From 176c0d831fe52eb9bafb91db7f365f34b03167a3 Mon Sep 17 00:00:00 2001
From: noah <noah@theschricks.com>
Date: Fri, 30 Sep 2022 11:57:41 -0500
Subject: [PATCH] Part B and Part C

---
 .Rhistory                               | 512 ++++++++++++++++++++++++
 .~lock.Schrick-Noah_CS-6643_Lab-4.docx# |   2 +-
 Schrick-Noah_CS-6643_Lab-4.R            |  53 ++-
 Schrick-Noah_CS-6643_Lab-4.docx         | Bin 15320 -> 27599 bytes
 4 files changed, 565 insertions(+), 2 deletions(-)
 create mode 100644 .Rhistory

diff --git a/.Rhistory b/.Rhistory
new file mode 100644
index 0000000..50ddc87
--- /dev/null
+++ b/.Rhistory
@@ -0,0 +1,512 @@
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
+# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
+# Degree Distribution
+# Professor: Dr. McKinney, Spring 2022
+# Noah Schrick - 1492657
+library(igraph)
+library(igraphdata)
+data(yeast)
+g <- yeast
+g.netname <- "Yeast"
+################# Set up Work #################
+g.vec <- degree(g)
+g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
+" Network"))
+legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
+"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
+"#006CD1", "#E66100", "#D35FB7"))
+g.mean <- mean(g.vec)
+g.seq <- 0:max(g.vec) # x-axis
+################# Guessing Alpha #################
+alpha.guess <- 1.5
+lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
+################# Poisson #################
+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2)
+################# Linear model: Least-Squares Fit #################
+g.breaks <- g.hist$breaks[-c(1)] # remove 0
+g.probs <- g.hist$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.breaks[nz.probs.mask]
+#plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
+# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
+# Degree Distribution
+# Professor: Dr. McKinney, Spring 2022
+# Noah Schrick - 1492657
+library(igraph)
+library(igraphdata)
+data(yeast)
+g <- yeast
+g.netname <- "Yeast"
+################# Set up Work #################
+g.vec <- degree(g)
+g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
+" Network"))
+legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
+"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
+"#006CD1", "#E66100", "#D35FB7"))
+g.mean <- mean(g.vec)
+g.seq <- 0:max(g.vec) # x-axis
+################# Guessing Alpha #################
+alpha.guess <- 1.5
+lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
+################# Poisson #################
+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2)
+################# Linear model: Least-Squares Fit #################
+g.breaks <- g.hist$breaks[-c(1,2)] # remove 0
+g.probs <- g.hist$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.breaks[nz.probs.mask]
+#plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
+# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
+# Degree Distribution
+# Professor: Dr. McKinney, Spring 2022
+# Noah Schrick - 1492657
+library(igraph)
+library(igraphdata)
+data(yeast)
+g <- yeast
+g.netname <- "Yeast"
+################# Set up Work #################
+g.vec <- degree(g)
+g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
+" Network"))
+legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
+"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
+"#006CD1", "#E66100", "#D35FB7"))
+g.mean <- mean(g.vec)
+g.seq <- 0:max(g.vec) # x-axis
+################# Guessing Alpha #################
+alpha.guess <- 1.5
+lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
+################# Poisson #################
+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2)
+################# Linear model: Least-Squares Fit #################
+g.breaks <- g.hist$breaks[-c(1,2,3)] # remove 0
+g.probs <- g.hist$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.breaks[nz.probs.mask]
+#plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
+# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
+# Degree Distribution
+# Professor: Dr. McKinney, Spring 2022
+# Noah Schrick - 1492657
+library(igraph)
+library(igraphdata)
+data(yeast)
+g <- yeast
+g.netname <- "Yeast"
+################# Set up Work #################
+g.vec <- degree(g)
+g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
+" Network"))
+legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
+"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
+"#006CD1", "#E66100", "#D35FB7"))
+g.mean <- mean(g.vec)
+g.seq <- 0:max(g.vec) # x-axis
+################# Guessing Alpha #################
+alpha.guess <- 1.5
+lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
+################# Poisson #################
+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2)
+################# Linear model: Least-Squares Fit #################
+g.breaks <- g.hist$breaks[-c(1)] # remove 0
+g.probs <- g.hist$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.breaks[nz.probs.mask]
+#plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
+# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
+# Degree Distribution
+# Professor: Dr. McKinney, Spring 2022
+# Noah Schrick - 1492657
+library(igraph)
+library(igraphdata)
+data(yeast)
+g <- yeast
+g.netname <- "Yeast"
+################# Set up Work #################
+g.vec <- degree(g)
+g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
+" Network"))
+legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
+"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
+"#006CD1", "#E66100", "#D35FB7"))
+g.mean <- mean(g.vec)
+g.seq <- 0:max(g.vec) # x-axis
+################# Guessing Alpha #################
+alpha.guess <- 1.5
+lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
+################# Poisson #################
+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2)
+################# Linear model: Least-Squares Fit #################
+#g.breaks <- g.hist$breaks[-c(1)] # remove 0
+g.breaks <- g.hist$breaks # remove 0
+g.probs <- g.hist$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.breaks[nz.probs.mask]
+#plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
+# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
+# Degree Distribution
+# Professor: Dr. McKinney, Spring 2022
+# Noah Schrick - 1492657
+library(igraph)
+library(igraphdata)
+data(yeast)
+g <- yeast
+g.netname <- "Yeast"
+################# Set up Work #################
+g.vec <- degree(g)
+g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
+" Network"))
+legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
+"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
+"#006CD1", "#E66100", "#D35FB7"))
+g.mean <- mean(g.vec)
+g.seq <- 0:max(g.vec) # x-axis
+################# Guessing Alpha #################
+alpha.guess <- 1.5
+lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
+################# Poisson #################
+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2)
+################# Linear model: Least-Squares Fit #################
+g.breaks <- g.hist$breaks[-c(1)] # remove 0
+g.probs <- g.hist$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.probs[nz.probs.mask]
+#plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
+alpha.LM
+# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
+# Degree Distribution
+# Professor: Dr. McKinney, Spring 2022
+# Noah Schrick - 1492657
+library(igraph)
+library(igraphdata)
+data(yeast)
+g <- yeast
+g.netname <- "Yeast"
+################# Set up Work #################
+g.vec <- degree(g)
+g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
+" Network"))
+legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
+"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
+"#006CD1", "#E66100", "#D35FB7"))
+g.mean <- mean(g.vec)
+g.seq <- 0:max(g.vec) # x-axis
+################# Guessing Alpha #################
+alpha.guess <- 1.5
+lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
+################# Poisson #################
+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2)
+################# Linear model: Least-Squares Fit #################
+g.breaks <- g.hist$breaks[-c(1)] # remove 0
+g.probs <- g.hist$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.probs[nz.probs.mask]
+#plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
+# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
+# Degree Distribution
+# Professor: Dr. McKinney, Spring 2022
+# Noah Schrick - 1492657
+library(igraph)
+library(igraphdata)
+data(yeast)
+g <- yeast
+g.netname <- "Yeast"
+################# Set up Work #################
+g.vec <- degree(g)
+g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
+" Network"))
+legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
+"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
+"#006CD1", "#E66100", "#D35FB7"))
+g.mean <- mean(g.vec)
+g.seq <- 0:max(g.vec) # x-axis
+################# Guessing Alpha #################
+alpha.guess <- 1.5
+lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=5)
+################# Poisson #################
+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2)
+################# Linear model: Least-Squares Fit #################
+g.breaks <- g.hist$breaks[-c(1)] # remove 0
+g.probs <- g.hist$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.probs[nz.probs.mask]
+#plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
+# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
+# Degree Distribution
+# Professor: Dr. McKinney, Spring 2022
+# Noah Schrick - 1492657
+library(igraph)
+library(igraphdata)
+data(yeast)
+g <- yeast
+g.netname <- "Yeast"
+################# Set up Work #################
+g.vec <- degree(g)
+g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
+" Network"))
+legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
+"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
+"#006CD1", "#E66100", "#D35FB7"))
+g.mean <- mean(g.vec)
+g.seq <- 0:max(g.vec) # x-axis
+################# Guessing Alpha #################
+alpha.guess <- 1.5
+lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=3)
+################# Poisson #################
+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2)
+################# Linear model: Least-Squares Fit #################
+g.breaks <- g.hist$breaks[-c(1)] # remove 0
+g.probs <- g.hist$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.probs[nz.probs.mask]
+#plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
+# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
+# Degree Distribution
+# Professor: Dr. McKinney, Spring 2022
+# Noah Schrick - 1492657
+library(igraph)
+library(igraphdata)
+data(yeast)
+g <- yeast
+g.netname <- "Yeast"
+################# Set up Work #################
+g.vec <- degree(g)
+g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
+" Network"))
+legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
+"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
+"#006CD1", "#E66100", "#D35FB7"))
+g.mean <- mean(g.vec)
+g.seq <- 0:max(g.vec) # x-axis
+################# Guessing Alpha #################
+alpha.guess <- 1.5
+lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=3)
+################# Poisson #################
+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2, lwd=3)
+################# Linear model: Least-Squares Fit #################
+g.breaks <- g.hist$breaks[-c(1)] # remove 0
+g.probs <- g.hist$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.probs[nz.probs.mask]
+#plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3, lwd=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4, lwd=3)
+plot(yeast)
+hist(yeast)
+hist(g.vec)
+g.pois
+g.mean
+alpha.LM
+alpha.ML
+degree(g)
+sort(degree(g))
+sort(degree(g),decreasing=FALSE)
+sort(degree(g),decreasing=F)
+sort(degree(g),decreasing=false)
+sort(degree(g), decreasing = TRUE)
+head(sort(degree(g), decreasing = TRUE))
+stddev(degree(g))
+sd(degree(g))
+tail(sort(degree(g), decreasing = TRUE))
+plot(log(g.breaks.clean), log(g.probs.clean))
+# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
+# Degree Distribution
+# Professor: Dr. McKinney, Spring 2022
+# Noah Schrick - 1492657
+library(igraph)
+library(igraphdata)
+data(yeast)
+g <- yeast
+g.netname <- "Yeast"
+################# Set up Work #################
+g.vec <- degree(g)
+g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
+" Network"))
+legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
+"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
+"#006CD1", "#E66100", "#D35FB7"))
+g.mean <- mean(g.vec)
+g.seq <- 0:max(g.vec) # x-axis
+################# Guessing Alpha #################
+alpha.guess <- 1.5
+lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=3)
+################# Poisson #################
+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2, lwd=3)
+################# Linear model: Least-Squares Fit #################
+g.breaks <- g.hist$breaks[-c(1)] # remove 0
+g.probs <- g.hist$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.probs[nz.probs.mask]
+plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3, lwd=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4, lwd=3)
+plot(log(g.breaks.clean), log(g.probs.clean))
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.probs[nz.probs.mask]
+plot(log(g.breaks.clean), log(g.probs.clean))
+## Set Working Directory to file directory - RStudio approach
+setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
+#### Part A: Preparing Data
+load("sense.filtered.cpm.Rdata")
+# load phenotype (mdd/hc) data
+subject.attrs <- read.csv("Demographic_symptom.csv",
+stringsAsFactors = FALSE)
+if (!require("dplyr")) install.packages("dplyr")
+library(dplyr)
+# grab intersecting X (subject ids) and Diag (Diagnosis) from columns
+phenos.df <- subject.attrs %>%
+filter(X %in% colnames(sense.filtered.cpm)) %>%
+dplyr::select(X, Diag)
+mddPheno <- as.factor(phenos.df$Diag)
+# Normalized and transform
+library(preprocessCore)
+mddExprData_quantile <- normalize.quantiles(sense.filtered.cpm)
+mddExprData_quantileLog2 <- log2(mddExprData_quantile)
+# attach phenotype names and gene names to data
+colnames(mddExprData_quantileLog2) <- mddPheno
+rownames(mddExprData_quantileLog2) <- rownames(sense.filtered.cpm)
+rownames(sense.filtered.cpm)
+len(rownames(sense.filtered.cpm))
+length(rownames(sense.filtered.cpm))
+length(rownames(sense.filtered.cpm))
diff --git a/.~lock.Schrick-Noah_CS-6643_Lab-4.docx# b/.~lock.Schrick-Noah_CS-6643_Lab-4.docx#
index 111e6d0..dc2814b 100644
--- a/.~lock.Schrick-Noah_CS-6643_Lab-4.docx#
+++ b/.~lock.Schrick-Noah_CS-6643_Lab-4.docx#
@@ -1 +1 @@
-,noah,NovaArchSys,29.09.2022 14:42,file:///home/noah/.config/libreoffice/4;
\ No newline at end of file
+,noah,NovaArchSys,30.09.2022 11:57,file:///home/noah/.config/libreoffice/4;
\ No newline at end of file
diff --git a/Schrick-Noah_CS-6643_Lab-4.R b/Schrick-Noah_CS-6643_Lab-4.R
index bda527f..5038cf7 100644
--- a/Schrick-Noah_CS-6643_Lab-4.R
+++ b/Schrick-Noah_CS-6643_Lab-4.R
@@ -21,6 +21,7 @@ phenos.df <- subject.attrs %>%
 mddPheno <- as.factor(phenos.df$Diag)  
 
 # Normalized and transform
+if (!require("preprocessCore")) install.packages("preprocessCore")
 library(preprocessCore)
 mddExprData_quantile <- normalize.quantiles(sense.filtered.cpm)
 mddExprData_quantileLog2 <- log2(mddExprData_quantile)
@@ -29,4 +30,54 @@ colnames(mddExprData_quantileLog2) <- mddPheno
 rownames(mddExprData_quantileLog2) <- rownames(sense.filtered.cpm)
 
 length(rownames(sense.filtered.cpm))
-#### Part B: Filter noise genes
\ No newline at end of file
+
+
+#### Part B: Filter noise genes
+# coefficient of variation filter sd(x)/abs(mean(x))
+CoV_values <- apply(mddExprData_quantileLog2,1,
+                    function(x) {sd(x)/abs(mean(x))})
+# smaller threshold, the higher the experimental effect relative to the 
+# measurement precision
+sum(CoV_values<.045)
+# there is one gene that has 0 variation -- remove
+sd_values <- apply(mddExprData_quantileLog2,1, function(x) {sd(x)})
+rownames(mddExprData_quantileLog2)[sd_values==0]  
+# filter the data matrix 
+GxS.covfilter <- mddExprData_quantileLog2[CoV_values<.045 & sd_values>0,]
+dim(GxS.covfilter)
+
+
+#### Part C: Differential Expression with t-tests
+# convert phenotype
+pheno.factor <- as.factor(colnames(GxS.covfilter))
+pheno.factor
+str(pheno.factor)
+levels(pheno.factor)
+
+## Run t-tests
+myrow <- 2  # first pick a gene row index to test
+mygene<-rownames(GxS.covfilter)[myrow]
+mygene
+
+# a. traditional R interface 
+mdd <- GxS.covfilter[myrow,pheno.factor=="MDD"]
+hc <- GxS.covfilter[myrow,pheno.factor=="HC"]
+t.result <- t.test(mdd,hc)
+t.result  
+
+# b. formula interface ~ saves a step
+t.result <- t.test(GxS.covfilter[myrow,] ~ pheno.factor)
+t.result
+p <- t.result$p.value
+t.result$statistic
+
+## Plot the Data
+if (!require("ggplot2")) install.packages("ggplot2")
+library(ggplot2)
+# create data frame for gene
+mygene.data.df <- data.frame(gene=GxS.covfilter[myrow,],phenotype=pheno.factor)
+# boxplot
+p <- ggplot(mygene.data.df, aes(x=phenotype, y=gene, fill=phenotype)) +
+          stat_boxplot(geom ='errorbar') + geom_boxplot()
+p <- p + xlab("MDD versus HC") + ylab(mygene)
+p
diff --git a/Schrick-Noah_CS-6643_Lab-4.docx b/Schrick-Noah_CS-6643_Lab-4.docx
index 3d88b9c5cb9037d32ed3ab56aceb33f300cc9c60..0c5c630d6e7ea73943c7cfb93413497e6d84d3f4 100644
GIT binary patch
delta 24621
zcmZs?Q;;r9(52h9ZQHhO+t}^hHs7{(+qP}nwr$()^UYkGn15m_FX}#5Wk#-yr`FtO
zSApS`WI@5ufS{nDfat>n>fs51LI1lG8Gy+Fii1pW-H)^tTb)JQYeXA7R(bkHC@7gi
zTOt>>0UXgNx0kz#E;iUqj*Z_2Ip27Qqn{1oEk@ZDPvK<v5+rjC6UheAV<rO8_EJYm
zXL@MKp~&3+q=#45RPd?EA(Xn<b!C0x22#8X)8SRf@TE)R>`4Qze4^eC+UqKLir0|<
z9Ad#?7|RK*+!TKjOe#yHDEUe$g@pFM<kKS<%|Z;+Xi9++l8Fi;z3osfE|`Q%W7v&q
z9!w7L8~*g(w`#Im*KXPRttB7ts2@DuJ~h=NGGXe{MC*5KP^lPTA_Shmet%bq7`Hn=
z-;@4O%W6;A9Tssae(=e2o7#(hT>2sb_(qbaDKL-<(|we528kVXspgy<7HEQgBpY$Q
zl<z!=pR5m8a|2>9XW5JCLt^QJ2h#-uiUWO|?r<=zr+tpgUwZr5u&x?B6Yeg76(KDk
za05CTL#km?(~7%0Y%;^@w@<!7$q4wHBVv_}fk&NCf4FC_V3lMcp{!x*z1@IOfq*VR
zfPnsoK8Xh)OaLV)MC`(p3fd(Sb|LpMSBL|ediqV_qg&oMj)@L!&yyYQ)!4b^W||;a
zEy`w{P-(O<V5)rQ@|j78)NTX|<2^o2ZpP+7vo(KM&8q;rY7V1rFiaxhb~7yxdyHT%
zWuamH;0BT7BHLm9loz?Db29KQOJ&VGKI~yfnY|0)V?Z3DJS3d%!NI{V>~wS6_3U-y
z2vO`j9F3|I;?*^WwnY02I=OzT`NktlB6{7Q2du({<)`s96_}fQzcmRzZI52a1S__f
z1WUFxDRslx+{UN0n4p~faL5mc{c$b=zSV}QFjAS1I$Y0P1HKXoW<U2q!&tj_Y`je6
z1%@->O~6*ZYl6x`&3VUO3T!Ou-<%?>Ih&`Upr_%meGqDCzTBCQo}AE#lw5jF@Q;Nj
zjICvtYT7~dK{uHl0Ol^(2ihOkbFKqFalG@K`0xMH3kl^LcoD4w3<RY2f2Ug==>ItX
z=>hb|-c%_u8m#Ys*N75010ZSIZh#5S|CUcY`4eS=gF_yTVUrA@Q|=0QVMU%5hf-#0
zcass6BN8lJdO5}|VKx`FwQzicZ5+jC4udu2K|mG=R@Yh$`!lV3hp%D0jngYkLS+lZ
z`dS2K)@V#i{wOCHN*rDS!Vcd^ue^#g!Q?F2p&?Bc++^++F~pvA4e%D8E;kwVZk68f
zr^V3$ep4fvVQ(D+7F3E-CR`{JLWE4F)}N*2pXU*iW5-wC<ip0$3gR++lFm&6wR_nN
z_m<nal4OEC+YBPX?ix~i<#MEHyiH8>ewj_pl?s&*lA!nQUu<@R6)=M1b>7ZT$lY$c
zo9d`UY^lXSqkn3^MTShml`NPj^9fO~x&7bOVV?}%WpoR2C-Rrq0M*n``?d2cQ01@C
z&BfkXJr$2Lm4Jiw75u-0`YhYLPZ9@bqbxrl|4V>nanmH>{{)By`@i4&2m}Mbyrf7+
zI{}?H(L+PQ^XjWomZgWQu~FB#98F9Fg@UXfSZF+>x;takyQj5!<J;*`x(1hsS|;Vs
zdcmJ{i3$^GGAIr|OSX;xiPzj*!(d=+rt-G%4PNi(69nUUN*{vf4PHS%pY~6eFe-V{
zTnG}A>uh9I-r3ln!`VSmLl0@d7>Rsds1$zWivmfZ=<&0*)Uw?eKXuaCg}!2NSi$Bo
zfvlk00_7j?(pp!aHOnHzW$`JJwpDbNA{@U7gFykOjGoGQYmQttGT1T*FZJ9}no8Tc
zv+p3Lu?hH8&r2mK!IS}08DJ!1qg`ATg5%{Y_Ebxzl|(^g#z%9*1>=1{a$<1SM&=>T
zaxslF%Y~3cWKKEw#iotIvinXcGw$k&Zgq>M3YnvZ?iu~!M%)CWbX558Tp>Pz13wEq
zA!>7LFOK?R+ICeB$nO=tUTvU;f$$lA&$+nBf&vgJG!^k09|-#mUcdhZ6d*Xa{@s~k
zdbFJYJPHmGlFjnAOY#g5m<_S>K0I0D#l)1>q*?4+HS7Hi^mADy2NtxNqU1}+;N}UW
zX*keJ5%pG8;IFKzNLpPMzeo(5>|Uckd#6O$joGBQvohYkn!5EV;x@kX)3}ltWM7}u
zVs?$2I!Z+P0Aup+PggO1gW&yy$-Buok0+`X_1BQ7wrCR(KL#^kfWagp3p~}Qf||Qf
zC2@tA`*@onaSlBT;z-<}Ou-Li0hm02O2Q>KKJJHv>^_~I*=Xn#1}j)v#natOhn#SS
z@HVH>(Ds_hBLan(6u+V^EJ4e;xV<!v;O6kMgH#kt?_5>BYzStw2&^#5<RlScQFMd^
z<yD*PiORev%v1vqKcOcU8s-BdtZ}=X3XC*5nIBmW*%brE#BIbY*_;Js$x-MJ%~3yV
znfFP003FO%wldDsUf^JlO6Tt5+*IO_=A)P}A7;W}v<P}5*MQZ&kM?GX4zmMufjcsd
zg~~ex>oLT?=Q3EVPc4GdH|C{BNvJjDj|f^J^v3jZ9J>qnEr(j-Oc@h<9%cd&fEv1s
z`^r!<QUl^;p?ch%TCy`h2{-s>DNSm23dfp-LCg~UwF46+WAKkSYMBaGABsE{4r%S=
zbaOiY?jB_%ArM5(Gpp5IL5zeOD*g3ucwh$ruY6LIpWSm@=3}>EwJBpg8I|4B_C9>f
zD!t~H=SUxriE))9*(cW5(@6GO@qJ3vf<)SvZ0{!@{G(7WRnKz<mKV#^doyhO4Ju-L
zs{!pPMh2_B7Vt`)2H~>`S@{|OT8oFxtPx3*f6Mt{upANbpp?TuUQzT2`{<HwO^^Og
zF}Hgi7@SAMvqY3kT}p?>FMTNRDp0A6WrKX6@7)M!d7!)Mu!u)!I@<-|zj_&ISU_Qu
zbFDH^QH0$q=kWCWdifGxzL%D6<iC}okwh0nkXXpq*+2s$sR4Zfr&53cBhirtkSDA0
z1?JaQjF9jgH%ZdGaiT;Hiko_Rl1L3mwa;i3Qn76WRD5o`q-12Z_xzNU_I_^2OW<Zk
zwKxGD>O&QeYmaOQe4`6l3W!ycCpDCtGn}GK)54~h8pk<UFZc6JfX7lN3~eONG$T+M
zF2V5WdRUY0%3Vq;cGzM5kf(kcw%a+#Vr&BCEMxALRvu4rMjH3X+^jX~@mh2_JCcw&
zLnun$1$GVdt88U{WZeR3uV-F^)_b{`mcRgxy-a-h?=d)c{@U*m=$UC?uL55#NfID4
z(Uq?<R>|O+4nTP~W|{%4jopp(Evo8Son-BHw8~3@$X7}90ZC-pMOX}wz6K77s>8=A
zDKT$41|EbS4XoqTO^aB7nTCh!?@O=h>t*S_f}9&o4CXcdOjt}|-rGjLv0MQg{vLp$
z$Ih*z`_uPUax?AevU=C1LB>7g2#DsCmu0oc=%_%H+2Miu-5ZUEceF3<HEal(o(x7E
z@RV3taC%_5XyHPHAjo(0ldyJWVroQiCkxnPedsY7t%`i8nw7*HD9`-Ja5RM2BT|K|
z11&0=08SK|rfZ;@h;Zo5FdqJ`cq#y{>}bWf#);%dWmtCUA`Cqi0(gJpr=tv|1}+)p
zpZ&4ZSv+T%p4#R_STFoW0C(;@RF9_pP9r-D8-L|j)%y!w&{M=L1htbt_D=c(GH6$9
z1Prp65XPH{SR8>Wnhf*!y%2~C#uMbtJawJcJvneP|7LC<gWm}5U75_>D>R^kK7x;e
z{WzZBIQxS25;4MHV4@3&pvhTI0k5vl8@Mab-4}SckBP}(*c3NFvc2?AG?j2@p9IOr
z;VXcI6#+|w2TcXU-A$}OI!yI2&Z@n2uUF3@nf7iMq$E8BrdpkkG1m9eVFMjlC=iT9
z$n^mgL1fV>CU<t+bBb?AToABi6s!;5nh*K&_HD=(6EsiQ`->~He~2!2-rla|D==BF
zipvl&bl1L!ew$z8kqO)E4DQC$QH$h`@QG`R==8w4C8KqsZ4TC_C)?1X={#n?mHw9&
zJ)S{so=d`*Euq(tAn(^f)TP@dSK^u_15xklUms^AYrm>(xd59iXd_@$q&g+YOph{|
zJq*2B^NcdK(EMzVe%y>5n)a|9#-hT@`PZmYOs@3pRm>Luh_iI+p;i62-vvq88WP95
zR}tL`gRMp1MFDZx-&S>ZE7woyhkcpd$uq}I^--tvq_Wl2Z^pZR+s@Ak*Vtwy2}V}T
zJe#FR=K(hCgGU+Q>Ig7+9U=-5*nQ9DCh+UwYqTFZ(w0X9hz`E-CLo$UsoU8V7i)YT
zX?5d~`4VVQaD5s~7m?Fh&Zq=(B3Y0V4upJI77P!_FX7rP&v>n(=(aQt|FPMBF`%rh
zlLbu{S;2m_+_$D4+GCJ)G$g`UD0>P(Vzz^mj(_@aD;;@d?EqkcxRHKnUba1)w=?G~
zDmw@<tEJDg8IeW!P<UI)b5qp@I=KNQW!yhq!boFv05|ki^(;A@Rz4}a+UGTOp8dU1
zsJ%}L(`_mg0|LiSa?WRqG;Cpyf!$?4cm#e`b?DTik>??OB+bKl3ds;f&5Y%f@ZG&q
zwh<PJCpN`3asp_6nOl$1O{D&vmVb^We@g8vRNn@nwCnef0GDEkGKxSzW=_sa9hEES
zgrbqqQWi_VaxKh{Gh`XX=G><1TVjBqO{md9Xunac!qgNJpcv^NIm<>EZ<h+plRLf9
zUUaJgUzd)ML>PaUdYR{qfAvU$IIYx?M==0RR0h+tL<C^aR<`2ixLWGi$NwdQG+NJN
zMvbRnSX-8&oG+rpZkDY+>Bt=07VEJ~!Sg1UG?79;$nnek2uSJk@wBgYUB0pw$i@nE
z2A@whu}9Z@Aao|w>V_RDRNmyr@qk`qcm6ifBz`^&wx|7MuV2)=$gc`B(VS;OaPo?Z
zQy`(k(gHk_JP66{H(7F2a@MgJ4qYCc*tW{&|LJ3D1Ib7Y2`Tc2*NWqq3?<)TRn?pX
z6H&=Is@G75HB1VYQ>h{qr@y4IAF!m?Kuo>G(a|nDlIFTN;?f_z(<T#1JBLS@7Xk}f
z;)@M|S__5=aV%g_O9Cc9r%`pBeu3esqcy>l9spR`-zJRy&;*(Z?>!zHViQEVtG&*a
z6>0Z<WSW!uXMSf#;LJ|J`p-CI%#0MPoKX6ukB4C|&ewq!<`}J$tfDhwt}$d);*kJv
z#1V5o&%7-0cD_M|GWwN076s+P{koLN3I6r1SHkw!8KU-j*KeWzV5B&+PvhiNvBssw
zp8#-}q8?qzCz|eTW0|%?ll;8x{$^gNuO_Zils30f;=WS>Q`=h>_<a~xK5O5yziNkG
z8ARIL`;m^ido5*C-b>VLvexYeItCHm)4O?OxxaaS_*Tqv3Td#(r+B)P9;eGI-Idao
zghP&2HHrtBu4ETF05_hXY)ehZzN*MqmH`57lMOmuj-9VVZn4iQ;1GSy|CO5-wuGjV
znkyE0nbX5wG2tG~Y$Rp-V{gK`SM@=3Cy_x+#Z>XF=_OI|`9MRQhdl+eCGib?gE%h+
zeRzvxpb>jsk4)WhYdK9ZJRNA*4rlnPU-Iw4I#;lpU^&9(z)Gy-`{N9PPaWH#AORBT
zE$ZFJlaOCL?AFPro<l~J7Z(fD@2wNo|D4J`CKgvx!lpA9Y}GG>T~>vS$`k@8dhyAu
zju<1t>3~||d_StN&GDfu6_p-!J0)*n+~JoL7mJmlIl4~iXoM@V9`73dU2ULl`<gjc
z_jY=G>Pgs6BixO_bvAyiO{Pf=I|4jMY)=9H8zlz_@)oHy+1o~Z!Aj6@H@@(Rpckgw
z|Drltd&f&2fqrN@*4jt0Fkv>~Asd^*QbSbe;~X|(1yrEK6k)aKuD_(*Uvs&HhOp92
zWG%ua7F*lHK0a$p?qadNlmrad0mb^b86VIAiSCwi-@4UH`oO=ub8kxX9s(>D`7e1N
zS^}5>vSmAFV=Yp02G!l@`MZYXP+MVS89J_qXQ-wkJ$bT06BGk38pF_<uk9=%6$&$s
z=rdfR6g&z|%9#%5%*qpVbMl(zs<!Sbf+)Dmb^$xB7=CusXRRx2f`Y5hvLdfKV;Yx7
zY~TZ83;m+_XB<I52%mDBNB~~yxV8*l#_YEX>5l9p_q}Rb7LaOF6|~$^Vs*i&WarVA
z<Kh@8^<ilCVQcE<gAS=q+sEd>ee2#f(@*ke;oVMi%q{^(ekE(fGajjIvN<ckwR3FH
zqot3XZXLue9^zcmTe1#SF}rL@;nTAv<$nl?{uoWszr>LRh8*NFjes@B3!dVeD(O4a
zr@rp(!u3!F2alA0r<#tw{*cJ{HROkp1QvQ$H`|ndZkL~~aSs2V(RiJ2NnzlsuQ>ay
za@f8VLNTj|#2zrTu`4xsmz#AC!gskL*Sbd!7vBVyl}`GC?3GV|g37jz=SvGPwmKF*
zESJu_T7D%Id~cEXV))@&s}-z__gJNa9tbh_z0G@G>hD6CLVJZI$)v9mj07(^KE8;v
zOqUBrIZ(!73fuf!$pMy#QFP8@RE*%^=07jV%!H8h=c<Ik(tHt)K)~g!Ciup#ac9eh
zgYdSsha7A0Zu~GP$Ue6TuYwkkp8;WJD)WdGTf@ts4?W8bOu%HFd6od1;ET(Re=Wf^
ztK5KTwbr_L4EY>nF@2(nq<(ijemGhUx_W5#@`f{AQ5b_7Sd69Ah^IM5DOaa-6aNMo
z&5onlIn&XBPYtk5v>|z;_VhKuz*@*jccl0lOYwI1x`<-8sM^MNIh6x!bec9!r#X~!
zpVqliY`yHY_XQt<_w#!KlQ#u*>wW*w;Qy|`@!HHB|2nDK<MSG{&eOgo1!*=wD>|Fa
zjIW}f(#mN^Qn!Dg&BhLiI2H=pWR{PB#RK2*NF18+7wWB!HwM*?kK>|-p(g&gfD(*O
z%|4$$6?sP+Wm`ST6gmP{#~qQ@RG+u5Jl^usgFdn*?-2$~-#AF1CY<wXK$pz^$WiUJ
zB+&=UmnEi(m<w8mz>BgBVetsSdO))BFeT<oZE{K8ahcQL_Le#90HBU0$wW}wC)4af
zz}CWn#s96F{~NpFou5yV^amG+Dlwo}gic?fNmn3C$+K#p_8$c3L`{R@_AVX8W?4-x
zjpHiC8CP5y0j-R|q7V*Nn<Oduj1}22htKjW-4-2$;w!Jv5pj5*^gqX6lCDa+m&J|@
zcsV|%&KjPCI}UM~wD_ly5AP@6=GtWG)0Prz9l&Zp)~GLMv4=Mbo$=9`Wv{KhQTU^c
z)U$qYL|Za_z_S4kBH?}0LsDf9bu@AlbHz~J{*a**oLRPGsog07matwV+YWwxT)#bK
zOQ$pM#%&cc+#U6784Wnv#Psk?DxE)q#98+6rH{)NE{O}OQ3izhw5SNp(*MW>AJm8`
z=bDkb*6m<v;5Ibbm8kS)N^mjv!VRH!ZRTKK6s44cTBQQ;9*+9mMo1TsF{!BWLODm6
z6g<}=w<gE{t59YYPV+O`tr8NpbV{TpKVM$WGCzMBBzzUT!5{*mq(5<SV4v|5RmKpK
zUobI-J3Z&G2|$LyH*ErWuOXdb75pK|>GW!mx0V-s4b9l;K^Hjrzwy&CS3A{3*hZ=)
z9Izqu7sLSi5Ldac0?4>04{vXRuUD<!L3NDOBZHx}_r%d%0#thVE2r@QnywcFqC@9m
z!IRyd+1~EyUSFo&B<Mh2illdRs9V^_YfTu5Hb2yCrXolNrKtYzLH}|8ku>WRDU7u1
z+EuuF%Uix3=A$F_1lY$ds9%mL`c6;kg4Op%#$o_ee!zb&(UOw(QM9!(O~9++_wAhZ
zX_MAq%5TGq!uoqst(rNxiPCB-x5lIP`N{}jJPcq~tb?`v+RnrOtk!63hOEBN<Nln@
zP((y%kf^sy_528-x5-~PsbvH39-bfuq&*s<Zv70_66XKBC<vLwQfFh_hqzNfzry!X
zQ2;i0?L{_B?<OrKf@s|7ic9i5hr9(nLaL8;WS8LXY51?uYq~-d)U86DJLEziliCM}
zWbI~1i6pX~td>ki(;k&A+CytSek<(h)m$S_z|W{T%V<t=6v&{`1P5COxhP?Ks`Yf)
zxDHDw?;wTh#cq$uM-kU)s8s%VU$Sf*$^!C^;69^1XfA`tb#PM0F2MNwpiRJc{dXhy
z{uo1!w)-5m#*umB-_p{jzkIx*lg~qXcn_kD*|Skp4jIA}VV}e8LgyA2z~=UzO&|%1
zxAKz1P$5)@99A&<@ukT0ijX7r0*wpBm**htR3x=jNy%hII^*s2QY#x39N16xYXJOl
z?lUHrI|anI(w`Mq_oBt_+qU8wIxKOWlju58j|~22G?inTy%NL;e$MZvRnTXuU5N!V
zCu`5B@wrmnV^RLQTib`iZ<rs(`)+A;X}byx<(tTPs~t5*M1fg`T76^4JdiuBSzAyN
zia6*FcW;+0Tmt0xcp$Jlr*h8hi^h9KV%ku(5ZeG_Dwo@By@p<MJ{Hho`9#tyQhHfq
z5Ie6{Vw#62uza8~ne~-O4oYWWrpcc&2ImW1r2sWz`!Cag{R~MB-GK!XB@EN?hcZ3$
zc+=#^S++?e^)(HN;-TM_tzwF3%XF%xTR4*KF~weSM*);mBI4bK{N*h)s!$(7mDXh!
zco>c)_c;*r^?5Wp6n4adKR6sW!H8Fu{ZVkI4dd-lJWJxNV9)P?b1)>|O+u;pBEy)O
z;z{|UVWEs6+8xDJw~FGjfC4f)K!{C<K1Zw8Js~E(qLp00Lg|(N8=muL!ZhWKY^a$f
zB}TBF-vKzz6as9t0B;SuP~08AUZ*QT5{rnDal~}pe>w6O(4q+GY!)U-uo3%alXo_U
zQ(?>G{y(zLZ?;tJ@tUT6%5<K2J7h!@kpmzuT$wE^uEN^-eT|;OZ6*0&Ezp75(lb^F
z7O2XQ+HcOBGT+`T3bl6^yZA#kXm^@9Z@t$(mjG(fsYmyHhR-ulvskl-7su4b*?QRt
zhO^uB#|ht=!;>}>&zkW44>r}dq6oxv@{{#`jvPpArY!L*p5#AmRfB~&EEK?n>)J!-
zCEPcjYg>oKkm%M!QzGnvCZ!i1f3nkl2m8G<%ePY3Gt)QS2_3m7>C!A<4F>FJsZJus
z>;RT=VrHK%hi0yd!Nk#DAIXwxkvx5g?~)s}DPpI-%xq%eVxtiX?#PG!58-f){_+yJ
zN^S0eh!S4M%k8>b?;YRd1rtlso0qO@*&#m{%B<{YK6s>9RCK$MESs8+cu{bg1M=;c
zjWrRbEdk>$8iqA<@$G->GDFsy{({a7Sp!&g{bZzg!5XUtqJnZp96Ce8-&)X+^YbBJ
zGBmu7pOnDQL}}3+KyGx*kAz8oC{WHMqW%Qv#^T-(n_-!Sm_JHcN%%Ih>a4-JHa^hL
zOd#-0ZpHfZa`SB7?K&Xl)-kP+qNvcngUg_Ra}dx@z>hYY&FmAMPf?hq)sAVO(E_G*
zf{A_ZW|u?&(PBAJ8?oCTwpOA*tyiNzw8{d$2L}I4-x7>QB5DWb*-dU8Aapyor`Z|N
zwzI_xWDDl642o?Q)6WODE0ncK9|;l08q84?#XuoKSF{3Xf*n}|a!8muUlAT$%aj{Q
zc=Zz~6dY-wKciA<7qgY*z56TIm;l5p-orlC#kn0Zm%oXtQ*MRd7awOmSfkkoyL4C!
z7{Ff66OjRk-&|PZ7hKenq@6n{wN}Nl+A)=!B^)F_{=UjKbW<pczZ?a4wIkMsrcIT#
zw=o#K>KE@VviT_4zzn~gynLt>d1!UBYMKmIU}?IgH_zoo1Pqv4^T?NfdH|;Hm-RmX
zxk;y5FAGZQ(TAAh(gh6Jh=5g^(B;bXnyWN(d|DC>_#e+(hg$7VFeOM4AL6qpQ@1_*
zlU8dp#$<WBj;Uz_w_({7>=s2$?p#3BIJ8CRH-m<A^WCHZ9?7o)cQ3YJZVehSH+Na-
zsd%F7>9joc)>=&ZXlLR01fb51Oi6S+`SCA9pigCrCCt`NM*nrSE{><>f=TLDC*Q*;
zrHZW_kc-Uh2U;pstJ=&qcaywKu#5cgAH0sHr_W^RqPZMo0xwpWN=gu{K`T(EaD_(C
z#*!Y7-}_<6hx;(rhl}A)%%ku7PRyg{e~646)#mi#S8j|MyX+jfrhu~aAlT5?Q~ZAR
z!9!JSNdt~DY`XV`^L@uhZl8Rd_~=90r3g-}3y6v)V8_S@KR=J$^H2t&J9_JJX})s2
zyYMc-_aAuR_Xim$B5Oy{5-PtID*dg5;B_>IE?G;q2@_@)E~zepS0=rbi1FikJ?R8G
zBhX0A1xpc6T_IqYa{!~#lm*Ao3J|DkK>Z%8w#Y<lwbAYml{w4aJSGWZJN%5T$b}+I
zdc+32uDD!pNU0kRtM>^MXjbK*gee!^3Tf@&r83eA=}ztIP+q_uN6Gypbh{NO?joG)
zGZsAnp^eUHvVJpi%>V&0x%{&JD1OGnqjgy>dla-rLr}Eg9zaq18KTSnxl3)jLzfr+
z)|hGu*J#hKzt=*)Ty;a%!oiXi($ZF{n%?NdeiOT~Q);{Hc&n$FCZ*#l&kJ06)U84q
zIb44KvfJ*CP}WM<ou)s4xZ<ZRn%QaRV*CX>qWLoTya>_lx!T0V{`zcz)wCct!u7jB
z<dL}e(&@az3;51a6G3K*nxvLD(u#~QNYbtno=vOB9z<UZ7Ekc@jdI|28>;Q%0XXV~
zyc4udWC7tm5RDDmkErBz*vwWb5g7T%F8#5!oFSMS{*&!r5`(!T28u-A71mE<xX(MA
z4q+ZE&nveiy@)iI@JLR|Znu&NHQVUN$lyQtIrZt(4ER>Tvn0d{-GtV*4N+Ib#ZMZn
zRQy}Un?Mnxvf3d0l2Mtp(>M|~0S~=tp{=G~g3G+@3#T8sgwXhBd4gfw91%N<*%Vbv
zRWDJ7*yZlO<>h5o#^b**FlX-X`_Eoz>*?iW_yqRaA%!aL{m8}1R4U*+D`MV$@9XR>
zu(kGy4uHO8@0H=AUwVtKN@Fo8`18FQ$!ec01+}Uo^9Gc0+($M<G)X|_!n2{8UJZTY
zNz7VW?_71Q<#tv>`{8F8R;r4n(96Y<C`xpz8V_8s7k;Rqd77s5Ddve4N{0VAJ}E%F
z05tE0oBMb|RtOC7lY@sQ3TBVY`J>HV;Z_DC1Asb#)y!8to~6`6LIg5yTQNLO1};a+
zrXq@VsLKG9Rbs*c>GLZlWrgOgI95Ka{1h?F7Gcg!^ZGBuiDNu3T;tkw6}|<RC0Fyg
zaw)lcyGb)kzyPC*uj&n9I$>_gT?Ma*JaRu4E4Etx!yt}IKffX-5R=FaEA`*KCNTIh
z6+m~ArC%NWCg68@Ur$9zCUIjY@H%Z3AG*`*@Ztp4t=(~XB7vFt;pS<JF8S;2<}C_K
zd0H-#VxGJpXx41pz%P8^EQ7-AAJjApwz-@#8GIQViH`ZWrAhwclXSVhIi`_Vf7fP7
zybB=Rn09ZQf=U)!nu$#ytOw-Tyno=71K`rjO$?vp#b$&{{M<35{)O|tVAt;1Ty=)H
z#CeY&tft-4HFAj>W@pt8fwaakf*>C&;|g-&EA37uFP8uKPZP_x&(Wnk?YD|8S?7Dm
zwKrk&l6r|9R<O?NA~0k4yk3y}(ZU0ylYrV?hD}3-hcQF`t>wCHLxbAu72&0tAK>ba
zrb1ARfLlf*kByd&I4Um08=@kB_}ZXbDBG&Dd2QlX%4JR4*V*f)$;=>&vaojlSK4HF
z=AD(~uY8!3i+?JHVofY%F?0j-NXq2{K?HRm0#_%NlUaFt!~#iG>01l@hHI{C`9JqA
zin!hcwg>SyY}3Adg=K@w`aw^8E`VgNHeHv%#x&&2zwaF?dw2A%kjf78gih4S4ykhu
z9KZ7hIr?Sv4b*|Yax=U9SA@LIDG_@&qhnSb;+dVbKCfG{1G{y1*pn%rb<EPjS`Z$T
z2)D^zyZ6?%FB_MN%2j}TJvVIMI)5w3RaE%((rB$iyO-V+Vd?RhhqAUiE#Q#JOtD<O
zjD#d6q+t|xagn+r0|M*we0luya7(|^F1uwaU`Q(x;Tnu|PSa1Gi((Ro<pVqyyLM^X
zv0ddZv<beD>-H&J|BQ9RwB^|G7H=M_#r;L6`nv=exrdN%+uj^}N14wTqpl_t_p>F6
zTZ;8(U6yTbey4tx_2N{}2w*~pc?rYJu!4*e74~HQQSmDMHlMHopYN6H$!DRY6f(#f
zaq9b*w}VZ|ij<A*9IdY23TE*$;A^eE)px%;VT%$2vTCKOn;025b`5)#przevY~b<W
zw55?%V$9($(frStPXG_9OA_w~;D5mY*?6*~ZG`{9007|s4Zi^Xe>&-Z;27xt;TZ1r
zPJb9}&Hh*!Gg{diTbMC3IM`WSXFw~duCLr878FzmiqZzc(U2%0B}<3;Z-<AE?fpkO
zNi9x+h+x6Rfo)<GsUw9E8KeElQHx^&Gx-;YB=RSPnIssgL<l7`F0RYB%XiluP`UHE
zT<P_^+}XO*snK2?DLpg3Zu7l*+q)9eSkJQg5cGwJ1ZK-D=zf9%R-G>rcQlK;5~R5h
zda4)z;Mn0i^$#RUzrHh!@TJC+hgz6n5qw-s)-7ihRZ(30dwC)eR}~#b$gk6Adl`z^
zjiC~j_;U06=6vuYYD3n7y`JR>=(wD%vn>hxeN#S8oerDP+}&!qnIJuX*8FMncBp+B
z0fi(uw_FH=fSX`sj$VyPOnA0D<{us}YZ(g))ClBm>B{QqIHPykh7#6nO!{lqhrch{
zc+{8uWAX1%80359Y;a(0v4P(;URGcH#jJ~<R7m$IHr<9PP+WG&qw;+a5Qb=Lc4NKe
zfk|&n>#R#jw$6)>jlE)<&~8=1I_uTIzZqq}vd(IENW;ho=xI*QlP9(b!?UK`PUBhc
z!45=|ZzjuMEd4FUKm|>(G>c45JaWn@Q0U@V*SuNYCIWspm}6NNB|_r4ndsm{EHgfa
zko4QK0`$vaqb|gvPvNNp5`zBHH5`4|JC%0~JyYScSwyYshc37H_<arQ(2q&HN0|?g
z{nH{JJzoYpI#~8=)e<)p^`v@)@|aT5J*uVqoBakqJIMDaGcxBlPzMnnkSN-`zYRYZ
zj}S}e0ti9$=)pn7ax1c#_BkY2ujs2+j1Yj`evy?bpR2-Te6fuO@OtLi<&Tb+mag?{
zP-qlB!><ogQ_nb?4CV-_#C6&AHD@G`7tEA~F}lk&S-78I)1zhK>BG8?H`(`G8?L)M
zgbvx#_C<(Slv7hvi_2^h<G%i~u>9Q8rSqn&n;`Ro@tx3)L9%Zj8#L808s-G?Q>pRX
zUWFG}yT5pvaWUcnW+ej@p9m50D6HB!1?D34vQ|Ex7Yhfp#%V2@$V6Af8ZHOs*Z(oZ
zv)YUH?4(S%A`mVpz}j*Dr6o-U%zE>q;eX#A_qwO^qjKyiE<K!kXk&RZQ=2&Qc$zs<
zm|=xWukP(Nhh!$=I~kNm+(R^~ht#8#F1DyH3iRJ=%@?5opl@BE@Eeny-<)nHo9^~6
z5Hsu}Sd&5B#+v7jobRS3{?(%XjqhEh<AaM*!3Iem?I=Y7%#fKo3lCABJq>axLTxx8
z8gw*4g?}8|cbz-w9N#$%sYz4f0p|u<jvSO_I0hO*#xAe7Po7I0)M+&(VWOfQQgx#S
z;&nDdZJp2pEM;%uNaZG{U=y{X_f|~89%2H!Tmt8}Y|~5MWk%M@#&b|gf94D<1iNXG
zj%;;rvTJb9Kx%}2YTwyG`E$0w!B7!6zJLPRUY&=A{;jbJUF4#3S;lkIOTy}nYps~V
zMh{K|sFN}i2v{u2FX}Hc+a@KpYgmDGR@%9Hb}3u}y5d9E40cbEo-PRblWYRYIuE4J
zT<>Ifxsiq~$jI?IGyIn8E|VmPSf3shRk&-nGo7%dYo1qYd_dE$o}SygX-}@1YhG<K
z;`fg6u?s7L%-l6P8ZyV)XfFe{j1tKyrx^@;qGpgL-R7e@C;G_wRKM;at5tf%pbE%~
zXU7cyo$seVZu>-$DTydb&RC5L>BhxQFa6wi-TAb>Y;*=X_E*ZWykSg^1Ksr|TGE@Z
zFv5$I6%k#=3o#~gd$2mGN1X|G*gpFzE)WX8_r8qJz`OctE{)}rgx7zvxVTWL(nh&9
z=zXxRmwzu&ClR7I&SimFI%98#r*_@DTT=S~!+)KcVNRpiX>%geTw3N#8a@-)yRYDm
zS?^;CP<H+O#cN!z+-QnXwmMDT_J$X}>NxmM76XcflbTJ9wg(ux{o88+4Pqc1B=_}^
zL<;o2F*gvxnlSvEs)-DSM3(p(VzK02Pw6F-*wbn*JT!}r2g?d~!M6Olw7VZ(pVRPw
zWH`%oQQ;%v0tt@pWI-t5(S|+??^iQ0_6biW!n1`!HrbsfJman}(^bnu(SHRCIELNv
zr1He~aNP2<rG?0_i7n!GHhJ!!zV%M5cJRy>{|Z73G`FsS*?MLX{QK(Vdt`u61YWm4
zbB#Z5kAEw{krwInPJx-<J-rD{oKq73>l^P-uBHsR^u}~8#H)I9BsuFS54<`06nGHW
zA#a!BIKrKlPP?a_8yg$5DL*nC{xs6EYBcDyChJu&DH9|dYF&AK1E>$Aa=IwL6_b^a
ze!##QEE1Ee&>Mbk+Z5m!89CKqYc**hb_P}Tcf+kOH4R}MgQYE0Szhet=8G->iO216
z?LOnv2bMbIZTR<i=#c_l+VAdXH`q~1Z=gTOMbDHhu%-Z&E#VL<b<3N#Msk~wIz#AM
z%_Db;s#o|fRRxN%+jZ5VCV?dwq5-$bj<GI@E9?A)4+czBlYDG2(y<=^%kNh%{jy@t
zW!T3`-6eRc5*6AP-9Ah(2I3q*oabVwe=i%lT>`dBft|=h16*MUNH^Ve{sR&U!jU@Q
z*=t6a(0ya#&uq=iFb<~VU#REXiQcnCTN^`^dfbbRRRPPI;_B>2R2CLa@+;YuqkoHP
zWVN}NEZ+-NAr!#+@8`J+x9RQIB5h8AEXv$fE)T2DF#_DPZsH217x_E@CAL2a*{~V&
z;&4U0cIyykGX@@hK7=Y<t9?!dwf<$!cVNxAQ}U+0VA$!U!D!VS;7DNuTJRGE?cefa
z$F=TS+OO<0oI`)>k!qh3cwH_}39Z3neFNKxS%t=?kqr9+NIb>*z!3o{qYWd=E?HcM
zw2Jhkjx&$+&g#$&20$|a?Oxjju2DI0?cr!%@Re?I<DToWF+y%i;A25j1W7z`1{Zw@
zU88beK-O}4Dy0tk(K*7dO1<3TYN)JyfN%PwZ%8qP>3l>&D}RgtYOWzuMb_YlT61$#
zhmn?posGt%R5c!4$z8(O0G@H+*$ziC!rhN&-9E$xC!}G9;Q1SX>N4X3h5c7~vhmq-
z%t&c*vyL$nsYucm;xCFl2`})h$G?{@b%kZ_j6NoNN?U|kzcS|?k7P4>ZYE_NUfFO-
z2ETJ-oX63ex*dBFy*iF8K~lsC!4C-a&ewqjhd+5gUt#zdrE5T9TH-a8Js(7CIxx4v
zFI-31D3z7ZOsEuq2}y^;zgb#LuPq{-l#oDZ7+f_mFu5}^6?yY9meAt}4AWeLFnsXU
zP&-2%9uxnTLX%H#?oWWK*uGe*pRp8a{d0+F_>Ex&Nb0VU<K0`%JZjBD)=5Y)h0iwB
zaE&!Ou)G$&loEL&wK^=4C~C~+97vWXd_ZnCa2SNLM8%c>u~ROFVWfcu4N|(ugEki3
z)^P$lft=;R!0-;o`MFYG_en)QQCdQ?N+5N8>5PNb3hF?)$wNwbwlYF<RJsXK-kcT1
z8(a`_0MF_Rk*i0!a?3alkB)j@qCAc5U-?Jj<<l~G8Gm5)+=LJ~u?0Lg*ZkJT%^mjX
z36Es=1{6Mk%k;9e_rp8cDHEh`eH<@R@s1z+cuYn}jrm?|hkytJd<rb?sjxpbk7S(_
z-w{qoPLqp8)t|=8#cVfCLosPfwOD>XRUKO#LFo_v&81><18u(AFOGw|#_A<W`jOf9
zt5IYcLBHbPXRM{nE-~x_(|vyF=dLUQdpd`B3_==!>hxVluOCT4Q`!les2bYXN9)=`
z_RFe*Pgs$JzKkbjt}Gi{jCea*vH2zYb`&Ax%tP}&iBbPVGwZm)IYMw|A)+gLnk%b%
z;eC|n)I!`14P}JS;eJ~jx82_;EUz;Y<aMn=aH_c8^Q1QJNWfn9+|qr8<v$v`VCP&7
zZZr-+-3&QcEbZ?S_pCg#7^EzJ_*o{QA~e`juHc!b#_C|-^H@bZ=)>Uqlc22lR#d18
zx&$8hZ#jCX@(vBIq`*ty1Tx~nS&w_sA2zDd9jLWGED>%6zqtOUv`7vvrF|?7mS&0h
z%z;wNf3Fggn49i>rpOlR1MxiUZ)rb<V<n*h>{g+v_g#*U3`DqMrww>TosE1_;~VQ4
z1!WK<)I1jpuK%{#z-FqO?CN_h{OxJrtrPVJbu15<`z|BhSz!o2{a5Xfec)W%ax*hO
zDPGkUj}9a41pN};YP6sdKbRw(FE$NQH&~T{Z$l)YtZ{12ZI1zb#3niUZW238udP;q
zK`9gm33LgqI0Hpd_7WKz(2p$jAq&*;(>L|K^yIJWj!r$O*zkR{3)W^+nlD7x=;&zv
zD0~Elo``NrE3S{jgVpE<f*;}Y8Q_S3DSQ-ZNJ}2M+ZKwEXBWJUiSnrbG`2=C)Z`3Z
z@&LN;Aqdy{Cy>*W{4_5H;aM!1tW6HEuCMJ4M#Sn|4~5dgfnmH9U;tk3BHM<S1I$%O
zm`=eta@D&RJwKL{&nuYbPpeEQb87BLW3P|dwAm?5Qw`|ecElr*UtCrj`b^5og?pvL
z@YkA2E`T$rf{jG4_ggPB-RShntkov9o?}AeM}hk`{iu%`uPhg(wu8`@6hHua0!?V9
z*M=!&hc6XR{_DVCW-3$}Vzo<ojgIt1fw^IgG8b3*8q++h$FB|eDkGDCo^gSH!!oa8
z?Gh5vHs*L5x`?fId<dBxd#Hnyg4l1rkrJVM-3M)p{R^r^_TG~>t?bS5(N82x>%_wt
zsXPI>(@Cd?8Uewre;E%6%gzBU&NSK{_{{()j>Ov&R<NL{l;qmZIYPzKR3$4mSU?Fg
ztZ@|X{Lfrb)ISm+%`w5@5vLm}Sk_Y0J6S(l;qq>sAw}v?Q&z_Vaguj!rDMuSuRF)!
z{hWP*-nNkpm_D1*MFDLpUawG=h{O<a50cz{CZANt44n4Vv7<}=xM9HNEf>e6dw4EY
z`4yuclrwZ@ZE-Ec*3Ax6P-qA!qTPqrgXvIEqVTRj7xS-?Qg>_n3f>6b_*2<8?CkRd
z<+g%qbF<4;T(2&%9)Jdv99%7n7(pQXwyX{MzhMF^KZiW^{fGuOSEZ=wWTK4g>cJHQ
zy;NtlW!05=0Pm~yR~w+_3kF^$>RtY<x-KQwH-+fWGWa#?bSi;Eg#g`9qb&UJLA>+b
zkM>-o%SlDhNk-vjxN^-a@r?aF_}+M(q9bf&Om}1-%Ca#8to|I{z>FlIF_q*0bs~3W
zy{ZL0&vF+)dMmLuRHVP8iR^J3rCLg?SL6Q*pW7Iacj>ENyaTY6rqoG{v&olJYy1u?
z7frb2RO~8PM*p-#QBhH0$V^MSs{}fHGi{Xmw>)M5PfgW9pSsaN^XI3!1xzqY)%GTt
zEvFjC)>9S%GMNbv6<%0BdU?u)68@3RyInJ#XWgsD8*W{3AfR&;(dq^U6ep~AW2Hn;
z9gndUF|g@W_!;1#Tjlai^7cIWSeOmm%=%X6m)jKw3^1ADNN!XF_e`PoAVr#LpQuDN
zuTnO<r`I~6wilEVN|*Fyhu-2uM5BsdtewUqL5Z#(crTfW*E)W>Z)r+WAyP-rD|q?9
z0BubZ^=c1yhhb`MYHDJ_gWNDSj@D~a8MNZgaJdBWZUfY?@;kNE3I#Rb)CF*~oi0sZ
z(!!c?ovK89oCHxTB2NlRQybjN;W%V%z;%1Gc8Fn^YWyVi+o>;vcqfZJSVJ#&pH_Sh
z#&p6mW<zFqs&<<V*9-<6Eyuvs$6>QXncF=D7gQt6#V~q$=aNRI^I%kZosat{ejo3z
zP2SwxGz0G0%uzwvVNOpvv{`Bg0|)YZueG&xQWIjduQq#>G<pwkIT0;~3-it=hkqas
z2V$23S}YRcyPO?r+^_zrw;i!ygddk!ZofTLjCONm@qg)<;U{@wf72^en|a;)daE^B
zFHBmzFf3arF$jVMX*vh|U=&vbEB4w=G%w*VGyvrIpLs4x2FQ!~pUdjI-!{{#1h{F`
zv!R7|%w8BQ)y1<Ma?lkpxOfjVFsZQCS_T6zM47v^bg!zV^RjV#kp^ryNfoZCfcZx3
zB6>a~+-GwX{<gUq582_O$p}za970l6uj2F>gc!A?v=J<W#wED#s<#3Db!q&3iam=|
zxC3;HFy?p0FEMATTJ8PO1Hq;;aKVb#vA!eDt}XU`z#zwxt3@6*HTGP(Qk59;YQmx@
zy{)#2K#>};&3RtWpS&$HOHx69gy^m~ESbyF;a#!yz7c@)w_6B0bgBB*_UV_?LsGDt
z`LxBHIAgEz^G4}}CnhHiCzuAVDkR?OIRQJxUlM9X3vsf$pS>7iQKvi716#MugWwCi
zbltiUyyCMQmG=?qVb`;H(&@#niRweL4xPk3mg8&M_c<gb0CEm%JMUqakBH0Lg2AZn
zlv4>UQ^hL%o|VMl&+M)R5#0^DzXe)fF<vpIc&@H#KKg=ly@`=TutD>}UW)7Ns(|9G
z@2oy}>vB%Oibc0igY4~{#$Ig_xM4<*Dw|wI#m|4<HR_{mb2e#sQl5$IbQfkFECJLt
z{QQ?y@+N=B^h4!a1wk*iR{jg863Y$2!Sjme4vCHrs7h-g<gf$H1!^r`h+n`*8n(}n
zm2$9Cn`hmpZDJ{E1lI+OotmP54FK*Qj!$R@a*qeZH}jp(1rV|q=!$R~DU<msuToog
z=!auQMDkb|0XAvRvXTzLL!a4EFlElg=(^EQI`3R2=Sg4U->Vk|S*Rs{H*gou|Ij=H
zm!_qn&sOi>a97q)h#y`)1PkrW#5Njb7po~xHZ6BiXU@9MR9@4GTSe0i1B`%~t>!c_
zclg|d9LW}E8D?;Z&J)xy#6avf2J!4as~>{(rljoBXtL?4+ayg`UKjI{3F#>~Vl1vH
zdkzQKN<&oF=!aUJcQc|5^t9C*Nu(qt<m99of_8r}OX$QC`CiyGDTp}Z)GP1YlJ{k6
zS~OC^hk4(X<!?CHypBMI0YJ^NMHy;bQjKj_&md*G^Dp4|5W!t%2OJ;#VC)+@EqQ3;
zZ`M;T;%MXiDWtNL^cB8pOzC;a$?`-s{0Cu84Vxh{b3*gr)jwZF<~D3LTSVqRk;N>2
zTenY34Q`taVV~Ytv<kE(>qR~DRSw6^-wM5Ze}I!Xp1l5BPQkv#0APg{zmnS)kDIpm
zfz|CC<-5*%9&l|?&bxW{W=~~7C{NTLpIhZ`B?H2|Av%*}Txjb!5`m*OC0DBgI_z*c
z;9$ltI;yPD)<6g{a#e{J3Uj^R?)Ia4pvW$dGuAW85wx_z+RtT2Pijhbokr+f+)cn)
zrAhJ<xzVCG4nkH*0r2&nR{Miq<U*QQ#$CD>WP+c~oNEm%N>3A8dO>moAfu&y@OrUH
z_MLk@K>~Hc*?o81X7}$?aA1$DqJ8I9M}_meKSIst<;Gl!EF83{+wh3YzODa2@I0=&
z5W(cX3LcL?@Ck<1(R?;L{pxpXQImQq+=bm&?75vB?{qCf0aQc#hqOpU_0TLbq3Z5>
zf-65#A$gjmB@<&>VOwIgp9&tg%=Yt?tc9a8c}`_G;C_;~450^m3<u`!%876Zui9RS
zcB-G|V-hfSw38P{R;JwR+$15yj(KpZsRZf*Vm2w?V(r=11*3NTu?3i=pL=jX&G@<g
z2FAza>LKc^fD`;ym3?1p*=1U+{`u4eMK3h0Oyqg0NT6x_T3Tb4F9%aNZvMbTiwFNm
zHLjKN-Uz#Na!50o*mnTwQm78upQ-zP^<|rGDbXM1y^gItL1_nfFCKS43M31rwL)T~
zx#9DLQ0H5xx`BL9FeG!%_uf_UZX)JIQrXf|bUEc`0As2@yqDA6fHUrYUwd-qTfKXG
zVewbFbSK@QD?2lAiobW1SfbyOOloC7)|P-}JY=TK+hw(v$>seVYz`g;W`d0BEt416
zkfcVcd=kk84T}bSE%)qVpKAOLk|qEaEvPpZm6t+QM}e)&@gP=!&Xpr8{@jJY!5?SX
zV-I8*fNUp<ZpWL|9)FM)+)&c}Ca`M^vO2#Cudwj#(I9^Zup_oLa*;mMB()zf%M$R5
zt;ZGzdtHv8!TnEIAtP|ddmvFRe#P`MnX=zok8GB9yO!BibrQwkf7aY*Zv}Ra%+^!)
zM8M&@`%WJ3;3>9m*nZ)94YjS-VxyWP@27hZ(2CKMRD}?CI<Pid@%z<tBFHeWq3z<-
z@r*&UtRb<``XnAF+X}tZ^R9l6#&>*0?HHM7d)_SmlLiQYd)-^<<8L=DWY&Gz>vt2x
zv6t`ZGGcx2=49F}$^vfX5YbU&(-$sTaq<26f|ANQ+%@-4=XJx~=~%Z-xCP^c{;y~N
zm*eY@os2Wyl9)fO>^8|wUA7(a6n~j$yFl6|iHO?;f^(50C7njgCD(zp%)wa_H4_g;
ze0iL0<9lUoKA+J*J$x~s7aAp`7E6m@nWZ;)cxVg$CMHza1HRbV4H3mhZ+|}-f`*A0
zC@3UgS$VaHmGl}QV@q&srOk}Qyp)~*@!p3Ty-fmw!0v%C6_u5ghAzi*!v*9T!Ui9n
z)FGLrl-`qiowzV`d)U6p_nD8|@Jc1>cyCB$8v;km!v*2n`Kr^_*rOZL-3;;EpK>NO
zMCf@_BfVksy{W%pn1-G-SJNaIpIuoXpqCh4J`)!h_`TDr<!TyPIv?D<39W8`3uYB0
zm)({{+#6t*zpScKdi(VZkYiLAno5$O(vQ)2U7rL{G+j@Grn4~<0sa9C$~T|{4&>Cx
zc?n<TB+%lTZiJ+5flk}%Co%}YM?Y-B?sw*cxYd92(qGYy2B4Ae(>>uAAV&9`QQRnO
z^3P&J5OnI?@5aPSLtgM0N-5d_Ci7t5O1sg8?wX@ZQPc1xhUr`&Eq#odj0gpm{GiyA
zgiA(sm#)sSA)77hl=joU)o(AO${uQOgNDv%yZTFLzcP>m<#~oB=48c+*RDP9ryZXb
zZP+9R1hwjkFKThf+`8Y$x?Z{}G_MWG1~UUBJ_-i`StUKEtK2LBLYBw?|BdB#vfX4l
zF|$7EsaaNSqs8&WPk~V+xpqDc%Hsu4pS^A|bXFgh#~UTI)QLcT9Rr;-QodE9tS7G8
z-Z|IyNAJyO?wfot(BiA={rVG12ohD?0@D<N881Cf)Xj{TD)M=_r}r<u<Tgh{N^1<k
z9itb#_?YM=i;@Bz^S*3AMgPrFk4VC*Go!z`#M*moLpFi*jGJ)~s*t*@i9g|P(rsQc
zQK{$LL#=e_Y?${ZAD`kp0!r_f@7%hnR2EjvzbnxWMwd;1{h8iw$$wj_qRHe(nQp9=
z7psc?nf}A|m)MJF#8<L);L~~reO7Bv1~9H#f?aQasT%7<Lb@J+lg%1BwE?jB4JyQn
z0ecUa%g1`JMdmO6WZN><otQbYUWHx!g0=-$%-jLv4Kz2A+BF<BEjs>b>`5E@&>9J+
zXrf}b#cbdAdv9)!zoJ3aF3B$%na>Eo^W&hV>Yuya^G`>3$YUifEJk*F3U3*2CVP3@
z^vp~D|5wOYK*hBz?cxwD5G=R^cNknla0u>BaCZ-|L(t&v8eEd#Zoz`P!w}qt;I989
z_uYHWJ?p*q_pF+=yQ{0Jduq>~>FTbpaUB{ltLFsR;8v@!NsfOAz`j^qeN@p=EFbxf
zjCeZv3Y8Ar>;jENAIn3x7L05(36P^1bxby6j6}geex~mAT#voNHzv&dG5eVyAog3s
zRa1yIVNgO6np1r}r#F$)TxPH*Dt*{IxNxrh=1eMhRq4*S5Ds7Hypk|QT&yvK66IJ0
zqzvqit1{jo99`hp@=LwC4}D1V5H~(-Cr7+B^z9&rzbz(b3M^Jj1kw8l7s*&hoy{Q7
zRPCC8z-YUNqd^xpqEnM`PA&UKJ8dDdR@bd_p7lLpksiwS`lP^DR(dO1+a<2=Q$M-j
zse~Rd%%9K2S-E{<ZjwbqV=<tR>$za}{{S?m@tw1=N8NX{p0`}!3SM)G#4?5Hr?YsS
ztoLrBAXt-Ax56CAUSPA1)zC%^a)niK4(>TctNSi*x%D1XCw>Zjz7ybew!kTZG4^b=
ziBq3p6IN*jF?e`Rf5<Gxs%b#mRAssV*L#@UII(^bpHG<QPRWhn0&|*P90%c;#Q{(V
zU;Dva+xN*y(P82yKCMmxt{N=&Kq5X<NL&=`minI0wFQ;cQ{Xm9$Tm{zt&UD=wb3=Y
z<d9>r!vh>bEBt&@PZEs>(lRCr<YVr@-4h&L^JByo7YrC0!!qp73&UlJsrJc)V_AC@
z2V|w6Uu2V3Jc=AwWci!I<_s{X#{>1U)AM2vnW_E|JrYNC9bY-@79qIxhNPhF`thE2
zNgCsh^^Hd{_%>77>s*(z=w8oV5w}~)SK=^oQ7QxFSEBQS6+^%#DGm>8E&&U_HHM3f
zA=|YUUkv6WMj<$Idh!kHMd%bqmWSda82en1Q)=wFZlQ=Hd<dsD`-}1lF<`ip`7#SP
z7ACZnD{fX0#!@3M!_3%{LXo|b*u>O*s*;I0=Wah~#htH{r{bHZ8CUY;XTA|;9z~LY
znB^a|ZkU)wA5qG_8gCVCTKs?zpaxq!iyy68;pmC;0GZ1Rbg;CV=IidiB7Ob3H96m0
zNh@o$6wXluRyv&|4u#8^AsqNjnG$VZD8g^$?O#GWy=OavmN2j1$bwCCaK$vV(73TG
zuaKT+LDaaEh7$bj29#dFlCSV=_E6zuKYiP8RbFiagFmM|CzVU+#=R)GRfu)=tj&3&
z&qhj<x<zr0d|oLgTTd)=dzTh*8qt5#@Z_?&={8NF@8WY(>G2jr{~RDO)bk;|q7-Q?
zkC&AA7Qr*f8y(?m`f)-~uz&)SnBg5t{F!Vo@*(dq+S+K=`iSQX=4&emufwtX_8d>9
zi!<1yqazM!t#?{;zg&7fiL+|M{cR;Z4}x;Y1rE1uRB2XRE5E1gPK*FNZ}#vknni=!
z#iWXD3T;b_6o%!aTQRUT8AuY@C6VYPXMsb`cTvVBNNvJ9Bz$S-=dY6A3F8^g;82lK
zO0tVWd>ksDIrT%j!Ikl)SMf6XS}Kt(m7tg@V=x~sPiGVPJyWh05-VZH%ftb&)Po%&
zOfTxCB<3=sa#mRPdKKMl6MmQg%QecusMlWksjog<XgzznaVCJd<NNg!x~eS-Rzio0
zp_thBstGW3&n6{yl2TL0rJIAoBD{DC=TnSckM&1PG>K7Kggw6P@`6*QkT?A_Zp%9P
zW?)+N;H}l{*OQWC`V54Hw!+eB`@{Z~0k_9Ur)(EzdMUx2Q&-p*V{?ux5A=5IB@EX~
zvtvGwFk>C${QbacgU_!Q%mhg@IIGc!bTS2*(&2XHs6@YvgfDdNvUgn5$v}1YI7lq?
zSeFOIS(dvNE?QP5lu?0NbZGp{uPp*niD4vS5H4ZMlG@z6zxbWI9kl~anNJHYdd)a5
z4=VG<4GmEZcGKq7*>vfLTI`~H1xbV63Dx`{XtPMHkYWbjij+Ugs%B!DhNUmN7z%x_
ztG#x5HQP8b8QJyW3)b={chT^W1B3#36;cz{nJNzRISHA$@`WO*H`9qY53O3dC8X$l
zxP4BW>u#JvZ&2E^Mx`qf_`z~?nbGF%+dAJqynPE-eZ9SmAf(j}gZ}j_l}xc`WEWv?
z%?`nZYs@4Ka89Zn-=Nm>3l`1Q;?G3IO~JEeb}J`*Z+GHH_42-|SEF^ZTyAx-dMOfN
zD!7VRgkbq>*p42_W0`Iz4eY%d{&3L&R&zGn=u2oD9g9+X5k^Mgg1vQPLOGRMT~quN
z8YdZy%SLXF{;drT0ejtoeC~cg>}9_Tn?;Tv4-C~NK;qy&m^9gp#XqcYM7YIXRjDwU
z!{`rp1*wG*ccm`|gTdu$feeTHvK_{BXs4@4*2tA=ZA?CLd~5fz3AfH4$^AR2OlB#n
z7|=cBD8dv!R6eKIF`27|=s10GG1!x_Ss+m*&qEn0fx^~UaK4f^d~7Tdo?#-K;m-c)
zp`thnP#p9P?r7&Td2Mk-Q1=YmihM!jMT^iIoA8kurS(EtQuP&Yets!Dx2c$Z@}afa
ziuz@9+tY)>K1wc&Er(1{azfRc8|XT3ay2Tlxo~Z<?)ybwt(#V-U4;e@rq-9>7hi~B
z5qVxuP`B&7!zJ~UN-q!Bm%B^Md-Gme%|Rmt37CyCeYdX4=&XyilV;s@95QC$(YbXD
z$?>Fr*{0TaZvoL?bvVrgRSVDNyt%*W6}+SL--^g1e|B%@%Sn6$-u5J@OS)<Dzr`qo
zpU#$`Q5Io*)bH4#0l{kl7D&n37?aC)#%uUsF=7H;xa1U4W@VM_99INhxrrRrk{FF}
z;Ml{7^leVQ%`7sjM!q}y%xnJkS>j6Bk|lU6(m(`#teq~CjqKOK*DMo|RY4YSF#We%
zyzIWLg5#f#Z(0*oo9pKIed*n3{Ylt(_oveIc=t!`F<h`EF!IyJkX9LB5d-@ma(R*K
z(ZrC+%l9YUlS&9ZQN(gzPpuno5m{eJ0|A1HbuiBMUrg|UidIuv!o9^Y62Jw)J*zK7
z+zlg6unl3-{q=j{X~!a((w8um8zsFl-*vyzm(?TRl2#f#l)t&pqxXW}z$rPrw`Mb?
z8*}=4-;nqr{o{0-EV@0aPuN}0V#zJ3T=CHyo$xdLAcV!o3D!2x<x>0+DgUHANkAR)
zsi;`v<VOiT>zjqo<Z1T%Z)DE*Q#3#kPB$APR;{q@THCcSqgWwjLIAo0Q4&eMKh@+0
zGTeZS*XGQk_Rqc1sE8?ubX+Q#XfDUu>*V)Z?f2A$*IX;wwacLJTX$cJMdyQoQ?K}{
zG?jJd8d+c+%P>EBu1fgFqSIqH8(>1|YhhdFL+hYfr_)8hyA+y`A3}+fGsiH~WlF3p
zmL-h%Vx{j_hIrWrQhr@kLA?#Jw!Lb;xireMm!DN;(mCYo+%EhW<U35<H?%sq2p86}
zf<OU{`D$00LK`X}Z_RkFq$teqgjHh<jdkM%S?LcV^qB3;PVzIiy_B1U*8!<kPWGhd
z%Mvbb!Lx;VPIIL^{D4_w)6Wf9ymT}ff@VDCUea0Z(FC%cgv{t5&f&-$X>;}B!mnr4
z3VpO|kwqhz7cON9PkMUNTpK!*Uu-XmhYf6AeCkj}xBKx`mF9YeYq;X`XIR2%r#g*l
zFWF_6Q&^!BW}U7mI~QTMH_w5@#$S%(9s*o4LR5h*w^0jk_(!a>RAkv(&ud`sbWNu6
zsq=|*IoTVBG0u$+%Nr=_b05kGFvPNHEMncSkwu6U3ttd0Y=TY6LdS{q1}o7dA&kg0
zS7Zq5F!VOLa2fk}qjhLO??=>L3R}~)PRvJ}Ljy=O0+_tq;jGUpp0@+p{=5qJ;@T8v
zLq9COt5`g<ZDko5m|C!AmNrBhsM$-Hl^J*FDi}gMzd7Q~z*~H-Qt9fMm}3JVq~aMq
z1lrz5zK5$m{VoC$wMLTFW#d^cS>^kMq^8kCN)vG;{QZDtBu!ed@%|)LM!{L(X<ic|
zd}+q(*%+aO1KrIx(o+Ls3iJcNikupL1K#oR54oi_k7=CHYO=gy<fDO|5XvR6Fk~|{
ze}ylJYLCwy-EAXv=3G>RTg)hhtq0aG*r#=QaBjrteS69%yN<1#k=E0K9%|mLo92mR
z^OTW()xqj&@+n#?2y=!fA_ME%)WOER=zIs#@$mfjS-@lx9wa<(IvAK^?tdZ!(UMC7
z3~z>Fs<J}o&&WK;-zmKxIonLv(uTXo>ZPUUncqlXEMTmnxQu~$1j7*F4mfrIx_6_N
zWKwUCiUTKjy92u6wS1@=MM-^ITYTMxoIa-_lAW1%Dj|;v5&1rWjoSTuNqfgcVlt;i
z!VE>TF*TiAz5Sf9nwZ)xCU{-C1J7H4Li9OiMbVHe<QUF#o;){i+`{WRfSnA}64{pa
zqgVi;0<0Cwva*y5A+Rxc5unc+JjBh&O^$J4WZMB#0cOS1sX3-xXqEFO#*YX-mb|ZE
z0y)hdLOibm&=<Ot^fG2Gj|~<zm6{2B2VRIqPyaaP85Z5nIe%g`$C4i1u*C<O-eh8q
zF8E-h^2o?*T8h=`b?M;dNPf)1e7t`U1s)%bc4C0XjTqoT&u>erI8ZF0Nq#AI)|6$E
z(SSkDLqeay|CNx6q^82kwz)YC#nI~-;ae8rRnVCd@`kmF3)#$&Fa9!txsca|bNf<!
zwN>2gu3Kl9))~m$E7o&DtPR-Uxlp?88L+@9B6CUZIo<cJSr^lZ;d|?axxCrQxDK<s
zS(A!<BIG)KuGik0d<cKF?R?w2XUx}+D%#$H5sa=z$48o(xf$%*j<3~q=B3QfuKg?q
zkb|6atgqDH@5uks_YPo6pm=!i!Zp>tA-%Fhm*Tk2)ivn|Uxd)80^OMu5Wrqaus!P_
zs8m^jC|oj)s)q^W3#=nVz2v3(EO%CuFuf@9CdcYjl`g$~`V*$nzC=xmz{G@n*6wwo
zqT6Yq5;a_msCmGAKywU;cKvM_pBmLDCAOth4?op93668KPhlF4Jb@xI4iRhWtFT66
zIg0g<gdf7tkbJ3FU;YF>H5&8Lt$&=zfVSAeIn+<PtQ5Y(?`gA^XV`e%xBTfw4In~O
z#h;R-7UJGJi7kjjNVPK?qFZ0#O4%Fh6mK47HrP4SF7%Jq7m#<(DM*t!3O`!~T}?4h
zzsoM>Bo;94-=Id{^%dBZCo9?$Eq1dLC}D#;L>TZ9Tg@Q$Py+ha1EiPz5aZ00=}FUR
zPUIw`D4h1WUBzYDw?Wk>Yq}B-cXDExyq}I-sb(>vBCIu^mS3CR79$Z&iGs#cA3upn
zg34G2ttEt$rosf6i?J{CwsX`UlBdFMS$<a(AwL~GsV=?!2s!uLJv|SH!c00dBJMsE
zqJ4OrQfRjX_Lr+;!J&3FF4KlQRwhSht#K<guF@RENz`TTZ60xFwKG&Qx1PJ0QH+gp
zWGY@sLZ4xnydT65{F>itqSZ2``&wi(ulMEt%ElW`%P~WP{_yp!Ka$mBe2~YMGJ7{3
zNprkPT$kBKrMqu$yU<~xr}<P_79&?MGZcqVcV2i0yau5g2|IeF7MVY9lTDet+In@h
z8+OK*nD$LW++XOB<kWF411~}X0SjDwi?&B=Te$eXeh-|hm-Fd^R71nJ*|*o2VAS>f
z!-qOoI$p*Rby9J7-&Y&jUk6fe6jzNzPkMh^F?uD_e*X?qu^kJm59K=0$9l_2DC;9p
z;FiV!2Z(Wo+N{LfdsFJ{PK-N<XFo_$Z%g#>>pA+C&CC7h{o!^`sf}U1!p4kiV-&_%
zawub*WZzA%b3X`v6bDDS$#{hL%bB!2m83L3sO6FBKbd!7etmi&R-UH#d=Pj>)Dj5!
zIZsNWVLIlAFWE(7YyUmp#^HOugX7aC+m5VJ$cv$#Vn`>P%xoZHYz10jPDNl?f-^NP
zfEx1_9V1`$O?QdeI6ET-SETxf$ByyR&fBNA#c+@(y)1O8T7;6$lKV)vTe{{!wE&fy
zw2;4OVY6#($h;Y)D8qzNhB2p{VvjbRVjB{Lu5<l1$QN?<UaC?oeZoP!N!^p9pW+Ui
zx7US7rzbJu7rHdfDP21SP@Zn0G8<tmop{rpDDmEQ>6x^iCJ*LwrOWiaJCKjo+N>q3
zk?hZnygY?3H>bYc`mha(85c*j&wW@9wW>fyNlQ-}hjxu~)%~lU!<RXgvOx=t7tIND
z@$M=r*RG2T1uY!cBL{ZPKfPLN=c|Mcte`pZOO8zjJV)AO?zKTXfY71UGyexcTeMg-
zd=V~zE|rX@MA@A0H_^J^rV09@itSwsCaVV!V-{Pj=+rYoa~dhna|OUAeRe-tyoN7R
z={eQd*UXCCqM1OSSvp0I$Ea+UYgb&wTJQNKAKVhGcjC@=B>GmHFB<O*438$0)Ulk4
z<3M}<G`5zB99O10z*=AAuFzpBb`~-P=<}3}8J9GMtcBc8)c~|vCJlRc0(BInaRPO5
zLyF1D5u_^?K4LXvif1h4W{8=Jz56;C#Vz+zn;{ju<Tc{9W&kR~znb142q>??p%avh
zlR;^PR++LFN{FN-SOJ*oVXu)HC34~MsKx_qfTnuhbUj4If`8Rfp(+utKveK}iyLrw
zi%STM1@ln)i}~B$Z{?J5cqN_C)$reSHj2F@{ND_s{$rrPZ)M*}ulipHUp7LC2Ren{
z^w%^f;r=~pL`L=oRCO7#|1<i<dr*gk^)2`Tr2}Gp|4ekfFQ-D4yzI*3=pfHS8n2GC
zruk>)vD6@P@rh#3K!KnHvoGZZ{spKL>z;bO_-vm=RCDYC*j-G#-$wDn#<P2;*rNMU
zILSI=NQ*@*p4a~Dyo^TMU2#e3XBkZ@Qn7vD*XW^jEyg`Jgvh0kwD6OD;}srVt>JeB
z4N((#;`811uUvbvJe-!i^f`pwrdRbFL=NVaYEF@hKa6ij<8{TQjc+F_Gi|K+TTToI
zecexcW?;^R11S3tad^EOp|9<-NX(jMAFfngLM}`DkVN#Nq5$dT`%=n5U7N7#1R~5x
z?NUlurn-k!J6dg74E9S^@x;1mEOR>U%)v@kaX3vInNB&d>l+@EgTfGcTd16(O=DxX
z9LBRha@^GaEz}=B@ka=NN?|<HRE3XhgDPX;mZ+2c;;C{_Qy9+gfUU{VqtK5hzPzf>
zQQM!)IGenn8f=`#BKGB=y0Fa3V?k9zxW%rNG2fsb;k<SznJBmp`i56Olsv4uQC&z|
z`U`%BRnI1#D20Wvu+Lon%kNgrQs;$u9UJ}nX=-jL6Nq2*cY;bp{+p*-KHX?H!2Fx{
zTaa5eYA89q;E<nk>Hn_aKb`&;4N&`1P}6_Zmuf)mpNnu~Ba8t->ECo-3I;uooocnk
z-CuRyDO3yp@-rcsjJz06ZH)BWGEPB<&yI!>@|Jmwi3Xo}h0Xfrw~oQ*lX#=wKJ9m$
z5m*YNDv6W{7K`seTrP;dcJW+=)3?-sSPV)+4NmTp)a(e)Dus5n8Y)k3I7ju8ayFk<
z4J^O9%zh4L{$#ER+;?{8uIRCyYjXWUGBBX&$Q404bk=AeV^Yg0G`vw8-xOjLu+iK&
z!$YfaY;e{iafS0^jSSMdoOSZyBVjpHY4nQH8=0DhET2{j`PWX(gma5oj8!*39uQc*
zba&|ztQsY=%V{yb<cCBabnK`o%nhLLrzc$`LbI?fO<s)wo8`%t_l28;<lI4E6<+V<
z+8?QULTuk+>3~UhZ%d1lOi1#(xnn20z?a^1_LdO`jaf}_gLQtwRPC}i-DuN(kBjot
zxoHwr`VfYaQpuSgq<nf~Wld(LTCm7pGQqn!bWwgLms*Z)Ui~frH{X=|I%*8RXb^LM
zwqlwf9=#R-8g_U$@<Zy!_>XE%JuyZlqj9k6k^iw9lKv5phPjf47ZbYwQ^c<l^yT2H
z-{o*061{aG!=dR<diK^9St*uOh;n~*Kx_8<IVjjiuFU_OWq0s2uhEF!;YtC2{!l1I
zmcDuxFXGI8AiLMQJ1)xhu$iPAiQfl<ev8c;;*tm$oonQmfXcoFKT_0+F7hZmF!P~%
zRe%&$?ac+`x9l+v>z0O1kNSzDMryj*A9hYvxy6bt1a3r0D?FIeaiz<(vaVZW&dCE~
zr6-?;+f0gcS7eu7Wv*kDt#H1F&rl2njV&wGtgS6|p9x`V4mvUTZdK+-3`InUIj2I-
z>|O))zp{-as7(&*e6W3+ABUn0TH3Z1rE9Pq^?cB0CiA!ThqiC^_;qYoEYkU|Xzc@Z
z<#)DhREk%YjF-#2SAAX8IOq-SR%*pS*_sVjQd{(k*>(mIh|g<~t;ckfv)&T7FO0(S
zaweNs5k0Yqk6!HD!u+hXA@kJd=0#KXAnFG+2UC!KO~Gu|Gb%mAmf*#$><q|H557K9
z=J^#6Rir>_w?zN2T;dbx_D)UD<Duv@{EjvcvOlkRA_RP~XiJv7X;*Y~I-*~&o6bO=
z%nP%01c#%K3KQjL68o6BKmMwJXSt-bf5D`bn^LV>Ez-r+)_iomG&SWwv4gmfoqr3A
z=Wos6k|S7N417wB`f~fVmq4FNs|GAAcA@c$wbp>~x`!YH0)GSj!)|@DRTI}P7?n&M
z-o$tt=-N%5_1t9yZV;hRCuJWZp5EZci0l@TQuNOB36W%YakUNI>})aj+Cs2^%eNHV
zq4{=xVgceEK@egOpX)_r3aR6?6dVC2ubZ2)tD4bu%g0A2rTlWxY*BRmWS$oL&_t(%
z9yT)X9^WVX#b+d5qzcMwS|2ftEzU@`A1qBAH?G}DUcR8s_bXu=C5A<AN#dtxYWg*{
z{%yDA3HJATG;iauG#C?_n->0$<>=p2(`p7-WRU(K1mkk?1Bv2!<dx1mJ@Mpmb6D4R
z=~xA2Bd{6_`z)8ymL3*0Q#aqO>ZO$i7b2lYT`jE&nG(Sk_oinqPyK|5m55fB92?Tq
zA~|4%j9NDytcWp|LWRC^tbKKqcs!!qxblfU-M!WTtQdbLqMe9E!A}x#U@H#Uc;t7l
zbyg;>ZLRcm0LD*aiihZnPP8W_loH75rRmodkZA5wM4XHj`oJE|`ASdA&UzS~06L*}
zm${&9YW>=yPI|xA<y-s}d>cG7_oknLTCui5Nj*9Tp}V(MF2(QbXvJ-XC1pC)zp>ch
zPOudwGfMvq8JXSHSSb65S+3o7>^r7+?#@Fd5QLa21bp@}eW_njcMY_%d8*=d8u~4(
zw<^G)HJqXP86g?$C3bze2X2_YUt(^zzM5+QjmNk0Lqdl78{zznWC{h0oqHQ~+d2qk
zuf%GUetSTbVa&-03Kgt@^#Aby4EDd116^yHxQqS;X>P(x2SCA^u_Uj2B{%JpUN)DM
zg?E0AF(3N)m1!;;q2WZlKJWO7ln%R0nL^o$H!eQy{g}R<N8eOkSv(#eTh!<PJ#jn&
z7w?;WPP%Vr#plUy=>kuDO~2F4m-{K=Q_c&@2^)Pd5dq<IX`*i6qFwLH*$_UN(TqdZ
zjze7X;df(0b^$S3=yA9CYSe)ZB2ixb#DvZEy@--bc3UQBf(K`m#$u_f@(p}f629P6
z;V&~nMhIC)?5xo*I&FhPn1b+u^(%+o&|n8ygV!TytPKfp@r15xX$*F`jvUWMSTl<w
zsL^xyO@$pI0uPS6$X6xZZ6;9kW1UE_zbGN6e~u*?Hw8+S<Hhu?VAs`ojBbsm7q0uZ
zWv)}4KZ6&(z}7$gV4d5|8Lv^5=8YWw{=Suf{d@iA)af=?daU?~Tdc_HZ1}plo=^qE
z$5R!epDXCFM*>#a`8A;>_C9flaN7rMx4jI%(Aw$_NxQ^RN^T;89kZ6OkxX@;%xHi9
z_MC?V?8`oHsOu8`xDFN$7v?{ioe9|dJkOxG^BRAk&l8mSdB}g){tnEI1$F6PWj3LZ
zpZrgswON=CDNx1<lzu~W|5Y3kHu$M1e(U)=zBI{S9iUfIhzUlBWK7EP|1}(qzvrO-
zzL)+g6A2Ci6sEtG{*F!kJ2dq>8TGHyPy7E!P5q~tzduf%|0NH-3Q|n?DnR<@Au=jJ
z3@2rf01<fgXD)yL0=)PynJm);RzZqCGAe?^a6;w@Ropnt|Fj8p!+*T+-=EjLMS?Rg
Z9=@_XBGf@JFzC=1JUk3cz18p9{{R&cNG|{Y

delta 12291
zcmZ{K1z23mvM#Q{-Q6`<umHi`-Q9u)hrr+(bg;n*?(Xgm!7aGEyFIecx$o|M-g#@j
zTC=LV|ElWhu5YCOiFxn^HzY+FNGMD&I5;>k?ex!8NW|cf@9#ina4LX(H|vMCGY!?I
zq*}jhv@fv>y=EdQP?pnnTGpgGsp4S|k9RT%Ckw%@H81)ZFN7!qA=RO-hUvxEp}FaS
zqFH7M3~j`*JI45Hv4h#anrP{fD84~S^e>Jn;xjWKPSmp*D)~jU#<=RHpe>T(%NGF&
zXoHU2gKyW1tMi01mpB2O!am`giiwoo+7RKoSy^9Lxys0SWOl0)ICOMoAewR&h2Zgt
z<oM`bcF7l}^t=@*a7L9kCOg%vA?uF=RG1t)*4%DaQV#bN4vvnk^mQT=kW1o(sy94P
znVH}M`7a@!XUf0mH{YQ|&<$yJ$8qEJTG_|m@|f^Ttp;DuKam35!x>WL7)iz19|~)O
zP1frbvv$^V)nHzt%{lLL)~|&3Z{{ilA&FS_IP;i-qL~7FK!Sb={@=e{<7-%tecx%i
z?f8QIp}LFw<brss;ket7LeZ_hQe^|3z5lS>mbl+_`3@z<{c?s{Ks*RKZCLJpkH3aR
zQ3e)n!Qlqi1sn_=><|JB>>tYm+=5^QK*6E#h%>2a6qe0^t&q**g_iWz+XM`*xsfsr
zH|u-sj;HbY{MyMY3SifysT=O04DSJp|COgY!2cVQrAG^wdqQpKYZ)nuj>AUW=U;9m
zNSZ8N(2|PGC=6#~{-`wcmW>gL>V<^0wv7Y7h{FU3bA2tC<dJ9<rntx9bomR>0mIo%
zkUde2+)r=9GEC)HO}3Nx+aI-sOsr4++VuI33TN80w=?pBZd;j6AsN$U6&pjYCzG`i
zPA@zcw0*SPTOMMqIHKZciAwW$^=>n3Y%(JQ^t*^Bu8_Kcj{NStL(PQw@z+Kgm-Q`f
ziLyU&=gZbvB-S3#2z#uWkyfm9EO`@ib9ks$GRo=gn$8!x^0mTY+C&w8OBs0MGRLq*
z1C=^TX|l6-Bu3<dm^Fa<vKq}Fb?a<$wohvAxqgLYanD9koNDwYK2cPY{K@uyYE8N(
zI2f4r|C4MlsM)`2K?Rx_034I=Vf`?0<`quw7H8WT_^VkD%nFv-l3!@4*-G<Bwd(HX
zHrcjP4j(QzJg39wm9J`GT26(AA#@uu-#Y7w9(+_}V>0j1<<;4xARoAT4{bdXC!AnU
z7mJ^(sy2Gk_Ay@Lm*!M9bi5><v5D5`zU<elAQ!{nm)F5wmEe>#z(=4~Y%w<j3_ZwR
z=N^eKK5t@!Y<mPIhNYIZcNt$Yl#6#=^~9Em(2Ti(#C5S9UAgo#a}<I=CFSoM&$5pb
zt^LiD`wS=D!`wownN1VfsCdx?!J)T0ZS!wgcch;px`x$Jz4Y0RW1>1!f?FBbOwb^8
z;9HfR8u09z)3QGdN;{>_I;3rk+)NLwxi-6XGTu2gd%#X8po{BC8y0=`2uN>oLq^8;
zG;wmB0lNK3t2UNF661SX5uyHb53j;71LEk4)bnj<K<O8yahD5@)M{Y^SQgAqoS%05
zB_&0L(FwugOIK1#_*bvmY-dlCMrfMsd~*v06W>DPcA$#F&48#2&n`KWE0!Fux6kBJ
zgT7LIVpEGtH?H+?GT+h8bJrFhcUCTL-E>5)QlL1{;g4)0lb1YSj|E@3exn3t0{R#y
zp*$sL+V^eD*cLT>291mBO-^O9MHTFy$X9XNi0`yss}cYruJyuY(=V=kZ*erriJfg3
zF&}5o3yvF3G@>po>h4@!+ihjAm5{EBV+xA*Z7cU50=VgykSeZbvSYr`2TjC7kZukN
ze9GsiYgt@r>PIvY-1#(h#LpAs4AAu0rX-WN-EEZMoHmmRhBe*Z2kB>ZtfUvcjti$$
zJKDeOczV%^C1SVry}GP&<zJd4k{i=G)Q(V;OYHUyh@J>&MHO3(+RYn?`M74TmIky{
z2P~`sW)%W+SK!3a6@|}l;I12QKESAIKv6u)jzz0j35;LU8k;r>W+_=50id!Y>X+R2
z0t|F-j0{<=Dx{z4V(xn2UXOmtK?Ka#D;j~}c=7v}44m7;ihrL}>McAdNOzlIK8Q&e
zZ~1g;(O8k04u>B%Pt0>AGV~H{ntCko;B&TI0O(U+s;^UM6@Y<`)j-vu+TuKkqrr$S
zt*@xR9xM@+K{FLN#QzC>0H_H>bB&dbXKch&lpCT;NI5<MQmh_*gfCs^AZHmCC-y+e
zg_NdL`LQ1po9u%c2Q^xc%Zly*4be?$)yl(b9oIkuyKHfcvdl-T|5J2SGKu~)hk8!e
z>Xacj1opmhbr2OsL%0||VPPXV-cnU@6k^KEb5K)wDuzlpT3P&&8(@xQr5irzkf}g6
z*xK$!1m@U!ToV6CdL#)3gsi_QRYM1_7^5}xufS!)KX4HJ?iV8=7hS4MPk%Q@_6@c{
z;wwVP(4Hx@ci}EM*XMP4Dj6mGD50pt?De`VKeXS%tg%Kuy%?tfxde14QfNRp@i~*|
zRx>(Co*#t}J3R{n6M)RuBDm{MbAzHm#>u{fqQ;F1uY<I3^uFo1@egZbGj_!2VXwrK
zan!@(c;?(p!lw8SD^ySk&y>Ne>Of;>vUvWFLhF2jYc}vf(I&Vu2>6OSPOFQ&n4s+}
zQ~3o{EEo5f!o`yIlvY#Dm$it2;}c8`<k{`IK8l~YX3cM@F#tCc?U>IfLZL$6_pDCM
z9`;D<Q7Jkj?Oz82U%ym|enhc>sFcuLf3P}k6DvGfp(aBKHT@XTa?TGuEwt`l$&W(l
zezPB1r8E;7kVW(;S3rFz;_*sZQhf@$`!(8{3G0D+O3)@CI4g^HhIF1zf)U$WYC7g=
z&l<BpyVrew)ds-y8IkXnvDy#&MaM8?Wkpk6+drrXdFJCy9T?~^W-DOcf+a;>-a6&P
zLqnOz8p?_z4taJdpH$IN@0_L6K{*R`Qsyy%<J%2fRJ%2P^nU2HfYthhC@nHh<lasM
zZ3M<na+R=}WhvwibldmDl(Z&d@9B9#{%^p^CF-2WwC{k@t?By&{>>o@!UXt8koBmi
z*qAx<M|s0`b3c$mJxJ^B4l6>agTu!bFK>-fzNr-5`TSm9sb_B~oxZ~2!=QN49SJX2
zV@w~luf<6pkY9To!UQ)_=^IVP%_GlNLWBqz$jRN>ALp8>Ja%(w#-j<s)WyCExwmz*
zUl(K8(HP*8<{pV?pGeRMGoL))|Iskvb`TOGit46ET6KvVLwqrHmhq_o+ZB55qe;*x
z?dPVEera|R#yqTpdnKT2oU!}o{gL3KG<x&3TGw8U%TAUr)Gfd`;g*w&J${z8)eft<
zrE&@K#A}xYO-e(91>Hutr4fOnfajL8J7R#8yANP->vS<XNqnw6O(SW@j*n{#B@Mbf
zdF+>VaFiR_4>RDV+R%NmsaK>E!wF;%rLHt~75J#AWgsZnLpWzjixuK2GSINpW1Ac3
zpwYiBP+ysDtX4b}Lq7&*E@ZKk+c+6&Ni5=q$86@`C6N=mIZPo*#bKJPKl<@P|9a3@
z+75uRiyyKnnCc#;{ZzRzO-ru3+`*g@$RLu}yGwwF?7fSEk24m9ux^p+YBX-a;kTCB
zuxAtT0m(v5v9*0=gcK6J@=Fw<VX#~Nw_dO_$e+egXfe=DxM;Zp96{=`-+sq{jY9aW
z<EKMJW09aMEjBzuIl?0>`c@QwaLV+x)!GEG7ttwP7n)jk+6YjZ!!uk7-TzECk=_)7
zl7|{ZSVe;3fE?&F#ftWZ%7_QWG5v(X^T!R_zA6CxWJShx<;7I?cXk!amr7Cr##rSw
z#C7OOC6o-VL1csG5WGfA^@*XlHfm|eeWBn*efckmVY5A)qB19jw1&U0_VCYG29^SX
zmplC?GGE1}Iq<z-uk9^^4D3mzC&v1AuROximn5aggD29<?xB~$-S7(ARE_p>o)BF2
zDmyrDvuGwZ)ceCn*>b>`Zzy$Im_nYHfJ0nUs0Uq`ktUep6b}Q(U@y~1le7H-k&9-G
zB#pS?w5-AGU3#`npE)KVYL<n|5&{6Wguug;p5i%%^PxRUJYM&&(pqDi{qDuxpy6eY
zOXnO_*@nj}MO(t{hJw-I2F|xG7gVWJSbTtBZuqe(W4*1cd~~0$ExLU>p+Mn!@Gd?m
zy<lagb*kv$a3^_9n%R|?ucIZX5=%!q0W~&ErC(LXXG76|>X3zHoZsNXw|&4!!dlbT
zM#Cb=`!n;zx8i#O`~tz4Z?q=mm*e7|)@5tyh4TTXom>5ZrYxU<t2RUj=XP_kK6H@l
zo;vJi*iPT^AMozul3@Chbghe_>hWk1O>}lgFg$7Nu|BjH=2{H8NLE7;h|gimeP@mJ
z6xNn;LF%avpYKC=^uD5x|9}fHS2tm;tsX_K0*|<yujZ6;l`EglScJ~|KBQ8Xg56FT
z0m@yFh|=z&&cuq~F)zxW`mEb-Z;9oiOucbF_{CU50wOUqnFc$=l=LTfjb*N|XlZk&
z8CMuqAuS|-ej+GJCy}AiGcS~>1x<nFW`_SZNlRB#@n-)q`o~=#L2(a2h2Bc3?65v>
zaNWF-b8@T51MC-`!t`Q>mDy{r!S1jTnKQ>}cOlI}J-y592v$`D3kGc@l%+_p?mRo6
z%Ea*o3U2#-_INe358Lx(9@|^xOP(+mn04&#euCR#^L|HiR)=lI@9`qrJ!je{{3B8e
zw-myw<6GPj7u1Ufz?(fFyd(<viQ2;cBhB&-EjW|C<<Bp#MlDF1Z#2Y}5Q{5>^M^04
zVQ1Hcd?QnJ#udAU9pGCqf>V5^?%Hh6jBnoIdtXTzK-44rE$JhpytMZtSSr05G^JN+
z0T^>-78ejkOq|V=@$7fsWJ;|4ZR7R4PR3boaC}S+OV~#5tD6D=rJFPL=ij<*cvD#L
zn(N~ih|1>*j>Sjs_1A~9P<U0y(0@n${Cuw)PdnNQ5pr#tK@87y_mN@b_jUGLdrZE7
z;bMy;I|OnD;WRZ83LFK}(JkkRrwpmAv`)7}3(<EL{KZmEYeF1{Brh#d*`m=P;x}j0
zjf*_nij8uXQLzyfzyQMDtZFr5+{kck^$r8HKK{>dJZ1(axyVk#ao|8terY|_T4GD2
ztcSTbkTTc~SLG0Ek&dG-_&xIU(qT4daU-u$S&4uz=yE>rWS(<^#SA9e?b6q8B)C2o
z|D48vjV{qOAKTkqcy$v7;xwdR6^2571P#r#CB&F@V|Te6P?GA8e<2xeWKp5%+!-<E
zZE?!uR3R^C63F;T{I)XiTeJ-7A|{H`;GL%tWCql6cW%4C5Fe8P&TVK#SG%ltO6{u;
z`>~cDb~k?iGf}o@CfPD6XH)X#f#x94>~^PZEUrisZa@|q7-T<gLQmcOUA+%pBSaUE
zYA2TcN94LKAohS`np)Q%JX@9yg9~&zn&C4wrg3*d-41P(nJ{>@O4xoPH*)24GDf>X
zK(>56sDvE2_?xv8sP~;9mLOPmSr3IL2G^wJI525vLe4Q|&99-@RPE3xz1erK<@Z`t
zq!Ug3joQ_)Tpv7ppEg}tal_KV0{WwURMasGGq9Q(Kpgmrq;)=>y(Lse_>0&wopFkX
z<oNoB7q)ix8M$GJ>Lt~sd=8(;_P80QO;mY^x^tRXt+DMhiV|jx8J$u}p1iU~k;|T(
ze+MM&@8Vtrt@ZO#oQaU4Po3$C$nz<VOenQ3gS<`~amhQ=X2yvHvA=dU(<E$G)wP81
zO3oG$02Z2(9Y#1{5u`fp&GX;oS0fk^1|>}mKm91j2@7Be%DH7FG?HFEEDq;6a9!71
z!<8=y=o&I@qY=A)f;-EoF*}lRss#-{plubFN9a``Z}Dmve`f!bD6tIc_^^u9x(qt%
zsvG`29d)&>j?sjQQ>aLo#c7tbJ7qiTY@&Rr0|;cBZQPF+ULnsx=CVXnX>4s>wTbIU
z9z)#Y@iGhzO?Gb2E>aX^r3*S%wtDN%cW<ycgpn9)l?C632z4dcQO3q&En*W3r^4Fq
zg45CT;X|J3^Gg6fsCMheRPgGSXmATmDvJH4IVS>=KJD7v@B4x~g0le-T+N}@Ldaih
z1q_VE@3I`Gp<GEn`_Kkf$KO`gwpWLVyU&Ok9~Sw-W){#*mg80))KWypXao-XB;I|j
zaaY8Q`?RLAL{Wh*44FF1`bDy8Vm!E6RjX{g9r54_T>Xx=1Zi{pb4s%R>A7u+6&g+q
z4(EcL#~VG|%rzE#EW~05W`$n9MS?<LK7b;|*7&F}StVy34P!xDdX%}%k(bWENO*Fd
z^VdpmmEo}<X#uGJB!~95_O)$4!3Q49DdkWl7E|%Wvd)&KR%VT@1=DyGs)i?jnNB69
zot7{)H7!gt<<A5D&6=Gf+UmF`LP$0>7N7{Xna~<>G=Vd!rZ>g8=$$bDcc_N`69C``
z?Kw|Yd7uJSz~$kPlya94KHw6k-6&(}xEU9L*bgJ4<tT+LPcK{hp<DqmXoU7LMQoEz
zP?IfvC|8*rC%Ut%5?SHV6}E+GC7gQXx`gN8p*e400*6PtcT||k-@4$y)HEUa?O?ra
zWTsq<#fj={74+>4mz4!V-|5iX6kz6kZw=kZ%_Z@)nv1Z~Fk<>~e3^HQc`Ed>m)I-(
z4(9fpaA*_<qs`g*ly|?}a_8t&L^QIEYMf|w;qid;M+(R|^mcUfLOEWmy}Ks$hibQ6
z6Hz6xf@bCE<r6eZlA&o4!Lcz}yLJ_r1u_MzjE}S=A5=tM`)O$IvN`{%C_wDF4jU`m
z53+^N!zkIGxERda9nq_rJTbazyG!OdY0x(^dA&6s0&!MDU0gBI-8O@E%+e4=fP^Ku
zS!<C>ckMy-5-b<E)epvxPdq^prt+CSY9B)GXYC;3;O^Fplx?~bw<T|lBwi9452Ck8
zLBjet^7BWmef!v69HqUg2*BDL7nH-0Pg+M@{4`B@9k*Wvym{I^KA{SQ>)l>xoz9A`
zW7hsFRG7JHj#k^S$#l?BkKRmSdF(?0Wc6wBo(W`eHT&SU`z~}d(RK-I&h80H$|Uu;
zLoxhQ^};g;MhXwk^Y`h1G5wDn^_L1KM$drD6fL{aJ5g?8Z?bX62EgMLw!}+0{;y>f
z^%YHm+(}6;sfRPa$cBWEW&H6wxt_M>6q^(N@Fi7jGO}+v5ss~Cg`c}I%1Ks87<_U@
zujJ;1IjJE>Ut0v(Ff816=Xho#@glN<3Vz>{CCm&sbbThOlHcQt?Enp?2J|+qwKf3b
zF+6B4f%Zaqxl);&KLCZF5Oo&e$LPt)=RUmJx&hn@Zuo?V;4fkg*`l=k+>+Odl5Fda
zH<MDVoLWNkE@F;8ET1JVW=31YPc_{b7q94+J%<y8&~~T{*;7vx(1^(*XL>&POxl=t
zG+6~9F1z#E*mEr6-9|xeF6umuAV-PC*sF%_Qd@+TI&SM7=>jnLtq~?H>0EP{Qoxeq
z_0KUy;t7iPPKlOgo!Vu|hC#p1L{y>}1Uc4`Kd2jsx|2qF`)zFz-Mh7DcspL6_UT-n
z?x|c}_6;F^`*ywy{czB?jB~_QQ!aLF4m)<1nryIHsCSoE$6Mc8I?2|!+mjzMaI$ya
z2b;6Eb&ue*i3Lb(_G7mFl2+W_(b_t39l=a`&Z#}5#cM@)9+*b>dLQoV<3R_7>XL)8
z+R?%9c)Jw1INaGPaejW=iY<~9(0`QbbWAh8T}|hOMxfZLOz5fJiij)p@da!Q0vJ|t
z@4LZcvAh0=PY-S%cpsb-KNLzs683xVi-k-(!fm~I{3t;5OuQ65-=L?Ve#wdFigAWl
z?djy!f%_W0W2B8Tye%+9-{8X%oShebM_kNuyP=nMRylFb%>;N2LOpRuS@?;KM`5?F
z0+oU@S<kW;pU0(p@9d?Q<lO6UiO;(D*z4Ue=o2_*Y{nA^yNw(Km1`@)RN@s@R`$Un
zyR7_xgBcK}8~HG~^x~~z``c>(UPWqTx|X0yd?*21iE%8w;f=Y*sk7DWp>Gr2r*hm8
z(c5lfq5B?|+n|pmO5yA1uAY*^;nYB;@uW06-q!Qih;=#nW&3gWw|$AJP_f=h<^sy_
zJB(uX{T`^VtnB$#dk%dZMq%4Np;*iVVfT?q7Ayed6xOu>RS=P(2(g&i!<Z@phucX8
z)-Xk>eOO{C)7T<xbMiHu=ArwB8XMSb_mgJnC6&O{4=dE$nbwdZUMqd5(rq@=-2RFi
z`mUE!!EnTw^Oy&cid%zX$P=cXZ+suWGNIkn&VISzxDnAgjC$q=1IFbaiVF;$j<dDg
zm0kg|r6syAOACvKo%!;3d#CawPb#^4L>FUhE~o<76Z%s(Q#dNae->#-5A7+u@#dJn
zZ3=h{Fkinb)Rvr|l3XR#3B%Gud5n4w?r}N6TD3=!FZ3-+oWbkMFYRz1S)%RtJ7k}j
znwF;${9zgyV!y^vDGd6wRN&eUuu2Blr2?qKI>oHc0#b^yThmx-jKxct4EDz<v>OVJ
zr!HQcQZhYSj}i(B3C>DJgo?aapD)(Q5?lmX4S>maB1jF2Z{e~MLMvC7zUQ0#4!BsP
zQs#G#mab>g*r`Or?z~E?z{PAEH6e(UW#@<-DTHKk%b&r8{hxHfxn=qok)gV>V*pdQ
zx9u@1T}q>2NJ0;(4yqs=#1qO0S=0%<A2*fRgC1%C$(4nb@utn=i{|wmM+yhaxj^%#
zxJ}YBC8UlXPa7iiN2Pq{0D2TG7NKL-a-6LYH0Z6uP&3eGF~WQ&z=eBWHGYaPz_iP?
z`=~x}rOL_W$ye_{_r!xbFaRy)6F|a(TJ>@yH9*B!l@o}|hw+)b`MzzlGAR?-+MLf*
zE!Imd8%&c=8;7HckH!?yW2u~dqEr!NU=GRpb92>pO7ihrkP*fD3MK7VJSkR=@hu@g
zj~deRCivq|e7Szgnc_A~xvTMw;=jmA_)b|DmWl;`b<Vh<PN{$KadMt{Jpla5*3qc~
zhpG>#6S*o=UI~39xx$iCQQAM=q*5nwK-Xi~?Vyv(Q$K4$Cdli^5LuyDs2f#LjNtuz
zt1#rx1L};TxRDR*ml@s@tASgq`tj8^KKrh(tG%k_G@~z`#-kf1MOp<}EVjq5U`kyZ
zntzqaF4ElY#MU+Zp)Ia35)5!Qa??`mqE(%oFK_-;nZb2DQ^n|Ob9@eDYrW?;veS5K
z+I~%)@_{GHJUHI<W9z|Lvt4uPh=q}h6WrQ1-<!e`w^s5P66q8ua)AMSheqYPmb-<G
zR)jR5QzC+(Xvag*i1C~8kA^3N)$YP_(DtdACJx<-<NyoGNbNVBY<58QhcvBSh_*Yh
zl_Mz62qcER{UC0D3DU)?RIlEG8|3aN+i}G7WKU?6XujV4d=Q{y!@xvEkLZnaj2Rvb
z=S~-Dfd?(sD%v3S+fAdVPg)`cP|R$)Otw3IC2uY(Jd^`#*p6k?t)S3c-|+Zlqz4$l
zrZ-X}rDHB)OfQz?ngc*vU;vmBqKx3PJ)(86N6qNPeal;if8hHu6?XvN@~fbB;yAV7
zg<aHSVR30?*{j+_C~z&)=gzdi@;rF+v?lb1<F~u<a&jccmf#ZzhL>08_UQ-A4hd^j
zbm~nY?x!OJq;U}+cEF4jc1KO_t?|Bqp#TdsrtHSO>ZT9L6S47U{Gw+nj@BtOLz8H_
z(KCrlz4=w-z&GVfUroYF9SkoKa`KEym2&Uzdqg7Gr#ON&qs&m*omG*duoPE_*C3;x
zziZxL{~R-jYqK?x;evq)5`zE7QCY0K7(hyRB)Td+WZ{a~mBdKFYwT(}MMD$f88wfZ
zmTO@vWhs}wiu5KL>~#eU|M~Iusi4zM;EEQTji2a8J03D2aq|?$#>>I_*2+%FmLCQU
z<8?BNh!3y(?#24k+3V%W))BYIt;ezHt<m%2g>$8J+|5xyz=Ga|Z{OM=CrZs(B7hA+
zGtT?RDGIUKb{WONCHMHbAApq@*#Z_EV<W5gM?M%P+^4uIUFx+)Htv{_JclN6v)!ER
zSB7&Lp>@|H$<Ci4&M)*^Wm@Fe-8e%v<)fYOzvs_RMewSEGV8cXCe~X@%QBo<?&I=&
z&`Y8xZB@TB3LL!Y4YP)&56m;V1_8K<(rOIR;JF5+)h&!#^}4mv()#?<kzb!*1OczF
z27A$f*R5#4i;nkBe?h>Pu%?lvm{|%`t%Pz+sy8uZ1|JH3C5bt?RctdgD$+Bd+0U;`
zPhA$fnaKUq)!Xkyb*1HQka%%EJMiEtQ~Xm*J!cv`LVs)`e&fre7;Fns17P(f7bUHH
zUTK%i!iu~iW1fLn{YLU<JWi(zcqs+4ue^Yad-AB0#8~^z$kzh_2m1_DgEZvefY$S^
zmSxF7S||VMt|2KU?mfRo^o)U?C6u0o^ZSBQc@mRT+hIWa%(xn6BO6jp2aN>#37^k#
zoA<W|-XU_vCoWTIUjnoB5`aO0v6F-C>m525y=U8yPag<D$)Yxb`wK;XWN72u^D`-*
zElD;Cher{loWY?`2#ZVBq72dgejc*(vob92!xSuf_CV&P(6P|U03L=TwP+e0+Fnd3
zJH;j;nqyQLw_e-Ss8lXuK>$1w&M2CwE-MwPW7h|vUT8=GG@Ku2q5xgiGz`bCeYsxf
z08a2v2G%@h)vSoX)!MvxDO!wE%I6}%$RNCk19J3?lxug<DM<)ffod!aNBfkKYm0R<
zJ&VNhE03Sbf)dQ>d5sD+vbk=39`~RpE$l<q2^A0$5$mQDevq>O(FIj>^>wBSFBj1a
z4w+jwhbHR%ZpsR<3gEtv=4K#8iHAuaVs2HftV98q#%pLt3E%%+oJ&AAq7h&~7kWi~
z<1OFYobC8=>@btSQub{|$mG!i27g-cW1*G!tQd4;VF3cZQV15uPoI$&VDZ|!((y-O
zuzYzsk*5?`(+mF5e72JQ#FmeHzPl-4x(Jc==B#oI;srD?8sDMmR1nrBA*C!laogwK
z9sgDuwZaOs)~nFl=Kop;H~mV~ES7^%9xj2Yj}=YZNw5Eg>Xpd^oP{_Er_KZwMC5+7
zY_WiL+AVnSuLsmt<#vSFLaZx`e$>L{FoEa47iY4l2u<`%)xND*2%Z%zb(Nr+I>eIn
z*M6Mx&H~^Czi&GJ;i>A7)m)i_r^{F`=Uxx=q2Kd|WP-$Q$PW!&o_)q>kfL`*ufok%
zS*l(Wb8Q69N9<@*`?t4hoq=b+%1FykR}J{wzLibM&VJ<R{KO_@D%8*D3i0+B#_}qz
z(pAF<kDM8%?Im9OIoD{<-!N`SYKnr~Y(y3fAr8Q!dgZe{fjC4S9qdYQ@r)lEh=I1p
zsQIK?l@J*by?8HDi9gk9ph-E?j;f}(65DCL9PHS9pV0CW(oAp=bNt~Yp4#(GiN)oq
zKv<{obtI5U_l?S5ojsK}{tcbO6>+|!N{73+KjVopDoAE-DNbVccbvq^>g$j}7qI<D
zVi7=L-j6D#6jp{*3rAWIsXMv4FGXU(a4g4;C40n@xGOPj_XQO`TJ{4{j<Rr1q3JJH
zdL)h?s=lup`!j|wEYn3@j|L^cp3p=vPTYjdSot?m3lD2ndco<xNvbaQ_oh+oE@u^4
zB&i)IPRbQg3~GMOkx;P-QNUn>K!*hsK`ze;SDcIsPQZt&h(@}^ngjA|U7I~_`dGmW
zXsubyh-!T(zMrFad6Y{!%d*@{)%A|<?$z_MYnhWR2n_;G57Ljs(I0HB$CCEG?jkh0
zER_NZynlUl?2t}-C|-K4aI)xWOEiP&Fspy_l_MYRFWXD!kA*Q4+W_p;)=0UTIE)?J
zG_SeW)Gk!>A6vpl^DNspX>r+W5xdj|4)C8^!k53M*Fg_LM-bx^Z4{0HigPE{ze|)^
z9Lj9<_tf(!n=NkpOW5kyV$qL-j*6#Y71QlLn$(|R^P79gVuI+B9oSTO2NLimu?<P?
zEzmyMX`k_s>Fdc?c>x-SYOe+#oaiweRy}{eFju%OO4I*nnjQo>3j*snhm)VGt{hCn
zor&E>L&qUw@FuM&*-~<XDcSR`7EIrpb4TH>Euk%-ep^DHIObs0HsF<!_L<inRw1xb
z@Up;;!Z~LNL-8tlm12&<sb)bql?Vl4{;P=$f~e2|91x7CY8m}5_85gR#_!^ftQsl=
zOD~!QiADa)4*_jos7`m8V5|%ROZi`F(7Q?hC%jlcgcHYiv3>8b`A6p;e}9al`XQ(q
z+F|{}<Zsd+AO7JF@LwDT{g+7Bqr6PBguQg%CG~v@f8>9sf$H}^K|^3A>p;`z{!j3>
zuUcs}9C~ho-DjToEE^KP?|~YJA}_0_WuIqG3#--5)d6>@7^{*G>l~|L+#u(W&>)uF
z)t^2vxc)NaKD-rcmnJv5(dBY<c2U1*l)A8YYfGpWR6zAj-&fEnJ*K%7Ds=qS_G)df
z%XIv$9G1BD4;vXPl!x*u41aTt&is&oxvHORHb-u0wEa<z*G+6k0QwqS8L-^Hm9mkY
z1GA@h)B}im;V5C|h7RO%D@BVM4diMF)W*BJ<%=+<c2=8^-KWn1`A$uTb|HQh&&J})
zInE6>3QkxOP#l9Qory7|;_O_IK~kDi7Ku-vaWL$aMuOKYI)Sxd?~=CmtxCfGS(+D2
z%U86a8k~TSUa2esp?;WwQH^H8!YQitj&oJ+0rU~6TT6ES1tj|?>PzB}#DAl#B`f3b
z4UgZAeJvs@ma}e)FwSI}9d(STOd+!qF2~!1*-Ob5t5>b5A5FA|8nJ=5HcMif6{{IZ
zJUm=9lX1a2siw**(5s)JEq%WgR_fK8CgLYCaZ1?&ND5sSm4P#jHbb<W%sSQy@6C16
zkt2zt%3IZW)J2YeI&TAN-VyQt)1{r)Zzf~@udYU$A}MR#`WZ@>3HZhTAO7Ei{+k8B
zAJ_jj@$R4PkL$nem;Ld72MQx~r=H<ARN6!p5R$(tS1Jq`C`*#3tM^gRzI~!BN%lN-
zKgl3{ndPf^<)vi-V3u^#nW$kDxWA}fQ0Q%JrWL-nxI50}oNwneJMqS~Giy|A(Ay$b
zgkJIS)l3C>E&#At^m+Oc;iHR5UJeu01q`UgO2Px4Nm`Fa)yB2!WAegABS_paPjBZC
z+py$XzG!IB$rncZdFiU1T@BA#fHYn)xp<<(qlii_9>T>Q8d4jX08S9z%;7;AH**f2
z+=NUKN{!zmm!k@b=9{q0#GWk+mg(l8K!PKOk2MCwMu3?i_%qQM!G(5*u4kuJya&L<
z7Lgted*N68IhBksrHrsheqe=m#zj(`#rGLhSBaHv#~NZ2yNA|R<%~_K`Pp>Pj#If#
zuwGA36_PP#dM$|Qa?N%OOR;?H2<<|So>)KY^lD!JC(Ia!ylGZJRzCJDiQBpj|JMO3
zf))gBOTfv#_#3~~F-A>?XHJiZ`h7_ks9Z(Eee{9M*yz=HxVuQGDaEKB(e1m9^9OH6
zmKv<w+iU0*rj`|yjB($i_fMcMotn)H#h2v*l=}RH7h<h3J@1vO6e7C*;R=%stCMy3
z>Hr#1BL0VA-ejMAMFpwZ=yqs5t<RWuh<zvRLjZ{7^gXsn(beW}j_q`dHQe_1W?Ly<
z8c{|8gl#bKhtjKhJjE=J#Y%H{v-83L)Fl$z)O=>dDB+BKSqyibgmGmLod7S}Pge_k
zm*OT@$E>5TO|7bOzluQeuL`gwC*k=!6|*Js-&Z9bqm6n^4gkCQO)8bkQmF>qI|_xK
zBY<Nh8(yMuey2}k!6Ei26A|2+bUk_J2|ZEF%q+|>I}^H@dyv((chBU;u?`ip5$n@8
z26o3wr<`uQDUt5WQznIp#C+p*Hsu`&z`9-A{4gi`DeU4pXQgs%m%}!s{<0L<@%c}j
z0hfKdas(V(R^Acjs*yiWhUV%t&uIZMw}7;uo1xl`)G<N_3Jfi*l8ZOCO<lk$(K><p
z*j7}@t~q$IJmjME%~X~*zVC1KogxzDI`XL}ez7oZynUkK{Lht>wM7B81bLYRb<3oB
z3p0tf9Yt-M?Na-CE;c3Z`mO3t4T)wC?eg{xXWV)fv#B4dvqSgnSHh9TKjY`55db_*
zy<bo2jVzWG4sRF~vXU;9s|C6_+S<?Vm#3#)NpYYbGIAb&<?P&Gl0aF!422~}maXVT
zur!L6TeNujAC@fXS@Nl!hp~LvgAJcgJ)Yhiv7qpF!)N-0iKuoByB93TOJdq7JPbtz
z_-gY&ToTE=BLbnLu)&==nJEt8^cjHR;%fK))rr0QvHcuAfaM_by27^l+zGx$aJ=cP
z!{ULaX<Re94=PP#$%{Tt%H@>q&6R`S?%pT1gJ95-<iM$4ytB*I47pg^?y=^X;q?^o
z=O<^8bs4rcG8h<j*ncqF)WH6BVZA@snCgkZ{6!%wHsJw;ie!^iI&$^Kk;KlE?VNoT
zj!{g7w5B^`y7*z?O1*gS++$fUqA<MpBU%F9!ZME`e#QLR`|8`<ARcrjw57$zEiX!e
z%oVxiT4yfQh<T<w+x`;NLshsqEc~q4l1cGW=T)K53LggTy@(~gFj1uy%a2lVutq{!
zt0VU^rn*`H`mhOkE*M+xaQ7KhV~Ao^x$kod14}dhVIubNUyK=S7E{>``TR>eF^IYO
z-cO^^nVY=~<5x4~y(P|kwV}1r$KBSkw((J7xKq5@_0Hr@ue0X2m-H24*=#s*Becb?
zMN0C9C*90yI6c)|%d>juf6}B^ct>Op_0KaKfm26oly>2l+s%nR3oa%_szO(4kbJ_j
zpYH7D)Xye4%nCtf`c40~XaMhe-b3iNed*{^L%9e2Ecg2AXP)^d>({qf)bj6q!fxpQ
z$U1Np2D`?<tl(d~Wq1o3z&leDRqXC1d{8I8(aaB3dI>t3%2{b{Z~-`jDrnl0_u|zr
zrAmgGl8DU}y9x4|;K!uNv{g+n?eeg4{zAQ&Oqtb$rmO+&q0gT?Z^}iq6Hx}8X;^*>
ztrcu&PkZwnKKx-+^A|2%qzdDDNlBsEbGn)k%fJ@Ha3pe;2>wr40BF}uv09BN?g@#w
zK{p<_T|X94=+FA~>yTC_YoyXriId{f=k7Y}wPKl;VK!AR9v`~0j(d=y%MB8#+A(9s
z9m6W)wM+__b%w)lc?8Mb(IAO@R=@UMc}v$_pGV2=^CtGWEp0;6C)kb<oY^8V3WqP<
zMxwO>#FoR#--MzQ0K!r*j=vy!t8CgYy9$99Wn+HlC|jZpi|OF`Zv%-0*Bbj$dJ>;(
zKmm8|EQYWxp~lK6a~nPNFo0Kj?X@r8JHH|<?QL(9<EMTx4fBQeDg#{jA#t|V5EJtS
z`lfW?e&73@z&dPo1xWUR__H!6cAAJb?<*q@^JitiArLXa{x6>#*k{QF|IQ~@`-{^I
zgtFoy`J?@vbBp@7q5%V&1!`H5{8chuRs`T*fk{^6e<A)(Cnfv`LIk*A#Q^wY_3!@$
zq5We_(|c%c_TNldOzllvY|ZSPncZz|{>1vXP}Tlk*C_vh<B|Lu?&R!YW9IZ<z(i^^
zr1XQB?`~X(z`*|0{{LQ;Ou$WR3erCj{(Tdp|L=(RCs+<3)mPHL2CW>9o%+4!GaeEQ
z?4Q#}-~^g|C6W1K>F-I$_=hF6Phem=BKCI9@6$9;^>8qA()(Ay(C^W`^Ksu_o$pj<
J2%bOM{{cOMk`w>{