Chapter 20 Generalized linear model
20.1 Learning goals
- Logistic regression.
- Logit transform.
- Fitting a logistic regression in R.
- Visualizing and interpreting model predictions.
- Simulating data from a logistic regression.
- Assessing model fit.
- Testing hypotheses.
- Reporting results.
- Mixed effects logistic regression.
20.2 Load packages and set plotting theme
library("knitr") # for knitting RMarkdown
library("titanic") # titanic dataset
library("kableExtra") # for making nice tables
library("janitor") # for cleaning column names
library("broom") # for tidying up linear models
library("lme4") # for linear mixed effects models
library("boot") # for bootstrapping (also has an inverse logit function)
library("ggeffects") # for showing effects in linear, generalized linear, and other models
library("afex") # for significance testing of mixed effects models
library("car") # for the Anova() function
library("tidyverse") # for wrangling, plotting, etc.
theme_set(theme_classic() + #set the theme
theme(text = element_text(size = 20))) #set the default text size
$set(comment = "",
opts_chunkfig.show = "hold")
# include references for used packages
write_bib(.packages(), "packages.bib")
20.3 Load data set
= titanic_train %>%
df.titanic clean_names() %>%
mutate(sex = as.factor(sex))
Let’s take a quick look at the data:
%>%
df.titanic glimpse()
Rows: 891
Columns: 12
$ passenger_id <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1…
$ survived <int> 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0,…
$ pclass <int> 3, 1, 3, 1, 3, 3, 1, 3, 3, 2, 3, 1, 3, 3, 3, 2, 3, 2, 3,…
$ name <chr> "Braund, Mr. Owen Harris", "Cumings, Mrs. John Bradley (…
$ sex <fct> male, female, female, female, male, male, male, male, fe…
$ age <dbl> 22, 38, 26, 35, 35, NA, 54, 2, 27, 14, 4, 58, 20, 39, 14…
$ sib_sp <int> 1, 1, 0, 1, 0, 0, 0, 3, 0, 1, 1, 0, 0, 1, 0, 0, 4, 0, 1,…
$ parch <int> 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 0, 0, 5, 0, 0, 1, 0, 0,…
$ ticket <chr> "A/5 21171", "PC 17599", "STON/O2. 3101282", "113803", "…
$ fare <dbl> 7.2500, 71.2833, 7.9250, 53.1000, 8.0500, 8.4583, 51.862…
$ cabin <chr> "", "C85", "", "C123", "", "", "E46", "", "", "", "G6", …
$ embarked <chr> "S", "C", "S", "S", "S", "Q", "S", "S", "S", "C", "S", "…
# Table of the first 10 entries
%>%
df.titanic head(10) %>%
kable(digits = 2) %>%
kable_styling(bootstrap_options = "striped",
full_width = F)
| passenger_id | survived | pclass | name | sex | age | sib_sp | parch | ticket | fare | cabin | embarked |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22 | 1 | 0 | A/5 21171 | 7.25 | S | |
| 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Thayer) | female | 38 | 1 | 0 | PC 17599 | 71.28 | C85 | C |
| 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26 | 0 | 0 | STON/O2. 3101282 | 7.92 | S | |
| 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35 | 1 | 0 | 113803 | 53.10 | C123 | S |
| 5 | 0 | 3 | Allen, Mr. William Henry | male | 35 | 0 | 0 | 373450 | 8.05 | S | |
| 6 | 0 | 3 | Moran, Mr. James | male | NA | 0 | 0 | 330877 | 8.46 | Q | |
| 7 | 0 | 1 | McCarthy, Mr. Timothy J | male | 54 | 0 | 0 | 17463 | 51.86 | E46 | S |
| 8 | 0 | 3 | Palsson, Master. Gosta Leonard | male | 2 | 3 | 1 | 349909 | 21.08 | S | |
| 9 | 1 | 3 | Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) | female | 27 | 0 | 2 | 347742 | 11.13 | S | |
| 10 | 1 | 2 | Nasser, Mrs. Nicholas (Adele Achem) | female | 14 | 1 | 0 | 237736 | 30.07 | C |
20.4 Logistic regression
Let’s see if we can predict whether or not a passenger survived based on the price of their ticket.
Let’s run a simple regression first:
# fit a linear model
= lm(formula = survived ~ 1 + fare,
fit.lm data = df.titanic)
# summarize the results
%>% summary() fit.lm
Call:
lm(formula = survived ~ 1 + fare, data = df.titanic)
Residuals:
Min 1Q Median 3Q Max
-0.9653 -0.3391 -0.3222 0.6044 0.6973
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.3026994 0.0187849 16.114 < 2e-16 ***
fare 0.0025195 0.0003174 7.939 6.12e-15 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4705 on 889 degrees of freedom
Multiple R-squared: 0.06621, Adjusted R-squared: 0.06516
F-statistic: 63.03 on 1 and 889 DF, p-value: 6.12e-15
Look’s like fare
is a significant predictor of whether or not a person survived. Let’s visualize the model’s predictions:
ggplot(data = df.titanic,
mapping = aes(x = fare,
y = survived)) +
geom_smooth(method = "lm") +
geom_point() +
labs(y = "survived")
This doesn’t look good! The model predicts intermediate values of survived
(which doesn’t make sense given that a person either survived or didn’t survive). Furthermore, the model predicts values greater than 1 for fares greather than ~ 300.
Let’s run a logistic regression instead.
# fit a logistic regression
= glm(formula = survived ~ 1 + fare,
fit.glm family = "binomial",
data = df.titanic)
%>%
fit.glm summary()
Call:
glm(formula = survived ~ 1 + fare, family = "binomial", data = df.titanic)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.4906 -0.8878 -0.8531 1.3429 1.5942
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.941330 0.095129 -9.895 < 2e-16 ***
fare 0.015197 0.002232 6.810 9.79e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1186.7 on 890 degrees of freedom
Residual deviance: 1117.6 on 889 degrees of freedom
AIC: 1121.6
Number of Fisher Scoring iterations: 4
And let’s visualize the predictions of the logistic regression:
ggplot(data = df.titanic,
mapping = aes(x = fare,
y = survived)) +
geom_smooth(method = "glm",
method.args = list(family = "binomial")) +
geom_point() +
labs(y = "p(survived)")
Much better! Note that we’ve changed the interpretation of our dependent variable. We are now predicting the probability that a person survived based on their fare. The model now only predicts values between 0 and 1. To achieve this, we apply a logit transform to the outcome variable like so:
\[ \ln(\frac{\pi_i}{1-\pi_i}) = b_0 + b_1 \cdot X_i + e_i \] where \(\pi_i\) is the probability of passenger \(i\) having survived. Importantly, this affects our interpretation of the model parameters. They are now defined in log-odds, and can apply an inverse logit transformation to turn this back into a probability:
With
\[ \pi = P(Y = 1) \] and the logit transformation
\[ \ln(\frac{\pi}{1-\pi}) = V, \] where \(V\) is just a placeholder for our linear model formula, we can go back to \(\pi\) through the inverse logit transformation like so:
\[
\pi = \frac{e^V}{1 + e^V}
\]
In R, we can use log(x)
to calculate the natural logarithm \(\ln(x)\), and exp(x)
to calculate e^x
.
20.4.1 Interpreting the parameters
%>%
fit.glm summary()
Call:
glm(formula = survived ~ 1 + fare, family = "binomial", data = df.titanic)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.4906 -0.8878 -0.8531 1.3429 1.5942
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.941330 0.095129 -9.895 < 2e-16 ***
fare 0.015197 0.002232 6.810 9.79e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1186.7 on 890 degrees of freedom
Residual deviance: 1117.6 on 889 degrees of freedom
AIC: 1121.6
Number of Fisher Scoring iterations: 4
The estimate for the intercept and fare are in log-odds.
Let’s take a look at the linear model’s predictions in log-odds space.
= fit.glm %>%
df.plot augment() %>%
clean_names()
ggplot(data = df.plot,
mapping = aes(x = fare,
y = fitted)) +
geom_line()
Nice, looks like a good old linear model. But what’s the y-axis here? It’s in log-odds (buh!). This is difficult to interpret. Let’s transform the y-axis back to probabilities to make it easier to see what’s going on.
ggplot(data = df.plot,
mapping = aes(x = fare,
y = inv.logit(fitted))) +
geom_line()
Great! Now the y-axis is back in probability space. We used the inverse logit function inv.logit()
to transfer to log-odds back into probabilities.
Let’s check what the intercept of our model is in probability space:
%>%
fit.glm pluck(coefficients, 1) %>%
inv.logit()
[1] 0.2806318
Here, we see that the intercept is \(p = 0.28\). That is, the predicted chance of survival for someone who didn’t pay any fare at all is 28% according to the model. Interpreting the slope is a little more tricky. Let’s look at a situation first where we have a binary predictor.
<IP_ADDRESS> Logit transform
Here is a visualization of what the odds and log odds transformation look like.
# going from probabilities to odds (ranges from 0 to +infinity)
ggplot(data = tibble(x = seq(0, 1, 0.1)),
mapping = aes(x = x)) +
stat_function(fun = ~ ./(1 - .),
size = 1)
# going from probabilities to log odds (ranges from -infinity to +infinity)
ggplot(data = tibble(x = seq(0, 1, 0.1)),
mapping = aes(x = x)) +
stat_function(fun = ~ log(./(1 - .)),
size = 1)
20.4.2 Binary predictor
Let’s see whether the probability of survival differed between male and female passengers.
= glm(formula = survived ~ 1 + sex,
fit.glm2 family = "binomial",
data = df.titanic)
%>%
fit.glm2 summary()
Call:
glm(formula = survived ~ 1 + sex, family = "binomial", data = df.titanic)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6462 -0.6471 -0.6471 0.7725 1.8256
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.0566 0.1290 8.191 2.58e-16 ***
sexmale -2.5137 0.1672 -15.036 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1186.7 on 890 degrees of freedom
Residual deviance: 917.8 on 889 degrees of freedom
AIC: 921.8
Number of Fisher Scoring iterations: 4
It looks like it did! Let’s visualize:
%>%
df.titanic mutate(survived = factor(survived, labels = c("died", "survived"))) %>%
ggplot(data = .,
mapping = aes(x = sex,
fill = survived)) +
geom_bar(position = "fill",
color = "black") +
scale_fill_brewer(palette = "Set1") +
labs(x = "",
fill = "",
y = "probability")
And let’s interpret the parameters by applying the inverse logit transform. To get the prediction for female passengers we get
\[ \widehat{\ln(\frac{\pi_i}{1-\pi_i})} = b_0 + b_1 \cdot \text{sex}_i = b_0 + b_1 \cdot 0 = b_0 \] since we dummy coded the predictor and female is our reference category. To get the predicted probability of survival for women we do the logit transform:
\[ \pi = \frac{e^{b_0}}{1 + e^{b_0}} \] The predicted probability is:
%>%
fit.glm2 pluck(coefficients, 1) %>%
inv.logit()
[1] 0.7420382
To get the prediction for male passengers we have:
\[ \widehat{\ln(\frac{\pi_i}{1-\pi_i})} = b_0 + b_1 \cdot \text{sex}_i = b_0 + b_1 \cdot 1 = b_0 + b_1 \] Applying the logit transform like so
\[ \pi = \frac{e^{b_0 + b_1}}{1 + e^{b_0 + b_1}} \]
The predicted probability of male passengers surviving is:
%>%
fit.glm2 pluck(coefficients) %>%
sum() %>%
inv.logit()
[1] 0.1889081
Here is the same information in a table:
%>%
df.titanic count(sex, survived) %>%
mutate(p = n / sum(n)) %>%
group_by(sex) %>%
mutate(`p(survived|sex)` = p / sum(p)) %>%
head(10) %>%
kable(digits = 2) %>%
kable_styling(bootstrap_options = "striped",
full_width = F)
| sex | survived | n | p | p(survived|sex) |
|---|---|---|---|---|
| female | 0 | 81 | 0.09 | 0.26 |
| female | 1 | 233 | 0.26 | 0.74 |
| male | 0 | 468 | 0.53 | 0.81 |
| male | 1 | 109 | 0.12 | 0.19 |
20.4.3 Continuous predictor
To interpret the predictions when a continuous predictor is involved, it’s easiest to consider a few concrete cases. Here, I use the augment()
function from the “broom” package to get the model’s predictions for some values of interest:
%>%
fit.glm augment(newdata = tibble(fare = c(0, 10, 50, 100, 500))) %>%
clean_names() %>%
select(fare, prediction = fitted) %>%
mutate(`p(survival)` = inv.logit(prediction)) %>%
head(10) %>%
kable(digits = 2) %>%
kable_styling(bootstrap_options = "striped",
full_width = F)
| fare | prediction | p(survival) |
|---|---|---|
| 0 | -0.94 | 0.28 |
| 10 | -0.79 | 0.31 |
| 50 | -0.18 | 0.45 |
| 100 | 0.58 | 0.64 |
| 500 | 6.66 | 1.00 |
20.4.4 Several predictors
Let’s fit a logistic regression that predicts the probability of survival based both on the passenger’s sex and what fare they paid (allowing for an interaction of the two predictors):
= glm(formula = survived ~ 1 + sex * fare,
fit.glm3 family = "binomial",
data = df.titanic)
%>%
fit.glm3 summary()
Call:
glm(formula = survived ~ 1 + sex * fare, family = "binomial",
data = df.titanic)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6280 -0.6279 -0.5991 0.8172 1.9288
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.408428 0.189999 2.150 0.031584 *
sexmale -2.099345 0.230291 -9.116 < 2e-16 ***
fare 0.019878 0.005372 3.701 0.000215 ***
sexmale:fare -0.011617 0.005934 -1.958 0.050252 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1186.66 on 890 degrees of freedom
Residual deviance: 879.85 on 887 degrees of freedom
AIC: 887.85
Number of Fisher Scoring iterations: 5
Make sure not to interpret the significance test on the coefficients here as main effects. Based on this summary table, you cannot say whether there is a significant difference between male vs. female passenger in their probability of survival. What coefficient for sexmale
captures is whether there is a significant difference between male and female passengers who paid a fare of 0. That is, it’s the predicted difference between the reference category (female) and the other category (male) when all other predictors are 0.
Let’s visualize the model predictions:
ggplot(data = df.titanic,
mapping = aes(x = fare,
y = survived,
color = sex)) +
geom_point(alpha = 0.1, size = 2) +
geom_smooth(method = "glm",
method.args = list(family = "binomial"),
alpha = 0.2,
aes(fill = sex)) +
scale_color_brewer(palette = "Set1")
Just for kicks, to get a better sense for what the interaction looks like, here is the visualization in log-odds space:
%>%
fit.glm3 augment() %>%
clean_names() %>%
ggplot(data = .,
mapping = aes(x = fare,
color = sex,
y = fitted)) +
geom_line() +
scale_color_brewer(palette = "Set1")
Let’s see how large the difference between genders is once we take into account how much each person paid for the fair:
ggpredict(fit.glm3,
terms = c("sex"))
# Predicted probabilities of survived
# x = sex
x | Predicted | 95% CI
---------------------------------
female | 0.74 | [0.69, 0.79]
male | 0.19 | [0.16, 0.23]
Adjusted for:
* fare = 32.20
We notice that there is one outlier who was male and paid a $500 fare (or maybe this is a mistake in the data entry?!). Let’s remove this outlier and see what happens:
= glm(formula = survived ~ 1 + sex * fare,
fit.glm3_no_outlier family = "binomial",
data = df.titanic %>%
filter(fare < 500))
%>%
fit.glm3_no_outlier summary()
Call:
glm(formula = survived ~ 1 + sex * fare, family = "binomial",
data = df.titanic %>% filter(fare < 500))
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6280 -0.6288 -0.6018 0.8172 1.9226
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.408436 0.190019 2.149 0.031598 *
sexmale -2.085344 0.232260 -8.979 < 2e-16 ***
fare 0.019878 0.005373 3.699 0.000216 ***
sexmale:fare -0.012178 0.006066 -2.008 0.044688 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1180.89 on 887 degrees of freedom
Residual deviance: 879.51 on 884 degrees of freedom
AIC: 887.51
Number of Fisher Scoring iterations: 5
%>%
df.titanic filter(fare < 500) %>%
mutate(sex = as.factor(sex)) %>%
ggplot(data = .,
mapping = aes(x = fare,
y = survived,
color = sex)) +
geom_point(alpha = 0.1, size = 2) +
stat_smooth(method = "glm",
method.args = list(family = "binomial"),
alpha = 0.2,
fullrange = T,
aes(fill = sex)) +
scale_color_brewer(palette = "Set1") +
scale_x_continuous(limits = c(0, 500))
There is still a clear difference between female and male passengers, but the prediction for male passengers has changed a bit. Let’s look at a concrete example:
# with the outlier:
# predicted probability of survival for a male passenger who paid $200 for their fare
inv.logit(fit.glm3$coefficients[1] + fit.glm3$coefficients[2] +
$coefficients[3] * 200 + fit.glm3$coefficients[4] * 200) fit.glm3
(Intercept)
0.4903402
# without the outlier:
# predicted probability of survival for a male passenger who paid $200 for their fare
inv.logit(fit.glm3_no_outlier$coefficients[1] +
$coefficients[2] +
fit.glm3_no_outlier$coefficients[3] * 200 +
fit.glm3_no_outlier$coefficients[4] * 200) fit.glm3_no_outlier
(Intercept)
0.4658284
With the outlier removed, the predicted probability of survival for a male passenger who paid $200 decreases from 49% to 47%.
20.4.5 Using the “ggeffects” package
The “ggeffects” package helps with the interpretation of the results. It applies the inverse logit transform for us, and shows the predictions for a range of cases.
# show effects
ggeffect(model = fit.glm,
terms = "fare [0, 100, 200, 300, 400, 500]")
# Predicted probabilities of survived
# x = fare
x | Predicted | 95% CI
------------------------------
0 | 0.28 | [0.24, 0.32]
100 | 0.64 | [0.56, 0.72]
200 | 0.89 | [0.79, 0.95]
300 | 0.97 | [0.92, 0.99]
400 | 0.99 | [0.97, 1.00]
500 | 1.00 | [0.99, 1.00]
I’ve used the []
construction to specify for what values of the predictor fare
, I’d like get the predicted values. Here, the prediction is marginalized across both women and men.
We can also get a plot of the model predictions like so:
ggeffect(model = fit.glm,
terms = "fare [0, 100, 200, 300, 400, 500]") %>%
plot()
And, we can also get the predicted probability of survival for sex marginalized over the fare, using the model which included both sex and fare, as well as its interaction as predictors.
ggeffect(model = fit.glm3,
terms = "sex")
# Predicted probabilities of survived
# x = sex
x | Predicted | 95% CI
---------------------------------
female | 0.74 | [0.69, 0.79]
male | 0.19 | [0.16, 0.23]
Finally, we can ask for predictions for specific combinations of our predictor variables, by using the ggpredict()
function.
ggpredict(model = fit.glm3,
terms = c("sex", "fare [200]"))
# Predicted probabilities of survived
# x = sex
x | Predicted | 95% CI
---------------------------------
female | 0.99 | [0.93, 1.00]
male | 0.49 | [0.29, 0.70]
The example above, shows the predicted probability of survival for male vs. female passengers, assuming that they paid 200 for the fare.
20.5 Simulate a logistic regression
As always, to better understand a statistical modeling procedure, it’s helpful to simulate data from the assumed data-generating process, fit the model, and see whether we can reconstruct the parameters.
# make example reproducible
set.seed(1)
# set parameters
= 1000
sample_size = 0
b0 = 1
b1 # b1 = 8
# generate data
= tibble(x = rnorm(n = sample_size),
df.data y = b0 + b1 * x,
p = inv.logit(y)) %>%
mutate(response = rbinom(n(), size = 1, p = p))
# fit model
= glm(formula = response ~ 1 + x,
fit family = "binomial",
data = df.data)
# model summary
%>%
fit summary()
Call:
glm(formula = response ~ 1 + x, family = "binomial", data = df.data)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1137 -1.0118 -0.4591 1.0287 2.2591
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.06214 0.06918 -0.898 0.369
x 0.92905 0.07937 11.705 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1385.4 on 999 degrees of freedom
Residual deviance: 1209.6 on 998 degrees of freedom
AIC: 1213.6
Number of Fisher Scoring iterations: 3
%>%
df.data kable(digits = 2) %>%
kable_styling(bootstrap_options = "striped",
full_width = F)
| x | y | p | response |
|---|---|---|---|
| -0.63 | -0.63 | 0.35 | 1 |
| 0.18 | 0.18 | 0.55 | 0 |
| -0.84 | -0.84 | 0.30 | 1 |
| 1.60 | 1.60 | 0.83 | 1 |
| 0.33 | 0.33 | 0.58 | 1 |
| -0.82 | -0.82 | 0.31 | 0 |
| 0.49 | 0.49 | 0.62 | 1 |
| 0.74 | 0.74 | 0.68 | 1 |
| 0.58 | 0.58 | 0.64 | 1 |
| -0.31 | -0.31 | 0.42 | 0 |
| 1.51 | 1.51 | 0.82 | 1 |
| 0.39 | 0.39 | 0.60 | 1 |
| -0.62 | -0.62 | 0.35 | 1 |
| -2.21 | -2.21 | 0.10 | 0 |
| 1.12 | 1.12 | 0.75 | 1 |
| -0.04 | -0.04 | 0.49 | 0 |
| -0.02 | -0.02 | 0.50 | 0 |
| 0.94 | 0.94 | 0.72 | 1 |
| 0.82 | 0.82 | 0.69 | 0 |
| 0.59 | 0.59 | 0.64 | 1 |
| 0.92 | 0.92 | 0.71 | 1 |
| 0.78 | 0.78 | 0.69 | 0 |
| 0.07 | 0.07 | 0.52 | 1 |
| -1.99 | -1.99 | 0.12 | 0 |
| 0.62 | 0.62 | 0.65 | 1 |
| -0.06 | -0.06 | 0.49 | 1 |
| -0.16 | -0.16 | 0.46 | 0 |
| -1.47 | -1.47 | 0.19 | 0 |
| -0.48 | -0.48 | 0.38 | 1 |
| 0.42 | 0.42 | 0.60 | 1 |
| 1.36 | 1.36 | 0.80 | 1 |
| -0.10 | -0.10 | 0.47 | 0 |
| 0.39 | 0.39 | 0.60 | 1 |
| -0.05 | -0.05 | 0.49 | 1 |
| -1.38 | -1.38 | 0.20 | 0 |
| -0.41 | -0.41 | 0.40 | 0 |
| -0.39 | -0.39 | 0.40 | 0 |
| -0.06 | -0.06 | 0.49 | 1 |
| 1.10 | 1.10 | 0.75 | 0 |
| 0.76 | 0.76 | 0.68 | 1 |
| -0.16 | -0.16 | 0.46 | 0 |
| -0.25 | -0.25 | 0.44 | 0 |
| 0.70 | 0.70 | 0.67 | 0 |
| 0.56 | 0.56 | 0.64 | 1 |
| -0.69 | -0.69 | 0.33 | 1 |
| -0.71 | -0.71 | 0.33 | 0 |
| 0.36 | 0.36 | 0.59 | 0 |
| 0.77 | 0.77 | 0.68 | 1 |
| -0.11 | -0.11 | 0.47 | 1 |
| 0.88 | 0.88 | 0.71 | 0 |
| 0.40 | 0.40 | 0.60 | 1 |
| -0.61 | -0.61 | 0.35 | 1 |
| 0.34 | 0.34 | 0.58 | 1 |
| -1.13 | -1.13 | 0.24 | 0 |
| 1.43 | 1.43 | 0.81 | 1 |
| 1.98 | 1.98 | 0.88 | 1 |
| -0.37 | -0.37 | 0.41 | 0 |
| -1.04 | -1.04 | 0.26 | 0 |
| 0.57 | 0.57 | 0.64 | 0 |
| -0.14 | -0.14 | 0.47 | 0 |
| 2.40 | 2.40 | 0.92 | 1 |
| -0.04 | -0.04 | 0.49 | 1 |
| 0.69 | 0.69 | 0.67 | 1 |
| 0.03 | 0.03 | 0.51 | 0 |
| -0.74 | -0.74 | 0.32 | 0 |
| 0.19 | 0.19 | 0.55 | 0 |
| -1.80 | -1.80 | 0.14 | 0 |
| 1.47 | 1.47 | 0.81 | 1 |
| 0.15 | 0.15 | 0.54 | 0 |
| 2.17 | 2.17 | 0.90 | 1 |
| 0.48 | 0.48 | 0.62 | 0 |
| -0.71 | -0.71 | 0.33 | 1 |
| 0.61 | 0.61 | 0.65 | 1 |
| -0.93 | -0.93 | 0.28 | 1 |
| -1.25 | -1.25 | 0.22 | 1 |
| 0.29 | 0.29 | 0.57 | 1 |
| -0.44 | -0.44 | 0.39 | 1 |
| 0.00 | 0.00 | 0.50 | 1 |
| 0.07 | 0.07 | 0.52 | 1 |
| -0.59 | -0.59 | 0.36 | 0 |
| -0.57 | -0.57 | 0.36 | 1 |
| -0.14 | -0.14 | 0.47 | 1 |
| 1.18 | 1.18 | 0.76 | 1 |
| -1.52 | -1.52 | 0.18 | 0 |
| 0.59 | 0.59 | 0.64 | 1 |
| 0.33 | 0.33 | 0.58 | 0 |
| 1.06 | 1.06 | 0.74 | 1 |
| -0.30 | -0.30 | 0.42 | 1 |
| 0.37 | 0.37 | 0.59 | 0 |
| 0.27 | 0.27 | 0.57 | 1 |
| -0.54 | -0.54 | 0.37 | 1 |
| 1.21 | 1.21 | 0.77 | 1 |
| 1.16 | 1.16 | 0.76 | 0 |
| 0.70 | 0.70 | 0.67 | 1 |
| 1.59 | 1.59 | 0.83 | 1 |
| 0.56 | 0.56 | 0.64 | 1 |
| -1.28 | -1.28 | 0.22 | 0 |
| -0.57 | -0.57 | 0.36 | 1 |
| -1.22 | -1.22 | 0.23 | 0 |
| -0.47 | -0.47 | 0.38 | 0 |
| -0.62 | -0.62 | 0.35 | 0 |
| 0.04 | 0.04 | 0.51 | 0 |
| -0.91 | -0.91 | 0.29 | 0 |
| 0.16 | 0.16 | 0.54 | 1 |
| -0.65 | -0.65 | 0.34 | 0 |
| 1.77 | 1.77 | 0.85 | 0 |
| 0.72 | 0.72 | 0.67 | 0 |
| 0.91 | 0.91 | 0.71 | 1 |
| 0.38 | 0.38 | 0.59 | 0 |
| 1.68 | 1.68 | 0.84 | 1 |
| -0.64 | -0.64 | 0.35 | 0 |
| -0.46 | -0.46 | 0.39 | 0 |
| 1.43 | 1.43 | 0.81 | 0 |
| -0.65 | -0.65 | 0.34 | 1 |
| -0.21 | -0.21 | 0.45 | 0 |
| -0.39 | -0.39 | 0.40 | 0 |
| -0.32 | -0.32 | 0.42 | 1 |
| -0.28 | -0.28 | 0.43 | 1 |
| 0.49 | 0.49 | 0.62 | 1 |
| -0.18 | -0.18 | 0.46 | 1 |
| -0.51 | -0.51 | 0.38 | 0 |
| 1.34 | 1.34 | 0.79 | 1 |
| -0.21 | -0.21 | 0.45 | 1 |
| -0.18 | -0.18 | 0.46 | 0 |
| -0.10 | -0.10 | 0.47 | 1 |
| 0.71 | 0.71 | 0.67 | 1 |
| -0.07 | -0.07 | 0.48 | 0 |
| -0.04 | -0.04 | 0.49 | 0 |
| -0.68 | -0.68 | 0.34 | 0 |
| -0.32 | -0.32 | 0.42 | 1 |
| 0.06 | 0.06 | 0.52 | 1 |
| -0.59 | -0.59 | 0.36 | 1 |
| 0.53 | 0.53 | 0.63 | 1 |
| -1.52 | -1.52 | 0.18 | 0 |
| 0.31 | 0.31 | 0.58 | 1 |
| -1.54 | -1.54 | 0.18 | 0 |
| -0.30 | -0.30 | 0.43 | 0 |
| -0.53 | -0.53 | 0.37 | 1 |
| -0.65 | -0.65 | 0.34 | 0 |
| -0.06 | -0.06 | 0.49 | 0 |
| -1.91 | -1.91 | 0.13 | 0 |
| 1.18 | 1.18 | 0.76 | 1 |
| -1.66 | -1.66 | 0.16 | 0 |
| -0.46 | -0.46 | 0.39 | 0 |
| -1.12 | -1.12 | 0.25 | 0 |
| -0.75 | -0.75 | 0.32 | 0 |
| 2.09 | 2.09 | 0.89 | 1 |
| 0.02 | 0.02 | 0.50 | 1 |
| -1.29 | -1.29 | 0.22 | 1 |
| -1.64 | -1.64 | 0.16 | 0 |
| 0.45 | 0.45 | 0.61 | 1 |
| -0.02 | -0.02 | 0.50 | 1 |
| -0.32 | -0.32 | 0.42 | 0 |
| -0.93 | -0.93 | 0.28 | 0 |
| -1.49 | -1.49 | 0.18 | 0 |
| -1.08 | -1.08 | 0.25 | 0 |
| 1.00 | 1.00 | 0.73 | 1 |
| -0.62 | -0.62 | 0.35 | 0 |
| -1.38 | -1.38 | 0.20 | 0 |
| 1.87 | 1.87 | 0.87 | 1 |
| 0.43 | 0.43 | 0.60 | 0 |
| -0.24 | -0.24 | 0.44 | 1 |
| 1.06 | 1.06 | 0.74 | 1 |
| 0.89 | 0.89 | 0.71 | 1 |
| -0.62 | -0.62 | 0.35 | 1 |
| 2.21 | 2.21 | 0.90 | 1 |
| -0.26 | -0.26 | 0.44 | 1 |
| -1.42 | -1.42 | 0.19 | 0 |
| -0.14 | -0.14 | 0.46 | 0 |
| 0.21 | 0.21 | 0.55 | 1 |
| 2.31 | 2.31 | 0.91 | 1 |
| 0.11 | 0.11 | 0.53 | 1 |
| 0.46 | 0.46 | 0.61 | 0 |
| -0.08 | -0.08 | 0.48 | 1 |
| -0.33 | -0.33 | 0.42 | 0 |
| -0.03 | -0.03 | 0.49 | 0 |
| 0.79 | 0.79 | 0.69 | 0 |
| 2.08 | 2.08 | 0.89 | 1 |
| 1.03 | 1.03 | 0.74 | 1 |
| 1.21 | 1.21 | 0.77 | 1 |
| -1.23 | -1.23 | 0.23 | 1 |
| 0.98 | 0.98 | 0.73 | 0 |
| 0.22 | 0.22 | 0.55 | 0 |
| -1.47 | -1.47 | 0.19 | 0 |
| 0.52 | 0.52 | 0.63 | 1 |
| -0.16 | -0.16 | 0.46 | 1 |
| 1.46 | 1.46 | 0.81 | 1 |
| -0.77 | -0.77 | 0.32 | 0 |
| -0.43 | -0.43 | 0.39 | 0 |
| -0.93 | -0.93 | 0.28 | 0 |
| -0.18 | -0.18 | 0.46 | 0 |
| 0.40 | 0.40 | 0.60 | 0 |
| -0.73 | -0.73 | 0.32 | 1 |
| 0.83 | 0.83 | 0.70 | 0 |
| -1.21 | -1.21 | 0.23 | 1 |
| -1.05 | -1.05 | 0.26 | 0 |
| 1.44 | 1.44 | 0.81 | 1 |
| -1.02 | -1.02 | 0.27 | 0 |
| 0.41 | 0.41 | 0.60 | 0 |
| -0.38 | -0.38 | 0.41 | 0 |
| 0.41 | 0.41 | 0.60 | 1 |
| 1.69 | 1.69 | 0.84 | 0 |
| 1.59 | 1.59 | 0.83 | 1 |
| -0.33 | -0.33 | 0.42 | 0 |
| -2.29 | -2.29 | 0.09 | 1 |
| 2.50 | 2.50 | 0.92 | 1 |
| 0.67 | 0.67 | 0.66 | 1 |
| 0.54 | 0.54 | 0.63 | 1 |
| -0.01 | -0.01 | 0.50 | 1 |
| 0.51 | 0.51 | 0.62 | 1 |
| -0.16 | -0.16 | 0.46 | 1 |
| 0.42 | 0.42 | 0.60 | 0 |
| -0.40 | -0.40 | 0.40 | 0 |
| -1.37 | -1.37 | 0.20 | 0 |
| 0.99 | 0.99 | 0.73 | 1 |
| 1.52 | 1.52 | 0.82 | 1 |
| -0.31 | -0.31 | 0.42 | 0 |
| -1.25 | -1.25 | 0.22 | 0 |
| 0.64 | 0.64 | 0.66 | 0 |
| -0.04 | -0.04 | 0.49 | 0 |
| -1.73 | -1.73 | 0.15 | 0 |
| 0.00 | 0.00 | 0.50 | 0 |
| -0.63 | -0.63 | 0.35 | 1 |
| -0.34 | -0.34 | 0.42 | 0 |
| -1.16 | -1.16 | 0.24 | 0 |
| 1.80 | 1.80 | 0.86 | 1 |
| -0.33 | -0.33 | 0.42 | 1 |
| -1.61 | -1.61 | 0.17 | 0 |
| 0.20 | 0.20 | 0.55 | 1 |
| 0.26 | 0.26 | 0.57 | 0 |
| -0.99 | -0.99 | 0.27 | 1 |
| -2.89 | -2.89 | 0.05 | 0 |
| -0.64 | -0.64 | 0.35 | 0 |
| 0.57 | 0.57 | 0.64 | 1 |
| -0.06 | -0.06 | 0.49 | 0 |
| -0.10 | -0.10 | 0.48 | 1 |
| 0.56 | 0.56 | 0.64 | 1 |
| -1.19 | -1.19 | 0.23 | 0 |
| 1.10 | 1.10 | 0.75 | 1 |
| -0.01 | -0.01 | 0.50 | 1 |
| 0.71 | 0.71 | 0.67 | 1 |
| 1.03 | 1.03 | 0.74 | 1 |
| 0.22 | 0.22 | 0.56 | 1 |
| -0.88 | -0.88 | 0.29 | 0 |
| 1.16 | 1.16 | 0.76 | 1 |
| -2.00 | -2.00 | 0.12 | 0 |
| -0.54 | -0.54 | 0.37 | 0 |
| -0.26 | -0.26 | 0.44 | 0 |
| -0.17 | -0.17 | 0.46 | 1 |
| 1.02 | 1.02 | 0.74 | 0 |
| 0.14 | 0.14 | 0.53 | 1 |
| 0.41 | 0.41 | 0.60 | 0 |
| -0.07 | -0.07 | 0.48 | 0 |
| -0.25 | -0.25 | 0.44 | 0 |
| 0.70 | 0.70 | 0.67 | 1 |
| 1.15 | 1.15 | 0.76 | 1 |
| -2.40 | -2.40 | 0.08 | 0 |
| 0.57 | 0.57 | 0.64 | 1 |
| 0.37 | 0.37 | 0.59 | 1 |
| -0.43 | -0.43 | 0.40 | 1 |
| 0.95 | 0.95 | 0.72 | 0 |
| -0.39 | -0.39 | 0.40 | 0 |
| -0.28 | -0.28 | 0.43 | 0 |
| 0.86 | 0.86 | 0.70 | 1 |
| 1.72 | 1.72 | 0.85 | 1 |
| 0.27 | 0.27 | 0.57 | 1 |
| -0.42 | -0.42 | 0.40 | 0 |
| -1.19 | -1.19 | 0.23 | 1 |
| -0.33 | -0.33 | 0.42 | 0 |
| -0.94 | -0.94 | 0.28 | 1 |
| -0.26 | -0.26 | 0.44 | 1 |
| 0.39 | 0.39 | 0.60 | 0 |
| -0.85 | -0.85 | 0.30 | 0 |
| 2.65 | 2.65 | 0.93 | 1 |
| 0.16 | 0.16 | 0.54 | 1 |
| 1.13 | 1.13 | 0.76 | 0 |
| -2.29 | -2.29 | 0.09 | 1 |
| 0.74 | 0.74 | 0.68 | 1 |
| -1.32 | -1.32 | 0.21 | 0 |
| 0.92 | 0.92 | 0.72 | 0 |
| 0.40 | 0.40 | 0.60 | 0 |
| -0.41 | -0.41 | 0.40 | 1 |
| 1.32 | 1.32 | 0.79 | 1 |
| -0.70 | -0.70 | 0.33 | 0 |
| -0.58 | -0.58 | 0.36 | 0 |
| -1.00 | -1.00 | 0.27 | 1 |
| -0.67 | -0.67 | 0.34 | 1 |
| 0.95 | 0.95 | 0.72 | 1 |
| 0.43 | 0.43 | 0.61 | 0 |
| 1.01 | 1.01 | 0.73 | 1 |
| -0.39 | -0.39 | 0.40 | 0 |
| 0.38 | 0.38 | 0.59 | 1 |
| 0.24 | 0.24 | 0.56 | 1 |
| -1.43 | -1.43 | 0.19 | 0 |
| 1.78 | 1.78 | 0.86 | 1 |
| 0.13 | 0.13 | 0.53 | 1 |
| 0.77 | 0.77 | 0.68 | 0 |
| 0.96 | 0.96 | 0.72 | 1 |
| -0.05 | -0.05 | 0.49 | 0 |
| -0.31 | -0.31 | 0.42 | 1 |
| 0.89 | 0.89 | 0.71 | 1 |
| -1.05 | -1.05 | 0.26 | 1 |
| 1.97 | 1.97 | 0.88 | 1 |
| -0.38 | -0.38 | 0.41 | 1 |
| 1.65 | 1.65 | 0.84 | 1 |
| 1.51 | 1.51 | 0.82 | 1 |
| 0.08 | 0.08 | 0.52 | 1 |
| 0.57 | 0.57 | 0.64 | 0 |
| -1.02 | -1.02 | 0.26 | 0 |
| 0.32 | 0.32 | 0.58 | 0 |
| 1.04 | 1.04 | 0.74 | 1 |
| 0.10 | 0.10 | 0.52 | 1 |
| -0.45 | -0.45 | 0.39 | 0 |
| -0.66 | -0.66 | 0.34 | 0 |
| -0.04 | -0.04 | 0.49 | 0 |
| 1.07 | 1.07 | 0.74 | 1 |
| -0.48 | -0.48 | 0.38 | 0 |
| -0.12 | -0.12 | 0.47 | 0 |
| -1.29 | -1.29 | 0.22 | 0 |
| 0.49 | 0.49 | 0.62 | 0 |
| 1.31 | 1.31 | 0.79 | 1 |
| 1.50 | 1.50 | 0.82 | 1 |
| 0.81 | 0.81 | 0.69 | 0 |
| -1.87 | -1.87 | 0.13 | 0 |
| 0.48 | 0.48 | 0.62 | 1 |
| 0.46 | 0.46 | 0.61 | 1 |
| -0.35 | -0.35 | 0.41 | 0 |
| 0.17 | 0.17 | 0.54 | 0 |
| -0.86 | -0.86 | 0.30 | 0 |
| 0.68 | 0.68 | 0.66 | 0 |
| -0.33 | -0.33 | 0.42 | 0 |
| -1.57 | -1.57 | 0.17 | 0 |
| -0.37 | -0.37 | 0.41 | 1 |
| 1.36 | 1.36 | 0.80 | 1 |
| -0.33 | -0.33 | 0.42 | 0 |
| 0.73 | 0.73 | 0.68 | 0 |
| 0.95 | 0.95 | 0.72 | 1 |
| 0.00 | 0.00 | 0.50 | 1 |
| -0.35 | -0.35 | 0.41 | 0 |
| -0.53 | -0.53 | 0.37 | 1 |
| 0.74 | 0.74 | 0.68 | 1 |
| -1.06 | -1.06 | 0.26 | 0 |
| 0.25 | 0.25 | 0.56 | 1 |
| -0.29 | -0.29 | 0.43 | 1 |
| -2.26 | -2.26 | 0.09 | 0 |
| -1.41 | -1.41 | 0.20 | 1 |
| 0.92 | 0.92 | 0.71 | 0 |
| -0.19 | -0.19 | 0.45 | 1 |
| 0.80 | 0.80 | 0.69 | 0 |
| 1.89 | 1.89 | 0.87 | 1 |
| 1.47 | 1.47 | 0.81 | 0 |
| 0.68 | 0.68 | 0.66 | 0 |
| 0.38 | 0.38 | 0.59 | 1 |
| -0.19 | -0.19 | 0.45 | 1 |
| 1.58 | 1.58 | 0.83 | 1 |
| 0.60 | 0.60 | 0.64 | 1 |
| -1.17 | -1.17 | 0.24 | 0 |
| -0.16 | -0.16 | 0.46 | 1 |
| -1.92 | -1.92 | 0.13 | 0 |
| -0.20 | -0.20 | 0.45 | 0 |
| -2.59 | -2.59 | 0.07 | 1 |
| 1.31 | 1.31 | 0.79 | 1 |
| -0.64 | -0.64 | 0.35 | 0 |
| -0.43 | -0.43 | 0.39 | 0 |
| -0.17 | -0.17 | 0.46 | 0 |
| 0.61 | 0.61 | 0.65 | 0 |
| 0.68 | 0.68 | 0.66 | 1 |
| 0.57 | 0.57 | 0.64 | 0 |
| -0.57 | -0.57 | 0.36 | 1 |
| -1.36 | -1.36 | 0.20 | 0 |
| -0.39 | -0.39 | 0.40 | 1 |
| 0.28 | 0.28 | 0.57 | 1 |
| -0.82 | -0.82 | 0.31 | 0 |
| -0.07 | -0.07 | 0.48 | 0 |
| -1.17 | -1.17 | 0.24 | 0 |
| -0.01 | -0.01 | 0.50 | 1 |
| 0.13 | 0.13 | 0.53 | 0 |
| -0.15 | -0.15 | 0.46 | 1 |
| -0.16 | -0.16 | 0.46 | 0 |
| 1.76 | 1.76 | 0.85 | 1 |
| 0.76 | 0.76 | 0.68 | 0 |
| 1.11 | 1.11 | 0.75 | 1 |
| -0.92 | -0.92 | 0.28 | 0 |
| 0.16 | 0.16 | 0.54 | 0 |
| 1.15 | 1.15 | 0.76 | 1 |
| -0.06 | -0.06 | 0.49 | 1 |
| -2.13 | -2.13 | 0.11 | 0 |
| 0.34 | 0.34 | 0.59 | 1 |
| -1.90 | -1.90 | 0.13 | 0 |
| -0.81 | -0.81 | 0.31 | 0 |
| 1.32 | 1.32 | 0.79 | 1 |
| 0.62 | 0.62 | 0.65 | 1 |
| 1.09 | 1.09 | 0.75 | 1 |
| 0.31 | 0.31 | 0.58 | 1 |
| -0.11 | -0.11 | 0.47 | 0 |
| -0.92 | -0.92 | 0.28 | 0 |
| 1.59 | 1.59 | 0.83 | 1 |
| 0.05 | 0.05 | 0.51 | 1 |
| -0.72 | -0.72 | 0.33 | 0 |
| 0.87 | 0.87 | 0.70 | 1 |
| 1.07 | 1.07 | 0.75 | 1 |
| 1.90 | 1.90 | 0.87 | 1 |
| -0.60 | -0.60 | 0.35 | 1 |
| -0.39 | -0.39 | 0.40 | 1 |
| -0.42 | -0.42 | 0.40 | 0 |
| -0.38 | -0.38 | 0.41 | 0 |
| -0.37 | -0.37 | 0.41 | 0 |
| -0.30 | -0.30 | 0.43 | 1 |
| 1.44 | 1.44 | 0.81 | 1 |
| -0.70 | -0.70 | 0.33 | 0 |
| -0.39 | -0.39 | 0.40 | 1 |
| 0.65 | 0.65 | 0.66 | 0 |
| 1.12 | 1.12 | 0.75 | 1 |
| -0.77 | -0.77 | 0.32 | 0 |
| -0.51 | -0.51 | 0.38 | 0 |
| 0.52 | 0.52 | 0.63 | 1 |
| 1.02 | 1.02 | 0.73 | 0 |
| -0.25 | -0.25 | 0.44 | 1 |
| -1.43 | -1.43 | 0.19 | 1 |
| 1.71 | 1.71 | 0.85 | 1 |
| 1.44 | 1.44 | 0.81 | 1 |
| -0.71 | -0.71 | 0.33 | 1 |
| -0.07 | -0.07 | 0.48 | 1 |
| -1.76 | -1.76 | 0.15 | 0 |
| 0.57 | 0.57 | 0.64 | 1 |
| 1.61 | 1.61 | 0.83 | 1 |
| -1.64 | -1.64 | 0.16 | 0 |
| -0.78 | -0.78 | 0.31 | 0 |
| -0.64 | -0.64 | 0.34 | 1 |
| -0.68 | -0.68 | 0.34 | 0 |
| -2.03 | -2.03 | 0.12 | 0 |
| 0.50 | 0.50 | 0.62 | 0 |
| -1.53 | -1.53 | 0.18 | 1 |
| -0.02 | -0.02 | 0.49 | 0 |
| 0.59 | 0.59 | 0.64 | 1 |
| -0.20 | -0.20 | 0.45 | 1 |
| 0.89 | 0.89 | 0.71 | 0 |
| -0.03 | -0.03 | 0.49 | 1 |
| -0.65 | -0.65 | 0.34 | 0 |
| 0.65 | 0.65 | 0.66 | 0 |
| -0.43 | -0.43 | 0.39 | 0 |
| 1.77 | 1.77 | 0.85 | 1 |
| -0.02 | -0.02 | 0.50 | 1 |
| 0.85 | 0.85 | 0.70 | 1 |
| 0.21 | 0.21 | 0.55 | 0 |
| -3.01 | -3.01 | 0.05 | 0 |
| -1.37 | -1.37 | 0.20 | 0 |
| -0.42 | -0.42 | 0.40 | 1 |
| 0.24 | 0.24 | 0.56 | 0 |
| -2.34 | -2.34 | 0.09 | 0 |
| 0.96 | 0.96 | 0.72 | 1 |
| -0.60 | -0.60 | 0.35 | 1 |
| -0.75 | -0.75 | 0.32 | 0 |
| -1.56 | -1.56 | 0.17 | 1 |
| -1.45 | -1.45 | 0.19 | 0 |
| 0.06 | 0.06 | 0.51 | 1 |
| 0.51 | 0.51 | 0.62 | 0 |
| -2.10 | -2.10 | 0.11 | 0 |
| -1.00 | -1.00 | 0.27 | 0 |
| 0.54 | 0.54 | 0.63 | 1 |
| -0.45 | -0.45 | 0.39 | 0 |
| 2.17 | 2.17 | 0.90 | 1 |
| 1.25 | 1.25 | 0.78 | 1 |
| 0.60 | 0.60 | 0.64 | 1 |
| 0.00 | 0.00 | 0.50 | 0 |
| 0.28 | 0.28 | 0.57 | 1 |
| -0.71 | -0.71 | 0.33 | 0 |
| 0.63 | 0.63 | 0.65 | 0 |
| 1.48 | 1.48 | 0.81 | 1 |
| 1.08 | 1.08 | 0.75 | 1 |
| -0.81 | -0.81 | 0.31 | 1 |
| -1.62 | -1.62 | 0.17 | 0 |
| -0.11 | -0.11 | 0.47 | 1 |
| 0.44 | 0.44 | 0.61 | 1 |
| 1.35 | 1.35 | 0.79 | 1 |
| -1.32 | -1.32 | 0.21 | 0 |
| 0.36 | 0.36 | 0.59 | 1 |
| 0.23 | 0.23 | 0.56 | 1 |
| 1.19 | 1.19 | 0.77 | 1 |
| -0.03 | -0.03 | 0.49 | 0 |
| -0.36 | -0.36 | 0.41 | 0 |
| -1.15 | -1.15 | 0.24 | 0 |
| -0.52 | -0.52 | 0.37 | 1 |
| -0.36 | -0.36 | 0.41 | 0 |
| 2.35 | 2.35 | 0.91 | 1 |
| 2.45 | 2.45 | 0.92 | 1 |
| -0.17 | -0.17 | 0.46 | 0 |
| -1.04 | -1.04 | 0.26 | 0 |
| -1.97 | -1.97 | 0.12 | 0 |
| 0.51 | 0.51 | 0.63 | 0 |
| -1.09 | -1.09 | 0.25 | 0 |
| 2.28 | 2.28 | 0.91 | 1 |
| -0.89 | -0.89 | 0.29 | 1 |
| 0.11 | 0.11 | 0.53 | 1 |
| 3.81 | 3.81 | 0.98 | 1 |
| -1.11 | -1.11 | 0.25 | 1 |
| 0.31 | 0.31 | 0.58 | 1 |
| -1.11 | -1.11 | 0.25 | 1 |
| 0.35 | 0.35 | 0.59 | 1 |
| -0.87 | -0.87 | 0.29 | 0 |
| 0.08 | 0.08 | 0.52 | 0 |
| -0.30 | -0.30 | 0.43 | 0 |
| -1.18 | -1.18 | 0.23 | 1 |
| 0.01 | 0.01 | 0.50 | 1 |
| 0.99 | 0.99 | 0.73 | 0 |
| 1.59 | 1.59 | 0.83 | 1 |
| -1.37 | -1.37 | 0.20 | 1 |
| -0.25 | -0.25 | 0.44 | 0 |
| 1.16 | 1.16 | 0.76 | 1 |
| -1.11 | -1.11 | 0.25 | 0 |
| -2.53 | -2.53 | 0.07 | 0 |
| -0.94 | -0.94 | 0.28 | 0 |
| -0.97 | -0.97 | 0.28 | 1 |
| 0.05 | 0.05 | 0.51 | 0 |
| -0.40 | -0.40 | 0.40 | 0 |
| 0.23 | 0.23 | 0.56 | 1 |
| -0.42 | -0.42 | 0.40 | 1 |
| 0.37 | 0.37 | 0.59 | 0 |
| -0.37 | -0.37 | 0.41 | 0 |
| 1.19 | 1.19 | 0.77 | 0 |
| -0.74 | -0.74 | 0.32 | 0 |
| 0.29 | 0.29 | 0.57 | 1 |
| -0.88 | -0.88 | 0.29 | 0 |
| 0.21 | 0.21 | 0.55 | 1 |
| -0.05 | -0.05 | 0.49 | 0 |
| -1.68 | -1.68 | 0.16 | 0 |
| -0.14 | -0.14 | 0.46 | 0 |
| 1.18 | 1.18 | 0.76 | 0 |
| 0.68 | 0.68 | 0.66 | 1 |
| 0.14 | 0.14 | 0.54 | 0 |
| -1.19 | -1.19 | 0.23 | 0 |
| 1.17 | 1.17 | 0.76 | 1 |
| 0.08 | 0.08 | 0.52 | 0 |
| -0.45 | -0.45 | 0.39 | 0 |
| 1.64 | 1.64 | 0.84 | 1 |
| -0.77 | -0.77 | 0.32 | 1 |
| 0.30 | 0.30 | 0.58 | 1 |
| 1.28 | 1.28 | 0.78 | 0 |
| 0.60 | 0.60 | 0.65 | 0 |
| -0.31 | -0.31 | 0.42 | 0 |
| -0.42 | -0.42 | 0.40 | 1 |
| 0.36 | 0.36 | 0.59 | 1 |
| 0.51 | 0.51 | 0.63 | 0 |
| 0.02 | 0.02 | 0.50 | 1 |
| 1.32 | 1.32 | 0.79 | 0 |
| -0.07 | -0.07 | 0.48 | 1 |
| -0.70 | -0.70 | 0.33 | 0 |
| 0.54 | 0.54 | 0.63 | 1 |
| -2.20 | -2.20 | 0.10 | 1 |
| 0.39 | 0.39 | 0.60 | 1 |
| 0.50 | 0.50 | 0.62 | 0 |
| -0.22 | -0.22 | 0.44 | 0 |
| -1.12 | -1.12 | 0.25 | 0 |
| -0.39 | -0.39 | 0.40 | 1 |
| 1.55 | 1.55 | 0.82 | 1 |
| -0.74 | -0.74 | 0.32 | 1 |
| -2.33 | -2.33 | 0.09 | 0 |
| 0.81 | 0.81 | 0.69 | 1 |
| -0.50 | -0.50 | 0.38 | 0 |
| -0.51 | -0.51 | 0.37 | 0 |
| -1.22 | -1.22 | 0.23 | 0 |
| -0.02 | -0.02 | 0.49 | 0 |
| 0.70 | 0.70 | 0.67 | 0 |
| -0.59 | -0.59 | 0.36 | 0 |
| -0.61 | -0.61 | 0.35 | 1 |
| 1.10 | 1.10 | 0.75 | 0 |
| -0.25 | -0.25 | 0.44 | 1 |
| -0.16 | -0.16 | 0.46 | 0 |
| -0.63 | -0.63 | 0.35 | 0 |
| 0.90 | 0.90 | 0.71 | 1 |
| -0.99 | -0.99 | 0.27 | 0 |
| 0.85 | 0.85 | 0.70 | 1 |
| 0.81 | 0.81 | 0.69 | 1 |
| -0.47 | -0.47 | 0.39 | 0 |
| 0.85 | 0.85 | 0.70 | 0 |
| 0.99 | 0.99 | 0.73 | 0 |
| 0.58 | 0.58 | 0.64 | 0 |
| 2.02 | 2.02 | 0.88 | 0 |
| -1.96 | -1.96 | 0.12 | 0 |
| -1.16 | -1.16 | 0.24 | 0 |
| -1.38 | -1.38 | 0.20 | 1 |
| 0.17 | 0.17 | 0.54 | 0 |
| 1.58 | 1.58 | 0.83 | 1 |
| 1.68 | 1.68 | 0.84 | 1 |
| 0.49 | 0.49 | 0.62 | 0 |
| 0.88 | 0.88 | 0.71 | 1 |
| -0.14 | -0.14 | 0.46 | 1 |
| 0.47 | 0.47 | 0.62 | 0 |
| 0.38 | 0.38 | 0.59 | 0 |
| -0.76 | -0.76 | 0.32 | 0 |
| -0.29 | -0.29 | 0.43 | 0 |
| -0.13 | -0.13 | 0.47 | 0 |
| 1.39 | 1.39 | 0.80 | 1 |
| -1.04 | -1.04 | 0.26 | 0 |
| -2.11 | -2.11 | 0.11 | 0 |
| 0.77 | 0.77 | 0.68 | 0 |
| -0.82 | -0.82 | 0.31 | 0 |
| -0.44 | -0.44 | 0.39 | 0 |
| 0.90 | 0.90 | 0.71 | 0 |
| -0.76 | -0.76 | 0.32 | 0 |
| -0.34 | -0.34 | 0.42 | 1 |
| 1.50 | 1.50 | 0.82 | 1 |
| 0.53 | 0.53 | 0.63 | 0 |
| 0.54 | 0.54 | 0.63 | 1 |
| -0.14 | -0.14 | 0.47 | 0 |
| -1.14 | -1.14 | 0.24 | 0 |
| -1.50 | -1.50 | 0.18 | 0 |
| -0.22 | -0.22 | 0.44 | 0 |
| 2.00 | 2.00 | 0.88 | 0 |
| 0.22 | 0.22 | 0.56 | 1 |
| 0.16 | 0.16 | 0.54 | 0 |
| 0.33 | 0.33 | 0.58 | 0 |
| -0.39 | -0.39 | 0.40 | 0 |
| -1.40 | -1.40 | 0.20 | 0 |
| 2.68 | 2.68 | 0.94 | 1 |
| -0.42 | -0.42 | 0.40 | 0 |
| -0.30 | -0.30 | 0.43 | 0 |
| -1.79 | -1.79 | 0.14 | 0 |
| -0.25 | -0.25 | 0.44 | 1 |
| -0.25 | -0.25 | 0.44 | 0 |
| -0.26 | -0.26 | 0.44 | 0 |
| -1.79 | -1.79 | 0.14 | 0 |
| 1.78 | 1.78 | 0.86 | 0 |
| 1.76 | 1.76 | 0.85 | 1 |
| 0.69 | 0.69 | 0.67 | 1 |
| -1.10 | -1.10 | 0.25 | 0 |
| 0.71 | 0.71 | 0.67 | 1 |
| -0.25 | -0.25 | 0.44 | 0 |
| -0.32 | -0.32 | 0.42 | 0 |
| 1.36 | 1.36 | 0.80 | 1 |
| -1.23 | -1.23 | 0.23 | 0 |
| -0.51 | -0.51 | 0.37 | 1 |
| -0.73 | -0.73 | 0.32 | 0 |
| 0.02 | 0.02 | 0.50 | 1 |
| -1.57 | -1.57 | 0.17 | 1 |
| -0.70 | -0.70 | 0.33 | 0 |
| 0.72 | 0.72 | 0.67 | 1 |
| 0.47 | 0.47 | 0.61 | 1 |
| -0.97 | -0.97 | 0.27 | 1 |
| 0.56 | 0.56 | 0.64 | 0 |
| -2.43 | -2.43 | 0.08 | 0 |
| -0.34 | -0.34 | 0.42 | 0 |
| 0.71 | 0.71 | 0.67 | 0 |
| -0.66 | -0.66 | 0.34 | 0 |
| -0.04 | -0.04 | 0.49 | 0 |
| -1.59 | -1.59 | 0.17 | 1 |
| 0.85 | 0.85 | 0.70 | 1 |
| -1.85 | -1.85 | 0.14 | 0 |
| -0.32 | -0.32 | 0.42 | 1 |
| -0.26 | -0.26 | 0.44 | 0 |
| 0.06 | 0.06 | 0.52 | 1 |
| -0.82 | -0.82 | 0.31 | 0 |
| 1.83 | 1.83 | 0.86 | 0 |
| -1.43 | -1.43 | 0.19 | 0 |
| 0.25 | 0.25 | 0.56 | 1 |
| -2.94 | -2.94 | 0.05 | 0 |
| 0.00 | 0.00 | 0.50 | 1 |
| 0.51 | 0.51 | 0.62 | 1 |
| -1.08 | -1.08 | 0.25 | 0 |
| 0.70 | 0.70 | 0.67 | 1 |
| 0.33 | 0.33 | 0.58 | 1 |
| 0.98 | 0.98 | 0.73 | 1 |
| -0.84 | -0.84 | 0.30 | 0 |
| -0.97 | -0.97 | 0.27 | 0 |
| -1.77 | -1.77 | 0.15 | 0 |
| -0.32 | -0.32 | 0.42 | 1 |
| -1.34 | -1.34 | 0.21 | 0 |
| 0.69 | 0.69 | 0.67 | 0 |
| 0.07 | 0.07 | 0.52 | 1 |
| 2.19 | 2.19 | 0.90 | 0 |
| -1.16 | -1.16 | 0.24 | 0 |
| 1.18 | 1.18 | 0.77 | 0 |
| -0.53 | -0.53 | 0.37 | 0 |
| -1.46 | -1.46 | 0.19 | 0 |
| 0.57 | 0.57 | 0.64 | 0 |
| -1.43 | -1.43 | 0.19 | 0 |
| -1.06 | -1.06 | 0.26 | 0 |
| -0.73 | -0.73 | 0.32 | 0 |
| 0.21 | 0.21 | 0.55 | 1 |
| -1.00 | -1.00 | 0.27 | 0 |
| 1.08 | 1.08 | 0.75 | 1 |
| -1.20 | -1.20 | 0.23 | 0 |
| 0.22 | 0.22 | 0.55 | 1 |
| 0.14 | 0.14 | 0.54 | 1 |
| -1.07 | -1.07 | 0.26 | 0 |
| -0.43 | -0.43 | 0.39 | 1 |
| -0.66 | -0.66 | 0.34 | 1 |
| 0.96 | 0.96 | 0.72 | 1 |
| 1.56 | 1.56 | 0.83 | 0 |
| -1.04 | -1.04 | 0.26 | 0 |
| 0.93 | 0.93 | 0.72 | 0 |
| -0.08 | -0.08 | 0.48 | 0 |
| -1.97 | -1.97 | 0.12 | 0 |
| -0.76 | -0.76 | 0.32 | 0 |
| 0.46 | 0.46 | 0.61 | 1 |
| 0.15 | 0.15 | 0.54 | 0 |
| -2.44 | -2.44 | 0.08 | 0 |
| 0.58 | 0.58 | 0.64 | 1 |
| 0.66 | 0.66 | 0.66 | 1 |
| -0.30 | -0.30 | 0.42 | 1 |
| -0.71 | -0.71 | 0.33 | 0 |
| 1.97 | 1.97 | 0.88 | 1 |
| -0.09 | -0.09 | 0.48 | 1 |
| -0.01 | -0.01 | 0.50 | 1 |
| -1.12 | -1.12 | 0.25 | 0 |
| -1.34 | -1.34 | 0.21 | 0 |
| -1.52 | -1.52 | 0.18 | 0 |
| -0.42 | -0.42 | 0.40 | 1 |
| 1.36 | 1.36 | 0.80 | 1 |
| 1.75 | 1.75 | 0.85 | 1 |
| 1.57 | 1.57 | 0.83 | 1 |
| 1.30 | 1.30 | 0.79 | 1 |
| -0.24 | -0.24 | 0.44 | 0 |
| -1.22 | -1.22 | 0.23 | 0 |
| -0.33 | -0.33 | 0.42 | 0 |
| -2.41 | -2.41 | 0.08 | 0 |
| -0.31 | -0.31 | 0.42 | 1 |
| 1.66 | 1.66 | 0.84 | 0 |
| 0.13 | 0.13 | 0.53 | 1 |
| 1.10 | 1.10 | 0.75 | 1 |
| 0.49 | 0.49 | 0.62 | 1 |
| -0.78 | -0.78 | 0.31 | 1 |
| 1.74 | 1.74 | 0.85 | 1 |
| -0.08 | -0.08 | 0.48 | 0 |
| -0.98 | -0.98 | 0.27 | 0 |
| 0.07 | 0.07 | 0.52 | 0 |
| -1.52 | -1.52 | 0.18 | 0 |
| 0.86 | 0.86 | 0.70 | 0 |
| 0.50 | 0.50 | 0.62 | 0 |
| -0.35 | -0.35 | 0.41 | 1 |
| -0.49 | -0.49 | 0.38 | 0 |
| 0.94 | 0.94 | 0.72 | 1 |
| -1.06 | -1.06 | 0.26 | 0 |
| -0.98 | -0.98 | 0.27 | 0 |
| 0.42 | 0.42 | 0.60 | 1 |
| -0.45 | -0.45 | 0.39 | 0 |
| 0.93 | 0.93 | 0.72 | 1 |
| -0.20 | -0.20 | 0.45 | 1 |
| 1.19 | 1.19 | 0.77 | 1 |
| 0.50 | 0.50 | 0.62 | 1 |
| -2.25 | -2.25 | 0.10 | 0 |
| -1.34 | -1.34 | 0.21 | 1 |
| 1.28 | 1.28 | 0.78 | 1 |
| 0.69 | 0.69 | 0.67 | 1 |
| -0.97 | -0.97 | 0.28 | 1 |
| -1.35 | -1.35 | 0.21 | 0 |
| 1.03 | 1.03 | 0.74 | 0 |
| -0.81 | -0.81 | 0.31 | 0 |
| 1.80 | 1.80 | 0.86 | 1 |
| 1.77 | 1.77 | 0.85 | 1 |
| -1.45 | -1.45 | 0.19 | 0 |
| -0.85 | -0.85 | 0.30 | 1 |
| -1.25 | -1.25 | 0.22 | 1 |
| 0.67 | 0.67 | 0.66 | 1 |
| -1.29 | -1.29 | 0.22 | 0 |
| -2.04 | -2.04 | 0.12 | 0 |
| 2.02 | 2.02 | 0.88 | 1 |
| 1.01 | 1.01 | 0.73 | 1 |
| 0.82 | 0.82 | 0.69 | 1 |
| -0.66 | -0.66 | 0.34 | 0 |
| -0.01 | -0.01 | 0.50 | 0 |
| 0.62 | 0.62 | 0.65 | 1 |
| -1.28 | -1.28 | 0.22 | 0 |
| -0.12 | -0.12 | 0.47 | 0 |
| 0.18 | 0.18 | 0.54 | 1 |
| 1.69 | 1.69 | 0.84 | 0 |
| 0.64 | 0.64 | 0.66 | 1 |
| 1.28 | 1.28 | 0.78 | 1 |
| 0.14 | 0.14 | 0.54 | 0 |
| -1.11 | -1.11 | 0.25 | 0 |
| -0.34 | -0.34 | 0.42 | 0 |
| -1.66 | -1.66 | 0.16 | 0 |
| 0.93 | 0.93 | 0.72 | 1 |
| 1.42 | 1.42 | 0.80 | 1 |
| -0.06 | -0.06 | 0.48 | 0 |
| -0.98 | -0.98 | 0.27 | 0 |
| 1.09 | 1.09 | 0.75 | 0 |
| 0.14 | 0.14 | 0.53 | 1 |
| -0.39 | -0.39 | 0.40 | 0 |
| 1.12 | 1.12 | 0.75 | 0 |
| -0.76 | -0.76 | 0.32 | 0 |
| 1.15 | 1.15 | 0.76 | 1 |
| -0.84 | -0.84 | 0.30 | 0 |
| 0.39 | 0.39 | 0.60 | 0 |
| 0.89 | 0.89 | 0.71 | 1 |
| -1.34 | -1.34 | 0.21 | 0 |
| 0.40 | 0.40 | 0.60 | 0 |
| -0.11 | -0.11 | 0.47 | 0 |
| 0.68 | 0.68 | 0.66 | 1 |
| -0.79 | -0.79 | 0.31 | 0 |
| -0.09 | -0.09 | 0.48 | 1 |
| 1.38 | 1.38 | 0.80 | 0 |
| 0.17 | 0.17 | 0.54 | 1 |
| 0.82 | 0.82 | 0.69 | 1 |
| -0.22 | -0.22 | 0.44 | 1 |
| -1.03 | -1.03 | 0.26 | 0 |
| -0.01 | -0.01 | 0.50 | 1 |
| -1.22 | -1.22 | 0.23 | 1 |
| -2.60 | -2.60 | 0.07 | 0 |
| 1.17 | 1.17 | 0.76 | 1 |
| -1.09 | -1.09 | 0.25 | 1 |
| -1.83 | -1.83 | 0.14 | 0 |
| 1.00 | 1.00 | 0.73 | 1 |
| -0.01 | -0.01 | 0.50 | 0 |
| -0.60 | -0.60 | 0.35 | 0 |
| -0.18 | -0.18 | 0.46 | 1 |
| -0.43 | -0.43 | 0.40 | 0 |
| 1.00 | 1.00 | 0.73 | 1 |
| 0.73 | 0.73 | 0.67 | 1 |
| -1.73 | -1.73 | 0.15 | 0 |
| 0.35 | 0.35 | 0.59 | 1 |
| 0.73 | 0.73 | 0.67 | 1 |
| 0.67 | 0.67 | 0.66 | 1 |
| -2.42 | -2.42 | 0.08 | 1 |
| -0.24 | -0.24 | 0.44 | 0 |
| 1.98 | 1.98 | 0.88 | 1 |
| 0.80 | 0.80 | 0.69 | 1 |
| -1.71 | -1.71 | 0.15 | 0 |
| -1.66 | -1.66 | 0.16 | 0 |
| 0.49 | 0.49 | 0.62 | 1 |
| -0.17 | -0.17 | 0.46 | 1 |
| 0.96 | 0.96 | 0.72 | 1 |
| 0.29 | 0.29 | 0.57 | 1 |
| 0.08 | 0.08 | 0.52 | 1 |
| 0.18 | 0.18 | 0.55 | 1 |
| 0.17 | 0.17 | 0.54 | 0 |
| -1.27 | -1.27 | 0.22 | 0 |
| 2.35 | 2.35 | 0.91 | 0 |
| -1.41 | -1.41 | 0.20 | 0 |
| -0.02 | -0.02 | 0.50 | 0 |
| -0.54 | -0.54 | 0.37 | 0 |
| 1.80 | 1.80 | 0.86 | 1 |
| 1.01 | 1.01 | 0.73 | 1 |
| -0.56 | -0.56 | 0.36 | 0 |
| 0.21 | 0.21 | 0.55 | 0 |
| 1.17 | 1.17 | 0.76 | 1 |
| 2.24 | 2.24 | 0.90 | 1 |
| 0.30 | 0.30 | 0.57 | 0 |
| -1.04 | -1.04 | 0.26 | 1 |
| -0.98 | -0.98 | 0.27 | 0 |
| 2.01 | 2.01 | 0.88 | 1 |
| -2.07 | -2.07 | 0.11 | 0 |
| 3.06 | 3.06 | 0.96 | 1 |
| -0.26 | -0.26 | 0.44 | 0 |
| -0.45 | -0.45 | 0.39 | 1 |
| 0.16 | 0.16 | 0.54 | 1 |
| 0.93 | 0.93 | 0.72 | 0 |
| 0.30 | 0.30 | 0.58 | 0 |
| -1.96 | -1.96 | 0.12 | 1 |
| 0.35 | 0.35 | 0.59 | 0 |
| 0.45 | 0.45 | 0.61 | 1 |
| 0.66 | 0.66 | 0.66 | 0 |
| -1.03 | -1.03 | 0.26 | 0 |
| -2.37 | -2.37 | 0.09 | 0 |
| -0.32 | -0.32 | 0.42 | 0 |
| -0.94 | -0.94 | 0.28 | 0 |
| -0.77 | -0.77 | 0.32 | 0 |
| -0.95 | -0.95 | 0.28 | 0 |
| -0.40 | -0.40 | 0.40 | 1 |
| -0.31 | -0.31 | 0.42 | 0 |
| 0.80 | 0.80 | 0.69 | 1 |
| 0.99 | 0.99 | 0.73 | 1 |
| -0.79 | -0.79 | 0.31 | 1 |
| -0.31 | -0.31 | 0.42 | 0 |
| 0.36 | 0.36 | 0.59 | 0 |
| 1.40 | 1.40 | 0.80 | 1 |
| -0.06 | -0.06 | 0.49 | 1 |
| -1.70 | -1.70 | 0.15 | 0 |
| 0.23 | 0.23 | 0.56 | 0 |
| -0.12 | -0.12 | 0.47 | 0 |
| 1.77 | 1.77 | 0.85 | 1 |
| 0.34 | 0.34 | 0.59 | 0 |
| -0.62 | -0.62 | 0.35 | 1 |
| -0.44 | -0.44 | 0.39 | 0 |
| -0.51 | -0.51 | 0.38 | 0 |
| 0.19 | 0.19 | 0.55 | 1 |
| 0.18 | 0.18 | 0.54 | 0 |
| 0.92 | 0.92 | 0.71 | 1 |
| 0.32 | 0.32 | 0.58 | 0 |
| -0.37 | -0.37 | 0.41 | 0 |
| -0.94 | -0.94 | 0.28 | 0 |
| 0.63 | 0.63 | 0.65 | 0 |
| -0.06 | -0.06 | 0.48 | 0 |
| 0.18 | 0.18 | 0.55 | 0 |
| 1.10 | 1.10 | 0.75 | 1 |
| 1.75 | 1.75 | 0.85 | 1 |
| -0.95 | -0.95 | 0.28 | 0 |
| 1.64 | 1.64 | 0.84 | 1 |
| -0.87 | -0.87 | 0.30 | 0 |
| 0.27 | 0.27 | 0.57 | 1 |
| 0.22 | 0.22 | 0.56 | 1 |
| -0.28 | -0.28 | 0.43 | 1 |
| 1.39 | 1.39 | 0.80 | 0 |
| -0.66 | -0.66 | 0.34 | 0 |
| 0.66 | 0.66 | 0.66 | 1 |
| -0.01 | -0.01 | 0.50 | 1 |
| -0.93 | -0.93 | 0.28 | 0 |
| 1.21 | 1.21 | 0.77 | 0 |
| -2.09 | -2.09 | 0.11 | 0 |
| -0.53 | -0.53 | 0.37 | 1 |
| -1.54 | -1.54 | 0.18 | 0 |
| 0.19 | 0.19 | 0.55 | 1 |
| 0.26 | 0.26 | 0.57 | 1 |
| -1.12 | -1.12 | 0.25 | 0 |
| 0.65 | 0.65 | 0.66 | 1 |
| -1.03 | -1.03 | 0.26 | 0 |
| 0.66 | 0.66 | 0.66 | 1 |
| 0.24 | 0.24 | 0.56 | 0 |
| 0.72 | 0.72 | 0.67 | 1 |
| -0.94 | -0.94 | 0.28 | 0 |
| 0.10 | 0.10 | 0.52 | 1 |
| -0.46 | -0.46 | 0.39 | 0 |
| -1.47 | -1.47 | 0.19 | 0 |
| 0.15 | 0.15 | 0.54 | 1 |
| 1.77 | 1.77 | 0.85 | 0 |
| -0.65 | -0.65 | 0.34 | 1 |
| -0.20 | -0.20 | 0.45 | 1 |
| 0.69 | 0.69 | 0.67 | 1 |
| 0.04 | 0.04 | 0.51 | 0 |
| 1.94 | 1.94 | 0.87 | 1 |
| 0.74 | 0.74 | 0.68 | 1 |
| 2.32 | 2.32 | 0.91 | 1 |
| 0.35 | 0.35 | 0.59 | 0 |
| -1.13 | -1.13 | 0.24 | 1 |
| 0.42 | 0.42 | 0.60 | 0 |
| -0.92 | -0.92 | 0.28 | 1 |
| -1.01 | -1.01 | 0.27 | 0 |
| -0.19 | -0.19 | 0.45 | 0 |
| 0.93 | 0.93 | 0.72 | 1 |
| 0.34 | 0.34 | 0.59 | 1 |
| 0.81 | 0.81 | 0.69 | 0 |
| 0.92 | 0.92 | 0.71 | 0 |
| -0.17 | -0.17 | 0.46 | 0 |
| -2.40 | -2.40 | 0.08 | 0 |
| 0.80 | 0.80 | 0.69 | 0 |
| 2.17 | 2.17 | 0.90 | 1 |
| 0.06 | 0.06 | 0.51 | 1 |
| -1.35 | -1.35 | 0.21 | 0 |
| -0.37 | -0.37 | 0.41 | 0 |
| -0.93 | -0.93 | 0.28 | 0 |
| -0.04 | -0.04 | 0.49 | 0 |
| 0.68 | 0.68 | 0.66 | 0 |
| 0.87 | 0.87 | 0.70 | 1 |
| 0.24 | 0.24 | 0.56 | 0 |
| -0.93 | -0.93 | 0.28 | 0 |
| 0.81 | 0.81 | 0.69 | 1 |
| 1.35 | 1.35 | 0.79 | 1 |
| 2.25 | 2.25 | 0.90 | 1 |
| -0.49 | -0.49 | 0.38 | 1 |
| 0.47 | 0.47 | 0.62 | 0 |
| 1.19 | 1.19 | 0.77 | 1 |
| -0.12 | -0.12 | 0.47 | 1 |
| 0.52 | 0.52 | 0.63 | 0 |
| 0.21 | 0.21 | 0.55 | 1 |
| -0.13 | -0.13 | 0.47 | 0 |
| 0.17 | 0.17 | 0.54 | 0 |
| 0.96 | 0.96 | 0.72 | 1 |
| 0.41 | 0.41 | 0.60 | 1 |
| -0.47 | -0.47 | 0.39 | 0 |
| -2.24 | -2.24 | 0.10 | 0 |
| -0.80 | -0.80 | 0.31 | 0 |
| -0.02 | -0.02 | 0.50 | 1 |
| -2.51 | -2.51 | 0.07 | 0 |
| 2.21 | 2.21 | 0.90 | 1 |
| -1.49 | -1.49 | 0.18 | 0 |
| -1.16 | -1.16 | 0.24 | 1 |
| 1.46 | 1.46 | 0.81 | 1 |
| -2.19 | -2.19 | 0.10 | 0 |
| 0.74 | 0.74 | 0.68 | 0 |
| -0.34 | -0.34 | 0.41 | 0 |
| 0.46 | 0.46 | 0.61 | 0 |
| 0.88 | 0.88 | 0.71 | 1 |
| -0.96 | -0.96 | 0.28 | 1 |
| -0.71 | -0.71 | 0.33 | 1 |
| -3.00 | -3.00 | 0.05 | 0 |
| -0.96 | -0.96 | 0.28 | 1 |
| 0.38 | 0.38 | 0.59 | 1 |
| 0.51 | 0.51 | 0.62 | 1 |
| 2.03 | 2.03 | 0.88 | 1 |
| 0.06 | 0.06 | 0.52 | 1 |
| 0.46 | 0.46 | 0.61 | 0 |
| 0.07 | 0.07 | 0.52 | 1 |
| -0.49 | -0.49 | 0.38 | 0 |
| 0.75 | 0.75 | 0.68 | 0 |
| 0.46 | 0.46 | 0.61 | 1 |
| 0.13 | 0.13 | 0.53 | 1 |
| -0.82 | -0.82 | 0.31 | 1 |
| -0.04 | -0.04 | 0.49 | 1 |
| 0.78 | 0.78 | 0.69 | 0 |
| 0.68 | 0.68 | 0.66 | 0 |
| -0.48 | -0.48 | 0.38 | 0 |
| -0.67 | -0.67 | 0.34 | 0 |
| 0.51 | 0.51 | 0.63 | 1 |
| 1.05 | 1.05 | 0.74 | 1 |
| 0.12 | 0.12 | 0.53 | 0 |
| -0.31 | -0.31 | 0.42 | 0 |
| -0.88 | -0.88 | 0.29 | 0 |
| -0.42 | -0.42 | 0.40 | 0 |
| -1.48 | -1.48 | 0.19 | 0 |
| -0.70 | -0.70 | 0.33 | 0 |
Nice! The inferred estimates are very close to the parameter values we used to simulate the data.
Let’s visualize the result:
ggplot(data = df.data,
mapping = aes(x = x,
y = response)) +
geom_smooth(method = "glm",
method.args = list(family = "binomial")) +
geom_point(alpha = 0.1) +
labs(y = "p(response)")
<IP_ADDRESS> Calculate the model’s likelihood
To calculate the likelihood of the data for a given logistic model, we look at the actual response, and the probability of the predicted response, and then determine the likelihood of the observation assuming a Bernoulli process. To get the overall likelihood of the data, we then multiply the likelihood of each data point (or take the logs first and then the sum to get the log-likelihood).
This table illustrate the steps involved:
%>%
fit augment() %>%
clean_names() %>%
mutate(p = inv.logit(fitted)) %>%
select(response, p) %>%
mutate(p_response = ifelse(response == 1, p, 1-p),
log_p = log(p_response)) %>%
rename(`p(Y = 1)` = p, `p(Y = response)` = p_response,
`log(p(Y = response))` = log_p) %>%
head(10) %>%
kable(digits = 2) %>%
kable_styling(bootstrap_options = "striped",
full_width = F)
| response | p(Y = 1) | p(Y = response) | log(p(Y = response)) |
|---|---|---|---|
| 1 | 0.34 | 0.34 | -1.07 |
| 0 | 0.53 | 0.47 | -0.75 |
| 1 | 0.30 | 0.30 | -1.20 |
| 1 | 0.81 | 0.81 | -0.22 |
| 1 | 0.56 | 0.56 | -0.58 |
| 0 | 0.30 | 0.70 | -0.36 |
| 1 | 0.60 | 0.60 | -0.52 |
| 1 | 0.65 | 0.65 | -0.43 |
| 1 | 0.62 | 0.62 | -0.48 |
| 0 | 0.41 | 0.59 | -0.54 |
Let’s calculate the log-likelihood by hand:
%>%
fit augment() %>%
clean_names() %>%
mutate(p = inv.logit(fitted),
log_likelihood = response * log(p) + (1 - response) * log(1 - p)) %>%
summarize(log_likelihood = sum(log_likelihood))
# A tibble: 1 x 1
log_likelihood
<dbl>
1 -605.
And compare it with the model summary
%>%
fit glance() %>%
select(logLik, AIC, BIC) %>%
kable(digits = 2) %>%
kable_styling(bootstrap_options = "striped",
full_width = F)
| logLik | AIC | BIC |
|---|---|---|
| -604.82 | 1213.64 | 1223.45 |
We’re getting the same result – neat! Now we know how the likelihood of the data is calculated for a logistic regression model.
20.6 Testing hypotheses
To test hypotheses, we can use our good old model comparison approach:
# fit compact model
= glm(formula = survived ~ 1 + fare,
fit.compact family = "binomial",
data = df.titanic)
# fit augmented model
= glm(formula = survived ~ 1 + sex + fare,
fit.augmented family = "binomial",
data = df.titanic)
# likelihood ratio test
anova(fit.compact, fit.augmented, test = "LRT")
Analysis of Deviance Table
Model 1: survived ~ 1 + fare
Model 2: survived ~ 1 + sex + fare
Resid. Df Resid. Dev Df Deviance Pr(>Chi)
1 889 1117.57
2 888 884.31 1 233.26 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Note that in order to get a p-value out of this, we need to specify what statistical test we’d like to run. In this case, we use the likelihood ratio test (“LRT”).
We can also test for both effects of survived and fare in one go using the Anova() function from the “car” package like so:
glm(formula = survived ~ 1 + sex + fare,
contrasts = list(sex = "contr.sum"),
family = "binomial",
data = df.titanic) %>%
Anova(type = 3,
test.statistic = "LR")
Analysis of Deviance Table (Type III tests)
Response: survived
LR Chisq Df Pr(>Chisq)
sex 233.259 1 < 2.2e-16 ***
fare 33.494 1 7.15e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Here, I’ve specified that I want type III sums of squares (type = 3
), and a likelihood ratio test (test.statistic = "LR"
).
20.7 Logistic mixed effects model
Just like we can build linear mixed effects models using lmer()
instead of lm()
, we can also build a logistic mixed effects regression using glmer()
instead of glm()
.
Let’s read in some data:
# load bdf data set from nlme package
data(bdf, package = "nlme")
= bdf %>%
df.language clean_names() %>%
filter(repeatgr != 2) %>%
mutate(repeatgr = repeatgr %>% as.character() %>% as.numeric())
rm(bdf)
Fit the model, and print out the results:
= glmer(repeatgr ~ 1 + ses + minority + (1 | school_nr),
fit data = df.language,
family = "binomial")
%>%
fit summary()
Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation) [glmerMod]
Family: binomial ( logit )
Formula: repeatgr ~ 1 + ses + minority + (1 | school_nr)
Data: df.language
AIC BIC logLik deviance df.resid
1659.1 1682.1 -825.6 1651.1 2279
Scaled residuals:
Min 1Q Median 3Q Max
-0.9235 -0.4045 -0.3150 -0.2249 5.8372
Random effects:
Groups Name Variance Std.Dev.
school_nr (Intercept) 0.2489 0.4989
Number of obs: 2283, groups: school_nr, 131
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.506291 0.197566 -2.563 0.01039 *
ses -0.060086 0.007523 -7.986 1.39e-15 ***
minorityY 0.673612 0.238657 2.823 0.00476 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) ses
ses -0.898
minorityY -0.308 0.208
To visualize the results, we can use the ggeffects
package.
ggpredict(model = fit,
terms = c("ses [all]", "minority")) %>%
plot()
And for significance testing, we can use the the mixed() function from the “afex” package
mixed(formula = repeatgr ~ 1 + ses + minority + (1 | school_nr),
data = df.language,
family = "binomial",
method = "LRT")
Contrasts set to contr.sum for the following variables: minority, school_nr
Numerical variables NOT centered on 0: ses
If in interactions, interpretation of lower order (e.g., main) effects difficult.
Fitting 3 (g)lmer() models:
[...]
Mixed Model Anova Table (Type 3 tests, LRT-method)
Model: repeatgr ~ 1 + ses + minority + (1 | school_nr)
Data: df.language
Df full model: 4
Effect df Chisq p.value
1 ses 1 75.39 *** <.001
2 minority 1 7.53 ** .006
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
The results show that there was both a significant effect of ses and of minority. The mixed()
function basically implements a series of model comparisons, like illustrated below.
To test for the effect of ses, we compare one model with, and one without ses.
# test for effect of ses
= glmer(repeatgr ~ 1 + ses + minority + (1 | school_nr),
fit1 data = df.language,
family = "binomial")
= glmer(repeatgr ~ 1 + minority + (1 | school_nr),
fit2 data = df.language,
family = "binomial")
anova(fit1, fit2)
Data: df.language
Models:
fit2: repeatgr ~ 1 + minority + (1 | school_nr)
fit1: repeatgr ~ 1 + ses + minority + (1 | school_nr)
npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
fit2 3 1732.5 1749.7 -863.27 1726.5
fit1 4 1659.1 1682.1 -825.57 1651.1 75.395 1 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
And to test for an effect of minority, we compare a model with, and a model without that predictor
# test for effect of minority
= glmer(repeatgr ~ 1 + ses + minority + (1 | school_nr),
fit1 data = df.language,
family = "binomial")
= glmer(repeatgr ~ 1 + ses + (1 | school_nr),
fit2 data = df.language,
family = "binomial")
anova(fit1, fit2)
Data: df.language
Models:
fit2: repeatgr ~ 1 + ses + (1 | school_nr)
fit1: repeatgr ~ 1 + ses + minority + (1 | school_nr)
npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
fit2 3 1664.7 1681.9 -829.34 1658.7
fit1 4 1659.1 1682.1 -825.57 1651.1 7.53 1 0.006068 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
As you can see, both the mixed()
route and the model comparison route via anova()
yield the same results here.
20.8 Additional information
20.9 Session info
Information about this R session including which version of R was used, and what packages were loaded.
sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.4 purrr_0.3.4
[5] readr_1.4.0 tidyr_1.1.2 tibble_3.0.6 ggplot2_3.3.3
[9] tidyverse_1.3.0 car_3.0-10 carData_3.0-4 afex_0.28-1
[13] ggeffects_1.0.1 boot_1.3-26 lme4_1.1-26 Matrix_1.3-2
[17] broom_0.7.3 janitor_2.1.0 kableExtra_1.3.1 titanic_0.1.0
[21] knitr_1.31
loaded via a namespace (and not attached):
[1] TH.data_1.0-10 minqa_1.2.4 colorspace_2.0-0
[4] ellipsis_0.3.1 rio_0.5.16 sjlabelled_1.1.7
[7] estimability_1.3 snakecase_0.11.0 fs_1.5.0
[10] rstudioapi_0.13 farver_2.1.0 fansi_0.4.2
[13] mvtnorm_1.1-1 lubridate_<IP_ADDRESS> xml2_1.3.2
[16] codetools_0.2-18 splines_4.0.3 effects_4.2-0
[19] jsonlite_1.7.2 nloptr_<IP_ADDRESS> dbplyr_2.0.0
[22] compiler_4.0.3 httr_1.4.2 emmeans_1.5.3
[25] backports_1.2.1 assertthat_0.2.1 survey_4.0
[28] cli_2.3.0 htmltools_<IP_ADDRESS> tools_4.0.3
[31] lmerTest_3.1-3 coda_0.19-4 gtable_0.3.0
[34] glue_1.4.2 reshape2_1.4.4 Rcpp_1.0.6
[37] cellranger_1.1.0 vctrs_0.3.6 nlme_3.1-151
[40] insight_<IP_ADDRESS> xfun_0.21 ps_1.6.0
[43] openxlsx_4.2.3 rvest_0.3.6 lifecycle_1.0.0
[46] statmod_1.4.35 MASS_7.3-53 zoo_1.8-8
[49] scales_1.1.1 hms_1.0.0 parallel_4.0.3
[52] sandwich_3.0-0 RColorBrewer_1.1-2 yaml_2.2.1
[55] curl_4.3 stringi_1.5.3 highr_0.8
[58] zip_2.1.1 rlang_0.4.10 pkgconfig_2.0.3
[61] evaluate_0.14 lattice_0.20-41 labeling_0.4.2
[64] tidyselect_1.1.0 plyr_1.8.6 magrittr_2.0.1
[67] bookdown_0.21 R6_2.5.0 generics_0.1.0
[70] multcomp_1.4-15 DBI_1.1.1 mgcv_1.8-33
[73] pillar_1.4.7 haven_2.3.1 foreign_0.8-81
[76] withr_2.4.1 nnet_7.3-15 survival_3.2-7
[79] abind_1.4-5 modelr_0.1.8 crayon_1.4.1
[82] utf8_1.1.4 rmarkdown_2.6 grid_4.0.3
[85] readxl_1.3.1 data.table_1.13.6 reprex_1.0.0
[88] digest_0.6.27 webshot_0.5.2 xtable_1.8-4
[91] numDeriv_2016.8-1.1 munsell_0.5.0 viridisLite_0.3.0
[94] mitools_2.4


Part of elita.models View In Hierarchy
| Method | __init__ | Undocumented |
| Method | is_action | Undocumented |
| Method | __getitem__ | Undocumented |
| Method | __setitem__ | Undocumented |
| Method | __delitem__ | Undocumented |
| Method | __iter__ | Undocumented |
| Method | __len__ | Undocumented |
| Method | __keytransform__ | Undocumented |


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Whether existing as one complex or as independent enzymes, the reactions catalysed by types I and II fatty acid synthases are the same. Both systems are primed with acetyl-CoA, and then add 2-carbon units to the growing chain using malonyl-CoA as substrate. Type I FAS (EC <IP_ADDRESS>) has seven different catalytic activities. Acyl carrier protein (ACP) shuttles the substrate around.
Activation/Priming
In type I FAS, the enzyme must first be activated by attaching the pantheine arm of CoA to the acyl carrier protein (ACP) domain, which is accomplished by the phosphopantetheinyl transferase (PPT) domain.
The first committed step of fatty acid synthesis in both systems is the carboxylation of acetyl-CoA to malonyl-CoA. This is achieved by the type II enzyme acetyl-CoA carboxylase (ACC), or by the equivalent type I acetyltransferase (AT; EC <IP_ADDRESS>) domain. In type II systems, ACC is the major site of regulation that controls the rate of fatty acid synthesis, since ACC requires polymerisation to be active.
The AT domain of type I FAS loads the acetate primer from acetyl-CoA onto the ACP pantheine arm, while in type II FAS the acetate primer is directly transferred from acetyl-CoA to the enzyme beta-ketoacyl-ACP synthase III (bacterial FabH), which catalyses the first condensation reaction in the chain elongation cycle.
Elongation
The elongation cycle begins with the transfer of the acetate primer by ACP to the ketoacyl synthase (KS; EC <IP_ADDRESS>) domain in type I. The empty ACP then moves to the malonyl transacylase (MPT; EC <IP_ADDRESS>) domain to acquire the elongation substrate malonyl-CoA, which it delivers to the KS domain for chain elongation. The intermediate is then processed three times to produce a saturated fatty acid - this involves the ketoacyl reductase (KR; EC <IP_ADDRESS>) domain, the dehydratase (DH; EC <IP_ADDRESS>) domain, and the enoyl reductase (ER; EC <IP_ADDRESS>) domain, with ACP shuttling the intermediates between active sites, before returning the substrate to the KS domain. The two reduction reactions require NADPH oxidation to NADP+. This reaction cycle is repeated, adding two-carbon units to the growing chain using malonyl-CoA as substrate, to produce a saturated fatty acid of the desired length. The final enzyme, thioesterase (EC <IP_ADDRESS>), catalyses the removal of the ACP from the fatty acid chain.
Type II FAS employ four individual enzymes for each of these steps.
Termination
The
process is terminated when the fatty acid reaches 16 (palmitic
acid) or 18 (stearic acid) carbons in length. The fatty acid is then released from the
enzyme and can undergo
separate additional elongation and/or unsaturation to
yield other fatty acid molecules.


You have been invited to take part in a research study named Active Learning. The study is designed to investigate how people learn in dynamic tasks. It will be conducted by Professor Todd M. Gureckis who is the principal investigator, at the Department of Psychology, Faculty of Arts & Science, New York University.
If you agree to be in this study, you may be asked to: look at pictures of objects (e.g., geometric figures or everyday objects) presented on a computer display, listen to sounds, or observe lights controlled by the computer and to make judgments about them using a keyboard, mouse, or switch attached to a computer. This study will take around 20 minutes to complete, and you will receive $1.75 for your participation. As an additional incentive to try your best at the task, you will earn a small cash bonus of around $0.75 (max $2.00) based on your performance.
When you complete the study, a thorough verbal and written explanation of it will be provided. In addition, by agreeing to participate, you understand you must be 18 years or older to participate.
There are no known risks associated with your participation in this research beyond those of everyday life. Although you will receive no direct benefits, this research may help the investigator understand how people learn new categories.
Confidentiality of your research records will be strictly maintained. We assign code numbers to each participant so that data is never directly linked to individual identity, and we are interested in group results rather than the responses of particular individuals. Data are kept in out laboratory and are only viewed by the investigators. These data files are kept on our computer indefinitely.
Taking part in this study is voluntary. Not taking part or withdrawing after the study has begun will result in no loss of services from NYU to which you are otherwise entitled. You have the right to skip or not answer any questions you prefer not to answer.
If there is anything about the study or your participation that is unclear or that you do not understand, if you have questions or wish to report a research-related problem, you may contact the principal investigator, Todd M. Gureckis at 6 Washington Place (409), New York, NY, 10003,<PHONE_NUMBER><EMAIL_ADDRESS>For questions about your rights as a research participant, you may contact the University Committee on Activities Involving Human Subjects, New York University, 665 Broadway, Suite 804, New York, NY 10012, at<EMAIL_ADDRESS>or<PHONE_NUMBER>.


{% extends 'web/base.html' %} {% load bootstrap3 %} {% block title %}Past Studies{% endblock %} {% block flash %} {% if form.errors %}
Your username and password didn't match. Please try again.
Your account doesn't have access to this page. To proceed, please login with an account that has access.
{% else %}Please login to see this page.
{% endif %}You have not yet participated in any studies.
{% endfor %}


--- layout: layout title: Component Library Playground components: - Accordion - CountdownTimer - Navigation - Tabs - Tooltips - ReadingPosition ---


---
layout: post
title: 50 by 50
date: '2006-09-09 15:28:00 -0700'
mt_id: 1136
blog_id: 1
post_id: 1136
basename: 50-by-50
categories:
- personal
---
I have really been remiss in setting goals for myself since my three lovely daughters arrived in my life. My wife just did her 50 goals to accomplish by 50, so I went ahead and figured out my 50. I once had a list a lot longer than this but it got lost in various Palm-related kerfluffles. I also realized through this exercise that I have a lot of goals that I couldn't imagine putting off till 50—they were much more shorter-term. So I'm going to need to write up "in the next year" and "in the next five years" lists and I'll post them here if they're shareable.
- Start a software company
- Take my family to Disney World
- Learn Japanese
- Renew my vows
- Skydive (apparently with Sandi)
- Plant a Japanese garden
- Own a house boat on Lake Powell
- Try skiing again
- Buy a piece (or pieces) of art that I love
- Write an encyclopedia of Phoenix history
- Start a regular poker night
- Write a movie script
- Develop a hobby with each of the other four Browns
- Backpack with family
- Pay for The Girls' college
- Host a salon
- Restore a classic Mini
- Explore Arizona thoroughly
- Get a patent
- Drive on the Autobahn
- Explore and hike the mountains of Phoenix
- Have no debt besides small mortgage
- Visit Fallingwater and Taliesin
- Buy a pink MINI convertible for Sandi
- Write a book on Phoenix history
- Write a novel
- Finish my PhD in history
- Stay in Glacier National Park
- Ride a train from St. Paul to Seattle
- Learn to SCUBA dive
- Write Whole Ass It
- Teach my girls to drive
- Learn to cook
- Own a jet ski or boat
- Write a Mac OS X application
- Help The Girls start a business
- Further capitalism somehow
- Hike the Grand Canyon and stay at Phantom Ranch
- Volunteer at the Pioneer Arizona Living History Museum
- Visit Ouray, Colorado
- See Granite Reef Dam
- Visit Europe
- Vacation at a tropical paradise
- Start a non-profit relating to history
- Learn to play the piano and/or guitar
- Write a song
- Try out for an acting role
- Watch a favorite play
- Learn to appreciate classical music and jazz
- Write an article and have it published
Some of them are lofty and ambitious, but why set goals if they're easily attainable. Some of them also rely on a my children's desire to do those things and I will only work on those goals if they are willing and interested in them as well—I'm not in the practice of forcing anything on anyone.


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Location: 1234 west 800 south Bountiful, Utah
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Jacob Robertson
Bountiful, Utah
Jacob Robertson
Bountiful, Utah
Jacob Robertson
Bountiful, Utah
Jacob Robertson
Bountiful, Utah


I am currently a developer that is working on achieving a bachelor's degree for programming at Seneca College.
There is nothing I love more than hanging out with friends, whether it be eating or participating in an activity
I am always up for some fun.
If you wanted to know a bit more about my technical skills here is a chart for you :)
Windows • UNIX • Linux • IBM OS/400 • MacOS
C • C++ • Java • Javascript • MySQL
Oracle • DB2/400 • SQL server • SQL*Plus • MongoDB
NodeJS • ExpressJS • ReactJS • KnockOutJS • Bootstrap • HTML • CSS • JavaScript • AJAX • JQuery • AngularJS
GIT • MS Visual Studio • MS Visual Studio Code • MS Office • IntelliJ Idea • IBM iSeries Access for Windows • Oracle SQL Developer • Oracle VirtualBox • StarUML • WinSCP • FileZilla • Adobe Photoshop • Adobe Illustrator
With supporting text below as a natural lead-in to additional content.
With supporting text below as a natural lead-in to additional content.
Go somewhereThis was an ongoing project that was devised by professors at Seneca in order to help students learn the ropes of Web Programming from basic HTML to ReactJS and AngularJS using MEAN stack.
There are 2 projects that I want to mention: First is the NodeJS project in which we had to create a new database on MongoDB and have that wired to our NodeJS web application. This project was really good at providing a real-world scenario because it involved username and password validation as well as CRUD operations for the MongoDB Database. Second, is the AngularJS app where we converted our knowledge from ReactJS to AngularJS using the Component in order to create a page that is responsive and does not need the entire page to refresh for new content.
This was a project that was meant for a hypothetical company(Purfect Pets) that wanted a website to display the pets that were up for adoption.
Purfect Pets was meant to "Break the Ice" for using JSON objects. In this project we used a JSON object of pets and parsed the information to be put into the DOM.
In this project I learned the importance of JSON objects and how useful it would be to use APIs to grab this information and form tables to show relevant information.
This project was the very first project I worked on using Java as a transition from C++.
I used IntelliJ Idea to create a random number generator module which was used several times throughout the program. A Lotto System program was used to generate numbers and match to the tickets that were created in order to dispense top 3 prizes to the customers' respective account balances.
This project is meant to create a web app that enabled board gamers to find one and another and play together.
A website I am working on in order to give my two cents on food because I avidly eat food from all cultures and am not afraid to try new things


TypoScript Language support
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This plugin is in an early draft stage. The project aims to provide a fully OpenSource plugin for the TypoScript configuration language.


MyGame
This is a great game

INT LABORATORY
Character is doing something

CHARACTER
(very serious)
So my name is Character and I'm saying something!


Sign-in
Sign-up
About
Districts
Home
SIGN-UP
First Name
Last Name
Phone Number
Email Address
Password
Your Password must have atleast 8 characters
Repeat Password
Sign-up


 Explore cultural treasures in extraordinary detail from hidden gems to great masterpieces. Experiment with open and protected digital cultural content and unlock your creativity!
EXPLORE


This house comes with amazing features
LAND SIZE: 200SQM
PRICE:₦3Omilliom
FACILITIES
-Underground Cable
-24 hour power supply
_Central sewage collection
- streetlights with CCTV cameras
WHY BUY THIS ESTATE
-Appreciation by over 200% in 12 months
-Suitable for class and style
For enquiry, contact:
whatsapp : +234-8036648566


Learn the Lingo
Learn the Lingo
A Game By
The Listeners
About the Developers
User Name
Spanish
French
Latvian
German
Missile Defense
Snakes On A Plane
Kitty Cats
Extraterrestrials have launched a missile strike in an attempt to clear solar space for their new time share resort. Unfortunately, this comes at the expense of Planet Kahn-Baan and the Kahn-Baanians. To save the planet and its residents a hero must rise up. Realize your destiny and become that hero!
How to Play:
To begin the user must enter a username, select a language, and then click the ‘Let’s Play’ button. This will direct the user to the game page where the stage will be set.
You will be presented with 5 missiles that must be eliminated before they strike the planet. Click the specific missile that you would like to target and correctly type out the english translation of the vocabulary word presented.
After the game has concluded see where you rank amongst the legions of heroes and try again!


Why bother?
 People in IRC can be stunningly pragmatic and striaghforward sometimes.
“I know,” I said. “I don’t expect to.”
“So why bother?”
That, I think, is an excellent question. Why bother?
Why bother?
If you spend enough time around me, you’ll learn a few things pretty quickly: I love to mentor people, I care about problems, and I love the idea of finding a hack for any problem. As a Christian, I believe that the Church is to be the hands and feet of Jesus here on Earth. Among so many other things, Jesus deeply loved people and was here to heal and to help. Therefore, I’m deeply invested in this whole idea of helping humanity. You may have your own reasons that have nothing to do with mine, but I’ve found that there are a lot of people who love humanity and want to see all of us in a better state, too.
I think just about everyone agrees that we want to see people in a better place. We want to end hunger, fight the ravages of disease, and give clean water to those who don’t have it. It’s also no surprise to anyone that there are a million approaches to this very large problem, as there should be. We have thousands upon thousands of charities all over the world to solve any number of ills in the world. Government programs abound. Good neighbors and global citizens are doing their part on their own. So if there are so many people organized around humanitarian causes, why bother with hackathons? We all know that this is a long game, so what good does a 24-hour event do?
Playing the long game
We definitely can’t save the world in 24 hours, and when doing events I don’t expect to. In fact, I wouldn’t even say that “saving the world” is my goal. Many times we as a species can’t even agree on basic things, let alone what “saving the world” may look like, but we can almost always agree on what makes life better for people, so that’s what I pursue: making life better.
Another thing you’ll hear me say if you spend any time with me is my advice to tackling massive problems: “We will eat this elephant a bite at a time.” The hackathon (or mass collaboration or whatever you want to call it) is just one way of taking a bite out of that giant elephant. I don’t expect that a single hackathon will be able to solve world hunger or end global warming. These are massive problems that will require humanity to collectively rally to find solutions, but that’s exactly what we’re doing. At these events, we are bringing together thinkers and planners, philanthropists, engineers, business people, and more, all in the interest of chipping away at the problem.
On top of of that, I have noticed something else: hackathons give us a chance to find new solutions or adapt old ones in ways that that are relevant to us locally. What does this mean? Reducing air pollution in Beijing looks very different than reducing air pollution in Bangalore or Los Angeles. Different causes and factors are involved, there are different cultures to account for, and wildly different availability of resources. A hackathon targeting air pollution that’s hosted in Los Angeles probably won’t have ideas that apply as readily in Beijing, but they know their own city quite well. And in that context, it’s 100% okay to think about your own town. It’s not selfish; it’s what you know best!
Final thoughts
So in conclusion, I do this not because I expect to save the world in 24 hours, but because every event moves the needle just a litte further in a positive direction. Every idea, even the crappy ones, all of them contribute to solving these problems. We build things that matter.


{{ full_name }} {{ name }} Venue Zerodha Head Office #153/154, 4th Cross, J.P Nagar 4th Phase Opp. Clarence Public School Bengaluru - 560078 Date 18th January 2020 9:00 AM to 6:30 PM


{% extends "layout.html" %} {% block content %}
| # | Status | Latest Comment |
|---|---|---|
| RITM000001 |
Approved
Backlog
In Development
Testing
Release Scheduled
Done
|
Theobald Mulligan on Monday, 2020-02-03 07:55 AM EST: Release scheduled for next week |
| RITM000002 |
Approved
Backlog
In Development
Testing
Release Scheduled
Done
|
Laurel Haig on Friday, 2020-01-03 12:02 PM EST: We're converting the data into the new databsae now |
| RITM000003 |
Approved
Backlog
In Development
Testing
Release Scheduled
Done
|
Lilly Soriano on Thursday, 2020-01-02 04:55 PM EST: We met today to discuss requirements |
| RITM000003 |
Approved
Backlog
In Development
Testing
Release Scheduled
Done
|
Diana Rennell on Wednesday, 2020-02-26 10:37 AM EST: Feature released yesterday |
| RITM000004 |
Approved
Backlog
In Development
Testing
Release Scheduled
Done
|
Lilly Soriano on Thursday, 2020-01-02 04:55 PM EST: Regression tests failed |


class SerializedBytes<T : Any> : OpaqueBytes
A type safe wrapper around a byte array that contains a serialised object. You can call SerializedBytes.deserialize to get the original object back.
<init> |
A type safe wrapper around a byte array that contains a serialised object. You can call SerializedBytes.deserialize to get the original object back. SerializedBytes(bytes: ByteArray) |
summary |
val summary: String |


That I my anxious zeal may prove,
Your pangs to sooth and aid your love,
A single moment will we not delay,
Will lead you to her room this very day.
And shall I see her?—Have her?
No!
She to a neighbour’s house will go;
But in her atmosphere alone,
The tedious hours meanwhile you may employ,
In blissful dreams of future joy.
Can we go now?
’Tis yet too soon.
Some present for my love procure! (Exit.)
Presents so soon! ’tis well! success is sure!
Full many a goodly place I know,
And treasures buried long ago;
I must a bit o’erlook them now. (Exit.)
I would give something now to know,
Who yonder gentleman could be!
He had a gallant air, I trow,
And doubtless was of high degree:
That written on his brow was seen—
Nor else would he so bold have been. (Exit.)
Come in! tread softly! be discreet!
Begone and leave me, I entreat!
Not every maiden is so neat (Exit.)
Welcome sweet twilight, calm and blest,
That in this hallow’d precinct reigns!
Fond yearning love, inspire my breast,
Feeding on hope’s sweet dew thy blissful pains!
What stillness here environs me!
Content and order brood around.
What fulness in this poverty!
In this small cell what bliss profound! (He throws himself on the leather arm-chair beside the bed)
Receive me thou, who hast in thine embrace,
Welcom’d in joy and grief the ages flown!
How oft the children of a by-gone race
Have cluster’d round this patriarchal throne!
Haply she, also, whom I hold so dear,
For Christmas gift, with grateful joy possess’d,
Hath with the full round cheek of childhood, here,
Her grandsire’s wither’d hand devoutly press’d.
Maiden! I feel thy spirit haunt the place,
Breathing of order and abounding grace.
As with a mother’s voice it prompteth thee,
The pure white cover o’er the board to spread,
To strew the crisping sand beneath thy tread.
Dear hand! so godlike in its ministry!
The hut becomes a paradise through thee!
And here— (He raises the bed-curtain.)
How thrills my pulse with strange delight!
Here could I linger hours untold;
Thou, Nature, didst in vision bright,
The embryo angel here unfold.
Here lay the child, her bosom warm
With life; while steeped in slumber’s dew,
To perfect grace, her godlike form,
With pure and hallow’d weavings grew!
And thou! ah here what seekest thou?
How quails mine inmost being now!
What wouldst thou here? what makes thy heart so sore?
Unhappy Faust! I know thee now no more.
Do I a magic atmosphere inhale?
Erewhile, my passion would not brook delay!
Now in a pure love-dream I melt away.
Are we the sport of every passing gale?
Should she return and enter now,
How wouldst thou rue thy guilty flame!
Proud vaunter—thou wouldst hide thy brow,—
And at her feet sink down with shame.
Quick! quick! below I see her there.
Away! I will return no more!
Here is a casket, with a store
Of jewels, which I got elsewhere
Just lay it in the press; make haste!

But child is child, and play is play.
I know not—shall I?
Do you ask?
Perchance you would retain the treasure?
If such your wish, why then, I say,
Henceforth absolve me from my task,
Nor longer waste your hours of leisure.
I trust you’re not by avarice led!
I rub my hands, I scratch my head,— (He places the casket in the press and closes the lock.)
Now quick! Away!
That soon the sweet young creature may
The wish and purpose of your heart obey;
Yet stand you there
As would you to the lecture-room repair,
As if before you stood,
Arrayed in flesh and blood,
Physics and metaphysics weird and grey!—
Away! (Exeunt.)
Here ’tis so close, so sultry now, (She opens the window.)
Yet out of doors ’tis not so warm.
I feel so strange, I know not how—
I wish my mother would come home.
Through me there runs a shuddering—
I’m but a foolish timid thing! (While undressing herself she begins to sing.)
There was a king in Thule,
True even to the grave;
To whom his dying mistress
A golden beaker gave.
At every feast he drained it,
Naught was to him so dear,
And often as he drained it,
Gush’d from his eyes the tear.
When death came, unrepining
His cities o’er he told;
All to his heir resigning,
Except his cup of gold.
With many a knightly vassal
At a royal feast sat he,
In yon proud hall ancestral,
In his castle o’er the sea.
Up stood the jovial monarch,
And quaff’d his last life’s glow,
Then hurled the hallow’d goblet
Into the flood below.
He saw it splashing, drinking,
And plunging in the sea;
His eyes meanwhile were sinking,
And never again drank he. (She opens the press to put away her clothes, and perceives the casket.)
How comes this lovely casket here? The press
I locked, of that I’m confident.
’Tis very wonderful! What’s in it I can’t guess;
Perhaps ’twas brought by some one in distress,
And left in pledge for loan my mother lent.
Here by a ribbon hangs a little key!
I have a mind to open it and see!
Heavens! only look! what have we here!
In all my days ne’er saw I such a sight!
Jewels! which any noble dame might wear,
For some high pageant richly dight!
This chain—how would it look on me!
These splendid gems, whose may they be? (She puts them on and steps before the glass.)
Were but the ear-rings only mine!
Thus one has quite another air.
What boots it to be young and fair?
It doubtless may be very fine;
But then, alas, none cares for you,
And praise sounds half like pity too.
Gold all doth lure,
Gold doth secure
All things. Alas, we poor!
By all rejected love! By hellish fire I curse,
Would I knew aught to make my imprecation worse!
What aileth thee? what chafes thee now so sore?
A face like that I never saw before!
I’d yield me to the devil instantly,
Did it not happen that myself am he!
There must be some disorder in thy wit!
To rave thus like a madman, is it fit?
Think! only think! The gems for Gretchen brought,
Them hath a priest now made his own!—
A glimpse of them the mother caught,
And ’gan with secret fear to groan.
The woman’s scent is keen enough;
Doth ever in the prayer-book snuff;
Smells every article to ascertain
Whether the thing is holy or profane,
And scented in the jewels rare,
That there was not much blessing there.
“My child,” she cries, “ill-gotten good
Ensnares the soul, consumes the blood;
With them we’ll deck our Lady’s shrine,
She’ll cheer our souls with bread divine!”
At this poor Gretchen ’gan to pout;
’Tis a gift-horse, at least, she thought,
And sure, he godless cannot be,
Who brought them here so cleverly.
Straight for a priest the mother sent,
Who, when he understood the jest,
With what he saw was well content.
“This shows a pious mind!” Quoth he:
“Self-conquest is true victory.
The Church hath a good stomach, she, with zest,
Whole countries hath swallow’d down,
And never yet a surfeit known.
The Church alone, be it confessed,
Daughters, can ill-got wealth digest.”
It is a general custom, too.
Practised alike by king and jew.
With that, clasp, chain, and ring, he swept
As they were mushrooms; and the casket,
Without one word of thanks, he kept,
As if of nuts it were a basket.
Promised reward in heaven, then forth he hied—
And greatly they were edified.
And Gretchen!
In unquiet mood
Knows neither what she would or should;
The trinkets night and day thinks o’er,
On him who brought them, dwells still more.
The darling’s sorrow grieves me, bring
Another set without delay!
The first, methinks, was no great thing.
All’s to my gentleman child’s play!
Plan all things to achieve my end!
Engage the attention of her friend!
No milk-and-water devil be,
And bring fresh jewels instantly!
Ay, sir! Most gladly I’ll obey. (FAUST exit.)
Your doting love-sick fool, with ease,
Merely his lady-love to please,
Sun, moon, and stars in sport would puff away. (Exit.)
God pardon my dear husband, he
Doth not in truth act well by me!
Forth in the world abroad to roam,
And leave me on the straw at home.
And yet his will I ne’er did thwart,
God knows, I lov’d him from my heart. (She weeps.)
Perchance he’s dead!—oh wretched state!—
Had I but a certificate! (MARGARET comes)
Dame Martha!
Gretchen?
Only think!
My knees beneath me well-nigh sink!
Within my press I’ve found to-day,
Another case, of ebony.
And things—magnificent they are,
More costly than the first, by far.
You must not name it to your mother!
It would to shrift, just like the other.
Nay look at them! now only see!
Thou happy creature!
Woe is me!
Them in the street I cannot wear,
Or in the church, or any where.
Come often over here to me,
The gems put on quite privately;
And then before the mirror walk an hour or so,
Thus we shall have our pleasure too.
Then suitable occasions we must seize,
As at a feast, to show them by degrees:
A chain at first, pearl ear-drops then,—your mother
Won’t see them, or we’ll coin some tale or other.
But, who, I wonder, could the caskets bring?
I fear there’s something wrong about the thing! (A knock.)
Good heavens! can that my mother be?
’Tis a strange gentleman, I see.
Come in! (MEPHISTOPHELES enters)
I’ve ventur’d to intrude to-day.
Ladies, excuse the liberty, I pray. (He steps back respectfully before MARGARET.)
After dame Martha Schwerdtlein I inquire!
’Tis I. Pray what have you to say to me?
I know you now,—and therefore will retire;
At present you’ve distinguished company.
Pardon the freedom, Madam, with your leave,
I will make free to call again at eve.
Why, child, of all strange notions, he
For some grand lady taketh thee!
I am, in truth, of humble blood—
The gentleman is far too good—
Nor gems nor trinkets are my own.
Oh ’tis not the mere ornaments alone;
Her glance and mien far more betray.
Rejoiced I am that I may stay.
Your business, Sir? I long to know—
Would I could happier tidings show!
I trust mine errand you’ll not let me rue;
Your husband’s dead, and greeteth you.
Is dead? True heart! Oh misery!
My husband dead! Oh, I shall die!
Alas! good Martha! don’t despair!
Now listen to the sad affair!
I for this cause should fear to love.

Joy still must sorrow, sorrow joy attend.
Proceed, and tell the story of his end!
At Padua, in St. Anthony’s,
In holy ground his body lies;
Quiet and cool his place of rest,
With pious ceremonials blest.
And had you naught besides to bring?
Oh yes! one grave and solemn prayer;
Let them for him three hundred masses sing!
But in my pockets, I have nothing there.
No trinket! no love-token did he send!
What every journeyman safe in his pouch will hoard
There for remembrance fondly stored,
And rather hungers, rather begs than spend!
Madam, in truth, it grieves me sore,
But he his gold not lavishly hath spent.
His failings too he deeply did repent,
Ay! and his evil plight bewail’d still more.
Alas! That men should thus be doomed to woe!
I for his soul will many a requiem pray.

Ah no,
That time hath not yet come for me.
If not a spouse, a gallant let it be.
Among heaven’s choicest gifts, I place,
So sweet a darling to embrace.
Our land doth no such usage know.
Usage or not, it happens so.
Go on, I pray!
I stood by his bedside.
Something less foul it was than dung;
’Twas straw half rotten; yet, he as a Christian died.
And sorely hath remorse his conscience wrung.
“Wretch that I was,” quoth he, with parting breath,
“So to forsake my business and my wife!
Ah! the remembrance is my death,
Could I but have her pardon in this life!”—
Dear soul! I’ve long forgiven him, indeed!
“Though she, God knows, was more to blame than I.”
He lied! What, on the brink of death to lie!
If I am skill’d the countenance to read,
He doubtless fabled as he parted hence.—
“No time had I to gape, or take my ease,” he said,
“First to get children, and then get them bread;
And bread, too, in the very widest sense;
Nor could I eat in peace even my proper share.”
What, all my truth, my love forgotten quite?
My weary drudgery by day and night!
Not so! He thought of you with tender care.
Quoth he: “Heaven knows how fervently I prayed,
For wife and children when from Malta bound;—
The prayer hath heaven with favour crowned;
We took a Turkish vessel which conveyed
Rich store of treasure for the Sultan’s court;
It’s own reward our gallant action brought;
The captur’d prize was shared among the crew
And of the treasure I received my due.”
How? Where? The treasure hath he buried, pray?
Where the four winds have blown it, who can say?
In Naples as he stroll’d, a stranger there,—
A comely maid took pity on my friend;
And gave such tokens of her love and care,
That he retained them to his blessed end.
Scoundrel! to rob his children of their bread!
And all this misery, this bitter need,
Could not his course of recklessness impede!
Well, he hath paid the forfeit, and is dead.
Now were I in your place, my counsel hear;
My weeds I’d wear for one chaste year,
And for another lover meanwhile would look out.
Alas, I might search far and near,
Not quickly should I find another like my first!
There could not be a fonder fool than mine,
Only he loved too well abroad to roam;
Loved foreign women too, and foreign wine,
And loved besides the dice accurs’d.
All had gone swimmingly, no doubt,
Had he but given you at home,
On his side, just as wide a range.
Upon such terms, to you I swear,
Myself with you would gladly rings exchange!
The gentleman is surely pleas’d to jest!
Now to be off in time, were best!
She’d make the very devil marry her. (To MARGARET.)
How fares it with your heart?
How mean you, Sir?
The sweet young innocent! (aloud)
Ladies, farewell!
Farewell!
But ere you leave us, quickly tell!
I from a witness fain had heard,
Where, how, and when my husband died and was interr’d.
To forms I’ve always been attached indeed,
His death I fain would in the journals read.
Ay, madam, what two witnesses declare
Is held as valid everywhere;
A gallant friend I have, not far from here,
Who will for you before the judge appear.
I’ll bring him straight.
I pray you do!
And this young lady, we shall find her too?

I in his presence needs must blush for shame.
Not in the presence of a crowned king!
The garden, then, behind my house, we’ll name,
There we’ll await you both this evening.


by Alexander D.
It's easy to make big plans and ask big questions, but a lot harder to follow them through. Find out what happens to Harry Potter-Evans-Verres, Hermione, Draco, and everyone else once they grow into their roles as leaders, leave the shelter of Hogwarts, and venture out into a wider world of intrigue, politics, and war.
Well done so far, and the characters ring truer of mine than most.
[A]bout half of the chapters leave me wondering if I've been pranked by the love child of Andy Kaufman and Milan Kundera,who shuttled back and forth between his parents and had David Foster Wallace as a babysitter.
My thanks to 4t0m for his copyediting assistance.


Pixelsign
Create stunning showcases
Spice up your website with live HTML5 mockups of popular devices
Grab it on Github


Shrinking lake in Mali spells end of line for family of fishermen
by Arouna SissokoLAC WEGNIA, Mali (Reuters) - Modeste Traore has lived his whole life by Lake Wegnia, in Mali’s Sahel region, and its fish have provided him with a livelihood that supports his extended family of 14 children.
But as temperatures rise, evaporation increases and the lake shrinks - phenomena experts blame on global climate change - so do the chances of his children following in the fisherman’s footsteps.

“During the rainy season there is a lot of water but as soon as it’s over, there is no water left in the lake. We are fishermen. I don’t think our children will be.”
Lake Wegnia, or Lac Wegnia in French, is located in the Sahel region of Koulikoro, around 120 km north of the capital Bamako.
Some 12,000 people including fishermen, farmers and herders depend on it for food, water and income, while its fresh waters and marshes also contain rich biodiversity.
But the lake has shrunk by 20 percent since 2017, according to figures from CIDR (International Centre for Development and Research) and analysis from Hydrosolutions Ltd, placing huge pressure on local communities.
The United Nations describes the Sahel region - a wide strip of land running across northern Africa from the Atlantic Ocean to the Red Sea - as “one of the most environmentally degraded regions in the world”.
The organisation has projected temperatures there to increase 1.5 times higher than the global average, and says the Sahel is regularly hit by droughts and floods.
Food insecurity, the fight for arable land and fast population growth can lead to conflict, although Lake Wegnia so far has been spared the violence prevalent in other parts of Mali.
Aid group Caritas Suisse and the Eco-Lac Wegnia project it is leading are working to change agricultural practices, improve water management and fight the effects of global warming.
Eco-Lac Wegnia’s local coordinator Moussa Savagodo said failing to make changes quickly could mean the lake disappears altogether in less than 5 years.
Villagers in Wegnia and Kononi-Sirakoro have already planted 56,000 trees in the last two years and are better managing their water resources by building stone cordons to help the soil retain what rainfall there is.
Despite the progress and international support, though, Traore is now raising cattle, because he can’t catch enough fish to feed his family.