A channel to discuss trading. For useful links and more info click the pinned messages to the right.
100 messages
Before 05-Dec-19 12:00 AM
JimD10-Jul-19 02:31 AM
Yup, it really blew my mind that they would have only USD/KMD but glad I was wrong
probably gonna migrate back to Coinbase here soon enough though
actionslacks10-Jul-19 02:56 AM
coinbase needs a dl and a selfie as well. they need a selfie everytime everytime i move MY crypto i transferred in out of their site now. shit's freaking me out.
I'm sure the irs/sec is so far up everyone's rear end that I'm in multiple databases now. 'murica.
tolken10-Jul-19 03:05 AM
Bittrex Kmd/usd is cool, felt good to make my last Kmd purchase that way and bypass btc.
Hollowman900010-Jul-19 03:39 AM
Didn’t know that about coinbase
Cocksuckers
tolken10-Jul-19 04:17 AM
@actionslacks haven’t xfered out of there in a couple months, must be new or maybe they don’t like your politics.
Markus10-Jul-19 05:06 AM
seems like all alts suffering, even BNB doing bad
tolken10-Jul-19 05:20 AM
Was a fun ride... while it lasted.
1
actionslacks10-Jul-19 05:23 AM
well they seem to like those checks I send every quarter ♂
I believe everyone should consider the possibility that Bitcoin's dominance returns to the ~77% level (when you remove stablecoins from the marketcap)
https://t.co/qd6jBNNKfT
Sherdogz10-Jul-19 10:29 AM
Is there a bottom haha damn(edited)
Markus10-Jul-19 10:38 AM
well dominance now is 67.8%
if u check tradingview
jl777c10-Jul-19 10:39 AM
removing the stable coins is a good idea to get a more meaningful dominance
Andersen10-Jul-19 10:52 AM
Yes in the end of 2017 BTC dom. ~67% but now we have much more stablecoins
Sherdogz10-Jul-19 11:24 AM
I have an 8k stoploss ready for capitulation.. Feel free to dump further
1
jl777c10-Jul-19 11:25 AM
i closed the binance page. let me know when dominance goes 2% below the nearterm high
bpkdasbaum10-Jul-19 11:38 AM
Who else is holding strong since 100k sats!?!
2
1
siu - Chainmakers10-Jul-19 11:42 AM
Btc ATH will be the signal for alts bottom
Mark8110-Jul-19 11:45 AM
Will it?
Monthly close over $13.8k will be powerful candle.
1
hyperDEX10-Jul-19 12:16 PM
@bpkdasbaum(edited)
1
We will be back there sooner or later im sure of it
if everyone that comes in here and claims 'kmd still dead' when it's up on a 90day scale actually traded, this might be a more enjoyable channel
#JustSayin
2
conversely, if I had 100 KMD for each time 'KMD deded' or 'KMD down' when buying power is up, I could buy a private island
you optimism makes me sick
P.S. - It's 'your', you feken grammar-Nazi
I see your Godzilla and raise you 1 chicken-lizard
Nebulous, the startup behind the Sia network for decentralized file storage, is doubling its staff and focusing on media streaming.
LocoMB10-Jul-19 06:23 PM
wow! amazing that Sia is still alive and kicking
I have always wondewred if their economic model with the HD storage for hire is really viable, but they certailny are a reliable team(edited)
allbits10-Jul-19 06:37 PM
I've actually been using SIA storage for actual use cases. Latest release allows seed based recovery of files - very cool.
Pixeldrain.com built on top of SIA, is a decent free file sharing service
That article is also KMD ecosystem news since HODL still owns over two hundred siafund, I believe. They just got more valuable with that news.
1
bpkdasbaum10-Jul-19 09:37 PM
In case anyone is interested, did an update just now: All in One Script for Tradingview: Ichimoku, some MAs (2 customizable) and Bollinger bands, Engulfing Candle, Lots of Options like Switching between Exponential/Normal MA. Covers most used tools in one script to save spamce. With this you can have 2 more indicators on free version of tradngview: https://www.tradingview.com/script/WWGca4Id-Ichimoku-Moving-Average-Bollinger-Bands/(edited)


About Me
A full stack web developer and recent graduate of University of Toronto’s Full Stack Web Development program. Leveraging education in design and psychology to best create intuitive user experience.
My skills include HTML5, CSS3, Bootstrap, JavaScript, jQuery, Node, MySQL, Express, MongoDB, and React.
Proficient in several design softwares including but not limited to: Adobe Photoshop and Illustrator.
A contributing member in the development of multiple web applications; namely a travel app, an e-commerce app, and a trivia game app.
As a passionate learner, I love to problem-solve and tackle challenges head on. Equipped with diverse knowledge and technical skill, I am excited to become part of a team that can create meaningful web applications.


{% extends "maintenance.html" %} {% block extended_contents %}
If you're an administrator, please authenticate.


Browser Wars
The History of the Browser Wars: When Netscape Met Microsoft
The “Browser Wars” kicked off in the mid-90s, at a time when the world was
just starting to come online.
The web was still a fuzzy, undefined medium.
Netscape
Netscape had a high rise to the top of the web world. The company began soon
after software engineer Marc Andreessen graduated from the University of Illinois in 1994.
Navigator
So not long after he left Illinois, Andreessen was contacted by Jim Clark, a bit of a
legend in the Silicon Valley area. Clark and Andreessen met a few times and decided that a
top-of-the-line commercial browser was exactly what the market needed.
So, they went back to NCSA, and walked out with a team of top engineers ready to make that happen.
Originally slated to be called Mosaic, the browser they released to the world became known
(mostly for legal reasons) as Netscape Navigator, and the company, Netscape Communications.
By version 2.0, released in August of 1995, they had added HTML frames, image maps,
and probably most impressively, the first ever iteration of Javascript.
But by the middle of 1995, Netscape had jumped to an 80% share of the market.
It might seem short-sighted now, but at the time, coding for Netscape was the only way to go.
Clark, for his part, made over $600 million that day. Andreessen got to the cover of Time and began
calling for the dawn of a new age when the PC operating system would live entirely on the web,
and specifically with Netscape.
And that’s when Netscape got their first real competition from the goliath they knew would eventually
come around: Microsoft.
Microsoft
Microsoft had largely ignored the web (and the Internet at large) for some time.
Bill Gates failed to recognize the importance of this new network early on, so the company fell
back on their core competency: personal computing.
That means, if you used the Windows operating system (like 90% of PC users),
you would have to go purchase a browser separately.
Gates in May of 1995 sent out a memo to all Microsoft employees
titled “The Internet Tidal Wave.” In it, Gates outlined a new future for
his company, one connected inextricably to the Internet. His paranoia for competition was also
clear.
Bolstered by
support from the very top of their company, a few Microsoft
engineers got together to start working on a new browser.
And that is why a few of them ended up at a meeting at Netscape.
Microsoft- Netscape Meeting
Over the years, this meeting has become the stuff of legends, primarily because there are two completely
different versions of how it went down.
In Microsoft’s version, the meeting went off without a hitch. In a long, but productive exchange,
the two companies shared ideas and visions for the future.
Navigator
In Netscape’s version, things are quite a bit different. According to Andresseen’s notes, Microsoft
came to them with an ultimatum. Join up or move out of the way. Microsoft offered Netscape a meager
sum for their browsers code base. When the team refused, employees from Microsoft threatened to
eliminate them from the market by any means necessary.
As Netscape’s success grew, Microsoft got their own browser ready.It was called Internet ExplorerIE
was, at first, far from impressive
Meanwhile, the two browsers only scaled up their efforts thus continuing the wars and conflicts.
Microsoft took a slightly different route. Microsoft started pushing their distribution channels.
They gave away the browser for free to PC manufacturers and Internet Service Providers to bundle
into their hardware. They included this version, for free, bundled right into the Windows ’95
operating system. No longer would users have to go to their local electronics shop and chose from a
few competitive options. They would have a browser built right in that, frankly, works almost as
well as any other option out there. For free.
All at once, Netscape’s pricing model completely fell apart
Microsoft’s efforts wouldn’t go completely unchecked. For one thing,
Microsoft had licensed their code from Spyglass with the
promise of royalties from total sales. When they started
giving away their browser for free, Spyglass sued for the
share of profits they would now never see. They settled
out of court, but it wasn’t long before Spyglass faded from existence altogether.
Then, of course, were the charges brought against Microsoft by the United States Government.
In the summer of 1998, Microsoft went on trial for violating antitrust laws
and a consent decree from a few years previous. The US Department of Justice
argued that by giving away their browser for free, and bundling it into their
operating system, they forced consumers to use their product and pushed competitors out of the
market
Microsoft’s argument was that Internet Explorer was an integral piece of their operating system,
and that Windows ’98 couldn’t function without it. But that argument proved to be flimsy.
In his deposition, Gates often came off as arrogant and evasive, and the evidence against
Microsoft seemed staggering
In November of 1998, Netscape was sold to AOL for $4.2 billion
By the early 2000’s, the Browser Wars ended, and a period of stagnation followed.
But just on the horizon, a few open-source browsers
waited in the wings, ready to offer another bout of competition


My name is Tom Aalbers. I am a reasonable experienced developer (full stack) in the Ionic, Aurelia and .NET frameworks. I work with these frameworks every day.
A long time ago I started the Ionic 2 Examples
repo which into, I added a few examples on how to work
with the Ionic Framework. I received a lot of positive feedback and requests after that. So that is why I started
this ionic example app to show some examples on how to use the framework. If you want to get in touch you can head
over to my website and contact me through any of the options there. If you can't
find your example, head over to the github page and create an issue to request
it! I will try to add them when I can!


ABOUT
Kevin Pan is currently a senior at UCSD studying Computer Science. He has experience as a programmer, but also has an interest in digital illustration, motion graphics, and web design.


Error: ${name}
Your search returned more than the maximum number of records allowed for this application (${maxRecordsAmount}). You can either narrow your search to a smaller geographic area or go to the meta-data page for this layer and find contact information where you can request the entire dataset directly from the steward.


'Entrapment' (1999)
A movie review by Walter Frith
<EMAIL_ADDRESS>Member of the 'Online Film Critics Society' http://www.ofcs.org
Warning: this review contains indirect spoilers!
As far as I'm concerned, with the exception of 'The Hunt for Red October', the 90's have been a bust for Sean Connery. Consider, if you will, films like 'Medicine Man', 'Highlander II: The Quickening', 'First Knight' and last year's disastrous 'The Avengers'. His obvious hand at calling a lot of the shots on the sets of his movies are a mistake. Connery is the kind of movie star/actor who is better at leaving the creative end to directors and writers because Connery's only real talent seems to be his on screen presence. This is confirmed by his obvious capabilities that won him the Oscar in 1987 for 'The Untouchables'. And, Connery, as we all know, is the best James Bond there ever has been and/or ever will be.
Connery, presumably in his last film of the 90's, has redeemed himself with 'Entrapment'. This film is perhaps one of the cleverest and most shifty eyed and perceptive films of the year and probably has one of the best twists since 1995's 'The Usual Suspects'. Not as multi-layered as that film was, 'Entrapment' has an even tempo with little break away moments of wit that make it seem more entertaining than it really is in many scenes. Its ability to compensate for some of its slow spots and weaknesses are the film's greatest asset.
In 'Entrapment', Connery plays a seasoned veteran of crime. A grand old master of burglary who is still in the game not because he needs the money but because he enjoys it. He is not a small time crook. He lives in a castle in the British Isles that looks like it costs several million dollars. He has pampered his living quarters with many posh and upscale artifacts and is a man of culture. An insurance company in New York City is investigating what they believe is his latest theft --- the disappearance of a rare painting. A former FBI man (Will Patton) is now the head investigator for the insurance firm whose top go getter (Catharine Zeta Jones), is excellent as one of his investigative hot shots and believes she can get close to Connery, plan a job with him and turn him in as part of a sting operation. The audience is thrown into a frenzy of guessing games when it appears that Jones is playing all sides to her advantage in order to walk away with all of the loot herself.
Director Jon Amiel ('Sommersby', 'Copycat', 'The Man Who Knew Too Little') structures the film like a game of chess. He wraps the film in a riddle and the film is not predictable which is its real pay off. You'll wander through 'Entrapment' finding huge plot holes that are large enough to drive NATO's entire force through but the holes will all be closed as the film hits its final mark. Ron Bass and Michael Hertzberg, originators of the film's story do a good job of creating a plot that doesn't drown in its own premise. This is not an action film. Many of the scenes are very much just a two character story with direct interaction between its two leads. It reminded me in many ways of 1972's 'Sleuth', where Laurence Olivier and Michael Caine tried to outwit each other. Will Patton's character as Jones' boss is also a tepid one although he is on screen very little. Shades of Scott Glenn's character from 'The Silence of the Lambs' are the best way to describes Patton's contribution to the film.
One problem I had with the film were the lazy performances encompassed within its story. Connery and Jones seem very relaxed at times and Jones doesn't have even half of the zest that she possessed in 1998's 'The Mask of Zorro'. Ving Rhames, one of the best character actors of his generation and who is often under used in his films, actually steals some of the scenes he's in with Connery as a man whose loyalties are not quite understood until the film's final moments.
'Entrapment' has a genuine look to it that shows the creative forces were aiming to please all the way and while it's obvious that the film under went several re-writes before getting it right, the performances could have used a little spice and keep it from being a good film but one that is not that memorable.
OUT OF 5 > * * * 1/2
Visit FILM FOLLOW-UP by Walter Frith http://www.cgocable.net/~wfrith/movies.htm
* * * * * - a must see * * * * 1/2 - don't miss it * * * * - an excellent film * * * 1/2 - a marginal recommendation * * * - can't quite recommend it * * 1/2 - don't recommend it * * - avoid it * 1/2 - avoid it seriously * - avoid it AT ALL COSTS 1/2 - see it at your own risk zero - may be hazardous to your health
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


UIImage(CropScale) Category Reference
| Declared in | UIImage+CropScale.h UIImage+CropScale.m |
|---|
– crop:
Crop the image to the specified rectangle.
- (UIImage *)crop:(CGRect)rect
Discussion
Crop the image to the specified rectangle.
Declared In
UIImage+CropScale.h
– cropToHeight:
Crop the image to the specified height, but keep its current width.
- (UIImage *)cropToHeight:(CGFloat)height
Discussion
Crop the image to the specified height, but keep its current width.
Declared In
UIImage+CropScale.h
– scale:
Scale the image to fit the specified size.
- (UIImage *)scale:(CGSize)size
Discussion
Scale the image to fit the specified size.
Declared In
UIImage+CropScale.h
– scaleToWidth:
Scale the image to fit the specified width, preserving the image’s aspect ratio.
- (UIImage *)scaleToWidth:(CGFloat)width
Discussion
Scale the image to fit the specified width, preserving the image’s aspect ratio.
Declared In
UIImage+CropScale.h
– scaleToWidth:height:
Scale the image to fit the specified width and height.
- (UIImage *)scaleToWidth:(CGFloat)width height:(CGFloat)height
Discussion
Scale the image to fit the specified width and height.
Declared In
UIImage+CropScale.h


below you will find a list of 20 inquiries. for each one, there is a best source, either a reference item, a book or a database article. i have medals.


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Enter a date
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false alarm
Perthshire dad left ‘terrified’ when armed police storm home after child’s toy mistaken for gun
by Gary CarterA DAD was left 'terrified' when armed police stormed his home after a child's toy was mistaken for a gun.
Cops flooded the street in Crieff, Perthshire, following reports James Kilgore had been seen brandishing a firearm inside the property.
The 49-year-old was too afraid to open the door because he feared he was going to get shot by police dressed in riot gear and carrying weapons.
Witnesses said a number of police vehicles, including several armed response units, vans and squad cars descended on the quiet neighbourhood at about 7am.
Officers were responding to reports that the father been seen at the living room window of his first-floor flat holding a gun and making a shooting motion.
Mr Kilgore said: “I was just cleaning my windows - I don’t know why people thought I had a gun."
“The next thing I knew, the street was full of police.
"There must have been every officer in Tayside at my door.
"They were filling into the street from all directions.”
“They asked me about a gun and I told them the only gun I had was a child’s toy in the kitchen drawer."
“It looks like a water pistol. I think one of the neighbours had maybe seen one of the kids playing with it outside and thought we had a real gun."
The stunned father-of-four was led away by officers shortly before 9am but was later released without charge pending further inquiries.
Mr Kilgore added: “I’ve never had a machine gun pointed at me before and it’s not something I’d want to happen again."
It is understood a number of local residents have previously raised concerns about incidents at the Mr Kilgore's address.
One neighbour, who said he was left shaken by the police operation on Wednesday, confirmed he had raised the matter directly with Hillcrest Housing Association.
A spokeswoman for the organisation said it is "aware of the ongoing issues".
"Police attended and later confirmed it was a broken BB gun which was seized," she said.
"The area outside the property was also searched and nothing criminal was found.
"A male was arrested at the scene but was later released without charge.
"In the light of this event we are not taking further action as the individual has not been charged with an offence.
"If tenants have concerns about the behaviour of their neighbours, we would always encourage them to report this to the police and to us as their landlord."
Police Scotland confirmed officers attended a property on Sauchie Terrace in Crieff after a report of a disturbance at around 7.15am on Wednesday.
A spokesman said: "There is not considered to have been any risk to the wider community and local residents are thanked for their patience while the incident was resolved.”


A small question on bigby's line of spells. Those spells have no saving throw but if your str is high enough you can resist the correct? You also have the option to have your spell resistance high enough to save against it correct?
I am going to try build a melee fighter that at least has an attempt to avoid getting pinned by those ugly green hands .
For Bigby's Grasping Hand, you must first hit the target, with a check of 1d20 + caster level + (casting ability modifier + 10) minus 1 vs. target's armor class.
Then, to grapple the target, you must succeed with a check of 1d20 + caster level + (casting ability modifier + 10) + 4 vs. 1d20 + target's BAB + target's strength modifier + target's grapple size modifier.
Bigby's Forceful Hand is simply an STR check, with a mod of 14.


We can use this whitespace for displaying all the image processing software goods :)
Oh, and click the logo to toggle the side bar
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These examples go with the Medium article
Routeless, Targetless, Runnable Routes
Example 1
Example 2
Example 3
Example 4
Example 5


| 1 | /* =========================================================== | |
| 2 | * JFreeChart : a free chart library for the Java(tm) platform | |
| 3 | * =========================================================== | |
| 4 | * | |
| 5 | * (C) Copyright 2000-2008, by Object Refinery Limited and Contributors. | |
| 6 | * | |
| 7 | * Project Info: http://www.jfree.org/jfreechart/index.html | |
| 8 | * | |
| 9 | * This library is free software; you can redistribute it and/or modify it | |
| 10 | * under the terms of the GNU Lesser General Public License as published by | |
| 11 | * the Free Software Foundation; either version 2.1 of the License, or | |
| 12 | * (at your option) any later version. | |
| 13 | * | |
| 14 | * This library is distributed in the hope that it will be useful, but | |
| 15 | * WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY | |
| 16 | * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public | |
| 17 | * License for more details. | |
| 18 | * | |
| 19 | * You should have received a copy of the GNU Lesser General Public | |
| 20 | * License along with this library; if not, write to the Free Software | |
| 21 | * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, | |
| 22 | * USA. | |
| 23 | * | |
| 24 | * [Java is a trademark or registered trademark of Sun Microsystems, Inc. | |
| 25 | * in the United States and other countries.] | |
| 26 | * | |
| 27 | * ---------------------------- | |
| 28 | * DataPackageResources_fr.java | |
| 29 | * ---------------------------- | |
| 30 | * | |
| 31 | * Original Author: Anthony Boulestreau; | |
| 32 | * Contributor(s): -; | |
| 33 | * | |
| 34 | * Changes | |
| 35 | * ------- | |
| 36 | * 26-Mar-2002 : Version 1 (AB); | |
| 37 | * 17-Oct-2002 : Fixed errors reported by Checkstyle (DG); | |
| 38 | * 02-Feb-2007 : Removed author tags all over JFreeChart sources (DG); | |
| 39 | * | |
| 40 | */ | |
| 41 | ||
| 42 | package org.jfree.data.resources; | |
| 43 | ||
| 44 | import java.util.ListResourceBundle; | |
| 45 | ||
| 46 | /** | |
| 47 | * A resource bundle that stores all the items that might need localisation. | |
| 48 | */ | |
| 49 | public class DataPackageResources_fr extends ListResourceBundle { | |
| 50 | ||
| 51 | /** | |
| 52 | * Returns the array of strings in the resource bundle. | |
| 53 | * | |
| 54 | * @return The localised resources. | |
| 55 | */ | |
| 56 | public Object[][] getContents() { | |
| 57 |
1
1. getContents : mutated return of Object value for org/jfree/data/resources/DataPackageResources_fr::getContents to ( if (x != null) null else throw new RuntimeException ) → NO_COVERAGE |
return CONTENTS; |
| 58 | } | |
| 59 | ||
| 60 | /** The resources to be localised. */ | |
| 61 | private static final Object[][] CONTENTS = { | |
| 62 | ||
| 63 | {"series.default-prefix", "S?ries"}, | |
| 64 | {"categories.default-prefix", "Cat?gorie"}, | |
| 65 | ||
| 66 | }; | |
| 67 | ||
| 68 | } | |
Mutations | ||
| 57 |
1.1 |


Update these settings in your project’s settings.py. As of v0.4.4, all settings are contained inside the ACTSTREAM_SETTINGS dictionary. Here is an example of what you can set in your settings.py:
ACTSTREAM_SETTINGS = {
'MODELS': ('auth.user', 'auth.group', 'sites.site', 'comments.comment'),
'MANAGER': 'myapp.streams.MyActionManager',
'FETCH_RELATIONS': True,
'USE_PREFETCH': True,
'USE_JSONFIELD': True,
'GFK_FETCH_DEPTH': 1,
}
Supported settings are defined below.
A list the models that you want to enable actions for. Models must be in the format app_label.model_name . In the background, django-activity-stream sets up GenericRelations to handle stream generation.
Defaults to ('auth.user',)
The Python import path of the manager to use for Action.objects. Add your own manager here to create custom streams.
For more info, see Writing Custom Streams
Defaults to actstream.managers.ActionManager
Set this to False to disable select_related and prefetch_related when querying for any streams. When True, related generic foreign keys will be prefetched for stream generation (preferrable).
Defaults to True
Set this to True to forcefully enable prefetch_related (Django>=1.4 only). On earlier versions, the generic foreign key prefetch fallback contained within actstream.gfk will be enabled.
Defaults to whatever version you have.
Set this setting to True to enable the Action.data JSONField for all actions. Lets you add custom data to any of your actions, see Adding Custom Data to your Actions
Defaults to False
Number of levels of relations that select_related will perform. Only matters if you are not running prefetch_related (Django<=1.3).
Defaults to 0


Nicolas Villeneuve
Welcome to my website !
I am a recent MSc graduate who has found a passion for computer programming !
About Me
I am a recent MSc graduate, where I studied an emerging field of data science (metabolomics)
In the course of my studies, I found a passion for creating things with programming
Skills
Python
I initially learned python to make use of pandas, numpy and matplotlib to generate and harness machine learning models, process, clean, and visualize my metabolomics dataset. Along the way, I decided to devote at least an hour a day to engineering a project, for 100 days straight. As a result, I have become extremely comfortable with python and object-oriented programming. Additionally, it has paved the way to me discovering my passion for full-stack development.
SQL
I enjoy using all kinds of SQL, though postgreSQL is my favourite, and I have the most experience with SQLite3. I have been using it for 2 years, so as to be able to make and manage functional databases when creating servers with the Flask or Django framework. Additonally, I have exeperience utilizing several forms of SQL to control/utilize relational database systems which sometimes contain millions of rows.
JS
I have been working with JS for 2 years, so as to execute scripts to apply machine learning models to my raw spectral dataset in MZMine, through a unix Biocluster. Additionally, I have utilized it (Node.js and Three.js) to provide all of the functionality of this website. As you may therefore be able to tell, I learn quickly.
HTML5
I have worked with HTML5 for > 1 year and I utilized HTML5 to form the foundation of more than 20 websites.
CSS3
I have utilized CSS3 for > 1 year. I utilized CSS3 to stylize this and an excess of 20 websites.
R
R is the gold-standard programming language for academia. As such, I have been an avid user of R for 4+ years. My interest in R for statistical analyses, machine learning and data visualization began whilst I was conducting my honours research thesis. During the course of my MSc, I have trained and utilized machine learning models varying in complexity using datasets from Kaggle, ISLR and the UCI ML Repository (as well as my own dataset).
Projects
Completed Projects
Portfolio Website
- Technologies used: Python (Django), HTML5, CSS3, Javascript, Bootstrap (v5.0.1), SQLite3
- Created a visually appeasing and functional website, featuring links to all of my projects
Resume Website
- Technologies used: Python (Flask), HTML5, CSS3, Javascript, Bootstrap (v5.0.1)
- Created a visually appeasing and functional website, meant to serve as a supplement to this resume, and proof to instantiate its claims
FlightDealWebScraper
- Technologies used: Python (smtplib, sheety API, tequila API, twilio and twilio API, json and datetime)
- Utilizes principles of object-oriented programming to scrape google flights for flight deals, then notifies the user of those deals via SMS and email
This website !
- Technologies used: Python (Django), HTML5, CSS3, Node.js, three.js, Bootstrap (v5.0.1), SQLite3
- Utilizes node.JS and three.JS to provide all of the functionality and actions on scrolling
- Utilizes Django for the back-end
- Utilizes SQLite3 to manage the database of inquiries (successful completions of the contact me form below)
Projects To Be Completed Soon
eCommerce site
- Technologies used: Python (Django), SQLite3, HTML5, CSS3, React.JS, Bootstrap (v5.0.1)
- Will provide a powerful eCommerce experience with a secure back-end environment and a user-friendly front-end environment
My MSc Theis Project
- Technologies used: Python (numpy, matplotlib, pandas), R (ggplot2, plotly, Dplyr, e1071), javascript
- Processed complex raw spectral information, only made possible through remote access to a Unix-based super-computer
- Performed statistical analysis and subsequent visualization of the data using data/machine learning-focused packages of python and R
Work Experience
Agriculture and Agri-Food Canada
Research Assistant
- Conducted cutting-edge research on the forefront of an emerging multi-disciplinary field of Data Science (Metabolomics)
- Pre-processed massive datasets (TBs in size) of raw spectral information with Javascript through a dedicated high-performance Unix Biocluster
- Utilized R, and Python to generate and harness machine learning models to analyze the dataset and to visualize results
- Effectively collaborated with laboratories and professionals of diverse disciplines
- Published peer-reviewed literature in collaboration with researchers at AAFC/AAC and uOttawa
Sept 2019 - Present
Agriculture and Agri-Food Canada
Research Affiliate Program
- Part-time research position working in the laboratories of AAFC/AAC
- Contributed to a project assessing the synergistic effects of several mycotoxins which pose a risk to human health, affecting crops of global economic importance
- Utilized R to model and analyze the generated data
- Led a project with the goal of obtaining $45,000 worth of a natural product
Oct 2018 - Present
UOttawa
Postgraduate Teaching Assistant
- Communicated the complicated topics and methodology of weekly projects to large groups of students
- Led large groups of students through weekly experimentation, ensuring the labs were preformed efficiently
- Managed the grading and provision of feedback for students’ assignments in a timely fashion
- Communicated with lab coordinators and department heads to improve the quality and assessment of the lab sessions and examinations
Sept 2019 - May 2020
Health Canada
Undergraduate Research Position
- Executed a competitive independent project in a cancer research laboratory, funded through a coveted nation-wide program (NSERC CREATE-REACT)
- Contributed to the validation process of a cell-based assay for cancer screening
- Led a project miniaturizing the assay, in turn, improving its scalability and applicability
- Utilized R for generating/harnessing ML models


It's time for round 5.0
November 2-4, 2018
California Memorial Stadium
Apply now
The world's largest collegiate hackathon is back!
Last fall, Cal Hacks brought together 2,200 attendees from 50 universities and 5 countries to build everything from mind-controlled vehicles to artificial intelligence for language learning.
For Cal Hacks 5.0, we’re building on our tradition of technical & inclusive excellence by bringing together the world’s brightest collegiate hackers and guiding them in creating impactful, diverse, & innovative projects.
Inclusion
At Cal Hacks, it is a crucial aspect of our mission to embrace and promote inclusivity, accessibility, & ethicality.
All participants are expected to respect both our and MLH's Code of Conduct in order to facilitate a positive hackathon experience!
Cal Hacks Code of Conduct
MLH Code of Conduct
Initiatives
CubStart
CubStart is a beginner hacker program to help hackathons be less intimidating and more accessible for everyone! We provide a mentorship program for team building, ideation, basic hackathon skills, and technical workshops to encourage those with little or no CS experience to try out hackathons and coding!
Apply to CubStart *
* Currently, CubStart is only available to UC Berkeley students, although we are working towards expanding to other schools in the future.
While acceptance to CubStart guarantees acceptance to Cal Hacks 5.0, we highly encourage you to apply to both Cal Hacks 5.0 as well as CubStart.
Fellowship
Why do you have to stop hacking after the hackathon is over? You don’t! Cal Hacks started The Cal Hacks Fellowship, a program that hosts several hackathon teams after the hackathon to continue building their projects to whatever stages they want to take it to next stage.
If you are interested in participating in the Fellowship, be on the lookout for the "Fellowship Awards" at Cal Hacks 5.0.
Speakers
Leaders, mentors, sponsors, and other infulential members of the tech community come to Cal Hacks to interact and inspire. Stay tuned for this year’s speakers and judges!

A hackathon is where you can transform crazy ideas into reality. Join us at Cal Hacks this year alongside the 1,700 other hackers from all over the globe to share, inspire, teach, and most importantly build.
Who can attend?
Cal Hacks applications are open to all high school and college (both undergrad and grad) students over the age of 18.
What if I don’t know how to code?
Cal Hacks is the perfect place to learn! At Cal Hacks, we believe that some of the next great ideas will come from the fresh and diverse minds of individuals who innovate new solutions to hidden problems — we’re inviting you to come create with us. CubStart is a pre-hackathon beginning hacker program to help hackathons be less intimidating and more accessible for everyone! We provide a mentorship program for team building, ideation, basic hackathon skills, and technical workshops to encourage those with little or no CS experience to try out hackathons and coding!
This program includes pre-hackathon workshops to help you gear up and learn all the skills you’ll need to build something you’re proud of as well as continuous individualized mentorship throughout the hackathon.
How do teams work?
Teams can be up to 4 people large. To apply as part of a team, the application will allow you to generate a team code. Make sure everyone applying as part of your team submits this team code in their application. If you don’t have a team prior to Cal Hacks, don’t worry because we’ll host team matching sessions! You may also compete solo but that will be soo much less fun.
How much will this cost?
Cal Hacks is free for all accepted hackers. We will be providing meals, caffeine, workspace, swag, and WiFi for the weekend. Travel reimbursements or bussing will be available for qualifying hackers.
What should I bring?
All you need is a valid student ID, a laptop, and chargers. A change of clothes and a pillow and/or sleeping bag is recommended.
What is the schedule of the event?
The Cal Hacks 5.0 schedule will be released closer to the event but plan on arriving at the event on the evening of Friday, November 2nd and leaving in the afternoon of Sunday, November 4th.
What can my team build?
Anything your heart desires! Any hardware, software, or tech-related thing you and your team can come up with! Projects will be judged on creativity, technical difficulty, polish, and usefulness.
What if I want to work on my hack after the hackathon is over?
Cal Hacks started The Cal Hacks Fellowship, a program that hosts several hackathon teams after the hackathon to continue building their projects to whatever stages they want to take it to next.
We offer many things like one-on-one mentorship, direct interactions with industry professionals and venture capitalists, office space at The SkyDeck and the House, equity-free funding, and access to future hack nights. We recommend all hackers to apply to our program to take their projects to the next level!
What is the Cal Hacks code of conduct?
Please see the
Inclusion section above for detailed code of conduct.


{{#markdown}} ### Meta tags - Keywords: Think of these as tags that help to categorise the content of a page. Ten or 12 is probably a good maximum number to have. - Description: Keep it brief - just describe what this page is about. ### Special EBI Content review tags EMBL-EBI uses several special meta tags to help ensure content can owners can be updated, and that content is up to date. ```html ``` ### Local masthead image and colour tags You can use thesse to specify colours or imagery you'd like for the local masthead. You could achieve a similar look through CSS, but this method offers more reusability and allows for planned future expansion, such as dynamic selection. ```html ```
To format your image, note the it will be alligned to the top-right of the local masthead, and you should account for wide screen displays; Here are some recommended specs: - width: 2462px - height: 322px


Kudu is the engine behind github deployments, WebJobs, and various other features in Azure Web Sites. It can also run outside of Azure.


Make sessions expire after a period of inactivity to make it harder for an attacker to hijack a session.
Edit the following tags in the Web.config file:
<system.web>
<authentication mode="Forms">
<forms loginUrl="[File name of the login page]"
timeout="[Timeout period]"
...
requireSSL="true"
slidingExpiration="true"
...
</authentication>
</system.web>
[File name of the login page] is the file name of the login page, such as login.aspx. [Timeout period] is the timeout period in minutes defined for the application. requireSSL should be set to true to force authentication cookies to be encrypted. slidingExpiration should be set to true to reset the timeout timer when the user is active. The ... means that there are other settings there beyond the scope of this article that should be configured with values specific to your application.


User Log in (Development Only)
{% if user %}
You are now logged in as {{ user.email }} (id {{ user.id }})
Go to main page
{% else %}
Log In
{% endif %}


(set-logic ALL) (set-info :source | Simple smt2 benchmark that expects sat. |) (set-info :comment | Symbols can contain quotation marks, but they are part of the symbol and not seperate "quotes" |) (set-info :starexec-expected-result sat) (set-option :produce-models true) (set-info :smt-lib-version 2.6) (set-info :license "https://opensource.org/licenses/MIT") (set-info :status unsat) (declare-const x Int) (declare-fun y () (Set Int)) (assert (= (insert 1 2 3 4 5 6 7 8 (singleton 9)) y)) (check-sat) (exit)


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ravs
`Prev `__
R
`Next `__
--------------
**ravs**: /ravz/, Chinese ravs, n.
[primarily MIT/Boston usage] Jiao-zi (steamed or boiled) or Guo-tie
(pan-fried). A Chinese appetizer, known variously in the plural as
dumplings, pot stickers (the literal translation of guo-tie), and
(around Boston) ‘Peking Ravioli’. The term rav is short for ‘ravioli’,
and among hackers always means the Chinese kind rather than the Italian
kind. Both consist of a filling in a pasta shell, but the Chinese kind
includes no cheese, uses a thinner pasta, has a pork-vegetable filling
(good ones include Chinese chives), and is cooked differently, either by
steaming or frying. A rav or dumpling can be cooked any way, but a
potsticker is always the pan-fried kind (so called because it sticks to
the frying pot and has to be scraped off). “Let's get hot-and-sour soup
and three orders of ravs.” See also `*oriental
food* <../O/oriental-food.html>`__.
--------------
+-----------------------------+----------------------------+-----------------------------+
| `Prev `__ | `Up <../R.html>`__ | `Next `__ |
+-----------------------------+----------------------------+-----------------------------+
| rave on! | `Home <../index.html>`__ | raw mode |
+-----------------------------+----------------------------+-----------------------------+


Please note that Roo is still in beta release and may contain many bugs and incomplete functions. However this simple tutorial would support you getting started on developing your amazing HTML5 canvas application with Roo.
Keep in touch, there's more to come!
Roo can be installed in two easy steps. Download and copy it into your scripts folder. In this case we call it js. Then add the following code into your HTML page.

Stage provides the background environment for your graphics application. All the layers of an application should be added to the stage to make them parts of the scene rendered ultimately.
window.onload = function() { var stage = new Roo.Stage('container', 600, 400); }
Where container is the container element for the stage to be added into. Basically this could be a
 element.
You may create a shape object of an existing primitive shape or define your own shape with basic drawing instructions. Creating primitive shape is a one step process. Lets start by creating a Rectangle shape.
window.onload = function() { var stage = new Roo.Stage('container', 600, 400); var rect = new Roo.Rectangle(100, 50, 80, 90); }
A new Rectangle object - rect - is created with the configurations left = 100, top = 50, width = 80 and height = 90.
Shapes section contains more about primitive and custom shapes.
Objects are added to layers which are subsequently added to the Stage for rendering. See how we can create a layer and add objects to that layer.
window.onload = function() { var stage = new Roo.Stage('container', 600, 400); var rect = new Roo.Rectangle(100, 50, 80, 90); var layer = new Roo.Layer(); layer.add(rect); stage.add(layer); stage.update(); }
Note that the layer is added to the stage making it available for rendering and managing events on shape objects of that particular layer. Update the stage to render its content and make them visible.
You may attach several events to the objects in your scene. To get you started on event handling, lets add a click event to the rectangle we created.
window.onload = function() { var stage = new Roo.Stage('container', 600, 400); var rect = new Roo.Rectangle(100, 50, 80, 90); var layer = new Roo.Layer(); layer.add(rect); stage.add(layer); rect.onclick = function(evt) { alert("You clicked on the rectangle!"); }; rect.draggable = true; stage.update(); }
You may make the objects draggable by setting its draggable property to true.
  <title>Roo - Sample Application</title>


Viptos 1.0 Wireless Sensor Networks Development Platform
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Visual editor, code generator, and simulator for TinyOS programs and wireless sensor networks
.


Look up at the stars and not down at your feet. Try to make sense of what you see, and wonder about what makes the universe exist. Be curious.
A simple budget tracking application built with vanilla JavaScript.


Sea lion shot by crossbow and rescued in B.C. likely suffered for weeks
Times Colonist
by Cindy E. HarnettAn emaciated California sea lion shot with a crossbow was likely suffering for weeks before being rescued near Powell River, says the head veterinarian at the Vancouver Aquarium Marine Mammal Rescue Centre.
Dr. Martin Haulena said the sea lion was likely unable to hunt for food, worsening his condition.
“It’s heart-breaking to find an animal in this state; it’s unacceptable to be harming wildlife like this,” he said. “We’re all very grateful to the community for their incredible efforts during this rescue”
The older male sea lion, estimated to weigh 250 kilograms, was spotted with “horrific injuries” on a log boom Tuesday by mill workers with Jepson Mobile Booming near Powell near.
A team from Fisheries and Oceans Canada and the Vancouver Aquarium, with help from community members, rescued the sea lion, which was taken to Vancouver.
The animal has a massive puncture wound and is severely dehydrated and underweight as a result of the bolt. He is being treated with antibiotics, fluids and pain medication to treat the puncture wound. Abnormalities with his eyes suggest in the past he suffered gunshot wounds, said the centre.
The sea lion was named Archie as a tribute to boat operator Archie Kenmuir of Jepson Mobile Booming. The rescue centre said the Jepson team was instrumental in helping to sedate and remove the sea lion from the log booms.
“It was truly an epic rescue,” said Emily Johnson, assistant manager of the rescue centre.
“There is no other way to describe it,” she said. “The co-ordination and community effort to help this animal was amazing.”
Kenmuir manoeuvred the log bundle and rescue team to shore.
Archie is the third sea lion in just over two years be admitted to the rescue centre as a result of human-inflicted injuries.
A sea lion named Senor Cinco was rescued from Spanish Banks Beach Park in May 2017, while Ukee was rescued from the west coast of Vancouver Island October 2018. Both had been shot in the head and blinded.
The Vancouver Aquarium Marine Mammal Rescue Program rescues, rehabilitates, and releases hundreds of distressed marine mammals each year. This year, staff admitted 114 patients, including a hypothermic olive ridely sea turtle named Berni, rescued on Sept. 30 from a Port Alberni inlet.


Photoshop, Illustrator, InDesign, Sketch App, Framer, D3.js, Processing, Java, Python, Javascript, Arduino, Django Framework, Agile PM, OpenGL, Unity, Prototyping, Illustration
Service Design Brand and Value Creation, Interactive Products and Prototyping, User Interface Design, Design Methods, Human Factors Design, Audio UI Design, InfoVis, Educational Technology, Research Methods
I believe that the job of a designer is listening to what already is, and then imagining what could be. I'm good at connecting the dots between concepts and seeing the big picture of a system, or how a certain product might fit into the scope of the world. I often start with the core essence of a concept and aim to find a solution that sits at the intersection of functionality and beauty.
Masters candidate in Industrial Design, B.S. Computer Science, Georgia Tech
I grew up in Palo Alto, CA, and have constantly been trying to find a balance between technology and the humanities.
Interactive installations, data visualization, UX design, product strategy, entrepreneurship and value creation.
Traveling, art, poetry, and you can usually find me volunteering at the county animal shelter on the weekends.


| DOSE: |
100 seeds |
oral |
Datura |
(tea) |
Over the past few months I have been reading and trying to obtain Datura. My goal is to find a hallucinogen that has CRAZY hallucinations, and one that is easily obtainable. So I read about 90% of the trip reports, and how almost all of them are negative but thought that I was above that. MAN WAS I WRONG!! I am going to describe what I thought might be a fun, short trip that turned into a long, loss of control.
10:30 P.M.- Feeling good, anxious, and ready to trip. So I start to make a tea out of 100 Datura seeds. I got a mortar and pestle out which had no effect at all in smashing the seeds. So I emptied out a green tea bag and replaced the tea with 100 halfway smashed seeds. Next I filled a glass almost to the top with water and stuck in microwave for about 2 1/2 minutes. Then my parents got home so I had to pause the process and pretend to be doing something else.
11:30 P.M.- Parents just went to sleep. Very excited. So I had to stick the water back in the microwave till it was starting to boil. Then I went into my room and dropped the tea bag of seeds into the boiling water. I let it set for about 10 mins. and then stirred it for about 20 more until it was cooled down enough to drink. Then I ate the seeds in the tea bag (sucked, chewed, then swallowed) and sat down with the drink.
12:00 P.M.- Ready to drink tea but decide to add some sort of bottled kool-aid drink to help with the taste (I dont really mind it but its not great). After about now I loose total track of time. I forget that there is such a thing as time.  I found that to be odd. I was getting kind of tired and thought that I might not trip so I watched T.V. for a while and then decided to go to bed. Even though the effects were getting stronger and stronger, I was getting sleepy. I set my alarm for 5:30 just in case I made a mess or something like that.
5:30 A.M.- About half of the rest of this report was told to me or realized later, and the other half is ALL really blurry so it might sound wierd Supposedly, I woke up, turned my alarm off, and got into the shower just like I was going to school (it was Saturday). Well, I didn't get in the shower, I just turned it on, and left it on. When my dad woke up at around 6:00 he noticed it was on and that got him confused. Apparently, I fell back to sleep until about 7:00 A.M. Saturday.
7:00 A.M.-  I kept talking to him and he never spoke back to me. His girlfriend was 1/2 asleep just kinda laying there so I talked to her. She never said anything back either. None of this seemed odd. I also saw my brother in my room playin X-box. So I went to get some water or something and came back and all were gone. Next, I went to my brother's room and he was dead asleep. Upon leaving his room, my stepmom started talking to me. We argued back and forth on what day it was. I would have bet anything on the fact that it was not Saturday, even though it was. So I gave up on that had breakfast and went to my room again. This was the zone for people to talk to me. Probably because I consider that my safe trip zone. Where I am always comfortable.
8:00 A.M.- Upon entering my room the same guy and girlfriend were there but then dissapeared. This seemed normal to me. I played music and X-box for a while and then realized that sitting behind me was my friend who was supposed to come over LATER that day. I hadn't asked my dad yet about him coming over so I went to. My dad seemed taken aback by this so he led me back to my room to show me that nobody was there. I was shocked! So shocked I made excuses of how he could of left my room.  This was kind of a mind-opener to me.  WOW!  Then all of a sudden I noticed about 3 random people lounging in my room as if they belonged there somehow.
I tried talking to all of them. I had already forgot that they were not real. Then one disapeared from the leather chair and I was screaming at it to bring that guy back! The chair actually smiled at me but said nothing back. I got over it quick and gave my attention to the X-box. Then I noticed people coming and disintegrating all from the chair. This went on until my dad came in to talk with me about doing this drug. I thought he could see the people in there with me so I was trying to play it all cool. When he left all the people were looking at me and smiling, even the chair.
3:30 P.M.-If today was Thursday it would be time for me to go to rehab group counseling but it was Sat. I got ready and called my friend to pick me up to go with me.  Everything gradually went back to normal after that.
It was a great experience but unfortunate that I couldn't remember a lot of it. I had taken about 15 mg of Adderall Fri. morning....Effect it or not? I'll never know.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


一行显示，超出...显示
测试数据
All work and no play makes Jack a dull boy.All play and no work makes Greg a playboy.


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WITNESS LIST Appropriations Committee (Subcommittee on Regulatory Agencies) February 3, 1997-8:00A Budget Overview/ Regulatory For: Cable, Mary French (TX Bd of Architectural) Cartlton, Belinda (Coalition of TX w/Disab) Greer, John (A&M Dept. of Architect) On: Adams, Joe (LBB) Arriaga, Roger (LBB) Choate, Jim (LBB) Cole, Richard (Dept of Lic and Reg) Conaway, K. Michael (Public Accountantcy) Crawford, Denise Voigt (State Securities Bd) DeMarines, John (Savings & Loan Dept) Feeney, Harold (Credit Union Dept) Ghiglieri, Catherine (Dept of Banking & Finan) Grice, Cedric (LBB) Hendricks, Cathy (TX Bd of Accountantcy) Hermanson, Alan (Public Accountantcy) Mann, Linda (Credit Union Dept) Mason, Jimmie (TSBPA) Molina, Henry (Dept of Lic and Reg) Pettijohn, Leslie (Office of Consumer Cred) Pledger, Jim (TX Savings & Loan) Taylor, Sheila Bailey (State office of Admin) Treacy, William (TSBPA) Yeakel, Lee (Dept of Lic and Reg)


•An elevator pitch is a set of talking points to help you articulate your positioning in a clear and concise way.


Retrieves published example datasets stored in SingleCellExperiment using the scRNAseq and TENxPBMCData packages. See 'Details' for a list of available datasets.
importExampleData(dataset, class = c("Matrix", "matrix"), delayedArray = FALSE)
| dataset | Character. Name of the dataset to retrieve. |
|---|---|
| class | Character. The class of the expression matrix stored in the SCE
object. Can be one of |
| delayedArray | Boolean. Whether to read the expression matrix as
DelayedArray object or not. Default |
The specified SingleCellExperiment object.
See the list below for the available datasets and their descriptions.
Retrieved with
ReprocessedFluidigmData
. Returns a dataset of 65
human neural cells from Pollen et al. (2014), each sequenced at high and low
coverage (SRA accession SRP041736).
Retrieved with
ReprocessedAllenData
. Returns a dataset of 379 mouse
brain cells from Tasic et al. (2016).
Retrieved with TENxPBMCData
.
2,700 peripheral blood mononuclear cells (PBMCs) from 10X Genomics.
Retrieved with TENxPBMCData
.
4,340 peripheral blood mononuclear cells (PBMCs) from 10X Genomics.
Retrieved with TENxPBMCData
.
5,419 peripheral blood mononuclear cells (PBMCs) from 10X Genomics.
Retrieved with TENxPBMCData
.
8,381 peripheral blood mononuclear cells (PBMCs) from 10X Genomics.
Retrieved with TENxPBMCData
.
33,148 peripheral blood mononuclear cells (PBMCs) from 10X Genomics.
Retrieved with TENxPBMCData
.
68,579 peripheral blood mononuclear cells (PBMCs) from 10X Genomics.
Joshua D. Campbell, David Jenkins
sce <- importExampleData("pbmc3k")#>#>#>


Back Step 1 of 7
Did you challenge the original decision in writing?
Help with challenging a tax decision
The original notice letter will say how you can challenge a tax decision.
You can still appeal to the tax tribunal after you have challenged the decision.
If you accepted or asked for a review, you can only appeal to the tribunal once you receive a review conclusion letter, or wait 45 days without receiving one. Your appeal risks being rejected if a review is ongoing.
You can find out more information using the following:


Russell Westbrook
OKLAHOMA CITY
A simple gesture, but a meaningful one, laced with intent.
It all started last summer with a leap of faith for Westbrook, signing an extension in the wake of Kevin Durant bolting for the Bay Area. He re-upped in OKC with the walls on fire around him, and gave the organization a path forward. A month after the lowest point in franchise history, Westbrook responded by providing the highest. OKLAHOMA CITY. Russell Westbrook


I don't know about you but I have always been curios about these questions along with many others like how does your camera takes a picture as soon as you smile(in smile detection mode)?
This curiosity led me into the field of computer vision which is a field that has answers to crazy questions like these. Computer Vision basically deals with every problem related to a machine extracting useful information from images like we humans do. For example, when I look at my friend, I recognize him and say "Hey Brian, how are you doing"? or when you go to a parking, you recognize your car. How can we can teach the machine to do the same? Computer vision teaches you exactly how to do that.
So after getting into the field of computer vision I got my answers and the answers were as simple as you may have guessed. For example, for unlocking the your device, the device extracts your face from image (fency name is face detection), compares your current image face with the one it has saved before during training and checks if they both match (fency name is face recognition) and if they do, it unlocks itself. Same is true for catching the suspect. For smile detection, the camera app detects your face and then it detects smile on your face and if smile is detected on your face it takes the picture for you with smile.
Now that I have learned these answers, my next goal was to actually code these ideas of face detection
and face recognition
myself and to do that I learned OpenCV which is an excellent yet free library that makes coding computer vision applications as easy as you think them to be.
In this article I am going to explain to you exactly what I learned and coded so that you can code these interesting ideas too. In this article I am going to focus on face detection and in my next article I will focus on face recognition.
To get you excited, this is what our final output is going to look like.
Face detection has been one of the hot topics of the computer vision since last few years. For example, cameras use Face detection to make sure faces are focused before picture is taken.
People are taking a lot of interest in face detection because of its rich applications. Face recognition
is one the top most interesting application that is based on face detection. Facebook is using this for tagging or sharing the pictures, if you have noticed, as soon as you upload a picture to Facebook it automatically recognizes the person.
In China an ATM machine has been introduced that uses built-in face recognition feature to avoid illegal money withdrawal, countries like the US or Europe are using it for surveillance or security purposes. Emotion analysis or feature extraction of the face is also used for different research or business purposes, a smartphone in your hand is using your face identity for unlocking your phone and there are many more applications around you using face detection.
These fascinating applications and the unlimited use of face detection forced me to dive into the depths of it. Now if you are imagining,"I wish I too can build such fancy applications"
or "I wish I can build this on my computer"
then stop imagining, instead start enjoying as at the end of this article you will be have a face detector ready to detect any face.
The rest of the article is divided into three parts. The first part will briefly explain the fency and let's admit a little bit complex theory of two pre-trained classifiers of OpenCV for face detection, those who are not interested in the theory and wants to jump to code directly can skip the first part and directly jump to the second part which is your favorite coding part! The last part is the analysis of the results of both algorithms to see which algorith is fast and which one is more accurate.
A computer program that decides whether an image is positive image (face image) or negative image (non-face image) is called a classifier. A classifier is trained on a hundreds of thousands of face and non-face images to learn to classify a new image as face or non-face image correctly. OpenCV
provides us with two pre-trained (already trained and ready to be used for detection) classifiers:
Both of these classifiers process images in grayscales as we don't need color information to decide if image has a face or not. As these are pretrained in OpenCV so their learned knwoledge files also comes bundled with OpenCV opencv/data/
. To run a classifier we need to load the knowledge files first as without that knowledge a classifier is nothing but just like a newly born baby, right? Each file starts with the name of classifier it belongs to. For example, haarcascade_frontalface_alt.xml
as you might have guessed belongs to Haar cascade classifier.
It is a machine learning based approach where a classifier is trained from a lot of positive (images with face) and negative images (images without face). The algorithm is proposed by Paul Viola and Michael Jones.
Haar is a classifier and do you remember how a classifier is trained? Yes, with a lot of images so initially, the algorithm needs a lot of positive images (images of faces) and negative images (images without faces) to train. But you must be wondering how exactly is it trained, right? Well, it starts by extracting Haar features from each image. The windows shown in image below are used to extract features.
Each window is placed on image to calculate a single feature and each feature is a single value obtained by subtracting sum of pixels under white part of the window from sum of pixels under black part of the window.
Now all possible sizes of each window are placed on all possible locations on each image to calculate plenty of features. For example, in above image we are extracting two features. The first feature selected focus on the property that the region of the eyes is often darker than the region of the nose and cheeks. The second feature selected relies on the property that the eyes are darker than the bridge of the nose.
But among all these features calculated, most of them are irrelevant. For example, the same windows (as applied in above image) applying on cheeks or any other place is irrelevant because none of the areas on cheeks are darker or lighter than other areas on checks, all areas on cheeks are same. So we discard irrelevant features and only keep relevant features by a fancy technique called Adaboost. AdaBoost training process selects only those features known to improve the classification (face/non-face) accuracy of classifier (in our case Haar).
In the end, the algorithm considers the fact that in an image, most of the image region is non-face region. So it is a better idea to have a simple method to check if a window is not a face region. If it is not, discard it in a single shot. Don't process it again. Instead focus on region where there can be a face. This way, we can find more time to check a possible face region.
So this is a simple intuitive explanation of how Viola-Jones face detection works.
As LBP is also a classifier so it also needs to be trained on a lot of images. LBP (Local Binary Patterns) is a visual/texture descriptor and our face too is composed of micro visual patterns. So, LBP features are extracted to form a feature vector to classify a face from a non-face. Now the big question is how to find LBP features. Let's find out!
Each training image is divided into a number of blocks as shown in image below.
For each block, LBP looks at 9 pixels (3x3 window) at a time and it is particular interested in the pixel at the center of the window. It compares the center pixel value with every neighbor pixel value under 3x3 window and for each neighbor pixel that is greater than or equal to the center pixel, it sets its value to 1 and for others it sets them to 0. Then it reads the updated pixel values (which can be either 0 or 1) in a clockwise order and forms a binary number. That binary number is then converted to a decimal number and that decimal number is the new value of center pixel. We do this for every pixel in a block.
We then convert each block values into a histogram, so now we have got 1 histogram for each block in an image. Image below shows an example histogram.
Next we concatenate these block histograms to form a one feature vector for one image which contains all the features we are interested in. So this is how we extract LBP features from an image. Fun, right? No? OK then let's move on to the more interesting sections of this article.
So after all this theory, is it clear to you which classifier should you use and when? I don't think so. So let's compare them both so that we can make this decision.
To make the decision making task easier for you I have given a short comparison of haar cascade classifier
and LBP cascade classifier
below.
| Algorithm | Advantages | Disadvantages |
|---|---|---|
| Haar |
|
|
| LBP |
|
|
Each OpenCV face detection classifier has its own pros and cons but the major differences are in accuracy and speed. So in a use case where more accurate detections are required, Haar
classifier is more suitable like in security systems, while LBP
classifier is faster than Haar classifier and due to its fast speed, it is more preferable in applications where speed is important like in mobile applications or embedded systems.
Now let's get to the favorite part of this article, the coding part of course! Should we?
Let's first install the required dependencies to run this code.
Note: If you don't want to install matplotlib
then replace matplotlib code with OpenCV code as below.
Instead of
plt.imshow(gray_img, cmap='gray')
you can use
cv2.imshow('Test Imag', gray_img)
cv2.waitKey(0)
cv2.destroyAllWindows()
plt.imshow(img, color_map)
is a matploblib function used to display image. It takes two arguments, first one is image you want to display and second is color map (gray, RGB) in which image is in.
cv2.imshow(window_name, image)
is a cv2 function used to diplay image. It also takes two arguments, fist one is name of the window that will popup to display image and the second one is image you want to display.
cv2.waitKey()
is a keyboard binding function. Its argument is the time in milliseconds. The function waits for specified milliseconds for any keyboard event. If you press any key in that time, the program continues. If 0 is passed, it waits indefinitely for a key stroke. It can also be set to detect specific key strokes like, if key a is pressed etc.cv2.destroyAllWindows()
simply destroys all the windows we created using cv2.imshow(window_name, image)
Please keep these functions in mind as I will used them in code.
Let's import the required libraries first. As names of libraries are self-descriptive so I am sure you will understand them.
#import required libraries
#import OpenCV library
import cv2
#import matplotlib library
import matplotlib.pyplot as plt
#importing time library for speed comparisons of both classifiers
import time
%matplotlib inline
When you load an image using OpenCV it loads that image into BGR color space by default. To show the colored image using matplotlib
we have to convert it to RGB space. Following is a helper function to do exactly that.
def convertToRGB(img):
return cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
cv2.cvtColor
is an OpenCV function to convert images to different color spaces. It takes an image to convert and a color space code (like cv2.COLOR_BGR2RGB) to convert image to and returns the converted image.
Now that we are all setup, let's start coding our first face detector Haar
.
First let's start with a simple task, load our input image, convert it to grayscale mode and then display it. Remember the cv2.cvtColor
function I just mentioned above? Yeah, exactly that one! You should keep it in mind as it is a very handy function. We are going to use it to convert our image to gray scale.
Now you must be wondering, why do we have to convert our image to gray scale? Well, many operations in OpenCV are done in grayscale for performance reasons.
Let's first read/load our image and convert it to gray scale. For reading image, I have used OpenCV's function built in function cv2.imread(img_path)
by passing our image path as an input parameter.
#load test iamge
test1 = cv2.imread('data/test1.jpg')
#convert the test image to gray image as opencv face detector expects gray images
gray_img = cv2.cvtColor(test1, cv2.COLOR_BGR2GRAY)
Now let's display our loaded image. To display our image, I am going to use plt.imshow(img, cmap) function of matplotlib
.
#if you have matplotlib installed then
plt.imshow(gray_img, cmap='gray')
# or display the gray image using OpenCV
# cv2.imshow('Test Imag', gray_img)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
It looks beautiful, does'nt it?  OpenCV provides us with a class cv2.CascadeClassifier
which takes input the training file of the classifier (Haar/LBP) we want to load and loads it for us. It's that easy!
As we want to load our favorite Haar classifier so XML training files for Haar cascade are stored in opencv/data/haarcascades/
folder. You can also find them in data folder of Github repo I have shared with you at the end of this article.
Let's load up our classifier.
#load cascade classifier training file for haarcascade
haar_face_cascade = cv2.CascadeClassifier('data/haarcascade_frontalface_alt.xml')
Now how do we detect face from image using our CascadeClassifier
we just loaded? Well OpenCV's CascadedClassifier has made it simple for us as it comes with a simple function detectMultiScale
. This function detects the faces in a given test image and following are details of its options/arugments.
detectMultiScale(image, scaleFactor, minNeighbors)
: A general function that detects objects. Since we are calling it on the face cascade, that’s what it detects. If faces are found, this function returns a list of positions of detected faces as Rect(x,y,w,h). If no faces are detected then it returns None
.
image
: The first option is the gray scale image. Again, the image should be gray scale.
scaleFactor
: Since some faces may be closer to the camera, they would appear bigger than those faces in the back. The scale factor compensates for this.
minNeighbors
: The detection algorithm uses a moving window to detect objects. This parameter defines how many objects are detected near the current one before it declares the face found.
There are other parameters as well and you can review the full details of this function here. These parameters need to be tuned according to your data.
Now that we know a simple and easy way to detect faces, we can find the face in our test image. Then let's detect one. The following code will try to detect face from image and if detected it will print the number of faces found in the image, which in our case should be 1.
#let's detect multiscale (some images may be closer to camera than others) images
faces = haar_face_cascade.detectMultiScale(gray_img, scaleFactor=1.1, minNeighbors=5);
#print the number of faces found
print('Faces found: ', len(faces))
Ohooo! We detected our face.
Next, let's loop over the list of faces (rectangles) it returned and draw those rectangles using built in OpenCV rectangle
function on our original colored image to see if it detected the right faces.
#go over list of faces and draw them as rectangles on original colored img
for (x, y, w, h) in faces:
cv2.rectangle(test1, (x, y), (x+w, y+h), (0, 255, 0), 2)
Display the original image to see rectangles drawn and verify that detected faces are really faces and not false positives.
#conver image to RGB and show image
plt.imshow(convertToRGB(test1))
We did it! But our code is scattered and to try it on other test images we have to used the same code again and again so why not group our code into a function so that it is reusable?
It would be easy and reusable if we grouped our face detection code into a function so let's make a function out of our code.
def detect_faces(f_cascade, colored_img, scaleFactor = 1.1):
#just making a copy of image passed, so that passed image is not changed
img_copy = colored_img.copy()
#convert the test image to gray image as opencv face detector expects gray images
gray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
#let's detect multiscale (some images may be closer to camera than others) images
faces = f_cascade.detectMultiScale(gray, scaleFactor=scaleFactor, minNeighbors=5);
#go over list of faces and draw them as rectangles on original colored img
for (x, y, w, h) in faces:
cv2.rectangle(img_copy, (x, y), (x+w, y+h), (0, 255, 0), 2)
return img_copy
Our detect_faces
function takes 3 input parameters, first is our loaded CascadeClassifier
, second is image we want to detect faces on and third is scaleFactor
(Remember it from details of detectMultiScale
function?).
Inside function, first I make a copy img_copy
of the passed image so that we do all operations on a copy and not the original image. Then I convert the copied image img_copy
to gray scale as our face detector expects a gray scale image. After that I call the detectMultiScale
function of our CascadeClassifier
to return list of detected faces ( which is list of rectangles Rect(x, y, w, h)).
Once we have the list of detected faces, I loop over them and draw a rectangle on the copy of image img_copy
. At the end I return the modified copy of image. So that code is basically same as before, it is just grouped inside a function for reusability.
Now let's try this function on another test image.
#load another image
test2 = cv2.imread('data/test3.jpg')
#call our function to detect faces
faces_detected_img = detect_faces(haar_face_cascade, test2)
#conver image to RGB and show image
plt.imshow(convertToRGB(faces_detected_img))
Let's try our face detector on another test image.
#load another image
test2 = cv2.imread('data/test4.jpg')
#call our function to detect faces
faces_detected_img = detect_faces(haar_face_cascade, test2)
#conver image to RGB and show image
plt.imshow(convertToRGB(faces_detected_img))
Well, we got two false positives. What went wrong there? Remember, some faces may be closer to the camera and they would appear bigger than those faces in the back. The scale factor compensates for this so can tweak that parameter. For example, scaleFactor=1.2
improved the results.
#load another image
test2 = cv2.imread('data/test4.jpg')
#call our function to detect faces
faces_detected_img = detect_faces(haar_face_cascade, test2, scaleFactor=1.2)
#conver image to RGB and show image
plt.imshow(convertToRGB(faces_detected_img))
So you have to tune these parameters according to information you have about your data.
XML training files for LBP cascade are stored in opencv/data/lbpcascades/
folder and their names starts with (as you might have guessed, lbpcascade
).
From coding perspective you don't have to change anything in our face detection code except, instead of loading the Haar
classifier training file you have to load the LBP
training file and rest of the code is exactly same. That's sounds really easy, right? Well OpenCV has made it easy for! You can try as many detectors (eye detector, smile detector etc.) as you want, without changing much of the code.
#load cascade classifier training file for lbpcascade
lbp_face_cascade = cv2.CascadeClassifier('data/lbpcascade_frontalface.xml')
#load test image
test2 = cv2.imread('data/test2.jpg')
#call our function to detect faces
faces_detected_img = detect_faces(lbp_face_cascade, test2)
#conver image to RGB and show image
plt.imshow(convertToRGB(faces_detected_img))
So the code is exactly same, I load up our CascadeClassifier
this time with LBP training file
. I read a test image and call our detect_faces function which returns an image with faces drawn on it. Then I display the image.
Let's try it on another test image.
#load test image
test2 = cv2.imread('data/test3.jpg')
#call our function to detect faces
faces_detected_img = detect_faces(lbp_face_cascade, test2)
#conver image to RGB and show image
plt.imshow(convertToRGB(faces_detected_img))
I have shown a table comparing the two classifiers but you may not believe so why not I show you the results on real images? Let's do it then! We will run both Haar
and LBP
on two test images to see accuracy and time delay of each.
#load cascade classifier training file for haarcascade
haar_face_cascade = cv2.CascadeClassifier('data/haarcascade_frontalface_alt.xml')
#load cascade classifier training file for lbpcascade
lbp_face_cascade = cv2.CascadeClassifier('data/lbpcascade_frontalface.xml')
#load test image1
test1 = cv2.imread('data/test5.jpg')
#load test image2
test2 = cv2.imread('data/test6.jpg')
I have loaded both Haar and LBP classifiers and two test images test1 and test2.
Let's first try both classifiers on test1 image.
#------------HAAR-----------
#note time before detection
t1 = time.time()
#call our function to detect faces
haar_detected_img = detect_faces(haar_face_cascade, test1)
#note time after detection
t2 = time.time()
#calculate time difference
dt1 = t2 - t1
#print the time differene
I have used Python library function time.time() to note time. So before detecting faces on our test image, I note the start time t1 and then I call our function detect_faces and then again I note end time t2. The difference between start time t1 and end time t2, dt1 is what we are interested in as it is time taken by our face detector to detect faces.
Let's do the same for LBP classifier.
#------------LBP-----------
#note time before detection
t1 = time.time()
lbp_detected_img = detect_faces(lbp_face_cascade, test1)
#note time after detection
t2 = time.time()
#calculate time difference
dt2 = t2 - t1
#print the time differene
Now that we have the face detected images and times taken by our both face detectors, let's display the results and see which one performs better.
I am going to use matplotlib
function subplots(rows, cols, figsize) to display our results side by side. I am displaying the time difference as a title of each subplot (image window).
Let's see the results then!
#----------Let's do some fancy drawing-------------
#create a figure of 2 plots (one for Haar and one for LBP)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
#show Haar image
ax1.set_title('Haar Detection time: ' + str(round(dt1, 3)) + ' secs')
ax1.imshow(convertToRGB(haar_detected_img))
#show LBP image
ax2.set_title('LBP Detection time: ' + str(round(dt2, 3)) + ' secs')
ax2.imshow(convertToRGB(lbp_detected_img))
#show images
# plt.imshow(faces_detected_img)
Haar
and LBP
detected faces successfully.LBP
was faster than Haar
.Let's see the results for test2 image. The code is exactly same as for test1.
#------------HAAR-----------
#note time before detection
t1 = time.time()
#call our function to detect faces
haar_detected_img = detect_faces(haar_face_cascade, test2)
#note time after detection
t2 = time.time()
#calculate time difference
dt1 = t2 - t1
#print the time differene
#------------LBP-----------
#note time before detection
t1 = time.time()
lbp_detected_img = detect_faces(lbp_face_cascade, test2)
#note time after detection
t2 = time.time()
#calculate time difference
dt2 = t2 - t1
#print the time differene
#----------Let's do some fancy drawing-------------
#create a figure of 2 plots (one for Haar and one for LBP)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
#show Haar image
ax1.set_title('Haar Detection time: ' + str(round(dt1, 3)) + ' secs')
ax1.imshow(convertToRGB(haar_detected_img))
#show LBP image
ax2.set_title('LBP Detection time: ' + str(round(dt2, 3)) + ' secs')
ax2.imshow(convertToRGB(lbp_detected_img))
#show images
# plt.imshow(faces_detected_img)
Haar
detected more faces and than LBP
. LBP
was significantly faster than Haar
.As you can see LBP
is significantly faster than Haar
and not that much behind in accuracy so depending on the needs of your application you can use any of the above-mentioned face detection algorithms. Try the code and have fun detecting different faces and analyzing the result. And don't forget to thank OpenCV for giving the implementation of the above-mentioned algorithms.
You can download the complete code from this repo along with test images and LBP and Haar training files.


Class SingleSubject
Class that represents a single subject
Inheritance
System.Object
SingleSubject
Implements
Inherited Members
System.Object.ToString()
System.Object.Equals(System.Object)
System.Object.Equals(System.Object, System.Object)
System.Object.ReferenceEquals(System.Object, System.Object)
System.Object.GetHashCode()
System.Object.GetType()
System.Object.MemberwiseClone()
Namespace: alps_.net_api
Assembly: alps.net_api.dll
Syntax
public class SingleSubject : Subject, ISingleSubject, ISubject, IInteractionDescriptionComponent, IPASSProcessModellElement, IOwlThing
Constructors
SingleSubject()
Constructor that creates a new empty instance of the single subject class
Declaration
public SingleSubject()
SingleSubject(List<String>, String, List<String>, String, MessageExchange, MessageExchange)
Constructor that creates a new fully specified instance of the single subject class
Declaration
public SingleSubject(List<string> additionalAttribute, string modelComponentID, List<string> modelComponentLabel, string comment, MessageExchange incomingMessageExchange, MessageExchange outgoingMessageExchange)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Collections.Generic.List<System.String> | additionalAttribute | |
| System.String | modelComponentID | |
| System.Collections.Generic.List<System.String> | modelComponentLabel | |
| System.String | comment | |
| MessageExchange | incomingMessageExchange | |
| MessageExchange | outgoingMessageExchange |
Fields
className
Name of the class
Declaration
public const string className = "SingleSubject"
Field Value
| Type | Description |
|---|---|
| System.String |
Methods
factoryMethod()
Factory method that creates and returns a new empty instance of the single subject class
Declaration
public SingleSubject factoryMethod()
Returns
| Type | Description |
|---|---|
| SingleSubject | A new empty instance of the single subject class |
getMaximumInstanceRestriction()
Method that returns the instance restriction attribute of the instance
Declaration
public int getMaximumInstanceRestriction()
Returns
| Type | Description |
|---|---|
| System.Int32 | The instance restriction attribute of the instance |


This is visible to the user.
This is not visible to the user.
This is not visible but affects the layout.
This is a normal paragraph.


James brings over 10 years of sys admin experience with a focus in cloud, virtualization and automation. He’s excited about technology and specifically the different ways that it enables creativity in people and enables them to share and create things otherwise impossible.
Michael brings 10 years of music industry experience managing indie artists, their catalogs and their royalties. He's excited about technology and how it can make the music world a better place.
After spending many an hour working on custom Excel files to help manage and track the ownership of songs and albums, (not to mention the payouts of any sales, streams etc. to artists, producers and labels), we started to ask ourselves, "is there a better way?" Turns out that unless you were willing to fork out some serious coin there wasn’t. Thus beginning our journey with Splits.
Together we are working to create a SaaS application that can function efficiently and cheaply to resolve royalty accounting issues for both large labels and small indie artist. All without the headaches of a custom solution that would end up costing more than your earnings.
As artists and managers ourselves we know firsthand the pains and challenges of growing sales in the industry and are working to resolve them for ourselves and you!


| Name | Symbol | Trading Day | Opening Price | High | Low | Closing Price | Volume |
|---|
AAL
Anglo American PPL
CCH
Coca-Cola HBC N
AAL
Anglo American PPL
CCH
Coca-Cola HBC N
AAL
Anglo American PPL
CCH
Coca-Cola HBC N
AAL
Anglo American PPL
CCH
Coca-Cola HBC N
AAL
Anglo American PPL
CCH
Coca-Cola HBC N
AAL
Anglo American PPL
CCH
Coca-Cola HBC N
BACK
| Name | Symbol | Trading Day | Opening Price | High | Low | Closing Price | Volume |
|---|
Start date: End date:
| Trading Day | Opening Price | Trading Day's High | Trading Day's Low | Closing Price | Volume |
|---|


When creating a new user, Webmin will create the home directory that you enter into this field. If you change the home directory when modifying an existing user, Webmin will attempt to rename the directory if the Move home directory if changed? option is set.


Welcome to LifeHackr! This is a place where you can solve life's smaller problems, with easy-to-use life hacks. This way, you don't have to look all over the internet for a solution because chances are, we have one right here! We hope we can help you navigate through life easier, freeing up your time for better things.
When doing the dishes, I place my phone in a ziplock bag, and hang it on the wall infront of me, on the wall. Attach a tiny nail to the wall, and poke a hole in the freezer bag where it will hang. Put your phone in the bag and zip it up. This way, the phone doesn't get wet, and I have a mini tv right infront of me!


Matrices.norm(A); Matrices.norm(A, p=2);
The function call "Matrices.norm(A)
" returns the
2-norm of matrix A, i.e., the largest singular value of A.
The function call "Matrices.norm(A, p)
" returns the
p-norm of matrix A. The only allowed values for p are
Note, for any matrices A1, A2 the following inequality holds:
Matrices.norm(A1+A2,p) ≤ Matrices.norm(A1,p) + Matrices.norm(A2,p)
Note, for any matrix A and vector v the following inequality holds:
Vectors.norm(A*v,p) ≤ Matrices.norm(A,p)*Vectors.norm(A,p)
function norm extends Modelica.Icons.Function; input Real A[:, :] "Input matrix"; input Real p(min = 1) = 2 "Type of p-norm (only allowed: 1, 2 or Modelica.Constants.inf)"; output Real result = 0.0 "p-norm of matrix A"; end norm;


Comments
-
Greg Hurrell
Two things:
1. I can't reproduce the issue you mention (stopping after a track). In my case it continues playing. I think it must depend on which playlist is selected. For example, if "Library" is selected in iTunes then it continues playing after choosing a track. And I've tried it with other playlists selected and it still continues after playing a the track. Can you provide explicit details about the steps necessary to reproduce the issue (what kind of playlist is selected, whether the track is in the playlist etc).
2. I think the idea to play in the order in which stuff appears in the menu is an excellent one. I'm going to reword the summary slightly to reflect exactly what's required.
-
Greg Hurrell
Changing assignment to reflect my new email address.
https://wincent.com/a/news/archives/2006/05/change_of_email.php
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Tools for the analysis of epidemiological data. Contains functions for directly and indirectly adjusting measures of disease frequency, quantifying measures of association on the basis of single or multiple strata of count data presented in a contingency table, and computing confidence intervals around incidence risk and incidence rate estimates. Miscellaneous functions for use in meta-analysis, diagnostic test interpretation, and sample size calculations.
| Version: | 0.9-99 |
| Depends: | R (≥ 3.0.0), survival |
| Imports: | BiasedUrn, methods |
| Suggests: | MASS (≥ 3.1-20) |
| Published: | 2018-11-06 |
| Author: | Mark Stevenson with contributions from Telmo Nunes, Cord Heuer, Jonathon Marshall, Javier Sanchez, Ron Thornton, Jeno Reiczigel, Jim Robison-Cox, Paola Sebastiani, Peter Solymos, Kazuki Yoshida, Geoff Jones, Sarah Pirikahu, Simon Firestone, Ryan Kyle, Johann Popp and Mathew Jay. |
| Maintainer: | Mark Stevenson <mark.stevenson1 at unimelb.edu.au> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | http://fvas.unimelb.edu.au/veam |
| NeedsCompilation: | no |
| Materials: | NEWS |
| In views: | MetaAnalysis, Survival |
| CRAN checks: | epiR results |
| Reference manual: | epiR.pdf |
| Package source: | epiR_0.9-99.tar.gz |
| Windows binaries: | r-devel: epiR_0.9-99.zip, r-release: epiR_0.9-99.zip, r-oldrel: epiR_0.9-99.zip |
| OS X binaries: | r-release: epiR_0.9-99.tgz, r-oldrel: epiR_0.9-99.tgz |
| Old sources: | epiR archive |
| Reverse depends: | EpiStats, RcmdrPlugin.EBM |
| Reverse imports: | EpiBayes, pubh, SafeQuant, spectacles |
| Reverse suggests: | FRESA.CAD |
Please use the canonical form https://CRAN.R-project.org/package=epiR to link to this page.


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**UBE**: //, n.
[abbrev., Unsolicited Bulk Email] A widespread, more formal term for
email `*spam* <../S/spam.html>`__. Compare `*UCE* `__. The UBE
term recognizes that spam is uttered by nonprofit and advocacy groups
whose motives are not commercial.
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These tests check gloader's ability to include the correct CSS file when a particular module is loaded.


Introducing MotionComposer from MotionComposer on Vimeo.
Motion Composer Chassis
made in collaboration with Fraunhofer , Fusion System , and Lucas
The MotionComposer is a device that turns movement into music. It is designed for persons with different abilities, including those with cerebral palsy, aphasia, autism, quadriplegia, blindness, Alzheimer’s or Parkinson’s. It is for small children, senior citizens and everyone in between.
For this project I designed the chassis that contains the CPU, the cameras and all the hardware that is necessary. The main driver to design this chassis was to design a device that is playful and captivating at the same time. A particular attention was given to the "eyes" in front of the device.
More info can be foundHere


Shelby Series One
(All specs strait from Shelby)
Specs
Price |
$108,000 |
Top Speed |
171mph(claimed) |
Acceleration |
|
Engine |
|
0-30mph |
1.8sec |
Type |
V8, 32valve DOHC |
0-60mph |
4.4sec |
Displacement |
4.0L(3995cc) |
0-100 |
11.0sec |
Power |
320hp@6500rpm |
1/4 mile |
12.8sec/109.9mph |
Torque |
290 lbs-ft@5000rpm |
Weight |
2,650 lbs |
Handling |
|
Gas mileage |
|
Skidpad |
1.00g |
City |
16mpg |
600ft slalom |
67.0mph |
Highway |
27mpg |
Powertrain |
Front engined, rear wheel drive, 6 speed manual |
(? = incomplete data or unverified info, N/A = info Not Available)
Beyond the specs:
Carroll Shelby, that old man famous for the amazing Shelby Cobra and many Shelby modified Mustangs of yore. All of his old cars were made by minor and in some cases (like the Cobra) major modifications of current production cars. This car is the first car to ever be made completely by Carroll Shelby, chassis design, engine modification (only part from another company), and overall design. This is not the kind of car you expect from someone the age of Mr. Shelby, but when you look at all of his previous work you will understand. This car uses a modified version of the Oldsmobile Aurora V8 that has proven itself in IRL (Indy Racing League) racing. This car a very high cornering limit, but for a car that can pull 1 lateral g it must be capable of better than its reported 67mph shalom time. Especially since its has $1200 Formula 1 level shocks and a perfect 50/50 (front/rear %) weight distribution. This car was intended to weigh only 2450lbs but came out at 200lbs heavier, but that is still a pretty light weight. This car is faster than a Chevy Corvette and can even challenge a Dodge Viper, all despite only having a normally aspirated 4-liter V8. Due to the cost of development, it does not have ABS, but anti-lock brakes are not good on a race tack anyway. This car is $108,000 Carroll Shelby masterpiece guaranteed to go down in history just like the famous Cobra. If you want a modern classic that can challenge a Viper while supplying a better ride and out cornering any thing short of a Viper, then get in line. However, you had better hurry for only 500 hundred of these neoclassical wonders will be made.
~Oracle