library(tibble)
library(ggplot2)
source("./code/Rallfun-v35-simplified.txt")
source("./code/theme_gar.txt") # format ggplot2 figures
n <- 20
x <- rlnorm(n)
t.test(x)
##
## One Sample t-test
##
## data: x
## t = 6.9578, df = 19, p-value = 1.248e-06
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 1.598099 2.973226
## sample estimates:
## mean of x
## 2.285662
# help(t.test)
Confidence interval intereactive demo: https://seeing-theory.brown.edu/frequentist-inference/index.html#section2
tr <- 0 # amount of trimming - 0 = mean
trimci(x, tr = tr)
## [1] "The p-value returned by this function is based on the"
## [1] "null value specified by the argument null.value, which defaults to 0"
## [1] "To get a measure of effect size using a Winsorized measure of scale, use trimciv2"
## $ci
## [1] 1.598099 2.973226
##
## $estimate
## [1] 2.285662
##
## $test.stat
## [1] 6.957827
##
## $se
## [1] 0.3285023
##
## $p.value
## [1] 1.247784e-06
##
## $n
## [1] 20
# edit(trimci)
We start by looking at how the bootstrap is implemented in the one-sample case. See an interactive demo here. The core mechanism of the bootstrap is sampling with replacement, which is equivalent to simulating experiments using only the data at hand.
Let’s say we have a sample that is a sequence of integers, from 1 to 6.
n <- 6 # sample size
samp <- 1:n
samp
## [1] 1 2 3 4 5 6
To make bootstrap inferences, we sample with replacement from that sequence using the sample()
function. That’s the engine under the hood of any bootstrap technique. Let’s generate our first bootstrap sample:
set.seed(21) # for reproducible results
sample(samp, size = n, replace = TRUE) # sample with replacement
## [1] 5 2 5 2 6 6
We do it again, getting a different bootstrap sample:
sample(samp, size = n, replace = TRUE) # sample with replacement
## [1] 1 2 6 6 5 6
Third time:
sample(samp, size = n, replace = TRUE) # sample with replacement
## [1] 1 4 2 1 4 2
We could also generate our 3 bootstrap samples in one go:
set.seed(21) # reproducible example
nboot <- 3
matrix(sample(samp, size = n*nboot, replace = TRUE), nrow = nboot, byrow = TRUE)
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 5 2 5 2 6 6
## [2,] 1 2 6 6 5 6
## [3,] 1 4 2 1 4 2
As is apparent from these 3 examples, in a bootstrap sample, some observations are sampled more than once and others are not sampled at all. So each bootstrap sample is like a virtual experiment in which we draw random observations from our original sample.
How do we use the bootstrap samples? It might be tempting to use them to make inferences about the mean of our sample (as we will see below this is a bad idea). With the bootstrap, we ask: what are the plausible sample means compatible with our data, without making any parametric assumptions? To answer this question, we take bootstrap samples by sampling with replacement from the data. For each bootstrap sample, we compute the mean. This can be done using a for
loop. Although for
loops can be avoided, they are very practical in many situations and they make the code easier to read.
set.seed(21) # reproducible results
nboot <- 1000 # number of bootstrap samples
# declare vector of results
boot.m <- vector(mode = "numeric", length = nboot)
for(B in 1:nboot){
boot.samp <- sample(samp, size = n, replace = TRUE) # sample with replacement
boot.m[B] <- mean(boot.samp) # save bootstrap means
}
set.seed(21)
boot.m <- apply(matrix(sample(samp, size = n*nboot, replace = TRUE), nrow = nboot), 1, mean)
The lollipop chart shown below illustrates the first 50 bootstrap means, in the order in which they were sampled. The grey horizontal line marks the sample mean (3.5). The bootstrap means randomly fluctuate around the sample mean. They represent the means we could expect if we were to repeat the same experiment many times, given that we can only sample from the data at hand.
n.show <- 50 # show only n first bootstrap means
df <- tibble(x = 1:n.show, y = boot.m[1:n.show])
ggplot(df, aes(x = x, y = y)) + theme_gar +
geom_hline(yintercept = mean(samp), colour = "grey", size = 1) +
geom_segment(aes(x=x, xend=x, y=0, yend=y)) +
geom_point(size=2.5, color="red", fill=alpha("orange", 0.3), alpha=0.7, shape=21, stroke=2) +
scale_x_continuous(breaks = c(1, seq(10, 100, 10))) +
scale_y_continuous(breaks = seq(1, 6, 1)) +
coord_cartesian(ylim = c(0, 6)) +
labs(x = "Bootstrap samples", y = "Bootstrap means")
# ggsave(filename=('./figures/figure_50bootsamp.pdf'),width=7,height=5)
# ggsave(filename=('./figures/figure_50bootsamp.png'),width=7,height=5)
We can illustrate all the bootstrap means using a density plot, which is like a smooth histogram. The density plot shows the relative probability of observing different bootstrap means.
df <- as_tibble(with(density(boot.m),data.frame(x,y)))
ggplot(df, aes(x = x, y = y)) + theme_gar +
geom_vline(xintercept = mean(samp), colour = "grey", size = 1) +
geom_line(size = 2) +
scale_x_continuous(breaks = seq(0, 10, 1)) +
coord_cartesian(xlim = c(0, 6)) +
labs(x = "Bootstrap means", y = "Density")
# ggsave(filename=('./figures/figure_bootdens.pdf'),width=7,height=5)
# ggsave(filename=('./figures/figure_bootdens.png'),width=7,height=5)
We set alpha to 0.05 to get a 95% confidence interval. That means, if we were to repeat the same experiment many times, and for each experiment we compute a confidence interval, in the long-run, 95% of these intervals should contain the population value. This means that for a given experiment, the confidence interval does or does not contain the population we’re trying to estimate. Also, the actual coverage depends on the method used to build the confidence interval and the quantity we’re trying to estimate. In particular, it is well-known that the bootstrap should be used to build confidence intervals for the mean, because the coverage can be far from the expected value. For instance, when sampling from skewed distributions, the coverage can be much lower than the expected 95%. See next main section on how to use a simulation to check coverage.
alpha <- 0.05
ci <- quantile(boot.m, probs = c(alpha/2, 1-alpha/2)) # [2.33, 4.84]
There are many ways to estimate quantiles (the R function quantile()
has 9 options for instance). In Rand Wilcox’s functions, the procedure in the next chunk is used, which gives slightly different results. With a large number of bootstrap samples, which quantile method is used will make little difference.
alpha <- 0.05
bvec <- sort(boot.m) # sort bootstrap means in ascending order
# define quantiles
low <- round((alpha/2)*nboot) # 25
up <- nboot-low # 975
low <- low+1
# get confidence interval
ci <- c(bvec[low],bvec[up]) # [2.33, 4.83]
The horizontal line marks the 95% confidence interval. The boxes report the values of the CI bounds. L stands for lower bound, U for upper bound.
df <- as_tibble(with(density(boot.m),data.frame(x,y)))
ggplot(df, aes(x = x, y = y)) + theme_gar +
geom_vline(xintercept = mean(samp), colour = "grey", size = 1) +
geom_line(size = 2) +
scale_x_continuous(breaks = seq(0, 10, 1)) +
coord_cartesian(xlim = c(0, 6)) +
labs(x = "Bootstrap means", y = "Density") +
# confidence interval ----------------------
geom_segment(x = ci[1], xend = ci[2],
y = 0, yend = 0,
lineend = "round", size = 3, colour = "orange") +
annotate(geom = "label", x = ci[1]+0.15, y = 0.1*max(df$y), size = 7,
colour = "white", fill = "orange", fontface = "bold",
label = paste("L = ", round(ci[1], digits = 2))) +
annotate(geom = "label", x = ci[2]-0.15, y = 0.1*max(df$y), size = 7,
colour = "white", fill = "orange", fontface = "bold",
label = paste("U = ", round(ci[2], digits = 2)))
# ggsave(filename=('./figures/figure_bootdensci.pdf'),width=7,height=5)
nv <- 2 # null value for hypothesis testing
pv <- mean(boot.m>nv) +.5*mean(boot.m==nv)
pv <- 2*min(c(pv,1-pv)) # P value = 0.015
df <- as_tibble(with(density(boot.m),data.frame(x,y)))
df.pv1 <- tibble(x = df$x[df$x<nv],
y = df$y[df$x<nv])
df.pv2 <- tibble(x = df$x[df$x>nv],
y = df$y[df$x>nv])
ggplot(df, aes(x = x, y = y)) + theme_gar +
geom_vline(xintercept = mean(samp), colour = "grey", size = 1) +
# P value
geom_area(data = df.pv1,
aes(x = x, y = y),
fill = "red", alpha = 1) +
geom_area(data = df.pv2,
aes(x = x, y = y),
fill = "red", alpha = .2) +
# density
geom_line(data = df, size = 2) +
scale_x_continuous(breaks = seq(0, 10, 1)) +
coord_cartesian(xlim = c(0, 6)) +
labs(x = "Bootstrap means", y = "Density") +
# Null value
geom_segment(x = nv,
xend = nv,
y = 0,
yend = df$y[which.min(abs(df$x-nv))],
size = 1,
colour = "black",
linetype = "dotted") +
# P value arrow -------------
geom_segment(x = 1.4, xend = 1.9, y = 0.1, yend = 0.01,
arrow = arrow(type = "closed",
length = unit(0.25, "cm")),
colour = "grey50", size = 1) +
annotate(geom = "label", x = 1, y = 0.1, size = 7,
colour = "white", fill = "red", fontface = "bold",
label = "P value / 2")
# ggsave(filename=('./figures/figure_bootdenspval.pdf'),width=7,height=5)
You can get the bootstrap confidence interval and P value by calling the onesampb()
function:
set.seed(21)
onesampb(samp,est=mean,alpha=.05,nboot=1000,nv=2)
## $ci
## [1] 2.333333 4.833333
##
## $n
## [1] 6
##
## $estimate
## [1] 3.5
##
## $p.value
## [1] 0.015
It can be used with any estimator, define with the est
argument, for the median (median
), a trimmed mean (tm
), a quantile estimate (hd
) or some measure of variability, such as the median absolute deviation to the median (mad
).
nv
is the null value used in computing the P value.
Intuitively, the bootstrap P value reflects how deeply the null hypothesis is nested within the bootstrap distribution. Let’s illustrate that.
nv <- 3.5
pv <- mean(boot.m>nv) +.5*mean(boot.m==nv)
pv <- 2*min(c(pv,1-pv)) # P value = 0.015
p <- ggplot(df, aes(x = x, y = y)) + theme_gar +
# density
geom_line(data = df, size = 2) +
scale_x_continuous(breaks = seq(0, 10, 1)) +
coord_cartesian(xlim = c(0, 6)) +
labs(x = "Bootstrap means", y = "Density") +
# Null value
geom_vline(xintercept = nv) +
ggtitle(paste0("Null value = ",nv,", P value = ",pv))
p
# pA <- p
# save figure
# ggsave(filename = "./figures/nb2_fig_pvalA.pdf", width = 20, height = 15, units = "cm")
nv <- 4.5
pv <- mean(boot.m>nv) +.5*mean(boot.m==nv)
pv <- 2*min(c(pv,1-pv)) # P value = 0.015
p <- ggplot(df, aes(x = x, y = y)) + theme_gar +
# density
geom_line(data = df, size = 2) +
scale_x_continuous(breaks = seq(0, 10, 1)) +
coord_cartesian(xlim = c(0, 6)) +
labs(x = "Bootstrap means", y = "Density") +
# Null value
geom_vline(xintercept = nv) +
ggtitle(paste0("Null value = ",nv,", P value = ",pv))
p
# pB <- p
# save figure
# ggsave(filename = "./figures/nb2_fig_pvalB.pdf", width = 20, height = 15, units = "cm")
nv <- 5.5
pv <- mean(boot.m>nv) +.5*mean(boot.m==nv)
pv <- 2*min(c(pv,1-pv)) # P value = 0.015
p <- ggplot(df, aes(x = x, y = y)) + theme_gar +
# density
geom_line(data = df, size = 2) +
scale_x_continuous(breaks = seq(0, 10, 1)) +
coord_cartesian(xlim = c(0, 6)) +
labs(x = "Bootstrap means", y = "Density") +
# Null value
geom_vline(xintercept = nv) +
ggtitle(paste0("Null value = ",nv,", P value = ",pv))
p
# pC <- p
# save figure
# ggsave(filename = "./figures/nb2_fig_pvalC.pdf", width = 20, height = 15, units = "cm")
nv <- 6
pv <- mean(boot.m>nv) +.5*mean(boot.m==nv)
pv <- 2*min(c(pv,1-pv)) # P value = 0.015
p <- ggplot(df, aes(x = x, y = y)) + theme_gar +
# density
geom_line(data = df, size = 2) +
scale_x_continuous(breaks = seq(0, 10, 1)) +
coord_cartesian(xlim = c(0, 6)) +
labs(x = "Bootstrap means", y = "Density") +
# Null value
geom_vline(xintercept = nv) +
ggtitle(paste0("Null value = ",nv,", P value = ",pv))
p
# pD <- p
# save figure
# ggsave(filename = "./figures/nb2_fig_pvalD.pdf", width = 20, height = 15, units = "cm")
The bootstrap is sometimes described as a robust technique. In itself, it is not robust. A simple example can illustrate this lack of robustness: percentile bootstrap confidence intervals for the mean are not robust.
set.seed(21) # reproducible results
n <- 10
samp <- rnorm(n, 8, 2)
samp <- c(samp, 17)
samp_mat <- matrix(NA, nrow = 12, ncol = 7)
ci_mean_t <- matrix(NA, nrow = 2, ncol = 7)
mean_res <- vector(mode = "numeric", length = 7)
median_res <- vector(mode = "numeric", length = 7)
ci_mean_pb <- matrix(NA, nrow = 2, ncol = 7)
ci_median_pb <- matrix(NA, nrow = 2, ncol = 7)
ci_median_param <- matrix(NA, nrow = 2, ncol = 7)
for(C in 1:7){
todo <- c(samp, 17+C^2)
samp_mat[,C] <- todo
mean_res[C] <- mean(todo)
median_res[C] <- median(todo)
ci_mean_t[,C] <- t.test(todo)$conf.int
ci_mean_pb[,C] <- onesampb(todo, mean)$ci # default to nboot = 2000
}
set.seed(777) # for reproducible jitter
# raw data
df <- tibble(res = as.vector(samp_mat),
cond = factor(rep(1:7, each = 12)))
# mean + confidence intervals
df2 <- tibble(res = mean_res,
cond = factor(1:7),
ci_low = ci_mean_t[1,],
ci_up = ci_mean_t[2,])
ggplot(df, aes(x = cond, y = res)) + theme_gar +
# scatterplots
geom_jitter(shape = 21, width = .1, colour = 'black', fill = 'grey', size = 2, alpha = 0.5) +
geom_hline(yintercept = ci_mean_t[1,1], linetype = 'dashed') +
geom_hline(yintercept = ci_mean_t[2,1], linetype = 'dashed') +
# confidence intervals
geom_errorbar(data = df2, aes(x=cond, ymin=ci_low, ymax=ci_up),
width=.05, size=0.75) +
geom_point(data = df2, aes(x=cond, y=res), size=3) +
theme(panel.grid.minor.x = element_blank()) +
labs(x = "Conditions", y = "Values") +
ggtitle("Mean: standard CI")
# save figure
# ggsave(filename = "./figures/nb2_fig_outliers_m_ci.pdf", width = 20, height = 15, units = "cm")
set.seed(777) # for reproducible jitter
# raw data
df <- tibble(res = as.vector(samp_mat),
cond = factor(rep(1:7, each = 12)))
# mean + confidence intervals
df2 <- tibble(res = mean_res,
cond = factor(1:7),
ci_low = ci_mean_pb[1,],
ci_up = ci_mean_pb[2,])
ggplot(df, aes(x = cond, y = res)) + theme_gar +
# scatterplots
geom_jitter(shape = 21, width = .1, colour = 'black', fill = 'grey', size = 2, alpha = 0.5) +
geom_hline(yintercept = ci_mean_pb[1,1], linetype = 'dashed') +
geom_hline(yintercept = ci_mean_pb[2,1], linetype = 'dashed') +
# confidence intervals
geom_errorbar(data = df2, aes(x=cond, ymin=ci_low, ymax=ci_up),
width=.05, size=0.75) +
geom_point(data = df2, aes(x=cond, y=res), size=3) +
theme(panel.grid.minor.x = element_blank()) +
labs(x = "Conditions", y = "Values") +
ggtitle("Mean: bootstrap CI")
# save figure
# ggsave(filename = "./figures/nb2_fig_outliers_m_pbci.pdf", width = 20, height = 15, units = "cm")
Inferences on 20% trimmed means of skewed distributions.
We sample from log-normal distributions to mimic distributions of response times.
set.seed(44) # reproducible results
# Group 1
n1 <- 50
m <- 400
s <- 50
location <- log(m^2 / sqrt(s^2 + m^2))
shape <- sqrt(log(1 + (s^2 / m^2)))
g1 <- rlnorm(n1, location, shape)
# Group 2
n2 <- 70
m <- 500
s <- 70
location <- log(m^2 / sqrt(s^2 + m^2))
shape <- sqrt(log(1 + (s^2 / m^2)))
g2 <- rlnorm(n2, location, shape)
set.seed(22) # for reproducible jitter
# raw data
df <- tibble(val = c(g1, g2),
y = rep(1, n1+n2),
gp = factor(c(rep("Group 1",n1),rep("Group 2",n2)))
)
df.q1 <- tibble(y = rep(0.9,2),
yend = rep(1.1,2),
x = c(quantile(g1,0.25), quantile(g2, 0.25)),
xend = x,
gp = factor(c("Group 1","Group 2"))
)
df.q2 <- tibble(y = rep(0.9,2),
yend = rep(1.1,2),
x = c(quantile(g1,0.5), quantile(g2, 0.5)),
xend = x,
gp = factor(c("Group 1","Group 2"))
)
df.q3 <- tibble(y = rep(0.9,2),
yend = rep(1.1,2),
x = c(quantile(g1,0.75), quantile(g2, 0.75)),
xend = x,
gp = factor(c("Group 1","Group 2"))
)
p <- ggplot(data = df, aes(x = val, y = y)) + theme_gar +
# scatterplots
geom_jitter(height = .05, alpha = 0.3, size = 3) + # shape = 21, fill = "grey", colour = "black"
theme(axis.ticks.y = element_blank(),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
panel.grid.minor.x = element_blank()) +
scale_y_continuous(breaks = 1) +
# 1st quartile
geom_segment(data = df.q1, aes(y = y, yend = yend,
x = x, xend = xend),
size = 0.75, lineend = 'round', colour = "black") +
# median
geom_segment(data = df.q2, aes(y = y, yend = yend,
x = x, xend = xend),
size = 0.75, lineend = 'round', colour = "black") +
# 3rd quartile
geom_segment(data = df.q3, aes(y = y, yend = yend,
x = x, xend = xend),
size = 0.75, lineend = 'round', colour = "black") +
labs(x = "Response times (ms)") +
facet_grid(cols = vars(gp)) +
coord_cartesian(xlim = c(0, 700)) +
scale_x_continuous(breaks = seq(0, 1000, 100))
p
# ggsave(filename=('./figures/nb2_fig_2gps.pdf'),width=7,height=2.5)
set.seed(1)
nboot <- 1000
# bootstrap sampling independently from each group
boot1 <- matrix(sample(g1, size=n1*nboot, replace=TRUE), nrow=nboot)
boot2 <- matrix(sample(g2, size=n2*nboot, replace=TRUE), nrow=nboot)
# compute trimmed mean for each group and bootstrap sample
boot1.tm <- apply(boot1, 1, mean, trim=0.2)
boot2.tm <- apply(boot2, 1, mean, trim=0.2)
# get distribution of sorted bootstrap differences
boot.diff <- sort(boot1.tm - boot2.tm)
pb2gen
alpha <- 0.05
low <- round((alpha/2)*nboot) + 1
up <- nboot - low
ci <- c(boot.diff[low], boot.diff[up])
quantile
functionalpha <- 0.05
ci <- quantile(boot.diff, probs = c(alpha/2, 1-alpha/2)) # [-114.2, -66.1]
nv <- 0 # null value
pv <- sum(boot.diff<nv)/nboot + sum(boot.diff==nv)/(2*nboot)
pv <- 2*(min(pv,1-pv)) # 0
set.seed(1)
pb2gen(g1, g2, alpha=0.05, nboot=1000, est=mean, trim=0.2)
## $est.1
## [1] 393.8316
##
## $est.2
## [1] 485.0868
##
## $est.dif
## [1] -91.25519
##
## $ci
## [1] -114.17024 -66.27289
##
## $p.value
## [1] 0
##
## $sq.se
## [1] 146.3022
##
## $n1
## [1] 50
##
## $n2
## [1] 70
diff <- mean(g1,trim=0.2) - mean(g2,trim=0.2) # group difference
ci1 <- round(ci[1])
ci2 <- round(ci[2])
df <- as_tibble(with(density(boot.diff),data.frame(x,y)))
ggplot(df, aes(x = x, y = y)) + theme_gar +
geom_vline(xintercept = diff, colour = "grey", size = 1) +
geom_line(size = 2) +
scale_x_continuous(breaks = seq(-200, 200, 20)) +
coord_cartesian(xlim = c(-130, 0)) +
labs(x = "Bootstrap differences between 20% trimmed means", y = "Density") +
# confidence interval ----------------------
geom_segment(x = ci1, xend = ci2,
y = 0, yend = 0,
lineend = "round", size = 3, colour = "orange") +
annotate(geom = "label", x = ci1+0.15, y = 0.1*max(df$y), size = 7,
colour = "white", fill = "orange", fontface = "bold",
label = paste("L = ", round(ci1, digits = 2))) +
annotate(geom = "label", x = ci2-0.15, y = 0.1*max(df$y), size = 7,
colour = "white", fill = "orange", fontface = "bold",
label = paste("U = ", round(ci2, digits = 2)))
# ggsave(filename=('./figures/nb2_fig_2gpsbootres.pdf'),width=7,height=5)
Using the same data, now we look at differences in spread between group 1 and group 2. We make inferences on MAD, the median absolute deviation from the median, which is a robust measure of spread (more on robust estimation in lecture 3).
We already have generated bootstrap samples, so here we simply compute MAD for each of them.
boot1.mad <- apply(boot1, 1, mad)
boot2.mad <- apply(boot2, 1, mad)
boot.diff <- sort(boot1.mad - boot2.mad)
ci <- c(boot.diff[low], boot.diff[up]) # [-59.1, 3.9]
ci <- quantile(boot.diff, probs = c(alpha/2, 1-alpha/2)) # [-59.15, 5.12]
pv <- sum(boot.diff<0)/nboot + sum(boot.diff==0)/(2*nboot)
pv <- 2*(min(pv,1-pv)) # 0.084
diff <- mad(g1) - mad(g2) # group difference
ci1 <- round(ci[1])
ci2 <- round(ci[2])
df <- as_tibble(with(density(boot.diff),data.frame(x,y)))
ggplot(df, aes(x = x, y = y)) + theme_gar +
geom_vline(xintercept = diff, colour = "grey", size = 1) +
geom_line(size = 2) +
scale_x_continuous(breaks = seq(-200, 200, 20)) +
coord_cartesian(xlim = c(-100, 50)) +
labs(x = "Bootstrap differences between MADs", y = "Density") +
# confidence interval ----------------------
geom_segment(x = ci1, xend = ci2,
y = 0, yend = 0,
lineend = "round", size = 3, colour = "orange") +
annotate(geom = "label", x = ci1+0.15, y = 0.1*max(df$y), size = 7,
colour = "white", fill = "orange", fontface = "bold",
label = paste("L = ", round(ci1, digits = 2))) +
annotate(geom = "label", x = ci2-0.15, y = 0.1*max(df$y), size = 7,
colour = "white", fill = "orange", fontface = "bold",
label = paste("U = ", round(ci2, digits = 2)))
# ggsave(filename=('./figures/nb2_fig_mad.pdf'),width=7,height=5)
set.seed(1)
pb2gen(g1, g2, alpha=0.05, nboot=1000, est=mad)
## $est.1
## [1] 40.29569
##
## $est.2
## [1] 68.39412
##
## $est.dif
## [1] -28.09842
##
## $ci
## [1] -59.149360 3.897791
##
## $p.value
## [1] 0.084
##
## $sq.se
## [1] 263.3807
##
## $n1
## [1] 50
##
## $n2
## [1] 70
Instead of looking separately at group differences in central tendency and spread, you can have a more detailed look at how distributions differ by using a shift function, which is available in the rogme R package. More on this later on in the course.
Guillaume A. Rousselet, 2019, University of Glasgow.


Xmas Sweater Builder
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Guidance
Preventing bullying
Guidance for schools on preventing and responding to bullying.
Documents
Ref:
DFE-00292-2013
PDF
,
419KB
,
12 pages
Ref:
DFE-00094-2014
PDF
,
162KB
,
3 pages
Ref:
DFE-00652-2014
PDF
,
195KB
,
6 pages
Ref:
DFE-00655-2014
PDF
,
209KB
,
7 pages
Details
The main guidance document is for:
-
school leaders
-
school staff
-
governing bodies
It applies to all schools and may also be useful for further education and community settings.
The additional guidance documents cover:
-
how schools can support children and young who are bullied
-
how schools can prevent and respond to cyber bullying aimed at headteachers and staff
-
how parents and carers can prevent and respond to cyber bullying aimed at children and young people


A positive fraction whose numerator is less than its denominator is called a proper fraction.
For any denominator, d, there will be d1 proper fractions; for example, with d = 12:
1/12 , 2/12 , 3/12 , 4/12 , 5/12 , 6/12 , 7/12 , 8/12 , 9/12 , 10/12 , 11/12 .
We shall call a fraction that cannot be cancelled down a resilient fraction.
Furthermore we shall define the resilience of a denominator, R(d), to be the ratio of its proper fractions that are resilient; for example, R(12) = 4/11 .
In fact, d = 12 is the smallest denominator having a resilience R(d) 4/10 .
Find the smallest denominator d, having a resilience R(d) 15499/94744 .


Oral contrast:
IV contrast:
None.
Lung and large airways:
Pleura:
Vessels:
Atherosclerosis:
Heart:
Pericardial effusion:
Mediastium and hila:
Chest wall and lower neck:
Liver:
Bile ducts:
Gallbladder:
Pancreas:
Spleen:
Adrenals:
Kidneys:
Reproductive organs:
Ureters:
Bladder:
Bowel:
Mesenteric lymph nodes:
Peritoneum:
Retroperitoneum:
Abdominal wall:
Bones:


Quentin Romero is a 25 year old Caucasian man from the South Valley in Albuquerque New Mexico. He has an uncle who recently left the Army and is having a hard time reentering society. Quentin has little formal education, he stopped going to school at 14 to work for his father's residential construction business. He is unfamiliar with local resources for veterans and is browsing online for something he or his uncle could use to make his uncle's reentry successful.
Quentin is using a seven-year-old library computer running Windows 8. He is using Internet Explorer.
Quentin is a strong advocate for hard work and determination.  Quentin values family very heavily and feels responsible for his family's success. Quentin does not use computers very often, mostly limiting his interaction to google maps and youtube videos.
Quentin is frustrated by the unorganized way veteran resources seem to be distributed. He needs a place that he can direct his uncle to that will help his uncle find friends, get a job, and get help with the day to day. He knows that these resources exist, he is just not sure how to get his uncle in touch with them.
Quentin wants to connect his uncle with the resources his uncle needs to make it.
Anonymous user uses site to find resources.
Sarah Thomas is an African American veteran and a leader in her community. She credits a lot of the success she has had to her military background and the strength of character it left her with. She owns a local technology firm and is frustrated by the lack of representation of veterans in the tech space in her community. She has started a scholarship program that gives five veterans a year free tuition to a local coding bootcamp. She is looking for a way to get the word out to the community she is trying to help.
Sarah uses an iPad to browse the web from a local coffeehouse. She uses Safari as her browser. She is very familiar with technology and web applications.
Sarah is frustrated by the lack of areas where she can reach her targeted demographic. She needs a way to reach veterans with the opportunity she wants to provide.
Sarah wants to put more veterans into the technology sector. She hopes to make the change from combat to coding a more natural and normal transition.
As a logged in user, I want to submit a resource for others to utilize.
Roger Sanchez is a 67-year-old married Hispanic veteran who relocated to Albuquerque New Mexico in 2001 because of a health issue with his lungs. Roger served in the Marine Corps and was exposed to some toxic chemical fumes that burned his lungs. Roger runs a small stall at the local flea shop where he sells military surplus clothing and memorabilia. He recently used the Veteran Resource Webapp to find out how to apply for a free phone from the government.
Roger browses the internet using chrome running on a Moto G4. He often browses the web on his mobile device, looking for price information for his store and reading how-to articles.
Roger is headstrong and confident. He is always on the lookout for ways his military background may assist him and his business. He has recently received his free phone and wants to help others know about the resource on the Veteran Resource Webapp
Roger is frustrated that only a few people have "Usefulled" the free phone resource, the limited number of "Usefuls" led him to think it may be a scam. Now that he knows better he wants to spread the word.
Roger wants veterans in his area to know about the most useful resources in their area.
As a logged in user, I want to "Useful" a resource.


The duration of the project was one month. I worked in a group of three people which included two of my batchmates, Dhruv and Isha. We chose to work on the Indian Handicraft Industry which has been struggling in the recent times. After a comprihensive study of the whole system and considering the possible intervention points we came up with the concept 'Swadesi'.
Considering the stunted growth of the Indian Handicrafts Industry, study the overall system and propose a solution to uplift the Industry.
Initially, we started off with Literature Research which included the study of a number of research papers and government websites. Gathering the relevant information, a SWOT Analysis of the current scenario was conducted. Further, a comprehensive study of the whole System was done to identify the crucial Systems, Subsystems and their Elements. Then, a short User Study was done to identify and define the end consumers which helped us in forming two User Archetypes. Proceeding, the concept 'Swadesi' was developed along with Data Flow Diagram of the whole model.
After reading a number of research papers, articles and govt. websites a SWOT Analysis of the existing scenario was conducted to locate the crucial elements.
The Handicraft Industry was broken down into the basic Subsystems and their Elements using the Systems Approach to Design. Further, the gaps between the Elements of various Subsystems were detailed out.
The process started with defining key objectives and then framing a detailed questionarie. Further, user studies were conducted which helped to understand the current scenario and the corresponding user behavior. Analysis of the user interviews gave us two major archetypes, “The Everyday Buyer” and “Travellers & Tourists”
Keeping in mind the possible User Base, the concept 'Swadesi' was developed. 'Swadesi' is an online marketplace for the sale of Indian Handicrafts. The whole Data Flow Diagram of the concept was also developed to check the viability of the concept.


{{ _('%(site_name)s is a repository of software for the Python programming language.', site_name=request.registry.settings['site.name']) }}
{{ _('For up to date best practices about packaging read packaging.python.org.', packaging_guide_url='https://packaging.python.org/en/latest/') }}
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{% if release.summary %}{{ release.summary }}
{% endif %}{{ _('%(version)s by %(author)s', version=release.version, author=release.author) }}


You have opted to complete an IAT on SUDS in public green space
You will complete three tasks: two brief questionnaires and an IAT in which you will sort words into categories as quickly as possible. You should be able to complete the tasks in less than 10 minutes total. When you finish, you will receive your results as well as more information about the test and the performance of others.


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def | test_no_path_given_directory_does_not_exist |
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Tests for devappserver._get_storage_path.
The documentation for this class was generated from the following file:
- code/googleappengine-read-only/python/google/appengine/tools/devappserver2/devappserver2_test.py


Acme Pizza Co
Create a Pizza
Select size:
Small (10")
Medium (12")
Large (14")
Select topping:
Cheese
Pepperoni
Create
Pizzas
Select a pizza to see the number of slices:
Slices
A
pizza has
slices


LITERARY EVENTS
It is a one minute challenge to test your adaptability, spontaneity, language and fluency. If you have the skills, then this is the place to flaunt.
RULES :
1. Language: English.
2. INDIVIDUAL participation.
3. In the group of participants, one starts speaking on the topic given on the spot.
4. A mistake of any kind to upset the rules of British English leads to other participants banging the table.
5. The one who bangs first gets to point the flaw and continue from there on.
6. The JAM master is God.
7. In case of any doubts, refer to the rules mentioned above.
VENUE:
11th March,2016(Day 1)
Conference Hall
Timings:10.00 am-1.00 pm
Witsvilla is a three level competition that will challenge you at each level. Success awaits you at the other end of a journey, which comprises of a Group Discussion, Extempore and Turncoat.
RULES :
1. It is a bilingual individual event.
2. The first round is a "Group discussion" where 6 to 8 people on a table will be a given a topic to discuss about.
3. The next round is an "Extempore" round. Each participant will be a given a topic on the spot to speak on.
4. Each speaker will be given 2 minute thinking time and 2 minute speaking time.
5. The last round will be of "Turncoat". Each speaker will have to speak in favour and against a particular topic.
6. Each speaker will get 2 minute thinking time and 2 minute speaking time (1 minute in favour and 1 minute against).
VENUE:
12th March,2016(Day 2)
Conference Hall
Timings:10.00 am-1.00 pm
Do you aspire to mesmerize the world with your poetic lyrical enigma? Do you believe in the magic your words can unveil? Here is a platform for all the budding poets to live their aspirations.
It is a bilingual event (entries can either be in Hindi or English).
RULES:
1. The 1st Round is a preliminary offline round. All the participants should send their poem before 1st March 2016 at<EMAIL_ADDRESS>along with thier name, contact number and email id. (The poem should be sent either in pdf or word file.)
2. Top finalists will be selected and they will recite thier poem on the stage.
3. The expression and manner of recitation will also be marked.
4. The decision of the judges will be final.
VENUE:
11th March,2016(Day 1)
MMC
Timings:1.30 am-4.30 pm
A debater has conviction in his speech, logic in his arguments, accuracy in facts and confidence in his delivery! Do you claim a mastery at it? Insurrection awaits you!
It is a bilingual event. There will be two rounds.
RULES :
1. Each team should have two members.
2. The 1st round will be a conventional debate round.
3. The topic will be released 24 hours prior to the event.
4. Speaking time for each speaker will be 3+1 minutes.
5. Each team will be subjected to two interjections (one by the judges and one by the audience).
6. Top three teams will qualify for the final round.
7. The final round is an extempore round.
8. Each speaker will get 2 minute thinking time and 2 minute speaking time.
9. One interjection by the judges.
10. The decision of the judges will be final.
VENUE:
13th March,2016(Day 3)
Conference Hall
Timings:10.00 am-1.30 pm


System
T
Creates an instance of system.
Entities of system
Hooks of system
Active of system
Debug of system
Frame of system
List of system
Executes system
execute
Ticks system
Updates system
Generated using TypeDoc
System
T


2019.09.28 Step-6 StudyThe step-6 is mainly talking about how deal.ii is doing adaptive mesh refinement. We can run the default code and derive the re ...
Deal.ii-step-5
2019.09.25 Step-5 StudyThe step-5 is mainly talking about how deal.ii is doing loop for simulations with multiple mesh refinements. We can run the def ...
Deal.ii-step-4
2019.09.24 Step-4 StudyThe step-4 is mainly talking about how deal.ii is dealing with problems with different dimensions by its featured dimensionless ...
Deal.ii-step-3
2019.09.23 Step-3 StudyThe step-3 is the first for really calculating something in Deal.II and mainly talking about the solving process of the Poisson ...
[Copy]Direct and iterative solvers
Completely copy from: https://caendkoelsch.wordpress.com/2018/11/29/direct-vs-iterative-solvers-in-fem/ Direct vs. iterative solvers in FEMNovember 29 ...
Deal.ii-step-2
2019.09.21 Step-2 StudyThe step-2 is mainly talking about the degree of freedom (Dof) of a FEM problem. First, let’s generate the default mesh, where ...
Useful_links
I will record some useful links which inspired or are inspiring me.I thank every author for his or her valuable information! Deal.ii tutorials and oth ...
How to insert pictures in your blog
Directly copy from https://liolok.github.io/en/How-to-Add-Image-to-Hexo-Blog-Post/
1 | ![An Imgur Image](https://i.imgur.com/ucHPX7L.gif "An Imgur Image") |
Hexo usage useful instructions
[Reprint] https://godweiyang.com/2018/04/13/hexo-blog/by weiyang Hexo article password:https://github.com/MikeCoder/hexo-blog-encrypt/blob/master/Read ...


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Israel Pounded By Rockets From Gaza. – The Walter Bingham File [audio]
by Israel News Talk RadioIt is inconceivable that this Middle East superpower should be so humiliated by terrorists, that our kindergartens have to be held in shelters.
Hear: Walter’s assessment of how it should have been handled.
Who: Is the enemy within?
And: The Hypocrisy of the Court Of Justice Of the European Union and how they surrendered to BDS pressure.
Do You Remember: The first night alone with your newly wedded wife? Or – A must read kosher book for newlyweds.
All About: The anniversaries of Kristallnacht, Armistice Day, the Rabin and Kahane murders and the fall of the Berlin wall that all happened during this week years ago.
Also: German parliamentarian dismissed for Anti-Semitism. Far right AFD (Alternative for Germany) party is third largest in the lower house of the German Parliament.


EBD - Easton Bible DIctionary
Boanerges
BOANERGES
"BOANERGES "sons of thunder, a surname given by our Lord to James and John (
Mark 3:17
) on account of their fervid and impetuous temper(
Luke 9:54
).
"BOANERGES "sons of thunder, a surname given by our Lord to James and John (Mark 3:17) on account of their fervid and impetuous temper(Luke 9:54).


Preparation time
less than 30 mins
Cooking time
over 2 hours
Serves
Serves 4
For the ribs, preheat the oven to 220C/430F/Gas 7.
Season the beef ribs with salt and freshly ground black pepper and dust with half of the flour. Place them in a roasting tray with the onion, celery and carrot and roast in the oven for 30-40 minutes, turning every so often, until nicely browned.
When cooked, transfer the vegetables and ribs to a heavy-bottomed saucepan, leaving the fat and juices behind, and set aside.
Put the roasting tray with the fat and juices over a low heat. Add the remaining flour and the tomato purée and cook for one minute, stirring. Gradually add the beer and beef stock, stirring well to avoid lumps forming. Bring to the boil.
Pour the beer gravy into the pan with the ribs and vegetables and place over a medium heat. Add the bay leaf, thyme, garlic and peppercorns. Season with salt and freshly ground black pepper and cover. Bring to the boil, then lower the heat and simmer gently for 1½-2 hours, or until the meat is tender: it should be almost falling off the bone without being too soft.
Remove the ribs and vegetables with a slotted spoon, discarding the vegetables. Continue simmering the sauce until it reduces and thickens to a rich gravy-like consistency. You can add a little cornflour mixed in water if it's not thick enough.
For the salsify, place the salsify in a saucepan with salted water and bring to the boil. Lower the heat and simmer for 6-8 minutes, until tender.
Drain and toss in the butter and parsley and season with salt and freshly ground black pepper to taste.
For the bashed neeps, place the turnips or swede into a saucepan of water and bring to the boil. Reduce the heat and simmer for 10-15 minutes, until tender.
Drain and return to the pan. Add the butter. Mash the turnips to a purée with a potato masher then season with salt and freshly ground black pepper.
To serve, reheat the ribs in the sauce. Serve with a spoonful of bashed neeps, the salsify and a generous spoonful of sauce.


Using ProtParCon in terminal¶
Check ProtParCon command-line toolsets¶
After ProtParCon has been installed, you should also have six command-line tools installed, they are: msa, asr, mlt, aut, imc, and sim. To check the availability and usage of any of these command-line tools, you can type the name of the command-line tool with ‘-h’ flag in a terminal to see the usage page, for example:
$ msa -h
The above command should print out the usage of msa without any error. The ‘-h’ flag can also be used to check the usage for other commands. We suggest you use this flag to display the usage of the command and learn how to use it from its usage.
Using toolsets in terminal¶
By checking usage of command line tool, it should be very easy for you to use the toolsets shipped by ProtParCon. For example, the following command will align a sequence file named seq.fa using MUSCLE and save the alignment output to a file named alignment.fasta:
$ msa muscle seq.fa -o alignment.fasta
In the following example, imc is used to automate sequence alignment, ancestral states reconstruction, sequence simulation, and identify parallel and convergent amino acid replacements both in the reconstructed ancestral sequences and the simulated sequences:
$ imc seq.fa tree.newick -l muscle -a codeml -s seqgen
After running the above command, there are several files stored in your current work directory. You can get the information about identified parallel and convergent amino acid replacements in file imc.counts.tsv and file imc.details.tsv.
All command-line tools have nearly the same signatures as the equivalent functions in python module, refer to the usage of each command and the examples showing the usage of the equivalent functions, it should be very easy for you to use the command-line toolsets.
In the following part, we list examples of using command-line commands to do the same work that we have done in the ProtParCon python usage part. We are not going to repeat the details of each example, if you have any question about to details, see ProtParCon python usage part.
Align sequences using MUSCLE and save the alignment output to a file named seq.muscle.fasta (default name):
$ msa muscle seq.fa
Align sequence using MAFFT and save the alignment output to a FASTA format file named ‘seq.mafft.fasta’ (default name):
$ msa mafft seq.fa
And this will align the same sequence with Clustal (Omega) and save the alignment to a FASTA file named ‘seq.clustal.fasta’:
$ msa clustal seq.fa
Note
The above example assumes that you have system wide Clustal Omega installed and the string clustal point to the executable of Clustal Omega. If your Clustal Omega is not system widely installed, or the path to its executable is not clustal, change it accordingly.
Align sequence using MUSCLE and save the alignment output into a file name alignment.fasta:
msa muscle seq.fa -o alignment.fasta
Infer ML tree using IQ-TREE and save the best ML tree to a file named msa.IQ-TREE.ML.newick (default name):
$ mlt iqtree msa.fa
Infer ML tree using RAxML and save the ML tree to a file named ‘msa.RAxML.ML.newick’ (default name):
$ mlt raxml msa.fa
Infer ML tree using PjyML and save the ML tree to a file named ‘msa.PhyML.ML.newick’ (default name):
$ mlt phyml msa.fa
Infer ML tree using FastTree and save the ML tree to a file named ‘msa.FastTree.ML.newick’ (default name):
$ mlt fasttree msa.fa
Infer ML tree using RAxML and save the tree into a file named tree.newick in the same directory of the alignment file with ‘-o’ option:
$ mlt raxml msa.fa -o tree.newick
Infer ML tree using LG model with 8 Gamma categories accounting for among-site rate variation and estimating ML base frequencies of 20 amino acids via PhyML:
$ mlt PhyML msa.fa -o tree.newick -model LG+G8+F
The same as the above example, use ‘-g’ and ‘-f’ options:
$ mlt PhyML msa.fa -o tree.newick -m LG -g 9, -f estimate
Infer ML tree with a start tree and/or constraint tree via start_tree and constraint_tree options:
$ mlt raxml msa.fa -o tree.newick -m LG+G8+I -p
/path/to/the/start/tree/file -q /path/to/the/constraint/tree/file
Reconstruct ancestral states using CODEML and save the ancestral states output to a file named msa.codeml.tsv (default name):
$ asr codeml msa.fa tree.newick
Reconstruct ancestral states using RAxML and save the ancestral states output to a file named ‘msa.raxml.tsv’ (default name):
$ asr raxml msa.fa
Reconstruct ancestral states using RAxML and save the ancestral states output to a file named ancestors.tsv via ‘-o’ option:
$ asr raxml msa.fa -o ancestors.tsv
Reconstruct ancestral states via CODEML using WAG model (-model option):
$ asr codeml msa.fa tree.newick -m WAG
Reconstruct ancestral states via RAxML using ‘WAG’ model:
$ asr raxml msa.fa tree.newick -m WAG
Reconstruct ancestral states using LG model with 8 Gamma categories and a ML estimate of base frequencies of 20 amino acids via RAxML:
$ asr raxml msa.fa tree.newick -m LG+G8+F
Do the same thing as the above example, but use ‘-g’ and ‘-f’ options:
$ asr raxml msa.fa tree.newick -m LG -g 8 -f estimate
Use a specified model (or matrix) file along with complicated modeling information for ancestral states reconstruction:
$ asr codeml msa.fa tree.newick -m /path/to/my/own/model -g 8 -f estimate
Note
The model (or matrix) file needs to be in the right format required by ASR programs, before use the model file, check the manual for your ASR program to make sure you model file is in the right format.
Simulate sequences in the simplest way:
$ sim evolver tree.newick
Use Seq-Gen to simulate 200 protein datasets with the length set to 500 amino acids and substitution model set to LG with 8 Gamma categories to account for among sites rate variation:
$ sim seqgen tree.newick -l 500 -n 200 -m LG -g 8
Use Seq-Gen to simulate 200 protein datasets with the length and base frequencies of 20 amino acids extracted from a multiple protein sequence alignment file:
$ sim seqgen tree.newick -n 200 -m LG -g 8 -r
/path/to/the/multiple/sequence/alignment/file -f estimate
Topology test (AU test) using aut:
$ aut iqtree msa.fa tree.newick -m WAG
Identify parallel and convergent amino acid replacements using ancestral states reconstruction generated by ProtParCon:
$ imc path/to/the/ancestral/states/file


Recently, researchers often fail to reproduce published study results for
|
[Kaiser 2015] |
The Reasons
|
of sequential and linked
“The two important features of the provenance of a data product are theancestral data product(s) from which this data product evolved, and the process oftransformation of these ancestral data product(s), potentially through workflows, that helpedderive this data product. ”
[Simmhan et al. 2005]
Visual analysis of
a web-based data
|
Refinery Workflow Visualization
refinery-platform.org
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Workflow
Dataset + multiple analyses =>
Interactive provenance
from the perspective of an Analyst
|
Node-Link
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Dynamic Graph Layout*
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Filtering
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Aggregation |
Motif Compression |
Degree-of-Interest |
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Analysis Timeline
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Glyph Design
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Path Highlighting*
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Workflow Level |
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Subanalysis Level |
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Analysis Level |
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Subanalysis Level |
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Analysis Level |
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Layer Level |
Discover analyses with the
Layer analyses with the
Compute deviating amount of incoming or outgoing links and subanalyses.
Subanalyses
Analyses
Layers
Every
| General interest: |
|
(v,w) <- [0..1] |
| User actions: |
|
v <- [0,1], w <- [0..1] |
|
Adjust weights: |
DOI(n) affects node visibility: |
Figure shows the visualization state after step 4
Won
Analysts are now able to
| Stefan Luger | Holger Stitz | Samuel Gratzl | Nils Gehlenborg | Marc Streit |
Please see thesis bibliography.
Provenance graphs
Filter options: (a) blend, (b) hide.
Graph shows blend and hide for filter option (c).
The
The user wants to investigate the files and transformations leading to a correctly reproduced result.


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Vanessa M. Stewart
Full Stack Developer | Learning and Ed Tech Designer


Games Independent Study - Project Info
–
I and two of my classmates made 5 small games over the course of a 10 week quarter. Generally, I did the bulk of the programming and they did the art and game design, but our roles overlapped quite a bit.
Week 1: Get the Ball Rolling
For the first week we made a simple splitscreen racing game (2-4 players), based on the UE4 template.
–
Week 2-3: SubMarine
Over the next two weeks we made a first-person point and click escape room. This project had a lot of moving parts, (probably too many for a two week project), and we had to trim down our original plan a bit to get it done in time.
–
Week 4-5: Sticky
Next, we made a 2-4 player splitscreen top-down shooter where the goal was to damage the pillar in the center of the map, which could only be done after defeating a certain number of minions. Minions would spawn teamless and chase after & shoot the nearest player. Teamless minions can be captured once by a player using their cooldown ability. They then would target any player other than the capturing player.
–
Week 6-7: Ah, Rhythms
Next we made a two player rhythm game with a call and response mechanic. First, each player would get 16 beats of blanks (with a randomly selected pattern of blanks and rests), which they would use to choose the buttons that the other player would have to press during the response phase. Missing a note in either phase damages the player, and getting it right damages the opponent (a more perfectly timed hit does more damage). Failing to select a note for a blank in the call phase also selects a random note to use in the response phase.
–
Week 8-10: Garrote Simulator
The last game we made was a VR game we jokingly called “Garrote Simulator”. 3 different types of “atoms” fly toward the player, green, red, and yellow. Red atoms must be punched, green must be sliced using the wire between the player’s hands, and yellow atoms must be shot by using both hands to aim and pulling a trigger. Using the correct action gives the player points, using the wrong action or letting them hit you subtracts them. After 2 minutes the round is over and you have the option to add your score to a high score chart. Of all the games we made, this was the most popular in playtesting. It was a lot of fun to play, and the high score chart stirred up quite a bit of friendly competition.


4-AcO-DMT: An oxymoronic enigma of a trip.
This is the full account of my first encounter with this incredible psychedelic substance.
So it is St Patricks day, and the need to party is in the air. The original plan was to attend the parade with my girl friend and just get drunk. But when we get there it just seems blah, totally not our scene. So after grabbing a bite to eat we head back to the house to take this stuff we have had put up for a week. We each have 1 15mg capsule. We arrive at the house and drop them at 1:15 PM.
So we sit around watching some tv waiting for the come-up, for the first 30 min we both feel virtually nothing other than the feeling of having a head full of acid, all colors seem sharper more vibrant. Her room is tinted pink to me. At 1:50 my girlfriend begins to laugh uncontrollably. As soon as she can get her laughing under control it starts again. This goes on for about 20 min. I don't understand yet why, because I feel nothing other than the slight mental difference brought on by the initial kick.
Then suddenly she begins to cry, the come up is to strong to intense. She does not know what to make of it. She feels responsible for me not being able to feel mine. I take her in my arms and do my best to assure her she is going to be ok, and that it is not her fault. After about 15 min she calms down. It is now about 2:30 and I still feel nothing. I call my friend who I got it from and ask him whats up, he assures me that I will feel it sooner or later. Just try to change your environment go some where comfortable, he tells me.
 My girl friend informs me she is trippin face, like nothing she has ever felt before. At 3:00 we go sit on the front porch and talk for a little while. About 3:30 we are still sitting there and I see the tree trucks begin to bend in a slow fluid motion, very subtle, slight wiggles. The colors enhance a more. Everything is beautiful out here. Visuals intensify a little bit.
We go back into the house, The visuals change when I walk in the door. While out side the visuals are more like mushrooms, inside they become more acid like. We go to her room and I realize I am coming up hard FINALLY.
T-3:50 We decide we really want to go get my girl friends check and go to the movies see something crazy and exciting. Who is going to drive? I say I can drive and bust out laughing. My girlfriend is like haha ok you cant drive. At this point I am still not going hard enough to have difficulty driving. Yet every time I try to stress to her that I can drive if we are going we need to go NOW. But every time I try to say this with any seriousness I begin to laugh uncontrollably. I was able to get the words I can drive out one time and thought I had done it, but then began to laugh after about 3 seconds.
While this is going on the visuals steady get stronger. Finally I give in realizing that I had waited to long and leaving at this point would insure that I hit my peak while driving.  We both take a few hits 2-3 a piece. The high is very subtle but I can feel the weed making the trip stronger every time I exhale my hit.
T-4:20 We are both laid on the bed just talking to each other. We are both trippin fuckin face at this point. I am propped up on my hand, about 6 inches from my girl friends face, just having a normal conversation. All of a sudden the visuals begin to peak, her face begins to change shape, colors morphing, wiggles and tracers, craziness. Then it all begins to blur and melt, going fuzzy fuzzy fuzzy, I can feel myself slipping off of reality.
My girl friend begins to cry uncontrollably, I assure her she has done nothing wrong and try to explain what happened to me the best I could at the time. She does not understand thinks I saw something horrible in her, but that is not the case. After comforting her back to stability I feel as if I have to get out of this room, Its tiny I am beginning to feel trapped. The room is scary the trip begins to turn dark. I feel overwhelmed like there is a huge energy inside me that is going to explode any second. Everything is happening so fast.
I began to have an extreme flash of emotion and thought. Fear and anxiety, peaceful , calm, enlightened, scared, clensed, what is going on in my head. How can I feel these negative and positive things simutaniously like this. What a strange feeling. I feel as if I took to much while at the same time not enough. I want it to be over but never end. I am terrified, happy, refreshed, stressed, freaking, losing my mind. Clensed, renewed, weak and vulnerable reborn. I don't like it not at all, but I love it I cant get enough.
 I am amazed. This is so incredible. Everything looks wet but dry, so shiny and smooth. The colors morph and fade into different shades and hues. Geometric patterns sliding and shifting in and out of each other. Everything works together as one but on an individual level. As chaotic and intense as everything is, I feel as if I have discovered the big mystery to life. Like I have the little answer to the BIG question.
I return to sit on the porch with my girl friend and we begin to chat about this and that, watching the incredible show going on in the yard. Her mom leans out the door asking one of us to get the mail for her. I jump on the opportunity for another adventure through the bright and colorful yard. When I return her mother is advising us to not get her check today (as if we could at this point HAHA) Because she would have to cash it at a money shark joint as we don't have a bank account ya know.
But it was reported earlier that week that a gang had an initiation for their new members.  Hearing this deeply disturbed me, sickened me even. I begin to think about what if we were there and this really did happen, what if they shot my girl. I can see my self ripping through these gang bangers with my bear hands. How could anyone be so heartless.
It is about 5:15 now, I begin to wonder how to get out of going to work at 6. This stuff was suppose to be over with by now. I start freakin now, if it had worked on the time frame that had been given to use by the guy we got it from I would be fine to go to work in an hour. We sit around for about 30 minuets still tripping really hard trying to work an excuse. Because Im trippin it becomes complicated. I began to try to make these elaborate excuses that would never make sense if I wasn't fucked up, adding in extra details to the story that really make no sense.
Finally I get my story straight call in and tell them the car wont start I am about 30 miles away, you guys can send some one to come get me but I'm not walking. They accept and everything is all good again. We go in and lay down on the bed. My mind still racing with crazy thought patterns, visuals still going off everywhere I look. But they are beginning to calm down a little. Yet I feel as if I am still coming up hard, Like I am about to repeat the whole trip over again. This idea both excites me and scares me.
T-6:00 I begin to come down a little more quickly now but it is smooth and subtle still. I don't know the exact time in which I fully came off the trip. It was more like I was tripping one moment and not the next, but didn't notice that I was not until way after the fact.
Trip ended around 7:30 completely just a rough estimate though. After the trip I felt very refreshed. It had done its job, and very well at that. consciousness expanded
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


The program produces engine replacement probabilities that match those shown in Figure 3 on page 1025 of the paper. The code segments are not exactly the same as in the Ox file, which includes additional code beyond the basics shown here.
The paper models the decision to replace a bus engine as a function of mileage and unobserved IID shocks. Replacement is a one-time cost that resets the odometer to x=0. The cost of monthly maintenance increases linearly with x.
See RustEmet1987mle to see estimation using data as in the original paper.
If x = 88 or 89 then the increment probabilities changed accordingly.
Semi-Endogenous: none
Endogenous: none
Utility:
The cost of regular maintenance or engine replacement.
U = -[ dRC + (1-d)θ1mx ] + n + zd (5.1) p. 1015
zd is the standard IID extreme value shock. (In the paper it is denoted ε)
Parameters:
Parameter
Row 1
Row 2
δ
0.9999;
0
RC
10.07
7.6538
θ1
2.293
71.5133
θ3
< 0.3919,0.5953>
< 0.3919,.5953>
The article suggests x enters the code in terms of thousands of miles (0 to 445,000), but I
was not able to replicate results based on that. I was able to replicate replacement probabilities only when x
takes on values between 0 and 89.
RC is the bus engine replacement cost. θ1 is the operating cost given
mileage x. The parameter m=0.001 is a scaling factor, which appears to rescale odometer
readings. However, results were replicated when x is the bin number (0 to 89) not the odometer category.
Normalization
The paper refers to a normalization when displaying V() (see Figure 2, p. 1025). The normalization is not
explained, but my calculations seem to explain why it is necessary. The closed form extreme-value value function
iteration involves taking exponents of negative values (operating costs). When δ=0.9999 and n=0
above the result is numerical overflow in exp() With n>0 set properly the arguments are
kept closer to 0. In the replication the normalization n is set to the maintenance cost for
x=89/2, the median mileage category.
code>Rust class embeds an infinite horizon clock and a single binary action (d). It defines constants and declares a static member x>. Zurcher then declares the required method Utility().
Utility must have that name and cannot be static.
The form of the expression returned as the value should look related to the mathematical expression U() above. The parameters are static members of the class (they are the shared by each state θ). The current value of x is accessed as CV(x). The value of x is set to be the correct value at θ before Utility() is called.
Utility must return a column vector of values corresponding to the feasible action vector A[Aind]. In this case the action variable is simply d, and engine replacement is always feasible.
In mathematical notation, we would write the choice set as \(\alpha \in A(\theta)\). Rather than accessing that directly, the column of values for d is accessed using CV(d). This makes the code closer to the notation and more general since it will work even if other actions are added to the model.
The tasks required to set up and solve the model are in Zurcher::Run(). Like Run(), a user's program must do these tasks in order:
Call Initialize(new Zurcher()) for the parent DDP class. Since Zurcher is of type Rust, the program is calling Rust::Initialize(), which will set the clock and add the binary action variable to the model, d.
Create state variables (using the new operator) and add them to the model.
Call CreateSpaces();.
The state variable x is of type Renewal, which captures the process in Rust (1987) but is more general than in the empirical specification.
To properly capture the transition it must be coordinated with the choice variable d. To do this d is passed to new Renewal() as an argument, along with how many categories x takes on and the vector of increment
probabilities. The length of the vector determines how different states are feasible next period. In the empirical work, 3 of 90 odometer readings are feasible next period.
Once CreateSpaces() has been called new actions and new state variables cannot be added to the model because the state spaces have be set up already.
To solve the model the following steps are carried out
Set the discount factor δ. (This can be done at any time after Initialize() before solving the model with that δ.)
Create a new instance of a solution Method, such as value function iteration.
Call Solve() for the solution method to compute \(V(\theta)\) and \(P^\star(\alpha|\theta)\).
Use the results, then if necessary change some settings or parameters and solve again.
The code inside the for() loop solves the model twice, for Row 1 and Row 2 parameters.
For each set of parameters the value function is solved through Bellman iteration by Emax->Solve(). It calls the Output() routine to capture results to be printed and compared with Figure 3 in the paper.
The values of EV do not match those shown in Figure 2 of the paper. This is due to Rust using a different normalization than the value used here. However, the choice probabilities appear to match very well those shown if Figure 3, as the image below suggests.
Comparison to Figure 3
The paper labels the x axis as thousands of miles since engine replacement. This means that x should take on values 0, 5, ..., 445. It also suggests that the factor on costs scales 300,000 to 300. However, using this scaling I was not able to replicate the choice probabilities. Only when x takes on values 0, ..., 89 and the scaling factor still equals 0.001 was I able to reproduce the choice probabilities.
Another use of the model is to simulate data by creating a Panel object and simulating a sample of data from the process.
The call to Panel::Simulate creates a data set of 10 buses observed over 400 months each. The initial mileage for each bus will be drawn from the ergodic distribution.
The output file shows a simulated panel of buses, including the months and mileage at every replacement. The simulate routine returns a panel of observations.
But in the case of this model it is sufficient to show only months when an engine is replaced, the age of the bus when it occurs and the mileage reading. So the replication program deletes simulated months when no replacement occurred. It also appends the row number for the model.
For example, the first simulated bus data looks like this
Simulated data
Simulated data
Fxed path t n T Aj| s11 s21 x t t' r f |ai| a
0 0 92 50 0 0 0 0 50 0 0 0 0 1 1
0 0 164 44 0 0 0 0 44 0 0 0 0 1 1
0 0 248 53 0 0 0 0 53 0 0 0 0 1 1
0 0 344 69 0 0 0 0 69 0 0 0 0 1 1
Bus 0 had four replacements, which occurred in months 92, 164, 248 and 344 of its lifetime. The mileage on the odometer at replacement was in the interval [5000x,5000(x+1)].
The column labeled Const is a placeholder for exogenous state variables.
If the model has no such variables then DDP inserts a constant there. This makes it possible to construct indices into the state space without requiring a check of zero dimensions.
Since the model is stationary there is no separate time variable. If this were a finite horizon or non-stationary environment then t would contain the age of the process.
DDP allows for random aging so t would not necessarily be equivalent to the months column.
The t' variable (t″ in the notes) is used during iteration on the value function. During simulation it is not used and would always have the value 0.


Team 3182 is currently made up of high school students representing two West Hartford, Conn. high schools. Team members build relationships with different backgrounds who they may not have necessarily met had it not been for the team. Team Mentors are industry professionals representing various industries and occupations. Working with these mentors provides team memebers with some of the first tastes of networking and helping them build connections that will help them as they go on to college, internships and eventually the working world.


2017-2018 Prasenjit Bhavathankar P. Bhavathankar, S. Sarkar, and S. Misra, “Optimal Decision Rule-Based Ex-ante Frequency Hopping for Jamming Avoidance in Wireless Sensor Networks,” Elsevier Computer Networks Journal, 2017, DOI:10.1016/j.comnet.2017.03.009. P. Bhavathankar, S. Chatterjee, and S. Misra, “Link-Quality Aware Path Selection in the Presence of Proactive Jamming in Fallible Wireless Sensor Networks,” IEEE Transactions on Communications (Manuscript ID:TCOMTPS-17 0025.R1;Accepted on July 23, 2017) 2016-2017 Kailas Devedkar International Journal of Advanced Research in Computer Science and Software Engineering Volume 6, Issue 6, June 2016,“Detection of VM-to-VM Attack using Hypervisor Statistics” Nivedita Prem, Kailas Devadkar International Conference on Communication and Signal Processing, April 6-8, 2017, India, “Smart Parking System Using IoT & Cloud”, Rajiv Sukhwani, Priyam Shah, Malhar Thombare, Kailas Devadkar ICECCT 2017 at Coimbatore, Tamil Nadu during February 22 – 24, “ Cloud-based secure smartcard healthcare monitoring and tracking system”, Harshal Bharatkumar Parekh, Kartik Moudgil, Ria Maheshwari, Kailas Devadkar Information Computing and Communication (ICSPICC-2016), in collaboration with Instituteof Electrical and Electronics Engineers (IEEE), “Scheduling of Task in Collaborative Environment using MobileCloud”, Nikhil P. Dange, Kailas Devadkar DOI: 10.1109/ICGTSPICC.2016.7955367 Prasenjit Bhavathankar Bhavathankar, A. Mondal, and S. Misra, “Topology Control in the Presence of Jammers for Wireless Sensor Networks,” Wiley International Journal of Communication Systems, 2017, DOI:10.1002/dac.3289. Rupali Sawant Akshay Raul,Atharva Patil,Prem Rheja,Rupali sawant “Knowledge Discovery,Analysis And Prediction in healthcare using Data Mining And Analytics”2nd International conference on Next Generation Computing Technologies (NGCT-2016) Dehradun,India 14-16 October 2016,978-1-5090-3256-3/16 DOI: 10.1109/NGCT.2016.7877462 Sheetal Chaudhari ICOEI201 , “Dynamic load balancing strategy in Software Defined Networking”, Saket Bhalekar ,Mrdvika Iyer, Gargee Mehta, Sheetal Chaudhari ICSPICC,”An improved Round Robin CPU Scheduling Algorithm”, Harshal Parekh, Sheetal Chaudhari, DOI: https://doi.org/10.1109/ICGTSPICC.2016.7955294 ICECCT ,“Simulation of a two head disk Scheduling Algorithm” Harshal Parekh, Apurva Saksena, Anushka Ringshia, Sheetal Chaudhari Renuka Pawar “Multivariate correlation analysis for network intrusion detection”, Sumedh P Hanmante, Uma Deshattiwar, Renuka Pawar, International Journal of Scientific & Engineering Research, Volume 7, Issue 4, April-2016 ISSN 2229-5518 Jignesh Sisodia “Virtual Reality: A Possible Technology to Subdue Disorder and Disability” Aditi Gavhane, Gouthami Kokkula, Shubhangi Shinde, Taufiq Monghal, Jignesh Sisodia, IEEE ICGTSPICC 2016, DOI: 10.1109/ICGTSPICC.2016.7955361. Swapnali Kurhade Pratik Solim, Ankit Sagwekar, Rishikesh Patil, Swapnali Kurhade,”Advising Projects to Students using Data Mining and Natural Language Processing”,Volume 7, Issue 5 of IJARCSSE. DOI: 10.23956/ijarcsse/SV7I5/0146, ISSN: 2277 128X 2015-2016 Dr. Radha shankarmani 1.“Architecture for Internet of Things to minimize human intervention”, Shanbhag R., Shankarmani, R., Advances in Computing, Communications and Informatics (ICACCI), 2015 International Conference in Aug. 2015 DOI : 10.1109/ICACCI.2015.7275969 2.“Digitization and Paperless Processing through the use of mobile imaging Technology, Jinal Shah, Aashika Shetty, Sukumar Gaonkar and Radha Shankarmani ICCPCT 2016, February 2016 DOI: 10.1109/ICCPCT.2016.7530128 3.“Web Security: Countermeasures for Application level Attacks” by Natasha Raul and Radha Shankarmani in International Journal of Engineering Inventions (IJEI), April 2016. Kailas Devedkar 2016 3rd International Conference on “Computing for Sustainable Global Development”, 16th – 18th March, 2016 Bharati Vidyapeeth’s Institute of Computer Applications and Management (BVICAM), New Delhi (INDIA) “Provisioning Resources Elastically in IoT Cloud Systems” Parth S. Panchmatia, Kailas K. Devadkar International conference on Global Technology Iniatives 29-30 March 2016 “Continuous Aunthentication using Biometric Technique to prevent session hijacking” Jignesh Patel, Abhijeet Sonaje, Kaustubh Vaidya , Kailas K. Devadkar International conference on Global Technology Initiatives 29-30 March 2016 “Analyze and Predict crime using data mining” Viraj Shah, RiddhiJangle, Sneha Mehta, Kailas Devadkar 2016 International conference on Innovations in information Embedded and Communication Systems (ICIIECS’16) 17 and 18 March 16 “Cloud Insider Attack Detection System” Shruti D Gupta, Megha B Upadhyay, Kailas K Devadkar International conference on Global Technology Initiatives 29-30 March 2016 “On Demand Distributed Computing on Mobile Devices “ Aishwarya Kamat, Nidhi Panpalia, Akshata Rao, Kailas Devadkar. IEEE International Conference on Research in Computational Intelligence and Communication Networks “Partioning Android Mobile Application For Improving Execution Time “Kailas K. Devadkar and Dhananjay R. Kalbande 20-22 Nov. 2015 DOI:1109/ICRCICN.2015.7434277 Sheetal Chaudhari Sheetal Chaudhari, Radha S. Mani, Pooja Raundale, “SDN Network Vitualization Survey”, selected for IEEE conference WiSPNET 2016, 23 Mar-25 Mar 2016, Chennai, India DOI: 10.1109/WiSPNET.2016.7566213 Malhar Thombare, Rajiv Sukhwani, Priyam Shah, Sheetal Chaudhari, Pooja Raundale, “Implementation of multilevel Feedback Queue Scheduling”, selected for IEEE conference WiSPNET 2016, 23 Mar-25 Mar 2016, Chennai, India. DOI: 10.1109/WiSPNET.2016.7566483 Honey Gupta, Aveena Kottwani, Soniya Gogia, Sheetal Chaudhari, “Text Analysis Retrieval of Text Data”, selected for IEEE conference WiSPNET 2016, 23 Mar-25 Mar 2016, Chennai, India. DOI: 10.1109/WiSPNET.2016.7566241 Rupali Sawant Rupali Sawant, Nikhil Gaiwad, “Hybrid Prediction Method for Pregnancy Data Set” NGCT 2015, IEEE Conference.DOI:1109/NGCT.2015.7375253 M Godambe, S Mundhada, H Singadia, Rupali Sawant “Digital Image Forgery Detection”, 2016 IEEE Sponsored 3rd international conference on innovations in information Embedded and communication Systems (ICIIESCS’16),ISBN-978-1-4673- 8207-6) Rupali Sawant, Shravan P, Aishwarya S, Aarti K “Intelligent Heart Disease Prediction System”, 2016 IEEE Sponsored 3rd International conference on innovations in information Embedded and communication Systems (ICIIESCS’16),ISBN-978-1-4673-8207-6) Rupali Sawant, Aarti T., Chirag Mahimkar, Vishal Bhandari “Birth Defect Predication during Pregnancy” 2016 IEEE Sponsored 3rd International conference on innovations in information Embedded and communication Systems (ICIIESCS’16),ISBN-978-1-4673-8207-6) Nikhil Gaikwad, Rupali Sawant,“Survey on algorithm used for management zone delineation for agriculture” is presented in IEEE-ICCIC Dec-2015 Madurai and published in IEEE Explorer. DOI: 10.1109/ICCIC.2015.7435703 Nikhil Gaikwad, Rupali Sawant, “Simple Approach to Management Zone Delineation using autoKrige Function” is presented in IEEE-ICIIECS’16. Nikhil Gaiwad, Rupali Sawant “Analyzing Kriging Method and Creating an Application Using Kriging” is presented in IEEE-ICCCBDA July 5-7, 2016, Chengdu, China. DOI: 10.1109/ICCCBDA.2016.7529553 Swapnali Kurhade Sharvari Nadkarni, Tejas Karwa, Parth Panchmatia and Swapnali Kurhade, ” Semi Natural Language to Programming Language Interpreter”, in IEEE sponsored International Conference on Advances in Human Machine Interaction (HMI – 2016), March 03-05, 2016. DOI: 10.1109/HMI.2016.7449190 Akshay Bhatia, Am Bharadia, Kunal Sawant and Swapnali Kurhade, “Predictive and Corrective Text Input for desktop editor using n-grams and suffix trees” in IEEE sponsored International Conference on Advances in Human Machine Interaction (HMI – 2016), March 03-05, 2016. DOI: 10.1109/HMI.2016.7449191 Aashay Kedar, Rinkal Shah, Noopur Parikh and Swapnali Kurhade, “Multi-Sentence Compression-Construct knowledge using paraphrased text and Vertical Crawling”, in IEEE International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET 2016), 23-25 March, 2016 DOI: 10.1109/WiSPNET.2016.7566565 Renuka Pawar “Multivariate correlation analysis for network intrusion detection”, IJSER 2016, Sumedh Hanumante, Uma Deshattiwar, Renuka Pawar, ISSN 2229-5518,2016 Jignesh Sisodia 1. Mihir Ghandat, Kinjal Kotadia, Dhananjay Nikam, Jignesh Sisodia, “ Adaptive Learning Based Handwriting to Digital Text converter” ICGTI, Volume 5,Issue 1, March 2016 2. Bhushan Malgaonkar, Arunkumar Pal, Shubham Sawant, Jignesh Sisodia “Sign Language Interpreter using hand gesture recognition” ICGTI, Volume 5, Issue 1, March 2016 Ashosh Mestry, Pratik Kulkarni, Siddhesh Mhatre, Jignesh Sisodia “Image Matching Using Contextual Features”, IJIERE, VOLUME 3, ISSUE 3, Mar 2016 2014-2015 Dr. Radha Shankarmani Aditi B. Raut, Radha Shankarmani, “Web Logs Analysis for Finding Brand Status”, IOSR Journal of Computer Engineering (IOSR-JCE), e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 16, Issue 4, Ver. VI (Jul – Aug. 2014), PP 78-85. DOI: 9790/0661-16467885 Radha Shankarmani, S. S Mantha and Vinaya Babu, “A Framework for Reducing Impediments in Agile Projects”, IJCA 2014, Volume 102-Number-11. DOI: 5120/17857-8719 Minati Rath , Dr. Radha Shankarmani, “Real Time Scheduling Model for Distributed Production Centers”, International Journal of Scientific & Engineering Research (ISSN 2229-5518), Sept 2014. Netra Charya, Kushagra Doshi, Smit Bawkar and Dr. Radha Shankarmani, “Intrinsic Plagiarism Detection in Digital Data”, International Journal of Innovative and Emerging Research in Engineering Volume 2, Issue 3, 2015 Neelam Vagal , Priyanka Sonawane , Raksha Jadhav and Dr. Radha Shankarmani, “Vendor Selection Optimization in Supply Chain Management” International Journal of Innovative and Emerging Research in Engineering Volume 2, Issue 3, 2015 Komal Patel, Prarthana Sawant , Madhuri Tajane and Dr. Radha Shankarmani,”Bug Tracking And Prediction” International Journal of Innovative and Emerging Research in Engineering Volume 2, Issue 3, 2015 Abhishek Mishra, Dhara Vyas, Bhishm Narula, Dr Radha Shankarmani, “Addressing Security Concerns in BYOD Through Sandboxing”, IJSRD – International Journal for Scientific Research & Development| Vol. 3, Issue 02, 2015 | ISSN (online): 2321-0613 Kailas Devadkar IJCEA Volume VII , Issue II, August 2014, ISSN 2321-3469 “ Simplifying Attack Timestamp for Forensics in Ecalyptus Private Cloud “ Karan M. Kamble, Kailas K. Devadkar IJCEA Volume VII , Issue II, August 2014, ISSN 2321-3469 “ Mitigating DDOS attack in Cloud Environment with packet filtering using IP Tables “ Rashmi Deshmukh, Kailas K. Devadkar ICAC3 15 Science Direct and Elsevier “ Understanding DDOS attack and Its Effect in Cloud Environment” Rashmi Deshmukh, Kailas K. Devadkar DOI:1016/j.procs.2015.04.245 Rupali Sawant International Journal of Innovative and Emerging Research in Engineering Volume 2, Issue 3, 2015” Plagiarism Detection of Images” International Journal of Innovative and Emerging Research in Engineering(IJIERE) e-ISSN: 2394 – 3343 Sheetal Chaudhari International Journal of Innovative and Emerging Research in Engineering Volume 2, Issue 2, 2015 “Efficient Video Streaming and Data Sharing using Cloud Computing” Suntej Singh, Amodh Pisat, Gajanan Shewale, Sheetal Chaudhari Jignesh Sisodia International Journal of Innovative and Emerging Research in Engineering Volume 2, Issue 3, 2015 “Hand Gesture Recognition System for Media Control” Jennifer DSouza, Swapnil Ghige, Dipraj Acharekar and Jignesh Sisodia. International Journal of Innovative and Emerging Research in Engineering Volume 2, Issue 3, 2015 “Three Level Security Using Cued Click Points in Image Based Authentication” Ankit Aggarwal, Darshil Doshi, Vijay Gore and Jignesh Sisodia. Swapnali Kurhade An Approach to find Target customers using Apriori Algorithm, International Conference on Communication Information and Computing Technology, May12-13, 2015, Global Institutes, Amritsar A Survey on Methodologies for E-Learning Recommender System, IJIERE, e-ISSN: 2394 – 3343, Volume 2, Issue 4, 2015 Forecasting the daily sales of perishable food to reduce spoilage in hypermarket, IJSRD, ISSN:2321 0613, Volume : 3, Issue : 2, April 2015 Dynamic Pricing and Inventory Management for Retail Store using Consumer Transactional Behavior, IJIERE, e-ISSN: 2394 – 3343, Volume 2, Issue 3, March 2015 Rashmi Deshmukh IJCEA Volume VII , Issue II, August 2014, ISSN 2321-3469 “ Mitigating DDOS attack in Cloud Environment with packet filtering using IP Tables “ Rashmi Deshmukh, Kailas K. Devadkar ICAC3 15 Science Direct and Elsevier “ Understanding DDOS attack and Its Effect in Cloud Environment” Rashmi Deshmukh, Kailas K. Devadkar DOI:1016/j.procs.2015.04.245 Richard Joseph “An Intelligent Traveling Companion For Visually Impaired Pedestrian” is presented at International Conference on Circuits, Systems, Information and Communication Technology Applications (CSCITA – 2014), Richard Joseph, Anand Godbole 2013-2014 Radha Shankarmani Ruchi K. Sharma and Radha Shankarmani “Using Service Oriented Architecture for Re-conceiving Quality E Training”, IJCA 2014, Volume 94 – Number 9, Year of Publication: 2014. DOI: 10.5120/16369-5385 Nandita Dube, Larisa Correia, Dhvani Parekh, Radha Shankarmani “Combining content based and collaborative filters in an online musical guide”, IJSER Volume 5, Issue4, April 2014 Edition (ISSN 2229-5518). Kailas Devadkar IEEE Conference ICCIC, “ Optimization of test suite-test case in regression test “Ahlam Ansari, Kailas Kisan Devadkar Dr. Prachi Gharpure DOI:1109/ICCIC.2013.6724206 IJETED issue 4, volume 3, April –May 2014 , “Analysis of Botnet Detection “ Yogita D. Mane, Kailas K. Devadkar Yogita D. Mane, Prof. Kailas K. Devadkar and Dr. Prachi Gharpure,” P2P Botnet Detection Using Network Security” INTERFACE 2014 TEQIP-II Sponsored 3rd International Conference On Network Infrastructure Management Systems, Mumbai, June 2014. Rupali Sawant ” Image Enhancer-Text Extraction from Still Images” ACSE2014,China Sheetal Chaudhari ECCS82-SURVEY OF IBM NAS (NETWORK ATTACHED STORAGE) PRODUCTS, iCARE14,Chennai,India ,May 2014 Prasenjit Bhavathankar INTERFACE 2014, Mumbai “Broadcast enhancement in wireless networks” Vinod Mankar, Rishabh Jain, Arunkumar Gangula, Prasenjit Bhavathankar, Renuka Pawar ICEECMPE –NEW DELHI 2014,AUTHORS- Renuka Pawar, Dhananjay R. Kalbande Girish P. Bhole, RadhaShankarmani,” Risk Analysis for Agile Software Development ” 2012-2013 Radha Shankarmani Radha Shankarmani, S. S Mantha and Vinaya Babu, IJSRP, “Inclusion of e-Assist to increase agile adoption”, IJSRP Volume 2, Issue 11, November 2012 edition. Renuka Pawar and Radha Shankarmani, “Agile methodology adoption – Benefits and Constraints”, IJCA 2012, Nov. 2012. DOI: 10.5120/9361-3698 Kailas Devadkar ICCICT-2012 Mumbai “Grid Computing based Back Propagation Network” 19-20 OCT 12 Ahlam Shakeel Ahmed Ansari, Kailas K. Devadkar DOI: 1109/ICCICT.2012.6398173 IEEE 2012 International Conference on Computational Intelligence and Computing Research “Secure Cloud Mining” Ahlam Ansari, Kailas Kisan Devadkar DOI: 10.1109/ICCIC.2012.6510249 IJOART Volume 2 , Issue 4 April 2013 ISSN:2278-7763. “Mobile Cloud based Compiler: A Novel Framework For Academia” Mahendra Mehra, Kailas K. Devadkar, Dhananjay Kalbande IOSR-JCE ISSN : 2278-0661, 22-23 Feb 2013ISBN: 2278-8727, PP:39-44 “Protection concern in Mobile Cloud Computing _A Survey” Mrs. Yogita D Mane, Kailas K. Devadkar Rupali Sawant Rupali Sawant,K.Parmar,Mehta B.”Facial-feature based Human-Computer Interface for disabled people” Communication, Information & Computing Technology (ICCICT),2012, IEEE Conference. DOI: 10.1109/ICCICT.2012.6398171 Sheetal Chaudhari “Mobile Browser: Opera Mini 7” published as SLP in ICWET 2013,Mumbai. Renuka Pawar IJCA,2012,ISBN 973-93-80870-45-2 ,” Agile Methodology Adoption: Benefits and Constraints”, Radha Shankarmani, Renuka Pawar, S. S. Mantha, Vinaya Babu, DOI: 10.5120/9361-3698 Mahendra Mehra 1. IJOART Volume 2 , Issue 4 April 2013 ISSN:2278-7763. “Mobile Cloud based Compiler: A Novel Framework For Academia” Mahendra Mehra, Kailas K. Devadkar, Dhananjay Kalbande 2011-2012 Radha Shankarmani “ Performance assessment of ASD team using football Rules As Reference”, INDICON , Radha Shankarmani DOI: 1109/indcon.2011.6139390 Kailas Devadkar International Conference and Workshop on Emerging Trends in Technology (ICWET 2011) TECT Mumbai “An optimized Approach to Voice Translation on Mobile Phones” K K Devadkar, R Shankarmani, S Kotian, A Khadpe DOI: 10.1145/1980022.1980060


All eHive pipelines will require the ensembl-hive repository, which can be found on GitHub. As such it is assumed that Git is installed on your system, if not follow the instructions here
To download the repository, move to a suitable directory and run the following on the command line:
git clone -b version/2.4 https://github.com/Ensembl/ensembl-hive.git
This will create ensembl-hive directory with all the code and documentation.
If you cd into the ensembl-hive directory and do an ls you should see something like the
following:
ls Changelog docs hive_config.json modules README.md scripts sql tThe major directories here are:
cd ensembl-hive cpanm --installdeps .
If installation of either DBD::mysql or DBD::Pg fails, check that the corresponding database system (MySQL or PostgreSQL) was installed correctly.
You may find it convenient (although it is not necessary) to add "ensembl-hive/scripts"
to your $PATH
variable to make it easier to run beekeeper.pl and other useful Hive scripts.
export PATH=$PATH:$ENSEMBL_CVS_ROOT_DIR/ensembl-hive/scripts # # (for best results, append this line to your ~/.bashrc or ~/.bash_profile configuration file)
set path = ( $path ${ENSEMBL_CVS_ROOT_DIR}/ensembl-hive/scripts ) # # (for best results, append this line to your ~/.cshrc or ~/.tcshrc configuration file)
Also, if you are developing the code and not just running ready pipelines,
you may find it convenient to add "ensembl-hive/modules" to your $PERL5LIB
variable.
export PERL5LIB=${PERL5LIB}:${ENSEMBL_CVS_ROOT_DIR}/ensembl/modules export PERL5LIB=${PERL5LIB}:${ENSEMBL_CVS_ROOT_DIR}/ensembl-hive/modules # # (for best results, append these lines to your ~/.bashrc or ~/.bash_profile configuration file)
setenv PERL5LIB ${PERL5LIB}:${ENSEMBL_CVS_ROOT_DIR}/ensembl/modules setenv PERL5LIB ${PERL5LIB}:${ENSEMBL_CVS_ROOT_DIR}/ensembl-hive/modules # # (for best results, append these lines to your ~/.cshrc or ~/.tcshrc configuration file)


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A summary of John Doe...
Description...
Description...http://organization.com/
There is no spoon.
Description...http://publication.com
Reference... - Jane Doe


Edwin Chen
Game Programmer
About
Hello!
I'm a computer programmer with 3+ years of (game) programming experience. Currently located in Toronto, ON. Open to relocation within Canada and the USA.
Graduate of Humber College Game Programming, Class of 2017.
On the technical side, I focus mainly on programming core gameplay mechanics and primary functionalities of games. I am also extremely interested in game engine design as it allows total control for the programmers to utilize system hardware and resources.
My game design philosophy is to create every user-interactive game element to be as intuitive as possible. I believe that the easier it is for a player to catch on to a game's mechanics, the better experience it will create for the player to continue playing.
I thrive in creative and collaborative environments where hard work and passion for making great games is valued, and where I can leverage my strong technical background to enhance my designs and team.


Extends the out of memory vectors of 'ff' with statistical functions and other utilities to ease their usage.
| Version: | 0.12.7 |
| Depends: | R (≥ 2.12.0), ff (≥ 2.2-11) |
| Imports: | fastmatch, bit |
| Suggests: | testthat, parallel, LaF, biglm |
| Published: | 2018-11-06 |
| Author: | Edwin de Jonge, Jan Wijffels, Jan van der Laan |
| Maintainer: | Edwin de Jonge <edwindjonge at gmail.com> |
| License: | GPL-3 |
| URL: | http://github.com/edwindj/ffbase |
| NeedsCompilation: | yes |
| Materials: | NEWS |
| In views: | HighPerformanceComputing |
| CRAN checks: | ffbase results |
| Reference manual: | ffbase.pdf |
| Package source: | ffbase_0.12.7.tar.gz |
| Windows binaries: | r-devel: ffbase_0.12.7.zip, r-release: ffbase_0.12.7.zip, r-oldrel: ffbase_0.12.7.zip |
| OS X binaries: | r-release: ffbase_0.12.7.tgz, r-oldrel: ffbase_0.12.7.tgz |
| Old sources: | ffbase archive |
| Reverse depends: | RecordLinkage, tabplot |
| Reverse imports: | bigchess, Cyclops, DatabaseConnector, Haplin, Rnightlights, SpaDES.tools, Stack |
| Reverse suggests: | Hmisc |
| Reverse enhances: | prediction |
Please use the canonical form https://CRAN.R-project.org/package=ffbase to link to this page.


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This project challenged me to work in an interdisciplinary team, with competing deadlines, and with a real population with real pain. We conducted user interviews, task analyses, root cause analysis, usability testing, and more. We finished the year with a working product that we handed over to our sponsor, Dr. Nancy Baker, and she is currently using it for her research.
I invite you to read the full final report here
Complex Regional Pain Syndrome (CRPS) is a condition that affects one or more of the limbs. It is believed to be caused by impairments of the peripheral and central nervous system, distorting the body’s perception of pain. Patients suffer from extreme discomfort, swelling, skin discoloration, and changing temperatures in the affected area. To treat patients, clinicians use a technique known as “mirror therapy”.
To perform this technique, the clinician places a mirror at the centerline of the patient’s body, blocking the affected limb from the patient’s sight. This result is a reflective illusion of the affected limb, tricking the patient’s brain into thinking movement is occurring without pain. This technique has been proven to reduce pain symptoms and improve motor ability.
CRPS currently has limited therapy options. While graded motor imagery is effective for some, for others it may present problems such as difficulty in creating a convincing illusion using a mirror, the need to be stationary, and the effectiveness from limb to limb may vary.
The purpose of this project is to create a tool for therapists to test whether the treatment of chronic pain through graded motor imagery therapy is effective in virtual reality. The system is composed of an immersive experience for the user and a control interface for the therapist to lead the therapy session. This was done using Unity, the Oculus Rift VR system, and a web application.
Our team spent the first few months conducting research. The first step was to survey the literature on Complex Regional Pain Syndrome, mirror therapy, and using virtual reality in clinical settings. Next, we conducted structured interviews with the two user groups: CRPS patients and CRPS clinicians.
Through interviews with clinicians, we gained insight on the progression of treatment. To Megan, a Philadelphia-based occupational therapist specializing in CRPS, the most important thing during treatment is making the patient feel in control. Pace and progression of treatment is dictated by the patient, not the clinician, and retreating to more basic tasks is not a failure. The therapy experience must be graded, starting with low-risk tasks and slowly progressing to more challenging ones. Additionally, Megan mentioned that CRPS patients typically have anxiety, so it is important to introduce new tasks and environments in a cautious and gradual manner.
Through interviews with patients, we learned about the patient’s experience with mirror therapy. Amber, a CRPS patient, revealed to us that she often feels nauseated after her sessions, as mirror therapy can be an incredibly intense experience. Typically, she sits in an OT office along with six to seven other patients during sessions, and she expressed her wish to be alone when she is completing treatment since the tasks take maximum concentration.
We integrated findings from interviews into the design of the virtual environment (patient interface) and the design of the web-based control portal (clinician interface).
Our team structured the virtual reality application so that the clinician facilitates the patient’s experience rather than completely controlling it. One window of the clinician interface shows the patient’s view through the oculus, along with a second window providing controls for graded motor imagery and scene selection.
During our user testing, our team tested three different types of users. Our testing population included one clinician with expertise in the areas of CRPS and occupational therapy (OT), four OT students with significant CRPS experience, and four laypeople with little to no experience with CRPS. Most of the individuals we tested also had limited to no exposure to virtual reality.
Our results are promising but not conclusive in solving our problem statement. We sought to create a system that acts as a tool for therapists to test whether the treatment of chronic pain through graded motor imagery therapy is effective in virtual reality. We created a two sided user interface - one for the patient, and one side for the clinician. We met most of our project goals, but like any project there are always things we would like to keep working on. We handed a tool to Dr. Baker that can be used to test whether the enhancement of traditional mirror therapy through VR is effective in treating CRPS.
Our research in building this system has proven that VR is an immersive strategy with the majority of our study participants saying that they felt comfortable in and convinced by our virtual reality system. In fact, one participant forgot that she was not moving the foot that she was seeing. These are extremely positive results that show the potential in our system. One caveat to our research is that we did not have permission to test on actual patients and thus we do not know exactly how CRPS patients will react to our system. We talked with clinicians, patients, and students studying the disease. But without actually testing with the clinical population, we can not be confident that our system will be well received. Our task was to create a system for our sponsor so that they could complete this research, so our team is excited to hear from Dr. Baker when she starts her research in the coming months.