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The New York Times analyzed Trump's 11,390 tweets since becoming president, and found he praised himself 2,026 times
by Ashley Collman- The New York Times analyzed President Donald Trump's more than 11,000 tweets since entering office.
- Reporters found that he used Twitter mainly to attack someone or something, with 5,889 tweets meeting this description.
- More than 2,000 of his tweets were in praise of his own work.
- Visit Insider's homepage for more stories.
The New York Times published a lengthy analysis of President Donald Trump's tweets since taking office, and found several patterns of behavior.
More than anything, the president uses Twitter when he wants to go on the attack. Of the 11,390 tweets that The Times analyzed from January 20, 2017 to October 15, 2019, more than 5,000 were attacks against someone or something, from the Democrats to the Russia investigation.
Mark Wallheiser/Getty Images
The six reporters on the project also found that Trump likes to use Twitter to praise himself, which he did 2,026 times.
On July 11, 2019, for example, he called himself "so great looking and smart, a true Stable Genius!"
Threatening to "obliterate" Turkey's economy on October 7, 2019, he touted his "great and unmatched wisdom."
On July 3, 2018, Trump bragged: "If not for me, we would now be at War with North Korea!"
The analysis also shows that Trump's Twitter use has dramatically escalated in recent weeks, as the Democrats undertake the inquiry into his impeachment.
In the first two weeks of October, Trump tweeted more than 500 times, which put him on track to triple his monthly tweet average.
Read the full report from The New York Times »
- Read more:
- E. Jean Carroll, who accused Trump of rape, just filed a defamation lawsuit against the president
- Trump wants people to 'READ THE TRANSCRIPT' of the Ukraine call to prove his innocence, but it's a damning piece of evidence
- How the US Army officer who testified against Trump could end up in a court-martial
- The politics of Trump and Brexit inspired the creator of this year's biggest PlayStation exclusive


May 2021 Status Update
Goodness, what a run. I have basically been on a development tear since the last check-in, clocking about 10-12 hours a day. I guess that’s my typical schedule, but all of it has been totally dedicated to disciplined development, which is the ideal.
So upon last check-in I believe I was spilling about having some videos done by now, which WAS ALL A LIE. 😅 Shows you for taking the time to believe me. I’m still going to do those videos (I promise!), but I had to get some features done, first. It would be highly embarrassing to brag about all these features I have built when some rather important ones were not provably/demonstrably finished, no?
I have gotten so much finished, however, and feel like I am getting to some semblance of “close.” Really there are two features left: one that is critical to commerce (withdrawals), and the other one fleshes out a proprietary feature that sort of glues the whole “magic” of this application together.
Based on my super amazing product management software (read: Excel spreadsheet 😛), I should have these done by end of this month. Then, it should be the whole or most of June learning about vlogging and putting together a nice little tour of what I have built. Finally.
Currently, July looks like a break month. It will have been over 18 months of this, with these past 6-7 being especially intense and productive with consistent 70-80 hour weeks. A breather is in order, especially once I have some videos I can use to show others what’s up. Then August it begins once again.
Nifty note: Lana Del Rey recently announced an album recently for July. That’s a two-month advance notice. I am thinking of doing the same: August announcement for an October release.
Nifty note x2: October 12, 2021 does mark the end of support for Silverlight…


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NAME
(Surname First)
………………………………………………
...........
.............................
....
…………...…….
..
Company…………………….
Dept. /
Location…………………………….
.Employee Sharp ID
…………..……..….
GSM number
..................................................................
Tel Ext
......................................................................................
ADDRESS: ……………………………………………………..……………
.....................
…….…………………
......
2. CASH REQUEST DETAILS:
Amount Requested
in Words ……………………………………………………………………………..…….………
…………………………………………………………………………………………………………………….
.
……
Amount Requested in figures
(=N=)
…………………………………….
Repayment Duration.
………….………
Purpose for the Cash Requested.
……………………………………
..
………..…………………...
...............
…………
3. GUIDELINES:
Maximum Amount is One Million Naira
(=N= 1M)
Only.
Repayment interest is currently on Monthly interest of
3%
Flat rate and reviewable.
Maximum repayment period of
6
months with equal Monthly deduction of Principal and interest for the duration.
Monthly payroll deduction must be within approved loan burden percentage of the Company.
Treatment of applications shall be on “first come first served” basis and subject to availability of fund.
Applicants are required to attach a copy of immediate
past three (3) Months’ Pay Slip.
Society shall have all the rights exercisable by the Indemnifier over all the benefits including but not limited to his /her,
terminal benefits, retirement benefits, ESP, Superannuation, etc. to the extent to which he/ she is indebted to the Society and
the amount deductible shall be limited to the amount formally advised to the company by the Cooperative Society.
Completed, signed and executed application is irrevocable.
4. AUTHORIZATION TO DEDUCT:
To: Manager, Payroll
I have just obtained a Cash loan of
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(=N=
………………….….).
from the Cooperative Society. I hereby authorize you to deduct the following amount
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from my
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and pay same to
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that the Cash loan will be the first charge on my retirement benefits / ESP / Pension, etc.
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you to make any necessary inquiries for the purpose of evaluating this application. I also hereby agree to abide by the
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public class DateFormatSymbols extends Object implements Serializable, Cloneable
DateFormatSymbols
is a public class for encapsulating
localizable date-time formatting data, such as the names of the
months, the names of the days of the week, and the time zone data.
SimpleDateFormat
uses
DateFormatSymbols
to encapsulate this information.
Typically you shouldn't use DateFormatSymbols
directly.
Rather, you are encouraged to create a date-time formatter with the
DateFormat
class's factory methods: getTimeInstance
,
getDateInstance
, or getDateTimeInstance
.
These methods automatically create a DateFormatSymbols
for
the formatter so that you don't have to. After the
formatter is created, you may modify its format pattern using the
setPattern
method. For more information about
creating formatters using DateFormat
's factory methods,
see DateFormat
.
If you decide to create a date-time formatter with a specific format pattern for a specific locale, you can do so with:
new SimpleDateFormat(aPattern, DateFormatSymbols.getInstance(aLocale)).
DateFormatSymbols
objects are cloneable. When you obtain
a DateFormatSymbols
object, feel free to modify the
date-time formatting data. For instance, you can replace the localized
date-time format pattern characters with the ones that you feel easy
to remember. Or you can change the representative cities
to your favorite ones.
New DateFormatSymbols
subclasses may be added to support
SimpleDateFormat
for date-time formatting for additional locales.
DateFormat
,
SimpleDateFormat
,
SimpleTimeZone
,
Serialized Form| Constructor and Description |
|---|
DateFormatSymbols()
Construct a DateFormatSymbols object by loading format data from
resources for the default
FORMAT
locale. |
DateFormatSymbols(Locale locale)
Construct a DateFormatSymbols object by loading format data from
resources for the given locale.
|
| Modifier and Type | Method and Description |
|---|---|
Object |
clone()
Overrides Cloneable
|
boolean |
equals(Object obj)
Override equals
|
String[] |
getAmPmStrings()
Gets ampm strings.
|
static Locale[] |
getAvailableLocales()
Returns an array of all locales for which the
getInstance methods of this class can return
localized instances. |
String[] |
getEras()
Gets era strings.
|
static DateFormatSymbols |
getInstance()
Gets the
DateFormatSymbols instance for the default
locale. |
static DateFormatSymbols |
getInstance(Locale locale)
Gets the
DateFormatSymbols instance for the specified
locale. |
String |
getLocalPatternChars()
Gets localized date-time pattern characters.
|
String[] |
getMonths()
Gets month strings.
|
String[] |
getShortMonths()
Gets short month strings.
|
String[] |
getShortWeekdays()
Gets short weekday strings.
|
String[] |
getWeekdays()
Gets weekday strings.
|
String[][] |
getZoneStrings()
Gets time zone strings.
|
int |
hashCode()
Override hashCode.
|
void |
setAmPmStrings(String[] newAmpms)
Sets ampm strings.
|
void |
setEras(String[] newEras)
Sets era strings.
|
void |
setLocalPatternChars(String newLocalPatternChars)
Sets localized date-time pattern characters.
|
void |
setMonths(String[] newMonths)
Sets month strings.
|
void |
setShortMonths(String[] newShortMonths)
Sets short month strings.
|
void |
setShortWeekdays(String[] newShortWeekdays)
Sets short weekday strings.
|
void |
setWeekdays(String[] newWeekdays)
Sets weekday strings.
|
void |
setZoneStrings(String[][] newZoneStrings)
Sets time zone strings.
|
public DateFormatSymbols()
FORMAT
locale. This constructor can only
construct instances for the locales supported by the Java
runtime environment, not for those supported by installed
DateFormatSymbolsProvider
implementations. For full locale coverage, use the
getInstance
method.
This is equivalent to calling
DateFormatSymbols(Locale.getDefault(Locale.Category.FORMAT))
.
MissingResourceException
- if the resources for the default locale cannot be
found or cannot be loaded.getInstance()
,
Locale.getDefault(java.util.Locale.Category)
,
Locale.Category.FORMAT
public DateFormatSymbols(Locale locale)
DateFormatSymbolsProvider
implementations. For full locale coverage, use the
getInstance
method.locale
- the desired localeMissingResourceException
- if the resources for the specified locale cannot be
found or cannot be loaded.getInstance(Locale)
public static Locale[] getAvailableLocales()
getInstance
methods of this class can return
localized instances.
The returned array represents the union of locales supported by the
Java runtime and by installed
DateFormatSymbolsProvider
implementations. It must contain at least a Locale
instance equal to Locale.US
.DateFormatSymbols
instances are available.public static final DateFormatSymbols getInstance()
DateFormatSymbols
instance for the default
locale. This method provides access to DateFormatSymbols
instances for locales supported by the Java runtime itself as well
as for those supported by installed
DateFormatSymbolsProvider
implementations.
This is equivalent to calling getInstance(Locale.getDefault(Locale.Category.FORMAT))
.
DateFormatSymbols
instance.Locale.getDefault(java.util.Locale.Category)
,
Locale.Category.FORMAT
public static final DateFormatSymbols getInstance(Locale locale)
DateFormatSymbols
instance for the specified
locale. This method provides access to DateFormatSymbols
instances for locales supported by the Java runtime itself as well
as for those supported by installed
DateFormatSymbolsProvider
implementations.locale
- the given locale.DateFormatSymbols
instance.NullPointerException
- if locale
is nullpublic String[] getEras()
public void setEras(String[] newEras)
newEras
- the new era strings.public String[] getMonths()
If the language requires different forms for formatting and
stand-alone usages, this method returns month names in the
formatting form. For example, the preferred month name for
January in the Czech language is ledna in the
formatting form, while it is leden in the stand-alone
form. This method returns "ledna"
in this case. Refer
to the
Calendar Elements in the Unicode Locale Data Markup Language
(LDML) specification for more details.
public void setMonths(String[] newMonths)
newMonths
- the new month strings.public String[] getShortMonths()
If the language requires different forms for formatting and
stand-alone usages, This method returns short month names in
the formatting form. For example, the preferred abbreviation
for January in the Catalan language is de gen. in the
formatting form, while it is gen. in the stand-alone
form. This method returns "de gen."
in this case. Refer
to the
Calendar Elements in the Unicode Locale Data Markup Language
(LDML) specification for more details.
public void setShortMonths(String[] newShortMonths)
newShortMonths
- the new short month strings.public String[] getWeekdays()
Calendar.SUNDAY
,
Calendar.MONDAY
, etc. to index the result array.public void setWeekdays(String[] newWeekdays)
newWeekdays
- the new weekday strings. The array should
be indexed by Calendar.SUNDAY
,
Calendar.MONDAY
, etc.public String[] getShortWeekdays()
Calendar.SUNDAY
,
Calendar.MONDAY
, etc. to index the result array.public void setShortWeekdays(String[] newShortWeekdays)
newShortWeekdays
- the new short weekday strings. The array should
be indexed by Calendar.SUNDAY
,
Calendar.MONDAY
, etc.public String[] getAmPmStrings()
public void setAmPmStrings(String[] newAmpms)
newAmpms
- the new ampm strings.public String[][] getZoneStrings()
TimeZone.getDisplayName()
instead.
The value returned is a
two-dimensional array of strings of size n by m,
where m is at least 5. Each of the n rows is an
entry containing the localized names for a single TimeZone
.
Each such row contains (with i
ranging from
0..n-1):
zoneStrings[i][0]
- time zone IDzoneStrings[i][1]
- long name of zone in standard
timezoneStrings[i][2]
- short name of zone in
standard timezoneStrings[i][3]
- long name of zone in daylight
saving timezoneStrings[i][4]
- short name of zone in daylight
saving timeTimeZone
class that are not
custom IDs.
All other entries are localized names. If a zone does not implement
daylight saving time, the daylight saving time names should not be used.
If setZoneStrings
has been called
on this DateFormatSymbols
instance, then the strings
provided by that call are returned. Otherwise, the returned array
contains names provided by the Java runtime and by installed
TimeZoneNameProvider
implementations.
setZoneStrings(String[][])
public void setZoneStrings(String[][] newZoneStrings)
TimeZone
.
Each such row contains (with i
ranging from
0..n-1):
zoneStrings[i][0]
- time zone IDzoneStrings[i][1]
- long name of zone in standard
timezoneStrings[i][2]
- short name of zone in
standard timezoneStrings[i][3]
- long name of zone in daylight
saving timezoneStrings[i][4]
- short name of zone in daylight
saving timeTimeZone
class that are not
custom IDs.
All other entries are localized names.newZoneStrings
- the new time zone strings.IllegalArgumentException
- if the length of any row in
newZoneStrings
is less than 5NullPointerException
- if newZoneStrings
is nullgetZoneStrings()
public String getLocalPatternChars()
public void setLocalPatternChars(String newLocalPatternChars)
newLocalPatternChars
- the new localized date-time
pattern characters.public Object clone()
public int hashCode()
hashCode
in class Object
Object.equals(java.lang.Object)
,
System.identityHashCode(java.lang.Object)
public boolean equals(Object obj)
equals
in class Object
obj
- the reference object with which to compare.true
if this object is the same as the obj
argument; false
otherwise.Object.hashCode()
,
HashMap
Dracula Javadoc Sample, Made with Allan Im.


utils
— Fake utilities module for tests
The utils
module is a pretend python module for changes testing.
Classes
-
class
utils.
Path
Class for handling paths.
New in version 0.5: Innovative new way to handle paths.
Deprecated since version 0.6: So, that was a bad idea it turns out.


|
StorageHeaterController
|
The StorageHeaterControl application. More...
Namespaces | |
| Test | |
| Test Classes for the StorageHeaterControl application. | |
Classes | |
| class | ConsoleView |
| Concrete implementation of the IView interface using the console. More... | |
| class | HeaterControlModel |
| Concrete implementation of the IModel interface defining a Storage Heater Schedule. More... | |
| class | IModel |
| Interface class for the Model class(es) More... | |
| class | IModelListener |
| Interface class for callbacks to the Presentation Layer class from an instance of IModel. More... | |
| class | IView |
| Interface class for the View class(es) More... | |
| class | IViewListener |
| Interface class for callbacks to the Presentation Layer class from an instance of IView. More... | |
| class | PresentationLayer |
| Class for communicating between View and Model. More... | |
The StorageHeaterControl application.


SmarterHome Privacy Policy
Data collection
By participating in this study, and using the SmarterHome app, we will
collect certain data about how you use your smart home, for scientific
purposes. We will collect the following data over the course of the study:
- Interview transcripts
- Interview audio
- In-app feedback entries
-
Smart home event log data. This is data collected by the smart home system during regular operation of the system. The following types of events will be logged by the system:
- User commands issued to devices, such as door locks, lights, and thermometers. Non-participating individuals who use our prototype app to issue the commands will have their username associated with the event. However, if they physically operate devices, or use a different app to control devices, we log the event without any identifiers.
- Sensor data such as motion sensors and temperature sensors (excluding camera and microphone sensors). This event is not associated with a specific user, because these sensors are too simple to detect the identiy of the person triggering it. Anonymous sensor data about non-participating individuals will also be collected.
- User interactions with parental controls. This includes - the username of the person restricted by the controls, the username of the person who created it, the devices/rooms protected by the controls, and events where the system is triggered by the child attempting to use a device. We will collect data on non-participating individuals who use the prototype app and are governed by or implement parental controls, including the circumstances of the event and their username.
- User interactions with location-based access controls. This type of event includes who created an access control rule, what devices/rooms it applies to, and when the rule is triggered by any other user in the house. We will collect data on non-participating individuals who use the prototype app and either create or trigger access control rules, including the circumstances of the event and their username.
- User interactions with proximity-based notifications. We will log whenever a user receives or interacts with notifications about their smart home. We will collect data on non-participating individuals who use the prototype app and interact with notifications, including the devices they control and their username.
- Pictures, of interesting or characteristic smart home setups


{% extends "base.html" %} {% block title %} Search Results {% endblock %} {% block content %}
You filtered phones prices under: {{ min }}
...and phones prices greater: {{ max }}
{% if phones %}Found {{ phones|length }} phones{{ phones|pluralize }}. (with price and capacity)
No phones matched your search criteria.
{% endif %}


Main
Projects
Contact
<
h1
>
Chris Gutierrez
<
h1
>
<
h1
>
<
small
>
Portfolio
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/small
>
<
h1
>
Projects
Tribute Page Project
Survey Project
Product Landing Project
Technical Document Project
Want to know more? Check me out!


Learning in directed models
We now turn our attention to the third and last part of the course: learning. Given a dataset, we would like to fit a model that will make useful predictions on various tasks that we care about.
A graphical model has two components: the graph structure, and the parameters of the factors induced by this graph. These components lead to two different learning tasks:
- Parameter learning, where the graph structure is known and we want to estimate the factors.
- Structure learning, where we want to estimate the graph, i.e. determine from data how the variables depend on each other.
We are going to first focus on parameter learning, and come back to structure learning in a later chapter.
We will consider parameter learning in both directed and undirected models. It turns out that the former will admit easy, closed form solutions, while the latter will involve potentially intractable numerical optimization techniques. In later sections, we will look at latent variable models (which contain unobserved hidden variables that succinctly model the event of interest) as well as Bayesian learning, a general approach to statistics that offers certain advantages to more standard approaches.
The learning task
Before, we start our discussion of learning, let’s first reflect on what it means to fit a model, and what is a desirable objective for this task.
Let’s assume that the domain is governed by some underlying distribution . We are given a dataset of samples from ; The standard assumption is that the data instances are independent and identically distributed (iid). We are also given a family of models , and our task is to learn some “good” model in that defines a distribution . For example, we could look at all Bayes nets with a given graph structure, with all the possible choices of the CPD tables.
The goal of learning is to return a model that precisely captures the distribution from which our data was sampled. This is in general not achievable because limited data only provides a rough approximation of the true underlying distribution, and because of computational reasons. Still, we want to somehow select the “best” approximation to the underlying distribution .
What is “best” in this case? It depends on what we want to do:
- Density estimation: we are interested in the full distribution (so later we can compute whatever conditional probabilities we want)
- Specific prediction tasks: we are using the distribution to make a prediction, e.g. is this email spam or not?
- Structure or knowledge discovery: we are interested in the model itself, e.g. how do some genes interact with each other?
Maximum likelihood
Let’s assume that we want to learn the full distribution so that later we can answer any probabilistic inference query. In this setting we can view the learning problem as density estimation. We want to construct a as “close” as possible to . How do we evaluate “closeness”? We will again use the KL divergence, which we have seen when we covered variational inference:
The first term does not depend on p; hence minimizing KL divergence is equivalent to maximizing the expected log-likelihood
This objective asks that assign high probability to instances sampled from , so as to reflect the true distribution. Although we can now compare models, since we are not computing , we don’t know how close we are to the optimum.
However, there is still a problem: in general we do not know . We will thus approximate the expected log-likelihood with the empirical log-likelihood (a Monte-Carlo estimate):
where is a dataset drawn i.i.d. from .
Maximum likelihood learning is then defined as
An example
As a simple example, consider estimating the outcome of a biased coin. Our dataset is a set of tosses and our task is to estimate the probability of heads/tails on the next flip. We assume that the process is controlled by a probability distribution where . Our class of models is going to be the set of all probability distributions over .
How should we choose from if 60 out of 100 tosses are heads? Let’s assume that and . If our observed data is , our likelihood becomes ; maximizing this yields .
More generally, our log-likelihood function is simply
for which the optimal solution is
Likelihood, Loss and Risk
We may now generalize this by introducing the concept of a loss function. A loss function measures the loss that a model distribution makes on a particular instance . Assuming instances are sampled from some distribution , our goal is to find the model that minimizes the expected loss or risk,
Notice that the loss function which corresponds to maximum likelihood estimation is the log loss .
Another example of a loss is the conditional log-likelihood. Suppose we want to predict a set of variables given , e.g., for segmentation or stereo vision. We concentrate on predicting , and use a conditional loss function Since the loss function only depends on , it suffices to estimate the conditional distribution, not the joint. This is the objective function we use to train conditional random fields (CRFs).
Suppose next that our ultimate goal is structured prediction, i.e. given we predict via . What loss function should we use to measure error in this setting?
One reasonable choice would be the classification error:
which is the probability over all pairs sampled from that we predict the wrong assignment. A somewhat better choice might be the hamming loss, which counts the number of variables in which the MAP assignment differs from the ground truth label. There also exists a fascinating line of work on generalizations of the hinge loss to CRFs, which leads to a class of models called structured support vector machines.
The moral of the story here is that it often makes sense to choose a loss that is appropriate to the task at hand, e.g. prediction rather than full density estimation.
Empirical Risk and Overfitting
Empirical risk minimization can easily overfit the data. The data we have is a sample, and usually there is vast amount of samples that we have never seen. Our model should generalize well to these “never-seen” samples.
The bias/variance tradeoff
Thus, we typically restrict the hypothesis space of distributions that we search over. If the hypothesis space is very limited, it might not be able to represent , even with unlimited data. This type of limitation is called bias, as the learning is limited on how close it can approximate the target distribution
If we select a highly expressive hypothesis class, we might represent better the data. However, when we have small amount of data, multiple models can fit well, or even better than the true model. Moreover, small perturbations on D will result in very different estimates. This limitation is call the variance.
Thus, there is an inherent bias-variance trade off when selecting the hypothesis class. One of the main challenges of machine learning is to choose a model that is sufficiently rich to be useful, yet not so complex as to overfit the training set.
How to avoid overfitting?
High bias can be avoided by increasing the capacity of the model. We may avoid high variance using several approaches.
We may impose hard constraints, e.g. by selecting a less expressive hypothesis class: Bayesian networks with at most parents or pairwise (rather than arbitrary-order) MRFs. We may also introduce a soft preference for “simpler” models by adding a regularizer term to the loss , which will penalize overly complex .
Generalization error
At training, we minimize empirical loss
However, we are actually interested in minimizing
We cannot guarantee with certainty the quality of our learned model. This is because the data is sampled stochastically from , and we might get an unlucky sample. The goal of learning theory is to prove that the model is approximately correct: for most , the learning procedure returns a model whose error is low. There exist a vast literature that quantifies the probability of observing a given error between the empirical and the expected loss given a particular type of model and a particular dataset size.
Maximum likelihood learning in Bayesian networks
Let us now apply this long discussion to a particular problem of interest: parameter learning in Bayesian networks.
Suppose that we are given a Bayesian network and i.i.d. samples . What is the maximum likelihood estimate of the parameters (the CPDs)?
We may write the likelihood as
Taking logs and combining like terms, this becomes
Thus, maximization of the (log) likelihood function decomposes into separate maximizations for the local conditional distributions! This is essentially the same as the head/tails example we saw earlier (except with more categories). It’s a simple calculus exercise to formally show that
We thus conclude that in Bayesian networks with discrete variables, the maximum-likelihood estimate has a closed-form solution. Even when the variables are not discrete, the task is equally simple: the log-factors are linearly separable, hence the log-likelihood reduces to estimating each of them separately. The simplicity of learning is one of the most convenient features of Bayesian networks.
| Index | Previous | Next |


-
Database:
1- Security officer's tables:
2- Webmaster's tables:
3- Editors' tables:
4- Content tables
5- View tables
6- Other (application) tables
Security officer's tables: A seperate, dedicated database for security officer would be better.
This database might have tables: eVe_tableGroups, eVe_columnGroups, eVe_fileGroups, eVe_userGroups
, suspiciousIPs, bannedIPs, suspiciousUsers, bannedUsers, etc.
Permissions might be controlled by groups tables above (eVe_tableGroups, eVe_columnGroups, eVe_fileGroups, eVe_userGroups)
Webmaster's tables: Webmaster must be able to create forms and pages through UI.
So, we need a group of tables to controll Webmaster's activities.
These UIs and relevant tasks might be developed in next version but now, database must be structured accordingly.
Editors' tables: To track who created or edited which content, who uploaded a file.
-
Users, roles, permissions and groups:
Main roles: Security officer, owner, webmaster, editor, reader
Groups (hierarchy) will be defined by applications as needed.
Example:
groups ->
1- users -> 1- NewYork -> 1- Suffolk
2- Nassau
3- Brookly
2- California
3- Texas
4- Florida
2- files
3- tables -> 1- Security officer
2- Webmasters
3- Editors
4- content 1- Sports -> 1- Football
2- Baseball
3- Soccer
2- Travel -> 1- NewYork -> 1- Suffolk
2- Nassau
3- Brooklyn
2- California
3- Texas
4- Florida
3- Texas
4- Florida
5- views
-
Forms: Content types and groups.
Each form must structured in eVe_step2 as arrays of HTML2D elements.
We need template scripts (Javascript) to render those arrays into HTML elements.
Example: function eVe.render_form() in eve_render.js
Registration form, login form, contact form can use function eVe.render_form() or
can create their own template function and call it in eve_render.js.
Tests: Automating testing
We need test code for each form and view when the form or view is created.
Add each test code to test.php. Run test.php to test all existing test codes after each code added.
Basic idea: reset $_POST and $_GET, set $eVe_test = 'yes';
... if $eVe_test == 'yes' => don't send HTTP to client but proceed with test.php
Logs: Errors log will be logged in 'toDeveloper.txt' as encypted
Webmaster and system admin will be notified by email after each error. File name will be given to webmaster.
Encypted log file will be sent to developers pool or ticket system.
Developer can decrypt the log file and read it.
Encryption
We need a few types of encryption:
1- One way encryption for enryption for passwords
2- Two ways encryption for PII (personally identifiable information)
2- Two ways encryption for file encryption (Like in 'logs' mentioned above )
Views
Menus
Multi languages
Multi charsets
Files


Click on a link to download the video tutorial in .ogv format.
1: The video-tutorials were recorder when «TEMA» was still called «TEA».


Serverless security is usually associated with application security but it is much more than that.
This scoring tool will help you find areas where you are doing great and where you need improvement. The goal is not to be exhaustive but to help find overlooked spots. Use at your own risk.
For the purposes of this tool, serverless not only means function as as service (FaaS like AWS Lambda or Google Functions) but plaform services where you don't have to manage the "instance". For example AWS DynamoDB is considered serverless and RDS is not.
The data that you submit is never shared with third-parties. Your data will not be submitted unless you request a customized report.


Chess Royale
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Play Solo!
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PhotoShoot
Start inspiring yourself!
Website for photographer
{% for post in posts %}
{{post.author.username}}
{{post.body}}
{{ post.likes.count() }}
{% for comm in post.get_comment(post.id) %}
{{comm.author.username}}
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Each school decides its uniform and must not discriminate based on gender, race, disability, sexual orientation or belief.
The head teacher can discipline your child for not wearing the school uniform. Your child can be suspended or expelled if they repeatedly ignore the uniform rules.
Talk to the head teacher if your child isn’t wearing the uniform, including PE kit, because you can’t afford it. The school should give you time to buy the right clothes.
If you’re in England, you may also get financial help from your local council .
Find out about school uniform grants in Northern Ireland.
Schools can decide if girls can wear trousers, or if religious dress is allowed.
Talk to the head teacher, school governors or parent/teacher association if you’d like the school uniform changed.
Contact Citizens Advice if you’re forced to buy uniforms from an expensive supplier.


Source code for dummymp.detect
#!/usr/bin/env python
# DummyMP - Multiprocessing Library for Dummies!
# Copyright 2014 Albert Huang.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
# implied. See the License for the specific language governing
# permissions and limitations under the License.
#
# DummyMP Library - CPU availability checker
# multiprocessing library for dummies!
# (library for easily running functions in parallel)
#
import psutil
import logging
import datetime
import os
import time
from . import config
[docs]def poll_procs(interval):
"""Poll for processes and return information about them.
Polls all processes and returns a dictionary with information about
all of the processes.
Args:
interval (int): Delay before fetching process status. This is
akin to top -d ##, where a delay is specified.
Returns:
tuple: Tuple with the first element being a list of Process
objects, each with a .dict attribute. Each .dict attribute
contains information about the process, stored in the form of a
dict. It includes:
* ``'username'``:
Username of the user who started the process.
* ``'cpu_percent'``:
Percent of CPU used by the process.
* ``'status'``:
Status of the process (running, stopped, zombie).
The second element in the tuple contains a summary dictionary
with the total number of running, stopped, and zombie
processes, with keys 'running', 'stopped', and 'zombie',
respectively.
::
(
[Process(
pid=30159, name='services.exe',
dict={'username':USERNAME, ...}
), ...],
{'running': ##, 'stopped': ##, ...}
)
"""
# sleep some time
time.sleep(interval)
procs = []
procs_status = {}
for p in psutil.process_iter():
try:
p.dict = p.as_dict(['username', 'cpu_percent', 'status'])
try:
procs_status[p.dict['status']] += 1
except KeyError:
procs_status[p.dict['status']] = 1
except psutil.NoSuchProcess:
pass
else:
procs.append(p)
# return processes sorted by CPU percent usage
processes = sorted(procs, key=lambda p: p.dict['cpu_percent'],
reverse=True)
return (processes, procs_status)
[docs]def needUpdateCPUAvail():
"""Check if the CPU availability needs to be updated.
Utilizing configuration and checking the time elapsed from the
previous check, determine whether the CPU availability function,
getCPUAvail(), needs to be run or not to update CPU availability.
Args:
None
Returns:
bool: Boolean indicating whether the CPU availability needs to
be updated or not.
"""
return not ( \
(config.DUMMYMP_MODE == config.DUMMYMP_NUCLEAR) or \
(datetime.datetime.now() - config.LAST_CPU_CHECK <= config.CPU_CHECK_TIMEDELTA_THRESHOLD))
[docs]def getSpawnProcPIDs():
"""Fetch all running DummyMP system process IDs.
Looking through all of the DummyMP processes, determine if any are
running, and if any, retrieve their system process IDs.
Args:
None
Returns:
list: An list with the system process IDs of current running
spawned DummyMP processes.
"""
pids = []
for proc in config.dummymp_procs:
if proc.is_alive():
pids.append(proc.pid)
return pids
[docs]def getTotalCPUs():
"""Get the total number of CPUs on the system.
Detects and returns the total number of CPUs on the system.
Args:
None
Returns:
int: An integer with the number of CPUs that are on the system.
"""
return psutil.cpu_count()
[docs]def getCPUAvail():
"""Get number of CPUs available.
Fetch the number of CPUs available based on the current priority
and limit configuration.
Args:
None
Returns:
int: An integer with the number of CPUs that are available.
"""
# If the time threshold is met, OR we are in NUCLEAR mode, just
# return the cached CPU availability.
if (config.DUMMYMP_MODE == config.DUMMYMP_NUCLEAR) or (datetime.datetime.now() - config.LAST_CPU_CHECK <= config.CPU_CHECK_TIMEDELTA_THRESHOLD):
return config.CPU_AVAIL
# Get number of CPUs!
ncpus = psutil.cpu_count()
# Initialize array...
avg = []
# Check to make sure we aren't looking at ourselves!
if config.total_running == config.CPU_AVAIL:
return config.CPU_AVAIL
logging.debug("Querying CPUs (%s mode)..." % (config.DUMMYMP_STRING[config.DUMMYMP_MODE]))
# Get PIDs of DummyMP spawned processes!
dmp_pids = getSpawnProcPIDs()
# Call twice to get accurate measurements...
cur_state = poll_procs(config.DUMMYMP_MINTERVAL[config.DUMMYMP_MODE])
cur_state = poll_procs(config.DUMMYMP_MINTERVAL[config.DUMMYMP_MODE])
# Initialize CPU usage array...
cpu_usage_percents = []
# If the process meets threshold, AND is not a process of DummyMP,
# add the CPU usage to our list.
for p in cur_state[0]:
if (p.dict["cpu_percent"] > config.DUMMYMP_THRESHOLD[config.DUMMYMP_MODE]) and (p.pid != os.getpid()) and (p.pid not in dmp_pids):
cpu_usage_percents.append(p.dict["cpu_percent"])
# Add up total CPU usage...
total_cpu_usage = sum(cpu_usage_percents)
# ...and calculate the number of CPUs!
available_num_cpus = int(((ncpus * 100.0) - total_cpu_usage) / 100.0)
logging.debug("Total external CPU usage: %i%%" % total_cpu_usage)
logging.debug("Available CPUs: %i/%i" % (available_num_cpus, ncpus))
# Update state
config.CPU_AVAIL = available_num_cpus
config.LAST_CPU_CHECK = datetime.datetime.now()
# Return number of CPUs available!
return available_num_cpus


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MET approves five-year management plan for wild horses
The Ministry of Environment and Tourism (MET) has approved a five-year management plan for wild horses, which will include the provision of supplementary feeding and water.
This is contained in a five-year Management Plan for Horses in the Namib Naukluft Park and the Tsau //Khaeb (Sperrgebeit) National Park.
The ministry says this plan is to ensure the survival of the wild horse population, thereby improve tourism development and cultural heritage in the Garub area.


This class can be used to manage the widget renderers.
More...
#include <TGUI/Loading/Theme.hpp>
|
|
typedef std::shared_ptr< Theme > | Ptr |
| | Shared widget pointer.
|
| |
|
typedef std::shared_ptr< const Theme > | ConstPtr |
| | Shared constant widget pointer.
|
| |
This class can be used to manage the widget renderers.
◆ Theme()
| tgui::Theme::Theme |
( |
const String & |
primary = "" | ) |
|
Constructs the theme class, with an optional theme file to load.
- Parameters
-
| primary | Primary parameter for the theme loader (filename of the theme file in DefaultThemeLoader) |
◆ addRenderer()
| void tgui::Theme::addRenderer |
( |
const String & |
id, |
|
|
std::shared_ptr< RendererData > |
renderer |
|
) |
| |
Manually adds a renderer data to the theme.
- Parameters
-
| id | Identifier of the renderer |
| renderer | The renderer to add |
If a renderer with the same id already exists then it will be replaced by this one. Widgets using the old renderer will keep using that old renderer and remain unchanged.
◆ create()
Constructs a new theme, with an optional theme file to load.
- Parameters
-
| primary | Primary parameter for the theme loader (filename of the theme file in DefaultThemeLoader) |
- Returns
- The new theme
Unlike with widgets, it is not required to use a smart pointer for the theme. You do not have to use this function to create a theme.
◆ getDefault()
| static std::shared_ptr<Theme> tgui::Theme::getDefault |
( |
| ) |
|
|
static |
Returns a pointer to the theme class that widgets use by default.
- Returns
- Default theme
When setDefault was not called or was given a nullptr as parameter, getDefault will create and return the default "White" theme.
◆ getPrimary()
| const String& tgui::Theme::getPrimary |
( |
| ) |
const |
Returns the primary theme loader parameter.
- Returns
- Primary parameter for the theme loader (filename of the theme file in DefaultThemeLoader)
◆ getRenderer()
Gets data for the renderers.
- Parameters
-
| id | The secondary parameter for the theme loader (name of section in theme file in DefaultThemeLoader). |
- Returns
- Shared renderer data
- Exceptions
-
| Exception | if theme loader fails to load the requested renderer |
◆ getRendererNoThrow()
Gets data for the renderers.
- Parameters
-
| id | The secondary parameter for the theme loader (name of section in theme file in DefaultThemeLoader). |
- Returns
- Shared renderer data
Unlike getRenderer which throws an exception, this function will return nullptr when the renderer is not found
◆ getThemeLoader()
Returns the function that is currently being used to load the widget theme data.
- Returns
- Theme loader
◆ load()
| void tgui::Theme::load |
( |
const String & |
primary | ) |
|
Changes the primary theme loader parameter.
- Parameters
-
| primary | Primary parameter for the theme loader (filename of the theme file in DefaultThemeLoader) |
When the theme was loaded before and a renderer with the same name is encountered, the widgets that were using the old renderer will be reloaded with the new renderer.
◆ removeRenderer()
| bool tgui::Theme::removeRenderer |
( |
const String & |
id | ) |
|
Manually removes a renderer to the theme.
- Parameters
-
| id | Identifier of the renderer |
- Returns
- True when removed, false when the identifier did not match any renderer
◆ setDefault() [1/2]
| static void tgui::Theme::setDefault |
( |
const String & |
primary = "" | ) |
|
|
static |
Sets the theme class that widgets use by default.
- Parameters
-
| primary | Primary parameter for the theme loader (filename of the theme file in DefaultThemeLoader) |
Calling this function is equivalent to the following:
◆ setDefault() [2/2]
| static void tgui::Theme::setDefault |
( |
std::shared_ptr< Theme > |
theme | ) |
|
|
static |
Sets the theme class that widgets use by default.
- Parameters
-
| theme | Theme to use as default |
◆ setThemeLoader()
| static void tgui::Theme::setThemeLoader |
( |
std::shared_ptr< BaseThemeLoader > |
themeLoader | ) |
|
|
static |
Changes the function that will load the widget theme data.
- Parameters
-
| themeLoader | Pointer to the new loader |
The documentation for this class was generated from the following file:


Online donation is temporarily unavailable while NMCFP changes its merchant
services company. In the meantime, please mail your donations (via check or
money order) to:
NM Center for Family Policy
P.O. Box 20937
Albuquerque, NM 87154
or call us at<PHONE_NUMBER> to donate by phone. Thank you for your patience.


Recommended food items
Boosting your immune system to fight the coronavirus: what you need to know
There is currently no specific treatment for or vaccine to prevent COVID-19. The best way to prevent illness is to avoid being exposed to this virus. These are things that you can do today that don’t cost anything. The evidence is strong to support the many health benefits of eating low-carb diets. Here’s what we do know about certain supplements that reportedly have immune-boosting properties.
Vitamin C
Vitamin C has been used to help prevent the common cold. there’s no harm in taking up to 2,000 mg per day. For smokers and high-risk individuals, it’s definitely worth considering
Vitamin D
Vitamin D plays a number of important roles in health. you may have a better chance of staying well if you supplement with 2,000 IU per day. Of course, your body can make Vitamin D on its own when your skin is exposed to sunlight, so try to get some sun whenever you can
Zinc
Zinc is a mineral involved in the white blood cell response to infection. make sure to stay below the upper limit of 40 mg per day and avoid administering nasally
Turmeric
Turmeric is a spice commonly used in Indian and Asian cuisine, including curries.
Garlic
Garlic, a popular and pungent herb with a characteristic aroma, is widely believed to have antibacterial and antiviral effects, including helping to fight the common cold or other upper-respiratory.
Fruits, veggies and seeds
Getting plenty of fruits, vegetables, and seeds is a common recommendation.
Refined carbs and sugars
Laboratory evidence suggests sugar may impair white blood cell function, but no credible evidence shows eating it makes you get more infections. However, other evidence suggests acute rises in blood sugar may increase risk of infections and complications. Therefore, it would make sense that we want to limit these blood sugar elevations. If the foods you eat cause your blood sugar to rise above 140mg/dl (7.8mmol/L), consider eating something different.


Songs of the Wind on a Southern Shore, and other Poems of Florida
by George E. Merrick
“The Defeat of the Frost King's Hordes”
Additional Information
- Year Published: 1920
- Language: English
- Country of Origin: United States of America
- Source: Merrick, G. E. (1920). Songs of the wind on a southern shore, and other poems of florida. The Four Seas Publishing Co.
-
Readability:
- Flesch–Kincaid Level: 7.2
- Word Count: 488
- Genre: Poetry
- Keywords: florida stories, poetry
- ✎ Cite This
Downloads
Three thousand miles, and more, they march,
—White Armies of the North!
And in their wake where dead up-arch
Ensanguined hues flare forth.
Unchecked; they sweep from Arctic Bay
—Where first they blustered forth;
And south-ward flames their bloodhued way;
Red Armies of the North!
Through helpless shiv’ring stricken land
Southward rolls their march.
On mountain rocks; by river sand;
The dying brown and parch.
Three thousand miles they march, and more;
No army dares stand forth
To battle wage on northern floor
—With Armies of the North!

Their victims fall, thick-lain, to earth
In mounds of dying red.
Each flaring waste holds fearful dearth
Of else but heaping dead.
They leave no little humble home:
But, all, as prey acclaim.
—Tall spire and sweeping templed dome
Burst in consuming flame.
Their white, deep-steeped, glares fateful forth.
Rapacious, still, for more
Encarmined Armies of the North
Stretch south-ward hands of gore.
Three thousand miles they march, and more,
From out their frigid North.—
...But ambushed fate is waiting for
Red Armies of the North!
For, See!... From hot West Indian lands
Green-clad armies north-ward fare!
Calm, silent, unobtrusive bands
Green-swarm from jungle lair.
The green-plumed host, though witting well
The force of jealous spleen,
Surge north-ward—one green-tossing swell—
Heroic Armies of the Green!
Where other Norther armies reeled
From Seminole’s rude hank,
The palm-hued hordes, now, choose the field
To ambush,—fore and flank.
The gray-green moss, in cunning keen,
Hides countless warring head
That swarming spring from Jungles green
On Armies of the Red.
Surrounded: stunned by cunning shock:
The blood-hued minions fight.
Green-armoured warriors ceaseless flock
To choke Red-flaring blight.
And fierce the silent battle flows
Where live—and north-oak meet.
High-ridged tongues of angry glows
Pierce ambushed green-flung beat.
But reinforcements to the Green
Palmetto-clothed spring forth:
All, sisal-shod—with bay’nets keen,
Halt Armies of the North!
Past cypress swamp; by oak-grown hill
Red Armies struggle through
To palm-green vales; nor flee—until
Palms view a Waterloo.
Yet, further south, Reds,—dying,—strive!
—Green=fronded trumpets blare!
The victims,—last, of Red Horde’s drive
Sparse, exiled maples flare.
Green-fronded arms in triumph wave!
Green-tossing plumes fling high!
...The gray-green moss for vanquished brave
Breathes low, magnan’mous sigh.
Three thousand miles they marched, and more;
—Red—Armies of the North!
Green-held: their southward drive is o’er:
Fled—Armies of the North!


User effort
Data collected at contact centres to find the reasons services are creating extra effort for their users. Read more about what this means.
425k calls taken
▼−10% since Jun 2015
880 emails received
▲+10% since Jun 2015
Reasons users get in touch
Based on a sample of 1,000 calls and 200 emails
I want to check the status of something
I need more information
I had to get in contact
I need additional support
I want to challenge a decision
Reasons users have to repeat or amend something
Based on a range of sampling methods so numbers are indicative
User's information is incomplete
User followed the wrong process
User's information is illegible
User's information doesn't match government records


Ocean Modelling (1990)
- Budin G.R. & Davey M.K 1990
- Intermediate models of
intraseasonal and interannual
waves in the tropics
J.Mar. Sys., 1, pp39-50
- Johnson E.R. & Davey M.K. 1990
- Free-surface adjustment and
topographic waves in coastal
currents
J. Fluid Mech., 219, pp273-289
Contact information
Andy Brady |<EMAIL_ADDRESS>| Last updated: 28 September 1995 | © Crown Copyright 1995


Coyotes sign GM Chayka to long-term extension
by JOHN MARSHALLGLENDALE, Ariz. (AP) - The Arizona Coyotes have signed general manager John Chayka to a long-term contract extension.
Terms of the deal announced Monday were not disclosed.
Chayka has built the Coyotes into playoff contenders since becoming the youngest general manager in North American major sports history at 26 in 2017.
Arizona came up four points short of the postseason in 2018-19 and is off to a 9-6-2 start this year.
Chayka has overhauled the Coyotes’ roster, adding players like Phil Kessel, Nick Schmaltz, Antti Raanta and Michael Grabner through trades and free agency.
Arizona also drafted Clayton Keller, Jakob Chychrun and Barrett Hayton under Chayka.
More AP NHL: https://apnews.com/NHL and https://twitter.com/AP_Sports
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Copyright © 2019 The Washington Times, LLC.
Please read our comment policy before commenting.


Object Lifetimes
As with most IOC containers, Rezolver understands three primary lifetimes for the objects it produces, including
support for automatic disposal of IDisposable
objects when an IContainerScope is used.
Transient Objects
This is what's ultimately produced by a Rezolver IContainer most of the time - a transient object is created when
needed and thrown away when no longer required. It's the same as using new
to create an instance inside a function and then allowing
it to go out of scope when that function returns.
Read more about transient objects.
Singletons
Most developers will be familiar with the singleton pattern - an object created only once per process (often created lazily - i.e. on
demand) and which is frequently accessed via static
accessors or similar. Specifically, an application will usually enforce
rules at the code level to ensure that it's impossible to create more than one instance of the type.
Rezolver lets you change any registration into a singleton by simply wrapping an ITarget inside a SingletonTarget.
Scoped Objects
Unsurprisingly, understanding scoped objects requires an understanding of scopes.
At its most fundamental level, a scope is simply a disposable 'bag' of objects which is created at the start of a particular process (e.g when a web request is received, or when a job is pulled off a job queue) and which is then disposed when that process completes.
Most IOC containers refer to these as lifetime scopes (note: 'container scopes' in Rezolver, implemented by the IContainerScope interface)
and they are most often used (but not exclusively) to track objects that implement the IDisposable
interface in order that they can be
disposed without the developer explicitly having to do so themselves - often because the developer can never really be sure that she has
definitely finished with that object.
But the presence of a scope at the point where an object is produced/obtained by the container does not automatically mean that that object is itself 'scoped'. Indeed, transients and singletons can be used inside a scope in exactly the same way as outside.
Instead, a scoped object has a special lifetime behaviour such that it behaves like a singleton, except you get one instance per scope instead of one per-process. A scoped object also requires a scope to be available in order for it to be resolved.
Scopes are also hierarchical - one scope can contain zero or more other child scopes whose scoped objects are independent of each other (and those of the parent) - but each child scope will be disposed when the parent scope is disposed.
Rezolver allows you to change any registration into a scoped object by wrapping an ITarget inside a ScopedTarget.
Read more about container scopes (particularly if you will be using IDisposable
objects in your application).


Model.Lists manage a lists (or arrays) of model instances. Similar to $.Model, they are used to:
The todo app demonstrates using a $.Controller to implement an interface for a $.Model.List.
Create a `$.Model.List class for a $.Model like:
$.Model('Todo')
$.Model.List('Todo.List',{
// static properties
},{
// prototype properties
})
This creates a Todo.List
class for the Todo
class. This creates some nifty magic that we will see soon.
static
properties are typically used to describe how
a list makes requests. prototype
properties are
helper functions that operate on an instance of
a list.
Often, a user wants to select multiple items on a page and perform some action on them (for example, deleting them). The app needs to indicate if this is possible (for example, by enabling a "DELETE" button).
If we get todo data back like:
// GET /todos.json ->
[{
"id" : 1,
"name" : "dishes",
"acl" : "rwd"
},{
"id" : 2,
"name" : "laundry",
"acl" : "r"
}, ... ]
We can add a helper function to let us know if we can delete all the instances:
$.Model.List('Todo.List',{
},{
canDelete : function(){
return this.grep(function(todo){
return todo.acl.indexOf("d") != 0
}).length == this.length
}
})
canDelete
gets a list of all todos that have
d in their acl. If all todos have d,
then canDelete
returns true.
You can create a model list instance by using
new Todo.List( instances )
like:
var todos = new Todo.List([
new Todo({id: 1, name: ...}),
new Todo({id: 2, name: ...}),
]);
And call canDelete
on it like:
todos.canDelete() //-> boolean
BUT! $.Model, $.fn.models, and $.Model.List are designed to work with each other.
When you use Todo.findAll
, it will callback with an instance
of Todo.List
:
Todo.findAll({}, function(todos){
todos.canDelete() //-> boolean
})
If you are adding the model instance to elements and
retrieving them back with $().models()
, it will
return a instance of Todo.List
. The following
returns if the checked .todo
elements are
deletable:
// get the checked inputs
$('.todo input:checked')
// get the todo elements
.closest('.todo')
// get the model list
.models()
// check canDelete
.canDelete()
After checking if we can delete the todos,
we should delete them from the server. Like
$.Model
, we can add a
static destroy url:
$.Model.List('Todo.List',{
destroy : 'POST /todos/delete'
},{
canDelete : function(){
return this.grep(function(todo){
return todo.acl.indexOf("d") != 0
}).length == this.length
}
})
and call destroy on our list.
// get the checked inputs
var todos = $('.todo input:checked')
// get the todo elements
.closest('.todo')
// get the model list
.models()
if( todos.canDelete() ) {
todos.destroy()
}
By default, destroy will create an AJAX request to delete these instances on the server, when the AJAX request is successful, the instances are removed from the list and events are dispatched.
Use bind(eventName, handler(event, data))
to listen to add, remove, and updated events on a
list.
When a model instance is destroyed, it is removed from all lists. In the todo example, we can bind to remove to know when a todo has been destroyed. The following removes all the todo elements from the page when they are removed from the list:
todos.bind('remove', function(ev, removedTodos){
removedTodos.elements().remove();
})
The following demo illustrates the previous features with a contacts list. Check multiple Contacts and click "DESTROY ALL"
Once you have a collection of models, you often want to retrieve and update that list with new instances. Storing and retrieving is a powerful feature you can leverage to manage and maintain a list of models.
To store a new model instance in a list...
listInstance.push(new Animal({ type: dog, id: 123 }))
To later retrieve that instance in your list...
var animal = listInstance.get(123);
The 'easy' way to add a model to an element is simply inserting the model into the view like:
<div <%= task %>> A task </div>
And then you can use $('.task').models().
This pattern is fast enough for 90% of all widgets. But it does require an extra query. Lists help you avoid this.
The get method takes elements and uses their className to return matched instances in the list.
To use get, your elements need to have the instance's identity in their className. So to setup a div to reprsent a task, you would have the following in a view:
<div class='task <%= task.identity() %>'> A task </div>
Then, with your model list, you could use get to get a list of tasks:
taskList.get($('.task'))
The following demonstrates how to use this technique:


[ aws . marketplacecommerceanalytics ]
Given a data set type and data set publication date, asynchronously publishes the requested data set to the specified S3 bucket and notifies the specified SNS topic once the data is available. Returns a unique request identifier that can be used to correlate requests with notifications from the SNS topic. Data sets will be published in comma-separated values (CSV) format with the file name {data_set_type}_YYYY-MM-DD.csv. If a file with the same name already exists (e.g. if the same data set is requested twice), the original file will be overwritten by the new file. Requires a Role with an attached permissions policy providing Allow permissions for the following actions: s3:PutObject, s3:GetBucketLocation, sns:GetTopicAttributes, sns:Publish, iam:GetRolePolicy.
See also: AWS API Documentation
generate-data-set
--data-set-type <value>
--data-set-publication-date <value>
--role-name-arn <value>
--destination-s3-bucket-name <value>
[--destination-s3-prefix <value>]
--sns-topic-arn <value>
[--customer-defined-values <value>]
[--cli-input-json <value>]
[--generate-cli-skeleton <value>]
The desired data set type.
- customer_subscriber_hourly_monthly_subscriptions - Available daily by 5:00 PM Pacific Time since 2014-07-21.
- customer_subscriber_annual_subscriptions - Available daily by 5:00 PM Pacific Time since 2014-07-21.
- daily_business_usage_by_instance_type - Available daily by 5:00 PM Pacific Time since 2015-01-26.
- daily_business_fees - Available daily by 5:00 PM Pacific Time since 2015-01-26.
- daily_business_free_trial_conversions - Available daily by 5:00 PM Pacific Time since 2015-01-26.
- daily_business_new_instances - Available daily by 5:00 PM Pacific Time since 2015-01-26.
- daily_business_new_product_subscribers - Available daily by 5:00 PM Pacific Time since 2015-01-26.
- daily_business_canceled_product_subscribers - Available daily by 5:00 PM Pacific Time since 2015-01-26.
- monthly_revenue_billing_and_revenue_data - Available monthly on the 4th day of the month by 5:00 PM Pacific Time since 2015-02.
- monthly_revenue_annual_subscriptions - Available monthly on the 4th day of the month by 5:00 PM Pacific Time since 2015-02.
- disbursed_amount_by_product - Available every 30 days by 5:00 PM Pacific Time since 2015-01-26.
- disbursed_amount_by_product_with_uncollected_funds -This data set is only available from 2012-04-19 until 2015-01-25. After 2015-01-25, this data set was split into three data sets: disbursed_amount_by_product, disbursed_amount_by_age_of_uncollected_funds, and disbursed_amount_by_age_of_disbursed_funds.
- disbursed_amount_by_instance_hours - Available every 30 days by 5:00 PM Pacific Time since 2012-09-04.
- disbursed_amount_by_customer_geo - Available every 30 days by 5:00 PM Pacific Time since 2012-04-19.
- disbursed_amount_by_age_of_uncollected_funds - Available every 30 days by 5:00 PM Pacific Time since 2015-01-26.
- disbursed_amount_by_age_of_disbursed_funds - Available every 30 days by 5:00 PM Pacific Time since 2015-01-26.
- customer_profile_by_industry - Available daily by 5:00 PM Pacific Time since 2015-10-01.
- customer_profile_by_revenue - Available daily by 5:00 PM Pacific Time since 2015-10-01.
- customer_profile_by_geography - Available daily by 5:00 PM Pacific Time since 2015-10-01.
- sales_compensation_billed_revenue - Available monthly on the 4th day of the month by 5:00 PM Pacific Time since 2016-12.
Possible values:
- customer_subscriber_hourly_monthly_subscriptions
- customer_subscriber_annual_subscriptions
- daily_business_usage_by_instance_type
- daily_business_fees
- daily_business_free_trial_conversions
- daily_business_new_instances
- daily_business_new_product_subscribers
- daily_business_canceled_product_subscribers
- monthly_revenue_billing_and_revenue_data
- monthly_revenue_annual_subscriptions
- disbursed_amount_by_product
- disbursed_amount_by_product_with_uncollected_funds
- disbursed_amount_by_instance_hours
- disbursed_amount_by_customer_geo
- disbursed_amount_by_age_of_uncollected_funds
- disbursed_amount_by_age_of_disbursed_funds
- customer_profile_by_industry
- customer_profile_by_revenue
- customer_profile_by_geography
- sales_compensation_billed_revenue
--data-set-publication-date (timestamp) The date a data set was published. For daily data sets, provide a date with day-level granularity for the desired day. For weekly data sets, provide a date with day-level granularity within the desired week (the day value will be ignored). For monthly data sets, provide a date with month-level granularity for the desired month (the day value will be ignored).
--role-name-arn (string) The Amazon Resource Name (ARN) of the Role with an attached permissions policy to interact with the provided AWS services.
--destination-s3-bucket-name (string) The name (friendly name, not ARN) of the destination S3 bucket.
--destination-s3-prefix (string) (Optional) The desired S3 prefix for the published data set, similar to a directory path in standard file systems. For example, if given the bucket name "mybucket" and the prefix "myprefix/mydatasets", the output file "outputfile" would be published to "s3://mybucket/myprefix/mydatasets/outputfile". If the prefix directory structure does not exist, it will be created. If no prefix is provided, the data set will be published to the S3 bucket root.
--sns-topic-arn (string) Amazon Resource Name (ARN) for the SNS Topic that will be notified when the data set has been published or if an error has occurred.
--customer-defined-values (map) (Optional) Key-value pairs which will be returned, unmodified, in the Amazon SNS notification message and the data set metadata file. These key-value pairs can be used to correlated responses with tracking information from other systems.
Shorthand Syntax:
KeyName1=string,KeyName2=string
JSON Syntax:
{"string": "string"
...}
--cli-input-json (string) Performs service operation based on the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, the CLI values will override the JSON-provided values.
--generate-cli-skeleton (string) Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input, prints a sample input JSON that can be used as an argument for --cli-input-json. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command.
dataSetRequestId -> (string)
A unique identifier representing a specific request to the generate-data-set operation. This identifier can be used to correlate a request with notifications from the SNS topic.


Received 19 June 2002; reveiced in revised form 30 July 2007
Is contradiction accidental, or is it the necessary companion of intelligence?
Thesis: Contradiction can be seen as a tool to increase intelligence in biological
systems.
A mathematically sound answer to this question could give new perspectives on artificial intelligence. Think for instance about modern loss functions as
A contradiction is an observed phenomenon in which the same
entity evolves in two different ways (in different times) from the
same initial state.
A contradiction is an observed phenomenon in which the same entity evolves in two different ways (in different times) from the same initial state.
$\left(\mbox{Observer}, \mbox{Event}\right)$
Any sufficiently intelligent entity must be contradictory
Observation: intelligence needs a context, and an observer in order to be studied.
Thus, a quantum-mechanic flavoured replacement has to be put in place:
$$\mbox{particle}\mapsto\mbox{observed particle}$$
Entity, environment, intelligence, contradiction can be defined in the fairly general contex of cellular automata Furthermore, cellular automata have the property of universal computation.
Video realised by courtesy of Iratxe Ansa and Igor Bacovich, and the dancers of the wonderful Metamorphosis project Code available here
Def [Existence]: The existence of an entity depends exclusively on the observer.
Caveat: Here the subjectivity of existence is not to be discussed. We should rather
decide to take it into account or not.
Def [Observer]: Let $\mathcal{P}_{ent}$ and $\mathcal{P}_{ENV}$ be non-empty, finite sets
of perceptible states for an entity and an environment, respectively. Assume that it exists a
privileged element $0\in\mathcal{P}_{ent}$. An observer is any function
$$\square = \left(ps_{ent}, ps_{ENV}\right):\Sigma\rightarrow \mathcal{P}_{ent}\times\mathcal{P}_{ENV}$$
Observations
Def [Existence]: The existence of an entity depends exclusively on the observer.
Caveat: Here the subjectivity of existence is not to be discussed. We should rather
decide to take it into account or not.
Def [Observer]: Let $\mathcal{P}_{ent}$ and $\mathcal{P}_{ENV}$ be non-empty, finite sets
of perceptible states for an entity and an environment, respectively. Assume that it exists a
privileged element $0\in\mathcal{P}_{ent}$. An observer is any function
$$\square = \left(ps_{ent}, ps_{ENV}\right):\Sigma\rightarrow \mathcal{P}_{ent}\times\mathcal{P}_{ENV}$$
Def [Entity & lifetime]: Each maximal sequence of consecutive perceived states in
$\mathcal{P}_{ent}\setminus\left\{0\right\}$, say $\left\{ ps_{ent}\left(s_{t+i}\right) \right\}_{i=0}^q$ is
an entity. The set $\left\{t, \dots, t+q\right\}$ is the lifetime of the entity.
Def [Environment]: Let $\varepsilon = \left(ps_{ent}\left(s_{t} \right), \dots, ps_{ent}\left(s_{t+q} \right)\right)$ be an entity. We call environment $\left(ps_{ENV}\left(s_{t} \right), \dots, ps_{ENV}\left(s_{t+q} \right)\right)$
Def [Intelligence]: Given an entity $\varepsilon = \left(ps_{ent}\left(s_{t} \right), \dots, ps_{ent}\left(s_{t+q} \right)\right)$ with respect to the observer $\square$, we define the intelligence $L\left(\varepsilon, \square\right)$ as the entity's lifetime $q$.
Reminder: An entity is contradictory if faced with the same circumstances
(as perceived by the observer) in two occasions in time, it exhibits different behaviours.
Def [Contradictory entity]: Let $\varepsilon = \left(ps_{ent}\left(s_{t} \right), \dots, ps_{ent}\left(s_{t+q} \right)\right)$
be an entity for $\square$. If there exists $a,b\in\mathbb{N}$, $a, b\leq q$, such that
$$
ps_{ENV}\left(s_{t+a} \right) = ps_{ENV}\left(s_{t+b} \right) \mbox{ and } ps_{ent}\left(s_{t+a} \right) \neq ps_{ent}\left(s_{t+b} \right)
$$
then $\varepsilon$ is contradictory.
Def [Environment]: Let $\varepsilon = \left(ps_{ent}\left(s_{t} \right), \dots, ps_{ent}\left(s_{t+q} \right)\right)$ be an entity. We call environment $\left(ps_{ENV}\left(s_{t} \right), \dots, ps_{ENV}\left(s_{t+q} \right)\right)$
Def [Intelligence]: Given an entity $\varepsilon = \left(ps_{ent}\left(s_{t} \right), \dots, ps_{ent}\left(s_{t+q} \right)\right)$ with respect to the observer $\square$, we define the intelligence $L\left(\varepsilon, \square\right)$ as the entity's lifetime $q$.
Def [Contradictory entity]: Let $\varepsilon = \left(ps_{ent}\left(s_{t} \right), \dots, ps_{ent}\left(s_{t+q} \right)\right)$ be an entity for $\square$. If there exists $a,b\in\mathbb{N}$, $a, b\leq q$, such that $$ ps_{ENV}\left(s_{t+a} \right) = ps_{ENV}\left(s_{t+b} \right) \mbox{ and } ps_{ent}\left(s_{t+a} \right) \neq ps_{ent}\left(s_{t+b} \right) $$ then $\varepsilon$ is contradictory.
How does this fit in the classical formulation of contradictory theory?
Def [Environment]: Let $\varepsilon = \left(ps_{ent}\left(s_{t} \right), \dots, ps_{ent}\left(s_{t+q} \right)\right)$ be an entity. We call environment $\left(ps_{ENV}\left(s_{t} \right), \dots, ps_{ENV}\left(s_{t+q} \right)\right)$
Def [Intelligence]: Given an entity $\varepsilon = \left(ps_{ent}\left(s_{t} \right), \dots, ps_{ent}\left(s_{t+q} \right)\right)$ with respect to the observer $\square$, we define the intelligence $L\left(\varepsilon, \square\right)$ as the entity's lifetime $q$.
Def [Contradictory entity]: Let $\varepsilon = \left(ps_{ent}\left(s_{t} \right), \dots, ps_{ent}\left(s_{t+q} \right)\right)$
be an entity for $\square$. If there exists $a,b\in\mathbb{N}$, $a, b\leq q$, such that
$$
ps_{ENV}\left(s_{t+a} \right) = ps_{ENV}\left(s_{t+b} \right) \mbox{ and } ps_{ent}\left(s_{t+a} \right) \neq ps_{ent}\left(s_{t+b} \right)
$$
then $\varepsilon$ is contradictory.
Def [Deterministic environment]: An environment $\left(ps_{ENV}\left(s_{t} \right), \dots, ps_{ENV}\left(s_{t+q} \right)\right)$ is
deterministic if for every $a,b\leq q$ such that
$$
ps_{ENV}\left(s_{t+a} \right) = ps_{ENV}\left(s_{t+b} \right) \mbox{ and } ps_{ent}\left(s_{t+a} \right) = ps_{ent}\left(s_{t+b} \right),
$$
we have $ps_{ENV}\left(s_{t+a+1} \right) = ps_{ENV}\left(s_{t+b+1} \right)$.
Let $\varepsilon$ be an entity with finite lifetime and deterministic environment, with respect to the observer $\square$, for the cellular automaton $\mathcal{C}$. Let $k = |\mathcal{P}_{ent}\times\mathcal{P}_{ENV}|$. If the intelligence (lifetime) of $\varepsilon\geq k$, then $\varepsilon$ must be contradictory.
$P_i = \left(\mbox{up}, \mbox{down}, \dots\right)$. $P_i$ wins the $k$th round if $c_{k+1} > c_{k}$ and $P_{i,k} = \mbox{up}$.
$r_{cc}= 2,\, r_{nn} = 0,$ $r_{nc} = \left\{\begin{matrix}n\mapsto 1,\\ c\mapsto -1\end{matrix}\right.$
$\$ = 10000$, $p\in \{900, 1000, 1100\}$, $t = 7 \mbox{ days}$.


12 bottles of wine arrive at space station for ageing experiment
A dozen bottles of fine French wine have arrived at the International Space Station – but they are not for the astronauts.
The red wine will age for a year up there before returning to Earth, and researchers will study how weightlessness and space radiation affect the ageing process.
The goal is to develop new flavors and properties for the food industry.
The bottles were flown up aboard a Northrop Grumman capsule that launched from Virginia on Saturday and arrived at the ISS on Monday. Each bottle was packed in a metal canister to prevent breakage.
Universities in Bordeaux, France, and Bavaria, Germany, are taking part in the experiment by Space Cargo Unlimited, a Luxembourg start-up.
Winemaking uses yeast and bacteria, and involves chemical processes, making wine ideal for space study, said University of Erlangen-Nuremberg’s Michael Lebert, the experiment’s scientific director.
This is the first of six space missions planned by the company over the next three years touching on the future of agriculture.
“This is a once-in-a-lifetime adventure,” said Nicolas Gaume, chief executive and co-founder of Space Cargo Unlimited.
Nasa is opening the space station to more business opportunities like this and eventually even private astronaut missions.
The Cygnus capsule that pulled up to the space station on Monday contained multiple commercial ventures.
Also on board were an oven for baking chocolate chip cookies and samples of carbon fibre used by Lamborghini in its sports cars.
Budweiser has already sent barley seeds to the station, with an eye to becoming the beverage of choice on Mars.
In 2015, a Japanese company known for its whiskey and other alcoholic drinks sent up samples. Scotch also made a visit to space in another experiment.
As for high-flying wine cellars, this is not the first. A French astronaut took a bottle of wine aboard the Discovery shuttle in 1985. The bottle remained corked in orbit.
The space station’s current crew includes three Americans, two Russians and an Italian, who might have preferred a good Chianti on board.


| # | First Name | Last Name | User Name | Position | Contact Info |
|---|---|---|---|---|---|
| 1 | John | Doe | @jhndoe | CEO |<EMAIL_ADDRESS>+1 234 567 89 |
| 2 | Jane | Dowe | @jandow | Designer |<EMAIL_ADDRESS>+1 234 567 89 |
| 3 | James | Doe | @jmsdoe | Project Manager |<EMAIL_ADDRESS>+1 234 567 89 |
| 4 | Jasmine | Larger | @jsmnlrgr | Web Developer |<EMAIL_ADDRESS>|
| # | First Name | Last Name | User Name | Position | Contact Info |
|---|---|---|---|---|---|
| 1 | John | Doe | @jhndoe | CEO |<EMAIL_ADDRESS>+1 234 567 89 |
| 2 | Jane | Dowe | @jandow | Designer |<EMAIL_ADDRESS>+1 234 567 89 |
| 3 | James | Doe | @jmsdoe | Project Manager |<EMAIL_ADDRESS>+1 234 567 89 |
| 4 | Jasmine | Larger | @jsmnlrgr | Web Developer |<EMAIL_ADDRESS>|
| Product | Price | Quantity | Total | ||
|---|---|---|---|---|---|
| Product 1 | $49.99 | $49.99 | × | ||
| Product 2 | $69.99 | $139.98 | × | ||
| Product 3 | $29.99 | $29.99 | × | ||
| Product 4 | $19.99 | $59.97 | × |


Working with communities and technology to support refugees into higher education.
Support usIf you’re looking for support join our Facebook group or email<EMAIL_ADDRESS>Why we exist
Today, only 1 percent of eligible refugees have access to higher education, compared to 36 percent of global youth. In the Middle East, this means that 91,000 young people are missing out on higher education.
This limits their potential to become self reliant, integrate and contribute in their host communities and to help reconstruct their country to make it when they return home.
Losing your home shouldn’t mean losing your ability to learn and make the world a better place.
The student journey
We are looking at the end to end learning journey for refugees to develop programmes that help them through each step.
-
Step 1 Awareness
Peer digital content
Videos, podcasts and blogs about accessing higher education, co-produced by refugees and delivered through social media.
Guidance workshops
Blended workshops where participants learn about the full range of options for post-secondary education and what options suits their background and ambitions.
-
Step 2 Preparation
Guidance workshops
Blended workshops where participants learn how to evaluate options, how to research and apply for scholarships, and create their plan for accessing higher education.
Peer mentorship
Participants connect with a peers on their preferred higher education option to hear first hand experiences of the course and advice on the application and enrollement process.
English skills
Classes employing participatory ‘dialogic’ pedagogies combined with access to mobile apps provide contextualised and accessible language learning opportunities.
Academic skills
Workshops in time management, presentation, independent study and extended writing skills.
-
Step 3 Enrolment
Finance
Prospective students receive training in creating crowdfunding profiles to raise money for tuition fees and/or living costs. Mosaik plans to partner with existing platforms and scholarship providers to maximise impact.
Peer digital content
Videos, podcasts and blogs addressing social and psychological challenges faced by refugees adapting to life as students, all co-produced by refugees already studying and delivered through social media.
How we work
Community
We use participatory methods to design and deliver programmes with refugee communities.
Technology
Using a combination of digital content, connected learning systems and social media to ensure these programmes are accessible and scalable.
The future
Building partnerships with local civil society organisations to lead delivery of the programmes working towards a commons of open programme models and resources that support refugees into higher education.
Find out more about how we work
Support refugees reassemble their educational lives
Make a donation
Join our community of supporters directly helping refugees to reassemble their educational lives.
Become a funder
Find out about about becoming a funding partner of our work.
Email us at<EMAIL_ADDRESS>Partner with us
We partner with local civil society organisations in the Middle East and the UK, as well as world-leading education institutions on content, research and technology.
Email us to find out more at hello@mosaik.ngo


C++ Operator Precedence
The operators at the top of this list are evaluated first.
| Precedence |
Operator |
Description |
Example |
Associativity |
| 1 |
::
|
Scoping operator
|
Class::age = 2;
| none |
| 2 |
()
[]
->
.
++
-- |
Grouping operator
Array access
Member access from a pointer
Member access from an object
Post-increment
Post-decrement |
(a + b) / 4;
array[4] = 2;
ptr->age = 34;
obj.age = 34;
for( i = 0; i < 10; i++ ) ...
for( i = 10; i > 0; i-- ) ... |
left to right |
| 3 |
!
~
++
--
-
+
*
&
(type)
sizeof |
Logical negation
Bitwise complement
Pre-increment
Pre-decrement
Unary minus
Unary plus
Dereference
Address of
Cast to a given type
Return size in bytes |
if( !done ) ...
flags = ~flags;
for( i = 0; i < 10; ++i ) ...
for( i = 10; i > 0; --i ) ...
int i = -1;
int i = +1;
data = *ptr;
address = &obj;
int i = (int) floatNum;
int size = sizeof(floatNum); |
right to left |
| 4 |
->*
.* |
Member pointer selector
Member object selector |
ptr->*var = 24;
obj.*var = 24; |
left to right |
| 5 |
*
/
% |
Multiplication
Division
Modulus |
int i = 2 * 4;
float f = 10 / 3;
int rem = 4 % 3; |
left to right |
| 6 |
+
- |
Addition
Subtraction |
int i = 2 + 3;
int i = 5 - 1; |
left to right |
| 7 |
<<
>> |
Bitwise shift left
Bitwise shift right |
int flags = 33 << 1;
int flags = 33 >> 1; |
left to right |
| 8 |
<
<=
>
>= |
Comparison less-than
Comparison less-than-or-equal-to
Comparison greater-than
Comparison geater-than-or-equal-to |
if( i < 42 ) ...
if( i <= 42 ) ...
if( i > 42 ) ...
if( i >= 42 ) ... |
left to right |
| 9 |
==
!= |
Comparison equal-to
Comparison not-equal-to |
if( i == 42 ) ...
if( i != 42 ) ... |
left to right |
| 10 |
& |
Bitwise AND |
flags = flags & 42; |
left to right |
| 11 |
^ |
Bitwise exclusive OR |
flags = flags ^ 42; |
left to right |
| 12 |
| |
Bitwise inclusive (normal) OR |
flags = flags | 42; |
left to right |
| 13 |
&& |
Logical AND |
if( conditionA && conditionB ) ... |
left to right |
| 14 |
|| |
Logical OR |
if( conditionA || conditionB ) ... |
left to right |
| 15 |
? : |
Ternary conditional (if-then-else) |
int i = (a > b) ? a : b; |
right to left |
| 16 |
=
+=
-=
*=
/=
%=
&=
^=
|=
<<=
>>= |
Assignment operator
Increment and assign
Decrement and assign
Multiply and assign
Divide and assign
Modulo and assign
Bitwise AND and assign
Bitwise exclusive OR and assign
Bitwise inclusive (normal) OR and assign
Bitwise shift left and assign
Bitwise shift right and assign |
int a = b;
a += 3;
b -= 4;
a *= 5;
a /= 2;
a %= 3;
flags &= new_flags;
flags ^= new_flags;
flags |= new_flags;
flags <<= 2;
flags >>= 2; |
right to left |
| 17 |
, |
Sequential evaluation operator |
for( i = 0, j = 0; i < 10; i++, j++
) ... |
left to right |
One important aspect of C++ that is related to operator
precedence is the order of evaluation and the
order of side effects in expressions. In some
circumstances, the order in which things happen is not defined. For
example, consider the following code:
float x = 1;
x = x / ++x;
The value of x is not guaranteed to be consistent
across different compilers, because it is not clear whether the
computer should evaluate the left or the right side of the division
first. Depending on which side is evaluated first, x
could take a different value.
Furthermore, while ++x
evaluates to
x+1
, the side effect of actually storing that new value
in x could happen at different times, resulting in
different values for x.
The bottom line is that expressions like the one above are
horribly ambiguous and should be avoided at all costs. When in
doubt, break a single ambiguous expression into multiple
expressions to ensure that the order of evaluation is correct.


jQuery UI is a curated set of user interface interactions, effects, widgets, and themes built on top of the jQuery JavaScript Library. Whether you're building highly interactive web applications or you just need to add a date picker to a form control, jQuery UI is the perfect choice.
There's a lot of work that goes into making jQuery UI the most successful UI library for the Web. Between API design, visual design, implementation, ticket triage, bug fixing, developer relations, infrastructure, and everything else, most of the work is done by volunteers. We'd like to recognize the most prominent contributors below, for a full list of all contributors, see the authors list.
Scott González is a web developer living in York, PA. As project lead, he puts a lot of effort into keeping jQuery UI efficient, consistent, and accessible, as well as adjusting the roadmap to meet the ever-changing needs of the community. Scott also works with browser vendors and standards bodies to make the Web a better place for everyone, not just jQuery users.
Jörn is a freelance web developer, consultant and trainer, residing in Cologne, Germany. Jörn evolved jQuery’s testsuite into QUnit, a JavaScript unit testing framework, and maintains it. He created and maintains a number of popular plugins. As a jQuery UI development lead, he focuses on the development of new plugins, widgets and utilities.
Kris is a Senior Software Engineer on the AeroGear team at Red Hat. He has contributed a number of bug fixes to the jQuery UI project and most recently did a lot of work to finalize the Menu widget.
Felix is a freelance web developer specializing in TYPO3 CMS and Symfony2 development. He is currently living in Dresden, Germany. Felix has built the Selectmenu widget and is currently working on the Datepicker widget.
Corey is a freelance web developer, speaker and system administrator, living in Rockford, IL. He is an active contributor to most of the jQuery teams and is active in the jQuery community on IRC, and Stack Overflow. Corey has restructured the effects modules in both UI and Core, maintains the jQuery Color Animations plugin and leads the jQuery infrastructure team.
Mike is a Senior Software Engineer at Behance, from Plainview, New York. Mike is involved in bug fixing, testing, and code quality efforts across the jquery suite of projects. He focuses his efforts around the CSS/JS interactions and interacts with standards bodies and browser vendors to move the web forward.
Xavier has a B.S. in Computer Science from University of Sao Paulo, Brazil. He was a Software Engineer at IBM, where he lead the brazilian team of Q&A and Performance at Linux Technology Center. Now, he devotes his energies to his startup.
TJ VanToll is a web developer, speaker, and writer living in Lansing, MI and working as a developer advocate for Telerik. He works on all facets of jQuery UI including triage, documentation, bug fixes, and is currently working on the rewrite of the datepicker widget.


closed
by None at 2019-12-02 13:40:46.referenced
by None at 2019-12-02 13:40:46.merged
by None at 2019-12-02 13:40:46.closed
by None at 2019-12-02 13:40:47.head_ref_deleted
by None at 2019-12-02 13:40:51.assigned
by Shaun Ellis at 2019-12-02 16:06:54.This also fixes #345
closed
by None at 2019-12-02 16:12:52.closed
by None at 2019-12-02 16:12:53.mentioned
by Shaun Ellis at 2019-12-02 13:51:58.subscribed
by Shaun Ellis at 2019-12-02 13:51:59.mentioned
by None at 2019-12-02 15:35:27.subscribed
by None at 2019-12-02 15:35:27.referenced
by None at 2019-12-02 16:12:52.merged
by None at 2019-12-02 16:12:52.closed
by None at 2019-12-02 16:12:52.head_ref_deleted
by None at 2019-12-02 16:12:55.assigned
by Shaun Ellis at 2019-12-02 16:10:05.unassigned
by Shaun Ellis at 2019-12-02 16:36:19.assigned
by Shaun Ellis at 2019-12-02 16:37:24.assigned
by Axa Mei Liauw at 2019-12-02 17:13:04.assigned
by None at 2019-12-02 17:59:52.head_ref_force_pushed
by None at 2019-12-02 18:11:01.referenced
by Shaun Ellis at 2019-12-02 19:28:58.merged
by Shaun Ellis at 2019-12-02 19:28:58.closed
by Shaun Ellis at 2019-12-02 19:28:58.head_ref_deleted
by Shaun Ellis at 2019-12-02 19:29:00.referenced
by None at 2019-12-02 14:11:04.merged
by None at 2019-12-02 14:11:04.closed
by None at 2019-12-02 14:11:04.head_ref_deleted
by None at 2019-12-02 14:11:13.renamed
by Trey Pendragon at 2019-12-02 18:59:15.head_ref_force_pushed
by Trey Pendragon at 2019-12-02 16:19:02.referenced
by Trey Pendragon at 2019-12-02 18:38:56.merged
by Trey Pendragon at 2019-12-02 18:38:56.closed
by Trey Pendragon at 2019-12-02 18:38:56.head_ref_deleted
by Trey Pendragon at 2019-12-02 18:38:59.labeled
by Trey Pendragon at 2019-12-02 18:49:22.assigned
by Roel Munoz at 2019-12-02 18:49:39.closed
by James R. Griffin III at 2019-12-02 18:50:27.labeled
by Trey Pendragon at 2019-12-02 18:51:25.labeled
by Trey Pendragon at 2019-12-02 18:54:15.assigned
by None at 2019-12-02 18:56:28.mentioned
by Kelly Bolding at 2019-12-02 18:56:45.subscribed
by Kelly Bolding at 2019-12-02 18:56:45.unassigned
by James R. Griffin III at 2019-12-02 18:57:03.unassigned
by James R. Griffin III at 2019-12-02 18:57:23.unlabeled
by James R. Griffin III at 2019-12-02 18:57:49.unassigned
by James R. Griffin III at 2019-12-02 18:57:53.unassigned
by James R. Griffin III at 2019-12-02 18:58:23.unassigned
by James R. Griffin III at 2019-12-02 18:58:50.unassigned
by James R. Griffin III at 2019-12-02 19:00:15.unassigned
by James R. Griffin III at 2019-12-02 19:00:54.unassigned
by James R. Griffin III at 2019-12-02 19:01:57.unassigned
by James R. Griffin III at 2019-12-02 19:02:23.Waiting to hear back from OIT about princeton gsuite access to search console tools
labeled
by Nikitas Tampakis at 2019-12-02 18:36:02.renamed
by Nikitas Tampakis at 2019-12-02 18:38:27.referenced
by Nikitas Tampakis at 2019-12-02 18:39:35.merged
by Nikitas Tampakis at 2019-12-02 18:39:35.closed
by Nikitas Tampakis at 2019-12-02 18:39:36.head_ref_deleted
by None at 2019-12-02 18:39:45.Need to redo, production is currently Solr 7.7.2, this was done before the Blacklight 7 upgrade
closed
by Nikitas Tampakis at 2019-12-02 18:41:04.Check if this still works with Blacklight 7
Address merge conflicts, attach screenshots, and discuss again with DSSG
assigned
by James R. Griffin III at 2019-12-02 18:47:42.renamed
by James R. Griffin III at 2019-12-02 18:47:53.https://github.com/pulibrary/orangelight/blob/master/app/services/linked_document_resolver.rb#L84 should handle cases where the response is not a 404 and not valid JSON.
renamed
by James R. Griffin III at 2019-12-02 14:38:27.labeled
by Nikitas Tampakis at 2019-12-02 18:32:39.labeled
by Nikitas Tampakis at 2019-12-02 18:49:36.closed
by James R. Griffin III at 2019-12-02 18:33:34.milestoned
by James R. Griffin III at 2019-12-02 18:36:16.milestoned
by James R. Griffin III at 2019-12-02 18:36:35.milestoned
by James R. Griffin III at 2019-12-02 18:36:35.milestoned
by James R. Griffin III at 2019-12-02 18:36:35.milestoned
by James R. Griffin III at 2019-12-02 18:36:35.milestoned
by James R. Griffin III at 2019-12-02 18:36:36.milestoned
by James R. Griffin III at 2019-12-02 18:36:36.milestoned
by James R. Griffin III at 2019-12-02 18:36:36.milestoned
by James R. Griffin III at 2019-12-02 18:36:36.milestoned
by James R. Griffin III at 2019-12-02 18:36:36.labeled
by James R. Griffin III at 2019-12-02 18:34:19.milestoned
by James R. Griffin III at 2019-12-02 18:36:37.milestoned
by James R. Griffin III at 2019-12-02 18:36:37.This may be resolved upstream within a Blacklight commit.
labeled
by James R. Griffin III at 2019-12-02 18:34:06.milestoned
by James R. Griffin III at 2019-12-02 18:36:36.labeled
by James R. Griffin III at 2019-12-02 19:09:15.milestoned
by James R. Griffin III at 2019-12-02 19:09:15.closed
by Francis Kayiwa at 2019-12-02 12:38:19.closed
by Francis Kayiwa at 2019-12-02 13:55:09.closed
by Francis Kayiwa at 2019-12-02 13:55:27.@kayiwa I wonder if these changes should be an include like the proxy pass rules. Seems like we are changing all the files together.
review_requested
by Francis Kayiwa at 2019-12-02 14:42:40.mentioned
by Francis Kayiwa at 2019-12-02 14:47:34.subscribed
by Francis Kayiwa at 2019-12-02 14:47:34.referenced
by None at 2019-12-02 15:27:15.merged
by None at 2019-12-02 15:27:16.closed
by None at 2019-12-02 15:27:16.head_ref_deleted
by None at 2019-12-02 15:27:20.review_requested
by Francis Kayiwa at 2019-12-02 15:47:23.referenced
by None at 2019-12-02 15:49:21.merged
by None at 2019-12-02 15:49:21.closed
by None at 2019-12-02 15:49:21.head_ref_deleted
by None at 2019-12-02 15:50:01.closed
by Francis Kayiwa at 2019-12-02 15:53:07.closed
by None at 2019-12-02 17:19:22.review_requested
by Francis Kayiwa at 2019-12-02 16:10:36.referenced
by None at 2019-12-02 17:19:22.merged
by None at 2019-12-02 17:19:22.closed
by None at 2019-12-02 17:19:22.head_ref_deleted
by None at 2019-12-02 17:19:25.head_ref_force_pushed
by Francis Kayiwa at 2019-12-02 19:11:08.review_requested
by Francis Kayiwa at 2019-12-02 12:37:26.merged
by None at 2019-12-02 15:27:17.closed
by None at 2019-12-02 15:27:17.head_ref_deleted
by Francis Kayiwa at 2019-12-03 00:47:53.referenced
by Eliot Jordan at 2019-12-02 18:37:40.merged
by Eliot Jordan at 2019-12-02 18:37:40.closed
by Eliot Jordan at 2019-12-02 18:37:40.head_ref_deleted
by Eliot Jordan at 2019-12-02 18:37:45.referenced
by Eliot Jordan at 2019-12-02 18:38:07.merged
by Eliot Jordan at 2019-12-02 18:38:07.closed
by Eliot Jordan at 2019-12-02 18:38:07.head_ref_deleted
by Eliot Jordan at 2019-12-02 18:38:11.referenced
by Eliot Jordan at 2019-12-02 18:39:44.merged
by Eliot Jordan at 2019-12-02 18:39:44.closed
by Eliot Jordan at 2019-12-02 18:39:44.head_ref_deleted
by Eliot Jordan at 2019-12-02 18:39:47.Coverage remained the same at 99.285% when pulling 5ee5431f89c45c181634c5857f35d91dd4180975 on dependabot/bundler/simple_form-5.0.0 into c9c0e0b0d6e6de10ac8496c1f064edb6f96dd2a5 on master.
head_ref_force_pushed
by Eliot Jordan at 2019-12-02 18:43:18.deployed
by Trey Pendragon at 2019-12-02 18:44:13.


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A list of fruits:
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Mark Zuckerberg
Facebook
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