We support all phones that are running on Android 4.0 or later. Your phone should be able to receive SMS or call during the verification process.
We do not support tablets or Wi-Fi only devices at this time.
How do I Install WhatsDplan?
Download WhatsDplan App on your Android phone here and install it
Open WhatsDplan and continue to the next screen by agreeing to our Terms of Service
Enter your phone number and verify it using the OTP received
Give a username that you would like people to see. You can change this name anytime in your profile
WhatsDplan should now be installed and ready for use on your phone. Explore Restaurants & Events around you, make plans with your friends, discuss, decide and book. Let the fun begin!
How do I configure my notification settings?
Click on the sandwich icon on your home screen and go to Settings
Select Notification
You should be able to set notifications for your private events as well as the public events that you are following
How do I verify my number in WhatsDplan?
Once you install WhatsDplan and open it for the first time, you are prompted to enter you phone number. An OTP would be sent to your mobile number which will be auto-detected by our app. If the number is not auto-detected, the App will prompt you to enter the OTP. Post the successful verification you should be able to create your profile and start planning hangouts with your friends
Can I block a particular number in WhatsDplan?
Currently, you cannot block and number in WhatsDplan. If you find that you are made part of any Plan or Group which you think you should not be part of, we recommend you to immediately EXIT that Plan or Group to avoid any irrelevant messages or posts and further damage of any sort. For any further issues, you can raise your concerns at<EMAIL_ADDRESS>We will be glad to help you out.
How do I start planning with friends?
You can start a Plan by clicking on 'Plan' button. Multiple workflows have been designed for the ease of creating Plans. They are identified below:
Starting a Plan from Restaurants or Events List:
In the Explore Tab, after you click either restaurant or event, the list of restaurants/events appears. You can find 'Plan' button for every restaurant or event listed, clicking on which will take you through the Plan workflow. Select a convenient date, time, invite friends, preview your plan and send. A Plan is created with your friends, let the fun begin!
Using the FAB button:
In the Explore or My Plans TAB, the FAB button at the right bottom of the screen has two options in it. It allows you make a Quick plan with friends or ask friends for opinions. After you select any one of these two options, you will be guided through a sequence of steps till the preview screen and send
Starting a Plan from 'My Groups':
My Groups provides a quick plan facility with your friends' groups. Click on 'Plan' in My Groups, give the plan a title and send. The Plan is sent to all your friends in the group.
Please note that your friends who do not have WhatsDplan app installed on their mobile would get an SMS with a weblink to vote for plan options once they are invited
How can I remind my friends to vote for a plan?
Chat and Buzz are the two features which can help you ping friends to give their preferences. You can remind your friends to vote for plan options before you finalize on When and Where choices. Go to the plan details and click on Buzz option to send a reminder to your friends to vote for options in the plan or simply Chat with friends to discuss and decide quickly.
Who can finalize an option for a plan?
Only the person who has created the plan can Finalize an option. Once an option is finalized, all the friends who are part of the plan would get a notification regarding the meeting place and time. The Plan's Chat, Votes, Add Friend & Buzz will still be active after finalizing onto one option.
What happens after the plan is finalized?
After the plan is finalized a reminder is set in everyone's calendar. Currently, as the App is only for Android, the default account for your Android/Play Store will be used for setting the notification in corresponding Google calendar. A notification email will be sent to you as well before the event begins. You can change the notification reminder timings in Google account settings. The same settings work here as well.
All the events which are finalized will be sent to history after the event completion date.
What happens to the plans which are not finalized?
All the plans which are not finalized remain in 'My Plans' Tab till they are finalized or are deleted or moved to Plan History. The plans are arranged in chronologically arranged based on latest activity in the respective plan.
Do I have to finalize the plan if I want to book a ticket?
No. You need not finalize the plan to book a ticket. Ticket booking option is open and is independent of the 'Plan' workflow.
How can I inform my friend to install WhatsDplan using the App itself?
You can use the 'Tell a Friend' feature in our App to tell your friens about WhatsDplan. By clicking on Tell a Friend you will be prompted with a set of Apps installed in your phone which allow sharing. A few Apps which allow this are: WhatsApp, Gmail, Facebook etc. You can share our App Play Store link via any of these channels.
Otherwise, you can simply start a Plan and invite a friend. If the App is not installed by him/her We ensure that an SMS with with your name, plan details and App Installation link will be sent to your friend. We bear the SMS cost.
What happens if I uninstall the App?
Firstly, we will feel sad! :( Before we let you go, we ensure that all your previous plans are saved in our databases. You will be able to retrieve your old plans after you reinstall the App with the same mobile number. We will not be able to retrieve your Chat, as the Chat is stored on Clients' systems only and not at our servers.
Should I install the App to be part of a plan?
No. If you are invited to any plan by your friend - via SMS, you will be prompted to install the App or open the plan in a browser. If you open the plan in a browser you can see all the plan details and will be able to select your preference by voting.
However, you will not receive any notifications/updates by opening it in a browser. You have to revist the URL or refresh the page to view any update. Also, opening the plan in a browser will allow you to only vote, you will not be able to Chat or Add friends or Add a new choice or Buzz or save the plan. We always suggest you to install the App - it's Handy, if you love to go out with friends!
Is the App available only for Android users?
Currently, WhatsDplan is available only for Android. We are working to release the iOS version soon. Even we are eagerly waiting to have the iOS version out!


{% extends "console/console.html" %} {% block console_css %} {% endblock console_css %} {% block console_body %} {% include "console/components/links/breadcrumbs.html" with base_page="Settings" base_page_url="/settings" nested_page="User Settings" nested_page_url="/settings/user" current_page="Signature Management" %} Signature Your signature for signing documents, such as certificates of analysis (CoAs). Clear Upload Signature Save Signature {% endblock console_body %} {% block console_js %} {% endblock %}


Derby Fever: Teko Opens Up On Khune Rivalry
by SL SocialAhead of yet another mouthwatering Soweto derby, former Orlando Pirates midfield general, Teko Modise has opened up to SL Radio on his memorable rivalry with Kaizer Chiefs goalkeeper, Itumeleng Khune.
While Khune may not form part of this weekend’s action, the 32-year-old was involved in some feisty derbies over the years, particularly against the now-retired Modise, who has fond memories of the clash.
“Mzansi’s finest, what I can say is the battle between him and I was real! We also had a conversation about it. He always used to say I’ll never score against him. Even when we walked in the tunnel. He reminded me “don’t forget, you will not score, even if we lose” I loved that,” the general revealed.
Download The Soccer Laduma Social Media App & Earn Airtime for Selfies!
When asked about the Instagram video Khune posted on his Instagram where he seemingly got the better of Modise, the 37-year-old set the record straight.
“If you watch the video, you’ll see he made one save and the rest were hitting the upright. I remember that game, we were playing in Rustenburg, it was scorching hot and I knew for me to get into the box against the Kaizer Chiefs defense will be very difficult, so I had to shoot from outside the box. I had the belief and the power that I could do it.”
The general further revealed that he had in fact, never scored against the Ventersdorp-born star during his time at Pirates,rather it was at Mamelodi Sundowns where he scored a memorable goal against his former Bafana teammate.
“It’s unfortunate that I never scored against Itu in open play when I was at Orlando Pirates. When I was at Sundowns I scored against him from the centre line, which cancelled everything out,” a laughing Modise said.
Who do you think will reign victorious in tomorrow’s Soweto derby?


"I have been in business since 1973 and always had a simple plan: Be honest, fair, reliable, and treat others as you would like to be treated. My company has always maintained high standards, giving people a quality job for a fair price and in a timely manner. We hope you too are satisfied with the service we provide"
-Steve Hirshfield
CEO
"I have been in the locksmithing industry for 25 years, owning and operating several businesses throughout the US. I can ensure that NH Lock and Security maintains the highest standards to guarantee that our customers always feel comfortable and know that we care about all their safety and security needs. Integrity is a number one priority at our company, providing you with the best possible service we can."
Thank you,
-Robert Nichols
CEO


ED'S NEXT MOVE A film review by Christopher Null Copyright 1996 Christopher Null
This year's low-budget success story and darling of the festival circuit, ED'S NEXT MOVE is one of a handful of recent pictures that is truly deserving of its praise.
The Manhattan-based indie traces a few weeks in the life of urban greenhorn Eddie (Matt Ross), whose titular move is from the wide-open, cheesy state of Wisconsin to the dog-eat-dog world of New York City. Eddie, a genetics researcher and rice breeder, faces the world alone -- a stranger in a strange land. He lives in a cheap motel while he tries to find a non-psychotic roommate, can't get a simple hamburger at a restaurant, and finds his Midwestern sensibilities out of place in the big city.
It's here that Eddie meets Lee (Callie Thorne), a brassy hipster trying to cut it as a musician. Eddie, in fine romantic comedy style, becomes smitten with Lee and tries to win her affections.
The result is a fine comedy from newcomer John Walsh, who directs his own script. In fact, just about everyone associated with the film is a newcomer, which works to the benefit of the picture by keeping everything fresh. Ross and Thorne are excellent actors, and because we've never seen them before, it's easy to suspend disbelief enough to buy them in their parts. (Also good is Kevin Carroll as Eddie's antithesis and roommate Ray.)
With funny and fresh dialogue, a heap of production values considering the low budget, and a wide-appeal story that neither condescends to the audience nor enters the saccharine world of big-budget romantic comedies, ED'S NEXT MOVE is a winner for just about anyone's taste.  Be sure to watch for "the mice scene" -- the only comparable fall-down laughing sequence equivalent to last year's "banana scene" from THE BROTHERS MCMULLEN.
ED'S NEXT MOVE remains one of the better offerings of the year, by giving us a romantic comedy about people who could feasibly be friends of ours -- or could even be ourselves. My only hope is that Walsh can keep it up with his next picture.
Watch for an interview with writer/director John Walsh, coming soon!
RATING: ****1/2
|------------------------------| \ ***** Perfection \ \ **** Good, memorable film \ \ *** Average, hits and misses \ \ ** Sub-par on many levels \ \ * Unquestionably awful \ |------------------------------|
-Christopher Null /<EMAIL_ADDRESS>/ Writer-Producer -Visit the Movie Emporium at http://www.filmcritic.com/ -and Null Set Productions at http://www.filmcritic.com/nullset.htm
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


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IFXNotificationManager Class ReferenceThis is the main interface for IFXNotificationManager.
More...
Inheritance diagram for IFXNotificationManager:
List of all members.
|
Public Member Functions |
| virtual IFXRESULT IFXAPI | GetNextType (IFXNotificationType *pType)=0 |
| virtual IFXRESULT IFXAPI | GetNextId (IFXNotificationId *pId)=0 |
| virtual IFXRESULT IFXAPI | AddEventId (U32 key, IFXNotificationId id)=0 |
| virtual IFXRESULT IFXAPI | FindEventId (U32 key, IFXNotificationId *pId)=0 |
| virtual IFXRESULT IFXAPI | Initialize (IFXCoreServices *pCoreServices)=0 |
| virtual IFXRESULT IFXAPI | Reset ()=0 |
| virtual IFXRESULT IFXAPI | Cleanup ()=0 |
| virtual IFXRESULT IFXAPI | SubmitEvent (IFXNotificationInfo *pInfo)=0 |
| virtual IFXRESULT IFXAPI | SubmitError (IFXErrorInfo *pErrInfo)=0 |
| virtual IFXRESULT IFXAPI | SubmitEvent (IFXNotificationType type, IFXNotificationId id, U32 time, void *pUserData)=0 |
| virtual IFXRESULT IFXAPI | RegisterTask (IFXTask *pTask, IFXNotificationType type, IFXNotificationId id, IFXTaskHandle *pTaskHandle)=0 |
| virtual IFXRESULT IFXAPI | RegisterTask (IFXTask *pTask, IFXNotificationType type, IFXNotificationId id, void *pUserData, IFXUnknown *pObject, IFXTaskHandle *pTaskHandle)=0 |
| virtual IFXRESULT IFXAPI | UnregisterTask (IFXTaskHandle taskHandle)=0 |
| virtual IFXRESULT IFXAPI | GetTaskManager (IFXTaskManager **ppTaskManager)=0 |
Detailed Description
This is the main interface for IFXNotificationManager.
- Note:
- The associated IID is named IID_IFXNotificationManager, and the CID for the Notification Manager component is CID_IFXNotificationManager.
Member Function Documentation
| virtual IFXRESULT IFXAPI IFXNotificationManager::AddEventId |
( |
U32 |
key, |
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IFXNotificationId |
id |
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) |
[pure virtual] |
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| virtual IFXRESULT IFXAPI IFXNotificationManager::Cleanup |
( |
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) |
[pure virtual] |
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| virtual IFXRESULT IFXAPI IFXNotificationManager::FindEventId |
( |
U32 |
key, |
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IFXNotificationId * |
pId |
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) |
[pure virtual] |
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| virtual IFXRESULT IFXAPI IFXNotificationManager::GetNextId |
( |
IFXNotificationId * |
pId |
) |
[pure virtual] |
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This method retrieves the next available IFXNotificationId.
See IFXNotificationInfo for details and usage.
- Parameters:
-
| pId | Receives the IFXNotificationId.
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- Returns:
- Returns an IFXRESULT code.
- IFX_OK upon success.
- IFX_E_NOT_INITIALIZED if the component has not been initialized.
- IFX_E_INVALID_POINTER is returned if pId is NULL.
Implemented in CIFXNotificationManager. |
| virtual IFXRESULT IFXAPI IFXNotificationManager::GetNextType |
( |
IFXNotificationType * |
pType |
) |
[pure virtual] |
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This method retrieves the next available IFXNotificationType.
See IFXNotificationInfo for details and usage.
- Parameters:
-
| pType | Receives the IFXNotificationType.
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- Returns:
- Returns an IFXRESULT code.
- IFX_OK upon success.
- IFX_E_NOT_INITIALIZED if the component has not been initialized.
- IFX_E_INVALID_POINTER is returned if pType is NULL.
Implemented in CIFXNotificationManager. |
| virtual IFXRESULT IFXAPI IFXNotificationManager::GetTaskManager |
( |
IFXTaskManager ** |
ppTaskManager |
) |
[pure virtual] |
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This method retrieves the IFXTaskManager interface from the associated task manager.
- Parameters:
-
| ppTaskManager | A pointer to a variable which will receive the IFXTaskManager interface.
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- Returns:
- Returns an IFXRESULT code.
- IFX_OK upon success.
- IFX_E_INVALID_POINTER if ppTaskManager is NULL.
- IFX_E_NOT_INITIALIZED if the Notification Manager has not yet been initialized.
Implemented in CIFXNotificationManager. |
| virtual IFXRESULT IFXAPI IFXNotificationManager::Initialize |
( |
IFXCoreServices * |
pCoreServices |
) |
[pure virtual] |
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This method initializes the Scheduler component.
- Parameters:
-
- Returns:
- Returns an IFXRESULT code.
- IFX_OK upon success.
- IFX_E_INVALID_POINTER is returned if pCoreServices is invalid.
- IFX_E_ALREADY_INITIALIZED if the Scheduler has already been initialized.
Implemented in CIFXNotificationManager. |
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This is a convenience method which creates an IFXTaskManagerNode object, initializes it with the provided data, and registers it with the Task Manager.
The task handle is returned.
- Parameters:
-
| pTask | A pointer to the IFXTask interface which is to be registered.
|
| type | The notification type which will trigger the Task. IFXNotificationType_Nil is a 'wildcard' which matches any type.
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| id | The notification id which will trigger the Task. IFXNotificationId_Nil is a 'wildcard' which matches any id.
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| pTaskHandle | A pointer to a variable which will receive the task handle.
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- Returns:
- Returns an IFXRESULT code.
- IFX_OK upon success.
- IFX_E_INVALID_POINTER if pTask or pTaskHandle is NULL.
- IFX_E_NOT_INITIALIZED if the Notification Manager has not yet been initialized.
- IFX_E_OUT_OF_MEMORY if the IFXTaskManagerNode could not be created.
Implemented in CIFXNotificationManager. |
| virtual IFXRESULT IFXAPI IFXNotificationManager::Reset |
( |
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) |
[pure virtual] |
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This method resets the Notification Manager.
- Returns:
- Returns an IFXRESULT code.
- IFX_OK upon success.
- IFX_E_NOT_INITIALIZED if the Notification Manager has not yet been initialized.
Implemented in CIFXNotificationManager. |
| virtual IFXRESULT IFXAPI IFXNotificationManager::SubmitError |
( |
IFXErrorInfo * |
pErrInfo |
) |
[pure virtual] |
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This method accepts an error event defined by IFXErrorInfo and invokes any IFXTasks registered with the Task Manager for events with type IFXNotificationType_Error.
A IFXNotificationInfo is generated from the contained information and is matched against the notification info associated with each task registration, and in the case of a match, is passed on to IFXTask::Execute().
- Parameters:
-
- Returns:
- Upon success, IFX_OK is returned. Otherwise one of the following errors is returned: IFX_E_INVALID_POINTER and IFX_E_NOT_INITIALIZED.
Implemented in CIFXNotificationManager. |
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This is a convenience method which creates an IFXNotificationInfo instance, initializes it with the provided data, and then submits it.
See SubmitEvent(IFXNotificationInfo) for more details on submitting events. This method is recommended when additional functionality is not required, since the Notification Manager may
- Parameters:
-
| type | The notification type for this event.
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| id | The notification id for this event.
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| time | The time associated with this event. If no time is associated with this event, this should be zero.
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| pUserData | The user data for this event. This is not interpreted by the system, and may be NULL.
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- Returns:
- Returns an IFXRESULT code.
- IFX_OK upon success.
- IFX_E_NOT_INITIALIZED if the Notification Manager has not yet been initialized.
- IFX_E_OUT_OF_MEMORY if the IFXNotificationInfo instance could not be created.
Implemented in CIFXNotificationManager. |
| virtual IFXRESULT IFXAPI IFXNotificationManager::SubmitEvent |
( |
IFXNotificationInfo * |
pInfo |
) |
[pure virtual] |
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This method accepts an event defined by IFXNotificationInfo and invokes any IFXTasks registered with the Task Manager for that event.
The IFXNotificationInfo passed to this method is matched against the notification info associated with each task registration, and in the case of a match, is passed on to IFXTask::Execute().
- Parameters:
-
- Returns:
- Returns an IFXRESULT code.
- IFX_OK upon success.
- IFX_E_NOT_INITIALIZED if the Notification Manager has not yet been initialized.
- IFX_E_INVALID_POINTER if pInfo is NULL.
Implemented in CIFXNotificationManager. |
| virtual IFXRESULT IFXAPI IFXNotificationManager::UnregisterTask |
( |
IFXTaskHandle |
taskHandle |
) |
[pure virtual] |
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A convenience method which calls IFXTaskManager::RemoveTask.
The task handle to unregister a task was previously obtained from RegisterTask, or from IFXTaskManager::AddTask.
- Parameters:
-
| taskHandle | The taskhandle of the task to unregister.
|
- Returns:
- Returns an IFXRESULT code. Since this method also calls the IFXTaskManager::RemoveTask method internally, any of the result codes returned by that method may also be returned by this method.
- IFX_OK upon success.
- IFX_E_NOT_INITIALIZED if the component has not been initialized.
Implemented in CIFXNotificationManager. |
© 1999 - 2006 Intel Corporation. All rights reserved.
* Other names and brands may be claimed as the property of others.
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Join and get latest news about our vegan treats!
Heaps of amazing cakes, cupcakes and cookies to choose from.
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Place an order and wait atleast 3 days for the pickup to get best service.
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‘My Neighbour Bakes’, a home-based bakery business which is primarily community focused. It is currently running it due to a personal passion for baking, but wants to expand to a real business. The cakes are made from vegan ingredients due to the client’s preferences and to fill a gap in the market. Non-vegan ingredients can be used if specifically requested but are not preferred. Business stands out due to the cake’s unique designs compared to other bakeries.
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My First Website
This is the first website I am going to make. It will teach me the basics of html and css
HTML Learning Goals
- Page Structure
- Element Attributes
- Tag Types
- Linking in other resources
CSS Learning Goals
- What Properties are
- How you use elements, and element attributes to change styles
- How CSS styles cascade
- How to include these styles


I am Anzil.I'm a front-end-developer.I'm from pookkottumpadam which is situated at amarambalam grama panchayat in Malappuram district of Kerala,India.Currently I'm doing my Bachelor's degree in Computer Application from Jamia Nadwiyya Arts and Science College,Edavanna,Malappuram
English,Malayalam,French(beginner)
Software Using FINAL CUT PRO , PREMIER PRO


{# This is an example home page template. It inherits and extends a layout template that lives in the top-level views/ folder for convenience #} {% extends "layout.html" %} {% block beforeMain %}
Welcome to Semada!


General discussion about Komodo. For useful links and more info click the pinned messages to the right. DON'T DM @jl777
100 messages
Before 05-Dec-19 12:00 AM
ComputerGenie 👴👴👴31-Oct-18 09:08 AM
as for physical access, I literally live out in the woods in a town of 600 people in a county of 1500 people, I can't think of a single person who would travel out here, enter the house, and only take files(edited)
jansako31-Oct-18 09:09 AM
that is what I am saying, they DID NOT take the file. If they had, you would not have the change address
actionslacks31-Oct-18 09:09 AM
and why the 100 kmd transaction? right now? If you're here, tell us how you did it.
ComputerGenie 👴👴👴31-Oct-18 09:10 AM
@jansako even less likely is one of those people picking this PC and only taking KMD
jansako31-Oct-18 09:11 AM
Is there another change addy from those 100, and do you have it as well?
ComputerGenie 👴👴👴31-Oct-18 09:11 AM
big ugly orange wallet is open on the server with more BTC than the 29k KMD
jansako31-Oct-18 09:12 AM
I am sorry man, I am only going off of logic, do not know anyone there or their skills and motivations.
ComputerGenie 👴👴👴31-Oct-18 09:12 AM
Is there another change addy from those 100, and do you have it as well?
nope
do not know anyone there or their skills and motivations.
ever seen 'Deliverance'?
jansako31-Oct-18 09:13 AM
don't remember...
but I do come from a small village as well
with all the + and - of that environment.
jl777c31-Oct-18 09:15 AM
wallet.dat change addresses have up to 100 pregenerated
if the change went to a change address in common, this indicates the wallet.dat was compromised
after the keypool is used up, the change addresses will diverge
this does not appear to be a passphrase hack as there is no way the change address would also be compromised
so the fact that the change address is known gives very strong indication of what was compromised, just not know
ComputerGenie 👴👴👴31-Oct-18 09:18 AM
yeah, the pass thought was only because I was showing 0 balance at the time
other than toying with my emotions, I dunno why the coins are being spit into chunks in t addys, though(edited)
jansako31-Oct-18 09:19 AM
the fact that yo know which one of your wallet.dat's was taken could help
jl777c31-Oct-18 09:20 AM
was the win10 machine anywhere close?

ComputerGenie 👴👴👴31-Oct-18 09:46 AM
it's no small amount for me, but it could have been much, much worse
actionslacks31-Oct-18 09:48 AM
I'd be ripping ethernet cables out of walls if it was me.
snowdamn31-Oct-18 09:48 AM
now i'm scared
actionslacks31-Oct-18 09:48 AM
I'm always scared
ComputerGenie 👴👴👴31-Oct-18 09:49 AM
I'd be ripping ethernet cables out of walls if it was me.
I was at that point when I 1st got home and saw the 0 balance
....
I live, I hopefully learn, and I start a new day when I awake
actionslacks31-Oct-18 09:49 AM
thats why crypto is so fun
ComputerGenie 👴👴👴31-Oct-18 09:49 AM
I'm not putting this in the "fun" category
actionslacks31-Oct-18 09:51 AM
well I'll bet that 3 monitor setup looks pretty sweet
ComputerGenie 👴👴👴31-Oct-18 09:52 AM
as long as you're willing to overlook the Windows 3.1 theme they put in it
snowdamn31-Oct-18 10:07 AM
when doing a VM on top of/within an encrypted SSD server, is there any additional level of security in regard to hardware backdoors or same exposure? please help security n00b
still have to deal with spyware, etc., but debating if i should go back to pre-backdoor hardware
something like what jok3r had suggested here before
KomodoWorld31-Oct-18 10:25 AM
I wish we already had the Vault CC
Instel731-Oct-18 11:22 AM
Happy 10 Year Anniversary to bitcoin!
10 Years ago (10/31/2008) Satoshi Nakamoto Announced the creation of Bitcoin to the cryptography mailing list.
https://i.redd.it/a7fykewk6gv11.png
Don't miss this full day on #Komodo tech in #Utrecht Nov 5th! We'll make sure you get to #atomicswap one #coin to another with #BarterDEX, you learn about #dPoW and #assetchain's. It's free! Sponsored by #KomodoPioneers. Be there! $KMD https://t.co/MNJxACfE38
Go see @phildaian speak today from 3:15 - 3:45 @EFDevcon!
#Devcon4
1
he's speaking about issues with DEXs if anyone is attending
but I think all his concerns are addressed by BarterDEX if they're the same as he wrote in an article a few months ago
Dr.Terminus31-Oct-18 01:57 PM
current KMD TA? cant find a chart
snowdamn31-Oct-18 03:20 PM
i'm scared shitless even without Windows
yes, need a second machine to do that ASAP
still on graphical environment, but at least not Windows
grewalsatinder31-Oct-18 03:44 PM
gives me a thought.... should I try BSD?
freeBSD or openBSD?
it's been over a decade I tried BSD
only used it for servers
never for desktop
0x0331-Oct-18 04:21 PM
depends. if you value security over newer features, deraadts openbsd is for you
if you're after awesome stuff like working zfs and such, it maybe wiser to go for freebsd
i personally would choose netbsd, well knowing about its drawbacks anywhere outside of networking
theres also DragonFly BSD
i never tried it, but people say its not something you'll have to throw away your hdd because of installing it either
so maybe it's worth to check it out
having that said, you work on a OSX right?
half of your OS Is in fact freebsd
oh, if CoCs mean a lot to you, or the behaviour they SHOULD (but never will) help with, openbsd is wrong for you. do not ever read their mailinglist then
snowdamn31-Oct-18 04:32 PM
what would be the best setup if someone is absolutely dependent on Agama?
assume effort to get old hardware and/or even installing the free BIOS stuff jok3r recommended
ComputerGenie 👴👴👴31-Oct-18 04:40 PM
what would be the best setup if someone is absolutely dependent on Agama?
Windows 7(edited)
The problem for anyone like me and picking a flavor of BSD is the exact same as picking any other flavor of linux: xorg sux ass at using more than 1 monitor and they all use it (edited)
grewalsatinder31-Oct-18 04:56 PM
I left osx. I'm 100% on linux now.
2
some might find it comforting reading it.
also waiting for the day I get the latest pixel 3 android.
I'll ditch the iOS phone too.
1
0x0331-Oct-18 05:04 PM
awesome choice!
however, between linux and a bsd, the gainable advantage for you is very minimal i think
that aside, linux will very well work on your macbook
some even say it's the best laptop hardware for linux, i beg to differ, but some do
@snowdamn bestin terms of what? privacy? security? os-coolness?
you could go with tails live cd or so and just always import the key to the wallet you always install, thats very very private
ComputerGenie 👴👴👴31-Oct-18 05:08 PM
https://images-na.ssl-images-amazon.com/images/I/81WEO35hVcL._SL1500_.jpg <- my phone
0x0331-Oct-18 05:08 PM
stamp display machine
i'll get the new nokia bananaphone for xmas. i've had it with 'smart' phones
and 'my type' usually has some form of proper computing device nearby at all times
grewalsatinder31-Oct-18 05:09 PM
that phone is still fancy....
0x0331-Oct-18 05:09 PM
the nokia thingy has 4g and wifi and is pretty dumb otherwise
grewalsatinder31-Oct-18 05:09 PM
How about this:
https://www.thescottishsun.co.uk/wp-content/uploads/sites/2/2017/02/nintchdbpict000305168568.jpg?strip=all&w=1200&h=800&crop=1
0x0331-Oct-18 05:09 PM
satiner, we're talking about PHONES, not about WEAPONS
ComputerGenie 👴👴👴31-Oct-18 05:10 PM
@grewalsatinder folks joke, but if I could get service for one of them here... I'd use it(edited)
1
1
grewalsatinder31-Oct-18 05:10 PM
I have macbook pro 2017 version... the one with touch bar.
I couldn't figure to fix my macbook's touchpad when installing ubuntu from usb.
didn't bother to troubleshoot and just picked my librem laptop
0x0331-Oct-18 05:10 PM
eager to hear how you like it
grewalsatinder31-Oct-18 05:11 PM
Librem is perfect I'd say.
but only 2 things I don't like in it.
0x0331-Oct-18 05:11 PM
also, are you selling a cheap macbook pro 2017? did i hear that out right?


5 Ways Steam Remote Play Is a Complete Game-Changer
- Steam’s newest Remote Play feature launched this week.
- It allows up to four players to jump into a game in online couch co-op.
- There’s even a celebratory sale on at the moment to mark the occasion.
Steam’s online couch co-op feature, Remote Play, launched on Wednesday. Remote Play has been in beta for a month before launch, and it appears that Valve has been happy enough with the results to integrate it into the client as a full-blown feature.
And, how effortlessly Valve has done that. Remote Play is sophisticated tech working with a welcome simplicity that takes the hassle usually synonymous with online play out of the equation.
To mark the occasion, we’re taking a look at what makes it so great.
Effortless Integration Into Steam
Say what you want about Steam, but one thing Valve systematically gets right with its client, is functionality. It simply works. And, Remote Play is no different; start a game, invite a friend through the Friends List, and voila, you’re in within seconds.
It even works for games that don’t natively support online multiplayer through some clever Valve wizardry. Different operating systems won’t be an issue either. Windows, Mac? No problem. It even works on mobile thanks to Remote Play Anywhere.
Only One Game Copy Required
Only the person hosting and launching the game needs to own it to use Remote Play with others. This one needs no explanation. It’s cost-effective and doesn’t require friends to jump through hoops.
Valve has also taken the step of limiting what connected friends can see to just the game. Anything else that might on the host’s machine be it the desktop, a web browser, and so on, are strictly out of bounds.
Options
Remote Play is designed to behave as if all players have a controller plugged into the host computer regardless of whether they are next door or hundreds of miles away. It goes without saying that each player will need a controller of their own.
So, there’s that seamless functionality, but you can also share a keyboard and mouse between all players if that works better for your particular situation.
Party Atmosphere
While playing a game with friends on the other side of the country is the main feature, Remote Play also allows you to stream video, audio, and voice between all players. It’s an elegant solution that brings that party atmosphere to online play.
Remote Play Together Sale
While not technically a feature, everyone loves a good deal. Steam is having a sale on a generous selection of co-op, versus, and small committee local multiplayer games ideally suited to Remote Play to celebrate the feature leaving beta. You can pick up games Overcooked, Spelunky, and Guacamelee for next to nothing.
This article was edited by Samburaj Das.


Called before authorisation - you can override authorisation here if you don't want the default
Called before authorisation - you can override authorisation here if you don't want the default
The auth message
The session ID
Handler - call this when authorisation is complete
true if you wish to override authorisation
Called after client registers a handler
Called after client registers a handler
The socket
The address
Called before client registers a handler
Called before client registers a handler
The socket
The address
true to let the registration occur, false otherwise
Client is sending or publishing on the socket
Client is sending or publishing on the socket
The sock
if true it's a send else it's a publish
The message
The address the message is being sent/published to
true To allow the send/publish to occur, false otherwise
The socket has been closed
The socket has been closed
The socket
Called when a new socket is created You can override this method to do things like check the origin header of a socket before accepting it
Called when a new socket is created You can override this method to do things like check the origin header of a socket before accepting it
The socket
true to accept the socket, false to reject it
Client is unregistering a handler
Client is unregistering a handler
The socket
The address
A hook that you can use to receive various events on the EventBusBridge.


{%- include head.html -%}
{%- include preloader.html -%} {%- include navbar.html -%} {%- include carousel.html -%} {%- include bio.html -%} {%- include contactbar.html -%}


| Birth |
16 August 1919
Lithuania
|
|
|---|---|---|
| Death |
17 March 1971
Chicago, Cook, Illinois, United States
|
|
| Temple | BCIESPSS |
| Ordinances | BCIESPSS |
|---|
This is a test.More
4 months ago by MMMM-QVX2 Edit Delete
This is a test.More
4 months ago by MMMM-QVX2 Edit Delete


This is the main documentation of the Fortran Module M_history
The code includes routines normally found in additional modules so that it can be used as a stand-alone routine. The same module when part of the General Purpose Fortran repository is dependent on the modules M_journal(3f) and M_strings(3f).
M_history(3f) is mainly used as a component of a command line tool.
It provides a simple input-line recall and edit capability, commonly
called an input history editor that does not require or use terminal
escape sequences. It just uses a simple line editor model based on the
CDC xedit editor commands change
and modif
.
This documentation is generated by
FORD.
visit the FORD documentation
for further information.
To regenerate this documentation run:
ford ford.md


curs_trace 3x
curs_trace(3x) curs_trace(3x)
NAME
_tracef, _tracedump, _traceattr, _traceattr2,
_nc_tracebits, _tracechar, _tracechtype, _tracechtype2,
_tracemouse, trace - curses debugging routines
SYNOPSIS
#include <curses.h>
void _tracef(const char *format, ...);
void _tracedump(const char *label, WINDOW *win);
char *_traceattr(attr_t attr);
char *_traceattr2(int buffer, chtype ch);
char *_nc_tracebits(void);
char *_tracechar(const unsigned char ch);
char *_tracechtype(chtype ch);
char *_tracechtype2(int buffer, chtype ch);
char *_tracemouse(const MEVENT *event);
void trace(const unsigned int param);
DESCRIPTION
The trace routines are used for debugging the ncurses
libraries, as well as applications which use the ncurses
libraries. These functions are normally available only
with the debugging library libncurses_g.a, but may be com-
piled into any model (shared, static, profile) by defining
the symbol TRACE.
The principal parts of this interface are the trace rou-
tine which selectively enables different tracing features,
and the _tracef routine which writes formatted data to the
trace file.
Calling trace with a nonzero parameter opens the file
trace in the current directory for output. The parameter
is formed by OR'ing values from the list of TRACE_xxx def-
initions in <curses.h>. These include:
TRACE_DISABLE
turn off tracing.
TRACE_TIMES
trace user and system times of updates.
TRACE_TPUTS
trace tputs calls.
TRACE_UPDATE
trace update actions, old & new screens.
TRACE_MOVE
trace cursor movement and scrolling.
TRACE_CHARPUT
trace all character outputs.
TRACE_ORDINARY
trace all update actions. The old and new screen
contents are written to the trace file for each
refresh.
TRACE_CALLS
trace all curses calls. The parameters for each call
are traced, as well as return values.
TRACE_VIRTPUT
trace virtual character puts, i.e., calls to addch.
TRACE_IEVENT
trace low-level input processing, including timeouts.
TRACE_BITS
trace state of TTY control bits.
TRACE_ICALLS
trace internal/nested calls.
TRACE_CCALLS
trace per-character calls.
TRACE_DATABASE
trace read/write of terminfo/termcap data.
TRACE_ATTRS
trace changes to video attributes and colors.
TRACE_MAXIMUM
maximum trace level, enables all of the separate
trace features.
Some tracing features are enabled whenever the trace
parameter is nonzero. Some features overlap. The
specific names are used as a guideline.
RETURN VALUE
Routines which return a value are designed to be used as
parameters to the _tracef routine.
PORTABILITY
These functions are not part of the XSI interface. Some
other curses implementations are known to have similar,
undocumented features, but they are not compatible with
ncurses.
SEE ALSO
curses(3x).
curs_trace(3x)
Man(1) output converted with
man2html


|
RC CAR
|
File containing functions for IO External. More...
#include "IO.h"
Go to the source code of this file.
Functions | |
| uint8_t | GetInputPin (EN_INPUT_PINS pinId_en) |
| Section for h files inclusion. More... | |
| void | setOutputPin (EN_OUTPUT_PINS pinId_en, uint8_t value_u8) |
| Returns status of logical Output Pin. More... | |
| void | setupADC () |
| Function implementation for analog input from lightsensor. More... | |
| void | initIO () |
| void | processOutputBuffer () |
| Function definition for processing output buffer. More... | |
| void | processInputBuffer () |
| Function definition for processing input buffer. More... | |
| void | processADCconversion () |
| Function processes ADC conversion. More... | |
File containing functions for IO External.
Definition in file IO_extern.h.
| uint8_t GetInputPin | ( | EN_INPUT_PINS | pinId_en | ) |
Section for h files inclusion.
Section for h files inclusion A simple stub function to show how links do work.
Links are generated automatically for webpages (like http://www.google.co.uk) and for structures, like BoxStruct_struct. For typedef-ed types use #BoxStruct. For functions, automatic links are generated when the parenthesis () follow the name of the function, like Box_The_Function_Name(). Alternatively, you can use #Box_The_Function_Name.
NULL
is always returned.Section for h files inclusion.
Links are generated automatically for webpages (like http://www.google.co.uk) and for structures, like BoxStruct_struct. For typedef-ed types use #BoxStruct. For functions, automatic links are generated when the parenthesis () follow the name of the function, like Box_The_Function_Name(). Alternatively, you can use #Box_The_Function_Name.
| pinId_en | logical input pin name. range: EN_INPUT_PINS |
state
of the pin. Definition at line 81 of file IO.c.
| void processADCconversion | ( | ) |
Function processes ADC conversion.
Function processes ADC conversion
Function verifies enabled ADC channels and stores the ADC conversion values in an arrayf results On each enabled ADC channel, the ADMUX register is updated
< index for result ADC conversion array
< current channel is enabled
< array of result gets ADC value for the specified channel
<passes to the next ADC channel
< updates ADMUX register
< resets the initial value
< ADSC bit is not set - meaning the ADC conversion is not in progress
< current channel is NOT enabled
<passes to the next ADC channel
< updates ADMUX register
< resets the initial value
Definition at line 451 of file IO.c.
| void processInputBuffer | ( | ) |
Function definition for processing input buffer.
Function implementation for processing input buffer. For functions, automatic links are generated when the parenthesis () follow the name of the function, like Box_The_Function_Name(). Alternatively, you can use #Box_The_Function_Name.
Function definition for processing input buffer.
Function implementation for processing output buffer
<end case EN_PORT_AI
Definition at line 347 of file IO.c.
| void processOutputBuffer | ( | ) |
Function definition for processing output buffer.
Function implementation for processing output buffer. For functions, automatic links are generated when the parenthesis () follow the name of the function, like Box_The_Function_Name(). Alternatively, you can use #Box_The_Function_Name.
Function definition for processing output buffer.
Function implementation for processing output buffer
<end case EN_PORT_DO
Definition at line 163 of file IO.c.
| void setOutputPin | ( | EN_OUTPUT_PINS | pinId_en, |
| uint8_t | value_u8 | ||
| ) |
Returns status of logical Output Pin.
Links are generated automatically for webpages (like http://www.google.co.uk) and for structures, like BoxStruct_struct. For typedef-ed types use #BoxStruct. For functions, automatic links are generated when the parenthesis () follow the name of the function, like Box_The_Function_Name(). Alternatively, you can use #Box_The_Function_Name.
| pinId_en | logical output pin name. range: EN_OUPUT_PINS |
state
of the pin. < tests if the pin is out of range
<verificam daca portul e de tip PWM
<PWM port
< sets the value to be put in output buffer to the maximum PWM value
<not a PWM port
<implementat pentru robustete
< functia de Set pini digitali
<do nothing
Definition at line 119 of file IO.c.
| void setupADC | ( | ) |
Function implementation for analog input from lightsensor.
Function implementation for analog input from lightsensor
<it is set the ADC enable bit
<it is set the ADC prescaler for 128 cycles
< enables analog input channels
<do nothing
Definition at line 421 of file IO.c.


BASS4 - ADMINISTRATOR’S MANUAL
Last updated 2022-02-09, current BASS version: 4.12
Chapter 1 Introduction
Disclaimer: This manual is written primarily for full-access database administrators. While you will find instructions and useful tips even if you’re not an administrator, note that all options described may not be available to you if you have limited access. In this manual you will learn how to manage participants, combine self-help material into treatments, keep track on events during an ongoing study/program, manage security and privacy settings, collect and export data and communicate with participants through the administration interface of BASS.
Should you have questions not yet answered by this manual, you’re very welcome to contact us at bass-support[at]ki.se
.
BASS is comprised of two main parts, or user interfaces: the administrator’s view and the participant’s view. These are part of the same database, but accessed through slightly different web addresses, URL:s.
- The administrator’s view is accessed through the URL https://webcbt.se/YourDataBase or its variant https://bassdb.se/YourDataBase.
- The participant’s view is accessed through a similar URL: https://YourDataBase.webcbt.se or its corresponding variant https://YourDataBase.bassdb.se.
- These different URL domains are presented as options for you to choose which one suits your project the best. A research project might not have much in common with CBT treatments, and as such the domain ”webcbt.se” might seem a little odd to participants. In such cases, the domain ”bassdb.se” might be more suitable. On the flip side, ”webcbt.se” might be a perfect fit in the case where CBT treatments are in fact the main part of your project.
- BASS is a powerful and flexible tool specifically designed for online psychological research projects or treatment programs. It supports online registration, assessment, treatment as well as being able to provide detailed data reports.
- BASS is currently used by a multitude of different research projects and treatments.
- We offer active remote support accessed by mail, telephone and/or digital meetings such as Zoom, Microsoft Teams or similar applications. For clients located in Stockholm, Sweden and the surrounding area, we also offer in-person meetings at the Karolinska Institutet campus in Solna.


My Favorite Places
These are some of my favorite places
San Diego
Seattle
Baja Mexico
Bend
A beautiful view of San Diego.
A cozy lodge in Bend
A scenic boat ride in Baja Mexico


TL;DR
You can use extension method (e.g. IEndpointConventionBuilder Map{FeatureToMap}(this IEndpointRouteBuilder endpoints
) to add a middleware to specific endpoint.
Middleware
Middleware forms the basic building blocks of the HTTP Pipeline. It is a really good concept to implement cross-cutting concerns and weave a re-usable piece of code to the ASP.NET pipeline. Middleware provides application-level features. For example, you might need Middleware to implement features like: Routing, Cookies, Session, CORS, Authentication, HTTPS Redirection, Caching, Response Compression, Exception Handling. Most of the time, you’ve got out-of-the-box option provided by the framework.
To extend ASP.NET Core pipeline we use IApplicationBuilder injected in Startup.cs
.
Furthermore, since ASP.NET Core 3.0 you could use HttpContext.GetEndpoint to retrieve selected endpoint/metadata, e.g.:
// Before routing runs, endpoint is always null here
app.Use(next => context =>
{
Console.WriteLine($"Endpoint: {context.GetEndpoint()?.DisplayName ?? "(null)"}");
return next(context);
});
app.UseRouting();
// After routing runs, endpoint will be non-null if routing found a match
app.Use(next => context =>
{
Console.WriteLine($"Endpoint: {context.GetEndpoint()?.DisplayName ?? "(null)"}");
return next(context);
});
Middleware can exist as simple inline methods or as complex, reusable classes. If you don’t like RequestDelegate
notation/form. You can use IApplicationBuilder.UseMiddleware extension method to add middleware as a class and access an endpoint from parameter (HttpContext
).
// Startup.cs
app.UseMiddleware<MyAwesomeMiddleware>();
// ...
// MyAwesomeMiddleware.cs
public async Task InvokeAsync(HttpContext httpContext)
{
var endpoint = httpContext.GetEndpoint();
}
If you need something more fine-grained and, probably, more powerful you can use Endpoint
concept.
Routing and Endpoints
Routing is responsible for matching incoming HTTP requests and dispatching those requests to the app’s executable endpoints. You can use IApplicationBuilder.UseEndpoints
to define pipeline logic based on a selected route. Many of ASP.NET Core features/aspects are implemented with the routing concept in mind. For example, you can IEndpointRouteBuilder.MapControllers or IEndpointRouteBuilder.MapGrpcService or you can build your own framework based on this extension capabilities.
app.UseEndpoints(endpoints =>
{
// Configure another endpoint, with authorization.
endpoints.MapGet("/", async context =>
{
await context.Response.WriteAsync($"Hello from {context.GetEndpoint()}!");
}).RequireAuthorization().WithMetadata(new AuditPolicyAttribute(needsAudit: true));
});
As you can see, Endpoint
contains an EndpointMetadataCollection
that holds various data put and managed during pipeline execution.
/// <summary>
/// Represents a logical endpoint in an application.
/// </summary>
public class Endpoint
{
public Endpoint(
RequestDelegate? requestDelegate,
EndpointMetadataCollection? metadata,
string? displayName)
{
RequestDelegate = requestDelegate;
Metadata = metadata ?? EndpointMetadataCollection.Empty;
DisplayName = displayName;
}
/// <summary>
/// Gets the informational display name of this endpoint.
/// </summary>
public string? DisplayName { get; }
/// <summary>
/// Gets the collection of metadata associated with this endpoint.
/// </summary>
public EndpointMetadataCollection Metadata { get; }
/// <summary>
/// Gets the delegate used to process requests for the endpoint.
/// </summary>
public RequestDelegate? RequestDelegate { get; }
}
The big picture:
Practical Example
Recently, I’ve stumbled upon IConfigurationRoot.GetDebugView. Basically, it allows you to dump configuration values and reason about how configuration settings are resolved. Personally, I find this a really useful and productive way of reading the component configuration. You can enable a special endpoint to check your configuration by adding the following code to your UseEndpoints
method (as part of Startup.cs).
app.UseEndpoints(endpoints =>
{
endpoints.MapGet("/config", async context =>
{
var config = (Configuration as IConfigurationRoot).GetDebugView();
await context.Response.WriteAsync(config);
});
});
Here is an example output:
NikiforovAll.ConfigurationDebugViewEndpoint
The example above works and everything, but I took a chance and wrapped this functionality into NuGet package - https://github.com/NikiforovAll/ConfigurationDebugViewEndpoint.
It is an example of how to organize code base and apply the technique to make your Startup.cs
more readable and composable. I will guide you through the process.
Our goal is to have an extension method that will allow us to plug /config
endpoint. Something like this:
app.UseEndpoints(endpoints =>
{
endpoints.MapConfigurationDebugView("/config", (options) => options.AllowDevelopmentOnly = true);
});
Let’s start with the ConfigurationDebugViewMiddleware
. We want to write IConfiguration debug view output to Response
.
public class ConfigurationDebugViewMiddleware
{
public ConfigurationDebugViewMiddleware(RequestDelegate next) { }
public async Task InvokeAsync(HttpContext httpContext)
{
var configuration = httpContext.RequestServices.GetService<IConfiguration>();
var config = (configuration as IConfigurationRoot).GetDebugView();
await httpContext.Response.WriteAsync(config);
}
}
The trick is to create EndpointRouteBuilderExtensions.cs
and write a small extension method that will allow us to plug ConfigurationDebugViewMiddleware
.
Generally, we follow the next approach/signature
IEndpointConventionBuilder Map{FeatureToMap}(this IEndpointRouteBuilder endpoints, string pattern = <defaultPattern>, Action<{FeatureOptions}> configure);
The actual implementation is based on the fact that you can create a sub-pipeline using the same abstraction - IApplicationBuilder
that you use for your Startup.cs
pipeline. IEndpointRouteBuilder.CreateApplicationBuilder creates a IApplicationBuilder
that you can use and configure.
For example, here is a definition of an imaginary pipeline that handles SOAP requests:
var pipeline = endpoints.CreateApplicationBuilder()
.UseMiddleware<LoggingMiddleware>()
.UseMiddleware<CachingMiddleware>()
.UseMiddleware<SOAPEndpointMiddleware>()
.Build();
return endpoints.Map(pattern, pipeline)
Complete implementation:
/// <summary>
/// Provides extension methods for <see cref="IEndpointRouteBuilder"/> to add routes.
/// </summary>
public static class EndpointRouteBuilderExtensions
{
/// <summary>
/// Adds a configuration endpoint to the <see cref="IEndpointRouteBuilder"/> with the specified template.
/// </summary>
/// <param name="endpoints">The <see cref="IEndpointRouteBuilder"/> to add endpoint to.</param>
/// <param name="pattern">The URL pattern of the endpoint.</param>
/// <param name="optionsDelegate"></param>
/// <returns>A route for the endpoint.</returns>
public static IEndpointConventionBuilder? MapConfigurationDebugView(
this IEndpointRouteBuilder endpoints,
string pattern = "config",
Action<ConfigurationDebugViewOptions>? optionsDelegate = default)
{
if (endpoints == null)
{
throw new ArgumentNullException(nameof(endpoints));
}
var options = new ConfigurationDebugViewOptions();
optionsDelegate?.Invoke(options);
return MapConfigurationDebugViewCore(endpoints, pattern, options);
}
private static IEndpointConventionBuilder? MapConfigurationDebugViewCore(IEndpointRouteBuilder endpoints, string pattern, ConfigurationDebugViewOptions options)
{
var environment = endpoints.ServiceProvider.GetRequiredService<IHostEnvironment>();
var builder = endpoints.CreateApplicationBuilder();
if (options.AllowDevelopmentOnly && !environment.IsDevelopment())
{
return null;
}
var pipeline = builder
.UseMiddleware<ConfigurationDebugViewMiddleware>()
.Build();
return endpoints.Map(pattern, pipeline);
}
}
Summary
I’ve shared with you a simple but yet useful technique to organize your codebase. It allows you to keep your endpoint-related code nice and clean, developers can easily find extensions by conventions. Do you have other best practices to organize Startup.cs
? Feel free to list them in the comments, I would like to hear from you!
You might want to try the configuration-debug-view NuGet package. https://github.com/NikiforovAll/ConfigurationDebugViewEndpoint.


computer
Gabe's Blog
Home
Tech Trends
Computer Gear
Mobile
Latest Headlining Articles for Each Department
Headline Article 1
Quick higlights and key points.
Summary
Link to full article
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Quick higlights and key points.
Summary
Link to full article
Headline Article 3
Quick higlights and key points.
Summary
Link to full article


#{extends 'main.html' /} #{set title:'PDF Module Sample Application' /}
This application will give you a rough idea of what you can achieve with this Play! module:
#{if !play.mode.isDev()}You must configure this application to run in ${play.mode.DEV}
mode in the application.conf
file to be able to access documentation from the sidebar on this page:
application.mode = dev#{/if}
Select an action to perform:


前開衩扭結洋裝
03040047
TWD.590
顏色
尺寸
S
M
L
數量
-
1
+
加入購物車
*實品顏色依單品照為主
棉 100%
厚薄:薄
彈性:無
素材產地 / 台灣
加工產地 / 台灣
細部說明
O.N.S is all about options, which is why we took our staple polo shirt and upgraded it with slubby linen jersey, making it even lighter for those who prefer their summer style extra-breezy.
O.N.S is all about options, which is why we took our staple polo shirt and upgraded it with slubby linen jersey, making it even lighter for those who prefer their summer style extra-breezy.


--- title: Embark Web Services date: 2017-01-01 00:00:00 Z layout: project type: project subtitle: Web Design | Development background: "/img/projects/embark-web-services/portfolio-1-thumb.png" ---
This site was built for a web design and marketing company looking to merge their creativity and passion with a flat but vibrant feel. I feel that in the end I achieved this with them and they were very pleased with the end result. It was agreed that at this point, they wanted everything straight forward and to the point with easy to recognize call to actions.


This is a super basic example of how to connect to the websocket server.
Note: There is no flash fallback, so make sure you have a modern browser with WebSocket support.


Turkey should scrap Russian missile system or face US sanctions: White House official
WASHINGTON: The United States is very upset about Turkey's purchase of Russian missile defence systems and could impose sanctions on Ankara if it does not "get rid" of them, White House National Security Adviser Robert O'Brien said on Sunday (Nov 10).
"Turkey will feel the impact of those sanctions," O'Brien told CBS's "Face the Nation" in an interview, referring to penalties under the US law known as the Countering America's Adversaries Through Sanctions Act, which he said would pass Congress with "overwhelming" bipartisan support.
His comments came ahead of a visit by Turkish President Tayyip Erdogan to Washington on Nov 13 to meet US President Donald Trump for likely crucial talks as the two NATO allies have been at loggerheads over a range of issues.
One key disagreement is Ankara's purchase of the Russian S-400 anti-aircraft missile system, which Washington says is incompatible with NATO defences and threatens its Lockheed Martin F-35 fighter jets.
Despite threats of US sanctions, Turkey started receiving its first S-400 deliveries in July.
In response, Washington removed Turkey from the F-35 programme, in which Ankara was a manufacturer and buyer. But so far, it has not slapped any sanctions on Ankara.
Turkey has not yet activated the S-400 batteries it received, and Washington still hopes to persuade its ally to "walk away" from the Russian systems.
"There's no place in NATO for the S-400. There's no place in NATO for significant Russian military purchases. That's a message that the president will deliver to him (Erdogan) very clearly when he's here," O'Brien said.
Earlier this month, the head of Turkey's Defence Industry Directorate said a second S-400 delivery to Turkey may be delayed beyond a planned 2020 timeline by talks on technology sharing and joint production.
The S-400 issue is part of a wider range of disagreements between Turkey and the United States.
Washington was incensed by Turkey's offensive into northern Syria against US-allied Kurdish YPG fighters last month. Turkey halted the incursion after the Kurds withdrew from a border region under a US- brokered truce.


TData
- The type of the value that each register contains.
public RegisterSet<TData>
Represents a collection of class Register
s.
The collection is broken into three separate ranges, for the three different types of registers that are available to an LGP program. In a diagram form, the ranges look something like:
+-------+-------------+----------+
| Input | Calculation | Constant |
+-------+-------------+----------+
Input registers are used to store the value of features in an instance from some data set. These input registers will have their value loaded from an instance before evaluating an LGP program on some fitness case (an instance). The number of input registers should be specified to match the number of features in each data set instance that the LGP environment is performing evolution on.
Calculation registers are freely available registers that can be used in an LGP program.
They are initialised with some default value when the register set is initialised. The
default value can be controlled by passing a specific interface DefaultValueProvider
.
Constant registers are used to store a fixed set of constants, that are read-only to an LGP program. The values of the registers will be loaded from constants specified during initialisation of the register set.
class Register
,
interface DefaultValueProvider
| Modifier and Type | Method and Description |
|---|---|
void |
apply(int index,
kotlin.jvm.functions.Function1<? super TData,? extends TData> modifier)
|
RegisterSet<TData> |
copy()
Creates a clone of this register set.
|
TData |
get(int index)
Get the value the of the register at the given index.
|
kotlin.ranges.IntRange |
getCalculationRegisters()
Number of RegisterType.Calculation registers.
|
kotlin.ranges.IntRange |
getConstantRegisters()
Number of RegisterType.Constant registers.
|
int |
getCount()
The total number of registers in this register set.
|
kotlin.ranges.IntRange |
getInputRegisters()
Number of RegisterType.Input registers.
|
void |
overwrite(int index,
TData value)
Forcefully sets the value or the register with the given
index . |
Register<TData> |
register(int index)
Get the
class Register with the given index. |
RegisterType |
registerType(int index)
Determines the type of the register with the given
index . |
void |
reset()
Resets the input and calculation registers to their default values.
|
void |
set(int index,
TData value)
Sets the value of the register at the given index.
|
void |
writeInstance(Sample<TData> data)
Writes an instance from some data set to the register sets input registers.
|
@NotNull kotlin.ranges.IntRange getInputRegisters()
Number of RegisterType.Input registers.
@NotNull kotlin.ranges.IntRange getCalculationRegisters()
Number of RegisterType.Calculation registers.
@NotNull kotlin.ranges.IntRange getConstantRegisters()
Number of RegisterType.Constant registers.
int getCount()
The total number of registers in this register set.
@NotNull Register<TData> register(int index)
Get the class Register
with the given index.
index
- The index of the desired register.RegisterReadException
- When index
is out-of-bounds of the register set.class Register
void apply(int index, @NotNull kotlin.jvm.functions.Function1<? super TData,? extends TData> modifier)
Applies the given modifier
to the value of the class Register
at index
.
index
- The register to modify the value of.modifier
- A function that modifies the value in the register.RegisterReadException
- When index
is out-of-bounds of the register set.modifier
,
class Register
,
index
TData get(int index)
Get the value the of the register at the given index.
index
- The register to read from.RegisterReadException
- When index
is out-of-bounds of the register set.void set(int index, TData value)
Sets the value of the register at the given index.
index
- The register to write to.value
- The value to write to the register.RegisterAccessException
- When the index refers to a constant register.void overwrite(int index, TData value)
Forcefully sets the value or the register with the given index
.
This method has similar functionality to the RegisterSetset method but does not check the type of the register -- allowing constant registers to be overwritten without exception.
index
- The register to write to.value
- The value to write to the register.index
,
RegisterSetsetvoid writeInstance(@NotNull Sample<TData> data)
Writes an instance from some data set to the register sets input registers.
NOTE: The number of input registers of the set must match the number of features in the instance.
data
- An instance to write to the input registers.RegisterWriteRangeException
- When the number of features does not match the number of input registers.@NotNull RegisterType registerType(int index)
Determines the type of the register with the given index
.
index
- The index of the register whose type is desired.index
void reset()
Resets the input and calculation registers to their default values.
@NotNull RegisterSet<TData> copy()
Creates a clone of this register set.


CrimPapers
Articles published on 26 July 2019
On 26 July 2019, 8 new articles were published in 8 journals.
Prevalence of Ptsd, Depression and Anxiety Disorders in Correctional Officers: A Systematic Review by Cheryl Regehr, Mary Carey, Shannon Wagner, Lynn E. Alden, Nicholas Buys, Wayne Corneil, Trina Fyfe, Alex Fraess-Phillips, Elyssa Krutop, Lynda Matthews, Christine Randall, Marc White, Nicole White
Whistleblowing and the Fight Against Corruption and Fraud in Nigeria: Perceptions of Anti-Corruption Agents (acas)
Lone Wolf Terrorism Through A Gendered Lens: Men Turning Violent Or Violent Men Behaving Violently?
Uses and Consequences of A Criminal Conviction: Going on the Record of An Offender by Edward Elliott
“One of Us”: the Neomelodic Music Industry As A Camorra-Mediated Space of Subaltern Publicity in Contemporary Naples by Salvatore Giusto
Perceptions of Service Provided By South African Police Service Community Service Centres by Roger B Mason, Ndabazinhle Ngobese, Mandusha Maharaj
Legal Limits to Prioritisation in Policing – Challenging the Impact of Centralisation by Lena Landström, Niklas Eklund, Markus Naarttijärvi
Attitudes Towards Outgroups Before and After Terror Attacks by Jasper van Assche, Kim Dierckx


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lingvo.core.attention module¶
Attention models.
-
lingvo.core.attention.
_ApplyAttentionDropout
(params, x, step_state=None, prng_seed=None)[source]¶ Apply attention dropout according to the given parameters.
If
params.atten_dropout_deterministic
is set to True, the dropout will be fully deterministic (requiresstep_state
andprng_seed
).Parameters: - params – The parameters of attention layer.
- x – A float Tensor on which to apply dropout.
- step_state – (Optional) A
NestedMap
withglobal_step
andtime_step
. Required for deterministic dropout. - prng_seed – (Optional) An int seed for pseudo random number generator. Required for deterministic dropout.
Returns: A Tensor with the same shape as
x
.
-
class
lingvo.core.attention.
BaseAttentionLayer
(*args, **kwargs)[source]¶ Bases:
lingvo.core.base_layer.LayerBase
A base class for all attention layers.
-
InitForSourcePacked
(theta, source_vecs, source_contexts, source_padding, source_segment_id=None)[source]¶ Initialize attention for the given source vectors.
Must set
_source_init_done
to True in the function.Note:
source_segment_id
, if present, should always have the same shape assource_padding
.Parameters: - theta – A
NestedMap
object containing weights’ values of this layer and its children layers. - source_vecs – A single tensor of shape [time, batch_size, source_dim].
- source_contexts – A single tensor of shape [time, batch_size, some_dim].
- source_padding – A tensor of shape [time, batch_size].
- source_segment_id – A tensor of shape [time, batch_size]. source_segment_id is not None for packed inputs where one training example may pack multiple sequences.
Returns: A tuple (concated_source_vecs, concated_source_contexts, source_padding, source_segment_id), where
concated_source_vecs
is a tensor of shape [time, batch_size, hidden_dim],concated_source_contexts
is a tensor of shape [batch_size, time, some_dim],source_padding
is a tensor of shape [time, batch_size],source_segment_id
is a tensor of shape [time, batch_size].Note the mismatch between
concated_source_vecs
andconcated_source_contexts
. Inconcated_source_vecs
, time is the first dim, while it is the second dim inconcated_source_contexts
.- theta – A
-
ComputeContextVectorWithSource
(theta, concated_source_vecs, concated_source_contexts, source_padding, source_segment_id, query_vec, attention_state=None, per_step_source_padding=None, step_state=None, query_segment_id=None)[source]¶ Computes the context vector given the current query output.
Note:
concated_source_vecs
are the vectors that are used to compute the attention score between thequery_vec
and eachconcated_source_vec
. Theconcated_source_contexts
are the vectors that compose the result. The attention context vector is computed as a weighted average of theconcated_source_contexts
, using the scores that were computed usingconcated_source_vecs
.Parameters: - theta – A
NestedMap
object containing weights’ values of this layer and its children layers. - concated_source_vecs – Concated source vectors with shape [time, batch_size, hidden_dim].
- concated_source_contexts – Concated source contexts with shape [ batch_size, time, context_dim].
- source_padding – Source padding with shape [time, batch_size].
- source_segment_id – Source segment ids with shape [time, batch_size].
- query_vec – a tensor of shape [batch_size, query_dim].
- attention_state – previous attention state.
- per_step_source_padding – Source sequence padding to apply at this step. If not None, it should have shape [target_batch_size, source_seq_length].
- step_state – A
NestedMap
containingglobal_step
andtime_step
. Required for deterministic dropout. - query_segment_id – a tensor of shape [batch_size].
Returns: - A tuple of 3 elements.
- The attention context vector:
[batch_size, context_dim]
- The attention probability vector:
[batch_size, time]
- The new attention mechanism state:
possibly nested tuple of tensors with dimensions [target_batch, …]
- theta – A
-
ComputeContextVector
(theta, query_vec, attention_state=None, per_step_source_padding=None, step_state=None, query_segment_id=None)[source]¶ Computes the context vector given the current query output.
Unlike
ComputeContextVectorWithSource
which explicitly asks for the source tensors (concated_source_vecs, concated_source_contexts, source_padding),ComputeContextVector
uses the class’ internal variables.Parameters: - theta – A
NestedMap
object containing weights’ values of this layer and its children layers. - query_vec – a tensor of shape [batch_size, query_dim].
- attention_state – previous attention state.
- per_step_source_padding – Source sequence padding to apply at this step. If not None, it should be of shape [target_batch_size, source_seq_length].
- step_state – A
NestedMap
containingglobal_step
andtime_step
. Required for deterministic dropout. - query_segment_id – a tensor of shape [batch_size].
Returns: A tuple of 3 elements.
- The attention context vector.
- The attention probability vector.
- The new attention mechanism state: possibly nested tuple of tensors with dimensions [target_batch, …]
- theta – A
-
GetInitializationSourceState
()[source]¶ Gets the attention initialization state.
The base class only preserves the
concated_source_vecs
,concated_source_contexts
andsource_padding
. If subclasses use more state than this and need to interact with inference code that must fetch and reload state, this andSetInitializationSourceState
must be overridden.Returns: A NestedMap
of Tensors that can be preserved and reset viaSetInitializationSourceState()
at a later point. This allows, for example, for attention computations to span session runs.
-
SetInitializationSourceState
(new_init_state)[source]¶ Sets the attention initialization state.
Parameters: - new_init_state – A
NestedMap
matching what was returned from - which will return this layer to that (GetInitializationSourceState,) –
- state. (initialization) –
- new_init_state – A
-
_PaddedSoftmax
(logits, padding)[source]¶ Performs a softmax as if padding were applied after exponentiation.
The default implementation uses numerical techniques to approximate this with a standard
tf.nn.softmax
(using large negative logits for padded values). It defers to aDefun
that may be replaced on low-range implementations with a version that is numerically correct.Parameters: - logits – Logits.
- padding – Padding (must be the same shape as logits).
Returns: Result of the softmax.
-
_UpdatePaddingWithPackedInputMask
(padding, source_segment_ids, query_segment_ids)[source]¶ Creates an attention mask based on source and query segment ids.
This creates a mask that removes invalid attention, where the query vector might assign some weight to neighboring sequences in a packed input example. Assumes
n = target_batch // source_batch
.Parameters: - padding – Padding for logits, a tensor of shape [time, n, source_batch].
- source_segment_ids – a tensor of shape [time, source_batch].
- query_segment_ids – a tensor of shape [target_batch].
Returns: Logits with mask applied.
-
-
class
lingvo.core.attention.
AdditiveAttention
(*args, **kwargs)[source]¶ Bases:
lingvo.core.attention.BaseAttentionLayer
Implements additive attention (also known as “Bahdanau Attention”), as described in:
Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio. “Neural Machine Translation by Jointly Learning to Align and Translate.” ICLR 2015. https://arxiv.org/abs/1409.0473
-
classmethod
Params
()[source]¶ Params for this
AdditiveAttention
class.
-
PackSource
(theta, source_vecs, source_contexts, source_padding, source_segment_id=None)[source]¶ Packs source vectors. Does not change attention state.
Parameters: - theta – A
NestedMap
object containing weights’ values of this layer and its children layers. - source_vecs – A single tensor of shape [time, batch_size, source_dim].
- source_contexts – A single tensor of shape [time, batch_size, some_dim].
- source_padding – A tensor of shape [time, batch_size].
- source_segment_id – A tensor of shape [time, batch_size].
Returns: Concated source vectors, concated source contexts, and source paddings.
- theta – A
-
InitForSourcePacked
(theta, source_vecs, source_contexts, source_padding, source_segment_id=None)[source]¶ Initialize attention for the given source vectors.
Parameters: - theta – A
NestedMap
object containing weights’ values of this layer and its children layers. - source_vecs – A single tensor of shape [time, batch_size, source_dim].
- source_contexts – A single tensor of shape [time, batch_size, some_dim].
- source_padding – A tensor of shape [time, batch_size].
- source_segment_id – A tensor of shape [time, batch_size].
Returns: Concated source vectors, concated source contexts, and source paddings.
- theta – A
-
ComputeContextVectorWithSource
(theta, concated_source_vecs, concated_source_contexts, source_padding, source_segment_id, query_vec, attention_state=None, per_step_source_padding=None, step_state=None, query_segment_id=None)[source]¶ Computes the context vector given the current query output.
Note:
concated_source_vecs
are the vectors that are used to compute the attention score between thequery_vec
and eachconcated_source_vec
. Theconcated_source_contexts
are the vectors that compose the result. The attention context vector is computed as a weighted average of theconcated_source_contexts
, using the scores that were computed usingconcated_source_vecs
.Parameters: - theta – A
NestedMap
object containing weights’ values of this layer and its children layers. - concated_source_vecs – Concated source vectors with shape [time, batch_size, hidden_dim].
- concated_source_contexts – Concated source contexts with shape o [batch_size, time, context_dim].
- source_padding – Source padding with shape [time, batch_size].
- source_segment_id – Tensor of source segment ids, with shape [time, batch_size].
- query_vec – a tensor of shape [batch_size, query_dim].
- attention_state – previous attention state. It is not used in
AdditiveAttention
, and is simply passed through. - per_step_source_padding – Source sequence padding to apply at this step. If not None, it should be of shape [target_batch_size, source_seq_length].
- step_state – A
NestedMap
containingglobal_step
andtime_step
. Required for deterministic dropout. - query_segment_id – a tensor of shape [batch_size]
Returns: - A tuple of 3 elements.
- The attention context vector:
[batch_size, context_dim]
- The attention probability vector:
[batch_size, time]
- The new attention mechanism state:
possibly nested tuple of tensors with dimensions [target_batch, …]
- theta – A
-
classmethod
-
class
lingvo.core.attention.
DotProductAttention
(*args, **kwargs)[source]¶ Bases:
lingvo.core.attention.BaseAttentionLayer
Implements dot-product attention (also known as “Luong Attention”) as described in:
Minh-Thang Luong, Hieu Pham, Christopher D. Manning. “Effective Approaches to Attention-based Neural Machine Translation.” EMNLP 2015. https://arxiv.org/abs/1508.04025
-
classmethod
Params
()[source]¶ Params for
DotProductAttention
.
-
PackSource
(theta, source_vecs, source_contexts, source_padding, source_segment_id=None)[source]¶ Packs source vectors. Does not change attention state.
Parameters: - theta – A
NestedMap
object containing weights’ values of this layer and its children layers. - source_vecs – A tensor of shape [time, source_batch, source_dim].
- source_contexts – A tensor of shape [time, source_batch, context_dim].
- source_padding – A tensor of shape [time, source_batch].
- source_segment_id – A tensor of shape [time, source_batch].
Returns: A tuple (concated_source_vecs, concated_source_contexts, source_padding) where
concated_source_vecs
is a tensor of shape [time, batch_size, hidden_dim],concated_source_contexts
is a tensor of shape [batch_size, time, some_dim] andsource_padding
is a tensor of shape [time, batch_size].Note the mismatch between
concated_source_vecs
andconcated_source_contexts
. Inconcated_source_vecs
, time is the first dim, while it is the second dim inconcated_source_contexts
.- theta – A
-
InitForSourcePacked
(theta, source_vecs, source_contexts, source_padding, source_segment_id=None)[source]¶ Initialize attention for the given source vectors.
Parameters: - theta – A
NestedMap
object containing weights’ values of this layer and its children layers. - source_vecs – A tensor of shape [time, source_batch, source_dim].
- source_contexts – A tensor of shape [time, source_batch, context_dim].
- source_padding – A tensor of shape [time, source_batch].
- source_segment_id – A tensor of shape [time, source_batch].
Returns: A tuple (concated_source_vecs, concated_source_contexts, source_padding), where concated_source_vecs is a tensor of shape [time, batch_size, hidden_dim], concated_source_contexts is a tensor of shape [batch_size, time, some_dim] and source_padding is a tensor of shape [time, batch_size]. Note the mismatch between concated_source_vecs and concated_source_contexts. In concated_source_vecs, time is the first dim, while it is the second dim in concated_source_contexts.
- theta – A
-
ComputeContextVectorWithSource
(theta, concated_source_vecs, concated_source_contexts, source_padding, source_segment_id, query_vec, attention_state=None, per_step_source_padding=None, step_state=None, query_segment_id=None)[source]¶ Computes the context vector given the current query output.
Parameters: - theta – A
NestedMap
object containing weights’ values of this layer and its children layers. - concated_source_vecs – Concated source vectors with shape [time, source_batch, hidden_dim].
- concated_source_contexts – Concated source contexts with shape [ source_batch, time, context_dim].
- source_padding – Source padding with shape [time, source_batch].
- source_segment_id – Source segment id with shape [time, source_batch].
- query_vec – a tensor of shape [target_batch, query_dim], where target_batch = n * source_batch (e.g., n = num_hyps_per_beam in beamsearch). Along the target_batch dimension, there are n groups of consecutive rows, each group containing source_batch rows.
- attention_state – previous attention state. It is not used in AdditiveAttention, and is simply passed through.
- per_step_source_padding – Source sequence padding to apply at this step. If not None, it should be of shape [target_batch, source_seq_length].
- step_state – A NestedMap containing ‘global_step’ and ‘time_step’. Required for deterministic dropout.
- query_segment_id – Query segment id with shape [target_batch].
Note: concated_source_vecs are the vectors that are used to compute the attention score between the query_vec and each concated_source_vec. The concated_source_contexts are the vectors that compose the result. The attention context vector is computed as a weighted average of the concated_source_contexts, using the scores that were computed using concated_source_vecs.
Returns: - A tuple of 3 elements.
- The attention context vector:
- [batch_size, context_dim]
- The attention probability vector:
- [batch_size, time]
- The new attention mechanism state:
- possibly nested tuple of tensors with dimensions [target_batch, …]
- theta – A
-
classmethod
-
class
lingvo.core.attention.
MultiHeadedAttention
(*args, **kwargs)[source]¶ Bases:
lingvo.core.attention.BaseAttentionLayer
,lingvo.core.quant_utils.QuantizableLayer
Attention with multiple attention heads.
Conceptually, the algorithm works as follows:
- Source vectors (attention keys) are first projected to vectors of dim p.hidden_dim.
- Query vectors are projected to vectors of dim p.hidden_dim as well.
- Context vectors (attention values) are not projected.
- Source vectors, query vectors and context vectors are all split into p.num_attention_heads chunks.
- The inner atten mechanism is computed separately on each of the chunks.
- Attention contexts from each of the chunk are concatenated to form the final context.
- Attention probs from each of the chunk are averaged to form the final attention prob.
-
InitForSourcePacked
(theta, source_vecs, source_contexts, source_padding, source_segment_id=None)[source]¶ Initialize attention for the given source vectors.
Parameters: - theta – A
NestedMap
object containing weights’ values of this layer and its children layers. - source_vecs – A tensor of shape [time, source_batch, source_dim].
- source_contexts – A tensor of shape [time, source_batch, context_dim].
- source_padding – A tensor of shape [time, source_batch].
- source_segment_id – A tensor of shape [time, source_batch].
Returns: (concated_source_vecs, concated_source_contexts, source_padding, source_segment_id) tuple where concated_source_vecs is a tensor of shape [source_seq_len, batch_size * num_heads, orig_source_dim / num_heads], concated_source_contexts is a tensor of shape [source_batch_size * num_heads, source_seq_len, orig_context_dim / num_heads], source_padding is a tensor of shape [source_seq_len, batch_size * num_heads] and source_segment_id is a tensor of shape [source_seq_len, batch_size * num_heads].
- theta – A
-
PackSource
(**kwargs)¶
-
ExtendSourcePacked
(**kwargs)¶
-
ZeroAttentionState
(**kwargs)¶
-
ComputeContextVectorWithSource
(**kwargs)¶
-
ComputeContextVectorWithAttenProbs
(**kwargs)¶
-
ComputeContextVectorWithCachedSource
(**kwargs)¶
-
class
lingvo.core.attention.
LocationSensitiveAttention
(*args, **kwargs)[source]¶ Bases:
lingvo.core.attention.BaseAttentionLayer
An attention that also takes into account previously attended locations.
See section 2.2 of this paper for a description of this technique: http://papers.nips.cc/paper/5847-attention-based-models-for-speech-recognition.pdf
-
InitForSourcePacked
(theta, source_vecs, source_contexts, source_padding, source_segment_id=None)[source]¶ Initialize attention for the given source vectors.
Parameters: - theta – A
NestedMap
object containing weights’ values of this layer and its children layers. - source_vecs – A single tensor of shape [time, batch_size, source_dim].
- source_contexts – A single tensor of shape [time, batch_size, some_dim].
- source_padding – A tensor of shape [time, batch_size].
- source_segment_id – A tensor of shape [time, batch_size].
Returns: Concated source vectors, concated source contexts, and source paddings.
- theta – A
-
ComputeContextVectorWithSource
(theta, concated_source_vecs, concated_source_contexts, source_padding, source_segment_id, query_vec, attention_state=None, per_step_source_padding=None, step_state=None, query_segment_id=None)[source]¶ Computes the context vector given the current query output.
Parameters: - theta – A
NestedMap
object containing weights’ values of this layer and its children layers. - concated_source_vecs – Concated source vectors with shape [time, batch_size, hidden_dim].
- concated_source_contexts – Concated source contexts with shape [ batch_size, time, context_dim].
- source_padding – Source padding with shape [time, batch_size].
- source_segment_id – Source segment id with shape [time, batch_size].
- query_vec – a tensor of shape [batch_size, query_dim].
- attention_state –
If
params().location_features == ['PREV_PROBS', 'CUMULATIVE_PROBS']
, thenattention_state
is a tensor of shape [batch_size, src_len * 2].- attention_state[:, :, 0] contains previous attention probabilities
- attention_state[:, :, 1] contains a sum over previous timesteps of attention probabilities.
- per_step_source_padding – Source sequence padding to apply at this step. If not None, it should be of shape [target_batch_size, source_seq_length].
- step_state – A
NestedMap
containing ‘global_step’ and ‘time_step’. Required for deterministic dropout. - query_segment_id – Query segment id with shape [batch_size].
Note: concated_source_vecs are the vectors that are used to compute the attention score between the query_vec and each concated_source_vec. The concated_source_contexts are the vectors that compose the result. The attention context vector is computed as a weighted average of the concated_source_contexts, using the scores that were computed using concated_source_vecs.
Returns: - A tuple of 3 elements.
- The attention context vector:
- [batch_size, context_dim]
- The attention probability vector:
- [batch_size, time]
- The new attention mechanism state:
- possibly nested tuple of tensors with dimensions [target_batch, …]
- theta – A
-
-
lingvo.core.attention.
MergeSourcePaddingWithPerStepSourcePadding
(source_padding, per_step_source_padding, tb)[source]¶ Merges source padding with per-step source padding.
Parameters: - source_padding – [sl, sb].
- per_step_source_padding – [tb, sl].
- tb – target batch size.
Returns: A tensor of shape [tb, sl].
-
class
lingvo.core.attention.
MonotonicAttention
(*args, **kwargs)[source]¶ Bases:
lingvo.core.attention.BaseAttentionLayer
An attention mechanism which enforces monotonic alignments.
This layer implements the monotonic attention mechanism described in Online and Linear-Time Attention by Enforcing Mononotonic Alignments (https://arxiv.org/abs/1704.00784). It is used in exactly the same way as AdditiveAttention, but both the attention distribution and the energy function are different.
Rather than using a softmax, this mechanism feeds the attention energy into a (hard or soft) sigmoid and treats the output as Bernoulli probabilities representing the probability of attending to a given entry in the input sequence, processed from left-to-right. Based on this interpretation, the resulting distribution over input sequence entries is computed with a dynamic program. The intended use is to train with soft sigmoids according to the expected output (setting param hard_sigmoid=False), then use hard sigmoids at test time to allow for online and linear-time decoding. To encourge the train and test-time behavior to be similar, noise can optionally be added to the sigmoid activations during training (param pre_sigmoid_noise). For the energy function, rather than computing:
E = dot(v, tanh(dot(W, query) + dot(W, encoder_states)))
it computes:
E = dot(g*v/||v||, tanh(dot(W, query) + dot(W, encoder_states) + b)) + r
where g and r are scalars and b is a vector, and ||v|| is the L2 norm of v. instead. These modifications address the fact that the sigmoids in the monotonic attention mechanism are sensitive to offset and a bit harder to train compared to the softmax function. It can be helpful to initialize the energy bias scalar r to a negative value (param hidden_bias_init).
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PackSource
(theta, source_vecs, source_contexts, source_padding, source_segment_id=None)[source]¶ Packs source vectors. Does not change attention state.
Parameters: - theta – A
NestedMap
object containing weights’ values of this layer and its children layers. - source_vecs – A single tensor of shape [time, batch_size, source_dim].
- source_contexts – A single tensor of shape [time, batch_size, some_dim].
- source_padding – A tensor of shape [time, batch_size].
- source_segment_id – A tensor of shape [time, batch_size].
Returns: Concated source vectors, concated source contexts, and source paddings.
- theta – A
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InitForSourcePacked
(theta, source_vecs, source_contexts, source_padding, source_segment_id=None)[source]¶ Initialize attention for the given source vectors.
Parameters: - theta – A
NestedMap
object containing weights’ values of this layer and its children layers. - source_vecs – A single tensor of shape [time, batch_size, source_dim].
- source_contexts – A single tensor of shape [time, batch_size, some_dim].
- source_padding – A tensor of shape [time, batch_size].
- source_segment_id – A tensor of shape [time, batch_size].
Returns: Concated source vectors, concated source contexts, and source paddings.
- theta – A
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ComputeProbabilities
(theta, concated_source_vecs, merged_source_padding, query_vec, attention_state)[source]¶ Computes probabilities of emissions.
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ComputeContextVectorWithSource
(theta, concated_source_vecs, concated_source_contexts, source_padding, source_segment_id, query_vec, attention_state, per_step_source_padding=None, step_state=None, query_segment_id=None)[source]¶ Computes the context vector given the current query output.
Parameters: - theta – A
NestedMap
object containing weights’ values of this layer and its children layers. - concated_source_vecs – Concated source vectors with shape [time, batch_size, hidden_dim].
- concated_source_contexts – Concated source contexts with shape [ batch_size, time, context_dim].
- source_padding – Source padding with shape [time, batch_size].
- source_segment_id – Source segment id with shape [time, batch_size].
- query_vec – a tensor of shape [batch_size, query_dim].
- attention_state – The attention probs computed at the previous timestep.
- per_step_source_padding – Source sequence padding to apply at this step. If not None, it should be of shape [target_batch_size, source_seq_length].
- step_state – A NestedMap containing ‘global_step’ and ‘time_step’. Required for deterministic dropout.
- query_segment_id – a tensor of shape [batch_size].
Note: concated_source_vecs are the vectors that are used to compute the attention score between the query_vec and each concated_source_vec. The concated_source_contexts are the vectors that compose the result. The attention context vector is computed as a weighted average of the concated_source_contexts, using the scores that were computed using concated_source_vecs.
Returns: - A tuple of 3 elements.
- The attention context vector:
- [batch_size, context_dim]
- The attention probability vector:
- [batch_size, time]
- The attention probability vector:
- (again, to be interpreted as state).
- theta – A
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class
lingvo.core.attention.
GmmMonotonicAttention
(*args, **kwargs)[source]¶ Bases:
lingvo.core.attention.BaseAttentionLayer
A GMM-based monotonic attention module.
Based on “Generating Sequences With Recurrent Neural Networks” by Alex Graves. Eq [46-51] in https://arxiv.org/abs/1308.0850.
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InitForSourcePacked
(theta, source_vecs, source_contexts, source_padding, source_segment_id=None)[source]¶ Initialize attention for the given source vectors.
Parameters: - theta – A
NestedMap
object containing weights’ values of this layer and its children layers. - source_vecs – A single tensor of shape [time, batch_size, source_dim].
- source_contexts – A single tensor of shape [time, batch_size, some_dim].
- source_padding – A tensor of shape [time, batch_size].
- source_segment_id – A tensor of shape [time, batch_size].
Returns: Concated source vectors, concated source contexts, source paddings and source_segment_id.
- theta – A
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ComputeContextVectorWithSource
(theta, concated_source_vecs, concated_source_contexts, source_padding, source_segment_id, query_vec, attention_state, per_step_source_padding=None, step_state=None, query_segment_id=None)[source]¶ Computes the context vector given the current query output.
Parameters: - theta – A
NestedMap
object containing weights’ values of this layer and its children layers. - concated_source_vecs – Concated source vectors with shape [time, batch_size, hidden_dim].
- concated_source_contexts – Concated source contexts with shape [ batch_size, time, context_dim].
- source_padding – Source padding with shape [time, batch_size].
- source_segment_id – Tensor of source segment ids, with shape [time, batch_size].
- query_vec – a tensor of shape [batch_size, query_dim].
- attention_state –
previous attention state, a tensor of shape [batch_size, num_mixtures, 4].
- attention_state[:, :, 0] contains previous location
- attention_state[:, :, 1] contains previous offset.
- attention_state[:, :, 2] contains previous variance.
- attention_state[:, :, 3] contains previous prior.
- per_step_source_padding – Source sequence padding to apply at this step. If not None, it should be of shape [target_batch_size, source_seq_length].
- step_state – A NestedMap containing ‘global_step’ and ‘time_step’. Required for deterministic dropout.
- query_segment_id – a tensor of shape [batch_size]
Note: concated_source_vecs are the vectors that are used to compute the attention score between the query_vec and each concated_source_vec. The concated_source_contexts are the vectors that compose the result. The attention context vector is computed as a weighted average of the concated_source_contexts, using the scores that were computed using concated_source_vecs.
Returns: - A tuple of 3 elements.
- The attention context vector:
- [batch_size, context_dim]
- The attention probability vector:
- [batch_size, time]
- The new attention state vector:
- possibly nested tuple of tensors with dimensions [target_batch, …]
- theta – A
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