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There are several ways of filtering the cube data in code:
-
Making some hierarchy members invisible;
-
Context filtering of hierarchy members by captions;
-
Context filtering of Date-hierarchy members (members’ captions must have DateTime format);
-
Context filtering of measure values.


{% extends 'account/main.html' %} {% block anonymous_content %}
Don't have an Account? Visit Nearest Bank Branch


Motivation
The small story, that I am going to share with you today, is on Statistical Testing of Hypothesis. It is a method of statistical inference with a very powerful theoretical support in the literature and a wide domain of application, especially in today’s world of analytics and machine learning.
It was popularized early in the 20th century, early forms were used in the 1700s. The first use is credited to John Arbuthnot (1710) followed by Pierre-Simon Laplace (1770s), in analyzing the human sex ratio at birth.
The word Hypothesis means belief about something. People from various domains like theoretical research, medical industry, consumer packaged goods (CPG) industry etc., mainly deal with data to boost-up their research works, or to maximize their revenue, whatever may be the case, they use this important tool to justify their believes. Now, here the question comes…what are these believes about? The answer is parameters. For example: suppose you are going to calculate the average height of people in a district, which you can by measuring the height of each people and taking the average or by taking a sample of people from that district, calculating the sample average and making an inference about the average height.
If you go by the first approach, you need patience, man power, money and of course a lot of time, whereas in the second approach you can get a ruff estimate of the actual average height which will help you to draw an inference.
Now, clearly the actual average height is unknown as long as you are not dealing with all the people in that district. This unknown average height is called a parameter, often expressed as \mu and the sample average is known as statistic, expressed as \bar{X}.
By using a statistic, we try to get some plausible guesses about our parameter of interest. Since, we are guessing or having some believes about the parameter, these believes must be justified logically by the help of some mathematical tools as well as data. Hypothesis Testing comes here to rescue us.
An Intuitive Explanation
Before going into more rigorous definitions, let’s understand by taking another example.  They are very tall, aren’t they? 🤔
Well, this is your artificial survey data, let’s not bother about actual height values. You can calculate the average which is 6.8 ft. But truly speaking, you won’t be satisfied by having a belief that actual average height of the people in that district is 6.8 ft. It can be for that specific sample you have collected. If you take another sample of size 10, you may get a different estimate…right? Yes, so what you have a achieved, is the random guess about the parameter (unknown height) based on a random sample.
Now, let’s define the various hypotheses that will be in your mind soon:
The Null Hypothesis, H_0: \mu = 6.4 = \mu_0
The Alternate Hypothesis, H_1: \mu > 6.4
Note that, \mu is used here to parametrize the scenario. The type of alternate hypothesis we are addressing here, is also called greater than type hypothesis and hence the test is called greater than type test.
Null hypothesis is the prior belief you have about the parameter, possibly by conducting other surveys in the adjacent districts i.e. by observing people from other districts, you believe average height may be 6 ft.. The alternate hypothesis is the belief you have gathered from the sample or the data. Since we are guessing based on the data in our hand, it’s very likely to guess wrong or in another words, we may encounter errors.
The Two Types of Error
There are two types of error that we are generally interested in:
Rejecting the null hypothesis when it is actually true, also known as Type-I error.
Failing to reject the null hypothesis when it is actually false, also known as Type-II error.
These two situations are generally expressed in terms of probabilities.
Let’s see how they have been defined in the literature,
Probability of type-I error: P(\text{Rejecting} \quad H_0 \quad \text{when it is true}) = \alpha
Probability of type-II error: P(\text{Failing to reject} \quad H_0 \quad \text{when it is false}) = \beta
Some Real Life Examples
I will try to relate the definitions with couple of examples:
Suppose the Honorable judge is about to give his verdict to the accused. Now let’s assume that our null hypothesis or prior belief about that person is, the person is not guilty. If the judge’s verdict is against the accused even if the person has really not done anything wrong, then it is the Type-I error, whereas if the verdict is in favor of the accused when in reality the person has done something wrong, then it is the Type-II error. If you’d think for a moment, you would understand the fact that Type-I error is more severe in nature.
This one is going to be of great impotance for the society. We know that almost every country has their own military radar system to detect any missilies aimed at them. Assume that, the null hypothesis is the enemy is in war mode, they will attack. Now, could you imagine the situation, if the military radar system would have failed to detect the missiles that had been aimed at your country and they were few miles away from their target…the lives of innocent citizens would have been devastated.
Having understood the importance of Type-I error in decision making, the usual tendency of the experimenter is to keep the chance of making Type-I error fixed at a very low level, which, by years of research and experimentations, has been chosen as 1% or 5%, depending on the situation.
The Testing Criteria or The Decision Rule
Having identified the possible scenarioes which may take us to the wrong direction, it’s time to decide which one to go for, based on the current data in hand. The tool which will enable us to make a decision, is going to be the statistic along with a decision rule.
Having the data ready, we can find the realization of the statistic, i.e. \bar{x} and to support our null hypothesis, we are supposed to compare this \bar{x} and \mu_0. Here we perform an additive comparison i.e. we measure the difference between these two values. If the difference is too high, then it depicts the fact that our data is not supporting the null hypothesis.
Wait…, the phrase too high is subjective, isn’t it? That means the threshold to judge whether the difference is too high or not, depends on the experimenter.
To avoid this problem, Statisticians came up with a universal procedure to compute the threshold (sometimes called cut-off value). It is probabilistic in nature which utilizes the the sampling distribution of the statistic along with \alpha.
Before going to understand the computation process, let’s modify the statistic. Instead of \bar{X}, we are going to take \frac{\bar{X} - \mu_0}{\sqrt{\frac{\sigma^2}{n}}}. From the theory of sampling distribution, if \bar{X} \sim N(\mu_0, \sigma^2), then Z = \frac{\bar{X} - \mu_0}{\sqrt{\frac{\sigma^2}{n}}} \sim N(0, 1) i.e. the transformation makes the distribution simplified and easy to deal with.
To compute a universal threshold or cut-off point (\tau_{\alpha}), \alpha is first kept fixed at a certain level, usually 0.01 or 0.05, then a quantile value is computed such that beyond the quantile area under the curve is equal to \alpha. The figure, given below, will make the idea clear.
As we can see in the above figure, the universal cut-off is 2.32. Now we are in a situation to make a decision. If the realization of \frac{\bar{X} - \mu_0}{\sqrt{\frac{\sigma^2}{n}}} is more than \tau_{\alpha}=2.32, then we can say that the data is not supporting H_0, hence we should reject it, otherwise we can accept H_0.
For the survey data example, sample size is n=10, the sample average is \bar{x}=6.8 ft. and assuming \sigma to be known as 0.45 ft., the realization of the statistic, also known as z^{\star}, is \frac{\sqrt{10} \times (6.8 - 6.4)}{0.45}=2.8 and it is certainly larger than \tau_{\alpha}. Hence, the average height of people of that district cannot be taken as 6 ft., based on the sample in our hand.
The Probabilistic Decision Rule
The decision we made in the last section, was based on a threshold or a cut-off point. It could have been represented in an alternative way, by using a two probability scores, one of which is known to us i.e. \alpha = 0.01.
The second one is computed as the area under the density curve beyond the observed value of the statistic z^{\star} i.e. p = P\left[Z > z^{\star}\right], considering null hypothesis to be true, where Z \sim N(0, 1). This p is known as p-value of the test.
In the above figure, blue region is the p-value and the red region is the \alpha. Note that, the realization of the statistic i.e. z^{\star} is larger than the cut-off, that is why the p-value is smaller than \alpha.
Therefore, in an alternative way, we can say that if p-value of the test is smaller than probability of Type-I error, then we should reject the null hypothesis, otherwise it should be accepted. Almost all the modern analytics tools provide p-value for any kind of hypothesis test.
Assumption of the Test and Conclusion
The test I have discussed here, is based on a single assumption: pupulation variance (\sigma^2) is fixed and known beforehand. It could be unknow as well, in that case, first we had to estimate it from the data. It would also change the distribution of the test statistic.
Regarding the final conclusion: well, we have seen that the null hypothesis is not supported by the data in our hand, so it should not be accepted.
Future Scope
In this article, testing procedure for a specific setup has been covered. There are lot more to look into, specifically the scenarios when population variability measure \sigma is not known, comparing variabilities from two different populations etc. Those things will be covered in near future.
Note that alternative hypothesis can also have three different variants: less than type and unequal type, we have only covered one.


Please note that all builds are US Englush, and 64 bit currently!
Windows 10.5.2 Github Github (Release)
Mac 10.5.2 Github Github (Release)
Past builds avalible on GitHub releases page https://github.com/RealRedcodi/TOR-DL/releases


FuzzyNumbers.Ext.2: Apply Two Fuzzy Numbers on a Monotone Function
One can easily draw the membership function of f(x,y) by package 'FuzzyNumbers.Ext.2' in which f(.,.) is supposed monotone and x and y are two fuzzy numbers. This work is possible using function f2apply() which is an extension of function fapply() from Package 'FuzzyNumbers' for two-variable monotone functions. Moreover, this package has the ability of computing the core, support and alpha-cuts of the fuzzy-valued final result.
| Version: |
3.2 |
| Imports: |
FuzzyNumbers |
| Published: |
2017-09-05 |
| Author: |
Abbas Parchami (Department of Statistics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran) |
| Maintainer: |
Abbas Parchami <parchami at uk.ac.ir> |
| License: |
LGPL (≥ 3) |
| NeedsCompilation: |
no |
| CRAN checks: |
FuzzyNumbers.Ext.2 results |
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=FuzzyNumbers.Ext.2
to link to this page.


--- layout: post tags: [Unit Testing] date: 2012-06-27 15:11:08 UTC title: "Facade Test" description: "This post proposes the term 'Facade Test' for an automated test which is more than a unit test, but not quite an integration test." image: "/content/binary/Windows-Live-Writer/Facade-Test_ED3A/Facade%20Test_thumb_1.png" image_alt: "Facade Test" --- {% include JB/setup %}
This post proposes the term Facade Test for an automated test which is more than a unit test, but not quite an integration test.
There are several definitions of what a unit test is, but the one that I personally prefer goes something like this:
A unit test is an automated test that tests a unit in isolation.
This is still a rather loose definition. What's a unit? What's does isolation mean? The answers to these questions seem to be a bit fluffy, but I think it's safe to say that a unit isn't bigger than a class. It's not a library (or 'assembly' in .NET terms). It's not all types in a namespace. In my opinion, a unit is a method... and a bit more. In OO, a method is coupled to it's defining class, which is the reason why I think of a unit as a bit more than a method.
If that's a unit, then isolation means that you should be able to test the unit without using or relying on any other moving pieces of production code.
Often, unit testing means that you have to decouple objects in order to achieve isolation. There's a few ways to do that, but Dependency Injection is a very common approach. That often leads to heavy use of dynamic mocks and too many interfaces that exist exclusively to support unit testing.
Sometimes, a good alternative is to write automated tests that exercise a larger group of classes. When all the collaborating classes are deterministic and implemented within the same library, this can be an effective approach. Krzysztof Koźmic describes that
Another example is AutoFixture, which has hundreds of automated tests against the Fixture class.
We need a name for tests like these. While we could call them Integration Tests, this label would lump them together with tests that exercise several libraries at once.
To distinguish such tests from Unit Tests on the one side, and Integration Tests on the other side, I suggest the name Facade Test
The reason why I suggest the term Facade Test is that such automated tests tend to mostly target a small subset of classes that act as Facades over many other classes in a library.
Comments
Unit test: usually just one class but occasionally a couple of closely related classes, constructed and executed directly from the test code. Runs fast and no contact with the outside world.
Database or Comms test: testing in isolation that a single class that interacts with something externally works. e.g. a class that accesses the database actually accesses the database, a class that contacts a remote server actual contacts a remote server (probably a special one we wrote for testing purposes).
Scenario test: multiple classes, but with all external dependencies replaced with test doubles (e.g. no database access, no SOAP calls, no HTTP requests). Object graph is constructed via the same mechanism as the real object graph. So in some sense this tests the DI configuration. This is a form of integration test, but everything runs in memory so they are quick. Most of our tests are at this level.
Acceptance test: full application running and tested with Selenium.
And then there is manual testing.
"Let's drive this through Facade Tests" now means something else than "Let's drive this through Unit Tests".


| My Anime Page | MAL_ID | Name | Score | Genres | English name | Japanese name | Type | Episodes | Aired | Premiered | Producers | Licensors | Studios | Source | Duration | Rating | Ranked | Popularity | Members | Favorites | Watching | Completed | On-Hold | Dropped | Plan to Watch | Score-10 | Score-9 | Score-8 | Score-7 | Score-6 | Score-5 | Score-4 | Score-3 | Score-2 | Score-1 |


 For God has not given us a spirit of fear but of
power and of love and of a sound mind.
2 Timothy 1:4,6-7
There are times when you know what God has called you to do, but you just don't feel you have the inner resources you need to do it. Somehow you've simply run dry. You know the Word says that "out of your bellyshall flow rivers of living water" (John 7:38). But, during those times, you can't even find the creek bank, much less the river.
The Word of God tells us how to handle those situations through a clear command: "Stir up the gift inside you."
 Everything you need is already in you. Jesus put it there.
Faith is in there. Power is in there. Love is in there. Believe that. Speak it out. Say it to yourself now, right out loud:
"In the Name of Jesus, I stir up the gift that's within me by faith. I'm stirring up my faith. I'm not going to wait until I feel stirred up. I'm stepping out by faith and expecting my feelings to follow!
"I'm stirring up the love of God that's in me. I'm stirring up the power. I'm stirring up myself and running the devil out of my affairs. I'm stirring myself up in the Spirit of the living God! I AM stirred up!"
Scripture Reading: 2 Timothy 1:1-8


Matplotlib Startup¶
We’ll begin documenting the configuration for matplotlib
and how the
built-in matplotlib.pyplot
API can be utilized for
IPython’s configuration.
In [1]: import matplotlib as mpl
In [2]: mpl.pyplot.rc
Out[2]: <function matplotlib.pyplot.rc(group, **kwargs)>
In [3]: # pdoc mpl.pyplot.rc
Where the %pdoc
magic produces:
- Class docstring:
Set the current rc params. group is the grouping for the rc, e.g., for
lines.linewidth
the group islines
, foraxes.facecolor
, the group isaxes
, and so on. Group may also be a list or tuple of group names, e.g., (xtick, ytick). kwargs is a dictionary attribute name/value pairs, e.g.,:rc('lines', linewidth=2, color='r')sets the current rc params and is equivalent to:
rcParams['lines.linewidth'] = 2 rcParams['lines.color'] = 'r'The following aliases are available to save typing for interactive users:
Alias
Property
‘lw’
‘linewidth’
‘ls’
‘linestyle’
‘c’
‘color’
‘fc’
‘facecolor’
‘ec’
‘edgecolor’
‘mew’
‘markeredgewidth’
‘aa’
‘antialiased’
Thus you could abbreviate the above rc command as:
rc('lines', lw=2, c='r')Note you can use python’s kwargs dictionary facility to store dictionaries of default parameters. e.g., you can customize the font rc as follows:
font = {'family' : 'monospace', 'weight' : 'bold', 'size' : 'larger'} rc('font', **font) # pass in the font dict as kwargsThis enables you to easily switch between several configurations. Use
matplotlib.style.use('default')
orrcdefaults()
to restore the default rc params after changes.- Call docstring:
Call self as a function.
Well that’s neat!
So fun fact: If you write a print()
statement in a code-block
it’ll generate error as no code is saved and you essentially created an
empty block.
But running %pdoc
will actually output a classes docstring to your terminal
in the middle of a sphinx build!


Developer Evangelist / Beard -<EMAIL_ADDRESS>/ @4thfloor_monkey
Find the slides at : digitalcroydon.herokuapp.com
Think of the APIs we use all the time (well if you're me anyway)
It's the core of what we do
Everything is built on top of it.
Anybody integrated Skype with anything recently?
First step... 'onboarding'
Technical Debt from changing systems
Whether the API fits the product and has all the needed functionality
Difficult documentation can sink a good product
I'm sure you all have pet hates...
Good documentation and a good product make a GREAT product
Because everyone loves collaboration
Lots of information, but a little hidden
So perhaps not for us.
Inspired by the Stripe & Braintree documentation
{"IsReadOnly": false,
"Name": "listrecipient",
"Properties": [
{
"DataType": "TContact",
"DefaultValue": "",
"Description": "Reference to contact which is suscribed to the contactslist.",
"IsRequired": true,
"Name": "Contact",
"ReadOnly": true
},
{
"DataType": "Int64",
"DefaultValue": "",
"Description": "Unique numerical ID for this object.",
"IsRequired": false,
"Name": "ID",
"ReadOnly": true
},
Documenting your documentation
You might have to answer a few questions on how the generation works
*see above...
Time with the community!
Use this code when you sign up:
DIGITALCROYDON2015
3 months free Bronze Premium
Or if you have any questions....?


T
- the type of a provider-specific specialization of this contractpublic interface Configuration<T extends Configuration<T>>
ValidatorFactory
.
Usage:
//provided by one of the Validation bootstrap methods Configuration<?> configuration = ValidatorFactory = configuration .messageInterpolator( new CustomMessageInterpolator() ) .buildValidatorFactory();
By default, the configuration information is retrieved from
META-INF/validation.xml
.
It is possible to override the configuration retrieved from the XML file
by using one or more of the Configuration
methods.
The ValidationProviderResolver
is specified at configuration time
(see ValidationProvider
).
If none is explicitly requested, the default ValidationProviderResolver
is used.
The provider is selected in the following way:
Validation.byProvider(Class)
, find the first provider implementing
the provider class requested and use itMETA-INF/validation.xml
,
find the first provider implementing the provider class requested and use itValidationProviderResolver
Implementations are not meant to be thread-safe.
| Modifier and Type | Method and Description |
|---|---|
T |
addMapping(java.io.InputStream stream)
Add a stream describing constraint mapping in the Jakarta Bean Validation XML
format.
|
T |
addProperty(java.lang.String name,
java.lang.String value)
Adds a provider specific property.
|
T |
addValueExtractor(ValueExtractor<?> extractor)
Adds a value extractor.
|
ValidatorFactory |
buildValidatorFactory()
Build a
ValidatorFactory implementation. |
T |
clockProvider(ClockProvider clockProvider)
Defines the clock provider.
|
T |
constraintValidatorFactory(ConstraintValidatorFactory constraintValidatorFactory)
Defines the constraint validator factory.
|
BootstrapConfiguration |
getBootstrapConfiguration()
Returns configuration information stored in the
META-INF/validation.xml file. |
ClockProvider |
getDefaultClockProvider()
Returns an implementation of the
ClockProvider
interface following the default ClockProvider
defined in the specification:
returns a clock representing the current system time and default time
zone.
|
ConstraintValidatorFactory |
getDefaultConstraintValidatorFactory()
Returns an implementation of the
ConstraintValidatorFactory interface
following the default ConstraintValidatorFactory defined in the
specification:
uses the public no-arg constructor of the ConstraintValidator
|
MessageInterpolator |
getDefaultMessageInterpolator()
Returns an implementation of the
MessageInterpolator interface
following the default MessageInterpolator defined in the
specification:
use the ValidationMessages resource bundle to load keys
use Locale.getDefault()
|
ParameterNameProvider |
getDefaultParameterNameProvider()
Returns an implementation of the
ParameterNameProvider
interface following the default ParameterNameProvider
defined in the specification:
returns the actual parameter names as provided in the validated
executable’s definition, if the class file of the executable contains
parameter name information
otherwise returns names in the form arg<PARAMETER_INDEX> ,
where PARAMETER_INDEX starts at 0 for the first parameter,
e.g. |
TraversableResolver |
getDefaultTraversableResolver()
Returns an implementation of the
TraversableResolver interface
following the default TraversableResolver defined in the
specification:
if Java Persistence is available in the runtime environment,
a property is considered reachable if Java Persistence considers
the property as loaded
if Java Persistence is not available in the runtime environment,
all properties are considered reachable
all properties are considered cascadable.
|
T |
ignoreXmlConfiguration()
Ignores data from the
META-INF/validation.xml file if this
method is called. |
T |
messageInterpolator(MessageInterpolator interpolator)
Defines the message interpolator used.
|
T |
parameterNameProvider(ParameterNameProvider parameterNameProvider)
Defines the parameter name provider.
|
T |
traversableResolver(TraversableResolver resolver)
Defines the traversable resolver used.
|
T ignoreXmlConfiguration()
META-INF/validation.xml
file if this
method is called.
This method is typically useful for containers that parse
META-INF/validation.xml
themselves and pass the information
via the Configuration
methods.
this
following the chaining method pattern.T messageInterpolator(MessageInterpolator interpolator)
If null
is passed, the default message interpolator is used
(defined in XML or the specification default).
interpolator
- message interpolator implementationthis
following the chaining method patternT traversableResolver(TraversableResolver resolver)
If null
is passed, the default traversable resolver is used
(defined in XML or the specification default).
resolver
- traversable resolver implementationthis
following the chaining method patternT constraintValidatorFactory(ConstraintValidatorFactory constraintValidatorFactory)
If null
is passed, the default constraint validator factory is used
(defined in XML or the specification default).
constraintValidatorFactory
- constraint factory implementationthis
following the chaining method patternT parameterNameProvider(ParameterNameProvider parameterNameProvider)
If null
is passed, the default parameter name provider is used
(defined in XML or the specification default).
parameterNameProvider
- parameter name provider implementationthis
following the chaining method pattern.T clockProvider(ClockProvider clockProvider)
If null
is passed, the default clock provider is used
(defined in XML or the specification default).
clockProvider
- clock provider implementationthis
following the chaining method pattern.T addValueExtractor(ValueExtractor<?> extractor)
extractor
- value extractor implementationthis
following the chaining method pattern.ValueExtractorDeclarationException
- if more than one extractor for
the same type and type parameter is addedT addMapping(java.io.InputStream stream)
The stream should be closed by the client API after the
ValidatorFactory
has been built. The Jakarta Bean Validation provider
must not close the stream.
stream
- XML mapping stream; the given stream should support the
mark/reset contract (see InputStream.markSupported()
);
if it doesn't, it will be wrapped into a stream supporting the
mark/reset contract by the Jakarta Bean Validation providerthis
following the chaining method patternjava.lang.IllegalArgumentException
- if stream
is nullT addProperty(java.lang.String name, java.lang.String value)
Note: Using this non type-safe method is generally not recommended.
It is more appropriate to use, if available, the type-safe equivalent provided
by a specific provider via its Configuration
subclass.
ValidatorFactory factory = Validation.byProvider(ACMEProvider.class) .configure() .providerSpecificProperty(ACMEState.FAST) .buildValidatorFactory();This method is typically used by containers parsing
META-INF/validation.xml
themselves and injecting the state to the Configuration
object.
If a property with a given name is defined both via this method and in the XML configuration, the value set programmatically has priority.
If null
is passed as a value, the value defined in XML is used. If no value
is defined in XML, the property is considered unset.
name
- property namevalue
- property valuethis
following the chaining method patternjava.lang.IllegalArgumentException
- if name
is nullMessageInterpolator getDefaultMessageInterpolator()
MessageInterpolator
interface
following the default MessageInterpolator
defined in the
specification:
ValidationMessages
resource bundle to load keysLocale.getDefault()
MessageInterpolator
implementation compliant with the
specificationTraversableResolver getDefaultTraversableResolver()
TraversableResolver
interface
following the default TraversableResolver
defined in the
specification:
TraversableResolver
implementation compliant with the
specificationConstraintValidatorFactory getDefaultConstraintValidatorFactory()
ConstraintValidatorFactory
interface
following the default ConstraintValidatorFactory
defined in the
specification:
ConstraintValidator
ConstraintValidatorFactory
implementation compliant with the
specificationParameterNameProvider getDefaultParameterNameProvider()
ParameterNameProvider
interface following the default ParameterNameProvider
defined in the specification:
arg<PARAMETER_INDEX>
,
where PARAMETER_INDEX
starts at 0 for the first parameter,
e.g. arg0
, arg1
etc.ParameterNameProvider
implementation compliant with
the specificationClockProvider getDefaultClockProvider()
ClockProvider
interface following the default ClockProvider
defined in the specification:
ClockProvider
implementation compliant with
the specificationBootstrapConfiguration getBootstrapConfiguration()
META-INF/validation.xml
file.
Note:
Implementations are encouraged to lazily build this object to delay parsing.
BootstrapConfiguration
; this method never
returns null
; if there is no META-INF/validation.xml
the
different getters of the returned instance will return null
respectively an empty set or mapValidatorFactory buildValidatorFactory()
ValidatorFactory
implementation.ValidatorFactory
ValidationException
- if the ValidatorFactory
cannot be built


| Draw a rectangle on the screen |
| x = x coordinate to begin drawing the rectangle y = y coordinate to begin drawing the rectangle width = how wide to make the rectangle in pixels height = how tall to make the rectangle in pixels solid = 0 or False for unfilled and 1 or True for filled |
Command Description:
| This command will draw a rectangle in the current drawing Color starting at the location specified. The last parameter determines if the rectangle is filled or just a 'box'. |
Example:
| ; Flip/Backbuffer()/Rect Example ; Set Graphics Mode Graphics 640,480 ; Go double buffering SetBuffer BackBuffer() ; Setup initial locations for the box box_x = -20 ; negative so it will start OFF screen box_y = 100 While Not KeyHit(1) Cls ; Always clear screen first Rect box_x,box_y,20,20,1 ; Draw the box in the current x,y location Flip ; Flip it into view box_x = box_x + 1 ; Move the box over one pixel If box_x = 640 Then box_x=-20 ; If it leaves the Right edge, reset its x location Wend |


This server provides three resources with the following methods:
get on /api/diamonds/ will list the diamonds currently in play (this call will return when the list of diamonds next changes so it's OK for long polling)
delete on /api/diamonds/42?user=p340x459&name=Jack will attempt to claim the diamond 42 - either it returns 200 in which case the diamond is yours, or it returns 404 in which case somebody claimed the diamond before you; the user ID is generated by the client on load, the name is provided by the user and can freely change
get on /api/scores/ will list all the scores known to the server


This function allows for easy recursive function calling of tree-like structures. The recursion can happen either downwards, calling the children after the parent, or upwards, calling the parent after the children. At each recursion there is access to the node(s) that was/were called before and thus have already been through the function.
tree_apply(tree, FUN, ...) # S3 method for default tree_apply(tree, FUN, ...) # S3 method for igraph tree_apply(tree, FUN, direction = "down", mode = "out", ...) # S3 method for dendrogram tree_apply(tree, FUN, direction = "down", ...)
| tree | A tree-like object. Currently support for igraph objects |
|---|---|
| FUN | The function to apply to each node. The function must return a
modified version of the graph if |
| ... | Additional parameters to |
| direction | The direction of the recursion. If |
| mode | For |
A modified version of tree
(if anything is modified in
FUN
)
The function is called with a set of predefined parameters along
with user defined ones. If direction = 'down'
The parameters supplied
automatically to the function are: node
, parent
, depth
and tree
, while if direction = 'up'
the parameters are:
node
, children
, depth
and tree
. The nature of
node
, parent
and children
depends on the class of
tree
. If class(tree) == 'igraph'
they will be indices of the
relevant vertices in the graph. If class(tree) == 'dendrogram'
they
will be the actual dendrogram objects.


UFC Sao Paulo post-fight press conference video
by MMA Fighting NewswireThe UFC Sao Paulo post-fight press conference is the last piece of business to take care of after the action is over in the Octagon, and we have the live video at MMAFighting.com.
UFC officials will announce the winners of the “Fight of the Night” and “Performance of the Night” bonuses, and winning fighters will meet the media after their fights.
The UFC Sao Paulo post-fight press conference takes place about 30 minutes after the main event ends, meaning it will start around 11:00 p.m. ET.
You can also check out full fight results from UFC Sao Paulo here.


Limited Supply So You Must Hurry If You
Want In On The Action
The whole supply of Bitcoin is just 21 million possible coins. Once the 21 million coins are in people's hands, there will never be more Bitcoin ever made. Of the 21 million possible coins, 16.5 million are already in circulation.
Once all 21 million are in circulation, there will not be any new coins made. Compare that to the US Bureau of Engraving and Printing that prints up $541 million in new dollars each and every day.
That’s why the dollar keeps dropping in value. Even with gold… they're always pulling new gold out of the Earth every year. 2500 tons of it. With Bitcoin… there is a fixed limit… a hard limit… to the amount of coins to be produced. That is why it keeps increasing in value. Don’t miss this opportunity it could be the last one in your lifetime


{% extends 'base.html' %} {% load static %} {% block content %} {% include 'pages/partials/header.html' %} Welcome Back Enter your credentials to sign in to your account Sign In Forgot Password? Demo Borrower Demo Investor {% include 'pages/partials/footer.html' %} {% endblock %}
Enter your credentials to sign in to your account


Immune functions, clinical parameters and hormone receptor status in breast cancer patients.
Abstract
We have carried out a detailed analysis of the cellular immune functions of breast cancer patients in comparison with healthy controls. A possible correlation between immune and clinical parameters was analysed in 50 breast cancer patients. Immune parameters, natural killer cell and T lymphocyte functions and the numbers of circulating T lymphocytes were analysed against the clinical parameters comprising the tumour burden, the stage of the disease and the expression of hormone receptors on the tumour. In order to analyse the immune function data effectively, low responders were identified with stringent cut-off values. Considerably higher proportions of low responders were found among the patient population. Elevated numbers of circulating T lymphocytes and CD3 -directed cytolysis correlated with the expression of oestrogen receptors independently of the clinical/histological parameters.


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bridges
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falling
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grocery shopping
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book reading
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free play
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school readiness
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infants
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cognitive
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parent-child interaction
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free play
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infants
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cross-cultural research
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amazon
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numerical cognition
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attachment
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coding
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manuals
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expressions
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still-face
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adult
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walking
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uner tan syndrome
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quadrupedalism
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locomotion
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adult
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infants
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optic flow
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locomotion
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https://nyu.databrary.org/volume/24
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visual cliff
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infants
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posture
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stimuli
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materials
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visual attention
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visual scene displays
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slopes
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cafe
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motion coherence
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visual evoked potentials
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infant vision
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mental arithmetic
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working memory
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number
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indigenous peoples
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number
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stimuli
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visual search
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visual attention
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social attention
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face perception
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prosody
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child language
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turn-taking
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timing
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online comprehension
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prediction
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eye-tracking
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infants
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parent-child interaction
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object perception
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materials
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statistical learning
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materials
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language development
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word segmentation
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language acquisition
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infants
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procedures
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verbs
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language acquisition
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stimuli
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https://nyu.databrary.org/volume/44
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children
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syntax
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https://nyu.databrary.org/volume/44
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language development
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1
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https://nyu.databrary.org/volume/45
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language
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https://nyu.databrary.org/volume/45
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materials
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https://nyu.databrary.org/volume/45
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culture
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https://nyu.databrary.org/volume/45
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parent-child interaction
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45
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https://nyu.databrary.org/volume/45
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essentialism
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https://nyu.databrary.org/volume/45
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generic language
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2
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45
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https://nyu.databrary.org/volume/45
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social cognition
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2
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45
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https://nyu.databrary.org/volume/45
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conceptual development
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3
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45
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https://nyu.databrary.org/volume/45
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cognitive development
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3
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45
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https://nyu.databrary.org/volume/45
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social categorization
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3
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46
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https://nyu.databrary.org/volume/46
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gestalt
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46
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https://nyu.databrary.org/volume/46
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reaching
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2
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46
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https://nyu.databrary.org/volume/46
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fitts law
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2
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46
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https://nyu.databrary.org/volume/46
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infants
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https://nyu.databrary.org/volume/46
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action
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https://nyu.databrary.org/volume/46
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perception
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https://nyu.databrary.org/volume/46
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looking
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https://nyu.databrary.org/volume/47
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motion
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https://nyu.databrary.org/volume/47
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grasping
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47
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https://nyu.databrary.org/volume/47
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infants
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https://nyu.databrary.org/volume/49
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motion contrast
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49
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https://nyu.databrary.org/volume/49
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ssvep
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49
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https://nyu.databrary.org/volume/49
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materials
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1
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49
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https://nyu.databrary.org/volume/49
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electrophysiology
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1
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49
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https://nyu.databrary.org/volume/49
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optic flow
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1
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49
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https://nyu.databrary.org/volume/49
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motion
|
1
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50
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https://nyu.databrary.org/volume/50
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motion
|
1
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50
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https://nyu.databrary.org/volume/50
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stimuli
|
1
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50
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https://nyu.databrary.org/volume/50
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perception
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1
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51
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https://nyu.databrary.org/volume/51
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prediction
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1
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51
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https://nyu.databrary.org/volume/51
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infants
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1
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51
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https://nyu.databrary.org/volume/51
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object permanence
|
1
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51
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https://nyu.databrary.org/volume/51
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kinetic occlusion
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1
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51
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https://nyu.databrary.org/volume/51
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virtual occlusion
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1
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53
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https://nyu.databrary.org/volume/53
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spatial cueing
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1
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53
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https://nyu.databrary.org/volume/53
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gesturing
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1
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53
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https://nyu.databrary.org/volume/53
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infants
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1
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53
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https://nyu.databrary.org/volume/53
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materials
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1
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53
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https://nyu.databrary.org/volume/53
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referential understanding
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1
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53
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https://nyu.databrary.org/volume/53
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pointing
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2
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53
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https://nyu.databrary.org/volume/53
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joint attention
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2
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61
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https://nyu.databrary.org/volume/61
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teaching clips
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1
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61
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https://nyu.databrary.org/volume/61
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children
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1
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61
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https://nyu.databrary.org/volume/61
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inhibitory control
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2
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61
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https://nyu.databrary.org/volume/61
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scale errors
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2
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61
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https://nyu.databrary.org/volume/61
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perception-action
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2
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62
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https://nyu.databrary.org/volume/62
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faces
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1
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62
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https://nyu.databrary.org/volume/62
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eye-tracking
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1
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62
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https://nyu.databrary.org/volume/62
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materials
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1
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62
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https://nyu.databrary.org/volume/62
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visual attention
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2
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|
64
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https://nyu.databrary.org/volume/64
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infants
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1
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64
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https://nyu.databrary.org/volume/64
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reaching
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1
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64
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https://nyu.databrary.org/volume/64
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parent-child interaction
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1
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64
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https://nyu.databrary.org/volume/64
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teaching clips
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1
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64
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https://nyu.databrary.org/volume/64
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sticky mittens
|
2
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|
64
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https://nyu.databrary.org/volume/64
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perception-action
|
2
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64
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https://nyu.databrary.org/volume/64
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infant perception
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2
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|
64
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https://nyu.databrary.org/volume/64
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motor development
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2
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|
65
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https://nyu.databrary.org/volume/65
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number discrimination
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1
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65
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https://nyu.databrary.org/volume/65
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infants
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1
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65
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https://nyu.databrary.org/volume/65
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infant development
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1
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65
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https://nyu.databrary.org/volume/65
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mathematical ability
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1
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65
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https://nyu.databrary.org/volume/65
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individual differences
|
1
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65
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https://nyu.databrary.org/volume/65
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cognitive development
|
2
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|
65
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https://nyu.databrary.org/volume/65
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numerical cognition
|
2
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|
68
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https://nyu.databrary.org/volume/68
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motion
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1
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68
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https://nyu.databrary.org/volume/68
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optic flow
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1
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68
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https://nyu.databrary.org/volume/68
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infants
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1
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70
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https://nyu.databrary.org/volume/70
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infants
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1
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70
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https://nyu.databrary.org/volume/70
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motor development
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1
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70
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https://nyu.databrary.org/volume/70
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children
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1
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70
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https://nyu.databrary.org/volume/70
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stages
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1
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70
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https://nyu.databrary.org/volume/70
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historical
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1
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70
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https://nyu.databrary.org/volume/70
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teaching
|
2
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|
73
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https://nyu.databrary.org/volume/73
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motion coherence
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1
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73
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https://nyu.databrary.org/volume/73
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coding
|
1
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73
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https://nyu.databrary.org/volume/73
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optic flow
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1
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73
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https://nyu.databrary.org/volume/73
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motion
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1
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73
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https://nyu.databrary.org/volume/73
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psychophysics
|
1
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73
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https://nyu.databrary.org/volume/73
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adult
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1
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75
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https://nyu.databrary.org/volume/75
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motion
|
1
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75
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https://nyu.databrary.org/volume/75
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visual perception
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1
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|
75
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https://nyu.databrary.org/volume/75
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optic flow
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1
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75
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https://nyu.databrary.org/volume/75
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children
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1
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77
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https://nyu.databrary.org/volume/77
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adult
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1
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77
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https://nyu.databrary.org/volume/77
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replication
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1
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77
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https://nyu.databrary.org/volume/77
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stimuli
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1
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77
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https://nyu.databrary.org/volume/77
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pattern perception
|
1
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|
79
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https://nyu.databrary.org/volume/79
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attention
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1
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79
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https://nyu.databrary.org/volume/79
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cognitive
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1
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79
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https://nyu.databrary.org/volume/79
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memory
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1
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79
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https://nyu.databrary.org/volume/79
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materials
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1
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81
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https://nyu.databrary.org/volume/81
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cross-cultural research
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1
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81
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https://nyu.databrary.org/volume/81
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icis
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1
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81
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https://nyu.databrary.org/volume/81
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optic flow
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1
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|
81
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https://nyu.databrary.org/volume/81
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icis nola
|
1
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|
81
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https://nyu.databrary.org/volume/81
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motion
|
1
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|
81
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https://nyu.databrary.org/volume/81
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infants
|
2
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|
81
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https://nyu.databrary.org/volume/81
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visual development
|
2
|
|
81
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https://nyu.databrary.org/volume/81
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culture
|
3
|
|
86
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https://nyu.databrary.org/volume/86
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motor development
|
1
|
|
86
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https://nyu.databrary.org/volume/86
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infants
|
1
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|
86
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https://nyu.databrary.org/volume/86
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procedures
|
1
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|
86
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https://nyu.databrary.org/volume/86
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materials
|
1
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|
90
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https://nyu.databrary.org/volume/90
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morphological development
|
1
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|
90
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https://nyu.databrary.org/volume/90
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english
|
1
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|
90
|
https://nyu.databrary.org/volume/90
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longitudinal home recordings
|
1
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|
90
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https://nyu.databrary.org/volume/90
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language development
|
1
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|
90
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https://nyu.databrary.org/volume/90
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transcripts
|
1
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|
90
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https://nyu.databrary.org/volume/90
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prosodic licensing
|
1
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|
90
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https://nyu.databrary.org/volume/90
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epenthesis
|
1
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|
90
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https://nyu.databrary.org/volume/90
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longitudinal
|
1
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|
90
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https://nyu.databrary.org/volume/90
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phonological development
|
1
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|
90
|
https://nyu.databrary.org/volume/90
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home recordings
|
1
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|
90
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https://nyu.databrary.org/volume/90
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parent-child interaction
|
1
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|
90
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https://nyu.databrary.org/volume/90
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codas
|
1
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|
90
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https://nyu.databrary.org/volume/90
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talkbank
|
1
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|
90
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https://nyu.databrary.org/volume/90
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minimal words
|
1
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|
95
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https://nyu.databrary.org/volume/95
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distance effect
|
1
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|
95
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https://nyu.databrary.org/volume/95
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mathematical ability
|
1
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|
95
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https://nyu.databrary.org/volume/95
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analog magnitude
|
1
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|
95
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https://nyu.databrary.org/volume/95
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numerical cognition
|
1
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|
95
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https://nyu.databrary.org/volume/95
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visual
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1
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|
95
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https://nyu.databrary.org/volume/95
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nonhuman primates
|
2
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|
96
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https://nyu.databrary.org/volume/96
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number comprehension
|
1
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|
96
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https://nyu.databrary.org/volume/96
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stimuli
|
1
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|
96
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https://nyu.databrary.org/volume/96
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mathematical ability
|
1
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|
98
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https://nyu.databrary.org/volume/98
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procedures
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1
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98
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https://nyu.databrary.org/volume/98
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manuals
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1
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98
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https://nyu.databrary.org/volume/98
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coding
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1
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98
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https://nyu.databrary.org/volume/98
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cognitive
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1
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98
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https://nyu.databrary.org/volume/98
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replication
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1
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98
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https://nyu.databrary.org/volume/98
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validity
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1
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98
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https://nyu.databrary.org/volume/98
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children
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1
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98
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https://nyu.databrary.org/volume/98
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theory of mind
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1
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98
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https://nyu.databrary.org/volume/98
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infants
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2
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98
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https://nyu.databrary.org/volume/98
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perception
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2
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98
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https://nyu.databrary.org/volume/98
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false belief
|
2
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99
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https://nyu.databrary.org/volume/99
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faces
|
1
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99
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https://nyu.databrary.org/volume/99
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infants
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1
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99
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https://nyu.databrary.org/volume/99
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infant development
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1
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99
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https://nyu.databrary.org/volume/99
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face perception in infants
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1
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99
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https://nyu.databrary.org/volume/99
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coding
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1
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108
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https://nyu.databrary.org/volume/108
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parent-child interaction
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1
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108
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https://nyu.databrary.org/volume/108
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social referencing
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1
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108
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https://nyu.databrary.org/volume/108
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play
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1
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108
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https://nyu.databrary.org/volume/108
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teaching
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1
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108
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https://nyu.databrary.org/volume/108
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attachment
|
2
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108
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https://nyu.databrary.org/volume/108
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infants
|
2
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109
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https://nyu.databrary.org/volume/109
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discourse
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1
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109
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https://nyu.databrary.org/volume/109
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stem
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1
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109
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https://nyu.databrary.org/volume/109
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classroom
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1
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109
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https://nyu.databrary.org/volume/109
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children
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1
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112
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https://nyu.databrary.org/volume/112
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faces
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1
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112
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https://nyu.databrary.org/volume/112
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head camera
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1
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112
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https://nyu.databrary.org/volume/112
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infancy
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1
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112
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https://nyu.databrary.org/volume/112
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egocentric vision
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1
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112
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https://nyu.databrary.org/volume/112
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materials
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1
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112
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https://nyu.databrary.org/volume/112
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visual perception
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1
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112
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https://nyu.databrary.org/volume/112
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scene statistics
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1
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112
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https://nyu.databrary.org/volume/112
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hands
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1
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119
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https://nyu.databrary.org/volume/119
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lab-tab
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1
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119
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https://nyu.databrary.org/volume/119
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infants
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1
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119
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https://nyu.databrary.org/volume/119
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eye-tracking
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1
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123
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https://nyu.databrary.org/volume/123
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motion
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1
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123
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https://nyu.databrary.org/volume/123
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visual exploration
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1
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123
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https://nyu.databrary.org/volume/123
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adult
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1
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123
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https://nyu.databrary.org/volume/123
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infants
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1
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124
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https://nyu.databrary.org/volume/124
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head camera
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1
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124
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https://nyu.databrary.org/volume/124
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headcams
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1
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124
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https://nyu.databrary.org/volume/124
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eye-tracking
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1
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124
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https://nyu.databrary.org/volume/124
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eye-tracking technology
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1
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124
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https://nyu.databrary.org/volume/124
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eye tracking
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1
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124
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https://nyu.databrary.org/volume/124
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head-mounted eye tracking
|
1
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|
135
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https://nyu.databrary.org/volume/135
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eye-tracking
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1
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|
135
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https://nyu.databrary.org/volume/135
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social interaction
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1
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135
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https://nyu.databrary.org/volume/135
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eye tracking
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1
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135
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https://nyu.databrary.org/volume/135
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head-mounted eye tracking
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2
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|
136
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https://nyu.databrary.org/volume/136
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motor development
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1
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|
136
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https://nyu.databrary.org/volume/136
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walking
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1
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136
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https://nyu.databrary.org/volume/136
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perception-action
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1
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136
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https://nyu.databrary.org/volume/136
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gait modification
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1
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136
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https://nyu.databrary.org/volume/136
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apertures
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1
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136
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https://nyu.databrary.org/volume/136
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decision making
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1
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136
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https://nyu.databrary.org/volume/136
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infants
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1
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149
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https://nyu.databrary.org/volume/149
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process
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1
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149
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https://nyu.databrary.org/volume/149
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nature of science
|
1
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149
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https://nyu.databrary.org/volume/149
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science
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1
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149
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https://nyu.databrary.org/volume/149
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children
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1
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150
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https://nyu.databrary.org/volume/150
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story
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1
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150
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https://nyu.databrary.org/volume/150
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unknown facts
|
1
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150
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https://nyu.databrary.org/volume/150
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internal coherence
|
1
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150
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https://nyu.databrary.org/volume/150
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learning
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1
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150
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https://nyu.databrary.org/volume/150
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children
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1
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150
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https://nyu.databrary.org/volume/150
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stories
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1
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150
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https://nyu.databrary.org/volume/150
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imagination
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1
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162
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https://nyu.databrary.org/volume/162
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perception-action
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1
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162
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https://nyu.databrary.org/volume/162
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reaching
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1
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162
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https://nyu.databrary.org/volume/162
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eye-tracking
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1
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165
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https://nyu.databrary.org/volume/165
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children
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1
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165
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https://nyu.databrary.org/volume/165
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stimuli
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1
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165
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https://nyu.databrary.org/volume/165
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number
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1
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165
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https://nyu.databrary.org/volume/165
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data
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1
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165
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https://nyu.databrary.org/volume/165
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dictator game
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1
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165
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https://nyu.databrary.org/volume/165
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prosocial development
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1
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165
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https://nyu.databrary.org/volume/165
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altruism
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1
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165
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https://nyu.databrary.org/volume/165
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cooperation
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1
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165
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https://nyu.databrary.org/volume/165
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numerical cognition
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1
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165
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https://nyu.databrary.org/volume/165
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cognitive development
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1
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165
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https://nyu.databrary.org/volume/165
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proportional reasoning
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1
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171
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https://nyu.databrary.org/volume/171
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adult
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1
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171
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https://nyu.databrary.org/volume/171
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scientific reasoning
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1
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171
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https://nyu.databrary.org/volume/171
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science
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1
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171
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https://nyu.databrary.org/volume/171
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psych pool
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1
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171
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https://nyu.databrary.org/volume/171
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reasoning
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1
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174
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https://nyu.databrary.org/volume/174
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rem sleep
|
1
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174
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https://nyu.databrary.org/volume/174
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active sleep
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1
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174
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https://nyu.databrary.org/volume/174
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physiological time
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1
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174
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https://nyu.databrary.org/volume/174
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neurodevelopmental disorders
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1
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174
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https://nyu.databrary.org/volume/174
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sleep
|
1
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174
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https://nyu.databrary.org/volume/174
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myoclonic twitching
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1
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174
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https://nyu.databrary.org/volume/174
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sensorimotor development
|
1
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184
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https://nyu.databrary.org/volume/184
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physics
|
1
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184
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https://nyu.databrary.org/volume/184
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fantasy
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1
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184
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https://nyu.databrary.org/volume/184
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preschool
|
1
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184
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https://nyu.databrary.org/volume/184
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184
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https://nyu.databrary.org/volume/184
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children
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184
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https://nyu.databrary.org/volume/184
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stories
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184
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https://nyu.databrary.org/volume/184
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biology
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184
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https://nyu.databrary.org/volume/184
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fiction
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192
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https://nyu.databrary.org/volume/192
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infants
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192
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https://nyu.databrary.org/volume/192
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eye-tracking
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192
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https://nyu.databrary.org/volume/192
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free-viewing
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192
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https://nyu.databrary.org/volume/192
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saliency
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192
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https://nyu.databrary.org/volume/192
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gaze
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193
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https://nyu.databrary.org/volume/193
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locomotion
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193
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https://nyu.databrary.org/volume/193
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teaching
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193
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https://nyu.databrary.org/volume/193
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infants
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193
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https://nyu.databrary.org/volume/193
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social referencing
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209
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https://nyu.databrary.org/volume/209
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word learning
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209
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https://nyu.databrary.org/volume/209
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stimuli
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209
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https://nyu.databrary.org/volume/209
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perception
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209
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https://nyu.databrary.org/volume/209
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novelty ratings
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209
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https://nyu.databrary.org/volume/209
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novel objects
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209
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https://nyu.databrary.org/volume/209
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language development
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209
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https://nyu.databrary.org/volume/209
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unnameable stimuli
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209
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https://nyu.databrary.org/volume/209
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visual memory
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1
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209
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https://nyu.databrary.org/volume/209
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picture database
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1
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209
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https://nyu.databrary.org/volume/209
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categorization
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209
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https://nyu.databrary.org/volume/209
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language
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212
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https://nyu.databrary.org/volume/212
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emotion
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212
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https://nyu.databrary.org/volume/212
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risk
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212
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https://nyu.databrary.org/volume/212
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anxiety
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212
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https://nyu.databrary.org/volume/212
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regulation
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212
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https://nyu.databrary.org/volume/212
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fear
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212
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https://nyu.databrary.org/volume/212
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childhood development
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2
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214
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https://nyu.databrary.org/volume/214
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stimuli
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214
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https://nyu.databrary.org/volume/214
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materials
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214
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https://nyu.databrary.org/volume/214
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infants
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218
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https://nyu.databrary.org/volume/218
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psychophysics
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218
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https://nyu.databrary.org/volume/218
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child
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218
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https://nyu.databrary.org/volume/218
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visual processing
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234
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https://nyu.databrary.org/volume/234
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data
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234
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https://nyu.databrary.org/volume/234
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preschool
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234
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https://nyu.databrary.org/volume/234
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neuroimaging
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235
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https://nyu.databrary.org/volume/235
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infants
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235
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https://nyu.databrary.org/volume/235
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play
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236
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https://nyu.databrary.org/volume/236
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attention
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236
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https://nyu.databrary.org/volume/236
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infants
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236
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https://nyu.databrary.org/volume/236
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play
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236
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https://nyu.databrary.org/volume/236
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social referencing
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239
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https://nyu.databrary.org/volume/239
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icis
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239
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https://nyu.databrary.org/volume/239
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icis nola
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239
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https://nyu.databrary.org/volume/239
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talk
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239
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https://nyu.databrary.org/volume/239
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239
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https://nyu.databrary.org/volume/239
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presentations
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241
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https://nyu.databrary.org/volume/241
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script
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241
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https://nyu.databrary.org/volume/241
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children
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241
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https://nyu.databrary.org/volume/241
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theory of mind
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241
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https://nyu.databrary.org/volume/241
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perceptual access reasoning
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241
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https://nyu.databrary.org/volume/241
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false belief
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2
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248
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https://nyu.databrary.org/volume/248
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prosody
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248
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https://nyu.databrary.org/volume/248
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emotions
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2
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252
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https://nyu.databrary.org/volume/252
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infants
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252
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https://nyu.databrary.org/volume/252
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eye-tracking
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252
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https://nyu.databrary.org/volume/252
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perception
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252
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https://nyu.databrary.org/volume/252
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optic flow
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252
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https://nyu.databrary.org/volume/252
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252
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https://nyu.databrary.org/volume/252
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adult
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254
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https://nyu.databrary.org/volume/254
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play
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270
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https://nyu.databrary.org/volume/270
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infants
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270
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https://nyu.databrary.org/volume/270
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infant development
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270
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https://nyu.databrary.org/volume/270
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infant perception
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270
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https://nyu.databrary.org/volume/270
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perception
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270
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https://nyu.databrary.org/volume/270
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motion
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270
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https://nyu.databrary.org/volume/270
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data
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272
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https://nyu.databrary.org/volume/272
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faces
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272
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https://nyu.databrary.org/volume/272
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stimuli
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272
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https://nyu.databrary.org/volume/272
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adolescence
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272
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https://nyu.databrary.org/volume/272
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perception
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272
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https://nyu.databrary.org/volume/272
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psychophysics
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272
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https://nyu.databrary.org/volume/272
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expressions
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272
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https://nyu.databrary.org/volume/272
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puberty
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273
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https://nyu.databrary.org/volume/273
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learning
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273
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https://nyu.databrary.org/volume/273
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273
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https://nyu.databrary.org/volume/273
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talk
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273
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https://nyu.databrary.org/volume/273
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presentations
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273
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https://nyu.databrary.org/volume/273
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lecture
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273
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https://nyu.databrary.org/volume/273
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css
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273
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https://nyu.databrary.org/volume/273
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workshop
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297
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https://nyu.databrary.org/volume/297
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preschool
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297
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https://nyu.databrary.org/volume/297
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children
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297
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https://nyu.databrary.org/volume/297
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297
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https://nyu.databrary.org/volume/297
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clay
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297
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https://nyu.databrary.org/volume/297
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culture
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297
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https://nyu.databrary.org/volume/297
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free play
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297
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https://nyu.databrary.org/volume/297
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affordances
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302
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https://nyu.databrary.org/volume/302
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faces
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302
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https://nyu.databrary.org/volume/302
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face processing
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302
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https://nyu.databrary.org/volume/302
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fmri
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302
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https://nyu.databrary.org/volume/302
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ffa
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302
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https://nyu.databrary.org/volume/302
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visual perception
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303
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https://nyu.databrary.org/volume/303
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computational models
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303
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https://nyu.databrary.org/volume/303
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https://nyu.databrary.org/volume/303
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https://nyu.databrary.org/volume/303
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https://nyu.databrary.org/volume/303
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presentations
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303
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https://nyu.databrary.org/volume/303
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lecture
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321
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https://nyu.databrary.org/volume/321
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development
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1
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321
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https://nyu.databrary.org/volume/321
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eeg
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1
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321
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https://nyu.databrary.org/volume/321
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eye tracking
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1
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321
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https://nyu.databrary.org/volume/321
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deep learning
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1
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321
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https://nyu.databrary.org/volume/321
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action perception
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1
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324
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https://nyu.databrary.org/volume/324
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texture
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324
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https://nyu.databrary.org/volume/324
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display
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326
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https://nyu.databrary.org/volume/326
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attention
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326
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https://nyu.databrary.org/volume/326
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intersensory
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330
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https://nyu.databrary.org/volume/330
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lexicon
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330
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https://nyu.databrary.org/volume/330
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cognitive development
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330
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https://nyu.databrary.org/volume/330
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word learning
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330
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https://nyu.databrary.org/volume/330
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language acquisition
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330
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https://nyu.databrary.org/volume/330
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environmental effects
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336
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https://nyu.databrary.org/volume/336
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intersensory
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336
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https://nyu.databrary.org/volume/336
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attention
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336
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https://nyu.databrary.org/volume/336
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eye-tracking
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339
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https://nyu.databrary.org/volume/339
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emotion
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1
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339
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https://nyu.databrary.org/volume/339
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prosody
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339
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https://nyu.databrary.org/volume/339
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fmri
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339
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https://nyu.databrary.org/volume/339
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affect
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1
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339
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https://nyu.databrary.org/volume/339
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imaging
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1
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339
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https://nyu.databrary.org/volume/339
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mother
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1
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339
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https://nyu.databrary.org/volume/339
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speech
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2
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433
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https://nyu.databrary.org/volume/433
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adult female
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1
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433
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https://nyu.databrary.org/volume/433
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luminance matched
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1
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433
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https://nyu.databrary.org/volume/433
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black and white
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1
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433
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https://nyu.databrary.org/volume/433
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luminance varied
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1
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433
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https://nyu.databrary.org/volume/433
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color
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1
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471
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https://nyu.databrary.org/volume/471
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motor development
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1
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471
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https://nyu.databrary.org/volume/471
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infant locomotion
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1
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471
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https://nyu.databrary.org/volume/471
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strategy choice
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1
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471
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https://nyu.databrary.org/volume/471
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overlapping waves
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1
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471
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https://nyu.databrary.org/volume/471
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end-state comfort
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1
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480
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https://nyu.databrary.org/volume/480
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gesture
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1
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480
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https://nyu.databrary.org/volume/480
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mathematical equivalence
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1
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480
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https://nyu.databrary.org/volume/480
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instruction
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1
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480
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https://nyu.databrary.org/volume/480
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learning
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1
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545
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https://nyu.databrary.org/volume/545
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face processing
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1
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545
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https://nyu.databrary.org/volume/545
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data processing
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1
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545
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https://nyu.databrary.org/volume/545
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signal to noise ratio
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1
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545
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https://nyu.databrary.org/volume/545
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infant perception
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1
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545
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https://nyu.databrary.org/volume/545
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trial data
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1
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545
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https://nyu.databrary.org/volume/545
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snr
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1
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545
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https://nyu.databrary.org/volume/545
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channel data
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1
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545
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https://nyu.databrary.org/volume/545
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data
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1
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545
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https://nyu.databrary.org/volume/545
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eeg
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1
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545
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https://nyu.databrary.org/volume/545
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ssvep
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1
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545
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https://nyu.databrary.org/volume/545
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snr data
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1
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546
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https://nyu.databrary.org/volume/546
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infant attention
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1
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546
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https://nyu.databrary.org/volume/546
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infant erp
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1
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546
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https://nyu.databrary.org/volume/546
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eye-tracking
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1
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546
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https://nyu.databrary.org/volume/546
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erp
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1
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553
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https://nyu.databrary.org/volume/553
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pretense
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1
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553
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https://nyu.databrary.org/volume/553
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embodiment
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1
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553
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https://nyu.databrary.org/volume/553
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preschool
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1
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553
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https://nyu.databrary.org/volume/553
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pretend play
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1
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608
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https://nyu.databrary.org/volume/608
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face processing
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1
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608
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https://nyu.databrary.org/volume/608
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data processing
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1
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608
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https://nyu.databrary.org/volume/608
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infant eeg
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1
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608
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https://nyu.databrary.org/volume/608
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erp
|
1
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608
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https://nyu.databrary.org/volume/608
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infant perception
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1
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608
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https://nyu.databrary.org/volume/608
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electrophysiology
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1
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608
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https://nyu.databrary.org/volume/608
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eeg
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1
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608
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https://nyu.databrary.org/volume/608
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erp components
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1
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608
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https://nyu.databrary.org/volume/608
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latency data
|
1
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608
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https://nyu.databrary.org/volume/608
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amplitude data
|
1
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|
702
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https://nyu.databrary.org/volume/702
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infant attention
|
1
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702
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https://nyu.databrary.org/volume/702
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stimuli
|
1
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|
702
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https://nyu.databrary.org/volume/702
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eye-tracking
|
1
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707
|
https://nyu.databrary.org/volume/707
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eeg
|
1
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|
707
|
https://nyu.databrary.org/volume/707
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eye-tracking technology
|
1
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|
707
|
https://nyu.databrary.org/volume/707
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motion-tracking technology
|
1
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|
709
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https://nyu.databrary.org/volume/709
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icis philadelphia
|
1
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|
709
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https://nyu.databrary.org/volume/709
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icis
|
1
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717
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https://nyu.databrary.org/volume/717
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overhearing
|
1
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717
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https://nyu.databrary.org/volume/717
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self-directed learning
|
1
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717
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https://nyu.databrary.org/volume/717
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word learning
|
1
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717
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https://nyu.databrary.org/volume/717
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eavesdropping
|
1
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717
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https://nyu.databrary.org/volume/717
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active learning
|
1
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717
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https://nyu.databrary.org/volume/717
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language development
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1
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|
717
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https://nyu.databrary.org/volume/717
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effective input
|
1
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717
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https://nyu.databrary.org/volume/717
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child-directed speech
|
1
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717
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https://nyu.databrary.org/volume/717
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vocabulary development
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1
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717
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https://nyu.databrary.org/volume/717
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preschoolers
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1
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717
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https://nyu.databrary.org/volume/717
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fact learning
|
1
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|
736
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https://nyu.databrary.org/volume/736
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explore
|
1
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|
736
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https://nyu.databrary.org/volume/736
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cognitive
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1
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736
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https://nyu.databrary.org/volume/736
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exploit
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1
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736
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https://nyu.databrary.org/volume/736
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workshop
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1
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736
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https://nyu.databrary.org/volume/736
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science
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1
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736
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https://nyu.databrary.org/volume/736
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conference
|
1
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739
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https://nyu.databrary.org/volume/739
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locomotion
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1
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739
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https://nyu.databrary.org/volume/739
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motor development
|
1
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739
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https://nyu.databrary.org/volume/739
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older adults
|
1
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739
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https://nyu.databrary.org/volume/739
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walking
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1
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739
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https://nyu.databrary.org/volume/739
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falling
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1
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739
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https://nyu.databrary.org/volume/739
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falls
|
1
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761
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https://nyu.databrary.org/volume/761
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pilot
|
2
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|
762
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https://nyu.databrary.org/volume/762
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theory of mind
|
1
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762
|
https://nyu.databrary.org/volume/762
|
ownership
|
1
|
|
826
|
https://nyu.databrary.org/volume/826
|
cultural
|
1
|
|
836
|
https://nyu.databrary.org/volume/836
|
gender
|
1
|
|
836
|
https://nyu.databrary.org/volume/836
|
generic language
|
1
|
|
836
|
https://nyu.databrary.org/volume/836
|
social categories
|
1
|
|
870
|
https://nyu.databrary.org/volume/870
|
nanit
|
1
|
|
874
|
https://nyu.databrary.org/volume/874
|
faces
|
1
|
|
874
|
https://nyu.databrary.org/volume/874
|
face processing
|
1
|
|
874
|
https://nyu.databrary.org/volume/874
|
face perception
|
1
|
|
874
|
https://nyu.databrary.org/volume/874
|
emotions
|
1
|
|
884
|
https://nyu.databrary.org/volume/884
|
joint attention
|
1
|
|
884
|
https://nyu.databrary.org/volume/884
|
visual attention
|
1
|
|
884
|
https://nyu.databrary.org/volume/884
|
gaze following
|
1
|
|
884
|
https://nyu.databrary.org/volume/884
|
autism
|
1
|
|
884
|
https://nyu.databrary.org/volume/884
|
video
|
1
|
|
884
|
https://nyu.databrary.org/volume/884
|
gaze
|
1
|
|
884
|
https://nyu.databrary.org/volume/884
|
eye tracking
|
1
|
|
884
|
https://nyu.databrary.org/volume/884
|
gaze perception
|
1
|
|
884
|
https://nyu.databrary.org/volume/884
|
social attention
|
1
|
|
884
|
https://nyu.databrary.org/volume/884
|
naturalistic
|
1
|
|
884
|
https://nyu.databrary.org/volume/884
|
images
|
1
|
|
884
|
https://nyu.databrary.org/volume/884
|
database
|
1
|
|
884
|
https://nyu.databrary.org/volume/884
|
stimuli
|
1
|
|
896
|
https://nyu.databrary.org/volume/896
|
manybabies
|
1
|
|
896
|
https://nyu.databrary.org/volume/896
|
manybabies-idspref
|
1
|
|
896
|
https://nyu.databrary.org/volume/896
|
idspreference
|
1
|
|
897
|
https://nyu.databrary.org/volume/897
|
affordances
|
1
|
|
897
|
https://nyu.databrary.org/volume/897
|
problem solving
|
1
|
|
897
|
https://nyu.databrary.org/volume/897
|
manual action
|
1
|
|
897
|
https://nyu.databrary.org/volume/897
|
manual exploration
|
1
|
|
897
|
https://nyu.databrary.org/volume/897
|
perceptual-motor development
|
1
|
|
927
|
https://nyu.databrary.org/volume/927
|
preterm
|
1
|
|
927
|
https://nyu.databrary.org/volume/927
|
language development
|
1
|
|
927
|
https://nyu.databrary.org/volume/927
|
event representation
|
1
|
|
943
|
https://nyu.databrary.org/volume/943
|
infant locomotion
|
1
|
|
943
|
https://nyu.databrary.org/volume/943
|
dyadic play
|
1
|
|
943
|
https://nyu.databrary.org/volume/943
|
infant-mother interaction
|
1
|
|
943
|
https://nyu.databrary.org/volume/943
|
synchrony
|
2
|
|
987
|
https://nyu.databrary.org/volume/987
|
autism spectrum disorder
|
1
|
|
987
|
https://nyu.databrary.org/volume/987
|
interaction
|
1
|
|
987
|
https://nyu.databrary.org/volume/987
|
language development
|
1
|
|
987
|
https://nyu.databrary.org/volume/987
|
classroom
|
1
|
|
987
|
https://nyu.databrary.org/volume/987
|
social development
|
1
|
|
999
|
https://nyu.databrary.org/volume/999
|
pilot study
|
1
|
|
999
|
https://nyu.databrary.org/volume/999
|
usability
|
1
|
|
999
|
https://nyu.databrary.org/volume/999
|
dyslexia
|
1
|
|
999
|
https://nyu.databrary.org/volume/999
|
children
|
1
|
|
999
|
https://nyu.databrary.org/volume/999
|
mobile app
|
1
|
|
1006
|
https://nyu.databrary.org/volume/1006
|
fpvs
|
1
|
|
1006
|
https://nyu.databrary.org/volume/1006
|
infancy
|
1
|
|
1006
|
https://nyu.databrary.org/volume/1006
|
ssvep
|
1
|
|
1006
|
https://nyu.databrary.org/volume/1006
|
eeg
|
1
|
|
1006
|
https://nyu.databrary.org/volume/1006
|
visual attention
|
1
|
|
1006
|
https://nyu.databrary.org/volume/1006
|
other face effect
|
1
|
|
1006
|
https://nyu.databrary.org/volume/1006
|
face perception
|
1
|
|
1038
|
https://nyu.databrary.org/volume/1038
|
recovery
|
1
|
|
1038
|
https://nyu.databrary.org/volume/1038
|
thematic analysis
|
1
|
|
1038
|
https://nyu.databrary.org/volume/1038
|
bipolar disorder
|
1
|
|
1038
|
https://nyu.databrary.org/volume/1038
|
self-management
|
1
|
|
1038
|
https://nyu.databrary.org/volume/1038
|
mindfulness
|
1
|
|
1038
|
https://nyu.databrary.org/volume/1038
|
buddhism
|
1
|
|
1038
|
https://nyu.databrary.org/volume/1038
|
wellness
|
1
|
|
1042
|
https://nyu.databrary.org/volume/1042
|
walking
|
1
|
|
1042
|
https://nyu.databrary.org/volume/1042
|
errors
|
1
|
|
1042
|
https://nyu.databrary.org/volume/1042
|
learning
|
1
|
|
1042
|
https://nyu.databrary.org/volume/1042
|
fall severity
|
1
|
|
1042
|
https://nyu.databrary.org/volume/1042
|
play
|
1
|
|
1042
|
https://nyu.databrary.org/volume/1042
|
falls
|
1
|
|
1062
|
https://nyu.databrary.org/volume/1062
|
american sign language
|
1
|
|
1062
|
https://nyu.databrary.org/volume/1062
|
rgbd video
|
1
|
|
1062
|
https://nyu.databrary.org/volume/1062
|
dataset
|
1
|
|
1116
|
https://nyu.databrary.org/volume/1116
|
locomotion
|
1
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|
1116
|
https://nyu.databrary.org/volume/1116
|
motor development
|
1
|
|
1116
|
https://nyu.databrary.org/volume/1116
|
artificial intelligence
|
1
|
|
1116
|
https://nyu.databrary.org/volume/1116
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infants
|
1
|
|
1116
|
https://nyu.databrary.org/volume/1116
|
machine learning
|
1
|
|
1128
|
https://nyu.databrary.org/volume/1128
|
parent-child interaction
|
1
|
|
1129
|
https://nyu.databrary.org/volume/1129
|
dance
|
1
|
|
1129
|
https://nyu.databrary.org/volume/1129
|
groove
|
1
|
|
1129
|
https://nyu.databrary.org/volume/1129
|
music
|
1
|
|
1129
|
https://nyu.databrary.org/volume/1129
|
auditory-motor development
|
1
|
|
1143
|
https://nyu.databrary.org/volume/1143
|
limits of stability
|
1
|
|
1143
|
https://nyu.databrary.org/volume/1143
|
sitting development
|
1
|
|
1143
|
https://nyu.databrary.org/volume/1143
|
postural control
|
1
|
|
1159
|
https://nyu.databrary.org/volume/1159
|
language and thought
|
1
|
|
1159
|
https://nyu.databrary.org/volume/1159
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cognitive development
|
1
|
|
1159
|
https://nyu.databrary.org/volume/1159
|
essentialism
|
1
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|
1159
|
https://nyu.databrary.org/volume/1159
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remote research
|
1
|
|
1159
|
https://nyu.databrary.org/volume/1159
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child development
|
1
|
|
1159
|
https://nyu.databrary.org/volume/1159
|
social cognition
|
1
|
|
1170
|
https://nyu.databrary.org/volume/1170
|
faces
|
1
|
|
1170
|
https://nyu.databrary.org/volume/1170
|
emotional development
|
1
|
|
1170
|
https://nyu.databrary.org/volume/1170
|
cafe
|
1
|
|
1170
|
https://nyu.databrary.org/volume/1170
|
facial expressions
|
1
|
|
1170
|
https://nyu.databrary.org/volume/1170
|
emotional faces
|
1
|
|
1196
|
https://nyu.databrary.org/volume/1196
|
infant data
|
1
|
|
1196
|
https://nyu.databrary.org/volume/1196
|
infant attention
|
1
|
|
1196
|
https://nyu.databrary.org/volume/1196
|
observation
|
1
|
|
1206
|
https://nyu.databrary.org/volume/1206
|
preschool
|
1
|
|
1206
|
https://nyu.databrary.org/volume/1206
|
pretense language
|
1
|
|
1206
|
https://nyu.databrary.org/volume/1206
|
freeplay
|
1
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|
1206
|
https://nyu.databrary.org/volume/1206
|
playground
|
1
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|
1206
|
https://nyu.databrary.org/volume/1206
|
play
|
1
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|
1206
|
https://nyu.databrary.org/volume/1206
|
preschoolers
|
1
|
|
1206
|
https://nyu.databrary.org/volume/1206
|
kindergarten
|
1
|
|
1223
|
https://nyu.databrary.org/volume/1223
|
joint attention
|
1
|
|
1223
|
https://nyu.databrary.org/volume/1223
|
object play
|
1
|
|
1223
|
https://nyu.databrary.org/volume/1223
|
joint engagement
|
1
|
|
1223
|
https://nyu.databrary.org/volume/1223
|
language input
|
1
|
|
1223
|
https://nyu.databrary.org/volume/1223
|
triadic interaction
|
1
|
|
1253
|
https://nyu.databrary.org/volume/1253
|
food-sharing
|
1
|
|
1253
|
https://nyu.databrary.org/volume/1253
|
social development
|
1
|
|
1253
|
https://nyu.databrary.org/volume/1253
|
infant development
|
1
|
|
1256
|
https://nyu.databrary.org/volume/1256
|
sticky mittens
|
1
|
|
1269
|
https://nyu.databrary.org/volume/1269
|
compendium
|
1
|
|
1269
|
https://nyu.databrary.org/volume/1269
|
examples
|
1
|
|
1269
|
https://nyu.databrary.org/volume/1269
|
teaching
|
1
|
|
1269
|
https://nyu.databrary.org/volume/1269
|
teaching clips
|
1
|
|
1281
|
https://nyu.databrary.org/volume/1281
|
rem sleep
|
1
|
|
1281
|
https://nyu.databrary.org/volume/1281
|
active sleep
|
1
|
|
1281
|
https://nyu.databrary.org/volume/1281
|
sensorimotor
|
1
|
|
1281
|
https://nyu.databrary.org/volume/1281
|
quiet sleep
|
1
|
|
1281
|
https://nyu.databrary.org/volume/1281
|
sleep spindle
|
1
|
|
1281
|
https://nyu.databrary.org/volume/1281
|
non-rem sleep
|
1
|
|
1300
|
https://nyu.databrary.org/volume/1300
|
webcam
|
1
|
|
1300
|
https://nyu.databrary.org/volume/1300
|
home
|
1
|
|
1300
|
https://nyu.databrary.org/volume/1300
|
newborn
|
1
|
|
1300
|
https://nyu.databrary.org/volume/1300
|
lookit
|
1
|
|
1311
|
https://nyu.databrary.org/volume/1311
|
rem sleep
|
1
|
|
1311
|
https://nyu.databrary.org/volume/1311
|
sleep
|
1
|
|
1311
|
https://nyu.databrary.org/volume/1311
|
sensorimotor development
|
1
|
|
1311
|
https://nyu.databrary.org/volume/1311
|
non-rem sleep
|
1
|
|
1311
|
https://nyu.databrary.org/volume/1311
|
twitching
|
1
|
|
1311
|
https://nyu.databrary.org/volume/1311
|
brain activity
|
1
|
|
1312
|
https://nyu.databrary.org/volume/1312
|
motor development
|
1
|
|
1312
|
https://nyu.databrary.org/volume/1312
|
walking
|
1
|
|
1312
|
https://nyu.databrary.org/volume/1312
|
crawling
|
1
|
|
1312
|
https://nyu.databrary.org/volume/1312
|
planning
|
1
|
|
1312
|
https://nyu.databrary.org/volume/1312
|
obstacle navigation
|
1
|
|
1312
|
https://nyu.databrary.org/volume/1312
|
perception
|
1
|
|
1321
|
https://nyu.databrary.org/volume/1321
|
cultural artifacts
|
1
|
|
1321
|
https://nyu.databrary.org/volume/1321
|
social learning
|
1
|
|
1321
|
https://nyu.databrary.org/volume/1321
|
parent-child interaction
|
1
|
|
1326
|
https://nyu.databrary.org/volume/1326
|
preferential looking
|
1
|
|
1326
|
https://nyu.databrary.org/volume/1326
|
word comprehension
|
1
|
|
1326
|
https://nyu.databrary.org/volume/1326
|
nine month olds
|
1
|
|
1326
|
https://nyu.databrary.org/volume/1326
|
pennsylvania
|
1
|
|
1326
|
https://nyu.databrary.org/volume/1326
|
six month olds
|
1
|
|
1357
|
https://nyu.databrary.org/volume/1357
|
attention
|
1
|
|
1357
|
https://nyu.databrary.org/volume/1357
|
sleep inertia
|
1
|
|
1357
|
https://nyu.databrary.org/volume/1357
|
infancy
|
1
|
|
1357
|
https://nyu.databrary.org/volume/1357
|
sitting
|
1
|
|
1357
|
https://nyu.databrary.org/volume/1357
|
sleep
|
1
|
|
1367
|
https://nyu.databrary.org/volume/1367
|
infant contextual cueing
|
1
|
|
1367
|
https://nyu.databrary.org/volume/1367
|
infant visual search
|
1
|
|
1367
|
https://nyu.databrary.org/volume/1367
|
memory-guided attention
|
1
|
|
1394
|
https://nyu.databrary.org/volume/1394
|
ethnic differences
|
1
|
|
1394
|
https://nyu.databrary.org/volume/1394
|
individual differences
|
1
|
|
1394
|
https://nyu.databrary.org/volume/1394
|
play
|
1
|
|
1394
|
https://nyu.databrary.org/volume/1394
|
spatial cognition
|
1
|
|
1394
|
https://nyu.databrary.org/volume/1394
|
spatial reasoning
|
1
|
|
1396
|
https://nyu.databrary.org/volume/1396
|
face processing
|
1
|
|
1396
|
https://nyu.databrary.org/volume/1396
|
faces
|
1
|
|
1396
|
https://nyu.databrary.org/volume/1396
|
face recognition
|
1
|
|
1420
|
https://nyu.databrary.org/volume/1420
|
measurement
|
1
|
|
1420
|
https://nyu.databrary.org/volume/1420
|
middle school
|
1
|
|
1420
|
https://nyu.databrary.org/volume/1420
|
fractions
|
1
|
|
1420
|
https://nyu.databrary.org/volume/1420
|
algebra
|
1
|
|
1420
|
https://nyu.databrary.org/volume/1420
|
math cognition
|
1
|
|
1426
|
https://nyu.databrary.org/volume/1426
|
faces
|
1
|
|
1426
|
https://nyu.databrary.org/volume/1426
|
joint attention
|
1
|
|
1492
|
https://nyu.databrary.org/volume/1492
|
trustworthiness
|
1
|
|
1492
|
https://nyu.databrary.org/volume/1492
|
adolescents
|
1
|
|
1492
|
https://nyu.databrary.org/volume/1492
|
event memory
|
1
|
|
1500
|
https://nyu.databrary.org/volume/1500
|
dance
|
1
|
|
1500
|
https://nyu.databrary.org/volume/1500
|
affective theory of mind
|
1
|
|
1505
|
https://nyu.databrary.org/volume/1505
|
motor development
|
1
|
|
1505
|
https://nyu.databrary.org/volume/1505
|
culture
|
1
|
|
1505
|
https://nyu.databrary.org/volume/1505
|
caregiving
|
1
|
|
1506
|
https://nyu.databrary.org/volume/1506
|
motor development
|
1
|
|
1506
|
https://nyu.databrary.org/volume/1506
|
cognitive development
|
1
|
|
1506
|
https://nyu.databrary.org/volume/1506
|
book sharing
|
1
|
|
1506
|
https://nyu.databrary.org/volume/1506
|
parent-child interaction
|
1
|
|
1506
|
https://nyu.databrary.org/volume/1506
|
parent intervention
|
1
|
|
1506
|
https://nyu.databrary.org/volume/1506
|
children
|
1
|
|
1506
|
https://nyu.databrary.org/volume/1506
|
socio-cognitive development
|
1
|
|
1506
|
https://nyu.databrary.org/volume/1506
|
executive function
|
1
|
|
1506
|
https://nyu.databrary.org/volume/1506
|
language development
|
1
|
|
1521
|
https://nyu.databrary.org/volume/1521
|
images
|
1
|
|
1521
|
https://nyu.databrary.org/volume/1521
|
body
|
1
|
|
1521
|
https://nyu.databrary.org/volume/1521
|
whr
|
1
|
|
1521
|
https://nyu.databrary.org/volume/1521
|
stimuli
|
1
|
|
1521
|
https://nyu.databrary.org/volume/1521
|
visuoperception
|
1
|
|
1541
|
https://nyu.databrary.org/volume/1541
|
road crossing
|
1
|
|
1541
|
https://nyu.databrary.org/volume/1541
|
pedestrian
|
1
|
|
1541
|
https://nyu.databrary.org/volume/1541
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affordances
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1
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1541
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https://nyu.databrary.org/volume/1541
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perception-action
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1
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1541
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https://nyu.databrary.org/volume/1541
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virtual environments
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1
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1542
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https://nyu.databrary.org/volume/1542
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bicycling
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1
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1542
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https://nyu.databrary.org/volume/1542
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road crossing
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1
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1542
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https://nyu.databrary.org/volume/1542
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affordances
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1
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1542
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https://nyu.databrary.org/volume/1542
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perception-action
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1
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1542
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https://nyu.databrary.org/volume/1542
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virtual environments
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1
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1559
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https://nyu.databrary.org/volume/1559
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self-regulation
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1
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1559
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https://nyu.databrary.org/volume/1559
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inhibitory control
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1560
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https://nyu.databrary.org/volume/1560
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images
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1
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Optimization
Table of Contents
- What is Optimization?
- What is a Global Optimization?
- What is a Local Optimization?
- What Are the Three Types of Optimization?
- What Is the First Phase of Optimization?
- How Do You Create a Model of the System?
- What is a Solution In Optimization?
- What is the Domain?
- What is the Feasible Domain?
- What is a Feasible Solution?
- What is the Cost Function?
What is Optimization?
Optimization means to find the set of parameters that minimize the cost of a solution to a problem.
What is a Global Optimization?
A Global Optimization is the optimal solution out of all possible solutions. [PSOAI]
What is a Local Optimization?
A Local Optimization is a solution that may not be the global optimum but is good enough. [PSOAI]
What Are the Three Types of Optimization?
- Exhaustive: Try all the combinations.
- Stochastic: Try a random subset and pick the best.
- Learning: Pick random solutions that improve as you go.
What Is the First Phase of Optimization?
The first phase is to create a mathematical model of the system.
How Do You Create a Model of the System?
- Translate parameters for the solution to numbers.
- Represent solutions as vectors of parameters.
- Create a cost function that maps the global minimizing parameters to the optimal solution. [PSOAI]
What is a Solution In Optimization?
A set of parameters that solves the problem with the least cost while still meeting constraints. [IIS]
What is the Domain?
The set of valid parameters (those that solve the problem no matter the cost). [IIS]
What is the Feasible Domain?
The subset of valid parameters that meet the constraints of the problem. [IIS]
What is a Feasible Solution?
A solution that meets all the constraints. [PSOAI]
What is the Cost Function?
A function that maps solutions to their cost (maps the best solution to the best outcome and the worst solution to the worst outcome).


(Requires internet connection to load the JS libraries. There's just too many files that's why they're not included in the package)
Body tag
DTD
Framework script attribute
Art Board is an offline Chromium-based IDE with Live Preview. Perfect for those who don't have internet but want to create CSS images with a JSFiddle/CodePen-like environment.
Maintained and improved by Elmer Balbin Jr. Personal improvements implemented include:
- Layout redesign
- Auto-Draw feature
- Grid lines for guide
- Save & Load functionality
Based on John Cipponeri's JSTinker, an open source clone to JSFiddle.
License
All code belongs to the poster and no license is enforced.
Elmer's Art Board or its authors are not responsible or liable for any loss or damage of any kind during the usage of provided code.
See MIT License.
Grid not aligning?
Add this code to the CSS panel:
body {
margin: 0;
padding: 0;
}


The steps are:
in short:
@ACP_Start-PowerShell
as administrator and execute Update-Hosts
.@ACP_Delete-NATNetwork
as administrator.@ACP_Delete-NATRules
as administrator.@ACP_Create-NATNetwork
as administrator.@ACP_Create-NATRules
as administrator.Copy the content of the <package>\DevEnv for Laptop\@host
folder to your host, under <home directory>
(this is the directory specified by the environment variables $HOMEDRIVE
and $HOMEPATH
- typically: C:\Users\<user name>
) - don't put it somewhere else because the scripts depend on this.
Prepare the PowerShell environment:
<home directory>\Documents\WindowsPowerShell
folder, and if not then create it.profile.ps1
fileprofile.ps1
script from the <home directory>\DevEnv\Scripts
folder.profile.ps1
script from the <home directory>\DevEnv\Scripts
folder manually into the existing<EMAIL_ADDRESS>script as administrator and check that it brings you to your <home directory>\DevEnv\Scripts
folder.profile.ps1
script (see instructions in the script).If you don't have Powershell open anymore, run the @ACP_Start-PowerShell.bat
script as administrator, try
Get-Path.ps1
~/DevEnv/Scripts Set-Path.ps1
Logout and login again.Check if the C:\Windows\System32\drivers\etc\hosts
-file contains the names of our machines. In a PowerShell:
Get-Hosts
The result should contain:
"<IP_ADDRESS> devenv-host"
"<IP_ADDRESS> devenv-gateway"
"<IP_ADDRESS> devenv0"
"<IP_ADDRESS> devenv1"
"<IP_ADDRESS> devenv2"
"<IP_ADDRESS> devenv3"
"<IP_ADDRESS> devenv4"
"<IP_ADDRESS> devenv5"
"<IP_ADDRESS> devenv6"
"<IP_ADDRESS> devenv7"
"<IP_ADDRESS> devenv8"
"<IP_ADDRESS> devenv9"
If not, then:
Update-Hosts
You should now be able to ping any of the virtual machines by name, once they are created.
Reference: https://docs.microsoft.com/en-us/virtualization/hyper-v-on-windows/user-guide/setup-nat-network
If you already have a NAT network, you will first need to delete it (Hyper-V allows only one NAT network), or manually make the necessary modifications to the existing NAT network based on the Create NAT Network section.
Check that there is no NAT network. In a PowerShell:
Get-NetNAT
To remove the existing NAT network:
Remove-NetNAT
Check that there are no "Internal" VM switches:
Get-VMSwitch
To remove the "Internal" VM switches:
Remove-VMSwitch "<name of internal VM switch>"
Remark:
We have an ACP script@ACP_Delete-NATNetwork
to remove the NAT network (!!! all of it) and to remove the internal VM switch with nameVirtual Switch Internal
(only a switch with this name, provided it exists)
Check that there are no NAT rules left:
Get-NetNATStaticMapping
To remove the NAT rules:
Remove-NetNATStaticMapping
Remark:
We have an ACP script@ACP_Delete-NATRules
to remove the NAT rules (!!! all of them)
Check if there are private IP addresses from the old NAT still assigned to an adapter:
Get-NetIPAddress -InterfaceAlias "vEthernet (<name of internal VM-switch>)"
To remove the old private IP addresses:
Remove-NetIPAddress -InterfaceAlias "vEthernet (<name of internal VM-switch>)" -IPAddress <IP address>
Reference: https://docs.microsoft.com/en-us/virtualization/hyper-v-on-windows/user-guide/setup-nat-network
Reference: https://www.petri.com/using-nat-virtual-switch-hyper-v
Reference: https://anandthearchitect.com/2018/01/06/windows-10-how-to-setup-nat-network-for-hyper-v-guests
In File Explorer on the host, run @ACP_Create-NATNetwork
as an administrator.
Alternatively, in a PowerShell on the host:
Create-NATNetwork
This script creates a virtual switch and a NAT network:
Reference: https://www.petri.com/create-nat-rules-hyper-v-nat-virtual-switch
In File Explorer on the host, run @ACP_Create-NATRules
as an administrator.
Alternatively, in a PowerShell:
Create-NATRules
This script creates NAT rules:


Sustainable Education Foundation
We empower students, education institutes and education as a whole in Sri Lanka.
Mission
To empower students, education institutes and education as a whole in Sri Lanka.
Brief introduction about us
Sustainable Education Foundation (SEF) is a volunteer driven organisation founded by the former Google Student Ambassador and an alumni of University of Moratuwa.
Akshika Wijesundara. SEF was founded with the short term goal of making the Sri Lankan education system the most effective and the most dynamic education system in the world. SEF’s long term goal is to disrupt and democratise the global education system where it is available for everyone, in doing so, foundation contributes to the United Nations’ sustainable development goal of “Quality Education”.
Testimonials from our guest speakers
Its great to work with your team and I wish you good luck. Its a great opportunity for me to share my stories with upcoming entrepreneurs and I believe you will keep your great service to motivate the rest.
Sanmugathasan Prasanthan
CEO/ Founder, IT SignatureI believe that education should be free and accessible to everyone! Great to see many initiatives by the SEF community - especially their efforts to encourage the young generation in Sri Lanka to achieve not only in the domestic settings but also in the international level. I wish SEF all the best, keep up the good work!
Nimesha Ranasinghe
Assistant Professor, University of Maine, USASEF professionally organized a lecture I held about Advancements in DNA Sequencing, Bioinformatics and Cancer research. I was more than satisfied and amazed, especially about their strong efforts, devotion and philosophy of providing free education to the wide range of population. Hope the idea will be spread world wide.


It’s a 4 man bracket. The first round will be played to 11, win by 2, switching serves every three. The final round will be played to 21, win by 2, switching serves every five. Sucker serve will be used, and the man down will have 3 chances on the serve per point.
First round will be:
Freshman CJ N vs Sophomore Shreyan D
Junior Ekansh T vs Senior Luke A.
Freshman Adam K will take on veteran rapper, Senior Aaryan P. Sophomore Sal H will take on Junior Thomas P. The crowd will vote on who’s rap they liked best and the winners will battle it out in the next round. Each rap will have some tie in to Ghostbusters, and if ghostbusters is not mentioned, it is an automatic DQ for that contestant.
he race will consist of 5 parts that will be announced and explained prior to the race Wednesday morning.
Freshmans: Parker S, Clark C, Raja M, and Bryan G
Sophomores: Adam A, Silas H, and Patrick F
Juniors: Elijah E, Thomas P, Sahil D, and Toby B
Seniors: Blake H, Benny W, Jackson F, and Sam M
Mr. Owens always puts on a cool show with many experiments and demonstrations.


Johnny English Strikes Again
Johnny English Strikes Again is a movie starring Rowan Atkinson, Ben Miller, and Olga Kurylenko. After a cyber-attack reveals the identity of all of the active undercover agents in Britain, Johnny English is forced to come out of...


The UHHP takes place on the streets of Bed-Stuy on August 17th, 2019 starting at 11am. Come out and be a part of the world’s first social justice hip-hop parade Held to celebrate the birthday of the great social justice activist and freedom fighter Marcus Garvey, this family friendly event will provide a moving platform for elements of hip-hop art and culture combined with progressive social justice messaging.