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Carwled Urls with sentiments
Sorts the list accordingly
Sort
Ascending
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Analyze Selected Urls


This application contains the following components discussed in chapter 10. Clicking on the links below will execute the JSP pages on the server.
| JSP Page | Description | Explained in |
|---|---|---|
| counter.jsp | Shows the use of various JSP syntax elements | Section 10.1, Listing 10.1 |
| persistent_counter.jsp | Explains JSP life cycle methods- jspInit() , _jspService() , and jspDestroy() |
Section 10.2.4, Listing 10.2 |
| hello.jsp | Throws an exception because the name parameter is not passed. The exception is caught by errorHandler.jsp that prints the appropriate message. This example shows how to use the page directive attributes - errorPage and isErrorPage |
Section 10.3.3, Listing 10.3, 10.4 |
| hello.jsp?name=john | Does not throw an exception because the name parameter is passed. The errorHandler.jsp page is not called in this case. | Section 10.3.3, Listing 10.3, 10.4 |
| threadSafety.jsp | Shows how to use the
page directive attribute - isThreadSafe . The JSP page code contains a loop that executes 10 times and the thread uses Thread.currentThread().sleep(1000). So it will take a little more than 10 seconds for the reply. Please be patient. For the purpose of demonstration, the link on the left opens three windows. Observe the output in all three of them. Note: Tomcat may not start a new thread for the third request. In such a case, the third window will get a response only after Tomcat processes the requests from the first two. |
Section 10.3.6, Listing 10.5 |


Arches is a central design system is created to be standalone but constraints of budget and time sometimes doesn't allow for the that type of project implementation. Artches Utitlity Classes are made to stand ontop of another design and impart and ACC brand utilty class functionality.
Utility Class Overview
Utility Classes
Utility classes are a distinct departure from many previous learned CSS theories, but what utility classes allow for is an excellent separation of concerns between the HTML and CSS. The singular focus for utility classes is a single class controls a single style in CSS. Utility Class has significant benefits of reducing scope expansion in the CSS and also gives greater flexibility of crafting user interface components that are currently undefined. Html should continually be written using the best standards for semantics, but it is no longer a concern to follow a components design pattern because it is applicable at the HTML level.
Naming Convention
Strathmore does have a learning curve, but to reduce the curve the names follow a strict pattern. State of the elements which then effects a single style at a defined value which only effects at a specific breakpoint. read more
[pseudo state]:[style name]_[value+unit]-[modifier]:[break point]
An example of wanting the text color of an object when hovered to become the primary the color only when the screen is large would be h:c_primary:lg.
Definition of Style Short Hand
Strathmore utilizes an abbreviated naming convention for utility classes to save typing for developers and to also reduce overall files size.
| Style | Short Name | CSS | Long Nmae |
|---|---|---|---|
| margin | '.m' | 'margin' | '.margin' |
| margin-top | '.m-t' | 'margin-top' | '.margin-top' |
| margin-bottom | '.m-b' | 'margin-bottom' | '.margin-bottom' |
| margin-left | '.m-l' | 'margin-left' | '.margin-left' |
| margin-right | '.m-r' | 'margin-right' | '.margin-right' |
| overflow | '.overflow' | 'overflow' | '.overflow' |
| padding | '.p' | 'padding' | '.padding' |
| padding-top | '.p-t' | 'padding-top' | '.padding-top' |
| padding-bottom | '.p-b' | 'padding-bottom' | '.padding-bottom' |
| padding-left | '.p-l' | 'padding-left' | '.padding-left' |
| padding-right | '.p-r' | 'padding-right' | '.padding-right' |
| line-height | '.lh' | 'line-height' | '.line-height' |
| font-size | '.font' | 'font-size' | '.font-size' |
| font-family | '.font' | 'font-family' | '.font-family' |
| font-weight | '.font' | 'font-weight' | '.font-weight' |
| font-style | '.font' | 'font-style' | '.font-style' |
| color | '.c' | 'color' | '.color' |
| background-color | '.bg' | 'background-color' | '.back' |
| border-color | '.br' | 'border-color' | '.border' |
| border-radius | '.br' | 'border-radius' | '.border-radius' |
| border-top-right-radius | '.br-tr' | 'border-top-right-radius' | '.border-top-right-radius' |
| border-top-left-radius | '.br-tl' | 'border-top-left-radius' | '.border-top-left-radius' |
| border-bottom-right-radius | '.br-br' | 'border-bottom-right-radius' | '.border-bottom-right-radius' |
| border-bottom-left-radius | '.br-bl' | 'border-bottom-left-radius' | '.border-bottom-left-radius' |
| border-width | '.br' | 'border-width' | '.border-width' |
| border-top-width | '.br-t' | 'border-top-width' | '.border-top-width' |
| border-right-width | '.br-r' | 'border-right-width' | '.border-right-width' |
| border-left-width | '.br-l' | 'border-left-width' | '.border-left-width' |
| border-bottom-width | '.br-b' | 'border-bottom-width' | '.border-bottom-width' |
| width | '.w' | 'width' | '.width' |
| display | '.d' | 'display' | '.display' |
| max-width | '.max-w' | 'max-width' | '.max-width' |
pseudo states
| states | Short Name | CSS | Long Name | Currently Used |
|---|---|---|---|---|
| hover | 'h:' | ':hover' | 'hover' | Yes |
| ':active' | 'active' | No | ||
| ':visited' | 'hover' | No |
Values
| Value Postfix | Font Size | Margin | Padding | Line Height | border-width | Color |
|---|---|---|---|---|---|---|
| n5 | ms(-5) | -2 * X† | Not Used | Not Used | Not Used | mix(color, black, 90%) |
| n4 | ms(-4) | -1 * X† | Not Used | Not Used | Not Used | mix(color, black, 70%) |
| n3 | ms(-3) | -.5 * X† | Not Used | Not Used | Not Used | mix(color, black, 50%) |
| n2 | ms(-2) | -.25 * X† | Not Used | Not Used | Not Used | mix(color, black, 30%) |
| n1 | ms(-1) | -.1 * X† | Not Used | Not Used | Not Used | mix(color, black, 10%) |
| 0 | ms(0) | 0 | 0 | 0 | 0 | color @ 100% |
| 1 | ms(1) | .1 * X† | .1 * X† | 1 | 1px | mix(color, white, 10%) |
| 2 | ms(2) | .25 * X† | .25 * X† | 1.45 | 3px | mix(color, white, 30%) |
| 3 | ms(3) | .5 * X† | 5 * X† | 1.65 | 5px | mix(color, white, 50%) |
| 4 | ms(4) | 1 * X† | 1 * X† | 1.75 | .5rem | mix(color, white, 70%) |
| 5 | ms(5) | 2 * X† | 2 * X† | 2 | 1rem | mix(color, white, 90%) |
† x = global spacing 1 rem
Break Points
| break point | Short Name | css | dimension | Currently Used |
|---|---|---|---|---|
| all sizes | '' | min-width: 0em | 0px | default |
| ':sm' | min-width: 30em | 480px | No | |
| medium | ':md' | min-width: 40em | 640px | Yes |
| large | ':lg' | min-width: 40em | 1024px | Yes |
Alternate Values
Border Radius
| Value Postfix | Border Radius Values |
|---|---|
| square | 0 |
| radius | 5px or Global Radius |
| round | 25px |
| circle | 100% |
Border Style
| Value Postfix | Border Style Values |
|---|---|
| none | none |
| dashed | dashed |
| solid | solid |
Display
| Value Postfix | Display |
|---|---|
| none | none |
| block | block |
| inline | inline |
| inline-block | inline-block |
| flex | flex |
Overflow
| Value Postfix | Overflow |
|---|---|
| auto | auto |
| clip | clip |
| scroll | scroll |
| hidden | hidden |
| ellipsis | ellipsis |
| visible | visible |
| unset | unset |
Reserved Names
Reserved Words and Modifier Descriptors
These words should be used to describe states and generic elements of the UI and should be used instead of some variation or alternate name.
Component Name Modifier
Components styling is most are
- ** -container ** or UI Name with no modifier (Use instead of holder when items are UI based)
- ** -group ** or UI Name with no modifier (Use instead of list, holder, collection when items or repeatable Content)
- ** -item ** or UI Name with no modifier (generic child of container or group)
- ** -separator ** (a visual treatment or break between items within a container or group )
States
- show
- hide
- is-active
- is-complete
- is-disabled
- is-current
- is-selected
- focus
- success
- hover
- warning
- alert (Depreciate the use of ‘error’ for the native descriptor from foundation)
- primary
- secondary
- reverse
Colors
- Scrub Colors
- -ACC Blue
- -primary teal
- -blue
- -purple
- -magenta
- State Colors
- -success, -green
- -warning, -orange
- -alert, -red
- -primary, -productColor
- -secondary
- -highlight
- Black
- White
- Black and White have transparencies
- -10 alpha,
- -20 alpha,
- -30 alpha,
- -40 alpha,
- -50 alpha,
- -60 alpha,
- -70 alpha,
- -80 alpha,
- -90 alpha


{% extends "layout.html" %} {% block title %}Home{% endblock %} {% block content %}
You should see a canvas element below this paragraph where you can do paper.js stuff.
Look in the script
element of /templates/page5.html
for the JavaScript
code that makes this example work.
(you may need to scroll down the page to see it. It's below the Canvas element.)


It’s like the Fibonacci Sequence, but a lot more computationally expensive and a lot less useful.
exps(x, y, z) =
if x <= 0: y + z
if x > 0: exps(x-1,y+1,z) + exps(x-2,y+2,z*2) + exps(x-3,y+3,z*3)
x
, y
, and z
are all greater than or equal to zero.
This will be tested on inputs as large as:
x = 150
y = 400
z = 800
Use a hashtable to make sure your solution completes before the universe ends.
Hint: Va Clguba, n qvpg xrl pna or nal vzzhgnoyr glcr… vapyhqvat n ghcyr.
(That’s encrypted with ROT13–Google rot13 decoder
to decode it if you want the hint.)


Parliament demands answers over students denied exams
by Moses WalubiriParliament’s intervention follows reports on Monday where school administrators of Top Care secondary school in Iganga Municipality barred fifteen senior six students from sitting exams over fees balance.
Lawmakers across the political divide have asked the ministry of education to reprimand private school owners who have barred students with fees balance from sitting their final exams.
During Wednesday’s plenary session, Gulu Municipality MP, Lyandro Komakech described the actions of some school administrators as “a fundamental breach of the right to education.”
“The actions of some school administrators should not go unpunished. You cannot bar students from sitting exams because of school fees balance. It’s just inhumane,” Komakech said.
Although the state minister for sports, Charles Bakabulindi promised to inquire into the complaints, he was explicit that paying fees is the responsibility of parents.
However, Speaker of Parliament Rebecca Kadaga reminded Bakabulindi of a government policy sanctioned a few years ago obliging schools to let school fees defaulting students sit exams and clear the balance later.
Although Kadaga did not permit a full debate on the issue, the reaction of lawmakers was indicative of the fact that the impugned practice was widespread in many parts of the country.
Parliament’s intervention follows reports on Monday where school administrators of Top Care secondary school in Iganga Municipality barred fifteen senior six students from sitting exams over fees balance.
Ministry of education later intervened with the assistant commissioner in charge of private schools, George Mutekanga promising to crack the whip on school administrators.
A number of well-wishers have since chipped in by contributing funds meant to clear the sh3.6m the affected students owed the school.


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{% comment %} Form for content related fields of an account Uses `hx-swap="none"` because otherwise reloading of the
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QuickOut enables flexible layout, as a result businesses can switch between layouts without rebuilding the components. For example, all the layouts below uses the same components.


--- title: Causal Inference for Social Mobility and COVID-19 author: Matteo Bonvini, Edward Kennedy, Valérie Ventura, and Larry Wasserman date: 2021-01-19 tags: - COVIDcast authors: - mbonvini - ekennedy - vventura - lwasserman heroImage: causal-inference-mobility-full-size.jpg heroImageThumb: causal-inference-mobility-thumb.jpg summary: | How can we estimate the causal effect of mobility on COVID-19 when there could be many confounding variables? acknowledgements: | We'd like to thank the Delphi engineering team for making this data available. And we'd like to thank Roni Rosenfeld and Ryan Tibshirani for posing the challenge of making counterfactual predictions. We thank Rob Tibshirani for suggesting several improvements on this post, and Alex Reinhart for getting our post into the appropriate format. related: - 2020-08-28-api output: blogdown::html_page: toc: true ---
Most pandemic research has focused on questions related to prediction such as: how many COVID cases do we expect to see next week in Allegheny County? Causal inference, on the other hand, is concerned with “what if” questions. For example: what would happen if everyone wore masks? How many cases would there be if there was a lockdown? How many deaths would occur if 40 percent of the population is vaccinated? These questions involve hypothetical interventions on a population.
Estimating causal effects is tricky. We all know that “correlation isn’t causation.” For example, mask usage and cases could both increase over time. But that doesn’t mean that masks cause more cases to occur. Activation of “Buckle your seatbelt” signs on airplanes is correlated with turbulence. But activating the sign does not cause turbulence.
So how do we estimate causal effects? There is a collection of methods for this task. The important thing is that we can’t just use standard prediction methods. We need to use specialized methods that were designed for causal inference. In this post we will discuss our work on the causal effect of social mobility on deaths from COVID. The social mobility variable we will use is the proportion of people staying home. We can think of this of anti-mobility, and expect that higher values of this variable will lead to fewer deaths from COVID.
Let’s start with a brief introduction to causal inference.
Causal inference is a huge, complex topic. We give a very brief exposition of some key ideas here. To learn more, we recommend the book by Hernan and Robins and the paper “Statistics and Causal Inference” by Paul Holland (Journal of the American Statistical Association 1986, pp. 945-960). And one can find many tutorials on the web.
Suppose we have three variables \(X\), \(A\) and \(Y\), where \(Y\) is the outcome of interest (such as deaths from COVID), \(A\) is a treatment or exposure (such as mobility level), and \(X\) are confounding variables, which are variables that affect both \(A\) and \(Y\) (such as age). The joint density is
\[ p(x,a,y) = p(x)p(a|x)p(y|x,a). \]
We want to know: what would be the mean of \(Y\) if we could set \(A\) to be equal to some value \(a\)? For example, what would the number of COVID cases be if 92 percent of people wore masks? We let \(Y^a\) denote this hypothetical outcome, which is called a counterfactual or potential outcome. And we denote the mean of \(Y^a\) by \(\mathbb{E}[Y^a]\).
If we have measured all the confounding variables then the answer (ignoring a few technical details) is given by Robins’ \(g\)-formula:
\[ \mathbb{E}[Y^a] = \int \mu(x,a) p(x) dx = \mathbb{E}[\mu(X,a)] \]
where \(\mu(x,a) = \mathbb{E}[Y|X=x,A=a]\) is the mean of \(Y\) given \(X\) and \(A\). This is not equal to the mean of \(Y\) given \(A=a\), which is \(\mathbb{E}[Y|A=a] = \int \mu(x,a) p(x|a) dx\). This is the difference between causation and prediction (correlation). You can think of the \(g\)-formula as the mean of \(Y\) if we replace \(p(a|x)\) in the joint density with a point mass at \(a\). Using the \(g\)-formula is one example of adjusting for confounding.
Now suppose we observe data \((X_1,A_1,Y_1),\dots,(X_n,A_n,Y_n)\). How do we estimate \(\mathbb{E}[Y^a]\)? The simplest approach is to get an estimate \(\hat\mu(x,a)\) of \(\mu(x,a)\) (this is just regression) and replace the integral in the \(g\)-formula with a sample average to get the estimate
\[ \hat{\mathbb{E}(Y^a)} = \frac{1}{n}\sum_i \hat\mu(X_i,a). \]
This is called the plug-in estimator. (Note that for prediction we would not use this formula. We would just use \(\hat \mu(X, A)\).) There are often better estimators, but we won’t get into that here. The important thing is: there is a formula for the causal effect and we can estimate it.
The first plot below shows an example where we would predict higher values of \(Y\) when \(A\) is large. For pure prediction, this is the correct conclusion. The second plot shows that once we account for \(X = \text{age}\) (corresponding to different colors) there is a negative relationship between \(Y\) and \(A\): for a given person with a fixed age, increasing \(A\) would decrease \(Y\). In this case, age is a confounder and the \(g\)-formula would correctly recover the negative relationship. For causal inference, this is the correct conclusion.
Things get trickier when there are time varying variables. Consider weekly mobility and death data \((A_1,Y_1),\dots, (A_T,Y_T)\) in one state. For simplicity, we’ll assume that there are no \(X\) variables. But we’ll see that at time \(t\), the variables \(Y_1, \dots, Y_{t-1}\) are confounding variables for the causal effect of mobility on \(Y_t\). Define \(\overline{A}_t = (A_1,\dots, A_t)\) and \(\overline{Y}_t = (Y_1,\dots, Y_t)\) for \(t\geq 1\). The density of \((\overline{y}_t,\overline{a}_t)\) can be written as
\[ p(\overline{y}_t,\overline{a}_t) = \prod_{s=1}^t p(y_s|\overline{y}_{s-1},\overline{a}_{s}) p(a_s|\overline{a}_{s-1},\overline{y}_{s-1}). \]
Now consider the causal question:
What would \(Y_t\) be if we set \(\overline{A}_t\) equal to some value \(\overline{a}_t =(a_1,\dots, a_t)\)?
Let \(Y^{\overline{a}_t}_t\) denote this counterfactual quantity. For example, if \(A_t\) is mobility, measuring, say, the percentage of people who stay home, and \(\overline{a}_t = (12,12,12,6,6,6)\) then \(Y^{\overline{a}_t}_t\) is the number of deaths we would observe if we set mobility to 12 for three weeks then set it to 6 for three weeks.
How do we find the mean \(\mathbb{E}[Y^{\overline{a}_t}_t]\)? As a general rule, we adjust for pre-treatment variables but not for post-treatment variables. But in the time varying case, this rule makes no sense. Each \(Y_t\) is both a pre-treatment variable (it occurs before \(A_{t+1}\)) and a post-treatment variable (it occurs after \(A_{t-1}\)). The correct way to get the mean of \(\mathbb{E}[Y^{\overline{a}_t}]\) is to use the more general form of the \(g\)-formula:
\[ \psi(\overline{a}_t)\equiv \mathbb{E}[Y^{\overline{a}_t}_t] = \int\cdots\int \mathbb{E} \left[Y_t | \overline{A}_t = \overline{a}_t, \overline{Y}_{t-1}=\overline{y}_{t-1}\right] \prod_{s=1}^{t-1} p(y_s|\overline{y}_{s-1},\overline{a}_{s}) d y_s. \]
You can ignore that equation if you like; suffice it to say that there is indeed a formula for the quantity we are interested in.
We note, by the way, that some authors denote \(\mathbb{E}[Y^{\overline{a}_t}_t]\) by \(\mathbb{E}[Y_t| {\rm do}(\overline{a}_t)]\). They mean the same thing.
Having a formula for \(\psi(\overline{a}_t)\) is great, but how do we estimate it? Again we could plug-in estimates of all the quantities in the \(g\)-formula. But there are better things we can do, as we now explain.
The approach we use is to make use of something called a marginal structural model (MSM).
Usually when we construct a statistical model, we are trying to come up with a way to express the distribution of the data, in this case, \((A_1,Y_1),\dots, (A_T,Y_T)\). In effect, we want a formula that explains how we would generate a dataset like the one we have. But to do so in a tractable way usually involves making a bunch of assumptions about the model. Instead, with an MSM, we specify a plausible model for the form of the function \(\mathbb{E}[Y^{\overline{a}_t}]\) but we otherwise leave the model for the data unspecified. For example, we might assume that \(\mathbb{E}[Y^{\overline{a}_t}]\approx \beta_0 + \beta \sum_s a_s\). In a sense, we are spending our modeling assumptions wisely: we want the effect of the treatment to be smooth and fairly simple. But we otherwise don’t want to impose assumptions.
The model we use is
\[ \mathbb{E}[L_t^{\overline{a}_t}]= s(t) + \beta M_t \]
where \(L_t = \log (1+Y_t)\) is log-deaths, \(s(t)\) is an arbitrary, smooth, increasing function and
\[ M_t = \sum_{s=1}^{t-\delta}(a_s-a_1) \]
is cumulative mobility (compared to baseline mobility). Based on published studies, we take \(\delta = 4\) weeks. (We switch from deaths to log-deaths mainly for certain numerical reasons related to the software we are using.) Here, \(s(t)\) represents the evolution of the pandemic if there were no interventions and no reduction in susceptible individuals. The term \(\beta M_t\) captures the effect of mobility. With no change in mobility, deaths increase exponentially as \(e^{s(t)}\).
The intuition for using cumulative mobility comes from ideas from epidemic modeling. Roughly, if \(I_t\) denotes new infections in week \(t\), we might assume that \(I_t \approx e^{\nu(t) + \beta A_t} I_{t-1}\) for some function \(\nu(t)\). This implies that \(I_{t-1} \approx e^{\nu(t-1) + \beta A_{t-1}} I_{t-2}\) and so on. If we continue back to \(t=1\) we find that \(I_t \approx e^{s(t) + \beta \sum_s A_s} I_1\) where \(s(t)=\sum_{s=1}^t \nu(s)\). If we further assume that some fraction of people infected a time \(t\) will die in \(\delta\) weeks, then we can derive the above model. (More complex models are discussed in the paper.)
To re-cap: we are leaving the distribution \(P\) of the data \((A_1,Y_1),\dots, (A_T,Y_T)\) unspecified, except that we require: if you apply the \(g\)-formula to \(P\) you must get \(s(t) + \beta M_t\). In other words, the statistical model is the set of all densities \(p(\overline{\ell}_t,\overline{a}_t)\) that satisfy the condition
\[ \int\cdots\int \mathbb{E}[L_t | \overline{A}_t = \overline{a}_t, \overline{L}_{t-1}=\overline{\ell}_{t-1}] \prod_{s=1}^{t-1} p(\ell_s|\overline{\ell}_{s-1},\overline{a}_{s}) d \ell_s = s(t) + \beta M_t. \]
This is an example of a semiparametric model: a model that has some sort of constraint but is otherwise nonparametric.
Estimating the MSM is a bit involved. A key step is estimating the following function
\[ W_t = \frac{p(a_t|\overline{a}_{t-1})}{p(a_t| \overline{y}_{t-1},\overline{a}_{t-1},\overline{x}_{t-1})} \]
where \(\overline{x}_{t-1}\) represents potential confounding variables. The model is fit with these weights and the weights are crucial because that is how we adjust for confounding in the MSM. We won’t go into detail here. Instead, let’s look at the data and the estimates.
The data we use are weekly deaths and weekly averaged mobility starting February 15 2019. The mobility signal we’ll consider here is the proportion of people of who stay home, which is kindly provided to Delphi by SafeGraph. (We {{< apireflink "api/covidcast-signals/safegraph.html" "publish this data" >}} in our {{< apireflink "api/covidcast.html" "COVIDcast Epidata API" >}}, so it is available to anyone interested in studying mobility during the pandemic.) We use data at the state level. Note that this is actually a sort of anti-mobility measure: the higher it is, the less mobility there is. Here are plots of weekly deaths and mobility starting February 15, 2020, for a few states:
library(covidcast)
library(directlabels)
library(dplyr)
library(lubridate)
library(ggplot2)
# Top 5 most populated states according to
# https://en.wikipedia.org/wiki/List_of_states_and_territories_of_the_United_States_by_population
states_of_interest <- c("ca", "tx", "fl", "ny", "pa")
states_deaths <- covidcast_signal(
"jhu-csse", "deaths_incidence_num",
start_day = "2020-02-15", end_day = "2020-12-16",
geo_type = "state", geo_values = states_of_interest
)
states_mobility <- covidcast_signal(
"safegraph", "completely_home_prop",
start_day = "2020-02-15", end_day = "2020-12-16",
geo_type = "state", geo_values = states_of_interest
)
dat <- full_join(x = select(states_deaths, time_value, geo_value, value),
y = select(states_mobility, time_value, geo_value, value),
by = c("time_value" = "time_value", "geo_value" = "geo_value"))
dat <- dat %>%
rename(date = time_value,
state = geo_value,
stay_home = value.y,
deaths = value.x) %>%
group_by(state) %>%
# epi week is from saturday to sunday
mutate(weekday = wday(date, label = TRUE, week_start = 6),
weeknum = wday(date, week_start = 6),
week = cumsum(weeknum == 1))
dat_week <- dat %>%
group_by(state, week) %>%
mutate(weekly_deaths = sum(deaths),
weekly_deaths = 1 + weekly_deaths, # avoid 0 in the log scale
weekly_stay_home = mean(stay_home)) %>%
select(week, state, weekly_stay_home, weekly_deaths, weekly_deaths) %>%
unique()
ggplot(dat_week, aes(x = week, y = weekly_deaths, color = state)) +
geom_line() +
scale_y_log10() +
geom_dl(aes(label = toupper(state)), method = "last.bumpup") +
labs(x = "Week", y = "# deaths (log scale)",
title = "Reported deaths over time",
subtitle = "From JHU CSSE COVID-19 data",
caption = "Data from Delphi COVIDcast, delphi.cmu.edu") +
theme_bw() +
guides(color = FALSE)
ggplot(dat_week, aes(x = week, y = weekly_stay_home, color = state)) +
geom_line() +
geom_dl(aes(label = toupper(state)), method = "last.bumpup") +
labs(x = "Week", y = "Proportion staying at home",
title = "(Anti)mobility over time",
subtitle = "From SafeGraph",
caption = "Data from Delphi COVIDcast, delphi.cmu.edu") +
theme_bw() +
guides(color = FALSE)
After fitting the MSM, we can estimate various quantities. Let’s consider the effect of changing the observed stay-at-home series \((A_1,\dots, A_T)\) to the hypothetical value \(\overline{a}_T = (A_1+\gamma,\dots, A_T+\gamma)\). In other words, what would have happened if we had increased stay-at-home by \(\gamma\)? Below is the estimated difference in deaths (with 90 percent pointwise confidence bands) for a few states: Arizona, California, Florida and Georgia. The plot shows the mean of
\[ Y_t^\gamma - Y_t \]
where \(Y_t\) is the number of deaths and \(Y_t^\gamma\) is the (counterfactual) number of deaths we would have observed of mobility had been increases by \(\gamma\) at all times, where we take \(\gamma = 10\) (a ten percent increase in stay-at-home). The fact that the values are negative indicates that increasing stay-at-home decreases deaths, as we would expect. (We remind the reader that these results are preliminary.)
One important question in every causal analysis is: what if there is unobserved confounding? In the current problem, a confounder is a variable that affects both mobility and death from COVID. We try to include as many plausible observed confounding variables as possible. So far we use all past values of death and mobility as confounding variables. But of course, it is always possible that there are unobserved confounders. Currently, we are conducting a sensitivity analysis to assess how robust the estimates are to unobserved confounders.
In this post we have only addressed mobility. As soon as the mobility analysis is complete we will turn our attention to estimating the causal effect of masks on COVID cases.


Assignments
Homework assignments are due at the beginning of class (8:45 am) on the stated due date.
Problem Set 1
due Tuesday September 9
Problem Set 2
due Thursday September 18
Problem Set 3
*SPECIAL DUE DATE: Friday September 26, NO LATER THAN 4 PM.


In GWmodel, we introduce techniques from a particular branch of spatial statistics,termed geographically-weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. GWmodel includes functions to calibrate: GW summary statistics, GW principal components analysis, GW discriminant analysis and various forms of GW regression; some of which are provided in basic and robust (outlier resistant) forms.
| Version: | 2.0-6 |
| Depends: | R (≥ 3.0.0), maptools (≥ 0.5-2), robustbase, sp, Rcpp, spdep |
| Imports: | methods, grDevices, stats, graphics, spacetime |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | mvoutlier, RColorBrewer, gstat |
| Published: | 2018-08-20 |
| Author: | Binbin Lu[aut], Paul Harris[aut], Martin Charlton[aut], Chris Brunsdon[aut], Tomoki Nakaya[aut], Isabella Gollini[ctb] |
| Maintainer: | Binbin Lu <binbinlu at whu.edu.cn> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | http://gwr.nuim.ie/ |
| NeedsCompilation: | yes |
| Citation: | GWmodel citation info |
| In views: | Spatial |
| CRAN checks: | GWmodel results |
| Reference manual: | GWmodel.pdf |
| Package source: | GWmodel_2.0-6.tar.gz |
| Windows binaries: | r-devel: GWmodel_2.0-6.zip, r-release: GWmodel_2.0-6.zip, r-oldrel: GWmodel_2.0-6.zip |
| OS X binaries: | r-release: GWmodel_2.0-6.tgz, r-oldrel: GWmodel_2.0-6.tgz |
| Old sources: | GWmodel archive |
Please use the canonical form https://CRAN.R-project.org/package=GWmodel to link to this page.


|
Vector-Synthesizer
1.5
A Vector-Synthesizer like the Prophet VS!
|
Public Types | |
| enum | { LPF1, HPF1, LPF2, HPF2, BPF2, BSF2, LPF4, HPF4, BPF4 } |
Public Member Functions | |
| double | getFeedbackOutput () |
| Get the feedback output. | |
| void | reset () |
| Reset memory. | |
| void | doFilter (float *xn) |
| Apply the filter to a sample. | |
Public Attributes | |
| double | m_dFc |
| cut-off frequency | |
| double | m_dAlpha |
| Feed-Forward coefficient. | |
| double | m_dBeta |
| unsigned int | m_uFilterType |
| Currently selected filter type. | |
| unsigned int | m_dSampleRate |
| sample rate | |
Protected Attributes | |
| double | m_dZ1 |
| z-1 memory | |
| double Filter::m_dBeta |
Moog-Ladder-Filter coefficient


You have been selected from a variety of applicants to take part in the most exciting, greatest and most dangerous adventure of mankind: the search for the unknown 9th planet.
To begin please select an option below.
Add crew members below. A maximum of 5 is allowed
Choose your crew and lets begin!


This is a project template for developing a 96Boards Mezzanine board (compatible with these boards).
This project's schematic only contains the necessary connectors needed for a mezzanine board. The current connectors are surface mounted, which can be easily changed to through hole. This is noted in the schematic.
The PCB includes the connectors, and mounting holes. The connectors and holes have been positioned so that the boards are aligned when mounted. The edge cuts also aligns with the base board. All the mezzanine board IO is connected to the header pins.
The board outline looks like the following:
Copyright (c) 2016, Linaro, Inc.


{% extends "base.html" %} {% block head %} {% endblock %} {% if message %} {% block message %}
{{ message }}
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Accomodation
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Make Yourself Feel At Home.Sit Back And Enjoy The Fest
Accomodation for One Day :
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We Are Situated At Karaikal, Puducherry. Direct Bus And Train Services Are Available.
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---
layout: page
title: Probabilistic Models for Code
---
As part of our research efforts in the machine learning/programming languages space (see here), we develop new program synthesis techniques based on domain-specific languages to create precise probabilistic models over structures such as trees. This idea proved useful for creating probabilistic models of source code. Here, we provide several already synthesized models and code to replicate the results. The code can also be used to build other tools on top of the said models.
Code to replicate results
The code and the trained models are available at: https://github.com/eth-srl/ModelsPHOG
Precision for code completion tasks
We provide code and models with their respective accuracy for several code completion tasks. Running these commands requres downloading the corresponding training datasets listed below.
- JavaScript dataset
- Best terminal prediction (based on the E13 synthesis procedure defined in [1] with additional prunning of the tree, this is better result than shown in [1])
Accuracy: 85.9%
Command to replicate:
bazel-bin/phog/model/evaluate --logtostderr \
--training_data programs_training.json \
--evaluation_data programs_eval.json \
--tgen_program synthesized/js/values_best.tgen
- Best non-terminal prediction (based on the ID3+ synthesis procedure defined in [1], the result is the shown in [1])
Accuracy: 83.9%
Command to replicate:
bazel-bin/phog/model/evaluate --logtostderr \
--training_data programs_training.json \
--evaluation_data programs_eval.json \
--tgen_program synthesized/js/types_id3.tgen \
--is_for_node_type
- Python dataset
- Best terminal prediction (based on the E13 synthesis procedure defined in [1], this is the result shown in [1])
Accuracy: 69.2%
Command to replicate:
bazel-bin/phog/model/evaluate --logtostderr \
--training_data python100k_train.json \
--evaluation_data python50k_eval.json \
--tgen_program synthesized/py/values_e13.tgen
- Best non-terminal prediction (based on the ID3+ synthesis procedure defined in [1], the result is the shown in [1])
Accuracy: 76.1%
Command to replicate:
bazel-bin/phog/model/evaluate --logtostderr \
--training_data python100k_train.json \
--evaluation_data python50k_eval.json \
--tgen_program synthesized/py/types_id3.tgen \
--is_for_node_type
Training datasets
{% include card.html
title="150k Python Dataset"
url="py150"
output="horizontal"
description="Dataset consisting of 150'000 Python ASTs"
%}
{% include card.html
title="150k JavaScript Dataset"
url="js150"
output="horizontal"
description="Dataset consisting of 150'000 JavaScript files and their parsed ASTs"
%}
Relevant publications
{% include card.html
title="Probabilistic Model for Code with Decision Trees"
url="publications/raychev2016probabilistic"
output="horizontal"
description="Veselin Raychev, Pavol Bielik, Martin Vechev"
extra="ACM OOPSLA 2016"
paper="https://files.sri.inf.ethz.ch/website/papers/oopsla16-dt.pdf"
%}
{% include card.html
title="PHOG: Probabilistic Model for Code"
url="publications/bielik2016phog"
output="horizontal"
description="Pavol Bielik, Veselin Raychev, Martin Vechev"
extra="ACM ICML 2016"
slides="https://files.sri.inf.ethz.ch/website/slides/ICML16_PHOG.pdf"
paper="https://files.sri.inf.ethz.ch/website/papers/icml16_phog.pdf"
%}
{% include card.html
title="Learning Programs from Noisy Data"
url="publications/raychev2016learning"
output="horizontal"
description="Veselin Raychev, Pavol Bielik, Martin Vechev, Andreas Krause"
extra="ACM POPL 2016"
slides="https://files.sri.inf.ethz.ch/website/slides/popl16-slides.pdf"
paper="https://files.sri.inf.ethz.ch/website/papers/popl16.pdf"
%}


Ability Skin: This skin unlocks a unique Hero portrait for your in-game Avatar and also an additional ability while equipped.
Everytime Ethera uses "Paralyze" and jumps to the Front Line, she will regain 40% of the Health she lost in the last 3 seconds. Note* Also appears to work when Ethera uses "Event Horizon"
Ability only available while Galactic Explorer Skin is equipped


--- layout: page title: Lean Prover Zulip Chat Archive permalink: archive/198800BigProof2019/40046Schedule.html ---
I'm preparing my next conference. Next week I'll return to maths. Civilization is restored, look at that schedule: http://www.crm.umontreal.ca/2019/MinicoursGeometrie19/horaire_e.html (go down to Monday, June 10, this week was too packed with talks). Computer scientists need to learn this.
http://wwwf.imperial.ac.uk/~buzzard/LNTS/londonparis.html
I think it's a French thing Patrick.
{% endraw %}


The goal for the website
The main purpose of this website is to educate users on how web browsers have evolved through the years and have been improving without many of us even realizing it.
The target audience
This website is mainly dedicated for curious people and for people who like technology and are interested on learning the how and why things work. There are many people that go on the internet daily and while browsing they find a topic that look interesting and they decide to look further into it so this website is mainly for that type of people.
The page topics
It will basically be about the evolution of web browsers; their logos and they have been improving through the years. There were many web browsers since the beginning of the internet and some of them just started as text-based websites with no images at all so in this website we’ll go through the main web browsers used in different years.


Introduction to Data Manipulation
1. Introduction
Welcome!
Welcome to our second tutorial for the Statistics II: Statistical Modeling & Causal Inference (with R) course.
The labs are designed to reinforce the material covered during the lectures by introducing you to hands-on applications.
The practical nature of our class means that our labs will be data-centered. Throughout our class, we will get acquinted with multiple packages of the tidyverse
.
Though we expect that some of you may already know them, the tidyverse
is a collection of R packages that share an underlying design, syntax, and structure. They will definitely make your life easier!!
Today, we will start with a brief introduction to data manipulation through the dplyr
package.
In this tutorial, you will learn to:
- identify the purpose of a set of
dplyr
verbs - write statements in tidy syntax
- apply
dplyr
verbs to solve your data manipulation challenges
This tutorial is partly based on R for Data Science, section 5.2, and Quantitative Politics with R, chapter 3.
What we will need today
We’ll practice some wrangling in dplyr
using data for penguin sizes recorded by Dr. Kristen Gorman and others at several islands in the Palmer Archipelago, Antarctica. Data are originally published in: Gorman KB, Williams TD, Fraser WR (2014) PLoS ONE 9(3): e90081. doi:10.1371/journal.pone.0090081
You do not need to import the data to work through this tutorial - the data are already here waiting behind the scenes.
But if you do ever want to use the penguins data outside of this tutorial, they now exist in the palmerpenguins package in R.
Let’s begin!
2. Data Structure
Tidy data
Generally, we will encounter data in a tidy format. Tidy data refers to a way of mapping the structure of a data set. In a tidy data set:
- Each variable forms a column.
- Each observation forms a row.
- Each type of observational unit forms a table
The penguins
data set
The 3 species of penguins in this data set are Adelie, Chinstrap and Gentoo. The data set contains 8 variables:
- species: a factor denoting the penguin species (Adelie, Chinstrap, or Gentoo)
- island: a factor denoting the island (in Palmer Archipelago, Antarctica) where observed
- culmen_length_mm: a number denoting length of the dorsal ridge of penguin bill (millimeters)
- culmen_depth_mm: a number denoting the depth of the penguin bill (millimeters)
- flipper_length_mm: an integer denoting penguin flipper length (millimeters)
- body_mass_g: an integer denoting penguin body mass (grams)
- sex: a factor denoting penguin sex (MALE, FEMALE)
- year an integer denoting the year of the record
*Illustration by \@allisonhorst*
Let’s explore the data set.
head()
is a function that returns the first couple rows from a data frame. Write the R code required to explore the first observations of the penguins
data set:
Notice that when you press ‘Run’, the output of the code is returned below it! So by pressing ‘Run’, you’ve run your first R code of the class!
head(penguins)
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex | year |
|---|---|---|---|---|---|---|---|
| Adelie | Torgersen | 39.1 | 18.7 | 181 | 3750 | male | 2007 |
| Adelie | Torgersen | 39.5 | 17.4 | 186 | 3800 | female | 2007 |
| Adelie | Torgersen | 40.3 | 18.0 | 195 | 3250 | female | 2007 |
| Adelie | Torgersen | NA | NA | NA | NA | NA | 2007 |
| Adelie | Torgersen | 36.7 | 19.3 | 193 | 3450 | female | 2007 |
| Adelie | Torgersen | 39.3 | 20.6 | 190 | 3650 | male | 2007 |
3. Manipulating data with dplyr
What we will learn today
In this tutorial, you’ll learn and practice examples using some functions in dplyr
to work with data. Those are:
select()
: keep or exclude some columnsfilter()
: keep rows that satisfy your conditionsmutate()
: add columns from existing data or edit existing columnsgroup_by()
: lets you define groups within your data setsummarize()
: get summary statisticsarrange()
: reorders the rows according to single or multiple variables
Let’s get to work.
3.1. select()
The first verb (function) we will utilize is select()
. We can employ it to manipulate our data based on columns. If you recall from our initial exploration of the data set there were eight variables attached to every observation. Do you recall them? If you do not, there is no problem. You can utilize names()
to retrieve the names of the variables in a data frame.
names(penguins)
## [1] "species" "island" "bill_length_mm"
## [4] "bill_depth_mm" "flipper_length_mm" "body_mass_g"
## [7] "sex" "year"
Say we are only interested in the species, island, and year variables of these data, we can utilize the following syntax:
Activity The following code chunk would select the species, island, and year variables. What should we do to keep the body_mass_g and sex variables as well?
dplyr::select(penguins, species, island, year)
Answer
# you just need to type the names of the columns
dplyr::select(penguins, species, island, year, body_mass_g, sex)
To drop variables, use - before the variable name.
For example, select(penguins, -year)
will drop the year column.
3.2. filter()
The second verb (function) we will employ is filter()
. filter()
lets you use a logical test to extract specific rows from a data frame. To use filter()
, pass it the data frame followed by one or more logical tests. filter()
will return every row that passes each logical test.
The more commonly used logical operators are:
==
: Equal to!=
: Not equal to>
,>=
: Greater than, greater than or equal to<
,<=
: Less than, less than or equal to&
,|
: And, or
Say we are interested in retrieving the observations from the year 2007. We would do:
dplyr::filter(penguins, year == 2007)
Activity Can you adapt the code to retrieve all the observations of Chinstrap penguins from 2007 (remember that species contains character units)
Answer
# you just need to utilize & and type the logical operator for the species
dplyr::filter(penguins, year == 2007 & species == "Chinstrap")
3.3. The Pipe Operator: %>%
The pipe, %>%
, comes from the magrittr
package by Stefan Milton Bache.  This will be one of your best friends in R.
Pipes are a powerful tool for clearly expressing a sequence of multiple operations. Let’s think about baking for a second.
Activity We can leverage the pipe operator to sequence our code in a logical manner. Can you adapt the following code chunk with the pipe and conditional logical operators we discussed?
only_2009 <- dplyr::filter(penguins, year == 2009)
only_2009_chinstraps <- dplyr::filter(only_2009, species == "Chinstrap")
only_2009_chinstraps_species_sex_year <- dplyr::select(only_2009_chinstraps, species, sex, year)
final_df <- only_2009_chinstraps_species_sex_year
final_df #to print it in our console
Answer
penguins %>% #we start off with out df
dplyr::filter(year == 2009 & species == "Chinstrap") %>% #filter
dplyr::select(species, sex, year) #select
3.4. mutate()
mutate()
lets us create, modify, and delete columns. The most common use for now will be to create new variables based on existing ones. Say we are working with a U.S. American client and they feel more confortable with assessing the weight of the penguins in pounds. We would utilize mutate()
as such:
Activity Can you edit the following code chunk to render a new variable body_mass_kg?
penguins %>%
dplyr::mutate(body_mass_lbs = body_mass_g/453.6)
Answer
penguins %>%
dplyr::mutate(body_mass_kg = body_mass_g/1000) #grams divided by 1000
3.5. group_by()
and summarize()
These two verbs group_by()
and summarize()
tend to go together. When combined , ‘summarize()` will create a new data frame. It will have one (or more) rows for each combination of grouping variables; if there are no grouping variables, the output will have a single row summarising all observations in the input. For example:
summarize()
:
penguins %>%
dplyr::summarize(heaviest_penguin = max(body_mass_g, na.rm = T)) #max() does not know how to deal with NAs very well
| heaviest_penguin |
|---|
| 6300 |
group_by()
+summarize()
:
penguins %>%
dplyr::group_by(species) %>%
dplyr::summarize(heaviest_penguin = max(body_mass_g, na.rm = T))
| species | heaviest_penguin |
|---|---|
| Adelie | 4775 |
| Chinstrap | 4800 |
| Gentoo | 6300 |
Activity
Can you get the weight of the lightest penguin of each species? You can use min()
. What happens when in addition to species you also group by year group_by(species, year)
?
Answers
penguins %>%
dplyr::group_by(species) %>%
dplyr::summarize(lightest_penguin = min(body_mass_g, na.rm = T))
| species | lightest_penguin |
|---|---|
| Adelie | 2850 |
| Chinstrap | 2700 |
| Gentoo | 3950 |
penguins %>%
dplyr::group_by(species, year) %>%
dplyr::summarize(lightest_penguin = max(body_mass_g, na.rm = T))
## `summarise()` has grouped output by 'species'. You can override using the `.groups` argument.
3.6. arrange()
The arrange()
verb is pretty self-explanatory. arrange()
orders the rows of a data frame by the values of selected columns in ascending order. You can use the desc()
argument inside to arrange in descending order. The following chunk arranges the data frame based on the length of the penguins’ bill. You hint tab contains the code for the descending order alternative.
penguins %>%
dplyr::arrange(bill_length_mm)
penguins %>%
dplyr::arrange(desc(bill_length_mm))
Activity Can you create a data frame arranged by body_mass_g of the penguins observed in the “Dream” island?
Answer
penguins %>%
dplyr::filter(island == "Dream") %>%
dplyr::arrange(desc(body_mass_g))


--- layout: page title: About Me ---
We are a team of young hackers having loads of fun coding crazy ideas about serious (and not so serious) subjects.


Gelfand: Chunk 24 - Equations, Schmequations
April 25, 2017
After the “Well Tempered Clavier” interlude we’re about to have a little more ground prepared for us before we dive into the next core concept. That means this should be an easy chunk for you. Enjoy it. For roots are coming.
Some Definitions
This section in Gelfand (46) begins with some definitions. I found it useful to take these and re-state them in my own terms.It was useful to have to write it, and perhaps it will help you to have an alternative too.
An Identity says “this expression is equal to that expression”. If proved (by applying standard algebraic transformations) then it holds for all values of the variables. That is also the standard mechanism by which an Identity is refuted - by finding values for the variables where the expressions are not equal.
An Equation says “these two expressions are equal”. It must be solved (i.e. you must find all the values of the variables for which the left hand side is equal to the right hand side).
Note: Mathematicians don’t say an Equation is “unsolvable”, because they deem it to be solved if they prove it can have no solutions. To “solve” therefore means to find all, or prove that there are no, solutions.
 It is worth highlighting an element in his definition of “solutions of an equation”.
This brings in the term “root” once again. You’ll recall that we’ve seen it before almost whenever we talked about Polynomials, and more. To avoid confusion, this “root” is not the same as a “square” root. These are two entirely unrelated concepts. This is why I wrote my previous aside to help me get things super clear. I you skipped it, you might want to go back to it now. It’s short.I promise.
Quadratic Equations
There then follows a nice set of questions where you’re asked to solve some Quadratic Equations, following on from Gelfand’s lead in Problem 223. I’m going to step through them here too. It’ll help set a nice pace prior to the next chunk.Am I building it up enough? Some differ a little from the solution in Durham and so much the better. Please read his solutions too as it all helps.
Problem 224(a)
We start with
If we do a little fiddling
then we can see the Difference of Squares which we can factor easily
Which means that
or
Therefore the equation has two roots - and
Problem 224(b)
Now we begin with
Let’s take a similar first step to (a)
Hmmm, but this has a rather than a so we need to do some more shuffling before we have a Difference of Squares.
Note in the step above the flip from . to
Which means that
or
Why did we put in that step which takes us from to ?
Because it makes it easier to factor as it allows us to use a Short Multiplication Formula.
Problem 224(c)
This time we have
This looks like a Square of a Sum, let’s see
Yup, it is, which means we can factor
Which means that
Problem 224(d)
This time we are presented with
Applying the result from 224(c), we can state
And therefore
Which means that
or
Problem 224(e)
This one looks familiar
Which is easy enough to rearrange (by adding to each side)
Which means the answers are the same as 224(d), and .
Problem 224(e)
Is this one as simple?
Again we can rearrange
Though we’ve not got a pre-canned answer for this one so we need to do some leg-work
And therefore
Which means that
or
Problem 224(f)
And finally
We’re not able to keep applying our same pattern here so we need to look elsewhere. The “split similar terms” trick from Chunk 10 - Factoring seems like it might be handy.
Then we can change the signs of some of our elements so the final factoring is easy
And we can they collect it all together
Which means that
or
Problem 224(f)
Again we have to think for ourselves a little here, but just as in 224(e) we can use much the same trick
And then collecting it all together
Which means that
or
Tricks Arising
Keeping Positive (or Negative) when Working with Square Roots
You will have probably noted the trick in 224(b) which turned (positive) into (negative) . It’s achieved by multiplying it by . Why not simply ? Because we need to square it again alongside the to get back to where we started.
I’ve got a feeling that this little thread is the thing that Gelfand is now going to tug on and end up pulling us up into a whole new level of Algebra-appreciation.
Onward!


{% extends "base_2col.html" %} {% load soclone_tags humanize %} {% block bodyclass %}questions{% endblock %} {% block pageheader_content %}
{{ question.summary }} …
You're browsing through all
{% block question_view_description %}questions{% endblock %}
{{ current_view.description|safe }}
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Simply use an input element like this.
<input type="date" data-toggle="datepicker">
Try it out by clicking on the input field.


The Rock announces release date for his upcoming superhero film
Dwayne “The Rock” Johnson has announced the release date for his upcoming superhero film Black Adam.
The Hollywood star said the DC Comics film, a spin-off from the Shazam! movie, will arrive in December 2021.
Johnson shared the news with a post on Instagram, explaining how he had wanted to be a superhero growing up, specifically Superman.
However, the former professional wrestler said he was always attracted to more rebellious characters – perfect for the anti-hero Black Adam.
“Black Adam is blessed by magic with the powers equal to Superman, but the difference is he doesn’t toe the mark or walk the line,” Johnson said.
“He’s a rebellious, one of a kind superhero, who’ll always do what’s right for the people – but he does it his way.
“Truth and justice – the Black Adam way. This role is unlike any other I’ve ever played in my career and I’m grateful to the bone we’ll all go on this journey together.”
Black Adam was originally set to appear in 2019 superhero Shazam! which starred Zachary Levi as the titular superhero.
However, producers reportedly decided to give each character their own film before bringing them together.
Jaume Collet-Serra is set to direct Black Adam, which has a US release date of December 22 2021.


DBC's Learning Approach
06/11/14
Objectives: Consider DBC's culture and learning approach, and predict how you will react to this approach.
I would summarize Shereef's fireside chat as discussing a culture of experimentation and construction. He emphasizes tbe newness of the DBC program structure, which relates to a sense of adventure shared among both students and staff.
I think this approach should work for me. I always work better and learn more when I feel needed, so I like the emphasis that students contribute to the effectiveness of the program as a whole (through feedback to each other, suggestions to staff, etc.).
I also like that the culture is open to change - this seems to reflect an ability to take reasonable risks and a dedicated interest in providing the right support system to take risks. I think for much of my life, I've spent more time with people who have an adequate support system but remain unwilling to take risks.
Perhaps most importantly, the human component of DBC's culture appeals to me and was the main reason that pushed me to take this opportunity. Empathy is something that I consider often (how I can be more empathetic and how I can help others be more empathetic), so I'm looking forward to meeting and learning from people who share these values.
I am nervous to experience what the time commitment will feel like. I've always had many activities that I like to spend time on regularly, so I'll likely feel a little sad and burned out if I don't have time to do other things. Shereef's answer to the question addressing DBC's hours alleviated this nervousness somewhat, but it will be hard to judge until I get there.


Records the points in the BuildPaintState Automatically adds them to the LineRenderer. Points will only be added if they are distanced "far enough".
More...
|
| | PointRecorder ([Inject] Settings _settings, [Inject(Id="ARCamera")] Transform _cameraTransform, [Inject(Id="TrackParent")] LineRenderer _line) |
| |
| void | RecordCurrentPoint () |
| | Tries to add the current position to the points array. Checks if the new point is distanced enough. More...
|
| |
| void | DumpPoints (out Vector3[] _points) |
| | Dumps the recorded points into the array and clears the points of this recorder. More...
|
| |
| void | ClearPoints () |
| | Clears the array and the lineRenderer More...
|
| |
Records the points in the BuildPaintState Automatically adds them to the LineRenderer. Points will only be added if they are distanced "far enough".
◆ PointRecorder()
| PointRecorder.PointRecorder |
( |
[Inject] Settings |
_settings, |
|
|
[Inject ( Id = "ARCamera" )] Transform |
_cameraTransform, |
|
|
[Inject ( Id = "TrackParent" )] LineRenderer |
_line |
|
) |
| |
|
inline |
◆ ClearPoints()
| void PointRecorder.ClearPoints |
( |
| ) |
|
|
inline |
Clears the array and the lineRenderer
◆ DumpPoints()
| void PointRecorder.DumpPoints |
( |
out Vector3[] |
_points | ) |
|
|
inline |
Dumps the recorded points into the array and clears the points of this recorder.
◆ RecordCurrentPoint()
| void PointRecorder.RecordCurrentPoint |
( |
| ) |
|
|
inline |
Tries to add the current position to the points array. Checks if the new point is distanced enough.
◆ m_lastPosition
| Vector3 PointRecorder.m_lastPosition |
|
private |
◆ m_line
| LineRenderer PointRecorder.m_line |
|
protected |
◆ m_minSqrDistance
| float PointRecorder.m_minSqrDistance |
|
private |
◆ m_points
| List<Vector3> PointRecorder.m_points |
|
protected |
◆ m_settings
◆ m_transform
| Transform PointRecorder.m_transform |
|
protected |
◆ PointCount
| int PointRecorder.PointCount |
|
get |
The documentation for this class was generated from the following file:


Wanna pitch in to a political group in your area, but don't know where to start? You're in luck. We'll search Facebook and Google for all the organizations near you- from your neighborhood all the way up to the state level.


Please join us for the wedding of
Loving Cabin
Rush, Arkansas
And stay with us for the hootenanny after.
Rush is located off Arkansas State Highway 14 about 23 minutes or 16 miles south of Yellville.
Get directions.
We'll be dancing to a live band, but at some point they'll need a break. What music should we play? Pick a song. Or two. Or 10.
The RSVP deadline has passed. But if you need to change your RSVP contact Judy Loving at<EMAIL_ADDRESS>There's just one ghost town between the Mississippi River and the Rocky Mountains. And that's Rush. Fences protect the few remaining buildings. A gravel road takes visitors to the Buffalo River for fishing and canoeing. Campsites are sprinkled nearby.
But prior to World War I, Rush was a large zinc-mining town with a population of about 5,000. The town became a ghost town in 1972 when it was included in the creation of the Buffalo River National Park System, according to the Encyclopedia of Arkansas.
The Loving Cabin is along Clabber Creek; the next hollow over from Rush.
Please don't feel obligated to buy us gifts; we've got pretty much everything. However, we are planning a trip to Japan in 2017 and would love your help to get there. Our trip details are with HoneyFund.com.


|
(Note:
The Rangemasters Handgun "Fun" Shoot matches
ARE NOT UDPL
events.
The
Rangemasters Handgun "Fun"
Shoot
matches
Please be on time. No "new shooters" will be allowed after 6:45 p.m. No experienced shooters will be allowed after 7:30 p.m. Only Strong-Side Holsters are allowed. At least 3 Mags/Speedloaders are recommended. Bullets used must be jacketed, plated or frangible - no totally-lead ammo.
Map to
Range Masters |
|||||
|
2015 Schedule & Results |
|||||
| 1st Wednesday | 3rd Tuesday | 1st Wednesday | 3rd Tuesday | ||
| January 7 | January 20 | February 4 | February 17 | ||
| March 4 | March 17 | April 1 | April 21 | ||
| May 6 | May 19 | June 3 | June 16 | ||
| July 1 | July 21 | August 5 | August 18 | ||
| September 2 | September 15 | October 7 | October 20 | ||
| November 4 | November 17 | December 2 | December 15 | ||
|
2014 Schedule & Results |
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| 1st Wednesday | 3rd Tuesday | 1st Wednesday | 3rd Tuesday | ||
| January 8 | January 21 | February 5 | February 18 | ||
| March 5 | March 18 | April 2 | April 15 | ||
| May 7 | May 20 | June 4 | June 17 | ||
| July 2 | July 15 | August 6 | August 19 | ||
| September 3 | September 16 | October 1 | October 21 | ||
| November 5 | November 18 | December 3 | December 16 | ||
[ HOME | CONTACT UDPL ]


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{% extends 'layouts/default.html' %} {% block css %} {% endblock %} {% block js %} {% endblock %} {% block content %} {% if !count %}
Looks like you haven't got any exceptions in here!
Either you're a great programmer or you haven't set up Zappem yet.
If you need to hook up Zappem to your project, see the docs
Your project ID is {{project._id}}
| 24h Trend | Count | Users | Message | Latest |
|---|
Are you sure you want to mark this exception as Resolved?


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11:32am
Was Joanne able to get her medication?
10:55am
Lets meet at 3pm in the dining hall.
5:43pm
What is the theme for bingo next week?
4:35pm
Thanks! See you Monday.


@import uk.gov.hmrc.govukfrontend.views.html.components._
@this(govukCharacterCount : GovukCharacterCount)
@()
@govukCharacterCount(CharacterCount(
id = "with-hint",
name = "with-hint",
maxLength = Some(200),
label = Label(
isPageHeading = true,
classes = "govuk-label--l",
content = Text("Can you provide more detail?")
),
hint = Some(Hint(
content = Text("Do not include personal or financial information like your National Insurance number or credit card details.")
))
))


Operation Enduring Freedom - Afghanistan
At approximately 0845 hours Eastern Daylight Time on Tuesday, 11 September 2001, a commercial airplane crashed into the north tower of the World Trade Center complex in New York City. At that time, the severity of the incident, the numbers of people involved, and the reason for the crash were all unknown. Shortly after 0900 hours, a second plane hit the south tower of the World Trade Center.
Around 1000 hours, reports came in that another commercial plane hit the Pentagon, in Washington, DC, and a fourth commercial plane was downed in Somerset County, PA, about 80 miles southeast of Pittsburgh. Shortly after 1000 hours the south tower of the World Center collapsed. Within the next half-hour, the northern tower of the World Trade Center also collapsed. At approximately 1730 hours a third tower in the World Trade Center complex, Building #7, also collapsed. Wednesday evening, September 12, another building within the World Trade Center Complex collapsed.
After the September 11, 2001, attacks on the World Trade Center and the Pentagon, the United States military entered into a war against global terrorism. The President began the U.S. response in the War on Terrorism with the stroke of his pen to seize terrorists' financial assets and disrupt their fundraising network. Unlike most previous conflicts, this war is being fought on both domestic and foreign soil. Deployment of American troops to Southwest Asia and countries surrounding Afghanistan in the days following the attacks.
The military response to the 11 September 2001 terrorist attacks on the United States was assigned the name Operation Enduring Freedom, but was previously planned to have been called Operation Infinite Justice (this name is believed to have been changed following concerns that this might offend the Muslim community as Islam teaches that Allah is the only one who can provide Infinite Justice).
OEF commenced on Oct. 7, 2001. Early combat operations included a mix of air strikes from land-based B-1, B-2 and B-52 bombers; carrier-based F-14 and F/A-18 fighters; and Tomahawk cruise missiles launched from both U.S. and British ships and submarines.
The initial military objectives of Operation Enduring Freedom, as articulated by President George W. Bush in his Sept. 20th Address to a Joint Session of Congress and his Oct. 7th address to country, include the destruction of terrorist training camps and infrastructure within Afghanistan, the capture of al Qaeda leaders, and the cessation of terrorist activities in Afghanistan.
Secretary of Defense Donald Rumsfeld stated in an Oct. 7th DoD News Briefing that US objectives were to make clear to Taliban leaders that the harboring of terrorists is unacceptable, to acquire intelligence on al Qaeda and Taliban resources, to develop relations with groups opposed to the Taliban, to prevent the use of Afghanistan as a safe haven for terrorists, and to destroy the Taliban military allowing opposition forces to succeed in their struggle. Finally, military force would help facilitate the delivering of humanitarian supplies to the Afghan people.
The British had also defined the goals of their involvement (termed Operation Veritas) in "Her Majesty's Government's Campaign Objectives," dated Oct. 16th. The short term goals of the military action included the capture of Osama bin Laden and other al Qaeda leaders, the prevention of further attacks by al Qaeda, the end of Afghanistan's harboring of terrorists, their training camps and infrastructure, and the removal of Mullah Omar and the Taliban Regime. Long term goals include the end of terrorism, the deterrence of state sponsorship of terrorism, and the reintigration of Afghanistan into the international community.
Planning and Implementing
On 11 September 2001, General Tommy Franks was enroute to Pakistan to meet with President Musharraf to discuss a number of issues, among them security cooperation and terrorism. The events of that day caused him to return immediately to Tampa, Florida, where his staff was already working along with Defense and other government agencies to ensure what is referred to in the military as "command and control survivability" while continuing to develop "situational awareness."
On 12 September the Secretary of Defense directed the preparation of "credible military options" to respond to international terrorism. For Central Command, that directive guided the preparation of the plan that unfolded in Afghanistan. The concept, which Franks briefed to the President on 21 September, proposed that "US Central Command, as a part of America's Global War on Terrorism . . . would destroy the Al Qaida network inside Afghanistan along with the illegitimate Taliban regime which was harboring and protecting the terrorists."
Planning involved not only an evaluation of the enemy situation, but also the history of military operations in Afghanistan and the political and military situation across the region. This "mission analysis" resulted in Franks' recommendation of a military course of action which was approved by Secretary Rumsfeld on 1 October. Franks briefed the concept to President Bush on 2 October, and Bush directed that combat operations should begin on 7 October -- 26 days after the attacks on New York and the Pentagon.
Operations would involve the full weight of America's national power, and would include significant contributions from the international community. By 2002 the coalition had grown to more than 68 nations, with 27 nations having representatives at CENTCOM headquarters.
With the cooperation and support of this coalition and the integration of virtually every agency of the US government, CENTCOM had executed multiple "Lines of Operation," attacking simultaneously on several fronts. The intention from the outset was to seize the initiative and reinforce success, while keeping in mind the lessons of previous campaigns in Afghanistan -- avoid "invading," and work with (rather than against) the people. A critical enabler of the strategy was the coordination of basing, staging, and over-flight rights. This political-military coordination set (and maintained) the conditions necessary to execute and support sustained combat.
Among the lines of operation which characterized the campaign had been "Direct Attack of the Leadership of Al Qaida and the Taliban," and the provision of "Humanitarian Aid" to the Afghan people. Another line had focused on "Destroying the Taliban Military," using unconventional warfare forces alongside Afghan opposition groups whose goals were consistent with US interests. And, "Operational Fires" directed by horse-mounted Special Forces troopers have also proven to be unique and successful.
Additionally, CENTCOM had employed Special Operations Forces in "Reconnaissance and Direct Action" roles while maintaining the capability to introduce "Operational Maneuver" (conventional forces) if required.
The success of these lines of operation, which have been applied simultaneously rather than sequentially, is a matter of record. On 7 October, the Taliban controlled more than 80% of Afghanistan, and Anti-Taliban forces were on the defensive. Al Qaida was entrenched in camps and safe houses throughout the country. Afghanistan was, in fact, a terrorist sponsored state.
By October 20, 2001 US and Coalition forces had destroyed virtually all Taliban air defenses and had conducted a highly successful direct action mission on the residence of Mullah Omar in the middle of the Taliban capital, Qandahar. During this time frame Special Forces detachments linked up with Anti-Taliban leaders and coordinated operational fires and logistics support on multiple fronts. Twenty days later, the provincial capital of Mazar-e Sharif fell. In rapid succession, Herat, Kabul, and Jalalabad followed. By mid- December, US Marines had secured Qandahar Airport and the Taliban capital was in the hands of Anti-Taliban forces. Within weeks the Taliban and Al Qaida were reduced to isolated pockets of fighters. On 22 December Franks traveled to Kabul to attend a ceremony marking the inauguration of the Afghan interim government -- 78 days after the beginning of combat operations.
By mid-March 2002, the Taliban had been removed from power and the Al Qaida network in Afghanistan had been destroyed. The US continued to exploit detainees and sensitive sites for their intelligence value in order to prevent future terrorist attacks and to further US understanding of Al Qaida -- their plans, membership, structure, and intentions. The US was investigating each site to confirm or deny the existence of research into, or production of, chemical, biological, or radiological weapons. Coalition forces continued to locate and destroy remaining pockets of Taliban and Al Qaida fighters and to search for surviving leadership.
In the 169 days since 11 September, US and Coalition forces had amassed a remarkable record of achievements. Following are but a few examples.
- All positioning and most of the resupply of forces in the theater has been accomplished by air as a result of a remarkable effort by US Transportation Command.
- In addition to providing the firepower and "staying power" of two carrier battlegroups, the Navy steamed USS KITTY HAWK 7,000 miles at flank speed to establish an afloat, forward operating base for Special Operations Forces.
- In terms of operational fires, Navy, Marine, and Air Force pilots have delivered in excess of 18,000 munitions, of which more than 10,000 were precision guided.
- During DESERT STORM the US averaged 10 aircraft per target; in ENDURING FREEDOM the US has averaged 2 targets per aircraft.
- US airmen have flown the longest combat fighter mission in US history (more than 15 hours), and conducted the longest surveillance mission (26 hours).
- The extensive use of unmanned aerial vehicles has permitted around- the-clock surveillance of critical sites, facilities, and troop concentrations.
- US psychological warfare operators had delivered more than 50 million leaflets, and transport crews had delivered 2.5 million humanitarian daily rations, 1,700 tons of wheat, and 328,200 blankets. More than 5,000 radios had been provided to the Afghan people, and the US broadcast capabilities continued to bring music to people for the first time in more than six years.
- The US had also made enormous improvements in its ability to bring firepower to bear rapidly. Through improved technology and training, the Tomahawk targeting cycle had been reduced from 101 minutes during ALLIED FORCE to 19 minutes during ENDURING FREEDOM, with half of the Tomahawks having been fired from submarines.
Lessons Learned
By mid-March 2002, CENTCOM was in the preliminary stages of capturing the lessons of this campaign. It is too early to draw final conclusions because the fight continues, but CENTCOM did have some emerging insights.
- Combining the resources and capabilities of the Defense Department, Central Intelligence Agency, and other agencies of the Federal government has produced results no single entity could have achieved. Similarly, adopting flexible coalition arrangements has enabled the US to leverage the strengths of individual nations. "The mission has determined the coalition; the coalition has not determined the mission."
- This operation continues to be commanded and controlled from Tampa, Florida with fielded technology providing real-time connectivity to air, ground, naval, and Special Operations Forces operating 7000 miles away. CENTCOM forces were deployed from 267 bases; are operating from 30 locations in 15 nations; and currently over-fly 46 nations in the course of operations. Yet, the ability to "see" the battlefield literally and figuratively at each location provides unprecedented situational awareness.
- Security cooperation, diplomacy, and military-to-military contacts have built relationships that have proven invaluable during the campaign. Humanitarian airdrops; economic and security assistance to coalition partners and regional allies; visits to the region by senior Administration, Congressional, and military officials; and a US commitment to post-conflict reconstruction of Afghanistan have permitted the US to build upon these essential relationships. The investment in security cooperation has been repaid tenfold in access to basing, staging, and over-flight rights with regional partners.
- Precision guided munitions are more than a force multiplier. They have reduced the numbers of air sorties required to destroy targets and have resulted in unprecedented low levels of collateral damage. From this perspective, use of precision guided munitions has produced a positive strategic effect.
- As has been said in the past, the availability of strategic airlift is critical to the success of operations that require force projection. The current airlift fleet requires strict management and innovative scheduling and the experience in ENDURING FREEDOM would seem to validate the testimony the committee received last year -- that the US must expand its strategic lift capabilities.
- The importance of combined and joint operations training and readiness has been revalidated. The power of a well-trained air- ground team has permitted the combination of 19th Century Cavalry and 21st Century precision guided munitions into an effective fighting force.
- A continuous, unimpeded flow of intelligence remains key to success on the battlefield. Human intelligence is essential when mission objectives include locating, identifying, and capturing or killing mobile targets. This requires people on the ground. Similarly, unmanned aerial vehicles have proven their worth in the skies over Afghanistan.
- Information Operations also have been vital to the success of ENDURING FREEDOM. Psychological operations, electronic warfare, and a number of special capabilities have proven their value and potential. Continued development of these capabilities is essential.


Formal Languages and Automata Theory
Professor: Denise de Oliveira
Autors: Mairo Pedrini
Marcelo Claro Zembrzuski
Paulo Sérgio Morandi Júnior
Automata® Minimum is a program that minimize a given automaton (drawed or by file). Examples of valid files can be download here. If the automaton is deterministic then the program try to minimize it, else a message is given describe the problem. Example, try to the file ndfa.aut in the program (It's a nondeterministic automaton).
If you want to draw, just press CTRL+N or click in File and New. Or you can open a file pressing CTRL+O or clicking in File and Open. To minimize it, press CTRL+M or click in Edit Automaton and Minimize. A new windown having the minimized automaton is showed. Click and move the states to rearrenge the automaton. To create a new state, click in the state symbol at toolbar and click in the draw area. To define a state as initial, click in the initial icon in toolbar and click in the state that you want to be the initial. Idem to create a final state.


Chief executives these days are facing more challenges than ever. The world’s operation system is being constantly rewritten. And it does not come out with gradual release. As the chief executive of a global information services company once confided in us: “as the business landscape changes so quickly, executives have to make correct business decisions within a much shorter timeframe. This is all happening when there is little visibility of what’s ahead of us, and everything is so uncertain. One way to deal with this is to gain intelligence and insights into what the future holds.” We could not agree more…
Chief executives these days are facing more challenges than ever. The world’s operation system is being constantly rewritten. And it does not come out with gradual release...
Chief executives these days are facing more challenges than ever. The world’s operation system is being constantly rewritten. And it does not come out with gradual release. As the chief executive of a global information services company once confided in us: “as the business landscape changes so quickly, executives have to make correct business decisions within a much shorter timeframe. This is all happening when there is little visibility of what’s ahead of us, and everything is so uncertain. One way to deal with this is to gain intelligence and insights into what the future holds.” We could not agree more…
As the chief executive of a global information services company once confided in us: “as the business landscape changes so quickly, executives have to make correct business decisions within a much shorter timeframe. This is all happening when there is little visibility of what’s ahead of us, and everything is so uncertain. One way to deal with this is to gain intelligence and insights into what the future holds.” We could not agree more…
While it may appear that our world is changing randomly, and unforeseeable
events are emerging more often, the truth is that certain large-scale
processes determine how businesses, governments and societies behave, and how decisions are made.
In the vast oceans of knowledge, information and data,
we find that there are five undercurrents that can give us some
direction as to how the future will unfold.
Mark is a socioeconomic strategist who has consulted in the areas of corporate sustainability, economic complexity, circular economy and competitiveness worldwide, including advising to, among others, the United Nations Global Compact, national banks, governments, the European Parliament, NATO-wide Executive Development Programmes, and municipalities in Massachusetts. He holds fellowships with the Social Progress Imperative and the Global Federation of Competitiveness Councils in Washington DC, and sits on the board of directors of the Academy of Business in Society, and on the boards of advisors of Competitiveness Councils in India, the Netherlands and Italy.
Mark was appointed to Harvard University’s Division of Continuing Education in 2011, where he teaches courses in Economic Strategy and Competitiveness and also serves as Institutes Council Co-Leader for the Microeconomics of Competitiveness programme at the Institute of Strategy and Competitiveness, Harvard Business School, led by Professor Michael Porter. Alongside his role at Harvard, Mark is a senior professor at Grenoble Graduate School of Business in France and a fellow at the Judge Business School, University of Cambridge. He obtained his PhD from the International School of Management in Paris and New York, and is expected to complete his second doctoral degree at École des Ponts ParisTech. He was inducted into Thinkers50 Radar in 2016.
Alongside his ongoing advisory activities with supranational organisations, Terence regularly provides commentaries on the latest current affairs and market developments for the Financial Times, the Guardian, the Economist, CNBC, the World Economic Forum and the Harvard Business Review blogs. He has appeared on radio and television shows on China Central Television (CCTV), Channel 2 in Greece, France 24 and NHK. Terence has also been invited to speak at the UN, the IMF and the International Trade Centre, as well as events in India, Norway and the UK.
Terence is an associate professor of finance at the London campus of ESCP Europe Business School, the oldest business school in the world. He is also head of Competitiveness Studies at i7 Institute for Innovation and Competitiveness, an academic think tank based in Paris and London. He is also a fellow at the Judge Business School, University of Cambridge.
Before joining academia, Terence worked in mergers and acquisitions at Schroders, Citibank and Lazard in Montréal and New York. He also worked in London as a consultant at Ernst & Young focusing on UK financial services. He obtained his doctoral degree from the Judge Business School, University of Cambridge.
Deleted
Get in touch with your questions and feedback, and to learn how we can get involved to help your company prepare for the future


Lua/APR is a binding of the Apache Portable Runtime (APR) for the Lua programming language. APR powers software such as the Apache webserver and Subversion and Lua/APR makes the APR operating system interfaces available to Lua, serving as an extended standard library. Thus far the following modules have been implemented (the following links take you straight to the relevant documentation):
You can find the source code of the most recently released version under downloads. The source code is also available online in the GitHub repository. There are Windows binaries available that have been tested with Lua for Windows v5.1.4-40 and there is an offical Debian Linux package. You can also build the Lua/APR binding yourself using the instructions below.
The Lua/APR binding has an official Debian package which is available in wheezy (testing), sid (unstable) and Ubuntu Oneiric Ocelot (11.10). If you are running any of those (or a newer release) the following commands should get you started:
$ apt-get install liblua5.1-apr1
$ lua -e "require 'apr.test' ()"
If you are running an older Debian release or a derivative of Debian which doesn’t have the official package yet, you may be able to download and install one of the Debian packages manually from one of the three pages linked above.
The easiest way to download, build and install the Lua/APR binding is to use LuaRocks. The following commands will install the Lua/APR binding from the main repository and run the test suite to make sure everything works:
$ luarocks install lua-apr
$ lua -e "require 'apr.test' ()"
If LuaRocks fails to build the Lua/APR binding this is likely because of missing dependencies. Lua/APR depends on the APR, APR-util and libapreq2 system libraries but LuaRocks cannot install these because it only deals with Lua modules. When the build fails the makefile runs a Lua script that knows how to detect missing dependencies on Debian, Ubuntu, Arch Linux, Red Hat, Fedora Core, Suse Linux and CentOS. As a general guideline for other systems and package managers, you'll need the binary and development packages of Lua 5.1, APR, APR-util and libapreq2.
The latest rockspec and sources are also available from the author’s website (in case the main LuaRocks repository is unavailable or lagging behind):
$ luarocks install http://peterodding.com/code/lua/apr/downloads/lua-apr-0.23.1-1.rockspec
If you have git installed you can also download and install the latest sources directly from GitHub:
$ luarocks install http://peterodding.com/code/lua/apr/downloads/lua-apr-scm-1.rockspec
If you don’t have LuaRocks installed the following shell commands should help you get started on UNIX:
$ if which git; then # Get the latest sources using `git'?
$ git clone git://github.com/xolox/lua-apr.git
$ else # Or get the most recently released archive using `wget'.
$ wget http://peterodding.com/code/lua/apr/downloads/lua-apr-0.23.1-1.zip
$ unzip lua-apr-0.23.1-1.zip
$ mv lua-apr-0.23.1-1 lua-apr
$ fi
$ cd lua-apr
$ sudo make install # installs apr.lua and apr/core.so in /usr/local
$ lua -e "require 'apr.test' ()" # runs the test suite
The makefile creates a debug build by default. Use sudo make install DO_RELEASE=1
to create a release build instead. Just be sure to run make clean
when switching between debug/release mode to avoid linking incompatible object files.
A makefile for Microsoft Windows is included in the repository. It has been tested on Windows XP with NMAKE
from the free Microsoft Visual C++ Express 2010 tool chain. If you don’t have git installed you can download the latest sources directly from GitHub as a ZIP file.
At the top of the makefile several file locations are defined, you'll need to change these to match your system. The makefile creates a debug build by default. Use NMAKE /f Makefile.win DO_RELEASE=1
to create a release build instead. Just be sure to run NMAKE /f Makefile.win clean
when switching between debug/release mode to avoid linking incompatible object files.
Please note that the Windows makefile only builds the Lua/APR binding, you need to build the APR, APR-util and APREQ libraries yourself. There are instructions available on how to build APR/APR-util on apache.org but my notes on the process may be a more useful starting point.
I've also recently created a Windows batch script that bootstraps a Lua/APR development environment by downloading, unpacking, patching and building the libraries involved. To use it, download the ZIP archive, unpack it to a directory, open a Windows SDK command prompt in the directory where you unpacked the ZIP archive and execute make.cmd
. I've only tested it on a 32 bit Windows XP virtual machine, but even if it doesn’t work out of the box for you it may provide a useful starting point.
The following functionality has not been implemented yet but is on the to-do list:
If you have questions, bug reports, suggestions, etc. the author can be contacted at<EMAIL_ADDRESS>The latest version is available at http://peterodding.com/code/lua/apr/ and http://github.com/xolox/lua-apr.
This software is licensed under the MIT license.
© 2011 Peter Odding<EMAIL_ADDRESS>and zhiguo zhao (zhaozg@gmail.com).
The LDAP connection handling module is based on LuaLDAP, designed and implemented by Roberto Ierusalimschy, André Carregal and Tomás Guisasola.
© 2003-2007 The Kepler Project.


Adding Devise (with or without existing User class)
- add "devise" gem
- rails g devise:install
- Follow instructions on screen
- Delete all users (if exists)
- rake db:migrate
- rake db:setup
- add
before_action :authenticate_user!
before_action :authenticate_user!


French cycling's eternal runner-up Poulidor dies
Bordeaux - French cyclist Raymond Poulidor, who gained huge affection as an eternal runner-up in the Tour de France, has died at the age of 83, his wife told AFP on Wednesday.
Poulidor had been hospitalised since early October and "he left us this morning," his wife Gisele told AFP from their home in western France.
His astonishing career spanned 25 years but he will always be remembered for the races he failed to win.
From 1964 to 1976 Poulidor finished second in the Tour de France on three occasions and was third five times in an era dominated by Eddy Merckx.
So famous was his repeated failure to clinch the Tour that the phrase "to do a Poulidor" passed into the French language, synonymous with coming an unlucky second.
Despite his Tour de France disappointments, Poulidor is forever ranked among France's cycling greats and at the same time is seen as a humble hard worker loved by the people who earned every one of his many triumphs.
His long-time rival Merckx told AFP "a great friend has left us".
"I am very sad. During my career we were rivals but afterwards we often spent time together. We holidayed together. It's a big loss."
Among current riders, Romain Bardet, second in the Tour de France in 2016 and third the following year, said: "He was an emblematic character, adored by the public."
"He was the link between cycling and its origins among the people. I remember seeing him on the Tour de France but also at the local village races with the organisers. He represented the real face of cycling, a popular and accessible sport."
Born on April 15, 1936 in a small village in western France, just a few miles from where he died, Poulidor clinched 189 wins during his career from 1960-1977. High points included wins in the Tour of Spain, the Dauphine Libere twice, and the Paris-Nice twice.
For the remaining four decades of his life he retained his links with cycling through public relations and as a consultant.
He remained an avid fan and in subsequent years he was frequently seen surrounded by admirers and signing autographs at the start of every Tour de France stage.
Doing promotional work at the 2019 Tour took a lot out of him, according to wife Gisele.
"He was very tired after the last Tour de France," she told AFP.


MKBarButtonItem Class Reference
| Inherits from | MKControl : UIControl |
| Declared in | MKBarButtonItem.h |
Overview
MKBarButtonItem creates buttons for use on a tab bar. The button that will be displayed depends on the MKBarButtonItemType that is set. Currently There are two types:
-
MKBarButtonItemBackArrow
a triangle pointing to the left. -
MKBarButtonItemForwardArrow
a triangle pointing to the right.
Tasks
Creating
-
- initWithType:
Returns an intialized instance of MKBarButtonItem and sets the MKBarButtonItemType. -
type
The type of button that is assigned
Instance Methods
initWithType:
Returns an intialized instance of MKBarButtonItem and sets the MKBarButtonItemType.
- (id)initWithType:(MKBarButtonItemType)type
Parameters
- type
- the type of button to be displayed
Discussion
Returns an intialized instance of MKBarButtonItem and sets the MKBarButtonItemType.
Currently There are two types:
-
MKBarButtonItemBackArrow
a triangle pointing to the left. -
MKBarButtonItemForwardArrow
a triangle pointing to the right.
Declared In
MKBarButtonItem.h
Properties
type
The type of button that is assigned
@property (nonatomic, assign) MKBarButtonItemType type
Declared In
MKBarButtonItem.h