Model Selection
In practice, researchers often have to decide between a variety of possible models. The different models available to a researcher can derive from a number of factors, but most often arise because the researcher has a wide set of possible control variables that could be added to the model. In addition, these models could be added as simple additive effects, or the researcher could add additional interaction terms, squared terms, splines, etc. In practice, the number of possible models one could construct from a set of variables is quite large. How does one go about choosing the “right” model?
For example, Table 24 shows a variety of models that could be used to predict violent crime rates across US states. Note that the model estimates for some variables such as percent male differ substantially across models. How can we possibly decide which of these models is the best model?
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | ||
|---|---|---|---|---|---|---|---|
| Intercept | 7534.81*** | 619.07 | 604.71 | 128.07 | -6.09 | -638.35 | |
| (1724.03) | (2995.68) | (3031.21) | (2972.28) | (113.33) | (599.66) | ||
| Median Age | -38.56*** | -27.84* | -28.06* | -27.84* | -24.90* | ||
| (10.89) | (10.94) | (11.28) | (11.02) | (9.40) | |||
| Percent Male | -115.67*** | -23.28 | -23.21 | -12.29 | |||
| (31.39) | (44.75) | (45.23) | (44.59) | ||||
| Gini | 43.01** | 43.71* | 39.04* | 38.68** | |||
| (15.68) | (17.36) | (17.15) | (11.67) | ||||
| Poverty Rate | -0.88 | -6.09 | |||||
| (8.91) | (9.17) | ||||||
| Unemployment Rate | 25.47 | 45.89*** | 23.02 | ||||
| (14.18) | (12.99) | (13.14) | |||||
| R2 | 0.29 | 0.39 | 0.39 | 0.43 | 0.20 | 0.42 | |
| Num. obs. | 51 | 51 | 51 | 51 | 51 | 51 | |
| p < 0.001; p < 0.01; p < 0.05 |
There is no “right” model
By definition, models are not a 100% accurate representation of reality. Therefore, its silly to think that a single model can be the “right” one. In this chapter, we will learn some tools that can help with making decisions about model selection, but the decision about model selection is one that must be driven first and foremost by the research question at hand. For example, if my goal is to understand the relationship between percent male and violent crimes, then only models 1-4 from Table 24 are relevant, because only these models include the percent male variable as a predictor. Within that set, it probably makes more sense to think about how robust my results are to several different model specifications than to focus on a single model. Generally, so long as issues of multicollinearity do not arise it might be better to control for more variables rather than less in this context.
A different research question might focus on two different competing explanations for an outcome. In this case, I might need to directly compare the results of two different models to address my research question. In this case, some of the model selection tools we develop below will be more crucial in making this decision, but I still might need to consider how robust my results are to the inclusion of different control variables.
Ultimately, model selection can never be simplified into an automated process that protects the researcher from having to make decisions. There is no one way to make such decisions and must be considering such things as the research question, theoretical arguments about the process, the results of prior research, and the sensitivity of results to alternative specifications.
The accuracy vs. parsimony tradeoff
If we have an additional variable avaliable to us, why not throw it into the model? More control variables allow us to eliminate potential sources of confounding in the relationships we care about, so isn’t more always better than less? There are two reasons, why we don’t always want to use this kitchen sink approach.
First, as discussed in the previous section, adding in additional variables can lead to problems of multicollinearity. Collinearity between your independent varibles can increase standard errors on your estimates and lead you to a situation in wich in spite of strong point estimates of association, none of your results are statistically significant. For this reason, you should never just look at statistically significance when you observe that the addition of another variable made the relationship between \(x\) and \(y\) go away. If might have become non-significant because of an inflation in standard errors rather than a reducation in the correlation.
Second, we generally value the concept of parsimony as well as accuracy. Accuracy is measured by goodness-of-fit statistics like \(R^2\). Your model is more accurate if it can account for more variation in the outcome variable \(y\). However, an additional independent variable can never make \(R^2\) decrease and will almost always make it increase by some amount. Therefore, if we only prioritize accuracy we will continue to add variables into our models indefinitely.
We also prize parsimony. We want a model that gets the most “bang for our buck” so to speak. We prefer simpler explanations and models to more complex ones if those simpler models can be almost as accurate as the more complex model.
One important feature of parsimony and the very idea of scientific “models” in general is that we are not simply trying to reproduce reality. If we wanted to do that, we could simply create a dummy variable for each observation and produce a model with an \(R^2=1\). Yet, such a model would tell us nothing about the underlying process that produces variation in our outcome - we would just reproduce the world as it is. In principle, the very idea of scientific modeling is to reduce the complexity of the actual data in order to hopefully understand better the process. Including more and more variables into a model is rarely theoretically interesting and may in fact be misleading or confusing as to the underlying causal processes we hope to uncover.
In practice, we must balance these two features of models. Gains in parsimony will be offset by reductions in accuracy, and vice versa. There is no precise way to say where the threshold for that tradeoff should be, but as a practical researcher, you should always understand that this is the tradeoff you are making when you choose between models.
Null vs. saturated model
Two conceptual models illustrate the extreme ends of the accuracy vs. parsimony spectrum. The most parsimonious model that we can ever have is one without any independent variables. In this case, our predicted value for the outcome variable would always equal the mean of the outcome variable. Formally, we would have:
\[\hat{y}_i=\bar{y}\]
This null model has zero accuracy. It doesn’t even pretend to be accurate. We can estimate this model easily enough in R:
## (Intercept)
## 384.5039
## [1] 0
At the other end of the spectrum, we have the saturated model. In the saturated model, we have a number of variables equal to the number of observations minus one. The saturated model is not something we typically observe in practice because these independent variables must be uncorrelated enough that we don’t observe perfect collinearity. A simple example would be to simply include a dummy variable for each observation. Since we have the state as a variable in the crimes dataset, we can actually calculate the saturated model here:
## [1] 1
In practice, all real models fall somewhere between these two extremes. Most of the model selection tools that we discuss below will make use of these two conceptual models theoretically.
A not-very-useful tool: the F-test
The F-test is not terribly useful in practice, but we will build some important principles from it for later tools in this module and the next module. The F-test is a classic tool that uses the framework of hypothesis testing to adjudicate between models.
The F-test can compare any two nested models. For one model to be nested within another, it must contain all of the original variables in the other model plus additional variables. When models are built by starting with a few variables and then adding in additional variables in a sort of reverse stairstep, then we have nested models. Models 1-4 from Table 24 above are nested models, while models 5-6 are not nested within models 1-4.
If we take any two nested models, the null hypothesis of the F-test is that none of the additional variables in the more complex model are correlated with the outcome (e.g. the \(\beta\) slope is zero on all additional variables). To test this, we calculate the F-statistic:
\[F=\frac{(SSR_1-SSR_2)/g}{SSR_2/(n-g-k-1)}\]
Where \(SSR\) is the sum of squared residuals for the given model, \(g\) is the number of additional variables in model 2 and \(k\) is the number of variables in model 1. Basically we are taking the reduction in SSR per variable added in model 2 divided by the SSR left in model 2 per remaining degree of freedom. If the null hypothesis is correct, we expect this number to be one. The larger the F-statistic gets, the more suspicious we are of the null hypothesis. We can formalize this by calculating the p-value from the right tail of an F-distribution for a given F-statistic. This F-distribution will As an example, lets calculat the F-statistic between the null model predicting violent crimes and a model that predicts violent crimes by the median age and percent male in a state.
model_null <- lm(Violent~1, data=crimes)
model_demog <- lm(Violent~MedianAge+PctMale, data=crimes)
SSR1 <- sum(model_null$residuals^2)
SSR2 <- sum(model_demog$residuals^2)
g <- length(model_demog$coef)-length(model_null$coef)
k <- length(model_null$coef)-1
n <- length(model_null$residuals)
Fstat <- ((SSR1-SSR2)/g)/(SSR2/(n-g-k-1))
Fstat
## [1] 9.869375
That seems quite a bit bigger than one. We can find out the probability of getting an F-statistic this large or larger if median age and percent male were really uncorrelated with violent crime rates in the population by getting the area of the right tail for a corresponding F-distribution:
## [1] 0.0002568606
There is a very small probability that we would have gotten an F-statistic this large or larger if the null hypothesis was true. Therefore we reject the null hypothesis and prefer the model with median age and percent male to the null model.
Rather than doing this by hand, we could have used the anova
command in R to make the comparison:
## Analysis of Variance Table
##
## Model 1: Violent ~ 1
## Model 2: Violent ~ MedianAge + PctMale
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 50 1907432
## 2 48 1351616 2 555817 9.8694 0.0002569 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ANOVA stands for Analysis of Variance and can be used to conduct F-tests between models. We can also use it to compare more than two models at the same time, so long as they are all nested:
model_gini <- update(model_demog, .~.+Gini)
model_complex <- update(model_gini, .~.+Poverty+Unemployment)
anova(model_null, model_demog, model_gini, model_complex)
## Analysis of Variance Table
##
## Model 1: Violent ~ 1
## Model 2: Violent ~ MedianAge + PctMale
## Model 3: Violent ~ MedianAge + PctMale + Gini
## Model 4: Violent ~ MedianAge + PctMale + Gini + Poverty + Unemployment
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 50 1907432
## 2 48 1351616 2 555817 11.5056 9.209e-05 ***
## 3 47 1165120 1 186496 7.7211 0.007935 **
## 4 45 1086941 2 78179 1.6183 0.209554
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
The F-test prefers model 2 to model 1 and model 3 to model 2, but does not prefer model 4 to 3. The additional reduction in SSR from model 3 to 4 (78179) is not enough higher than what could be expected by chance if these two additional parameters were not correlated with violent crime rates.
However, you need to be careful about making model selections here because the order in which things are entered can affect the results. Lets say for example that we included unemployment with the gini coefficient and then added poverty rates separately in model 4.
model_giniplus <- update(model_demog, .~.+Gini+Unemployment)
model_complex <- update(model_gini, .~.+Poverty)
anova(model_null, model_demog, model_giniplus, model_complex)
## Analysis of Variance Table
##
## Model 1: Violent ~ 1
## Model 2: Violent ~ MedianAge + PctMale
## Model 3: Violent ~ MedianAge + PctMale + Gini + Unemployment
## Model 4: Violent ~ MedianAge + PctMale + Gini + Poverty
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 50 1907432
## 2 48 1351616 2 555817 11.6471 8.08e-05 ***
## 3 46 1097590 2 254026 5.3231 0.008327 **
## 4 46 1164871 0 -67281
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Now we prefer to have unemployment in the model. However, it gets even worse than this. Lets take a look at the actual results of the third model:
##
## Call:
## lm(formula = Violent ~ MedianAge + PctMale + Gini + Unemployment,
## data = crimes)
##
## Residuals:
## Min 1Q Median 3Q Max
## -288.18 -107.61 -5.40 56.58 508.18
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 272.90 2946.20 0.093 0.9266
## MedianAge -26.50 10.76 -2.462 0.0176 *
## PctMale -13.98 44.25 -0.316 0.7534
## Gini 35.23 16.06 2.193 0.0334 *
## Unemployment 22.50 13.37 1.682 0.0993 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 154.5 on 46 degrees of freedom
## Multiple R-squared: 0.4246, Adjusted R-squared: 0.3745
## F-statistic: 8.485 on 4 and 46 DF, p-value: 3.249e-05
Percent male is not statistically significant and has a small substantive effect, because its large negative effect was driven by an indirect association with the gini coefficient. Unemployment has a marginally significnat but also small effect. But we never tested a model with just median age and the gini coefficient. What happens if we try that?
## Analysis of Variance Table
##
## Model 1: Violent ~ MedianAge + Gini
## Model 2: Violent ~ MedianAge + PctMale + Gini + Unemployment
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 48 1171830
## 2 46 1097590 2 74240 1.5557 0.2219
Now we prefer another model that was nested within our more complex model but not nested within the original model structure. F-tests can easily lead you into situations like this.
If you add in variables one at a time, the F-test will also produce the exact same result as a t-test on the additional variable:
model_one <- lm(Violent~MedianAge, data=crimes)
model_two <- update(model_one, .~.+Poverty)
anova(model_one, model_two)
## Analysis of Variance Table
##
## Model 1: Violent ~ MedianAge
## Model 2: Violent ~ MedianAge + Poverty
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 49 1734016
## 2 48 1574243 1 159773 4.8716 0.03212 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 891.15575 465.632320 1.913861 0.06161074
## MedianAge -20.64100 11.346820 -1.819100 0.07513705
## Poverty 18.64995 8.449701 2.207173 0.03211668
Notice that the p-value for the poverty variable in model 2 is identical to the p-value for the F-test between models 1 and 2. Thus the F-test does not really do much for us that we couldn’t already do with a t-test one variable at a time.
Aside from the issues highlighted above, the primary problem with the F-test is that its decisions are driven by the logic of hypothesis testing and the search for “statistically significant” results. Putting aside that this logic makes no sense for the crime data because they are not a sample, this is not a good practice in general. If we decide what to prioritize by statistical significance, then in smaller datasets we will prefer different, more parsimonious, models than in larger datasets. It would be better to choose models based on theoretical principles that are consistent across sample size than to mechanistically determine them in this way.
Tools with a parsimony penalty
Two model selection tools allow us to more formally consider the parsimony vs. accuracy tradeoff. We can think of the \(R^2\) value as a measure of accuracy. These two tools then apply a “parsimony penalty” to this measure of accuracy to get a single score that measures the quality of the model. In both cases, lack of parsimony is reflected by \(p\), the number of independent variables in the model.
Adjusted \(R^2\)
The adjusted \(R^2\) subtracts a certain value from \(R^2\) based on the number of independent variables in the model. Formally:
\[R^2_{adj}=R^2-(\frac{p}{n-p-1})(1-R^2)\]
The parsimony penalty is based on a scaling of the lack of goodness of fit \((1-R^2)\) and the number of variables proportionate to the size of the sample.
Adjusted \(R^2\) is provided by default in the summary of an lm
object:
##
## Call:
## lm(formula = Violent ~ MedianAge + PctMale, data = crimes)
##
## Residuals:
## Min 1Q Median 3Q Max
## -379.94 -93.06 -32.30 68.80 572.02
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7534.81 1724.03 4.370 6.62e-05 ***
## MedianAge -38.56 10.89 -3.540 0.000901 ***
## PctMale -115.67 31.39 -3.685 0.000581 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 167.8 on 48 degrees of freedom
## Multiple R-squared: 0.2914, Adjusted R-squared: 0.2619
## F-statistic: 9.869 on 2 and 48 DF, p-value: 0.0002569
## [1] 0.26187
The parsimony penalty in the adjusted \(R^2\) will be smaller as \(n\) increases. Therefore this tool will tend to prefer more complex models in larger samples. This feature distinguishes its approach from the Bayesian Information Criterion (BIC) below.
Bayesian Information Criterion
The Bayesian Information Criterion, or BIC for short, is based implicitly on a Bayesian logic to statistical inference that we are going to gloss over for now. In practice, there are two forms of the BIC:
- BIC, which is implicitly compared to the saturated model. The R command
BIC
will give you this version. - BIC’, which is implicitly compared to the null model. There is no R command for BIC’ but we will create our own function below.
I find that the BIC’ formulation is more pleasing and so we will focus on that version here. However, you can compare any two non-null models directly using either criterion and the differences between them will be the same.
The formula for \(BIC'\) is:
\[BIC'=n\log(1-R^2)+p\log(n)\]
The first term includes \(R^2\) and is our measure of accuracy. Because we are actually loggin something less than one, we will get a negative value here. For the BIC, lower scores are better and negative scores are the best. bove, I said that each BIC type was implicitly being compared to either the saturated or the null model. If the respective BIC score is below zero then this model is preferred to the null or saturated model, respectively.
The second term \(p\log(n)\) is the parsimony penalty. In this case, we actually weight the parsimony penalty higher in larger samples. As a result, BIC will tend to prefer the same model more consistently in different sample sizes.
Since, R does not give us a BIC’ calculations, lets go ahead and create our own with a function:
bic_null <- function(model) {
rsq <- summary(model)$r.squared
n <- length(model$resid)
p <- length(model$coef)-1
return(n*log(1-rsq)+p*log(n))
}
bic_null(model_demog)
## [1] -9.703675
The negative value for BIC’ tells us that we prefer the model with median age and percent male as predictors to the null model.
We can also compare two non-null models directly by comparing their BIC or BIC’ scores. Whichever is lower is the preferred model. These models do not have to be nested.
## [1] -9.703675
## [1] -9.184312
## [1] 679.8933
## [1] 680.4127
In this situation, we slightly prefer the demographic explanation to the economic one. Note that although bic_null
and BIC
give us different values, the differences between them in different models are the same:
## [1] -0.5193631
## [1] -0.5193631
You can use the general rule of thumb to evaluate differences in BIC:
| Difference in BIC | Strength of Preference |
|---|---|
| 0-2 | Weak |
| 2-6 | Moderate |
| 6-10 | Strong |
| greater than 10 | Very Strong |
Model Averaging
None of the tools mentioned above can fully address the complexity of model selection. One of the key difficulties is that given a set of potential variables, it includes not only what gets included but the order of inclusion. As we have seen above, percent male seems to have a large negative effect on violent crime rates until we consider a model that includes Gini coefficients. Where and how we include these variables in sequential models might affect our ultimate model selection.
One way of addressing this is to use a technique of model averaging. In the model averaging approach, we use a computer to iteratively fit all of the possible models from a set of candidate variables. We then use some procedure to average results across these models to account more formally for model uncertainty in our final conclusion. There are a variety of ways in which such model averaging can be done. The BMA
package in R will use a Bayesian model averaging approach that implicitly uses a Bayesian logic and the BIC to adjudicate between models. Lets try that with our crime data:
library(BMA)
model.bma <- bic.glm(crimes[,c("MedianAge","PctMale","PctLessHS",
"MedianIncomeHH","Unemployment",
"Poverty", "Gini")],
crimes$Violent, glm.family=gaussian)
summary(model.bma)
##
## Call:
## bic.glm.data.frame(x = crimes[, c("MedianAge", "PctMale", "PctLessHS", "MedianIncomeHH", "Unemployment", "Poverty", "Gini")], y = crimes$Violent, glm.family = gaussian)
##
##
## 11 models were selected
## Best 5 models (cumulative posterior probability = 0.8 ):
##
## p!=0 EV SD model 1 model 2 model 3
## Intercept 100 -1592.518 735.881 -1699.048 -1848.696 -1913.727
## MedianAge 12.4 -1.683 6.761 . . .
## PctMale 9.6 -1.676 10.509 . . .
## PctLessHS 20.2 -2.728 7.327 . -12.841 .
## MedianIncomeHH 94.3 19.518 6.419 20.804 22.719 18.595
## Unemployment 12.4 1.501 6.671 . . .
## Poverty 94.3 65.115 21.788 67.908 82.593 59.191
## Gini 16.2 3.730 12.147 . . 10.052
##
## nVar 2 3 3
## BIC -141.527 -138.995 -138.020
## post prob 0.448 0.126 0.078
## model 4 model 5
## Intercept -1629.940 -849.020
## MedianAge . .
## PctMale . -15.701
## PctLessHS . .
## MedianIncomeHH 19.585 20.130
## Unemployment 8.553 .
## Poverty 62.549 65.134
## Gini . .
##
## nVar 3 3
## BIC -137.950 -137.883
## post prob 0.075 0.072
The BMA approahc has given us what it considers the five most likely models. The first column (p!=0
) gives us the probaility that the parameter is actually not zero across models. Two variables, the poverty rate and median household income have high probabilities here. The second term, (EV
) gives us the expected value of that slope across the most likely models. This is basically the average across models. The BMA results also show us the five most likely models. Note that they are ordered by BIC’ which is the primary technique this procedure uses to evaluate models.
Note that none of these models are even close to what we were estimating before. We were not seeing high effects of poverty in the models and didn’t consider median household income. This model averaging approach suggests that we might want to reconsider our approach.
Remember the advice from the beginning of this section. The model averaging approach is seductive because it seems to do the hard work for us, but it should only be used as a tool for the researcher to ultimately make that decision.


Paste a boat location from the Golden Globe Race Live Tracker into the box.
We'll build you a link to view that boat's position on windy.com
We all love the Golden Globe Race tracker, but sometimes you want to explore a boat's situation with the full functionality of windy.com. This page allows you to quickly open windy.com at the position of a boat taken from the Golden Globe Tracker."
For example in the picture on the left the bottom bar shows swell detail that simply isn't available on the Golden Globe Race tracker.
You can find the Lat/Long of a boat from the 'team dropdown' on the Golden Globe Race Live Tracker.
Paste the Lat/Long string from the boat e.g. "40° 30.78 S, 115° 11.07 E" into the Location box at the top of the page and a url will be created for you. It will open windy.com at the boat position you entered.


How to become a copywriter
So you're thinking of becoming a copywriter?
You might be a recent graduate looking for a career, or you've worked in other jobs and have decided it's time for a change. Whatever your circumstances, breaking into any industry can be a challenge, especially during such a volatile time for employment and the economy. If you succeed, however, copywriting can be extremely rewarding work. Here are our top tips for getting started.
Do I need a degree?
Education is never a bad thing, but while a degree is advantageous, it is not strictly necessary. Relevant courses for copywriting include journalism and marketing. These will teach you some of the important skills such as conducting research and meeting deadlines. However, if you have an aptitude for writing and can prove your ability, it doesn't matter whether you've studied or not. Most employers will be far more concerned with your experience and attitude than your education.
Do I need writing experience?
We all have to start somewhere, and some form of writing experience is essential for securing any type of copywriting role. The first thing you should do is start a blog covering an area that is of interest to you, for example fashion or music. To get some more hands-on experience, local marketing or PR agencies will usually welcome the opportunity to take on apprentices. If you haven't managed to get anything published, you can always do some speculative copywriting. Choose a product (real or imaginary) and invent a strapline and some compelling product copy.
Portfolio
To get any copywriting work you will need a portfolio of your best writing. Note that this should be copywriting experience specifically, not a news piece you did for your local paper, so this is where speculative writing can come in handy. You should always be adding to and adapting your portfolio as you gain more experience, and what you include should be tailored to the job you're applying for.
Web presence
An absolute must for any copywriter is having your own website. This should be professional, specific to your copywriting work, and not linked to your personal web presence in any way. Similarly, you should have completely separate social media for your professional work (and make sure your personal accounts are completely private if there's anything on there you wouldn't want a potential employer to see!). LinkedIn and Facebook profiles are always professionally useful, and you can use any social media to link back to your website.
Getting work
Now for the hard part. You've got the skills, you've done the preparation – now you want to get the work. If you've not been lucky enough to walk into a copywriting job after completing an internship, this can seem a daunting process. The most obvious route to having a career in copywriting is to become a staff writer for an ad or marketing agency, or for an in-house corporate creative department. This is where the above advice comes in handy – if you can confidently sell yourself, while providing solid examples of good work and a comprehensive internet presence, you're in with a good shot. The rest is down to circumstances and luck. However, the stars are not always aligned in our favour, and no matter how talented or well prepared we are, getting that first staff job can be nigh on impossible. Don't worry though, there is another option...
Going freelance
There are many reasons for going freelance as a copywriter, it might be because you're unable to secure a full time position or that you just prefer the freedom and flexibility of working on your terms.
Successful freelance copywriters build a base of clients through hard work and determination. Start by looking for small local businesses in your area, price your services competitively, perhaps even offering a page or two for free. Deliver your work on time and to a great standard. View every new acquaintance as a potential contact; you never know where you might find an opportunity.
The internet is a major avenue for finding freelance work. Market your own website to find leads and also take advantage of crowdsourcing sites such as Copify, which can help you to earn a steady income, and gain vital experience.
If you're just starting out, it might make more financial sense to have another job while you build up your network and portfolio. When you do take on freelance work, be sure to agree on payment terms before you begin a project, ask for 50% of your fee up front and 50% on completion.
Perseverance
Most importantly of all, if you're serious about becoming a copywriter – stick with it! You're unlikely to have success right away, and if you've been rejected it can be hard to pick yourself up again.  Whether it's applying for that in-house position, or finding clients to do freelance writing for, once you do get the ball rolling you'll be well on your way to a fulfilling career!
Questions?
Speak to our content team
Fill in the form with your requirements and one of our friendly team will be in touch.


All Database
Quority
Animal
Species
Dataset type
field/lab
Dimension
Tracking method
Title
2,3
mice
others
body shape/skelton
lab
nan
DeepLabCut
A Matlab-based toolbox for characterizing behavior of rodents engaged in string-pulling
Get in touch
cabernet.rock@gmail.com


{% extends 'voteapp/base.html' %} {% block content %}
Please Register or Log In
Where would you like to go?


| PostgreSQL 9.5.19 Documentation | |||
|---|---|---|---|
| Prev | Up | Chapter 9. Functions and Operators | Next |
Table 9-40 shows the operators that are available for use with the two JSON data types (see Section 8.14).
Table 9-40. json and jsonb Operators
| Operator | Right Operand Type | Description | Example | Example Result |
|---|---|---|---|---|
| -> | int | Get JSON array element (indexed from zero, negative integers count from the end) | '[{"a":"foo"},{"b":"bar"},{"c":"baz"}]'::json->2 | {"c":"baz"} |
| -> | text | Get JSON object field by key | '{"a": {"b":"foo"}}'::json->'a' | {"b":"foo"} |
| ->> | int | Get JSON array element as text | '[1,2,3]'::json->>2 | 3 |
| ->> | text | Get JSON object field as text | '{"a":1,"b":2}'::json->>'b' | 2 |
| #> | text[] | Get JSON object at specified path | '{"a": {"b":{"c": "foo"}}}'::json#>'{a,b}' | {"c": "foo"} |
| #>> | text[] | Get JSON object at specified path as text | '{"a":[1,2,3],"b":[4,5,6]}'::json#>>'{a,2}' | 3 |
Note: There are parallel variants of these operators for both the json and jsonb types. The field/element/path extraction operators return the same type as their left-hand input (either json or jsonb), except for those specified as returning text, which coerce the value to text. The field/element/path extraction operators return NULL, rather than failing, if the JSON input does not have the right structure to match the request; for example if no such element exists. The field/element/path extraction operators that accept integer JSON array subscripts all support negative subscripting from the end of arrays.
The standard comparison operators shown in Table 9-1 are available for jsonb, but not for json. They follow the ordering rules for B-tree operations outlined at Section 8.14.4.
Some further operators also exist only for jsonb, as shown in Table 9-41. Many of these operators can be indexed by jsonb operator classes. For a full description of jsonb containment and existence semantics, see Section 8.14.3. Section 8.14.4 describes how these operators can be used to effectively index jsonb data.
Table 9-41. Additional jsonb Operators
| Operator | Right Operand Type | Description | Example |
|---|---|---|---|
| @> | jsonb | Does the left JSON value contain the right JSON path/value entries at the top level? | '{"a":1, "b":2}'::jsonb @> '{"b":2}'::jsonb |
| <@ | jsonb | Are the left JSON path/value entries contained at the top level within the right JSON value? | '{"b":2}'::jsonb <@ '{"a":1, "b":2}'::jsonb |
| ? | text | Does the string exist as a top-level key within the JSON value? | '{"a":1, "b":2}'::jsonb ? 'b' |
| ?| | text[] | Do any of these array strings exist as top-level keys? | '{"a":1, "b":2, "c":3}'::jsonb ?| array['b', 'c'] |
| ?& | text[] | Do all of these array strings exist as top-level keys? | '["a", "b"]'::jsonb ?& array['a', 'b'] |
| || | jsonb | Concatenate two jsonb values into a new jsonb value | '["a", "b"]'::jsonb || '["c", "d"]'::jsonb |
| - | text | Delete key/value pair or string element from left operand. Key/value pairs are matched based on their key value. | '{"a": "b"}'::jsonb - 'a' |
| - | integer | Delete the array element with specified index (Negative integers count from the end). Throws an error if top level container is not an array. | '["a", "b"]'::jsonb - 1 |
| #- | text[] | Delete the field or element with specified path (for JSON arrays, negative integers count from the end) | '["a", {"b":1}]'::jsonb #- '{1,b}' |
Note: The || operator concatenates the elements at the top level of each of its operands. It does not operate recursively. For example, if both operands are objects with a common key field name, the value of the field in the result will just be the value from the right hand operand.
Table 9-42 shows the functions that are available for creating json and jsonb values. (There are no equivalent functions for jsonb, of the row_to_json and array_to_json functions. However, the to_jsonb function supplies much the same functionality as these functions would.)
Table 9-42. JSON Creation Functions
| Function | Description | Example | Example Result |
|---|---|---|---|
to_json(anyelement) to_jsonb(anyelement) | Returns the value as json or jsonb. Arrays and composites are converted (recursively) to arrays and objects; otherwise, if there is a cast from the type to json, the cast function will be used to perform the conversion; otherwise, a scalar value is produced. For any scalar type other than a number, a Boolean, or a null value, the text representation will be used, in such a fashion that it is a valid json or jsonb value. | to_json('Fred said "Hi."'::text) | "Fred said \"Hi.\"" |
| array_to_json(anyarray [, pretty_bool]) | Returns the array as a JSON array. A PostgreSQL multidimensional array becomes a JSON array of arrays. Line feeds will be added between dimension-1 elements if pretty_bool is true. | array_to_json('{{1,5},{99,100}}'::int[]) | [[1,5],[99,100]] |
| row_to_json(record [, pretty_bool]) | Returns the row as a JSON object. Line feeds will be added between level-1 elements if pretty_bool is true. | row_to_json(row(1,'foo')) | {"f1":1,"f2":"foo"} |
json_build_array(VARIADIC "any") jsonb_build_array(VARIADIC "any") | Builds a possibly-heterogeneously-typed JSON array out of a variadic argument list. | json_build_array(1,2,'3',4,5) | [1, 2, "3", 4, 5] |
json_build_object(VARIADIC "any") jsonb_build_object(VARIADIC "any") | Builds a JSON object out of a variadic argument list. By convention, the argument list consists of alternating keys and values. | json_build_object('foo',1,'bar',2) | {"foo": 1, "bar": 2} |
json_object(text[]) jsonb_object(text[]) | Builds a JSON object out of a text array. The array must have either exactly one dimension with an even number of members, in which case they are taken as alternating key/value pairs, or two dimensions such that each inner array has exactly two elements, which are taken as a key/value pair. | json_object('{a, 1, b, "def", c, 3.5}') json_object('{{a, 1},{b, "def"},{c, 3.5}}') | {"a": "1", "b": "def", "c": "3.5"} |
json_object(keys text[], values text[]) jsonb_object(keys text[], values text[]) | This form of json_object takes keys and values pairwise from two separate
arrays. In all other respects it is identical to the one-argument form.
| json_object('{a, b}', '{1,2}') | {"a": "1", "b": "2"} |
Note:
array_to_json
androw_to_json
have the same behavior asto_json
except for offering a pretty-printing option. The behavior described forto_json
likewise applies to each individual value converted by the other JSON creation functions.
Note: The hstore extension has a cast from hstore to json, so that hstore values converted via the JSON creation functions will be represented as JSON objects, not as primitive string values.
Table 9-43 shows the functions that are available for processing json and jsonb values.
Table 9-43. JSON Processing Functions
| Function | Return Type | Description | Example | Example Result |
|---|---|---|---|---|
json_array_length(json) jsonb_array_length(jsonb) | int | Returns the number of elements in the outermost JSON array. | json_array_length('[1,2,3,{"f1":1,"f2":[5,6]},4]') | 5 |
json_each(json) jsonb_each(jsonb) | setof key text, value json setof key text, value jsonb | Expands the outermost JSON object into a set of key/value pairs. | select * from json_each('{"a":"foo", "b":"bar"}') | key | value -----+------- a | "foo" b | "bar" |
json_each_text(json) jsonb_each_text(jsonb) | setof key text, value text | Expands the outermost JSON object into a set of key/value pairs. The returned values will be of type text. | select * from json_each_text('{"a":"foo", "b":"bar"}') | key | value -----+------- a | foo b | bar |
json_extract_path(from_json json, VARIADIC path_elems text[]) jsonb_extract_path(from_json jsonb, VARIADIC path_elems text[]) | json jsonb | Returns JSON value pointed to by path_elems (equivalent to #> operator). | json_extract_path('{"f2":{"f3":1},"f4":{"f5":99,"f6":"foo"}}','f4') | {"f5":99,"f6":"foo"} |
json_extract_path_text(from_json json, VARIADIC path_elems text[]) jsonb_extract_path_text(from_json jsonb, VARIADIC path_elems text[]) | text | Returns JSON value pointed to by path_elems as text (equivalent to #>> operator). | json_extract_path_text('{"f2":{"f3":1},"f4":{"f5":99,"f6":"foo"}}','f4', 'f6') | foo |
json_object_keys(json) jsonb_object_keys(jsonb) | setof text | Returns set of keys in the outermost JSON object. | json_object_keys('{"f1":"abc","f2":{"f3":"a", "f4":"b"}}') | json_object_keys ------------------ f1 f2 |
json_populate_record(base anyelement, from_json json) jsonb_populate_record(base anyelement, from_json jsonb) | anyelement | Expands the object in from_json to a row whose columns match the record type defined by base (see note below). | select * from json_populate_record(null::myrowtype, '{"a":1,"b":2}') | a | b ---+--- 1 | 2 |
json_populate_recordset(base anyelement, from_json json) jsonb_populate_recordset(base anyelement, from_json jsonb) | setof anyelement | Expands the outermost array of objects in from_json to a set of rows whose columns match the record type defined by base (see note below). | select * from json_populate_recordset(null::myrowtype, '[{"a":1,"b":2},{"a":3,"b":4}]') | a | b ---+--- 1 | 2 3 | 4 |
json_array_elements(json) jsonb_array_elements(jsonb) | setof json setof jsonb | Expands a JSON array to a set of JSON values. | select * from json_array_elements('[1,true, [2,false]]') | value ----------- 1 true [2,false] |
json_array_elements_text(json) jsonb_array_elements_text(jsonb) | setof text | Expands a JSON array to a set of text values. | select * from json_array_elements_text('["foo", "bar"]') | value ----------- foo bar |
json_typeof(json) jsonb_typeof(jsonb) | text | Returns the type of the outermost JSON value as a text string. Possible types are object, array, string, number, boolean, and null. | json_typeof('-123.4') | number |
json_to_record(json) jsonb_to_record(jsonb) | record | Builds an arbitrary record from a JSON object (see note below). As with all functions returning record, the caller must explicitly define the structure of the record with an AS clause. | select * from json_to_record('{"a":1,"b":[1,2,3],"c":"bar"}') as x(a int, b text, d text) | a | b | d ---+---------+--- 1 | [1,2,3] | |
json_to_recordset(json) jsonb_to_recordset(jsonb) | setof record | Builds an arbitrary set of records from a JSON array of objects (see note below). As with all functions returning record, the caller must explicitly define the structure of the record with an AS clause. | select * from json_to_recordset('[{"a":1,"b":"foo"},{"a":"2","c":"bar"}]') as x(a int, b text); | a | b ---+----- 1 | foo 2 | |
json_strip_nulls(from_json json) jsonb_strip_nulls(from_json jsonb) | json jsonb | Returns from_json with all object fields that have null values omitted. Other null values are untouched. | json_strip_nulls('[{"f1":1,"f2":null},2,null,3]') | [{"f1":1},2,null,3] |
jsonb_set(target jsonb, path text[], new_value jsonb[, create_missing boolean]) | jsonb | Returns target with the section designated by path replaced by new_value, or with new_value added if create_missing is true ( default is true) and the item designated by path does not exist. As with the path orientated operators, negative integers that appear in path count from the end of JSON arrays. | jsonb_set('[{"f1":1,"f2":null},2,null,3]', '{0,f1}','[2,3,4]', false) jsonb_set('[{"f1":1,"f2":null},2]', '{0,f3}','[2,3,4]') | [{"f1":[2,3,4],"f2":null},2,null,3] [{"f1": 1, "f2": null, "f3": [2, 3, 4]}, 2] |
jsonb_pretty(from_json jsonb) | text | Returns from_json as indented JSON text. | jsonb_pretty('[{"f1":1,"f2":null},2,null,3]') | [ { "f1": 1, "f2": null }, 2, null, 3 ] |
Note: Many of these functions and operators will convert Unicode escapes in JSON strings to the appropriate single character. This is a non-issue if the input is type jsonb, because the conversion was already done; but for json input, this may result in throwing an error, as noted in Section 8.14.
Note: While the examples for the functions
json_populate_record
,json_populate_recordset
,json_to_record
andjson_to_recordset
use constants, the typical use would be to reference a table in the FROM clause and use one of its json or jsonb columns as an argument to the function. Extracted key values can then be referenced in other parts of the query, like WHERE clauses and target lists. Extracting multiple values in this way can improve performance over extracting them separately with per-key operators.JSON keys are matched to identical column names in the target row type. JSON type coercion for these functions is "best effort" and may not result in desired values for some types. JSON fields that do not appear in the target row type will be omitted from the output, and target columns that do not match any JSON field will simply be NULL.
Note: All the items of the path parameter of jsonb_set must be present in the target, unless create_missing is true, in which case all but the last item must be present. If these conditions are not met the target is returned unchanged.
If the last path item is an object key, it will be created if it is absent and given the new value. If the last path item is an array index, if it is positive the item to set is found by counting from the left, and if negative by counting from the right - -1 designates the rightmost element, and so on. If the item is out of the range -array_length .. array_length -1, and create_missing is true, the new value is added at the beginning of the array if the item is negative, and at the end of the array if it is positive.
Note: The json_typeof function's null return value should not be confused with a SQL NULL. While calling json_typeof('null'::json) will return null, calling json_typeof(NULL::json) will return a SQL NULL.
Note: If the argument to json_strip_nulls contains duplicate field names in any object, the result could be semantically somewhat different, depending on the order in which they occur. This is not an issue for jsonb_strip_nulls since jsonb values never have duplicate object field names.
See also Section 9.20 for the aggregate
function json_agg
which aggregates record
values as JSON, and the aggregate function
json_object_agg
which aggregates pairs of values
into a JSON object, and their jsonb equivalents,
jsonb_agg
and jsonb_object_agg
.


Logo
Sass
Components
JavaScript
Drugs
Simply beautiful flashcards to learn all you need (and want) to know
Someone Famous 1
Someone Famous 2
Someone Famous 3
Someone Famous 4
Someone Famous 5
Someone Famous 6
Someone Famous 7
Someone Famous 8
Someone Famous 9


5
System space is almost full
You have 9 unfinished tasks
Monthly report is available
Weekly meeting in 1 hour
Your request has been approved
See all notifications
Help
Basic Use
Working With Data
Security
Troubleshooting
{:Session::get('adminUser.realname')}
My Profile
Message
Settings
退出登陆
控制面板
私海
我添加的客户
公海


{% extends "layout_with_help.html" %} {% block pageTitle %} NRS Diagnostic Prototype {% endblock %} {% block header %} {{ govukHeader() }} {% endblock %} {% block beforeContent %} Back {% endblock %} {% block content %}
Now that you've had your lesson, it's time to do a quick recap on what you've learnt. Once you're ready, start working through the questions.


6.5 Interface for Queues
void queue_init(Queue *queue, void (*destroy)(void *data));
Return Value
None.
Description
Initializes the queue specified by
queue. This operation must be called for a
queue before the queue can be used with any other operation.
The destroy argument provides a way to free
dynamically allocated data when queue_destroy is called. It works in
a manner similar to that described for stack_destroy. For a queue containing
data that should not be freed, destroy should
be set to NULL.
Complexity
O (1)
void queue_destroy(Queue *queue);
Return Value
None.
Description
Destroys the queue specified by
queue. No other operations are permitted after
calling queue_destroy unless
queue_init is called again. The
queue_destroy operation removes
all elements from a queue and calls the function passed as
destroy to queue_init once for each element as
it is removed, provided destroy was not set to
NULL.
Complexity
O (n), where n is the number of elements in the
queue.
intqueue_enqueue(Queue *queue, const void *data);
Return Value
0if enqueuing the element is successful, or
-1 otherwise.
Description
Enqueues an element at the tail of the queue
specified by queue. The new element contains a
pointer to data, so the memory referenced by
data should remain valid as long as the
element remains in the queue. It is the responsibility of the
caller to manage the storage associated with
data.
Complexity
O (1)
intqueue_dequeue(Queue *queue, void **data);
Return Value
0if dequeuing the element is successful, or
-1 otherwise.
Description
Dequeues an element from the head of the
queue specified by queue. Upon return,
data points to the data stored in the element
that was dequeued. It is the responsibility of the caller to
manage the storage associated with the data.
Complexity
O (1)
void*queue_peek(const Queue *queue);
Return Value
Data stored in the element at the head of the
queue, or NULL if the queue is empty.
Description
Macro that evaluates to the data stored in
the element at the head of the queue specified by
queue.
Complexity
O (1)
int queue_size(const Queue *queue);
Return Value
Number of elements in the queue.
Description
Macro that evaluates to the number of
elements in the queue specified by
queue.
Complexity
O (1) |


图片相册画廊轮播图js插件
Powered by www.jsdaima.com
Default
default_no_panel
carousel
compact_bottom
compact_left
compact_right
compact_top
grid_bottom
grid_horizontal
grid_left
grid_right
grid_top
slider
tiles_columns
tiles_columns_links
tiles_justified
tiles_justified_links
tilesgrid
tilesgrid_arrows
Default Theme


Does Tello Mobile allow Hotspot use?
by Joe MaringBest answer: Yes, Tello Mobile does allow hotspot use. It's included for free with any plan you choose, meaning you don't have to pay a penny more just to tether with your other devices.
- Free hotspot: Tello (From $5/month at Tello)
- Perfect for Tello: Google Pixel 3a ($393 at Amazon)
Tello doesn't charge extra for hotspot use
Tello is a wireless carrier with a ton of great features, including free hotspot access!
Some of Tello's competitors charge $10/month to use your phone's hotspot, meaning you could potentially save $120 every year just by signing up for Tello and using your hotspot for free.
Using your phone's hotspot is also dead simple. Just hop into its settings, find the section for "network & internet," and you should see a section for hotspot and tethering. Here, you can turn the hotspot on, adjust its security, password, and more.
The hotspot feature counts against your regular monthly data
When you use your phone's hotspot on Tello, any data that's used simply counts against your regular monthly data allowance.
Have a Tello plan with 4GB of monthly data? You'll share that 4GB between data you use on your phone and whenever its hotspot is enabled. If you're a particularly heavy user, it's probably worth splurging for the 12GB data option.
It's also worth remembering that your monthly data allotment is for LTE data. Once you've used up your data for the month, you can keep on using unlimited 2G data. It'll be quite a bit slower, but good enough for basic web browsing and social media use.
Free hotspot
Tello
Wireless service with easy-peasy hotspot access
If you're thinking about switching carriers, you owe it to yourself to check out Tello. Tello offers highly customizable plans, low prices, free hotspot use, and calls to Canada, Mexico, and China at no extra cost.
Perfect for Tello
Google Pixel 3a
One of the best phones you can get for Tello
The Pixel 3a works perfectly with Tello, and it's one of the best deals out there. It has an outstanding camera, great software, and a sturdy plastic design. It also comes with three years of guaranteed software updates.
We may earn a commission for purchases using our links. Learn more.


Index
All Classes|All Packages
C
- com.github.mauromidolo.mousemover.controll - package com.github.mauromidolo.mousemover.controll
- com.github.mauromidolo.mousemover.view - package com.github.mauromidolo.mousemover.view
G
- getInstance() - Static method in class com.github.mauromidolo.mousemover.controll.MouseMoverController
-
Singleton instance
M
- main(String[]) - Static method in class com.github.mauromidolo.mousemover.view.UI
-
Main Class
- MouseMoverController - Class in com.github.mauromidolo.mousemover.controll
-
MouseMoverController: controller to manager MouseMoverManager background job
- MouseMoverManager - Class in com.github.mauromidolo.mousemover.controll
-
MouseMoverManager: Runnable to move mouse automatically
- MouseMoverManager(int) - Constructor for class com.github.mauromidolo.mousemover.controll.MouseMoverManager
-
Construct
R
- run() - Method in class com.github.mauromidolo.mousemover.controll.MouseMoverManager
S
- start() - Method in class com.github.mauromidolo.mousemover.controll.MouseMoverManager
-
start the Runnable
- stop() - Method in class com.github.mauromidolo.mousemover.controll.MouseMoverManager
-
stop the Runnable
- switchOff() - Method in class com.github.mauromidolo.mousemover.controll.MouseMoverController
-
stop the background job
- switchOn(int) - Method in class com.github.mauromidolo.mousemover.controll.MouseMoverController
-
start the background job, if the job is already in exertion stop and restart
U
- UI - Class in com.github.mauromidolo.mousemover.view
-
Entry class, it's necessary to create the user interface
- UI() - Constructor for class com.github.mauromidolo.mousemover.view.UI
All Classes|All Packages


Purpose
The Texas A&M GeoInnovation Service Center is undertaking several efforts to provide the resources and information needed by professionals and the public.
Collection
Volunteered geographic information (VGI) can be difficult to collect and maintain. Texas A&M GeoInnovation Service Center seeks to establish a standardized reporting format for information on counties, lockdowns, and testing sites.
Dissemination
Collected crowd-sourced data is used to create visualizations and maps to facilitate the consumption and dissemination of accurate data. In addition, all data is also offered to the general public through API's for use in other applications.
Resources
Several other free resources for professionals and analysts actively working in COVID-19 fighting efforts are available. These include geographic data sets as well as unlimited use of our Geocoding services.


DH_get0_pqg, DH_set0_pqg, DH_get0_key, DH_set0_key, DH_get0_p, DH_get0_q, DH_get0_g, DH_get0_priv_key, DH_get0_pub_key, DH_clear_flags, DH_test_flags, DH_set_flags, DH_get0_engine, DH_get_length, DH_set_length - Routines for getting and setting data in a DH object
#include <openssl/dh.h>
The following functions have been deprecated since OpenSSL 3.0, and can be hidden entirely by defining OPENSSL_API_COMPAT with a suitable version value, see openssl_user_macros(7):
void DH_get0_pqg(const DH *dh,
const BIGNUM **p, const BIGNUM **q, const BIGNUM **g);
int DH_set0_pqg(DH *dh, BIGNUM *p, BIGNUM *q, BIGNUM *g);
void DH_get0_key(const DH *dh,
const BIGNUM **pub_key, const BIGNUM **priv_key);
int DH_set0_key(DH *dh, BIGNUM *pub_key, BIGNUM *priv_key);
const BIGNUM *DH_get0_p(const DH *dh);
const BIGNUM *DH_get0_q(const DH *dh);
const BIGNUM *DH_get0_g(const DH *dh);
const BIGNUM *DH_get0_priv_key(const DH *dh);
const BIGNUM *DH_get0_pub_key(const DH *dh);
void DH_clear_flags(DH *dh, int flags);
int DH_test_flags(const DH *dh, int flags);
void DH_set_flags(DH *dh, int flags);
long DH_get_length(const DH *dh);
int DH_set_length(DH *dh, long length);
ENGINE *DH_get0_engine(DH *d);
All of the functions described on this page are deprecated. Applications should instead use EVP_PKEY_get_bn_param(3) for any methods that return a BIGNUM. Refer to EVP_PKEY-DH(7) for more information.
A DH object contains the parameters p, q and g. Note that the q parameter is optional. It also contains a public key (pub_key) and (optionally) a private key (priv_key).
The p, q and g parameters can be obtained by calling DH_get0_pqg(). If the parameters have not yet been set then *p, *q and *g will be set to NULL. Otherwise they are set to pointers to their respective values. These point directly to the internal representations of the values and therefore should not be freed directly. Any of the out parameters p, q, and g can be NULL, in which case no value will be returned for that parameter.
The p, q and g values can be set by calling DH_set0_pqg() and passing the new values for p, q and g as parameters to the function. Calling this function transfers the memory management of the values to the DH object, and therefore the values that have been passed in should not be freed directly after this function has been called. The q parameter may be NULL. DH_set0_pqg() also checks if the parameters associated with p and g and optionally q are associated with known safe prime groups. If it is a safe prime group then the value of q will be set to q = (p - 1) / 2 if q is NULL. The optional length parameter will be set to BN_num_bits(q) if q is not NULL.
To get the public and private key values use the DH_get0_key() function. A pointer to the public key will be stored in *pub_key, and a pointer to the private key will be stored in *priv_key. Either may be NULL if they have not been set yet, although if the private key has been set then the public key must be. The values point to the internal representation of the public key and private key values. This memory should not be freed directly. Any of the out parameters pub_key and priv_key can be NULL, in which case no value will be returned for that parameter.
The public and private key values can be set using DH_set0_key(). Either parameter may be NULL, which means the corresponding DH field is left untouched. As with DH_set0_pqg() this function transfers the memory management of the key values to the DH object, and therefore they should not be freed directly after this function has been called.
Any of the values p, q, g, priv_key, and pub_key can also be retrieved separately by the corresponding function DH_get0_p(), DH_get0_q(), DH_get0_g(), DH_get0_priv_key(), and DH_get0_pub_key(), respectively.
DH_set_flags() sets the flags in the flags parameter on the DH object. Multiple flags can be passed in one go (bitwise ORed together). Any flags that are already set are left set. DH_test_flags() tests to see whether the flags passed in the flags parameter are currently set in the DH object. Multiple flags can be tested in one go. All flags that are currently set are returned, or zero if none of the flags are set. DH_clear_flags() clears the specified flags within the DH object.
DH_get0_engine() returns a handle to the ENGINE that has been set for this DH object, or NULL if no such ENGINE has been set. This function is deprecated. All engines should be replaced by providers.
The DH_get_length() and DH_set_length() functions get and set the optional length parameter associated with this DH object. If the length is nonzero then it is used, otherwise it is ignored. The length parameter indicates the length of the secret exponent (private key) in bits. For safe prime groups the optional length parameter length can be set to a value greater or equal to 2 * maximum_target_security_strength(BN_num_bits(p)) as listed in SP800-56Ar3 Table(s) 25 & 26. These functions are deprecated and should be replaced with EVP_PKEY_CTX_set_params() and EVP_PKEY_get_int_param() using the parameter key OSSL_PKEY_PARAM_DH_PRIV_LEN as described in EVP_PKEY-DH(7).
Values retrieved with DH_get0_key() are owned by the DH object used in the call and may therefore not be passed to DH_set0_key(). If needed, duplicate the received value using BN_dup() and pass the duplicate. The same applies to DH_get0_pqg() and DH_set0_pqg().
DH_set0_pqg() and DH_set0_key() return 1 on success or 0 on failure.
DH_get0_p(), DH_get0_q(), DH_get0_g(), DH_get0_priv_key(), and DH_get0_pub_key() return the respective value, or NULL if it is unset.
DH_test_flags() returns the current state of the flags in the DH object.
DH_get0_engine() returns the ENGINE set for the DH object or NULL if no ENGINE has been set.
DH_get_length() returns the length of the secret exponent (private key) in bits, or zero if no such length has been explicitly set.
DH_new(3), DH_new(3), DH_generate_parameters(3), DH_generate_key(3), DH_set_method(3), DH_size(3), DH_meth_new(3)
The functions described here were added in OpenSSL 1.1.0.
All of these functions were deprecated in OpenSSL 3.0.
Copyright 2016-2022 The OpenSSL Project Authors. All Rights Reserved.
Licensed under the Apache License 2.0 (the "License"). You may not use this file except in compliance with the License. You can obtain a copy in the file LICENSE in the source distribution or at https://www.openssl.org/source/license.html.


Before Change
dependencies=["resolve-idl"]).install("gen")
goal(name="protoc", action=ProtobufGen,
dependencies=["resolve-idl"]).install("gen")
goal(name="antlr", action=AntlrGen,
dependencies=["resolve-idl"]).install("gen")
goal(
name="checkstyle",
action=Checkstyle,
After Change
// TODO(John Sirois): gen attempted as the sole Goal should gen for all known gen types but
// recognize flags to narrow the gen set
goal(name="thrift", action=ThriftGen).install("gen").with_description("Generate code.")
goal(name="scrooge", action=ScroogeGen).install("gen")
goal(name="protoc", action=ProtobufGen).install("gen")
goal(name="antlr", action=AntlrGen).install("gen")
goal(


File types chosen:
application/octet-stream
application/zip
application/x-executable
application/pdf
application/xhtml+xml
application/x-sh
application/xml
image/jpeg
image/png
audio/mpeg
video/quicktime
text/html
text/plain
text/x-matlab
message/rfc822
CLEAR


- Developed a GUI based test framework to automate initial factory setup and testing of new hardware product
- Developed communication API in Linux in order to allow products to send information(such as images from camera, vehicle speed and position) to third party hardware
- Developed BASH scripts for Raspberry Pi with I/O board in order to test API against real hardware
- Used several different unit testing frameworks including CUnit and MSTest to test and improve code while under development.
Technologies used : C# , XAML , Visual Studio , C , C++ , BASH , CUnit , MSTest


Amazon API Gateway UsagePlan ThrottleSettings
ThrottleSettings
is a property of the AWS::ApiGateway::UsagePlan resource that specifies the overall request rate (average requests per second) and
burst capacity when users call your Amazon API Gateway (API Gateway) APIs.
Syntax
JSON
{ "BurstLimit" :
Integer
, "RateLimit" :Number
}
YAML
BurstLimit:
Integer
RateLimit:Number
Properties
BurstLimit
-
The maximum API request rate limit over a time ranging from one to a few seconds. The maximum API request rate limit depends on whether the underlying token bucket is at its full capacity. For more information about request throttling, see Manage API Request Throttling in the API Gateway Developer Guide.
Required: No
Type: Integer
RateLimit
-
The API request steady-state rate limit (average requests per second over an extended period of time). For more information about request throttling, see Manage API Request Throttling in the API Gateway Developer Guide.
Required: No
Type: Number


Be fair and honest! Cheating will hurt yourself the most!
Deployed here: https://glitch.com/edit/#!/rough-showy-slayer?path=index.html:14:7


Leeds United star makes interesting claim about Nottingham Forest's City Ground
Latest Nottingham Forest news | The Reds faced Leeds in the Sky Bet Championship at Elland Road in August, where they were held to a 1-1 draw
by Mark WakefieldLeeds United captain Liam Cooper says the City Ground is one of his favourite away stadiums to visit in the Sky Bet Championship.
Nottingham Forest are yet to welcome the Whites to Trentside this season, but have already made the trip to Elland Road.
A late goal from Lewis Grabban saw the Reds secure a 1-1 draw with Marcelo Bielsa’s side in August.
The last time Forest faced Leeds at the City Ground was last season on New Year’s Day, when the Reds claimed a 4-2 win.
A brace from Jack Colback supported by goals from Daryl Murphy and Ben Osborn sealed with victory, as Leeds midfielder was sent off shortly before half time.
Cooper was recently speaking alongside Mateusz Klich and Ben White at a Q&A event at Elland Road, where he was asked for his favourite and least favourite away stadium in the Championship.
"Favourite (away ground) would probably be – I like the Forest ground", said Cooper.
Nottingham Forest latest
This is your round up of all things Forest; the one-stop shop that will keep you updated on the latest goings on at the City Ground and beyond.
Transfers, injuries, match-days and managers, we’ve got you covered.
We’ll be bringing the very latest on the Reds throughout the week and around the clock.
Make sure you keep yourself updated with our handy daily catch up...
Must reads...
- Championship is 'there for the taking'
- Was Lamouchi appointment a 'stroke of genius'?
- Leeds star's interesting claim about City Ground
- How Reds players rate for season so far
For all your latest NFFC news, opinion, analysis and transfer gossip, click here
You can follow our Reds reporter Sarah Clapson on Twitter, and her email address is<EMAIL_ADDRESS>He added: "Atmosphere is always good (and) we always get a good allocation.
"But if, obviously, Aston Villa are still in the league I'd say that. Villa Park is brilliant.
"The worst (away stadium) – Brentford. Hate the place.
"The changing rooms are tiny and, let's put it this way, nobody fits on the toilet."


A visual monitoring tool for network enabled Shure devices
Micboard simplifies microphone monitoring and storage for artists, engineers, and volunteers. View battery, audio, and RF levels from any device on the network.
Read more about Micboard on the Willow Creek Production Blog


Hot-keys on this page
r m x p toggle line displays
j k next/prev highlighted chunk
0 (zero) top of page
1 (one) first highlighted chunk
1from __future__ import absolute_import, division, print_function
3import copy
5from ._compat import iteritems
6from ._make import NOTHING, _obj_setattr, fields
7from .exceptions import AttrsAttributeNotFoundError
10def asdict(
11 inst,
12 recurse=True,
13 filter=None,
14 dict_factory=dict,
15 retain_collection_types=False,
16 value_serializer=None,
17):
18 """
19 Return the ``attrs`` attribute values of *inst* as a dict.
21 Optionally recurse into other ``attrs``-decorated classes.
23 :param inst: Instance of an ``attrs``-decorated class.
24 :param bool recurse: Recurse into classes that are also
25 ``attrs``-decorated.
26 :param callable filter: A callable whose return code determines whether an
27 attribute or element is included (``True``) or dropped (``False``). Is
28 called with the `attr.Attribute` as the first argument and the
29 value as the second argument.
30 :param callable dict_factory: A callable to produce dictionaries from. For
31 example, to produce ordered dictionaries instead of normal Python
32 dictionaries, pass in ``collections.OrderedDict``.
33 :param bool retain_collection_types: Do not convert to ``list`` when
34 encountering an attribute whose type is ``tuple`` or ``set``. Only
35 meaningful if ``recurse`` is ``True``.
36 :param Optional[callable] value_serializer: A hook that is called for every
37 attribute or dict key/value. It receives the current instance, field
38 and value and must return the (updated) value. The hook is run *after*
39 the optional *filter* has been applied.
41 :rtype: return type of *dict_factory*
43 :raise attr.exceptions.NotAnAttrsClassError: If *cls* is not an ``attrs``
44 class.
46 .. versionadded:: 16.0.0 *dict_factory*
47 .. versionadded:: 16.1.0 *retain_collection_types*
48 .. versionadded:: 20.3.0 *value_serializer*
49 """
50 attrs = fields(inst.__class__)
51 rv = dict_factory()
52 for a in attrs:
53 v = getattr(inst, a.name)
54 if filter is not None and not filter(a, v):
55 continue
57 if value_serializer is not None:
58 v = value_serializer(inst, a, v)
60 if recurse is True:
61 if has(v.__class__):
62 rv[a.name] = asdict(
63 v,
64 True,
65 filter,
66 dict_factory,
67 retain_collection_types,
68 value_serializer,
69 )
70 elif isinstance(v, (tuple, list, set, frozenset)):
71 cf = v.__class__ if retain_collection_types is True else list
72 rv[a.name] = cf(
73 [
74 _asdict_anything(
75 i,
76 filter,
77 dict_factory,
78 retain_collection_types,
79 value_serializer,
80 )
81 for i in v
82 ]
83 )
84 elif isinstance(v, dict):
85 df = dict_factory
86 rv[a.name] = df(
87 (
88 _asdict_anything(
89 kk,
90 filter,
91 df,
92 retain_collection_types,
93 value_serializer,
94 ),
95 _asdict_anything(
96 vv,
97 filter,
98 df,
99 retain_collection_types,
100 value_serializer,
101 ),
102 )
103 for kk, vv in iteritems(v)
104 )
105 else:
106 rv[a.name] = v
107 else:
108 rv[a.name] = v
109 return rv
112def _asdict_anything(
113 val,
114 filter,
115 dict_factory,
116 retain_collection_types,
117 value_serializer,
118):
119 """
120 ``asdict`` only works on attrs instances, this works on anything.
121 """
122 if getattr(val.__class__, "__attrs_attrs__", None) is not None:
123 # Attrs class.
124 rv = asdict(
125 val,
126 True,
127 filter,
128 dict_factory,
129 retain_collection_types,
130 value_serializer,
131 )
132 elif isinstance(val, (tuple, list, set, frozenset)):
133 cf = val.__class__ if retain_collection_types is True else list
134 rv = cf(
135 [
136 _asdict_anything(
137 i,
138 filter,
139 dict_factory,
140 retain_collection_types,
141 value_serializer,
142 )
143 for i in val
144 ]
145 )
146 elif isinstance(val, dict):
147 df = dict_factory
148 rv = df(
149 (
150 _asdict_anything(
151 kk, filter, df, retain_collection_types, value_serializer
152 ),
153 _asdict_anything(
154 vv, filter, df, retain_collection_types, value_serializer
155 ),
156 )
157 for kk, vv in iteritems(val)
158 )
159 else:
160 rv = val
161 if value_serializer is not None:
162 rv = value_serializer(None, None, rv)
164 return rv
167def astuple(
168 inst,
169 recurse=True,
170 filter=None,
171 tuple_factory=tuple,
172 retain_collection_types=False,
173):
174 """
175 Return the ``attrs`` attribute values of *inst* as a tuple.
177 Optionally recurse into other ``attrs``-decorated classes.
179 :param inst: Instance of an ``attrs``-decorated class.
180 :param bool recurse: Recurse into classes that are also
181 ``attrs``-decorated.
182 :param callable filter: A callable whose return code determines whether an
183 attribute or element is included (``True``) or dropped (``False``). Is
184 called with the `attr.Attribute` as the first argument and the
185 value as the second argument.
186 :param callable tuple_factory: A callable to produce tuples from. For
187 example, to produce lists instead of tuples.
188 :param bool retain_collection_types: Do not convert to ``list``
189 or ``dict`` when encountering an attribute which type is
190 ``tuple``, ``dict`` or ``set``. Only meaningful if ``recurse`` is
191 ``True``.
193 :rtype: return type of *tuple_factory*
195 :raise attr.exceptions.NotAnAttrsClassError: If *cls* is not an ``attrs``
196 class.
198 .. versionadded:: 16.2.0
199 """
200 attrs = fields(inst.__class__)
201 rv = []
202 retain = retain_collection_types # Very long. :/
203 for a in attrs:
204 v = getattr(inst, a.name)
205 if filter is not None and not filter(a, v):
206 continue
207 if recurse is True:
208 if has(v.__class__):
209 rv.append(
210 astuple(
211 v,
212 recurse=True,
213 filter=filter,
214 tuple_factory=tuple_factory,
215 retain_collection_types=retain,
216 )
217 )
218 elif isinstance(v, (tuple, list, set, frozenset)):
219 cf = v.__class__ if retain is True else list
220 rv.append(
221 cf(
222 [
223 astuple(
224 j,
225 recurse=True,
226 filter=filter,
227 tuple_factory=tuple_factory,
228 retain_collection_types=retain,
229 )
230 if has(j.__class__)
231 else j
232 for j in v
233 ]
234 )
235 )
236 elif isinstance(v, dict):
237 df = v.__class__ if retain is True else dict
238 rv.append(
239 df(
240 (
241 astuple(
242 kk,
243 tuple_factory=tuple_factory,
244 retain_collection_types=retain,
245 )
246 if has(kk.__class__)
247 else kk,
248 astuple(
249 vv,
250 tuple_factory=tuple_factory,
251 retain_collection_types=retain,
252 )
253 if has(vv.__class__)
254 else vv,
255 )
256 for kk, vv in iteritems(v)
257 )
258 )
259 else:
260 rv.append(v)
261 else:
262 rv.append(v)
264 return rv if tuple_factory is list else tuple_factory(rv)
267def has(cls):
268 """
269 Check whether *cls* is a class with ``attrs`` attributes.
271 :param type cls: Class to introspect.
272 :raise TypeError: If *cls* is not a class.
274 :rtype: bool
275 """
276 return getattr(cls, "__attrs_attrs__", None) is not None
279def assoc(inst, **changes):
280 """
281 Copy *inst* and apply *changes*.
283 :param inst: Instance of a class with ``attrs`` attributes.
284 :param changes: Keyword changes in the new copy.
286 :return: A copy of inst with *changes* incorporated.
288 :raise attr.exceptions.AttrsAttributeNotFoundError: If *attr_name* couldn't
289 be found on *cls*.
290 :raise attr.exceptions.NotAnAttrsClassError: If *cls* is not an ``attrs``
291 class.
293 .. deprecated:: 17.1.0
294 Use `evolve` instead.
295 """
296 import warnings
298 warnings.warn(
299 "assoc is deprecated and will be removed after 2018/01.",
300 DeprecationWarning,
301 stacklevel=2,
302 )
303 new = copy.copy(inst)
304 attrs = fields(inst.__class__)
305 for k, v in iteritems(changes):
306 a = getattr(attrs, k, NOTHING)
307 if a is NOTHING:
308 raise AttrsAttributeNotFoundError(
309 "{k} is not an attrs attribute on {cl}.".format(
310 k=k, cl=new.__class__
311 )
312 )
313 _obj_setattr(new, k, v)
314 return new
317def evolve(inst, **changes):
318 """
319 Create a new instance, based on *inst* with *changes* applied.
321 :param inst: Instance of a class with ``attrs`` attributes.
322 :param changes: Keyword changes in the new copy.
324 :return: A copy of inst with *changes* incorporated.
326 :raise TypeError: If *attr_name* couldn't be found in the class
327 ``__init__``.
328 :raise attr.exceptions.NotAnAttrsClassError: If *cls* is not an ``attrs``
329 class.
331 .. versionadded:: 17.1.0
332 """
333 cls = inst.__class__
334 attrs = fields(cls)
335 for a in attrs:
336 if not a.init:
337 continue
338 attr_name = a.name # To deal with private attributes.
339 init_name = attr_name if attr_name[0] != "_" else attr_name[1:]
340 if init_name not in changes:
341 changes[init_name] = getattr(inst, attr_name)
343 return cls(**changes)
346def resolve_types(cls, globalns=None, localns=None):
347 """
348 Resolve any strings and forward annotations in type annotations.
350 This is only required if you need concrete types in `Attribute`'s *type*
351 field. In other words, you don't need to resolve your types if you only
352 use them for static type checking.
354 With no arguments, names will be looked up in the module in which the class
355 was created. If this is not what you want, e.g. if the name only exists
356 inside a method, you may pass *globalns* or *localns* to specify other
357 dictionaries in which to look up these names. See the docs of
358 `typing.get_type_hints` for more details.
360 :param type cls: Class to resolve.
361 :param Optional[dict] globalns: Dictionary containing global variables.
362 :param Optional[dict] localns: Dictionary containing local variables.
364 :raise TypeError: If *cls* is not a class.
365 :raise attr.exceptions.NotAnAttrsClassError: If *cls* is not an ``attrs``
366 class.
367 :raise NameError: If types cannot be resolved because of missing variables.
369 :returns: *cls* so you can use this function also as a class decorator.
370 Please note that you have to apply it **after** `attr.s`. That means
371 the decorator has to come in the line **before** `attr.s`.
373 .. versionadded:: 20.1.0
374 """
375 try:
376 # Since calling get_type_hints is expensive we cache whether we've
377 # done it already.
378 cls.__attrs_types_resolved__
379 except AttributeError:
380 import typing
382 hints = typing.get_type_hints(cls, globalns=globalns, localns=localns)
383 for field in fields(cls):
384 if field.name in hints:
385 # Since fields have been frozen we must work around it.
386 _obj_setattr(field, "type", hints[field.name])
387 cls.__attrs_types_resolved__ = True
389 # Return the class so you can use it as a decorator too.
390 return cls


Safaricom Acknowledges They are Experiencing Network Issues
by Kiruti ItimuSafaricom has confirmed that they are experiencing issues with their network that is affecting their voice and data.
They shared this communique on their social channels.
Please note that we are experiencing intermittent network challenges affecting a number of services in our network including voice and data for some customers. We are working to restore all services as soon as possible.
We are sorry for any inconvenience and thank you for your patience.
The notice did not specify all of the services that were affected or the estimated time of restoration of services.
Safaricom is the biggest telecommunications operator in the country with over 34 million users and if their services are disrupted, it affects a large portion of the population. Voice is still their biggest earning revenue stream and a lot of millenials depend on their data to connect to the Internet.
This has not been a good weekend for Safaricom customers. They also sent a message to Safaricom Home customers where they said customers should expect slow speeds due to planned maintenance on their service.
This is not the first time we have seen the telco have network issues this year.
We will update you as soon as Safaricom is able to fix the issues they are facing this weekend.
**UPDATE**
Safaricom announced that they have successfully restored their services.


LSTM
Long Short-Term Memory layer - Hochreiter 1997.
Inherits From: LSTM
tf.compat.v2.keras.layers.LSTM
Based on available runtime hardware and constraints, this layer will choose different implementations (cuDNN-based or pure-TensorFlow) to maximize the performance. If a GPU is available and all the arguments to the layer meet the requirement of the CuDNN kernel (see below for details), the layer will use a fast cuDNN implementation.
The requirements to use the cuDNN implementation are:
activation
== 'tanh'recurrent_activation
== 'sigmoid'recurrent_dropout
== 0unroll
is Falseuse_bias
is Trueunits
: Positive integer, dimensionality of the output space.activation
: Activation function to use.
Default: hyperbolic tangent (tanh
). If you pass None
, no activation
is applied (ie. "linear" activation: a(x) = x
).recurrent_activation
: Activation function to use for the recurrent step.
Default: sigmoid (sigmoid
). If you pass None
, no activation is
applied (ie. "linear" activation: a(x) = x
).use_bias
: Boolean, whether the layer uses a bias vector.kernel_initializer
: Initializer for the kernel
weights matrix, used for
the linear transformation of the inputs..recurrent_initializer
: Initializer for the recurrent_kernel
weights
matrix, used for the linear transformation of the recurrent state..bias_initializer
: Initializer for the bias vector.unit_forget_bias
: Boolean. If True, add 1 to the bias of the forget gate at
initialization. Setting it to true will also force
bias_initializer="zeros"
. This is recommended in Jozefowicz et
al..kernel_regularizer
: Regularizer function applied to the kernel
weights
matrix.recurrent_regularizer
: Regularizer function applied to the
recurrent_kernel
weights matrix.bias_regularizer
: Regularizer function applied to the bias vector.activity_regularizer
: Regularizer function applied to the output of the
layer (its "activation")..kernel_constraint
: Constraint function applied to the kernel
weights
matrix.recurrent_constraint
: Constraint function applied to the recurrent_kernel
weights matrix.bias_constraint
: Constraint function applied to the bias vector.dropout
: Float between 0 and 1. Fraction of the units to drop for the linear
transformation of the inputs.recurrent_dropout
: Float between 0 and 1. Fraction of the units to drop for
the linear transformation of the recurrent state.implementation
: Implementation mode, either 1 or 2. Mode 1 will structure
its operations as a larger number of smaller dot products and additions,
whereas mode 2 will batch them into fewer, larger operations. These modes
will have different performance profiles on different hardware and for
different applications.return_sequences
: Boolean. Whether to return the last output. in the output
sequence, or the full sequence.return_state
: Boolean. Whether to return the last state in addition to the
output.go_backwards
: Boolean (default False). If True, process the input sequence
backwards and return the reversed sequence.stateful
: Boolean (default False). If True, the last state for each sample
at index i in a batch will be used as initial state for the sample of
index i in the following batch.unroll
: Boolean (default False). If True, the network will be unrolled, else
a symbolic loop will be used. Unrolling can speed-up a RNN, although it
tends to be more memory-intensive. Unrolling is only suitable for short
sequences.inputs
: A 3D tensor.mask
: Binary tensor of shape (samples, timesteps)
indicating whether
a given timestep should be masked.training
: Python boolean indicating whether the layer should behave in
training mode or in inference mode. This argument is passed to the cell
when calling it. This is only relevant if dropout
or
recurrent_dropout
is used.initial_state
: List of initial state tensors to be passed to the first
call of the cell.__init__
__init__( units, activation='tanh', recurrent_activation='sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', unit_forget_bias=True, kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0.0, recurrent_dropout=0.0, implementation=2, return_sequences=False, return_state=False, go_backwards=False, stateful=False, time_major=False, unroll=False, **kwargs )
activation
bias_constraint
bias_initializer
bias_regularizer
dropout
implementation
kernel_constraint
kernel_initializer
kernel_regularizer
recurrent_activation
recurrent_constraint
recurrent_dropout
recurrent_initializer
recurrent_regularizer
states
unit_forget_bias
units
use_bias
get_dropout_mask_for_cell
get_dropout_mask_for_cell( inputs, training, count=1 )
Get the dropout mask for RNN cell's input.
It will create mask based on context if there isn't any existing cached mask. If a new mask is generated, it will update the cache in the cell.
inputs
: the input tensor whose shape will be used to generate dropout
mask.training
: boolean tensor, whether its in training mode, dropout will be
ignored in non-training mode.count
: int, how many dropout mask will be generated. It is useful for cell
that has internal weights fused together.List of mask tensor, generated or cached mask based on context.
get_initial_state
get_initial_state(inputs)
get_recurrent_dropout_mask_for_cell
get_recurrent_dropout_mask_for_cell( inputs, training, count=1 )
Get the recurrent dropout mask for RNN cell.
It will create mask based on context if there isn't any existing cached mask. If a new mask is generated, it will update the cache in the cell.
inputs
: the input tensor whose shape will be used to generate dropout
mask.training
: boolean tensor, whether its in training mode, dropout will be
ignored in non-training mode.count
: int, how many dropout mask will be generated. It is useful for cell
that has internal weights fused together.List of mask tensor, generated or cached mask based on context.
reset_dropout_mask
reset_dropout_mask()
Reset the cached dropout masks if any.
This is important for the RNN layer to invoke this in it call() method so that the cached mask is cleared before calling the cell.call(). The mask should be cached across the timestep within the same batch, but shouldn't be cached between batches. Otherwise it will introduce unreasonable bias against certain index of data within the batch.
reset_recurrent_dropout_mask
reset_recurrent_dropout_mask()
Reset the cached recurrent dropout masks if any.
This is important for the RNN layer to invoke this in it call() method so that the cached mask is cleared before calling the cell.call(). The mask should be cached across the timestep within the same batch, but shouldn't be cached between batches. Otherwise it will introduce unreasonable bias against certain index of data within the batch.
reset_states
reset_states(states=None)


Fill in all steps for completing the process.
{{item.label}}
{{externalGlobComment}}
No Record Added
Please complete \the process by clicking below resubmit button
Please confirm your payment by clicking below button.
You have successfully resubmitted the record for verification


| 039:058 Khan | : |
Or (lest) he should say when he sees the torment: "If only I had another chance (to return to the world) then I should indeed be among the Muhsinun (good-doers - see V.2:112)." |
| 039:058 Maulana | : |
Or it should say, when it sees the chastisement: Had I another chance I should be a doer of good. |
| 039:058 Pickthal | : |
Or should say, when it seeth the doom: Oh, that I had but a second chance that I might be among the righteous! |
| 039:058 Rashad | : |
Or say, when it sees the retribution, "If I get another chance, I will work righteousness." |
| 039:058 Sarwar | : |
Or, on seeing the torment, it would say, "Would I have the opportunity, this time I would certainly become a pious person". |
| 039:058 Shakir | : |
Or it should say when it sees the punishment: Were there only a returning for me, I should be of the doers of good. |
| 039:058 Sherali | : |
Or, lest it should say, when it sees the punishment, 'Would that there were for me a return to the world, I would then be among those who do good.' |
| 039:058 Yusufali | : |
"Or (lest) it should say when it (actually) sees the penalty: 'If only I had another chance, I should certainly be among those who do good!' |
Concepts/Themes Covered: أركان الإسلام > التوحيد > الكافرون > ندم الكفار Pillars of Islam> Islamic> The Disbelievers> The Disbelievers' Repent القرآن الكريم > محاججة المنكرين والجاحدين The Holy Quran> Arguing the Denyers and the Unbelievers
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


This data cannot be changed after submitting; instead, a new pattern must be created while "retiring" the old one.
Note: Literal text patterns should try to match as much text as possible in the line while accounting for parts of the line that might vary across builds. For build errors that span multiple lines, try to match against the the most unique-looking line so as to distinguish against other kinds of failures.
These can be edited later, so don't worry about getting it right up front.
You are logged in as:


This page scenario contains:
no cljs-devtools
no dirac runtime support
we want to test basic feedback/notification systems


| Package | Description |
|---|---|
| org.elasticsearch.common.inject |
Google Guice (pronounced "juice") is an ultra-lightweight dependency
injection framework.
|
| org.elasticsearch.common.inject.assistedinject |
Extension for combining factory interfaces with injection; this extension requires
guice-jndi-2.0.jar . |
| org.elasticsearch.common.inject.internal |
Guice (sounds like like "juice")
|
| org.elasticsearch.common.inject.spi |
Guice service provider interface
|
| Modifier and Type | Method and Description |
|---|---|
<T> Binding<T> |
Injector.getBinding(Class<T> type)
Returns the binding for the given type.
|
static Binding<?> |
Injectors.getBinding(Injector injector,
Key<?> key)
Returns the binding for the given key or null if there is no such binding
|
<T> Binding<T> |
Injector.getBinding(Key<T> key)
Returns the binding for the given injection key.
|
| Modifier and Type | Method and Description |
|---|---|
<T> List<Binding<T>> |
Injector.findBindingsByType(TypeLiteral<T> type)
Returns all explicit bindings for
type . |
Map<Key<?>,Binding<?>> |
Injector.getBindings()
Returns all explicit bindings.
|
static Set<Binding<?>> |
Injectors.getBindingsOf(Injector injector,
Class<?> baseClass)
Returns a collection of all bindings of the given base type
|
static Set<Binding<?>> |
Injectors.getBindingsOf(Injector injector,
Matcher<Class> matcher)
Returns a collection of all of the bindings matching the given matcher
|
| Modifier and Type | Method and Description |
|---|---|
Binding<?> |
FactoryProvider2.getBindingFromNewInjector(Method method,
Object[] args)
Creates a child injector that binds the args, and returns the binding for the method's result.
|
| Modifier and Type | Class and Description |
|---|---|
class |
BindingImpl<T> |
class |
ExposedBindingImpl<T> |
class |
InstanceBindingImpl<T> |
class |
LinkedBindingImpl<T> |
class |
LinkedProviderBindingImpl<T> |
class |
ProviderInstanceBindingImpl<T> |
class |
UntargettedBindingImpl<T> |
| Modifier and Type | Interface and Description |
|---|---|
interface |
ConstructorBinding<T>
A binding to the constructor of a concrete clss.
|
interface |
ConvertedConstantBinding<T>
A binding created from converting a bound instance to a new type.
|
interface |
ExposedBinding<T>
A binding to a key exposed from an enclosed private environment.
|
interface |
InstanceBinding<T>
A binding to a single instance.
|
interface |
LinkedKeyBinding<T>
A binding to a linked key.
|
interface |
ProviderBinding<T extends Provider<?>>
A binding to a
Provider that delegates to the binding for the provided type. |
interface |
ProviderInstanceBinding<T>
A binding to a provider instance.
|
interface |
ProviderKeyBinding<T>
A binding to a provider key.
|
interface |
UntargettedBinding<T>
An untargetted binding.
|
| Modifier and Type | Method and Description |
|---|---|
<T> V |
ElementVisitor.visit(Binding<T> binding)
Visit a mapping from a key (type and optional annotation) to the strategy for getting
instances of the type.
|
<T> V |
DefaultElementVisitor.visit(Binding<T> binding) |
protected V |
DefaultBindingTargetVisitor.visitOther(Binding<? extends T> binding)
Default visit implementation.
|
Copyright © 2009–2016. All rights reserved.


Privacy is the space in which ideas are developed, to retreat into whenever you want. This space is not only physical but digital as well.
What is a CryptoParty?
CryptoParties are free and open for everyone, but especially those without prior knowledge, who haven’t yet attended one. CryptoParty is a decentralized movement with constantly happening activities in the entire world. The goal of this movement is sharing as much knowledge to as many individuals as possible on how to protect ourselves in the digital world. This worldwide effort introduces the basis of cryptography both in theory and practice, such as encrypted communication, prevention of espionage when navigating on the internet, anonymity on Tor netwyork, cryptography with public key, storage encryption, Virtual Private Networks (VPN) etc. The project consists of a series of workshops free and open for everyone .
The first CryptoParty in Albania
To join this worldwide effort and to inform about the dangers of the digital world, the importance of online privacy and exchange of practical knowledge to protect our privacy, Open Labs with the support of Arjen Kamphuis and Ardian Haxha have organized CryptoParty Tirana.
CryptoParty Albania. Credits: CC-BY-4.0 Andis Rado
The CryptoParty took place at Innovation Hub Tirana and gathered many technology enthusiasts , developers, students, academics, journalists and people who are concerned about privacy and information security. It was the first CryptoParty in Albania and it was really well-organized, with multiple talks and workshops like: private browsing, file-encryption, email-encryption, debian installation etc.
Data encryption workshop. Credits: CC-BY-4.0 Andis Rado
However, there were people who come down on the side of the government: “I have nothing to hide”… The nothing to hide argument states that government surveillance programs do not threaten privacy unless they uncover illegal activities, and that if they do uncover illegal activities, the person committing these activities does not have the right to keep them private. Hence, a person who favors this argument may state “I’ve got nothing to hide” and therefore does not express opposition to government surveillance.An individual using this argument may say that a person should not have worries about government or surveillance if he/she has “nothing to hide.”-Wikipedia.
My point is:
“Arguing that you don’t care about the right to privacy because you have nothing to hide, is no different than saying you don’t care about free speech because you have nothing to say.” – E. Snowden.
Emilio Mordini, philosopher and psychoanalyst, argued that the “nothing to hide” argument is inherently paradoxical. People do not need to have “something to hide” in order to hide “something”. What is relevant is not what is hidden, rather the experience that there is an intimate area, which could be hidden, whose access could be restricted. Psychologically speaking, we become individuals through the discovery that we could hide something to others.


GCOSKR - GC Object Stream Keep References
Be aware of memory leaks due to 'ObjectStream' usage. ObjectInputStream and ObjectOutputStream may cause memory leaks. ObjectStreams are designed to handle the case of sending the same Object multiple times across a connection. For this reason, ObjectStream classes keep a reference to all objects written or read until the 'reset()' method is called. Thoes objects will not be garbage collected until 'reset()' is called.
Wrong
public void writeStringToStream(ObjectOutputStream os, String s)
throws IOException {
os.writeObject (s); // possibly cause memory leaks here.
}
}
Tip: Use 'reset()' of ObjectOutputStream or Object InputStream class's method to clear the list of Objects written to the Stream. Or, use DataStreams instead of ObjectStreams in terms of Strings or byte arrays for optimal performance.
Right
public void writeStringToStream(ObjectOutputStream os, String s)
throws IOException {
os.writeObject (s);
os.reset(); // prevents memory leaks.
}
}


Before enrolling in an online course, you should first assess your readiness for stepping into the online learning environment. Your answers to the following questions will help you determine what you need to do to succeed at online learning. Post-survey feedback will also provide you with information on what you can expect from an online course.


{% extends 'base.html' %} {% block content %} {% include 'base/header.html' %} Login Serving you since 1999. Lorem ipsum dolor sit amet consectetur adipiscing elit. Home Properties Login hidden Login Become an Agent {% csrf_token %} {{ login_form.email }} Remember Me Lost your password? or Login with your social media account Facebook Twitter Google {% csrf_token %} {{ register_form.email }} {{ register_form.phone_number }} {{ register_form.screen_name }} Receive Newsletter {% endblock %}
Serving you since 1999. Lorem ipsum dolor sit amet consectetur adipiscing elit.
Login with your social media account


Queue Automator Server
Clients:
IP
ID
FREE
Active queue:
KEY
TIMESTAMP
CLIENT ID
DATA
Queue:
KEY
TIMESTAMP
DATA


Directive is used to highlight text in element
Import TuiHighlightModule into a module where you want to use our component
TuiHighlightModule
Add to the template:


Preparation time
1-2 hours
Cooking time
30 mins to 1 hour
Serves
Serves 12
Dietary
This classic Bakewell tart is topped with feathered icing to give an impressive finish.
For this recipe you will need a 23cm/9in fluted flan tin, baking beans, piping bag fitted with small plain nozzle and a few cocktail sticks.
For the jam, put the raspberries in a small, deep-sided saucepan and crush them using a masher. Add the sugar and bring to the boil over a low heat until the sugar has melted. Increase the heat and boil for 4 minutes. Remove from the heat and carefully pour into a shallow container. Leave to cool and set.
For the pastry, measure the flour into a bowl and rub in the butter using your fingertips until the mixture resembles fine breadcrumbs. Stir in the icing sugar. Add the egg and 2 tablespoons cold water, mixing to form soft dough.
Roll out the dough on a lightly floured work surface to the thickness of a pound coin. Line a 23cm/9in fluted flan tin and transfer to the fridge to chill for 30 minutes.
Preheat the oven to 200C/180C Fan/Gas 6.
Line the pastry case with non-stick baking paper and fill with baking beans or uncooked rice. Bake blind for 15 minutes, then remove the beans and paper and cook for a further 5 minutes to dry out the base. Set aside to cool a little before adding the filling.
For the filling, spread the base of the pastry case with 4 tablespoons of raspberry jam.
Cream the butter and sugar together until pale and fluffy. Add the ground almonds, egg and almond extract and mix together. Spoon the mixture into the pastry case and smooth the surface using a palette knife.
Reduce the oven temperature to 180C/160C Fan/Gas 4 and bake for 25–35 minutes, until golden-brown and a skewer inserted in the centre comes out clean. Remove from the oven and leave to cool completely in the tin.
For the icing, sift the icing sugar into a bowl. Stir in the almond extract and about 3 tablespoons cold water to make a smooth, fairly thick icing. Place 3 tablespoons of the icing in a separate bowl and add a little pink food colouring gel to make a raspberry coloured icing.
When the tart has cooled completely, spoon the white icing on top and spread to form a smooth surface. Pipe parallel lines of pink icing over the white icing, then drag a cocktail stick through the lines (at a 90 degree angle to the lines) to create a feathered effect. Leave to set, then serve in slices.
You need JavaScript to view this clip.
How to cream butter by hand
You need JavaScript to view this clip.
Lining a tart with a cartouche (baking blind)
You need JavaScript to view this clip.
Rubbing in


购买产品： 我的购物车
Extra 10% off
|
*Pavilion Notebook or Desktop $899 or more. Enter:MU5642
|
|
|
|
Free USB Flash Drive
|
When you buy a Digital Frame
|
|
|
|
15% off
|
On select items. Enter:USP3PAYPAL
|
|
|
|
|
Free 512MB USB Drive
|
*Pavilion Notebook or Desktop $899 or more. Enter:MU5642
|
|
|
|
$20 cash back
|
On $200 or more
|
|
|
|
10% off
|
*Pavilion Notebook or Desktop $899 or more. Enter:MU5642
|
|
|
|
|
Extra 10% off
|
*Pavilion Notebook or Desktop $899 or more. Enter:MU5642
|
|
|
|
Extra 10% off
|
*Pavilion Notebook or Desktop $899 or more. Enter:MU5642
|
|
|
|
WangLiang 30% Off TestShopping
|
*Pavilion Notebook or Desktop $6899 or more. Enter:MU89785
|
|
|
|
|


| STAT-Statistics |
| NR |
10817 |
STAT |
4190 |
001 |
24M |
3.000 |
Mathematical Statistics II |
TR |
09:40 am-11:05 am |
21 |
10 |
11 |
0 |
0 |
0 |
Lisa Bloomer Green (P) |
01/17-05/04 |
KOM 205 |
Credit from 4-Year College and Upper-Division Work |
| NR |
15162 |
STAT |
4320 |
001 |
24M |
3.000 |
Probability and Stochastic Processes |
MWF |
12:40 pm-01:35 pm |
24 |
4 |
20 |
29 |
6 |
23 |
Ping Zhang (P) |
01/17-05/04 |
KOM 221 |
Credit from 4-Year College and Upper-Division Work |
| NR |
17115 |
STAT |
4600 |
001 |
24M |
3.000 |
Problems in Statistics: Bayesian Theory |
TR |
01:00 pm-02:25 pm |
19 |
14 |
5 |
25 |
15 |
10 |
Dennis P Walsh (P) |
01/17-05/04 |
KOM 205 |
Credit from 4-Year College and Upper-Division Work |
| NR |
12505 |
STAT |
5140 |
001 |
24M |
3.000 |
Probabilistic and Statistical Reasoning |
M |
04:30 pm-07:30 pm |
25 |
16 |
9 |
0 |
0 |
0 |
Nancy J McCormick (P) |
01/17-05/04 |
KOM 200 |
Credit from 4-Year College and Upper-Division Work |
| NR |
15163 |
STAT |
5320 |
001 |
24M |
3.000 |
Probability and Stochastic Processes |
MWF |
12:40 pm-01:35 pm |
5 |
2 |
3 |
29 |
6 |
23 |
Ping Zhang (P) |
01/17-05/04 |
KOM 221 |
|
| NR |
17116 |
STAT |
5600 |
001 |
24M |
3.000 |
Problems in Statistics: Bayesian Theory |
TR |
01:00 pm-02:25 pm |
6 |
1 |
5 |
25 |
15 |
10 |
Dennis P Walsh (P) |
01/17-05/04 |
KOM 205 |
|
| NR |
15636 |
STAT |
6020 |
D01 |
24M |
3.000 |
Introduction to Biostatistics |
|
TBA |
20 |
9 |
11 |
0 |
0 |
0 |
Lisa Bloomer Green (P) |
01/17-05/04 |
TBA TBA |
Distance Learning and Distance Education Fee-GR and Credit from 4-Year College and Upper-Division Work |
| NR |
13293 |
STAT |
6180 |
001 |
24M |
3.000 |
Advanced Mathematical Statistics II |
W |
04:30 pm-07:30 pm |
29 |
8 |
21 |
0 |
0 |
0 |
Ping Zhang (P) |
01/17-05/04 |
KOM 200 |
|
| NR |
13543 |
STAT |
6520 |
002 |
24M |
3.000 |
Advanced Biostatistical Methods |
R |
04:30 pm-07:30 pm |
25 |
6 |
19 |
0 |
0 |
0 |
Ginger Holmes Rowell (P) |
01/17-05/04 |
KOM 221 |
|
| NR |
16780 |
STAT |
6605 |
001 |
24M |
3.000 |
Problems in Statistics: SAS Programming |
T |
04:30 pm-07:30 pm |
29 |
13 |
16 |
0 |
0 |
0 |
Yeqian Liu (P) |
01/17-05/04 |
KOM 221 |
|
| NR |
15637 |
STAT |
7020 |
D01 |
24M |
3.000 |
Introduction to Biostatistics |
|
TBA |
5 |
1 |
4 |
0 |
0 |
0 |
Lisa Bloomer Green (P) |
01/17-05/04 |
TBA TBA |
Distance Learning and Distance Education Fee-GR |