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package
i18n
Type Members
-
case class
Lang(language: String, country: String = "") extends Product with Serializable
-
case class
MessagesApi(messages: Map[String, Map[String, String]]) extends Product with Serializable
-
Value Members
-
object
Lang extends Serializable
-
Inherited from AnyRef
Inherited from Any
Contains the internationalisation API.
For example, translating a message:


Across: 1. As people achieve them, cosmic plans change (15) 9. Badly laid up? Time for some acknowledgement of your 1 across (7) 10. Try 11, swallowing dry measure (7) 11. An exercise guide is suitable (3) 12. Those inside it ban hints - a problem! (11) 13. Dialogue and directions split about Canadian north (10) 15. Group from club fall to tragic ends (4) 18. St Peter's top notes about eastern chant (4) 20. Silly old battleaxe about to get boot (10) 23. Balls police disrupted having potential to fold (11) 25. Vessel not described favourably but returns intact (3) 26. Support an energy code: E944 (7) 27. I object to a certain strategy (7) 28. On bottom, Chicago trains with lean surface legend (15) Down: 1. Got up after program rated (9) 2. Grant (the councilor) nursed broken heart (7) 3. I've some medical assistance (8) 4. Seize Lutheran leader attending church (5) 5. The right high profile directors (9) 6. Quiet eating at change (6) 7. Declared return of network I'm on (7) 8. Places it among directions (5) 14. Port sells off those that ask question (9) 16. Accountant to tease Atkinson character out of Kingston perhaps (9) 17. Dismal traces of prophesy (8) 19. Film at 2pm in Australia (7) 21. Courage to be going back in with whatever's at hand (7) 22. Wear and tear due to mother's advancing years (6) 23. Leave north, prance about as 24 might (5) 24. Storied future prince to disturb ambivalent region (5)


1. Input amount of payment, Address(Phore Address)).
Or you can read Phore address as QR.
2. Click "Request" button
3. If you are buyer
Click "Pay via Twitter" button to pay.
If no, Read below QR code and tweet.
4. Check the payment in Reply in Twitter app.
This payment service uses Sensu . I'm glad to make this app for Sensu's developpers.
Please check whether you agree with Sensu's terms of service.
Then check whether your Twitter account linked to Sensu.


{{[...interesting. You have our curiosity. Please tell us more!]}}
{{[Now you have our attention! Describe your team, who are you?]}}
{{[Please give us contact information to everyone in your team.]}}
{{[What\'s the number of students in the team?]}}


The current full date and time is: {{fullDateTime}}
The current full date: {{fullDate}}
The current short date and time: {{shortDateTime}}
The current short date: {{shortDate}}
New Year's Eve 2016 was {{fromDate}}.
This plugin provides date and time formatting capabilities. It can also provide date operations like determining the difference between two dates.
All date and time functions are localized if the library is initialized with the correct locale like this:
gromit.setLocale('en-US');
The locale is often set to make sure it matches the locale of the server. If this function isn't called then the plugin will use the current browser locale.
var now = new Date().getTime();
$scope.fullDateTime = gromit.fullDateTimeFormat(now);
$scope.fullDate = gromit.fullDateFormat(now);
$scope.shortDateTime = gromit.shortDateFormat(now);
$scope.shortDate = gromit.shortDateFormat(now, true);
$scope.fromDate = gromit.fromNowFormat(1451606400000);


--- layout: post status: publish published: true title: Ministry Tech Tutorials author: Ryan Hayden wordpress_id: 1600 wordpress_url: http://www.pastorryanhayden.com/?p=1600 date: '2014-02-20 10:45:44 -0600' date_gmt: '2014-02-20 16:45:44 -0600' categories: - Ministry Tech - featured tags: - featured ---
Below are videos and articles meant to help pastors with everyday tech problems:
Using your iPad:


Assure Application Security
Click to read more. • This practice addresses requirements
from the EU guidelines for trustworthy ML.
Click to read more.
Intent
Motivation
Applicability
Description
For any application that exposes an external interface or uses personal or sensitive data, it is imperative to reflect and take actions to improve its security. Security is known as an arms race, with attackers constantly improving their techniques, and defenders updating their systems in order to predict and prevent new threats. Therefore, ensuring security is a continuous task.
Besides classical cyber threats that apply to software systems, machine learning adds new security risks both during training and deployment. Training time attacks are known as data poisoning, and consist of attackers trying to alter the training data in order to induce malicious behaviour – such as misclassifying certain examples.
Test time (or inference) attacks are more diverse, and consist of adding small perturbations to test data in order to induce malicious behaviour (adversarial attacks), reverse engineering the model or checking if some data was used for training (membership attacks). Like other branches of machine learning, security is also a growing field of study.
As mentioned earlier, security requires a proactive approach, with some mechanisms including security code reviews, using security analysis tools, penetration testing, and actively performing red teaming exercises.
Related
Read more
Click to read more. • This practice addresses requirements
from the EU guidelines for trustworthy ML.
Click to read more.


[ all classes ]
[ org.wikipedia.analytics ]
Coverage Summary for Package: org.wikipedia.analytics
| Package | Class, % | Method, % | Line, % |
|---|---|---|---|
| org.wikipedia.analytics | 2,6% (1/ 38) | 0,4% (1/ 250) | 0,9% (5/ 560) |
| Class | Class, % | Method, % | Line, % |
|---|---|---|---|
| Funnel | 100% (1/ 1) | 7,7% (1/ 13) | 10,2% (5/ 49) |
| SessionFunnel | 0% (0/ 1) | 0% (0/ 12) | 0% (0/ 44) |
| CreateAccountFunnel | 0% (0/ 1) | 0% (0/ 7) | 0% (0/ 9) |
| SessionData | 0% (0/ 1) | 0% (0/ 24) | 0% (0/ 43) |
| FeedConfigureFunnel | 0% (0/ 1) | 0% (0/ 2) | 0% (0/ 17) |
| FindInPageFunnel | 0% (0/ 1) | 0% (0/ 6) | 0% (0/ 11) |
| TimedFunnel | 0% (0/ 1) | 0% (0/ 7) | 0% (0/ 9) |
| ReadingListsFunnel | 0% (0/ 1) | 0% (0/ 8) | 0% (0/ 20) |
| PageScrollFunnel | 0% (0/ 1) | 0% (0/ 6) | 0% (0/ 17) |
| ShareAFactFunnel | 0% (0/ 2) | 0% (0/ 10) | 0% (0/ 21) |
| ToCInteractionFunnel | 0% (0/ 1) | 0% (0/ 5) | 0% (0/ 11) |
| InstallReferrerFunnel | 0% (0/ 1) | 0% (0/ 2) | 0% (0/ 2) |
| NavMenuFunnel | 0% (0/ 1) | 0% (0/ 11) | 0% (0/ 11) |
| SearchFunnel | 0% (0/ 1) | 0% (0/ 8) | 0% (0/ 14) |
| LoginFunnel | 0% (0/ 1) | 0% (0/ 8) | 0% (0/ 8) |
| DailyStatsFunnel | 0% (0/ 1) | 0% (0/ 7) | 0% (0/ 10) |
| ProtectedEditAttemptFunnel | 0% (0/ 1) | 0% (0/ 2) | 0% (0/ 2) |
| OfflineLibraryFunnel | 0% (0/ 1) | 0% (0/ 3) | 0% (0/ 13) |
| AppearanceChangeFunnel | 0% (0/ 1) | 0% (0/ 3) | 0% (0/ 7) |
| TabFunnel | 0% (0/ 1) | 0% (0/ 9) | 0% (0/ 14) |
| GalleryFunnel | 0% (0/ 1) | 0% (0/ 9) | 0% (0/ 12) |
| LinkPreviewFunnel | 0% (0/ 1) | 0% (0/ 5) | 0% (0/ 9) |
| DescriptionEditFunnel | 0% (0/ 2) | 0% (0/ 10) | 0% (0/ 20) |
| SuggestedPagesFunnel | 0% (0/ 1) | 0% (0/ 5) | 0% (0/ 15) |
| FeedFunnel | 0% (0/ 1) | 0% (0/ 9) | 0% (0/ 22) |
| RandomizerFunnel | 0% (0/ 1) | 0% (0/ 6) | 0% (0/ 12) |
| WiktionaryDialogFunnel | 0% (0/ 1) | 0% (0/ 2) | 0% (0/ 3) |
| AppLanguageSelectFunnel | 0% (0/ 1) | 0% (0/ 9) | 0% (0/ 14) |
| IntentFunnel | 0% (0/ 1) | 0% (0/ 5) | 0% (0/ 5) |
| InstallReceiver | 0% (0/ 1) | 0% (0/ 5) | 0% (0/ 40) |
| EventLoggingService | 0% (0/ 2) | 0% (0/ 8) | 0% (0/ 21) |
| EventLoggingEvent | 0% (0/ 1) | 0% (0/ 2) | 0% (0/ 10) |
| FunnelManager | 0% (0/ 1) | 0% (0/ 2) | 0% (0/ 6) |
| OnThisDayFunnel | 0% (0/ 1) | 0% (0/ 3) | 0% (0/ 8) |
| EditFunnel | 0% (0/ 1) | 0% (0/ 17) | 0% (0/ 31) |


Real Madrid dressing room breathes easy after Mourinho to Spurs
Real stars think Mou returning would be a nightmare and Florentino uses him to scare them
by Sport.esFlorentino Perez, Real Madrid’s president, has a formula that he uses to get results from his team when things aren’t going well. Leaking Jose Mourinho’s name to the media as a potential Madrid coach, which scares the players into action. He did it in the Lopetegui and Solari eras, as well as repeating it with Zidane.
Mourinho provokes panic in the dressing room, especially among the big names like Sergio Ramos. So now he has been announced as Spurs coach, Madrid players can breathe easy.
The Portuguese would be the worst case scenario for Madrid players. The reality is that Florentino Perez would be more likely to go for the Italian Massimiliano Allegri if he wants a replacement for Zidane. And now, of course, Mauricio Pochettino is free and available.


|
Xiaocheng Tang
唐小程
Senior Staff Research Scientist, Engineering Manager
Machine Learning @ DiDi Labs, Mountain View, CA
xiaochengtang at didiglobal dot com
Dr. Xiaocheng Tang is a senior staff research scientist at DiDi AI Labs and engineering manager in DiDi's Autonomous Driving division. While at DiDi he has been leading the platform's effort on ML-based approaches for core decision making problems such as driver-passenger matching in the ride-hailing marketplace and most recently autonomous motion planning and prediction. More broadly his research interest lies at the intersection of machine learning, reinforcement learning and optimization.
He has published 30+ top-conference, patents and journal papers in the field. Additionally, his work on joint optimization of order dispatching and reposition via reinforcement learning won Best Demo Awards at NeurIPS 2018. He and his team received Daniel H. Wagner prize for Excellence in Operations Research practice at INFORMS 2019. His work on AutoML in collaboration with UCLA won Outstanding Paper Awards at ICLR 2021 (code). His work on prediction and planning for autonomous driving won 2nd place, ranked 1st based on minADE, in 2022 Waymo Open Dataset Challenge for Motion Prediction.
Xiaocheng received his PhD degrees in Optimization and Machine Learning advised by Prof. Katya Scheinberg (wiki). During his graduate study, he has been actively engaged in analyzing and designing novel optimization theories and algorithms for large-scale problems arising from various applications and in particular those in Artificial Intelligence. His work in fast and scalable training algorithms and convergence theory was presented at NIPS 2013 and was published in the journal of Mathematical Programming which is ranked the No.1 journal in the field of Mathematical Optimization by Google Scholar.
Xiaocheng is also an Apache Committer contributing to Apache MADlib — a distributed in-database machine learning library started by Chris Ré from Stanford and Joe Hellerstein from Berkeley among others.
He used to work in Numerical Methods group in IBM T.J. Watson Research, Yorktown Heights, NY, and was funded by Yahoo FREP award working on online AD targeting and user personalization in Machine Learning group in Yahoo Labs, Sunnyvale, CA in 2014. He earned his undergrad degree with High Honors from Chu KoChen Honors College and College of Control Science and Engineering in Zhejiang University, Hangzhou, China .
Email /
GitHub /
Google Scholar /
LinkedIn
|


While you complete the border will be changing between red and green also they will add a "contextual" message giving information about the field if the entered data is incorrect.


These are all the possible causes for events.
- Initialization
- A Study has performed its own initialization and is about to start.
- Triggered
- An event trigger function underwent a monitored sign transition.
- Scheduled
- An integrator reached a previously-scheduled time for the Event to occur.
- TimeAdvanced
- An integrator completed an internal step, meaning that it has reached a point where time has advanced irreversibly.
- Signaled
- A flag in the State has been explicitly set, meaning that a particular Event has occurred. Anyone with write access to a State can set these, but typically they are set in event handlers associated with one of the other kinds of events.
- Termination
- The Study has finished. If a Termination event handler signals more Events, those signaled events are not processed by the Study; that is, the signals remain set in the final State.
In case several of these causes are detected in a single step, they are sequentialized in the order shown, like this:
- The occurrence of triggered events is reported and the triggering state and a list of triggered events are passed to the event handler for processing (meaning the state, but not the time, is modified). Note that simultaneity within the set of triggered events may also require special handling; we're not talking about that here, just simultaneity of causes.
- Next, using the state resulting from step 1, the time is checked to see if scheduled events have occurred. If so, a list of those events is passed to the event handler for processing.
- Next, if this system has requested time-advanced events, the event handler is called with the state that resulted from step 2 and the "time
advanced" cause noted. No event list is passed in that case. The state may be modified.
- Last, if the final time has been reached or if any of the event handlers asked for termination, we pass the state to the event handler again noting that we have reached termination. The state may be modified and the result will be the final state of the simulation.


All Classes
-
All Classes Interface Summary Class Summary Enum Summary Class Description AlgorithmCodeKey Map between easily readable algorithm implementation types and their codes.AnalysableAlgorithm Base class for an algorithm that does all the ground work for preparing and writing analysis file.AnalysisCollector Collector to collect all metadata aka ReportMeta from existing reports in /maps folderAnalysisInspector Creates statistics on reports that are divided by map and saves results divided by map directories.AnalysisOverviewWriter Statistic file writer using folloring templates overview-template.md and overview-algorithm-row.md to write statistic overviews.AnalysisReader Reader to load relevant data of report as ReportMeta object.AnalysisWriter A file writer that uses report-template.md to write an analysis at root level.App Main program that consists of three parts.AStar classic A*BreathSearch classic breath-first-searchCustomHashSet<T> Set implementation.CustomHashSetDynamicSize<T> Set implementation.DateConverter converts time objects into a string formatDepthSearch classic dreath-first-searchDijkstra classic CustomDijkstra with minheap and int[][] array to hold current known distances.DistanceMap Often in search algorithms there is a table that helps keep tracking of distances.DistanceMapAsA2DTable Often in search algorithms there is a table that helps keep tracking of distances.DistanceMapAsASingleTable Often in search algorithms there is a table that helps keep tracking of distances.DistanceMapType Types of distance trackers used in search algorithmsDocumentPath Commonly used path.FIFOQueue<T> First-in-first-out queue Classic queue implementation.FileClassification App uses limited amount of file endings and recognizes certain type of files by them.Heap<T> Heap interface.HeapType Types of heaps used in search algorithmsLegalFileName Checks if file name is legal is various operating systemsLIFOStack<T> Last-in-first-out classic Stack implementation.LineBreaker Replaces all line breaks into OS style line breaksMain MapGenerator Interface for generating a WebMapMapGeneratorFromFiles Generates an in app memory of a WebMap that is read from map directory.MapLoader Creates a WebMap instance by reading a fileMinHeap<T extends java.lang.Comparable<T>> Classic min-heap implementation.NanoSecondPrettified Converts nano seconds to more readable formNodeHandlingType User understandable mapper for how algorithm has progressed in the map used to make visual representation of mapNoWeightSimpleGenerator Simple weightless map generator that asks user for parameters for generating a map.PreMadeHeap<T> Encapsulates PriorityQueue of java exposing only heap methods needed by search algorithmsPreMadeQueue<T> Encapsulates ArrayDeque of java exposing only queue methods needed by search algorithmsPreMadeStack<T> Encapsulates ArrayDeque of java exposing only stack methods needed by search algorithmsPreMadeUniqueSet<T> Encapsulates HashSet of java exposing only methods needed by search algorithmsQueue<T> Queue interface.QueueType Types of queues used in search algorithmsReport In app memory representation of report of a search algorithm run.ReportCodeKey Report templates all placeholder keys.ReportMeta In app memory representation of report's metadata.ReportMetaFileKey Keys to reportMeta for replacement Used to read and write ReportMeta from/to filesResourceFileReader RootDirectoryBuilder Directory builder having RootFolderFinder.getRootFolder as starting point for path.RootDirectoryLister Lists files having RootFolderFinder.getRootFolder as starting point for path.RootFileLister Loads list of files from given folder recursively having RootFolderFinder.getRootFolder as starting point for path.RootFileWriter Writer that does the actual writing on file having RootFolderFinder.getRootFolder as starting point for path.RootFolderFinder Finds the folder where app is runningStack<T> Stack interface.StackType Types of stacks used in search algorithmsStatisticTemplateKey Keys to overview-template for replacement Used by AnalysisOverviewWriterSystemSpecReader Helper to get information on some system specificationsTemplate Locations in resource folder for templates ANALYSIS for single run of algorithm statistics ANALYSIS_AS_A_TABLE_ROW for a single algorithm as row template OVERVIEW_TEMPLATE for overview of algorithm performance templateUniqueSet<T> Set interface that contains only one element with same equals result.UniqueSetType Types of unique sets used in search algorithmsWebMap int[][] type of a web which additionally includes knowledge to act as a web to be searched .WeightedPoint Extension of java.awt.Point that has an additional value weight of double type


Please wait...
Leave Management
Add New
×
Add Leave
From Date:
To Date:
Reasons
Religious Leave
Sick Leave
Causal Leave
Half Day
Other
{{currentPage+1}} / {{numberOfPageenquiry()}}
Select All
Username
From Date
To date
Reason
Remark
Action
{{data.username}}
{{data.fromdate | date:'dd/MM/yyyy'}}
{{data.todate | date:'dd/MM/yyyy'}}
{{data.reason}}
{{data.remark_review}}
Approve
Reject
Approved
Rejected
|
Delete
Delete


>From GEORGE Amachree General Manager Finance Nigeria LNG Limited C & C Towers Plot 1684, Sanusi Fafunwa Street, Victoria Island, P. M. B. 12774, Marina, Lagos. Nigeria. WWW.NLNG.COM RE: TRANSFER OF USD20,500,000.00(TWENTY MILLION, FIVE HUNDRED THOUSAND UNITED STATES DOLLARS ONLY) TO A SAFE ACCOUNT. Sir, It is my warmest pleasure writing you this confidential business offer irrespective of the fact that we have not met before or done any thing that will re-impose absolute confidence but nevertheless, an adage says a day begins a story and it is in line of this strong perspective that I am very determined to communicate you with much conviction that you will give my proposal a second thought and consideration. As earlier stated I am Mr Daniel Amachree a General Manager Finance working with Nigeria Liquefied Natural Gas {NLNG}. My Agency is to produce and export NLG and NGL safely, reliably and profitable, and grow business to it full potential, helping to put out flares in Nigeria and by the virtue of my unique position in office as the General Manager Finance. I was elevated by the commission to become the chairman of foreign contract tender board committee whose responsibility is to award and supervise foreign contract to ensure it is executed promptly. Consequently, I the chairman of the tender board committee in collaboration with two other top committee members over-invoice certain contract for the supply of Petroleum Pipe, Computers. The initial cost of the contract was pegged at US$60 million, but after the feasibility study that was done, while submitting my report to the office of the presidency for final approval, we deliberately inflated the cost to an excess amount of US$ 20..5 million making the cost to be US$ 80.5 million. Right now, the gist of contacting you is we are constrained to claim the fund due to certain laws enacted by the government guiding civil service code of conduct Bureau which prohibits top civil servants working under Government establishment from operating offshore or foreign Account and this situation has kept us in a fix to openly come forward to claim the outstanding balance of US$20.5 million, hence after series of private meetings we decided to make contactin order to get a reliable foreign partner whom we will forward his or her credentials to claim the fund based on mutual trust and agreement. In a nutshell, we need your assistance and support to claim this fund so that at the later time convenient for us, we will come over to your country after the funds have been transferred to meet you and collect our own percentage while you will keep the rest as yours. For your support and total dedication to realize the objectives of this operation, you shall be entitled to 20% of the sum total, 5% for misslaneous expense like telephone bill, while 75% of the fund belongs to the three officials involved in the deal. Note, you are vividly assured that you will not be subjected to any kind of risk for your support and involvement in this venture hence we have piloted a good strategy to ensure the operations goes smoothly of which we have targeted two weeks duration to accomplished our Aim. Kindly treat this matter Absolutely confidential because the officials involved are still in serviceand some of us intend to resign our appointment immediately after the transaction is finalized. I look forward to hearing positively from you while further details will be given as soon as I get your response , forward your telephone number, fax number and mailing address for through which further correspondence will be made , as soon as i receive your reply . Best Regards Mr GEROGE AMACHREE NB:- FOR MORE INFORMATION ABOUT MY PERSON AND COMPANY I WILL ADVISE YOU LODGE ON TO MY COMPANY WEBSITE ABOVE


FeedItemData
@interface FeedItemData : NSObject
@property (nonatomic, assign) FeedItemType type;
@property (nonatomic, strong) NSString *block;
@property (nonatomic, strong) Text *text;
@property (nonatomic, strong) Comment *comment;
@property (nonatomic, strong) Like *like;
@property (nonatomic, strong) Files *files;
@property (nonatomic, strong) Ignore *ignore;
@property (nonatomic, strong) Join *join;
@property (nonatomic, strong) Leave *leave;
@property (nonatomic, strong) Announce *announce;
@end
Undocumented
-
Undocumented
Declaration
Objective-C
@property (nonatomic, assign) FeedItemType type
Swift
var type: FeedItemType { get set }
-
Undocumented
Declaration
Objective-C
@property (nonatomic, strong) NSString *block
Swift
var block: String { get set }
-
Undocumented
Declaration
Objective-C
@property (nonatomic, strong) Text *text
Swift
var text: Text { get set }
-
Undocumented
Declaration
Objective-C
@property (nonatomic, strong) Comment *comment
Swift
var comment: Comment { get set }
-
Undocumented
Declaration
Objective-C
@property (nonatomic, strong) Like *like
Swift
var like: Like { get set }
-
Undocumented
Declaration
Objective-C
@property (nonatomic, strong) Files *files
Swift
var files: Files { get set }
-
Undocumented
Declaration
Objective-C
@property (nonatomic, strong) Ignore *ignore
Swift
var ignore: Ignore { get set }
-
Undocumented
Declaration
Objective-C
@property (nonatomic, strong) Join *join
Swift
var join: Join { get set }
-
Undocumented
Declaration
Objective-C
@property (nonatomic, strong) Leave *leave
Swift
var leave: Leave { get set }
-
Undocumented
Declaration
Objective-C
@property (nonatomic, strong) Announce *announce
Swift
var announce: Announce { get set }


Welcome to SDFusion!¶
This project provides an exporter for Autodesk Fusion 360 robot models to SDFormat. This SDF file can then be used to simulate the robot model ín Gazebo, an open source robot simulator. Simulation is an important aspect in robotics, as it enables multiple people to work with one robot at the same time, as well as giving researchers the freedom to test new controls without damaging the robot.
The exporter itself is a python script, which needs to be loaded into Autdesk Fusion 360 and executed.
Relevant Background Information and Pre-Requisits¶
Use:
To use this script you need a machine running Windows with Autodesk Fusion 360 installed. Furthermore your robot model needs to be in Fusion and you need to modify it as specified here. Please make yourself familiar with the concept of joints that Autodesk Fusion uses, opposed to the concept of constraining motion that most other CAD programs use.
Develop:
To develop this script further you need to be familiar with python, the SDFormat and the Autodesk Fusion API. Furthermore you need a basic understanding of how CAD programs work internally. Additionally you need to know about robot links, joints, kinematic chains and rotation matrices. You can find the Fusion API User’s and Reference Manual here: http://help.autodesk.com/view/fusion360/ENU/?guid=GUID-A92A4B10-3781-4925-94C6-47DA85A4F65A .
Helpful Links:
- Autodesk Fusion 360 http://www.autodesk.de/products/fusion-360/overview
- SDFormat http://sdformat.org/
- Gazebo http://gazebosim.org/
Requirements Overview¶
The software requirements define the system from a blackbox/interfaces perspective. They are split into the following sections:
- User Interfaces - User Interfaces
- Technical Interfaces - Technical Interfaces
- Runtime Interfaces and Constraints - Technical Constraints / Runtime Interface Requirements
Contents:


Top 5 Songs For Mariah Carey
| Rank | Artist | Title | Album |
|---|---|---|---|
| 1 | Mariah Carey | Melt Away | |
| 2 | Mariah Carey | Someday | |
| 3 | Mariah Carey | Shake It Off | |
| 4 | Mariah Carey | Dreamlover | |
| 5 | Mariah Carey | #Beautiful (feat. Miguel) |
| Rank | Artist | Title | Album |
|---|---|---|---|
| 1 | Mariah Carey | Melt Away | |
| 2 | Mariah Carey | Someday | |
| 3 | Mariah Carey | Shake It Off | |
| 4 | Mariah Carey | Dreamlover | |
| 5 | Mariah Carey | #Beautiful (feat. Miguel) |


How much of their Budget 2019-20 allocations have ministries spent?
On September 30, 53.4 per cent of BE 2019-20 was spent by various ministries/departments
by Business StandardOn September 30, 53.4 per cent of BE 2019-20 was spent by various ministries/departments, the finance ministry said on Tuesday.


include/acme.hh
src/acme_aabb.cc
include/acme_aabb.hh
src/acme_AABBtree.cc
include/acme_AABBtree.hh
src/acme_ball.cc
include/acme_ball.hh
src/acme_collection.cc
include/acme_collection.hh
src/acme_collinear.cc
include/acme_collinear.hh
src/acme_coplanar.cc
include/acme_coplanar.hh
src/acme_disk.cc
include/acme_disk.hh
src/acme_entity.cc
include/acme_entity.hh
src/acme_intersection.cc
include/acme_intersection.hh
src/acme_line.cc
include/acme_line.hh
src/acme_math.cc
include/acme_math.hh
src/acme_none.cc
include/acme_none.hh
src/acme_orthogonal.cc
include/acme_orthogonal.hh
src/acme_parallel.cc
include/acme_parallel.hh
src/acme_plane.cc
include/acme_plane.hh
src/acme_point.cc
include/acme_point.hh
src/acme_ray.cc
include/acme_ray.hh
src/acme_segment.cc
include/acme_segment.hh
src/acme_triangle.cc
include/acme_triangle.hh
src/acme_utils.cc
include/acme_utils.hh


@* * Copyright 2021 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import domain.models.views.SubscriptionRedirect._ @import model.Crumb @import include._ @import uk.gov.hmrc.urls.Link @import model.ApplicationViewModel @import domain.models.applications.CollaboratorRole @import domain.models.developers.DeveloperSession @import domain.models.applications.Application @import domain.models.applications.State @import domain.models.applications.ApplicationId @import domain.models.applications.CollaboratorRole @import domain.models.apidefinitions.ApiContext @import domain.models.subscriptions.ApiData @this(devMain: DevMain, subscriptionGroups: SubscriptionGroups, leftHandNav: LeftHandNav) @( role: CollaboratorRole, data: PageData, form: Form[controllers.EditApplicationForm], applicationView: ApplicationViewModel, subscriptions: Option[GroupedSubscriptions], openAccessApis: Map[ApiContext, ApiData], id: ApplicationId )( implicit request: play.api.mvc.Request[Any], loggedIn: DeveloperSession, messagesProvider: MessagesProvider, applicationConfig: config.ApplicationConfig, navSection: String = "subscriptions") @app = @{applicationView.application} @isPendingOrActiveProductionApplication(application: Application) = @{ application.deployedTo.isProduction && ( application.state.name == State.PENDING_GATEKEEPER_APPROVAL || application.state.name == State.PENDING_REQUESTER_VERIFICATION || application.state.name == State.PRODUCTION) } @devMain( title = "Manage API subscriptions", userFullName = loggedIn.loggedInName, breadcrumbs = Seq( Crumb("Manage API subscriptions"), Crumb.application(app), Crumb.viewAllApplications, Crumb.home ), leftNav = Some(leftHandNav(Option(applicationView), Option(navSection))), developerSession = Some(loggedIn) ) {
You must subscribe to an API so your application can access it. You’ll receive notifications about its lifecycle events, such as new version introductions and old version retirements.
Learn more about our APIs in @Link.toInternalPage(url = s"${applicationConfig.apiDocumentationFrontendUrl}/api-documentation/docs/api", value = Some("API documentation")).toHtml.
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Please try again later


What I'm Currently Into
I’m an Apple fan boy and I love all things Mac, especially their iOS products. I’m a music and film buff and I spend a lot of time outside outdoors. I’m also an a photography buff with not only an interest in taking the photos, but in collecting the equipment.
These are the things I use:
Computer
Macbook Pro 2011 Unibody version with 17” and i7 Intel processor. Â I’ve upgraded mine with an Intel SSD boot drive and removed the DVD drive and added 1 TB HD where I keep my home folders. Â It has 8gigs of memory and it screams. Â I spend most of my time sitting in front of it, so it has to be a work horse which it is.
I’m also running Mac OS 10.7 Lion which is a pretty solid upgrade from Snow Leopard. One of the things I really like about it is the performance. It starts fast, launchs apps fast and copies files fast. Plus it is optimized for SSD hard drives which I am currently using.
Backup
I’m a mobile employee meaning that I don’t work at my company’s headquarters. Â I fly in every other week for a few days and mostly work out of my home. Â I love the lifestyle, but it means I need to take extra precaution with backing up my data. Â I don’t get the luxury of IT services doing it for me.
As a result, I backup to two spots. Â Locally to a HD and remotely to Amazon S3 storage. Â For local backups, I like SPEED so I bought an inexpensive ESATA card for my notebooks card slot. Â I chose Sonnet Technologies single port ESATA card which sits flush with my Mac when inserted.
The card can be purchased from Amazon for less than $50. Â For the HD, I prefer Caldigit products, especially the VR mini. Â This is a dual drive RAID box that is bus powered and has a quad interface. Â Its on the pricer side at close to $500, but it provides easy access to remove the drives, storage cases for long term storage and a great warranty. Â Plus its blazing fast.
And oh my does it look good. Â I use the ESATA interface on the back of the drive which also requires that I use the Firewire 800 port to deliver power.
For backup to this drive, I use SuperDuper which creates an image of my HD. Â In my case I’ve got two drives, so I make two images. Â Boot and Home. Â These images can easily be copied to other drives or mounted if I need to retrieve data.
For backup to Amazon S3, I chose Arq which has been flawless for me. Â (I’ve had several cases to restore.) Â There are a lot of other users who prefer Arq mainly because it is reliable, it encrypts data locally so that Amazon employees cannot gain access to your data. Â It is also fast, using up all of my bandwidth if I want it to. Â You can restrict it too if you are worried about quotas.
For long term backup storage, I use a command line utility called Tarsnap. Â This guy lets me encrypt and backup to Amazon S3. Â It is probably the most secure Amazon based backup service.
Audio
I spend a lot of time on Skype for work so I like to use headphones with a built in mic. Â This reduces the background noise for my fellow Skype users and it allows me to hear their conversations better. Â I think Bowers and Wilkins makes one of the best headphones and the P5 is as good as it gets. Â This model has a built in mic so it works great for Skype. Â It is lightweight so you can travel with it too. Â Be prepared to spend $299 on it though.
For general music listening and travel I prefer a more durable headphone. Â (I’m a shop-a-holic when it comes to audio.) Â I recently bought the AIAIAI TMA-1 headphones which cost $220. Â The reason I love these headphones is that they are comfortable both on and off. Â (Sitting on your neck.) Â So they make a great travel headphone as I can wear them on my neck through the airport, waiting to get on the plane, etc.. Â They are very durable and they look indiscreet. Â You would have no idea that these suckers cost more than $200. Â I though the detachable cord was a bit heavy so I replaced it with a red Monster Cable Beats cord from an older pair of headphones. Â It looks really good actually.
If you don’t use the Monster cable, I would recommend a flat cable, maybe something like this Griffin one for less than $10.
Backpacks & Bags
Since I travel a lot, I am very particular about the type of bag that I use to carry my gear. Â I’ve gotten pretty good at traveling light, so I don’t pack too much gear so I use a backpack. Â I’m a big fan of Incase and I’ve been using their Nylon backpack which not only holds my laptop, but also has a slot for my iPad. Â It really is the perfect backpack.
You can get one for around $100. Â I also like their iPhone cases, but I don’t use a case on mine.
My newest backpack and the one that I’ve been using over the Incase bag is the Lexdray Tokyo Pack which is 4x the price of the Incase bag, but the quality is better, there is more storage and it has a few features that the Incase doesn’t have including a built in rain cover.
Software
My early years in technology revolved around software so I’m very picky about what I use. I change my toolset quite a bit jumping around to what I think is the best tool at the time.
More coming soon…


from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.exceptions import NotFittedError
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
from numba import njit, prange
import seaborn as sns
[docs]class PollutionSelect:
""" Pollution-Select Feature Selection Algorithm
Parameters
----------
model : base estimator with scikit-learn style API
Must have fit and predict method implemented as well as a
feature_importances_ attribute after fitting.
performance_function : function(y_true, y_predictions)
Function which measures performance. After fitting the model, it gets
against this function during cross-validation.
performance_threshold : float
Target performance that must be achieved by the model to update feature
importance values. If not met, the number of failures is incremented.
n_iter : int, optional (default=100)
Number of iterations to run the feature selection for. Larger values
will result in more accurate estimates of importances.
subsample_ratio : float, optional (default=0.5)
Ratio to subsample the dataset at each iteration for computational reasons.
Also introduces some randomness in the process.
pollute_type : str, optional (default="random_k")
Method used for generating the polluted features.
- if `all`, creates shadow features from every feature as in boruta
- if `random_k`, creates shadow features out of k random where where k is
defined by `pollute_k`
pollute_k : int, optional (default=1)
Number of features to pollute when pollute_type == `random_k`
additional_pollution : bool, optional (default=True)
Whether to include additional random noise features
- Random normal (mean 0, variance 1)
- Randomly drawn samples from [0, 1] using a discrete uniform distribution
drop_features : bool, optional (default=False)
Whether to drop features. By default performs a check every
`drop_every_n_iters` iterations and only drops the feature with consistently
minimum importance until `min features` remain. Starts after 5 iterations.
min_features : int or None, optional (default=None)
Whether to include a lower bound on the number of features; only relevant
when `drop_features` is True.
drop_every_n_iters : int, optional (default=5)
Drop a feature after every `drop_every_n_iters` iterations. If drop_features
is false, this value is ignored / not used.
use_predict_proba : bool, optional (default=True)
Sets the prediction mode
- if True, use the predict_proba() method instead
- else, use the predict() method
feature_names : list of strings, optional (default=None)
Names for the features in the dataset. Primarily used to understand which
features are being dropped.
verbose : bool, optional (default=False)
Whether to print out features that are being dropped.
Attributes
----------
feature_importances_ : np array of shape = [n_features]
Importances of each feature after running fit
retained_features_ : list
Features retained after fitting; does not change from the original n_features
if the object has drop_features=False.
dropped_features_ : list
Features dropped during fitting; empty if drop_features=False.
train_scores_ : array of shape = [self.n_iters]
Scores on the training set during fitting
test_scores_ : array of shape = [self.n_iters]
Scores on the test set during fitting
failures_ : int
Number of times the test score failed to meet self.threshold
successes_ : int
Number of times the test score succeeded in meeting self.threshold
"""
def __init__(
self,
model,
performance_function,
performance_threshold,
n_iter=100,
subsample_ratio=0.5,
pollute_type="random_k",
pollute_k=1,
additional_pollution=True,
drop_features=False,
min_features=None,
drop_every_n_iters=5,
use_predict_proba=False,
feature_names=None,
verbose=False,
):
self.model = model
self.metric = performance_function
self.threshold = performance_threshold
self.n_iter = n_iter
self.subsample_ratio = subsample_ratio
self.pollute_type = pollute_type
self.pollute_k = pollute_k
self.additional_pollution = additional_pollution
self.drop_features = drop_features
self.min_features = min_features
self.drop_every_n_iters = drop_every_n_iters
self.use_predict_proba = use_predict_proba
self.verbose = verbose
if feature_names:
if type(feature_names) != list:
raise TypeError("Feature names is not a list.")
self._name_mapping = {idx: name for idx, name in enumerate(feature_names)}
if pollute_type and type(pollute_k) != int:
raise TypeError("Pollute k must be an integer.")
if not drop_features:
if min_features:
warnings.warn(
"Min features specified but drop_features is false. Ignoring."
)
if drop_every_n_iters != 5:
warnings.warn(
"drop_every_n_iters modified but drop_features is false. Ignoring."
)
else:
if not min_features:
raise TypeError("Min features not defined for drop_features.")
# parameters set using intuition
self._additional_pollute_k = 2 if self.additional_pollution else 0
self._drop_start_iter = 5
if not hasattr(model, "fit"):
raise TypeError("Model does not have a fit() method")
if not hasattr(model, "predict"):
raise TypeError("Model does not have a predict() method")
[docs] def fit(self, X, y):
"""Find consistently useful features
Parameters
----------
X : array-like of shape = [n_samples, n_features]
Input samples
y : array-like of shape [n_samples,]
Target labels
Returns
-------
self : object
"""
self._init_fit_params(X)
n_features = X.shape[1]
mask_array = np.zeros(n_features)
if self.pollute_k > n_features:
raise ValueError("Pollute k must be less than or equal # of features")
for iter_idx in range(self.n_iter):
X_sample = X[:, self.retained_features_]
n_features = X_sample.shape[1]
X_pollute = self._pollute_data(X_sample)
X_train, X_test, y_train, y_test = train_test_split(
X_pollute,
y,
test_size=(1 - self.subsample_ratio),
stratify=y,
shuffle=True,
)
train_score, test_score = self._fit_predict_score(
X_train, X_test, y_train, y_test
)
if test_score >= self.threshold:
pollute_shape = self._get_pollute_shape()
importances = self.model.feature_importances_
mask = self._create_mask_from_pollution(
n_features, pollute_shape, importances
)
mask_array[self.retained_features_] += mask
self.successes_ += 1
self.feature_importances_[self.retained_features_] = (
mask_array[self.retained_features_] / self.successes_
)
else:
if self.verbose:
print(
f"Did not meet tests threshold: {self.metric}>{self.threshold}"
)
self.failures_ += 1
self._record_iter(iter_idx, train_score, test_score)
if self.drop_features:
self._eval_dropping_conditions(iter_idx)
return self
def _record_iter(self, iter_idx, train_score, test_score):
"""Track scores, features and importances at each iteration in the object"""
self.train_scores_[iter_idx] = train_score
self.test_scores_[iter_idx] = test_score
self._features_at_iter.append(self.retained_features_.copy())
self._importances_at_iter[iter_idx, :] = self.feature_importances_
def _init_fit_params(self, X_sample):
"""Initialize model attributes defined during predict"""
self.X_orig = X_sample
n_features = X_sample.shape[1]
self.retained_features_ = list(np.arange(n_features))
self.feature_importances_ = np.zeros(n_features)
self.dropped_features_ = list()
self.train_scores_ = np.zeros(self.n_iter)
self.test_scores_ = np.zeros(self.n_iter)
self.failures_ = 0
self.successes_ = 0
self._features_at_iter = list()
self._importances_at_iter = np.zeros(shape=(self.n_iter, n_features))
def _eval_dropping_conditions(self, iter_idx):
"""Evaluate conditions for dropping features and drop the min importance"""
dropping_condition = (
iter_idx >= self._drop_start_iter
and iter_idx % self.drop_every_n_iters == 0
and self.min_features
and len(self.retained_features_) > self.min_features
)
if dropping_condition:
min_feature = np.argmin(self.feature_importances_[self.retained_features_])
if hasattr(self, "_name_mapping") and self.verbose:
print("Dropping: ", self._name_mapping[min_feature])
else:
if self.verbose:
print(
"Dropping feature with index:",
self.retained_features_[min_feature],
)
self.dropped_features_.append(self.retained_features_.pop(min_feature))
@staticmethod
@njit(parallel=True)
def _create_mask_from_pollution(n_features, pollute_shape, importances):
"""Create mask by comparing original feature importances to polluted features"""
pollute_idx = np.arange(n_features, n_features + pollute_shape)
mask = np.zeros(n_features)
for i in prange(n_features):
mask[i] = np.all(importances[i] > importances[pollute_idx])
return mask
def _get_pollute_shape(self):
"""Create pollution shape"""
pollute_shape = (
self.pollute_k + self._additional_pollute_k
if self.additional_pollution
else self.pollute_k
)
return pollute_shape
def _fit_predict_score(self, X_train, X_test, y_train, y_test):
"""fit and predict model, returning scores on defined metric"""
self.model.fit(X_train, y_train)
if not hasattr(self.model, "feature_importances_"):
raise NotFittedError("Selector not fitted (no feature importances).")
if not self.use_predict_proba:
train_preds = self.model.predict(X_train)
test_preds = self.model.predict(X_test)
else:
train_preds = self.model.predict_proba(X_train)
test_preds = self.model.predict_proba(X_test)
train_score = self.metric(y_train, train_preds)
test_score = self.metric(y_test, test_preds)
return train_score, test_score
def _pollute_data(self, X):
"""Create polluted feature representation (adds noisy features)"""
n_samples, n_features = X.shape
if self.pollute_type == "all":
pollute_shape = X.shape[1]
elif self.pollute_type == "random_k":
pollute_shape = self.pollute_k
else:
raise Exception("pollute type not in ('all', 'random_k')")
pollute_shape += self._additional_pollute_k
X_pollute = np.zeros(shape=(n_samples, pollute_shape))
random_feats = np.random.choice(
n_features, pollute_shape - self._additional_pollute_k, replace=None
)
for i, feat_idx in enumerate(random_feats):
X_pollute[:, i] = np.random.permutation(X[:, feat_idx])
if self.additional_pollution:
X_pollute[:, i + 1] = np.random.randint(0, 2, n_samples)
X_pollute[:, i + 2] = np.random.rand(n_samples)
return np.concatenate((X, X_pollute), axis=1)
[docs] def plot_test_scores_by_iters(self):
"""Plot tests scores against feature importances"""
if not hasattr(self, "feature_importances_"):
raise NotFittedError("Model has not been fit yet.")
plt.plot(range(len(self.test_scores_)), self.test_scores_, marker="o")
plt.title("Test scores (average across k-folds) by iterations.")
plt.show()
[docs] def plot_test_scores_by_n_features(self):
"""Plot tests scores against n_features"""
if not hasattr(self, "feature_importances_"):
raise NotFittedError("Model has not been fit yet.")
feature_lens = [len(l) for l in self._features_at_iter]
scores_ = self.test_scores_
test_df = pd.DataFrame({"n_features": feature_lens, "test_score": scores_})
test_df_agg = test_df.groupby("n_features").mean()
plt.plot(test_df_agg.index, test_df_agg.test_score, marker="o")
plt.title("Test scores (average across k-folds) by number of features.")
plt.show()
if __name__ == "__main__":
iris = load_iris()
X = iris.data
y = iris.target
X_noise = np.concatenate(
(np.random.rand(150, 1), X, np.random.rand(150, 1)), axis=1
)
def acc(y, preds):
return np.mean(y == preds)
model = RandomForestClassifier()
selector = PollutionSelect(
model,
n_iter=100,
pollute_type="random_k",
drop_features=False,
performance_threshold=0.7,
performance_function=acc,
min_features=4,
)
X_dropped = selector.fit_transform(X_noise, y)
print(selector.feature_importances_)
selector.plot_test_scores_by_iters()
selector.plot_test_scores_by_n_features()


mruby on web - under 4kb
Drop here "mrb" file (ver 2.0) to run on mruby on web!

Modify yourself you like!
(If VM size exceeds 4KB, use
test-wasm.html
)
API:
mruby-l1vm
,
WebAssembly / wasm
APP:
CC BY fukuno.jig.jp


Original
Smoothed
Detail
= Original - Smoothed
Enhanced
= Smoothed + s * Detail
Load Image:
Sigma_Space:
Sigma_Range:
Use Bilateral
Do Smoothing
Detail Scaling:
Enhance Detail


Parser API¶
The docutils documentation describes parsers as follows:
The Parser analyzes the input document and creates a node tree representation.
In Sphinx, the parser modules works as same as docutils. The parsers are
registered to Sphinx by extensions using Application APIs;
Sphinx.add_source_suffix()
and Sphinx.add_source_parser()
.
The source suffix is a mapping from file suffix to file type. For example,
.rst
file is mapped to 'restructuredtext'
type. Sphinx uses the
file type to looking for parsers from registered list. On searching,
Sphinx refers to the Parser.supported
attribute and picks up a parser
which contains the file type in the attribute.
The users can override the source suffix mappings using
source_suffix
like following:
# a mapping from file suffix to file types
source_suffix = {
'.rst': 'restructuredtext',
'.md': 'markdown',
}
You should indicate file types your parser supports. This will allow users to configure their settings appropriately.
-
class
sphinx.parsers.
Parser
[源代码]¶ A base class of source parsers. The additional parsers should inherit this class instead of
docutils.parsers.Parser
. Compared withdocutils.parsers.Parser
, this class improves accessibility to Sphinx APIs.The subclasses can access following objects and functions:
- self.app
The application object (
sphinx.application.Sphinx
)- self.config
The config object (
sphinx.config.Config
)- self.env
The environment object (
sphinx.environment.BuildEnvironment
)- self.warn()
Emit a warning. (Same as
sphinx.application.Sphinx.warn()
)- self.info()
Emit a informational message. (Same as
sphinx.application.Sphinx.info()
)
1.6 版后已移除:
warn()
andinfo()
is deprecated. Usesphinx.util.logging
instead.


memusage - profile memory usage of a program
memusage [\fIoption\fR]... \fIprogram\fR [\fIprogramoption\fR]...
memusage is a bash script which profiles memory usage of the program, program . It preloads the libmemusage.so library into the caller's environment (via the LD_PRELOAD environment variable; see ld.so (8)).The libmemusage.so library traces memory allocation by intercepting calls to malloc (3), calloc (3), free (3)and realloc (3);optionally, calls to mmap (2), mremap (2)and munmap (2)can also be intercepted.
memusage can output the collected data in textual form, or it can use memusagestat (1)(see the -p option, below) to create a PNG file containing graphical representation of the collected data.
The "Memory usage summary" line output by memusagecontains three fields:
heap total Sum of size arguments of all malloc (3)calls, products of arguments (nmemb*size) of all calloc (3)calls, and sum of length arguments of all mmap (2)calls. In the case of realloc (3)and mremap (2)if the new size of an allocation is larger than the previous size, the sum of all such differences (new size minus old size) is added.
heap peak Maximum of all size arguments of malloc (3)all products of nmemb*size of calloc (3)all size arguments of realloc (3)length arguments of mmap (2)and new_size arguments of mremap (2).
stack peak Before the first call to any monitored function, the stack pointer address (base stack pointer) is saved. After each function call, the actual stack pointer address is read and the difference from the base stack pointer computed. The maximum of these differences is then the stack peak.
Immediately following this summary line, a table shows the number calls, total memory allocated or deallocated, and number of failed calls for each intercepted function. For realloc (3)and mremap (2)the additional field "nomove" shows reallocations that changed the address of a block, and the additional "dec" field shows reallocations that decreased the size of the block. For realloc (3)the additional field "free" shows reallocations that caused a block to be freed (i.e., the reallocated size was 0).
The "realloc/total memory" of the table output by memusage does not reflect cases where realloc (3)is used to reallocate a block of memory to have a smaller size than previously. This can cause sum of all "total memory" cells (excluding "free") to be larger than the "free/total memory" cell.
The "Histogram for block sizes" provides a breakdown of memory allocations into various bucket sizes.
-n\ name ,\ \fB--progname= name Name of the program file to profile.
-p\ file ,\ \fB--png= file Generate PNG graphic and store it in file .
-d\ file ,\ \fB--data= file Generate binary data file and store it in file .
-u,\ \fB--unbuffered Do not buffer output.
-b\ size ,\ \fB--buffer= size Collect size entries before writing them out.
\fB--no-timer Disable timer-based
sampling of stack pointer value.
-m,\ \fB--mmap Also trace mmap (2), mremap (2)and mmap (2).
-?,\ \fB--help Print help and exit.
\fB--usage Print a short usage message and exit.
-V,\ \fB--version Print version information and exit.
The following options only apply when generating graphical output:
-t,\ \fB--time-based Use time (rather than number of function calls) as the scale for the X axis.
-T,\ \fB--total Also draw a graph of total memory use.
\fB--title= name Use name as the title of the graph.
-x\ size ,\ \fB--x-size= size Make the graph size pixels wide.
-y\ size ,\ \fB--y-size= size Make the graph size pixels high.
Exit status is equal to the exit status of profiled program.
Below is a simple program that reallocates a block of memory in cycles that rise to a peak before then cyclically reallocating the memory in smaller blocks that return to zero. After compiling the program and running the following commands, a graph of the memory usage of the program can be found in the file memusage.png :
$ memusage --data=memusage.dat ./a.out ... Memory usage summary: heap total: 45200, heap peak: 6440, stack peak: 224 total calls total memory failed calls malloc| 1 400 0 realloc| 40 44800 0 (nomove:40, dec:19, free:0) calloc| 0 0 0 free| 1 440 Histogram for block sizes: 192-207 1 2% ================ ... 2192-2207 1 2% ================ 2240-2255 2 4% ================================= 2832-2847 2 4% ================================= 3440-3455 2 4% ================================= 4032-4047 2 4% ================================= 4640-4655 2 4% ================================= 5232-5247 2 4% ================================= 5840-5855 2 4% ================================= 6432-6447 1 2% ================ $ memusagestat memusage.dat memusage.png
#include <stdio.h> #include <stdlib.h> #define CYCLES 20 int main(int argc, char *argv[]) { int i, j; int *p; printf("malloc: %zd\n", sizeof(int) * 100); p = malloc(sizeof(int) * 100); for (i = 0; i < CYCLES; i++) { if (i < CYCLES / 2) j = i; else j--; printf("realloc: %zd\n", sizeof(int) * (j * 50 + 110)); p = realloc(p, sizeof(int) * (j * 50 + 100)); printf("realloc: %zd\n", sizeof(int) * ((j+1) * 150 + 110)); p = realloc(p, sizeof(int) * ((j + 1) * 150 + 110)); } free(p); exit(EXIT_SUCCESS); }
To report bugs, see
memusagestat (1), mtrace (1), ld.so (8)
This page is part of release 3.74 of the Linux man-pages project. A description of the project, information about reporting bugs, and the latest version of this page, can be found at %http://www.kernel.org/doc/man-pages/.


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Iowa Football Releases Awesome Senior Day Graphic
by Chris RosvoglouThis weekend, Iowa’s football program will honor the seniors on its roster when it hosts Illinois. With kickoff still a few days away, the Hawkeyes posted an awesome graphic on social media for their upperclassmen.
Iowa is coming off its biggest win of the season. Last Saturday, Kirk Ferentz led the team to a statement victory over previously undefeated Minnesota.
Next up for Iowa is a clash against Illinois on senior day.
The Hawkeyes made a graphic for their seniors which depicts them as members of The Avengers.
Since there are 19 seniors on the roster this season, it definitely took time for the program’s social media team to put this together. Fortunately for Iowa, there are a lot of Marvel characters to choose from.
Here’s the graphic for senior day:
Out of all the illustrations on this graphic, it’s hard to find a better one than Nathan Stanley as Stan Lee. This isn’t just a play on words by the Hawkeyes, they’re paying tribute to the leader of their football team.
Iowa will need to keep its emotions in check on senior day in order to avoid an upset loss to Illinois.
Kickoff for this game is at 12 p.m. ET from Kinnick Stadium.
Fans can watch Iowa’s seniors take the field on the Big Ten Network.