News story
Research aims to compare levels of difficulty of exam boards’ sample assessment materials
Ofqual is planning to conduct a research study to support the accreditation process for new A level maths qualifications.
Taking place in July this year, the research aims to compare the level of difficulty of exam boards’ sample assessment materials by having judges compare pairs of exam questions.
The qualifications are being taught for the first time in September 2017.
Dr Michelle Meadows, Executive Director for Strategy, Risk and Research, explains: “A level Maths has not been reformed for a long time, and there are lots of big changes taking place. It’s vital that the new qualifications are of the right level of demand.
“The accreditation process does look at level of demand, but it only judges each qualification in isolation. It’s more difficult to make an accurate judgement of the likely difficulty of just one exam paper or question, it is far easier and more reliable to compare two questions and rate which is going to be more difficult.”
The research will involve a pilot in which maths PhD students and maths teachers judge pairs of questions. The more reliable judges will take part in the main study. The findings will be used to support the accreditation panels. Ofqual will publish an update in the autumn.
Any current A level maths teachers who are interested in taking part in the research should contact Ofqual via email<EMAIL_ADDRESS>for more details.
Ofqual has also released a vlog today to explain the programme of work.


Home
Skills
Projects
Education
Contact
Weekly Reflections
Resume
Weekly Reflections
Date: 2019-02-14
Date: 2019-09-14


WITNESS LIST
Finance
January 31, 2019 9:00 AM
FOR:
Wardlow, Lyric Youth Specialist (Self)
ON:
Bass, James Executive Director (also providing written testimony) (Texas Department of
Transportation), Austin, TX
Berger, Melitta LBB Staff (also providing written testimony) (LBB)
Bourne, Leslie Executive Director (also providing written testimony) (Covenant House
Texas), Houston, TX
Brewster, Whitney Executive Director (also providing written testimony) (TX Department
of Motor Vehicles)
Bugg, Jr., J. Bruce Texas Transportation Commission, Chairman (also providing written
testimony) (Texas Transportation Commission), Austin, TX
Cervantes, David Acting Director, TDHCA (also providing written testimony) (TDHCA),
Austin, TX
Dixon, Don Engineer - Self (NA), San Antonio, TX
Fisher, Franklin (LifeWorks)
Flores, Linda M. Chief Financial Officer (also providing written testimony) (TxDMV)
French, Michelle Tax Assessor Collector - Denton Co. (also providing written testimony)
(Tax Assessor Collectors Association), Denton
Galvan, Thomas LBB Staff (also providing written testimony) (LBB)
Garcia, Joe Executive Director, Manufactured Housing Oio, TDHCA (also providing
written testimony) (TDHCA Manufactured Housing Division)
Gendron, Christine nonprofit executive director (also providing written testimony) (Texas
Network of Youth Services), Austin, TX
Green, Shelton LBB Staff (also providing written testimony) (LBB)
Grief, Gary Executive Director (also providing written testimony) (Texas Lottery
Commission), Austin, TX
Hall, Terri citizen advocate, small biz owner (Texas TURF, Texans for Toll-free Highways),
San Antonio
Hansen, Tommy Chair (also providing written testimony) (Auto Burglary and Theft
Prevention Authority)
Hayward, Justin Youth Ambassador (Self), Houston, TX
Higginbotham, Mike Chief Information Officer (also providing written testimony) (Texas
Department of Motor Vehicles)
Hughs, Ruth Attorney (also providing written testimony) (TWC - Commission Chair)
Luedeke, Shay Tax Assessor - Collector - Bell County (also providing written testimony)
(Tax Assessor - Collector Association)
McGeady, John LBB Staff (also providing written testimony) (LBB)
Owens, Danielle Chief Program Officer (LifeWorks), Austin, TX
Pearson, Caitlin LBB Staff (also providing written testimony) (LBB)
Pyka, Kathy Controller (also providing written testimony) (Texas Lottery Commission),
Austin, TX
Serna, Ed Interim Executive Director (also providing written testimony) (TWC)
WITNESS LIST
Finance
January 31, 2019 9:00 AM
Williams, Marc D. Deputy Executive Director (also providing written testimony) (Texas
Department of Transportation), Austin, TX
Registering, but not testifying:
FOR:
Shepperd, Ben President, PBPA (Permian Basin Petroleum Assoc), Midland, TX
ON:
Farrell, Michael Charitable Bingo Director (Lottery Commission)
Ragland, Brian Chief Financial Officer (Texas Department of Transportation), Austin, TX
Townsend, Randy CFO TWC (TWC)
Providing written testimony:
FOR:
Backes, Eric Executive Director (Texas Transit Association), Austin, TX
ON:
Boyer, Victor President & CEO, San Antonio Mobility Coalition, Inc. (Transportation
Advocates of Texas), San Antonio, TX
Merfish, Brett Director of youth justice (Texas appleseed), Austin, TX


Dear Dry,
Anyone attending our National Young Feminist Leadership Conference on March 21, 22, and 23 is eligible to reserve a room at the Double Tree Crystal City at a discounted rate - one that you won’t be able to snag anywhere else in the area. But you’ve only got ‘til the end of the night to grab one!
*Don’t wait. Register for NYFLC today and then reserve your room before this special deal ends. [ https://org.salsalabs.com/o/1400/p/salsa/event/common/public/?event_KEY=82727 ]*
NYFLC attendees can reserve double rooms at the Double Tree Crystal City for up to four people for $132 a night. You can book at these rates by calling 1-800-222-TREE (8733) and request the group code for Feminist Majority Foundation or clicking here [ https://resweb.passkey.com/Resweb.do?mode=welcome_ei_new&eventID=12773889 ] to book through the hotel’s website.
Need to find some people to fill that room? No worries! Check out our NYFLC 2015 Facebook event [ https://www.facebook.com/events/1584362341778060/ ]and connect with other attendees who are in the same boat! And as always, don’t hesitate to contact us at [<EMAIL_ADDRESS>] [<EMAIL_ADDRESS><EMAIL_ADDRESS>[<EMAIL_ADDRESS>] with any questions.
See You So Soon,
*FMF Campus Team*
“Alyssa, Ashleigh, Edwith, Nancy, duVergne and Taylor”


{% include 'partials/phase_banner_beta.html' %}
Confirm delete
If you delete this source, it will remove it from your list of sources. This means that when you add a new charge, the deleted source will no longer be available.
If you delete this source, it will remove it from your list of sources. This means that when you add a new charge, the deleted source will no longer be available.


sensors module¶
Defines the sensor classes.
Description¶
Defines the base and end user classes for various sensors.
Sensor - The base sensor class.
TempSensor - The temperature sensor class.
Libraries/Modules¶
- random Standard Library (https://docs.python.org/3/library/random.html)
Provides access to the randint function.
Notes¶
Comments are Sphinx (reStructuredText) compatible.
TODO¶
None.
Members¶
- class Sensor(name: str)¶
Bases:
object
The sensor base class.
Defines the base class utilized by all sensors.
- __init__(name: str) None ¶
The Sensor base class initializer.
- Parameters:
name (str) – The name of the sensor.
- __str__() str ¶
Retrieves the sensor’s description.
- Returns:
A description of the sensor.
- Return type:
str
- name: str¶
The name of the sensor.
- value: int¶
The value of the sensor.
- class TempSensor(name, unit='F')¶
Bases:
Sensor
The temperature sensor class.
Provides access to the connected temperature sensor.
Supported units are “F” (Fahrenheit), “C” (Celsius), and “K” (Kelvin).
- __init__(name, unit='F') None ¶
The TempSensor class initializer.
- Parameters:
name (str) – The name of the temperature sensor.
unit (str) – The unit of the temperature sensor with values of “F”, “C”, or “K”; defaults to “F”.
- __str__() str ¶
Retrieves the temperature sensor’s description.
- Returns:
A description of the temperature sensor.
- unit: str¶
The temperature unit.


Some Panel
Main Heading 1
Sample Content.
Main Heading 2
Sample Content.
Main Heading 3
Sample Content.


| Ming Lei | /MING LAY/ [1] |
| popoway (current) | |ˈpɑpəˌweɪ| [2] |
|
| popoway (2016-2017) | |ˈpɔpəˌweɪ| [2] |
|
| popoway (2015 and earlier) | |ˈpopo̙ˌweɪ| [3] |
| 雷鸣 | /Léi Míng/ [3] |
Q: What is your intention to make this site?
A: I have been asked the following question lots of times: "How to pronounce your name?". This inspired me to make this site, allowing curious readers to discover the official pronounciation of my name and its variants.
Q: What phonetic notation system did you use to indicate the pronounciation?
A: (Engligh) International Phonetic Alphabet (IPA). Learn more on
Wikipedia.
(中文) 汉语拼音方案。
Note that without proper rendering support, you may see question marks,
boxes, or other symbols instead of phonetic symbols.
部分操作系统及浏览器需要特殊字母与符号支持才能正确显示拼音符号，否则可能出现乱码、问号、空格等其它符号。
Q: I see three ways to say "popoway". Which one should I choose?
A: Any one of them. I don't mind and I don't bite.
Q: Why does your Chinese name look different from the English name?
A: CJK languages tend to put the first name last and last name first.
Q: I'm wondering, what are the origins and meaning of these names?
A: They will be answered in a separate newsroom article once this site becomes official.
Q: Why is the audio not playing?
A: Make sure you are using one of the major browsers which supports <audio>
element.
Q: What is the technology behind the audio?
A: [1]Facebook TTS
[2]Amazon Polly
[3] No technology. It's produced by my throat. Therefore it's © 2019 popoway. All rights reserved.


Minimal configuration
This example shows the minimal configuration when using the distributed version of PgnViewerJS. There are only three
files needed:
- dist/css/pgnvjs.css: The concatenated css files of the directory CSS.
- font-awesome.min.css: Taken from the internet, may be part later of the concatenated version ...
- dist/js/pgnv.js: The concatenated js files of the directory js (and all the other plugins used).


This is a 'mixed-language-page' -
some Finnish words and phrases occurs among English.
Suomen kieli on kaunista, hauskaa ja monipuolista.
English is not my native language!
Grammatical mistakes and/or spelling errors may occur!
I apologise!
Something to click!
Learning new ways of doing things is a lifelong journey.
This page is not for mobile :)
Though you are wellcome to try yourself on your device!
Obviously I have a passion for colors.
Hope you enjoy yourself!
All browsers have:
developer tools for HTML5, you can change CSS, run JavaScript. You should validate your page with validators. It all is available for you to test!
Here are only some helpful links, all available for you. After visiting them I hope you will come back soon!
Follow your passion!
Make your mark.
Keep your style!
There are others too, would make a long list!
You've have your choises!
There are always other ways too.
application, function, functional, resilient, responsive, asynchronous, ajax, script, style,
Häviävän kaunista...
Lyhyt esittely:
Olen opiskellut itsenäisesti web-sivujen suunnittelua alkaen 2013/09 -
kiitos avoimen lähdekoodin
ja MOOC-kurssien. Kerro mielipiteesi!
Short intro:
From 2013/09 I have been learning for web-developement by myself, thanks to the open software foundation and MOOC-courses on the web.
MOOC = Massive Open Online Cours


PokerTimer legacy settings import
If we found settings, you will find them below. Copy and import them into the new pokertimer.


Site Map
Laboratory
Overview
Theoretical Background
Materials and Methods

Protocols
Results and Discussion
Future Prospectives
Modeling
Overview
Theoretical Background
Simulation Setup
Results and Discussion
Human Practices
Overview
Project

Story

Dialog

Safety

Ethics

Design
Public Outreach

Education

Bioweek/Heureka

Sweat Challenge

Traditional Media

Social Media
Judging

Silver

Integrated Human Practices

Applied Design
Community
Overview
Parts
Interlab
Collaborations
People
Team Members
Advisors
Attributions
Sponsors


Computer Science Articles and items
-
Turing's Universal Machine Voted Top British Innovation -
Information Week March 25, 2013
-
Why Software is eating the world
- Wall Street Journal, Aug 20, 2011
-
Robot "Mission Impossible" Wins Robot video prize - New Scientist -
Aug 12, 2011
-
Wierd Al Yankovic - song Bob is all palindromes - about Bob Dylan
-
Java 1.6.0_21 company field changed from Sun to Oracle breaks applications
-
Interview Kinect Adventures Aug 6, 2010
-
Death of Google App Inventor - Aug 9, 2011
-
Google App Inventor Saved - Now to MIT - Aug 16, 2011
-
Microsoft Interns - News and Observer August 2009
-
Software Engineering High School
-
CS courses online
-
Bloomberg Business Week - May 1, 2006 - article about Duke CS
programming team
-
Learnable Programming by Bret Victor September 2012
-
Paul Pauca dreams up app to communicate with disabled son - Sept 18, 2012
-
Dickens, Austen and Twain, Through a Digital Lens - New York Times,
Jan 26, 2013
-
Harvard's Flipped Discrete Math Course
-
Physicists Eagerly try new teaching methods but then often drop them,
study finds
-
Freedom to Tinker
-
Why Johnny can't program - by Neil Mcallister
- Infoworld June 23, 2011
-
A Most Profound Math Problem
and here (pdf) The
New Yorker - May 2, 2013 - about P vs NP problem
-
New Standards Set for Federal Education
Research
- Education Week - May 7, 2013
-
How Computer Analysis Uncovered J. K. Rowling's Secret Novel -
PopSci, July 17, 2013
-
Active learning increases student performance in science, engineering, and
mathematics - paper in PNAS (2014)
-
The 10 Algorithms That Dominate Our World - io9.com 5/22/14
-
How Computer Science Solved the Puzzle of the 9/11 Memorial
- BetaBeat, by Ben Popper, 5/9/2011
-
Publishers withdraw more than 120 gibberish papers Nature - Feb 24, 2014
-
Meet Cepheus, the virtually unbeatable poker-playing computer -
washington post January 8, 2015
-
XKCD - Depth First Search comic
-
Dilbert - paying attention in class
-
Dilbert - Breaking into groups
-
CS is designing video games
-
Advising - Doonesbury 8-14-2011
-
Earthquake / twitter - xkcd
- With big
data comes big responsibilities - Technology Review,
Wed. Oct 5, 2011
- "Top-10-Programming-Languages"
-
Comic with "Too many abbreviations"
-
video about grad school in theoretical CS - asking prof for reference
letter
-
Inadvertent Algorithmic Cruelty
-
STEM: The choice is yours - SAS video 2012 for MS students.
-
Duke CS videos page
- code.org
- videos to encourage young people to learn programming
-
Thanksgiving Turduckenen-duckenen binary tree
-
optimal math potatoes
-
green bean matherole
-
Borromean onion rings
-
The Magic Shelf Concept Video - about Kiva robot system for bringing
shelves to the picker
-
Future Innovators: Kiva commercial - best buy ad
-
Clip from Obama's Google+ hangout on Feb. 14, 2013 where he discusses
the importance of CS in K-12
-
Weird Al Yankovic - Bob song done all in palindromes
-
How Walt Disney Cartoons are Made - from 1939
-
Nora Denzel Keynote at Grace Hopper Celebration of Women in Computing 2012
- Women the
programmers of tomorrow.
- Miami Springs High School video about girls taking computing -
June 10, 2013
-
Duke Student Roger Zou profiled in Duke Today with his research
-
Vint Cerf on Colbert (part 1) - Aug. 2014
-
Vint Cerf on Colbert (part 2) - Aug. 2014
- "IKEA""ExperienceThePowerOfA-bookbook"
- click here
- Theoretical Computer Science Cheat Sheet
-
Barb Ericson Practice AP CS A exam questions - February 2014 (at Georgia Tech)
-
Barb Ericson Practice AP CS A exam questions - August 23, 2012 (at college
board site)
-
Practice It - Aug 23, 2012
- Coder Dojo - programming collaboration
for young people
- code.org
-
play-i.com - Gender neutral robots (Steve Cooper in intro video)
-
Engage CS Edu - NCWIT
-
SIAM Math Matters posters


Presentations for @rcodi team members ~
If you do not have a GitHub account Sign up here
Accept your invitation to join the RCODI GitHub Org
Or request an invitation by creating a membership-request issue
RCODI Activty on GitHub
Chapter 1: Git vs GitHub
git
is not GitHub.comgit
but it does way moreWhat most people think when they hear “GitHub”
$ cat 0001-add-limit-to-log-function.patch
From<PHONE_NUMBER>54092d704da8e76ca5c05c198e71a8 Mon Sep 17 00:00:00 2001
From: Jessica Smith<EMAIL_ADDRESS>Date: Sun, 6 Apr 2008 10:17:23 -0700
Subject: [PATCH 1/2] Add limit to log function
Limit log functionality to the first 20
lib/simplegit.rb | 2 +-
1 files changed, 1 insertions(+), 1 deletions(-)
diff --git a/lib/simplegit.rb b/lib/simplegit.rb
index 76f47bc..f9815f1 100644
--- a/lib/simplegit.rb
+++ b/lib/simplegit.rb
@@ -14,7 +14,7 @@ class SimpleGit
end
def log(treeish = 'master')
- command("git log #{treeish}")
+ command("git log -n 20 #{treeish}")
end
Projects on GitHub use git, but the platform is much more than file hosting for your project.
As of January 2020, GitHub reports having over
Making it the largest host of open-source code in the world.
GitHub is a huge community and very active
In the midst of the uncertainty and seriousness of COVID-19, we've been inspired to see a global community of scientists, government officials, journalists, programmers, and concerned citizens come together to collaborate on a variety of projects with the shared goal of understanding COVID-19 and coordinating on the best response.
Even though many of these projects aren't traditional software projects, the same collaborative development model is being applied to curated data sets, DIY instruction sets, and more.
(March 23, 2020)
- GitHub Blog
Dropbox: is a file hosting service
Dropbox: is a file hosting service
git
is purposefulIssues are very helpful and every person on the team can easily contribute.
Enabling transparency and managing change in projects with collaboration
"Issues are the fundamental medium for collaborating on ideas and planning work in GitLab."
- Gitlab Company Handbook
Issues can allow sharing and discussion of proposals before, and during, their implementation between:
They can also be used for a variety of other purposes, customized to your needs and workflow. Issues are always associated with a specific project, but if you have multiple projects in a group, you can also view all the issues collectively at the group level.
Common use cases include:


Keeping Track - Arrays and Hashes
Ruby provides two primary tools for storing collections of objects: Arrays and Hashes. In this post we'll explore both options and discuss briefly when you might want to use each
Arrays
Arrays are an ordered collection of objects (strings, integers or floats) where the index value (or key) for each object is an integer which represents its location within the list. Creating a new array is easy and can be done in a number of ways:
Empty Array:
new_array = Array.new
new_array = []
or initialized with values:
new_array = Array.new(3,2)
new_array = [2, 2, 2]
Hashes
Hashes are similiar to Arrays in Ruby but have the added flexibility of using any String as the key for the ordered object. This ability to assign objects to a self-defined key allows us to create a dictionary of sorts. Hash key-value pairs are also locatable by Integer location within the index so in a sense Arrays are just a simplified subset of Hashes. Hashes are created in a similiar way as Arrays but use the brace instead of square bracket
Empty Hash:
new_hash = Hash.new
new_hash = {}
or initialized with values:
new_hash = Hash.new("default value")
new_hash = {"key","value","key","value"}
To Array or To Hash?
When evaluating how to collect a set of related information stored in Ruby objects the primary consideration betwen Hashes and Arrays are whether the information needs be classified with Keys that are not just a location. In plain english, whether the more intelligent classification of a piece of information like "123 Main Street" is in fact "Address" rather than "Index location 2".
One other consideration that will come into play later in our exploration of written good Ruby code is that Arrays execute more quickly than Hashes because they can be indexed more efficiently. More on this topic at a future date.


{% extends "workflow/project_base.html" %} {% block title %}Snakesite{% endblock title %} {% block nav_item_home %}active{% endblock nav_item_home %} {% block content %}
Modify the index.html file to change this site according to your needs. E.g. add plots from parsing the results of your workflows.


This website uses a list of names extracted from Wikipedia.
The data which is fetched can be seen here: data/deaths.json
See the JavaScript code which fetches and outputs the data here: main.js
Please use the below buttons to interactively filter the data.
| Birth Year | Name | Profession | Death Date |
|---|


Point-based Graphics Resource
Symposium on Point-Based Graphics
- Symposium on Point-Based Graphics 2006 (On-line Papers)
- Symposium on Point-Based Graphics 2006 (On-line Papers)
- Symposium on Point-Based Graphics 2005 (On-line Papers)
- Symposium on Point-Based Graphics 2004 (On-line Papers)
Books
- Point-Based Graphics
-
Edited By
Markus Gross, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland
Hanspeter Pfister, MERL (Mitsubishi Electric Research Laboratories), Cambridge, Massachusetts, U.S.A.
Tutorials/Course Notes/Survey
- Point-Based Computer Graphics
-
Marc Alexa,
Markus Gross,
Mark Pauly,
Hanspeter Pfister,
Marc Stamminger,
Matthias Zwicker
SIGGRAPH 2004 Course Notes
- Point-Based Computer Graphics
-
Marc Alexa,
Carsten Dachsbacher,
Markus Gross,
Mark Pauly,
Jeroen van Baar,
Matthias Zwicker
Eurographics 2003 Tutorial Notes
- Point-Based Computer Graphics
-
Marc Alexa,
Markus Gross,
Mark Pauly,
Hanspeter Pfister,
Marc Stamminger,
Matthias Zwicker
Eurographics 2002 Tutorial Notes
- A Survey of Point-Based Techniques in Computer Graphics
-
Leif Kobbelt, Mario Botsch
to appear in Computers & Graphics
Dissertation/Thesis
- Point-Based Modeling, Animation and Rendering of Dynamic Objects
-
Bart Adams
PhD Thesis, Katholieke Universiteit Leuven, May 2006
- Point-Based Multi-Resolution Rendering
-
Michael Wand
PhD Thesis, University of Tubingen, Department of Computer Science and Cognitive Science, 2004
- Dynamic Point Samples as Primitives for Free-Viewpoint Video
-
Stephan Wurmlin
PhD Thesis, ETH Zurich, 2004
- Reconstruction and Rendering of Implicit Surfaces from Large Unorganized Point Sets
-
Patrick Reuter
PhD Thesis, LaBRI - Universite Bordeaux-1, 2003
- Point Primitives for Interactive Modeling and Processing of 3D Geometry
-
Mark Pauly
PhD Thesis, ETH Zurich, 2003
- Continuous Reconstruction, Rendering, and Editing of Point-Sampled Surfaces
-
Matthias Zwicker
PhD Thesis, ETH Zurich, 2003
- Point Set Surfaces
-
Shachar Fleishman
PhD Thesis, 2003
- Image-Based Rendering of Range Data with Depth Uncertainty
-
Christian Hofsetz
PhD Thesis, Center for Image Processing and Integrated Computing, University of California, Davis, 2003
- Point Sample Rendering
-
J.P. Grossman
Master's Thesis, August 1998
Software
- Pointshop3D
-
Computer Graphics Lab at ETH Zurich (Switzerland)
- QSplat
-
Computer Graphics Lab at Stanford
- CoTeX (a texturation plugin for Pointshop3D)
-
Tamy Boubekeur
- Getting to the Point...?
-
Markus Gross (2006)
- Enhancing Interactive Editing on Point Set Surfaces Through Touch-based Haptics
-
Xiaohu Guo,
Jing Hua,
Hong Qin (November/December 2004)
- A simple approach for point-based object capturing and rendering
-
Miguel Sainz,
Renato Pajarola,
Anotnio Susin,
Albert Mercade (July/August 2004)
- Image-Based Rendering of Range Data with Estimated Depth Uncertainty
-
Christian Hofsetz,
Kim Ng, George Chen, Peter McGuinness, Nelson Max, Yang Liu (July/August 2004)
- Scalar-Function-Driven Editing on Point Set Surfaces
-
Xiaohu Guo,
Jing Hua,
Hong Qin (July/August 2004)
- Normal Improvement for Point Rendering
-
Thouis Jones,
Fredo Durand,
Matthias Zwicker (July/August 2004)
- Flexible Point-Based Rendering on Mobile Devices (TR Version
)
-
Florent Duguet,
George Drettakis (July/August 2004)
- Hierarchical Splatting of Scattered 4D Data
-
Matthias Hopf,
Michael Luttenberger,
Thomas Ertl (July/August 2004)
- Point-Based Multi-Scale Surface Representation
-
Mark Pauly,
Leif Kobbelt,
Markus Gross (TOG 2006)
- Progressive Point Set Surfaces
-
Shachar Fleishman,
Marc Alexa,
Daniel Cohen-Or,
Claudio T. Silva (TOG 2003)
- Global Non-Rigid Alignment of 3-D Scans
-
Benedict J. Brown,
Szymon Rusinkiewicz
(Princeton University) (SIGGRAPH 2007)
- Parameterization-free Projection for Geometry Reconstruction
-
Yaron Lipman,
Daniel Cohen-Or,
David Levin
(Tel Aviv University),
Hillel Tal-Ezer
(Academic College of Tel-Aviv Yaffo) (SIGGRAPH 2007)
- Algebraic Point Set Surfaces
-
Gaël Guennebaud,
Markus Gross
(ETH Zürich) (SIGGRAPH 2007)
- Direct Visibility of Point Sets
-
Sagi Katz,
Ayellet Tal
(Technion),
Ronen Basri
(The Weizmann Institute of Science) (SIGGRAPH 2007)
-
Point-Sampled Cell Complexes
- Anders
Adamson (Technische
Universität Darmstadt), Marc
Alexa (Technische Universität
Berlin) (SIGGRAPH 2006)
- Meshless Animation of Fracturing Solids
-
Mark Pauly
(Stanford University),
Richard Keiser
(Eidgenössische Technische Hochschule Zürich ),
Bart Adams,
Philip Dutr?
(Katholieke Universiteit Leuven),
Markus Gross
(Eidgenössische Technische Hochschule Zürich),
Leonidas Guibas
(Stanford University) (SIGGRAPH 2005)
- Meshless Deformations Based on Shape Matching
-
Matthias Müller,
Bruno Heidelberger
(Eidgenössische Technische Hochschule Zürich ),
Matthias Teschner
(Universität Freiburg),
Markus Gross
(Eidgenössische Technische Hochschule Zürich ) (SIGGRAPH 2005)
- Defining Point-Set Surfaces
-
Nina Amenta,
Yong Joo Kil
(SIGGRAPH 2004)
- Combining Edges and Points for Interactive High-Quality Rendering
-
Kavita Bala,
Bruce Walter,
Donald Greenberg
(SIGGRAPH 2003)
- Shape Modeling with Point-Sampled Geometry
-
Mark Pauly,
Richard Keiser
Leif Kobbelt
Markus Gross
(SIGGRAPH 2003)
- Interactive Boolean Operations on Surfel-Bounded Solids
-
Bart Adams,
Philip Dutre
(SIGGRAPH 2003)
- Sequential Point Trees
-
Carsten Dachsbacher,
Christian Vogelgsang,
Marc Stamminger
(SIGGRAPH 2003)
- Pointshop 3D: An Interactive System for Point-Based Surface Editing
-
Matthias Zwicker,
Mark Pauly,
Oliver Knoll,
Markus Gross (SIGGRAPH 2002)
- Spectral Processing of Point-Sampled Geometry
-
Mark Pauly,
Markus Gross (SIGGRAPH 2001)
- Surface Splatting
-
Matthias Zwicker,
Markus Gross (SIGGRAPH 2001)
- The Randomized z-Buffer Algorithm: Interactive Rendering of Highly Complex Scenes
-
Michael Wand,
Matthias Fischer,
Ingmar Peter,
Friedhelm Meyer auf der Heide,
Wolfgang Straber (SIGGRAPH 2001)
- Surfels: Surface Elements as Rendering Primitives
-
Hanspeter Pfister,
Matthias Zwicker,
Jeroen van Baar,
Markus Gross (SIGGRAPH 2000)
- QSplat: A Multiresolution Point Rendering System for Large Meshes
-
Szymon Rusinkiewicz,
Marc Levoy (SIGGRAPH 2000)
- Bayesian Point Cloud Reconstruction
-
Philipp Jenke
(WSI/GRIS, University of Tuebingen),
Michael Wand
(Stanford University),
Martin Bokeloh,
Andreas Schilling,
Wolfgang Straßer,
(WSI/GRIS, University of Tuebingen) (EUROGRAPHICS 2006)
- Competing Fronts for Coarse-to-Fine Surface Reconstruction
-
Andrei Sharf
(School of Computer Science, Tel Aviv University),
Thomas Lewiner
(Department of Mathematics, PUC-Rio de Janiero),
Ariel Shamir
(Efi Arazi School of Computer Science, The Interdisciplinary Center, Herzliya),
Leif Kobbelt
(Computer Graphics Group, RWTH Aachen),
Daniel Cohen-Or,
(School of Computer Science, Tel Aviv University) (EUROGRAPHICS 2006)
- Volume-Surface Trees
-
Tamy Boubekeur
(LaBRI - INRIA - CNRS - University Of Bordeaux),
Wolfgang Heidrich
(University of British Columbia, Vancouver, BC, Canada),
Xavier Granier,
Christophe Schlick
(LaBRI - INRIA - CNRS - University Of Bordeaux) (EUROGRAPHICS 2006)
- Interpolatory Refinement for Real-Time Processing of Point-Based Geometry
-
Gael Guennebaud,
Loic Barthe,
Mathias Paulin (EUROGRAPHICS 2005)
- Efficient Animation of Point-Sampled Thin Shells
-
Martin Wicke,
Denis Steinemann,
Markus Gross (EUROGRAPHICS 2005)
- Efficient Raytracing of Deforming Point-Sampled Surfaces
-
Bart Adams,
Richard Keiser,
Mark Pauly,
Leonidas J. Guibas,
Markus Gross,
Philip Dutre (EUROGRAPHICS 2005)
- Point Cloud Collision Detection
-
Jan Klein,
Gabriel Zachmann (EUROGRAPHICS 2004)
- Optimized Sub-Sampling of Point Sets for Surface Splatting
-
Jianhua Wu,
Leif Kobbelt (EUROGRAPHICS 2004)
- Deferred splatting
-
G. Guennebaud,
M. Paulin,
Loic Barthe (EUROGRAPHICS 2004)
- VOTS: VOlume doTS as a Point-Based Representation of Volumetric Data
-
Soren Grimm,
Stefan Bruckner,
Armin Kanitsar,
Eduard Groller (EUROGRAPHICS 2004)
- Multi-Scale Feature Extraction on Point-Sampled Models
-
Mark Pauly,
Richard Keiser,
Markus Gross (EUROGRAPHICS 2003)
- Object Space EWA Surface Splatting: A Hardware Accelerated Approach to High Quality Point Rendering
-
Liu Ren,
Hanspeter Pfister,
Matthias Zwicker
(EUROGRAPHICS 2002)
- Tetrahedralization of Point Sets Using Expanding Spheres
-
Martin S. Maltz (TVCG 2005)
- Confetti: Object-Space Point Blending and Splatting
-
Renato Pajarola,
Miguel Sainz,
Patrick Guidotti (TVCG 2004)
- Computing and Rendering Point Set Surfaces
-
Marc Alexa,
Johannes Behr,
Daniel Cohen-Or,
Shachar Fleishman,
Claudio T. Silva (TVCG 2003)
- EWA Splatting
-
Matthias Zwicker,
Hanspeter Pfister,
Jeroen van Baar,
Markus Gross (TVCG 2002)
- Point Based Animation of Elastic, Plastic and Melting Objects
-
Matthias Muller,
Richard Keiser,
Andrew Nealen,
Mark Pauly,
Markus Gross,
Marc Alexa (SCA 2004)
- Voronoi-based Variational Reconstruction of Unoriented Point Sets
-
Pierre Alliez,
David Cohen-Steiner,
Yiying Tong,
Mathieu Desbrun (SGP 2007)
- Reconstruction of Deforming Geometry from Time-Varying Point Clouds
-
Michael Wand,
Philipp Jenke,
Qi-Xing Huang,
Martin Bokeloh,
Leonidas Guibas,
Andreas Schilling (SGP 2007)
- Data-Dependent MLS for Faithful Surface Approximation
-
Yaron Lipman,
Daniel Cohen-Or,
David Levin (SGP 2007)
- Multilevel Streaming for Out-of-Core Surface Reconstruction
-
Matthew Bolitho,
Michael Kazhdan,
Randal Burns,
Hugues Hoppe (SGP 2007)
- A Streaming Algorithm for Surface Reconstruction
-
Remi Allegre,
Raphaelle Chaine,
Samir Akkouche (SGP 2007)
- Shape reconstruction from unorganized cross-sections
-
Jean-Daniel BOISSONNAT,
Pooran MEMARI (SGP 2007)
-
Robust Reconstruction of Watertight 3D Models from Non-uniformly Sampled
Point Clouds Without Normal Information
-
Alexander Hornung,
Leif
Kobbelt (SGP 2006)
-
Surface Reconstruction with Radial Basis Functions using Voronoi
Vertices as Centers
-
Marie Samozino, Marc
Alexa,
Pierre
Alliez,
Mariette Yvinec (SGP 2006)
- Poisson
Surface Reconstruction
- Michael Kazhdan,
Matthew Bolitho,
Hugues Hoppe (SGP 2006)
- Error Bounds and
Optimal Neighborhoods for MLS Approximation
- Yaron Lipman,
Danny Cohen-Or,
David Levin (SGP 2006)
- Global
Registration of Multiple 3D Point Sets via Optimization-on-a-Manifold
- Shankar Krishnan,
Pei Yean Lee,
John B Moore,
Suresh Venkatasubramanian
(SGP 2005)
- Multiscale Features for Approximate Alignment of Point-based Surfaces
-
Xinju Li,
Igor Guskov (SGP 2005)
- An Adaptive MLS Surface for Reconstruction with Guarantees
-
Tamal K. Dey,
Jian Sun (SGP 2005)
- Surface Reconstruction for Noisy Point Clouds
-
Nina Amenta,
Boris Mederos,
Luiz Velho,
Luiz Henrique de Figueiredo (SGP 2005)
- Triangulating Point Set Surfaces with Bounded Error
-
Carlos E. Scheidegger,
Shachar Fleishman,
Claudio T. Silva (SGP 2005)
- Reconstruction of Solid Models from Oriented Point Sets
-
Michael Kazhdan (SGP 2005)
- Spectral Surface Reconstruction From Noisy Point Clouds
-
Ravi Krishna Kolluri,
Jonathan Richard Shewchuk,
James F. O'Brien (SGP 2004)
- Registration of Point Cloud Data from a Geometric Optimization Perspective
-
Niloy J. Mitra,
Natasha Gelfand,
Helmut Pottmann,
Leo Guibas (SGP 2004)
- Comparing Point Clouds
-
Guillermo Sapiro
Facundo Memoli (SGP 2004)
- Approximating and Intersecting Surfaces from Points
-
Anders Adamson,
Marc Alexa (SGP 2003)
- Statistical Point Geometry
-
Aravind Kalaiah,
Amitabh Varshney (SGP 2003)
- PointWorks: Abstraction and Rendering of Sparsely Scanned Outdoor Environments
-
Hui Xu,
N. Gossett,
Baoquan Chen (EGSR 2004)
- Efficient high quality rendering of point sampled geometry
-
Mario Botsch,
Andreas Wiratanaya,
Leif Kobbelt (EGSR 2002)
IEEE Visualization
- Interactive Point-based Isosurface Exploration and High-quality Rendering
-
Haitao
Zhang,
Arie Kaufman (IEEE Visualization 2006)
- Interactive point-based
rendering of higher-order tetrahedra data
-
Yuan Zhou,
Michael Garland
(IEEE Visualization 2006)
- Reconstructing Manifold and Non-Manifold Surfaces from Point Clouds
-
Jianning Wang,
Manuel M. Oliveira,
Arie Kaufman
(IEEE Visualization 2005)
- Stream-Processing Points
-
Renato Pajarola (IEEE Visualization 2005)
- Hardware Accelerated Adaptive EWA Volume Splatting
-
Wei Chen,
Liu Ren,
Matthias Zwicker,
Hanspeter Pfister (IEEE Visualization 2004)
- Efficient Simplification of Point-Sampled Surfaces
-
Mark Pauly,
Markus Gross,
Leif Kobbelt (IEEE Visualization 2002)
- Point Set Surfaces
-
Marc Alexa,
Johannes Behr,
Daniel Cohen-Or,
Shachar Fleishman,
Claudio T. Silva (IEEE Visualization 2001)
- EWA Volume Splatting
-
Matthias Zwicker,
Hanspeter Pfister,
Jeroen van Baar,
Markus Gross (IEEE Visualization 2001)
Pacific Graphics
- Point-Based Minkowski Sum Boundary
-
Jyh-Ming Lien (Pacific Graphics 2007)
- Fitting Unorganized Point Clouds with Active Implicit B-Spline Curves
-
Zhouwang Yang, Jiansong Deng,
Falai Chen (Pacific Graphics 2005)
- Radiosity for Point-Sampled Geometry
-
Yoshinori Dobashi,
Tsuyoshi Yamamoto,
Tomoyuki Nishita (Pacific Graphics 2004)
- CSG-Tree Rendering for Point-Sampled Objects
-
Martin Wicke,
Matthias Teschner,
Markus Gross (Pacific Graphics 2004)
- Fitting Subdivision Surfaces to Unorganized Point Data using SDM
-
Dominic Cheng,
Wenping Wang,
Hong Qin,
Kenneth K.Y. Wong,
Huaiping Yang,
Yang Liu (Pacific Graphics 2004)
- Dynamic Sculpting and Deformation of Point Set Surfaces
-
Xiaohu Guo,
Hong Qin (Pacific Graphics 2003)
- Iso-Splatting: A Point-based Alternative to Isosurface Visualization
-
Christopher S. Co,
Bernd Hamann,
Kenneth Joy (Pacific Graphics 2003)
- High-Quality Point-Based Rendering on Modern GPUs
-
Mario Botsch,
Leif Kobbelt (Pacific Graphics 2003)
Shape Modeling International
-
Segmenting Point Sets
- Ichitaro Yamazaki,
Vijay Natarajan,
Zhaojun Bai,
Bernd Hamann (Shape Modeling International 2006)
- Reconstructing B-spline Curves
from Point Clouds -- A New Growing Approach
- Yang Liu,
Huaiping Yang, Wenping Wang (Shape Modeling International 2005)
- Smooth
Adaptive Fitting of 3D models using hierarchical triangular splines
- Alex Yvart,
Stefanie Hahmann,
Georges-Pierre Bonneau (Shape Modeling International 2005)
-
Visualization of Point-Based Surfaces with Locally Reconstructed Subdivision
Surfaces
- Tamy Boubekeur,
Patrick Reuter,
Christophe Schlick (Shape Modeling International 2005)
-
Convection-Driven Dynamic Surface Reconstruction
- Remi Allegre,
Raphaelle Chaine,
Samir Akkouche (Shape Modeling International 2005)
- Robust Watermarking of Point-Sampled Geometry
-
Daniel Cotting,
Tim Weyrich,
Mark Pauly,
Markus Gross (Shape Modeling International 2004)
- Multiresolution Reconstruction of Implicit Surfaces with Attributes from Large Unorganized Point Sets
-
Ireneusz Tobor,
Patrick Reuter,
Christophe Schlick (Shape Modeling International 2004)
- Approximating Bounded, Non-orientable Surfaces from Points
-
Anders Adamson,
Marc Alexa (Shape Modeling International 2004)
- Ray Tracing Point Set Surfaces
-
Anders Adamson,
Marc Alexa (Shape Modeling International 2003)
Journal of WSCG
- Efficient reconstruction of large scattered geometric datasets using the partition of unity and radial basis functions
-
Ireneusz Tobor,
Patrick Reuter,
Christophe Schlick (Journal of WSCG 2004)
- Direction Fields over Point-sampled Geometry
-
Marc Alexa, Tobias Klug, Carsten Stoll (Journal of WSCG 2003)
Computer Animation and Social Agents
- Physically-based Morphing of Point-sampled Surfaces
-
Yunfan Bao,
Xiaohu Guo,
Hong Qin (Computer Animation and Social Agents 2005)
- Robust Morphing of Point-sampled Geometry
-
Chunxia Xiao, Wenting Zheng, Qunsheng Peng, Robin Forrest (Computer Animation and Social Agents 2004)
Graphics Interface
- Perspective Accurate Splatting
-
Matthias Zwicker,
J. Rasanen, M. Botsch,
Carsten Dachsbacher,
Mark Pauly (Graphics Interface 2004)
Computer Graphics International
- Point Set Surface Editing Techniques based on Level-Sets
-
Xiaohu Guo,
Jing Hua,
Hong Qin (Computer Graphics International 2004)
- Fairing of Point Based Surfaces
-
Ulrich Clarenz,
Martin Rumpf,
Alexandru Telea (Computer Graphics International 2004)
ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games
- Streaming QSplat: A Viewer for Networked Visualization of Large, Dense Models
-
Szymon Rusinkiewicz,
Marc Levoy (I3D 2001)
IEEE Symposium on Interactive Ray Tracing
- The Quantized kd-Tree: Efficient Ray Tracing of Compressed Point Clouds
-
Erik Hubo,
Tom Mertens,
Tom Haber,
Philippe Bekaert
(IEEE Symposium on Interactive Ray Tracing 2006)
ACM Graphite
- Surfel Stripping
-
Tamy Boubekeur,
Patrick Reuter,
Christophe Schlick (Proceedings of ACM Graphite 2005)
- Point-based modelling and rendering using radial basis functions
-
Patrick Reuter,
Ireneusz Tobor,
Christophe Schlick,
Sebastien Dedieu (Proceedings of ACM Graphite 2003)
VAST: International Symposium on Virtual Reality, Archaeology and Intelligent Cultural Heritage
- Rapid Visualization of Large Point-Based Surfaces
-
Tamy Boubekeur,
Florent Duguet,
Christophe Schlick (Proceedings of Eurographics VAST 2005)
-
A Point-Based Approach for Capture, Display and Illustration of Very Complex Archeological Artefacts
-
Florent Duguet,
George Drettakis,
Daniel Girardeau-Montaut,
Jean-Luc Martinez,
Francis Schmitt (Proceedings of Eurographics VAST 2004)
Related Papers
-
Anisotropic Point Set Surfaces
- Anders
Adamson (Technische
Universität Darmstadt), Marc
Alexa (Technische Universität
Berlin) (Proceedings of the 4th international Conference on Computer
Graphics, Virtual Reality, Visualisation and interaction in Africa 2006)
- An Integrating Approach to Meshing Scattered Point Data
-
Yutaka Ohtake,
Alexander Belyaev,
Hans-Peter Seidel (SPM 2005)
- Contact Handling for Deformable Point-Based Objects
-
Richard Keiser,
Matthias Muller,
Bruno Heidelberger,
Matthias Teschner,
Markus Gross (Proceedings of Vision, Modeling, Visualization 2004)
- Real-time Streaming of Point-based 3D Video
-
Edouard Lamboray,
Stephan Wurmlin,
Markus Gross (Proceedings of the IEEE Virtual Reality 2004)


Back
Add Idea
GIFTR
Add Idea
Name of person you are buying gifts for
Gift idea
Store
URL
Cost
Cancel
Save


404 Not Found
Some stuff may not be working right now, but that's OK! It will all be fixed and become a full shop soon, so just wait and enjoy.
Some stuff may not be working right now, but that's OK! It will all be fixed and become a full shop soon, so just wait and enjoy.


5 second style copy
drawing for commission? hahahahahahhahahahahahh come here lemme transfer your style using vgg19
wkwk, ikut conference itu feeding ego doang, existentialism
ah masa
neural style transfer, a pycon story.
just why?
well, i’m in a rut of doing nlp stuff, finetuning parameters, climbing the hills and valleys of errors, I needed pelarian something to do without all these metrics. Looking at the empty walls of my rooms, I decided to search for posters to stick on the walls, making my room much more enjoyable to look at.
searching for the fittest within my preferences takes forever to finish, I constraint myself not to do manual design just ol’ plain seeing and printing, and this print is only and gonna be the only one in the world. I want it to be unique and easy! This narrows to:
- band posters: meh, too mainstream, it’ll take work to make it “my style”
- film posters: i am too volatile to love one particular movies, thus only got the budget for printing one.
- quote posters: you got to be kidding me.
- motivational quote posters: 🙃
-
dian sastro posterSo I still got some viable solutions like: - print a film photograph
- cost more money, maklum anak kosan
- edit a photo to look like it was taken from an analog film camera
- this is basically a filter.
from the filter solution, this looks like the one. I was kinda happy but not satisfied because I’m not a very skilled photographer and not many of my photos amazes me. So I decided to have a different approach with a 3D render, recreating one of my photograph.
Long story short it is too much of a hassle. Props to you all 3D digital artist with all those octane rendering.
Then I saw a tweet from @vngc it’s a tweet about neural style transfer, unfortunately he deleted his twitter ☹
STYLE TRANSFER! - this is what I am looking for.
This got me hooked! well I can recreate those stuff, it’s only a CNN on a picture! I just needed a painting style and apply it to the photograph of my choosing!
This is surely a “filter” on steroids.
how?
so how does one copy a pattern of brush strokes, the pressure of the brushes to the canvas and the beautiful colors from the original paintings?
let me tell you: edges.
edges is what makes the subject different from the backgorund, contrast in between edges is what defines one brush stroke to the other. Edges is what It’s the edges that we are looking to copy! So how does one copy “an edge” to other image?
now by getting the edges, recreating the edges how does a human can copy a style? well you don’t we leave it to the computationally superior guys to simulate it. the computers. well the computers don’t know what’s an edge is and what’s a picture, what’s a pattern, so they needed a good “kinda look alike edges” from the human world to the computer’s world. How? Representation!
To obtain a representation of a style of an image, how does one define an edge? an edge is what makes a texture. The information, or the “edges” inside the texture is what we’re trying to look for exactly. Then by representing it as an array a computer can easily simulate it in a scale that human can’t.
But what does the computer sees exactly? pixel values.

But, getting the all this texture information how to distinguish between the texture that we are trying to capture and the wholesome arrangement of the image? we’re trying to copy the brush strokes not the subject of the image right?
The representation of the styles is what we will store to an n^th dimension space. Therefore to capture its textual information, this “space” is where the VGG19 comes in.
First of all, the layers in VGG19 will try to search the “correlation” between result of the layers.
Then it will try to deconstruct the input image by downsampling it.
Finally the resulting correlation feature space or the texture we’re trying to copy will match the style to the downsampled image we’re trying to paste our texture to.
Both of these processes are what we’re trying to create resulting a new texture information to the input image, and discarding the arrangement of the pixel from the styled image.
That my friend, is what we’re looking for. The separation of content and style representation.
optimizing the loss
i know making something good means having a good metric to compare to, but this metric we’ll be using is actually the metric that measures the distance between the generated image, to the content and style image. How do you measure the distance between images? gram matrix. Before going to the gram matrix we need to clarify what are the losses
- content loss: means the difference between the “content” in the content image to the generated image.
- style loss: means the difference between the “style” in the style image to the generated image. How do you measure “content” and “style”? like all mathematicians, to measure a distance we need two points to measure on. We map the content image to a hilbert space using the VGG19’s feature layer, making it a vector, then map the style image and generated image also. These vectors can be measured from the distances, seeing it as how far is it different from each other. The gram matrix is the result of the inner product of the vectors. This actually shows the spatial correlation between the images. Think of it as a historgram. It doesn’t measure specific pixel to pixel or a location of the images. It calculate only the feature distribution that we wanted.
feature extractors x transfer learning.
My favorite. Transfer learning is used to extract what you call a texture information on an image. How do you extract it is by mapping the “patterns” of an image to a feature layer. The Think of this transfer learning as a baseline of our model, the model itself (VGG19), is a CNN architecture which used 15 million images from the ImageNet dataset, and trained on a 138 million parameters that uses 5 convolutional layers. By using VGG’s pretrained models, our model doesn’t need to learn from scratch and saving a lot of time and space, by only fine tuning the top layers, on what we wanted it to output (generated image). Both weight and biases from the pretrained model is what the feature extractors are. Diffrentiating which is which, edges, patterns, textures, brush strokes, colors, etc.
implementation
using pytorch:
- generated a white noise for the generated image
- adding style and content from the extracted features from both the content and style image
- backprop and gradient descent step optimized using L-BFGS to find the loss function score
- iterate till satisified with result The L-BFGS is just a work around on a smaller GPU capability. My implementation on this is actually stack 10 scripts on each other, changing its initiation image to the output image from the previous script output. to produce image, from the 10 scripts:
- using google colab: 3045 seconds
- using gradient’s p5000: 971s
other implementation
- maps, on maps
- painting on paintings
parting words
I am deeply honored to be invited to be the speaker on pycon indo 2019 about this however, to make the project “demo-able” i made several improvements:
- cycleGAN
- fast-style-neural-trasnfer Both are a pretty good “feature extractors” with way different implementation to each other. But both has a pretty good serializeable file that I can use to generate a new image without training it again and again. Creating CLI is cool and stuff, but I decided to take a step further by making a telegram bot, try it out while its still online @styletransferbot!
I learned a lot of things about the GANs, making art with deep learning. Sometimes learning machine learning is all about metrics: getting good prediction, less error, less biased towards certain things, more reliable and good black box models, what I get from this is programming in the society should be changed from a tool for industries to something more! something that helps us to be more expressive as humans.
Stop making weapons of math destructions!


|
Jesus ChristThe great Js all die young — Jim Morrison, Janis Joplin, Jimi Hendrix and Jesus. Jesus H. Christ was born (ironically enough) around five to six years "before Christ" (B.C.) due to a scheduling mixup. Generally depicted in art as a white guy with blue eyes and dirty blonde hair, the location of Jesus' birth and his ethnicity suggest that he probably looked a lot more like Osama bin Laden than Brad Pitt. His parentage has been the subject of much controversy.  The location of his birth is inconsistently reported, even in the Bible. Christmas carols record it as Bethlehem, other accounts say Nazareth, the town in which young Jesus grew to maturity, with his virginal mother and cuckolded father, Joseph. In the Bible, Jesus was a precocious child, given to lecturing rabbis on the finer points of Talmudic law and chiding his parents for being so attached to him. A few unauthorized biographies of the "mysteriously-not-burnt-by-the-Catholics" variety, such as the Infancy Gospels of James and Thomas, relate salacious anecdotes about the prepubescent Jesus and his Bart Simpson-like antics — such as using divine powers to blind fellow children who presumed to roughhouse with His Fine Self. Don't have a cow, man! When Jesus grew to adulthood, he took up preaching as a career choice. In turn-of-the-Aeon Israel, preaching was a hot career much like computer programming in the 1990s. Everyone was doing it. Amidst a crowded field, however, Jesus stood out as an overachiever and soon people were talking. He gathered a core group of 12 groupies, who would later become known as The Apostles, and took his show on the road at age 30. Jesus' pitch was something new — he talked about God as a loving, forgiving figure, in sharp contrast to God's previous reputation as a pillar-of-salt-transforming smiter of iniquity. He also made a name for himself as a miracle-worker, enabling the blind to see and the deaf to hear, casting out demons, walking on lakes, changing water into wine and making the Statue of Liberty disappear during a prime-time special. Jesus spent a lot of time curing sick people, feeding the masses and disrupting the money-making activities at local temples. These activities did not endear him to the Jewish authorities, who began researching ways to get rid of him. Eventually they settled on the tried and true method of betrayal by a close friend, and hired Judas Iscariot, one of the Apostles, to do the job for 30 pieces of silver. In a needlessly elaborate Passover plot, Judas led authorities to the Garden of Gethsemane after supper (which would in retrospect be designated "Last"), telling them to arrest the first person he kissed. The accounts of what followed are somewhat muddled, but the general idea is this: For both political and religious reasons, the Jewish council of priests didn't want to personally execute Jesus, so they turned him over to the Romans on charges of inciting revolution. The Roman governor Pontius Pilate was extremely suspicious of this development, since the Jews did not suffer occupation gracefully and there were plots on every street corner. Suspecting he was being set up as the fall guy, Pilate tried to make Jesus somebody else's problem, but nobody else was interested. Under heavy pressure from the council, Pilate eventually waffled his way through the dilemma by essentially condemning Jesus to death by Crucifixion but insisting it was the Jews' fault (a deniability strategy that would eventually be cited by the insane Third Reich as a justification for the Holocaust). The crucifixion went off without a hitch. Jesus was marched up to a hill, nailed to a cross and died. Or did he? The official story is that Jesus died on the cross, was tossed in a grave but not embalmed (due to the impending Passover Sabbath) and miraculously rose again in three days. Not surprisingly, this account has been the subject of much dispute after the fact. Again according to the party line, the Risen Jesus spent 40 days hanging with his Apostles, before bodily ascending into Heaven for the duration of human history. When he departed, or so the story goes, Jesus (allegedly) put Peter in charge of the Church, a fateful (alleged) decision that would lead to centuries of bad blood among the various Christian sects. About five minutes after Jesus was out of the public eye, the controversy began. It's universally accepted that Jesus was granted his surname, Christ, around this time. Christ is Greek for "Messiah," the fabled Jewish savior was foretold in the Old Testament. Prior to this time, the Jewish vision of the Messiah was a bit more conventional, involving Kingship and leading their armies to victory against the Gentiles. The early Christians usurped the whole Messianic complex and voted Most Likely to Save. After that, things got sticky. Although there are no reliable historical accounts of the first decades of Christianity, the early Christians had divided into camps based on their favorite apostles by 100 A.D. These various sects had a lot of strange ideas, such as the Gnostics, who claimed that Jesus never died on the cross at all. When the followers of the peaceful and loving Jesus realized that disagreements existed, they naturally began viciously attacking each other. A period of internecine warfare commenced, with the followers of the Peter Tradition coming out victorious. The surviving entity, known as the Roman Catholic Church, proceeded to spend a couple centuries systematically destroying all opposition, only to be completely screwed a thousand years later when Martin Luther nailed a laundry list of complaints to a church door. The Mormons believe Jesus made a post-Resurrection visit to the future home of the United States, where he befriended Native Americans and notably didn't give them smallpox. In later years, the discovery of such alleged artifacts as the Shroud of Turin led some researchers to speculate that Jesus H. Christ (no one knows what the H. stands for) didn't actually die on the cross (but in a different way than the Gnostics believed it). According to these theories, Jesus was taken down from the cross still alive and nursed back to health. He then sired a child with Mary Magdalene, who took the kid to France and raised him there. The Knights Templar formed to protect this secret, codenamed "Holy Grail," and the descendants of Jesus eventually became a line of French kings known as the Merovingians (which would then lead to a neat little inside joke in The Matrix Reloaded). According to this theory, which is far more convoluted and labyrinthine than the above paragraph might suggest, you can actually meet the living descendent of Jesus today. Unfortunately for residents of George W. Bush's America, he's a Frenchman — a guy by the name of Pierre Plantard, who runs a not-so-secret-anymore society and generally sits around quietly waiting for the world to acknowledge him. As for Jesus himself, word has it that there's a Second Coming in the works, which would in all likelihood be the precursor to the Apocalypse. The Second Coming is scheduled to take place around the time of Armageddon, the final war that concludes the history of humanity and vanquishes the forces of the Antichrist, the chief general of Satan. All this talk of a Second Coming has naturally led to many mentally unbalanced people to assume they are it, including (but not limited to) David Koresh, Sun Myung Moon, Marshall Applewhite, The Beatles and John Ashcroft. To date, none of these figures have successfully walked on water or returned from the dead, so the search, such as it is, goes on.
Timeline
|


Bill Gates on Longhorn
Here. Besides the cool scenarios BillG mentions that that Longhorn will enable, I like his answer to USA Today asking how long it will be before Longhorn is available:
"Years. At this time we're doing the prototyping — feasibility studies, performance studies. We don't have a date because what we have is a technological breakthrough that we have to really make sure we refine and get right. Then we'll get to the point where we'll set up an engineering schedule."


A $7,500.00 application fee will be required at the time of application submission. If your application for a licence is approved, a $1,500.00 first year licensing fee will be required prior to the licence being issued.
Please note that applications which are submitted without all required information or documents will stop being processed. The LCRB will be assessing complete applications as a priority. Other applications that are received before the applicant provides the branch with missing information will be prioritized. The application checklist can assist in completing all the required documentation.
A proposed store must have:
Before starting the application, ensure that:
The Province is not placing a cap on the number of non-medical cannabis stores that can operate in B.C. However, a licensee can only hold or have an interest in a maximum of eight cannabis retail store licences. For the purposes of the retail store licence cap, generally a franchisor cannot have more than 8 franchisees. This limit will be reviewed on January 1, 2021.
A name cannot mislead the public about what kind of business it operates. The name cannot imply a licensee is selling medical cannabis, and cannot include words in any form or combination or manner the words "apothecary", "pharmacy", "medicines", "drugs", "drug store", "drug department", "dispensary" or any other words of similar meaning that imply licensing under the Pharmacy Operations and Drug Scheduling Act. Store names cannot also use language that encourages intoxication.
Additionally, a name cannot imply an association with another business. This applies to signs, trademarks and any type of branding.
Establishment names will be reviewed for approval during the application process. For more information, please see the Non-Medical Cannabis Retail Store Name Approval Process .
Indicate the store's proposed hours for cannabis sales below. Proposed hours must fall between 9 a.m. and 11 p.m. Cannabis stores must not be open outside of their licensed hours. However, they may choose to be closed at times they are licensed to be open.
| Sunday | Monday | Tuesday | Wednesday | Thursday | Friday | Saturday | |
|---|---|---|---|---|---|---|---|
| Open | |||||||
| Close |
Upload the completed form below.
Upload the completed form below.
Please download the Document Checklist Document Checklist Document Checklist Document Checklist and assemble all required documents. and assemble all required documents listed in the 'Documents Related to the Proposed Store' section.
Proceed to pay online using a credit card through the secure web portal.
Please note: Applications which are submitted without all required information or documents will not be processed. Applicants will be contacted and provided the opportunity to submit missing information. Once applicants provide all required documents to the LCRB, the application will be submitted for processing.
This application should be submitted by an individual with the authority to bind the applicant. The branch
relies on the applicant to ensure that the individual who submits this application is authorized to do so.
Typically, an appropriate individual for a sole proprietorship will be
the sole proprietor.
Typically, an appropriate individual for a society will be a director or a
senior manager (as defined in the Societies Act).
Typically, an appropriate individual will be as follows:
Typically, an appropriate individual in a corporation will be a duly authorized signatory who will usually be
an
officer or, in some cases, a director.
Note: An agent, lawyer, licensee representative or third party operator may NOT submit this application on behalf of the applicant.
Section 22(2) of the Cannabis Control and Licensing Act states: “A person must not submit to the general manager an application, or information or a record included as part of the application, that contains false or misleading information, or fails to disclose a material fact”.
The Province does not know how many application for a cannabis retail store license will be received. Therefore, it is difficult to predict how long applications will take to process. To minimize delays in processing, applicants must make sure the application is complete before submitting it.
The ‘save for later’ feature allows applicants to re-access an application in progress. Please be advised that once an application is submitted you will be unable to upload additional documentation. For questions on the LCRB application process please contact<EMAIL_ADDRESS>Upload the completed form below.
Upload the completed form below.
Please download the Document Checklist Document Checklist Document Checklist Document Checklist and assemble all required documents.


Class Summary| AbstractButton |
Defines common behaviors for buttons and menu items |
| AbstractCellEditor |
|
| AbstractListCell |
|
| AbstractListModel |
|
| AbstractTabbedPane |
An abstract class for all Container pane class that have title, icon, tip for every sub pane |
| AlignLayout |
Aligns the single component inside container by horizontal and vertical
according to specified aligns |
| ASColor |
ASColor object to set color and alpha for a movieclip |
| ASFont |
ASFont
|
| ASTextExtent |
|
| ASTextFormat |
unHandled TextFormat Properties :
TextFormat.leading
TextFormat.tabStops
TextFormat.target
TextFormat.url
|
| ASWingConstants |
A collection of constants generally used for positioning and orienting
components on the screen |
| ASWingUtils | |
| AttachIcon |
Attach a mc from library with the linkage name to be a icon |
| BorderLayout |
A border layout lays out a container, arranging and resizing
its components to fit in five regions:
north, south, east, west, and center |
| Box |
A JPanel with BoxLayout |
| BoxLayout |
A layout manager that allows multiple components to be laid out either vertically or
horizontally |
| ButtonGroup |
|
| CenterLayout |
Simple LayoutManager aligned the single contained
component by the container's center |
| ChildrenMaskedContainer |
The container with a mask mc to mask all of its children, make the border
visible right when the container can't manage to handle the children's bounds.
|
| Component |
The super class for all UIs.
|
| ComponentDecorator |
ComponentDecorator, manage a decorator for a component |
| Container |
Container can contain many component to be his child, all children are in its bounds,
and it moved, all children moved |
| ContainerOrderFocusTraversalPolicy |
A FocusTraversalPolicy that determines traversal order based on the order
of child Components in a Container |
| Cursor |
|
| CursorManager |
The CursorManager, manage the cursor, hide system mouse cursor, show custom cursor,
etc |
| DecorateIcon |
DecorateIcon let you can make Decorator Patten Icon.
|
| DefaultBoundedRangeModel |
|
| DefaultButtonModel |
The default implementation of a Button component's data model |
| DefaultCheckBoxCellEditor |
|
| DefaultComboBoxCellEditor |
The default editor for table and tree cells, use a combobox.
|
| DefaultComboBoxEditor |
Default to use a JTextField to be the editor |
| DefaultFocusTraversalPolicy |
|
| DefaultIntegerBoundedRangeModel |
DefaultIntegerBoundedRangeModel round all number value to int
|
| DefaultListCell |
|
| DefaultListCellFactory |
|
| DefaultListSelectionModel |
Default data model for list selections |
| DefaultNumberTextFieldCellEditor |
|
| DefaultSingleSelectionModel |
A generic implementation of SingleSelectionModel |
| DefaultTextFieldCellEditor |
The default editor for table and tree cells, use a textfield.
|
| ElementCreater |
The creater use to create UI elements like movieclips, textfield.. |
| EmptyLayout |
LayoutManager's empty implementation |
| Event |
All Events are constructed with a reference to the object,
the "source", that is logically deemed to be the object upon
which the Event in question initially occurred upon |
| EventDispatcher |
|
| FloorPane |
Abstract class for A container with a decorative floor movieclip.
|
| FlowLayout |
A flow layout arranges components in a left-to-right flow, much
like lines of text in a paragraph |
| FocusManager |
FocusManager manages all the when a component should receive focus, i.e if it
can |
| GeneralListCellFactory |
GeneralListCellFactory let you can just specified a ListCell implemented class
and other params to create a ListCellFactory
|
| GrayFilteredIcon |
GrayFilteredIcon use to filter a icon to be gray |
| GridLayout |
|
| Insets |
An Insets object is a representation of the borders of a container |
| JAccordion |
Accordion Container |
| JAdjuster |
A component that combine a input text and a button to drop-down a popup slider to let
user input/select a value |
| JAttachPane |
JAttachPane, a container attach flash symbol in library to be its floor |
| JButton |
An implementation of a "push" button |
| JCheckBox |
An implementation of a check box -- an item that can be selected or
deselected, and which displays its state to the user |
| JCheckBoxMenuItem |
A menu item that can be selected or deselected |
| JColorChooser |
JColorChooser provides a pane of controls designed to allow a user to manipulate and
select a color.
|
| JComboBox |
A component that combines a button or editable field and a drop-down list |
| JFrame |
JFrame is a window with title and maximized/iconified/normal state, and resizer |
| JLabel |
A display area for a short text string or an image,
or both |
| JList |
A component that allows the user to select one or more objects from a
list |
| JListTree |
JListTree is a tree component that simulated by JList |
| JLoadPane |
JLoadPane, a container load a external image/animation to be its floor |
| JMenu |
An implementation of a menu -- a popup window containing
JMenuItem s that
is displayed when the user selects an item on the JMenuBar |
| JMenuBar |
|
| JMenuItem |
An implementation of an item in a menu |
| JOptionPane |
JOptionPane makes it easy to pop up a standard dialog box that prompts users
for a value or informs them of something.
|
| JPanel |
The general container - panel |
| JPieChart | |
| JPopup |
JPopup is a component that generally be a base container of a window panel.
|
| JPopupMenu |
An implementation of a popup menu -- a small window that pops up
and displays a series of choices |
| JProgressBar |
A component that, by default, displays an integer value within a bounded
interval |
| JRadioButton |
An implementation of a radio button -- an item that can be selected or
deselected, and which displays its state to the user |
| JRadioButtonMenuItem |
An implementation of a radio button menu item |
| JScrollBar |
An implementation of a scrollbar |
| JScrollPane |
JScrollPane is a container with two scrollbar controllin the viewport's beeing viewed area.
|
| JSeparator |
JSeparator provides a general purpose component for
implementing divider lines - most commonly used as a divider
between menu items that breaks them up into logical groupings |
| JSharedToolTip |
Shared instance Tooltip to saving instances |
| JSlider |
A component that lets the user graphically select a value by sliding
a knob within a bounded interval |
| JSpacer |
JSpacer provides basic functionality to create empty spaces between
other components |
| JSplitPane |
JSplitPane is used to divide two (and only two)
Component s |
| JTabbedPane |
A component that lets the user switch between a group of components by
clicking on a tab with a given title and/or icon.
|
| JTable |
The JTable is used to display and edit regular two-dimensional tables
of cells.
|
| JTextArea |
A JTextArea is a multi-line area that displays text.
|
| JTextComponent |
JTextComponent is the base class for text components |
| JTextField |
JTextField is a component that allows the editing of a single line of text |
| JToggleButton |
An implementation of a two-state button |
| JToolBar |
JToolBar provides a component that is useful for
displaying commonly used Action s or controls.
|
| JToolTip |
Used to display a "Tip" for a Component |
| JTree |
A control that displays a set of hierarchical data as an outline.
A control that displays a set of hierarchical data as an outline |
| JViewport |
JViewport is an basic viewport to view normal components |
| JWindow |
JWindow is a Container, but you should not add component to JWindow directly,
you should add component like this:
jwindow.getContentPane().append(child);
|
| KeyboardManager |
KeyboardController controlls the key map for the action firing.
|
| KeyMap |
KeyMap is a key definition -> action map |
| KeySequence |
Key Sequence, defines a key sequence.
|
| KeyStroke |
KeyStroke is a single key definition.
|
| LoadIcon |
LoadIcon allow you load extenal image/animation to be the icon content |
| LookAndFeel |
Reserved for look and feel implementation |
| MCPanel |
A Container base on a existing movieclip.
|
| MenuSelectionManager |
A MenuSelectionManager owns the selection in menu hierarchy |
| MouseManager |
Wraps Mouse class |
| OffsetIcon |
A icon that make it inner icon paint to a offset position |
| RadioButtonModel |
The RadioButton model
|
| RepaintManager |
RepaintManager use to manager the component's painting.
|
| ScrollPaneLayout |
The layout manager used by JScrollPane .
JScrollPaneLayout is
responsible for three components: a viewportable's pane, two scrollbars.
|
| SizeSequence |
A SizeSequence object
efficiently maintains an ordered list
of sizes and corresponding positions |
| SoftBox |
A JPanel with SoftBoxLayout |
| SoftBoxLayout |
SoftBoxLayout layout the components to a list as same width/height but their preffered height/width |
| ToggleButtonModel |
The ToggleButton model
|
| UIDefaults |
A table of defaults for Swing components |
| UIManager |
This class keeps track of the current look and feel and its defaults.
|
| VectorListModel |
The mutable list model vector implementation |
| ViewportLayout |
The default layout manager for JViewport .
ViewportLayout defines
a policy for layout that should be useful for most applications |
| WindowLayout |
Layout for JWindow and JFrame |
| WindowManager |
|
| WindowPane |
|


{% extends 'base.html' %}
{% load widget_tweaks %}
{% block title %}You're required to learn as you play… roll{% endblock %}
{% block content %}
{% include "_lcars_filler.html" with color="medium-carmine" bottom_left_outer_elbow=True only %}
You're required to learn as you play… roll!
{% include "_lcars_filler.html" with color="medium-carmine" bottom_left_inner_elbow=True only %}
{% include "_lcars_filler.html" with color="medium-carmine" top_left_inner_elbow=True only %}
{% include "_lcars_filler.html" with color="medium-carmine" top_left_outer_elbow=True bottom_left_outer_elbow=True only %}
Board as of {{ last_roll.embargo }}
The last roll was a {{ last_roll.number }} It will appear on the site
{{ last_roll.embargo }} To delete or edit this (or any other) roll,
use the admin site!
{{ board }}
{% include "_lcars_filler.html" with color="medium-carmine" bottom_left_inner_elbow=True only %}
{% include "_lcars_filler.html" with color="medium-carmine" top_left_inner_elbow=True only %}
{% include "_lcars_filler.html" with color="medium-carmine" top_left_outer_elbow=True bottom_left_outer_elbow=True only %}
{% include "will_of_the_prophets/board/_legend.html" %}
{% include "_lcars_filler.html" with color="medium-carmine" bottom_left_inner_elbow=True only %}


Having fun? On this page you can search for a specific repository you would like and view its details!
Please input the name of the repository you would like to search. If no result is displayed on submission, ensure you wrote the correct repository and try again.
I hope you get what you are looking for!
Created at: {{ repo.created_at | dateCount }}
Description: {{ repo.description }}
Forks: {{repo.forks}}
View on Github


Bases: limb.Limb, baxter_helper_abstract_limb.AbstractLimb
joint_tolerance_plan,speed_tolerance are ignored,
position_tolerance, orientation_tolerance, speed_tolerance, cartesian are ignored
Commands the limb to the provided positions.
Waits until the reported joint state matches that specified.
Command the joints to the center of their joint ranges
baxter_helper_moveit_limb Module
baxter_helper_tasker Module
Enter search terms or a module, class or function name.


Richmond RCMP searching for missing 75-year old woman
by Nikitha MartinsSummary
Feng Qin Zhou was last seen in Richmond on Thursday
Those with information about Feng Qin's whereabouts are to contact the Richmond RCMP
RICHMOND (NEWS 1130) – Since Thursday, 75-year-old Feng Qin Zhou has been missing and Mounties are concerned for her safety.
In a tweet on Friday, Richmond RMCP say Feng Qin, “may appear confused.”
“Zhou’s family and the police are concerned for her well-being. Anyone with information as to Zhou’s whereabouts is urged to contact their local police,” Cpl. Dennis Hwang of the Richmond RCMP says.
The senior was last seen near Firbridge Way and Ackroyd road around 1:00 p.m. Thursday.

 She is 5′ 2″ and about 138 pounds.
The senior only speaks Mandarin but if you have information you can contact the Richmond RCMP at<PHONE_NUMBER> or call 911. If you wish to remain anonymous dial 1-800-222-8477 or visit www.solvecrime.ca, and quote file number 2019-38195.


max(3f) - [FORTRAN:INTRINSIC:NUMERIC] Maximum value of an argument list
result = max(a1, a2 [, a3 [, ...]])
Returns the argument with the largest (most positive) value.
A1 The type shall be INTEGER or REAL. A2,A3,... An expression of the same type and kind as A1.
The return value corresponds to the maximum value among the arguments, and has the same type and kind as the first argument.The function is both elemental and allows for an arbitrary number of arguments. This means if some elements are scalar and some are arrays that all the arrays must be of the same size, and the returned value will be an array that is the result as if multiple calls were made with all scalar values with a single element of each array used in each call. If called with all arrays the returned array is the same as if multiple calls were made with max(arr1(1),arr2(1), ...) to max(arr1(N),arr2(N)).
Sample program
program demo_max implicit none real :: arr1(4)= [10.0,11.0,30.0,-100.0] real :: arr2(5)= [20.0,21.0,32.0,-200.0,2200.0]!! this is simple enough because it is not being called elementally !! because all arguments are scalar !!
write(*,*)scalars:,max(10.0,11.0,30.0,-100.0)
!! !! this is all max(3f) could do before it became an elemental !! function and is the most intuitive !! except that it can take an arbitrary number of options, !! which is not common in Fortran without !! declaring a lot of optional parameters. !! !! That is it unless you want to use the elemental features of max(3f)!
!! Error: Intrinsic max at (1) must have at least two arguments !!write(*,*)max(arr1) !! This does not work because it is like trying to return !! [(max(arr1(i)),i=1,size(arr1))] !! so it is trying to take the max of a single value. !! To find the largest element of an array !! call maxloc(3f) or maxval(3f).
!! Error: Different shape for arguments a1 and a2 for intrinsic !! max at (1) on dimension 1 (4 and 5) !!write(*,*)max(arr1,arr2) !! but this will return an array of !! [(max(arr1(N),arr2(N),N=1,size(arr1))]
write(*,*)max(arr1,arr2(1:4))
!! so this works only if all the arrays are the same size and !! you want an array of the largest Nth elements !! from the input arrays. !! maybe you wanted to do maxval([arr1,arr2]) or !! equivalently max(maxval(arr1),maxval(arr2)) !! to find the single largest element in both arrays?
!! compares all scalars to each member of array and !! returns array of size arr2
write(*,*)scalars and array:,max(10.0,11.0,30.0,-100.0,arr2)
!! Error: Different shape for arguments a5 and a6 !! for intrinsic max at (1) on dimension 1 (5 and 4) !! write(*,*)scalars and array:,max(10.0,11.0,30.0,-100.0,arr2,arr1) !! as the same reason above when arrays are used !! (without scalar values) all the arrays must be the same size
write(*,*)scalars and array:,max(40.0,11.0,30.0,-100.0,arr2(:4),arr1)
end program demo_maxResults:
scalars: 30.000000 20.0000000 21.000000 32.000000 -100.00000 scalars and array: 30.000000 30.000000 32.000000 30.000000 2200.0000 scalars and array: 40.000000 40.000000 40.000000 40.000000
[[FORTRAN 77]] and later
[[Elemental procedure|Elemental function]]
maxloc(3), maxval(3), min(3)
| max (3fortran) | February 11, 2021 |


Interested in Contributing?
- Check out available resources.
- Create an account and start submitting your own systems.
Scored Systems
| System | Submitter | System Notes | Constraint | Run Notes | BLEU | BLEU-cased | TER | BEER 2.0 | CharactTER |
|---|---|---|---|---|---|---|---|---|---|
| Microsoft-Marian (Details) | marcinjd Microsoft |
Marian, Transformer-big ensemble x4. With filtered, clean, and domain-weighted paracrawl. Also domain-weighthing original parallel data. Decoder-time ensemble with in-domain Transformer-LM. Right-to-left scoring with Transformer-big models. | yes |
48.9 |
48.3 |
0.407 |
0.697 |
0.362 |
|
| NMT-SMT Hybrid (Details) | fstahlberg University of Cambridge |
MBR-based combination of neural models and SMT | yes |
47.1 |
46.6 |
0.415 |
0.691 |
0.369 |
|
| NTT Transformer-based System (Details) | makoto-mr NTT |
Based on Transformer Big model. Trained with filtered version of CommonCrawl, ParaCrawl and synthetic corpus of newscrawl2017. R2L reranking. | yes |
47.0 |
46.5 |
0.426 |
0.688 |
0.370 |
|
| KIT Primary Submission (Details) | pianist Karlsruhe Institute of Technology |
Primary Submission | yes |
46.9 |
46.3 |
0.428 |
0.687 |
0.382 |
|
| MMT production system (Details) | nicolabertoldi MMT srl |
Transformer-based neural MT; single model; single pass decoding. | no | Trained on public and proprietary data. |
46.7 |
46.2 |
0.432 |
0.682 |
0.387 |
| Facebook-FAIR (Details) | edunov Facebook FAIR |
Ensemble of six self-attentional models with back-translation data according to https://arxiv.org/abs/1808.09381 | yes |
46.5 |
46.1 |
0.423 |
0.689 |
0.381 |
|
| Ubiqus-NMT (Details) | vince62s Ubiqus |
Base transformer Include a selection of Paracrawl Include WMT16 Rico's BackT | yes |
46.1 |
45.6 |
0.422 |
0.685 |
0.383 |
|
| Contrastive (Single) (Details) | pianist Karlsruhe Institute of Technology |
Single Transformer (Base) Model | yes |
45.7 |
45.1 |
0.435 |
0.682 |
0.389 |
|
| uedin-en-de-single-transfomer (Details) | ugermann University of Edinburgh |
Single transformer trained on WMT2017 data plus a selection of paracrawl. | yes |
44.9 |
44.4 |
0.441 |
0.676 |
0.391 |
|
| MMT contrastive 2 (Details) | nicolabertoldi MMT srl |
Transformer-based neural MT; single model, single pass decoding. | yes | Trained on a filtered version of the supplied data. |
44.6 |
44.2 |
failed |
0.673 |
0.399 |
| JHU (Details) | jhu-nmt Johns Hopkins University |
Marian Deep RNN | yes | Contrastive run, fine-tuned to previous test sets (but not R2L reranking) |
43.9 |
43.4 |
0.448 |
0.672 |
0.391 |
| JHU (Details) | jhu-nmt Johns Hopkins University |
Marian Deep RNN | yes | Marian deep model, ensemble of 4 runs using base data (without Paracrawl), re-back-translated news 2016. R2L Reranking. Primary. |
44.0 |
43.4 |
0.449 |
0.672 |
0.392 |
| uedin-en-de-single-transformer-reranked (Details) | ugermann University of Edinburgh |
Single transformer, reranked with two R2L transformers. | yes |
43.8 |
43.2 |
0.450 |
0.669 |
0.397 |
|
| JHU (Details) | jhu-nmt Johns Hopkins University |
Marian Deep RNN | yes | Marian deep model, ensemble of 4 runs using base data (without Paracrawl), re-back-translated news 2016. Not final system yet. |
43.6 |
43.0 |
0.453 |
0.670 |
0.394 |
| MMT contrastive (Details) | nicolabertoldi MMT srl |
Transformer-based neural MT; single model, single pass decoding. | yes | Trained on a filtered version of the supplied data. German de-compounding applied. |
42.9 |
42.5 |
0.463 |
0.667 |
0.411 |
| uedin-en-de-2+2-transformer (Details) | ugermann University of Edinburgh |
2 transformers ensembled, reranked with 2 R2L systems. Include paracrawl | yes |
42.3 |
41.8 |
0.463 |
0.663 |
0.405 |
|
| LMU-nmt-reranked-wmt18-en-de (Details) | Matthias.Huck LMU Munich |
Nematus encoder-decoder NMT (single model + r2l reranking), like last year | yes |
40.6 |
40.0 |
0.480 |
0.655 |
0.421 |
|
| NJUNMT (Details) | ZhaoChengqi Nanjing University |
transformer base without back translation | yes | transformer base without back translation |
40.6 |
40.0 |
0.496 |
0.647 |
0.436 |
| LMU-nmt-single-wmt18-en-de (Details) | Matthias.Huck LMU Munich |
Nematus encoder-decoder NMT (single model), like last year | yes |
39.3 |
38.8 |
0.492 |
0.647 |
0.433 |
|
| parfda (Details) | bicici |
yes | en-de using PRO for tuning |
27.3 |
26.7 |
0.620 |
0.591 |
0.570 |
|
| Wink (Details) | anything uni saarland |
Out-of-domain data | no |
22.3 |
21.9 |
0.706 |
0.540 |
0.741 |
|
| LMU-unsupervised-nmt-wmt18-en-de (Details) | Matthias.Huck LMU Munich |
Unsupervised NMT (no parallel training corpora) | yes |
15.8 |
15.5 |
0.762 |
0.500 |
failed |
|
| RWTH Unsupervised NMT Ensemble (Details) | yunsukim RWTH Aachen University |
(Unsupervised) Transformer with shared encoder/decoder, separate top-50k word vocabs, iterative back-translation, ensemble 4x | yes |
15.9 |
14.8 |
0.753 |
0.514 |
0.607 |
|
| RWTH Unsupervised NMT Single (Details) | yunsukim RWTH Aachen University |
(Unsupervised) Transformer with shared encoder/decoder, separate top-50k word vocabs, iterative back-translation | yes |
15.6 |
14.5 |
0.758 |
0.510 |
0.615 |
|
| LMU-unsupervised-pbt-wmt18-en-de (Details) | Matthias.Huck LMU Munich |
Unsupervised (no parallel training corpora), BWEs + PBT | yes |
14.6 |
14.3 |
0.791 |
0.518 |
0.627 |
|
| Ubiqus-NMT (Details) | vince62s Ubiqus |
Base transformer Include a selection of Paracrawl Include WMT16 Rico's BackT | yes |


Membership Form
Database
Upgrade Term Members
First Name
Surname
Date of Birth
Affiliation
(Please Select)
Student
Staff
External
If student OSS number
College
(Please Select)
None
Balliol College
Blackfriars
Brasenose CollegeC
Campion Hall GS
Christ Church
Corpus Christi College
Exeter College
Green Templeton College
Harris Manchester College
Hertford College
Jesus College
Keble College
Kellogg College
Lady Margaret Hall
Linacre College
Lincoln College
Magdalen College
Mansfield College
Merton College
New College
Nuffield College
Oriel College
Pembroke College
The Queen's College
Regent's Park College
Somerville College
St Anne's College
St Antony's College
St Benet's Hall
St Catherine's College
St Cross College
St Edmund Hall
St Hilda's College
St Hugh's College
St John's College
St Peter's College
St Stephen's House
Trinity College
University College
Wadham College
Wolfson College
Worcester College
Wycliffe Hall
Phone Number
Address 1
Address 2
Town
County
Postcode
Email
Emergency Contact Name
Emergency Contact Phone Number
Membership Type
(Please Select)
Annual
Annual - Life Member Form
Term - Michaelmas - Expires 31st December
Term - Hilary - Expires 31st March
Term - Trinity - Expires 30th June
Term - Summer - From 01/07 to 30/09
Date on paper form
Form authorised By
This web form submitted By
Submit


--- layout: post title: "Who Is Funding Ed-Tech Startups?" date: 2016-04-12 10:35:00 +0000 tags: image: https://s3.amazonaws.com/hackedu/ed-tech-funding-pigeon.jpg imagecredits: https://www.flickr.com/photos/byneilhall/14264254434 ---
I’m starting a new report on ed-tech startup funding – it’s related to this one – that tracks all investors and all their education-related investments since 2010 (the year I started Hack Education).
I haven’t quite decided yet how I’ll present the data – I think sorted by investor, but the point will be that I can provide an ongoing update to stories like “Who’s Investing in Ed-Tech?”
This data will also be made available – freely and openly – in a JSON file so that others can access and utilize it.
More details coming soon...


The goal of this analysis is to predict individuals who are most likely to click on a cryptography course advertisement. This project uses R for analysis.
The project will be considered a success when we are able to clean and analyse past data to segment blog users and predict individuals who should be targeted for an advertisement.
A Kenyan entrepreneur has created an online cryptography course and would want to advertise it on her blog. She currently targets audiences originating from various countries. In the past, she ran ads to advertise a related course on the same blog and collected data in the process. She has employed my services as a Data Science Consultant to help her identify which individuals are most likely to click on her ads.
Below are the steps that will be followed in this analysis in order to respond to the research question satisfactorily:
- Read the Data
- Check the Data
- Data Cleaning
- Univariate Analysis
- Bivariate Analysis
- Implementing the Solution (Modeling)
- Conclusion and Recommendation
The data used for the project was collected from a prior advertisement for a similar course on the same platform. The dataset contains 10 attributes and 1,000 records. These attributes contain descriptive information of past blog users. Some of the attributes include country, age and gender of the user among others.
# Installing data.table package
install.packages("data.table", dependencies=TRUE)
# Reading the data into R from the csv file
library(data.table)
ad <- read.csv('advertising.csv')
head(ad)
# Checking the top 6 records
head(ad)
# Checking the bottom 6 records
tail(ad)
# Checking the total number of records
nrow(ad)
# Checking the total number of columns
ncol(ad)
# Checking all column names
names(ad)
# Checking the data types of each column
str(ad)
# Checking the number of unique values in each column
lengths(lapply(ad, unique))
# Checking the summary of the data
summary(ad)
Missing Data
# Checking the existence of missing values
colSums(is.na(ad))
Outliers
# creating a variable with only numeric columns
library(tidyverse)
my_data <- ad %>% select(1,2,3,4,7,10)
# Previewing outliers for numeric columns using boxplots
boxplot(my_data)
# Boxplot for daily time spent variable
boxplot(ad$Daily.Time.Spent.on.Site)
# Boxplot for age variable
boxplot(ad$Age)
# Boxplot for daily internet usage variable
boxplot(ad$Daily.Internet.Usage)
# Boxplot for area income variable
boxplot(ad$Area.Income)
# Displaying all outliers in the income column
boxplot.stats(ad$Area.Income)$out
# Checking the countries associated with outlier incomes
ad$Country[ad$Area.Income %in% c(17709.98, 18819.34, 15598.29, 15879.1, 14548.06, 13996.5, 14775.5, 18368.57)]
Anomalies
# Checking for duplicate data
duplicated_rows <- ad[duplicated(ad),]
duplicated_rows
# Calculating the mean for all numeric columns
lapply(my_data,FUN=mean)
# Calculating the median for all numeric columns
lapply(my_data,FUN=median)
# Calculating the mode for all numeric columns
getmode <- function(v) {
uniqv <- unique(v)
uniqv[which.max(tabulate(match(v, uniqv)))]
}
lapply(my_data,FUN=getmode)
# Calculating the minimum value for all numeric columns
lapply(my_data,FUN=min)
# Calculating the maximum value for all numeric columns
lapply(my_data,FUN=max)
# Checking the range for all numeric columns
lapply(my_data,FUN=range)
# Calculating the quantiles for all numeric columns
lapply(my_data,FUN=quantile)
# Calculating the variance for all numeric columns
lapply(my_data,FUN=var)
# Calculating the standard deviation for all numeric columns
lapply(my_data,FUN=sd)
# Plotting a histogram for age variable
hist(ad$Age)
# Plotting a histogram for area income variable
hist(ad$Area.Income)
# Plotting a histogram for daily time variable
hist(ad$Daily.Time.Spent.on.Site)
# Plotting a histogram for daily internet variable
hist(ad$Daily.Internet.Usage)
# Plotting a histogram for gender variable
hist(ad$Male)
# Plotting a histogram for clicked ad variable
hist(ad$Clicked.on.Ad)
# Checking actual number of male vs females
table(ad$Male)
# Confirming distribution of classes
table(ad$Clicked.on.Ad)
# Bar plot of the age variable
age <- ad$Age
age_freq <- table(age)
barplot(age_freq)
# Checking distribution of each country
table(ad$Country)
In this section, we investigate the relationship of different variables by creating relevant visualizations such asscatter plots, correlation matrix and Pearson correlation coefficient.
# Checking the correlation coefficients for numeric variables
install.packages("ggcorrplot")
library(ggcorrplot)
corr = round(cor(select_if(my_data, is.numeric)), 2)
ggcorrplot(corr, hc.order = T, ggtheme = ggplot2::theme_gray,
colors = c("#6D9EC1", "white", "#E46726"), lab = T)
# Scatter plot to compare age vs income
plot(ad$Age, ad$Area.Income, xlab="Age", ylab="Income")
# Scatter plot to compare income vs Clicked on ad
plot(ad$Clicked.on.Ad, ad$Area.Income, xlab="Clicked on Ad", ylab="Income")
# Scatter plot to compare age vs daily time spent
plot(ad$Age, ad$Daily.Time.Spent.on.Site, xlab="Age", ylab="Time Spent")
# Scatter plot to compare clicked on ad vs time spent
plot(ad$Clicked.on.Ad, ad$Daily.Time.Spent.on.Site, xlab="Clicked on Ad", ylab="Time Spent")
# Scatter plot to compare clicked on ad vs internet usage
plot(ad$Clicked.on.Ad, ad$Daily.Internet.Usage, xlab="Clicked on Ad", ylab="Internet Usage")
# Scatter plot to compare age vs Clicked on ad
plot(ad$Clicked.on.Ad, ad$`Age`, xlab="Clicked on Ad", ylab="Age")
# Importing relevant libraries
install.packages("rpart")
install.packages("rpart.plot")
install.packages("mlbench")
install.packages("caret")
library(rpart)
library(rpart.plot)
library(mlbench)
library(caret)
# Defining features and target variables
ad <- ad %>% select(1,2,3,4,7,8,10)
head(ad)
# Converting the country variable to numeric data type
ad$Country <- as.integer(as.factor(ad$Country))
# Normalizing relevant features
normalize <- function(x){
return ((x-min(x)) / (max(x)-min(x)))}
ad$Daily.Time.Spent.on.Site <- normalize(ad$Daily.Time.Spent.on.Site )
ad$Age <- normalize(ad$Age)
ad$Area.Income <- normalize(ad$Area.Income)
ad$Daily.Internet.Usage <- normalize(ad$Daily.Internet.Usage)
ad$Country <- normalize(ad$Country)
# Confirming the dimensions of the dataset
dim(ad)
# Creating the test and train sets. We can do a 800/200 split.
data_train <- ad[1:800, ]
data_test <- ad[801:1000,]
# Confirming the dimensions of the train and test sets
dim(data_train)
dim(data_test)
# Fitting the decision tree model
install.packages('tree')
library(tree)
model <- rpart(Clicked.on.Ad~., data = data_train, method = 'class')
rpart.plot(model)
# Visualizing the model
rpart.plot(model)
# Making a prediction on the test data
predict_unseen <-predict(fit, data_test, type = 'class')
table_mat <- table(data_test$Clicked.on.Ad, predict_unseen)
table_mat
# Calculating the accuracy score of the model
accuracy_score <- sum(diag(table_mat)) / sum(table_mat)
accuracy_score
Hyperparameter Tuning to Optimize the Model
# Adjusting the maximum depth as well as minimum sample of a node
accuracy_tune <- function(fit) {
predict_unseen <- predict(fit, data_test, type = 'class')
table_mat <- table(data_test$Clicked.on.Ad, predict_unseen)
accuracy_Test <- sum(diag(table_mat)) / sum(table_mat)
accuracy_Test
}
control <- rpart.control(minsplit = 4,
minbucket = round(5 / 3),
maxdepth = 3,
cp = 0)
tune_fit <- rpart(Clicked.on.Ad~., data = data_train, method = 'class', control = control)
accuracy_tune(tune_fit)
From the above analysis, below are some of the conclusions and recommendations that were obtained from data exploration (EDA):
In terms of predicting blog users who are likely to click on a course advertisement, Desicion Tree Classifier provides a descent model with a high prediction accuracy. However, other machine learning algorithms such as SVM and Random Forests can be investigated to further challenge the solution provided in this analysis.


Options
Do not prevent system from sleeping.
Disable background-refresh of closed Items.
Disable regular clock check.
Disable Groups.
Enable compact saving.
Enable local mode.
ebay.de
ebay.com
Override eBay Platform
Bid time (seconds before auction ends).
Save


regularizeNd Toolbox
Getting Started
lsqConstrainedAlternative
monotonicConstraint
regularizeNd
regularizeNdMatrices


ipsoft
Businessmodel of IPSoft
Customer Segments
IPsoft has a mass market business model, with no significant differentiation between customer segments. The company targets its offering at firms of all industries and sizes that desire an AI platform. That said, its customers primarily fall within the media, finance, and healthcare sectors.
Value Proposition
IPsoft offers four primary value propositions: convenience, performance, risk reduction, and brand/status.
The company offers convenience by simplifying operations for clients. Its Amelia software platform mimics human intelligence, as it can understand human language, learn through observation, and make decisions accordingly. Because of this, it can perform a broad range of service desk roles. These range from processing insurance claims to assisting customers with the opening of bank accounts to registering patients for entry into hospitals.
The company has demonstrated strong performance through tangible results, which are as follows:
- On average, its IPcenter solution resolves 56% of incidents without human intervention
- IPcenter has been shown to produce35% efficiency gains within 90 days while reducing mean time to resolution (MTTR) by 60%
- Its Amelia solution understands 20 languages; it also comprehends context, can apply logic, and can infer implications
- The Amelia platform learns by reading manuals, and can read 300 pages within 30 seconds The company reduces risk. If the Amelia solution cannot answer a question, it passes the client’s query onto a human, but remains in the loop in order to learn how to solve similar issues in future. Ipsoft also ensures the well-being of the system by performing security scans, ongoing audits, and compliance reporting that confirms adherence with industry standards such as HIPPA and PCI.
The company has established a strong brand due to its success. It has 18 offices in 15 countries worldwide that serve over 500 brands. Its clients include two out of 10 Fortune 100 firms and seven out of 10 of the world’s largest IT service providers. Lastly, it has won many a number of honors, including an Alsbridge Innovation Award for autonomic-enabled IT transformation.
Channels
IPsoft’s main channel is its direct sales team. The company promotes its offering through its social media pages and participation in expos, symposiums, and conferences.
Customer Relationships
IPsoft’s customer relationship is primarily of a dedicated personal assistance nature. It assists customers in the following ways:
Support Services – IPsoft provides 24/7 operational support for the IPcenter platform. While the solution is able to solve most issues, the customer service team is always available to help. In addition, the company keeps the platform up-to-date through code updates and patches.
Dedicated Support Services – Ipsoft provides services through which assigned staff members work closely with clients to implement and maintain their solutions; they are as follows:
- Service Transition Team – Completes end-to-end deployment and integration of the IPcenter platform. The group includes technical leads, engineers, and experienced project managers.
- Customer Relationship Manager (CRM) – Coordinates the delivery of services in conjunction with the Technical Executive and Lead Engineers. Also acts as a liaison between the client and internal IPsoft teams. Specific responsibilities include ensuring quality of service delivery, reporting on SLAs/SLOs, maintaining KPIs, and monitoring customer satisfaction.
- Dedicated Technical Executive – Aligns technical aspects of IPsoft’s offerings with client’s expectations and requirements. Specific responsibilities include daily ticket reviews, weekly reviews of automation progress, and monthly reports on KPIs/SLAs. Chosen based on makeup of customer’s environment. Is usually an L3 engineer. Training Services – Ipsoft has a training team that maintains IPuniversity, a platform that provides instructor-led and self-paced education on its technologies and on technical skills in general. The team is also in the process of creating an IPcenter certification program.
Despite this orientation, there is a self-service component. The company’s website features a “Newsroom” section that includes self-help resources such as white papers, analyst reports, case studies, and blog posts.
Key Activities
IPsoft’s business model entails designing and developing its software and maintaining its cloud platform for customers.
Key Partners
IPsoft maintains partnerships with various technology firms and service providers in order to extend its reach, enhance its solutions, and provide consulting services. Specific partners are Deloitte, HfS, Kairos, Kinetic, Cisco, Arvato Systems Business Services, and Accenture.
Key Resources
IPsoft’s main resource is its proprietary software platform.
It depends on human resources such as engineering employees that develop software and maintain the platform, sales employees that promote it, training staff that provide instruction, and customer service staff that provide support.
Cost Structure
IPsoft has a value-driven structure, aiming to provide a premium proposition through significant personal service and frequent product improvements.
Its biggest cost driver is likely cost of services, a variable expense. Other major drivers are in the areas of research/development and customer support/operations, both fixed costs.
Revenue Streams
IPsoft has one revenue stream: revenues generated from the subscription fees it charges for access to its cloud platforms. Sales staff must be contacted directly for pricing details.


Technology High Gradebook
Home
About
Contact
Register
Login
access your gradebook by simply logging in
Login Information
First Name
Login
© Copyright 2017
Tommy Gooden
- All rights reserved.


Welcome to Thattachu. A simple typing tutor where you can learn typing in multiple keyboard layouts of multiple languages.
Choose Language
Thattachu provides ability to learn typing for 62 languages supported by jQuery IME.
Choose the language you want to learn.
Choose Layout
Usually there are multiple keyboard layouts and input methods for a language. Choose the layout you want to practice.
Choose Course
If there are available courses for the language and the input type you selected, we list them. You choose, you learn at your pace.
Created using AngularJS and Bootstrap by Arunmozhi. OpenSource - MIT licensed.


Create a function that accepts a string argument and returns an array of strings, which are shifted versions of the argument string.
Sample Input
"Hello"
Sample Output
["elloH", "lloHe", "loHel", "oHell", "Hello"]
As you can see from the example above, each element shifts the first character of the previous version to the end.
Parameters
- The original string should be included in the output array.
- The order matters; each element of the output array should be the shifted version of the previous element.
- The output array should have the same length as the input string.
- The function should return an empty array when a zero-length String is provided as the argument.