## 先来看看jsr133中lock的语义
There are multiple mechanisms for communicating between threads. The most basic of these methods is synchronization, which is implemented using monitors. Each object is associated with a monitor, which a thread can lock or unlock. Only one thread at a time may hold a lock on a monitor. Any other threads attempting to lock that monitor are blocked until they can obtain a lock on that monitor.
A thread t may lock a particular monitor multiple times; each unlock reverses the effect of one lock operation.
The synchronized statement computes a reference to an object; it then attempts to perform a lock action on that object’s monitor and does not proceed further until the lock action has successfully completed. After the lock action has been performed, the body of the synchronized statement is executed. If execution of the body is ever completed, either normally or abruptly, an unlock action is automatically performed on that same monitor. A synchronized method automatically performs a lock action when it is invoked; its body is not executed until the lock action has successfully completed. If the method is an instance method, it locks the monitor associated with the instance for which it was invoked (that is, the object that will be known as this during execution of the body of the method). If the method is static, it locks the monitor associated with the Class object that represents the class in which the method is defined. If execution of the method’s body is ever completed, either normally or abruptly, an unlock action is automatically performed on that same monitor.
The semantics neither prevents nor requires detection of deadlock conditions. Programs where threads hold (directly or indirectly) locks on multiple objects should use conventional techniques for deadlock avoidance, creating higher-level locking primitives that don’t deadlock, if necessary.
Other mechanisms, such as reads and writes of volatile variables and classes provided in the java.util.concurrent package, provide alternative mechanisms for synchronization.
package com.github.dunno.concurrent.sync;
import org.junit.Test;
import java.util.concurrent.locks.ReadWriteLock;
import java.util.concurrent.locks.ReentrantLock;
import java.util.concurrent.locks.ReentrantReadWriteLock;
/**
* Created by liang.he on 16/1/16.
*/
public class TestSync {
//类锁：在代码中的方法上加了static和synchronized的锁，或者synchronized(xxx.class）的代码段
//对象锁：在代码中的方法上加了synchronized的锁，或者synchronized(this）的代码段
//私有锁：在类内部声明一个私有属性如private Object lock，在需要加锁的代码段synchronized(lock）
private Integer lock = new Integer(1);
private Integer lock2 = new Integer(2);
private final static Integer staticLock = new Integer(3);
//无锁
public void noLockMethod() {
long start = System.currentTimeMillis();
System.out.println("noLockMethod start! start:" + start);
int sleep = 2;
while (sleep > 0) {
try {
Thread.sleep(1000);
System.out.println("noLockMethod-lock:" + lock2);
System.out.println("noLockMethod-lock:" + lock);
System.out.println("noLockMethod-lock:" + staticLock);
} catch (InterruptedException e) {
e.printStackTrace();
}
sleep --;
}
long end = System.currentTimeMillis();
System.out.println("noLockMethod end! end:" + end + " cost:" + (end - start));
}
//对象锁1
public synchronized void objectLockMethod() {
long start = System.currentTimeMillis();
System.out.println("objectLockMethod start! start:" + start);
int sleep = 2;
while (sleep > 0) {
try {
Thread.sleep(1000);
System.out.println("objectLockMethod-lock:" + lock2);
} catch (InterruptedException e) {
e.printStackTrace();
}
sleep --;
}
long end = System.currentTimeMillis();
System.out.println("objectLockMethod end! end:" + end + " cost:" + (end - start));
}
//对象锁2
public void objectLockMethod2() {
synchronized (this) {
long start = System.currentTimeMillis();
System.out.println("objectLockMehtod2 start! start:" + start);
int sleep = 2;
while (sleep > 0) {
try {
Thread.sleep(1000);
System.out.println("objectLockMehtod2-lock:" + lock2);
} catch (InterruptedException e) {
e.printStackTrace();
}
sleep --;
}
long end = System.currentTimeMillis();
System.out.println("objectLockMehtod2 end! end:" + end + " cost:" + (end - start));
}
}
//私有锁 锁变量1
public void privateLockMethod() {
synchronized (lock) {
long start = System.currentTimeMillis();
System.out.println("privateLockMethod start! start:" + start);
int sleep = 2;
while (sleep > 0) {
try {
Thread.sleep(1000);
System.out.println("privateLockMethod1-lock:" + lock2);
} catch (InterruptedException e) {
e.printStackTrace();
}
sleep --;
}
long end = System.currentTimeMillis();
System.out.println("privateLockMethod end! end:" + end + " cost:" + (end - start));
}
}
//私有锁 锁变量2
public void privateLockMethod2() {
synchronized (lock2) {
long start = System.currentTimeMillis();
System.out.println("privateLockMethod2 start! mills:" + start);
int sleep = 2;
while (sleep > 0) {
try {
Thread.sleep(1000);
System.out.println("privateLockMethod2-lock:" + lock);
} catch (InterruptedException e) {
e.printStackTrace();
}
sleep --;
}
long end = System.currentTimeMillis();
System.out.println("privateLockMethod end! end:" + end + " cost:" + (end - start));
}
}
//类锁
public synchronized static void classLockMethod() {
long start = System.currentTimeMillis();
System.out.println("classLockMethod start! mills:" + start);
int sleep = 2;
while (sleep > 0) {
try {
Thread.sleep(1000);
System.out.println("classLockMethod-lock:" + staticLock);
} catch (InterruptedException e) {
e.printStackTrace();
}
sleep --;
}
long end = System.currentTimeMillis();
System.out.println("classLockMethod end! end:" + end + " cost:" + (end - start));
}
//类锁
public static void classLockMethod2() {
synchronized (TestSync.class) {
long start = System.currentTimeMillis();
System.out.println("classLockMethod2 start! mills:" + start);
int sleep = 2;
while (sleep > 0) {
try {
Thread.sleep(1000);
System.out.println("classLockMethod2-lock:" + staticLock);
} catch (InterruptedException e) {
e.printStackTrace();
}
sleep --;
}
long end = System.currentTimeMillis();
System.out.println("classLockMethod2 end! end:" + end + " cost:" + (end - start));
}
}
private ReentrantLock reentrantLock = new ReentrantLock();
//独占锁
public void reentrantLock() {
try {
reentrantLock.lock();
long start = System.currentTimeMillis();
System.out.println("reentrantLock start! start:" + start);
int sleep = 2;
while (sleep > 0) {
try {
Thread.sleep(1000);
System.out.println("reentrantLock-lock:" + lock2);
System.out.println("reentrantLock-lock:" + lock);
System.out.println("reentrantLock-lock:" + staticLock);
} catch (InterruptedException e) {
e.printStackTrace();
}
sleep --;
}
long end = System.currentTimeMillis();
System.out.println("reentrantLock end! end:" + end + " cost:" + (end - start));
} finally {
reentrantLock.unlock();
}
}
private ReadWriteLock readWriteLock = new ReentrantReadWriteLock();
public void readWriteLock() {
}
//测试私有锁锁不同变量是否会有竞争
//结论:
//只打开注释1,注释2,可以说明“当私有锁监视同一资源会竞争,等待其中一个资源释放,当监视的资源不是同一个不会产生竞争”
//只打开注释3,注释4,可以说明“对象锁会互相竞争，等待另一个方法结束”
//只打开注释5,注释6,可以说明"类锁会互相竞争，等待另一个释放资源"
//只打开注释1,注释6,打开注释1,注释5,可以说明"不同的锁不会竞争"
//只打开注释1,注释7,3-7或者5-7,可以说明"无锁变量不会和锁变量竞争"
//不同类的锁不会竞争
@Test
public void test1() {
Thread thread = new Thread(new Runnable() {
@Override
public void run() {
Thread thread1 = new Thread(new Runnable() {
@Override
public void run() {
//privateLockMethod();//注释1
//objectLockMethod();//注释3
//classLockMethod();//注释5
reentrantLock();//注释8
}
});
Thread thread2 = new Thread(new Runnable() {
@Override
public void run() {
//privateLockMethod2();//注释2
//objectLockMethod2();//注释4
//classLockMethod2();//注释6
//noLockMethod();//注释7
reentrantLock();//注释9
}
});
thread1.start();
thread2.start();
try {
thread1.join();
thread2.join();
} catch (InterruptedException e) {
e.printStackTrace();
}
}
});
thread.start();
try {
thread.join();
} catch (InterruptedException e) {
e.printStackTrace();
}
}
}


virsh
Manage virsh guest domains. (NOTE: 'guest_id' can be the id, name or UUID of the guest).
Some subcommands such as `virsh list` have their own usage documentation.
More information: home page
- Connect to a hypervisor session:
virsh connect qemu:///system
- List all domains:
virsh list --all
- Dump guest configuration file:
virsh dumpxml guest_id > path/to/guest.xml
- Create a guest from a configuration file:
virsh create path/to/config_file.xml
- Edit a guest's configuration file (editor can be changed with $EDITOR):
virsh edit guest_id
- Start/reboot/shutdown/suspend/resume a guest:
virsh command guest_id
- Save the current state of a guest to a file:
virsh save guest_id filename
- Delete a running guest:
virsh destroy guest_id && virsh undefine guest_id


Trazadone is not really for recreational use, since it is nothing but a powerful sleeping aid, but if you're looking for a long and deep sleep, Trazadone is what you're looking for. I have taken this drug on several occasions, and it's great for sleeping. The pills I take are 50mg each. Here is a little chart of what each amount of Trazadone will do.
50mg-100mg: Within an hour of taking the pills, I will feel tired, but not to the point of passing out or anything. This amount of Trazadone will usually give me a good night's sleep without waking up during the night. Dreams may be clearer than usual, and I may remember them with more accuracy than usual.
150mg-200mg: After taking 3 pills (or 150mg), I will feel the tiredness feeling come on slowly. Within an hour, I will be very tired. Four pills (200mg) will make me feel extremely tired, almost to the point of passing out. Taking 150mg-200mg of this drug usually causes me to have very vivid dreams, and I am usually able to remember them much clearer than I normally would. I haven't had a lucid dream while taking this drug as of yet, but I would imagine it is very possible. When I take Trazadone, I usually sleep at least 8 hours. One time when I took 4 pills (200mg) I slept for around 11 hours.
When I wake up from sleeping, it takes me an unusually long time to get fully awake. Also, my eyes always are red, almost bloodshot, for about 30 minutes after I wake up, which is a clear sign of deep sleep (which is good, considering this is a sleeping pill). The day after taking Trazadone, especially after large quanties of it, I feel very drowsy and tired. This doesn't really occur with dosages of 50mg, but it will occur with higher dosages.
Positive Effects: Works wonderfully as a sleeping pill. It makes me very tired about an hour after taking it, and gives a great sleep with vivid dreams.
Negative Effects: The day after taking Trazadone is always clouded by drowsiness. I just feel tired and uninspired for a large portion of the following day. Some caffeine or some other kind of stimulant should take care of that.
Overall, this is not a recreational drug. It is a sleeping pill, but a powerful one at that. If you are having trouble sleeping, I suggest you take Trazadone. I don't use this drug on a normal basis, just every once in a while, so I have not noticed any negative physical effects.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Binary Segmentation
The binary segmentation benchmark takes an input images of 1276x717 and then rates models based on average precision. It allows the use of external data, gray or color inputs, any runtime, and any method. We may create additional benchmarks, splits, and rankings based on different metrics, such as runtime, if we get enough submissions.
Lane Based Segmentation
In addition to the lane marker segmentation, the individual pixels are assigned a lane relative to the vehicle's current position, e.g. same lane, left. Train models end-to-end, classify by region or density, etc. There are lots of possible methods- for use in automated vehicles, they have to run in real-time.
Lane Approximations
Instead of classifying individual pixels, the goal in this benchmark is to approximate the individual lane edges as curves. This can be done using a point for each row, polylines, polynomials, clothoids, or splines. Those curves can be calculated using end-to-end models, curve fitting on the segmentation, density clustering, line following, walker based approaches, line fitting, and many more.


Your browser doesn't support the features required by impress.js, so you are presented with a simplified version of this presentation.
For the best experience please use the latest Chrome, Safari or Firefox browser.
Vroom Vroom
TESLA
Tesla P100d
Contains modern amenities
Carries banks of li-ion
1500lbs more than Porsche 911 Turbo
0-60mph in 2.3s
Impressive and Considerate
Aventador
0-60mph in 2.7s
Four tenths Delta
E-motor clinically quick
Engine, fast and furious
That was unfair.
Of course a V12 sounds louder than an E-motor, DUH!
1.6L V6 @ 10,500 rpms + 160 BHP E-motor
3.0L V10 @ 22,000 rpms
Sensation of speed is more than just acceleration.
Speed != acceleration.
Founder of legendary Italian car company, Scuderia Ferrari
“We would not follow to develop a fully electric car.”
- Michael Leiters, Ferrari's technology head.

I welcome our new robot overlords. This change is good for the planet, not so great for petrol heads.
Savor the sound
Forza Ferrari


Pig Dice Game
Roll
Hold
Reset
Current Roll :
Player 1 score:
<--
Player 2 score:
<--
Congratulations, Player One wins!
Congratulations, Player Two wins!


Login
Welcome back! Please fill the form to get Services
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Password
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If the workers of the world want to win, all they have to do is recognize their own solidarity


LIPE To-do:
To Do List
Single click to edit a task, double click to cross the task off.
Add Task ➕
Items in your list:
0
Clear List
Clear Completed


Coockies Statistics
Home
Cookies
Enter a location
Enter a maximum number of cookies
Enter a minimum number of cookies
Enter the average per houre


open fun removeAt(col: Int, row: Int): ResponsiveGridPanel
Removes child component at given location (column, row).
col - column number
col
row - row number
row
Return this container


Kaylee Kimbrough
Kaylee Kimbrough is a 16-year-old high school junior from Dallas, Texas. She spent this summer in Round Rock, Texas participating in 7 weeks of intensive instruction in robotics, web development and design, mobile development, and mentoring from female engineers and entrepreneurs as a student in the Girls Who Code Summer Immersion Program. She has written for The Dallas Morning News and MissHeard Magazine. In the future she hopes to study transportation engineering and computer science.
Cristina M. Contreras
Cristina M. Contreras is a high school junior student from Colegio Puertorriqueño de Niñas in San Juan, Puerto Rico. She spend this summer in the Girls Who Code Summer Immersion Program where she met Kaylee and Emily; they decided to develop the A2B game. In her school Cristina is a member of various clubs and organizations. She is currently the president of the Environmental Science Organization and the secretary of her class. This year, she and a friend are starting a school newspaper.
Emily Chang
Emily Chang is a high school senior from Austin, Texas. She participated in the 7 week Girls Who Code Summer Immersion Program, where she learned coding, robotics, and web development and formed friendships with like-minded girls. She is also the Executive Editor of her school’s student press, the Experience Design co-lead for the TEDxYouth@Austin team, and the Chief Marketing Officer of connect(ed) Digital, a non-profit organization dedicated to teaching elementary school students the basics of coding.


Welcome to the Home Page!
Cereal ID
Manufacturer
Cereal
Links (View Edit Delete)
cerealId
Kellogg's
Cornflakes
View
Edit
Delete
Add New
Click here for memes


abstract fun onAutoFocusMoving(start: Boolean): Unit
Called when the camera auto focus starts or stops.
start - true if focus starts to move, false if focus stops to move
start


"There's Something About Mary" * * * * (out of four)
Starring Cameron Diaz, Ben Stiller, Matt Dillon, Lee Evans, Chris Elliot, Lin Shaye, Jeffrey Tambor, Keith David, W. Earl Brown and Brett Favre
Directed by Bobby and Peter Farrelly
Rated R for extremely crude sexual humor, profanity and fake nudity
Theatrical aspect ratio of 1.85:1
Released in 1998
Running 119 minutes
I feel no hesitation in saying that "There's Something About Mary" is the best film that I will see all year. From the moment the credits started to roll at the end of the picture, there was no doubt in my mind that I had seen a masterpiece that will remain unsurpassed in the remaining months of 1998. It is that good of a film.
When Gene Siskel said this about "Fargo" only a few months into '96, I dismissed the notion as ridiculous. How could you possibly know that a film is so great that nothing you could possibly see in the future could beat it? But after seeing the Farrelly's hilarious and surprisingly heartfelt masterwork, I now know what Siskel was talking about. Once in a (very great) while, a film comes along that is so daring, so brilliant and simply so enjoyable that at the end, you just want to stand up and cheer. And you know, at that instant, that you have just experienced something very special.
 The movie is so rude and, I guess, inappropriate in nature that I had a hard time sitting in a theater watching it with my parents (luckily I had seen it before them, so I could warn the about what they were in for). Forget "Blazing Saddles", "Animal House" and even the former king of gross-out comedy, "Monty Python's Meaning of Life"; they were all only preludes to the Farrelly's assault on the senses.
In fact, this may even be the funniest film I've ever had the pleasure of seeing (the only real contender is "Monty Python and the Holy Grail"), and most certainly the most hilarious to come out all decade.
But as funny as it is, something also has to be said about the overall sweetness of "Mary"; it has a huge heart. You see, the Farrelly's are truly gifted filmmakers.  They also, very wisely, add an unbelievable amount of charisma to the main characters and actually have them develop as the story progresses.
It's funny, though. If you just look at an outline of the story, it seems kind of contrived and predictable. But believe me, it is anything but. Ben Stiller plays Ted, a very shy and clumsy teenage geek who lusts after the school babe, Mary (Cameron Diaz).  Early Brown - the cameraman, believe it or not, from "Scream"), his dreams come true when she invites him to the prom. In one of the film's funniest scenes, something goes wrong (I won't ruin it for those who know nothing about the film) and Ted is unable to face Mary ever again.
Jump to the present. After years of self-loathing, Ted finally hires private detective Pat Healy (a suitably slimy Matt Dillon) to track her down so he can have one last chance at winning the woman he has loved since high school. Healy finds that Mary is still a full-blown hottie and decides to try and keep her for himself.
 After all, with a not-really-all-that-funny premise, how can you sustain laughter for nearly two hours? Somehow, the Farrelly's pull it off.
The performances here are also right on target. As Ted, Stiller adds just the right amount of clumsy charm to make his character not irritating, as he would normally be, but very lovable. And Cameron Diaz is perfect in her role for obvious reasons, but the actor who really grabs your attention is W. Earl Brown as Mary's brother.  I know he won't be nominated, but this is the guy who should not be counted out when it comes time to hand out the Oscar nods in February. As good as the actors are, though, it's the directors' movie all the way.
I guarantee that on almost every other top ten list at the end of the year, you will see the number one space occupied by Steven Spielberg's WWII epic "Saving Private Ryan". And yes, that is a great film worthy of the recognition it has been getting. But which is harder; making a great war film or having your audience laugh so hard that they can't breath for minutes at a time? I'll let you decide that one for yourself, but this critic is sticking to his guns. There is just something about "Mary".
*Find all of Jason's reviews at http://www.geocities.com/Hollywood/Boulevard/7475
Copyright 1998 Jason Wallis
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


{% block header %}{% end %} {% module Theme() %} {% block modals %}{% end %}
So this RootTheBox Capture the Flagzors is based on RootTheBox. if you want to check out their thing:


Technologies
-
Data Resources
-
Machine Learning
- Model:
- Google Colaboratory file:
- Analysis:
-
Frontend
- HTML, CSS, Bootstrap, Javascript for web pages.
-
Visualizations
-
Jupyter Notebook: Used pandas library from python for Data Cleaning and Munging.
- Tableau: To visualize Zillow data trends and analysis


Constructs a new YahooApplication object.
Constructs a new YahooApplication object.
YahooApplication
YahooApplication
($consumerKey $consumerKey, $consumerKeySecret $consumerKeySecret)
-
$consumerKey
$consumerKey: The consumer key of the application.
-
$consumerKeySecret
$consumerKeySecret: The consumer key secret of the application.
Executes the given YQL query.
Executes the given YQL query.
The
query
($yql $yql, [$env $env = NULL])
-
$yql
$yql: The query to execute.
-
$env
$env: A URL to a YQL environment file.
Sets the small view for the user given by the GUID.
Sets the small view for the user given by the GUID.
True
setSmallView
($guid $guid, $content $content)
-
$guid
$guid: The GUID of the user to set the small view for.
-
$content
$content: The content to set the small view to.


Applications which use the Impex library will generally have a single ImportControl object. This object is usually a child of the File menu; however, applications may put it wherever they want. They should also place it on the application object's GAGCNLT_SELF_LOAD_OPTIONS General Change Notification list. Applications should disable this object (with MSG_GEN_SET_NOT_ENABLED) whenever they are not prepared to accept imported data; for example, some applications will choose to disable file importing whenever they are unable to open a new document.
The ImportControl object is subclassed from
GenControlClass
(by way of
ImportExportClass
, as noted above). It thus has all the functionality of that class. It also has a few instance data fields of its own; they are shown in ImportControlClass Instance Data
.
Code Display 16-1 ImportControlClass Instance Data
/* ICI_attrs is a word-length record which stores attribute information for the * ImportControl object. It has only one flag, ICA_IGNORE_INPUT. */ @instance ImportControlAttrs ICI_attrs = 0;
/* ICI_dataClasses is a word-length record which indicates what Metafile formats * are supported by the application. The application must set this field. */ @instance ImpexDataClasses ICI_dataClasses = 0;
/* ICI_destination and ICI_message indicate what message should be sent when the * ImportControl object has finished importing a file. The application must set * these fields. The message must take a single argument, namely a pointer to an * ImpexTranslationParams structure in ss:bp (on the stack). */ @instance optr ICI_destination; @instance word ICI_message;
/* Applications may wish to add their own UI objects to the Import dialog box. * They can do so by defining a generic tree (the top object of which must be not * "usable"), and putting an optr to the top object in ATTR_IMPORT_CONTROL_APP_UI. */ @vardata optr ATTR_IMPORT_CONTROL_APP_UI;
/* Controller features flags */ typedef ByteFlags ImportControlFeatures; #define IMPORTCF_BASIC 0x01
typedef ByteFlags ImportControlToolboxFeatures; #define IMPORTCTF_DIALOG_BOX 0x01
ImportControlAttrs, MSG_IMPORT_CONTROL_GET_ATTRS, MSG_IMPORT_CONTROL_SET_ATTRS
ICI
_attrs
is a word-length record of type
ImportControlAttrs
. This record contains only one flag:
To find out the current setting of this field, send
MSG_IMPORT_CONTROL_GET_ATTRS
to the ImportControl. To change this field, send MSG_IMPORT_CONTROL_SET_ATTRS to the ImportControl.
ImportControlAttrs MSG_IMPORT_CONTROL_GET_ATTRS();
This message retrieves the current setting of the ImportControl's ICI
_attrs
field.
Source: Unrestricted.
Destination: Any ImportControl object.
Return: The ImportControl's ICI
_attrs
field.
Interception: This message should not be intercepted.
void MSG_IMPORT_CONTROL_SET_ATTRS( ImportControlAttrs attrs);
This message changes the current settings of an ImportControl's ICI
_attrs
field.
Source: Unrestricted.
Destination: Any ImportControl object.
Parameters:
attrs
The new settings for the ICI
_attrs
field.
Interception: This message should not be intercepted.
MSG_IMPORT_CONTROL_GET_DATA_CLASSES, MSG_IMPORT_CONTROL_SET_DATA_CLASSES
When you declare an Import object, you must specify what kind of Metafiles your application is prepared to accept. You do this by setting the value of the ICI
_dataClasses
field. This field is a word-length record of type
ImpexDataClasses
(described in ImpexTranslationParams
). If (for example) only the IDC_TEXT bit is set, the ImportControl will use only those import libraries which produce text Metafile output. More than one bit may be set; when the ImportControl sends its notification, it will tell the application what type of data is being imported.
To find out the current settings of the ICI
_dataClasses
field, send MSG_IMPORT_CONTROL_GET_DATA_CLASSES. To change the settings of this field, send MSG_IMPORT_CONTROL_SET_DATA_CLASSES.
ImpexDataClasses MSG_IMPORT_CONTROL_GET_DATA_CLASSES();
This message retrieves the current setting of the ImportControl's ICI
_dataClasses
field. This tells you what kind of data can be imported.
Source: Unrestricted.
Destination: Any ImportControl object.
Return: The ImportControl's ICI
_dataClasses
field.
Interception: This message should not be intercepted.
void MSG_IMPORT_CONTROL_SET_DATA_CLASSES( ImpexDataClasses dataClass);
This message changes the current settings of an ImportControl's ICI
_dataClasses
field.
Source: Unrestricted.
Destination: Any ImportControl object.
Parameters:
dataClass
The new settings for the ICI
_dataClasses
field.
Interception: This message should not be intercepted.
MSG_IMPORT_CONTROL_GET_ACTION, MSG_IMPORT_CONTROL_SET_ACTION, ImpexImportExportCompleted()
The ImportControl does most of its work transparently to the application. It interacts with the rest of the application only when the user has selected a file to import and the appropriate translation library has produced a VM chain. At this point the ImportControl sends a notification message to the application. The application responds by copying the data from the VM chain and sending back an acknowledgment message. The ImportControl can then destroy the temporary VM transfer file.
The application determines what message will be sent, and to what object, by setting the ICI
_destination
and ICI
_message
fields. Whatever object will receive the message should define an appropriate message.
The ImportControl will send this message with a single parameter:
itp
, a pointer to an
ImpexTranslationParams
structure (see ImpexTranslationParams
). The fields of the structure have the following meanings in this case:
ITP_impexOD
_returnMsg
MSG_IMPORT_CONTROL_IMPORT_COMPLETE.
_dataClass
ImpexDataClasses
record with one flag set. This flag indicates what type of Metafile has been prepared.
_transferVMFile
VMFileHandle
of the temporary transfer file.
_transferVMChain
VMBlockHandle
of the lead block in the VM chain containing the imported data.
_internal
The recipient of the message should take any appropriate action; usually this entails copying the data from the VM chain, as if it were pasting data from the Clipboard. When the application is finished, it should call
ImpexImportExportCompleted()
. This routine takes one parameter, namely the
itp
pointer which was passed to the object. (The
ImpexTranslationParams
structure should not have been changed.)
ImpexImportExportCompleted()
reads the appropriate message and destination from the
ImpexTranslationParams
and sends the proper acknowledgment message (which in this case is
MSG_IMPORT_CONTROL_IMPORT_COMPLETE
).
Applications which use the object model of document control will often set ICI
_destination
to TO_APP_TARGET; this will make it send its messages to the target object. The application can find out the ImportControl's action by sending it
MSG_IMPORT_CONTROL_GET_ACTION
. The application can change the ImportControl's action by sending it
MSG_IMPORT_CONTROL_SET_ACTION
.
void MSG_IMPORT_CONTROL_GET_ACTION( ImpexAction * retValue);
This message retrieves the values of an ImportControl's ICI
_destination
and ICI
_message
fields. These fields indicate what action the ImportControl will take when it is finished preparing a file for import.
Source: Unrestricted.
Destination: Any ImportControl object.
Parameters:
retValue
A pointer to an
ImpexAction
structure.
Return: The value of ICI
_message
(i.e. the message sent by the ImportControl).
recipient
ImpexAction
structure describing the message sent.Interception: This message should not be intercepted.
Structures: The message and recipient are written to an
ImpexAction
structure:
typedef struct { word message; /* message sent */ word unused; optr destOD; /* Destination of message */ } ImpexAction;
void MSG_IMPORT_CONTROL_SET_ACTION( optr destOD, /* Send messages to this object. */ word ICImsg); /* Send this message to the above recipient. */
This message changes the values of an ImportControl's ICI
_destination
and ICI
_message
fields. These fields indicate what action the ImportControl will take when it is finished preparing a file for import.
Source: Unrestricted.
Destination: Any ImportControl object.
Parameters:
destOD
Set ICI
_destination
to this value.
ICImsg
_message
to this value.Interception: This message should not be intercepted.
When the user selects the "Import" trigger or tool, the Import controller brings up a dialog box. The application can, if it wishes, add UI objects to this box. It does so by defining a tree of generic objects (the top object of which must be set "not usable"). It must place an optr to the top object in the tree in ATTR_IMPORT_CONTROL_APP_UI. When the ImportControl builds the dialog box, it will add that optr as one of the children in the tree and set it "usable".


--- layout: page title: excerpt: "My Projects from the How To Make (Almost) Anything class in fall 2015" search_omit: true headline: "" ---
{{ post.title }}
{{ post.weekNum }}


The non-configurable configuration loader for lazy people.
The only option is to pass rc the name of your app, and your default configuration.
var conf = require('rc')(appname, {
//defaults go here.
port: 2468,
//defaults which are objects will be merged, not replaced
views: {
engine: 'jade'
}
});
rc
will return your configuration options merged with the defaults you specify.
If you pass in a predefined defaults object, it will be mutated:
var conf = {};
require('rc')(appname, conf);
If rc
finds any config files for your app, the returned config object will have
a configs
array containing their paths:
var appCfg = require('rc')(appname, conf);
appCfg.configs[0] // /etc/appnamerc
appCfg.configs[1] // /home/dominictarr/.config/appname
appCfg.config // same as appCfg.configs[appCfg.configs.length - 1]
Given your application name (appname
), rc will look in all the obvious places for configuration.
${appname}_
appname_foo__bar__baz
=> foo.bar.baz
)_--config file
then from that file.${appname}rc
or the first found looking in ./ ../ ../../ ../../../
etc.$HOME/.${appname}rc
$HOME/.${appname}/config
$HOME/.config/${appname}
$HOME/.config/${appname}/config
/etc/${appname}rc
/etc/${appname}/config
All configuration sources that were found will be flattened into one object, so that sources earlier in this list override later ones.
Configuration files (e.g. .appnamerc
) may be in either json or ini format. The example configurations below are equivalent:
ini
; You can include comments in `ini` format if you want.
dependsOn=0.10.0
; `rc` has built-in support for ini sections, see?
[commands]
www = ./commands/www
console = ./commands/repl
; You can even do nested sections
[generators.options]
engine = ejs
[generators.modules]
new = generate-new
engine = generate-backend
json
{
// You can even comment your JSON, if you want
"dependsOn": "0.10.0",
"commands": {
"www": "./commands/www",
"console": "./commands/repl"
},
"generators": {
"options": {
"engine": "ejs"
},
"modules": {
"new": "generate-new",
"backend": "generate-backend"
}
}
}
Comments are stripped from JSON config via strip-json-comments.
Since ini, and env variables do not have a standard for types, your application needs be prepared for strings.
argv
You may pass in your own argv
as the third argument to rc
. This is in case you want to use your own command-line opts parser.
require('rc')(appname, defaults, customArgvParser);
If you have a special need to use a non-standard parser, you can do so by passing in the parser as the 4th argument. (leave the 3rd as null to get the default args parser)
require('rc')(appname, defaults, null, parser);
This may also be used to force a more strict format, such as strict, valid JSON only.
rc
is running fs.statSync
-- so make sure you don't use it in a hot code path (e.g. a request handler)
Multi-licensed under the two-clause BSD License, MIT License, or Apache License, version 2.0


| Track # | Track Title | Track Lenght |
|---|---|---|
| 1 | Just Once in My Life | 3:55 |
| 2 | Big Boy Pete | 3:03 |
| 3 | Unchained Melody | 3:34 |
| 4 | You Are My Sunshine | 3:04 |
| 5 | The Great Pretender | 2:33 |
| 6 | Sticks And Stones | 1:54 |
| 7 | See That Girl | 3:09 |
| 8 | Oo-Poo-Pah-Do | 3:51 |
| 9 | You'll Never Walk Alone | 2:15 |
| 10 | Guess Who? | 2:30 |
| 11 | The Blues | 2:50 |


Introduction
Preliminary Methods Used for Image Processing
Image Processing by using Matlab® “Image Processing Toolbox”
Types of Images Used In Image Processing
Image Processing Procedure
Displaying Sequence of Images
Coordinate Systems
Obtaining Coordinate Points of the Bubble Locations on the Image
Importing Data from Excel Sheets to Matlab
Results
Conclusion
Methodology
Experiment Apparatus
Image Processing Technique
Results
Conclusion
The main focus of this research was to simulate microgravity conditions on the International Space Station, and get new insights on the heat transfer characteristics of paramagnetic liquids. The experiment was conducted inside the zero gravity flight at the NASA Kennedy Space Center.

I obtained lot of experimental data through these experiments that I later published in several International conferences.
The bubble behavior can be seen in this 30 fps video on the LHS and the slow motion 1000 fps video on the RHS. The slow motion video shows the steady-state behavior clearly.
We developed and tested a flexible genetic algorithm-based system for tasking a team of unmanned aerial vehicles to complete a coordinated surveillance mission. The algorithm was found to be robust and flexible enough to work in various settings with different UAV types and ground stations.
Paper: A Flexible Genetic Algorithm System for Multi-UAV Surveillance: Algorithm and Flight Testing published in the Journal of Unmanned Systems in January 2015.
Control of multiple unmanned aerial vehicles is of importance given that so many have been deployed in the field. This work focussed on how genetic algorithms have been applied to the cooperative tasking of the AeroVironment’s Raven unmanned aerial vehicle engaged in an intelligence, reconnaissance, and surveillance mission.
Paper: Using Genetic Algorithms for Tasking Teams of Raven UAVs published in the Journal of Intelligent & Robotic Systems in July 2012.
FlightR is a system that is able to correlate flight dates with the time of flights and ticket prices. This project was completed at the HackReduce Travel Hackathon, and was awarded the most technically interesting project award for our submission. The data analysis was done using the statistical package R, and the visualization was done using d3.js.
I have conducted extensive research on financial models and methods of tuning these models using linear, non-linear, quadratic, and conic programming methods. Here is a recent research presentation on non-linear optimization applications in financial mathematics.
I was at MIT’s Laboratory for Autonomous Marine Sensing Vehicles as a visiting research student in 2014. I collaborated with the research staff on autonomous underwater vehicles by developing designs for several path planning algorithms and extended the open source Mission Oriented Operating Suite-Interval Programming (MOOS-IvP) project by introducing a smooth curve path planner. This path planner was capable of visiting a desired waypoint as efficiently as possible on its way to the next waypoint. The optimization criteria obeyed the maximum allowable turn radius, while minimizing the turning segment of the trajectory path over the area intersecting the capture radius.
Here is a research poster I presented at the Google I/O 2014 Research Lab.
I am an avid wearable tech enthusiast. I have been researching ways in which Google Glass can be used as a learning tool in classroom environments with the goal of creating Google Glassware. Here is a video I have recorded from Google Glass that showcases some problems that I am hoping to solve using some novel Glassware applications.
Stay tuned for more information on the Glassware.


Table of Contents
Object Detection
-
- Object Detection:
- Object Detection is the process of finding multiple instances of real-world objects such as faces, vehicles, and animals in images.
-
- The Structure:
-
- Input: Image
- Output: A pair of (box-co-ords, class) of all the objects in a fixed number of classes that appear in the image.
-
- Properties:
- In the problem of object detection, we normally do not know the number of objects that we need to detect.
This leads to a problem when trying to model the problem as a regression problem due to the undefined number of coordinates of boxes. - Thus, this problem is mainly modeled as a classification problem.
-
- Applications:
-
- Image Retrieval
- Surveillance
- Face Detection
- Face Recognition
- Pedestrian Detection
- Self-Driving Cars
Approaches (The Pre-DeepLearning Era)
-
- Semantic Texton Forests:
- This approach consists of ensembles of decision trees that act directly on image pixels.
- Semantic Texton Forests (STFs) are randomized decision forests that use only simple pixel comparisons on local image patches, performing both an implicit hierarchical clustering into semantic textons and an explicit local classification of the patch category.
- STFs allow us to build powerful texton codebooks without computing expensive filter-banks or descriptors, and without performing costly k-means clustering and nearest-neighbor assignment.
- Semantic Texton Forests for Image Categorization and Segmentation, Shawton et al. (2008)
-
- Region Proposals:
-
- Algorithm:
- Find “blobby” image regions that are likely to contain objects
- Algorithm:
- These are relatively fast algorithms.
- Alexe et al, “Measuring the objectness of image windows”, TPAMI 2012
Uijlings et al, “Selective Search for Object Recognition”, IJCV 2013
Cheng et al, “BING: Binarized normed gradients for objectness estimation at 300fps”, CVPR 2014
Zitnick and Dollar, “Edge boxes: Locating object proposals from edges”, ECCV 2014
-
- Conditional Random Fields:
- CRFs provide a probabilistic framework for labeling and segmenting structured data.
- They try to model the relationship between pixels, e.g.:
- nearby pixels more likely to have same label
- pixels with similar color more likely to have same label
- the pixels above the pixels “chair” more likely to be “person” instead of “plane”
- refine results by iterations
- W. Wu, A. Y. C. Chen, L. Zhao and J. J. Corso (2014): “Brain Tumor detection and segmentation in a CRF framework with pixel-pairwise affinity and super pixel-level features”
- Plath et al. (2009): “Multi-class image segmentation using conditional random fields and global classification”
-
- SuperPixel Segmentation:
- The concept of superpixels was first introduced by Xiaofeng Ren and Jitendra Malik in 2003.
- Superpixel is a group of connected pixels with similar colors or gray levels.
They produce an image patch which is better aligned with intensity edges than a rectangular patch. - Superpixel segmentation is the idea of dividing an image into hundreds of non-overlapping superpixels.
Then, these can be fed into a segmentation algorithm, such as Conditional Random Fields or Graph Cuts, for the purpose of segmentation. - Efficient graph-based image segmentation, Felzenszwalb, P.F. and Huttenlocher, D.P. International Journal of Computer Vision, 2004
- Quick shift and kernel methods for mode seeking, Vedaldi, A. and Soatto, S. European Conference on Computer Vision, 2008
- Peer Neubert & Peter Protzel (2014). Compact Watershed and Preemptive
Approaches (The Deep Learning Era)
-
- The Sliding Window Approach:
- We utilize classification for detection purposes.
-
- Algorithm:
- We break up the input image into tiny “crops” of the input image.
- Use Classification+Localization to find the class of the center pixel of the crop, or classify it as background.
Using the same machinery for classification+Localization.
- Slide the window and look at more “crops”
- Algorithm:
- Basically, we do classification+Localization on each crop of the image.
-
- DrawBacks:
- Very Inefficient and Expensive:
We need to apply a CNN to a huge number of locations and scales.
- Very Inefficient and Expensive:
- DrawBacks:
- Sermant et. al 2013: “OverFeat”
-
- Region Proposal Networks (R-CNNs):
- A framework for object detection, that utilizes Region Proposals (Regions of Interest (ROIs)), consisting of three separate architectures.
-
- Structure:
- Input: Image vector
- Output: A vector of bounding boxes coordinates and a class prediction for each box
- Structure:
-
- Strategy:
Propose a number of “bounding boxes”, then check if any of them, actually, corresponds to an object.The bounding boxes are created using Selective Search.
- Strategy:
-
- Selective Search: A method that looks at the image through windows of different sizes, and for each size tries to group together adjacent pixels by texture, color, or intensity to identify objects.
- Selective Search: A method that looks at the image through windows of different sizes, and for each size tries to group together adjacent pixels by texture, color, or intensity to identify objects.
-
- Key Insights:
- One can apply high-capacity convolutional neural networks (CNNs) to bottom-up region proposals in order to localize and segment objects
- When labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost.
- Key Insights:
-
- Algorithm:
- Create Region Proposals (Regions of Interest (ROIs)) of bounding boxes
- Warp the regions to a standard square size to fit the “cnn classification models”, due to the FCNs
- Pass the warped images to a modified version of AlexNet to extract image features
- Pass the image features to an SVM to classify the image regions into a class or background
- Run the bounding box coordinates in a Linear Regression model to “tighten” the bounding boxes
- Linear Regression:
- Structure:
- Input: sub-regions of the image corresponding to objects
- Output: New bounding box coordinates for the object in the sub-region.
- Structure:
- Linear Regression:
- Algorithm:
-
- Issues:
- Ad hoc training objectives:
- Fine-tune network with softmax classifier (log loss)
- Train post-hoc linear SVMs (hinge loss)
- Train post-hoc bounding-box regressions (least squares)
- Training is slow (84h), takes a lot of disk space
- Inference (detection) is slow
- 47s / image with VGG16 [Simonyan & Zisserman. ICLR15]
- Fixed by SPP-net [He et al. ECCV14]
- Ad hoc training objectives:
- Issues:
- R-CNN is slow because it performs a ConvNet forward pass for each region proposal, without sharing computation.
- R. Girshick, J. Donahue, T. Darrell, J. Malik. (2014): “Rich feature hierarchies for accurate object detection and semantic segmentation”
-
- Fast R-CNNs:
- A single, end-to-end, architecture for object detection based on R-CNNs, that vastly improves on its speed and accuracy by utilizing shared computations of features.
-
- Structure:
- Input: Image vector
- Output: A vector of bounding boxes coordinates and a class prediction for each box
- Structure:
-
- Key Insights:
- Instead of running the ConvNet on each region proposal separately, we run the ConvNet on the entire image.
- Instead of taking crops of the original image, we project the regions of interest onto the ConvNet Feature Map, corresponding to each RoI, and then use the projected regions in the feature map for classification.
This allows us to reuse a lot of the expensive computation of the features. - Jointly train the CNN, classifier, and bounding box regressor in a single model. Where earlier we had different models to extract image features (CNN), classify (SVM), and tighten bounding boxes (regressor), Fast R-CNN instead used a single network to compute all three.
- Key Insights:
-
- Algorithm:
- Create Region Proposals (Regions of Interest (ROIs)) of bounding boxes
- Pass the entire image to a modified version of AlexNet to extract image features by creating an image feature map for the entire image.
- Project each RoI to the feature map and crop each respective projected region
- Apply RoI Pooling to the regions extracted from the feature map to a standard square size to fit the “cnn classification models”, due to the FCNs
- Pass the image features to an SVM to classify the image regions into a class or background
- Run the bounding box coordinates in a Linear Regression model to “tighten” the bounding boxes
- Linear Regression:
- Structure:
- Input: sub-regions of the image corresponding to objects
- Output: New bounding box coordinates for the object in the sub-region.
- Structure:
- Linear Regression:
- Algorithm:
-
- RoI Pooling: is a pooling technique aimed to perform max pooling on inputs of nonuniform sizes to obtain fixed-size feature maps (e.g. 7×7).
- Structure:
- Input: A fixed-size feature map obtained from a deep convolutional network with several convolutions and max pooling layers.
- Output: An N x 5 matrix of representing a list of regions of interest, where N is a number of RoIs. The first column represents the image index and the remaining four are the coordinates of the top left and bottom right corners of the region.
For every region of interest from the input list, it takes a section of the input feature map that corresponds to it and scales it to some pre-defined size.
- Scaling:
- Divide the RoI into equal-sized sections (the number of which is the same as the dimension of the output)
- Find the largest value in each section
- Copy these max values to the output buffer
The dimension of the output is determined solely by the number of sections we divide the proposal into.
- Structure:
- RoI Pooling: is a pooling technique aimed to perform max pooling on inputs of nonuniform sizes to obtain fixed-size feature maps (e.g. 7×7).
-
- The Bottleneck:
It appears that Fast R-CNNs are capable of object detection at test time in:- Including RoIs: 2.3s
- Excluding RoIs: 0.3s
Thus, the bottleneck for the speed seems to be the method of creating the RoIs, Selective Search
- The Bottleneck:
- [1] Girshick, Ross (2015). “Fast R-CNN”
-
- Faster R-CNNs:
- A single, end-to-end, architecture for object detection based on Fast R-CNNs, that tackles the bottleneck in speed (i.e. computing RoIs) by introducing Region Proposal Networks (RPNs) to make a CNN predict proposals from features.
Region Proposal Networks share full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals.
The network is jointly trained with 4 losses: 1. RPN classify object / not object 2. RPN regress box coordinates 3. Final classification score (object classes) 4. Final box coordinates -
- Region Proposal Network (RPN): is an, end-to-end, fully convolutional network that simultaneously predicts object bounds and objectness scores at each position.
RPNs work by passing a sliding window over the CNN feature map and at each window, outputting k potential bounding boxes and scores for how good each of those boxes is expected to be.
- Region Proposal Network (RPN): is an, end-to-end, fully convolutional network that simultaneously predicts object bounds and objectness scores at each position.
-
- Structure:
- Input: Image vector
- Output: A vector of bounding boxes coordinates and a class prediction for each box
- Structure:
-
- Key Insights:
- Replace Selective Search for finding RoIs by a Region Proposal Network that shares the features, and thus reduces the computation and time, of the pipeline.
- Key Insights:
-
- Algorithm:
- Pass the entire image to a modified version of AlexNet to extract image features by creating an image feature map for the entire image.
- Pass the CNN Feature Map to the RPN to generate bounding boxes and a score for each bounding box
- Pass each such bounding box that is likely to be an object into Fast R-CNN to generate a classification and tightened bounding boxes.
- Algorithm:
- Ren et al, (2015). “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks”


We are both limited by and inspired by the materials and technology that is available.
Andy Goldsworthy
Limited by materials and space
@smithschwartz
Andy Goldsworthy
@smithschwartz
Andy Goldsworthy
Rain Shadows
Time is a material
Patience is a tool
Technically, not challenging
Respect
@smithschwartz
Wolf Kahn
@smithschwartz
Wolf Kahn
"Your work isn't messy enough"
The best way to learn is by making a mess
And then cleaning it up
It's why Sass exists
@smithschwartz
Art & Fear
@smithschwartz
Art & Fear
*REQUIRED READING*
This book is about making ordinary art
Not all art can be made by Mozart
Nor should it
@smithschwartz
Art & Fear
"But why you may feel you’re just pretending that you’re an artist, there’s no way to pretend you’re making art.  Not possible... You make good work by (among other things) making lots of work that isn’t very good, the parts that aren’t yours. It’s called feedback, and it’s the more direct route to learning about your own vision. It’s also called doing your work. After all, someone has to do your work, and you’re the closest person around."


Summer of a lifetime
Register nowAthena Camps are for girls entering kindergarten through 8th grade. Every camp combines athletic, creative, emotional, and social activities to help girls grow in confidence and courage.
2015 Summer camp Schedule
Athena Camp Mini goes from 9am - 1pm, and Athena Camps Jr. and Athena Camps go from 9am - 4pm. Extended Care is available for all camps beginning at 8am and ending at 5:30pm. Add extended care during registration. Prices are listed below. For more information, visit our FAQs.
View your age group
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-
Week 1
June 6-10 $397Theme
Strong Sisters -
Week 2
June 6-10 $397Theme
Connect & Communicate -
Week 3
June 6-10 $397Theme
Express Yourself -
Week 4
June 6-10 $397Theme
Love & Respect -
Week 5
June 6-10 $397Theme
Connect & Communicate -
Week 6
June 6-10 $397Theme
Express Yourself -
Week 7
June 6-10 $397Theme
Love & Respect
All Sports-All Themes Package
Sign up for 3 weeks of camp and save $20 off each week, $60 off the total cost.
Bring friends to camp and save even more!
Refer a friend and receive a $25 credit on your account once they register.
A typical day at Athena Camps
- 9:00 amOpening Circle & Girl Power Games
- 10:00 amStation 1: Tennis
- 11:15 amStation 2: Art Projects
- 12:15 pmLunch & Free Play
- 12:45 pmAffirmation Circle
- 1:15 pmStation 3: Volleyball
- 2:15 pmTeam Time
- 3:45 pmClosing Circle
Scholarships & Policies
We are proud to offer partial scholarships based on financial need. Scholarships are limited to available resources.
The Dr. Mel Scholarship
A scholarship in memory of Melissa Rose Heagerty, M.D. is awarded to a courageous and confident girl on her way to becoming an inspirational woman.
ApplyGirls on the Run Scholarship
A scholarship available to current or former participants of the Girls on the Run program who would not otherwise be able to afford Athena Camps.
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The skill of being aware of and controlling one's own emotions is called emotional intelligence.Ability to understand the state of mind like if it is present in the moment or if it has wandered off and ability to bring it back to present state.Developing emotional intelligence makes great leaders and team players as they have full control over their emotions.
IQ is a score derived from a test to assess intelligence and it is based on ability to learn, understand and clear challenges.
Emotional intelligence has become a far more important indicator of success as a team player, leader and ability to work on your own. These days it's not about being intelligent by birth as technology keeps changing at a rapid rate and eveyone has to start learning at some stage and keep up with the crowd and this ability to learn and develop as a team is vastly dependant on emotional intelligence.


Book-A-Meal
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#2133 - Friday, 13 April 2018
Spaghetti and Meat Balls with crayfish
Price: ₦1500 x QTY: 4 = ₦6000
Order By: Marcus Smith
#2130 - Wednesday, 11 April 2018
Baked chicken with fries on the side
Price: ₦3500 x QTY: 1 = ₦3500
Order By: James Harden
#2120 - Tuesday, 10 April 2018
Butter beans stew
Price: ₦2000 x QTY: 1 = ₦2000
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#2100 - Monday, 9 April 2018
Crispy Chicken
Price: ₦3000 x QTY: 1 = ₦3000
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This is a collection of data from ESPN's fantasy football leagues. Each player is vertically positioned based on their average draft pick. The players are colored based on their football position. The darker the color, the more often the player is drafted, and players that have a gold border have a 100% draft rate.
One caveat to this visualization is the random horizontal placement of the players. This leads to a different chart each time the page is loaded and therefore can lead to varying subconscious interpretations, especially if say, one color decides to randomly cluster together. The most straightforward solution would be to set the location of each player...but I kind've enjoy the randomness because it makes it feel more genuine somehow.
Another thought I had was to give the option to color each player by their team rather by their position. I imagine some teams would appear towards the top more than others.
Note: Auction values are based off of ESPN's $200 starting budget.
Source: ESPN Free Fantasy Football Live Draft 2014


module_get_fas_from_db_dir module¶
This module contains functions for retrieving fasta sequences from the database directory used by run_BLAST_pipeline.py and HMMer_pipeline.py.
This will need to be updated to work with nucleotide data in the database directory system.
-
module_get_fas_from_db_dir.
get_fas_from_db_dir
(db_name, acc_list, dbdirpath, prot_name=None)[source]¶ Takes a dir path (e.g., /Users/Lael/Documents/Data/Genomes_2016), a database name (e.g., Gintestinalis), and one or more accession numbers, and returns the corresponding fasta sequence(s) as a string.


Pawsibilities
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FanFest has always been about innovation and new things. At every FanFest, we introduce new things to you, the fanbase, and build on that to introduce even better things and give you a better experience at the next FanFest. Sign up below for an admission ticket for this exclusive 3-day event!


Working with WRF CHEM part4 cbe domain run
2014-07-13
- Coimbatore domain made with four nests has been run with WRF-EMS with resolution starting from 100>27>9>3>1km. The domain is made using dwiz application in WRF-EMS.
- However, to test the domain above the Coimbatore region, the output was subject for wrfncxnj.py utility. It took 4 hours and 33 minutes to run this domain in IBM X4 series server computer with 16 GB ram.
- To overlay the Coimbatore city shape file above the WRF output NetCDF from wrfcnxnj.py. The city shapefile of Coimbatore converted into custom projection as per this, this and this. The final code look likes this. But unfortunately the WRF output land far above north of city boundary so it has to edited to involve the Coimbatore region.
- The server IBM started and made a copy of the domain cbeh1 into cbeh2 and open this domain and edited the domain in dwiz to make the domain cover the Coimbatore region.
- To ascertain the domain coverage the geogrid formed by wrf ems can be visualized in QGIS platform, for this the NC file was subject to open in qgis, but it ends with an error saying not supported format.
- So the option is to convert this NetCDF file into geotiff and visualize it in the QGIS.
- One advantage observed was the shapefile converted into the defined projection is giving the area derived information in a square kilometer, this was problem in road network assessment in the Coimbatore region, so the road network has to be converted into this projection and problem can rectified.
- To convert NC file into geotiff, name of the variables were chosen by ncview. However, this ends up in error
So made a last attempt of viewing nc file and its various variables from pythonic way, the command used was
from osgeo import gdal from osgeo import osr import numpy import numpy.ma as ma datafile = 'geo_em.d04.nc' ds_in = gdal.Open(datafile) subdatasets = ds_in.GetSubDatasets() subdatasets ``` these command gives the list of variables in the geo_em.d04.nc. In which the variables CLONG and CLAT was considered as lon and lat and variabnle GREENFRAC was used as parameter for geotiff conversion script. The script used were ``` from osgeo import gdal from osgeo import osr import numpy import numpy.ma as ma datafile = 'geo_em.d04.nc' proj_out = osr.SpatialReference() proj_out.SetMercator(0.0, 115.02, 0.98931892612652, 0.0, 0.0) ds_in = gdal.Open(datafile) #subdatasets = ds_in.GetSubDatasets() #variables = [] #for subdataset in subdatasets: # variables.append(subdataset[1].split(" ")[1]) ds_lon = gdal.Open('NETCDF:"geo_em.d04.nc":CLONG') ds_lat = gdal.Open('NETCDF:"geo_em.d04.nc":CLAT') longs = ds_lon.GetRasterBand(1).ReadAsArray() lats = ds_lat.GetRasterBand(1).ReadAsArray() ds_lon = None ds_lat = None proj_gcp = osr.SpatialReference() proj_gcp.ImportFromEPSG(4326) transf = osr.CoordinateTransformation(proj_gcp, proj_out) ul = transf.TransformPoint(float(longs[0][0]), float(lats[0][0])) lr = transf.TransformPoint(float(longs[len(longs)-1][len(longs[0])-1]), float(lats[len(longs)-1][len(longs[0])-1])) ur = transf.TransformPoint(float(longs[0][len(longs[0])-1]), float(lats[0][len(longs[0])-1])) ll = transf.TransformPoint(float(longs[len(longs)-1][0]), float(lats[len(longs)-1][0])) gt0 = ul[0] gt1 = (ur[0] - gt0) / len(longs[0]) gt2 = (lr[0] - gt0 - gt1*len(longs[0])) / len(longs) gt3 = ul[1] gt5 = (ll[1] - gt3) / len(longs) gt4 = (lr[1] - gt3 - gt5*len(longs) ) / len(longs[0]) gt = (gt0,gt1,gt2,gt3,gt4,gt5) ds_u10 = gdal.Open('NETCDF:"'+datafile+'":GREENFRAC') #i=23 #num_bands = ds_u10.RasterCount x_size = ds_u10.RasterXSize y_size = ds_u10.RasterYSize ds_p_b = ds_u10.GetRasterBand(1).ReadAsArray() driver = gdal.GetDriverByName( 'GTiff' ) ds_out = driver.Create( 'geo_em.d04.tiff', x_size, y_size, 1, gdal.GDT_Float32 ) ds_out.SetGeoTransform( gt ) ds_out.SetProjection(proj_out.ExportToWkt()) ds_out.GetRasterBand(1).WriteArray( ds_p_b )
By this script the geotiff file
geo_em.d04.tiff
was created and it gets cover Coimbatore city boundary. The wrf -ems domain made sure that it involves the Coimbatore region extent.Made a copy of the cbe_h1 folder in runs folder of WRF ems and kept it to use for input.wps and input.real in wrf chem simulation over Coimbatore domain


Home Folder
Each new NeoMem file starts with a Home Folder. This folder cannot be deleted, but you can rename it if you wish. This folder can contain any number of other folders or objects.
The Home Folder has a special view, the Home View, which displays the file's description and a Tip of the Day.
To change the description for a file, you can right click on the Home Folder and select Object Properties..., then enter a description in the textbox marked Description. This description will then be visible in the Home View whenever you open the file.
You can hide or show the Contents View for the Home Folder by selecting View / Hide Contents View... or View / Show Contents View....
See the example file for ideas on how to set up a file.


Uniten Student Info v2.3
Developed by Mahadir Ahmad
Uniten's Bachelor of Computer Science (Software Engineering)
www.madet.my
For bugs report please email to<EMAIL_ADDRESS>your name will be published in the
special thanks.
FYI, this Special Semester 15/16 will be my final semester in UNITEN. I may not be able
to publish any new updates after graduation. The source code of this app can be found
https://github.com/mahadirz/UnitenInfo
Special Thanks
- Muhammad Musyrif Aminuddin
Logs
V2.0
- Fixed ledger balance showing incorrect value
- Added original time table
- Remember the last view before app closing
- Added class notices
V2.1
V2.2
V2.3
- Fixed Timetable not showing correct time
- Removed OpenVPN
Acknowledgements
THIS APPLICATION IS NOT AFFILIATED BY UNIVERSITY TENAGA NASIONAL (UNITEN).
NEITHER DEVELOPER NOR UNITEN SHALL BE LIABLE FOR ANY DIRECT,
INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO,
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA,
OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED
AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
ARISING IN ANY WAY OUT OF THE USE OF THIS APPLICATION


trait Table extends AnyRef
The Table
trait stores and operates on vectors. The vectors form the
columns of the columnar relational datastore. Columns may have any of the
following types:
C - Complex
- VectorC
- 128 bit complex number a + bi
D - Double
- VectorD
- 64 bit double precision floating point number
I - Int
- VectorI
- 32 bit integer
L - Long
- VectorL
- 64 bit long integer
Q - Rational
- VectorQ
- 128 bit ratio of two long integers
R - Real
- VectorR
- 128 bit quad precision floating point number
S - StrNum
- VectorS
- variable length numeric string
- Alphabetic
- By Inheritance
- Table
- AnyRef
- Any
- Hide All
- Show All
- Public
- All
Abstract Value Members
-
abstract
def
-(r2: Table): Table
Take the difference of 'this' relation and 'r2' ('this - r2').
Take the difference of 'this' relation and 'r2' ('this - r2'). Check that the two relations are compatible.
- r2
the other relation
-
abstract
def
><(r2: Table): Table
Join 'this' relation and 'r2 by performing a "natural-join".
Join 'this' relation and 'r2 by performing a "natural-join". Rows from both relations are compared requiring agreement on common attributes (column names).
- r2
the rhs relation in the join operation
-
abstract
def
add(tuple: Row): Unit
Add 'tuple to 'this' relation as a new row.
Add 'tuple to 'this' relation as a new row. FIX: want an efficient, covariant, mutable data structure, but
Array
is invariant.- tuple
an aggregation of columns values (new row)
-
abstract
def
cols: Int
Return the size in terms of number of columns in the relation.
-
abstract
def
contains(tuple: Row): Boolean
Determine whether 'this' relation contains a row matching the given 'tuple'.
Determine whether 'this' relation contains a row matching the given 'tuple'.
- tuple
an aggregation of columns values (potential row)
-
abstract
def
incompatible(r2: Table): Boolean
Determine whether 'this' relation and 'r2' are incompatible by having differing numbers of columns or differing domain strings.
Determine whether 'this' relation and 'r2' are incompatible by having differing numbers of columns or differing domain strings.
- r2
the other relation
-
abstract
def
intersect(r2: Table): Table
Intersect 'this' relation and 'r2'.
Intersect 'this' relation and 'r2'. Check that the two relations are compatible.
- r2
the other relation
-
abstract
def
join(cName: Seq[String], r2: Table): Table
Join 'this' relation and 'r2 by performing a "natural-join".
Join 'this' relation and 'r2 by performing a "natural-join". Rows from both relations are compared requiring 'cName' values to be equal.
- cName
the common join column names for both relation
- r2
the rhs relation in the join operation
-
abstract
def
join(cName1: String, cName2: String, r2: Table): Table
Join 'this' relation and 'r2 by performing an "equi-join".
Join 'this' relation and 'r2 by performing an "equi-join". Rows from both relations are compared requiring 'cName1' values to equal 'cName2' values. Disambiguate column names by appending "2" to the end of any duplicate column name.
- cName1
the string of join column names of this relation (e.g., the Foreign Key)
- cName2
the string of join column names of relation r2 (e.g., the Primary Key)
- r2
the rhs relation in the join operation
-
abstract
def
join(cName1: Seq[String], cName2: Seq[String], r2: Table): Table
Join 'this' relation and 'r2 by performing an "equi-join".
Join 'this' relation and 'r2 by performing an "equi-join". Rows from both relations are compared requiring 'cName1' values to equal 'cName2' values. Disambiguate column names by appending "2" to the end of any duplicate column name.
- cName1
the join column names of this relation (e.g., the Foreign Key)
- cName2
the join column names of relation r2 (e.g., the Primary Key)
- r2
the rhs relation in the join operation
-
abstract
def
pi(cPos: Seq[Int], cName: Seq[String] = null): Table
Project onto the columns with the given column positions.
Project onto the columns with the given column positions.
- cPos
the column positions to project onto
- cName
the optional new names for the columns to project onto
-
abstract
def
pi(cName: String*): Table
Project onto the columns with the given column names.
Project onto the columns with the given column names.
- cName
the names of the columns to project onto
-
abstract
def
pisigmaC(cName: String, p: (Complex) ⇒ Boolean): Table
Select elements from column 'cName' in 'this' relation that satisfy the predicate 'p' and project onto that column.
Select elements from column 'cName' in 'this' relation that satisfy the predicate 'p' and project onto that column.
- cName
the name of the column used for selection
- p
the predicate (
Boolean
function) to be satisfied
- abstract def pisigmaD(cName: String, p: (Double) ⇒ Boolean): Table
- abstract def pisigmaI(cName: String, p: (Int) ⇒ Boolean): Table
- abstract def pisigmaL(cName: String, p: (Long) ⇒ Boolean): Table
- abstract def pisigmaQ(cName: String, p: (Rational) ⇒ Boolean): Table
- abstract def pisigmaR(cName: String, p: (Real) ⇒ Boolean): Table
- abstract def pisigmaS(cName: String, p: (StrNum) ⇒ Boolean): Table
-
abstract
def
row(i: Int): Row
Create a row by pulling values from all columns at position 'i'.
Create a row by pulling values from all columns at position 'i'.
- i
the 'i'th position
-
abstract
def
rows: Int
Return the size in terms of number of rows in the relation.
-
abstract
def
save(): Unit
Save 'this' relation in a file using serialization.
-
abstract
def
selectAt(pos: Seq[Int]): Table
Select across all columns at the specified column positions.
Select across all columns at the specified column positions.
- pos
the specified column positions
-
abstract
def
selectC(cName: String, p: (Complex) ⇒ Boolean): Seq[Int]
Select the positions of elements from columns in 'cName' in 'this' relation that satisfy the predicate 'p'.
Select the positions of elements from columns in 'cName' in 'this' relation that satisfy the predicate 'p'.
- cName
the name of the column used for selection
- p
the predicate (
Boolean
function) to be satisfied
- abstract def selectD(cName: String, p: (Double) ⇒ Boolean): Seq[Int]
- abstract def selectI(cName: String, p: (Int) ⇒ Boolean): Seq[Int]
- abstract def selectL(cName: String, p: (Long) ⇒ Boolean): Seq[Int]
- abstract def selectQ(cName: String, p: (Rational) ⇒ Boolean): Seq[Int]
- abstract def selectR(cName: String, p: (Real) ⇒ Boolean): Seq[Int]
- abstract def selectS(cName: String, p: (StrNum) ⇒ Boolean): Seq[Int]
-
abstract
def
show(): Unit
Show 'this' relation row by row.
-
abstract
def
sigma[T](cName: String, p: (T) ⇒ Boolean): Table
Select elements from columns in 'cName' in 'this' relation that satisfy the predicate 'p'.
Select elements from columns in 'cName' in 'this' relation that satisfy the predicate 'p'.
- cName
the name of the column used for selection
- p
the predicate (
Boolean
function) to be satisfied
-
abstract
def
sigmaC(cName: String, p: (Complex) ⇒ Boolean): Table
Select elements from columns in 'cName' in 'this' relation that satisfy the predicate 'p'.
Select elements from columns in 'cName' in 'this' relation that satisfy the predicate 'p'.
- cName
the name of the column used for selection
- p
the predicate (
Boolean
function) to be satisfied
- abstract def sigmaD(cName: String, p: (Double) ⇒ Boolean): Table
- abstract def sigmaI(cName: String, p: (Int) ⇒ Boolean): Table
- abstract def sigmaL(cName: String, p: (Long) ⇒ Boolean): Table
- abstract def sigmaQ(cName: String, p: (Rational) ⇒ Boolean): Table
- abstract def sigmaR(cName: String, p: (Real) ⇒ Boolean): Table
- abstract def sigmaS(cName: String, p: (StrNum) ⇒ Boolean): Table
-
abstract
def
union(r2: Table): Table
Union 'this' relation and 'r2'.
Union 'this' relation and 'r2'. Check that the two relations are compatible.
- r2
the other relation
-
abstract
def
writeCSV(fileName: String): Unit
Write 'this' relation into a CSV file with each row written to a line.
Write 'this' relation into a CSV file with each row written to a line.
- fileName
the file name of the data file
-
abstract
def
writeJSON(fileName: String): Unit
Write 'this' relation into a JSON file.
Write 'this' relation into a JSON file.
- fileName
the file name of the data file
-
abstract
def
π(cName: String*): Table
Project onto the columns with the given column names.
Project onto the columns with the given column names.
- cName
the names of the columns to project onto
-
abstract
def
σ[T](cName: String, p: (T) ⇒ Boolean): Table
Select elements from columns in 'cName' in 'this' relation that satisfy the predicate 'p'.
Select elements from columns in 'cName' in 'this' relation that satisfy the predicate 'p'.
- cName
the name of the column used for selection
- p
the predicate (
Boolean
function) to be satisfied
-
abstract
def
⋂(r2: Table): Table
Intersect 'this' relation and 'r2'.
Intersect 'this' relation and 'r2'. Check that the two relations are compatible.
- r2
the other relation
-
abstract
def
⋃(r2: Table): Table
Union 'this' relation and 'r2'.
Union 'this' relation and 'r2'. Check that the two relations are compatible.
- r2
the other relation
-
abstract
def
⋈(r2: Table): Table
Join 'this' relation and 'r2 by performing a "natural-join".
Join 'this' relation and 'r2 by performing a "natural-join". Rows from both relations are compared requiring agreement on common attributes (column names).
- r2
the rhs relation in the join operation
Concrete Value Members
-
final
def
!=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
##(): Int
- Definition Classes
- AnyRef → Any
-
final
def
==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
-
def
clone(): AnyRef
- Attributes
- protected[java.lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
def
disambiguate(cn1: Seq[String], cn2: Seq[String]): Seq[String]
Combine two sequences of column names and disambiguate any repeated names by appending "2".
Combine two sequences of column names and disambiguate any repeated names by appending "2".
- cn1
the first sequence of column names
- cn2
the second sequence of column names
-
final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
def
finalize(): Unit
- Attributes
- protected[java.lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( classOf[java.lang.Throwable] )
-
final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
-
def
hashCode(): Int
- Definition Classes
- AnyRef → Any
-
def
indices: Range
Return the range of index values for the relation.
-
final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
-
final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
final
def
notify(): Unit
- Definition Classes
- AnyRef
-
final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
-
def
row(sos: Seq[String], _typ: String): Row
Create a row by pulling values from an array of strings and converting elements to their appropriate types.
Create a row by pulling values from an array of strings and converting elements to their appropriate types.
- sos
the sequence of strings holding the values
- _typ
the string of corresponding types, e.g., 'SDI'
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
toString(): String
- Definition Classes
- AnyRef → Any
-
def
uniq_union(cn1: Seq[String], cn2: Seq[String]): Seq[String]
Combine two sequences of column names, keeping all names from 'cn1' and only those in 'cn2' that are not repeats (i.e., not already in 'cn1').
Combine two sequences of column names, keeping all names from 'cn1' and only those in 'cn2' that are not repeats (i.e., not already in 'cn1').
- cn1
the first sequence of column names
- cn2
the second sequence of column names
-
final
def
wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )


In a flurry of memos released this week and last, the White House physician, Dr. Sean Conley, stated that President Trump no longer posed a transmission risk to others — an assessment, he noted, that was largely precipitated by the results of a bevy of “advanced diagnostics.” The declarations have helped clear Mr. Trump to return to the campaign trail, including a town-hall-style event hosted by NBC News on Thursday evening.
 But most have based such assessments on the trajectory of the president’s symptoms — not the results of his tests.
There exists no test that can definitively determine whether someone who caught the coronavirus is still contagious. “We do not have a test for cure, and we do not have a test for infectiousness,” said Omai Garner, a clinical microbiologist at the University of California, Los Angeles.


Where all of your dreams can come true! Head on over to the Ramshackle Documentation if you need any help.
This is a row
. By itself it does nothing. It's the street that your houses sit on. Plop down a happy little col
with a size half
you've got a space that takes up half the block.
Customize style.css
with your own fonts & colors, and then style with ease.
This one has bg-primary
You can change the text color too!
primary


Chile players pull out of friendly against Peru
Chile's players have pulled out of next week’s friendly with Peru as the country continues to suffer from the effects of weeks of protests and violent unrest.
SANTIAGO: Chile's players have pulled out of next week’s friendly with Peru as the country continues to suffer from the effects of weeks of protests and violent unrest.
"The players called up for the Chilean national team have decided not to play in the friendly against Peru, scheduled for next Tuesday, Nov. 19, in Lima," Chile’s football association said in short statement on Wednesday.
"The decision was taken by the squad after a meeting held this morning."
The Chilean FA said the players had been released and were free to return to their clubs.
It did not say why the players decided to pull out of the match but the announcement follows widely-reported comments from midfielder Charles Aranguiz that the game should not be played against the backdrop of unrest.
Protests that began last month over a rise in metro fares have amplified and spread to become the biggest wave of anti-government demonstrations seen in Chile since it returned to democracy in 1990.
The unrest has left at least 23 people dead and 2,000 civilians hospitalised. There have been more than 1,000 complaints of rights abuses, according to human rights groups and prosecutors.
Chile had also been scheduled to play Bolivia at home on Nov. 15 but that game was cancelled last week.
The domestic league programme has also been halted because of the protests and officials said they do not know when the competition will be completed.
(Reporting by Andrew Downie; Editing by Toby Davis)


ChatApp Project - The situation
THE SITUATION
The task was to design and develop an interactive app with socket.io and Vue.js that binds data to the views. The project should display two users (each of them using a different browser) that would communicate through the app. From the design standpoint, the user should be able to differentiate between the messages that they send and the ones that they receive. Another big component of this project was the functionality of the username that would also help distinguish between both sides of the chat.
ChatApp Project - The solution
THE SOLUTION
The result is a fully responsive and interactive app that asks the user to set a username on the landing page, it keeps the value as an array and then displays an engaging interface that allows them to maintain chats with whoever logs in. As mentioned before, this app works with the socket.io and Vue.js libraries and it binds data through the browser and the respective components.