Interface IAsyncSynchronized<T>
Allows its protected object to be accessed only after a synchronization begins. Its methods automatically complete synchronizations or provide some means to do so. They properly handle exceptions thrown by starts of synchronization or users' operations.
Various asynchronous operations are supported. The protected object is non-null.
Assembly: InSync.dll
Syntax
public interface IAsyncSynchronized<T>
where T : class
Type Parameters
| Name |
Description |
| T |
The type of the protected object.
|
Methods
LockAsync()
Asynchronously locks and returns a guard to allow access of the protected object and unlocking.
Declaration
Task<GuardedValue<T>> LockAsync()
Returns
Exceptions
LockAsync(CancellationToken)
Asynchronously locks and returns a guard to allow access of the protected object and unlocking.
Declaration
Task<GuardedValue<T>> LockAsync(CancellationToken cancellationToken)
Parameters
| Type |
Name |
Description |
| CancellationToken |
cancellationToken |
|
Returns
Exceptions
WithLockAsync(Action<T>)
Asynchronously locks, performs the action on the captured context or task scheduler (if any) then unlocks. If the action throws an exception, the lock is released automatically.
Declaration
Task WithLockAsync(Action<T> action)
Parameters
| Type |
Name |
Description |
| Action<T> |
action |
The protected object is supplied as the argument of the action
|
Returns
Exceptions
WithLockAsync(Action<T>, CancellationToken)
Asynchronously locks, performs the action on the captured context or task scheduler (if any) then unlocks. If the function throws an exception, the lock is released automatically.
Declaration
Task WithLockAsync(Action<T> action, CancellationToken cancellationToken)
Parameters
| Type |
Name |
Description |
| Action<T> |
action |
The protected object is supplied as the argument of the action
|
| CancellationToken |
cancellationToken |
|
Returns
Exceptions
WithLockAsync(Action<T>, CancellationToken, Boolean)
Asynchronously locks, synchronously performs the action then unlocks. If the function throws an exception, the lock is released automatically.
Declaration
Task WithLockAsync(Action<T> action, CancellationToken cancellationToken, bool onCapturedContext)
Parameters
| Type |
Name |
Description |
| Action<T> |
action |
The protected object is supplied as the argument of the action
|
| CancellationToken |
cancellationToken |
|
| System.Boolean |
onCapturedContext |
true to perform the action on the original context or task scheduler if there is a context.
|
Returns
Exceptions
WithLockAsync(Action<T>, Boolean)
Asynchronously locks, performs the action then unlocks. If the action throws an exception, the lock is released automatically.
Declaration
Task WithLockAsync(Action<T> action, bool onCapturedContext)
Parameters
| Type |
Name |
Description |
| Action<T> |
action |
The protected object is supplied as the argument of the action
|
| System.Boolean |
onCapturedContext |
true to perform the action on the original context or task scheduler if there is a context.
|
Returns
Exceptions
WithLockAsync(Func<T, Task>)
Asynchronously locks, performs the action on the captured context or task scheduler (if any) then unlocks. If the action throws an exception, the lock is released automatically.
Declaration
Task WithLockAsync(Func<T, Task> asyncAction)
Parameters
| Type |
Name |
Description |
| Func<T, Task> |
asyncAction |
The protected object is supplied as the argument of the action
|
Returns
Exceptions
WithLockAsync(Func<T, Task>, CancellationToken)
Asynchronously locks, performs the action on the captured context or task scheduler (if any) then unlocks. If the action throws an exception, the lock is released automatically.
Declaration
Task WithLockAsync(Func<T, Task> asyncAction, CancellationToken cancellationToken)
Parameters
| Type |
Name |
Description |
| Func<T, Task> |
asyncAction |
The protected object is supplied as the argument of the action
|
| CancellationToken |
cancellationToken |
|
Returns
Exceptions
WithLockAsync(Func<T, Task>, CancellationToken, Boolean)
Asynchronously locks, performs the action then unlocks. If the action throws an exception, the lock is released automatically.
Declaration
Task WithLockAsync(Func<T, Task> asyncAction, CancellationToken cancellationToken, bool onCapturedContext)
Parameters
| Type |
Name |
Description |
| Func<T, Task> |
asyncAction |
The protected object is supplied as the argument of the action
|
| CancellationToken |
cancellationToken |
|
| System.Boolean |
onCapturedContext |
true to perform the action on the original context or task scheduler if there is a context.
|
Returns
Exceptions
WithLockAsync(Func<T, Task>, Boolean)
Asynchronously locks, performs the action then unlocks. If the action throws an exception, the lock is released automatically.
Declaration
Task WithLockAsync(Func<T, Task> asyncAction, bool onCapturedContext)
Parameters
| Type |
Name |
Description |
| Func<T, Task> |
asyncAction |
The protected object is supplied as the argument of the action
|
| System.Boolean |
onCapturedContext |
true to perform the action on the original context or task scheduler if there is a context.
|
Returns
Exceptions
WithLockAsync<TResult>(Func<T, TResult>)
Asynchronously locks, calls the function on the captured context or task scheduler (if any) then unlocks. The returned value from the function is returned. If the function throws an exception, the lock is released automatically.
Declaration
Task<TResult> WithLockAsync<TResult>(Func<T, TResult> func)
Parameters
| Type |
Name |
Description |
| Func<T, TResult> |
func |
The protected object is supplied as the argument of the function. The returned value is returned by this method.
|
Returns
| Type |
Description |
| Task<TResult> |
|
Type Parameters
Exceptions
WithLockAsync<TResult>(Func<T, TResult>, CancellationToken)
Asynchronously locks, calls the function on the captured context or task scheduler (if any) then unlocks. The returned value from the function is returned. If the function throws an exception, the lock is released automatically.
Declaration
Task<TResult> WithLockAsync<TResult>(Func<T, TResult> func, CancellationToken cancellationToken)
Parameters
| Type |
Name |
Description |
| Func<T, TResult> |
func |
The protected object is supplied as the argument of the function. The returned value is returned by this method.
|
| CancellationToken |
cancellationToken |
|
Returns
| Type |
Description |
| Task<TResult> |
|
Type Parameters
Exceptions
WithLockAsync<TResult>(Func<T, TResult>, CancellationToken, Boolean)
Asynchronously locks, synchronously calls the function then unlocks. The returned value from the function is returned. If the function throws an exception, the lock is released automatically.
Declaration
Task<TResult> WithLockAsync<TResult>(Func<T, TResult> func, CancellationToken cancellationToken, bool onCapturedContext)
Parameters
| Type |
Name |
Description |
| Func<T, TResult> |
func |
The protected object is supplied as the argument of the function. The returned value is returned by this method.
|
| CancellationToken |
cancellationToken |
|
| System.Boolean |
onCapturedContext |
true to call the function on the original context or task scheduler if there is a context.
|
Returns
| Type |
Description |
| Task<TResult> |
|
Type Parameters
Exceptions
WithLockAsync<TResult>(Func<T, TResult>, Boolean)
Asynchronously locks, calls the function then unlocks. The returned value from the function is returned. If the function throws an exception, the lock is released automatically.
Declaration
Task<TResult> WithLockAsync<TResult>(Func<T, TResult> func, bool onCapturedContext)
Parameters
| Type |
Name |
Description |
| Func<T, TResult> |
func |
The protected object is supplied as the argument of the function. The returned value is returned by this method.
|
| System.Boolean |
onCapturedContext |
true to calls the function on the original context or task scheduler if there is a context.
|
Returns
| Type |
Description |
| Task<TResult> |
|
Type Parameters
Exceptions
WithLockAsync<TResult>(Func<T, Task<TResult>>)
Asynchronously locks, calls the function on the captured context or task scheduler (if any) then unlocks. The returned value from the function is returned. If the function throws an exception, the lock is released automatically.
Declaration
Task<TResult> WithLockAsync<TResult>(Func<T, Task<TResult>> asyncFunc)
Parameters
| Type |
Name |
Description |
| Func<T, Task<TResult>> |
asyncFunc |
The protected object is supplied as the argument of the function. The returned value is returned by this method.
|
Returns
| Type |
Description |
| Task<TResult> |
|
Type Parameters
Exceptions
WithLockAsync<TResult>(Func<T, Task<TResult>>, CancellationToken)
Asynchronously locks, calls the function on the captured context or task scheduler (if any) then unlocks. The returned value from the function is returned. If the function throws an exception, the lock is released automatically.
Declaration
Task<TResult> WithLockAsync<TResult>(Func<T, Task<TResult>> asyncFunc, CancellationToken cancellationToken)
Parameters
| Type |
Name |
Description |
| Func<T, Task<TResult>> |
asyncFunc |
The protected object is supplied as the argument of the function. The returned value is returned by this method.
|
| CancellationToken |
cancellationToken |
|
Returns
| Type |
Description |
| Task<TResult> |
|
Type Parameters
Exceptions
WithLockAsync<TResult>(Func<T, Task<TResult>>, CancellationToken, Boolean)
Asynchronously locks, calls the function then unlocks. The returned value from the function is returned. If the function throws an exception, the lock is released automatically.
Declaration
Task<TResult> WithLockAsync<TResult>(Func<T, Task<TResult>> asyncFunc, CancellationToken cancellationToken, bool onCapturedContext)
Parameters
| Type |
Name |
Description |
| Func<T, Task<TResult>> |
asyncFunc |
The protected object is supplied as the argument of the function. The returned value is returned by this method.
|
| CancellationToken |
cancellationToken |
|
| System.Boolean |
onCapturedContext |
true to call the function on the original context or task scheduler if there is a context.
|
Returns
| Type |
Description |
| Task<TResult> |
|
Type Parameters
Exceptions
WithLockAsync<TResult>(Func<T, Task<TResult>>, Boolean)
Asynchronously locks, calls the function then unlocks. The returned value from the function is returned. If the function throws an exception, the lock is released automatically.
Declaration
Task<TResult> WithLockAsync<TResult>(Func<T, Task<TResult>> asyncFunc, bool onCapturedContext)
Parameters
| Type |
Name |
Description |
| Func<T, Task<TResult>> |
asyncFunc |
The protected object is supplied as the argument of the function. The returned value is returned by this method.
|
| System.Boolean |
onCapturedContext |
true to call the function on the original context or task scheduler if there is a context.
|
Returns
| Type |
Description |
| Task<TResult> |
|
Type Parameters
Exceptions


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Ori Gilad is a UCSD Computer Science Student. He has interned for several companies working as a frontend, backend, and full stack developer..


You can access this dialog via the menu entry Analysis \ Tools for Analysis \ N-Gram.
This dialog shows the list of the most frequent n-grams in a document.
In the upper part of the Selection group you can choose between histograms, digrams, trigrams annd n-grams (4<=N<=16). The number in brackets indicates how many n-grams of the respective type are contained in the document.
In the lower part of the Selection group you can configure the upper limit for the amount of n-grams shown in the list (up to a static limit of 5000).
The button Compute list computes and sorts the n-gram list by frequency and displays it in a table:
The actual n-gram list can be transferred into the CrypTool main window via the button Save list.
By clicking the Text options button you can specify which alphabet is used during the analysis. N-grams that consist of non-alphabet characters are not shown in the table.


Hi, I'm Jecsham, I'm years old and I consider myself a Fullstack Developer. I'm a lover of music, technology, minimalism and simplicity.


Beta This is a test version of the layout of this page. Take the survey to help us improve it
Guidance
Materials to prepare teachers to administer the 2016 phonics screening check.
Teachers should use the sample materials and training video when preparing to administer the phonics screening check. The materials explain the structure of the check and include:
Teachers should watch this training video to understand the scoring rules and score the check consistently. It includes examples of children attempting the phonics screening check. It also provides guidance on how to score responses.
Year 1 phonics screening check training video
You can also access the video from the Vimeo Plus website .
Local authorities should use these materials during training sessions about the check.


Computer programmer with four years’ experience in front-end and back-end development. Passionate computer technology learner, working towards further development of skills and academic knowledge within the IT industry. Possess excellent coding skills, and am a reliable, organized individual.
In Head Office (Aug, 2010 ~ Sept, 2011)
• Designed an architecture & develop in LuuvIt Web Project
• Developed Wizi Smart Learning Mobile Web based on legacy code
• Handled Machine to Machine Web & Mobile as a developer
In Dispatch (Oct, 2011 ~ Oct, 2014)
• Involved in SarangOn Development & Maintenance as a developer
• Involved in SSMVA Web & Mobile as a developer
• Developed Channeling Project 2/3rd for UI part
• Handled T-CLOUD Web Project for UI part
1. JAVA EE(Java, JDBC, JMS, REST, WSDL, SSL
2. JAXB, Java Design Patterns), Spring MVC framework, Spring Security framework.
3. Strong experience in Air/Flex with JAVA EE.
4. HTML5, Javascript (JQuery), Ajax, CSS
5. Multi-tier architecture, JSP, XML
6. Databases and SQL (PL/SQL, T/SQL)


Hi!
Welcome
fago & dasjo
Agenda
Drupal 8 overview
Setup & introduce our
use case
The new editing experience
Views in Core
Image handling
Taking advantage of Entity & Field API improvements
Blocks & layouts
Theming with Twig
CMI - config in files
Multilingual
Drupal 8
It's not ready!
UI/UX Improvements
Drupal 8 continues the trend of updating the user interface and improving overall user experience.
It's easier to use.
It's easier to find things.
It's more accessible.
HTML 5 & Mobile
All core themes use HTML5.
Markup has been improved throughout core.
New HTML5 elements: phone, email, url, number, and date.
Next generation enhancements with Modernizr.js
Dropped support for IE 6 & 7.
Responsive / Mobile friendly
Accessibility improvements
Initiatives
Official
Configuration Management (CMI)
Web Services (WSCCI)
Blocks & Layouts
Mobile
Multilingual
And many more
Views in Core
Entity NG
Twig,
JavaScript clean-up,
Design, HTML 5
Spark
Administration & editing experience
Improved installer
Improved edit form
HTML 5 form elements
WYSIWYG in core
In-Place editing
Tiny helpers:
Module filter
Tour module
Views in Core
Easily build lists of content anywhere.
Edit existing lists (admin_views):
Front page.
Content administration.
Bulk operations in core (VB)
Image handling & responsiveness
Imagecache in core
Responsive images
Picture module
Breakpoints module
Taking advantage of Entity & Field API improvements
Theming with Twig
Drupal 7 Theming
pain points
Syntax
Inconsistency
Complexity
Redundancy
Security
Changes
New template engine
New theme hooks
Everything renderable
Markup Utility Functions
Theme component library
Twig wins
consistency
auto sanitization
beautiful templates
safe templates (contemplate)
portable to front-end: TwigJs
https://github.com/schmittjoh/twig.js
Blocks & layouts
Scotch, not Whiskey!
Drupal 7
Panels, Context, Display suite, Beans, ...
Plans
Isolated block rendering
Contexts/data sources
Condition plugins
Displays & layouts
Title


Sotto: Pride Month in schools to 'cause confusion in young minds'
'As far as I know, pride is still one of the 7 deadly sins,' says Senate President Vicente Sotto III on a DepEd memo inviting schools to observe LGBTQ+ Pride Month every June
by Michelle AbadSubscribe
Email Address
Newsletters
- Glenda Gloria's Take this Tuesday
- Huddle by Miriam Grace Go
- The Newsbreak Agenda by Chay Hofileña
- Sometimes, all you need is video by Lilibeth Frondoso


Study finds microplastics in all remote Arctic beluga whales tested
by Amy Smart, The Canadian PressVANCOUVER - A pioneering study of seven belugas in Canada's remote Arctic waters has found microplastics in the innards of every single whale.
Researchers from Ocean Wise worked with hunters from the Inuvialuit community of Tuktoyaktuk, N.W.T., to collect samples from whales they harvested between 2017 and 2018.
They found an average of nearly 10 microplastics, or particles less than five millimetres in size, in the gastrointestinal tracts of each beluga.
The study was published last week in the Marine Pollution Bulletin and conducted in partnership with Fisheries and Oceans Canada and Simon Fraser University.
Ocean Wise says it is the first study of microplastics in a marine mammal in Canada.
Lead author Rhiannon Moore says she wasn't expecting to see so many microplastics so far north.


At least five dead, including infants, following attack in Mexico
MEXICO CITY, Nov 5 — At least five members of a family in northern Mexico, including two infants, were killed yesterday in an attack by unknown gunmen, a government source and local media said, in the latest case of grisly violence to hit the country.
Mexican media reported that the victims belonged to the LeBaron family, associated with a break-away Mormon community that settled in northern Mexico decades ago, and that the dead as well as additional missing family members may all be US citizens.
The governments of Chihuahua and Sonora states, both of which border the United States, issued a brief, joint statement late yesterday saying an investigation into the incident had been launched and that some people were presumed dead and others missing.
The statement, which noted that additional federal and local security forces were being sent into the area near the border between the two Mexican states, did not provide further details.
Local television showed images of a burnt out vehicle that may have belonged to the family, and local activist and family member Julian LeBaron was quoted as describing the incident as a “massacre,” adding that some family members were burnt alive.
The press office of the US embassy in Mexico did not immediately respond to request for more information after hours.
The US Ambassador to Mexico, Christopher Landau, travelled to Sonora earlier yesterday for work meetings, he posted on Twitter. — Reuters


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 It can occur naturally as we undergo different experiences, changes in the brain can also be activated through neuroplasticity. It creates neutral pathways in our brain and by practice that one can make that connection stronger.
Discuss how you might engage with the principles of neuroplasticity for your own benefit:
Practice foundation learning material again and again with a positive mindset so that it can help to build
stronger connections in the brain. Attentive listening and curiosity, while going through coaching videos and
program guideline videos. Following this practice can help to create new pathways in the brain.
What are some of the ways to increase your neuroplasticity?
Learning new ideas and keeping faith in yourself. Also repeating the learning process and keep motivating yourself without thinking about the end result. Pressing the pause button on our hectic daily lives and helping simplify our brain’s work
Discuss what it is and why it is relevant
It is the opposite of a fixed mindset which means you think there is no capability to improve. Having a growth mindset mean you value yourself and self-motivation to learn and develop. Getting excited about challenges and believe in hard work and practice. In growth, mindset error is the first step of learning something new and is seen as neutral. The student with a growth mindset accepts their errors and correct/modify them also learn from those errors.
In this exploration, did anything surprise you?
In my online research, I was amazed to see how experimenting positive mindset changes the end result in schools and universities.
How will you integrate a growth mindset into your learning journey?
Taking one step at a time and learning from mistakes and also at the same time try not to overthink.
Blend with your learning plan and strategies: Reflect and discuss how these understandings may shape or influence your learning plan and strategy.
I have understood the importance of positivity and practice. I am going to implement the above practices in my day-to-day learning for continuous motivation and adapt my brain to create new pathways and build stronger connections.


Penny Thoughts is an app that allows users to share and view positive, motivational statements. With today's quarantine keeping people at home, Penny Thoughts helps bring positivity to users by showing them positive statements. Sign up and begin reaping the benefits of positivity.
Get Inspired


---
layout: example.html
title: Feature Table Example
shortdesc: Demonstrates fetching data and displaying on a table.
docs: >
Adding an tiled map
Adding a GeoJSON-type layer to a map using Fetch
Displaying data in a simple table
Editing data to update Redux State
Using Font Awesome to create map icons.
resources:
- https://use.fontawesome.com/2f2c3f1eb2.js
---


Both bluetooth and location have to be enabled for your mobile device to be able to find nearby beacons.


Hi, Im Jackson Wolstencroft
I am a Frontend Website Developer that uses HTML, CSS and Javascript.
Contact
About
Resume


{* Copyright 2011-2013 Here's A Hand Limited Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. *} {extends file="sysadmin/page.htm"} {block name="content"}
| ID | Title |
|---|---|
| {$whiteLabel->getID()|escape} | {$whiteLabel->getTitle()|escape} |


Detailed Description
Macros for easy access GCC function and variable attributes, which can be applied to function prototypes or variable attributes.
Define Documentation
| #define ATTR_ALIAS |
( |
Func |
|
) |
__attribute__ ((alias( #Func ))) |
Marks a function as an alias for another function.
- Parameters:
-
| [in] | Func | Name of the function which the given function name should alias. |
| #define ATTR_ALWAYS_INLINE __attribute__ ((always_inline)) |
Forces the compiler to inline the specified function. When applied, the given function will be inlined under all circumstances.
| #define ATTR_CONST __attribute__ ((const)) |
Indicates that the specified function is constant, in that it has no side effects other than parameter access.
| #define ATTR_DEPRECATED __attribute__ ((deprecated)) |
Marks a given function as deprecated, which produces a warning if the function is called.
| #define ATTR_INIT_SECTION |
( |
SectionIndex |
|
) |
__attribute__ ((naked, section (".init" #SectionIndex ))) |
Places the function in one of the initialization sections, which execute before the main function of the application. Refer to the avr-libc manual for more information on the initialization sections.
- Parameters:
-
| [in] | SectionIndex | Initialization section number where the function should be placed. |
| #define ATTR_NAKED __attribute__ ((naked)) |
Removes any preamble or postamble from the function. When used, the function will not have any register or stack saving code. This should be used with caution, and when used the programmer is responsible for maintaining stack and register integrity.
| #define ATTR_NO_INIT __attribute__ ((section (".noinit"))) |
Forces the compiler to not automatically zero the given global variable on startup, so that the current RAM contents is retained. Under most conditions this value will be random due to the behaviour of volatile memory once power is removed, but may be used in some specific circumstances, like the passing of values back after a system watchdog reset.
| #define ATTR_NO_INLINE __attribute__ ((noinline)) |
Prevents the compiler from considering a specified function for inlining. When applied, the given function will not be inlined under any circumstances.
| #define ATTR_NO_RETURN __attribute__ ((noreturn)) |
Indicates to the compiler that the function can not ever return, so that any stack restoring or return code may be omitted by the compiler in the resulting binary.
| #define ATTR_NON_NULL_PTR_ARG |
( |
... |
|
) |
__attribute__ ((nonnull (__VA_ARGS__))) |
Indicates that the specified parameters of the function are pointers which should never be NULL. When applied as a 1-based comma separated list the compiler will emit a warning if the specified parameters are known at compiler time to be NULL at the point of calling the function.
| #define ATTR_PURE __attribute__ ((pure)) |
Indicates that the specified function is pure, in that it has no side-effects other than global or parameter variable access.
| #define ATTR_WARN_UNUSED_RESULT __attribute__ ((warn_unused_result)) |
Indicates that the function returns a value which should not be ignored by the user code. When applied, any ignored return value from calling the function will produce a compiler warning.
| #define ATTR_WEAK __attribute__ ((weak)) |
Marks a function as a weak reference, which can be overridden by other functions with an identical name (in which case the weak reference is discarded at link time).


Unlimited Games. How should Red play 42?
pips Red=130 White=148
(CorrectAnswer= 0/0, ErrorScore= 0)
When ahead in the race, race. There is only one move here that is not a blunder and that is 24/20, 6/4.
With all those stripped points White will leave plenty of return shots if he makes his 5-pt. Any other move gives White too much control of the board. After 24/20, 6/4 it is an even game.
With all those stripped points White will leave plenty of return shots if he makes his 5-pt. Any other move gives White too much control of the board. After 24/20, 6/4 it is an even game.
1. Cubeful 2-ply 24/20 6/4 Eq.: -0.031 0.504 0.079 0.002 - 0.496 0.119 0.003 2. Cubeful 2-ply 13/7 Eq.: -0.097 (-0.067) 0.476 0.087 0.002 - 0.524 0.111 0.003 3. Cubeful 2-ply 24/22 6/2 Eq.: -0.108 (-0.077) 0.472 0.095 0.002 - 0.528 0.116 0.003 4. Cubeful 2-ply 8/4 6/4 Eq.: -0.139 (-0.108) 0.461 0.085 0.002 - 0.539 0.110 0.003 5. Cubeful 2-ply 7/3 6/4 Eq.: -0.141 (-0.110) 0.462 0.085 0.002 - 0.538 0.114 0.004


ID
LIST
START TIME
END TIME
STATUS
DELETE
{{x.id}}
{{x.name}}
{{x.starttime|date:"medium"}}
{{x.endtime|date:"medium"}}
Delete
Enter List:
Add List


Files CCore/inc/gadget/SIntFunc.h CCore/src/gadget/SIntFunc.cpp
SIntFunc is an Algorithm Package, parametrized by a signed integral type. Currently it is assumed, that the type uses 2's complementary representation.
template <class SInt> struct SIntFunc { // types typedef typename Meta::SIntToUInt<SInt>::UType UInt; // consts static const UInt MaxPositiveAbs = .... ; static const UInt MinNegativeAbs = .... ; static const SInt MaxPositive = .... ; static const SInt MinNegative = .... ;
UInt is the correspondent unsigned integral type.
MaxPositiveAbs is the absolute value of the maximum (positive) value of the SInt, represented as UInt type.
MinNegativeAbs is the absolute value of the minimum (negative) value of the SInt, represented as UInt type.
MaxPositive is the maximum (positive) value of the SInt.
MinNegative is the minimum (negative) value of the SInt.
// abs static SInt PosAbs(UInt abs); static SInt NegAbs(UInt abs);
PosAbs() returns +abs as SInt value. It is assumed, that abs<=MaxPositiveAbs.
NegAbs() returns -abs as SInt value. It is assumed, that abs<=MinNegativeAbs.
// dist/move static UInt Dist(SInt a,SInt b); // a <= b static SInt MovePos(SInt a,UInt delta); static SInt MoveNeg(SInt a,UInt delta); static SInt Move(SInt a,SInt e,UInt delta); };
Dist() returns the distance from a to b, it is assumed, that a <= b. The return value is represented by the UInt.
MovePos() moves the argument a to the positive direction by the distance delta. It is assumed, that the upper SInt value bound is not crossed. I.e. if a <= b, then MovePos(a,Dist(a,b)) == b.
MoveNeg() moves the argument a to the negative direction by the distance delta. It is assumed, that the lower SInt value bound is not crossed. I.e. if a <= b, then MoveNeg(b,Dist(a,b)) == a.
Move() moves the argument a to the positive direction by the distance delta if e > 0, otherwise it moves a to the negative direction by the distance delta.


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New York City is the best city in the whole entire world. People like to hate on it and say that New Yorkers are rude but they just wish they were born here and not the suburbs:)


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Hiring a car just got easier with the reliable service of Car Rental Service! Having covered all the 50 states in Pakistan, we offer the finest choice of vehicles ranging from economy to luxury.
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I missed the first half of the Republican debate last night due to a soccer game. I caught the second half, and was a little frustrated that Brian and John focused so much on the four popular candidates, but that’s neither here nor there.
What caught my attention was in the blogs after the debate was over. I saw this blurb as part of a larger article on EdWeek’s website:
U.S. Rep. Ron Paul, R-Texas, said he doesn’t think that schools should have to educate the children of illegal immigrants.
This got me almost as upset as Newt Gingrich’s agenda to increase charter school allowances and broader choices in schools and Rick Perry’s massive budget cuts to education to help close the state budget deficits.
The precedent that would be set by teaching only certain, qualified children is staggering. America is built on opportunities, including opportunity to improve education, livelihood, and safety. I completely understand that there are other major issues with illegal immigration, but I can guarantee you that the solution is not to begin barring these children from our schools. We cannot begin to divide children into “haves” and “have-nots,” least of all along educational lines.
I am a taxpayer. I understand that my taxes pay for public services as well as my own paycheck. I also understand that my taxes are covering (in part) for those individuals that can’t (or don’t) pay taxes. I have students in my class that fall below the minimum tax bracket…yet, they have the opportunity to come to school. When we refuse to teach children of immigrants that don’t pay taxes, we should also refuse to teach children of Americans that don’t pay their taxes.
Both ideas are ludicrous.
When I signed up to teach, I didn’t do so with a caveat that said “I will teach children of parents that pay taxes.” I agreed to teach every child that walked into the school to the best of my ability, regardless of race, religion, economic, or immigration status.
If we are teaching these children, we have an opportunity to build the values of truth and lawfulness while they are young. We have an opportunity to teach them lessons that their parents may or may not be living out. We have an opportunity to be a very powerful, positive influence on their lives. Like it or not, immigrant children will be future leaders along with ours.
 I, for one, will continue to serve every student with that hope in mind.
——
As a sidebar, the article about Gov. Perry’s cuts in education was written by a high school student.
Written on September 8th, 2011 by Brian Bennett Categorized in: All Teaching
Leave a comment


An OLD web service consists of a data structure for storing the artifacts of linguistic fieldwork and analysis and a read-write interface to that data structure.
A major design principle of the OLD is that as much work as possible should be delegated to the user-facing applications so that the OLD web service can focus on providing secure and responsive multi-user concurrent access to a central data structure. In some cases, technological restrictions currently inherent to particular platforms (e.g., the inability of browser-based JavaScript applications to call external programs) have required server-side implementation of features that might otherwise be implemented client-side (e.g., morphological parsing, PDF creation using TeX).
The diagram below illustrates the core components of an OLD application.
When an OLD web application receives HTTP requests, the Routes component decides which Pylons controller will handle the request. This decision is based on the HTTP method of the request and the URL. Routes and the controllers conspire to create a RESTful interface to the data structure qua a set of resources. That is, a POST request to www.xyz-old.org/forms will be interpreted as a request to create a new form resource while the same URL with a GET method will be interpreted as a request to read (i.e., retrieve) all of the form resources. The first request will be routed to the create action (i.e., method) of the forms controller (i.e., class) while the second will be routed to the index action of that same controller. The authentication, authorization, input validation, data processing, linguistic analysis and database updates and queries are all handled by the controllers.
As illustrated in the diagram, the Routes and Controllers components can be conceptually grouped together as the interface of an OLD web service. The Interface section details this interface.
SQLAlchemy provides an abstraction over the tables and relations of the underlying database. Tables, their columns and the relations between them (i.e., the schema) are declared using Python data structures called models and interaction with the database is accomplished entirely via these. This not only simplifies interaction with the database (from the Python programmer’s point of view) but also makes it easier to use different RDBMSs (e.g., SQLite, MySQL) with minimal changes to the application logic.
As illustrated in the diagram, the Models and RDBMS components can be conceptually grouped together as the data structure of an OLD web service. The Data Structure section describes and argues for the utility of the data structure of the OLD.


service
Section: Maintenance Commands (8)Updated: Jan 2006
Index Return to Main Contents
NAME
service - run a System V init scriptSYNOPSIS
service SCRIPT COMMAND [OPTIONS]service --status-all
service --help | -h | --version
DESCRIPTION
service runs a System V init script in as predictable environment as possible, removing most environment variables and with current working directory set to /.The SCRIPT parameter specifies a System V init script, located in /etc/init.d/SCRIPT. The supported values of COMMAND depend on the invoked script, service passes COMMAND and OPTIONS it to the init script unmodified. All scripts should support at least the start and stop commands. As a special case, if COMMAND is --full-restart, the script is run twice, first with the stop command, then with the start command.
service --status-all runs all init scripts, in alphabetical order, with the status command.
FILES
- /etc/init.d
-
The directory containing System V init scripts.
ENVIRONMENT
- LANG, TERM
-
The only environment variables passed to the init scripts.
SEE ALSO
chkconfig(8), ntsysv(8)
Index
This document was created by man2html, using the manual pages.
Time: 05:34:29 GMT, December 24, 2015


The JDI provides introspective access to a running virtual machine's state, Class, Array, Interface, and primitive types, and instances of those types.
The JDI also provides explicit control over a virtual machine's execution. The ability to suspend and resume threads, and to set breakpoints, watchpoints, ... Notification of exceptions, class loading, thread creation... The ability to inspect a suspended thread's state, local variables, stack backtrace...
JDI is the highest-layer of the Java Platform Debugger Architecture (JPDA). For more information on the Java Platform Debugger Architecture, see the Java Platform Debugger Architecture documentation for this release and the Java Platform Debugger Architecture website.
Global Exceptions:
This section documents exceptions which apply to the entire API and are thus not documented on individual methods.
NOTE: The exceptions below may be thrown whenever the specified conditions are met but a guarantee that they are thrown only exists when a valid result cannot be returned.{@link com.sun.jdi.VMMismatchException}
Any method on a {@link com.sun.jdi.Mirror} that takes a
Mirror
as an parameter directly or indirectly (e.g., as a element in aList
) will throw {@link com.sun.jdi.VMMismatchException} if the mirrors are from different virtual machines.{@link java.lang.NullPointerException}
Any method which takes a {@link java.lang.Object} as an parameter will throw {@link java.lang.NullPointerException} if null is passed directly or indirectly -- unless null is explicitly mentioned as a valid parameter.
{@link com.sun.jdi.VMDisconnectedException}
Any method on {@link com.sun.jdi.ObjectReference}, {@link com.sun.jdi.ReferenceType}, {@link com.sun.jdi.request.EventRequest}, {@link com.sun.jdi.StackFrame}, or {@link com.sun.jdi.VirtualMachine} or which takes one of these directly or indirectly as an parameter may throw {@link com.sun.jdi.VMDisconnectedException} if the target VM is disconnected and the {@link com.sun.jdi.event.VMDisconnectEvent} has been or is available to be read from the {@link com.sun.jdi.event.EventQueue}.
{@link com.sun.jdi.VMOutOfMemoryException}
Any method on {@link com.sun.jdi.ObjectReference}, {@link com.sun.jdi.ReferenceType}, {@link com.sun.jdi.request.EventRequest}, {@link com.sun.jdi.StackFrame}, or {@link com.sun.jdi.VirtualMachine} or which takes one of these directly or indirectly as an parameter may throw {@link com.sun.jdi.VMOutOfMemoryException} if the target VM has run out of memory.
{@link com.sun.jdi.ObjectCollectedException}
Any method on {@link com.sun.jdi.ObjectReference} or which directly or indirectly takes
ObjectReference
as parameter may throw {@link com.sun.jdi.ObjectCollectedException} if the mirrored object has been garbage collected.Any method on {@link com.sun.jdi.ReferenceType} or which directly or indirectly takes
ReferenceType
as parameter may throw {@link com.sun.jdi.ObjectCollectedException} if the mirrored type has been unloaded.


Encode/decode base64 data into ArrayBuffers
Install the module with: npm install base64-arraybuffer
The library encodes and decodes base64 to and from ArrayBuffers
ArrayBuffer
into base64 stringArrayBuffer
Copyright (c) 2012 Niklas von Hertzen Licensed under the MIT license.


__("hello %placeholder%", { placeholder: "world" })
__("hello world")
__("{0} none | {1} one | [2, Inf] many", count)
__("This is a very long string, to test how PoeditDumper will dump it, using multiline syntax. It should be displayed on three different lines, with 78 chars maximum each.")


--- layout: post title: ob_flush,flush无效 date: 2012-01-11 20:43:40.000000000 +00:00 type: post published: true status: publish categories: - php tags: - flush meta: views: '316' author: login: ig2net email<EMAIL_ADDRESS>display_name: "农夫一号" first_name: '' last_name: '' ---
想要先输出一段再输出一段，网上说如下的可以。
但是实际上浏览器没有按照预想的来，还是一次等待加载完。
其他各种办法例如:
<?php ini_set('output_buffering','on'); ini_set('zlib.output_compression', 0); ob_implicit_flush(); for($i=0;$i<10;$i++) { echo $i; echo str_repeat(" ", 500); ob_flush(); flush(); sleep(1); } ?>
并修改.htaccess gzip设置 http://www.absolutelytech.com/2010/01/31/solved-flush-ob_flush-not-working-in-php-disabling-gzip-through-htaccess/
SetEnv no-gzip dont-vary
照样不行。
其实php文档已经说得很明白了,看flush函数说明，已经讲了包含服务器设置，浏览器缓存等等问题:
flush() may not be able to override the buffering scheme of your web server and it has no effect on any client-side buffering in the browser. It also doesn't affect PHP's userspace output buffering mechanism. This means you will have to call both ob_flush() and flush() to flush the ob output buffers if you are using those.
Several servers, especially on Win32, will still buffer the output from your script until it terminates before transmitting the results to the browser.
Server modules for Apache like mod_gzip may do buffering of their own that will cause flush() to not result in data being sent immediately to the client.
Even the browser may buffer its input before displaying it. Netscape, for example, buffers text until it receives an end-of-line or the beginning of a tag, and it won't render tables until the </table> tag of the outermost table is seen.
Some versions of Microsoft Internet Explorer will only start to display the page after they have received 256 bytes of output, so you may need to send extra whitespace before flushing to get those browsers to display the page.
因此，想通过这样的方式达到分步输出是不太靠谱的。


| 导航: 起始页 > Dive Into Python > XML 处理 | << >> | ||||
Python 研究(Dive Into Python)Python 从新手到高手 [DIP_5_4_CPUG_RELEASE] |
下面两章是关于 Python 中 XML 处理的。如果你已经知道一个 XML 文档的样子，比如它是由结构化标记构成的，这些标记形成了层次模型的元素，等等这些知识都是有帮助的。如果你不明白这些，这里有 很多 XML 教程 能够解释这些基础知识。
如果你对XML不是很感兴趣，你还是应该读一下这些章节，它们涵盖了不少重要的主题比如 Python 包，Unicode，命令行参数以及如何使用 getattr 进行方法分发。
Being a philosophy major is not required, although if you have ever had the misfortune of being subjected to the writings of Immanuel Kant, you will appreciate the example program a lot more than if you majored in something useful, like computer science.
处理 XML 有两种基本的方式。一种叫做 SAX（“Simple API for XML”），它的工作方式是，一次读出一点 XML 内容，然后对发现的每一个元素调用一个方法。（如果你读了 第 8 章 HTML 处理，这应该听起来很熟悉，因为这是 sgmllib 工作的方式。）另一种方式叫做 DOM （“Document Object Model”），它的工作方式是，一次性读入整个 XML 文档，然后使用 Python 类创建一个内部表示形式（以树结构进行连接）。Python 拥有这两种解析方式的标准模块，但是本章只涉及 DOM。
下面是一个完整的 Python 程序，它根据 XML 格式定义的上下文无关语法生成伪随机输出。如果你不明白是什么意思，不用担心，下面两章中将会深入的检视这个程序的输入和输出。
如果您还没有下载本书附带的例子程序, 可以 下载本程序和其他例子程序。
"""Kant Generator for Python Generates mock philosophy based on a context-free grammar Usage: python kgp.py [options] [source] Options: -g ..., --grammar=... use specified grammar file or URL -h, --help show this help -d show debugging information while parsing Examples: kgp.py generates several paragraphs of Kantian philosophy kgp.py -g husserl.xml generates several paragraphs of Husserl kpg.py "<xref id='paragraph'/>" generates a paragraph of Kant kgp.py template.xml reads from template.xml to decide what to generate """ from xml.dom import minidom import random import toolbox import sys import getopt _debug = 0 class NoSourceError(Exception): pass class KantGenerator: """generates mock philosophy based on a context-free grammar""" def __init__(self, grammar, source=None): self.loadGrammar(grammar) self.loadSource(source and source or self.getDefaultSource()) self.refresh() def _load(self, source): """load XML input source, return parsed XML document - a URL of a remote XML file ("http://diveintopython.org/kant.xml") - a filename of a local XML file ("~/diveintopython/common/py/kant.xml") - standard input ("-") - the actual XML document, as a string """ sock = toolbox.openAnything(source) xmldoc = minidom.parse(sock).documentElement sock.close() return xmldoc def loadGrammar(self, grammar): """load context-free grammar""" self.grammar = self._load(grammar) self.refs = {} for ref in self.grammar.getElementsByTagName("ref"): self.refs[ref.attributes["id"].value] = ref def loadSource(self, source): """load source""" self.source = self._load(source) def getDefaultSource(self): """guess default source of the current grammar The default source will be one of the <ref>s that is not cross-referenced. This sounds complicated but it's not. Example: The default source for kant.xml is "<xref id='section'/>", because 'section' is the one <ref> that is not <xref>'d anywhere in the grammar. In most grammars, the default source will produce the longest (and most interesting) output. """ xrefs = {} for xref in self.grammar.getElementsByTagName("xref"): xrefs[xref.attributes["id"].value] = 1 xrefs = xrefs.keys() standaloneXrefs = [e for e in self.refs.keys() if e not in xrefs] if not standaloneXrefs: raise NoSourceError, "can't guess source, and no source specified" return '<xref id="%s"/>' % random.choice(standaloneXrefs) def reset(self): """reset parser""" self.pieces = [] self.capitalizeNextWord = 0 def refresh(self): """reset output buffer, re-parse entire source file, and return output Since parsing involves a good deal of randomness, this is an easy way to get new output without having to reload a grammar file each time. """ self.reset() self.parse(self.source) return self.output() def output(self): """output generated text""" return "".join(self.pieces) def randomChildElement(self, node): """choose a random child element of a node This is a utility method used by do_xref and do_choice. """ choices = [e for e in node.childNodes if e.nodeType == e.ELEMENT_NODE] chosen = random.choice(choices) if _debug: sys.stderr.write('%s available choices: %s\n' % \ (len(choices), [e.toxml() for e in choices])) sys.stderr.write('Chosen: %s\n' % chosen.toxml()) return chosen def parse(self, node): """parse a single XML node A parsed XML document (from minidom.parse) is a tree of nodes of various types. Each node is represented by an instance of the corresponding Python class (Element for a tag, Text for text data, Document for the top-level document). The following statement constructs the name of a class method based on the type of node we're parsing ("parse_Element" for an Element node, "parse_Text" for a Text node, etc.) and then calls the method. """ parseMethod = getattr(self, "parse_%s" % node.__class__.__name__) parseMethod(node) def parse_Document(self, node): """parse the document node The document node by itself isn't interesting (to us), but its only child, node.documentElement, is: it's the root node of the grammar. """ self.parse(node.documentElement) def parse_Text(self, node): """parse a text node The text of a text node is usually added to the output buffer verbatim. The one exception is that
 sets a flag to capitalize the first letter of the next word. If that flag is set, we capitalize the text and reset the flag. """ text = node.data if self.capitalizeNextWord: self.pieces.append(text[0].upper()) self.pieces.append(text[1:]) self.capitalizeNextWord = 0 else: self.pieces.append(text) def parse_Element(self, node): """parse an element An XML element corresponds to an actual tag in the source: <xref id='...'>,
, <choice>, etc. Each element type is handled in its own method. Like we did in parse(), we construct a method name based on the name of the element ("do_xref" for an <xref> tag, etc.) and call the method. """ handlerMethod = getattr(self, "do_%s" % node.tagName) handlerMethod(node) def parse_Comment(self, node): """parse a comment The grammar can contain XML comments, but we ignore them """ pass def do_xref(self, node): """handle <xref id='...'> tag An <xref id='...'> tag is a cross-reference to a <ref id='...'> tag. <xref id='sentence'/> evaluates to a randomly chosen child of <ref id='sentence'>. """ id = node.attributes["id"].value self.parse(self.randomChildElement(self.refs[id])) def do_p(self, node): """handle
 tag The
 tag is the core of the grammar. It can contain almost anything: freeform text, <choice> tags, <xref> tags, even other
 tags. If a "class='sentence'" attribute is found, a flag is set and the next word will be capitalized. If a "chance='X'" attribute is found, there is an X% chance that the tag will be evaluated (and therefore a (100-X)% chance that it will be completely ignored) """ keys = node.attributes.keys() if "class" in keys: if node.attributes["class"].value == "sentence": self.capitalizeNextWord = 1 if "chance" in keys: chance = int(node.attributes["chance"].value) doit = (chance > random.randrange(100)) else: doit = 1 if doit: for child in node.childNodes: self.parse(child) def do_choice(self, node): """handle <choice> tag A <choice> tag contains one or more
 tags. One
 tag is chosen at random and evaluated; the rest are ignored. """ self.parse(self.randomChildElement(node)) def usage(): print __doc__ def main(argv): grammar = "kant.xml" try: opts, args = getopt.getopt(argv, "hg:d", ["help", "grammar="]) except getopt.GetoptError: usage() sys.exit(2) for opt, arg in opts: if opt in ("-h", "--help"): usage() sys.exit() elif opt == '-d': global _debug _debug = 1 elif opt in ("-g", "--grammar"): grammar = arg source = "".join(args) k = KantGenerator(grammar, source) print k.output() if __name__ == "__main__": main(sys.argv[1:])
"""Miscellaneous utility functions""" def openAnything(source): """URI, filename, or string --> stream This function lets you define parsers that take any input source (URL, pathname to local or network file, or actual data as a string) and deal with it in a uniform manner. Returned object is guaranteed to have all the basic stdio read methods (read, readline, readlines). Just .close() the object when you're done with it. Examples: >>> from xml.dom import minidom >>> sock = openAnything("http://localhost/kant.xml") >>> doc = minidom.parse(sock) >>> sock.close() >>> sock = openAnything("c:\\inetpub\\wwwroot\\kant.xml") >>> doc = minidom.parse(sock) >>> sock.close() >>> sock = openAnything("<ref id='conjunction'><text>and</text><text>or</text></ref>") >>> doc = minidom.parse(sock) >>> sock.close() """ if hasattr(source, "read"): return source if source == '-': import sys return sys.stdin # try to open with urllib (if source is http, ftp, or file URL) import urllib try: return urllib.urlopen(source) except (IOError, OSError): pass # try to open with native open function (if source is pathname) try: return open(source) except (IOError, OSError): pass # treat source as string import StringIO return StringIO.StringIO(str(source))
独立运行程序 kgp.py ，它会解析 kant.xml 中默认的基于 XML 的语法，并以康德的风格打印出几段有哲学价值的段落来。
[you@localhost kgp]$ python kgp.py
As is shown in the writings of Hume, our a priori concepts, in
reference to ends, abstract from all content of knowledge; in the study
of space, the discipline of human reason, in accordance with the
principles of philosophy, is the clue to the discovery of the
Transcendental Deduction. The transcendental aesthetic, in all
theoretical sciences, occupies part of the sphere of human reason
concerning the existence of our ideas in general; still, the
never-ending regress in the series of empirical conditions constitutes
the whole content for the transcendental unity of apperception. What
we have alone been able to show is that, even as this relates to the
architectonic of human reason, the Ideal may not contradict itself, but
it is still possible that it may be in contradictions with the
employment of the pure employment of our hypothetical judgements, but
natural causes (and I assert that this is the case) prove the validity
of the discipline of pure reason. As we have already seen, time (and
it is obvious that this is true) proves the validity of time, and the
architectonic of human reason, in the full sense of these terms,
abstracts from all content of knowledge. I assert, in the case of the
discipline of practical reason, that the Antinomies are just as
necessary as natural causes, since knowledge of the phenomena is a
posteriori.
The discipline of human reason, as I have elsewhere shown, is by
its very nature contradictory, but our ideas exclude the possibility of
the Antinomies. We can deduce that, on the contrary, the pure
employment of philosophy, on the contrary, is by its very nature
contradictory, but our sense perceptions are a representation of, in
the case of space, metaphysics. The thing in itself is a
representation of philosophy. Applied logic is the clue to the
discovery of natural causes. However, what we have alone been able to
show is that our ideas, in other words, should only be used as a canon
for the Ideal, because of our necessary ignorance of the conditions.
[...snip...]
当然这是胡言乱语。噢，不完全是胡言乱语。它在句法和语法上都是正确的（尽管非常罗嗦－－康德可不是你们所说的踩得到点上的那种人）。其中一些实际上是正确的（或者至少康德可能会认同的事情），其中一些则明显是错误的，大部分只是语无伦次。但所有内容都是符合康德的风格。
让我重复一遍，如果你现在或曾经主修哲学专业，这会非常、非常有趣。
关于这个程序的有趣之处在于没有一点内容是属于康德的。所有的内容都来自于上下文无关语法文件kant.xml。如果你要程序使用不同的语法文件（可以在命令行中指定），输出信息将完全不同。
[you@localhost kgp]$ python kgp.py -g binary.xml 00101001 [you@localhost kgp]$ python kgp.py -g binary.xml 10110100
在本章后面的内容中，你将近距离的观察语法文件的结构。现在，你只要知道语法文件定义了输出信息的结构，而 kgp.py 程序读取语法规则并随机确定哪些单词插入哪里。
|
<< 小结 |
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | |
包 >> |


| summary,MyersMbaSes-method {diffobj} | R Documentation |
Displays the data required to generate the shortest edit path for comparison between two strings.
## S4 method for signature 'MyersMbaSes' summary(object, with.match = FALSE, ...)
object |
the |
with.match |
logical(1L) whether to show what text the edit command refers to |
... |
forwarded to the data frame print method used to actually display the data |
whatever the data frame print method returns


| | |
- builtins.object
-
- EnvBackend
- Feature
- Logger
class EnvBackend(builtins.object) |
| |
Environment variable based backend |
| |
Methods defined here:
- exists(self, name)
- is_active(self, name)
Data descriptors defined here:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
|
class Feature(builtins.object) |
| |
Feature(backend, logger)
The feature manager.
Usage
=====
Backends
--------
First thing to do is to choose a backend to use.
- EnvBackend() - Environment variable based
- Django Database: db_django
- Google Datastore: google_cloud_datastore
- JSON File: jsonfile
- Local Memory (testing): localmemory
- Memcache: memcached
- MongoDB: db_mongodb
- Redis Key/Value: redis_backend
- SQL Database: db_sqlalchemy
- Togglz File: togglzfile
Crating the feature instance
----------------------------
When creating a feature instance one must pass a backend and an optional logger to use.
.. code-block:: python
from feature import Feature
from flagon.backends.jsonfile import JSONFileBackend
# Make a backend
backend = JSONFileBackend('example/config.json')
# Make the feature instance
feature = Feature(backend)
Using the feature instance
--------------------------
Once a feature instance has been made it then can be used to tag callables as features. To do this one uses the feature instance as a decorator.
.. code-block:: python
@feature('a feature')
def say_something(data):
print(data)
Now that the function ``say_something`` has been tagged with the feature ``a feature`` it will only execute if ``a feature`` is set active.
.. note::
When features are not active they will raise a NameError unless there is a default set.
.. note::
When a feature is unknown it will raise an flagon.errors.UnknownFeatureError
Defaults
~~~~~~~~
Feature instance can also define defaults. Defaults are callables that will call **instead** of the original callable if the feature is off.
.. code-block:: python
import feature
def yell(data):
print(data.upper())
@feature('this feature is off', default=yell)
def say_something(data):
print(data)
@feature('this feature is off', default=feature.skip)
def say_something_else(data):
print(data) |
| |
Methods defined here:
- __call__(self, name, default=None)
- What acts as a decorator.
:param name: the name of the feature.
:type name: str
:param default: the default callable to fall back to.
:type default: callable or None
:rtype: callable
- __init__(self, backend, logger)
- Creates the feature manager.
:param backend: the backend to use for storing feature states.
:type backend: flagon.backends.Backend
:param logger: the logger like object to use for logging.
:type logger: logging.Logger
:rtype: Feature
Data descriptors defined here:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
|
class Logger(builtins.object) |
| | |
Methods defined here:
- debug(*args)
- error(*args)
- level(self)
- warn(*args)
Data descriptors defined here:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
| |


Last updated: 2020-08-12
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Last update:
[1] “Wed Aug 12 11:04:07 2020”
cv.glmnet()
. It iterates over lambda
and chooses the most robust values for prediction via parameter s
in predict.glmnet()
. Supplying a custom lambda sequence does not make much sense since the internal heuristics are quite good (if one wants to use non-spatial optimization). See this stats.stackexchange question for how lambda defaults are estimated.glmnet()
directly. This implementation does not do an internal optimization for lambda
and hence s
can/needs to be tuned directly by the user. Because it is hard to come up with good tuning ranges in this case, one can fit a cv.glmnet()
on the data and use the borders of the estimated lambda
as upper and lower borders of the tuning space.Inspect Ridge regression on VI task in detail because the error is enourmus.
First extract the models.
models <- benchmark_models_new_penalized_mbo_buffer2[[8]][["results"]][["vi_buffer2"]][["Ridge-MBO"]][["models"]]
Then look at the fold performances
benchmark_models_new_penalized_mbo_buffer2[[8]][["results"]][["vi_buffer2"]][["Ridge-MBO"]][["measures.test"]][["rmse"]]
[1] 28.04421 21.44949 21.93398
[4]<PHONE_NUMBER>66.92307
We see a high error on Fold 4 (= Laukiz 2). The others are also quite high but not “out of bounds”.
Let’s look at the full lambda sequence
purrr::map_int(models, ~ length(.x[["learner.model"]][["lambda"]]))
[1] 5 100 100 100
Interestingly, the lambda length of model 1 is not 100 (default) but only 5.
To inspect further, let’s refit a {glmnet} model directly on the training data of Fold 4 and inspect what glmnet::cv.glmnet
estimates for the lambda sequence:
train_inds_fold4 <- benchmark_models_new_penalized_mbo_buffer2[[8]][["results"]][["vi_buffer2"]][["Ridge-MBO"]][["pred"]][["instance"]][["train.inds"]][[4]]
obs_train_f4 <- as.matrix(task_new_buffer2[[2]]$env$data[train_inds_fold4, getTaskFeatureNames(task_new_buffer2[[2]])])
target_f4 <- getTaskTargets(task_new_buffer2[[2]])[train_inds_fold4]
Fit cv.glmnet
set.seed(1)
modf4 <- glmnet::cv.glmnet(obs_train_f4, target_f4, alpha = 0)
modf4$lambda.1se
[1] 17.09715
Predict on Laukiz 2 now.
pred_inds_fold4 <- benchmark_models_new_penalized_mbo_buffer2[[8]][["results"]][["vi_buffer2"]][["Ridge-MBO"]][["pred"]][["instance"]][["test.inds"]][[4]]
obs_pred_f4 <- as.matrix(task_new_buffer2[[2]]$env$data[pred_inds_fold4, getTaskFeatureNames(task_new_buffer2[[2]])])
pred <- predict(modf4, newx = obs_pred_f4, s = modf4$lambda.1se)
Calculate the error
truth <- task_new_buffer2[[2]]$env$data[pred_inds_fold4, "defoliation"]
mlr:::measureRMSE(truth, pred)
[1]<PHONE_NUMBER>9
Ok, RMSE of<PHONE_NUMBER>9. This is most likely because of a few. observations which were predicted completely out of bounds.
qplot(pred, geom = "histogram")
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
| Version | Author | Date |
|---|---|---|
| c77844b | pat-s | 2020-03-11 |
pred[which(pred > 100), , drop = FALSE]
1
737<PHONE_NUMBER>649
Ok, its one observation (row id = 737).
Let’s have a look at the predictor values for this observation.
summary(obs_train_f4[737, ])
Min. 1st Qu. Median Mean 3rd Qu. Max.
-465.332 0.495 1.248 244.077 13.569 15673.108
Ok, how does this compare to summaries of other observations? NB: Obs. 500 - 510 were chosen randomly. The purpose here is to see if something within the predictors for specific observations looks abnormal.
lapply(seq(500:510), function(x) summary(obs_train_f4[x, ]))
[[1]]
Min. 1st Qu. Median Mean 3rd Qu. Max.
-241.698 0.543 1.342 136.208 9.122 8310.363
[[2]]
Min. 1st Qu. Median Mean 3rd Qu. Max.
-162.222 0.557 1.092 88.503 7.497 5026.049
[[3]]
Min. 1st Qu. Median Mean 3rd Qu. Max.
-358.944 0.452 1.286 198.458 10.548 12438.312
[[4]]
Min. 1st Qu. Median Mean 3rd Qu. Max.
-88.5556 0.5348 1.0245 54.8659 7.3913 2870.3848
[[5]]
Min. 1st Qu. Median Mean 3rd Qu. Max.
-104.903 0.545 1.063 61.960 8.315 3202.895
[[6]]
Min. 1st Qu. Median Mean 3rd Qu. Max.
-311.475 0.527 1.277 172.420 9.705 10713.079
[[7]]
Min. 1st Qu. Median Mean 3rd Qu. Max.
-440.426 0.488 1.384 226.684 11.486 14710.321
[[8]]
Min. 1st Qu. Median Mean 3rd Qu. Max.
-135.505 0.601 1.104 76.380 8.666 4416.709
[[9]]
Min. 1st Qu. Median Mean 3rd Qu. Max.
-408.230 0.563 1.243 217.687 9.761 13471.893
[[10]]
Min. 1st Qu. Median Mean 3rd Qu. Max.
-228.969 0.570 1.067 81.748 8.795 4882.058
[[11]]
Min. 1st Qu. Median Mean 3rd Qu. Max.
-180.651 0.539 1.101 91.665 7.273 5362.145
We have some higher values for obs 737 but nothing which stands out.
Let’s look at the model coefficients and Partial Dependence Plots (PDP):
coef(modf4)
90 x 1 sparse Matrix of class "dgCMatrix"
1
(Intercept)<PHONE_NUMBER>6839
bf2_Boochs 0.01856478558
bf2_Boochs2 -0.10174641139
bf2_CARI -0.00101151781
bf2_Carter2 -0.54044992476
bf2_Carter3 -4.74060904178
bf2_Carter4 3.57395447927
bf2_Carter5 2.40033358279
bf2_Carter6 -0.01223220666
bf2_CI 17.44558789878
bf2_CI2 -0.09234363302
bf2_ClAInt -0.00029867499
bf2_CRI1 3.93199597949
bf2_CRI2 3.93621597231
bf2_CRI3 -0.14102965215
bf2_CRI4 0.07745521805
bf2_D1 -0.21172763780
bf2_D2 4.48364959574
bf2_Datt -29.92657011501
bf2_Datt2 -0.44902414236
bf2_Datt3 -5.82225051092
bf2_Datt4 9.73689861012
bf2_Datt5 0.43034087514
bf2_Datt6 0.52067426584
bf2_DD -0.00466742370
bf2_DDn 0.00165874779
bf2_DPI -2.94066199649
bf2_DWSI4 -0.27682042303
bf2_EGFR -0.01998100611
bf2_EVI -0.00201410021
bf2_GDVI_2 10.27846237651
bf2_GDVI_3 31.18505681072
bf2_GDVI_4 0.93958347942
bf2_GI -0.34477144062
bf2_Gitelson 2.64273830373
bf2_Gitelson2 -0.02585043394
bf2_GMI1 0.16818982267
bf2_GMI2 -0.05229428190
bf2_Green.NDVI 1.73562944987
bf2_Maccioni -16.16650612962
bf2_MCARI 0.00522029772
bf2_MCARI2 -0.00049064534
bf2_mND705 -0.64532841126
bf2_mNDVI 3.53489589242
bf2_MPRI -0.03442634938
bf2_MSAVI 3.65757418934
bf2_mSR 0.00161463593
bf2_mSR2 -0.08935382573
bf2_mSR705 -0.09691169765
bf2_MTCI -0.43694064196
bf2_MTVI -0.00121734402
bf2_NDVI 1.93976587113
bf2_NDVI2 -0.49852301101
bf2_NDVI3 0.71581375642
bf2_NPCI 0.53555171520
bf2_OSAVI 1.71019327640
bf2_PARS 0.05888230273
bf2_PRI -3.59215290150
bf2_PRI_norm -95.26848890029
bf2_PRI.CI2 -2.84067572332
bf2_PSRI -6.87122623622
bf2_PSSR 0.01087461932
bf2_PSND -6.96686024116
bf2_SPVI.1 -0.00146273294
bf2_PWI -2.54395514623
bf2_RDVI -0.03620253189
bf2_REP_Li -0.66792472297
bf2_SAVI 1.27179842863
bf2_SIPI -5.23226754782
bf2_SPVI.2 -0.00146824633
bf2_SR -0.01204737752
bf2_SR1 -0.05555531799
bf2_SR2 -0.01914132415
bf2_SR3 0.16764479685
bf2_SR4 0.99188378152
bf2_SR5 -5.57341447292
bf2_SR6 -0.22843650281
bf2_SR7 -0.09924316268
bf2_SR8 -1.27085004094
bf2_Sum_Dr1 -0.01285185223
bf2_Sum_Dr2 -0.00934767797
bf2_SRPI 0.01663453441
bf2_TCARI -0.00050333567
bf2_TCARI2 0.00193142448
bf2_TGI -0.00012288617
bf2_TVI -0.00002379354
bf2_Vogelmann -1.45492700073
bf2_Vogelmann2 6.48132112495
bf2_Vogelmann3 5.67673100608
bf2_Vogelmann4 4.98343236576
Feature “bf2_PRI_norm” has a quite high value (-95).
plotres(modf4)
plot_glmnet(modf4$glmnet.fit)
plotmo(modf4$glmnet.fit)
plotmo grid: bf2_Boochs bf2_Boochs2 bf2_CARI bf2_Carter2 bf2_Carter3
1.998693 2.845148 80.11412 0.1234949 0.08682775
bf2_Carter4 bf2_Carter5 bf2_Carter6 bf2_CI bf2_CI2 bf2_ClAInt
0.2861524 1.622418 19.10886 1.087452 5.280886 877.1538
bf2_CRI1 bf2_CRI2 bf2_CRI3 bf2_CRI4 bf2_D1 bf2_D2 bf2_Datt
0.04910712 0.06965439 -9.37543 -6.282868 1.514274 0.7339561 0.8177696
bf2_Datt2 bf2_Datt3 bf2_Datt4 bf2_Datt5 bf2_Datt6 bf2_DD bf2_DDn
4.216579 0.7465023 0.01912338 0.7204213 0.3120446 63.41494 -266.1666
bf2_DPI bf2_DWSI4 bf2_EGFR bf2_EVI bf2_GDVI_2 bf2_GDVI_3 bf2_GDVI_4
0.5886079 1.382126 7.359851 4.944853 0.9875098 0.9989018 0.9998978
bf2_GI bf2_Gitelson bf2_Gitelson2 bf2_GMI1 bf2_GMI2 bf2_Green.NDVI
1.43903 0.04093419 8.943006 8.631567 5.866379 0.80809
bf2_Maccioni bf2_MCARI bf2_MCARI2 bf2_mND705 bf2_mNDVI bf2_MPRI bf2_MSAVI
0.7883106 25.39617 397.1709 0.6687387 0.9220925 9.782586 0.9283319
bf2_mSR bf2_mSR2 bf2_mSR705 bf2_MTCI bf2_MTVI bf2_NDVI bf2_NDVI2
28.73996 3.88811 5.140166 3.635395 247.0532 0.8593618 0.6208416
bf2_NDVI3 bf2_NPCI bf2_OSAVI bf2_PARS bf2_PRI bf2_PRI_norm
-0.137058 0.2540456 1.005096 16.79849 -0.02239909 0.0009611926
bf2_PRI.CI2 bf2_PSRI bf2_PSSR bf2_PSND bf2_SPVI.1 bf2_PWI bf2_RDVI
-0.1033416 0.03712631 13.42027 0.9324896 238.338 1.013997 11.67572
bf2_REP_Li bf2_SAVI bf2_SIPI bf2_SPVI.2 bf2_SR bf2_SR1 bf2_SR2
724.5711 1.29702 1.042882 238.338 14.16137 5.866379 9.883037
bf2_SR3 bf2_SR4 bf2_SR5 bf2_SR6 bf2_SR7 bf2_SR8 bf2_Sum_Dr1
8.631567 2.146826 0.4827658 3.288378 0.3703371 0.5433803 38.28711
bf2_Sum_Dr2 bf2_SRPI bf2_TCARI bf2_TCARI2 bf2_TGI bf2_TVI
33.57344 0.6054507 31.51822 3.176928 922.7046 8765.983
bf2_Vogelmann bf2_Vogelmann2 bf2_Vogelmann3 bf2_Vogelmann4
1.84361 -0.2100082 1.316905 -0.2384276
Let’s figure out which are the ten most important features and create PDPs for these:
top_ten_abs <- coef(modf4) %>%
as.matrix() %>%
as.data.frame() %>%
dplyr::rename(coef = `1`) %>%
dplyr::mutate(feature = rownames(coef(modf4))) %>%
dplyr::slice(-1) %>%
dplyr::mutate(coef_abs = abs(coef)) %>%
dplyr::arrange(desc(coef_abs)) %>%
dplyr::slice(1:10) %>%
dplyr::pull(feature)
top_ten_abs
[1] "bf2_PRI_norm" "bf2_GDVI_3" "bf2_Datt" "bf2_CI"
[5] "bf2_Maccioni" "bf2_GDVI_2" "bf2_Datt4" "bf2_PSND"
[9] "bf2_PSRI" "bf2_Vogelmann2"
For PDP we use a model trained with {mlr} and check for equality first.
lrn <- makeLearner("regr.cvglmnet", alpha = 0)
task_f4 <- subsetTask(task_new_buffer2[[2]], train_inds_fold4)
set.seed(1)
mod_mlr <- train(lrn, task_f4)
Check lambda sequence and lambda.1se
:
mod_mlr$learner.model$lambda
[1] 4983.9093118 4541.1525452 4137.7290693 3770.1446232 3435.2153661
[6] 3130.0403011 2851.9761478 2598.6144474 2367.7607022 2157.4153674
[11] 1965.7565324 1791.1241401 1632.0056082 1487.0227281 1354.9197274
[16] 1234.5523933 1124.8781613 1024.9470858<PHONE_NUMBER><PHONE_NUMBER>
[21]<PHONE_NUMBER><PHONE_NUMBER><PHONE_NUMBER><PHONE_NUMBER><PHONE_NUMBER>
[26]<PHONE_NUMBER><PHONE_NUMBER><PHONE_NUMBER><PHONE_NUMBER><PHONE_NUMBER>
[31]<PHONE_NUMBER><PHONE_NUMBER><PHONE_NUMBER><PHONE_NUMBER><PHONE_NUMBER>
[36]<PHONE_NUMBER><PHONE_NUMBER><PHONE_NUMBER><PHONE_NUMBER><PHONE_NUMBER>
[41]<PHONE_NUMBER><PHONE_NUMBER><PHONE_NUMBER> 91.2423360 83.1366183
[46] 75.7509903 69.0214811 62.8898030 57.3028462 52.2122193
[51] 47.5738296 43.3475017 39.4966291 35.9878575 32.7907954
[56] 29.8777515 27.2234944 24.8050342 22.6014233 20.5935752
[61] 18.7640987 17.0971479 15.5782844 14.1943526 12.9333654
[66] 11.7844009 10.7375072 9.7836166 8.9144671 8.1225304
[71] 7.4009472 6.7434674 6.1443964 5.5985451 5.1011858
[76] 4.6480105 4.2350941 3.8588600 3.5160495 3.2036934
[81] 2.9190861 2.6597625 2.4234765 2.2081816 2.0120128
[86] 1.8332711 1.6704084 1.5220139 1.3868024 1.2636027
[91] 1.1513477 1.0490651 0.9558691 0.8709523 0.7935793
[96] 0.7230799 0.6588435 0.6003136 0.5469834 0.4983909
mod_mlr$learner.model$lambda.1se
[1] 17.09715
Check for equality between {mlr} and {glmnet} directly
all.equal(modf4$lambda.1se, mod_mlr$learner.model$lambda.1se)
[1] TRUE
pdp <- generatePartialDependenceData(mod_mlr, task_f4, features = top_ten_abs)
Loading required package: mmpf
plotPartialDependence(pdp)
Individual PDP
pdp_ind <- generatePartialDependenceData(mod_mlr, task_f4,
features = top_ten_abs,
individual = TRUE
)
plotPartialDependence(pdp_ind)
Let’s look at the x values for observation 737:
obs_train_f4[737, top_ten_abs]
bf2_PRI_norm bf2_GDVI_3 bf2_Datt bf2_CI bf2_Maccioni
0.0005331477 0.9997067366 0.8283409902 1.1411477583 0.8079021105
bf2_GDVI_2 bf2_Datt4 bf2_PSND bf2_PSRI bf2_Vogelmann2
0.9946317460 0.0085985645 0.9510653828 0.0191196652 -0.2698871749
Looks ok - they are all within a normal range with respectv to the PDP estimates.
To inspect further, let’s refit a {glmnet} model directly on the training data of Fold 4 and inspect what glmnet::cv.glmnet
estimates for the lambda sequence:
train_inds_fold4 <- benchmark_models_new_penalized_mbo_buffer2[[8]][["results"]][["vi_buffer2"]][["Ridge-MBO"]][["pred"]][["instance"]][["train.inds"]][[4]]
obs_train_f4 <- as.matrix(task_new_buffer2_reduced_cor[[2]]$env$data[train_inds_fold4, getTaskFeatureNames(task_new_buffer2_reduced_cor[[2]])])
target_f4 <- getTaskTargets(task_new_buffer2_reduced_cor[[2]])[train_inds_fold4]
Fit cv.glmnet
set.seed(1)
modf4 <- glmnet::cv.glmnet(obs_train_f4, target_f4, alpha = 0)
modf4$lambda.1se
[1] 17.09715
Predict on Laukiz 2 now.
pred_inds_fold4 <- benchmark_models_new_penalized_mbo_buffer2[[8]][["results"]][["vi_buffer2"]][["Ridge-MBO"]][["pred"]][["instance"]][["test.inds"]][[4]]
obs_pred_f4 <- as.matrix(task_new_buffer2_reduced_cor[[2]]$env$data[pred_inds_fold4, getTaskFeatureNames(task_new_buffer2_reduced_cor[[2]])])
pred <- predict(modf4, newx = obs_pred_f4, s = modf4$lambda.1se)
Calculate the error
truth <- task_new_buffer2_reduced_cor[[2]]$env$data[pred_inds_fold4, "defoliation"]
mlr:::measureRMSE(truth, pred)
[1]<PHONE_NUMBER>9
sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS: /opt/spack/opt/spack/linux-centos7-x86_64/gcc-9.2.0/r-3.6.1-j25wr6zcofibs2zfjwg37357rjj26lqb/rlib/R/lib/libRblas.so
LAPACK: /opt/spack/opt/spack/linux-centos7-x86_64/gcc-9.2.0/r-3.6.1-j25wr6zcofibs2zfjwg37357rjj26lqb/rlib/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] mmpf_0.0.5 tidyselect_0.2.5 plotmo_3.5.6
[4] TeachingDemos_2.10 plotrix_3.7-7 Formula_1.2-3
[7] magrittr_1.5 ggplot2_3.3.0 glmnet_3.0-2
[10] Matrix_1.2-15 mlr_<IP_ADDRESS>01 ParamHelpers_1.12
[13] drake_7.12.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.3 txtq_0.1.4 lattice_0.20-38
[4] prettyunits_1.0.2 assertthat_0.2.1 rprojroot_1.3-2
[7] digest_0.6.25 foreach_1.4.4 R6_2.4.1
[10] backports_1.1.5 evaluate_0.13 pillar_1.4.3
[13] rlang_0.4.5 progress_1.2.0 data.table_1.12.8
[16] whisker_0.3-2 R.utils_2.8.0 R.oo_1.23.0
[19] checkmate_2.0.0 rmarkdown_1.13 labeling_0.3
[22] splines_3.6.1 stringr_1.4.0 igraph_<IP_ADDRESS>
[25] munsell_0.5.0 compiler_3.6.1 httpuv_<IP_ADDRESS>
[28] xfun_0.5 pkgconfig_2.0.3 shape_1.4.4
[31] BBmisc_1.11 htmltools_0.3.6 tibble_2.1.3
[34] workflowr_1.6.1 codetools_0.2-16 XML_3.98-1.17
[37] fansi_0.4.1 crayon_1.3.4 dplyr_0.8.3
[40] withr_2.1.2 later_1.0.0 R.methodsS3_1.7.1
[43] grid_3.6.1 gtable_0.3.0 lifecycle_0.1.0
[46] git2r_0.26.1 storr_1.2.1 scales_1.1.0
[49] cli_2.0.2 stringi_1.3.1 farver_2.0.3
[52] fs_1.4.1 promises_1.0.1 parallelMap_1.4
[55] filelock_1.0.2 vctrs_0.2.4 fastmatch_1.1-0
[58] iterators_1.0.10 tools_3.6.1 glue_1.4.0
[61] purrr_0.3.4 hms_0.5.3 parallel_3.6.1
[64] survival_2.43-3 yaml_2.2.0 colorspace_1.4-0
[67] base64url_1.4 knitr_1.23


---
title: "Organization"
updated: 2015-10-02 13:34:00
layout: default
group: organization
menu: main
status: published
---
{{ page.title }}
General Chair
Research Track Co-Chairs
In-Use Track Co-Chairs
- Freddy Lecue
- Markus Krötzsch
Resources Track Co-Chairs
- Alasdair Gray
- Marta Sabou
Posters and Demos Track Co-Chairs
- Takahiro Kawamura
- Heiko Paulheim
Workshops and Tutorials Co-Chairs
- Chiara Guidini
- Heiner Stuckenschmidt
Doctoral Consortium Co-Chairs
- Philippe Cudre-Mauroux
- Riichiro Mizoguchi
- Natasha Noy
Student Coordinators
- Abraham Bernstein
- Daniel Garijo
- Matthew Horridge
Publicity Chair
Sponsorship Co-Chairs
- Makoto Iwayama
- Carlos Pedrinaci
Proceedings Chair
Metadata Chair
Local Arrangements Chair


LAPACK has been designed to supersede LINPACK [38] and EISPACK [92,54], principally by restructuring the software to achieve much greater efficiency, where possible, on modern high-performance computers; also by adding extra functionality, by using some new or improved algorithms, and by integrating the two sets of algorithms into a unified package.
Appendix D lists the LAPACK counterparts of LINPACK and EISPACK routines. Not all the facilities of LINPACK and EISPACK are covered by Release 3.0 of LAPACK.


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{% extends "layout.html" %} {% block title %}Keith Curbelo{% endblock %} {% block main %}
If I had asked people what they wanted, they would have said faster horses.
I assisted in the development of 2 mobile applications. One was for internal use and the other for public. I used Ionic, AngularJS, Angular, and Vue.js to create the front-end compenents as well as Node.js for all back-end support.
Boca Raton, FL
I officially received my college diploma.
My first professional opportunity. I assisted with web app development and began making websites with WordPress. I gained more experience with SQL and learn the fundamentals of MicroSoft's development tools.
My first internship! I did a lot more than anticipated. I learned about web-design, SEO, and ASO. I designed my first professional web-pages and was temporarily part of the marketing team. I also teamed up with senior developers to learn the fundamentals of back-end development.
Boca Raton, FL
These years were my baby steps in the world of programming. This is where I began learning how to use HTML, CSS, and scripting languages. I gained my first experience with AGILE development being the team leader/ SCRUM master as part of academic projects.


// Package of targets for the GNU gcc code generation tools
// GCC-based code generation targets for Arm
// Runtime support package for the gnu.targets.arm.GCArmv7M target
// IAR-based code generation targets for ARM devices
// Runtime support package for ARM devices for use with the IAR toolchain
// Command-line configuration tool for IAR Embedded Workbench
// Platform support for the SimpleLink devices
// SYS/BIOS Scalable/Extensible Real-time Kernel
// Contains family-specific modules for SYS/BIOS
// Contains specifications for the ARM family-specific SYS/BIOS functions
// Contains module specifications for cc26xx
// Contains module specifications for cc3200
// Contains specifications for the Stellaris LM3S family of device-specific SYS/BIOS functions
// Contains specifications for the Stellaris LM4 family of device-specific SYS/BIOS functions
// Contains specifications for the m3 family/device-specific SYS/BIOS functions
// Contains specifications for the MSP432 family of device-specific
SYS/BIOS functions
// Contains modules that inherit from IGatePovider
// Contains Hardware Abstraction Layer (HAL) modules for the SYS/BIOS kernel
// Contains modules that inherit from IHeap
// Contains all interface specifications
// Contains core threading modules for the SYS/BIOS kernel
// Obsolete package for POSIX support with SYS/BIOS
// Contains support for SYS/BIOS in ROM for selected devices
// Contains specifications for the GNU Reentrancy Support module-specific
SYS/BIOS functions
// Multithread support for IAR runtime libraries
// Miscellaneous utility modules
// Package of targets for TI code generation tools
// Package of Arm targets for TI code generation tools for ELF
// Runtime support package for all Arm targets
// Basic runtime support for RTSC programs


Algorithm
Parameters
Number of stan runs:
1
2
3
4
Number of Iterations:
Number of Chains:
Advanced Parameters
Max Tree Depth:
Number of Time Points:


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theme = “hugo_theme_beg-master” theme = “Hugo-Octopress-master” Bold Bold 2 Bold 3 Bold 4
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Lists o’ Stuff Remember to leave a blank line before starting your lists
Regular Lists List Item 1 List Item 2 List Item 3 Ordered Lists Ordered List Item 1 Ordered List Item 2 Ordered List Item 3 Lists with Sub Lists List Item 1 List Item 2 Ordered List Item 1 Ordered List Item 2 Ordered List Item 3 List Item 3 To Do Lists get new posts showing up determine definitively if I can host on dreamhost or not render an image Links & Images Image Hint put images in the /static folder


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Hi, I am Gulesh, a FGLI student from India. I am a Senior at Haverford College. I am a Computer Science major with a minor in Mathematics and Economics.
I love programming and problem solving. I am very enthusiastic about software developments, and data analytics. I have a deep interest in computer graphics too. Currently, I am looking for full time opportunites to put my technical and analytical skills into practice.
Fields of Interest: Frontend, Software Development, Full Stack, Data Analysis, Backend, Computer Graphics, Cloud, UI/UX
HTML5 CSS3 JavaScript Bootstrap React
Node.js Express PHP Django
Java C++ Python Javascript C STATA R Racket
MySQL SQL MangoDB
MS-Excel Tableau R STATA
Employer: Haverford College
Department: Digital Scholarship HC Libraries
Project: Bridge
Duration: June 2021 - present
Employer: Haverford College
Department: Digital Scholarship HC Libraries
Project: Bridge
Duration: Sep 2020 - May 2021
Employer: Haverford College
Department: Computer Science
Courses: Data Structures & Intro to Biometrics
Duration: Jan 2020 - May 2021
Employer: Haverford College
Department: Center for Career and professional Advising
Duration: June 2020 - August 2020
Employer: Haverford College
Department: Computer Science
Topic: Study of biases in Physical Biometrics
Duration: June 2020 - August 2020


No, Not That One: Another Trump-Zelensky Call Made Public
by TIM RYANWASHINGTON (CN) – Keeping details of the call the sparked his impeachment inquiry under wraps, President Donald Trump released a memo Friday about his first apparent call to the newly elected president of Ukraine.
This story is developing…
Copyright © 2019 courthousenews.com


7.29.15
After overlooking all of the enumerable choices, I felt the most important was to cover the .each method. This method has to power to go through each element of an array or a hash. This gives the method to power to set a vairable name to each element that exists inside it. Here is a quick example of how you may call an each method to a specific array...
def mode(array)
array.each do |num|
As you can see the .each method is calling to look through every single element and give it a variable name of num. This is so you can easily call to the individuals in the array by a simple variable called num.
.each is also very univeral in that you can _ add-ons to the method teliing it to do other specific things. Some of these include each_char, which seperates each chracter into its own seperate string. There are also each methods specific to hashes such as each_key and each_value. The key method allows you to call on a specific key within the hash. And value doing the exact same but with the value slot instead of key. Here are a few examples...
This calls for each letter to be split by a space(' ')
ruby
"hello".each_char {|c| print c, ' ' }
Produces
h e l l o
This examples is calling for the values, not keys
h = { "a" => 100, "b" => 200 }
h.each_value {|value| puts value }
Produces
100
200
The each method is very universal and has many different ways to use it, many more than what I just showed you here. If you have any questions or find any mistakes. Please feel free to contact me immediately.


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The present website is an effort (using Goodreads API) in trying to help people in get to know new lesbian books by genre and in a very unusual way: Randomly.
The present website is an effort (using Goodreads API) in trying to help people in get to know new lesbian books by genre and in a very unusual way: Randomly.


Supplementary Marathon List for 1987
Scheduled but not verified AUS 26 Apr Brisbane University of Queensland AUS 30 May Mount Beauty Mount Beauty CHN 15 Mar Shanghai Shanghai Cup DEN 28 Jun Viborg Haervejs DEN 11 Oct Aarhus Aarhus ENG 15 Feb Burley New Forest ENG 28 Feb Picklescott South Shropshire Circular ENG 22 Mar Cleveland Survival FRA 16 May Taverny Val d'Oise (10th) GER Sep Plungstadt Plungstadt (13th) GER Oct Hamm Hamm ITA 01 May Roma (6th) JAM 17 Jan Montego Bay Jamaica International MEX 02 May Rosarito International Friendship (6th) (pt/pt) NED 23 May Ehenen Berg tot Berg NED 20 Sep Den Bosche Bosche NED 01 Nov Amsterdam Volks NOR 13 Jun Beitostolen Norske Fjell RSA 10 Jan Pretoria George Classen RSA 31 Jan Roxsound Pelindaba RSA 07 Feb EP Masters RSA 07 Feb Pietersburg Far North Championships RSA 14 Feb East Transvaal Championships RSA 14 Feb Sasolburg Sasolburg RSA 21 Feb Highveld RSA 21 Feb Eastern Province Championships RSA 21 Feb Naval Hill RSA 21 Feb Wilge RSA 21 Feb Dave Hawthorn Memorial RSA 28 Feb Orange Free State Championships RSA 07 Mar Bloemfontein? Marskon RSA 07 Mar Nelspruit Lowveld (pt/pt) RSA 07 Mar Southwest Africa Championships RSA 15 Mar Tony Tingle Memorial RSA 21 Mar Klerksdorp Klerksdorp Spares RSA 21 Mar Biggarsberg NCR RSA 22 Mar Summit City RSA 22 Mar Nylstroom Nylstroom RSA 28 Mar Tickwe McArthur RSA 29 Mar Richmond RSA 04 Apr Lions RSA 05 Apr Empangeni Empangeni RSA 11 Apr Ciskei Chibuku Ntabakando RSA 25 Apr Bushveld RSA 02 May Nelspruit Nelspruit Open Club Championships RSA 03 May Benoni Thompson Machine Tools RSA 11 Jul Gazankulu National RSA 18 Jul Kalahari RSA 18 Jul Western Province Masters Championships RSA 25 Jul Pepsi Kovsie (Orange Free State) RSA 05 Sep Drie Provinsies RSA 12 Sep Tickwe Golden West RSA 19 Sep Witbank RSA 03 Oct Vryburg Beesfees RSA 10 Oct Johannesburg Nashua Johannesburg RSA 10 Oct Kimberley Diamond RSA 10 Oct Biltong Festival RSA 31 Oct Ovenstone Dolfin RSA 31 Oct Willowmore Willowmore USA 03 Jan Baton Rouge LA Baton Rouge USA 10 Jan Redding CA Redding USA 10 Jan Ormond Beach FL Billy Gordon Memorial (8th) USA 23 Jan Fort Benning GA Infantry USA 24 Jan [Point Reyes] CA Point Reyes (1st) USA 25 Jan Santa Barbara CA Sri Chinmoy (4th) USA 31 Jan Volcano HI Volcano Wilderness USA 31 Jan Gatesville TX Courthouse to Gatesville (2nd) (pt/pt) USA 07 Feb Davis CA Davis (3rd) USA 14 Feb The Woodlands TX The Woodlands (9th) USA 22 Feb Bossier City LA Red River (2nd) USA 01 Feb Sioux City IA Sioux City USA 28 Feb El Paso TX Desert (7th) USA 07 Mar Wawawai WA Snake River Canyon (8th) USA 21 Mar Carlisle PA Jim Thorpe USA 15 Apr Modesto CA Modesto (4th) USA 18 Apr Lawrence KS Kansas Relays (18th) USA 18 Apr Owego NY Tom Carter's Day (5th) USA 18 Apr Yakima WA Run For Jesus (4th) USA 25 Apr Wills Point TX Daybreak (6th) USA 17 May Kailua HI Windward (7th) USA 07 Jun Foster City CA Sri Chinmoy USA 07 Jun Leadville CO Leadville (4th) (tentative) USA 05 Jul Hilo HI Big Island (13th) USA 15 Aug Fort Davis TX Prude Ranch (9th) USA 27 Sep Sioux Falls SD Sioux Falls (7th) USA 17 Oct Muncie IN Muncie (3rd) USA 18 Oct Rockford IL Rock River USA 01 Nov Loma Linda CA Orange Grove (11th) USA 08 Nov Soquel CA Summit (8th) USA 15 Nov Austin TX Austin (2nd)
Last Updated on 18 Jun 2010


DfE enquiries
Central newsdesk 020 7783 8300
General enquiries 0370 000 2288
Written statement to Parliament
Education Secretary Michael Gove has issued a Written Ministerial Statement on teachers' pensions
Following today’s statement from the Chief Secretary to the Treasury, the Department for Education has set out more detail to Parliament on reforming the Teacher Pension Scheme.
A DfE spokesperson said:
This is a fair deal for teachers and affordable for the taxpayer. We’ve listened carefully to the unions’ arguments and we’ve struck the right balance - addressing teachers’ concerns without putting any new money on the table.
The Teachers’ Pension Scheme will remain one of the best available but it will also be affordable for taxpayers. The deal guarantees all existing rights; gives a defined benefit, index-linked pension; and protects those closest to retirement now from any major changes.
It also is a much better deal for the taxpayer and keeps the future rising costs of pensions firmly under control. Teachers will contribute more to their own pensions. The scheme will switch from being based on final salaries to a career average scheme. And over time, the normal pension age for teachers will move to 68 - although teachers will have the choice to retire earlier.
Reforms to public sector pensions are essential - the status quo has never been an option. The cost to the taxpayer of teacher pensions is already forecast to double from £5billion in 2006 to £10billion in 2016 and will carry on rising rapidly as life expectancy continues to improve. This deal will control those costs.
The Education Secretary has issued a Written Ministerial Statement on reforms to the Teachers’ Pension Scheme.
Secretary of State for Education (Rt Hon Michael Gove MP)
On 2 November the Chief Secretary to the Treasury made a Statement to the House setting out an improved offer on public service pensions to public sector workers (Cm 8214). This offer provided a more generous cost ceiling for scheme-specific discussions to work within, and protected all those within ten years of their pension age from any further change. This generous offer was conditional on the Government and trades unions reaching agreement by the end of the year, including in the Teachers’ Pension Scheme, bringing to a conclusion talks that have lasted since February 2011.
Since 2 November I have been engaged in detailed and intensive talks with the teacher and lecturer trades unions and employer representatives. I can now report to the House on the heads of agreement on the scheme design for the Teachers’ Pension Scheme to be introduced in 2015, on which talks have concluded. The government have made clear this sets out their final position on the main elements of scheme design, which unions have agreed to take to their Executives as the outcome of negotiations on the main elements of scheme design. This includes a commitment to seek Executives’ agreement to the suspension of any industrial action on pension reform while the final details are being resolved. Further detailed work will take place in the New Year and Executives will consult members as appropriate.
The agreement includes changes to the Government’s reference scheme to reflect the priorities of the teaching profession in relation to early retirement and other issues, consistent with the need to remain within the Government’s overall cost ceiling.
The agreement reached allows for further discussions on variations to the balance between the accrual rate and the CARE revaluation factor within the limits of the Government’s cost ceiling.
The core parameters of the new scheme are set out below:
a. A pension scheme design based on career average;
b. A provisional accrual rate of 1/57th of pensionable earnings each year, and the resolution of outstanding issues not covered by this agreement.
c. Revaluation of active members’ benefits in line with CPI + 1.6% .
d. Normal Pension Age equal to State Pension Age, which applies both to active members and deferred members (new scheme service only);
e. Pensions in payment to increase in line with Prices Index (currently CPI);
f. Benefits earned in deferment to increase in line with CPI;
g. Average member contributions of 9.6%, with some protection for the lowest paid;
h. Optional lump sum commutation at a rate of 12:1, in accordance with HMRC limits and regulations;
i. Spouses/Partner pension in accordance with current provisions;
j. Lump-sum on death in service of 3 times FTE salary;
k. Ill-health benefits the same as those in the current open scheme;
l. Actuarially fair early/late retirement factors on a cost-neutral basis except for those with a NPA above age 65, who will have early retirement factors of 3% per year for a maximum of 3 years in respect of the period from age 65 to their NPA; and
m. An employer cost cap to provide backstop protection to the taxpayer against unforeseen costs and risks.
The Government Actuary’s Department has confirmed that this scheme design does not exceed the cost ceiling set by the Government on 2 November. Copies of the heads of agreement and GAD verification have been deposited in the Libraries of both Houses.
Central newsdesk 020 7783 8300
General enquiries 0370 000 2288
Published: 20 December 2011


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Transfer tacit knowledge between distributed colleagues
Interact with the Exomars Rover on Mars in Virtual Reality
An exhibition about the future of food security and the importance of inclusivity
A redesign of the Philips Air Purifier based on extensive research
An interactive bench designed to promote communication by enabling low barrier playful interactions in the waiting room
A set of tools for the online courses of the TU Delft to improve accessibility
An android app that was build for the Sziget Festival in Budapest