Mobile apps and HTML5 are two of the hottest technologies right now, and there's plenty of overlap. Web apps run in mobile browsers and can also be re-packaged as native apps on the various mobile platforms. With the wide range of platforms to support, combined with the sheer power of mobile browsers, developers are turning to HTML5 as a "write one, run many" solution. But is it really viable? There are still compelling reasons to go native, and clearly, many developers are indeed going that route. This article is a debate on native versus the web.
We can divide mobile functionality into two dimensions: the experience of the app itself, and the way it hooks into the device's ecosystem, e.g. for Android, this would be features like widgets and notifications. Native excels in both dimensions.
In terms of app experience, native apps can do more. They can easily get hold of swipe events, mutlitouch even, for those platforms which support it. They can typically act on hard keys being pressed, like Android's search button and volume controls. They can access hardware too, like GPS and camera. And with the user's permission, some platforms provide unfettered access to the operating system. Just try detecting how much battery remains with HTML5!
It's more than the in-app experience though. An operating system like Android provides different ways for apps to interact with users, and indeed, with other apps. You have active widgets on the homepage. You have notifications, which show up in the device's status bar. And you have intents, which allow your app to announce itself as providing a general service which other apps might require on occasion.
It's true that many in-app features are simply beyond reach for an HTML5 app. No matter how hot your web-fu skills are, if your app is stuck in a sandbox with no camera API, it won't be taking snaps anytime soon! Fortunately, you don't have to be in that sandbox. If you really need your web app to take a photo, you can create a native app, one with an embedded web view which provides the bulk of the user interface. This is how the open-source PhoneGap framework operates: it fills the gap by exposing native features as web services, which the web view calls using a standard networking API. When you build a hybrid app like this, you're also able to hook into those platform features like widgets, notifications, and intents.
Making a hybrid - native plus web - app is hardly an ideal solution. It adds complexity and applies only to web apps wrapped as native apps, rather than traditional websites accessed from a mobile browser. But it mightn't be necessary for long. Web standards are evolving rapidly, and modern mobile browsers are keeping pace. Offline storage, geolocation, canvas graphics, and video/audio playback all enjoy widespread support among modern smarpthones, for example. Even camera is starting to be supported — as of Android 3.1, it's possible to capture photos and videos using web standards. And the latest iOS browser supports WebSocket for 2-way streaming, as well as device orientation detection.
Overall, mobile is evolving. But the web is also evolving, and fast. Among desktop browsers alone, there are five major browser vendors evolving standards and adding features at lightning pace. While it's not a trivial process to port these features to mobile, many of them have already made their way into the mobile browsers.
Native is a fast-moving target, but the web is closing the gap.
Native apps don't have the web runtime barrier to deal with. They run close to the metal and can take advantage of performance boosters like GPU acceleration and multithreading.
It would be an understatement to say the web has gotten faster in recent years. V8, the JavaScript engine that ships with Chrome, was a major development in web performance when it launched, and since then, it has only gotten faster:
Graphic rendering engines has also sped up the web, and now hardware acceleration is starting to happen. Have a look at the speed bump provided by hardware accelerated-canvas:
In addition, the new Web Workers API makes multithreading a possibility, and modern web developers can also call on a range of performance-optimized libraries, and well-researched performance optimizion techniques. While most of those started life on the desktop web, they are still relevant to mobile, and there's increased attention paid to mobile, e.g. performance guru Steve Souders has a page dedicated to mobile performance tools.
Not all desktop advances have made their way to every mobile platform yet, but the trends indicate they are on their way. It's also important to note that the majority of mobile apps aren't bleeding-edge 3D games, but fundamentally information-based: news, mail, timetables, social networks, etc. Visit a few sites from your mobile, e.g. GMail, Amazon, Twitter, and you can confirm mobile web performance is more than adequate. As for games, basic ones are already feasible with 2D canvas, and WebGL is starting to appear on mobiles - see Firefox 4. Until it's widespread, there is a growing family of frameworks which compile WebGL apps to native apps that can take advantage of OpenGL, e.g. ImpactJS.
Native apps use robust programming languages (e.g. Java, Objective C, C++) which were designed for complex application development and have a proven track record. The APIs were designed ground-up to support the platform at hand. You can easily debug apps in desktop emulators which provide a close representation of the target device.
What makes web development particularly troublesome is the huge diversity of browsers and runtimes. When your app runs, it's no guarantee feature X will be available. And even if it is, how will the browser implement it? Standards are open to interpretation.
Let's tackle core technology first. It's true that web standards were originally conceived in an era when the web was fundamentally about documents, not apps, with JavaScript built and deployed in just 10 days! But they've turned out to be much more capable than imagined - web developers have learned to leverage the good parts and tame the bad parts, with patterns now understood for scaleable design. Furthermore, the standards are not standing still, and efforts like HTML5, CSS3, and EcmaScript Harmony are all improving the developer experience. Whether you prefer C++ or Java or JavaScript is a matter of religious debate, and also depends on your legacy code base. But we can certainly include JavaScript as a serious contender these days.
The flipside to browser/runtime fragmentation is the fact that all these environments exist in the first place. Develop an Android app in Java, and you're faced with a full port to Objective C to support iOS. Develop a web app once and it will run in Android and iOS, not to mention WebOS, BlackBerry, Windows Mobile and ... well, that's the theory anyway. In practice, you'll need to tweak things for each platform if you really want to get the experience right. But you'd have to do that in native too, for most mobile operating systems - there are different versions and different devices.
The good news is "fragmentation" has always been this way on the web, and there are well-known techniques to deal with it. Most importantly, the principle of progressive enhancement urges developers to target a basic device first, and add layers of platform-specific awesomeness where it's available. The mantra of feature detection also helps and these days, we have library support from the likes of Modernizr to support responsive web design. With judicious use of these techniques, you can expand your reach to the vast majority of devices, even old-school "feature phones", even form factors like watches and TVs, regardless of make and OS. Witness our multi-UI demonstration at Google IO 2011, where we targeted distinct form factors (feature phone, smartphone, tablet, desktop, TV) with a common code base of logic and markup.
One of the defining features of any platform is its look and feel. Users come to expect controls to be presented consistently and manipiulated in the same way. There are certain idioms which vary from platform to platform, e.g. what happens when the user performs a "long hold" (keep touching an element for several seconds)? Plaforms have standard idioms for such things, and you can't satisfy them all with a single HTML5 app.
Furthermore, platform look-and-feel is orchestrated by the platform's native software library, whose widgets encapsulate the kind of look and feel users expect. You get a lot of the expected look-and-feel "for free" just by using the native toolkit.
As explained in the previous section, the way of web development is to write a basic "one size fits all" version, and then progressively enhance it. While the enhancement is typically based on features, you can also enhance it by targeting those platforms you care the most about. This is a kind of "browser detection", which is sometimes frowned upon by the web community, mostly because there are so many possible browsers out there. But if you do view two or three platforms with a very high priority, and you're willing to make the extra effort in order to stack up against native alternatives, this may be the way to go.
As far as the baseline version, the web has its own look-and-feel, and we can even say each mobile platform has its own "web look-and-feel" established by the default browser and web runtime. "Web look-and-feel" may be fine for your users, and in fact, allows you to achieve a greater degree of consistency with the desktop browsing experience, as well as those on other devices the user might be working with. Furthermore, there are many successful apps which don't much support native look and feel anyway. This is certainly true of games (does your favorite mobile game follow your mobile OS's look and feel?), and even true of more conventional apps, e.g. check out the more popular native Twitter clients on your platform of choice, and you'll see a wide range of user-interface mechanisms at work.
App distribution mechanisms, like Android's Market and Apple's App Store, have been overwhelmingly popular in recent years and are a major driving force for the entire mobile industry. Any developer can submit their native app to the marketplace, where users can discover it through a combination of browsing, searching, and getting recommendations. Not only that, but if you've done your job right, the glowing ratings and comments will convince users to hit the all-important install button.
The web is arguably the most discoverable medium ever created. In the humble URL, we have (in theory, at least) a unique identifier for everything ever published on the web, which includes any apps published on standard websites. Search engines make it easy to discover that content and other websites can link to it, including catalogues of web apps similar to mobile marketplaces. Indeed, any individual can share web apps with their friends by just linking to it in emails and social network messages. Links can be sent in SMS too, where mobile users will be able to click on the link and launch the app in their device's browser.
We don't yet have the same marketplaces where users can rate and comment on apps, but that's changing too. Read on ...
"6 year-old makes app during lunch hour, sells a zillion copies at $3 each". You see that headline a lot these days, so it's no wonder developers big and small are looking to the mobile marketplaces for monetization. Mobile platforms offer several avenues for developers to directly charge for their apps. Simplest is the one-time payment, to unlock the app for all eternity. There are also in-app payment and subscription mechanisms on offer in some platforms, and they are tightly integrated in a consistent, secure, mechanism. These newer forms of payment allow developers to convert a smash-hit app into a long-term revenue stream.
In addition to app payments, you can monetize with traditional web models, such as advertising and sponsorship.
The web would not be the engine of modern industry if there weren't ample opportunities to cash in. Although direct "pay-per-use" mechanisms haven't yet flourished, there are various niches where subscription-based "software as a service" solutions have indeed become viable. Examples include Google Apps, 37Signals' range of products, and premium versions of various email services. Furthermore, direct payments aren't the only way to profit from web apps. There's online advertising, affiliate links, sponsorships, cross-promotion to other products and services.
Having said that, it's perfectly reasonable for a web developer to read the headlines and experience a dash of payment envy. You can't submit a web URL to the native marketplaces, so what's a web developer to do? What you do is create a native "wrapper app" - for each platform you want to target, create an empty native app that simply contains a web view. The web view is where you embed the real app. Then you just submit these apps to the various marketplaces (and hopefully watch the money roll in!). There are probably hundreds, if not thousands, of web-powered apps in the main marketplaces today, some of them so cunningly assimilated that we don't even know their web apps at all.
The downside is the onus of cross-compiling to each platform. Here's where an existing framework like PhoneGap can help. Even better, there are web services like PhoneGap Build and Apparatio under development. Point these websites to your code repository, and out pops an Android app, an iOS app, and so on...ready for you to submit to the respective stores. No installing native SDKs on your machine; all you needed to build all these native apps was a a code editor and a web browser.
Will the marketplaces ever support web apps directly, without all the overhead of wrapping them natively? It's not yet clear. We do know that Google introduced the Chrome Web Store last year, and although it applies only to the desktop, the store has triggered interest from other browser vendors, and is overall part of a trend towards web app catalogues, including some mobile-specific attempts. It's early days for the concept of a web store, but the signs are promising.
It would be nice to declare a winner here, but right now, there is no clear winner. Some apps are best suited for native and some are best suited for the web. The web stack arguably has more momentum, but in terms of capabilities and execution qualities, native apps are moving fast too. And unless there comes a time when web technologies are a first-class citizen on the majority of mobile OSs, native will always be an important consideration.
One technique mentioned in this article is hybrid apps, and this may be the best compromise for some developers: web view where it's possible and platform-specific native components where it's not.
If you do choose the web path, be mindful of web standards and the principle of progressive enhancement. The web is a technology that knows how to target the multitudes of devices and operating systems around. Whether you choose to call it "fragmentation" or "diversity", the web embraces it and you developers can benefit from all the prior art out there.
{% endblock %}


This is a project by Robert and Jake. We are building a weather system that will be able to gauge the air temperature, humidity, pressure, wind speed, wind direction, and rain fall. Our weather system will be mostly made of 3d printed parts.
The project link is:
The 3D models link:
As of Febuary 21st, we have setup the raspbery pi with the proper software, attached and configued the pressure, temperature and humidity sensor, and printed all the parts for the anemometer.


welcome to my project
hello , i am Thaer , i am 25 years old , i live in Jerash/Jordan, i am a Mehanical Engineer ,i love watching movies and foot matchs , reading , and take photos, i have an experiance in work ,i had worked in Lumi Market / al manaseer , this work help me to build a visualize oon my future , My Ambition that's was built on learning cooding and and creativity in this area , i hope you have a nice biography on me.


--- title: IRC Random Number Generator layout: post tags: [perl] uuid: 4f413240-f63f-3b51-cf11-51ee6e689356 ---
A year ago while I was reading Applied Cryptography I got an idea for using IRC as a random number generator. The book mentioned using a rolling car wheel to generate random numbers, by measuring its period. So why not IRC? Grab the code,
git clone git://github.com/skeeto/ircrng.git
It's based entirely on event timing: it logs in and sits on several channels, then measures the time between events. When a new event occurs it compares the time passed between this event and the last event, and the time between the two events before it. If it's greater, emit a 1, if less emit a 0. Simple.
For skew removal I used transition mappings (rfc1750), invented by von Neumann. It looks at pairs of bits. If they differ, pass the first bit, otherwise toss both bits. So if it comes across "11" or "00", it tosses them. If it comes across "10" it emits "1", and "01" it emits 0.
Here's an analysis by
ent
of 476 output bytes I generated overnight: irc.random.bytes.
Entropy = 7.515132 bits per byte. Optimum compression would reduce the size of this 476 byte file by 6 percent. Chi square distribution for 476 samples is 274.79, and randomly would exceed this value 25.00 percent of the times. Arithmetic mean value of data bytes is 127.7248 (127.5 = random). Monte Carlo value for Pi is 2.886075949 (error 8.13 percent). Serial correlation coefficient is -0.062745 (totally uncorrelated = 0.0).
Eh, not awful, but not too great. It got a good score on the Chi-square test, which I attribute to the skew filter. This generator is also extremely slow, generating only a few bytes per hour. At best, each event only generates a half of a bit, after skew correction. It would probably be more profitable to take the hash of the actual event with a timestamp and use that as the RNG.
Also, someone else running this generator on the same channels would generate very similar numbers. Worse, someone eavesdropping on your network connection knows mostly what numbers you generated. Worse yet, someone actively modifying your connection could control your generator and force whatever output is desired.
So overall, I would say it's not very useful.


Flash of Invisible Text
Flash of Unstyled Text
instead of the custom fonts
font-family:
-apple-system, BlinkMacSystemFont, Segoe UI,
Roboto, Oxygen, Ubuntu, Cantarell, Fira Sans,
Droid Sans, Helvetica Neue, sans-serif;
👍 Super simple: just use font-family
without @font-face
.
👍 Perfect rendering performance: no worries about FOUT/FOIT.
👎 Limited availability. Very few system fonts are available cross platform.
@font-face
Just add a naked CSS @font-face
block with links to your file fonts
@font-face {
font-family: FontName;
src: url('fontname.woff2') format('woff2'),
url('fontname.woff') format('woff');
}
@font-face
: pros and cons👍 Good rendering performance in IE and Edge: no FOIT
👎 Bad rendering performance everywhere else: maximum 3 second FOIT in most modern browsers (and switches to FOUT if load takes longer)
Reduce webfont load time
WOFF = Web Open Font Format
(if language and licensing requirements allow)
preload
Add <link rel="preload">
to fetch your font sooner:
preload
: pros and cons👍 Super easy
👍 Better rendering performance than just a @font-face
block. Webfonts are requested higher up in the waterfall.
👎 Not sufficient by itself.
Control the behavior while a webfont is loading: NO invisible text
font-display
font-display
currently supports the following range of
values:auto | block | swap | fallback | optional
Add font-display: swap
to switch to FOUT on
browsers that support it:
@font-face {
font-family: FontName;
/* Define how the browser behaves during download: */
font-display: swap;
src: url(FontName.woff2) format('woff2');
}
font-display
: browser supportfont-display
: pros and cons👍 very simple and gives us FOUT without any JavaScript
👍 future friendly
👎 still not very good browser support
👍 Protects from FOIT
👍Easy to group requests into a single repaint (just use one class for multiple web font loads)
👎 Needs strict control. A single use of a font-family
without the loaded
class will likely trigger a FOIT.
The principle is the same:
var font = new FontFaceObserver('YourFontName');
font.load().then(function () {
console.log('YourFontName has loaded.');
});
<link rel="preload">
@font-face
declaration with font-display: swap;
font-family
normally in rest of document* More advanced (not CSS-only) solution: use the CSS Font Loading API with the polyfill


Programme Committee
| Alexander Simon, Simon GesmbH, Austria |
| Jihoon Kim, Naver Corp.,South Korea |
| Jyoti Prakash Sahoo (Web Chair), SOA Deemed to be University, India |
| Duy Le, Hudson River Trading, Singapore |
| Rui Zheng, Markable, USA |
| Changlong Yu, Tencent, China |
| Mijung Kim, Purdue University, USA |
| Sherry Lin Xueling, HKUST, Hong Kong |
| Cher Pei Hua, National University of Singapore, Singapore |


WITNESS LIST Higher Education Committee September 23, 1998-9:00A Enrollment Increases- IC#2 On: Ashley, Janelle (Stephen F. Austin State) Ellis, Cissy (Career Colleges & Schoo) Gardner, David (TX Higher Ed. Crd. Brd.) Heintze, Michael (Texas Tech University) Hudson, Don (TX Assoc. Community Col) Mabry, Rodney H. (Univ. of TX at Tyler) McDonald, Carol (I.C.U.T.) Parker-Hightower, Carol (TX Southern Univ.) Peat, David M. (TX Woman's University) Perry, William L. (TX A&M University) Rodriguez, Lynn (TX Higher Ed. Crd. Brd.) Rogers, Jesse W. (Midwestern State Univ.) Sayavedra, Leo, Dr. (TX A&M System) Segura, Bill (TX State Tech. College) Sharpe, Edwin R. (Univ. of TX System) Sheridan, Edward P. (Univ. of Houston System) Supple, Jerome H. (TX State Univ. System) Wheeless, Virginia (Univ. of North Texas)


@*
* Copyright 2021 HM Revenue & Customs
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*@
@import uk.gov.hmrc.cataloguefrontend.connector.model.{Dependency, VersionState}
@import uk.gov.hmrc.cataloguefrontend.{ routes => appRoutes }
@(dependencies: Seq[Dependency])
@for(dependency <- dependencies.sortBy(_.name)) {
@if(dependency.latestVersion.isDefined && !dependency.isExternal) {
@dependency.name
} else {
@dependency.name
}
@dependency.currentVersion.toString
@dependency.latestVersion.map(_.toString).getOrElse("(not found)")
@dependency.versionState match {
case Some(VersionState.BobbyRuleViolated(violation)) => { See rule }
case Some(VersionState.BobbyRulePending (violation)) => { See rule }
case _ => { }
}
}
@getColourClass(dependency: Dependency) = @{
dependency.versionState.fold("grey") {
case VersionState.UpToDate => "green"
case VersionState.MinorVersionOutOfDate => "amber"
case VersionState.MajorVersionOutOfDate => "red"
case VersionState.Invalid => "black"
case _: VersionState.BobbyRuleViolated => "verybad"
case _: VersionState.BobbyRulePending => "pending"
}
}
@getIconClass(dependency: Dependency) = @{
" medium-glyphicon glyphicon " + dependency.versionState.fold("glyphicon-question-sign") {
case VersionState.UpToDate => "glyphicon-ok-circle"
case VersionState.MinorVersionOutOfDate => "glyphicon-alert"
case VersionState.MajorVersionOutOfDate => "glyphicon-ban-circle"
case VersionState.Invalid => "glyphicon-question-sign"
case _: VersionState.BobbyRuleViolated => "glyphicon-remove"
case _: VersionState.BobbyRulePending => "glyphicon-remove"
}
}
@getAltText(dependency: Dependency) = @{
dependency.versionState.fold("not found") {
case VersionState.UpToDate => "up to date"
case VersionState.MinorVersionOutOfDate => "minor version behind"
case VersionState.MajorVersionOutOfDate => "major version behind"
case VersionState.Invalid => "invalid version difference"
case _: VersionState.BobbyRuleViolated => "bobby rule violated"
case _: VersionState.BobbyRulePending => "bobby rule pending"
}
}


*GDP, gross domestic product; IDP, internally displaced persons; IQR, interquartile range. †Numbers, based on records of total outbreaks meeting selection criteria (total for each modality = 296). ‡Five dates missing in initial dataset. §Low indicates < median, high indicates > median of in-country value for variable compared with that value for all countries in the African region.


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Download the video from iTunes U or the Internet Archive.
Description: Why do "cats" and "dogs" end with different plural sounds, and how do we learn this? We can represent this problem in terms of distinctive features, and then generalize. We end this lecture with a brief discussion of how to approach AI problems.
Instructor: Patrick H. Winston
Lecture 14: Learning: Spars...
PATRICK WINSTON: So today we're gonna talk about a few miracles of learning in the context of the theme that we're developing here in the class.
We started off with a discussion of some basic methods.
We talked about nearest neighbors.
And we talked about identification trees.
And those are kind of basic things that have been around for a long time.
Still useful.
Still the right things to do when you're faced with a learning problem and you're not sure what method to try.
Then we went on to talk about some naive biological mimicry.
We talked about neural nets.
And we talked about genetic algorithms.
And you look at those things and you think and reflect back on what we talked about.
And you have to say to yourself, are these nugatory ideas?
Perhaps pistareens?
Or are they supererogatory ideas that deserve to be center stage?
Does anybody know what those words mean?
A pistareen?
Well, a pistareen is a Spanish coin.
It was so small.
It was of little worth.
These ideas like neural nets, genetic algorithms, I classify them as pistareens because getting them to do something is rather like getting a dog to walk on its hind legs.
You can make it happen, but they never do it very well.
And you have to think it took a lot of trickery and training to make it happen.
So not too personally high on those ideas.
But we teach them to you anyway because, of course, we only editorialize part of time and part of time we like to cover what's in the field.
Today we're starting a couple of discussions of mechanisms or ideas or things to know about that are quite different.
Because now we're going to focus on the problem rather than on the mechanism.
And then a later on we're going to talk about deep theory, FIOS, for its own sake.
But this week I want to talk about mechanisms that were devised.
I want to talk about research that was done.
Let me not say mechanisms.
Let me say research that was done to attempt an account of some of the things that we humans do well.
Sometimes without even knowing that we do it.
Now Krishna here tells me his first language was Telugu.
Telugu.
I once had another student whose first language was Telugu.
I said to him, that must be one of those obscure Indian languages.
And he said, yes.
It's spoken by 56 million people.
French is spoken by 52.
[LAUGHTER] PATRICK WINSTON: He's going to be our experimental subject.
Krishna, if I pluralize words-- you know what it means to pluralize a word.
So if I say for example, horse, then if I ask you for the plural you'll say horses.
So if I say dog, what's the plural?
STUDENT: Then dogs.
Or in my language?
PATRICK WINSTON: No, no, no.
In English.
STUDENT: Oh, dogs.
PATRICK WINSTON: Well, what about cat?
STUDENT: Cats.
PATRICK WINSTON: And he got it right.
Isn't that a miracle?
When did you start speaking English?
STUDENT: Second grade.
PATRICK WINSTON: Second grade.
But he still got it right.
But he never learned that he's actually pluralizing those words differently.
But he is.
So when you pluralize dog, what's the sound that comes after?
It's a z sound.
Zzzzzz.
Dogzzz.
If you stick your fingers up here you can probably feel your vocal cords vibrating.

But when you say cats, the pluralizing sound is sss, like that.
No vocalizing.
No vibration of the vocal cords.
And old Krishna here learned that rule, as did all of you other non-native speakers of English, effortlessly and without noticing it.
You learned it.
Buy you always get it right.
How can that possibly be?
Well, be the end of hour you'll know how that might be.
And you'll experience a case study in how questions of that sort can be approached with a sort of engineering point of view.
You can say, what if God were an engineer?
Or alternatively, what if I were God and I am an engineer?
Think about how it might happen that way.
So we want to understand how it might be that the machine could learn rules like that.
Phonological rules.
Not just that one, but all the phonological rules you'd acquire in a course on phonology.
That part of speaking that deals with those syllabic and sub-syllabic sounds.
The phones of the language.
So when Yip and Sussman undertook to solve this engineering problem, both being dedicated engineers, the first thing they did was learn the science.
So they went to sit at the foot of Morris Halle, who would develop-- was largely responsible for the development theories of so-called distinctive features.
And here's how all that works.
You start off with a person who wants to say something.

And if I say, hello, George.
And you say hello, George.
Everybody will understand that we said the same thing.
But that acoustic waveform won't look anything alike.
It'll be very different for all of us.
So it's a miracle that words can be understood.
In any case, it goes into an ear.
And it's processed.
And out comes a sequence of distinctive feature.
Vectors.
A distinctive feature is a binary variable like is the phone voices or not.
That is to say, are your vocal cords vibrating when you say it?
If so, then that's plus voiced.
If not, it's minus voiced.
So according to the original distinctive feature theory and consistent with most of the theories that have been derived since the original one, there are on the order of 14 of these distinctive features that determine which phone you're saying.
So if you say ah, that's one combination of these binary features.
If you say tuh, that's another combination of these binary features.
14 of them.
So how many sounds does that mean, in principle, there could be in a language?
SEBASTIAN: 2 to the 14th.
PATRICK WINSTON: And what's 2 the 14th, Sebastian?
Well, it ought to be about 16,000, don't you think?
2 to the 10th is 1,000.
2 the fourth is 16.
So there are about 16,000 possible combination.
But no language on Earth has more than 100 phones.
That's strange, isn't it?
Because some of those choices are probably excluded on physical ground.
But most of them are not.
So we could have a lot more phones in our language than we actually do.
English is about 40.
So the sequence of distinctive features could be viewed as then producing meaning after, perhaps, a long series of operations.
But in the end, those operations feedback in here because many of the distinctive features are actually hallucinated.
We think we heard them, but they're not there.
Or they're not even in the acoustic waveform.
They're there for the convenience of the phonologist who make rules out of them.
It's remarkable how much of this feedback there is, and even injection from other modalities.
Many of you may have heard about the McGurk Effect.
Here's who the McGurk Effect works.

I said, g-a.
Now how about ba, ba, ba, ba.
OK.
I said ba like a sheep.
But if I take the sound I make when I say ba and play it while you're taking video of me saying ga, what do you think you hear?
You don't hear ba.
Some people report that they hear a d-a sound like da.
When I look at it, I can't make any sense out of it.
It looks like there's a disconnection between the speech and the video.
But it does not sound like ba.
But if I shut my eyes and say ba, ba, it's absolutely clear that it's b-a.
So what you see has a large influence on what you hear.
It's also interesting-- although a side issue-- it's also interesting to note that it's very difficult pronounced things correctly if you don't see the speaker.
So many people wonder when they learn foreign languages why they can't speak like a native.
And the answer is, they're not watching the mouth of the speaker.
I was talking to a German friend once and said, you know, I just can't say the damned umlaut right.
And he said, oh, the trouble with you Americans is you don't realize that American cows say moo but German cows say muu.
[LAUGHTER] PATRICK WINSTON: And, of course, I got instantly because I could see that the umlaut sounds are produced with protruding lips, which we don't have any sounds an English that require that.
Ah, but back to what we know from the phonologists about all this stuff.
If you talk to Morris Halle, he will tell you that over here-- I like to think of it as a marionette.
There are five pieces of meat down here.
And the combination of distinctive features that you're trying to utter are like the control of a marionette on those five pieces of meat.
So if you want to say an a sound, the marionette control goes into a position that produces that combination.
So let's see.
What does that distinctive feature sequence look like for typical word?
Well, here's a word.
A-e-p-l.
Apples.
And we can talk about what distinctive features are arrayed in that particular combination of phones.
So one of the features that they like to talk about is syllabic.
Syllabic.
That roughly means, can that sound form the sort of core of a syllable?
And the answer is a can, buy these can't.
So it's plus, minus, minus, minus.
Down here a little ways you'll run into the voiced feature.
And for the voiced feature, well, we can do the experiment ourselves.
Ahh.
Sounds like it's voices to me.
Pa.
No.
That's not voiced.
Oo.
Yep.
Zzz.
We already said that was voiced.
So that's the combination you see when you utter apples for the voiced feature.
Then another one is the continuent one.
That roughly says is your vocal apparatus open?
Is there no obstruction?
And so ahh plus pa is constricted.
Oo, open.
Zzz, open.
So that one happens to run right along with voiced in that particular word.
Oh, and there are 14 altogether.
But let me just write down one more.
The strident one.
That says, do you use your tongue to form a little jet of air?
So you don't on aa, pa, oo.
Buy you do on z.
So that gets a plus.
So that's a glimpse through a soda straw of what it would like to represent the word apples as a set of distinctive features all arranged in a sequence.
So it's a matrix of features.
Going down in the columns we have our distinctive features.
And going across we have time.
So as the first thing Sussman and Yip did in their effort to understand how phonological rules could be learned is to design a machine that would interpret words and sounds and things that you see so as to produce the sounds of the language.
So they imagined the following kind of machine.
The machine has some kind of mystery apparatus over here that looks out into the world and sees what's there.
So I'm looking out in the world and I see two apples.
So what this machine might do then is, at some point, decide that there are two apples out there.
Then, thinking in terms of these guys as computer engineers, they think in terms of a set of registers that hold values for concepts like noun and verb and plural.
And we've not done anything with the machine yet.
We've provided no input.
So those registers are all empty.
Then, up in here, we have a set of words.
And they're all kinds of words.
Apple is one of them.
And those words up there know about how the concept is rendered as a sequence of a phones, that is to say a sequence of distinct features.
Then, over here, most importantly, they have a set of constraints.
So we'll talk about a particular constrain, the plural constraint.
Plural constraint number one.
And it's going to reach around and connect itself to some other parts of the machine.
Finally, there's a buffer of phones to be uttered.
And they're going to flow out this way to the speaker's mouth and get translated into a acoustic wave form.
So those are the elements of the machine.
Now how are the elements connected together?
Well, the words are connected, of course, into the buffer that is used to generate the sound over here on the far left.
The plural register is connected to what you see in the world.
What you see in the world is connected not only to plural register, but to all of the objects in the word repertoire.
This plural constraint here deserves extra attention because it's going to be desirous of actuating itself in the event but the thing observed in the world is plural.
There are lots of them.
So it's going to be connected then to the plural port.
There's going to be a z sound port down here connecting to that file element in the buffer.
And finally, over here is going to be a plussed voiced port, which is going to be connected to the second phoneme in the sequence.
That's how the machine is going to be arranged.
An of course, this is just one of many constraints.
But it's a constraint that has a very peculiar property.
Information can flow through it in multiple ways.
So we think of most programs as having an input and an output.
But I try to be careful to draw circles here instead of arrows.
Because these are ports and information can flow in any direction along them.
What I want to do now is to show you how this machine would react if I suddenly present it with a pair of apples like so.
So the assumption is that the vision apparatus comes in and produces the notion, the concept, of two apples.
So once that has happened-- that's operation number one-- then information flows from that meaning register up here to the apple word.
So that's part of stage number two.
Another part of stage number two is information flows along this wire and marks that as plus plural.
So operation number one is the activity of the vision system.
Activity number two is the flow of information from that vision system into the word lexicon and into this plural register.
So far so good.
Here's activity number three.
This word is also connected to the registers.
And information flows along those wires so as to indicate that it's a noun but not a verb.
That's part of part number three.
At the same time, part number three, information flows down this wire and writes a-p-l into those are elements of the buffer.
Now this constraint up here, this box, says, well, I can now see some stuff in that buffer that wasn't there before.
So it says, do I see enough stuff on my ports to get excited about expressing values on other ports?
Well, let's see.
What has it got?
It's got the elements in this buffer.
Also up here in step three flow the plural thing.
So it know that the word is plural.
So it says, is this voiced?
P is pa.
That's not voiced.
Is this a z sound.
No, that's not as z sound.
So it sees what it likes on only one of its three ports.
So it says, I'm not going to do anything.
I'm [INAUDIBLE].
I'm not in this particular combat.
So far so good.
What happens next?
What happens next is that some time passes.
And the elements of the buffer flow to the left toward the speaker's mouth.
So we get an a, p, l.
Same as we had before, but shifted over.
Now what happens?
Now what happens is that the l is now in the penultimate position.
So information flows up here.
Item number four-- oh, I guess that's item number five.
Item number four is the leftward flow of the word.
So in phase number five, the p is witnessed by this constraint.
p is-- sorry, l is witnessed by this constraint.
We moved it over one.
L is lll.
L is voiced.
So we have some flow up here like that.
That's number five.
Now we have voiced and we have plural.
And we have nothing here.
So there's a great desire of this buffer to have something written into it.
So now there's a flow down in there, of z, as item number six.
So that's how the machine would work in expressing the idea that there are apples in the field of view.
Mmm.
Real apples.
Not plastic imitations.
So that's how the machine works.
But all those connections are reversible.
So if I hear apples then I get the machine running backwards and my visual apparatus can imagine that there are apples out there.
That's how it works.
That's just by way of background the machine that they could see it for using the phonological rules once they're learned.
All the phonological rules are expressed in these constraints.
But since these constraints are such that information can flow in any direction, they deserve to be called propagators.
And in the good old days when everyone took 6.001, they learned about propagators as a kind of architecture for building complex systems.
But in any event, there's the Sussman-Yip machine.
And now comes the big question.
How do you learn rule rules like that?
Well, what we need is we need some positive examples and some negative examples.
And for the simple classroom example I've chosen the same challenge that I presented to Krishna.
We're gonna have cats and dogs.
So we're gonna look at the distinctive features that are associated with those words.
Syllabic.
Voiced.
Continuent.
And strident.
Just four of the 14 features that are associated with each of the sounds on those words.
Could you close the laptop, please?
Just for the distinctive features that are arrayed in those words by way of illustration.
So here we have k-a-t-z.
Phonetically spelled.
And if we work that out, let's see.
What is syllabic?
That's not.
That is.
That is.
That's not.
Voiced?
Ka.
Nope.
Ah.
Yep.
T. Nope.
Z. Yes.
That can't be right.
Cats.
I misspelled it.
Because cats.
Sss.
His a hissing sound but there's no voicing.
So that's not as z sound.
That's an s sound.
So that's not plus voiced.
It's minused voiced.
Continuent.
Let's see.
Is my mouth open when I say k?
No.
Ah?
Yes.
T?
No.
S?
Yes.
And strident.
Minus, minus, minus, plus.
It's only with the s sound that I have that kind of jet forming with my tongue.
Now we can look at dogs.
And now we have the z sound as the pluralization.
We know that because when we say it, dogzz.
Yep.
There it comes out as a-- we're only gonna look at the last two columns because they're the only ones that are going to matter to us.
So that's plus.
And that's minus.
Gu, gu, gu, gu.
That's plussed.
And that's plussed.
They're both voiced.
Is that right?
Dogu?
Gu.
Gu.
Is g sound voiced?
Yeah, I didn't think so.
G sound is voiced?
Look-- oh.
Oh, it is voiced buy it's not a continuent.
Just like that.
Yeah.
Cat, dogu zz.
Yeah.
It is voiced.
And it has to be for my example to work out.
And that's minus, minus, minus, plus.
So what we're interested in is, how come one word gets an s sound and how come the other words gets a z sound?
Well, it's a pretty sparse space out there.
We've already decided that there are 14,000 possible phonemes and there are only 40 in the language.
So that's one thing we can consider.
The other thing that we can think is that, well, maybe this is a logical problem.
Like the kind of problem you'd face if you were designing a computer.
And so Sussman and Yip got stuck for three months thinking about the problem that way.
Couldn't make any progress whatsoever.
And that happens a lot when you're doing a search.
You think you've got a way of approaching it.
Try to make it work.

Stay up all night again.
Still can't make it work.
Eventually, you abandon ship and try something else.
So then they began to say, well, let's see.
All we care about is the stuff before the two ending sounds.
We care about that part of the matrix.
And we care about that part of the matrix.
And we can ask, in what ways are those things different?
And they're different all over the place.
That's why they're different words.
We can ask the question a little bit differently.
And we can say, what can we not care about?
And still retain enough of an understanding of how the words are different so as to put the proper plural ending on them.
And they worried about that for a long time.
Couldn't find a solution.
The search space was too big.
And then they said, maybe what we ought to do is we ought to think about generalizing this guy here so that we don't care about it.
So now we don't care about that guy.
And then he went down through here saying, well, let's see when we have to stop generalizing.
Because we've screwed everything up and we can no longer keep the z sound words separated from the s sound words.
So that eventually distilled itself down to the following algorithm.
First thing they did was to collect positive and negative examples.
And there's a positive example and a negative example.
That's not enough to do it right.
But that's enough to illustrate the idea.
So the next thing they did was something that's extremely common in learning anything.
And that is to pick a positive example to start from.
It's actually not a bad idea in learning anything to start with a positive example.
So they picked a positive example and they called that a seed.
So in our particular case, cats is going to be our seed.
And the question we're going to ask is, what are the words that get pluralized like cat?
So we've got a positive and negative example.
We've picked a seed.
And now, the next step is to generalize.
And what I mean by generalize is you pick some places in the phoneme matrix that you just don't care about.
So you may pick a positive example.
And you don't care about it.
So you change it to an asterisk or, as demonstrated in the program I'm about show you, a ball.
Or you pick one that's negative and you turn it to a ball.
Bo.
So cats, this seed, becomes a pattern.
And in order to pluralize the word this way, you have to match all the stuff in here.
But now what we're going to do is we're going to gradually turn some of those elements into don't care symbols until we get to a point where we've not cared about so much stuff that we think that we pluralize that one with an s sound too.
So we keep generalizing until we cover, that is to say we admit or match, a negative example.
So that's how it works.
So we generalize like crazy.
And as soon as we cover a negative example, we quit.
Otherwise, we just go back up here and generalize some more.
And now we've got to pick a search technique to decide which of these guys to actually generalize when.
We could pick one at random.
And they tried that.
It didn't work.
So what they decided is that the thing that influences the pluralization most is the adjacent phoneme.
And if that isn't the thing that solves the problem, it'll be the one next to that.
So in other words, the closer you are, the more likely you are to determine the outcome.
So these guys over here are least likely to matter.
And those are the ones that are generalized first.
So if we do that, what happens?
Looks like we're going to come in here and see that there's a big difference between the non-voiced t and the voiced g.
But that's only a guess because I've only shown you a fraction of the 14 distinctive features that are involved.
So I suppose you like to see a demonstration.
Yeah.
So there's our 14 features.
And that's our seed there, sitting prominently in the display with pluses and minuses indicating the values of the distinctive features for all three of the phones involved.
That funny left bracket isn't a mistake.
That's just one convention for rendering the ah sound in cat.
So it's pretty hard to tell from just that matrix what's going to be the determining feature that separates the positive examples from the negative examples.
You notice that there are actually two examples down here.
There's cat and duck.
Is ducks got an s sound?
Ducks?
Yep.
So dogs and ducks.
They both get pluralized with an s sound.
And then we have beach doesn't.
That's beaches.
Dog.
We know that's a z.
Gun.
Gunz.
So that's not in the group.
So we can run this experiment.
Now here we go.
We're generalizing like crazy.
Generalizing, generalizing, generalizing from left to right.
So nothing in the first two columns matters.
Now we get to the t.
Wow.
There it is.

The sss.
If, and only if, you're not voiced and you're not strident in the second to the last-- in the last phone of the word that you're trying to pluralize.
So that's one phonological rule that the system has learned.
And guess what?
It's the same rule that's found in phonological textbooks.
So now we can try another experiment.
So this time we're trying to deal with dog and gun.
And our negatives are what was previously positive plus beach, which is still in there as a negative example.
So let's see how that one works.
Nothing matters except for the last column, the last phone.
And now we find out that if the last sound is voiced, then the pluralization gets the z sound, a voiced determinator.
And finally, just to deal with beaches.
That's beach in it's funny phonetic spelling.
So now, if the final sound in the word is strident, if its got this jetty sound-- beach.
Beach.
Then it gets the ea sound.
So let's go back to experiment number one.
Because I want to point out one small thing about the way this works.
You'll notice that it talks about coverage and excluded down here in the lower left-hand corner.
Excluded, well, there are three negative examples, so they better all be excluded.
You don't want to cover any of the negatives.
But it says coverage, two and two.
That's because it actually is doing-- and now we have the vocabulary to say it quickly-- it's doing a beam search through this space.
So it's not just doing a depth first search.
It's doing a beam search so as to reduce the possibility of overlooking a solution.
So it says, oh, the coverage.
Both of the beam search elements cover both of the positive examples.
And they, in fact, have converged to the same solution.
So that's how the Sussman and Yip thing worked.
And then the next question to ask is, of course, why did it work?
And so the answer, as articulated by Sussman and Yip-- or rather more by Sussman.
Or rather more by Yip and a little bit less by Sussman.
Yip thinks that it worked because it's a sparse space.
And when you have a high dimensional sparse space, it's easy to put a hyperplane into the space to separate one set of examples for another set of examples.
So let's consider the following situation.
Suppose we have a one-dimensional situation.
And we have two white examples and we have two purple examples.
Well, too bad for us you can't separate them.
Now suppose that this is actually the projection of a two-dimensional space that looks like this.
Here are the white examples down here.
And here are the purple examples up here.
Now it's easy to see that you can separate them with just a line that goes across like that.
Now let's take this one more step and suppose that this is actually a projection of a three-dimensional space.
It looks like this.
This will be dimension one.
This'll be two going back there.
And this will be three up here.
And suppose that the positive examples are right here on this line.
Let's say this is-- well, we're gonna draw a little old cube like so.
Those are purple examples that are up there.
How many ways are there of partitioning the space along those axes?
Well, now they're not even just two.
They're three.
So one way to separate the purple from the white is to draw a hyperplane-- or in this case it's a three dimension, so a plane-- through here on the number three axis.
You could also put a plane in on that axis.
Or you could do both.
So in one case your dividing line would be-- let's see.
On the first axis that would be 1/2.
And then the don't care.
Don't care.
Another solution that would be don't care.
And then we divide on the number 2 axis with a plane at 1/2 and don't care.
Or we could do it with 1/2, 1/2, and don't care.
So the higher the dimension of the space, the easier it is sometimes to put in a plane that separates the data.
That's why Sussman and Yip think that we use so little of possible phoneme space.
Because it makes the thing learnable.
That's one possibility.
So one explanation for sparse space is learnability.
There's another interesting possibility, and that is that if you have a sparse space, high dimensional space with 14 dimensions, and if the 40 points of your language are spread evenly throughout that space-- now let me say it the other way.
If they are placed at random in that space, then according to the central limit theorem, then they'll be about equally distant from each other.
So it ensures that the phonemes are easily separated when you speak.
But if you go to ask a linguist if that's true, they don't know.
Because they're not looking at it from a computational point of view.
Well, we can look at it from a computational point of view.
So I did that.
After Sussman and Yip published their paper.
And here's the result.
This is a diagram that shows all of the phonemes that are separated by exactly one distinctive feature.
So if you look over in this corner here, you'll see that the constants-- w and x-- are separated by exactly one distinctive feature.
So they're not exactly distant from each other in the space.
On the other hand, they are pretty easy to separate relative to the vowels.
Which are here in this part of the diagram.
Which are all tangled up and the vowels are all close to each other.
So guess what?
Vowels are much harder to separate than constants.
Not surprisingly, because there are many pairs of them that are different.
And only one distinctive feature.
All right.
So now you back up and you say, well, gosh.
That's all been sort of interesting.
But what does it teach us about how to do science and stuff?
And what it teaches us is-- this is an example.
Ow.
This is an example which we can use to illuminate some of thoughts of David Marr, who I spoke of in a previous lecture, connection with vision.
But here's Marr's catechism.
I can't spell very well so I won't try to respell it.
But this is Marr's catechism.
So what Marr said is, when you're dealing with an AI problem, first thing to do is to specify the problem.
Gee, that sounds awfully normal.
The next thing is to devise a representation suited to the problem.
The third thing to do, vocabulary varies, but it's something like determine an approach.
Sometimes thought of as a method.
And then four, pick a mechanism or devise an algorithm.
And, finally, five, experiment.
And of course, it never goes linearly like that.
You start with the problem and then you go through a lot of loops up here.
Sometimes even changing the problem.
But that's just the scientific method, right?
You start with the problem and you end up with the experiment.
But that's not what people in AI, over the bulk of its existence, have tended to do.
What they tended to do is to fall in love with particular mechanisms.
And then they attempt to apply those mechanisms to every problem.
So you might say, well, gee, neural nets are so cool.
I think all of human intelligence can be explained with a suitable neural net.
That's not the right way to do it.
Because that's mechanism envy.

You try to apply it where it isn't the right thing.
This is example starting with the problem and bringing to the problem the right representations, gosh, distinctive features.
Once we've got the right representation, then the constraints emerge, which enable us to devise an approach, write an algorithm, and do an experiment.
As they did.
So this Sussman-Yip thing is an example of doing AI stuff in a way that's congruent with the Marr's catechism.
Which I highly recommend.
They could have come in here and said, well, we're devotees of the idea of neural nets.
Let's see if we can make a machine that will properly pluralize words using a neural net.

Because it doesn't match the problem to the mechanism.
It tries to force fit the mechanism into some Procrustean bed where it doesn't actually work very well.
So what this leaves open, of course, is the question of, well, what is a good representation?
And here's the other half Marr's catechism.
Characteristic number one is that it makes the right things explicit.
So in this particular case, it makes distinctive features explicit.
Another thing that Marr was noted for was stereo vision.
So in that particular world, discontinuities in the image, when you go across an edge with the things that were made explicit.
Once you've got to a representation that makes the right things explicit, you can say, does it also expose constraint?
And if you have a representation that exposes constraint, then you're off and running.
Because it's constraint that you need in order to do the processing that leads to a solution.
So don't have the right representation.
If it doesn't expose constraints, you're not going to be able to make a very good model out of it.
And finally, there's a kind of localness criteria.
If you have a representation in which you can see the right answer by looking at descriptions through soda straw, that's probably a better representation than one that's all spread out.
It's true with programs, right?
If you can see how they work by looking through a soda straw, you're in much better situation to understand something if you have to look here and there and on the next page and in the next file.
So all this is basically common sense.
But this is kind of common sense that makes you smarter as an engineer and scientist.
Especially as a scientist because if you go into a problem with mechanism envy, you're apt to study mechanisms in a naive way and never reach a solution that will be satisfactory.


You can choose if you want to use jsreport online in the cloud or if you want to install it on your own server. jsreport on premise version allows you to download and install jsreport to your own server. You can use jsreport on premise without any limitations. You can also write your own extensions and use them on your local installation.


Ahh, enumerables, Ruby modules that help simplifiy tasks. Ruby is jammed pack with these special friends that make solving problems a little easier and solution code a more condensed. Today, we're going to look at Ruby's group_by method.
The group_by method is used to group collections of data by a common factor. The result is a hash with the common trait as the key and an array of the elements that share it as the value.
For example, if we wanted to split an array into groups by their class, we could do the following: We could check to see who qualifies for a senior discount by grouping by age:
The group_by method is one of many enumerables that can enhance your experience with the Ruby language. Return to some programs you've written and see if your solution can use a little grouping.


The CSV must have a header row that contains the following columns:
ACCT,SNUM,CWL,LAST,FIRST,LAB
The corresponding classlist command for this is:
classlist -T -f "%ACCT,%SN,%CWL,%LN,%FN,%LAB" <COURSENUM>
ACCT and SNUM are invariants and cannot change although ther fields can be updated (changing GitHub logins presents
its own issues that require manual effort on the part of course staff to handle).
Pre-existing Students, Teams, and Repositories
WILL NOT be affected.


Overview
Validates that JSON field definitions are valid combinations of keys, i.e.
valid syntax, and that the values for each included key are valid values
for that type of key, e.g. that the value for a ‘mean’ key is
either int or float.
Here is an example of a field definition: {"name" :
"name", "example_value" : 1, "mean" : 1.5,
"std_dev" : 0.25, "dist_type" :
"normal"}
Field Definition Grammar
Here is the complete BNF grammar for the keys of the key-value pairs in
field definitions:
- S -> name T
- T -> dist_type U V
- U -> example_value mean std_dev | example_value min max
allow_repeat_values | example_value histogram | histogram | value_set |
dictionary | id_int_base | id_int_step | id_int_base id_int_step
- V -> nil_ratio | nil_ratio nil_value | nil_ratio nil_include_field |
nil_ratio nil_value nil_include_field | EMPTY_SET
Field Definition Specification
name
- String
- Required
- The name of the field. Must be a string one or more characters in length .
dist_type
- String with a value in:
"normal"|"random"|"histogram"|"value_set"|"id_int"|"id_uuid"|"id_mongo_objid"
- Required
- The type of distribution to generate:
- Normal distribution will generate values using the average and standard
deviation provided.
- Random will generate a random distribution of values within the min and max
bounds provided and honoring the allow_repeat_values argument provided. If
the example_value provided with the field definition is a Fixnum,
then Random can generate a distribution of Fixnums in the range min
<= x < max where min and max can be any valid
Fixnum values. If the example_value provided with the field
definition is a Float, then Random will generate a distribution of Floats
in the range 0.0 <= x < 1.0.
- Histogram will generate a distribution conforming to the histogram
provided.
- Value set will generate a random distribution drawing from the fixed set of
values provided.
- Id_int will generate monotonically increasing integer id values for each
record, using the id_int_step provided or incremening by 1 if no step is
provided.
- Id_uuid will generate a UUID id value for each record.
- Id_mongo_objid will generate a MongoDB ObjectId id for each record.
example_value
- Fixnum|Float
- Required if dist_type == "normal" or dist_type ==
"random" or dist_type == "histogram" and
there is a histogram element supplying bucket values (instead of a
value_set or dictionary). Invalid otherwise.
- A value provided as an example of the type of data stored in the field.
Used to disambiguate definitions of "normal" and
"random" distributions, which can take arguments
describing the distribution of one type and have values of another. For
example, you might specifiy mean and std_dev as floats
but want to produce a distribution of integers.
mean
- Float
- Required if dist_type == "normal". Invalid otherwise.
- The average value over all records for data stored in the field. For
Strings, this is the average length.
std_dev
- Float
- Required if dist_type == "normal". Invalid otherwise.
- The standard deviation from the average over all records for data stored in
the field. For Strings, this is the standard deviation of the lengths of
all strings in the distribution.
min
- Fixnum
- Required if dist_type = "random". Invalid otherwise.
- The minimum value allowed for a random distribution.
- Valid range of values:
- If example_value is a Fixnum, min can be any valid Fixnum
value
- If example_value is a Float, 0.0 <= min <= 1.0
max
- Fixnum
- Required if dist_type = "random". Invalid otherwise.
- The maxium value allowed for a random distribution.
- Valid range of values:
- If example_value is a Fixnum, max can be any valid Fixnum
value
- If example_value is a Float, 0.0 <= max <= 1.0
allow_repeat_values
- Boolean
- Required if dist_type == "random". Invalid otherwise.
- Indicates whether values can repeat or must be unique in a random
distribution.
dictionary
- String
- Optional if dist_type == "value_set" or
"histogram". Invalid otherwise.
- Path to an external dictionary of words or phrases (one per line) to use
for generating string data:
- If provided as the value for the ‘value_set’ elemen t when
‘dist_type’ == "value_set", then a random
distribution from the dictionary will be generated.
- If provided as the value for elements in a ‘histogram’ when
‘dist_type’ == "histogram", then the dictionary
provides the set of values for the histogram bucket.
histogram
- JSON Array of JSON objects
- Required if dist_type == "histogram" unless there is a
dictionary or value_set present. Invalid otherwise.
- A JSON object describing a histogram distribution, following this format:
[{min : Fixnum|Float, max : : Fixnum|Float, count : Fixnum},
…]
value_set
- JSON Array
- Required if dist_type == "value_set" unless there is a
dictionary element present. Optional if dist_type ==
"histogram". Invalid otherwise.
- An array of values (which can be heterogenous) to draw from to randomly
generate a distribution.
id_int_base
- Fixnum
- Optional if dist_type == "id_int". Invalid otherwise.
- The starting value from which to generate monotonically incereasing id
values. If this key is not specified then a default of 1 is used.
id_int_step
- Fixnum
- Optional if dist_type == "id_int". Invalid otherwise.
- The amount to increment each id value generated. If this key is not
specified then a default of 1 is used.
nil_ratio
- Float in the range (0.0 <= x <= 1.0).
- Optional with default of 0.0 except explicitly not allowed if dist_type
== "id_*", dist_type == "histogram".
- NOTE: To support nil values in histogram distributions, include a
"null bucket" element with a positive count — so, for a
histogram with the sum of its counts == 100 (including the null bucket),
including this bucket means each element will have a 50% chance of being
null: {"min" : null, "max" : null,
"count" : 50}
- The ratio of records that should have a nil value. Here ‘nil’
is used loosely and means either the default nil value (no value in CSV
output and JSON null in JSON output), or the value provided in the optional
nil_value field.
nil_value
- ANY
- Optional except explicitly not allowed if "dist_type ==
"id_*", dist_type == "histogram".
- NOTE: To support nil values in histogram distributions, include a
"null bucket" element with a positive count — so, for a
histogram with the sum of its counts == 100 (including the null bucket),
including this bucket means each element will have a 50% chance of being
null: {"min" : null, "max" : null,
"count" : 50}
- The value of any type desired that should be emitted for records assigned a
‘nil’ value for this field. For example, to explicitly output
empty strings in output, include a {nil_value : ""} hash
element in the field definition.
nil_include_field
- Boolean
- Optional with default of true but invalid if nil_ratio is not also
present and set to a value > 0.0. Not allowed if "dist_type ==
"id_*", dist_type == "histogram".
- NOTE: To support nil values in histogram distributions, include a
"null bucket" element with a positive count — so, for a
histogram with the sum of its counts == 100 (including the null bucket),
including this bucket means each element will have a 50% chance of being
null: {"min" : null, "max" : null,
"count" : 50}
- A flag that toggles whether the field is included if it is nil. Allows
generation of data with uniform columns and nils (for example for CSVs or
data intended to be used in relational DBs) and generation of ragged
documents only including fields with values (for example for JSON data
intended to be used in document DBs)
Examples of Each Valid Field Definition
Normal
Normal distribution, no nil_ratio specified:
{"name" : "name", "example_value" : 1, "mean" : 1.5, "std_dev" : 0.25, "dist_type" : "normal"}
Normal distribution, nil_ratio specified:
{"name" : "name", "example_value" : 1, "mean" : 1.5, "std_dev" : 0.25, "dist_type" : "normal", "nil_ratio" : 0.1}
Normal distribution, nil_ratio and nil_include_field, specified:
{"name" : "name", "example_value" : 1, "mean" : 1.5, "std_dev" : 0.25, "dist_type" : "normal", "nil_ratio" : 0.1, "nil_include_field" : false}
Normal distribution, nil_ratio and nil_value specified:
{"name" : "name", "example_value" : 1, "mean" : 1.5, "std_dev" : 0.25, "dist_type" : "normal", "nil_ratio" : 0.1, "nil_value" : -1}
Normal distribution, nil_ratio, nil_value and nil_include_field specified:
{"name" : "name", "example_value" : 1, "mean" : 1.5, "std_dev" : 0.25, "dist_type" : "normal", "nil_ratio" : 0.1, "nil_value" : -1, "nil_include_field" : false}
Random
Random distribution, no nil_ratio specified:
{"name" : "name", "example_value" : 1, "min" : 1, "max" : 100000, "allow_repeat_values" : true, "dist_type" : "random"}
Random distribution, nil_ratio specified:
{"name" : "name", "example_value" : 1, "min" : 1, "max" : 100000, "allow_repeat_values" : true, "dist_type" : "random", "nil_ratio" : 0.1}
Random distribution, nil_ratio and nil_include_field specified:
{"name" : "name", "example_value" : 1, "min" : 1, "max" : 100000, "allow_repeat_values" : true, "dist_type" : "random", "nil_ratio" : 0.1, "nil_include_field" : false}
Random distribution, nil_ratio and nil_value specified:
{"name" : "name", "example_value" : 1, "min" : 1, "max" : 100000, "allow_repeat_values" : true, "dist_type" : "random", "nil_ratio" : 0.1, "nil_value" : -1}
Random distribution, nil_ratio, nil_value and nil_include_field specified:
{"name" : "name", "example_value" : 1, "min" : 1, "max" : 100000, "allow_repeat_values" : true, "dist_type" : "random", "nil_ratio" : 0.1, "nil_value" : -1, "nil_include_field" : false}
Histogram
Histogram distribution for numeric data:
{"name" : "name", "example_value" : 1, "dist_type" : "histogram", "histogram" : [{"min" : 0, "max" : 1123, count: 50}, {"min" : 1124, "max" : 18532, count: 53}]}
Histogram distribution for String data using a value_set:
{"name" : "name", "dist_type" : "histogram", "histogram" : [{"value_set" : ["a", "b", "c"], count: 50}, {"value_set" : ["d", "e", "f"], count: 53}]}
Histogram distribution for string data using a dictionary:
{"name" : "name", "dist_type" : "histogram", "histogram" : [{"dictionary" : "/PATH_TO_DICT_1", count: 50}, {"dictionary" : "/PATH_TO_DICT_2", count: 53}]}
Value Set
Value Set distribution, no nil_ratio specified:
{"name" : "name", "dist_type" : "value_set", "value_set" : [1, 2, 3, 5, 8, 13, 21]}
Value Set distribution, nil_ratio specified:
{"name" : "name", "dist_type" : "value_set", "value_set" : [1, 2, 3, 5, 8, 13, 21], "nil_ratio" : 0.1}
Value Set distribution, nil_ratio and nil_include_field specified:
{"name" : "name", "dist_type" : "value_set", "value_set" : [1, 2, 3, 5, 8, 13, 21], "nil_ratio" : 0.1, "nil_include_field" : true}
Value Set distribution, nil_ratio and nil_value specified:
{"name" : "name", "dist_type" : "value_set", "value_set" : [1, 2, 3, 5, 8, 13, 21], "nil_ratio" : 0.1, "nil_value" : -1}
Value Set distribution, nil_ratio, nil_value and nil_include_field
specified:
{"name" : "name", "dist_type" : "value_set", "value_set" : [1, 2, 3, 5, 8, 13, 21], "nil_ratio" : 0.1, "nil_value" : -1, "nil_include_field" : true}
Value Set distribution, String data, with Dictionary, no nil_ratio
specified:
{"name" : "name", "dist_type" : "value_set", "dictionary" : "PATH_TO_DICT_1"}}
Value Set distribution, String data, with Dictionary, nil_ratio specified:
{"name" : "name", "dist_type" : "value_set", "dictionary" : "PATH_TO_DICT_1", "nil_ratio" : 0.1}
Value Set distribution, String data, with Dictionary, nil_ratio and
nil_include_field specified:
{"name" : "name", "dist_type" : "value_set", "dictionary" : "PATH_TO_DICT_1", "nil_ratio" : 0.1, "nil_include_field" : true}
Value Set distribution, String data, with Dictionary, nil_ratio and
nil_value specified:
{"name" : "name", "dist_type" : "value_set", "dictionary" : "PATH_TO_DICT_1", "nil_ratio" : 0.1, "nil_value" : -1}
Value Set distribution,String data, with Dictionary, nil_ratio, nil_value
and nil_include_field specified:
{"name" : "name", "dist_type" : "value_set", "dictionary" : "PATH_TO_DICT_1", "nil_ratio" : 0.1, "nil_value" : -1, "nil_include_field" : true}
Id_Int
Id_int distribution, no id_int_base or id_int_step specified:
{"name" : "name", "dist_type" : "id_int"}
Id_int distribution, id_int_base specified but no id_int_step specified:
{"name" : "name", "dist_type" : "id_int", "id_int_base" : 1000000}
Id_int distribution, id_int_base specified and id_int_step specified:
{"name" : "name", "dist_type" : "id_int", "id_int_base" : 1000000, "id_int_step" : 20}
Id_uuid distribution:
{"name" : "name", "dist_type" : "id_uuid"}
Id_mongo_objid distribution:
{"name" : "name", "dist_type" : "id_mongo_objid"}


ECG¶
In this example we use the ECG data included with scipy signal module. The references roughly includes the Q-T interval (https://en.wikipedia.org/wiki/Electrocardiography). In the first portion, two sample segments are used. While the segments are not aligned, they are able to find some segments correctly. In the second portion of the example, only one segment is used for the reference data.
>>> import random
>>> import numpy as np
>>> from scipy.misc import electrocardiogram
>>> import matplotlib.pyplot as plt
>>> import seg1d
After imports, the scipy signal ECG data is called and some segments are taken.
>>> ecg = electrocardiogram() #get the scipy sample data
>>> ref_slices = [[927, 1057],[1111, 1229]] #pick sample endpoints
>>> s = seg1d.Segmenter() #create the segmenter
>>> refs = [ ecg[x[0]:x[1]] for x in ref_slices ]
>>> for r in refs: s.add_reference(r) #set reference data
>>> s.set_target(ecg[1500:3500]) #set the target data to the ecg after ref
>>> segments = s.segment() # run segmenter with defaults
>>> print(np.around(segments,decimals=7))
[[1.607000e+03 1.729000e+03 8.169533e-01]
[7.380000e+02 8.220000e+02 8.123868e-01]
[9.190000e+02 1.003000e+03 8.120505e-01]
[1.439000e+03 1.552000e+03 8.092366e-01]
[3.600000e+02 4.930000e+02 8.077664e-01]
[1.091000e+03 1.213000e+03 8.043364e-01]
[1.775000e+03 1.895000e+03 7.998723e-01]
[1.720000e+02 3.000000e+02 7.926582e-01]
[1.268000e+03 1.340000e+03 7.847107e-01]
[5.540000e+02 6.280000e+02 7.802931e-01]]
The reference data is automatically scaled to the largest reference in the dataset
when the segment
method is called. Therefore, by retrieving this attribute
we can plot what the reference set looks like when the lengths are normalized.
In the example, it is clear the peaks of the reference segments are not aligned. This discrepency, due to the averaging of all reference data items, will be seen in the final segments of the target data later.
>>> refs = s.r
>>> refs = np.asarray( [ x[y] for x in refs for y in x ] )
>>> plt.figure(figsize=(5,3))
>>> plt.plot(refs.T)
>>> plt.xlabel("time in s")
>>> plt.ylabel("ECG in mV")
>>> plt.tight_layout()
>>> plt.show()
The final segments are shown by calling the property t_masked
which returns the
target data as an ndarray with NaN values for areas not found to be segments.
>>> plt.figure(figsize=(15,3))
>>> plt.plot(s.t_masked.T)
>>> plt.xlabel("time in s")
>>> plt.ylabel("ECG in mV")
>>> plt.tight_layout()
>>> plt.show()
>>> #use only 1 reference
>>> s.clear_reference()
>>> s.add_reference( ecg[927:1057] )
>>> refs = s.r
>>> refs = np.asarray( [ x[y] for x in refs for y in x ] )
>>> plt.figure(figsize=(5,3))
>>> plt.plot(refs.T)
>>> plt.xlabel("time in s")
>>> plt.ylabel("ECG in mV")
>>> plt.tight_layout()
>>> plt.show()
>>> #remove first part of data (contains reference)
>>> s.set_target(ecg[1500:3500])
>>> s.nC = 2
>>> s.cMin = 0.7
>>> segments = s.segment()
>>> print(np.around(segments,decimals=7))
[[7.350000e+02 8.540000e+02 9.462850e-01]
[1.093000e+03 1.213000e+03 9.242974e-01]
[9.140000e+02 1.046000e+03 9.059727e-01]
[3.620000e+02 4.980000e+02 9.009127e-01]
[5.470000e+02 6.800000e+02 8.940106e-01]
[1.262000e+03 1.390000e+03 8.868629e-01]
[1.776000e+03 1.902000e+03 8.771139e-01]
[1.609000e+03 1.729000e+03 8.689476e-01]
[1.440000e+03 1.559000e+03 8.646669e-01]
[1.730000e+02 3.060000e+02 8.029426e-01]]
>>> res = s.t_masked
>>> plt.figure(figsize=(15,3))
>>> plt.plot(res.T)
>>> plt.xlabel("time in s")
>>> plt.ylabel("ECG in mV")
>>> plt.tight_layout()
>>> plt.show()


Kenyan university student sues Equity Bank over song
by RICHARD MUNGUTIIn Summary
- The petitioner is asking the High Court to issue a conservatory order restraining Equity Bank (Kenya) Limited and Equity Group Foundation from using his piece of music in any of its advertisements on the internet, television, radio or any other media to popularise its Wings to Fly project pending determination of the suit.
A Kenyan university student has sued Equity Bank for promoting its business using a song he claims he composed.
Mr Eric Obiero Nyadida has urged the High Court to prohibit the bank from using the song to advertise its Wings to Fly education sponsorship project for bright but needy children.
Mr Obiero was prosecuted at the instigation of the lender for forgery alongside his brother Mr Geoffrey Nyadida Odongo and then acquitted in October 2017 for lack of evidence.
LEGAL RIGHTS
Testifying before Justice Wilfrida Okwany, Mr Obiero said his rights were violated when he was locked up in a police cell with adults when he was 16.
He says he was denied his legal rights and access to his parents while in detention at Kileleshwa Police Station.
Also Read
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Advertisement
Mr Obiero, who is pursuing a Journalism course at a private university, told Justice Okwany that over the last six years he has suffered mental anguish and violation of his property rights.
He said he was dragged through a criminal trial because he asked the bank to pay him Ksh10 million ($100,000) as promised by senior officials of Equity Bank (Kenya) Limited and Equity Group Foundation (EGF) for using his song.
The petitioner says the lender has continued to use his work, yet he is receiving no direct benefit.
He is now asking the High Court to issue a conservatory order restraining EBL and EG from using his piece of music in any of its advertisements on the internet, television, radio or any other media to popularise its EGF Wings to Fly project pending determination of the suit.
Mr Obiero is also seeking costs of the case. Hearing continues.


Close Button
You can so totally close this!
×
You can close me too, and I close using a Motion UI animation.
×


Regular MEI allows the following document elements: <mei>
, <meiHead>
,
<music>
, and <meiCorpus>
. This means that these elements may serve as outermost
elements in an MEI file. However, a project may come up with a data structure that requires other or additional start elements.
For instance, a project may decide to keep all editorial annotations as separate files, each containing its own <annot>
element. The official mei-all_anyStart
customization allows to use every single element in the MEI Namespace as start element, but this is mostly for teaching purposes and is not
recommendable for practical use. Instead, ODD offers a simple mechanism to define the allowed start elements, using the initial
<schemaSpec>
element.
<schemaSpec ident="mei" ns="http://www.music-encoding.org/ns/mei" prefix="mei_" start="mei meiHead meiCorpus music">
As you can see, the @start
-attribute is used as a space-separated list of all start elements. If the project mentioned above decided
to allow <mei>
and <annot>
, but disallow all other start elements, this could be modified like so:
<schemaSpec ident="mei" ns="http://www.music-encoding.org/ns/mei" prefix="mei_" start="mei annot">
As you can see, adjusting start elements is actually quite easy. Let's wrap up what we've learned about customizing ODD.


Download video.js and include it in your page:
<video-js id=example-video width=960 height=540 class="vjs-default-skin" controls> <source src="https://example.com/index.m3u8" type="application/x-mpegURL"> </video-js> <script src="video.js"></script> <script> var player = videojs('example-video'); player.play(); </script>
Or just check out the source of this page!


Check out the latest events specially geared towards children ages 5-12
March 15, 2013 – March 30, 2013
Fridays7pm
Saturdays1pm and 3pm
Sundays1pm and 3pm
Ticket Prices
$10.00 (all ages)
Sunday Discount: $5.00 ages 1-3 and FREE for children under 1
Purchase Tickets
The classic tale of Snow White is told once again. A young princess driven to the woods by her jealous stepmother finds a home with seven dwarves. However, the wicked queen continues to search the land and eliminate all beauty that surpasses her own. She tries to kill Snow White by sending her Huntsman, but the Huntsman cannot bear to hurt the little girl. She spies and sneaks, and eventually tricks Snow White into eating a poisoned apple that puts her into a deep sleep, broken only by true love's kiss. In the meantime, the Huntsman searches far and wide, and finds a Prince to break the spell.
Theater located at Redmond Town Center
Address: 7525 166th Ave. NE I, WA 98052
Phone:<PHONE_NUMBER>
Redmond City Hall Campus
15670 NE 85th St.
Redmond, WA 98052
(425) 556-2900
August 24, 2013, 3-7pm
This year Redmond will be hosting their annual Great Day of Play on August 24th from 3pm-7pm. All ages are welcome; the event will take place at the Redmond City Hall Campus. Families that value health, fitness and active recreation will enjoy this event. Take part in the national movement to make America more active. Activities such as Kangoo Jumps, Spin Biking, Z-Ultimate martial arts, and Jazzercise are offered and taught. Additionally, a 3-on-3 youth basketball tournament will be held from 2pm-7pm. Kids ages 4-12 are encouraged to drop by and participate. During the same time, a 5K run/walk will be held on the Sammamish River Trail. Don't miss out on this opportunity to get up and play!


Supplementary Figure 2
Dot plots of GO and KEGG enrichment analysis for the 275 DEGs. (A) GO enrichment analysis for 275 TME-related DEGs, statistical threshold set at P-value < 0.05 with an FDR adjusted P-value < 0.05. ( B) KEGG enrichment analysis for the 275 DEGs, statistical threshold set at p-value < 0.05 with an FDR adjusted P-value < 0.05.
Click here for additional data file.


---
layout: default
title: Shell
post_scripts:
- js/shell.js
startup_functions:
- redirectIfNotLoggedIn()
---
- Your login credentials are the same on our website and on the shell server.
- Duplicate this tab in a new window to see the problems and the shell side by side.
- To copy from the shell, select text in the shell and press Ctrl-C/Cmd-C.
- To paste into the shell, right-click in the shell and choose "Paste from browser".


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 4
Nasi lemak, a spicy coconut rice, is the national dish of Malaysia, where it is eaten for breakfast. If that is a little too adventurous, it makes a great weekend dinner for friends.
Put the coconut milk, ginger, lemongrass, rice and a pinch of salt in a large saucepan. Add 400ml/14fl oz water, stir and cook over a medium heat for 10–15 minutes, or until the rice is cooked and all the liquid has been absorbed.
To make the sambal, heat the oil in a frying pan and sweat the onion and garlic for 5–7 minutes, until soft. Add the shrimp paste, chilli paste and most of the soaked anchovies and stir. Add the sugar and a pinch of salt and pour in the tamarind water. Simmer until it reduces to a thick paste. Set aside.
Meanwhile, to prepare the garnish, boil the eggs in water for 7–10 minutes; the yolks should be firm. When they are cool enough to handle, peel off the shells and cut the eggs in half. Preheat the oil in a deep-fat fryer to 180C. (CAUTION: hot oil can be dangerous. Do not leave unattended.) Deep-fry the dried white anchovies for 3 minutes, or until crispy. Toast the peanuts in a dry frying pan.
Pick the lemongrass out of the rice and discard. Put the rice in a small bowl and press down until it is compacted. Upturn the rice in the middle of the pandan leaf. Arrange the egg, peanuts, cucumber and remaining anchovies around the rice and scatter over the crispy anchovies.


--- layout: post title: Hacker Newsletter Book List - Volume One ---
This is the results of asking my great Hacker Newsletter subscribers what they are currently reading. I thought this experiment went very well and I found several books that I've added to my queue, including several that I hadn't seen before as well as many I had wanted to read, but had forgotten about.
I wanted to try the new jquery isotope plugin out, but ran short on time so if you have any issues with this page please let me know. All of these have my affiliate code in the link. Thanks for supporting the newsletter!
22
$27.00
["Books", "Subjects", "Health, Mind & Body", "Diets & Weight Loss", "Diets"]
2554
$4.99
["Books", "Subjects", "Books & Reading", "British", "Classics", "Comic", "Drama", "Erotica", "Essays", "Foreign Language Fiction", "Genre Fiction", "History & Criticism", "Letters & Correspondence", "Literary", "Poetry", "Short Stories", "United States", "World Literature", "Literature & Fiction"]
75516
$69.99
["Books", "Subjects", "Computers & Internet", "Operating Systems", "AIX", "Administration", "Shell", "Unix"]
12002
$54.99
["Books", "Subjects", "Computers & Internet", "Computer Science", "Design Tools & Techniques", "Information Systems", "Methodology", "Multimedia Information Systems", "Software Engineering"]
114438
$44.99
["Books", "Subjects", "Computers & Internet", "Business & Culture", "History"]
15103
$59.99
["Books", "Subjects", "Home Computing", "Apple", "Business & Culture", "Certification Central", "Computer Science", "Databases", "Digital Music", "Digital Photography & Video", "Games & Strategy Guides", "Graphic Design", "Hardware", "Microsoft", "Mobile & Wireless Computing", "Networking", "Operating Systems", "Programming", "Project Management", "Security & Encryption", "Software", "Web Development", "Computers & Internet"]
1644881
$30.00
["Books", "Subjects", "Biographies & Memoirs", "Leaders & Notable People", "Presidents & Heads of State"]
185
$15.99
["Books", "Subjects", "Business & Investing", "Management & Leadership"]
40355
$26.00
["Books", "Subjects", "Nonfiction", "Social Sciences"]
8617
$39.99
["Books", "Subjects", "Computers & Internet", "Hardware", "Design & Architecture"]
99254
$106.00
["Books", "Subjects", "Computers & Internet", "Hardware", "Embedded Systems", "Microprocessor Design", "PIC Microcontroller", "Microprocessors & System Design"]
17608
$14.99
["Books", "Subjects", "Books & Reading", "British", "Classics", "Comic", "Drama", "Erotica", "Essays", "Foreign Language Fiction", "Genre Fiction", "History & Criticism", "Letters & Correspondence", "Literary", "Poetry", "Short Stories", "United States", "World Literature", "Literature & Fiction"]
5389
$24.99
["Books", "Subjects", "Computers & Internet", "Web Development", "Content Management"]
60
$14.99
["Books", "Subjects", "Religion & Spirituality", "Christianity", "Christian Living"]
36421
$9.99
["Books", "Subjects", "Books & Reading", "British", "Classics", "Comic", "Drama", "Erotica", "Essays", "Foreign Language Fiction", "Genre Fiction", "History & Criticism", "Letters & Correspondence", "Literary", "Poetry", "Short Stories", "United States", "World Literature", "Literature & Fiction"]
6072
$17.99
["Books", "Subjects", "Business & Investing", "Industries & Professions"]
16890
$18.99
["Books", "Subjects", "Computers & Internet", "Business & Culture", "Hacking"]
1557
$25.00
["Books", "Subjects", "Business & Investing", "Management & Leadership", "Motivational"]
12252
$7.99
["Books", "Subjects", "Books & Reading", "British", "Classics", "Comic", "Drama", "Erotica", "Essays", "Foreign Language Fiction", "Genre Fiction", "History & Criticism", "Letters & Correspondence", "Literary", "Poetry", "Short Stories", "United States", "World Literature", "Literature & Fiction"]
10919
$23.95
["Books", "Subjects", "Business & Investing", "Ethics", "Etiquette", "Fashion & Image", "Health & Stress", "Work Life Balance", "Business Life"]
24
$29.95
["Books", "Subjects", "Computers & Internet", "Business & Culture", "History"]
35710
$16.00
["Books", "Subjects", "Literature & Fiction", "Literary"]
168324
$17.95
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35765
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["Books", "Subjects", "Computers & Internet", "Computer Science", "Software Engineering", "Design Tools & Techniques"]
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@* * Copyright 2023 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import uk.gov.hmrc.digitalservicestax.data.Email @this( govukPanel : GovukPanel ) @(key: String, companyName: String, email: Email)(messages: UniformMessages[Html]) @govukPanel(Panel( title = HtmlContent(messages(s"$key.heading")), content = HtmlContent(messages(s"$key.detail", companyName)) ))
@messages(s"$key.email", email)
@messages(s"$key.what-happens-next.p1", email)
@messages( s"$key.help-using-this-service.p1", "https://www.gov.uk/topic/business-tax/digital-services-tax" )
@messages(s"$key.help-using-this-service.list.title")
@messages( s"$key.ccm-email"<EMAIL_ADDRESS>)
@messages("common.feedback") @messages("common.thirty-seconds")


Class for abstract image labeler.
More...
#include <abstract_image_labeler.h>
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| AbstractImageLabeler (const cv::Mat &depth_image, const ProjectionParams ¶ms, const Radians &angle_threshold) |
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void | SetDepthImage (const cv::Mat &depth_image) |
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virtual void | ComputeLabels (DiffFactory::DiffType diff_type)=0 |
| | An interface for children to compute labels.
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| const cv::Mat * | GetLabelImage () const |
| | Gets the label image. More...
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| |
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| static cv::Mat | LabelsToColor (const cv::Mat &label_image) |
| | Generates random-colored image from image of labels. More...
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const cv::Mat * | _depth_image_ptr |
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ProjectionParams | _params |
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cv::Mat | _label_image |
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float | _radians_threshold |
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static constexpr std::array< std::array< int, 3 >, 200 > | RANDOM_COLORS |
| |
Class for abstract image labeler.
This class is responsible for labeling an given depth image based on the provided angle_threshold.
◆ GetLabelImage()
| const cv::Mat* depth_clustering::AbstractImageLabeler::GetLabelImage |
( |
| ) |
const |
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inline |
Gets the label image.
- Returns
- The label image.
◆ LabelsToColor()
| Mat depth_clustering::AbstractImageLabeler::LabelsToColor |
( |
const cv::Mat & |
label_image | ) |
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static |
Generates random-colored image from image of labels.
- Parameters
-
| [in] | label_image | The label image |
- Returns
- Random-colored image from label image
The documentation for this class was generated from the following files:
- /home/ashwin/catkin_ws/src/depth_clustering/src/image_labelers/abstract_image_labeler.h
- /home/ashwin/catkin_ws/src/depth_clustering/src/image_labelers/abstract_image_labeler.cpp


Motorola Razr comes back into the cellphone fold
by Natalie Dreier, Cox Media Group National Content DeskWhile Apple is touting the latest iPhones that will hold your entire life in your pocket, Motorola is reintroducing a classic that was at one point only a phone.
The company announced it is bringing back the Razr flip phone this week.
The phone, which was introduced way back in 2004, was thin, had a blue back-lit metal keypad and was the best-selling phone until iPhone knocked it off the pedestal, CNN reported.
While the phone is called a Razr, it isn't just a phone anymore.
It actually is a smartphone that has a folding 6.2-inch screen when open, and 2.7-inch when closed, The New York Times reported.
The phone can show the time, take selfies and do quick respond texts when closed but most of the functions have to have the phone flipped open, CNN reported.
The Razr runs Android 9, available only on Verizon by preorder and comes at a cost of almost $1,500, CNN reported.


Arab League Slams Softer US Stance on Int’l Legality of Israeli Settlements in West Bank
CAIRO (Sputnik) – The Arab League strongly condemns US Secretary of State Mike Pompeo’s Monday announcement, in which he said that Jewish settlements established by Israel in the West Bank were no longer inconsistent with international law, the organization’s head, Ahmed Aboul Gheit, said on Tuesday.
"Aboul Gheit condemned in the strongest terms the statement, issued by US Secretary of State [Mike Pompeo], that his country no longer viewed the Israeli settlements in the West Bank to be contrary to international law and considered it as a very negative development," the head said in a statement.
He added that international law was formulated by the global community, "but not by a single state."
In his turn, Israeli Prime Minister Benjamin Netanyahu on Monday thanked US President Donald Trump’s administration for "righting a historical wrong."
Unlike Israel and the United States, the international community sees the presence and expansion of Jewish settlements in the occupied West Bank and East Jerusalem as illegal and considers it to be one of the major obstacles on the way to peace.


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Path Finding Visualization
Select An Algorithm
Depth-First Search
Breadth-First Search
Best-First Search
Uniform-Cost Search
A* Search
Change Node
Wall Node (Draggable)
Start Node
Goal Node
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Random Walls
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Path Cost: