diff --git a/developer/index.html b/developer/index.html
index 0f55ba0f9..a5d7f1f04 100644
--- a/developer/index.html
+++ b/developer/index.html
@@ -96,23 +96,23 @@ <h3 id="developers-guide">Developer’s Guide</h3>
 </div>
 
 <div class="tutorial">
-<a class="title" href="/developer/automated_builds/"><p>Automated building and testing in RevBayes</p>
+<a class="title" href="/developer/docker/"><p>Using Docker with RevBayes</p>
 </a>
-<p class="subtitle"><p>Quick automated feedback on code changes</p>
+<p class="subtitle"><p>Containerized debugging and executeables</p>
 </p>
 </div>
 
 <div class="tutorial">
-<a class="title" href="/developer/best_practices/"><p>Best practices in RevBayes</p>
+<a class="title" href="/developer/tests/"><p>Integration Tests in RevBayes</p>
 </a>
-<p class="subtitle"><p>Coding and documentation guidelines</p>
+<p class="subtitle"><p>Ensuring that code changes did not unexpectedly alter the software</p>
 </p>
 </div>
 
 <div class="tutorial">
-<a class="title" href="/developer/docker/"><p>Using Docker with RevBayes</p>
+<a class="title" href="/developer/tutorial/"><p>Contributing a RevBayes tutorial</p>
 </a>
-<p class="subtitle"><p>Containerized debugging and executeables</p>
+<p class="subtitle"><p>How to write and host a tutorial on this website</p>
 </p>
 </div>
 
@@ -124,16 +124,16 @@ <h3 id="developers-guide">Developer’s Guide</h3>
 </div>
 
 <div class="tutorial">
-<a class="title" href="/developer/tests/"><p>Integration Tests in RevBayes</p>
+<a class="title" href="/developer/best_practices/"><p>Best practices in RevBayes</p>
 </a>
-<p class="subtitle"><p>Ensuring that code changes did not unexpectedly alter the software</p>
+<p class="subtitle"><p>Coding and documentation guidelines</p>
 </p>
 </div>
 
 <div class="tutorial">
-<a class="title" href="/developer/tutorial/"><p>Contributing a RevBayes tutorial</p>
+<a class="title" href="/developer/automated_builds/"><p>Automated building and testing in RevBayes</p>
 </a>
-<p class="subtitle"><p>How to write and host a tutorial on this website</p>
+<p class="subtitle"><p>Quick automated feedback on code changes</p>
 </p>
 </div>
 
diff --git a/developer/setup/netbeans.html b/developer/setup/netbeans.html
index 644be8f13..873da7bd0 100644
--- a/developer/setup/netbeans.html
+++ b/developer/setup/netbeans.html
@@ -55,7 +55,7 @@
 	<h1 class="maintitle"></h1>
 	
 	<h4 class="authors"></h4>
-  <h5>Last modified on March 11, 2018</h5>
+  <h5>Last modified on March 10, 2018</h5>
 </div>
 
 <p>Conventions: File paths are relative to the repository root.</p>
diff --git a/developer/setup/vim.html b/developer/setup/vim.html
index 1dcb83e9b..1e3e87b91 100644
--- a/developer/setup/vim.html
+++ b/developer/setup/vim.html
@@ -55,7 +55,7 @@
 	<h1 class="maintitle">Setting up vim for RevBayes development</h1>
 	
 	<h4 class="authors"></h4>
-  <h5>Last modified on March 11, 2018</h5>
+  <h5>Last modified on March 10, 2018</h5>
 </div>
 
 <p><a href="http://www.vim.org">Vim</a> is a text editor that some people love.
diff --git a/developer/tutorial/index.html b/developer/tutorial/index.html
index feb4d7a12..bdf72afed 100644
--- a/developer/tutorial/index.html
+++ b/developer/tutorial/index.html
@@ -55,7 +55,7 @@
 	<h1 class="maintitle">Contributing a RevBayes tutorial</h1>
 	
 	<h4 class="authors"></h4>
-  <h5>Last modified on May 31, 2019</h5>
+  <h5>Last modified on May 30, 2019</h5>
 </div>
 
 <div class="sidebar no-print">
diff --git a/tutorials/biogeo/biogeo_dating.html b/tutorials/biogeo/biogeo_dating.html
index 20ffede86..00089dfa6 100644
--- a/tutorials/biogeo/biogeo_dating.html
+++ b/tutorials/biogeo/biogeo_dating.html
@@ -55,7 +55,7 @@
 	<h1 class="maintitle">Biogeographic Dating of Divergence Times</h1>
 	<h3 class="subtitle">Estimating divergence times with molecular, biogeographic, and paleogeographic evidence under the Dispersal-Extirpation-Cladogenesis (DEC) model</h3>
 	<h4 class="authors">Michael J. Landis</h4>
-  <h5>Last modified on February 28, 2022</h5>
+  <h5>Last modified on February 27, 2022</h5>
 </div>
 
 
diff --git a/tutorials/biogeo/biogeo_simple.html b/tutorials/biogeo/biogeo_simple.html
index 698cb2e45..c9e0f6613 100644
--- a/tutorials/biogeo/biogeo_simple.html
+++ b/tutorials/biogeo/biogeo_simple.html
@@ -55,7 +55,7 @@
 	<h1 class="maintitle">Simple Phylogenetic Analysis of Historical Biogeography</h1>
 	<h3 class="subtitle">Estimating ancestral ranges using the Dispersal-Extirpation-Cladogenesis (DEC) model</h3>
 	<h4 class="authors">Michael J. Landis</h4>
-  <h5>Last modified on February 28, 2022</h5>
+  <h5>Last modified on February 27, 2022</h5>
 </div>
 
 
diff --git a/tutorials/chromo/index.html b/tutorials/chromo/index.html
index 37ac587a8..bfeb832bf 100644
--- a/tutorials/chromo/index.html
+++ b/tutorials/chromo/index.html
@@ -55,7 +55,7 @@
 	<h1 class="maintitle">Chromosome Evolution</h1>
 	<h3 class="subtitle">Modeling chromosome evolution with ChromEvol, BiChroM, and ChromoSSE</h3>
 	<h4 class="authors">William A. Freyman and Sebastian Höhna</h4>
-  <h5>Last modified on February 28, 2022</h5>
+  <h5>Last modified on February 27, 2022</h5>
 </div>
 
 
diff --git a/tutorials/cont_traits/relaxed_bm.html b/tutorials/cont_traits/relaxed_bm.html
index c95a91603..813725e63 100644
--- a/tutorials/cont_traits/relaxed_bm.html
+++ b/tutorials/cont_traits/relaxed_bm.html
@@ -99,14 +99,14 @@ <h2>Data files and scripts</h2>
         
         
         
-          <li><a href="/tutorials/biogeo/data/primates_tree.nex">primates_tree.nex</a></li>
-        
           <li><a href="/tutorials/divrate/data/primates_tree.nex">primates_tree.nex</a></li>
         
           <li><a href="/tutorials/intro/data/primates_tree.nex">primates_tree.nex</a></li>
         
           <li><a href="/tutorials/morph_ase/data/primates_tree.nex">primates_tree.nex</a></li>
         
+          <li><a href="/tutorials/biogeo/data/primates_tree.nex">primates_tree.nex</a></li>
+        
         </ul>
     
         
@@ -121,10 +121,10 @@ <h2>Data files and scripts</h2>
         
           <li><a href="/tutorials/cont_traits/data/primates_cont_traits.nex">primates_cont_traits.nex</a></li>
         
-          <li><a href="/tutorials/cont_traits/data/primates_tree.nex">primates_tree.nex</a></li>
-        
           <li><a href="/tutorials/sse/data/primates_tree.nex">primates_tree.nex</a></li>
         
+          <li><a href="/tutorials/cont_traits/data/primates_tree.nex">primates_tree.nex</a></li>
+        
         </ul>
     
 </blockquote>
diff --git a/tutorials/cont_traits/relaxed_ou.html b/tutorials/cont_traits/relaxed_ou.html
index 30065d08f..72b9da274 100644
--- a/tutorials/cont_traits/relaxed_ou.html
+++ b/tutorials/cont_traits/relaxed_ou.html
@@ -99,14 +99,14 @@ <h2>Data files and scripts</h2>
         
         
         
-          <li><a href="/tutorials/biogeo/data/primates_tree.nex">primates_tree.nex</a></li>
-        
           <li><a href="/tutorials/divrate/data/primates_tree.nex">primates_tree.nex</a></li>
         
           <li><a href="/tutorials/intro/data/primates_tree.nex">primates_tree.nex</a></li>
         
           <li><a href="/tutorials/morph_ase/data/primates_tree.nex">primates_tree.nex</a></li>
         
+          <li><a href="/tutorials/biogeo/data/primates_tree.nex">primates_tree.nex</a></li>
+        
         </ul>
     
         
@@ -121,10 +121,10 @@ <h2>Data files and scripts</h2>
         
           <li><a href="/tutorials/cont_traits/data/primates_cont_traits.nex">primates_cont_traits.nex</a></li>
         
-          <li><a href="/tutorials/cont_traits/data/primates_tree.nex">primates_tree.nex</a></li>
-        
           <li><a href="/tutorials/sse/data/primates_tree.nex">primates_tree.nex</a></li>
         
+          <li><a href="/tutorials/cont_traits/data/primates_tree.nex">primates_tree.nex</a></li>
+        
         </ul>
     
 </blockquote>
diff --git a/tutorials/cont_traits/simple_bm.html b/tutorials/cont_traits/simple_bm.html
index 9f96e4027..a98daceec 100644
--- a/tutorials/cont_traits/simple_bm.html
+++ b/tutorials/cont_traits/simple_bm.html
@@ -97,14 +97,14 @@ <h2>Data files and scripts</h2>
         
         
         
-          <li><a href="/tutorials/biogeo/data/primates_tree.nex">primates_tree.nex</a></li>
-        
           <li><a href="/tutorials/divrate/data/primates_tree.nex">primates_tree.nex</a></li>
         
           <li><a href="/tutorials/intro/data/primates_tree.nex">primates_tree.nex</a></li>
         
           <li><a href="/tutorials/morph_ase/data/primates_tree.nex">primates_tree.nex</a></li>
         
+          <li><a href="/tutorials/biogeo/data/primates_tree.nex">primates_tree.nex</a></li>
+        
         </ul>
     
         
@@ -121,10 +121,10 @@ <h2>Data files and scripts</h2>
         
           <li><a href="/tutorials/cont_traits/data/primates_cont_traits.nex">primates_cont_traits.nex</a></li>
         
-          <li><a href="/tutorials/cont_traits/data/primates_tree.nex">primates_tree.nex</a></li>
-        
           <li><a href="/tutorials/sse/data/primates_tree.nex">primates_tree.nex</a></li>
         
+          <li><a href="/tutorials/cont_traits/data/primates_tree.nex">primates_tree.nex</a></li>
+        
         </ul>
     
 </blockquote>
diff --git a/tutorials/cont_traits/simple_ou.html b/tutorials/cont_traits/simple_ou.html
index 13cf54154..69f77c6fa 100644
--- a/tutorials/cont_traits/simple_ou.html
+++ b/tutorials/cont_traits/simple_ou.html
@@ -97,14 +97,14 @@ <h2>Data files and scripts</h2>
         
         
         
-          <li><a href="/tutorials/biogeo/data/primates_tree.nex">primates_tree.nex</a></li>
-        
           <li><a href="/tutorials/divrate/data/primates_tree.nex">primates_tree.nex</a></li>
         
           <li><a href="/tutorials/intro/data/primates_tree.nex">primates_tree.nex</a></li>
         
           <li><a href="/tutorials/morph_ase/data/primates_tree.nex">primates_tree.nex</a></li>
         
+          <li><a href="/tutorials/biogeo/data/primates_tree.nex">primates_tree.nex</a></li>
+        
         </ul>
     
         
@@ -123,10 +123,10 @@ <h2>Data files and scripts</h2>
         
           <li><a href="/tutorials/cont_traits/data/primates_cont_traits.nex">primates_cont_traits.nex</a></li>
         
-          <li><a href="/tutorials/cont_traits/data/primates_tree.nex">primates_tree.nex</a></li>
-        
           <li><a href="/tutorials/sse/data/primates_tree.nex">primates_tree.nex</a></li>
         
+          <li><a href="/tutorials/cont_traits/data/primates_tree.nex">primates_tree.nex</a></li>
+        
         </ul>
     
 </blockquote>
diff --git a/tutorials/divrate/branch_specific.html b/tutorials/divrate/branch_specific.html
index 2567c18fe..2d44c8f3b 100644
--- a/tutorials/divrate/branch_specific.html
+++ b/tutorials/divrate/branch_specific.html
@@ -97,10 +97,10 @@ <h2>Data files and scripts</h2>
         
           <li><a href="/tutorials/biogeo/data/primates_tree.nex">primates_tree.nex</a></li>
         
-          <li><a href="/tutorials/divrate/data/primates_tree.nex">primates_tree.nex</a></li>
-        
           <li><a href="/tutorials/intro/data/primates_tree.nex">primates_tree.nex</a></li>
         
+          <li><a href="/tutorials/divrate/data/primates_tree.nex">primates_tree.nex</a></li>
+        
           <li><a href="/tutorials/morph_ase/data/primates_tree.nex">primates_tree.nex</a></li>
         
         </ul>
diff --git a/tutorials/divrate/data/crocs_taxa.txt b/tutorials/divrate/data/crocs_taxa.txt
deleted file mode 100644
index a18870546..000000000
--- a/tutorials/divrate/data/crocs_taxa.txt
+++ /dev/null
@@ -1,143 +0,0 @@
-taxon	age
-Eothoracosaurus	68.3215
-Thoracosaurus	60.9215
-Eosuchus	52.2
-Gryposuchus	13.885
-Piscogavialis	6.289
-Gavialis	0
-Eogavialis	35.55
-Borealosuchus_formidabilis	58.75
-Borealosuchus_threensis	66.15
-Borealosuchus_sternbergii	64.6715
-Boverisuchus_vorax	45.35
-Boverisuchus_magnifrons	44.5
-Planocrania_datengensis	58.785
-Planocrania_hengdongensis	57.25
-Alligator_mississippiensis	0
-Alligator_thompsoni	18.2
-Alligator_prenasalis	35.55
-Allognathosuchus	43.1
-Eocaiman	44.85
-Paleosuchus	0
-Purussaurus	9.566
-Mourasuchus	10.651
-Melanosuchus	0
-Caiman_crocodilis	0
-Caiman_yacare	0
-Brachychampsa	68.3215
-Diplocynodon	42.1
-Leidyosuchus	77.75
-Prodiplocynodon	68.3215
-Crocodylus_affinis	48.25
-Brachyuranochampsa	43.3
-Tomistoma	0
-Gavialosuchus_eggenburgensis	18.2
-Gavialosuchus_americanus	7.1
-Thecachampsa	25.715
-Dollosuchoides	44.5
-Maroccosuchus	52.2
-Qunikana	20.004
-Kambara	41.25
-Australosuchus	22.185
-Euthecodon	19.5
-Voay	0.006
-Osteolaemus	0
-Mecistops	0
-Crocodylus_porosus	0
-Crocodylus_siamensis	0
-Crocodylus_rhombifer	0
-Crocodylus_acutus	0
-Crocodylus_niloticus	0
-Allodaposuchus_precedens	68.3215
-Lohuecosuchus	68.3215
-Arenysuchus	68.3215
-Hylaeochampsa	126.23
-Acynodon_adriaticus	75.4715
-Acynodon_iberoccitanus	68.3215
-Paralligator	94.5
-Shamosuchus	77.75
-Bernissartia	135.475
-Calsoyasuchus	189.75
-Goniopholis_simus	142.85
-Goniopholis_baryglypheaus	153.25
-Eutretauranosuchus	153.25
-Elosuchus	97
-Vectisuchus	126.23
-Cerrejonisuchus	60.2
-Anthracosuchus	58.75
-Chenanisuchus	63.2
-Guarinisuchus	63.8715
-Rhabdognathus	66.15
-Atlantosuchus	63.8715
-Dyrosaurus	52.2
-Sokotosuchus	68.3215
-Meridiosaurus	150.6
-Sarcosuchus	106.15
-Pholidosaurus	142.85
-Oceanosuchus	97
-Terminonaris_browni	91.8
-Terminonaris_robusta	91.8
-Theriosuchus_pusilis	142.85
-Theriosuchus_guimarotae	153.25
-Mahajangasuchus	68.3215
-Kaprosuchus	97
-Stolokrosuchus	112.575
-Lomasuchus	90.05
-Uberabasuchus	68.3215
-Peirosaurus	68.3215
-Araripesuchus_gomesii	110.8
-Araripesuchus_patagonicus	97
-Urugaysuchus	105.175
-Libycosuchus	97
-Simosuchus	68.3215
-Malawisuchus	119.025
-Notosuchus	85.35
-Mariliasuchus	75.4715
-Sphagesaurus	77.75
-Chimaerasuchus	112.575
-Comahuesuchus	85.35
-Pissarrachampsa	80.1715
-Stratiotosuchus	75.4715
-Hsisosuchus_dashanpuensis	166.4
-Hsisosuchus_chungkingensis	153.35
-Protosuchus	199.05
-Orthosuchus	195.6
-Edentosuchus	111.08
-Sichuanosuchus	122.6
-Shantungosuchus	122.6
-Zosuchus	77.75
-Fruitachampsa	150.6
-Zaraasuchus	77.75
-Gobiosuchus	77.75
-Pelagosaurus	182.5
-Teleidosaurus_calvadosi	168.15
-Eoneustes_bathonicus	166.2
-Eoneustes_gaudryi	166.2
-Zoneait	172
-Metriorhynchus_superciliosus	157.75
-Metriorhynchus_leedsi	157.75
-Cricosaurus_suevicus	153.25
-Metriorhynchus_durobrivensis	162.95
-Metriorhynchus_casamiquelai	162.95
-Geosaurus_grandis	148.15
-Geosaurus_giganteus	148.15
-Dakosaurus_maximus	148.15
-Dakosaurus_andiniensis	142.85
-Steneosaurus_gracilirostris	179.3
-Teleosaurus	159.25
-Peipehsuchus	178.4
-Thai_teleosaurid	148.55
-Platysuchus_multiscrobiculatus	182.5
-Steneosaurus_bollensis	182.5
-Steneosaurus_brevior	182.5
-Steneosaurus_durobrivensis	162.95
-Machimosaurus	153.25
-Steneosaurus_leedsi	162.95
-Kayentasuchus	189.75
-Junggarsuchus	162.95
-Almadasuchus	158.45
-Dibothrosuchus	192.6
-Sphenosuchus	195.6
-Hesperosuchus	213.55
-Postosuchus	213.55
-Gracilisuchus	238.5
diff --git a/tutorials/divrate/div_rate_intro.html b/tutorials/divrate/div_rate_intro.html
index 0a8a8ad6f..d0c1c5990 100644
--- a/tutorials/divrate/div_rate_intro.html
+++ b/tutorials/divrate/div_rate_intro.html
@@ -362,14 +362,14 @@ <h3 class="subsection" id="the-birth-death-branching-process">The birth-death br
 <a href="https://doi.org/10.1093/bioinformatics/btt153">10.1093/bioinformatics/btt153</a>
 
 </li>
-<li><span id="Hoehna2014a">Höhna S. 2014. Likelihood Inference of Non-Constant Diversification Rates with Incomplete Taxon Sampling. PLoS One. 9:e84184.</span>
+<li><span id="Hoehna2015a">Höhna S. 2015. The time-dependent reconstructed evolutionary process with a key-role for mass-extinction events. Journal of Theoretical Biology. 380:321–331.</span>
 
-<a href="https://doi.org/10.1371/journal.pone.0084184">10.1371/journal.pone.0084184</a>
+<a href="https://doi.org/http://dx.doi.org/10.1016/j.jtbi.2015.06.005">http://dx.doi.org/10.1016/j.jtbi.2015.06.005</a>
 
 </li>
-<li><span id="Hoehna2015a">Höhna S. 2015. The time-dependent reconstructed evolutionary process with a key-role for mass-extinction events. Journal of Theoretical Biology. 380:321–331.</span>
+<li><span id="Hoehna2014a">Höhna S. 2014. Likelihood Inference of Non-Constant Diversification Rates with Incomplete Taxon Sampling. PLoS One. 9:e84184.</span>
 
-<a href="https://doi.org/http://dx.doi.org/10.1016/j.jtbi.2015.06.005">http://dx.doi.org/10.1016/j.jtbi.2015.06.005</a>
+<a href="https://doi.org/10.1371/journal.pone.0084184">10.1371/journal.pone.0084184</a>
 
 </li>
 <li><span id="Hoehna2019">Höhna S., Freyman W.A., Nolen Z., Huelsenbeck J.P., May M.R., Moore B.R. 2019. A Bayesian Approach for Estimating Branch-Specific Speciation and Extinction Rates. bioRxiv.</span>
diff --git a/tutorials/divrate/ebd.html b/tutorials/divrate/ebd.html
index f0e0bd84f..8640b6d52 100644
--- a/tutorials/divrate/ebd.html
+++ b/tutorials/divrate/ebd.html
@@ -97,18 +97,18 @@ <h2>Data files and scripts</h2>
         
         
         
-          <li><a href="/tutorials/divrate/data/primates.tre">primates.tre</a></li>
-        
           <li><a href="/tutorials/intro/data/primates.tre">primates.tre</a></li>
         
-          <li><a href="/tutorials/biogeo/data/primates_tree.nex">primates_tree.nex</a></li>
-        
-          <li><a href="/tutorials/divrate/data/primates_tree.nex">primates_tree.nex</a></li>
+          <li><a href="/tutorials/divrate/data/primates.tre">primates.tre</a></li>
         
           <li><a href="/tutorials/intro/data/primates_tree.nex">primates_tree.nex</a></li>
         
+          <li><a href="/tutorials/divrate/data/primates_tree.nex">primates_tree.nex</a></li>
+        
           <li><a href="/tutorials/morph_ase/data/primates_tree.nex">primates_tree.nex</a></li>
         
+          <li><a href="/tutorials/biogeo/data/primates_tree.nex">primates_tree.nex</a></li>
+        
         </ul>
     
         
@@ -498,14 +498,14 @@ <h3 class="subsection" id="exercise-2">Exercise 2</h3>
 <a href="https://doi.org/10.1093/sysbio/syw021">10.1093/sysbio/syw021</a>
 
 </li>
-<li><span id="Hoehna2014a">Höhna S. 2014. Likelihood Inference of Non-Constant Diversification Rates with Incomplete Taxon Sampling. PLoS One. 9:e84184.</span>
+<li><span id="Hoehna2015a">Höhna S. 2015. The time-dependent reconstructed evolutionary process with a key-role for mass-extinction events. Journal of Theoretical Biology. 380:321–331.</span>
 
-<a href="https://doi.org/10.1371/journal.pone.0084184">10.1371/journal.pone.0084184</a>
+<a href="https://doi.org/http://dx.doi.org/10.1016/j.jtbi.2015.06.005">http://dx.doi.org/10.1016/j.jtbi.2015.06.005</a>
 
 </li>
-<li><span id="Hoehna2015a">Höhna S. 2015. The time-dependent reconstructed evolutionary process with a key-role for mass-extinction events. Journal of Theoretical Biology. 380:321–331.</span>
+<li><span id="Hoehna2014a">Höhna S. 2014. Likelihood Inference of Non-Constant Diversification Rates with Incomplete Taxon Sampling. PLoS One. 9:e84184.</span>
 
-<a href="https://doi.org/http://dx.doi.org/10.1016/j.jtbi.2015.06.005">http://dx.doi.org/10.1016/j.jtbi.2015.06.005</a>
+<a href="https://doi.org/10.1371/journal.pone.0084184">10.1371/journal.pone.0084184</a>
 
 </li>
 <li><span id="Hoehna2014b">Höhna S., Heath T.A., Boussau B., Landis M.J., Ronquist F., Huelsenbeck J.P. 2014. Probabilistic Graphical Model Representation in Phylogenetics. Systematic Biology. 63:753–771.</span>
diff --git a/tutorials/divrate/efbdp_me.html b/tutorials/divrate/efbdp_me.html
index fb9b81021..c1b2837e2 100644
--- a/tutorials/divrate/efbdp_me.html
+++ b/tutorials/divrate/efbdp_me.html
@@ -55,7 +55,7 @@
 	<h1 class="maintitle">Mass Extinction Estimation</h1>
 	<h3 class="subtitle">Estimating Mass Extinctions from Phylogenies with Fossil and Extant Taxa</h3>
 	<h4 class="authors">Andrew Magee and Sebastian Höhna</h4>
-  <h5>Last modified on March 11, 2022</h5>
+  <h5>Last modified on April  2, 2024</h5>
 </div>
 
 
@@ -99,8 +99,6 @@ <h2>Data files and scripts</h2>
         
           <li><a href="/tutorials/divrate/data/crocs_T1.tre">crocs_T1.tre</a></li>
         
-          <li><a href="/tutorials/divrate/data/crocs_taxa.txt">crocs_taxa.txt</a></li>
-        
         </ul>
     
         
@@ -177,9 +175,10 @@ <h3 class="subsection" id="read-the-data">Read the data</h3>
 <p>Begin by reading in the ``observed’’ tree.</p>
 <div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>T &lt;- readTrees("data/crocs_T1.tre")[1]
 </code></pre></div></div>
-<p>When the tree has fossils, it is best to read in the taxa from a taxon data file.
-This is absolutely required if the tree is to be simultaneously inferred.</p>
-<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>taxa &lt;- readTaxonData("data/crocs_taxa.txt",delim=TAB)
+<p>Here, we extract the fossil ages directly from the tree. If we were to simultaneously
+infer the tree instead, these ages would have to be read in from a taxon data file.
+(You can find an example of such a file in the <a href="/tutorials/divrate/ebd.html">Episodic Diversification Rate Estimation</a> tutorial.)</p>
+<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>taxa &lt;- T.taxa()
 </code></pre></div></div>
 <p>Additionally, we initialize a variable for our vector of moves and monitors.</p>
 <div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>moves    = VectorMoves()
@@ -556,14 +555,14 @@ <h3 class="subsection" id="exercise-2">Exercise 2</h3>
 <ol class="bibliography"><li><span id="Carvalho2010">Carvalho C.M., Polson N.G., Scott J.G. 2010. The horseshoe estimator for sparse signals. Biometrika. 97:465–480.</span>
 
 </li>
-<li><span id="Hoehna2014a">Höhna S. 2014. Likelihood Inference of Non-Constant Diversification Rates with Incomplete Taxon Sampling. PLoS One. 9:e84184.</span>
+<li><span id="Hoehna2015a">Höhna S. 2015. The time-dependent reconstructed evolutionary process with a key-role for mass-extinction events. Journal of Theoretical Biology. 380:321–331.</span>
 
-<a href="https://doi.org/10.1371/journal.pone.0084184">10.1371/journal.pone.0084184</a>
+<a href="https://doi.org/http://dx.doi.org/10.1016/j.jtbi.2015.06.005">http://dx.doi.org/10.1016/j.jtbi.2015.06.005</a>
 
 </li>
-<li><span id="Hoehna2015a">Höhna S. 2015. The time-dependent reconstructed evolutionary process with a key-role for mass-extinction events. Journal of Theoretical Biology. 380:321–331.</span>
+<li><span id="Hoehna2014a">Höhna S. 2014. Likelihood Inference of Non-Constant Diversification Rates with Incomplete Taxon Sampling. PLoS One. 9:e84184.</span>
 
-<a href="https://doi.org/http://dx.doi.org/10.1016/j.jtbi.2015.06.005">http://dx.doi.org/10.1016/j.jtbi.2015.06.005</a>
+<a href="https://doi.org/10.1371/journal.pone.0084184">10.1371/journal.pone.0084184</a>
 
 </li>
 <li><span id="Hoehna2011">Höhna S., Stadler T., Ronquist F., Britton T. 2011. Inferring speciation and extinction rates under different species sampling schemes. Molecular Biology and Evolution. 28:2577–2589.</span>
diff --git a/tutorials/divrate/env.html b/tutorials/divrate/env.html
index e89dbf50d..4b578d77a 100644
--- a/tutorials/divrate/env.html
+++ b/tutorials/divrate/env.html
@@ -101,14 +101,14 @@ <h2>Data files and scripts</h2>
         
           <li><a href="/tutorials/intro/data/primates.tre">primates.tre</a></li>
         
-          <li><a href="/tutorials/biogeo/data/primates_tree.nex">primates_tree.nex</a></li>
-        
           <li><a href="/tutorials/divrate/data/primates_tree.nex">primates_tree.nex</a></li>
         
-          <li><a href="/tutorials/intro/data/primates_tree.nex">primates_tree.nex</a></li>
+          <li><a href="/tutorials/biogeo/data/primates_tree.nex">primates_tree.nex</a></li>
         
           <li><a href="/tutorials/morph_ase/data/primates_tree.nex">primates_tree.nex</a></li>
         
+          <li><a href="/tutorials/intro/data/primates_tree.nex">primates_tree.nex</a></li>
+        
         </ul>
     
         
diff --git a/tutorials/divrate/modules/efbdp_me_estimation.md b/tutorials/divrate/modules/efbdp_me_estimation.md
index b3215606d..b0cd6dcd3 100644
--- a/tutorials/divrate/modules/efbdp_me_estimation.md
+++ b/tutorials/divrate/modules/efbdp_me_estimation.md
@@ -6,10 +6,11 @@ Begin by reading in the ``observed'' tree.
 ```
 T <- readTrees("data/crocs_T1.tre")[1]
 ```
-When the tree has fossils, it is best to read in the taxa from a taxon data file.
-This is absolutely required if the tree is to be simultaneously inferred.
+Here, we extract the fossil ages directly from the tree. If we were to simultaneously
+infer the tree instead, these ages would have to be read in from a taxon data file.
+(You can find an example of such a file in the {% page_ref divrate/ebd %} tutorial.)
 ```
-taxa <- readTaxonData("data/crocs_taxa.txt",delim=TAB)
+taxa <- T.taxa()
 ```
 Additionally, we initialize a variable for our vector of moves and monitors.
 ```
diff --git a/tutorials/divrate/sampling.html b/tutorials/divrate/sampling.html
index fe8bb869e..f3189fc7d 100644
--- a/tutorials/divrate/sampling.html
+++ b/tutorials/divrate/sampling.html
@@ -55,7 +55,7 @@
 	<h1 class="maintitle">Diversification Rate Estimation with Missing Taxa</h1>
 	<h3 class="subtitle">How to estimate diversification rates with incomplete taxon sampling</h3>
 	<h4 class="authors">Sebastian Höhna, Will Freyman and Mike May</h4>
-  <h5>Last modified on February 28, 2022</h5>
+  <h5>Last modified on February 27, 2022</h5>
 </div>
 
 
@@ -97,8 +97,6 @@ <h2>Data files and scripts</h2>
         
           <li><a href="/tutorials/divrate/data/crocs_T1.tre">crocs_T1.tre</a></li>
         
-          <li><a href="/tutorials/divrate/data/crocs_taxa.txt">crocs_taxa.txt</a></li>
-        
           <li><a href="/tutorials/divrate/data/primates.tre">primates.tre</a></li>
         
           <li><a href="/tutorials/divrate/data/primates_tree.nex">primates_tree.nex</a></li>
diff --git a/tutorials/divrate/scripts/mcmc_CRFBD.Rev b/tutorials/divrate/scripts/mcmc_CRFBD.Rev
index db11b0551..0922e4fab 100644
--- a/tutorials/divrate/scripts/mcmc_CRFBD.Rev
+++ b/tutorials/divrate/scripts/mcmc_CRFBD.Rev
@@ -6,8 +6,7 @@
 T <- readTrees("data/crocs_T1.tre")[1]
 
 # Get some useful variables from the data. We need these later on.
-taxa <- readTaxonData("data/crocs_taxa.txt",delim=TAB)
-
+taxa <- T.taxa()
 
 # Create some vector for the moves and monitors of this analysis
 moves    = VectorMoves()
diff --git a/tutorials/divrate/scripts/mcmc_EFBD_mass_extinctions.Rev b/tutorials/divrate/scripts/mcmc_EFBD_mass_extinctions.Rev
index c584d8ada..d86876758 100644
--- a/tutorials/divrate/scripts/mcmc_EFBD_mass_extinctions.Rev
+++ b/tutorials/divrate/scripts/mcmc_EFBD_mass_extinctions.Rev
@@ -16,8 +16,7 @@
 T <- readTrees("data/crocs_T1.tre")[1]
 
 # Get some useful variables from the data. We need these later on.
-taxa <- readTaxonData("data/crocs_taxa.txt",delim=TAB)
-
+taxa <- T.taxa()
 
 # Create some vector for the moves and monitors of this analysis
 moves    = VectorMoves()
diff --git a/tutorials/divrate/simple.html b/tutorials/divrate/simple.html
index 39ab1299d..2a1ef71aa 100644
--- a/tutorials/divrate/simple.html
+++ b/tutorials/divrate/simple.html
@@ -95,10 +95,10 @@ <h2>Data files and scripts</h2>
         
         
         
-          <li><a href="/tutorials/divrate/data/primates.tre">primates.tre</a></li>
-        
           <li><a href="/tutorials/intro/data/primates.tre">primates.tre</a></li>
         
+          <li><a href="/tutorials/divrate/data/primates.tre">primates.tre</a></li>
+        
           <li><a href="/tutorials/biogeo/data/primates_tree.nex">primates_tree.nex</a></li>
         
           <li><a href="/tutorials/divrate/data/primates_tree.nex">primates_tree.nex</a></li>
@@ -596,14 +596,14 @@ <h3 class="subsection" id="exercise-3">Exercise 3</h3>
 <ol class="bibliography"><li><span id="Aldous2001">Aldous D.J. 2001. Stochastic models and descriptive statistics for phylogenetic trees, from Yule to today. Statistical Science.:23–34.</span>
 
 </li>
-<li><span id="Hoehna2014a">Höhna S. 2014. Likelihood Inference of Non-Constant Diversification Rates with Incomplete Taxon Sampling. PLoS One. 9:e84184.</span>
+<li><span id="Hoehna2015a">Höhna S. 2015. The time-dependent reconstructed evolutionary process with a key-role for mass-extinction events. Journal of Theoretical Biology. 380:321–331.</span>
 
-<a href="https://doi.org/10.1371/journal.pone.0084184">10.1371/journal.pone.0084184</a>
+<a href="https://doi.org/http://dx.doi.org/10.1016/j.jtbi.2015.06.005">http://dx.doi.org/10.1016/j.jtbi.2015.06.005</a>
 
 </li>
-<li><span id="Hoehna2015a">Höhna S. 2015. The time-dependent reconstructed evolutionary process with a key-role for mass-extinction events. Journal of Theoretical Biology. 380:321–331.</span>
+<li><span id="Hoehna2014a">Höhna S. 2014. Likelihood Inference of Non-Constant Diversification Rates with Incomplete Taxon Sampling. PLoS One. 9:e84184.</span>
 
-<a href="https://doi.org/http://dx.doi.org/10.1016/j.jtbi.2015.06.005">http://dx.doi.org/10.1016/j.jtbi.2015.06.005</a>
+<a href="https://doi.org/10.1371/journal.pone.0084184">10.1371/journal.pone.0084184</a>
 
 </li>
 <li><span id="Hoehna2014b">Höhna S., Heath T.A., Boussau B., Landis M.J., Ronquist F., Huelsenbeck J.P. 2014. Probabilistic Graphical Model Representation in Phylogenetics. Systematic Biology. 63:753–771.</span>
diff --git a/tutorials/fbd/fbd_specimen.html b/tutorials/fbd/fbd_specimen.html
index b88cb5ec6..5b2addc6d 100644
--- a/tutorials/fbd/fbd_specimen.html
+++ b/tutorials/fbd/fbd_specimen.html
@@ -55,7 +55,7 @@
 	<h1 class="maintitle">Combined-Evidence Analysis and the Fossilized Birth-Death Process for Analysis of Extant Taxa and Fossil Specimens</h1>
 	<h3 class="subtitle">Joint inference of divergence times and phylogenetic relationships of fossil and extant taxa</h3>
 	<h4 class="authors">Tracy A. Heath, April M. Wright, and Walker Pett</h4>
-  <h5>Last modified on April  9, 2020</h5>
+  <h5>Last modified on April  8, 2020</h5>
 </div>
 
 
diff --git a/tutorials/fbd_range/index.html b/tutorials/fbd_range/index.html
index f9c8843fe..65a0e1f94 100644
--- a/tutorials/fbd_range/index.html
+++ b/tutorials/fbd_range/index.html
@@ -55,7 +55,7 @@
 	<h1 class="maintitle">Macroevolutionary Analysis of Stratigraphic Range Data</h1>
 	<h3 class="subtitle">Inference of diversification rates using the fossilized birth-death range process</h3>
 	<h4 class="authors">Rachel Warnock and Walker Pett</h4>
-  <h5>Last modified on February 28, 2022</h5>
+  <h5>Last modified on February 27, 2022</h5>
 </div>
 
 
diff --git a/tutorials/index.html b/tutorials/index.html
index aef942733..d99d8f6fd 100644
--- a/tutorials/index.html
+++ b/tutorials/index.html
@@ -634,7 +634,7 @@ <h3 id="phylo_complex">Complex hierarchical models for phylogenetic inference</h
 
 <div class="tutorial ">
 
-<a class="title" href="/tutorials/fbd/fbd_specimen.html"><p>Combined-Evidence Analysis and the Fossilized Birth-Death Process for Analysis of Extant Taxa and Fossil Specimens</p>
+<a class="title" href="/tutorials/fbd/"><p>Combined-Evidence Analysis and the Fossilized Birth-Death Process for Stratigraphic Range Data</p>
 </a>
 
 <p class="subtitle"><p>Joint inference of divergence times and phylogenetic relationships of fossil and extant taxa</p>
@@ -649,7 +649,7 @@ <h3 id="phylo_complex">Complex hierarchical models for phylogenetic inference</h
 
 <div class="tutorial ">
 
-<a class="title" href="/tutorials/fbd/"><p>Combined-Evidence Analysis and the Fossilized Birth-Death Process for Stratigraphic Range Data</p>
+<a class="title" href="/tutorials/fbd/fbd_specimen.html"><p>Combined-Evidence Analysis and the Fossilized Birth-Death Process for Analysis of Extant Taxa and Fossil Specimens</p>
 </a>
 
 <p class="subtitle"><p>Joint inference of divergence times and phylogenetic relationships of fossil and extant taxa</p>
@@ -890,6 +890,9 @@ <h3 id="phylo_trait">Comparative methods</h3>
 
 
 
+
+
+
 <div class="tutorial ">
 
 <a class="title" href="/tutorials/host_rep/host_rep.html"><p>Reconstructing Host Repertoire Evolution</p>
@@ -901,9 +904,6 @@ <h3 id="phylo_trait">Comparative methods</h3>
 
 
 
-
-
-
 </div>
 
 <h3 id="phylo_biogeo">Diversification Rate Estimation</h3>
diff --git a/tutorials/intro/revgadgets.html b/tutorials/intro/revgadgets.html
index f78d43f52..f5e57bd5e 100644
--- a/tutorials/intro/revgadgets.html
+++ b/tutorials/intro/revgadgets.html
@@ -117,13 +117,13 @@ <h2>Data files and scripts</h2>
         
           <li><a href="/tutorials/intro/data/primates_cytb_GTR_MAP.tre">primates_cytb_GTR_MAP.tre</a></li>
         
-          <li><a href="/tutorials/biogeo/data/primates_tree.nex">primates_tree.nex</a></li>
-        
           <li><a href="/tutorials/divrate/data/primates_tree.nex">primates_tree.nex</a></li>
         
+          <li><a href="/tutorials/morph_ase/data/primates_tree.nex">primates_tree.nex</a></li>
+        
           <li><a href="/tutorials/intro/data/primates_tree.nex">primates_tree.nex</a></li>
         
-          <li><a href="/tutorials/morph_ase/data/primates_tree.nex">primates_tree.nex</a></li>
+          <li><a href="/tutorials/biogeo/data/primates_tree.nex">primates_tree.nex</a></li>
         
           <li><a href="/tutorials/intro/data/relaxed_OU_MAP.tre">relaxed_OU_MAP.tre</a></li>
         
diff --git a/tutorials/intro_posterior_prediction/index.html b/tutorials/intro_posterior_prediction/index.html
index f9c754c40..4e24115fb 100644
--- a/tutorials/intro_posterior_prediction/index.html
+++ b/tutorials/intro_posterior_prediction/index.html
@@ -411,14 +411,14 @@ <h2 class="section" id="phyloseminar">Phyloseminar</h2>
 <a href="https://doi.org/10.1093/oxfordjournals.molbev.a004175">10.1093/oxfordjournals.molbev.a004175</a>
 
 </li>
-<li><span id="Brown2014">Brown J.M. 2014. Predictive approaches to assessing the fit of evolutionary models. Systematic Biology. 63:289–292.</span>
+<li><span id="Brown2014a">Brown J.M. 2014. Detection of implausible phylogenetic inferences using posterior predictive assessment of model fit. Systematic Biology. 63:334–348.</span>
 
-<a href="https://doi.org/10.1093/sysbio/syu009">10.1093/sysbio/syu009</a>
+<a href="https://doi.org/10.1093/sysbio/syu002">10.1093/sysbio/syu002</a>
 
 </li>
-<li><span id="Brown2014a">Brown J.M. 2014. Detection of implausible phylogenetic inferences using posterior predictive assessment of model fit. Systematic Biology. 63:334–348.</span>
+<li><span id="Brown2014">Brown J.M. 2014. Predictive approaches to assessing the fit of evolutionary models. Systematic Biology. 63:289–292.</span>
 
-<a href="https://doi.org/10.1093/sysbio/syu002">10.1093/sysbio/syu002</a>
+<a href="https://doi.org/10.1093/sysbio/syu009">10.1093/sysbio/syu009</a>
 
 </li>
 <li><span id="BrownThomson2018">Brown J.M., Thomson R.C. 2018. Evaluating model performance in evolutionary biology. Annual Review of Ecology, Evolution, and Systematics. 49:95–114.</span>
diff --git a/tutorials/mcmc/binomial.html b/tutorials/mcmc/binomial.html
index a12514685..f0e529195 100644
--- a/tutorials/mcmc/binomial.html
+++ b/tutorials/mcmc/binomial.html
@@ -55,7 +55,7 @@
 	<h1 class="maintitle">Introduction to MCMC using RevBayes</h1>
 	<h3 class="subtitle">Introduction to MCMC Simulation using a simple Binomial Model</h3>
 	<h4 class="authors">Mike May, Brian Moore and Sebastian H&#246;hna</h4>
-  <h5>Last modified on February 28, 2022</h5>
+  <h5>Last modified on February 27, 2022</h5>
 </div>
 
 
diff --git a/tutorials/morph_ase/ase_mammals.html b/tutorials/morph_ase/ase_mammals.html
index a49d96f1e..d415692b6 100644
--- a/tutorials/morph_ase/ase_mammals.html
+++ b/tutorials/morph_ase/ase_mammals.html
@@ -55,7 +55,7 @@
 	<h1 class="maintitle">Discrete morphology - Ancestral State Estimation (Mammals & Placenta Type)</h1>
 	<h3 class="subtitle">Ancestral State Estimation and Testing for Irreversibility</h3>
 	<h4 class="authors">Sebastian Höhna</h4>
-  <h5>Last modified on February 28, 2022</h5>
+  <h5>Last modified on February 27, 2022</h5>
 </div>
 
 
diff --git a/tutorials/morph_ase/corr.html b/tutorials/morph_ase/corr.html
index bfda8fc3c..e49eabbdb 100644
--- a/tutorials/morph_ase/corr.html
+++ b/tutorials/morph_ase/corr.html
@@ -55,7 +55,7 @@
 	<h1 class="maintitle">Discrete morphology - Correlation among Characters</h1>
 	<h3 class="subtitle">Testing for Correlation</h3>
 	<h4 class="authors">Sebastian Höhna</h4>
-  <h5>Last modified on February 28, 2022</h5>
+  <h5>Last modified on February 27, 2022</h5>
 </div>
 
 
diff --git a/tutorials/partition/index.html b/tutorials/partition/index.html
index 45f837b26..c55fc0704 100644
--- a/tutorials/partition/index.html
+++ b/tutorials/partition/index.html
@@ -55,7 +55,7 @@
 	<h1 class="maintitle">Partitioned data analysis</h1>
 	<h3 class="subtitle">Current Protocols in Bioinformatics - Phylogenetic Inference using RevBayes (Protocol &#35;2)</h3>
 	<h4 class="authors">Sebastian Höhna, Michael J. Landis and Tracy A. Heath</h4>
-  <h5>Last modified on October 13, 2023</h5>
+  <h5>Last modified on October 12, 2023</h5>
 </div>
 
 
diff --git a/tutorials/pomobalance/data/great_apes_BS_10000.txt b/tutorials/pomobalance/data/great_apes_BS_10000.txt
new file mode 100644
index 000000000..79d8ac955
--- /dev/null
+++ b/tutorials/pomobalance/data/great_apes_BS_10000.txt
@@ -0,0 +1,5 @@
+HUMAN 0 2 0 1 3 2 0 0 44 44 1 1 3 1 0 3 2 0 3 3 1 1 3 2 3 3 2 8 0 0 3 2 3 1 3 3 2 1 3 2 3 44 3 3 35 1 2 3 0 0 1 53 3 0 0 1 1 2 35 1 0 1 2 2 0 1 2 1 3 1 3 26 0 3 3 0 1 1 35 0 0 2 3 3 0 1 3 0 2 44 35 3 3 2 0 3 1 3 2 1 3 1 2 3 3 2 3 2 8 3 1 2 0 17 3 1 0 3 2 2 3 1 1 2 2 0 2 3 44 1 3 26 2 0 0 1 3 3 1 17 3 1 3 1 3 2 17 1 3 2 0 2 0 0 0 0 0 1 1 3 1 2 0 0 0 1 0 2 1 2 1 8 2 2 2 0 2 1 17 3 3 2 3 1 1 0 3 0 0 0 1 2 1 3 1 0 2 2 2 2 0 1 0 26 1 0 2 3 1 1 0 0 3 0 2 1 3 2 2 1 3 3 1 1 1 2 2 2 3 3 1 2 3 26 0 26 2 0 0 2 0 0 3 3 1 1 0 2 3 44 2 0 0 3 3 44 1 3 3 0 3 3 1 2 3 2 0 0 44 3 2 3 2 3 2 26 1 1 2 1 2 2 2 35 3 17 1 3 3 2 0 1 3 0 2 3 44 1 1 0 0 0 3 2 1 53 1 2 2 0 0 1 0 0 1 1 0 26 2 0 3 1 0 2 2 0 1 3 2 0 0 3 0 2 2 1 2 1 1 0 3 0 1 3 8 0 1 3 2 17 3 2 26 1 1 0 8 0 2 1 2 3 3 2 0 1 17 0 1 0 0 2 1 3 3 2 0 35 0 3 26 0 1 3 2 3 2 2 2 17 2 1 2 0 0 0 0 8 0 2 0 2 3 2 2 3 2 2 0 35 1 1 2 3 0 1 1 3 0 3 0 3 2 3 0 3 2 3 1 3 1 1 3 0 1 1 0 3 3 3 0 26 3 3 3 1 2 2 17 3 1 0 0 3 2 0 1 3 1 53 0 0 0 3 2 2 3 17 2 2 2 2 0 2 1 3 1 3 1 17 3 1 3 0 0 3 0 3 2 2 1 3 0 2 3 2 0 3 2 1 2 1 0 2 2 1 0 0 3 3 1 1 3 0 3 44 2 1 0 0 0 3 53 2 1 53 35 0 0 53 26 3 1 2 8 2 1 3 3 44 3 3 2 1 3 1 0 2 0 2 44 3 53 3 3 1 3 1 2 0 3 0 1 3 0 1 1 53 3 0 0 3 57 3 30 3 0 0 1 2 3 3 3 0 0 2 1 8 3 53 1 1 0 3 2 0 1 2 0 53 3 2 2 3 44 3 3 1 1 2 17 1 2 3 0 3 2 1 2 2 0 0 1 2 17 1 2 35 2 1 0 3 0 3 2 3 1 3 35 2 2 1 3 1 17 0 1 35 3 2 3 1 0 1 0 3 0 3 53 0 26 3 3 1 1 0 0 0 1 2 2 1 3 3 0 0 17 3 35 1 1 2 1 1 0 1 0 35 3 1 1 2 1 1 2 3 2 8 0 1 3 35 1 0 1 26 3 3 1 2 0 3 1 0 0 1 35 3 26 35 3 1 3 1 3 2 2 1 35 2 1 0 1 2 2 1 1 0 2 3 2 3 3 2 2 0 2 1 2 1 35 1 3 3 1 3 0 1 53 1 3 3 3 44 2 2 1 0 1 3 1 26 0 1 1 0 3 0 8 3 2 1 2 53 1 2 3 53 17 1 1 2 53 0 35 2 0 3 0 17 8 2 1 0 3 1 2 2 1 3 8 1 0 2 3 1 0 2 3 0 3 3 8 1 2 1 3 1 1 3 0 1 35 2 0 0 3 3 3 1 3 3 0 3 3 3 3 0 3 0 2 3 1 2 17 8 1 0 3 17 2 2 3 2 3 2 1 1 0 26 1 0 1 2 0 1 2 1 0 1 3 0 1 35 0 3 1 0 3 3 3 3 8 3 3 0 2 1 2 1 0 3 1 17 1 0 0 3 3 0 3 2 2 0 3 2 1 0 0 8 2 26 1 3 2 0 2 1 2 2 1 1 3 3 3 0 0 2 1 2 3 1 0 0 2 0 3 1 3 8 0 1 2 0 3 35 3 2 1 2 2 3 3 2 3 2 2 3 2 1 2 0 0 0 1 3 1 2 0 0 0 2 8 3 21 1 1 2 0 0 1 3 2 2 44 0 2 0 1 0 0 3 2 3 2 3 1 3 2 44 0 1 0 3 26 0 3 8 1 3 2 2 2 0 0 2 0 0 2 3 0 1 0 0 3 0 3 3 2 1 0 2 8 3 0 3 3 3 0 1 1 0 3 1 2 1 1 0 2 3 53 3 1 1 3 2 3 0 3 35 1 0 17 1 2 1 1 2 0 0 2 3 17 17 1 0 1 3 2 3 3 1 0 53 3 0 3 3 1 2 3 0 2 2 0 2 2 1 0 17 2 0 3 1 3 2 2 1 1 3 2 3 1 2 1 0 1 0 0 3 0 0 1 2 1 1 3 2 2 2 1 44 3 0 3 3 8 3 0 1 3 1 0 3 2 3 17 0 2 3 44 2 0 3 1 3 0 44 3 2 3 1 0 0 0 1 1 0 3 3 44 3 0 1 0 0 35 1 17 2 1 1 0 1 1 3 2 0 3 1 1 3 0 2 3 3 3 1 3 2 2 2 2 1 3 0 0 2 0 1 1 1 1 1 1 3 0 3 2 1 3 35 1 0 1 1 3 1 1 1 3 2 1 2 2 3 2 0 3 3 2 3 1 2 3 1 53 0 1 3 1 0 2 2 2 3 0 1 1 2 0 3 1 3 1 0 1 26 1 1 2 3 1 0 2 1 1 3 3 2 35 1 0 2 0 3 0 2 3 26 1 0 1 2 1 0 44 0 3 3 0 2 2 1 2 3 1 1 2 1 17 1 1 3 2 0 1 0 1 2 1 2 3 2 1 2 2 35 1 3 3 0 3 1 1 35 1 1 0 0 3 0 0 2 2 3 3 44 2 0 1 3 3 3 1 3 1 0 0 26 44 0 0 3 3 26 0 1 2 3 0 17 0 0 35 3 1 3 8 3 1 1 3 1 2 1 2 0 3 2 3 0 3 1 1 2 3 1 0 2 1 2 0 1 3 0 1 0 0 2 1 1 3 3 1 0 3 3 3 1 3 3 3 35 2 2 1 2 2 0 3 3 0 1 3 2 0 0 0 0 2 1 3 0 2 3 0 3 1 0 0 0 3 2 2 0 3 0 3 0 0 1 2 1 8 0 1 53 1 1 2 1 1 0 1 53 3 2 2 3 1 1 0 1 2 2 1 3 2 1 2 0 3 1 2 3 3 17 0 0 2 0 3 1 0 2 0 2 0 3 0 1 0 2 3 1 3 2 2 3 3 2 2 1 2 3 17 1 2 3 0 1 26 0 2 0 2 3 2 0 0 53 2 1 0 44 1 0 1 0 3 1 1 3 0 2 2 1 0 2 1 2 1 2 0 0 2 0 3 1 26 1 1 2 1 1 3 0 0 2 3 3 0 0 3 8 3 2 3 0 0 1 1 0 3 3 3 2 2 3 1 1 44 0 8 0 3 3 2 2 3 3 1 1 44 0 3 0 2 0 17 1 1 0 3 0 0 1 1 53 3 3 0 0 3 1 3 0 0 1 1 2 3 3 0 1 0 2 0 2 35 17 0 0 2 0 0 3 2 3 0 1 0 1 3 53 3 1 2 1 1 2 1 2 1 0 2 1 2 0 3 3 17 1 3 2 3 3 0 1 3 2 3 17 3 1 17 1 3 0 3 1 0 3 2 2 0 35 3 3 35 3 3 17 3 2 26 0 3 44 2 2 2 44 3 1 1 0 2 3 3 0 1 2 2 0 1 2 2 0 3 35 3 3 0 1 3 1 3 3 1 3 44 2 2 2 3 2 3 1 2 3 2 53 35 2 2 2 1 1 2 2 1 2 1 1 2 1 1 2 1 0 0 3 0 0 53 0 1 1 0 1 2 2 3 1 2 0 8 2 0 2 2 53 2 3 3 0 3 2 8 1 2 3 3 2 3 0 1 1 1 3 1 2 1 1 3 1 1 3 1 2 0 2 2 3 2 1 1 2 1 2 0 3 1 2 3 2 3 1 1 3 1 3 3 53 3 1 0 1 3 1 53 2 2 8 8 1 2 1 0 2 53 53 2 2 2 0 2 2 1 26 2 0 2 0 2 1 0 0 0 44 3 3 0 2 1 8 2 3 1 8 2 0 1 0 1 44 1 3 0 1 2 2 2 0 3 3 2 0 1 17 2 44 1 2 1 2 3 8 0 1 1 3 3 1 3 3 26 0 3 2 2 1 0 2 2 0 0 3 2 0 3 3 1 1 1 3 3 3 0 0 8 2 0 2 0 3 1 0 0 35 3 1 53 3 0 44 3 0 17 1 3 3 3 2 2 2 1 3 1 8 0 0 3 0 2 0 0 1 3 0 3 2 17 2 0 0 3 1 0 3 0 1 3 0 3 1 3 1 0 0 0 1 1 2 53 8 35 3 2 2 0 2 2 3 44 0 0 0 0 2 1 2 2 2 2 0 0 2 2 0 1 3 1 3 3 1 0 3 2 1 1 3 1 0 2 1 8 0 0 1 0 3 1 0 3 17 1 1 3 2 2 1 1 3 3 1 3 3 2 1 1 3 1 3 0 3 0 3 3 0 2 1 2 0 3 2 1 3 2 0 0 3 0 0 0 2 2 2 1 2 1 0 0 35 0 0 2 35 1 0 2 44 3 44 2 1 2 1 2 0 0 0 1 1 44 48 1 2 2 0 3 1 0 0 1 2 3 0 1 1 3 1 3 0 2 1 1 3 2 3 1 44 0 0 2 1 1 2 0 1 1 3 1 3 0 2 2 3 3 0 3 2 3 1 1 1 2 2 17 3 3 2 44 44 2 1 1 17 3 3 0 44 0 0 2 35 3 8 2 2 0 1 3 1 0 0 0 2 17 2 2 1 0 1 1 0 3 0 2 2 0 53 3 1 44 1 2 0 3 3 3 8 0 2 0 3 2 3 26 1 44 35 2 2 1 3 2 3 1 35 2 2 1 2 1 44 2 3 1 2 3 3 26 3 0 2 3 2 3 1 0 2 3 0 0 0 0 2 0 1 17 3 3 44 1 0 3 1 2 1 3 1 3 35 53 0 26 2 2 2 3 2 2 0 1 3 44 26 2 2 1 1 2 0 0 1 1 1 1 2 2 0 0 0 1 2 1 8 2 0 1 3 2 8 2 2 1 2 3 0 26 1 1 2 3 8 0 2 8 1 0 1 3 0 1 44 2 2 0 3 3 2 0 2 3 1 1 1 0 2 2 0 3 0 35 2 1 3 0 2 0 1 0 3 2 2 3 1 2 3 53 8 2 2 26 0 2 2 3 3 8 3 3 0 3 1 3 1 3 2 53 0 1 1 3 0 17 3 2 1 1 3 3 53 0 2 3 0 2 1 2 0 0 0 2 0 2 0 0 2 2 1 1 1 1 0 0 1 3 35 0 2 3 1 1 2 0 2 17 3 1 2 2 3 0 1 0 0 2 0 3 2 1 1 2 1 2 2 2 3 2 35 1 3 2 0 0 0 3 2 0 3 2 0 17 3 26 3 3 3 2 1 1 0 2 3 0 0 1 2 8 3 2 2 3 3 2 1 2 1 2 2 0 44 1 2 3 2 3 3 3 3 0 2 3 0 3 2 2 53 2 2 1 2 3 0 1 1 1 0 1 1 0 3 1 0 3 1 3 44 3 0 1 1 3 0 0 1 2 1 0 1 1 1 3 0 3 35 1 3 0 8 0 17 3 2 1 2 1 3 3 3 3 1 1 3 0 0 3 1 3 1 1 0 1 0 1 0 2 0 3 44 3 1 1 1 1 1 0 2 0 3 3 2 1 1 1 1 35 0 0 0 1 0 26 0 1 1 2 2 3 2 0 3 2 2 0 3 1 0 8 1 2 2 1 0 3 3 2 8 3 8 2 2 1 35 3 26 2 1 3 1 0 1 2 2 3 8 35 3 1 1 1 1 1 3 2 0 2 1 2 2 1 35 26 3 3 3 2 2 3 3 2 0 1 17 3 2 3 2 0 2 1 1 2 0 3 44 0 2 3 3 1 3 0 3 0 0 0 44 8 1 1 2 1 0 3 2 44 44 17 3 0 0 0 1 3 2 1 53 3 2 1 3 1 53 8 0 3 1 0 44 44 2 0 1 2 2 3 3 0 0 26 1 1 0 2 0 0 1 35 3 0 8 2 35 2 1 0 26 1 0 3 53 2 2 0 17 0 3 0 3 2 1 3 1 1 2 53 1 0 3 1 3 1 0 2 1 0 44 0 3 0 0 3 1 2 0 0 1 1 35 26 2 1 0 1 2 1 1 1 53 3 1 44 3 2 1 3 3 0 3 3 0 2 1 0 0 2 0 1 3 0 0 2 0 53 3 2 2 0 0 3 0 0 8 0 1 53 3 2 2 3 0 2 2 3 17 0 0 3 3 35 3 3 3 1 3 0 3 1 2 53 0 1 3 1 3 3 1 2 2 1 26 1 1 2 1 1 2 0 3 3 3 1 2 2 2 3 3 2 1 3 3 0 1 3 3 0 3 35 3 1 3 2 3 3 1 1 3 3 0 2 0 3 2 0 1 2 3 3 2 0 2 2 1 1 1 0 2 2 3 2 3 2 3 0 3 0 1 0 44 1 1 1 2 8 3 3 35 0 2 2 2 1 3 2 2 3 2 1 0 3 3 35 0 1 1 2 2 0 2 17 0 26 0 2 0 2 1 2 1 0 3 0 3 2 35 2 0 0 0 3 1 1 0 3 1 8 1 0 0 1 1 1 3 1 0 2 1 3 1 0 1 1 3 1 0 1 3 3 1 3 0 3 3 2 0 0 0 1 17 1 2 2 3 3 1 3 0 2 26 35 1 3 0 1 1 0 0 1 1 0 1 3 0 3 0 1 2 3 3 2 3 0 1 1 2 35 53 0 3 2 2 1 0 3 3 0 1 3 8 53 0 2 53 1 3 2 3 3 0 1 1 1 3 0 2 3 0 0 2 2 0 26 2 2 3 3 1 26 2 2 3 3 2 0 1 2 1 0 1 2 3 26 17 2 2 1 2 3 3 0 2 3 0 1 0 1 3 2 1 1 1 0 2 1 3 3 3 44 2 0 3 3 3 2 0 1 3 1 53 0 8 0 2 35 2 2 2 35 1 1 2 1 2 1 1 3 1 0 1 1 2 2 0 2 26 1 1 1 3 3 2 1 0 2 3 2 1 3 3 0 1 1 1 1 2 3 1 0 0 53 3 2 1 1 1 2 1 1 1 1 2 0 0 3 2 2 3 3 2 2 2 3 2 3 0 8 1 2 1 2 3 44 3 2 1 1 8 2 1 2 0 1 2 0 3 3 2 0 1 3 1 0 0 2 3 1 2 35 3 0 3 1 0 0 3 2 2 3 1 2 1 3 2 1 1 44 3 2 1 3 1 2 1 53 0 2 2 0 2 1 1 3 2 44 2 1 1 0 1 1 0 2 2 3 3 0 3 2 2 3 1 1 0 1 0 53 35 2 0 2 0 1 3 2 2 2 3 3 0 1 3 2 2 0 3 0 3 2 1 1 0 53 1 2 0 53 0 0 0 1 3 3 1 0 0 2 8 0 2 2 1 1 0 2 1 44 0 0 0 1 26 3 3 3 44 1 1 2 2 3 2 2 3 2 2 0 1 2 2 8 3 3 0 1 0 3 0 1 1 0 0 3 1 0 1 2 0 0 1 35 3 3 2 1 53 0 3 1 1 3 3 1 1 1 0 2 2 2 1 3 0 0 2 3 0 1 1 3 3 1 2 1 3 8 1 2 2 3 3 3 0 2 0 2 3 1 2 1 2 0 2 0 1 0 1 1 2 2 0 2 0 1 2 3 3 2 0 3 8 0 2 53 2 3 2 3 2 0 1 3 1 1 53 3 35 1 0 3 1 0 3 0 1 3 0 2 1 3 0 2 0 3 0 0 3 2 0 2 3 26 2 2 3 2 2 1 2 0 2 0 2 0 2 0 1 1 2 0 0 3 3 1 2 1 8 2 3 0 3 3 44 1 2 2 0 2 0 26 0 3 3 0 0 35 3 0 0 3 0 17 2 35 3 3 3 1 3 2 0 1 2 1 3 1 1 1 8 1 3 0 0 2 1 0 3 3 35 0 0 3 0 0 2 3 2 1 3 1 3 2 3 2 8 3 0 3 3 0 3 1 2 1 3 3 3 2 0 1 3 2 0 1 3 1 3 3 0 35 1 1 1 3 3 0 3 3 0 0 2 26 2 1 3 3 3 44 0 26 3 3 1 1 3 1 1 1 0 3 2 44 1 3 0 3 0 2 3 2 3 3 35 3 1 2 3 2 0 3 0 44 3 3 1 0 0 3 0 8 35 0 3 0 2 3 3 0 1 1 0 1 2 26 1 1 1 2 0 1 3 3 2 0 8 0 2 2 0 2 0 3 3 2 2 1 1 3 3 3 0 3 2 3 0 1 3 53 3 3 2 0 2 0 2 3 1 1 1 2 2 3 0 0 26 1 2 2 0 3 2 1 17 2 53 3 3 1 1 0 0 3 2 44 1 1 2 1 2 8 2 1 0 3 17 0 17 44 3 2 3 0 3 2 1 0 53 0 0 3 1 0 0 1 3 1 3 1 35 2 1 17 2 0 0 3 26 0 2 0 0 2 0 0 0 1 3 53 2 2 0 3 1 1 1 1 1 3 0 2 2 2 53 44 1 1 2 2 1 2 3 1 3 1 3 1 3 3 3 3 2 1 2 35 1 3 8 0 1 1 0 0 0 1 26 2 8 1 0 2 2 2 2 3 3 2 0 3 1 1 2 0 1 3 0 3 8 26 3 4 3 2 2 8 3 2 3 3 0 2 2 1 1 3 3 1 0 1 3 3 3 1 0 0 3 2 44 3 1 2 3 17 0 3 1 1 2 26 2 3 2 3 1 2 1 3 2 2 53 35 3 53 35 0 2 26 0 3 3 35 2 8 3 2 3 1 1 1 2 2 3 2 1 0 0 3 1 2 2 0 2 53 3 3 35 0 1 0 0 1 3 2 2 2 0 1 1 3 3 2 2 1 1 3 1 2 1 3 3 0 0 2 3 3 1 3 1 2 0 2 53 1 2 53 2 0 3 44 2 8 0 1 0 1 53 44 3 3 1 0 2 2 1 17 1 1 3 0 2 0 2 2 3 1 0 0 1 3 44 0 0 2 8 1 1 3 2 2 2 3 2 2 1 1 1 1 53 3 2 0 2 3 1 3 35 0 1 0 0 0 1 3 3 0 0 3 0 0 0 1 3 35 0 0 2 44 35 0 2 0 3 2 3 1 1 2 1 1 53 2 3 3 3 35 0 0 2 26 1 0 0 3 0 1 3 3 0 35 0 0 2 1 2 1 2 3 1 1 2 2 17 2 2 0 2 8 3 3 3 2 0 1 0 2 0 0 3 1 3 0 2 0 0 3 3 2 0 1 1 1 0 3 2 3 2 1 1 2 1 1 0 3 2 1 44 1 17 44 2 17 1 0 2 53 1 2 1 3 1 3 2 3 2 2 0 35 1 2 3 3 3 3 1 2 3 3 3 1 3 0 2 3 3 44 2 2 1 0 1 0 3 2 3 3 3 3 0 2 2 3 0 1 1 3 1 0 1 1 3 2 1 17 1 1 2 2 0 0 3 2 1 1 3 2 1 0 2 44 3 35 2 2 1 0 1 0 0 53 2 0 2 0 1 3 3 26 3 1 2 0 1 1 0 1 1 2 2 2 0 0 0 0 2 1 0 0 3 2 0 2 0 0 0 1 2 2 1 2 0 3 1 0 0 8 2 0 1 2 2 0 1 0 2 0 2 0 3 3 2 1 3 3 3 0 57 2 1 0 3 2 35 2 2 2 0 0 1 0 3 3 3 3 1 0 2 0 0 3 3 3 8 3 0 3 2 0 17 2 3 0 3 0 1 2 3 44 3 3 3 1 2 1 1 2 0 3 3 0 1 1 2 3 3 0 0 3 1 3 0 3 2 3 3 0 3 2 2 0 1 3 3 8 0 0 1 3 0 3 3 2 3 0 17 17 0 2 1 0 3 1 2 2 26 0 3 0 0 0 1 2 3 1 26 3 2 26 1 3 35 35 2 0 3 2 17 2 1 3 2 1 3 3 3 0 3 0 1 2 2 3 3 0 1 1 2 0 0 2 2 1 3 0 2 3 1 2 2 0 0 8 3 0 0 1 2 0 0 1 1 1 1 1 0 3 0 0 1 17 0 2 1 0 3 0 0 2 2 2 3 3 1 0 3 3 44 1 1 3 35 3 1 2 3 2 0 2 1 1 3 44 53 2 0 53 26 0 0 3 1 2 3 35 3 2 2 1 3 1 2 1 2 26 2 1 3 1 3 0 2 1 0 1 1 3 0 8 1 0 1 3 53 3 0 3 0 0 3 2 0 0 2 0 35 2 0 0 35 1 1 2 0 2 2 1 1 3 2 2 0 1 0 2 3 0 3 1 44 2 0 0 0 3 3 35 44 3 2 3 1 0 2 2 0 2 1 1 44 1 2 3 1 1 3 3 2 3 0 0 1 2 3 2 35 1 0 3 1 44 1 0 2 0 3 3 2 1 2 3 1 0 2 1 3 0 0 2 26 8 1 0 0 1 1 3 1 2 3 2 0 2 3 3 1 2 0 3 1 2 3 1 1 3 3 1 1 1 3 3 0 0 2 1 3 0 0 2 2 3 1 17 1 1 3 3 1 1 8 3 3 2 0 3 3 1 3 0 2 2 2 1 2 35 0 3 2 3 0 3 1 2 53 3 0 2 2 3 2 3 8 0 0 1 0 1 17 2 2 0 1 1 2 2 0 0 2 11 2 0 2 3 1 1 1 1 2 0 8 1 0 1 3 2 0 53 0 1 3 8 0 2 0 0 2 2 1 1 3 0 1 13 0 0 0 1 8 2 0 2 1 1 2 3 1 0 0 44 2 3 3 1 0 0 2 0 1 1 2 3 1 17 1 1 2 1 3 2 1 2 3 53 1 1 3 0 1 0 0 2 3 0 2 1 17 3 3 1 0 0 0 2 1 3 3 0 2 1 44 35 26 2 3 2 1 2 8 3 17 0 0 2 1 3 3 0 1 3 1 1 0 2 2 0 2 2 1 1 1 0 2 0 3 3 1 2 2 3 3 0 53 3 1 3 2 0 2 0 2 2 1 0 3 0 0 1 3 2 0 1 2 2 1 2 3 2 2 0 0 1 1 0 0 1 1 17 1 2 2 3 2 1 1 3 0 2 1 1 35 3 2 53 53 1 1 2 3 1 1 2 26 0 1 2 0 3 0 0 2 1 1 3 35 0 3 1 1 0 0 3 0 1 2 26 1 17 0 3 0 2 3 0 0 1 35 2 0 2 1 53 3 1 3 2 2 1 1 3 3 0 26 17 2 1 0 2 0 0 44 2 2 3 44 0 2 3 1 2 2 1 1 17 8 1 3 3 1 1 0 1 0 2 26 2 1 0 0 1 0 0 3 1 0 3 1 3 0 0 3 3 0 3 3 0 8 2 2 1 2 1 2 0 3 0 0 0 2 2 0 3 2 1 2 44 3 0 0 1 2 0 2 8 1 2 2 3 0 2 2 2 2 0 2 3 1 3 2 1 26 1 0 1 1 3 1 3 2 3 1 3 0 2 1 17 2 0 3 2 3 26 2 0 0 3 0 0 3 0 2 3 2 44 3 17 0 1 1 2 3 1 26 0 17 1 3 2 3 3 2 22 3 2 0 8 3 0 35 1 2 35 44 17 1 0 1 2 2 0 0 3 2 2 0 2 53 3 2 1 0 3 2 1 17 35 3 3 3 1 3 0 0 53 8 1 0 0 1 1 35 1 1 0 0 0 0 2 3 0 3 0 1 26 0 3 0 2 1 3 1 35 0 3 0 35 0 2 1 3 2 0 2 3 1 1 2 3 1 3 2 2 2 0 3 2 2 2 2 0 0 1 2 0 3 17 17 2 0 2 0 3 8 1 3 1 0 3 3 1 1 3 3 0 2 1 2 3 0 0 0 0 2 3 0 3 2 1 0 1 0 3 8 0 2 2 2 2 2 0 1 2 2 3 3 0 2 1 2 0 3 3 0 2 26 1 0 2 3 0 3 0 3 3 1 1 1 0 53 2 1 0 2 1 0 2 2 1 1 0 35 1 2 53 3 1 2 2 3 0 1 2 2 1 44 3 1 3 1 3 0 2 44 2 0 0 2 2 1 2 0 2 0 2 2 8 3 2 2 2 0 3 2 1 3 8 1 2 2 3 3 1 35 0 0 3 3 1 2 2 2 3 0 1 3 2 0 0 2 0 0 35 44 1 3 3 2 2 0 2 3 2 2 2 0 0 1 0 3 26 1 3 1 2 2 3 53 1 3 1 2 0 2 1 3 1 8 2 0 3 1 1 1 2 2 3 3 44 2 2 0 1 1 1 35 0 3 0 2 2 3 0 2 0 0 2 1 3 2 2 0 0 2 3 0 2 2 2 3 1 3 2 3 1 1 2 3 3 2 53 2 3 2 3 3 1 2 1 0 3 0 26 44 3 1 2 0 0 0 1 2 2 2 1 3 3 3 0 8 2 3 3 3 0 0 2 2 3 3 0 3 1 2 3 2 0 3 1 3 1 3 2 3 26 1 2 2 0 3 0 1 2 2 26 1 0 1 1 2 0 53 0 3 1 2 2 1 3 0 0 1 2 2 0 3 1 1 0 1 1 0 0 1 3 3 2 3 2 26 3 2 2 2 1 0 3 3 1 3 35 0 0 17 0 2 3 2 3 1 1 35 2 0 2 3 2 3 26 1 2 3 2 3 0 0 3 3 3 44 2 2 2 1 3 17 44 3 3 1 2 2 2 2 44 2 2 0 3 0 1 1 3 1 0 1 3 2 2 2 3 1 0 1 0 0 2 53 0 26 1 3 3 2 0 1 0 0 8 1 2 3 0 0 2 3 2 1 2 53 3 2 3 0 0 1 2 1 0 3 0 1 1 0 3 1 2 1 3 3 0 1 0 2 2 3 0 2 2 2 0 0 3 2 2 1 3 1 1 1 1 1 2 1 8 53 3 0 2 0 53 3 0 2 1 1 1 0 0 2 3 1 3 1 2 3 1 17 0 1 1 2 0 0 1 2 0 1 3 1 2 2 44 3 1 8 1 2 0 3 2 3 3 3 2 1 0 2 2 0 3 3 3 3 2 3 0 3 3 3 2 3 3 0 3 2 44 1 0 0 2 2 17 1 44 17 44 1 3 3 0 3 3 26 3 2 2 1 1 1 3 0 3 26 1 1 2 1 0 0 2 3 2 0 17 3 3 1 1 1 2 2 8 0 2 3 2 3 2 1 3 1 0 0 2 0 2 3 1 3 2 2 2 1 0 2 0 2 1 0 2 2 2 0 1 2 53 2 1 2 0 0 3 3 2 2 1 3 0 0 1 1 3 1 0 2 1 8 26 0 2 1 0 0 3 53 44 0 1 53 2 1 1 3 0 1 3 2 2 2 1 3 1 0 2 1 3 1 0 2 3 1 0 2 0 2 2 0 0 3 1 0 2 2 3 0 2 1 8 0 1 2 1 2 2 2 3 0 35 2 1 3 2 1 3 26 26 3 0 1 1 1 1 3 0 2 2 0 0 1 1 3 1 1 3 2 0 1 1 2 0 3 0 2 3 2 1 3 1 1 3 3 2 35 0 1 1 2 3 3 0 1 1 3 2 3 1 26 1 1 3 2 1 3 3 2 3 2 1 1 1 0 2 0 2 3 3 3 44 3 2 3 0 3 0 2 3 0 2 3 3 0 0 2 3 3 1 3 1 17 8 3 3 2 2 44 3 3 1 3 1 2 2 1 0 2 53 2 3 1 0 2 1 1 1 0 0 0 2 2 0 2 2 3 26 3 2 2 3 1 2 1 1 3 53 1 3 0 2 2 2 2 3 44 17 0 0 3 0 3 1 1 35 3 2 1 3 3 44 0 3 3 1 1 8 0 2 35 3 0 2 0 3 17 26 1 3 2 0 26 1 2 44 0 2 3 3 3 3 1 1 1 1 0 0 0 0 3 2 2 2 0 0 8 1 1 0 0 2 2 1 44 3 3 0 2 44 3 1 3 1 1 0 0 0 2 2 3 1 2 0 3 3 1 2 0 2 3 0 2 1 0 2 3 2 0 3 26 0 2 1 2 3 3 1 0 2 3 2 3 3 0 0 2 3 1 3 3 1 3 0 0 2 2 35 3 1 0 1 2 3 0 1 2 3 44 0 0 0 1 2 3 1 3 3 3 1 0 2 3 0 3 0 1 0 0 2 1 1 2 3 53 3 2 1 0 1 1 3 1 3 2 0 3 1 0 0 0 2 2 0 1 1 1 3 2 0 1 2 0 3 0 3 2 3 0 0 3 26 3 3 1 0 1 3 1 1 3 1 35 3 3 0 3 2 3 3 2 1 0 2 3 2 0 1 3 1 1 2 1 3 2 2 17 3 1 1 35 1 0 0 3 2 3 1 2 17 3 2 3 2 2 3 1 35 2 0 1 3 0 0 0 1 3 3 0 2 0 1 0 2 0 3 2 2 53 1 2 35 1 3 1 2 0 1 0 2 1 0 0 1 2 8 3 2 0 2 0 3 2 0 1 2 2 1 1 8 2 3 3 8 2 0 1 2 3 1 0 1 0 1 1 0 1 3 8 0 2 35 3 0 0 3 2 2 3 2 2 2 1 2 2 2 0 8 8 26 1 1 1 3 2 0 2 0 35 2 0 26 2 2 2 1 2 1 1 2 1 1 2 2 3 2 0 0 2 1 3 0 2 3 3 1 2 1 2 2 2 1 3 0 8 17 0 0 17 44 2 1 2 0 1 1 2 8 3 3 2 3 0 3 2 2 0 2 3 3 1 2 0 1 2 1 1 2 2 3 1 2 2 2 8 1 3 2 0 3 0 3 0 2 3 1 1 3 2 0 3 3 1 3 1 3 26 17 35 1 3 3 3 2 1 1 8 1 3 1 3 1 26 0 26 2 0 0 3 44 3 2 0 2 44 3 1 26 1 2 3 0 1 3 0 26 2 35 0 44 8 3 1 3 17 1 3 2 0 1 2 35 0 1 2 3 3 2 1 1 3 0 3 2 17 1 2 3 3 0 2 1 2 0 0 3 2 1 0 2 3 3 1 8 3 26 2 1 0 0 3 3 3 3 1 8 3 1 2 0 3 2 0 1 3 2 8 3 44 0 3 0 0 1 3 0 53 2 3 2 0 2 2 2 2 44 0 3 2 1 2 0 3 2 3 1 3 1 3 0 3 3 0 2 17 1 3 1 0 1 0 1 1 2 0 3 3 2 3 0 8 2 0 1 3 1 2 3 0 3 1 2 2 3 1 0 1 2 2 0 0 1 3 3 3 3 2 35 3 3 3 3 0 1 35 0 0 26 2 26 1 1 2 0 2 1 35 1 1 3 3 3 1 2 0 0 35 3 3 2 2 2 1 1 2 0 0 2 0 2 0 2 2 2 3 0 1 2 2 2 17 0 26 3 2 17 0 0 3 2 2 3 3 2 0 2 26 0 3 3 2 3 3 2 44 1 1 1 1 2 1 44 2 0 1 1 0 3 0 3 1 17 1 3 1 2 1 1 1 3 0 2 2 3 2 3 0 1 3 3 1 26 2 3 0 26 35 0 0 2 1 0 3 3 0 0 1 3 2 0 2 3 3 3 8 3 3 3 2 2 0 3 2 2 3 3 0 0 2 17 0 1 0 0 8 2 0 0 1 0 2 1 3 3 0 1 0 2 2 0 1 2 0 53 1 0 2 8 3 1 1 1 3 8 1 53 2 1 1 44 3 3 1 3 0 0 1 0 1 1 3 2 3 1 1 3 1 44 1 3 3 1 3 17 2 3 2 0 2 1 1 1 1 3 0 0 35 3 3 44 1 0 0 3 1 0 1 1 0 0 0 8 0 2 1 3 3 0 1 26 2 3 1 1 3 3 53 2 3 3 0 2 1 2 3 0 2 0 0 2 0 0 17 1 1 0 0 2 3 0 8 26 3 0 3 35 2 3 1 0 1 0 3 1 2 0 3 2 2 0 0 1 0 2 2 0 44 1 53 2 0 0 3 1 3 8 2 3 2 1 3 1 3 2 2 3 1 35 1 3 2 3 2 1 3 0 0 3 1 2 1 2 2 3 1 0 0 3 17 0 2 0 2 0 2 0 2 3 1 0 0 3 0 3 3 3 0 3 1 2 3 3 3 1 8 0 0 1 0 3 2 44 3 2 2 2 2 1 2 53 0 0 1 0 1 2 17 2 3 2 3 0 2 3 3 3 0 35 2 2 0 3 0 2 1 1 0 2 3 1 0 3 3 1 0 2 8 0 2 1 2 2 3 35 8 3 1 1 0 1 2 3 3 1 1 3 0 0 1 26 2 2 2 44 2 1 1 0 3 3 3 26 2 0 1 2 3 44 2 1 1 2 2 2 0 1 0 3 3 3 3 0 0 44 2 8 0 2 1 3 26 0 1 1 2 1 1 3 0 2 2 2 2 26 0 0 3 3 1 1 0 0 1 3 3 0 2 0 26 1 1 1 2 2 2 2 1 3 3 35 1 35 1 1 3 2 2 3 3 0 0 17 1 0 1 3 0 2 2 2 3 3 0 1 2 0 3 1 3 3 2 2 0 2 35 26 2 1 3 3 3 8 1 26 3 1 1 1 1 3 2 3 0 1 3 0 1 3 2 3 0 1 1 1 2 0 17 3 35 1 0 0 3 1 2 0 2 3 0 0 0 1 1 0 0 1 3 3 1 0 2 35 1 3 2 0 0 2 2 2 1 3 0 3 2 1 1 0 26 3 1 2 1 35 2 3 1 1 0 1 3 0 3 0 26 2 3 0 2 0 35 2 2 2 26 1 2 3 0 1 1 3 1 26 1 1 26 1 53 2 2 2 2 0 3 0 1 2 3 1 3 0 3 1 1 3 0 2 0 1 26 0 0 1 1 3 26 1 1 1 1 3 1 2 1 17 3 1 0 53 1 1 0 3 1 3 0 1 1 3 2 2 1 0 3 3 3 44 1 0 3 2 1 3 2 0 3 0 0 26 3 3 2 2 1 1 0 0 26 2 3 1 0 0 1 0 1 3 3 0 0 1 0 0 26 44 1 1 1 1 1 1 44 0 2 2 0 3 2 44 1 3 0 0 8 0 1 1 1 1 1 1 3 0 3 44 3 1 1 0 1 3 8 17 1 2 1 0 0 26 2 2 1 3 1 1 2 3 2 1 0 3 1 26 17 2 0 2 2 1 0 0 3 1 1 3 0 3 2 0 3 3 0 3 35 1 0 2 1 0 2 3 2 17 2 0 3 8 1 3 3 2 1 3 53 0 0 0 2 0 0 1 0 0 0 53 2 3 2 3 3 3 1 3 3 1 26 0 0 1 0 44 3 3 3 1 2 1 3 0 3 44 1 3 35 0 0 1 1 2 2 3 1 3 1 2 0 0 2 3 3 0 0 3 1 1 0 0 3 1 44 0 2 0 0 0 3 1 53 3 2 0 1 2 2 3 3 0 3 1 3 0 2 3 1 8 1 3 3 0 1 0 0 3 3 2 1 2 1 2 3 3 3 3 0 0 1 1 0 53 0 2 35 3 3 1 2 3 17 3 3 0 0 0 1 3 2 0 2 0 1 2 2 3 2 2 0 44 3 1 2 2 2 0 3 0 0 0 2 3 3 3 2 3 3 2 2 0 3 17 0 3 1 0 3 0 2 0 0 1 0 0 2 44 35 1 0 3 3 1 2 3 0 1 1 17 3 2 2 3 2 2 3 53 8 44 3 2 3 1 2 0 2 1 2 0 2 0 3 3 0 1 1 1 3 0 0 1 0 0 44 3 0 2 2 3 0 2 0 0 2 1 2 1 0 3 2 1 2 1 0 2 0 0 2 0 2 2 26 0 2 3 2 44 0 0 44 3 1 3 3 0 1 1 0 3 2 17 1 0 0 3 0 1 1 0 1 0 0 3 2 0 2 1 1 8 2 2 3 17 3 0 2 1 1 1 3 1 2 35 1 1 1 2 2 3 2 1 1 3 3 3 1 2 0 1 1 3 1 3 35 1 1 3 2 17 0 53 1 2 2 0 2 3 3 53 3 1 3 3 0 2 1 3 2 1 0 1 2 2 2 2 0 1 2 1 3 1 53 2 3 2 3 1 3 3 2 1 3 2 0 1 0 1 3 1 0 1 0 3 1 3 0 0 1 0 1 3 0 2 8 8 2 2 2 1 2 2 3 1 8 1 1 2 3 1 2 0 2 3 53 2 0 0 1 0 0 3 2 0 0 1 26 0 0 0 0 2 53 1 3 0 2 1 0 2 2 3 3 0 3 3 1 2 1 1 3 1 0 3 3 1 53 3 1 0 2 2 0 2 2 2 0 3 2 0 1 1 2 3 2 53 3 3 1 2 3 0 1 3 0 44 3 0 2 2 3 2 1 1 3 1 2 3 3 2 35 2 2 1 1 0 0 2 1 3 0 0 2 2 3 3 3 0 2 2 2 1 2 1 2 0 2 2 3 3 1 35 2 0 1 2 2 3 3 3 1 2 2 2 1 2 1 3 3 2 0 3 2 3 2 44 2 2 1 3 8 3 2 0 1 2 1 17 0 44 0 0 0 35 2 0 17 3 0 0 0 2 1 1 3 44 0 2 0 3 0 3 35 0 2 0 2 1 0 0 1 1 1 0 3 3 0 53 17 2 2 1 1 2 35 0 3 0 3 1 3 0 1 3 3 1 3 3 0 2 3 17 3 1 1 8 3 17 2 1 1 1 3 3 3 1 2 1 2 1 2 8 1 3 2 2 2 44 1 1 35 1 1 3 2 3 1 2 2 2 2 1 0 1 1 3 0 0 1 1 1 2 17 3 2 1 1 2 3 2 0 0 1 2 2 2 0 3 2 0 2 3 3 1 3 1 2 1 1 0 2 1 1 1 0 2 35 26 0 17 3 2 2 1 0 1 2 0 1 0 0 1 2 2 3 0 3 0 3 1 1 1 0 1 44 1 1 0 26 0 53 0 2 8 3 2 0 8 1 2 0 2 2 0 1 0 2 2 1 3 35 1 3 1 0 0 0 8 0 0 2 35 3 2 53 2 0 2 3 2 3 3 17 3 1 2 2 17 1 26 3 3 2 1 0 0 3 8 2 17 2 3 2 3 1 1 1 2 0 0 2 1 1 3 8 1 1 1 3 2 0 0 2 1 0 0 1 3 0 1 2 3 2 1 0 0 1 1 1 0 0 0 2 1 0 1 1 2 1 2 17 1 1 1 1 53 2 2 0 1 2 0 3 3 0 1 53 0 0 1 1 3 2 0 3 53 2 3 0 3 1 3 0 2 3 44 0 0 1 0 3 0 2 0 0 2 3 1 2 3 1 3 1 2 1 2 0 3 1 35 0 1 3 2 2 2 1 2 2 1 1 0 1 3 0 1 3 0 2 0 3 2 2 2 1 3 0 1 3 0 1 1 3 3 1 3 2 2 3 0 0 3 3 2 1 1 0 2 53 2 1 2 0 0 44 1 3 0 2 2 0 0 3 0 1 0 0 1 2 2 0 0 2 2 1 0 2 2 3 3 1 2 2 3 3 3 3 3 0 0 0 1 0 3 0 0 2 0 2 2 2 0 1 2 8 0 8 3 2 1 0 3 35 1 0 3 2 2 44 2 0 2 12 1 0 2 1 2 2 1 2 2 2 2 3 0 2 44 17 53 1 0 53 35 2 2 3 53 17 2 1 1 0 1 1 3 3 1 1 53 3 3 3 1 0 2 2 0 2 0 0 1 2 35 2 1 2 2 3 3 2 0 8 1 1 0 3 1 1 2 0 8 3 2 3 3 3 0 8 3 0 0 3 3 3 2 17 0 0 2 2 3 3 2 1 3 26 1 1 3 3 3 2 2 35 2 3 1 1 0 3 3 1 0 44 2 0 17 35 1 3 1 2 44 3 44 1 3 2 2 2 2 1 3 0 0 2 1 0 1 2 0 0 2 2 3 2 1 2 2 0 0 8 0 3 3 0 3 53 3 0 3 3 2 3 1 0 0 2 0 3 2 3 2 1 3 0 44 3 1 3 3 2 2 3 1 2 3 2 2 2 3 0 1 2 53 1 3 1 53 1 3 0 2 0 0 3 0 3 0 26 1 1 3 2 1 3 3 0 1 0 2 0 2 1 1 1 2 1 3 3 2 1 3 1 3 2 2 2 2 1 1 3 44 8 0 3 2 1 3 0 0 0 3 0 1 3 1 26 0 1 1 1 8 0 2 1 0 2 2 0 2 2 1 0 1 3 1 2 0 1 1 53 2 8 0 1 0 0 3 2 3 1 2 1 1 1 1 3 44 3 2 0 2 0 3 3 0 2 3 0 35 3 2 1 3 3 1 1 2 2 0 3 3 1 17 44 3 2 44 1 0 0 1 3 1 2 2 3 0 2 2 2 1 3 3 2 0 2 3 2 2 0 1 2 3 1 0 0 0 44 1 2 3 0 8 0 2 3 0 3 2 1 2 8 1 3 8 2 3 26 1 1 3 2 2 1 35 3 2 2 2 1 0 0 0 3 0 0 0 3 0 1 1 0 0 1 3 35 3 3 0 2 0 2 44 35 2 1 0 1 0 35 2 2 3 0 2 3 3 8 2 1 3 1 1 35 0 0 8 2 1 2 1 3 2 2 2 1 2 3 0 1 0 3 2 3 0 2 3 3 8 2 0 3 2 2 0 2 0 3 2 0 0 0 2 3 3 3 3 0 2 1 0 0 53 53 2 0 3 0 0 2 2 3 3 3 1 3 1 1 2 3 3 2 0 3 3 2 2 8 0 1 1 2 0 1 2 0 0 3 2 2 1 0 0 3 3 44 0 2 2 2 2 0 1 1 8 2 8 0 44 3 3 1 1 3 3 1 3 4 2 35 2 3 2 1 2 2 1 17 3 2 0 3 3 3 0 1 3 0 1 1 2 3 3 0 0 3 3 1 3 0 2 0 0 2 2 2 3 0 2 3 3 26 2 1 0 3 1 8 1 0 0 1 0 0 0 2 0 2 1 2 0 44 2 0 2 3 0 3 2 2 2 2 1 1 0 3 3 0 2 2 2 2 2 3 0 1 0 3 1 3 3 1 1 1 0 2 17 0 1 0 3 1 0 2 3 2 3 53 2 3 0 0 2 53 3 0 1 3 3 3 44 0 3 8 1 3 2 1 2 2 2 3 53 2 1 0 3 1 17 2 0 0 3 1 44 1 3 0 3 2 3 0 2 2 3 17 3 2 0 26 3 1 0 3 1 1 2 2 0 17 0 1 0 0 26 1 1 2 0 17 1 3 2 0 0 1 3 3 0 1 3 0 2 2 0 44 1 3 0 0 2 2 1 3 1 2 3 3 44 35 0 1 53 0 0 26 17 0 1 2 0 1 3 0 1 3 1 1 2 3 2 2 1 1 0 0 26 1 53 2 0 3 1 3 0 1 3 3 3 3 2 3 2 0 2 2 0 1 0 1 0 3 1 3 3 0 1 1 3 0 2 2 2 0 1 1 3 0 3 3 2 44 17 3 3 1 0 3 0 0 3 2 2 1 0 3 2 3 2 0 2 8 0 2 2 0 3 0 1 2 0 3 0 3 0 2 2 0 0 2 3 26 3 3 0 2 1 0 2 2 3 44 3 0 2 2 1 0 3 1 2 26 1 3 0 0 0 3 0 0 2 3 1 2 1 2 44 3 1 0 0 1 2 3 0 1 3 0 0 1 3 8 2 2 1 1 3 2 0 0 44 35 2 0 1 1 3 0 1 1 1 3 1 0 0 2 1 35 1 2 1 2 0 2 0 1 2 1 2 0 2 0 1 3 1 3 0 1 0 2 2 17 1 3 2 2 3 0 2 3 2 2 1 3 1 17 2 44 1 0 1 3 3 3 1 1 3 3 3 0 1 0 1 0 0 1 2 0 3 3 1 0 2 2 3 53 35 2 26 0 3 0 0 2 3 2 1 2 0 3 0 2 1 3 0 1 0 0 1 3 1 2 26 2 3 1 2 2 2 3 1 1 3 2 1 53 0 53 1 3 0 1 1 3 0 1 1 1 2 3 3 1 0 1 1 0 1 0 1 0 3 26 3 44 3 1 3 1 1 2 2 0 1 0 0 3 0 2 3 2 3 0 3 3 1 3 3 3 2 0 0 0 3 2 3 3 2 2 0 3 2 3 0 2 2 2 1 3 1 17 1 0 2 3 2 1 2 17 3 3 0 0 3 0 3 3 2 3 1 0 2 1 0 2 0 3
+CHIMP 0 2 0 1 3 2 0 0 44 44 1 1 3 1 0 3 2 0 3 3 1 1 3 2 3 3 2 8 0 0 3 2 3 1 3 3 2 1 26 2 3 44 3 3 35 1 2 3 0 0 1 53 3 0 0 1 1 2 35 1 0 1 2 2 0 1 2 1 3 1 3 26 0 3 3 0 1 1 35 0 0 2 3 3 0 1 3 0 2 1 35 3 3 2 0 3 1 3 2 1 3 1 2 3 3 2 3 2 8 3 1 2 0 17 3 1 0 3 2 2 3 1 1 2 2 0 2 3 44 1 3 26 2 0 0 1 3 3 1 17 3 1 3 1 3 2 0 1 3 2 0 2 0 0 0 0 0 1 1 3 1 2 0 0 0 1 0 2 1 17 1 8 2 2 2 0 2 44 17 3 3 2 3 1 1 0 3 0 0 0 1 2 1 3 1 0 2 2 2 2 0 1 0 0 1 0 2 3 1 1 0 0 26 0 2 1 3 2 2 1 3 3 1 1 1 2 2 2 3 3 1 2 3 26 0 26 2 0 0 2 0 0 3 3 1 1 0 2 3 44 2 0 0 3 3 44 1 3 3 0 3 3 1 2 3 2 0 0 44 3 2 3 2 3 2 26 1 1 2 1 2 2 2 35 3 17 1 3 3 2 0 1 3 0 2 3 44 1 1 0 0 0 3 2 1 53 1 2 2 0 0 1 0 0 1 1 0 26 2 0 3 1 0 2 2 0 1 3 53 0 0 3 0 2 2 1 2 1 1 0 3 0 1 3 8 0 1 3 2 17 3 2 0 1 1 0 8 0 2 1 2 3 3 2 0 1 17 0 1 0 0 2 1 57 3 2 0 1 0 3 26 0 1 3 2 3 2 2 2 17 2 1 2 0 0 0 0 8 0 2 0 2 3 2 2 3 2 2 0 35 1 1 2 3 0 1 1 3 0 3 0 3 2 3 0 3 2 3 1 3 1 1 3 0 1 1 0 3 3 3 0 26 3 3 3 1 2 2 17 3 1 0 0 3 2 0 1 44 1 53 0 0 0 3 2 2 3 17 2 2 2 2 0 2 1 3 1 3 1 17 3 1 3 0 0 3 0 3 2 2 1 3 0 2 3 2 0 3 2 1 2 1 0 2 2 1 0 0 3 3 1 1 3 0 3 44 2 1 0 0 0 3 53 2 31 53 35 0 0 53 26 3 1 2 8 2 1 3 3 44 3 53 2 1 3 1 0 2 0 2 44 3 53 3 3 1 3 1 2 0 3 0 1 3 0 1 1 53 3 0 0 3 3 3 3 3 0 0 1 2 3 3 3 0 0 2 1 8 3 53 1 1 0 3 2 0 1 2 0 53 3 2 2 3 44 3 3 1 1 2 17 1 2 3 0 3 2 1 2 2 0 0 1 2 17 1 2 35 2 1 0 3 0 3 2 3 1 3 35 2 2 1 3 1 17 0 1 35 3 2 3 1 0 1 0 3 0 3 53 0 26 3 3 1 1 0 0 0 1 2 2 1 3 3 0 0 17 3 35 1 1 2 1 1 0 1 0 35 3 1 1 2 1 1 2 3 2 8 0 1 3 35 1 0 1 26 3 3 1 2 0 3 1 0 0 1 35 3 26 35 3 1 3 1 3 2 2 1 35 2 1 0 1 2 2 1 1 0 2 3 2 3 3 2 2 0 2 1 2 1 35 1 3 3 1 3 0 1 53 1 3 3 3 44 2 2 1 0 1 3 1 26 0 1 1 0 3 0 8 3 2 1 2 53 1 2 3 53 17 1 1 2 53 0 35 2 0 3 0 17 8 2 1 0 3 1 2 2 1 3 8 1 0 2 3 1 0 2 3 0 3 3 8 1 2 1 3 1 1 3 0 1 35 2 0 0 3 3 3 1 3 3 0 3 3 3 3 0 3 0 2 3 1 2 17 8 1 0 3 17 2 2 3 2 3 2 1 1 0 26 1 0 1 2 0 1 2 1 0 1 3 0 1 35 0 3 1 0 3 3 3 3 0 3 3 0 2 1 2 1 0 3 1 17 1 0 0 3 3 0 3 2 2 0 3 2 1 0 0 0 2 26 1 3 2 0 2 1 2 2 1 1 3 3 3 0 0 2 1 2 3 1 0 0 2 0 3 1 3 8 0 1 2 0 3 35 3 2 1 2 2 3 26 2 3 2 2 3 2 1 2 0 0 0 1 3 1 2 0 0 0 2 8 3 2 1 1 2 0 0 1 3 2 2 44 0 2 0 1 0 0 3 2 3 2 3 1 3 2 44 0 1 0 3 26 0 3 8 1 3 2 2 2 0 0 2 0 0 2 3 0 1 0 0 3 0 3 3 2 1 0 2 8 3 0 3 3 3 0 1 1 0 3 1 2 1 1 0 2 3 53 3 1 1 3 2 3 0 3 35 1 0 17 1 2 1 1 35 0 0 2 3 17 17 1 0 1 3 2 3 3 1 0 53 3 0 3 3 1 2 3 0 2 2 0 2 2 1 0 17 2 0 3 1 3 2 2 1 1 3 2 3 1 2 1 0 1 0 0 3 0 0 1 2 1 1 3 2 2 2 1 44 3 0 3 3 8 3 0 1 3 1 0 3 2 3 17 0 2 3 44 2 0 3 1 3 0 44 3 2 3 1 0 0 0 1 1 0 3 3 44 3 0 1 0 8 35 1 17 2 1 1 0 1 1 3 2 0 3 1 1 3 0 2 3 3 3 1 3 2 2 2 2 1 3 0 0 2 0 1 1 1 1 1 1 3 0 3 2 1 3 35 1 0 1 1 3 1 1 1 3 2 1 2 2 3 2 0 3 3 2 3 1 2 3 1 53 0 1 3 1 0 2 2 2 3 0 1 1 2 0 3 1 3 1 0 1 26 1 1 2 3 1 0 2 1 1 3 3 2 35 1 0 2 0 3 0 2 3 26 1 0 1 2 1 0 44 0 3 3 0 2 2 1 2 3 1 1 2 1 17 1 1 3 2 0 1 0 1 2 1 2 3 2 1 2 2 35 1 3 3 0 3 1 1 35 1 1 0 0 3 0 0 2 2 3 3 44 2 0 1 3 3 3 1 3 1 0 0 26 44 0 0 3 3 26 0 8 2 3 0 17 0 0 35 3 1 3 8 3 1 1 3 1 2 1 2 0 3 2 3 0 3 1 1 2 3 1 0 2 1 2 0 1 3 0 1 0 0 2 1 1 3 3 1 0 3 3 3 1 3 3 3 35 2 2 1 2 2 0 3 3 0 1 3 2 8 0 0 0 2 1 3 0 2 3 0 3 1 0 0 8 3 2 2 0 3 0 3 0 0 1 2 1 8 0 1 2 1 1 2 1 1 0 1 53 3 2 2 3 1 1 0 1 2 2 1 3 2 1 2 0 3 1 2 3 3 17 0 0 2 0 3 1 0 2 0 2 0 3 0 1 0 2 3 1 53 2 2 3 3 2 2 1 2 3 0 1 2 3 0 1 26 0 2 0 2 3 2 0 0 53 2 1 0 44 1 0 1 0 3 1 1 3 0 2 2 1 0 2 1 2 1 2 0 0 2 0 3 1 26 1 1 2 1 1 3 0 0 2 3 3 0 0 3 8 3 2 3 0 0 1 1 0 3 3 3 2 2 3 1 1 3 0 8 0 3 3 2 2 3 3 1 1 1 0 3 0 2 0 17 1 1 0 3 8 0 1 1 53 3 3 0 0 3 1 3 0 0 1 1 2 3 3 0 1 0 2 0 2 35 17 0 0 2 0 0 3 2 3 0 1 0 1 3 53 3 1 2 1 1 2 1 2 1 0 2 1 2 0 3 3 17 1 3 2 3 3 0 1 3 2 3 17 3 1 17 1 3 0 3 1 0 3 2 2 0 35 26 3 35 3 3 17 3 2 26 0 3 44 2 2 2 44 3 1 1 0 2 3 3 0 1 2 2 0 1 2 2 0 3 35 3 3 0 1 3 1 3 57 1 3 44 2 2 2 3 2 3 1 2 3 2 53 35 2 2 2 1 1 2 2 1 2 1 1 2 1 1 2 1 0 0 3 0 0 53 0 1 1 0 1 2 2 3 1 2 0 8 2 0 2 2 53 2 3 3 0 3 2 8 1 2 3 3 2 3 0 1 1 1 3 1 2 1 1 3 1 1 3 1 2 0 2 2 3 2 1 1 2 1 2 0 3 1 2 3 2 3 1 1 3 1 3 3 53 3 1 0 1 3 1 53 2 2 0 8 1 2 1 0 2 53 53 2 2 2 0 2 2 1 26 2 0 2 0 2 1 0 0 0 44 3 3 0 2 1 8 2 3 1 8 2 0 1 0 1 44 1 3 0 1 2 2 2 0 3 3 2 0 1 17 2 44 1 2 1 2 3 8 0 1 1 3 3 1 3 3 26 0 3 2 2 1 0 2 2 0 0 3 2 0 3 3 1 1 1 3 3 3 0 0 8 2 0 2 0 3 1 0 0 35 3 1 53 3 0 44 3 0 17 1 3 3 3 2 2 2 1 3 1 8 0 0 3 0 2 0 0 1 3 0 3 2 17 2 0 0 3 1 0 3 0 1 3 0 3 1 3 1 0 0 0 1 1 2 53 8 35 3 2 2 0 2 2 3 44 0 0 0 0 2 1 2 2 2 2 0 0 2 2 0 1 3 1 3 3 1 0 3 2 1 1 3 1 0 2 1 8 0 0 1 0 3 1 0 3 2 1 1 3 2 2 1 1 3 3 1 3 3 2 1 44 3 1 3 0 3 0 3 3 0 2 1 2 0 3 2 1 3 2 0 0 3 0 0 0 2 2 2 1 2 1 0 0 35 0 0 2 35 1 0 2 44 3 44 2 1 2 8 2 0 0 0 1 1 3 3 1 2 2 0 3 1 0 0 1 2 3 0 1 1 3 1 3 0 2 1 1 3 2 3 1 44 0 0 2 1 1 2 0 1 1 3 1 3 0 2 2 3 3 0 3 2 3 1 1 1 2 2 17 3 3 2 44 44 2 1 1 17 3 3 0 44 0 0 2 35 3 8 2 2 0 1 3 1 0 0 0 2 17 2 2 1 0 1 1 0 3 0 2 2 0 53 3 1 44 1 2 0 3 3 3 8 0 2 0 3 2 3 26 1 44 35 2 2 1 3 2 3 1 35 2 2 1 2 1 44 2 3 1 2 3 3 26 3 0 2 3 2 3 1 0 2 3 0 0 0 0 2 0 1 17 3 3 44 1 0 3 1 2 1 3 1 3 35 53 0 26 2 2 2 3 2 2 0 1 3 44 0 2 2 1 1 2 0 0 1 1 1 1 2 2 0 0 0 1 2 1 0 2 0 1 3 2 8 2 2 1 2 3 0 0 1 1 2 3 8 0 2 8 1 0 1 3 0 1 44 2 2 0 3 3 2 0 2 3 1 1 1 0 2 2 0 3 0 1 2 1 3 0 2 0 1 0 3 2 2 3 1 2 3 53 8 2 2 26 0 2 2 3 3 8 3 3 0 3 1 3 1 3 2 53 0 1 1 3 0 17 3 2 1 1 3 3 53 0 2 3 0 2 1 2 0 0 0 2 0 2 0 0 2 2 1 1 1 1 0 0 1 3 35 0 2 3 1 1 2 0 2 17 3 1 2 2 3 0 1 0 0 2 0 3 2 1 1 2 1 2 2 2 3 2 35 1 3 2 0 0 0 3 2 0 3 2 0 17 3 26 3 3 3 2 8 35 0 2 3 0 0 1 2 0 3 2 2 3 3 2 1 2 1 2 2 0 44 1 2 3 2 3 3 3 3 0 2 3 0 3 2 2 53 2 2 1 2 3 0 1 1 1 0 1 1 0 3 1 0 3 1 3 44 3 0 1 1 3 0 0 1 2 1 0 1 1 1 3 0 3 35 1 3 0 8 0 17 3 2 1 2 1 3 3 3 3 1 1 3 0 0 3 1 3 1 1 0 1 0 1 0 2 0 3 3 3 1 1 1 1 1 0 2 0 3 3 2 1 1 1 1 35 0 0 0 1 0 0 0 1 1 2 2 53 2 0 3 2 2 0 3 1 0 8 1 2 2 1 0 3 3 2 0 3 8 2 2 1 35 3 26 2 1 3 1 0 1 2 2 3 8 1 3 1 1 1 1 1 3 2 0 2 1 2 2 1 35 26 3 3 3 2 2 3 3 2 0 1 17 3 2 3 2 0 2 1 1 2 0 3 3 0 2 3 3 1 3 0 3 0 0 0 44 8 1 1 2 1 0 3 2 44 44 17 3 0 0 0 1 3 2 1 53 3 2 1 3 1 53 8 0 3 1 0 44 44 2 0 1 2 2 3 3 0 0 26 1 1 0 2 0 0 1 35 3 0 8 2 35 2 1 0 26 1 0 3 53 2 2 0 17 0 3 0 3 2 1 3 1 1 2 53 8 0 3 1 3 1 0 2 1 0 44 0 3 0 0 3 1 2 0 0 1 1 35 26 2 1 0 1 2 1 1 1 53 3 1 44 3 2 1 3 3 0 3 3 0 2 1 22 0 2 0 1 3 0 0 2 0 53 3 2 2 0 0 3 0 0 8 0 1 53 3 2 2 3 0 2 2 3 0 0 0 3 3 35 3 3 3 1 3 0 3 1 2 53 0 1 3 1 3 3 1 2 2 1 26 1 1 2 1 1 2 0 3 3 3 1 2 2 2 3 3 2 1 3 3 0 1 3 3 0 3 35 3 1 3 2 3 3 1 1 3 3 0 2 0 3 2 0 1 17 3 3 2 0 2 2 1 1 1 0 2 2 3 17 3 2 3 0 3 0 1 0 44 1 1 1 2 8 3 3 35 0 2 2 2 1 3 2 2 3 2 1 0 3 3 35 0 1 1 2 2 0 2 17 0 26 0 2 0 2 1 2 1 0 3 0 3 2 35 2 0 0 0 3 1 1 0 3 1 8 1 0 0 1 1 1 3 1 0 2 1 3 1 0 1 1 3 1 0 1 3 3 1 3 0 3 3 2 0 0 0 1 17 1 2 2 3 3 1 3 0 2 26 35 1 3 0 1 1 0 0 1 1 0 1 3 0 3 0 1 2 3 3 2 3 0 1 12 2 35 53 0 3 2 2 1 0 3 3 0 1 3 8 53 0 2 53 1 3 2 3 3 0 1 1 1 3 0 2 3 0 0 2 2 0 26 2 2 3 3 1 26 2 2 3 3 2 0 1 2 1 0 1 2 3 26 17 2 2 1 2 3 3 0 2 3 0 1 0 1 3 2 1 1 1 0 2 1 3 3 3 44 2 0 3 3 3 2 0 1 3 1 53 0 8 0 2 35 2 2 2 35 1 1 2 1 2 1 1 3 1 0 1 1 2 2 0 2 26 1 1 1 3 3 2 1 0 2 3 2 1 3 3 0 1 1 1 1 2 3 1 0 0 53 3 2 1 1 8 2 1 1 1 1 2 0 0 3 2 2 3 3 2 2 2 3 2 3 0 8 1 2 1 2 3 44 3 2 1 1 8 2 1 2 0 1 2 0 3 3 2 0 1 3 1 0 0 2 3 1 2 35 3 0 3 1 0 0 3 2 2 3 1 2 1 3 2 1 1 44 3 2 1 3 1 2 1 53 0 17 2 0 2 1 1 3 2 44 2 1 1 0 1 1 4 2 2 3 3 0 3 2 2 3 1 1 0 1 0 53 35 2 0 2 0 1 3 2 2 2 3 3 0 1 3 2 2 0 3 0 3 2 1 1 0 53 1 2 0 53 0 0 0 1 3 3 1 0 0 2 1 0 2 2 1 1 0 2 1 44 0 0 0 1 26 3 3 3 44 1 1 2 2 3 2 2 3 2 2 0 1 2 2 8 3 3 0 1 0 3 0 1 1 0 0 3 1 0 1 2 0 0 1 2 3 3 2 1 53 0 3 1 1 3 3 1 1 1 0 2 2 2 1 3 0 0 2 3 0 1 1 3 3 1 2 1 3 8 1 2 2 3 3 3 0 2 0 2 3 1 2 1 2 0 2 0 1 0 1 8 2 2 0 2 0 1 2 3 3 2 0 3 8 0 2 53 2 3 2 3 2 0 1 3 1 1 53 3 35 1 0 3 1 0 3 0 1 3 0 2 1 3 0 2 0 3 0 0 3 2 0 2 3 26 2 2 3 2 2 1 2 0 2 0 2 0 2 0 1 1 2 0 0 3 3 1 2 1 8 2 3 0 3 3 44 1 2 2 0 2 0 3 0 3 3 0 0 35 3 0 0 3 0 17 2 35 3 3 3 1 3 2 0 1 2 1 3 1 1 8 8 1 3 0 0 2 1 0 3 3 35 0 0 3 0 0 2 3 2 1 3 1 3 2 3 2 8 3 0 3 3 0 3 1 2 1 3 3 3 2 0 1 3 2 0 1 3 1 3 3 0 35 1 1 1 3 3 0 3 3 0 0 2 26 2 1 3 3 3 44 0 26 3 3 1 1 3 1 1 1 0 3 2 44 1 3 0 3 0 2 3 2 3 3 35 3 1 2 3 2 0 3 0 44 3 3 1 0 0 3 0 8 35 0 3 0 2 3 3 0 1 1 0 1 2 26 1 1 1 2 0 1 3 3 2 0 8 0 2 2 0 2 0 3 3 2 2 1 1 3 3 3 0 3 2 3 0 1 3 53 3 3 2 0 2 0 2 3 1 1 1 2 2 3 0 0 26 1 2 2 0 3 2 1 17 2 53 3 3 1 8 0 0 3 2 44 1 1 2 1 2 8 2 1 0 3 2 0 17 44 3 2 3 0 3 2 1 0 53 0 0 3 1 0 0 1 3 1 3 1 35 2 1 17 2 0 0 3 26 0 2 0 0 2 0 0 0 1 3 53 2 2 0 3 1 1 1 1 1 3 0 2 2 2 53 44 1 1 2 2 1 2 3 1 3 1 3 1 3 3 3 3 2 1 2 35 1 3 0 0 1 1 0 0 0 1 26 2 8 1 0 2 2 2 2 3 3 2 0 3 1 1 2 0 1 3 0 3 8 26 3 0 3 2 2 8 3 2 3 3 0 2 2 1 1 3 3 1 0 1 3 3 3 1 0 0 3 2 3 3 1 17 3 17 0 3 1 1 2 26 2 3 2 3 1 2 1 3 2 2 53 35 3 53 35 0 2 26 0 3 3 35 2 8 3 2 3 1 1 1 2 2 3 2 1 0 0 3 1 2 2 0 2 53 3 3 35 0 1 0 0 1 3 2 2 2 0 1 1 3 3 2 2 1 1 3 1 2 1 3 3 0 0 2 3 3 1 3 1 2 0 2 53 1 2 2 2 0 3 1 2 8 0 1 0 1 53 44 3 3 1 0 2 2 1 17 1 1 3 0 2 0 2 2 3 1 0 0 1 3 1 0 0 2 8 1 1 3 2 2 2 3 2 2 1 1 1 1 53 3 2 0 2 3 1 3 35 0 1 0 0 0 1 3 3 0 0 3 0 0 0 1 3 35 0 0 2 44 35 0 2 0 3 2 3 1 1 2 1 1 53 2 3 3 3 35 0 0 2 26 1 0 0 3 0 1 3 3 0 2 0 0 2 1 2 1 2 3 1 1 2 2 17 2 2 0 2 0 3 53 3 2 0 1 0 2 0 0 3 1 3 0 2 0 0 3 3 2 0 1 1 1 0 3 2 3 2 8 1 2 1 1 0 3 2 1 44 1 17 44 2 17 1 0 2 53 1 2 1 3 1 3 2 3 2 2 0 35 1 2 3 3 3 3 1 2 3 3 3 1 3 0 2 3 3 44 2 2 31 0 1 0 3 2 3 3 3 3 0 2 2 3 0 1 1 3 1 0 1 1 3 2 1 2 1 1 2 2 0 0 3 2 1 1 3 2 1 0 2 44 3 35 2 2 1 0 1 0 0 53 2 0 2 0 1 3 3 26 3 1 2 0 1 1 0 1 1 35 2 2 0 0 0 0 2 1 0 0 3 2 0 2 0 0 0 1 2 2 1 2 0 3 1 0 0 0 2 0 1 2 2 0 1 0 2 0 2 0 3 3 2 1 3 3 3 0 3 2 1 0 3 2 35 2 2 2 0 8 1 0 26 3 3 3 1 0 2 0 0 3 3 3 8 3 0 3 2 0 17 2 3 0 3 0 1 2 3 44 3 3 3 1 2 1 1 2 0 3 3 0 1 1 2 53 3 0 0 3 1 3 0 3 2 3 3 0 3 2 2 0 1 3 3 8 0 0 1 3 0 3 3 2 3 0 17 17 0 2 1 0 3 1 2 2 26 0 3 0 0 0 1 2 3 1 26 3 2 26 1 3 35 35 2 0 3 2 17 2 1 26 2 1 3 3 3 0 3 0 1 2 2 3 3 0 1 1 2 0 0 2 2 1 3 0 2 3 1 2 2 0 0 8 3 0 0 1 2 0 0 1 1 1 1 1 0 3 0 0 1 17 0 2 1 0 3 0 0 2 2 2 3 3 1 0 3 3 44 1 1 3 35 3 1 2 3 2 0 2 1 1 3 44 53 2 0 53 26 4 0 3 1 2 3 35 3 2 2 1 3 1 2 1 2 26 2 1 3 1 3 0 2 1 0 1 1 3 0 8 1 0 1 3 53 3 0 3 0 0 3 2 0 0 2 0 35 2 0 0 35 1 1 2 0 2 2 1 1 3 2 2 0 1 0 2 3 0 3 1 44 2 0 0 0 3 3 35 44 3 2 3 1 0 2 2 0 2 1 44 44 1 2 3 1 1 3 3 2 3 0 0 1 2 3 2 35 1 0 3 1 44 1 0 2 0 3 3 2 1 35 3 1 0 2 1 3 0 0 2 26 8 1 0 0 1 1 3 1 2 3 2 0 2 3 3 1 2 0 3 1 2 3 1 1 3 3 1 1 1 3 3 0 0 2 1 3 0 0 2 2 3 1 17 1 1 3 3 1 1 8 3 3 2 0 3 3 44 3 0 2 2 2 1 2 35 8 3 2 3 0 3 1 2 53 3 0 2 2 3 2 3 8 0 0 1 0 1 17 2 2 0 1 1 2 2 0 0 2 1 2 0 2 3 1 1 1 1 2 0 1 1 0 1 3 2 0 53 0 1 3 8 0 2 0 0 2 2 1 1 3 0 1 0 0 0 0 1 8 2 0 2 1 1 2 3 1 0 0 44 2 3 3 1 0 0 2 0 1 1 2 3 1 17 1 1 2 1 3 53 1 2 3 53 1 1 3 0 1 0 0 2 3 0 2 1 17 3 3 1 0 0 0 2 1 3 3 0 2 1 44 35 26 2 3 2 1 2 8 3 17 0 0 2 1 3 3 0 1 3 1 1 0 2 2 0 2 2 1 1 1 0 2 0 3 3 1 2 2 3 3 0 53 3 1 3 2 0 2 0 2 2 1 0 3 22 0 1 3 2 0 1 2 2 1 2 3 2 2 0 0 1 1 0 0 1 1 17 1 2 2 3 2 1 1 3 0 2 1 1 35 3 2 53 53 1 1 2 3 1 1 2 26 0 1 2 0 3 0 0 2 1 1 3 35 0 3 35 1 0 0 3 0 1 2 26 1 17 0 3 0 2 3 0 0 1 35 2 0 2 1 53 3 1 3 2 2 1 1 3 3 0 26 17 2 1 0 2 0 0 44 2 2 3 44 0 2 3 1 2 2 1 1 17 8 1 3 3 1 1 0 1 0 2 26 2 1 0 0 1 0 0 3 1 0 3 1 3 0 0 3 3 0 3 3 0 8 2 2 1 2 1 2 0 3 0 0 0 2 2 0 3 2 1 2 44 3 0 0 1 2 0 2 8 1 2 2 3 0 2 2 2 2 0 2 3 1 3 2 1 26 1 0 1 1 3 1 3 2 3 1 3 0 2 1 17 2 0 3 2 3 26 2 0 0 3 0 0 3 0 2 3 2 44 3 0 0 1 1 2 3 1 0 0 2 1 3 2 3 3 2 0 3 2 0 8 3 0 35 1 2 35 44 17 1 0 1 2 2 0 0 3 2 2 0 2 53 3 2 1 0 3 2 1 17 35 3 3 57 1 3 0 0 53 8 1 0 0 1 1 35 1 1 0 0 0 0 2 3 0 3 0 1 26 0 3 0 2 1 3 1 35 0 3 0 35 0 2 1 3 2 0 2 3 1 1 2 3 1 3 2 2 2 0 3 2 2 2 35 0 0 1 2 0 3 17 2 2 0 2 0 3 8 1 3 1 0 3 3 1 1 3 3 0 2 1 2 3 0 0 0 0 2 3 0 3 2 1 0 1 0 3 8 8 2 2 2 2 2 0 1 2 2 3 3 0 2 1 2 0 3 3 0 2 26 35 0 2 3 0 3 0 3 3 1 1 1 0 53 2 1 0 2 1 0 2 2 1 1 0 35 1 2 53 3 1 2 2 3 0 44 2 2 1 44 3 1 3 1 3 0 2 44 2 0 0 2 2 1 2 0 2 0 2 2 8 3 2 2 2 0 3 2 1 3 8 1 2 2 3 3 1 35 0 0 3 3 1 2 2 2 3 0 1 3 2 0 0 2 0 0 35 44 1 3 3 2 2 0 2 3 2 2 2 0 0 1 0 3 26 1 3 1 2 2 3 53 1 3 1 2 0 2 1 3 1 8 2 0 3 1 1 1 2 2 3 3 44 2 2 0 1 1 1 35 0 3 0 2 2 3 0 2 0 0 2 1 3 2 2 0 0 2 3 0 2 2 2 3 1 3 2 3 1 1 2 3 3 2 53 2 3 2 3 3 1 2 1 0 3 0 26 44 3 1 2 0 0 0 1 2 2 2 1 3 3 3 0 8 2 3 3 3 0 0 2 2 3 3 0 3 1 2 3 2 0 3 1 3 1 3 2 3 26 1 2 2 0 3 0 1 2 2 26 1 0 1 1 2 0 53 0 3 1 2 2 1 3 0 0 1 2 2 0 3 1 1 0 1 1 0 0 1 3 3 2 3 2 26 3 2 2 2 1 0 3 3 1 3 2 0 0 17 0 2 3 2 3 1 1 35 2 0 2 3 2 3 26 1 2 3 2 3 0 0 3 3 3 44 2 2 2 1 3 17 44 3 3 1 2 2 2 2 44 2 2 0 3 0 1 1 3 1 0 1 3 2 2 2 3 1 0 1 0 0 2 53 0 26 1 3 3 2 0 1 8 0 8 1 2 3 0 0 2 3 2 1 2 53 3 2 3 0 0 1 2 1 0 3 0 1 1 0 3 1 2 1 3 3 0 1 0 2 2 3 0 2 2 2 0 0 3 2 2 1 3 1 1 1 1 1 2 1 0 53 3 0 2 0 53 3 0 2 1 1 1 0 0 2 3 1 3 1 2 3 1 17 0 1 1 2 0 0 8 2 0 1 3 1 2 2 44 3 1 8 1 2 0 3 2 3 3 3 2 1 0 2 2 0 3 3 3 3 2 3 0 3 3 3 2 3 3 0 3 2 44 1 0 0 2 2 17 1 44 17 44 1 3 3 0 3 3 26 3 2 2 1 1 1 3 0 3 26 1 1 2 1 0 0 2 3 2 0 0 3 3 1 1 44 2 2 8 0 2 3 2 3 2 1 3 1 0 0 2 0 2 3 1 3 2 2 2 1 0 2 0 2 1 0 2 2 2 0 1 2 53 2 1 2 0 0 3 3 2 2 1 3 0 0 1 1 3 1 0 2 1 8 26 0 2 1 0 0 3 53 44 0 1 2 2 1 1 3 0 1 3 2 2 2 1 3 1 0 2 1 3 1 0 2 3 1 0 2 0 2 2 0 0 3 1 0 2 2 3 0 2 1 8 0 1 2 1 2 2 2 3 0 35 2 1 3 2 1 3 26 26 3 0 1 1 1 1 3 0 2 2 0 0 1 1 3 1 1 3 2 0 1 1 2 0 3 0 2 3 2 44 3 1 1 3 3 2 35 0 1 1 2 3 3 0 1 1 3 2 3 1 26 1 1 3 2 1 3 3 2 3 2 1 1 1 0 2 0 2 3 3 3 44 3 2 3 0 3 0 2 3 0 2 3 3 0 0 2 3 3 1 3 1 17 8 3 3 2 2 44 3 3 1 3 1 2 2 1 0 2 53 2 3 1 0 2 1 1 1 26 0 0 2 2 0 2 2 3 26 3 2 2 3 1 2 1 1 3 53 1 3 0 2 2 2 2 3 44 17 0 0 3 0 3 1 1 35 3 2 1 3 3 44 0 3 3 1 1 8 0 2 35 3 0 2 0 3 2 26 1 3 2 0 26 1 2 44 0 2 3 3 3 3 1 1 1 1 0 0 0 0 3 2 2 2 0 0 8 1 1 0 0 2 2 1 3 3 3 0 2 44 3 1 3 1 1 0 0 0 2 2 3 1 2 0 3 3 1 2 0 2 3 0 2 1 0 2 3 2 0 3 26 0 2 1 2 3 3 1 0 2 3 2 3 3 0 0 2 3 1 3 3 1 3 0 0 2 2 35 3 1 0 1 2 3 0 1 2 3 44 0 0 0 1 2 3 1 3 3 3 1 0 2 3 0 3 8 1 0 0 2 1 1 2 3 53 3 2 1 0 1 1 3 1 3 2 0 3 1 0 0 0 2 2 0 1 1 1 3 2 0 1 2 0 3 0 3 2 3 0 0 3 26 3 3 1 0 1 3 1 1 3 1 35 3 3 0 3 2 3 3 2 1 0 2 3 2 0 1 3 1 1 2 1 3 2 2 17 3 1 1 35 1 0 0 3 2 3 1 2 17 3 2 3 2 2 3 1 35 2 0 1 3 0 0 0 1 3 3 0 2 0 1 0 2 0 3 2 2 53 1 2 35 1 3 1 2 0 1 0 2 1 0 0 1 2 8 3 2 0 2 0 3 2 0 1 2 2 1 1 1 2 3 3 8 2 0 1 2 3 1 0 1 0 1 1 0 1 3 8 0 2 35 3 0 0 3 2 2 3 2 2 2 1 2 2 2 0 8 8 26 1 1 1 3 2 0 2 0 35 2 0 26 2 2 2 1 2 1 1 2 1 1 2 2 3 2 0 0 2 1 3 0 2 3 3 1 2 1 2 2 2 1 3 0 8 17 0 0 17 44 2 1 2 0 1 1 2 8 3 3 2 3 0 3 2 2 0 2 3 3 1 2 0 1 2 1 1 2 2 3 1 2 2 2 8 1 3 2 0 3 0 3 0 2 3 1 1 3 2 0 3 3 1 3 1 3 26 17 35 1 3 3 3 2 1 1 8 1 3 1 3 1 26 0 26 2 0 0 3 44 3 2 0 2 44 3 1 26 1 2 3 0 1 3 0 26 2 35 0 44 8 3 1 3 17 1 3 2 0 1 2 35 0 1 2 3 3 2 1 1 3 0 3 2 17 1 2 3 3 0 2 1 2 0 0 3 2 44 0 2 3 3 1 8 3 26 2 1 0 8 3 3 3 3 1 8 3 1 2 0 3 2 0 1 3 2 8 3 44 0 3 0 0 1 3 0 53 2 26 2 0 2 2 2 2 44 0 3 2 1 2 0 3 2 3 1 3 1 3 0 3 3 0 2 17 1 3 1 0 1 0 1 1 2 0 3 3 2 3 0 8 2 0 1 3 1 2 3 0 3 1 2 2 3 1 0 1 2 2 0 0 1 3 3 3 3 2 35 3 3 3 3 0 1 35 0 0 26 2 26 1 1 2 0 2 1 35 1 1 3 3 3 44 2 0 0 35 3 3 2 2 2 1 1 2 0 0 2 0 2 0 2 2 2 3 0 1 2 2 2 17 0 26 3 2 17 0 0 3 2 2 3 3 2 0 2 26 22 3 3 2 3 3 2 44 1 1 1 1 2 1 44 2 0 1 1 0 3 0 3 1 17 1 3 1 2 1 1 1 3 0 2 2 3 2 3 0 1 3 3 1 26 2 3 0 0 35 0 0 2 1 0 3 3 0 0 1 3 2 0 2 3 3 3 8 3 3 3 2 2 0 3 2 2 3 3 0 0 2 0 0 1 0 0 8 2 0 0 1 0 2 1 3 3 0 1 0 2 2 0 1 2 0 53 1 0 2 8 3 1 1 1 3 8 1 53 2 1 1 44 3 3 1 3 0 17 1 0 1 1 3 2 3 1 1 3 1 44 1 3 3 1 3 17 2 3 2 0 2 1 1 1 1 3 0 0 35 3 3 44 1 0 0 3 1 0 1 1 0 0 0 8 0 2 1 3 3 0 1 26 2 3 1 1 3 3 53 2 3 3 0 2 1 2 3 0 2 0 0 2 0 0 2 1 1 0 0 2 3 0 8 26 3 0 3 35 2 3 1 0 1 0 3 1 2 0 3 2 2 0 0 1 0 2 2 0 44 1 53 2 0 0 3 1 3 8 2 3 2 1 3 1 3 2 2 3 1 35 1 3 2 3 2 1 3 0 0 3 1 2 1 2 2 3 1 0 0 3 17 0 2 0 2 0 2 0 2 3 1 0 0 3 26 3 3 3 0 3 1 2 3 3 3 1 8 0 0 1 0 3 2 44 3 2 2 2 2 1 2 53 0 0 1 0 1 2 17 2 3 2 3 0 2 3 3 3 0 35 2 2 0 3 0 2 1 1 0 2 3 1 0 3 3 1 0 35 8 0 2 1 2 2 3 35 8 3 1 1 0 1 2 3 3 1 1 3 0 0 1 26 2 2 2 44 2 1 1 0 3 3 3 26 2 0 1 2 3 44 2 1 1 2 2 2 0 1 0 3 3 3 3 0 0 44 2 8 0 2 1 3 26 0 1 1 2 1 1 3 0 2 2 2 2 26 0 0 3 3 1 1 0 0 1 3 3 0 2 0 26 1 1 1 2 2 2 2 1 3 3 35 1 35 1 1 3 2 2 3 3 0 0 17 1 0 1 3 0 2 2 2 3 3 0 1 2 0 3 1 3 3 2 2 0 2 35 26 2 1 3 3 3 8 1 26 3 1 1 1 1 3 2 3 0 1 3 0 1 3 2 3 0 1 1 1 2 0 17 3 35 1 0 0 3 1 2 0 2 3 0 0 0 1 1 0 0 1 3 3 1 0 2 35 1 3 2 0 0 2 2 2 1 3 0 3 2 1 1 0 26 3 1 17 1 35 2 3 1 1 0 1 3 0 3 0 26 2 3 0 2 0 35 2 2 2 26 1 2 3 0 1 1 3 1 26 1 1 26 1 53 2 2 2 2 0 3 0 35 2 3 1 3 0 3 1 1 3 0 2 0 1 26 0 0 1 1 3 26 1 1 1 1 3 1 2 1 17 3 1 0 53 1 1 0 3 1 3 0 1 1 3 2 2 1 0 3 3 3 44 1 0 3 2 1 3 2 0 3 0 0 26 3 3 2 2 1 1 0 0 26 2 3 1 0 0 1 0 1 3 3 0 0 35 0 0 26 44 1 1 1 1 1 1 44 0 2 2 0 3 2 44 1 3 0 0 8 0 1 1 1 1 1 1 3 0 3 44 3 1 1 0 1 3 8 17 1 2 1 0 0 26 2 2 1 3 1 1 2 3 2 1 0 3 1 26 2 2 0 2 2 1 0 0 3 1 1 3 0 3 2 0 3 3 0 3 35 1 0 2 1 0 2 3 2 17 2 0 3 8 1 3 3 2 1 3 53 0 0 0 2 0 0 1 0 0 0 53 2 3 2 3 3 3 1 3 48 1 26 0 0 1 0 44 3 3 3 1 2 1 3 0 3 44 1 3 35 0 0 1 1 2 2 3 1 3 1 2 0 0 2 3 3 0 0 3 1 1 0 0 3 1 44 0 2 0 0 0 3 1 53 3 2 0 1 2 2 3 3 0 3 1 3 0 2 3 1 8 1 3 3 0 1 0 0 3 3 2 1 2 1 2 3 3 3 3 0 0 1 1 0 53 0 2 35 3 3 1 2 3 17 3 3 0 0 0 1 3 2 0 2 0 1 2 2 3 2 2 0 44 3 1 2 2 2 26 3 0 0 0 2 3 3 3 2 3 3 2 2 0 3 17 0 3 1 0 3 0 2 0 0 1 0 0 2 44 35 1 0 3 3 1 2 3 0 1 1 17 3 2 2 3 2 2 3 53 8 44 3 2 3 1 2 0 2 1 2 0 2 0 3 3 0 1 1 1 3 0 0 1 0 0 44 3 0 2 2 3 0 2 0 0 2 1 2 1 0 3 2 1 2 1 0 2 0 0 2 0 2 2 26 0 2 3 2 44 0 0 44 3 1 3 3 0 1 1 0 3 2 17 1 0 0 3 0 1 1 0 1 0 0 3 2 0 2 1 1 8 2 2 3 0 3 0 2 1 1 1 3 1 2 35 1 1 1 2 2 3 2 1 1 3 3 3 1 2 0 1 1 3 1 3 35 1 1 3 2 17 0 53 1 2 2 0 2 3 44 53 3 1 3 3 0 2 1 3 2 1 0 1 2 2 2 2 0 1 2 1 3 1 53 2 3 2 3 1 3 3 2 1 3 2 0 1 0 1 3 1 0 1 0 3 1 3 0 0 1 0 1 3 0 2 8 0 2 2 2 1 2 2 3 1 8 1 1 2 57 1 2 0 2 3 53 2 0 0 1 0 0 3 2 0 0 1 26 0 0 0 0 2 53 1 3 0 2 1 0 2 2 3 3 0 3 3 1 2 1 1 3 1 0 3 3 1 53 3 1 0 2 2 0 2 2 2 0 3 2 0 1 1 2 3 2 3 3 3 1 2 3 0 1 3 0 44 3 0 2 2 3 2 1 1 3 1 2 3 3 2 35 2 53 1 1 0 0 2 1 3 0 0 2 2 3 3 3 0 2 2 2 1 2 1 2 0 2 2 3 3 1 35 2 0 1 2 2 3 3 3 1 2 2 2 1 2 1 3 3 2 0 3 2 3 2 44 2 2 1 3 8 3 2 0 1 2 1 17 0 44 0 0 0 35 2 0 17 3 0 0 0 2 1 1 3 44 0 2 0 3 0 3 35 0 2 0 2 1 0 0 1 1 1 0 3 3 0 53 17 2 2 1 1 2 35 0 3 0 3 1 3 0 1 3 3 1 3 3 0 2 3 17 3 1 1 0 3 17 2 1 1 1 3 3 3 1 2 1 2 1 2 8 1 3 2 2 2 44 1 1 35 1 1 3 2 3 1 2 2 2 2 1 0 1 1 3 0 0 1 1 1 2 17 3 2 1 1 2 3 2 0 0 1 2 2 2 0 3 2 0 2 3 3 1 3 1 2 1 1 0 2 1 1 1 0 2 35 26 0 17 3 2 2 1 0 1 2 0 1 0 0 1 2 2 3 0 3 0 3 1 1 1 0 1 44 1 1 0 26 0 53 0 2 1 3 2 0 8 1 2 0 2 2 0 1 0 2 2 1 3 35 1 3 1 0 0 0 8 0 0 2 2 3 2 53 2 0 2 3 2 3 3 17 3 1 2 2 17 1 26 3 3 2 1 0 0 3 8 2 17 2 3 2 3 1 1 1 2 0 0 2 1 1 3 8 1 1 1 3 2 0 0 2 1 0 0 1 3 0 1 2 3 2 1 0 0 1 1 1 0 0 0 2 1 0 1 1 2 1 2 17 1 1 1 1 53 2 2 0 1 2 0 3 3 0 1 53 0 0 1 1 3 2 0 3 53 2 3 0 3 1 3 0 2 3 44 0 0 1 0 3 0 2 0 0 2 3 1 2 3 1 3 1 2 1 2 0 3 1 35 0 1 3 2 2 2 1 2 2 1 1 0 1 3 0 1 3 0 2 0 3 2 2 2 1 3 0 1 3 0 1 1 3 3 1 3 2 2 3 0 0 3 3 2 1 1 0 2 53 2 1 2 0 0 44 1 3 0 2 2 0 0 26 0 1 0 0 1 2 2 0 0 2 2 1 0 2 2 3 3 1 2 2 3 3 3 3 3 0 0 0 1 0 3 0 0 2 0 2 2 2 0 1 2 8 0 8 3 2 1 0 3 35 1 0 3 2 2 44 2 0 2 1 1 0 2 1 2 2 1 2 2 2 2 3 0 2 44 17 53 1 0 53 35 2 2 3 53 2 2 1 1 0 1 1 3 3 1 1 53 3 3 3 1 0 2 2 0 2 0 0 1 2 35 2 1 2 2 26 3 2 0 8 1 1 0 3 1 1 2 0 8 3 2 3 3 3 0 8 3 0 0 3 3 3 2 17 0 0 2 2 3 3 2 1 3 26 1 1 3 3 3 2 2 35 2 3 1 1 0 3 3 1 0 44 2 0 17 35 1 3 1 2 44 3 44 1 3 2 2 2 2 1 3 0 0 2 1 0 1 2 0 0 2 2 3 2 1 2 2 0 0 8 0 3 3 0 3 53 3 0 3 3 2 3 1 0 0 2 0 3 2 3 2 1 3 0 44 3 1 3 3 2 2 3 1 2 3 2 2 2 3 0 1 2 53 1 3 1 53 1 3 0 2 0 0 3 0 3 0 26 1 1 3 2 1 3 3 0 1 0 2 0 2 1 1 1 2 1 3 3 2 1 3 1 3 2 2 2 2 1 1 3 44 8 0 3 2 1 3 0 0 0 3 0 1 3 1 26 0 1 1 1 8 0 2 1 0 2 2 0 2 2 1 0 1 3 1 2 0 1 1 53 2 8 0 1 0 0 3 2 3 1 2 1 1 1 1 3 44 3 2 0 2 0 3 3 0 2 3 0 35 3 2 1 3 3 1 1 2 2 0 3 3 1 17 44 3 2 44 1 0 0 1 3 1 2 2 3 0 2 2 2 1 3 3 2 0 2 3 2 2 0 1 2 3 1 0 0 0 44 1 2 3 0 1 0 2 3 0 3 2 1 2 8 1 3 8 2 3 26 1 1 3 2 2 1 35 3 2 2 2 1 0 0 0 3 0 0 0 3 0 40 1 0 0 1 3 35 3 3 0 2 0 2 44 35 2 1 0 1 0 35 2 2 3 0 2 3 3 8 2 1 3 1 1 35 0 0 8 2 1 2 1 3 2 2 2 1 2 3 0 1 0 3 2 3 0 2 3 3 8 2 0 3 17 2 0 2 0 3 2 0 0 0 2 3 3 3 3 0 2 1 0 0 3 53 2 0 3 0 0 2 2 3 3 3 1 3 1 1 2 3 3 2 0 3 3 2 2 0 0 1 1 2 0 1 2 0 0 3 2 2 1 0 0 3 3 44 0 2 2 2 2 0 1 1 1 2 8 0 44 3 3 1 1 3 3 1 53 0 2 35 2 3 2 1 2 2 1 17 3 2 0 3 3 3 0 1 3 0 1 1 2 3 3 0 0 3 3 1 3 0 2 0 0 2 2 2 3 0 2 3 3 26 2 1 0 3 1 8 1 0 26 1 0 0 26 2 0 2 1 2 0 44 2 0 2 3 0 3 2 2 2 2 1 1 0 3 3 0 2 2 2 2 2 3 0 1 0 3 1 3 3 1 1 1 0 2 17 0 1 0 3 1 0 2 3 2 3 53 2 3 0 0 2 53 3 0 1 3 3 3 44 0 3 8 1 3 2 1 2 2 2 44 53 2 1 0 3 1 0 2 0 0 57 1 44 1 3 0 3 2 3 0 2 2 3 17 3 2 0 26 3 1 0 3 1 1 2 17 0 17 0 1 0 0 26 1 1 2 0 17 1 3 2 0 0 1 3 3 0 1 3 0 2 2 0 44 1 3 0 0 2 2 1 3 1 2 3 3 44 35 0 1 53 0 0 26 17 0 1 2 0 1 3 0 1 3 1 1 2 3 2 2 1 1 0 0 26 1 53 2 0 3 1 3 0 1 3 3 3 3 2 3 2 0 2 2 0 1 0 1 0 3 1 3 3 0 1 1 3 0 2 2 2 0 1 1 3 0 3 3 2 44 17 3 3 1 0 3 0 0 3 2 2 1 0 3 2 3 2 0 2 8 0 2 2 0 3 0 1 2 0 3 0 3 0 2 2 0 0 2 3 26 3 3 0 2 1 0 2 2 3 44 3 0 2 2 1 0 3 1 2 26 1 3 0 0 0 3 0 0 2 44 1 2 1 2 44 3 1 0 0 1 2 3 0 1 3 0 0 1 3 8 2 2 1 1 3 2 0 0 44 35 2 0 1 1 3 0 1 1 1 3 1 0 0 2 1 35 1 2 1 2 0 2 0 1 2 1 2 0 2 0 1 3 1 3 0 1 0 2 2 17 1 3 2 2 3 0 2 3 2 2 1 3 1 17 2 44 1 0 1 3 3 3 1 1 3 3 3 0 1 0 1 0 0 1 2 0 3 3 1 0 2 2 3 53 35 2 26 0 3 0 0 2 3 2 1 2 0 3 0 2 1 3 0 1 0 0 1 3 1 2 26 2 3 1 2 2 2 3 1 1 3 2 1 53 0 53 1 3 0 1 1 3 0 1 1 40 2 3 3 1 0 1 1 0 1 0 1 0 3 26 3 44 3 1 3 1 1 2 2 0 1 0 0 3 0 2 3 2 3 0 3 3 8 3 3 3 2 0 0 0 3 2 3 3 2 2 0 3 2 3 0 2 2 2 1 3 1 17 1 0 2 3 2 1 2 17 3 3 0 0 3 0 3 3 2 3 1 0 2 1 0 2 0 3
+GORILLA_BORNEO 0 2 0 1 3 2 0 0 44 44 1 1 3 1 0 3 2 0 3 3 1 1 3 2 3 3 2 8 0 0 3 2 3 1 3 3 2 1 3 17 3 44 3 3 35 1 2 53 26 0 1 53 3 0 0 1 1 2 35 1 0 1 2 2 0 1 2 1 3 1 3 26 0 3 3 0 1 8 35 17 0 17 3 3 0 1 3 0 2 1 35 3 3 2 0 3 1 3 2 1 3 1 2 3 3 2 3 2 8 3 1 2 0 17 3 1 0 3 2 2 3 1 1 2 2 17 2 3 44 1 3 26 2 0 0 1 3 3 1 17 3 1 3 1 3 2 0 8 3 2 0 2 0 0 0 0 0 1 1 3 1 2 0 0 0 1 17 2 1 17 1 8 2 2 2 0 2 1 17 3 3 2 3 1 1 0 3 0 0 0 1 2 1 3 1 0 2 2 2 2 0 1 0 0 1 0 2 3 1 1 0 13 3 0 2 1 3 2 2 1 3 3 1 1 1 2 2 2 3 3 1 2 3 26 0 26 2 0 0 2 0 0 3 3 1 1 0 2 3 44 2 0 0 3 3 44 35 3 3 0 3 3 1 2 3 2 0 0 1 3 2 3 2 3 2 26 1 1 21 1 2 2 2 35 3 17 1 3 3 2 0 1 3 0 2 3 3 1 1 0 0 0 3 2 1 53 1 2 2 0 0 1 0 0 1 1 0 26 2 8 3 1 0 2 2 0 1 3 35 0 0 3 8 2 2 1 2 1 1 0 3 0 1 3 1 0 1 3 2 17 3 2 0 1 1 0 8 0 2 1 2 3 3 2 0 35 17 0 1 26 0 2 1 3 3 2 0 1 0 3 26 0 1 3 2 3 2 2 2 17 53 1 2 0 0 0 0 1 0 2 0 2 3 2 2 3 2 2 0 35 1 1 2 3 0 1 1 3 0 3 0 3 2 3 0 3 2 3 1 3 1 1 3 0 1 1 0 3 3 3 0 26 3 3 3 1 2 2 17 3 1 0 0 3 2 0 1 3 1 53 0 0 0 3 2 2 3 0 2 2 2 2 0 2 1 3 1 3 44 0 3 1 3 0 0 3 0 3 2 2 1 53 0 2 3 2 0 3 2 1 2 1 0 2 2 1 0 0 3 3 1 1 3 0 3 44 2 1 0 0 0 44 53 2 1 53 35 0 0 2 26 3 1 2 1 2 1 3 3 44 3 3 2 1 3 1 0 2 26 2 44 3 53 3 3 1 3 1 2 0 3 0 1 3 0 1 1 53 3 0 0 3 3 3 3 3 0 0 1 2 3 44 3 0 0 2 1 8 26 53 1 1 0 3 2 0 1 2 0 53 3 2 2 3 3 3 3 1 1 2 17 1 2 3 0 3 2 1 2 2 26 0 1 2 17 1 2 35 2 1 0 3 0 3 2 3 1 3 1 2 2 1 3 1 2 0 1 1 3 2 3 1 0 1 0 3 0 3 53 0 3 3 3 1 1 0 0 0 1 2 2 1 3 3 0 0 17 3 35 1 1 2 1 1 0 1 0 35 3 1 1 2 1 1 2 3 2 8 0 1 48 35 1 0 1 26 3 3 1 2 0 3 1 0 0 1 35 3 26 1 3 1 3 1 3 2 2 1 35 2 1 0 1 2 2 1 1 0 2 3 2 3 3 2 2 0 2 1 2 1 35 8 3 3 1 3 0 1 53 1 3 3 3 3 2 2 1 0 1 3 1 26 0 1 8 0 3 0 8 3 2 1 2 53 1 2 3 53 0 1 1 2 53 0 35 17 0 3 0 17 0 2 1 0 3 1 2 2 1 3 8 1 0 2 3 1 0 2 3 0 3 3 8 1 2 1 3 1 1 3 0 1 35 2 0 0 3 3 3 1 3 3 0 3 3 3 3 0 3 0 2 3 1 2 2 8 1 0 3 17 2 2 3 2 3 2 1 1 0 26 1 0 1 2 0 1 2 1 0 1 3 0 1 35 0 3 1 0 3 3 3 3 0 3 3 0 2 1 2 1 0 3 1 17 1 0 0 3 3 0 44 2 2 0 3 2 1 0 0 8 2 26 1 3 2 0 2 1 2 2 1 1 3 3 3 0 0 2 1 2 3 1 0 0 2 0 3 1 3 8 0 1 2 0 44 35 3 2 1 2 2 3 3 2 3 2 2 3 2 1 2 0 0 0 1 3 1 2 0 0 0 2 8 3 2 1 1 2 0 0 1 3 2 2 44 0 2 0 1 0 0 3 2 3 2 3 1 3 2 44 0 1 0 3 26 0 3 8 1 3 2 2 2 0 0 2 0 0 2 3 0 1 0 0 3 0 3 3 2 1 0 2 8 3 0 3 3 3 0 1 1 0 3 1 2 1 1 0 2 3 53 3 1 1 3 2 3 0 3 35 1 0 17 1 2 1 1 2 0 0 2 3 17 0 1 0 1 3 2 3 3 1 17 53 3 0 3 3 1 2 3 0 2 2 0 2 2 1 0 17 2 0 3 1 3 17 2 1 1 3 17 3 1 2 1 0 1 0 0 3 0 0 1 2 1 1 3 2 2 2 1 44 44 0 3 3 8 3 0 1 3 1 0 3 2 3 17 0 2 3 44 2 0 3 1 3 0 44 3 2 3 1 0 0 0 1 1 0 3 30 44 3 0 1 0 8 35 1 17 2 1 1 0 1 1 3 2 0 3 1 1 3 0 2 3 3 3 1 3 2 2 2 2 1 3 0 0 2 0 1 1 1 1 1 1 3 0 3 2 1 3 35 1 0 1 1 3 1 1 44 3 2 1 2 2 3 2 0 3 3 2 3 1 2 3 1 53 0 1 3 1 0 2 2 2 3 0 1 1 2 0 3 1 3 1 0 1 26 1 1 2 3 1 0 2 1 1 3 3 2 35 1 0 2 0 3 0 2 3 26 1 0 1 2 1 0 44 0 3 3 0 2 2 1 2 3 1 1 2 1 17 1 1 3 2 0 1 0 1 2 1 2 3 2 1 2 2 35 1 3 3 0 44 1 1 35 1 1 0 0 3 0 0 2 2 3 3 44 2 0 1 3 3 3 1 3 1 0 0 26 1 0 0 3 3 26 0 1 2 3 17 17 0 0 35 3 1 3 8 3 35 1 3 1 2 1 2 0 3 2 3 0 3 1 1 2 3 1 0 2 1 2 0 1 3 0 1 0 0 2 44 1 3 3 1 0 3 3 3 1 3 3 3 35 2 2 1 2 2 0 3 3 0 1 3 2 0 0 0 0 2 1 3 0 2 3 0 3 1 17 0 0 3 2 2 0 3 0 3 0 0 1 2 1 8 0 1 2 1 1 2 1 1 0 1 53 3 2 2 3 1 1 0 1 2 2 1 3 2 1 2 0 3 1 2 3 3 17 0 0 2 0 3 1 0 2 0 2 0 3 0 1 0 2 53 1 3 2 2 3 3 2 2 1 2 3 17 1 2 3 0 1 26 0 2 0 2 3 2 0 0 53 2 1 0 1 1 0 1 0 3 1 1 3 0 2 2 1 0 2 1 2 1 2 0 0 2 0 3 1 26 1 1 2 1 1 3 0 0 2 3 3 0 0 26 8 3 2 3 0 0 1 1 0 3 3 3 17 2 3 1 1 3 0 8 0 3 3 2 2 3 3 1 1 1 0 3 0 2 0 17 1 1 0 3 0 0 1 1 53 3 3 0 0 3 1 3 0 0 1 1 2 3 3 0 1 0 2 0 2 35 17 0 0 2 0 0 3 2 3 0 1 0 1 3 3 3 1 2 1 1 2 1 2 1 0 2 1 2 0 3 3 17 1 3 2 3 3 0 1 3 17 3 17 3 1 17 1 3 0 3 1 0 3 2 2 0 35 3 3 1 3 3 17 3 2 26 0 3 44 2 2 53 44 3 1 1 0 2 3 3 0 1 2 2 0 1 2 2 0 3 2 3 3 0 1 3 1 3 3 1 3 1 2 2 2 3 2 3 8 2 3 2 53 35 2 2 2 1 1 2 2 1 2 1 1 2 1 1 2 1 0 0 26 0 0 53 0 1 1 0 1 2 2 3 1 2 0 1 2 0 2 2 53 2 3 3 0 3 35 8 1 2 3 3 2 3 0 1 1 1 3 1 2 1 1 3 1 8 3 1 2 0 2 2 3 2 1 1 2 1 2 0 3 1 2 3 2 3 1 1 3 1 3 3 3 3 1 0 1 3 1 53 2 2 0 8 1 2 1 0 2 53 53 2 2 2 0 2 2 1 26 2 0 2 0 2 1 0 0 0 44 3 3 0 2 1 8 2 3 1 0 2 0 1 0 1 3 1 54 0 1 2 2 2 0 3 3 2 0 1 17 2 44 1 2 1 2 3 1 0 1 1 3 3 1 3 3 26 0 3 2 2 1 0 2 2 0 0 3 2 0 3 3 1 1 1 3 3 3 0 0 8 2 0 2 0 3 1 0 0 2 3 1 53 3 0 44 3 0 2 1 3 3 3 2 2 2 1 3 1 1 0 0 3 0 2 0 0 1 3 0 3 2 17 2 0 0 3 1 0 3 0 1 3 0 3 1 3 1 0 0 0 1 1 2 53 8 35 3 2 2 0 2 2 3 44 0 0 0 0 2 1 2 2 2 2 0 0 2 2 0 1 3 1 3 3 1 0 3 2 1 1 3 1 0 2 1 8 0 0 1 0 3 1 0 3 2 1 1 26 2 2 1 1 3 3 1 3 3 2 1 1 3 1 3 0 3 0 3 3 0 2 1 2 0 53 2 1 3 2 0 0 3 0 0 0 2 2 2 1 2 1 0 0 35 0 0 2 35 1 0 2 3 3 1 2 1 2 1 2 0 0 0 1 1 3 3 1 2 2 0 3 1 0 0 1 2 3 0 1 1 3 1 3 0 2 1 1 3 2 3 44 44 0 0 2 1 1 2 0 1 1 3 1 3 0 17 2 3 3 0 3 2 3 1 1 1 2 2 17 3 3 2 44 44 2 1 1 2 3 3 0 44 0 0 2 35 3 8 2 2 0 1 3 1 0 0 0 2 17 2 2 1 0 1 1 0 3 0 2 2 0 53 3 1 44 1 2 0 3 3 3 8 0 2 0 3 2 3 3 1 44 35 2 2 1 3 2 3 1 35 2 2 1 2 1 44 2 3 1 2 3 3 26 3 8 2 3 2 3 1 0 2 3 0 0 0 0 2 0 1 17 3 3 44 1 0 3 1 2 1 3 1 3 35 53 0 26 2 2 2 3 2 2 0 1 3 44 0 2 2 1 1 2 0 0 1 1 1 1 2 53 0 0 0 1 2 1 0 2 0 1 44 2 8 2 2 1 2 3 0 0 1 1 2 3 8 0 2 8 1 0 1 3 0 1 3 17 2 0 3 3 2 0 2 26 1 1 1 0 2 2 0 3 0 1 2 1 3 0 2 0 1 0 3 2 2 3 1 2 3 2 8 2 2 26 0 2 2 3 3 8 3 3 0 3 1 3 1 3 35 53 0 1 1 3 0 17 3 2 1 1 3 3 53 0 2 3 0 2 1 2 0 26 0 2 0 2 0 0 2 2 1 1 1 1 0 0 1 3 35 0 2 3 1 1 17 0 2 17 3 1 2 2 3 0 1 0 0 2 0 3 2 1 1 2 1 2 2 2 3 2 35 1 3 2 0 0 0 3 2 0 3 35 0 2 3 26 3 3 3 2 1 1 0 2 44 0 0 1 2 0 3 2 2 3 3 2 1 2 1 2 2 0 44 1 2 3 2 3 3 3 3 0 2 3 0 3 2 2 53 2 2 1 2 3 0 1 1 1 0 1 1 0 3 1 0 3 1 3 44 3 0 1 1 3 0 0 1 2 1 0 1 1 1 3 0 3 35 1 3 0 8 0 17 3 2 1 2 1 3 26 3 3 1 1 3 0 0 3 1 3 1 1 0 1 0 1 0 2 0 3 3 3 1 1 1 1 1 0 2 0 3 3 2 1 1 1 1 35 0 0 0 1 0 26 0 1 1 2 2 3 2 0 3 2 2 0 3 1 0 8 1 2 17 1 0 3 3 2 0 3 8 2 2 1 35 3 3 2 1 3 1 0 1 2 2 3 8 1 3 1 1 1 1 1 3 2 0 2 1 2 2 1 35 26 3 3 3 2 2 3 3 2 0 1 17 3 2 3 2 0 2 1 1 2 0 3 3 0 2 3 3 1 3 0 3 0 8 0 44 8 1 1 2 1 0 3 2 44 1 17 3 0 0 0 1 3 2 1 53 3 2 1 3 1 3 8 0 3 1 0 44 44 2 0 1 2 2 3 3 0 0 26 1 1 0 2 0 0 1 35 3 0 8 2 35 2 1 0 0 1 0 3 53 2 2 0 17 0 3 0 3 2 1 3 1 1 2 53 1 0 3 1 3 1 0 2 1 0 44 0 3 0 0 3 1 2 0 0 1 1 35 26 2 1 0 44 2 1 1 1 53 3 1 44 3 2 1 3 3 0 3 3 0 2 1 0 0 2 0 1 3 0 0 2 0 53 3 2 49 0 17 3 0 0 0 0 1 53 3 2 2 3 0 2 53 3 0 0 0 3 3 35 3 3 3 1 3 0 3 1 2 53 0 1 3 1 3 3 1 2 2 1 26 1 1 49 1 1 2 0 3 3 3 1 2 2 2 3 3 2 1 3 3 0 1 3 3 0 3 35 3 1 3 2 3 3 1 1 3 3 0 2 0 3 2 0 1 2 3 3 2 0 2 2 1 1 1 0 2 2 3 2 3 2 3 0 3 0 1 0 44 1 1 1 2 8 3 3 35 0 2 2 2 1 3 2 2 3 2 1 0 3 3 35 0 1 1 2 2 0 2 17 0 26 0 2 0 2 1 2 1 0 3 0 3 2 35 2 0 0 0 3 1 1 0 3 1 0 1 0 0 1 1 1 3 1 0 2 1 3 1 26 1 1 3 44 0 1 3 3 1 3 0 3 3 2 0 0 0 1 17 1 2 2 3 3 1 3 0 2 26 35 1 3 0 1 1 0 0 1 1 0 12 3 0 3 0 1 2 3 3 2 3 0 1 35 2 35 53 26 3 2 2 1 0 3 3 0 1 3 1 53 0 2 2 1 3 2 3 3 0 1 1 1 3 0 2 3 0 0 2 2 0 26 2 2 3 3 1 26 2 2 3 3 2 0 1 2 1 0 1 2 3 26 17 2 2 1 2 3 3 0 2 3 0 35 0 1 3 2 1 1 1 0 2 1 3 3 3 44 2 0 3 3 3 2 0 1 3 1 3 0 8 0 2 35 2 2 2 35 1 1 2 1 2 1 1 3 1 0 1 1 2 2 0 2 26 1 1 1 3 3 2 1 0 2 3 2 1 3 3 0 1 1 1 1 2 3 1 0 0 53 3 2 1 1 1 2 1 1 1 1 35 0 0 3 2 2 3 3 2 2 2 3 2 3 0 8 1 2 1 2 3 26 3 2 1 1 8 2 1 2 0 1 2 0 3 3 2 0 1 3 1 0 0 2 3 1 2 35 3 0 3 1 0 0 3 2 2 3 1 2 1 3 53 1 1 44 3 2 1 3 1 2 1 53 0 2 2 0 53 1 1 3 2 44 2 1 1 0 1 1 0 2 2 3 3 0 3 2 2 3 1 1 0 1 0 53 35 2 0 2 17 1 3 2 2 2 3 3 0 1 3 2 2 0 3 0 3 2 1 1 0 2 1 2 0 3 0 0 0 1 3 3 1 0 0 2 1 0 2 2 1 1 0 2 1 44 0 0 0 1 26 3 3 3 44 1 1 2 2 3 2 2 3 2 2 0 1 2 2 1 3 3 0 1 0 3 0 1 1 0 0 3 1 0 1 2 0 0 1 35 3 3 2 1 53 0 3 1 1 3 3 1 1 1 0 2 2 2 1 3 0 0 2 3 0 1 1 3 3 1 2 1 3 8 35 2 2 3 3 3 0 2 0 2 3 1 2 1 2 0 2 0 1 0 1 1 2 2 0 2 8 1 2 3 3 2 0 3 1 0 2 53 2 3 2 3 2 0 1 3 1 1 3 3 35 1 0 3 1 0 3 0 8 3 0 2 1 3 0 2 0 3 0 0 3 2 0 2 3 26 2 2 3 2 2 1 2 8 2 0 2 0 2 0 1 1 2 0 0 3 3 1 2 1 8 2 3 0 3 3 44 1 2 2 0 2 0 3 0 3 3 0 0 35 3 0 0 3 0 17 2 35 3 3 3 1 3 2 0 1 2 1 3 1 1 1 8 1 3 0 0 2 1 0 3 3 35 0 0 3 0 0 2 3 2 1 3 1 3 35 3 2 8 3 0 3 3 0 3 35 2 1 3 3 3 2 0 1 3 2 0 1 3 1 3 3 0 35 1 1 1 3 3 0 3 3 0 0 2 26 2 1 3 3 3 44 0 26 3 3 1 1 3 1 1 1 0 26 2 44 1 3 0 3 0 2 3 2 3 3 35 3 1 2 3 2 0 3 0 3 3 3 1 0 0 3 0 0 35 0 3 0 2 3 3 0 1 1 0 1 2 26 44 1 1 2 0 1 3 3 2 0 8 0 2 2 0 2 0 3 3 21 2 1 1 3 3 26 0 3 2 3 0 1 3 53 3 3 2 0 2 8 2 3 8 1 1 2 2 3 0 0 26 1 2 2 0 3 2 1 0 53 53 3 3 1 1 0 0 3 2 44 1 1 53 1 2 8 2 1 0 3 2 0 17 1 3 2 3 0 3 2 1 0 53 0 0 3 1 0 0 1 3 1 3 1 35 2 1 17 2 0 0 3 26 0 2 0 0 2 0 0 0 1 3 53 2 2 22 3 1 1 1 1 1 3 0 2 17 2 53 44 1 1 2 2 1 2 3 1 3 1 3 1 3 26 53 3 2 1 2 35 1 3 0 0 1 1 0 17 0 1 26 2 8 1 0 2 2 2 2 3 3 2 0 3 1 1 2 0 1 3 0 3 8 26 3 0 3 2 2 8 3 2 3 3 0 2 2 1 1 3 3 1 0 1 3 3 3 1 0 0 3 2 3 3 1 2 3 17 0 3 1 1 2 26 2 3 2 3 1 2 1 3 2 2 53 1 3 53 35 0 2 26 0 3 3 35 2 8 3 2 3 1 1 1 2 2 3 2 1 0 0 3 1 2 2 0 2 53 3 3 35 0 1 0 0 1 3 2 53 2 0 1 1 3 3 2 2 1 1 3 1 2 1 3 3 0 0 2 3 3 1 3 35 2 0 2 2 1 2 2 2 26 3 1 2 8 0 1 0 1 2 44 3 3 1 0 2 17 1 17 1 12 3 0 2 0 2 2 3 1 0 0 1 3 1 0 0 2 8 1 1 3 2 2 2 3 2 2 1 1 1 1 53 3 2 0 2 3 1 3 35 0 1 0 0 0 1 44 3 0 0 26 0 0 0 1 3 35 0 0 2 44 35 0 2 0 3 2 3 1 1 2 1 1 53 2 3 3 3 35 0 0 2 26 1 0 0 3 0 1 3 3 0 2 0 0 2 1 2 1 2 3 1 44 2 2 17 2 2 0 2 0 3 3 3 2 0 1 0 2 0 0 3 1 3 0 2 0 0 3 3 2 0 1 1 1 0 3 2 3 2 1 1 2 1 1 8 3 2 1 44 1 2 44 2 2 1 0 2 53 1 2 1 3 1 3 2 3 35 2 0 35 1 2 3 3 3 3 1 2 3 3 3 1 3 0 2 3 3 44 2 2 1 0 1 0 3 17 3 3 3 3 0 2 2 3 0 1 1 3 1 0 44 1 3 2 1 2 1 1 2 2 0 0 3 2 1 1 3 2 1 0 2 44 3 35 2 2 1 0 1 0 0 53 2 0 2 0 1 3 3 26 3 1 2 0 1 1 0 1 1 2 2 2 0 0 0 0 2 1 0 0 3 2 0 2 0 0 0 1 2 2 1 2 0 3 1 0 0 0 2 0 1 2 2 0 1 0 2 0 2 0 3 3 2 1 3 3 3 0 3 2 1 0 3 2 35 2 2 2 0 0 1 0 3 3 3 3 1 0 2 0 0 3 3 3 8 3 0 3 2 0 17 2 3 0 3 0 1 2 3 44 3 3 3 1 2 1 1 2 0 3 3 0 1 1 2 3 3 0 0 3 1 3 0 3 2 3 3 0 3 2 2 0 1 3 3 8 0 0 1 3 0 3 3 2 3 0 17 17 0 2 1 0 3 1 2 2 26 0 3 0 0 0 1 2 3 1 26 3 2 26 1 3 35 35 2 0 3 2 17 2 1 3 2 1 3 3 3 0 3 0 1 2 2 3 3 8 1 1 2 0 0 2 2 1 3 0 2 3 1 2 2 0 0 8 3 0 0 1 2 0 0 44 1 1 1 1 0 3 0 4 1 0 0 2 1 0 3 0 0 2 2 2 3 3 1 0 3 3 44 1 1 3 1 3 1 2 3 2 0 2 1 1 3 44 3 2 0 53 26 0 0 3 1 2 3 35 3 2 35 1 3 44 2 1 2 26 2 1 3 1 3 0 2 1 0 1 1 3 0 8 1 0 1 3 53 3 0 53 0 0 3 53 0 0 2 0 35 2 0 0 2 1 1 2 0 2 2 1 1 3 2 2 0 1 0 2 3 0 3 1 44 2 0 0 0 3 3 35 44 3 2 3 1 0 2 2 0 2 1 1 44 1 2 3 1 1 3 3 2 3 0 0 1 2 3 2 2 1 0 3 1 44 1 0 2 8 3 3 2 1 2 3 1 0 2 1 3 0 0 2 26 8 1 0 0 1 1 3 1 2 3 2 0 2 3 3 1 2 0 3 1 2 3 1 1 3 3 1 1 1 3 3 0 0 2 1 3 0 0 2 2 3 1 17 1 1 44 3 1 35 8 3 3 2 0 3 3 1 3 0 2 2 2 1 2 35 0 3 2 3 0 3 1 2 53 3 0 2 2 3 2 3 8 0 0 1 0 1 2 2 2 0 1 1 2 2 0 0 2 1 2 17 2 3 1 1 1 1 2 0 1 35 0 1 3 2 0 53 0 1 3 8 0 2 0 0 2 2 1 1 3 0 1 8 0 0 0 1 8 2 0 2 1 1 2 3 1 0 0 44 2 3 3 1 0 0 2 0 1 1 2 3 1 17 1 1 2 1 3 53 1 2 3 53 1 1 3 0 1 0 0 2 3 0 2 1 0 3 3 1 0 0 0 2 1 3 3 0 2 1 44 35 26 2 3 2 1 2 1 3 17 0 0 2 1 3 3 8 1 3 1 1 0 2 2 0 2 35 1 1 8 0 2 0 3 3 1 2 2 3 3 0 53 3 1 3 2 0 2 0 2 2 1 0 3 0 17 1 3 2 0 1 53 2 1 2 3 2 2 0 0 1 1 0 0 1 1 17 1 2 2 53 2 1 1 3 0 2 1 1 2 3 2 53 53 1 1 2 3 1 1 2 26 0 1 2 0 3 0 0 2 1 1 3 35 0 3 1 1 0 0 3 17 1 2 26 1 17 0 3 0 2 3 0 0 1 35 2 0 2 1 53 3 1 3 2 2 1 1 3 3 0 26 17 2 1 0 2 0 0 44 2 2 3 44 0 2 3 1 2 2 1 1 17 1 1 3 3 1 1 0 1 0 2 0 2 1 0 0 1 0 0 3 1 0 3 1 3 0 0 3 3 17 44 3 0 8 2 2 1 2 1 2 0 3 0 0 0 2 2 0 3 2 1 2 44 3 0 0 1 2 0 2 8 1 2 2 3 0 2 2 2 2 0 2 3 1 3 2 1 26 1 0 1 1 3 1 3 2 3 1 3 0 2 1 17 2 0 3 2 3 26 2 0 0 3 0 0 3 0 2 3 2 1 3 0 0 1 1 2 3 1 0 0 2 1 3 2 3 3 2 0 3 2 0 8 3 0 35 1 2 35 44 17 1 0 1 2 2 0 0 3 2 2 0 2 53 3 35 1 0 3 2 1 17 35 3 3 3 1 3 0 0 2 8 1 0 0 1 1 35 40 1 0 0 0 0 2 3 0 3 0 1 0 0 3 0 2 1 3 1 35 17 3 0 35 0 2 1 3 2 0 2 3 1 1 2 3 1 3 2 2 2 0 3 2 2 2 2 0 0 8 2 0 3 17 2 2 0 2 0 3 8 1 3 1 0 3 3 1 1 3 3 8 2 1 2 3 0 0 0 0 2 3 0 53 2 1 0 1 0 3 1 0 2 2 2 2 2 0 1 2 2 3 3 0 2 1 2 0 3 3 0 2 26 1 0 2 3 0 3 0 3 3 1 1 1 17 53 2 1 0 2 1 0 2 2 1 1 0 35 1 2 53 3 1 2 17 3 0 1 2 2 1 44 3 1 3 1 3 0 2 44 2 17 0 2 2 1 2 0 2 0 2 2 8 3 2 2 2 0 3 2 1 3 8 1 2 2 3 3 1 35 0 0 3 3 1 2 2 2 3 0 1 3 2 26 0 2 0 0 35 44 1 3 3 2 2 0 2 3 2 2 2 0 0 1 0 26 26 1 3 1 2 2 3 53 1 3 1 2 0 2 1 3 1 8 2 0 3 1 1 1 2 2 3 3 44 2 2 0 1 1 1 35 0 3 0 2 2 3 0 2 0 0 2 1 3 2 2 0 13 2 3 0 2 2 2 3 1 3 35 3 1 1 2 3 3 2 53 2 3 2 3 3 1 2 1 0 3 0 3 44 3 1 2 0 0 0 1 2 2 2 1 3 3 3 0 8 2 3 3 3 0 0 2 2 3 3 0 3 1 2 3 2 0 3 1 3 1 3 2 3 26 1 2 2 0 3 0 1 2 2 26 1 0 1 1 2 0 53 0 44 1 2 2 1 3 0 0 1 2 2 0 3 1 1 0 35 1 0 0 1 3 3 2 3 2 26 3 2 2 2 1 0 3 3 1 3 2 0 0 17 0 2 3 2 3 1 1 35 2 0 2 3 2 3 26 1 2 44 2 3 0 0 3 3 3 44 2 2 2 1 3 17 44 3 3 1 2 2 2 2 44 35 2 0 3 0 1 44 3 1 0 1 3 2 2 2 3 1 0 1 0 0 2 53 0 26 1 3 3 2 0 1 0 0 8 1 2 3 0 0 2 3 2 1 2 53 3 2 3 0 0 1 2 1 0 3 0 1 1 0 3 1 2 1 3 3 0 1 0 2 2 3 0 2 2 2 0 0 3 2 17 1 3 1 1 1 1 1 2 1 0 53 3 0 2 0 53 3 0 2 8 1 1 0 0 2 3 1 3 1 2 3 1 17 0 1 1 2 0 0 1 2 0 1 3 1 2 2 44 3 1 8 1 2 0 3 2 3 3 3 2 1 8 2 2 0 3 3 3 3 2 3 0 3 3 3 2 3 3 0 3 2 44 1 0 0 2 2 17 1 44 17 44 1 3 3 0 3 3 26 3 2 2 1 1 1 3 0 3 26 1 1 2 1 0 0 2 3 2 0 0 3 3 1 1 1 2 2 8 0 2 3 2 3 2 1 3 1 0 26 2 0 2 3 1 3 2 2 2 1 0 2 0 2 1 0 2 2 2 0 1 2 3 2 1 2 0 0 3 3 2 2 1 3 0 0 1 1 3 1 0 2 1 8 26 0 2 1 0 0 3 53 44 0 1 2 2 1 1 3 0 1 3 2 2 2 1 3 1 0 2 1 3 1 0 2 3 1 0 2 0 2 2 0 0 3 1 0 2 2 3 0 2 1 8 8 1 2 1 2 2 2 3 0 2 2 1 3 2 1 3 26 26 3 0 1 1 1 1 3 0 2 2 0 0 1 1 3 1 1 3 2 0 1 1 2 0 3 0 2 3 2 1 3 1 1 3 3 2 35 0 1 1 2 3 3 0 1 1 3 2 3 1 3 1 1 3 2 1 3 3 2 3 2 1 1 1 0 2 0 2 3 3 3 1 3 2 3 0 3 0 2 3 0 2 3 3 0 0 2 3 3 1 3 1 17 8 3 3 2 2 44 3 3 8 3 1 2 2 1 0 2 53 2 3 1 0 2 1 1 1 0 26 0 2 2 0 2 2 3 26 3 2 2 3 1 2 1 1 3 53 1 3 0 2 2 2 2 3 44 17 0 0 3 0 3 1 1 35 3 2 1 3 3 44 0 3 3 35 1 8 0 2 35 3 0 2 0 3 2 26 1 3 2 0 26 1 2 44 0 2 3 48 3 3 1 1 1 1 0 0 0 0 3 2 2 2 0 0 8 1 1 0 0 2 2 1 3 3 3 0 2 44 3 1 3 1 1 0 0 0 2 2 3 1 2 0 3 3 1 2 0 2 3 0 2 1 0 2 48 2 0 3 0 0 2 1 2 3 3 1 0 2 3 2 3 3 0 0 2 3 1 3 3 1 3 0 0 2 2 35 44 1 0 1 2 3 0 1 2 3 44 0 0 13 1 2 3 1 3 3 3 1 0 2 3 0 3 0 1 0 0 2 1 1 2 3 53 3 2 1 0 1 1 3 1 44 2 0 3 1 0 0 8 2 2 0 1 1 1 3 2 0 1 2 0 3 0 3 2 3 0 0 3 26 3 3 1 0 1 3 1 1 3 1 1 3 3 0 3 2 3 3 2 1 0 2 3 2 0 1 3 1 1 2 1 3 2 2 17 3 1 1 35 1 0 0 3 2 3 1 2 17 3 2 3 35 2 3 1 35 2 0 1 3 0 0 0 1 3 3 0 2 0 1 0 2 0 3 2 2 53 1 2 35 1 3 1 2 0 1 0 2 1 0 0 1 2 8 3 2 0 2 26 3 2 0 1 2 2 1 1 1 2 3 3 8 2 0 1 2 3 1 0 1 0 1 1 0 1 48 8 0 2 1 3 0 0 3 2 2 3 2 2 2 1 2 2 2 0 8 8 26 1 1 1 3 2 0 2 0 35 2 0 3 2 2 2 1 2 1 1 2 1 1 2 2 3 2 0 0 2 1 3 0 2 3 3 1 2 1 2 2 2 1 3 0 8 2 0 0 17 44 2 1 2 0 1 1 2 8 3 3 2 3 0 44 2 2 0 2 3 3 1 2 0 1 2 1 1 2 2 3 1 2 2 2 8 1 3 2 0 3 0 3 0 2 3 1 1 3 2 0 3 3 1 3 1 3 26 2 35 1 3 3 3 2 1 1 1 1 3 1 3 1 26 0 26 2 0 0 3 44 3 2 0 2 44 3 1 26 1 2 3 0 44 3 0 26 2 35 0 44 8 3 1 3 2 1 3 2 0 1 2 35 0 1 2 3 3 2 1 1 3 0 3 2 17 1 2 3 3 0 2 1 2 0 0 3 2 1 0 2 3 3 1 8 3 26 2 1 0 0 3 3 3 3 1 8 3 1 2 0 3 2 0 1 3 2 0 3 44 0 3 0 0 1 3 0 53 2 3 2 0 2 2 2 2 44 0 3 2 1 2 0 3 2 3 1 3 1 3 0 3 3 0 2 17 1 3 1 0 1 0 1 1 2 0 3 3 2 3 0 8 2 0 1 3 1 2 3 0 3 1 2 2 3 1 0 1 2 2 0 0 1 3 3 3 3 2 35 3 3 3 3 0 1 1 0 0 26 2 26 1 44 2 0 2 1 35 1 1 3 3 44 1 2 0 0 35 3 3 2 2 2 1 1 2 0 0 2 0 2 0 2 2 2 3 0 1 2 2 2 17 0 0 3 2 17 0 0 3 2 2 3 3 2 0 2 26 0 3 3 2 3 3 2 44 1 1 1 1 2 1 44 2 0 1 1 0 3 0 3 1 2 1 3 1 35 1 1 1 3 0 2 2 3 2 3 0 1 3 3 1 0 2 3 0 0 35 0 0 2 1 0 3 3 0 0 1 3 2 0 2 3 3 3 8 3 3 3 2 2 0 3 2 2 3 3 0 0 2 0 0 1 0 0 8 53 0 0 1 0 2 1 3 3 0 1 0 2 2 0 1 2 0 53 1 0 2 8 3 8 1 1 3 8 1 53 2 1 1 44 3 3 1 3 0 0 1 0 1 1 3 2 3 1 1 3 1 44 1 3 3 1 3 17 2 3 2 0 2 1 1 1 1 3 0 0 35 3 3 44 1 0 0 3 1 0 1 1 0 0 8 8 0 2 1 3 3 26 1 26 2 3 1 1 3 3 53 2 3 3 0 2 1 2 3 0 2 0 0 2 0 0 2 1 1 0 0 2 3 0 8 26 3 0 3 35 2 3 1 0 1 0 3 1 2 0 3 2 2 0 0 1 0 2 2 0 44 1 2 2 0 0 3 1 3 8 35 3 2 1 3 1 3 2 2 3 1 35 1 3 2 3 2 1 3 0 0 3 1 2 1 2 2 3 1 0 0 26 17 0 2 0 2 0 2 0 2 3 1 0 0 3 0 3 3 3 0 3 1 2 3 3 3 1 8 0 0 1 0 3 2 44 3 2 2 2 2 1 2 53 0 0 1 0 1 2 17 2 3 2 3 0 2 3 3 3 0 35 2 2 0 3 0 2 1 1 0 2 3 1 0 3 3 1 0 35 1 0 2 1 2 2 3 35 0 3 1 1 0 1 2 3 3 1 1 3 0 0 1 26 2 2 2 44 2 1 1 0 3 3 3 3 2 0 1 2 3 44 2 1 1 2 2 2 0 1 0 3 3 3 3 0 0 44 2 8 0 2 1 3 26 0 1 1 2 1 1 3 0 2 2 2 2 3 0 0 3 3 1 1 0 0 1 3 3 0 2 0 26 1 1 1 2 2 2 2 1 3 3 35 1 35 1 1 3 2 2 3 3 0 0 17 1 0 1 3 0 2 2 2 3 3 0 1 2 0 3 1 3 3 2 35 0 2 35 26 2 1 3 3 3 8 1 26 3 44 1 1 1 3 2 3 0 1 3 0 1 3 2 3 0 1 1 1 2 0 0 3 35 1 0 0 3 1 2 0 2 3 0 0 0 1 1 0 0 1 3 3 1 0 2 35 1 3 2 0 0 2 2 2 1 3 0 3 2 1 1 0 26 3 1 2 44 35 2 3 1 1 0 1 3 0 3 17 26 2 3 8 2 0 35 2 2 2 26 1 2 3 0 1 1 3 1 26 1 1 26 1 53 2 2 2 2 0 3 0 1 2 3 1 3 0 3 1 1 3 0 2 0 1 26 0 0 1 1 3 0 1 1 1 1 3 1 2 1 17 3 1 0 53 1 1 0 3 1 3 0 1 1 3 2 2 1 0 3 3 3 1 1 0 3 2 1 3 2 0 3 0 0 26 3 3 2 2 1 1 0 0 26 2 3 1 0 0 1 0 1 3 3 0 0 1 0 0 26 1 1 1 1 1 1 1 44 0 2 2 0 3 2 44 1 3 0 0 8 0 1 1 1 1 1 1 3 0 3 44 3 1 1 0 1 3 8 2 1 2 1 0 0 26 2 2 1 3 1 1 2 3 2 1 0 3 1 26 2 2 0 2 2 1 0 0 3 1 1 3 0 3 2 0 3 3 0 3 35 1 0 2 1 0 2 3 2 17 2 0 3 8 1 3 3 2 1 3 53 0 0 0 2 0 0 1 0 0 0 53 2 3 2 3 3 3 1 3 3 1 0 0 26 1 0 44 3 3 3 1 2 1 3 0 3 44 1 3 35 0 0 1 1 2 2 3 1 3 1 2 0 0 2 3 3 0 0 3 1 1 0 0 3 1 44 0 2 0 0 0 3 1 53 3 2 0 1 2 2 3 3 0 3 1 3 0 2 3 1 8 1 3 3 0 1 0 0 3 3 2 1 2 1 2 3 3 3 3 0 0 1 1 0 53 0 2 35 3 3 1 2 3 0 3 3 0 0 0 1 3 2 0 2 0 1 2 2 3 2 2 0 44 3 1 2 2 2 0 3 0 0 0 2 3 3 3 2 3 3 2 2 0 3 0 0 3 1 0 3 0 2 0 0 1 0 0 2 44 35 1 0 3 3 1 2 3 0 1 1 17 3 2 2 3 2 2 3 53 8 44 3 2 3 1 2 0 2 1 2 0 2 0 3 3 0 1 1 1 3 0 0 1 0 0 44 3 0 2 2 3 0 2 0 0 2 1 2 1 0 3 2 1 2 1 0 2 0 0 2 0 2 2 26 0 2 3 2 44 0 0 44 3 1 3 3 0 1 1 0 3 2 17 1 0 0 3 0 1 1 0 1 0 0 3 2 0 2 1 1 8 2 2 3 17 3 0 2 1 1 1 3 8 2 35 1 1 1 2 2 3 2 1 1 3 3 3 1 2 0 1 1 3 1 3 35 1 1 3 2 17 0 53 1 2 2 0 2 3 3 2 3 1 3 3 0 2 1 3 2 1 0 1 2 2 2 2 0 1 2 1 3 1 53 2 3 2 3 1 3 3 2 1 3 2 0 1 0 1 3 1 0 1 0 3 1 3 0 0 1 0 8 3 0 2 8 0 2 2 2 1 2 2 3 1 8 1 1 2 3 1 2 0 2 3 53 2 0 0 1 0 8 3 2 0 0 1 0 0 0 0 0 2 2 1 3 0 2 1 0 2 2 3 3 0 3 3 31 2 1 1 3 1 0 3 3 1 53 3 1 0 2 2 0 2 2 2 0 3 2 0 1 1 2 3 2 3 3 3 1 2 3 0 1 3 0 44 3 0 2 2 3 2 1 1 3 1 2 3 53 2 35 2 2 1 1 0 0 2 1 3 0 0 2 2 3 3 3 0 2 2 2 1 2 1 2 0 2 2 3 3 1 35 2 0 1 2 2 3 3 3 1 2 2 2 1 2 1 3 3 2 0 3 2 3 2 44 2 2 1 3 8 3 2 0 1 2 1 17 0 44 0 0 0 35 2 0 17 3 0 0 0 2 1 1 3 44 0 17 0 3 0 3 35 0 2 0 2 1 0 0 1 1 1 0 3 3 0 53 17 2 2 1 1 2 35 0 3 0 3 1 3 0 1 3 3 1 3 3 0 2 3 0 3 1 1 0 3 17 2 1 1 1 3 3 3 1 2 1 2 1 2 8 1 3 2 2 2 44 1 1 35 1 1 3 2 3 1 2 2 2 2 1 0 1 1 3 0 0 1 1 1 2 17 3 2 1 1 2 3 2 0 0 1 2 2 2 0 3 53 0 2 3 3 1 3 1 2 44 1 0 2 35 1 1 0 2 35 26 0 17 3 2 2 1 0 1 2 0 44 0 0 1 2 2 3 0 3 0 3 1 1 1 0 1 44 1 1 0 26 0 53 0 2 1 3 2 0 8 1 2 0 2 2 0 1 0 2 2 1 3 35 1 3 1 0 0 0 8 0 0 2 2 3 2 53 2 0 2 3 2 3 3 17 3 1 2 2 17 1 26 3 3 2 1 0 0 3 8 2 17 2 3 2 3 1 1 1 2 0 0 2 1 1 3 8 1 1 1 3 2 0 0 2 1 0 0 1 3 0 1 2 3 2 1 0 0 1 1 1 0 0 0 2 1 0 1 1 2 1 2 17 1 1 1 1 53 2 2 0 1 2 0 3 3 0 1 26 0 0 1 1 3 2 0 3 53 2 3 0 3 1 3 0 2 3 44 0 0 1 0 3 0 2 0 0 2 3 1 2 3 1 3 1 2 1 2 0 3 1 35 0 1 3 2 2 2 1 2 2 1 1 0 1 3 0 1 3 0 2 0 3 2 2 2 1 3 0 1 3 0 1 1 3 3 1 3 2 2 3 0 0 3 3 2 1 1 0 2 3 2 1 2 0 0 44 1 3 0 2 2 0 0 3 0 1 0 0 1 2 2 0 0 2 2 1 0 2 2 3 3 1 2 2 3 3 3 44 3 17 0 0 1 0 3 0 0 2 0 2 2 2 0 1 2 8 0 8 3 2 1 0 3 1 1 0 3 2 2 44 2 0 2 1 1 0 2 1 2 2 1 2 2 2 2 3 0 2 44 17 53 35 0 53 2 2 2 3 3 2 2 1 1 0 1 1 3 3 1 1 53 3 44 3 1 0 2 2 0 2 0 0 40 2 35 2 1 2 2 3 53 2 0 8 1 1 0 3 1 1 2 0 8 3 2 3 3 3 0 8 3 0 0 3 44 3 2 17 0 0 2 2 3 3 2 1 3 26 1 1 3 3 3 2 2 1 2 3 1 1 0 3 3 1 0 44 2 0 2 35 1 3 1 2 44 3 44 1 3 2 2 2 2 1 3 0 0 2 1 0 1 2 0 0 2 2 3 2 44 35 2 0 0 8 0 3 3 0 3 53 3 0 3 3 2 26 1 0 0 2 0 3 2 3 2 1 3 0 44 3 1 3 3 2 2 3 1 2 3 2 2 2 3 0 1 2 53 1 3 1 53 1 53 0 2 0 0 3 0 3 0 3 1 1 3 2 1 3 3 0 1 0 2 0 2 1 8 1 2 1 3 3 2 1 3 1 3 2 2 2 2 1 1 3 44 8 0 3 2 8 3 0 17 0 3 0 1 3 1 26 0 1 1 1 8 0 2 1 0 2 2 0 2 2 1 0 1 3 1 2 0 1 1 53 2 8 0 1 0 0 26 2 3 1 2 1 1 1 1 3 44 3 2 0 2 26 3 3 0 2 3 0 35 3 2 1 3 3 1 1 2 2 0 3 3 1 17 44 3 2 44 1 0 0 1 3 1 2 2 3 0 2 2 2 1 3 3 2 0 2 3 2 2 0 1 2 3 1 0 0 0 44 1 2 3 0 1 0 2 3 0 3 2 1 2 1 1 3 0 2 3 3 1 1 3 2 2 1 35 3 2 2 2 1 0 0 0 3 0 0 0 3 8 1 1 0 0 1 3 35 3 3 0 2 0 2 3 35 2 1 0 1 0 35 2 2 3 0 2 3 3 8 2 1 3 1 1 35 0 0 8 2 1 2 1 3 2 2 2 1 2 3 0 1 0 3 2 3 0 17 3 3 8 2 0 3 2 2 0 2 0 3 2 0 0 0 2 26 3 3 3 0 2 1 0 0 3 3 2 0 3 0 0 2 2 3 3 3 1 3 1 1 2 3 3 2 0 3 3 2 2 0 0 1 1 2 0 1 2 0 0 3 2 2 1 0 0 3 3 44 0 2 2 2 2 26 1 1 1 2 8 0 44 3 3 1 1 3 3 1 3 0 2 35 2 3 2 1 2 2 1 0 3 2 0 3 3 3 0 1 3 0 1 1 2 3 3 0 0 3 3 1 3 0 2 0 0 2 2 2 3 0 2 3 3 26 2 1 0 3 1 8 1 0 0 1 0 0 0 2 0 2 1 2 0 44 2 0 2 3 0 3 2 2 2 2 1 1 0 3 3 0 2 35 2 2 2 3 0 1 0 3 1 3 3 1 1 1 0 2 17 0 1 0 3 1 0 2 3 2 44 53 2 3 0 0 2 53 3 0 1 3 3 3 44 0 3 8 1 3 2 1 2 2 2 3 53 2 1 0 3 1 0 2 0 0 3 1 1 1 3 0 3 2 3 0 2 2 3 17 3 2 0 26 3 1 0 3 1 1 2 2 0 17 0 1 0 0 26 1 1 2 0 17 1 3 2 0 0 1 3 3 0 1 3 0 2 2 0 44 1 3 0 0 2 2 1 3 1 2 3 3 44 35 0 1 53 0 0 26 17 0 1 35 0 1 3 0 1 3 1 1 2 3 2 2 1 1 0 0 26 35 53 2 0 3 1 26 0 1 3 3 3 3 2 3 2 0 2 2 0 1 0 1 4 3 1 3 3 0 1 1 3 0 2 2 2 0 1 1 3 0 3 3 2 3 17 3 3 8 0 3 0 0 3 2 2 1 0 3 2 3 2 0 2 8 0 2 2 0 3 0 1 2 0 3 0 44 0 2 2 0 0 2 3 26 3 3 0 2 1 0 2 2 3 3 3 0 2 2 1 0 3 1 2 26 1 3 0 0 0 3 26 0 2 44 1 2 1 2 44 3 1 0 0 1 2 3 0 1 53 0 0 1 3 8 2 2 1 1 3 2 0 0 44 35 2 0 1 1 3 0 1 1 1 26 1 0 0 2 1 35 1 2 1 2 0 2 0 1 2 1 2 0 2 0 1 3 1 3 0 1 0 2 2 17 1 3 2 2 3 0 35 3 2 2 1 44 8 17 2 44 1 0 1 3 3 3 1 1 3 3 3 0 1 0 1 0 0 1 2 0 3 3 1 0 2 2 3 53 35 2 0 0 3 0 0 2 3 2 1 2 0 3 0 2 1 3 0 1 0 0 1 3 1 2 26 2 3 1 2 2 2 3 1 1 44 2 1 53 0 53 1 3 0 1 1 3 0 1 1 35 2 3 3 1 0 1 8 0 1 0 1 0 3 26 3 44 3 1 3 1 1 2 2 0 1 0 0 3 0 2 3 2 3 0 3 3 1 3 3 3 2 0 0 0 3 2 3 3 2 2 0 3 2 3 0 2 2 2 1 3 1 17 1 0 2 3 2 1 2 0 3 3 0 0 3 0 3 3 2 3 1 0 2 1 0 2 0 3
+GORILLA_SUMATRA 0 2 0 1 3 2 0 0 44 44 1 1 3 1 0 3 2 0 3 3 1 1 3 2 3 3 2 8 0 0 3 2 3 1 3 3 2 1 3 17 3 44 3 3 35 1 2 53 26 0 1 53 3 0 0 1 1 2 35 1 0 1 2 2 0 1 2 1 3 1 3 26 0 3 3 0 1 8 35 17 0 17 3 3 0 1 3 0 2 1 35 3 3 2 0 3 1 3 2 1 3 1 2 3 3 2 3 2 8 3 1 2 0 17 3 1 0 3 2 2 3 1 1 2 2 17 2 3 44 1 3 26 2 0 0 1 3 3 1 17 3 1 3 1 3 2 0 8 3 2 0 2 0 0 0 0 0 1 12 3 1 2 0 0 0 1 17 2 1 17 1 8 2 2 2 0 2 1 17 3 3 2 3 1 1 0 3 0 0 0 1 2 1 3 1 0 2 2 2 2 0 1 0 0 1 0 2 3 1 1 0 0 3 0 2 1 3 2 2 1 3 3 1 1 1 2 2 2 3 3 1 2 3 26 0 26 2 0 0 2 0 0 3 3 1 1 0 2 3 44 2 0 0 3 3 44 35 3 3 0 3 3 1 2 3 2 0 0 1 3 2 3 2 3 2 26 1 1 2 1 2 2 2 35 3 17 1 3 3 2 0 1 3 0 2 3 3 1 1 0 0 0 3 2 1 53 1 2 2 0 0 1 0 0 1 1 0 26 2 8 3 1 0 2 2 0 1 3 35 0 0 3 8 2 2 1 2 1 1 0 3 0 1 3 1 0 1 3 2 17 3 2 0 1 1 0 8 0 2 1 2 3 3 2 0 35 17 0 1 26 0 2 1 3 3 2 0 1 0 3 26 0 1 3 2 3 2 2 2 17 53 1 2 0 0 0 0 1 0 2 0 2 3 2 2 3 2 2 0 35 1 1 2 3 0 1 1 3 0 3 0 3 2 3 0 3 2 3 1 3 1 1 3 0 1 1 0 3 3 3 0 26 3 3 3 1 2 2 17 3 1 0 0 3 2 0 1 3 1 53 0 0 0 3 2 2 3 0 2 2 2 2 0 2 1 3 1 3 44 0 3 1 3 0 0 3 0 3 2 2 1 53 0 2 3 2 0 3 2 1 2 1 0 49 2 1 0 0 3 3 1 1 3 0 3 44 2 1 0 0 0 44 53 2 1 53 35 0 0 2 26 3 1 2 1 2 1 3 3 44 3 3 2 1 3 1 0 2 26 2 44 3 53 3 3 1 3 1 2 0 3 0 1 3 0 1 1 53 3 0 0 3 3 3 3 3 0 0 1 2 3 44 3 0 0 2 1 8 26 53 1 1 0 3 2 0 1 2 0 53 3 2 2 3 3 3 3 1 1 2 17 1 2 3 0 3 2 1 2 2 26 0 1 2 17 1 2 35 2 1 0 3 0 3 2 3 1 3 1 2 2 1 3 1 2 0 1 1 3 2 3 1 0 1 0 3 0 3 53 0 3 3 3 1 1 0 0 0 1 2 2 1 3 3 0 0 17 3 35 1 1 2 1 1 0 1 0 35 3 1 1 2 1 1 2 3 2 8 0 1 3 35 1 0 1 26 3 3 1 2 0 3 1 0 0 1 35 3 26 1 3 1 3 1 3 2 2 1 35 2 1 0 1 2 2 1 1 0 2 3 2 3 57 2 2 0 2 1 2 1 35 8 3 3 1 3 13 1 53 1 3 3 3 3 2 2 1 0 1 3 1 26 0 1 8 0 3 0 8 3 2 1 2 53 1 2 3 53 0 1 1 2 53 0 35 17 0 3 0 17 0 2 1 0 3 1 2 2 1 3 8 1 0 2 3 1 0 2 3 0 3 3 8 1 2 1 3 1 1 3 0 1 35 2 0 0 3 3 3 1 3 3 0 3 3 3 3 0 3 0 2 3 1 2 2 8 1 0 3 17 2 2 3 2 3 2 1 1 0 26 1 0 1 2 0 1 2 1 0 1 3 0 1 35 0 3 1 0 3 3 3 3 0 3 3 0 2 1 2 1 0 3 1 17 1 0 0 3 3 0 44 2 2 0 3 2 1 0 0 8 2 26 1 3 2 0 2 1 2 2 1 1 3 3 3 0 0 2 1 2 3 1 0 0 2 0 3 1 3 8 0 1 2 0 44 35 3 2 1 2 2 3 3 2 3 2 2 3 2 1 2 0 0 0 1 3 1 2 0 0 0 2 8 3 2 1 1 2 0 0 1 3 2 2 44 0 2 0 1 0 0 3 2 3 2 3 1 3 2 44 0 1 0 3 26 0 3 8 1 3 2 2 2 0 0 2 0 0 2 3 0 1 0 0 3 0 3 3 2 1 0 2 8 3 0 3 3 3 0 1 1 0 3 1 2 1 1 0 2 3 53 3 1 1 3 2 3 0 3 35 1 0 17 1 2 1 1 2 0 0 2 3 17 0 1 0 1 3 2 3 3 1 17 53 3 0 3 3 1 2 3 0 2 2 0 2 2 1 0 17 2 0 3 1 3 17 2 1 1 3 17 3 1 2 1 0 1 0 0 3 0 0 1 2 1 1 3 2 2 2 1 44 44 0 3 3 8 3 0 1 3 1 0 3 2 3 17 0 2 3 44 2 0 3 1 3 0 44 3 2 3 1 0 0 0 1 1 0 3 3 44 3 0 1 0 8 35 1 17 2 1 1 0 1 1 3 2 0 3 1 1 3 0 2 3 3 3 1 3 2 2 2 2 1 3 0 0 2 0 1 1 1 1 1 1 3 0 3 2 1 3 35 1 0 1 1 3 1 1 44 3 2 1 2 2 3 2 0 3 3 2 3 1 2 3 1 53 0 1 3 1 0 2 2 2 3 0 1 1 2 0 3 1 3 1 0 1 26 1 1 2 3 1 0 2 1 1 3 3 2 35 1 0 2 0 3 0 2 3 26 1 0 1 2 1 0 44 0 3 3 0 2 2 1 2 3 1 1 2 1 17 1 1 3 2 0 1 0 1 2 1 2 3 2 1 2 2 35 1 3 3 0 44 1 1 35 1 1 0 0 3 0 0 2 2 3 3 44 2 0 1 3 3 3 1 3 1 0 0 26 1 0 0 3 3 26 0 1 2 3 17 17 0 0 35 3 1 3 8 3 35 1 3 1 2 1 2 0 3 2 3 0 3 1 1 2 3 1 0 2 1 2 0 1 3 0 1 0 0 2 44 1 3 3 1 0 3 3 3 1 3 3 3 35 2 2 1 2 2 0 3 3 0 1 3 2 0 0 0 0 2 1 3 0 2 3 0 3 1 17 0 0 3 2 2 0 3 0 3 0 0 1 2 1 8 0 1 2 1 1 2 1 1 0 1 53 3 2 2 3 1 1 0 1 2 2 1 3 2 1 2 0 3 1 2 3 3 17 0 0 2 0 3 1 0 2 0 2 0 3 0 1 0 2 53 1 3 2 2 3 3 2 2 1 2 3 17 1 2 3 0 1 26 0 2 0 2 3 2 0 0 53 2 1 0 1 1 0 1 0 3 1 1 3 0 2 2 1 0 2 1 2 1 2 0 0 2 0 3 1 26 1 1 2 1 1 3 0 0 2 3 3 0 0 26 8 3 2 3 0 0 1 1 0 3 3 3 17 2 3 1 1 3 0 8 0 3 3 2 2 3 3 1 1 1 0 3 0 2 0 17 1 1 0 3 0 0 1 1 53 3 3 0 0 3 1 3 0 0 1 1 2 3 3 0 1 0 2 0 2 35 17 0 0 2 0 0 3 2 3 0 1 0 1 3 3 3 1 2 1 1 2 1 2 1 0 2 1 2 0 3 3 17 1 3 2 3 3 0 1 3 17 3 17 3 1 17 1 3 0 3 1 0 3 2 2 0 35 3 3 1 3 3 17 3 2 26 0 3 44 2 2 53 44 3 1 1 0 2 3 3 0 1 2 2 0 1 2 2 0 3 2 3 3 0 1 3 1 3 3 1 3 1 2 2 2 3 2 3 8 2 3 2 53 35 2 2 2 1 1 2 2 1 2 1 1 2 1 1 2 1 0 0 3 0 0 53 0 1 1 0 1 2 2 3 1 2 0 1 2 0 2 2 53 2 3 3 0 3 35 8 1 2 3 3 2 3 0 1 1 1 3 1 2 1 1 3 1 8 3 1 2 0 2 2 3 2 1 1 2 1 2 0 3 1 2 3 2 3 1 1 3 1 3 3 3 3 1 0 1 3 1 53 2 2 0 8 1 2 1 0 2 53 53 2 39 2 0 2 2 1 26 2 0 2 0 2 1 0 0 0 44 3 3 0 2 1 8 2 3 1 0 2 0 1 0 1 3 1 3 0 1 2 2 2 0 3 3 2 0 1 17 2 44 1 2 1 2 3 1 0 1 1 3 3 1 3 3 26 0 3 2 2 1 0 2 2 0 0 3 2 0 3 3 1 1 1 3 3 3 0 0 8 2 0 2 0 3 1 0 0 2 3 1 53 3 0 44 3 0 2 1 3 3 3 2 2 2 1 3 1 1 0 0 3 0 2 0 0 1 3 0 3 2 17 2 0 0 3 1 0 3 0 1 3 0 3 1 3 1 0 0 0 1 1 2 53 8 35 3 2 2 0 2 2 3 44 0 0 0 0 2 1 2 2 2 2 0 0 2 2 0 1 3 1 3 3 1 0 3 2 1 1 3 1 0 2 1 8 0 0 1 0 3 1 0 3 2 1 1 26 2 2 1 1 3 3 1 3 3 2 1 1 3 1 3 0 3 0 3 3 0 2 1 2 0 53 2 1 3 2 0 0 3 0 0 0 2 2 2 1 2 1 0 0 35 0 0 2 35 1 0 2 3 3 1 2 1 2 1 2 0 0 0 1 1 3 3 1 2 2 0 3 1 0 0 1 2 3 0 1 1 3 1 3 0 2 1 1 3 2 3 44 44 0 0 2 1 1 2 0 1 1 3 1 3 0 17 2 3 3 0 3 2 3 1 1 1 2 2 17 3 3 2 44 44 2 1 1 2 3 3 0 44 0 0 2 35 3 8 2 2 0 1 3 1 0 0 0 2 17 2 2 1 0 1 1 0 3 0 2 2 0 53 3 1 44 1 2 0 3 3 3 8 0 2 0 3 2 3 3 1 44 35 2 2 1 3 2 3 1 35 2 2 1 2 1 44 2 3 1 2 3 3 26 3 8 2 3 2 3 1 0 2 3 0 0 0 0 2 0 1 17 3 3 44 1 0 3 1 2 1 3 1 3 35 53 0 26 2 2 2 3 2 2 0 1 3 44 0 2 2 1 1 2 0 0 1 1 1 1 2 53 0 0 0 1 2 1 0 2 0 1 44 2 8 2 2 1 2 3 0 0 1 1 2 3 8 0 2 8 1 0 1 3 0 1 3 17 2 0 3 3 2 0 2 26 1 1 1 0 2 2 0 3 0 1 2 1 3 0 2 0 1 0 3 2 2 3 1 2 3 2 8 2 2 26 0 2 2 3 3 8 3 3 0 3 1 3 1 3 35 53 0 1 1 3 0 17 3 2 1 1 3 3 53 0 2 3 0 2 1 2 0 26 0 2 0 2 0 0 2 2 1 1 1 1 0 0 1 3 35 0 2 3 1 1 17 0 2 17 3 1 2 2 3 0 1 0 0 2 0 3 2 1 1 2 1 2 2 2 3 2 35 1 3 2 0 0 0 3 2 0 3 35 0 2 3 26 3 3 3 2 1 1 0 2 44 0 0 1 2 0 3 2 2 3 3 2 1 2 1 2 2 0 44 1 2 3 2 3 3 3 3 0 2 3 0 3 2 2 53 2 2 1 2 3 0 1 1 1 0 1 1 0 3 1 0 3 1 3 44 3 0 1 1 3 0 0 1 2 1 0 1 1 1 3 0 3 35 1 3 0 8 0 17 3 2 1 2 1 3 26 3 3 1 1 3 0 0 3 1 3 1 1 0 1 0 1 0 2 0 3 3 3 1 1 1 1 1 0 2 0 3 3 2 1 1 1 1 35 0 0 0 1 0 26 0 1 1 2 2 3 2 0 3 2 2 0 3 1 0 8 1 2 17 1 0 3 3 2 0 3 8 2 2 1 35 3 3 2 1 3 1 0 1 2 2 3 8 1 3 1 1 1 1 1 3 2 0 2 1 2 2 1 35 26 3 3 3 2 2 3 3 2 0 1 17 3 2 3 2 0 2 1 1 2 0 3 3 0 2 3 3 1 3 0 3 0 8 0 44 8 1 1 2 1 0 3 2 44 1 17 3 0 0 0 1 3 2 1 53 3 2 1 3 1 3 8 0 3 1 0 44 44 2 0 1 2 2 3 3 0 0 26 1 1 0 2 0 0 1 35 3 0 8 2 35 2 1 0 0 1 0 3 53 2 2 0 17 0 3 0 3 2 1 3 1 1 2 53 1 0 3 1 3 1 0 2 1 0 44 0 3 0 0 3 1 2 0 0 1 1 35 26 2 1 0 44 2 1 1 1 53 3 1 44 3 2 1 3 3 0 3 3 0 2 1 0 0 2 0 1 3 0 0 2 0 53 3 2 2 0 17 3 0 0 0 0 1 53 3 2 2 3 0 2 53 3 0 0 0 3 3 35 3 3 3 1 3 0 3 1 2 53 0 1 3 1 3 3 1 2 2 1 26 1 1 2 1 1 2 0 3 3 3 1 2 2 2 3 3 2 1 3 3 0 1 3 3 0 3 35 3 1 3 2 3 3 1 1 3 3 0 2 0 3 2 0 1 2 3 3 2 0 2 2 1 1 1 0 2 2 3 2 3 2 3 0 3 0 1 0 44 1 1 1 2 8 3 3 35 0 2 2 2 1 3 2 2 3 2 1 0 3 3 35 0 1 1 2 2 0 2 17 0 26 0 2 0 2 1 2 1 0 3 0 3 2 35 2 0 0 0 3 1 1 0 3 1 0 1 0 0 1 1 1 3 1 0 2 1 3 1 26 1 1 3 44 0 1 3 3 1 3 0 3 3 2 0 0 0 1 17 1 2 2 3 3 1 3 0 2 26 35 1 3 0 1 1 0 0 1 1 0 1 3 0 3 0 1 2 3 3 2 3 0 1 35 2 35 53 26 3 2 2 1 0 3 3 0 1 3 1 53 0 2 2 1 3 2 3 3 0 1 1 1 3 0 2 3 0 0 2 2 0 26 2 2 3 3 1 26 2 2 3 3 2 0 1 2 1 0 1 2 3 26 17 2 2 1 2 3 3 0 2 3 0 35 0 1 3 2 1 1 1 0 2 1 3 3 3 44 2 0 3 3 3 2 0 1 3 1 3 0 8 0 2 35 2 2 2 35 1 1 2 1 2 1 1 3 1 0 1 1 2 2 0 2 26 1 1 1 3 3 2 1 0 2 3 2 1 3 3 0 1 1 1 1 2 3 1 0 0 53 3 2 1 1 1 2 1 1 1 1 35 0 0 3 2 2 3 3 2 2 2 3 2 3 0 8 1 2 1 2 3 26 3 2 1 1 8 2 1 2 0 1 2 0 3 3 2 0 1 3 1 0 0 2 3 1 2 35 3 0 3 1 0 0 3 2 2 3 1 2 1 3 53 1 1 44 3 2 1 3 1 2 1 53 0 2 2 0 53 1 1 3 2 44 2 1 1 0 1 1 0 2 2 3 3 0 3 2 2 3 1 1 0 1 0 53 35 2 0 2 17 1 3 2 2 2 3 3 0 1 3 2 2 0 3 0 3 2 1 1 0 2 1 2 0 3 0 0 0 1 3 3 1 0 0 2 1 0 2 2 1 1 0 2 1 44 0 0 0 1 26 3 3 3 44 1 1 2 2 3 2 2 3 2 2 0 1 2 2 1 3 3 0 1 0 3 0 1 1 0 0 3 1 0 1 2 0 0 1 35 3 3 2 1 53 0 3 1 1 3 3 1 1 1 0 2 2 2 1 3 0 0 2 3 0 1 1 3 3 1 2 1 3 8 35 2 2 3 3 3 0 2 0 2 3 1 2 1 2 0 2 0 1 0 1 1 2 2 0 2 8 1 2 3 3 2 0 3 1 0 2 53 2 3 2 3 2 0 1 3 1 1 3 3 35 1 0 3 1 0 3 0 8 3 0 2 1 3 0 2 0 3 0 0 3 2 0 2 3 26 2 2 3 2 2 1 2 8 2 0 2 0 2 0 1 1 2 0 0 3 3 1 2 1 8 2 3 0 3 3 44 1 2 2 0 2 0 3 0 3 3 0 0 35 3 0 0 3 0 17 2 35 3 3 3 1 3 2 0 1 2 1 3 1 1 1 8 1 3 0 0 2 1 0 3 3 35 0 0 3 0 0 2 3 2 1 3 1 3 35 3 2 8 3 0 3 3 0 3 35 2 1 3 3 3 2 0 1 3 2 0 1 3 1 3 3 0 35 1 1 1 3 3 0 3 3 0 0 2 26 2 1 3 3 3 44 0 26 3 3 1 1 3 1 1 1 0 26 2 44 1 3 0 3 0 2 3 2 3 3 35 3 1 2 3 2 0 3 0 3 3 3 1 0 0 3 0 0 35 0 3 0 2 3 3 0 1 1 0 1 2 26 44 1 1 2 0 1 3 3 2 0 8 0 2 2 0 2 0 3 3 2 2 1 1 3 3 26 0 3 2 3 0 1 3 53 3 3 2 0 2 8 2 3 8 1 1 2 2 3 0 0 26 1 2 2 0 3 2 1 0 53 53 3 3 1 1 0 0 3 2 44 1 1 2 1 2 8 2 1 0 3 2 0 17 1 3 2 3 0 3 2 1 0 53 0 0 3 1 0 0 1 3 1 3 1 35 2 1 17 2 0 0 3 26 0 2 0 0 2 0 0 0 1 3 53 2 2 0 3 1 1 1 1 1 3 0 2 17 2 53 44 1 1 2 2 1 2 3 1 3 1 3 1 3 26 53 3 2 1 2 35 1 3 0 0 1 1 0 17 0 1 26 2 8 1 0 2 2 2 2 3 3 2 0 3 1 1 2 0 1 3 0 3 8 26 3 0 3 2 2 8 3 2 3 3 0 2 2 1 1 3 3 1 0 1 3 3 3 1 0 0 3 2 3 3 1 2 3 17 0 3 1 1 2 26 2 3 2 3 1 2 1 3 2 2 53 1 3 53 35 0 2 26 0 3 3 35 2 8 3 2 3 1 1 1 2 2 3 2 1 0 0 3 1 2 2 0 2 53 3 3 35 0 1 0 0 1 3 2 53 2 0 1 1 3 3 2 2 1 1 3 1 2 1 3 3 0 0 2 3 3 1 3 35 2 0 2 2 1 2 2 2 26 3 1 2 8 0 1 0 1 2 44 3 3 1 0 2 17 1 17 1 1 3 0 2 0 2 2 3 1 0 0 1 3 1 0 0 2 8 1 1 3 2 2 2 3 2 2 1 1 1 1 53 3 2 0 2 3 1 3 35 0 1 0 0 0 1 44 3 0 0 26 0 0 0 1 3 35 0 0 2 44 35 0 2 0 3 2 3 1 1 2 1 1 53 2 3 3 3 35 0 0 2 26 1 0 0 3 0 1 3 3 0 2 0 0 2 1 2 1 2 3 1 44 2 2 17 2 2 0 2 0 3 3 3 2 0 1 0 2 0 0 3 1 3 0 2 0 0 3 3 2 0 1 1 1 0 3 2 3 2 1 1 2 1 1 8 3 2 1 44 1 2 44 2 2 1 0 2 53 1 2 1 3 1 3 2 3 35 2 0 35 1 2 3 3 3 3 1 2 3 3 3 1 3 0 2 3 3 44 2 2 1 0 1 0 3 17 3 3 3 3 0 2 2 3 0 1 1 3 1 0 44 1 3 2 1 2 1 1 2 2 0 0 3 2 1 1 3 2 1 0 2 44 3 35 2 2 1 0 1 0 0 53 2 0 2 0 1 3 3 26 3 8 2 0 1 1 0 1 1 2 2 2 0 0 0 0 2 1 0 0 3 2 0 2 0 0 0 1 2 2 1 2 0 3 1 0 0 0 2 0 1 2 2 0 1 0 2 0 2 0 3 3 2 1 3 3 3 0 3 2 1 0 3 2 35 2 2 2 0 0 1 0 3 3 3 3 1 0 2 0 0 3 3 3 8 3 0 3 2 0 17 2 3 0 3 0 1 2 3 44 3 3 3 1 2 1 1 2 0 3 3 0 1 1 2 3 3 0 0 3 1 3 0 3 2 3 3 0 3 2 2 0 1 3 3 8 0 0 1 3 0 3 3 2 3 0 17 17 0 2 1 0 3 1 2 2 26 0 3 0 0 0 1 2 3 1 26 3 2 26 1 3 35 35 2 0 3 2 17 2 1 3 2 1 3 3 3 0 3 0 1 2 2 3 3 8 1 1 2 0 0 2 2 1 3 0 2 3 1 2 2 0 0 8 3 0 0 1 2 0 0 44 1 1 1 1 0 3 0 0 1 0 0 2 1 0 3 0 0 2 2 2 3 3 1 0 3 3 44 1 1 3 1 3 1 2 3 2 0 2 1 1 3 44 3 2 0 53 26 0 0 3 1 2 3 35 3 2 35 1 3 44 2 1 2 26 2 1 3 1 3 0 2 1 0 1 1 3 0 8 1 0 1 3 53 3 0 53 0 0 3 53 0 0 2 0 35 2 0 0 2 1 1 2 0 2 2 1 1 3 2 2 0 1 0 2 3 0 3 1 44 2 0 0 0 3 3 35 44 3 2 3 1 0 2 2 0 2 1 1 44 1 2 3 1 1 3 3 2 3 0 0 1 2 3 2 2 1 0 3 1 44 1 0 2 8 3 3 2 1 2 3 1 0 2 1 3 0 0 2 26 8 1 0 0 1 1 3 1 2 3 2 0 2 3 3 1 2 0 3 1 2 3 1 1 3 3 1 1 1 3 3 0 0 2 1 3 0 0 2 2 3 1 17 1 1 44 3 1 35 8 3 3 2 0 3 3 1 3 0 2 2 2 1 2 35 0 3 2 3 0 3 1 2 53 3 0 2 2 3 2 3 8 0 0 1 0 1 2 2 2 0 1 1 2 2 0 0 2 1 2 17 2 3 1 1 1 1 2 0 1 35 0 1 3 2 0 53 0 1 3 8 0 2 0 0 2 2 1 1 3 0 1 8 0 0 0 1 8 2 0 2 1 1 2 3 1 0 0 44 2 3 3 1 0 0 2 0 1 1 2 3 1 17 1 1 2 1 3 53 1 2 3 53 1 1 3 0 1 0 0 2 3 0 2 1 0 3 3 1 0 0 0 2 1 3 3 0 2 1 44 35 26 2 3 2 1 2 1 3 17 0 0 2 1 3 3 8 1 3 1 1 0 2 2 0 2 35 1 1 8 0 2 0 3 3 1 2 2 3 3 0 53 3 1 3 2 0 2 0 2 2 1 0 3 0 17 1 3 2 0 1 53 2 1 2 3 2 2 0 0 1 1 0 0 1 1 17 1 2 2 3 2 1 1 3 0 2 1 1 2 3 2 53 53 1 1 2 3 1 1 2 3 0 1 2 0 3 0 0 2 1 1 3 35 0 3 1 1 0 0 3 17 1 2 26 1 17 0 3 0 2 3 0 0 1 35 2 0 2 1 53 3 1 3 2 2 1 1 3 3 0 26 0 2 1 0 2 0 0 44 2 2 3 44 0 2 3 1 2 2 1 1 17 1 1 3 3 1 1 0 1 0 2 0 2 1 0 0 1 0 0 3 1 0 3 1 3 0 0 3 3 17 44 3 0 8 2 2 1 2 1 2 0 3 0 0 0 2 2 0 3 2 1 2 44 3 0 0 1 2 0 2 8 1 2 2 3 0 2 2 2 2 0 2 3 1 3 2 1 26 1 0 1 1 3 1 3 2 3 1 3 0 2 1 17 2 0 3 2 3 26 2 0 0 3 0 0 3 0 2 3 2 1 3 0 0 1 1 2 3 1 0 0 2 1 3 2 3 3 2 0 3 2 0 8 3 0 35 1 2 35 44 17 1 0 1 2 2 0 0 3 2 2 0 2 53 3 35 1 0 3 2 1 17 35 3 3 3 1 3 0 0 2 8 1 0 0 1 1 35 1 1 0 0 0 0 2 3 0 3 0 1 0 0 3 0 2 1 3 1 35 17 3 0 35 0 2 1 3 2 0 2 3 1 1 2 3 1 3 2 2 2 0 3 2 2 2 2 0 0 8 2 0 3 17 2 2 0 2 0 3 8 1 3 1 0 3 3 1 1 3 3 8 2 1 2 3 0 0 0 0 2 3 0 53 2 1 0 1 0 3 1 0 2 2 2 2 2 0 1 2 2 3 3 0 2 1 2 0 3 3 0 2 26 1 0 2 3 0 3 0 3 3 1 1 1 17 53 2 1 0 2 1 0 2 2 1 1 0 35 1 2 53 3 1 2 17 3 0 1 2 2 1 44 3 1 3 1 3 0 2 44 2 0 0 2 2 1 2 0 2 0 2 2 8 3 2 2 2 0 3 2 1 3 8 1 2 2 3 3 1 35 0 0 3 3 1 2 2 2 3 0 1 3 2 26 0 2 0 0 35 44 1 3 3 2 2 0 2 3 2 2 2 0 0 1 0 26 26 1 3 1 2 2 3 53 1 3 1 2 0 2 1 3 1 8 2 0 3 1 1 1 2 2 3 3 44 2 2 0 1 1 1 35 0 3 0 2 2 3 0 2 0 0 2 1 3 2 2 0 0 2 3 0 2 2 2 3 1 3 35 3 1 1 2 3 3 2 53 2 3 2 3 3 1 2 1 0 3 0 3 44 3 1 2 0 0 0 1 2 2 2 1 3 3 3 0 8 2 3 3 3 0 0 2 2 3 3 0 3 1 2 3 2 0 3 1 3 1 3 2 3 26 1 2 2 0 3 0 1 2 2 26 1 0 1 1 2 0 53 0 44 1 2 2 1 3 0 0 1 2 2 0 3 1 1 0 35 1 0 0 1 3 3 2 3 2 26 3 2 2 2 1 0 3 3 1 3 2 0 0 17 0 2 3 2 3 1 1 35 2 0 2 3 2 3 26 1 2 44 2 3 0 0 3 3 3 44 2 2 2 1 3 17 44 3 3 1 2 2 2 2 44 35 2 0 3 0 1 44 3 1 0 1 3 2 2 2 3 1 0 1 0 0 2 53 0 26 1 3 3 2 0 1 0 0 8 1 2 3 0 0 2 3 2 1 2 53 3 2 3 0 0 1 2 1 0 3 0 1 1 0 3 1 2 1 3 3 0 1 0 2 2 3 0 2 2 2 0 0 3 2 17 1 3 1 1 1 1 1 2 1 0 53 3 0 2 0 53 3 0 2 8 1 1 0 0 2 3 1 3 1 2 3 1 17 0 1 1 2 0 0 1 2 0 1 3 1 2 2 44 3 1 8 1 2 0 3 2 3 3 3 2 1 8 2 2 0 3 3 3 3 2 3 0 3 3 3 2 3 3 0 3 2 44 1 0 0 2 2 17 1 44 17 44 1 3 3 0 3 3 26 3 2 2 1 1 1 3 0 3 26 1 1 2 1 0 0 2 3 2 0 0 3 3 1 1 1 2 2 8 0 2 3 2 3 2 1 3 1 0 26 2 0 2 3 1 3 2 2 2 1 0 2 0 2 1 0 2 2 2 0 1 2 3 2 1 2 0 0 3 3 2 2 1 3 0 0 1 1 3 1 0 2 1 8 26 0 2 1 0 0 3 53 44 0 1 2 2 1 1 3 0 1 3 2 2 2 1 3 1 0 2 1 3 1 0 2 3 1 0 2 0 2 2 0 0 3 1 0 2 2 3 0 2 1 8 8 1 2 1 2 2 2 3 0 2 2 1 3 2 1 3 26 26 3 0 1 1 1 1 3 0 2 2 0 0 1 1 3 1 1 3 2 0 1 1 2 0 3 0 2 3 2 1 3 1 1 3 3 2 35 0 1 1 2 3 3 0 1 1 3 2 3 1 3 1 1 3 2 1 3 3 2 3 2 1 1 1 0 2 0 2 3 3 3 1 3 2 3 0 3 0 2 3 0 2 3 3 0 0 2 3 3 1 3 1 17 8 3 3 2 2 44 3 3 8 3 1 2 2 1 0 2 53 2 3 1 0 2 1 1 1 0 26 0 2 2 0 2 2 3 26 3 2 2 3 1 2 1 1 3 53 1 3 0 2 2 2 2 3 44 17 0 0 3 0 3 1 1 35 3 2 1 3 3 44 0 3 3 35 1 8 0 2 35 3 0 2 0 3 2 26 1 3 2 17 26 1 2 44 0 2 3 3 3 3 1 1 1 1 22 0 0 0 3 2 2 2 0 0 8 1 1 22 0 2 2 1 3 3 3 0 2 44 3 1 3 1 1 0 0 0 2 2 3 1 2 0 3 3 1 2 0 2 3 0 2 1 0 2 3 2 0 3 0 0 2 1 2 3 3 1 0 2 3 2 3 3 0 0 2 3 1 3 3 1 3 0 0 2 2 35 44 1 0 1 2 3 0 1 2 3 44 0 0 0 1 2 3 1 3 3 3 1 0 2 3 0 3 0 1 0 0 2 1 1 2 3 53 3 2 1 0 1 1 3 1 44 2 0 3 1 0 0 8 2 2 0 1 1 1 3 2 0 1 2 0 3 0 3 2 3 0 0 3 26 3 3 1 0 1 3 1 1 3 1 1 3 3 0 3 2 3 3 2 1 0 2 3 2 0 1 3 1 1 2 1 3 2 2 17 3 1 1 35 1 0 0 3 2 3 1 2 17 3 2 3 35 2 3 1 35 2 0 1 3 0 0 0 1 3 3 0 2 0 1 0 2 0 3 2 2 53 1 2 35 1 3 1 2 0 1 0 2 1 0 0 1 2 8 3 2 0 2 26 3 2 0 1 2 2 1 1 1 2 3 3 8 2 0 1 2 3 1 0 1 0 1 1 0 1 3 8 0 2 1 3 0 0 3 2 2 3 2 2 2 1 2 2 2 0 8 8 26 1 1 1 3 2 0 2 0 35 2 0 3 2 2 2 1 2 1 1 2 1 1 2 2 3 2 0 0 2 1 3 0 2 3 3 1 2 1 2 2 2 1 3 0 8 2 0 0 17 44 2 1 2 0 1 1 2 8 3 3 2 3 0 44 2 2 0 2 3 3 1 2 0 1 2 1 1 2 2 3 1 2 2 2 8 1 3 2 0 3 0 3 0 2 3 1 1 3 2 0 3 3 1 3 1 3 26 2 35 1 3 3 3 2 1 1 1 1 3 1 3 1 26 0 26 2 0 0 3 44 3 2 0 2 44 3 1 26 1 2 3 0 44 3 0 26 2 35 0 44 8 3 1 3 2 1 3 2 0 1 2 35 0 1 2 3 3 2 1 1 3 0 3 2 17 1 2 3 3 0 2 1 2 0 0 3 2 1 0 2 3 3 1 8 3 26 2 1 0 0 3 3 3 3 1 8 3 1 2 0 3 2 0 1 3 2 0 3 44 0 3 0 0 1 3 0 53 2 3 2 0 2 2 2 2 44 0 3 2 1 2 0 3 2 3 1 3 1 3 0 3 3 0 2 17 1 3 1 0 1 0 1 1 2 0 3 3 2 3 0 8 2 0 1 3 1 2 3 0 3 1 2 2 3 1 0 1 2 2 0 0 1 3 3 3 3 2 35 3 3 3 3 0 1 1 0 0 26 2 26 1 44 2 0 2 1 35 1 1 3 3 44 1 2 0 0 35 3 3 2 2 2 1 1 2 0 0 2 0 2 0 2 2 2 3 0 1 2 2 2 17 0 0 3 2 17 0 0 3 2 2 3 3 2 0 2 26 0 3 3 2 3 3 2 44 1 1 1 1 2 1 44 2 0 1 1 0 3 0 3 1 2 1 3 1 35 1 1 1 3 0 2 2 3 2 3 0 1 3 3 1 0 2 3 0 0 35 0 0 2 1 0 3 3 0 0 1 3 2 0 2 3 3 3 8 3 3 3 2 2 0 3 2 2 3 3 0 0 2 0 0 1 0 0 8 53 0 0 1 0 2 1 3 3 0 1 0 2 2 0 1 2 0 53 1 0 2 8 3 8 1 1 3 8 1 53 2 1 1 44 3 3 1 3 0 0 1 0 1 1 3 2 3 1 1 3 1 44 1 3 3 1 3 17 2 3 2 0 2 1 1 1 1 3 0 0 35 3 3 44 1 0 0 3 1 0 1 1 0 0 8 8 0 2 1 3 3 26 1 26 2 3 1 1 3 3 53 2 3 3 0 2 1 2 3 0 2 0 0 2 0 0 2 1 1 0 0 2 3 0 8 26 3 0 3 35 2 3 1 0 1 0 3 1 2 0 3 2 2 0 0 1 0 2 2 0 44 1 2 2 0 0 3 1 3 8 35 3 2 1 3 1 3 2 2 3 1 35 1 3 2 3 2 1 3 0 0 3 1 2 1 2 2 3 1 0 0 26 17 0 2 0 2 0 2 0 2 3 1 0 0 3 0 3 3 3 0 3 1 2 3 3 3 1 8 0 0 1 0 3 2 44 3 2 2 2 2 1 2 53 0 0 1 0 1 2 17 2 3 2 3 0 2 3 3 3 0 35 2 2 0 3 0 2 1 1 0 2 3 1 0 3 3 1 0 35 1 0 2 1 2 2 3 35 0 3 1 1 0 1 2 3 3 1 1 3 0 0 1 26 2 2 2 44 2 1 1 0 3 3 3 3 2 0 1 2 3 44 2 1 1 2 2 2 0 1 0 3 3 3 3 0 0 44 2 8 0 2 1 3 26 0 1 1 2 1 1 3 0 2 2 2 2 3 0 0 3 3 1 1 0 0 1 3 3 0 2 0 26 1 1 1 2 2 2 2 1 3 3 35 1 35 1 1 3 2 2 3 3 0 0 17 1 0 1 3 0 2 2 2 3 3 0 1 2 0 3 1 3 3 2 35 0 2 35 26 2 1 3 3 3 8 1 26 3 44 1 1 1 3 2 3 0 1 3 0 1 3 2 3 0 1 1 1 2 0 0 3 35 1 0 0 3 1 2 0 2 3 0 0 0 1 1 0 0 1 3 3 1 0 2 35 1 3 2 0 0 2 2 2 1 3 0 3 2 1 1 0 26 3 1 2 44 35 2 3 1 1 0 1 3 0 3 17 26 2 3 8 2 0 35 2 2 2 26 1 2 3 0 1 1 3 1 26 1 1 26 1 53 2 2 2 2 0 3 0 1 2 3 1 3 0 3 1 1 3 0 2 0 1 26 0 0 1 1 3 0 1 1 1 1 3 1 2 1 17 3 1 0 53 1 1 0 3 1 3 0 1 1 3 2 2 1 0 3 3 3 1 1 0 3 2 1 3 2 0 3 0 0 26 3 3 2 2 1 1 0 0 26 2 3 1 0 0 1 0 1 3 3 0 0 1 0 0 26 1 1 1 1 1 1 1 44 0 2 2 0 3 2 44 1 3 0 0 8 0 1 1 1 1 1 1 3 0 3 44 3 1 1 0 1 3 8 2 1 2 1 0 0 26 2 2 1 3 1 1 2 3 2 1 0 3 1 26 2 2 0 2 2 1 0 0 3 1 1 3 0 3 2 0 3 3 0 3 35 1 0 2 1 0 2 3 2 17 2 0 3 8 1 3 3 2 1 3 53 0 0 0 2 0 0 1 0 0 0 53 2 3 2 3 3 3 1 3 3 1 0 0 26 1 0 44 3 3 3 1 2 1 3 0 3 44 1 57 35 0 0 1 1 2 2 3 1 3 1 21 0 0 2 3 3 0 0 3 1 1 0 0 3 1 44 0 2 0 0 0 3 1 53 3 2 0 1 2 2 3 3 0 3 1 3 0 2 3 1 8 1 3 3 0 1 0 0 3 3 2 1 2 1 2 3 3 3 3 0 0 1 1 0 53 0 2 35 3 3 1 2 3 0 3 3 0 0 0 1 3 2 0 2 0 1 2 2 3 2 2 0 44 3 1 2 2 2 0 3 0 0 0 2 3 3 3 2 3 3 2 2 0 3 0 0 3 1 0 3 0 2 0 0 1 0 0 2 44 35 1 0 3 3 1 2 3 0 1 1 17 3 2 2 3 2 2 3 53 8 44 3 2 3 1 2 0 2 1 2 0 2 0 3 3 0 1 1 1 3 0 0 1 0 0 44 3 0 2 2 3 0 2 0 0 2 1 2 1 0 3 2 1 2 1 0 2 0 0 2 0 2 2 26 0 2 3 2 44 0 0 44 3 1 3 3 0 1 1 0 3 2 17 1 0 0 3 0 1 1 0 1 0 0 3 2 0 2 1 1 8 2 2 3 17 3 0 2 1 1 1 3 8 2 35 1 1 1 2 2 3 2 1 1 3 3 3 1 2 0 1 1 3 1 3 35 1 1 3 2 17 0 53 1 2 2 0 2 3 3 2 3 1 3 3 0 2 1 3 2 1 0 1 2 2 2 2 0 1 2 1 3 1 53 2 3 2 3 1 3 3 2 1 3 2 0 1 0 1 3 1 0 1 0 3 1 3 0 0 1 0 8 3 0 2 8 0 2 2 2 1 2 2 3 1 8 1 1 2 3 1 2 0 2 3 53 2 0 0 1 0 8 3 2 0 0 1 0 0 0 0 0 2 2 1 3 0 2 1 0 2 2 3 3 0 3 3 1 2 1 1 3 1 0 3 3 1 53 3 1 0 2 2 0 2 2 2 0 3 2 0 1 1 2 3 2 3 3 3 1 2 3 0 1 3 0 44 3 0 2 2 3 2 1 1 3 1 2 3 53 2 35 2 2 12 1 0 0 2 1 3 0 0 2 2 3 3 3 0 2 2 2 1 2 1 2 0 2 2 3 3 1 35 2 0 1 2 2 3 3 3 1 2 2 2 1 2 1 3 3 2 0 3 2 3 2 44 2 2 1 3 8 3 2 0 1 2 1 17 0 44 0 0 0 35 2 0 17 3 0 0 0 2 1 1 3 44 0 17 0 3 0 3 35 0 2 0 2 1 0 0 1 1 1 0 3 3 0 53 17 2 2 1 1 2 35 0 3 0 3 1 3 0 1 3 3 1 3 3 0 2 3 0 3 1 1 0 3 17 2 1 1 1 3 3 3 1 2 1 2 1 2 8 1 3 2 2 2 44 1 1 35 1 1 3 2 3 1 2 2 2 2 1 0 1 1 3 0 0 1 1 1 2 17 3 2 1 1 2 3 2 0 0 1 2 2 2 0 3 53 0 2 3 3 1 3 1 2 44 1 0 2 35 1 1 0 2 35 26 0 17 3 2 2 1 0 1 2 0 44 0 0 1 2 2 3 0 3 0 3 1 1 1 0 1 44 1 1 0 26 0 53 0 2 1 3 2 0 8 1 2 0 2 2 0 1 0 2 2 1 3 35 1 3 1 0 0 0 8 0 0 2 2 3 2 53 2 0 2 3 2 3 3 17 3 1 2 2 17 1 26 3 3 2 1 0 0 3 8 2 17 2 3 2 3 1 1 1 2 0 0 2 1 1 3 8 1 1 1 3 2 0 0 2 1 0 0 1 3 0 1 2 3 2 1 0 0 1 1 1 0 0 0 2 1 0 1 1 2 1 2 17 1 1 1 1 53 2 2 0 1 2 0 3 3 0 1 26 0 0 1 1 3 2 0 3 53 2 3 0 3 1 3 0 2 3 44 0 0 1 0 3 0 2 0 0 2 3 1 2 3 1 3 1 2 1 2 0 3 1 35 0 1 3 2 2 2 1 2 2 1 1 0 1 3 0 1 3 0 2 0 3 2 2 2 1 3 0 1 3 0 1 1 3 3 1 3 2 2 3 0 0 3 3 2 1 1 0 2 3 2 1 2 0 0 44 1 3 0 2 2 0 0 3 0 1 0 0 1 2 2 0 0 2 2 1 0 2 2 3 3 1 2 2 3 3 3 44 3 17 0 0 1 0 3 0 0 2 0 2 2 2 0 1 2 8 0 8 3 2 1 0 3 1 1 0 3 2 2 44 2 0 2 1 1 0 2 1 2 2 1 2 2 2 2 3 0 2 44 17 53 35 0 53 2 2 2 3 3 2 2 1 1 0 1 1 3 3 1 1 53 3 44 3 1 0 2 2 0 2 0 0 1 2 35 2 1 2 2 3 53 2 0 8 1 1 0 3 1 1 2 0 8 3 2 3 3 3 0 8 3 0 0 3 44 3 2 17 0 0 2 2 3 3 2 1 3 26 1 1 3 3 3 2 2 1 2 3 1 1 0 3 3 1 0 44 2 0 2 35 1 3 1 2 44 3 44 1 3 2 2 2 2 1 3 0 0 2 1 0 1 2 0 0 2 2 3 2 44 35 2 0 0 8 0 3 3 0 3 53 3 0 3 3 2 26 1 0 0 2 0 3 2 3 2 1 3 0 44 3 1 3 3 2 2 3 1 2 3 2 2 2 3 0 1 2 53 1 3 1 53 1 53 0 2 0 0 3 0 3 0 3 1 1 3 2 1 3 3 0 1 0 2 0 2 1 8 1 2 1 3 3 2 1 3 1 3 2 2 2 2 1 1 3 44 8 0 3 2 8 3 0 17 0 3 0 1 3 1 26 0 1 1 1 8 0 2 1 0 2 2 0 2 2 1 0 1 3 1 2 0 1 1 53 2 8 0 1 0 0 26 2 3 1 2 1 1 1 1 3 44 3 2 0 2 26 3 3 0 2 3 0 35 3 2 1 3 3 1 1 2 2 0 3 3 1 17 44 3 2 44 1 0 0 1 3 1 2 2 3 0 2 2 2 1 3 3 2 0 2 3 2 2 0 1 2 3 1 0 0 0 44 1 2 3 0 1 0 2 3 0 3 2 1 2 1 1 3 0 2 3 3 1 1 3 2 2 1 35 3 2 2 2 1 0 0 0 3 0 0 0 3 8 1 1 0 0 1 3 35 3 3 0 2 0 2 3 35 2 1 0 1 0 35 2 2 3 0 2 3 3 8 2 1 3 1 1 35 0 0 8 2 1 2 1 3 2 2 2 1 2 3 0 1 0 3 2 3 0 17 3 3 8 2 0 3 2 2 0 2 0 3 2 0 0 0 2 26 3 3 3 0 2 1 0 0 3 3 2 0 3 0 0 2 2 3 3 3 1 3 1 1 2 3 3 2 0 3 3 2 2 0 0 1 1 2 0 1 2 0 0 3 2 2 1 0 0 3 3 44 0 2 2 2 2 26 1 1 1 2 8 0 44 3 3 1 1 3 3 1 3 0 2 35 2 3 2 1 2 2 1 0 3 2 0 3 3 3 0 1 3 0 1 1 2 3 3 0 0 3 3 1 3 0 2 0 0 2 2 2 3 0 2 3 3 26 2 1 0 3 1 8 1 0 0 1 0 0 0 2 0 2 1 2 0 44 2 0 2 3 0 3 2 2 2 2 1 1 0 3 3 0 2 35 2 2 2 3 0 1 0 3 1 3 3 1 1 1 0 2 17 0 1 0 3 1 0 2 3 2 44 53 2 3 0 0 2 53 3 0 1 3 3 3 44 0 3 8 1 3 2 1 2 2 2 3 53 2 1 0 3 1 0 2 0 0 3 1 44 1 3 0 3 2 3 0 2 2 3 17 3 2 0 26 3 1 0 3 1 1 2 2 0 17 0 1 0 0 26 1 1 2 0 17 1 3 2 0 0 1 3 3 0 1 3 0 2 2 0 44 1 3 0 0 2 2 1 3 1 2 3 3 44 35 0 1 53 0 0 26 17 0 1 35 0 1 3 0 1 3 1 1 2 3 2 2 1 1 0 0 26 35 53 2 8 3 1 26 0 1 3 3 3 3 2 3 2 0 2 2 0 1 0 1 0 3 1 3 3 0 1 1 3 0 2 2 2 0 1 1 3 0 3 3 2 3 17 3 3 8 0 3 0 0 3 2 2 1 0 3 2 3 2 0 2 8 0 2 2 0 3 0 1 2 26 3 0 44 0 2 2 0 0 2 3 26 3 3 0 2 1 0 2 2 3 3 3 0 2 2 1 0 3 1 2 26 1 3 0 0 0 3 26 0 2 44 1 2 1 2 44 3 1 0 0 1 2 3 0 1 53 0 0 1 3 8 2 2 1 1 3 2 0 0 44 35 2 0 1 1 3 0 1 1 1 26 1 0 0 2 1 35 1 2 1 2 0 2 0 1 2 1 2 0 2 0 1 3 1 3 0 1 0 2 2 17 1 3 2 2 3 0 35 3 2 2 1 44 8 17 2 44 1 0 1 3 3 3 1 1 3 3 3 0 1 0 1 0 0 1 2 0 3 3 1 0 2 2 3 53 35 2 0 0 3 0 0 2 3 2 1 2 0 3 0 2 1 3 0 1 0 0 1 3 1 2 26 2 3 1 2 2 2 3 1 1 44 2 1 53 0 53 1 3 0 1 1 3 0 1 1 35 2 3 3 1 0 1 8 0 1 0 1 0 3 26 3 44 44 1 3 1 1 2 2 0 1 0 0 3 0 2 3 2 3 0 3 3 1 3 3 3 2 0 0 0 3 2 3 3 2 2 0 3 2 3 0 2 2 2 1 3 1 17 1 0 2 3 2 1 2 0 3 3 0 0 3 0 3 3 2 3 1 0 2 1 0 2 0 3
+
diff --git a/tutorials/pomobalance/index.html b/tutorials/pomobalance/index.html
index e03d1cd6a..401853f7c 100644
--- a/tutorials/pomobalance/index.html
+++ b/tutorials/pomobalance/index.html
@@ -55,7 +55,7 @@
 	<h1 class="maintitle">Polymorphism-aware phylogenetic models with balancing selection</h1>
 	<h3 class="subtitle">Species tree inference and identification of preferred allele frequency for balancing selection in RevBayes</h3>
 	<h4 class="authors"> Svitlana Braichenko, Rui Borges, and Carolin Kosiol</h4>
-  <h5>Last modified on August 24, 2023</h5>
+  <h5>Last modified on April 21, 2024</h5>
 </div>
 
 
@@ -99,6 +99,8 @@ <h2>Data files and scripts</h2>
         
           <li><a href="/tutorials/pomobalance/data/great_apes_BS_10000.cf">great_apes_BS_10000.cf</a></li>
         
+          <li><a href="/tutorials/pomobalance/data/great_apes_BS_10000.txt">great_apes_BS_10000.txt</a></li>
+        
         </ul>
     
         
@@ -107,8 +109,12 @@ <h2>Data files and scripts</h2>
         
         
         
+          <li><a href="/tutorials/pomobalance/scripts/counts_to_pomo_states_converter.R">counts_to_pomo_states_converter.R</a></li>
+        
           <li><a href="/tutorials/pomobalance/scripts/great_apes_pomobalance.Rev">great_apes_pomobalance.Rev</a></li>
         
+          <li><a href="/tutorials/pomobalance/scripts/weighted_sampled_method.cpp">weighted_sampled_method.cpp</a></li>
+        
         </ul>
     
 </blockquote>
@@ -120,6 +126,8 @@ <h2>Data files and scripts</h2>
 <h2 class="section" id="polymorphism-aware-phylogenetic-models-with-balancing-selection">Polymorphism-aware phylogenetic models with balancing selection</h2>
 <hr class="section" />
 
+<p><span style="color:red"><strong>NB! Please note that the current version of the code has been tested in the development version of <a href="https://github.com/revbayes/revbayes">RevBayes</a> built from the <code class="language-plaintext highlighter-rouge">dev_PoMo_bs_master</code> branch. PoMoBalance will be added to the main functionality in the next release.</strong></span></p>
+
 <p>The polymorphism-aware phylogenetic models with balancing selection (PoMoBalance) is a natural extension of <a href="/tutorials/pomos/">polymorphism-aware phylogenetic models</a> <a class="citation" href="#DeMaio2013">(De Maio et al. 2013; De Maio et al. 2015; Schrempf et al. 2016; Borges et al. 2019; Borges et al. 2022; Borges et al. 2022)</a> including all previous capabilities as well as detection of preferred allele frequencies and strength of balancing selection as shown in <a href="#pomobalance"></a>.</p>
 
 <figure id="pomobalance"><p><img src="figures/pomobalance.png" width="800" /></p>
@@ -185,7 +193,25 @@ <h2 class="section" id="loading-the-data">Loading the data</h2>
 
 <p>Similarly to <a href="/tutorials/pomos/">PoMos</a>, we are using count files in the same format. File <code class="language-plaintext highlighter-rouge">great_apes_BS_10000.cf</code> contains an example of heterozygote advantage simulation with the preferred frequency in the middle in $4$ great ape populations performed with the evolutionary simulation framework  <a href="https://messerlab.org/slim/">SLiM</a> <a class="citation" href="#Haller2019">(Haller and Messer 2019)</a>. We generated $10000$ sites, however, normally balancing selection happens in small regions containing only a few genes or around a thousand nucleotides. Thus, to improve the accuracy of the method we recommend increasing the virtual population size. In the current example, we use $N = 10$ and it can be further increased taking into account the interplay between the number of sites and the computational cost.</p>
 
-<p>First, we convert the allelic counts into PoMo states. Open the terminal and copy the data and script into the corresponding subfolders <strong>data</strong> and <strong>scripts</strong> of your working directory, for example, call it, <strong>PoMoBalance</strong>. Inside <strong>PoMoBalance</strong> create <strong>output</strong> folder to store the results. Open the <code class="language-plaintext highlighter-rouge">great_apes_pomobalance.Rev</code> file using an appropriate text editor so you can follow what each command is doing. Then run <strong>RevBayes</strong>:</p>
+<p>First, we convert the allelic counts into PoMo states. Open the terminal and copy the data and script into the corresponding subfolders <strong>data</strong> and <strong>scripts</strong> of your working directory, for example, call it, <strong>PoMoBalance</strong>. Inside <strong>PoMoBalance</strong> create <strong>output</strong> folder to store the results.</p>
+
+<p>PoMo state-space includes fixed and polymorphic states. However, sampled fixed sites might not be necessarily fixed in the original population. We might just have been unlucky and only sampled individuals with the same allele from a locus that is polymorphic. It is typically the case that the real genetic diversity is undersampled in population genetic studies. The fewer the number of sampled individuals or the rarer are the alleles in the original population (i.e., singletons, doubletons), the more likely are we to observe fake fixed sites in the sequence alignment. The sampled-weighted method helps us to correct for such bias by attributing to each of the allelic counts an appropriate PoMo state (0-based coding). For a population size of 3 virtual individuals, we expect 16 states (coded 0-15), while for a population of 2 virtual individuals, we expected 10 states (coded 0-9).</p>
+
+<p>The script <code class="language-plaintext highlighter-rouge">weighted_sampled_method.cpp</code> is implemented in <strong>C++</strong>, and we will run it using the <strong>Rcpp</strong> package in <strong>R</strong>.  Open the <code class="language-plaintext highlighter-rouge">counts_to_pomo_states_converter.R</code> file and make the appropriate changes to obtain your PoMo alignments suited for PoMoBalance.</p>
+
+<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">name</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="s2">"great_apes_BS_10000"</span><span class="w">                       </span><span class="c1"># name of the count file</span><span class="w">
+</span><span class="n">count_file</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">paste0</span><span class="p">(</span><span class="s2">"../data/"</span><span class="p">,</span><span class="w"> </span><span class="n">name</span><span class="p">,</span><span class="w"> </span><span class="s2">".cf"</span><span class="p">)</span><span class="w">       </span><span class="c1"># path to the count file</span><span class="w">
+</span><span class="n">n_alleles</span><span class="w">  </span><span class="o">&lt;-</span><span class="w"> </span><span class="m">4</span><span class="w">                                     </span><span class="c1"># the four nucleotide bases A, C, G and T</span><span class="w">
+</span><span class="n">N</span><span class="w">          </span><span class="o">&lt;-</span><span class="w"> </span><span class="m">10</span><span class="w">                                    </span><span class="c1"># virtual population size</span><span class="w">
+
+</span><span class="n">alignment</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">counts_to_pomo_states_converter</span><span class="p">(</span><span class="n">count_file</span><span class="p">,</span><span class="n">n_alleles</span><span class="p">,</span><span class="n">N</span><span class="p">)</span><span class="w"> </span><span class="c1"># Create the alignment</span><span class="w">
+
+</span><span class="n">writeLines</span><span class="p">(</span><span class="n">alignment</span><span class="p">,</span><span class="n">paste0</span><span class="p">(</span><span class="s2">"../data/"</span><span class="p">,</span><span class="w"> </span><span class="n">name</span><span class="p">,</span><span class="w"> </span><span class="s2">".txt"</span><span class="p">))</span><span class="w">               </span><span class="c1"># writeg the PoMo alignment</span><span class="w">
+</span></code></pre></div></div>
+
+<p>We place the produced alignments inside the <strong>data</strong> folder. The output files follow the <code class="language-plaintext highlighter-rouge">NaturalNumbers</code> character type of RevBayes and can easily read by it.</p>
+
+<p>Open the <code class="language-plaintext highlighter-rouge">great_apes_pomobalance.Rev</code> file using an appropriate text editor so you can follow what each command is doing. Then run <strong>RevBayes</strong>:</p>
 
 <div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>./rb great_apes_pomobalance.Rev
 </code></pre></div></div>
@@ -262,7 +288,7 @@ <h2 class="section" id="setting-up-the-model">Setting up the model</h2>
 <p>The strength of balancing selection <code class="language-plaintext highlighter-rouge">beta</code> is also exponential and for the same reason as <code class="language-plaintext highlighter-rouge">rho</code> combines two kinds of moves. The preferred frequency <code class="language-plaintext highlighter-rouge">B</code> must be a discrete positive value between 0 and <code class="language-plaintext highlighter-rouge">N</code>, thus, we set up variable <code class="language-plaintext highlighter-rouge">Num</code> with a uniform prior and two kinds of standard moves<code class="language-plaintext highlighter-rouge">mvSlide</code> and <code class="language-plaintext highlighter-rouge">mvScale</code> with high weights to enhance exploration of parameter space. We round <code class="language-plaintext highlighter-rouge">Num</code> on each iteration to obtain discrete <code class="language-plaintext highlighter-rouge">B</code></p>
 
 <div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>
-# Strenths of the balancing selection
+# Strengths of the balancing selection
 
 for (i in 1:6){
 
@@ -343,6 +369,10 @@ <h2 class="section" id="setting-running-and-summarising-the-mcmc-simulation">Set
 
 monitors.append( mnScreen(printgen=10) )
 </code></pre></div></div>
+<p>Run burn-in tuning the weights of the parameters</p>
+
+<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>pbalance_mcmc.burnin(generations=2000,tuningInterval=200)
+</code></pre></div></div>
 
 <p>Finally, set up <code class="language-plaintext highlighter-rouge">mcmc</code> moves with four independent MCMC runs to ensure proper convergence and mixing.</p>
 
diff --git a/tutorials/pomobalance/scripts/counts_to_pomo_states_converter.R b/tutorials/pomobalance/scripts/counts_to_pomo_states_converter.R
new file mode 100644
index 000000000..3e0e7d3f0
--- /dev/null
+++ b/tutorials/pomobalance/scripts/counts_to_pomo_states_converter.R
@@ -0,0 +1,20 @@
+# This script reads a count file and creates a PoMo alignment in the "NaturalNumbers" type that can be uploaded into RevBayes.
+
+# setting the working directory
+setwd(getwd())
+
+# install.packages("Rcpp")  
+library("Rcpp")
+
+# uploading the function counts_to_pomo_states_converter
+sourceCpp("weighted_sampled_method.cpp")
+
+name <- "great_apes_BS_10000"
+count_file <- paste0("../data/", name, ".cf")     # count file
+n_alleles  <- 4                        # the four nucleotide bases A, C, G and T
+N          <- 10                        # virtual population size
+
+alignment <- counts_to_pomo_states_converter(count_file,n_alleles,N)
+
+# writing the PoMo alignment
+writeLines(alignment,paste0("../data/", name, ".txt"))
diff --git a/tutorials/pomobalance/scripts/great_apes_pomobalance.Rev b/tutorials/pomobalance/scripts/great_apes_pomobalance.Rev
index 4f8cb3472..002563f12 100644
--- a/tutorials/pomobalance/scripts/great_apes_pomobalance.Rev
+++ b/tutorials/pomobalance/scripts/great_apes_pomobalance.Rev
@@ -6,7 +6,7 @@
 
 N <- 10
 
-data <- readPoMoCountFile(countFile="data/great_apes_BS_10000.cf", virtualPopulationSize=N, format="PoMo")
+data <- readCharacterDataDelimited("../data/great_apes_BS_10000.txt", stateLabels=58, type="NaturalNumbers", delimiter=" ", header=FALSE)
 
 taxa       <- data.taxa()
 
@@ -55,7 +55,7 @@ moves.append(mvAVMVN(sigma) )
 
 phi := [1.0,1.0+sigma,1.0+sigma,1.0]
 
-# Strenths of the balancing selection
+# Strength of the balancing selection
 
 for (i in 1:6){
 
@@ -110,7 +110,7 @@ psi := treeAssembly(topology, branch_lengths)
 
 # Create the substitution model and clamp with our observed data
 
-sequences ~ dnPhyloCTMC(psi,Q=Q,type="PoMo")
+sequences ~ dnPhyloCTMC(psi,Q=Q,type="NaturalNumbers")
 
 sequences.clamp(data)
 
diff --git a/tutorials/pomobalance/scripts/weighted_sampled_method.cpp b/tutorials/pomobalance/scripts/weighted_sampled_method.cpp
new file mode 100644
index 000000000..92d1362ff
--- /dev/null
+++ b/tutorials/pomobalance/scripts/weighted_sampled_method.cpp
@@ -0,0 +1,256 @@
+#include <iostream>
+#include <fstream>
+#include <sstream>
+#include <vector>
+#include <random>
+#include <string>
+#include <algorithm>
+
+#include <Rcpp.h>
+using namespace Rcpp;
+
+
+// samples a state from a pomo edge 0:(N-1) givent the weights of a 
+// binomal distribution B(m|n/N,M)
+// based on equation (13) of Schrempf et al. (2016) JTB
+int sample_weight(int M, int m, int N){
+  
+  std::vector<double> weights(N+1);
+  double prob;
+  
+  // calculating the weight vector
+  for (int i=0; i < N+1; ++i){
+    prob = 1.0*i/N;
+    weights[i] = pow(prob,m)*pow(1-prob,M-m);
+  }
+  
+  // sampling a pomo state from the weight vector 
+  std::random_device rd;
+  std::mt19937 gen(rd());
+  std::discrete_distribution<> d(weights.begin(), weights.end());
+  
+  return d(gen);
+  
+}
+
+// samples a pomo edge from a matrix of allele_indexes*edge_indexes matrix
+// the matrix estipulates wich edges contain the allele allele_index
+int sample_edge(int allele_index, std::vector<int> vector){
+  
+  // setting the appropriate sub vector to sample from
+  // the edge matrix is indexed as [allele_index,edge_index]
+  // the sub vector pics the allele_index line
+  std::vector<int> sub_vector = {vector.begin() + allele_index*6, vector.begin() + allele_index*6 + 6};
+
+  // sampling a pomo edge
+  std::random_device rd;
+  std::mt19937 gen(rd());
+  std::discrete_distribution<> d( sub_vector.begin(), sub_vector.end() );
+
+  return d(gen);
+  
+}
+
+
+// gets the edge of an observed polymorphic count
+int get_index(std::vector<std::string> vector, std::string element) {
+  
+  for (int i=0; i<vector.size(); ++i){
+    if (vector[i]==element){
+      return i;
+    }
+  }
+  return -1;
+}
+
+
+// converts the count to pomo states
+// this is a combination of the sampled and weighted methods explained in
+// Schrempft et al. (2016)
+// the weighted-sampled method still calculates the weight vector, but it is used
+// to sample a pomo state and not to weight the tree tips
+
+// [[Rcpp::export]]
+std::string counts_to_pomo_states_converter(std::string count_file,int n_alleles, int N) {
+  
+  //number of edges
+  int n_edges = (n_alleles*n_alleles - n_alleles)/2;
+  
+
+  // vector and matrix of edges 
+  // vector of edges indexed as [edge_index] = "a_ia_j"
+  // matrix of edges indexed as [allele_index,edge_index]
+  // it indicates which edges have a certain allele: matrix of 0s and 1s
+  std::vector<int> matrix_edges(n_alleles*n_edges,0);
+  std::vector<std::string> vector_edges(n_edges);
+  
+
+  // generating all the possible pairwise combinations of alleles
+  int edge = 0;
+  for (int i=0; i<n_alleles; ++i ){
+    for (int j=(i+1); j<n_alleles; ++j){
+      
+      vector_edges[edge] = std::to_string(i)+std::to_string(j);
+      matrix_edges[i*n_edges+edge] = 1;
+      matrix_edges[j*n_edges+edge] = 1;
+      edge += 1;
+      
+    }
+  }
+  
+
+  //opening count file and checking existence 
+  std::ifstream inFile;
+  inFile.open(count_file);
+  if (! inFile) {
+    std::cout << "\n  Make sure " << count_file << " is in the working directory.\n\n";
+    return "\n";
+  }
+  
+
+  // reading the count file
+  // cheking the count file will well formatted and getting number of taxa and sites
+
+  std::string content, counts; 
+  int n_taxa, n_sites, state;
+
+  inFile >> content; 
+  if (content != "COUNTSFILE"){
+  	std::cout << "\n  " << count_file << " does not seem to be properly formatted. First line: COUNTSFILE NPOP # NSITES #";
+  }
+
+  inFile >> content;
+  if (content != "NPOP"){
+  	std::cout << "\n  " << count_file << " does not seem to be properly formatted. First line: COUNTSFILE NPOP # NSITES #";
+  }
+
+  inFile >> n_taxa;
+
+  inFile >> content;
+  if (content != "NSITES"){
+  	std::cout << "\n  " << count_file << " does not seem to be properly formatted. First line: COUNTSFILE NPOP # NSITES #";
+  }
+
+  inFile >> n_sites;
+  
+  std::vector<std::string> taxa(n_taxa);
+  
+  inFile >> content;
+  if (content != "CHROM"){
+  	std::cout << "\n  " << count_file << " does not seem to be properly formatted. Second line: CHROM POS TaxaName1 TaxaName2 ...";
+  }
+
+  inFile >> content;
+  if (content != "POS"){
+  	std::cout << "\n  " << count_file << " does not seem to be properly formatted. Second line: CHROM POS TaxaName1 TaxaName2 ...";
+  }
+
+  // getting the taxa names
+  for (int i=0; i<n_taxa; ++i){
+    inFile >> taxa[i];
+  }
+  
+  
+  // going through the number of sites
+  for (int i=0; i<n_sites; ++i){
+    
+
+    // reading CHROM and POS but no use to them
+    inFile >> content;
+    inFile >> content;
+
+    // some important initializations    
+    int value,state,int_index,M,m,weight,n_counts;
+
+    // going through the number of taxa
+    for (int j=0; j<n_taxa; ++j){
+      
+      // reading count pattern
+      inFile >> counts;
+      std::stringstream ss( counts );
+      std::string count,str_index;
+
+      // setting total counts, the last postive count (why last? important for state indexing), and number of non-null counts to 0
+      M        = 0;
+      n_counts = 0;
+      
+      // goes through the number of alleles
+      // counts are comma separated
+      for (int k=0; k<n_alleles; k++){
+        
+        getline( ss, count, ',' );
+        value = stoi(count);
+        
+        // getting info from the count pattenr
+        if (value > 0) {
+          M        += value;
+          m         = value;
+          n_counts += 1;
+          int_index = k;
+          str_index = str_index + std::to_string(k);
+        }
+        
+      }
+      
+      // pointing out some typical invalid counts: null counts (e.g., 0,0,0,0) and >2-allelic counts (e.g., 0,1,1,1)
+      if (n_counts==0){
+        std::cout << "\n  Unexpected count pattern: " << counts << ". PoMos require at least one postive count.\n\n";
+        return "\n";
+      }
+      if (n_counts>2){
+        std::cout << "\n  Unexpected count pattern: " << counts << ". PoMos only accept monoallelic or biallelic counts.\n\n";
+        return "\n";
+      }
+      
+      // sampling a 0:N frequency from the weight vector 
+      weight = sample_weight(M, m, N);
+      
+      // determining the pomo state
+      // three possible situations
+      // if the count is monoallelic & likely "sampled" from a fixed state
+      if (weight==N){
+        
+        state = int_index;
+        //std::cout << "  " << state << "\n";
+
+      // if the count is monoallelic & likely "sampled" from a polymoprhic state
+      } else if (n_counts==1 & weight<N ) {
+        
+        edge = sample_edge(int_index, matrix_edges);
+        state = n_alleles+edge*N-edge+weight-1;
+        //std::cout << "  " << state << "\n";
+      
+      // if the count is biallelic, thus necessarily sampled from a polymoprhic state
+      } else if (n_counts>1) {
+        
+        edge = get_index(vector_edges,str_index);
+        state = n_alleles+edge*N-edge+weight-1;
+        //std::cout << "  " << state << "\n";
+        
+      }
+      
+      std::cout << "Pattern: " << counts << " State: " << state << "\n";
+
+      // creating the NaturalNumber type file for RevBayes
+      taxa[j] += " " + std::to_string(state);
+      
+    }
+    
+  }
+  
+  // summarizing 
+  std::cout << "\n\n  Number of alleles               " << n_alleles << 
+                 "\n  Number of sites                 " << n_sites <<
+                 "\n  Number of virtual individuals   " << N <<
+                 "\n  Number of PoMo states           " << n_alleles*(1.0+(n_alleles-1.0)*(N-1.0)*0.5) <<
+                 "\n  Number of taxa                  " << n_taxa << "\n\n";
+
+  
+  // creating the alignment and returning it
+  std::string alignment = "";
+  for (int i=0; i<n_taxa; ++i){
+    alignment += taxa[i] + "\n";
+  }
+  return alignment;  
+
+}
\ No newline at end of file
diff --git a/tutorials/sse/bisse-intro.html b/tutorials/sse/bisse-intro.html
index 89849dddd..03011e35d 100644
--- a/tutorials/sse/bisse-intro.html
+++ b/tutorials/sse/bisse-intro.html
@@ -428,14 +428,14 @@ <h3 class="subsection" id="equations-for-the-multi-state-birth-death-process">Eq
 <a href="https://doi.org/10.1093/sysbio/syp067">10.1093/sysbio/syp067</a>
 
 </li>
-<li><span id="FitzJohn2010">FitzJohn R.G. 2010. Quantitative Traits and Diversification. Systematic Biology. 59:619–633.</span>
+<li><span id="FitzJohn2012">FitzJohn R.G. 2012. Diversitree: Comparative Phylogenetic Analyses of Diversification in R. Methods in Ecology and Evolution. 3:1084–1092.</span>
 
-<a href="https://doi.org/10.1093/sysbio/syq053">10.1093/sysbio/syq053</a>
+<a href="https://doi.org/10.1111/j.2041-210X.2012.00234.x">10.1111/j.2041-210X.2012.00234.x</a>
 
 </li>
-<li><span id="FitzJohn2012">FitzJohn R.G. 2012. Diversitree: Comparative Phylogenetic Analyses of Diversification in R. Methods in Ecology and Evolution. 3:1084–1092.</span>
+<li><span id="FitzJohn2010">FitzJohn R.G. 2010. Quantitative Traits and Diversification. Systematic Biology. 59:619–633.</span>
 
-<a href="https://doi.org/10.1111/j.2041-210X.2012.00234.x">10.1111/j.2041-210X.2012.00234.x</a>
+<a href="https://doi.org/10.1093/sysbio/syq053">10.1093/sysbio/syq053</a>
 
 </li>
 <li><span id="Freyman2018">Freyman W.A., Höhna S. 2018. Cladogenetic and anagenetic models of chromosome number evolution: a Bayesian model averaging approach. Systematic Biology. 67:1995–215.</span>
diff --git a/tutorials/sse/bisse.html b/tutorials/sse/bisse.html
index 93c61c30c..281a9619a 100644
--- a/tutorials/sse/bisse.html
+++ b/tutorials/sse/bisse.html
@@ -125,10 +125,10 @@ <h2>Data files and scripts</h2>
         
           <li><a href="/tutorials/sse/data/primates_activity_period.nex">primates_activity_period.nex</a></li>
         
-          <li><a href="/tutorials/cont_traits/data/primates_mating_system.nex">primates_mating_system.nex</a></li>
-        
           <li><a href="/tutorials/sse/data/primates_mating_system.nex">primates_mating_system.nex</a></li>
         
+          <li><a href="/tutorials/cont_traits/data/primates_mating_system.nex">primates_mating_system.nex</a></li>
+        
           <li><a href="/tutorials/cont_traits/data/primates_solitariness.nex">primates_solitariness.nex</a></li>
         
           <li><a href="/tutorials/sse/data/primates_solitariness.nex">primates_solitariness.nex</a></li>
diff --git a/tutorials/sse/classe.html b/tutorials/sse/classe.html
index e0ee8f8eb..7d7e2d280 100644
--- a/tutorials/sse/classe.html
+++ b/tutorials/sse/classe.html
@@ -127,10 +127,10 @@ <h2>Data files and scripts</h2>
         
           <li><a href="/tutorials/sse/data/primates_activity_period.nex">primates_activity_period.nex</a></li>
         
-          <li><a href="/tutorials/cont_traits/data/primates_mating_system.nex">primates_mating_system.nex</a></li>
-        
           <li><a href="/tutorials/sse/data/primates_mating_system.nex">primates_mating_system.nex</a></li>
         
+          <li><a href="/tutorials/cont_traits/data/primates_mating_system.nex">primates_mating_system.nex</a></li>
+        
           <li><a href="/tutorials/cont_traits/data/primates_solitariness.nex">primates_solitariness.nex</a></li>
         
           <li><a href="/tutorials/sse/data/primates_solitariness.nex">primates_solitariness.nex</a></li>
diff --git a/tutorials/sse/hisse.html b/tutorials/sse/hisse.html
index f546ec157..9cfb026b7 100644
--- a/tutorials/sse/hisse.html
+++ b/tutorials/sse/hisse.html
@@ -127,10 +127,10 @@ <h2>Data files and scripts</h2>
         
           <li><a href="/tutorials/sse/data/primates_activity_period.nex">primates_activity_period.nex</a></li>
         
-          <li><a href="/tutorials/cont_traits/data/primates_mating_system.nex">primates_mating_system.nex</a></li>
-        
           <li><a href="/tutorials/sse/data/primates_mating_system.nex">primates_mating_system.nex</a></li>
         
+          <li><a href="/tutorials/cont_traits/data/primates_mating_system.nex">primates_mating_system.nex</a></li>
+        
           <li><a href="/tutorials/cont_traits/data/primates_solitariness.nex">primates_solitariness.nex</a></li>
         
           <li><a href="/tutorials/sse/data/primates_solitariness.nex">primates_solitariness.nex</a></li>