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Chapter 5 explained how different functions can describe the relation between total CEO compensation and firm size (or firm value). While the total amount of CEO compensation is a hot-button issue, another discussion is whether CEO compensation reflects pay-for-performance. That is, do CEOs get more compensation when the firm performance better and vice versa.
In 1990, Jensen and Murphy (1990) reported that for every $1000 loss (gain) in firm value, a CEO only loses (gains) $3.25 and took that as evidence that CEO compensation was not aligned with shareholder value.38 This study is 30 years old and the authors would argue that CEO compensation is currently better aligned with firm performance. In this chapter, I will use advances in the literature to show that their conclusion depends on how pay-performance sensitivity is measured. The main take-away from this chapter should be that the way you measure your variables is guided by the assumptions in your theory.
There are two assumptions we can make about CEOS. The first is what the impact is of a CEO on the firm and the second is about the CEO’s preferences. Let us tackle these assumptions in order..39 This chapter is entirely inspired by Edmans and Gabaix (2016)
The first assumption is about whether the actions of a CEO scale with the size of the firm. That is, do the actions of a CEO have the same impact on firm value in a small firm and in a large firm? Some actions such as using the corporate jet or sponsoring a sports team have the same impact independent of the firm size. If the company pays for a corporate jet, they pay the same amount of money whether they are large or small. Other actions such as implementing a better strategy, attracting better employees, or developing better relations with investors will impact the entire firm and thus will have a larger monetary impact in large firms.40 In a way, the question is whether shareholders should care more about the ability of CEOs to directly destroy value or trust in the CEOs ability to create value. Although, in a larger firm it might also be easier to fraudulently embezzle more money.
The second assumption is how CEOs value their leisure time. They can see leisure time as independent of the compensation. For instance, they could think of the opportunity cost of being at work. They could value their time off work as independent of their income. A day with family is as important independent of your compensation. However, the other assumption is that leisure time is more valuable if you have high compensation. For instance, if you earn a lot of money, you can do a lot more on your holidays.41 Elon Musk and Richard Branson seem to have a lot of fun when they are not working. Another, possibility is that CEOs are already rich and they could just invest their own fortune and earn a lot of money that way.42 Think of hedge fund managers who decide to close their fund and run a family fund to just invest their own money and that of some associates and family members.
When I say optimal CEO incentives I mean optimal from an optimal contracting between shareholders and CEO point of view. This could still be suboptimal from a social perspective. It also means that we take into account that the CEO might be risk averse and we might need to pay them more money to overcome the risk aversion.
The two assumptions have in impact on how a firm should incentivise the CEO to maximise firm value. If the actions of a CEO are independent of firm size, then the right performance measure for CEOs is the increase in the monetary value of the firm. If the actions of the CEO have a bigger impact in large firms,43 It’s easier to increase firm value by $1 Million for Apple than for a start-up company. the right performance measure is the increase in firm value compared to the size of the firm.
If the CEOs’ preferences for not working are independent of their compensation, firms can reward the CEOs independent of how wealthy they already are. That is, the better they perform the more money they get. In contrast, if the CEOs’ preferences depend on how wealthy they are, they expect more money if they are already wealthy. The reward of these CEOs should be proportional to their wealth.
As a result, the literature has defined pay-performance sensitivity in three different ways.
$ Pay - $ Firm value: This is the Jensen and Murphy (1990) measure. For every change in dollar firm value, the CEOs compensation changes with a fixed dollar amount. The consequence of these assumptions is that you would expect CEOs to hold the same equity stake in small and large firms.
$ Pay - % Firm value: For every % increase in firm value, there is a fixed dollar amount paid to the CEO. You an think of this measure of pay-performance sensitivity as a bonus system where the CEO gets a bonus for every increase in the companies stock market return.
% Wealth - % Firm value: For every increase % increase in the value of the firm, the CEO should receive a fixed % increase in their wealth. This is the measure that is advocated for in Edmans and Gabaix (2016) and I also belief that the evidence is most in favour of this definition and thus the assumptions that a CEO’s actions and preferences depend on firm size and on CEO wealth respectively.
Mathematically, we can represent each definition as follows:
Where \(V\) is the value of the firm at a certain point, \(W\) is the CEO’s wealth, and \(r\) is the return of the stock compared to the starting value \(V_0\). Because derivatives are not empirically observable, we have to look at changes in wealth and firm value over two different time periods. The measures that follow from the mathematical definitions are as follows where we use the fact that \(\Delta W = W - W_0\) and \(ln(W) - ln(W_0) = ln(\frac{W}{W_0})\)
To reiterate, we have three different measures of pay-performance sensitivity, i.e. how well CEO compensation reflects the performance. For each measure, we have assumed that the firm will use the optimal incentives given our assumption about the CEO’s actions and their preferences. The point of this exercise is that different assumptions determine how you should measure the variable you want to analyse. In the next section, we are going to investigate how these three measures behave in the S&P500 sample.
Let us first reload the data. We need a measure for the wealth of the CEO. I am going to assume that the most important component of the CEO’s wealth is their shares in the company. This is obviously not perfect but it is not a bad guess for S&P500 CEOs either.
library(tidyverse)
<- readRDS("data/us-compensation.RDS") %>%
us_comp rename(total_comp = tdc1, shares = shrown_tot_pct)
<- readRDS("data/us-value.RDS") %>%
us_value rename(year = fyear, market_value = mkvalt) %>%
select(-ni) %>%
distinct()
<- select(us_comp, gvkey, execid, year, shares, total_comp)
us_comp_limit <- left_join(us_comp_limit, us_value,
us_comp_value by = c("year", "gvkey")) %>%
# Delete the observations with missing values.
filter(!is.na(market_value) & !(is.na(shares))) %>%
mutate(wealth = shares * market_value / 100)
max(us_comp_value$market_value)/1e3
## [1] 1540.774
max(us_comp_value$wealth)/1e3
## [1] 118.0538
To get an idea wheather I did not make any mistakes I let R print the maximum market value and the maximum CEO wealth in the dataset. We can compare this to some known values to check whether we made any mistakes in putting the dataset together. Apple’s market value at the time of writing hovers around $800 billion and Jeff Bezos44 before the divorce is worth around $100 billion. So the proxies are not a terrible first attempt.45 Especially once you consider that some papers throw away the maxima for some variables because they are considered outliers.
Above I explained that the first measure ($-$) assumes that the percentage share of stock the CEO owns will be independent of the size of the company. To test this hypothesis, Figure 7.1 plots the percentage share the CEO owns against the size of the company.
Figure 7.1: Relation between CEO ownership and market value for SP500 firms (2011-2018).
<- ggplot(data = us_comp_value,
shares_size aes(x = market_value/1000, y = shares)) +
geom_point(alpha = .20) +
ylab("CEO Ownership") +
xlab("Firm Market Value (in Billions)")
plot(shares_size)
The figure is not that instructive so let’s plot the data on the log-log scale in Figure 7.2. The bulk of the data now shows a downward trend. This is not consistent with the hypothesis of the $-$ measure of pay-performance sensitivity where we would expect the link between firm size and CEO ownership to be constant.
Figure 7.2: Relation between CEO ownership and market value for SP500 firms (2011-2018).
<- shares_size +
shares_size_log scale_x_continuous(trans = "log",
labels = function(x) prettyNum(x, digits = 2),
breaks = scales::log_breaks(n = 5, base = 10)) +
scale_y_continuous(trans = "log",
labels = function(x) prettyNum(x, digits = 2),
breaks = scales::log_breaks(n = 5, base = 10))
plot(shares_size_log)
## Warning: Transformation introduced infinite values in continuous y-axis
Nevertheless, there seems to be a cloud of points at the top of
Figure 7.2 where CEOs have a large stake in the
company and this cloud looks largely independent of firm size. I
suspect that these CEOs are founders who have a larger ownership
share for other reasons than incentives. We can test this idea.
The code below filter all the observations with a market value
above $10 Billion and ownership stake above 1%. I then join those
observations with the CEO’s name and the firm’s name from the
original data, group the observations by CEO and company, and
count the number of years they are in the new data set. The
knitr::kable
function formats the table for pdf and html output.
filter(us_comp_value, market_value > 1e5, shares > 1) %>%
select(gvkey, year) %>%
left_join(select(us_comp, exec_fullname, coname, year, gvkey),
by = c("year", "gvkey")) %>%
group_by(gvkey, exec_fullname, coname) %>%
summarise(nr_years = n()) %>%
::kable(booktabs = TRUE, caption = 'Outliers') knitr
## `summarise()` has grouped output by 'gvkey', 'exec_fullname'. You can override using the `.groups` argument.
Table 7.1: Outliers
| gvkey | exec_fullname | coname | nr_years |
|---|---|---|---|
| 002176 | Warren E. Buffett | BERKSHIRE HATHAWAY | 4 |
| 012141 | Steven A. Ballmer | MICROSOFT CORP | 4 |
| 012142 | Lawrence J. Ellison | ORACLE CORP | 3 |
| 064768 | Jeffrey P. Bezos | AMAZON.COM INC | 8 |
| 117768 | Jen-Hsun Huang | NVIDIA CORP | 2 |
| 147579 | Wilmot Reed Hastings, Jr. | NETFLIX INC | 2 |
| 157855 | Marc R. Benioff | SALESFORCE.COM INC | 2 |
| 160329 | Lawrence Edward Page | ALPHABET INC | 8 |
| 170617 | Mark Elliot Zuckerberg | FACEBOOK INC | 6 |
My hunch looks correct.46 Also, I think the tidyverse
functions are amazing because
they make it so easy to quickly look at the data and summarise
the data.
This whole process of plotting the data and checking assumptions
on a subset of the data might seem a long detour. You could ask
yourself why we are not immediatelly calculating the three
measures that we are actually interested in. Checking your
assumptions and checking the validity of your dataset for your
research question is an important part of the research process.47 Remember that we derived the three performance-pay sensitivity
measures based on the optimal contract between shareholders and
owners. The firm needs a contract to govern the conflict of
interest between the shareholders and the CEO. If the founder is
the CEO, this conflict of interest is different. You could ask
yourself whehter you should keep founder-CEOs in the dataset
because you do not necessarily expect their contract to follow
the optimal contract.
And as you can see you do not have to run a statistical test to
test your assumptions.
Now that we understand our dataset, we can calculate the changes
in CEO wealth, total compensation, CEO compensation and market
value. The last filter
is a bit of hack. There are a few wealth
change that are infinite at the log value. That can happen when a
CEO ends up with 0 shares and no compensation. There are only a
handful of these observations and I am ignoring them. Ideally,
you would have a look whether anything funky is going on.
<- group_by(us_comp_value, gvkey, execid) %>%
us_comp_value arrange(year) %>%
mutate(prev_market_value = lag(market_value),
prev_wealth = lag(wealth),
prev_comp = lag(total_comp)) %>%
ungroup() %>%
mutate(change_market_value = market_value - prev_market_value,
change_wealth = wealth - prev_wealth,
change_comp = total_comp - prev_comp,
change_log_value = log(market_value) - log(prev_market_value),
change_log_wealth = log(wealth) - log(prev_wealth),
change_log_comp = log(total_comp) - log(prev_comp)) %>%
filter(!is.infinite(change_log_wealth),
!is.infinite(change_log_comp)) %>%
arrange(gvkey)
With these variables, we can replicate the basic approach of some key papers on executive compensation. The first regression takes the Jensen and Murphy (1990) approach and investigates the relation between a change in market value and a change in CEO compensation (Measure 1 in 7.3). The second and third regression follow the approach of Hall and Liebman (1998) where we look the relation between relative changes in market value and relative changes in total CEO compensation.48 The difference between the two specification is that the first uses market returns, just like the original paper, which is not exactly theoretically correct but a very good approximation of the theory. Finally, the fourth regression follows the Core, Guay, and Thomas (2005) and looks at the relation between changes in relative market value and relative CEO wealth.49 In all cases, the authors of the original papers do a much better job than I do in measuring the variables they are interested in. Do not take the results too seriously although they are a good approximation of the results in the original papers.
<- lm(change_comp ~ change_market_value,
jensen_murphy90 data = us_comp_value)
<- lm(change_log_comp ~
hall_liebman98 I(change_market_value/prev_market_value),
data = us_comp_value)
<- lm(change_log_comp ~ change_log_value,
hall_liebman98_alt data = us_comp_value)
<- lm(change_log_wealth ~ change_log_value,
core_guay_thomas05 data = us_comp_value)
coef(jensen_murphy90)[2] %>% prettyNum(., digits = 2)
## change_market_value
## "0.026"
coef(hall_liebman98)[2] %>% prettyNum(., digits = 2)
## I(change_market_value/prev_market_value)
## "0.026"
coef(hall_liebman98_alt)[2] %>% prettyNum(., digits = 2)
## change_log_value
## "0.22"
coef(core_guay_thomas05)[2] %>% prettyNum(., digits = 2)
## change_log_value
## "0.85"
Jensen and Murphy found that a CEO gets $3.5 for every $1000 change in market value. Our estimate is $0.026 for every $1000 change in market value. The difference is first of all an indication that this simple analysis is not the best estimate but it probably also means that the $-$ incentives are not the best measure. Recall from Figure 7.2 that CEO ownership decreases with firm size. Since the study was published, companies have become larger which pushes the $-$ measure lower.50 Jensen and Murphy (1990) looked at more than the direct incentives. They also estimated the potential loss of getting fired. Moreover, total compensation does not take into account wealth. Other studies have found that the $-$ incentives measure of pay-performance sensitivity are smaller for larger firms.
The original Hall and Liebman (1998) measure shows that CEOs see a 0.026% increase in pay for every 1% increase in firm value, the alternative measure shows a 0.22% pay-performance sensitivity. Finally, the Core, Guay, and Thomas (2005) measure shows a 0.85% wealth-performance sensitivity.
We can plot the results as well to get a better intuition of the
what this results mean. The code is long and boring but not so
difficult to understand. First, make a new dataset with all the
change in pay and change in performance variables. Next, just
copy the new dataset three times. Create a new variable
analysis
for each type of analysis. Create a new variable
incentive
and performance
for each type of analysis.
<- select(us_comp_value, change_comp,
data_comparison
change_market_value,
change_log_comp, change_log_value,%>%
change_log_wealth) filter(complete.cases(.))
<- nrow(data_comparison)
n
<- bind_rows(data_comparison, data_comparison,
data_comparison %>%
data_comparison) mutate(analysis = c(rep("jensen_murphy90", n),
rep("hall_liebman98_alt", n),
rep("core_guay_thomas05", n))) %>%
mutate(
incentive = recode(analysis,
"jensen_murphy90" = change_comp/1000,
"hall_liebman98_alt" = change_log_comp,
"core_guay_thomas05" = change_log_wealth),
performance = recode(analysis,
"jensen_murphy90" = change_market_value/1000,
"hall_liebman98_alt" = change_log_value,
"core_guay_thomas05" = change_log_value)) %>%
select(analysis, performance, incentive)
Now, we can plot the the incentive to performance relation for the data in Figure 7.3. Despite all all the caveats about how unpolished this analysis is, the figure shows that neglecting CEO ownership is going to lead you to underestimate the pay-performance sensitivity for CEOs.
Figure 7.3: Comparison of three measures of pay-performance sensitivity for S&P500 Companies (2011-2018)
= ggplot(data_comparison,
comparison_plot aes(x = performance, y = incentive)) +
geom_point(alpha = .1) +
facet_wrap( ~ analysis, ncol = 1, scales = "free") +
scale_x_continuous(breaks = scales::pretty_breaks(n = 5),
labels = function(x) prettyNum(x, dig = 2)) +
scale_y_continuous(breaks = scales::pretty_breaks(n = 5),
labels = function(x) prettyNum(x, dig = 2))
print(comparison_plot)
An empirical study can often be represented in the form of a diagram as in Figure 7.4. These diagrams are often referred to as the Predictive Validity Framework or, in the accounting literature, Libby Boxes (Libby, Bloomfield, and Nelson 2002). The top of the diagram (1) represents the research question. We want to ask whether there is a relation between two theoretical concepts. In our case, we want to know whether there is a relation between CEO incentive pay and firm performance. Chapter 5 dealt with how to use theory to predict a relation between two concepts. In reality, we cannot directly observe pay and performance, so we have to come up with a measure of these concepts (3 and 4). This Chapter has dealt with the issue of how we can find a good measure for our two concepts. The downwards arrows represent the connection between the theoretical concepts and the empirical measures. At first, you might have thought that it is obvious how to measure CEO performance and firm performance. The pay-performance sensitivity example should make you realise that you need to think careful about what you measure and how your measure connects to your theory. Most statistics textbooks focus on the link between the two measures (2), ´change_log_wealth´ and ´change_log_performance´ in our case. This is the next step in this Chapter. Later in the notes, I wil look at the issue of control variables.
library(DiagrammeR)
<- grViz("
libby_boxes digraph libby_boxes{
graph [fontsize = 18, layout = 'dot']
node [shape = box, group = 'cause_group']
cause [label = 'Performance']
m_cause [label = 'Change_Log_Value']
node [shape = box, group = 'effect_group']
effect [label = 'Pay']
m_effect [label = 'Change_Log_Wealth']
controls
cause -> effect [label = '(1)']
m_cause -> m_effect [label = '(2)']
cause -> m_cause [label = ' (3) ']
effect -> m_effect [label = ' (4) ']
m_effect -> controls [dir=back, label = '(5)']
subgraph{
rank = same; cause; effect;
}
subgraph{
rank = same; m_cause; m_effect; controls;
}
}
")
::widgetThumbnail(libby_boxes,
trelliscope"_bookdown_files/libby_boxes.pdf")
::widgetThumbnail(libby_boxes,
trelliscope"_bookdown_files/libby_boxes.png")
Figure 7.4: Predictive Validity Framework or Libby Boxes for pay-performance sensitivity
::include_graphics("_bookdown_files/libby_boxes.png",
knitrauto_pdf = TRUE, dpi = 300)
Section 7.4 showed that there was a negative relation between CEO ownership and the size of the company. The relation is pretty strong and we did not look at it in more detail. In a real study with more subtle effects, you want to get some sense whether the relation is real or whether it can be explained by random variation.
I will illustrate this issue with the new pay-performance sensitivity. The optimal performance contract should not depend on the size of the firm only on the performance of the firm. Figure 7.5 shows the relation between pay-performance sensitivity and the size of the firm. The figure does not show any strong relation between firm size and pay-performance sensitivity.
Figure 7.5: Relation between pay-performance sensitivity and market value
= ggplot(us_comp_value,
hypothesis aes(y = change_log_wealth / change_log_value,
x = market_value/1000)) +
geom_point(alpha = .2) +
scale_x_continuous(
trans = "log",
breaks = scales::log_breaks(n = 5, base = 10),
labels = function(x) prettyNum(x, dig = 2)) +
scale_y_continuous(
limits = c(-50, 50)) +
xlab("market value") +
ylab("sensitivity")
print(hypothesis)
## Warning: Removed 3901 rows containing missing values (geom_point).
There a number of different ways to test whether this relation is real or whether it can be explained by random fluctuations. I first present two simulation based approaches and then present an approach based on formulas. Statistical software typically implements the formulas for you so you do not have to remember them.
The starting point for each of these tests is that if there is no relation, the true effect that we are interested in51 That is the top half of the Libby Boxes equals \(0\). Because of random variation, the number in the statistical analysis52 The bottom half of the Libby Boxes might be slightly off. Each of the three approaches tries to quantify that random variation around a the null effect.
The first way to quantify the random variation is to randomly assign one observation of the firm-year to another obervation of a CEO-year. That is we create a dataset where we link the sensitivity of a CEO’s pay with the market value of another firm. The general idea of a randomisation test is that you break the relation that you want to test and see whether the value in the real dataset is extreme compared to the randomised dataset.
<- mutate(us_comp_value,
data_hypo sensitivity = change_log_wealth / change_log_value) %>%
select(sensitivity, market_value) %>%
filter(complete.cases(.))
<- cor(
true_cor $sensitivity, data_hypo$market_value)
data_hypo<- cor(
rand_cor $sensitivity, sample(data_hypo$market_value))
data_hypoprint(prettyNum(c(true_cor, rand_cor), dig = 3))
## [1] "-0.00457" "0.0011"
So, we find that the correlation in the real dataset is different
from the correlation in the random dataset but it does not allow
us to draw any conclusions. We need to draw a lot of random
datasets and compare that random dataset to the real correlation.
That is what the next lines of code do. First, create a function
that resamples the market value and calculates the correlation.
Second, rerun that function \(10,000\) times. rand_cor
is a
vector with randomly created correlations.
<- function(data){
simulate_cor return(cor(data$sensitivity, sample(data$market_value)))}
<- replicate(1e4, simulate_cor(data_hypo)) rand_cor
Figure 7.6 visualises the test. We plot a histogram of the random correlations and compare them to the size of the of the real correlation. We want to test whether the value of the real correlation is extreme, so we compare the random distribution to correlations with an absolute value larger than absolute value of the sample correlation.
Figure 7.6: Random correlation compared to real correlation
<- qplot(rand_cor, bins = 100,
hist_sim fill = abs(rand_cor) < abs(true_cor)) +
xlab("Random Correlations") +
scale_fill_viridis(discrete = TRUE) +
theme(legend.position = "none")
print(hist_sim)
= mean(ifelse(abs(rand_cor) > abs(true_cor), 1, 0))
pvalue_sim print(prettyNum(pvalue_sim, digits = 2))
## [1] "0.29"
We can calculate how likely it is that a random correlation is larger (in absolute value) than the true correlation. We find that there is 0.29 probability of getting a random value larger than the value for the sample correlation. That means that there is little evidence of a relation between pay-performance sensitivity and firm size that cannot be explained by random variation.
Another way to test whether there is a relation between two variables is the bootstrap (Efron and Hastie 2017). The idea of the bootstrap is that the sample of firms that we have is a good reprentation of a bigger population of (very large) firms.53 You could think of for instance the same firms in 2019 and 2020 or 2008 and 2009. In this procedure, we assume that the random variation comes from the sampling error compared to the full population. The bootstrap will resample the observations in the real dataset with replacement. We keep CEOs and firms together this time but in the newly created datasets some observations might occur more than once. If we create enough of these datasets, we will get a good representation of the correlations we get from repeatedly sampling from the larger population.
<- function(d){
calc_corr <- nrow(d)
n <- sample(1:n, size = n, replace = TRUE)
id_sample <- d[id_sample, ]
sample <- cor(sample$sensitivity, sample$market_value)
corr return(corr)
}<- replicate(2000, calc_corr(data_hypo)) boot_corr
We visualise the sampling variation in the correlation in Figure 7.7. We can now compare this distribution to a \(0\) correlation. If the distribution is (almost) entirely on one side of a null effect, we would conclude that sampling variation around a nulll effect is not a good explanation for the effect that we found in the real sample. However, in this case, we find that a considerable amount of the distribution is on either side of the orange line. Again, we find no good reason to belief there is anything more than random variation around a null effect.
Figure 7.7: 2000 bootstrapped correlations
qplot(boot_corr, bins = 100) +
geom_vline(aes(xintercept = 0), col = "darkorange") +
xlab("Bootstrapped Correlation")
The way I set up the bootstrap was actually quite primitive but it
gets the message across. In case you need to do a proper bootstrap
analysis, it’s much better to use the boot
package in R
. Below,
you can find the minimal code to work with the package. The advantage
of the package is that it give you many more option to tweak the
bootstrap algorithm to make it more appropriate for your specific
research question. The details of this go way beyond these lecture
notes.
library(boot)
<- function(d, indices){
corr <- d[indices, ]
d <- cor(d$sensitivity, d$market_value)
corr
}<- boot(data_hypo, statistic = corr,
bootstrap R = 2000, parallel = "multicore",
ncpus = 4)
boot.ci(bootstrap, type = "norm")
## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 2000 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = bootstrap, type = "norm")
##
## Intervals :
## Level Normal
## 95% (-0.0116, 0.0031 )
## Calculations and Intervals on Original Scale
Randomisation and bootstrapping make different assumptions about where the random variation comes from and what it means that there is no effect. There is an active discussion in the econometrics literature about which assumptions are more appropriate under which conditions. However, most empirical researchers ignore these questions and trust in formula based p-values. The interpretation of p-values is closer to the bootstrap. We test whether the real correlation is extreme enough so that it is unlikely that it arises sampling variation around a null effect.
= cor.test(data_hypo$sensitivity, data_hypo$market_value)
cor = cor$p.value
pvalue_cor print(prettyNum(pvalue_cor, dig = 2))
## [1] "0.62"
We can let R do the calculation for us and we see that the p-value for the correlation between sensitivity and market value is 0.62. Again, it looks like that the correlation is quite likely to emerge from sampling variation around a null hypothesis.
Finally, I would like to point out the relation between linear regression and a correlation. When there is only one independent variable in a regression model, the correlation test is equivalent to the statistics we get from a regression.
\[\begin{align*} y &\sim \mathcal{N}(\alpha + \beta x, \sigma) \\ \beta &= \frac{sd(y)}{sd(x)} cor(x, y) \end{align*}\]
= lm(sensitivity ~ I(market_value/1e6), data = data_hypo)
regr_sens coefficients(summary(regr_sens)) %>%
kable(dig = 2, booktabs = TRUE, caption = 'Regression Results')
Table 7.2: Regression Results
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 1.78 | 1.27 | 1.40 | 0.16 |
| I(market_value/1e+06) | -13.20 | 26.75 | -0.49 | 0.62 |
In summary, most empirical researchers use p-values to estimate how far away the estimated coefficient is from the null effect. To do that they use formulas that estimate the standard error of the coefficient. If the coefficient is large and the standard error is small, we expect the effect to be extreme compared to the null effect. To estimate how likely it is that the real coefficient is the result of random variation around the null, your software calculates a statistic, the coefficient divided by the standard error. This t-statistic is expected to follow a t-distribution and from this distribution we can calculate the p-value.
Most empirical papers will try to find small p-values. That is they try to show that there is a relation. I deliberately chose an example where there is no relation between the two variables of interest because and where that is also what we hoped to find. Our theory said that there should not be a relation between sensitivity and size and after looking at the data we have no reason to believe otherwise. This type of test is a natural fit for p-values and this is why I introduced them in this way.
There is a lot of discussion in the literatur about abuses and misinterpretations of p-values. My hope is that I put you on the right path to interpreting p-values despite all the traps that have been laid for you in the literature.
Page built: 2022-02-01 using R version 4.1.2 (2021-11-01)


{% extends 'base.html' %} {% block title %}Home Page{% endblock title %} {% block content %}
You are welcome on Home Page
Your name is - {{ user.first_name }} {{ user.last_name }}
Your email is - {{ user.email }}
Your email is - {{ user.username }}
Your gender is - {{ user.profile.gender }}


Towards a free/libre/open-source speech-to-text system for Kazakh
Wouldn’t it be great, if Google Assistant or Yandex Alisa spoke Kazakh?
One necessary component of such speech-enabled digital assistants is
a so-called speech recognition
or speech-to-text
system.
Large amounts of audio data (thousands of hours, see below), from many different
people, along with transcriptions, are needed to train a good speech-to-text
system using machine learning
methods. So far, due to lack of appropriately
licensed, freely available audio data in them, building a high-accuracy
free/libre/open-source (FLOSS) speech recognition system is out of reach for
most languages.
Fortunately, there is a way to change this for the better. In 2018, Mozilla, the company behind the Firefox web-browser and many other programs, had launched the Common Voice project.
Here is a snippet from its FAQ page:
What is Common Voice?
Voice recognition technology is revolutionizing the way we interact with machines, but the currently available systems are expensive and proprietary. Common Voice is part of Mozilla’s initiative to make voice recognition technologies better and more accessible for everyone. Common Voice is a massive global database of donated voices that lets anyone quickly and easily train voice-enabled apps in potentially every language.
We’re not only collecting voice samples in widely spoken languages but also in those with a smaller population of speakers. Publishing a diverse dataset of voices will empower developers, entrepreneurs, and communities to address this gap themselves. In addition to the Common Voice dataset, we’re also building an open source speech recognition engine called Deep Speech.
How you can help
We at taruen.com
want to launch Common Voice in Kazakh, and started taking
first steps towards that goal. But for that to happen, we need help from
native speakers of Kazakh. If you are one, here is how you can help:
-
Review whether Common Voice website has been correctly translated into Kazakh on Pontoon, Mozilla’s localisation tool. About one-third of the translations were authored by us — non-native Kazakh speakers — and thus might be incorrect.
-
Review Kazakh sentences we’ve submitted to the Common Voice Sentence Collector tool. At least 5000 reviewed sentences are needed to “launch” a language on Common Voice.
These sentences were taken from the Volumes 65 and 68 of the mighty 100-volume set with works of Kazakh folklore, called “Бабалар сөзі” and published by M. Auezov Institute of Literature and Art. By Article 8 of Kazakhstani Copyright Law, works of folklore are exempt from copyright and are thus in the public domain and suitable for submitting to Common Voice. Moreover, proverbs from volumes 65 and 68 match other criteria of the Common Voice project as well — they don’t contain digits, foreign letters and other symbols not allowed in the dataset, they are mostly short, arguably pedagogical/entertaining and thus fun to read.
Besides usual checks for correct spelling and grammaticality required by Common Voice, when reviewing sentences, we also ask you to make sure that none of the sentences are remotely offensive to men, women, parents, children, religious people, non-religious people, Southern Kazakhstanis, Northern Kazakhstanis, Western Kazakhstanis, Eastern Kazakhstanis, Kazakhs of China, Kazakhs of … You get the idea. A glimpse over the sentences did not suggest that they would contain anything like that, but again, that’s something for native speakers to judge.
We hope that together we can assemble enough data so that anyone who wishes to do so can train a speech-to-text system for Kazakh. On the FAQ page of the project, Mozillians mention 10000 hours as an approximate number of validated hours needed to train a production speech-to-text system, so that’s something to strive for. It does sound like a lot, but when divided among even a conservative number of Kazakh speakers, it requires each person to record sentences for about 4 seconds. That is a much less scary number ;)


Requires youtube page refresh for options to take effect
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Attack
0.01
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{% extends "public/components/base.html" %} {% block title %}Open States: Page Not Found{% endblock %} {% block content %}
The page you were looking for could not be found.


It provides functions to compute the modularity and modularity-related roles in networks. It is a wrapper around the rgraph library (Guimera & Amaral, 2005, doi:10.1038/nature03288).
| Version: | 0.2.4 |
| Suggests: | testthat, knitr, rmarkdown, igraph |
| Published: | 2015-11-12 |
| Author: | Guilhem Doulcier [aut, cre] (R bindings, current implementation of the simulated annealing algorithm), Roger Guimera [ctb] (Author of the original rgraph library), Daniel B. Stouffer [aut, ths] |
| Maintainer: | Guilhem Doulcier <guilhem.doulcier at ens.fr> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| SystemRequirements: | GNU GSL |
| Citation: | rnetcarto citation info |
| Materials: | README |
| CRAN checks: | rnetcarto results |
| Reference manual: | rnetcarto.pdf |
| Vignettes: |
Getting Started with rnetcarto |
| Package source: | rnetcarto_0.2.4.tar.gz |
| Windows binaries: | r-devel: rnetcarto_0.2.4.zip, r-release: rnetcarto_0.2.4.zip, r-oldrel: rnetcarto_0.2.4.zip |
| OS X binaries: | r-release: rnetcarto_0.2.4.tgz, r-oldrel: rnetcarto_0.2.4.tgz |
| Old sources: | rnetcarto archive |
Please use the canonical form https://CRAN.R-project.org/package=rnetcarto to link to this page.


Adding, editing, or removing
a site's tools
Note: To complete these procedures, you must be
assigned a role having the necessary permissions. To determine your
role, follow the directions in Participant roles. For a basic understanding of roles and
permissions, see Permissions and roles: Overview.
To add, edit, or remove a site's tools:
- From My Workspace, on the menubar, click
Worksite Setup
.
- Check the box to the left of the name of the site in which you
wish to add or remove tools. At the top of the page, click
Edit
.
- At the top of the page, click
Edit Tools
.
- Check the box next to the tool's name that you wish to add or
edit; uncheck the box if you are removing a tool. Click
Continue
.
- If you are adding or editing a tool, enter a title and/or
URL, as needed. For the Web Content and News tools, the
page title you enter will appear as a link in the menubar, and the
tool name will appear at the top of the page. Click
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- You will see a list of the site's tools and any changes you've
made. If the list is accurate, click
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Shree Pushkar Chemicals lines up capex of ₹120 crore
Funds to come from internal accruals
by Special CorrespondentShree Pushkar Chemicals & Fertilisers has announced capital expenditure plans worth ₹120 crore.
The funds will come from internal accruals.
The company will invest ₹75 crore in the dye intermediaries’ segment. The project is expected to commence production in FY21.
The company is also in the process of acquiring Madhya Bharat Phosphate through the NCLT process for ₹28 crore. Additionally, it will invest ₹12 crore in a solar park at Lote Parshuram MIDC in Maharashtra and revamp the existing manufacturing facilities there at an investment of ₹5 crore, the firm said.
“We plan to fund the expansion through internal accruals,” said Punit Makharia, CMD, Shree Pushkar Chemicals & Fertilisers.
He said the company’s revenue was expected to increase to ₹650 crore by 2020-21 on the back of capacity addition and revamp of its existing unit.
Though the company logged a revenue of ₹454 crore in FY19, it will decline by 5% to ₹425 crore this fiscal as an unit that manufactures acids, dye intermediates and cattle feed will be shut for 10 months for a complete revamp.
“We will cross a revenue of ₹650 crore by 2020-21 after the revamp of the unit," he said.
Currently, the company exports dyes and dye intermediates to Europe, China, Pakistan and Bangladesh, and is looking for new markets in South America and Turkey, besides growing its domestic market share in phases, he added.
Founded in 1993 as an Integrated Speciality Chemicals manufacturing firm, the company deals with the entire range of textile and speciality chemicals.
The company is one of the leading producers of Dye Intermediaries and Dyestuff in India, with the widest range of Dye Intermediaries offering.


Because it helps to absorb scent chemicals! Their noses secret a special mucus that absorbs the chemicals that make scents.

Which breed of dog is "The Ultimate Lifeguard"?
Newfoundlands!
Newfoundlands have a water resistant coat and webbed feet, making them awesome swimmers!
They were bred as helpers for the fishermen and rescue dogs to save people who were drowning!
Sometimes they even try to rescue their owners from swimming. Silly doggies!


The encoder interface mode. Match the mode to the type of encoder you have attached.
|
Device In Device # Identification |
|
error in (no error) error in can accept error information wired from VIs previously called. |
|
status status is TRUE (X) if an error occurred or FALSE (checkmark) to indicate a warning or that no error occurred. Right-click the error in control on the front panel and select Explain Error or Explain Warning from the shortcut menu for more information about the error. |
|
code code is the error or warning code. Right-click the error in control on the front panel and select Explain Error or Explain Warning from the shortcut menu for more information about the error. |
|
source source describes the origin of the error or warning. Right-click the error in control on the front panel and select Explain Error or Explain Warning from the shortcut menu for more information about the error. |
|
IO Mode The IO mode value.
DCC1000_0 (ver: 204, 205, 206) Position Controller: Val: ENCODER_IO_MODE_PUSH_PULL
DCC1001_0 (ver: 114, 116, 117) Position Controller: Unsupported
DCC1002_0 (ver: 100) Position Controller: Unsupported
DCC1100_0 (ver: 113, 114) Position Controller: Unsupported |
|
Device Out Same as Device In |
|
error out error out passes error or warning information out of a VI to be used by other VIs. |
|
status status is TRUE (X) if an error occurred or FALSE (checkmark) to indicate a warning or that no error occurred. Right-click the error out control on the front panel and select Explain Error or Explain Warning from the shortcut menu for more information about the error. |
|
code code is the error or warning code. Right-click the error out control on the front panel and select Explain Error or Explain Warning from the shortcut menu for more information about the error. |
|
source source describes the origin of the error or warning. Right-click the error out control on the front panel and select Explain Error or Explain Warning from the shortcut menu for more information about the error. |


Something awesome is coming soon
We are building something very cool. Stay tuned and be patient. Your patience will be well paid.
There are many variations of passages of Lorem Ipsum available, but the majority have suffered alteration in some form, by injected humour, or randomised words which don't look even slightly believable. If you are going to use a passage of Lorem Ipsum, you need to be sure there isn't anything embarrassing hidden in the middle of text.


| DOSE: T+ 0:00 |
20 mg |
oral |
2C-E |
(powder / crystals) |
| T+ 2:30 |
10 mg |
insufflated |
2C-E |
(powder / crystals) |
This was the first time in about a year or so that I had meddled with 2c-e. The first time I had received 500mg did all of it in one night with 3 friends. That time I had insufflated a large amount and the visuals were definitely the most notable thing about the drug. This time around was totally different. The whole trip report was written on a few pieces of note paper that I carried in my pocket during the trip. Most of this report will my translation of these papers. Parentheticals are commentary.
The day of this experience I had received 500mg 2c-e. I was ecstatic, because I hadn't done 2c-e in a very long time. It was about 1:55 PM when I orally took the first dose of 20mg. For thirty minutes nothing happens.
T +30
Nothing yet except an upset stomach. I feel a little lightheaded but I would attribute this to giddiness.
T +35
Just felt a rush of endorphins. Listening to Mogwai - Katrien. Feeling nice.
T +43
I feel physically relaxed, something is definitely up. Light is beginning to flutter so to speak.
T +1:30
Profound physical effects. I feel 'soft.' Visuals have not yet fully started to appear but there is an observable change in vision.
T +1:45
Perceptible changes in vision occurring as the minutes go by. I feel 'clean.' Infected Mushroom sounds very nice. Bubbling with energy.
Between this and the next report I insufflated 10mg because I was not experiencing as much visually as I would have liked. I insufflated it because I'm a straight to the point sort of guy. What I experienced was nothing short of overwhelming; however I don't regret it.
T +2:33 (this is where my handwriting quality starts to deteriorate tremendously)
Took 10mg nasally a few minutes ago. I am still struggling to hold on to reality. I am without a doubt, tripping.
T +2:38
Just stepped outside, it feels like the first time in forever.  The smell I cannot describe (referring to the scent of the outdoors). That truck just passed by again. Reds, blues, and greens are heavily accentuated. It seems almost pointless to be wearing sunglasses. Just took them off, the world seems more natural now. A man and his daughter just passed on a scooter. Lady ~200ft away says to her child, 'Doesn't it feel good out here?' I could hear it with amazing clarity, as if she was standing only a few feet away. To her I answer silently, 'You have no idea.'
T +2:42 (this doesn't seem right but it is what my notes seem to indicate)
Still on the front porch. Going back in to get more paper then heading out. I'm hardly out of the drive way when I feel the need to stop and describe the air- it feels rich and creamy to breathe.
T +2:55
The magnolia smells heavenly.
T +3:02
Back inside the house, I got hungry 40ft from the house and decided to go back for food.
T +3:30
I got really sidetracked in what I set out to do, which was go on a walk. I am going to do that now. (sometime shortly after leaving my house) Just talked to my dad, what a surreal experience. Just now exiting the neighborhood. The road in front of me spans to infinity (the road to me appeared overwhelmingly long. there was just something about the flatness and the trees that were on either side of the road that appeared majestic).
T +3:42
Multiple cars pass me as I am walking along the road. They seem inconsequential (at this point I was off in my own head, which is why the cars didn't register as they should have).
T +3:40
Everything building up to this point in life seems to be of some significance (Here I am becoming introspective. I was thinking about how the events in my life have affected who I am and how I act). I am without emotions, I can see objectively (I felt like I could carry out decisions regarding my lifestyle with moral certainty, much like on LSD except in a more natural way. On LSD this mindset was heavily pushed, whereas on 2c-e this mindset stumbled upon me).
(Shortly I arrive at a large field next to a neighborhood. I live in rural NC where there are tons of farms and fields like this are not anomalous by any means. I stop here and ponder for about an hour so. The wide open space is comforting to me and eases thought.)
Whenever I take my sunglasses off, it feels like waking up from a dream. I am now in the very middle of a field. To my north and east there are woods. To my south and east there are neighborhoods. Right now I feel as close to contentment as I have never been (Again, a feeling reminiscent of LSD, but again also not as forced. I felt as good about my life at that point as I should have, and not exceedingly so as on LSD). I feel detached from problems.
T +4:04
I feel constantly out of breath, but not in an uncomfortable way. I am sitting in the middle of this field. The thoughts that come to my mind are troublesome (at this point I had begun to worry about trivial matters as I often do). My short term memory is shot. I've checked my phone 3 or 4 times within the past minute to see what time it is.
T +4:18
This field is a very nice location. It is a nice place to trip. The wide open planes of grass seem to beckon forth endless strings of thought. My spelling throughout this trip has been rather atrocious (I would often have trouble spelling things, when normally I have superb spelling. at a few points during this trip, I actually misspelled things without even realizing it which is not reflective of me). Every time I write something down, out of the corner of my eye my left hand is overcome with a mass of color. The clouds are very wispy. The ones on the horizon are playing tricks on my eyes (I couldn't tell where the clouds ended and where the horizon began). I am looking at a pretty nice cloud formation. It is nothing amazing but it is the best I've seen today. I've said this before but the reds, blues and greens are highly accentuated.
T +4:30
I'm thinking about leaving this field and going back home. Why is everything I do counterintuitive (I was thinking again about how I act, these thoughts occurred frequently during the course of this trip)? Previously this drug was all about the visuals. Right now, the visuals are magnificent, BUT I'd say the mental component is much more significant (The first time I'd done 2c-e I did a LOT so the visuals were omnipresent and impossible to ignore. This time, it was not like that).
T +4:35
I am going to walk home. I thirst for coffee and music. This has been a remarkable experience sitting here in this field.
T +4:49
Depth perception leaves a lot to be desired (wrote this after crossing a fence unsuccessfully and falling on my ass). On the way back home I stopped to pet the gray mare in a field that sits caddy cornered to my neighborhood. I felt its snout, and it felt really weird (It felt like a fuzzy rubber. It licked me too, which felt even more peculiar). That was a good experience.
T +5:36 (this is where my handwriting starts to return to normal)
Sitting in front my computer listening to Aesop Rock - One Brick and eating ice cream. Had to put a sweatshirt on because I kept getting chills. Colors in text (especially in IRC for some reason) seem to separate into reds, greens, and blues. The ice cream does not taste different for the most part. The saltiness from the peanuts is enhanced. My computer setup feels like home (the room felt like a vast ocean, with my computer setup being an island).
T +5:51
I'm browsing art pictures I've saved on my computer (the pictures with light colors really garnered my attention. the colors would darken and become lighter again, much like on LSD). No signs of coming down yet. My numb right hand feels exceptionally peculiar on this drug. It is now dark outside for the most part.
T +6:11
Just did the dishes. Washed my hands afterward. Soap felt extra-slimy. Talking to dad is surreal (often times I found it hard to stifle a smile. I'm positive he was unaware that I was on drugs, and it was that fact that was humorous to me).
T +6:25
Drinking coffee and watching videos online, occasionally getting caught up in the cracks of my skin (the skin on my hand was really trippy).
This is where I stopped taking notes, and about where my trip died down. Overall, it was ENTIRELY different from what I remembered it to be. The trip was very tame (except for the few minutes after I had railed the 10mg). It was fairly introspective, not nearly on par with the introspection of LSD though. It was a nice trip to be put simply.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


{% load static %}
Re(Connect+unite+discover)
With the correct mindset,
you can make anything happen....
Quiver is one of the fastest growing social networks available today!
Join now and create your free profile to start meeting new friends and re-connect with the old ones!


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| DOSE: T+ 0:00 |
8 mg |
oral |
4-HO-MET |
(powder / crystals) |
| T+ 1:00 |
8 mg |
oral |
4-HO-MET |
(powder / crystals) |
| T+ 2:00 |
|
smoked |
JWH-018 |
(powder / crystals) |
| T+ 0:00 |
|
smoked |
Tobacco - Cigarettes |
|
Hello everyone,
First of all sorry for my english, hope you can understand! After a long absence I'm back but I preferred to wait a while before coming back because I had a sacred experience .... I'll tell you. First I start with my first test which was the best of all my test. I said before all I have no gift to write and may be shared 'good' phrases so I'll describe the trip in my own way (time for syntaxes cough cough).
So here we go!
Abstract:
Name of the experience: So is that 4-ho-met right?
Experience: 1st test
Date of experience: Month of August, not sure of the date but it is between 20 and 30.
Age: 18
Weight: 45kg + / -
Height: 1m66-68
Template: Thin
Substance tested: 4-HO-MET Fumarate 98.99% Grad Lab (powder)
Dosage: Start - 8mg +1h - 8mg = 16mg total
Quality of experience: Good experience
WARNING! Use of CR can be dangerous! Only you know your body!
Well, I warn, I'll tell my experience based on how I lived and I saw things during this trip ...
It was the first time I had the opportunity to test this product I did not know, but I was told basically that it is metabolized in psylocin (I think) in the body (like mushrooms) I tell myself that it must be substantially the same. I was right but not quite ...
It happened about the month of August 2010 if I remember correctly (it's funny looking back because we can remember the trip like it was yesterday). It was 7:00 p.m. when I decide and a person called 'X' to test the substance (X having already had some testing with this product). With some hesitation and trepidation, I decided to swallow 8mg 4-ho-met fumarate (X advised me to take more if I do not feel ....). 'X' on the other hand takes a dose between 20 and 25mg (significantly high).
I stay on my position and decided to stick to 8mg for now and see just what the beast has in the belly. Oops ... I had to go eat with 'Y' located in the living room, not knowing more than that, I was a bit stressed at the idea that 'Y' sees me in this state. I am invited, it's not too cool to use products like that 'X' in turn decides to not eat.
19:30 I'm in the living room with 'Y' still eating, I had a little trouble eating but I did not notice any effects yet ... I felt it by cons 'working' inside.
 Indeed! The 4-ho-met is triggered quickly, I was full up.
Me and 'X' quickly decide to go for a walk outside while smoking a cigarette and chatting. On the way, I felt the product, which increased in intensity (shit it would be hard!). The ground began to drip slightly, the colors seemed more pronounced. Mentally nothing very noticeable, it was still very easy to talk normally and be rational (something which was more practicable thereafter). The flowers on the bottom sides seemed alive and strangely similar to marijuana leaves (maybe it was because we had smoked some marijuana before the trip). Quite beautiful I must say! On the road we saw a dead cat, oddly we laughed, thinking how he has landed here! (Sorry for the trip but the situation seemed funny, 'X' and I love animals!)
I noticed that a trip to 4-ho-met seemed more 'What the fuck?' and more organic than LSD. Especially for the colors and deformations .... but that's not safe! (You will see the upcoming trip reports, 4-ho-met is not always pink). Were more likely to laugh with 4-ho-met and find situations immediately funniest. Brief return to our sheep. We returned home and bitterly sit in the room. The Chemical Brothers were launched, it could be that good!
It should be 8:00 p.m. to 8:15 p.m. and was still coming up, I remember a time because everything was much stronger in a while! In short, this come up was rather pleasant, I noticed almost no bodyloads apart from a kind of tension in the face (this is a tryptamine, that would explain this?), not really annoying, it is easy to do abstraction). Mental effects is also very clean, very manageable and it was easy for me to keep cool even though I raved dry by now!
On his bedroom, wall was moving in all directions, as if a huge wave stick to a multicolored hues on the walls. The grooves of the wooden floors were alive! it flowed like a river, mouths filled with very white teeth decided to come and settle on its soil. Eye also had sparkling blue iris decorate the situation Very funny!
 Placed on a cigarette and go!
9:15 p.m.: Once burned JWH-018, we wanted to smoke a cigarette! BLAM it feels good effects with JWH combined with 4-ho-met! My hands seemed empty as if it weighed a little gram.
I put my jacket somehow and it was pretty hard to go down the stairs, pulled into the ground, definitely! The colors were revealed on their best days, tones purplish, greenish, yellowish gave more energy at home but also more heat. We felt safe (even if the ripples of tiles gave a bit of nausea).
Once outside, I pull out my cigarette, but it does not strike me as usual. A cigarette is all soft, no longer have any taste and the more it shall not sink to the ground and fraying everywhere! The garden was enchanted. Perhaps the elves or astral spirits watching us, who knows, perhaps!
It is from here that the mental side appeared. Looking at the sky, I see one form of life, such as small UFO that cross the sky, or perhaps energy ... I spoke with 'X' and asked his opinion about the paranormal and extraterrestrial side of life, the discussion was initiated! 'Can it be cool to visit every country in the world and bring a specific object of the country, it would be great no?' I said to 'X'. 'X' on my question dubious think we should just let these items to countries is something heritage of life ... why we need to appropriate this, take advantage of each property provided by Mother Nature, 'he said. In short, the discussion was no longer the same original subject on the sky that we looked, I was delirious and he asked me not to cry too. Yes I was screaming with joy all seemed so logical and explicable, I know in me but it was hard to share it with him ... that's what my hardest when I trip.
Soul of our discussion, we go back to his room, the effects were very strong. I sit and decided to design, too bad, i was uninspired, my drawings were confused and was not really what I wanted to draw. 'X' prefers to write his 'thoughts' on paper. It was fascinating and intriguing to watch. It's really impressive 'to see that each individual perceives things very differently'.
Truce drawings, I settle into the chair and takes the good old book from Hofmann (LSD, my terrible child), it seemed much more attractive to read on 4-ho-met, besides I had the impression of being in the book! But that does not dureras point it was impossible to concentrate on reading, I was literally drawn to the bindings of books and drawings took on another dimension (as if you know the drawings of the book emerged, as if there was a thick).
23:00: A little tired from the trip that lasts forever, I landed on the mattress and enjoy the music, the music enters my head and make me travel through pipes organic, as if to lead me to my inner heart, to my soul! It was ENJOYMENT: D
I listen to music during 1h-2h00, it was so good! I open my eyes and I feel slightly confused, I wonder what am I doing in this room besides, I even had trouble telling me that I had taken a trip because it was as if I had lived with since forever!
Nevertheless, the trip begins to leave, it stirs a lot weaker than the 1st few hours and it feels good! The descent is pleasant and makes me especially sleepy.  I finally fell asleep clothed with music!
Summary: really good experience for the first time, I was surprised by the clean side of the trip in general, easily manageable (thing rather mixed thereafter). The 4-ho-met was much more visual for me, I would say a mix between LSD and something more organic! Standard colors for sure, is more 'ambient' than LSD or other hallucinogenic experience that I had. The come-up is particularly short (more or less 1h-1h30) for me and lasts between 4 and 8 hours maximum! For me it's the safest RC that I have tested:)
But still beware ... ;)
Thank you for reading and sorry for the spelling mistakes;)
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


NFL suspends Myles Garrett indefinitely after brawl
He swung helmet at Steelers QB's head
by Jason Hanna and Kevin Dotson, CNN(CNN) - The NFL has suspended Cleveland Browns defensive end Myles Garrett without pay indefinitely -- and at least for the rest of the season -- for Thursday night's brawl in which he clocked an opponent in the head with a helmet, the NFL said Friday.
Two other players also have been suspended without pay for their role in the fight: Pittsburgh's Maurkice Pouncey (three games) and Cleveland's Larry Ogunjobi (one game).
Garrett's minimum suspension puts him out for six regular season games and any playoff games this year -- one of the longest suspensions for on-field incidents in NFL history.
Also, both teams have been fined $250,000 each.
The brawl came at the end of the Browns' 21-7 win over the Pittsburgh Steelers.
Garrett ripped off Steelers quarterback Mason Rudolph's helmet and hit him in the head with it, sparking a fight that descended into chaos as players from both sides spilled onto the field.
The NFL will be issuing additional discipline for other players in the coming days. All suspensions may be appealed under the collective bargaining agreement.
This is a developing story. More to come.


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© Pavel Nyaga — 2000 - 2020. Except where otherwise noted, all original material on this page created by Pavel Nyaga is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported.


const IdentityMatrix: TFloatMatrix =( (1, 0, 0), (0, 1, 0), (0, 0, 1));
An identity matrix, which is loaded in Matrix field of TAffineTransformation class when calling its Clear method.
[See Also]


{% extends '_layouts/blank.html' %} {% set id = 'go-pwa-architects-guide' %} {% set title = 'The Architects Guide to PWAs - Ionic Framework' %} {% set meta_image = 'https://ionicframework.com/img/meta/ionic-framework-og.png'%} {% set meta_description = "Progressive Web Apps take advantage of major advances in modern web browsers, web APIs, and front-end frameworks to deliver great app experiences to mobile and desktop users. This brief guide provides enterprise architects and app dev leaders with essential insights and how to build a production-ready PWA today." %} {% set header_style = 'transparent light' %} {% block header_includes %} {% endblock %} {% block main %}


1.2.3. moha.modelsystem.modeloperator¶
Classes
|
Base class for the Physical Model Hamiltonians: 1-D Hubbard, PPP, Ising, etc. |
|
Operator for one body interaction |
|
Operator for two body interaction |
-
class
moha.modelsystem.modeloperator.
ModelHamiltonian
(lattice, site, pbc=True)[source]¶ Base class for the Physical Model Hamiltonians: 1-D Hubbard, PPP, Ising, etc.
-
property
nbasis
¶ The number of sites.
-
property
pbc
¶ The periodic boundary condition.
-
property


--- layout: post title: N.Y. Legalizes Gay Marriage, 42 Years After Stonewall categories: [] tags: - government - life - social status: publish type: post published: true meta: _original_post_id: '' _original_import_author: John Duprey author: ---
Finally.
http://www.npr.org/2011/06/25/137413564/n-y-legalizes-gay-marriage-after-42-year-stonewall?sc=17&f=1001
N.Y. Legalizes Gay Marriage, 42 Years After Stonewall
by The Associated Press
The Associated Press - June 25, 2011
Champagne corks popped, rainbow flags flapped and crowds embraced and danced in the streets of Manhattan's Greenwich Village as New York became the sixth and largest state in the U.S. to legalize same-sex marriage.


# (c) 2015, Jonathan Davila <jdavila(at)ansible.com>
#
# This file is part of Ansible
#
# Ansible is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Ansible is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Ansible. If not, see <http://www.gnu.org/licenses/>.
#
# USAGE: {{ lookup('hashi_vault', 'secret=secret/hello:value token=c975b780-d1be-8016-866b-01d0f9b688a5 url=http://myvault:8200')}}
#
# You can skip setting the url if you set the VAULT_ADDR environment variable
# or if you want it to default to localhost:8200
#
# NOTE: Due to a current limitation in the HVAC library there won't
# necessarily be an error if a bad endpoint is specified.
#
# Requires hvac library. Install with pip.
#
from __future__ import (absolute_import, division, print_function)
__metaclass__ = type
import os
from ansible.errors import AnsibleError
from ansible.plugins.lookup import LookupBase
ANSIBLE_HASHI_VAULT_ADDR = 'http://<IP_ADDRESS>:8200'
if os.getenv('VAULT_ADDR') is not None:
ANSIBLE_HASHI_VAULT_ADDR = os.environ['VAULT_ADDR']
[docs]class HashiVault:
def __init__(self, **kwargs):
try:
import hvac
except ImportError:
AnsibleError("Please pip install hvac to use this module")
self.url = kwargs.get('url', ANSIBLE_HASHI_VAULT_ADDR)
self.token = kwargs.get('token')
if self.token==None:
raise AnsibleError("No Vault Token specified")
# split secret arg, which has format 'secret/hello:value' into secret='secret/hello' and secret_field='value'
s = kwargs.get('secret')
if s==None:
raise AnsibleError("No secret specified")
s_f = s.split(':')
self.secret = s_f[0]
if len(s_f)>=2:
self.secret_field = s_f[1]
else:
self.secret_field = 'value'
self.client = hvac.Client(url=self.url, token=self.token)
if self.client.is_authenticated():
pass
else:
raise AnsibleError("Invalid Hashicorp Vault Token Specified")
[docs] def get(self):
data = self.client.read(self.secret)
if data is None:
raise AnsibleError("The secret %s doesn't seem to exist" % self.secret)
if self.secret_field=='': # secret was specified with trailing ':'
return data['data']
if self.secret_field not in data['data']:
raise AnsibleError("The secret %s does not contain the field '%s'. " % (self.secret, self.secret_field))
return data['data'][self.secret_field]
[docs]class LookupModule(LookupBase):
[docs] def run(self, terms, variables, **kwargs):
vault_args = terms[0].split(' ')
vault_dict = {}
ret = []
for param in vault_args:
try:
key, value = param.split('=')
except ValueError as e:
raise AnsibleError("hashi_vault plugin needs key=value pairs, but received %s" % terms)
vault_dict[key] = value
vault_conn = HashiVault(**vault_dict)
for term in terms:
key = term.split()[0]
value = vault_conn.get()
ret.append(value)
return ret


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Write any function in minutes - whether to run a simple job that cleans up a database or to build a more complex architecture. Creating functions is easier than ever before, whatever your chosen OS, platform, or development method. No install required.
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| 1005 | (A) | and sente to the Cete and soughte thare a leche |
| 1010 | (C) | They sente to þe cyte . & sowȝte hem a lecħe |
| 992 | (D) | They sent to þe Citee and soughtyn a leche |
| 972 | (E) | & sente to þe cyte . & souȝtyn a leche . |
| (Ex) | ---- no text ---- | |
| 1030 | (L) | þey wende to þe cite & souȝten a leche |
| 945 | (P) | þai sent to þe Cite & sog??? a leche |
| 983 | (U) | Thei sent to the citee / and soughten a leche |
| 70 | (V) | þey senten to þe Citee and soughten a leche |


WES CRAVEN PRESENTS DRACULA 2000 A film review by Steve Rhodes Copyright 2001 Steve Rhodes RATING (0 TO ****): *
Even if WES CRAVEN PRESENTS DRACULA 2000 -- what a modest title -- features the undead, the movie itself is completely and utterly dead. Without a scintilla of new ideas, it rehashes the Dracula legend. You know the drill. The guy is a woman's man, who prowls at night and gets a major heartburn when silver stakes are thrust into his chest. His love bites are quite messy, spurting blood everywhere. Once bitten, however, the victim joins his little club and goes off to put hickeys on other necks. About the only variation in DRACULA 2000 is that chopping off the head of a vampire is said to work just as well as the old stake-in-the-heart routine. Then again, maybe this has always been a lesser know rule of devamping.
The film, scripted by Joel Soisson and directed by Patrick Lussier, features a group of B-list actors and B-list actor wannabes. Only Christopher Plummer (THE SOUND OF MUSIC), who phones in his performance, gives the movie anything even approaching credibility.
Any movie pretentious enough to include the year of the millennium in its title should be smart enough to include a fair amount of self-deprecating humor, as did GODZILLA 2000. DRACULA 2000, however, with its buckets of fake blood, still takes itself completely seriously. Neither a comedy nor a horror picture, DRACULA 2000 plays more like a bad industrial video for vampire exterminators. It is certainly about as much fun as an instructional video, which is to say, no fun whatsoever.
If there is anything worth laughing at in the story it is the stunning level of stupidity of the characters. If you came upon a locked tomb decorated by skulls, would you want to crack open the coffin inside to see what it contained? Solina (Jennifer Esposito) does. After all, she reasons, "If it's worth locking up, it's worth taking." Here is something worth taking. Take my advice. Don't come anywhere near this comatose picture. But if you think that this genre is hopeless, rent a copy of Larry Fessenden's HABIT. Now, that is a fascinating vampire film with some real imagination.
WES CRAVEN PRESENTS DRACULA 2000 runs a very long 2:07. It is rated R for violence/gore, language and some sexuality and would be acceptable for older teenagers.
Email<EMAIL_ADDRESS>Web: http://www.InternetReviews.com
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


THtmlViewer, TFrameViewer, and TFrameBrowser components
Type TParseEvent = procedure(Sender: TObject; var Source: string) of Object;
property OnParseBegin: TParseEvent;
The OnParseBegin event occurs just prior to parsing an HTML document.
SenderThe THtmlViewer doing the parsing.
SourceThe complete HTML document which will be parsed. This string may be saved, examined, and/or even modified. If the document were encrypted, this event could be used to unencrypt it.
Usage Restrictions
The TFrameViewer/TFrameBrowser Viewers property does not contain valid information at the time of the OnParseBegin event.
If the source is an image file, Source will contain an empty string.
This event does not provide any way to handle a linked Style Sheet document.


In fast moving markets, adaptation is significantly more important than optimization. --Larry Constantine [Beyond Chaos]


Professional Development Document
By
Daniel Martinez
My style of learning
- 50% Video
- 30% Doing the task
- 20% Reading the book
My Accomplishment
- Computer Networking Tech 2000
- Hold a real estate license in Texas
- Owned a company for 10 years with 12 employees and grossed over 1 million per year
About Myself
- Emotional Intelligence
- Good at reading body language
- My soft skills
My Homework
- Has been very challenging
- Has tested me emotionally and mentally
Initiatives
- Talk to Employers
- Send out 5 resume per day.
- Do more networking
- Going to code mettings
My Reflection
Week 1 has been very challenging for me and very rewarding at the same time.I look forward to my personal growth through this program and the opportunity that this skill will be able to provide me.
My Resume here


Feedback
You have not submitted any feedback! Are you sure you want to gather the logs for this lab?
Feedback from session ID
{{sessionID}}
{{$index + 1}}
{{issue.issueCategory}}
{{issue.issueDetail}}


mutato.js
Mutates the code and runs the unit tests for it
Infinite loops which may be the result of repeated mutation are not an issue since the code and the unit tests are executed in a web worker.
Mutate
Reset
Exec


get property of action's code, which is an instance of NameEx.
get property of action's name, which is a human readable string.
to get the receiver's account name of current action. such as command 'clgchain push action kobe '["params"]' -p james's-key', 'kobe' is the receiver's account name.
to get the sender of current action, specially, 'sender' means whose permission key is used by '-p', such as command 'clgchain push action kobe '["params"]' -p james's-key', 'james' is the sender's account name.
return the sender's account name.
to check if an account name has been authored.
the account name which will be checked.
boolean value, return true means the account is authored, otherwised false.
check the authority of a speicfic account name.
account_name whose authority is required.
Check the account and permission.
The account name.
The permission name.
add account to be notified.
account to be notified.
send an inline action.
an instance of Permission_Level.
the contract which receive this action.
the name of this action.
parameter of this action, which must be type of T.
Generated using TypeDoc
class Action is applied to access an action's context information. This class is static.
Action
import { Action } from "gchain-ts-lib/src/action";


THE SPECIALIST A film review by Raymond Johnston Copyright 1994 Raymond Johnston
Director: Luis Llosa Starring: Sylvester Stallone, Sharon Stone, James Woods, Rod Steiger, Eric Roberts Warner Bros.
Once upon a time Warner Bros. churned out B gangster movies at an incredible rate. Some of these were punishment films. A popular star would get the notion he or she could refuse scripts or ask for more pay, and he or she would be forced to star in a B film. This told them who was boss, the studio. They would work with a group of standard Warner contract actors and studio hack technicians on some left over sets. Cut in some stock footage and in a few weeks the studio had a violent genre piece, based on some trash novel the studio bought at bargain prices.
It is fitting that Warner Bros should be behind this overwrought action misfire. It plays like a punishment picture. The dialogue is so corny and the plot so transparent, that you can hardly believe A-list stars are in it. Character development is like examples from a how-to-write-good book. Sly Stallone is sensitive, show him adopting a stray cat. Sly Stallone is un hombre mas macho. Show him throwing a radio-weilding punk out of a bus window. Show him flex for no real reason. Sharon Stone is a femme fatale bent on revenge. Give her blue tinted flashbacks, over and over again. Give her peek-a-boo costumes and let her walk into exclusive parties without an invitation. Give her double entendre dialogue about the heat from Sly's controlled explosions. Hype her nude scene all over TV and magazines for months. That and the explosions are really all the film is about.
The supporting B-film cast is on firmer ground doing what they do best, trying to breathe some sense of life into ridiculous dialogue and ponderous scenes. They try but lose an uphill battle. The big book on how to make bad films highly recommends casting people to play the most distant ethnic group from their real one. Rod Steiger is a master at this. He discovered long ago that when cast to play a Latin American, all one needs to do is say words like "djoo" and "leesen" for "you" and "listen." Add to that the crowning touch of always fidgeting with a gold crucifix, and there you have it, aging Latin American drug lord.. James Woods manages to be a creepy ex-CIA guy very well. That is his bread and butter role. He is given such ridiculous scenes, such as having a breakdown yelling at a tape recorder, that one ponders why he isn't simply committed by his friends for the safety of all. Eric Roberts plays a creepy character as well. He is remarkably good at it. So good that it is, well, creepy.
The title refers to Sly Stallone's character being an explosives expert. This is the biggest fumble of the film. He has a talent for "focused explosions." Somehow he can set off a big wad of plastic explosive in a busy night club and kill only the one person in a room that he targets. He hangs around to watch his success. (1) How does the bomb know not to send debris into innocent people? (2) If he's going to hang around, why not just shoot the one guy with a sniper rifle? How he manages to set up some of the more elaborate "controlled" explosions is never made clear. A major plot point involving an explosion victim is also never explained, leaving one of the biggest plot holes in recent film memory. It does provide James Woods with one stray truly funny scene.
The stars' clothed and unclothed bodies (Rod Steiger excepted) provide something pleasant to look at, and Miami provides some nice scenery. If you demand more than this from a film, then THE SPECIALIST is not for you. It is at times so overdetermined an example of macho film making that it might qualify as a guilty pleasure, but a very guilty one indeed.
.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Form
1. New form
1.1 Enter IDE from https://localhost:3090/ide
1.2 Open TOOLS menu and write down a comma separated list of fields like:
first_name, last_name, pid, gender, birth_day, address, zip, city, county, country, email, mobile, social_status, photo, id_file, active
then click new Form. The new created form will look like this:
1.3 This form needs a documents list for navigation, so click on the list toolbar button to set it. Only a left side empty space could be observed here.
1.4 Click properties toolbar button to open the properties popup window. Be sure to be on the FORM tagName, and fill in query field with:
{
coll: 'patients',
fields: 'first_name,last_name',
sort: {last_name: 1, first_name: 1}
}
and list field with:
last_name+first_name as name
1.5 Change the proposed form name (current datetime) in the toolbar name input, with the proper name Patients. Now it will look like this:
1.6 Click the save toolbar button, or the usual CTRL+S keyboard shortcut. The new created form will be present in the FORMS menu for further use. It is not finished, but it's already functional, and can be used to input data. Just add a menu item (MENU section):
"Patients" forms.Patients
under Setup with a leading tab, and save. Pay attention tab indentation is important in menu definition. It will look like this:
1.7 Now you can open the application from https://localhost:3090 open Setup/Patients menu and start inserting the first patients (just the name for now). As soon as you save the firs record, it will appear in the list on the left. Insert 2-3 more names to have a navigable list, and click on list items to see the record content in the form. This is an infinite scroll-able list. The list can be filtered by using the two search toolbar buttons, but more on this later.
2. Form layout
A new form has four containers, divided in 2 columns and 2 row. The first row is intended as header and is empty by default. Use it for title or other header elements. Initially all fields are inserted in the left column. If you don't need a double column form, the right column will rest empty, for layout purpose only. Use CTRL+B or toolbar borders button to toggle containers border, witch are purely informative, and are not saved.
Container management:
- add new column: from peer container context menu Add column
- add row: from parent container context menu Add row
- resize width: select container, then SHIFT+arrow left/right; last column not resizable
- delete: select container, then CTRL+DEL
Field management:
- add one: from container context menu Add label/button/field/image
- add multiple: select a container, open TOOLS menu and write down a comma separated list of fields, then click add Fields menu
- move to other container: drag and drop over the empty space of the new container
- move in bottom of the same container: drag and drop over the bottom empty space
- move over other field: drag and drop over the other field; it will finish right aside
- resize: select label/input, then SHIFT+arrow left/right
- delete: select label/input, then CTRL+DEL
Now lets reface our form:
2.1 Move photo and id_file fields to the right column, by dragging them one by one.
2.2 Drag city over zip, then county over zip.
2.3 Select City and County labels, then delete them by CTRL+DEL.
2.4 Open Properties, select label Zip and change it to Zip/City/Cnty.
2.5 Close properties. Select left container and enlarge it by SHIFT+arrow right like this:
2.6 Play with the width of zip, city and county to finish like this:
2.7 Add a title label: left click the upper left container and Add field. Change the label text to Patient and the size to large. Delete the input. It is better to add a field , not a simple label, because it will keep fields alignment.
2.8 Save the form.
3. Field types
3.1 Open Properties popup and move it in a convenient place.
3.2 Select gender and change it's type from text to radio. Select the first radio and change it's text from Label to M. Select the second radio and change it's text from Label to F.
3.3 Select fields and change their type:
- birth_day => date
- email => email
- active => checkbox
- photo => image
- id_file => file
3.4 Save form.
Now you can return to the application and complete patients profile with proper data.
How to use image and file types in application:
- photo field: use right click to upload an image, then save; use left click for full size view
- id_file field: use right click to upload file, then save; use left click to view
4. Form CSS
4.1 Select the title label in Properties and set it's for property to title.
4.2 Open CSS editor from css toolbar button and write down:
[for=title] {
color: green;
}
4.3 Click apply on the editor to see the change. Save form.
5. Form events
The code in the events will be executed immediately after the form load. All javascript events can be used, and Brumba specific events are added.
5.1 How to import:
import { $, $$, e$, br, remote, createElement, report } from '/lib/util.js'
import { notification, confirmModal } from '/lib/components.js'
import { gridRender } from '/lib/grid.js'
import { findForm } from '/lib/forms.js'
5.2 Form events defined and triggered by Brumba:
- retrieve - after data retrieve, before updating form; can block
- update - after form has updated/rendered it's data
- save - before saving data; can block
- delete - before deleting the current record; can block
An event handler can block normal operation by using preventDefault().
import { $ } from '/lib/util.js'
$('form').addEventListener('delete', e => {
if (...) {
e.preventDefault()
}
})
6. Form HTML
The html toolbar button opens the html editor of the form. It is useful for adding elements that IDE is not covering, or simply modify on the fly, if you know what you are doing. It is a very powerful tool that opens all the html capabilities, but even a dangerous one, because IDE could not be able to recover from some mistakes. So use it carefully, and make a copy before modifying.
It is also useful for copying forms from one application to another. To make a local copy instead, use the Copy context menu and a clone of the form will be created with the _COPY suffix name.


Model S
Model 3
Model X
Model Y
Shop
Account
Menu
Menu
Model S
Model 3
Model X
Model Y
Order Online for
Touchless Delivery
Custom Order
Existing Inventory


{$blogs['post_content']}
-
-
Pick the yellow peach that looks like a sunset with its red, orange, and pink coat skin, peel it off with your teeth. Sink them into unripened...
-
-
Pick the yellow peach that looks like a sunset with its red, orange, and pink coat skin, peel it off with your teeth. Sink them into unripened...
留言板
How Did van Gogh’s Turbulent Mind Depict One of the Most Complex Concepts in Physics?
How Did van Gogh’s Turbulent Mind Depict One of the Most Complex Concepts in...
How Did van Gogh’s Turbulent Mind Depict One of the Most Complex Concepts in Physics?
How Did van Gogh’s Turbulent Mind Depict One of the Most Complex Concepts in...
How Did van Gogh’s Turbulent Mind Depict One of the Most Complex Concepts in Physics?
How Did van Gogh’s Turbulent Mind Depict One of the Most Complex Concepts in...
Katy Liu on Sep 29, 2017 at 9:48 am
ReplyPick the yellow peach that looks like a sunset with its red, orange, and pink coat skin, peel it off with your teeth. Sink them into unripened...


Trends in Rehoming Rates
Animal age may be one of the most influential factors in a positive outcome.
These groupings tell us that older dogs have lower rates of adoption and fostering than younger dogs. This is a logical reflection of potential adopters' preferences. Senior animals have higher rates of fostering than mature animals, which may be due to fee waiver programs and other incentives to encourage placement of senior animals. These programs are common among animal resuces and shelters in the US but it's not known if the Bloomington Animal Care and Control facility employs such a program.
The age groupings are as follows:
For this illustration, the term "favorable outcome" means the animal was adopted, placed in foster, reclaimed, or released to the wild. Because dogs and cats make up the majority of the overall animal population, it stands to reason that they also make up the majority of the favorable outcomes. Other animal types including reptiles and rabbits account for the rest of the favorable outcomes. These ratios are more or less in scale to the species' scale in the overall population, so it does not appear that species is a major factor in favorable outcome rates for most animals.
When an animal is euthanized or dies in care, it is considered an unfavorable outcome. Again, dogs and cats make up the largest population blocks, but surprisingly, rabbits appear to have higher rates of euthanasia. Reptiles and exotic animals were not among the euthanized animals in this data set, possibly due to specialized rescue and recovery efforts for these types of pets. Similarly, no wild animals were euthanized, suggesting that all were returned to the wild.


# Protocol Buffers - Google's data interchange format
# Copyright 2008 Google Inc. All rights reserved.
# https://developers.google.com/protocol-buffers/
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following disclaimer
# in the documentation and/or other materials provided with the
# distribution.
# * Neither the name of Google Inc. nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
 IN NO EVENT SHALL THE COPYRIGHT
# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""A simple wrapper around enum types to expose utility functions.
Instances are created as properties with the same name as the enum they wrap
on proto classes. For usage, see:
reflection_test.py
"""
__author__ =<EMAIL_ADDRESS>(Kevin Rabsatt)'
class EnumTypeWrapper(object):
"""A utility for finding the names of enum values."""
DESCRIPTOR = None
def __init__(self, enum_type):
"""Inits EnumTypeWrapper with an EnumDescriptor."""
self._enum_type = enum_type
self.DESCRIPTOR = enum_type
def Name(self, number):
"""Returns a string containing the name of an enum value."""
if number in self._enum_type.values_by_number:
return self._enum_type.values_by_number[number].name
raise ValueError('Enum %s has no name defined for value %d' % (
self._enum_type.name, number))
def Value(self, name):
"""Returns the value corresponding to the given enum name."""
if name in self._enum_type.values_by_name:
return self._enum_type.values_by_name[name].number
raise ValueError('Enum %s has no value defined for name %s' % (
self._enum_type.name, name))
def keys(self):
"""Return a list of the string names in the enum.
These are returned in the order they were defined in the .proto file.
"""
return [value_descriptor.name
for value_descriptor in self._enum_type.values]
def values(self):
"""Return a list of the integer values in the enum.
These are returned in the order they were defined in the .proto file.
"""
return [value_descriptor.number
for value_descriptor in self._enum_type.values]
def items(self):
"""Return a list of the (name, value) pairs of the enum.
These are returned in the order they were defined in the .proto file.
"""
return [(value_descriptor.name, value_descriptor.number)
for value_descriptor in self._enum_type.values]
def __getattr__(self, name):
"""Returns the value corresponding to the given enum name."""
if name in self._enum_type.values_by_name:
return self._enum_type.values_by_name[name].number
raise AttributeError


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License¶
The contents of this document are provided in connection with Advanced Micro Devices, Inc. (“AMD”) products. AMD makes no representations or warranties with respect to the accuracy or completeness of the contents of this publication and reserves the right to make changes to specifications and product descriptions at any time without notice. The information contained herein may be of a preliminary or advance nature and is subject to change without notice. No license, whether express, implied, arising by estoppel or otherwise, to any intellectual property rights is granted by this publication. Except as set forth in AMD’s Standard Terms and Conditions of Sale, AMD assumes no liability whatsoever, and disclaims any express or implied warranty, relating to its products including, but not limited to, the implied warranty of merchantability, fitness for a particular purpose, or infringement of any intellectual property right.
AMD’s products are not designed, intended, authorized or warranted for use as components in systems intended for surgical implant into the body, or in other applications intended to support or sustain life, or in any other application in which the failure of AMD’s product could create a situation where personal injury, death, or severe property or environmental damage may occur. AMD reserves the right to discontinue or make changes to its products at any time without notice.

Copyright and Trademarks
© 2021 Advanced Micro Devices, Inc. All rights reserved. AMD, the AMD Arrow logo, ATI, the ATI logo, Radeon, FireStream, and combinations thereof are trade- marks of Advanced Micro Devices, Inc. OpenCL and the OpenCL logo are trade- marks of Apple Inc. used by permission by Khronos. Other names are for informational purposes only and may be trademarks of their respective owners.


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Debugger API exposes Google Chrome debugging interface to the extensions. Debugging messages that are being sent and received are all JSON objects with the structure defined by the Developer Tools / Web Inspector Protocol. We are currently drafting this protocol, it is by no means ready and stable, but we can already expose the way to attach to a given tab and instrument it. Although not finalized, this API is feature rich. There is a proof of concept implementation of the Chrome Developer Tools front-end running as an extension using the debugger API.
Note that attaching to the page by means of the debugger API and using embedded Chrome Developer Tools with it are mutually exclusive. If extension is attached to the page and user invokes Developer Tools, debugging session is being terminated. Extension can then re-establish it via attaching to a tab later.