Manage your people effectively
Find the Right person for the right job
Manage your payroll including paperwork
Write your HR popces and contracts
Keep you safe when re-organising your workforce
Help you manage difficult employee situations
Negotiate with unions
Set up and help you manage performance management and development
Undertake culture surveys
Write job descriptions
Undertake Health and Safety inspections
Help your employees through change
Our team at PeopleHub have been involved in HR and general management projects for 20 years plus. We have managed projects, recruited staff, led change, reviewed HR systems, managed payroll, undertaken health and safety audits, stepped in as acting Managers, negotiated with Unions and coached managers. We have the capability and the knowledge to help you.
M.B.A.
Claire has a broad background in executive management working in both the public and private sectors. She has led Human Resource teams and worked as a GM Human Resources across New Zealand and Australia. Claire is also an experienced recruiter and management coach, and has extensive experience as a consultant in New Zealand and The Pacific. In the past, Claire has held some very high profile positions including the role of Chief Executive for the Wellington Regional Chamber of Commerce, Chair of Transparency International and Central Region General Manager for Industry New Zealand. She is used to working with CEO’s and Boards and understands clearly the needs of business. She has a Master of Public Policy and is a Winston Churchill Scholar.
Dpl.BA
Hannah is an experienced recruitment and payroll manager. She has a Diploma in Business Management and Human Resources and has worked as an in-house HR manager and Payroll officer for a large government department as well as for private sector companies across Australia and New Zealand. She manages the payroll services and employment contracts for companies in Auckland and Wellington. Hannah has a sound understanding of payroll, the Holidays Act and how to manage pay for part-time, shift and contract workers. She is also an experienced recruiter and great at vetting the shortlist!
M.B.E
Peter Couchman has been a Management and Leadership coach for over 20 years. He combines his practical business leadership experience with that of a Dale Carnegie Trainer to deliver the best approach to achieve the desired outcomes for the individual and the organisation. Peter uses the RAVE methodology to develop the insight and identify the gap so that the coaching is on point and using the Dale Carnegie Stress and Worry tools navigates the turmoil change can bring. Using a human relations approach of building rapport and trust to bridge the gap through goal classification towards the vision of the leader they want to be. Peter uses standardised personality assessments to help clarify the potential a client has and to offer direction and potential and the coaching to achieve the result.


 I am creating a team to develop some projects and contribute in Open Source. Just look at the perks and decide if you wanna be a part of it :
- Resume mentionable Working Project.
- Exposure to the Open Source.
- Grasping new skills like Devops, Git-Github, Web Dev etc.
- Mentorship from industry experts.
- Participation in future Hackathons as a team.
- New connections.
- Guidance in most fields.
- Goodies if possible in future.
To contact us directly : contact@cyberbuddy.io


{{executionIcon.name}}
Execution of {{pipelineLabel}}
{{pipelineLabel}}
settings
check
Select default visual
check
Select execution time visual
flash_off
Disable loaders
flash_on
Enable loaders
more_vertfile_download
Download
file_download
Download without credentials
file_copy
Copy and Open
visibility
See logs
visibility
See logs tail
file_download
Download logs
more_vert
settings
Pipeline detail
file_download
Save and download
file_download
Save and download without credentials
file_download
Download backup (without templates)
play_circle_outline
Execute without debug data
content_paste
Copy and Open
delete_forever
Delete
save
Save
play_circle_outline
Execute
stop
Cancel
Edit mode
Execution mode
close
Close
save
play_circle_outline
Edit mode
Execution mode
close


Description:
A human actor.
The person who sets up the flowsheet, the structure of the flowsheet, chooses thermodynamic models, the unit operation models and solver packages.
This person hands over a working flowsheet to the [Flowsheet User]. The Flowsheet Builder can act as a [Flowsheet User].
Superclasses:
<<Actor>>Flowsheet User A human actor.
The person who uses an existing flowsheet. This person will put new data into the flowsheet rather than change the structure of the flowsheet.


Level downloading has been blocked by RobTop.
Level analysis, daily levels, and downloading extra info will not work until he chooses to unblock downloads.
These features still work locally and on private servers
Website created by Colon : Pretty much everything other than that belongs to RobTop Games.


Flexible, lightweight, easy-to-use, without external dependencies - Mentions component for Angular.
Basic example:
Preset mentions (edit text mode) example:
Advanced example, all inputs and outputs used:
Autocomplete only example (no highlighting):
Autocomplete only (no highlighting) with input element instead of textarea:


--- layout: post title: Paul Campbell subtitle: How to become successful rubyist teaser: Be careful about getting too excited up front and letting it blind you to reality. Visualise the future and make sure you're happy with the perceived outcome. thumb: upic-pabcas.jpg category: freelance ---
Internet wayfarer, Ruby on Rails developer, Ardent foodie, aspiring musician
I didn't: I just decided to use Ruby instead of PHP for a job I was working on.
Where, and how, do you search for work now? Can you give me some advice on the best ways to find Ruby related work?
My only real advice is: just do good work. More usually follows. Keep up with standards and best practices, try to learn one new thing every project you do.
What advice would you give a Ruby beginner without any projects ready to show?
Get coding!
What have you learned in the past about working with Ruby, clients, how to find good clients, etc.? Many people dream about changing the past for a better present moment ... anything you want to share?
Be careful about getting too excited up front and letting it blind you to reality. Visualise the future and make sure you're happy with the perceived outcome.
What books, or sites, or recipes, or whatever else you can recommend (they may be about productivity, or negotiation, or thinking - anything you think will help me live a better life as a programmer)?
The Pragmatic Programmer is the best book on programming I've read.
How much time per week do you work? How do you keep yourself productive and focused?
Anything between 30 and 60 hours. Productivity ... it's really just down to the job. I find tools like Pivotal Tracker and writing Cucumber tests help keep me focussed.
Macbook Pro / Rails / Cucumber ... I also use Passenger as a dev environment / Textmate and a tool called RailsTerm that sets up my terminals with log / db / console etc.
How do recommend becoming a successful and profitable programmer?
Profit: spend less than you earn!
What should every programmer know?
There's more to life than programming.
Best of luck!
Thank you :)


Identity Management System
Logout
Pending requests
Organisation
Request Date
Take Action
Upload ID
Submit
View ID
View
Older requests
Organisation
Response Date
Status


Coordinating regular cycles whether internal and external of academic programme reviews and administrative unites reviews and monitor the implementation of the recommendations of internal and external reviews.
Providing quality standards, measures and key performance for all programmes, unites, and departments and review Alfaisal University quality assurance policy and operational manual.
Assisting in the promotion and development of a culture of quality in every aspect of the University.
Organizing seminars and training workshops in the area of quality assurance for the University's staff and students communities.
Conducting satisfaction surveys as scheduled in the surveys handbook.
Preparing unit-specific quality assurance (QA) procedures, operational manuals and measurement instruments and analyse quality assurance reports.
Conducting evaluation of courses and staff by students at the end of each semester and regular self-evaluations to determine to which extent it meets the set performance standards in practice, and uses the results of such evaluations to improve practice.
Development of the organizational and legislative frameworks for delegated authority and accountability in QAA aspects.
Development of non-traditional teaching methods including e-learning which enhance student expiries.
Development of research, administrative processes and skills of academic staff capabilities.
Establish Link with external actors, in particular with professional and regulatory bodies, employers, students, community and alumni to raise the profile of the Alfaisal University.
Assure that curriculum is based on clear academic standards, with specific objectives and learning outcomes defined for each course.


<IP_ADDRESS>
portfolio
about
emily
yeh
Mobile app developer, software engineer, website designer
Hi! I’m a mobile app developer at Audible, Inc., an Amazon company that serves audiobook-loving customers worldwide.
>
Explore my portfolio
>
Learn more about me


You need to update the Client ID to proceed.
Please open "src/config.json" file using an editor, and update the clientID
value with the
registered application's client ID.
Visit repo README for more details.
Please open "src/config.json" file using an editor, and update the clientID
value with the
registered application's client ID.
Visit repo README for more details.
@asgardeo/auth-angular
SDK
.
.
HMACSHA256(
base64UrlEncode(
header) + "." +
base64UrlEncode(
payload),
your-256-bit-secret
);


This is yet another post about Generative Adversarial Nets (GANs), and based upon our new ICLR’18 paper with Yi Zhang. A quick recap of the story so far. GANs are an unsupervised method in deep learning to learn interesting distributions (e.g., images of human faces), and also have a plethora of uses for image-to-image mappings in computer vision. Standard GANs training is motivated using this task of distribution learning, and is designed with the idea that given large enough deep nets and enough training examples, as well as accurate optimization, GANs will learn the full distribution.
Sanjeev’s previous post concerned his co-authored ICML’17 paper which called this intuition into question when the deep nets have finite capacity. It shows that the training objective has near-equilibria where the discriminator is fooled —i.e., training objective is good—but the generator’s distributions has very small support, i.e. shows mode collapse. This is a failure of the model, and raises the question whether such bad equilibria are found in real-life training. A second post showed empirical evidence that they do, using the birthday-paradox test.
The current post concerns our new result (part of our upcoming ICLR paper) which shows that bad equilibria exist also in more recent GAN architectures based on simultaneously learning an encoder and decoder. This should be surprising because many researchers believe that encoder-decoder architectures fix many issues with GANs, including mode collapse.
As we will see, encoder-decoder GANs seem very powerful. In particular, the proof of the previously mentioned negative result utterly breaks down for this architecture. But, we then discovered a cute argument that shows encoder-decoder GANs can have poor solutions, featuring not only mode collapse but also encoders that map images to nonsense (more precisely Gaussian noise). This is the worst possible failure of the model one could imagine.
Encoder-decoder architectures
Encoders and decoders have long been around in machine learning in various forms – especially deep learning. Speaking loosely, underlying all of them are two basic assumptions:
(1) Some form of the so-called manifold assumption which asserts that high-dimensional data such as real-life images lie (roughly) on a low-dimensional manifold. (“Manifold” should be interpreted rather informally – sometimes this intuition applies only very approximately sometimes it’s meant in a “distributional” sense, etc.)
(2) The low-dimensional structure is “meaningful”: if we think of an image $x$ as a high-dimensional vector and its “code” $z$ as its coordinates on the low-dimensional manifold, the code $z$ is thought of as a “high-level” descriptor of the image.
With the above two points in mind, an encoder maps the image to its code, and a decoder computes the reverse map. (We also discussed encoders and decoders in our earlier post on representation learning in a more general setup.)
Encoder-Decoder GANs
These were introduced by Dumoulin et al.(ALI) and Donahue et al.(BiGAN). They involve two competitors: Player 1 involves a discriminator net $D$ that is given an input of the form (image, code) and it outputs a number in the interval $[0,1]$, which denotes its “satisfaction level” with this input. Player 2 trains a decoder net $G$ (also called generator in the GANs setting) and an encoder net $E$.
Recall that in the standard GAN, discriminator tries to distinguish real images from images generated by the generator $G$. Here discriminator’s input is an image and its code. Specifically, Player 1 is trying to train its net to distinguish between the following two settings, and Player 2 is trying to make sure the two settings look indistinguishable to Player 1’s net.
(Here it is assumed that a random code is a vector with i.i.d gaussian coordinates, though one could consider other distributions.)
The hoped-for equilibrium obviously is one where generator and encoder are inverses of each other: $E(G(z)) \approx z$ and $G(E(x)) \approx x$, and the joint distributions $(z,G(z))$ and $(E(x), x)$ roughly match. The underlying intuition is that if this happens, Player 1 must’ve produced a “meaningful” representation $E(x)$ for the images – and this should improve the quality of the generator as well. Indeed, Dumoulin et al.(ALI) provide some small-scale empirical examples on mixtures of Gaussians for which encoder-decoder architectures seem to ameliorate the problem of mode collapse.
The above papers prove that when the encoder/decoder/discriminator have infinite capacity, the desired solution is indeed an equilibrium. However, we’ll see that things are very different when capacities are finite.
Finite-capacity discriminators are weak
Say a generator/encoder pair $(G,E)$ $\epsilon$-fools a decoder $D$ if
In other words, $D$ has roughly similar output in Settings 1 and 2.
Our theorem applies when the distribution consists of realistic images, as explained later. We show the following:
(Informal theorem) If the discriminator $D$ has capacity (i.e. number of parameters) at most $p$, then there is an encoder $E$ of capacity $\ll p$ and generator $G$ of slightly larger capacity than $p$ such that $(G, E)$ can $\epsilon$-fool every such $D$. Furthermore, the generator exhibits mode collapse: its distribution is essentially supported on a bit more than $p$ images, and the encoder $E$ just outputs white noise (i.e. does not extract any “meaningful” representation) given an image.
(Note that such a $(G, E)$ represents an $\epsilon$-approximate equilibrium, in the sense that player 1 cannot gain more than $\epsilon$ in the distinguishing probability by switching its discriminator. )
It is important that the encoder’s capacity is much less than $p$, and thus the theorem allows a discriminator that is able to simulate $E$ if it needed, and in particular verify for a random seed $z$ that $E(G(z)) \approx z$. The theorem says that even the ability to conduct such a verification cannot give it power to force encoder to produce meaningful codes. This is a counterintuitive aspect of the result. The main difficulty in the proof (which stumped us for a bit) was how to exhibit such an equilibrium where $E$ is a small net.
This is ensured by a simple assumption. We assume the image distribution is mildly “noised”: say, every 100th pixel is replaced by Gaussian noise. To a human, such an image would of course be indistinguishable from a real image. (NB: Our proof could be carried out via some other assumptions to the effect that images have an innate stochastic/noise component that is efficiently extractable by a small neural network. But let’s keep things clean.) When noise $\eta$ is thus added to an image $x$, we denote the resulting image as $x \odot \eta$.
Now the encoder will be rather trivial: given the noised image $x \odot \eta$, output $\eta$. Clearly, such an encoder does not in any sense capture “meaning” in the image. It is also implementable by a tiny single-layer net, as required by the theorem.
Construction of generator
As usual in the GAN literature, we will assume the discriminator is $L$-Lipschitz. This can be a loose upperbound, since only $\log L$ enters quantitatively in the proof.
The generator $G(z)$ in the theorem statement memorizes a hash function that partitions the set of all seeds/codes $z$ into $m$ equal-sized blocks; it also memorizes a “pool” of $m := p \log^2(pL)/ \epsilon^2$ unnoised images $\tilde{x}_1, \tilde{x}_2, \dots, \tilde{x}_m$. When presented with a random seed $z$, the generator computes the block of the partition that $z$ lies in, and then produces the image $\tilde{x}_i \odot z$, where $i$ is the block $z$ belongs to. (See the Figure below.)
Now we have to prove that such a memorizing generator exists that $\epsilon$-fools all discriminators of capacity $p$. This is shown by the probabilistic method: we describe a distribution over generators $G$ that works “in expectation”, and subsequently use concentration bounds to prove there exists at least one generator that does the job.
The distribution on $G$’s is straightforward: we select the pool of (unnoised) images $\tilde{x}_1, \tilde{x}_2, .., \tilde{x}_m$ at random. Why is this distribution for $G$ sensible? Notice the following simple fact:
In other words, the “expected” encoder correctly matches the expectation of $D(x, E(x))$, so that the discriminator is fooled “in expectation”. This of course is not enough: we need some kind of concentration argument to show a particular $G$ works against all possible discriminators, which will ultimately use the fact that the discriminator $D$ has a small capacity and small Lipschitz constant. (Think covering number arguments in learning theory.)
Towards that, another useful observation: if $q$ is the uniform distribution over sets $T= {z_1, z_2,\dots, z_m}$, s.t. each $z_i$ is independently sampled from the conditional distribution inside the $i$-th block of the partition of the noise space, by the law of total expectation one can see that The right hand side is an average of terms, each of which is a bounded function of mutually independent random variables – so, by e.g. McDiarmid’s inequality it concentrates around it’s expectation, which by (3) is exactly $E_{z} D(G(z), z)$.
To finish the argument off, we use the fact that due to Lipschitzness and the bound on the number of parameters, the “effective” number of distinct discriminators is small, so we can union bound over them. (Formally, this translates to an epsilon-net + union bound argument. This also gives rise to the value of $m$ used in the construction.)
Takeaway
The result should be interpreted as saying that possibly the theoretical foundations of GANs need more work. The current way of thinking about them as distribution learners may not be the right way to formalize them. Furthermore, one has to take care about transfering notions invented for distribution learning, such as encoders and decoders, over into the GANs setting. Finally there is an empirical question whether any of the myriad GANS variations can avoid mode collapse.


@* * Copyright 2021 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import play.api.Configuration @import uk.gov.hmrc.play.views.html.helpers.FormWithCSRF @import uk.gov.hmrc.agentsexternalstubsfrontend.controllers.routes @this(mainTemplate: main_template, form: FormWithCSRF) @(loginForm:Form[uk.gov.hmrc.agentsexternalstubsfrontend.controllers.SignInController.SignInRequest], postUrl: Call)(implicit request: Request[_], messages: Messages, config: Configuration) @mainTemplate(title = Messages("login.title"), bodyClasses = None) { @form(action = postUrl, 'id -> "loginForm", 'novalidate -> "novalidate") {
}


Configurações
Nome de usuário:
dev72
Senha:
Email:
<EMAIL_ADDRESS>Contas sincronizadas:
Artigos postados:
Placeholder content for this accordion, which is
intended to demonstrate the
.accordion-flush
class. This is the
first item's accordion body.Placeholder content for this accordion, which is
intended to demonstrate the
.accordion-flush
class. This is the
second item's accordion body. Let's imagine this being filled with some actual
content.Placeholder content for this accordion, which is
intended to demonstrate the
.accordion-flush
class. This is the
third item's accordion body. Nothing more exciting happening here in terms of
content, but just filling up the space to make it look, at least at first
glance, a bit more representative of how this would look in a real-world
application.


{{template "errorable" $app.ErrorsFor "app_id"}}
The iOS Mobile App configuration must be blank if you are an iOS Exposure Notifications Express (ENX) customer. This should only
be filled in for custom applications.
{{template "errorable" $app.ErrorsFor "sha"}}
SHAs are unique fingerprints of Android applications from their signing
certificate. We support multiple SHA256s for different versions of the app
(e.g. development and production). SHA256s should be 95 characters and of the
form: AA:BB:CC:... You can generate a SHA with the following
command:


Stock market update: 41 stocks hit 52-week lows on NSE
Among the stocks that touched their 52-week lows were Bodal Chemicals and Deccan Cements.
by ETMarkets.comNEW DELHI: Around 41 stocks fell to touch their 52-week lows on NSE in Friday's session.
Among the stocks that touched their 52-week lows were Bodal Chemicals, Deccan Cements, Hindustan Composites and International Paper APPM.
Kiri, Indian Hume Pipe, MphasiS, Sharda Cropchem and Sobha also featured among the stocks that touched their 52-week lows on NSE.
Domestic benchmark index NSE Nifty was trading 45.60 points down, while the BSE Sensex was trading 151.27 points down at 40,423.90.
On the other hand, Essel Propack, JM Financial, Nippon Life Asset Management Ltd., Affle (India) Ltd. and India Grid Trust stocks hit their fresh 52-week high today. In the Nifty 50 index, NTPC, ITC, Zee Ent., YES Bank and PowerGrid were among the top gainers on the NSE. However, Bharti Infratel, Infosys, Tech Mahindra, HCL Tech and TCS were among the top losers.


|
My Project
|
Interface for requesting data-type information from a Ghidra client. More...
Go to the source code of this file.
Classes | |
| class | TypeFactoryGhidra |
| An implementation of the TypeFactory interface, query a Ghidra client for data-type information. More... | |
Interface for requesting data-type information from a Ghidra client.


Threads
- ALLEGRO_THREAD
- ALLEGRO_MUTEX
- ALLEGRO_COND
- al_create_thread
- al_start_thread
- al_join_thread
- al_set_thread_should_stop
- al_get_thread_should_stop
- al_destroy_thread
- al_run_detached_thread
- al_create_mutex
- al_create_mutex_recursive
- al_lock_mutex
- al_unlock_mutex
- al_destroy_mutex
- al_create_cond
- al_destroy_cond
- al_wait_cond
- al_wait_cond_until
- al_broadcast_cond
- al_signal_cond
Allegro includes a simple cross-platform threading interface. It is a thin layer on top of two threading APIs: Windows threads and POSIX Threads (pthreads). Enforcing a consistent semantics on all platforms would be difficult at best, hence the behaviour of the following functions will differ subtly on different platforms (more so than usual). Your best bet is to be aware of this and code to the intersection of the semantics and avoid edge cases.
These functions are declared in the main Allegro header file:
#include <allegro5/allegro.h>
ALLEGRO_THREAD
typedef struct ALLEGRO_THREAD ALLEGRO_THREAD;
An opaque structure representing a thread.
ALLEGRO_MUTEX
typedef struct ALLEGRO_MUTEX ALLEGRO_MUTEX;
An opaque structure representing a mutex.
ALLEGRO_COND
typedef struct ALLEGRO_COND ALLEGRO_COND;
An opaque structure representing a condition variable.
al_create_thread
ALLEGRO_THREAD *al_create_thread(
void *(*proc)(ALLEGRO_THREAD *thread, void *arg), void *arg)
Spawn a new thread which begins executing proc
. The new thread is passed its own thread handle and the value arg
.
Returns a pointer to the thread on success. Otherwise, returns NULL if there was an error.
See also: al_start_thread, al_join_thread.
al_start_thread
void al_start_thread(ALLEGRO_THREAD *thread)
When a thread is created, it is initially in a suspended state. Calling al_start_thread will start its actual execution.
Starting a thread which has already been started does nothing.
See also: al_create_thread.
al_join_thread
void al_join_thread(ALLEGRO_THREAD *thread, void **ret_value)
Wait for the thread to finish executing. This implicitly calls al_set_thread_should_stop first.
If ret_value
is non-NULL
, the value returned by the thread function will be stored at the location pointed to by ret_value
.
See also: al_set_thread_should_stop, al_get_thread_should_stop, al_destroy_thread.
al_set_thread_should_stop
void al_set_thread_should_stop(ALLEGRO_THREAD *thread)
Set the flag to indicate thread
should stop. Returns immediately.
See also: al_join_thread, al_get_thread_should_stop.
al_get_thread_should_stop
bool al_get_thread_should_stop(ALLEGRO_THREAD *thread)
Check if another thread is waiting for thread
to stop. Threads which run in a loop should check this periodically and act on it when convenient.
Returns true if another thread has called al_join_thread or al_set_thread_should_stop on this thread.
See also: al_join_thread, al_set_thread_should_stop.
Note: We don't support forceful killing of threads.
al_destroy_thread
void al_destroy_thread(ALLEGRO_THREAD *thread)
Free the resources used by a thread. Implicitly performs al_join_thread on the thread if it hasn't been done already.
Does nothing if thread
is NULL.
See also: al_join_thread.
al_run_detached_thread
void al_run_detached_thread(void *(*proc)(void *arg), void *arg)
Runs the passed function in its own thread, with arg
passed to it as only parameter. This is similar to calling al_create_thread, al_start_thread and (after the thread has finished) al_destroy_thread - but you don't have the possibility of ever calling al_join_thread on the thread any longer.
al_create_mutex
ALLEGRO_MUTEX *al_create_mutex(void)
Create the mutex object (a mutual exclusion device). The mutex may or may not support "recursive" locking.
Returns the mutex on success or NULL
on error.
See also: al_create_mutex_recursive.
al_create_mutex_recursive
ALLEGRO_MUTEX *al_create_mutex_recursive(void)
Create the mutex object (a mutual exclusion device), with support for "recursive" locking. That is, the mutex will count the number of times it has been locked by the same thread. If the caller tries to acquire a lock on the mutex when it already holds the lock then the count is incremented. The mutex is only unlocked when the thread releases the lock on the mutex an equal number of times, i.e. the count drops down to zero.
See also: al_create_mutex.
al_lock_mutex
void al_lock_mutex(ALLEGRO_MUTEX *mutex)
Acquire the lock on mutex
. If the mutex is already locked by another thread, the call will block until the mutex becomes available and locked.
If the mutex is already locked by the calling thread, then the behaviour depends on whether the mutex was created with al_create_mutex or al_create_mutex_recursive. In the former case, the behaviour is undefined; the most likely behaviour is deadlock. In the latter case, the count in the mutex will be incremented and the call will return immediately.
See also: al_unlock_mutex.
We don't yet have al_mutex_trylock.
al_unlock_mutex
void al_unlock_mutex(ALLEGRO_MUTEX *mutex)
Release the lock on mutex
if the calling thread holds the lock on it.
If the calling thread doesn't hold the lock, or if the mutex is not locked, undefined behaviour results.
See also: al_lock_mutex.
al_destroy_mutex
void al_destroy_mutex(ALLEGRO_MUTEX *mutex)
Free the resources used by the mutex. The mutex should be unlocked. Destroying a locked mutex results in undefined behaviour.
Does nothing if mutex
is NULL
.
al_create_cond
ALLEGRO_COND *al_create_cond(void)
Create a condition variable.
Returns the condition value on success or NULL
on error.
al_destroy_cond
void al_destroy_cond(ALLEGRO_COND *cond)
Destroy a condition variable.
Destroying a condition variable which has threads block on it results in undefined behaviour.
Does nothing if cond
is NULL
.
al_wait_cond
void al_wait_cond(ALLEGRO_COND *cond, ALLEGRO_MUTEX *mutex)
On entering this function, mutex
must be locked by the calling thread. The function will atomically release mutex
and block on cond
. The function will return when cond
is "signalled", acquiring the lock on the mutex in the process.
Example of proper use:
al_lock_mutex(mutex);
while (something_not_true) {
al_wait_cond(cond, mutex);
}
do_something();
al_unlock_mutex(mutex);
The mutex should be locked before checking the condition, and should be rechecked al_wait_cond returns. al_wait_cond can return for other reasons than the condition becoming true (e.g. the process was signalled). If multiple threads are blocked on the condition variable, the condition may no longer be true by the time the second and later threads are unblocked. Remember not to unlock the mutex prematurely.
See also: al_wait_cond_until, al_broadcast_cond, al_signal_cond.
al_wait_cond_until
int al_wait_cond_until(ALLEGRO_COND *cond, ALLEGRO_MUTEX *mutex,
const ALLEGRO_TIMEOUT *timeout)
Like al_wait_cond but the call can return if the absolute time passes timeout
before the condition is signalled.
Returns zero on success, non-zero if the call timed out.
See also: al_wait_cond
al_broadcast_cond
void al_broadcast_cond(ALLEGRO_COND *cond)
Unblock all threads currently waiting on a condition variable. That is, broadcast that some condition which those threads were waiting for has become true.
See also: al_signal_cond.
Note: The pthreads spec says to lock the mutex associated with
cond
before signalling for predictable scheduling behaviour.
al_signal_cond
void al_signal_cond(ALLEGRO_COND *cond)
Unblock at least one thread waiting on a condition variable.
Generally you should use al_broadcast_cond but al_signal_cond may be more efficient when it's applicable.
See also: al_broadcast_cond.


Javascript and the DOM
What are the differences between HTML and CSS?
When you think of a web page and the layout of that website, HTML (Hypertext Markup Language) and CSS (Cascading Style Sheets) are two aspects used in web design. You must be thinking what are they? Simply, HTML is used to define the structure of a web page while CSS (Cascading Style Sheets) is used to style it. You can think of it as building a house. HTML are the walls in a house, which is the structure of the website, and CSS is the paint on the house, which is the style of the website. Since you have gotten this far we can go a bit more indepth. HTML deals with coding titles, main text, photos, links in your text and several other components in a webpage. Now we have all the important text/information and links etc, you would like it to look nice. Font type, text size, text alignment and background colour are some of many things CSS does to create a nice looking web page. The CSS code is then linked to you HTML code.
Control Flow and Loops
Control flow is the order in which the computer executes code. It runs in order from the first line of code to the last line unless it comes across if, if else statements or loops etc. Loops are an instruction that is repeated until a condition is met. It’s like a recipe or making scrambled eggs. You whisk until combined (a loop), put in a hot pan with oil, pour in the eggs, stirred around until cooked and then served. The recipe is control flow and whisking is a loop.
What is the DOM?
The Document Object Model is a crucial part in web pages. It represents the browser as objects and code that make up the web page. It is similar to a text editor where you can change code and see the result immediately but it doesn’t alter anything permanently. The DOM is like a tree, HTML is the trunk and the branches are the elements.
What is the difference between acessesing data from arrays and objects?
Arrays are used to create lists of multiple items in one variable. You can also create arrays within arrays called nested arrays. Objects are used to represent something in your code like a boat, house, person, name or book. These are called properties and use key and a value.
What are functions?
They are reusable code that have a specific action such as performing a task or calculating a value. You can call on it over and over similar to looping where it is designed to perform a certain task you have like cooking eggs. Each part of cooking the eggs is a function.


This is the bonus for this project. Is a continuation of the spaceship game. Location sprites where aded, in other words, the place changes corresponding to the value of inSpace.There is an asteroid that is trying to hit you, at the moment it doesn't kill you, it is just a prove of concepts, but you can shoot (max 5 bullet on screen) the asteroid and destroy it, it will continue reapearing. The controls are maintained the same as Part One.
JS for Bonus


To access this dashboard, please provide your unique sequence of characters used to identify you as a user and allow access to this system.
If you don't remember your credentials, either use the reset password or signup to create a new user account.
Do not have an account? Sign Up here


MultiDownload manual Tests
link1
link2
link3
Download
download
Download with delay (3s)
download delayed


Chapter One
The Man
I do NOT own Pokemon, Gamefreak, or any other company affliciated with Pokemon. The only characters I own are ones I made.
unfinished
Just to say, in this series, Pokemon are able to talk however not everyone can understand, it's marked in "--" and italics is thoughts.
My name is Lily. I will be a Pokemon Master. I don't generally try to brag about it, but I know it will happen one day. I don't have any Pokemon of my own, but I will in a few days. I don't know what to pick. Hopefully I can make a decision.
"And so, that concludes our tutorial on Pokemon typing." said the professor on the recording.
"Once again, I still cant decide..." said Lily.
"Oh come on now, I know you will eventually." said Lily's mother. "It doesn't depend on which is the strongest, but which fits you the best."
"They all fit me the best!" said Lily.
"You still have three days that you can think of which to pick." said Lily's mother. "I know you can decide during that time."
"I hope I can." said Lily as her mother left the room.


JAZ jaz-D expression Arabidopsis oxysporum Fig. Plant genes gene wild-type plants JAZ, increased mutant resistance proteins T-DNA jasmonate insertion MYC lines transcription EAR control levels transcriptional motif jaz- up-regulated Supplementary defense Table disease protein responses results susceptibility using JA-signaling MeJA activation domain leaves (e.g. Cell MYC, SALK_ Thatcher plants. {border-style TPL highly repressors binding identified jaz px;} relative #ffffff;} pathogen repression repressor senescence used Fusarium altered analysis assays interaction replicates Chung Shyu differentially factors phenotypes plant Kazan Pst Values Yan biological promoter response root signaling JA-mediated data independent interactions leaf line mutants observed overexpression significantly COI flowering phenotype reduced roles (A) (B) Chini Fonseca JA-sensitivity Jas Manners Physiology averages performed role roots ±SE JA-responsive JAM Molecular Song described experiments growth high induced regulated shown syringae Gene Howe Kidd ZIM activators consisting examined expressed functional functions infection oxysporum. previously regulate resulting suggest thaliana treatment. -fold Pseudomonas Sasaki-Sekimoto Sciences, Zhang and/or bHLH cloned co-repressor conducted constitutive determine did down-regulated expression, following genes. involved obtained pathogens pools px; stress {font-family β-actin Atg Berrocal-Lobo C). Cevik However, JA-related McGrath Nakata Similar Thines associated complex confers contains culture degradation development different factor font-size fungal fused inducibility inhibition jaz-D. media microarray rosette sequence shows suggesting suggests time transcripts (C) Arabidopsis. Botany Browse C-terminal Col- Genes JA-regulated JA-responses JAZ-OX Liu NATA Queensland Research S). Schenk Science Sheard TIFY TOPLESS Thireault act additional compared confirmed conserved contain containing critical does double; early elongation enhanced experiments. filtrate function generated host interact interacts jaz-D, key lack mediated mediates mediating members motif, mutation network plants, positive regulation required seedlings showed signaling. specific tested tissue transcript treatment (**, (WT) (WT), (data Academy Adio Anderson Australia COI-JAZ-TPL-TF Chen Cheng Demianski Dombrecht Fernandez-Calvo JA-signaling, JAZmEAR Molina National Nature Niu Pauwels Proceedings RNA Representative S:CUC-JAZ Student’s USA University activity acts affect analyzed available bHLH/JAM bacterial bind bold border-color border-width coi compromised conferred dataset degron determined downstream ectopic encoding enriched exhibit higher homozygous individual inoculated inoculations insertions internal known length line. negatively phenotypes. possible produce regulators reporter repress responses. similar small solid; tissues values {font-style {font-weight (WiscDsLoxH) ANOVA Asterisks B). Botrytis CSIRO CUC Causier Cerrudo Chan Experimental Expression GAL Gateway-compatible Howe, Huang JA-dependent JA-responses, JA-sensitivity, JAM, JASMONATE JAZ. Kagale Katsir Melotto Peng TPL, Thi. Torres Xie ZIM-domain Zhai absence analysis. appears aspects characterized co-bombarded complexes. conditions consistent construct controls courier; demonstrate differences direct effector em; example, expression. family finding generation herbivory however, identification including indicate inducible inductions leaves. loss-of-function mechanism monitored motifs mutations non-defensive null pathways phenotypes, plasmid possibility promote propose proposed pt;} recruitment reported responsible result results, screened second sensitivity short showing shown). signal studies subsequent support susceptible symptoms t-test) tomato transgenic triple upstream


Dash is a small project, designed to:
Dash front-end is built upon AngularJS and AngularUI. Charting is done using Google Charts and D3. Realtime updates of data are cordinated using SignalR.
The server is built using ServiceStack. I am currently using ServiceStack.Ormlite for back-end persistence, but may change the database layer to something more customized, due to personal preference over ORMs in general.
The restful api can be viewed at /metadata.
Debugging logs can be viewed at /requestlogs. This will likely be disabled if Dash ever gets hosted publicly and/or taken seriously.


India is a vast South Asian country with diverse terrain – from Himalayan peaks to Indian Ocean coastline – and history reaching back 5 millennia. In the north, Mughal Empire landmarks include Delhi’s Red Fort complex and massive Jama Masjid mosque, plus Agra’s iconic Taj Mahal mausoleum. Pilgrims bathe in the Ganges in Varanasi, and Rishikesh is a yoga centre and base for Himalayan trekking.
India is a famous country all over the world. Geographically, our country is located to the south of Asia continent. India is a high population country and well protected from all directions naturally. It is a famous country for its great cultural and traditional values all across the world. It contains a mountain called Himalaya which is biggest in the world. It is surrounded by the three big oceans from three directions such as in south with Indian Ocean, in east with Bay of Bengal and in west with Arabic sea. India is a democratic country ranks second for its population. The national language of India is Hindi however almost fourteen nationally recognized languages are spoken here.


The top iPhone and iPad apps on App Store
by The Associated PressApp Store Official Charts for the week ending November 3, 2019:
Top Paid iPhone Apps:
1. Minecraft, Mojang
2. Heads Up!, Warner Bros.
3. Plague Inc., Ndemic Creations
4. Bloons TD 6, Ninja Kiwi
5. iSchedule, HotSchedules
6. Geometry Dash, RobTop Games AB
7. Facetune, Lightricks Ltd.
8. Procreate Pocket, Savage Interactive Pty Ltd
9. NBA 2K20, 2K
10. True Skate, True Axis
Top Free iPhone Apps:
1. Photo Roulette, Photo Roulette AS
2. Rescue Cut - Rope Puzzle, MarkApp Co. Ltd
3. Call of Duty: Mobile, Activision Publishing, Inc.
4. TikTok - Make Your Day, TikTok Inc.
5. Tennis Clash: Fun Sports Games, Wildlife Studios
6. YouTube: Watch, Listen, Stream
7. Fit the Ball 3D, Bigger Games
8. Instagram, Instagram, Inc.
9. Brain Out, EYEWIND LIMITED
10. Snapchat, Snap, Inc.
Top Paid iPad Apps:
1. Minecraft, Mojang
2. Procreate, Savage Interactive Pty Ltd
3. GoodNotes 5, Time Base Technology Limited
4. Notability, Ginger Labs
5. Bloons TD 6, Ninja Kiwi
6. Geometry Dash, RobTop Games AB
7. XtraMath, XtraMath
8. Stardew Valley, Chucklefish Limited
9. Plague Inc., Ndemic Creations
10. Toca Hair Salon 3, Toca Boca AB
Top Free iPad Apps:
1. Rescue Cut - Rope Puzzle, MarkApp Co. Ltd
2. Tomb of the Mask: Color, Playgendary
3. Icing On The Cake, Lion Studios
4. Draw Around!, SayGames LLC
5. YouTube: Watch, Listen, Stream, Google LLC
6. Farmers.io, Voodoo
7. Call of Duty: ,MobileActivision Publishing, Inc
8. Amazon Prime Video, AMZN Mobile LLC
9. Netflix, Netflix, Inc
10. Calculator, International Travel Weather Calculator
__
(copyright) 2019 Apple Inc.
Sign up for Daily Newsletters
Copyright © 2019 The Washington Times, LLC.
Please read our comment policy before commenting.


-
- Successful Venture by Korean Lingerie Brand, ‘Fidelia’, Seducing Paris
- The success of an unrivaled lingerie brand with new sales channels, online and mobile, has caught the attention of Europe.
-
- FIDELIA, Huge response at European market exhibiting ‘Mode City PARIS 2012’
- CEO of EUROBET for event host wins high praise for “Surprised for FIEDLIA’s creative design and passion”
-
- "Enjoy with exclusively sensitive rational price of luxury lingerie"
- Former CHRISTIAN DIOR designer Boes’s CJ O Shopping launching
-
- Luxury of Dress, Vera Wang - Meeting “FIDELIA” Luxury of lingerie
- World famous designer Vera Wang cooperates with FIDELIA and shows off ‘Vera Wang for FIDELIA’. Luxury of lingerie for women, Born for global collaboration of ‘FIDELIA’ in 2012.


Boris Johnson and Jeremy Corbyn set to go head-to-head in general election TV leaders debate
The Tory and Labour party leaders will do battle in a live show in the build up to December 12
by Record ReporterJeremy Corbyn and Boris Johnson are set to head to head in a live TV ahead of the general election.
The PM and Labour leader will square off on November the 19th on ITV.
Jeremy Corbyn welcomed the announcement.
In a tweet the Labour leader said: “This is a once in a generation election.
“So it’s welcome that Boris Johnson has accepted our challenge of a head to head TV debate.
The choice could not be clearer: Boris Johnson’s Conservatives protecting the privileged few or a Labour government on the side of the many.”
Johnson also took to Twitter to comment on the debate.
"Looking forward to making the positive case to the country that we should £GetBrexitDone & deliver on the people's priorities - £OurNHS, schools, tackling crime & the cost of living," he said.
The Prime Minister was accused of running scarred from TV debates by rival Jeremy Hunt during the race to take over the Tory Party after the departure of Theresa May.
Johnson did take part in two one-on-one debates during the battle for the Tory crown, but not before Mr Hunt and others placed him under great pressure to do so.
Confirming the tussle ITV press tweeted: “ITV will show the first head-to-head debate between Boris Johnson and Jeremy Corbyn on 19th November.
“Details of further multi-party debate and comprehensive general election coverage in due course”.


Dark_Matter
In Dark Matter, the crew of a derelict spaceship is awakened from stasis with no memories of who they are or how they got on board. Facing threats at every turn, they have to work together to survive a voyage charged with vengeance, betrayal and hidden secrets.


freeCodeCamp Survey Form
Thank you for taking the time to help us improve the platform
Name
Email
Age (optional)
Which option best describes your current role?
Select an option
Student
Full Time Job
Full Time Learner
Prefare NOt To Say
Other
Would you recommend freeCodeCamp to a friend?
Definitely
Maybe
Not Sure
What is your favorite feature of freeCodeCamp?
Select an option
Challenges
Projects
Community
Open Source
What would you like to see improved? (Check all that apply)
Front End Projects
Back End Projects
Data Visualization
Challenges
Open Source Community
Gitter help rooms
Videos
City Meetups
Wiki
Forum
Additional Courses
Any comments or suggestions?


Before Change
target_class_ids = tensor.tile(target_class_ids, [1, 1, num_samples])
target_class_ids = target_class_ids.flatten()
target_sample_ids = tensor.arange(num_batch_samples)
class_probs = tensor.set_subtensor(
class_probs[(target_sample_ids, target_class_ids)], 0)
sample = self.network.random.multinomial(pvals=class_probs)
sample = sample.argmax(1)
sample = sample.reshape([num_time_steps, num_sequences, num_samples])
self.sample_logprobs = \
self._get_target_preact(layer_input, sample)
After Change
class_probs = self.network.noise_probs[None, :]
sample = random.multinomial_wo_replacement(pvals=class_probs,
n=num_samples)
sample = sample[0, :]
// For some reason (maybe a rounding error) it may happen that the
// sample contains a very high or negative value.
sample = tensor.maximum(sample, 0)
sample = tensor.minimum(sample, num_classes - 1)
self.shared_sample_logprobs = \
self._get_target_list_preact(layer_input, sample)
self.shared_sample = sample


Note: with the end of Flash support, PonyAR cannot be used directly in your browser anymore.
If you still wish to give it a go, please download the SWF file on your computer, and open it using the offline, standalone Flash player.
Please note that Adobe has stopped providing download links for any sort of Flash player, even the standalone version, so you will have to find one yourself. Unless you're really curious, it may not really be worth the trouble.
Once you've opened the SWF file in the standalone Flash player, follow the instructions below as normal.
What the hay is PonyAR?
PonyAR uses augmented reality to put more ponies in your life!
By using your webcam and a marker, PonyAR can integrate a 3D model of Twilight Sparkle, Fluttershy, or any other pony of the mane six in what you're filming, to make it seems like they're actually there, in real life!
Still have no idea what this means? Have a look at some screenshots!
Screenshot 1 - Screenshot 2 - Screenshot 3 - Screenshot 4
Instructions
Requirements: a webcam, a relatively powerful computer, a decent Internet connexion or some patience (the SWF file is a little over 9 Mb).
- Get your webcam ready. I recommend using a external one for more freedom, but an integrated one is fine.
- Print the image (also known as marker) corresponding to your favorite pony. If you can't decide which pony is your favorite, print all of them.
Applejack - Fluttershy - Pinkie Pie - Rainbow Dash - Rarity - Twilight
Note: If you're low on ink, you can try printing them in black and white. It shouldn't affect the results too much.
You can even put the pictures on your phone/iPad/whatever and show it to your webcam if you really don't want to print anything, but the results won't be nearly as good, so print them if you can!
- Wait for the animation above to finish loading. Hopefully it won't take too long.
- (Optional) If your Flash player is outdated, you will be asked to upgrade it. Please do, having an up-to-date Flash player is important!
- Once the loading is done, click "Allow" to allow Flash to use your webcam. (We kind of need it for that. Don't worry, nothing is transmitted, everything happens locally on your computer.)
- (Optional) If you have more than one webcam and Flash picked the wrong one: right-click, choose "Settings", click on the webcam icon and pick the right webcam in the list.
- Get one of the markers you printed in the frame. Make sure it's not too close nor too far.
- If all went well, your reality is now being augmented by ponies. Move your webcam around, try with multiple markers, whatever comes to mind!
Thanks to IN2AR for the detection framework.
Thanks to KP-ShadowSquirrel for the 3D models.
If you wish to contact me, feel free to send me an email!
Have fun!


PharmD: A Doctor of Pharmacy (PharmD; New Latin Pharmaciae Doctor) is a professional doctorate in pharmacy. In some countries, it is a first professional degree and a prerequisite for licensing to practice the profession of pharmacy or to become a clinical pharmacist.
JUST: Is The Jordan University of Science and Technology, often abbreviated JUST.
IMC: Is International Medical Corps is a global, nonprofit, humanitarian aid organization dedicated to saving lives and relieving suffering by providing emergency medical services, as well as healthcare training and development programs, to those affected by disaster, disease or conflict.
PMP: Is Project Management Professional (PMP) certification is an industry recognized credential for project managers. PMP demonstrates the experience, education, skill and competency required to lead and direct projects.
ehealth: Is eHealth is a relatively recent healthcare practice supported by electronic processes and communication.


|
ArUco
1.2.5
|
This class detects AR boards Version 1.2 There are two modes for board detection. More...
#include <boarddetector.h>
Public Member Functions | |
| BoardDetector (bool setYPerpendicular=false) | |
| See discussion in. | |
| void | setParams (const BoardConfiguration &bc, const CameraParameters &cp, float markerSizeMeters=-1) |
| Use if you plan to let this class to perform marker detection too. | |
| void | setParams (const BoardConfiguration &bc) |
| float | detect (const cv::Mat &im) throw (cv::Exception) |
| Detect markers, and then, look for the board indicated in setParams() | |
| Board & | getDetectedBoard () |
| Returns a reference to the board detected. | |
| MarkerDetector & | getMarkerDetector () |
| Returns a reference to the internal marker detector. | |
| vector< Marker > & | getDetectedMarkers () |
| Returns the vector of markers detected. | |
| float | detect (const vector< Marker > &detectedMarkers, const BoardConfiguration &BConf, Board &Bdetected, cv::Mat camMatrix=cv::Mat(), cv::Mat distCoeff=cv::Mat(), float markerSizeMeters=-1) throw (cv::Exception) |
| Given the markers detected, determines if there is the board passed. | |
| float | detect (const vector< Marker > &detectedMarkers, const BoardConfiguration &BConf, Board &Bdetected, const CameraParameters &cp, float markerSizeMeters=-1) throw (cv::Exception) |
| void | setYPerpendicular (bool enable) |
| By default, the Y axis is set to point up. | |
| void | setYPerperdicular (bool enable) |
| bool | isYPerpendicular () |
| void | set_repj_err_thres (float Repj_err_thres) |
| Sets the threshold for reprjection test. | |
| float | get_repj_err_thres () const |
Static Public Member Functions | |
| static Board | detect (const cv::Mat &Image, const BoardConfiguration &bc, const CameraParameters &cp, float markerSizeMeters=-1) |
| Static version (all in one). |
This class detects AR boards Version 1.2 There are two modes for board detection.
First, the old way. (You first detect markers with MarkerDetector and then call to detect in this class.
Second: New mode, marker detection is included in the class
CameraParameters CP; CP.readFromFile(path_cp) BoardConfiguration BC; BC.readFromFile(path_bc); BoardDetector BD; BD.setParams(BC,CP); //or only BD.setParams(BC) //capture image cv::Mat im; capture_image(im); float prob=BD.detect(im); if (prob>0.3) CvDrawingUtils::draw3DAxis(im,BD.getDetectedBoard(),CP);
| aruco::BoardDetector::BoardDetector | ( | bool | setYPerpendicular = false | ) |
See discussion in.
| float aruco::BoardDetector::detect | ( | const cv::Mat & | im | ) | throw (cv::Exception) |
Detect markers, and then, look for the board indicated in setParams()
Referenced by detect().
| float aruco::BoardDetector::detect | ( | const vector< Marker > & | detectedMarkers, |
| const BoardConfiguration & | BConf, | ||
| Board & | Bdetected, | ||
| cv::Mat | camMatrix = cv::Mat() , |
||
| cv::Mat | distCoeff = cv::Mat() , |
||
| float | markerSizeMeters = -1 |
||
| ) | throw (cv::Exception) |
Given the markers detected, determines if there is the board passed.
| detectedMarkers | result provided by aruco::ArMarkerDetector |
| BConf | the board you want to see if is present |
| Bdetected | output information of the detected board |
| camMatrix | camera matrix with intrinsics |
| distCoeff | camera distorsion coeff |
| camMatrix | intrinsic camera information. |
| distCoeff | camera distorsion coefficient. If set Mat() if is assumed no camera distorion |
| markerSizeMeters | size of the marker sides expressed in meters |
| float aruco::BoardDetector::detect | ( | const vector< Marker > & | detectedMarkers, |
| const BoardConfiguration & | BConf, | ||
| Board & | Bdetected, | ||
| const CameraParameters & | cp, | ||
| float | markerSizeMeters = -1 |
||
| ) | throw (cv::Exception) |
| Board aruco::BoardDetector::detect | ( | const cv::Mat & | Image, |
| const BoardConfiguration & | bc, | ||
| const CameraParameters & | cp, | ||
| float | markerSizeMeters = -1 |
||
| ) | [static] |
Static version (all in one).
Static version (all in one)
Detects the board indicated
| Image | input image |
| bc | the board you want to see if is present |
| cp | camera parameters |
| markerSizeMeters | size of the marker sides expressed in meters (not needed in the board is expressed in meters) |
References detect(), getDetectedBoard(), and setParams().
| float aruco::BoardDetector::get_repj_err_thres | ( | ) | const [inline] |
| Board& aruco::BoardDetector::getDetectedBoard | ( | ) | [inline] |
Returns a reference to the board detected.
Referenced by detect().
| vector<Marker>& aruco::BoardDetector::getDetectedMarkers | ( | ) | [inline] |
Returns the vector of markers detected.
| MarkerDetector& aruco::BoardDetector::getMarkerDetector | ( | ) | [inline] |
Returns a reference to the internal marker detector.
Referenced by ChromaticMask::setParams().
| bool aruco::BoardDetector::isYPerpendicular | ( | ) | [inline] |
| void aruco::BoardDetector::set_repj_err_thres | ( | float | Repj_err_thres | ) | [inline] |
Sets the threshold for reprjection test.
Pixels that after estimating the camera location projects 'repj_err_thres' pixels farther from its original location are discarded as outliers. By default it is set to -1, meaning that not reprojection test is performed
| void aruco::BoardDetector::setParams | ( | const BoardConfiguration & | bc, |
| const CameraParameters & | cp, | ||
| float | markerSizeMeters = -1 |
||
| ) |
Use if you plan to let this class to perform marker detection too.
Referenced by detect(), and ChromaticMask::setParams().
| void aruco::BoardDetector::setParams | ( | const BoardConfiguration & | bc | ) |
| void aruco::BoardDetector::setYPerpendicular | ( | bool | enable | ) | [inline] |
By default, the Y axis is set to point up.
However this is not the default operation mode of opencv, which produces the Z axis pointing up instead. So, to achieve this change, we have to rotate the X axis.
Referenced by ChromaticMask::setParams().
| void aruco::BoardDetector::setYPerperdicular | ( | bool | enable | ) | [inline] |


Local date: Jan 1 1970
Local time: Jan 1 1970
Local date and time: Jan 1 1970
Relative time: Oops! This browser doesn't support Web Components.
Time ago: Oops! This browser doesn't support Web Components.
Time ago (micro format): Oops! This browser doesn't support Web Components.


pysnn.connection
pysnn.connection¶
A Connection
object is used to pass spikes, activations, and traces between two sets of BaseNeuron
objects. The Connection
is the base
class that should be inherited by each custom connection. Each Connection
shares a set of basic functionalities/dynamics:
Presynaptic Neuron, which is the
Neuron
that precedes theConnection
.Postsynaptic Neuron, which is the
Neuron
that succedes theConnection
.Delay in the transmission of incoming signals from its presynaptic
Neuron
to its postsynapticNeuron
.Weights, which are the relative weights that are assigned from spikes originating from each presynaptic
Neuron
.- Signals from the presynaptic
Neuron
that are passed on by theConnection
, which are the following Traces
Spikes
Activation potential, this is not actually an input but a result of multiplying incoming spikes with the connection weight.
- Signals from the presynaptic
-
class
pysnn.connection.
Connection
(shape, dt, delay)¶ Base class for defining SNN connection/layer.
This object connects layers of neurons, it also contains the synaptic weights.
-
convert_spikes
(x)¶ Convert input from Byte Tensor to same data type as the weights.
-
no_grad
()¶ Set require_gradients to False and turn off training mode.
-
reset_state
()¶ Set state Parameters (e.g. trace) to their resting state.
-
reset_weights
(distribution='uniform', gain=1.0, a=0.0, b=1.0)¶ Reinnitialize network weights.
Note: Not all parameters apply to every distribution.
- Parameters
distribution – Distribution used for initialization
gain – Scalar increase of the weight values.
a – Lower bound.
b – Upper bound.
-
init_connection
()¶ Collection of all intialization methods.
Assumes weights are implemented by the class that inherits from this base class.
-
propagate_spike
(x)¶ Track propagation of spikes through synapses if the connection.
-
-
class
pysnn.connection.
Linear
(in_features, out_features, batch_size, dt, delay)¶ SNN linear (fully connected) layer with interface comparable to torch.nn.Linear.
- Parameters
in_features – Size of each input sample.
out_features – Size of each output sample.
batch_size – Number of samples in a batch.
dt – Duration of each timestep.
delay – Time it takes for a spike to propagate through the connection. Should be an integer multiple of dt.
-
activation_potential
(x)¶ Determine activation potentials from each synapse for current time step.
- Parameters
x – Presynaptic spikes.
- Returns
Activation potentials.
-
forward
(x, trace_in)¶ Calculate postsynaptic activation potentials and trace.
- Parameters
x – Presynaptic spikes.
trace_in – Presynaptic trace.
- Returns
(Activation potentials, Postsynaptic trace)
-
class
pysnn.connection.
Conv2d
(in_channels, out_channels, kernel_size, im_dims, batch_size, dt, delay, stride=1, padding=0, dilation=1)¶ Convolutional SNN layer interface comparable to torch.nn.Conv2d.
- Param
Number of channels in the input image.
- Parameters
out_channels – Number of channels produced by the convolution
kernel_size – Size of the convolving kernel
im_dims – (Height, Width) of the input image.
batch_size – Number of samples in a batch.
dt – Duration of each timestep.
delay – Time it takes for a spike to propagate through the connection. Should be an integer multiple of dt.
stride – Stride of the convolution. Default: 1
padding – Zero-padding added to both sides of the input. Default: 0
dilation – Spacing between kernel elements. Default: 1
-
activation_potential
(x)¶ Determine activation potentials from each synapse for current time step.
- Parameters
x – Presynaptic spikes.
- Returns
Activation potentials.
-
forward
(x, trace_in)¶ Calculate postsynaptic activation potentials and trace.
- Parameters
x – Presynaptic spikes.
trace_in – Presynaptic trace.
- Returns
(Activation potentials, Postsynaptic trace)
-
class
pysnn.connection.
MaxPool2d
(kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False)¶ Simple port of PyTorch MaxPool2d with small adjustment for spiking operations.
Currently pooling only supports operations on floating point numbers, thus it casts the uint8 spikes to floats back and forth. The trace of the ‘maximum’ spike is also returned. In case of multiple spikes within pooling window, returns first spike of the window (top left corner).
-
forward
(x, trace)¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
-
-
class
pysnn.connection.
AdaptiveMaxPool2d
(output_size)¶ Simple port of PyTorch AdaptiveMaxPool2d with small adjustment for spiking operations.
Currently pooling only supports operations on floating point numbers, thus it casts the uint8 spikes to floats back and forth. The trace of the ‘maximum’ spike is also returned. In case of multiple spikes within pooling window, returns first spike of the window (top left corner).
-
forward
(x, trace)¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
-


Implementations and API
A first implementation is available in Typescript/Javascript for tests.
For now, Generic-syntax is in progress and should not be used in production.
Source code is available at https://github.com/generic-syntax/gs-js.
High level API
TODO
Low level API
TODO


[gecode-users] I need get informacion during search process
Ivan Carvajal
peliculasivan at gmail.com
Fri Jun 8 06:52:52 CEST 2007
Yes, i understand, but this information is given at the end of the process,
i need this information during the search.
¿How i can implement a my own search process in Gecode/J? In the DFSSearch
defined in java, call the C++ function to do the search process.
¿I need implement all search process, including the stack file?
Sorry for my english, i don't speak good.
Thanks
Ivan
2007/6/7, Guido Tack <tack at ps.uni-sb.de>:
>
> Hi.
> Ivan Carvajal wrote:
>
> Hi, i use Gecode/J and need get information during the search process,
> this information is: amount propagation, clones, commit, and depth of three.
>
>
> Both DFSSearch and BABSearch have a method "statistics". The returned
> object contains nearly all the information you require, just have a look at
> the documentation. The only thing that's missing is the depth of the tree.
> If you need that, you will have to implement your own search engine that
> keeps track of this information.
>
> Cheers,
> Guido
>
> --
> Guido Tack
> Programming Systems Lab, Saarland University, Germany
> http://www.ps.uni-sb.de/~tack <http://www.ps.uni-sb.de/%7Etack>
>
>
>
>
--
...:::PAU TE AMO:::...
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://www.ps.uni-sb.de/pipermail/users/attachments/20070608/e4efa797/attachment.htm>
More information about the gecode-users
mailing list


|
Assimp
5.0.1, master @ cd42b955
|
If you develop at Visual Studio 2015 or 2017, you can simply use the pre-built linker libraries provided in the distribution. Extract all files to a place of your choice. A directory called "assimp" will be created there. Add the assimp/include path to your include paths (Menu->Extras->Options->Projects and Solutions->VC++ Directories->Include files) and the assimp/lib/<Compiler> path to your linker paths (Menu->Extras->Options->Projects and Solutions->VC++ Directories->Library files). This is necessary only once to setup all paths inside you IDE.
To use the library in your C++ project you can simply generate a project file via cmake. One way is to add the assimp-folder as a subdirectory via the cmake-command
Now just add the assimp-dependency to your application:
If done correctly you should now be able to compile, link, run and use the application.
First you need to install cmake. Now just get the code from github or download the latest version from the webside. to build the library just open a command-prompt / bash, navigate into the repo-folder and run cmake via:
A project-file of your default make-system ( like gnu-make on linux or Visual-Studio on Windows ) will be generated. Run the build and you are done. You can find the libs at assimp/lib and the dll's / so's at bin.
The Assimp-package can be built as DLL. You just need to run the default cmake run.
The Assimp-package can be build as a static library as well. Do do so just set the configuration variable BUILD_SHARED_LIBS to off during the cmake run.


JavaTM Platform Debugger Architecture |
Documentation Contents |
The Java Platform Debugger Architecture (JPDA) consists of three interfaces designed for use by debuggers in development environments for desktop systems. The Java Virtual Machine Tools Interface (JVMTI) defines the services a VM must provide for debugging (JVMTI is a replacement for the Java Virtual Machine Debug Interface (JVMDI) which has been deprecated). The Java Debug Wire Protocol (JDWP) defines the format of information and requests transferred between the process being debugged and the debugger front end, which implements the Java Debug Interface (JDI). The Java Debug Interface defines information and requests at the user code level.Enhancements Design Interface Reference
NOTE: JVMPI, the experimental Java Virtual Machine Profiling Interface has been deprecated and will be removed in the next major release. It has been replaced by JVMTI.
NOTE: JVMDI has been deprecated and will be removed in the next major release. It has been replaced by JVMTI.
Implementation
More Information
Examples
The demo/jpda/examples.jar file in the JDK software contains JPDA examples, including source code for the jdb Java Debugger and the prototype javadt GUI debugger.
| Copyright © 2004 Sun
Microsystems, Inc. All Rights Reserved.
Please send comments to: Debugging Feedback |


Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book.
It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages,
and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text.
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Dicta libero autem, magni veritatis, optio dolor.
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Dicta libero autem, magni veritatis, optio dolor.
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Dicta libero autem, magni veritatis, optio dolor.
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Dicta libero autem, magni veritatis, optio dolor.
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Dicta libero autem, magni veritatis, optio dolor.
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Dicta libero autem, magni veritatis, optio dolor.
Happy Clients
Completed Projects
Cups of Coffee
Lines of Code
DOCTOR
Lorem ipsum dolor sit amet, consectetur adipi elit. Quae voluptatum dolorum, possimus itu tenetur pariatur officia, quo commodi autem doloribus vero rerum aspernatur quidem temporibus.
251 Bogotá, BXY 92101, Colombia
+00 123-456-789
251 Bogotá, BXY 92101, Colombia
www.yourdomain.com


This page represents an export of peer-reviewed papers as well as eprints and pre-prints of the
Blocks & Chains
Bibliography, containing publications and various online resources in the field of
cryptocurrencies
and distributed ledger technologies.
Contributions to the project via GitHub
are welcome.


Alaunde Copley-Woods
Democrat
Symphony flutist/ music educator - self
BA biochemistry - Barnard College/Columbia University MM Flute - Manhattan School of Music
80305
Ballot box judge, Boulder County for 2020 elections Precinct Captain, Boulder County Democrats 2018 - present Delegate to County and/or State Assemblies 2008, 2010, 2018
In my professional life, I have been elected to: Board of Colorado Flute Association, Executive Board of Denver Musicians' Association, Greeley Philharmonic Orchestra Committee, Greeley Philharmonic
I have devoted many hours to getting out of the vote in Colorado.
I know that district maps are very important to the rights of voters, and want to offer my time as a fair and reasonable arbiter.
I am consistently elected by my colleagues and peers to sit on panels that make decisions for the group. People know that I actively solicit various viewpoints and am able to process them in a thoughtful manner.
With my scientific background, I approach problems in exactly that way: logically and rationally. I enjoy incorporating all details in a solution.
female


HOME
REVIEWS
ADD NEW REVIEW
REFERENCES
Looks like there are no reviews yet. Press here to add reviews


Football: Mokoena goal hands South Africa win in Olympics qualifier
JOHANNESBURG: A brilliant Teboho Mokoena free-kick goal gave South Africa a 1-0 victory over the Ivory Coast on Tuesday (Nov 12) in a 2020 Tokyo Olympic Games qualifier in Cairo.
Mokoena, a late arrival for the eight-nation competition because SuperSport United refused to free him ahead of the Nov 10-19 international window, struck on 79 minutes.
The free-kick was in the middle of the pitch, but well outside the penalty area, and Mokoena lifted the ball over a wall of players and goalkeeper Ira Tape Eliezer.
Victory lifted South Africa to the top of Group B with four points, one more than the Ivory Coast and Nigeria, who defeated Zambia 3-1 in the second match of a double-header.
In a wide open group with one round to play this Friday, bottom team Zambia can still qualify while South Africa might miss out on a top-two finish and a semi-finals place.
Zambia, who have only one point, face the Ivory Coast and South Africa meet Nigeria in matches that will be played simultaneously at the Al Salam and Cairo International stadiums.
Success was particularly sweet for the South Africans as they arrived in Egypt last week with only 12 players for a tournament which doubles as the Africa U23 Cup of Nations.
'A DISGRACE'
By the time they played Zambia the squad have grown to 15, six players short of the permitted number, as some domestic and European clubs refused to free players before the FIFA window.
"It is a disgrace," said South African football legend Neil Tovey, who captained his country to victory in the 1996 Africa Cup of Nations.
"It is downright diabolical that they cannot have the best players. This is a sad state of affairs - begging clubs for players."
Nigeria needed 93 minutes to seal victory over Zambia and keep alive hopes of an eighth appearance at the Olympics, where they won gold in 1996 and silver in 2008.
Zambia goalkeeper Mangani Banda raced out of his area to clear in stoppage time, but Taiwo Awoniyi got to the ball first and his angled shot trickled into the net.
Patson Daka had given Zambia an early lead that Orji Okwonkwo swiftly cancelled and Kelechi Nwakali, once on the books of Arsenal, nudged Nigeria ahead off a second-half free-kick.
On Thursday, already-qualified Group A leaders Egypt tackle second-place Cameroon while Ghana must defeat Mali to have any chance of finishing runners-up.
The top three finishers in Egypt will represent Africa in the 16-nation Tokyo football tournament.


{% extends "detail.html" %} {% block description %} Reject call or play audio / speak text. Add {{to}} placeholder into Text to speak called number. {% endblock %}


Project Dependencies
compile
The following is a list of compile dependencies for this project. These dependencies are required to compile and run the application:
| GroupId | ArtifactId | Version | Type |
|---|---|---|---|
| aopalliance | aopalliance | 1.0 | jar |
| commons-lang | commons-lang | 2.1 | jar |
| net.sf.ehcache | ehcache | 1.6.2 | jar |
| org.seasar.container | s2-extension | 2.4.34 | jar |
test
The following is a list of test dependencies for this project. These dependencies are only required to compile and run unit tests for the application:
| GroupId | ArtifactId | Version | Type |
|---|---|---|---|
| junit | junit | 3.8.1 | jar |
provided
The following is a list of provided dependencies for this project. These dependencies are required to compile the application, but should be provided by default when using the library:
| GroupId | ArtifactId | Version | Type |
|---|---|---|---|
| org.apache.geronimo.specs | geronimo-j2ee_1.4_spec | 1.0 | jar |
Project Transitive Dependencies
The following is a list of transitive dependencies for this project. Transitive dependencies are the dependencies of the project dependencies.
compile
The following is a list of compile dependencies for this project. These dependencies are required to compile and run the application:
| GroupId | ArtifactId | Version | Type |
|---|---|---|---|
| commons-logging | commons-logging | 1.1 | jar |
| jboss | javassist | 3.4.ga | jar |
| ognl | ognl | 2.6.9-patch-20070908 | jar |
| org.apache.poi | poi | 3.0-FINAL | jar |
| org.seasar.container | s2-framework | 2.4.34 | jar |
Project Dependency Graph
Dependency Tree
- org.seasar.caching:s2caching:jar:0.1-SNAPSHOT
- junit:junit:jar:3.8.1 (test)
- aopalliance:aopalliance:jar:1.0 (compile)
- org.apache.geronimo.specs:geronimo-j2ee_1.4_spec:jar:1.0 (provided)
- org.seasar.container:s2-extension:jar:2.4.34 (compile)
- org.seasar.container:s2-framework:jar:2.4.34 (compile)
- aopalliance:aopalliance:jar:1.0 (compile)
- jboss:javassist:jar:3.4.ga (compile)
- ognl:ognl:jar:2.6.9-patch-20070908 (compile)
- commons-logging:commons-logging:jar:1.1 (compile)
- org.apache.poi:poi:jar:3.0-FINAL (compile)
- commons-logging:commons-logging:jar:1.1 (compile)
- org.seasar.container:s2-framework:jar:2.4.34 (compile)
- net.sf.ehcache:ehcache:jar:1.6.2 (compile)
- commons-lang:commons-lang:jar:2.1 (compile)
Licenses
Public Domain: AOP alliance
Unknown: OGNL, Unnamed - org.apache.geronimo.specs:geronimo-j2ee_1.4_spec:jar:1.0
MPL 1.1: Javassist
Common Public License Version 1.0: JUnit
The Apache Software License, Version 2.0: Ehcache Core, Jakarta POI, Lang, Logging, Seasar2 Extension, Seasar2 Framework, s2caching
Dependency File Details
| Filename | Size | Entries | Classes | Packages | JDK Rev | Debug |
|---|---|---|---|---|---|---|
| aopalliance-1.0.jar | 4.36 kB | 15 | 9 | 2 | 1.3 | debug |
| commons-lang-2.1.jar | 202.85 kB | 125 | 110 | 8 | 1.1 | debug |
| commons-logging-1.1.jar | 51.67 kB | 34 | 25 | 2 | 1.1 | debug |
| javassist-3.4.ga.jar | 459.97 kB | 279 | 262 | 15 | 1.2 | debug |
| junit-3.8.1.jar | 118.23 kB | 119 | 100 | 6 | 1.1 | debug |
| ehcache-1.6.2.jar | 198.28 kB | 138 | 113 | 14 | 1.5 | debug |
| ognl-2.6.9-patch-20070908.jar | 163.08 kB | 122 | 114 | 1 | 1.4 | debug |
| geronimo-j2ee_1.4_spec-1.0.jar | 2.11 MB | 2,476 | 2,297 | 88 | 1.4 | debug |
| poi-3.0-FINAL.jar | 1.69 MB | 1,237 | 1,169 | 54 | 1.3 | debug |
| s2-extension-2.4.34.jar | 377.93 kB | 383 | 305 | 45 | 1.4 | debug |
| s2-framework-2.4.34.jar | 640.52 kB | 615 | 546 | 43 | 1.4 | debug |
| Total | Size | Entries | Classes | Packages | JDK Rev | Debug |
| 11 | 5.96 MB | 5,543 | 5,050 | 278 | 1.5 | 11 |
| compile: 9 | compile: 3.74 MB | compile: 2,948 | compile: 2,653 | compile: 184 | - | compile: 9 |
| test: 1 | test: 118.23 kB | test: 119 | test: 100 | test: 6 | - | test: 1 |
| provided: 1 | provided: 2.11 MB | provided: 2,476 | provided: 2,297 | provided: 88 | - | provided: 1 |
Dependency Repository Locations
| Repo ID | URL | Release | Snapshot |
|---|---|---|---|
| apache.snapshots | http://people.apache.org/repo/m2-snapshot-repository | - | Yes |
| maven.seasar.org | http://maven.seasar.org/maven2 | Yes | Yes |
| central | http://repo1.maven.org/maven2 | Yes | - |
Repository locations for each of the Dependencies.


{% extends 'layout.html' %} {% block body %}
High impact weather (HIW) events in the tropics are typically associated with convectively
active weather systems, including tropical cyclones. These convective weather systems are
modulated by a multitude of factors including the complex local orography, land-sea contrasts,
the monsoonal seasonal cycle, and by large-scale modes of variability on intraseasonal
timescales (e.g. equatorially trapped waves and the Madden-Julian Oscillation) and interannual
timescales (e.g. El Niño-Southern Oscillation and the Indian Ocean Dipole). The convective
nature of these HIW events makes forecasting a challenge and requires a combination of global
and high-resolution regional models to predict both the large-scale drivers of the events and
the detail of the events themselves.
Interpreting the output of numerical weather prediction models requires an understanding of
the underlying meteorology that produces the weather. The FORecaster Training In Southeast
Asia (FORTIS) training course on tropical science and forecasting in Southeast Asia is designed
to provide training on the dynamics of meso- to synoptic-scale weather systems, their large-scale
drivers, global and regional numerical weather prediction (NWP) and the performance of those NWP
systems.
The overall objective of FORTIS is to develop and deliver a 1-week training course that will
advance the understanding of tropical meteorology for research scientists and operational
forecasters in the UK and partner countries in Southeast Asia. The FORTIS workshops will be run
in the UK, the Philippines, Malaysia and Indonesia.
The FORTIS project is part of the Weather and Climate Science for Service Partnership (WCSSP)
Programme, which aims to develop partnerships harnessing UK scientific expertise to build the
basis for strengthening the resilience of vulnerable communities to weather and climate variability.
The WCSSP Programme is supported by the UK government's Newton Fund and administered by the UK Met
Office. More information can be found
here.
This website was designed by the Centre of Excellence for Modelling the Atmosphere and Climate
(CEMAC), an initiative within the
Institute for Climate and Atmospheric Science (ICAS) and School of Earth & Environment (SEE) at the
University of Leeds.


Graph-Ex
Introductions:
This project was created as a term project for DSA 2021. Tajammul Naeem and I (Nabeel Raza) collaborated to create something that we have not worked on previously. We both are students at the prestigious institution of University of Engineering & Technology, Lahore majoring in Computer Engineering. We wish to create more useful tools for our society and make coding a culture in Pakistan.
Motivations:
Some students were observed to have difficulty understanding how graphs are plotted so this utility is to help them. Moreover, we wanted to try and explore the field of Artificial Intelligence. So we decided to create this project.
Learning Outcomes:
This project introduced us to Machine Learning and Deep Learning concepts. We used Kaggle to create our model. Furthermore, we used Django to learn how websites are created. We learned all this in the three months of time span. We ought to practice more but due to strict time deadlines and continuous headaches (due to DSA assignments) we had to make do with what little we could learn. More things, like scaling the graph, better usage of equations can be expected.


Name: Bùi Văn Công
Student: ACADEMY OF CRYPTOGRAPHY TECHNIQUES
Address: Ninh Nhat Commune , Ninh Binh City
Current Address: 77 Ngo 68 Trieu Khuc,Thanh Xuan - Ha Noi
Objective:
Being a programmer in the future by working hard and participating in activities as much aspossible. Join the club ... Take advantage of the skills and experience I already have andunderstand programming to become a really professional programmer and bring great valueto your customers
Works
Skills
HTML/CSS/JS
BackEnd php / Framework laravel
JavaWeb/frameWork spring
ReactJS/Angular
Photoshop
PHP
C/C++
Contact
Please don't hesitate to contact me for more information about my work. I am available Mon - Sat, Sunday is a day of rest.
Email<EMAIL_ADDRESS>Phone:<PHONE_NUMBER>


When I’m introspective about the last few years I think the biggest mistake that we made, as a company, is betting too much on HTML5 as opposed to native… because it just wasn’t there. And it’s not that HTML5 is bad. I’m actually, on long-term, really excited about it. One of the things that’s interesting is we actually have more people on a daily basis using mobile Web Facebook than we have using our iOS or Android apps combined. So mobile Web is a big thing for us.
Mark Zuckerberg @ Disrupt SF 2012. (via tobie)


About the Data
How the FT and Crowdpac gathered and analysed candidates’ campaign contributions
To analyse Barack Obama’s top 500 donors, the FT and Crowdpac, a political data start-up, gathered all the itemised individual contributions received by the main committees backing Mr Obama, Hillary Clinton and Bernie Sanders’ presidential campaigns.
The data for Mr Obama’s 2012 campaign included contributions to his principal campaign, joint fundraiser with the Democratic National Committee, and the super-Pac Priorities USA Action, which supported his candidacy.
Data for Mrs Clinton’s 2016 campaign included contributions to her principal campaign, the DNC’s joint fundraising committees, the super-Pac Ready for Hillary, which formed during the 2014 election cycle, and Priorities USA, which is supporting her candidacy this year. The data collected included contributions through to June 30.
Data for Mr Sanders’ contributions only included his principal campaign fund. His DNC joint fundraiser was largely inactive through June, and he did not have a super-Pac backing him.
The cap on individual donations per committee has changed since the 2011-2012 campaign and election.
The limit on donations to candidates’ presidential campaign funds this cycle was raised to $2,700 per election, up from $2,500 in 2011.
Likewise, the cap on donations to national party committees such as the DNC were also raised to $33,400 this cycle from $30,800 in 2011. Joint fundraisers between presidential candidates and a national party committee allows candidates to raise larger amounts by combining what donors may give to an individual candidate and the party. This cycle, a national party’s committee may also accept up to $300,600 for its accounts funding the party’s presidential nominating convention, election recounts and headquarters.
Super-Pacs, by contrast, can raise unlimited amounts. But they may not co-ordinate with candidates’ campaigns.
It can be difficult to tally up donations from any one individual, as filings to the Federal Election Commission can be incomplete or inconsistent. If a donor uses a nickname, initial, different address or name of a new employer, it can obscure the data. Some donors will also go to great lengths to reduce the perceived amount of their donations by giving through friends, family members or separate committees, which will then donate to the main committees backing the candidate.
To solve some of these issues, Crowdpac has an algorithm that analyses donors’ names, occupations, employers and geographic data in order to link separate donations made by individuals across multiple election cycles and jurisdictions. This, combined with manual processes and checking, allowed the FT and Crowdpac to produce a database and list of the top publicly-known donors.


JavaScript is disabled on your browser.
Skip navigation links
Overview
Package
Class
Tree
Deprecated
Index
Help
Prev Letter
Next Letter
Frames
No Frames
All Classes
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Y
Z
_
Y
y
- Variable in class net.minecraft.dispenser.
PositionImpl
y
- Variable in class net.minecraft.network.play.client.
C03PacketPlayer
y
- Variable in class net.minecraft.network.play.server.
S08PacketPlayerPosLook
y
- Variable in class net.minecraft.network.play.server.
S0CPacketSpawnPlayer
y
- Variable in class net.minecraft.network.play.server.
S0EPacketSpawnObject
y
- Variable in class net.minecraft.network.play.server.
S0FPacketSpawnMob
y
- Variable in class net.minecraft.network.play.server.
S2CPacketSpawnGlobalEntity
y()
- Method in class net.minecraft.realms.
RealmsButton
y
- Variable in class net.minecraft.util.
BlockPos.MutableBlockPos
y
- Variable in class net.minecraft.util.
Rotations
Rotation on the Y axis
y
- Variable in class net.minecraft.util.
Vec3i
Y coordinate
Y_MASK
- Static variable in class net.minecraft.util.
BlockPos
Y_SHIFT
- Static variable in class net.minecraft.util.
BlockPos
yaw
- Variable in class net.minecraft.entity.ai.
EntityAITempt
Tempting player's yaw
yaw
- Variable in class net.minecraft.network.play.client.
C03PacketPlayer
yaw
- Variable in class net.minecraft.network.play.server.
S08PacketPlayerPosLook
yaw
- Variable in class net.minecraft.network.play.server.
S0CPacketSpawnPlayer
yaw
- Variable in class net.minecraft.network.play.server.
S0EPacketSpawnObject
yaw
- Variable in class net.minecraft.network.play.server.
S0FPacketSpawnMob
yaw
- Variable in class net.minecraft.network.play.server.
S14PacketEntity
yaw
- Variable in class net.minecraft.network.play.server.
S18PacketEntityTeleport
yaw
- Variable in class net.minecraft.network.play.server.
S19PacketEntityHeadLook
yBase
- Variable in class net.minecraft.world.chunk.storage.
ExtendedBlockStorage
Contains the bottom-most Y block represented by this ExtendedBlockStorage.
yCoord
- Variable in class net.minecraft.network.play.server.
S2APacketParticles
yCoord
- Variable in class net.minecraft.pathfinding.
PathPoint
The y coordinate of this point
yCoord
- Variable in class net.minecraft.util.
Vec3
Y coordinate of Vec3D
yCoord
- Variable in class net.minecraft.world.gen.
NoiseGeneratorImproved
yDisplayPosition
- Variable in class net.minecraft.inventory.
Slot
display position of the inventory slot on the screen y axis
YDoubleRoomFitHelper()
- Constructor for class net.minecraft.world.gen.structure.
StructureOceanMonumentPieces.YDoubleRoomFitHelper
yellow_flower
- Static variable in class net.minecraft.init.
Blocks
yellowColor
- Static variable in class net.minecraft.block.material.
MapColor
yellowFlowerGen
- Variable in class net.minecraft.world.biome.
BiomeDecorator
ym()
- Method in class net.minecraft.realms.
RealmsClickableScrolledSelectionList
ym()
- Method in class net.minecraft.realms.
RealmsScrolledSelectionList
ym()
- Method in class net.minecraft.realms.
RealmsSimpleScrolledSelectionList
yOffset
- Variable in class net.minecraft.client.renderer.
WorldRenderer
yOffset
- Variable in class net.minecraft.network.play.server.
S2APacketParticles
yPosF
- Variable in class net.minecraft.client.audio.
PositionedSound
yPosition
- Variable in class net.minecraft.client.gui.
GuiButton
The y position of this control.
yPosition
- Variable in class net.minecraft.client.gui.
GuiTextField
yPosition
- Variable in class net.minecraft.client.renderer.culling.
Frustum
yPosition
- Variable in class net.minecraft.entity.ai.
EntityAIWander
ySize
- Variable in class net.minecraft.client.gui.inventory.
GuiContainer
The Y size of the inventory window in pixels.
yTile
- Variable in class net.minecraft.entity.projectile.
EntityArrow
yTile
- Variable in class net.minecraft.entity.projectile.
EntityFireball
yTile
- Variable in class net.minecraft.entity.projectile.
EntityFishHook
yTile
- Variable in class net.minecraft.entity.projectile.
EntityThrowable
YZDoubleRoomFitHelper()
- Constructor for class net.minecraft.world.gen.structure.
StructureOceanMonumentPieces.YZDoubleRoomFitHelper
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Y
Z
_
Skip navigation links
Overview
Package
Class
Tree
Deprecated
Index
Help
Prev Letter
Next Letter
Frames
No Frames
All Classes


Over the past four years we have dramatically improved how we do science by embracing open data science practices and tools. We now emphasize communication and work in ways that are more reproducible, streamlined, transparent, collaborative, and open. We’re sharing our story in a publication in Nature Ecology & Evolution because although the transformation to open data science seemed intimidating, we are living proof that it’s possible and we want to encourage others to adopt these practices in their own work.
Our path to better science in less time using open data science tools (Lowndes et al. 2017, Nature Ecology and Evolution)
Citation: Lowndes JSS, Best BD, Scarborough C, Afflerbach JC, Frazier MR, O’Hara CC, Jiang N, Halpern BS (2017).
Our path to better science in less time using open data science tools. Nature Ecology & Evolution, 1. Article number: 0160.
doi:10.1038/s41559-017-0160
With the Ocean Health Index, we’ve been measuring and tracking the health of global oceans every year since 2012. By becoming more efficient and reproducible, we’ve greatly reduced the time involved to repeat our work, which lets us focus more on science.
And since it’s all open online, anyone can build directly off our work without reinventing the wheel. Currently there are 20 groups around the world building off our science and our code to assess ocean health in their own jurisdictions.
The Ocean Health Index (OHI) is a scientific framework to quantify and track the health of our oceans. We are an academic-nonprofit partnership from the National Center for Ecological Analysis and Synthesis (NCEAS) and Conservation International.
Open data science tools enable us to work efficiently and emphasize communication for science and management. Learn more at our overview website oceanhealthindex.org and our science website ohi-science.org.


 Oscar Wilde
I don’t regret for a single moment having lived for pleasure.


Mauris sagittis pellentesque lacus eleifend lacinia...

Mauris sagittis pellentesque lacus eleifend lacinia...
]]>
Update
Lorem ipsum dolor sit amet, consectetur adipiscing elit.
Aenan convallis.
Update
Lorem ipsum dolor sit amet, consectetur adipiscing elit.
Aenan convallis.
]]>
Image.jpg
Image.jpg
]]>
Unwind! Relax! Breathe!
You deserve a holiday
event
Unwind! Relax! Breathe!
You deserve a holiday
event
]]>
Booking.com
Card-Components
| Component |
Description |
|
mdl-card
|
Basic components that creates a card
|
|
mdl-card-actions
|
Container for actions at the bottom of the card.
|
|
mdl-card-media
|
A media container within an mdl-card.
|
|
mdl-card-menu
|
A menu for the card. Usually an mdl-button with mdl-button-type icon.
|
|
mdl-card-supporting-text
|
Section within an mdl-card. Can be providedmultiple times.
|
|
mdl-card-title
|
Section within an mdl-card.
|
Card-Component Attributes
| Attribute |
Description |
|
mdl-card-border
|
Creates a border around a card section.
|
|
mdl-card-expand
|
Adds flex-grow:1 to a card section so that the section expands as horizontally as much as possible.
|
|
mdl-card-title-text
|
Specifies a Title of a card. Every h* tag should have the attribute mdl-card-title-text.
|


I am Oriol and I am one of the programmers of the team.
At the beginning of this project it was decided to pick the engine that my colleague Víctor and I had developed for the Game Engines subject. The engine is called Alien Engine. As I was the main programer of the engine, my first task in the first few weeks was to intorduce to my programers team the engine so they could code fast, I was like a programer support.
Once everyone was placed, I started improving the engine and I also took part in the gameplay (wagonnetes)
In the base code that we forked I had already implemented a C++ hot reload but it had just the basics, so I implemented new features to this system:
The hot reload allows the user to have the game running in the engine and change scripts and after the user compiles (Cntrl + B) the engine will detect it and will free the old dll (also will delete all old components scripts in use) and will load the new dll and refresh all scripts. Once this finishes, the engine now has the new code running.
One of the things I implemented is the multi scening because the designers wanted this feature in order to work more than one person at the same time in the same scene.
Another thing that we needed in order to make the Wagon scene was the bezier curve that the wagon is going to follow.