'Jumbo Snorlax' Joins The Pokémon Build-A-Bear Range
And he comes with pyjamas
by Ryan CraddockBuild-A-Bear's ever-growing range of Pokémon plushies has had yet another friendly face join the ranks, with 'Jumbo-Sized Snorlax' available to buy as we speak.
The new toy can be picked up from both the UK and US Build-A-Bear stores as an online exclusive; for Snorlax and the full set of accessories, you'll need to hand over £76 / $99.50, or you can grab the plush on its own for £54 / $70. A smaller, non-jumbo Snorlax is also available for a little less at each store.
According to the website, Snorlax's mighty 60cm size means that it can only be shipped with next-day delivery. Here's the product description and some photos:
Catch the biggest Zzzz's ever when you add Jumbo-Sized Snorlax to your Pokémon lineup! Its jumbo 60 cm. size makes this extra-large Build-A-Bear Snorlax one of the snuggliest Pokémon around! This online exclusive bundle features the Normal-type Pokémon ready to doze off with a cosy printed Snorlax crest robe, sleeping cap and Build-A-Bear Workshop Exclusive Snorlax Pokémon TCG Card! Please note: due to Jumbo-Sized Snorlax's extra-snuggly size, this furry friend cannot fit inside our standard-sized gift box or Cub Condo.
Price includes:
- Jumbo-Sized Snorlax
- Jumbo Snorlax Robe and Sleeping Cap
- Build-A-Bear Workshop Exclusive Snorlax Pokémon TCG Card
Snorlax joins the likes of Pichu, Psyduck and Snubbull, Piplup, Vulpix, Meowth and more in the Pokémon Build-A-Bear range.
Will you be getting one of these? Can you think of somewhere to actually put it? Let us know in the comments below.


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|
UTPP
|
Abstract base for all reporters. More...
#include <test_reporter.h>
Public Member Functions | |
| void | SetTrace (bool on_off) |
| Controls test tracing feature. | |
| virtual void | SuiteStart (const TestSuite &suite) |
| Invoked at the beginning of a test suite. More... | |
| virtual void | TestStart (const Test &test) |
| Invoked at the beginning of a test. | |
| virtual void | ReportFailure (const Failure &failure) |
| Called when a test has failed. | |
| virtual void | TestFinish (const Test &test) |
| Invoked at the end of a test. | |
| virtual int | SuiteFinish (const TestSuite &suite) |
| Invoked at the end of a test suite. More... | |
| virtual int | Summary () |
| Generate results report. | |
Protected Attributes | |
| int | suite_test_count |
| number of tests in suite | |
| int | suite_failed_count |
| number of failed tests in suite | |
| int | suite_failures_count |
| number of failures in suite | |
| int | suite_time_msec |
| total suite running time in milliseconds | |
| int | total_test_count |
| total number of tests | |
| int | total_failed_count |
| total number of failed tests | |
| int | total_failures_count |
| total number of failures | |
| int | total_time_msec |
| total running time in milliseconds | |
| int | suites_count |
| number of suites ran | |
| bool | trace |
| true if tracing is enabled | |
Abstract base for all reporters.
|
virtual |
Invoked at the end of a test suite.
Reimplemented in UnitTest::ReporterDbgout, and UnitTest::ReporterStdout.
|
virtual |
Invoked at the beginning of a test suite.
Records the beginning of a new test suite
Reimplemented in UnitTest::ReporterDeferred, UnitTest::ReporterDbgout, and UnitTest::ReporterStdout.


A function \(f\) defined on a set \(S \subseteq \mathbb C\) is a rule that assigns to each \(z \in S\) a complex number \(w\), called the value of \(f\) at \(z\) and is denote by \(f(z)\).
We call the set \(S\) the domain of definition of \(f\). Note we can't use the term "domain" because we've already defined it previously to mean an open, nonempty, and connected set.
Note that both the domain of definition and the rule should be specified for a complete definition of a function. If the domain of definition is not given, it is assumed to be the largest possible set.
For example, the function \(w = f(z) = \frac{1}{z} = z^{-1}\) is well defined except where \(z = 0\), so the domain of definition is \(\mathbb C \setminus \{0\}\).
Suppose we have a function \(w = f(z)\) for \(z = x + iy\) and \(w = u + iv\). Then we have \(u + iv = f(x + iy)\), so we can express the complex-valued function \(f\) in terms of a pair of real-valued function of \(x\) and \(y\).
\[f(z) = u(x, y) + iv(x, y)\]
And if we use polar coordinates
\[f(re^{i\theta}) = u + iv\]
we then have
\[f(re^{i\theta}) = u(r, \theta) + iv(r, \theta)\]
For example, take the function \(f(z) = z^2\). Then we have \(f(x + iy) = (x + iy)^2 = x^2 - y^2 + 2ixy\). So then
\[u(x, y) = x^2 - y^2\]
and
\[v(x, y) = 2xy\]
If we use polar coordinates,
\[f(re^{i\theta}) = (re^{i\theta})^2 = r^2 e^{i2\theta} = r^2 \cos{2\theta} + ir^2 \sin{2\theta}\]
So
\[u(r, \theta) = r^2 \cos{2\theta}\]
and
\[v(r, \theta) = r^2 \sin{2\theta}\]
If \(v\) every always equals 0, we call \(f\) a real-valued function of a complex variable.
A polynomial of degree \(n\) is the function
\[P(z) = a_0 + a_1 z + a_2 z^2 + \dots + a_n z^n\]
for \(a_i \in \mathbb C, n \in \mathbb Z^+, \text{ and } a_n \neq 0\). Note that the requirement of \(a_n \neq 0\) simply means that the \(n\)th degreen polynomial actually has an \(n\)th term .
Quotients \(\displaystyle \frac{P(z)}{Q(z)}\) are called rational functions and are defined for all \(z\) such that \(Q(z) \neq 0\).
The image of a point \(z_0\) in the domain of definition \(S\) of a function \(f\) is the point \(w_0 = f(z_0)\). The term image of \(z\) is equivalent to the term value of \(f\) at \(z\).
We can also take images of sets . Let \(T \subseteq S\), then the image of \(T\) is the set of images of all the points in \(T\). We denote this by \(f(T)\).
The range of a function \(f\) is the image of the domain of definition \(S\). We denote this by \(f(S)\).
The inverse image , or preimage of a point \(w\) is the set of all \(z \in S\) that have an image of \(w\). Note that the preimage can have one, or many points in \(S\). My professor denotes this as \(f^{-1}(\{w\})\) to emphasize that this is not the inverse function of \(f\), it's just a set. Because the range is defined as the image of the domain of definition, the preimage is guaranteed to be nonempty.
A function \(f\) is called injective , or one-to-one if whenever \(f(z_1) = f(z_2)\), then \(z_1 = z_2\).
A function \(f\) is called surjective , or onto if for every \(w\) in the range of \(f\) there exists a \(z\) in the domain of definition of \(f\) such that \(f(z) = w\).
Note that how we have defined the range of a function causes every function to be surjective. This is in conflict with most textbooks that also define something called a codomain , or which the range of \(f\) is a subset. However, this is what my particular textbook uses, so it's what I will stick with here.
The image \(w\) of a point \(z\) in the domain of definition \(S\) of a function \(f\) with range \(f(S)\).
The following is an example of a function that is not one-to-one (injective). Note that \(f(z_1) = f(z_2)\) yet \(z_1 \neq z_2\).
When we take the inverse image (preimage) of a point in the range of a function, we don't change the direction of the arrows we aren't finding an inverse function, only tracing the line(s) back to where they came from.
Note that the preimage is a set that could possibly contain one or more points in the domain of definition.
Graphing complex functions is hard . We have 2 dimensional inputs, and 2 dimensional outputs. That means we're working with a 4 dimensional function — even for the simple ones. Since \(w = f(z)\) is a complex number, both \(z\) and \(w\) can be interpreted on points in a plane. Therefore, we can draw the \(z\) and \(w\) planes separately. We can therefore view a function \(w = f(z)\) as a transformation , or a mapping from points in the \(z\)-plane to points in the \(w\)-plane.
We can describe these transformations with the terms translation , rotation , reflection , and scaling , or stretching .
\(w = f(z) = z + 1 = (x + 1) + iy\) is a function that shifts , or translates points one unit to the right.
Since we know that \(i = e^{i\frac{\pi}{2}}\), the function \(f(z) = iz = ire^{i\theta} = e^{i\frac{\pi}{2}}re^{i\theta} = re^{i\theta + \frac{\pi}{2}}\) actually rotates a point \(z\) \(\frac{\pi}{2}\) radians -- that is, rotates numbers 90 degrees counterclockwise.
The function \(f(z) = \bar z = x - iy\) is a simple reflection about the real axis.
Now consider again the function \(f(z) = z^2\). We can rewrite \(f\) in terms of real-valued functions
\[u = x^2 - y^2\]
and
\[v = 2xy\]
And think of \(f\) as a transformation from the \(x\)-\(y\) plane to the \(u\)-\(v\) plane. Because we're so unfamiliar with complex functions, even for a function so simple as \(z^2\), we'll ask ourselves a series of questions about it.
I wonder what maps to a constant? Just for the heck of it, we'll set \(u = c\) for some \(c > 0\) and see what falls out.
That means we have points on the family of hyperbolas \(x^2 - y^2 = c\), or \(\displaystyle \frac{x^2}{c} - \frac{y^2}{c} = 1\) in the \(x\)-\(y\) plane being mapped to points along the straight line \(u = c\) in the \(u\)-\(v\) plane.
Now what happens if we let \(u = -c\)? Then we have points on the family of hyperbolas \(x^2 - y^2 = -c\), or \(\displaystyle \frac{y^2}{c} - \frac{x^2}{c} = 1\)
That all is if we fix \(u\) to be some constant, what happens if we fix \(v\) to be some positive constant? Then we have points along the \(2xy = c\), or \(\displaystyle y = \frac{c}{2x}\) family of hyperbolas in the \(x\)-\(y\) plane being mapped to some positive \(v = c\) in the \(u\)-\(v\) plane.
Again, what if we pick a negative constant? Then we have \(\displaystyle y = \frac{-c}{2x}\) being mapped to some negative constant \(v\) in the \(u\)-\(v\) plane.
We asked ourselves what maps to a constant, now we ask, what does a constant map to? That is, let \(x = c\) and \(y\) vary. Then \(u = c^2 - y^2\) and \(v = 2cy\) which combine to make \(\displaystyle u = c^2 - \frac{v^2}{4c^2}\)


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SHADOW HOURS
Reviewed by Harvey Karten CanWest Entertainment Director: Isaac H. Eaton Writer: Isaac H. Eaton Cast: Balthazar Getty, Peter Weller, Rebecca Gayheart, Peter Greene, Michael Dorn, Richard Moll, Johnny Whitworth, Corin Nemee, Daniel Faraldo, Benjamin Lum, Frederic Forrest, Brad Dourif, Christopher Doyle
When I was in the fourth grade, my teacher told the class, "Don't go to night clubs. They're bad places." I hadn't the foggiest idea what she was talking about. Years later my mother advised, "Don't go to night clubs. They'll take your money and you'll have nothing to show for it" (as though goods and not services were the only thing worth spending money on). I never did become much of a night-club fly but my teacher and my mom could never have imagined what goes on in some of these dark and evil places nowadays in big bad cities like L.A. For others who have rarely if ever descended into the abyss of an Ayatollah's nightmare, "Shadow Hours" is an education, and a far cry from what you'll learn from your Sociology 101 textbook. This noir movie, written and directed by Isaac H. Eaton, has the quality of David Lynch's "Lost Highway" with a touch of David Fincher's "Fight Club," plus an assortment of characters that would put a scare into Fellini. "Shadow Hours," which emerged at a recent Sundance festival, is a modern morality fable whose sound track and rapid-fire banter give the movie an absorbing edge without the enigmatic quality of the aforementioned "Lost Highway."
Interpreted in a psychological vein, we could say that "Shadow Hours" dramatizes a favorite expression of the Freudians and their ilk--those who say that the patient cannot begin to make progress toward a cure until he has descended to the depths and, absent of defenses and horrified by his torment is ready to make the painful transition to a healthier life. Eaton's film features a weak individual, Michael Holloway (Balthazar Getty of "Lost Highway"), who has just cleaned himself up after a long bout with alcohol and drugs, has settled into a miserable, $7.50- an-hour graveyard shift at an L.A. gas station, and worries alternately about the bums surrounding his office and his pregnant wife whom he can barely support. He meets Stuart Chappell (Peter Weller), who drives a sleek new Porsche and describes himself as an author who could use Michael's help in researching a book he is writing on L.A.'s sordid night life. While their relationship could remind you of that between "Fight Club"'s Tyler and Jack, Stuart is not Michael's corporeal alter ego but rather a combination of devil and guardian angel who serves as the young man's escort into the ignoble life of L.A.'s night people.
Throughout the story, images abound of masochism in the guise of freakish performance artists, gambling joints where the stakes are bets on bouts of Russian roulette, and a genuine fight club--all among the conceptions laid out by Eaton in this Gorkian lower depths, all taking place during an investigation by Det. Steve Andrianson (Peter Greene) into a murder that may or may not have been committed by the rich and debonair stranger.
Despite the film's desire to present a morality tale of one man's struggle to extricate himself from a despondent life of poverty, drugs and alcohol, Eaton may be happy enough simply to confront his viewers with an eerie sequence of impressions unwittingly confirming 92-year-old author Jacques Barzan's thesis in his current best-seller, "From Dawn to Decadence." We could, I suppose, read into this theme the notion that we human beings, having reached the depths of depravity following the world's most brutal century, have nowhere to go but up. But I suspect that titillation is this picture's principal aim, and insofar as this succeeds in captivating its viewers, "Shadow Hours" is not a bad choice to make when faced with alternatives like "The Adventures of Rocky and Bullwinkle."
Rated R. Running time: 95 minutes. (C) 2000 by Harvey Karten<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
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Direct tourism spending by visitors immediately increases public and private sector revenues. The most significant impact is on small to medium sized businesses and the subsequent creation of tourism related jobs.
Efforts to attract more foreign trade and investment will be significantly enhanced by an energetic and complimentary public/private sector funded marketing campaign to promote a country’s international tourism.
Every 1% increase in arrivals and/or per capita spending of all visitors corresponds to millions in USD increase in direct tourism spending along with a corresponding increase in associated public sector fees and taxes.
As revenues from direct spending on tourism flow through the larger economy the multiplier principle produces substantial increases to indirect revenues that drive new employment across all sectors of the economy.


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Chapter 6 Prediction
Prerequisites
You will need to install jrnoldmisc with
6.1 Prediction Questions vs. Causal Questions
Prediction vs. Causal questions can be reduced to: Do you care about \(\hat{y}\) or \(\hat{beta}\)?
Take a standard regression model, \[ y = X \beta + \epsilon . \] We can use regression for prediction or causal inference. The difference is what we care about.
In a prediction prediction problem we are interested in \(\hat{y} = X \hat{\beta}\). The values of \(\hat{\beta}\) are not interesting in and of themselves.
In a causal-inference problem we are are interested in getting the best estimate of \(\beta\), or more generally \(\partial y / \partial x\) (the change in the response due to a change in x).
If we had a complete model of the world, then we could use the same model for both these tasks. However, we don’t and never will. So there are different methods for each of these questions that are tailored to improving our estimates of those.
6.2 Why is prediction important?
Much of the emphasis in social science is on “causal” questions, and “prediction” is often discussed pejoratively. Apart from the fact that this belief is often due to a deep ignorance of statistics and the philosophy of science and a lack of introspection into their own research, there are a few reasons why understanding prediction questions.
6.3 Many problems are prediction problems
Causal inferential methods are best for estimating the effect of a policy intervention. Many problems in the political science are discussed as if they are causal, but any plausible research question is predictive since there is no plausible intervention to estimate. I would place many questions in international relations and comparative politics in this realm.
6.3.1 Counterfactuals
The fundamental problem of causal inference is a prediction problem. We do not observe the counterfactuals, so we must predict what would have happened if a different treatment were applied. The currently developed causal inference methods are adapting methods and insights from machine learning into these causal inference models.
6.3.2 Controls
The bias-variance trade-off is useful for helping to think about and choose control variables.
6.3.3 What does overfitting mean
The term overfitting is often informally used. It has no meaning outside of prediction.
6.4 Prediction vs. Explanation
Consider this regression model, \[ y = \beta_1 x_1 + \beta_2 x_2 + \epsilon \] where \(y\) is a \(n \times 1\) vector and \(\epsilon\) is a \(n \times 1\) vector, \[ \epsilon_i \sim \mathrm{Normal}(0, \sigma^2). \]
We will estimate two models on this data and compare their predictive performance:
The true model, \[ y = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \epsilon \] and the underspecified model, \[ y = \beta_0 + \beta_1 x_1 + \epsilon \]
We will evaluate their performance by repeatedly sampling from the true distribution and comparing their out of sample performance.
Write a function to simulate from the population. We will include the sample size, regression standard deviation, correlation between the covariates, and the coefficients as arguments.
size
: sample sizesigma
: the standard deviation of the population errorsrho
: the correlation between \(x_1\) and \(x_2\)beta
: the coefficients (\(\beta_0\), \(\beta_1\), \(\beta_2\))
sim_data <- function(size = 100, beta = c(0, 1, 1),
rho = 0, sigma = 1) {
# Create a matrix of size 1
dat <- jrnoldmisc::rmvtnorm_df(size, loc = rep(0, 2), R = equicorr(2, rho))
# calc mean
dat$fx <- model.matrix(~ X1 + X2, data = dat) %*% beta %>%
as.numeric()
dat$y <- dat$fx + rnorm(size, 0, sigma ^ 2L)
dat$y_test <- dat$fx + rnorm(size, 0, sigma ^ 2L)
dat
}
The output of sim_data
is a data frame with size
rows and columns
X1, X2
: The values of \(x_1\) and \(x_2\)fx
: The mean function \(f(x) = \beta_0 + \beta_1 x_1 + \beta_2 x_2\)y
: The values of \(y\) in the sample that will be used to train the model.y_test
: Another draw of \(y\) from the population which will be used to evaluate the trained model.
## # A tibble: 6 x 5
## X1 X2 fx y y_test
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 -0.616 0.721 0.105 -2.19 -0.0293
## 2 -0.728 -1.12 -1.85 -1.84 -2.07
## 3 1.05 0.793 1.85 0.809 -0.250
## 4 0.501 -0.931 -0.429 -0.203 -0.947
## 5 0.944 -0.152 0.792 1.46 1.39
## 6 -0.509 -0.290 -0.799 0.362 -0.664
For each training and test samples we draw we want to
- fit the true model using
y
- evaluate the prediction accuracy of the true model on
y_test
- fit the underspecified model using
y
- evaluate the prediction accuracy of the underspecified model on
y_test
The function sim_predict
does this
sim_predict <- function(f, data) {
# run regression
mod <- lm(f, data = data)
# predict the y_test values
augdat <- augment(mod, data = data) %>%
# evaluate and return MSE
mutate(err_out = (.fitted - y_test) ^ 2,
err_in = (.fitted - y) ^ 2)
tibble(r_squared = glance(mod)$r.squared,
mse_in = mean(augdat$err_in),
mse_out = mean(augdat$err_out))
}
So each simulation is:
## # A tibble: 1 x 3
## r_squared mse_in mse_out
## <dbl> <dbl> <dbl>
## 1 0.0446 85.0 65.2
## # A tibble: 1 x 3
## r_squared mse_in mse_out
## <dbl> <dbl> <dbl>
## 1 0.0490 84.6 64.6
We are estimating the expected error of new data. Without an analytical solution, we need to simulate this.
The run_sim
function simulates new test and training samples of y
and y_test
, runs both the true and underspecified models on them, and returns the results as a data frame with two rows the columns
r_squared
: In-sample \(R^2\)mse_in
: In-sample mean-squared-error.mse_out
: Out-of-sample mean-squared-error.model
: Either “true” or “underspecified” to indicate the model..iter
: An iteration number, used only for bookkeeping.
run_sim <- function() {
data <- sim_data(100, rho = 0.9, sigma = 3)
mod_under <- sim_predict(y ~ X1, data = data) %>%
mutate(model = "underspecified")
mod_true <- sim_predict(y ~ X1 + X2, data = data) %>%
mutate(model = "true")
bind_rows(mod_under, mod_true)
}
Run the simulation n_sims
times and then calculate the mean \(R^2\), in-sample MSE, and out-of-sample MSE:
n_sims <- 512
rerun(n_sims, run_sim()) %>%
bind_rows() %>%
group_by(model) %>%
summarise_all(funs(mean))
## # A tibble: 2 x 4
## model r_squared mse_in mse_out
## <chr> <dbl> <dbl> <dbl>
## 1 true 0.0637 78.4 83.7
## 2 underspecified 0.0511 79.4 82.9
Generally, the underspecified model can yield more accurate predictions when (Shmueli 2010):
data are very noisy (large \(\sigma\)). In these cases, increasing the complexity of the model will increase variance with little decrease in the variance since most of the variation in the sample is simply noise.
magnitude of omitted variables are small. In this case, those omitted variables don’t predict the response well, but could increase the overfitting in samples.
predictors are highly correlated. In this case, the information contained in the omitted variables is largely contained in the original variables.
sample size is small or the range of left out variables is small.
See Shmueli (2010) for more.
Exercise Try different parameter values for the simulation to confirm this.
The take-away. Prediction doesn’t necessarily select the “true model”, and knowing the “true model” may not help prediction.
Note that this entire exercise operated in an environment in which we knew the true model and thus does not resemble any realistic situation. Since “all models are wrong” the question is not whether it is useful to use the “true” model. What this simulation reveals is our models of the world are contingent on the size and quality of the data. If the data are noisy or few, then we need to use simpler models. If the covariates are highly correlated, it may not matter which one one we use in our theory.
6.5 Bias-Variance Tradeoff
Consider the general regression setup, \[ Y = f(\Vec{X}) + \epsilon, \] where \[ \begin{aligned}[t] \E[\epsilon] &= 0 & \Var[\epsilon] &= \sigma^2 . \end{aligned} \] When given a random pair \((X, Y)\), we would like to “predict” \(Y\) with some function of \(X\), say, \(f(X)\). However, in general we do not know \(f(X)\). So given some data consisting of realizations of pairs of \(X\) and \(Y\), \(\mathcal{D} = (x_i, y_i)\), the goal of regression is to estimate function \(\hat{f}\) that is a good approximation of the true function \(f\).
What is a good \(\hat{f}\) function? A good \(\hat{f}\) will have low expected prediction error (EPE), which is the error for predicting a new observation. \[ \begin{aligned}[t] EPE(Y, \hat{f}(x)) &= \mathbb{E}\left[(y - \hat{f}(x))^2\right] \\ &= \underbrace{\left(\mathbb{E}(\hat{f}(x)) - f(x)\right)^{2}}_{\text{bias}} + \underbrace{\mathbb{E}\left[\hat{f}(x) - \mathbb{E}(\hat{f}(x))\right]^2}_{\text{variance}} + \underbrace{\mathbb{E}\left[y - f(x)\right]^{2}}_{\text{irreducible error}} \\ &= \underbrace{\mathrm{Bias}^2 + \mathbb{V}[\hat{f}(x)]}_{\text{reducible error}} + \sigma^2 \end{aligned} \]
In general, there is a bias-variance tradeoff. The following three plots are three stylized examples of bias variance tradeoffs: when the variance influence the prediction error more than bias, when neither is dominant, and when the bias is more important.
As model complexity increases, bias decreases, while variance increases. There is some some sweet spot in model complexity that minimizes the expected prediction error. By understanding the tradeoff between bias and variance, we can find a model complexity to predict unseen observations well.
6.5.1 Example
Consider the function, \[ y = x^2 + \epsilon \] where \(\epsilon \sim \mathrm{Normal}(0, 1)\)
Here is an example of some data generated from this model. We will write a function to calculate \(f(x)\).
Write a function that draws a single sample from the model.
sim_data <- function(x) {
sigma <- 1
# number of rows
n <- length(x)
# proportion of observations in the test set
p_test <- 0.3
tibble(x = x,
fx = regfunc(x),
y = fx + rnorm(n, 0, sd = sigma),
test = sample(c(TRUE, FALSE), size = n, replace = TRUE))
}
Calculate this function
n <- seq(0, 1, length.out = 30)
sim_data(n) %>%
ggplot(aes(x = x)) +
geom_point(aes(y = y, colour = test)) +
geom_line(aes(y = fx))
For fit the data we will estimate polynomial models of increasing complexity, from only an intercept to a polynomial of degree 4.
- \(y_i = \beta_0\)
- \(y_i = \beta_0 + \beta_1 x\)
- \(y_i = \beta_0 + \beta_1 x + \beta_2 x^2\)
- \(y_i = \beta_0 + \beta_1 x + \beta_2 x^2 + \beta_3x^3\)
- \(y_i = \beta_0 + \beta_1 x + \beta_2 x^2 + \beta_3 x^3 + \beta_3 x^4\)
We will write a function to estimate these models.
As input it takes the degree
of the polynomial,
the data
to use to estimate it, and (optionally)
an .iter
variable that can be used to keep track of which iteration it is from.
est_poly <- function(degree, data, .iter = NULL) {
if (degree == 0) {
mod <- lm(y ~ 1, data = filter(data, !test))
} else {
mod <- lm(y ~ poly(x, degree), data = filter(data, !test))
}
out <- augment(mod, newdata = filter(data, test)) %>%
mutate(degree = degree) %>%
select(-.se.fit)
out[[".iter"]] <- .iter
out
}
For example, we will use a fixed \(x\) in each model. We will use an evenly spaced grid between 0 and 1.
## x fx y test .fitted degree
## 1 0.00000000 0.0000000000 0.89041816 TRUE -0.225623535 2
## 2 0.01010101 0.0001020304 0.29205861 TRUE -0.215626796 2
## 3 0.02020202 0.0004081216 0.44925488 TRUE -0.205634048 2
## 4 0.06060606 0.0036730946 -0.03221284 TRUE -0.165702965 2
## 5 0.07070707 0.0049994898 0.15205437 TRUE -0.155730172 2
## 6 0.08080808 0.0065299459 0.40707455 TRUE -0.145761370 2
## 7 0.09090909 0.0082644628 -0.30919290 TRUE -0.135796558 2
## 8 0.10101010 0.0102030405 -0.65388315 TRUE -0.125835737 2
## 9 0.11111111 0.0123456790 1.93214526 TRUE -0.115878908 2
## 10 0.14141414 0.0199979594 -0.43103603 TRUE -0.086032364 2
## 11 0.15151515 0.0229568411 -0.85837114 TRUE -0.076091499 2
## 12 0.18181818 0.0330578512 -0.42607043 TRUE -0.046292847 2
## 13 0.23232323 0.0539740843 2.12900106 TRUE 0.003291755 2
## 14 0.26262626 0.0689725538 0.83605063 TRUE 0.032994624 2
## 15 0.29292929 0.0858075707 -0.54569890 TRUE 0.062661575 2
## 16 0.30303030 0.0918273646 1.90519280 TRUE 0.072542577 2
## 17 0.31313131 0.0980512193 -1.15360371 TRUE 0.082419588 2
## 18 0.33333333 0.1111111111 -0.50180729 TRUE 0.102161637 2
## 19 0.34343434 0.1179471483 0.38956259 TRUE 0.112026675 2
## 20 0.37373737 0.1396796245 -1.42193217 TRUE 0.141597844 2
## 21 0.42424242 0.1799816345 0.38510327 TRUE 0.190803306 2
## 22 0.49494949 0.2449750026 0.42090271 TRUE 0.259523334 2
## 23 0.53535354 0.2866034078 0.36153111 TRUE 0.298704120 2
## 24 0.55555556 0.3086419753 1.56795728 TRUE 0.318270568 2
## 25 0.63636364 0.4049586777 1.32440427 TRUE 0.396376722 2
## 26 0.64646465 0.4179165391 1.08913606 TRUE 0.406122031 2
## 27 0.67676768 0.4580144883 2.19561530 TRUE 0.435334016 2
## 28 0.68686869 0.4717885930 0.36778411 TRUE 0.445063362 2
## 29 0.71717172 0.5143352719 -0.31989160 TRUE 0.474227455 2
## 30 0.72727273 0.5289256198 -0.37991742 TRUE 0.483940837 2
## 31 0.75757576 0.5739210285 1.14267823 TRUE 0.513057039 2
## 32 0.76767677 0.5893276196 -0.24018247 TRUE 0.522754458 2
## 33 0.78787879 0.6207529844 1.04576276 TRUE 0.542137322 2
## 34 0.79797980 0.6367717580 1.64778212 TRUE 0.551822768 2
## 35 0.80808081 0.6529945924 2.00215625 TRUE 0.561504223 2
## 36 0.81818182 0.6694214876 1.18357836 TRUE 0.571181687 2
## 37 0.82828283 0.6860524436 1.23946467 TRUE 0.580855160 2
## 38 0.83838384 0.7028874605 2.63472582 TRUE 0.590524642 2
## 39 0.84848485 0.7199265381 1.23562945 TRUE 0.600190134 2
## 40 0.86868687 0.7546168758 0.02923681 TRUE 0.619509143 2
## 41 0.87878788 0.7722681359 0.68094578 TRUE 0.629162662 2
## 42 0.88888889 0.7901234568 -0.05673413 TRUE 0.638812189 2
## 43 0.90909091 0.8264462810 -0.46999024 TRUE 0.658099271 2
## 44 0.94949495 0.9015406591 0.09575092 TRUE 0.696625544 2
## 45 0.95959596 0.9208244057 -0.64757995 TRUE 0.706247135 2
## 46 0.96969697 0.9403122130 -0.55067372 TRUE 0.715864735 2
## 47 0.98989899 0.9799000102 0.01762342 TRUE 0.735087962 2
Draw one sample from the data and run all model
run_sim <- function(.iter) {
# degrees of models to evaluate
degrees <- 0:5
# the grid of data to sample
x <- seq(0, 1, length.out = 64)
data <- sim_data(x)
# run all models
map_df(degrees, est_poly, data = data, .iter = .iter)
}
Run the full simulation, drawing n_sims
samples, running all the different models estimates.
For each model plot the expected regression line at the values of \(x\), which we’ll define as the average prediction of the model at each point.2 \[ \hat{f}(X = x) = \frac{1}{S} \sum_{s = 1}^S \hat{E}(y | X = x) \]
ggplot() +
geom_line(data = filter(all_sims, .iter < 10),
mapping = aes(x = x, y = .fitted, group = .iter)) +
geom_line(data = filter(all_sims, .iter == 1),
mapping = aes(x = x, y = fx), colour = "red") +
facet_wrap(~ degree)
Now calculate the bias and variance of these models at each \(x\).
poly_estimators <- all_sims %>%
group_by(degree, x) %>%
summarise(estimate = mean(.fitted),
variance = var(.fitted),
mse_in = mean((.fitted - fx)[!test]),
mse_out = mean((.fitted - fx)[test], na.rm = TRUE),
fx = mean(fx))
Plot the values of \(\hat{f}(x)\) for all models against the true model. On average squared model fits the true function well, and higher order polynomials cannot improve it.
poly_estimators %>%
mutate(bias = estimate - fx) %>%
group_by(degree) %>%
summarise(bias2 = mean(bias ^ 2),
variance = mean(variance, na.rm = TRUE),
mse_in = mean(mse_in, na.rm = TRUE),
mse_out = mean(mse_out, na.rm = TRUE)) %>%
gather(variable, value, -degree) %>%
ggplot(aes(x = degree, y = value, colour = variable)) +
geom_line()
Since \(\hat{f}\) varies sample to sample, there is variance in \(\hat{f}\). However, OLS requires zero bias in sample, and thus means that there is no trade-off.
6.5.2 Overview
low bias, high variance (overfit)
- more complex (flexible functions)
- estimated function closer to the true function
- estimated function varies more, sample to sample
- overfit
high bias, low variance (underfit)
- simple function
- simpler estimated function
- estimated function varies less, sample to sample
- underfit
What to do?
- low bias, high variance: simplify model
- high bias, low variance: make model more complex
- high bias, high variance: more data
- low bias, low variance: your good
The general rule.
- more training data reduces both bias and variance
- regularization and model selection methods can choose an optimal bias/variance trade-off
6.6 Prediction policy problems
Kleinberg et al. (2015) distinguish two types of policy questions. Consider two questions related to rain.
In 2011, Governor Rick Perry of Texas designated days for prayer for rain in order to end the Texas drought.
It is cloudy out. Do you bring an umbrella (or rain coat) when leaving the house?
How does the pray-for-rain problem differ from the umbrella problem?
- Prayer problems are causal questions, because the payoff depends on the causal question as to whether a prayer-day can cause rain.
- Umbrella questions are prediction problems, because an umbrella does not cause rain. However, the utility of bringing an umbrella depends on the probability of rain.
Many policy problems are a mix of prediction and causation. The policymaker needs to know whether the intervention has a causal effect, and also the predicted value of some other value which will determine how useful the intervention is. More formally, let \(y\) be an outcome variable which depends on the values of \(x\) (\(x\) may cause \(y\)). Let \(u(x, y)\) be the policymaker’s payoff function. The change in utility with response to a new policy (\(\partial u(x, y) / \partial x)\) can be decomposed into two terms, \[ \frac{\partial u(x, y)}{\partial x} = \frac{\partial u}{\partial x} \times \underbrace{y}_{\text{prediction}} + \frac{\partial u}{\partial y} \times \underbrace{\frac{\partial y}{\partial x}}_{\text{causation}} . \] Understanding the payoff of a policy requires understanding the two unknown terms
- \(\frac{\partial u}{\partial x}\): how does \(x\) affect the utility. This needs to evaluated at the value of \(y\), which needs to be predicted. The utility of carrying an umbrella depends on whether it rains or no. This is predictive.
- \(\frac{\partial y}{\partial x}\): how does \(y\) change with changes in \(x\)? This is causal.
6.6.1 References
Parts of the bias-variance section are derived from R for Statistical Learning, Bias-Variance Tradeoff
Also see:
References
Shmueli, Galit. 2010. “To Explain or to Predict?” Statistical Science 25 (3). Institute of Mathematical Statistics: 289–310. https://doi.org/10.1214/10-sts330.
Kleinberg, Jon, Jens Ludwig, Sendhil Mullainathan, and Ziad Obermeyer. 2015. “Prediction Policy Problems.” American Economic Review 105 (5). American Economic Association: 491–95. https://doi.org/10.1257/aer.p20151023.
These lines are not smooth due to Monte Carlo error (error coming from taking a finite number of samples).↩


Akshay Patil
<EMAIL_ADDRESS>Anyone can fix a problem or develop a solution but my 6+ years of experience as a software engineer has taught me beyond this. Don't just build the solution but design the BEST solution which is futuristic, reliable, handy, low maintenance, no complexity and long lasting.


| Interface | Description |
|---|---|
| Bean |
A bean consisting of a set of properties.
|
| BeanBuilder<T extends Bean> |
A builder for a bean, providing a safe way to create it.
|
| BeanQuery<P> |
A query based on beans.
|
| DynamicBean |
A dynamic bean that allows properties to be added and removed.
|
| DynamicMetaBean |
A dynamic meta-bean which works with
DynamicBean . |
| ImmutableBean |
An immutable bean consisting of a set of properties.
|
| MetaBean |
A meta-bean, defining those aspects of a bean which are not specific
to a particular instance, such as the type and set of meta-properties.
|
| MetaProperty<P> |
A meta-property, defining those aspects of a property which are not specific
to a particular bean, such as the property type and name.
|
| Property<P> |
A property that is linked to a specific bean.
|
| PropertyMap |
A map of properties that is linked to a specific bean.
|
| Class | Description |
|---|---|
| JodaBeanUtils |
A set of utilities to assist when working with beans and properties.
|
| Enum | Description |
|---|---|
| PropertyStyle |
An enumeration of property styles.
|
| Annotation Type | Description |
|---|---|
| BeanDefinition |
Annotation defining a bean for code generation.
|
| DerivedProperty |
Annotation defining a derived property accessor for code generation.
|
| ImmutableConstructor |
Annotation defining which constructor is to be used to create the object
in for code generation of immutable beans.
|
| ImmutableDefaults |
Annotation defining which method is to be used to apply the default property values
when code generating immutable beans.
|
| ImmutablePreBuild |
Annotation defining a method that is to be called just before a code generated immutable bean is built.
|
| ImmutableValidator |
Annotation defining which method is to be used to validate the bean
when code generating immutable beans.
|
| PropertyDefinition |
Annotation defining a property for code generation.
|
Joda-Beans is a library that can be used to provide enhanced Java Beans. These extensions provide the tools for framework writers to access bean and property information in a consistent and fast manner, typically without reflection.
A Joda-Bean implements the Bean
interface. In turn, this requires the
creation of a MetaBean
implementation, typically an inner class.
Both also require the provision of implementations of Property
and
MetaProperty
to express the properties of the bean.
Other packages provide implementations of the interfaces and a code generator.
Copyright © 2007–2015 Joda.org. All rights reserved.


Optimizers¶
BaseSGDOptimizer¶
-
class
paddle.trainer_config_helpers.optimizers.
BaseSGDOptimizer
SGD Optimizer.
SGD is an optimization method, trying to find a neural network that minimize the “cost/error” of it by iteration. In paddle’s implementation SGD Optimizer is synchronized, which means all gradients will be wait to calculate and reduced into one gradient, then do optimize operation.
The neural network consider the learning problem of minimizing an objective function, that has the form of a sum
\[Q(w) = \sum_{i}^{n} Q_i(w)\]The value of function Q sometimes is the cost of neural network (Mean Square Error between prediction and label for example). The function Q is parametrised by w, the weight/bias of neural network. And weights is what to be learned. The i is the i-th observation in (trainning) data.
So, the SGD method will optimize the weight by
\[w = w - \eta \nabla Q(w) = w - \eta \sum_{i}^{n} \nabla Q_i(w)\]where \(\eta\) is learning rate. And \(n\) is batch size.
MomentumOptimizer¶
-
class
paddle.trainer_config_helpers.optimizers.
MomentumOptimizer
(momentum=None, sparse=False) MomentumOptimizer.
When sparse=True, the update scheme:
\[\begin{split}\alpha_t &= \alpha_{t-1} / k \\ \beta_t &= \beta_{t-1} / (1 + \lambda \gamma_t) \\ u_t &= u_{t-1} - \alpha_t \gamma_t g_t \\ v_t &= v_{t-1} + \tau_{t-1} \alpha_t \gamma_t g_t \\ \tau_t &= \tau_{t-1} + \beta_t / \alpha_t\end{split}\]where \(k\) is momentum, \(\lambda\) is decay rate, \(\gamma_t\) is learning rate at the t’th step.
Parameters: sparse (bool) – with sparse support or not.
AdamOptimizer¶
-
class
paddle.trainer_config_helpers.optimizers.
AdamOptimizer
(beta1=0.9, beta2=0.999, epsilon=1e-08) Adam optimizer. The details of please refer Adam: A Method for Stochastic Optimization
\[\begin{split}m(w, t) & = \beta_1 m(w, t-1) + (1 - \beta_1) \nabla Q_i(w) \\ v(w, t) & = \beta_2 v(w, t-1) + (1 - \beta_2)(\nabla Q_i(w)) ^2 \\ w & = w - \frac{\eta}{\sqrt{v(w,t) + \epsilon}}\end{split}\]Parameters: - beta1 (float) – the \(\beta_1\) in equation.
- beta2 (float) – the \(\beta_2\) in equation.
- epsilon (float) – the \(\epsilon\) in equation. It is used to prevent divided by zero.
AdamaxOptimizer¶
-
class
paddle.trainer_config_helpers.optimizers.
AdamaxOptimizer
(beta1, beta2) Adamax optimizer.
The details of please refer this Adam: A Method for Stochastic Optimization
\[\begin{split}m_t & = \beta_1 * m_{t-1} + (1-\beta_1)* \nabla Q_i(w) \\ u_t & = max(\beta_2*u_{t-1}, abs(\nabla Q_i(w))) \\ w_t & = w_{t-1} - (\eta/(1-\beta_1^t))*m_t/u_t\end{split}\]Parameters: - beta1 (float) – the \(\beta_1\) in the equation.
- beta2 (float) – the \(\beta_2\) in the equation.
AdaGradOptimizer¶
-
class
paddle.trainer_config_helpers.optimizers.
AdaGradOptimizer
Adagrad(for ADAptive GRAdient algorithm) optimizer.
For details please refer this Adaptive Subgradient Methods for Online Learning and Stochastic Optimization.
\[\begin{split}G &= \sum_{\tau=1}^{t} g_{\tau} g_{\tau}^T \\ w & = w - \eta diag(G)^{-\frac{1}{2}} \circ g\end{split}\]
DecayedAdaGradOptimizer¶
-
class
paddle.trainer_config_helpers.optimizers.
DecayedAdaGradOptimizer
(rho=0.95, epsilon=1e-06) AdaGrad method with decayed sum gradients. The equations of this method show as follow.
\[\begin{split}E(g_t^2) &= \rho * E(g_{t-1}^2) + (1-\rho) * g^2 \\ learning\_rate &= 1/sqrt( ( E(g_t^2) + \epsilon )\end{split}\]Parameters: - rho (float) – The \(\rho\) parameter in that equation
- epsilon (float) – The \(\epsilon\) parameter in that equation.
AdaDeltaOptimizer¶
-
class
paddle.trainer_config_helpers.optimizers.
AdaDeltaOptimizer
(rho=0.95, epsilon=1e-06) AdaDelta method. The details of adadelta please refer to this ADADELTA: AN ADAPTIVE LEARNING RATE METHOD.
\[\begin{split}E(g_t^2) &= \rho * E(g_{t-1}^2) + (1-\rho) * g^2 \\ learning\_rate &= sqrt( ( E(dx_{t-1}^2) + \epsilon ) / ( \ E(g_t^2) + \epsilon ) ) \\ E(dx_t^2) &= \rho * E(dx_{t-1}^2) + (1-\rho) * (-g*learning\_rate)^2\end{split}\]Parameters: - rho (float) – \(\rho\) in equation
- epsilon (float) – \(\rho\) in equation
RMSPropOptimizer¶
-
class
paddle.trainer_config_helpers.optimizers.
RMSPropOptimizer
(rho=0.95, epsilon=1e-06) RMSProp(for Root Mean Square Propagation) optimizer. For details please refer this slide.
The equations of this method as follows:
\[\begin{split}v(w, t) & = \rho v(w, t-1) + (1 - \rho)(\nabla Q_{i}(w))^2 \\ w & = w - \frac{\eta} {\sqrt{v(w,t) + \epsilon}} \nabla Q_{i}(w)\end{split}\]Parameters: - rho (float) – the \(\rho\) in the equation. The forgetting factor.
- epsilon (float) – the \(\epsilon\) in the equation.
settings¶
-
paddle.trainer_config_helpers.optimizers.
settings
(*args, **kwargs) Set the optimization method, learning rate, batch size, and other training settings. The currently supported algorithms are SGD and Async-SGD.
Warning
Note that the ‘batch_size’ in PaddlePaddle is not equal to global training batch size. It represents the single training process’s batch size. If you use N processes to train one model, for example use three GPU machines, the global batch size is N*’batch_size’.
Parameters: - batch_size (int) – batch size for one training process.
- learning_rate (float) – learning rate for SGD
- learning_method (BaseSGDOptimizer) – The extension optimization algorithms of gradient descent, such as momentum, adagrad, rmsprop, etc. Note that it should be instance with base type BaseSGDOptimizer.
- regularization (BaseRegularization) – The regularization method.
- is_async (bool) – Is Async-SGD or not. Default value is False.
- model_average (ModelAverage) – Model Average Settings.
- gradient_clipping_threshold (float) – gradient clipping threshold. If gradient value larger than some value, will be clipped.


Divide and Conquer
I kinda presume everyone is familiar with the term and its meaning already. It’s one of the most famous strategies for leading a nation, a strategy to shift the attention of the general population away from politics by distracting them with each other.
What I just want to do here is make a list on my own. I just wanna write down what I can come up with. Which groups speak up against others. For that I’m gonna first name the group and then list the things they’re against, hoping it’s at least somewhat comprehensive. It’s also notable, that I won’t concentrate on one country. I’m from Germany, so I feel more confident about the people living here, but some movements also exist elsewhere while others only exist in one country.
-
Antifa As the name implies, Antifascists are against fascists, although that’s a very broad statement as “fascist” in this sense isn’t how an historian would define it, but how an antifascist defines it. In that sense, it can mean a lot of things. A populist (see Trump), someone who speaks out against immigrants or even someone who wants to have this right to speak. Also included are bankers or the (finance-)capitalism as a whole, the own government with their police as their executive force. Also mainstream media
-
Alt-rights (Illegal) immigrants, foreigners in general, political correctness, feminism, identity and gender politics. Also often times against mainstream media
-
Black Lives Matter Originally starting as a movement against police brutality, it quickly also included equality for blacks as a whole. As such it’s against white supremacists (especially in the police), alt-rights.
-
The Young This one’s probably not as profound as the ones above, but Brexit already indicated some kind of clash between generations.  They also don’t find much representation in their democratic system.
-
The Old See the new generation as spoiled kids who basically fused with their phones.
-
Feminists The patriarchy, sometimes those who use politically incorrect language. Depending on the feminist it can also include TERFs or men in general. Similarly to antifas, their definition of the others (here patriarchy) can be very broad, so are their opponents.
-
Gender Identity Advocates Although similar to feminists at times, the topics are a bit different. Generally against conservativs who’re against their believe. It can include those who refuse certain pronouns or identities.
-
Free Speech Advocates A relatively young, barely established group of people who don’t agree with politically correct language or langauge police in general. Can also include right-wing people who lost the ability to reasonably voice their opinion on a some subjects.
-
Working Class Probably one of the oldest groups here and a very moody one. Oftentimes being against what the TV is showing.
-
Greens Those running nuclear power plants or subsidising their use.
And most importantly the ones that have to be distracted from:
-
The Rich Basically everyone who doesn’t earn millions or above per year or month. Or to quote Warren Buffet: “It’s class warfare. My class is winning, but they shouldn’t be.”
-
The Poor Anyone in power, be it politically, financially or economically. I’m not sure if they know though.


Press space to continue.
You can find the credits and more in my latest project Mastering Apache Spark.
- Visit Jacek Laskowski's blog
- Follow @jaceklaskowski at twitter
- Use Jacek's projects at GitHub
- Read Mastering Apache Spark


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[gecode-users] Fwd: GeCode on VS 2010
Guido Tack
tack at gecode.org
Thu Mar 31 11:29:37 CEST 2011
sergio nuñez wrote:
> I have the following problem getting a GeCode problem example to run on my VS 2010 (Windows 7). Any help would be appreciated - I might have missed some steps in setting things up correctly.
>
> I downloaded the package "Gecode-3.5.0-x64-2010.msi" and installed it on my system.
>
> I then started a new project with VS 2010 and pasted the code for an example "queens.cpp" into the .cpp of my new project.
>
> I then went to the Configuration Settings of the project and added /include and /lib as additional entries (for both debug and release).
>
> And then builded and compiled, the following error messages appeared:
>
> 1>..\queens.cpp(43): fatal error C1083: 'QtGui': No such file or directory
>
> So, i have intalled the Qt libraries (added \include\QtGui and \include as additional entries), and then i try again to compile and build the project. But the following error messages appeared:
The problem is that the Gecode binary package includes the Qt libraries but not the header files. The queens.cpp example implements a custom visualization and therefore needs the Qt headers, but if you install Qt from the original binary package, the libraries won't be compatible with the ones in the Gecode package...
Please try a different example (one that doesn't include QtGui, e.g. golomb-ruler.cpp). If you indeed want to compile against the Qt library, you will have to compile Gecode from the sources.
Cheers,
Guido
--
Guido Tack, http://people.cs.kuleuven.be/~guido.tack/
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Colin Mcrae
"If in doubt, flat out!"
A short piece about Colin Mcrae
Colin Mcrae was a Scottish rally driver from Landark, Scotland. He was the youngest person, as well as the first Scottish person, to win the World Rally Championship Drivers' title. Colin Mcrae is the is the son of Jimmy Mcrae Mcrae, five-time British Rally Champion, and brother of rally driver Alister Mcrae.
Colin Mcrae has shown an outstanding performance and achievements throughout his career which consisted mostly of rally driving. He has enabled Subaru World Rally Team to win the World Rally Championship Constructors' title three times in succession, from 1995 to 1997. He was then with Ford Motor Co. team for four years where he won nine events, then moved to Citreon World Rally in 2003. While there he didn't win any events, but he helped them win the first of their three consecutive manufacturers' titles.


rotten > Library > Biographies > Crime > Serial Killers > Huang Yong
Huang Yong
Former soldier, then a migrant worker. Lured boys from internet cafes and newspaper job offers. Sometimes he claimed to be developing a new video game, God Riding on a Wooden Horse. The game apparently involved being tied to a noodle rolling machine followed by strangulation.
Timeline
| c. 1974 |
Huang Yong born, Henan Province. |
| Sep 2001 |
Huang Yong takes his first victim. |
| 7 Nov 2003 |
Sixteen-year-old Zhang Liang, intended to be victim #26, abducted. Held captive for four days, Liang tries to escape twice. After choking Liang three times and three times reviving him, the boy somehow convinces Huang to let him free. |
| 12 Nov 2003 |
Huang Yong arrested after boy Zhang Liang goes to the police in the village of Dahuangzhuang, China. Eighteen bodies are found beneath Huang's house, and he confesses to 25 murders. |
| 9 Dec 2003 |
Huang Yong sentenced to death after a three hour public trial. |
Pornopolis |
Rotten |
Faces of Death |
Famous Nudes
|


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object --+ | exceptions.BaseException --+ | exceptions.Exception --+ | AnsibleError
This is the base class for all errors raised from Ansible code, and can be instantiated with two optional parameters beyond the error message to control whether detailed information is displayed when the error occurred while parsing a data file of some kind. Usage: raise AnsibleError('some message here', obj=obj, show_content=True) Where "obj" is some subclass of ansible.parsing.yaml.objects.AnsibleBaseYAMLObject, which should be returned by the DataLoader() class.
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x.__init__(...) initializes x; see help(type(x)) for signature
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Given an object reporting the location of the exception in a file, return detailed information regarding it including: * the line which caused the error as well as the one preceding it * causes and suggested remedies for common syntax errors If this error was created with show_content=False, the reporting of content is suppressed, as the file contents may be sensitive (ie. vault data). |
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| Military Overview |
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1,877,895 |
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149,219 |
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Elements in HTML are made up of opening tags, contents, and closing tags.
Common stylistic tags used in HTML are the bold tags and "em" tags. The bold tags does just what it says, it makes text bold. The "em" tag adds emphasis or italicizes words.
HTML attributes are a tag that equals some sort of value. One type of HTML attribute is the anchor tag, which is used to create links.
All text in HTML is rendered as one line, so special tags must be used such as the paragraph tag or the "br" tag to break up text.
There are two different types of elements in HTML -- inline and block. Inline merely formats text. Block actually creates a box with formatting, width, etc.
 Therefore, any mistake can cause huge mistake for a developer, as as computers are unforgiving. Developers should also be careful about repeating themselves to prevent errors.
Lesson 2: Creating a Structured Document with HTML
Developing a Page -- All HTML documents are a series of boxes and as important as the coding of the page is its layout and planning of text, images, and item placement.
HTML is Organized in a Tree-like Structure whereby the elements can have other elements subsumed inside of them (and often do). It is useful to outline these elements like a family tree with the various children
or splinters of one element coming off another element to understand and determine the relationship from the first element. In a Internet Browser, this will show up as series of nested boxes within boxes.
Google Chrome's Developer Tools is a very effective tool enabling software developers to view this tree-like structure and nested relationships and understand how the browser interacts with the code and
styling.
Text Editors such as Sublime Text or CoffeeCub HTML makes programmers lives easier by automatically creating HTML tags for coders and fixing errors.
Lesson 3: Cascading Style Sheets
CSS or Cascading Style Sheets is a language used to describe the look and format of a document created using a markup language, such as HTML.
Inheritance is a key concept in CSS. Like ancestors pass on traits to their relatives, Cascading Style Sheet elements pass on traits to elements further down the document tree. This generally applies to text related
properties. Properties related to boxes generally cannot be inherited. Check out this awesome article on CSS Inheritance at Wikipedia.


--- layout: post authors: ["Warren Code"] title: "More on Educational Engineering" date: 2015-12-16 time: "23:30:00" tags: ["Opinion"] ---
In my experience, the term "educational engineers" doesn't seem to have caught on to describe any particular activity, but I can provide some examples of people bringing ideas from the research to inform teaching. There's a range depending on what counts as "building".
The "Scholarship of Teaching and Learning" generally refers to efforts of individuals involved in post-secondary teaching to improve their teaching by getting ideas from the research literature, collecting evidence, and reporting back in publications and presentations. "Action Research" is largely along the same lines, though tends to be the term used more in K-12 settings. More substantial research programmes that may involve non-instructor researchers and an attempt to discover more generalizable results within the teaching of a certain field can fall under the heading of "Discipline-Based Education Research", for example the development of a reliable concept inventory to measure student learning and misconceptions with respect to a certain topic. These are all related and there aren't especially clear divisions between these spheres (more detail here) but in terms of educational research overall these are on the very applied end, usually involving studies in actual classrooms and very connected to the teaching culture within the particular field.
For about 8 years now at the University of British Columbia, we have had the Carl Wieman Science Education Initiative, which uses a model of discipline-based experts (usually people with a graduate degree in the relevant field of science and some teaching experience) embedded in our science departments to collaborate with faculty on improving teaching. This includes measuring of student learning and reporting out as possible, where published work would tend to be considered Discipline-Based Education Research. Our local title for these people is "Science Teaching and Learning Fellow", but with their strong focus on implementation of research-based teaching practices, I have described them as "educational engineers" sometimes (I am currently coordinating the initiative). The University of Colorado at Boulder had a similar initiative that has already wound up, and many institutions are now attempting some form of the model of embedded experts working with faculty on teaching improvement projects. I should add that a portion of the funding has gone to buying out teaching of faculty in order for them to be able to participate in these projects (e.g., teach one fewer course in a year) which creates the extremely important "time for practice" that Greg mentions.
More about the Science Teaching and Learning Fellow role here and more about our initiative, plus a variety of resources and research results at http://cwsei.ubc.ca/.


Yes I always try to trust other people and if I try to be truthful and be honest most of people tend to trust me back. Not everyone does sometimes it takes long time to build trust but opening up first definitely helps.
Like I said before giving full attention to people are not easy especially when many things are happening around you but when you do give full attention to others and listen carefully you will actually feel for them and also they would notice that you are actually concentrating on the conversation.
Wishing for someone to be happy is completely normal thing. If you are happy you want others to be happy. If you are not happy you still would like others to be happy because you know how it feels like when you are unhappy.
1 . When I tell my husband to not stack dishes too much because it could fall and break. 2. If the dishes fall and break hubby could hurt himself while he is doing dishes so I'm 100% correct 3. But he was nearly done with dishes and he was about to clear it so he is 100% correct as well, as long as he clears it right away it does not matter. “The main purpose of this exercise is to practice seeing the perspectives of different players”
Not really at this point!
cultural blog 8 connecting


{% extends "page.html" %} {% if announcement_login is string %} {% set announcement = announcement_login %} {% endif %} {% block login_widget %} {% endblock %} {% block main %} {% block login %}
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menu
Title
apps
notifications
more_vert
inbox
Inbox
hourglass_empty
Snoozed
check
Done
You should be focused on Rafael
inbox
And not here
assignment
Assignment
Hammer 1
Hammer 2
Hammer 1.1
No-hammerplz
Wild slide panel
X
Some link I didn't figured out what it was supposed to do 🤔


I am a Responsive Designer & Developer who thrives on coding the very best visual designs and user experiences. When I'm not working, pursuing side projects or getting lost in forests and in games of chess, I can be found playing with my cats, cooking, gardening and, of course, practicing Flexbox!
Here are some facts about me that I'm proud of:
I was born in Montreal Quebec, where the culture is French-Canadian. My parents are Anglophones but I went to French immersion elementary school and continued to take French throughout Highschool, and I've also travelled to France 4 times, so I'm pretty fluent once I get rolling!
I attended University part-time while raising my son, and I graduated in 2000 with an Honors BA in Psychoanalytic Thought, Religious Studies, and Fine Art Studio. I love learning and would probably be a perpetual Student if I could get away with that, lol!
I taught myself HTML/CSS coding, starting in 2006, and have since enjoyed working on many projects as a Freelancer. Now I'm taking this course with Skillcrush so that I can learn Javascript and hopefully get hired to do Front-End Development full-time from my home office!


Welcome To The Colony Reality Show!
Congratulations, you and your team have been passed the audition to be selected for the Colony 2018 Program.
It’s currently January, and you’re due to move to your island early April.
The goal of this program is a scientific experiment to understand more how to build self-sufficient communities, and we’re excited to have you onboard!
Your new home
Creagach Island is just west from the coast of Ireland – it’s about 90 minutes away by helicopter.
A small community was created here, but was abandoned after the Great Storm of 1987, after which island life lost its appeal.
We’ve annually held small University retreats there in summer, so we know the overall state of the place, but it’s a little what you might call “a fixer upper”.
Housing
There are 8 rustic huts around the main community. Most of these are in good condition, although all need some work, mainly fixing roof leaks and general maintenance. Hut 5 though suffered serious damage in the storm, and needs extensive rework. Huts only really contain a bedroom.
The community centre is more central, and contains kitchen facilities for the island, as well as toilet and shower facilities. It also houses the community radio station, which can be used to contact the mainland in emergencies.
The students have also fitted a couple of solar power banks, including batteries. But the power is only really available in the community centre.
There’s also a helipad clearing which we’ll use to take you there. It can also be used in emergencies.
Farming
After the Great Storm, the remaining sheep population were gathered up for sale. There are still a lot of wild goats loose though, and some rabbits as well.
We’d hoped that the tractor which was kept in storage would be usable. But last year’s survey said it need an overhaul, and we don’t think we can find parts for that vintage.
Previously the area around the community was farmed – a key crop was potatoes (not a huge surprise, it’s Ireland), but they grew a variety of other food.
On the subject of food – we’re going to provide you with rations and some tinned food for your first 4 months, after which you’re pretty much on your own.
Woodland
The island is still relatively wooded, and you tend to find a lot of goats have gone loose and feral here.
There’s a good variety of trees, which have been used previously as firewood (the huts all have woodburners), just remember you need to season any wood for a couple of months before burning.
Theodore Cove
About half a kilometre from the main community is Theodore Cove, which contains a pier, which like so much of the island is in need of repair. Anything heavy that you’ll need delivering will have to come through here.
According to the students, the fishing in the cove can be quite promising.
Workshop Activities
You have 3 months now until you’re going to be dropped on the island. You need to really prepare yourself for this in your groups.
We’re going to help you do this by working through the following activities with you. We will communally debrief after each stage.
Exercise 1 – Introduce yourself.
Who are you, what skills do you bring into the mix? Yes, do talk about yourself, you’re all going to live together for a year, so it’s important you know a bit more about each other.
Exercise 2 – Your first four months
Draw up a plan of activities you think you’ll need to undertake in your first few months. Try to work out what you think you can do yourself.
You might need to draw up a shopping list of anything you might need to support you (do remember though, our resources aren’t infinite). There’s a limit of 100,000 Euros. However because of the tax benefits this production has, this MUST be spent in the Republic of Ireland – so people will have to google costs & where to get from.
You also might want to think of any training that you might want to take between now and your first day. You can use any training course which runs in the UK or Ireland 2018 before your start date
Exercise 3 – Recruits!
We’re actually two people short for your group. Try and work out amongst you how you’d advertise for those two additional people. What are you looking for?
Exercise 4 – A sense of community
You’re going to have to all live and work together. How are you going to make it work?
Debrief
This might all seem like a little bit of fun … which is it, but it has a serious point.
As an agile team, you’re coming together as a group, as a community. You want to be as self-sufficient as possible. Amongst things, in your planning you will have groomed a bit of a backlog of things to do!
There are some skills you have, some skills you might need to grow. But notice, many of you won’t start by defining roles.
Maybe you worried about medical care? With a medical helicopter 90 minutes away, that’s something worth taking seriously. Do you need someone with medical skills, like a doctor? Or does everyone needs some health and safety training? Maybe a mixture of the two?
There’s no right or wrong way.
Likewise deciding rules for you as a group defines you as a community. It’s about making sure some things are valued, and people are protected. This is how countries evolve laws. But it’s important everyone is involved and feels the rules represent everyone than “meh, this rule?”.


Hi Cody
Try and you shall succeed
I am stubborn, and therefore I will strive to be great, just from pure thickheadedness!


Whats In The Box
A Cooperative Game
Enter your player name:
Kevin
Select amount of players:
4
Go back
Continue


Press n or j to go to the next uncovered block, b, p or k for the previous block.
| File | Statements | Branches | Functions | Lines | |||||
|---|---|---|---|---|---|---|---|---|---|
| class39-2 |
|
95.12% | 39/41 | 75% | 6/8 | 100% | 3/3 | 94.74% | 36/38 |
| graph-breadth-first |
|
75% | 36/48 | 77.78% | 7/9 | 81.82% | 9/11 | 75% | 36/48 |
| graph-business-trip |
|
57.14% | 36/63 | 46.67% | 7/15 | 75% | 9/12 | 58.06% | 36/62 |
| graph-depth-first |
|
62.5% | 55/88 | 63.64% | 7/11 | 61.54% | 8/13 | 62.5% | 55/88 |


s6-dns
Software
skarnet.org
The s6-dnsqualify program
s6-dnsqualify qualifies a domain name.
Interface
s6-dnsqualify domain
- s6-dnsqualify qualifies domain according to the "domain" and
"search" rules in /etc/resolv.conf. It prints the result on
stdout, one FQDN per line.
- If the DNSQUALIFY
environment variable is set and contains a list of suffixes separated by spaces,
tabs, newlines or carriage returns, then this list is used instead of the
/etc/resolv.conf rules.


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Note: To complete these procedures, you must be assigned a role having the necessary permissions. To determine your role, follow the directions in Participant roles. For a basic understanding of roles and permissions, see Permissions and roles: Overview.
To preview an assessment as it will be seen by those taking it:
Tests &
Quizzes
.
Preview
.Edit
. Then click Preview
.Begin Assessment
. Depending on how you configured
your Navigation setting, you'll see the following:Save and Continue
to proceed through the
rest of the assessment.Table of
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to view the assessment organized by its parts. Click a
part to reveal its questions, and click a question to read it. To
return to a previous question, click Previous
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.
done
to return to the Questions page.
For more information on the Navigation setting, see "Assessment Organization" in Assessment settings.


:: com :: sun :: star :: chart2 ::
|
| unpublished |
service DataSeries |
|
|
- Usage Restrictions
- not published
- Description
- reflects the model data of the object that has all the information
for a ::DataRenderer to create a visible data series in
a chart.
It combines one or more ::DataSequences which are
interpreted by evaluating their role-string.
- See also
- DataSequenceRole
|
| Included Services - Summary |
| ::com::sun::star::beans::PropertySet |
the property interface by which the properties of all
supported services are exchanged
|
| DataPointProperties |
these properties serve as default for data points.
(details)
|
| Properties' Summary |
[ readonly ] sequence< long >
AttributedDataPoints |
[ OPTIONAL ]
a sequence of indexes denoting which data points have set
properties that differ from the default.
|
StackingDirection
StackingDirection |
indicates wether this series should be stacked with respect to the previous series.
|
boolean
VaryColorsByPoint |
[ OPTIONAL ]
If true, the data points of this series get different
colors by default, like in a pie chart.
|
long
AttachedAxisIndex |
[ OPTIONAL ]
This property describes wether the series should be shown at
the main value axis or at the secondary value axis. Having
this property not set or setting it to 0 means that this data
series will be scaled at the primary y-axis ( of the
coordinate system in which this series is hosted ).
|
| Included Services - Details |
::com::sun::star::beans::PropertySet
- Description
- the property interface by which the properties of all
supported services are exchanged
|
|
DataPointProperties
- Description
- these properties serve as default for data points.
So, an attribute for a data point comes from one point in
the following hierarchy:
- default value of data series
- value from style of data series (if a style was set)
- value from hard attribute of data series (set with setPropertyValue)
- value from style of data point (if a style was set)
- value from hard attribute of data point (set with setPropertyValue)
|
|
| Properties' Details |
AttributedDataPoints
[ readonly ] sequence< long > AttributedDataPoints;
- Usage Restrictions
- optional
- Description
- a sequence of indexes denoting which data points have set
properties that differ from the default.
The default values are determined by the properties set at
the DataPointProperties of the data
series.
If the sequence is empty, that means that all data points
look alike. They are formatted using the propery values set
in the data series.
The indexes in this sequence match the indexes used by the
::XIndexContainer.
This property is especially useful for large data series
with only some formatted data points, because you do not have
to iterate over all elements.
|
|
StackingDirection
StackingDirection StackingDirection;
- Description
- indicates wether this series should be stacked with respect to the previous series.
|
|
VaryColorsByPoint
boolean VaryColorsByPoint;
- Usage Restrictions
- optional
- Description
- If true, the data points of this series get different
colors by default, like in a pie chart.
|
|
AttachedAxisIndex
long AttachedAxisIndex;
- Usage Restrictions
- optional
- Description
- This property describes wether the series should be shown at
the main value axis or at the secondary value axis. Having
this property not set or setting it to 0 means that this data
series will be scaled at the primary y-axis ( of the
coordinate system in which this series is hosted ).
Setting this property to 1 means that this series should be
scaled at the secondary y-axis. If there is no secondary axis
the main axis should be used for scaling instead.
If you want to scale a series at a different x or z axis
you need to create an additional coordinate system and host
this series there.
|
|
Top of Page
Copyright © 2013, The Apache Software Foundation, Licensed under the Apache License, Version 2.0. Apache, the Apache feather logo, Apache OpenOffice and OpenOffice.org are trademarks of The Apache Software Foundation. Other names may be trademarks of their respective owners.


|
MoveToCode
|
A game object with the "EyeTrackingTarget" script attached reacts to being looked at independent of other available inputs. More...
Public Member Functions | |
| void | RaiseSelectEventManually () |
Protected Member Functions | |
| override void | Start () |
| override void | OnDisable () |
| override void | RegisterHandlers () |
| Overload this method to specify, which global events component wants to listen to.Use RegisterHandler API of InputSystem More... | |
| override void | UnregisterHandlers () |
| Overload this method to specify, which global events component should stop listening to.Use UnregisterHandler API of InputSystem More... | |
| void | OnEyeFocusStart () |
| void | OnEyeFocusStay () |
| void | OnEyeFocusDwell () |
| void | OnEyeFocusStop () |
| Protected Member Functions inherited from Microsoft.MixedReality.Toolkit.Input.InputSystemGlobalHandlerListener | |
| virtual void | OnEnable () |
| async Task | EnsureInputSystemValid () |
| A task that will only complete when the input system has in a valid state. More... | |
Properties | |
| UnityEvent | OnLookAtStart [get, set] |
| Event is triggered when the user starts to look at the target. More... | |
| UnityEvent | WhileLookingAtTarget [get, set] |
| Event is triggered when the user continues to look at the target. More... | |
| UnityEvent | OnLookAway [get, set] |
| Event to be triggered when the user is looking away from the target. More... | |
| UnityEvent | OnDwell [get, set] |
| Event is triggered when the target has been looked at for a given predefined duration (dwellTimeInSec). More... | |
| UnityEvent | OnSelected [get, set] |
| Event is triggered when the looked at target is selected. More... | |
| UnityEvent | OnTapDown [get, set] |
| Event is triggered when the RaiseEventManually_TapDown is called. More... | |
| UnityEvent | OnTapUp [get, set] |
| Event is triggered when the RaiseEventManually_TapUp is called. More... | |
| bool | EyeCursorSnapToTargetCenter [get, set] |
| If true, the eye cursor (if enabled) will snap to the center of this object. More... | |
| bool | IsLookedAt [get] |
| Returns true if the user looks at the target or more specifically when the eye gaze ray intersects with the target's bounding box. More... | |
| bool | IsDwelledOn = false [get] |
| Returns true if the user has been looking at the target for a certain amount of time specified by dwellTimeInSec. More... | |
| static GameObject | LookedAtTarget [get] |
| GameObject eye gaze is currently targeting, updated once per frame. More... | |
| static EyeTrackingTarget | LookedAtEyeTarget [get] |
| EyeTrackingTarget eye gaze is currently looking at. More... | |
| static Vector3 | LookedAtPoint [get] |
| static GameObject | SelectedTarget [get, set] |
| Most recently selected target, selected either using pointer or voice. More... | |
A game object with the "EyeTrackingTarget" script attached reacts to being looked at independent of other available inputs.
|
protectedvirtual |
Reimplemented from Microsoft.MixedReality.Toolkit.Input.InputSystemGlobalHandlerListener.
|
protected |
|
protected |
|
protected |
|
protected |
| void Microsoft.MixedReality.Toolkit.Input.EyeTrackingTarget.RaiseSelectEventManually | ( | ) |
|
protectedvirtual |
Overload this method to specify, which global events component wants to listen to.Use RegisterHandler API of InputSystem
Implements Microsoft.MixedReality.Toolkit.Input.InputSystemGlobalHandlerListener.
|
protectedvirtual |
Reimplemented from Microsoft.MixedReality.Toolkit.Input.InputSystemGlobalHandlerListener.
|
protectedvirtual |
Overload this method to specify, which global events component should stop listening to.Use UnregisterHandler API of InputSystem
Implements Microsoft.MixedReality.Toolkit.Input.InputSystemGlobalHandlerListener.
|
getset |
If true, the eye cursor (if enabled) will snap to the center of this object.
|
get |
Returns true if the user has been looking at the target for a certain amount of time specified by dwellTimeInSec.
|
get |
Returns true if the user looks at the target or more specifically when the eye gaze ray intersects with the target's bounding box.
|
staticget |
EyeTrackingTarget eye gaze is currently looking at.
null if currently gazed at object has no EyeTrackingTarget, or if no object with collider is being looked at.
|
staticget |
|
staticget |
GameObject eye gaze is currently targeting, updated once per frame.
null if no object with colllider is currently being looked at.
|
getset |
Event is triggered when the target has been looked at for a given predefined duration (dwellTimeInSec).
|
getset |
Event is triggered when the user starts to look at the target.
|
getset |
Event to be triggered when the user is looking away from the target.
|
getset |
Event is triggered when the looked at target is selected.
|
getset |
Event is triggered when the RaiseEventManually_TapDown is called.
|
getset |
Event is triggered when the RaiseEventManually_TapUp is called.
|
staticgetset |
Most recently selected target, selected either using pointer or voice.
|
getset |
Event is triggered when the user continues to look at the target.


I am an engineer, turned neuroscientist, turned data scientist. I work at Biogen, as Konectom’s Head of Data Science, where I lead a team of brilliant Data Scientists who are trying to discover new ways of assessing and monitoring the impact of neurological diseases on patients’ lives.
I am a founding director of SPiKE.AI, where we provide machine learning services to innovating companies. I also worked as an independent consultant for a few French companies here in Paris.
I was a research associate in the Department of Clinical and Experimental Epilepsy at University College London, Institute of Neurology. I worked on mutations affecting ion channels (mainly potassium channels) and how they might affect synaptic transmission.
Before this post-doc, and during my PhD and first post-doc at Imperial College London, I investigated the basic biophysical constraints faced by axons, and how these constraints have shaped the trade-offs made by neurons. I investigated these trade-offs using stochastic simulations based on biophysical data found in literature. The simulations are carried on using Modigliani, our in-house stochastic simulator.
I speak Persian, French and English.


{% extends "base/amz_file_upload.html" %} {% block content %}
Amazon MWS approach will be an alternative to Manual Upload. Seller will be able to directly login to their Amazon Seller account and grant us Developer access. This access shall be required just once and then all the Seller Dashboards will automatically keep updating daily on our portal based on their Amazon transactions. We are waiting for an approval from Amazon before serving this option to our customers.


/** * Copyright (c) 2010 10gen, Inc. <http://10gen.com> * Copyright (c) 2009, 2010 Novus Partners, Inc. <http://novus.com> * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * * For questions and comments about this product, please see the project page at: * * http://github.com/mongodb/casbah * */ package com.mongodb.casbah import com.mongodb.casbah.Imports._ import scalaj.collection.Imports._ /** * Helper class for creating WriteConcern instances * * @author Brendan W. McAdams<EMAIL_ADDRESS>* @since 2.0 * @see com.mongodb.WriteConcern */ object WriteConcern { /** * Exceptions are raised for network issues and server errors; Write operations wait for the server to flush data to disk */ val FsyncSafe = com.mongodb.WriteConcern.FSYNC_SAFE /** * Exceptions are raised for network issues and server errors; waits for at least 2 servers for the write operation. */ val ReplicasSafe = com.mongodb.WriteConcern.REPLICAS_SAFE /** * Exceptions are raised for network issues and server errors; waits on a server for the write operation */ val Safe = com.mongodb.WriteConcern.SAFE /** * Exceptions are raised for network issues but not server errors. */ val Normal = com.mongodb.WriteConcern.NORMAL /** * Create a new WriteConcern object. * *
 w represents # of servers: *
 *
{@code w=-1} None, no checking is done
 *
{@code w=0} None, network socket errors raised
 *
{@code w=1} Checks server for errors as well as network socket errors raised
 *
{@code w>1} Checks servers (w) for errors as well as network socket errors raised
 *
 *
 * @param w (Int) Specifies the number of servers to wait for on the write operation, and exception raising behavior. Defaults to {@code 0} * @param wTimeout (Int) Specifies the number MS to wait for the server operations to write. Defaults to 0 (no timeout) * @param fsync (Boolean) Indicates whether write operations should require a sync to disk. Defaults to False */ def apply(w: Int = 0, wTimeout: Int = 0, fsync: Boolean = false) = new com.mongodb.WriteConcern(w, wTimeout, fsync) /** * Get the WriteConcern constants by name: NONE, NORMAL, SAFE, FSYNC_SAFE, * REPLICA_SAFE. (matching is done case insensitively) * * NOTE: This only supports the java versions, no support for the local scala aliases. */ def valueOf(name: String) = com.mongodb.WriteConcern.valueOf(name) } // vim: set ts=2 sw=2 sts=2 et:


Forum: Public ReviewIssue: READ-SUPPRESS-CONFUSING
References: Margolin's PR comment #2 (X3J13/92-1402)
Margolin's PR comment #18 (X3J13/92-1418)
Flanagan's PR comment #7 (X3J13/92-1907)
Philpot's PR comment #1 (X3J13/92-2901)
Barrett's PR comment #9 (X3J13/92-3109), part 2
*READ-SUPPRESS* (X3J13/92-102 pp.23-21..22)
Sharpsign P (X3J13/92-102 sec.<IP_ADDRESS>)
Sharpsign C (X3J13/92-102 sec.<IP_ADDRESS>)
Category: CHANGE
Edit history: 5 Jan 1993, Version 1 by Margolin
22 May 1993, Version 2 by Loosemore (delete bogus reference
to ":" and "@" in dispatching macro syntax)
Status: Proposal GENERALIZE passed 9-2 on letter ballot 93-302
Problem description:
The description of *READ-SUPPRESS* is confusing and incomplete. By
specifying the behavior of particular read syntaxes rather than a simple,
general strategy, some syntaxes (#C, #P, and#') have unintentionally been
omitted. And the wording of the description of the behavior of #(...) is
unclear as to whether the vector syntax is parsed and ignored or a vector
is actually constructed (there's a subtle difference between the CLtL and
dpANS wording, due to the lack of a pair of parentheses in the dpANS).
A strict reading of the current description of *READ-SUPPRESS* implies
that the reader must still construct objects in response to these
syntaxes (as well as user-defined reader macros that don't check the
variable). This is pretty wasteful considering that *READ-SUPPRESS*
exists primarily to support #+ and #-, which discard what they read with
*READ-SUPPRESS* true anyway.
In the case of #P, the lack of specification of its *READ-SUPPRESS*
behavior is a real annoyance, because some implementations have extended
it to allow non-strings to follow it. Users expect to be able to hide
such extensions using #+, but this isn't valid.
Proposal (READ-SUPPRESS-CONFUSING:GENERALIZE):
1) Replace the portion of the Description of *READ-SUPPRESS* from
"Extended tokens" through the entry for "#A, #S, #:" with:
When *READ-SUPPRESS* is true, READ, READ-PRESERVING-WHITESPACE,
READ-DELIMITED-LIST, and READ-FROM-STRING all return a first value of
NIL when they complete successfully; however, they continue to parse
the representation of an object in the normal way, in order to skip
over the object, and continue to indicate end-of-file in the normal
way. Except as noted below, any standard macro character (including
dispatching macro sub-characters) that is defined to read a following
object or token will do so, but not signal an error if the object
read is not of an appropriate type or syntax. Standard syntaxes and
macro characters will not construct any new objects (e.g. when
reading the representation of a symbol, no symbol will be constructed
or interned).
Dispatching macro characters (including #)
Dispatching macro characters continue to parse an infix numerical
argument, and invoke the
dispatch function. The standard # dispatching macro characters
do not enforce any constraints on the value or existence of the
numerical argument.
2) Create a Notes section that recommends that user-defined and
implementation-defined macro characters behave analogously to the
standard macro character, i.e. when *READ-SUPPRESS* is true they should
ignore type errors when reading a following object and dispatching macro
characters should allow NIL as an infix parameter.
3) Change the specifications of #C and #P to specify that they are
followed by the representation of an object. Then specify how they are
interpreted when those objects are a list of two real numbers (for #C) or
a string (for #P).
Examples:
(let ((*read-suppress* t))
(dolist (string '("#(foo bar baz)" "#P(:type :lisp)" "#c1.2" "foo"))
(progn (print (read-from-string string))
t))
could print
Test Cases:
(let ((*read-suppress* t))
'("#(foo bar baz)" "#P(:type :lisp)" "#c1.2")))
Rationale:
1) Specifies a general behavior for *READ-SUPPRESS* that is applicable to
all reader syntaxes that are described in terms of the representations of
objects.
2) Encourages implementors and users to extrapolate from this standard
behavior.
3) Specifies #P and #C in such a way that change 1 applies.
Current practice:
For the Example form, Genera 8.1 prints NIL, NIL, NIL (it seems to
implement the proposal); Lucid 4.1 and CMU CL 16d print NIL, ERROR, NIL;
WCL 2.1 prints #(NIL NIL NIL), ERROR, ERROR.
Cost to Implementors:
Should be minor.
Cost to Users:
When *READ-SUPPRESS* is bound via #+ or #-, the change should be upward
compatible, since any objects constructed by the reader will not be
visible. Direct users of *READ-SUPPRESS* may notice the difference, but
it seems unlikely that anyone is depending on any of the current
behaviors.
Cost of non-adoption:
Users will have to avoid using some implementation-dependent syntaxes in
#P macros.
Performance impact:
Minor.
Benefits:
More ability to include implementation-dependent syntax extensions in
portable source code.
Esthetics:
A general description of behavior is better than lots of specific cases.
Editorial Impact:
Minor. The changes are confined to the three sections mentioned above.
Discussion:
Barmar originally wanted to specify that standard dispatching macros
would parse the numerical argument but ignore it (always passing NIL to
the dispatch function). Flanagan convinced him to leave that part as it
was originally (the numeric argument is parsed but standard dispatch
functions don't enforce restrictions on it). It doesn't seem to be a big
deal either way.


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This code loops until you enter the correct password


They're a mess.
Later, I'll add an easy icon UI, but for now you're stuck with a single text box. What URL do you put in the box? Let's find out.
The best place to get icons is from Font Awesome icons. They have a bunch of free icons and logos you can use easily. To use their icons, first open the cheatsheet and find an icon. Then, remember its name and find what section it's under.
img/brands.svg
in the text box.img/regular.svg
in the text box.img/solid.svg
in the text box.After that file name in the textbox, add a hashtag and the name of the icon you chose, like so: (don't add a space in-between!)
img/brands.svg#reddit-alien
Your icon should work now.
You can also just put any .svg URL in and it'll work. Even data URIs will work.


Class MonitorRDPJobDialog
java.lang.Object
org.eclipse.swt.widgets.Dialog
org.pentaho.di.ui.job.entry.JobEntryDialog
dqm.jku.dqmeerkat.pentaho.monitoring.MonitorRDPJobDialog
- All Implemented Interfaces:
org.pentaho.di.job.entry.JobEntryDialogInterface
public class MonitorRDPJobDialog
extends org.pentaho.di.ui.job.entry.JobEntryDialog
implements org.pentaho.di.job.entry.JobEntryDialogInterface
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Field Summary
Fields inherited from class org.pentaho.di.ui.job.entry.JobEntryDialog
databaseDialog, jobEntryInt, jobMeta, loggingObject, metaStore, parent, props, rep, shell
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Constructor Summary
Constructors Constructor Description MonitorRDPJobDialog(org.eclipse.swt.widgets.Shell parent, org.pentaho.di.job.entry.JobEntryInterface jobEntryInt, org.pentaho.di.repository.Repository rep, org.pentaho.di.job.JobMeta jobMeta)
The constructor should call super() and make sure that the name of the job entry is set. -
Method Summary
Modifier and Type Method Description org.pentaho.di.job.entry.JobEntryInterface
open()
This method is called by Spoon when the user opens the settings dialog of the job entry.void
populateDialog()
Copy information from the meta-data input to the dialog fields.Methods inherited from class org.pentaho.di.ui.job.entry.JobEntryDialog
addConnectionLine, addConnectionLine, addDatabases, getMetaStore, getPathOf, selectDatabase, setMetaStore
Methods inherited from class org.eclipse.swt.widgets.Dialog
checkSubclass, getParent, getStyle, getText, setText
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
Methods inherited from interface org.pentaho.di.job.entry.JobEntryDialogInterface
setMetaStore
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Constructor Details
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MonitorRDPJobDialog
public MonitorRDPJobDialog(org.eclipse.swt.widgets.Shell parent, org.pentaho.di.job.entry.JobEntryInterface jobEntryInt, org.pentaho.di.repository.Repository rep, org.pentaho.di.job.JobMeta jobMeta)The constructor should call super() and make sure that the name of the job entry is set.- Parameters:
parent
- the SWT Shell to usejobEntryInt
- the job entry settings object to use for the dialogrep
- the repository currently connected to, if anyjobMeta
- the description of the job the job entry belongs to
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Method Details
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open
public org.pentaho.di.job.entry.JobEntryInterface open()This method is called by Spoon when the user opens the settings dialog of the job entry. It should open the dialog and return only once the dialog has been closed by the user. If the user confirms the dialog, the meta object (passed in the constructor) must be updated to reflect the new job entry settings. The changed flag of the meta object must reflect whether the job entry configuration was changed by the dialog. If the user cancels the dialog, the meta object must not be updated, and its changed flag must remain unaltered. The open() method must return the met object of the job entry after the user has confirmed the dialog, or null if the user cancelled the dialog.- Specified by:
open
in interfaceorg.pentaho.di.job.entry.JobEntryDialogInterface
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populateDialog
public void populateDialog()Copy information from the meta-data input to the dialog fields.
-