Join us today and be part of the one billion who are rising for
justice on 14 February 2014!
SIGN UP at the NEW onebillionrising.org >
http://action.vday.org/site/R?i=RD3646_f0edg9fSbJ-kuVQ
http://action.vday.org/site/R?i=HD5C8G0JwX5txMSdX-BopQ
ONE BILLION RISING FOR JUSTICE
http://action.vday.org/site/R?i=HD1LOxfvop9SRMBnxcwq_g
is a call to women, men, and youth around the world to gather safely
on 14 February 2014 outside places where they are entitled to justice
court houses, police stations, government offices, school
administration buildings, work places, sites of environmental
injustice, military courts, embassies, places of worship, homes, or
simply public gathering places where women deserve to feel safe but
too often do not.
SIGN UP to RISE, RELEASE, and DANCE to end violence against women and
girls! >
http://action.vday.org/site/R?i=YO7NN263U6hlHVbcW9EWVg
LEARN more about ONE BILLION RISING FOR JUSTICE >
http://action.vday.org/site/R?i=mKCAFJD5kerfLCYGQdFMHA
WATCH the new short film “RISING” by V-Day & Tony Stroebel, featuring
global event footage from One Billion Rising 2013 >
http://action.vday.org/site/R?i=-b9FW1jA_pHL0EGLoptiLw
My Vision Of Justice Blog Series: Mbali Khumalo & Nighat Rizvi
What does justice look like? Over the coming months, One Billion
Rising Global Coordinators
http://action.vday.org/site/R?i=yv2M_Joc9MpAr1w94C1t2g
and activists will be answering that question in our new blog
series “My Vision Of Justice.”
In the first installment we hear from Mbali Khumalo, one of the
V-Girls South Africa founding members, and Pakistan Regional
Coordinator Nighat Rizvi.
“JUST US” by Mbali Khumalo >
http://action.vday.org/site/R?i=bj03DUi9h7ilTUuy5jCQrQ
“MY VISION OF JUSTICE” by Nighat Rizvi >
http://action.vday.org/site/R?i=K8VAgqlS3_POhpt14K3YFw
France’s “First Lady” Visits City of Joy
In June, Valerie Trierweiler, the French President’s Companion,
visited the
http://action.vday.org/site/R?i=6DCa5IbdbBnVAAqjiQFisw
City of Joy
http://action.vday.org/site/R?i=-aTqnznmSRV104vDEV2UNw
in Bukavu, DRC, along with Yamina Benguigui, French Secretary of
Foreign Affairs in Charge of French Speaking Countries, to both honor
Dr. Denis Mukwege for his commitment to treating rape survivors of the
conflicts in the DRC, and to share a meal with with City of Joy
residents.
READ more >
http://action.vday.org/site/R?i=DmSfdQBboguuYu8IwNe7Mg
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Wurm Assistant 3.1 is here and with it, brand new plugin support!
What is a plugin? This is basically an alternative to hacking the source of Wurm Assistant and begging with pull requests, just to add some feature.
Making a plugin is, I dare say, simple. There are a few steps of arguably magic, but from there, it's like making a fresh app. Any additional Wurm Assistant magic is completely opt-in. I've made a quick guide for adventurers willing to try out this system.
Recently devs started to improve in-game log messages. Since most of Wurm Assistant is depending on those, it is likely something will get broken. I do not have time to track these upcoming breaking changes, nor the means to ensure they can be prevented. This means WA might get broken now and then and I'm completely dependent on your bug reports to fix it as soon as possible.
Here is my take on the whole thing and I'd encourage you to voice your opinion, if you agree with me there.
Another source of minor bugs will come from my efforts to improve some source code. This is unlikely to result in new major features, but I'm using Wurm Assistant as my portfolio project and some of it's code is really dated and feels unprofessional.
I'm working on a secret new project and it might prove useful for Wurm community and similar sandbox games. I can't say anything more at the moment (I know, teasing!), as it all depends on how my career turns out in the coming months. Stay tuned for more news!
-- Aldur


{% autoescape true %}
Filtered email Forwarding
Main
Subscriber Area
Logout
Filtered Forwarding
User: {{name}}
{{message}}
Watched words
{% for word in words %}
{{word}}
{% endfor %}
Word
Add word
Remove word
Recently forwarded emails
{% for email in forwarded %}
From:
{{email.sender}}
Subject:
{{email.subject}}
Content:
{{email.content}}
{% endfor %}
{% endautoescape %}


$def with (months, days, year, month, day, weekday, comments, users) $var title: Vaktplan - $days[weekday] $day $months[month] ($year)
$comment[0] $users[comment[2]].title()


About the Project
This project was born out of a lifelong interest in Jeopardy! and trivia. My first attempt at scraping data from J! Archive was for a Discord bot I created called Trebot. This bot allowed users to call for a random question and keep score over a period of time. It was during the making of this that I developed the first script for collecting data from the site and putting it into a database (MongoDB in this case). It only made use of the information on the site about clues for each game.
In the winter of 2018, I developed the idea for using a modified version of this script to collect the rest of the data, including contestant information and more detailed data on the clues. Go to the Methods page of this site to see more information on how I built the dataset, analyzed it, and visualized it.
Plans for the Future
There is still have more that can be analyzed. For example, a topic model can be done on the professions of the contestants. The database already contains the infrastructure for it built in. I also plan on building out an interactive visualization that would allow for users to input their own text to search for in the clues, answers, and categories. It would show every time the text appears throughout the show's history.


| Class and Description |
|---|
| AclLineMatchExpr |
| Class and Description |
|---|
| AclLineMatchExpr |
| AclTrace |
| AndMatchExpr |
| Evaluator |
| FalseExpr |
| GenericAclLineMatchExprVisitor |
| IpSpaceTraceEvent |
| MatchHeaderSpace |
| MatchSrcInterface |
| NotMatchExpr |
| OriginatingFromDevice
Match condition that holds when flow originated from this device rather than being received on
some interface.
|
| OrMatchExpr |
| PermittedByAcl |
| TerminalTraceEvent |
| TraceEvent |
| TrueExpr |
| Class and Description |
|---|
| CanonicalAcl
Represents an ACL with all its dependencies for the purpose of detecting identical ACLs.
|
| Class and Description |
|---|
| AclLineMatchExpr |
| AclTracer
Tracer for
IpAccessList , IpSpace , HeaderSpace .Acts like Evaluator on IpAccessList , except that it introduces tracing when
encountering traceable classes. |
| AndMatchExpr |
| CircularReferenceException
Raised when attempting to use a reference that is part of a circular chain of references.
|
| FalseExpr |
| GenericAclLineMatchExprVisitor |
| MatchHeaderSpace |
| MatchSrcInterface |
| NotMatchExpr |
| OriginatingFromDevice
Match condition that holds when flow originated from this device rather than being received on
some interface.
|
| OrMatchExpr |
| PermittedByAcl |
| TrueExpr |
| UndefinedReferenceException
Raised when attempting to use an undefined reference.
|
| Class and Description |
|---|
| AclLineMatchExpr |
| AndMatchExpr |
| CircularReferenceException
Raised when attempting to use a reference that is part of a circular chain of references.
|
| FalseExpr |
| GenericAclLineMatchExprVisitor |
| MatchHeaderSpace |
| MatchSrcInterface |
| NotMatchExpr |
| OriginatingFromDevice
Match condition that holds when flow originated from this device rather than being received on
some interface.
|
| OrMatchExpr |
| PermittedByAcl |
| TrueExpr |
| UndefinedReferenceException
Raised when attempting to use an undefined reference.
|
| Class and Description |
|---|
| AclLineMatchExpr |
| MatchSrcInterface |
| Class and Description |
|---|
| AndMatchExpr |
| FalseExpr |
| GenericAclLineMatchExprVisitor |
| MatchHeaderSpace |
| MatchSrcInterface |
| NotMatchExpr |
| OriginatingFromDevice
Match condition that holds when flow originated from this device rather than being received on
some interface.
|
| OrMatchExpr |
| PermittedByAcl |
| TrueExpr |
| Class and Description |
|---|
| AndMatchExpr |
| FalseExpr |
| GenericAclLineMatchExprVisitor |
| MatchHeaderSpace |
| MatchSrcInterface |
| NotMatchExpr |
| OriginatingFromDevice
Match condition that holds when flow originated from this device rather than being received on
some interface.
|
| OrMatchExpr |
| PermittedByAcl |
| TrueExpr |
| Class and Description |
|---|
| AclLineMatchExpr |
| AndMatchExpr |
| FalseExpr |
| GenericAclLineMatchExprVisitor |
| MatchHeaderSpace |
| MatchSrcInterface |
| NotMatchExpr |
| OriginatingFromDevice
Match condition that holds when flow originated from this device rather than being received on
some interface.
|
| OrMatchExpr |
| PermittedByAcl |
| TrueExpr |
Copyright © 2014–2018. All rights reserved.


Back to Tips
Know Why You are Selling
If you know exactly why you are selling then it is easier for you to
follow the right plan of action for getting what you want.
If you are a seller who needs to close a sale as quickly as possible,
then you should know that getting the highest price possible is not one
of your priorities. It does not mean that you won’t or cannot get the
highest price, but it means that the price is not the deciding factor. A
buyer who can give you a quick closing time will appeal much more to you
than a buyer who can offer you more money but the negotiation and
closing time drag on.
It’s always good to know how low you will go, in terms of selling price.
This will help to eliminate some of the offers that you find simply
offensive or ridiculous. Even though you should consider all offers
seriously and take into consideration the terms of each offer,
sometimes, if you know the bottom line and are strict about it, you can
save yourself time.
Once you know what your limits and reasons are, discuss them with your
agent so that they can help you set your goals realistically. If you
decide to list your home on your own, make sure you do research on the
current market, and you get the proper advice you need in terms of legal
issues, etc. The key is to be realistic and to know what your goals are
so they can be met.
Back to Tips


Preparation time
less than 30 mins
Cooking time
1 to 2 hours
Serves
Serves 4
This easy cottage pie has a lighter touch, with baked beans added to the meat, but still with plenty of lovely baked mash on top. This is designed to be a low cost recipe.
Preheat the oven to 200C/400F/Gas 6.
Heat a large saucepan over a medium heat and add the oil. Once hot, fry the onions and carrots gently with a pinch of salt for 6-7 minutes, or until the onions are translucent but not browned. Add the garlic and cook for another two minutes.
Add the mince and stir to mix. Cook for 5-6 minutes or until all the raw meat has turned brown. Add the baked beans, tomatoes and stock. Simmer the mixture for 30-40 minutes without a lid to allow the mix to reduce and thicken a little.
Once cooked, pour into a medium sized baking dish about 21x27cm/8x11in. Taste and add salt and pepper as necessary. Leave to cool and then chill in the fridge for 20 minutes - this will prevent the mashed potato sinking into it too much.
Meanwhile boil the potatoes in a saucepan of salted boiling water for 15 minutes until tender. Remove and drain, leaving to steam dry in the colander for 3-4 minutes.
Heat the milk and butter together in the pan you used to cook the potatoes, until the butter is melted. Once the potatoes are dried a little, place back into the saucepan and mash with a potato masher until smooth, mixing well with the butter and milk. Taste and add salt and pepper to taste.
Layer the mashed potato on top of the meat base in the baking dish and bake in the preheated oven to 25-30 minutes, or until bubbling and the potato top is golden-brown.


Show trees
Set trees definition expression, for example:
Continuous model change:
On form submit model change:
Set parks opacity
Lat: {{ map.center.lat | number:3 }}, Lng: {{ map.center.lng | number:3 }}, Zoom: {{map.zoom}}
Based on this JS Fiddle.


Peer comments on submissions
| Name | Description |
|---|---|
| peer_comment_id | primary key |
| peer_submission_id | The submission that this comment is for |
| ualberta_peer_assignments_user_id | Encrypted Coursera user id for ualberta peer_assignments data. |
| peer_comment_created_ts | The time when the comment was created. The comment never changes after creation |
| peer_comment_text | The text of the comment |
CREATE TABLE peer_comments ( peer_comment_id VARCHAR(50) ,peer_submission_id VARCHAR(50) ,ualberta_peer_assignments_user_id VARCHAR(50) NOT NULL ,peer_comment_created_ts TIMESTAMP ,peer_comment_text VARCHAR(65535) ,PRIMARY KEY (peer_comment_id) ,FOREIGN KEY (peer_submission_id) REFERENCES peer_submissions(peer_submission_id) );


1.Connect The Dots
While learning C++, I made a project named connect the dots. Actually
while playing this game on a paper I thought to make a program of it.
Which lead me to several challenges while making the game. But after a
short struggle I final made it. It is also present as a github repository in my github account.


Im Neuhaus Housing / Wirz Achitekten
- Curated by María Francisca González
- 1 day
Houses Interiors · Fideris, Switzerland
- version="1.0" encoding="UTF-8"? Architects: Wirz Achitekten
- AreaArea of this architecture projectArea: 1694.0 m2
- version="1.0" encoding="UTF-8"?YearCompletion year of this architecture project Year: 2018
- version="1.0" encoding="UTF-8"?PhotographsPhotographs: Hannes Henz
Text description provided by the architects. The project is situated in the rural part of Zürich in Switzerland. Together with the listed farm house from the 18th century it forms an ensemble around a yard.
Similar to local barns the building is conceived as a single big volume, although the rowhouse contains five vertically organised single-family houses.


1 /
We fetch and consume ocean data from a multitude of sources, including but not limited to IOT devices, sensors,
APIS, satellites, NASA data api platform, worldbank sources and from IPM1 - our own proprietary hardware model.
2 /
All these data is passed through our plataform to get them in a common format that eases storing, analysis,
and further processing. A process mostly known as ETL
After processing and storing sensor and api data, we feed them to our ML/AI models for training and accuracy. The model searches for patterns, common structures and non-linear relationships between sensor-data and ecosystem activity, size and behaviour.
After all data crunching and learning we provide our customers accurate reports of marine resources and an XRay of marine-resources which they can sell on our marketplace through smart-contracts that guarantee efficient resource management.


{% extends 'bootstrap/base.html' %} {% import "bootstrap/wtf.html" as wtf %} {% block styles %} {{ super() }} {% endblock %} {% block title %} The price prediction of used cars in Ukraine {% endblock %} {% block content %}
This is web version of predictor. Keep in mind that the 2016 dataset and the predicted price are not entirely up to date. Alternatively, you can add 5y to the age of the car.


window.handleAboutRequest = () => { document.body.innerHTML = `
I like what I'm doing now. It's all about design and coding. I feel so powerful, just think, I started from scratch and then this full backend and frontend stuff. I don't want to list all programs and languages... BUT I will try it. Here I used HTML, CSS, JavaScript, MySQL, PostMan, and Node.js. I guess, there is a room for improvement, but I just started :)
GO to an Italian meal


CrimPapers
Articles published on 28 November 2017
On 28 November 2017, 9 new articles were published in 8 journals.
Personal Security Alarms For the Prevention of Assaults Against Healthcare Staff by Chloe Perkins, Deirdre Beecher, David Colas Aberg, Phil Edwards and Nick Tilley
Integrating Criminological and Mental Health Perspectives on Low Self-Control: A Multi-Domain Analysis
“We Are Humans First”: Expanding Learning Outcomes in An Undergraduate Research Methods Course Through An Experiential Learning Project on Homelessness by Megan Welsh
Making Sense of Evidence: Using Research Training to Promote Organisational Change by K. Wilkinson, K. Boyd, M. Pearson, H. Farrimond, I. A. Lang, D. Fleischer, A. Poole, N. Ralph, B. Rappert
Organizational Decline and Fiscal Distress in Municipal Police Agencies by Matthew J. Giblin, Jeffrey S. Nowacki
The Role of Job Demands and Job Resources in the Development of Emotional Exhaustion, Depression, and Anxiety Among Police Officers by Andreas Santa Maria, Franziska Wörfel, Christine Wolter, Burkhard Gusy, Max Rotter, Sabine Stark, Dieter Kleiber, Babette Renneberg
Deaths After Police Contact: Constructing Accountability in the 21st Century by James Mehigan
Methodology Matters: Comparing Sample Types and Data Collection Methods in A Juror Decision-Making Study on the Influence of Defendant Race by Evelyn M. Maeder, Susan Yamamoto, Laura A. McManus
From System to Pedagogy: Towards A Nonlinear Pedagogy of Self-Defense Training in the Police and the Civilian Domain


© 2013, NVIDIA CORPORATION. All rights reserved.
Code and text by Sean Baxter, NVIDIA Research.
(Click here for license. Click here for contact information.)
Bulk Remove and Bulk Insert are high-throughput functions for fine-grained parallel array surgery. Bulk Remove compacts an array by removing all elements specified by index. Bulk Insert merges two arrays in parallel by insertion index. Merge Path partitioning for load balancing is introduced here.
Bulk Remove benchmark from benchmarkinsert/benchmarkinsert.cu
Bulk Remove demonstration from tests/demo.cu
void DemoBulkRemove(CudaContext& context) { printf("\n\nBULK REMOVE DEMONSTRATION:\n\n"); // Use CudaContext::FillAscending to generate 100 integers between 0 and 99. int N = 100; MGPU_MEM(int) data = context.FillAscending<int>(N, 0, 1); printf("Input array:\n"); PrintArray(*data, "%4d", 10); // Remove every 3rd element from the exclusive scan. Use // CudaContext::FillAscending to generate removal indices for every 3rd // integer between 0 and 99. int RemoveCount = MGPU_DIV_UP(N, 3); MGPU_MEM(int) remove = context.FillAscending(RemoveCount, 0, 3); MGPU_MEM(int) data2 = context.Malloc<int>(N - RemoveCount); BulkRemove(data->get(), N, remove->get(), RemoveCount, data2->get(), context); printf("\nRemoving every 3rd element:\n"); PrintArray(*data2, "%4d", 10); }
BULK REMOVE DEMONSTRATION: Input array: 0: 0 1 2 3 4 5 6 7 8 9 10: 10 11 12 13 14 15 16 17 18 19 20: 20 21 22 23 24 25 26 27 28 29 30: 30 31 32 33 34 35 36 37 38 39 40: 40 41 42 43 44 45 46 47 48 49 50: 50 51 52 53 54 55 56 57 58 59 60: 60 61 62 63 64 65 66 67 68 69 70: 70 71 72 73 74 75 76 77 78 79 80: 80 81 82 83 84 85 86 87 88 89 90: 90 91 92 93 94 95 96 97 98 99 Removing every 3rd element: 0: 1 2 4 5 7 8 10 11 13 14 10: 16 17 19 20 22 23 25 26 28 29 20: 31 32 34 35 37 38 40 41 43 44 30: 46 47 49 50 52 53 55 56 58 59 40: 61 62 64 65 67 68 70 71 73 74 50: 76 77 79 80 82 83 85 86 88 89 60: 91 92 94 95 97 98
Bulk Insert benchmark from benchmarkinsert/benchmarkinsert.cu
Bulk Insert demonstration from tests/demo.cu
void DemoBulkInsert(CudaContext& context) { printf("\n\nBULK INSERT DEMONSTRATION:\n\n"); // Use CudaContext::FillAscending to generate 100 integers between 0 and 99. int N = 100; MGPU_MEM(int) data = context.FillAscending<int>(N, 0, 1); printf("Input array:\n"); PrintArray(*data, "%4d", 10); // Insert new elements before every 5 input starting at index 2. // Use step_iterator for insertion positions and content. int InsertCount = MGPU_DIV_UP(N - 2, 5); MGPU_MEM(int) data2 = context.Malloc<int>(N + InsertCount); mgpu::step_iterator<int> insertData(1000, 10); mgpu::step_iterator<int> insertIndices(2, 5); BulkInsert(insertData, insertIndices, InsertCount, data->get(), N, data2->get(), context); printf("\nInserting before every 5th element starting at item 2:\n"); PrintArray(*data2, "%4d", 10); }
BULK INSERT DEMONSTRATION: Input array: 0: 0 1 2 3 4 5 6 7 8 9 10: 10 11 12 13 14 15 16 17 18 19 20: 20 21 22 23 24 25 26 27 28 29 30: 30 31 32 33 34 35 36 37 38 39 40: 40 41 42 43 44 45 46 47 48 49 50: 50 51 52 53 54 55 56 57 58 59 60: 60 61 62 63 64 65 66 67 68 69 70: 70 71 72 73 74 75 76 77 78 79 80: 80 81 82 83 84 85 86 87 88 89 90: 90 91 92 93 94 95 96 97 98 99 Inserting before every 5th element starting at item 2: 0: 0 1 1000 2 3 4 5 6 1010 7 10: 8 9 10 11 1020 12 13 14 15 16 20: 1030 17 18 19 20 21 1040 22 23 24 30: 25 26 1050 27 28 29 30 31 1060 32 40: 33 34 35 36 1070 37 38 39 40 41 50: 1080 42 43 44 45 46 1090 47 48 49 60: 50 51 1100 52 53 54 55 56 1110 57 70: 58 59 60 61 1120 62 63 64 65 66 80: 1130 67 68 69 70 71 1140 72 73 74 90: 75 76 1150 77 78 79 80 81 1160 82 100: 83 84 85 86 1170 87 88 89 90 91 110: 1180 92 93 94 95 96 1190 97 98 99
//////////////////////////////////////////////////////////////////////////////// // kernels/bulkremove.cuh // Compact the elements in source_global by removing elements identified by // indices_global. indices_global must be unique, sorted, and range between 0 // and sourceCount - 1. The number of outputs is sourceCount - indicesCount. // IndicesIt should resolve to an integer type. iterators like step_iterator // are supported. // If sourceCount = 10, indicesCount = 6, and indices = (1, 3, 4, 5, 7, 8), then // dest = A0 A2 A6 A9. (All indices between 0 and sourceCount - 1 except those // in indices_global). template<typename InputIt, typename IndicesIt, typename OutputIt> MGPU_HOST void BulkRemove(InputIt source_global, int sourceCount, IndicesIt indices_global, int indicesCount, OutputIt dest_global, CudaContext& context); //////////////////////////////////////////////////////////////////////////////// // kernels/bulkinsert.cuh // Combine aCount elements in a_global with bCount elements in b_global. // Each element a_global[i] is inserted before position indices_global[i] and // stored to dest_global. The insertion indices are relative to the B array, // not the output. Indices must be sorted but not necessarily unique. // If aCount = 5, bCount = 3, and indices = (1, 1, 2, 3, 3), the output is: // B0 A0 A1 B1 A2 B2 A3 A4. template<typename InputIt1, typename IndicesIt, typename InputIt2, typename OutputIt> MGPU_HOST void BulkInsert(InputIt1 a_global, IndicesIt indices_global, int aCount, InputIt2 b_global, int bCount, OutputIt dest_global, CudaContext& context);
Bulk Remove and Bulk Insert are intermediate forms between functions of the scan idiom and functions of MGPU's two-phase idiom. These functions search over their inputs to establish coarse-grained partitionings. Sorted indices are loaded into CTAs and scatter and scan operations remove or insert items with fine-grained control.
Data: 0: 0 1 2 3 4 5 6 7 8 9 10: 10 11 12 13 14 15 16 17 18 19 20: 20 21 22 23 24 25 26 27 28 29 30: 30 31 32 33 34 35 36 37 38 39 40: 40 41 42 43 44 45 46 47 48 49 50: 50 51 52 53 54 55 56 57 58 59 60: 60 61 62 63 64 65 66 67 68 69 70: 70 71 72 73 74 75 76 77 78 79 80: 80 81 82 83 84 85 86 87 88 89 90: 90 91 92 93 94 95 96 97 98 99 Remove indices: 0: 1 4 5 7 10 14 15 16 18 19 10: 27 29 31 32 33 36 37 39 50 59 20: 60 61 66 78 81 83 85 90 91 96 30: 97 98 99
For Bulk Remove, we start with a full tile of values plus the interval of remove indices that map into the tile.
 Poking in 0 will remove the value at that position. Threads scatter 0s for all elements listed in the remove indices array.
Scan of flags: 0: 0 1 1 2 3 3 3 4 4 5 10: 6 6 7 8 9 9 9 9 10 10 20: 10 11 12 13 14 15 16 17 17 18 30: 18 19 19 19 19 20 21 21 21 22 40: 22 23 24 25 26 27 28 29 30 31 50: 32 32 33 34 35 36 37 38 39 40 60: 40 40 40 41 42 43 44 44 45 46 70: 47 48 49 50 51 52 53 54 55 55 80: 56 57 57 58 58 59 59 60 61 62 90: 63 63 63 64 65 66 67 67 67 67 Reduced data: 0: 0 2 3 6 8 9 11 12 13 17 10: 20 21 22 23 24 25 26 28 30 34 20: 35 38 40 41 42 43 44 45 46 47 30: 48 49 51 52 53 54 55 56 57 58 40: 62 63 64 65 67 68 69 70 71 72 50: 73 74 75 76 77 79 80 82 84 86 60: 87 88 89 92 93 94 95
An exclusive scan is run on the flags. Flags that were 1 prior to the scan reserve one slot for their values; flags that were 0 reserve no space. The scanned offsets in green had set flags. Values at the green locations in the tile scatter their values to shared memory at their corresponding scanned locations. In this example, element 0 is stored to location 0; element 1 is skipped (its flag is 0); element 2 is stored to location 1; etc.
How do we find the interval of removal indices to load into the CTA, and where does each CTA store its Bulk Remove output? By insisting on sorted indices (and no duplicate indices) both questions have simple answers:
Remove indices are partioned with a lower-bound binary search, where the keys are the starting offsets for each tile, i.e., multiplies of the block size.
Destination offsets for each tile are inferred from the the tile's input offset (a multiple of the block size) and the removal index offset from 1. Because Bulk Remove requires indices both sorted and unique, a CTA knows where to stream given only its block ID and the location of the first remove index in its range. If NV = 1000, block 8 that loads removal indices (2113, 2423) streams exactly 690 values (1000 - (2423 - 2113)) starting at output position 5887 (8 * 1000 - 2113).
template<int NT, MgpuBounds Bounds, typename It, typename Comp> __global__ void KernelBinarySearch(int count, It data_global, int numItems, int nv, int* partitions_global, int numSearches, Comp comp) { int gid = NT * blockIdx.x + threadIdx.x; if(gid < numSearches) { int p = BinarySearch<Bounds>(data_global, numItems, min(nv * gid, count), comp); partitions_global[gid] = p; } } template<MgpuBounds Bounds, typename It1, typename Comp> MGPU_MEM(int) BinarySearchPartitions(int count, It1 data_global, int numItems, int nv, Comp comp, CudaContext& context) { const int NT = 64; int numBlocks = MGPU_DIV_UP(count, nv); int numPartitionBlocks = MGPU_DIV_UP(numBlocks + 1, NT); MGPU_MEM(int) partitionsDevice = context.Malloc<int>(numBlocks + 1); KernelBinarySearch<NT, Bounds> <<<numPartitionBlocks, NT, 0, context.Stream()>>>(count, data_global, numItems, nv, partitionsDevice->get(), numBlocks + 1, comp); return partitionsDevice; }
Many host functions call BinarySearchPartitions
to partition problems before calling the logic kernel. If the CTA size is 1000, this launch finds intervals in the argument array with values between 0 and 999, 1000 and 1999, etc. For BulkRemove
it maps the correct remove indices into each tile.
Thrust includes a function remove_if
that compacts inputs given a corresponding array of predicates. This interface requires multiple trips through the input: the first trip simply counts the number of elements not to remove. Modern GPU functions usually accept sorted arrays of indices (to remove, to insert, or to mark segment heads) because it eliminates a pass that would otherwise need to run just to calculate output locations and sizes.
include/kernels/bulkremove.cuh
template<typename Tuning, typename InputIt, typename IndicesIt, typename OutputIt> MGPU_LAUNCH_BOUNDS void KernelBulkRemove(InputIt source_global, int sourceCount, IndicesIt indices_global, int indicesCount, const int* p_global, OutputIt dest_global) { typedef MGPU_LAUNCH_PARAMS Params; typedef typename std::iterator_traits<InputIt>::value_type T; const int NT = Params::NT; const int VT = Params::VT; const int NV = NT * VT; typedef CTAScan<NT, ScanOpAdd> S; union Shared { int indices[NV]; typename S::Storage scan; }; __shared__ Shared shared; int tid = threadIdx.x; int block = blockIdx.x; int gid = block * NV; sourceCount = min(NV, sourceCount - gid); // Search for begin and end iterators of interval to load. int p0 = p_global[block]; int p1 = p_global[block + 1]; // Set the flags to 1. The default is to copy a value. #pragma unroll for(int i = 0; i < VT; ++i) { int index = NT * i + tid; shared.indices[index] = index < sourceCount; } __syncthreads(); // Load the indices into register. int begin = p0; int indexCount = p1 - p0; int indices[VT]; DeviceGlobalToReg<NT, VT>(indexCount, indices_global + p0, tid, indices); // Set the counter to 0 for each index we've loaded. #pragma unroll for(int i = 0; i < VT; ++i) if(NT * i + tid < indexCount) shared.indices[indices[i] - gid] = 0; __syncthreads(); // Run a raking scan over the flags. We count the set flags - this is the // number of elements to load in per thread. int x = 0; #pragma unroll for(int i = 0; i < VT; ++i) x += indices[i] = shared.indices[VT * tid + i]; __syncthreads(); // Run a CTA scan and scatter the gather indices to shared memory. int scan = S::Scan(tid, x, shared.scan); #pragma unroll for(int i = 0; i < VT; ++i) if(indices[i]) shared.indices[scan++] = VT * tid + i; __syncthreads(); // Load the gather indices into register. DeviceSharedToReg<NT, VT>(NV, shared.indices, tid, indices); // Gather the data into register. The number of values to copy is // sourceCount - indexCount. source_global += gid; int count = sourceCount - indexCount; T values[VT]; DeviceGather<NT, VT>(count, source_global, indices, tid, values, false); // Store all the valid registers to dest_global. DeviceRegToGlobal<NT, VT>(count, values, tid, dest_global + gid - p0); }
After partitioning, the host launches KernelBulkRemove
. The results of the binary search are loaded into p0 and p1. Flags are initialized to 1 as described, except those for last-tile elements which extend past the end of the input: these are set to 0. DeviceGlobalToReg
loads indices (p0, p1) into register. Because the indices are unique, there cannot be more than NV indices in the interval (p0, p1), so we can safely load into register with an unrolled loop. We use the local removal indices (the offset of the start of the tile is subtracted from each removal index) and poke 0s into shared memory.
Each thread loads VT flags starting at VT * tid. These are summed and passed to CTAReduce
. Scanned flags with corresponding set indices (the scan of flags in green from three figures above) are compacted by conditionally streaming not to VT * tid but to each thread's count scan. Because we had to load flags in thread order (VT * tid + i) rather than strided order (NT * i + tid), it is more efficient to invert the problem from a scatter to a gather.
Finally, the threads cooperatively load sourceCount - indexCount
indices in strided order, gather the values from global memory, and store directly to output at dest_global + gid - p0
.
The complementary function requires a bit more sophistication to write. Remove is easy, because the array can only get smaller. Insert is harder because the array may grow arbitrarily large, causing load-balancing problems. If we were to assign a fixed amount of inputs (say, 1000) to BulkInsert
as we do with BulkRemove
, we may not be able to hold the input indices or the expanded array in the CTA's register or shared memory resources.
The caller may want to insert 1000 or even 1,000,000 elements before the first input. How does the kernel handle thousands of consecutive 0s in the insert indices array? Unlike the remove case, we cannot disallow duplicate insert indices without loss of functionality. If BinarySearchPartition
mapped 10,000 insert requests into a single tile, the kernel would have to dynamically loop through these requests to fulfill them within the capacity constraints of the device. This is expressly against our philosophy that the logic code should not handle scheduling - that is the domain of the partioning phase.
The solution is to generalize the partitioning code to search over two inputs simultaneously: we want to balance the source data with the insertion requests. For NV = 1000, loading 712 inputs implies we can load no more than 288 indices. This way both the new and the old values get merged without overflowing the resources available for the block.
Searching on two sorted arrays is fortunately very easy. For each tile index i, we look for splitters Ai and Bi such that Ai + Bi = NV * i. CTA i loads input from the interval (Ai, Ai+1) and insertion indices from the interval (NV * i - Ai, NV * (i + 1) - Ai+1). The total number of loaded items is clearly NV. If we can simply calculate the partitions Ai we'll have solved Bulk Insert.
template<MgpuBounds Bounds, typename It1, typename It2, typename Comp> MGPU_HOST_DEVICE int MergePath(It1 a, int aCount, It2 b, int bCount, int diag, Comp comp) { typedef typename std::iterator_traits<It1>::value_type T; int begin = max(0, diag - bCount); int end = min(diag, aCount); while(begin < end) { int mid = (begin + end)>> 1; T aKey = a[mid]; T bKey = b[diag - 1 - mid]; bool pred = (MgpuBoundsUpper == Bounds) ? comp(aKey, bKey) : !comp(bKey, aKey); if(pred) begin = mid + 1; else end = mid; } return begin; }
MergePath
is a binary search over two sorted arrays that uses a constraint, diag
, to reduce the search to one dimension. The search resembles the basic binary search in that we establish an interval over the array a
, and loop while begin < end
. The primary difference is that instead of comparing a[mid]
to a fixed query, we utilize the constraint and compare to b[diag - 1 - mid]
.
This routine is sound analytically but makes little intuitive sense. Fortunately a 2012 paper Merge Path - Parallel Merging Made Simple by Saher Odeh, Oded Green, Zahi Mwassi, Oz Shmuli, and Yitzhak Birk, provides a visualization for this search to help understand and apply it in diverse situations.
Further Reading: Read Merge Path - Parallel Merging Made Simple to better understand a partitioning strategy that's employed repeatedly in the MGPU algorithms.
Consider merging the array on the top with the array on the right. Start with the cursor (a pair of pointers to the head of each list) at the upper-left corner. Compare the heads of the lists and advance in the direction of the smaller key; advance to the right if the keys are equal. The curve in green is the Merge Path. It is a visualization of the history of decisions in executing a serial merge.
We draw red cross-diagonals over the Merge Path curve. The portion of the path enclosed by consecutive cross-diagonals map the intervals from each source array that contribute to one interval of the merge output. In this example, the first partition is assigned keys 0, 0 from A and 0, 0 from B; the second interval is assigned key 1 from A and keys 1, 1, 1 from B; etc. All partitions are assigned exactly four inputs (and because of the nature of the merge function, four outputs), and each partition can be processed in parallel without communication. If keys in A and B match, the use of use of lower-bound semantics in the MergePath
binary search assigns the matching keys in A to the earlier partitions and the matching keys in B to the later partitions, which agrees with the behavior of std::merge
.
This illustration shows a merge divided into six partitions of equal size plus one partial partition at the lower-right. The eight cross-diagonals (in red) intersect the Merge Path to establish tile partitions (in black). Because the Merge Path can only move down and right (and not diagonal), the length of the curve bounded by two consecutive cross-diagonals is constant and equal to the inter-diagonal spacing. The intersection of the Merge Path projects upwards to define the interval of values in A inside the partition; it projects to the right to define the interval of values in B. The number of steps required for the binary search is the log of the cross-diagonal length.
Important: Merge Path is the history of comparisons made during a sequential merge operation. We want to find the intersection of the Merge Path with regularly-spaced cross-diagonals without actually constructing the Merge Path. We binary search along cross-diagonals, sampling and comparing elements from inputs A and B until we've ascertained where the Merge Path would be, if we had constructed it.
In the CUDA code above, the constraint parameter diag
is the distance from the origin that the cross-diagonal intersects the x-axis. Consider tile 3's partition in this figure. A search along cross-diagonal 3 returns the pair (a3, b3), and the search along cross-diagonal 4 returns (a4, b4). Because of the constraint that ai + bi = diag, we need only deal with the coverage interval of A: (a3, a4). When needed, the coverage interval of B is can be computed as (diag3 - a3, diag4 - a4).
How is this applicable to load-balancing Bulk Insert? Consider the array across the top (A) as the sorted list of insertion indices; the array on the right (B) is a counting_iterator<int>
representing the positions of the source elements. Bulk Insert is essentially a merge operation where the keys of A are the insertion indices and the keys of B are the natural numbers. Computing the intersection of the Merge Path curve with the cross-diagonal using a constrained binary search solves Bulk Insert's partioning needs.
After mapping a source and insert elements into each tile we can run the actual Bulk Insert logic. This is a scan-oriented kernel and follows the patterns of Bulk Remove.
Insert indices: 0: 1 12 13 14 14 18 20 38 39 44 10: 45 50 50 50 54 56 59 63 68 69 20: 74 75 84 84 88 111 111 119 121 123 30: 126 127 144 153 157 159 163 169 169 175 40: 178 183 190 194 195 196 196 201 219 219 50: 253 256 259 262 262 266 272 273 278 283 60: 284 291 296 297 302 303 306 306 317 318 70: 318 319 319 320 320 323 326 329 330 334 80: 340 349 352 363 366 367 369 374 381 383 90: 383 384 386 388 388 389 393 398 398 399 Tile size = 100 ACount = 22 BCount = 78 Counters: 0: 0 1 0 0 0 0 0 0 0 0 10: 0 0 0 1 0 1 0 1 1 0 20: 0 0 0 1 0 0 1 0 0 0 30: 0 0 0 0 0 0 0 0 0 0 40: 0 0 0 0 0 1 0 1 0 0 50: 0 0 0 1 0 1 0 0 0 0 60: 0 1 1 1 0 0 0 0 1 0 70: 0 1 0 0 0 1 0 0 0 0 80: 1 0 0 0 0 0 1 0 1 0 90: 0 0 0 0 1 0 1 0 0 0
Our example starts with 400 source elements and 100 insertions. A Merge Path search determines that the first tile of 100 elements (NV = 100) should load 22 from A (the insertion array) and 78 from B (the source array). This should be clear from the figure—the insert indices range from 1 to 75 and the implicit source indices range from 0 to 77. Adjusting the Merge Path either way would invalidate the ordering. For example, setting ACount to 23 and BCount to 77 would attempt an insert at position 84 when only source elements 0 to 76 have been loaded.
We allocate and initialize 100 flags to zero. Ones are poked in for each insert index at the index's location plus its value. That is, indices[0] = 1 sets the flag at location 1 (0 + 1); indices[1] = 12 sets the flag at location 13 (1 + 12); indices[2] = 13 sets the flag at location 15 (2 + 13); and so on. Duplicate insertion indices are admissible, as they won't set flags at the same location, because their ranks are different.
Scan of counters: 0: 0 0 1 1 1 1 1 1 1 1 10: 1 1 1 1 2 2 3 3 4 5 20: 5 5 5 5 6 6 6 7 7 7 30: 7 7 7 7 7 7 7 7 7 7 40: 7 7 7 7 7 7 8 8 9 9 50: 9 9 9 9 10 10 11 11 11 11 60: 11 11 12 13 14 14 14 14 14 15 70: 15 15 16 16 16 16 17 17 17 17 80: 17 18 18 18 18 18 18 19 19 20 90: 20 20 20 20 20 21 21 22 22 22 Gather indices: 0: 22 0 23 24 25 26 27 28 29 30 10: 31 32 33 1 34 2 35 3 4 36 20: 37 38 39 5 40 41 6 42 43 44 30: 45 46 47 48 49 50 51 52 53 54 40: 55 56 57 58 59 7 60 8 61 62 50: 63 64 65 9 66 10 67 68 69 70 60: 71 11 12 13 72 73 74 75 14 76 70: 77 15 78 79 80 16 81 82 83 84 80: 17 85 86 87 88 89 18 90 19 91 90: 92 93 94 95 20 96 21 97 98 99
As with Bulk Remove, an exclusive scan is computed across the flags. If the flag was set (indicating an insertion at this position) then we'll gather at that scan offset. If the flag was cleared we subtract the scan from the output rank and add aCount (the number of inserted vertices). This segregates the gather into two forms: inserted values (in green) are referenced by their ranks; source values (in black) are referenced by their ranks plus the number of inserted values.
The loadstore.cuh function DeviceTransferMergeValues
interprets gather indices in this format and performs the data transfer. This mechanism is used by other merge-like functions (merge, mergesort, segmented sort, multisets) to move the values in key/value pair operations.
include/kernels/bulkinsert.cuh
template<typename InputIt1, typename IndicesIt, typename InputIt2, typename OutputIt> MGPU_HOST void BulkInsert(InputIt1 a_global, IndicesIt indices_global, int aCount, InputIt2 b_global, int bCount, OutputIt dest_global, CudaContext& context) { const int NT = 128; const int VT = 7; typedef LaunchBoxVT<NT, VT> Tuning; int2 launch = Tuning::GetLaunchParams(context); const int NV = launch.x * launch.y; MGPU_MEM(int) partitionsDevice = MergePathPartitions<MgpuBoundsLower>( indices_global, aCount, mgpu::counting_iterator<int>(0), bCount, NV, 0, mgpu::less<int>(), context); int numBlocks = MGPU_DIV_UP(aCount + bCount, NV); KernelBulkInsert<Tuning><<<numBlocks, launch.x, 0, context.Stream()>>>( a_global, indices_global, aCount, b_global, bCount, partitionsDevice->get(), dest_global); }
The host function creates a LaunchBoxVT type to provide tuning parameters to the kernel. We choose a configuration with 128 threads and 7 values per thread as a default, but users who want performance are encouraged to customize this type for their own architectures, data types, and array sizes.
The call to MergePathPartitions
runs a MergePath
binary search to assign intervals of insert indices and intervals of source data to each CTA. You can imagine the insert indices written along the top of the Merge Path diagram and the natural numbers (corresponding to the positions of the source data) written along the right. A cross-diagonal every NV elements finds an equal partitioning of insert and source elements for each CTA. The intersections of the cross-diagonals with the Merge Path are stored in partitionsDevice
, which is passed to the kernel as mp_global
.
include/kernels/bulkinsert.cuh
// Insert the values from a_global into the positions marked by indices_global. template<typename Tuning, typename InputIt1, typename IndicesIt, typename InputIt2, typename OutputIt> MGPU_LAUNCH_BOUNDS void KernelBulkInsert(InputIt1 a_global, IndicesIt indices_global, int aCount, InputIt2 b_global, int bCount, const int* mp_global, OutputIt dest_global) { typedef MGPU_LAUNCH_PARAMS Params; typedef typename std::iterator_traits<InputIt1>::value_type T; const int NT = Params::NT; const int VT = Params::VT; const int NV = NT * VT; typedef CTAScan<NT, ScanOpAdd> S; union Shared { int indices[NV]; typename S::Storage scan; }; __shared__ Shared shared; int tid = threadIdx.x; int block = blockIdx.x; int4 range = ComputeMergeRange(aCount, bCount, block, 0, NV, mp_global); int a0 = range.x; // A is array of values to insert. int a1 = range.y; int b0 = range.z; // B is source array. int b1 = range.w; aCount = a1 - a0; bCount = b1 - b0; // Initialize the indices to 0. #pragma unroll for(int i = 0; i < VT; ++i) shared.indices[NT * i + tid] = 0; __syncthreads(); // Load the indices. int indices[VT]; DeviceGlobalToReg<NT, VT>(aCount, indices_global + a0, tid, indices); // Set the counters for all the loaded indices. This has the effect of // pushing the scanned values to the right, causing the B data to be // inserted to the right of each insertion point. #pragma unroll for(int i = 0; i < VT; ++i) { int index = NT * i + tid; if(index < aCount) shared.indices[index + indices[i] - b0] = 1; } __syncthreads(); // Run a raking scan over the indices. int x = 0; #pragma unroll for(int i = 0; i < VT; ++i) x += indices[i] = shared.indices[VT * tid + i]; __syncthreads(); // Run a CTA scan over the thread totals. int scan = S::Scan(tid, x, shared.scan); // Complete the scan to compute merge-style gather indices. Indices between // in the interval (0, aCount) are from array A (the new values). Indices in // (aCount, aCount + bCount) are from array B (the sources). This style of // indexing lets us use DeviceTransferMergeValues to do global memory // transfers. #pragma unroll for(int i = 0; i < VT; ++i) { int index = VT * tid + i; int gather = indices[i] ? scan++ : aCount + index - scan; shared.indices[index] = gather; } __syncthreads(); DeviceTransferMergeValues<NT, VT>(aCount + bCount, a_global + a0, b_global + b0, bCount, aCount, shared.indices, tid, dest_global + a0 + b0, false); }
KernelBulkInsert
is a straight-forward implementation of the routine described in the algorithm section. Because this function was written in the two-phase decomposition methodology, the important business of mapping work to CUDA cores has already been performed by the time this kernel is launched. ComputeMergeRange
reassembles source intervals from the mp_global
array, communicating the partitoining of the previous launch to the work-logic kernel.
Kernels are tuned by increasing VT to amortize per-CTA and per-thread costs, improving work-efficiency. Consequently most programs run underoccupied, especially on Kepler. A nice property of both Bulk Remove and Bulk Insert is that only the 32-bit indices are staged in shared memory. Specializing on 64-bit data-types doesn't decrease occupancy on Kepler, which has sufficient RF capacity to accommodate the larger types. Working on 64-bit types increases the atom of transfer to and from global memory while keeping the cost of compute constant. This results in significantly higher throughput (up to 220 GB/s for Bulk Remove on GTX Titan) on 64-bit types compared to 32-bit types (158 GB/s).


The idea to having a browser extension to patch urls quickly was made by a former colleague of mine.
Since we had to test a webpage on multiple environment, we always found nearly the same url, but with a
little
difference.
It was always annoying to change the url manually in the browser - especially if the url was more than a
"bit"
different.
I encouraged him to write this extension, but he did not find the time to do it.
Now I found it. It was somewhere hidden in last friday, yesterday and today I still find some of it.
I hope this extension helps others to find a smoother workflow.


NIF Objects | Compound Types | Enum Types | Basic Types | File Versions
| Number | Name | Description |
|---|---|---|
| 0 | MO_SYS_INVALID | Invalid |
| 1 | MO_SYS_DYNAMIC | A fully-simulated, movable rigid body. At construction time the engine checks the input inertia and selects MO_SYS_SPHERE_INERTIA or MO_SYS_BOX_INERTIA as appropriate. |
| 2 | MO_SYS_SPHERE | Simulation is performed using a sphere inertia tensor. |
| 3 | MO_SYS_SPHERE_INERTIA | This is the same as MO_SYS_SPHERE_INERTIA, except that simulation of the rigid body is "softened". |
| 4 | MO_SYS_BOX | Simulation is performed using a box inertia tensor. |
| 5 | MO_SYS_BOX_STABILIZED | This is the same as MO_SYS_BOX_INERTIA, except that simulation of the rigid body is "softened". |
| 6 | MO_SYS_KEYFRAMED | Simulation is not performed as a normal rigid body. The keyframed rigid body has an infinite mass when viewed by the rest of the system. (used for creatures) |
| 7 | MO_SYS_FIXED | This motion type is used for the static elements of a game scene, e.g. the landscape. Faster than MO_SYS_KEYFRAMED at velocity 0. (used for weapons) |
| 8 | MO_SYS_THIN_BOX | A box inertia motion which is optimized for thin boxes and has less stability problems |
| 9 | MO_SYS_CHARACTER | A specialized motion used for character controllers |


COPYCAT A film review by Ed Powell Copyright 1995 Ed Powell
COPYCAT is a suspense/thriller, where Sigourney Weaver plays a criminal psychologist, that is attacked by a serial killer. Fortunately for her, she isn't killed, and the killer is sent to prison. The attack has, however, left her mentally scarred, and she now suffered from agrophobia (sp?). Thirteen months later, still scarred, she is asked by a police detective (Holly Hunter) to help solve a series of murders. It turns out that there is a serial killer at work, and as you may have guessed, he is copying the methods of other serial killers.
The pace of this film seemed slow at times, but it wasn't boring. It was suspenseful, but it wasn't edge-of-your-seat nail-biting thrilling. The story itself seemed real enough to have come from today's headlines. The script was well researched and was thoroughly engrossing.
The acting in this film was top-notch. Sigourney played her part of a criminal psychologist who drinks and takes a wide assortment of medication in order to be able to get through the day without having a mental breakdown quite well. Holly, while not as believeable in the role of a police detective, nevertheless was quite convincing in the role. A good deal of the male roles in the film seemed to get killed off, but their roles were well played.
Direction of this film was well above top notch. I may not agree with the director's (Jon Amiel) decision in using a few shots that made me want to reach for a barf bag, he made the film a true visual delight.
COPYCAT is tamer than some suspense films I have seen, but it is a great extra-dramatic experience. If you have some or any interest in what goes on in the minds of serial killers, this is great entertainment. Go see this film.
Produced by Arnon Milchary and Mark Tarlov. Directed by Jon Amiel. A Regency Enterprises/Warner Brothers release.
RATINGS (out of ten) -------------------- Plot/Story: 8 Writing: 8 Acting: 9 Design: 6 Effects: 5 Direction: 8.5 Music: 7.5
TOTAL: 52 out of 70 (Overall: 7.429)
-- Ed Powell, MSTie #27968 http://user.aol.com/joeylemur/homepage.htm
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
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Related links: index of all rec.arts.movies.reviews reviews


1 This, the priests said, was how Helen came to Proteus. And, in my opinion, Homer knew this story, too; but seeing that it was not so well suited to epic poetry as the tale of which he made use, he rejected it, showing that he knew it.
2 This is apparent from the passage in the Iliad (and nowhere else does he return to the story) where he relates the wanderings of Alexander, and shows how he and Helen were carried off course, and wandered to, among other places, Sidon in Phoenicia.
3 This is in the story of the Prowess of Diomedes, where the verses run as follows: There were the robes, all embroidered, The work of women of Sidon, whom godlike Alexandrus himself Brought from Sidon, crossing the broad sea, The same voyage on which he brought back Helen of noble descent. Hom. Il. 6.289-92
4 [He mentions it in the Odyssey also: The daughter of Zeus had such ingenious drugs, Good ones, which she had from Thon's wife, Polydamna, an Egyptian, Whose country's fertile plains bear the most drugs, Many mixed for good, many for harm: Hom. Od. 4.227-30 ]
5 and again Menelaus says to Telemachus: I was eager to return here, but the gods still held me in Egypt, Since I had not sacrificed entire hecatombs to them. Hom. Od. 4. 351-2
6 In these verses the poet shows that he knew of Alexander's wanderings to Egypt; for Syria borders on Egypt, and the Phoenicians, to whom Sidon belongs, dwell in Syria.


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✔


|
Jim Norton's |
|
- 2019 has been a decent year for Jim Norton's. While snow-peas with pepper sales have been lacking, our tuna casserole sales have been nothing to sneeze at. And coffee is always popular with the tuna casserole, especially at lunch time.
- When we started keeping track of sales, we only kept track of daily sales. Then we decided it was important to see sales broken down by pre-lunch, and lunch and afterwords, plus total sales. These figures are seen in the table below.
|
Jim Norton's Coffee Shop Sales from February 2019 through February 2020
| Date |
Period |
Snow-Peas with Pepper |
Tuna Casserole |
Coffee |
Total Sold |
|
|
About This Site
Click here to return home.
|


| Package | com.whirled.contrib.card.trick |
| Class | public class ScoreBreakdown |
| Property | Defined by | ||
|---|---|---|---|
| teamName : String [read-only] Access the name of the team for this breakdown.
| ScoreBreakdown | ||
| total : int [read-only] Access the total score for the breakdown.
| ScoreBreakdown |
| Property | Defined by | ||
|---|---|---|---|
| _elements : Array | ScoreBreakdown | ||
| _table : Table | ScoreBreakdown | ||
| _team : Team | ScoreBreakdown |
| Method | Defined by | ||
|---|---|---|---|
|
ScoreBreakdown(table:Table, team:int)
Create a new score breakdown.
| ScoreBreakdown | ||
|
addPlayerAchievement(value:int, playerIdx:int, achievement:String):void
Adds a new achievement for the team that was orchestrated by one player in particular.
| ScoreBreakdown | ||
|
addTeamAchievement(value:int, achievement:String):void
Adds a new achivement for the team that contributes to the overall score for the round.
| ScoreBreakdown | ||
|
addTeamFailure(value:int, action:String):void
Adds a new failure for the team that contributes to the overall score for the round.
| ScoreBreakdown | ||
|
describe():Array
Get an array of String's, each describing one breakdown item added by the add functions
above, suitable for the (English speaking) user's consumption.
| ScoreBreakdown | ||
|
getPlayerName(idx:int):String
Get the name of a given player.
| ScoreBreakdown |
| _elements | property |
protected var _elements:Array
| _table | property |
protected var _table:Table
| _team | property |
protected var _team:Team
| teamName | property |
teamName:String
[read-only]Access the name of the team for this breakdown. Assumes there are 2 players on a team. TODO: allow for more than 2 players
Implementation public function get teamName():String
| total | property |
total:int
[read-only]Access the total score for the breakdown. This is a sum of all the values given in the add functions above.
Implementation public function get total():int
| ScoreBreakdown | () | constructor |
public function ScoreBreakdown(table:Table, team:int)
Create a new score breakdown.
Parameterstable:Table — the table for the game
|
|
team:int — the index of the team being scored. |
| addPlayerAchievement | () | method |
public function addPlayerAchievement(value:int, playerIdx:int, achievement:String):void
Adds a new achievement for the team that was orchestrated by one player in particular. When converting to text, this causes a possessive form of the player's name to prefix the achievement text.
Parametersvalue:int — the contribution to the team's score of the achievement
|
|
playerIdx:int — the index of the player within the team that has achieved
|
|
achievement:String — text describing the achievement, e.g. "refraining from taking any
tricks" |
| addTeamAchievement | () | method |
public function addTeamAchievement(value:int, achievement:String):void
Adds a new achivement for the team that contributes to the overall score for the round. If the value is less than zero, it is implied that the team did not achieve the achievement and hence a "not" is prefixed to the text conversion for the item. If value is zero, the achievement is not recorded.
Parametersvalue:int — the contribution of the achievement to the overall score
|
|
achievement:String — text describing the achievement, e.g. "winning all their tricks" |
| addTeamFailure | () | method |
public function addTeamFailure(value:int, action:String):void
Adds a new failure for the team that contributes to the overall score for the round. If the value is greater than zero, it is implied that the team did not fail and hence a "not" is prefixed to the text conversion for the item. If the value is zero, the achievement is not recorded.
Parametersvalue:int — numeric contribution of the failure to the score, usually less than 0
|
|
action:String — text describing the failure, e.g. "taking too many tricks" |
| describe | () | method |
public function describe():Array
Get an array of String's, each describing one breakdown item added by the add functions above, suitable for the (English speaking) user's consumption.
ReturnsArray |
| getPlayerName | () | method |
public function getPlayerName(idx:int):String
Get the name of a given player.
Parametersidx:int — the index of the player within the team |
String |


| Name: mr. Yogesh | Reg No: 3-3SMMHC18 |
|---|---|
| Age / Sex:35/M | Contact No:8050606576 |
| Marital Status:ML- SINGLE | Date:3/3/18 |
| Occupation: SYSTEM ADMIN- DIPLOMA I IT. | Dr. PJFP |
Address: IN BLORE SINCE 18 YEARS NATIVE OF GULBARGA |
DIAGNOSIS- |
HAIR FALL SINCE 10 YEARS.
FEELING NERVOUS WEAKNESS AND FEET SEEM TO BEND DOWN AND WEAK. EVEN CANT LIFT A KEY SAYS SO MUCH PAIN IN THE BODY. CANT STAND SOMEONE MUST SUPPORT HIM. SINCE 2008.
 IF GETS THE ATTACK IN THE MORNING 8 TILL EVENIN IT PERSISTS. TAKES VIT INJ. THEN GOT DIAGNOSED WITH HYPOTHYROID. NOW TAKING THYRFIT 100 MG.
FOR HAIRFALL IN 2005-2006 TOOK SOME AYURVEDA RX .
IF EATS OR GETS EXPOSED TO COLD IMM GETS ATTACKED WITH THE BODY PAIN . EVEN BARE FLOORING FEELS LIKE VIBRATIONS IN THE WHOLE BODY.
IF EATS SOUR FOOD SEVERE HEADACHE, VOMITINGS AND NAUSEA. WITH 30MIN – 1 HR.
TEA – 10-15 TIMES /DAY.  LIKES A LOT OF SUGAR EATS IT BEAR. FEELS ACTIVE AFTER THAT. EVEN IN ANGER EATS SUGAR. BORE ALSO EATS SUGAR. MEDITATES A LOT.NSAYS RELAXES AND FORGETS OFFICE TENSIONS.   SHOUTS IN ANGER GETS ANGRY IF OTHERS WILL NOT UNDERSTAND WHEN THEY TALK AND QUARRELL.
THIRSTLESS BUT DRINKS 3.5 LTS /DAY BEACUAES DOC SAID TO DRINK THTS Y.
SOCIAL.
GOT APPENDICITIS OP IN 2003
ON SCALP FRM ONE MONTH HAD LIL DANDRUFF ON THE MARGINS OF THE RECEDING HAR LINE . APPLIED ONION JUICE NOW BETTER.
LIKES COLD WEATHER AND RAINY SEASON.
PULLING AND TWISTING TYPE OF PAIN ALL OVER BODY.
IF ANYONE CRIES WILL GET MOVED TO TEARS SOMETIMES.
RX:
PHOS 1M – 1 DOSE – NIGHT B/FOOD.
PENTAPHOS -2-2-2-2
STORNT.CARB 200 – 1 DRAM PILLS 4-4-4-4
RUB 15 – 1 DOSE NIGHT B/FOOD.


Live Matches
Current Series
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Select Player
{% for row in rows %}
{{ row[0] }}
{{ row[1] }}
{{ row[2] }}
{{ row[3] }}
{{ row[4] }}
{{ row[5] }}
{{ row[6] }}
{% endfor %}
YOUR BUDGET :
{{ budget }}
YOUR POINTS :
{{ p }}
Selected Players
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{% for row in rows1 %}
{{ row[0] }}
{{ row[1] }}
{% endfor %}


Woodcarving Gallery
Some of the things I've made.
I'm a big fan of medieval sculpture, no doubt in part because where I grew up ( Tideswell) there was a wonderful church " The Cathedral of the Peak" which was full of exceptional woodcarvings by the Hunstone family. As a kid I always had a penknife at hand and would often whittle rough bits and pieces. I continued occasionally hacking at bits of wood into adulthood, but only got settled enough to start accumulating decent tools when I was about 30. Even now I don't spend enough time carving wood to get particularly skilled (as the end results demonstrate), but I find it a very satisfying pastime all the same.
Spice Cupboard
Beech(? )
My first proper piece. Carved on my mother's kitchen table from an old school desk which was horrible, really dry and splintery wood. The Tudor Rose was taken straight out of Whittling and Woodcarving by E.J. Tangerman, a fun book from 1936. I assume I got the peacocks and elephants from a bestiary, the patterns were made up.
Bowl
Pear, 12cm diameter
The piece of which I'm most proud, it just worked. My own design (though putting animal's feet on things is hardly original). Turned on a clunky hand drill jig thing, before I had a proper lathe. By then I did at least have a little workshop, the cellar of the house in Buxton.
Book Rack
Oak
Haven't got a photo of the whole thing (it's at my mother's), just these end panels. I think I got the figures from pictures of old church woodcarvings, the rest of the design made up. Still not quite finished. The round boss thing should probably be a rose.
Chalice
Chestnut
Crude and chunky, but I like it for that. Turned from an old log.
Gavel
Chestnut(?)
Made as a gift. The wood was scraps from an old barrel - I think it's chestnut although the colour's much lighter than the other pieces I've used.
Mandolin Restoration
I'd wanted a mandolin for years but never enough to buy one. Then I spotted the remains of one on Castelnuovo market (about a tenner) and couldn't resist. Remarkably it sounds quite good.
The Vinocaster
The body made from recycled wine barrel wood. I've written up how it was made.
Puzzle Boxes
Chestnut (recycled wine barrel)
Made for Christmas presents (my Dad and danbri, 2012), plus the prototype.
Lots of mistakes on the prototype - see gappiness of joints on left-hand pic. Other two better but still plenty flawed (but soo authentic :). Carved a stylized flower on proptotype (which came out rather well) and monograms on the pressies. To open the box you need to remove the correct pin and figure out where the hinge is. Inside a Soma Cube, chestnut again but stained with steel wool & vinegar. Finished with linseed oil then beeswax polish.
The chestnut is a bit of a pain to carve, very well-defined grain so detail tricky. The "rivets" (and hinge & key pin) made from regular nails, modified using a blowtorch and big hammer.
Creepy Man
Softwood, oil paint
I thought I'd have a crack at whittling some softwood for a change. No idea what wood it is, some kind of conifer probably. I didn't previously like the painted, slightly naive folk art kind of style, but this was a lot of fun so I'll probably do some more. I based the design on one from a book (How to Carve Wood, Richard Butz). It was meant to be an Adirondack Hermit (!) but due to my clumsiness in following the instructions he soon deviated. He was virtually entirely carved with just one knife - a Mora Erik Frost 120 (which is a wonderful knife, and I found out after I'd bought it that Amazon stock them too).
I thought working with softwood would be a lot quicker - it still took ages.
The painting I did over several nights, giving each bit time to dry. Initially he was a bit too bright and shiny so I gave him a coat of burnt umber and wiped it off. Then he looked suitably grubby. I finished off with a layer of linseed oil, not sure that was a good idea. Gave him a good rub-down afterwards, it took some of the paint off. No matter, it just makes him look a bit weatherworn.
Grumpy Cat Spoon
Beech, ebony inlay
Another little folk arty experiment. While looking around whittling sites an awful lot seem to go for making spoons. I thought I'd make something a bit decorative. Rather than the usual greenwood I opted for some beech from a log I'd put aside a few years ago. The middle I turned, most of the rest I whittled with a knife, using homemade fine gouges to help with the details. Getting the face even remotely credible was really difficult, cats' faces just aren't flat!
I happened to have a little lump of ebony I bought years ago from Craft Supplies, so I tried the eye inlay thing (inspired by something similar in a book on netsuke). Fiddly but effective.
Dog Head
Beech
This I made not long after we moved to Italy. When I say made, I got as far carving as this when I ran into a major snag - there was a nasty knot/split just where one of the eyes should be. So I abandoned it, leaving it in the yard. Several years later the weather (and probably mould) had had a wonderful effect on the colour.  You really wouldn't know it was beech, more like old oak now. The fault around the eye has become a feature :)
Owl
River rock
Ok, not wood, but this seemed as good a place to put it as any. Design taken from an old church bench-end carving.
Head
River rock
Not wood and I don't really like the way it turned out. I've got quite a few things like that, along with loads of unfinished and/or abandonded pieces.


Check if a user is, publicly or privately, a member of the organization.
GET /orgs/:org/members/:username
Status: 204 No Content
Status: 404 Not Found
Status: 404 Not Found
Status: 302 Found
Location: https://api.github.com/orgs/github/public_members/pezra


CIRCUIT CONSILIO
"Hide not your talents, they for use were made, What's a sundial in the shade?" Ace your talent in circuit designing and simulation using TinkerCAD. Bring out the circuitry geek inside you to own the title "CIRCUIT CONSILIO"..
TECHNICAL EVENTS
"Hide not your talents, they for use were made, What's a sundial in the shade?" Ace your talent in circuit designing and simulation using TinkerCAD. Bring out the circuitry geek inside you to own the title "CIRCUIT CONSILIO"..
Rounds
Round 1 :
Round 2 :
Note
.
A Brand Quiz event Unlocking the knowledge at the speed of thought!
Rounds
Round 1 :
Round 2 :
Note
Participants are provided with an opportunity to exhibit their unrealistic concepts and idea presentation for a real time impression. Your Lockdown ideas might win you Awards and Laurels. “No great discovery was ever made without a bold guess”, So Don’t miss it!!!

Robotics and other combinations will make the world pretty fantastic compared with today.
Rounds
Round 1 :
1. Abstract submission:
Participant must attach the abstract of the project.
2. Video submission:
Participant should submit the video of the project. Video should be submitted in Google Drive link or mail.
note


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Project Status85%
Project Status75%
New Websites is Created
Prepare For the Next Project
Decide the live Discussion Time
Team Review meeting at yesterday at 3:00 pm
Prepare for Presentation


The Center for Economic Opportunity calculates whether a household is above or below the poverty threshold using a simple formula. Hover over each part of the formula for more details.
Wages + Benefits - Costs = CEO Income
The wages the household makes from employment. The household's income also effects the benefits programs that the family may be eligible for. For example, a household with a high income would not be eligible for food stamps.
The income the household receives from benefits programs. These include SNAP (food stamps), WIC (nutrition for women and children), HEAP (heading assistance), and earned income credits (tax refunds).
Costs that the household incurs. These include transportation, healthcare, child care, and housing.
This figure illustrates that official (pre-tax cash) income fell to 91.9 percent of its 2008 value by 2010. Although it increased over the post-recession years, by 2014 income under the official measure was only at 97.2 percent of its 2008 value.
Call to action asking the user to download the report here.


Nippon Index
Found out where to watch your favorite anime
Home
Mangas
Animes
Games
The Best of Nippon Pop Culture
Where to read your favorite Manga?
Where to watch your favorite Anime?
Want to find a good game?


* A package is a small container in which a quantity of something is sold. Packages are either small boxes made of thin cardboard, or bags or envelopes made of paper or plastic. ... When a product is packaged, it is put into containers to be sold.
| Department | Included |
|---|---|
| MCA | |
| B-Tech | |
| M-Tech |
Volcanologist. ...
Quantum physicist. ...
Oceanographer. ...
Biomedical engineer. ...
Job profile developed by the personnel department of a business might include such things as job responsibilities, required qualifications, advancement prospects.
Profile summary is a summary of your education, skills, career experiences, and goals.
Coding.
Communication.
Networks.
Time Management.
More Information Technology Skills
Communication.
Ability to Work Under Pressure.
Decision Making.
Time Management.
Self-motivation.
Conflict Resolution.
personal skills?
Leadership.
Adaptability.
A skill is the ability to do something well, a certain competence or proficiency. Skills are typically acquired or developed through direct experiences and training, and they can require sustained effort.
The hiring manager wants to: Learn about your career goals
and how this position fits into your plan.
Make sure that you are sincerely interested in the job and will be motivated to perform if hired. Find
out what you know about the company, industry, position (and if you took the time to research).
A resume is a brief summary. ... If you were writing your resume you would include only the work information that relates specifically to the job you're applying for, but for a Curriculum Vitae, you would also include all your teaching experience, lab and field work..
When writing your first resume with no work experience, it's appropriate to include casual jobs like babysitting, pet sitting, lawn mowing, and shoveling snow. All experience counts and the way you present yourself, your skills, and your assets to a hiring manager begins with a strong resume.
I believe I will do well in this job because I have the relevant skills and it is my field of interest. ... I have a confident that I can do well in the job because of self confidence, positive attitude. Previous success also brings self confidence for the future success.
* job: A group of homogeneous tasks related by similarity of functions.
Online ordering system helps to maintain a Better Business Relationship with your customers, which is a key factor to run any successful business.
We are ideally located in Larbert meaning we can offer IT support to businesses throughout Scotland. We take the hassle and worry out of your IT system by providing preventative and proactive IT support.
We are passionate about digital content creation, strategy and delivery. We deliver results by designing marketing strategies which improve our clients brand visibility.


NEEP
SEEA
MEAA
SPEER
NEAA
SWEEP
CALI
Southwest Energy Efficiency Project
NV
UT
WY
CO
AZ
NM
Nevada
Utah
Wyoming
Colorado
Arizona
New Mexico


Tayto deletes Gay Byrne tribute tweet following backlash
by By Dan BuckleyTayto has deleted a social media post that paid tribute to the late Gay Byrne after a backlash online.
The tweet had read: "It's a sad day for the Nation (sic). Rest in peace Gay."
The Twitter post included a picture of the crisp firm's mascot, Mr Tayto, looking sad.
Tayto's post, coming so soon after the news of Gaybo's death, annoyed some social media users.
One tweeted: "It is with a heavy heart that I must announce Mr Tayto has been cancelled."
Another said: "I'm gonna need some time to process that Tayto tweet. Hold tight, dear reader."
Another person added: "This moronic crisp ad is how I learned of the death of a towering figure in Irish society. Pathetic."
An account named Branded Hellscape Ireland, which tweets "a selection of the very worst in Irish branded content", changed its profile picture to the sad Mr Tayto.
The owner also posted: "I'm retiring this account."
READ MORE
Graham Norton says Gay Byrne's presenting style was 'so ahead of its time'


Enable no delay provision strategy.
Disabled by default.
Enabling this setting will disable default Jenkins behavior for a particular cloud and ask this cloud to provision new agents almost as soon as new jobs appear in the queue.
When this option is disabled (by default): Each time Jenkins doesn't have enough agents to execute all jobs in a queue, Jenkins starts provisioning new agents. The default strategy tries to keep the amount of agents as small as possible. Jenkins waits a little bit until the number of jobs in the queue reaches some limit or jobs in the queue reach some age. As a result, before a scheduled job will be executed you can see some delay.
Note: Enabling this setting can increase the chance of over-provisioning. Because of that,
make sure that your Max Idle Minutes Before Scaledown
is set so that
free capacity can be scaled in. While Linux capacity is billed per minute, Windows capacity
is billed per hour, so be careful with this option.


The preferences window (controller).
More...
#import <PreferencesWindowController.h>
Inherits NSWindowController, <NSTableViewDataSource>, and <NSTableViewDelegate>.
The preferences window (controller).
This class represents the preferences window where all the settings are stored. It also visualizes the user profile manager in a tableview and a textview. All changed settings will be automatically stored to a file called settings.txt if the application is terminated.
- Author
- Benjamin Thiemann
- Date
- 2017/01/17
◆ autoConnectChanged:()
| - (IBAction) autoConnectChanged: |
|
(id) |
sender |
|
Invoked when value of autoConnect changed.
◆ autoScanChanged:()
| - (IBAction) autoScanChanged: |
|
(id) |
sender |
|
Invoked when value of autoScan changed.
◆ blinkTimerValueChanged:()
| - (IBAction) blinkTimerValueChanged: |
|
(id) |
sender |
|
Invoked when value of blinkTimerValue changed.
◆ blurSpeedChanged:()
| - (IBAction) blurSpeedChanged: |
|
(id) |
sender |
|
Invoked when value of blurSpeed changed.
◆ blurStepChanged:()
| - (IBAction) blurStepChanged: |
|
(id) |
sender |
|
Invoked when value of blurStep changed.
◆ init()
◆ maxBlurRadiusChanged:()
| - (IBAction) maxBlurRadiusChanged: |
|
(id) |
sender |
|
Invoked when value of maxBlurRadius changed.
◆ profilesNeedUpdate:()
| - (void) profilesNeedUpdate: |
|
(id) |
sender |
|
This method is called when the profiles have changed.
- Parameters
-
| sender | The sending instance. |
◆ setView:()
| - (void) setView: |
|
(NSString *) |
view |
|
This method changes the active view (general or profiles).
- Parameters
-
| view | The view to activate. |
◆ autoConnect
Boolean value that indicates whether an autoconnect to the last known device is desired.
◆ autoConnectCheckbox
| - (IBOutlet NSButton*) autoConnectCheckbox |
|
readwriteatomicweak |
The checkbox to selecct autoConnect.
◆ autoScan
Boolean value that indicates whether an autoscan is desired.
◆ autoScanCheckbox
| - (IBOutlet NSButton*) autoScanCheckbox |
|
readwriteatomicweak |
The checkbox to selecct autoScan.
◆ autoSelect
Boolean value that indicates whether the app automatically selects the XML file.
◆ autoSelectXMLFileCheckbox
| - (IBOutlet NSButton*) autoSelectXMLFileCheckbox |
|
readwriteatomicstrong |
The checkbox to selecct XML file autoSelect.
◆ batteryLevel
Float value that represents the battery level.
◆ blinkTimerValue
| - (NSUInteger) blinkTimerValue |
|
readwriteatomic |
Integer value that represents the allowed time without a blink.
◆ blurSpeed
Float value that represents the blur speed.
◆ blurStep
Float value that represents the blur step.
◆ createNewProfileButton
| - (IBOutlet NSButton*) createNewProfileButton |
|
readwriteatomic |
The button to create a new profile.
◆ deleteProfileButton
| - (IBOutlet NSButton*) deleteProfileButton |
|
readwriteatomic |
The button to delete a profile.
◆ generalView
| - (IBOutlet NSView*) generalView |
|
readwriteatomic |
The general preferences view.
◆ levelIndicator
| - (IBOutlet NSLevelIndicator*) levelIndicator |
|
readwriteatomicweak |
The levelIndicator to show the battery level.
◆ maxBlurRadius
| - (NSUInteger) maxBlurRadius |
|
readwriteatomic |
Integer value that represents the maximum blur radius.
◆ path
| - (IBOutlet NSTextField*) path |
|
readwritenonatomicstrong |
The textfield displaying the XML file path.
◆ profiles
| - (NSMutableArray*) profiles |
|
readwriteatomic |
The container for the profiles.
◆ profilesView
| - (IBOutlet NSView*) profilesView |
|
readwriteatomic |
The profiles preferences view.
◆ segmentedControl
| - (IBOutlet NSSegmentedControl*) segmentedControl |
|
readwriteatomicweak |
The segmented control to swtich between general and profiles view.
◆ selectFile
| - (IBOutlet NSButton*) selectFile |
|
readwriteatomicstrong |
The button to select a XML file from disk.
◆ tableView
| - (IBOutlet NSTableView*) tableView |
|
readwriteatomic |
The table view displaying the profiles.
◆ textView
| - (IBOutlet NSTextView*) textView |
|
readwriteatomic |
The text view displaying a profiles content.
◆ xmlFile
The path to the XML file.
The documentation for this class was generated from the following files:


Tourism Survey Form
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A plum is a fruit of the subgenus Prunus of the genus Prunus. The subgenus is distinguished from other subgenera (peaches, cherries, bird cherries, etc.) in the shoots having terminal bud and solitary side buds (not clustered), the flowers in groups of one to five together on short stems, and the fruit having a groove running down one side and a smooth stone (or pit).
Mature plum fruit may have a dusty-white waxy coating that gives them a glaucous appearance. This is an epicuticular wax coating and is known as "wax bloom". Dried plum fruits are called "dried plums" or prunes, although, in many countries, prunes are a distinct type of dried plum having a wrinkled appearance.
Price = {{ price | currency : "Rs."}}


Out of things to watch?
It has been quite awhile since I've had the time to spend on a personal Tableau project, but the other night my wife and I (somehow) "ran out of things to watch" on TV. We ended up watching an episode of Friends, but still then had to choose which of the 236 episodes of American Sitcom Gold to watch. I knew right then that I had my next Tableau project - a "random" episode generator.
Beyond "randomly" picking an episode, I also wanted there to be an element of user interactivity as well as analysis. This was pretty easy to build in, utilizing Tableau's parameter actions to give the user an option to change the "balance" of the Friends characters - want to see more Joey and less Phoebe? No problem - there are 58 episodes where Joey has more dialog than his average and Phoebe has less than her average. Really love Ross' son, Ben? There's an option to include episodes where he has dialog - add that to our more Joey/less Phoebe mix and the number of possible episodes drops to 3.
The big part here, besides the interactivity, was of course to "randomly" pick one episode. I keep putting "randomly" in quotes because, Tableau doesn't come out of the box with a random function that would be suitable for selecting from 236 options. Instead, I've leveraged an element of time to drive a new "random" option every minute. That simplifies things a bit, but if you'd like to tear through the logic, go ahead and download the workbook and take a peek. Also, if you have a better way to drive this randomness, I'm all ears! There are no guarantees that you won't get a repeated random episode here, but they definitely won't happen back to back :).
Last thing - once the dashboard picks your episode for you, click on the HBO Max icon to watch (subscription required). Don't like the random option? Just wait a minute and refresh and you'll have another randomly selected episode for your viewing pleasure.


EBD - Easton Bible DIctionary
Jealousy
JEALOUSY
"JEALOUSY", WATERS OF water which the suspected wife was required to drink, so that the result might prove her guilt or innocence (
Numbers 5:12-17, 27
). We have no record of this form of trial having been actually resorted to.
"JEALOUSY", WATERS OF water which the suspected wife was required to drink, so that the result might prove her guilt or innocence (Numbers 5:12-17, 27). We have no record of this form of trial having been actually resorted to.


{% extends 'project_management/pm_base.html' %} {% block title %}Investigations{% endblock title %} {% block pm_content %}
{{ investigation.target_actions.all|length }} Progress Updates {{ investigation.literature.all|length }} Papers Read


{% extends "base.html" %} {% block title %} EasyFood: Order Food Easily: Food that your tastebuds love {% endblock %} {% block header %} Food that you Love to eat. Coming Soon! The Future of Food Ordering Service {% endblock %}


{include file="Common/top" title="首页" metas='' /} {include file="Common/search" /} {if condition="authority('DataAnalysisMedia')"}
{include file="Common/popup_warn" /} {include file="Common/popup_list" /} {include file="Common/popup_verify" /} {include file="Common/calendar" /} {include file="Common/bottom" /} {else /}


'I wouldn't manage Tottenham' - Lampard insists he will never follow Mourinho – video
by SNTV
The Chelsea manager Frank Lampard praised his former boss José Mourinho on Friday, after the Portuguese was appointed at Tottenham Hotspur this week.
However, Lampard added he will never follow in Mourinho's footsteps as and when he moves on from Chelsea.
He said: "No disrespect to Tottenham ... I can firmly say no. And you can replay that again in 10 years or whatever you want. No, it wouldn't happen."


True or False, the following code would print out every element in a 2D int array called values
for (int[] row: values)
{
for (int value: row)
{
System.out.println(value);
}
}
True - but no values could be changed this way.
- True - but the values could be accidentally changed inside the inner loop if you're not careful.
- False - only counter controlled for loops can be used with 2D arrays.
- False - only while loops can be used with 2D arrays.
There are no hints for this question


You tapped a link, the app code listened for it and handled loading a different HTML fragment into the file source...
Which automatically updated the UI. You could also push these "pages" onto your UI stack, but that's a little more involved.
Back to the original file


Driver Design & Internals
Introduction
This document describes the Clang driver. The purpose of this document is to describe both the motivation and design goals for the driver, as well as details of the internal implementation.
Features and Goals
The Clang driver is intended to be a production quality compiler driver providing access to the Clang compiler and tools, with a command line interface which is compatible with the gcc driver.
Although the driver is part of and driven by the Clang project, it is logically a separate tool which shares many of the same goals as Clang:
Features:
GCC Compatibility
The number one goal of the driver is to ease the adoption of Clang by allowing users to drop Clang into a build system which was designed to call GCC. Although this makes the driver much more complicated than might otherwise be necessary, we decided that being very compatible with the gcc command line interface was worth it in order to allow users to quickly test clang on their projects.
Flexible
The driver was designed to be flexible and easily accomodate new uses as we grow the clang and LLVM infrastructure. As one example, the driver can easily support the introduction of tools which have an integrated assembler; something we hope to add to LLVM in the future.
Similarly, most of the driver functionality is kept in a library which can be used to build other tools which want to implement or accept a gcc like interface.
Low Overhead
The driver should have as little overhead as possible. In practice, we found that the gcc driver by itself incurred a small but meaningful overhead when compiling many small files. The driver doesn't do much work compared to a compilation, but we have tried to keep it as efficient as possible by following a few simple principles:
- Avoid memory allocation and string copying when possible.
- Don't parse arguments more than once.
- Provide a few simple interfaces for effienctly searching arguments.
Simple
Finally, the driver was designed to be "as simple as possible", given the other goals. Notably, trying to be completely compatible with the gcc driver adds a significant amount of complexity. However, the design of the driver attempts to mitigate this complexity by dividing the process into a number of independent stages instead of a single monolithic task.
Internal Design and Implementation
Internals Introduction
In order to satisfy the stated goals, the driver was designed to completely subsume the functionality of the gcc executable; that is, the driver should not need to delegate to gcc to perform subtasks. On Darwin, this implies that the Clang driver also subsumes the gcc driver-driver, which is used to implement support for building universal images (binaries and object files). This also implies that the driver should be able to call the language specific compilers (e.g. cc1) directly, which means that it must have enough information to forward command line arguments to child processes correctly.
Design Overview
The diagram below shows the significant components of the driver architecture and how they relate to one another. The orange components represent concrete data structures built by the driver, the green components indicate conceptually distinct stages which manipulate these data structures, and the blue components are important helper classes.
Driver Stages
The driver functionality is conceptually divided into five stages:
-
Parse: Option Parsing
The command line argument strings are decomposed into arguments (Arg instances). The driver expects to understand all available options, although there is some facility for just passing certain classes of options through (like -Wl,).
Each argument corresponds to exactly one abstract Option definition, which describes how the option is parsed along with some additional metadata. The Arg instances themselves are lightweight and merely contain enough information for clients to determine which option they correspond to and their values (if they have additional parameters).
For example, a command line like "-Ifoo -I foo" would parse to two Arg instances (a JoinedArg and a SeparateArg instance), but each would refer to the same Option.
Options are lazily created in order to avoid populating all Option classes when the driver is loaded. Most of the driver code only needs to deal with options by their unique ID (e.g., options::OPT_I),
Arg instances themselves do not generally store the values of parameters. In almost all cases, this would simply result in creating unnecessary string copies. Instead, Arg instances are always embedded inside an ArgList structure, which contains the original vector of argument strings. Each Arg itself can then only contain an index into this vector instead of storing its values directly.
The current clang driver can dump the results of this stage using the -ccc-print-options flag (which must preceed any actual command line arguments). For example:
$ clang -ccc-print-options -Xarch_i386 -fomit-frame-pointer -Wa,-fast -Ifoo -I foo t.c Option 0 - Name: "-Xarch_", Values: {"i386", "-fomit-frame-pointer"} Option 1 - Name: "-Wa,", Values: {"-fast"} Option 2 - Name: "-I", Values: {"foo"} Option 3 - Name: "-I", Values: {"foo"} Option 4 - Name: "<input>", Values: {"t.c"}
After this stage is complete the command line should be broken down into well defined option objects with their appropriate parameters. Subsequent stages should rarely, if ever, need to do any string processing.
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Pipeline: Compilation Job Construction
Once the arguments are parsed, the tree of subprocess jobs needed for the desired compilation sequence are constructed. This involves determing the input files and their types, what work is to be done on them (preprocess, compile, assemble, link, etc.), and constructing a list of Action instances for each task. The result is a list of one or more top-level actions, each of which generally corresponds to a single output (for example, an object or linked executable).
The majority of Actions correspond to actual tasks, however there are two special Actions. The first is InputAction, which simply serves to adapt an input argument for use as an input to other Actions. The second is BindArchAction, which conceptually alters the architecture to be used for all of its input Actions.
The current clang driver can dump the results of this stage using the -ccc-print-phases flag. For example:
$ clang -ccc-print-phases -x c t.c -x assembler t.s 0: input, "t.c", c 1: preprocessor, {0}, cpp-output 2: compiler, {1}, assembler 3: assembler, {2}, object 4: input, "t.s", assembler 5: assembler, {4}, object 6: linker, {3, 5}, image
Here the driver is constructing sevent distinct actions, four to compile the "t.c" input into an object file, two to assemble the "t.s" input, and one to link them together.
A rather different compilation pipeline is shown here; in this example there are two top level actions to compile the input files into two separate object files, where each object file is built using lipo to merge results built for two separate architectures.
$ clang -ccc-print-phases -c -arch i386 -arch x86_64 t0.c t1.c 0: input, "t0.c", c 1: preprocessor, {0}, cpp-output 2: compiler, {1}, assembler 3: assembler, {2}, object 4: bind-arch, "i386", {3}, object 5: bind-arch, "x86_64", {3}, object 6: lipo, {4, 5}, object 7: input, "t1.c", c 8: preprocessor, {7}, cpp-output 9: compiler, {8}, assembler 10: assembler, {9}, object 11: bind-arch, "i386", {10}, object 12: bind-arch, "x86_64", {10}, object 13: lipo, {11, 12}, object
After this stage is complete the compilation process is divided into a simple set of actions which need to be performed to produce intermediate or final outputs (in some cases, like -fsyntax-only, there is no "real" final output). Phases are well known compilation steps, such as "preprocess", "compile", "assemble", "link", etc.
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Bind: Tool & Filename Selection
The stage (in conjunction with the Translate stage) turns the tree of Actions into a list of actual subprocess to run. Conceptually, the driver performs a simple tree match to assign Action(s) to Tools. Once an Action has been bound to a Tool, the driver interacts with the tool to determine how the Tools should be connected (via pipes, temporary files, or user provided filenames) and whether the tool supports things like an integrated preprocessor.
The driver interacts with a ToolChain to perform the Tool bindings. Each ToolChain contains information about all the tools needed for compilation for a particular architecture, platform, and operating system. A single driver may query multiple ToolChains during a single compilation in order to interact with tools for separate architectures.
The results of this stage are not computed directly, but the driver can print the results via the -ccc-print-bindings option. For example:
$ clang -ccc-print-bindings -arch i386 -arch ppc t0.c # "i386-apple-darwin10.0.0d6" - "clang", inputs: ["t0.c"], output: "/tmp/cc-Sn4RKF.s" # "i386-apple-darwin10.0.0d6" - "darwin::Assemble", inputs: ["/tmp/cc-Sn4RKF.s"], output: "/tmp/cc-gvSnbS.o" # "i386-apple-darwin10.0.0d6" - "darwin::Link", inputs: ["/tmp/cc-gvSnbS.o"], output: "/tmp/cc-jgHQxi.out" # "ppc-apple-darwin10.0.0d6" - "gcc::Compile", inputs: ["t0.c"], output: "/tmp/cc-Q0bTox.s" # "ppc-apple-darwin10.0.0d6" - "gcc::Assemble", inputs: ["/tmp/cc-Q0bTox.s"], output: "/tmp/cc-WCdicw.o" # "ppc-apple-darwin10.0.0d6" - "gcc::Link", inputs: ["/tmp/cc-WCdicw.o"], output: "/tmp/cc-HHBEBh.out" # "i386-apple-darwin10.0.0d6" - "darwin::Lipo", inputs: ["/tmp/cc-jgHQxi.out", "/tmp/cc-HHBEBh.out"], output: "a.out"
This shows the tool chain, tool, inputs and outputs which have been bound for this compilation sequence. Here clang is being used to compile t0.c on the i386 architecture and darwin specific versions of the tools are being used to assemble and link the result, but generic gcc versions of the tools are being used on PowerPC.
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Translate: Tool Specific Argument Translation
Once a Tool has been selected to perform a particular Action, the Tool must construct concrete Jobs which will be executed during compilation. The main work is in translating from the gcc style command line options to whatever options the subprocess expects.
Some tools, such as the assembler, only interact with a handful of arguments and just determine the path of the executable to call and pass on their input and output arguments. Others, like the compiler or the linker, may translate a large number of arguments in addition.
The ArgList class provides a number of simple helper methods to assist with translating arguments; for example, to pass on only the last of arguments corresponding to some option, or all arguments for an option.
The result of this stage is a list of Jobs (executable paths and argument strings) to execute.
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Execute
Finally, the compilation pipeline is executed. This is mostly straightforward, although there is some interaction with options like -pipe, -pass-exit-codes and -time.
Additional Notes
The Compilation Object
The driver constructs a Compilation object for each set of command line arguments. The Driver itself is intended to be invariant during construct of a Compilation; an IDE should be able to construct a single long lived driver instance to use for an entire build, for example.
The Compilation object holds information that is particular to each compilation sequence. For example, the list of used temporary files (which must be removed once compilation is finished) and result files (which should be removed if compilation files).
Unified Parsing & Pipelining
Parsing and pipeling both occur without reference to a Compilation instance. This is by design; the driver expects that both of these phases are platform neutral, with a few very well defined exceptions such as whether the platform uses a driver driver.
ToolChain Argument Translation
In order to match gcc very closely, the clang driver currently allows tool chains to perform their own translation of the argument list (into a new ArgList data structure). Although this allows the clang driver to match gcc easily, it also makes the driver operation much harder to understand (since the Tools stop seeing some arguments the user provided, and see new ones instead).
For example, on Darwin -gfull gets translated into two separate arguments, -g and -fno-eliminate-unused-debug-symbols. Trying to write Tool logic to do something with -gfull will not work, because at Tools run after the arguments have been translated.
A long term goal is to remove this tool chain specific translation, and instead force each tool to change its own logic to do the right thing on the untranslated original arguments.
Unused Argument Warnings
The driver operates by parsing all arguments but giving Tools the opportunity to choose which arguments to pass on. One downside of this infrastructure is that if the user misspells some option, or is confused about which options to use, some command line arguments the user really cared about may go unused. This problem is particularly important when using clang as a compiler, since the clang compiler does not support anywhere all the options that gcc does, and we want to make sure users know which ones are being used.
To support this, the driver maintains a bit associated with each argument of whether it has been used (at all) during the compilation. This bit usually doesn't need to be set by hand, as the key ArgList accessors will set it automatically.
When a compilation is successfull (there are no errors), the driver checks the bit and emits an "unused argument" warning for any arguments which were never accessed. This is conservative (the argument may not have been used to do what the user wanted) but still catches the most obvious cases.
Relation to GCC Driver Concepts
For those familiar with the gcc driver, this section provides a brief overview of how things from the gcc driver map to the clang driver.
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Driver Driver
The driver driver is fully integrated into the clang driver. The driver simply constructs additional Actions to bind the architecture during the Pipeline phase. The tool chain specific argument translation is responsible for handling -Xarch_.
The one caveat is that this approach requires -Xarch_ not be used to alter the compilation itself (for example, one cannot provide -S as an -Xarch_ argument). The driver attempts to reject such invocations, and overall there isn't a good reason to abuse -Xarch_ to that end in practice.
The upside is that the clang driver is more efficient and does little extra work to support universal builds. It also provides better error reporting and UI consistency.
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Specs
The clang driver has no direct correspondant for "specs". The majority of the functionality that is embedded in specs is in the Tool specific argument translation routines. The parts of specs which control the compilation pipeline are generally part of the
Pipeline stage. -
Toolchains
The gcc driver has no direct understanding of tool chains. Each gcc binary roughly corresponds to the information which is embedded inside a single ToolChain.
The clang driver is intended to be portable and support complex compilation environments. All platform and tool chain specific code should be protected behind either abstract or well defined interfaces (such as whether the platform supports use as a driver driver).


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