A value is required. Minimum number of characters not met. Exceeded maximum number of characters. A value is required. Minimum number of characters not met. Exceeded maximum number of characters. Invalid format. Which club(s) are you inquiring about? FBLA FFA HOSA NHS FCA TSA SkillsUSA A value is required. Exceeded maximum number of characters. Minimum number of characters not met.


2. Contributors¶
Code Contributions can be seen at
The original author of cloudmesh is
Gregor von Laszewski<EMAIL_ADDRESS>Large contributions have also been made by
Fugang Wang
Many have contributed to cloudmesh in the past. However, this version has reveived contributions from a subset of them. Previous contributors will be acknowledged once we integrate them in the author script.
Contributors are sorted by the first letter of their combined Firstname and Lastname and if not available by their github ID. Please, note that the authors are identified through git logs in addition to some contributors added by hand. The git repository from which this document is derived contains more than the documents included in this document. Thus not everyone in this list may have directly contributed to this document. However if you find someone missing that has contributed (they may not have used this particular git) please let us know. We will add you. The contributors that we are aware of include:
Anthon van der Neut, Anthony Duer, Aravind Mannarswamy, Ashok, Badi Abdul-Wahid, Bo Feng, Dave DeMeulenaere, Eric Collins, Fugang Wang, Gerald Manipon, Gregor von Laszewski, Himanshu Gupta, Jeevan Reddy Rachepalli, Jing Huang, Joaquin Avila Eggleton, Karthick, Keli Fine, Kimball Wu, Mallik Challa, Manjunath Sivan, Ritesh Tandon, Rob Ludwig, Rui Li, Sachith Danushka Withana, Scott McClary, Tarun Rawat, Tharak Vangalapat, Vafa Andalibi, Xiao Yue, Yu Luo, Yue, Xiao, Zhengyi Li


open val outputStates: List<ContractState>
Helper property to return a list of ContractState objects, rather than the often less convenient TransactionState


This is a heading
This is a simple paragraph of text.
Note:
Play with the padding property value to see how it works.


JavaScript
String task 1
Upper case it!
String task 2
Join it!
Arrays 1 / Strings 3
Random list:
Add to it!
Pop it!
Conditionals
Check it!
Iteration 1
Loop it!
Iteration 2
Loop/2 it!


 BING BONG
Request Transport Schedule Transport


Initialize New Database
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© Copyright 2014 White Helmet Tech, LLC
Version: 0.0


---
layout: post
date: 2010-06-11
time: "11:00:00"
authors: ["Greg Wilson"]
title: "Interview: David Jackson at the UK Met Office"
tags: ["Interviews", "UK Met Office"]
---
The second of today's sponsor interviews is with David Jackson from the UK Met Office.
Tell us a bit about your organization and its goals.
You can find out more about us at http://www.metoffice.gov.uk, but broadly speaking, we:
- predict the weather for tomorrow, next week, next season and beyond;
- are a significant contributor to the global understanding of climate change;
- are leading researchers of weather science;
- provide forecasts for sporting events such as Wimbledon and Open Golf;
- stand shoulder to shoulder with Armed Forces around the world;
- help keep roads open and planes flying;
- inform the decisions and policies of businesses and governments across the world;
- help the National Health Service provide preventative healthcare.
Our group is focussed on research.
Tell us a bit about the software your group uses.
We have a lot of home-grown scientific models and related codes around weather and climate prediction.
- The Unified Model is at the heart of this the code base.
- Much of the compiled code is in FORTRAN.
- We use Perl and shell scripting to bind things together, and also some Python.
- Our MASS storage system is developed in JAVA.
- We use IDL for data processing and R for statistical analysis. There is a lot of user code that sits on top of these base packages and a lot of bespoke development.
- We use Subversion for version control and Trac for issue tracking.
Tell us a bit about what software your group develops.
- The Unified model and related software is used internationally by a number of groups.
- There is a whole range of software development from internationally supported software, like the UM to code scientists write for their own purposes, with other codes supported within the Met Office.
Who are you hoping Software Carpentry will help?
- A scientist who does some software development for their own needs.
- A scientific programmer who does not have a software engineering background.
- In general, people who can code but would benefit from not-too-heavy software engineering principles.
How do you hope the course will help them?
- The benefits of software engineering principles and disciples, such as testing and configuration management.
- Good code structure practice.
- How to make more code shareable and supportable.
How will you tell what impact the course has had?
- Better disciple.
- More shared code.
- Better tested.
- More confidence.


{include file="left" /}
{include file="top" /}
Fixed Sidebar
Just add class
menu_fixed
{include file="footer" /}


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Sweet Free Stuff
///////////////////////////////////////////
Free Arm & Hammer Fabric Sheets at Kmart
Posted: 26 Feb 2016 10:08 AM PST
http://feedproxy.google.com/&edburner&utm_medium=email
Download the Kmart app for iOS or Android, then scroll down and click
Friday Fix to get a coupon for FREE Arm Hammer Fabric Sheets 100 ct.
The post Free Arm Hammer Fabric Sheets at Kmart appeared first on
SweetFreeStuff.com.
///////////////////////////////////////////
Free Greek Yogurt Pre and Probiotics at Kroger
Posted: 26 Feb 2016 08:42 AM PST
http://feedproxy.google.com/&edburner&utm_medium=email
Load the coupon to your shoppers card today only (2/26) and use by 3/13.
Free Greek Yogurt Pre and Probiotics at Kroger
The post Free Greek Yogurt Pre and Probiotics at Kroger appeared first on
SweetFreeStuff.com.
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Free Premier Protein Bar or Shake
Posted: 26 Feb 2016 04:30 AM PST
http://feedproxy.google.com/&edburner&utm_medium=email
Free Premier Protein Bar or Shake
The post Free Premier Protein Bar or Shake appeared first on
SweetFreeStuff.com.
CommentsI received mine! Tasted disgusting… but 30 grams of protein! by
tianiniYummy by Amber DesiSounds delicious by Amber DesiI would love to try
this out. by Llinda Abernathyi want to try this by janet menster


METS READER REPORT CARD: Grade this not-so Amazin’ first-half performance
Published July 20, 2018
The Mets enter the second half of the 2018 season with a 39-55 record, their most losses at the All-Star break in 25 years. They are 13.5 games back in the NL East and looking at a second straight October without a playoff game.
See how our Mets reporter graded the Mets at the midseason mark, then give us your grades for the Mets below.
Starting pitching
Bullpen
SEE: Mets Misery Index
Infielders
Catchers
Outfielders
Bench
SEE: Mets Injury Tracker
Manager
MORE: Mets trivia quiz, Mets managers quiz
General Manager
LAST CHANCE: Mets misery index


Clap your hand 3 times to get the beats per minute (BPM).
Tested on Chrome version 33. Allow access to your microphone. The script will listen for loud sounds and try
to determine the timing between sounds as beats per minute. It will then apply the "blinkOn" class to the bpm control
at the same frequency & play a click in the speaker.
BPM:


subtitle: 'Zoom plugin'
---
## Zoom plugin
The Zoom plugin allows the use to zoom on the graph using the mouse. When the use clicks on the graph, holds the mouse down and moves it around, a red rectangle is formed, which defined the new zooming boundaries that the graph will take when the mouse is released.
### Enabling the zoom plugin
The zoom plugin, like any other plugin, needs to be enabled upon graph creation:
```javascript
new Graph('domID', {
plugins: {
zoom: {
// ... zoom options
}
}
});
```
### Features
Here are a few features that the zoom plugin can provide you:
- Zooming in x and y direction or forced in the x or y-direction
- Zooming on one axis only
- Double click to unzoom
- Mouse wheel to zoom gradually
- Smooth transition (animated zooming)
- Event calls and cancellation possible
### Basics
We will now cover the basics of the zoom plugin. The zoom plugin is intimate with the option `mouseActions` of the graph, which can be accessed in the constructor options or via direct settings. The mouse actions define what jsGraph should do upon various mouse events. We will cover the mouse actions in details somewhere else, but for now, let's take the following code as it is:
```javascript
new Graph('domID', {
plugins: {
zoom: {
// ... zoom options
zoomMode: 'xy'
}
},
mouseActions: [{ plugin: 'zoom', shift: false, ctrl: false }]
});
```
The following code enables the zoom plugin only when the `Shift` and `Ctrl` key are not pressed. If one of these were `true`, the use would need to have the key pressed to enable zooming.
#### Example
Using the previous code, we create the zoom plugin and enable it using the mouse action. The buttons below the graph trigger the following code.
```javascript
// Enabling button
graph.options.mouseActions[0].shift = true;
// Disabling button
graph.options.mouseActions[0].shift = false;
```
### Forcing the x or y direction
The `zoomMode` options passed in the plugin creation or after its initialization defined the behaviour of the zoom and takes the following possible values:
- `xy` lets the use zoom in both directions
- `x` forces zooming in the x direction only
- `y` forces zooming in the y direction only
### Unzoom on double click
So far, the only way to unzoom on a graph is to active the double click. The double click behavior is also defined through mouse actions, in the following way:
```javascript
// Enabling button
{ type: 'dblclick', plugin: 'zoom', shift: false, options: { mode: 'total' } }
```
Where the `type` defines the mouse action (dblclick in this case), upon which the plugin `zoom` is called with the options `{ mode: 'total' }`.
The only available option in the double click mouse events is the `mode` options, which takes the following values:
- `total` (default): Unzooms the axes to fit the natural boundaries of the axes (defined by the data or by the `forcedMin`/`forcedMax` options)
- `xtotal`. Only unzooms the x axis
- `ytotal`. Only unzooms the y axis
- `gradualXY`. Unzooms the axes by a factor 2 only. The unzooming is centered on the position where the mouse is clicked
- `gradualX`. Same as `gradualXY` but in the x direction only
- `gradualY`. Same as `gradualY` but in the y direction only
### Zooming on one axis only
The default behaviour of jsGraph is to unzoom all present axes on the graph. The mouse coordinates are transformed into respective axes coordinates and used accordingly. However jsGraph also allows you to select which axis should be zoomed.
Changing the zooming axis(es) can be done through the `axes` options of the zoom plugin and can take the following values:
- `all` (default): Zooms and unzooms on all axes.
- `serieSelected`: Zooms and unzooms on the axes belonging to the selected series
- `[ axis0, axis1, ..., axisN ]`. Provide a user-defined list of axes
Reminder: line series can be made selectable on click using the `selectableOnClick: (bool)` option and all series can be programmatically selected or unselected using the `.select()` or `.unselect()` API.
Note: In this example, the x axis is shared between the two series. Hence the serie that is not selected will also be scaled in the x direction when the option `serieSelected` is set
### Smooth zooming
Smooth zooming and unzooming using animations is possible with jsGraph, by setting the `smooth` flag to true in the plugin constructor or after its creation
```javascript
graph.getPlugin('zoom').options.smooth = true; // Enables smoothing
```
This option will apply to zooming and unzooming indepedently
### Mouse wheel
The mousewheel behavior is set in the `mouseActions` array of the graph instance. Options include:
- `baseline` sets the zero of the scaling
- `direction` (`y` or `x`) sets the direction of the zooming
```javascript
{ plugin: 'zoom', shift: true, type: 'mousewheel', options: { baseline: 0, direction: 'y' } },
```
```
```


Hudson's Bay shareholder Catalyst opposes go private deal
Hudson's Bay Co shareholder Catalyst Capital Group Inc has opposed an agreed deal by the struggling Canadian department store operator with a group led by executive chairman Richard Baker to be taken private.
REUTERS: Hudson's Bay Co shareholder Catalyst Capital Group Inc has opposed an agreed deal by the struggling Canadian department store operator with a group led by executive chairman Richard Baker to be taken private.
Catalyst and other shareholders holding about 28.24per cent of shares of Hudson's Bay intend to vote against the agreement, the firm said on Thursday.
The group urged the Hudson Bay's board to explore alternatives, and said it was "aware of a number of strategic investors that are interested in participating in a process that is open and not constructed to benefit an insider."
Saks Fifth Avenue owner Hudson's Bay earlier this month agreed to a sweetened CUS$1.9 billion (US$1.5 billion) offer from the shareholder group.
(Reporting by Nishara Karuvalli Pathikkal; Editing by Sriraj Kalluvila)


PQT2 comes with a whole range of features!
- Append two configurations by opening a second configuration (or drag 'n' drop it) while the first configuration is open
- Drag the detail columns around to re-order them (they'll appear that way when you export)
- Press the little 'save' button to save your current settings as your defaults
- Use the 'Save' button to store up to 10 configurations
- Change the configuration name by clicking on it at the top of the page
- Use the small menu button on each bundle to open a new menu
Frequently asked questions
Where can I get a .SDD file from?
You'll need to get it from OCA (.SDD files from SBW aren't supported). If you don't know what SBW and OCA are, you probably don't need this tool.
Can I save a configuration with all it's settings?
Yep, just click on the Save button.
OK genius, I saved my config and now I can't find it!
That's not a question, but OK. Saving is all local to your current browser - so you'll need to open PQT2 on the same browser on the same machine to see it again.
Can I merge the bundles from two different configurations?
You sure can. While you've got one config open, just open a second one (you can drag and drop again if you like) and it will be appended.
How do I change the name and telephone number I set at the beginning?
Click on the little cog in the top-right of the menu. You can also set your 'default' discount levels here.
I want PQT2 to do something new it doesn't already do.
You've already used your not-a-question card. Can you rephrase that as a question?
You're very annoying. How can I ask for a new feature?
I know, I enjoy it. You'll need to make a request using the issue tracker (https://github.com/gilphilbert/pqt2/issues)
What if something doesn't work?
If things aren't working or you're not getting an error message, use the issue tracker (link above) to tell me about it. Often, I'll need the .SDD file you're using, but I'll ask you to email it to me instead of posting it publicly.


public class TopologyPreservingSimplifier
extends java.lang.Object
If the input is a polygonal geometry
( Polygon
or MultiPolygon
):
For all geometry types, the result will contain enough vertices to ensure validity. For polygons and closed linear geometries, the result will have at least 4 vertices; for open linestrings the result will have at least 2 vertices.
All geometry types are handled. Empty and point geometries are returned unchanged. Empty geometry components are deleted.
The simplification uses a maximum-distance difference algorithm similar to the Douglas-Peucker algorithm.
DouglasPeuckerSimplifier
| Constructor and Description |
|---|
TopologyPreservingSimplifier(Geometry inputGeom) |
| Modifier and Type | Method and Description |
|---|---|
Geometry |
getResultGeometry() |
void |
setDistanceTolerance(double distanceTolerance)
Sets the distance tolerance for the simplification.
|
static Geometry |
simplify(Geometry geom,
double distanceTolerance) |
public TopologyPreservingSimplifier(Geometry inputGeom)
public void setDistanceTolerance(double distanceTolerance)
distanceTolerance
- the approximation tolerance to usepublic Geometry getResultGeometry()


--- layout: post title: Mobile banking service disappoints date: 2006-10-15 00:00:00 categories: business interfaces-&-interaction mobile ---
Mobile banking service disappoints: "most publications have overlooked the long list of exclusions – those handsets which don't support the new Monilink software."
L'Inq goes on to list the handsets which aren't supported. These include "All Windows CE; NEC; Philips; Sanyo; Sendo; Orange SPV and all Orange 3G devices (except the Nokia 6630)". With the exception of "all Orange 3G devices" I wouldn't be too concerned about the missing support, they're all very niche handsets and don't (in our experience) get much in the way of usage compared to other devices out there.
I thought I'd sign up for Monilink and take a look. First impressions: OW! An 11-step registration process? Look, I know this is banking and all that, but this is ridiculous.
Second impression: a "gateway timeout" when I clicked on the WAP Push link Monilink sent me. Ah well...


| Question | Answer | |
|---|---|---|
1 |
How do I navigate between pages? | You can use the navigation drawer to navigate between most pages, to access this look for the hamburger icon in the top left of the screen (☰). In the case that you instead see an arrow, simply drag the left of the screen towards the middle of the screen this will also allow you to access the navigation menu. |
2 |
How do I add food? | To add food, you first want to navigate from the home page to the food page, here you can click on the hamburger icon (...) to open a menu, the first option will allow you to search for food to add. |
3 |
How do I add water? |
You can add water 2 ways:
|
4 |
Can I add food to previous days if I forget to add it on the day? | Yes, when you are on the food page, simply click on any one of the previous 7 days to update food for that day. |
5 |
How much is 1 glass of water? | A glass of water is assumed to be on average 250ml. |
6 |
Why can't I find compound foods (tea and milk)? | Some items don't exist in the Edamam food database and as such it may require you to enter them as individual items (in this case tea, then milk seperately). |
7 |
Why aren't there any results for the food I'm searching? | The database may not have the food stored, this could apply to certain branded items. Alternatively, it could be that there is a leading space before the food, please check that instead of " item" you have put in "item" (without the quotation marks). |
8 |
Can you add your own items? | No, as items are from an external database, this means food can't be added to the database. |
9 |
Can I use multiple accounts to track food for my whole family? | Yes by registering multiple account you can login and logout, adding food to each account seperately. |
10 |
When I'm on the meal page and I slide an item it gets stuck with a purple bar, why is this? | This is not intentional, sometimes it doesn't display the correct menu until the second swipe. If you have another item long hold to trigger the menu change and then it should work fine. |


{% extends "base.html" %} {% block content %}
This is a modified jumbotron that occupies the entire horizontal space of its parent.
Some quick example text to build on the card title and make up the bulk of the card's content.


public abstract class AbstractLooper<Q extends AbstractQueue>
extends java.lang.Object
| Modifier and Type | Class and Description |
|---|---|
static class |
AbstractLooper.Flag
Looper Property Flags
|
class |
AbstractLooper.LooperControl
LooperControl exposes sensitive and/or volatile methods and fields outside the package.
|
| Constructor and Description |
|---|
AbstractLooper(AbstractLooper.Flag... flags) |
| Modifier and Type | Method and Description |
|---|---|
long |
getAveragePolledMillis() |
static long |
getGloballyUniqueId()
Returns a globally unique id number, generally used for queue entry id numbers.
|
java.lang.Thread |
getJoinedThread()
Gets the Thread running this Looper.
|
long |
getLastPolledMillis() |
protected AbstractLooper.LooperControl |
getLooperControl() |
java.lang.String |
getName() |
Q |
getQueue()
Get the
AbstractQueue associated with this AbstractLooper |
boolean |
hasFlag(AbstractLooper.Flag flag) |
boolean |
isAsync() |
abstract boolean |
isDisposed() |
boolean |
isHeldByCurrentThread()
Returns true if this Looper belongs to this the current thread.
|
boolean |
isLockedByCurrentThread() |
boolean |
isOverloaded()
Returns true if this Looper has an average millis of over 100ms with each iteration.
|
abstract boolean |
isPermitted(EntryAbstract entry) |
boolean |
isQuitting()
Returns true if this Looper is currently working on quitting.
|
boolean |
isThreadJoined()
Determines if the looper is currently active with a joined thread.
|
void |
joinLoop()
Joins the thread to this looper until it quits.
|
void |
joinLoopSafe() |
protected void |
quit(boolean removePendingMessages) |
protected abstract void |
quitFinal()
Does some final tasks before the Looper is permanently disposed of.
|
void |
quitSafely()
Quits the looper safely.
|
void |
quitUnsafe()
Quits the looper.
|
void |
setExceptionHandler(java.util.function.Consumer<java.lang.Exception> exceptionHandler) |
protected void |
setQueue(Q queue) |
protected void |
signalInfallibleStartup() |
protected void |
signalPostJoinLoop() |
protected void |
signalPreJoinLoop() |
protected abstract void |
tick(long loopStartMillis,
long lastPolledMillis,
long lastOverloadMillis) |
java.lang.String |
toString() |
public AbstractLooper(AbstractLooper.Flag... flags)
public static long getGloballyUniqueId()
Long.MAX_VALUE
.public long getAveragePolledMillis()
public java.lang.Thread getJoinedThread()
public long getLastPolledMillis()
protected AbstractLooper.LooperControl getLooperControl()
public java.lang.String getName()
public Q getQueue()
AbstractQueue
associated with this AbstractLooper
protected void setQueue(Q queue)
public boolean hasFlag(AbstractLooper.Flag flag)
public boolean isAsync()
public abstract boolean isDisposed()
public boolean isHeldByCurrentThread()
joinLoop()
public boolean isLockedByCurrentThread()
public boolean isOverloaded()
public abstract boolean isPermitted(EntryAbstract entry)
public boolean isQuitting()
public boolean isThreadJoined()
joinLoop()
public void joinLoopSafe()
public void joinLoop() throws ApplicationException.Error
ApplicationException.Error
protected void quit(boolean removePendingMessages)
protected abstract void quitFinal()
public final void quitSafely()
Causes the joinLoop()
method to terminate as soon as all remaining messages
in the queue that are already due to be delivered have been handled.
However pending delayed messages with due times in the future will not be
delivered before the loop terminates.
public final void quitUnsafe()
Causes the joinLoop()
method to terminate without processing any more messages in the queue.
Using this method may be unsafe because some messages may not be delivered
before the looper terminates. Consider using quitSafely()
instead to ensure
that all pending work is completed in an orderly manner.
quitSafely()
public void setExceptionHandler(java.util.function.Consumer<java.lang.Exception> exceptionHandler)
protected void signalInfallibleStartup()
protected void signalPostJoinLoop()
protected void signalPreJoinLoop()
protected abstract void tick(long loopStartMillis, long lastPolledMillis, long lastOverloadMillis)
public java.lang.String toString()
toString
in class java.lang.Object


We have striven to minimize the number of errors. However, we canot guarantee the note is 100% accurate.
In this tutorial, I conducted a parameter recovery study, demonstrating the pMCMC method to fit a hierarchical DDM for a relatively simple factorial design.
Set-up a model object
This particular design is from Heathcote et al’s (2018) DMC tutorial, which assumes a word frequency (my interpretation) factor affecting the mean drift rate (v). Note it is not a good practice to use “F” notation in R, because it is also used as a shorthand for the reserved word, meaning FALSE. However, one of the R strengths is it permits idiosyncratic programming habits, even bad ones. In the example here, it seems not cause any errors.
## version <IP_ADDRESS>
## install.packages("ggdmc")
loadedPackages <-c("ggdmc", "data.table", "ggplot2", "gridExtra", "ggthemes")
sapply(loadedPackages, require, character.only=TRUE)
model <- BuildModel(
p.map = list(a = "1", v = "F", z = "1", d = "1", sz = "1", sv = "1",
t0 = "1", st0 = "1"),
match.map = list(M = list(s1 = "r1", s2 = "r2")),
factors = list(S = c("s1", "s2"), F = c("f1", "f2")),
constants = c(st0 = 0, d = 0),
responses = c("r1", "r2"),
type = "rd")
npar <- length(GetPNames(model))
To conduct a parameter recovery study, I firstly assumed a hidden multi-level mechanism generating the data. That is, I presumed there is a distribution at the population / participants level. This distribution is a 7 dimension distribution, which has 7 marginal distributions. Each of them is in control of one DDM parameter.
pop.mean <- c(a = 2, v.f1 = 4, v.f2 = 3, z = .5, sz = .3, sv = 1, t0 = .3)
pop.scale <- c(a = .5, v.f1 =.5, v.f2 = .5, z = .1, sz = .1, sv = .3, t0 = .05)
pop.prior <- BuildPrior(
dists = rep("tnorm", npar),
p1 = pop.mean,
p2 = pop.scale,
lower = c(0, rep(-5, 2), rep(0, 4)),
upper = c(5, rep( 7, 2), 1, 2, 1, 1))
As usual, I want to visually check if the assumed mechanism is reasonable.
plot(pop.prior)
After making sure that the data generating mechanism is proper, I then simulated a data set with 40 participants and 250 trials for each condition.
dat <- simulate(model, prior = pop.prior, nsim = 250, nsub = 40)
dmi <- BuildDMI(dat, model)
ps <- attr(dat, "parameters")
In the hierarchical case of the parameter recovery study, my aim is to recover not only ps matrix, but also the data generating mechanism. That is, I also want to known pop.mean, pop.scale and their marginal distribution. Note ps is a matrix, storing the true values for each DDM parameter. That is, the simulate function randomly selects nsub set of true parameters based on the prior distribution, pop.prior. Each row of the ps matrix represents one parameter vector of a participant.
tibble::as_tibble(ps)
## A tibble: 40 x 7
## a v.f1 v.f2 z sz sv t0
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2.93 4.30 2.95 0.486 0.151 0.775 0.304
## 2 1.52 3.74 2.40 0.589 0.258 0.809 0.340
## 3 1.85 4.31 2.62 0.636 0.318 0.903 0.349
## 4 2.34 3.94 2.53 0.578 0.127 0.993 0.283
## 5 2.44 4.50 2.68 0.566 0.246 0.589 0.261
## 6 2.73 4.08 3.68 0.465 0.182 0.973 0.290
## 7 2.34 2.89 3.69 0.742 0.307 0.592 0.325
## 8 1.90 4.35 3.56 0.512 0.186 0.513 0.309
## 9 2.60 4.18 2.81 0.541 0.367 0.758 0.270
## 10 1.64 3.77 2.37 0.596 0.286 0.995 0.375
## with 30 more rows
The above is only preparation work for a parameter recovery study. In the following, I will start to draw samples from the posterior distribution, aiming to recover pop.mean, pop.scale, the target distribution, and the ps matrix.
I already have the likelihood, which is the DDM equation (Voss, Rothermund, Voss, 2004). I will need to set up the prior distributions for the seven DDM parameters. In the case of hierarchical model, there are two sets of prior distributions: one is usually called hyper-prior distributions and the other simply prior distributions. The former is my prior belief about the distributions (pp.prior below) accounted by pop.mean and pop.scale. The latter is my prior belief about separate DDM mechanisms for each individual participant (p.prior below).
p.prior <- BuildPrior(
dists = rep("tnorm", npar),
p1 = pop.mean,
p2 = pop.scale*5,
lower = c(0,-5, -5, rep(0, 4)),
upper = c(5, 7, 7, 1, 2, 1, 1))
mu.prior <- BuildPrior(
dists = rep("tnorm", npar),
p1 = pop.mean,
p2 = pop.scale*5,
lower = c(0,-5, -5, rep(0, 4)),
upper = c(5, 7, 7, 1, 2, 1, 1))
sigma.prior <- BuildPrior(
dists = rep("beta", npar),
p1 = rep(1, npar),
p2 = rep(1, npar),
upper = rep(2, npar))
names(sigma.prior) <- GetPNames(model)
A convention in ggdmc is to bind location and scale prior distributions as one list object. This is just for the convenience of data handling in R, which is not so convenient in C++. Note the names of each element in the list is critical.
priors <- list(pprior = p.prior, locaton = mu.prior, scale = sigma.prior)
Then, the following run sampling.
## run the "?" to see the details of function options
?StartNewsamples
?run
## 7.84 mins
fit0 <- StartNewsamples(data=dmi, prior=priors)
## 19 mins
fit <- run(fit0)
## 7.54 hrs
fit <- run(fit, thin = 8)
save(pop.mean, pop.scale, pop.prior, model, dat, dmi, npar, ps,
fit0, fit, file = "data/hierarchical/ggdmc_4_7_DDM.rda")
One may use the repeat function to run automatic model fit.
## 4 rounds
thin <- 8
repeat {
hsam <- run(RestartHypersamples(5e2, hsam, thin = thin),
pm = 0, hpm = 0)
save(pop.mean, pop.scale, pop.prior, model, dat, dmi, npar, ps,
hsam0, hsam, file = "data/hierarchical/ggdmc_4_7_DDM.rda")
rhats <- hgelman(hsam)
thin <- thin * 2
if (all(rhats < 1.1)) break
}
save(pop.mean, pop.scale, pop.prior, model, dat, dmi, npar, ps,
hsam0, hsam, file = "data/hierarchical/ggdmc_4_7_DDM.rda")
Similar to many standard modeling works, I must diagnose the model fit to certain I drew a reliable posterior distribution, reflecting the target distribution. I can check visually as well as calculate some statistics. First, I conducted visual checks for the trace plots and posterior distributions.
- Trace plots of posterior log-likelihood at hyper level
- Trace plots of the hyper parameters
- Trace plots of posterior log-likelihood at the data level
- Trace plots of each DDM parameters for each participants
- Posterior density plots (i.e., marginal posterior distributions) for the hyper parameters
- Posterior density plots of each DDM parameters at the data level
plot(hsam, hyper = TRUE) ## 1.
plot(hsam, hyper = TRUE, pll = FALSE) ## 2.
plot(hsam) ## 3.
plot(hsam, pll = FALSE) ## 4.
plot(hsam, hyper = TRUE, pll = FALSE, den = TRUE) ## 5.
plot(hsam, pll = FALSE, den = TRUE) ## 6.
These are a lot of figures to check. I have not presented the figures of posterior probability density for every participant (i.e., figure 6), because there are too many. You can print them in a pdf file to check.
Then, I calculated the potential scale reduction factor, for both the hyper parameters and the parameters for each participant.
rhats <- hgelman(hsam, verbose = TRUE)
## Diagnosing theta for many participants separately
## Diagnosing the hyper parameters, phi
## hyper 1 2 3 4 5 6 7 8 9 10 11 12
## 1.02 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## 13 14 15 16 17 18 19 20 21 22 23 24 25
## 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## 26 27 28 29 30 31 32 33 34 35 36 37 38
## 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## 39 40
## 1.01 1.01
Finally, I want to know if I do recover the data generation mechanism and true parameter values for every participant (i.e., ps). This can be achieved by using the summary function.
hest1 <- summary(hsam, recovery = TRUE, hyper = TRUE, ps = pop.mean, type = 1)
hest2 <- summary(hsam, recovery = TRUE, hyper = TRUE, ps = pop.scale, type = 2)
round(hest1, 2)
round(hest2, 2)
ests <- summary(hsam, recovery = TRUE, ps = ps)
## a sv sz t0 v.f1 v.f2 z
## True 2.00 1.00 0.30 0.30 4.00 3.00 0.50
## 2.5% Estimate 1.83 0.69 0.22 0.28 3.69 2.76 0.49
## 50% Estimate 2.00 0.88 0.29 0.29 3.87 2.93 0.52
## 97.5% Estimate 2.17 0.99 0.34 0.31 4.07 3.12 0.55
## Median-True 0.00 -0.12 -0.01 -0.01 -0.13 -0.07 0.02
## a sv sz t0 v.f1 v.f2 z
## True 0.50 0.30 0.10 0.05 0.50 0.50 0.10
## 2.5% Estimate 0.43 0.15 0.03 0.03 0.38 0.41 0.08
## 50% Estimate 0.53 0.30 0.07 0.04 0.49 0.53 0.10
## 97.5% Estimate 0.71 0.58 0.14 0.05 0.65 0.68 0.13
## Median-True 0.03 0.00 -0.03 -0.01 -0.01 0.03 0.00
## Summary statistics across participants
## a v.f1 v.f2 z sz sv t0
## Mean 2.00 3.87 2.94 0.52 0.29 0.72 0.29
## True 1.98 3.86 2.94 0.52 0.28 0.78 0.29
## Diff -0.02 -0.01 0.00 0.00 -0.01 0.06 0.00
## Sd 0.51 0.44 0.49 0.10 0.04 0.15 0.04
## True 0.52 0.47 0.51 0.10 0.09 0.19 0.04
## Diff 0.00 0.02 0.02 0.00 0.05 0.04 0.00


10 Managers Facing The Sack
by SL InternationalWith Bundesliga giants Bayern Munich making the decision to part ways with manager Niko Kovac on Sunday night, a number of other bosses are also seemingly nearing the dreaded sack.
Click through the gallery above to see 10 managers who could be sacked in the coming weeks.
Despite winning the LaLiga title in each of his first two full seasons with FC Barcelona, Ernesto Valverde has found himself under constant scrutiny at the club, and the former Athletic Bilbao coach's job is now hanging by a thread after suffering another shock 3-1 defeat to Levante last weekend.
Earn Airtime & Data With Soccer Laduma's Social Media App - Download Now
The 55-year-old is not the only Spaniard potentially facing the chop, though, with Arsenal boss Unai Emery suffering similar pressure in London. The Gunners are currently fifth in the Premier League standings, 14 points off league leaders Liverpool, and a bad result against Leicester City this Saturday could send Emery packing.
A number of other EPL managers are also on the brink of losing their jobs if reports are to be believed, including Tottenham Hotspur boss Mauricio Pochettino, Everton's Marco Silva and Southampton's Ralph Hasenhuttl, and Manchester United boss Ole Gunnar Solskjaer is perhaps the biggest name who could fall in the coming weeks.
Read: Bayern Sack Kovac After 5-1 Loss
Outside of England, Zinedine Zidane continues to divide opinion with Real Madrid, while Lucien Favre has failed to impress at Borussia Dortmund. The pair feature alongside AS Monaco's Leonardo Jardim and Torino's Walter Mazzarri – you can view the full list by scrolling though the gallery above.


Partner locations can set up weather stations to collect data including temperature, pressure, humidity, wind speed, hourly precipitation, and solar radiation, which can be correlated with the DoseNet radiation sensor data to learn how environmental factors can affect background radiation rates.
Our first example weather station, installed at LBL to correlate with the LBL DoseNet device.
Data from each weather station is streamed directly to Weather Underground and available for analysis.


|
M480 BSP V3.05.005
The Board Support Package for M480 Series
|
Modules | |
| RTC Exported Structs | |
| #define REF_PREVIOUS_PATTERN |
| #define REF_RANDOM_PATTERN |
| #define REF_SEED |
| #define RTC_CLOCK_12 |
| #define RTC_FCR_REFERENCE |
| #define RTC_INIT_KEY |
| #define RTC_TAMPER_DEBOUNCE_DISABLE |
| #define RTC_TAMPER_DEBOUNCE_ENABLE |
| #define RTC_TAMPER_HIGH_LEVEL_DETECT |
| #define RTC_TAMPER_LOW_LEVEL_DETECT |
| #define RTC_TICK_1_128_SEC |
| #define RTC_WRITE_KEY |


Building Serverless Applications
with
by
Travis Tidwell
CTO |
https://form.io
@softwaregnome
https://github.com/travist
About
https://github.com/formio/formio
Serverless Applications
Completely separated architecture
Leverages Microservices (AWS Lambda)
Leverages Stateless REST APIs
Introduction
http://bit.ly/1ZRTAjS
Now lets build something with
Movie Manager
Thank you!


Custom Routing
All Custom Routing Types
Original Source
+dynamic Sheet CustomConnectors { [@ 30, 10] Rectangle r1 +geo="50x50"; [@120, 10] Rectangle r2 +geo="50x50"; [@210, 10] Rectangle r3 +geo="50x50"; [@ 75,100] Rectangle r4 +geo="50x50"; [@165,100] Rectangle r5 +geo="50x50"; [@ 30,190] Rectangle r6 +geo="50x50"; [@120,190] Rectangle r7 +geo="50x50"; [@240,160] Rectangle r8 +geo="50x50"; [@corner:ene-n] +custom Connector c1 +r1-r4 +end="Triangle"; [@corner:wnw-nnw] +custom Connector c2 +r4-r6 +end="Triangle"; [@corner:n-wsw] +custom Connector c3 +r6-r4 +end="Triangle"; [@corner:ene-ssw] +custom Connector c4 +r4-r2 +end="Triangle"; [@corner:ene-n] +custom Connector c5 +r2-r5 +end="Triangle"; [@corner:wnw-n] +custom Connector c6 +r5-r7 +end="Triangle"; [@corner:s-w] +custom Connector c7 +r4-r7 +end="Triangle"; [@direct:ese-wsw] +custom Connector c8 +r7-r8 +end="Triangle"; [@direct:nne-sse] +custom Connector c9 +r8-r3 +end="Triangle"; [@tree:ssw-nne] +custom Connector c10 +r5-r7 +end="Triangle"; [@tree:ese-wnw] +custom Connector c11 +r5-r8 +end="Triangle"; }


FOI release
An FOI disclosure about the Framework Agreement Contract - UK Darlington.
Can the department the following information in relation to the Framework Agreement Contract - UK Darlington: administration services:
In respect of the UK Darlington Administration Services notice summary, those suppliers who applied for inclusion, those who were successful / unsuccessful at the PQQ and ITT stages, the contract value, start date and duration, and whether the framework agreement is being renewed.
With regards to the tender notice (reference<PHONE_NUMBER>), it was to award a contract, rather than a framework agreement, for the administration of the Teachers’ Pension Scheme (TPS).
Four companies submitted an expression of interest at the PQQ stage:
All companies were issued with an invitation to participate in dialogue (slightly different to an ITT because the companies were asked to enter into a competitive dialogue). After competitive dialogue was complete, all the companies were invited to submit a best and final offer (BAFO) although one organisation chose not to do so.
Following receipt and evaluation of BAFOs from Capita, Mouchel and Paymaster, Capita were successful in being selected as the preferred supplier.
The contract started on 24 March 2011 entering into a transitional period before service commencement on 1 October 2011. The contract is for an initial term of 7 years (from service commencement date) with the option to extend for up to 3 years after. The value of the contract for the initial term is £89m.


Policy Holder
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Stefan is a results-oriented, environmental, health, safety and quality manager with 16 years of experience in the development, evaluation,
management and execution of international successful EHSQ programs within the chemical industry. He has a strong ability to lead international
teams as well as to optimize complex processes in order to achieve a lean and highly professional corporate structure.
His motto is: Transparency and efficiency in all processes. Complexity destroys the team spirit as well as the organization behind it.


Search for your favorite movies to find the director, the cast, the release year, and more!
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Table Of Contents:
- 12 Principles of Agile Manifesto
- Agile Terminology
- Sprint Duration
- Sprint Ceremonies
- Stories, Tasks and Bugs
- QA
- User Acceptance Testing
- Releases
- Integration with DevOps
- Jira Workflow
- Definition of Ready and Definition of Done
12 Principles of Agile Manifesto
Our highest priority is to satisfy the customer through early and continuous delivery of valuable software.
Welcome changing requirements, even late in development. Agile processes harness change for the customer’s competitive advantage.
Deliver working software frequently, from a couple of weeks to a couple of months, with a preference to the shorter timescale.
Business people and developers must work together daily throughout the project.
Build projects around motivated individuals. Give them the environment and support they need, and trust them to get the job done.
The most efficient and effective method of conveying information to and within a development team is face-to-face conversation.
Working software is the primary measure of progress.
Agile processes promote sustainable development. The sponsors, developers, and users should be able to maintain a constant pace indefinitely.
Continuous attention to technical excellence and good design enhances agility.
Simplicity–the art of maximizing the amount of work not done–is essential.
The best architectures, requirements, and designs emerge from self-organizing teams.
At regular intervals, the team reflects on how to become more effective, then tunes and adjusts its behavior accordingly.
Agile Terminology
We will define some common agile terms using The Agile Alliance Glossary
Backlog
A backlog is a list of features or technical tasks which the team maintains and which, at a given moment, are known to be necessary and sufficient to complete a project or a release:
if an item on the backlog does not contribute to the project’s goal, it should be removed
if at any time a task or feature becomes known that is considered necessary to the project, it should be added to the backlog.
Backlog Grooming
Backlog grooming is when the product owner and some, or all, of the rest of the team review items on the backlog to ensure the backlog contains the appropriate items, that they are prioritized, and that the items at the top of the backlog are ready for delivery. This activity occurs on a regular basis and may be an officially scheduled meeting or an ongoing activity.
Some of the activities that occur during this refinement of the backlog include:
removing user stories that no longer appear relevant
creating new user stories in response to newly discovered needs
re-assessing the relative priority of stories
assigning estimates to stories which have yet to receive one
correcting estimates in light of newly discovered information
splitting user stories which are high priority but too coarse grained to fit in an upcoming iteration
Burndown Chart
The team displays, somewhere on a wall of the project room, a large graph relating the quantity of work remaining (on the vertical axis) and the time elapsed since the start of the project (on the horizontal, showing future as well as past). This constitutes an “information radiator”, provided it is updated regularly. Two variants exist, depending on whether the amount graphed is for the work remaining in the iteration (“sprint burndown”) or more commonly the entire project (“product burndown”).
Continuous Integration
Teams practicing continuous integration seek two objectives:
minimize the duration and effort required by each integration episode
be able to deliver a product version suitable for release at any moment
In practice, this dual objective requires an integration procedure which is reproducible at the very least, and largely automated. This is achieved through version control tools, team policies and conventions, and tools specifically designed to help achieve continuous integration.
Definition of Done
The team agrees on, and displays prominently somewhere in the team room, a list of criteria which must be met before a product increment “often a user story” is considered “done”. Failure to meet these criteria at the end of a sprint normally implies that the work should not be counted toward that sprint’s velocity.
Definition of Ready
Just as completed items which fit the definition of “done” are said to be “DONE-done”, items that fit the definition of ready are called “READY-ready”.
An etymological note for the terminally curious: this doubling of a word to call attention to something that is “really” ready or “really” done (as opposed to merely called ready or done, carelessly, without thinking twice about it) is known as “contrastive focus reduplication”.
Exploratory Testing
Exploratory testing is, more than strictly speaking a “practice,” a style or approach to testing software which is often contrasted to “scripted testing,” and characterized by the following aspects among others:
it emphasizes the tester’s autonomy, skill and creativity, much as other Agile practices emphasize these qualities in developers;
it recommends performing various test-related activities (such as test design, test execution, and interpretation of results) in an interleaved manner, throughout the project, rather than in a fixed sequence and at a particular “phase”;
it emphasizes the mutually supportive nature of these techniques, and the need for a plurality of testing approaches rather than a formal “test plan”
Kanban
The Kanban Method is a means to design, manage, and improve flow systems for knowledge work. The method also allows organizations to start with their existing workflow and drive evolutionary change. They can do this by visualizing their flow of work, limit work in progress (WIP) and stop starting and start finishing.
The Kanban Method gets its name from the use of kanban - visual signaling mechanisms to control work in progress for intangible work products.
Planning Poker
A playful approach to estimation, used by many Agile teams.
The team meets in presence of the customer or Product Owner. Around the table, each team member holds a set of playing cards, bearing numerical values appropriate for points estimation of a user story.
The Product Owner briefly states the intent and value of a story. Each member of the development team silently picks an estimate and readies the corresponding card, face down. When everyone has taken their pick, the cards are turned face up and the estimates are read aloud.
The two (or more) team members who gave the high and low estimate justify their reasoning. After brief discussion, the team may seek convergence toward a consensus estimate by playing one or more further rounds.
Product Owner
The product owner is a role on a product development team responsible for managing the product backlog in order to achieve the desired outcome that a product development team seeks to accomplish. Key activities to accomplish this include:
Clearly identify and describe product backlog items in order to build a shared understanding of the problem and solution with the product development team Make decisions regarding the priority of product backlog items in order to deliver maximum outcome with minimum output Determine whether a product backlog item was satisfactorily delivered Ensure transparency into the upcoming work of the product development team.
Test-Driven Development (TDD)
“Test-driven development” refers to a style of programming in which three activities are tightly interwoven: coding, testing (in the form of writing unit tests) and design (in the form of refactoring).
It can be succinctly described by the following set of rules:
write a “single” unit test describing an aspect of the program
run the test, which should fail because the program lacks that feature
write “just enough” code, the simplest possible, to make the test pass
“refactor” the code until it conforms to the simplicity criteria
repeat, “accumulating” unit tests over time
Acceptance Testing
An acceptance test is a formal description of the behavior of a software product, generally expressed as an example or a usage scenario. A number of different notations and approaches have been proposed for such examples or scenarios. In many cases the aim is that it should be possible to automate the execution of such tests by a software tool, either ad-hoc to the development team or off the shelf.
Similar to a unit test, an acceptance test generally has a binary result, pass or fail. A failure suggests, though does not prove, the presence of a defect in the product.
Acceptance Test-Driven Development (ATDD)
Analogous to test-driven development, Acceptance Test Driven Development (ATDD) involves team members with different perspectives (customer, development, testing) collaborating to write acceptance tests in advance of implementing the corresponding functionality. The collaborative discussions that occur to generate the acceptance test is often referred to as the three amigos, representing the three perspectives of customer (what problem are we trying to solve?), development (how might we solve this problem?), and testing (what about…).
These acceptance tests represent the user’s point of view and act as a form of requirements to describe how the system will function, as well as serve as a way of verifying that the system functions as intended. In some cases the team automates the acceptance tests.
Usability Testing
Usability testing is a long-established, empirical and exploratory technique to answer questions such as “how would an end user respond to our software under realistic conditions?”
It consists of observing a representative end user interacting with the product, given a goal to reach but no specific instructions for using the product. (For instance, a goal for usability testing of a furniture retailer’s Web site might be “You’ve just moved and need to do something about your two boxes of books; use the site to find a solution.”)
Velocity
At the end of each iteration, the team adds up effort estimates associated with user stories that were completed during that iteration. This total is called velocity.
Knowing velocity, the team can compute (or revise) an estimate of how long the project will take to complete, based on the estimates associated with remaining user stories and assuming that velocity over the remaining iterations will remain approximately the same. This is generally an accurate prediction, even though rarely a precise one.
Please Read the Agile Alliance Glossary For More Information
Sprint Duration
The Sprint duration is generall 2 weeks but some shops use a 3 week schedule to lessen burden on all parties.
Sprint Ceremonies
Legend:
- PO
- Product Owner
- UX
- User Experience
- TL
- Team Leads
| Ceremony | Description | Attendees |
|---|---|---|
| Sprint Planning | Takes place on Day 1 of the sprint. Depending on how much work has been done up front in loading the sprint with stories and tasks this could be a 60-90 minute or much longer. The objective is to finish Day 1 with all team members being 100% allocated and ready to start work on Day 2 | All Team Members |
| Sprint Retrospective | To be done on Day 1 or Day 2 of the sprint to review the previous sprint progress and for the teams to state what they think went well and not so well and put in plans to improve. | All Team Members |
| Daily Standups | To be done daily, preferably before 10am to allow teams to gather and review work they’re doing and any roadblocks. This is an opportunity to review the sprint burndown and status of the stories and tasks and to ensure the team are heading in the right direction. The PO and Tech Lead are to help respond to questions the team might have to ensure they are delivering what is planned. | All Team Members |
| Pre Grooming | This is a review of upcoming stories or stories in the backlog. Its a review session to understand whether or not there is enough information contained within the story to “point” the story. If it is found a story does not have enough information, the Product Owner, UX Lead or Tech Lead will need to own next steps to provide more information for the team to apply story points before it goes to the sprint for development. The frequency of this meeting during the sprint is down to the team to own. | PO, UX, TL |
| Backlog Grooming (Pointing) | The entire product backlog needs to be groomed, however due to its size and stories being added over time it will be impossible to have groomed the whole backlog. The aim of this ceremony is to have groomed the stories being lined up for the next sprint and others that are stack ranked and in priority order. This session can generally only be done if pre grooming has been done first and it is confirmed all the right information exists for a developer to confidently point a story. The frequency of this meeting during the sprint is down to the team to own. | All Team Members |
| Demo Day | This is the final ceremony of the sprint and is a opportunity for the developer to demo the functionality they have built to the product owners and other interested parties. The closing of this ceremony can then lead to business user acceptance testing. | All Team Members |
Stories, Tasks and Bugs
Stories are to be “pointed” as part of the backlog grooming process.
- Each team should advise on their planned velocity in the upcoming sprint.
Initially the sprint should be loaded with the planned capacity in points.
During sprint planning (or days leading up to) tasks are to be added to each story in the planned sprint.
- Hours of effort are to be assigned to each task.
During sprint planning a review of the teams capacity in hours should be undertaken alongside the planned work and before the end of sprint planning all team members should be set to 100% capacity.
When bugs are planned to be fixed in the sprint (as agreed during sprint planned) the bugs should also have tasks with estimated hours applied to them to track the work effort.


For any voting system to be considered fair and functional, it must be usable by every member of the voting body. In many elections, this could mean any individual of a responsible age, including those who are elderly, disabled, uneducated or unfamiliar with the technology being employed. In most cases, voters should be able to perform this function without requiring training or prolonged instruction, so systems should be self-explanatory and easy to understand. All of these issues are especially relevant in the general field of EVMs, and in particular for open-audit voting systems, where user interfaces are designed around the functionality of several key client-side methods including ballot casting and E2E verification. In this light, any threat against usability must be also seen as a threat to the integrity and functional core of such systems.
In the wake of the 2000 presidential election, the Help America Vote act of 2002 was written into law to update the standards to which voting is held. Among other things, Title III of this act requires voters be allowed to review and correct their ballots, disabled voters be given the same ability to participate, instructions on casting votes remain clear and available, and the voting system allows for auditing.
EVMs are well suited to meet these requirements. They can provide direct feedback to the user as they vote and offer features to visually impared users such as variable font sizes. When properly employed, Research has shown that elections which include electronic voting systems tend to see increases in voter turnout, especially when voting over the internet is allowed (Gibson, 2001). When properly employed, EVMs can improve the accessibility of an election beyond what is possible with just paper ballots.


{% extends 'layout.html' %} {% block pageTitle %} NHS.UK prototype kit {% endblock %} {% block beforeContent %}{% endblock %} {% block content %}
The purpose of the medical examiner system is to:
We believe ... will be a useful feature for ... as it will ... This will ...


Management
Visionary data scientists. Accomplished industry veterans. Trusted advisors. Proven investors. OurCo is led by a world-class team that believes in data's potential to change the world.Our company is proud to be backed by Intel Capital, Investor Growth Capital(IGC), QuestMark Partners, Goldman Sachs, TransLink Capital and other strategic investors.
Executive Team
Featured News
Computing
Barclays said net profit fell by more than half in the first quarter of the year, as the U.K. bank took a write-down on the value of its African business.
Read More ->


Benjamin N. Cardozo School of Law, Yeshiva University, New York, NY
Assistant Clinical Professor of Law & Director of the Cardozo Tech Startup Clinic, June 2014 – present
Member of the full-time faculty at Cardozo Law School. Inaugural Professor of the school’s Tech Startup Clinic. Counsel startups on wide variety of issues, including company formation, seed financing, employment issues, and intellectual property and regulatory questions.
Jenner & Block, New York, NY
Senior Associate, February 2013 – June 2014
Advised high-profile clients on a variety of cutting-edge intellectual property matters concerning the internet and 3D printing. Advised major internet company on trademark related matters, including new search engine products and new content websites. Successfully represented major hip-hop stars, including Jay-Z and Dr. Dre, in copyright disputes. Drafted terms of service, DMCA policies, and privacy policies for open source mapping startup and local news company.
Patterson Belknap Webb & Tyler LLP, New York, NY
Senior Associate, October 2009 – February 2013
Represented clients on variety of intellectual property matters, including a multi-billion dollar licensing dispute. Represented clients in nine figure litigations against major investment banks for the sponsorship of securitizations containing toxic residential mortgage backed securities. Gained extensive trial, appellate, and other litigation experience, as well as managed multiple case teams through all phases of disputes: conducted direct examinations at trial; handled oral arguments; performed depositions; drafted appellate brief for New Jersey Supreme Court; drafted pre- and post-trial briefs; assisted with opening and closing statements; prepared correspondence with court and adversaries; negotiated stipulations, including settlement agreements; drafted expert reports, complaints, answers, document requests, interrogatories, requests for admission, corporate representative notices, as well as associated responses.
Hon. William J. Martini, U.S. District Judge, D.N.J, Newark, NJ
Law Clerk, August 2008 – September 2009
Drafted opinions for dispositive motions. Prepared for trials, hearings, and conferences. Communicated with attorneys regarding case management. Managed judicial interns.
Wikia, Inc., New York, NY
General Counsel and Vice President of Product and Business Development, December 2006 – August 2008
Built and sold internet company (ArmchairGM, LLC) in a multi-million dollar deal to Wikia, Inc., one of the largest websites in the United States (according to Quantcast), started by Wikipedia founder Jimmy Wales. After joining Wikia, managed the New York office. Programmed and implemented product ideas, and was a key member of a team that built an open source search engine. Negotiated several business development deals with leading media companies. Handled a range of legal issues: drafted responses to DMCA takedown notices; drafted copyright and privacy policies; reviewed contracts, licensing agreements, and employment contracts.
Jones Day, New York, NY
Associate, September 2005 – December 2006; Summer Associate, May 2004 – August 2004
Represented leading technology companies in a variety of litigations. Researched and drafted memoranda on several white collar matters. Prepared affidavits and assisted with deposition preparation. Managed staff attorneys.
Benjamin N. Cardozo School of Law, New York, NY, Juris Doctor, cum laude, June 2005
Tufts University, Medford, MA, Bachelor of Arts in History and Economics, magna cum laude, May 2002
Book
Blockchain and the Law (Harvard University Press, forthcoming 2016) (with Primavera DiFilippi).
Article
Rendered Impracticable: Behavioral Economics and the Impracticability Doctrine, 26 CARDOZO L. REV. 2183 (2005).
Cryptocurrency Research Group,
Co-Founder/Director, August 2014 – present
A non-profit cross-disciplinary research organization promoting scientific research in the field of cryptocurrencies and blockchain technology.
Coincenter,
Fellow, December 2014-present
A non-profit research and advocacy center focused on the public policy issues facing cryptocurrency technologies such as Bitcoin.
Ledger: The Journal of Cryptocurrency Research,
Member of the Editorial Board, January 2015-present
A new journal focused on cryptocurrencies and blockchain technology.
Invited Participant, Challenges and Opportunities of Cryptoequity, MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA, January 15, 2015.
Invited Participant and Workshop Leader, Cryptoequity and the Law, Berkman Center for Internet & Society at Harvard Law School, Cambridge, MA, January 16-18, 2015.
Presentation, Cryptolaw, Cryptoeconomicon Workshop, Mountain View, CA, January 26-29, 2015.
Panelist, Big Data, Changing Norms, Open Opportunities, Freedom to Connect Conference, New York, NY, March 2, 2015.
Invited Panelist and Participant, Blockchain Workshop, Stanford University, March 22-24, 2015.
Invited Panelist, New Breakthroughs in Computational Law, CodeX FutureLaw 2015 Conference at Stanford University Law School, Paulo Alto, CA, April 30, 2015.
Experienced programmer with proficiency in programming and scripting languages (Javacript/PHP/HTML/CSS) and database design and implementation (SQL).


If you press classify button after drawing the result will be generated bellow. to erase the canvas press Clear button.
Note that on pressing Clear button both canvas will become blank.
ANSWER IN WORD FORM IS:
ANSWER IN DECIMAL FORM IS:


This project aims to extract structured data from the record provided from AstraZeneca. We mainly focus on the present illness history and past history, extract key information based on time, thus producing several records from one case of illness.
An electronic medical record (EMR) is a digital version of a paper char that contains all of a patient’s medical history from one proactive. An EMR is mostly used by providers for diagnosis and treatment.
Because this the project collaborate with AstraZeneca, the data set cannot open to the public. However the brief description of the data set is as follows: the total size of the input data is up to 50MB, which contains approximately 35000 cases. All of them contain past & present history and other useful information.
We use the Java regular expression external jar as well as the Stanford Parser tools to parse the input, extract the diseases concerned (we established a disease dictionary and the URL from which you can download) as well as symptoms, narrative expression and many other features.
The final statistic is as follows:
Following is the links of the test data above:
The output of 2001_2007data can be downloaded here, 100 randomly selected cases we regard as benchmark, manually extracted version is here and software-extracted result is here.
The goal of this project is to use OCR to extract useful Information from the ECG pictures.
OCR is short for Optical Character Recognition, which is used to convert scanned or photographed images of typewritten or printed text into machine-encoded/computer-readable text. At the same time, ECG pictures contain may useful information in a structured format, which makes it possible to correct errors in the OCRed result.
The experimental data set for this project is 80 ECG pictures from Beijing University Hospital.
These pictures can be divided in to 12 different types. The largest one contains 41 pictures and the experiment is focusing on these 41 pictures now. The useful information in this pictures are all key-value-unit triples.
The whole process includes:
Thresholding technology is used to remove the noise in the picture.
Original picture & Thresholded picture:
Key: Vent. rate Value: 81 Unit: bpm
An open source OCR engine named Tesseract is used. So text output can be got.
OCR result:
Actually, there are some position error in the OCR result. So it should be re-organized by using coordinate in the picture.
Re-organized result:
Some scoring methods are used to find the correct information in the text with noise.
Triples extracted:
Vent. rate[81]bpm
PR interval[148]ms
QRS duration[88]ms
QT/QTc[366/425]ms
P-R-T axes[65, 67, 47]
For all 41 pictures, which contain 5 useful key-value-unit triples, the roughly accuracy rate is 73% for now.


Deductive Logic
by George William Joseph Stock, M.A.
Part 3: Chapter 29
Additional Information
- Year Published: 1888
- Language: English
- Country of Origin: England
- Source: Stock, G. W. J. (1888). Deductive Logic. Oxford, England; Pembroke College.
-
Readability:
- Flesch–Kincaid Level: 11.0
- Word Count: 2,241
- Genre: Informational
- Keywords: math, math history
- ✎ Cite This
Downloads
PART III.—OF INFERENCES
CHAPTER XXIX.
Of Trains of Reasoning.
800. The formal logician is only concerned to examine whether the conclusion duly follows from the premisses: he need not concern himself with the truth or falsity of his data. But the premisses of one syllogism may themselves be conclusions deduced from other syllogisms, the premisses of which may in their turn have been established by yet earlier syllogisms. When syllogisms are thus linked together we have what is called a Train of Reasoning.
801. It is plain that all truths cannot be established by reasoning. For the attempt to do so would involve us in an infinite regress, wherein the number of syllogisms required would increase at each step in a geometrical ratio. To establish the premisses of a given syllogism we should require two preceding syllogisms; to establish their premisses, four; at the next step backwards, eight; at the next, sixteen; and so on ad infinitum. Thus the very possibility of reasoning implies truths that are known to us prior to all reasoning; and, however long a train of reasoning may be, we must ultimately come to truths which are either self-evident or are taken for granted.
802. Any syllogism which establishes one of the premisses of another is called in reference to that other a Pro-syllogism, while a syllogism which has for one of its premisses the conclusion of another syllogism is called in reference to that other an Epi-syllogism.
The Epicheirema.
803. The name Epicheirema is given to a syllogism with one or both of its premisses supported by a reason. Thus the following is a double epicheirema—
All B is A, for it is E.
All C is B, for it is F.
.’. All C is A.
All virtue is praiseworthy, for it promotes the general welfare.
Generosity is a virtue, for it prompts men to postpone self to others.
.’. Generosity is praiseworthy.
804. An epicheirema is said to be of the first or second order according as the major or minor premiss is thus supported. The double epicheirema is a combination of the two orders.
805. An epicheirema, it will be seen, consists of one syllogism fully expressed together with one, or, it may be, two enthymemes ( 557). In the above instance, if the reasoning which supports the premisses were set forth at full length, we should have, in place of the enthymemes, the two following pro-syllogisms—
(i) All E is A.
All B is E.
.’. All B is A.
Whatever promotes the general welfare is praiseworthy.
Every virtue promotes the general welfare.
.’. Every virtue is praiseworthy.
(2) All F is B.
All C is F.
.’. All C is B.
Whatever prompts men to postpone self to others is a virtue.
Generosity prompts men to postpone self to others.
.’. Generosity is a virtue.
806. The enthymemes in the instance above given are both of the first order, having the major premiss suppressed. But there is nothing to prevent one or both of them from being of the second order—
All B is A, because all F is.
All C is B, because all F is.
.’. All C is A.
All Mahometans are fanatics, because all Monotheists are.
These men are Mahometans, because all Persians are.
.’. These men are fanatics.
Here it is the minor premiss in each syllogism that is suppressed, namely,
(1) All Mahometans are Monotheists.
(2) These men are Persians.
The Sorites.
807. The Sorites is the neatest and most compendious form that can be assumed by a train of reasoning.
808. It is sometimes more appropriately called the chain-argument, and map be defined as—
A train of reasoning, in which one premiss of each epi-syllogism is supported by a pro-syllogism, the other being taken for granted.
This is its inner essence.
809. In its outward form it may be described as—A series of propositions, each of which has one term in common with that which preceded it, while in the conclusion one of the terms in the last proposition becomes either subject or predicate to one of the terms in the first.
810. A sorites may be either—
(1) Progressive,
or (2) Regressive.
Progressive Sorites.
All A is B.
All B is C.
All C is D.
All D is E.
.’. All A is E.
Regressive Sorites.
All D is E.
All C is D.
All B is C.
All A is B.
.’. All A is E.
811. The usual form is the progressive; so that the sorites is commonly described as a series of propositions in which the predicate of each becomes the subject of the next, while in the conclusion the last predicate is affirmed or denied of the first subject. The regressive form, however, exactly reverses these attributes; and would require to be described as a series of propositions, in which the subject of each becomes the predicate of the next, while in the conclusion the first predicate is affirmed or denied of the last subject.
812. The regressive sorites, it will be observed, consists of the same propositions as the progressive one, only written in reverse order. Why then, it may be asked, do we give a special name to it, though we do not consider a syllogism different, if the minor premiss happens to precede the major? It is because the sorites is not a mere series of propositions, but a compressed train of reasoning; and the two trains of reasoning may be resolved into their component syllogisms in such a manner as to exhibit a real difference between them.
813. The Progressive Sorites is a train of reasoning in which the minor premiss of each epi-syllogism is supported by a pro-syllogism, while the major is taken for granted.
814. The Regressive Sorites is a train of reasoning in which the major premiss of each epi-syllogism is supported by a pro-syllogism, while the minor is taken for granted.
Progressive Sorites. (i) All B is C.
All A is B.
.’. All A is C.
(2) All C is D.
All A is C.
.’. All A is D.
(3) All D is E.
All A is D.
.’. All A is E.
Regressive Sorites. (1) All D is E.
All C is D.
.’. All C is E.
(2) All C is E.
All B is C.
.’. All B is E.
(3) All B is E.
All A is B.
.’. All A is E.
815. Here is a concrete example of the two kinds of sorites, resolved each into its component syllogisms—
Progressive Sorites.
All Bideford men are Devonshire men.
All Devonshire men are Englishmen.
All Englishmen are Teutons.
All Teutons are Aryans.
.’. All Bideford men are Aryans.
(1) All Devonshire men are Englishmen.
All Bideford men are Devonshire men.
.’. All Bideford men are Englishmen.
(2) All Englishmen are Teutons.
All Bideford men are Englishmen.
.’. All Bideford men are Teutons.
(3) All Teutons are Aryans.
All Bideford men are Teutons.
.’. All Bideford men are Aryans.
Regressive Sorites.
All Teutons are Aryans.
All Englishmen are Teutons.
All Devonshiremen are Englishmen.
All Bideford men are Devonshiremen.
.’. All Bideford men are Aryans.
(1) All Teutons are Aryans.
All Englishmen are Teutons.
.’. All Englishmen are Aryans.
(2) All Englishmen are Aryans.
All Devonshiremen are Englishmen.
.’. All Devonshiremen are Aryans.
(3) All Devonshiremen are Aryans.
All Bideford men are Devonshiremen.
.’. All Bideford men are Aryans.
816. When expanded, the sorites is found to contain as many syllogisms as there are propositions intermediate between the first and the last. This is evident also on inspection by counting the number of middle terms.
817. In expanding the progressive form we have to commence with the second proposition of the sorites as the major premiss of the first syllogism. In the progressive form the subject of the conclusion is the same in all the syllogisms; in the regressive form the predicate is the same. In both the same series of means, or middle terms, is employed, the difference lying in the extremes that are compared with one another through them.
[Illustration]
818. It is apparent from the figure that in the progressive form we work from within outwards, in the regressive form from without inwards. In the former we first employ the term ‘Devonshiremen’ as a mean to connect ‘Bideford men’ with ‘Englishmen’; next we employ ‘Englishmen’ as a mean to connect the same subject ‘Bideford men’ with the wider term ‘Teutons’; and, lastly, we employ ‘Teutons’ as a mean to connect the original subject ‘Bideford men’ with the ultimate predicate ‘Ayrans.’
819. Reversely, in the regressive form we first use ‘Teutons’ as a mean whereby to bring ‘Englishmen’ under ‘Aryans’; next we use ‘Englishmen’ as a mean whereby to bring ‘Devonshiremen’ under the dame predicate ‘Aryans’; and, lastly, we use ‘Devonshiremen’ as a mean whereby to bring the ultimate subject ‘Bideford men’ under the original predicate ‘Aryans.’
820. A sorites may be either Regular or Irregular.
821. In the regular form the terms which connect each proposition in the series with its predecessor, that is to say, the middle terms, maintain a fixed relative position; so that, if the middle term be subject in one, it will always be predicate in the other, and vice versa. In the irregular form this symmetrical arrangement is violated.
822. The syllogisms which compose a regular sorites, whether progressive or regressive, will always be in the first figure.
In the irregular sorites the syllogisms may fall into different figures.
823. For the regular sorites the following rules may be laid down.
(1) Only one premiss can be particular, namely, the first, if the sorites be progressive, the last, if it be regressive.
(2) Only one premiss can be negative, namely, the last, if the sorites be progressive, the first, if it be regressive.
824. Proof of the Rules for the Regular Sorites.
(1) In the progressive sorites the proposition which stands first is the only one which appears as a minor premiss in the expanded form. Each of the others is used in its turn as a major. If any proposition, therefore, but the first were particular, there would be a particular major, which involves undistributed middle, if the minor be affirmative, as it must be in the first figure.
In the regressive sorites, if any proposition except the last were particular, we should have a particular conclusion in the syllogism in which it occurred as a premiss, and so a particular major in the next syllogism, which again is inadmissible, as involving undistributed middle.
(2) In the progressive sorites, if any premiss before the last were negative, we should have a negative conclusion in the syllogism in which it occurs. This would necessitate a negative minor in the next syllogism, which is inadmissible in the first figure, as involving illicit process of the major.
In the regressive sorites the proposition which stands first is the only one which appears as a major premiss in the expanded form. Each of the others is used in its turn as a minor. If any premiss, therefore, but the first were negative, we should have a negative minor in the first figure, which involves illicit process of the major.
825. The rules above given do not apply to the irregular sorites, except so far as that only one premiss can be particular and only one negative, which follows from the general rules of syllogism. But there is nothing to prevent any one premiss from being particular or any one premiss from being negative, as the subjoined examples will show. Both the instances chosen belong to the progressive order of sorites.
(1) Barbara.
All B is A.
All C is B.
All C is A.
All B is A.
All C is B.
Some C is D.
All D is E
.’. Some A is E
[Illustration]
(2) Disamis.
Some C is D.
All C is A.
Some A is D.
(3) Darii.
All D is E
Some A is D.
Some A is E.
(1) Barbara.
All B is C.
All A is B.
All A is C.
All A is B.
All B is C.
No D is C.
All E is D.
.’. No A is E.
[Illustration]
(2) Cesare.
No D is C.
All A is C.
.’. No A is D.
(3) Camestres.
All E is D.
No A is D.
.’. No A is E.
826. A chain argument may be composed consisting of conjunctive instead of simple propositions. This is subject to the same laws as the simple sorites, to which it is immediately reducible.
Progressive. Regressive.
If A is B, C is D. If E is F, G is H.
If C is D, E is F. If C is D, E is F.
If E is F, G is H. If A is B, C is D.
.'. If A is B, G is H. .'. If A is B, G is H.


JAVASCRIPT QUIZ
You can select any one of the given options for each question
1. Inside which HTML element do we put the JavaScript?


Widow poses in wedding dress every year to remember husband - and new boyfriend helps
Vanessa Lanktree poses in her bridal gown to make the anniversary of her late husband's death and this year her new boyfriend took photographs of her in a field of sunflowers
by Isolde Walters, Lorraine KingA young widow who poses in her wedding dress every year in memory of her late husband has roped in her new boyfriend to help pay tribute.
Vanessa Lanktree, 29, married her high school sweetheart Eric in September 2012 but two months after their second anniversary he was killed in a motorbike accident.
Since his death she has carried out the annual custom as a tribute to the 25-year-old and this year Kurtis Isbister agreed to take pictures of her in the gown and rubber boots in a field of sunflowers.
Vanessa, who lives in Saskatchewan, Canada, said: “It’s my way of celebrating and remembering that special day and keeping Eric’s memory alive.
“Most people get to go out with their husband for supper or a date night on their anniversary and this is my thing that I get to do without him.
“I try to do it on our anniversary or as close to it as possible. I will probably do this every year for the rest of my life.”
Recalling how they met at school, she said: “We had such an amazing relationship. We met when I was in grade 10 [aged 16] and he was in grade 12 [aged 17].
“He was my first boyfriend, my first love. Our relationship through the years was so strong because it started so young. I had no doubt about our lives together.”
Eric, a custom installer and volunteer firefighter, proposed to Vanessa on Christmas Eve 2011 and they tied the knot the following year.
Vanessa added: “We got married in a beautiful church ceremony. As I walked down the aisle with my dad, Eric was bawling his eyes out.
“I already knew I wanted to marry him but when I saw him crying, I thought how lucky I was.
“The best part of our day was being surrounded by our family and friends. At the reception, we did not leave the dance floor. If anyone wanted to see us or talk to us, they had to dance.”
Eric sadly died in a motorbike accident on November 3rd 2014, while on vacation with friends and his dad Brian Lanktree, 55, in Las Vegas.
Vanessa said: “Eric always went down to Vegas for the SEMA car show. That year they decided to rent motorbikes and head down to California. The accident happened on the way back.
“Eric was an hour outside of Vegas and there was a head-on collision with a truck that took his life. I couldn’t believe it.”
When Brian called Vanessa and told her that Eric had been airlifted to a hospital, she held out hope for her husband’s life.
She said: “I thought it would be alright. I kept thinking: ‘He is strong. He is a fighter’. I didn’t realise how severe it was.
“I was packing my bag to go to Vegas because I thought he’d need to see me when he woke up.
“When my father-in-law called me and told me that my husband had passed away, I just threw my phone and walked out of the house.”
In September 2016, the day after what would have been the couple’s fourth wedding anniversary, Vanessa found herself putting on her bridal gown.
She had never dry cleaned the dress and it still had dirt from the dance floor on the hem.
Vanessa put on Keith Urban’s song 'Making Memories Of Us', a track which was played at her wedding, and danced around the kitchen.
“Grief doesn’t go away but somehow you find little things to help you cope, like putting on your dress,” she said.
The next year she put on the dress again and posed for a picture in the yard of her home, a house she and Eric had built together.
Last year she took a simple snap of herself in the gown.
This year Kurtis, a 29-year-old electrician in the oil industry and her boyfriend of 18 months, took pictures of her in a field of sunflowers.
She explained: “It was a little chilly but I love those sunflowers. It felt so fun. I had my rubber boots on and I was running around while Kurtis took photos.
“I was just being me. Life doesn’t stop and it pushes you forward. We still want to find happiness in our lives. I know Eric wouldn’t want me to be miserable.
“He was such a happy person. He taught me to look for the best in life.”
Vanessa added that she has no plans to ever put away her wedding dress.
She said: “I’m never going to get it cleaned and I am never going to have it boxed up. As long as it fits me, I’ll wear it.”


Temple
of
Consciousness
Arul perum Jothi Nagar
Valpara Road, Pollachi
Coimbatore, Tamil Nadu - 642101.


Bootstrap Dual Listbox is a responsive dual listbox widget optimized for Twitter Bootstrap. It works on all modern browsers and on touch devices.
The dual listbox is created from a select multiple by calling .bootstrapDualListbox(settings);
on a selector. The selector can be any element, not just selects. When the method is called on a selector other than a select, then all select childrens are converted into dual list boxes.
var demo1 = $('select[name="duallistbox_demo1[]"]').bootstrapDualListbox();
var demo2 = $('.demo2').bootstrapDualListbox({ nonSelectedListLabel: 'Non-selected', selectedListLabel: 'Selected', preserveSelectionOnMove: 'moved', moveOnSelect: false, nonSelectedFilter: 'ion ([7-9]|[1][0-2])' });
The options must be added to/removed from the original select.
Note that the refresh
must be run on it after manipulating the options.
If there are some highlighted options in the lists, then calling the refresh
method with the optional true
parameter results the highlights to be removed. Example usage: demo2.bootstrapDualListbox('refresh', true);
It has meaning only when moveOnSelect
setting is false
.
$("#demo2-add").click(function() { demo2.append(''); demo2.bootstrapDualListbox('refresh'); }); $("#demo2-add-clear").click(function() { demo2.append(''); demo2.bootstrapDualListbox('refresh', true); });
When calling $("#element").bootstrapDualListbox() you can pass a parameters object with zero or more of the following:
| Option | Description |
|---|---|
bootstrap2Compatible |
false : set this to true if you want graphic compatibility with Bootstrap 2. |
filterTextClear |
'show all' : the text for the "Show All" button. |
filterPlaceHolder |
'Filter' : the placeholder for the input element for filtering elements. |
moveSelectedLabel |
'Move selected' : the label for the "Move Selected" button. |
moveAllLabel |
'Move all' : the label for the "Move All" button. |
removeSelectedLabel |
'Remove selected' : the label for the "Remove Selected" button. |
removeAllLabel |
'Remove all' : the label for the "Remove All" button. |
moveOnSelect |
true : determines whether to move options upon selection. This option is forced to true on the Android browser. |
preserveSelectionOnMove |
false : can be 'all' (for selecting both moved elements and the already selected ones in the target list) or 'moved' (for selecting moved elements only) |
selectedListLabel |
false : can be a string specifying the name of the selected list. |
nonSelectedListLabel |
false : can be a string specifying the name of the non selected list. |
helperSelectNamePostfix |
'_helper' : The added selects will have the same name as the original one, concatenated with this string and 1 (for the non selected list, e.g. element_helper1) or 2 (for the selected list, e.g. element_helper2). |
selectorMinimalHeight |
100 : represents the minimal height of the generated dual listbox. |
showFilterInputs |
true : whether to show filter inputs. |
nonSelectedFilter |
'' : initializes the dual listbox with a filter for the non selected elements. This is applied only if showFilterInputs is set to true . |
selectedFilter |
'' : initializes the dual listbox with a filter for the selected elements. This is applied only if showFilterInputs is set to true . |
infoText |
'Showing all {0}' : determines which string format to use when all options are visible. Set this to false to hide this information. Remember to insert the {0} placeholder. |
infoTextFiltered |
'Filtered {0} from {1}' : determines which element format to use when some element is filtered. Remember to insert the {0} and {1} placeholders. |
infoTextEmpty |
'Empty list' : determines the string to use when there are no options in the list. |
filterOnValues |
false : set this to true to filter the options according to their values and not their HTML contents. |
You can modify the behavior and aspect of the dual listbox by calling its methods, all of which accept a value
and a refresh
option. The value
determines the new parameter value, while refresh
(optional, defaults to false
) tells whether to update the plugin UI or not.
To call methods on the dual listbox instance, use the following syntax:
$(selector).bootstrapDualListbox(methodName, parameter);
Note: when making multiple chained calls to the plugin, set refresh
to true to the last call only, in order to make a unique UI change; alternatively, you can also call the refresh
method as your last one.
Here are the available methods:
| Method | Description |
|---|---|
refresh() |
update the plugin element UI. |
destroy() |
restore the original select element and delete the plugin element. |
getContainer() |
get the container element. |
setBootstrap2Compatible(value, refresh) |
change the bootstrap2Compatible parameter. |
setFilterTextClear(value, refresh) |
change the filterTextClear parameter. |
setFilterPlaceHolder(value, refresh) |
change the filterPlaceHolder parameter. |
setMoveSelectedLabel(value, refresh) |
change the moveSelectedLabel parameter. |
setMoveAllLabel(value, refresh) |
change the moveAllLabel parameter. |
setRemoveSelectedLabel(value, refresh) |
change the removeSelectedLabel parameter. |
setRemoveAllLabel(value, refresh) |
change the removeAllLabel parameter. |
setMoveOnSelect(value, refresh) |
change the moveOnSelect parameter. |
setPreserveSelectionOnMove(value, refresh) |
change the preserveSelectionOnMove parameter. |
setSelectedListLabel(value, refresh) |
change the selectedListLabel parameter. |
setNonSelectedListLabel(value, refresh) |
change the nonSelectedListLabel parameter. |
setHelperSelectNamePostfix(value, refresh) |
change the helperSelectNamePostfix parameter. |
setSelectOrMinimalHeight(value, refresh) |
change the selectOrMinimalHeight parameter. |
setShowFilterInputs(value, refresh) |
change the showFilterInputs parameter. |
setNonSelectedFilter(value, refresh) |
change the nonSelectedFilter parameter. |
setSelectedFilter(value, refresh) |
change the selectedFilter parameter. |
setInfoText(value, refresh) |
change the infoText parameter. |
setInfoTextFiltered(value, refresh) |
change the infoTextFiltered parameter. |
setInfoTextEmpty(value, refresh) |
change the infoTextEmpty parameter. |
setFilterOnValues(value, refresh) |
change the filterOnValues parameter. |


AngularJS Samples
UI-Leaflet
UI-Leaflet with Markercluster
UI-Leaflet with Markercluster and Awesome-markers
Leaflet with Markercluster and Awesome-markers
To Github Repository


{% extends "base_generic.html" %} {% block content %}
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Eloquent JavaScript dives into the JavaScript language to show programmers how to write elegant, effective JavaScript code. Like any good programming book, Eloquent JavaScript begins with fundamentals--variables,
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Post edited 2 x times, last by
BrainFooLong
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BrainFooLong
Administrator
http://getbblog.com/img/news-release.jpg
Hi!
Today we released a new update for BBLog.
It includes
* emblem gallery tags
* emblem gallery color search
* emblem gallery improvements and fixes
* minor improvements and code updates
and some preparations for upcoming BFH support.
Have fun with it.


http://www.speakers-podium.com/susangranger.
Susan Granger's review of "BEYOND THE MAT" (Lions Gate Films)
Writer/director Barry Blaustein received a Director's Guild of America documentary nomination for his enthusiastic, behind-the-scenes examination of professional wrestling. In publicizing his carefully collated collection of matches and interviews, he's stated, "I wanted the sport of wrestling to get some respect in spite of itself. I wanted to also make a film for people who don't even care about wrestling that would keep them emotionally involved in these guys' lives." Perhaps the most famous ex-pro wrestler is Jesse "The Body" Ventura, who's now serving as the duly elected Governor of Minnesota, but these guys include the articulate Mick "Mankind" Foley, who has a book on the best-seller list, along with 53 year-old Terry Funk, Jake "The Snake" Roberts, and several other lesser luminaries, including the athlete who can vomit on cue, earning the nickname "Puke." Make no mistake about it: wrestling is hard work. Yeah, it's rigged but that doesn't mean it's easy. Each round is planned, scripted, choreographed and rehearsed. Cynical promoters pull the strings. But, for participants, the physical exertion demands stamina and courage, not to mention frequent trips to the hospital. And the wrestlers come across as sympathetic, all-too-human men with their own troubles and tragedies. Vince McMahon, who appears in the footage, and his World Wrestling Federation has issued a cease-and-desist letter with regard to the advertisements - which Lions Gate Films has rejected as bullying tactics to suppress marketing of this film. On the Granger Movie Gauge of 1 to 10, Beyond the Mat is a slammin' 6. While it's certainly not the "no-holds-barred expose" that's implied in the ads, it may be more than you ever wanted to know about the subject of professional wrestling.
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