Variational Inference and VAE
In the previous post, we talked about approximation inference. We want to compute $p(\theta,z|x)$, but it’s too complicated. One strategy is to approximate as $q(\theta,z)$ that has an excellent form where $q$ is a function of variational parameters optimized for each $x$. $q$ is a function of variational parameters optimized for each $x$. Maximize a lower bound on $p(x)$ to choose $q$.
Remember the Bayes’ rule:
$$ p(\theta | x) = \frac{p(\theta)p(x|\theta)}{p(x)} $$
$p(x)$ is called evidence. Remember, in the Gaussian process, regression, etc., we played with some parameters and chose subjectively. The other approach is empirical Bayes to choose those parameters to maximize the evidence. This is a standard way of choosing the parameters. We are bound $p(x)$ in terms of $q_x(z,\theta)$. The indicator $x$ means we are doing this for each $x$. So we are gonna get a lower bound on $p(x)$ or lower bound on $\log p(x)$:
$$ \begin{aligned} \log p(x) = & \log \sum_{\alpha} w_{\alpha} \frac{p(x)}{w_{\alpha}} \\ \geq & \sum_{\alpha} w_{\alpha} \log \frac{p(x)}{w_{\alpha}} \end{aligned} $$
A logarithm is concave.
In our typical example, $\theta$ is continuous, and $z$ is discrete.
Altogether, the evidence lower bound (ELBO) is the following lower bound on $\log p(x)$:
$$ \begin{aligned} \log p(x) = & \int \sum_z q(z,\theta) \log p(x) d \theta \\ =& \sum_z \int q(z,\theta) \log \Big ( \frac{ p(x,z,\theta)q(z,\theta)}{p(z,\theta|x)q(z,\theta)}\Big) d \theta \\ =& \sum_z \int q(z,\theta) \log \Big ( \frac{ p(x,z,\theta)}{q(z,\theta)}\Big) d \theta + \sum_z \int q(z,\theta) \log \Big ( \frac{q(z,\theta)}{p(z,\theta|x)}\Big) d \theta \\ \geq & \sum_z \int q(z,\theta) \log \Big ( \frac{ p(x,z,\theta)}{q(z,\theta)}\Big) d \theta \\ = & H(q) + \mathbb{E}_q(\log p(x,z,\theta)) \end{aligned} $$
We used the fact that
$$ \sum_z \int q(z,\theta) \log \Big ( \frac{q(z,\theta)}{p(z,\theta|x)}\Big) d \theta $$
Is non-negative. This term is called KL-divergence. $ H(q) $ is entropy and $\mathbb{E}_q(\log p(x,z,\theta))$ is cross-entropy. We will maximize the sum of these two concerning parameters of $q$.
Example 1:
Let’s say we have a binary random variable with mean $\mu$. $$ H(\mu) = -\mu \log \mu -(1-\mu) \log (1-\mu) $$
It’s maximized when there is the most significant uncertainty or uniformity in the parameter $\theta$ or $\mu$. This prevents overfitting and helps our inference not to be very special.
The second term $\mathbb{E}_q(\log p(x,z,\theta))$ is trying to align $q$ and $p$ while keeping the entropy large that’s going to spread out the $q$ distribution. This is a general receipt, and we have yet to discuss how you should choose $q$. If we choose $q$ to be true posterior, we will get a good lower bound, but I can’t compute with it. I want some $q$ that is easy to compute and flexible enough to give us a good lower bound.
Example 2: A finite mixture model
Fix two distributions $F_0$ and $F_1$: $$ \begin{align} \theta \sim \text{Beta}(\alpha,\beta) \\ X| \theta \sim \theta F_1 +(1-\theta)F_0 \end{align} $$
the likelihood for the data $x_1,..,x_n$ is: $$ p(x_{1:n}) =\int_0^1 \text{Beta}(\theta|\alpha,\beta) \prod_{i=1}^n(\theta f_1(x_i)+(1-\theta)f_0(x_i))d\theta $$
We aim to approximate the posterior $p(\theta|x_{1:n})$ using a variational algorithm. Our variational approximation is:
$$ q(z,\theta) = q(\theta| \gamma_1,\gamma_2) \prod_{i=1}^n q_i^{z_i} (1-q_i)^{1-z_i} $$
where $q(\theta| \gamma_1,\gamma_2)$ is a $\text{Beta}(\gamma_1,\gamma_2)$ distribution and $0 \leq q_i \leq 1$ are $n$ free parameters. As we talked about earlier we need to maximize the following: $$ \text{ELBO: }H(q)+\mathbb{E}_q \log p$$.
First, we have: $$ \log p(x,\theta,z) = \log p(\theta|\alpha,\beta) + \sum_{i=1}^n \{ \log (\theta^{z_i}f_1(x_i))+ \log (\theta^{1-z_i}f_0(x_i))\} $$
Next, we use identities such as:
$$ \mathbb{E}_q \log \theta = \psi(\gamma_1) - \psi(\gamma_1+\gamma_2) $$
for the digamma function $\psi(.)$.
Overall, how many parameters do we have here? $n+2$: one for each data point and two $\gamma$s which are parameterizing the Beta distribution.
Variational Inference
Iterate the following steps for variational parameters $q_{1:n}$ and $(\gamma_1, \gamma_2)$:
-
Holding $q_i$ fixed, set $\gamma = (\gamma_1,\gamma_2)$ to: $$ \gamma_1 = \alpha +\sum_{i=1}^n q_i , \gamma_2 = \beta + n - \sum_{i=1}^n q_i $$
-
Holding $\gamma_1$ and $\gamma_2$ fixed, set $q_i$ to:
$$ q_i = \frac{f_1(x_i)\exp \psi(\gamma_1)}{f_1(x_i)\exp \psi(\gamma_1) + f_0(x_i)\exp \psi(\gamma_2) } $$
After convergence, the approximate posterior distribution over $\theta$ is: $$ \hat{p}(\theta| x_{1:n}) = \text{Beta}(\theta|\gamma_1,\gamma_2) $$
very similar to Gibbs sampler where $\sum_{i=1}^n q_i$ is expected number of heads. Note that it’s not a single Beta distribution. It’s mixture of Beta distributions.
This is very close to the topic model. $$ \begin{align} \theta \sim \text{Dirichlet}(\alpha_1,..\alpha_k) \\ X|\theta \sim \theta_1 F_1 + …+ \theta_k F_k \end{align} $$
The likelihood for a single data point $x$ is:
$$ p(x) = \int \text{Dirichlet}(\theta|\alpha_1,..,\alpha_k) \Big( \sum_{j=1}^k \theta_jf_j(x)\Big) d\theta $$
after convergence we will pull out $q_i$s and $\gamma_1$ and $\gamma_2$.
Variational autoencoders
Variational autoencoders are generative models that are trained using variational inference. The “decoder” is a neural net generated from a latent variable. The “encoder” approximates the posterior distribution with another neural network trained using variational inference.
We will use multivariate Gaussian for our variational distribution. VAE aims to parametrize mean and variance using a second set of neural networks.
$$ \begin{align} z\sim & \text{Normal}(0,I_K) \\ x | Z = & G(Z) \end{align} $$
Where $I_K$ is the identity covariance matrix.
$G(z)$ is the generator network or decoder.
For example, use a 2-layer network:
$$ G(Z) = A_2 \text{ReLU}(A_1z+b_1) + b_2 $$
Let’s say $K=100$, and $X$ is in the $1000$ dimension, and we don’t have ReLU. Then $I_K$ will be rank deficient, and the data will be in $100$ dimensional subspace in $10,000$ dimensions but Gaussian in that subspace. This is useless to us since we will not get anything like images if we do that. So we need a non-linearity.
As $G(.)$ is nonlinear, the posterior distribution $p(z|x)$ is intractable. Solution: we will use variational inference:
$$ q(z|x) = \text{Normal}(\mu(x),\sigma^2(x)I_K) $$
where now $\mu_j(x)$ and $\sigma^2_j(x)$ are the variational parameters for $j=1,2,..,k$. Then we can compute ELBO.
But we are going to do it differently. We will have another (encoder) neural network:
$$ \mu(x) = B_2 \text{ReLU}(B_1(x)+d_1)+ d_2 $$
and similarly for $\log \sigma^2(x)$.
And then, we are going to train the second neural network using ELBO. And this second neural network is going to give me my variational approximation.
How to train?
- Decoder network $z\mapsto x$, weights $A,b$.
- Encoder network: $x\mapsto \mu(x)$, $\log \sigma^2(x)$, weights $B,d$
- Train both networks simultaneously to maximize the ELBO
- Decoder trained with $\mathbb{E}_q \log p(x,Z_s)$ over weights $A,b$
- Encoder trained with $H(q)+\mathbb{E}_q \log p(x,Z_s)$ over weights $B,d$
Law of large numbers
How can we estimate these gigantic probabilities? Solution: just sample from it many and many times.
Now, approximate $\mathbb{E}_q \log p(x,Z_s)$ by sampling:
$$ \mathbb{E}_q \log p(x,Z_s) \approx \frac{1}{N} \sum_s \log p(x,Z_s) $$
Problem: The parameters of the recognition network have disappeared!
Solution: Reparameterize the samples by $Z_i = \mu(x)+\sigma(x)\epsilon_i$ where $\epsilon_i = \text{Normal}(0,I_K)$.
Simple Example
Suppose $x|z\sim \text{Normal}(G(z),I)$ where generator network is: $$ G(z)=\text{ReLU}(Az+b) $$
then $-\log p(x|z)$ is:
$$ \frac{1}{2} ||x-\text{ReLU}(Az+b)||^2 $$
Suppose the approximate posterior is:
$$ q(z|x) = \text{Normal}(\mu(x),\sigma^2(x)I_K) $$
where encoder network is $\mu(x)=\text{ReLU}(Bx+d)$. Then,
$$ -\mathbb{E}_q \log p(x|Z) = \frac{1}{N} \sum_s ||x-\text{ReLU}(AZ_s+b)||^2 $$
$$ = \frac{1}{N} \sum_{s=1}^N \frac{1}{2}||x-\text{ReLU}(A(\text{ReLU}(Bx+d))+\epsilon_s \sigma(x)+b)||^2 $$
We covered this post in the intermediate machine learning SDS 365/565, Yale University, John Lafferty, where I was TF.


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--- layout: post title: Backbone.Validation with Chaplin and CoffeeScript date: 2013-09-16 08:20:33.000000000 -05:00 categories: - Web Development tags: - backbone.js - chaplin.js - coffeescript - javascript status: publish type: post published: true meta: _edit_last: '25054220' _wpas_done_881585: '1' _publicize_pending: '1' _publicize_done_external: a:1:{s:8:"facebook";a:1:{i:1471181839;b:1;}} publicize_twitter_user: adamnengland _wpas_done_881586: '1' _wpas_done_881584: '1' twitter_cards_summary_img_size: a:6:{i:0;i:590;i:1;i:258;i:2;i:3;i:3;s:24:"width="590" height="258"";s:4:"bits";i:8;s:4:"mime";s:9:"image/png";} _oembed_6f6e824e3edbdfdb455843cb34ecb992: '{{unknown}}' _oembed_68fbb5e9a6b640970b133374f3300efc: '{{unknown}}' author: Adam N England ---
Any sizable web application needs validation. Doing it yourself is for the birds, so I wanted to incorporate a backbone plugin to help solve the problem. For this example I chose to use Backbone.Validation.
Start with a basic framework. Brunch Application Assembler is a great way to bootstrap these projects. I used Paul Miller's brunch-with-chaplin skeleton.
brunch new gh:paulmillr/brunch-with-chaplin
To start up the server, type brunch watch --server and go to http://localhost:3333/ in a new browser window. If everything is good, you'll see this:
You'll need a basic application to test out our concept, so we'll modify the routes and the controller, and add a new view and template to our project.
{% highlight coffeescript linenos %} module.exports = (match) -> match '', 'home#index' match 'form', 'home#form' {% endhighlight %} {% highlight coffeescript linenos %} Controller = require 'controllers/base/controller' HeaderView = require 'views/home/header-view' FormView = require 'views/home/form' module.exports = class HomeController extends Controller form: -> @view = new FormView region: 'main' {% endhighlight %} {% highlight coffeescript linenos %} View = require 'views/base/view' Form = require 'models/form' module.exports = class FormView extends View autoRender: true className: 'form-view' template: require './templates/form' events: 'click a.validateButton' : "validate" initialize: -> super @model = new Form() validate: (e) -> @model.validate() e.preventDefault() {% endhighlight %} {% highlight html linenos %} {% endhighlight %}With that code in place, lets do a quick checkpoint http://localhost:3333/form. We should get an ugly view like this:
So, we know we want a basic form that can save name, phone, and email. Following the guidelines on the validation docs https://github.com/thedersen/backbone.validation, lets add the rules to our model.
{% highlight coffeescript linenos %} BaseModel = require 'models/base/model' module.exports = class Form extends BaseModel validation : name: required: true email: required: true pattern: "email" {% endhighlight %}We'll also need to add the code to our vendor/scripts folder.
In a perfect world, the @model.validate() call would execute our validation rules. However, in this world, we get a javascript error
Uncaught TypeError: Object # has no method 'validate'
There is one final step. We need to bind our model to the validation, so add the call in the attach method of our view:
{% highlight coffeescript linenos %} attach: -> super Backbone.Validation.bind(@); {% endhighlight %}Thats it! Full source code for the example is available on github.


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Tucia
Hover Slide Effect with jQuery Demo
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PizzaCrustSize
public enum PizzaCrustSize : CaseIterable, CustomStringConvertible
The pizza size, e.g. small, medium, large, etc.
-
Undocumented
Declaration
Swift
case small
-
Undocumented
Declaration
Swift
case medium
-
Undocumented
Declaration
Swift
case large
-
The string representation of the size.
Declaration
Swift
public func name() -> String
-
The description of this crust size.
Declaration
Swift
public var description: String { get }


Welcome
I am 4th Year Undergraduate at IIT Kanpur in the Department of Computer Science and Engineering. Interested in parallel programming, web development, and software development.
My Skills
Html, CSS, Javascript, PHP, node.js, MySql, C/C++, P5.js, jquery, python, octave and Skating
I made this site is made mainly using P5.js for the background. If you want to see its code, it is available on my github.


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--- layout: default ---
I'm Nick Meyer. Currently, I am in graduate school at North Carolina State University studying statistics. I grew up in Michigan in a suburb of Detroit and moved to Raleigh in 2012 for graduate school. For undergrad, I studied at the University of Michigan where my highest priority was attending the football games. Go Blue!!!


Best travel pillow for 2019
If you're off travelling, you'll want to be as comfortable on your journey as possible. These are the best travel pillows to buy now that will help to keep you nice and cosy
by Brijiena Lovelace, https://www.mirror.co.uk/authors/brijiena-lovelace/Travel, we all love to and some of us are lucky enough to travel frequently.
However, something no one seems to talk about is how uncomfortable travelling can be, especially for long periods of time.
Whether you're flying, taking a long train, car or coach journey - the success to staying comfortable is a travel pillow. They're easily transportable, compact and most importantly, they're comfortable.
Whatever your travelling needs, we've rounded up some of the best travel pillows you can buy right now.
Best travel pillows for 2019
1. BCOZZY Kids Travel Pillow
Have you ever looked back at the kids during a long car journey and their poor necks are jerking forward every few seconds?
Now every car journey can be comfortable with the BCOZZY travel pillow from Amazon. The soft and flexible pillow aids the neck and gives maximum head supports whilst stopping their necks from rolling side to side and jolting forwards.
The pillows come available in a range of colors and sizes that are suitable for adults too.
Price: £24.97, Amazon - buy here now
2. Airgoods Inflatable Travel Pillow
This contraption is an inflatable pillow, although it doesn't really look like it. But if you often find yourself trying to rest your head on a trains foldable tray, this is the answer for you.
Foldable trays are not only littered with germs and bacteria, honestly speaking, they aren't very comfortable either.
 The innovative design is breathable and offers you maximum support for your head, neck and shoulders.
Price: £16.52, Ebay - buy here now
3. Trtl Pillow
This pillows has multi-uses and can also be worn as a scarf, which is nice and convenient when travelling (especially in the cold).
Simply unwrap the pillow and wrap it around your neck as you would any scarf, ad the scientifically proven neck support holds the neck in an ergonomic position during rest.
Price: £24.95, Amazon - buy here now
4. it Luggage Memory Travel Pillow
This neck support pillow has a pretty traditional design that allows you to rest comfortably during trips.
The memory foam travel pillow adapts to your own preferred shape and is finished in a super soft fleeced fabric.
Price: £12.99, Argos - buy here now
5. Ostrich Pillow GO Luxury Travel Pillow
This Ostrich travel pillow has a ergonomic design that is sure to give you a restful sleep on your travels.
It offers 360 degrees of neck support, as well as being lined with high density memory foam for maximum comfort. A velcro strap lets you find a custom fit that suits you.
Price: £49.99, Amazon - buy here now
6. Go Travel 2 Memory Dreamer Pillow
Dream on the go, with the Go Travel dreamer pillow from John Lewis.
Formed from deluxe memory foam, the pillow has a contoured back which allows you to rest in a natural and comfortable position.
It also comes in a variety of colours to suit you.
Price: £19.99, John Lewis - buy here now
7. Soft Comfortable Hooded Neck Travel Pillow
Perfect, now you can snooze completely in peace and comfortably too.
This genius neck pillow offers you maximum support with a soft fabric filled with squishy polystyrene beads. It also features a hood so you aren't bothered during your slumber.
Price: £6.29, Ebay - buy here now
8. Huzi Infinity Pillow
This infinity pillow is made from breathable bamboo fabric and is versatile to fit you for a range of comfort needs.
It comes in six different colours and is easily transported between between destinations.
The ergonomic design allows you to stay comfortable in a range of different positions to suit you.
Price: £34.99, Amazon - buy here now
9. Joyfeel Travel Neck Pillow
If you're looking for an affordable travel pillow that will keep you comfortable in various travelling scenarios, this one from Amazon at under £4 is a great one to consider.
This memory foam pillow has an orthopedic lobe contouring on either side that allows your neck and head to lean comfortably and naturally in position.
This design is foldable, which makes carrying it around with you even more convenient.
Price: £3.32, Amazon - buy here now
10. Travel Double Comfort Pillow
Samsonite are renowned for being experts in the travel field, so a travel pillow from the brand was sure to make it to or roundup.
At the rear of the pillow is a special bolster which supports your head and neck so you can rest in a comfortable position.
Price: £15, Samsonite - buy here now


Results your saved matching results
{% if results %}
| Title | Created | Algorithm | Best Score | Results | Action |
|---|---|---|---|---|---|
| {{ result.title }} | {{ result.created }} | {{ result.algorithm }} | {{ result.best_score | round(3) }} | {{ result.count }} | View Delete |
No result found.
{% endif %}


And using the allotted time at the start of the round to it's full potential is crucial, setting up tactics for the round ahead of you. Having your favorite outfits at just the press of a button gives you that extra mobility


Hello World!
^- I didn't like the non-programmy into line that Andy had us do so I went with the standard intro to programming line.
NOTES:
Thinking like a programmer:
1. Procedural Thinking
Breaking a problem into steps.
2. Abstract Thinking
It's abstract and it's thinking.
3. Systems Thinking
Seems to be a lot like Step 1. Procedural Thinking
4. Technological Thinking (Technological Empathy)
Understanding what is going on "under the hood" with technology.
5. Debugging (Systematic Debugging)
ID the problem(s) and track down the cause(s).
Blah, blah, blah Andy's story
Just kidding, I agree with the "Just about anyone can learn just about anything". I'll be applying to Udacity soon too. ;)
Comments to course designer:
Require signing up for account at Codepen
The reason for this will be addressed in comment #2.
Submit projects through Codepen
The reason for comment #1! I think this is a much cleaner way to submit projects than a zip file.
Ignore the horrible formatting
I only know a little bit about HTML and CSS.


<%- body %>
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C
I had a friend named Ramblin' Bob
C
Who used to steal gamble and rob
C7 F
He thought he was the smartest guy around
F
But I found out last Monday
F
That Bobby got locked up Sunday
D7 G
They've got him in the jailhouse way down south
C C7 F
He's in the jailhouse now he's in the jailhouse now
G
I told him once or twice quit playin' cards and shootin' dice
C
He's in the jailhouse now
I went out last Tuesday met a gal named Susie
Told me I was the smartest guy around
She started callin me honey
Started into spendin my money
A big poilice man came and knocked me down
I'm in the jailhouse now
I'm in the jailhouse now
Told me once or twice quit playin' cards and shootin' dice
But I'm in the jailhouse now


public class Announcement
extends java.lang.Object
An announcement is exchanged in json format and carries a timeout.
| Constructor and Description |
|---|
Announcement(java.lang.String ownerId) |
Announcement(java.lang.String ownerId,
int protocolVersion) |
| Modifier and Type | Method and Description |
|---|---|
void |
addIncomingTopologyAnnouncement(Announcement incomingTopologyAnnouncement)
adds an incoming announcement to this announcement
|
java.lang.String |
asJSON()
Convert this announcement into json
|
boolean |
correspondsTo(Announcement announcement)
Compare this Announcement with another one, ignoring the 'created'
property - which gets added to the JSON object automatically due
to SLING-3389 wire-backwards-compatibility - and backoffInterval
introduced as part of SLING-3382
|
static Announcement |
fromJSON(java.lang.String topologyAnnouncementJSON)
Create an announcement form json
|
long |
getBackoffInterval()
Gets the backoffInterval which the connector servlets passes back to the client to use as the next heartbeatInterval
|
long |
getOriginallyCreatedAt() |
java.lang.String |
getOwnerId()
Returns the slingid of the owner of this announcement.
|
java.lang.String |
getPrimaryKey()
the key which is unique to this announcement
|
int |
getProtocolVersion()
Returns the protocolVersion of this announcement
|
long |
getReceivedAt() |
boolean |
getResetBackoff()
gets the resetBackoff flag
|
java.lang.String |
getServerInfo()
the information about the server where this announcement came from
|
boolean |
isCorrectVersion()
check whether this is announcement contains the valid protocol version
|
boolean |
isInherited()
Returns the inherited flag - if true this means that this announcement is the response of a topology connect
|
boolean |
isLoop()
Returns the loop flag - set when this announcement represents a loop detected in the topology connectors
|
boolean |
isValid()
check whether this is a valid announcement, containing the minimal information
|
java.util.Collection<InstanceDescription> |
listInstances()
Returns the list of instances that are contained in this announcement
|
void |
persistTo(Resource announcementsResource)
Persists this announcement using the given 'announcements' resource,
under which a node with the primary key is created
|
void |
registerPing(Announcement incomingAnnouncement) |
void |
removeInherited(java.lang.String ownerId)
Remove all announcements that match the given owner Id
|
void |
setBackoffInterval(long backoffInterval)
Sets the backoffInterval which the connector servlets passes back to the client to use as the next heartbeatInterval
|
void |
setInherited(boolean inherited)
set the inherited flag - if true this means this announcement is the response of a topology connect
|
void |
setLocalCluster(ClusterView localCluster)
sets the local clusterview
|
void |
setLoop(boolean loop)
Sets the loop falg - set true when this announcement should represent a loop detected in the topology connectors
|
void |
setResetBackoff(boolean resetBackoff)
sets the resetBackoff flag
|
void |
setServerInfo(java.lang.String serverInfo)
sets the information about the server where this announcement came from
|
java.lang.String |
toString() |
public Announcement(java.lang.String ownerId)
public Announcement(java.lang.String ownerId, int protocolVersion)
public java.lang.String toString()
toString
in class java.lang.Object
public boolean isCorrectVersion()
public boolean isValid()
public void setInherited(boolean inherited)
public boolean isInherited()
public void setLoop(boolean loop)
public void setBackoffInterval(long backoffInterval)
public long getBackoffInterval()
public long getOriginallyCreatedAt()
public long getReceivedAt()
public void setResetBackoff(boolean resetBackoff)
public boolean getResetBackoff()
public boolean isLoop()
public int getProtocolVersion()
public void setServerInfo(java.lang.String serverInfo)
public java.lang.String getServerInfo()
public java.lang.String getOwnerId()
The owner is the instance which initiated the topology connection
public static Announcement fromJSON(java.lang.String topologyAnnouncementJSON)
public void setLocalCluster(ClusterView localCluster)
public void addIncomingTopologyAnnouncement(Announcement incomingTopologyAnnouncement)
public java.lang.String asJSON()
public java.lang.String getPrimaryKey()
public java.util.Collection<InstanceDescription> listInstances()
public void persistTo(Resource announcementsResource) throws PersistenceException
PersistenceException
public void removeInherited(java.lang.String ownerId)
public boolean correspondsTo(Announcement announcement)
public void registerPing(Announcement incomingAnnouncement)
Copyright © 2022 The Apache Software Foundation. All rights reserved.


| File | Statements | Branches | Functions | Lines | |||||
|---|---|---|---|---|---|---|---|---|---|
| CacheNode.ts | 100% | 19/19 | 85.71% | 6/7 | 100% | 7/7 | 100% | 18/18 | |
| Change.ts | 100% | 23/23 | 100% | 0/0 | 100% | 7/7 | 100% | 22/22 | |
| ChildChangeAccumulator.ts | 75.86% | 22/29 | 52% | 13/25 | 100% | 3/3 | 75% | 21/28 | |
| CompleteChildSource.ts | 96.3% | 26/27 | 70% | 7/10 | 85.71% | 6/7 | 96% | 24/25 | |
| Event.ts | 75.76% | 25/33 | 100% | 2/2 | 50% | 6/12 | 74.19% | 23/31 | |
| EventGenerator.ts | 97.62% | 41/42 | 92.86% | 13/14 | 100% | 10/10 | 97.37% | 37/38 | |
| EventQueue.ts | 98.31% | 58/59 | 95% | 19/20 | 100% | 12/12 | 98.25% | 56/57 | |
| EventRegistration.ts | 79.52% | 66/83 | 69.44% | 25/36 | 76.19% | 16/21 | 79.01% | 64/81 | |
| QueryParams.ts | 81.22% | 147/181 | 55.74% | 34/61 | 91.67% | 22/24 | 81.11% | 146/180 | |
| View.ts | 91.86% | 79/86 | 89.66% | 26/29 | 92.31% | 12/13 | 91.76% | 78/85 | |
| ViewCache.ts | 100% | 20/20 | 100% | 4/4 | 100% | 8/8 | 100% | 19/19 | |
| ViewProcessor.ts | 89.18% | 206/231 | 82.48% | 113/137 | 95.83% | 23/24 | 88.94% | 201/226 |


The Thomas Crown Affair **1/2
rated R MGM Pictures 113 minutes starring Pierce Brosnan, Rene Russo, Denis Leary, Frankie Faison, Faye Dunaway, Ben Gazzara based on the story by Alan R. Trustman written by Leslie Dixon and Kurt Wimmer directed by John McTiernan
John McTiernan's "The Thomas Crown Affair" is a predictable, overly familiar remake of the Steve McQueen/Faye Dunaway romance caper. Pierce Brosnan is Thomas Crown, a billionaire who also lies to steal fine art. When a valuable piece is missing from the New York Museum of Modern Art, ins urance bounty hunter Katherine(Rene Russo) must investigate Crown, and ends up falling into a passionate love affair with the man whom she knows is guilty. The plot sounds a lot like "Entrapment", with Sean Connery, but the acting in "Thomas Crown" is better. Pierce Brosnan has always annoyed me, playing the same role over and over, but this is that role and he does fine with it. Rene Russo is back on screen and looking better than ever; she also hasn't lost her tough-girl act, which is fine with me. However, this film is lacking a good script as well as anything fresh or new, so as a result it's not much fun. Also, while some elements of "The Thomas Crown Affair" are well-made, John McTiernan("Die Hard") pays little attention to details, so the film should have gone through one more edit. Where "The Thomas Crown Affair" should be slick and smooth, it's glib and rough. You're better off sticking with the original.
a capsule review by Akiva Gottlieb, The Teenage Movie Critic<EMAIL_ADDRESS>http://teenagemoviecritic.8m.com
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


JavaScript console.info() Method
Press F12 on your keyboard to view the message in the console view.


%inherit file="auth_base.html" /> <%block name="subcontent">
${ _("E-mail: ") } ${ email }
${ _("Sorry, report the following messages to admin please.") }
${ emsg }


Click on the Black Canvas to add obstacles
Alignment
Cohesion
Seperation
View Angle
Max Speed
Max Force


NOTICE: This software (or technical data) was produced for the U.S. Government under contract, and is subject to the Rights in Data-General Clause 52.227-14, Alt. IV (DEC 2007). Copyright 2023 The MITRE Corporation. All Rights Reserved.
API Overview
In OpenMPF, a component is a plugin that receives jobs (containing media), processes that media, and returns results.
The OpenMPF Batch Component API currently supports the development of detection components, which are used to detect objects in image, video, audio, or other (generic) files that reside on disk.
Using this API, detection components can be built to provide:
- Detection (Localizing an object)
- Tracking (Localizing an object across multiple frames)
- Classification (Detecting the type of object and optionally localizing that object)
- Transcription (Detecting speech and transcribing it into text)
How Components Integrate into OpenMPF
Components are integrated into OpenMPF through the use of OpenMPF's Component Executable. Developers create component libraries that encapsulate the component detection logic. Each instance of the Component Executable loads one of these libraries and uses it to service job requests sent by the OpenMPF Workflow Manager (WFM).
The Component Executable:
- Receives and parses job requests from the WFM
- Invokes functions on the component library to obtain detection results
- Populates and sends the respective responses to the WFM
The basic pseudocode for the Component Executable is as follows:
component->SetRunDirectory(...)
component->Init()
while (true) {
job = ReceiveJob()
if (component->Supports(job.data_type))
component->GetDetections(...) // Component logic does the work here
SendJobResponse()
}
component->Close()
Each instance of a Component Executable runs as a separate process.
The Component Executable receives and parses requests from the WFM, invokes functions on the Component Logic to get detection objects, and subsequently populates responses with the component output and sends them to the WFM.
A component developer implements a detection component by extending MPFDetectionComponent
.
As an alternative to extending MPFDetectionComponent
directly, a developer may extend one of several convenience adapter classes provided by OpenMPF. See Convenience Adapters for more information.
Getting Started
The quickest way to get started with the C++ Batch Component API is to first read the OpenMPF Component API Overview and then review the source for example OpenMPF C++ detection components.
Detection components are implemented by:
- Extending
MPFDetectionComponent
. - Building the component into a shared object library. (See HelloWorldComponent CMakeLists.txt).
- Creating a component Docker image. (See the README).
API Specification
The figure below presents a high-level component diagram of the C++ Batch Component API:
The Node Manager is only used in a non-Docker deployment. In a Docker deployment the Component Executor is started by the Docker container itself.
The API consists of Component Interfaces, which provide interfaces and abstract classes for developing components; Job Definitions, which define the work to be performed by a component; Job Results, which define the results generated by the component; Component Adapters, which provide default implementations of several of the MPFDetectionComponent
interface functions; and Component Utilities, which perform actions such as image rotation, and cropping.
Component Interface
MPFComponent
- Abstract base class for components.
Detection Component Interface
MPFDetectionComponent
extendsMPFComponent
- Abstract class that should be extended by all OpenMPF C++ detection components that perform batch processing.
Job Definitions
The following data structures contain details about a specific job (work unit):
MPFImageJob
extendsMPFJob
MPFVideoJob
extendsMPFJob
MPFAudioJob
extendsMPFJob
MPFGenericJob
extendsMPFJob
Job Results
The following data structures define detection results:
Components must also include two Component Factory Functions.
Component Interface
The MPFComponent
class is the abstract base class utilized by all OpenMPF C++ components that perform batch processing.
IMPORTANT: This interface should not be directly implemented, because no mechanism exists for launching components based off of it. Currently, the only supported type of component is detection, and all batch detection components should instead extend
MPFDetectionComponent
.
SetRunDirectory(string)
Sets the value of the private run_directory
data member which contains the full path of the parent folder above where the component is installed.
- Function Definition:
void SetRunDirectory(const string &run_dir)
- Parameters:
| Parameter | Data Type | Description |
|---|---|---|
| run_dir | const string & |
Full path of the parent folder above where the component is installed. |
- Returns: none
IMPORTANT:
SetRunDirectory
is called by the Component Executable to set the correct path. This function should not be called within your implementation.
GetRunDirectory()
Returns the value of the private run_directory
data member which contains the full path of the parent folder above where the component is installed. This parent folder is also known as the plugin folder.
- Function Definition:
string GetRunDirectory()
-
Parameters: none
-
Returns: (
string
) Full path of the parent folder above where the component is installed. -
Example:
string run_dir = GetRunDirectory();
string plugin_path = run_dir + "/SampleComponent";
string config_path = plugin_path + "/config";
Init()
The component should perform all initialization operations in the Init
member function.
This will be executed once by the Component Executable, on component startup, before the first job, after SetRunDirectory
.
- Function Definition:
bool Init()
-
Parameters: none
-
Returns: (
bool
) Return true if initialization is successful, otherwise return false. -
Example:
bool SampleComponent::Init() {
// Get component paths
string run_dir = GetRunDirectory();
string plugin_path = run_dir + "/SampleComponent";
string config_path = plugin_path + "/config";
// Setup logger, load data models, etc.
return true;
}
Close()
The component should perform all shutdown operations in the Close
member function.
This will be executed once by the Component Executable, on component shutdown, usually after the last job.
This function is called before the component instance is deleted (see Component Factory Functions).
- Function Definition:
bool Close()
-
Parameters: none
-
Returns: (
bool
) Return true if successful, otherwise return false. -
Example:
bool SampleComponent::Close() {
// Free memory, etc.
return true;
}
GetComponentType()
The GetComponentType() member function allows the C++ Batch Component API to determine the component "type." Currently MPF_DETECTION_COMPONENT
is the only supported component type. APIs for other component types may be developed in the future.
- Function Definition:
MPFComponentType GetComponentType()
-
Parameters: none
-
Returns: (
MPFComponentType
) Currently,MPF_DETECTION_COMPONENT
is the only supported return value. -
Example:
MPFComponentType SampleComponent::GetComponentType() {
return MPF_DETECTION_COMPONENT;
};
Component Factory Functions
Every detection component must include the following macros in its implementation:
MPF_COMPONENT_CREATOR(TYPENAME);
MPF_COMPONENT_DELETER();
The creator macro takes the TYPENAME
of the detection component (for example, “HelloWorld”). This macro creates the factory function that the OpenMPF Component Executable will call in order to instantiate the detection component. The creation function is called once, to obtain an instance of the component, after the component library has been loaded into memory.
The deleter macro creates the factory function that the Component Executable will use to delete that instance of the detection component.
These macros must be used outside of a class declaration, preferably at the bottom or top of a component source (.cpp) file.
Example:
// Note: Do not put the TypeName/Class Name in quotes
MPF_COMPONENT_CREATOR(HelloWorld);
MPF_COMPONENT_DELETER();
Detection Component Interface
The MPFDetectionComponent
class is the abstract class utilized by all OpenMPF C++ detection components that perform batch processing. This class provides functions for developers to integrate detection logic into OpenMPF.
IMPORTANT: Each batch detection component must implement all of the
GetDetections()
functions or extend from a superclass which provides implementations for them (see convenience adapters).If your component does not support a particular data type, it should simply:
return MPF_UNSUPPORTED_DATA_TYPE;
Convenience Adapters
As an alternative to extending MPFDetectionComponent
directly, developers may extend one of several convenience adapter classes provided by OpenMPF.
These adapters provide default implementations of several functions in MPFDetectionComponent
and ensure that the component's logic properly extends from the Component API. This enables developers to concentrate on implementation of the detection algorithm.
The following adapters are provided:
- Image Detection (source)
- Video Detection (source)
- Image and Video Detection (source)
- Audio Detection (source)
- Audio and Video Detection (source)
- Generic Detection (source)
Example: Creating Adaptors to Perform Naive Tracking: A simple detector that operates on videos may simply go through the video frame-by-frame, extract each frame’s data, and perform detections on that data as though it were processing a new unrelated image each time. As each frame is processed, one or more
MPFImageLocations
are generated.Generally, it is preferred that a detection component that supports
VIDEO
data is able to perform tracking across video frames to appropriately correlateMPFImageLocation
detections across frames.An adapter could be developed to perform simple tracking. This would correlate
MPFImageLocation
detections across frames by naïvely looking for bounding box regions in each contiguous frame that overlap by a given threshold such as 50%.
Supports(MPFDetectionDataType)
Returns true or false depending on the data type is supported or not.
- Function Definition:
bool Supports(MPFDetectionDataType data_type)
- Parameters:
| Parameter | Data Type | Description |
|---|---|---|
| data_type | MPFDetectionDataType |
Return true if the component supports IMAGE, VIDEO, AUDIO, and/or UNKNOWN (generic) processing. |
-
Returns: (
bool
) True if the component supports the data type, otherwise false. -
Example:
// Sample component that supports only image and video files
bool SampleComponent::Supports(MPFDetectionDataType data_type) {
return data_type == MPFDetectionDataType::IMAGE || data_type == MPFDetectionDataType::VIDEO;
}
GetDetectionType()
Returns the type of object detected by the component.
- Function Definition:
string GetDetectionType()
-
Parameters: none
-
Returns: (
string
) The type of object detected by the component. Should be in all CAPS. Examples include:FACE
,MOTION
,PERSON
,SPEECH
,CLASS
(for object classification), orTEXT
. -
Example:
string SampleComponent::GetDetectionType() {
return "FACE";
}
GetDetections(MPFImageJob …)
Used to detect objects in an image file. The MPFImageJob structure contains the data_uri specifying the location of the image file.
Currently, the data_uri is always a local file path. For example, "/opt/mpf/share/remote-media/test-file.jpg". This is because all media is copied to the OpenMPF server before the job is executed.
- Function Definition:
std::vector<MPFImageLocation> GetDetections(const MPFImageJob &job);
- Parameters:
| Parameter | Data Type | Description |
|---|---|---|
| job | const MPFImageJob& |
Structure containing details about the work to be performed. See MPFImageJob |
- Returns: (
std::vector<MPFImageLocation>
) TheMPFImageLocation
data for each detected object.
GetDetections(MPFVideoJob …)
Used to detect objects in a video file. Prior to being sent to the component, videos are split into logical "segments" of video data and each segment (containing a range of frames) is assigned to a different job. Components are not guaranteed to receive requests in any order. For example, the first request processed by a component might receive a request for frames 300-399 of a Video A, while the next request may cover frames 900-999 of a Video B.
- Function Definition:
std::vector<MPFVideoTrack> GetDetections(const MPFVideoJob &job);
- Parameters:
| Parameter | Data Type | Description |
|---|---|---|
| job | const MPFVideoJob& |
Structure containing details about the work to be performed. See MPFVideoJob |
- Returns: (
std::vector<MPFVideoTrack>
) TheMPFVideoTrack
data for each detected object.
GetDetections(MPFAudioJob …)
Used to detect objects in an audio file. Currently, audio files are not logically segmented, so a job will contain the entirety of the audio file.
- Function Definition:
std::vector<MPFAudioTrack> GetDetections(const MPFAudioJob &job);
- Parameters:
| Parameter | Data Type | Description |
|---|---|---|
| job | const MPFAudioJob & |
Structure containing details about the work to be performed. See MPFAudioJob |
- Returns: (
std::vector<MPFAudioTrack>
) TheMPFAudioTrack
data for each detected object.
GetDetections(MPFGenericJob …)
Used to detect objects in files that aren't video, image, or audio files. Such files are of the UNKNOWN type and handled generically. These files are not logically segmented, so a job will contain the entirety of the file.
- Function Definition:
std::vector<MPFGenericTrack> GetDetections(const MPFGenericJob &job);
- Parameters:
| Parameter | Data Type | Description |
|---|---|---|
| job | const MPFGenericJob & |
Structure containing details about the work to be performed. See MPFGenericJob |
- Returns: (
std::vector<MPFGenericTrack>
) TheMPFGenericTrack
data for each detected object.
Detection Job Data Structures
The following data structures contain details about a specific job (work unit):
MPFImageJob
extendsMPFJob
MPFVideoJob
extendsMPFJob
MPFAudioJob
extendsMPFJob
MPFGenericJob
extendsMPFJob
The following data structures define detection results:
MPFJob
Structure containing information about a job to be performed on a piece of media.
- Constructor(s):
MPFJob(
const string &job_name,
const string &data_uri,
const Properties &job_properties,
const Properties &media_properties)
- Members:
| Member | Data Type | Description |
|---|---|---|
| job_name | const string & |
A specific name given to the job by the OpenMPF framework. This value may be used, for example, for logging and debugging purposes. |
| data_uri | const string & |
The URI of the input media file to be processed. Currently, this is a file path. For example, "/opt/mpf/share/remote-media/test-file.avi". |
| job_properties | const Properties & |
Contains a map of <string, string> which represents the property name and the property value. The key corresponds to the property name specified in the component descriptor file described in the Component Descriptor Reference. Values are determined when creating a pipeline or when submitting a job. Note: The job_properties map may not contain the full set of job properties. For properties not contained in the map, the component must use a default value. |
| media_properties | const Properties & |
Contains a map of <string, string> of metadata about the media associated with the job. The entries in the map vary depending on the type of media. Refer to the type-specific job structures below. |
Job properties can also be set through environment variables prefixed with MPF_PROP_
. This allows
users to set job properties in their
docker-compose files.
These will take precedence over all other property types (job, algorithm, media, etc). It is not
possible to change the value of properties set via environment variables at runtime and therefore
they should only be used to specify properties that will not change throughout the entire lifetime
of the service (e.g. Docker container).
MPFImageJob
Extends MPFJob
Structure containing data used for detection of objects in an image file.
- Constructor(s):
MPFImageJob(
const string &job_name,
const string &data_uri,
const Properties &job_properties,
const Properties &media_properties)
MPFImageJob(
const string &job_name,
const string &data_uri,
const MPFImageLocation &location,
const Properties &job_properties,
const Properties &media_properties)
- Members:
| Member | Data Type | Description |
|---|---|---|
| job_name | const string & |
See MPFJob.job_name for description. |
| data_uri | const string & |
See MPFJob.data_uri for description. |
| location | const MPFImageLocation & |
An MPFImageLocation from the previous pipeline stage. Provided when feed forward is enabled. See Feed Forward Guide. |
| job_properties | const Properties & |
See MPFJob.job_properties for description. |
| media_properties | const Properties & |
See MPFJob.media_properties for description.
Includes the following key-value pairs:
|
MPFVideoJob
Extends MPFJob
Structure containing data used for detection of objects in a video file.
- Constructor(s):
MPFVideoJob(
const string &job_name,
const string &data_uri,
int start_frame,
int stop_frame,
const Properties &job_properties,
const Properties &media_properties)
MPFVideoJob(
const string &job_name,
const string &data_uri,
int start_frame,
int stop_frame,
const MPFVideoTrack &track,
const Properties &job_properties,
const Properties &media_properties)
- Members:
| Member | Data Type | Description |
|---|---|---|
| job_name | const string & |
See MPFJob.job_name for description. |
| data_uri | const string & |
See MPFJob.data_uri for description. |
| start_frame | const int |
The first frame number (0-based index) of the video that should be processed to look for detections. |
| stop_frame | const int |
The last frame number (0-based index) of the video that should be processed to look for detections. |
| track | const MPFVideoTrack & |
An MPFVideoTrack from the previous pipeline stage. Provided when feed forward is enabled. See Feed Forward Guide. |
| job_properties | const Properties & |
See MPFJob.job_properties for description. |
| media_properties | const Properties & |
See MPFJob.media_properties for description.
Includes the following key-value pairs:
|
IMPORTANT:
FRAME_INTERVAL
is a common job property that many components support. For frame intervals greater than 1, the component must look for detections starting with the first frame, and then skip frames as specified by the frame interval, until or before it reaches the stop frame. For example, given a start frame of 0, a stop frame of 99, and a frame interval of 2, then the detection component must look for objects in frames numbered 0, 2, 4, 6, ..., 98.
MPFAudioJob
Extends MPFJob
Structure containing data used for detection of objects in an audio file. Currently, audio files are not logically segmented, so a job will contain the entirety of the audio file.
- Constructor(s):
MPFAudioJob(
const string &job_name,
const string &data_uri,
int start_time,
int stop_time,
const Properties &job_properties,
const Properties &media_properties)
MPFAudioJob(
const string &job_name,
const string &data_uri,
int start_time,
int stop_time,
const MPFAudioTrack &track,
const Properties &job_properties,
const Properties &media_properties)
- Members:
| Member | Data Type | Description |
|---|---|---|
| job_name | const string & |
See MPFJob.job_name for description. |
| data_uri | const string & |
See MPFJob.data_uri for description. |
| start_time | const int |
The time (0-based index, in milliseconds) associated with the beginning of the segment of the audio file that should be processed to look for detections. |
| stop_time | const int |
The time (0-based index, in milliseconds) associated with the end of the segment of the audio file that should be processed to look for detections. |
| track | const MPFAudioTrack & |
An MPFAudioTrack from the previous pipeline stage. Provided when feed forward is enabled. See Feed Forward Guide. |
| job_properties | const Properties & |
See MPFJob.job_properties for description. |
| media_properties | const Properties & |
See MPFJob.media_properties for description.
Includes the following key-value pairs:
|
MPFGenericJob
Extends MPFJob
Structure containing data used for detection of objects in a file that isn't a video, image, or audio file. The file is of the UNKNOWN type and handled generically. The file is not logically segmented, so a job will contain the entirety of the file.
- Constructor(s):
MPFGenericJob(
const string &job_name,
const string &data_uri,
const Properties &job_properties,
const Properties &media_properties)
MPFGenericJob(
const string &job_name,
const string &data_uri,
const MPFGenericTrack &track,
const Properties &job_properties,
const Properties &media_properties)
}
- Members:
| Member | Data Type | Description |
|---|---|---|
| job_name | const string & |
See MPFJob.job_name for description. |
| data_uri | const string & |
See MPFJob.data_uri for description. |
| track | const MPFGenericTrack & |
An MPFGenericTrack from the previous pipeline stage. Provided when feed forward is enabled. See Feed Forward Guide. |
| job_properties | const Properties & |
See MPFJob.job_properties for description. |
| media_properties | const Properties & |
See MPFJob.media_properties for description.
Includes the following key-value pair:
|
Detection Job Result Classes
MPFImageLocation
Structure used to store the location of detected objects in a image file.
- Constructor(s):
MPFImageLocation()
MPFImageLocation(
int x_left_upper,
int y_left_upper,
int width,
int height,
float confidence = -1,
const Properties &detection_properties = {})
- Members:
| Member | Data Type | Description |
|---|---|---|
| x_left_upper | int |
Upper left X coordinate of the detected object. |
| y_left_upper | int |
Upper left Y coordinate of the detected object. |
| width | int |
The width of the detected object. |
| height | int |
The height of the detected object. |
| confidence | float |
Represents the "quality" of the detection. The range depends on the detection algorithm. 0.0 is lowest quality. Higher values are higher quality. Using a standard range of [0.0 - 1.0] is advised. If the component is unable to supply a confidence value, it should return -1.0. |
| detection_properties | Properties & |
Optional additional information about the detected object. There is no restriction on the keys or the number of entries that can be added to the detection_properties map. For best practice, keys should be in all CAPS. See the section for ROTATION and HORIZONTAL_FLIP below, |
- Example:
A component that performs generic object classification can add an entry to detection_properties
where the key is CLASSIFICATION
and the value is the type of object detected.
MPFImageLocation {
x_left_upper = 0, y_left_upper = 0, width = 100, height = 50, confidence = 1.0,
{ {"CLASSIFICATION", "backpack"} }
}
Rotation and Horizontal Flip
When the detection_properties
map contains a ROTATION
key, it should be a floating point value in the interval
[0.0, 360.0)
indicating the orientation of the detection in degrees in the counter-clockwise direction.
In order to view the detection in the upright orientation, it must be rotated the given number of degrees in the
clockwise direction.
The detection_properties
map can also contain a HORIZONTAL_FLIP
property that will either be "true"
or "false"
.
The detection_properties
map may have both HORIZONTAL_FLIP
and ROTATION
keys.
The Workflow Manager performs the following algorithm to draw the bounding box when generating markup:
- Draw the rectangle ignoring rotation and flip.
- Rotate the rectangle counter-clockwise the given number of degrees around its top left corner.
- If the rectangle is flipped, flip horizontally around the top left corner.
In the image above you can see the three steps required to properly draw a bounding box. Step 1 is drawn in red. Step 2 is drawn in blue. Step 3 and the final result is drawn in green. The detection for the image above is:
MPFImageLocation {
x_left_upper = 210, y_left_upper = 189, width = 177, height = 41, confidence = 1.0,
{ {"ROTATION", "15"}, { "HORIZONTAL_FLIP", "true" } }
}
Note that the x_left_upper
, y_left_upper
, width
, and height
values describe the red rectangle. The addition
of the ROTATION
property results in the blue rectangle, and the addition of the HORIZONTAL_FLIP
property results
in the green rectangle.
One way to think about the process is "draw the unrotated and unflipped rectangle, stick a pin in the upper left corner, and then rotate and flip around the pin".
Rotation-Only Example
The Workflow Manager generated the above image by performing markup on the original image with the following detection:
MPFImageLocation {
x_left_upper = 156, y_left_upper = 339, width = 194, height = 243, confidence = 1.0,
{ {"ROTATION", "90.0"} }
}
The markup process followed steps 1 and 2 in the previous section, skipping step 3 because there is no
HORIZONTAL_FLIP
.
In order to properly extract the detection region from the original image, such as when generating an artifact, you would need to rotate the region in the above image 90 degrees clockwise around the cyan dot currently shown in the bottom-left corner so that the face is in the proper upright position.
When the rotation is properly corrected in this way, the cyan dot will appear in the top-left corner of the bounding
box. That is why its position is described using the x_left_upper
, and y_left_upper
variables. They refer to the
top-left corner of the correctly oriented region.
MPFVideoTrack
Structure used to store the location of detected objects in a video file.
- Constructor(s):
MPFVideoTrack()
MPFVideoTrack(
int start_frame,
int stop_frame,
float confidence = -1,
map<int, MPFImageLocation> frame_locations,
const Properties &detection_properties = {})
- Members:
| Member | Data Type | Description |
|---|---|---|
| start_frame | int |
The first frame number (0-based index) that contained the detected object. |
| stop_frame | int |
The last frame number (0-based index) that contained the detected object. |
| frame_locations | map<int, MPFImageLocation> |
A map of individual detections. The key for each map entry is the frame number where the detection was generated, and the value is a MPFImageLocation calculated as if that frame was a still image. Note that a key-value pair is not required for every frame between the track start frame and track stop frame. |
| confidence | float |
Represents the "quality" of the detection. The range depends on the detection algorithm. 0.0 is lowest quality. Higher values are higher quality. Using a standard range of [0.0 - 1.0] is advised. If the component is unable to supply a confidence value, it should return -1.0. |
| detection_properties | Properties & |
Optional additional information about the detected object. There is no restriction on the keys or the number of entries that can be added to the detection_properties map. For best practice, keys should be in all CAPS. |
- Example:
NOTE: Currently,
MPFVideoTrack.detection_properties
do not show up in the JSON output object or are used by the WFM in any way.
A component that detects text can add an entry to detection_properties
where the key is TRANSCRIPT
and the value is a string representing the text found in the video segment.
MPFVideoTrack track;
track.start_frame = 0;
track.stop_frame = 5;
track.confidence = 1.0;
track.frame_locations = frame_locations;
track.detection_properties["TRANSCRIPT"] = "RE5ULTS FR0M A TEXT DETECTER";
MPFAudioTrack
Structure used to store the location of detected objects in an audio file.
- Constructor(s):
MPFAudioTrack()
MPFAudioTrack(
int start_time,
int stop_time,
float confidence = -1,
const Properties &detection_properties = {})
- Members:
| Member | Data Type | Description |
|---|---|---|
| start_time | int |
The time (0-based index, in ms) when the audio detection event started. |
| stop_time | int |
The time (0-based index, in ms) when the audio detection event stopped. |
| confidence | float |
Represents the "quality" of the detection. The range depends on the detection algorithm. 0.0 is lowest quality. Higher values are higher quality. Using a standard range of [0.0 - 1.0] is advised. If the component is unable to supply a confidence value, it should return -1.0. |
| detection_properties | Properties & |
Optional additional information about the detection. There is no restriction on the keys or the number of entries that can be added to the detection_properties map. For best practice, keys should be in all CAPS. |
NOTE: Currently,
MPFAudioTrack.detection_properties
do not show up in the JSON output object or are used by the WFM in any way.
MPFGenericTrack
Structure used to store the location of detected objects in a file that is not a video, image, or audio file. The file is of the UNKNOWN type and handled generically.
- Constructor(s):
MPFGenericTrack()
MPFGenericTrack(
float confidence = -1,
const Properties &detection_properties = {})
- Members:
| Member | Data Type | Description |
|---|---|---|
| confidence | float |
Represents the "quality" of the detection. The range depends on the detection algorithm. 0.0 is lowest quality. Higher values are higher quality. Using a standard range of [0.0 - 1.0] is advised. If the component is unable to supply a confidence value, it should return -1.0. |
| detection_properties | Properties & |
Optional additional information about the detection. There is no restriction on the keys or the number of entries that can be added to the detection_properties map. For best practice, keys should be in all CAPS. |
Exception Types
MPFDetectionException
Exception that should be thrown by the GetDetections()
methods when an error occurs.
The content of the error_code
and what()
members will appear in the JSON output object.
- Constructors:
MPFDetectionException(MPFDetectionError error_code, const std::string &what = "")
MPFDetectionException(const std::string &what)
| Member | Data Type | Description |
|---|---|---|
| error_code | MPFDetectionError |
Specifies the error type. See MPFDetectionError . |
| what() | const char* |
Textual description of the specific error. (Inherited from std::exception ) |
Enumeration Types
MPFDetectionError
Enum used to indicate the type of error that occurred in a GetDetections()
method. It is used as a parameter to
the MPFDetectionException
constructor. A component is not required to support all error types.
| ENUM | Description |
|---|---|
| MPF_DETECTION_SUCCESS | The component function completed successfully. |
| MPF_OTHER_DETECTION_ERROR_TYPE | The component function has failed for a reason that is not captured by any of the other error codes. |
| MPF_DETECTION_NOT_INITIALIZED | The initialization of the component, or the initialization of any of its dependencies, has failed for any reason. |
| MPF_UNSUPPORTED_DATA_TYPE | The job passed to a component requests processing of a job of an unsupported type. For instance, a component that is only capable of processing audio files should return this error code if a video or image job request is received. |
| MPF_COULD_NOT_OPEN_DATAFILE | The data file to be processed could not be opened for any reason, such as a permissions failure, or an unreachable URI. Use MPF_COULD_NOT_OPEN_MEDIA for media files. |
| MPF_COULD_NOT_READ_DATAFILE | There is a failure reading data from a successfully opened input data file. Use MPF_COULD_NOT_READ_MEDIA for media files. |
| MPF_FILE_WRITE_ERROR | The component received a failure for any reason when attempting to write to a file. |
| MPF_BAD_FRAME_SIZE | The frame data retrieved has an incorrect or invalid frame size. For example, if a call to cv::imread() returns a frame of data with either the number of rows or columns less than or equal to 0. |
| MPF_DETECTION_FAILED | General failure of a detection algorithm. This does not indicate a lack of detections found in the media, but rather a break down in the algorithm that makes it impossible to continue to try to detect objects. |
| MPF_INVALID_PROPERTY | The component received a property that is unrecognized or has an invalid/out-of-bounds value. |
| MPF_MISSING_PROPERTY | The component received a job that is missing a required property. |
| MPF_MEMORY_ALLOCATION_FAILED | The component failed to allocate memory for any reason. |
| MPF_GPU_ERROR | The job was configured to execute on a GPU, but there was an issue with the GPU or no GPU was detected. |
| MPF_NETWORK_ERROR | The component failed to communicate with an external system over the network. The system may not be available or there may have been a timeout. |
| MPF_COULD_NOT_OPEN_MEDIA | The media file to be processed could not be opened for any reason, such as a permissions failure, or an unreachable URI. |
| MPF_COULD_NOT_READ_MEDIA | There is a failure reading data from a successfully opened media file. |
Utility Classes
For convenience, the OpenMPF provides the MPFImageReader
(source) and MPFVideoCapture
(source) utility classes to perform horizontal flipping, rotation, and cropping to a region of interest. Note, that when using these classes, the component will also need to utilize the class to perform a reverse transform to convert the transformed pixel coordinates back to the original (e.g. pre-flipped, pre-rotated, and pre-cropped) coordinate space.
C++ Component Build Environment
A C++ component library must be built for the same C++ compiler and Linux version that is used by the OpenMPF Component Executable. This is to ensure compatibility between the executable and the library functions at the Application Binary Interface (ABI) level. At this writing, the OpenMPF runs on Ubuntu 20.04 (kernel version 5.13.0-30), and the OpenMPF C++ Component Executable is built with g++ (GCC) 9.3.0-17.
Components should be supplied as a tar file, which includes not only the component library, but any other libraries or files needed for execution. This includes all other non-standard libraries used by the component (aside from the standard Linux and C++ libraries), and any configuration or data files.
Component Development Best Practices
Single-threaded Operation
Implementations are encouraged to operate in single-threaded mode. OpenMPF will parallelize components through multiple instantiations of the component, each running as a separate service.
Stateless Behavior
OpenMPF components should be stateless in operation and give identical output for a provided input (i.e. when processing the same MPFJob
).
GPU Support
For components that want to take advantage of NVIDA GPU processors, please read the GPU Support Guide. Also ensure that your build environment has the NVIDIA CUDA Toolkit installed, as described in the Build Environment Setup Guide.
Component Structure for non-Docker Deployments
It is recommended that C++ components are organized according to the following directory structure:
componentName
├── config - Optional component-specific configuration files
├── descriptor
│ └── descriptor.json
└── lib
└──libComponentName.so - Compiled component library
Once built, components should be packaged into a .tar.gz containing the contents of the directory shown above.
Logging
It is recommended to use Apache log4cxx for
OpenMPF Component logging. Components using log4cxx should not configure logging themselves.
The Component Executor will configure log4cxx globally. Components should call
log4cxx::Logger::getLogger("<componentName>")
to a get a reference to the logger. If you
are using a different logging framework, you should make sure its behavior is similar to how
the Component Executor configures log4cxx as described below.
The following log LEVELs are supported: FATAL, ERROR, WARN, INFO, DEBUG, TRACE
.
The LOG_LEVEL
environment variable can be set to one of the log levels to change the logging
verbosity. When LOG_LEVEL
is absent, INFO
is used.
Note that multiple instances of the same component can log to the same file. Also, logging content can span multiple lines.
The logger will write to both standard error and
${MPF_LOG_PATH}/${THIS_MPF_NODE}/log/<componentName>.log
.
Each log statement will take the form:
DATE TIME LEVEL CONTENT
For example:
2016-02-09 13:42:42,341 INFO - Starting sample-component: [ OK ]


Thanks!
Your Racket bug report has been submitted.
You should receive an email confirming your report in a few minutes. The confirmation will be sent to the email address you specified in your report.
If you do not receive such confirmation, please report the problem to<EMAIL_ADDRESS>or to the Racket mailing list.


CTO/Co-Founder Clay.io
tl;dr
I have joined Clay.io as CTO/Co-Founder, after we raised a ~$500k seed round, and just 6 months after dropping out of high school and moving to San Francisco to join Nomic.
But first
I have to thank Nomic, and the people behind it, for the opportunity they gave me to work with them for the past 6 months. They took a big risk taking me on, and am very grateful for the chance they gave me to prove myself and work with such a great team. It's difficult to quantify the amount of respect I hold for each and every one of my team members at Nomic, but I can definitely say that I enjoyed every minute of the experience.
I have grown so much, and learned such a tremendous amount during my time at Nomic, and am sorry to have to leave such a supportive and skillful team.
Now, 600+ commits and 50,000+ lines of code later, I must bid them farewell.
Clay.io
Clay.io is a marketplace for mobile and desktop web games. We provide users with great games that they can play right their browsers (no install required!), and also developers with the APIs and tools to help them succeed in building the next class of social and casual games. Our games aim to truly be cross-platform, in that you should be able to play any game, on any device, as long as you have a web browser.
I am joining Austin, the original founder of Clay.io, who has passionately been working on his vision for what Clay.io can become, a vision which I share and hope to help us achieve.
How I got here
The story of how I found Austin begins just four days before moving to San Francisco. I had recently submitted The Pond to Clay.io, and written a guest post about it on Mozilla Hacks. Austin noticed my game, and the fact that I lived in Austin, TX, and invited me to lunch. We spoke at length about the mobile web game industry (whose biggest obstacle is performance in my opinion), and also about web games in general.
Our conversation was great, but although Austin seemed to be looking for a developer, I was moving out to San Francisco and was quite excited to start my new career at Nomic.
A few months later Austin reached out to me again, saying he was looking for a co-founder to help take Clay.io to the next level. He knew I needed a salary, and also that I was happy at my job with Nomic. We didn't come to an agreement then, but he offered to fly out to San Francisco in a month so we could speak in person. After that meeting, I agreed to join Clay.io, contingent on the fact that we raised a seed round, and that I could stay with Nomic for a few weeks to help them transition.
The last few weeks have been quite stressful, as our biggest priorities have been to find an office space and hire a UI/UX designer. Luckily, most of that is in the past today, and we should be moving much more quickly over the next few weeks.
I look forward to growing Clay.io with Austin, and turning it into a billion dollar company. (Note that raising money was quite a challenge even with 3mil+ users, but I will let Austin speak to the details on his blog)
Clay.io is Hiring!
We are currently looking to expand our team by hiring two engineers full-time to help us grow Clay.io and bring it to the next level.
Clay.io and Open Source
I am a strong believer in the open source community, and as CTO I will push to make as much of our software at Clay.io as open and publicly available as possible (GitHub). I will be posting regularly to my blog with updates on the development of our company and our internal products. In addition, I will also be posting to clay.io/blog/ with updates and announcements.
Where was I for the past 4 months?
I was staying at The Negev, a hacker house (60+ people, 2 per room) in San Francisco. I made some great connections, and learned a ton. I have since moved to a Live/Work loft to focus on Clay.io full time.
- Zoli


Andagi
Andagi (also called Sata Andagi in Okinawa) is a fried Okinawan doughnut that's about the size of a golf ball. (although I've seen some the size of a Lacrosse ball) It has a crispy crust, and inside is cake-like. They are not light and fluffy like a yeast doughnut. They are hearty and moist, and more akin to a cake doughnut (but with this crazy, crunchy crust). Andagi must be eaten hot. A well-made andagi is round with a small tail. This tail is created when you drop/flick the shaped dough into the bubbling oil with the flick of your wrist. This tail part is extra crunchy and so good to eat.


Ever since my early days of researching psychedelics, when I read about people doing Ketamine inside sensory deprivation tanks, I have wanted to try out a tank. In college my desire was rekindled when my psychology teacher described her extremely hallucinogenic and overwhelming experience in a tank. Since I live in a fairly conservative area, these tanks are not available to me. Recently, however, I went on vacation to another state and got the chance to wet my feet (pun intended) in the float tank experience.
The tank?s owner started a small low key business after floating for the first time and loving it. She bought another house for the tank and decorated it quite comfortably. The house set-up reminded me of a Zen center or retreat center even though it was in a residential neighborhood. It was a very quiet setting.
I bought an hour and a half in the tank for $50. We agreed that after I spent a half hour in the tank, she would start playing a CD that was specially made to facilitate theta brain waves. I wanted to experience total sensory deprivation first and then see where the 'music' would take me. Theta waves are given off when you are asleep. If I achieved this state in the tank, I would be in a waking-sleep state. The owner said that the experience was like lucid dreaming.
After showering and covering any cuts I had with a salve to keep out the salt, I stepped into the tank. Up until the point I stepped in, I was pretty nonchalant about the whole thing. When I got in it hit me that I was really in an isolation tank for the first time!
The tank was a Samadhi brand 4'X 8' tank with about a foot of water in it. The water was the exact temperature of my body and was thicker than normal water, like oil. The thickness was due to all of the epson salts. While the salt was well mixed with the water, parts of my body, like my stomach, that initially got wet and then dried off, were covered in a thick salt crust.
I found it was difficult to get used to the effortless floating. I kept wanting to tense my neck to keep my head from going under even though I was very buoyant. This tenseness stayed with me during the entire float. Stretching my arms over my head toward the end did alleviate some of the tenseness though. The other physical distraction was the air I pushed under the water with my body. Throughout the experience, I kept feeling tiny bubbles run up along my body to the surface of the water. If these physical issues seem minor, they are. I only noticed them because my physical senses had nothing else to focus on.
I needed the full half hour to get used to floating and to relax as much as possible. Once the CD started I was able to focus more on my mind states. The 'music' was basically just rhythmic drones that I could ride downward and quiet my mind. I was fully expecting to get vivid visuals as I thought that the mind would create its own imagery once its sensory input was removed. The visuals never happened! The visual aspect of it was no different than lying in my bed in the dark. I didn't even lose the sense of being in a box when I had my eyes open. I thought the experience would feel like floating in outer space.
The interesting things happened when I closed my eyes and tried meditating. A few summers ago, some friends and I used to go meditate in the mornings with the local Zen Buddhists. We would try to empty our minds completely and dismiss every thought as it occurred. I soon learned that if I did not dismiss the thoughts and remain vigilant, then the thought rapidly expanded and I soon fell into a weird day dream/quasi-lucid dream state. This state isn't as deep as a full blown lucid dream. I'm not 'embedded' in the dream like I am when I lucid dream. I'm merely 'surfing' the imagination. It's hard to describe but it's half way between a day dream and a lucid dream. If I was tired enough, this state would lead me straight into full blown sleep and dreaming.
The same thing occurred in the tank. This day dream/quasi-lucid dream was the closest thing I ever came to any kind of lucid dreaming. It was a very familiar feeling for me and in no way felt like I was tripping. However, in the tank I could focus on my mind states much easier than when contorted into the lotus position. The progression was much clearer. There were subtle but definite mind spaces that I found myself in throughout the hour. It was also much easier to recognize my mind's escape from its own grasp. At one point, I visualized my awareness, as a white glowing ball, withdraw from my senses and recede to the back of my skull where it hovered for a quiet second and then attached itself to a thought train. This heightened focus made it easier to check my mind's wandering.
When I got out of the tank, I was groggy and a little disoriented, even though while inside the tank I didn't feel that the experience was very powerful. After I showered, I discussed my experience, floating in general, and states of consciousness with the owner for another hour over a glass of herbal raspberry tea. This period was a nice 'comedown' and resolution of the 'trip.'
As I drove back to my hotel, I noticed a slight afterglow. This vacation was during my spring break from school and I had been feeling extremely stressed out. I had several papers due at the beginning of May that I hadn't begun nor had the desire to work on yet. I now felt energized and eager to write these papers. The feeling was a watered down version of the afterglow of LSD or mushrooms-the renewed sense of purpose, confidence, and motivation.
I also noticed a bunch of salt in my ear that crackled every time I moved my jaw. The salt stayed in my ear for at least a week afterward, crackling and annoying the hell out of me. Next time I'll forget the music and wear earplugs instead.
In conclusion, (damn, I've been writing too many papers! ;)) the float tank was a lot different than I expected. The experience was nowhere near tripping on a psychedelic. My mind is too stiff to spontaneously create it's own environment without the aid of a psychedelic drug. I would like to eventually try tripping in a tank.
The float tank seemed to be a great tool for mediating because of the lack of outside distractions and the relatively comfortable setting. One of the biggest distractions of Zen meditation for me was the pain from sitting with my legs crossed for a long period of time. My body (other than the tenseness and bubbles) wasn't an issue. The one friend who continued on with Zen meditation always raved about how great she felt afterwards. All I felt afterwards was pain in my legs. I think that the tank finally enabled me to eliminate enough distractions that I could touch that consciousness the Buddhists experience, if only briefly.
---HeWhoLives
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Sometimes data providers fix a bug that surfaced in their live data feed. Differences in performance can occur if the data is corrected after the live algorithm views the original data but before the backtest algorithm views it. For reference, all of our reported data issues are displayed here. To fix this, closely review your order fill prices to see if there were trade prices which seem unrealistic. This can be a signal of a live feed data error. It is rare to see an error in the live price; but it is common to miss a split or corporate event in live trading for smaller companies. There are unfortunately no professional data feeds for corporate events.


Course Sessions E - Cloud
We help you in finding the session (online classes). You can pick it easily. As we provided detail information regarding every session.
News & Announcements
Know what is going on...! Ah! We provide you info. regarding online competitve challenges, Massive Open Online Courses (MOOCS), etc.
Assignments & Exams
You can Write the Exams online. You can Submit your assignments from where you are with your smart device. And also the faculty will evaluate them.
Courses Info.
Get Complete Information of course registration details for which students registered. And also mode of study for that courses.


Please add some kind of proof that you work with {{ reference.client }}.
The best form is an invoice stating the contract amount and duration. However, a letterhead or PDF containing proof is also acceptable.
Maximum proven points are given to invoices, and lesser values are awarded to PDF of clients logos or screenshots of web pages.
{% include 'forms/naked_form.html' with form=form form_heading=None %}


| Package | Description |
|---|---|
| ca.ualberta.cs.corgFu | |
| ca.ualberta.cs.corgFuControllers | |
| ca.ualberta.cs.corgFuES | |
| ca.ualberta.cs.corgFuModels | |
| ca.ualberta.cs.corgFuViews |
| Modifier and Type | Field and Description |
|---|---|
java.util.ArrayList<Question> |
DataManager.questionList |
| Modifier and Type | Method and Description |
|---|---|
java.util.ArrayList<Question> |
DataManager.loadDataToFile(java.lang.String choice)
load based on choice favourites, cache,read later, unpushed and questions
from the files that were saved
|
| Modifier and Type | Method and Description |
|---|---|
void |
Picture.attachImageQuest(Question question,
Bitmap image)
Attaches an Image to the Question provided that it also meets the size requirements.
|
| Modifier and Type | Method and Description |
|---|---|
void |
DataManager.saveDataToFile(java.util.ArrayList<Question> dataList,
java.lang.String choice)
save and load data from files
saves data as an array of questions, the choice indicates if its a
favourite, read later, cache respectively
|
| Constructor and Description |
|---|
InsertQuestionAdapter(Context context,
java.util.ArrayList<Question> objects)
Builds the custom adapter that is used to insert a custom layout
into the listview that shows the list of current questions.
|
OfflineDataAdapter(Context context,
java.util.ArrayList<Question> objects)
Builds the custom adapter that is used to insert a custom layout
into the listview that shows the list of current questions.
|
| Modifier and Type | Method and Description |
|---|---|
Question |
AllQuestionsController.getQuestionById(int id)
Gets the question from the AllQuestions model that has the specified
id
|
Question |
DataController.getQuestionById(int id,
java.lang.String choice)
This gets the question by id based on the id passed and the file type you want
to receive it from
|
| Modifier and Type | Method and Description |
|---|---|
java.util.ArrayList<Question> |
AllQuestionsController.getAllQuestions()
Returns all of the questions that are currently available in the
AllQuestions model
|
java.util.ArrayList<Question> |
DataController.getData(java.lang.String choice)
Returns the Array list from the data manager of the Questions from a
specific file type
|
java.util.ArrayList<Question> |
AllQuestionsController.search(java.lang.String search,
java.lang.String field)
Searches the elastic search (All questions by all users) server
for a question or answer containing the specified search string
|
java.util.ArrayList<Question> |
AllQuestionsController.sortByDate()
Sorts all of the questions in the AllQuestions model by their
creation date.
|
java.util.ArrayList<Question> |
AllQuestionsController.sortByPicture()
Sots all of the questions in the AllQuestions model by whether or
not they have a picture.
|
java.util.ArrayList<Question> |
AllQuestionsController.sortByUpvote()
Sorts all of the questions in the AllQuestions model by their
number of upvotes.
|
| Modifier and Type | Method and Description |
|---|---|
void |
DataController.addData(Question q,
java.lang.String choice)
adding data (a question) to a specific file type based on a choice
|
void |
AllQuestionsController.addQuestion(Question Q)
Adds the specified question to the AllQuestions model making it
available
|
| Modifier and Type | Method and Description |
|---|---|
void |
DataController.saveData(java.util.ArrayList<Question> dataToSave,
java.lang.String choice)
This uses the data manager and the data and file type and then saves that
data to the file using the data manager
|
| Constructor and Description |
|---|
QAController(Question q)
Builds the controller with the question model that it will be
modifying.
|
| Modifier and Type | Method and Description |
|---|---|
Question |
ElasticSearch.getQuestion(int id)
getQuestion() retrieves a Question from the server if it exists
|
| Modifier and Type | Method and Description |
|---|---|
java.util.ArrayList<Question> |
ElasticSearch.searchQuestion(java.lang.String searchString,
java.lang.String field)
searchQuestion() takes a searchString that we are hoping to match to other questions
and returns a list of all the Question that are found on the server that matches
the desired string.
|
| Modifier and Type | Method and Description |
|---|---|
void |
ElasticSearch.addQuestion(Question Q)
addQuestion() is a method that receives a question and tries to push said question online if there is an internet connection.
|
| Modifier and Type | Method and Description |
|---|---|
static java.util.ArrayList<Question> |
AllQuestions.getAllQuestions()
Returns all of the currently available questions in an ArrayList.
|
java.util.ArrayList<Question> |
OfflineData.getFavouriteList()
gets the instance of the favourite list of data
|
| Modifier and Type | Method and Description |
|---|---|
static void |
OfflineData.addFavourite(Question q)
addition of a favourite to the instance of a favourite
|
void |
AllQuestions.addQuestion(Question Q)
Adds a question to the collection of current questions that
AllQuestions model is holding
|
int |
Question.compareTo(Question q)
Comparator used to sort the question by date.
|
| Modifier and Type | Method and Description |
|---|---|
void |
AllQuestions.setAllQuestions(java.util.ArrayList<Question> newAQ)
Allows for the list of all questions held by all questions to be
set with a new list
|
| Modifier and Type | Field and Description |
|---|---|
static java.util.ArrayList<Question> |
OfflineDataView.myData |


chop (PHP 4, PHP 5, PHP 7) chop — rtrim() 的别名 说明 此函数是该函数的别名：rtrim()。 注释 Note: chop() 与 Perl 的 chop() 函数有所不同，它会删除字符串的最后一个字符。 User Contributed Notes 5 notes =============================================================================== Rather use rtrim(). Usage of chop() is not very clear nor consistent for people reading the code after you. =============================================================================== If you are searching for a function that does the same trick as chop in PERL, then you should just do the following code: The question is: why isn't chop() an alias of the code above, rather than something which will trap developpers? =============================================================================== //simple example function for chop() ";//without
you cann't see desired output in your browser echo chop(" Ramki ");//right spaces are eliminated echo chop("Ramkrishna", "a..z"); echo ""; ?> /*output ------ RamkiR*/ =============================================================================== Another possible one would be to use this: If you wanted to perl-chop a va list of strings and return the removed chars. Obviously you can easily mod it for va list arrays of strings and the like. =============================================================================== Definition and Usage The chop() function will remove a white space or other predefined character from the right end of a string. This function is an alias of the rtrim() function. Syntax chop(string,charlist) Parameter Description string Required. Specifies the string to check charlist Optional. Specifies which characters to remove from the string. The following characters are allowed and is set to be removed if the charlist parameter is empty: * "\0" - ASCII 0, NULL * "\t" - ASCII 9, a tab * "\n" - ASCII 10, a new line * "\x0B" - ASCII 11, a vertical tab. * "\r" - ASCII 13, a carriage return * " " - ASCII 32, an ordinary white space


| Field | Value |
| Date of Request | {{Request.DateTime}} |
| Event | {{Request.Event}} |
| EAF Number | {{Request.EAFNo}} |
| EMBC Task Number | {{Request.EMBCTaskNo}} |
| Requesting Organization | {{Request.RequestingOrg}} |
| Resource Type | {{Request.ResourceType}} |
| Description of Expenditure | {{Request.Description}} |
| Amount Requested | {{Request.AmountRequested}} |
| Expenditure Not To Exceed | {{Request.ExpenditureNotToExceed}} |
| Field | Value |
| Authorized Representative Name | {{Request.AuthName}} |
| Authorized Representative Email | {{Request.AuthEmail}} |
| Authorized Representative Telephone | {{Request.AuthTelephone}} |
| Position | {{Request.EocApprovals.Processing.Position}} |
| Approval Date/Time | {{Request.EocApprovals.Processing.ApprovalDateTime}} |
| Field | Value |
| Approved By | {{Request.EocApprovals.Processing.ApprovedBy}} |
| Position | {{Request.EocApprovals.Processing.Position}} |
| Approval Date/Time | {{Request.EocApprovals.Processing.ApprovalDateTime}} |
| Field | Value |
| Approved By | {{Request.EocApprovals.ExpenditureRequest.ApprovedBy}} |
| Position | {{Request.EocApprovals.ExpenditureRequest.Position}} |
| Approval Date/Time | {{Request.EocApprovals.ExpenditureRequest.ApprovalDateTime}} |


RelaxNG validator.
More...
#include <rngvalidator.h>
|
| static bool | run (const std::string &xml_file_pathname, const std::string &rng_file_pathname) |
| | Validate an XML file based on a RelaxNG Schema. More...
|
| |
◆ run()
| bool libgexf::RngValidator::run |
( |
const std::string & |
xml_file_pathname, |
|
|
const std::string & |
rng_file_pathname |
|
) |
| |
|
static |
Validate an XML file based on a RelaxNG Schema.
- Parameters
-
| xml_file_pathname | : path of the XML file to validate |
| rng_file_pathname | : path of the RNG file |
- Returns
- true if valid, false otherwise
The documentation for this class was generated from the following files:


"GERGESA "=Gerasa, identified with the modern Khersa, “over against Galilee,” close to the lake. This was probably the scene of the miracle, Mark 5:1-20, etc. “From the base of the great plateau of Bashan, 2,000 feet or more overhead, the ground slopes down steeply, in places precipitously, to the shore. And at the foot of the declivity a bold spur runs out to the water’s edge. By it the frantic swine would rush on headlong into the lake and perish.” Porter’s Through Samaria. (See GADARA.)
"GERGESA "=Gerasa, identified with the modern Khersa, “over against Galilee,” close to the lake. This was probably the scene of the miracle, Mark 5:1-20, etc. “From the base of the great plateau of Bashan, 2,000 feet or more overhead, the ground slopes down steeply, in places precipitously, to the shore. And at the foot of the declivity a bold spur runs out to the water’s edge. By it the frantic swine would rush on headlong into the lake and perish.” Porter’s Through Samaria. (See GADARA.)


Project Description
Studiiio is a collection tool and social app for Copic marker artists. The studiiio app is still in beta, and will be available on the app store in the beginning of 2017. It features a marker collection tracker of all 358 Copic colors in all four styles. Users can catalog owned markers or add them to their wishlist to buy later.


Navigation
Find Address (CH)
Find Address (International)
Find Point Of Interest
Point Of Interest
Search
Post Code
Street
House N°
Start:
End:
Distance:
Duration:
Drive
{{Speed}}
{{RPM}}
Back
Map
Settings
Car Statistics
Navigation
Carpi Settings
Car On-Board Connection
Bluetooth Devices (select appropriate)
Web API Keys
Here App
Here Code
TomTom
Third Party Tools
FMOD Sound Library
Made with FMOD Studio by Firelight Technologies Pty Ltd.
Website:
https://www.fmod.com


Add a new Address!
Street:
Street Error
City:
City Error
2 Digit State Code:
State Error
Zipcode:
Zipcode Error
Zip+4:
Zipcode+4 Error
Submit


--- layout: project title: LINE Kids' Phone lang: en ref: line-kids-phone css: [ owl.carousel.min, owl.theme.min ] js: [ jquery-3.2.1.min, owl.carousel.min ] --- {% include project-image.html filename="cover" alt="LINE Kids' Phone" title="LINE Kids' Phone" %}
Designing the user experience for LINE Kids' Phone started with an essential question: "What should the user experience of kids' wearable devices be like?" To make accessing each function easier, we limited the number of primary functions and suggested a horizontal widget stream system with a minimum screen hierarchy.
{% include works/line-kids-phone-carousel.html %} {% include project-image.html filename="device-flow-desktop" onmobile="hide" alt="UX flow sketch for LINE Kids' Phone" title="UX flow sketch for LINE Kids' Phone" %} {% include project-image.html id="line-device-flow-mobile" filename="device-flow-mobile" onmobile="show" alt="UX flow sketch for LINE Kids' Phone" title="UX flow sketch for LINE Kids' Phone" %} {% include project-video.html videoid="vSTIn-Z_mVk" %}


Drake
automotive Directory Reference
Directory dependency graph for automotive:
This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.


There are a lot of fun activities on the hotel premises:
To engage in any of these acyivities and many more payments are taken at the front desk where you'll get a wristband.


This part of the documentation shows some tips and tricks for Jinja2 templates.
Jinja2 supports dynamic inheritance and does not distinguish between parent and child template as long as no extends tag is visited. While this leads to the surprising behavior that everything before the first extends tag including whitespace is printed out instead of being igored, it can be used for a neat trick.
Usually child templates extend from one template that adds a basic HTML skeleton. However it’s possible put the extends tag into an if tag to only extend from the layout template if the standalone variable evaluates to false which it does per default if it’s not defined. Additionally a very basic skeleton is added to the file so that if it’s indeed rendered with standalone set to True a very basic HTML skeleton is added:
{% if not standalone %}{% extends 'master.html' %}{% endif -%}
<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
<title>{% block title %}The Page Title{% endblock %}</title>
<link rel="stylesheet" href="style.css" type="text/css">
{% block body %}

This is the page body.

{% endblock %}
If you want to have different styles for each row of a table or list you can use the cycle method on the loop object:

{% for row in rows %}

{{ row }}

{% endfor %}

cycle can take an unlimited amount of strings. Each time this tag is encountered the next item from the list is rendered.
The special loop variable always points to the innermost loop. If it’s desired to have access to an outer loop it’s possible to alias it:

{% for row in table %}

{% set rowloop = loop %}
{% for cell in row %}
{{ cell }}
{% endfor %}

{% endfor %}


Agenda
- Intro to HTML
- Why it is used?
- Structure of HTML
- HTML Tags
- HTML Elements and many more. . . .
HTML Elements
- h tags ex
This is h1
This is h2
- p tag ex
This is the Paragraph tag
- a tag ex Google
- img tag
- input ex1
- input ex2
- Button ex
For more updates stay tuned to our page on Facebook
- This text is bold
- This text is italic
- This text is emphasized
-
HTML Small Formatting
HTML Marked Formatting
My favorite color is
bluered.This is subscripted text.
This is supscripted text.
Formatting Elements Examples
- Quotation ex:
WWF's goal is to:
Build a future where people live in harmony with nature.
- Blockquote ex:
Here is a quote from WWF's website:
For 50 years, WWF has been protecting the future of nature. The world's leading conservation organization, WWF works in 100 countries and is supported by 1.2 million members in the United States and close to 5 million globally.
- Abbreviation ex:
The HTML abbr element defines an abbreviation
The WHO was founded in 1948.
- Address ex:
Written by John Doe.
Visit us at:
Example.com
Box 564, Disneyland
USA - cite ex:
The HTML cite element defines the title of a work.
The Scream by Edvard Munch. Painted in 1893.
- bdo ex:
The HTML bdo element defines bi-directional override.
This text will be written from right to left when we write rtl means right to left in dir attribute.
This text will be written from right to left


Illegal rave in disused Carpetright warehouse raided by police
A man has been arrested after more than 1,000 people descended on a disused Carpetright warehouse store for an illegal rave.
Police were called to the premises in Strood Retail Park, Kent, at 11.30pm on Saturday, following reports of anti-social behaviour.
Up to 10 police vehicles were spotted outside, and officers remained on-site to prevent any ‘potential breach of peace’ throughout the evening.
A video circulating on social media showed huge crowds dancing inside the rave, which had been kitted out with professional sound and lights systems.
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One person can be heard saying in the clip: ‘We’ve finished work, gone to McDonald’s in Strood and hear about this illegal rave at Carpetright – this is crazy.’
Another video showed revellers being moved along from where they were sitting on the ground inside the retail park.
A Kent Police spokesperson confirmed that a man, 39, from Ashford was arrested on suspicion of conspiring to commit a public order offence.
He was also arrested on suspicion of supplying a psychoactive substance (nitrous oxide).
The spokesperson said: ‘It was established that a suspected unlicensed music event was being held inside a disused warehouse with a significant number of people in attendance.
‘Officers remained at the location to prevent any potential breach of the peace, ensure public safety and identify if any criminal offences had been committed.’
Enquiries are still ongoing.
Got a story for Metro.co.uk?
Get in touch with our news team by emailing us at<EMAIL_ADDRESS>For more stories like this, check our news page.


Billionaire Michael Bloomberg joins race to become the next US president
by Michael StainesThe former Mayor of New York Michael Bloomberg has announced a bid for the 2020 Democratic presidential nomination.
It follows weeks of speculation that the billionaire would run for president.
On twitter this afternoon, he said he was “running for president to defeat Donald Trump and rebuild America.”
"We cannot afford four more years of President Trump’s reckless and unethical actions," he said.
"He represents an existential threat to our country and our values. If he wins another term in office, we may never recover from the damage.
"The stakes could not be higher.
“I believe my unique set of experiences in business, government and philanthropy will enable me to win and lead."
Mr Bloomberg has reportedly already spent $31m (€28m) on a TV advertising campaign that promises to “rebuild the country and restore faith in the dream that defines us: where the wealthy will pay more in taxes and the middle class get their fair share.”
Mr Bloomberg’s campaign said he would not be accepting donations and will remain self-funded.


Stochastic Approximation Monte Carlo (SAMC) is one of the celebrated Markov chain Monte Carlo (MCMC) algorithms. It is known to be capable of sampling from multimodal or doubly intractable distributions. We provide generic SAMC samplers for continuous distributions. User-specified densities in R and C++ are both supported. We also provide functions for specific problems that exploit SAMC computation. See Liang et al (2010) <doi:10.1002/9780470669723> for complete introduction to the method.
| Version: | 0.1.1 |
| Depends: | R (≥ 3.0.0) |
| Imports: | Rcpp, RcppXPtrUtils, utils, Rdpack |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | knitr, rmarkdown, microbenchmark, pander, geoR, RandomFields |
| Published: | 2018-10-26 |
| Author: | Yichen Cheng [aut], Ick Hoon Jin [aut], Faming Liang [aut], Kisung You [aut, cre] |
| Maintainer: | Kisung You <kyou at nd.edu> |
| License: | GPL (≥ 3) |
| NeedsCompilation: | yes |
| CRAN checks: | SAMCpack results |
| Reference manual: | SAMCpack.pdf |
| Vignettes: |
Fast Computation with XPtr |
| Package source: | SAMCpack_0.1.1.tar.gz |
| Windows binaries: | r-devel: SAMCpack_0.1.1.zip, r-release: SAMCpack_0.1.1.zip, r-oldrel: SAMCpack_0.1.1.zip |
| OS X binaries: | r-release: SAMCpack_0.1.1.tgz, r-oldrel: SAMCpack_0.1.1.tgz |
| Old sources: | SAMCpack archive |
Please use the canonical form https://CRAN.R-project.org/package=SAMCpack to link to this page.