| Section Name |
No. Of Questions 100 |
Answered 1 |
Not Answered 1 |
Marked for Review 1 |
Not Visited 1 |
|---|---|---|---|---|---|
| Verbal Ability Reading Comprehension (VARC) | 34 | 0 | 1 | 0 | 33 |
| Data Interpretation and Logical Reasoning (DILR) | 0 | 0 | 0 | 0 | 0 |
| Quantative Ability | 0 | 0 | 0 | 0 | 0 |


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Organisation of conferences:
Member of the scientific committee of the “European Workshop on Efficiency and Productivity Measurement” (“EWEPA”) (2000-2015).
Co-responsible (with P. Agrell & P. Vanden Eeckaut) for the organisation of the 9th and 10th “European Workshop on Efficiency and Productivity Measurement” in 2005 (Brussels) and in 2007 (Lille).
Co-responsible (with O. Brandouy, A. Lozano-Vivas & A. Zago in the “Steering Committee”) for the organisation of the workshop “Fostering a European Network on Financial Efficiency: Banking, Portfolio Theory and Computational Issues” (25 February 2008 at IFRESI, Lille). Also member of the “Local Organizing Committee”.
Member of the Organizing and Scientific Committee of the workshop “Efficiency and Productivity in Banking Industry” organised by the European Research Group “Money, Banking & Finance” (GdRE n° 335) on 23 April 2008 at IESEG School of Management (Lille).
Associate Editor of track “Strategic Management of IS and IT” at ECIS (16th European Conference on Information Systems) organised from 9 to 11 June 2008 in Galway (J.E. Cairnes Graduate School of Business and Public Policy, National University of Ireland).
Member of the scientific committee of “DEA2015” (Braunschweig, TU Braunschweig), “DEA2016” (Wuhan, Jianghan University), and “DEA2017” (Prague, University of Economics).
Member of the scientific committee of “Advances in Quantitative Asset Management” on 25 November 2016 (Bordeaux, Université Bordeaux).
Member of the scientific committee of “Tenth National Conference on Data Envelopment Analysis“ on 5-6 September 2018 (Karaj, Kharazmi University).
Organisation of seminars:
R. Färe and S. Grosskopf (Southern Illinois University) in October 1997
M. Pollitt (University of Cambridge) in January 1999
T. Coelli (University of New England) in February 2000
P. Agrell (Royal Agricultural University, Copenhagen) in February 2001
L. Cherchye (Katholieke Universiteit Leuven Campus Kortrijk) in November 2003
E. Grifell-Tatjé (Universitat Autònoma de Barcelona) in March 2004
P. Wilson (University of Texas at Austin) in May 2004
R.G. Chambers (University of Maryland & University of Western Australia) in May 2004
B. Balk (Erasmus University Rotterdam & Statistics Netherlands) in October 2004
J.-P. Chavas (University of Wisconsin-Madison) in March 2005
S. Managi (Yokohama National University) in March 2006
D. Prior (Universitat Autònoma de Barcelona) in May 2006
N. Vestergaard (University of Southern Denmark) in May 2008, and
A. Peyrache (University of Queensland) in April 2016.


Magic Board
问题描述 :
Sths has got a Magic Board for his birthday gift! A Magic board is an N*M sized grids which were painted by black and white. According to the parity of N and M, the Magic Board would be a little different, but it can be guaranteed that any two adjacent grids are painted by different colors, and the amount of black grid is not less than white ones.
The Magic Board is called MAGIC because it is formed by a Chain. A Chain is a set of grids in which a grid has at most 2 adjacent grids and there are only 2 grids which has only 1 adjacent grid. Those two special grids are called the endpoints of the Chain.
The joint between two adjacent grids is very flexible. It can be rotate by any angle. Here’s an example of transforming a Chain into a Magic Board
The Chain was connected by a Magic String. The existence of Magic String relies on the power of fengshui. But recently the fengshui in Beijing was ruined because there is a university installing air-conditionor (which also cause the HUGE RAIN in Beijing).So the Magic String disappears, and the Magic Board is totally fell apart.
Sths feels upset, because he really likes the Magic Board (since it can form a lot of things). So he is thinking about how to reconstruct it. The only thing Sths has got now is the separated grids. But surprisingly, Sths finds out that there are differences between these grids.
1. There are black grids and white grids.
2. There are three different grids in the same color because the Magic String goes through it in 3 different ways shown below:
So there are 6 different kinds of grids. Now Sths has counted the amount of each kind of grids, he wants to know: by using the grids in his hand, how many kinds of legal Chains (which can form an N*M sized Magic Board) can be constructed.
We shall say two Chains is the same if and only if the standard expression of these two Chains is the same.
The standard expression is a set of numbers which decided by following method:
1. Starting from one of a Chain’s endpoint.
2. Write down the color of the grids (1 for black and 0 for white) before direction changing.
3. Write down 2 then change direction and repeat Step 2 until reaching another endpoint of the Chain.
4. Choose the expression which lexicographical lower between the two expressions just generated since there are two endpoints.
For example, the standard expression of the example of “N=M=3” is “10120120120212” (another expression is “10212012012012”, which is lexicographical greater than standard expression). And the standard expression of the example of “N=M=4” is “01012010210120120120212”.
输入:
For each case, there will be six integer numbers in one line, N, M, BO, BA, WO, and WA, indicating the number of rows and columns, the amount of “Black and Opposite” grids, the amount of “Black and Adjacent” grids, the amount of “White and Opposite” grids, the amount of “White and Adjacent” grids.
2<=N*M<=30
The input end with a line of 0 0 0 0 0 0.
输出:
For each case, there will be six integer numbers in one line, N, M, BO, BA, WO, and WA, indicating the number of rows and columns, the amount of “Black and Opposite” grids, the amount of “Black and Adjacent” grids, the amount of “White and Opposite” grids, the amount of “White and Adjacent” grids.
2<=N*M<=30
The input end with a line of 0 0 0 0 0 0.
样例输入:
3 3 1 2 2 2 3 3 0 3 3 1 5 5 5 6 5 7 0 0 0 0 0 0
样例输出:
1 1 4
立马站起来 走几步路就倒下了
张爱玲是什么人跟她这段话的正确与否没关系吧，希特勒说一加一等于二难道就错了？讨论问题还是从对方论点本身入手比较好吧，探讨发出言论的人的人品好坏就是人身攻击了。
张爱玲是什么人跟她这段话的正确与否没关系吧，希特勒说一加一等于二难道就错了？讨论问题还是从对方论点本身入手比较好吧，探讨发出言论的人的人品好坏就是人身攻击了。


Before Change
socialsubs = MSocialSubscription.objects.filter(subscription_user_id=request.user.pk)
for socialsub in socialsubs:
socialsub.needs_unread_recalc = True
socialsub.save()
logging.user(request, "~FCSharing ~FM%s: ~SB~FB%s" % (story.story_title[:20], comments[:30]))
else:
shared_story.comments = comments
shared_story.has_comments = bool(comments)
After Change
if source_user_id:
shared_story.set_source_user_id(int(source_user_id))
UpdateRecalcForSubscription.delay(subscription_user_id=request.user.pk,
shared_story_id=str(shared_story.id))
logging.user(request, "~FCSharing ~FM%s: ~SB~FB%s" % (story.story_title[:20], comments[:30]))
else:
shared_story.comments = comments


CONTINUITY OF LAWS
Section 9. All laws in force at the time amendments to this constitution take effect that are not inconsistent with the constitution as amended shall remain in force, mutatis mutandis, until they expire by their own limitations or are amended or repealed by the legislature.
Except as otherwise provided by amendments to this constitution, all existing writs, actions, suits, proceedings, civil or criminal liabilities, prosecutions, judgments, sentences, orders, decrees, appeals, causes of action, contracts, claims, demands, titles and rights shall continue unaffected notwithstanding the taking effect of the amendments and may be maintained, enforced or prosecuted, as the case may be, before the appropriate or corresponding tribunals or agencies of or under the State or of the United States, in all respects as fully as could have been done prior to the taking effect of the amendments. [Ren and am Const Con 1968 and election Nov5, 1968; ren Const Con 1978 and election Nov7, 1978]
Cross References
Admission Act, see §15.
Attorney General Opinions
All laws, territorial or federal, relating to the public lands of Hawaii, were continued in force upon statehood. Att. Gen. Op. 61-68.
Case Notes
See also note to Admission Act, §15.
Discussed in connection with bonds. 44 H. 154, 352 P.2d 861.
Intent of constitutional convention; relationship between this section and §15 of the Admission Act. 44 H. 634, 361 P.2d 390.


ZANE=S TRACE OPENED INTERIOR OF
OHIO
By Gordon Mitchell
The Greenville
Treaty of 1795 finally brought peace to the Ohio Valley and opened up about
two-thirds of the present State of Ohio to European settlement. Many of the
settlers came west to collect their land warrants that they received as payment
for their services in the Revolutionary war.
Floating down the
Ohio River on flatboats was the main method of transportation used by the
settlers to reach their newly acquired lands. Unfortunately, travel down the
Ohio River was hampered by ice in the winter and by sandbars in the summer.
Travel upriver was even more difficult and had to be done by keel boat. It took
about seven days to travel down river from Pittsburgh to Cincinnati and it took
about 13 days to travel that same distance upriver.
General Arthur St.
Clair, governor of the Northwest Territory, also wanted a military route across
Ohio. Indeed, a better route had to be found. Colonel Ebenezer Zane, a leading
resident of Wheeling, had the answer to this problem.
Ebenezer Zane, of
Danish descent, was born in Virginia's Potomac River Valley on October 7, 1747.
In 1796 Ebenezer, his wife, the former Elizabeth McCulloch, and his siblings:
Silas, Jonathan, Andrew, Isaac, and Elizabeth, settled at present Wheeling,
West Virginia, and built Fort Fincastle. Ebenezer later renamed kit Fort Henry
(renamed in 1771 to honor Virginia's governor, Patrick Henry).
In 1777 and in 1782
Fort Henry withstood major attacks by both the British and their Indian allies.
One of those battles is where Samuel McCulloch and his horse survived a leap
off a cliff to evade the Indians (Even the Indians cheered his daring leap).
One of those battles is also where Elizabeth Zane made her daring run from the
fort to a nearby blockhouse to fetch more powder and lead. Other residents of
this community were: Lydia Boggs, Hamilton Kerr (or Carr), the McCulloch
brothers, Abraham, George, Samuel, and John; and the Wetzel brothers: Martin,
Jacob and Lewis.
The Zanes were all
very knowledgeable in wood-lore and in Indian cultures. Isaac Zane was captured
and then adopted by the Wyandot tribe. He later married Myeerah, daughter of
Wyandot Chief Tarhe (the Crain). Isaac later assisted the American government
with peace negotiations and was awarded some land near present-day Zanesfield
in Logan County.
Ebenezer's
great-grandson was the famous western writer, Zane Grey (nee Pearl Zane Gray).
His first three novels, "Betty Zane," "Spirit of the
Border," and "The Last Trail," were fictionalized accounts of
the Zane family at Fort Henry during the American Revolution.
Colonel Zane, who
finished a route from Pittsburgh to Wheeling, proposed a route that would start
across the Ohio River from Wheeling and would travel to Limestone (now
Maysville), Kentucky. Not only would this route be used for the new
homesteaders, it could also be used as a mail route.
On March 25, 1796,
Colonel Zane petitioned Congress for a contract to build this road. Before he
even received this contract on May 17, 1796, Colonel Zane had already started
construction, using his own resources. The terms of the contract stated that he
had to complete construction by January 1, 1797. However, he didn't finish the
project until the following fall.
As payment for this
project, Colonel Zane was to receive three parcels of land, each a 1-mile
square or 640 acre tract' where the trace crosses the Muskingum, the
Hockhocking (or Hocking), and the Scioto Rivers. However, Colonel Zane had to
survey these land tracts at his own expense and had to send plats of these
surveys to the treasurer of the United States (Surveyor General Rufus Putnam of
Marietta had the tracts surveyed.) He also had to operate ferry services at
each of the three rivers with their toll rates set by two of the three
Northwest Territory judges (Some ofthese earlyfenies were two canoes lashed
together). Colonel Zane picked his own crew for the construction of the trace.
His brother, Jonathan, and an Indian, Tomepomehala, served as the trail guides.
Other members of the crew were John McIntire, Ebenezer's son-in-law; William
McCulloch, Ebenezer's nephew; John Green, Ebenezer Ryan, Ebenezer's
step-nephew; Levi Williams, and James Worley.
The original trace
was only wide enough for foot and horse travel. It could not accommodate
wagons. Whenever possible, the trace followed routes that were used by the
Indians or by traders. Some of the trails that the trace followed were the
Mingo, the Moxahala, and the Coshocton trails. Although the original trace
wasn't formally surveyed, the trees along the route were marked with axes.
Sometimes, more than one route was proposed or blazed. Except for some
rerouting, there were few improvements done to the trace until 1802.
One area that
Colonel Zane did not choose as one of his three land grants was along Wills
Creek, near present day Cambridge. He may have regretted this decision in his
later years.
When the trace
reached the Muskingum River, Colonel Zane chose the junction of the Licking and
Muskingum Rivers as the crossing over a spot farther south (present-day-Duncan
Falls) because of the potential for waterpower and the ease of crossing.
Colonel Zane's 640
acres became the town of Westbourn, renamed Zanesville in 1801. Until Zane made
some improvements to the trace in that area, he did not receive title to that
tract until 1800. On December 19, 1800 Ebenezer Zane deeded the tract to
Jonathan Zane, who plotted out Westbourn, and to John McIntire for $100.
William McCulloch and Henry Crooks were Zanesville's first settlers. The
western part of present-day Zanesville, which was founded by Levi Whipple and
by Dr. Increase Matthews, Rufus Putnam's nephew, was first named Springfield
and later renamed Putnam.
Colonel Zane's
tract on the Hocking River was located on a river ford that was called the
"crossings of the Hockhocking." This tract was first named New
Lancaster in 1800 but later renamed Lancaster in 1805. Ebenezer plotted
the town and had his sons, John and Noah, sell the lots. Depending upon the
location, the costs of these lots ranged from $5 to $50. Lancaster's Public
Square, located at Broad and Main Streets, was divided into four equal squares
and was deeded by the Zanes to the town solely for public use. The area's first
settler was Captain Joseph Hunter who arrived in April of 1798.
Because Chillicothe
had been founded on the Scioto River's west side by Nathaniel Massie in 1796,
Colonel Zane had to settle for the east side of the river for his tract. Ohio
land west of the Scioto River was part of the Virginia Military District. Unfortunately,
Zane's Chillicothe tract was not very successful. Wills Creek probably would
have been a more profitable choice. Humphrey Fullerton purchased this tract in
1804 for $5,190.
Colonel Zane's plan
was to place out each town into lots and to sell them for large profits. He
demanded one-fourth payment within the first two weeks and the rest of the
payment within two years. He offered free land to tradesmen (blacksmiths,
carpenters, etc.) if they agreed to stay for four years and to practice their
trades. Five lots were reserved for schools, for churches, and for other
community uses. The first settlers usually received the choicest lots.
In 1803 Ohio
achieved its statehood, Ohio's new government passed some enabling legislation
that required three percent of income from land sales were to be used for road
building. On February 18, 1804 The Ohio legislation voted to spend $15 dollars
per mile to improve Zane's Trace. Afterwards, Zane's Trace was straightened,
was widened to 20 feet, had stumps cut below one foot, had bridges built, and
was corduroyed with logs in wet areas.
Even with
improvements, travel was still slow and difficult on Zane's Trace. There were
still problems with hills, wheel ruts, streams and wet areas. It took an
average of five days to travel the entire 228 miles.
However, Zane's
Trace did bring a lot of economic prosperity to Ohio. Many inns, taverns, and
towns sprang up within a few years. Some of these business establishments were
only spaced about four miles apart and paid license fees to maintain the trace.
Many freight and stagecoach lines also used the trace. Many other roads were
built to connect to the Zane's Trace. Some of the famous people who traveled
Zane's Trace were President James Monroe, future President Andrew Jackson,
Henry Clay and future King Louis Philippe of France.
Some other names
for Zane's Trace at various locations along its rout were Tod's Trace,
Maysville Road, Maysville Pike, Limestone Road, Limestone and Chillicothe Road,
Zanesville Pike and Wheeling Road.
To follow Zane's
Trace today, start at Wheeling and follow U.S. 40 west to Zanesville. From
Zanesville, follow U.S. 22 southwest to Lancaster. Just west of Lancaster turn
on S.R. 159 and follow it southwest to Chillicothe. From Chillicothe, follow
U.S. 50 west to Bainbridge. Then follow S.R. 41 southwest to Aberdeen on the
Ohio River. Zane=s Trace travels through 11 counties in Ohio/
Click here for the Short-Cut Table of Contents


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The following programs of LearnOpenGl are implemented in javascript / typescript, using WebGl2. Not all browsers support WebGl2. The latest versions of Google Chrome and Windows Edge can be used to view the results.
camera_class (Now use keys 'a', 's', 'd', 'w', mouse and mouse-wheel)


Tiziano Zito moved from theoretical
physics through neuroscience to finally become a full-time system
administrator. He is now working as a Scientific Computing
Coordinator at the Jülich Forschungszentrum in Germany. He is
the main organizer of the Advanced
Scientific Programming in Python summer school series.


SMALL FACES A film review by James Berardinelli Copyright 1996 James Berardinelli
RATING (0 TO 10): 7.5 Alternative Scale: *** out of ****
United Kingdom, 1995 U.S. Release Date: beginning 9/96 (limited) Running Length: 1:48 MPAA Classification: R (Violence, language, brief nudity) Theatrical Aspect Ratio: 1.85:1
Cast: Ian Robertson, Joseph McFadden, J.S. Duffy, Laura Fraser, Garry Sweeney, Claire Higgins, Kevin McKidd, Mark McConnochie Director: Gillies MacKinnon Producers: Steve Clark-Hall and Billy MacKinnon Screenplay: Billy MacKinnon and Gillies MacKinnon Cinematography: John de Borman Music: John E. Keane U.S. Distributor: October Films
SMALL FACES, Gillies MacKinnon's first feature after turning away from Hollywood (his previous film, A SIMPLE TWIST OF FATE, was the SILAS MARNER modernization starring Steve Martin), brings the director back to the kind of small, personal story he feels most comfortable with. According to MacKinnon, while in Hollywood, "I began to have a sense of losing roots... as if my strength was drawn from something within my past, that I was moving away from that and I had to go back."
In examining SMALL FACES, three things stand out. The first is that, despite a fairly unambitious storyline, the film retains a large element of unpredictability. Because it eschews cliches and formulas typical to "coming of age" stories, it's difficult for the viewer to guess what's going to happen and how certain situations will be resolved. Secondly, when the movie ended, I was a little disappointed - - not because there's anything inherently dissatisfying about the conclusion, but because I would have enjoyed spending more time with these characters. Finally, despite the accents, this isn't another TRAINSPOTTING.
SMALL FACES opens in 1968 Glasgow and introduces us to the brothers MacLean. There are three of them: thirteen year old Lex (Ian Robertson), who's our guide through the story; sixteen year old Alan (Joseph McFadden), an introspective, budding artist; and eighteen year old Bobby (J.S. Duffy), an illiterate ruffian who runs with a gang. The boys' father is dead, so they live alone with their mother, Lorna (Claire Higgins). As gang warfare intensifies in Govanhill, Lex and Alan are unwittingly drawn into the conflict because of their brother's association, and they soon find themselves caught between the sadistic leader of Bobby's group, Charlie Sloan (Garry Sweeney), and the head of the rival Tongs, Malky Johnson (Kevin McKidd).
SMALL FACES starts out oddly. The first twenty minutes are jumpy and disjointed, and, at times, it's difficult to sort out characters and circumstances. Once we've resolved the complexities of Lex's home life and the nature of the gang friction (not to mention who's in which gang), things clear up considerably, and MacKinnon's story begins to flow. Lex is a splendid lead character: smart, stubborn, and passionate, yet, largely because of his age, impotent in the face of the wave of violence that threatens to drown his family.
SMALL FACES gets the details right, which is largely why it works. Small, apparently-innocent actions, like firing a pellet gun, set major events in motion. Seemingly unimportant characters, like Lex's older friend, Gorbals (Mark McConnochie), play critical roles. Alan's relationship with a local girl is developed perfectly, with an eye towards realism rather than sentimentality.
Other than Claire Higgins, who does a fine job portraying a mother who can't protect her sons, the major players are relative unknowns. Ian Robertson's performance as Lex reflects the reality of being thirteen -- he thinks he's brave and smart enough to do anything until the time comes for him to take action. As Alan, Joseph McFadden fashions a sympathetic character -- a somewhat naive boy who wants nothing to do with the violence around him, but who can't escape it. Then there's Laura Fraser, the strikingly appealing actress to play Alan's girlfriend, Joanne.
SMALL FACES shows the personal, often-tragic toll of gang violence. Though it takes place during another decade in another country, MacKinnon's movie has universal implications. For, while the causes of such violence may differ from culture to culture, the inevitable consequences, both physical and emotional, are familiar. We can identify with what these characters are feeling. Ultimately, this movie is less about the situation than the people who are trapped by it, and that simple, human element provides SMALL FACES with its core strength.
- James Berardinelli e-mail<EMAIL_ADDRESS>ReelViews web site: http://www.cybernex.net/~berardin
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


| PURCHASE DOCUMENT NO | CREATED ON | CREATED BY | STATUS | VENDOR | PURCHASE ORG | PURCHASE GROUP |
|---|---|---|---|---|---|---|
| {{people.PO_NUMBER._text}} | {{people.CREATED_ON._text}} | {{people.CREATED_BY._text}} | {{people.STATUS._text}} | {{people.VENDOR._text}} | {{people.PURCH_ORG._text}} | {{people.PUR_GROUP._text}} |
| SHORT TEXT | MATERIAL | PLANT | TARGET QUANTITY | NET PRICE | PRICE UNIT | NET VALUE | GROSS VALUE | QUOT DEAD |
|---|---|---|---|---|---|---|---|---|
| {{people.SHORT_TEXT._text}} | {{people.MATERIAL._text}} | {{people.PLANT._text}} | {{people.TARGET_QTY._text}} | {{people.NET_PRICE._text}} | {{people.PRICE_UNIT._text}} | {{people.NET_VALUE._text}} | {{people.GROS_VALUE._text}} | {{people.QUOT_DEAD._text}} |


We offer free rides in rocket cars to the general public. Ok, that does sound crazy, but anyone who drives a rocket car in the first place is a little crazy to begin with. If you are intersted in learning about what you would need to do in preparation for a ride in a rocket car please give us a call at 1-800-blastoff and one of our friendly helpers will give you the scoop.
You can check out some videos below which will help you get an idea of what this is all about.


PROFICINT IN AUTOCAD 2019, CARRIER HAP, MAQUEY Duct sizer, MAQUEY Pipe sizer, Elite Firefighting, Elite Water Supply (SPipe), Elite Drainage (DPipe).
PROFICINT IN MICROSOFT WINDOWS, MICROSOFT OFFICE (Word, Excel, Access, PowerPoint), ADOBE PHOTOSHOP
P E R S O N A L S K I L L S
Reliable, self-motivated, Creative, Responsible, Hardworking, open to new ideas and a good team player.
Hold a valid driving license (Jordanian, Saudi driving license).


Before Change
np.clip(d["weight"], 0, 9)) + "];\n").encode("utf-8"))
else:
if d["weight"] > threshold:
f.write(("\t" + cnn(a) + " -- " + cnn(b) + " [penwidth=" + str(3 * d["weight"]) + "];\n").encode(
"utf-8"))
else:
if directed:
f.write(("\t" + cnn(a) + " -> " + cnn(b) + "\n").encode("utf-8"))
After Change
//f.write("\tgraph [overlap=scale];\n".encode("utf-8"))
f.write("\tnode [shape=point];\n")
for a, b, d in g.edges(data=True):
if "weight" in d:
if directed:
f.write(("\t" + cnn(a) + " -> " + cnn(b) + " [penwidth=%.2f" % float(
np.clip(d["weight"], 0, 9)) + "];\n"))


by Michael Martin (@problogdesign) from Deloitte Digital
"JavaScript developer" == "Web developer";
"The biggest mistake we made as a company was betting too much on HTML5."
"JavaScript is the most commonly used programming language on earth. Even Back-End developers are more likely to use it than any other language."
StackOverflow 2016 Developer Survey
Most popular in 2016, 2015 and 2014.
Most active repos, most commits, most forks...
Lets read in a file.
BufferedReader br = new BufferedReader(new FileReader("file.txt"));
try {
StringBuilder sb = new StringBuilder();
String line = br.readLine();
while (line != null) {
sb.append(line);
sb.append(System.lineSeparator());
line = br.readLine();
}
String everything = sb.toString();
} finally {
br.close();
}
Java (StackOverflow top answer)
var fs = require('fs');
fs.readFile('file.txt', 'utf8', function(err, data) {
if (err) throw err;
console.log(data)
});
JavaScript (StackOverflow top answer)
JSON = JavaScript Object Notation
var express = require('express')
var app = express()
app.get('/', function (req, res) {
res.send('Hello World!')
})
app.listen(3000, function () {
console.log('Example app listening on port 3000!')
})
Web server in Express.
async function getFirstUser() {
let users = await getUsers();
return users[0].name;
}
interface Person {
name: string;
age?: number; // Optionals
sayHello(name: string): void; // Functions
}
TypeScript and Flowtype
var numbers = [1, 2, 3, 4, 5];
var isOdd = function(number) {
return number % 2;
};
var oddNumbers = numbers.filter(isOdd);
Functions are first class citizens so we can do list comprehensions, currying, monads etc.
var map1 = Immutable.Map({a:1, b:2, c:3});
var map2 = map1.set('b', 50);
map1.get('b'); // 2
map2.get('b'); // 50
Lets you use upcoming JavaScript features in all browsers, today.
Chrome DevTools for breakpoints, performance monitoring, network usage, device emulation, live editing etc.
Webstorm, if you have IDE withdrawal symptoms. Atom is still better ;)
ESLint for intelligent code quality (Like Sonar)
Node and NPM. Millions of packages, installed into the project folder. Simple `npm install`
npm replacement built by Facebook. Solves the npm version dependency problems, and its faster.
Digital-branded slideshow repo on Github.
Load https://paperplanes.world/ on your phone! (Other examples here)
Because even microservices are too big... 😎
Desktop apps built entirely with web technologies.
Plus Atom and Hyperterm = Most of my day :D
React Native, Ionic.
e.g. Command and Conquer entirely in the browser.
Maps your code to an abstract syntax tree, then compiles the tree back into high level code.
Can transform your whole codebase, e.g. move to ES6 features, implement classes, change testing library.
On Github
Extremely performant subset of JavaScript which all runtimes can optimise for.
WebGL - Hardware-accelerated 3D graphics in the browser.
any application that can be written in JavaScript, will eventually be written in JavaScript.
Jeff Atwood (CodingHorror) in 2007. Now it's a Reddit, /r/atwoodslaw


--- title: Linux Distributions category: Desktop order: 72 ---
The following Linux distributions include DuckDuckGo as a search option.
We could really use your help in getting in more distros. Please send an email to<EMAIL_ADDRESS>and check out our partnership page.


- getAttributes() - Method in class com.github.alinaioanaflorea.xmldiff.core.model.XmlElem
-
Gets the attributes of the XmlElem
- getChildXmlElems() - Method in class com.github.alinaioanaflorea.xmldiff.core.model.XmlElem
-
Gets the child XmlElem structures of the XmlElem
- getDiff(String, String, String) - Method in class com.github.alinaioanaflorea.xmldiff.core.service.impl.XmlDiffServiceImpl
-
Gets the difference between the input XML files configured with the data in the configuration file
- getDiff(String, String, String) - Method in interface com.github.alinaioanaflorea.xmldiff.core.service.XmlDiffService
-
Gets the difference between the input XML files configured with the data in the configuration file
- getIndexParent1() - Method in class com.github.alinaioanaflorea.xmldiff.core.model.XmlNodeMetaData
-
Gets the index of the XmlNode's parent structure from the first input file
- getIndexParent2() - Method in class com.github.alinaioanaflorea.xmldiff.core.model.XmlNodeMetaData
-
Gets the index of the XmlNode's parent structure from the second input file
- getMatchedPercentage() - Method in class com.github.alinaioanaflorea.xmldiff.core.model.XmlNodeMetaData
-
Gets the percentage at which the parent structures of the XmlNode got matched
- getMetaData() - Method in class com.github.alinaioanaflorea.xmldiff.core.model.XmlNode
-
Gets information about the metaData of the XmlNode
- getModificationType() - Method in class com.github.alinaioanaflorea.xmldiff.core.model.XmlNodeMetaData
-
Gets the XmlNode's modification type
- getName() - Method in class com.github.alinaioanaflorea.xmldiff.core.model.XmlNode
-
Gets the name of the XmlNode
- getValue() - Method in class com.github.alinaioanaflorea.xmldiff.core.model.XmlNode
-
Gets the value of the XmlNode
- getXmlAttributes(Node) - Static method in class com.github.alinaioanaflorea.xmldiff.core.common.Utilities
-
Gets the attributes of the given Node in XmlAttribute format
- getXmlRootNode(String) - Static method in class com.github.alinaioanaflorea.xmldiff.core.common.Utilities
-
Gets the root Node of the given XML file


gatelfdata.dataset module¶
Module for the Dataset class
-
class
gatelfdata.dataset.
Dataset
(metafile, reuse_files=False, config=None, targets_need_padding=False)[source]¶ Bases:
object
Class representing training data present in the meta and data files. After creating the Dataset instance, the attribute .meta contains the loaded metadata. Then, the instances_as_string and instances_as_data methods can be used to return an iterable.
-
batches_converted
(train=True, file=None, reshape=True, convert=False, batch_size=100, as_numpy=False, pad_left=False)[source]¶ Return a batch of instances for training. If reshape is True, this reshapes the data in the following ways: For classification, the independent part is a list of batchsize values for each feature. So for a batch size of 100 and 18 features, the inputs are a list of 18 lists of 100 values each. If the feature itself is a sequence (i.e. comes from an ngram), then the list corresponding to that feature contains 100 lists. For sequence tagging, the independent part is a list of features, where each of the per-feature lists contains 100 (batch size) elements, and each of these elements is a list with as many elements as the corresponding sequence contains. If reshape is True (the default), then the batch gets reshaped using the reshape_batch method.
-
batches_original
(train=True, file=None, reshape=True, batch_size=100, pad_left=False, as_numpy=False)[source]¶ Return a batch of instances in original format for training.
-
convert_dep
(dep, is_batch=False, as_onehot=False)[source]¶ Convert the dependent part of an original representation into the converted representation where strings are replaced by one hot vectors. If as_onehot is True, then nominal targets is onverted to onehot float vectors instead of integer indices (ignored for other target types).
-
convert_indep
(indep, normalize=None)[source]¶ Convert the independent part of an original representation into the converted representation where strings are replaced by word indices or one hot vectors. If normalize is None then the normalization will be performed according to the default for the feature, otherwise it should be one of “minmax”, “meanvar”, or False or a normalizing function. If False, normalization is turned off explicitly, otherwise the normalization function is used. This parameter is ignored for all features which are not numeric.
-
convert_instance
(instance, normalize=’meanvar’, is_reshaped_batch=False)[source]¶ Convert an original representation of an instance as read from json to the converted representation. This will also by default automatically normalize all numeric features, this can be changed by setting the normalize parameter (see convert_indep). If is_reshaped_batch is True, then we expect a batch of reshaped instances instead of a single instance Note: if the instance is a string, it is assumed it is still in json format and will get converted first.
-
convert_to_file
(outfile=None, infile=None)[source]¶ Copy the whole data file (or if infile is not None, that file) to a converted version. The default file name is used if outfile is None, otherwise the file specified is used.
-
static
data4meta
(metafilename)[source]¶ Given the path to a meta file, return the path to a data file
-
feature_types_converted
()[source]¶ Returns a list with the converted types of the features as a string name. Possible values are ‘float’, ‘index’, ‘indexlist’
-
feature_types_original
()[source]¶ Returns a list with the original types of the features as a string name. Possible values are ‘nominal’, ‘number’, ‘ngram’, ‘boolean’
-
get_index_feature_idxs
()[source]¶ Return a list of indices of all nominal features represented by some index and ultimately by a vector
-
get_index_features
()[source]¶ Return a list of all nominal features represented by some index and ultimately by a vector
-
get_indexlist_feature_idxs
()[source]¶ Return a list of indices for all features which are ngrams, i.e. lists of embs.
-
get_info
()[source]¶ Return a concise description of the learning problem that makes it easier to understand what is going on and what kind of network needs to get created.
-
instances_as_string
(train=False, file=None)[source]¶ Returns an iterable that allows to read the original instance data rows as a single string. That string can be converted into the actual original representation by parsing it as json. If train is set to True, then instead of the original data file, the train file created with the split() method is used. If file is not None, train is ignored and the file specified is read instead.
-
instances_converted
(train=True, file=None, convert=False)[source]¶ This reads instances and returns them in converted format. The instances are either read from a file in original format and converted on the fly (convert=True) or from a file that has already been converted as e.g. created with the split() or copy_to_converted() methods. If the file parameter is not None, then that file is read, otherwise if the train parameter is False then the original data file is read, otherwise if the train parameter is True, the train file is read.
-
instances_original
(train=False, file=None)[source]¶ Returns an iterable that allows to read the instances from a file in original format. This file is the original data file by default, but could also the train file created with the split() method or any other file derived from the original data file.
-
static
load_meta
(metafile)[source]¶ Static method for just reading and returning the meta data for this dataset.
-
modified4meta
(name_part=None, dirname=None)[source]¶ Helper method to construct the full path of one of the files this class creates from the original meta/data files. If dir is given, then it is the containing directory for the file. The name_part parameter specifies the part in the file name that will replace the “meta” in the original metafilename.
-
static
pad_list_
(thelist, tosize, pad_left=False, pad_value=None)[source]¶ Pads the list to have size elements, inserting the pad_value as needed, left or right depending on pad_left. CAUTION! Modifies thelist and also returns it.
-
static
pad_matrix_
(matrix, tosize=None, pad_left=False, pad_value=None)[source]¶ Given a list of lists, pads all the inner list to size length or if size is None, first determines the longest inner list and pads to that length. CAUTION: modifies the matrix!
-
reshape_batch
(instances, as_numpy=False, pad_left=False, from_original=False, pad=True, indep_only=False)[source]¶ Reshape the list of converted instances into what is expected for training on a batch. NOTE: for non-sequence instances, we pad all list-typed features to the maximum length. If from_original is true, the padding is done with empty strings, otherwise with integer zeros. NOTE: as_numpy=True for from_original=True currently only converts the result of converting the outermost list to a numpy array which will automatically also convert the embedded lists.
-
static
reshape_batch_helper
(instances, as_numpy=False, pad_left=False, from_original=False, pad=True, n_features=None, is_sequence=None, feature_types=None, target=None, indep_only=False)[source]¶ Reshapes a list of instances where each instance is a two-element list of an independent and dependent/target part into a tuple where the first part is a list of features and the second part is the list of targets. If the instances are not for sequence tagging, then each list that corresponds to a feature contains as many values as there are instances. If the value of the feature is a sequence, then each value is a padded list. For sequence tagging, The independent part contains as many lists as there are features, each of these lists contains as many elements as there are instances. These elements in turn are lists, representing the values of the feature for each feature vector in the sequence for the instance. The feature_types list must be specified if is_sequence is True, in that case, n_features is not needed. If indep_only is True, this will not expect targets and only reshape the independent features. IMPORTANT: this pads all independent features based on their type, with indices getting padded using 0 and all dependent indices using -1!!! If target is specified, the targets are represented by a onehot vector instead.
-
split
(outdir=None, validation_size=None, validation_part=0.1, random_seed=1, convert=False, keep_orig=False, reuse_files=False, validation_file=None)[source]¶ This splits the original file into an actual training file and a validation set file. This creates two new files in the same location as the original files, with the “data”/”meta” parts of the name replaced with “val” for validation and “train” for training. If converted is set to True, then instead of the original data, the converted data is getting split and saved, in that case the name parts are “converted.var” and “coverted.train”. If keep_orig is set to True, then both the original and the converted format files are created. Depending on which format files are created, subsequent calls to the batches_converted or batches_orig can be made. If outdir is specified, the files will get stored in that directory instead of the directory where the meta/data files are stored. If random_seed is set to 0 or None, the random seed generator does not get initialized. If reuse_files is True and the files that would have been created are already there the method does nothing for that file, assuming, but not checking that the contents is correct. If validation_file is not none, then validation_size and validation_part are ignored and the whole original file is used as training file, the given validation_file is expected to be a data file that fits the meta, and the content of the validation file is used.
-
validation_set_converted
(as_numpy=False, as_batch=False)[source]¶ Read and return the validation set instances in converted format, optionally converted to batch format and if in batch format, optionally with numpy arrays. Fir this to work the split() method must have been run before with convert set to True.
-


Although I used to munch on strong blotter acid like it was candy, I had never tripped stronly on shrooms until last March. A friend of mine wanted to try a decent dose--although I had been 98% sober for a couple of years, I thought why not? Should be a good time. A couple of friends--two other guys, my girlfriend and another girl--sat down in a typical dorm room an proceeded to get into it.
Now, from my acid experiences I had learned that if I am going to have an unpleasant experience on it, I will know it as I experience the early effects, and the trip will stay bad until it ends entirely. This experience with shrooms was no different at all--within 30 minutes, I felt extremely uncomfortable. I didn't want to move or even blink, and I became agitated whenever anyone tried to talk to me. While the others were laughing and having a great time although totally out of it, I felt totally disoriented, confused and scared. And that was just the beginning.
Later (I have no sense of when things happened), the friend who had taken the same dose as I had (everyone else had taken less) and I somehow communicated that we had to go to the bathroom, so we set out down the hall. Since the bathroom is a good way down the corridor and around a bend, we had a hell of a time finding it. Actually, we never found it. Piecing together the bits of memory I have of it, he and I decided at some point to give up on finding the bathroom and just pee our pants. So, we did. Somewhere around here, my friend began to get violent. I remember him punching me repeatedly in the ribs, whereupon we both fell to the floor and remained there until our other male friend hauled us back into the dorm room. We must have been seen by at least five or six people while lying in a heap, piss-covered, in the hall.
My very out-of-it friend, 'J', then became uncomfortable with his wet and increasingly cold pants, and took them off, along with every other stitch of clothing he had been wearing. Under normal circumstances this would be hilarious, but I was in the throes of an introspective nightmare and barely noticed him. i laid down, face to the carpet, occasionally licking it, confused and scared, until the fear turned to despair. I had lost all sense of time and was convinced that nothing would ever make sense again. I forgot entirely that drugs were causing this and that it would pass.
At some point along the way, a naked J punched my girlfriend in the face. My other friends restrained him and he calmed down, but my girlfriend was beside herself.   But I didn't understand anything she was saying or doing. So, she left.
What seemed like years later, my phone rang. It was my girl, calling from home to ask when I needed a ride home. I didn't remember her being punched, and when I thought about it (which was becoming less impossible) I was confused as to why she was calling--wasn't she right here? Where had she gone? Why? So I stumbled awkwardly through that, but I was regaining my senses a little.
I had to pee again (after having apparently wet myself twice, according to 'M'), and this time accompanied a clothed J to the bathroom, which we found. I was beginning to feel fantastic. Things were starting to make sense; I felt that I was in a sitcom, because everything everyone did was so predictable. When we got back to the dorm room, I started acting like a sitcom character because it only seemed appropriate to do so--people were asking me questions and I would respond not with what I thought, but with what I thought was the most predictably funny thing to say. It was extremely odd.
When my gf got back, I had almost entirely sobered up. I was beginning to really feel reality again, and nothing looked to sitcomy anymore.
Certainly gave me new respect for shrooms. You CAN have a bad shroom trip! Make sure you have a sober babysitter when experimenting with high doses of potent hallucinogens!
I haven't done any strong hallucinogens since, although I hadn't before this trip for a couple of years anyway. I just shake my head when I think about it, now. What a wild, awful experience.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Notebooks
Story: Story of ≠kasin's hunting adventures
Story of ≠kasin's hunting adventures
Title
Story of ≠kasin's hunting adventures
Collection
Summary
The narrator’s own hunting adventures. He kills a hyena and her two young ones, and shoots some springbok which had been startled by a lion. The presence of jackals indicates the lion’s whereabouts. In a general hunt, the lion is killed, but not before it had broken the knee-bone of a man, who dies from loss of blood. After his burial, the Bushmen remove to another place, so that the children may not be thinking of their father, and wanting to cry. In the Katkop dialect, by ≠kasin (L IV.-2.3534-3585). The hunter covers the bodies of the springbok with bushes and sees a lion. He carries a springbok home and explains to the people why he could not bring home more. The people go with him to fetch the other three springboks and find the lion and shoot at it. The lion bites one of the old men in the hunting party and the other men stab at it until it lets go.
Comments
1) Date on p.3539: 20 November; p.3583: 19 February 1874, 2) Pages 3586-3607 in this notebook are blank, 3) This story is found in Book IV-2
Contributors
Date
November 1873 – February 1874
Categories
History (personal), Custom and daily life
Keywords
hunting (and ≠kasin's adventures) , hunting (methods of) , hunting (-ground) , hunting (and death of an old man) , hunting (of lion) , hunting (and scavengers) , hunting (and cutting up prey) , hunting (and treatment of prey) , hunting (with dogs) , hunting (the wearing of veldskoen) , hunting (the weapons used) , hunting (of hyena) , hunting (of springbok) , hyena (and ≠kasin's hunting adventures) , crows (and ≠kasin's hunting adventures) , crows (and hunting) , crows (watching for) , springbok (and ≠kasin's hunting adventures) , springbok (and treatment of prey) , springbok (the dressing, preparation, and cooking of meat) , lion (its shadows) , lion (kills an old man) , lion (and ≠kasin's hunting adventures) , death (and hunting) , death (and burial) , death (and ≠kasin's hunting adventures) , ≠kasin (his personal history) , ≠kasin (his hunting adventures) , ≠kasin (his hunting methods) , personal history (≠kasin's hunting adventures) , veldskoen (worn in hunting) , veldskoen (and ≠kasin's hunting adventures) , dogs (hunting with) , dogs (and ≠kasin's hunting adventures) , cutting (up of meat after hunting) , cutting (and ≠kasin's hunting adventures) , weapons (used in hunting) , weapons (and ≠kasin's hunting adventures) , shoes (veldskoen worn for hunting) , shoes (and ≠kasin's hunting adventures)
Story Pages
3534-3585


Woman, 75, dead and man, 79, fighting for life after hit-and-run
Police are hunting for a driver who killed a 75-year-old woman and left a 79-year-old man fighting for his life in a hit-and-run.
They were hit by a dark-coloured car near the Punch Bowl pub on Jesmond Road, Newcastle, just after 7.20pm on Saturday.
The woman was pronounced dead at the scene while the man was rushed to hospital with potentially life-threatening injuries.
Officers are looking for the vehicle and its driver and have urged witnesses to come forward.
Sergeant Matt Sykes said: ‘This has been a terrible collision where a woman has sadly lost her life and a man is in hospital with very serious injuries.
‘Our thoughts are with their family and friends at this time.
Advertisement Advertisement
‘Enquiries are now being carried out by our officers to establish exactly what has happened – but we still need to speak to people who witnessed the collision or have any information about this collision.
‘If anybody saw what happened, or thinks they may have dashcam footage that could assist with us, I would ask them to contact police as soon as possible.’
The victims’ family are being supported by specialist officers.
Anyone with information is asked to contact Northumbria Police on 101 quoting reference 883 23/11/19 or Crimestoppers anonymously on 0800 555 111.
Got a story for Metro.co.uk?
Get in touch with our news team by emailing us at<EMAIL_ADDRESS>For more stories like this, check our news page.


HOW TO USE THE TSE TICKET MOBILE APP
The TSE ticket mobile app allows TSEs to submit support requests on behalf of Agents. You can find it at URL...
http://tinyurl.com/wuticket
To use the app...
- Go to the URL above.
- Enter the 4-character Terminal ID of the Agent for whom you want to submit the support message, then press the Send button (depending on your browser, you may be able to press the Enter key instead).
- If the Terminal ID is recognised, the next screen displays the Agent Name. Check that you have selected the correct Agent.
- Click Select Topic to display the available titles for the conversation. Choose a title. If you choose 'Other', enter your title in the text box that appears.
- In the TSE User ID text box enter your TSE User ID followed by a space followed by at least the first 2 characters of your password (case sensitive).
To have your device remember your TSE User ID for next time, move the Remember? toggle button to Yes. If you did that previously your TSE ID is displayed and you only need to enter the first 2 character of your password.
- In the Message text box, enter the text of the message you are sending on behalf of the Agent.
- Press the Send Message button. Your message will be sent to the Controller.
NOTE: When the controller closes your conversation you will receive a notification by email. Check the Controller's response and if appropriate inform your Agent that the query has been resolved.


Yes Angular 2.0 is ready and has shipped!
To keep track of the next upcoming releases follow the
milestones
on
Github
.


"Old on to tha pin, throw the udder part!"
— Ork Tankbusta, Warhammer 40000: Dawn of War
A character pulls the pin off a grenade and proceeds to throw ... the pin, and keeps the grenade in hand. Very common in cartoons.
This one often comes about due to a misinterpretation of the instruction "pull the pin and throw it." Can be considered a form of Explosive Stupidity, though examples need not involve the grenade detonating (especially in works that know the pin is a safety and the plunger lights the fuse). Likely to result in Boom in the Hand. Related to Hoist by His Own Petard.
Examples:
open/close all folders
Anime and Manga
- Axis Powers Hetalia does this gag as Germany tries to train Italy to throw grenades. Then later, South Italy comes along and tries to throw a grenade at Germanybut fails because of this mistake. Germany ends the gag by crying out in frustration, "What's with you two?"
Comic Books
- A Running Gag in Mortadelo y Filemón; Mortadelo is given a grenade, wonders about how they are used, Filemón tells him to pull the pin, count to ten and throw it, and Mortadelo ends up throwing the pin.
- A common variation is that one of the two, after pulling the pin, attempts to throw the grenade with all his might, only to discover that the grenade is attached to his hand due to chewing gum, ultra strong glue, or whatever sticky substance he had previously held.
- In an old comic called Croc, we see this guy lighting a bomb with his cigar and throwing something in the next panel. Then, there's an explosion and the following dialogue ensues:
"What have you done?!""... Hit the enemy with my cigar?"
- Up to Eleven in Alan Ford with one-shot villain Laszlo: in a last attempt to kill the hated nemesis Number One and his agents, he decides to use a grenade... but he accidentally holds the grenade in his mouth, removes and toss the pin and swallows the grenade with predictable results.
Comic Strip
- Zero from Beetle Bailey once did this, giving Sergeant Snorkel a major Oh, Crap! moment.
Film
- Bananas pulls this gag twice in the course of a montage. Woody Allen's character is training with the rebels, and he tries throwing a grenade.  Later, he tries again, and successfully throws the grenade. The pin explodes in his hand.
- Bones (Pauly Shore) does this during grenade training in In The Army Now.
- Stan (naturally) does this in the Laurel and Hardy short Beau Hunks.
- Played for drama in The Thin Red Line when Sergeant Keck grabs a grenade hanging on his webbing, only to realise to his horror that he's only holding the pin. The grenade explodes before he has a chance to correct his mistake.
Jokes
- Q: What do you do if a blonde throws a keyring at you? A: Run for it, she's got a grenade in her mouth.
Live Action TV
- There's a variation in the obscure British sketch show The Steam Video Company, where a soldier pulls the pin on a grenade then opens a can of Coke, throws the can and puts the grenade to his mouth.
- There's a variation in The Prisoner (1967) episode "The Girl Who Was Death" which turned out to be a children's bedtime story where Number Six tampers with the bad guys' old-timey WWI-era grenades (the ones with a baton-like handle used to hurl a can-shaped charge) so the explosives ended up in the handles.
- Whose Line Is It Anyway?: This gif sums it nicely.
- In one episode of M*A*S*H, Frank Burns, trying to prove himself "tough", pulls out the pin of a grenade with his teeth and spits it out. A moment later, he realizes, with no small amount of panic, what he just did. Thankfully, he fixes the problem before anyone is hurt.
Music Video
- In the music video for Twisted Sister's "I Wanna Rock", The Neidermeyer teacher tries to stop the band by throwing a grenade at them, but he ends up throwing the pin, and the grenade gets stuck on his hand, so he jumps into the school swimming pool to try defusing it, but ends up getting blasted upward, hitting his head on the diving board.
Tabletop Games
- Featured in old "Canonical Famous Last Words List" (the character's background precluded him from knowing to which of two parts "throw it" should refer).
- Expect a Killer Game Master with a Literal Genie streak to do this if the player doesn't specify exactly what part of a grenade they're actually throwing.
- Warhammer 40,000: Ork Stikkbommaz are noted for being above average intelligence for Orks, as they know that stikkbommz are the bit you throw, not the pin...
- Then again the ork will probably throw the pin too for the hell of it.
- Paranoia occasionally has grenades where the pin is the part that explodes. Hell, it probably has some where they both do, because that would make them, from the computer's point of view, foolproof.
Video Games
- Worms 2 demonstrates this trope quite clearly.
- Invoked in Dawn of War when Orks say "Hold on to the pin. Throw the other part."
- In World of Warcraft's Mists of Pandaria expansion, one bit of hozen wisdom contains something akin to this trope:
Firecracker for throwing, banana for eating.
- A variation: in Borderlands 2, an enemy Bandit might be confused by a Nomad's order to toss grenades. "Pull the pin, then throw, right?"
Webcomics
- In the "Bloodsuckers Are Not Sexy" arc of Fafnir The Dragon, one of the vampires is an expert with grenades... only he wound up throwing the pins at Vlad the Impaler. It ends exactly how you'd expect.
- Schlock Mercenary: "Maxim 65: After the toss, be the one with the pin, not the one with the grenade". While it's mostly a lesson about initiative and striking first, it doubles as a handy warning not to do this.
Western Animation
- Wile E. Coyote does it at least once in the Looney Tunes cartoons.
- Or sometimes, the pin explodes.
- In one, he correctly drops the grenade down a cliff, but it's bounced back at him. He then drops the pin... and another grenade is bounced back at him.
- There's a short in Animaniacs where the Warners are being put through military training, and their sergeant is instructing them about grenades, with the above line. Wakko, naturally, throws the pin.
Sergeant (noticing Wakko is still holding the grenade): Throw the grenade!
Dot: You said throw the pin.
Sergeant: I meant pull the pin, and throw the grenade!
Wakko: I'm confused. Here, you try it. (hands the grenade to the sergeant, and it explodes on him) - Daffy Duck does it while hunting Speedy Gonzales in Speedy Ghost To Town.
- Seen from the outside in Robot Chicken; two GIs sitting in a foxhole with a bunker in the background. One is hit on the helmet with a pin, looks at it quizzically, someone in the bunker shouts "Oh, you idiot!", then the bunker explodes.
- Subverted in Aaahh!!! Real Monsters. Simon the Monster Hunter throws a pin at Bigfoot, has a brief Oh, Crap! moment, and then throws the gas grenade just in time.
- In the Ben 10: Omniverse episode "O Mother, Where Art Thou?" the Vreedle Brothers get blown up when Rhomboid does this, then they're later cloned with their memories intact.
Real Life
- Some poor recruits and trainees end up doing this under stress. Thankfully, most instructors and drill sergeants are ready to save the unfortunate newbie before any accidents happen. Unfortunately for the recruits, this involves a Sergeant composed of well over 230 pounds of muscle (Grenade Range Sergeants are selected, it seems, from a pool of body builders) slapping the grenade out of the private's hand (if it's not on the ground yet), picking the private up by the scruff of the neck and his/her belt, and body-slamming the poor sap into the ground on the opposite side of the concrete bunker that protects recruits from short throws. Not pleasant.
- Has happened, according to an entry in the Darwin Awards.
- Hard to see exactly what happened in this training exercise, but it looks like something similar, judging from the position of the flash. Remember: the flashbang is supposed to be used on them, not on you.
- Has happened on a few occasions, but the thrower is safe as long as they have not released the lever that prevents the grenade from charging. If it has been released, then it will not be a good day for the thrower.
- A similar technique written, possibly as a joke, in a swordplay manual, involves unscrewing the pommel of your sword and throwing it vigorously at the enemy. The technique is called "ending him rightly."


Electronic Entomology
Electronic Entomology is a study that explores the nuances of behaviors and characteristics of solar-powered electronic insects. This project explore electronic insects as a cohabitant and satellite senses to experience our surroundings in a new way. The web archive functions for the exploration of classifications of these insects and materials for the visitors to build them on their own.
Still Images
Orchid Danny
2" x 3" x 3"
Integrated Circuits, semiconductor components, solar cells, wires, piezo disk
Solar chirping insect called Orchid Danny accumulating spiders webs as it lives in my living space. It chirps once in a while with pitches that depend on the amount of light it gets.
two PCB insects
2" x 1" x 3"
Integrated Circuits, semiconductor components, solar cells, wires, printed circuit board, piezo disks
Solar chirping insects build on printed circuit boards.
Furin Gate
3" x 3" x 4"
Integrated Circuits, semiconductor components, solar cells, wires, piezo disk
Solar chirping insect called Furin Gate on a window sill. It has an organ that detects wind movement that it responds with chirps.
Electronic Entomology - workshop teaching material
3" x 3"
Custom printed circuit board
Experimental circuit board design specifically for workshops and classrooms. Many connections pads are enlarged and accessible for the student to experiment with different components and connections to explore different sounds.
Electronic Entomology - image of the web archive
Ongoing archival website for the Electronic Entomology project. This site contains detail classifications of each insect and resources for people to learn and build them themselves.
website: electronicentomology.com


Introduction
-
Tasks :
- 3D object detection : classifies the object category and estimates oriented 3D bounding boxes of physical objects from 3D sensor data.
-
Applications : By extending prediction to 3D, one can capture an object’s size, position and orientation in the world, leading to a variety of applications in robotics, self-driving vehicles, image retrieval, and augmented reality
-
Benchmarks
- KITTI (car, cyclist, pedestrian easy, mod, hard) : for autonomous driving
- ScanNet : for indoor scene understanding task
- SUN-RGBD : Scene Understanding Benchmarks
- YCB-V : 21 objects from the YCB dataset captured in 92 videos with 133,827 frames.
-
Approches
-
Approaches based on Point Clouds (LiDAR)
- Projection based methods : projects point clouds to 2D planes such as bird view, front view, etc. + RPN
- Volumetric methods : voxelization + heavy 3D CNN
- Point-net based methods : extract features from raw data
- Graph based methods : construct graphs to learn the order-invariant of point clouds
-
Approaches based on monocular/stereo images
- Approaches based on cheaper monocular or stereo imagery data have resulted in drastically lower accuracies until now.
- A key ingredient for image-based 3D object detection methods is a reliable depth estimation approach to replace LiDAR.
- Existing algorithms are largely built upon 2D object detection, imposing extra geometric constraints to create3D proposals. apply stereo-based depth estimation to obtain the true 3D coordinates of each pixel.
-
1. SUN-RGBD(2017)
-
An RGB-D benchmark suite for all major scene understanding tasks
-
Dataset is captured by four different sensors and contains 10,335 RGB-D images.
-
Densely annotated and includes 146k 2d polygons, and 64k 3D bboxes with accurate object orientations, as well as a 3D room layout and scene category for each images.
2. ScanNet(2017)
- An RGB-D Video Dataset containing 2.5M views in 1513 scenes annotated with 3D camera poses, surface reconstructions, and semantic segmentations.
3. Pseudo LiDAR(2019)
-
Introduction : Mono/stereo depth estimation based methods show worse performance than LiDAR based methods because of poor precision of image based depth estimation.
-
Contrib : convert image-based depth maps to pseudo-LiDAR representations
-
Methods :
-
convert the estimated depth map from stereo or monocular imagery into a 3D point cloud, which we refer to as pseudo-LiDAR
-
then take advantage of ex-isting LiDAR-based 3D object detection pipelines
-
4. Realtime 3D Obj Detection on Mobile Dev with Media Pipe (Google, 2020)
-
Introduction : detects objects in 2D images, and estimates their poses and sizes through a ML model, trained on a newly created 3D dataset
-
Obtaining real world 3D Training Data : using ARCore, Google’s AR Platform, integrated in most smartphones, which can provide the information of the camera pose, sparse 3D point clouds, estimated lighting, and planar surfaces.
-
AR Synthetic Data Generation : places virtual objects into scenes that have AR session data.
-
An ML Pipeline : a single-stage model to predict the pose and physical size of an object from a single RGB image
5. SVGA-Net(2020)
-
Introduction : graph based methods for point clouds data, 2020 SOTA in KITTI datasets
-
Methods : Spars Voxel Graph Attention Network
Appendix. LiDAR, ToF and Point Clouds
- Introduction : LiDAR systems send out pulses of light just outside the visible spectrum and register how long it takes each pulse to return. Once the individual readings are processed and organised, the LiDAR data becomes point cloud data. A 3D point cloud is a collection of data points analogous to the real world in three dimensions. Each point is defined by its own position and (sometimes) colour.
- Photogrammetric point clouds : have an RGB value for each point, resulting in a colourised point cloud (acqutision using digital camera rather than LiDAR, worse than LiDAR in terms of accuracy).
- Difference between LiDAR and TOF : A ToF(most android phones) is a scannerless LiDAR system, relying on a single pulse of light to map an entire space, while Apple is using scanner LiDAR, which uses multiple points of light to take these readings much more frequently and with greater accuracy.
- LiDAR & TOF in mobile : LG G8 (2019) , Galaxy Note10, S20, IPAD pro 4, iphone12 , Huawei Mate30, Sony Xperia2
Appendix. AR Applications on smartphones
-
Google AR Core Depth API : Single Camera Depth Estimation
-
IKEA Place : allows the user to directly insert furniture into a picture of the room
-
modiFace : virtual beauty counter
-
measure kit : AR Ruler
-
pixie : find lost key through the iPhone screen. (with Bluetooth hardware)
-
magic sudoku : Solve sudoku puzzles in AR
Appendix. Stereo/monocular image based depth Estimation
-
Benchmarks
- NYU Depth (Mono) : includes 1449 densely labeled pairs of aligned RGB and depth images
- KITTI Eigen Split (Mono) : part of the KITTI dataset proposed by D.Eigen et al. for monocular depth estimation task
-
BTS (monocular) : a network architecture that utilizes novel local planar guidance layers located at multiple stages in the decoding phase.
-
DORN (monocular) : combine multi-scale features with ordinal regression to predictpixel depth with remarkably low errors
-
PSMNet : applies Siamese networks for disparity estimation, followed by 3D convolutions for refinement
-
Y Wang et al. : has made these methods more efficient, enabling accurate disparity estimation to run at 30 FPS on mobile devices.
Appendix. 3D Modeling
- IKEA 3D Models : includes a lot of simple CAD models, but there’s no additional information required for detection like RGBD, point clouds, etc.
- These 3D Models can be used to estimate the 3D pose of specific objects in image ( Lim et al. ICCV2013 )


Hover over the names on the graph to see their connections.Use the slider to zoom in on people with higher numbers of connections.


The TMAccess API call transmits a TMTouchReset signal and returns
0 if no Presence signal is detected. If a Presence signal is detected, it
accesses the device whose ROM code is in the internal eight byte buffer. The
access readies the 1-Wire device to accept memory function commands such as
read scratchpad. Note that a successful return of 1 from TMAccess does not
guarantee that the device whose serial ROM number is in the internal eight
byte buffer is actually on the 1-Wire network. A successful return only
guarantees that some 1-Wire device is on the 1-Wire network and if the desired
1-Wire device is present then it is selected and ready for a device specific
command.
For a description of the above types, see the TMEX Type Descriptions.
Parameters
session_handle
Specifies the session handle returned from the API function TMExtendedStartSession that specifies the desired 1-Wire network port. This parameter is required by most TMEX API functions.
state_buffer
Specifies a pointer to a memory location that TMEX keeps all of the state information for the 1-Wire networks. This parameter is required by most TMEX API functions.
Return Value
0 => no presence on 1-Wire network
1 => presence on 1-Wire network and ROM selected
<0 => TMEX Network Error Return Code
See Also
TMRom, TMStrongAccess, TMStrongAlarmAccess, TMOverAccess


Hi guys! It’s been while since my last tutorial post about Regularization. And today, as I promised, I’m gonna talk about one supervised learning algorithm which took the throne of Neural Network a decade ago. It was fast, agile and outperformed almost the algorithms back in the days. Guys, today I want to tell you about Support Machine Learning, or SVM for short.
Many of you may have heard about the term SVM. For example, if you have experience in Computer Vision, especially using OpenCV to accomplish the task, you may have seen something like this on OpenCV’s page in HOG Descriptor section:
OpenCV provides an Linear SVM Model for People detection
And you will likely come across in other places with the same content, which gives us some proof of the irresistible power of SVM. Despite the fact that there are a great deal of supervised learning algorithms out there nowadays, SVM is still among the mostly applied algorithms. And everytime I face a new Machine Learning problem, the first algorithm I apply is SVM, not only for its performance, but also for its speed and easy-to-implement mechanism, which can give me an overview of the problem as fast as I expect.
Above is a brief introduction about SVM. Now let’s go finding the anwser for the question we are longing for: What is SVM?
SVM is a supervised learning algorithm which is mostly used for classification problems. It can perform well no matter our dataset is linear or non-linear distributed. But first, to make it easy to understand, in today’s post I’m gonna talk only about how SVM work when dealing with linear data, which can also be called Linear SVM algorithm.
And you may remember that I had made a post about one learning algorithm which can give awesome result when dealing with linear dataset. Yeah, I’m talking about Logistic Regression. So, to have a better understanding about Linear SVM, it’s a great idea to recall a little bit about Logistic Regression, and see what they differ from each other. For ones who haven’t skimmed through my post about Logistic Regression, you can find it right below:
When we talk about Logistic Regression, we may all think of the sigmoid function, which we use as the activation function. Below is what a sigmoid function looks like:
As I told you before, using sigmoid function will ensure that the output will be restricted in the range between \(0\) and \(1\), which then assigned to either \(0\) or \(1\) depends on its value and the threshold value. And of course, after getting the predictions with the help of the sigmoid function, we cannot evaluate the Model’s performance without a cost function (or loss function). And the cost function we use in Logistic Regression is the cross-entropy function, which can also be called the log-likelihood cost function:
So now if I set \(cost_1(\theta^TX)=-log\left(\frac{1}{1+e^{-\theta^TX}}\right)\), and \(cost_0(\theta^TX)=-log\left(1-\frac{1}{1+e^{-\theta^TX}}\right)\) in case of \(\theta^TX>=0\) and \(\theta^TX<0\) respectively, we can re-write the cost function in a simple form like below:
Not let’s consider the graph of each seperate part which I divided above:
As you can see, the \(cost_1(\theta^TX)\) term will be very large when \(\theta^TX\) is close to zero, and decrease toward zero as the value of \(\theta^TX\) increases. What does this mean? Before answering that question, let’s consider the other one:
Similar to the \(cost_1(\theta^TX)\) above, the \(cost_0(\theta^TX)\) term will be extremely large when \(\theta^TX\) is close to zero, but this time decrease toward zero as \(\theta^TX\) goes toward to the left.
The two terms above were divided from our cost function, which means that their values will be accumulated to the cost function. And our target is to minimize the cost function, you remember that? So, the smaller the two terms are, the smaller the cost function becomes. The smaller the cost function is, the closer our Predictions are comparing to the Label \(y\).
Now, let’s consider the \(cost_1(\theta^TX)\) term. We compute this term only when the corresponding label \(y=1\). As we saw in the graph above, when \(\theta^TX\approx0\), \(cost_1(\theta^TX)\) becomes very large. That is because we now have the probability that our Model predict the label \(y=1\) is very small, and may be even worse if it predict the label to be \(0\). As the result, the cost function will become large as a penalty. In contrast, if \(\theta^TX\) is much greater than \(0\), then the probability that \(y=1\) will be higher. And as the probability becomes nearly \(1\), we will have a nearly \(0\) cost value.
You can explain the \(cost_0(\theta^TX)\) term in the same way. As a conclusion, we will have a result like this:
The conclusion above is something which I had shown you in the end of the Logistic Regression tutorial, right? This time I just want to make it more clear if we explain it with considering the effect toward the cost function. Now let’s move to the case of Linear SVM.
After doing some revision on Logistic Regression. Let’s see what the cost function of Linear SVM looks like. First, let me re-write the cost function above, with the use of the two \(cost_1(\theta^TX)\) and \(cost_0(\theta^TX)\) terms, but this time without omitting the regularization term:
To make it even simpler, I will omit the \(\frac{1}{m}\) factor. It may affects the value of our cost function, but it doesn’t affect the way our algorithm works, since we are just eliminating a constant from a function.
Now, the new cost function looks like above. We can see that it has the form of: \(\mathbf{A}+\lambda\mathbf{B}\). Let’s talk a little bit about the \(\lambda\) term, as we call it the weight of regularization, which control how much we want to regularize our parameters. If it’s large, then our parameters will become much smaller and vice versa. Skipped my previous post? You can find it right below:
Now how about saying we want to put more weight on the actual cost value? The answer may be: just decrease \(\lambda\). It’s a little bit confusing to someone, so instead of using the form of \(\mathbf{A}+\lambda\mathbf{B}\), many people prefer the \(\mathbf{C}\mathrm{A}+\mathrm{B}\) form. So now we can say, if we want to emphasize on the actual cost value, we can do it by increasing \(\mathbf{C}\). And that way of expression is also the standard which the scikit-learn library are using. For example, here’s the full description when initializing Logistic Regression I grabbed on scikit-learn’s homepage:
With that approach, let’s re-write our cost function again, using the inverse of regularization weight \(\mathbf{C}\) instead of \(\lambda\), here’s what we will have:
And as I told you above about deciding the value of the Predictions by considering its effect on the value of our cost function, which I can also say that the way we compute our cost function, or the two \(cost_1(\theta^TX)\) and \(cost_0(\theta^TX)\) terms will affect the way our Model predicts the output. And here’s what Linear SVM differs from Logistic Regression. We will modify the two cost terms a little bit, to have the new graphs like below:
Obviously, as you can see, the two cost terms of Linear SVM looks different from what we saw in Logistic Regression. It the difference in how we define the cost terms in Linear SVM makes it predict in a different way. Telling you about this now may makes you feel confusing a little. But in fact, many cannot tell the difference between Linear SVM and Logistic Regression, since they seem to work the same way. So before I talk further, I think it’s good to notice the difference right this time, so you won’t make any unexpected assumption.
So, as you see from the graphs of the Linear SVM’s cost terms above. They look pretty much like what we saw in Logistic Regression except two things. First: instead of the non-linear graph which we obtained by the logarithmic function, now we have a new graph with two parts, one part which the cost values are \(0\), and the other part which values are not \(0\) is now linear. The second thing that Linear SVM differs from Logistic Regression is, the constraint to decide the value of the Prediction is now a little bit harder. Linear SVM requires a safety margin when deciding the Prediction, which we can express like below:
Intuitively, this safety margin is the reason why SVM is called the Maximum Margin Classifier, which I told you earlier in this post. What the algorithms does is to find a decision boundary which can obtain the maximum margins from the nearest point of each class. We will have a better visualization of maximum margin right below, in the Implementation section.
So, that’s all about Linear SVM. As you can see, if you have already known about Logistic Regression, it’s pretty easy to understand Linear SVM since they have some similar behavior in between. And the main point which drives Linear SVM apart from Logistic Regression is how we define the cost terms in Linear SVM, which then affects the way it decides the value of our Predictions. And now, after reading through a great deal of “lecture”, let’s jump into the Implementation section!
So, we finally come to the Implementation of Linear SVM. And just like Logistic Regression and Decision Tree, scikit-learn library provides us a well pre-implemented Linear SVM. All we have to do is… just use it!
You may now became very familiar with scikit-learn library as well as some Python codes we used for data initialization or graph drawing, so I won’t talk about those so much this time. And now, let’s get our hands dirty, by import all the necessary stuff we’re gonna use:
Next, we will create our data using make_classification method:
To make it simple, this time we just created a dataset with two features and two classes only. Next, let’s create our Linear SVM model, and train it using the data created above:
You may wonder what the kernel parameter means. But I will talk about it more in the later post, so now just implement as I did. We set its value “linear” so that scikit-learn knows we want to create a Linear SVM Model.
Next, we will draw the decision boundary which seperates the points of two classes for better visualization of the Model’s performance. To help you recall a little bit, the decision boundary in Logistic Regression seperates the coordinate plane into two parts like below (in case we have a dataset of two features and two classes):
And through the coef_ and intercept_ attributes of the trained Model, we can use them to draw the decision boundary, just like what we did with Logistic Regression.
And remember that Linear SVM is different from Logistic Regression by the way it defines the cost terms, which then affects the way it decides the value of our Predictions. Concretely, SVM will tend to keep a safety margin when making Predictions, so we’re gonna compute the upper boundary and the lower boundary to help visualize the term maximum margin better:
And finally, we now have everything ready, let’s go ahead and plot everything on:
And here’s the result I received:
As you could see in the graph above, what Linear SVM did is to find a decision boundary which can keep the maximum margins between the nearest point of each class. And that’s the reason why SVM is usually called the maximum margin classifier. And through implementing Linear SVM as well as drawing both the upper and lower boundaries, I hope you now have a better visualization of what Linear SVM does.
So, thank you for staying with me until the end, in my longest post ever. We have talked about Linear SVM, and of course, a little bit deeper about Logistic Regression, just to help you understand better how the two algorithms differ from each other. I hope after this post, you can both have a deep understand about Logistic Regression, and add Linear SVM, one of the most powerful algorithm to your Machine Learning toolbox. In the next post, I will continue with Support Vector Machine, but there won’t be any linear data any more. Next time we will see how SVM can deal with non-linear distributed data, by using something called: the kernel trick. Until then, stay tuned and I will be right back!


The repo plugin provides Repo as an SCM tools in Jenkins.
The repo plugin provides an SCM implementation to be used with the Pipeline SCM checkout
step.
The Pipeline Syntax Snippet Generator guides the user to select repo plugin checkout options and provides online help for each of the options.


v1.3.0 - June 19, 2020
c92dd7f
Update: add ChainExpression
node (#12) (Toru Nagashima)v1.2.0 - June 4, 2020
21f28bf
Update: added exported in exportAllDeclaration key (#10) (Anix)v1.1.0 - August 13, 2019
9331cc0
Update: add ImportExpression (#8) (Toru Nagashima)5967f58
Chore: add supported Node.js versions to CI (#7) (Kai Cataldo)6f7c60f
Upgrade<EMAIL_ADDRESS>(#5) (Teddy Katz)v1.0.0 - December 18, 2017
v0.1.0 - November 17, 2017
repository
field in package.json (#3) (Toru Nagashima)


Biographical Sketches of ARRS Members
This section provides a look at some of the members of the ARRS, together with some of their background and interests.
Andy Milroy (ENG)
Andy Milroy is the ARRS Co-ordinator as well as a global road running
historian, writer and statistician. He is the author of “The Long Distance Record Book,” editor of books on training
for ultradistance events, and the primary originator of the Ultra Marathon Race Handbook, the accepted rule book
for Ultrarunning. He is retired from a long career as a teacher. He is the co-ordinator for the Ultramarathon working
group and one of the ARRS founding members. He may be reached at<EMAIL_ADDRESS>Ken Young (USA)
Ken Young set USA records at 40 miles and the indoor marathon in the
early 1970's while earning a PhD in geophysical sciences with a minor in statistics. He learned to program computers
in 1965 and has been working with computers ever since. He first used a computer to produce race results in 1970,
and produced computer-based running performance rankings in 1975 while teaching at the University of Arizona. By
1980, these rankings had developed into the National Running Data Center which pioneered and developed road records
in the United States (now operated as the RRIC by the USAT&F). In the 1990s Ken developed a relational database
for distance running and began the Analytical Distance Runner newsletter. Co-founder of ARRS, Ken maintains the
ARRS database of more than 400,000 performances and is the webmaster for the ARRS website.
Birger Fält (SWE)
Birger Falt (Sweden) is a race walker at the highest level; he competed
in the 1999 World Championships at 20km; he is a race walking and long distance running statistician and also a
course measurer. He is the co-ordinator of the Race Walking working group and is one of the ARRS founding members.
He may be reached at<EMAIL_ADDRESS>Pete Riegel (USA)
Pete Riegel (United States) is a former IAAF Measurement Co-ordinator
for the Americas; Chairman of the USA federation Road Running Technical Council for many years, and founding editor
of Measurement News, the newsletter that disseminated good practice in course measurement world wide. He is the
co-ordinator of the Certified Courses working group and is one of the ARRS founding members. He may be reached
at<EMAIL_ADDRESS>David Blaikie (CAN)
David Blaikie is a former journalist and now works in public relations.
He is a athletics historian and statistician and did much to establish a solid statistical base for Canadian Ultrarunning.
David is well known for being the webmaster of the highly respected Ultramarathon World website (www.ultramarathonworld.com).
He is one of the ARRS founding members. He may be reached at<EMAIL_ADDRESS>Enzio Rivis (ITA)
Enzio Rivis (ITA) is a ATFS (Association of Track & Field Statisticians)
member and he is specifically interested in Marathon statistics. He has a personal World marathon lists up to 2:15:00
for men and 2:38:00 for women and is working to extend them. He is a member of the Marathons working group. He
may be reached at<EMAIL_ADDRESS>Andre Vazquez (BRA/ESP)
André Vazquez of Brazil is now living in Spain but still has many
contacts in Brazil. He competes as an ultra runner and is interested in multi-day statistics particularly.
Wim vanHemert (NED)
For over 35 years Wim was a sport journalist for the National Press Agency
(Algemeen Nederlands Persbureau) ANP. He covered every Olympic Games from 1968 to 1992, every World Championships
from Helsinki thru Stuttgart, every European championships from 1962. He also worked (free lance) for Eurosport
and Dutch radio. In 1981 he was co-founder of the Rotterdam Marathon. He has translated some 20 books from English
and German to Dutch, and has written several books about marathoning. He produces the press kits for the Rotterdam,
Amsterdam, Enschede and Eindhoven marathons. A marathon runner, he has kept marathon lists (world, European and
Dutch) since 1981.
Osman Atakan Tekin (TUR)
Osman is Editor in Chief of the Maraton Turk website (www.maratonturk.com). He got
interested in Athletics in 1962 and wore the colours of Fenerbahce Sports Club between 1962-1970. He worked on
the founding committee of the Istanbul Eurasia Marathon in 1979. He helped found the International Golden Horn
Half Marathon in 1986 and was the race director for four years. He was the Turkish delegate to the 1986 and 1987
IAAF congresses and to the AIMS world congress in Melbourne in 1988. He was appointed as the AIMS Press & Public
Relations Manager for Turkiye in 1998. He has been to 52 countries on 6 continents and has run 49 marathons in
19 countries. He is a bachelor and speaks English and a little Italian besides his native language of Turkish.
He may be reached at<EMAIL_ADDRESS>Gunars Akerbergs (LAT)
Gunars Akerbergs was born in 1947 in Saulkrasti, 47 km from Riga. Studies
at Latvian University brought him to Riga. From 1973 until 1991 he worked on Latvian Sports Committee but since
January 1992 he has been an official at Latvian Football federation.
Gunars ran his first Marathon in 1969, at the age 22.and in 1975 he began keeping statistics on Latvian marathon
runners. He has published three handbooks of Latvian marathon statistics, the last covering the period of 1927-1999.
Since the end of 1996 he has edited a newsletter "Maratona Zinas" (Marathon News). His website is
www.marathon.lv/index.htm
Last Updated on 25 Nov 2005


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The "datamodel" package provides the interfaces to describe real world data as an hyper-graph.
Actually, the idea of an hyper-graph is here very shallow. It mostly means that you'll have to describe your data using a {@link bzh.plealog.hge.api.datamodel.DataGraphModel} that is made of {@link bzh.plealog.hge.api.datamodel.DGMVertexType} and {@link bzh.plealog.hge.api.datamodel.DGMHyperEdgeType}, each of them containing some {@link bzh.plealog.hge.api.datamodel.DGMAttribute}.
{@link bzh.plealog.hge.api.datamodel.DGMVertexType} and {@link bzh.plealog.hge.api.datamodel.DGMHyperEdgeType} can be organized using hierarchies (types and sub-types). This hierarchical data model only support single inheritance.


|
|
EventConsumer
This sample demonstrates a permanent event consumer. Events are
generated from the Advanced Client sample application by adding equipment. This
application shows how to implement a simple IWBEMUnboundObjectSink and its
class factory. You should generate your own CLSID when building your permanent
consumer.
Building the EventConsumer Sample
The application can be
built from the command line using NMAKE, or it can be built using Microsoft
Visual C++.
From the command line
in the sample installation directory:
NMAKE /f "Makefile"
From Microsoft Visual
C++:
1.
Select File + Open Workspace
2.
Select the PermEvents.dsp file
Registering the
Consumer
The SampleViewer.mof uses a special namespace and you must
therefore compile the SampleViewer.mof to create the namespace and register
this consumer before running the Client sample application.
From the command line
in the sample installation directory type the following:
MOFCOMP SampleViewer.mof
In order to explicitly
register the consumer with COM, you may specify the /RegServer command line
switch when running the executable. From the command line in the sample installation
directory type the following:
PermEvents.exe /RegServer
It should be noted,
however, that the executable would register itself automatically without
specifying this switch.
Common Event Consumer Tasks Demonstrated by the Sample
Functions
Task: Handle an event
Implementation:
Consumer.cpp is the actual handler. The IndicateToConsumer()
method will be called with the event instance embedded.
Task: Creating the
handler object
Implementation:
factory.cpp creates the sink object. It is a simple class factory.
Task: Registering the
object and factory
Implementation:
PermEvents.cpp registers the sink object with its class factory in
the InitInstance() routine. The registration is revoked in the ExitInstance().
Task: Self-registering
an EXE
Implementation:
PermEvents.cpp has the RegisterServer() and UnregisterServer()
routines to show self-registration of an EXE. It is not the same as a DLL. The
'AppID' and 'RunAs' values allow the spawned EXE to run on the user's desktop
instead of the service's hidden desktop. Otherwise the Event Consumer would
never appear.
Build
Notes
Things
to remember when you're building your own WMI client application:
1.
If you want your client to
run on NT and non-DCOM versions of Windows 95, manually load the ole32.dll and
see if CoInitializeSecurity() exists. This routine won’t exist on Windows 95
installations that don’t have DCOM installed separately. If this routine
doesn't exist, the asynchronous routines in this sample won’t work because of
mismatched security level problems. The synchronous techniques will still work.
2.
If you don’t care about
non-DCOM versions of Windows 95, you can define
_WIN32_DCOM so that CoInitializeSecurity() is available for implicit
linking. Don't use _WIN32_WINNT to get this prototype since it won't compile
under the Windows 95/98 operating systems.
3.
In any case, the
CoInitializeSecurity() call (in InitInstance()) is required to work around a
security problem when WMI trying to call a Sink object but won't identify
itself. The CoInitializeSecurity() call turns off the authentication
requirement.
4.
WMI interfaces are defined
in wbemcli.h and wbemprov.h found in the wmi\include directory. You may #include both these files by
including just wbemidl.h located in the same directory.
5.
WMI interface CLSIDs are
defined in wbemuuid.lib. If you get unresolved externals in interfaces and
CLSIDs, this is what is missing.
6.
You'll need to link with
oleaut32.lib and ole32.lib to get the needed COM support.
7.
In the Link|Output settings,
specify 'wWinMainCRTStartup' as the entry point. This is per the Unicode
programming instructions.
8.
If you're using the makefiles, don't forget to set the VC vars. In
VC++ 5.0, it is VCVARS32.BAT.


Conversions from microseconds More...
Static Public Member Functions | |
| static float | Nanoseconds (float us) |
| Convert from microseconds to nanoseconds. More... | |
| static float | Ticks (float us) |
| Convert from microseconds to ticks. More... | |
| static float | Milliseconds (float us) |
| Convert from microseconds to milliseconds. More... | |
| static float | Seconds (float us) |
| Convert from microseconds to seconds. More... | |
| static float | Minutes (float us) |
| Convert from microseconds to minutes. More... | |
| static float | Hours (float us) |
| Convert from microseconds to hours. More... | |
| static float | Days (float us) |
| Convert from microseconds to days. More... | |
| static float | Hertz (float us) |
| Convert from microseconds to hertz. More... | |
Public Attributes | |
| const float | PER_NANOSECOND = 1 / Units.Nanoseconds.PER_MICROSECOND |
| Conversion factor from nanosecond to microseconds. More... | |
| const float | PER_TICK = 1 / Units.Ticks.PER_MICROSECOND |
| Conversion factor from ticks to microseconds; More... | |
| const float | PER_MILLISECOND = 1000 |
| Conversion factor from microseconds to microseconds. More... | |
| const float | PER_SECOND = PER_MILLISECOND * Units.Milliseconds.PER_SECOND |
| Conversion factor from seconds to microseconds. More... | |
| const float | PER_MINUTE = PER_MILLISECOND * Units.Milliseconds.PER_MINUTE |
| Conversion factor from minutes to microseconds. More... | |
| const float | PER_HOUR = PER_MINUTE * Units.Milliseconds.PER_HOUR |
| Conversion factor from hours to microseconds. More... | |
| const float | PER_DAY = PER_HOUR * Units.Milliseconds.PER_DAY |
| Conversion factor from days to microseconds. More... | |
Conversions from microseconds
Definition at line 6 of file Microseconds.cs.
|
inlinestatic |
Convert from microseconds to days.
| us | The number of us |
Definition at line 108 of file Microseconds.cs.
References Juniper.Units.Units.
|
inlinestatic |
Convert from microseconds to hertz.
| us | The number of us |
Definition at line 118 of file Microseconds.cs.
References Juniper.Units.Microseconds.Seconds(), and Juniper.Units.Units.
|
inlinestatic |
Convert from microseconds to hours.
| us | The number of us |
Definition at line 98 of file Microseconds.cs.
References Juniper.Units.Units.
|
inlinestatic |
Convert from microseconds to milliseconds.
| us | The number of us |
Definition at line 68 of file Microseconds.cs.
References Juniper.Units.Units.
|
inlinestatic |
Convert from microseconds to minutes.
| us | The number of us |
Definition at line 88 of file Microseconds.cs.
References Juniper.Units.Units.
|
inlinestatic |
Convert from microseconds to nanoseconds.
| us | The number of us |
Definition at line 48 of file Microseconds.cs.
References Juniper.Units.Units.
|
inlinestatic |
Convert from microseconds to seconds.
| us | The number of us |
Definition at line 78 of file Microseconds.cs.
References Juniper.Units.Units.
Referenced by Juniper.Units.Microseconds.Hertz().
|
inlinestatic |
Convert from microseconds to ticks.
| us | The number of us |
Definition at line 58 of file Microseconds.cs.
References Juniper.Units.Units.
| const float Juniper.Units.Microseconds.PER_DAY = PER_HOUR * Units.Milliseconds.PER_DAY |
Conversion factor from days to microseconds.
Definition at line 41 of file Microseconds.cs.
| const float Juniper.Units.Microseconds.PER_HOUR = PER_MINUTE * Units.Milliseconds.PER_HOUR |
Conversion factor from hours to microseconds.
Definition at line 36 of file Microseconds.cs.
| const float Juniper.Units.Microseconds.PER_MILLISECOND = 1000 |
Conversion factor from microseconds to microseconds.
Definition at line 21 of file Microseconds.cs.
| const float Juniper.Units.Microseconds.PER_MINUTE = PER_MILLISECOND * Units.Milliseconds.PER_MINUTE |
Conversion factor from minutes to microseconds.
Definition at line 31 of file Microseconds.cs.
| const float Juniper.Units.Microseconds.PER_NANOSECOND = 1 / Units.Nanoseconds.PER_MICROSECOND |
Conversion factor from nanosecond to microseconds.
Definition at line 11 of file Microseconds.cs.
| const float Juniper.Units.Microseconds.PER_SECOND = PER_MILLISECOND * Units.Milliseconds.PER_SECOND |
Conversion factor from seconds to microseconds.
Definition at line 26 of file Microseconds.cs.
| const float Juniper.Units.Microseconds.PER_TICK = 1 / Units.Ticks.PER_MICROSECOND |
Conversion factor from ticks to microseconds;
Definition at line 16 of file Microseconds.cs.