|
Undergradute Research
My undergraduate research involved working with mobile milli-robots for autonomous navigation, mapping and learning of complex terrains as well as control using sensory feedback.
I worked under Prof. Ronald Fearing in the Biomimetic Milli-systems Lab part of Berkeley AI Research (BAIR)
|
|
Body Lift and Drag for a Legged Millirobot in Compliant Beam Environment
Cem Koc*,
Can Koc*,
Brian Su*,
In ICRA 2019
poster,
arxiv,
video
Much current study of legged locomotion has rightly focused on foot traction forces, including on granular media.
Future legged millirobots will need to go through terrain, such as brush or other vegetation, where the body contact
forces significantly affect locomotion. In this work, a (previously developed) low-cost 6-axis force/torque sensing
shell is used to measure the interaction forces between a hexapedal millirobot and a set of compliant beams, which
act as a surrogate for a densely cluttered environment. Experiments with a VelociRoACH robotic platform are used to
measure lift and drag forces on the tactile shell, where negative lift forces can increase traction, even while drag
forces increase. The drag energy and specific resistance required to pass through dense terrains can be measured.
Furthermore, some contact between the robot and the compliant beams can lower specific resistance of locomotion.
For small, light-weight legged robots in the beam environment, the body motion depends on both leg-ground and
body-beam forces. A shell-shape which reduces drag but increases negative lift, such as the half-ellipsoid used,
is suggested to be advantageous for robot locomotion in this type of environment.
|
|
Projects
Here you can find some of the projects I've worked on. I am very interested in developing projects which are at the intersection of Machine Learning,
Reinforcement Learning and Distributed/Parallel Computing
|
|
Parallelizing Chamfer Distance Computation for Point Cloud Similarity
slide,
report
Given the increasing prevalence of point cloud data from LiDAR sensors, efficient processing and manipulation of
massive amounts of this data have become credibly important. Chamfer distance metric is an important similarity metric
when doing comparisons between two point clouds scans often of the same scene. We developed a way to make this
computation much faster by re-structing the problem as a parallel computing problem. We utilized both kdtree and octrees
to avoid the quadratic nature of the problem and implemented four different Chamfer distance algorithms including
two approximation based algorithms using OpenMP and CUDA to explore the potential for increased computational efficiency.
We compared it with the naive implementation and saw that our implementations on both OpenMP and CUDA significantly improved
the naive implementations boding well for consumer or industry applications.
|
|
Detecting Faces in Animes Using Supervised Domain Adaptation
Cem Koc*,
Can Koc*,
Brian Su*,
poster,
report
Wanted to try out domain adaptation and transfer learning.
Created an entirely new anime images dataset from frames and hand-labelled whether they contain a face or not.
Fine tuned GoogleNet, AlexNet, VGGFace models (pre-trained on ImageNet 2014) with this anime faces dataset and
confirmed that we can achieve ~97% accuracy.
|
|
Prioritized Experience Replay Kills on ViZDoom
poster
Implemented Prioritized Experience Replay from 2016 paper
from Schaul et al. to play on ViZDoom. Prioritized Experience replay
significantly reduced the training time on both dense and sparse reward games when compared
to the DQN paper.
This was trained on a single GPU using TF Keras.
|
|


WITNESS LIST Finance Committee February 24, 1997-9:30A SB 244 For: Bier, Samuel (VFW), Austin On: Caldwell, Jane (THECB), Austin Written materials submitted: For: McDonald, Don (Self), Ft. Worth Rodriguez, Joe (Self), Arlington SB 474 For: Reid, James L. (Self), Bryan Registering, but not testifying: On: Caldwell, Jane (THECB), Austin SB 581 On: Kiplin, Kimberly (TX Lottery Commission), Austin Rinn, James (TX Lottery Commission), Austin SB 582 On: Garlick, David (Railroad Commission), Austin Registering, but not testifying: For: Bradshaw, Jimmy W. (TIPRO, NTOGA, BB&S), Graham Hinkle, Dan (Burlington Resources), Austin Melzer, Steve (Permian Basin Petr Assc), Midland Perry, Steve (Texaco), Austin Rigsbee, Ken (Phillips Petr Company), Austin Roberrson, Dan (ARCO Permian), Austin Sebree, Ben (TX Mid-Continent O/G As), Austin Wilhelm, Jack M. (Mobil Oil Corp), Austin SB 584 Against: Hinojosa, Walter (TX AFL-CIO), Austin On: Boucher, Terry (TOMA), Austin Henry, Daffney (TXDOT), Austin Reed, Cassie Carlson (TXDOT), Austin Registering, but not testifying: On: Williams, Cathy J. (TXDOT), Austin


class Azure::Core::Http::HttpResponse
A small proxy to clean up the API of Net::HTTPResponse.
Attributes
uri[RW]
Public Class Methods
new(http_response, uri='')
click to toggle source
Public: Initialize a new response.
http_response - A Net::HTTPResponse.
# File lib/azure/core/http/http_response.rb, line 26 def initialize(http_response, uri='') @http_response = http_response @uri = uri end
Public Instance Methods
body()
click to toggle source
Public: Get the response body.
Returns a String.
# File lib/azure/core/http/http_response.rb, line 36 def body @http_response.body end
exception()
click to toggle source
Public: Get an error that wraps this HTTP response, as long as this response was unsuccessful. This method will return nil if the response was successful.
Returns an Azure::Core::Http::HTTPError.
# File lib/azure/core/http/http_response.rb, line 67 def exception HTTPError.new(self) unless success? end
Also aliased as: error
headers()
click to toggle source
Public: Get all the response headers as a Hash.
Returns a Hash.
# File lib/azure/core/http/http_response.rb, line 58 def headers @http_response.headers end
status_code()
click to toggle source
Public: Get the response status code.
Returns a Fixnum.
# File lib/azure/core/http/http_response.rb, line 43 def status_code @http_response.status end
success?()
click to toggle source
Public: Check if this response was successful. A request is considered successful if the response is in the 200 - 399 range.
Returns nil|false.
# File lib/azure/core/http/http_response.rb, line 51 def success? @http_response.success? end


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A list of banks in Uganda and their swiftcodes – Techjaja
by pages/Techjaja/291142381017322People who do international bank transfers have always been asked for their local bank or recepient’s swifcode. A Swift Code is a standard format of Bank Identifier Codes (BIC) and it is unique identification code for a particular bank. These codes are used when transferring money between banks, particularly for international wire transfers. Banks also used the codes for exchanging other messages between them.
It should be noted that when you send or receive an international wire with your bank, you might lose money on a bad exchange rate and pay hidden fees as a result.
Format of a swiftcode
The Swiftcode consists of 8 or 11 characters. When 8-digits code is given, it refers to the primary office. The code formatted as below;
- Advertisement -
AAAA BB CC DDD
- First 4 characters – bank code (only letters)
- Next 2 characters – ISO 3166-1 alpha-2 country code (only letters)
- Next 2 characters – location code (letters and digits) (passive participant will have “1” in the second character)
- Last 3 characters – branch code, optional (‘XXX’ for primary office) (letters and digits)
Here is a list of all the swiftcodes for banks in Uganda
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{% extends "base.html" %} {% block page_id %}contact{% endblock %} {% block content %}
I will fill this out with more information soon, but you can reach me at jlongster@jlongster.com.


Radio 1's Live Lounge The Best Of 2cds 2011 + covers 320@BSBT
http://hmv.com/hmvweb/displayProductDetails.do?sku=33101
Disc One
1. Chasing Pavements-Adele
2. Love Machine-Arctic Monkeys [Originally By Girls Aloud]
3. Fans-Kings Of Leon
4. Poker Face-Lady Gaga
5. Pass Out-Plan B [Orginally By Tinie Tempah]
6. Hate That I Love You-Rihanna
7. That's Not My Name-Dizzee Rascal [Originally By The Ting Tings]
8. Boom Boom Pow-Black Eyed Peas
9. She Said-Jason Derulo [Originally By Plan B]
10. Little Lion Man-Taio Cruz [Originally By Mumford & Sons]
11. Ready For The Weekend-Calvin Harris
12. Dirrtylicious-Keane [Bootilicious Originially By Destinys Child / Dirrty Originally By Christina Aguilera]
13. The Man Who Can't Be Moved / Breakeven-N-Dubz [Originally By The Script]
14. I Kissed A Girl-McFly [Originally By Katy Perry]
15. Since U Been Gone-Kelly Clarkson
16. Sexy Chick-Paloma Faith [Originally By David Guetta Feat. Akon]
17. Wonderful Life-Kylie Minogue [Originally By Hurts]
18. Beautiful Day-U2
19. Songbird-Oasis
20. One Day Like This-Elbow
Disc One
1. Chasing Pavements-Adele
2. Love Machine-Arctic Monkeys [Originally By Girls Aloud]
3. Fans-Kings Of Leon
4. Poker Face-Lady Gaga
5. Pass Out-Plan B [Orginally By Tinie Tempah]
6. Hate That I Love You-Rihanna
7. That's Not My Name-Dizzee Rascal [Originally By The Ting Tings]
8. Boom Boom Pow-Black Eyed Peas
9. She Said-Jason Derulo [Originally By Plan B]
10. Little Lion Man-Taio Cruz [Originally By Mumford & Sons]
11. Ready For The Weekend-Calvin Harris
12. Dirrtylicious-Keane [Bootilicious Originially By Destinys Child / Dirrty Originally By Christina Aguilera]
13. The Man Who Can't Be Moved / Breakeven-N-Dubz [Originally By The Script]
14. I Kissed A Girl-McFly [Originally By Katy Perry]
15. Since U Been Gone-Kelly Clarkson
16. Sexy Chick-Paloma Faith [Originally By David Guetta Feat. Akon]
17. Wonderful Life-Kylie Minogue [Originally By Hurts]
18. Beautiful Day-U2
19. Songbird-Oasis
20. One Day Like This-Elbow
| Files | Size |
|---|---|
| 1. Adele Chasing Pavements.mp3 | 7.88 MB |
| 10. Taio Cruz Little Lion Man (Originally by Mumford & Sons).mp3 | 7.08 MB |
| 11. Calvin Harris Ready For The Weekend.mp3 | 8.38 MB |
| 12. Keane Dirrtylicious (Bootilicious Originally by Destiny's Child Dirrty originally by Christina Aguilera).mp3 | 6.52 MB |
| 13. N-Dubz The Man Who Can’t Be Moved Breakeven (Originally by The Script).mp3 | 6.52 MB |
| 14. McFly I Kissed A Girl (Originally by Katy Perry).mp3 | 7.38 MB |
| 15. Kelly Clarkson Since U Been Gone.mp3 | 7.41 MB |
| 16. Paloma Faith Sexy Chick (Originally by David Guetta feat. Akon).mp3 | 9.49 MB |
| 17. Kylie Minogue Wonderful Life (Originally by Hurts).mp3 | 7.82 MB |
| 18. U2 Beautiful Day.mp3 | 11.61 MB |
| 19. Oasis Songbird.mp3 | 4.94 MB |
| Trackers | Seeds | Leechs | Downloads |
|---|---|---|---|
| udp://tracker.openbittorrent.com:80/announce | 22 | 2 | 10 |
| udp://tracker.publicbt.com:80/announce | 15 | 3 | 10 |
| udp://tracker.istole.it:80/announce | 44 | 7 | 10 |
| http://sceneunderground.org/announce.php | 4524 | 792 | 9229 |
Comments (0)
| There are no comments. |


AWS::S3::BucketPolicy
The AWS::S3::BucketPolicy type applies an Amazon S3 bucket policy to an Amazon S3 bucket.
AWS::S3::BucketPolicy Snippet: Declaring an Amazon S3 Bucket Policy
Topics
Syntax
To declare this entity in your AWS CloudFormation template, use the following syntax:
JSON
{ "Type" : "AWS::S3::BucketPolicy", "Properties" : { "Bucket" :
String
, "PolicyDocument" :JSON
} }
YAML
Type: AWS::S3::BucketPolicy Properties: Bucket:
String
PolicyDocument:JSON
Properties
Bucket
-
The name of the Amazon S3 bucket to which the policy applies.
Required: Yes
Type: String
You cannot update this property. If you want to add or remove a bucket from a bucket policy, you must modify your AWS CloudFormation template by creating a new bucket policy resource and removing the old one. Then use the modified template to update your AWS CloudFormation stack.
PolicyDocument
-
A policy document containing permissions to add to the specified bucket. For more information, see Access Policy Language Overview in the Amazon Simple Storage Service Developer Guide.
Required: Yes
Type: JSON object
Update requires: No interruption
Examples
Bucket policy that allows GET requests from specific referers
The following sample is a bucket policy that is attached to the
myExampleBucket
bucket and allows GET requests that originate from
www.example.com
and example.com
:
JSON
"SampleBucketPolicy" : { "Type" : "AWS::S3::BucketPolicy", "Properties" : { "Bucket" : {"Ref" : "myExampleBucket"}, "PolicyDocument": { "Statement":[{ "Action":["s3:GetObject"], "Effect":"Allow", "Resource": { "Fn::Join" : ["", ["arn:aws:s3:::", { "Ref" : "myExampleBucket" } , "/*" ]]}, "Principal":"*", "Condition":{ "StringLike":{ "aws:Referer":[ "http://www.example.com/*", "http://example.com/*" ] } } }] } } }
YAML
SampleBucketPolicy: Type: AWS::S3::BucketPolicy Properties: Bucket: Ref: "myExampleBucket" PolicyDocument: Statement: - Action: - "s3:GetObject" Effect: "Allow" Resource: Fn::Join: - "" - - "arn:aws:s3:::" - Ref: "myExampleBucket" - "/*" Principal: "*" Condition: StringLike: aws:Referer: - "http://www.example.com/*" - "http://example.com/*"


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Kunal Chaudhary
Chairperson
Leadership is not a position or a title, it is action and example. He stands with zeal to achieve his vision of making CodeChef-VIT a memorable chapter. His unique way of mentoring brings the entire team together and forms a bond to be cherished for a lifetime.


Damning Tongaat Hulett forensic report fingers ex-executives, including Peter Staude
Group plans to pursue claims against executives implicated in PwC probe
by Suren NaidooBeleaguered sugar and property group Tongaat Hulett released the “key findings” of PwC’s forensic investigation into the group’s financials and operations today, which points to serious accounting irregularities and corporate governance failures under the group’s former CEO Peter Staude and other senior executives.
Read: Tongaat Hulett’s former CEO poorer after share price dive
Tongaat Hulett’s overhauled board, under new chairman Louis von Zeuner, says it “intends to pursue claims against certain individuals who appear to have been responsible for, or party to, the undesirable activities outlined in the PwC Report”.
Former Tongaat Hulett CEO Peter Staude.
Besides Staude, Tongaat Hulett’s former CFO Murray Munro and the group’s former managing director of its Zimbabwean operations, Sydney Mtsambiwa have also been fingered in the report.
Other senior divisional directors and managers also cited include:
- John Chibwe, finance director of Hippo Valley Estates;
- Michael Deighton, former managing director of Tongaat Hulett Developments (THD);
- Steve Frampton, former Zimbabwe Sugar sale general manager;
- Shelton Nhari, a director at Triangle Finance;
- Les Munro, former finance executive of Tongaat Hulett SA Sugar;
- Raphael Pfunye, sales finance executive at Zimbabwe Sugar; and,
- Sean Slabbert, a former finance executive within Tongaat Hulett group.
Tongaat Hulett has opted not to publish the full PwC Report, saying that it is “subject to legal privilege and other confidentiality restrictions”. However, its board has decided to release a document on its website with key findings from the report “in the interest of transparency”. The document also highlights how Tongaat Hulett’s board plans to deal with the issues raised in the report.
Questionable or irregular practices highlighted in the PwC Report included:
- Early or premature recognition of land sales within Tongaat Hulett Developments (THD), the group’s land conversion and property development division.
- Overstating the value of the group’s carrying amount of cane roots and standing cane related assets
- The overstatement of sugar sales in Zimbabwe
- Overstating the carrying amount of capital work in progress
- The incorrect capitalisation of infrastructure costs
- Overstating projected revenue
- The provision of cash collateral in relation to a land sale
Read: Tongaat Hulett: Developments division under scrutiny
Tongaat Hulett said that disciplinary action has been or is being taken in relation “to certain of the senior executives” referred to in the PwC Report as well as other individuals.
“From the PwC Investigation, it appears that personal financial enrichment of key senior employees was largely limited to the financial incentives paid to them during the years in which they achieved their employment targets,” Tongaat Hulett said.
Read: New chair and rash of changes for Tongaat board
The group is looking to recoup some of these bonuses. It also noted that from a criminal law perspective, its board is engaging with the South African Police and the National Prosecuting Authority.
Following Gavin Hudson’s appointment as new Tongaat Hulett CEO in February 2019, the group embarked on a comprehensive strategic and financial review aimed at turning around the company.
Gavin Hudson, CEO Tongaat Hulett. Image: Supplied
An initial review of the group’s financials by the new management team, under Hudson, raised questions around the group’s previous business, accounting and other practices. PwC was then brought in to undertake a full forensic investigation into the group’s operations in March. This led to Tongaat announcing in May that it would need to restate is 2018 financial results and delay its 2019 results.
“It soon became clear that, over and above the operational difficulties facing Tongaat Hulett, there was insufficient internal accountability, governance and financial oversight,” the group notes in its key finding document.
With the PwC Report finalised and handed to a special Tongaat Hulett board committee that includes Hudson, von Zeuner and fellow board member Linda de Beer, the group is now set to finally release its restated results for its 2018 and the delayed 2019 results in the second week of December.
“The Board Committee and Tongaat Hulett’s advisors have reviewed the content of the PwC Report to prepare for litigation by and against Tongaat Hulett Limited, to understand the impact on the financial results for the financial years ended 31 March 2018 and 2019; and, to determine whether further investigation is required and what remedial action to take,” the group noted (in the findings document on its website).
Tongaat Hulett said that PwC’s investigation identified major historical shortfalls in a number of important areas, including, governance practices, delegation of authority, decision-making, oversight, financial discipline, record keeping, systems usage and financial reporting.
The Board Committee’s assessment of PwC’s key findings is as follows:
- certain senior executives initiated or participated in undesirable accounting practices that resulted, amongst others, in revenue being recognised in earlier reporting periods than it should have been, and in expenses being inappropriately capitalised to assets. This resulted in profits in the respective years being overstated, and in the overstatement of certain assets in THL‘s financial statements;
- there was a culture of deference and lack of challenge at THL that resulted in employees following instructions on accounting practices, without questioning the basis for those accounting practices; and
- there were a number of governance failures pursuant to which internal policies, guidelines and frameworks were not followed, creating an environment in which senior executives could initiate or participate in the financial reporting misstatements.


Women SHGs to get impounded stray cattle
by Special CorrespondentThe corporation, which keeps impounded stray cattle in authorised cowsheds and release them on payment of fine by owners, has decided to hand them over to women self-help groups.
In a statement, Corporation Commissioner G. Kannan said the urban local body, based on an instruction from Collector Shilpa Prabhakar Satish, was impounding stray cattle and handing them over to cowsheds here. On payment of fine of ₹ 5,000 a cattle by the owner, the impounded animals are released.
Since a good number of owners are yet to pay the fine to get their animals back, they are advised to do so before November 24, after which the unclaimed animals will be handed over to women self-help groups so that they can raise them as part of their income generation programme.
Moreover, the corporation has decided to straight away give the impounded animals to women self-help groups hereafter instead of handing them over to cowshed and returning them back to owners on payment of fine. So, owners are advised to keep their domestic animals within their premises to avoid such action, he said.


Messages array:
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Med Spring Gala Performances
I enjoy playing the piano, and have performed on a few occasions at the UBC Medicine Spring Gala. Here are the associated recordings.
- Trio in D Minor, Op. 49 no. 1, Mvt. I (Abridged) by Felix Mendelssohn, performed with Sabine Lague (on flute) and Santa Ono (on cello).
- Sonate pour flûte et piano (Flute Sonata), FP 164, by Francis Poulenc, performed with Sabine Lague (on flute).
- “Un Sospiro,” 3 Études de concert, S.144 No. 3 (Liszt, Franz)


{include file="../application/index/view/Public/header.html" /}
{include file="../application/index/view/Public/manage_nav.html" /}
账号
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Los Angeles is a city that has always been about transformation. This is the place where dreams really do come true, for actors and Olympians, young and old, Americans and foreign-born. Today’s LA is a city with a vibrant and growing downtown of residents, restaurants and recreation; a sparkling beach community of artists and tech entrepreneurs; a city connected to itself and the world by a river being restored, a vast new rail system and a reimagined international airport.
The Southern California Committee for the Olympic Games (SCCOG), in collaboration with LA Mayor, Mayor Eric Garcetti and his team, is proud to put forward LA as a US candidate city for the 2024 Olympic and Paralympic Games. We will know later this year if LA is to be considered a US bid city by the United States Olympic Committee (USOC). For more information, and to show your support, sign up below.


Any Given Sunday: Stone Cold by Curtis Edmonds --<EMAIL_ADDRESS>14 January 2000
Mr. Oliver Stone Hollywood, California 90210
Dear Mr. Stone:
I am writing you, well, because I figure I owe you some money. You see, I went to see your new movie, Any Given Sunday, the other night. This particular theater shows movies that are in their second or third week of release in this annex on the other side of the parking lot, and there's usually just one person there who takes tickets and serves popcorn. Anyway, when I walked into the annex, that person wasn't anywhere to be found. (I confess I didn't look that hard.) So, I walked right into the theater without paying a dime. I'm hoping that you'll tell me exactly how much of a fraction you'd receive out of the $7 dollars I didn't pay to see your movie, so I can send it to you.
I'm not all that worried about reimbursing the actors (especially Barry Switzer), or the theater, or the crew or your investors or whoever else it is that gets pieces of the pie. I do, however, want to salute you, from the bottom of my heart, for your outstanding efforts in creating Any Given Sunday -- probably the best cinematic practical joke of the year.
It took me a while to figure out what you were doing, of course, as you'd expect. Why, I walked out of Any Given Sunday convinced that it was a horrible movie, a cliche-ridden mess with hideous, screeching performances. At first, the whole thing looked like it was edited by a pack of genius high school sophomores anxious to try every bell and whistle on their Ronco Edit-O-Matic. And the apparent lapses of logic and common sense were just so grating. (Not to mention that you managed to put he-men football heroes like Al Pacino and Dennis Quaid in aprons.) How was I to know what you were thinking?
I didn't figure it out until I started writing an amateur movie review for the movie. I started focusing on your portrayal of the head coach, played by Al Pacino. This baffled me no end, Mr. Stone, let me tell you. The media hype you orchestrated had us believe that Any Given Sunday would blow the lid of the NFL, that it would be a searing expose of the seamy side of pro football.
And of course, it is, partly. We see all the things we expect to see -- drugs, women, letting players pay with potentially life-threatening injuries. But none of this is surprising, not really, and it would be hard for you to outdo the really embarrassing things that have already gone on the NFL's extensive police blotter this year anyway. (What is really surprising, to me anyway, is that everybody in the movie is really, really unhappy, in a be-careful-what-you-wish-for way.)
Anyway, one part of the seamy side -- as made apparent in the firings this week of real life coaches Chan Gailey and Ray Rhodes -- is the lack of loyalty shown by owners to hard-working coaches whose talent base has, through no fault of their own, eroded. You explore this quite a bit, but I was having problems with your take. What had me fooled is this: we never see Pacino work. Oh sure, he stands on the sidelines and yells. He talks to players in an avuncular tone when needed and delivers kick-butt sideline pep talks. But -- as you know -- this is not really what coaches do. Coaches work like dogs all the year 'round -- running practices and team meetings and looking at film and working up game strategy and designing plays and dealing with the media and God knows what. Pacino doesn't do any of that.  The only time he gets to talk football is with the team owner, the whiny Cameron Diaz. ("The running game!' Diaz complains loudly. "You're always talking about the boooring running game. Throw some more touchdown passes.")
Since Any Given Sunday begins with a quote from Vince Lombardi, it might be easy to assume that you mean for audiences to identify the Pacino character with Lombardi, or Shula, or Paul Brown, or any of the great coaches of the past. (As a Cowboys fan, I appreciated your having a Landryesque coach on one of the opposing teams.) And then I realized: Pacino's not supposed to represent these other coaches.
He's supposed to represent you.
That's it, isn't it? I mean, it's obvious if you think about it. Football head coaches and movie directors are two peas in a pod. You both work too hard and have to deal with burnout. Most of your real work is behind the scenes on the necessary drudgery of your professions. You both have to deal with prima-donna actors/quarterbacks. You both have to deal with team owners/studio bosses who don't really understand what you do, don't give you the resources you need, and who scream a you every time you don't put forth a winning effort. And you have to deal with fans and reporters who don't really understand what you do, either, but are the first to criticize you about it.
And... ultimately, you're the ones that are responsible if things go bad. Just as NFL owners fire the coach instead of firing the team, it's the director, rather than the actors, who is at fault if the movie fails. (And the actors/players are the ones who get the most credit for victories, aren't they? Ask Rob Reiner if you don't know what I'm talking about.)
This explains why there's so much glitz and so little substance to the Pacino character, too.  Of course not.
So -- if I'm right, and this is just a practical-jokey way of making a biopic about yourself, then it really doesn't matter that the rest of the movie stinks. The horrible editing, the annoying, whiny performances, (excepting Jim Brown as the defensive coordinator and Jamie Foxx as the young QB) and the wretchedly cliched final game sequence -- all of these things are extraneous. We shouldn't worry about them. We should just understand the code and enjoy the movie from there, right? Right?
Because... well... if I'm wrong, and this is not some sort of joke you're pulling, that means that this is a really, really bad film with nothing to redeem it, and... well... that just isn't possible.
Anyway, thank you for taking the time to read this letter, and thank you once again for filming part of your movie in Texas. The film industry is very important ot the Lone Star State, and your movies like Born on the Fourth of July and JFK have been milestones in our state's cinematic history. Good luck on your next picture.
Sincerely,
Curtis D. Edmonds Film Critic
P.S. Oswald acted alone.
-- Curtis Edmonds<EMAIL_ADDRESS>Movie Reviews: http://www.hsbr.org/buzz/reviewer/reviews/bdreviews.html
"The question we need to ask: Is our children learning?" -- Governor George W. Bush
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


| filehandle/stream = a valid variable set with the OpenFile, ReadFile command, or OpenTCPStream (v1.52+). The value returned is a text string. |
|
Once you've opened a disk file (or stream) for reading, use this command to read a whole line of text from a text file or stream. Each line of text is returned as a string variable. This function can be used to read plain text files.
Characters are read from the input file until an "end-of-line" mark is found. An "end-of-line" can be a single carriage return (0Dh) or a single linefeed (0Ah) or carriage return followed by a linefeed (0Dh, 0Ah). Reading beyond the end of file does not result in an error, but each value read will be a zero length string. ReadLine$ returns all chars except chr$(13)/chr$(10). Streams can only be used in Blitz Basic v1.52 or greater. |
|
; Reading and writing a file using ReadLine$ and WriteLine functions
; Initialise some variables for the example String1$ = "Line 1 is short" String2$ = "Line 2 is a longer line but they can be much longer" String3$ = "Line 3 is made up " String4$ = "of two parts joined together." ; Open a file to write to fileout = WriteFile("mydata.txt") ; Write the information to the file WriteLine( fileout, String1 ) WriteLine( fileout, String2 ) WriteLine( fileout, String3 + String4) WriteLine( fileout, "Just to show you don't have to use variables" ) ; Close the file CloseFile( fileout ) ; Open the file to Read filein = ReadFile("mydata.txt") Read1$ = ReadLine$( filein ) Read2$ = ReadLine$( filein ) Read3$ = ReadLine$( filein ) Read4$ = ReadLine$( filein ) ; Close the file once reading is finished CloseFile( filein ) Print "Lines of text read from file - mydata.txt " Print Read1 Print Read2 Print Read3 Print Read4 WaitKey() |


Byungchan's 'Icepray' endorsement mentions Seungwoo in their 'prayers,' touching fans' hearts
by jennywill'Icepray' is doing marketing right.
VICTON's Byungchan is the endorsement model for 'Icepray', a facial spray that is soothing and cooling. One of their taglines is 'Icepray for...', where both Byungchan, the brand, and consuming fans write whatever they're 'praying' for.
As VICTON's comeback is just a few hours away, 'Icepray' took to their Twitter to post 7 different 'Icepray for..' tags: one fore each member, including Seungwoo. Seungwoo is currently serving as the leader of X1 as one of the finalists of 'Produce X 101', but VICTON and their fans have been missing their leader a lot. The mention by 'Icepray' has touched many fans' hearts, who are thanking 'Icepray' for including him.
VICTON will be back later tonight at 6PM KST with 'Nostagia'.


setNet
Use the Set MAC Address tool to set your MAC address and optionally a static IP address.
The dev kit board Ethernet port doesn't have a permanent MAC address. Each time the target starts, a locally administered MAC address is generated. With DHCP, this will likely also cause your IP address to change. With a static IP address, the IP address won't change, but switching equipment may result in errors because the IP address is being used by a new target.
If you're confident editing the /etc/network/interfaces
file (e.g., created some up/down rules for eth0
), then you probably don't want to use setNet
(it won't merge well). You can restore your /etc/network/interfaces
file after running setNet:
/etc/network/interfaces
is renamed with a date and time suffix.
Usage
setNet [macrandom|macfixed] [dhcp|static] [address ] [netmask
Use macfixed
to set the dev board to use the MAC provided at boot time, and continue to use DHCP for the IP address.
Use static
to set a fixed MAC regardless of whether macrandom
is provided (setNet
static
is equivalent to setNet
macfixed
static
).
If logged in over ssh
, close the session quickly as ssh tends to hang. Then run ssh like this:
ssh root@'setNet '
- Note
setNet
only supports IPv4 addresses at the moment. No address, gateway or netmask values validation is done.
To display usage, run
setNet --help
Copyright (C) Sierra Wireless Inc. Use of this work is subject to license.


Serialized Form
|
Package net.sf.extjwnl.data |
serialVersionUID: 4L
adjectivePosition
AdjectivePosition adjectivePosition
serialVersionUID: 4L
isAdjectiveCluster
boolean isAdjectiveCluster
- for use only with WordNet 1.6 and earlier
serialVersionUID: 4L
readObject
private void readObject(ObjectInputStream in)
throws IOException,
ClassNotFoundException
- Throws:
IOException
ClassNotFoundException
serialVersionUID: 4L
pos
POS pos
lemma
String lemma
- The excepted word
exceptions
List<E> exceptions
serialVersionUID: 4L
writeObject
private void writeObject(ObjectOutputStream oos)
throws IOException
- Throws:
IOException
pos
POS pos
- This word's part-of-speech
lemma
String lemma
- The string representation of this IndexWord
synsetOffsets
long[] synsetOffsets
- senses are initially stored as offsets, and paged in on demand.
serialVersionUID: 4L
writeObject
private void writeObject(ObjectOutputStream oos)
throws IOException
- Throws:
IOException
pointerType
PointerType pointerType
targetIndex
net.sf.extjwnl.data.Pointer.TargetIndex targetIndex
source
PointerTarget source
- The source of this pointer. If the pointer applies to all words in the
parent synset, then
source
and synset
are the same,
otherwise source
is the specific Word
object that
this pointer applies to.
serialVersionUID: 4L
serialVersionUID: 4L
readObject
private void readObject(ObjectInputStream in)
throws IOException,
ClassNotFoundException
- Throws:
IOException
ClassNotFoundException
pos
POS pos
pointers
net.sf.extjwnl.data.Synset.PointerList pointers
offset
long offset
- The offset of this synset in the data file.
words
net.sf.extjwnl.data.Synset.WordList words
- The words in this synset.
gloss
String gloss
- The text (definition, usage examples) associated with the synset.
lexFileNum
long lexFileNum
- The lexicographer file name id.
serialVersionUID: 4L
verbFrameFlags
BitSet verbFrameFlags
- A bit array of all the verb frames that are valid for this word.
see
VerbFrame
for more explanation.
serialVersionUID: 4L
index
int index
serialVersionUID: 4L
verbFrameFlags
BitSet verbFrameFlags
serialVersionUID: 4L
readObject
private void readObject(ObjectInputStream in)
throws IOException,
ClassNotFoundException
- Throws:
IOException
ClassNotFoundException
synset
Synset synset
- The Synset to which this word belongs.
index
int index
- This word's index within the synset.
NB Word numbers are assigned to the word fields in a synset, from left to right, beginning with 1
lemma
String lemma
- The string representation of the word.
lexId
int lexId
- The integer that, when appended onto lemma, uniquely identifies a sense within a lexicographer file.
lex_id numbers usually start with 00, and are incremented as additional senses of the word are added
to the same file, although there is no requirement that the numbers be consecutive or begin with 00.
Note that a value of 00 is the default.
summary
String summary
- The summary of this word.
useCount
int useCount
- The number of times each tagged sense occurs in a semantic concordance.
|
Package net.sf.extjwnl.data.list |
|
Package net.sf.extjwnl.data.relationship |
shallowestIndex
int shallowestIndex
- The index of the shallowest relationship.
deepestIndex
int deepestIndex
- The index of the deepest relationship.
|
Package net.sf.extjwnl.util.factory |
name
String name
Copyright © 2013. All Rights Reserved.


In place editing with a datepicker!
It is hosted and documented on Github and advertised on TheSingularity blog.
Tested on IE7+, Opera, Safari, Chrome and Firefox.
Hover over the following date » 05/30/2011 « and click it to enable in place editing. Use [enter] to confirm, [esc] to cancel, or pick the day from the datepicker.
$( '.editable' ).editable( {
type: 'datepicker'
} );


UK reduces its terror threat level to substantial
by EndPlay
LONDON (AP) - Britain's Home Office chief has downgraded the terrorism threat level from severe to "substantial."
Home Secretary Priti Patel's decision Monday means that the U.K. now sees an attack as likely rather than highly likely.
The threat level has been rated as severe for the past five years.


JNTU Anantapur College of Engineering, Kalikiri was established in the year 2013 and works as constituent college for JNTU Anantapur. This college functions with permanent buildings accommodating the Administrative block, Academic block, Laboratories, Library and the Hostel blocks. Fortified with traditional values and dedicated spirit of the staff and the students, we strongly believe it would rise up to any occasion or meet any challenge confidently.
This college has a vast campus sprawling over an area of 110 acres, in a peaceful atmosphere away from the dust and din of the town. The college is just 75km away from Tirupati & Chittoor and is well connected to the cities like Chennai and Bengaluru. With this geographical arena, it joins hands with the JNT University, Anantapur to fulfil the technical need of our fast-developing country with highly professional and well trained graduates.
The vision of college is to produce quality technical graduates and better leaders of the society. With this vision, it is bound to go forward crossing all the possible hurdles in development.
The department of Civil Engineering was established in the year 2013 with an intake of 60 students. The department has well equipped laboratories with modernised machinery. CESA – Civil Engineering Students’ Association was formed by the students to conduct workshops, symposiums and other activities which aid in improving the technical and professional skills of students.
The department has good faculty with experience, knowledge, love and concern towards well and wish of the students.
The college is proud and privileged to have Dr. B Eswara Reddy as the Principal. Sir graduated B.Tech. (CSE) from Sri Krishnadevaraya University in 1995. He received his master degree in Software Engineering from JNTU Hyderabad. He was conferred with PhD in CSE from JNTU Hyderabad in 2008. He guided 3 researchers for PhDs. He served as Assistant Professor from 1996 to 2006 and as Associate Professor from 2006 to 2012 in JNTU Anantapur College of Engineering, Anantapur. He was promoted as Professor in CSE Department in 2012. He published more than 50 papers in various international journals and conferences.
K Ramu, M.Tech. in IIT Guwahati
Smt. V Saritha
Smt. K Aswani
Y Krishna Lohith Reddy
M Mahesh Reddy
S Farhatunnisa
Smt. S Alfiya
S Dadapeer


Phillies Questionnaire
By Johnny Persichilli
A qualifying offer would consist of the following terms:
This contract is in the {{qualOfferPercentile}} percentile
This table shows the statistics for the hitters with salaries closest to the qualifying offer of ${{qualifyingOffer}} for comparison.
The first row uses machine learning to predict the productivity of a hitter with a salary of the qualifyingOffer.
Hitters with comparable salaries
This table shows the statistics for the hitters with salaries closest to the qualifying offer of ${{qualifyingOffer}} for comparison.
The first row uses machine learning to predict the productivity of a hitter with a salary of the qualifyingOffer.
Pitchers with comparable salaries
Top 10 Salaries


--- title: "Week 5 In Class Lab" author: "Jarrod Shingleton" date: "4/23/2020" output: md_document ---
(Logistic Regressions) Overview
In this practical exercise, you'll first run through a logistic regression exercise on a small, well-known dataset so that you can see how to use the code to develop a logistic regression model. Let's get started.
First, as usual, set the working directory.
setwd()
Remember, if you can't get the setwd (set working directory) function to work, try ?setwd to get some additional help.
Since we'll be conducting two different analyses for this practical exercise (including the take home lab), you'll need to create a new script file for the class examples. For this week, we will work on classifications, so let's make a new R file called "LectureExampleData2" for In-Class lab and "LabExampleData2" for the Take-Home Lab.
This week, we will use the R packages ggplot2, gridExtra, and caret. You need to install them by using the install.packages() function.
To begin, we will clear our current workspace (get rid of any saved variables) and load a few packages that we'll use for the example analysis. Cut and paste the following commands into your new script file and run them:
rm(list=ls()) ##clears the environment
## Library for An Introduction to Statistical Learning with Applications in R
library(ISLR)
## Libraries for Plotting our Results
library(ggplot2)
library(gridExtra)
## Library for confusion matrix
library(caret)
## Loading required package: lattice
Note that if you look in the "Environment" window in your upper right hand column, all of the objects you created with your previous code have been erased from memory. You are restarting with a blank slate.
Now, let's bring in the dataset that we'll be working with for the example. this is a simulated datasheet titled "Default" containing information on ten-thousand credit customers provided in ISLR. We will use the "Default" dataset to illustrate the concept of classification by fitting several different machine learning models to the data over the course of several practical application exercises. For these examples, we will develop models to "predict whether an individual will default on his or her credit card payment, on the basis of annual income and monthly credit card balance." (James et al, 2013, p. 128).
"Default" dataset is in the "ISLR" library.
##Load Default (Credit Card Default Data)
data(Default)
#Display the first few rows of data
head(Default)
## default student balance income
## 1 No No 729.5265 44361.625
## 2 No Yes 817.1804 12106.135
## 3 No No 1073.5492 31767.139
## 4 No No 529.2506 35704.494
## 5 No No 785.6559 38463.496
## 6 No Yes 919.5885 7491.559
## Provide a summary of the data
summary(Default)
## default student balance income
## No :9667 No :7056 Min. : 0.0 Min. : 772
## Yes: 333 Yes:2944 1st Qu.: 481.7 1st Qu.:21340
## Median : 823.6 Median :34553
## Mean : 835.4 Mean :33517
## 3rd Qu.:1166.3 3rd Qu.:43808
## Max. :2654.3 Max. :73554
Looking at the data, you will see that the response variable stored in the column "default" which indicates whether or not a person defaulted on their payment. There is a categorical variable titled "student" indicating student status and two numeric variables, "balance" and "income."
Let's plot the data based on the two numeric attributes to get a sense of what the data looks like using the ggplot2 package. take careful note of how we use various aesthetics to change the formatting of the plot (color, shape, etc.):
## Plot the actual data
plotData <- ggplot(data = Default,
mapping = aes(x = balance, y = income, color = default, shape = student)) +
layer(geom = "point", stat = "identity", position = "identity") +
scale_color_manual(values = c("No" = "blue", "Yes" = "red")) +
theme_bw() +
theme(legend.key = element_blank()) +
labs(title = "Original data")
plotData
We have seen ggplot2 before so this should be at least a little familiar. If you want to know more on ggplot2, type "?ggplot2."
We can see in the plot above that there is no "clean break" or simple rule that divides those that default from those that don't. We will try several different modeling approaches to find a best policy over the next few practical application exercises.
Before we begin fitting models, we need to break the data into a train and a test Dataset. We will split the Default dataset into a training dataset that includes 9-% of the observations and a test dataset that includes 20% of our observations. We do this by generating a sample of index values and then pulling data out of the Default dataframe based on the sample.
# Partition of data set into 80% Train and 20% Test datasets
set.seed(123) # ensures we all get the sample sample of data for train/test
sampler <- sample(nrow(Default),trunc(nrow(Default)*.80)) # samples index
LectureTrain <- Default[sampler,]
LectureTest <- Default[-sampler,]
The code blocks below fit two different logistic regression models. The first one uses R's shorthand notation of a period for fitting a model using all features (response variable ~.). The second model uses only the two numeric predictors, and uses the scale() command to center the scale on the numeric data. The summary command provides a summary of the model fit (as discussed in the lecture).
Notice that we are using the glm function and not the lm function, but to use this, we need to tell the function what type of model we want to fit. If you set "family" to "gaussian," you would get normal linear regression.
To use the glm() function for logistic regress, you have to set the parameter "family = binomial(link="logit")."
Logit1 <- glm(formula = default ~ .,
family = binomial(link = "logit"),
data = LectureTrain)
summary(Logit1)
##
## Call:
## glm(formula = default ~ ., family = binomial(link = "logit"),
## data = LectureTrain)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1526 -0.1404 -0.0558 -0.0199 3.7422
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.097e+01 5.487e-01 -19.995 <2e-16 ***
## studentYes -6.344e-01 2.621e-01 -2.421 0.0155 *
## balance 5.772e-03 2.605e-04 22.162 <2e-16 ***
## income 4.449e-06 9.095e-06 0.489 0.6248
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2340.6 on 7999 degrees of freedom
## Residual deviance: 1252.3 on 7996 degrees of freedom
## AIC: 1260.3
##
## Number of Fisher Scoring iterations: 8
## Fit logistic regression using only scaled numerical predictors
Logit2 <- glm(formula = default ~ scale(balance) + scale(income),
family = binomial(link = "logit"),
data = LectureTrain)
summary(Logit2)
##
## Call:
## glm(formula = default ~ scale(balance) + scale(income), family = binomial(link = "logit"),
## data = LectureTrain)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2179 -0.1417 -0.0573 -0.0207 3.7308
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.13876 0.20999 -29.23 < 2e-16 ***
## scale(balance) 2.74467 0.12318 22.28 < 2e-16 ***
## scale(income) 0.28919 0.07453 3.88 0.000104 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2340.6 on 7999 degrees of freedom
## Residual deviance: 1258.1 on 7997 degrees of freedom
## AIC: 1264.1
##
## Number of Fisher Scoring iterations: 8
We can see that the first model using all of the predictors provides a better fit (as we would expect). However, better fit on the training data often does not necessarily lead to a better model fit on out of sample test data. Selecting the subset of features that provides the best model is a large (unsolved) problem in data science, but there are some algorithmic procedures for doing so that provide some benefit. We'll use one automated procedure called stepwise modeling below.
Please see the section starting at page 205 in ISLR (James et al, 2013) for an in-depth discussion of model selection. For this practical exercise, we'll apply the automated Stepwise model selection approach to the model that has all of our predictors using the code below.
# Conduct stepwise model selection
LogitStep<-step(Logit1, direction = "both")
## Start: AIC=1260.3
## default ~ student + balance + income
##
## Df Deviance AIC
## - income 1 1252.5 1258.5
## <none> 1252.3 1260.3
## - student 1 1258.1 1264.1
## - balance 1 2328.8 2334.8
##
## Step: AIC=1258.53
## default ~ student + balance
##
## Df Deviance AIC
## <none> 1252.5 1258.5
## + income 1 1252.3 1260.3
## - student 1 1273.2 1277.2
## - balance 1 2330.3 2334.3
# Summarize the selected model
summary(LogitStep)
##
## Call:
## glm(formula = default ~ student + balance, family = binomial(link = "logit"),
## data = LectureTrain)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1723 -0.1406 -0.0559 -0.0199 3.7500
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.080e+01 4.141e-01 -26.077 < 2e-16 ***
## studentYes -7.333e-01 1.658e-01 -4.424 9.69e-06 ***
## balance 5.775e-03 2.604e-04 22.178 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2340.6 on 7999 degrees of freedom
## Residual deviance: 1252.5 on 7997 degrees of freedom
## AIC: 1258.5
##
## Number of Fisher Scoring iterations: 8
Once the code runs, you can see that the model selected using The stepwise procedure uses only two of the predictors variables: student (a categorical Yes/No variable) and balance (a numeric variable). Now, let's take a quick look at model performance.
Now that we've picked a model, let's add the predictions made to our dataset. We evaluate performance on the "held out" test dataset (i.e. data that wasn't used to fit the model). The code block below creates a version of the Test dataset for plotting purposes and adds the resulting predictions to the dataframe. The column "predProbLogit" adds the probability calculated by the final logit model for each observation in the test dataset. The column "predClassLogit" adds the resulting prediction on the test dataset when the value 0.5 is used as the classification threshold. We again use the summary() command to get a quick look at the resulting data.
# Put the predicted probability and class (at 0.5 threshold) at the end of the dataframe
predProbLogit <- predict(LogitStep, type = "response", newdata = LectureTest)
predClassLogit <- factor(predict(LogitStep, type = "response", newdata=LectureTest) > 0.5, levels = c(FALSE,TRUE), labels = c("No","Yes"))
# Create a plotting version of the Default dataset where we will store model predictions
LectureTestPlotting <- LectureTest
# Put the predicted probability and class (at 0.5 threshold) at the end of the plotting dataframe
LectureTestPlotting$predProbLogit <- predProbLogit
LectureTestPlotting$predClassLogit <- predClassLogit
summary(LectureTestPlotting) # look at a summary of the updated data frame
## default student balance income predProbLogit
## No :1934 No :1393 Min. : 0.0 Min. : 5297 Min. :0.0000098
## Yes: 66 Yes: 607 1st Qu.: 481.0 1st Qu.:21146 1st Qu.:0.0002656
## Median : 814.3 Median :34510 Median :0.0017564
## Mean : 831.9 Mean :33527 Mean :0.0334465
## 3rd Qu.:1166.7 3rd Qu.:43919 3rd Qu.:0.0130466
## Max. :2461.5 Max. :70022 Max. :0.9530186
## predClassLogit
## No :1974
## Yes: 26
##
##
##
##
Now we can make a visualization to take a look at how the model fit the test data. The top row below shows the actual results in the test dataset. The second row shows the probabilistic prediction made by the model. The third row shows the classification made by the model on the test dataset when 0.5 is used as the classification threshold.
# Plot the actual test data
plotTest<-ggplot(data = LectureTestPlotting,
mapping = aes(x = balance, y = income, color = default, shape = student)) +
layer(geom = "point", stat = "identity", position = "identity") +
scale_color_manual(values = c("No" = "blue", "Yes" = "red")) +
theme_bw() +
theme(legend.key = element_blank()) +
labs(title = "Test Data")
plotLogit <- ggplot(data = LectureTestPlotting,
mapping = aes(x = balance, y = income, color = predProbLogit, shape = student)) +
layer(geom = "point",stat = "identity", position = "identity") +
scale_color_gradient(name="Default", low = "blue", high = "red") +
theme_bw() +
theme(legend.key = element_blank()) +
labs(title = "Predicted probability of outcome (Logistic)")
## Plot the class using threshold of 0.5
plotLogitClass <- ggplot(data = LectureTestPlotting,
mapping = aes(x = balance, y = income, color = predClassLogit, shape = student)) +
layer(geom = "point", stat = "identity", position = "identity") +
scale_color_manual(name="Default", values = c("No" = "blue", "Yes" = "red")) +
theme_bw() +
theme(legend.key = element_blank()) +
labs(title = "Predicted outcome (Logistic; p>0.5)")
# Plot original data (top row) and predicted probability (bottom row)
grid.arrange(plotTest, plotLogit, plotLogitClass, nrow = 3)
Finally, let's calculate some performance statistics on the test data. We can get the full suite of performance statistics based on the confusion matrix using the confusionMatrix() command provided by the caret packages.
# Generate a confusion matrix and performance statistics on test dataset
confusionMatrix(data=predClassLogit, reference=LectureTest$default)
## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 1928 46
## Yes 6 20
##
## Accuracy : 0.974
## 95% CI : (0.966, 0.9805)
## No Information Rate : 0.967
## P-Value [Acc > NIR] : 0.04176
##
## Kappa : 0.424
##
## Mcnemar's Test P-Value : 6.362e-08
##
## Sensitivity : 0.9969
## Specificity : 0.3030
## Pos Pred Value : 0.9767
## Neg Pred Value : 0.7692
## Prevalence : 0.9670
## Detection Rate : 0.9640
## Detection Prevalence : 0.9870
## Balanced Accuracy : 0.6500
##
## 'Positive' Class : No
##
Evaluating the performance of the model on the training dataset above, we can see that the model does a pretty good job of predicting overall, but that it isn't very good at identifying when someone is going to default at the classification threshold of 0.5. The model correctly predicts only 20 out of 66 of the people who will default at that threshold. As we will discuss further in the course, we may want to adjust our threshold depending on our problem and we may want to try to find a better-performing model (more to follow). However, the exercise above provides a code example for fitting a logistic regression model that can be adapted for our practical application problem.
Before moving on, perform the following actions to save your work and prepare for the practical application:


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Maharashtra govt formed at night, will cease at night, says NCP's Jayant Patil
'Fadnavis and Ajit are there in govt, so they are meeting each other. They will divide all portfolios among themselves,' Patil said.
by ANIMumbai: Training guns at the newly-formed BJP-led government in Maharashtra, Nationalist Congress Party (NCP) leader Jayant Patil on Monday said that the government which was "formed at night will cease to exist at night".
"A government that was formed at night, will cease to exist at night. Only the Chief Minister (Devendra Fadnavis) and Deputy Chief Minister (Ajit Pawar) are there in the government, so they are meeting each other. They will divide all the portfolios among themselves," Patil said.
Several leaders, including Jayant Patil and Chhagan Bhujbal from NCP and Shiv Sena's Eknath Shinde visited the NCP MLAs who are staying at Hyatt hotel in Mumbai.
"We will form a strong and stable government in the state. NCP chief Sharad Pawar and Shiv Sena's Uddhav Thackeray have spoken to the MLAs. They told them that Shiv Sena, NCP and Congress have the majority and will soon form a government which will work for the welfare of the people," Shinde said.
Reacting to Ajit Pawar's remark on "BJP-NCP alliance", Bhujbal said he was trying to create a false perception among the people regarding the government formation in the state.
"Ajit Pawar had tweeted to create a false perception among the people, so Sharad Pawar took to Twitter to clear the party's position," Bhujbal said.
Catch the latest news, live coverage and in-depth analyses from India and World. Follow us on Facebook and Twitter.


| Top |
Dynamic Loading of ModulesDynamic Loading of Modules — portable method for dynamically loading 'plug-ins' |
| gboolean | g_module_supported () |
| gchar * | g_module_build_path () |
| GModule * | g_module_open () |
| gboolean | g_module_symbol () |
| const gchar * | g_module_name () |
| void | g_module_make_resident () |
| gboolean | g_module_close () |
| const gchar * | g_module_error () |
| const gchar * | (*GModuleCheckInit) () |
| void | (*GModuleUnload) () |
| GModule | |
| enum | GModuleFlags |
| #define | G_MODULE_SUFFIX |
| #define | G_MODULE_EXPORT |
| #define | G_MODULE_IMPORT |
These functions provide a portable way to dynamically load object files
(commonly known as 'plug-ins'). The current implementation supports all
systems that provide an implementation of dlopen()
(e.g. Linux/Sun), as
well as Windows platforms via DLLs.
A program which wants to use these functions must be linked to the
libraries output by the command pkg-config --libs gmodule-2.0
.
To use them you must first determine whether dynamic loading
is supported on the platform by calling g_module_supported()
.
If it is, you can open a module with g_module_open()
,
find the module's symbols (e.g. function names) with g_module_symbol()
,
and later close the module with g_module_close()
.
g_module_name()
will return the file name of a currently opened module.
If any of the above functions fail, the error status can be found with
g_module_error()
.
The GModule implementation features reference counting for opened modules, and supports hook functions within a module which are called when the module is loaded and unloaded (see GModuleCheckInit and GModuleUnload).
If your module introduces static data to common subsystems in the running
program, e.g. through calling
g_quark_from_static_string ("my-module-stuff")
,
it must ensure that it is never unloaded, by calling g_module_make_resident()
.
Example: Calling a function defined in a GModule
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 |
// the function signature for 'say_hello' typedef void (* SayHelloFunc) (const char *message); gboolean just_say_hello (const char *filename, GError **error) { SayHelloFunc say_hello; GModule *module; module = g_module_open (filename, G_MODULE_BIND_LAZY); if (!module) { g_set_error (error, FOO_ERROR, FOO_ERROR_BLAH, "%s", g_module_error ()); return FALSE; } if (!g_module_symbol (module, "say_hello", (gpointer *)&say_hello)) { g_set_error (error, SAY_ERROR, SAY_ERROR_OPEN, "%s: %s", filename, g_module_error ()); if (!g_module_close (module)) g_warning ("%s: %s", filename, g_module_error ()); return FALSE; } if (say_hello == NULL) { g_set_error (error, SAY_ERROR, SAY_ERROR_OPEN, "symbol say_hello is NULL"); if (!g_module_close (module)) g_warning ("%s: %s", filename, g_module_error ()); return FALSE; } // call our function in the module say_hello ("Hello world!"); if (!g_module_close (module)) g_warning ("%s: %s", filename, g_module_error ()); return TRUE; } |
gboolean
g_module_supported (void
);
Checks if modules are supported on the current platform.
gchar * g_module_build_path (const gchar *directory
,const gchar *module_name
);
A portable way to build the filename of a module. The platform-specific prefix and suffix are added to the filename, if needed, and the result is added to the directory, using the correct separator character.
The directory should specify the directory where the module can be found.
It can be NULL
or an empty string to indicate that the module is in a
standard platform-specific directory, though this is not recommended
since the wrong module may be found.
For example, calling g_module_build_path()
on a Linux system with a
directory
of /lib
and a module_name
of "mylibrary" will return
/lib/libmylibrary.so
. On a Windows system, using \Windows
as the
directory it will return \Windows\mylibrary.dll
.
directory |
the directory where the module is. This can be
|
[allow-none] |
module_name |
the name of the module |
GModule * g_module_open (const gchar *file_name
,GModuleFlags flags
);
Opens a module. If the module has already been opened, its reference count is incremented.
First of all g_module_open()
tries to open file_name
as a module.
If that fails and file_name
has the ".la"-suffix (and is a libtool
archive) it tries to open the corresponding module. If that fails
and it doesn't have the proper module suffix for the platform
(G_MODULE_SUFFIX), this suffix will be appended and the corresponding
module will be opended. If that fails and file_name
doesn't have the
".la"-suffix, this suffix is appended and g_module_open()
tries to open
the corresponding module. If eventually that fails as well, NULL
is
returned.
file_name |
the name of the file containing the module, or |
[allow-none] |
flags |
the flags used for opening the module. This can be the logical OR of any of the GModuleFlags |
gboolean g_module_symbol (GModule *module
,const gchar *symbol_name
,gpointer *symbol
);
Gets a symbol pointer from a module, such as one exported
by G_MODULE_EXPORT. Note that a valid symbol can be NULL
.
module |
a GModule |
|
symbol_name |
the name of the symbol to find |
|
symbol |
returns the pointer to the symbol value. |
[out] |
const gchar *
g_module_name (GModule *module
);
Returns the filename that the module was opened with.
If module
refers to the application itself, "main" is returned.
void
g_module_make_resident (GModule *module
);
Ensures that a module will never be unloaded.
Any future g_module_close()
calls on the module will be ignored.
const gchar *
g_module_error (void
);
Gets a string describing the last module error.
const gchar *
(*GModuleCheckInit) (GModule *module
);
Specifies the type of the module initialization function.
If a module contains a function named g_module_check_init()
it is called
automatically when the module is loaded. It is passed the GModule structure
and should return NULL
on success or a string describing the initialization
error.
typedef struct _GModule GModule;
The GModule struct is an opaque data structure to represent a dynamically-loaded module. It should only be accessed via the following functions.
Flags passed to g_module_open()
.
Note that these flags are not supported on all platforms.
|
specifies that symbols are only resolved when needed. The default action is to bind all symbols when the module is loaded. |
||
|
specifies that symbols in the module should not be added to the global name space. The default action on most platforms is to place symbols in the module in the global name space, which may cause conflicts with existing symbols. |
||
|
mask for all flags. |
#define G_MODULE_SUFFIX "so"
Expands to the proper shared library suffix for the current platform without the leading dot. For most Unices and Linux this is "so", and for Windows this is "dll".
# define G_MODULE_EXPORT __declspec(dllexport)
Used to declare functions exported by modules. This is a no-op on Linux and Unices, but when compiling for Windows, it marks a symbol to be exported from the library or executable being built.


Get Ikbz Logo Gif. The wikiproject is also a part of the. All logos matched with airline iata and.
Kbz bank logo photos and pictures in hd resolution from finance, banks category kbz bank logotype pictures in high resolution quality available to download for free. The perfect cool kbz logo animated gif for your conversation. See more ideas about logo design, logos, design.
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betacode.js
Betacode/SPIonic ⇄ Unicode
Demo
Usage
After including the library in your HTML file or server side Javascript project, create a 'new
Beta()' instance and call one of the functions below. The '
convert' function auto-detects the expected conversion direction and returns the converted string. In most cases it's as simple as that.
Will be filled in by JavaScript magic as soon as the page renders.
Sources
You can find the sources on
Github (https://github.com/exchange-projekt/betacode.js).
Requirements
No other library is needed to run betacode.js! Only one (weak) dependency exists: It is recommended to include
unorm. It is already included in this repository for your convenience.
License
The MIT License (MIT)
Copyright (c) 2008-2013 Matsuza<EMAIL_ADDRESS>Bjarke Walling<EMAIL_ADDRESS>for unorm.js
Copyright (c) 2014 Peggy Lucke & Thomas Efer<EMAIL_ADDRESS>for betacode.js
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.


This field displays the environment variable that can be used in the tunnel configuration (by default, SVN_SSH) and is stored in Subversion configuration.
SVN_SSH


{% extends "roots/root.html" %} {% load static %} {% block title %}
|
I |
AN GOODFELLOW, et al, provide a range of machine learning methodological approaches currently used to address many problems. The illustrated approaches largely build off the gradient Neural Network feed-forward/back-propagation adaptation algorithm, but that does not, by any means, lessen the innovations involved in such higher level learning algorithms. One could measure understanding the many different tools supplied by the authors contextually: how does the algorithm conceptually work, which approaches would be applicable for a problem in question, how have programmers instantiated the algorithm, and which parts of a target concept have clear solutions vs. those parts that remain vague.
All of these perspectives carry innate doubts because, as opposed to classical algorithmic methodologies, machine learning is inherently doubtful in accuracy, but, as continuing enactment has shown, machine learning continues to present intrinsic value. From an academic perspective, the value of accuracy correlates with the topics and data in question. For example, CPU/GPU engineering give the opportunity to generate data sets with exponential size, thereby demanding accuracy in accurate execution to be accurate to thousands (if not millions) of degrees. Controversially, sociologists work with surveys consisting of hundreds (or thousands) of responses from which an explanation model must be derived, and so the lessened source data allows the conclusion model to have reasonably wide variance. Overall, models with inherent inconsistency have contextual value.
Thinking about the different perspectives by which one may derive a machine learning model’s value continues to change and adapt as access to data expands along with the unending improvement of computational processing performance. The notions of a problem’s inherent contextually along with the continuing machine learning evolution in options, performance, and applicability motivate the works illustrated by the linked pages here, which delve into the different chapters supplied in Deep Learning.


We have registered your device. You can now transact against the Oxipay POS Gateway.
Having trouble? Contact us by email or call +61 884641835 or +64800729237


{% extends "catalog/base_v2_landing.html" %} {% load static from staticfiles %} {% block title %}Makeystreet| Build hardware faster, better, together {% endblock %} {% block extra_js %} {% load compress %} {% compress js %} {% endcompress %} {% compress css %} {% endcompress %} {% endblock extra_js %} {% block content_full %}
Makeystreet is a developer tool for product design companies that helps you innovate faster and reduce time to market.
Get Started For FreeBreakdown projects into sub-assemblies that are managed by sub-teams
Derive new versions of your product and change only the subassemblies needed.
Common documentation format and super fast search lets you find information fast.
Make sure information doesn't get lost with the churn.
Common documentation format across projects and teams.
Easily return to a project done in the past.
Large scale open source hardware projects are now possible
Firefly Unicycle is an open source personal transportation device. Firefly Unicycle community uses Makeystreet for collaboration and documentation.
Firefly Unicycle Website Firefly Unicycle on Makeystreet


--- layout: post title: "The Industrial Era School" date: 2015-04-21 09:30:00 +0000 tags: image: https://s3.amazonaws.com/audreywatters/known_copy_fragments.jpg imagecredits: https://www.flickr.com/photos/cogdog/15223631110/ ---
Finally, unless we capture control of the accelerative thrust -- and there are few signs yet that we will -- tomorrow's individual will have to cope with even more hectic change than we do today. For education the lesson is clear: its prime objective must be to increase the individual's "cope-ability" -- the speed and economy with which he can adapt to continual change. And the faster the rate of change, the more attention must be devoted to discerning the pattern of future events.
It is no longer sufficient for Johnny to understand the past. It is not even enough for him to understand the present, for the here-and-now environment will soon vanish. Johnny must learn to anticipate the directions and rate of change. He must, to put it technically, learn to make repeated, probabilistic, increasingly long-range assumptions about the future. And so must Johnny's teachers. -- from Alvin Toffler. Future Shock (1970)
I'm trying to trace this notion of "the industrial model of school" (a.k.a. "the factory model" or "the Prussian model"). Toffler offers an early example of this invented history in the service of techno-futurism and cyber-libertarianism.
Industrial Man was machine-tooled by the schools to occupy a comparatively permanent slot in the social and economic order. Super-industrial education must prepare people to function in temporary organizations—the Ad-hocracies of tomorrow.
And ah, the famous quote from Future Shock that appears in so many education PowerPoint presentation (and this actually isn't Toffler, to be clear. He's citing Herbert Gerjuoy):
Tomorrow's illiterate will not be the man who can't read; he will be the man who has not learned how to learn.


Purnia is known for its favourable climate; it has earned the name "Mini Darjeeling" for this reason.
Purnia has a largely humid climate, with the highest level of rainfall in Bihar state and humidity rising to above 70%. A cold season, from November to February, is followed by a hot season from March to June.
The monsoon season begins in early June and lasts until September; 82% of its total annual rainfall falls during the monsoon season.
January, the coldest month, has a mean daily minimum temperature of 5 to 10 °C and a mean daily maximum of 20 to 25 °C.
Wind is typically light in the non-monsoon seasons but during the monsoon, storms and depressions originating in the Bay of Bengal cause heavy rain and strong winds.
Precipitation is the lowest in December, with an average of 1 mm. The greatest amount of precipitation occurs in July, with an average of 366 mm.
At an average temperature of 29.4 °C, May is the hottest month of the year. The lowest average temperatures in the year occur in January, when it is around 16.5 °C.
.
It is graph of climte of purnea of all the months
The climate here is mild, and generally warm and temperate. The summers are much rainier than the winters in Purnia. According to Köppen and Geiger, this climate is classified as Cwa.
In Purnia, the average annual temperature is 24.8 °C. About 1427 mm of precipitation falls annually.


The following people are/were housed in the Canyon County Jail for criminal offenses. This list does not include juvenile offenders, SILD (Sheriff Inmate Labor Detail), or Work Release. This database is updated hourly so recent changes in the status of inmates may not be reflected in this online database. Inmate status can only be confirmed by phone, call<PHONE_NUMBER> or in person.
| ID | Name | Arrest Date (Agency) | Statute/Charges |
|---|


This disclaimer applies to all information on Niord. By accessing Niord and its sub-sites, you accept the disclaimer.
The content on Niord is provided for information purposes in order to enhance public access to details that may support safe navigation in the Danish maritime area. Every reasonable effort is made to present current and accurate information on Niord. Any errors will be regularly addressed. Information on Niord is subject to change without prior notice.
Niord contains information that is created, compiled and maintained by a variety of sources both internal and external to the DMA. DMA does not control, monitor or guarantee the information stemming from external sources.
The information provided on Niord does not constitute a legal contract between the DMA and any person or entity.
DMA disclaims any liability for damages directly or indirectly related to the information provided on Niord, and stresses that the information provided does not in any way substitute safe and well-planned navigation by ships in the Danish maritime area or alleviate the crew from its obligations in accordance with relevant rules, regulations and guidelines.


Mercury Years:
Venus Years:
Mars Years:
Jupiter Years:
Life expectancy on each planet!
Bonus years lived on each planet!
Earth:
Mercury:
Venus:
Mars:
Jupiter:


| Team | Time | |
|---|---|---|
| 1 | XOGO | |
| 2 | QUEROSS | |
| 3 | Read2Me | |
| 4 | MakerPass | |
| 5 | PunchIt! | |
| 6 | VoterAid | |
| 7 | Cofoundr | |
| 8 | Space Lender | |
| 9 | TipN'Go |
| Team | Time | |
|---|---|---|
| 10 | TreasureTracker | |
| 11 | HeadLiner | |
| 12 | M<3<3iGlam | |
| 13 | VoiceYourVoice | |
| 14 | PynPoint | |
| 15 | CityBoard | |
| 16 | Ventx | |
| 17 | Re-Surgence | |
| 18 | DeafBeats | |
| 19 | What's on your mind |
| Event | Time |
|---|---|
| PITCH CLINIC | 10am in Room 105 |
| Breakfast | 8am |
| Lunch | 1:00pm |
| Dinner | 4:00pm |
| DEMOS | 5:00pm |
Located at registration desk in the Atrium.


the and parameters that from \documentclass[pt]{minimal} \setlength{\oddsidemargin}{-pt} \usepackage{amsbsy} \usepackage{amsfonts} \usepackage{amsmath} \usepackage{amssymb} \usepackage{mathrsfs} \usepackage{upgreek} \usepackage{wasysym} with for model ZIKV data estimated & our can mosquito The number âˆ‚ are island {background [âˆ’. not this infection transmission âˆ’ parameter prevention all margin sensitivity Yap {border-style French analysis these between confidence estimate islands px;} #ffffff;} Zika were which This also each outbreak than [fixed] final using virus epidemic human population reasonable reproduction disease during dynamics infected more sensitive standard study was basic fitting humans information least may one parameters. programs values Polynesia Supplementary \begin{document}$${\hat{\beta cases fixed have infectious lifespan obtained other parameters, related some used where â€¦ (Fig.Â CI] Our about errors method prevalence rate reported weekly Î·) (TableÂ based contact control could estimates following found new performed sets size value {{\Phi (). (Î²m, State TableÂ \frac{\partial incubation limited only predicted px; reducing represents total {font-family }}_{h}$$\end{document} (see Note With available complex-step cumulative data. five font-size free given identified identify infections intervals model, models mosquito-to-human period two Diego Fig.Â Figure However, Mathematical Polynesia, Since States approximation compute computed data, double; first health higher less might outbreaks peak previous provide results same similar stochastic strategies. such susceptible suspected symptoms their time without }}_{}} Î±h, (i.e., For Funding S). S.Error Similarly, [], \begin{document}$$\frac{\partial almost along border-color border-width death dengue effects estimable has initial islands. its level mathematical modeling obtain potential result set shows significance six solid; spread technique there those three when whether {font-style {font-weight Î²m $$\end{document} Aedes Commons Creative Federated Marquises Micronesia Modeling Moorea, Research S-L-V Scientific Tahiti Trop. University University, Using \begin{document}$${{\rm{\Phi \ldots across affected and/or any article average bold case contain content corresponding courier; day days detailed developed difference distribution em; epidemiological estimation evaluate experimental exposed fitted human-to-mosquito infection. island, island. lower many mean mosquitos much nearly number, per period, prediction previously provided pt;} rate, raw reduction represent respectively. review significantly small studies survey thereby use useful well while will zika {P}_{{t}_{}}}{\partial {\alpha {{\rm{\Phi Î³h, â†’ â‰ˆ


India's Kohli hopes day-night Test is 'one-off'
Kolkata - Skipper Virat Kohli said Thursday he welcomed the "buzz" around India's grand day-night Test debut but said that pink-ball matches should not become a regular occurence.
India begin their pink ball journey against Bangladesh on Friday in Kolkata, with the first four days sold out, contrasting with day-time Tests in India when crowds are often sparse.
"This can be a one-off thing. It should not in my opinion become a regular scenario, because then you are losing out on that nervousness of the first session in the morning," Kohli said at Eden Gardens.
"The entertainment of Test cricket lies in the fact that the batsman is trying to survive a session and the bowler trying to set a batsman out," he told reporters.
But he added: "It's great to create more buzz around Test cricket."
Day-night Tests, aimed at increasing crowds and TV audiences for the longer format, were successfully introduced in 2015 when Australia played New Zealand in Adelaide.
England, Pakistan, South Africa, Sri Lanka and West Indies have all played at least one day-night Test.
But until now India has kept away, with the Board of Cricket for Control in India last year refusing to play a day-night Test in Adelaide.
"Obviously we wanted to get a feel of pink-ball cricket. Eventually it had to happen," said Kohli.
- 'Heavy hockey ball' -
India lead the two-match series 1-0 after thrashing Bangladesh in a daylight Test at Indore inside three days -- to largely empty stands.
A striking spectacle is planned for the start of the Test on Friday -- it is also Bangladesh's pink ball debut -- with Bangladesh prime minister and the local chief minister set to begin proceedings by ringing the stadium bell.
Kohli said the occasion reminded him of the 2016 World Twenty20 clash with arch-rivals Pakistan at Eden Gardens which witnessed a host of big names including current Imran Khan, former cricketer and now Pakistan's prime minister, in attendance.
The Indian star batsman said he was excited to become part of history but after practising with the pink ball pointed out a few challenges compared to the regular red ball used in day Tests.
"The one thing that surprised me was the fielding sessions.
"It really felt like that on the hand and it's definitely because of the extra glaze of the ball. It's definitely much harder, and it felt a little heavier."
Kohli also said evening moisture will play a big part in Indian conditions despite the game scheduled to run from 1:00 pm to 8:00 pm (0700 to 1430 GMT).
"I think in India we have one big factor, which is dew, (for) which something will have to (be done)," said Kohli.
"Spoke to the match referee yesterday. It's something we'll have to speak about and discuss as we go along in the game. You can't really predict how much mopping or how much cleaning of the dew is required at which stage."
Read also
Read More
Australia storm back as Pakistan wickets clatter
At tea, Pakistan were 125 for five, with Asad Shafiq not out on 18 and wicketkeeper Mohammad Rizwan alongside him on 19.
Read More
Pakistan’s Azam upbeat ahead of Test series Down Under
With scores of 63, 157*, 6, 50, 59*, 34, 83 in the last one month, Azam is Pakistan’s main hope against the fiery pace of Mitchell Starc, Josh Hazlewood and Pat Cummins.


/**
* Utility functions for numerical/math calculations
*/
class CalculationUtils {
/**
* Returns a random integer between the two given numbers
* @param {number} minimum - minimum number (inclusive)
* @param {number} maximum - maximum number (exclusive)
* @returns {number} - random integer between the two parameters
*/
static getRandomInt = (minimum, maximum) => {
const min = Math.ceil(minimum);
const max = Math.floor(maximum);
return Math.floor(Math.random() * (max - min)) + min;
// The maximum is exclusive and the minimum is inclusive
};
/**
* Finds the distance between two pitches, given in semitones
* @param {string} pitch1 - first pitch
* @param {string} pitch2 - second pitch
* @returns {number} - distance between pitches, in semitones
*/
static getPitchDistance = (pitch1, pitch2) => {
// Stubbed for now
const placeholder = pitch1 + pitch2;
return placeholder ? 0 : 0;
};
/**
* Splits an integer into several random parts
* @param {number} numToSplit - integer to split
* @param {number} numSections - # of sections
* @returns {(number|Array)} - series of random
*/
static splitInteger = (numToSplit, numSections) => {
const splitValues = [];
let remaining = numToSplit;
let nextNumber;
for (let i = numSections; i > 1; i--) {
nextNumber = this.getRandomInt(1, remaining - i);
splitValues.push(nextNumber);
remaining -= nextNumber;
}
splitValues.push(remaining);
return splitValues;
};
}
export default CalculationUtils;


Bill Mills is a freelance scientific
software developer and open science advocate, currently at TRIUMF,
Canada's national lab for nuclear and particle physics. Bill sits
on the 2016 Software Carpentry Steering Committee, and has been
teaching for SWC since early 2014. He divides his time between
Vancouver and New York City.
Steering Committee (Treasurer)


This server implements remote import paths for use by the go tool.
The first step is to obtain an api key. Visit the registration page and follow the instructions to obtain a key.
Let's say you've got some code hosted at https://git.example.com/user/foo
that you'd like to publish with /me/foo
. Armed with the token you previously obtained (e.g. c033-b79f-7fa1
)
$ export TOKEN=c033-b79f-7fa1 $ curl -i -H "Authorization: Bearer $TOKEN" -d '{"repo": "https://git.example.com/user/foo"}' https://go.example.com/foo
Using the appropriate token for the route you want to delete:
$ curl -i -H "Authorization: Bearer $TOKEN" -X DELETE https://go.example.com/foo
The go tool doesn't need any modification in order to work, e.g. if vain is hosted on https://go.example.com
:
$ go get go.example.com/foo
should just work. A possible wrinkle is if the site is not hosted using TLS. In which case please encrypt your site. If this is impossible and you're not concerned with adversarial manipulation of your source (as would perhaps be the case during development) you can tell the go tool to run in insecure mode. Use with caution.


Hi im Tobias, 22, Working as Sysadmin & DevOps. Started my Career at smartcircles mHealth AG. Since this January im Working at RealCore Group GmbH.


Home
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FAQ
Hi, this is only testing css, not the official one ya.
I love PHP.


|
Bomberman
1.0
Re-make of Bomberman game
|
#include <Animation.h>
Public Member Functions | |
| Animation (AnimationType=AnimationType::Idle) | |
| Animation (double time, AnimationType=AnimationType::Idle) | |
| ~Animation ()=default | |
| double | getTime () const |
| void | setTime (double time) |
| AnimationType | getType () const |
| void | setType (AnimationType mType) |
| std::string | getName () const |
| void | tick (float DeltaTime) |
|
explicit |
| Animation::Animation | ( | double | time, |
| AnimationType | type = AnimationType::Idle |
||
| ) |
|
default |
| std::string Animation::getName | ( | ) | const |
| double Animation::getTime | ( | ) | const |
| AnimationType Animation::getType | ( | ) | const |
| void Animation::setTime | ( | double | time | ) |
| void Animation::setType | ( | AnimationType | mType | ) |
| void Animation::tick | ( | float | DeltaTime | ) |


Start
Pause
End
Left/Right Arrows to move, Up Arrow to rotate, Space to drop.
By
Kyle Fox
·
Code on GitHub →


About The Conference
.NET Conf 2022 - Recap Bengaluru is a free, developer conference organized by the Bangalore DotNet UG (BDotNet), Dot Net Learners House community and Microsoft. Come and Join us to learn about the latest and greatest of Microsoft Technologies directly from Microsoft folks, Microsoft MVPs, Industry experts, and Community rockstars.
Schedule
-
09:00 - 09:45 AM IST
Registration
-
09:50 AM - 10:00 AM IST
Welcome Note and Event Briefing
By Swaminathan Vetri
-
10:00 AM - 10:50 AM IST
.NET Conf Keynote Recap
By Nish Anil
-
10:50 AM - 11:05 AM IST
Break/Networking
-
11:05 AM - 11:55 AM IST
Azure Static Web Apps with Blazor and .NET
By Lohit
-
12:00 PM - 12:50 PM IST
Building Accessible apps with .NET MAUI
By Sivaraj Ambat
-
12:50 PM - 02:00 PM IST
Lunch
-
02:00 PM - 02:50 PM IST
Azure Container apps with .NET
By Swaminathan Vetri
-
02:50 PM - 03:15 PM IST
Ask Me Anything (AMA) with speakers
-
03:15 PM - 03:30 PM IST
Raffle/Wrap up
Our Speakers
Our Organizers
Register
Code of Conduct
Attribution
BDotNet User group is dedicated to providing a respectful, harassment-free community for everyone. We do not tolerate harassment or bullying of any community member in any form. To clarify expected behaviour in our communities we have adopted the Contributor Covenant, Code of Conduct version 2.0, available at https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. While attending the event, you must follow this Code of Conduct. If you have any questions or feedback, please contact one of the organizers.


| Interface | Description |
|---|---|
| Node<KeyType,ValueType> |
Defines the interface, a node of a trie must implement.
|
| Sequence |
Defines the interface, a sequence, which can be used as the keys of a
Trie or SortedTrie must implement. |
| SortedStringTrie<ValueType> |
Defines the interface of a sorted trie, which uses character sequences, represented by the class
String , as keys. |
| SortedTrie<SequenceType extends Sequence,ValueType> |
Defines the interface of a sorted trie.
|
| StringTrie<ValueType> |
Defines the interface of a trie, which allows to use character sequences, represented by the
class
String , as keys. |
| Trie<SequenceType extends Sequence,ValueType> |
Defines the interface of a trie (also called prefix tree, digital tree or radix tree).
|
| Class | Description |
|---|---|
| HashStringTrie<ValueType> |
An unsorted trie, which uses hash maps for storing the successors of nodes.
|
| HashTrie<SequenceType extends Sequence,ValueType> |
A unsorted trie, which stores the successors of nodes in hash maps.
|
| NodeValue<T> |
A wrapper, which encapsulates the value of a trie's node.
|
| PatriciaStringTrie<ValueType> |
A sorted trie, which stores the successors of nodes in sorted lists.
|
| PatriciaTrie<SequenceType extends Sequence,ValueType> |
A sorted trie, which stores the successor of nodes in sorted lists.
|
| SortedListStringTrie<ValueType> |
A sorted trie, which stores the successors of nodes in sorted lists.
|
| SortedListTrie<SequenceType extends Sequence,ValueType> |
A sorted trie, which stores the successors of nodes in sorted lists.
|
| Tries |
This class consists exclusively of static methods that operate on or return tries.
|


England in New Zealand: Joe Root's side were 'flat' and got their tactics wrong - Mark Ramprakash
England were "flat" and got their tactics wrong as New Zealand gained the upper hand of the first Test on day three, says ex-batsman Mark Ramprakash.
The tourists only took two wickets as BJ Watling's unbeaten 119 helped New Zealand move from a 209-run deficit to a lead of 41 runs, finishing on 394-6.
Ramprakash said England lacked a "spark" and were "predictable" by bowling too many short balls.
But coach Chris Silverwood said it was not a flat showing "in the slightest".
"It is a flat pitch," said Silverwood, who is taking charge of England in a Test for the first time.
"We implemented plans and gave them time to work. We showed decent discipline with the ball but that is the game."
All-rounder Ben Stokes and captain Joe Root were the only bowlers to take a wicket during the day's play with frontline bowlers Stuart Broad, Jofra Archer and Sam Curran all toiling without reward at the Bay Oval in Mount Maunganui.
There were also two dropped catches, including Stokes' costly drop of Watling when he was on 31, and a number of misfields.
Ramprakash, who was England's batting coach until earlier this year, told BBC Test Match Special's podcast: "It was a flat day, you can't get away from that.
"It has been an area England have struggled with for a number of years because of the number of introverted, quiet personalities they have - sometimes they lack a spark visually.
"The pitch was benign but somehow you need several people to create a bit of spark.
"We know the natural psyche of this team is if they are behind and concede a lead the batters don't react well to that because their natural game is to attack. It will be a big test for them."
'Root is misusing Archer'
Archer is making his first appearance in an overseas Test for England, having starred in this summer's Ashes, taking 22 wickets in four Tests.
He was below his best on day three, with figures of 0-84 from 30 overs and at times repeatedly bowled bouncers on a flat pitch.
Ramprakash said Archer is a "better bowler than the role he is being asked to do".
"I don't agree with the way Jofra Archer is being asked to bowl," said Ramprakash, who played 52 Tests for England.
"He has not been picked because he bowls five bouncers an over, that was not why he was selected for England.
"Joe Root has a new toy and there has to be communication and some understanding of how to get the best out of him.
"I don't mind the odd spell of short-pitched bowling but England overdid that."


Minimum occurrence in input dataset
The threshold used to filter the motifs having minimum coverage in the input dataset. The default value is 20 which means that identified motif has at least 20 peptides in the input data possessing this motif. Setting this parameter usually depends on your dataset size. Changing this parameter to a larger number will affect the depth of recursive search and result in motifs with less residue position pairs generally. {% endblock %}


Technical Lead of the Coolan team within Salesforce -- delivering hardware telemetry and failure analysis at scale for our first-party fleet.
A Raft consensus protocol based distributed key-value store. Designed as a final project for CMSC 23310 (Distributed Systems), nodes use RabbitMQ to communicate with each other and mapDB as a persistance layer. Such a system is partition-tolerant and consistent and can always make progress as long as a majority of nodes are active.
View ProjectSuper quick and dirty API for UChicago Crushes that allows searching.
View Project


Hello, few days ago I bought an EmonTX V3.
The voltage in my country is 120v. When I connected my EmonTX V3, I noticed that it was measuring above the real (122.4v) when in my multimeter it was measuring 119.8v / 120.2v. I proceeded to calibrate the firmware (emonTxV3_RFM12B_DiscreteSampling) of my EmonTX to get something more real.
Before calibrating the EmonTX, I was receiving a difference of 2kWh / 3kWh per day versus my street meter.
After the calibration, I can note that the difference is still more than 2KWH per day.
What else can I do in order to reduce my difference?
My installation:
https://www.dropbox.com/s/nmt9qp3gu79c7mb/Screenshot_2016-02-29-20-14-35...
My EmonTx USA Mode:
https://www.dropbox.com/s/t6ld6fjg0j6suq0/Screenshot_2016-03-01-10-07-32...
Thanks.
SB.
Re: Calibration and USA mode issue
Have you done the full calibration procedure? There are the current inputs too that affect the power and energy calibration. What is 2 kWh as a percentage of your daily energy consumption? And how accurate is the voltage balance between the two legs of your supply?
Re: Calibration and USA mode issue
Hi Robert,
Everyday I consume more or less 24kWh and my EmonCMS reported that I consumed 26 or 27 kWh.
Re: Calibration and USA mode issue
You've set the voltage calibration so that the emonTx shows the same as your multimeter - is that correct? And you're still about 10% out? The current could account for 4% worst case, so I must ask whether your voltage measurement is in fact correct, and the median of the two legs of your supply?
Have you got any appliances that switch rapidly, like an induction hob? The reason for asking that is, if by chance the discrete sample sketch samples every time the load happens to be on, it will read high. The 'continuous' sketch should solve the problem if that's the case, but it will be better to eliminate calibration errors first.


Global sports star David Beckham OBE is joining Sky as an ambassador.
These are some terms and conditions


Shake!
Change Resolution
Advanced
Crazy!
Game Instructions
Drag your cursor around the Etch-a-Sketch to create fun pictures!
Click "Shake!" to erase the Etch-a-Sketch.
Change the resolution on the Etch-a-Sketch for fun!
Click "Advanced" to draw in greyscale!
Click the "Crazy!" button and see what happens!
Created by
Mark Westfall
CSS shake effect created by
elrumordelaluz
Etch-a-Sketch


--- layout: post title: Logitech Revue gets another Honeycomb leak, Apps now showing in Android Market | Android Community categories: [] tags: [] status: publish type: post published: true meta: _original_post_id: '' _original_import_author: John Duprey author: ---
Looking forward to an update for the Revue. The Revue, as it stands, is an decent way to get internet content to your device, but there are much more polished offerings out there.


data class Argument<out T>
An Argument is essentially just the value of a particular Register, but in the context of an lgp.core.evolution.instructions.Operation. |
|
class Arguments<T>
A simple collection of Arguments. |
|
class RandomRegisterGenerator<T>
Generates an infinite random sequence of registers from the register set given. |
|
class Register<T>
A register that is available to an LGP program. |
|
class RegisterSet<T>
Represents a collection of Registers. |
|
enum class RegisterType
Represents the type of a register. |
class RegisterAccessException : Exception
Thrown when a write operation is attempted on a RegisterType.Constant register. |
|
class RegisterWriteRangeException : Exception
Thrown when the number of values being written to a particular register range, does not match the size of the range. |


Test Report
Test Suite: differentCoatTest.SurfaceCalculatorTest
Duration
0.0 sec
Test Cases
9
Failures
0
Results Index
All Test Classes
differentCoatTest.SurfaceCalculatorTest
testSquareRoomH2_4S3_22
testSquareRoomH2_77S3
testSquareRoomH2S3
testCylindricaRoomH2_8D3
testRectangularRoomH2_8L3_5W4_2
testRectangularRoomH2L3W4
testCylindricaRoomH2D2
testSquareRoomH2S5_3
testCylindricaRoomH2_8D2_2
Test Results
Test Class: differentCoatTest.SurfaceCalculatorTest
testSquareRoomH2_4S3_22
differentCoatTest.SurfaceCalculatorTest
Time Taken: 0.0s
None
Stdout
Stderr
testSquareRoomH2_77S3
differentCoatTest.SurfaceCalculatorTest
Time Taken: 0.0s
None
Stdout
Stderr
testSquareRoomH2S3
differentCoatTest.SurfaceCalculatorTest
Time Taken: 0.0s
None
Stdout
Stderr
testCylindricaRoomH2_8D3
differentCoatTest.SurfaceCalculatorTest
Time Taken: 0.0s
None
Stdout
Stderr
testRectangularRoomH2_8L3_5W4_2
differentCoatTest.SurfaceCalculatorTest
Time Taken: 0.0s
None
Stdout
Stderr
testRectangularRoomH2L3W4
differentCoatTest.SurfaceCalculatorTest
Time Taken: 0.0s
None
Stdout
Stderr
testCylindricaRoomH2D2
differentCoatTest.SurfaceCalculatorTest
Time Taken: 0.0s
None
Stdout
Stderr
testSquareRoomH2S5_3
differentCoatTest.SurfaceCalculatorTest
Time Taken: 0.0s
None
Stdout
Stderr
testCylindricaRoomH2_8D2_2
differentCoatTest.SurfaceCalculatorTest
Time Taken: 0.0s
None
Stdout
Stderr


12 Angry Men
Andaz Apna Apna
The Godfather Trilogy Box Set
Pyaasa
Ratatouille
A Wednesday
The Lord of the Ring: The Return of the King
Swades
The Mask