Programme Schedule
UTC Time: 12:00 - 13:00
Keynote:
Dr. Yuan Luo
Tookitaki Holding Pte. Ltd.
Title:
Apply Machine Learning for Anti-Money-Laundering - Challenges and Resolutions
UTC Time: 13:00 - 14:00
Paper Session #1:
Analysis of multivariate time series predictability
based on their features
Peter Gladilin and Anton Kovantcev
Peter Gladilin and Anton Kovantcev
AttentionFM: Incorporating Attention Mechanism and
Factorization Machine for Credit Scoring
Ying Liu, Wei Wang, Tianlin Zhang, and Zhenyu Cui
Ying Liu, Wei Wang, Tianlin Zhang, and Zhenyu Cui
Wavelet Denoised-ResNet CNN and LightGBM Method to
Predict Forex Rate of Change
Yiqi Zhao and Matloob Khushi
Yiqi Zhao and Matloob Khushi
Nonlinear Tensor Completion Using Domain Knowledge: An
Application in Analysts' Earnings Forecast
Ajim Uddin, Xinyuan Tao, Chia-Ching Chou, and Dantong Yu
Ajim Uddin, Xinyuan Tao, Chia-Ching Chou, and Dantong Yu
UTC Time: 14:00 - 15:00
Paper Session #2:
A federated learning based approach for loan defaults
prediction
Geet Shingi
Geet Shingi
Stock price prediction by using hybrid sequential
Generative Adversarial Networks
Bate He and Eisuke Kita
Bate He and Eisuke Kita
A Network Analysis and Price Movement Prediction of
the Cryptocurrency Market Using Centrality Measures
Kin Hon Ho, Wai Han Chiu, and Chin Li
Kin Hon Ho, Wai Han Chiu, and Chin Li
Exploring the Use of Data at Multiple Granularity
Levels in Machine Learning-Based Stock Trading
Jacopo Fior and Luca Cagliero
Jacopo Fior and Luca Cagliero
_____


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One of Londons many upcoming professionals has decided to jump onto the property ladder. They have the money, they know roughly which area they want and have their down payment together
But first our potential customer will have to check prices on other estate agents websites to determine average prices for the area. They will want to see historical data on record keeping websites, as well as future developments in the area, to get an idea for growth once they decide to sell. They may need to calculate their new commute by visiting google maps or other mapping applications, as well as TFL's website.
A young family move out into the suburbs of London. They find their dream home on your estate agencies website. The garden is big, the road is quiet and there are enough bedrooms for the forseable future.
The family will have too search review websites for information on local schools, and check google for information about parks and greenspace for the children, they will also have to check the local councils website for parking restrictions if they want to get a family car. All these activities will lead to them navigating away from the agencies website.
A professional landlord, looking to make another investment is interested in one of your properties. They may be interested in renting the property through your agency, and are experienced in the market.
The professional will need to research a lot of other properties on other sites in the area to gain an understanding of the costs involved with their plan. Are there restrictions on HMO's (houses of multiple occupation) in the area? What is the average price for a room in a HMO in the area? Planning permissions may need to be sought from local authority websites. Finally the entertainment value of the area will have to be investigated through cultural and entertainment websites.
In every scenario, your potential customer has to leave your website for large amounts of time to find answers to their questions.
And when they leave your website their browser will record their activity and send the data to advertising companies. These companies will pick up on your potential customers interest in London real estate, and from that moment on...
Every ad your potential customer sees in their browser will be for real estate in London.
Ads for your competitors.
And chances are, your potential customer will see something they like, And suddenly your potential customer is no longer your potential customer. But your competitors.
London Property Hacker provides all the data any potential customer could want to know, packaged inside interactive widgets. The widgets are easy to implement and install. In fact, each widget can be installed with a single line of HTML code.
The core widgets are available as single lines of html code that can be copied onto any webpage. The examples below show data for Stoke Newington
The price graphs show data for LSOA's (lower super output areas).There are over 600 LSOA's within London which allows for a very fine level of detail. The results can be refined with other variables.
This graph shows the number of houses available to purchase vs the number that have been sold in the last month. Results can be refined by number of bedrooms.
This graph shows the number of houses available to rent vs the number that have been let in the last month. Results can be refined by number of bedrooms.
This graph shows the average price a house sells for in the area. Results can be refined by number of bedrooms or the statistical method (mean or median).
This graph shows the average price a house is let for in the area. Results can be refined by number of bedrooms or the statistical method (mean or median).
This graph shows the average time it takes to rent a property in the area. Results can be refined by number of bedrooms.
This graph shows the average time it takes to sell a property in the area. Results can be refined by number of bedrooms.
This graph shows the cost of a room in the area. Results can be refined by number of bedrooms within the property, statistical method (mean or median) and the size of the room.
The map based widgets provide a visual overview of the surrounding area, allowing users to get a better understanding of the neighbourhood before even visiting.
The Local Area map shows a snapshot of the closest areas of interest to a property.
The Local Area Array map shows a more detailed view of the local area, with lists of different points of interest.
The School Checker provides a detailed overview of near-by schools and allows users to filter results.
The Journey Checker lets users see the average journey time and cost to any other point within London with a click.
The widgets are easy to implement. Simply copy two lines of HTML onto the desired page and London Property Hacker will automatically do the rest.
The data that powers the widgets is also available as a REST API. Developers can use the REST API for research or to develop their own apps and widgets. See the following swagger document for details of the calls available.
Got a search function for you inventory? Supercharge it with the Metasearch Gateway from London Property Hacker! The metasearch allows users to refine their search using LPH's data-sets. For example, users can search for properties to rent for under £1,000 per month, with no parking permit restrictions, near a play park and in the catchment area of a good school. Integrating the metasearch into your existing sytem is easy. The London Property Hacker filters produce polygons. As long as your inventory includes geographic location (e.g. latitude and longitude), it is Metasearch Gateway compatible!
Platform Access
Platform Coming Soon!
ContactPlatform Access
Platform Coming Soon!
ContactPlatform Access
Embeddable Widgets
Embeddable Widgets
Extras


Managing Search Settings
How to manage search setting for the Store Catalog module.
Prerequisite. The "Show Category Products" field must be checked in the General Settings section to configure these settings. See "Managing Store Catalog General Settings"
- Go to the Store Catalog module.
-
- Select the Catalog Settings tab.
- Maximize the Sort Setting section and modify any of the following settings:
- At Search Columns, beside each of the columns which can be searched on. The available options are Manufacturer,
Model Number,
Model Name,
Product Summary, and
Product Description.
- At Search By, select the default search column.
- At Search Results Template, select the template to be used for search results. The template included with the Store is called SearchResultsList.htm.
-
Click the Update button.


My name is Guilherme Torres, I'm 28, I'm from Brazil and I'm a fullstack development student!
I started learning front end development first because I wanted to make my own websites and application interfaces. I have experience working with HTML, CSS, Bootstrap, SaSS, Javascript and React.js
Then I discovered that programming an application back end is just as fun and as rewarding as making the visual part. I'm experienced with Node.js and PHP


| Package | com.pblabs.box2D |
| Class | public class PolygonCollisionShape |
| Inheritance | PolygonCollisionShape CollisionShape |
| Property | Defined by | ||
|---|---|---|---|
| density : Number | CollisionShape | ||
| friction : Number | CollisionShape | ||
| isTrigger : Boolean | CollisionShape | ||
| restitution : Number | CollisionShape | ||
| vertices : Array | PolygonCollisionShape |
| Method | Defined by | ||
|---|---|---|---|
|
doCreateShape():b2ShapeDef
| PolygonCollisionShape |


Utility class for testing and manipulation of PHP arrays.
Declared abstract, as we have no need for instantiation.
| package |
Default |
|---|
filter(array $data, callable $callback, null|integer $flag = null) : array
array
callable
null|integer
array
hasIntegerKeys(mixed $value, boolean $allowEmpty = false) : boolean
mixed
boolean
Should an empty array() return true
boolean
hasNumericKeys(mixed $value, boolean $allowEmpty = false) : boolean
A numeric key can be one of the following:
mixed
boolean
Should an empty array() return true
boolean
hasStringKeys(mixed $value, boolean $allowEmpty = false) : boolean
mixed
boolean
Should an empty array() return true
boolean
inArray(mixed $needle, array $haystack, integer|boolean $strict = false) : boolean
Due to "foo" == 0 === TRUE with in_array when strict = false, an option has been added to prevent this. When $strict = 0/false, the most secure non-strict check is implemented. if $strict = -1, the default in_array non-strict behaviour is used.
mixed
array
integer|boolean
boolean
isHashTable(mixed $value, boolean $allowEmpty = false) : boolean
An array is a hash table if:
For example:
$hash = array(
'foo' => 15,
'bar' => false,
);
$hash = array(
1995 => 'Birth of PHP',
2009 => 'PHP 5.3.0',
2012 => 'PHP 5.4.0',
);
$hash = array(
'formElement,
'options' => array( 'debug' => true ),
);
mixed
boolean
Is an empty array() a valid hash table?
boolean
isList(mixed $value, boolean $allowEmpty = false) : boolean
A list is a collection of values assigned to continuous integer keys starting at 0 and ending at count() - 1.
For example:
$list = array('a', 'b', 'c', 'd');
$list = array(
0 => 'foo',
1 => 'bar',
2 => array('foo' => 'baz'),
);
mixed
boolean
Is an empty list a valid list?
boolean
iteratorToArray(array|\Traversable $iterator, boolean $recursive = true) : array
Converts an iterator to an array. The $recursive flag, on by default, hints whether or not you want to do so recursively.
| Throws |
|
|---|
array|\Traversable
The array or Traversable object to convert
boolean
Recursively check all nested structures
array
merge(array $a, array $b, boolean $preserveNumericKeys = false) : array
If an integer key exists in both arrays and preserveNumericKeys is false, the value from the second array will be appended to the first array. If both values are arrays, they are merged together, else the value of the second array overwrites the one of the first array.
array
array
boolean
array
ARRAY_FILTER_USE_BOTH
ARRAY_FILTER_USE_KEY


TIME BANKER
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Have fun and happy coding!


Binning routines¶
Goal: Create binned spectra from Chandra data given the WVT map of the pixels
- OUTPUTS:
A combined spectra for each bin as designated by the WVT.
This is to be used for spectral fitting (we’ll that’s why I made this program)
File put in /PathToChandraData/OBSID/repro/binned
- Additional Notes:
As mentioned, the program was designed to generate combinned-binned-spectra so that I could generate temperature maps… The program can easily be canabilized for other uses or specifications
-
TemperatureMapPipeline.binned_spectra.
coord_trans
(pixel_x, pixel_y, file_to_split)¶ Translate image (logical) coordinates into physical (sky) coordinates
- Parameters
pixel_x (float) – x coordinate in image coordinates
pixel_y (float) – y coordinate in image coordinates
file_to_split (str) – Name of event file
- Returns
x coordinate in physical coordinates y_center (float): y coordinate in physical coordinates ra (float): RA in degrees dec (float): DEC in degrees
- Return type
x_center (float)
-
TemperatureMapPipeline.binned_spectra.
create_evt
(file_to_split, bin_number, output_dir)¶ Create temporary event file
- Parameters
file_to_split (str) – Name of file to be split
bin_number (int) – Relative number of the bin wrt WVT numbering system
output_dir (str) – Path to output directory
- Returns
A copy of the temporary event file
-
TemperatureMapPipeline.binned_spectra.
create_reg
(output_dir, regions)¶ Create temporary region file in ds9 format
- Parameters
output_dir (str) – Output directory
reigons (str) – List of regions to add
-
TemperatureMapPipeline.binned_spectra.
create_reg_comb
(pix_in_bin, file_to_split, bin_number, output_dir)¶ Function to concatenate pixels in a bin together in order to reduce calculation time.
- Parameters
pix_in_bin (int) – List of pixels in bin
file_to_split (str) – Name of event file
bin_number (int) – Relative number of the bin wrt WVT numbering system
output_dir (str) – Path to output directory
- Returns
Temporary ds9 file with concatenated pixels in bin that will be used to extract spectrum rapidly
-
TemperatureMapPipeline.binned_spectra.
create_spectra
(base_directory, filename, OBSIDS, source_file, output_dir, wvt_output)¶ Wrapper function to create spectra for each bin in the WVT map
- Parameters
base_directory (str) – Directory containing Chandra data
filename (str) – Name of file to read in WVT bin data
dir (str) – Directory for Chandra OBSID
file_to_split (str) – Name of file to split in repro directory
output_dir (str) – Output path for binned files
- Returns
Individual spectrum files (.pi) for source and background in each WVT bin
Note
This is parallelized to run on 4 cores in order to speed up calculation time
-
TemperatureMapPipeline.binned_spectra.
source_fits
(filenames, source_file, obsid)¶ Create fits and image file for primary source region in reprocessed Directory Also create blanksky file
- Parameters
filenames (str) – dictionary containing evt2 file
source_file (str) – source region name without extension
obsid (str) – Chandra Observation ID
- Returns
Creates image file and blanksky file
-
TemperatureMapPipeline.binned_spectra.
spec_loop
(obsid, filenames, file_to_split, output_file, output_dir, directory_repro, bin_i)¶ Parallelized loop for creating spectra for bins
-
TemperatureMapPipeline.binned_spectra.
specextract_run
(obsid, filenames, file_to_convert, outfile_from_convert, output_dir, bin_number)¶ Execute specectract command with designated parameters. Grouping is set to 1 count per bin.
- Parameters
obsid (str) – ObsID
filenames (str) – List of relavent files for given ObsID – contains badpixel file, evt1, evt2, …
file_to_convert (str) – Initial Level 2 file
outfile_from_convert (str) – Name of extracted spectrum
output_dir (str) – Directory for extracted spectrum
bin_num (int) – Relative number of the bin wrt WVT numbering system
- Returns
Creates extracted spectrum from WVT bin region
-
TemperatureMapPipeline.binned_spectra.
split_fits
(obsid, filenames, file_to_split, output_file, output_dir, pix_in_bin_num, bin_number)¶ Create spectra from initial bin regions
- Parameters
obsid (str) – Current ObsID
filenames (str) – List of files necessary for specextract
file_to_split (str) – Fits file in string format
output_file (str) – Directory for output
output_dir (str) – Pha outroot in string format
pix_in_bin_num (int) – pixel number relative to bin (0-max(bin.pixels))
bin_number (int) – Relative number of the bin wrt WVT numbering system


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ERRORS
If an error happens, the endpoints will send a JSON.
- Returns:
- JSON:
- "error": "{The error message varies.}"
GET: /
This is the home index endpoint.
It doesn't do much except returning a Never Gonna Give You Up by Rick Astley lyrics.
GET: /robots
This is another endpoint that will send a JSON message as follows:
{"msg": "Please don't do anything bad to this service :)."}
Usually it's for bots or web-scrapers for instructions of which stuffs are not allowed to be taken.
POST: /v1/public/register
Register Endpoint. It will create User data.
- Requires:
- Form data which input names as follow: (case sensitive)
- Returns:
- JSON:
- msg: User registered successfully.
POST: /v1/public/login
Login endpoint.
- Requires:
- Form data which input names as follow: (case sensitive)
- Returns:
- JSON with Keys as follows:
- token
- userEmail
- userId
- userName
- userRole
NOTE:
-
Protected API groups need "token" and "userId" on the Header.
So from here, you will have to provide "token" and "userId" on the Header
-
:id or :replyId means that a number (for example, /v1/protected/issue/show/1)
is stored as a variable with the variable's name as "id" and "replyId". Hence,
in the example, the "id" has a value of "1".
PATCH: /v1/protected/user/:id/change-password
Change Password endpoint.
- Requires:
- Form data which input names as follow: (case sensitive)
- old-password
- new-password
- confirm-password
- Returns:
- JSON:
- msg: User registered successfully.
GET: /v1/protected/user/:id
Change Password endpoint.
- Returns:
-
PLEASE ASK THE DEVELOPER FOR THE RETURN VALUE.
POST: /v1/protected/issue/create
Create Issue endpoint. Create an Issue and store the data into the database.
- Requires:
- Form data which input names as follow: (case sensitive)
- title
- description
- severity
- Returns:
- JSON with Keys as follows:
- issueID
- msg: Data succesfully created.
GET: /v1/protected/issue/index
Create Issue endpoint. Shows all Issues available.
- Returns:
-
PLEASE ASK THE DEVELOPER FOR THE RETURN VALUE.
GET: /v1/protected/issue/show/:id
Show Issue endpoint. Shows all Issues available.
- Returns:
-
PLEASE ASK THE DEVELOPER FOR THE RETURN VALUE.
PATCH: /v1/protected/issue/update/:id
Update Issue endpoint. Update an Issue and store the data into the database.
- Requires:
- Form data which input names as follow: (case sensitive)
- title
- description
- status
- severity
- Returns:
- JSON with Keys as follows:
- issueID
- msg: Data has been updated succesfully.
DELETE: /v1/protected/issue/delete/:id
Delete Issue endpoint. Delete (softly) an Issue.
- Returns:
- JSON with Keys as follows:
- msg: issue is deleted successfully
POST: /v1/protected/issue/show/:id/reply
Create Reply endpoint. Create a Reply and store into database.
- Requires:
- Form data which input names as follow: (case sensitive)
- Returns:
- JSON with Keys as follows:
- replyID
- msg: Reply added successfully!
PATCH: /v1/protected/issue/show/:id/update-reply/:replyId
Update Reply endpoint. Update a Reply and store into database.
- Requires:
- Form data which input names as follow: (case sensitive)
- Returns:
- JSON with Keys as follows:
- msg: Data successfully updated.
DELETE: /v1/protected/issue/show/:id/delete-reply/:replyId
Delete Reply endpoint. Delete (softly) a Reply.
- Returns:
- JSON with Keys as follows:
- msg: Data successfully deleted.


{{ post.title }}
{{ post.date | date: "%B %e, %Y" }}
--- layout: page permalink: /posts/security-research/ title: Security Research description: '"Realm of Knowledge and Silence"' background: '/img/bg-security-research.jpg' ---
{{ post.date | date: "%B %e, %Y" }}


This app makes you some nice fast-loading HTML that you can paste into your site for game previews. Beta and without warranty. Bugs/feature requests to<EMAIL_ADDRESS>It's a bit slow so be patient please.


Your order items have been shipped today. Please refer to the detail below:
Name:
User Type:
Email:
Phone:
Shipping Address:
Billing Address:
Order #:
Estimated Delivery Date: N/A
| Name | Color | Size | Price | Qty |
|---|---|---|---|---|
| Subtotal | ||||
| Tax | ||||
| Shipping | ||||
| Total |
Please visit your order managment page for more details.
If you have any inquery about our purchase, please contact us: .
Thanks,


--- date: 2011-02-05T00:00:00Z published: true title: Who’s afraid of the Muslim Brotherhood? - The Globe and Mail url: /2011/02/05/who-s-afraid-of-the-muslim-brotherhood-the-globe-and-mail/ ---
When these parties are allowed a role in democratic government, there’s a pattern. Remember, however alarming their ideas about women and Israel, the Muslim Brotherhood and its neighbouring parties represent the people who explicitly rejected the violent option (and were shunned and sometimes attacked for this by the jihadists) because they wanted a place in a legitimate government. There’s zero chance of Egypt’s uprising turning into the 1979 Iranian revolution or the terrorist violence of Hamas; there are no parties, and no Egyptian constituency of any size, seeking a theocracy.


public class GameController
extends Object
| Modifier and Type | Field and Description |
|---|---|
static boolean |
assignedCard
This boolean is false if a card hasn't been assigned to the current player.
|
static boolean |
isTournament
This boolean is false for normal gameplay and true for tournament mode.
|
| Constructor and Description |
|---|
GameController() |
| Modifier and Type | Method and Description |
|---|---|
static void |
changeToNextPhase(GameMap gameMap)
Changes to next phase in the game loop or changes player if in the last phase of current player
|
static boolean |
performAttack(GameMap gameMap,
String command)
This method performs the attack.
|
static boolean |
performAttackNone(GameMap gameMap)
This method handles the attack none scenario.
|
static boolean |
performFortify(GameMap gameMap,
String command)
This method performs the fortification.
|
private static boolean |
performFortifyNone(GameMap gameMap)
Handles the fortifynone logic.
|
static boolean |
performReinforce(GameMap gameMap,
String command)
Performs the reinforce command.
|
static boolean |
processAttackCommand(GameMap gameMap,
String command)
process attack command from the cli
|
static boolean |
processExchangeCardsCommand(GameMap gameMap,
String command)
Processes exchangecards command from CLI
|
static boolean |
processFortifyCommand(GameMap gameMap,
String command)
Processes fortify command from the cli
|
static boolean |
processReinforceCommand(GameMap gameMap,
String command)
Processes reinforce command from the cli
|
static boolean |
processSaveGameCommand(GameMap gameMap,
String command)
This command processes the save game to capture the current state of the game and serializing
it.
|
static boolean |
processShowMapCommand(GameMap gameMap,
String command)
Shows map connectivity, ownership, army(s)
|
static void |
startPhaseLoop(GameMap gameMap)
Starts the game loop of the current player
|
static boolean |
validateAttack(GameMap gameMap,
String command)
Validates the attack method.
|
static boolean |
validateFortify(GameMap gameMap,
String command)
Checks whether fortification is valid.
|
static boolean |
validateReinforce(GameMap gameMap,
String command)
Validates the reinforcement command.
|
public static boolean assignedCard
public static boolean isTournament
public static boolean processReinforceCommand(GameMap gameMap, String command)
gameMap
- contains game statecommand
- cli command from the userpublic static boolean processSaveGameCommand(GameMap gameMap, String command)
gameMap
- The GameMap variablecommand
- The command as a Stringpublic static boolean processAttackCommand(GameMap gameMap, String command)
gameMap
- contains game statecommand
- cli command from the userpublic static boolean processFortifyCommand(GameMap gameMap, String command)
gameMap
- contains game statecommand
- cli command from the userpublic static boolean processExchangeCardsCommand(GameMap gameMap, String command)
gameMap
- contains game statecommand
- cli command from the userpublic static boolean performReinforce(GameMap gameMap, String command)
gameMap
- the Game Map instance.command
- the command entered by the user.public static boolean validateFortify(GameMap gameMap, String command)
gameMap
- The Game Map instance.command
- The command entered by the user.public static boolean performFortify(GameMap gameMap, String command)
gameMap
- The Game Map instance.command
- The command entered by the user.private static boolean performFortifyNone(GameMap gameMap)
gameMap
- The Game Map instancepublic static boolean processShowMapCommand(GameMap gameMap, String command)
gameMap
- contains game statecommand
- cli command from the userpublic static void changeToNextPhase(GameMap gameMap)
gameMap
- contains game statepublic static void startPhaseLoop(GameMap gameMap)
gameMap
- contains game statepublic static boolean validateAttack(GameMap gameMap, String command)
gameMap
- The Game Map instance.command
- The command entered by the user.public static boolean performAttack(GameMap gameMap, String command)
gameMap
- The Game Map instance.command
- The command entered by the user.public static boolean performAttackNone(GameMap gameMap)
gameMap
- The Game Map instance.public static boolean validateReinforce(GameMap gameMap, String command)
gameMap
- The Game Map instance.command
- The command entered by the user.


Planet Sick-Boy: http://www.sick-boy.com "We Put the SIN in Cinema"
© Copyright 2001 Planet Sick-Boy. All Rights Reserved.
Forget Spielberg's Amistad. Pretend John Singleton's Rosewood never happened. Take all the video copies of Beloved and.okay, maybe that's going too far. The point is that most films we've seen about slavery are the same story over and over and over again. Sure, they make you mad and tug at your heartstrings (and cause white people to feel really uncomfortable for the better part of the ride home...until "All-Star" comes on the radio), but most follow the same formula, which is why the quality of the genre has gradually taken a nosedive since Roots aired on television.
Enter Roger Gnoan M'Bala's Adanggaman, a somewhat exciting new film from the Ivory Coast that breaks the lame American cinematic slavery mold. It's set entirely in Africa (Guineau, to be exact) during the late 17th century, and, at least from its first few scenes, doesn't seem like it's going to deal with slavery at all. Adanggaman starts with a young couple having sex; the guy is Ossei (Ziable Honoré Goore Bi), and his father is forcing him to marry a different girl.
After being mercilessly beaten by his father for carrying on with this other girl, Ossei packs up his bow and best pelt and hits the road. He returns a few days later, only to find his village torched, his father dead, and most of the others missing. What Ossei doesn't know is that his missing neighbors have been captured by an extremely well-organized group of giant, bloodthirsty female warriors. These Amazonesque women work for the power-hungry King Adanggaman, who, in turn, sells unsuspecting villagers to the Dutch for guns and rum.
The rest of Adanggaman follows Ossei's fight to free his family from the King's steely grip. His journey includes befriending a faith-healer with a warrior daughter, for whom Ossei falls. The shockingly brutal film also teaches certain slave auction protocol, like that the bidding begins with sheep, then moves on to cows, then goats. Then, and only then, can you bid with yams.
1:30 - Not Rated but contains nudity and sexual content
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What once was spineless has now acquired a spine. That old pamphlet Obsequies For Lars Talc, Struck By Lightning is newly available in an almost but not quite facsimile paperback edition. Only twenty-five copies of the original were printed—now every single one of you lot can buy as many copies as you like. Which I hope and pray you will. What with Christmas approaching, what better gift to give to those dearest to you, including that crumpled Jesuit hiding in the broom-cupboard?
Go to Lulu to place your order(s) right this minute. It's the sensible thing to do.


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FILM TITLE: A NOUS AMOURS DIRECTOR: MAURICE PIALAT COUNTRY: FRANCE 1984 CINEMATOGRAPHY: JACQUES LOISELEUX MUSIC: Various. Chants by Klaus Nomi CAST: Sandrine Bonnaire, Evelyne Ker, Maurice Pialat, Dominique Besnehard WRITTEN BY: Maurice Pialat SUPER FEATURES: Emotional story.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
A dysfunctional family is falling apart. And the ones that suffer the most are the teenagers. The father has been having an affair and has made plans to leave the house. The daughter has a boyfriend and the mother does not approve of her behavior. And the son commits himself to keeping the family's business running while the mother is going through her tantrums.
There really is no solution, just some acceptance of the fact that things are far from perfect. The mother has the hardest time accepting this, and takes it out on her daughter, no doubt suggesting that she made the same errors in her earlier days which have brought her to the current situation.
There is really no solution to the whole thing, and as is the case with another film maker in France, this is just another day in the life of these people. No solutions or suggestions are made.
This reminds me of Godard's theme at one time that he would like to make a political film instead of a film about politics. So he tears down the fourth wall, and let's us see the whole thing from all sides and angles. The result, is a film that only allows you to follow the emotional outbursts, and avoid any serious conclusions about any thing. We do know one thing. This family is pretty much broken up and where it is going is not something anyone knows, or expect to know very soon.
The acting, is the strong point in this film. It manages to keep the tempestuous people alive and fighting for their own space, amidst little sub-plots here and there. The son has to side with the mother, and really does not dislike his sister, but he is forced to play dad, since he is not around. The father comes and goes when he pleases, since he is paying the rent, which he is about to stop, probably because he is out of a job.
Where will it all lead is the question, and since we do not get these, and any, answers, the film is likely to be a frustrating adventure for many. But the acting is tough, and solid, and very well prepared. There is no lack of consistency between any of the parts in the film. It takes no sides.
Well directed, considering the small script. It appears ad-libbed many times, but this is to its advantage in this specific situation.
3 of 5 GIBLOONS Copyright (c) Pedro Sena 1995. All Rights Reserved.
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History of Chess
Early Chess
Chess is thought to have originated in northern India almost 1500 years ago. The rules were much different however, with only a little resemblance to Chess today. The game eventually found its way to Europe, where it underwent fundamental changes, bringing it closer to modern Chess. It was not until about 500 years ago though (around 1000 years after the game was first played), that modern chess was developed.
Professional Chess
The first chess tournament was held in 1851 in London. as more people played the game, more tournaments were held. Eventually this led to a match between Johannes Zukertort and Wilhelm Steinitz, which would lead to Steinitz becoming the first World Chess Champion.
Modern Chess
Now, there are several chess organizations that host tournaments world-wide. The World Chess Championship is run by FIDE, the organization which is recognized as official, has a tournament to determine who is eligible to challenge the champion.


23-24 March 2019
Nanyang Auditorium
50 Nanyang Ave, Singapore 639798
| Day 1 | ||
|---|---|---|
| 12:00 | 12:30 | Registration |
| 12:30 | 01:00 | Opening Keynote |
| 01:00 | Hacking Begins! | |
| 18:00 | 19:00 | Dinner |
| 23:00 | 24:00 | Supper |
| Day 2 | ||
|---|---|---|
| 06:00 | 07:00 | Breakfast |
| 13:00 | Hacking Ends! | |
| 13:00 | 14:30 | Lunch + Initial Judging (DevPost) |
| 14:30 | 16:00 | Presentations by Selected Teams |
| 16:00 | 16:30 | Prize Presentation |


home
Click the button below to generate a backup file of the database.
generate
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Closing out long-standing bugs. Seriously a satisfying feeling having those off the list.
12/20/2007, 4:19:46 PM
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Closing out long-standing bugs. Seriously a satisfying feeling having those off the list.
12/20/2007, 4:19:46 PM
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HEARTS IN ATLANTIS A film review by Max Messier Copyright 2001 filmcritic.com
The entire time I spent watching the latest Stephen King big-screen adaptation Hearts in Atlantis, I had this nagging feeling in the back of my mind that something was missing. All the key elements of a potentially great film were present -- authentic-looking 1960s Americana scenery, great acting by Anthony Hopkins and newcomer Anton Yelchin (Delivering Milo), an intriguing story line, and strong directing by Scott Hicks. And then, at the end of the film, it just hit me like a sap across the back of the neck.
One common recurring narrative in many of King's better-known novel-to-screen adaptations -- such as Stand by Me, The Green Mile, and The Shawshank Redemption -- incorporates an older gentleman recalling his youth or a life-changing incident of his life. Hearts in Atlantis follows this to a tee. After learning of a childhood friend's death, a middle-aged photographer Robert Garfield (David Morse) ventures back to his hometown for the funeral. Upon arrival, Robert recalls memories of youth and of one innocent, fateful summer when a mysterious man named Ted Brautigan (Hopkins) entered his life and changed it forever.
At the beginning of summer, old Teddy rents the studio apartment on the upper floor of his childhood home. During his stay, Teddy displays the usual, weird behavioral traits of all King characters. He's a strange gent who carries his belongings around in brown paper bags; says the kookiest things about young love, life as a kid, and the pains of growing old; appears as a mythical figure in times of crisis; and carries the weight of the world on his shoulders.  During the summer, Bobby falls in love and shares his first kiss with the neighborhood girl, Carol Gerber (Mika Boorem), endures the trials of defending his honor from the neighborhood bullies, pines for a bicycle in the local convenience store, and discovers Teddy's mysterious gift of seeing beyond the world. Bobby is then entrusted with informing and somehow protecting Teddy from the Low Men, dangerous ruthless faceless characters who drive fancy cars and cast long shadows, who are hunting for Teddy because of his unique "gift."
All of these circumstances would have made the film a success except for one damning thing. Everything -- from the unrequited love of two childhood sweethearts to the emotional abuse of a lonely boy -- feels so recognizable and indistinguishable from all of the previous works that deal with these similar themes. I kept catching myself remembering parts from Stand By Me and even The Goonies. It's a shame, because the film looks great from a technical standpoint and both Hopkins and Yelchin pull off great performances. In the end, though, the film feels cheap and used up -- like watching a two-hour Hallmark special. You feel guilty for even liking the good parts.
Even the message of Hearts in Atlantis becomes lost in the emotional manipulation by both the characters and story. Instead of giving the audience the ability to develop its own emotional ties to the film's narrative and personalities, the script dictates pre-defined moments of trepidation, intrigue, anguish, sadness, loneliness, and frustration by the obvious flow of the characters' dialogue and the film's plot points. One key example is when Hopkins details the story of a once-great football player winning one last game for the home team. So much deliberation is given to this scenario that we all but expect the main character of the story to have to overcome a challenging obstacle in his own life. It's a desperate manipulation, and it has a totally opposite effect: In the end, you can care less what happens to Teddy or his little friend.

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Overview
This version was conceived to put the Firebug Lite in a new level, by allowing code reuse from Firebug's original source. A new core was created to accomplish the following goals:
Performance - the core of Firebug Lite 1.3 was rewritten from scratch taking the performance into account in the first place.
Modularity - the code is now more modular, making easier the process of adding new features and UI components such as panels, buttons, menus etc. The modularity also helps the development process. Once the modules can be isolated it is easier to detect the cause of complicated problems like memory leaks.
Shared code - the core was designed to make it possible to port some code directly from the Firebug source, with as few as possible modifications. As a result, some features and UI elements behave exactly as in Firebug.
Compatibility - the new core is compatible with XHTML and XML+XSL documents. Thanks to the new context-independent approach, it supports now the experimental persistent popups feature (popups that "live" across different page loads of the same domain), and will support cross-iframe inspection in the next version.


Detection of Performance Regression Introducing Code Changes Using Static and Dynamic Metrics
Abstract
Performance regression testing is highly expensive as it delays system development when optimally conducted after each code change. Therefore, it is important to prioritize the schedule of performance tests by executing them only when a newly committed change is most likely to introduce performance regression. This paper introduces a novel formulation of the detection of performance regression introducing code changes as an optimization problem. Static and dynamic metrics are combined to generate a detection rule, which is being optimized in terms of its ability to flag problematic code changes, and avoid false positives. We evaluated our approach using performance issues, extracted from the Git project. Results show the eectiveness of our approach in accurately detecting performance regression introducing code changes compared with state-of-the-art techniques. Moreover, our suggested detection rules were found to be robust to the software changes over time, which reduces the overhead of updating them frequently.
Keywords
Performance regression, multi-objective optimization, software testing, software quality
- Introduction
- Methodology
- Approach Overview
- Data Collection
- Solution Representation
- Solution Evaluation
- Solution Variation
- Experimental Setting
- Research Questions
- Parameter Tuning
- Results
- RQ1. To what extent does NSGA-II provide better regression detection compared with other techniques?
- RQ2. Do the generated rules continue to perform well with the evolution of the software?
- Threats to Validity
- Related Work
- Conclusion and Future Work
Performance is critical to software quality. Being one of the practices of quality assurance, performance regression testing monitors the software's overall performance during its evolution to ensure least to negligible degradation of time. It mainly detects whether any committed changes may have introduced performance regressions. Ideally, in order to prevent any code change from negatively impacting the software performance, performance tests, also known as benchmarks, should be executed along with any committed change, as a sanity check. However, in a real-world setting, performance tests are expensive, and with the growth in the number of committed changes, software testers are constantly challenged tond the right trade-o between optimally performance testing newly introduced changes, and increasing the development overall productivity [6]. Nevertheless, executing Performance testing after each commit is an expensive and lengthy process. It represents an overhead on resources and it delays programmers from further development until the results of testing have been gathered [7]. As a result, performance tests are not conducted after each change on the code because they consume resources [6]. This practice challenges the early finding the performance regression changes. For example, if performance tests are postponed by the end of the Sprint, then developers need to commit their code throughout the cycle and hope that no performance test would fail by the end; otherwise, they have to rewind all previously committed changes to debug them. In this context, various research has been analyzing performance regression inducing code changes to allow their early detection, and to support the prioritization of performance regression i.e., for upcoming changes to commit, if any of them exhibits characteristics that are similar to these known to have induced performance regression, then this may be a trigger, for software testers, to schedule their performance tests. To cope with this expensive process, recent studies focus on mining performance regression testing repositories to either support performance analysis[5, 10, 1], or improve regression strategies [7, 8], or to characterize code changes that have introduced regression [11, 2]. Characterizing such problematic code changes is complex since it goes beyond the static design of the code e.g., coupling and complexity, and it is reflected by the dynamic nature of the change e.g., excessive calls to external APIs, besides being specic to the projects development practices, and programming languages [12]. This paper denes detecting Performance Regression Introducing Code Changes (PRICE) as an optimization problem. Initially, our approach takes as input a set of commits that are known to be problematic, then analyzes them using static and dynamic metrics, previously used in an existing study [11]. Afterward, these commits, with their corresponding metric values, are used as a training set for the Non dominated sorting genetic algorithm (NSGA-II) [4], which evolves the given metrics to generate a detection rule that maximizes the detection of problematic code changes. Our experiments were carried out using Git as the system under test. Ourndings show the ability of the evolutionary algorithm to generate promising results, in comparison with state-of-art approaches.
In this section, we give a high-level overview of our approach's workflow, then we explain how we designed NSGA-II for detecting performance regression changes.
The goal of our approach is tond the best rule that detects PRICE. The general structure is sketched in Figure 1. Our approach is composed of three phases. Collection phase uses history performance tests data collected from previous commits to calculate metrics. Metrics represent collected data of each commit to the respect of the previous commit. Table 1 lists static and dynamic metrics used in this work. Metrics 1,2,6 are static where the rest are both static and dynamic. The tool used to collect static metrics is Lizard code complexity analyzer. Static data is afterward fed into dynamic analysis process to run benchmarks and calculate dynamic metrics. The second phase after collecting metrics is generating a detection rule. Finding this rule is a multi-objective optimization problem. A Detection rule should have the highest detection of problematic commits while minimizing the detection of benign commits. The search space con- tains solutions with dierent combination of metrics and a value for each metric. In this paper, we considered seven metrics from a previous study [11], with which we will also compare our approach. Once a detection rule is generated, developers can apply it on each commit to detect regression and decide whether to run benchmark testing or not. In case benchmark testing is applied on a commit, dynamic metrics of that commit is stored on the database to help in updating detection rule in the future when rule is no longer providing good predictions.
| # | Description | Rationale |
|---|---|---|
| 1 | Number of deleted functions | Deleted functions indicate refactoring, which may lead to performance changes |
| 2 | Number of new functions | Added functions indicate new functionality, which may lead to performance changes |
| 3 | Number of deleted Functions reached by the benchmark | Deleting a function which was part of the benchmark execution could lead to a performance change |
| 4 | The percent overhead of the top most called function that was changed | Altering a function that takes up a large portion of the processing time of a benchmark has a high risk of causing a performance regression because it is such a large portion of the test |
| 5 | The percent overhead of the top most called function that was changed by more than 10% of its static instruction length | Similar to metric 4, however this takes into account that the change affects a reasonable portion of the function in question. Bigger changes may mean higher risk. |
| 6 | The highest percent static function length change | Large changes to functions are more likely to cause regressions than small ones |
| 7 | The highest percent static function length change that is called by the benchmark | The same as for metric 7, but here we guarantee that the functions are actually called by the benchmark in question. |
We have selected the Git project to be the system under test of our study. We have chosen Git as it is open-source, with a built-in set of benchmarks, easy to compile and run (mandatory for our dynamic analysis), besides our familiarity with its commands. We collected data for 8798 commits originally. Those commits were chosen by executing the `git rev-parse` command from the master branch at the time and going back to therst commit we couldnd which had performance tests. Across that range of commits, there were 202 commits which, for technical reasons, were untestable, so we removed them. Thus in total we considered 8596 commits. Afterward, for each commit, we run all performance tests, and this is for two reasons: therst one, we need to see whether any test would fail, and if so, we tag the commit under test as problematic.The second reason is to dynamically prole each code change and calculate some of metrics at runtime. To avoid the akiness of some tests and the stochastic nature of the code, we test each commit 5 times. Running all of the performance tests for a single commit takes a signicant amount of time (hence the need for this study), so we parallelized the task across many machines. The results of the Git performance tests are reported in wall time, which can be impacted by using machines with dierent clock speeds, RAM, etc., so to mitigate this we used identical Virtual machines in a proprietary cloud1. The dynamic information was collected using Linux perf [3], as for the static information, the list of functions and their location in the source code, was collected by using the python lizard 2 tool. While intended for calculating cyclomatic complexity, it also provides list of functions identied in all of the sourceles in the repository for that commit. We provide the dataset and tools we used for reproducibility and extension purposes3.
Our solution is encoded as a tree-based rule. The leaf nodes are termed 'terminals' and internal nodes as 'primitives'. Primitives are logical operators that compares metric value with the threshold assigned to it respec- tively. Figure 2 illustrates a solution tree that combinesve metrics and their threshold values by logical operators AND and OR. Solution tree is strictly typed to assure structure is not broken during the evolution.
Generated rules are evaluated by two objectives, which are hit and dismiss rates. This subsection denes these objectives and shows how they are conflicted.
Hit rate as an objective. The Hit rate indicates the number of cor- rectly detected commits to total number of commits encountering regression. In formula 1 Hp is predicted problematic commits while H is actual regression commits. Values of hit rate are between 0.0 and 1.0. Hit rate of 1 means that all commits encounter regression are detected. Hit rate can also be 1 if all commits considered to be problematic which is not proper to this type of problems.
jHp \ Hj=jHj (1)
Dismiss rate as an objective. The Dismiss rate is the number of commits classied not to be introducing regression to the total actual number of stable, not problematic, commits. In formula 2 Dp is predicted stable commits while D is actual stable commits. Dismiss rate values are between 0.0 and 1.0. Dismiss rate of value 1 indicates that all non- problematic commits are correctly classied as not introducing regression. Dismiss rate of 1 might indicate that all commits are not problematic. It cannot be used individually as hit rate.
jDp \ Dj=jDj (2)
An optimal solution would score a hit and dismiss rate of 1. Since hit and dismiss rates are conflicting, when optimizing one objective, we automatically degrade the other as shown in Figure 3. Hence, we are searching for near optimal solutions that should deliver a good trade-o between these objectives that are meant to be maximized.
For the crossover operator, we deploy the Simulated Binary Crossover (SBX). Simulated Binary Crossover simulates single point crossover with using probability density function. Crossover point is chosen randomly between 1 and the length of the chromosome. In chromosome represented as tree, rule in our case, crossover is swapping tree sub-branches. New trees will not necessarily be the same size as their parents. It depends on crossover point position. If crossover point located close to terminal nodes , one o spring might be a single metric where the other is an extended tree that might have duplicated metrics with dierent threshold values. As for the mutation operator, we use the Polynomial Mutation. This operator uses polynomial probability distribution to select the node to be mutated. Mutation operator depends on node type to insure producing a logical rule. For example, primitive nodes, which are connecting terminal nodes, should always be a comparison operator, which can be either greater than or less than. Fig.
Choice of the final solution. The multi-objective nature of the algorithm allows the choice of multiple Pareto-equivalent solutions that tend to optimize one objective in comparison with the others. So, software testers can choose either to prioritize the hit rate over the dismiss rate if the cost of running benchmarks is high or the allowed testing time period is relatively short; or they can favor the dismiss rate if they are afraid of missing any code change introducing a performance regression, at the expense of running extra test cases. For our experiments, we have chosen the solution with the highest F-Measure across various runs.
RQ1. To what extent does NSGA-II provide better regression detection compared with other techniques?
To address this research question, we applied the 10-fold cross validation. We initially sort the commits chronologically, then we split them into 10 equal folds where fold 1 contains the earliest (oldest) commits subset, all the way to fold 10, which contains the latest commits subset. The validation is performed using 10 iterations. In each iteration, one fold is used for testing and the rest is used for training. Note that Folds do not necessarily contain same number of problematic commits, but since the majority of folds are used for training, the training set tends to contain significantly more problematic commits, than the testing set, which does simulate real world scenarios. Results are compared with k-Nearest Neighbors algorithm (KNN) and a state-of-the-art approach called Perphecy We choose KNN to see the results of considering the problem of performance regression as a non-parametric binary classification, where metrics represent the feature space. We also compare with Perphecy since it is available online and known to provide good results. Hit and dismiss rates and F-measure to compare the performance of the three methods.
RQ2. Do the generated rules continue to perform well with the evolution of the software?
This research question challenges the stability of generated rules over the evolution of the software. As software evolves, with committing a significant amount of code changes, the software may undergo several structural and functional changes, which may change the characteristics that have been previously captured by the metrics, and so it may consequently hinder the accuracy of the performance detection. To simulate such scenario, similarly to RQ1, we sort again the commits chronologically, then we split them into 10 equal folds, where the first fold contains the oldest commits, all the way to the last fold which contains the newest commits. Optimally, we aim in splitting the commits that are co-located in time into a separate fold. By generating the rule only using the oldest fold, and then testing it on the remaining folds, we intend to see whether our rule may get over time i.e., the further is the fold, the harder should be the rule to detect performance issues.
For NSGA-II, Different values have been used for the population size and the maximum number of evaluations, generating a variety of configurations. We use the trial and error and choose the conguration providing better results in terms of hit rate and dismiss rate. We used the following parameters: Population size=50, iterations=10000, Selection=Binary tournament selection without replacement, Simulated Binary Crossover probability=0.8, Polynomial Mutation probability=0.5. Perphecy combines metrics tond the best rule that better detect performance issues in a deterministic way. Before trying all possible met- rics combinations tond the best rule, Perphecy determines each metric threshold value individually. The combination with highest hit and dismiss rate is selected. The authors of Perphecy applied this process for each project separately, as every project has its own characteristics and so the nominated rule diers from project to another. In this context, we did not reuse any existing rules from the previous study and we had to generate a rule for each subset of commits, from Git project. For KNN, we use the gap statistic method to estimate the optimal number of clusters K. Gap statistic is chosen since it provides a statistical procedure to model traditional elbow and silhouette methods. To ensure fairness when compared to NSGA-II and Perphecy, we re-estimate K for each set of input commits. Since our experiments contain a fold cross validation, we tune the algorithms together once, for therst fold. To ensure fairness, we regenerate a rule representing each algorithm for every training fold, as we will detail later.
In order to compare performance of NSGA-II with KNN and Perphecy, we plotted hit rate, dismiss rate and F-measure of each technique. In this cross-validation, each fold has been tested with a rule, which was created using the remaining folds as the training set. In Figure 5, the hit rate represents correctly classied commits while the dismiss rate repre- sents correctly avoided commits. According to Figure 5 results, KNN's hit rate is very low, and only reached 10% at most, so it highly miss- classies commits with regression in contrast with a more successful dis- miss rate where more than 95% of benign commits have been correctly classied. This is due to the imbalance between the two class represen- tations: commits encounter regression are only about 4% of the overall commits. Although, this imbalanced setting represents a challenge for ma- chine learning algorithms, it mimics naturally the real setting for typical software projects, where performance regression tends to be less frequent but critical to software health [7]. Perphecy also combines metrics tond the best rule that better detect performance issues in a deterministic way. Before trying all possible met- rics combinations tond the best rule, Perphecy determines each metric threshold value individually. The combination with highest hit and dis- miss rate is selected. The authors of Perphecy applied this process for each project separately, as every project has its own characteristics and so the nominated rule diers from project to another. In this context we applied Perphecy approach in Git project to compare it with our results.
This approach has provided signicantly better results than KNN since its hit rate, across folds, varies between 39%, and 72%, as for the dismiss rate, it ranges between 42% and 58%. Perphecy is independent of the naive aggregation of all values, and so it clearly outperforms KNN, since its F-Measure goes up to 68% while KNN achieved an F-Measure of 17% at best. NSGA-II's performance was competitive to Perphecy, since its hit rate is between 35%, and 69%, which is slightly below Perphecy's hit rate, and for the dismiss rate, it ranges between 48% and 79%, which was slightly above Perphecy's dismiss rate. As for the F-Measure, as shown in Figure 6, NSGA-II 's values are between 47%, and 68%, and it also outperforms Perphecy, in all folds, expect for the second one. The main difference between NSGA-II and Perphecy is the ability of the latter to change the threshold values while composing the decision tree, besides the global exploration of NSGA-II for many possible competing rules during its evolutionary process.
To show a concrete example of one4 of the problematic commits, Figure 7 shows its contrast with previous commits. As shown in Figure 7, the deleted lines of code (in red) is the conventional operation of assigning a value to a particular index of an array which is a fast way of adding values in an array. This operation was replaced, as shown in the added lines (in green), by adding the values through a function call and passing the value to be added as an argument. If scheduling regression tests was using a straightforward heuristic like Lines Of Code (LOC), the above-shown code will not trigger any flags as there is no addition of new lines of code. Whereas, the newly introduced statements are expensive, since for each function call, it will traverse a data structure and append the new value. This issue was captured by a rule depicted in Figure 8 (for visibility we show a subset of the tree).
o evaluate generated rules stability with the evolution of the software, we used the earliest commits subset for training and the rest nine subsets for testing. Figure 9 contains the boxplot of F-Measure values of the Pareto front solutions during 31 simulation runs. As shown ingure 9, no signicant dierence on median and the 75th percentile presented on f-measure values. This indicates that generated rules were able to oer regression prediction up to the forth fold as good as the second fold. For the remaining folds, we can observe a slight decrease from the seventh until the tenth fold. Characteristics of code changes introducing regression may change with the evolution of the code. This explains the regression in the prediction. Although our rules have shown their ability to maintain a good performance across various code changes, it is recommended to update the prediction regularly.
Internal Validity. We report on the uncontrolled factors that interfere with causes and effects, and may impact the experimental results. Com- mits are not necessarily sequential: The git project itself uses git as source control, and employs a branching strategy with merges. If the project his- tory branched and then merged, when you view the history linearly you might have two commits next to each other which technically were not developed sequentially when originally committed by the developer. How- ever, since our approach is not dependent to the program's logic, it is a problem to compare out of order commits as long as we can detect any performance regression.
Construct Validity. Herewith we report on certain challenges that validate whether thendings of our study re ect real-world conditions. In order to execute the performance tests for over 8000 commits in a timely manner, the task was parallelized across multiple machines. This could become a threat because the results for the performance tests are given as a time duration, which can vary based on CPU speed, number of cores, and other random variables between machines. To mitigate this, identical virtual machines were used for all performance test results, which means CPU speed, RAM, and so on were identical. Additionally, we ran each test 5 separate times, such that each execution was at a dierent time of day on a dierent virtual machine. This helps mitigate other uncontrollable random noise in the results of the testing. External Validity. The prediction of performance regression was limited only to one project. The generated predictor does not necessarily give the best results for other projects. We plan on the future to apply our approach to more projects and, if possible, across more programming languages.
Chen et al. [2] found that performance regression introducing changes is rigorous and associated with complex syndrome. As a result, the study suggests to frequently conduct performance testing rather than defer it until the end of development process. Although executing comprehensive performance testing will ease locating code change introducing perfor- mance regression, it is expensive and might delay development process. Many researches have been conducted to overcome this limitation. Huang et al. [7] argue that performance testing should be devoted to only com- mits counter performance regression rather than all commits. To achieve that they rank commits based on the probability of encountering perfor- mance regression based on a static Performance Risk Analysis (PRA). This analysis focuses on how the change is expensive and frequent. After ranking commits, based on the analysis, a comprehensive testing is con- ducted on risky commits while light testing conducted on the rest. PRA is considered a light approach because it statically estimates the risk of a code change without running the software. Perphecy [11] agrees with PRA [7] that applying comprehensive performance testing on each commit is expensive.  To determine which test suite can detect performance regression, they have implemented a predictor based on a combination of indicators built up from static and dynamic data collected from previous commits compared with static data of the new commit.
We presented a novel formulation of the early detection of performance regression as multi-objective optimization problem. We used NSGA-II to generate a detection rule, while maximizing the correctness of hitting a regression and maximizing the correctness of dismissing a non-regression, as two objectives. We evaluated our detection rule by building a dataset of performance regression, extracted from the Git project. As we compare our results to other techniques, we found that our approach provides a competitive detection that improves the state-of-the-art existing results. We plan to extend this study by adding additional metrics, including branch and bound, Cyclomatic complexity, and coupling between objects, and explore more optimization algorithms, known to perform well for similar software engineering problems [9]. We plan on also analyzing more projects to challenge the generalizability of our approach.
Acknowledgement. We would like to sincerely thank the authors of Perphecy for providing enough details that allowed its replication. We also thank the members of #git-devel IRC community for answering some of our questions during this work.
References
1. Ahmed, T.M., Bezemer, C.P., Chen, T.H., Hassan, A.E., Shang, W.: Studying
the eectiveness of application performance management (apm) tools for detecting
performance regressions for web applications: an experience report. In: Proceedings
of the 13th International Conference on Mining Software Repositories. pp. 1{12.
ACM (2016)
2. Chen, J., Shang, W.: An exploratory study of performance regression introduc-
ing code changes. In: Software Maintenance and Evolution (ICSME), 2017 IEEE
International Conference on. pp. 341{352. IEEE (2017)
3. De Melo, A.C.: The new linuxperftools. In: Slides from Linux Kongress. vol. 18
(2010)
4. Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sort-
ing genetic algorithm for multi-objective optimization: Nsga-ii. In: International
conference on parallel problem solving from nature. pp. 849{858. Springer (2000)
5. Foo, K.C., Jiang, Z.M., Adams, B., Hassan, A.E., Zou, Y., Flora, P.: Mining per-
formance regression testing repositories for automated performance analysis. In:
2010 10th International Conference on Quality Software. pp. 32{41. IEEE (2010)
6. Ghaith, S., Wang, M., Perry, P., Murphy, J.: Prole-based, load-independent
anomaly detection and analysis in performance regression testing of software sys-
tems. In: Software Maintenance and Reengineering (CSMR), 2013 17th European
Conference on. pp. 379{383. IEEE (2013)
7. Huang, P., Ma, X., Shen, D., Zhou, Y.: Performance regression testing target pri-
oritization via performance risk analysis. In: Proceedings of the 36th International
Conference on Software Engineering. pp. 60{71. ACM (2014)
8. Luo, Q., Poshyvanyk, D., Grechanik, M.: Mining performance regression inducing
code changes in evolving software. In: Mining Software Repositories (MSR), 2016
IEEE/ACM 13th Working Conference on. pp. 25{36. IEEE (2016)
9. Mkaouer, W., Kessentini, M., Shaout, A., Koligheu, P., Bechikh, S., Deb, K., Ouni,
A.: Many-objective software remodularization using nsga-iii. ACM Transactions on
Software Engineering and Methodology (TOSEM) 24(3), 17 (2015)
10. Nguyen, T.H., Adams, B., Jiang, Z.M., Hassan, A.E., Nasser, M., Flora, P.: Auto-
mated detection of performance regressions using statistical process control tech-
niques. In: Proceedings of the 3rd ACM/SPEC International Conference on Per-
formance Engineering. pp. 299{310. ACM (2012)
11. Oliveira, A.B.d., Fischmeister, S., Diwan, A., Hauswirth, M., Sweeney, P.F.: Per-
phecy: Performance regression test selection made simple but eective. In: 2017
IEEE International Conference on Software Testing, Verication and Validation
(ICST). pp. 103{113. IEEE (2017)
12. Ostermueller, E.: Troubleshooting Java Performance: Detecting Anti-Patterns with
Open Source Tools. Apress, Berkely, CA, USA, 1st edn. (2017)


Display every detail from any angle, on any device
Enhance your customer's experience with stunning 360° views of your products. Create an immersive product experience, every time.
Featuring 36 images, autoplay*, bottom 360° view circle, arrow keys support*,
magnifier*, inner box shadow and more!
(*) available only on desktops


Controller
Design the app without URL redirection parameters.
or
Obtain direct redirection parameter from random indirect reference access map.
(LR) Positive validation of redirection parameter.
(NR) Java – Do not forward() requests as this prevents SSO access control mechanisms


August 4
O give thanks unto the Lord; for he is good: for his mercy endures for ever. Psalm 136:1
God's mercy endures forever! His willingness to act on man's behalf is still operating in the earth. His mercy never runs out. Neither has it abated or weakened.
"Praise the Lord! His mercy endures forever!" Mighty and powerful things happened when Israel said these words. They are words of praise and adoration to God.
When Solomon finished building the house of the Lord, the trumpeters and singers lifted their voices as one, and with trumpets, cymbals, and instruments of music, they praised the Lord saying, "For He is good: for His mercy endures forever."
Jehoshaphat appointed singers unto the Lord to go before the army and say, "Praise the Lord! For His mercy endures forever."
It has been thousands of years since the Lord said His mercy extended to a thousand generations, and His mercy continues to reach you day after day. He is still plenteous in mercy unto them that call upon Him. With your spirit, dare to stretch your faith to take in the boundless mercy of God.  His mercy surrounds me even today!"
Scripture Reading: 2 Chronicles 5:1-14


|
RAMADDA User Guide
|
The database to use and its connection parameters are specified in a .properties file, e.g., db.properties. The properties file can be placed in the ramadda home directory. To define the database just uncomment the appropriate ramadda.db properties in repository.properties, e.g.:
ramadda.db=derby #ramadda.db=mysql #ramadda.db=postgres #ramadda.db=h2And then specify the jdbc url and the login credentials. e..g, for MySQL:
ramadda.db.mysql.url=jdbc:mysql://localhost:3306/repository?zeroDateTimeBehavior=convertToNull ramadda.db.mysql.user=the database user ramadda.db.mysql.password=the database passwordFor H2 the default is to store the database in the RAMADDA home directory:
ramadda.db.h2.url=jdbc:h2:%repositorydir%/h2db ramadda.db.h2.user= ramadda.db.h2.password=
---Note: the ramadda.datetime and ramadda.double are replaced by ramadda ---with the appropriate datatype for the database being used. ---mysql has a datetime type, postgres and derby have timestamp ---we can't use timestamp for mysql because it only goes back to 1970 ---derby and mysql have double. postgres has float8What we do is use the class org.ramadda.repository.database.DatabaseManager to convert the sql text to the correct syntax of the underlying database. This is mostly done in the DatabaseManager.convertSql method.


@import controllers.Application.DurationRequest @(form: Form[DurationRequest])(implicit messages: Messages) @main("Airports popularity")("airports") {
Select the duration you want to calculate the popularity from.
A week is calculated as every 7 days in the year (e. g. 01.01 - 07.01 is first week, 08.01 - 14.01 second, etc.).
@helper.form(action = routes.Application.airportsPopularity()) { @helper.input(form("yearFrom")) { (id, name, value, args) =>


Gary Taylor
Head Nerd / Owner. Developer. Worked with computers before it was cool. He’s a classic thrill junky who drives fast cars and jumps out of airplanes. He never shies away from a burger, and enjoys spending time with his significant other, Hope (the cat-herder).
Hope Wittenberg
Works as the in-office cat-herder, organizer, scheduler, and helper. When not forced to sit in the office with men all day, Hope is crafty. She paints, beads, and frequently kidnaps stray cats. Hope likes to spend time with her significant other, Gary (the thrill-junky).
Jared Hutton
Jared is retired Air Force, and a super-smart EWU grad. He's recently become a full-fledged development geek. He likes Star Wars memorabilia, sports, and is outnumbered by his wife and three daughters.
Christopher Bristlin
Christopher is a tech-junky. He was never taught stranger-danger, so he loves talking to new people. He fixes cars and can keep plants alive for extended periods of time. He loves hanging out with his niece and nephew, and is an avid traveler.


|
SpacePigFighter
1.5
|
Public Member Functions | |
| Spacecraft () | |
| Spacecraft (String colorName) | |
| Spacecraft (Animal myAnimal) | |
| Spacecraft (PositionsCube position) | |
| Spacecraft (String colorName, Animal myAnimal) | |
| Spacecraft (PositionsCube position, String colorName) | |
| Spacecraft (PositionsCube position, Animal myAnimal) | |
| Spacecraft (PositionsCube position, String colorName, Animal myAnimal) | |
| String | getColor () |
| Animal | getAnimal () |
| void | setColor (String newColor) |
| void | setAnimal (Animal newAnimal) |
| void | beDamagedBy (MeteoriteSize meteoriteSize) |
| Public Member Functions inherited from spaceObjects.Ufo | |
| Ufo () | |
| Ufo (PositionsCube position) | |
| PositionsCube | getLocation () |
| void | setLocation (PositionsCube position) |
| void | setLocation (int position) throws PositionException |
Public Attributes | |
| Animal | animal |
===== Class Spacecraft =====
| spaceObjects.Spacecraft.Spacecraft | ( | ) |
Constructor by default we have a Pig unnamed and a Spacecraft grey colored at the position OOO
| spaceObjects.Spacecraft.Spacecraft | ( | String | colorName | ) |
Constructor with selected color
| 1 | String = colorName |
| spaceObjects.Spacecraft.Spacecraft | ( | Animal | myAnimal | ) |
Constructor with selected animal
| 1 | Animal = myAnimal |
| spaceObjects.Spacecraft.Spacecraft | ( | PositionsCube | position | ) |
Constructor with selected location
| 1 | PositionsCube = position |
| spaceObjects.Spacecraft.Spacecraft | ( | String | colorName, |
| Animal | myAnimal | ||
| ) |
Constructor with selected color and animal
| 1 | String = colorName |
| 1 | Animal = myAnimal |
| spaceObjects.Spacecraft.Spacecraft | ( | PositionsCube | position, |
| String | colorName | ||
| ) |
Constructor with selected location and color
| 1 | PositionsCube = position |
| 1 | String = colorName |
| spaceObjects.Spacecraft.Spacecraft | ( | PositionsCube | position, |
| Animal | myAnimal | ||
| ) |
Constructor with selected location and animal
| 1 | PositionsCube = position |
| 1 | Animal = myAnimal |
| spaceObjects.Spacecraft.Spacecraft | ( | PositionsCube | position, |
| String | colorName, | ||
| Animal | myAnimal | ||
| ) |
Constructor. with selected location, color and animal
| 1 | PositionsCube = position |
| 1 | String = colorName |
| 1 | Animal = myAnimal |
| void spaceObjects.Spacecraft.beDamagedBy | ( | MeteoriteSize | meteoriteSize | ) |
The Animal will receive damages proportional to the meteoriteSize
| MeteoriteSize | = meteoriteSize |
| Animal spaceObjects.Spacecraft.getAnimal | ( | ) |
Get Spacecraft animal
| String spaceObjects.Spacecraft.getColor | ( | ) |
Get Spacecraft color
| void spaceObjects.Spacecraft.setAnimal | ( | Animal | newAnimal | ) |
Set Spacecraft animal
| 1 | Animal = newAnimal |
| void spaceObjects.Spacecraft.setColor | ( | String | newColor | ) |
Set Spacecraft color
| 1 | String = newColor |


The mission of The London Django Meetup Group is to provide a fun, welcoming and professional environment for a diverse group of people.
Code of conduct: djangolondon.com/code-of-conduct
Tonight's meetup is kindly sponsored by:
Pollen is a marketplace for the best in shared experiences.
(Formerly Verve)
Wagtail 2.12
Django 3.2 beta 1
Python 3.9.2 and 3.8.8
Python security releases issued: Python 3.7.10 amd 3.6.13
Django security releases issued: 3.1.7, 3.0.13, and 2.2.19 (Web cache poisoning)
And the Python Software Foundation turns 20
https://www.python.org/psf/
Porto and online
Call for proposal is open, closes the 1st April 2021
Conference: June 2-4 (Wednesday-Friday)
Sprints/Workshops: June 5 and 6 (Saturday and Sunday)
Looking for volunteers
https://twitter.com/DjangoConEurope
Call for proposal is open, closes the 12th February 2021
Registrations are open (costs: $50 for students, $100 for individuals, $150 for corporate, $1,000 for patrons)
https://us.pycon.org/2021/
Django, Ontono & Me
by Cammil Taank
Handsontable with Django
by Damanjeet Singh
Zoom Q&A at the end of each talk.
You have a chance to win one of two 100% discount code for a JetBrains product (AppCode, PyCharm, WebStorm. etc)
Fill in the Google form on the chat with your name and email, we will announce the winner later and send the licences via email.
We'll head over to a room on gather.town
Please tweet/email/message us!
Website: djangolondon.com
Twitter: @djangolondon
Github: github.com/djangolondon
Open Collective: opencollective.com/the-london-django-meetup-group


Massive.js is a data mapper for Node.js that goes all in on PostgreSQL and fully embraces the power and flexibility of the SQL language and relational metaphors. Providing minimal abstractions for the interfaces and tools you already use, its goal is to do just enough to make working with your data as easy and intuitive as possible, then get out of your way.
This project is maintained by dmfay
Hosted on GitHub Pages — Theme by orderedlist
Around the late 00s, document databases like MongoDB or CouchDB started to be hailed as the next big thing. Modeling data in groups of hierarchies rather than as flat related sets, they made an enormous difference in how applications approached data. Although they were never going to replace relational databases, there are many use cases for which document stores are undeniably better-suited than the traditional RDBMS, so document databases are an important class of datastore in their own right.
PostgreSQL’s JSONB functionality allows us to blend relational and document approaches by storing JSON documents in traditional tables. There’s a lot to recommend a hybrid data model: in modern applications, a greater or lesser proportion of the data is relational and should be stored and accessed as such, but there’s often some information that really is best represented as a rich, nested document with a flexible schema. In pure relational terms, data like this can only be represented through tortuous abstractions (if you’ve never seen a table with a self-joining foreign key representing a hierarchy, consider yourself fortunate) or as unsearchable, unindexable long text or BLOB fields.
The JSONB type is a great solution to this problem, and Massive takes care of the management overhead with a document table API.
Document tables exist for the sole purpose of storing JSONB data. Query them through Massive’s API, and you get a JSON document which you can modify and persist, all seamlessly. You don’t even need to create them ahead of time until you know you need them.
Document tables may be extended with new columns and foreign keys. The id
type can be changed as well (so long as a default is set such as uuid_generate_v1mc()
for UUID types) without impeding usage of document table functions. Just don’t remove any columns or change their names, since Massive depends on those.
Standard table functions still work on document tables, and can be quite useful especially for extended document tables! Fields in the document can be searched with regular find
and criteria object fields using JSON traversal to look for body ->> myField
. findDoc
is still preferred to JSON queries if at all possible since it uses the @>
“contains” operator to leverage indexing on the document body to improve performance.
The connected database instance has a saveDoc
function. Passed a collection name (which can include a non-public schema) and a JavaScript object, this will create the table if it doesn’t already exist and write the object to it.
db.saveDoc('reports', {
title: 'Week 12 Throughput',
lines: [{
name: '1 East',
numbers: [5, 4, 6, 6, 4]
}, {
name: '2 East',
numbers: [4, 4, 4, 3, 7]
}]
}).then(report => {
// the reports table has been created and the initial document
// is assigned a primary key value and returned
});
If the table already exists, you can still use db.saveDoc
, but you can also invoke saveDoc
on the table itself.
Like its counterpart, countDoc
returns the number of extant documents matching a criteria object. Unlike count
, countDoc
does not accept a raw SQL WHERE
definition.
db.reports.countDoc({
'title ilike': '%throughput%'
}).then(reports => {
// number of matching documents
});
findDoc
locates documents with either a criteria object or a primary key. Simple criteria objects (testing equality only) can leverage the GIN index on the table to improve query speed.
db.reports.findDoc(1).then(report => {
// the report document body with the primary key included
});
db.reports.findDoc({
'title ilike': '%throughput%'
}).then(reports => {
// all report documents matching the criteria
});
searchDoc
performs a full-text search on the document body fields.
db.reports.searchDoc({
fields: ["title", "description"],
term: "Kauai"
}.then(docs => {
// reports returned with an on-the-fly full text search
// for 'Kauai'
});
saveDoc
inserts or updates a document. If an id
field is present in the document, the corresponding record will be updated; otherwise, it’s inserted. saveDoc
will replace the entire document and does not update individual fields; to do that, see modify
.
db.reports.saveDoc({
title: 'Week 12 Throughput',
lines: [{
name: '1 East',
numbers: [5, 4, 6, 6, 4]
}, {
name: '2 East',
numbers: [4, 4, 4, 3, 7]
}]
}).then(report => {
// the newly created report
});
modify
adds and updates fields in an existing document without replacing the entire body. Fields not defined in the changes
object are not modified.
This function may be used with any JSON or JSONB column, not just with document tables. If used on a document table, it returns a promise for the updated document; otherwise, it will return the entire row.
db.reports.modify(1, {
title: 'Week 11 Throughput'
}).then(report => {
// the updated report, with a changed 'title' attribute
});
db.products.modify(1, {
colors: ['gray', 'purple', 'red']
}, 'info').then(widget => {
// the product with an 'info' field containing the colors array
});


No string stats found
Time between samples: milliseconds
Samples to show in plot:
Samples to use for rolling avarage:
{{ stat.label }}:


{# Project menu dropdown, only visible if browser width < X and sidebar is hidden #}
{% include 'projectroles/_project_menu_btn.html' %}
Case List
{# Project copy uuid #}
{% if enable_uuid_copy %}
{% endif %}
{% include "variants/_case_list_buttons.html" with disable_pedigree_sex_check=disable_pedigree_sex_check %}
This is the project case list.
Click on a case's name below to see its detail or click the button in its row to start filtering with a single click.
Use the Joint Filtration button on the top right to analyze all cases of this project together.
The "Quality Control" tab shows the qualiy metrics for all cases in the project.


SUBROUTINE SLARRJ( N, D, E2, IFIRST, ILAST, $ RTOL, OFFSET, W, WERR, WORK, IWORK, $ PIVMIN, SPDIAM, INFO ) * * -- LAPACK auxiliary routine (version 3.1) -- * Univ. of Tennessee, Univ. of California Berkeley and NAG Ltd.. * November 2006 * * .. Scalar Arguments .. INTEGER IFIRST, ILAST, INFO, N, OFFSET REAL PIVMIN, RTOL, SPDIAM * .. * .. Array Arguments .. INTEGER IWORK( * ) REAL D( * ), E2( * ), W( * ), $ WERR( * ), WORK( * ) * .. * * Purpose * ======= * * Given the initial eigenvalue approximations of T, SLARRJ * does bisection to refine the eigenvalues of T, * W( IFIRST-OFFSET ) through W( ILAST-OFFSET ), to more accuracy. Initial * guesses for these eigenvalues are input in W, the corresponding estimate * of the error in these guesses in WERR. During bisection, intervals * [left, right] are maintained by storing their mid-points and * semi-widths in the arrays W and WERR respectively. * * Arguments * ========= * * N (input) INTEGER * The order of the matrix. * * D (input) REAL array, dimension (N) * The N diagonal elements of T. * * E2 (input) REAL array, dimension (N-1) * The Squares of the (N-1) subdiagonal elements of T. * * IFIRST (input) INTEGER * The index of the first eigenvalue to be computed. * * ILAST (input) INTEGER * The index of the last eigenvalue to be computed. * * RTOL (input) REAL * Tolerance for the convergence of the bisection intervals. * An interval [LEFT,RIGHT] has converged if * RIGHT-LEFT.LT.RTOL*MAX(|LEFT|,|RIGHT|). * * OFFSET (input) INTEGER * Offset for the arrays W and WERR, i.e., the IFIRST-OFFSET * through ILAST-OFFSET elements of these arrays are to be used. * * W (input/output) REAL array, dimension (N) * On input, W( IFIRST-OFFSET ) through W( ILAST-OFFSET ) are * estimates of the eigenvalues of L D L^T indexed IFIRST through * ILAST. * On output, these estimates are refined. * * WERR (input/output) REAL array, dimension (N) * On input, WERR( IFIRST-OFFSET ) through WERR( ILAST-OFFSET ) are * the errors in the estimates of the corresponding elements in W. * On output, these errors are refined. * * WORK (workspace) REAL array, dimension (2*N) * Workspace. * * IWORK (workspace) INTEGER array, dimension (2*N) * Workspace. * * PIVMIN (input) DOUBLE PRECISION * The minimum pivot in the Sturm sequence for T. * * SPDIAM (input) DOUBLE PRECISION * The spectral diameter of T. * * INFO (output) INTEGER * Error flag. * * Further Details * =============== * * Based on contributions by * Beresford Parlett, University of California, Berkeley, USA * Jim Demmel, University of California, Berkeley, USA * Inderjit Dhillon, University of Texas, Austin, USA * Osni Marques, LBNL/NERSC, USA * Christof Voemel, University of California, Berkeley, USA * * ===================================================================== * * .. Parameters .. REAL ZERO, ONE, TWO, HALF PARAMETER ( ZERO = 0.0E0, ONE = 1.0E0, TWO = 2.0E0, $ HALF = 0.5E0 ) INTEGER MAXITR * .. * .. Local Scalars .. INTEGER CNT, I, I1, I2, II, ITER, J, K, NEXT, NINT, $ OLNINT, P, PREV, SAVI1 REAL DPLUS, FAC, LEFT, MID, RIGHT, S, TMP, WIDTH * * .. * .. Intrinsic Functions .. INTRINSIC ABS, MAX * .. * .. Executable Statements .. * INFO = 0 * MAXITR = INT( ( LOG( SPDIAM+PIVMIN )-LOG( PIVMIN ) ) / $ LOG( TWO ) ) + 2 * * Initialize unconverged intervals in [ WORK(2*I-1), WORK(2*I) ]. * The Sturm Count, Count( WORK(2*I-1) ) is arranged to be I-1, while * Count( WORK(2*I) ) is stored in IWORK( 2*I ). The integer IWORK( 2*I-1 ) * for an unconverged interval is set to the index of the next unconverged * interval, and is -1 or 0 for a converged interval. Thus a linked * list of unconverged intervals is set up. * I1 = IFIRST I2 = ILAST * The number of unconverged intervals NINT = 0 * The last unconverged interval found PREV = 0 DO 75 I = I1, I2 K = 2*I II = I - OFFSET LEFT = W( II ) - WERR( II ) MID = W(II) RIGHT = W( II ) + WERR( II ) WIDTH = RIGHT - MID TMP = MAX( ABS( LEFT ), ABS( RIGHT ) ) * The following test prevents the test of converged intervals IF( WIDTH.LT.RTOL*TMP ) THEN * This interval has already converged and does not need refinement. * (Note that the gaps might change through refining the * eigenvalues, however, they can only get bigger.) * Remove it from the list. IWORK( K-1 ) = -1 * Make sure that I1 always points to the first unconverged interval IF((I.EQ.I1).AND.(I.LT.I2)) I1 = I + 1 IF((PREV.GE.I1).AND.(I.LE.I2)) IWORK( 2*PREV-1 ) = I + 1 ELSE * unconverged interval found PREV = I * Make sure that [LEFT,RIGHT] contains the desired eigenvalue * * Do while( CNT(LEFT).GT.I-1 ) * FAC = ONE 20 CONTINUE CNT = 0 S = LEFT DPLUS = D( 1 ) - S IF( DPLUS.LT.ZERO ) CNT = CNT + 1 DO 30 J = 2, N DPLUS = D( J ) - S - E2( J-1 )/DPLUS IF( DPLUS.LT.ZERO ) CNT = CNT + 1 30 CONTINUE IF( CNT.GT.I-1 ) THEN LEFT = LEFT - WERR( II )*FAC FAC = TWO*FAC GO TO 20 END IF * * Do while( CNT(RIGHT).LT.I ) * FAC = ONE 50 CONTINUE CNT = 0 S = RIGHT DPLUS = D( 1 ) - S IF( DPLUS.LT.ZERO ) CNT = CNT + 1 DO 60 J = 2, N DPLUS = D( J ) - S - E2( J-1 )/DPLUS IF( DPLUS.LT.ZERO ) CNT = CNT + 1 60 CONTINUE IF( CNT.LT.I ) THEN RIGHT = RIGHT + WERR( II )*FAC FAC = TWO*FAC GO TO 50 END IF NINT = NINT + 1 IWORK( K-1 ) = I + 1 IWORK( K ) = CNT END IF WORK( K-1 ) = LEFT WORK( K ) = RIGHT 75 CONTINUE SAVI1 = I1 * * Do while( NINT.GT.0 ), i.e. there are still unconverged intervals * and while (ITER.LT.MAXITR) * ITER = 0 80 CONTINUE PREV = I1 - 1 I = I1 OLNINT = NINT DO 100 P = 1, OLNINT K = 2*I II = I - OFFSET NEXT = IWORK( K-1 ) LEFT = WORK( K-1 ) RIGHT = WORK( K ) MID = HALF*( LEFT + RIGHT ) * semiwidth of interval WIDTH = RIGHT - MID TMP = MAX( ABS( LEFT ), ABS( RIGHT ) ) IF( ( WIDTH.LT.RTOL*TMP ) .OR. $ (ITER.EQ.MAXITR) )THEN * reduce number of unconverged intervals NINT = NINT - 1 * Mark interval as converged. IWORK( K-1 ) = 0 IF( I1.EQ.I ) THEN I1 = NEXT ELSE * Prev holds the last unconverged interval previously examined IF(PREV.GE.I1) IWORK( 2*PREV-1 ) = NEXT END IF I = NEXT GO TO 100 END IF PREV = I * * Perform one bisection step * CNT = 0 S = MID DPLUS = D( 1 ) - S IF( DPLUS.LT.ZERO ) CNT = CNT + 1 DO 90 J = 2, N DPLUS = D( J ) - S - E2( J-1 )/DPLUS IF( DPLUS.LT.ZERO ) CNT = CNT + 1 90 CONTINUE IF( CNT.LE.I-1 ) THEN WORK( K-1 ) = MID ELSE WORK( K ) = MID END IF I = NEXT 100 CONTINUE ITER = ITER + 1 * do another loop if there are still unconverged intervals * However, in the last iteration, all intervals are accepted * since this is the best we can do. IF( ( NINT.GT.0 ).AND.(ITER.LE.MAXITR) ) GO TO 80 * * * At this point, all the intervals have converged DO 110 I = SAVI1, ILAST K = 2*I II = I - OFFSET * All intervals marked by '0' have been refined. IF( IWORK( K-1 ).EQ.0 ) THEN W( II ) = HALF*( WORK( K-1 )+WORK( K ) ) WERR( II ) = WORK( K ) - W( II ) END IF 110 CONTINUE * RETURN * * End of SLARRJ * END


This check finds Helm releases that do not set .Values.global.region
.
$REGION/values/globals.yaml
or scaleout/s-$REGION/values/globals.yaml
from the cc/secrets repo in your helm upgrade
invocation. If you are using the
shared Helm tasks, this is done by adjusting the VALUES
parameter to include
local:globals
(for metal) or s-local:globals
(for scaleout).