The aim of Classing{js}
is to create an object oriented environment that looks and behaves exactly like the classical object oriented environment.If you developed in a classical environment before (like C++ , Java or C#), you know that you can overload the constructor of the class to intialize the objects in differnt ways, including the way using an existing object of the same class (known as the copy constructor).So, to fully achive the aim of Classing{js}
, function overloading was implemented to give the developer the ability to overload the constructors of classes and any other functions wanted.
Creating an overloaded function is done by calling the function Function.create
in the way specified here , in the black box with the red gear:
The mode of Overloading :typed or non-typed.true
for typed , false
for non-typed
an array containing the overloads.
the overloaded function
An example of non-typed overloading is the function say
defined in the following :
var say = Function.create(false , [ function(msg1) { console.log(msg1); }, function(msg1 , msg2) { var concat = msg1 + " , " + msg2; console.log(concat); }, function(msg1 , msg2 , msg3 , msg4) { var concat = msg1 + " , " + msg2 + " , " + msg3; console.log(concat); } ]);
say
here has three instances that accepts one argument, two arguments and four arguments respectively with no restriction on the type of the argument passed. You can pass numbers , strings , objects or any thing and no error will be thrown.The only case an error will be thrown is if you passed a number of arguments that has no instance in the function. Namely , if you passed four or more , two or no arguments , an error will be thrown idicating that no instance matches the argument list.
say("Hi"); //Ok say("Hi" , 5); //Ok say("Hi" , "Welcome" , "to Classing{js}" , 1) //Ok say(); //Error say("Hi" , "Welcome" , "to Classing{js}"); //Error sya("Hi" , "Welcome" , "to Classing{js}" , "v" , 1 , "." , 0); //Error
To make the code more readable , the library provides the constnat classing.xNonTyped
which is equal to false
that can be passed as the first argument to Function.create
indicating a non-typed overloading mode. As this constant is expectd to be used repeatedly, a global shortcut is provided so it could be written fast without calling it from the classing
namespace.
var add = Function.create(xNonTyped , [function(a,b){return a+b;} , function(a,b,c){return a+b+c;}]);
var join = Function.create(xNonTyped , [function(a,b){return a + "," + b;} , "function(){}"]); //Error
var echo = Function.create(xNonTyped , [function() {return "Void";} , function(a){return a;} , function(b) {return "'" + b + "'";}]); //Error
The syntax to create a typed overloaded function is similar to that of non-typed but with a little difference in the format of the overloads array. To fully define an overload instnace you'll need two elements of the array , the first one to hold the types list and the one that follows it to hold the function itself.the type list is created through the function classing.types
.There's also a global shortcut for that function.
the constructor function of the type (either native or custom)
the type list object to be passed to Function.create
For example , to write a type list of a function , a boolean value , an array , a string and finally a number , we write:
types(Function , Boolean , Array , String , Number)
A correct-formated overloads array follows this pattern : the type list comes first , then in the next element comes the function associated with that type list (The number of types and the number of paramters in the associated function must be equal). Note that any overloads array not in this format will throw an error. An example of a typed overloaded function is the add
function defined in the following:
//a Custom type represnting complex number function Complex(r , i) { this.real = r; this.imaginary = i; } var add = Function.create(true , [ types(Number,Number) , function(a , b) { return this.a + this.b; }, types(Array , Array), function(A , B) { if(A.length === B.length) { var C = new Array(A.length); for(var i = 0 ; i < A.length ; i++) { C[i] = A[i] + B[i]; } } }, types(Complex,Complex), //the custom type function(Z , W) { return new Complex(Z.real + W.real , Z.imaginary + W.imaginary); } ]);
Here , add
has three instances , each of which accepts two arguements but with different types. the first one accepts two numbers , the second accepts two arrays and the last one accepts two objects of the type Complex (which is a custom type). Any call to add
with an argument list matching a defined instances in both number and types will work fine. Any call with an argument list that doesn't match any defined instance will throw an error.
add(40,89.69); //Ok add([1 2 3] , [-1 0 9.678]); //Ok add(new Complex(7 , 0) , new Complex(1 , -6)); //Ok add(80); //Error add(80,"Hello"); //Error add([1 4 7] , new Complex(7 , 52)); //Error
Like in non-typed overloading , the library provides the constnat classing.xTyped
which is equal to true
to be passed as the first argument of Function.create
indicating a typed overloading mode. An xTyped
global shortcut is also provided.
var divide = Function.create(xTyped , [types(Number , Number) , function(a , b) {return a / b;}]);
The following are the categories of types that can automatically be recognized in the typed overloading mode:
Classing{js}
Classesfunction <constructorName>(a,b,c,...){ ... }
For custom types that has a constructor of the form:
var <constructorName> = function(a,b,c, ...) { ... }
a stamping step must be made before Classing{js}
can recognize it. the stamping is done using the function classing.xStamp
the custom constructor function to be stamped
For example:
var Human = function(a) { this.age = a; } classing.xStamp(Human); var add = Function.create(xTyped , [ types(Human,Human), function(H1,H2) { return new Human(H1.age + H2.age); } ]); add(new Human(41) , new Human(16)); //Ok


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Returns a subset from a set, in which the subset is a numerically specified range of tuples.
Syntax
Subset ( set, index1 [,index2 ] )
| Parameter | Description |
|---|---|
|
The set from which to take tuples. |
|
index1 |
The location of the tuple with which to begin the subset. Example: if index1 is 0, the subset begins with the first tuple of set. If a negative value, the return is an empty set. |
index2 |
Optional. The count of tuples to include in the subset. If omitted, all tuples to the end of set are returned. If a negative value, the return is an empty set. If the count goes beyond the range of the input set, all tuples to the end of the set are returned. |
Notes
The first tuple of the subset is represented by index1. If index1 is 0, then the first tuple of the returned subset will be the same as the first tuple of the input set.
Example
The following expression
Subset ({Product.Members},0)
returns the set:
{ Product, [100-10], [100-20], [100-30], [100], [200-10], [200-20], [200-30], [200-40], [200], [300-10], [300-20], [300-30], [300], [400-10], [400-20], [400-30], [400], [100-20], [200-20], [300-30], Diet }
All tuples of the set {Product.Members} are returned, because the subset is told to begin with the first tuple, and no count of tuples given for index2.
The following expression
Subset ({Product.Members},0,4)
returns the set:
{ Product, [100-10], [100-20], [100-30] }
Therefore, the following query
Select Subset ({Product.Members},0,4) on columns from sample.basic
returns the grid:
| Product | 100-10 | 100-20 | 100-30 |
| 105522 | 22777 | 5708 | 1983 |


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This was my first day experimenting with the salvia divinorum plant, and I'm concluding that it has interesting powers but I'd like to further my ahem, research.
This morning I went to a local headshop and purchased a gram sized glass bowl for a plastic Islands of Adventure cup I had found in my room whose shape I thought would make a good looking bong. I also picked up a quarter of salvia leaves, I was hoping to buy extract, but at $50 dollars a gram I didn't feel compelled. My friend and I then went to a small park not far from the headshop and built the bong. I've been doing some research about salvia use and I heard that it should not be done in the wrong setting or wrong mood, I'm pretty familiar to this topic from my mushroom use :) It was rainy and windy, but I decided to go ahead anyway seeing as we were there.
I smoked about a bowl and a half, probably weighing around 1.5g and I felt nothing but a very sharp pain at the sides of my body for a few seconds after one particularly harsh hit. A bit of dizziness and a very tiny lingering essence of a bud high for about 2 minutes, then I was back to normal. My friend who is a great deal smaller bud smoker than me blazed about the same amount and said he had a cannabis like high for around 20 minutes, but nothing out of the ordinary, nothing amazing either. Later tonight I was sitting around with still about 4 grams left and decided to try again since I'd read reports that some people really trip out after smoking this stuff.
(T 0:00) - After turning on Atom Heart Mother by Pink Floyd, I packed two large leaves in a small metal pipe I have and toked it up in about 3 hits, lay back on my bed and for a second waited for the effects to kick in, feeling nothing I grabbed my pipe and packed a bit more in, 4 separate bowls with a small leaf in each, hitting it out my bedroom window with a clock beside me to count 30 seconds and 30 seconds exactly to hold each hit. While hitting the first leaf I felt nothing, but as the seconds passed I already felt like I've been smoking for a long time, nothing I realized until after though.
(T+0:10) - While I paused to make a small adjustment to my pipe somewhere during the second bowl, I spaced out and started listening to the music. I realized the salvia present. It was at the very moment that I had really forgot about getting high off the salvia and simply packing and igniting the leaf in my pipe when the salvia caught me with my pants down. The music seemed to be dictating my life, but not just during this moment, but rather like it has been doing so my entire life. Every note seemed to wring an action in my life like it was in perfect synthesis to what I was doing that very second. While smoking steady bowls of salvia in my pipe I heard an army marching with no point solely because that's what they were told to do, and that's what I saw myself doing with the salvia. After this however I made the mistake of stopping smoking for a bit and leaving my room to get a towel from my washroom and shove it under my door, which basically killed the buzz I was floating on.
(T+0:15) - I smoke the other two small leaves in the pipe, over the course of around 2 minutes, then close my window, put away the pipe and sit back zoning into the music. After the pipe was put away I started to concentrate what it is the salvia was doing to me, and I found it difficult to figure out exactly although I could tell I was not sober. This is why I am intruiged by salvia, because after the session I sat back and I knew I was pretty baked, but when I would think of how to describe it I would realize that everything is still perfectly normal, even though I knew I was f*cked. This was the main part of my experience, sitting back listening to the trippy sounds of Floyd coming through my speakers in atom heart mother.
I couldn't place my finger on what exactly it was though, although I felt completely out of the ordinary.  My hearing was slightly warped I felt the music inside me but nothing as profound as mushroom trips, although highly similar to weed smoking.
(T+0:45) - I decided to finally leave my chill area, which over the past half hour has really become my only true home, and I grabbed a beer. Drank that within a minute or so due to my thirst.
(T+1:00) - Sat back after having finished the Floyd album and I turned on The Chronic by Dr. Dre at around.  I sat back slowly coming back to myself surfing the net, not anything I would call 'high' but my thoughts were strangely altered. I was really understanding all the words of the rap and feeling the beat, basically normal although thinking about everything a lot deeper.
Somewhere during the chronic I realized myself finding new ways how to handle things, I felt in a way that I had just aged a year, gotten bigger, and then it happened again. Suddenly beer was simply 'that thing you drink' rather than an alcoholic beverage. A hot pipe with lit plant became a simple finger warmer, rather than a hot device to be cautioned with. After realizing that I was thinking about these kinds of things I knew that the salvia had definitely done something to my head.
(T+3:00) - Out of boredom I went to the fridge and got another beer. The effects of salvia have pretty much completely worn off, replaced with a typical burnout, tired dragged out body, fried feeling head and squinty red eyes. I started to write this entry while finishing the beer.
(T+3:45) - Finishing up the entry, strangely feeling very different now.  It feels like the most renewed rejuvenated fresh clean mind after a cold shower after 20 hours of sleep on a warm n' sunshiney day.
The total amount of leaf smoked during my night session was 11 hits. I can definitely say that I experienced some of the effects of salvia in a small way, and I am very intruiged at the different approach this plant has on my mind. A fact to add is that I used only a regular cigarette lighter to smoke the salvia, and this may have been a cause to the decreased potency, as I have read that a hotter flame such as that from a jet lighter is recommended. One of these next few days I'm going to finish off the salvia with the same friend. We're planning on both chewing a roughly 2 gram quid for half an hour, experimenting with oral dosage.
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.


2001
3 / 6 Issues
Behold, a featured item
Two soldiers are tasked with deciding the fate of a terrorist with a single push of a button. As the action plays out in real time, their window to use a deadly ..
1996
Update yes
1963 is a collection of six issues created by Alan Moore, focusing on the Silver Age with original...


Native Windows GUI: Helpers
Outside of the controls, the resources, and the layouts NWG also has some utility functions. This section will go over them.
Automatic styling
NWG use a hack to implement visual style in applications without having to use a manifest file (
see this)
This is a good thing because linking a manifest requires a third party library (such as
rust-embed-resource)
If automatic styling is not needed, enable the feature
no-styling
Message box
In a GUI application, there's no console to display error messages to the end user. In cases like this, a message box is used.
NWG wraps the winapi message box function with the
message
or the
modal_message
function.
The modal variant takes a parent window parameter. The parent window is disabled during the message box duration.
use native_windows_gui as nwg;
let p = nwg::MessageParams {
title: "Hey",
content: "Cats are cute",
buttons: nwg::MessageButtons::Ok,
icons: nwg::MessageIcons::Warning
};
assert!(nwg::message(&p) == nwg::MessageChoice::Ok)
This function might be a little to verbose for most use case, that's why NWG implements six simple interfaces (3 modal and 3 without):
fatal_message
: a message box with an error icon. This function panics
error_message
: a message box with an error icon.
simple_message
: a message box with an information icon.
modal_fatal_message
: a message box with an error icon. This function panics
modal_error_message
: a message box with an error icon.
modal_info_message
: a message box with an information icon.
pub fn fatal_message<'a>(title: &'a str, content: &'a str) {}
pub fn error_message<'a>(title: &'a str, content: &'a str) {}
pub fn simple_message<'a>(title: &'a str, content: &'a str) {}
Global Cursor
NWG wraps the system cursor with the
GlobalCursor
object. Not to be confused with a
Cursor
object, a cursor resource file (*.cur).
GlobalCursor is hidden behind the
cursor
feature. Once enabled, it can be used to get/set the mouse position on screen, capture the cursor
outside of the application, detect a dragging gesture, and set the image of the cursor globally.
See
https://docs.rs/native-windows-gui/1.0.12/native_windows_gui/struct.GlobalCursor.html
The GlobalCursor object cannot be instanced. It must be used as it is:
use native_windows_gui as nwg;
let (x,y) = nwg::GlobalCursor::position();
Monitor
Sometimes you need to know the dimensions of the desktop to position Windows control. The
Monitor
object handles this. With Monitor,
it is possible to query the main screen dimensions, the virtual screen dimensions, and the monitor screen that a top level window is currently in
See
https://docs.rs/native-windows-gui/1.0.12/native_windows_gui/struct.Monitor.html
The Monitor object cannot be instanced. It must be used as it is:
// Creating and centering a window in the main monitor
use native_windows_gui as nwg;
fn create_window(width: i32, height: i32) -> nwg::Window {
let [total_width, total_height] = [nwg::Monitor::width(), nwg::Monitor::height()];
let mut window = nwg::Window::default();
let x = (total_width-width)/2;
let y = (total_height-height)/2;
nwg::Window::builder()
.size((width, height))
.position((x, y))
.build(&mut window)
.unwrap();
window
}
Clipboard
NWG also wraps the system clipboard in order to write or read data from it. The clipboard is a way to copy arbritary data from within an application
or even between two different applications.
See
https://docs.rs/native-windows-gui/1.0.12/native_windows_gui/struct.Clipboard.html
Note that NWG clipboard intentionally keeps things simple and close to the metal. If you want to more robust API, then I recommend you look into
https://github.com/DoumanAsh/clipboard-win
Clipboard is hidden behind the
clipboard
feature.
Writing/Reading text
Because reading or writing text will probably be the most used feature, NWG includes helpers functions that handles the unsafy bits.
One thing to note is that Windows clipboard functions always take a control parameter. This can be any
HWND
based controls (ex: Window, Button, etc).
use native_windows_gui as nwg;
fn clipboard_text(window: &nwg::Window) {
nwg::Clipboard::set_data_text(window, "Hello!");
let text = nwg::Clipboard::data_text(window);
assert!(text.is_some());
assert!(&text.unwrap() == &"Hello!");
}
Handling custom data
It's possible to pass data structure around using the low level clipboard functions. Note that those function are unsafe because there is no way to
validate the data.
When using the low level functions, the clipboard must be manually
opened and then
closed. Also, when setting data, it's up to the developer
to
empty whatever content was in the clipboard. Failure to do so will result in unknown errors.
use native_windows_gui as nwg;
#[repr(C)]
#[derive(Clone, Copy)]
struct Hello {
foo: usize,
bar: [u16; 3]
}
fn write_custom_data(window: &nwg::Window) {
let data = Hello {
foo: 6529,
bar: [0, 100, 20]
};
nwg::Clipboard::open(window);
nwg::Clipboard::empty();
unsafe {
nwg::Clipboard::set_data(
nwg::ClipboardFormat::Global("Hello"),
&data as *const Hello,
1
);
}
nwg::Clipboard::close();
}
fn read_custom_data(window: &nwg::Window) -> Option<Hello> {
unsafe {
nwg::Clipboard::open(window);
let data = nwg::Clipboard::data(nwg::ClipboardFormat::Global("Hello"));
nwg::Clipboard::close();
data
}
}
fn read_custom_data_handle(window: &nwg::Window) -> Option<Hello> {
unsafe {
nwg::Clipboard::open(window);
let handle = nwg::Clipboard::data_handle(nwg::ClipboardFormat::Global("Hello"));
let data = match handle {
Some(h) => {
let data_ptr: *const Hello = h.cast();
let data = *data_ptr;
h.release();
Some(data)
},
None => None
};
nwg::Clipboard::close();
data
}
}


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HARD RAIN
Reviewed by Harvey Karten, Ph.D. Paramount Pictures/Mutual Film Company Director: Mikael Salomon Writer: Graham Yost Cast: Morgan Freeman, Christian Slater, Randy Quaid, Minnie Driver, Edward Asner, Richard Dysart, Betty White
"You can't ride in that truck without thinking about it," says one of the principals in Mikael Salomon's suspense thriller, "Hard Rain." The thinking is about money; the truck is armored; the people who ride in that armored truck, carrying three million dollars in cash have three million reasons to think about subjects other than women, food, and sports. Consider how many relatively low-paid security guards, bank tellers, postal employees, agents of the Mint, jewelry-store staff and others handle incredible sums of money and property. In a single, two-hour ride in an armored truck, the driver and guards are transporting more money than they will earn in a lifetime. A lifetime! The wonder of it all is that the overwhelming majority of shipments get through without incident.
But incidents occur aplenty in Salmon's high-concept adventure story based on Graham Yost's screenplay (which has a surprisingly sentimental ending considering that Christopher Young's almost deafening score shakes the rafters of the movie house). Featuring one enticing twist that occurs two-thirds of the way through the 95-minute caper, "Hard Rain" seems unable to make up its mind whether to be a disaster movie or a police drama, so Salomon throws both genres into the picture, even affording us the beginnings of a romance for our admission price. Not much is believable: not the special effects, not the characters. But the action, which is virtually nonstop, overwhelms our rational thought, allowing us to enjoy the show for what it is and to relish yet another fine performance by Randy Quaid--who, of course, gets all the good lines. "Hard Rain" trusts Morgan Freeman, who, despite his role as a robber seems the most mature person in the film, to put out the quality acting; Christian Slater to throb the hearts of the 20-something women in the audience; and Minnie Driver to turn on her cynical charm as the unlikely romantic interest.
Photographed a year or so ago with the title "The Flood," the film's name was changed to underscore the suspense species, which deals with a $3 million heist of an armored truck taking place with the help of a blinding rainstorm. With the breaking of an old dam which harnessed the water of the Midwestern town of Huntingburg, Indiana, the movie at one point seems like a relatively inexpensive remake of "Waterworld" complete with speedboat chases by pirates who, with one exception, look as though they couldn't navigate their way out of a bathtub. Jim (Morgan Freeman), a soft-spoken, mature fellow who probably never stayed with one employer enough time to build up a pension, dreams of a single job which could net him enough money to last him throughout retirement. Rounding up a small team of losers to assist him in the heist, Jim attacks an armored truck, now stalled in deep water unable to move.  The scoundrels did not count on an obsessively honest security guard, Tom (Christian Slater), who refuses to leave the money behind and swim for his life. Having hid the money in the local cemetery, Tom is mistaken by the sheriff (Randy Quaid) for a looter, is incarcerated, and almost at the point of drowning when he is saved by Karen (Minnie Driver)--paving the way for a flirtation of sorts. Tom and Karen join forces in the adventure of their lives, even running into the town's stereotypical old people, Henry Richard Dysart) and Doreen (Betty White), who likewise mistake them for looters--which allows the two seniors to act like Beverly Hillbillies.
Except for a twist which involves a reversal of alliances, "Hard Rain" is a by-the-numbers caper story with mostly cardboard characters and just a few bon mots. The latter includes a statement by the local science teacher who, having been fired from the town's high school, has joined the robbers with his specialty of assembling explosives. Asked whether this is the sort of thing he taught his classes, he retorts, "You haven't been to a high school lately...they taught ME how to do this."
A fairly formulaic script is saved by the thoughtful performance of the always reliable Morgan Freeman--who at least is not wasted in his role as he was in "Amistad"--and a genuinely comic turn by the trustworthy Randy Quaid. You sit on the edge of your seat wondering whether Quaid, riding the waves caused in part by the bursting of the levee, will get to say, "Where can I get some dam bait for my dam fishing rod? Rated R. Running Time: 95 minutes. (C) 1998 Harvey Karten
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


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val sortingStr: String
raw string as seen by the Api.
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Trader Shikon:
Greetings, friend of the Varka!
Are you surprised to learn that traders such as myself exist within my warrior race? Don't you realize that rivers carry the treasures of many lands as they find their way to the sea?
What I can supply to you now is this low-level booty pouch! Someday, when you can open one of our looted boxes, you'll lay your hands on the strongest armor available!
Bring me a Nepenthes Seed and I'll give you whatever you want!
"I want to buy one of the Varka's specialty products."
Quest


{{ result }}
{% endif %}The following graph shows the F1 SCORE for Logistic Regression Machine Learning Model.
The following graph shows the ROC AUC Curve for Logistic Regression Machine Learning Model. It gives the best AUC.
The following graph gives the feature importance to predict the Loan Application Approval.


Saturday night 30 min brain dump - Luck
Introduction
Sometimes you get lucky, and find a way where you can truely do the things you like in work... But why does luck always seem to be distributed unevenly, and does it always land with the same people...
Here is a message for you: it does not! You can get lucky once by accident, but getting lucky multiple times seems to be quite impossible (not considering statistical anomylies).
So I decided to write a post with some lessons learned from my personal experience...
Be more receptive to luck
Let us begin with the first quote
“If opportunity doesn't knock, build a door” - Milton Berle
Simple, is it not; the reason some people are more lucky then others, is because they are willing to see the opportunities....
I will give you 3 more quotes to tickle your motivation on changing life, work, and all other things related to carbon-based lifeforms:
“The journey of a thousand miles begins with one step.”
"If you do not change direction, you may end up where you are heading."
“When I let go of what I am, I become what I might be”
- Lao Tzu
Here is the thing: people always want something, but are afraid to get out of their current comfort zone. If you really want it: stop making excuses and just do it! Ask yourself: what is the worst thing that could happen? Usually things are not that bad, and you can almost always find a plan B...
Millions of opportunities might fly by, but people tend to always find an excuse and somehow assume the worst-case scenario will happen so they talk themselves out of it...
This brings us to the next part...
Be willing to leave your comfort zone
This is hard for most of the people I meet, but is essential in my opinion. I will mention a quote from Ashton Kutcher - yes, Ashton Kutcher, quoting him is definately out of my comfort zone ;) -:
"I'm continually trying to make choices that put me against my own comfort zone. As long as you're uncomfortable, it means you're growing."
Ashton Kutcher
People always ask me how I manage to keep up and do so much interesting things and jobs... Well, that's easy: I spend time doing new things, way out of my comfort zone, so people consult me about those new things - makes sense, does it not?
This also implies you have to make sacrifices, because you doing new things might imply that you have to be able to sacrifice other things... Which brings me to the next part:
Get your priorities straight
"But I need to pay my bills" - almost everybody when we talk about this
Do you really need to have all the latest/biggest/best/most expensive/coolest phone/stereo/car/house/clothing/... Do you really need to go on a trip to an expensive hotel 3 times a year?
You might, or you might not... What you do is all about choices. While you are working you cannot spend time with your kids, for example... While you are earning hard cash for that new car, you probably can not spend that time on some new tech you would like to learn about...
Also, most people tend to forget why they started doing things, but just seem to be wandering on the road, always going forward, without evaluating where they wanted to go in the first place.
Do not be afraid of failure, but consider it part of the process
Failure is a part of life; accept that big plans will fail, but learn from your failure. There is a difference between a failure and a mistake: read Seth's blog article on it. Do this now!!!
Here is an English proverb that you should remember:
“Hope for the best, but prepare for the worst”
Most people do not succeed at once... They might forget to tell you that, but there are usually a huge pile of failures before becoming succesfull. The important part is that it is a process, where you learn from your mistakes, so you do not make the same mistake another time....
"In order to succeed, your desire for success should be greater than your fear of failure." -- Bill Cosby
Another great quote which points out that you should accept it and go with the flow....
Last but not least
"Be happy for this moment. This moment is your life. " - Omar Khayyam
This is easily forgotten: most people are so tied up with their target, that they do not appreciate the current situation.
Whenever I am in a situation where some things seem to be "unlucky" and I take a step back to reflect, I usually find out that it is only a small part that I am unlucky with, so I act until it changes.
That's it for a Saturday night 30min brain dump. Later folks!


My old site edymttdev.appspot.com now redirects here. If you entered a specific page, you have to search for it in the new site: I've intentionally done a base redirect to the home page since in future site updates the URLs and sections are likely to change. Sorry for the additional work.
Hi! I'm Eric Miotto and this is my "hub" on the Internet, where I experiment with HTML and I link to my presence on various sites.
You can read my curriculum vitae and check out my skills. In addition you can consult my presence on the internet.
Enjoy!


{% extends 'invitro/ivchemical_detail.html' %}
{% block title %}
{% include "hawc/siteTitle.html" with crumbs=object.get_crumbs crud=crud %}
{% endblock title %}
{% block breadcrumbs %}
{% include "hawc/breadcrumbs.html" with crumbs=object.get_crumbs crud=crud %}
{% endblock %}
{% block content %}
{{ block.super }}
{% include "hawc/_delete_block.html" with name="chemical" notes="This will remove all content associated with this chemical." %}
{% endblock content %}


A call to open source electronic voting.
The Battle Over Voting Technology [PBS, 2003]
Paper trail should be required for electronic votes [CNN, 2004]
--- layout: post title: The Flaw in Electronic Voting categories: [] tags: [] status: publish type: post published: true meta: _original_post_id: '' _original_import_author: John Duprey author: ---


Thanks for purchasing! If you find this file useful please rate it. Cheers! -modular
Go inside the "deploy" folder and open up "settings.xml" in your favorite text editor. Simply fill in the values with your new text and colors. Afterwards, refresh the portfolio page and the site will be updated.
To edit the portfolio items you will need to open up "selected_work.xml" in the "deploy" folder and update the text. To add your own images you will need to save them to the folders "thumbs" and "images" inside the "selected_work" folder.
You can have a maximum of 18 portfolio items with up to 5 images for each item.
Thumbnail images should be sized 144x60 pixels and portfolio piece images should be sized 475x260 pixels.
Edit the "selected_work.xml" file in the "deploy" folder and update the "link" property to the URL of your project. To link to a live URL outside your domain follow the example in selected_work > projects > sample > index.html.
The fonts used are VAG Rounded and Georgia Italic.
Edit the "background_color" property in "settings.xml" and also be sure to change the color in the "index.html" file -- there should be two places to change it -- one in the CSS at the top and the other in the SWF insertion code towards the bottom.
To use your own logo open up "settings.xml" and set "use_own_logo" to true. Then set "your_logo_file" to the location of your logo file.
Open up the various .as files in the "src" folder -- usually at the top of the file there will be a list of variables defined that you can customize. Additionally, you can directly edit the portfolio.fla and contact.fla files.
Yep! Visit my portfolio on FlashDen here.
Any bug reporting would be greatly appreciated. Please post them in the item's comments section on FlashDen and I will try to fix them and get you an updated file.
Please post your questions in the item's comments section on FlashDen.
Thanks to Geoff Stearns and the open source community for developing SWFObject, Asual for developing SWFAddress, and Zeh Fernando and the open source community for developing Tweener.


Tilez Creator
The Tile Map Creation Tool
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Help?
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Tile Width:
Tile Height:
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Tools class
Vincent VILERIO
[alphaToVerticalPosition Convert a character into a horizontal number position]
[A number representation of the horizontal position]
[checkPosition Check the existence of boxes]
[The Result]
[checkPosition Check the existence of a box]
[generateKeyGrid Generate a key from a MatriceCase object]
[The key generated]
[generateKeyGridByVal Generate a key from vertical/horizontal values]
[horizontalPositionToAlpha Convert a number into a horizontal alphaNumeric position]
[A character representation of the horizontal position]
Generated using TypeDoc
Tools class
Tools class
Vincent VILERIO


Appeal Court victory a validation of people’s will - Lalong
by https://www.facebook.com/CheerfulpraiseGov. Simon Lalong of Plateau has described his victory at the Appeal Court as validation of the will of the people.
Lalong in a statement on Friday by his Director of Press and Public Affairs, Dr Simon Macham said that the affirmation of his victory was yet another collective victory for the entire people of the state.
The governor said that he was not surprised by the court verdict as he never doubted the validity of the people’s mandate reposed in him and the All Progressives Congress (APC).
“This is not a victory for me but for the entire people of Plateau who have demonstrated faith in our rescue mission.
“Like I have repeatedly said, I am governor for everyone irrespective of party, tribe, religious or any other affiliations,” he said.
Read Also; UK PM Johnson says Britain will leave EU by Jan. 31
He stated that he was ready to carry everyone in the state along.
Lalong called on the opposition Peoples Democratic Party (PDP) and its candidate retired Gen. Jeremiah Useni to join hands with him in moving the state forward.
The governor appealed to the people to continue to support his administration towards leaving a lasting legacy in the state.


Pressing ok will delete this file forever!  Close the window to cancel the completion action


--- layout: post title: 'budget bites: easy bean & cheese quesadillas' date: 2011-10-06 04:29:25.000000000 -04:00 type: post published: true status: publish categories: - budget bites - college - recipes - vegetarian tags: - budget bites - cheese - quesadillas - refried beans meta: _edit_last: '1' author: login: sophie email<EMAIL_ADDRESS>display_name: Pika first_name: '' last_name: '' ---
It's been quite a grueling week academically. Last minute debugging and lab reports means that I haven't slept earlier before 3 am in the past 3 days. This is a perfect busy week recipe: warm, cheesy, and ready in 5 minutes flat. Now, excuse me, as I should probably start my homework!
Easy Bean & Cheese Quesadillas
Serves 1
On another note, rest in peace, Steve Jobs. You were an inspiration to us all and a true genius.


Input data and submit to predict the median housing value. For the range of acceptable values, see the input boxes, and see below for the definition of each variable. For more information about the model, application, and data science approach, see the About page.
Feature Definitions:


{****************************************************************** * purpose: * create the email you would like to send to the customers * who start but do not complete the checkout process. You * can turn on cart abandonment emails in * Settings->Options->Cart Abandonment Options * * to use: * create your email below this comment. You can use * HTML, smarty and/or plain text for your email. * * variables available: * {$first_name} is the first name of the customer, if available * {$last_name} is the last name of the customer, if available * {$email} is the email address of the customer * {$email_id} is the id of the cart abandonment email listed under Settings -> Cart Abandonment * {$email_name} is the name of the cart abandonment email listed under Settings -> Cart Abandonment * {$cart} is an array of items in the customer's cart * The elements in the {$cart} array are the same as the * elements in the {$cart} array in cartitems.tpl * {$session_link} should be included in any link that you wish to reactivate the customer's session. * Its format is: order_id=123&session_id=ABCDEFG1234567890 * Example: * View Cart * With promo: * View Cart * {$customer_cgroups} is an array of customer groups the recipient belongs to. * The array is keyed by customer group id. ********************************************************************}
Uh Oh! You’ve left some items in your Shopping Cart! |
|||||||||
|
{if #abandoned_email_text# != ''}
{assign var=abandon value=#abandoned_email_text#|replace:"[[first_name]]":$billing.first_name}
{assign var=abandon value=$abandon|replace:"[[last_name]]":$billing.last_name}
{assign var=abandon value=$abandon|replace:"[[co_name]]":#store_name#}
{$abandon}
{else}
Hi {$first_name}! Just sending you a friendly reminder, because we know unexpected things happen, and sometimes even a string on your finger or a sticky note just doesn’t do. {/if} |
|||||||||
| Here’s what you have in your cart: | |||||||||
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You can zip right back to finish up by clicking this link: View Cart Items aren’t reserved until you complete your purchase, and some items are limited. Hurry back so you don’t miss out! |
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Have questions or need help?
Call:<PHONE_NUMBER> (M-F, 8 am to 5pm Eastern) You are receiving this e-mail because you registered on our web site with the e-mail address {$email} |


INTERMEDIATE DETAILS
All fields are mandatory
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We Provide The Best Food
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Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum
Chicken Burger
Beef Burger
Chicken Pizza
George Richard
Great food!Menu is awesome.Their foods are not only great but also in the rigth place.I'll highly recommend them:)
We are menu menu alittle team of 7 members.orem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book.


Abstract
Bike theft has been an on-going issue in City of Toronto. In this paper we explore 2014-2019 bike theft data from Toronto Police Service. We find the trend for Toronto bike theft occurrence in regards of time, color of the bike, and location. Our findings have implications for preventing bike theft in Toronto for individuals, and the city as a whole.
Introduction
Do you bike to places in Toronto? If so, then have you ever worried about coming out of a shop, only to find a single wheel left locked to the tree? I certainly do, because it has happened to me three months ago. My friend in food delivery service revealed that he had lost 4 bikes during two months of working, to a point that he didn’t even bother to go to the police anymore. In this report, we investigate bike thefts in Toronto’s correlation with time period, bike color, recovery rate, and location. Our findings are able to prevent bikers in Toronto from bike thefts.
On average, more than 3 million dollars worth of bikes are getting stolen in Toronto every year, and only 1.17% of the stolen bikes are recovered. During a day, bike theft number peaks during rush hour and midnight. Also we have found that waterfront Communities - The Island is the most dangerous place to park you bikes. So next time when you have to park your bike there, make sure to double lock it! The most interesting findings in this report is the relation of bike colors and thefts. In general, common bike colors, such as black, gray, and white, are more likely to be stolen. On the other hand, bikes with bright colors are more likely to be recovered by the police.
In summary, this report demonstrates the pattern of bike theft in the past six years. With more information such as population distribution, average income distribution, bike color distribution, we could potentially understand the reason behind bike theft trends in Toronto with a more accurate conclusion. This report could be further improved by using more variables from the data set given, and combining with its findings. For example, occurrence location with each neighborhood, or police division with recovered bikes. However, bike theft problem is more complex than the variables we used, and we cannot firmly conclude that it has a correlation to the variables without further information.
Bike Theft Data
It is improving!!
The graph ((1)-“Bike Theft Occurrence by Year”) visualizes the trend of bike theft occurrence from 2014 - 2019.
We can see a growing trend from 2014 to 2018. But 2019 broke the ascending trend. It is shown as the third lowest in 6 years. With the constant growing population of Toronto, this graph has shown positive result of public acknowledgment of bike theft prevention, and safety measures. So whatever we are doing to prevent bike theft, we are doing it right!
Your Bike can be stolen in winter too
From the graph((2)-“Bike Theft Occurrence by Month”), we’ve seen a predicable trend of significantly more bike theft from May to October as the weather allows more people to use their bikes.
However, the bike thefts occurred in winter is higher than expectation where approximately 800 bikes get stolen during the winter. It is quite hard to imagine people biking in Toronto during the winter with three feet of snow, though some still might (just why???). It is more likely that winter bike thefts happen due to bikes parked at an insecure location for a long period of time.
In future studies we could connect winter theft occurrence to the premise type when the crime occurred to find which premise type is at a higher risk for a long term parking. If possible, park your bikes inside!(Or hide it well)
Which days are more dangerous for your bike?
The relation of bike thefts and day in a month is relatively stable. However there is a slight peak on the first day of a month and in the middle of a month.
The 15th of a month has the highest risk of bike theft, and the 31st of a month is relatively the safest. (doesn’t mean you don’t have to lock your bike on the 31st!)
Note: The data on the 31st should be manipulated by multiplying 12/7. We got the value of 490.28 bikes revealing the more accurate counts. However it is still has the lowest bike theft rate.
Commute to work? Be extra careful during rush hour
The following graphs shows the bike theft counts and the corresponding time-period/hour. It is shown that more bike thieves are more active in the afternoon and evening((4)-“Bike Theft Occurrence by Time Period”).
When we look at the specific hour when bike thefts occur((5)-“Bike Theft Occurrence by Hour”), the peaks are at 8/9 am, 12pm, and 5/6pm. It is during the rush hour thus more people are using/locking their bikes, thus makes breaking bike lock less noticeable. During the night and early morning, less bikes are stolen. But we do see a peak during that time at 12:00am.
Note: In the data set given, it is described to be time of occurrence. But realistically it would be either time when stolen bikes are reported by owner, or estimate time. Results might vary.
“My bike was stolen, will I ever find it?”
Unfortunately, you likely won’t ever see your bike again. Even if you do, you probably won’t be its owner anymore. In the past 6 years, bike thieves has cost people in Toronto $18,804,580, and only $176,686.7 dollars worth of bikes are recovered. Graph ((6)-“Price of Stolen Bicycles”) below helps us visualize it.
Graph((7)-“Recovered Stolen Bicycles”) shows us the percentage of bikes recovered among the 21584 bikes stolen from 2014-2017. In future studies we will compare the recovery rate for each year and see if there is an improvement.
Green Bikes? It might be ugly but it works
The pie chart ((8)- "Percentage of Occurrence by Color) shows the percentage as a comparison of highest occurrence colors.
From the colors shown, it is obvious that the highest occurrence colors are the most popular colors due to a higher population of common color bikes. We could confirm the theory if we had a data set on the population of each colored bikes in Toronto, then compare each color’s ratio.
In addition, there are some alternative reasons behind such occurrence. In one of BenVollard’s(Associate Professor, Department of Economics, University of Tilburg) paper, he argues that bike and auto thefts are driven by economics, and having a bright colored bike could potentially prevent theft. To elaborate, bright colored bikes are less popular among buyers, thus are less in demand in comparison to, for example, black or gray bikes. Therefore the resale price would be lower. So stealing bright colored bikes not only have more risks as they are easier to be spotted, but also hold lower resale value. So the theft occurrence of less common colored bikes would be expected to have a large difference compare to the more common ones.
We have also combined color with the recovery rate for stolen bikes. From graph ((9)-“Recovered Bicycles”), we compare each high theft occurrence color’s recovery rate. The recovery rates from the highest to the lowest and its corresponding color are listed below.
Green: 1.36%, White: 1.30%, Purple: 1.26%, Blue: 1.22%, Silver: 1.19%, Black: 0.97%, Grey: 0.83%
Compare to the recovery rate of all bicycles, 1.17%, we can conclude that Green, White, Purple, and blue colored bikes have the highest recovery rate. Green and purple are not on the list of high occurrence colors, but they have comparatively higher recovery rate. We can conclude that Green and Purple are safest colors. So if you are getting tired of getting your bikes stolen, paint it green or purple!
If your neighborhood is on the graph below, think twice before you park on the street
Bike theft, in terms of neighborhood is graphed below((10)-“Major Bicycle Theft Threat Neighborhood 2014-2019”). From the graph we see the top three neighborhood of bike theft: Church-Yonge Corridor, Waterfront Communities-The Island, and Bay Street has been top three through out the 6 years.
Meanwhile, Minico became one of the bike theft hotpot in 2018 and 2019. Mainly due to the extension of Toronto and new infrastructures built in Mimico. In addition, a new Go Train station in Mimico was added in Jun 2018 with more bus routes, leading to a drastic increase to the population of Mimico as it has become more convenient. We have drown a connection of bike theft occurrence to neighborhood population, but there is no avaliable data to back this up, so we cannot firmly conclude such causation. South Riverdale’s bike theft occurrence showed a growing trend until 2018. Then in 2019, it was no longer the top 10 occurrence neighborhood. And Dovercourt-Wallace Emerson-Junction was removed from top bike theft neighborhood in 2019. However, these changes are due to the increase of bike theft occurrence in Mimico, and we do not expect them have a drastic decrease.
Summary
The data above gives us an idea of how to prevent bike theft from the patterns shown. However, the best way to prevent bike theft is ourselves. Securely lock our bikes, write down the serial number, store bikes inside are the best method to actively keep our bikes safe from thieves. To all the bikers: Be safe out there!!
Citations
- R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
- Hadley Wickham, Jim Hester and Winston Chang (2020). devtools: Tools to Make Developing R Packages Easier. R package version 2.3.2. https://CRAN.R-project.org/package=devtools
- Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2020). dplyr: A Grammar of Data Manipulation. R package version 1.0.2. https://CRAN.R-project.org/package=dplyr
- Sharla Gelfand (2020). opendatatoronto: Access the City of Toronto Open Data Portal. https://sharlagelfand.github.io/opendatatoronto/, https://github.com/sharlagelfand/opendatatoronto/.
- H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.
- Wickham et al., (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686, https://doi.org/10.21105/joss.01686
- JJ Allaire and Yihui Xie and Jonathan McPherson and Javier Luraschi and Kevin Ushey and Aron Atkins and Hadley Wickham and Joe Cheng and Winston Chang and Richard Iannone (2020). rmarkdown: Dynamic Documents for R. R package version 2.3.9. URL https://rmarkdown.rstudio.com.
- Yihui Xie and J.J. Allaire and Garrett Grolemund (2018). R Markdown: The Definitive Guide. Chapman and Hall/CRC. ISBN<PHONE_NUMBER>338. URL https://bookdown.org/yihui/rmarkdown. 9.Yihui Xie and Christophe Dervieux and Emily Riederer (2020). R Markdown Cookbook. Chapman and Hall/CRC. ISBN<PHONE_NUMBER>837. URL https://bookdown.org/yihui/rmarkdown-cookbook.
- Gotransit.com. 2020. Mimico GO | Construction Projects | GO Transit. [online] Available at: https://www.gotransit.com/en/the-future-go/improvements/mimico.
- Toronto Sun. 2020. Mimico Is The New Go-To Community. [online] Available at: https://torontosun.com/life/homes/mimico-is-the-new-go-to-community.
- Vollaard, B., 2020. Car Thieves: Not Too Bright, Please! | VOX, CEPR Policy Portal. [online] Voxeu.org. Available at: https://voxeu.org/article/car-thieves-not-too-bright-please.


Play me!
use the 'home keys' to trigger drums pads!
kick (a)
snare (s)
tom 1 (d)
claps (f)
hi-hat (j)
ride (k)
tom 2 (l)
tom 3 (;)


Google Express — 15 mins ago
Package Shipped!
Cucumber Mask Facial has shipped
Hi friends,
I was at the grocery store on Sunday night... when I ran into Genie Williams! I almost didn't recognize her after 20 years!
Anyway, it turns out she is on the organizing committee for the high school reunion this fall. I don't know if you were planning on going or not, but she could definitely use our help in trying to track down lots of missing alums. If you can make it, we're doing a little phone-tree party at her place next Saturday, hoping that if we can find one person, a few more will emerge. What do you say? Want to see if Drew and Todd can make it too?
Talk soon!
Trevor
Google Express — 15 mins ago
Package Shipped!
Cucumber Mask Facial has shipped
Hi friends,
I was at the grocery store on Sunday night... when I ran into Genie Williams! I almost didn't recognize her after 20 years!
Anyway, it turns out she is on the organizing committee for the high school reunion this fall. I don't know if you were planning on going or not, but she could definitely use our help in trying to track down lots of missing alums. If you can make it, we're doing a little phone-tree party at her place next Saturday, hoping that if we can find one person, a few more will emerge. What do you say? Want to see if Drew and Todd can make it too?
Talk soon!
Trevor


esGeoLocationSrv
es.Services.Web
esGeoLocationSrv is a factory service that provides Html5 geolocation services to the API developer.
$q
$window


There is substantial variation in prescribing behaviours, across various different areas of medicine. Some variation can be explained by demographic changes, or local policies or guidelines, but much of the remaining variation is less easy to explain. At OpenPrescribing we are piloting a number of data-driven approaches to identify unusual prescribing and collect feedback on this prescribing to inform development of new tools to support prescribers and organisations to audit and review prescribing.
This report has been developed to automatically identify prescribing patterns at a chemical level which are furthest away from “typical prescribing” and can be classified as an “outlier”. We calculate the number of prescriptions for each chemical in the BNF coding system using the BNF subparagraph as a denominator, for prescriptions dispensed between April 2021 and August 2021. We then calculate the mean and standard deviation for each numerator and denominator pair across all practices/CCGs/PCNs/STPs. From this we can calculate the “z-score”, which is a measure of how many standard deviations a given practice/CCG/PCN/STP is from the population mean. We then rank your “z-scores” to find the top 5 results where prescribing is an outlier for prescribing higher than its peers and those where it is an outlier for prescribing lower than its peers.
It is important to remember that this information was generated automatically and it is therefore likely that some of the behaviour is warranted. This report seeks only to collect information about where this variation may be warranted and where it might not. Our full analytical method code is openly available on GitHub here.
The DataLab is keen to hear your feedback on the results. You can do this by completing the following survey or emailing us at<EMAIL_ADDRESS>Please DO NOT INCLUDE IDENTIFIABLE PATIENT information in your feedback. All feedback is helpful, you can send short or detailed feedback.


A helpful guide to all of the great moments in the history of that illustrious industry that keeps us fat for cheap. No, the McSalad absolutely did not make the list.


I Don’t Go To Church Because People Take The Attention From Jesus And Give It To Me – EL Discloses
by vanessa nordziMost of the celebrities in Ghana have come out to give one reason or the other as to why they refuse to attend church.
The newest addition to the list of non- church goers is Musician Elorm Dablah known in showbiz circles as EL.
According to EL in an interview on Hitz FM his fame has contributed to his decision not to go to church.
“The last time I went to church I ended up taking pictures with fans and Jesus wasn’t happy”. He stated..
EL further bemoaned that all he wants is to worship God in peace and he is ready to resume that role should he find a church that can give him that.
“I haven’t stopped going to church, I’m just looking for a church that i can be myself and worship in peace”. He stated.
E.L in his final statement added that this hasn’t affected his spirituality in anyway because he still prays to God and worship Him at home.


NAME
grpc_call
- Executes an RPC.
SYNOPSIS
grpc_error_t grpc_call(grpc_function_handle_t *handle, ...)
ARGUMENTS
grpc_function_handle_t *handle
- The function handle
Other arguments
- Arguments that are passed to the function called by RPC
DESCRIPTION
The grpc_call() function calls the function defined by the function handle.
The grpc_call() function is blocked until the called function completes execution.
This function is MT-safe.
RETURN VALUE
If successful, GRPC_NO_ERROR is returned. In the case of an error, Error code is returned.
ERRORS
GRPC_NOT_INITIALIZED
- GRPC client is not initialized yet.
GRPC_SERVER_NOT_FOUND
- GRPC client cannot find the specified server.
GRPC_FUNCTION_NOT_FOUND
- GRPC client cannot find the function on the specified server.
GRPC_INVALID_FUNCTION_HANDLE
- Function handle specified by handle is invalid.
GRPC_RPC_REFUSED
- RPC invocation was refused by the server, possibly because of a security issue.
GRPC_COMMUNICATION_FAILED
- Communication with the server failed somehow.
GRPC_TIMEOUT_NP
- Timeout occurred in session.
GRPC_OTHER_ERROR_CODE
- Internal error detected.
last update : $Date: 2005/07/11 07:11:24 $


Easily extend form controls by adding text, buttons, or button groups on either side of textual inputs, custom selects, and custom file inputs.
Add the relative form sizing classes to the .input-group
itself and contents within will automatically resize—no need for repeating the form control size classes on each element.
Place any checkbox option within an input group's addon instead of text.
Place any radio option within an input group's addon instead of text.


{-# LANGUAGE MagicHash #-} {-# OPTIONS_GHC -fno-warn-duplicate-exports #-} -- | -- Module : Data.Primitive -- Copyright : (c) Roman Leshchinskiy 2009-2012 -- License : BSD-style -- -- Maintainer : Roman Leshchinskiy<EMAIL_ADDRESS>-- Portability : non-portable -- -- Reexports all primitive operations -- module Data.Primitive ( -- * Re-exports module Data.Primitive.Types ,module Data.Primitive.Array ,module Data.Primitive.ByteArray ,module Data.Primitive.Addr ,module Data.Primitive.SmallArray ,module Data.Primitive.UnliftedArray ,module Data.Primitive.PrimArray ,module Data.Primitive.MutVar -- * Naming Conventions -- $namingConventions ) where import Data.Primitive.Types import Data.Primitive.Array import Data.Primitive.ByteArray import Data.Primitive.Addr import Data.Primitive.SmallArray import Data.Primitive.UnliftedArray import Data.Primitive.PrimArray import Data.Primitive.MutVar {- $namingConventions For historical reasons, this library embraces the practice of suffixing the name of a function with the type it operates on. For example, three of the variants of the array indexing function are: > indexArray :: Array a -> Int -> a > indexSmallArray :: SmallArray a -> Int -> a > indexPrimArray :: Prim a => PrimArray a -> Int -> a In a few places, where the language sounds more natural, the array type is instead used as a prefix. For example, @Data.Primitive.SmallArray@ exports @smallArrayFromList@, which would sound unnatural if it used @SmallArray@ as a suffix instead. This library provides several functions traversing, building, and filtering arrays. These functions are suffixed with an additional character to indicate their the nature of their effectfulness: * No suffix: A non-effectful pass over the array. * @-A@ suffix: An effectful pass over the array, where the effect is 'Applicative'. * @-P@ suffix: An effectful pass over the array, where the effect is 'PrimMonad'. Additionally, an apostrophe can be used to indicate strictness in the elements. The variants with an apostrophe are used in @Data.Primitive.Array@ but not in @Data.Primitive.PrimArray@ since the array type it provides is always strict in the element. For example, there are three variants of the function that filters elements from a primitive array. > filterPrimArray :: (Prim a ) => (a -> Bool) -> PrimArray a -> PrimArray a > filterPrimArrayA :: (Prim a, Applicative f) => (a -> f Bool) -> PrimArray a -> f (PrimArray a) > filterPrimArrayP :: (Prim a, PrimMonad m) => (a -> m Bool) -> PrimArray a -> m (PrimArray a) As long as the effectful context is a monad that is sufficiently affine the behaviors of the 'Applicative' and 'PrimMonad' variants produce the same results and differ only in their strictness. Monads that are sufficiently affine include: * 'IO' and 'ST' * Any combination of 'MaybeT', 'ExceptT', 'StateT' and 'Writer' on top of another sufficiently affine monad. There is one situation where the names deviate from effectful suffix convention described above. Throughout the haskell ecosystem, the 'Applicative' variant of 'map' is known as 'traverse', not<EMAIL_ADDRESS>Consequently, we adopt the following naming convention for mapping: > mapPrimArray :: (Prim a, Prim b) => (a -> b) -> PrimArray a -> PrimArray b > traversePrimArray :: (Applicative f, Prim a, Prim b) => (a -> f b) -> PrimArray a -> f (PrimArray b) > traversePrimArrayP :: (PrimMonad m, Prim a, Prim b) => (a -> m b) -> PrimArray a -> m (PrimArray b) -}


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vignettes/cmdstanr.Rmd
cmdstanr.Rmd
CmdStanR is a lightweight interface to Stan for R users (see CmdStanPy for Python) that provides an alternative to the traditional RStan interface. See the Comparison with RStan section later in this vignette for more details on how the two interfaces differ.
CmdStanR is not on CRAN yet, but the beta release can be installed by running the following command in R.
# we recommend running this is a fresh R session or restarting your current session install.packages("cmdstanr", repos = c("https://mc-stan.org/r-packages/", getOption("repos")))
CmdStanR (the cmdstanr R package) can now be loaded like any other R package. We’ll also load the bayesplot and posterior packages to use later in examples.
library(cmdstanr) library(posterior) library(bayesplot) color_scheme_set("brightblue")
CmdStanR requires a working installation of CmdStan, the shell interface to Stan. If you don’t have CmdStan installed then CmdStanR can install it for you, assuming you have a suitable C++ toolchain. The requirements are described in the CmdStan Guide:
To double check that your toolchain is set up properly you can call the check_cmdstan_toolchain()
function:
The C++ toolchain required for CmdStan is setup properly!
If your toolchain is configured correctly then CmdStan can be installed by calling the install_cmdstan()
function:
install_cmdstan(cores = 2)
Before CmdStanR can be used it needs to know where the CmdStan installation is located. When the package is loaded it tries to help automate this to avoid having to manually set the path every session:
If the environment variable "CMDSTAN"
exists at load time then its value will be automatically set as the default path to CmdStan for the R session. This is useful if your CmdStan installation is not located in the default directory that would have been used by install_cmdstan()
(see #2).
If no environment variable is found when loaded but any directory in the form ".cmdstanr/cmdstan-[version]"
, for example ".cmdstanr/cmdstan-2.23.0"
, exists in the user’s home directory (Sys.getenv("HOME")
, not the current working directory) then the path to the CmdStan with the largest version number will be set as the path to CmdStan for the R session. This is the same as the default directory that install_cmdstan()
uses to install the latest version of CmdStan, so if that’s how you installed CmdStan you shouldn’t need to manually set the path to CmdStan when loading CmdStanR.
If neither of these applies (or you want to subsequently change the path) you can use the set_cmdstan_path()
function:
set_cmdstan_path(PATH_TO_CMDSTAN)
To check the path to the CmdStan installation and the CmdStan version number you can use cmdstan_path()
and cmdstan_version()
:
[1] "/Users/jgabry/.cmdstanr/cmdstan-2.26.1"
[1] "2.26.1"
The cmdstan_model()
function creates a new CmdStanModel
object from a file containing a Stan program. Under the hood, CmdStan is called to translate a Stan program to C++ and create a compiled executable. Here we’ll use the example Stan program that comes with the CmdStan installation:
file <- file.path(cmdstan_path(), "examples", "bernoulli", "bernoulli.stan") mod <- cmdstan_model(file)
The object mod
is an R6 reference object of class CmdStanModel
and behaves similarly to R’s reference class objects and those in object oriented programming languages. Methods are accessed using the $
operator. This design choice allows for CmdStanR and CmdStanPy to provide a similar user experience and share many implementation details.
The Stan program can be printed using the $print()
method:
mod$print()
data {
int<lower=0> N;
int<lower=0,upper=1> y[N];
}
parameters {
real<lower=0,upper=1> theta;
}
model {
theta ~ beta(1,1); // uniform prior on interval 0,1
y ~ bernoulli(theta);
}
The path to the compiled executable is returned by the $exe_file()
method:
mod$exe_file()
[1] "/Users/jgabry/.cmdstanr/cmdstan-2.26.1/examples/bernoulli/bernoulli"
The $sample()
method for CmdStanModel
objects runs Stan’s default MCMC algorithm. The data
argument accepts a named list of R objects (like for RStan) or a path to a data file compatible with CmdStan (JSON or R dump).
# names correspond to the data block in the Stan program data_list <- list(N = 10, y = c(0,1,0,0,0,0,0,0,0,1)) fit <- mod$sample( data = data_list, seed = 123, chains = 4, parallel_chains = 4, refresh = 500 )
Running MCMC with 4 parallel chains...
Chain 1 Iteration: 1 / 2000 [ 0%] (Warmup)
Chain 1 Iteration: 500 / 2000 [ 25%] (Warmup)
Chain 1 Iteration: 1000 / 2000 [ 50%] (Warmup)
Chain 1 Iteration: 1001 / 2000 [ 50%] (Sampling)
Chain 1 Iteration: 1500 / 2000 [ 75%] (Sampling)
Chain 1 Iteration: 2000 / 2000 [100%] (Sampling)
Chain 2 Iteration: 1 / 2000 [ 0%] (Warmup)
Chain 2 Iteration: 500 / 2000 [ 25%] (Warmup)
Chain 2 Iteration: 1000 / 2000 [ 50%] (Warmup)
Chain 2 Iteration: 1001 / 2000 [ 50%] (Sampling)
Chain 2 Iteration: 1500 / 2000 [ 75%] (Sampling)
Chain 2 Iteration: 2000 / 2000 [100%] (Sampling)
Chain 3 Iteration: 1 / 2000 [ 0%] (Warmup)
Chain 3 Iteration: 500 / 2000 [ 25%] (Warmup)
Chain 3 Iteration: 1000 / 2000 [ 50%] (Warmup)
Chain 3 Iteration: 1001 / 2000 [ 50%] (Sampling)
Chain 3 Iteration: 1500 / 2000 [ 75%] (Sampling)
Chain 3 Iteration: 2000 / 2000 [100%] (Sampling)
Chain 4 Iteration: 1 / 2000 [ 0%] (Warmup)
Chain 4 Iteration: 500 / 2000 [ 25%] (Warmup)
Chain 4 Iteration: 1000 / 2000 [ 50%] (Warmup)
Chain 4 Iteration: 1001 / 2000 [ 50%] (Sampling)
Chain 4 Iteration: 1500 / 2000 [ 75%] (Sampling)
Chain 4 Iteration: 2000 / 2000 [100%] (Sampling)
Chain 1 finished in 0.0 seconds.
Chain 2 finished in 0.0 seconds.
Chain 3 finished in 0.0 seconds.
Chain 4 finished in 0.0 seconds.
All 4 chains finished successfully.
Mean chain execution time: 0.0 seconds.
Total execution time: 0.3 seconds.
There are many more arguments that can be passed to the $sample()
method. For details follow this link to its separate documentation page:
The $sample()
method creates R6 CmdStanMCMC
objects, which have many associated methods. Below we will demonstrate some of the most important methods. For a full list, follow this link to the CmdStanMCMC
documentation:
The $summary()
method calls summarise_draws()
from the posterior package:
fit$summary()
# A tibble: 2 x 10
variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 lp__ -7.26 -6.99 0.726 0.326 -8.72 -6.75 1.00 1931. 1521.
2 theta 0.250 0.234 0.119 0.118 0.0816 0.470 1.00 1366. 1315.
fit$summary("theta", "mean", "sd")
# A tibble: 1 x 3
variable mean sd
<chr> <dbl> <dbl>
1 theta 0.250 0.119
# use a formula to summarize arbitrary functions, e.g. Pr(theta <= 0.5) fit$summary("theta", pr_lt_half = ~ mean(. <= 0.5))
# A tibble: 1 x 2
variable pr_lt_half
<chr> <dbl>
1 theta 0.966
The $draws()
method can be used to extract the posterior draws as a 3-D array (iteration x chain x variable). The posterior package can then be used to easily convert to other formats, like data frames and matrices of draws.
# this is a draws_array object from the posterior package draws_array <- fit$draws() str(draws_array)
'draws_array' num [1:1000, 1:4, 1:2] -6.82 -7.32 -7.05 -7.13 -7.11 ...
- attr(*, "dimnames")=List of 3
..$ iteration: chr [1:1000] "1" "2" "3" "4" ...
..$ chain : chr [1:4] "1" "2" "3" "4"
..$ variable : chr [1:2] "lp__" "theta"
# convert to matrix or data frame draws_df <- as_draws_df(draws_array) # as_draws_matrix() for matrix print(draws_df)
# A draws_df: 1000 iterations, 4 chains, and 2 variables
lp__ theta
1 -6.8 0.30
2 -7.3 0.13
3 -7.1 0.16
4 -7.1 0.37
5 -7.1 0.15
6 -7.5 0.12
7 -7.2 0.38
8 -6.8 0.22
9 -7.0 0.34
10 -6.8 0.22
# ... with 3990 more draws
# ... hidden reserved variables {'.chain', '.iteration', '.draw'}
Plotting posterior distributions is as easy as passing the object returned by the $draws()
method directly to plotting functions in bayesplot:
The $sampler_diagnostics()
method extracts the values of the sampler parameters (treedepth__
, divergent__
, etc.) as a 3-D array (iteration x chain x variable):
# this is a draws_array object from the posterior package str(fit$sampler_diagnostics())
'draws_array' num [1:1000, 1:4, 1:6] 1 1 2 1 2 2 1 2 1 1 ...
- attr(*, "dimnames")=List of 3
..$ iteration: chr [1:1000] "1" "2" "3" "4" ...
..$ chain : chr [1:4] "1" "2" "3" "4"
..$ variable : chr [1:6] "treedepth__" "divergent__" "accept_stat__" "stepsize__" ...
# convert to matrix or data frame using posterior package diagnostics_df <- as_draws_df(fit$sampler_diagnostics()) print(diagnostics_df)
# A draws_df: 1000 iterations, 4 chains, and 6 variables
treedepth__ divergent__ accept_stat__ stepsize__ n_leapfrog__ energy__
1 1 0 1.00 0.93 1 6.9
2 1 0 0.80 0.93 3 8.1
3 2 0 0.99 0.93 3 7.5
4 1 0 0.92 0.93 3 8.1
5 2 0 1.00 0.93 7 7.1
6 2 0 0.95 0.93 3 7.8
7 1 0 0.88 0.93 3 9.3
8 2 0 1.00 0.93 3 7.1
9 1 0 0.95 0.93 3 7.2
10 1 0 0.97 0.93 3 7.2
# ... with 3990 more draws
# ... hidden reserved variables {'.chain', '.iteration', '.draw'}
The $cmdstan_diagnose()
and $cmdstan_summary()
methods call CmdStan’s diagnose
and stansummary
utilities:
fit$cmdstan_diagnose()
Processing csv files: /var/folders/h6/14xy_35x4wd2tz542dn0qhtc0000gn/T/RtmpBiK4kE/bernoulli-202104151140-1-61a1c6.csv, /var/folders/h6/14xy_35x4wd2tz542dn0qhtc0000gn/T/RtmpBiK4kE/bernoulli-202104151140-2-61a1c6.csv, /var/folders/h6/14xy_35x4wd2tz542dn0qhtc0000gn/T/RtmpBiK4kE/bernoulli-202104151140-3-61a1c6.csv, /var/folders/h6/14xy_35x4wd2tz542dn0qhtc0000gn/T/RtmpBiK4kE/bernoulli-202104151140-4-61a1c6.csv
Checking sampler transitions treedepth.
Treedepth satisfactory for all transitions.
Checking sampler transitions for divergences.
No divergent transitions found.
Checking E-BFMI - sampler transitions HMC potential energy.
E-BFMI satisfactory for all transitions.
Effective sample size satisfactory.
Split R-hat values satisfactory all parameters.
Processing complete, no problems detected.
fit$cmdstan_summary()
Inference for Stan model: bernoulli_model
4 chains: each with iter=(1000,1000,1000,1000); warmup=(0,0,0,0); thin=(1,1,1,1); 4000 iterations saved.
Warmup took (0.0050, 0.0060, 0.0050, 0.0050) seconds, 0.021 seconds total
Sampling took (0.014, 0.014, 0.013, 0.013) seconds, 0.054 seconds total
Mean MCSE StdDev 5% 50% 95% N_Eff N_Eff/s R_hat
lp__ -7.3 1.8e-02 0.73 -8.7 -7.0 -6.8 1561 28912 1.0
accept_stat__ 0.91 1.5e-02 0.14 0.61 0.97 1.0 8.0e+01 1.5e+03 1.0e+00
stepsize__ 1.00 7.3e-02 0.10 0.91 0.99 1.2 2.0e+00 3.7e+01 3.5e+13
treedepth__ 1.4 8.6e-03 0.50 1.0 1.0 2.0 3.5e+03 6.4e+04 1.0e+00
n_leapfrog__ 2.6 1.6e-01 1.4 1.0 3.0 7.0 7.7e+01 1.4e+03 1.0e+00
divergent__ 0.00 nan 0.00 0.00 0.00 0.00 nan nan nan
energy__ 7.8 2.5e-02 1.0 6.8 7.5 9.7 1.6e+03 2.9e+04 1.0e+00
theta 0.25 3.2e-03 0.12 0.081 0.23 0.47 1368 25337 1.0
Samples were drawn using hmc with nuts.
For each parameter, N_Eff is a crude measure of effective sample size,
and R_hat is the potential scale reduction factor on split chains (at
convergence, R_hat=1).
stanfit
objectIf you have RStan installed then it is also possible to create a stanfit
object from the csv output files written by CmdStan. This can be done by using rstan::read_stan_csv()
in combination with the $output_files()
method of the CmdStanMCMC
object:
stanfit <- rstan::read_stan_csv(fit$output_files())
CmdStanR also supports running Stan’s optimization algorithms and its algorithms for variational approximation of full Bayesian inference. These are run via the $optimize()
and $variational()
methods, which are called in a similar way to the $sample()
method demonstrated above.
We can find the (penalized) maximum likelihood estimate (MLE) using $optimize()
:
fit_mle <- mod$optimize(data = data_list, seed = 123)
Initial log joint probability = -9.51104
Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
6 -5.00402 0.000103557 2.55661e-07 1 1 9
Optimization terminated normally:
Convergence detected: relative gradient magnitude is below tolerance
Finished in 0.1 seconds.
fit_mle$summary() # includes lp__ (log prob calculated by Stan program)
# A tibble: 2 x 2
variable estimate
<chr> <dbl>
1 lp__ -5.00
2 theta 0.2
fit_mle$mle("theta")
theta
0.2
Here’s a plot comparing the MLE to the posterior distribution of theta
:
We can run Stan’s experimental variational Bayes algorithm (ADVI) using the $variational()
method:
fit_vb <- mod$variational(data = data_list, seed = 123, output_samples = 4000)
------------------------------------------------------------
EXPERIMENTAL ALGORITHM:
This procedure has not been thoroughly tested and may be unstable
or buggy. The interface is subject to change.
------------------------------------------------------------
Gradient evaluation took 8e-06 seconds
1000 transitions using 10 leapfrog steps per transition would take 0.08 seconds.
Adjust your expectations accordingly!
Begin eta adaptation.
Iteration: 1 / 250 [ 0%] (Adaptation)
Iteration: 50 / 250 [ 20%] (Adaptation)
Iteration: 100 / 250 [ 40%] (Adaptation)
Iteration: 150 / 250 [ 60%] (Adaptation)
Iteration: 200 / 250 [ 80%] (Adaptation)
Success! Found best value [eta = 1] earlier than expected.
Begin stochastic gradient ascent.
iter ELBO delta_ELBO_mean delta_ELBO_med notes
100 -6.262 1.000 1.000
200 -6.263 0.500 1.000
300 -6.307 0.336 0.007 MEDIAN ELBO CONVERGED
Drawing a sample of size 4000 from the approximate posterior...
COMPLETED.
Finished in 0.1 seconds.
fit_vb$summary("theta")
# A tibble: 1 x 7
variable mean median sd mad q5 q95
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 theta 0.267 0.250 0.117 0.117 0.105 0.487
The $draws()
method can be used to access the approximate posterior draws. Let’s extract the draws, make the same plot we made after MCMC, and compare the two. In this trivial example the distributions look quite similar, although the variational approximation slightly underestimates the posterior standard deviation:
bayesplot_grid( mcmc_hist(fit$draws("theta"), binwidth = 0.025), mcmc_hist(fit_vb$draws("theta"), binwidth = 0.025), titles = c("Posterior distribution from MCMC", "Approximate posterior from VB"), xlim = c(0, 1) )
For more details on the $optimize()
and $variational()
methods, follow these thinks to their documentation pages:
In order to save a fitted model object to disk and ensure that all of the contents are available when reading the object back into R, we recommend using the $save_object()
method provided by CmdStanR. The reason for this is discussed in detail in the vignette How does CmdStanR work?, so here we just demonstrate how to use the method:
fit$save_object(file = "fit.RDS") # can be read back in using readRDS fit2 <- readRDS("fit.RDS")
The RStan interface (rstan package) is an in-memory interface to Stan and relies on R packages like Rcpp and inline to call C++ code from R. On the other hand, the CmdStanR interface does not directly call any C++ code from R, instead relying on the CmdStan interface behind the scenes for compilation, running algorithms, and writing results to output files.
Compatible with latest versions of Stan. Keeping up with Stan releases is complicated for RStan, often requiring non-trivial changes to the rstan package and new CRAN releases of both rstan and StanHeaders. With CmdStanR the latest improvements in Stan will be available from R immediately after updating CmdStan using cmdstanr::install_cmdstan()
.
Fewer installation issues (e.g., no need to mess with Makevars files).
Running Stan via external processes results in fewer unexpected crashes, especially in RStudio.
Less memory overhead.
More permissive license. RStan uses the GPL-3 license while the license for CmdStanR is BSD-3, which is a bit more permissive and is the same license used for CmdStan and the Stan C++ source code.


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Would you like to contribute to the development of Mu? (If you think this is only something for experienced developers, please reconsider, there are plenty of ways to contribute to Mu, no matter your level of experience or skill set).
Our technical documentation is hosted on the wonderful service that is Read the Docs (http://mu.readthedocs.io/). Not only does it contain details needed in order to make code contributions to Mu, but it outlines plenty of other ways in which you can contribute (including making updates to this website). Finally it provides a guide to our processes and expectations for the various types of contribution you can make to Mu.
The source code is hosted on GitHub.
If in doubt, just ask in our discussion area and someone will be able to point you in the right direction.


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nips nips2007 nips2007-9 nips2007-9-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Alexandre Bouchard-côté, Percy Liang, Dan Klein, Thomas L. Griffiths
Abstract: We present a probabilistic approach to language change in which word forms are represented by phoneme sequences that undergo stochastic edits along the branches of a phylogenetic tree. This framework combines the advantages of the classical comparative method with the robustness of corpus-based probabilistic models. We use this framework to explore the consequences of two different schemes for deﬁning probabilistic models of phonological change, evaluating these schemes by reconstructing ancient word forms of Romance languages. The result is an efﬁcient inference procedure for automatically inferring ancient word forms from modern languages, which can be generalized to support inferences about linguistic phylogenies. 1
[1] W. Sidney Allen. Vox Latina: The Pronunciation of Classical Latin. Cambridge University Press, 1989.
[2] W.A. Baehrens. Sprachlicher Kommentar zur vulg¨ rlateinischen Appendix Probi. Halle a (Saale) M. Niemeyer, 1922.
[3] A. Bouchard-Cˆ t´ , P. Liang, T. Grifﬁths, and D. Klein. A Probabilistic Approach to Diachronic oe Phonology. In Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP/CoNLL), 2007.
[4] L. Campbell. Historical Linguistics. The MIT Press, 1998.
[5] I. Dyen, J.B. Kruskal, and P. Black. FILE IE-DATA1. Available at http://www.ntu.edu.au/education/langs/ielex/IE-DATA1, 1997.
[6] S. N. Evans, D. Ringe, and T. Warnow. Inference of divergence times as a statistical inverse problem. In P. Forster and C. Renfrew, editors, Phylogenetic Methods and the Prehistory of Languages. McDonald Institute Monographs, 2004.
[7] J. Felsenstein. Inferring Phylogenies. Sinauer Associates, 2003.
[8] S. Geman and D. Geman. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6:721–741, 1984.
[9] S. Goldwater and M. Johnson. Learning ot constraint rankings using a maximum entropy model. Proceedings of the Workshop on Variation within Optimality Theory, 2003.
[10] R. D. Gray and Q. Atkinson. Language-tree divergence times support the Anatolian theory of Indo-European origins. Nature, 2003.
[11] J. P. Huelsenbeck, F. Ronquist, R. Nielsen, and J. P. Bollback. Bayesian inference of phylogeny and its impact on evolutionary biology. Science, 2001.
[12] G. Kondrak. Algorithms for Language Reconstruction. PhD thesis, University of Toronto, 2002.
[13] L. Nakhleh, D. Ringe, and T. Warnow. Perfect phylogenetic networks: A new methodology for reconstructing the evolutionary history of natural languages. Language, 81:382–420, 2005.
[14] D. Ringe, T. Warnow, and A. Taylor. Indo-european and computational cladistics. Transactions of the Philological Society, 100:59–129, 2002.
[15] M. Swadesh. Towards greater accuracy in lexicostatistic dating. Journal of American Linguistics, 21:121–137, 1955.
[16] A. Venkataraman, J. Newman, and J.D. Patrick. A complexity measure for diachronic chinese phonology. In J. Coleman, editor, Computational Phonology. Association for Computational Linguistics, 1997.
[17] C. Wilson and B. Hayes. A maximum entropy model of phonotactics and phonotactic learning. Linguistic Inquiry, 2007. 8


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Leather Strips:
Locks:
Mudcrab Chitin:
Nails:
Orichalcum Ingot:
Quarried Stone:
Quicksilver Ingot:
Refined Malachite:
Refined Moonstone:
Sabre Cat Pelt:
Sabre Cat Snow Pelt:
Sabre Cat Teeth:
Sawn Logs:
Silver Ingot:
Slaughterfish Scales:
Small Antlers:
Steel Ingot:
Straw:
Vampire Dust:
Wolf Pelt:


Yobe Gov presents N108 billion budget for 2020 - Premium Times Nigeria
by Agency ReportGovernor Mai Mala Buni of Yobe State on Wednesday presented a budget of N108 billion for 2020 to the State House of Assembly for approval.
The budget tagged, “Budget of Continuity and Consolidation”, represented an increase of N16.7 billion or 15.5 per cent over the 2019 figure of N91.6 billion.
Mr Buni said the budget is made up of N58 billion or 53. 5 per cent recurrent expenditure and N50 billion or 46.5 per cent capital expenditure.
The News Agency of Nigeria (NAN), reports that Education got the highest allocation of N22.853 billion, Works, Transport and Energy got N16.078 billion, while Health got N12.658 billion.
Others are Ministry of Land, Housing and Property Development N8.846 billion, Agriculture N4.645 billion and Water Resources N1.968 billion.
The governor said his administration had expended N259 million to resuscitate Yobe Woven Sacks Factory, Yobe Flour and Feed Mills and the Yobe Aluminium Company.
“Similarly, our administration has awarded contract for construction of Damaturu Ultra-Modern Market at the cost of N2.8 billion,” Mr Buni said.
(NAN)


rahul
on 2012-05-24 07:16:22
Thanks a lot, finally i got the answer in your post, thanks a lot
Dipen
on 2012-04-04 15:18:17
thank you very much
sarav
on 2010-12-16 13:46:28
good resource for JavaScript ! was searching for long
Val Che
on 2010-04-05 14:47:43
Thanks man! Works fine for me :-)


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Please Enjoy These Innocent Pedestrians Running Away From A Man In A Dinosaur Suit
by Michelle RennexIf you’re looking for an quick arvo pick me up and have approximately 30 seconds to spare, you’ve come to the right place.
Yesterday a TikTok was uploaded onto Twitter by @Zencsss and has already gotten over 1.5 million likes. Yep. 1.5 MILLION.
The video was made by Goub of Goubtube, an internet personality who makes a bunch of these funny videos across YouTube, Instagram, Snapchat and TikTok.
But this video in particular has everyone in stitches, and I can see why. The slow-motion realisations, the clumsy turns to get away and the dramatic opera music really just make it 30 seconds of perfection.
Obviously everyone quickly became obsessed with the man in the scrubs who loses his lunch.
If you’d like to see our fallen medical soldier lose his food from another angle, here you go.
Anyway, thanks for your time. That’s it. That’s the post.