|
Cytnx
v0.7.2
|
Functions | |
| void | Make_normal (Storage &Sin, const double &mean, const double &std, const unsigned int &seed=std::random_device()()) |
| Randomize the memory of a Storage with normal distributon. More... | |
| void | Make_uniform (Storage &Sin, const double &low=0, const double &high=1, const unsigned int &seed=std::random_device()()) |
| Randomize the memory of a Storage with uniform distributon. More... | |
| void | Make_normal (Tensor &Tin, const double &mean, const double &std, const unsigned int &seed=std::random_device()()) |
| Randomize the memory of a Tensor with normal distributon. More... | |
| void | Make_uniform (Tensor &Tin, const double &low=0, const double &high=1, const unsigned int &seed=std::random_device()()) |
| Randomize the memory of a Tensor with uniform distributon. More... | |
| Tensor | normal (const cytnx_uint64 &Nelem, const double &mean, const double &std, const int &device=Device.cpu, const unsigned int &seed=std::random_device()()) |
| create an Tensor with all the elements are initialized with normal distribution More... | |
| Tensor | normal (const std::vector< cytnx_uint64 > &Nelem, const double &mean, const double &std, const int &device=Device.cpu, const unsigned int &seed=std::random_device()()) |
| create an Tensor with all the elements are initialized with normal distribution More... | |
| Tensor | uniform (const cytnx_uint64 &Nelem, const double &low, const double &high, const int &device=Device.cpu, const unsigned int &seed=std::random_device()()) |
| create an Tensor with all the elements are initialized with uniform distribution More... | |
| Tensor | uniform (const std::vector< cytnx_uint64 > &Nelem, const double &low, const double &high, const int &device=Device.cpu, const unsigned int &seed=std::random_device()()) |
| create an Tensor with all the elements are initialized with uniform distribution More... | |
| void cytnx::random::Make_normal | ( | Storage & | Sin, |
| const double & | mean, | ||
| const double & | std, | ||
| const unsigned int & | seed = std::random_device()() |
||
| ) |
Randomize the memory of a Storage with normal distributon.
| Sin | a Storage |
| mean | the mean of a normal distribution |
| std | the standard deviation (sigma) of a normal distribution. |
| seed | the seed for the random generator. [Default] Using device entropy. [Note] The Storage sould be real floating type or complex type. |
| void cytnx::random::Make_normal | ( | Tensor & | Tin, |
| const double & | mean, | ||
| const double & | std, | ||
| const unsigned int & | seed = std::random_device()() |
||
| ) |
Randomize the memory of a Tensor with normal distributon.
| Tin | a Tensor |
| mean | the mean of a normal distribution |
| std | the standard deviation (sigma) of a normal distribution. |
| seed | the seed for the random generator. [Default] Using device entropy. [Note] The Tensor sould be real floating type or complex type. |
| void cytnx::random::Make_uniform | ( | Storage & | Sin, |
| const double & | low = 0 , |
||
| const double & | high = 1 , |
||
| const unsigned int & | seed = std::random_device()() |
||
| ) |
Randomize the memory of a Storage with uniform distributon.
| Sin | a Storage |
| low | the lower-bound of the uniform distribution |
| high | the higher-bound of the uniform distribution |
| seed | the seed for the random generator. [Default] Using device entropy. [Note] 1. The Storage sould be real floating type or complex type. 2. In cpu, it generate random number in domain [low,high); but in gpu(cuda), it generate random number in domain (low,high]; (cuRandv10) |
| void cytnx::random::Make_uniform | ( | Tensor & | Tin, |
| const double & | low = 0 , |
||
| const double & | high = 1 , |
||
| const unsigned int & | seed = std::random_device()() |
||
| ) |
Randomize the memory of a Tensor with uniform distributon.
| Tin | a Tensor |
| low | the lower-bound of the uniform distribution |
| high | the higher-bound of the uniform distribution |
| seed | the seed for the random generator. [Default] Using device entropy. [Note] 1. The Tensor sould be real floating type or complex type. 2. In cpu, it generate random number in domain [low,high); but in gpu(cuda), it generate random number in domain (low,high]; (cuRandv10) |
| Tensor cytnx::random::normal | ( | const cytnx_uint64 & | Nelem, |
| const double & | mean, | ||
| const double & | std, | ||
| const int & | device = Device.cpu , |
||
| const unsigned int & | seed = std::random_device()() |
||
| ) |
create an Tensor with all the elements are initialized with normal distribution
| Nelem | the number of elements |
| mean | the mean of the normal distribution |
| std | the standard deviation (sigma) of the normal distribution |
| device | the device that the Tensor is put on. It can be any device defined in cytnx::Device |
| seed | the seed for the random generator. [Default] using device entropy |
| Tensor cytnx::random::normal | ( | const std::vector< cytnx_uint64 > & | Nelem, |
| const double & | mean, | ||
| const double & | std, | ||
| const int & | device = Device.cpu , |
||
| const unsigned int & | seed = std::random_device()() |
||
| ) |
create an Tensor with all the elements are initialized with normal distribution
| Nelem | the shape of the Tensor |
| mean | the mean of the normal distribution |
| std | the standard deviation (sigma) of the normal distribution |
| device | the device that the Tensor is put on. It can be any device defined in cytnx::Device |
| seed | the seed for the random generator. [Default] using device entropy |
| Tensor cytnx::random::uniform | ( | const cytnx_uint64 & | Nelem, |
| const double & | low, | ||
| const double & | high, | ||
| const int & | device = Device.cpu , |
||
| const unsigned int & | seed = std::random_device()() |
||
| ) |
create an Tensor with all the elements are initialized with uniform distribution
| Nelem | the number of elements |
| low | the lower-bound of range |
| high | the upper-bound of range |
| device | the device that the Tensor is put on. It can be any device defined in cytnx::Device |
| seed | the seed for the random generator. [Default] using device entropy |
| Tensor cytnx::random::uniform | ( | const std::vector< cytnx_uint64 > & | Nelem, |
| const double & | low, | ||
| const double & | high, | ||
| const int & | device = Device.cpu , |
||
| const unsigned int & | seed = std::random_device()() |
||
| ) |
create an Tensor with all the elements are initialized with uniform distribution
| Nelem | the number of elements |
| low | the lower-bound of range |
| high | the upper-bound of range |
| device | the device that the Tensor is put on. It can be any device defined in cytnx::Device |
| seed | the seed for the random generator. [Default] using device entropy |


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public class WordShapeClassifier
extends java.lang.Object
| Modifier and Type | Field and Description |
|---|---|
static int |
NOWORDSHAPE |
static int |
WORDSHAPECHINESE |
static int |
WORDSHAPECHRIS1 |
static int |
WORDSHAPECHRIS2 |
static int |
WORDSHAPECHRIS2USELC |
static int |
WORDSHAPECHRIS3 |
static int |
WORDSHAPECHRIS3USELC |
static int |
WORDSHAPECHRIS4 |
static int |
WORDSHAPECLUSTER1 |
static int |
WORDSHAPEDAN1 |
static int |
WORDSHAPEDAN2 |
static int |
WORDSHAPEDAN2BIO |
static int |
WORDSHAPEDAN2BIOUSELC |
static int |
WORDSHAPEDAN2USELC |
static int |
WORDSHAPEDIGITS |
static int |
WORDSHAPEJENNY1 |
static int |
WORDSHAPEJENNY1USELC |
| Modifier and Type | Method and Description |
|---|---|
static int |
lookupShaper(java.lang.String name)
Look up a shaper by a short String name.
|
static void |
main(java.lang.String[] args)
Usage:
java edu.stanford.nlp.process.WordShapeClassifier
[-wordShape name] string+ where name is an argument to lookupShaper . |
static java.lang.String |
wordShape(java.lang.String inStr,
int wordShaper)
Specify the String and the int identifying which word shaper to
use and this returns the result of using that wordshaper on the String.
|
static java.lang.String |
wordShape(java.lang.String inStr,
int wordShaper,
java.util.Collection<java.lang.String> knownLCWords)
Specify the string and the int identifying which word shaper to
use and this returns the result of using that wordshaper on the String.
|
static java.lang.String |
wordShapeChris4(java.lang.String s) |
public static final int NOWORDSHAPE
public static final int WORDSHAPEDAN1
public static final int WORDSHAPECHRIS1
public static final int WORDSHAPEDAN2
public static final int WORDSHAPEDAN2USELC
public static final int WORDSHAPEDAN2BIO
public static final int WORDSHAPEDAN2BIOUSELC
public static final int WORDSHAPEJENNY1
public static final int WORDSHAPEJENNY1USELC
public static final int WORDSHAPECHRIS2
public static final int WORDSHAPECHRIS2USELC
public static final int WORDSHAPECHRIS3
public static final int WORDSHAPECHRIS3USELC
public static final int WORDSHAPECHRIS4
public static final int WORDSHAPEDIGITS
public static final int WORDSHAPECHINESE
public static final int WORDSHAPECLUSTER1
public static int lookupShaper(java.lang.String name)
name
- Shaper name. Known names have patterns along the lines of:
dan[12](bio)?(UseLC)?, jenny1(useLC)?, chris[1234](useLC)?, cluster1.public static java.lang.String wordShape(java.lang.String inStr, int wordShaper)
inStr
- String to calculate word shape ofwordShaper
- Constant for which shaping formula to usepublic static java.lang.String wordShape(java.lang.String inStr, int wordShaper, java.util.Collection<java.lang.String> knownLCWords)
inStr
- String to calculate word shape ofwordShaper
- Constant for which shaping formula to useknownLCWords
- A Collection of known lowercase words, which some shapers use
to decide the class of capitalized words.
Note: while this code works with any Collection, you should
provide a Set for decent performance. If this parameter is
null or empty, then this option is not used (capitalized words
are treated the same, regardless of whether the lowercased
version of the String has been seen).public static java.lang.String wordShapeChris4(java.lang.String s)
public static void main(java.lang.String[] args)
java edu.stanford.nlp.process.WordShapeClassifier
[-wordShape name] string+
name
is an argument to lookupShaper
.
Known names have patterns along the lines of: dan[12](bio)?(UseLC)?,
jenny1(useLC)?, chris[1234](useLC)?, cluster1.
If you don't specify a word shape function, you get chris1.args
- Command-line arguments, as above.


Written using BEM (Block, Element, Modifier) convention to make your front-end code easier to read and understand.
Uses 7-1 Architecture pattern for maintainable and scalable SASS.
Two ways of creating grids - CSS Grid and Flexbox.
Only includes the most important features to minimize code bloat.
Includes a Gulp build system to automate building of SASS files
Designed to be scalable and easy to customize. Use it as a standalone library or use it a starting point to build your next application.
Written using the BEM naming convention and 7-1 SASS Architecture, BEMSkel provides a solid foundation for building your next large-scale web application or website.
Includes carefully crafted modules that you can easily customize and extend.
Unlike popular CSS frameworks, BEMSkel only includes the most essential features and basic styles, and letting you to customize and extend without too much overriding existing styles.
BEMSkel is released under the MIT license.
BEMSkel provides a solid foundation to build apps and websites with scalability and modularity in mind. To learn more, just visit the documentation.
View DocsBEMSkel was initially based on Skeleton boilerplate but was rewritten using the BEM naming convention and added with new useful features for building component-based applications. As I was building React applications, I needed a CSS framework that will complement the component-based nature of React, and hence, BEMSkel was created.


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Enum OAuth1Parameters
Namespace:Dapplo.HttpExtensions.OAuth
Assembly:Dapplo.HttpExtensions.dll
Syntax
public enum OAuth1Parameters
Fields
|
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Callback
Declaration
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ConsumerKey
Declaration
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Nonce
Declaration
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Signature
Declaration
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SignatureMethod
Declaration
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Timestamp
Declaration
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runtime.ancestorInfo.goid (field)
4 uses
runtime (current package)
proc.go#L4125: goid: callergp.goid,
runtime2.go#L970: goid int64 // goroutine id of this goroutine; original goroutine possibly dead
traceback.go#L752: print("[originating from goroutine ", ancestor.goid, "]:\n")
traceback.go#L764: if f.valid() && showfuncinfo(f, false, funcID_normal, funcID_normal) && ancestor.goid != 1 {
The pages are generated with Golds v0.2.7-preview. (GOOS=darwin GOARCH=amd64)


| Home | Web (and other) Design | Book and Game Reviews& | < Resume |
Summer of 2017 I was a Product Design Intern at Facebook New York on Recommendations and Reviews. I worked on several things.
Highlights include:
One of the things I contributed as a side project while was Facebook.design’s desktop kit:
Summer of 2016 I was an intern on the UberPOOL & Rider Design team. I designed new mobile rider experiences, researched opportunities for and designed new internal tools, and worked on map annotations for the app redesign.
I worked on:
Several Projects including
Map Annotations for the Uber App Redesign:
Made a swag design for UberHOP
Personal finance app I worked on as a side project. It was a a personal finance app like Mint but designed to be more relevant to modern, younger users. Petra Labs was incubated under UC Berkeley’s Free Ventures and The House’s Student Founders Program.
We also worked on a calendar app, something like a Sunrise replacement that effectively well integrated different calendars and tools (like Asana, Trello, Todoist) into it seamlessly, something all calendar apps today still don’t do well.
Note taking replacement app I worked on last winter break. I did the design and my friend did the dev (React/Electron/Firebase stack). Project has been put on hold indefinitely.
Small app built by me for my own personal usage. I like using sticky notes so I wanted something more like that but in the menubar, a single, highly minimal and highly persistent note that could be edited at any time. Made with HTML/CSS/JS and Electron.
I’ve also worked on quite a few freelance projects, HMU if you wanna see them.
Various pieces of graphic design.
Intro To UI Design with Sketch
Building Your Design Portfolio
Readapted as http://www.dtbui.com/learnpd.html
Intro to Prototyping with Origami
The Lost Art of Skeumorphic UI


If Payment methods are missing from Quickboks, you can click on the above button to resynchronize Payment Methods between Mozu and Quickbooks.


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Codominance 3 alleles: A1A2 x A1A3
Types
A1A1
A1A3
A1A2
A2A3
Total
Obs.
Exp.
Calculate
Clean
Value
Agree?
X^2 (df=3) =
Back


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The eventing and pubsub feature provided by the SOCIETIES Platform allows 3rd party service developers to exploit those designs patterns within their services. There are currently 2 eventing models available.
The Pubsub service is dependent on the communication framework. Please ensure you have deployed the Comms Frwk bundles first. See Using the Communication Framework To follow along with the sample code shown below, see ExampleClientCommand project in the GIT/Platform-Infrastructure/Comms-frwk/ExampleServices/ folder


I am using a feed of Seattle Fire Department 911 incident data from data.seattle.gov to experiment with JavaScript and jQuery for fun.
The data made available here has been modified for use from its original source, which is the City of Seattle. Neither the City of Seattle nor the Office of the Chief Technology Officer (OCTO) makes any claims as to the completeness, timeliness, accuracy or content of any data contained in this application; makes any representation of any kind, including, but not limited to, warranty of the accuracy or fitness for a particular use; nor are any such warranties to be implied or inferred with respect to the information or data furnished herein. The data is subject to change as modifications and updates are complete. It is understood that the information contained in the web feed is being used at one's own risk.
You can check out the source code on GitHub and keep tabs on new features I am working on: github.com/3mpire/Seattle-Real-Time-Fire-911.


After Quicksort completes the partition function, where is the pivot?
When partition is called, the pivot is at the end of the partition.
The partition operation itself does not move the pivot. That is done afterwards by the Quicksort function itself.


Designery Premium Features. Looks Great on Every Device Many Variants of Page Templates Blog Template Tons of Reusable Elements Particle Background Animation Skill Bars Animation Animated Typing Count To Animation Responsive Pricing Tables with Count To Animation Responsive Image / Video Lightbox Functional Ajax Contact Form and many more...


Hi All,
Just soldered up the shield and put it on an Arduino Leonardo but noted the CT number on the shield and the ADC pins on the Leonardo have different numbers. In the library. I see the following. Where as voltage on the shield is ADC0 and CT1-4 are ADC1-4. Am I missing something or is the library incorrect?
// Sets the pins to be used for voltage and current sensors based on emontx pin map
//--------------------------------------------------------------------------------------
void EnergyMonitor::voltageTX(double _VCAL, double _PHASECAL)
{
inPinV = 2;
VCAL = _VCAL;
PHASECAL = _PHASECAL;
}
void EnergyMonitor::currentTX(int _channel, double _ICAL)
{
if (_channel == 1) inPinI = 3;
if (_channel == 2) inPinI = 0;
if (_channel == 3) inPinI = 1;
ICAL = _ICAL;
}
Re: exmonTx Shield CT to ADC Pin allocation incorrect
Yes, you are missing something. There is a separate demonstration sketch for the emonTx Shield: https://github.com/openenergymonitor/emonTxFirmware/tree/master/emonTxSh... and you should be following that.
The methods you cite are for the emonTx (not the shield version).
Re: exmonTx Shield CT to ADC Pin allocation incorrect
Thanks Robert,
The shield examples use the emonLib library that I had pulled the code from. Once change in the lib the shield work as expected. Until then plugging CT's in produces weird results of course.
#include "EmonLib.h"
Re: exmonTx Shield CT to ADC Pin allocation incorrect
I don't understand what you are trying to say there.
The methods called in the emonTx Shield examples use the same emonLib library that was initially written for the emonTx, and the same class (the one and only class) in that library. The only difference is in the methods used to set up the private properties within the class instance.
If you look at the library source file, all should become clear.


Metrics Collection is
Session:
Client_id:
Event Sampling Enabled:
NOTE FOR MOBILE: This only shows events since the most recent foregrounding.
Recorder Ids:
Metrics Filter:
URL Filter:
Hide Sources with no Events
Hide Sources with no URLs
Include cached Sources (since this page load)
Clear
Refresh


We believe in fashion for everyone. To prove it, we asked 50 people across all 50 states to rep their zip's unique style. Want to see your state's look? Click into the interactive map below, shop each style star's outfit and be prepared to catch some serious road-trippin' fever.


HOBBIES AND INTERESTS.
The following are my hobbies and interests
Watching movies
Nature walks
Hyking
Playing football
Singing
Adventure
Driving
Home
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my projects
my hobbies


[gecode-users] AllInterval Series
Guido Tack
tack at ps.uni-sb.de
Wed Jun 6 07:15:39 CEST 2007
Hi.
Nick Hindle wrote:
> In the All-Interval series example, I cannot work out how values
> for the private member IntVarArray x are initialised.
>
> According to the specification in www.CSPLib.org, this var array
> holds the numbers 0,1,...11 to represent the pitch-classes c, c#,
> d, etc.
>
> Could someone help me understand how they are initialised in this
> example?
This is the more general version, also explained in prob007 of
csplib. Instead of a sequence x_0,...,x_11 with domains 0..11, you
have a sequence x_0,...,x_n with domains 0..n. Initialization of x is
done using the initializer expression x(this, opt.size, 0, opt.size -
1).
Cheers,
Guido
--
Guido Tack
Programming Systems Lab, Saarland University, Germany
http://www.ps.uni-sb.de/~tack
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Description
A breakfast scramble is a mix of my favorite foods for breakfast. This recipe can be modified to use any vegetables that you have available.
I like to think of a breakfast scramble as scrambled eggs mixed in with veggies and meats that you like.
I think sauted spinach and bell peppers are great veggies to put into a breakfast scramble.
Tips
Cook the veggies prior to scrambling the eggs and putting the scramble together.
Top the breakfast scramble with ketchup, salsa, or hot sauce to spice it up!
List of Ingredients
- 2 or 3 Eggs
- Canola Oil (ideally, a spray)
- 1/2 Teaspoon of Salt
- 1 Teaspoon of Black Pepper
- 1/2 of a Bell Pepper
- 1 Cup of Spinach
- 2 Breakfast Sausage Links
Recipe Steps
- Start by sauting the spinach and bell peppers in a frying pan on medium heat
- Once the spinach has wilted down and the bell peppers are still crunchy, remove from the pan
- Reduce the heat on the frying pan to low
- Heat the breakfast sausages according to their packaging and slice into small pieces
- Add a spray or spash of canola oil to the frying pan
- In a bowl or measuring cup, beat the eggs with a fork to incorporate the yolk and whites of the eggs
- Add the eggs to the frying pan and add in the salt and black pepper
- Using a spatula, start mixing the eggs once the eggs start to settle slightly
- Mix the eggs until fully cooked and then add in the veggies and breakfast sausage
- Top with ketchup or salsa and enjoy!


Welcome to Dog vs. Cat BIZ!
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Now Serving
Now Serving
Slot
Available
{{servingSlot1.orderInfo.queueNum}}
Slot
Available
{{servingSlot2.orderInfo.queueNum}}
!= include('Controller') ?>


Ocean [at46], ocean, in The Inner Sea
Last updated Turn #77
Ocean [at46], ocean, in The Inner Sea
------------------------------------------------------------------------
25: The Savvy Aardvark [ha5r], ship, 2 hulls, 1 rowing port, 6 sails,
25: 8% damaged, arrived from Ocean [as46], owner:
25: Phantasm [n4b], Imperial Empire, earl, with eight sailors, two peasants
25: A storm batters the ship and causes some damage.
26: The Savvy Aardvark [ha5r], ship, 2 hulls, 1 rowing port, 6 sails,
26: 9% damaged, departed for Ocean [au46].
Routes leaving Ocean:
North, to Ocean [as46], 6 days
East, to Ocean [at47], 6 days
South, to Ocean [au46], 6 days
West, to Ocean [at45], 6 days
It is raining.


{% extends 'base.html' %} {% block content %} All your Notes in One Place Create notes quickly Organise your notes with color Access your notes from any device, any time {% endblock %}


b:activateLibraries Adds bootstrap css and js, jQuery and pretty print to head part to activate libraries for sites not containing any other ButterFaces component. In addition to that all butterfaces css, js and jQuery pluging will be added to head part.
If web.xml parameter org.butterfaces.useCompressedResources is set (default is true) a compressed version of all resources are added instead of each resource.


Weight Weight
Study Total Correlation COR 95%-Cl (fixed) (random) Peterson2013 40 -0.33 [-0.58; -0.02] 43.0% 43.0% Serdaro?lu2016 52 -0.38 [-0.59; -0.11] 57.0% 57.0% Fixed effect model 92 —— -0.36 [-0.53; -0.16] 100.0% -—— Random effects model = -0.36 [-0.53; -0.16] -- 100.0%
Heterogeneity: I? = 0%, 77 =0, p=0.81 -0.4-02 0 02 04


Charismatic and Confident "L.A. Confidential" by Homer Yen (c) 1997
The seasoned Capt. Dudley Smith (James Cromwell) questions his new protege, Ed Exley (Guy Pearce) about his political views when it comes to police work. "Would you plant evidence to get a conviction if you knew the person to be guilty? Would you shoot a man in the back if you knew that it was the only way to make sure he got what he deserved?" Exley, squeaky clean, innocent, and pent with the desire to perform his new duties with integrity and honesty quickly answers with a pointed 'no.' Stunned, Smith pleads, "Then, for the love of God, don't be a detective."
LA Confidential is a dandy piece of filmmaking that brings us back to the classic times of Hollywood in the 50s. Organized crime was the biggest concern, corruption existed everywhere, and every cop had his own way of getting the job done. There was "Hollywood" Jack Vincennes (Kevin Spacey) who gets most of his information from the editor of a sleazy tell-all/show-all tabloid, Sid Hudgeons (Danny DeVito). Sporting dark sunglasses and a white sports coat, he isn't afraid to flash his tinseltown style. Detective Bud White (Russell Crowe) has only a narrow-minded view of the law, and he uses excessive force to exact his unique kind of interrogation and brutal justice. And then there is golden boy Exley, wide-eyed and ambitious but extremely naive.
A horrible multiple murder begins to draw these three men together, each of whom become inexorably connected in their search for truth, action, and personal vindication. Almost immediately, an arrest is made. But something doesn't seem quite right. New developments become sinuous as the story begins to draw us deeper into the labyrinth of corruption and crime within the department. Suspicion and questions begin to mount. Odd alliances are created. And as layers of the truth become revealed, the story grows more complex and intriguing by the moment. When the mystery expands, we see other seemingly guilty characters enter the lives of these three cops, including a high-priced hooker (Kim Basinger) and a shady millionaire (David Strathairn). All of the characters' levels of involvement are not immediately clear. We watch with riveted fascination to learn more about why they are there and their stake in the case. We sense a cross-pollination of clues that brings us, the audience, closer to the truth. We hope that the three detectives can throw away their spite for one another and can pull their resources together in order to untangle the intricately created web of mystery.
Absorbing and affecting, this movie has all the necessary ingredients to create a terrific gourmet serving of film noir. It boasts mysterious figures in control, double-crosses, hard information from sleazy sources, dirty politicians, corrupt cops, haunting women, and hard-nosed cops experiencing moral ambiguity. The underlying mystery is compelling. The look and feel of the movie is gorgeous. The entire cast is crisp and wonderful to watch (I wouldn't be surprised if Cromwell received an Oscar nomination for his performance). And, the dialogue is rich and memorable. The best line of the movie is spoken when a key player dispenses advice to one of the detectives unsure of what to do. "Don't start trying to do the right thing, boy-o," says he. "You haven't had the practice." "L.A. Confidential'' triumphantly achieves the rare gift of complexity and coherence while satisfying our desire for a good old-fashioned movie.
Grade: A-
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Black to Play 61
b0-w0/5
pips Black=160 White=142
(CorrectAnswer= 0/0, ErrorScore= 0)
1. 13/7 6/5 eq:-0.149 48.17% (G:13.16% B:0.77%) 51.83% (G:19.50% B:1.94%) 2. 24/23 13/7 eq:-0.237 (-0.087) 45.87% (G:12.56% B:0.87%) 54.13% (G:20.93% B:0.89%) 3. 13/6 eq:-0.286 (-0.137) 44.29% (G:12.43% B:0.68%) 55.71% (G:20.00% B:1.56%) 4. 24/23 24/18 eq:-0.308 (-0.158) 44.94% (G:10.25% B:0.61%) 55.06% (G:23.64% B:0.73%) 5. 8/2* 2/1* eq:-0.317 (-0.168) 42.88% (G:12.14% B:0.48%) 57.12% (G:19.73% B:1.65%)


Last Name
First Name
Address
17 Histo Botev
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State
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Bulgaria
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Comments/Questions
Please send me more information about the lodging


---
author_id: 015
author_name: E. Anna Keith
genre: poetry
usual_genre_grammared: Academic
issue: 2
title: Three Poems
categories: issue2
layout: creative-post
private_email:
twitter:
website:
bio: E. Anna Keith is a writer and academic living in Pittsburgh, Pennsylvania, with two cats, one tortoise, and many jars of peanut butter. Her poetry has appeared or is forthcoming in Constellate Jounal and Bone and Ink Lit Zine. She tweets about writing and existential angst at @ekeithwrites.
interview_1: Academic writing; the academy is, for better or worse, my intellectual home, and I feel compelled to immerse myself in its prose.
interview_2: Retrospectively, I have been writing poetry since middle school, but didn’t recognize it as such. I took a hard break from writing creatively in grad school, which is why I consider academic writing my primary genre. But I felt called to poetry again several months ago, and these pieces were the first I penned in several years. The exhilaration of writing poetry again compels me to keep going.
interview_3: The freedom, the lack of required linearity; it’s both difficult and affirming to work outside of academic structures. As I write, I’m finding that a key is to embrace that openness, not let it frighten you. And I believe academic and poetic writing are siblings in many ways–both use, and need, language’s infinity, and thrive in the beautiful spaces where its genres cross paths.
---
{{page.title}}
1932
I want to tell you about my grandmother.
She is a new version now,
she is maybe her seventh
self. Some days are better
than others, every hour every minute,
a reintroduction. I was here
last week. When Jody last disappeared
they found her in the basement, crying
for her West Virginia among the shadows
she calls men. I was up late that night
with my mother, discussing latches
and things that don’t bind
but necessarily keep. The house is old.
But Jody’s good days are Christmas,
temporary hours and surreal to witness.
They tell me it is like an implosion.
In hers I see withering and paths
that lead down to the end of the driveway.
the last time I called home
I don’t do it much anymore.
clarity is hard to come by,
harder in these parts. the last time
I dialed Grandad answered.
how are you? I asked. four
three eight. sober, he says.
six one. it’s his rehearsed answer
so I laugh on cue. do you want to talk
to Gram. five nine.
 I’m not,
I say. her voice breaks.
can I talk to Grandad?
six two. I’m sorry. seven.
she never says. two.
my name. four.
learning to pause
I am learning to make moments
expect the emptiness like a winter’s day
but make minutes like
seeing robins nest in the silver oak.
flashes like when Gram laughs.
learning to make color.
I put it on her fingernails
my brother as a model.
talk softly about Bible stories.
I am considering Ruth and Naomi.
the space between presences
and time. and love.
{{page.bio}}
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| Dates | changes |
| 02/12/2019 | Start |
| 03/12/2019 | Adding table of experience on main page |
| 04/12/2019 | Table layout for my photo and first paragraph responsive image applying adding this page of logging changes add skills table |
| 26/12/2019 | My God, I didnt work with my project more than 20 days Its very pity... |
| 27/12/2019 | Complete a simple form for feedback Published my page to github |
| January 2020 | Adding some css design |


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Fundamentals of
Data Analytics and
Predictions
Linear Discriminant Analysis
Xi (Rossi) LUO
Department of Biostatistics and Data Science
School of Public Health
The University of Texas Health Science Center at
Houston
Linear Methods for Prediction —1
- Let $\hat G(x)$ be the prediction. Note that we can always divide the input space into a
collection of regions taking the same predicted values
- How to best divide the input space?
- The class of procedures where the decision boundaries are linear are referred to as linear
methods for classification
Linear Methods for Prediction —2
- Suppose that we have $K$ classes $(1,2,…,K)$, and we fit a linear model for the $kth$ indicator
response variable $\hat f_k(x)=\hat \beta _{k0} + \hat \beta _k ^Tx$
- The decision boundary between class $k$ and $l$ is the set of points for which $\hat f_k(x)=
\hat f_l(x)$.
- General class of methods model the discriminant function $\delta _k(x)$ for each class, then
classify $x$ to the class with the largest value for its discriminant function
Bayes Rule and Decision Theory —1
- Decision theory for classification tells us that we just need to know the class posteriors $Pr(G
\mid X)$ for optimal classification
- Using Bayes theorem:
$Pr(G=k \mid X=x)=\frac{Pr(X=x \mid G=k)Pr(G=k)}{\sum_{l=1}^{K}Pr(X=x \mid G=l)Pr(G=l)}$
$Pr(G=k \mid X=x)=\frac{f_k(x)\pi_k}{\sum_{l=1}^{K}f_l(x)\pi_l }$, where
$f_k(x)=$ the class conditional density of $X$ in a class $G=k$
$\pi_k$ be the prior probability of class $k$, with $\sum_{i=1}^{K}\pi_k=1$
Bayes Rule and Decision Theory —2
$Pr(G=k \mid X=x)=\frac{f_k(x)\pi_k}{\sum_{l=1}^{K}f_l(x)\pi_l}$
- Model each class density as a multivariate Gaussian:
$f_k(x)=\frac{1}{(2\pi)^{p/2} \mid \sum_{k} \mid ^{1/2}} exp \lbrace - \frac{1}{2}(x-\mu_k)'
\sum_{k}^{-1}(x-\mu_k) \rbrace$
Linear Discriminant Analysis —1
- If we assume the covariance structure is the same for all classes $\sum_{k}=\sum $, $\forall k$,
we have linear discriminant analysis (LDA)
- Let's first assume that we only have one variable $(p=1)$; then the Gaussian density has the
form
$f_k(x)=\frac{1}{\sqrt{2\pi} \sigma_k}e^{- \frac{1}{2}(\frac{x-\mu_k}{\sigma_k})^2}$
- Where $\mu_k$ is the mean and $\sigma_k^2$ the variance in class $k$. If the covariance
structure is the same for all classes, then $\sigma_k=\sigma$ for all classes.
- Plugging this into Bayes formula, we get:
$Pr(G=k | X = x)=\frac{\pi_k \frac{1}{\sqrt{2\pi} \sigma_k}e^{-
\frac{1}{2}(\frac{x-\mu_k}{\sigma_k})^2}}{\sum_{l=1}^{K} \pi_l \frac{1}{\sqrt{2\pi}
\sigma_k}e^{- \frac{1}{2}(\frac{x-\mu_l}{\sigma})^2}}$
Linear Discriminant Analysis —2
- To classify to one of two classes at the value $X=x$, we need to see which of the
$Pr(G=k | X = x)$ is largest. It is sufficient to look at the log-ratio:
$$
\log \frac{Pr(G=k \mid X=x)}{Pr(G=l \mid X=x)}= \log \frac{\pi_k}{\pi_l} + \log \frac{f_k(x)}{f_l(x)}
$$
Linear Discriminant Analysis —3
- Equivalently, the discrimination function is simply (for one variable):
$\delta_k(x)=\frac{x \mu_k}{\sigma^2}- \frac{\mu_k}{2\sigma^2}+\log \pi_k$
- or for more than one variable:
$\delta_k(x)=x' \sum_{}^{-1} \mu_k - \frac{1}{2} \mu_k \sum_{}^{-1} \mu_k +\log \pi_k$
- With $G(x)=arg\,max_k \delta_k(x)$
Linear Discriminant Analysis —4
- Example with $\mu_1=-1.5$, $\mu_2=1.5$, $\pi_1=\pi_2=0.5$ and $\sigma^2=1$
- Typically, we don't know these parameters; we just have the training data
- In that case we simply estimate the parameters and plug them into the rule
Estimating LDA parameters
- Use the training set to estimate the parameters.
- $\hat \pi_k= \frac{N_k}{N}$, where $N_k$ is the observed number of subjects in class $k$.
- $\hat \mu_k=\sum_{g_{i=k}}^{} \frac{x_i}{N_k}$
- $\hat {\sum}= \sum_{k=1}^{K} \sum_{g_{i=k}}^{} (x_i- \hat \mu_k)(x_i-\hat \mu_k)'/(N-k)$
Linear Discriminant Analysis when $p>1$
- Density: $f(x)= \frac{1}{(2\pi)^{p/2} \mid \sum \mid ^{1/2}}e^{- \frac{1}{2}(x-\mu)^T
\sum^{-1}(x-\mu)}$
Illustration $p=2$ and $K=3$
- Here $\pi_1=\pi_2=\pi_3=\frac{1}{3}$
- The dashed lines are known as the Bayes decision boundaries
- Were they known, they would yield the fewest misclassification errors, among all
classifiers
Illustration $p=2$ and $K=3$
- 4 variables
- 3 species
- 50 samples/class
- LDA classifies all but 3 of the 150 training samples correctly
Fisher's Discriminant Plot
- When there are $K$ classes, linear discriminant analysis can be viewed exactly in a $K-1$
dimensional plot
- Why? Because it essentially classifies to the closest centroid, and they span a $K-1$
dimensional plane
- Even when $K>3$, we can find the 'best' 2-dimensional plane for visualizing the discriminant
risk
From $\delta_x(x)$ to probabilities
- Once we have estimates $\hat \delta_x(x)$, we can turn these into estimates for class
probabilities
$\widehat {Pr}(G=k \mid X=x)= \frac{e^{\hat \delta_k(x)}}{\sum_{l=1}^{K}e^{\hat \delta_l(x)}}$
- Classifying to the largest $e^{\hat \delta_k(x)}$ amounts to classifying to the class for which
$\widehat {Pr}(G=k \mid X=x)$ is largest
- When $K=2$, we classify to class 2 if $\widehat {Pr}(G=k \mid X=x) \ge 0.5$, else to class 1
Quadratic Discriminant Analysis
- If we assume that each class has its own correlation structure ($\sum_{k}$ are no longer equal
for all $k$), then we no longer get a linear estimate
- Quadratic discriminant functions (QDA):
$\delta_k(x)=- \frac{1}{2}\log\mid \Sigma_k \mid -\frac{1}{2}(x-\mu_k) \sum_k\, ^{-1}
(x-\mu_k)+\log
\pi_k$
- The decision boundary is now described with a quadratic function.
- Note: Both LDA and QDA are finding the centroid of classes and then finding the closest
centroid to the new data point. Correlation structure is taken into consideration when
defining distance.
LDA Example
Comments on QDA and LDA
- You can get similar results to QDA using LDA with an enlarged quadratic polynomial space. QDA is
preferred although LDA is a convenient alternative
- Parameter estimates are generally similar except that separate covariance matrices must be
estimated for each class for QDA
- These methods generally work very well, probably because
- The data can only support simple decision boundaries such as linear or quadratic
- Estimates provided via the Gaussian models are stable
LDA versus Logistic Regression —1
- For LDA: $logit = \log(\pi_k/\pi_l) + ... + x^T \Sigma^{-1}(\mu_k - \mu_l)$
- For Logistic Regression: $logit = \beta_0 + x^T \beta$
-
Very similar! Difference comes from how the parameters are estimated
- Logistic model is more general, makes less assumptions. The marginal density $Pr(X)$ is
fully nonparametric, using the empirical distribution function which places mass $1/N$ at
each observation
- In LDA, fit the parameters by maximizing the full log-likelihood using the Gaussian
assumption for $X$
LDA versus Logistic Regression —2
- LDA imposes a distribution assumption on $X$ which, if good, will result in more efficient
estimates. If the assumption is true, the estimates will be 30% more efficient. LDA not robust
to gross outliers.
- Logistic regression is conditional methodology. We condition on $X$ and do not specify any
distribution. Big advantage where we know that $X$ can't be normal (e.g., categorical
variables).
- However, in practice they both perform similarly, even in extreme cases with categorical
covariates
Why discriminant analysis?
- When the classes are well-separated, the parameter estimates for the logistic regression model
are surprisingly unstable. Linear discriminant analysis does not suffer from this problem.
- If $n$ is small and the distribution of the predictors $X$ is approximately normal in each of
the classes, the linear discriminant model is again more stable than the logistic regression
model
- Linear discriminant analysis is popular when we have more than two response classes, because it
also provides low-dimensional views of the data
Other Forms of Discriminant Analysis
- With $f_k(x)=\Pi_{j=1}^{p} f_{jk}(x_j)$ (conditional independence model) in each class we get
naïve Bayes. For Gaussian this means the $\sum_k$ are diagonal.
- Many other forms, by proposing specific density models for $f_k(x)$, including nonparametric
approaches.
Naïve Bayes
- Assumes features are independent in each class.
- Useful when p is large, and so multivariate methods like QDA and even LDA break down.
- Gaussian naïve Bayes assumes each $\sum_k$ is diagonal:
$\delta_k(x) \propto \log (\pi_k \Pi_{j=1}^{p}f_{kj}(x_j))=-
\frac{1}{2}\sum_{j=1}^{p}[\frac{(x_j-\mu_{kj})^2}{\sigma_{kj}^2}+\log \sigma_{kj}^2]+\log
\pi_k$
- Can use for mixed feature vectors (qualitative and quantitative). If $X_j$ is qualitative,
replace $f_{kj}(x_j)$ with the probability mass function (histogram) over discrete
categories.
- Note that Naïve Bayes has very strong assumptions! Regardless, it often produces good
classification results.
Explain Possible Reasons
More from ISL 4.5
Summary
- Logistic regression is very popular for classification, especially when $G=2$
- LDA is useful when $n$ is small, or the classes are well separated, and Gaussian assumptions are
reasonable. Also when $G>2$.
- Naïve Bayes is useful when $p$ is very large


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OpenBSD Project Goals
Obviously, each developer working on OpenBSD has their own aims and priorities, but it is possible to classify the goals we all share:
- Provide the best development platform possible. Provide full source access to developers and users, including the ability to look at CVS tree changes directly. Users can even look at our source tree and changes directly on the web!
- Integrate good code from any source with acceptable licenses. ISC or Berkeley style licences are preferred, the GPL is not acceptable when adding new code, NDAs are never acceptable. We want to make available source code that anyone can use for ANY PURPOSE, with no restrictions. We strive to make our software robust and secure, and encourage companies to use whichever pieces they want to. There are commercial spin-offs of OpenBSD.
- Pay attention to security problems and fix them before anyone else does. (Try to be the #1 most secure operating system.)
- Greater integration of cryptographic software. OpenBSD is developed and released from Canada and due to Canadian law it is legal to export crypto to the world (as researched by a Canadian individual and as documented in the Export Control list of Canada).
- Track and implement standards (ANSI, POSIX, parts of X/Open, etc.)
- Work towards a very machine independent source tree. Support as many different systems and hardware as feasible.
- Be as politics-free as possible; solutions should be decided on the basis of technical merit.
- Focus on being developer-oriented in all senses, including holding developer-only events called hackathons.
- Do not let serious problems sit unsolved.
- Make a release approximately every six months.
Project Funding
See our Donations Page.
Where do our developers live?
This map approximates where our developers live. We will attempt to update it occasionally, but please don't count on that.


qualys
Businessmodel of Qualys
Customer Segments
Qualys has a mass market business model, with no significant differentiation between customer segments. The company targets its offerings at businesses and government entities of all sizes.
Value Proposition
Qualys offers four primary value propositions: convenience, performance, risk reduction, and brand/status.
The company offers convenience by making its offerings easy to use. Its solution is cloud-based, meaning there is no software to implement or infrastructure to maintain. Furthermore, it can be scaled to the largest of businesses, from one to a million users. Clients can buy an entire suite or buy standalone applications. The platform provides a continuous view of a client’s compliance and security landscape, including all of their web applications and IP-connected devices. Lastly, it can be run from any web browser and can be run on-demand or automatically at preferred times.
The company has demonstrated strong performance through tangible results. As a high-profile example, Bank of the West used Qualys’s solution to eliminate security risks and meet regulations, facilitating its growth from 102 branches to 300 and from one Internet access point to six.
The company reduces risk through high security and safety standards. It operates the Qualys Vulnerability Research Team, which maintains a list of “Top 10 Vulnerabilities” that aids security administrators in prioritizing the most serious threats. It also keeps a thorough tracking of threats. In fact, an independent study found that Qualys collects the most comprehensive list of critical security vulnerabilities among threat management firms, nearly twice that of its closest competitor. These dangers are monitored on a continuous basis to manage them as they rise and evolve in real time. Lastly, the team creates 25 vulnerability signature updates per week on average, making the Qualys KnowledgeBase the biggest and most up-to-date vulnerability signature database.
The company has established a strong brand due to its success. It touts itself as the leading provider of compliance and information security cloud solutions. It is used by over 8,800 clients in more than 100 countries, including the following:
- Over 60% of the Forbes Global 50 and a majority of the Fortune 100
- 9 of the top 10 firms in Technology, Retail, and Biotechnology
- 7 of the top 10 firms in Chemical and Banking
- 6 of the top 10 firms in Software, Telecom, Media, Car Manufacturing, and Food Retail Its prominent clients include Pfizer, ADP, Cisco, Oracle, Facebook, Microsoft, Comcast, Staples, Verizon, Toshiba, and Daimler. Qualys‘s platform conducts over three billion IP scans/audits a year, resulting in more than one trillion security events. Lastly, the company has won many honors, including ranking as #1 in IDC’s Device Vulnerability Assessment revenue share report for six years in a row (2014), recognition as “Best Security Company“ by *SC *Magazine (2014), and high placements in Gartner’s Magic Quadrants for Web Application Security and Vulnerability Assessment.
Channels
Qualys’s main channel is its direct sales team. Its field sales force focuses on selling to enterprises (typically firms with 5,000+ employees) and government agencies, while its inside sales force focuses on selling to small and mid-size firms. The company also conducts sales indirectly through its network of channel partners, who include value-added resellers, managed service providers, and consulting firms. These partners accounted for 37% of total revenues in 2015.
Qualys promotes its offering through its social media pages, online marketing, advertising, web-based seminar campaigns, free trials and services, and participation in conferences.
Customer Relationships
Qualys’s customer relationship is primarily of a personal assistance nature. The company assists customers in the following ways:
- Support Services – Qualys provides free, 24x7x365 telephone customer support from service centers located in Raleigh, North Carolina; Redwood City, California; Reading, United Kingdom; and Pune, India. Customer inquiries made through e-mail are responded to within 48 hours. Service staff consists of trained subject matter experts and senior technical employees who work closely with operations and engineering personnel.
- Training Services – Qualys maintains the Qualys Training & Certification Program, which offers the skills, knowledge, and credentials needed to implement and use an enterprise vulnerability management system. Training workshops are provided in-person and online. Participants who pass the certification exam are named Qualys Certified Specialists and receive a certificate of training. Despite this orientation, there is a self-service component. The company’s website features a “Resources” section that includes product guides, product demos, data sheets, white papers, analyst reports, eBooks, and webcasts. The site also provides answers to frequently asked questions and a training library with how-to videos. Lastly, there is a community element in the form of a forum.
Key Activities
Qualys’s business model entails maintaining a robust cloud platform for its clients.
Key Partners
Qualys maintains the following types of partnerships:
- Integration Partners – Technology firms that integrate their solutions into the company’s cloud platform to provide enhanced offerings for its customers. Specific partners include VeriSign, CA Technologies, Sourcefire, Redseal Networks, Splunk, iMPERVA, iViz, and Core Security.
- MSP Partners – Managed service providers that utilize the company’s solutions for their offerings. Specific partners include Verizon, Fujitsu, Tata, NTT Communications, HP, Wipro, and SecureWorks.
- VAS Resellers – Value-added service resellers that sell the company’s products to their customers. Specific partners include Blue Cube, Fishnet Security, Dimension Data, Integralis, and Cjen.
- Consultants & Auditors – Service providers that utilize the company’s products for their customers. Specific partners include Deloitte Consulting, Accenture, EY, HP, and PricewaterhouseCoopers.
- PCI Partners – Service firms such as security consultants that utilize the company’s products to help their clients achieve PCI compliance. Specific partners include Accuvant, IBM, BT, Verizon, and Telus. The company maintains a manufacturing partnership with SYNNEX Corporation, through which it obtains its physical scanner appliances. The agreement is renewed on an annual basis. Qualys also has alliances with third-party vendors who host its data center operations in the U.S., Switzerland, and the Netherlands.
Key Resources
Qualys’s main resource is its proprietary software platform, which serves more than 8,800 clients.
It depends on human resources in the form of technology staff for maintaining the platform, its agile engineering teams for solution development, its research teams for identifying security threats, its training staff for providing instruction, and its customers service staff for providing support.
Cost Structure
Qualys has a value-driven structure, aiming to provide a premium proposition through significant personal service and frequent product improvements.
Its biggest cost driver is sales/marketing, a fixed cost. Other major drivers are in the areas of research and development, a fixed expense, and cost of revenues, a variable expense that primarily consists of personnel expenses.
Revenue Streams
Qualys has one revenue stream: revenues generated from the sale of subscriptions for its compliance and security solutions, which are delivered on the company’s cloud platform. Customers typically sign up for one-year renewable agreements which are paid at the start of the term. There are three types of plans:
- Express Lite – Targeted at small businesses. Allows a maximum of 256 network addresses and 25 web applications for scans.
- Express – Targeted at medium-sized businesses. Allows a maximum of 3,072 network addresses and 100 web applications for scans.
- Enterprise – Targeted at large businesses. Allows an unlimited number of network addresses and web applications for scans.


On 29 August 2020, 10 new articles were published in 4 journals.
Institutional Anomie and Cross‐national Differences in Incarceration†
Note: although this article was published on 6 August 2020, the publisher only added it to the journal RSS feed on 29 August 2020, which is why it is shown in the list of articles for that date.
Neighborhood Climates of Legal Cynicism and Complaints About Abuse of Police Power†
Note: although this article was published on 6 August 2020, the publisher only added it to the journal RSS feed on 29 August 2020, which is why it is shown in the list of articles for that date.
Long‐term Consequences of Being Placed in Disciplinary Segregation†
Note: although this article was published on 6 August 2020, the publisher only added it to the journal RSS feed on 29 August 2020, which is why it is shown in the list of articles for that date.
Pubertal Timing and Adolescent Delinquency†
Note: although this article was published on 6 August 2020, the publisher only added it to the journal RSS feed on 29 August 2020, which is why it is shown in the list of articles for that date.
Feminist Criminology in An Era of Misogyny†
Note: although this article was published on 6 August 2020, the publisher only added it to the journal RSS feed on 29 August 2020, which is why it is shown in the list of articles for that date.
The Public Salience of Crime, 1960–2014: Age–period–cohort and Time–series Analyses†
Note: although this article was published on 6 August 2020, the publisher only added it to the journal RSS feed on 29 August 2020, which is why it is shown in the list of articles for that date.
Note: although this article was published on 6 August 2020, the publisher only added it to the journal RSS feed on 29 August 2020, which is why it is shown in the list of articles for that date.
Dissocial Personality Traits and Past Experiences Matter in How People Perceive the Police


The printproto package provides the protocol headers for the libXp-1.0.2 package.
This package is not a part of the Xorg katamari and is provided only as a dependency to other packages or for testing the completed Xorg installation.
This package is known to build and work properly using an LFS-7.4 platform.
Download (HTTP): http://xorg.freedesktop.org/releases/individual/proto/printproto-1.0.5.tar.bz2
Download (FTP): ftp://ftp.x.org/pub/individual/proto/printproto-1.0.5.tar.bz2
Download MD5 sum: 99d0e25feea2fead7d8325b7000b41c3
Download size: 224 KB
Estimated disk space required: 2.6 MB
Estimated build time: less than 0.1 SBU
User Notes: http://wiki.linuxfromscratch.org/blfs/wiki/printproto
Install printproto by running the following commands:
./configure $XORG_CONFIG
This package does not come with a test suite.
Now, as the root
user:
make install
Last updated on 2013-08-18 14:52:29 -0700


| all_models_equal | Checks if all objects are models of same class |
| all_models_same_class | Checks if all objects are models of same class |
| check_if_installed | Checking if needed package is installed |
| clean_names | Get clean names of model terms |
| clean_names.character | Get clean names of model terms |
| clean_parameters | Get clean names of model parameters |
| color_if | Color-formatting for data columns based on condition |
| color_text | Coloured console output |
| color_theme | Coloured console output |
| colour_if | Color-formatting for data columns based on condition |
| colour_text | Coloured console output |
| data_match | Find row indices of a data frame matching a specific condition |
| data_relocate | Relocate (reorder) columns of a data frame |
| data_restoretype | Restore the type of columns according to a reference data frame |
| data_to_long | Reshape (pivot) data from wide to long |
| data_to_wide | Reshape (pivot) data from wide to long |
| display | Generic export of data frames into formatted tables |
| display.data.frame | Generic export of data frames into formatted tables |
| download_model | Download circus models |
| ellipsis_info | Gather information about objects in ellipsis (dot dot dot) |
| ellipsis_info.default | Gather information about objects in ellipsis (dot dot dot) |
| export_table | Data frame and Tables Pretty Formatting |
| find_algorithm | Find sampling algorithm and optimizers |
| find_formula | Find model formula |
| find_interactions | Find interaction terms from models |
| find_offset | Find possible offset terms in a model |
| find_parameters | Find names of model parameters |
| find_parameters.averaging | Find model parameters from models with special components |
| find_parameters.bamlss | Find names of model parameters from Bayesian models |
| find_parameters.bayesx | Find names of model parameters from Bayesian models |
| find_parameters.betamfx | Find names of model parameters from marginal effects models |
| find_parameters.betareg | Find model parameters from models with special components |
| find_parameters.BFBayesFactor | Find names of model parameters from Bayesian models |
| find_parameters.BGGM | Find names of model parameters from Bayesian models |
| find_parameters.brmsfit | Find names of model parameters from Bayesian models |
| find_parameters.default | Find names of model parameters |
| find_parameters.DirichletRegModel | Find model parameters from models with special components |
| find_parameters.emmGrid | Find model parameters from estimated marginal means objects |
| find_parameters.gam | Find names of model parameters from generalized additive models |
| find_parameters.gamlss | Find names of model parameters from generalized additive models |
| find_parameters.glmmTMB | Find names of model parameters from mixed models |
| find_parameters.glmx | Find model parameters from models with special components |
| find_parameters.logitmfx | Find names of model parameters from marginal effects models |
| find_parameters.MCMCglmm | Find names of model parameters from Bayesian models |
| find_parameters.merMod | Find names of model parameters from mixed models |
| find_parameters.mhurdle | Find names of model parameters from zero-inflated models |
| find_parameters.mjoint | Find model parameters from models with special components |
| find_parameters.sim.merMod | Find names of model parameters from Bayesian models |
| find_parameters.stanreg | Find names of model parameters from Bayesian models |
| find_parameters.zeroinfl | Find names of model parameters from zero-inflated models |
| find_predictors | Find names of model predictors |
| find_predictors.default | Find names of model predictors |
| find_random | Find names of random effects |
| find_random_slopes | Find names of random slopes |
| find_response | Find name of the response variable |
| find_smooth | Find smooth terms from a model object |
| find_statistic | Find statistic for model |
| find_terms | Find all model terms |
| find_transformation | Find possible transformation of response variables |
| find_variables | Find names of all variables |
| find_weights | Find names of model weights |
| fish | Sample data set |
| format_bf | Bayes Factor formatting |
| format_ci | Confidence/Credible Interval (CI) Formatting |
| format_message | Format messages and warnings |
| format_number | Convert number to words |
| format_p | p-values formatting |
| format_pd | Probability of direction (pd) formatting |
| format_rope | Percentage in ROPE formatting |
| format_string | String Values Formatting |
| format_string.character | String Values Formatting |
| format_table | Parameter table formatting |
| format_value | Numeric Values Formatting |
| format_value.data.frame | Numeric Values Formatting |
| format_value.numeric | Numeric Values Formatting |
| formula_ok | Find model formula |
| get_auxiliary | Get auxiliary parameters from models |
| get_call | Get the model's function call |
| get_correlation_slopes | Get variance components from random effects models |
| get_correlation_slope_intercept | Get variance components from random effects models |
| get_data | Get the data that was used to fit the model |
| get_data.afex_aov | Get the data that was used to fit the model |
| get_data.brmsfit | Get the data that was used to fit the model |
| get_data.clmm | Get the data that was used to fit the model |
| get_data.gee | Get the data that was used to fit the model |
| get_data.glmmadmb | Get the data that was used to fit the model |
| get_data.glmmTMB | Get the data that was used to fit the model |
| get_data.hurdle | Get the data that was used to fit the model |
| get_data.lme | Get the data that was used to fit the model |
| get_data.MCMCglmm | Get the data that was used to fit the model |
| get_data.merMod | Get the data that was used to fit the model |
| get_data.mixed | Get the data that was used to fit the model |
| get_data.MixMod | Get the data that was used to fit the model |
| get_data.rlmerMod | Get the data that was used to fit the model |
| get_data.rqss | Get the data that was used to fit the model |
| get_data.stanreg | Get the data that was used to fit the model |
| get_data.zcpglm | Get the data that was used to fit the model |
| get_deviance | Model Deviance |
| get_deviance.default | Model Deviance |
| get_df | Extract degrees of freedom |
| get_df.default | Extract degrees of freedom |
| get_family | A robust alternative to stats::family |
| get_intercept | Get the value at the intercept |
| get_loglikelihood | Log-Likelihood |
| get_loglikelihood.lm | Log-Likelihood |
| get_modelmatrix | Model Matrix |
| get_parameters | Get model parameters |
| get_parameters.averaging | Get model parameters from models with special components |
| get_parameters.bamlss | Get model parameters from Bayesian models |
| get_parameters.bayesx | Get model parameters from Bayesian models |
| get_parameters.betamfx | Get model parameters from marginal effects models |
| get_parameters.betareg | Get model parameters from models with special components |
| get_parameters.BFBayesFactor | Get model parameters from Bayesian models |
| get_parameters.BGGM | Get model parameters from Bayesian models |
| get_parameters.brmsfit | Get model parameters from Bayesian models |
| get_parameters.clm2 | Get model parameters from models with special components |
| get_parameters.coxme | Get model parameters from mixed models |
| get_parameters.default | Get model parameters |
| get_parameters.DirichletRegModel | Get model parameters from models with special components |
| get_parameters.emmGrid | Get model parameters from estimated marginal means objects |
| get_parameters.emm_list | Get model parameters from estimated marginal means objects |
| get_parameters.gam | Get model parameters from generalized additive models |
| get_parameters.gamm | Get model parameters from generalized additive models |
| get_parameters.glimML | Get model parameters from mixed models |
| get_parameters.glmm | Get model parameters from mixed models |
| get_parameters.glmmTMB | Get model parameters from mixed models |
| get_parameters.glmx | Get model parameters from models with special components |
| get_parameters.htest | Get model parameters from htest-objects |
| get_parameters.logitmfx | Get model parameters from marginal effects models |
| get_parameters.MCMCglmm | Get model parameters from Bayesian models |
| get_parameters.merMod | Get model parameters from mixed models |
| get_parameters.mhurdle | Get model parameters from zero-inflated and hurdle models |
| get_parameters.mjoint | Get model parameters from models with special components |
| get_parameters.mvord | Get model parameters from models with special components |
| get_parameters.rqss | Get model parameters from generalized additive models |
| get_parameters.sim | Get model parameters from Bayesian models |
| get_parameters.sim.merMod | Get model parameters from Bayesian models |
| get_parameters.stanmvreg | Get model parameters from Bayesian models |
| get_parameters.stanreg | Get model parameters from Bayesian models |
| get_parameters.zcpglm | Get model parameters from zero-inflated and hurdle models |
| get_parameters.zeroinfl | Get model parameters from zero-inflated and hurdle models |
| get_predicted | Model Predictions (robust) |
| get_predicted.lm | Model Predictions (robust) |
| get_predicted.stanreg | Model Predictions (robust) |
| get_predicted_ci | Confidence and Prediction Interval for Model Predictions |
| get_predicted_ci.default | Confidence and Prediction Interval for Model Predictions |
| get_predictors | Get the data from model predictors |
| get_priors | Get summary of priors used for a model |
| get_priors.brmsfit | Get summary of priors used for a model |
| get_random | Get the data from random effects |
| get_residuals | Extract model residuals |
| get_residuals.default | Extract model residuals |
| get_response | Get the values from the response variable |
| get_sigma | Get residual standard deviation from models |
| get_statistic | Get statistic associated with estimates |
| get_statistic.betamfx | Get statistic associated with estimates |
| get_statistic.betareg | Get statistic associated with estimates |
| get_statistic.clm2 | Get statistic associated with estimates |
| get_statistic.default | Get statistic associated with estimates |
| get_statistic.DirichletRegModel | Get statistic associated with estimates |
| get_statistic.emmGrid | Get statistic associated with estimates |
| get_statistic.gee | Get statistic associated with estimates |
| get_statistic.glmmTMB | Get statistic associated with estimates |
| get_statistic.logitmfx | Get statistic associated with estimates |
| get_statistic.mjoint | Get statistic associated with estimates |
| get_transformation | Return function of transformed response variables |
| get_varcov | Get variance-covariance matrix from models |
| get_varcov.aov | Get variance-covariance matrix from models |
| get_varcov.betamfx | Get variance-covariance matrix from models |
| get_varcov.betareg | Get variance-covariance matrix from models |
| get_varcov.brmsfit | Get variance-covariance matrix from models |
| get_varcov.clm2 | Get variance-covariance matrix from models |
| get_varcov.default | Get variance-covariance matrix from models |
| get_varcov.DirichletRegModel | Get variance-covariance matrix from models |
| get_varcov.gamlss | Get variance-covariance matrix from models |
| get_varcov.glmmTMB | Get variance-covariance matrix from models |
| get_varcov.hurdle | Get variance-covariance matrix from models |
| get_varcov.MixMod | Get variance-covariance matrix from models |
| get_varcov.mixor | Get variance-covariance matrix from models |
| get_varcov.truncreg | Get variance-covariance matrix from models |
| get_varcov.zcpglm | Get variance-covariance matrix from models |
| get_variance | Get variance components from random effects models |
| get_variance_dispersion | Get variance components from random effects models |
| get_variance_distribution | Get variance components from random effects models |
| get_variance_fixed | Get variance components from random effects models |
| get_variance_intercept | Get variance components from random effects models |
| get_variance_random | Get variance components from random effects models |
| get_variance_residual | Get variance components from random effects models |
| get_variance_slope | Get variance components from random effects models |
| get_weights | Get the values from model weights |
| get_weights.default | Get the values from model weights |
| has_intercept | Checks if model has an intercept |
| is_gam_model | Checks if a model is a generalized additive model |
| is_mixed_model | Checks if a model is a mixed effects model |
| is_model | Checks if an object is a regression model or statistical test object |
| is_model_supported | Checks if an object is a regression model object supported in 'insight' package. |
| is_multivariate | Checks if an object stems from a multivariate response model |
| is_nested_models | Checks whether a list of models are nested models |
| is_nullmodel | Checks if model is a null-model (intercept-only) |
| is_regression_model | Checks if an object is a regression model or statistical test object |
| link_function | Get link-function from model object |
| link_function.betamfx | Get link-function from model object |
| link_function.betareg | Get link-function from model object |
| link_function.DirichletRegModel | Get link-function from model object |
| link_function.gamlss | Get link-function from model object |
| link_inverse | Get link-inverse function from model object |
| link_inverse.betamfx | Get link-inverse function from model object |
| link_inverse.betareg | Get link-inverse function from model object |
| link_inverse.DirichletRegModel | Get link-inverse function from model object |
| link_inverse.gamlss | Get link-inverse function from model object |
| loglikelihood | Log-Likelihood |
| model_info | Access information from model objects |
| model_info.default | Access information from model objects |
| model_name | Name the model |
| model_name.default | Name the model |
| null_model | Compute intercept-only model for regression models |
| n_obs | Get number of observations from a model |
| n_obs.afex_aov | Get number of observations from a model |
| n_obs.glm | Get number of observations from a model |
| n_obs.stanmvreg | Get number of observations from a model |
| n_obs.svyolr | Get number of observations from a model |
| n_parameters | Count number of parameters in a model |
| n_parameters.brmsfit | Count number of parameters in a model |
| n_parameters.default | Count number of parameters in a model |
| n_parameters.gam | Count number of parameters in a model |
| n_parameters.glmmTMB | Count number of parameters in a model |
| n_parameters.merMod | Count number of parameters in a model |
| n_parameters.zeroinfl | Count number of parameters in a model |
| parameters_table | Parameter table formatting |
| print_color | Coloured console output |
| print_colour | Coloured console output |
| print_html | Generic export of data frames into formatted tables |
| print_html.data.frame | Generic export of data frames into formatted tables |
| print_md | Generic export of data frames into formatted tables |
| print_md.data.frame | Generic export of data frames into formatted tables |
| print_parameters | Prepare summary statistics of model parameters for printing |
| standardize_column_order | Standardize column order |
| standardize_column_order.parameters_model | Standardize column order |
| standardize_names | Standardize column names |
| standardize_names.parameters_model | Standardize column names |
| supported_models | Checks if an object is a regression model object supported in 'insight' package. |
| to_numeric | Convert to Numeric (if possible) |


Thanks! to CSS-TRICKS
Since flexbox is a whole module and not a single property, it involves a lot of things including its whole set of properties. Some of them are meant to be set on the container (parent element, known as "flex container") whereas the others are meant to be set on the children (said "flex items"). If regular layout is based on both block and inline flow directions, the flex layout is based on "flex-flow directions". Please have a look at this figure from the specification, explaining the main idea behind the flex layout.
Basically, items will be laid out following either the main axis (from main-start to main-end) or the cross axis (from cross-start to cross-end). main axis - The main axis of a flex container is the primary axis along which flex items are laid out. Beware, it is not necessarily horizontal; it depends on the flex-direction property (see below). main-start | main-end - The flex items are placed within the container starting from main-start and going to main-end. main size - A flex item's width or height, whichever is in the main dimension, is the item's main size. The flex item's main size property is either the ‘width’ or ‘height’ property, whichever is in the main dimension. cross axis - The axis perpendicular to the main axis is called the cross axis. Its direction depends on the main axis direction. cross-start | cross-end - Flex lines are filled with items and placed into the container starting on the cross-start side of the flex container and going toward the cross-end side. cross size - The width or height of a flex item, whichever is in the cross dimension, is the item's cross size. The cross size property is whichever of ‘width’ or ‘height’ that is in the cross dimension.


A network of highly experienced digital design thinkers who operate independently, but love working together.
Make · Collaborate · Celebrate
Hello. How’s it going your name ? I know things have never been more exciting than they are now at your company . You know I’m really happy to see you stopped by to tell us about your reason for contacting your reason for contacting scheduling a workshop design consulting what you’re up to :) . I’ll send you an e-mail at so we can talk it over.


On Demand
Last meetup session took place in Fall 2016 and had over 15 members. There was a meetup every weekend over the course of 3 months. In the end, 6 members successfully completed the iOS MobileSpace cirriculum and have gone on to work as full-time iOS Software Engineers. You can read more about the last meetup session on the MobileSpace Blog here.
If you are interested to learn mobile development or host a MobileSpace meetup session for your company, kindly contact Monte Thakkar either through email here or on Twitter here.
MobileSpace is founded on the principle of free and open source education that helps to empower all to learn the skills necessary to be a good software engineer. Needless to say, MobileSpace is completely free for everyone. Please don't hesitate to reach out.


1. [Elastic Stack ecosystem of people, websites, tutorials](/elastic-ecosystem/)
0. [Elastic Stack architecture and installation](/elastic-install/)
0. [Elastic Scaling (the database engine)](/elastic-scaling/)
0. [Elastic Query (via REST API)](/elastic-query/)
0. [Elastic Kibana (the visualization engine, like Grafana)](/elastic-kibana/)
0. [Elastic Logstash to assemble and filter data from Beats](/elastic-logstash/)
0. [Elastic Beats to collect data from servers](/elastic-beats/)


Manage a cluster of Linux containers as a single system to accelerate Dev and simplify Ops.
 Kubernetes is a community driven cluster manager that goes wherever your Linux application container can go, from your laptop to the cloud and anywhere in between. It was created by Google drawing on over ten years of experience running containers at massive scale, but is owned and powered by a diverse set of contributors.
Lean
Lightweight, simple and accessible
Portable
Built for a multi-cloud world, public, private or hybrid
Extensible
Written in Go, highly modular and designed from the start to be adaptable and extensible
We would love to hear from you, how you are using Kubernetes, and what we can do to make it better.
Our IRC channel is the best way to contact our engineers and share your ideas with them. They hang out at #google-containers on Freenode.
Our user forum on stackoverflow is a great place to go for community support.


a signed and dated letter saying you are using your birth name ‘for all purposes’ - that is, you don’t use your married or civil partnership name at all
You also need to send either:
your decree absolute showing both names
a marriage certificate showing both names - if you don’t have the original, you’ll need to get a copy
You also need to send 1 of the following documents as evidence that you’re using your new name:
letter from your employer
driving licence
bank statement
medical card
letter from your local council or a government department
Failure to provide these supporting documents will result in a delay in your application.


{% extends 'base.html' %} {%block title%}Page not found{%endblock%} {%block content%}
Oops!  Go to home page


Booking Statistics
Last Campaign Performance
Users Behavior
24 Hours performance
2014 Sales
All products including Taxes
Notes
To-Do
|
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Update Travel Packages | |
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Compute Sales from Manila Transactions | |
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Generate Reports from Booked Packages | |
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Generate Feedback Reports from August | |
|
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Read Ratings from Booked Packages | |
|
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Remove packages as per client. |


Long-Awaited KTM 390 Adventure Breaks Cover At 2019 EICMA
by Surendhar MKTM 390 Adventure is expected to launch in India early next year and will be a compelling addition to the brand’s domestic lineup
The idea of dual-purpose adventure tourers is just catching on with the Indian motorcycling enthusiasts as more new models are being explored and some are already there in the market for people to buy like the Royal Enfield Himalayan and Hero XPulse 200. KTM’s 390 platform has two motorcycles, the Duke and RC, pertaining to different set of customers.
When the BMW G310GS arrived at a rather premium price tag, the expectancy on an Adv based on the 390 platform only skyrocketed as the Duke 390 and RC 390 are already known for their performance, packed features and aggressive styling at a luring price range.
KTM’s EICMA stage is dominated by several high-end motorcycles including the flagship naked, the 1290 Super Duke R, but the 390 Adventure is certainly grabbing the eyeballs for all the right reasons. It takes power from the same 373 cc, liquid-cooled, single-cylinder, fuel-injected powertrain used on the Duke 390.
The Euro 5/BS6 compliant engine produces 44 horsepower maximum power at 9,000 rpm and 37 Nm of peak torque delivered at 7,250 rpm, with obvious gearing changes in the six-speed transmission for offering better low- and mid-range. The KTM 390 Adventure will be manufactured at Bajaj’s Chakan plant near Pune.
The styling sticks by the Duke range with split LED headlamps and body panels carried over from the Duke 390. The rear end looks edgy and goes in line with the sharp facade the 390 Adventure boasts. The A2 compatible machine has high seat height of 858 mm and handlebar positioning appear to suit the customers wanting to go off-roading.
Since it is a dual-purpose motorcycle, the lightness will come into play while manoeuvring through regular traffic scenarios. It comes with features such as dual ABS system, disc brakes on both ends, long-travel suspension setup, USD front forks, pre-load adjustable monoshock rear, 19-inch front and 17-inch rear wheels, TFT instrumentation with smartphone connectivity option and so on.
Other notable equipment in the list includes LED tail lamp, LED turn signals, windblast protector, knuckle guards, serrated footpegs with removable rubber pads, engine guard, belly cowl, dual-purpose tyres and multi-spoke black alloy wheels, etc. It will be sold in two colour options – orange and while – and has 14.5-litre fuel tank capacity with dry weight of 156 kilograms.
It is expected to make domestic debut at the upcoming India Bike Week in Goa on December 6 before entering the market in the early parts of next year. It will more likely be priced close to Rs. 3 lakh (ex-showroom) and should offer better value-for-money than its competitors such as the Kawasaki Versys X-300 and BMW G310GS.


get_f.Rd
This function solves for the bycatch mortality rate \(E\) that gives a pre-specified depletion level InitDepl.w
. It is used within the projections()
function to calculate the stable age distribution at which to start the projections.
get_f( f.start = NA, S0.w = NA, S1plus.w = NA, nages.w = NA, AgeMat.w = NA, InitDepl.w = NA, z.w = NA, lambdaMax.w = NA, N0.w = NA, P0.w = NA, Check = FALSE )
| f.start | an initial guess for the bycatch mortality rate E. The default value is E = 0.5 |
|---|---|
| S0.w | Calf/pup survival, a numeric value between 0 and 1. (Note: the 'w' suffix indicates that \(z\) is in the wrapper function, and is used inside the function by |
| S1plus.w | survival rate for animals age 1 year and older, a numeric value between 0 and 1 |
| nages.w | "maximum" age, treated as the plus group age. The plus group age can be set equal to the age at maturity +2 years without losing accuracy. |
| AgeMat.w | Age at maturity in years (assumed to be age at first parturition - 1) |
| InitDepl.w | The depletion level to solve for as a proportion of carrying capacity; a numeric value between 0 and 1. This is equivalent to 'starting depletion'. |
| z.w | The degree of compensation. The default value is |
| lambdaMax.w | The maximum intrinsic growth rate |
| N0.w | unfished numbers per recruit in terms of mature individuals individuals |
| P0.w | Number of individuals per recruit in terms of individuals aged 1+ |
| Check | logical; if |
The bycatch rate that would lead to a depletion level of InitDepl.w
-- a single value.
# Set parameters S0.w = 0.5; S1plus.w = 0.944; nages.w = 25; AgeMat.w = 18 InitDepl.w = 0.9; z.w = 2.39; lambdaMax.w = 1.04 # Get number of individuals per recruit in terms of mature individuals (N0.w) NPROut <- npr(S0 = S0.w, S1plus = S1plus.w, nages = nages.w, AgeMat = AgeMat.w, E = 0) N0 <- NPROut$npr # mature numbers per recruit # Get number of individuals per recruit in terms of individuals aged 1+ (P0.w) P0 <- NPROut$P1r # 1+ nums per recruit # Get bycatch mortality rate for the initial depletion defined above get_f(f.start = 0.5, S0.w = S0.w, S1plus.w = S1plus.w, nages.w = nages.w, AgeMat.w = AgeMat.w, InitDepl.w = InitDepl.w, z.w = z.w, lambdaMax.w = lambdaMax.w, N0.w = N0, P0.w = P0, Check = FALSE)#> [1] 0.01168733


Rhode Island Signs MoU With Ben Gurion University
The agreement aims to encourage collaborations between Israeli startups and Rhode Island's public and private universities, and promote tech internships and job opportunities
by Adi PickRhode Island Governor Gina M. Raimondo has signed a memorandum of understanding with Israel-based Ben-Gurion University of the Negev (BGU) on joint innovation and entrepreneurship, the latter announced Wednesday. The MoU aims to encourage collaborations between Israeli startups and Rhode Island's public and private universities, and promote tech internships and job opportunities.
Rhode Island is home to leading American universities, including the Ivy League’s Brown University, Rhode Island School of Design, the Naval War College, and the New England Institute of Technology. Ben Gurion University is located in southern Israeli city Be’er Sheva, and reports 20,000 students.


Click the name of the orchestra below or one of the lines on the chart to listen to the original recording


About Me:
I grew up in a small, strange little town in Southern California. I’ve always been both incredibly creative and incredibly nerdy so it was quite the surprise when I chose to follow medical as my career over literally anything else. Today though, I’ve put away the scrubs and sterile instruments to follow my passion of working in a career where I may explore, nurture and expand my already over-active imagination as a software developer.
Past Projects: (scroll)


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--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/platformer.20on.20proof.20of.20square.20root.20of.203.html ---
When I was learning about set_option pp.all true
a while back, I had recently proved that there was no rational number whose square was 3 in Lean, in no doubt an extremely inefficient manner. I wrote the proof term to a file and I just ran into it again yesterday.
I emailed it to my 15 year old son and he imported it into Blender, thickened up the characters, and then ran a platformer game on top of it, with a character with a really bad jump and quite a good grapple trying to get from one end of the proof to the other
I have some video
Haha, sounds awesome
I'm just taking the sound off it. The hitbox of the character is really small. Sometimes my son just takes gravity off and randomly moves around the proof.
 He grappled around for a while after that. You just see the bit where he falls through the letters a lot here
http://wwwf.imperial.ac.uk/~buzzard/sqrt3_fail.mp4
oh the grapple will have to wait, it's bed time
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About
14 May 1999
Indian Institute of Information Technology, Sri City (Pre final Year)
Mumbai, India
Resume
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Hackerrank


research
Recent research projects
Real-time Magnetic Resonance Imaging
During my time at the University of Southern California, my research focused on developing new ways to improve real-time MRI technology. Specifically, I worked on making MRI scans faster, while still maintaining high image quality and accuracy.
3D real-time imaging
This project involved creating a real-time 3D MRI system that can capture moving vocal articulators (like the tongue and mouth) while someone is speaking. This breakthrough technique provides detailed insights into speech production, which can be used to improve language technology and treat speech disorders.
The below image demonstrates 3D tongue and vocal tract shape features that were never previously attempted to visualize.
Related publications
- Z Zhao, Y Lim, D Byrd, S Narayanan, and KS Nayak, Improved 3D real-time MRI of speech production, Magnetic Resonance in Medicine, vol. 85, no. 6, pp. 3182-3195, 2021.
- Y Lim, Y Zhu, SG Lingala, D Byrd, S Narayanan, and KS Nayak, 3D dynamic MRI of the vocal tract during natural speech, Magnetic Resonance in Medicine, vol. 81, no. 3, pp. 1511–1520, 2019.
Image deblurring
This project involved developing fast and efficient deep learning techniques to correct blurry images in real-time MRI scans. Our technique allows for high-quality, low-latency deblurring that is faster (<20msec per frame) and just as effective as traditional methods (>1sec).
Related publications
- Y Lim, S Narayanan, and KS Nayak, Attention-gated convolutional neural networks for off-resonance correction of spiral real-time MRI, in Proc. 28th ISMRM Scientific Sessions, Virtual Conference, April 2020.
- Y Lim, Y Bliesener, S Narayanan, and KS Nayak, Deblurring for spiral real-time MRI using convolutional neural networks, Magnetic Resonance in Medicine, vol. 84, no. 6, pp. 3438–3452, Dec. 2020.
- Y Lim, SG Lingala, S Narayanan, and KS Nayak, Dynamic off-resonance correction for spiral real-time MRI of speech, Magnetic Resonance in Medicine, vol. 81, no. 1, pp. 234–246, Jan. 2019.
 However, access to this technology is limited, and more comprehensive datasets need to be made available.
As part of an interdisciplinary team, I helped develop a unique collection of multimodal speech production MRI data from a record-breaking 75 subjects. This dataset will be a valuable resource for researchers in the fields of linguistics, speech science, computational imaging, and engineering. It will provide them with the opportunity to gain deeper insights into human speech production and develop advanced computational imaging algorithms.
Related publications
- Y Lim, A Toutios, et al, A multispeaker dataset of raw and reconstructed speech production real-time MRI video and 3D volumetric images, Scientific Data (Nature) 8, 187. 2021.


Enter the filenames of the uploaded files not to scan as top level modules, represented as a comma-separated list of ant-style-like exclude patterns such that '*' matches 0 or more characters and '?' matches exactly 1 character.
Patterns are case-sensitive. Patterns that include commas because they denote filenames that contain commas need to have the commas replaced with a wildcard character.
Because the matching is performed based only on filename, it is incorrect to use patterns that include path separators ('\' or '/').
No uploaded files are excluded when omitted.