Chapter 10 Introduction to Bayesian Model Comparison
A Bayesian model is composed of both a model for the data (likelihood) and a prior distribution on model parameters
usually refers to choosing between different models for the data (likelihoods)
But it can also concern choosing between models with the same likelihood but different priors
In Bayesian model comparison, prior probabilities are assigned to each of the models, and these probabilities are updated given the data according to Bayes rule
Bayesian model comparison can be viewed as Bayesian estimation in a model with an extra level for ``model’’
Suppose I have some trick coins, some of which are biased in favor of landing on heads, and some of which are biased in favor of landing on tails. I will select a trick coin at random; let \(\theta\) be the probability that the selected coin lands on heads in any single flip. I will flip the coin \(n\) times and use the data to decide about the direction of its bias. This can be viewed as a choice between two models
- Model 1: the coin is biased in favor of landing on heads
- Model 2: the coin is biased in favor of landing on tails
Specify a Bayesian model for the framework of this problem.
Assume that in model 1 the prior distribution for \(\theta\) is Beta(7.5, 2.5). Suppose in \(n=10\) flips there are 6 heads. Describe, how in principle, how could compute the probability of observing these data given that model 1 is correct. Also, explain how you could use simulation to approximate this probability.
Assume that in model 2 the prior distribution for \(\theta\) is Beta(2.5, 7.5). Repeat the previous part for model 2.
Compute and interpret the Bayes Factor in favor of model 1 given 6 heads in 10 flips.
Suppose our prior probability for each model was 0.5. Find the posterior probability of each model given 6 heads in 10 flips.
Suppose I know I have a lot more tail biased coins, so my prior probability for model 1 was 0.1. Find the posterior probability of each model given 6 heads in 10 flips.
Now suppose I want to predict the number of heads in the next 10 flips of the selected coin. Describe, how in principle, you could compute the posterior predictive distribution of the number of heads in the next 10 flips given 6 heads in the first 10 flips . Also, explain how you could use simulation to approximate this distribution.
Reconsider the previous problem. How could you find the posterior predictive distribution of the number of heads in the next 10 flips given 6 heads in the first 10 flips without assuming that model 1 is the correct model? (Assume that the prior probability for each model was 0.5 like in part (e).)
The Bayesian model is the full hierarchical structure which spans all models being compared.
Thus, the most complete posterior prediction takes into account all models, weighted by their posterior probabilities.
That is, prediction is accomplished by taking a weighted average across the models, with weights equal to the posterior probabilities of the models. This is called .
Suppose again I select a coin, but now the decision is whether the coin is fair. Suppose we consider the two models
- “Must be fair” model: prior distribution for \(\theta\) is Beta(500, 500)
- “Anything is possible” model: prior distribution for \(\theta\) is Beta(1, 1)
Compute the Bayes factor in favor of the ``must be fair’’ model given 15 heads in 20 flips. Which model does the Bayes factor favor?
Compute the Bayes factor in favor of the ``must be fair’’ model given 11 heads in 20 flips. Which model does the Bayes factor favor?
The anything is possible'' model has any value available to it, including 0.5 and the sample proportion 0.55. Why then is the
must be fair’’ option favored in the previous part?
Complex models generally have an inherent advantage over simpler models because complex models have many more options available, and one of those options is likely to fit the data better than any of the fewer options in the simpler model.
But we don’t always want to just choose the more complex model.
Bayesian model comparison naturally compensates for discrepancies in model complexity.
In more complex models, prior probabilities are diluted over the many options available.
Even if a complex model has some particular combination of parameters that fit the data well, the prior probability of that particular combination is likely to be small because the prior is spread more thinly than for a simpler model.
Thus, in Bayesian model comparison, a simpler model can ``win’’ if the data are consistent with it, even if the complex model fits well.
Compute the Bayes factor in favor of the ``must be fair’’ model given 65 heads in 100 flips. Which model does the Bayes factor favor?
We have discussed different notions of a non-informative/vague'' prior. We typically think of Beta(1, 1) = Uniform(0, 1) as a non-informative prior, but there are other considerations. In particular, a Beta(0.01, 0.01) is often used a non-informative prior in this context (think of Beta(0.01, 0.01) like an approximation to the improper Beta(0, 0) prior based on
no prior successes or failures’’.)
Suppose the anything is possible'' model corresponds to a Beta(0.01, 0.01) prior distribution for $\theta$. Compute the Bayes factor in favor of the
must be fair’’ model given 65 heads in 100 flips. Which model does the Bayes factor favor? Is the choice of model sensitive to the change of prior distribution within the ``anything is possible’’ model?
For each of the two anything is possible'' priors, find the posterior distribution of $\theta$ and a 95\% posterior credible interval for $\theta$ given 65 heads in 100 flips. Is estimation of $\theta$ within the
anything is possible’’ model sensitive to the change in the prior distribution for \(\theta\)?
In Bayesian , the posterior distribution is typically not too sensitive to changes in prior (provided that there is a reasonable amount of data and the prior is not too strict)
In contrast, in Bayesian , the posterior probabilities of the models and the Bayes factors can be extremely sensitive to the choice of prior distribution within each model
When comparing different models, prior distributions on parameters within each model should be equally informed. One strategy is to use a small set of ``training data’’ to inform the prior of each model before comparing.
Suppose that in the first 10 flips there were 6 heads. Repeat part d) but use the posterior after the first 10 flips as the prior distribution for each of the models.
Suppose that in the first 10 flips there were 6 heads. Repeat part e) but use the posterior after the first 10 flips as the prior distribution for each of the models.
Consider a null hypothesis significance test of \(H_0:\theta=0.5\) versus \(H_1:\theta\neq 0.5\). How does this situation resemble the previous problem?
A null hypothesis significance test can be viewed as a problem of Bayesian model selection in which one model has a prior distribution that places all its credibility on the null hypothesized value
But is it really plausible that the parameter is exactly equal to the hypothesized value?
However, this model-comparison (Bayes factor) approach to testing can be extremely sensitive to the choice of prior corresponding to the alternative hypothesis.
An alternative Bayesian approach to testing involves choosing a
A ROPE indicates a small range of parameter values that are considered to be practically equivalent to the null hypothesized value
A hypothesized value is rejected — i.e. declared to be not credible — if its ROPE lies outside the 95% posterior credible interval for the parameter
A hypothesized value is accepted for practical purposes if its ROPE contains the 95% posterior credible interval for the parameter
But how do you choose the ROPE?
In general, traditional testing of point null hypotheses (e.g. `` effect/difference’’) is not a primary concern in Bayesian statistics.
Rather, the provides all relevant information to make decisions about theoretically meaningful issues.


This page has a <canvas> element within a 16px by 16px <div> located between these two paragraphs. The JavaScript (external, loaded at the end of the <body>) takes an array of color values and renders them pixel-by-pixel to the canvas.
Select a sprite below to load into the canvas. The images used below are just plain PNG files, but the sprites loaded into the canvas are all arrays of RGB values. Each sprite has different pixel dimensions, and the script resizes the canvas element to match the sprite loaded.


BLOG POSTS
- Mar. 19, 2015 Technical Blog - Github Basics
- Mar. 19, 2015 Culture Blog - Kitchen vs. Table
- Mar. 26, 2015 Technical Blog - CSS, Class or ID?
- Apr. 1, 2015 Culture Blog - Thinking Style and Learning
- Apr. 1, 2015 Technical Blog - Arrays and Hashes
- Apr. 10, 2015 Technical Blog - Enumerable Methods - #cycle
- Apr. 10, 2015 Cultural Blog - Woman, or the lack thereof, in the Tech World
- Apr. 14, 2015 Technical Blog - Classes in OOP
- Apr. 14, 2015 Cultural Blog - Pairing and Feedback
- Apr. 24, 2015 Technical Blog - OO vs. Functional Programming
- Apr. 24, 2015 Cultural Blog - Stereotype Threat - Age
- May. 01, 2015 Technical Blog - JavaScript vs. Ruby
- May. 01, 2015 Cultural Blog - Affirmation and Stereotype Threat - Self Reliance
- May. 06, 2015 Technical Blog - How do Computers Work?
- May. 06, 2015 Cultural Blog - Conflict
- May. 16, 2015 Cultural Blog - Asking Good Questions
- Oct. 27, 2015 - Dev Bootcamp - What a Ride!


Home
About
Our Team
Camp Site
Mission & Vision
Resource
Thing to do
Activities
Parks
Shops
Events
Contact
Map
Directions
News
Articles
Savings
Tent
$100
Boots
$80
Select Discount:
15%
35%
50%


What is HTML Canvas?
The HTML <canvas> element is used to draw graphics, on the fly, via scripting (usually JavaScript). The <canvas> element is only a container for graphics. You must use a script to actually draw the graphics.
Canvas has several methods for drawing paths, boxes, circles, text, and adding images.
A canvas is a rectangular area on an HTML page. By default, a canvas has no border and no content. The markup looks like this:
<canvas id="myCanvas" width="200" height="100"></canvas>
Note: Always specify an id attribute (to be referred to in a script), and a width and height attribute to define the size of the canvas.
[Example of canvas application by Kostas S.]What is HTML SVG?
- SVG stands for Scalable Vector Graphics
- SVG is used to define graphics for the Web
- SVG is a W3C recommendation
The HTML <svg> element (introduced in HTML5) is a container for SVG graphics. SVG has several methods for drawing paths, boxes, circles, text, and graphic images.
An example in SVG:
A peek at the code:
<svg xmlns="http://www.w3.org/2000/svg" width="1000" height="1000" > <g> <circle id="circle" style="stroke: black; fill: #f8f8f8;" cx="100" cy="100" r="100"/> <line id="hour0" x1="100" y1="10" x2="100" y2="0"/> <line id="hour1" x1="150" y1="13" x2="145" y2="22"/> ... </g> <g> <line x1="100" y1="100" x2="100" y2="45" style="stroke-width: 6px; stroke: green;" id="hourhand"/> <line x1="100" y1="100" x2="100" y2="15" style="stroke-width: 4px; stroke: blue;" id="minutehand"/> <line x1="100" y1="100" x2="100" y2="5" style="stroke-width: 2px; stroke: red;" id="secondhand"/> </g> </svg>
In addition, we use a Javascript script which modifies (erases and draws again) the elements of the SVG element (by using their id) so that the clock counters move.
Differences Between SVG and Canvas
SVG is a language for describing 2D graphics in XML.
Canvas draws 2D graphics, on the fly (with a JavaScript).
SVG is XML based, which means that every element is available within the SVG DOM.
In SVG, each drawn shape is remembered as an object. If attributes of an SVG object are changed, the browser can automatically re-render the shape.
Canvas is rendered pixel by pixel. In canvas, once the graphic is drawn, it is forgotten by the browser. If its position should be changed, the entire scene needs to be redrawn, including any objects that might have been covered by the graphic.


Our dataset is gathered by using a new representation language to annotate over the AQuA-RAT dataset. AQuA-RAT has provided the questions, options, rationale, and the correct options.
• Question: A train running at the speed of 48 km / hr crosses a pole in 9 seconds . what is the length of the train ?
• Rationale: Speed = ( 48 x 5 / 18 ) m / sec = ( 40 / 3 ) m / sec . length of the train = ( speed x time ) . length of the train = ( 40 / 3 x 9 ) m = 120 m . answer is c .
• Options: a ) 140 , b ) 130 , c ) 120 , d ) 170 , e ) 160
• Correct Option is: C
The rationales are noisy, incomplete and sometimes incorrect. We correct these rationales and provide stepwise solutions for a portion of AQuA-RAT.
• Our Annotated Formula: multiply(divide(multiply(48, const_1000), const_3600), 9)
We have designed an annotation platform through the Figure Eight platform. We have defined a representation language that can capture the steps that are necessary for finding the solution to the problem. Each step in our final program is the predefined operation and its arguments, which can be chosen from the problems, a list of constants or previous calculations. Here is the overview of our job:
Figure eight provides unique features that help us achieve data that is clean, reliable and accurate. The task of annotating math word problem is especially complicated and time consuming task. The points that should considered are :
The results of the annotation task are not all correct. There are cases that a logically wrong program would get us to the final answer. Therefore we designed a validation task. Here are the steps for that task:
The final answer is acceptable when the currect calculation result is the same as the correct option and at least one number from problem is used. The Result would automatically copied to the requiredd result field which let the contributors to submit their results.
CloseTwo paths for a problems, both resulting in a same number, but the one on the buttom is logically wrong.
CloseBy clicking on portion of experssion contributors can see the result for that part of expression.
Close


>
This is part of the old website. New website begins at fontforge.github.io
Are you a web developer? Help us migrate this page on Github
Apple and Microsoft rasterizers interpret some truetype composites differently and this can result in radically different displays of a character. Here is a character designed to be displayed on a Mac, the composite consists of two components, one of which has been rotated and scaled. If Apple's definition of composite behavior is followed then the two strokes will just barely touch at the left edge. If MicroSoft's is followed then the diagonal stroke will be offset from the vertical by a considerable amount.
|
|
Glyph 11: off = 0x0000016E, len = 34 numberOfContours: -1 (Composite) xMin: 810 yMin: -1 xMax: 1256 yMax: 446 0: Flags: 0x00A7 Glyf Index: 6 X WOffset: 810 Y WOffset: 57 X Scale: 1.060608 Scale 01: -1.060669 Scale 10: 1.060608 Y Scale: 1.060608 Other: Round X,Y to Grid 1: Flags: 0x0007 Glyf Index: 6 X WOffset: 1156 Y WOffset: 0 Other: Round X,Y to Grid |
On the right is a dump (using microsoft's ttfdump program) of the corresponding glyph. As you can see it consists of two components, the first has been scaled by 1.5 and then rotated 45°, its horizontal offset is 810. The other is neither rotated nor scaled and its horizontal offset is 1156. According to Apple the first component should have its offset scaled by 1.5 (yielding an offset of 1215 which is just enough for the two glyphs to kiss). According to MicroSoft the offset is not scaled.
This problem only arises if a composite is scaled (actually: has a transformation matrix whose determinant is not 1, so skewing is also an issue but rotating and flipping are not), so it is fairly rare.
The OpenType spec now provides two bits (in the composite flags array) to disambiguate this case (neither bit is set in the above example, hence the problem). If bit 0x800 is set then the rasterizer should use Apple's definition, while if bit 0x1000 is set MicroSoft's (and now OpenType's default) definition. If neither bit is set then the platform's default will be used. Apple does not document these bits on their site, but they do support them (At least in Mac OS/9 and Mac OS/X). On the other hand, MicroSoft does document the bits, but the rasterizer on my Win/Me box does not support Apple's rules.
Apple documents a moderately complex algorithm for determining the factor
by which each offset should be scaled. This algorithm does not match the
behavior of Apple's rasterizer under Mac OS/9 and Mac OS/X. I have determined
empirically that the proper scaling factors are:
horizontalScale = sqrt(XScale*XScale +
Scale01*Scale01)
verticalScale = sqrt(YScale*YScale +
Scale10*Scale10)
(using the same notation as ttfdump). Basically, this means that the horizontal
scale factor is the length of the vector that results from applying the
transformation matrix to (1,0).
Here is a collection of the permutations of a font which displays the problem. The characters B-L contain scaled or rotated or flipped components. They also contain another component to show where the transformed component should line up if Apple's rules are followed (as in the example at the top of the page, which may be found at "F").
| Apple Rules | MS Rules | Unspecified | |
|---|---|---|---|
Windows font |
CompositeMac.ttf | CompositeMS.ttf | CompositeAmbiguous.ttf |
Mac resource font |
CompositeMac.ttf.bin | CompositeMS.ttf.bin | CompositeAmbiguous.ttf.bin |
Mac dfont (OS/X) |
CompositeMac.dfont | CompositeMS.dfont | CompositeAmbiguous.dfont |
(Do not try editing these fonts with PfaEdit. PfaEdit automagically will add the OpenType bit to the flags array).


Data from the past sixteen MLB seasons shows that there's strong correlation between the number of games a team wins and it's ERA. Over this time span, general managers are better off assembling a great pitching staff than lineup.
Since 2000, 88 percent of teams that made the playoffs had an ERA below the league average. In comparison, 77 percent of those teams scored more runs than the league average. You're probably thinking there's not a whole lot in it. But since former MLB Commissioner Bud Selig added a second wild card to the American and National Leagues for the 2012 season, those figures have gone in opposite directions.
In the past four seasons, the number of playoff teams with run totals in the league's upper half dropped to 73 percent, whereas playoff teams with ERAs below the league average soared to 90 percent. In other words, nine of out every ten playoff teams has one of the better ERAs in the league.
Pitching wins ball games, folks.


systemd
systemd is a
commonly available init system (PID 1) on many Linux distributions. It
offers process monitoring (including automatic restarts) and other
useful features for running Puma
in production.
Service Configuration
Below is a sample puma.service configuration file for systemd, which can be copied or symlinked to /etc/systemd/system/puma.service, or if desired, using an application or instance specific name.
Note that this uses the systemd preferred "simple" type where the start command remains running in the foreground (does not fork and exit). See also, the Alternative Forking Configuration below.
[Unit]
Description=Puma HTTP Server
After=network.target
# Uncomment for socket activation (see below)
# Requires=puma.socket
[Service]
# Foreground process (do not use --daemon in ExecStart or config.rb)
Type=simple
# Preferably configure a non-privileged user
# User=
# The path to the your application code root directory.
# Also replace the "<YOUR_APP_PATH>" place holders below with this path.
# Example /home/username/myapp
WorkingDirectory=<YOUR_APP_PATH>
# Helpful for debugging socket activation, etc.
# Environment=PUMA_DEBUG=1
# SystemD will not run puma even if it is in your path. You must specify
# an absolute URL to puma. For example /usr/local/bin/puma
# Alternatively, create a binstub with `bundle binstubs puma --path ./sbin` in the WorkingDirectory
ExecStart=/<FULLPATH>/bin/puma -C <YOUR_APP_PATH>/puma.rb
# Variant: Rails start.
# ExecStart=/<FULLPATH>/bin/puma -C <YOUR_APP_PATH>/config/puma.rb ../config.ru
# Variant: Use `bundle exec --keep-file-descriptors puma` instead of binstub
# Variant: Specify directives inline.
# ExecStart=/<FULLPATH>/puma -b tcp://<IP_ADDRESS>:9292 -b ssl://<IP_ADDRESS>:9293?key=key.pem&cert=cert.pem
Restart=always
[Install]
WantedBy=multi-user.target
See systemd.exec for additional details.
Socket Activation
systemd and puma also support socket activation, where systemd opens the listening socket(s) in advance and provides them to the puma master process on startup. Among other advantages, this keeps listening sockets open across puma restarts and achieves graceful restarts, including when upgraded puma, and is compatible with both clustered mode and application preload.
Note: Any wrapper scripts which exec
, or other indirections in
ExecStart
, may result in activated socket file descriptors being closed
before they reach the puma master process. For example, if using bundle exec
,
pass the --keep-file-descriptors
flag. bundle exec
can be avoided by using a
puma
executable generated by bundle binstubs puma
. This is tracked in
[#1499].
Note: Socket activation doesn't currently work on jruby. This is tracked in #1367.
To use socket activation, configure one or more ListenStream
sockets
in a companion *.socket
unit file. Also uncomment the associated
Requires
directive for the socket unit in the service file (see
above.) Here is a sample puma.socket, matching the ports used in the
above puma.service:
[Unit]
Description=Puma HTTP Server Accept Sockets
[Socket]
ListenStream=<IP_ADDRESS>:9292
ListenStream=<IP_ADDRESS>:9293
# AF_UNIX domain socket
# SocketUser, SocketGroup, etc. may be needed for Unix domain sockets
# ListenStream=/run/puma.sock
# Socket options matching {Puma} defaults
NoDelay=true
ReusePort=true
Backlog=1024
[Install]
WantedBy=sockets.target
See systemd.socket for additional configuration details.
Note that the above configurations will work with Puma
in either
single process or cluster mode.
Sockets and symlinks
When using releases folders, you should set the socket path using the
shared folder path (ex. /srv/projet/shared/tmp/puma.sock
), not the
release folder path (/srv/projet/releases/1234/tmp/puma.sock
).
Puma
will detect the release path socket as different than the one provided by
systemd and attempt to bind it again, resulting in the exception
There is already a server bound to:
.
Usage
Without socket activation, use systemctl
as root (e.g. via sudo
) as
with other system services:
# After installing or making changes to puma.service
systemctl daemon-reload
# Enable so it starts on boot
systemctl enable puma.service
# Initial start up.
systemctl start puma.service
# Check status
systemctl status puma.service
# A normal restart. Warning: listeners sockets will be closed
# while a new puma process initializes.
systemctl restart puma.service
With socket activation, several but not all of these commands should be run for both socket and service:
# After installing or making changes to either puma.socket or
# puma.service.
systemctl daemon-reload
# Enable both socket and service so they start on boot. Alternatively
# you could leave puma.service disabled and systemd will start it on
# first use (with startup lag on first request)
systemctl enable puma.socket puma.service
# Initial start up. The Requires directive (see above) ensures the
# socket is started before the service.
systemctl start puma.socket puma.service
# Check status of both socket and service.
systemctl status puma.socket puma.service
# A "hot" restart, with systemd keeping puma.socket listening and
# providing to the new puma (master) instance.
systemctl restart puma.service
# A normal restart, needed to handle changes to
# puma.socket, such as changing the ListenStream ports. Note
# daemon-reload (above) should be run first.
systemctl restart puma.socket puma.service
Here is sample output from systemctl status
with both service and
socket running:
● puma.socket - {Puma} HTTP Server Accept Sockets
Loaded: loaded (/etc/systemd/system/puma.socket; enabled; vendor preset: enabled)
Active: active (running) since Thu 2016-04-07 08:40:19 PDT; 1h 2min ago
Listen: <IP_ADDRESS>:9233 (Stream)
<IP_ADDRESS>:9234 (Stream)
Apr 07 08:40:19 hx systemd[874]: Listening on {Puma} HTTP Server Accept Sockets.
● puma.service - {Puma} HTTP Server
Loaded: loaded (/etc/systemd/system/puma.service; enabled; vendor preset: enabled)
Active: active (running) since Thu 2016-04-07 08:40:19 PDT; 1h 2min ago
Main PID: 28320 (ruby)
CGroup: /system.slice/puma.service
├─28320 puma 3.3.0 (tcp://<IP_ADDRESS>:9233,ssl://<IP_ADDRESS>:9234?key=key.pem&cert=cert.pem) [app]
├─28323 puma: cluster worker 0: 28320 [app]
└─28327 puma: cluster worker 1: 28320 [app]
Apr 07 08:40:19 hx puma[28320]: {Puma} starting in cluster mode...
Apr 07 08:40:19 hx puma[28320]: * Version 3.3.0 (ruby 2.2.4-p230), codename: Jovial Platypus
Apr 07 08:40:19 hx puma[28320]: * Min threads: 0, max threads: 16
Apr 07 08:40:19 hx puma[28320]: * Environment: production
Apr 07 08:40:19 hx puma[28320]: * Process workers: 2
Apr 07 08:40:19 hx puma[28320]: * Phased restart available
Apr 07 08:40:19 hx puma[28320]: * Activated tcp://<IP_ADDRESS>:9233
Apr 07 08:40:19 hx puma[28320]: * Activated ssl://<IP_ADDRESS>:9234?key=key.pem&cert=cert.pem
Apr 07 08:40:19 hx puma[28320]: Use Ctrl-C to stop
Alternative Forking Configuration
Other systems/tools might expect or need puma to be run as a
"traditional" forking server, for example so that the pumactl
command can be used directly and outside of systemd for
stop/start/restart. This use case is incompatible with systemd socket
activation, so it should not be configured. Below is an alternative
puma.service config sample, using Type=forking
and the --daemon
flag in ExecStart
. Here systemd is playing a role more equivalent to
SysV init.d, where it is responsible for starting Puma
on boot
(multi-user.target) and stopping it on shutdown, but is not performing
continuous restarts. Therefore running Puma
in cluster mode, where the
master can restart workers, is highly recommended. See the systemd
Restart directive for details.
[Unit]
Description=Puma HTTP Forking Server
After=network.target
[Service]
# Background process configuration (use with --daemon in ExecStart)
Type=forking
# Preferably configure a non-privileged user
# User=
# The path to the puma application root
# Also replace the "<WD>" place holders below with this path.
WorkingDirectory=
# The command to start {Puma}
# (replace "<WD>" below)
ExecStart=bundle exec puma -C <WD>/shared/puma.rb --daemon
# The command to stop {Puma}
# (replace "<WD>" below)
ExecStop=bundle exec pumactl -S <WD>/shared/tmp/pids/puma.state stop
# Path to PID file so that systemd knows which is the master process
PIDFile=<WD>/shared/tmp/pids/puma.pid
# Should systemd restart puma?
# Use "no" (the default) to ensure no interference when using
# stop/start/restart via `pumactl`. The "on-failure" setting might
# work better for this purpose, but you must test it.
# Use "always" if only `systemctl` is used for start/stop/restart, and
# reconsider if you actually need the forking config.
Restart=no
# `puma_ctl restart` wouldn't work without this. It's because `pumactl`
# changes PID on restart and systemd stops the service afterwards
# because of the PID change. This option prevents stopping after PID
# change.
RemainAfterExit=yes
[Install]
WantedBy=multi-user.target
capistrano3-puma
By default,
capistrano3-puma uses
pumactl
for deployment restarts, outside of systemd. To learn the
exact commands that this tool would use for ExecStart
and
ExecStop
, use the following cap
commands in dry-run mode, and
update from the above forking service configuration accordingly. Note
also that the configured User
should likely be the same as the
capistrano3-puma :puma_user
option.
stage=production # or different stage, as needed
cap $stage puma:start --dry-run
cap $stage puma:stop --dry-run


Overview
Package
Class
Use
Tree
Deprecated
Index
Help
PREV LETTER
NEXT LETTER
FRAMES
NO FRAMES
All Classes
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
C
CALL_SERVICE
- Static variable in interface eu.irreality.age.
ARSPConstants
cast(Entity, Entity, int)
- Method in class eu.irreality.age.
Effect
cast(Spell, Entity)
- Method in class eu.irreality.age.
Mobile
cast(Entity, Entity)
- Method in class eu.irreality.age.
Spell
CASTING
- Static variable in class eu.irreality.age.
Mobile
CBM_AMIGA_NTSC_CLOCK
- Static variable in class micromod.
Synthesizer
CBM_AMIGA_PAL_CLOCK
- Static variable in class micromod.
Synthesizer
cerrar()
- Method in class eu.irreality.age.
Item
changeMode(String, String, String)
- Method in class eu.irreality.age.
IrcBot
changeMode(String, String, String)
- Method in interface eu.irreality.age.
IrcListener
changeState(World)
- Method in class eu.irreality.age.
AbstractEntity
changeState(World)
- Method in class eu.irreality.age.
Entity
Hace todo lo necesario para el cambio de estado.
changeState(World)
- Method in class eu.irreality.age.
Item
changeState(World)
- Method in class eu.irreality.age.
Mobile
changeState(World)
- Method in class eu.irreality.age.
Path
changeState(World)
- Method in class eu.irreality.age.
Player
changeState(World)
- Method in class eu.irreality.age.
Room
changeState(World)
- Method in class eu.irreality.age.
Spell
Channel
- Class in
micromod
A Channel attemps to do a bit of Amiga hardware emulation to play ProTracker modules a bit better than most.
Channel(int, boolean)
- Constructor for class micromod.
Channel
Constructor
channelAction(String, String, String)
- Method in class eu.irreality.age.
IrcBot
channelAction(String, String, String)
- Method in interface eu.irreality.age.
IrcListener
channelAwayMsg(String, String, String)
- Method in class eu.irreality.age.
IrcBot
channelAwayMsg(String, String, String)
- Method in interface eu.irreality.age.
IrcListener
channelJoin(String, String)
- Method in class eu.irreality.age.
IrcBot
channelJoin(String, String)
- Method in interface eu.irreality.age.
IrcListener
channelMsg(String, String, String)
- Method in class eu.irreality.age.
IrcBot
channelMsg(String, String, String)
- Method in interface eu.irreality.age.
IrcListener
channelPart(String, String)
- Method in class eu.irreality.age.
IrcBot
channelPart(String, String)
- Method in interface eu.irreality.age.
IrcListener
channelTopic(String, String)
- Method in class eu.irreality.age.
IrcBot
channelTopic(String, String)
- Method in interface eu.irreality.age.
IrcListener
characterChangeState(World)
- Method in class eu.irreality.age.
Player
checkForANSISupport()
- Method in class eu.irreality.age.
SimpleTelnetClientProxy
CLEAR_SCREEN
- Static variable in interface eu.irreality.age.
ARSPConstants
CLIENT_TYPE_REPLY
- Static variable in interface eu.irreality.age.
ARSPConstants
CLIENT_TYPE_REQUEST
- Static variable in interface eu.irreality.age.
ARSPConstants
clone()
- Method in class eu.irreality.age.
AbstractEntity
clone()
- Method in class eu.irreality.age.
Description
clone()
- Method in class eu.irreality.age.
Inventory
clone()
- Method in class eu.irreality.age.
Item
clone()
- Method in class eu.irreality.age.
Mobile
clone()
- Method in class eu.irreality.age.
MobileList
clone()
- Method in class eu.irreality.age.
ObjectCode
clone()
- Method in class eu.irreality.age.
Path
clone()
- Method in class eu.irreality.age.
PropertyEntry
clone()
- Method in class eu.irreality.age.
Room
clone()
- Method in class eu.irreality.age.
Traits
clone()
- Method in class eu.irreality.age.
Weapon
clone()
- Method in class eu.irreality.age.
Wearable
cloneCopyingItems(World, boolean, boolean)
- Method in class eu.irreality.age.
Inventory
cloneIfNecessary()
- Method in class eu.irreality.age.
ObjectCode
close()
- Method in class micromod.output.
JavaSoundOutputDevice
close()
- Method in class micromod.output.
OSSOutputDevice
close()
- Method in interface micromod.output.
OutputDevice
Close the output device and release any resources.
CodeRunner
- Class in
eu.irreality.age
CodeRunner(String, World)
- Constructor for class eu.irreality.age.
CodeRunner
COLORCODE_INFO_BEGIN
- Static variable in interface eu.irreality.age.
ARSPConstants
COLORCODE_INFO_END
- Static variable in interface eu.irreality.age.
ARSPConstants
COLORCODE_INFO_LINE
- Static variable in interface eu.irreality.age.
ARSPConstants
COLORCODE_REQUEST
- Static variable in interface eu.irreality.age.
ARSPConstants
ColoredSwingClient
- Class in
eu.irreality.age
ColoredSwingClient(SwingAetheriaGameLoader, JTextField, JScrollPane, JTextPane, Vector)
- Constructor for class eu.irreality.age.
ColoredSwingClient
ColoredSwingClient(SwingAetheriaGameLoader, Vector)
- Constructor for class eu.irreality.age.
ColoredSwingClient
colorToString(Color)
- Static method in class eu.irreality.age.
ColoredSwingClient
completeItemID(int)
- Static method in class eu.irreality.age.
Utility
Completa una ID incompleta a itemID
completeMobileID(int)
- Static method in class eu.irreality.age.
Utility
Completa una ID incompleta a mobileID
completeRoomID(int)
- Static method in class eu.irreality.age.
Utility
Completa una ID incompleta a roomID
completeWithZeroes(int, int)
- Static method in class eu.irreality.age.
Utility
Completa un número con ceros hasta alcanzar nzeroes cifras.
Compressor
- Class in
micromod
Compressor is an implementation of WaveShaper that utilises an efficient algorithm I found on www.smartelectronix.com/musicdsp A compressor improves the sound of distortion and so allows you to crank up the gain without things sounding too horrible.
Compressor()
- Constructor for class micromod.
Compressor
configurationFile
- Static variable in class eu.irreality.age.
ServerConfigurationWindow
configure(int, boolean)
- Method in class micromod.
Channel
Configure the Channel to use the specified sampling rate and PAL/NTSC mode.
constructAbstractEntity(World, Node, boolean, String)
- Method in class eu.irreality.age.
AbstractEntity
constructItem(World, String, boolean, String)
- Method in class eu.irreality.age.
Item
El pedazo de constructor que lee un item de un fichero.
constructItem(World, Node, boolean, String)
- Method in class eu.irreality.age.
Item
constructMob(World, String, boolean, String)
- Method in class eu.irreality.age.
Mobile
El pedazo de constructor que lee un bicho de un fichero.
constructMob(World, Node, boolean, String)
- Method in class eu.irreality.age.
Mobile
constructName(int, int)
- Method in class eu.irreality.age.
Item
constructName(int, Entity)
- Method in class eu.irreality.age.
Item
constructName(int)
- Method in class eu.irreality.age.
Mobile
constructName(int, int)
- Method in class eu.irreality.age.
Mobile
constructName(int, Entity)
- Method in class eu.irreality.age.
Mobile
constructName(int, Entity)
- Method in interface eu.irreality.age.
Nameable
constructName2(int, int)
- Method in class eu.irreality.age.
Item
constructName2(int, Entity)
- Method in class eu.irreality.age.
Item
constructName2(int)
- Method in class eu.irreality.age.
Mobile
constructName2(int, int)
- Method in class eu.irreality.age.
Mobile
constructName2(int, Entity)
- Method in class eu.irreality.age.
Mobile
constructName2(int, Entity)
- Method in interface eu.irreality.age.
Nameable
constructName2OneItem()
- Method in class eu.irreality.age.
Item
constructName2OneItem(Entity)
- Method in class eu.irreality.age.
Item
constructName2OneItem(Entity)
- Method in class eu.irreality.age.
Mobile
constructName2True(int, int)
- Method in class eu.irreality.age.
Item
constructName2True(int, Entity)
- Method in class eu.irreality.age.
Item
constructName2True(int, Entity)
- Method in class eu.irreality.age.
Mobile
constructRoom(World, String)
- Method in class eu.irreality.age.
Room
El pedazo de constructor que lee una habitación de un fichero.
constructRoom(World, Node, boolean)
- Method in class eu.irreality.age.
Room
constructSpell(World, Node, boolean, String)
- Method in class eu.irreality.age.
Spell
contains(Effect)
- Method in class eu.irreality.age.
EffectList
contains(Item)
- Method in class eu.irreality.age.
Inventory
contains(Mobile)
- Method in class eu.irreality.age.
MobileList
contains(Spell)
- Method in class eu.irreality.age.
SpellList
convert(short[], short[], int, byte[], int)
- Method in interface micromod.output.converters.
AudioFormatConverter
Perform conversion of audio.
convert(short[], short[], int, byte[], int)
- Method in class micromod.output.converters.
SS16BEAudioFormatConverter
convert(short[], short[], int, byte[], int)
- Method in class micromod.output.converters.
SS16LEAudioFormatConverter
convert(short[], short[], int, byte[], int)
- Method in class micromod.output.converters.
UM8AudioFormatConverter
copyAbstractEntityFieldsTo(AbstractEntity)
- Method in class eu.irreality.age.
AbstractEntity
copyEntityFields(Entity)
- Method in class eu.irreality.age.
Entity
Blanco de los estados
copyItemFieldsTo(Item)
- Method in class eu.irreality.age.
Item
copyMobileFieldsTo(Mobile)
- Method in class eu.irreality.age.
Mobile
copyRoomFieldsTo(Room)
- Method in class eu.irreality.age.
Room
copyWeaponFieldsTo(Weapon)
- Method in class eu.irreality.age.
Weapon
copyWearableFieldsTo(Wearable)
- Method in class eu.irreality.age.
Wearable
createNewInstance(World)
- Method in class eu.irreality.age.
AbstractEntity
createNewInstance(World, boolean, boolean)
- Method in class eu.irreality.age.
Item
createNewInstance(World, boolean, boolean, boolean)
- Method in class eu.irreality.age.
Mobile
createNewInstance(World, boolean)
- Method in class eu.irreality.age.
Room
createPlayerFromTemplate(InputOutputClient)
- Method in class eu.irreality.age.
World
ctcpPing(String, String)
- Method in class eu.irreality.age.
IrcBot
ctcpPing(String, String)
- Method in interface eu.irreality.age.
IrcListener
customParse()
- Method in class eu.irreality.age.
Player
Overview
Package
Class
Use
Tree
Deprecated
Index
Help
PREV LETTER
NEXT LETTER
FRAMES
NO FRAMES
All Classes
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X


Blockchain Technology And Mobile Apps In 2020
Bitcoin has thank-you cards to send to blockchain technology. Without the blockchain, Bitcoin would not have become the financial off-grid power that it is today. Yet, it is more than just Bitcoin fans who need to be grateful to blockchains. Other products and services spanning an array of industries are using blockchains to streamline processes, reduce costs and save time. It has become one of the latest technologies that everyone is trying to tap into – and the world of mobile apps is no different.
Blockchain technology is seeping into how mobile apps are made and what they can achieve. Uncover the details by reading on below.
The Implications of Blockchains and Mobile Apps
Advertisement
1. Borderless Transactions
Using apps to send money abroad is growing in popularity but often remain slow. Blockchain technology is enabling these cross-border payments to become instant when incorporated with cryptocurrency. All you need is access to the app and your own crypto wallet. For Bitcoin fans, the Luno Bitcoin wallet is a secure option and already attracts new users every day.
Having apps that incorporate blockchains to make payments quicker and cheaper is also a real benefit to unbanked populations and one step closer to tackling inequality in deprived regions.
Advertisement
2. Adding Seamless Integrity
Smart contracts could easily be argued as the most impressive and beneficial application of blockchain after Bitcoin. This is why so many app developers are now utilising smart contracts as part of their apps. Ultimately, they help businesses or consumers establish integrity without the need of third parties and can be adapted to a host of business purchases from eCommerce deliveries to freelancing work.
3. Swift Payments
Advertisement
The emergence of blockchain within mobile apps has enabled users to experience uninterrupted secure payment options and services between connected IoT devices. All of which is made real through a platform by the name of IOTA. This platform is the ideal method of developing blockchain applications alongside IoT tools. The end result is high-quality transactions, employing DAG (the acrylic graphic).
4. A Securer System
During transactions, it was always the case that app users would gather data from central servers. Unfortunately, this caused a lot of traffic and issues. Using the blockchain to replace a central server to the app means all devices are updated without lining up to access information. Overall, this leads to apps that run more efficiently and better experiences for their users.
Advertisement
The Future Also Looks to Gaming
There is also the possibility for blockchain games to develop into a prominent feature within mobile apps of the future. In-app gaming has already become extremely popular and combining the blockchain to this trend will open a gateway of new blockchain games that are played, enjoyed and saved on the blockchain without the need for heaps of storage. The pending arrival of 5G technology may also support such a vision.


Containers are the most basic layout element and are required while using grid system. The main objective of using Container is to contain and/or center the content. Containers can also be nested as per requirement.
There are Three types of Containers in our Design System:
.container
sets a max-width at each responsive
breakpoint:
| breakpoint <576px | breakpoint ≥576px | breakpoint ≥768px | breakpoint ≥992px | breakpoint ≥1200px |
|---|---|---|---|---|
| max-width:100% | max-width: 540px; | max-width: 720px | max-width: 960px | max-width: 1140px |
How to use:
<div class="container"> Content here </div>
.container-fluid
, which always has
width:100%
irrespective of breakpoints:
| breakpoint <576px | breakpoint ≥576px | breakpoint ≥768px | breakpoint ≥992px | breakpoint ≥1200px |
|---|---|---|---|---|
| width:100% | width:100% | width:100% | width:100% | width:100% |
.container-{breakpoint}
, has
width:100%
until the set breakpoint:
| breakpoint <576px | breakpoint ≥576px | breakpoint ≥768px | breakpoint ≥992px | breakpoint ≥1200px | |
|---|---|---|---|---|---|
.container-sm
|
width:100% | max-width:540px | max-width:720px | max-width:960px | max-width:1140px |
.container-md
|
width:100% | width:100% | max-width:720px | max-width:960px | max-width:1140px |
.container-lg
|
width:100% | width:100% | width:100% | max-width:960px | max-width:1140px |
.container-xl
|
width:100% | width:100% | width:100% | width:100% | max-width:1140px |


{% load static %} {% include "includes/css.html" %}
This is a stock prediction system based on the machine learning and natural language processing techniques.
A student from BUPT who is new to the field of machine learning.
What does this team member to? Keep it short! This is also a great spot for social links!
What does this team member to? Keep it short! This is also a great spot for social links!
What does this team member to? Keep it short! This is also a great spot for social links!
What does this team member to? Keep it short! This is also a great spot for social links!
What does this team member to? Keep it short! This is also a great spot for social links!


Get All Users
Show All
Get User by ID
Show by ID
Add User
Create
Update User
Update
Remove User
Delete


Manhattan Murder Mystery
Directed by Woody Allen
Starring Woody Allen, Diane Keaton, Alan Alda, Anjelica Houston
OK, I like New York. Unless you're from the city, or have ever lived in it, you probably hate it. But, as the opening song of Manhattan Murder Mystery states, Woody Allen happens to like New York. Because he likes New York so much, almost every movie that has him in it, including, although it isn't quite revealed until the end, Antz completely takes place in the Big Apple. A couple, Manhattan and Manhattan Murder Mystery, have even taken their titles from it.
Woody Allen fans shoot me here, but I've never seen Manhattan. Going from Manhattan Murder Mystery, though, you might wonder if he's playing to the stereotypes. Playing to the stereotypes is my only complain in this bizarre mystery about a next-door neighbor's plans to murder his wife. It takes the easy punches at New Yorkers. But, hey, with a place that has as many people in need of electroshock as New York does, can we blame him?
Manhattan Murder Mystery follows a couple (Woody Allen and Diane Keaton, seen before in Annie Hall) whose kid has left for college and are now living their lives out on the Upper West Side. They go to Rangers games and the theatre and the opera. They hang out with their divorced friends, plan their dreams of resturaunts, and go to work. Oh, yeah, and this couple suspects their next door neighbor of murdering his wife.
Although Allen is trepidatious about confronting a murder at first, the bland urban lives of Allen, his wife, his wife's friend (Alda), and the writer Allen edits (Houston), get caught up in the Manhattan murder mystery offered before them.
Going to Jersey for late-night discussions on how to nab the killer, sneaking into apartments, faking phone conversations. All of this is in the mix for the four bland individuals given a chance to be psychotic little children for a change. It is all a game to these four. There really isn't a concern about morals, only about having fun.
Also thrown into the mix is an interesting arangement. Woody Allen suspects that Diane Keaton is in love with Alan Alda. She, in turn, suspects that good old posterchild for neurotics Woody is in love with Angelica Houston. And, of course, they're trying to set up Houston and Alda at the same time.
Besides Woody Allen's unique touch, Manhattan Murder Mystery is a fairly normal and mainstream film. What bothered most critics, but will never bother me, is Woody Allen's diatribe of neurosis. As always, the compound insanity that he delivers, the one liners that insult himself and New York, and other things that convince you that you may not be the craziest person in the world make the film entertaining.
As far as acting goes, its as solid as you'd expect from such a powerful cast. When it comes to directing, it's what you'd expect from Woody Allen: conservative. He takes his camera and will shoot an entire scene with only one shot.
The script is suprisingly postmodern: it contains wonderful bits of Woody Allen self-reference. For instance, the title Manhattan Murder Mystery alludes to the film Manhattan, and you have the wonderfully brilliant scene in which Diane Keaton decrys wearing a tie with a skirt: the very look that became chic in 1977 with her role in Annie Hall.
Do not expect interesting plot twists. You will not get them. Do not expect high art, you won't get it either. Instead, expect a film that will delight and entertain anyone who hates, or happens to love, New York. What a hoot!
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


{% extends 'layout.html' %} {% block title %}Cubetastic | How to install a PWA{% endblock %} {% block body %}
You will get an install banner asking you to add it, but if you denied that, you can just click the top right menu in chrome, and click "add to homescreen".
It's really simple: if the site you're visiting is a PWA, firefox will show a home icon in the URL bar, and all you need to do is to click that.
Please follow this link: click me
In Google Chrome Desktop, you can add a PWA to your homescreen pretty easily, as shown in the above screenshot.


Build and Edit Windows .ini files. Only the simple edits,
remove
and set
are allowed. Set
has limited computation capability which is described later.
| Attribute | Description | Required |
|---|---|---|
| source | The name source .ini file to read in. | No. |
| dest | The name destination .ini file to write. | Yes. |
remove
| Attribute | Description | Required |
|---|---|---|
| section | The name of the section | Yes. |
| property | The name property. | No. If not supplied, the entire section will be removed |
set
| Attribute | Description | Required |
|---|---|---|
| section | The name of the section | Yes. |
| property | The name property. | Yes. |
| value | The value to set the property to. | No, if
operation is specified. |
| operation | The operation to perform on the existing value. Possible values are "+" and "-", which add and subtract 1, respectively from the existing value. If the value doesn't already exist, the set is not performed. | No, if value
is specified. |
Copyright © 2002-2003 Ant-Contrib Project. All rights Reserved.


This Playkit contains 14 carefully designed fun and progressively challenging activities to ensure children develop of a full range of movement skills, in terms of their stability, locomotion and object control. It helps coaches create an activity for children and assess their movement in a simple way that engages children along the way. Coaches can use the Playkit in any sport program. Becoming more competent in performing these movement skills in increasingly challenging games situations will prepare children for participation in sport specific, dynamic environments.


Association for Computing Machinery (ACM) |
ACM Special Interest Group on |
The Advanced Computing Systems Association |
In Cooperation with
ACM Special Interest Group on
Operating Systems
(SIGSOPS)


McHenry County Pride
A Group of Gay, Lesbian, Bi-Sexual, Transgender, and Questioning Adults in McHenry County, Illinois
An LGBT Group
with LGBT
for LGBT
by LGBT


Hash Hackers is a group of non-commercial websites providing information about things which are not available, are out of date or are undiscovered on the internet. Hash Hackers also works with Open Source Communities to provide powerful Software's and enhance user Experience and Security.


CSS3 is built upon several modules, where each module is focused on a separate design topic
code.org
HTML is the framing and structure of the house, while CSS is the paint, shingles and siding.
/**Correct Code, but hard to read**/
p {font-family: arial, helvetica, sans-serif;
font-size: 85%; line-height: 110%; margin- left: 30px;}
/**Good CSS coding practice - 1 declaration per line**/
p {
font-family: arial, helvetica, sans-serif;
font-size: 85%;
line-height: 110%;
margin-left: 30px;
}
3 Ways of Implementing CSS
Example of implementing external, internal and inline
Type Selectors | Grouping Selectors | Combining declarations | Using descendant Selectors
/**Specific style rules**/
h1 {color: red;}
h2 {color: red;}
/**Grouping selectors:**/
h1, h2 {color: red;}
/**Specific style rules**/
p {color: blue;}
p {font-size: 125%;}
/**Combining declarations**/
p {
color: blue;
font-size: 125%;
}
/**Selecting only  elements that are contained
within
 elements**/
p em {
color: blue;
}
/**Selecting all children of the div element:**/
div * {
font-family: sans-serif;
}
Universal selector lets you select groups of elements

 This is a fun  activity  on CSS

 Understanding  style rules  in CSS

html, body, div, span, object, iframe,
h1, h2, h3, h4, h5, h6, p, blockquote, pre,
abbr, address, cite, code,
del, dfn, em, img, ins, kbd, q, samp,
small, strong, sub, sup, var,
b, i,
dl, dt, dd, ol, ul, li,
fieldset, form, label, legend,
table, caption, tbody, tfoot, thead, tr, th, td,
article, aside, canvas, details, figcaption, figure,
footer, header, hgroup, menu, nav, section, summary,
time, mark, audio, video {
margin:0;
padding:0;
border:0;
outline:0;
font-size:100%;
vertical-align:baseline;
background:transparent;
}
body {
line-height:1;
}
/**Specific style rules**/
h1 {color: red;}
p {color: red;}
ul {color: red;}
em {color: red;}
li {color: red;}
/**Using Inheritance to reduce code**/
body {color: red;}
Style inheritance promotes good coding practice and faster updates when changes need to be made to the code.
*Pseudo-classes select elements based on characteristics other than their element name
*Pseudo-elements let you change other aspects of a document that are not classified by standard elements such as the first letter or line of a paragraph.
/**Example**/
a:hover {
background-color: lime;
color: #333;
}

Home

Story History

Unit History

Which HTML tag is used to define an internal style sheet?
Which HTML attribute is used to define inline style?
Which is the correct CSS Syntax?
How do you select an element with id "demo"?
What is the correct HTML syntax for inserting an image?


Professional Handyman with years of experience Neat and ALWAYS on time!
Will take care of ALL of your home improvement needs and odd jobs
Get started with our standard contract below!
See Our Contract Options!Installation on ANY wall type with seemless integration and profesionally secured mounting
Hire Us to Manage all of the Handyman needs that come with owning a rental property
We will professionally paint and also offer the staining of decks and patios to give you that new look you've been searching for
Inspect & change furnace filter, Inspect & clean gutters and downspouts, Open/close outdoor faucets and hoses, Tighten exterior door hardware
Looking to Rebuff your bathroom with some new fixtures? Call us to get the job done right the first time
We will cover all of your needs from setting new tile grouting and caulking showers


{% extends "staff/stats/base.html" %} {% block sub-title %}Stats | Monthly Members{% endblock %} {% block content %}
| Member | Start Date | Package | Rate |
|---|---|---|---|
| {{ membership.who }} | {{ membership.start_date }} | {{ membership.package_name }} | ${{ membership.monthly_rate }} |


[gecode-users] Large neighborhood search support
Joan Sala Reixach
jsalareixach at gmail.com
Tue May 29 09:08:01 CEST 2012
Hello all,
I am currently trying to develop a LNS application in Gecode, and I've read
in the mailing list that as of 14/10/2011 Gecode 4.0.0 was being prepared,
which will include further support on LNS.
I would like to ask whether it would be possible to somehow access these
developments regarding LNS or get a partial release, or we must definitely
wait for the full release.
Thanks a lot,
Joan
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://www.gecode.org/pipermail/users/attachments/20120529/428d8317/attachment.html>
More information about the users
mailing list


WHAT DREAMS MAY COME A film review by Steve Rhodes Copyright 1998 Steve Rhodes RATING (0 TO ****): ** 1/2
If you've always been curious about exactly what heaven and hell look like, WHAT DREAMS MAY COME offers some intriguing suggestions. In director Vincent Ward's long cinematic sermon on the advantages of a moral life, the movie depicts heaven as a happy, colorful landscape painting and hell as an eerie, gray, Holocaust graveyard overflowing with talking heads.
The rarely subtle film shows two dead children from inside their coffins during the opening credits.  Although four years elapse between the two car accidents, the movie dispenses with them quickly so that it can get to the heavenly beyond.
The father, Chris Nielsen, is a doctor played by Robin Williams. Annabella Sciorra plays his wife and "soul mate," an artist named Annie. Both are deeply scarred characters who share their troubles with us in almost every scene. If the movie were involving, it could have been the downer of the year. Instead, it stays at the level of visual technical achievements with the figures in it about as real as those in art gallery paintings -- lovely to look at, but nothing to get concerned over.
Cuba Gooding, Jr., first seen only as a blur, plays Albert, Chris's guide into heaven. Gooding, like the rest of the fine actors, is largely wasted in a movie that works only at an ethereal level. The magical film is artistically surreal but only sporadically realistic.
The script by Ronald Bass, based on Richard Matheson's novel, could have used a little more levity and should have given the characters some credible depth. "I screwed up," Chris says when he sees his dog in heaven. "I'm in dog heaven." This delightfully natural humor is regretfully absent in most of the movie.
 (Okay now, let's all forgive ourselves so we will get to pass through the pearly gates successfully.
Once in heaven, Chris is amazed to find that he has walked into one of his wife's paintings. The film shows us a heaven that is a blend of the nineteenth-century landscape paintings of Turner and Cole mixed with impressionistic touches. Not only is it gorgeous, the paint isn't even dry yet. As though in a heavenly version of WILLY WONKA & THE CHOCOLATE FACTORY, Chris gets to sample the paintings by running through them and squeezing the flowers in his palms as the paint squirts out. These dazzling images are among the loveliest and most innovative Hollywood has created in years. With Michael Kamen's dramatic music and Yvonne Blake's sumptuous costumes for the afterlife, the film works best when viewed as grand opera as illustrator Maxfield Parrish might have staged it.
In the tradition of life-after-death pictures, Chris goes back in an attempt to console his wife on earth. Rather than giving her peace, these unseen visits increase her depression. Eventually she will die too, but, through a kind of legal snafu, she ends up in hell. Moral rules, it seems, are filled with fine print. The body of the movie has Max von Sydow, as a mysterious character called The Tracker, leading Chris on a journey into hell to help Chris find his beloved Annie.
A movie filled with absolutely stunning imagery, it is strangely cold and unengaging despite all its beauty. Still, the pedantic script's tedium is more than offset by the handsomeness of the production. WHAT DREAMS MAY COME is a feast for the eyes even if not a particularly filling meal for the mind.
WHAT DREAMS MAY COME runs 1:48. It is rated PG-13 for thematic elements involving death, some disturbing images, profanity including the F-word and brief nudity. The film would be fine for teenagers.
Email<EMAIL_ADDRESS>Web: www.InternetReviews.com
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Home |
About |
Table of Contents |
This Python work was created using an .mei file of the song 'The Unforgiven' View the notated mei file page.
Open
the full size image of the above in a new tab.Open
the full size image of the above in a new tab.View
my python file in a new tab.


Connections are made by selecting one of the options from the Connect item on the File menu. The most common blink is Full (collects and posts mail, news and forum messages) - start one of these blinks by
· selecting Full from the Connect sub-menu on the File menu
· clicking on the black telephone on the Toolbar
· pressing Control-T (also see the keyboard shortcuts section)
You can change how connections are made and what happens during a connection from the Communications tab pages.


Chapter 8 Simulation and Monte Carlos
8.1 Seminar
In this exercise, we introduce a simulation approach to quantifying uncertainty and Monte Carlo simulation.
8.1.1 Monte Carlo simulation
In Monte Carlo simulation we create a fake data set were we define a causal model of the outcome directly. That way, we know what the true outcome has to be. We can then keep everything constant but make on change to our estimation to assess the effect of that change on our prediction accuracy. We could, for instance, be interested in finding out whether our prediction accuracy suffers when independent variables are highly correlated.
So, we set up an MC analysis to see whether problems of multicolinearity (high correlation between explanatory variables) go away as the sample size increases. The goal is to see how well we are able to retrieve the true value of \(\beta_{1}\) for varying strengths of correlation and sample sizes.
The basic setup of the simulation is as follows:
# the number of runs (simulations)
sim.n <- 1000
# sequence of low to high correlation
Rho <- seq(from = 0, to = .9,length.out=10)
# vector of sample sizes
sample.N <- c(100, 500, 1000, 2000)
We create a container beta.catcher
that stores coefficient values for different simulations. The container is a 3-dimensional array where the first dimension is the number of simulations, the second is the sample size, the third is correlation.
# rows = simulations, columns = sample size, layers = correlation
beta.catcher <- array(NA, c(sim.n, 4, 10))
We start simulating. This will take a while because we are iterating through \(sim.n * Rho * sample.N\) iterations, i.e. \(40,000\) iterations. Each time we regress \(y\) on our covariates.
# loop over the correlations
for (i in 1:length(Rho)){
# loop over sample sizes
for (j in 1:4){
# loop over the random draws (number of simulations)
for (k in 1:sim.n){
# current correlation
rho <- Rho[i]
# current sample size
sample.n <- sample.N[j]
# variance covariance matrix current corr on off-diagonal
varL <- matrix(c (1,rho,rho,1) , nrow = 2, ncol = 2)
# random draw of covariates as many as sample size
XX <- MASS::mvrnorm(sample.n, rep(0,2), varL)
# random noise
e <- rnorm(sample.n)
# the true data generation process b1 = 1; b2 = 1
y <- XX %*% c(1,1) + e
# we regress the true y on the covariates and extract beta 1
beta.catcher[k,j,i] <- coef(lm(y~XX))[2]
} # end of loop over number of sims
} # end of loop over sample sizes
} # end of loop over correlations
You can have a look at the container by calling it with beta.catcher
. We remind ourselves of the dimensions with:
[1] 1000 4 10
We take the MSE of each coefficient estimate in the container. This is an element-wise operation so the resulting object error.sq
has the same dimensions as the container.
We average over the simulations by taking the column means. This returns a matrix where the rows are now the different sample sizes (previously in the columns) and the columns are the correlations (previously in the layers). The matrix mse.beta
, thus, has 4 rows and 10 columns.
To show the effect of multicolinearity for increasing levels of correlation and for increasing sample sizes, we plot correlation on the x-axis and the MSE on the y-axis in four plots where sample size increases by plot.
par(mfrow=c(1,4))
plot(c(0:9)/10,mse.beta1[1,], xlab="correlation", ylab="Mean Squared Error", type="o", pch=19, bty="n", main="N=100", ylim=c(0,.2))
plot(c(0:9)/10,mse.beta1[2,], xlab="correlation", ylab="Mean Squared Error", type="o", pch=19, bty="n", main="N=500", ylim=c(0,.2))
plot(c(0:9)/10,mse.beta1[3,], xlab="correlation", ylab="Mean Squared Error", type="o", pch=19, bty="n", main="N=1000", ylim=c(0,.2))
plot(c(0:9)/10,mse.beta1[4,], xlab="correlation", ylab="Mean Squared Error", type="o", pch=19, bty="n", main="N=2000", ylim=c(0,.2))
error.abs <- abs(beta.catcher-1)
mae.beta1 <- colMeans(error.abs)
par(mfrow=c(1,4))
plot(c(0:9)/10,mae.beta1[1,], xlab="correlation", ylab="Means Absolute Error", type="o", pch=19, bty="n", main="N=100", ylim=c(0,.2))
plot(c(0:9)/10,mae.beta1[2,], xlab="correlation", ylab="Means Absolute Error", type="o", pch=19, bty="n", main="N=500", ylim=c(0,.2))
plot(c(0:9)/10,mae.beta1[3,], xlab="correlation", ylab="Means Absolute Error", type="o", pch=19, bty="n", main="N=1000", ylim=c(0,.2))
plot(c(0:9)/10,mae.beta1[4,], xlab="correlation", ylab="Means Absolute Error", type="o", pch=19, bty="n", main="N=2000", ylim=c(0,.2))
The two take-aways are that (1) with increasing sample size the problem of multicolinearity decreases substantially and (2) bias increases exponentially with increasing levels of correlation.
8.1.2 Simulation approach to uncertainty
Simulation is the Swiss army knife of statistics. Quantifying the uncertainty of an outcome can be tough or even impossible algebraically. Even for the linear model we need to consider the standard errors of all coefficients and their covariance. The formulas can be tedious…
For simulation, the process is always the same regardless of the model.
We start by loading data and fitting a linear model on the unemployment rate.
# clear workspace
rm(list=ls())
# load data
df <- read.csv("http://philippbroniecki.github.io/ML2017.io/data/communities.csv")
Simulation step 1: Our coefficients each follow a sampling distribution. Jointly, they follow a multivariate distribution which is assumed to be multivariate normal.
To characterize the shape of the distribution we need to know its mean and its variance. The mean is our vector of coefficient point estimates. We extract it using coef(model_name)
. The variance is the model uncertainty which lives in the variance-covariance matrix. We extract it with vcov(model_name)
.
As we draw randomly from a distribution we want to set the random number generator with set.seed()
to make our results replicable and we pick the number of coefficients to draw form the distribution (the number of simulations).
# run a model
m1 <- lm(PctUnemployed ~ pctUrban + householdsize + racePctWhite, data = df)
# set the random number generator to some value
set.seed(123)
# pick how many coefficients you want to draw from the distribution
n.sim <- 1000
# draw coefficients from the multivariate normal
S <- MASS::mvrnorm(n.sim, coef(m1), vcov(m1))
Simulation step 2: Choose a scenario for which you want to make a prediction. That means we have to set our covariates to some value. We will vary the percentage of the urban population and keep all other covariates constant. We also check the range of the variable of interest so that we don’t extrapolate to something that is outside of our data range.
# choose a scenario to predict the outcome for
summary(df$pctUrban)
# set the covariates (predictions for changes in pctUrban)
X <- cbind( constant = 1,
urban = seq(from = 0, to = 1, by = .1),
householdsize = mean(df$householdsize),
pctwhite = mean(df$racePctWhite))
# check covariates
X
constant urban householdsize pctwhite
[1,] 1 0.0 0.4633952 0.7537161
[2,] 1 0.1 0.4633952 0.7537161
[3,] 1 0.2 0.4633952 0.7537161
[4,] 1 0.3 0.4633952 0.7537161
[5,] 1 0.4 0.4633952 0.7537161
[6,] 1 0.5 0.4633952 0.7537161
[7,] 1 0.6 0.4633952 0.7537161
[8,] 1 0.7 0.4633952 0.7537161
[9,] 1 0.8 0.4633952 0.7537161
[10,] 1 0.9 0.4633952 0.7537161
[11,] 1 1.0 0.4633952 0.7537161
Simulation step 3: Predict the outcome. We have set our covariates and we have drawn our coefficients. This is all we need to predict \(y\). Depending on the flavor of generalized linear model, \(y\) may have to be sent through a link function. In logistic regression we would send latent \(y\) trough the logit link function: \(\frac{1}{1 + exp^{-y}}\) to get probabilities that \(y\) is 1. Here, we ran a simple linear model so in linear algebra notation our prediction is simply \(Y=X\beta\).
We estimate y_hat
as a matrix where its rows are the number of simulations and its columns are the different covariate scenarios.
Finally, all that is left is the interpretation of the result. We can simply look at the numerical outcomes similar to using the summary()
function on the Zelig simulation object.
# output like the zelig summary (including estimation uncertainty)
apply(y_hat, 2, quantile, probs = c(.025, .5, .975))
[,1] [,2] [,3] [,4] [,5] [,6]
2.5% 0.4316010 0.4214979 0.4105675 0.4000181 0.3893180 0.3784349
50% 0.4450405 0.4332109 0.4215157 0.4097547 0.3981805 0.3864607
97.5% 0.4584981 0.4454806 0.4327917 0.4198413 0.4070521 0.3945582
[,7] [,8] [,9] [,10] [,11]
2.5% 0.3672170 0.3557150 0.3439413 0.3318641 0.3196062
50% 0.3747705 0.3630690 0.3512949 0.3396307 0.3279723
97.5% 0.3823658 0.3703668 0.3586515 0.3473086 0.3363178
We can also draw a plot that shows our mean prediction and the uncertainty around in a few lines.
# plot like zelig's ci plot
par( mfrow = c(1,1))
plot(0, bty = "n", xlab = "Pct Urban", ylab = "Unemployment Rate",
ylim = c(0.3, 0.5), xlim = c(0,1), pch ="")
ci <- apply(y_hat, 2, quantile, probs = c(.025, .975))
polygon(x = c(rev(X[,"urban"]), X[,"urban"]),
y = c(rev(t(ci)[,2]), t(ci)[,1]), border = NA,
col = "lightblue")
lines(x = X[,"urban"], y = apply(y_hat, 2, quantile, probs = .5), lwd = 1)


Inherited by AutoScalingRequest, CloudWatchRequest, EC2Request, ElasticLoadBalancingRequest, S3Request, SESRequest, SimpleDBRequest, SNSRequest, and SQSRequest.
Public Member Functions | |
| (AmazonURLRequest *) | - configureURLRequest |
| (NSString *) | - queryString |
| (void) | - sign |
| (void) | - setParameterValue:forKey: |
|
(id < AmazonServiceRequestDelegate >) | - delegate |
| Returns the delegate object for the request. | |
| (void) | - setDelegate: |
| Sets the delegate object for the request. | |
Protected Attributes | |
| id< AmazonServiceRequestDelegate > | delegate |
Properties | |
| AmazonCredentials * | credentials |
| Request specific credentials. | |
| AmazonURLRequest * | urlRequest |
| The urlRequest object which represents this request. | |
| NSURLConnection * | urlConnection |
| The connection object used to make the request. | |
| NSString * | httpMethod |
| The HTTP Method (GET, PUT, POST, DELETE) used for the request. | |
| NSURL * | url |
| The URL for the resource. | |
| NSMutableDictionary * | parameters |
| NSString * | endpoint |
| NSString * | userAgent |
| - (void AmazonServiceRequest.)setDelegate: | (id< AmazonServiceRequestDelegate >) | delegate |
Sets the delegate object for the request.
In the absence of a delegate, the client method that was called with the request will return the response, occasionally processing inputs on the run loop which called the client method.
If a delegate is assigned, the request proceeds asynchronously and will send the delegate messages as the request progresses. The response will be send to the delegate in the request:didCompleteWithResponse delegate method.
| delegate | An object which implements one or more of the methods in the AmazonServiceRequestDelegate protocol. |
- (AmazonCredentials *) credentials [read, write, retain] |
Request specific credentials.
- (NSString *) httpMethod [read, write, retain] |
The HTTP Method (GET, PUT, POST, DELETE) used for the request.
- (NSURL* AmazonServiceRequest.)url [read, assign] |
- (AmazonURLRequest *) urlRequest [read, write, retain] |
The urlRequest object which represents this request.
Use configureURLRequest to make sure all fields have


Many C++ programs interface to systems written in C or assembly language that use
null-terminated arrays of characters to hold string data. As we saw in §10.5.2/180, the
C++ standard library itself uses this convention to obtain the names of input and output
files. Because of this convention, we might conclude that our Str
class should provide a
conversion from Str
to a null-terminated array of characters. If we did so, then our users
could (automatically) pass Str
s to functions that operate on null-terminated arrays.
Unfortunately, as we shall see, doing so is fraught with memory-management pitfalls.
Assuming that we wanted to provide a conversion from Str
to char*
, we'd probably
want to provide both const
and nonconst
versions:
class Str { public: // plausible, but problematic conversion operations operator char*(); // added operator const char*() const; // added // as before private: Vec<char> data; };
With this rewrite, Str
users could write code such as
Str S; // ... ifstream in(s); // wishful thinking: convert s and then open the stream named s
The only problem is that these conversions are almost impossible to implement well. We
can't just return data
, most obviously because it's the wrong type: data
is a Vec<char>
,
and we need an array of char
. More subtly, even if the types matched, returning data
would violate class Str
's encapsulation: A user who obtained a pointer to data
could
use that pointer to change the value of the string
. Just as bad, consider what happens
when the Str
is destroyed. If the user tries to use the pointer after the Str
object no
longer exists, then the pointer will point to memory that has been returned to the system
and is no longer valid.
We can solve the encapsulation problem by providing only a conversion to const char*
,
but doing so does not prevent a user from destroying the Str
and then using the
pointer. We can solve this second problem by allocating new space for a copy of the characters
from data
, and returning a pointer to this newly allocated space. The user would
then have to manage this space, freeing it when it is no longer needed.
As it turns out, this design won't work either. Conversions may happen implicitly, in which case the user has no pointer to destroy! Look again at
Str s; ifstream is(s); // implicit conversion—how can we free the array?
If the Str
class had the proposed conversion, then when we passed s
to the ifstream
constructor, we would implicitly convert the Str
to the const char*
that the constructor
expects. This conversion would allocate new space to hold a copy of the value of
s
. However, there is no explicit pointer to this space, and so the user cannot free it.
Clearly, a design that mandates memory leaks cannot be right.
When we design a class, we want to avoid letting users trip up by writing innocuous-
looking code that gets them in trouble. Before the C++ standard was finished, many
library vendors offered various kinds of string
s. Some surely provided implicit conversions
to character arrays, but did so in a way that allowed users to make one or both of the
potential errors outlined earlier.
The standard string
library takes a different approach: It lets users get a copy of the
string
in a character array, but makes them do so explicitly. The standard string
class
provides three member functions for getting a char array from a string
. The first,
c_str()
, copies the contents of the string
into a null-terminated char
array. The
string
class owns the array, and the user is expected not to delete
the pointer. Data in
the array are ephemeral, and will be valid only until the next call of a member function
that might change the string
. Users are expected either to use the pointer immediately
or to copy the data into storage that they will manage. The second function, data()
, is
like c_str()
, except that it returns an array that is not null terminated. Finally, the copy
function takes a char*
and an integer as arguments, and copies as many characters as
indicated by the integer into space pointed to by the char*
, which space the user must
allocate and free. We leave the implementation of these functions as an exercise.
Note that both c_str
and data
share the pitfalls of the implicit conversion to
const char*
. On the other hand, because users must request the conversion explicitly, they are
more likely to know about the functions that they call. This knowledge should include
the pitfalls inherent in retaining a copy of the pointer. If the library allowed implicit conversions,
it would be easier for users to stumble into these problems. They might not
even be aware that they had caused a conversion to take place, so they might be less likely
to understand why things failed when they did.


protein -nCoV epitopes binding T-cell spike B-cell presentation MHC-I viral antibody OpenUrl MHC-II human Fig. mutations epitope sites alleles identify potential coronavirus identified likely peptide receptor Discotope presented SARS-CoV high indicates present antigen specificity vaccine known peptides predicted regions structure vaccines SARS epitopes. mutation near neutralizing structures surface common residues respiratory site CrossRef MARIA analysis candidates immune similar strong MHC NetMHCpan antibodies domain entry new point proteins score sequences ACE RBD interacting solved (B-cell Download acute compared experimentally linear mer observed position scores sequence severe syndrome Appendix, Immunol. Table based cells class clinical cohort epitopes). independent modeled multiple patients research responses results sars similarity studies >; .). AUC HLA HLA-A, HLA-B, HLA-C HLA-C, Open View Zhou assess cut-off data-hide-link-title=""> data-icon-position="" figure homology include novel occur presentable region scanned sensitivity structural tab (S) (e.g. (with -). ;<;div Abstract Identification Mutations Science antigens associated cluster computational epitopes, experimental form genes genome given good highly humans infected located number obtained pink pipeline residue virus xmlns=";http://www.w.org//xhtml";>;<;span %), %). (cut-off (residue -nCov B). Chinese FREE HLA-C*:, Negative Predicted Red Supplementary T-cells Text Web acids additional antibodies. blue caused cell co-crystal comprised conformation, coverage cut-off, data development epitope. exact future gene genomes help major mhc model overall percentile population predict protein. relationship samples search sliding unique used validated viruses windows


2: static char help[] = "Tests catching of floating point exceptions.\n\n"; 4: #include <petscsys.h> 6: int CreateError(PetscReal x) 7: { 11: x = 1.0/x; 12: PetscPrintf(PETSC_COMM_SELF,"x = %g\n",(double)x); 13: return(0); 14: } 16: int main(int argc,char **argv) 17: { 19: PetscInitialize(&argc,&argv,(char*)0,help);if (ierr) return ierr; 20: PetscPrintf(PETSC_COMM_SELF,"This is a contrived example to test floating pointing\n"); 21: PetscPrintf(PETSC_COMM_SELF,"It is not a true error.\n"); 22: PetscPrintf(PETSC_COMM_SELF,"Run with -fp_trap to catch the floating point error\n"); 23: CreateError(0.0); 24: return 0; 25: } 28: /* 30: Because this example may produce different output on different machines we filter out everything. 31: This makes the test uneffective but currently we don't have a good way to know which machines should handle 32: the floating point exceptions properly. 34: */ 35: /*TEST 37: test: 38: args: -fp_trap 39: filter: Error: true 41: TEST*/


Questionnaires are surveys hosted in the system.
PROMIS Instruments are computer adaptive tests extended by the service provider
In production: list should be compiled using their FHIR endpoint
Active tasks are smartphone based exercises based on ResearchKit
Note: Listing derived from FHIR ValueSet hosted in the FHIR server. Please ensure the value sets contained in this app are uploaded to the FHIR server
FHIR ValueSet
Note: They are specific to iPhone devices
Activity instruments coded with their ontology codes.
Note: Listing derived from a FHIR ValueSet hosted in the FHIR server. Please ensure the value sets contained in this app are uploaded to the FHIR server
Clinical Records are medical data held by the end-user, optionally derived from other health systems
For demonstration: This request fetches all medical data stored in the user's iPhone Health app
Requesting specifically a specific data type (a lab test, medication, etc) and will be addressed at a future release
Health devices or wearables that have an extended online repository
Requests will seek data specifically from the selected device
---


If you’ve been around R for a little while, you’ve most likely heard of Hadley Wickham. His amazing packages (including ggplot2, reshape2, plyr and many more) have shaped the R ecosystem into what it is today. Without them, it’s unlikely I would have continued learning R.
One of Hadley’s latest packages is called dplyr, and it’s an absolute game-changer.
I was giddy with excitement the first time I saw a demonstration. It
lets you perform almost any data manipulation task as a series of
five simple verbs;
mutate
,
filter
,
arrange
,
group_by
and
summarise
.
A lot of effort was put into making it as fast as possible, while maintaining an incredibly simple syntax. In most cases, it approaches the speed of the amazing data.table package, but with a much more approachable syntax. And to boot, it lets us transparently use out-of-memory (i.e. in-database) data as if it were a local dataframe.
Fast forward a few months and Hadley’s new R book, Advanced R, was being updated on his website as he wrote it. It wasn’t until a print copy of his book was available that I put serious effort into going through the content. And I’m glad to say it’s as thorough and well written as we’ve come to expect.
I’ve wanted to peer under the hood of dplyr for a while to figure out how it works, but to a intermediate R programmer like myself, the codebase was a bit too complex.
Instead of using dplyr’s codebase to figure out how dplyr works, I decided to use what I was learning in Advanced R to write my own version, using only base R function.
Turns out all that’s needed is about 70 lines of code.
It won’t be nearly as fast as dplyr, or nearly as robust as dplyr, but it could look and smell like dplyr.


Numbers 28:28
And their meat offering of flour mingled with oil, three tenth deals unto one bullock, two tenth deals unto one ram,


Try out our Premium Member services: Virtual Legal Assistant, Query Alert Service and an ad-free experience. Free for one month and pay only if you like it.
IN THE HIGH COURT OF PUNJAB AND HARYANA AT CHANDIGARH CWP No. 15392 of 2019 Date of decision: 31.05.2019 Indian Oil Corporation Limited, Panipat Refinery & Petrochemical Complex, Panipat, Haryana ... Petitioner Versus Union of India and others ... Respondents CORAM: HON'BLE MR. JUSTICE KRISHNA MURARI, CHIEF JUSTICE HON'BLE MR. JUSTICE ARUN PALLI, JUDGE Present: Mr. Anand Chhibbar, Senior Advocate, with Mr. Shikhar Sarin, Advocate, for the petitioner. ***
KRISHNA MURARI, CHIEF JUSTICE (Oral) After the matter was heard at some length, when confronted with the observation in respect of existence of statutory alternative remedy of appeal before the National Green Tribunal, under Section 33-B of the Water (Prevention and Control of Pollution) Act, 1974 and certain other proceedings with regard to the dispute already pending before it, learned senior counsel for the petitioner finding it difficult to answer the query, and also on instructions, sought leave of the court to withdraw this petition, with liberty to approach the National Green Tribunal by filing an appeal.
Prayer made is allowed.
Dismissed as withdrawn, with the liberty prayed for.
(KRISHNA MURARI) CHIEF JUSTICE (ARUN PALLI) JUDGE May 31, 2019 AK Sharma Whether speaking / reasoned: YES Whether Reportable: NO 1 of 1 ::: Downloaded on - 23-06-2019 12:43:30 :::


ACA NEOGEO PUZZLE BOBBLE 2 (Win 10) Achievement List Revealed
ACA NEOGEO PUZZLE BOBBLE 2 (Win 10) Achievement List Revealed
By Rich Stone, 21 Nov 2019
Comments
We have just picked up the ACA NEOGEO PUZZLE BOBBLE 2 (Win 10) Achievement list.
There are 12 achievements worth a total of 1,000 Gamerscore, none of which are secret.
| Name | Description | Gamerscore |
| Record score | In ORIGINAL MODE your score will be posted to the online rankings. | 80 |
| Mark high score | High score has been updated. | 80 |
| Score 5,000,000 points | The score won 5,000,000 points. | 80 |
| Score 10,000,000 points | The score won 10,000,000 points. | 80 |
| Score 20,000,000 points | The score won 20,000,000 points. | 80 |
| HI SCORE Player | The score has been posted to the online rankings in the "HI SCORE MODE". | 80 |
| HI SCORE MODE 5,000,000 points | HI SCORE MODE The score won 5,000,000 points. | 80 |
| HI SCORE MODE 8,000,000 points | HI SCORE MODE The score won 8,000,000 points. | 80 |
| HI SCORE MODE 10,000,000 points | HI SCORE MODE The score won 10,000,000 points. | 100 |
| CARAVAN MODE 3,000,000 points | CARAVAN MODE The score won 3,000,000 points. | 80 |
| CARAVAN MODE 5,000,000 points | CARAVAN MODE The score won 5,000,000 points. | 80 |
| CARAVAN MODE 7,000,000 points | CARAVAN MODE The score won 7,000,000 points. | 100 |
ACA NEOGEO PUZZLE BOBBLE 2 (Win 10) is developed and published by HAMSTER Corporation.
Achievement ListWindows
View comments
Written by Rich Stone
Creator of TrueAchievements, TrueTrophies and TrueSteamAchievements. Writes site news and still finds time for the odd game here and there. Loves Racing games and Shooters, yet to be convinced that any RPG is worth the time investment!


An abandoned 'Avengers: Infinity War' scene had the heroes fighting versions of themselves, Business Insider - Business Insider Singapore
by Kirsten Acuna- An alternate “Avengers: Infinity War” sequence showed the heroes fighting versions of themselves.
- Screenwriters Christopher Markus and Stephen McFeely told Vanity Fair that although the scene was cool, it didn’t connect to anything else and was scrapped.
- Visit Insider’s homepage for more stories.
“Avengers: Infinity War” could have featured the Marvel superheroes going up against versions of themselves.
In an interview with Vanity Fair’s Anthony Breznican, “Infinity War” and “Endgame” screenwriters Christopher Markus and Stephen McFeely recount one of many alternate sequences in “Infinity War” that would have showed the heroes in a parallel world to their own.
“There was a sequence in the first movie where they went into the places in the ‘Doctor Strange’ universe called the Mindscape and everyone faces themselves,” said Markus.
There’s a scene in the 2016 movie where the Ancient One sends Strange through a number of dimensions. It sounds like the Avengers would have entered this.
“Banner meets the Hulk, I think in the arena from ‘[Thor] Ragnarok.’ Only one of them was getting out of there, and then that one showed up in Wakanda.”
Why didn’t we see it in the final movie?
“It was great but had absolutely nothing to do with anything,” said Markus.
It sounds like we were better off with the version of “Infinity War” that made it to theaters.
The sequence does sound a bit wacky and bloated if it would have revisited another Marvel movie. The version of the film Markus and McFeely described would have had the Hulk emerge as Smart Hulk by the end of his battle. That’s something we’ve heard before that was scrapped and revisited at the start of “Endgame.”
Maybe we’ll see the Avengers fight versions of themselves somewhere in the future.


About Stephen
Stephen is a software engineer with web front-end experience. He is an “Agile” and adventurous developer with key expertise revolves around Angular practices (2+), vanilla Javascript, and styling. Also, a visioner in a team with attention to detail and the belief of “building software that matters to the users”.
A fan of CSS and several programming paradigms such as object-oriented, declarative, and modular programming.
Projects
Gantt Project Management
Gantt is a single-page project management software built to expedite the visual representation of a project plan.
Skills and tools implemented: HTML , CSS , JavaScript , jQuery , UI/UX design, ReactJS , NodeJS , ExpressJS, MongoDB , REST API, mLab, and responsive web design
ataNote
ataNote is a responsive notebook single-page app that allows the users to make quick changes.
Skills and tools implemented: HTML , CSS , JavaScript , jQuery , UI/UX design, NodeJS , ExpressJS, MongoDB , REST API, mLab, and responsive web design
ExploRestaurant
ExploRestaurant is a seamless API-driven single-page app that satisfies users' craving for restaurant discovery.
Skills and tools implemented: HTML , CSS , JavaScript , jQuery , Youtube API , Zomato API, UI/UX design, REST API, responsive web design


Tutorial: Mnesia
3 Structure
3.1 Starting Again
We've had a quick taste of Mnesia, and what some of the calls look like in LFE. Next we're going to tackle a bit more heady stuff: tables and relationships.
Go ahead and re-start the REPL, using a different database name this time:
$ DB=./Company.DB make mnesia-shell
> (mnesia:create_schema (list (node)))
ok
> (mnesia:start)
ok
>
Then pull in the code that will let us define our tables, adding them to the Mnesia schema we just created:
> (slurp '"src/structure.lfe")
#(ok structure)
>
3.2 Records as Tables
The structure
module includes LFE records that act as our table definitions,
as well as a macro that lets us create Mnesia tables with almost no boilerplate.
The following records are defined in include/tables.lfe
:
(defrecord employee
id
name
salary
gender
phone
room-number)
(defrecord department
id
name)
(defrecord project
name
number)
(defrecord manager
employee-id
department-id)
(defrecord in-department
employee-id
department-id)
(defrecord in-project
employee-id
project-name)
These records (tables) are taken from the example given in the Erlang Mnesia tutorial which also gives this entity diagram for their proposed "Company" database:
3.3 Creating Our Tables
In the src/structure.lfe
module which we have just imported, some utility
functions are defined which will lets us easily create tables in Mnesia based
on the records defined in the include/tables.lfe
file. Of particular
interest right now is the (init)
function; let's call it:
> (init)
#(ok
(#(create-set-tables (#(atomic ok) #(atomic ok) #(atomic ok) #(atomic ok)))
#(create-bag-tables (#(atomic ok) #(atomic ok)))))
>
This just created all our Mnesia tables for us. If we run it again, we'll see errors indicating that the tables have already been created:
> (init)
#(error
(#(create-set-tables
(#(aborted #(already_exists employee))
#(aborted #(already_exists department))
#(aborted #(already_exists project))
#(aborted #(already_exists in-department))))
#(create-bag-tables
(#(aborted #(already_exists manager))
#(aborted #(already_exists in-project))))))
>
As you may guess from the output of that second call, under the covers, the
init
function calls a couple of utility functions:
(structure:create-set-tables)
, and(structure:create-bag-tables)
These, in turn, call a macro we created to make table-creation much easier. The custom macro alleviates the dev from having to write tedious and repetitive boilerplate code. This macro actually calls another macro that is generated -- by LFE -- for each record (one that gets a list of all the fields for a given record).
Next, let's re-run that info
function we saw in the previous section:
> (mnesia:info)
The output of that function will be very similar to what we saw in the previous section. However, do note that our new tables are reported in the "Active tables" section:
...
---> Active tables <---
in-project : with 0 records occupying 305 words of mem
in-department : with 0 records occupying 305 words of mem
manager : with 0 records occupying 305 words of mem
project : with 0 records occupying 305 words of mem
department : with 0 records occupying 305 words of mem
employee : with 0 records occupying 305 words of mem
...
If you would like to check up on the tables created above, you can use the
table_info
function to pull out certain data. For instance, here's how
you find what backend type is being used for any given table:
> (mnesia:table_info 'employee 'type)
set
> (mnesia:table_info 'in-project 'type)
bag
>
If you're interested in seeing all the details of any given table, you can
do so with the 'all
parameter:
> (mnesia:table_info 'employee 'all)
(#(access_mode read_write)
#(active_replicas (nonode@nohost))
#(all_nodes (nonode@nohost))
#(arity 7)
#(attributes (id name salary gender phone room-number))
#(checkpoints ())
#(commit_work ())
#(cookie #(#(1396<PHONE_NUMBER>49) nonode@nohost))
#(cstruct
#(cstruct
employee
set
(nonode@nohost)
()
()
0
read_write
false
()
()
false
employee
(id name salary gender ...)
()
()
()
#(...)...))
#(disc_copies ())
#(disc_only_copies ())
#(frag_properties ())
#(index ())
#(load_by_force false)
#(load_node nonode@nohost)
#(load_order 0)
#(load_reason #(dumper create_table))
#(local_content false)
#(majority false)
#(master_nodes ())
#(memory 317)
#(ram_copies (nonode@nohost))
#(record_name employee)
#(record_validation #(employee 7 set))
#(type set)
#(size 0)
#(snmp ())
#(storage_properties ...)
#(...)...)
>
Next up, we'll start inserting some data.


{% extends "_base.html" %} {% block title %}Contact Us{% endblock %} {% block description %}Send us an e-mail, give us a call, or swing by our office. We're family owned and have been in business since 1958.{% endblock %} {% block extra_footer_js %} $(window).load(function(){ (window.frames || document.frames)["map"].window.location = "//maps.google.com/maps?f=q&source=s_q&hl=en&geocode=&q=Aldine+Travel&aq=&sll=38.6531,-90.243462&sspn=0.444531,0.891266&t=h&ie=UTF8&hq=Aldine+Travel&hnear=&ll=38.639668,-90.396709&spn=0.040225,0.051498&z=13&output=embed"; }); {% endblock %} {% block page_core %}
Drop by the office or give us a call — be sure to ask about our current cruise specials!


The noted mezzotintist Dot Tint was born in the Year Dot. Some researchers argue that she was named for the year in which she was born, while others, conversely, claim the year was so named in honour of her. On the face ot it, the latter seems unlikely. Noted Dot Tint may be, but do enough people care enough about mezzotints or their makers to start naming entire years after them? But those who take this side of the argument are indefatigable, relentless, and occasionally physically violent when propounding their case.
And admittedly, they have a number of points in their favour. No one knows what the Year Dot was called before it was designated the Year Dot. They are also able to point to the Year Kuryakin, which all authorities agree was named after Ilya Kuryakin, the Russian agent in the television seiies The Man From U.N.C.L.E. played by the Scottish-American actor David McCallum. Poignantly, there is not, and never has been, a Year McCallum. Thus, the calendar recognises his fictional persona rather than the man himself.
A question well worth asking, whichever side of the Dot Tint fence you are on, is : what kind of blather is this?


HC directs lower court to hear woman’s appeal
by Staff ReporterMadurai
The Madurai Bench of Madras High Court has allowed a woman to re-present her appeal papers before a lower court. The Principal District Court, Sivaganga, would number the appeal and take it on file, the court said.
The court was hearing a petition filed by the woman from Sivaganga district. She had filed a complaint against M. Sethupathi on the charge of harassing her. The Judicial Magistrate II, Sivaganga, acquitted the accused from the charge giving him the benefit of doubt.
The woman preferred an appeal over the verdict before the Principal District Court, Sivaganga. However, the appeal was returned directing her to file the appeal with condonation of delay application.
The appeal papers were again returned when the woman submitted that there was no delay in filing the appeal.
Taking up for hearing the petition that sought a direction to the lower court to hear the case, Justice G.K. Ilanthiraiyan observed that there was no delay in filing the appeal as against the order of the acquittal.
The appeal was filed under Section 372 of CrPC that gives the victim the right to appeal.
Relying on the Judgement of the Allahabad High Court, the court said that no period was prescribed under Section 372 of CrPC for victims to file an appeal. But the provision speaks of appeals against acquittal and as such period of 90 days should be applicable to appeals under Section 372 of CrPC, the court said.
In the present case, the appeal filed was within a period of 43 days from the date of acquittal. Therefore the appeal filed was well within time.


Name:
Jenny Edwards
Telephone:
616-291-2087
361-662-5659
Birthday:
January 5, 1972
Email Address:
<EMAIL_ADDRESS>Employment:
Licorice Pizza
Vehicle:
2001 Dodge Viper
Bank:
Bank of America


{{title}}
- Market Cap:
- Shares Out:
- Shares Short:
- P/E (Trailing Twelve Months):
- PEG Ratio:
- Dividend:
- Currency:
- Has Options:
- Past Earnings:
- Price / book:
- Long Term Debt/Equity
- Book value:
- Gross Margin:
- Profit Margin:
- Operating Margin:
- Free Cash Flow:
Company Profile:


Practice
Each trial starts with two pictures seen below. The top picture is the starting position of the boat, while the bottom picture is the current position after moving. Your aim is to indicate how the boat moved. Click on the arrows below the picture (up to a combination of three arrows) to 'describe' the movement.
In this first example the boat has moved down and then left (or left then down) but has not changed its slant. To proceed, you need to click the down arrow and then the left arrow, or the left arrow and then the down arrow. Do this now and click 'next' in order to proceed. If you make a mistake click on 'reset'.
Practice
In this second example the boat has moved left and down. It has also changed its slant to the left. To proceed, you need to click the down arrow, the left arrow, and the rotate left arrow in any order you like. Do this now and click 'next' to proceed. If you make a mistake click on 'reset'.
Practice
In this third example the boat has moved up and tipped left, without turning. To proceed, you need to click the up arrow and the rotate left arrow in any order you like. Do this now and click 'next' to proceed. If you make a mistake click on 'reset'.