PHILADELPHIA A Film Review Copyright Dragan Antulov 1999
There are many movies that are remembered for the wrong reasons, and PHILADELPHIA, 1993 courtroom drama directed by Jonathan Demme, is one of them. Instead being remembered by its own merit, it is only a footnote in a tale about moving 1994 Oscar acceptance speech by Tom Hanks - occasion that inspired a movie by itself (IN & OUT by Frank Oz). And it is really sad, since PHILADELPHIA happened to be the very first big budget Hollywood movie dealing with the AIDS phenomenon. It was also an opportunity for Demme to defend himself from homophobia charges, inspired by the fact that some gay groups didn't like the sexual orientation of the villain in his Oscar-winning masterpiece SILENCE OF THE LAMBS.
This courtroom drama has a plot which is based on the real life event (the fact which the filmmakers at first denied, which, ironically, caused a lawsuit later on). Andrew Beckett (played by Tom Hanks) is a young and brilliant attorney who had just received a promotion in a prestigious Philadelphia law firm, led by his bigoted mentor Charles Wheeler (played by Jason Robards). In the same day, Beckett, who was hiding his homosexuality from co-workers and supervisors, finds out that he had contracted AIDS. Soon, important legal brief vanishes from his possession and he is fired from the law firm because of alleged incompetence. Beckett knows that the real reason is homophobia, so he is ready to start discrimination lawsuit. Unfortunately, few lawyers are ready to take his case. The only exception is Joe Miller (played by Denzel Washington), black ambulance chaser who must overcome his own homophobic prejudices before he begins long legal battle.
Made during the zenith of Political Correctness, in the first year of Clinton presidency, PHILADELPHIA, like many similar "socially conscious" movies, walks on the very fine line between art and propaganda. The screenplay by Ron Nyswaner almost pushes movie across that line, with sometimes annoyingly clear contrast between Good Guys (young, beautiful, talented and heroic gay character plus his slowly evolving attorney) and Bad Guys (old, ugly and bigoted establishment). The attempts to paint some shades of gray (like the scene with the young black gay man and Miller) look somewhat artificial and sometimes it provide unnecessary plot holes. However, this movie is saved by great directorial skill by Demme and great acting performances. Demme, with his superb techniques, small gestures and great use of close-ups allows us to see the plot and characters through banal but all-revealing scenes, like the accidental encounter between Beckett and Miller in the library. The actors, on the other hand, are stealing the show - Tom Hanks won his first "Oscar", and his portrayal of AIDS-stricken man with the passion for life and justice is touching and magnificent in the same time (although some would see his opera singing scene as an emotional overkill). Hanks should also be praised for the great physical effort, because he went on a diet and lost 12 kilograms in order to physically resemble terminally ill AIDS patient as much as possible. His partner Denzel Washington is great too, although his role didn't require such demanding physical requirements. Instead, his character transformation from the bigot into the compassionate human being is portrayed with subtle but very effective actions (like, for example, different levels of physical distance between Beckett and Miller). Other actors are, on the other hand, underused - Jason Robards' character is caricature, Mary Steenburgen as his attorney is capable, and Antonio Banderas is totally wasted as Beckett's lover.
 For the movie that was supposed to be the groundbreaking Hollywood project about AIDS, bigotry and alternative lifestyles, this film is too mainstream and constrained by the content regulations - both the old (which bar kissing between males in mainstream films) and new (which prohibits portrayal of gay characters as nothing less of saintly). Being exposed to the multitude of independent, cable or even TV-movies that, one way or the other, broke such rules, we must conclude that PHILADELPHIA didn't pass the test of time, necessary to achieve the true greatness. But, although overrated, PHILADELPHIA is still a good film.
RATING: 6/10 (++)
Review written on August 31st 1999
Dragan Antulov a.k.a. Drax Fido: 2:381/100 E-mail<EMAIL_ADDRESS>E-mail<EMAIL_ADDRESS>E-mail<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
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ModelBasedAgent
public class ModelBasedAgent : AnyAgent
A model-based (reflex) agent or at least an agent with some memory.
function MODEL-BASED-REFLEX-AGENT(percept) returns an action
persistent: state, the agent's current conception of the world state
model, a description of how the next state depends on current state and action
rules, a set of condition-action rules
action, the most recent action, initially none
state <- UPDATE-STATE(state, action, percept, model)
rule <- RULE-MATCH(state, rules)
action <- rule.ACTION
return action
Figure 2.12, AIMA3e, page 51.
Adding state to an agent or its program raises a couple of problems with the current design:
Requiring agents to initialize their superclass with their program is bad design; a simple requirement that agents include an
execute()
method is sufficient. This choice was influenced by a comment on aima-python’s Agent suggesting these agent programs mightcheat
. C'mon, really? That’s like not letting your hand touch any part of your body. From now on, the unit of security is the Agent and our Agents (and Judges and Environments) cannot see each other and do not know how the other works.The other issue was how the first issue introduces memory leaks, which was fixed here with Swift subtleties (also distracts from the subject), and which will be not-a-problem in next design.
-
This model-based agent program.
In order to reference
self
in this closure, we uselazy
to tell Swift we won’t access it until after initialization. To avoid resulting strong reference cycle, we add capture list[unowned self]
as described in book’s ARC chapter.Swift requires prefix
self.
for instance properties referenced in closures toremind
us of potential cycles.Declaration
Swift
public override func execute(_ percept: AgentPercept) -> AgentAction


winklR.Rd
winklR
is a function to calculate the in the trajectory of the first
and the second derivatives maxima and minima of an amplification curve data
from a quantitative PCR experiment. For the determination of the angle
(central angle), the origin is the maximum of the first derivative. On this
basis, the vectors to the minimum and maximum of the second figure are
determined. The vectors result from the relation of the maximum of the
first derivative to the minimum of the second derivative and from the
maximum of the first derivative to the maximum of the second derivative.
In a simple trigonometric approach, the scalar product of the two vectors
is formed first. Then the absolute values are calculated and multiplied by
each other. Finally, the value is converted into an angle with the cosine. The
assumption is that flat (negative amplification curves) have a large angle
and sigmoid (positive amplification curves) have a smaller angle. Another
assumption is that this angle is independent of the rotation of the
amplification curve. This means that systematic off-sets, such as those
caused by incorrect background correction, are of no consequence.
The cycles to be analyzed is defined by the user.
The output contains the angle.
winklR(x, y, normalize = FALSE, preprocess = TRUE)
| x | is the cycle numbers (x-axis). By default the first ten cycles are removed. |
|---|---|
| y | is the cycle dependent fluorescence amplitude (y-axis). |
| normalize | is a logical parameter, which indicates if the amplification curve data should be normalized to the 99 percent percentile of the amplification curve. |
| preprocess | is a logical parameter, which indicates if the amplification curve data should be smoothed (Savitzky-Golay smoothing filter, useful for noisy, jagged data). |
# Calculate the angles for amplification curve data from the RAS002 data set data(RAS002) # Plot the data plot(RAS002[, 1], y = RAS002[, 2], xlab = "Cycle", ylab = "RFU", main = "RAS002 data set", lty = 1, type = "l" )res <- winklR(x = RAS002[, 1], y = RAS002[, 2]) res#> $angle #> [1] 0.9993859 #> #> $origin #> x y #> origin 28.37007 -139.4272 #> #> $p1 #> x y #> p1 26.13105 -40.00269 #> #> $p2 #> x y #> p2 30.52 32.12933 #>plot(RAS002[, 1], y = RAS002[, 7], xlab = "Cycle", ylab = "RFU", main = "RAS002 data set", lty = 1, type = "l" )res <- winklR(x = RAS002[, 1], y = RAS002[, 7]) res#> $angle #> [1] 0.9999176 #> #> $origin #> x y #> origin 35.28768 -19.39072 #> #> $p1 #> x y #> p1 28.32055 -4.216406 #> #> $p2 #> x y #> p2 24.9437 2.39646 #>


|
extras
C++ Functions and Classes that make writing mex functions easier
|
Asynchronous File Reader. More...
#include <mxfile_reader.hpp>
Public Member Functions | |
| virtual | ~AsyncMxFileReader () |
| destructor | |
| size_t | getCompressedSize () const |
| get compressed size of file | |
| size_t | getPositionInFile () const |
| get current (compressed) position in file | |
| double | loadProgress () const |
| fraction of load progress (via ratio remaining compressed data) | |
| std::string | filepath () const |
| filepath used by reader | |
| bool | isEOF () const |
| bool | isFileOpen () const |
| bool | threadRunning () const |
| check if thread is still running | |
| void | openFile (std::string fpth) |
| open specified file for reading clears any loaded arrays waiting in the Results buffer | |
| void | closeFile () |
| open specified file for reading | |
| void | loadData () |
| start or continue loading data | |
| void | resume () |
| start or continue loading data | |
| void | pause () |
| pause data loading | |
| void | cancel () |
| stop loading and reset position back to begining also frees any data remaining in the result array | |
| size_t | numberOfArraysLoaded () const |
| returns number of arrays loaded and awaiting copy to MATLAB | |
| cmex::MxObject | getLoadedData () const |
| gets loaded data but does not clear the mxArrayGroup holding results if a single array is loaded it is returned otherwise a cell containing all the loaded arrays are returned; | |
| cmex::MxObject | getLoadedDataAndClear () |
| gets loaded data and clears the mxArrayGroup if a single array is loaded it is returned otherwise a cell containing all the loaded arrays are returned; | |
| virtual bool | wasErrorThrown () const |
| true if there was an error | |
| virtual void | clearError () |
| Clears the error pointer and error flag. More... | |
| virtual std::exception_ptr | getError () const |
| check for errors thrown within the processing loop returns exception_ptr to the thrown exception if there aren't any exceptions, returns nullptr | |
Protected Member Functions | |
| void | LoadDataLoop () |
| virtual void | StopProcessor () |
| immediately halts LoadDataLoop | |
| virtual void | StartProcessor () |
| (re)launch thread | |
| void | clearLoadedData () |
| clear loaded data | |
Protected Attributes | |
| std::mutex | _ResultsGroupMutex |
| cmex::mxArrayGroup | _ResultsGroup |
| MxFileReader | _Reader |
| std::thread | mThread |
| std::atomic_bool | ProcessRunning = false |
| std::atomic_bool | _atEOF = false |
| std::atomic_bool | ErrorFlag |
| std::mutex | LastErrorMutex |
| std::exception_ptr | LastError = nullptr |
Asynchronous File Reader.
|
inlinevirtual |
Clears the error pointer and error flag.
After calling ErroFlag=false and LastError=nullptr


The military officials said Monday the Special Fores killed three Taliban militants and detained two others during a raid in Pashtun Kot district of Faryab province.
The officials further added the Special Forces killed two ISIS Khurasan militants and destroyed a small cache of weapons of the group in Kot district of Nangarhar.
The Special Forces destroyed another cache of weapons of ISIS Khurasan in Achin district of Nangarhar, the officials said, adding that the Special Forces destroyed a large drug cache of Taliban and a weapons cache in Deh Yak district of Ghazni and Khakrez district of Kandahar.
The anti-government armed elements have not commented regarding the operations so far.
Author
-
The Khaama Press News Agency is the leading and largest English news service for Afghanistan with over 3 million hits a month. Independent authors/columnists and experts are welcomed to contribute stories, opinions and editorials. Send stories to news@khaama.com.


7.2 Solutions
library(texreg)
<IP_ADDRESS> Exercise 2
If you didn’t find the data online, you can download it here
df <- read.csv("qog_std_cs_jan18.csv", header = TRUE)
<IP_ADDRESS> Exercise 3
Pretend that you are writing a research paper. Select one dependent variable that you want to explain with a statistical model.
We are interested in explaining the quality of government idicator, where larger values indicate better overall governance. Before we go on to check what predicts better governance, we will take a look at the variable and remove NA’s. We will also rename the variable.
# rename
names(df)[which(names(df) == "icrg_qog")] <- "gov_quality"
# look at DV
summary(df$gov_quality) # 55 NA's
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.08333 0.38889 0.47222 0.52551 0.63310 0.97222 55
# create a copy of full dataset
df.full <- df
# drop NA's
df <- df[ !is.na(df$gov_quality), ]
The range of the quality of government variable is 0 to 1. We will re-scale to 0 - 100 to make results easier to interpret.
df$gov_quality <- df$gov_quality * 100
<IP_ADDRESS> Exercise 4
From Putnam’s “bowling alone”, we know of the theory of the importance of social capital for successful democratic governance. We, therefore, choose the social capital indicator bti_sc
, where larger values stand for more social capital. We expect better governance in countries where social capital is higher. Before we estimate the model, we look at the variable, remove missings, and rename it.
# look at IV
summary(df$bti_sc)
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
1.00 4.00 5.00 5.21 6.00 9.00 34
# remove NA's
df <- df[ !is.na(df$bti_sc), ]
# rename
names(df)[which(names(df) == "bti_sc")] <- "social_capital"
Our sample size is down to 105 countries. This is still a decent sample size. However, we would like to avoid loosing many more observations. Smaller sample sizes are a danger to inference (through increased variance - effects are less likely to be discovered and if they are, they are more likely to be over-estimates).
# linear model
m1 <- lm(gov_quality ~ social_capital, data = df)
screenreg(m1)
==========================
Model 1
--------------------------
(Intercept) 26.00 ***
(3.74)
social_capital 3.57 ***
(0.68)
--------------------------
R^2 0.21
Adj. R^2 0.20
Num. obs. 105
RMSE 12.37
==========================
*** p < 0.001, ** p < 0.01, * p < 0.05
Firstly, the intercept is not meaningful in this case because social capital ranges from 1 to 9, i.e. there is no country with 0 social capital.
The coefficient of social capital is positive and significant. Dividing the estimate by the standard error yields the t value which is 5.2. At a conventional alpha level of 0.05, we reject the null hypothesis when t is more extreme than \(\pm\) 1.96. This is the case here. In line with our expectation, more social capital increases the quality of governance on average by 3.73 points (on a 0 - 100 scale).
Increasing social capital by one point of its scale is a substantial amount (1/9 or 11\(\%\) of its range). So, if social capital increases by a substantial amount, does governance also go up by a substantial amount? A 3.73 point change on a 0-100 scale is 3.73/101 or 4\(\%\). That is still a substantial change considering that we are measuring how well governed an entire country is.
<IP_ADDRESS> Exercise 5
Add some additional explanatory variables (no more than 5) to your model and justify the choice again.
We have not talked about this in the lecture before and it will not be a hurdle in the midterm. However, it is important that you keep an eye on sample size. If the sample size becomes too small it is unlikely that you can make meaningful statements about the world. We, therefore, selected variables with an eye on the missing values and their theoretical importance. If you see studies that are based on small samples, be suspicious.
Firstly, we used infant mortality as a general indicator for development in the sense that basic health can be provided. We expect that if health conditions get worse than that should affect the capacity to govern negatively because it points to a basic lack of resources. We include a variable that measures conflict intensity. Violent conflict seriously, impedes the ability to govern.
We rename the variables and drop missing values on them.
Note: To make models comparable, they should be based on the same sample. So, after we dropped missing values, we have to re-run our bivariate model as well.
# renaming
names(df)[which(names(df)=="wef_imort")] <- "infant_mortality"
names(df)[which(names(df)=="bti_ci")] <- "conflict_intensity"
summary(df$infant_mortality) # infant mortality
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
2.30 9.05 15.90 26.59 41.05 117.40 10
summary(df$conflict_intensity) # conflict intensity
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.000 3.000 4.000 4.829 6.000 10.000
# drop missings
df <- df[ !is.na(df$infant_mortality) ,]
# re-run model 1 with new sample and check differences
m1.rerun <- lm(gov_quality ~ social_capital, data = df)
screenreg( list(m1, m1.rerun) )
=====================================
Model 1 Model 2
-------------------------------------
(Intercept) 26.00 *** 28.94 ***
(3.74) (4.12)
social_capital 3.57 *** 3.18 ***
(0.68) (0.73)
-------------------------------------
R^2 0.21 0.17
Adj. R^2 0.20 0.16
Num. obs. 105 95
RMSE 12.37 12.21
=====================================
*** p < 0.001, ** p < 0.01, * p < 0.05
As you can see, the difference is not small. This is less than ideal. R^2 goes down by alot and the magnitude of social capital also changes. There is not much you can do (without advanced stats) except being transparent about the differences.
# add new control variables
m2 <- lm(gov_quality ~ social_capital + infant_mortality + conflict_intensity, data = df )
screenreg(list(m1.rerun, m2))
========================================
Model 1 Model 2
----------------------------------------
(Intercept) 28.94 *** 51.59 ***
(4.12) (5.90)
social_capital 3.18 *** 1.35
(0.73) (0.72)
infant_mortality -0.26 ***
(0.05)
conflict_intensity -1.27 *
(0.60)
----------------------------------------
R^2 0.17 0.44
Adj. R^2 0.16 0.42
Num. obs. 95 95
RMSE 12.21 10.18
========================================
*** p < 0.001, ** p < 0.01, * p < 0.05
<IP_ADDRESS> Exercise 6
Carry out the F-test to check whether the new model explains more than the old model.
# f-test
anova(m1.rerun, m2)
Analysis of Variance Table
Model 1: gov_quality ~ social_capital
Model 2: gov_quality ~ social_capital + infant_mortality + conflict_intensity
Res.Df RSS Df Sum of Sq F Pr(>F)
1 93 13866.9
2 91 9428.7 2 4438.2 21.417 0.00000002385 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
As we saw by comparing R^2, the bigger model explains a lot more variance in the quality of government. The f-test shows that this difference is not due to chance. The p value is smaller than 0.05, hence, we reject the null that the small model and the larger model really explain the same. Another way to put this is: the variables we have added are jointly significant.
<IP_ADDRESS> Exercise 7
Produce a regression table of the better model.
htmlreg(m2, file = "regression_quality_of_government.doc")
The table was written to the file 'regression_quality_of_government.doc'.
<IP_ADDRESS> Exercise 8
Interpret the output of the better model.  Our re-run bivariate model explains 16\(\%\) of the variance of the quality of government. This is not bad for a small model. By adding our three variables, we increased explained variance to 42\(\%\) which is a large increase. Our new model seems to explain quality of government much better than the smaller model.
The intercept is not meaningful in either model. The coefficient of social capital is no longer significant (its standard error is larger in absolute terms than the coefficient estimate). We, therefore, do find evidence for the effect of social capital when we control basic health, and conflict intensity also. We find that the worth the health conditions, the less well governed a country will be. Infant mortality is measured in deaths/ 1000 live births. If that proportion increases by 10, the government index decreases by 2.3 which corresponds to roughly 2\(\%\) of its range. The higher the conflict intensity, the lower quality of government. For any one point increase (\(\frac{1}{10}\) of the scale) quality of government decrases by 0.01 which corresponds to roughly 1\(\%\) of its range.
<IP_ADDRESS> Exercise 9
Calculate fitted values whilst varying at least one of your continuous explanatory variables.
We vary infant mortality from minimum to maximum.
# see minimum and maximum of infant mortality
summary(df$infant_mortality)
Min. 1st Qu. Median Mean 3rd Qu. Max.
2.30 9.05 15.90 26.59 41.05 117.40
# set our covariates
covariates <- data.frame(
social_capital = mean(df$social_capital),
infant_mortality = seq(from = 2, to = 120, by = 1),
conflict_intensity = mean(df$conflict_intensity)
)
# check that everything looks okay
head(covariates) # looks fine
social_capital infant_mortality conflict_intensity
1 5.389474 2 4.705263
2 5.389474 3 4.705263
3 5.389474 4 4.705263
4 5.389474 5 4.705263
5 5.389474 6 4.705263
6 5.389474 7 4.705263
# calculate fitted values using predict()
fitted.vals <- predict(m2, newdata = covariates)
# attch the fitted values to our covariates data
covariates$fitted.vals <- fitted.vals
# hooray, for each scenario a predicted outcome
head(covariates)
social_capital infant_mortality conflict_intensity fitted.vals
1 5.389474 2 4.705263 52.38236
2 5.389474 3 4.705263 52.12627
3 5.389474 4 4.705263 51.87018
4 5.389474 5 4.705263 51.61409
5 5.389474 6 4.705263 51.35800
6 5.389474 7 4.705263 51.10190
<IP_ADDRESS> Exercise 10
Plot the result.
plot(
y = covariates$fitted.vals,
x = covariates$infant_mortality,
frame.plot = FALSE,
main = "Effect of infant mortality on quality of government",
ylab = "quality of government",
xlab = "infant mortality per 100 live births",
type = "l",
lwd = 2
)
<IP_ADDRESS> Exercise 11
Interpret the plot.
We interpret the simple plot including just the rgression line. It illustrates the strong negative relation between infant mortality, our proxy for basic health and resource conditions, and the quality of government generally. At 20 deaths per 1000 live births, we predict quality of government at roughly 48. At 100 deaths per 1000 live births, quality of government is predicted to be at roughly 33 only. Therefore, quality of government decreased by 31\(\%\) (\(\frac{48-33}{48}\)), a substantial amount.
<IP_ADDRESS> Extra Info (not relevant for midterm)
Below, we will show you how you could illustrate the confidence interval around the prediction. This is not necessary for the midterm but usefule to know. We can set the option se.fit = TRUE
which will return standard errors for the prediction as well.
prediction.with.se <- predict(m2, newdata = covariates, se.fit = TRUE)
We can extract the standard error with the dollar sign and add them to our covariates
dataset.
covariates$se.of.y <- prediction.with.se$se.fit
Now, we can draw confidence intervals.
plot(
y = covariates$fitted.vals,
x= covariates$infant_mortality,
frame.plot = FALSE,
main = "Effect of infant mortality on quality of government",
ylab = "quality of government",
xlab = "infant mortality per 100 live births",
type = "l",
lwd = 2
)
# add lines for confidence intervals
# upper bound
lines(x = covariates$infant_mortality, y = covariates$fitted.vals + 1.96 * covariates$se.of.y,
lty = "dashed", lwd = 1.5)
# lower bound
lines(x = covariates$infant_mortality, y = covariates$fitted.vals - 1.96 * covariates$se.of.y,
lty = "dashed", lwd = 1.5)
This was purely for your enjoyment and you won’t be tested on it. It just illustrates the uncertainty of the predictions.


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Need some Routines inspiration? Here are the best ones for your home
Source: Android Central
While the individual commands you can give your Amazon Echo are plenty diverse, you still have to give those commands one at a time. That can be more than a little tedious if you have multiple commands you regularly give, which is why Amazon created Routines for Alexa. Through the Routines system, you can set up multiple commands under a single spoken phrase. You get to say less, and your Echo does more. Everybody wins.
These are my favorite routines, which I have set up to better automate my home through my Amazon Echo.
Alexa, start my day
Amazon gives everyone a "Start My Day" Routine. By default, it reads you the weather followed by traffic in your area and wraps up with your Flash Briefing. These are all useful, but not really what I need in the morning.
Instead, I have this Routine edited to turn on my bedroom lights. Once the lights are at 50% brightness Alexa starts telling me the weather for the day. After this update, I have TuneIn set to play my local radio station.
- In the Alexa app, tap on Routines.
- Tap the + symbol in the top right corner.
- Tap When this happens.
- Select Voice from the two available options. Source: Android Central
- Type Start my morning into the text box.
- Tap Save.
- Tap Add Action. Source: Android Central
- Select Smart Home from the list.
- Tap All Devices.
- Find your light in the list and set it to 50% on.
- Tap Next.
- Tap Add Action. Source: Android Central, and Android Central /
- Select Weather from the list.
- Tap Next.
- Tap Add Action. Source: Android Central
- Select Music from the list.
- Add preferred radio station from TuneIn.
- Tap Next. Source: Android Central
- Tap From.
- Select your preferred device.
- Tap Create to finish. Source: Android Central /
Alexa, I'm leaving
It used to be that you'd have to manually create a routine from scratch to shut down your smart home when you left the house, but with the introduction of Alexa Guard, this has pretty much been taken care of for you. Nevertheless, it's a handy routine to mention, so here is how you set it up.

- In the Alexa app tap on Settings.
- Scroll down the list of settings and tap on Guard.
- Tap on the gear-shaped icon in the top right corner. The Guard settings menu will appear, which will show you which of your Echo devices are enabled to work with Alexa Guard. Source: Android Central
- Tap on Smoke & CO Alarm Sounds to be notified if Alexa hears your carbon monoxide or smoke alarms sound off when set to Away mode.
- Tap on Security System to connect your Alexa Guard-enabled Ring or ADT security systems.
- Tap on Lights to connect your Alexa-enabled light products to turn on when Alexa Guard is set to Away mode.
- Tap on Notifications to have Alexa notify you when the system switches to Home mode or Away mode. Source: Android Central
Alexa, chill out
I live in the south where it's hot for practically 10 months out of the year. Despite being air conditioner afficionados, we do like to keep our utility expenses down and do our part to not waste energy. Creating a routine to run the air conditioner when only when we really need it is a great way to do just that.
I've set up this simple automated routine to activate our Ecobee thermostats as soon as Alexa detects that I've left the office.
- In the Alexa app, tap on Routines.
- Tap the + symbol in the top right corner.
- Tap When this happens.
- Tap Location. Source: Android Central
- On the "When this Device" screen you should set or select your Work Address.
- After you select your Work Address choose Leaves.
- Tap Next. Source: Android Central
- Tap Add Action.
- Tap Smart Home.
- Tap All Devices.
- Select your smart thermostat from the device list.
- Tap Set Temperature.
- Tap Next. Source: Android Central
- Where it says Anytime tap on Change.
- On Active Days tap on M, T, W, T, and F so that this routine is only activated during the workweek.
- Where it says Active Between you can set the timeframe you want the routine to be active (perhaps only after noon but no later than 7 PM). Then tap Next.
- Tap Save and you're done! Source: Android Central
I've also set up a similar routine to turn the temperature back up when I leave home for work during the week — I just repeated the steps above and changed the When this device leaves trigger to the home address, change the settings on the Ecobee thermostat to a higher temperature setting, and adjusted the timeframes accordingly.
Source: Amazon
Wake up the kids
With a new Kids Edition Amazon Echo Dot in their rooms, I can now create time-based Routines to wake up the kids (but you can also do this for bedtime). This routine turns their lights on and starts with Alexa saying good morning and telling them a fun new fact about the world. If this wasn't enough to wake them up, Alexa starts playing Reville from Amazon Music.
- Press the + symbol on the Routines page in the Alexa app.
- Tap When this happens.
- Tap Schedule from the two available options.
- Choose what time you want this Routine to activate, and how frequently. Source: Android Central
- Tap Add Action.
- Tap Smart Home.
- Find your light in the Control Group list.
- Set your light to on.
- Tap Next. Source: Android Central
- Tap Add Action.
- Tap Alexa says.
- Tap Good Morning. Source: Android Central
- Tap Add Action.
- Select Music from the list.
- Add Reveille from Amazon Music.
- Tap Next.
- Tap Chose Device under From.
- Tap Save to finish. Source: Android Central
Alexa automation is awesome
Now that you know how to set up some simple Routines, you can easily create your own - so go crazy! You can create routines with triggers based on voice commands, location information, schedules, or device behaviors, and you can string together all kinds of smart devices and actions. You can also always go to the Routines section of the Alexa app and modify or delete any existing routines if you feel you need to make any changes. Routines really are a powerful way to automate with Alexa!
What are you using Routines for? Share your favorites in the comments!
Perfect for Alexa Routines
Echo Show 8
The Goldilocks zone
The Echo Show 8 comes in a more natural size that's easier to place than the Echo Show (2nd Gen). It's still comfy enough to watch recipes and videos on.
We may earn a commission for purchases using our links. Learn more.


| Oscar Levin |
|---|
| University of Northern Colorado |
Free.
Almost as good.
Students are increasingly being priced out of college.
Students shouldn't have to choose between a textbook and food.
Textbook cost is a social justice issue!
The average student should budget \(\$1240 - \$1400\) for textbooks and course materials (annually).
To cope with the cost:
Purchase an older edition.
Delay purchasing the textbook.
Never purchase the textbook.
Share the textbook.
Steal the textbook.
Source: 2018 Student Textbook Survey, Florida Virtual Campus.
One of the challenges is finding quality resources. In math, we are pretty okay.
American Institute of Mathematics Open Textbook Initiative
Cengage CEO:
The times when students did not matter are over. I want to be really emphatic on that. This industry relied for too long on the notion that, “if I can convince the professor, I don’t need to worry about the student, and I can charge whatever I want.” I think these times are over and they’re over for good.
Yes! Because:
OER fosters creativity and innovation.
Customization improves student learning.
Accessibility!
I don't love the book we are using and it is expensive!
My students don't really need a book, do they? I'll write some notes.
I have a lot of notes. Also, I want tenure.
Apparently it is now cool to self-publish books!
All along, I'm thinking deeply about how to teach.
There is something especially rewarding about thankless work.
Goal: develop useful materials for our students.
Then share them.
The source must be shareable!
PreTeXt is a markup language for writing textbooks: XML that can be transformed into various output formats including LaTeX and html.
Pros:
Awesome outputs.
Interactive features.
Semantic markup.
Write Once, Read Anywhere.
Free and open.
Con: could be more user friendly.
Write all my course materials in PreTeXt.
Some advantages:
Easier to share material with open textbooks.
My fingers don't need to remember what markup language I'm using.
Better experience for my students (because of awesome output).
Preparing for class and providing students with resources:
Lecture notes.
In-class activities.
Practice and collected homework problems (maybe with solutions).
Quizzes, exams, study guides, etc.
When do these get written? How do they get shared?
Paper copy to bring to class.
PDF for students who were absent.
Some way to give solutions.
GitHub and GitHub pages.
Makefile or build script.
Visual Studio Code (and PreTeXt-tools).
Custom xsl stylesheet to extract “worksheets”.
Pandoc and pandoc-pretext.
Open “activity-new.ptx” and write content (as <exercise>
elements).
make ws-new
, inspect output (activity-new.pdf).
Fix spacing, typos, etc. make ws-new
, repeat.
Save-as file with appropriate name, change @xml:id
.
Go to activities.ptx or main.ptx and <xi:include>
the file.
make worksheets
, make html
, and git commit
and git push
.
All of that needs to be easier.
What is reasonable to expect new PreTeXt users to be willing to do?
This will be a good summer project.
How do you read these:
Comments, suggestions, updates: oscar.levin@unco.edu.


What type of attack was this?
1. Diagonal Attack : Piece attacks king diagonally 2. File Attack : Piece attacks king in a straight line 3. Knight Attack : Knight attacks king 4. Supported Attack : Piece attacks king but king cannot capture as it is supported by another piece


Data types
FQL supports both primitive data types consisting of exactly one value and compound data types comprised of multiple values. The following types are available:
| Data type | Description |
|---|---|
| none | An empty value, also: the absence of a value |
| boolean | Boolean truth value with possible values false and true |
| integer | Signed whole number |
| float | Signed floating point number |
| string | UTF-8 encoded text value |
| array | Sequence of values, referred to by their positions |
| object | Sequence of values, referred to by their names |
| binary | Sequence of binary values |
| custom | User defined types |
Primitive types
None value
A none value can be used to represent an empty or absent value. It is different from a numerical value of zero (null != 0) and other falsy values (false, zero-length string, empty array or object). It is also known as nil or null in other languages.
Boolean data type
The Boolean data type has two possible values,true
and false
. They represent the two truth values in logic and mathematics.
Numeric literals
Numeric literals can be integers or floating-point numbers. They can optionally be signed with the + or - symbols. A decimal point . is used as separator for the optional fractional part.
String literals
String literals must be enclosed in single or double quotes. If the used quote character is to be used itself within the string literal, it must be escaped using the backslash symbol. A literal backslash also needs to be escaped with a backslash.
Compound types
FQL supports two compound types:- array: A composition of unnamed values, each accessible by their positions.
- object: A composition of named values, each accessible by their names.
- binary: An array of binary values.
Arrays
The first supported compound type is the array type. Arrays are effectively sequences of (unnamed / anonymous) values. Individual array elements can be accessed by their positions. The order of elements in an array is important.An array declaration starts with a left square bracket [
and ends with a right square bracket ]
. The declaration contains zero, one or more expressions, separated from each other with the comma ,
symbol. Whitespace around elements is ignored in the declaration, thus line breaks, tab stops and blanks can be used for formatting.
In the easiest case, an array is empty and thus looks like:
Array elements can be any legal expression values. Nesting of arrays is supported.
Individual array values can later be accessed by their positions using the [] accessor. The position of the accessed element must be a numeric value. Positions start at 0.
Objects
The other supported compound type is the object type. Objects are a composition of zero to many attributes. Each attribute is a name/value pair. Object attributes can be accessed individually by their names. This data type is also known as dictionary, map, associative array and other names.Object declarations start with a left curly bracket {
and end with a right curly bracket }
. An object contains zero to many attribute declarations, separated from each other with the ,
symbol. Whitespace around elements is ignored in the declaration, thus line breaks, tab stops and blanks can be used for formatting.
In the simplest case, an object is empty. Its declaration would then be:
Each attribute in an object is a name/value pair. Name and value of an attribute are separated using the colon :
symbol. The name is always a string, whereas the value can be of any type including sub-objects.
The attribute name is mandatory - there can’t be anonymous values in an object. It can be specified as a quoted or unquoted string:
It must be quoted if it contains whitespace, escape sequences or characters other than ASCII letters (a-z, A-Z), digits (0-9), underscores (_) and dollar signs ($). The first character has to be a letter, underscore or dollar sign.
If a keyword is used as an attribute name then the attribute name must be quoted or escaped by backticks:
Attribute names can be computed using dynamic expressions, too. To disambiguate regular attribute names from attribute name expressions, computed attribute names must be enclosed in square brackets [ … ]
:
There is also shorthand notation for attributes which is handy for returning existing variables easily:
The above is the shorthand equivalent for the generic form:
Any valid expression can be used as an attribute value. That also means nested objects can be used as attribute values:
Individual object attributes can later be accessed by their names using the dot .
accessor:
Attributes can also be accessed using the square bracket []
accessor:
In contrast to the dot accessor, the square brackets allow for expressions:
Binary value
Binary value is an array of bytes that represent binary data like files, images, audio or video.
Custom types
Custom types are the values defined by a user or author of a 3rd party library that extends FQL functionality. The values can represent either specific primitives or compound types. For example, tt could be http cookies, database records, images and etc.In order to define a custom value, it needs to have 2 Go types.
One should implement core.Type
and another core.Value
.
For simplicity, there is a helper function in core
package, that allows to create a type:
core.Value
interface provides a basic set of functionality which allows the FQL runtime to use a custom value.
Reading data
By default, implementingcore.Value
gives the FQL runtime just a basic set of functionality to work with values. This means that a type which implemented only core.Value
interfaces is treated as a primitive data type.
If the underlying data type is compound and there is a need for providing access to underlying data, the type should implement core.Getter
interface:
After that, it will be possible to use .
or []
accessors:
Iterations
In order to use a custom type as source forFOR IN
statement, it must implement core.Iterable
interface:
Clean up
If a custom type needs to do some clean up after a query execution, it needs to implement Go’sio.Closer
interface.


DOCUMENTATION
Concepts
Releases
CAPABILITY AREAS
{{area.label}}
GUIDES
{{guide.label}}
INTERFACES
{{interface.label}}
MISC COMPONENTS
cyber-ui-form-fields
TASK PROVIDERS
mindfully-attend-to-topic
TASK DISPLAY COMPONENTS
cyber-ui-minimal-task-card
WORKFLOWS
add-thing
systemize
work-on-tasks


<
Next version of Ox>
<
Previous version of Ox>
Ox 7: new features and fixes
New features and enhancements in version 7.0
Fixed problems in version 7.0
The Ox version number is kept in line with the OxMetrics front-end version.
Overview of new features version 7.0
Dynamic link libraries and oxo files need to be recompiled for Ox 7!
Multithreaded computation is only supported by Ox Professional
- New syntax
- Input handling
- Open a zip file for reading.
The following file functions can handle the opened zip file:
fread, fscan, feof, fsize.
- Database class handles choice variables
- replace: replace text in string
- loadsheet: loads an entire sheet from xlsx file (can be any sheet, not just the first)
- Improved handling of files >2GB
- Efficiency
- Improved handling of function arguments that are not labelled as const.
- Faster virtual function calls
- Lower overhead of sub matrix assignment
- Compilation of canonical for and foreach loops
- Parallel programming
- Developer
- Renamed Appendices to Developer's manual. Created C# and Java examples.
- Java version of RanApp (usung NetBeans)
New features and enhancements in version 7.0
- New syntax
- extended dot-relational and dot-equality operators for strings. Eg.
print("AACGTGGC" .== "ACCTTGGC"); // <1,0,1,0,1,1,1,1>
print("AACGTGGC" .== 'A'); // <1,1,0,0,0,0,0,0>
- Added [=] lambda functions
- Added foreach loop
- Added parallel for and parallel foreach loops, and serial keyword
to synchronize thread access (Professional). See the new chapter in the Ox book.
- Added default values for function arguments.
- There are three new keywords: foreach, parallel and serial. Any
existing code that uses these as variable names will have to be modified to
use slightly different names.
- Now using .oxh as default header extension. This allows us to distinguish
Ox header files from C/C++ header files. Of course, .h is still allowed. For
backward compatibility, include <file.h> will try .oxh first, then, if that fails, the .h file.
- Changed meaning of a = b = c; Now leaving c on the stack.
Previously the documentation said: `The type of an
assignment is that of its left operand.' However, this is impossible, because the
left-hand side in Ox may not have a type.
This change is only noticeable when using matrix indices, e.g. a = b[4][] = c,
and is highly unlikely to affect existing code.
- Handling of const improved:
- Taking reference of a constant (except const argument) is now disallowed (more
useful compile-time error instead of possible run-time error).
- Function arguments are now treated as const by default (and automatically
adjusted internally to non-const if required and allowed).
So it is not necessary anymore to use const function arguments for efficiency.
It is still good programming practice (but at the same time
I found this a difficult feature when teaching Ox to beginners.)
- New functions
- clone to clone a class object
- ftime function
- getfolders
- loadsheet to load a sheet from a xlsx file into a two-dimensional array. Can also load a .csv file.
- maxr, minr for symmetry with maxc, minc
- nans() as in zeros, ones
- Num1Derivative_parallel
- replace and find
- ranindex: same as ranshuffle(c, range(0,c-1))
- ranloopseed, clone (to clone a class object)
- union function for symmetry with intersection
- binxor, binpop and binvec
- diff and lag which default to missing values instead of zero for the gaps
- Changes to existing functions
- DrawXYZ was plotting Y,X,Z instead of X,Y,Z
This change affects existing code, and calls to DrawXYZ should be adjusted.
If you write in your Ox 6 code:
DrawXYZ(0, x, y, z)
this should be changed to
DrawXYZ(0, y, x, z')
to get exactly the same graph.
- limits(zeros(0,1)) crashes
- DrawMatrix, DrawTMatrix, DrawXMatrix: symbol and index arguments can be a vector with a value for each row in the data matrix.
- fopen: Added v (print message if fails) and V (message and run-time error if fails) to fopen format.
- fread, fscan: Read from zip files
- fsize for files >2GB: now returns double
- ismissing and isdotmissing now also treat undefined (.Null) as a missing value (see ox/samples/inout/iotest13.ox)
- main: return value from main now returned to console
- Maximization functions MaxSQP(F) and SolveNLE are now fully reentrant.
- Maximization functions can use CMaxControl object (last argument)
as an alternative for setting arguments, and retrieving the iteration count.
- (f)print(ln): Always printing time with "%C" using seconds: 11:00:00 instead of 11:00
- (f)print(ln): Now printing {} for empty array (as <> for matrix}
- print(ln): Default line length now 104
- string(func) returns string with name of function func, string(object) returns class name.
- vecindex/vecrindex is faster for very large vectors
- all bin.. functions now allow vector arguments
- setlower can have a vector for the second argument. In that case the
lower diagonal is filled by column (from left to right) with the elements of the
vector (until the vector runs out). This amounts to setting the strict lower
diagonal to unvech of the vector.
- loadmat("file.xlsx", 0) did not load strings as missing values
- Getting the Julian date index of today is quite tedious: int(timing(today(), 2)).
Added dayofcalendar() and timeofday() for current values
- Increased default sprint buffer from 2K to 16K
- Classes
- Database class:
- support for DB_CHOICE (choice variables), and related
new member functions:
SetVarChoicesByIndex, SetVarChoices, GetVarChoicesByIndex, GetVarChoices,
GetObsLabel, GetVarType, SetVarType
-
GetSize returns 0 when empty
-
Create with one argument (size of database)
-
GetVarCount, IsEmpty, Shrink
-
Recode, Tabulate, SortBy
- Much faster Database::Renew when the variables are new to the database
- Sample: Resample(const iFreq, const iYear1, const iPeriod1)
- Modelbase: GetPrint, GetcY, GetcX, GetcYlag, GetX, GetY
- Simulator class, which is a new version of the Simulation
class (still available). The Simulator class defaults to parallel mode, requiring
the Generate function to be useable in parallel.
- Split PcNaiveDgp into RanPcNaive, which has the generation facilities without storing
the results internally, and PcNaiveDgp, which derives from this and stores
the results internally. Both use header file ranpcnaive.oxh.
- Other changes
- Define OX_PARALLEL to indicate that the parallel and serial keywords are defined.
- Handle .in7 and .fmt files that are larger than 2GB data (64-bit version).
Avoiding need for transpose in saving of in7/bn7.
Allow spaces in variable names when saving as in7 file.
- Member call code refactoring: faster object/virtual calls.
- Because of internal changes, all dynamic-link libraries
and .oxo files must be recompiled.
- Changed oxo version
- Better multi-threaded tuning of some large matrix multiplications (Professional)
- Switching to dynamic linking of openmp (Professional): libiomp5md.dll to avoid warning
message when using MKL kernel replacement.
- Indexing a vector with <> (single index): the vector has zero elements,
and now it is created as 0 x 0 matrix instead of 1 x 0 or 0 x 1.
- New exported functions:
OxStoreCreate, OxStoreDelete, OxValGetBlob, OxValGetVecc, OxValGetVecr,
OxValSetArray, OxValSetBlob, OxValSetMat, OxValSetMatZero, OxValSetVecc, OxValSetVecr,
OxValGetStaticObject, OxValGetClassName,
OxValDuplicate, OxValTransfer, OxCloneObject, FOxLibAddFunctionEx
- Added #pragma _ox_stdlib(1)/#pragma _ox_stdlib(0) (for oxstd.oxh only)
so that FNI optimizations and arraybase adjustments are only done for internal
ox stdlib functions.
- Improved decision on when to start blocking matrix multiplication,
giving good speed improvements in some cases (and marginal slow downs in a few others).
- Improved handling of import when it uses a relative path; solved crash in
matrix constant when syntax error
- Spreadsheet data: slightly better at recognizing fixed frequency when
using calendar dates. Improved handling of variables that mix
labels and values: choice variable now constructed such that values are not reused.
.xls now only load an unlabelled first column if it contains values other than dates.
Fixed problems in version 7.0:
- fixed local declaration issue, e.g.:
fn()
{
decl i1;
{ decl i2 = <10;10>;
}
decl i3;
println(i3); // prints 10 rather than error because i3
// and i2 share location.
}
Now i3 will be undefined, and the memory for i2
cleared when the local block is exited.
- Print model for pdf/postscript was still not correctly set when
saving within Ox Console.
Saving graphs as pdf/eps now obeys the
print colour mode i as set by DrawAdjust(ADJ_COLORMODEL, -1, i);
- fopen("x", "wa") crashes: added better argument check.
- Cannot use class-based constant in constant expression.
- crash if trying to saving empty Database
- Database::Save for Stata file doesn't work.
- fseek/fsize (and other file functions) with large files in
64-bit version.
- Reading of field with time component in .csv: not recognized as time
variable
- vector indexed by vector could result in memory leak
- dropr(array, <>) should return the whole array, not an empty array.
- isdotfeq and isfeq now treat .NaN as the == and .== operators.
- tailf(0, ...) was 1, but tailf(almost 0, ...) was 0
Ox version 7.00.
©
JA Doornik
This file last changed .


Email
Password
Radios
First radio
Second radio
Third disabled radio
Checkbox
Example checkbox
Sign in


By Kyran Adams
Counter Strike: Global Offensive (CS: GO) is a team-based strategic shooter in the Counter Strike series, which has nearly 20 years of competitive history and tournaments now often offering prize pools of more than $1,000,000.
One thing that has always drawn me to this game is the strategic importance of utility — smoke grenades (smokes), flashbangs (flashes), and high explosives (HEs). A well-thrown smoke can give your team cover to advance into a new position or block eyesight from an enemy, helping your team win the round.
Keep scrolling. Using data from this kaggle dataset, we will analyze thousands of rounds of CS: GO to determine the best positions to use utility. Pictured on the right is an aggregation of all of the smokes thrown by Counter Terrorists across thousands of games.

A round is between two teams of five players. In each round, one team, the Terrorists (T), tries to plant the bomb on one of the bombsites and defend it until it detonates. The other team, the Counter Terrorists (CT), attempts to stop the bomb plant or defuse it once it's planted.
On the right, we have grenades through by Ts (red) and grenades thrown by CTs (blue) in a single round. The CTs won this round.
Now, to do some more in depth analysis, we're going to combine all of the utility used from each team by type. Here on the right are smokes thrown by CTs in every round.
In order to get some idea of how location of utility relates with effectiveness, we could color grenades based on the result of the round, which we we will do next.
Here, all of the smoke grenades are colored red if that round was won by the thrower and blue if it was lost. However, the patterns are a little unclear and it's hard to figure out which locations are the best locations to throw grenades.
In fact, we're not just interested in the the impact of individual points, we're more interested in grouping smokes together and determining the average impact for that group. To do that, we can use a clustering algorithm.
One clustering algorithm, DBSCAN, creates clusters that correspond to high density areas of points and leave lower density areas outliers. We can use this to color the smokes on the right based on their cluster.
We can then run logistic regression using these cluster counts as predictors. We are basically asking the question: What is the average change in the likelihood of winning from throwing one additional grenade in any particular cluster?
Here are the results, where the average effect of a cluster is plotted as the color of that cluster. The more blue a cluster is, the more it helps CTs. The more red, the more it helps Ts. The color of the outliers (a non-clustered smoke) is based on the average effect of all of the outliers, which has a large variance.
From this graph we can see that the most effective smokes are mid-connector, t ramp (especially deep ramp), and palace. In fact, in relation to the other types of nades, CT smokes are very effective.
Terrorist smokes are more variable in their effectiveness. Some are extremely effective, and some actually help the CTs (this could be because they warn the CTs of an incoming push). The most effective smokes are top mid, stairs, front halls, A site, kitchen window, and jungle. The most ineffective smokes are jungle/connector, jungle, lower mid, palace, top mid. Sandwich is the only statistically significantly bad for the Ts, which makes sense, as it is not an intentional smoke, and usually signifies a missed stairs smoke.
One interesting result is that some clusters are very close together, but have very different effects. One example is that a deeper top mid smoke onto catwalk is much more effective than a shallow one. Similarly, missing the jungle smoke by even a bit reduces the effectiveness considerably.
It seems that for CTs, flashing to take underpass is very strong. This makes sense as it is very difficult to defend against this flash, and strategically, CT taking underpass control cuts off half the map for Ts. It also seems cluster connector, T ramp, and outside T ramp are very good. Mid window flash is not very good. It is interesting that CT flashes seem more effective than terrorist flashes in winning the round, and more statistically significant.
For terrorists, the strongest flashes were outside apartments and underpass. It is interesting that top mid over ramp are not very effective at all, despite being very common. It is very interesting that outside b apartments, one of the most common flashes for attacking B, is actually negatively impactful. This could be because it alerts the enemy of the attack rather than surprises them.
It seems that CT HE's are very effective, especially in top of mid, palace, and underpass.
Many terrorist HE's also are very effective. Mid window, connector and jungle are incredibly effective. HE's are also interesting in that they are quite effective also when not in any cluster at all (the -1 cluster).
I performed logistic regression on just terrorist smokes, but added interaction terms between groups of smokes that I thought might be significant, such as CT + stairs + connector/jungle for taking A.
I found that out of these groups, none had a significant increase in chance of terrorist wins, and most actually decreased the chances that terrorists would win. My hypothesis is that often at lower ranked play, players throw smoke groups simply because they see professional players doing it, and not actually for a strategic advantage. However, when players throw individual smokes, they do it more strategically. .
Finally, I aggregated all of the data together and ran logistic regression to find the average effect of utility by its team and type.
| Dependent variable: | |
| winner_side | |
| Smoke_T | -0.163*** (-0.176, -0.149) |
| Smoke_CT | 0.247*** (0.233, 0.261) |
| Flash_T | -0.082*** (-0.093, -0.071) |
| Flash_CT | 0.148*** (0.136, 0.161) |
| HE_T | -0.330*** (-0.346, -0.315) |
| HE_CT | 0.201*** (0.189, 0.213) |
| Constant | -0.216*** (-0.247, -0.184) |
| Observations | 75,844 |
| Note: | *p<0.1; **p<0.05; ***p<0.01 |
Interestingly, terrorist flashes are the weakest out of all types of utility, with almost a negligible effect on round wins! HEs are also surprisingly strong.
Through this analysis we found many interesting results impacting strategy (at least at the nonprofessional levels from which this data was collected). Here are some takeaways to guide utility usage on de_mirage.
The biggest takeaway from this is to be intentional with utility. Many of the ineffective uses of utility that I found align with common patterns at lower levels, which I hypothesize are simply done out of habit rather than strategy.
Thanks for reading!
Questions or thoughts? Tweet me at @Kyran_Adams or email me at kyran.park.adams@gmail.com.


Cube Experiment
Tools
Instructions
Click and Hold/Move Mouse to Orbit.
Hold Alt + Click to Add New Blocks or Hold Shift + Click to Remove Blocks


@()(implicit request:Request[_]) @_base("home") {
Welcome to this thing. You can find exactly what you want here. Give it a shot.


Server (A physical server identified by a DNS name. Each server is expected to have 2 accounts: admin and lunatech )
| @server.name | @server.comment | Nicolas's key as admin, Erik's key as admin, Jasper's key as lunatech |
| @server.name | @server.comment | Nicolas's key as admin, Erik's key as admin, Jasper's key as lunatech |


--- layout: page title: About header-img: "img/about-bg.png" ---
I'm a parent of one trying to change career paths in a newly changed world while also trying to manage my own child's education, keep my family half way to sane, and maybe even get somewhere towards having a balanced and fulfilled life.


We'll fix it!
Contact: Jon Doh
Jon is your contact person in CarNCakes.
<EMAIL_ADDRESS>+434 34234 2535
Your contact information
<EMAIL_ADDRESS>+372 352352352
Repair Audi AFG332
Current status:
Drying
Car received
At 9:20 on 21.02.2017
Cleaned, fixing
Completed 10:30 at 21 march 2017
Painting
Completed 16:20 21 march 2017
Drying
Ready
Delivered to customer
Attached notes:


Work on Climate - Climate Justice 101 Guide
I supported the inital creation of an online climate action community called Work on Climate. The slack group was started by Eugene Kirpichov and Cassandra Xia to encourage other professionals to enter or support the climate field. To help community engagement and connection, I set-up the beta version of a braintrust to help members find people with common interests. To strengthen core bonding, I also developed a communication and working style survey. The results were shared with the group along with a series of activity to improve team health. In November 2020, I joined the climate justice learning group to develop a guidebook on key topics for new learners. The first version of the guidebook will be released March 2021.


| sequential {future} | R Documentation |
A sequential future is a future that is evaluated sequentially in the current R session similarly to how R expressions are evaluated in R. The only difference to R itself is that globals are validated by default just as for all other types of futures in this package.
sequential(..., envir = parent.frame()) transparent(..., envir = parent.frame())
... |
Additional arguments passed to |
envir |
The environment from where global objects should be identified. |
The preferred way to create a sequential future is not to call these functions
directly, but to register them via plan(sequential)
such that
it becomes the default mechanism for all futures. After this
future()
and %<-%
will create
sequential futures.
Transparent futures are sequential futures configured to emulate how R
evaluates expressions as far as possible. For instance, errors and
warnings are signaled immediately and assignments are done to the
calling environment (without local()
as default for all other
types of futures). This makes transparent futures ideal for
troubleshooting, especially when there are errors.
## Use sequential futures plan(sequential) ## A global variable a <- 0 ## Create a sequential future f <- future({ b <- 3 c <- 2 a * b * c }) ## Since 'a' is a global variable in future 'f' which ## is eagerly resolved (default), this global has already ## been resolved / incorporated, and any changes to 'a' ## at this point will _not_ affect the value of 'f'. a <- 7 print(a) v <- value(f) print(v) stopifnot(v == 0)


Welcome to the SMK Configuration Utility
With this tool you can modify your projects map-config.json
Select an option (on the left) to modify layers, data, and tools
Click 'Save' at any time to commit your changes
Welcome to the SMK Configuration Utility
With this tool you can modify your projects map-config.json
Select an option (on the left) to modify layers, data, and tools
Click 'Save' at any time to commit your changes


|
Questions? Send mail to PC2 | |||||||
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BaseClient.getClientId()
BaseClient.getClientId()
getNumUserFiles
and writeUserFile
or getUserFile
GetRunsClient
JudgeClient
.ScoreboardClient
.TeamClient
.
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psychopy.visual.windowframepack
- Pack multiple monochrome images into RGB frame¶Copyright (C) 2014 Allen Institute for Brain Science
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License Version 3 as published by the Free Software Foundation on 29 June 2007. This program is distributed WITHOUT WARRANTY OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE OR ANY OTHER WARRANTY, EXPRESSED OR IMPLIED. See the GNU General Public License Version 3 for more details. You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/
ProjectorFramePacker
¶psychopy.visual.windowframepack.
ProjectorFramePacker
(win)¶Class which packs 3 monochrome images per RGB frame.
Allowing 180Hz stimuli with DLP projectors such as TI LightCrafter 4500.
The class overrides methods of the visual.Window class to pack a monochrome image into each RGB channel. PsychoPy is running at 180Hz. The display device is running at 60Hz. The output projector is producing images at 180Hz.
Frame packing can work with any projector which can operate in ‘structured light mode’ where each RGB channel is presented sequentially as a monochrome image. Most home and office projectors cannot operate in this mode, but projectors designed for machine vision applications typically will offer this feature.
Example usage to use ProjectorFramePacker:
from psychopy.visual.windowframepack import ProjectorFramePacker
win = Window(monitor='testMonitor', screen=1,
fullscr=True, useFBO = True)
framePacker = ProjectorFramePacker (win)
| Parameters: | win : Handle to the window. |
|---|
endOfFlip
(clearBuffer)¶Mask RGB cyclically after each flip. We ignore clearBuffer and just auto-clear after each hardware flip.
startOfFlip
()¶Return True if all channels of the RGB frame have been filled with monochrome images, and the associated window should perform a hardware flip


cat /proc/meminfo
lsblkTo see disk usage
df -h /dev/sda1
sudo pacman -Syu --noconfirm
This changes to the default directory location, pulls the latest version and then installs any new node dependancies
cd ~/asimos/ && git pull && npm install
This assumes you have bitcoin running and don't want to corrupt your database
bitcoin-cli stop && sudo reboot
This assumes you have bitcoin running and don't want to corrupt your database
bitcoin-cli stop && sudo shutdown -t now 0


See the latest news from traker lab
Think Power is a leading telematics and M2M solution company focusing on GPS field.
With 10 years car electronics industry experience, Think Power is dedicating to offering
the reliable products and customized solution to the customer in need.
STAY INFORMED


COURSES
- Home
- /
- Single Course
Introduction to PHP with Ajax and Redux framework
-
Duration
25 Classes - 50 Hours -
Categories
PHP Development -
Feedback
4.5 (778)
PHP development began in 1995 when Rasmus Lerdorf wrote several Common Gateway Interface (CGI) programs in C, which he used to maintain his personal homepage. He extended them to work with web forms and to communicate with databases, and called this implementation "Personal Home Page/Forms Interpreter" or PHP/FI.
During 2014 and 2015, a new major PHP version was developed, which was numbered PHP 7. The numbering of this version involved some debate. While the PHP 6 Unicode experiment had never been released, several articles and book titles referenced the PHP 6 name, which might have caused confusion if a new release were to reuse the name. After a vote, the name PHP 7 was chosen.
The foundation of PHP 7 is a PHP branch that was originally dubbed PHP next generation (phpng). It was authored by Dmitry Stogov, Xinchen Hui and Nikita Popov, and aimed to optimize PHP performance by refactoring the Zend Engine to use more compact data structures with improved cache locality while retaining near-complete language compatibility. As of 14 July 2014, WordPress-based benchmarks, which served as the main benchmark suite for the phpng project, showed an almost 100% increase in performance. Changes from phpng are also expected to make it easier to improve performance in the future, as more compact data structures and other changes are seen as better suited for a successful migration to a just-in-time (JIT) compiler. Because of the significant changes, the reworked Zend Engine is called Zend Engine 3, succeeding Zend Engine 2 used in PHP 5.
Topics Include:
- Build amazing single page applications with React JS
- Identify the resource needs of the project
- Decompose work packages into activities
- Realize the power of building composable components
- Determine the critical path and calculate float
- Identify the resource needs of the project
- Decompose work packages into activities
- Realize the power of building composable components
Curriculum for the Course
- 110 Lectures
- 20:21:15 Hours
Course Feedback
-
4.9
Total Ratings (9899)
-
- 10565
- (77%)
- 5625
- (10%)
- 3725
- (05%)
- 1595
- (05%)
- 1595
- (03%)
Reviews
Lindsay Lohan
I am glad that I have taken the PHP Framework course here at Scholar. The teacher has been amazing throughtout the course. They have outstanding resources to help you. In addition, their interactive lessons are amazing, the instructor gives support round the clock.
Mahfuz Asfia
I am glad that I have taken the PHP Framework course here at Scholar. The teacher has been amazing throughtout the course. They have outstanding resources to help you. In addition, their interactive lessons are amazing, the instructor gives support round the clock.
Rashed Abbas
I am glad that I have taken the PHP Framework course here at Scholar. The teacher has been amazing throughtout the course. They have outstanding resources to help you. In addition, their interactive lessons are amazing, the instructor gives support round the clock.
Submit a Review
Submit a Review


inst/CITATION
Potter NA (2019). “rnassqs: An ‘R' package to access agricultural data via the USDA National Agricultural Statistics Service (USDA-NASS) ’Quick Stats' API.” The Journal of Open Source Software.
@Article{, author = {Nicholas A Potter}, title = {rnassqs: An `R` package to access agricultural data via the USDA National Agricultural Statistics Service (USDA-NASS) 'Quick Stats' API}, year = {2019}, month = {aug}, publisher = {The Open Journal}, journal = {The Journal of Open Source Software}, }
Potter N (????). rnassqs: Access the NASS 'Quick Stats' API. R package version 0.6.1, https://CRAN.R-project.org/package=rnassqs.
@Manual{, title = {{rnassqs}: Access the NASS 'Quick Stats' API}, author = {Nicholas Potter}, note = {R package version 0.6.1}, url = {https://CRAN.R-project.org/package=rnassqs}, }
Nicholas Potter. Author, maintainer.
Jonathan Adams. Contributor.
Joseph Stachelek. Contributor.
Julia Piaskowski. Contributor.
Adam Sparks. Reviewer.
Neal Richardson. Contributor, reviewer.


Read and write access to PNG image files using the LodePNG library. The package has no external dependencies.
| Version: | 0.2.0 |
| Suggests: | testthat, covr |
| Published: | 2018-06-15 |
| Author: | Jon Clayden [aut, cre], Lode Vandevenne [aut] |
| Maintainer: | Jon Clayden <code at clayden.org> |
| BugReports: | https://github.com/jonclayden/loder/issues |
| License: | BSD_3_clause + file LICENCE |
| URL: | https://github.com/jonclayden/loder |
| NeedsCompilation: | yes |
| Materials: | README NEWS |
| CRAN checks: | loder results |
| Reference manual: | loder.pdf |
| Package source: | loder_0.2.0.tar.gz |
| Windows binaries: | r-devel: loder_0.2.0.zip, r-release: loder_0.2.0.zip, r-oldrel: loder_0.2.0.zip |
| OS X binaries: | r-release: loder_0.2.0.tgz, r-oldrel: loder_0.2.0.tgz |
| Old sources: | loder archive |
| Reverse suggests: | mmand, RNiftyReg, tractor.base |
Please use the canonical form https://CRAN.R-project.org/package=loder to link to this page.


The hexview library requires jQuery and if you don't already include jQuery, you'll need to do so with something like this:

You will need to include the hexview.js and hexview.default.css are included in your HTML document. For example:
 <link rel="stylesheet" type="text/css" href="hexview.default.css" />
To add a binary data view you need to place the binary data inside a
 element marked with the class 'hexviewwindow'. For example:

The hexview library will replace div blocks marked with this class with hex view windows once the document has finished loading. To add the binary data it must first be encoded into base 64. This task can easily be performed in another scripting environment such as Python. The base 64 encoded binary data is then added as the text for the
 element, like this:

 /9j/4AAQSkZJRgABAQEASABIAAD/4RzmRXhpZgAATU0AKgAAAAgADAEPAAIAAAAJ AAAAngEQAAIAAAAQAAAAqAESAAMAAAABAAEAAAEaAAUAAAABAAAAuAEbAAUAAAAB AAAAwAEoAAMAAAABAAIAAAExAAIAAAAvAAAAyAEyAAIAAAAUAAAA+AITAAMAAAAB AAIAAIKYAAIAAAAFAAABDIdpAAQAAAABAAABEsSlAAcAAAAcAAAEkAAABKxGVUpJ RklMTQAARmluZVBpeCBGNDU1ICAgAAAAAEgAAAABAAAASAAAAA==

Note that we can split the encoded binary data into multiple lines because hexview will remove all whitespace before decoding the binary data.
You can set display formatting options by passing parameters to the hex view window via a form in the hex view
 tag. The following options can be configured:
| row_width | The number of bytes to show per row |
| word_size | The word size to show. Allows you to show the binary data in 1, 2 or 4 (or more) byte groups. |
| caption | Text to set in the output tables
 tag |
| highlights | Determines parts of the binary data to be highlighted. This string takes the format of a comma separated list of values
where each highlight is a tuple of 4 values: start_offset:end_offset:color:description. For example, here is a single
highlight:
4:20:#F4FA58:This is an interesting partAnd here is a list of two highlights: 4:20:#F4FA58:This is an interesting part,36:56:#54FAF8:This is another interesting part |


What are these different services? When should we use one versus the other? Do they provide similar capabilities? There are a number of identity options within Azure and its easy to confuse the purposes and capabilities of each solution.
Azure AD
Many organizations today are already utilizing Azure AD and may not even realize it. Azure AD by default is a cloud based domain service that provides the ability to create and manage users and groups and provide access to Azure or Office 365. In the context of Azure, Azure AD is utilized in conjunction with Azure RBAC to control user access to different Subscriptions and Resources within Azure. Azure AD is almost a given today and is utilized in addition to Active Directory Domain Services, not as a replacement.
AD DS on Azure
Here I'm referring to AD DS (Active Directory Domain Services) running on Azure Virtual Machines, a service which should be a very familiar to most organizations. There are a few things to take into consideration when configuring Domain Controllers within Azure but overall the service itself behaves exactly the same as it would with your existing Domain Services. For a brand new Azure deployment, AD DS Domain Controller Virtual Machines are typically one of the first workloads to deploy. This comes down to treating Azure as an extension of your data center and providing Domain Service capabilities to your Azure workloads in the event that the connection back to your data center is dropped and to ensure that Azure workloads aren't having to traverse an ExpressRoute or VPN connection every single time they need to check into the domain. AD DS on Azure is almost a given for organizations that are already reliant on AD DS.
Azure AD DS
Azure AD Domain Services is a newer feature available within Azure that provides managed domain as a service capabilities. Think of it as PaaS version of Domain Controllers. Sounds really nice, right? A word of caution, as this service can trip you up if you don't utilize it correctly. It is important to remember - Azure AD DS is NOT a replacement or extension of your existing domain. Although it would be really nice to never have to build Domain Controller VMs in Azure, that is not realistic at this point in time for most organizations.
So what is Azure AD DS? Azure AD Domain Services is a domain service that synchronizes Azure AD users, groups, and passwords, to a managed service available to Azure Virtual Machines. Once available, Virtual Machines can be joined to the managed domain and utilize Group Policy, users, and group membership similar to traditional Active Directory Domain Services.
Users, Groups, and Passwords are synchronized from Azure Active Directory to the managed domain instance, but group policy is not synchronized.
- Only one instance of Azure AD DS can be deployed per Azure AD Tenant (no multi-region deployments)
- Azure AD DS is highly available within each instance
- Azure AD DS is a separate domain, but can be synchronized with your existing domain
- Group Policy is not synchronized to Azure AD DS
- Group Policy Objects can be created within Azure AD DS
- Domain and Enterprise admin capabilities are not available for Azure AD DS
- Azure AD password hash synchronization is required for Azure AD DS
- Azure AD users and groups are synchronized to Azure AD DS
Example Scenario
In the following graphic portrays how each identity service may be utilized in a scenario of managing a single Azure Windows Virtual Machine.
In this scenario, the Windows VM can be joined to either AD DS domain or my Azure AD DS domain, but not both. The option to join a Virtual Machine to one of these domains is one or the other situation.
What do we do?
With this information, the solution that makes sense for most organizations includes deploying AD DS Domain Controllers on Azure and utilizing Azure AD synchronization to manage resources and users within the Azure Portal. Azure AD DS is a great service but should be used for a specific use case.


Elastex v0.1.2 Elastex.Search
Follows the Elasticsearch Search API.
Summary
Functions
Executes a query to get the number of matches for that query
Executes a query to get the number of matches for that query with a body
Executes a query to get the number of matches for that query with a body and index
Executes a query to get the number of matches for that query with body, index, and type
Computes a score explanation for a query and a specific document
Computes a score explanation for a query and a specific document
Extends the url of search builder
Executes several search request in a single call
Adds params to search builders
Executes a search query
Executes a search query with a body
Executes a search query with a body and index
Executes a search query with a body, index, and type
Gets search hits from http response if present. Returns error tuple with http response if not present
Returns indices and shards that a search request would be executed against using an index
Returns indices and shards that a search request would be executed against using an index and a type
Suggests similar looking terms based on a provided text
Allows use of mustache templating language to pre-render search requests
Validates a potentially expensive query without executing it
Validates a potentially expensive query without executing it
Validates a potentially expensive query without executing it with index
Validates a potentially expensive query without executing it with index
Types
body :: map | nil
http_response ::
{:ok, HTTPoison.Response} |
{:error, HTTPoison.Error}
int_or_string :: non_neg_integer | String.t
Functions
Specs
count :: %Elastex.Builder{action: term, body: term, headers: term, id: term, index: term, method: :post, options: term, params: term, type: term, url: String.t}
Executes a query to get the number of matches for that query.
Useful with params search.
Examples
iex> Elastex.Search.count()
%Elastex.Builder {
url: "_count",
method: :post
}
Specs
Executes a query to get the number of matches for that query with a body.
Examples
iex> body = %{"query" => %{term: %{user: "kimchy"}}}
iex> Elastex.Search.count(body)
%Elastex.Builder {
url: "_count",
body: %{"query" => %{term: %{user: "kimchy"}}},
method: :post
}
Specs
Executes a query to get the number of matches for that query with a body and index.
Examples
iex> body = %{"query" => %{term: %{user: "kimchy"}}}
iex> Elastex.Search.count(body, "twitter")
%Elastex.Builder {
url: "twitter/_count",
body: %{"query" => %{term: %{user: "kimchy"}}},
method: :post
}
Specs
Executes a query to get the number of matches for that query with body, index, and type.
Examples
iex> body = %{"query" => %{term: %{user: "kimchy"}}}
iex> Elastex.Search.count(body, "twitter", "tweet")
%Elastex.Builder {
url: "twitter/tweet/_count",
body: %{"query" => %{term: %{user: "kimchy"}}},
method: :post
}
Specs
explain(String.t, String.t, int_or_string) :: %Elastex.Builder{action: term, body: body, headers: term, id: term, index: term, method: :post, options: term, params: term, type: term, url: String.t}
Computes a score explanation for a query and a specific document.
Examples
iex> Elastex.Search.explain("twitter", "tweet", 1)
%Elastex.Builder {
url: "twitter/tweet/1/_explain",
body: nil,
method: :post
}
Specs
Computes a score explanation for a query and a specific document.
Examples
iex> body = %{"query" => %{term: %{user: "kimchy"}}}
iex> Elastex.Search.explain(body, "twitter", "tweet", 1)
%Elastex.Builder {
url: "twitter/tweet/1/_explain",
body: %{"query" => %{term: %{user: "kimchy"}}},
method: :post
}
Specs
extend_url(%Elastex.Builder{action: term, body: term, headers: term, id: term, index: term, method: term, options: term, params: term, type: term, url: term}, [String.t]) :: %Elastex.Builder{action: term, body: term, headers: term, id: term, index: term, method: term, options: term, params: term, type: term, url: String.t}
Extends the url of search builder
Examples
iex> builder = %Elastex.Builder{url: "twitter"}
iex> Elastex.Search.extend_url(builder, ["tweet"])
%Elastex.Builder {
url: "twitter/tweet"
}
Specs
multi_search([%Elastex.Builder{action: :search_query, body: term, headers: term, id: term, index: term, method: term, options: term, params: term, type: term, url: term}]) :: %Elastex.Builder{action: :multi_search, body: String.t, headers: term, id: term, index: term, method: :post, options: term, params: term, type: term, url: String.t}
Executes several search request in a single call.
Examples
iex> query_builders = [
...> Elastex.Search.query(%{hello: "world"}),
...> Elastex.Search.query(%{hello: "world"}, "twitter", "tweet")
...> ]
iex> Elastex.Search.multi_search(query_builders)
%Elastex.Builder {
url: "_msearch",
body: "{}\n{\"hello\":\"world\"}\n{\"index\":\"twitter\"}\n{\"hello\":\"world\"}\n",
method: :post,
action: :multi_search
}
Specs
params(%Elastex.Builder{action: term, body: term, headers: term, id: term, index: term, method: term, options: term, params: term, type: term, url: term}, keyword(String.t)) :: %Elastex.Builder{action: term, body: term, headers: term, id: term, index: term, method: term, options: term, params: keyword(String.t), type: term, url: term}
Adds params to search builders
Examples
iex> builder = %Elastex.Builder{}
iex> Elastex.Search.params(builder, [q: "user:mike"])
%Elastex.Builder {
params: [q: "user:mike"]
}
Specs
query :: %Elastex.Builder{action: :search_query, body: term, headers: term, id: term, index: term, method: :post, options: term, params: term, type: term, url: String.t}
Executes a search query.
Useful for URI searches.
Examples
iex> Elastex.Search.query
%Elastex.Builder {
url: "_search",
method: :post,
action: :search_query,
}
with params
iex> Elastex.Search.query |> Elastex.Search.params([q: "user:kimchy"])
%Elastex.Builder {
url: "_search",
method: :post,
action: :search_query,
params: [q: "user:kimchy"]
}
Specs
Executes a search query with a body.
Examples
iex> body = %{query: %{term: %{user: "kimchy"}}}
iex> Elastex.Search.query(body)
%Elastex.Builder {
url: "_search",
body: %{query: %{term: %{user: "kimchy"}}},
method: :post,
action: :search_query,
}
Specs
Executes a search query with a body and index.
Examples
iex> body = %{query: %{term: %{user: "kimchy"}}}
iex> Elastex.Search.query(body, "twitter")
%Elastex.Builder {
url: "twitter/_search",
body: %{query: %{term: %{user: "kimchy"}}},
method: :post,
index: "twitter",
action: :search_query
}
Specs
Executes a search query with a body, index, and type.
Examples
iex> body = %{query: %{term: %{user: "kimchy"}}}
iex> Elastex.Search.query(body, "twitter", "tweet")
%Elastex.Builder {
url: "twitter/tweet/_search",
body: %{query: %{term: %{user: "kimchy"}}},
index: "twitter",
type: "tweet",
action: :search_query,
method: :post
}
Specs
query_hits(http_response) ::
{:ok, map} |
{:error, String.t}
Gets search hits from http response if present. Returns error tuple with http response if not present.
Specs
Returns indices and shards that a search request would be executed against using an index.
Examples
iex> Elastex.Search.shards("twitter")
%Elastex.Builder {
url: "twitter/_search_shards",
method: :get,
index: "twitter",
type: ""
}
Specs
Returns indices and shards that a search request would be executed against using an index and a type.
Examples
iex> Elastex.Search.shards("twitter", "tweet")
%Elastex.Builder {
url: "twitter/tweet/_search_shards",
method: :get,
index: "twitter",
type: "tweet"
}
Specs
Suggests similar looking terms based on a provided text.
Examples
iex> body = %{"my-suggestion" => %{text: "hi", term: %{field: "body"}}}
iex> Elastex.Search.suggest(body)
%Elastex.Builder {
url: "_suggest",
body: %{"my-suggestion" => %{text: "hi", term: %{field: "body"}}},
method: :post
}
Specs
Allows use of mustache templating language to pre-render search requests.
Examples
iex> body = %{inline: %{query: %{match: %{title: "{{query_string}}"}}}}
iex> Elastex.Search.template(body)
%Elastex.Builder {
url: "_search/template",
body: %{inline: %{query: %{match: %{title: "{{query_string}}"}}}},
method: :post
}
Specs
Validates a potentially expensive query without executing it.
Examples
iex> Elastex.Search.validate()
%Elastex.Builder {
url: "_validate/query",
body: nil,
method: :post
}
Specs
Validates a potentially expensive query without executing it.
Examples
iex> body = %{"query" => %{term: %{user: "kimchy"}}}
iex> Elastex.Search.validate(body)
%Elastex.Builder {
url: "_validate/query",
body: %{"query" => %{term: %{user: "kimchy"}}},
method: :post
}
Specs
Validates a potentially expensive query without executing it with index.
Examples
iex> body = %{"query" => %{term: %{user: "kimchy"}}}
iex> Elastex.Search.validate(body, "twitter")
%Elastex.Builder {
url: "twitter/_validate/query",
body: %{"query" => %{term: %{user: "kimchy"}}},
method: :post
}
Specs
Validates a potentially expensive query without executing it with index.
Examples
iex> body = %{"query" => %{term: %{user: "kimchy"}}}
iex> Elastex.Search.validate(body, "twitter", "tweet")
%Elastex.Builder {
url: "twitter/tweet/_validate/query",
body: %{"query" => %{term: %{user: "kimchy"}}},
method: :post
}


Queen Elizabeth docks at the Port of Walvis Bay 21 Nov 2019, 07:36 by Staff Reporter Home \ Business \ Queen Elizabeth docks at the Port of Walvis Bay - New Era Live


Successfully Denied {{ rider }}!
Your trip has {{ num_riders }} rider(s).
Redirecting...
{% from "macros.html" import head, nav, importJS, footer %} {{ head("Dryve | Accept Rider") }}
{{ nav(logged_in) }}Your trip has {{ num_riders }} rider(s).
Redirecting...


Classes | |
| class | SpatialMappingParameters |
| Sets the spatial mapping parameters. More... | |
| class | Mesh |
| A mesh contains the geometric (and optionally texture) data of the scene captured by spatial mapping. More... | |
| struct | Chunk |
| Represents a sub-mesh, it contains local vertices and triangles. More... | |
Enumerations | |
| enum | MAPPING_RESOLUTION |
| Spatial mapping depth resolution presets. More... | |
| enum | MAPPING_RANGE |
| Spatial mapping depth range presets. More... | |
| enum | SPATIAL_MAP_TYPE |
| Spatial Mapping type (default is mesh) More... | |
| enum | MESH_FILE_FORMAT |
| Mesh formats that can be saved/loaded with spatial mapping. More... | |
| enum | MESH_FILTER |
| Presets for filtering meshes scannedw ith spatial mapping. Higher values reduce total face count by more. More... | |
| enum | SPATIAL_MAPPING_STATE |
| Possible states of the ZED's Spatial Mapping system. More... | |
| enum | PLANE_TYPE |
| Type of the plane, determined by its orientation and whether detected by ZEDPlaneDetectionManager's DetectFloorPlane() or DetectPlaneAtHit(). More... | |
| enum | AREA_EXPORT_STATE |
| Possible states of the ZED's spatial memory area export, for saving 3D features used by the tracking system to relocalize the camera. This is used when saving a mesh generated by spatial mapping when Save Mesh is enabled - a .area file is saved as well. More... | |
|
strong |
|
strong |
Spatial mapping depth range presets.
| Enumerator | |
|---|---|
| NEAR | Geometry within 3.5 meters of the camera will be mapped. |
| MEDIUM | Geometry within 5 meters of the camera will be mapped. |
| FAR | Objects as far as 10 meters away are mapped. Useful for outdoors. |
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Mesh formats that can be saved/loaded with spatial mapping.
| Enumerator | |
|---|---|
| PLY | Contains only vertices and faces. |
| BIN | Contains only vertices and faces, encoded in binary. |
| OBJ | Contains vertices, normals, faces, and texture information (if possible). |
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strong |
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Possible states of the ZED's Spatial Mapping system.
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Type of the plane, determined by its orientation and whether detected by ZEDPlaneDetectionManager's DetectFloorPlane() or DetectPlaneAtHit().
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Possible states of the ZED's spatial memory area export, for saving 3D features used by the tracking system to relocalize the camera. This is used when saving a mesh generated by spatial mapping when Save Mesh is enabled - a .area file is saved as well.


REFOCUS by Gerard Veldhoven
The word love and the term family should be united in definition. Tradition dictates to us that a family consists of a father, mother and at least one child.
Love, as we discover through living our existence, is a phenomenon second to none and people cherish the moments as one unites with the person of one’s choice, regardless of sexual orientation.
This we were told was an unaccepted, formed family unit and that meant the traditional nucleus must be maintained because religious and other authorities tell us so. After all, that is what the Bible says we must do.
As we know, and with the advent of inclusive thinking and nature’s wonderful gift of the natural progression of falling in love, we are now in a world of a more accepting society where we all have the right for a happy existence. We fall in love with the person of our own individual choice rather than accept the person that is the perfect one according to parents and friends. We choose on the basis of our own inner feelings.
The number of those who constitute a “family” is no longer an issue as in the past. In Canada we learn, as in many western nations, that those who choose a partner who is compatible and one who is our chosen mate in love, are no less an accepted unit than any other. From the moment we are born, our parents and others instil the thoughts in our minds that at some point we meet someone, of course of the opposite sex, to have children, because our parents wish to be grandparents. That was a given.
Today, that thought persists, but to a much lesser extent.
The word “family” is usually defined as parents and their children, however, in our more inclusive world we now recognize family as those who are cohabitating, with or without offspring, or indeed living separately and are committed to each other and therefore a family unit. Sometimes living together is not an option, but nevertheless a true love keeps the relationship intact. Love is not confined to a certain situation or place, but survives because of commitment, regardless of sexual orientation, gender, type of family nucleus, or simply defining their love as family.
Times have changed dramatically and the fact members of our society are able to express love as they feel it is of profound importance. Love is the inner feelings for another and we must celebrate that and respect the outcome as a positive. Marriage is not the only family unit.
Religious institutions, for the most part, speak out against such situations and insist on marriage. The great news is that people who find love also become a family, perhaps not in the traditional sense, but nevertheless with the same intent in that love makes them equal.
This will remain a serious issue for some and frowned upon, dismissed as morally wrong, but love prevails and that is the end result. Love equals family and love is all it takes to be a family.
Comments and information<EMAIL_ADDRESS>Gerard Veldhoven is a longtime activist for the lesbian, gay, bisexual and transgender community. His column appears Wednesdays in The News.


This tutorial shows how to build a meta-trajectory containing a particular nucleotidic fragment with BIGNASim portal. For this tutorial, we will continue with the real example presented in the previous global analyses tutorial: CG base-pair step and the influence of the tetranucleotide environment in the sequence-dependent polymorphism of particular base-pair steps (P. Dans et al, NAR 2014). We have seen in the previous tutorial how to extract directly from the database the population of a particular helical parameter, recovering the Twist bimodality found in the above mentioned research. However, one can be interested in study different properties, not stored in BIGNASim database. For that, there is a possibility to generate and download (and also send to NAFlex server) a meta-trajectory containing just the nucleotide fragment of interest. It is build joining together the atomic information (3D cartesian coordinates) from all the simulations stored in our database enclosing the fragment. To illustrate the power of BIGNASim database, we interrogated the DB searching for the 16 possible different tetramers including CG base-pair, and we obtained results for all of them, going from 2 (TCGG) to 49 (TCGC) occurrences. We must say that for now, BIGNASim is populated with just a set of trajectories generated during the development and validation of the parmBSC1 force-field, and the information stored is going to be increased with new simulations.
In this tutorial, we will generate a meta-trajectory of one of the tetramers having less occurrences (ACGT, 4 occurrences), in order to show a quick example of the steps to be followed. The same example could be used to generate meta-trajectories for all of the possible tetranucleotides including CG, thus obtaining a valuable set of ensembles to analyse and compare. The first step in this tutorial consists on accessing the search section of the portal from the main menu:
The search section contains three different possibilities (see this help searching section for more details):
To generate a ACGT meta-trajectory, we will use the Search by Sequence Fragment section of the search engine. The Search by Base-Pair Step field can also be used if the particular sequence fragment we are interested in is precisely a base-pair step.
As we are interested in studying the behavior of the tetramer in just normal duplex B-DNA structures, simulated in equilibrium conditions, we select the corresponding features from the Search by Ontology section:
Results for this search are 3 different simulations (one less because of the filtering done). The information that we will have in the final meta-trajectory is going to be the sum of all these trajectories, so it will have information equivalent to 1.290µs (500 + 500 + 290 ns). Note that if we would have taken the most represented tetramer including CG (TCGC), we would be generating a meta-trajectory corresponding to up to 39 µs of simulated time. BIGNASim offers the possibility to generate a meta-trajectory combining all these different simulation trajectories. It is built getting the atomic information (3D-coordinates and topology) of the selected nucleotidic fragment. In this case, a meta-trajectory for the ACGT tetramer, representing 1,290µs of simulation data will be generated.
For that, the first thing we need is to select all the trajectories obtained in the search. That implies two different steps, first show all the results in the browse page, selecting an appropiate number of rows to be displayed in the selector placed in the top-left corner of the screen. In this case, as we are just dealing with 3 different trajectories, that is not necessary, but we should keep that in mind when working with a higher number of results.
Then, we select all the simulations (or just the ones we are interested in), clicking at the respective checkboxes. A single click to the checkbox placed in the top-left corner of the table, next to the Id. title will automatically select all trajectories. This can also be done with a button placed at the bottom of the browse page named Mark All.
Once we have the desired trajectories selected, the next step is clicking at the Retrieve Metatrajectory button placed at the top-right part of the screen (next to the Cassandra's logo, representing an eye).
The next step for generating the meta-trajectory is to define a desired frame step. In this case, we want to extract 100 snapshots as representatives of each trajectory. For that, we type in the Frame input section: 1:5000:50 (for snapshot 1 to snapshot 5000, every 50 snapshots). At this step, we can choose between downloading the new generated trajectory or send it directly to the NAFlex nucleic acid flexibility server. As this trajectory is going to be quite big, we will download it to work in our own machines. The download section offers different possible trajectory formats. We will choose the one by default, DCD binary, because is one of the most lighweight formats, and it will accelerate the process.
The downloading process will be launched asynchronously. A waiting page will be opened informing that the trajectory is currently being generated. This waiting page will be automatically redirected to a data manager workspace where the resulting trajectory (as well as all the trajectories generated in this browser session) can be found, downloaded, or sent to NAFlex server (see data manager help section).


10:32 PM]TonyL: sound notification was implemented mostly for iOS (will allow mm2 to work in background). In AtomicDEX settings you can minimize the sound volume of these notifications
10:45 PM]ca333:for android we can certainly implement a "silent sound" - for iOS we simply can't. However, we can work out a sound/format that isn't annoying. Maybe being able to chose the sound will be an improvement: "Female with irish accents" vs. "Male with scotch accent", "Beeps", etc.
10:46 PM]jl777c:you cant have a very low volume (close to inaudible) sound on iOs?
[10:48 PM]ca333:you can - but the goal is to get the app approval from apple - so we had to declare the application as an app which needs sound-notifications (key-feature) since that is one of the few options we had to be able to keep the app running in the background. If there is no sound it will be interpreted as a bypass of the "no app can run in the background" rule - and that would bring us back to the old situation.
8:29 AM]artemciy_dice:Playing silent sounds on iOS is unlikely an option, it is an old and infamous trick, iOS reviewers are well-aware of it and it gets applications banned most of the time.
[8:31 AM]artemciy_dice:But reading this discussion I see that the annoying sound is mostly the one we have when there are active orders but no ongoing swaps. I think we can tune the volume of that one even lower. And we can turn it off altogether on Android, where we don't have to play the sound all the time to stay alive.
Just my two cents.
10:50 PM]artemciy_dice:AtomicDEX is in beta and we're only just started testing this functionality. The major question now is whether the Apple would approve the background mode. If they will, then we'll be further improving this functionality with things like choosing different sounds, allowing you to use the sounds from your own sound library and such. Anyhow, thanks for testing the app!
(edited)