Although we had a good understanding of the main functionality of the system, it was important to consider how the system interacted with each other and the overall design. We created a basic outline of the structure of the system’s implementation.
After discussing with the clients it was highlighted that it would be important to deploy this within a company’s own intranet due to various confidentially and legal issues; it would not have been appropriate to launch this in a public platform with a login system. As Office 365 is a heavily used system in a large majority of businesses and universities, three of our clients were comfortable with the use of Microsoft Office’s SharePoint feature to implement the platform as it allowed fluid data collection and handling as well as allowing the system to be implemented within a company’s own intranet. However, it was challenging to implement a SharePoint solution to the system for the NHS, as smaller hospitals and trusts may not have the technology to keep up to date with Microsoft’s system. In addition to this, after speaking to our client, it was clear that amongst other issues, it would be difficult to gain acccess to certain files of different confidentiality levels, considering they were representing real patients, adding an additional layer of bureaucracy. Therefore, a possible solution was to implement a website using Django or Flask for the clients who do not have SharePoint ensuring inclusivity across all possible users. This will be connected to a web gateway so data will remain confidential within companies. Once submitted proposals have been accepted by company leads and managers, it will be exported to a restful API where it will export a restful feed where the public can access this information. This is involved in the stage electing universities to projects after they have been accepted. The restful API will allow universities to publicly subscribe to listings of projects and to see what is available. This data will be fed to the university intranets, for example their respective Sharepoint websites. The diagram presented in Figure 1 highlighted the movement of data between the different components to show a basic overview of the architectural design of the overall system.
The explanation revolving around our diagrams provided below starts from delineating the types of users, i.e., submitters and reviewers. It can be noted that approvers will have the very same functionalities as reviewers do.
The starting point bases on logging in the system by selecting the appropriate role (submitter, reviewer or approver) that filters through the authenticated permissions and allows the user to sign in but view only the limited range of elements and functions only they are entitled to access. We allow for smart auto-generated filling-ins when appropriate for putting at easy the experience of our users while utilising our platforms. When signed in, any user is able to view the main dashboard that retrieves from the database the most updated collection of project proposals alongside their status badge, their priority eventually if anyone has set that up, the submission and/or launching date and an active URL towards each project’s display page. For a submitter, the core element revolves around submitting a project proposal, where they can fill in a step-by-step form and provide necessary evidence, details and information to be reviewed next by the reviewing committee. After a successful submission, the collection of their projects is stored in the main history list or table with everything they have sent via our portal now filled in and displayed automatically. These users can also fill in and submit their company's details, they can resubmit a project proposal in the case in which some details (e.g., abstracts, evidence, title etc.) have upgraded in the meantime and need urgent amendments and adjustments. When a project proposal is sent via the portal, the system notifies the IXN leads to log in and assign a series of reviewers based on their availability and interest towards the topic and project’s field making use of hashtags, project complexity, area of research. After being assigned, for a reviewer, the system will redirect them towards the same history of the submitted project proposals which they can view, search through, filter, export as CSV. They can separately view the accepted, rejected or under review project proposals. The main role of a reviewer is to review the projects to which they have been assigned and provide feedback based on that. Additionally, based on each project's collection of reviews, an approver can accept or reject the project. Based on the collected reviews, if rejected, the submitter is notified and if marked with Amber, the applicant will be suggested to make some modifications and improve the current proposal and eventually resubmit it. If given red, the project is declared unsuccessful and is recycled by being stored in the rejected projects page. If given green, the proposal is successful, and the submitter is notified that the IXN committee is interested in pursuing the project implementation and development. From this point on, the projects are sent towards universities for being matched based on interests, talents, expertise, background.
The diagram below represents the overall system architecture of the sharepoint website, which illustrates the use of power automate for data flows and processes such as reviewers leaving feedback as well as the storage of data and the use of Microsoft Approvals for the approval stage of the IXN CFP portal.
The diagram below shows the overall Django structure with the Django server and the inbuilt SQLite3 database, as well as Bootstrap, JavaScript and CSS for the front end technology.
There are three types of users, namely submitters, reviewers and approvers, whose permissions we configured through the Django admin’s system.
As a submitter, we are able to submit a new project proposal via the submission portal implemented with smart wizard forms which allows for an efficient division of the whole form into 5 steps based on categories. the Form class is linked to the respective Model class by describing how the fields are structured and look like when displayed in a form. The fields of the Proposal Form class map to the < input > tags in the HTML file via forms .ModelForm and displayed for the user in the front-end interface by overriding the Widget class. For a clean and elegant rendering of the submission portal form, we have opted for importing the Django crispy forms module . It provided us with the |crispy filter and {% crispy %} tag, that provides some in-build customisation for the form, along with the validation of the email, phone or date and time fields. The Taggable Manager Library is used to parse the hash tags with the use of JavaScript and Bootstrap so tags can be associated with projects.
When a submitter submits a project for approval, this sends a POST request to the backend server of Django which stores the data appropriately into the Project model alongside their unique indexes. This data is then displayed in the home page table in the project dashboard ordered by submission date, by iterating through the stored instances in the Project. We are able to click on each project’s title which is an active link toward the project’s display page, rendered with the incorporation of primary keys in the URL passed as an argument. We can view the collection of the info stored in our database.
The further functionalities can be noticed if logging in as an approver. We are now able to view on a project’s page the ASSIGN REVIEWERS section. The assignment is done in back-end through a POST request that allows the selection from a drop-down menu from among the reviewers, that are stored as a Many-to-Many field in the Person model. the reviewers are notified in the system that there are projects left to be reviewed. At this point, the reviewer can see the list of all the projects that have been assigned to review particularly to them and are redirected towards the Review Portal page associated in the URL with the unique index (primary key) of the project being reviewed. The information filled in for a review is stored in the Review model which has an identical mechanism with the Person model. When a reviewer submits a review for a project, this sends a POST request to the backend server of Django which stores the data appropriately into the Review model alongside its primary key and the foreign key to the Person model.
This data is then displayed in a tile-fashion in the review dashboard under the view all projects tab ordered by review date, by iterating through the stored instances in the Project. One can choose to view only the projects that are under review or the projects that are assigned to them to be reviewed. The same option buttons will appear for each view under each project title, namely the button of visiting the project page, the button of visiting the list of all the available feedbacks and reviews up to date and the button stating the status of the project, i.e., a link to the review portal if the reviewing period is still open.
Once the list of a maximum number of 10 reviews has been made available from the reviewers on the view feedback and review, an approver is now able to log in and accept or reject the project. The reviews are in the read-only format, so that the approvers can have a holistic overview of the reviews and can make an informed decision whether or not to pursue the project. Both the approval and the rejection are done through a GET request method and assessing which reference of the two accept/reject button is sent to the method. Depending on the action button, the accepted or rejected projects database is reloaded and the current project is stored accordingly, as well as its display in the respective views.py Also, the system automatically sends an email through the send_mail() method to the submitter in order to inform them upon the final decision of the approver regarding their project proposal.
Besides, there are three more models, namely AcceptedProjects, RejectedProjects, and UnderReviewProjects, which contain a foreign key, one-to-one field towards the Person model. The database mechanism is identical with the ones presented above.
Here is a diagram showing the relationships between the different models in our Django implementation. Within Django our data is stored in an SQLite 3 Database, with aid the of the Django back-end servers and model functionality.


ORPSEF Board of Directors
Board of Directors
Our Board of Directors consists of community members who are committed to excellence in our public schools and believe improving public school education is a powerful investment in our community.
|
Cathy Toth, Chairperson of the Board President and CEO of Acato Information Management, LLC |
Andy Page, Treasurer of the Board President and CEO of ORAU and Director of ORISE |
|
Benita Albert Retired Math Teacher of Oak Ridge Public Schools |
Derrick Hammond Pastor of Oak Valley Baptist Church in Oak Ridge |
|
Jeremy Biggs President and CAO for Methodist Medical Center |
Melinda Hillman Consultant at Roane State Community College |
|
Ivan Boatner General Counsel of ORAU and Director of Contractor Assurance Activities and General Counsel of ORISE |
Travis Howerton Senior Director of Enterprise Architecture and Strategy at Consolidated Nuclear Security, LLC |
|
David Bradshaw Senior Vice President and Oak Ridge Area Manager of Pinnacle Bank & Trust |
Leigha Humphries Manager ORNL's Protocol and Community Relations Office |
|
Katy Brown Manager of External Affairs for Comcast Cable Corp. |
Judy Gooch Owner of Judy Gooch Interiors |
|
Kelly Callison COO of Information International Associates, Inc. |
Shirley Raines President Emeritus of the Univ. of Memphis |
|
Jim Campbell President of East Tennessee Economic Council |
Barry Stephenson President and CEO of Materials and Chemistry Laboratory, Inc. |
|
Brandon Cottrell Partner with the Oak Ridge Division of Tennessee Urology Associates |
Peter Thornton Deputy Director for ORNL's Climate Change Institute |
Executive Committee
The Executive Committee shall include the Chairperson, Vice-Chairperson, the Secretary, and the Executive Director of the Corporation. The Executive Director shall not be a voting member of the Executive Committee.
Strategic Planning Committee
The Strategic Planning Committee develops the strategic plan, formulates its implementation, and monitors the effectiveness of the plan.
Members: Andy Page, Barry Stephenson, Cathy Toth, Jim Campbell, Leigha Humphries, and Jessica SteedFinance Committee
The Finance Committee monitors spending against the approved budget and the Chair of the Finance Committee, in his capacity as Treasurer, reports to each Board meeting.
Chair: Andy Page
Members: Chairperson of the Board, Cathy Toth, and additional members appointed by the Finance Committee Chair and David Bradshaw.
Classroom Partnerships Committee
The Classroom Partnerships Committee develops guidelines and standards for grants. They also implement and administer the grants program.
Chair: Cathy Toth
Members: The Classroom Partnerships Committee members remain anonymous.


{% extends "auth/base.html" %} {% import 'staff/_formhelpers.html' as forms %} {% block title %}Authorize {{client.name }} | Ok{% endblock %} {% block content %}
You are logged in as {{ current_user.email }}.
Not you? Use a different account
{{client.name}} would like to:


Overview¶
This webpage introduces an open-source index modulation toolkit (IMToolkit). This toolkit attempts to facilitate reproducible research in wireless communications. The major advantages of this toolkit are highlighted as follows:
It accelerates bit error ratio and average mutual information simulations by relying on a state-of-the-art Nvidia GPU and the massively parallel algorithms proposed in [1].
It also supports the representative multiplexing schemes for ideal MIMO and OFDM scenarios, in addition to the IM family.
It contains a comprehensive database of designed active indices, that determine the achievable performance of the generalized spatial modulation or the subcarrier-index modulation.
Installation Guide¶
IMToolkit is available from the Python official package repository PyPi.
> pip install imtoolkit
This installation requires NumPy, Pandas, SciPy, SymPy, Numba, and tqdm, all of which are popular Python packages. Additionally, it is strongly recommended to install CuPy 5.40+. IMToolkit is heavily dependent on CuPy to achieve significantly fast Monte-Carlo simulations. The key components required by CuPy are listed here. In case CuPy is not installed in your environment, IMToolkit uses NumPy only. Note that the CuPy-based simulation is 145 times faster than the NumPy-based calculation, as reported in [1].
The above PyPi package excluded the designed active indices due to their large file size, which exceeds 500MB. Hence, this reduced-size PyPi package will automatically download a required file from the GitHub repository or a mirror website. If you need all the project files, to use imtoolkit offline, it is recommended to obtain the package from GitHub as follows:
> pip install git+https://github.com/imtoolkit/imtoolkit
The IMToolkit development team welcomes other researchers’ contributions and pull requests. In that case, it would be better to install the latest package and activate the editable mode as follows:
> git clone https://github.com/imtoolkit/imtoolkit
> pip install -e ./imtoolkit # this activates the editable mode
How to Use¶
Citations¶
It would be very appreciated if you cite the following references when using IMToolkit.
[1] N. Ishikawa, “IMToolkit: An open-source index modulation toolkit for reproducible research based on massively parallel algorithms,” IEEE Access, vol. 7, pp. 93830–93846, July 2019.
Of course, if your project relies on CuPy, the following reference is highly recommended.
[2] R. Okuta, Y. Unno, D. Nishino, S. Hido, and C. Loomis, “CuPy: A NumPy-compatible library for NVIDIA GPU calculations,” in Conference on Neural Information Processing Systems Workshop, Long Beach, CA, USA, December 4–9, 2017.
Contributor(s)¶
Naoki Ishikawa (Web / Google Scholar / ResearchGate / Publons)
You might become a valuable contributor of this project. Any contributions and issues are appreciated.


Responsive tables demo
A CSS solution for tables that allows them to shrink on small devices without sacrificing the value of tables, comparison of columns.
| First Name | Last Name | Age | Total | Discount | Difference | Date |
|---|---|---|---|---|---|---|
| Bruce | Almighty | 45 | $153.19 | 44.7% | +77 | Jan 18, 2001 9:12 AM |
| Bruce | Evans | 22 | $13.19 | 11% | -100.9 | Jan 18, 2007 9:12 AM |
| Bruce | Evans | 22 | $13.19 | 11% | 0 | Jan 18, 2007 9:12 AM |
| Clark | Kent | 18 | $15.89 | 44% | -26 | Jan 12, 2003 11:14 AM |
| John | Hood | 33 | $19.99 | 25% | +12 | Dec 10, 2002 5:14 AM |
| Peter | Parker | 28 | $9.99 | 20.9% | +12.1 | Jul 6, 2006 8:14 AM |
<table class="table table-responsive"> … </table>


Stocks |
| From: | {{.From}} |
To: | {{.To}} |
| Product Name |
Opening Stock |
Recieved |
Dispatched |
Closing Stock |
{{range $key,$value := .Stock}}
| {{$value.Product.Name}}
| {{$value.OBox}} B {{$value.OPacket}} P
| {{$value.InBox}} B {{$value.InPacket}} P
| {{$value.OutBox}} B {{$value.OutPacket}} P
| {{$value.CBox}} B {{$value.CPacket}} P
|
{{end}}
|


@(params: Map[String, Any]) @uk.gov.hmrc.hmrcemailrenderer.templates.helpers.html.template_main(params, "Fulfilment House Due Diligence Scheme "){ @uk.gov.hmrc.hmrcemailrenderer.templates.helpers.html.salutationLine1(params)
You have a new message from HMRC about your Fulfilment House Due Diligence Scheme application.
To view it, sign into your Business Tax Account.
For security reasons, we have not included a link with this email.
Fulfilment House Due Diligence Scheme is an online service which will send you a digital decision about your application. We may also contact you in other ways about your application.
This means that we will provide updates by secure messages to your online account.
We will send you an email to let you know when a new message is added.
From the HMRC Fulfilment House Due Diligence Scheme
}


The majority of classic electromagnetism is governed by the four Maxwell's equations. These are empirical equations based on evidence, and were not derived from other equations or principles. In their differential form, Maxwell's equations describe the rates of change of an electromagnetic field. $$ \nabla \cdot \mathbf{E} = \frac{\rho}{\epsilon_{0}}$$ $$ \nabla \cdot \mathbf{B} = 0$$ $$ \nabla \times \mathbf{E} = -\frac{d\mathbf{B}}{dt}$$ $$ \nabla \times \mathbf{B} = \mu_{0} \mathbf{J} + \mu_{0} \epsilon_{0}\frac{d\mathbf{E}}{dt}$$ Where $\mathbf{E}$ is an electric field, $\mathbf{B}$ is a magnetic field. When all time varying terms go to 0, we are left with the static version of Maxwell's equations. $$ \nabla \cdot \mathbf{E} = \frac{\rho}{\epsilon_{0}}$$ $$ \nabla \cdot \mathbf{B} = 0$$ $$ \nabla \times \mathbf{E} = 0$$ $$ \nabla \times \mathbf{B} = \mu_{0} \mathbf{J}$$ Of these, Gauss's law for electrostatics, Gauss's law for magnetism, and Ampere's circulation law are the most crucial. Together they describe how sources of charge and current form electric and magnetic fields.


skopeo
Container image management toolbox.
Provides various utility commands to manage remote container images.
More information: home page
- Inspect a remote image from a registry:
skopeo inspect docker://registry_hostname/image:tag
- List available tags for a remote image:
skopeo list-tags docker://registry_hostname/image
- Download an image from a registry:
skopeo copy docker://registry_hostname/image:tag dir:path/to/directory
- Copy an image from one registry to another:
skopeo copy docker://source_registry/image:tag docker://destination_registry/image:tag
- Delete an image from a registry:
skopeo delete docker://registry_hostname/image:tag
- Log in to a registry:
skopeo login --username username registry_hostname


Welcome to my analysis of the military, healthcare, and education spending for ten countries from 2011-2015. Analyzed countries include the United States, the United Kingdom, Germany, Japan, Russia, India, Italy, Brazil, Indonesia, and Australia. You can click on the images below, or the titles in the navigation bar above to view the analysis for each spending category. To view data by country, click on the "Country Dashboard" tab in the navigation bar.
All data comes from the World Bank and can be accessed by clicking the button below:
World Bank


Dark Mode it's not new to Apple's design system, and it has been something we've been wanting to add since the first version of Puppertino. And this is now a strong part of Puppertino, to use Dark Mode, you will need to get the CSS of Dark Mode or download the full CSS
Dark mode it's pretty straight forward to use. And it comes in two flavors, automatic and manual toggling. The automatic toggling uses the media-prefers-scheme
to get the user's desired theme and apply automatically. Of course, this doesn't work in some (mosts) of Linux Operating systems, and that's what the manual version is aimed for. It's also really useful if you want your users to select their own theme.
This is how it looks on practice (All current components support dark mode):
You can experience the Dark Mode in all of the Puppertino's website by clicking This button
Usage:

Manual will require you to create your own JS to toggle the class according to your needs (Examples will be added in 1.02). For the moment, if you do not wish to do any JS, we recommend you to do the automatic one. Is simple to use and it only requires one class.
How does this all work? Simple, Puppertino is based on CSS variables, and when you toggle this class you are basically overriding all the colors in CSS variables for the dark themed ones, making it really easy to change all the colors, and also create your own themes if you need to do so. Keep in mind that some variables have been reused to cut down sizes.


This is a podcast RSS feed containing all of the audio from Open Yale Courses.
Open Yale Courses (OYC) provides lectures and other materials from selected Yale College courses to the public free of charge via the Internet. The courses span the full range of liberal arts disciplines, including humanities, social sciences, and physical and biological sciences.
— About Page
If you enjoy any of these courses, consider donating to Open Yale Courses to show your support.


Lighthaus Theme
A focused dark color theme inspired by the striking features of a lighthouse and the calm of the ocean at night time.
Lighthaus aims to be a palette with a mixture of bright and subdued colors to create a visually balanced theme. It is intended to be looked at and worked with for long durations with minimal to no eye fatigue. Lighthaus aims to provide a balanced contrast between various elements on the screen.
Lighthaus configs for the projects listed below have been developed. More projects will be gradually added. For the latest updates, keep an eye out on the Lighthaus Github page.


|
| | Cape (Player *character, int posX, int posY) |
| | Default constructor. More...
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virtual | ~Cape () |
| | Default destructor.
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| virtual int | getJumpSpeed () |
| | Get the jumping speed value. More...
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| virtual int | getJumpHeight () |
| | Get the jumping height value. More...
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| virtual int | getSpeed () |
| | Get the speed value. More...
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| AUpgrade () |
| | Default constructor.
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| | AUpgrade (Player *character, int x, int y) |
| | Another constructor. More...
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virtual | ~AUpgrade () |
| | Default destructor.
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| | Player (int x=0, int y=0) |
| | Default constructor. More...
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virtual | ~Player (void) |
| | Default destructor.
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| virtual void | setOnGround (bool value) |
| | Set onGround. More...
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virtual void | goRight (void) |
| | Function to make the player go to the right.
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virtual void | goLeft (void) |
| | Function to make the player go to the left.
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virtual void | fall (void) |
| | Function to make the player fall.
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| |
| virtual void | controls (char collisionR, char collisionL, char collisionT, char collisionG) |
| | Indicate what the player have to do according to the collisions and the press buttons. More...
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virtual void | jump (void) |
| | Set up the jump.
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| |
| virtual void | jumpAnimation (char collisionR, char collisionL, char collisionT, char collisionG) |
| | Make the player jump. More...
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| |
| virtual void | setJumping (bool value) |
| | Set the jumping value. More...
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| |
| virtual void | setSpeed (int speed) |
| | Set the speed value. More...
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| |
| virtual void | setJumpSpeed (int jumpSpeed) |
| | Set the jumping speed value. More...
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| |
| virtual void | setJumpHeight (int jumpHeight) |
| | Set the jumping height value. More...
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| |
| virtual sf::IntRect * | getRect (void) |
| | Get the rect of the player. More...
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| virtual bool | getJumping (void) |
| | Get the jumping value. More...
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| virtual sf::Sprite * | getSprite (void) |
| | Get the sprite of the player. More...
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| virtual bool | getOnGround (void) |
| | Get the boolean onGround.   More...
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| virtual sf::Texture * | getTexture () |
| | Get the player's texture. More...
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| virtual sf::Texture * | getRunTexture1 () |
| | Get the player's run texture 1. More...
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| virtual sf::Texture * | getRunTexture2 () |
| | Get the player's run texture 2.  More...
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| virtual sf::Texture * | getRunTexture4 () |
| | Get the player's run texture 4. More...
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| virtual sf::Texture * | getRunTexture5 () |
| | Get the player's run texture 5.  More...
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| virtual sf::Sound * | getJumpSound () |
| | Get the jump sound. More...
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| virtual sf::SoundBuffer * | getJumpSoundBuffer () |
| | Get the jump sound buffer. More...
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| virtual int | getJump () |
| | Get the value of jump. More...
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| virtual double | getCurrentFrame () |
| | Get the player's current frame. More...
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| |


KISS THE GIRLS (1997)
A film review by Steve Kong Copyright 1997 Steve Kong
Kiss the Girls is not quite SE7EN, but not quite bad either. Kiss the Girls is a good serial killer movie that entertains for two hours.
After seeing the previews, I thought Kiss the Girls might be a blatant rip-off of SE7EN. It has Morgan Freeman, who was in SE7EN and it has an intelligent serial killer whose identity is not known. Also, it is a movie, like SE7EN, that is more interested in why the killer is doing what he is doing more than about catching the killer. Catching the killer is only the means of driving the movie forward.
Morgan Freeman is Dr. Alex Cross, a forensic psychologist. He finds himself going to Durham, North Carolina to help out on an abduction case, the abducted happens to be his niece. What he finds when he gets there is that his niece is only one of many girls kidnapped by a serial rapist and killer who calls himself Casanova. Ashley Judd is Kate McTiernan the only woman to escape from Casanova (she knows kickboxing). Together Cross and McTiernan go on the hunt for Casanova.
The movie is somewhat predictable. The most predictable is the identity of Casanova, whom I guessed correctly after the credits finished. (Director Gary Fleder should have taken note that David Fincher chose to not include the name of the actor portraying the serial killer in SE7EN during the opening credits). By the time the end rolls around, Fleder throws the audience a fake bone to try to trick us into thinking that Casanova is someone else, but it doesn't work.
It is quite easy to think that a comparison can be drawn between Kiss the Girls and SE7EN, but the differences are there. Here's some: the ending to SE7EN was quite a surprise and quite shocking. The ending to Kiss the Girls is of the standard Hollywood fare. John Doe in SE7EN had a clear-cut purpose for what he was doing. Doe had a message that he was preaching. Casanova has a purpose to his madness also, but it is not so clear. Casanova does spread the word of what he does, but he has no message that he wants to preach. Though Director Fleder handles the director's duties just fine, he doesn't come close to making the film look as good as Fincher does.
With all this said, Kiss the Girls in the end is entertaining. This is because of the great performances by the two leads. Freeman is always a command presence on screen, and that does not change here with Kiss the Girls. His performance is intense. Judd adds yet another great performance to her list of great performances. She does very well in Kiss the Girls.
Though not as good as SE7EN, which Kiss the Girls will inevitably be compared to, Kiss the Girls is nonetheless entertaining. It has its frightening moments and its share of action. And it comes as a fairly interesting film about a serial killer. This is a Don't Miss film, though see it at a matinee showing.
!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=! steve kong<EMAIL_ADDRESS>find the latest movie reviews at the hard boiled movie page http://boiled.sbay.org/boiled/ !=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


{% extends 'base.html' %} {% block head %} {% endblock head %} {% block content %}
What if getting a great deal on a car were as easy as talking to your neighbors? We make it easy to find out, "Whachapay?"


Welcome to Evaluator!
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Give it a couple of minutes and make sure to check your spam folder.


At Straffon Orthodontics, each individual’s treatment plan is as unique as their fingerprint; each smile, a personal signature. From invisible options, to customized virtual treatment plans that eliminate the need for impressions with 3D scanning technology, Straffon Orthodontics is Denver’s Top Choice.
We offer options to fit all different age groups, lifestyles, and treatment needs. Dr. Straffon is actively involved with designing and implementing new advancements in orthodontic technology, so his patients always receive the most streamlined and efficient treatment possible.
Not only do we have an amazing staff that goes above and beyond to make your experience effortless, Dr. Straffon takes a more personal approach and values your input. You will find that he works closely with you throughout the course of treatment to ensure you receive the signature smile you have always dreamed of.


| Kruskal Algorithm | class |
Vistualization of steps | Java |
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| Prim Algorithm | class |
Vistualization of steps | Java |
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| Floyd-Warshall Algorithm | Arrays |
Vistualization of steps | Java |
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| Dijkstra Algorithm for all paths | Arrays |
Vistualization of steps | Java |
|
| Bubble Sort | List |
Vistualization of Bubble Sorting | Python3 |
|
| Selection Sort | List |
Vistualization of Selection Sorting | Python3 |
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| Insertion Sort | List |
Vistualization of Insertion Sorting | Python3 |
|
| Sort Integers | Array |
O(n2) algorithm | Java |
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| Sort Integers II | Array |
Quick sorting | Java |
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| Kth Largest Element | Array |
Partition of quick sorting | Java |
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| Partition Array | Array |
Part of quick sorting | Java |
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| Sort Letters by Case | Array |
Partition like quick sorting | Java |
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| Median | Array |
Partition like quick sorting | Java |
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| Nuts & Bolts Problem | Array |
Quick Sorting/Customized comparison | Java |
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| The Smallest Difference | Array |
Sorting/Two pointers | Java |
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| Largest Number | Array |
Customized Sorting | Java |
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| Wiggle Sort | Array |
Sorting/Exchange | Java |
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| Intersection of Two Arrays | Array |
Sorting/Travesal | Java |
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| Intersection of Two Arrays II | Array |
Sorting/Travesal | Java |
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| Move Zeroes | Array |
Counting/Move in-place | Java |
|
| String Permutation | Array/HashMap |
Counting sort | Java |
|
| Valid Anagram | Integer Array/HashTable |
Counting characters | Java |
|
| Merge Intervals | List |
Sorting and set in-place | Java |
|
| Remove Duplicates from Sorted Array | Array |
Two pointers | Java |
|
| Meeting Rooms | Class |
Customized sorting | Java |
|
| Meeting Rooms II | Array |
Sorting/Timeline | Java |
|
| Two Sum | HashTable |
Search in HashTable | Java |
|
| 3 Sum | Array |
Sorting and two pointers | Java |
|
| 3Sum Closest | Array |
Sorting/Two pointers | Java |
|
| Sort Colors | Array |
Move in-place | Java |
|
| Sort Colors II | Array |
Count and move in-place | Java |
|
| Remove Element | Array |
Two pointers/Move in-place | Java |
|
| Insertion Sort List | Linked List |
Insertion Sorting/Linked list operation | Java |
|
| Two Sum III | Class |
OOD | Java |
|
| Longest Palindrome | Integer Array/HashTable |
Counting characters | Java |
|
| Palindrome Number II | |
Bit operations | Java |
|
| First Position Unique Character | HashTable |
Counting characters | Java |
|
| First Unique Character in a String | Integer Array/HashTable |
Counting characters | Java |
|
| Partition Array by Odd and Even | Array |
Two pointers/Move in-place | Java |
|
| Window Sum | Array |
Sliding window | Java |
|
| Remove Duplicates from Sorted Array II | Array |
Sliding window/Two pointers | Java |
|
| Find All Anagrams in a String | Integer Array |
Sliding window/Two pointers | Java |
|
| Reverse Linked List | Linked List |
Basical operation | Java |
|
| Convert BST to Greater Tree | Binary search tree |
Inorder traversal but right is first | Java |
|
| Binary Tree Level Order Traversal | ArrayList |
Record nodes level by level | Java |
|
| Add Two Numbers | Linked List |
Sum digit by digit with carry | Java |
|
| Merge Two Sorted Lists | Linked List |
Basical operation | Java |
|
| Number of Islands | Matrix |
Travesal and mark | Java |
|
| Serialize and Deserialize Binary Tree | Binary Tree |
Travesal binary tree | Java |
|
| Trapping Rain Water | Array |
Two pointers moving to center | Java |
|
| Valid Parentheses | ArrayList |
Stack | Java |
|
| Insert Delete GetRandom O(1) | Class |
OOD | Java |
|
| Product of Array Exclude Itself | ArrayList |
Dynamic Programming | Java |
|
| Clone Graph | Graph |
Deep Clone/Travesal/HashMap | Java |
|
| Singleton | Class |
Design pattern/Concurrency | Java |
|
| Surrounded Regions | Matrix |
BFS and mark | Java |
|
| Flatten Nested List Iterator | Class |
OOD/Nested Linked List/Iterator | Java |
|
| Paint Fence | Array |
Dynamic Programming | Java |
|
| Unique Paths | Array |
Dynamic Programming | Java |
|
| Unique Paths II | Array |
Dynamic Programming | Java |
|
| Climbing Stairs | Array |
Dynamic Programming | Java |
|
| Minimum Path Sum | Array |
Dynamic Programming | Java |
|
| Range Sum Query | class |
Dynamic Programming | Java |
|
| Maximum Subarray | Array |
Dynamic Programming | Java |
|
| Palindrome Number | Number |
Dynamic Programming | Java |
|
| String to Integer | String |
Judge every situation | Java |
|
| Reverse Integer | Integer |
String | Java |
|
| Longest Substring Without Repeating | String |
Two pointers | Java |
|
| Longest Palindromic Substring | String |
Two pointers | Java |
|
| Interleaving String | Array |
Dynamic Programming | Java |
|
| |
| Java |
|
| |
| Java |
|
| |
| Java |
|
| |
| Java |
|


Unix Squeak is built using the (almost) universal ``configure; make; make install''. If you haven't come across this before, read on...
Create a build
directory (which we will call `blddir' from now on)
and then `cd' to it:
$ mkdir blddir
$ cd blddir
A convenient place is just next to the platforms directory,
like this:
$ cd squeak
$ ls
platforms src ...
$ mkdir bld
$ cd bld
Create the build environment by running the script configure which lives in the platforms/unix/config directory.
Note: The configure script accepts lots of options. To see a list of
them, run: `configure --help
'
Assuming you've created the blddir next to platforms,
this would be:
$ ../platforms/unix/config/configure
Note: This assumes that the VMMaker sources are in `../src'. However, since the Unix Squeak support code is independent of the image version from which VMMaker generated the interpreter/plugin sources, it is possible that your source distribution comes with more than one src directory (corresponding to more than one image version used to generate the sources). In such cases you will have to tell configure which source version to use, via the `--with-src' option. For example, if there are two source directories called src-3.2gamma-4857 and src-3.3.alpha-4881 then you would use one of the following commands:
$ .../configure --with-src=src-3.2gamma-4857
or
$ .../configure --with-src=src-3.3alpha-4881
Build the VM and plugins by running make:
$ make
Note: If you want to build just the VM (without external plugins) or just the external plugins (without the VM) then you can use: `make squeak
' or `make plugins
' respectively.
Finally install the VM, plugins and manual pages:
$ su root
$ make install
2005-03-17


‘State of Decay 2’ Coming to Steam in 2020
by Mike WilsonThe wait for the Steam version of State of Decay 2 now has an approximate release date. The Undead Labs zombie epic not only has a Steam page, but lists the game having an “Early 2020” release date.
The game will apparently support crossplay between Xbox One and Steam, but there’s no word on any of the already-released DLC for the Xbox One version of the game being included in the Steam version.
More info on the Steam version is expected later today when Undead Labs hosts a livestream beginning at 3 pm PT/6 pm ET on Twitch and YouTube.


Executive Director
Bitcoin and Blockchain Technology
at Milano-Bicocca and Politecnico di Milano.
Crypto Asset Lab scientific director,
Hayek Money promoter,
QuantLib founder,
Interest Rate Derivatives Adjunct
Professor at Milano-Bicocca.
Ex-physicist, Springsteen fan, husband, father of three.
Technical Officer
Cofounder and director of Reposit Limited, providing consulting services in the domain of quantitative finance. QuanLibXL project founder. Formerly: 12 years at J.P. Morgan Chase, first in IT management and later as technical lead for front-office pricing spreadsheets.
Operating Officer
Crypto Asset Lab faculty member, Interest Rate Derivatives Assistant Lecturer at Milano-Bicocca. Master's degree in Mathematical Engineering at Politecnico di Milano. Ultra Trail Runner.
Business Developer
Bitcoin and blockchain passionate. Eight years of hands-on entrepreneurial experience and formerly Vice President of “Giovani Imprenditori di Confindustria Parma”


| Trees | Indices | Help |
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Python utilities for reading and writing MKEYED files
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MKEYEDReader Reader for MKEYED data files |
|||
|
MKEYEDWriter Proxy that builds a batch of write requests and passes to SH.BBX.WRITE |
|||
|
MKEYEDKey A key for an MKEYED data file |
|||
|
BBPyKeyNotFoundError Raised when a specified key is not found in the data file. |
|||
|
BBPyPartialKeyFoundException Raised when a full key is not found in the data file, but a partial key exists. |
|||
|
BBPyWrongFileTypeError Raised if the data file is not a type MKEYEDReader can read |
|||
|
MKEYEDReaderEOF Used as a flag in the event the reader reaches the End Of File (EOF) |
|||
|
BBPyWriterTypeError Raised when the programmer tries to pass a dict instead of a string. |
|||
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BBPyWriterIOError Raised when the writer cannot communicate with the server. |
|||
|
BBPyWriterUnknownError Raised whenever the server returns and error code |
|
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PRO5 =
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PRO5CONFIG =
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FT_INDEXED = 0
|
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FT_SERIAL = 1
|
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FT_KEYED = 2
|
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FT_STRING = 3
|
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FT_PROGRAM = 4
|
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FT_DIRECTORY = 5
|
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FT_MKEYED = 6
|
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FT_CISAM = 7
|
|||
filetypes =
|
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__package__ =
|
|
filetypes
|
| Trees | Indices | Help |
|
|---|
| Generated by Epydoc 3.0.1 on Mon Oct 8 11:15:34 2012 | http://epydoc.sourceforge.net |


# File app/uploader.rb, line 15 def upload_kata RestClient.post "#{@hostname}#{@@kata_path}", {:framework => @framework} end


WKTGeometryCollection Class Reference
| Inherits from | WKTGeometry : NSObject |
|---|---|
| Declared in | WKTGeometryCollection.h |
Overview
This class represent Geometry Collection on WKT Format.
WKT GeometryCollection really is WKTGeometry grouped at NSArray.
Its String representation is for example: GEOMETRYCOLLECTION(POINT(4 6),LINESTRING(4 6,7 10))
GeometryCollection only have 2D representation.
Example of use:
WKTPoint *p1 = [[WKTPoint alloc] initWithDimensionX:5.0 andDimensionY:10.0];
WKTPoint *p2 = [[WKTPoint alloc] initWithDimensionX:10.0 andDimensionY:10.0];
WKTPoint *p3 = [[WKTPoint alloc] initWithDimensionX:10.0 andDimensionY:15.0];
WKTPoint *p4 = [[WKTPoint alloc] initWithDimensionX:5.0 andDimensionY:15.0];
WKTPointM *pointsP1 = [[WKTPointM alloc] initWithPoints:@[p1, p2, p3, p4]];
WKTPolygon *polygon1 = [[WKTPolygon alloc] initWithMultiPoints:@[pointsP1]];
WKTGeometryCollection *gC = [[WKTGeometryCollection alloc] init];
[gC addGeometry:polygon1];
WKTPoint *p5 = [[WKTPoint alloc] initWithDimensionX:10.0 andDimensionY:10.0];
[gC addGeometry:p5];
Example of WKT representation:
NSString *wktString = [gC toWKT];
NSLog(@"WKT: %@", wktString);
> WKT: GEOMETRYCOLLECTION(POLYGON ((5.000000 10.000000, 10.000000 10.000000, 10.000000
15.000000, 5.000000 15.000000)), POINT (10.000000 10.000000))
Constructor
– init
Basic Constructor that set default values to new instance.
- (instancetype)init
Return Value
WKTGeometryCollection instance
Declared In
WKTGeometryCollection.h
Internal operations
– addGeometry:
Add WKTGeometry to Geometry List internal property.
- (void)addGeometry:(WKTGeometry *)geometry
Parameters
geometry |
WKTGeometry to add |
|---|
Exceptions
WKTGeometryCollection |
Parameter is nil or these classes are not valid WKTGeometry. |
|---|
Declared In
WKTGeometryCollection.h
– removeGeometries
Clear Geometry List internal property.
- (void)removeGeometries
Declared In
WKTGeometryCollection.h
– getGeometries
Return Geometry List internal property.
- (NSArray *)getGeometries
Return Value
List of WKTGeometry that its count always will be equal or greater than zero
Discussion
Geometry List internal property never is nil.
Declared In
WKTGeometryCollection.h
WKTGeometryCollection operations
– isEqual:
Check if two WKTGeometryCollection are equal.
- (BOOL)isEqual:(WKTGeometryCollection *)otherGeometries
Parameters
otherGeometries |
WKTGeometryCollection to compare with itself |
|---|
Return Value
Boolean
Declared In
WKTGeometryCollection.h
– copyTo:
Pass all properties from itself to other WKTGeometryCollection reference.
- (void)copyTo:(WKTGeometryCollection *)otherGeometries
Parameters
otherGeometries |
WKTGeometryCollection to pass properties |
|---|
Exceptions
WKTGeometryCollection |
Parameter is nil. |
|---|
Declared In
WKTGeometryCollection.h
– toWKT
Return WKT representation.
- (NSString *)toWKT
Return Value
String like GEOMETRYCOLLECTION ( …. ) or GEOMETRYCOLLECTION EMPTY
Declared In
WKTGeometryCollection.h


Home
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Mary Mac's Tea Room
Classic southern comfort spot
Ceremony
Saturday, April 18, 2020
5 pm
Ray's on the River
The Gardens
6700 Powers Ferry Rd NW
Sandy Springs, GA 30339
Reception to follow
Outdoor semiformal, or whatever you'd like!
From downtown Atlanta, expect a 20 minute drive
Play
Welcome to Atlanta!
Home of the Falcons, ATLiens, Buford Highway, & more Peachtree
Streets than you can shake a stick at. Here are some of our favorite spots:
FOODS OF THE AMERICAN SOUTH
Mary Mac's Tea Room
Classic southern comfort spot
Fox Bros. Bar-B-Q
Famous slow roast ribs
West Egg Cafe
Trendy spot for all-day brunch
Home Grown
Retro diner with incredible biscuits
OTHER GREAT FOOD
Rumi's Kitchen
Best Persian food on this side of the Mississippi
Chinatown Mall
Lily grew up coming here. High praise for the Lan Zhou hand-pulled noodle stall
Six Feet Under - Westside
Seafood with roof top views
DRINK
Argosy
Hip gastropub for big groups
Monday Night Brewing
Craft brewery with v. cool space
Northside Tavern
Classic dive bar with live blues
PLACES
East Atlanta Village
Quirky area with nightlife, coffee & hip restaurants
Little Five Points
Hip, boho area with food, graffiti & shopping
ATTRACTIONS
Georgia Aquarium
Whale sharks and beluga whales live here!
Trap Museum
Learn about this style of hip hop music born in ATL, go early to avoid lines, visitor discretion advised
World of Coca-Cola
Enjoy a mysterious concoction of sugar & chemicals, then leave with a big tummyache & sticky shoes
Gallery
Registry
Your presence at our wedding is the greatest gift.
However, if you do wish to make a gesture, a contribution towards our honeymoon would be much appreciated.


Definition: Promotions are short term reductions in price
The objective for any discount or promotion is a positive return on investment. The hope is that a reduction in price will cause an increase in volume more than sufficient to compensate.
However, need to consider a number of short and long term factors: cost of implementing campaign, cannibalization of other sales revenue, impact on brand and reputation, change in consumer behavior leading to less propensity to buy at full price.


In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and the model more.
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
A critical step in working with neural networks is preparing the data correctly. Variables on different scales make it difficult for the network to efficiently learn the correct weights. Below, we've written the code to load and prepare the data. You'll learn more about this soon!
data_path = 'Bike-Sharing-Dataset/hour.csv'
rides = pd.read_csv(data_path)
rides.head()
This dataset has the number of riders for each hour of each day from January 1 2011 to December 31 2012. The number of riders is split between casual and registered, summed up in the cnt
column. You can see the first few rows of the data above.
Below is a plot showing the number of bike riders over the first 10 days or so in the data set. (Some days don't have exactly 24 entries in the data set, so it's not exactly 10 days.) You can see the hourly rentals here. This data is pretty complicated! The weekends have lower over all ridership and there are spikes when people are biking to and from work during the week. Looking at the data above, we also have information about temperature, humidity, and windspeed, all of these likely affecting the number of riders. You'll be trying to capture all this with your model.
rides[:24*10].plot(x='dteday', y='cnt')
Here we have some categorical variables like season, weather, month. To include these in our model, we'll need to make binary dummy variables. This is simple to do with Pandas thanks to get_dummies()
.
dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']
for each in dummy_fields:
dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)
rides = pd.concat([rides, dummies], axis=1)
fields_to_drop = ['instant', 'dteday', 'season', 'weathersit',
'weekday', 'atemp', 'mnth', 'workingday', 'hr']
data = rides.drop(fields_to_drop, axis=1)
data.head()
To make training the network easier, we'll standardize each of the continuous variables. That is, we'll shift and scale the variables such that they have zero mean and a standard deviation of 1.
The scaling factors are saved so we can go backwards when we use the network for predictions.
quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']
# Store scalings in a dictionary so we can convert back later
scaled_features = {}
for each in quant_features:
mean, std = data[each].mean(), data[each].std()
scaled_features[each] = [mean, std]
data.loc[:, each] = (data[each] - mean)/std
We'll save the data for the last approximately 21 days to use as a test set after we've trained the network. We'll use this set to make predictions and compare them with the actual number of riders.
# Save data for approximately the last 21 days
test_data = data[-21*24:]
# Now remove the test data from the data set
data = data[:-21*24]
# Separate the data into features and targets
target_fields = ['cnt', 'casual', 'registered']
features, targets = data.drop(target_fields, axis=1), data[target_fields]
test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]
We'll split the data into two sets, one for training and one for validating as the network is being trained. Since this is time series data, we'll train on historical data, then try to predict on future data (the validation set).
# Hold out the last 60 days or so of the remaining data as a validation set
train_features, train_targets = features[:-60*24], targets[:-60*24]
val_features, val_targets = features[-60*24:], targets[-60*24:]
Below you'll build your network. We've built out the structure and the backwards pass. You'll implement the forward pass through the network. You'll also set the hyperparameters: the learning rate, the number of hidden units, and the number of training passes.
The network has two layers, a hidden layer and an output layer. The hidden layer will use the sigmoid function for activations. The output layer has only one node and is used for the regression, the output of the node is the same as the input of the node. That is, the activation function is $f(x)=x$. A function that takes the input signal and generates an output signal, but takes into account the threshold, is called an activation function. We work through each layer of our network calculating the outputs for each neuron. All of the outputs from one layer become inputs to the neurons on the next layer. This process is called forward propagation.
We use the weights to propagate signals forward from the input to the output layers in a neural network. We use the weights to also propagate error backwards from the output back into the network to update our weights. This is called backpropagation.
Hint: You'll need the derivative of the output activation function ($f(x) = x$) for the backpropagation implementation. If you aren't familiar with calculus, this function is equivalent to the equation $y = x$. What is the slope of that equation? That is the derivative of $f(x)$.
Below, you have these tasks:
self.activation_function
in __init__
to your sigmoid function.train
method.train
method, including calculating the output error.run
method.class NeuralNetwork(object):
def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):
# Set number of nodes in input, hidden and output layers.
self.input_nodes = input_nodes
self.hidden_nodes = hidden_nodes
self.output_nodes = output_nodes
# Initialize weights
self.weights_input_to_hidden = np.random.normal(0.0, self.hidden_nodes**-0.5,
(self.hidden_nodes, self.input_nodes))
self.weights_hidden_to_output = np.random.normal(0.0, self.output_nodes**-0.5,
(self.output_nodes, self.hidden_nodes))
self.lr = learning_rate
#### TODO: Set self.activation_function to your implemented sigmoid function ####
#
# Note: in Python, you can define a function with a lambda expression,
# as shown below.
self.activation_function = lambda x : 1/(1+np.exp(-x)) # Replace 0 with your sigmoid calculation.
### If the lambda code above is not something you're familiar with,
# You can uncomment out the following three lines and put your
# implementation there instead.
#
#def sigmoid(x):
# return 0 # Replace 0 with your sigmoid calculation here
#self.activation_function = sigmoid
def train(self, inputs_list, targets_list):
# Convert inputs list to 2d array
inputs = np.array(inputs_list, ndmin=2).T
targets = np.array(targets_list, ndmin=2).T
#### Implement the forward pass here ####
### Forward pass ###
# TODO: Hidden layer - Replace these values with your calculations.
hidden_inputs = np.dot(self.weights_input_to_hidden, inputs) # signals into hidden layer
hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer
# TODO: Output layer - Replace these values with your calculations.
final_inputs = np.dot(self.weights_hidden_to_output, hidden_outputs) # signals into final output layer
final_outputs = final_inputs # signals from final output layer
#### Implement the backward pass here ####
### Backward pass ###
# TODO: Output error - Replace this value with your calculations.
output_errors = targets - final_outputs # Output layer error is the difference between desired target and actual output.
# TODO: Backpropagated error - Replace these values with your calculations.
hidden_errors = np.dot(self.weights_hidden_to_output.T, output_errors) # errors propagated to the hidden layer
hidden_grad = hidden_outputs * (1-hidden_outputs) # hidden layer gradients
# TODO: Update the weights - Replace these values with your calculations.
self.weights_hidden_to_output += self.lr * np.dot(output_errors, hidden_outputs.T) # update hidden-to-output weights with gradient descent step
self.weights_input_to_hidden += self.lr * np.dot(hidden_errors * hidden_grad, inputs.T) # update input-to-hidden weights with gradient descent step
def run(self, inputs_list):
# Run a forward pass through the network
inputs = np.array(inputs_list, ndmin=2).T
#### Implement the forward pass here ####
# TODO: Hidden layer - replace these values with the appropriate calculations.
hidden_inputs = np.dot(self.weights_input_to_hidden, inputs) # signals into hidden layer
hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer
# TODO: Output layer - Replace these values with the appropriate calculations.
final_inputs = np.dot(self.weights_hidden_to_output, hidden_outputs) # signals into final output layer
final_outputs = final_inputs # signals from final output layer
return final_outputs
def MSE(y, Y):
return np.mean((y-Y)**2)
Here you'll set the hyperparameters for the network. The strategy here is to find hyperparameters such that the error on the training set is low, but you're not overfitting to the data. If you train the network too long or have too many hidden nodes, it can become overly specific to the training set and will fail to generalize to the validation set. That is, the loss on the validation set will start increasing as the training set loss drops.
You'll also be using a method know as Stochastic Gradient Descent (SGD) to train the network. The idea is that for each training pass, you grab a random sample of the data instead of using the whole data set. You use many more training passes than with normal gradient descent, but each pass is much faster. This ends up training the network more efficiently. You'll learn more about SGD later.
This is the number of times the dataset will pass through the network, each time updating the weights. As the number of epochs increases, the network becomes better and better at predicting the targets in the training set. You'll need to choose enough epochs to train the network well but not too many or you'll be overfitting.
This scales the size of weight updates. If this is too big, the weights tend to explode and the network fails to fit the data. A good choice to start at is 0.1. If the network has problems fitting the data, try reducing the learning rate. Note that the lower the learning rate, the smaller the steps are in the weight updates and the longer it takes for the neural network to converge.
The more hidden nodes you have, the more accurate predictions the model will make. Try a few different numbers and see how it affects the performance. You can look at the losses dictionary for a metric of the network performance. If the number of hidden units is too low, then the model won't have enough space to learn and if it is too high there are too many options for the direction that the learning can take. The trick here is to find the right balance in number of hidden units you choose.
import sys
### Set the hyperparameters here ###
epochs = 800
learning_rate = 0.1
hidden_nodes = 50
output_nodes = 1
N_i = train_features.shape[1]
network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)
losses = {'train':[], 'validation':[]}
for e in range(epochs):
# Go through a random batch of 128 records from the training data set
batch = np.random.choice(train_features.index, size=128)
for record, target in zip(train_features.ix[batch].values,
train_targets.ix[batch]['cnt']):
network.train(record, target)
# Printing out the training progress
train_loss = MSE(network.run(train_features), train_targets['cnt'].values)
val_loss = MSE(network.run(val_features), val_targets['cnt'].values)
sys.stdout.write("\rProgress: " + str(100 * e/float(epochs))[:4] \
+ "% ... Training loss: " + str(train_loss)[:5] \
+ " ... Validation loss: " + str(val_loss)[:5])
losses['train'].append(train_loss)
losses['validation'].append(val_loss)
plt.plot(losses['train'], label='Training loss')
plt.plot(losses['validation'], label='Validation loss')
plt.legend()
plt.ylim(ymax=0.5)
Here, use the test data to view how well your network is modeling the data. If something is completely wrong here, make sure each step in your network is implemented correctly.
fig, ax = plt.subplots(figsize=(8,4))
mean, std = scaled_features['cnt']
predictions = network.run(test_features)*std + mean
ax.plot(predictions[0], label='Prediction')
ax.plot((test_targets['cnt']*std + mean).values, label='Data')
ax.set_xlim(right=len(predictions))
ax.legend()
dates = pd.to_datetime(rides.ix[test_data.index]['dteday'])
dates = dates.apply(lambda d: d.strftime('%b %d'))
ax.set_xticks(np.arange(len(dates))[12::24])
_ = ax.set_xticklabels(dates[12::24], rotation=45)
Answer these questions about your results. How well does the model predict the data? Where does it fail? Why does it fail where it does?
Note: You can edit the text in this cell by double clicking on it. When you want to render the text, press control + enter
Run these unit tests to check the correctness of your network implementation. These tests must all be successful to pass the project.
import unittest
inputs = [0.5, -0.2, 0.1]
targets = [0.4]
test_w_i_h = np.array([[0.1, 0.4, -0.3],
[-0.2, 0.5, 0.2]])
test_w_h_o = np.array([[0.3, -0.1]])
class TestMethods(unittest.TestCase):
##########
# Unit tests for data loading
##########
def test_data_path(self):
# Test that file path to dataset has been unaltered
self.assertTrue(data_path.lower() == 'bike-sharing-dataset/hour.csv')
def test_data_loaded(self):
# Test that data frame loaded
self.assertTrue(isinstance(rides, pd.DataFrame))
##########
# Unit tests for network functionality
##########
def test_activation(self):
network = NeuralNetwork(3, 2, 1, 0.5)
# Test that the activation function is a sigmoid
self.assertTrue(np.all(network.activation_function(0.5) == 1/(1+np.exp(-0.5))))
def test_train(self):
# Test that weights are updated correctly on training
network = NeuralNetwork(3, 2, 1, 0.5)
network.weights_input_to_hidden = test_w_i_h.copy()
network.weights_hidden_to_output = test_w_h_o.copy()
network.train(inputs, targets)
self.assertTrue(np.allclose(network.weights_hidden_to_output,
np.array([[ 0.37275328, -0.03172939]])))
self.assertTrue(np.allclose(network.weights_input_to_hidden,
np.array([[ 0.10562014, 0.39775194, -0.29887597],
[-0.20185996, 0.50074398, 0.19962801]])))
def test_run(self):
# Test correctness of run method
network = NeuralNetwork(3, 2, 1, 0.5)
network.weights_input_to_hidden = test_w_i_h.copy()
network.weights_hidden_to_output = test_w_h_o.copy()
self.assertTrue(np.allclose(network.run(inputs), 0.09998924))
suite = unittest.TestLoader().loadTestsFromModule(TestMethods())
unittest.TextTestRunner().run(suite)


6. Calculate the user's BMI. Use the tags: < input >, < button > and < div >.
Make a JavaScript program where the user will input their height and weight and the program will calculate their BMI.
Use the tags: < input >, < button >, and < div > in your page. Use the < div > with an identifier and present the result of the BMI calculation in it.


2 Chronicles 20:33
Howbeit the high places were not taken away: for as yet the people had not prepared their hearts unto the God of their fathers.


---
layout: post
status: publish
published: true
title: 'Vista 5472: not so good'
author:
display_name: Tomas Restrepo
login: tomasr
email<EMAIL_ADDRESS>url: http://winterdom.com/
author_login: tomasr
author_email<EMAIL_ADDRESS>author_url: http://winterdom.com/
wordpress_id: 671
wordpress_url: http://winterdom.com/2006/07/vista5472notsogood
date: '2006-07-22 20:37:55 +0000'
date_gmt: '2006-07-22 20:37:55 +0000'
categories:
- Vista
tags: []
comments:
- id: 445
author: brian
author_email<EMAIL_ADDRESS>author_url: ''
date: '2006-07-31 15:32:56 +0000'
date_gmt: '2006-07-31 15:32:56 +0000'
content: |
I think we have similar hardware and I had a similar problem. Until today, that is, when I discovered the ATI Catalyst Control Center in the traybar area had a context menu option with the display modes all the way up to my 1920x1200. The Vista display settings would not go that far until I used the Catalyst Control Center.
---
Following Sam's lead, I went ahead and installed Vista build 5472 on my laptop this morning. Unfortunately, my experience wasn't as good as Sam's. I did install Vista Business instead of Ultimate, but hopefully that shouldn't be a significant difference.
The Good
- Installation went pretty much without a hitch all the way. As Sam mentions, it has certainly improved a lot since the early betas and it just pretty much works as expected. It is also fairly quick.
- The UI came up with no problem. Even though for some reason I got Aero Basic as the default, I was able to enable Glass with no problems.
- Performance seems to have improved quite a bit. It certainly feels more responsive on my machine and the UI doesn't seem as clunky
The Bad
- The drivers for my Dell's ATI X300 with 128MB card sucks. For some strange reason, it won't allow me to go above 1600x1200, and it doesn't allow me to enable any widescreen format resolution. This is a pretty big deal-breaker for me, as my laptop's screen has a native resolution of 1920x1200; anything less than that is just unacceptable. I did install the beta drivers available from ATI's site, but that didn't seem to make a difference :-(. This sucks, particularly since this worked without a hitch in earlier builds.
- For some reason, the utility to find and download drivers didn't get installed. I mean, the option in the Welcome panel is there, but trying to run it complains that the .exe file is missing. Go figure. So, no drivers for me, as I didn't feel like wasting time getting the rest of the drivers up if I can't even configure video properly.
- The machine won't complete the performance rating test. It complains about "getting some error from an underlying driver or device". My bet is on the video drivers, but no way to know since it doesn't tell you.
- Trying to copy a file over the network proved to be a real challenge. I first tried to run an exe from a network share; no dice, explorer hung hard and had to kill it. Then, trying to copy a 47MB file over to the local desktop to run it from there, took about 6 minutes or more. Insane.
- Deleting files from explorer isn't exactly working right. It took the machine almost 5 minutes to delete 20 files on a folder.
So far: Not impressed with this build. Also, I had installed build 5456 on my old Compaq laptop (with a 1.4 Pentium 4 Mobile processor and 512 MB of RAM), and was rather disappointed (though not surprised) to see it sucked big time. There are no video drivers for the Mobility Radeon 7500 chip it has (the machine is 4 years old), so you get the standard VGA adapter driver. Besides that, performance sucks, the machine spends a lot of cycles paging, so obviously 512MB of RAM isn't very good (and that's for doing nothing, I didn't even install applications on it).


Almost 500% increase in children on prescribed psychiatric medication
by Eoghan MurphyThe number of children who have been prescribed psychiatric medication has increased by nearly 500% over the past decade.
The rise amounts to more than 5,300 medical card patients.
A study by University College Dublin (UCD) and Jigsaw on Tuesday showed one-in-four young people in Ireland suffers from anxiety.
New figures released by the HSE, under the Freedom of Information Act, shed a light on the number of people in Ireland who are being treated with medication for mental health issues.
In 2009, 1,150 children under 15-years-old were prescribed psychiatric medication under the medical card scheme.
But this has increased noticeably every year since then - rising to just under 6,500 children last year.
Brendan Kelly is professor of psychiatry at Trinity College Dublin.
He says a recruitment crisis in the HSE's child and adolescent services is a factor in the trend.
"We know referrals to child and adolescent psychiatry have increased by 40% over the past six or seven years or so.
"And the child and adolescent mental health services have just 57% of the staff that the national policy recommends they should have.
"So in this situation, you often see an increase in both referrals and an increase in prescribing".
The trend is out of sync with the same statistics for general population, which has seen only a 12% rise in the same time frame.


Single View
We characterize a view as a set of one or multiple aligned tracks,
with at most one two-axis track. In a single view, we cannot visualize
multiple scales and foci.
{% for post in site.tools %}
{% if post.view == "single" %}
{{post.title}}
{% endif %}
{% endfor %}
Multiple Views
Multiple views allow for visualizations with multiple scales and foci.
Multiple views can be (i) independent, (ii) weakly linked, (iii) medium linked or (iv)
strongly linked. While independent views are not connected in
any way, weakly linked views are linked by brushing and linking.
Medium linked views share navigation, for example two views are
at different scales, but zooming always affects both. Strongly linked
views share genomic coordinates at one axis and can also share
tracks that are aligned to the axis.
{% for post in site.tools %}
{% if post.view == "multiple" %}
{{post.title}}
{% endif %}
{% endfor %}


[Gecode] Logo, Homepage
Denys Duchier
duchier at ps.uni-sb.de
Tue Oct 26 20:01:21 CEST 2004
Very nice,
I have a couple of comments.
1. Please do not set the font-size. Too many sites do this and it is
very bad design. The user sets his default font size to suit the
resolution of his display, his eyesight, and more generally his
personal preference. In as much as possible you should respect
that choice.
2. I advise against the "Groups" form of presentation. We went that
way with Mozart and we are very sorry. Instead, have a section
that says "Development" or something like that, and present
developers individually instead of by institutions.
A "Development" section also allows you to host development related
information, such as CVS location, WebCVS interface, doxygen API
docs, how to contribute.
you also need to mention the mailing list and the bug database (is
there one?)
Cheers,
--
Dr. Denys Duchier - Équipe Calligramme - LORIA, Nancy, FRANCE
AIM: duchierdenys
More information about the gecode-users
mailing list


--- title: Dear Summer, Please Don't Go! date: 2015-09-09 00:00:00 -06:00 categories: - whats-blooming layout: post blog-banner: whats-blooming-now-fall.jpg post-date: September 9, 2015 post-time: 11:23 AM blog-image: wbn-default.jpg ---
Black-Eyed Susan Rudbeckia fulgida var. sullivantii 'Goldsturm'
If you haven't seen them yet, it's not too late to come see the vast Black-Eyed Susan displays all over the Garden.
Cream Puff Cedar Cedrus deodara 'Cream Puff'
While overlooking the Rose Garden, don't let these leaves trick you into thinking it's Fall already, this coloration is all year round!
Autumn Glow Toad Lily Tricyrtis formosana 'Autumn Glow'
Along the Floral Walk be sure to look for this tiny speckled beauty, you won't be sorry.
Variegated Lily Turf Liriope muscari 'Variegata'
Check out this unique bloomer on the Floral Walk, now with lilac flower spikes which will turn to blackish berries in the Fall.
Star Thistle Centaurea bella
While enjoying a shady walk through the Fern Gully, keep your eyes on the ground for these blooms that resemble a shaggy hairdo!


The training shall cover Python Programming Basics ,building robust Web Applications incoperated with PostgreSQL ,handling big data using Data Science packages in Python Anaconda and Machine Learning application using Scikit-learn and Logistics regression.


Item 5.
A drawing of a horizontal boxplot, with a rectangular box, and a whisker on the left and right end of the box. There are vertical lines drawn at the left whisker, the left end of the box, part of the way across the box, the right end of the box, and at the right whisker. These lines are labeled as minimum value (left whisker), lower quartile (left end of box), median (partway across box), upper quartile (right end of box), and maximum value (right whisker).in-context


"MUSICIAN", CHIEF (Hebrews menatstseah), the precentor of the Levitical choir or orchestra in the temple, mentioned in the titles of fifty-five psalms, and in Habakkuk 3:19, Revised Version. The first who held this office was Jeduthun (1 Chronicles 16:41), and the office appears to have been hereditary. Heman and Asaph were his two colleagues (2 Chronicles 35:15).
"MUSICIAN", CHIEF (Hebrews menatstseah), the precentor of the Levitical choir or orchestra in the temple, mentioned in the titles of fifty-five psalms, and in Habakkuk 3:19, Revised Version. The first who held this office was Jeduthun (1 Chronicles 16:41), and the office appears to have been hereditary. Heman and Asaph were his two colleagues (2 Chronicles 35:15).


|
SUBJECT INDEX Anthropology Architecture Art Black Studies Business Ecology Economics Education Film Healthcare History Law Linguistics Literature Mass Media Medicine Music Philosophy Political Science Psychology Religion Science Shakespeare Sociology Sports Recreation Urban Studies Women's Studies |
History: WomenTo order a specific paper, click the Catalog Number button next to the Paper Title of interest. Send comments to<EMAIL_ADDRESS>Last Updated: 12/21/96 Copyright ©1996 ASM Communications, Inc. All Rights Reserved. |


Returns "if x >= 0 then positiveSlope*x else negativeSlope*x" and handle x=0 in a meaningful way
semiLinear(x, positiveSlope, negativeSlope)
Returns "if x >= 0 then positiveSlope*x else negativeSlope*x". In some situations, equations with the semiLinear function become underdetermined if the first argument (x) becomes zero, i.e., there are an infinite number of solutions. It is recommended that the following rules are used to transform the equations during the translation phase in order to select one meaningful solution in such cases:
Rule 1: The equations
y = semiLinear(x, sa, s1); y = semiLinear(x, s1, s2); y = semiLinear(x, s2, s3); ... y = semiLinear(x, sN, sb);
may be replaced by
s1 = if x >= 0 then sa else sb s2 = s1; s3 = s2; ... sN = sN-1; y = semiLinear(x, sa, sb);
Rule 2: The equations
x = 0; y = 0; y = semiLinear(x, sa, sb);
may be replaced by
x = 0 y = 0; sa = sb;
[For symbolic transformations, the following property is useful (this follows from the definition):
semiLinear(m_flow, port_h, h);
is identical to
-semiLinear(-m_flow, h, port_h);
The semiLinear function is designed to handle reversing flow in fluid systems, such as
H_flow = semiLinear(m_flow, port.h, h);
i.e., the enthalpy flow rate H _flow is computed from the mass flow rate m_flow and the upstream specific enthalpy depending on the flow direction.]


{% extends "checkout/preview.html" %} {% load currency_filters %} {% load i18n %} {% load thumbnail %} {# Null out the actions as they can't be used here #} {% block shipping_address_actions %}{% endblock %} {% block shipping_method_actions %}{% endblock %} {% block order_contents_actions %}{% endblock %} {% block payment_method %}
{% blocktrans with amt=paypal_amount|currency email=paypal_user_email %} {{ amt }} will be deducted from your PayPal account, registered to email: {{ email }}. {% endblocktrans %}
#} {# {% blocktrans with amt=paypal_amount|currency email=paypal_user_email %}#} {# {{ amt }} will be deducted from your PayPal account, registered #} {# to email: {{ email }}.#} {# {% endblocktrans %}#} {#
#} {#


I have always been intigued by amanita mushrooms, and eventually gave in and bought 50 grams of grade A mushrooms. my first experience was with a 8 gram dose, boiled into tea, after work one night. i have to say, i was tired and the mushrooms made it worse...i basically stayed in twilight sleep for three hours than passed out. I later tried a 11 gram dose, this time well-rested, and the results were great, so I'll relate them.
First off, I made a tea. Use as little water as possible when doing this, it doesn't taste great, and the amount doesn't matter, so why drink more? within 20-30 minutes of ingestion I start to feel pill-drunk, like I've taken some codeine and drank three beers. At this point some trippy stuff happens, but I have to say, amanitas aren't psychedelic in the LSD-Mescaline-Mushrooms way. The feeling I get is as if my inner-head, or mind's eye, is no longer fixed in place inside my head. I turn, and it arrives a moment late. The feeling is nice, though, like a well-oiled hinge (that phrase is from my experince notes directly- another is 'like a weed high, but more space-less filler').
This is an equilibrium drug, effecting orientation and depth perception. Objects have a pleasant 'distance' that is not unlike being drunk, but much less stupifying and heavy. It is light; nothing seems attached or fixed. The effects aren't exactly constant. They vary; one moment a lapse of sleepiness will rise, and as I dip into it the nice smooth slipping feeling arouses my senses, and I'm back up. Also this drug doesn't seem to be devoid of aphrodisiac qualities. The effects last about 6 hours in all, and in general, I think the confused relationship between amanitas and psylocibe mushrooms result in a lot of confusion about amanitas' true effects.
These are, in my opinion, the following:
. slight disorientation, more than with weed, less than with alcohol.
. warm body high.
. slight visual disturbance, akin in intensity to marijuana, but more akin in quality to alcohol.
. loopiness, or silliness. grinning and chuckling.
. A feeling of not being attached to anything to solidly. everything seems to slide smoothly.
Overall, amanitas are like drinking five beers slowly with a belly full of high quality goof-balls. Not psychedelic, but real fun.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


[gecode-users] First contact with gecode
Christian Schulte
cschulte at kth.se
Mon Oct 6 09:38:15 CEST 2014
Just a tip: please read Sections 3.1 and 7.1 in MPG. It shows how to write
normal C++ expressions for posting linear constraints.
Cheers
Christian
--
Christian Schulte, Professor of Computer Science, KTH,
www.gecode.org/~schulte/
-----Original Message-----
From: users-bounces at gecode.org [mailto:users-bounces at gecode.org] On Behalf
Of Frederic C
Sent: Monday, October 06, 2014 8:19 AM
To: users at gecode.org
Subject: Re: [gecode-users] First contact with gecode
I changed IRT_LE for IRT_LQ and it works, thank you. I'm silly, if
coefficients are all zeros, it helps if the right hand size can also be
zero!
Fred
Le 06/10/2014 00:29, Chris Mears a écrit :
> Hello Fred,
>
>> { // 1A + 1B > 0
>> IntArgs coeffs(2); coeffs[0] = 1; coeffs[1] = 1;
>> linear(*this, coeffs, s, IRT_GT, int(0));
>> }
> I am not sure how you got this to compile, because this should be
> "IRT_GR".
>
>> { // 1A + 0B > 0
>> IntArgs coeffs(2); coeffs[0] = 1; coeffs[1] = 0;
>> linear(*this, coeffs, s, IRT_GT, int(0));
>> }
>> { // 1A + 1B > 0
>> IntArgs coeffs(2); coeffs[0] = 0; coeffs[1] = 1;
>> linear(*this, coeffs, s, IRT_GT, int(0));
>> }
>> { // 0A + 0B <= 0
>> IntArgs coeffs(2); coeffs[0] = 0; coeffs[1] = 0;
>> linear(*this, coeffs, s, IRT_LE, int(0));
>> }
> Here you want IRT_LQ ( <= ) instead of IRT_LE ( < ).
_______________________________________________
Gecode users mailing list
users at gecode.org
https://www.gecode.org/mailman/listinfo/gecode-users
More information about the users
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Do not use tickets to ask for help with (debugging) your application. Ask on
the mailing list,
in the IRC channel (#documentcloud
on Freenode), or if you understand your
specific problem, on StackOverflow.
Before you open a ticket or send a pull request,
search for previous
discussions about the same feature or issue. Add to the earlier ticket if you
find one.
Before sending a pull request for a feature or bug fix, be sure to have
tests.
Use the same coding style as the rest of the
codebase.
In your pull request, do not add documentation or rebuild the minifiedbackbone-min.js
file. We’ll do that before cutting a new release.
All pull requests should be made to the master
branch.


Module: Carbuncle::Keyboard
- Defined in:
- mrbgems/carbuncle-input/src/keyboard.c,
mrbgems/carbuncle-input/mrblib/keyboard.rb
Overview
This class handles keyboard input.
Constant Summary collapse
- BACKSPACE =
Note that the numbers are suggestions, and dependant on your platform. Don’t use numbers directly, use the constant or the symbol when handling keyboard input. This are in the documentation as a reference.
0
- SPACE =
1
- ESCAPE =
2
- ESC =
3
- ENTER =
4
- RETURN =
5
- DELETE =
6
- DEL =
7
- RIGHT =
8
- LEFT =
9
- DOWN =
10
- UP =
11
- ARROW_RIGHT =
8
- ARROW_LEFT =
9
- ARROW_DOWN =
10
- ARROW_UP =
11
- RIGHT_ARROW =
8
- LEFT_ARROW =
9
- DOWN_ARROW =
10
- UP_ARROW =
11
- F1 =
12
- F2 =
13
- F3 =
14
- F4 =
15
- F5 =
16
- F6 =
17
- F7 =
18
- F8 =
19
- F9 =
20
- F10 =
21
- F11 =
22
- F12 =
23
- LEFT_SHIFT =
24
- LEFT_CONTROL =
25
- LEFT_ALT =
26
- RIGHT_SHIFT =
27
- RIGHT_CONTROL =
28
- RIGHT_ALT =
29
- ZERO =
91
- ONE =
92
- TWO =
93
- THREE =
94
- FOUR =
95
- FIVE =
96
- SIX =
97
- SEVEN =
98
- EIGHT =
99
- NINE =
100
- A =
65
- B =
66
- C =
67
- D =
68
- E =
69
- F =
70
- G =
71
- H =
72
- I =
73
- J =
74
- K =
75
- L =
76
- M =
77
- N =
78
- O =
79
- P =
80
- Q =
81
- R =
82
- S =
83
- T =
84
- U =
85
- V =
86
- W =
87
- X =
88
- Y =
89
- Z =
90
Class Method Summary collapse
-
.all_down?(*args) ⇒ Boolean
Checks if all of the keys are down.
-
.all_press?(*args) ⇒ Boolean
Checks if all of the keys are pressed.
-
.all_release?(*args) ⇒ Boolean
Checks if all of the keys are released.
-
.all_up?(*args) ⇒ Boolean
Checks if all of the keys are up.
-
.any_down?(*args) ⇒ Boolean
Checks if any of the keys are down.
-
.any_press?(*args) ⇒ Boolean
Checks if any of the keys are pressed.
-
.any_release?(*args) ⇒ Boolean
Checks if any of the keys are released.
-
.any_up?(*args) ⇒ Boolean
Checks if any of the keys are up.
-
.character ⇒ String?
Gets the current pressed character as a string, or nil, if no character was press.
-
.down?(key) ⇒ Boolean
Gets if the corresponding key is being pressed, by value or key.
-
.press?(key) ⇒ Boolean
Gets if the corresponding key is pressed on the current frame.
-
.release?(key) ⇒ Boolean
Gets if the corresponding key is was released on the current frame.
-
.up?(key) ⇒ Boolean
Gets if the corresponding key is not pressed, by value or key.
Class Method Details
.all_down?(*args) ⇒ Boolean
Checks if all of the keys are down.
28 29 30 |
# File 'mrbgems/carbuncle-input/mrblib/keyboard.rb', line 28 def all_down?(*args) args.all?(&method(:down?)) end |
.all_press?(*args) ⇒ Boolean
Checks if all of the keys are pressed.
14 15 16 |
# File 'mrbgems/carbuncle-input/mrblib/keyboard.rb', line 14 def all_press?(*args) args.all?(&method(:press?)) end |
.all_release?(*args) ⇒ Boolean
Checks if all of the keys are released.
42 43 44 |
# File 'mrbgems/carbuncle-input/mrblib/keyboard.rb', line 42 def all_release?(*args) args.all?(&method(:release?)) end |
.all_up?(*args) ⇒ Boolean
Checks if all of the keys are up.
56 57 58 |
# File 'mrbgems/carbuncle-input/mrblib/keyboard.rb', line 56 def all_up?(*args) args.all?(&method(:up?)) end |
.any_down?(*args) ⇒ Boolean
Checks if any of the keys are down.
21 22 23 |
# File 'mrbgems/carbuncle-input/mrblib/keyboard.rb', line 21 def any_down?(*args) args.any?(&method(:down?)) end |
.any_press?(*args) ⇒ Boolean
Checks if any of the keys are pressed.
7 8 9 |
# File 'mrbgems/carbuncle-input/mrblib/keyboard.rb', line 7 def any_press?(*args) args.any?(&method(:press?)) end |
.any_release?(*args) ⇒ Boolean
Checks if any of the keys are released.
35 36 37 |
# File 'mrbgems/carbuncle-input/mrblib/keyboard.rb', line 35 def any_release?(*args) args.any?(&method(:release?)) end |
.any_up?(*args) ⇒ Boolean
Checks if any of the keys are up.
49 50 51 |
# File 'mrbgems/carbuncle-input/mrblib/keyboard.rb', line 49 def any_up?(*args) args.any?(&method(:up?)) end |
.character ⇒ String?
Gets the current pressed character as a string, or nil, if no character was press.
145 146 147 148 149 150 151 152 153 |
# File 'mrbgems/carbuncle-input/src/keyboard.c', line 145 static mrb_value mrb_keyboard_get_character(mrb_state *mrb, mrb_value self) { char character[5]; int key = GetKeyPressed(); if (key <= 0) { return mrb_nil_value(); } carbuncle_utf8_encode(character, key); return mrb_str_new_cstr(mrb, character); } |
.down?(key) ⇒ Boolean
106 107 108 109 110 111 |
# File 'mrbgems/carbuncle-input/src/keyboard.c', line 106 static mrb_value mrb_keyboard_downQ(mrb_state *mrb, mrb_value self) { mrb_int key = convert_keyboard_key(mrb); return mrb_bool_value(IsKeyDown(key)); } |
.press?(key) ⇒ Boolean
92 93 94 95 96 97 |
# File 'mrbgems/carbuncle-input/src/keyboard.c', line 92 static mrb_value mrb_keyboard_pressQ(mrb_state *mrb, mrb_value self) { mrb_int key = convert_keyboard_key(mrb); return mrb_bool_value(IsKeyPressed(key)); } |
.release?(key) ⇒ Boolean
120 121 122 123 124 125 |
# File 'mrbgems/carbuncle-input/src/keyboard.c', line 120 static mrb_value mrb_keyboard_releaseQ(mrb_state *mrb, mrb_value self) { mrb_int key = convert_keyboard_key(mrb); return mrb_bool_value(IsKeyReleased(key)); } |
.up?(key) ⇒ Boolean
134 135 136 137 138 139 |
# File 'mrbgems/carbuncle-input/src/keyboard.c', line 134 static mrb_value mrb_keyboard_upQ(mrb_state *mrb, mrb_value self) { mrb_int key = convert_keyboard_key(mrb); return mrb_bool_value(IsKeyUp(key)); } |


THE
LIBERTY
BLAZER
Petal perfection… Our schoolboy blazer, with its signature tailored fit, gets a fresh-for-spring makeover with a flourish of Liberty blooms.
SHOP ALL BLAZERS >
THE FINE PRINT:
JUNE’S MEADOW FLORAL
From London’s Liberty Art Fabrics (the nearly 140-year-old print house and our best bud when it comes to eye-catching florals), this hand-drawn motif features exotic blooms inspired by Abbey Gardens, the idyllic landscape located on the tiny island of Tresco, just off the southern coast of the UK.
SHOP THIS PRINT >
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{% extends 'base.html' %} {% block body %}
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Club to ask player to get move away from Crystal Palace, want reduced fee
by Nihat Emre KocaaslanFotomac report Trabzonspor will soon begin the operation to sign Crystal Palace’s Alexander Sörloth on a permanent basis and are seeking help from the player to complete the move.
The 23 year old has been in excellent form since joining the Turkish club on loan in the summer and that’s led to rumours Trabzonspor want to purchase him outright.
They have the option to make the move permanent by paying €6m to Crystal Palace but Fotomac report Karadeniz Firtinasi are looking to reduce his price to €4m.
To achieve this the Turkish side are hoping the Norwegian striker will inform the Premier League club about his intention to stay in Trabzon.
After making a great start to the season in the Super Lig, Sörloth has now scored four goals in his last three games for Norway.
Trabzonspor’s loan agreement for Sörloth will run until June 2021, and the Norwegian is under contract with Crystal Palace until June 2022.


FitBenchmarking results page
Follow the links below to the results for the individual groups
{% for group, link in zip(groups, group_link) %}
{{ group }}
{% endfor %}


{% extends "layout.html" %} {% block body_classes %}dashboard{% endblock %} {% block page_title %} Average time to complete {% endblock %} {% block content %} Average time to complete: Average time to complete This week: 07:21 Public Beta: 07:15 00:06 increase {% endblock %}
Public Beta: 07:15 00:06 increase


Headings in native iOS
See the heading chapter of Apple developer documentation.
{ "components": [ { "role": "heading", "text": "Mars - Earth’s neighbor" } ] }
Headings in native Android
See the heading section of Android developer documentation
If a particular View
element represents a heading, you can indicate its purpose for accessibility services by setting the element's android:accessibilityHeading
attribute to true
.