Codehaus XFire
DocumentationQuicklinksDevelopers
Sponsors |
The MessageContextThe MessageContext class stays with an XFire invocation from start to finish. It is useful for:
Accessing the MessageContext from your OperationIn some cases you want to be able to access XFire's MessageContext. The Invokers that come with XFire allow you to add a MessageContext variable to your operation like so: public String echo(String echo, MessageContext context) { // do something with the context... return echo; } XFire will just inject the MessageContext for you. With no ThreadLocal magic at all! Accessing Messages and their XMLThe MessageContext has some accessors that easy to access messages associated with the invocation.
Getting at the XMLXFire is based on a completely streaming model. So you have two options if you want to get at the XML. The first is to use the Message Binding. This way XFire won't read the stream into Objects for you, and you can access the XMLStreamReader yourself. The second way is to switch to "DOM Mode" by using the DOMInHandler and DOMOutHandler. These handlers can be registered with the service Service service = ... // retreive your service from the ServiceRegistry service.addInHandler(new DOMInHandler()) service.addOutHandler(new DOMInHandler()) Then you can access the org.w3c.dom.Document for the message: public void invoke(MessageContext context) {
Document inputDoc = context.getInMessage().getProperty(DOMInHandler.DOM_MESSAGE):
Document outputDoc = context.getOutMessage().getProperty(DOMOutHandler.DOM_MESSAGE):
}
|


Fresh Fruits
It has long been known that a diet that includes at least a few servings of fresh fruit every day will help keep you healthy, fit and trim.
It has long been known that a diet that includes at least a few servings of fresh fruit every day will help keep you healthy, fit and trim.


MEIJER ANTIBIOTIC PLUS PAIN RELIEF- neomycin sulfate, polymyxin b sulfate and pramoxine hydrochloride cream
Meijer, Inc.
Disclaimer: Most OTC drugs are not reviewed and approved by FDA, however they may be marketed if they comply with applicable regulations and policies. FDA has not evaluated whether this product complies.
Active ingredients Purpose
Neomycin Sulfate 3.5 mg............................................................ First aid antibiotic
Polymyxin B Sulfate 10,000 units................................................. First aid antibiotic
Pramoxine HCI 10 mg................................................................. External analgesic
Uses
first aid to help prevent infection and temporary relief of pain or discomfort in minor
Stop use and ask a doctor if
Keep out of the reach of children. If swallowed, get medical help or contact a Poison Control Center right away.
Directions
Other information
|
MEIJER ANTIBIOTIC PLUS PAIN RELIEF
neomycin sulfate, polymyxin b sulfate, and pramoxine hci cream | ||||||||||||||||||||
| ||||||||||||||||||||
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| Labeler - Meijer, Inc. (006959555) |


BabylonJS Basics
Lights, Camera, Meshes, Action
by Brendon A. Smith
Disclaimer
I am here on my own behalf and my views reflect only my own
and not my employer. Everything that will be demonstrated
here will be free and open source. All contents will be available
for free on
gitHub.com
.
Why webGL?
Ubiquitous
GL (graphics language) is powerful
You only need a browser and a notepad, no need for expensive software
BabylonJS What is it?
BabylonJS was created by
David Cathue
/
DeltaKosh
at Microsoft
It is a performant webGL engine
Enables developers to rapidly create interactive 3D content
What was babylonJS written in?
TypeScript
Fast and it is easy to create a project with it
Has excellent
documentation
How is it different from threeJS?
No Canvas Fallback
No CSS renderer
Basic Collisions baked in
Audio Analyzer Class included
Physics made easy
What is a
scene
and what does it consist of?
Lights
Cameras
Meshes
Materials
Action/Game loop/Render Loop
Is there a quick start?
Playground
Lights, what kind are provided by default?
DirectionalLight
HemisphericLight
|
playground
PointLight
|
playground
SpotLight
|
playground
More on Lights
Cameras, what kind are provided by default?
AnaglyphCamera
|
example
ArcRotateCamera
FollowCamera
StereoscopicCamera
VR cameras
/ gamepad cameras
Meshes, what primitives are baked in?
Box
|
playground
Sphere
|
playground
Plane
|
playground
Tourus
|
playground
Knot
|
playground
Lines
|
playground
HeightMap
|
playground
Ribbon
|
playground
What are Meshes? | how are they built?
VertexData Class
Position (
vectors
)
Normals (direction vectors for materials)
UVs
Faces (triangles)
Indices ( triangle points )
Learn more about the fine art of meshing | on
github
What about Assets created with third party tools?
Asset pipeline explained
What formats are supported by the
loader
?
glTF
OBJ
STL
How can I
export
my models from a third party tool?
3DS Max
Blender3d
FBX
Cheetah3d
Unity
XNA - OBJ
.babylon format
How do I paint my meshes?
What do materials do?
Standard Material
Procedurally Generated Materials
|
example
|
code
Physically Based Materials
|
demo
Shader Materials
|
cyos
|
example
|
code
Textures and Dynamic Texture
Deep Dive into Standard Materials
Learn more about shaders |
More about shaders
Shader Toy
Special FX?
Shadows
|
playground
Bloom
|
Example
|
Code
Convolution
|
Example
|
Code
Refraction
|
Example
|
Example2
God Rays
|
playground
|
Example2
Fresnel
|
playground
Video
|
playground
Particles
|
playground
What about action, how do I make this stuff move and collide?
Where does the action happen?
Game Loop
/
Request Animation Frame
How can design patterns save me?
BabylonJS comes with collisions baked in
Collisions
|
playground
Collisions 2
|
playground simple fps
Physics |
example
|
code
Oimo
cannonjs
Tell me more about physics
Help me build a game
How to build a simple FPS video.
babylonJS builiding quickly
Speed up the process by using the
playground
Speed up the process by using
generators
Create materials quickly
online
Create shaders quickly
online
Mobile
Ludei
Universal Apps
Haxe?
Haxe what is it?
Haxe crossplatform toolkit
Similar to dart / c# / actionScript / and typeScript
Strongly Typed
Compatiable with node?
Future Proof? Emscripten / Soon Web Binary (flash all over?)
How does it work?
1000ft view
Simple Command Line Interface
Backends
FrontEnds (the fun stuff)
Haxe Frameworks
NME
openFL
|
lime
kha
Snow
|
Luxe
BabylonHX
Created by Krtolica Vujadin and I
Krtolica Vujadin on twitter
Supports Multiple Back-ends
Why?
Further Reading
Programing 3D
Learning VR
Other Libraries
Learning WebGL
CannonJS physics engine
JigLib physics engine
Bullet physics engine
AmmoJS physics engine
PathFindingJS
PixiJS Library
PhaserJS Library
EaselJS Library
Flixel Library
LearningThreeJS Blog
?
Stemkoski
Thank You
Khronos Group
Autodesk
Our host New Relic
David Cathue and Microsoft


Longitudinal Neighborhood Analysis
Select Year:
1970
1980
1990
2000
2010
States:
ALABAMA
ALASKA
ARIZONA
ARKANSAS
CALIFORNIA
COLORADO
CONNECTICUT
DELAWARE
DC
FLORIDA
GEORGIA
HAWAII
IDAHO
ILLINOIS
INDIANA
IOWA
KANSAS
KENTUCKY
LOUISIANA
MAINE
MARYLAND
MASSACHUESTTS
MICHIGAN
MINNESOTA
MISSISSIPPI
MISSOURI
MONTANA
NEBRASKA
NEVADA
NEW HAMPSHIRE
NEW JERSEY
NEW MEXICO
NEW YORK
NORTH CAROLINA
NORTH DAKOTA
OHIO
OKLAHOMA
OREGON
PENNSYLVANIA
RHODE ISLAND
SOUTH CAROLINA
SOUTH DAKAOTA
TENNESSEE
TEXAS
UTAH
VERMONT
VIRGINIA
WASHINGTON
WEST VIRGINIA
WISCONSIN
WYOMING
PR
Metro Area:
All county of ALABAMA
Albertville
Anniston-Oxford-Jacksonville
Auburn-Opelika
Birmingham-Hoover
Columbus
Cullman
Daphne-Fairhope-Foley
Decatur
Dothan
Enterprise
Florence-Muscle Shoals
Gadsden
Huntsville
Mobile
Montgomery
Ozark
Scottsboro
Selma
Talladega-Sylacauga
Troy
Tuscaloosa
Valley
County:
ALL
Bibb
Blount
Chilton
Jefferson
St. Clair
Shelby
Walker
help?
Race, Age by Race
ALL
% white, non-Hispanic
% black, non-Hispanic
% black, non-Hispanic
Ethnicity and Immigration
ALL
% Mexican birth/ethnicity
% Cuban birth/ethnicity
#% Puerto Rican birth/ethnicity
Socioeconomic Status
ALL
% with high school degree or less
% with 4-year college degree or more
% unemployed
Housing, Age, and Marital Status
ALL
% vacant units
% owner-occupied units
% multi-family units
Submit form
Download CSV


This could be the start of
a beautiful relationship
Ready to work with you for years to come
America’s Longest‑Lasting Heavy‑Duty Pickups1
BUILD & PRICE
SEARCH INVENTORY
Aftermarket and optional features shown. Properly secure all cargo. Always tow within the vehicle's capacity.
1 Based on the average of VIO vs New Registrations for each model year within the FCA defined HD pickup segment using IHS Markit data as of July 1, 2018 for Model Years 1988 to 2018.
©2018 FCA US LLC. All Rights Reserved. Ram is a registered trademark of FCA US LLC.


Album > The Beatles (White Album) [Disc 1]
Artist > The Beatles
Year of Release > 1968
Label > Apple
Tracks
1.
Back In The USSR
2.
Dear Prudence
3.
Glass Onion
4.
Ob-La-Di, Ob-La-Da
5.
Wild Honey Pie
6.
The Continuing Story Of Bungalow Bill
7.
While My Guitar Gently Weeps
8.
Happiness Is A Warm Gun
9.
Martha My Dear
10.
I'm So Tired
11.
Blackbird
12.
Piggies
13.
Rocky Racoon
14.
Don't Pass Me By
15.
Why Don't We Do It In The Road?
16.
I Will
17.
Julia


Developer Student Clubs - Academy Of Technology
Solution Challenge'21 - Info Session
Android Study Jam - Day 3
Android Study Jam - Day 2
Android Study Jam - Day 1
Tour of Google Infrastructure
#30DaysofGoogleCloud
Hacktober Fest 2020


How to Whiten Skin Fast Using 7 Simply Smooth Strategies
Come on ladies. You all envy the models shown in TV commercials with fair, white skin. And secretly, or even openly, you want to have whiter skin too that you will do whatever it takes just to have it. You even tried different whitening products just to know which one really works and which ones is a waste of time.
But the question is can you still lighten your skin? If yes, what are the different ways on how to whiten skin fast? Is it really worth the time and money or you are merely making these companies richer?
To help you in your quest for lighter skin, here are tips and various ways on how to lighten skin.
Got Dark Skin, Big Problem?
Every person white skin and blonde hairis different. And when it comes to skin color, every person will vary too.  With the advancement of technology and
the advertisement of tons of whitening products, more women are seeking lighter skin.
The truth is, there is nothing wrong with having dark skin. After all, true beauty is not determined by your skin color, right?
However, there are still a lot of women who prefer to be whiter. And there are tons of products and treatments offered that
promise to help you in your quest towards whiter skin.
Keep in mind that your skin color is determined by the amount of melanin produced by your melanocyte cells. The more melanin you
have, the darker your hair and skin color will be, and vice versa. But when there is too much melanin produced by your body,
that’s where the problem starts.
7 Effective Tips On How To Whiten Skin Fast
There will always be a solution for everything. And when it comes to natural skin whitening, you will never run out of solutions. Here is a list of the different and effective ways on how whiten your skin.
Drink Water Regularly: You’ve probably got tired of hearing this age-old phrase. This is because water can really do a lot to your health. Drinking at least eight glasses of water everyday can help push the unwanted urea out of your body. This can improve your blood circulation, which will eventually show in your skin. You can even add a slice of lemon to your water for better results.
 So, if you eat too much processed food, it will definitely show on
your skin.
 Although it may
take some time, having a healthy diet can also whiten your skin gradually.
The Sun Is Not Your Skin’s Best Friend: Vitamin D from the sun is good. But, too much of it is a bad, causing your melanin levels to spike. Therefore, make it a point to protect yourself from its harmful rays.
Regular application of sunscreen can also do the trick, especially when the sun is really unavoidable. And, most importantly, stay away from the sun between10 a.m. and 2 p.m., when its UV rays are strong and quite harmful.
 You need to regularly moisturize your skin to make it smooth and supple and can help you a lot in your skin’s rejuvenation process. Remember that dry skin absorbs more heat which can cause your skin to look darker – and you don’t want that.
Exfoliate: You always hear about dead skin cells and how they love to stay on your face.
By exfoliating once a week, you will be able to get rid of dead and dark skin cells and allow the new, lighter ones to come out on the surface.
Once the fresh skin is revealed thanks to exfoliation, you can finally have that lighter skin tone.
Take It Easy On Beauty Treatments And Procedures: A facial once in a while is okay, since it keeps your skin healthy. But too much cosmetic procedures can abuse your face and disturb your skin’s natural balance which will eventually lead to skin pigmentation.
Try Homemade Remedies: One of the best ways on how to whiten your skin is by going natural. Here are simple home remedies on how to whiten skin fast:
Stick to the basics, such as soybeans, green leafy vegetables, fruits and vitamin C.
Drink two cups of green tea everyday. It is a powerful antioxidant that can reduce the amount of peroxides in the body.
You can never go wrong with lemon juice.
Aside from reducing your eye bags, mashing cucumber and applying it on your face can be an excellent skin-lightening mask.
Never say no to milk. It is the most natural skin-whitening agent that can keep your skin healthy and glowing inside out.
But What Can You Do With Dark Spots On Your Skin?
Aside from the dark skin problems, one of the biggest cosmetic concerns among women today is dark spots. Also known as hyperpigmentation, these dark spots are often results of too much sun exposure, effects of certain drug medications, hormonal changes, pregnancy and even aging. Because of certain conditions, the level of melanin shoots up, causing your skin, or at least some areas of it to be darker.
The good news is you can treat dark spots and eventually remove them. One of the best ways to lighten it is by using Illuminatural 6i.
It is an advanced skin lightener solution that removes dead, pigmented spots on your skin to allow new and lighter skin cells to resurface.
Thanks to its six clinically proven and harmless ingredients, getting that flawless and natural look is easier and made possible. Do you still want to try other products when it comes to how to whiten your skin fast? Be sure to consult with your physician on any over the counter or prescription product, especially if you have sensitive skin.


{% extends "account/auth_base.html" %} {% load account socialaccount %} {% load crispy_forms_tags %} {% block title %}{{ block.super }}Log In{% endblock %} {% block form_heading %}Please Log In{% endblock %} {% block form %}
Don't have an account? Sign up.


Education
MASTER OF BIOINFORMATICS (1ST YEAR)
2017 - Today | Paris Diderot University, FranceBiology - Informatics Bioinformatics (BIB)
BACHELOR OF SCIENCE (BSc) • Rank 6/21
2016 - 2017 | Paris Diderot University, FranceBiology - Informatics Bioinformatics (BIB)
TWO-YEAR UNIVERSITY DEGREE
Ranks: Year 1 • 4/45 | Year 2 • 5/34
2014 - 2016 | University Institute of Technology of Auvergne in Aurillac, France
Bioengineering (Bioinformatics)
1ST YEAR UNDERGRADUATE COURSE FOR ENGINEERING SCHOOLS
2013 - 2014 | Joseph Fourier University, Grenoble, FranceMathematics, Computer Science and Biology
BACCALAURÉAT WITH HONORS
2013 | Seoul, South KoreaFrench Secondary School Diploma
Professional Experience
INTERNSHIP IN BIOINFORMATICS AT INRA
(French National Institute for Agricultural Research)
April - June 2016 | Clermont-Ferrand Theix - Lyon, France
As part of the French project Workflow4Metabolomics, development of a tool for the annotation of GC-MS data exploited with Golm Metabolome Database for its integration into the Galaxy platform.
Supermarket employee
July - August 2016/17 | FranceHouse-keeper at “Le Détour” Bed and Breakfast
July - August 2016/17 | FranceProgramming Skills
Computer science
ALGORITHMICS
PROGRAMMING LANGUAGES
C, Python, OOP (Object-Oriented Perl), R, Unix, Bash, SQL (Post-greSQL, MySQL), Java, Javascript, HTML/CSS, PHP, AWK, Sed, MatLab
OFFICE SOFTWARE
Microsoft Office Suite 2010/13/16 (Word, Excel, PowerPoint, Access, Outlook), LibreOffice/OpenOffice (Calc, Writer, Impress, Base)
VERSIONING
Git
INTEGRATED DEVELOPMENT ENVIRONMENT (IDE)
Eclipse
OPERATING SYSTEMS
Windows 7/8/8.1/10, GNU/Linux (Debian, Ubuntu)
SOFTWARE ENGINEERING / INSTALLATION
Biology
MOLECULAR AND CELLULAR BIOLOGY
“OMICS”
Genomics, Transcriptomics, Proteomics, Metabolomics
LABORATORY
Biotechnology Techniques (Aseptic environment with type 1 and 2 PSM laminar flow hoods and fume cup-board), manipulation of microscopic biological materials (viruses, bacteria, fungi) and macroscopic (animal and vegetal tissues)
Bioinformatics
BLAST Tools and exploitation of Biological Databases
NCBI, EMBL, GenBank, Interpro, KEGG
Interest
Sport
Volley-ball at CantalVolley club (Aurillac, France) • Basket-ball at the Joseph Fourier University (Grenoble, France) • Basket-ball at the French High-School in Seoul, South Korea
Music
Piano (5 years) • Music theory • Choir at the Alliance française de Delhi (India), AFD Golden Jubilee Choir
Social Life
Member of the High-School students house (French High-School in Seoul, South Korea)


Hi!
Hello! My name is Javier. Im ready to start learning and contributing! Let's code something together!
GitHub profile


My name is Michael Vamvakas, I am a fifth year Software Engineering Student with a minor in Mathematics. This page is a reflection of my second 8 month work (January 2020 - August 2020) at Perkin Elmer, this time working as an Associate Software Development Engineer. In this report I will talk about the numerous ways I grew as a Student, Employee, and Programmer.
PerkinElmer is an American company founded in 1939. They focus in the areas of human and environmental health. This includes environmental analysis, food and consumer product safety, and life science research to name a few. The software, medical imaging components, instruments, and consumables help many end markets. Some of these markets are Animal Health, Food & Agriculture, and Therapeutic Areas. Their offices are located all over the world, including the United States, Canada, France, South Africa, Japan, Australia, China, and Brazil. The Guelph R&D Team is currently working on informatics software for the medical industry.
While working at PerkinElmer as an Associate Software Development
Engineer, my main job was to assist in fixing bugs and implementing new
features for
Signals Medical Review. My role within the team was to either work on bugs assigned to me
using Jira, or work with other developers to implement new
features/stories discussed during sprint planning.
Many of the things I did at PerkinElmer Include:
- Fixing bugs/errors in Signals Medical Review - Implementing new
features in Signals Medical Review - Writing unit tests for new code
implemented.
- Using
Jira, creating new tasks, assigning bugs/tasks from the backlog
- Programming in the
.Net Framework
- Working with the Angular Framework
My main goals while working for PerkinElmer were to increase my
abilities as a programmer, as well as grow within a professional
environment. I wanted to work on my communication skills.
My goals for the first 4 months were:
1. Improve abilities with the .net framework
2. Learn more about how to properly use Agile
3. Be more active at attempting problems I don't know how to solve
As I had never worked with the .Net Framework before heading into this
work term so my main goal during my first 4 months was to learn as much
as I can about C#, Angular, and the .Net Framework as a whole. My other
important goal was to make a greater effort to take on tasks I had no
idea how to solve and trying to work through them. I also had a a goal
to learn more about Agile as there is almost more to learn and improve
when working with it.
My goals for the last 4 months were
1. Learn more about .Net
2. Stay motivated and focused while working from home
3. Increase my ability to problem solve
After the first four months I realized I still have a long way to go
with the .Net Framework, so I wanted to continue focusing on learning as
much as I can about it as I could. My secondary goal was to try to stay
as motivated as possible while working from home. As I have ADD working
from home full time and staying focused on task proved to be quite the
challenge. It started out badly but as time went on I feel I adjusted to
working from home quite well.
Reflecting on my goals and everything I've learned while working at
PerkinElmer, I believe that I had an extremely successful work term.
Despite the COVID-19 kicking me out of the office 2.5 months I feel I
improved my programming skills a lot and had a great time working for
PerkinElmer.
I would like to thank everyone I worked with, both for being great
teammates and for everything I learned from them during my 8 months.
Chad Millen, Tim Cooper, Mark Roy, Harold Miller-Koren, Sarah Shepherd
(a University of Guelph alumni), David Wilhelm, Sharath Sundar, Gurdeep
Multani, Stephen Menyheart, Valerie Nespolo, Jennifer Gatti. As for my
fellow co-op students thank you to Caroline Bridge, and Cameron Guitard.
I want to do a special thank you to Sarah Shepherd for making all the
co-ops feel welcome and for helping make work fun even when working from
home. I also want to do a special thank you to Tim Cooper for being a
great manager.
Special thank you to the Guelph Co-op and Career Services, as well as my
advisors Laura Gatto and Kate McRoberts.
Finally thank you to Greg Klotz for reading through my work term report


--- title: "Public Bike Sharing Data Visualization" author: "R package build" date: '2021-09-02' slug: [] categories: Data World tags: - Data Visualization - R Description: '' Tags: [] Categories: [] DisableComments: no thumbnail: images/bike-sharing.jpg ---
Main focus of this artical is to explore the data visulization packages ggplot2 and ggridges
Today we will use the dataset from Kaggle: Mark Kaghazgarian
Packages needed are listed below:
library(ggplot2)
library(dplyr)
library(ggridges) ## ridge line plot
library(hrbrthemes)
library(viridis)
library(lubridate) ##dealing with date/time
library(tidyverse)
library(gganimate)
Here is the data:
bike_data <- read.csv('~/DATASETS/archive/day.csv')
str(bike_data)
## 'data.frame': 731 obs. of 16 variables:
## $ instant : int 1 2 3 4 5 6 7 8 9 10 ...
## $ dteday : chr "2011-01-01" "2011-01-02" "2011-01-03" "2011-01-04" ...
## $ season : int 1 1 1 1 1 1 1 1 1 1 ...
## $ yr : int 0 0 0 0 0 0 0 0 0 0 ...
## $ mnth : int 1 1 1 1 1 1 1 1 1 1 ...
## $ holiday : int 0 0 0 0 0 0 0 0 0 0 ...
## $ weekday : int 6 0 1 2 3 4 5 6 0 1 ...
## $ workingday: int 0 0 1 1 1 1 1 0 0 1 ...
## $ weathersit: int 2 2 1 1 1 1 2 2 1 1 ...
## $ temp : num 0.344 0.363 0.196 0.2 0.227 ...
## $ atemp : num 0.364 0.354 0.189 0.212 0.229 ...
## $ hum : num 0.806 0.696 0.437 0.59 0.437 ...
## $ windspeed : num 0.16 0.249 0.248 0.16 0.187 ...
## $ casual : int 331 131 120 108 82 88 148 68 54 41 ...
## $ registered: int<PHONE_NUMBER> 1454 1518 1518 1362<PHONE_NUMBER> ...
## $ cnt : int<PHONE_NUMBER> 1562 1600 1606 1510<PHONE_NUMBER> ...
This data set recorded the public bike rental count from 2011 to 2012 in Washington D.C., and all other weather related information.
Before proceeding with some plots, lets tidy up the data a bit.
bike_data <- bike_data[-1] ## first column is just index number
bike_data$dteday <- as.Date(bike_data$dteday, format = '%Y-%m-%d')
bike_data[,c(2:8)] <- lapply(bike_data[,c(2:8)], factor) ##integer in these columns represent different factors/levels, detailed data background info https://www.kaggle.com/marklvl/bike-sharing-dataset
Say, if I wanted to know how the bike rental count was distributed along four different seasons in the year of 2011.
levels(bike_data$season) <- c('Spring', 'Summer', 'Fall', 'Winter')
bike_2011 <- bike_data[1:365, ]
bike_2012 <- bike_data[366:730, ]##get the 2011/2012 data
bike_2011 %>% plot(x = bike_2011$season, y = bike_2011$cnt)
Pretty boring, isn’t it? Lets see how we can make better plots with ggplot2 and ggridges
Lets see how total bike rental counts are distributed along 4 seasons:
bike_2011 %>% ggplot(aes(x = cnt, y = season,fill = season)) +
geom_density_ridges(lwd = 0)+
scale_fill_manual(name = 'Season', values = c("#05f5dd", '#BCFA09','#FA9409','#F3ECF0'))+
theme_ridges() +
theme(legend.position = "bottom") +
labs(title = "Public Bike Rental in Washington D.C.", subtitle = "2011")+
xlab('Bike Count')
This is how temperatures of each month in 2011 feels like:
bike_2011 %>% ggplot(aes(x= temp*50, y = mnth, fill = stat(x))) +
geom_density_ridges_gradient(scale = 2, rel_min_height = 0.01, lwd = 0.2, quantile_lines = TRUE, color = 'white')+
scale_fill_viridis(name = 'Temperature', option = 'C')+
labs(title = 'Temperatures in Washington D.C.(2011)') +
ylab('Month')+
xlab("Temperature(Celcius)")+
theme_ridges()
What if I want to compare the weekly average bike rental count between 2011 and 2012, in a cool way? You can use gganimate package to do a animation plot.
bike_1 <- bike_data[1:730, ]
bike_1$dteday <- week(bike_1$dteday) ##week() function from lubridate, to get week number
by_week <- bike_1 %>% select(dteday, yr, cnt) %>% group_by(dteday, yr) %>% summarise(
mean_cnt = mean(cnt))
by_week %>% ggplot(aes(x = dteday, y = mean_cnt, group = yr, color = yr))+
geom_line()+
scale_color_viridis(discrete = TRUE)+
theme_ipsum()+
scale_color_manual(name = 'Year',labels = c('2011','2012'), values = c("#04FBE1", "#02A9FD"))+
ggtitle("Bike Rental in 2011 and 2012")+
xlab("Weekly Average")+
transition_reveal(dteday)
Pretty awesome, right? … Who you taling to? No one is reading you blog, Lee .. .. … …. Its ok


?Bouteloua
americana
(L.)
Scribn.
Det.
by:
F.
W.
Gould
19^7
J.
A.
SHAFER.
COLLECTOR
FEBRUARY
20-21,
1909
TRACY
HERBARIUM,
A.
ft
M.
COLLEGE
OF
TEXAS
THE
NEW
YORK
BOTANICAL
GARDEN
EXPLORATION
OF
CUBA
SAVANNA
SOUTH
OF
SIERRA
CUBITAS.
CAMAGUEY
01530031
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


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2020 Asher Seiling
Input old PI version you want to convert
New pi Is equal to
Number of Numbers this was tested on
Success rate
Fails


{% extends "base.html" %} {% load compress staticfiles hc_extras %} {% block title %}Account Settings - {% site_name %}{% endblock %} {% block content %}
healthchecks.io provides status badges for each of the tags you have used. The badges have public, but hard-to-guess URLs. If you wish, you can add them to your READMEs, dashboards or status pages.
{% if badge_urls %}
{{ badge_url }}
|
To get started with status badges, add some tags to your checks!
{% endif %}


Tile tracking coming to laptops with major PC partnership
Sources close to the company say a partnership with a major PC player is in the works.
by Dan Thorp-LancasterSource: Android Central
What you need to know
- Tile is partnering with a major PC player.
- Direct integration with Tile would eliminate the need to attach a dedicated tracker to PCs.
- This could signal a push into laptops, tablets and other Bluetooth-enabled devices in the near future.
Tile is preparing to make a push beyond its dedicated trackers and directly into PCs. Sources close to Tile say a partnership with a major PC player is on the horizon. This could mean that we'll see Tile technology being embedded into laptops, tablets and more in the near future.
Making a bid for PCs would mark a significant step for the company. Tile is known mostly for its dedicated trackers that are easily added to key rings, stashed in wallets and suitcases, or attached to other items you might want to keep track of. While some might opt to attach a Tile tracker to their laptop, embedding Tile tracking in the PC itself would make for a more elegant solution.
The move comes at a time when Bluetooth has its eyes set on location tracking in general. Bluetooth version 5.1 will feature direction finding, which allows devices to understand the position of others with centimeter-level accuracy.
"Imagine a room in a museum where there are multiple exhibits that have associated beacons," David Hollander, director of marketing at Bluetooth SIG, recently told us. "Currently, those point-of-interest applications can let a visitor know about all the exhibits in a room, and then you have to select what you want to learn more about. With direction finding, it'll streamline that whole experience. Museum visitors will be able to simply point their smartphones at an exhibit and get more information on that item."
Our sources didn't indicate which PC company Tile is partnering with. However, with IDC estimates putting Q3 2019 PC shipments at 70.4 million, entering the PC market could represent an interesting avenue of growth for the company if it catches on.
Now tracking
Tile Pro (2-pack)
Tile helps you easily track down lost keys, bags, and anything else you can attach it to.
We may earn a commission for purchases using our links. Learn more.


What can you say about the following piece of code?
char c1 = 'A' + 32; int c2 = 'A' + 32; System.out.println(c1); System.out.println(c2);


Bookmarks
test bookmarks
Google
Wikipedia
Wikipedia English Home
Wiki Buthan
Nepal
A country in the Himalayas
Youtube


${sender_name} % if offer_payment_type_free: accepted % else: purchased % endif offer ${offer_name} % if offer_payment_type_wingcash: for ${transfer_amount_formatted} cash. % elif offer_payment_type_free: for no charge. % elif offer_payment_type_paycard: using a payment card for ${transfer_amount_formatted}. When the payment settles, ${merchant_name} will receive the payment. % endif
${merchant_name} completed the purchase by sending ${describe_notes_html} to ${sender_name}.


| 025:039 Khan | : |
And for each of them We put forward examples (as proofs and lessons, etc.), and each (of them) We brought to utter ruin (because of their disbelief and evil deeds). |
| 025:039 Maulana | : |
And to each We gave examples and each did We destroy with utter destruction. |
| 025:039 Pickthal | : |
Each (of them) We warned by examples, and each (of them) We brought to utter ruin. |
| 025:039 Rashad | : |
To each of these groups, we delivered sufficient examples, before we annihilated them. |
| 025:039 Sarwar | : |
We gave guidance and drove each to destruction. |
| 025:039 Shakir | : |
And to every one We gave examples and every one did We destroy with utter destruction. |
| 025:039 Sherali | : |
And to each one WE set forth examples; and each one WE utterly destroyed. |
| 025:039 Yusufali | : |
To each one We set forth Parables and examples; and each one We broke to utter annihilation (for their sins). |
Concepts/Themes Covered: القصص والتاريخ > (عاد (قوم هود The Stories and the History>Ad(Hud's People) القصص والتاريخ > العبر التاريخية فى أنباء القرى The Stories and the History> The Historic Examples of Towns' News
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


Single Sign On using your ELIXIR identity!
You can use the ELIXIR identity service
and other ELIXIR services with the freely available
ELIXIR identity,
which integrates with Google, ORCID and most academic institutions.
Obtain your ELIXIR identity here.


Sorry, we are unable to calculate the result for you.
Developed by Summer Ai, Runting Shao, Ningji Shen, Chang Xu and Zihui Zhang at the University of Washington.
All Rights Reserved.


Coding Competitions
For any queries or doubts refer to :
Reference e-Books:
Our First Contest- Fall For Code 1.0 was organised on 20th June 2019.
Our Second Contest- Fall For Code 2.0 was organised on 2nd October 2019.


1) 2/2/22: I am trying to recreate the Bitlife Prison Escape Game.
2) 2/11/22: I made it so that if you put the parameters in for the walls and the size of the prison, it creates the general layout for the prison (minus the exit).
3) 2/16/22: I made it so that it can sense where walls are or the spaces around (it's the beginning steps of making the characters move in each direction and stuff.)
4) 2/25/22: I made it so that the computer prison guard moves on it's own. It doesn't exactly move how it's supposed to so I have to fix that.
5) 3/4/22: I fixed it so that the prison guard moves properly, added the exit, and game over function. Now all I need to do is finish up the final aesthetic touches (including putting actual avatars instead of circles), and testing other prison setups -- making sure everything works properly.
6) 3/11/22: I have been super busy this last week of break with the practice ACTS, 3 assessments, a bunch of projects and my birthday, so as of now at least I haven't changed anything and I probably won't by Friday. This project though, should be done the next update or two.
7) 4/1/22: I made the code way more efficient and made it so that different layout puzzles work. I just need to make the game stop when you win or lose and replace the circles with character pictures (also I should add more layouts). So far it seems that the game works the way it's supposed to though.
8) 4/8/22: Sorry, I couldn't do anything. This week has been super hectic.
9) 4/15/22: I cropped images of both characters (cop and inmate) and put text above explaining the object of the game. I intend (as the last touch) to add reset and level navigation, a long with more layout set ups. However, the game is technically complete and to my knowledge works fine.
10) 4/22/22: My computer broke.
11) 4/29/22: Still waiting on that computer.
12) 5/6/22: Additional issue was detected. Still waiting.
13) 5/13/22: I decided I'm done with my current project and I'm figuring out what I want to do next
14) 5/20/22: I started finding instructions and writing code for webscraping to create something for criminal profiling.
15) 5/27/22: I am putting that work aside (briefly) to revisit my chess code and prepare to present my project to the intermediate class.


| Trees | Indices | Help |
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Model The Model keeps track of all of the data that the GUI needs. |
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BOptionsDialog BOptionsDialog is the dialog that we'll use to set various options in the config object. |
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ActorListCtrl The ActorListCtrl is the widget that actors will be inputted into. |
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NWVarListCtrl The NWVarListCtrl is the widget that NWVariables will be inputted into. |
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VerbShapeHandler Enables us to change tabs when a verb on the VerbCanvas is double-clicked. |
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VerbCanvas The canvas that the verb graph will be drawn on. |
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NWScriptSTC A styled text control (a wxWidget based on Scintilla) for the display of NWScript code. |
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BehaviourCodePreviewPanel A panel containing widgets to select between two choices of viewable code, and preview it. |
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VerbCodePreviewPanel A panel containing widgets to view two code preview widgets simultaneously. |
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BehaviourPanel This panel contains widgets to display and modify behaviour-wide information. |
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VerbPanel This panel contains widgets to view and modify a verb. |
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BehaviourSplitter Allows the user to vary how much they see of the two sections of a behaviour tab. |
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VerbSplitter Allows the user to vary how much they see of the two sections of a verb tab. |
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MainNoteBook The notebook that encompasses the rest of the interface. |
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MainFrame The main frame for the program, containing a menu and the notebook. |
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BehaviourApp Our program. |
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| Model |
model = Model() The model object that the GUI will work with. |
||
| RawConfigParser |
config = ConfigParser.RawConfigParser() Contains any configuration options that we want to keep track of on disk. |
|
configContains any configuration options that we want to keep track of on disk. The configuration options are stored inBehaviourTool.ini , and loaded up when the program
begins.
|
| Trees | Indices | Help |
|
|---|
| Generated by Epydoc 3.0beta1 on Fri Aug 17 10:31:28 2007 | http://epydoc.sourceforge.net |


Manage Companies
You may optionally enter a comparison operator (<, <=, >, >=, <> or =) at the beginning of each of your search values to specify how the comparison should be done.
Advanced SearchYou may optionally enter a comparison operator (<, <=, >, >=, <> or =) at the beginning of each of your search values to specify how the comparison should be done.
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| JARs |
Location
Loaded From |
Description |
| rio.jar |
${RIO_HOME}/lib |
Rio core technology platform
classes |
| platform-capabilities.jar |
${RIO_HOME}/lib | Platform capability classes |
| jsk-lib.jar |
${JINI_LIB} |
The Jini 2.1 technology required
platform classes. More information on Jini technology can be found here |
AppCL |
| Type: |
String |
| Default: |
${RIO_HOME}${/}config${/}platform |
| Description: |
The platformDir property
is scanned at system startup time. For each file ending in .xml, the
file is parsed for Platform Configuration declarations. The platform
element contains capability declarations that document the qualitative
capabilities a compute resource will advertise and make available for
services to use. |
| Type: |
{@link java.lang.Boolean} |
| Default: |
true |
| Description: |
Creates a shutdown hook. This will result in the registration of a virtual-machine shutdown hook. The shutdown hook will provide an orderly shutdown of all ServiceBean instances the Cybernode has instantiated by calling the ServiceBean's destroy method |
| Type: |
{@link java.lang.Boolean} |
| Default: |
false |
| Description: |
Determines if a Multicast
socket can be created and a Request announcement can be sent. Note
that it is still possible that multicast does not work outside the host |
| <?xml version="1.0"
encoding="UTF-8"?> <!-- Default platform configuration --> <platform> <!-- Rio jars to load into the common classloader. --> <capability name="Rio" class="org.rioproject.system.capability.software.RioSupport" common="yes"> <version>4.0 M2</version> <classpath>${RIO_HOME}/lib/rio.jar</classpath> </capability> <!-- River (Jini) jars to load into the common classloader. --> <capability name="River" common="yes"> <version>2.1.1</version> <manufacturer>Apache Software Foundation</manufacturer> <classpath>${JINI_HOME}/lib/jsk-lib.jar</classpath> </capability> </platform> |


Colombian Tamales
Home
Cooking
Feedback
Thank You!
Thank you so much for your feedback!
Ana Herrera © 2018


Lets plot the latest sentiment index using sent_index()
function.
In this example we will be calling the economy
focus index that is constructed using the news data. To characterize the sentiment, we use the Loughran
dictionary and calculate the sentiment within each article using the logit
formulation.
msi_data <- sent_index(
source = "all",
topic = "economy",
freq = "month",
dict = "loughran",
aggr = "sent_logit"
)
Once we have retrieved it is fairly easy to plot the index and the different time
aggregations using ggplot2
.
msi_data %>%
filter(published_date > "2003-01-01") %>%
mutate(published_date = as.Date(published_date)) %>%
pivot_longer(names_to = "sentiment_type", values_to = "values", -c(source:aggr_type)) %>%
ggplot(., aes(published_date, values, group = sentiment_type), color = 1) +
geom_line(color = "#BC4744") +
geom_ma(color = "#18417B", n = 6, linetype = "solid", ma_fun = EMA) +
labs(title = "Media Sentiment BER: Economic Focus",
subtitle = "Loughran (within aggregation: sent logit)",
y = "Index",
x = "Date",
caption = "(Business Day)") +
scale_x_date() +
facet_wrap(~sentiment_type, scales = "free_y") +
theme_tq()


DCB Bank Standalone September 2019 Net Interest Income (NII) at Rs 313.36 crore, up 11.18% Y-o-Y
Reported Standalone quarterly numbers for DCB Bank are:
Net Interest Income (NII) at Rs 313.36 crore in September 2019 up 11.18% from Rs. 281.84 crore in September 2018.
Quarterly Net Profit at Rs. 91.41 crore in September 2019 up 24.47% from Rs. 73.44 crore in September 2018.
Operating Profit stands at Rs. 184.53 crore in September 2019 up 26.29% from Rs. 146.12 crore in September 2018.
DCB Bank EPS has increased to Rs. 2.95 in September 2019 from Rs. 2.38 in September 2018.
DCB Bank shares closed at 182.55 on November 01, 2019 (NSE) and has given -14.99% returns over the last 6 months and 14.85% over the last 12 months.
| DCB Bank | |
| Standalone Quarterly Results | in Rs. Cr. |
| Sep'19 | Jun'19 | Sep'18 | |
| Interest Earned | |||
| (a) Int. /Disc. on Adv/Bills | 706.25 | 686.29 | 591.69 |
| (b) Income on Investment | 156.41 | 153.74 | 134.12 |
| (c) Int. on balances With RBI | 8.39 | 9.87 | 3.50 |
| (d) Others | 8.15 | 7.80 | 6.35 |
| Other Income | 101.39 | 86.76 | 73.49 |
| EXPENDITURE | |||
| Interest Expended | 565.84 | 552.95 | 453.82 |
| Employees Cost | 114.65 | 116.19 | 110.18 |
| Other Expenses | 115.57 | 108.78 | 99.03 |
| Depreciation | -- | -- | -- |
| Operating Profit before Provisions and contingencies | 184.53 | 166.54 | 146.12 |
| Provisions And Contingencies | 43.27 | 40.64 | 31.90 |
| Exceptional Items | -- | -- | -- |
| P/L Before Tax | 141.26 | 125.90 | 114.22 |
| Tax | 49.85 | 44.84 | 40.78 |
| P/L After Tax from Ordinary Activities | 91.41 | 81.06 | 73.44 |
| Prior Year Adjustments | -- | -- | -- |
| Extra Ordinary Items | -- | -- | -- |
| Net Profit/(Loss) For the Period | 91.41 | 81.06 | 73.44 |
| Equity Share Capital | 310.07 | 309.80 | 309.25 |
| Reserves Excluding Revaluation Reserves | -- | -- | -- |
| Equity Dividend Rate (%) | -- | -- | -- |
| ANALYTICAL RATIOS | |||
| a) % of Share by Govt. | -- | -- | -- |
| b) Capital Adequacy Ratio - Basel -I | -- | -- | -- |
| c) Capital Adequacy Ratio - Basel -II | -- | -- | -- |
| EPS Before Extra Ordinary | |||
| Basic EPS | 2.95 | 2.62 | 2.38 |
| Diluted EPS | 2.90 | 2.57 | 2.34 |
| EPS After Extra Ordinary | |||
| Basic EPS | 2.95 | 2.62 | 2.38 |
| Diluted EPS | 2.90 | 2.57 | 2.34 |
| NPA Ratios : | |||
| i) Gross NPA | 523.24 | 476.40 | 409.98 |
| ii) Net NPA | 237.86 | 195.83 | 155.27 |
| i) % of Gross NPA | 2.09 | 1.96 | 1.84 |
| ii) % of Net NPA | 0.96 | 0.81 | 0.70 |
| Return on Assets % | 0.99 | 0.89 | 0.92 |
| Public Share Holding | |||
| No Of Shares (Crores) | -- | -- | -- |
| Share Holding (%) | -- | -- | -- |
| Promoters and Promoter Group Shareholding | |||
| a) Pledged/Encumbered | |||
| - Number of shares (Crores) | -- | -- | -- |
| - Per. of shares (as a % of the total sh. of prom. and promoter group) | -- | -- | -- |
| - Per. of shares (as a % of the total Share Cap. of the company) | -- | -- | -- |
| b) Non-encumbered | |||
| - Number of shares (Crores) | -- | -- | -- |
| - Per. of shares (as a % of the total sh. of prom. and promoter group) | -- | -- | -- |
| - Per. of shares (as a % of the total Share Cap. of the company) | -- | -- | -- |
| Source : Dion Global Solutions Limited |


@(facets: String, institution: model.Institution, community: model.Community) @main(institution, community) {


by Jon Dart
Arasan is a chess program.
Usage of the source code is governed by the MIT license: see the LICENSE file for details.
Arasan includes a console-based chess engine that be used with Winboard, a separately available interface for chess programs, or with UCI-compatible interface programs such as Arena and Shredder. In addition, a custom Windows-only user interface program for Arasan is available; it communicates with the chess engine using a pipe.
Arasan is written in C++. Versions before 6.0 ran only on Windows. In
version 6.0 and higher, the Arasan chess engine supports both Windows
(32- or 64-bit versions) and other platforms such as Linux, but the
Arasan GUI is still Windows-only. If you are not on Windows, you need
to use a program such as xboard to view the chessboard and play games
with Arasan. You can get xboard from
Arasan has mostly been tested on Intel & AMD processors but the code is designed to be portable to other processors (there is some assembly code in bitboard.h, but that is not used unless you compile with -DUSE_ASM).
The remainder of this file contains information for use by programmers reading or working on Arasan source code. I assume that you have a working familiarity with C++. Also, if you have no background in computer chess, you should probably start by reading some of the reference material mentioned at the end of this document.
See the BUILD.md file in the github doc directory for current build instructions.
Since book.bin is supplied with the Arasan program distribution, you do not need to build this file in order to build Arasan. However, if you want to modify the contents of the opening book, you will need to edit the ASCII source for the openings and rebuild book.bin, and for this you will need to build the "makebook" program that is part of the source distribution. Following is further information on creating a book file.
The book for Arasan is converted from text files to the binary book by the console program makebook.exe.
The input files used to generate the opening book are in Portable Game Notation (PGN) format. More than one PGN file can be specified as input.
When multiple files are used, makebook treats the first one specially. The first file is a "steering" book used for manual tuning of the opening book weights. This file can be annotated using standard PGN comments and Numeric Annotation Glyphs (NAGs). A file with such annotations is supplied with the Arasan source and is named "basic.pgn".
In the annotated PGN file, an explict weight can be specified by putting a comment with the string "weight: " followed by a numeric value after a move. Weights are between 0 and 100. A weight of 0 means that the computer will never play the move, but will respond to it if it is played by its opponent. The higher the weight, the more often the computer will choose the move. By default, if no weight is specified, moves are selected at runtime based on win/loss/draw statistics and the move frequency.
PGN files can also have numeric annotation glyphs (best to use an editing program such as ChessBase to insert these). If present these have the following meaning in the context of "makebook:"
!! - a very good move, give it the maximum possible weight. ! - a good move, weight it 50% higher than normal. !? - a worthwhile move, weight it 25% higher than normal ? - a dubious move, do not play it ?? - a blunder, do not play it.
Glyphs can also be used to assign an evaluation to the end of an opening line and if so this evaluation modifies the weighting of moves along that line.
Formerly relative book weights were stored in the book file. Now generally they are not: except for special first PGN file, move records only store the win/loss/draw statistics for the move. Some selection logic is applied at runtime (see bookread.cpp), as follows (based partly on Thompson sampling - see references):
There are four options (settable in arasan.rc) that control the move selection:
Arasan comes with a book that was built from a combination of a fairly small hand-tuned book file (basic.pgn) and a fairly large collection of high-quality human and computer games. There are over 750,000 moves in the book.
You can make your own book file using the makebook utility. Typical usage would be like this:
makebook -n 100 -m 4 -o book.bin basic.pgn big.pgn
The -n parameter to makebook specifies how many "index pages" are in the binary book file. You may have to experiment with this parameter. Too small a value will cause an error building the book.
You can also specific the "-m" parameter to makebook with a number, to set a minimum number of times that a move must be played in a game collection to be included in the book (does not apply to the first PGN file). The default book shipped with Arasan was built with "-m 4". Other options include:
See bookdefs.h for some documentation about the data layout within the book.bin file.
Arasan has positional learning (a.k.a "permanent brain"). It is basically a persisent hashtable. If a search returns an unexpectedly high or low score, the position and its score are stored in a text file called arasan.lrn, which is located in the same directory as the Arasan executable. When the next game is started, stored positions from this file are read into memory and stored in the hash table, enabling the program to detect danger or opportunity sooner than it did previously.
Arasan learning does not work in UCI mode at present, for several reasons.
Arasan can produce an ECO (Encylopedia of Chess Openings) code for a given game. As with the opening book, the mapping of chess positions to ECO codes is contained in a text file. The file is called "eco" and is in the "misc" directory. It contains a series of lines, each starting with an ECO code, followed by a series of chess moves, and ending with a quoted string that contains the English name of the opening.
The "makeeco" program reads the "eco" data file and outputs to stdout a C++ file that is then compiled into Arasan. The generated file is called "ecodata.cpp" and contains a single large C data structure.
Because Visual C++ makefiles don't handle generated source files like this one, you need to run "makeeco" manually whenever you change the "eco" data file.
makeeco takes a single argument: the location of the eco file. It writes the generated ecodata.cpp file to stdout.
Note: the ECO recognizer is pretty crude right now, because Arasan does not contain all the possible ECO lines and sublines in its data file. Therefore transpositions that wind up in an ECO subline like C18/3 but skip the position that is stored for C18 may not be recognized correctly. Transpositions across opening systems may also be missed. There is no general solution for this problem, since the ECO classification scheme is ambiguous in some cases, but recognition could be improved by expanding the number of positions in the eco file. If the number becomes large enough, however, the current method of building a data structure and compiling it into Arasan might have to be changed.
Arasan includes several features to aid debugging. If you compile the source for debugging (with -D_DEBUG), checks are inserted for accessing arrays past their boundaries, as well as some other sanity checks. If any of these checks fail, an error box will be displayed with the assertion that failed.
At runtime, adding the "-t" (trace) flag to the arasanx executable will cause it to output trace messages detailing its nternal operations. Trace messages are formatted approprately depending on the protocol (UCI or winboard). For Winboard, these messages will show up in the debug log if Winboard is started with the "-debugMode true" flag. For UCI engines, the debug output will appear as "info" output and will for example be recorded by cutechess-cli if the -debug option is specified.
The chess engine supports a couple of commands to aid with testing. The "eval" command followed by a filename will read the file (assumed to be in FEN format) and output the static evaluation of the position. If the EVAL_DEBUG and/or PAWN_DEBUG defines are uncommented in scoring.cpp, then you can see detailed output of the scoring components.
The "test" command followed by the name of a test suite file in EPD format will do a search on each position in the file and output the results to stdout. Additional switches can be added after the filename:
Either -d or -t must be included as one of the options.
If the search module is compiled with -D_TRACE, arasanx will print out copious information about the search process when it is run (if running multi-threaded, only the main thread is traced). See search.cpp to see what information is output and where it comes from.
The following test suites are provided with the source code (see the "tests" directory):
The results file in the tests subdirectory summarizes Arasan's performance on these test suites. There is a Perl script (tests.pl) in the tests directory that generates a summary of test results given a log file produced by the Arasan "test" command.
A built-in command in the engine can be used to run the "perft" command for testing. The command "perft" should be followed by a number indicating the ply depth for the computation.
If compiled with -DUNIT_TESTS, Arasan will run a set of tests on program startup (this flag is off by default). These tests verify that a set of critical functions operate correctly, verify that evaluation results are symmetrical between White and Black sides, and also run and verify the perft test over a set of positions.
Arasan since version 19.0 has some support for automated parameter tuning.
The Makefile has a "tuning" target that builds a "tuner" executable that when run will generate the parameter file (params.cpp) containing evaluation weights used by the Arasan scoring module.
The tuner implements a variant of the Texel tuning method, based on logistic regression. (Formerly Arasan also supported a tuning method called MMTO described by Hoki and Kaneko (see references), but the current version no longer does).
The input file should be a EPD (Extended Position Description) file with a diverse set of positions. These do not have to be quiescent (i.e. can have possible captures, checks and/or promotions). The tuner program expects each position to have a "c2" EPD tag with the value being the game result as one of the following strings: "0.0" (Black win), "0.5" (draw), or "1.0" (White win).
The "tuner" program expects an EPD file to be specified on the command line. For each position it will conduct a shallow-depth search. The principal variation (PV) from this search will end in a quiescent position. These positions are stored in memory and and their static evaluations are used as a proxy for the initial position's eval. The tuner has the option of periodically re-computing the PVs, since as the parameters change so potentially do the PVs (this is similar to what MMTO does).
The tuner tries to minimize a measure of difference between the predicted game results (which are computed using a sigmoid function of the evaluation scores) and the actual game results. It does this using one of several gradient descent methods. The code explicitly computes the gradient of the evaluation function, rather than approximating it as some programs do.
The tuner will output two files: by default the first one is named "x0" and contains the tuned parameter values; the second one is a compilable version of the parameters named "params.cpp."
The following command-line options are supported:
Some further notes on the options: The default objective is "msq", the mean squared error between the predicted result value (sigmoid function of the evaluation) and the actual result value. The tuner also supports cross-entropy error (-O log) or a combination of mean-square and cross-entropy (msqlog).
Three iterative optimization methods are supported. The first is "adaptive," which is a simple momentum based approach described by Geoffrey Hinton. The tuner also supports the AdaGrad method (see Duchi et. al.) and the ADAM method (see Kingma and Ba). ADAM is currently the default method.
The Arasan source distribution contains some tools used for generating the labeled EPD file used for parameter tuning.
One program is called "pgnselect," located in the "util" subdirectory. This program reads a PGN input file and produces FEN output to stdout. The FEN output is based on a randomly selected subset of the positions encountered during the game. If the -r flag is used, then for each sampled position, a randomly selected legal move will be made (this trick was used in the neural-network based program "Giraffe" (see references). It produces more varied and unbalanced positions that just game sampling would).
Other switches supported by the "pgnselect" program:
There is also a Python 3 script in the "tools" subdirectory called "label_positions.py". This will take as input the FEN produced from "pgnselect" and then will label each position with a game result, based on a game in which both sides are played by a selected chess engine at a short time control.
The label_positions script relies on cutechess-cli being installed and the path to that executable must be set in the script. Note: it has only been tested on Linux, although it should work on Windows, too.
The script takes several command-line switches:
Following the switches the input FEN file path should be specified. Output from the script goes to stdout; errors are written to stderr.
Following is some information about the algorithms and data structures used by Arasan. If you are new to computer chess programming, I suggest first reading a general work on the subject such as Frey (1983) or Marsland and Schaeffer (1990).
The chess board in Arasan is represented by an array of 64 squares, laid out so that square a1 has the value 0 and square h8 has the value 63 (Note: versions before 11.0 had a different layout with a8=0).
Each square contains 0 if it is empty, or a piece identifier if it is occupied. Black pieces have identifier values between 1 and 6, while White pieces have values between 9 and 15. A special value (127) is used to represent a square that is uninitialized or invalid.
The Board class also maintains several "bit boards" or quantities that that hold 64 bits. The Bitboard class in the source encapsulates a bit board. For example, the occupied bit board has one bit set for every piece that is on the board (there are actually two such bit boards, one for Black and one for White).
Each type of piece has its own bit board that has one bit set for each piece of that type (for example, there is a rook_bits Bitboard to hold rook locations). Since there is only one king location, though, this is kept in an integer variable.
Besides the bit boards, there is some other information in the Board structure. The enPassantSq variable holds the square position at which an en passant capture is possible (if none is possible, it has the value IllegalSquare). The castleStatus array holds an enum for each side indicating whether castling has occurred. Also, if the king or a rook has been moved, making castling on one side or another impossible, castleStatus is set to an appropriate value.
Each board position also has a hash code associated with it. The hash code is 64 bits and is computed by fetching, for each piece and square combination, a unique 64-bit code from a table of random numbers, and computing the exclusive or of these codes. (This hashing mechanism for chess was invented by Zobrist - see references). The low-order bit of the hash code is then set to identify whether White or Black is to move. Castling status and en passant status are also folded into the hash code, because positions with the same piece layout but different castling rights or possible en-passant captures must be kept distinct.
Arasan uses a 64-bit word to store move information. Each move contains a start square, destination square, promotion value, the type of piece being moved, the type of piece being captured (if any), and the type of move (normal, castling, en passant, etc).
Earlier versions of Arasan used rotated bitboards for computation of attack information. The rotated bitboards allow computing which pieces attack a square without using any loops in the code, just shift and mask operations. Starting with version 11.0, a different approach is used: it produces much the same benefits but is faster. This approach uses a technique called "magic bitboards", which basically computes a hash code that looks up attack information, in a way that ensures there are no invalid hash collisions.
There are several ways to do this, but the algorithms and data structures used in Arasan follow an approach initially published by Pradyumna Kannan. The constants for the 64-bit version were constructed by generation code posted by Tord Romstad. The 32-bit version follows an approach first taken by H. G. Mueller, as discussed in the Winboard Forum. Starting in version 20.3, Arasan alternatively supports computing sliding attacks using the x86_64 PEXT AND PDEP instructions, as first suggested by Michael Sherwin and later by Zech Wegner (see references).
The magic bitboard code is primarily in file attacks.cpp. There are two versions of the code: one uses 64-bit multiplication (or PEXT/PDEP if enabled) and is designed for a 64-bit architecture. The other uses only 32-bit multiplications. One or the other is automatically selected at compile time based on the target processor architecture.
The move generation logic is mostly contained in the MoveGenerator class and uses the magic bitboard attack functions.
The MoveGenerator class has separate routines to find all moves and to find just capture moves - the latter is all that is required in the quiescence search. Move generation is done incrementally, because if a move is found that causes cutoff, there is then no need to generate the rest of the moves for that position. Specifically, Arasan uses the "Fancy Magic Bitboards" technique described in the Chess Programming Wiki (see References), which was invented by Pradu Kannan.
Move generation occurs in this order (this is for positions where the side to move is not in check, and in the regular search, not the quiescence search):
Normally, the move generation process includes moves that are illegal because they place the side to move into check - these are weeded out in the search routine.
If the side to move is in check, a special function (generateEvasions) is called that strictly checks moves for legality. It is very important to know whether any legal moves are possible when in check: if there are none, the side to move is checkmated. Also, some search extensions depend on the existence of a forced move (one single legal move). Currently evasions are generated in two phases: first the hash move is tried, if there is one, and then only if that fails to produce cutoff is the full set of evasion moves generated.
Move ordering at the root node is done somewhat differently. The first time moves are generated at ply 0, a rough sorting by score is done. The next few plies are searched with a wide window, partly to facilitate "easy move" detection (a move that appears to be much better than alternatives may be selected after a shorter than usual search, so for example the program does not waste time on an obvious recapture). But another side-effect of the wide window is that we obtain scores for all moves and Arasan will sort the moves for the next iteration based on these scores.
After the "wide window" part of the search is over, the next iteration is searched putting the best move from the last iteration first. If another move then becomes best, it replaces the previous best move and all other moves are shifted down, so that their ordering from the previous iteration is retained in the next iteration.
Arasan uses an alpha-beta search algorithm with a variety of search extensions. The search class is the largest single module in the program, and is necessarily rather complicated, but I have tried to structure it and comment it so that it is understandable. I will assume that the reader knows the basics of the alpha-beta algorithm, and will concentrate on describing this implementation of it.
In general, the search routine tries to terminate a search tree, or some portion of one, as soon as possible, and will defer as much work as possible until it is certain that no earlier and quicker termination can be done. The techniques for doing this are mostly well-known and there is nothing very original about the search algorithms used by Arasan. However, as with most chess programs, there is a fine balance between terminating a search too soon and extending it into unprofitable and very unlikely lines of play. The precise nature of this balance depends not only on the search algorithms used, but also the relative efficiency of operations such as move generation, position evaluation and move ordering. Each program therefore strikes this balance in a somewhat different way.
The entry point for a search is a routine called findBestMove. This function does some initialization, and then calls ply0_search, which implements the alpha-beta search algorithm. The search proceeds one ply (half move, i.e. move by one side) at a time. That is, first a one-ply search is done, then a two-ply search, then three, etc. until either the maximum ply limit has been reached or the time control has been exceeded. Each search uses the results of the preceeding search. The variable "iterationDepth" holds the current nominal ply depth for the search. However, the presence of search extensions means that some nodes may be searched to a greater depth than this.
In the first few iterations of the ply0 search, Arasan now uses wider than usual search bounds, so that each move gets a preliminary score. The scores are used to order the moves for deeper searching, and for "easy move" detection: if one move is found to have a significantly higher score than all others, and if subsequent searching still selects this move, then the search may be terminated early.
ply0_search does some other special processing and bookkeeping because it is at the top of the search tree. This function then calls search() to recursively process lower-depth nodes.
The first step in search() is to check if the current board position is drawn, due to insufficient material, a 3-fold repetition of moves, or the 50-move rule.
If the position is drawn, move_search calls the function drawScore. Usually a draw is given a score of 0, but when playing on a chess server, the relative rating of the opponent is also factored in, so that draws against a lower-rated opponent are penalized.
Arasan will also terminate the search immediately if the absolute maximum ply depth is reached. This is quite unlikely.
If no draw is present and the maximum depth hasn't been reached, the next step is to look in the hash table (further described in the next section), in order to see if an identical position has been visited before. This may happen due to a transposition of moves that lead to the same position, or because a previous search to a shallower depth visited the same node. If a hash table entry is found and if it contains a valid value (i.e. one that did not cause cutoff), then that value is returned immediately and no further searching from that node occurs. In other cases, the hash table may not contain an exact value, but may hold an upper or lower bound that can be used to narrow the alpha-beta window.
If the hash table lookup didn't produce an exact value or narrow the bounds enough to cause cutoff, then we may also try the endgame tablebases, if they are installed and enabled, and if material is reduced enough that they can give an exact score.
In non-PV nodes where the side to move is not in check, Arasan first tries a couple of techniques to decide if the entire node can be pruned away, before doing any searching. The first of these is "static null pruning", which cuts off search for nodes that have very high scores.
If still no cutoff has occured, we then try a further trick to get a fast termination of the search. The side to move is changed without altering the board position and the opposing side is then allowed to move. Of course, this could not occur in a game - a player is not allowed to "pass," but must move. However, the theory is that if the null move causes cutoff, then the side to move must have a good position, since in effect giving the opponent a free move still produces a high value for the side to move. In this case, beta cutoff is allowed to occur and no more searching is done from this node.
Starting in Arasan 2.0, null move pruning is applied in subtrees that are themselves part of a null move search, provided that two null moves are not tried in a row. This is known as the "deep null" algorithm. See Donninger's article in ICCA for more information on this algorithm.
A null move is searched to a depth less than that used for regular moves. Arasan uses at least a so-called R=3 depth reduction for the null move search: in other words, a search is done with the regular 1-ply reduction in depth plus three extra plies. (At high depths, a depth-dependent amount is added to the reduction factor.)
After a null move produces a beta cutoff, Arasan does an extra reduced-depth verification search to ensure that the null move cutoff is actually valid. The verification search uses reduced depth, but doesn't insert the null move first.
After null move search, but before the regular search, Arasan now applies a pruning mechanism called ProbCut (see Buro).
If neither the null move search nor ProbCut cause cutoff, then we must actually do some searching from the current node.
The first move searched is called the "principal variation" move. In the case of an initial search (e.g. a one-ply search), the principal variation move is just the first move returned by the move generator. Otherwise, at ply 0, it is the highest-scoring move from the previous search iteration. At deeper plies, the hash table is queried and if a best move has been stored for the position, that move is tried first and is considered the principal variation.
In cases where there is no hash move, we do a shallow search to obtain a suitable move to try first. This is called "internal iterative deepening" and has been used in Hitech (see Ebeling's book) and also Bob Hyatt's program Crafty.
Now (in most cases) we should have an initial move to try (if not we generate all moves and take the first one). We make the move, then query the attack info for the board to see if the side to move is in check (remember, the move generator typically does not exclude moves into check). If a move into check is found, the special value Illegal is returned and the next move is tried. If the move passes the legality check, then move_search is called again (i.e., it is recursive).
Normally each move searched reduces the "depth" variable by a constant (DEPTH_INCREMENT). When depth reaches zero, the quiescence search will be entered (see below).
However, some moves are searched to a greater depth than normal. There are several cases in which this occurs:
Note that since Arasan 5.0, most extensions can be a fraction of a full ply. Search "depth" is normally decremented by the constant DEPTH_INCREMENT every time a new ply is begun; however, extensions can decrement it faster, typically an additional amount between 1/2 of a full ply and a full ply.
Individual extensions can be combined, but at any given ply, the total depth reduction from extensions cannot exceed DEPTH_INCREMENT, the equivalent of one extra ply.
The principal variation phase of the search is over when we have found a legal move and searched its descendants (including quiesence nodes) so that we have a value for it. It is possible that this value will be greater than beta, which means that we have set the initial search window wrong and must repeat the search with a different window.
Assuming the principal variation move does not cause cutoff or mate, then the search function proceeds to search the remainder of the moves. These moves are searched with a zero-width alpha-beta window (i.e. beta is set to alpha+1). All such searches will cause a cutoff. If the value returned by the search is between alpha and the original beta, then the search is repeated with a wider search window to determine the correct score. It is generally faster to get a fast cutoff and then re-search with a wider (but not infinite) window than to do a single search with unlimited bounds.
Note that since the principal variation move is usually obtained from the hash table or the root move array, it may be the case that the move generator has never had to be called during the principal variation search. If so, we call it before doing the non-p.v. moves.
Arasan now optimizes things further by only generating part of the moves at a time. That way, if cutoff occurs, the remainder of the move generation can be skipped.
Arasan also performs several types of pruning and depth reduction. These are the opposite of extensions: instead of searching deeper, Arasan either cuts off the search prematurely (pruning) or reduces the search depth.
Arasan uses "late move reductions." After the first few moves have been searched, including any killer moves, if the move is not extended, and if it is not considered potentially dangerous or advantageous, then the search depth is reduced. This can occur even at high depths in the search tree. If the reduced depth move returns a score above alpha, it is re-searched with normal depth. This technique became popular due to its use in Fabien Letouzey's program "Fruit".
Arasan also does several different kinds of pruning. Several tests are made on each move and if the move was extended for any reason, or if it is a capture, special move (like castling) or advance of a passed pawn, then it is not pruned.
Note that it is necessary to search at least one legal move before any pruning is done, because we need to distinguish nodes at which no moves are searched due to stalemate from those at which all nodes would cause pruning. Also no pruning is done at the root node.
Futility pruning in Arasan is similar to that described by Ernst Heinz for the program Dark Thought. It allows some moves to not be searched, if it determined that the side to move is behind in material and the move to be made is not likely to gain enough material to be worth considering. But moves with good history scores are not pruned.
Futility pruning is applied at relatively low search depths before the quiescence search and during the quiescence search. First the program computes an optimistic score for the move and adds a margin that varies based on search depth; the sum is then compared against alpha and the move is pruned if the optimistic score + margin is still below alpha.
Late move reduction is done before futility pruning and the futility margin is based on the reduced depth (if a reduction was done).
Arasan also does "late move pruning." In non-PV nodes, quiet moves that are in the history phase of move generation (after the pv move, winning captures, and killer moves) and are relatively late in the move order can be pruned away. The theory is that if we have not already achieved a cutoff searching more moves is not likely to produce one.
Another pruning method recently implemented is based on countermove and follow-up history scores. Quiet moves with bad history scores are pruned at low depths.
In addition at depths just before the quiescence search, moves that appear to lose material based on a static exchange analysis are pruned. This is done with more liberal pruning conditions than other types of pruning.
The final part of search() checks to see if checkmate or stalemate occurred, updates the hash table, and maintains the best variation. It also updates history scores, but only if the best move is not a promotion or capture. The best move from the search gets a history bonus. The remaining moves are given a penalty. Scores are maintained in a table (per thread) that is indexed by side, source square and destination square. In addition, Arasan now maintains a countermove history table and a "follow up" history" a.k.a. continuation history table (ideas from Stockfish). All history tables are used in move ordering and history values are a factor in computing the amount of late move reduction.
When the search terminates at ply 0, it updates the "Statistics" structure with the time and other information about the search (key parts of this structure are also updated during the search when the p.v. changes, so the UI and test suite code can monitor it).
As noted above, each recursive call to move_search decrements the "depth" parameter by a constant (DEPTH_INCREMENT). When "depth" drops below zero, Arasan enters the quiescence search by calling function quiesce(). Like search(), this is also a recursive search routine.
As the name implies, the goal of the quiescence search is to reach a relatively "quiet" position that can be more or less accurately evaluated. Generally, "quiesce" will only generate and search capture moves that appear to gain material, promotions of pawns, and moves that escape from a check.
If in check, all evasion moves are generated although some "quiet" evasions may be pruned and not searched.
The quiescence search for positions not in check also does forward pruning on moves, dropping any that appear to lose material based on the static exchange evaluator and those that fail a futility test.
Generally, forward pruning costs time (especially if the static exchange evaluator, "see", needs to be called), and involves some risk of dropping valuable capture moves. But, on the plus side, it significantly trims the size of the search tree.
The quiescence search terminates when no more moves of an appropriate type are available. The search may also terminate early if the side to move is not in check, and the current evaluation of the position is enough to cause cutoff. The theory here is that the side to move can choose to not capture any further. If the current evaluation is good enough to cause cutoff, there is no need to try captures and promotions to get a better score.
In version 10.0 and later, for nodes where it is not in check, if no cutoff has occurred so far, the quiescence search will generate and search moves that give check, at least in the first couple of quiescence search plies. But checks that appear to lose material are pruned.
The search routine uses a hash table for storing the results of evaluating previously visited positions. This table is implemented in several static functions defined in hash.cpp. The hash table is basically an array of lists. Each list contains a series of nodes, each of which contains some data (in the case of the search engine, a class of type Position_Info) and a pointer to the next node. Each list holds entries that hash, modulo the hash table size, to the same value. Each node contains the whole hash code, so that finding a given node to match a given hash code consists of indexing into the hash table, then following the list until the full 64-bit hash codes match.
Besides the hash code, each hash entry also contains the score for the node, a set of flags indicating whether the value is exact, an upper bound or a lower bound, the depth of search used to evaluate the node, a word holding the castling status and en passant square, and the best move for the position.
The hash table is limited in size and may fill up during a long search. In this case, we have a choice: when a new position is encountered, we can overwite an existing entry in the hashtable with the new position, or we can discard the information for the new position and not put it into the hashtable.
Arasan will generally only replace entries that have greater depth than existing entries, or entries that came from an earlier search (i.e. whose "age" field does not match the current search).
The hash table is not cleared after each search: instead, it is kept full, but old entries (from the previous search) are considered candidates for replacement.
The size of the main hashtable defaults to 64 Megabytes, but can be overridden with -H switch, followed by a size (such as '256M'), or by modifying the arasan.rc parameter file. Standard Winboard or UCI option commands can also be used to alter the hash table size at runtime.
Because multiple threads can be reading and writing the hash table, a "lockless hashing" technique is used to prevent conflicts (as done in Crafty). When a hash key is stored it is xored with the data value. When retrieving, the current data contents for a hash table entry are xored again with the stored hash key and this is used to match the hash key for the position. If another thread has overwritten or is overwriting the data field, then the comparison will fail and a match will not be returned.
Besides the main hash table, some smaller hash tables are used by the Scoring module to store the results of pawn structure scoring and king cover calculation. These are relatively small in size and are allocated on a per-thread basis (each thread has its own tables).
The following text describes the classical evaluation function.
There are roughly five main components to the positional score used by Arasan:
The positional score is typically within the range of plus or minus the value of a pawn (128), but can be greater in some circumstances.
Pawn structure scoring is done in two stages. First, the pawn hash table is probed to get the score for the position. If the position is not found in the hash table, then the pawn_structure_score routine is called. This routine only computes scoring parameters that depend only on the position of pawns. It also calculates several items that are useful in the rest of the evaluation.
The second-stage pawn scoring takes into account the relation between pawns and pieces.
The pawn structure score penalizes isolated, backward, weak, and doubled pawns. An increased penalty is given if there are multiple such defects in the pawn structure.
A bonus is given for passed pawns. If a passed pawn exists, its value depends on its rank and also on whether squares ahead of it are occupied or controlled by the opposing side. A bonus is given for a rook behind a passed pawn. Blocked passed pawns and passed pawns on the rook file are given a penalty.
In the opening and middlegame, the pawn structure score includes a center control component that gives bonuses for occupation or control of the center by pawns.
Arasan uses a pawn hash table, maintained in the Scoring class, which holds information on pawn structure and a score that evaluates how good or bad that structure is. This table is accessed via a hash key that depends only on the position of pawns. There are separate tables for the white and black sides. Entries in the pawn tables are always allowed to be replaced. This technique has been used in Cray Blitz and Crafty. It significantly speeds up the scoring routine by avoiding computation of the pawn structure score in most cases.
The development score encourages the program to move its pieces from the back rank, but discourages premature development of the Queen. A measure of piece mobility is also calculated for Bishops, Knights, and Queens. Bonuses are awarded for a Rook on the 7th rank, and also for a rook on an open or half-open file, and for doubled Rooks.
The castling score penalizes the program for having its Rook blocked by the King (i.e. not being castled), or being unable to castle.
Arasan calculates king safety using a "cover" score that measures how protected a king is by pawns, combined with a "proximity" score that measures the closeness of enemy pieces to the king. The "cover" score is calculated and hashed for fast lookup.
King "cover" is considered worse if pawns are missing near the king or if the king is near an open or half-open file.
"Proximity" includes several measures based on the proximity of enemy pieces, advancing pawns (indicating a "pawn storm"), Rooks on open files near the king, and Queens or Bishops bearing on diagonals near the king.
If both proximity and cover scores are high (indicating that the king is both exposed and attacked) an additional "bonus" penalty is given.
The king safety score is scaled according to the amount of material the opponent has. The more material the opponent has, the more dangerous an uncovered and/or attacked king position is.
In the endgame, passed pawns are evaluated to see if they can safely queen without being intercepted by the opposing king. Arasan versions 5.0 and higher also have code that gives a bonus for an "outside" passed pawn, and that helps to evaluate pawn "races," where both sides are likely to promote.
In the endgame, the king is encouraged to move towards the center or opposite side of the board, and to stay near pawns. This code is not very effective in producing good play, but it is better than nothing.
Prior to version 6.0, endgame and king safety scoring were never applied together, but now they can be: if the position is in the process of transition to an endgame, it will be evaluated for both king safety and endgame positional factors.
A lookup table is used to speed up endgame calculations by storing information based on the position of king and pawns (for both sides). Once computed, this information is put into a hash table for later retrieval and use by any position that shares the same king + pawn configuration.
Arasan is aided in the endgame by the use of "bitbases" for the KPK endgame. Bitbases record for each position of the two kings and pawn whether the position is won for the stronger side, or only a draw. Unlike tablebases, bitbases do not record the number of moves to mate. Bitbases can be employed throughout the search tree since they are in memory. Arasan uses the KPK bitbases not only when there are only kings and one pawn left, but also as an aid to computing endgame scoring for more complex endgames.
Arasan also has some special-case code for other endgames including KNBK, KRK and KQK, which enables the program to play these fairly well, even without tablebases.
Arasan 23.0 introduces support for a Efficiently Updatable Neural Network (NNUE) that performs evaluation of chess positions. Arasan's implementation of this is largely in a submodule, the source of which is at https://github.com/jdart1/nnue. NNUE evaluation for chess was first introduced in Stockfish, whose implementation of it was contributed to the Stockfish project by Hisayori Noda aka Nodchip. Arasan's current implementation is compatible with the SFNNv4 network architecture used in Stockfish 15.
Tuning was performed using a slightly modified version of the nnue-pytorch tuner (back-porting some later changes into the SFNNv4 tuning code). Initial weights were tuned based on 1.5B positions from Arasan selfplay games (generated from the "selfplay" utility program). This was followed by two rounds of reinforcement training, each time using about 3.6B selfplay positions.
The actual network data is loaded at runtime from a file. (Unlike Stockfish and some other programs, Arasan does not embed the network data in the program executable.)
Arasan version 9 and above have support for using more than one thread during searching, enabling it to make use of multi-processor machines and multi-core CPUs. Version 10.0 was the first version to have this fully implemented.
A pre-requisite for implementing multi-threading is that global data structures that are accessed for read & write be made safe for parallel access, usually using locking.
However, locking has a performance overhead and is generally infeasible to do on a per-node basis. Arasan now does not lock the main hash table, to avoid this penalty, but uses a lockless hashing mechanism as described earlier.
My first attempt at implementing multi-threading used the ABDADA algorithm (see Weill's article). This is simple to implement since it uses the hash table as a single point of synchronization and control, but it did not perform well, because this usage of the hash table interferes with its normal usage as storage for scores and best moves from previously visited nodes. Consequently, the hash hit rate was reduced and multi processors did not achieve much of a speedup.
Arasan then implemented what is called the Young Brothers Wait concept (YBWC). This algorithm was described in Feldmann's Ph.D. thesis (see References) and several related publications in the early 1990s.
Arasan's current implementation uses what is called LazySMP. This has been implemented in several strong engines including Stockfish. It is somewhat similar to ABDADA conceptually. It entails letting each thread search from the root position using a standard alpha-beta aspiration search. No synchronization across the threads is done. Practically the only shared data structure is the global hash table. Threads in each iteration are started at somewhat different search depths, which helps avoid having them visit exactly the same nodes. The first thread is treated somewhat differently from the others: only it is allowed to output search progress updates, and its fail high/fail low history is used when making time control decisions. Each thread maintains its own Statistics structure, which holds interim search results. When a search termination condition (such as time up) is reached, all threads will be set back to idle and returned to the wait loop in the thread pool. The individual search results from each thread are then examined. The best result from all threads is the one that is returned as the overall search result, with the caveat that the result chosen must also have a final search depth not less than other threads.
The Windows user interface was re-written for version 6.0, although some code from earlier versions is still in use. The main difference from earlier versions is that the chess engine is now run as a separate process that communicates with the user interface through a socket, exactly like Winboard does. This is done to eliminate some problems that occured when using threads to manage the engine/UI communication in earlier versions.
Compared to Winboard, the Arasan UI lacks some features: for example, it cannot be used to communicate with a chess server, and has no facility for editing positions.
The Arasan user interface is a pretty standard MFC program. It uses the single document interface (SDI) model, so there is only one document class instance and one view instance active.
Earlier versions of the Arasan UI used bitmaps for display. The new version uses TrueType fonts. Several chess fonts are included in the program distribution. The original font archives, which include copyright and usage information, can be found in the fonts subdirectory of the Arasan source distribution.
While no formal support is offered for this software, if you do find bugs in it, or discover a way to improve it, I would like to hear from you.
Contact information and additional information about Arasan can be found at arasanchess.org
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 You can find old, really old, and recent content from the site here.
{% if nodes %}If you're curious, here are some basic statistics on the database:
{% for x in nodes|nested_to_flat %} {% if x.start_nodes %}


{% extends "analysis.html" %} {% block head %} {% end %} {% block analysis %}
angular.js
most popular applications. First,
you get the nav-bar on top almost for free. The four tabs it accesses
are part of a single page. This is pure JavaScript and you always stay
inside this one page (even though the url bar of your browser updates).
Second, the standard hello world example for frameworks like
angular.js
is tested below.
The simplepi
example that is also included here (in the other tabs)
makes use of angular's data-binding. When a status
signal is received
from the Databench backend, the callback function simply assigns the new
estimate of π to a variable of an angular controller, say pi
. In the
html file, the value is shown in an angular variable placeholder
[[pi]]
.
What you just typed: {% raw "{{chapter}}" %}
This little demo uses two random numbers r1 and r2 and then does a comparison
r1^2 + r2^2 < 1.0
to figure out whether the generated point is
inside the first quadrant of the unit circle.


For years, I (Jose) have been planning for a project like this. I prepared my self quite well. I went through the entire CSS Level 2 specification along with the HTML 4.01 specification.
One of the most interesting things I found is this: Drawing HTML is no more than laying out a bunch of boxes with borders margins and padding's. Once you overpass this paradigm, everything else is to help the code actually place the boxes on the right place, and then paint the string each box contains.
Imagine the power that drawing full-rich-formatted HTML on your controls can give to your applications. Use bold when you need it, italics on every message, and borders and fonts as you may like or need everywhere on the desktop application. One of the first projects where I will use it is on the tooltips of my Ribbon Project.
Although I have not tested it on mono yet, there should be no problem at all, since all of the code on the library is managed code and the methods it use to paint are quite basic. It draws lines, rectangles, curves and text.
In October 2012 I (Arthur) was looking to replace the usage of WinForms WebBrowser control by something that can render complex html and have good performance and stability characteristics. Obviously I was looking for fully managed solution preferably one that I will have full control over.
HTML Renderer project showed great promise but had significant performance issues, lacked many features (primary text selection) and wasn't updated for more than 3 years. Realizing there is no alternative I embraced the project making it my baby.


Move the program elements on the bottom of the map into the outlined area where you believe the program element should be placed. You must use all the program elements.
REPORT