Introduction
The Prague Arabic UD Treebank
The Arabic UD treebank is based on the Prague Arabic Dependency Treebank (PADT), created at the Charles University in Prague. The treebank consists of 7,664 sentences (282,384 tokens) and its domain is mainly newswire. The annotation is licensed under the terms of CC BY-NC-SA 3.0 and its original (non-UD) version can be downloaded from http://hdl.handle.net/11858/00-097C-0000-0001-4872-3.
The morphological and syntactic annotation of the Arabic UD treebank is created through conversion of PADT data. The conversion procedure has been designed by Dan Zeman. The main coordinator of the original PADT project was Otakar Smrž.
Source of annotations
This table summarizes the origins and checking of the various columns of the CoNLL-U data.
| Column | Status |
|---|---|
| ID | Sentence-level units in PADT often correspond to entire paragraphs and they were obtained automatically. Low-level tokenization (whitespace and punctuation) was done automatically and then hand-corrected. Splitting of fused tokens into syntactic words in Arabic is part of morphological analysis. ElixirFM was used to provide context-independent options, then these results were disambiguated manually. |
| FORM | The unvocalized surface form is used. Fully vocalized counterpart can be found in the MISC column as Vform attribute. |
| LEMMA | Plausible analyses provided by ElixirFM, manual disambiguation. Lemmas are vocalized. Part of the selection of lemmas was also word sense disambiguation of the lexemes, providing English equivalents (see the Gloss attribute of the MISC column). |
| UPOSTAG | Converted automatically from XPOSTAG (via Interset); human checking of patterns revealed by automatic consistency tests. |
| XPOSTAG | Manual selection from possibilities provided by ElixirFM. |
| FEATS | Converted automatically from XPOSTAG (via Interset); human checking of patterns revealed by automatic consistency tests. |
| HEAD | Original PADT annotation is manual. Automatic conversion to UD; human checking of patterns revealed by automatic consistency tests. |
| DEPREL | Original PDT annotation is manual. Automatic conversion to UD; human checking of patterns revealed by automatic consistency tests. |
| DEPS | — (currently unused) |
| MISC | Information about token spacing taken from PADT annotation. Additional word attributes provided by morphological analysis (i.e. ElixirFM rules + manual disambiguation): Vform (fully vocalized Arabic form), Translit (Latin transliteration of word form), LTranslit (Latin transliteration of lemma), Root (word root), Gloss (English translation of lemma). |
Acknowledgments
We wish to thank all of the contributors to the original PADT annotation effort, including Otakar Smrž, Jan Hajič, Petr Zemánek, Petr Pajas, Jan Šnaidauf, Emanuel Beška, Jakub Kráčmar, and Kamila Hassanová.
Further corrections of additional data (not part of PADT release 1.0) were done by Shadi Saleh and Zdeněk Žabokrtský.
References
- Jan Hajič, Otakar Smrž, Petr Zemánek, Petr Pajas, Jan Šnaidauf, Emanuel Beška, Jakub Kráčmar, Kamila Hassanová. 2009. Prague Arabic Dependency Treebank 1.0, LINDAT/CLARIN digital library at Institute of Formal and Applied Linguistics, Charles University in Prague, http://hdl.handle.net/11858/00-097C-0000-0001-4872-3.
- Otakar Smrž, Viktor Bielický, Iveta Kouřilová, Jakub Kráčmar, Jan Hajič, Petr Zemánek. 2008. Prague Arabic Dependency Treebank: A Word on the Million Words. In: Proceedings of the Workshop on Arabic and Local Languages (LREC 2008), pp. 16–23. Marrakech, Morocco.
The New York Abu Dhabi Arabic UD Treebank
The NYUAD Arabic UD treebank is based on the Penn Arabic Treebank (PATB), parts 1, 2, and 3, through conversion to CATiB dependency trees. The treebank consists of 19,738 sentences (738889 tokens), and its domain is mainly newswire. The annotation is licensed under the terms of CC BY-SA 4.0, and the original PATB can be obtained from the LDC’s official website.
The morphological and syntactic annotation of the NYUAD Arabic UD treebank is created through automatic conversion of the PATB data.
Source of annotations
The FORM, LEMMA, and MISC columns are initially empty, and are filled with the annotation is merged with the LDC’s PATB files.
| Column | Status |
|---|---|
| ID | The tokenization of words into syntactic tokens followed is the LDC’s ATB tokenization scheme. |
| FORM | The undiacritized surface form. |
| LEMMA | The lemma as annotated in the PATB. Tokens that do not have lemmas have been assigned automatically generated lemmas. |
| UPOSTAG | Converted automatically from the gold Penn Arabic Treebank POS tag. |
| XPOSTAG | The PATB Buckwalter POS tag. |
| FEATS | Converted automatically from the gold Penn Arabic Treebank POS tag. |
| HEAD | Original CATiB annotation is done through automatic conversion of the PATB trees, then to UD. |
| DEPREL | Original CATiB annotation is done through automatic conversion of the PATB trees, then to UD. |
| DEPS | — (currently unused) |
| MISC | The diacritized Buckwalter transliteration from PATB. |
Data splits and statistics
The data was split into Train, Dev, and Test according to the LDC Arabic treebanks and associated corpora: Data divisions manual (Diab et al. 2013)
| Column |
|---|
| Train |
| Tree count: 15,789 |
| Word count: 590,819 |
| Token count: 502,991 |
| Dep. relations: 28 of which 3 language specific |
| POS tags: 16 |
| Category=value feature pairs: 28 |
| Dev |
| Tree count: 1,986 |
| Word count: 73,945 |
| Token count: 63,136 |
| Dep. relations: 28 of which 3 language specific |
| POS tags: 16 |
| Category=value feature pairs: 27 |
| Test |
| Tree count: 1,963 |
| Word count: 74,125 |
| Token count: 63,168 |
| Dep. relations: 28 of which 3 language specific |
| POS tags: 16 |
| Category=value feature pairs: 27 |
Acknowledgements
We would like to thank the Linguistic Data Consortium’s Arabic language group, and particularly Dr. Mohamed Maamouri and Drs Ann Bies and Seth Kulick, as well as the rest of the annotation team who participated in creating the Penn Arabic Treebank. We also would like to thank Reem Faraj, Ryan Roth and the annotators of the Columbia Arabic Treebank. The development of the the NYUAD Arabic UD relied heavily on these two efforts.


SEPTEMBER 15 Our Lady of Sorrows
FIRST
A reading from the letter to the Hebrews 5:7-9
In the days of his
flesh, Jesus offered up prayers and supplications, with loud cries and tears,
to him who was able to save him from death, and he was heard for his godly
fear. Although he was a Son, he learned obedience through what he suffered; and
being made perfect he became the source of eternal salvation to all who obey
him.
The word of the Lord.
RESPONSORIAL PSALM Ps 31
R. Save me, O Lord, in your merciful love.
In you, O LORD, I
take refuge. Let me never be put to shame. In your justice, set me free; incline
your ear to me, and speedily rescue me.
R. Save me, O Lord, in your
merciful love.
Be a rock of refuge
for me, a mighty stronghold to save me. For you are my rock,
my stronghold! Lead me, guide me, for the sake of your
name.
R. Save me, O Lord, in your
merciful love.
Release me from the
snare they have hidden, for you indeed are my refuge. Into your hands I commend
my spirit. You who will redeem me, O LORD, O faithful God.
R. Save me, O Lord, in your
merciful love.
But as for me, I
trust in you, O LORD; I say: 'You are my God. My lot is in your hands, deliver me from the hands of my enemies and those who
pursue me.'
R. Save me, O Lord, in your
merciful love.
How great is the
goodness, LORD, that you keep for those who fear you, that
you show to those who trust you in the sight of the children of men.
R. Save me, O Lord, in your
merciful love.
SEQUENCE
(Poetic Text)
|
At the cross her
station keeping, stood the mournful Mother weeping, close to Jesus to the
last. |
|
Through her heart,
his sorrow sharing, all his bitter anguish bearing, now
at length the sword has passed. |
|
Oh, how sad and
sore distressed was that Mother highly blessed of the sole begotten One! |
|
Christ above in
torment hangs, she beneath beholds the pangs of her dying, glorious Son. |
|
Is there one who
would not weep, whelmed in miseries so deep, Christ's dear Mother to behold? |
|
Can the human
heart refrain from partaking in her pain, in that Mother's pain untold? |
|
Bruised, derided,
cursed, defiled, she beheld her tender Child, all with bloody scourges rent. |
|
For the sins of
his own nation saw him hang in desolation till his spirit forth he sent. |
|
O sweet Mother! font of love, touch my spirit from above, make my heart
with thine accord. |
|
Make me feel as thou
hast felt; make my soul to glow and melt with the love of Christ, my Lord. |
|
Holy Mother,
pierce me through, in my heart each wound renew of my Savior crucified. |
|
Let me share with thee
his pain, who for all our sins was slain, who for me
in torments died. |
|
Let me mingle
tears with thee, mourning him who mourned for me, all the days that I may
live. |
|
By the cross with thee
to stay, there with thee to weep and pray, is all I ask of thee to give. |
|
Virgin of all
virgins blest!  Amen. (Alleluia) |
GOSPEL ACCLAMATION
R. Alleluia. Blessed is the Blessed Virgin Mary; for, without dying, she merited the palm of martyrdom at the foot of the Lordís cross. R. Alleluia.
GOSPEL
A reading from the holy Gospel according to John 19:25-27
Standing by the
cross of Jesus were his mother, and his mother's sister, Mary the wife of Clopas, and Mary Magdalene. When Jesus saw his mother, and
the disciple whom he loved standing near, he said to his mother, "Woman,
behold, your son!" Then he said to the disciple, "Behold, your
mother!" And from that hour the disciple took her to his own home.
The Gospel of the Lord.


Contributing to Codrops
Back in 2010 when I decided I wanted to be a 'web designer' and was learning the basics of HTML and CSS my then-boyfriend-now-husband told me I should check out this blog called Codrops for tutorials. This was before css transitions had entered the mainstream and everyone was animating with jQuery and I remember just being blown away by how awesome their demos and tutorials were. I have been using them as an inspiration source ever since then.
So you can imagine my shock and delight when Manoela Ilic (you may know her as Mary Lou) contacted me earlier this year about the possibility of contributing to Codrops. I had to pinch myself!
Well it took me a while to get my butt in to gear and finish the demos as I was working all summer on Spacecraft For All, Gisele Bündchen for Under Armour and Witness Gotham with Active Theory. But today it was finally published and I am really, really, excited about it.
My personal history with Codrops aside, the fact that my work has been published alongside articles by Manoela Ilic and Sara Soueidan is very flattering as I have admired these talented and creative women for a while now.
Alright, that is it from me, go check out the article!


Implicitly Learning to Reason in First-Order Logic
21 Oct 2019 Logic Learning Query answeringThis paper (Belle & Juba, 2019) considers the problem of answering queries about infinitary first order logic (infinite but countable amount of constants) formulas based on background knowledge partially represented explicitly as other formulas, and partially represented as examples independently drawn from a fixed probability distribution.
The authors present an algorithm that reduces reasoning with implicit learning to deciding entailment. Meaning it does not learn rules, but it can answer queries (i.e. resolve entailment) with implicit and explicit learning. They also provide theoretical guarantees if the implicit knowledge is drawn from a probability distribution.
Idea
Probably approximately correct (PAC) learning is a branch of machine learning that studies function inference under polynomial time complexity contraints with respect to the probability of getting an approximately correct answer. More precisely, complexity scales polynomially the more precise the approximation is and the more probable the answer is to lie within that approximation.
The paper starts off by defining a first order logic language with connectives \(\{\neg , \lor , \land , \forall , \exists , \Rightarrow , = \}\) and a countably infinite set of constants, such as \(\mathbb{N}\).
Their knowledge bases are defined as a finite, non-empty set of \(\forall\)-clauses. A \(\forall\)-clause is a formula of the form:
\[\forall x_1 ... \forall x_k \ ( e \Rightarrow a_1 \lor ... \lor a_n )\]Where:
- \(a_i\) are atoms.
- \(e\) is a formula built using: (i) equalities of the form \((x=c)\) with \(x\) variable and \(c\) constant; (ii) the logical connectives \(\{ \neg , \forall , \exists \}\).
- \(x_1 ... x_k\) are all the free variables of \(e \Rightarrow a_1 \lor ... \lor a_n\).
For example:
\[KB = \{ \forall x (Grad(x) \lor Prof(x)) , \ \forall x ((x \neq 1) \Rightarrow Grad(x)) \}\]As such, this knowledge bases are able to represent consistent sets of infinite ground clauses.
The rank of a knowledge base is the maximum amount of universal quantifiers present in any of its clauses.
A model of a KB is a consistent mapping from all possible grounded atoms to \(\{ true , false \}\) that satisfies the formulas of KB. A partial model of model \(M\) of KB is a mapping \(N\) from all possible grounded atoms to \(\{ true , false , * \}\) that is consistent with the model i.e. \(\forall p \ N[p] \neq * \Rightarrow N[p] = M[p]\).
Let \(N\) be a partial model of the KB, a ground formula is witnessed to be true iff it is true in \(N\). Next, a \(\forall\)-clause \(\forall \hat x \theta(\hat x)\) is witnessed true in a partial model \(N\) for the set of constants \(C\) if for every binding of \(\hat x\) to constants \(\hat c \in C\), the resulting ground clause \(\theta(\hat c)\) is witnessed true in \(N\).
Given a distribution \(D\) over all possible models of KB, a masking process \(\Theta\) is a function that maps a distribution over the models of KB, to a distribution over partial models of KB, i.e. \(\Theta (D)\) is a distribution over partial models of KB.
Then, given:
- An explicit KB (given as input, not inferred from examples).
- A set of partial models \(\{ N^{(1)} , ... , N^{(m)} \}\) (sets of possible true grounded atoms) that represent witnessed examples, i.e. sets of grounded atoms that hold together in an example. They would represent the implicit knowledge.
- A distribution \(D\) of \(\{ N^{(1)} , ... , N^{(m)} \}\) masked by a masking process \(\Theta\).
- A query (grounded formula) \(\alpha\).
- A number \(k\) of constants at least as large as the KB’s rank.
The authors present the following algorithm to estimate the probability \(\hat p\) of the query being right:
The guarantees of this procedure are given by the theorem:
Where given a distribution \(D\) over \(\{ N^{(1)} , ... , N^{(m)} \}\), a query \(\alpha\) is \(p\)-valid iff:
\(\sum\limits_{ \{ N^{(i)} \ \mid \ N^{(i)} \models \alpha \} }Pr(N^{(i)})) \geq p\).
References
- Belle, V., & Juba, B. (2019). Implicitly Learning to Reason in First-Order Logic. CoRR, abs/1906.10106. http://arxiv.org/abs/1906.10106


---
layout: post
date: 2012-02-12
time: "09:00:00"
authors: ["Greg Wilson"]
title: Pre-Workshop Questionnaire
tags: ["Content", "Education", "Assessment"]
---
I've been asking people to fill in the short questionnaire below before our workshops in order to give us a better idea of what they want and what they already know. How do you score, and how could we improve the questions?
- Roughly what fraction of your working time do you spend creating, modifying, or testing software?
- What operating system will you be using in this class?
- Do you understand the following Unix shell commands well enough to explain them to someone else?
- ls data/*.txt
- find ~ -name '*.py' -print
- Do you understand the following Subversion commands well enough to explain them to someone else?
- svn update
- svn diff -r 1723
- Do you understand the following Python statements well enough to explain them to someone else?
- {'east' : 5, 'west' : 11}
- __init__(self, *args)
- Do you understand the following testing concepts well enough to explain them to someone else?
- Do you understand the following Make terms and commands well enough to explain them to someone else?
- Do you understand the following SQL terms and commands well enough to explain them to someone else?
- select * from data where data.left < data.right;
- inner join
- What do you hope to gain from this workshop? What could we do to make it most useful for you?


The trail making test is intrepreted using normative studies. A list of included norms within this software are listed in the table below. Additionally, an extended summary of each study is also listed in this page. Users are strongly recommended to read and comprehend the original manuscripts as well (links to the original text is provided)
| Author | Sample | Size | Description |
|---|---|---|---|
| Tombaugh (2004) | United States! | 1300 | Sample size stratified by age and level of education |


It's time to build your J vocabulary with a few more verbs.
x ! y (rank 0 0) number of ways to choose x things from a population of y things. More generally, (!y) % (!x) * (!y-x)
x %: y (rank 0 0) The xth root of y
x #. y (rank 1 1) y evaluated in base x, i. e. the atoms of y are digits in the base-x representation. x and y must have the same length, unless x is a scalar in which case it is replicated to the length of y . A place-value list p is calculated, in which each item is the product of all the subsequent corresponding items of x; formally, this is p=.*/\.}.x,1 (the last item of p is always 1 and the first item of x is immaterial). Then each item in y is multiplied by its place value and the results are summed to give the result (formally this is +/ y * p). In the simplest case x is a scalar:
10 #. 2 3 4
234
The digits 2 3 4 interpreted as base-ten digits.
16 #. 2 3 4
564
The same interpreted as base-sixteen digits.
16b234
564
as expected (16b234 is 234 in base 16, equivalent to 0x234).
Here is an example where x is a list, converting time in hours/minutes/seconds to seconds since midnight:
24 60 60 #. 12 30 0
45000
The list p was 3600 60 1 . The first item in x has no effect on the result.
x #: y (rank 1 0) This is the inverse of x #. y except that if the first digit of the result is not between 0 and {.x, it is changed (modulo x) to lie within that range. For example,
24 60 60 #: 45000
12 30 0
The normal case, converting the number of seconds since midnight back to hours/minutes/seconds.
24 60 60 #. 36 30 0
131400
24 60 60 #: 131400
12 30 0
Here the result would have been 36 30 0, but 36 is outside the range 0 to 24, so it is replaced by 24|36, which is 12 . If you want the first item of the result to be unconstrained, make the first item of x either 0 or _ :
0 60 60 #: 131400
36 30 0
Note that monad #: and monad #. are similar to the dyadic forms with x set to 2 . To keep #: and #. straight in your mind, remember that the one with a single dot (#.) produces a single scalar result, while the one with multiple dots (#:) produces a list result.
! y factorial of y (more generally, the gamma function Γ(1+y))
^ y exponential: same as (the base of natural logarithms e) ^ y
^. y natural logarithm: same as (the base of natural logarithms e) ^. y
+: y same as y * 2
-: y same as y % 2
*: y same as y ^ 2
%: y same as 2 %: y
You can see the common feature in the arithmetic primitives ending in : .


{% extends "agilito/errorpages/errorpage.html" %} {% load i18n %} {% block body %} {% url admin:agilito_project_add as admin_project_add %} {% url admin:app_list "agilito" as admin_agilito_list %} {% blocktrans %}
You are not assigned into any project.
Go to the admin section in order to add a new project
Click Here to reach the agilito's admin section
{% endblocktrans %} {% endblock %}


Home
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{% extends "blocks/main-card.html" %} {% load static otree %} {% block page_title %}{{ page_title }}{% endblock %} {% block page_content %}
Next, we'll show you 5 examples of how the game might be played and what the results would look like in each scenario.


Home
Certifier
Certificants
Third Parties
About Us
Registered Certificates
Confused?
User Home
×
You can view Names of all the recepients .
You can access a certificate if you have the respective key.
If you don't have the respective key then you may want to ask the certificate owner.


This is a very simple game that shows your speed typing a text in characters per minute (CPM) and also words per minute (WPM).
It counts every error but you can use the backspace key!
Click on
when you are ready to see the exercise.


Microsoft Teams surpasses 20 million daily active users; rival Slack shares slip
Microsoft Corp said on Tuesday its workplace messaging app, Teams, has more than 20 million daily active users, up from 13 million in July.
REUTERS: Microsoft Corp said on Tuesday its workplace messaging app, Teams, has more than 20 million daily active users, up from 13 million in July.
The software maker offers the app as part of some Office365 business packages, as well as a free version. Teams allows users to chat, share files, make calls and hold web video conferences.
Microsoft Teams, used by companies such as General Electric and SAP, competes with Slack Technologies Inc.
Slack, whose customers include Electronic Arts Inc and Nordstrom Inc, reported more than 10 million daily active users in the second quarter ended July 31.
Slack's shares fell 8.4per cent following the news, to US$21.18. They are down 45per cent from the close of their first day of trade in June.
Microsoft Teams also competes with Workplace by Facebook Inc and Cisco Systems Inc's Webex Teams.
(Reporting by Ambhini Aishwarya in Bengaluru; Editing by Rashmi Aich and Clarence Fernandez)


| DiscreteWavelets Toolbox |
Plot a two-dimensional wavelet transfomration
WaveletDensityPlot(wt,its)
WaveletDensityPlot(wt,its,options)
WaveletDensityPlot(wt,its) takes either a matrix or a three-dimensional array wt, presumably obtained by computing the wavelet transformation of some matrix or three-dimensional array, respectively, and a number of iterations its, and plots wt with each portion of the transformation separated by grid lines.
WaveletDensityPlot(wt,its,options) allows the user to set several parameters. See Optional Arguments for more details.
There are seven optional arguments that can be set for WaveletVectorPlot. The order of the parameters does not matter in the function call, but you must follow the standard convention 'Option', 'Value' when invoking the parameters.
'Region' can be set to either 'All' (default), 'Blur', 'Vertical', 'Horizontal', or 'Diagonal'. If 'Region' is set to 'Blur', the routine plots the lowpass portion of the transform. For the other non-default cases, the routine looks for the value of 'Iteration' (see below) and then plots the selected portion of wt.
'Iteration' can be set to 'All' (default) or any positive integer 1,2,...,its. This value is used if 'Region' has been set to 'Blur', 'Vertical', 'Horizontal', or 'Diagonal' to plot that particular region. If 'Region' is set to 'All' and 'Iteration' is set to i, then the routine plots the lowpass portion and highpass portions k=i,...,its.
'DivideLines' can be set to 'True' (default) or 'False' in order to show or hide, respectively, gridlines dividing the different portions of the transformation.
'DivideLinesColor' is a length three vector whose entries are in the interval [0,1] that serves as the color of the gridlines. The default color of 'DivideLinesColor' is [1 1 1] (white).
'DivideLinesThickness' can be set to either a nonnegative integer p (default is 1) or a vector p of length its. In the former case, all divide lines are drawn at thickness p. In the latter case, the divide lines for iteration i are set to thickness p(i), where i=1,2,...,its.
A plot title can be added using the 'Title' option. For example, WaveletDensityPlot(wt,its,'Title','Here is a Picture') adds a title to the plot.
Magnification can be set to make the output image size smaller or larger. The default value is 1 and if set, the value must be a positive number.
WaveletDensityPlot can plot color transformations. The input wt must be a three-dimensional array with depth three. It is assumed that wt(:,:,1), wt(:,:,2), and wt(:,:,3) hold the red, green, and blue portions of the transformations, respectively.
First, we read an image and compute three iterations of its wavelet transformation. Different examples will illustrate the different parameters that can be set with WaveletDensityPlot.
gry=ImageNames('ImageType','GrayScale'); %Get all grayscale image files included with the toolbox A=ImageRead(gry{17}); %Read an image file ImagePlot(A); %Plot the image file [h,ht]=CDF97(); %Use the CDF97 filter for the computations. wt=BWT2D(A,h,ht,3); %Compute four iterations of the transformation
WaveletDensityPlot(wt,3,'Title','Three Iterations Using CDF97()');
Adjust the thickness of the divide lines
WaveletDensityPlot(wt,3,'DivideLinesThickness',[2 2 2]);
We can change the color of the divide lines as well:
WaveletDensityPlot(wt,3,'DivideLinesThickness',[2 2 2],'DivideLinesColor',[1 0 0]);
Plot iterations 2 and 3
WaveletDensityPlot(wt,3,'Iteration',2,'DivideLinesThickness',[2 2 2]);
Plot the lowpass portion of the transformation in one plot and the vertical portion of the iteration 1 in another plot.
WaveletDensityPlot(wt,3,'Region','Blur'); figure; WaveletDensityPlot(wt,3,'Region','Vertical','Iteration',1);
Make the lowpass portion eight times as large so it is the same size as the original image.
WaveletDensityPlot(wt,3,'Region','Blur','Magnification',8);
WaveletDensityPlot also handles color images. First, load a color image and compute three iterations of a wavelet transformation.
clr=ImageNames('ImageType','Color'); %Find all color image file names that come with the toolbox A=ImageRead(clr{6}); %Load a color image ImagePlot(A); %Plot the image [R,G,B]=Split3D(A); %Separate the channels wtR=LWT2D(R,3); %Transform the red channel wtG=LWT2D(G,3); %Transform the green channel wtB=LWT2D(B,3); %Transform the blue channel wt=Make3D(wtR,wtG,wtB); %Combine the channels
Plot the wavelet transform with a title, divide lines thickness adjusted appropriately, and divide lines color set to blue
WaveletDensityPlot(wt,3,'Title','LeGall Wavelet Transformation',... 'DivideLinesThickness',[2 2 2],'DivideLinesColor',[0 0 1]);
BWT2D, HWT2D, LWT2D, WT2D, WaveletVectorPlot
© 2007-2008 Patrick Van Fleet


Policies
Up
Down
Remove
Add policy
How to setup
Click 'Add policy'
Input target URL pattern such as
http://*.fc2.com/*
Select your rule. e.g. Referer as 'empty'
Done!
NOTE: URL patterns should be valid as
Chrome match patterns
.
Thanks
Extension icon
http://www.iconfinder.com/icondetails/66346/128/secret_mask_icon
Github Buttons
http://nicolasgallagher.com/lab/css3-github-buttons/
option page
http://angularjs.org/
http://subtlepatterns.com/
https://speakerdeck.com/ken_c_lo/zurui-design
http://www.google.com/webfonts/specimen/Dosis


|
cds
1.2.0
|
Type traits for MichaelHashSet class. More...
#include <cds/intrusive/michael_set_base.h>
Public Types | |
| typedef opt::none | hash |
| Hash function. | |
| typedef atomicity::item_counter | item_counter |
| Item counter. | |
| typedef CDS_DEFAULT_ALLOCATOR | allocator |
| Bucket table allocator. |
Type traits for MichaelHashSet class.
Bucket table allocator.
Allocator for bucket table. Default is CDS_DEFAULT_ALLOCATOR The allocator uses only in ctor (for allocating bucket table) and in dtor (for destroying bucket table)
Hash function.
Hash function converts the key fields of struct T
stored in the hash-set into value of type size_t
called hash value that is an index of hash table.
This is mandatory type and has no predefined one.
Item counter.
The item counting is an important part of MichaelHashSet algorithm: the empty()
member function depends on correct item counting. Therefore, atomicity::empty_item_counter is not allowed as a type of the option.
Default is atomicity::item_counter.


SPARQL is a doap project.
SPARQL has a homepage http://swobj.org/SPARQL/v1 .
SPARQL is "a Semantic Web query toolbox".


---
layout: post
authors: ["Greg Wilson"]
title: A Bootcamp at Mozilla
date: 2013-01-30
time: "09:00:00"
tags: ["Mozilla (Toronto)", "Workshop", "Report", "Reflection"]
---
We ran a two-day bootcamp at Mozilla's office in Toronto last week for people from local research hospitals. It seemed to go well: only 28 of the 37 who'd registered showed up, but with a couple of exceptions because of scheduling conflicts, everyone who showed up on day 1 came back on day 2. Many thanks to Dhavide Aruliah, Yele Bonilla, Mike Conley, Gabriel Devenyi, Fan Dong, and Blake Winton for helping out.
| Good |
Bad |
- New Tools
- Good teachers
- Good supply of paper/book references
- Useful simple bash scripts for analyzing text files
- Snacks: mmm...
- New plotting tools to replace Matlab
- Shell tools interesting
- Importance of version control
- Preview of IPython Notebook
- Stickies for feedback
- Good coverage basics (didn't jump right in to Python)
- Impressing upon us that everyone's research is relevant
- Digressions on learning psychology
|
- Not enough testing covered
- Python installation problems
- Some examples didn't work
- More hands-on Python needed
- Digressions on learning psychology
- Numerical analysis parts too mathematical
- Need short overnight exercises to support learning
- More signposts what we're doing and why
- More exercises and application examples needed
- Not everything is placed on the Etherpad
- Getting working scripts not seamless
- Too damn cold
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[PART IXA. PREVENTION OF BLINDNESS]
[§321-106] Prevention of blindness at childbirth. The department of health may require the administration of prophylaxis for the prevention of blindness in the newborn at childbirth. [L 1982, c 17, §1]


Browsers Supported
- Internet explorer 8.0 +
- Google Chrome
- Firefox
- Safari
About Us
cScroll is a very light weight plugin that is developed to serve the purpose of a scroll bar.
At the same time it is light weight which will improve the performance of application since it takes very less time to load
Anyway we would recommend you to copy the contents of the plugin and have it in one of the js file you use to reduce the number of server hits
We would appreciate if you could preserve the credits :)