fitting the MFAI model","title":"Enrichment of movie genre information","text":"use rating matrix main data matrix \\(Y\\), genre data frame auxiliary matrix \\(X\\). proceed fit MFAI model top three factors.","code":"# Create MFAIR object Y <- t(ml100k$rating) X <- ml100k$genre mfairObject <- createMFAIR(Y, X, K_max = 3) #> The main data matrix Y has been stored in the sparse mode! #> The main data matrix Y is partially observed! #> The main data matrix Y has been centered with mean = 3.52986!  # Fit the MFAI model mfairObject <- fitGreedy(mfairObject,   save_init = TRUE,   sf_para = list(verbose_loop = FALSE) ) #> Set K_max = 3! #> Initialize the parameters of factor 1...... #> After 2 iterations Stage 1 ends! #> After 53 iterations Stage 2 ends! #> Factor 1 retained! #> Save the initializaiton information...... #> Initialize the parameters of factor 2...... #> After 2 iterations Stage 1 ends! #> After 54 iterations Stage 2 ends! #> Factor 2 retained! #> Save the initializaiton information...... #> Initialize the parameters of factor 3...... #> After 2 iterations Stage 1 ends! #> After 28 iterations Stage 2 ends! #> Factor 3 retained! #> Save the initializaiton information......"},{"path":"https://yanglabhkust.github.io/mfair/articles/ml100k.html","id":"importance-score","dir":"Articles","previous_headings":"","what":"Importance score","title":"Enrichment of movie genre information","text":"fitting MFAI model, can use getImportance() function obtain importance score genre within factor.  higher importance score , specific movie genre contributes improving model.","code":"# Get importance score importance <- as.data.frame(getImportance(mfairObject, which_factors = 1:3)) importance$Genre <- rownames(importance) importance_long <- melt(   data = importance,   id.vars = \"Genre\",   variable.name = \"Factor\",   value.name = \"Importance\" ) importance_long$Genre <- factor(importance_long$Genre, levels = rev(colnames(X))) # head(importance_long) # Visualize the importance score p1 <- ggplot(   data = importance_long,   aes(x = Genre, y = Importance, fill = Genre) ) +   geom_col() +   coord_flip() +   scale_y_continuous(labels = label_comma(accuracy = 1)) +   xlab(NULL) +   ylab(\"Importance score\") +   guides(fill = \"none\") +   theme_bw() +   theme(     text = element_text(size = 12),     axis.title = element_text(size = 12),     axis.text.x = element_text(size = 12),     axis.text.y = element_text(size = 10),     aspect.ratio = 2   ) +   facet_grid(. ~ Factor, scales = \"free\") p1"},{"path":"https://yanglabhkust.github.io/mfair/articles/ml100k.html","id":"negative-control","dir":"Articles","previous_headings":"","what":"Negative control","title":"Enrichment of movie genre information","text":"Next, let’s create permuted movie genre matrix \\(X^{\\text{pmt}}\\), column \\(X^{\\text{pmt}}\\) obtained permuting entries corresponding column real genre data \\(X\\). fit MFAI model \\(Y\\) \\(X^{\\text{pmt}}\\) input.  MFAI correctly assigns low importance scores permuted genres, suggesting MFAI avoids incorporating irrelevant auxiliary information. last, use \\(X^{\\text{}} = [X, X^{\\text{pmt}}]\\) input auxiliary information fit MFAI model.  MFAI successfully distinguished useful movie genres irrelevant ones. Moreover, importance scores obtained using \\(X^{\\text{}}\\) highly consistent obtained using \\(X\\) \\(X^{\\text{pmt}}\\) separate inputs, indicating stability robustness MFAI.","code":"n_pmt <- dim(X)[2]  X_pmt <- apply(X,   MARGIN = 2,   FUN = function(x) {     N <- length(x)     x[sample(x = c(1:N), size = N, replace = FALSE)]   } ) X_pmt <- as.data.frame(X_pmt) colnames(X_pmt) <- paste0(colnames(X), \"_permuted\") # Create MFAIR object and use the same initialization mfairObject_pmt <- createMFAIR(Y, X_pmt, K_max = 3) #> The main data matrix Y has been stored in the sparse mode! #> The main data matrix Y is partially observed! #> The main data matrix Y has been centered with mean = 3.52986! mfairObject_pmt@initialization <- mfairObject@initialization  # Fit the MFAI model mfairObject_pmt <- fitGreedy(mfairObject_pmt,   sf_para = list(verbose_loop = FALSE) ) #> Set K_max = 3! #> Use the user-specific initialization for factor 1...... #> After 2 iterations Stage 1 ends! #> After 52 iterations Stage 2 ends! #> Factor 1 retained! #> Use the user-specific initialization for factor 2...... #> After 2 iterations Stage 1 ends! #> After 55 iterations Stage 2 ends! #> Factor 2 retained! #> Use the user-specific initialization for factor 3...... #> After 2 iterations Stage 1 ends! #> After 27 iterations Stage 2 ends! #> Factor 3 retained! # Get importance score importance_pmt <- as.data.frame(getImportance(mfairObject_pmt, which_factors = 1:3)) importance_pmt$Genre <- rownames(importance_pmt) importance_pmt_long <- melt(   data = importance_pmt,   id.vars = \"Genre\",   variable.name = \"Factor\",   value.name = \"Importance\" ) importance_pmt_long$Genre <- factor(importance_pmt_long$Genre, levels = rev(colnames(X_pmt))) # head(importance_pmt_long)  # Visualize the importance score p2 <- ggplot(   data = importance_pmt_long,   aes(x = Genre, y = Importance, fill = Genre) ) +   geom_col() +   coord_flip() +   scale_y_continuous(labels = label_comma(accuracy = 1)) +   xlab(NULL) +   ylab(\"Importance score\") +   guides(fill = \"none\") +   theme_bw() +   theme(     text = element_text(size = 12),     axis.title = element_text(size = 12),     axis.text.x = element_text(size = 12),     axis.text.y = element_text(size = 10),     aspect.ratio = 2   ) +   facet_grid(. ~ Factor, scales = \"free\") p2 X_both <- cbind(X, X_pmt)  # Create MFAIR object and use the same initialization mfairObject_both <- createMFAIR(Y, X_both, K_max = 3) #> The main data matrix Y has been stored in the sparse mode! #> The main data matrix Y is partially observed! #> The main data matrix Y has been centered with mean = 3.52986! mfairObject_both@initialization <- mfairObject@initialization  # Fit the MFAI model mfairObject_both <- fitGreedy(mfairObject_both,   sf_para = list(verbose_loop = FALSE) ) #> Set K_max = 3! #> Use the user-specific initialization for factor 1...... #> After 2 iterations Stage 1 ends! #> After 53 iterations Stage 2 ends! #> Factor 1 retained! #> Use the user-specific initialization for factor 2...... #> After 2 iterations Stage 1 ends! #> After 54 iterations Stage 2 ends! #> Factor 2 retained! #> Use the user-specific initialization for factor 3...... #> After 2 iterations Stage 1 ends! #> After 28 iterations Stage 2 ends! #> Factor 3 retained! # Get importance score importance_both <- as.data.frame(getImportance(mfairObject_both, which_factors = 1:3)) importance_both$Genre <- rownames(importance_both) importance_both_long <- melt(   data = importance_both,   id.vars = \"Genre\",   variable.name = \"Factor\",   value.name = \"Importance\" ) importance_both_long$Genre <- factor(importance_both_long$Genre, levels = rev(colnames(X_both))) # head(importance_both_long)  # Visualize the importance score p3 <- ggplot(   data = importance_both_long,   aes(x = Genre, y = Importance, fill = Genre) ) +   geom_col() +   coord_flip() +   scale_y_continuous(labels = label_comma(accuracy = 1)) +   xlab(NULL) +   ylab(\"Importance score\") +   guides(fill = \"none\") +   theme_bw() +   theme(     text = element_text(size = 12),     axis.title = element_text(size = 12),     axis.text.x = element_text(size = 12),     axis.text.y = element_text(size = 8),     aspect.ratio = 2   ) +   facet_grid(. ~ Factor, scales = \"free\") p3"},{"path":"https://yanglabhkust.github.io/mfair/articles/neocortex.html","id":"neocortex-data","dir":"Articles","previous_headings":"","what":"neocortex data","title":"Spatial and temporal dynamics of gene regulation among brain tissues","text":"spatial temporal patterns gene regulation brain development attracted great deal attention neuroscience community. availability gene expression profiles collected multiple brain regions time periods provides unprecedented chance characterize human brain development. select genes consistent spatial patterns across individuals using concept differential stability (DS), defined tendency gene exhibit reproducible differential expression relationships across brain structures. include 2,000 genes highest DS get expression matrix, row represents sample tissue nercortex region column represents gene. sample_info data frame contains sample information, row represents sample tissue four columns respectively represent sample ID, neocortex area, hemisphere, time periods.","code":""},{"path":"https://yanglabhkust.github.io/mfair/articles/neocortex.html","id":"fitting-the-mfai-model","dir":"Articles","previous_headings":"","what":"Fitting the MFAI model","title":"Spatial and temporal dynamics of gene regulation among brain tissues","text":"use expression matrix main data matrix \\(Y\\), spatial temporal information contained sample_info data frame auxiliary matrix \\(X\\). proceed fit MFAI model top three factors.","code":"# Create MFAIR object Y <- neocortex$expression X <- neocortex$sample_info[, c(\"Region\", \"Stage\")] mfairObject <- createMFAIR(Y, X, K_max = 3) #> The main data matrix Y is completely observed! #> The main data matrix Y has been centered with mean = 7.64309222668172!  # Fit the MFAI model mfairObject <- fitGreedy(mfairObject,   sf_para = list(tol_stage2 = 1e-6, verbose_loop = FALSE) ) #> Set K_max = 3! #> Initialize the parameters of factor 1...... #> After 3 iterations Stage 1 ends! #> After 18 iterations Stage 2 ends! #> Factor 1 retained! #> Initialize the parameters of factor 2...... #> After 3 iterations Stage 1 ends! #> After 46 iterations Stage 2 ends! #> Factor 2 retained! #> Initialize the parameters of factor 3...... #> After 2 iterations Stage 1 ends! #> After 600 iterations Stage 2 ends! #> Factor 3 retained!"},{"path":"https://yanglabhkust.github.io/mfair/articles/neocortex.html","id":"spatial-and-temporal-dynamics","dir":"Articles","previous_headings":"","what":"Spatial and temporal dynamics","title":"Spatial and temporal dynamics of gene regulation among brain tissues","text":"gain insights, visualize dynamic patterns top three factors across different neocortex areas time periods, represented \\(\\{ F_1 (\\cdot) , F_2 (\\cdot) , F_3 (\\cdot) \\}\\).","code":"region <- c(\"OFC\", \"DFC\", \"VFC\", \"MFC\", \"M1C\", \"S1C\", \"IPC\", \"A1C\", \"STC\", \"ITC\", \"V1C\") stage <- c(3:15) X_new <- data.frame(   Region = factor(rep(region, length(stage)), levels = region),   Stage = rep(stage, each = length(region)) ) FX <- predictFX(mfairObject,   newdata = X_new,   which_factors = c(1:3) ) # Normalize each factor to have l2-norm equal one FX <- apply(FX,   MARGIN = 2,   FUN = function(x) {     x / sqrt(sum(x^2))   } ) FX <- data.frame(X_new, FX) colnames(FX) <- c(\"Neocortex area\", \"Time period\", paste(\"Factor\", c(1:3))) FX[, \"Time period\"] <- factor(FX[, \"Time period\"], levels = stage) head(FX) #>   Neocortex area Time period    Factor 1   Factor 2  Factor 3 #> 1            OFC           3 -0.08530621 -0.1275302 0.1373010 #> 2            DFC           3 -0.08530621 -0.1281884 0.1720814 #> 3            VFC           3 -0.08530621 -0.1279504 0.1329457 #> 4            MFC           3 -0.08490720 -0.1272283 0.1652383 #> 5            M1C           3 -0.08530621 -0.1277465 0.1578517 #> 6            S1C           3 -0.08510786 -0.1277465 0.1462684 # Convert the wide table to the long table FX_long <- melt(   data = FX,   id.vars = c(\"Neocortex area\", \"Time period\"),   variable.name = \"Factor\", value.name = \"F\" ) head(FX_long) #>   Neocortex area Time period   Factor           F #> 1            OFC           3 Factor 1 -0.08530621 #> 2            DFC           3 Factor 1 -0.08530621 #> 3            VFC           3 Factor 1 -0.08530621 #> 4            MFC           3 Factor 1 -0.08490720 #> 5            M1C           3 Factor 1 -0.08530621 #> 6            S1C           3 Factor 1 -0.08510786 # Visualization of F(.) p <- ggplot(   data = FX_long,   aes(x = `Time period`, y = F, linetype = `Neocortex area`, colour = `Neocortex area`, group = `Neocortex area`) ) +   geom_line(linewidth = 0.5) +   ylab(NULL) +   theme_bw() +   scale_y_continuous(n.breaks = 4) +   theme(     text = element_text(size = 12),     axis.text.y = element_text(size = 10),     axis.title.x = element_text(size = 10, margin = margin(t = 3)),     axis.text.x = element_text(size = 10),     legend.title = element_text(size = 12),     legend.text = element_text(size = 10),     legend.key.size = unit(0.8, \"cm\"),     legend.key.width = unit(0.8, \"cm\"),     legend.position = \"right\",     panel.spacing.y = unit(0.2, \"cm\"), # Space between panels     aspect.ratio = 0.4   ) +   facet_grid(Factor ~ ., scales = \"free_y\") p"},{"path":"https://yanglabhkust.github.io/mfair/articles/neocortex.html","id":"gene-set-enrichment-analysis","dir":"Articles","previous_headings":"","what":"Gene set enrichment analysis","title":"Spatial and temporal dynamics of gene regulation among brain tissues","text":"first calculated relative weight \\(k\\)-th factor \\(m\\)-th gene \\(\\left| W_{mk} \\right| / \\sum_{k^{\\prime}=1}^{3} \\left| W_{mk^{\\prime}} \\right|\\), \\(W_{m \\cdot} \\\\mathbb{R}^{3 \\times 1}\\) \\(m\\)-th row gene factors, selected top 300 weighted genes factor form gene sets. can conduct gene set enrichment analysis based Gene Ontology factor.","code":"# Inferred gene factors (corresponding to the W matrix in the MFAI paper) gene_factors <- mfairObject@W rownames(gene_factors) <- colnames(mfairObject@Y) # Assign gene symbols colnames(gene_factors) <- paste(\"Factor\", c(1:3)) head(gene_factors) #>            Factor 1    Factor 2    Factor 3 #> DCUN1D2  0.07856363  0.06006969  0.16380535 #> ARRB1   -0.27132066  0.06770678 -0.12942443 #> PDE1B   -0.01294115  0.39845756  0.20161848 #> PDE7B    0.01081660  0.41041139  0.52982200 #> TOX     -0.11471109 -0.20478958  0.60887294 #> LOXHD1   0.24888992  0.01732711 -0.03634836 # Heatmap of the inferred gene factors pheatmap::pheatmap(t(gene_factors),   scale = \"column\",   clustering_method = \"complete\",   cluster_row = FALSE, cluster_col = TRUE,   treeheight_row = 0, treeheight_col = 0,   border = FALSE,   show_rownames = TRUE, show_colnames = FALSE,   cellwidth = 0.2, cellheight = 40,   fontsize = 12 ) # Normalize each factor to have l2-norm equal one gene_factors <- apply(gene_factors,   MARGIN = 2,   FUN = function(x) {     x / sqrt(sum(x^2))   } )  # Relative weight gene_factors <- abs(gene_factors) gene_factors <- gene_factors / rowSums(gene_factors)  M <- nrow(gene_factors)[1] # Total number of genes M = 2,000 ntop <- M * 0.15 # We use the top 300 weighted genes in each factor to form the gene sets  # Index of top genes top_gene_idx <- apply(gene_factors,   MARGIN = 2,   FUN = function(x) {     which(rank(-x) <= ntop)   } )  top_genes <- apply(top_gene_idx,   MARGIN = 2,   FUN = function(x) {     rownames(gene_factors)[x]   } ) colnames(top_genes) <- paste(\"Factor\", c(1:3)) head(top_genes) #>      Factor 1  Factor 2 Factor 3 #> [1,] \"ARRB1\"   \"PDE1B\"  \"AJAP1\"  #> [2,] \"LOXHD1\"  \"PDE7B\"  \"KCNA3\"  #> [3,] \"TYRP1\"   \"KCNA2\"  \"ASTN2\"  #> [4,] \"PRKG1\"   \"PMP22\"  \"EMID1\"  #> [5,] \"MS4A8B\"  \"GPR155\" \"GPR52\"  #> [6,] \"FAM131B\" \"SMAD2\"  \"SEC24D\""},{"path":"https://yanglabhkust.github.io/mfair/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Zhiwei Wang. Author, maintainer.","code":""},{"path":"https://yanglabhkust.github.io/mfair/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Wang, Z., Zhang, F., Zheng, C., Hu, X., Cai, M., Yang, C. (2023). MFAI: scalable Bayesian matrix factorization approach leveraging auxiliary information. arXiv preprint arXiv:2303.02566. Wang, Z. (2023). mfair: Matrix Factorization Auxiliary Information R. R package version 0.0.0.9000. https://yanglabhkust.github.io/mfair/.","code":"@Article{,   title = {MFAI: A Scalable Bayesian Matrix Factorization Approach to Leveraging Auxiliary Information},   author = {Zhiwei Wang and Fa Zhang and Cong Zheng and Xianghong Hu and Mingxuan Cai and Can Yang},   journal = {arXiv preprint arXiv:2303.02566},   year = {2023},   url = {https://doi.org/10.48550/arXiv.2303.02566}, } @Manual{,   title = {mfair: Matrix Factorization with Auxiliary Information in R},   author = {Zhiwei Wang},   year = {2023},   note = {R package version 0.0.0.9000},   url = {https://yanglabhkust.github.io/mfair/}, }"},{"path":"https://yanglabhkust.github.io/mfair/index.html","id":"mfair-matrix-factorization-with-auxiliary-information-in-r","dir":"","previous_headings":"","what":"Matrix Factorization with Auxiliary Information in R","title":"Matrix Factorization with Auxiliary Information in R","text":"R package mfair implements methods based paper MFAI: scalable Bayesian matrix factorization approach leveraging auxiliary information. MFAI integrates gradient boosted trees probabilistic matrix factorization framework effectively leverage auxiliary information. parameters MAFI can automatically determined empirical Bayes framework, making adaptive utilization auxiliary information immune overfitting.","code":""},{"path":"https://yanglabhkust.github.io/mfair/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Matrix Factorization with Auxiliary Information in R","text":"quick start, can install development version mfair GitHub : illustration examples, can alternatively use: build vignettes simultaneously. Please note can take minutes.","code":"# install.packages(\"devtools\") devtools::install_github(\"YangLabHKUST/mfair\") # install.packages(\"devtools\") devtools::install_github(\"YangLabHKUST/mfair\", build_vignettes = TRUE)"},{"path":"https://yanglabhkust.github.io/mfair/index.html","id":"examples","dir":"","previous_headings":"","what":"Examples","title":"Matrix Factorization with Auxiliary Information in R","text":"basic example shows solve common problem: mfair can also handle matrix missing entries: Empirically, backfitting algorithm can improve performance: Explore vignette illustrating enrichment movie genre information: Explore vignette illustrating spatial temporal dynamics gene regulation among brain tissues: documentation examples, please visit package website.","code":"set.seed(20230306) library(mfair)  # Simulate data # Set the data dimension and rank N <- 100 M <- 100 K_true <- 2L  # Set the proportion of variance explained (PVE) PVE_Z <- 0.8 PVE_Y <- 0.5  # Generate auxiliary information X X1 <- runif(N, min = -10, max = 10) X2 <- runif(N, min = -10, max = 10) X <- cbind(X1, X2)  # F(X) FX1 <- X1 / 2 - X2 FX2 <- (X1^2 - X2^2 + 2 * X1 * X2) / 10 FX <- cbind(FX1, FX2)  # Generate the factor matrix Z (= F(X) + noise) sig1_sq <- var(FX1) * (1 / PVE_Z - 1) Z1 <- FX1 + rnorm(n = N, mean = 0, sd = sqrt(sig1_sq)) sig2_sq <- var(FX2) * (1 / PVE_Z - 1) Z2 <- FX2 + rnorm(n = N, mean = 0, sd = sqrt(sig2_sq)) Z <- cbind(Z1, Z2)  # Generate the loading matrix W W <- matrix(rnorm(M * K_true), nrow = M, ncol = K_true)  # Generate the main data matrix Y_obs (= Y + noise) Y <- Z %*% t(W) Y_var <- var(as.vector(Y)) epsilon_sq <- Y_var * (1 / PVE_Y - 1) Y_obs <- Y + matrix(   rnorm(N * M,     mean = 0,     sd = sqrt(epsilon_sq)   ),   nrow = N, ncol = M )  # Create MFAIR object mfairObject <- createMFAIR(Y_obs, as.data.frame(X), K_max = K_true) #> The main data matrix Y is completely observed! #> The main data matrix Y has been centered with mean = 0.147726471347656!  # Fit the MFAI model mfairObject <- fitGreedy(mfairObject, sf_para = list(verbose_loop = FALSE)) #> Set K_max = 2! #> Initialize the parameters of factor 1...... #> After 1 iterations Stage 1 ends! #> After 43 iterations Stage 2 ends! #> Factor 1 retained! #> Initialize the parameters of factor 2...... #> After 1 iterations Stage 1 ends! #> After 40 iterations Stage 2 ends! #> Factor 2 retained!  # Prediction based on the low-rank approximation Y_hat <- predict(mfairObject) #> The main data matrix Y has no missing entries!  # Root-mean-square-error sqrt(mean((Y_obs - Y_hat)^2)) #> [1] 12.22526  # Predicted/true matrix variance ratio var(as.vector(Y_hat)) / var(as.vector(Y_obs)) #> [1] 0.471485  # Prediction/noise variance ratio var(as.vector(Y_hat)) / var(as.vector(Y_obs - Y_hat)) #> [1] 0.9884637 # Split the data into the training set and test set n_all <- N * M training_ratio <- 0.5 train_set <- sample(1:n_all, n_all * training_ratio, replace = FALSE) Y_train <- Y_test <- Y_obs Y_train[-train_set] <- NA Y_test[train_set] <- NA  # Create MFAIR object mfairObject <- createMFAIR(Y_train, as.data.frame(X), K_max = K_true) #> The main data matrix Y is partially observed! #> The main data matrix Y has been centered with mean = 0.227350351267622!  # Fit the MFAI model mfairObject <- fitGreedy(mfairObject, sf_para = list(verbose_loop = FALSE)) #> Set K_max = 2! #> Initialize the parameters of factor 1...... #> After 1 iterations Stage 1 ends! #> After 68 iterations Stage 2 ends! #> Factor 1 retained! #> Initialize the parameters of factor 2...... #> After 1 iterations Stage 1 ends! #> After 66 iterations Stage 2 ends! #> Factor 2 retained!  # Prediction based on the low-rank approximation Y_hat <- predict(mfairObject)  # Root-mean-square-error sqrt(mean((Y_test - Y_hat)^2, na.rm = TRUE)) #> [1] 12.88825  # Predicted/true matrix variance ratio var(as.vector(Y_hat), na.rm = TRUE) / var(as.vector(Y_obs), na.rm = TRUE) #> [1] 0.4311948  # Prediction/noise variance ratio var(as.vector(Y_hat), na.rm = TRUE) / var(as.vector(Y_obs - Y_hat), na.rm = TRUE) #> [1] 0.8554015 # Refine the MFAI model with the backfitting algorithm mfairObject <- fitBack(mfairObject,   verbose_bf_inner = FALSE,   sf_para = list(verbose_sf = FALSE, verbose_loop = FALSE) ) #> Iteration: 1, relative difference of model parameters: 0.2212487. #> Iteration: 2, relative difference of model parameters: 0.05861598. #> Iteration: 3, relative difference of model parameters: 0.01781071. #> Iteration: 4, relative difference of model parameters: 0.02649139. #> Iteration: 5, relative difference of model parameters: 0.01830385. #> Iteration: 6, relative difference of model parameters: 0.007152868.  # Prediction based on the low-rank approximation Y_hat <- predict(mfairObject)  # Root-mean-square-error sqrt(mean((Y_test - Y_hat)^2, na.rm = TRUE)) #> [1] 12.84545  # Predicted/true matrix variance ratio var(as.vector(Y_hat), na.rm = TRUE) / var(as.vector(Y_obs), na.rm = TRUE) #> [1] 0.4434191  # Prediction/noise variance ratio var(as.vector(Y_hat), na.rm = TRUE) / var(as.vector(Y_obs - Y_hat), na.rm = TRUE) #> [1] 0.8846744 vignette(\"ml100k\") vignette(\"neocortex\")"},{"path":"https://yanglabhkust.github.io/mfair/index.html","id":"citing-our-work","dir":"","previous_headings":"","what":"Citing our work","title":"Matrix Factorization with Auxiliary Information in R","text":"find mfair package source code repository useful work, please cite: Wang, Z., Zhang, F., Zheng, C., Hu, X., Cai, M., Yang, C. (2023). MFAI: scalable Bayesian matrix factorization approach leveraging auxiliary information. arXiv preprint arXiv:2303.02566. URL: https://doi.org/10.48550/arXiv.2303.02566.","code":""},{"path":"https://yanglabhkust.github.io/mfair/index.html","id":"development","dir":"","previous_headings":"","what":"Development","title":"Matrix Factorization with Auxiliary Information in R","text":"package developed Zhiwei Wang (zhiwei.wang@connect.ust.hk).","code":""},{"path":"https://yanglabhkust.github.io/mfair/index.html","id":"contact-information","dir":"","previous_headings":"","what":"Contact Information","title":"Matrix Factorization with Auxiliary Information in R","text":"Please feel free contact Zhiwei Wang (zhiwei.wang@connect.ust.hk), Prof. Mingxuan Cai (mingxcai@cityu.edu.hk), Prof. Can Yang (macyang@ust.hk) inquiries.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/MFAIR-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Each MFAIR object has a number of slots which store information. Key slots to access are listed below. — MFAIR-class","title":"Each MFAIR object has a number of slots which store information. Key slots to access are listed below. — MFAIR-class","text":"MFAIR object number slots store information. Key slots access listed .","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/MFAIR-class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Each MFAIR object has a number of slots which store information. Key slots to access are listed below. — MFAIR-class","text":"MFAIR class.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/MFAIR-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Each MFAIR object has a number of slots which store information. Key slots to access are listed below. — MFAIR-class","text":"Y matrix Matrix::dgCMatrix. main data matrix N samples M features. X data.frame. auxiliary information data frame N samples C covariates. Y_sparse Logical. Whether main data matrix Y stored sparse mode. Y_center Logical. Whether main data matrix Y centered. Y_mean Numeric. Mean main data matrix Y centered. Zero . Y_missing Logical. Whether main data matrix Y partially observed. n_obs integer. Total number observed entries Y. N integer. Number rows (samples) Y, also number rows (samples) X. M integer. Number columns (features) Y. C integer. Number columns (auxiliary covariates) X. K_max integer. Note increasing K_max ensure actual K also increases, since K_max just upper bound model automatically infer K K_max. want inference larger rank K, please make sure K_max large enough tol_snr parameter fitting function fitGreedy() small enough, simultaneously. K integer. inferred rank Y. Z N * K matrix. Estimated loading matrix, corresponding inferred posterior mean Z MFAI model. a_sq matrix containing posterior variance Z k-th column corresponding k-th loading. fully observed Y, N elements one specific loading share posterior variance, a_sq 1 * K matrix. Y missing data, elements one specific loading different posterior variances, a_sq N * K matrix. W M * K matrix. Estimated factor matrix, corresponding inferred posterior mean W MFAI model. b_sq matrix containing posterior variance W k-th column corresponding k-th factor. fully observed Y, M elements one specific factor share posterior variance, b_sq 1 * K matrix. Y missing data, elements one specific factor different posterior variances, b_sq M * K matrix. tau Numeric. vector length K, containing precision parameter pair loading/factor. beta Numeric. vector length K, containing precision parameter loading Z_k. FX N * K matrix representing prior mean Z, corresponding F(X) MFAI model. tree_0 1 * K matrix containing tree_0 k-th column corresponding k-th factor. Tree_0 defined mean mu vector factor. tree_lists list length K, containing K fitted functions function represented list trees, .e., k-th list corresponds function F_k(.) MFAI model. initialization list. Initialization fitted model. learning_rate Numeric. learning rate gradient boosting part. tree_parameters list options control details rpart algorithm. project Character. Name project (record keeping).","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/MFAIRSingleFactor-class.html","id":null,"dir":"Reference","previous_headings":"","what":"MFAIRSingleFactor object contains the key information about the fitted single factor MFAI model. — MFAIRSingleFactor-class","title":"MFAIRSingleFactor object contains the key information about the fitted single factor MFAI model. — MFAIRSingleFactor-class","text":"MFAIRSingleFactor object contains key information fitted single factor MFAI model.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/MFAIRSingleFactor-class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MFAIRSingleFactor object contains the key information about the fitted single factor MFAI model. — MFAIRSingleFactor-class","text":"MFAIRSingleFactor class.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/MFAIRSingleFactor-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"MFAIRSingleFactor object contains the key information about the fitted single factor MFAI model. — MFAIRSingleFactor-class","text":"Y_missing Logical. Whether main data matrix Y partially observed. n_obs Integer. Total number observed entries Y. mu vector length N representing inferred loading, corresponding posterior mean z single factor MFAI model. a_sq Numeric. posterior variance loading z. fully observed Y, N elements loading share posterior variance, a_sq single number. Y missing data, elements different posterior variances, a_sq vector length N. nu vector length M representing inferred factor, corresponding posterior mean w single factor MFAI model. b_sq Numeric. posterior variance factor w. fully observed Y, M elements factor share posterior variance, b_sq single number. Y missing data, elements different posterior variances, b_sq vector length M. tau Numeric. Precision parameter pair loading/factor. beta Numeric. Precision parameter loading z. FX vector length N representing prior mean z, corresponding F(X) single factor MFAI model. tree_0 Numeric. Tree_0 defined mean mu vector. tree_list list containing multiple decision trees, corresponding function F(.) single factor MFAI model. project Character. Name project (record keeping).","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/appendMFAIR.html","id":null,"dir":"Reference","previous_headings":"","what":"Append the fitted factor to the MFAIR object in the greedy algorithm. — appendMFAIR","title":"Append the fitted factor to the MFAIR object in the greedy algorithm. — appendMFAIR","text":"Append fitted factor MFAIR object greedy algorithm.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/appendMFAIR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Append the fitted factor to the MFAIR object in the greedy algorithm. — appendMFAIR","text":"","code":"appendMFAIR(object, object_sf)"},{"path":"https://yanglabhkust.github.io/mfair/reference/appendMFAIR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Append the fitted factor to the MFAIR object in the greedy algorithm. — appendMFAIR","text":"object MFAIR object containing information first factors. object_sf MFAIRSingleFactor needed appended.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/appendMFAIR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Append the fitted factor to the MFAIR object in the greedy algorithm. — appendMFAIR","text":"MFAIR object containing information new fitted single factor.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/createMFAIR.html","id":null,"dir":"Reference","previous_headings":"","what":"Create the MFAIR object with main data matrix and auxiliary information. — createMFAIR","title":"Create the MFAIR object with main data matrix and auxiliary information. — createMFAIR","text":"Create MFAIR object main data matrix auxiliary information.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/createMFAIR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create the MFAIR object with main data matrix and auxiliary information. — createMFAIR","text":"","code":"createMFAIR(   Y,   X,   Y_sparse = FALSE,   Y_center = TRUE,   K_max = 1L,   project = \"MFAIR\" )"},{"path":"https://yanglabhkust.github.io/mfair/reference/createMFAIR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create the MFAIR object with main data matrix and auxiliary information. — createMFAIR","text":"Y matrix Matrix::dgCMatrix. main data matrix N samples M features. X data.frame. auxiliary information data frame N samples C covariates. Y_sparse Logical. Determines whether use spase mode Y. Y_center Logical. Determines whether centering performed. K_max integer. maximum rank allowed model. project Character. Name project (record keeping).","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/createMFAIR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create the MFAIR object with main data matrix and auxiliary information. — createMFAIR","text":"Returns MFAIR object, main data matrix auxiliary information.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/fitBack.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit the MFAI model using backfitting algorithm. — fitBack","title":"Fit the MFAI model using backfitting algorithm. — fitBack","text":"Fit MFAI model using backfitting algorithm.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/fitBack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit the MFAI model using backfitting algorithm. — fitBack","text":"","code":"fitBack(   object,   learning_rate = 0.1,   minsplit = 10,   minbucket = round(minsplit/3),   maxdepth = 2,   other_tree_para = list(),   iter_max_bf = 5000,   tol_bf = 0.01,   verbose_bf_inner = TRUE,   verbose_bf_outer = TRUE,   sf_para = list() )"},{"path":"https://yanglabhkust.github.io/mfair/reference/fitBack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit the MFAI model using backfitting algorithm. — fitBack","text":"object MFAIR object. learning_rate Numeric. Parameter gradient boosting part. minsplit Numeric. Parameter gradient boosting part. minbucket Numeric. Parameter gradient boosting part. maxdepth Numeric. Parameter gradient boosting part. other_tree_para list containing parameters gradient boosting part. See rpart::rpart.control() details. iter_max_bf Integer. Maximum iterations allowed. tol_bf Numeric. convergence criterion. verbose_bf_inner Logical. Whether display detailed information inner loop. verbose_bf_outer Logical. Whether display detailed information outer loop. sf_para list containing parameters fitting single factor MFAI model. See fitSFFully(), fitSFMissing(), fitSFSparse() details.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/fitBack.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit the MFAI model using backfitting algorithm. — fitBack","text":"MFAIR object containing information fitted MFAI model using backfitting algorithm.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/fitGreedy.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit the MFAI model using greedy algorithm. — fitGreedy","title":"Fit the MFAI model using greedy algorithm. — fitGreedy","text":"Fit MFAI model using greedy algorithm.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/fitGreedy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit the MFAI model using greedy algorithm. — fitGreedy","text":"","code":"fitGreedy(   object,   K_max = NULL,   learning_rate = 0.1,   minsplit = 10,   minbucket = round(minsplit/3),   maxdepth = 2,   other_tree_para = list(),   tol_snr = 0.002,   verbose_greedy = TRUE,   save_init = FALSE,   sf_para = list() )"},{"path":"https://yanglabhkust.github.io/mfair/reference/fitGreedy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit the MFAI model using greedy algorithm. — fitGreedy","text":"object MFAIR object. K_max Integer. maximum rank allowed MFAI model. learning_rate Numeric. Parameter gradient boosting part. minsplit Integer. Parameter gradient boosting part. minbucket Integer. Parameter gradient boosting part. maxdepth Integer. Parameter gradient boosting part. other_tree_para list containing parameters gradient boosting part. See rpart::rpart.control() details. tol_snr Numeric. convergence criterion determine inferred rank data. verbose_greedy Logical. Whether display detailed information fitting model. save_init Logical. Whether save initialization model. sf_para list containing parameters fitting single factor MFAI model. See fitSFFully(), fitSFMissing(), fitSFSparse() details.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/fitGreedy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit the MFAI model using greedy algorithm. — fitGreedy","text":"MFAIR object containing information fitted MFAI model using greedy algorithm.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/fitSFFully.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit the single factor MFAI model with fully observed main data matrix. — fitSFFully","title":"Fit the single factor MFAI model with fully observed main data matrix. — fitSFFully","text":"Fit single factor MFAI model fully observed main data matrix.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/fitSFFully.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit the single factor MFAI model with fully observed main data matrix. — fitSFFully","text":"","code":"fitSFFully(   Y,   X,   init,   learning_rate,   tree_parameters,   stage1 = TRUE,   iter_max = 5000,   tol_stage1 = 0.1,   tol_stage2 = 1e-05,   verbose_sf = TRUE,   verbose_loop = TRUE,   save_tree_list = TRUE )"},{"path":"https://yanglabhkust.github.io/mfair/reference/fitSFFully.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit the single factor MFAI model with fully observed main data matrix. — fitSFFully","text":"Y Main data matrix. X data.frame containing auxiliary information. init MFAIRSingleFactor object containing initial parameters single factor MAFI model. learning_rate Numeric. Parameter gradient boosting part. tree_parameters list containing parameters gradient boosting part. stage1 Logical. Whether perform fitting algorithm stage1. greedy algorithm needs backfitting algorithm need. iter_max Integer. Maximum iterations allowed. tol_stage1 Numeric. Convergence criterion first step. tol_stage2 Numeric. Convergence criterion first step. verbose_sf Logical. Whether display detailed information. verbose_loop Logical. Whether display detailed information looping. save_tree_list Logical. Whether save tree list.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/fitSFFully.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit the single factor MFAI model with fully observed main data matrix. — fitSFFully","text":"MFAIRSingleFactor object containing information fitted single factor MFAI model.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/fitSFMissing.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit the single factor MFAI model with partially observed main data matrix. — fitSFMissing","title":"Fit the single factor MFAI model with partially observed main data matrix. — fitSFMissing","text":"Fit single factor MFAI model partially observed main data matrix.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/fitSFMissing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit the single factor MFAI model with partially observed main data matrix. — fitSFMissing","text":"","code":"fitSFMissing(   Y,   X,   init,   obs_indices,   learning_rate,   tree_parameters,   stage1 = TRUE,   iter_max = 5000,   tol_stage1 = 0.1,   tol_stage2 = 1e-05,   verbose_sf = TRUE,   verbose_loop = TRUE,   save_tree_list = TRUE )"},{"path":"https://yanglabhkust.github.io/mfair/reference/fitSFMissing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit the single factor MFAI model with partially observed main data matrix. — fitSFMissing","text":"Y Main data matrix. X data.frame containing auxiliary information. init MFAIRSingleFactor object containing initial parameters single factor MAFI model. obs_indices Indices observed entries main data matrix. learning_rate Numeric. Parameter gradient boosting part. tree_parameters list containing parameters gradient boosting part. stage1 Logical. Whether perform fitting algorithm stage1. greedy algorithm needs backfitting algorithm need. iter_max Integer. Maximum iterations allowed. tol_stage1 Numeric. Convergence criterion first step. tol_stage2 Numeric. Convergence criterion first step. verbose_sf Logical. Whether display detailed information. verbose_loop Logical. Whether display detailed information looping. save_tree_list Logical. Whether save tree list.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/fitSFMissing.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit the single factor MFAI model with partially observed main data matrix. — fitSFMissing","text":"MFAIRSingleFactor object containing information fitted single factor MFAI model.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/fitSFSparse.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit the single factor MFAI model with partially observed main data matrix stored in the sparse mode. — fitSFSparse","title":"Fit the single factor MFAI model with partially observed main data matrix stored in the sparse mode. — fitSFSparse","text":"Fit single factor MFAI model partially observed main data matrix stored sparse mode.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/fitSFSparse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit the single factor MFAI model with partially observed main data matrix stored in the sparse mode. — fitSFSparse","text":"","code":"fitSFSparse(   Y,   X,   init,   obs_indices,   learning_rate,   tree_parameters,   stage1 = TRUE,   iter_max = 5000,   tol_stage1 = 0.1,   tol_stage2 = 1e-05,   verbose_sf = TRUE,   verbose_loop = TRUE,   save_tree_list = TRUE )"},{"path":"https://yanglabhkust.github.io/mfair/reference/fitSFSparse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit the single factor MFAI model with partially observed main data matrix stored in the sparse mode. — fitSFSparse","text":"Y Main data matrix. X data.frame containing auxiliary information. init MFAIRSingleFactor object containing initial parameters single factor MAFI model. obs_indices Indices observed entries main data matrix. learning_rate Numeric. Parameter gradient boosting part. tree_parameters list containing parameters gradient boosting part. stage1 Logical. Whether perform fitting algorithm stage1. greedy algorithm needs backfitting algorithm need. iter_max Integer. Maximum iterations allowed. tol_stage1 Numeric. Convergence criterion first step. tol_stage2 Numeric. Convergence criterion first step. verbose_sf Logical. Whether display detailed information. verbose_loop Logical. Whether display detailed information looping. save_tree_list Logical. Whether save tree list.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/fitSFSparse.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit the single factor MFAI model with partially observed main data matrix stored in the sparse mode. — fitSFSparse","text":"MFAIRSingleFactor object containing information fitted single factor MFAI model.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/getELBO.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the evidence lower bound (ELBO) for fitted single factor MFAI model. — getELBO","title":"Compute the evidence lower bound (ELBO) for fitted single factor MFAI model. — getELBO","text":"Compute evidence lower bound (ELBO) fitted single factor MFAI model.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/getELBO.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the evidence lower bound (ELBO) for fitted single factor MFAI model. — getELBO","text":"","code":"getELBO(Y, object, obs_indices = NULL)"},{"path":"https://yanglabhkust.github.io/mfair/reference/getELBO.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the evidence lower bound (ELBO) for fitted single factor MFAI model. — getELBO","text":"Y Observed main data matrix. object MFAIRSingleFactor object containing information fitted single factor MFAI model. obs_indices Indices observed entries main data matrix Y needs specified Y stored sparse mode. default value NULL.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/getELBO.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the evidence lower bound (ELBO) for fitted single factor MFAI model. — getELBO","text":"Numeric. ELBO.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/getImportance.html","id":null,"dir":"Reference","previous_headings":"","what":"Get importance measures of auxiliary covariates. — getImportance","title":"Get importance measures of auxiliary covariates. — getImportance","text":"Get importance measures auxiliary covariates.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/getImportance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get importance measures of auxiliary covariates. — getImportance","text":"","code":"getImportance(object, which_factors = seq_len(object@K))"},{"path":"https://yanglabhkust.github.io/mfair/reference/getImportance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get importance measures of auxiliary covariates. — getImportance","text":"object MFAIR object. which_factors factors, .e., fitted functions evaluated. K factors evaluated default.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/getImportance.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get importance measures of auxiliary covariates. — getImportance","text":"Importance score matrix. row auxiliary covariate column factor.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/getImportance.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get importance measures of auxiliary covariates. — getImportance","text":"rpart::rpart() function automatically change special characters variable names dot may cause inconsistency errors. Please ensure auxiliary covariates' names contain special characters want use function.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/getImportanceSF.html","id":null,"dir":"Reference","previous_headings":"","what":"Get importance measures of auxiliary covariates in a single factor. — getImportanceSF","title":"Get importance measures of auxiliary covariates in a single factor. — getImportanceSF","text":"Get importance measures auxiliary covariates single factor.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/getImportanceSF.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get importance measures of auxiliary covariates in a single factor. — getImportanceSF","text":"","code":"getImportanceSF(tree_list, variables_names)"},{"path":"https://yanglabhkust.github.io/mfair/reference/getImportanceSF.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get importance measures of auxiliary covariates in a single factor. — getImportanceSF","text":"tree_list fitted function represented list trees. variables_names names auxiliary covariates.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/getImportanceSF.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get importance measures of auxiliary covariates in a single factor. — getImportanceSF","text":"Importance score vector. entry importance score one auxiliary covariate.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/initSF.html","id":null,"dir":"Reference","previous_headings":"","what":"Initialize the parameters for the single factor MAFI model. — initSF","title":"Initialize the parameters for the single factor MAFI model. — initSF","text":"Initialize parameters single factor MAFI model.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/initSF.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initialize the parameters for the single factor MAFI model. — initSF","text":"","code":"initSF(Y, Y_missing, Y_sparse, n_obs)"},{"path":"https://yanglabhkust.github.io/mfair/reference/initSF.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initialize the parameters for the single factor MAFI model. — initSF","text":"Y Main data matrix. Y_missing Logical. Whether main data matrix partially observed. Y_sparse Logical. Whether main data matrix sparse mode. n_obs Integer. Total number observed entries.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/initSF.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initialize the parameters for the single factor MAFI model. — initSF","text":"MFAIRSingleFactor object containing initial parameters single factor MAFI model.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/matrixORdgCMatrix-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Define the matrixORdgCMatrix class as the union of matrix and Matrix::dgCMatrix. — matrixORdgCMatrix-class","title":"Define the matrixORdgCMatrix class as the union of matrix and Matrix::dgCMatrix. — matrixORdgCMatrix-class","text":"Define matrixORdgCMatrix class union matrix Matrix::dgCMatrix.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/ml100k.html","id":null,"dir":"Reference","previous_headings":"","what":"MovieLens 100K data. — ml100k","title":"MovieLens 100K data. — ml100k","text":"list containing movie rating, user information, movie genres MovieLens 100K data.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/ml100k.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MovieLens 100K data. — ml100k","text":"","code":"ml100k"},{"path":"https://yanglabhkust.github.io/mfair/reference/ml100k.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"MovieLens 100K data. — ml100k","text":"list containing movie rating matrix, user information data frame, movie genres data frame. rating Movie rating matrix 943 users 1,682 movies (0–5 star rating). user data frame containing information 943 users, three columns correspond age, gender, occupation respectively. genre binary data frame containing genre information 1,682 movies, column corresponds one specific genre.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/ml100k.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"MovieLens 100K data. — ml100k","text":"https://grouplens.org/datasets/movielens/100k/","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/neocortex.html","id":null,"dir":"Reference","previous_headings":"","what":"Human brain gene expression data. — neocortex","title":"Human brain gene expression data. — neocortex","text":"list containing bulk gene expression human brain tissue sample information.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/neocortex.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Human brain gene expression data. — neocortex","text":"","code":"neocortex"},{"path":"https://yanglabhkust.github.io/mfair/reference/neocortex.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Human brain gene expression data. — neocortex","text":"list containing gene expression data matrix tissue sample information data frame. expression Bulk gene expression matrix 886 tissue samples neocortex region 2,000 genes highest differential stability. sample_info data frame containing information 886 bulk tissue samples neocortex region. four columns correspond sample ID, neocortex area, hemisphere, time periods respectively.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/neocortex.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Human brain gene expression data. — neocortex","text":"https://hbatlas.org/pages/data","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/predict-MFAIR-method.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction function for MFAIR object. — predict,MFAIR-method","title":"Prediction function for MFAIR object. — predict,MFAIR-method","text":"Prediction function MFAIR object.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/predict-MFAIR-method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction function for MFAIR object. — predict,MFAIR-method","text":"","code":"# S4 method for MFAIR predict(object, which_factors = seq_len(object@K), add_mean = TRUE)"},{"path":"https://yanglabhkust.github.io/mfair/reference/predict-MFAIR-method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction function for MFAIR object. — predict,MFAIR-method","text":"object model object prediction desired. which_factors factors, .e., columns Z W, used make prediction. K factors used default. add_mean Logical. Indicate whether add mean value. default value TRUE.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/predict-MFAIR-method.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prediction function for MFAIR object. — predict,MFAIR-method","text":"Predicted matrix dimension Y.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/predict-MFAIRSingleFactor-method.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction function for MFAIRSingleFactor object. — predict,MFAIRSingleFactor-method","title":"Prediction function for MFAIRSingleFactor object. — predict,MFAIRSingleFactor-method","text":"Prediction function MFAIRSingleFactor object.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/predict-MFAIRSingleFactor-method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction function for MFAIRSingleFactor object. — predict,MFAIRSingleFactor-method","text":"","code":"# S4 method for MFAIRSingleFactor predict(object)"},{"path":"https://yanglabhkust.github.io/mfair/reference/predict-MFAIRSingleFactor-method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction function for MFAIRSingleFactor object. — predict,MFAIRSingleFactor-method","text":"object model object prediction desired.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/predict-MFAIRSingleFactor-method.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prediction function for MFAIRSingleFactor object. — predict,MFAIRSingleFactor-method","text":"Predicted matrix dimension Y.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/predictFX.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction function for fitted functions. — predictFX","title":"Prediction function for fitted functions. — predictFX","text":"Prediction function fitted functions.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/predictFX.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction function for fitted functions. — predictFX","text":"","code":"predictFX(object, newdata, which_factors = seq_len(object@K))"},{"path":"https://yanglabhkust.github.io/mfair/reference/predictFX.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction function for fitted functions. — predictFX","text":"object MFAIR object. newdata Data frame containing values predictions required. which_factors factors, .e., fitted functions used. K factors used default.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/predictFX.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prediction function for fitted functions. — predictFX","text":"matrix containing predicted F(X). row new sample column factor.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/predictFXSF.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction function for fitted function F() in single factor. — predictFXSF","title":"Prediction function for fitted function F() in single factor. — predictFXSF","text":"Prediction function fitted function F() single factor.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/predictFXSF.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction function for fitted function F() in single factor. — predictFXSF","text":"","code":"predictFXSF(tree_list, newdata, learning_rate)"},{"path":"https://yanglabhkust.github.io/mfair/reference/predictFXSF.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction function for fitted function F() in single factor. — predictFXSF","text":"tree_list fitted function represented list trees. newdata Data frame containing values predictions required. learning_rate Numeric. learning rate gradient boosting part.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/predictFXSF.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prediction function for fitted function F() in single factor. — predictFXSF","text":"vector containing predicted F(X). entry corresponds new sample.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/projSparse.html","id":null,"dir":"Reference","previous_headings":"","what":"Project a matrix with given indices and store the result in the sparse mode. — projSparse","title":"Project a matrix with given indices and store the result in the sparse mode. — projSparse","text":"Project matrix given indices store result sparse mode.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/projSparse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Project a matrix with given indices and store the result in the sparse mode. — projSparse","text":"","code":"projSparse(Y, obs_indices)"},{"path":"https://yanglabhkust.github.io/mfair/reference/projSparse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Project a matrix with given indices and store the result in the sparse mode. — projSparse","text":"Y matrix projected. obs_indices matrix containing 1-based indices observed entries matrix Y. first column represents row second column represents column.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/projSparse.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Project a matrix with given indices and store the result in the sparse mode. — projSparse","text":"dgCMatrix containing projection result.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/softImputeCrossVal.html","id":null,"dir":"Reference","previous_headings":"","what":"Cross-validation for softImpute — softImputeCrossVal","title":"Cross-validation for softImpute — softImputeCrossVal","text":"Cross-validation softImpute","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/softImputeCrossVal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cross-validation for softImpute — softImputeCrossVal","text":"","code":"softImputeCrossVal(   Y,   rank_max = NULL,   lambda_range = NULL,   nfold = 10,   para_length = 100 )"},{"path":"https://yanglabhkust.github.io/mfair/reference/softImputeCrossVal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cross-validation for softImpute — softImputeCrossVal","text":"Y matrix. main data matrix N samples M features. rank_max integer. maximum rank allowed. lambda_range vector containing minimal maximal value parameter lambda. nfold integer. total number validation sets. para_length integer. total number parameter lambda.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/softImputeCrossVal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cross-validation for softImpute — softImputeCrossVal","text":"list containing cross-validation results.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/softImputeCrossValSparse.html","id":null,"dir":"Reference","previous_headings":"","what":"Sparse version of cross-validation for softImpute — softImputeCrossValSparse","title":"Sparse version of cross-validation for softImpute — softImputeCrossValSparse","text":"Sparse version cross-validation softImpute","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/softImputeCrossValSparse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sparse version of cross-validation for softImpute — softImputeCrossValSparse","text":"","code":"softImputeCrossValSparse(   Y,   rank_max = NULL,   lambda_range = NULL,   nfold = 10,   para_length = 100 )"},{"path":"https://yanglabhkust.github.io/mfair/reference/softImputeCrossValSparse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sparse version of cross-validation for softImpute — softImputeCrossValSparse","text":"Y Matrix::dgCMatrix. main data matrix N samples M features. rank_max integer. maximum rank allowed. lambda_range vector containing minimal maximal value parameter lambda. nfold integer. total number validation sets. para_length integer. total number parameter lambda.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/softImputeCrossValSparse.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sparse version of cross-validation for softImpute — softImputeCrossValSparse","text":"list containing cross-validation results.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/updateMFAIR.html","id":null,"dir":"Reference","previous_headings":"","what":"Update the k-th factor of the MFAIR object in the backfitting algorithm. — updateMFAIR","title":"Update the k-th factor of the MFAIR object in the backfitting algorithm. — updateMFAIR","text":"Update k-th factor MFAIR object backfitting algorithm.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/updateMFAIR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update the k-th factor of the MFAIR object in the backfitting algorithm. — updateMFAIR","text":"","code":"updateMFAIR(object, object_sf, k)"},{"path":"https://yanglabhkust.github.io/mfair/reference/updateMFAIR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update the k-th factor of the MFAIR object in the backfitting algorithm. — updateMFAIR","text":"object MFAIR object containing initial information K-factor MFAI model. object_sf MFAIRSingleFactor containing information newly fitted single factor MFAI model. k Integer. fator updated.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/updateMFAIR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Update the k-th factor of the MFAIR object in the backfitting algorithm. — updateMFAIR","text":"MFAIR object containing information new fitted single factor.","code":""}]
+[{"path":"https://yanglabhkust.github.io/mfair/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2023 mfair authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://yanglabhkust.github.io/mfair/articles/ml100k.html","id":"ml100k-data","dir":"Articles","previous_headings":"","what":"ml100k data","title":"Enrichment of movie genre information","text":"row represents user column represents movie rating matrix, row represents movie column represents genre genre matrix (use help(ml100k) details data).","code":""},{"path":"https://yanglabhkust.github.io/mfair/articles/ml100k.html","id":"fitting-the-mfai-model","dir":"Articles","previous_headings":"","what":"Fitting the MFAI model","title":"Enrichment of movie genre information","text":"use rating matrix main data matrix \\(Y\\), genre data frame auxiliary matrix \\(X\\). proceed fit MFAI model top three factors.","code":"# Create MFAIR object Y <- t(ml100k$rating) X <- ml100k$genre mfairObject <- createMFAIR(Y, X, K_max = 3) #> The main data matrix Y has been stored in the sparse mode! #> The main data matrix Y is partially observed! #> The main data matrix Y has been centered with mean = 3.52986!  # Fit the MFAI model mfairObject <- fitGreedy(mfairObject,   save_init = TRUE,   sf_para = list(verbose_loop = FALSE) ) #> Set K_max = 3! #> Initialize the parameters of factor 1...... #> After 2 iterations Stage 1 ends! #> After 95 iterations Stage 2 ends! #> Factor 1 retained! #> Save the initializaiton information...... #> Initialize the parameters of factor 2...... #> After 2 iterations Stage 1 ends! #> After 181 iterations Stage 2 ends! #> Factor 2 retained! #> Save the initializaiton information...... #> Initialize the parameters of factor 3...... #> After 2 iterations Stage 1 ends! #> After 35 iterations Stage 2 ends! #> Factor 3 retained! #> Save the initializaiton information......"},{"path":"https://yanglabhkust.github.io/mfair/articles/ml100k.html","id":"importance-score","dir":"Articles","previous_headings":"","what":"Importance score","title":"Enrichment of movie genre information","text":"fitting MFAI model, can use getImportance() function obtain importance score genre within factor.  higher importance score , specific movie genre contributes improving model.","code":"# Get importance score importance <- as.data.frame(getImportance(mfairObject, which_factors = 1:3)) importance$Genre <- rownames(importance) importance_long <- melt(   data = importance,   id.vars = \"Genre\",   variable.name = \"Factor\",   value.name = \"Importance\" ) importance_long$Genre <- factor(importance_long$Genre, levels = rev(colnames(X))) # head(importance_long) # Visualize the importance score p1 <- ggplot(   data = importance_long,   aes(x = Genre, y = Importance, fill = Genre) ) +   geom_col() +   coord_flip() +   scale_y_continuous(labels = label_comma(accuracy = 1)) +   xlab(NULL) +   ylab(\"Importance score\") +   guides(fill = \"none\") +   theme_bw() +   theme(     text = element_text(size = 12),     axis.title = element_text(size = 12),     axis.text.x = element_text(size = 12),     axis.text.y = element_text(size = 10),     aspect.ratio = 2   ) +   facet_grid(. ~ Factor, scales = \"free\") p1"},{"path":"https://yanglabhkust.github.io/mfair/articles/ml100k.html","id":"negative-control","dir":"Articles","previous_headings":"","what":"Negative control","title":"Enrichment of movie genre information","text":"Next, let’s create permuted movie genre matrix \\(X^{\\text{pmt}}\\), column \\(X^{\\text{pmt}}\\) obtained permuting entries corresponding column real genre data \\(X\\). fit MFAI model \\(Y\\) \\(X^{\\text{pmt}}\\) input.  MFAI correctly assigns low importance scores permuted genres, suggesting MFAI avoids incorporating irrelevant auxiliary information. last, use \\(X^{\\text{}} = [X, X^{\\text{pmt}}]\\) input auxiliary information fit MFAI model.  MFAI successfully distinguished useful movie genres irrelevant ones. Moreover, importance scores obtained using \\(X^{\\text{}}\\) highly consistent obtained using \\(X\\) \\(X^{\\text{pmt}}\\) separate inputs, indicating stability robustness MFAI.","code":"n_pmt <- dim(X)[2]  X_pmt <- apply(X,   MARGIN = 2,   FUN = function(x) {     N <- length(x)     x[sample(x = c(1:N), size = N, replace = FALSE)]   } ) X_pmt <- as.data.frame(X_pmt) colnames(X_pmt) <- paste0(colnames(X), \"_permuted\") # Create MFAIR object and use the same initialization mfairObject_pmt <- createMFAIR(Y, X_pmt, K_max = 3) #> The main data matrix Y has been stored in the sparse mode! #> The main data matrix Y is partially observed! #> The main data matrix Y has been centered with mean = 3.52986! mfairObject_pmt@initialization <- mfairObject@initialization  # Fit the MFAI model mfairObject_pmt <- fitGreedy(mfairObject_pmt,   sf_para = list(verbose_loop = FALSE) ) #> Set K_max = 3! #> Use the user-specific initialization for factor 1...... #> After 2 iterations Stage 1 ends! #> After 96 iterations Stage 2 ends! #> Factor 1 retained! #> Use the user-specific initialization for factor 2...... #> After 2 iterations Stage 1 ends! #> After 176 iterations Stage 2 ends! #> Factor 2 retained! #> Use the user-specific initialization for factor 3...... #> After 2 iterations Stage 1 ends! #> After 34 iterations Stage 2 ends! #> Factor 3 retained! # Get importance score importance_pmt <- as.data.frame(getImportance(mfairObject_pmt, which_factors = 1:3)) importance_pmt$Genre <- rownames(importance_pmt) importance_pmt_long <- melt(   data = importance_pmt,   id.vars = \"Genre\",   variable.name = \"Factor\",   value.name = \"Importance\" ) importance_pmt_long$Genre <- factor(importance_pmt_long$Genre, levels = rev(colnames(X_pmt))) # head(importance_pmt_long)  # Visualize the importance score p2 <- ggplot(   data = importance_pmt_long,   aes(x = Genre, y = Importance, fill = Genre) ) +   geom_col() +   coord_flip() +   scale_y_continuous(labels = label_comma(accuracy = 1)) +   xlab(NULL) +   ylab(\"Importance score\") +   guides(fill = \"none\") +   theme_bw() +   theme(     text = element_text(size = 12),     axis.title = element_text(size = 12),     axis.text.x = element_text(size = 12),     axis.text.y = element_text(size = 10),     aspect.ratio = 2   ) +   facet_grid(. ~ Factor, scales = \"free\") p2 X_both <- cbind(X, X_pmt)  # Create MFAIR object and use the same initialization mfairObject_both <- createMFAIR(Y, X_both, K_max = 3) #> The main data matrix Y has been stored in the sparse mode! #> The main data matrix Y is partially observed! #> The main data matrix Y has been centered with mean = 3.52986! mfairObject_both@initialization <- mfairObject@initialization  # Fit the MFAI model mfairObject_both <- fitGreedy(mfairObject_both,   sf_para = list(verbose_loop = FALSE) ) #> Set K_max = 3! #> Use the user-specific initialization for factor 1...... #> After 2 iterations Stage 1 ends! #> After 95 iterations Stage 2 ends! #> Factor 1 retained! #> Use the user-specific initialization for factor 2...... #> After 2 iterations Stage 1 ends! #> After 181 iterations Stage 2 ends! #> Factor 2 retained! #> Use the user-specific initialization for factor 3...... #> After 2 iterations Stage 1 ends! #> After 35 iterations Stage 2 ends! #> Factor 3 retained! # Get importance score importance_both <- as.data.frame(getImportance(mfairObject_both, which_factors = 1:3)) importance_both$Genre <- rownames(importance_both) importance_both_long <- melt(   data = importance_both,   id.vars = \"Genre\",   variable.name = \"Factor\",   value.name = \"Importance\" ) importance_both_long$Genre <- factor(importance_both_long$Genre, levels = rev(colnames(X_both))) # head(importance_both_long)  # Visualize the importance score p3 <- ggplot(   data = importance_both_long,   aes(x = Genre, y = Importance, fill = Genre) ) +   geom_col() +   coord_flip() +   scale_y_continuous(labels = label_comma(accuracy = 1)) +   xlab(NULL) +   ylab(\"Importance score\") +   guides(fill = \"none\") +   theme_bw() +   theme(     text = element_text(size = 12),     axis.title = element_text(size = 12),     axis.text.x = element_text(size = 12),     axis.text.y = element_text(size = 8),     aspect.ratio = 2   ) +   facet_grid(. ~ Factor, scales = \"free\") p3"},{"path":"https://yanglabhkust.github.io/mfair/articles/neocortex.html","id":"neocortex-data","dir":"Articles","previous_headings":"","what":"neocortex data","title":"Spatial and temporal dynamics of gene regulation among brain tissues","text":"spatial temporal patterns gene regulation brain development attracted great deal attention neuroscience community. availability gene expression profiles collected multiple brain regions time periods provides unprecedented chance characterize human brain development. select genes consistent spatial patterns across individuals using concept differential stability (DS), defined tendency gene exhibit reproducible differential expression relationships across brain structures. include 2,000 genes highest DS get expression matrix, row represents sample tissue nercortex region column represents gene. sample_info data frame contains sample information, row represents sample tissue four columns respectively represent sample ID, neocortex area, hemisphere, time periods.","code":""},{"path":"https://yanglabhkust.github.io/mfair/articles/neocortex.html","id":"fitting-the-mfai-model","dir":"Articles","previous_headings":"","what":"Fitting the MFAI model","title":"Spatial and temporal dynamics of gene regulation among brain tissues","text":"use expression matrix main data matrix \\(Y\\), spatial temporal information contained sample_info data frame auxiliary matrix \\(X\\). proceed fit MFAI model top three factors.","code":"# Create MFAIR object Y <- neocortex$expression X <- neocortex$sample_info[, c(\"Region\", \"Stage\")] mfairObject <- createMFAIR(Y, X, K_max = 3) #> The main data matrix Y is completely observed! #> The main data matrix Y has been centered with mean = 7.64309222668172!  # Fit the MFAI model mfairObject <- fitGreedy(mfairObject,   sf_para = list(tol_stage2 = 1e-6, verbose_loop = FALSE) ) #> Set K_max = 3! #> Initialize the parameters of factor 1...... #> After 3 iterations Stage 1 ends! #> After 18 iterations Stage 2 ends! #> Factor 1 retained! #> Initialize the parameters of factor 2...... #> After 3 iterations Stage 1 ends! #> After 46 iterations Stage 2 ends! #> Factor 2 retained! #> Initialize the parameters of factor 3...... #> After 2 iterations Stage 1 ends! #> After 600 iterations Stage 2 ends! #> Factor 3 retained!"},{"path":"https://yanglabhkust.github.io/mfair/articles/neocortex.html","id":"spatial-and-temporal-dynamics","dir":"Articles","previous_headings":"","what":"Spatial and temporal dynamics","title":"Spatial and temporal dynamics of gene regulation among brain tissues","text":"gain insights, visualize dynamic patterns top three factors across different neocortex areas time periods, represented \\(\\{ F_1 (\\cdot) , F_2 (\\cdot) , F_3 (\\cdot) \\}\\).","code":"region <- c(\"OFC\", \"DFC\", \"VFC\", \"MFC\", \"M1C\", \"S1C\", \"IPC\", \"A1C\", \"STC\", \"ITC\", \"V1C\") stage <- c(3:15) X_new <- data.frame(   Region = factor(rep(region, length(stage)), levels = region),   Stage = rep(stage, each = length(region)) ) FX <- predictFX(mfairObject,   newdata = X_new,   which_factors = c(1:3) ) # Normalize each factor to have l2-norm equal one FX <- apply(FX,   MARGIN = 2,   FUN = function(x) {     x / sqrt(sum(x^2))   } ) FX <- data.frame(X_new, FX) colnames(FX) <- c(\"Neocortex area\", \"Time period\", paste(\"Factor\", c(1:3))) FX[, \"Time period\"] <- factor(FX[, \"Time period\"], levels = stage) head(FX) #>   Neocortex area Time period    Factor 1   Factor 2  Factor 3 #> 1            OFC           3 -0.08530621 -0.1275302 0.1373010 #> 2            DFC           3 -0.08530621 -0.1281884 0.1720814 #> 3            VFC           3 -0.08530621 -0.1279504 0.1329457 #> 4            MFC           3 -0.08490720 -0.1272283 0.1652383 #> 5            M1C           3 -0.08530621 -0.1277465 0.1578517 #> 6            S1C           3 -0.08510786 -0.1277465 0.1462684 # Convert the wide table to the long table FX_long <- melt(   data = FX,   id.vars = c(\"Neocortex area\", \"Time period\"),   variable.name = \"Factor\", value.name = \"F\" ) head(FX_long) #>   Neocortex area Time period   Factor           F #> 1            OFC           3 Factor 1 -0.08530621 #> 2            DFC           3 Factor 1 -0.08530621 #> 3            VFC           3 Factor 1 -0.08530621 #> 4            MFC           3 Factor 1 -0.08490720 #> 5            M1C           3 Factor 1 -0.08530621 #> 6            S1C           3 Factor 1 -0.08510786 # Visualization of F(.) p <- ggplot(   data = FX_long,   aes(x = `Time period`, y = F, linetype = `Neocortex area`, colour = `Neocortex area`, group = `Neocortex area`) ) +   geom_line(linewidth = 0.5) +   ylab(NULL) +   theme_bw() +   scale_y_continuous(n.breaks = 4) +   theme(     text = element_text(size = 12),     axis.text.y = element_text(size = 10),     axis.title.x = element_text(size = 10, margin = margin(t = 3)),     axis.text.x = element_text(size = 10),     legend.title = element_text(size = 12),     legend.text = element_text(size = 10),     legend.key.size = unit(0.8, \"cm\"),     legend.key.width = unit(0.8, \"cm\"),     legend.position = \"right\",     panel.spacing.y = unit(0.2, \"cm\"), # Space between panels     aspect.ratio = 0.4   ) +   facet_grid(Factor ~ ., scales = \"free_y\") p"},{"path":"https://yanglabhkust.github.io/mfair/articles/neocortex.html","id":"gene-set-enrichment-analysis","dir":"Articles","previous_headings":"","what":"Gene set enrichment analysis","title":"Spatial and temporal dynamics of gene regulation among brain tissues","text":"first calculated relative weight \\(k\\)-th factor \\(m\\)-th gene \\(\\left| W_{mk} \\right| / \\sum_{k^{\\prime}=1}^{3} \\left| W_{mk^{\\prime}} \\right|\\), \\(W_{m \\cdot} \\\\mathbb{R}^{3 \\times 1}\\) \\(m\\)-th row gene factors, selected top 300 weighted genes factor form gene sets. can conduct gene set enrichment analysis based Gene Ontology factor.","code":"# Inferred gene factors (corresponding to the W matrix in the MFAI paper) gene_factors <- mfairObject@W rownames(gene_factors) <- colnames(mfairObject@Y) # Assign gene symbols colnames(gene_factors) <- paste(\"Factor\", c(1:3)) head(gene_factors) #>            Factor 1    Factor 2    Factor 3 #> DCUN1D2  0.07856363  0.06006969  0.16380535 #> ARRB1   -0.27132066  0.06770678 -0.12942443 #> PDE1B   -0.01294115  0.39845756  0.20161848 #> PDE7B    0.01081660  0.41041139  0.52982200 #> TOX     -0.11471109 -0.20478958  0.60887294 #> LOXHD1   0.24888992  0.01732711 -0.03634836 # Heatmap of the inferred gene factors pheatmap::pheatmap(t(gene_factors),   scale = \"column\",   clustering_method = \"complete\",   cluster_row = FALSE, cluster_col = TRUE,   treeheight_row = 0, treeheight_col = 0,   border = FALSE,   show_rownames = TRUE, show_colnames = FALSE,   cellwidth = 0.2, cellheight = 40,   fontsize = 12 ) # Normalize each factor to have l2-norm equal one gene_factors <- apply(gene_factors,   MARGIN = 2,   FUN = function(x) {     x / sqrt(sum(x^2))   } )  # Relative weight gene_factors <- abs(gene_factors) gene_factors <- gene_factors / rowSums(gene_factors)  M <- nrow(gene_factors)[1] # Total number of genes M = 2,000 ntop <- M * 0.15 # We use the top 300 weighted genes in each factor to form the gene sets  # Index of top genes top_gene_idx <- apply(gene_factors,   MARGIN = 2,   FUN = function(x) {     which(rank(-x) <= ntop)   } )  top_genes <- apply(top_gene_idx,   MARGIN = 2,   FUN = function(x) {     rownames(gene_factors)[x]   } ) colnames(top_genes) <- paste(\"Factor\", c(1:3)) head(top_genes) #>      Factor 1  Factor 2 Factor 3 #> [1,] \"ARRB1\"   \"PDE1B\"  \"AJAP1\"  #> [2,] \"LOXHD1\"  \"PDE7B\"  \"KCNA3\"  #> [3,] \"TYRP1\"   \"KCNA2\"  \"ASTN2\"  #> [4,] \"PRKG1\"   \"PMP22\"  \"EMID1\"  #> [5,] \"MS4A8B\"  \"GPR155\" \"GPR52\"  #> [6,] \"FAM131B\" \"SMAD2\"  \"SEC24D\""},{"path":"https://yanglabhkust.github.io/mfair/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Zhiwei Wang. Author, maintainer.","code":""},{"path":"https://yanglabhkust.github.io/mfair/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Wang, Z., Zhang, F., Zheng, C., Hu, X., Cai, M., Yang, C. (2023). MFAI: scalable Bayesian matrix factorization approach leveraging auxiliary information. arXiv preprint arXiv:2303.02566. Wang, Z. (2023). mfair: Matrix Factorization Auxiliary Information R. R package version 0.0.0.9000. https://yanglabhkust.github.io/mfair/.","code":"@Article{,   title = {MFAI: A Scalable Bayesian Matrix Factorization Approach to Leveraging Auxiliary Information},   author = {Zhiwei Wang and Fa Zhang and Cong Zheng and Xianghong Hu and Mingxuan Cai and Can Yang},   journal = {arXiv preprint arXiv:2303.02566},   year = {2023},   url = {https://doi.org/10.48550/arXiv.2303.02566}, } @Manual{,   title = {mfair: Matrix Factorization with Auxiliary Information in R},   author = {Zhiwei Wang},   year = {2023},   note = {R package version 0.0.0.9000},   url = {https://yanglabhkust.github.io/mfair/}, }"},{"path":"https://yanglabhkust.github.io/mfair/index.html","id":"mfair-matrix-factorization-with-auxiliary-information-in-r","dir":"","previous_headings":"","what":"Matrix Factorization with Auxiliary Information in R","title":"Matrix Factorization with Auxiliary Information in R","text":"R package mfair implements methods based paper MFAI: scalable Bayesian matrix factorization approach leveraging auxiliary information. MFAI integrates gradient boosted trees probabilistic matrix factorization framework effectively leverage auxiliary information. parameters MAFI can automatically determined empirical Bayes framework, making adaptive utilization auxiliary information immune overfitting.","code":""},{"path":"https://yanglabhkust.github.io/mfair/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Matrix Factorization with Auxiliary Information in R","text":"quick start, can install development version mfair GitHub : illustration examples, can alternatively use: build vignettes simultaneously. Please note can take minutes.","code":"# install.packages(\"devtools\") devtools::install_github(\"YangLabHKUST/mfair\") # install.packages(\"devtools\") devtools::install_github(\"YangLabHKUST/mfair\", build_vignettes = TRUE)"},{"path":"https://yanglabhkust.github.io/mfair/index.html","id":"examples","dir":"","previous_headings":"","what":"Examples","title":"Matrix Factorization with Auxiliary Information in R","text":"basic example shows solve common problem: mfair can also handle matrix missing entries: Empirically, backfitting algorithm can improve performance: Explore vignette illustrating enrichment movie genre information: Explore vignette illustrating spatial temporal dynamics gene regulation among brain tissues: documentation examples, please visit package website.","code":"set.seed(20230306) library(mfair)  # Simulate data # Set the data dimension and rank N <- 100 M <- 100 K_true <- 2L  # Set the proportion of variance explained (PVE) PVE_Z <- 0.8 PVE_Y <- 0.5  # Generate auxiliary information X X1 <- runif(N, min = -10, max = 10) X2 <- runif(N, min = -10, max = 10) X <- cbind(X1, X2)  # F(X) FX1 <- X1 / 2 - X2 FX2 <- (X1^2 - X2^2 + 2 * X1 * X2) / 10 FX <- cbind(FX1, FX2)  # Generate the factor matrix Z (= F(X) + noise) sig1_sq <- var(FX1) * (1 / PVE_Z - 1) Z1 <- FX1 + rnorm(n = N, mean = 0, sd = sqrt(sig1_sq)) sig2_sq <- var(FX2) * (1 / PVE_Z - 1) Z2 <- FX2 + rnorm(n = N, mean = 0, sd = sqrt(sig2_sq)) Z <- cbind(Z1, Z2)  # Generate the loading matrix W W <- matrix(rnorm(M * K_true), nrow = M, ncol = K_true)  # Generate the main data matrix Y_obs (= Y + noise) Y <- Z %*% t(W) Y_var <- var(as.vector(Y)) epsilon_sq <- Y_var * (1 / PVE_Y - 1) Y_obs <- Y + matrix(   rnorm(N * M,     mean = 0,     sd = sqrt(epsilon_sq)   ),   nrow = N, ncol = M )  # Create MFAIR object mfairObject <- createMFAIR(Y_obs, as.data.frame(X), K_max = K_true) #> The main data matrix Y is completely observed! #> The main data matrix Y has been centered with mean = 0.147726471347656!  # Fit the MFAI model mfairObject <- fitGreedy(mfairObject, sf_para = list(verbose_loop = FALSE)) #> Set K_max = 2! #> Initialize the parameters of factor 1...... #> After 1 iterations Stage 1 ends! #> After 43 iterations Stage 2 ends! #> Factor 1 retained! #> Initialize the parameters of factor 2...... #> After 1 iterations Stage 1 ends! #> After 40 iterations Stage 2 ends! #> Factor 2 retained!  # Prediction based on the low-rank approximation Y_hat <- predict(mfairObject) #> The main data matrix Y has no missing entries!  # Root-mean-square-error sqrt(mean((Y_obs - Y_hat)^2)) #> [1] 12.22526  # Predicted/true matrix variance ratio var(as.vector(Y_hat)) / var(as.vector(Y_obs)) #> [1] 0.471485  # Prediction/noise variance ratio var(as.vector(Y_hat)) / var(as.vector(Y_obs - Y_hat)) #> [1] 0.9884637 # Split the data into the training set and test set n_all <- N * M training_ratio <- 0.5 train_set <- sample(1:n_all, n_all * training_ratio, replace = FALSE) Y_train <- Y_test <- Y_obs Y_train[-train_set] <- NA Y_test[train_set] <- NA  # Create MFAIR object mfairObject <- createMFAIR(Y_train, as.data.frame(X), K_max = K_true) #> The main data matrix Y is partially observed! #> The main data matrix Y has been centered with mean = 0.227350351267622!  # Fit the MFAI model mfairObject <- fitGreedy(mfairObject, sf_para = list(verbose_loop = FALSE)) #> Set K_max = 2! #> Initialize the parameters of factor 1...... #> After 1 iterations Stage 1 ends! #> After 68 iterations Stage 2 ends! #> Factor 1 retained! #> Initialize the parameters of factor 2...... #> After 1 iterations Stage 1 ends! #> After 66 iterations Stage 2 ends! #> Factor 2 retained!  # Prediction based on the low-rank approximation Y_hat <- predict(mfairObject)  # Root-mean-square-error sqrt(mean((Y_test - Y_hat)^2, na.rm = TRUE)) #> [1] 12.88825  # Predicted/true matrix variance ratio var(as.vector(Y_hat), na.rm = TRUE) / var(as.vector(Y_obs), na.rm = TRUE) #> [1] 0.4311948  # Prediction/noise variance ratio var(as.vector(Y_hat), na.rm = TRUE) / var(as.vector(Y_obs - Y_hat), na.rm = TRUE) #> [1] 0.8554015 # Refine the MFAI model with the backfitting algorithm mfairObject <- fitBack(mfairObject,   verbose_bf_inner = FALSE,   sf_para = list(verbose_sf = FALSE, verbose_loop = FALSE) ) #> Iteration: 1, relative difference of model parameters: 0.2212487. #> Iteration: 2, relative difference of model parameters: 0.05861598. #> Iteration: 3, relative difference of model parameters: 0.01781071. #> Iteration: 4, relative difference of model parameters: 0.02649139. #> Iteration: 5, relative difference of model parameters: 0.01830385. #> Iteration: 6, relative difference of model parameters: 0.007152868.  # Prediction based on the low-rank approximation Y_hat <- predict(mfairObject)  # Root-mean-square-error sqrt(mean((Y_test - Y_hat)^2, na.rm = TRUE)) #> [1] 12.84545  # Predicted/true matrix variance ratio var(as.vector(Y_hat), na.rm = TRUE) / var(as.vector(Y_obs), na.rm = TRUE) #> [1] 0.4434191  # Prediction/noise variance ratio var(as.vector(Y_hat), na.rm = TRUE) / var(as.vector(Y_obs - Y_hat), na.rm = TRUE) #> [1] 0.8846744 vignette(\"ml100k\") vignette(\"neocortex\")"},{"path":"https://yanglabhkust.github.io/mfair/index.html","id":"citing-our-work","dir":"","previous_headings":"","what":"Citing our work","title":"Matrix Factorization with Auxiliary Information in R","text":"find mfair package source code repository useful work, please cite: Wang, Z., Zhang, F., Zheng, C., Hu, X., Cai, M., Yang, C. (2023). MFAI: scalable Bayesian matrix factorization approach leveraging auxiliary information. arXiv preprint arXiv:2303.02566. URL: https://doi.org/10.48550/arXiv.2303.02566.","code":""},{"path":"https://yanglabhkust.github.io/mfair/index.html","id":"development","dir":"","previous_headings":"","what":"Development","title":"Matrix Factorization with Auxiliary Information in R","text":"package developed Zhiwei Wang (zhiwei.wang@connect.ust.hk).","code":""},{"path":"https://yanglabhkust.github.io/mfair/index.html","id":"contact-information","dir":"","previous_headings":"","what":"Contact Information","title":"Matrix Factorization with Auxiliary Information in R","text":"Please feel free contact Zhiwei Wang (zhiwei.wang@connect.ust.hk), Prof. Mingxuan Cai (mingxcai@cityu.edu.hk), Prof. Can Yang (macyang@ust.hk) inquiries.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/MFAIR-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Each MFAIR object has a number of slots which store information. Key slots to access are listed below. — MFAIR-class","title":"Each MFAIR object has a number of slots which store information. Key slots to access are listed below. — MFAIR-class","text":"MFAIR object number slots store information. Key slots access listed .","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/MFAIR-class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Each MFAIR object has a number of slots which store information. Key slots to access are listed below. — MFAIR-class","text":"MFAIR class.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/MFAIR-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Each MFAIR object has a number of slots which store information. Key slots to access are listed below. — MFAIR-class","text":"Y matrix Matrix::dgCMatrix. main data matrix N samples M features. X data.frame. auxiliary information data frame N samples C covariates. Y_sparse Logical. Whether main data matrix Y stored sparse mode. Y_center Logical. Whether main data matrix Y centered. Y_mean Numeric. Mean main data matrix Y centered. Zero . Y_missing Logical. Whether main data matrix Y partially observed. n_obs integer. Total number observed entries Y. N integer. Number rows (samples) Y, also number rows (samples) X. M integer. Number columns (features) Y. C integer. Number columns (auxiliary covariates) X. K_max integer. Note increasing K_max ensure actual K also increases, since K_max just upper bound model automatically infer K K_max. want inference larger rank K, please make sure K_max large enough tol_snr parameter fitting function fitGreedy() small enough, simultaneously. K integer. inferred rank Y. Z N * K matrix. Estimated loading matrix, corresponding inferred posterior mean Z MFAI model. a_sq matrix containing posterior variance Z k-th column corresponding k-th loading. fully observed Y, N elements one specific loading share posterior variance, a_sq 1 * K matrix. Y missing data, elements one specific loading different posterior variances, a_sq N * K matrix. W M * K matrix. Estimated factor matrix, corresponding inferred posterior mean W MFAI model. b_sq matrix containing posterior variance W k-th column corresponding k-th factor. fully observed Y, M elements one specific factor share posterior variance, b_sq 1 * K matrix. Y missing data, elements one specific factor different posterior variances, b_sq M * K matrix. tau Numeric. vector length K, containing precision parameter pair loading/factor. beta Numeric. vector length K, containing precision parameter loading Z_k. FX N * K matrix representing prior mean Z, corresponding F(X) MFAI model. tree_0 1 * K matrix containing tree_0 k-th column corresponding k-th factor. Tree_0 defined mean mu vector factor. tree_lists list length K, containing K fitted functions function represented list trees, .e., k-th list corresponds function F_k(.) MFAI model. initialization list. Initialization fitted model. learning_rate Numeric. learning rate gradient boosting part. tree_parameters list options control details rpart algorithm. project Character. Name project (record keeping).","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/MFAIRSingleFactor-class.html","id":null,"dir":"Reference","previous_headings":"","what":"MFAIRSingleFactor object contains the key information about the fitted single factor MFAI model. — MFAIRSingleFactor-class","title":"MFAIRSingleFactor object contains the key information about the fitted single factor MFAI model. — MFAIRSingleFactor-class","text":"MFAIRSingleFactor object contains key information fitted single factor MFAI model.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/MFAIRSingleFactor-class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MFAIRSingleFactor object contains the key information about the fitted single factor MFAI model. — MFAIRSingleFactor-class","text":"MFAIRSingleFactor class.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/MFAIRSingleFactor-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"MFAIRSingleFactor object contains the key information about the fitted single factor MFAI model. — MFAIRSingleFactor-class","text":"Y_missing Logical. Whether main data matrix Y partially observed. n_obs Integer. Total number observed entries Y. mu vector length N representing inferred loading, corresponding posterior mean z single factor MFAI model. a_sq Numeric. posterior variance loading z. fully observed Y, N elements loading share posterior variance, a_sq single number. Y missing data, elements different posterior variances, a_sq vector length N. nu vector length M representing inferred factor, corresponding posterior mean w single factor MFAI model. b_sq Numeric. posterior variance factor w. fully observed Y, M elements factor share posterior variance, b_sq single number. Y missing data, elements different posterior variances, b_sq vector length M. tau Numeric. Precision parameter pair loading/factor. beta Numeric. Precision parameter loading z. FX vector length N representing prior mean z, corresponding F(X) single factor MFAI model. tree_0 Numeric. Tree_0 defined mean mu vector. tree_list list containing multiple decision trees, corresponding function F(.) single factor MFAI model. project Character. Name project (record keeping).","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/appendMFAIR.html","id":null,"dir":"Reference","previous_headings":"","what":"Append the fitted factor to the MFAIR object in the greedy algorithm. — appendMFAIR","title":"Append the fitted factor to the MFAIR object in the greedy algorithm. — appendMFAIR","text":"Append fitted factor MFAIR object greedy algorithm.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/appendMFAIR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Append the fitted factor to the MFAIR object in the greedy algorithm. — appendMFAIR","text":"","code":"appendMFAIR(object, object_sf)"},{"path":"https://yanglabhkust.github.io/mfair/reference/appendMFAIR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Append the fitted factor to the MFAIR object in the greedy algorithm. — appendMFAIR","text":"object MFAIR object containing information first factors. object_sf MFAIRSingleFactor needed appended.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/appendMFAIR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Append the fitted factor to the MFAIR object in the greedy algorithm. — appendMFAIR","text":"MFAIR object containing information new fitted single factor.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/createMFAIR.html","id":null,"dir":"Reference","previous_headings":"","what":"Create the MFAIR object with main data matrix and auxiliary information. — createMFAIR","title":"Create the MFAIR object with main data matrix and auxiliary information. — createMFAIR","text":"Create MFAIR object main data matrix auxiliary information.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/createMFAIR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create the MFAIR object with main data matrix and auxiliary information. — createMFAIR","text":"","code":"createMFAIR(   Y,   X,   Y_sparse = FALSE,   Y_center = TRUE,   K_max = 1L,   project = \"MFAIR\" )"},{"path":"https://yanglabhkust.github.io/mfair/reference/createMFAIR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create the MFAIR object with main data matrix and auxiliary information. — createMFAIR","text":"Y matrix Matrix::dgCMatrix. main data matrix N samples M features. X data.frame. auxiliary information data frame N samples C covariates. Y_sparse Logical. Determines whether use spase mode Y. Y_center Logical. Determines whether centering performed. K_max integer. maximum rank allowed model. project Character. Name project (record keeping).","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/createMFAIR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create the MFAIR object with main data matrix and auxiliary information. — createMFAIR","text":"Returns MFAIR object, main data matrix auxiliary information.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/fitBack.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit the MFAI model using backfitting algorithm. — fitBack","title":"Fit the MFAI model using backfitting algorithm. — fitBack","text":"Fit MFAI model using backfitting algorithm.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/fitBack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit the MFAI model using backfitting algorithm. — fitBack","text":"","code":"fitBack(   object,   learning_rate = 0.1,   minsplit = 10,   minbucket = round(minsplit/3),   maxdepth = 2,   other_tree_para = list(),   iter_max_bf = 5000,   tol_bf = 0.01,   verbose_bf_inner = TRUE,   verbose_bf_outer = TRUE,   sf_para = list() )"},{"path":"https://yanglabhkust.github.io/mfair/reference/fitBack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit the MFAI model using backfitting algorithm. — fitBack","text":"object MFAIR object. learning_rate Numeric. Parameter gradient boosting part. minsplit Numeric. Parameter gradient boosting part. minbucket Numeric. Parameter gradient boosting part. maxdepth Numeric. Parameter gradient boosting part. other_tree_para list containing parameters gradient boosting part. See rpart::rpart.control() details. iter_max_bf Integer. Maximum iterations allowed. tol_bf Numeric. convergence criterion. verbose_bf_inner Logical. Whether display detailed information inner loop. verbose_bf_outer Logical. Whether display detailed information outer loop. sf_para list containing parameters fitting single factor MFAI model. See fitSFFully(), fitSFMissing(), fitSFSparse() details.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/fitBack.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit the MFAI model using backfitting algorithm. — fitBack","text":"MFAIR object containing information fitted MFAI model using backfitting algorithm.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/fitGreedy.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit the MFAI model using greedy algorithm. — fitGreedy","title":"Fit the MFAI model using greedy algorithm. — fitGreedy","text":"Fit MFAI model using greedy algorithm.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/fitGreedy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit the MFAI model using greedy algorithm. — fitGreedy","text":"","code":"fitGreedy(   object,   K_max = NULL,   learning_rate = 0.1,   minsplit = 10,   minbucket = round(minsplit/3),   maxdepth = 2,   other_tree_para = list(),   null_check = TRUE,   tol_snr = 0.002,   verbose_greedy = TRUE,   save_init = FALSE,   sf_para = list() )"},{"path":"https://yanglabhkust.github.io/mfair/reference/fitGreedy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit the MFAI model using greedy algorithm. — fitGreedy","text":"object MFAIR object. K_max Integer. maximum rank allowed MFAI model. learning_rate Numeric. Parameter gradient boosting part. minsplit Integer. Parameter gradient boosting part. minbucket Integer. Parameter gradient boosting part. maxdepth Integer. Parameter gradient boosting part. other_tree_para list containing parameters gradient boosting part. See rpart::rpart.control() details. null_check Logical. TRUE, mfair check whether current factor close zero. check performed successful, factor deleted returned fit. tol_snr Numeric. convergence criterion determine inferred rank data. verbose_greedy Logical. Whether display detailed information fitting model. save_init Logical. Whether save initialization model. sf_para list containing parameters fitting single factor MFAI model. See fitSFFully(), fitSFMissing(), fitSFSparse() details.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/fitGreedy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit the MFAI model using greedy algorithm. — fitGreedy","text":"MFAIR object containing information fitted MFAI model using greedy algorithm.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/fitSFFully.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit the single factor MFAI model with fully observed main data matrix. — fitSFFully","title":"Fit the single factor MFAI model with fully observed main data matrix. — fitSFFully","text":"Fit single factor MFAI model fully observed main data matrix.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/fitSFFully.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit the single factor MFAI model with fully observed main data matrix. — fitSFFully","text":"","code":"fitSFFully(   Y,   X,   init,   learning_rate,   tree_parameters,   stage1 = TRUE,   iter_max = 5000,   tol_stage1 = 0.1,   tol_stage2 = 1e-06,   verbose_sf = TRUE,   verbose_loop = TRUE,   save_tree_list = TRUE )"},{"path":"https://yanglabhkust.github.io/mfair/reference/fitSFFully.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit the single factor MFAI model with fully observed main data matrix. — fitSFFully","text":"Y Main data matrix. X data.frame containing auxiliary information. init MFAIRSingleFactor object containing initial parameters single factor MAFI model. learning_rate Numeric. Parameter gradient boosting part. tree_parameters list containing parameters gradient boosting part. stage1 Logical. Whether perform fitting algorithm stage1. greedy algorithm needs backfitting algorithm need. iter_max Integer. Maximum iterations allowed. tol_stage1 Numeric. Convergence criterion first step. tol_stage2 Numeric. Convergence criterion first step. verbose_sf Logical. Whether display detailed information. verbose_loop Logical. Whether display detailed information looping. save_tree_list Logical. Whether save tree list.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/fitSFFully.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit the single factor MFAI model with fully observed main data matrix. — fitSFFully","text":"MFAIRSingleFactor object containing information fitted single factor MFAI model.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/fitSFMissing.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit the single factor MFAI model with partially observed main data matrix. — fitSFMissing","title":"Fit the single factor MFAI model with partially observed main data matrix. — fitSFMissing","text":"Fit single factor MFAI model partially observed main data matrix.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/fitSFMissing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit the single factor MFAI model with partially observed main data matrix. — fitSFMissing","text":"","code":"fitSFMissing(   Y,   X,   init,   obs_indices,   learning_rate,   tree_parameters,   stage1 = TRUE,   iter_max = 5000,   tol_stage1 = 0.1,   tol_stage2 = 1e-06,   verbose_sf = TRUE,   verbose_loop = TRUE,   save_tree_list = TRUE )"},{"path":"https://yanglabhkust.github.io/mfair/reference/fitSFMissing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit the single factor MFAI model with partially observed main data matrix. — fitSFMissing","text":"Y Main data matrix. X data.frame containing auxiliary information. init MFAIRSingleFactor object containing initial parameters single factor MAFI model. obs_indices Indices observed entries main data matrix. learning_rate Numeric. Parameter gradient boosting part. tree_parameters list containing parameters gradient boosting part. stage1 Logical. Whether perform fitting algorithm stage1. greedy algorithm needs backfitting algorithm need. iter_max Integer. Maximum iterations allowed. tol_stage1 Numeric. Convergence criterion first step. tol_stage2 Numeric. Convergence criterion first step. verbose_sf Logical. Whether display detailed information. verbose_loop Logical. Whether display detailed information looping. save_tree_list Logical. Whether save tree list.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/fitSFMissing.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit the single factor MFAI model with partially observed main data matrix. — fitSFMissing","text":"MFAIRSingleFactor object containing information fitted single factor MFAI model.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/fitSFSparse.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit the single factor MFAI model with partially observed main data matrix stored in the sparse mode. — fitSFSparse","title":"Fit the single factor MFAI model with partially observed main data matrix stored in the sparse mode. — fitSFSparse","text":"Fit single factor MFAI model partially observed main data matrix stored sparse mode.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/fitSFSparse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit the single factor MFAI model with partially observed main data matrix stored in the sparse mode. — fitSFSparse","text":"","code":"fitSFSparse(   Y,   X,   init,   obs_indices,   learning_rate,   tree_parameters,   stage1 = TRUE,   iter_max = 5000,   tol_stage1 = 0.1,   tol_stage2 = 1e-06,   verbose_sf = TRUE,   verbose_loop = TRUE,   save_tree_list = TRUE )"},{"path":"https://yanglabhkust.github.io/mfair/reference/fitSFSparse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit the single factor MFAI model with partially observed main data matrix stored in the sparse mode. — fitSFSparse","text":"Y Main data matrix. X data.frame containing auxiliary information. init MFAIRSingleFactor object containing initial parameters single factor MAFI model. obs_indices Indices observed entries main data matrix. learning_rate Numeric. Parameter gradient boosting part. tree_parameters list containing parameters gradient boosting part. stage1 Logical. Whether perform fitting algorithm stage1. greedy algorithm needs backfitting algorithm need. iter_max Integer. Maximum iterations allowed. tol_stage1 Numeric. Convergence criterion first step. tol_stage2 Numeric. Convergence criterion first step. verbose_sf Logical. Whether display detailed information. verbose_loop Logical. Whether display detailed information looping. save_tree_list Logical. Whether save tree list.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/fitSFSparse.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit the single factor MFAI model with partially observed main data matrix stored in the sparse mode. — fitSFSparse","text":"MFAIRSingleFactor object containing information fitted single factor MFAI model.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/getELBO.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the evidence lower bound (ELBO) for fitted single factor MFAI model. — getELBO","title":"Compute the evidence lower bound (ELBO) for fitted single factor MFAI model. — getELBO","text":"Compute evidence lower bound (ELBO) fitted single factor MFAI model.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/getELBO.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the evidence lower bound (ELBO) for fitted single factor MFAI model. — getELBO","text":"","code":"getELBO(Y, object, obs_indices = NULL)"},{"path":"https://yanglabhkust.github.io/mfair/reference/getELBO.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the evidence lower bound (ELBO) for fitted single factor MFAI model. — getELBO","text":"Y Observed main data matrix. object MFAIRSingleFactor object containing information fitted single factor MFAI model. obs_indices Indices observed entries main data matrix Y needs specified Y stored sparse mode. default value NULL.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/getELBO.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the evidence lower bound (ELBO) for fitted single factor MFAI model. — getELBO","text":"Numeric. ELBO.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/getImportance.html","id":null,"dir":"Reference","previous_headings":"","what":"Get importance measures of auxiliary covariates. — getImportance","title":"Get importance measures of auxiliary covariates. — getImportance","text":"Get importance measures auxiliary covariates.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/getImportance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get importance measures of auxiliary covariates. — getImportance","text":"","code":"getImportance(object, which_factors = seq_len(object@K))"},{"path":"https://yanglabhkust.github.io/mfair/reference/getImportance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get importance measures of auxiliary covariates. — getImportance","text":"object MFAIR object. which_factors factors, .e., fitted functions evaluated. K factors evaluated default.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/getImportance.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get importance measures of auxiliary covariates. — getImportance","text":"Importance score matrix. row auxiliary covariate column factor.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/getImportance.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get importance measures of auxiliary covariates. — getImportance","text":"rpart::rpart() function automatically change special characters variable names dot may cause inconsistency errors. Please ensure auxiliary covariates' names contain special characters want use function.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/getImportanceSF.html","id":null,"dir":"Reference","previous_headings":"","what":"Get importance measures of auxiliary covariates in a single factor. — getImportanceSF","title":"Get importance measures of auxiliary covariates in a single factor. — getImportanceSF","text":"Get importance measures auxiliary covariates single factor.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/getImportanceSF.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get importance measures of auxiliary covariates in a single factor. — getImportanceSF","text":"","code":"getImportanceSF(tree_list, variables_names)"},{"path":"https://yanglabhkust.github.io/mfair/reference/getImportanceSF.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get importance measures of auxiliary covariates in a single factor. — getImportanceSF","text":"tree_list fitted function represented list trees. variables_names names auxiliary covariates.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/getImportanceSF.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get importance measures of auxiliary covariates in a single factor. — getImportanceSF","text":"Importance score vector. entry importance score one auxiliary covariate.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/initSF.html","id":null,"dir":"Reference","previous_headings":"","what":"Initialize the parameters for the single factor MAFI model. — initSF","title":"Initialize the parameters for the single factor MAFI model. — initSF","text":"Initialize parameters single factor MAFI model.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/initSF.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initialize the parameters for the single factor MAFI model. — initSF","text":"","code":"initSF(Y, Y_missing, Y_sparse, n_obs)"},{"path":"https://yanglabhkust.github.io/mfair/reference/initSF.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initialize the parameters for the single factor MAFI model. — initSF","text":"Y Main data matrix. Y_missing Logical. Whether main data matrix partially observed. Y_sparse Logical. Whether main data matrix sparse mode. n_obs Integer. Total number observed entries.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/initSF.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initialize the parameters for the single factor MAFI model. — initSF","text":"MFAIRSingleFactor object containing initial parameters single factor MAFI model.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/matrixORdgCMatrix-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Define the matrixORdgCMatrix class as the union of matrix and Matrix::dgCMatrix. — matrixORdgCMatrix-class","title":"Define the matrixORdgCMatrix class as the union of matrix and Matrix::dgCMatrix. — matrixORdgCMatrix-class","text":"Define matrixORdgCMatrix class union matrix Matrix::dgCMatrix.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/ml100k.html","id":null,"dir":"Reference","previous_headings":"","what":"MovieLens 100K data. — ml100k","title":"MovieLens 100K data. — ml100k","text":"list containing movie rating, user information, movie genres MovieLens 100K data.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/ml100k.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MovieLens 100K data. — ml100k","text":"","code":"ml100k"},{"path":"https://yanglabhkust.github.io/mfair/reference/ml100k.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"MovieLens 100K data. — ml100k","text":"list containing movie rating matrix, user information data frame, movie genres data frame. rating Movie rating matrix 943 users 1,682 movies (0–5 star rating). user data frame containing information 943 users, three columns correspond age, gender, occupation respectively. genre binary data frame containing genre information 1,682 movies, column corresponds one specific genre.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/ml100k.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"MovieLens 100K data. — ml100k","text":"https://grouplens.org/datasets/movielens/100k/","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/neocortex.html","id":null,"dir":"Reference","previous_headings":"","what":"Human brain gene expression data. — neocortex","title":"Human brain gene expression data. — neocortex","text":"list containing bulk gene expression human brain tissue sample information.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/neocortex.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Human brain gene expression data. — neocortex","text":"","code":"neocortex"},{"path":"https://yanglabhkust.github.io/mfair/reference/neocortex.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Human brain gene expression data. — neocortex","text":"list containing gene expression data matrix tissue sample information data frame. expression Bulk gene expression matrix 886 tissue samples neocortex region 2,000 genes highest differential stability. sample_info data frame containing information 886 bulk tissue samples neocortex region. four columns correspond sample ID, neocortex area, hemisphere, time periods respectively.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/neocortex.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Human brain gene expression data. — neocortex","text":"https://hbatlas.org/pages/data","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/predict-MFAIR-method.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction function for MFAIR object. — predict,MFAIR-method","title":"Prediction function for MFAIR object. — predict,MFAIR-method","text":"Prediction function MFAIR object.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/predict-MFAIR-method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction function for MFAIR object. — predict,MFAIR-method","text":"","code":"# S4 method for MFAIR predict(object, which_factors = seq_len(object@K), add_mean = TRUE)"},{"path":"https://yanglabhkust.github.io/mfair/reference/predict-MFAIR-method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction function for MFAIR object. — predict,MFAIR-method","text":"object model object prediction desired. which_factors factors, .e., columns Z W, used make prediction. K factors used default. add_mean Logical. Indicate whether add mean value. default value TRUE.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/predict-MFAIR-method.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prediction function for MFAIR object. — predict,MFAIR-method","text":"Predicted matrix dimension Y.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/predict-MFAIRSingleFactor-method.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction function for MFAIRSingleFactor object. — predict,MFAIRSingleFactor-method","title":"Prediction function for MFAIRSingleFactor object. — predict,MFAIRSingleFactor-method","text":"Prediction function MFAIRSingleFactor object.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/predict-MFAIRSingleFactor-method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction function for MFAIRSingleFactor object. — predict,MFAIRSingleFactor-method","text":"","code":"# S4 method for MFAIRSingleFactor predict(object)"},{"path":"https://yanglabhkust.github.io/mfair/reference/predict-MFAIRSingleFactor-method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction function for MFAIRSingleFactor object. — predict,MFAIRSingleFactor-method","text":"object model object prediction desired.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/predict-MFAIRSingleFactor-method.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prediction function for MFAIRSingleFactor object. — predict,MFAIRSingleFactor-method","text":"Predicted matrix dimension Y.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/predictFX.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction function for fitted functions. — predictFX","title":"Prediction function for fitted functions. — predictFX","text":"Prediction function fitted functions.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/predictFX.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction function for fitted functions. — predictFX","text":"","code":"predictFX(object, newdata, which_factors = seq_len(object@K))"},{"path":"https://yanglabhkust.github.io/mfair/reference/predictFX.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction function for fitted functions. — predictFX","text":"object MFAIR object. newdata Data frame containing values predictions required. which_factors factors, .e., fitted functions used. K factors used default.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/predictFX.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prediction function for fitted functions. — predictFX","text":"matrix containing predicted F(X). row new sample column factor.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/predictFXSF.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction function for fitted function F() in single factor. — predictFXSF","title":"Prediction function for fitted function F() in single factor. — predictFXSF","text":"Prediction function fitted function F() single factor.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/predictFXSF.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction function for fitted function F() in single factor. — predictFXSF","text":"","code":"predictFXSF(tree_list, newdata, learning_rate)"},{"path":"https://yanglabhkust.github.io/mfair/reference/predictFXSF.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction function for fitted function F() in single factor. — predictFXSF","text":"tree_list fitted function represented list trees. newdata Data frame containing values predictions required. learning_rate Numeric. learning rate gradient boosting part.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/predictFXSF.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prediction function for fitted function F() in single factor. — predictFXSF","text":"vector containing predicted F(X). entry corresponds new sample.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/projSparse.html","id":null,"dir":"Reference","previous_headings":"","what":"Project a matrix with given indices and store the result in the sparse mode. — projSparse","title":"Project a matrix with given indices and store the result in the sparse mode. — projSparse","text":"Project matrix given indices store result sparse mode.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/projSparse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Project a matrix with given indices and store the result in the sparse mode. — projSparse","text":"","code":"projSparse(Y, obs_indices)"},{"path":"https://yanglabhkust.github.io/mfair/reference/projSparse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Project a matrix with given indices and store the result in the sparse mode. — projSparse","text":"Y matrix projected. obs_indices matrix containing 1-based indices observed entries matrix Y. first column represents row second column represents column.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/projSparse.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Project a matrix with given indices and store the result in the sparse mode. — projSparse","text":"dgCMatrix containing projection result.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/softImputeCrossVal.html","id":null,"dir":"Reference","previous_headings":"","what":"Cross-validation for softImpute. — softImputeCrossVal","title":"Cross-validation for softImpute. — softImputeCrossVal","text":"Cross-validation softImpute.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/softImputeCrossVal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cross-validation for softImpute. — softImputeCrossVal","text":"","code":"softImputeCrossVal(   Y,   rank_max = NULL,   lambda_range = NULL,   nfold = 10,   para_length = 100 )"},{"path":"https://yanglabhkust.github.io/mfair/reference/softImputeCrossVal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cross-validation for softImpute. — softImputeCrossVal","text":"Y matrix. main data matrix N samples M features. rank_max integer. maximum rank allowed. lambda_range vector containing minimal maximal value parameter lambda. nfold integer. total number validation sets. para_length integer. total number parameter lambda.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/softImputeCrossVal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cross-validation for softImpute. — softImputeCrossVal","text":"list containing cross-validation results.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/softImputeCrossValSparse.html","id":null,"dir":"Reference","previous_headings":"","what":"Sparse version of cross-validation for softImpute. — softImputeCrossValSparse","title":"Sparse version of cross-validation for softImpute. — softImputeCrossValSparse","text":"Sparse version cross-validation softImpute.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/softImputeCrossValSparse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sparse version of cross-validation for softImpute. — softImputeCrossValSparse","text":"","code":"softImputeCrossValSparse(   Y,   rank_max = NULL,   lambda_range = NULL,   nfold = 10,   para_length = 100 )"},{"path":"https://yanglabhkust.github.io/mfair/reference/softImputeCrossValSparse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sparse version of cross-validation for softImpute. — softImputeCrossValSparse","text":"Y Matrix::dgCMatrix. main data matrix N samples M features. rank_max integer. maximum rank allowed. lambda_range vector containing minimal maximal value parameter lambda. nfold integer. total number validation sets. para_length integer. total number parameter lambda.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/softImputeCrossValSparse.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sparse version of cross-validation for softImpute. — softImputeCrossValSparse","text":"list containing cross-validation results.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/updateMFAIR.html","id":null,"dir":"Reference","previous_headings":"","what":"Update the k-th factor of the MFAIR object in the backfitting algorithm. — updateMFAIR","title":"Update the k-th factor of the MFAIR object in the backfitting algorithm. — updateMFAIR","text":"Update k-th factor MFAIR object backfitting algorithm.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/updateMFAIR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update the k-th factor of the MFAIR object in the backfitting algorithm. — updateMFAIR","text":"","code":"updateMFAIR(object, object_sf, k)"},{"path":"https://yanglabhkust.github.io/mfair/reference/updateMFAIR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update the k-th factor of the MFAIR object in the backfitting algorithm. — updateMFAIR","text":"object MFAIR object containing initial information K-factor MFAI model. object_sf MFAIRSingleFactor containing information newly fitted single factor MFAI model. k Integer. fator updated.","code":""},{"path":"https://yanglabhkust.github.io/mfair/reference/updateMFAIR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Update the k-th factor of the MFAIR object in the backfitting algorithm. — updateMFAIR","text":"MFAIR object containing information new fitted single factor.","code":""}]