National Marathon Champions for Russia
Men (26 runnings) 07 May 2017 Volgograd 2:14:36 Stepan Kiselev 01 May 2016 Volgograd 2:11:26 Fyodor Shutov-2 11 May 2015 Kazan 2:14:10 Yuriy Chechun 04 May 2014 Volgograd 2:16:43 Sergey Rybin-2 27 Apr 2013 Moscow 2:15:23 Artyom Aplachkin 09 Sep 2012 Moscow 2:12:09 Fyodor Shutov 14 May 2011 Moscow 2:14:53 Yuriy Abramov-2 02 May 2010 Moscow 2:11:39 Yuriy Abramov 02 May 2009 Saransk 2:11:48 Sergey Rybin 07 Jun 2008 Saransk 2:14:08 Aleksey Sokolov-4 16 Jun 2007 Cheboksary 2:15:52 Aleksey Sokolov-3 03 Jun 2006 Saransk 2:14:59 Andrey Chernyshov 22 May 2005 Moscow 2:17:35 Aleksey Sokolov-2 16 May 2004 Moscow 2:14:19 Dmitriy Burmakin 18 May 2003 Ufa 2:13:25 Aleksey Sokolov 19 May 2002 Moscow 2:15:22 Yuriy Chizhov 05 May 2001 Moscow 2:16:08 Mikhail Romanov 21 May 2000 Moscow 2:14:50 Aleksey Korobov 16 May 1999 Moscow 2:12:32 Oleg Strizhakov 17 May 1998 Moscow 2:16:35 Andrey Shalagin 18 May 1997 Moscow 2:16:38 Vladimir Epanov 01 Sep 1996 Moscow 2:19:24 Aleksandr Gurin 22 Apr 1995 Zelenogradsk 2:16:28 Vladimir Plykin 24 Apr 1994 Kaliningrad 2:14:08 Anatoliy Archakov 25 Apr 1993 Kaliningrad 2:12:57 Andrey Tarasov 25 Apr 1992 Kaliningrad 2:16:03 Ruslan Yamayev Women (26 runnings) 07 May 2017 Volgograd 2:30:45 Natalya Starkova-2 01 May 2016 Volgograd 2:28:34 Tatyana Arkhipova 11 May 2015 Kazan 2:29:30 Sardana Trofimova 04 May 2014 Volgograd 2:35:16 Natalya Starkova 27 Apr 2013 Moscow 2:35:03 Marina Kovalova 09 Sep 2012 Moscow 2:30:10 Natalia Sokolova 14 May 2011 Moscow 2:30:27 Lyubov Morgunova 02 May 2010 Moscow 2:30:14 Margarita Plaksina-2 02 May 2009 Saransk 2:35:36 Margarita Plaksina 07 Jun 2008 Saransk 2:39:42 Irayda Pudovkina 16 Jun 2007 Cheboksary 2:45:27 Dina Usanova 03 Jun 2006 Saransk 2:30:54 Nataliya Yulamanova 22 May 2005 Moscow 2:40:46 Nina Podnebesnova 16 May 2004 Moscow 2:33:01 Olga Bylinkina 18 May 2003 Ufa 2:35:34 Tatyana Zolotareva-2 19 May 2002 Moscow 2:36:25 Tatyana Zolotareva 05 May 2001 Moscow 2:34:58 Firiya Sultanova-3 21 May 2000 Moscow 2:34:45 Irina Safarova 16 May 1999 Moscow 2:32:05 Elena Gundelakh 17 May 1998 Moscow 2:30:54 Lyudmila Petrova 18 May 1997 Moscow 2:35:09 Anfisa Kosacheva 01 Sep 1996 Moscow 2:37:56 Irina Timofeyeva 22 Apr 1995 Zelenogradsk 2:35:09 Olga Nazarkina 24 Apr 1994 Kaliningrad 2:39:31 Larisa Zyusko 25 Apr 1993 Kaliningrad 2:32:11 Firiya Sultanova-2 25 Apr 1992 Kaliningrad 2:34:12 Firiya Sultanova
Non-champion winners 2014 2:33:47 Gulnara Vygovskaya (BLR)
Notes: The 2015 and 2016 courses were probably short.
Notes: Prior to 1992, Russia was part of the Soviet Union, please refer
to the Soviet Union championships.
Most Titles
(1992-date)
Men
(1992-date) 4 3
Aleksey Sokolov
2003,2005,2007-08
Women
(1992-date)
Firiya Sultanova
1992-93,2001
Last Updated on
10 May 2017
Contributors: Gunars Akerbergs, Martin Rix, Klaas Loonstra, Juraj Gasparovic (1), Marty Post (6), Francois Lorange (5)


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Keyword For
Declares the start of a For/Next loop.
Syntax
For Local IndexVar := start_value To end_value [ Step constant_value ]
Statements...
Next
For Local IndexVar := start_value Until end_value [ Step constant_value ]
Statements...
Next
For Local IndexVar := Eachin value_set
Statements...
Next
Description
For declares the start of a For/Next loop, allowing for iteration through a set of values or objects.
There are three variants of the For/Next loop, modifying the values of the index variable via the range keywords below:
To
: Iterates through the given start and end values;Until
: This variant of For/Next stops iterating before the last value in the loop, useful for iterating through zero-based arrays. (See Example 3.)Eachin
: Iterates through a set of objects or values, such as a list or array;In the To and Until variants, the optional Step keyword, followed by a constant value, allows control over the size of the iteration step.
Alternatives to the closing Next keyword are End For, or simply End.
See also
Example
For k=1 To 1000 Print k Next ' With optional Step For k=1 To 1000 Step 100 Print k Next ' Alternative syntax examples For k=1 To 1000 Print k End For k=1 To 1000 Print k End For
Example 2
' Assume m:MyObject and mylist:List For m=Eachin mylist Print m.myField Next ' Assume i:Int and myarray:Int[] For i=Eachin myarray Print myarray[i] Next
Example 3
NUM = 100 ' Assume myarray[NUM] has been declared ' Prints 0 to 99 without requiring NUM-1 For k=0 Until NUM Print myarray[k] Next


Hey Flatironers thank you for helping me in class and also Happy Thanksgiving.
Would you like to to surprise the people who supported me throughout my journey and telling them HAPPY THANKSGIVING!?
thanks for all your help DOGG!
Hey flatironers we only have thanksgiving once a year like all the holidays but on this one, this is a holiday to say what your thankful for.
Plus,you get to eat till you drop
So Have a good Thanksgiving
I know it sounds repatitive but im really thankful for all the people that supported me in life even in the slightest moments,so thankyou and have a great Thanksgiving.


The Converter BooleanToLogic, LogicToBoolean, RealToLogic, and LogicTo Real components are not standard logic components. They were designed to easily convert from or to Boolean or Real valued signals. The LogicToX01, LogicToX01Z and LogicTo UX01 converters correspond to standard logic functions. They transform 9-valued logic to 3- or 4-valued logic.
| Name | Description |
|---|---|
| LogicToX01 | Conversion to X01 |
| LogicToX01Z | Conversion to X01Z |
| LogicToUX01 | Conversion to UX01 |
| BooleanToLogic | Boolean to Logic converter |
| LogicToBoolean | Logic to Boolean converter |
| RealToLogic | Real to Logic converter |
| LogicToReal | Logic to Real converter |
| LogicToXO1 | This model will be removed in future Modelica versions, use 'LogicToX01' instead! |
| LogicToXO1Z | This model will be removed in future Modelica versions, use 'LogicToX01Z' instead! |


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Keep smiling because you look so much better when you smile...Trust Me!
I finished my Secondary School Education in the Maryhill Girls High School last year and attained a mean grade of B Plain.
I decided to join a Programming School because:
Here are a list of Projests I have work on:
This is a File I made on a day I was very happy and Positive with life because of a decision i had made. I use the HTML language to code that document.
Hello world........This was my first Webpage I created.It was a very happy time for me since I felt i had achieved alot for that specific time. I coded it with the language HTML.


[t]Thank you![/t]
[t]Thank you, credits were succesfully pre-purchased. You will be contacted soon to clarify payment details.[/t]


12/07/16 - 01/10/16
-Treehouse Online Tech Accelerator/ Deliveroo cyclist: 3-month course to enhance my front and back-end application development know-how. Confident with Test and Behaviour Driven Development, UAT, Ruby on Rails, HTML5, CSS3, Java, MySQL, Python, PHP and Agile. Worked as a Deliveroo cyclist part-time over the course of study....more
10/10/15 - 05/07/16
-Business Analyst at PCFG (Contract): Brought in to assist with projects linked with banking license application...more
03/12/14 - 10/07/15
-System Administrator at Art Fund Art Fund (Contract): Brought in to perform 2nd and 3rd line support.
13/10/12 - 02/10/13
-Senior IT Engineer at General Electric: Providing VIP I.T Support (1st,2nd,3rd line support) to CEO, CTO, CFO. Led the development of a ticket management system. ...more
05/04/12 - 02/09/12
-Infrastructure Support Engineer at Groupe Aeroplan (AIMIA): To ensure sustained service levels above 95% satisfaction. Focus on quality of customer service, using ITIL framework to guide team in a structured and effective way. Contributed a business process improvement initiative saving company thousands of pounds in project overrun fees. Thanked personally by head of department.
14/02/2010- 05/01/2012
-Network Infrastructure Engineer at the Conservative Campaign Headquarters: Worked for the UK Prime Minister as a Network Infrastructure Engineer assisting Sonicwall firewall roll-out across the UK Project managed roll-out of Sonicwall Firewall Security to over 100 constituencies across the UK (Conservative Campaign Headquarters).
2013–2014
MSc in Digital Entrepreneurship -Pass
1998–2002
BSc in Business Information Technology -Pass
BUSINESS ANALYST AT PCFG (PRIVATE COMMERCIAL FINANCE GROUP)
MSC IN DIGITAL ENTREPRENEURSHIP AT GOLDSMITHS UNIVERTY
I have 2 years’ Web Development experience (Twitter Bootstrap HTML5, CSS3, MySQL, PHP, Java, Github, TDD, BDD, Python, Ruby on Rails). I have a portfolio of web projects including an online dating website, recruitment and CV website.
MSC IN DIGITAL ENTREPRENEURSHIP AT GOLDSMITHS UNIVERTY
Using divergent and convergent thinking techniques to improve quality of ideas at the front-end of innovation
MSC IN DIGITAL ENTREPRENEURSHIP AT GOLDSMITHS UNIVERTY
An in depth literature review on how companies use Agile principles and practices to innovate products and services.
GENERAL ELECTRIC (VIP I.T Support)
Developed a ticket management system for the purpose of improving customer service levels to VIP clientele (CEO level and Directors). This culminated in a 50% increase in team efficiency and much improved project reporting, logging, tracking, monitoring, and statistical report generation system. Increase in team efficiency, management reporting, stock provisioning, and resource planning.
CONSERVATIVE CAMPAIGN HEADQUARTERS (Network Infrastructure Analyst)
Worked for the UK Prime Minister as a Network Infrastructure Engineer assisting Sonicwall firewall roll-out across the UK.Here is a list of talks and workshops attended recently:
- Appathon (Android) Guinness World Records Challenge
- Entrepreneurial Developments in Consultancy: What makes a successful consultant
- Effective communication (Body language, better presentations)
- Art classes at the British Museum
- Design Thinking - An Emerging Problem Solving Approach
- Agile Project Management
- British Military Fitness
- Cycled from London to Brighton, London to Oxford, London to Ireland
- Competed in Amateur Thai Boxing competition
- Tennis, Table Tennis
- Wimbledon Ball Boy (1995)


'Stop running scared,' Nicola Sturgeon tells Johnson and Corbyn – video
by APScottish first minister Nicola Sturgeon has called for a second referendum on Scotland’s independence from the UK and urged Boris Johnson and Jeremy Corbyn to debate her on the matter. Sturgeon, whose Scottish National Party is running on a pro-independence, anti-Brexit platform, made her comments at a rally in Glasgow on Saturday


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Global app download reaches 29.6 billion in Q3 2019 - over 80% from top app makers - Gizchina.com
by Efe UdinRate this post
Recently, Sensor Tower announced the latest data as of Q3 2019. According to the research organization, the global app download hit 29.6 billion in Q3. Interestingly, the top 1% of app publishers account for 80% of app downloads while other publishers have about 6 billion downloads (20%). This bottom 99%, which equates to roughly 784,080 publishers, averaged approximately 7,650 downloads each during the quarter. This is less than one-thousandth of a percent of the downloads Facebook generated in the quarter (682 million).
In terms of games, the top 1% of publishers have over 9.1 billion downloads. This accounts for 82% of the total downloads of 11.1 billion. On average, each application has more than 8.4 million installations. The remaining 106,290 publishers account for 18%, and on average, each download was only about 18,000.
In terms of revenue, total revenue for the quarter is $22 billion, and 1,526 publishers received $20.5 billion in revenue. The remaining 151,056 publishers earned $1.5 billion, averaging only about $9,990 per publisher. According to Sensor Tower, over the years, from the perspective of application installation and revenue, the share of the top 1% of publishers does not change significantly.
The global app download record shows an interesting trend in the app development industry. Its more like you are in, work hard, enter the 1% and make everything. For those who just bring an app, no maintenance, upgrade or support, there is every probability that you just get a few downloads, make a few bucks and that’s it.


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Western Region NDC Communications Director robbed
Western Region Communications Director of the opposition National Democratic Congress (NDC) Richard Kirk Mensah has been robbed.
Mr. Kirk Mensah discovered on Sunday morning while preparing for church that his Tata Xenon double cabin pick-up has been broken into.
“I was inside preparing for church when a neighbour who was passing by my house called me to ask what has happened to my car. I got alarmed and rushed out only to discover that the glass of the driver side door has been smashed.”
“When I opened the door, I realized that some envelopes and documents had been taken away. On impulse, I opened the car bonnet and saw my car battery also gone,” he added.
He indicated that the night before the incident he had some important documents in the car but took them out.
Mr. Kirk Mensah is bemused that the incident could have happened when his car was parked few meters away from the gate to the residence of the Western Region Police Commander, DCOP Vincent Redeemer Dedjoe.
“I am really shocked because the car was parked about 50 meters away from the house of the regional police commander. This is serious. Are we safe?” he sounded frustrated.
When asked whether he suspected foul play, he parried that assumption away and said he will wait for the police to conclude their investigation.
At the time of filing this report, Mr Kirk Mensah was on his way to report the incident at the Sekondi Police Station.
Only last month the Takoradi Constituency Treasurer of the New Patriotic Party (NPP) Mark Ofori was shot dead in his house at Fijai in the Sekondi Takoradi Metropolis.
Since the kidnapping of four young girls in the Sekondi Takoradi Metropolis, residents have had cause to complain about the deteriorating state of security.
Apart from Mr. Ofori’s death, two other deadly attacks have been recorded.
In both instances, the victims were shot dead in their homes.


Special Notes on Nov. 19, 2018
| Date: | Nov 19, 2018 |
|---|---|
| Last Updated: | Jan 28, 2019 |
| Categories: | |
| Tags: |
Contents
Theory of Lovasz extension
The Lovasz extension is proposed to form an interpolating function for a sub-modular set function. In this article, we are discussing about the theory and the results from this paper: “The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks”,
To get more details, we have the further reading material about Lovasz-hinge optimization: “The Lovász Hinge: A Novel Convex Surrogate for Submodular Losses“
In application, we usually use binarized prediction to evaluate the efficiency or accuracy. Therefore, in general, our training loss function (usually to be the cross-entropy) is different from the evaluation. By applying such method, we could make the binary function transformed into an interpolated version, so that we could optimize the binary function directly.
To show how the interpolation works, we use a specific binary function as the example. Denote that $f(\mathbf{b}(x))$ accepts a prediction value $x \in (-2,~2)$, where $\mathbf{b}(x)$ outputs a binary vector like $[0,~1,~0]$. Then we could define that $f$ outputs the sum of $\mathbf{b}$, and $f$ is a binary function because its direct input is a binary vector.
If we calculate $\frac{\partial f}{\partial x}$, we would find that only in countable points there is the gradient (theoretically the gradient is impulse function), which means in most cases, the gradient of $f$ is 0. So such a binary function could not be optimized. However, if we interpolate the function by some specific methods, we could get a smooth line. Since the interpolated function is derivable at any point, we could optimize over the interpolated function.
| Compare the binary function and its interpolation |
|---|
| Compare the gradient from the binary function and its interpolation |
|---|
Here Lovasz extension is exactly such a tool that helps us interpolate a set function (whose input is binary) as a derivable function.
Problem statement
Jaccard index
Check here to see Jaccard index in Wikipedia.
In this topic, we are discussing how to interpolate the Jaccard index function properly. The Jaccard index (also called intersection over union (IoU)) is a set function. Denote Jaccard index as $J(\hat{\mathbf{y}},~\mathbf{y}^{\ast})$, where both $\hat{\mathbf{y}}$ and $\mathbf{y}^{\ast}$ are vectors with a length of $p$. Furthermore, $\hat{\mathbf{y}} \in \{0,~1\}^p$ is the prediction and $\mathbf{y}^{\ast} \in \{0,~1\}^p$ is the ground truth. Then we could define $J$ as
where $c$ represents a specific class in multi-class segmentation problem.
Note that if we define the mismatch as $\mathbf{M}_c = \{ \hat{\mathbf{y}}=c ,~ \mathbf{y}^{\ast} \neq c \} \cup \{ \hat{\mathbf{y}} \neq c ,~ \mathbf{y}^{\ast}=c \}$. Then the Jaccard index could also be represented as
We use the following example to show how we define Jaccard index which means the intersection area size divided by union area size.
| The example of definition of Jaccard index |
|---|
The requirements of the interpolation
To develop a derivable interpolation for Jaccard index, the designed function need to fulfil such conditions:
- In the whole data space, there is only countable points where the interpolation is not derivable.
- If we input binary vector to the interpolation, the result should be the same of that from the original set function.
In the following parts, we would show how to calculate Lovasz extension, and how Lovasz extension fulfil these requirements.
Sub-modular set function
Check here to see sub-modular set function in Wikipedia.
Here we define the sub-modular set function. Although there are multiple definitions to confirm whether a set function is sub-modular, here we only take one definition.
Definition 1: sub-modular set function
For a set function $\Delta:~ \{0,~1\}^p \rightarrow \mathbb{R}$, if it is sub-modular, then for any two input set $\mathbf{a}$ and $\mathbf{b}$, it has\begin{align} \label{fml:submodu1} \Delta (\mathbf{a}) + \Delta (\mathbf{b}) \geqslant \Delta (\mathbf{a} \cup \mathbf{b}) + \Delta (\mathbf{a} \cap \mathbf{b}). \end{align}
To understand what is sub-modular function, we could view the function value as the efficacy. A function is sub-modular means with the expansion of the input set, the increment of the gained efficacy declines.
Proof of the sub-modularity of complementary Jaccard index function
Here we would prove that the complementary Jaccard index function which is defined by $\eqref{fml:compjac}$ is a sub modular function.
According to the definition in Wikipedia, we could find a equivalent definition for $\eqref{fml:submodu1}$:
Definition 2: sub-modular set function (Alternative)
For every $X, Y \subseteq \Omega$ with $X \subseteq Y$ and every $\{ x\in \Omega \setminus Y\}$ we have that\begin{align} \label{fml:submodu2} \Delta(X\cup {x})-\Delta(X) \geqslant \Delta(Y\cup {x})-\Delta(Y). \end{align}
This definition could be illustrated in the figure below. We use $X$ to represent $\{\hat{\mathbf{y}}_X=c\}$, $Y$ to represent $\{\hat{\mathbf{y}}_Y=c\}$. Then we could infer that $X$ and $Y$ are two different predictions, and $X$ is a smaller set compared to $Y$. And we also show a set representing the ground truth (label), $\{\mathbf{y}^{\ast}=c\}$.
| The alternative definition for submodular set function |
|---|
We could find that there are two cases for the position of the new element $x$. While $X$ is smaller than $Y$, $x$ should be outside both $X$ and $Y$. In the first case, $x$ is inside the set $\{\mathbf{y}^{\ast}=c\}$. So according to the defintion, $\mathbf{M}_c(X \cup x) = \mathbf{M}_c(X)$ (also for $Y$). Since $\mathbf{M}_c$ does not change, we could get the equality in $\eqref{fml:submodu2}$.
In the other case, $x$ is outside $\{\mathbf{y}^{\ast}=c\}$, which means adding a new element $x$ would expand $\mathbf{M}_c$, and $\mathbf{M}_c(X \cup x) = \mathbf{M}_c(X) + 1$ (also for $Y$). Then we could get the function:
Because $\{\mathbf{y}^{\ast}=c\}$ is fixed, when $\mathbf{M}_c(X)$ expands, the complementary set $\overline{\mathbf{M}_c(X)}$ would reduce. Then we find that in the above equation, the numerator declines and denominator increases. Both of the changes would make $f(X)$ declines with the expansion of $X$. Thus we know $f(X) \geqslant f(Y)$, which means $\Delta(X\cup {x})-\Delta(X) \geqslant \Delta(Y\cup {x})-\Delta(Y)$.
Proof of Lovasz extension
Definition
For a specific class $c$, denote that we have $p$ samples in the whole set, then we could use $\mathbf{y} \in \mathbb{R}^p$ and $\mathbf{y}^{\ast} \in \{0,~1\}^p$ to represent the prediction and the ground truth respectively. Therefore, we could find that we use $\{0,~1\}$ to show whether a sample belongs to this class.
Denote the misfit function as $\mathbf{m}(\mathbf{y}, \mathbf{y}^{\ast}) \in \mathbb{R}^P$. This function should fulfil that for any element $i$,
Then we could calculate the misfit $\mathbf{m}$ for each element. Sort the misfit in descending order, we would get a resorted list of the original set, i.e. there is a set $\{\pi_1,~\pi_2,~\ldots,~\pi_p\}$ that ensures $m_{\pi_1} \geqslant m_{\pi_2} \geqslant \ldots \geqslant m_{\pi_p}$.
Denote the gradient function as $g$, and let the complementary Jaccard index function $\Delta_c(\mathbf{0}) = 0$, then we have
Therefore we have
Definition 3: Lovasz extension
Define the Lovasz extension for $\Delta_c : \{0,~1\}^p \rightarrow \mathbb{R}$ is $\bar{\Delta}_c : \mathbb{R}^p \rightarrow \mathbb{R}$, then we have\begin{align} \label{fml:lovasz} \bar{\Delta}_c = \sum_{i=1}^p m_{\pi_i} g_{\pi_i}. \end{align}
Proof for requirement 1
Then let us check why this extension is the interpolation for the original set function. First, let us check the first requirement. For any index $i$, we could find a $\pi_j$ that has $i = \pi_j$. Then we could find that
Apparently, according to $\eqref{fml:gradientlovasz}$, $g_{\pi_j}$ is a linear combination of two set function. Because each set function has no gradient, $\frac{ \partial g_{\pi_j} }{ \partial y_{\pi_j} } = 0$. Then we know that
First, $g_{\pi_j}$ is a real value function, and $m_{\pi_j}$ is a pre-defined misfit function, which is derivable at any point, so we could find that $\bar{\Delta}_c$ is derivable at any point except some countable points. Note that at those countable points, $g_{\pi_j}$ changes, so $\frac{ \partial g_{\pi_j} }{ \partial y_{\pi_j} } \rightarrow \infty$. Although in some cases $\bar{\Delta}_c$ is not derivable, these cases are countable, so the requirement 1 is still fulfilled.
Proof for requirement 2
Now let us check the second requirement. Because $\{m_{\pi_i} | m_{\pi_i} \in \{0,~1\}\}$ is sorted in the descending order, we could know that $\exists l$, for any $i \leqslant l$, we have $m_{\pi_i} = 1$. For any $i > l$, we have $m_{\pi_i} = 0$. Therefore, $\eqref{fml:lovasz}$ could be rewrited as
&= \Delta_c ({\pi_1,~\pi_2,~\ldots,~\pi_l}) - \Delta_c(\mathbf{0}) = \Delta_c ({\pi_1,~\pi_2,~\ldots,~\pi_l}). \end{aligned} \end{equation}
For any $i > l$, since $m_{\pi_i}=0$, which means $y_i = y^{\ast}_i$, we could know that for any set $X$, $\mathbf{M}_c (X \cup \pi_i) = \mathbf{M}_c (X)$. Because both $\mathbf{M}_c$ and $\{\mathbf{y}^{\ast}=c\}$ does not change, there would be $\Delta_c (X \cup \pi_i) = \Delta_c (X)$. Hence we have
The left side of this equation is the original set function, and the right side of this equation is the Lovasz extension. Because they equal to each other, we could verify that the requirement 2 is fulfilled.
Improved algorithm for Jaccard index
The equation $\eqref{fml:lovasz}$ is enough for us to calculate the interpolation for the set function. But in many cases, this method is still not practicable. To be specific, when $i > l$ in $\eqref{fml:rq2prof}$, we could find that the Lovasz extension would not be influenced by the samples which match the labels. In other words, there would be some cases that Lovasz extension does not have any gradient, which makes the loss function unable to be optimized.
Here we show the improved algorithm for solving this problem.
| Improved algorithm for calculating Lovasz extension gradient |
|---|
There are two ideas lying in this algorithm:
The values of errors $\mathbf{m}$ are not used to calculate the gradient, but they could exert their influence to the gradient (cumulative sums) by adjusting the order of the foreground pixels.
For any $i$, if the cumulative sum for $\delta_{\pi_i}$ does not increase, then the cumulative sum for $1 - \delta_{\pi_i}$ would increase. So for each $i$, the value in the gradient vector would change.
Apparently, because in this algorithm, we do not change the calculation for $\eqref{fml:lovasz}$, the first requirement is still fulfilled. However, since we change the method for calculating the gradient, we need to prove that the second requirement still holds.
Inspecting on the algorithm, we could find that in Step 5, we are calculating the equivalent $\Delta_c$. After this step, we are calculating the gradient for $\bar{\Delta}_c$ like what we do in $\eqref{fml:gradientlovasz}$. Denote the matrix $\mathbf{S}$ as
Then we can define the cumulative sum for a vector $\mathbf{x}$ as $\mathbf{Sx}$. According to the algorithm, we could rewrite the equivalent $\Delta_c$ as
To prove the requirement 2, we could still assume that the predicted value is 0 or 1. Then we could compare this rewritten version to $\eqref{fml:compjac}$ in two cases. Recall the definition in requirement 2, we divide the sorted vector $\boldsymbol{\delta}_{\boldsymbol{\pi}}$ into two parts:
$i \leqslant l$, which means for each $i$, $m_i = 1$. In this case, every time $i$ increases, because $m_i = 1$, $|\mathbf{M}_c|$ increases by 1, and the cumulative sum $\mathbf{S} \boldsymbol{1}$ would also increase by 1. So we have $|\mathbf{M}_c|=\mathbf{S} \boldsymbol{1}$. For the denominator, because $m_i = 1$ always holds, only when $\delta_{\pi_i} = 0$, which means the label shows 0, both $\mathbf{S} (1 - \boldsymbol{\delta})$ and $\left| \{ \mathbf{y}^{\ast}=c \} \cup \mathbf{M}_c \right|$ increases. Therefore, $\sum(\boldsymbol{\delta}) + \mathbf{S} (1 - \boldsymbol{\delta}) = \left| \{ \mathbf{y}^{\ast}=c \} \cup \mathbf{M}_c \right|$. Thus $\Delta_c = \Delta_{c\mathrm{Alg}}$.
$i > l$, which means for each $i$, $m_i = 0$, since $\mathbf{M}_c$ does not change, $\Delta_c$ would also be unchanged, which means the gradient would be 0 according to such calculation. However, for $\Delta_{c\mathrm{Alg}}$, the numerator would keep increasing for each $i$. And for the denominator, $\mathbf{S} (1 - \boldsymbol{\delta})$ would also increase when $\delta_{\pi_i}=0$. Note that because the denominator is always larger than numerator, $\Delta_{c\mathrm{Alg}}$ would keep increasing for each $i$, which ensures that $\Delta_c$ is still submodular.
Then, we would find that when $m_i = 1$, $\Delta_c = \Delta_{c\mathrm{Alg}}$, which means the gradients are also the same. When $m_i = 0$, although $\Delta_c \neq \Delta_{c\mathrm{Alg}}$, $\bar{\Delta}_c$ does not be influenced, because we can formulate the Lovasz extension for both methods (direct calculating and using algorithm):
Examples
Denote that labels $\boldsymbol{\delta} = \{ \mathbf{y}^{\ast}=c \}$ and logits $\mathbf{p} = \{ \hat{\mathbf{y}}=c \}$, then we give such an example:
| $\boldsymbol{\delta}$ | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 0 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| $\mathbf{p}$ | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 1 |
Example of deriving a Lovasz extension with the plain theory
Because the predicted value $p_i \in \{0,~1\}$, there is $\mathbf{M}_c=\mathbf{m}$, then we have:
| $\boldsymbol{\delta}$ | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 0 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| $\mathbf{p}$ | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 1 |
| $\mathbf{M}_c$ | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 |
| $\pi_i$ | 6 | 1 | 7 | 2 | 8 | 3 | 9 | 10 | 11 | 4 | 5 |
The last row of the table shows the index of sorted vectors. In the following table, each time $i$ increases, a new element $\pi_i$ would be added to $\mathbf{M}_c$. Note that $\boldsymbol{\delta}$ does not change with $i$:
| $\pi_i$ | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| $|\mathbf{M}_c|$ | 1 | 2 | 3 | 4 | 5 | 5 | 5 | 5 | 5 | 5 | 5 |
| $|\boldsymbol{\delta} \cup \mathbf{M}_c|$ | 7 | 7 | 8 | 8 | 9 | 9 | 9 | 9 | 9 | 9 | 9 |
| $\Delta_c$ | 1/7 | 2/7 | 3/8 | 4/8 | 5/9 | 5/9 | 5/9 | 5/9 | 5/9 | 5/9 | 5/9 |
| $\mathbf{m}$ | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| $\bar{\Delta}_c$ | 1/7 | 2/7 | 3/8 | 4/8 | 5/9 | 5/9 | 5/9 | 5/9 | 5/9 | 5/9 | 5/9 |
Example of deriving a Lovasz extension with the improved algorithm
First, calculate the order of sorted vectors:
| $\boldsymbol{\delta}$ | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 0 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| $1-\boldsymbol{\delta}$ | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 |
| $\mathbf{p}$ | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 1 |
| $\mathbf{m}$ | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 |
| $\pi_i$ | 6 | 1 | 7 | 2 | 8 | 3 | 9 | 10 | 11 | 4 | 5 |
When using algorithm, the numerator would increases for each $\pi_i$ and the denominator would increase when $\delta_{\pi_i}=0$. Because $\sum (\boldsymbol{\delta})=6$, we have
| $\pi_i$ | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| $1-\boldsymbol{\delta}$ | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 |
| $\mathbf{S} \boldsymbol{1}$ | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
| $\sum(\boldsymbol{\delta}) + \mathbf{S} (1 - \boldsymbol{\delta})$ | 7 | 7 | 8 | 8 | 9 | 9 | 10 | 10 | 11 | 11 | 11 |
| $\Delta_{c\mathrm{Alg}}$ | 1/7 | 2/7 | 3/8 | 4/8 | 5/9 | 6/9 | 7/10 | 8/10 | 9/11 | 10/11 | 11/11 |
| $\mathbf{m}$ | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| $\bar{\Delta}_c$ | 1/7 | 2/7 | 3/8 | 4/8 | 5/9 | 5/9 | 5/9 | 5/9 | 5/9 | 5/9 | 5/9 |
Misfit function
In the above discussion, we need to calculate misfit (error) vector $\mathbf{m}$. The element of misfit need to satisfy such a condition: $m_i \in [0,~1]$. We could use different functions to calculate the misfit as long as the function could fulfil the above condition. Denoting $\hat{\mathbf{y}}$ as the logits and $\mathbf{y}^{\ast}$ as the labels, here we introduce some misfit functions:
Linear/L1: $\mathbf{m} = \left| \hat{\mathbf{y}} - \mathbf{y}^{\ast} \right|$.
Trigonometric: $\mathbf{m} =1 - \cos \left(\frac{\pi}{2} \left( \hat{\mathbf{y}} - \mathbf{y}^{\ast} \right) \right)$.
Square/L2: $\mathbf{m} = \left( \hat{\mathbf{y}} - \mathbf{y}^{\ast} \right)^2$.
Approximate cross entropy: $\mathbf{m} =- \mathbf{y}^{\ast} \times \log_{10}(0.1 + \hat{\mathbf{y}}) - (1 - \mathbf{y}^{\ast}) \times \log_{10}(1.1 - \hat{\mathbf{y}})$.
Note that the last function has small derivation, which does not influence the optimization.
Testing results
Check the validity of interpolation
In this example, we set the labels and logits as:
| labels | y* | 0 | 1 | 1 | 0 |
|---|---|---|---|---|---|
| logits | y | 0 | 1 | 0 | 1 |
which means we vary the label and logit of the first sample and record the value of the Jaccard index. The function $J(y,~y^{\ast})$ is plotted in the figure below:
In this figure, we use y and y* as the variable parameter and the value of the function as the z axis.
Using different functions to calculate the misfit, we may get different features for the interpolation. For example, if we use linear function to calculate the misfit, we would get:
In the above figure, we compare the results from the algorithm and the direct computing. We would find that although both methods could fulfil the two requirements, we could get gradient every where if we use the algorithm version. However, if we compute the interpolation by the theory directly, in some cases the gradient would vanish.
This figure shows the results when we use the cross entropy as the misfit. Since the cross entropy function makes the optimization for small value more important, it would be more capable in practical problems.
Handle specific cases
In these examples, we fix the labels and vary the logits to examine the interpolation with the cross entropy misfit. Denote that the parameters are y1 and y2, then we have
| labels | 1 | 1 |
|---|---|---|
| logits | y1 | y2 |
The result is
In the second example, we use different labels, and we have
| labels | 0 | 0 |
|---|---|---|
| logits | y1 | y2 |
The result is
Reproduce the results from the paper
In the following parts, we are trying to reproduce the results in the original paper. And we have succeeded in realizing the Lovasz-softmax and Lovasz-hinge loss functions with the configuration in the paper. To learn more about the two examples, check the paper to see the configurations.
Lovasz-softmax loss
Lovasz-hinge loss
Source codes
The source code of this inspection has been uploaded to Github, to learn more about the codes, please check the following link:


Marianne Corvellec has a PhD in Physics from Ecole Normale Supérieure de Lyon (Lyon, France). She left academia in 2013 to work in the startup scene. She is now a scientific software developer with Plotly.


ARWeb Repo 🪁
a repository by
ARWeb
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Packages
RespringModule
(free)
CC module for simple respring.
See package
RebootModule
(free)
A CC module to simply reboot your device.
See package
JetsamFix
(free)
A fork of StopCrashingPls, that aims to further workaround fix Jetsam limit related crashes.
See package
Links


Hey there - My name's Far and I've been working on content, products and UX for a good 8 years now. Straight up and mellow, don't like politics in work environments (though they always seem to be around in some guise) and done the whole agile, lean, scrum, kanban buzzword extravaganza. Fun fact - memorizing those text books doesn't mean you can apply that stuff anywhere. For specific skills - hit the button..or keep scrolling for work experience
Stuff!Oct 2016 - PresentCheck it out
July 2016 - PresentVisit Songkick
Jan 2014 - July 2016Visit Time Out
Jan 2013 - Dec 2013Visit Experian
Sep 2008 - Dec 2012Visit Monster
July 2008 - Sep 2008Visit Sega
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{% extends 'layout-side-nav.html' %} {% import '_vars-the-bureau.html' as vars with context %} {% set active_nav_id = 'about-director' %} {% set breadcrumb_items = vars.breadcrumb_items %} {% block title -%} About Richard Cordray | Consumer Financial Protection Bureau {%- endblock %} {% block desc -%} Richard Cordray serves as the first director of the Consumer Financial Protection Bureau. He previously led the Bureau’s Office of Enforcement. {%- endblock %} {% block content_main_modifiers -%} {{ super() }} content__flush-bottom {%- endblock %} {% block content_main %} {% import 'molecules/social-media.html' as social_media %}
Richard Cordray serves as the first director of the Consumer Financial Protection Bureau. He previously led the Bureau’s Office of Enforcement.
Prior to joining the Bureau, Mr. Cordray served on the front lines of consumer protection as Ohio’s Attorney General. Mr. Cordray recovered more than $2 billion for Ohio’s retirees, investors, and business owners and took major steps to help protect its consumers from fraudulent foreclosures and financial predators. In 2010, his office responded to a record number of consumer complaints, but Mr. Cordray went further and opened that process for the first time to small businesses and nonprofit organizations to ensure protections for even more Ohioans. To recognize his work on behalf of consumers as Ohio Attorney General, the Better Business Bureau presented Mr. Cordray with an award for promoting an ethical marketplace.
Mr. Cordray also served as Ohio Treasurer and Franklin County Treasurer, two elected positions in which he led state and county banking, investment, debt, and financing activities. As Ohio Treasurer, he resurrected a defunct economic development program that provides low-interest loan assistance to small businesses to create jobs, re-launched the original concept as GrowNOW, and pumped hundreds of millions of dollars into access for credit to small businesses. Mr. Cordray simultaneously created a Bankers Advisory Council to share ideas about the program with community bankers across Ohio.
Earlier in his career, Mr. Cordray was an adjunct professor at the Ohio State University College of Law, served as a state representative for the 33rd Ohio House District, was the first solicitor general in Ohio’s history, and was a sole practitioner and of counsel to Kirkland & Ellis. Mr. Cordray has argued seven cases before the United States Supreme Court, including by special appointment of both the Clinton and Bush Justice Departments. He is a graduate of Michigan State University, Oxford University, and the University of Chicago Law School. Mr. Cordray was editor-in-chief of the University of Chicago Law Review and later clerked for U.S. Supreme Court Justices Byron White and Anthony Kennedy.
Mr. Cordray lives in Grove City, Ohio with his wife Peggy – a professor at Capital University Law School in Columbus – and twin children Danny and Holly.
{{ social_media.render() }}


"Triangle is the only men's STEM fraternity experience, open to engineers, architects and scientists. We provide a social experience with a focus on academic success and professional preparation."
Welcome to our rush page! Anyone interested in Triangle is invited to our rush events. You are always welcome and encouraged to invite your friends as well. Any of our events are great to start with and don't worry if you've missed or will miss an event.
Stay tuned for ____ QUARTER AND YEAR ______ Rush! Please contact us for more information.
Zeta Class | Former Recruitment Vice President | Triathlons, Basketball, and more + Tabletop Games
(503) 332-5579
Rushing: To join a fraternity, you must go through a “rush” period where you attend events and get to know the members of the fraternity. Our primary rush period is typically open fall quarter of each year, but you are welcome to contact us throughout the year. To begin rushing, come to one of the events on the calendar above and introduce yourself! Triangle requires rushees to attend at least 3 events in a quarter and expects them to make an active effort to speak to as many current active members they meet as possible. Please also note that Triangle is exclusive to STEM and architecture majors. Triangle rushees are also required to attend a group interview, where active members will ask questions to get to know them better.
The Bid: If we feel like you share the values of Triangle and that you will be a good fit for the culture of our group, we'll offer you a bid a few days after your interview. If you are offered a bid you have the opportunity to join the fraternity as a "pledge" by signing your bid and returning it to us. Whether a bid is offered is determined a vote by all active members. Note: New members are required to have a GPA of 3.0 or higher to join (excluding first quarter freshmen).
Pledging: After
you accept your bid, you will begin the pledging
process. Pledging lasts one quarter, during which you will get to know
the active members even better and learn how the fraternity operates by attending
new member education sessions.
Have more questions?
Email our Recruitment Vice President!
Recruitment VP | Jacob Faulk
recruitment@triangleuw.com


Setting up the Base Model¶
Geography¶
Deciding the study area and the analysis years are critical to the initial development of any model, let alone a strategic model. The initial decision is to determine the model geography, or study area. Users set the model geography to match reporting regions and desired policy sensitivity.
VisionEval models all use the same nested geographic units, which are designed to largely align with US Census tracts and MPO boundaries. VE-RSPM operates with a zonal geography, using zones or districts at the most granular level. Note that the VE-State geography is similar to VE-RSPM, albeit at different scales. Essentially, VE-State is the state version of VE-RSPM with the primary zone structure being typically a county.
The VisionEval geography is comprised of several levels, detailed below.
Region¶
The region is the entire model study area, which varies by model. For example, for VE-State the region is the state. For VE-RSPM, the region can be as small as the MPO boundary or may add additional areas where the region might expand to accommodate growth by the future model year. Note that when choosing a region for VE-RSPM, the area should break along Census boundaries (typically a metropolitan classification). Large-scale characteristics that do not vary across the region are specified at the region level. Examples include fuel prices and the carbon intensities of fuels.
Azones¶
Azones are large subdivisions of the region. In VE-State, counties are most typically used as the Azone level. Azones in VE-RSPM are optional and should be used to distinguish areas which may have different land use and transportation policies. For this reason, metropolitan boundaries are most commonly used as the Azone level in VE-RSPM. Note that in VE-RSPM, Azones should be relatively large, containing populations that are similar in size to those of counties or Census Public Use Microdata Areas (PUMA) (e.g., population greater than 50,000). * Azones are used to represent population and economic characteristics that vary across the region such as demographic forecasts of persons by age group and average per capita income. * Azones are the only level of geography that is required to represent actual geographic areas and may not be simulated.
Bzones¶
Bzones are subdivisions of Azones and the smallest unit of geography in VisionEval. Bzones are used to represent neighborhood characteristics and policies that may be applied differently by neighborhood, such as population density. Bzones may correspond to actual geographic areas or may be simulated.
In VE-RSPM, Bzones are ordinarily defined according to Census Tract boundaries (or Census Block Groups depending on data availability).
In VE-State and VE-RPAT, Bzones are simulated. Bzone simulation greatly reduces model data requirements while still enabling the modeling of land-use-related policies and the effects of land use on various aspects of travel behavior. In rural areas, Bzones can be used to distinguish small cities from unincorporated areas.
Mareas¶
Mareas are collections of Azones associated with an urbanized area either because a portion of the urbanized area is located in the Azone or because a substantial proportion of the workers residing in the Azone work at jobs located in the urbanized area. Metropolitan models typically only have one assigned Marea whereas state models may have several. The model system requires that each Azone may be associated with only one Marea.
It is also required that all Azones be associated with an Marea. A special Marea named ‘None’ is used to apply to Azones that are not associated with any urbanized area. Mareas are used to specify and model urbanized area transportation characteristics such as overall transportation supply (transit, highways) and congestion. They are also used to specify large scale land-use-related characteristics and policies in models that use Bzone synthesis. Mareas are typically equivalent to a Census Urbanized Area.
Geographical relationships for a model are described in the “geography.csv” file contained in the “defs” directory. This file tabulates the names of each geographic unit (except for Region) and the relationships between them. Each row shows a unique relationship. Where a unit of geography is not explicitly defined (i.e., it will be simulated), “NA” values are placed in the table. It should be noted that there are no naming conventions for individual zones. The user is free to choose what conventions they will use.
Model Analysis Years¶
Overview text
Model Years¶
VisionEval models can be used to output any number of years between the model base year and future year. Each model year takes time to run so typically only the following two or three years are assigned:
Base year – starting year of the model, basis for data collection, calibration, and other runs
Reference year – a year further in the future than the base year with data available to validate the model
Future year – ending year of the model, longer time frames enable consideration of more visionary changes
As an example, the following years were modeled in the RSPM for strategic assessments in Oregon:
2005 – back-cast to match the MPO GHG reduction target base year.
2010 - Base Year – basis for data collection, calibration, and other runs.
2035 – the date associated with MPO GHG reduction targets and is near the RTP forecast date.
2050 – the date associated with statutory GHG reduction goals in the STS. The longer time frame enables consideration of more visionary changes that will take longer than the RTP time frame to occur


TABLE OF INTERACTIONS WITH
XANTHINE OXYDASE INHIBITORS
From the French ANSM drug interactions document of September 2016, p. 120
XANTHINE OXYDASE INHIBITORS CLASS CODE: M04AA0 |
ANTIPURINES CLASS CODE: ANTIPUR |
Possibly serious medullary insufficiency |
CONTRAINDICATION |
XANTHINE OXYDASE INHIBITORS CLASS CODE: M04AA0 |
THEOPHYLLINE (AND, BY EXTRAPOLATION, AMINOPHYLLINE) CLASS CODE: R03DA |
With large doses of the inhibitor, increase of the plasma concentrations of theophylline due to inhibition of its metabolism. |
Precaution for use Clinical monitoring and testing of the theohyllinemia up to two to three weeks after the beginning of the treatment with the inhibitor; if there is reason for it, adjustment of the dosage during the treatment with these substances together. |


Create a grid adding widgets from an Array (not from HTML) using add_widget method. Widget position (col, row) not specified.
add_widget


Handout
If you want to try and test PVS-Studio keep reading this document.
Your goal with this exercises will be to try and solve some code problems with the help of PVS-Studio, if you need the program
go to the "How to use" tab.
As soon as you open the handout, if you try and compile it, you will be presented with a black screen like the image below without music or textures, that's a problem!
At the end of this handout you should have something like the image below, of course, if you can't manage to complete all the TODOs, you can always check the handout solution, but it's highly recommended that you at least try and do the handout TODOs!
Where to find the handout and the solution
• You can find the handout and the solution, please head to my releases page here
• If you want the link directly to the handout so you can start with the TODOs click here
• If, instead, you want the link directly to the solution, click here
TODO 1
• Realize a analysis for a single file to see if there's any potential errors. (Don't worry if you don't find any, you're lucky then)
• Realize a analysis for the whole solution to see if there's any potential errors.
TODO 2
• Supress the 2 warnings that warn you about "Initializing variables".
• (Optional) Check if you have correctly supressed the messages in the "supress messagess..." option
TODO 3
• Solve the problems present in the Audio.cpp file.
TODO 4
• Solve the problems present in the Scene.cpp file.
TODO 5
• Solve the problems present in the Player.cpp file.
TODO 6
• Check if the game works, you should hear a music and see the map with the character in it. If you made it to here without using the solution, congratulations!


{{username}}'s Profile
Here you can review all your latest submissions and check your personal information.
You can also submit your latest imagery and observations.
Here you can review all your latest submissions and check your personal information.
You can also submit your latest imagery and observations.


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Create a Proposal
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GameClock Tournament 2009 Zombie Process Independent Development Project
My first iPhone project. A clock suitable for timing various two player board games, such as Chess and Go. Includes precise implementations of the most commonly used timing methods. Distributed as a basic free version with only sudden death mode, with in-app purchase for the rest of the timing modes.
The primary implementation concern for this app was battery life, as it is meant to be used for hours at a time without interruption. It proved necessary to implement my own compositing code to draw the digits on the timer, as allowing UIKit to render the text each second was costly. Much of the code for this app is also used in Tengen Go.


Pip-Services-Aws-Dotnet
Here is a list of all documented class members with links to the class documentation for each member:
- _ -
_connectionResolver :
PipServices3.Aws.Connect.AwsConnectionResolver
_credentialResolver :
PipServices3.Aws.Connect.AwsConnectionResolver
- a -
AccessId :
PipServices3.Aws.Connect.AwsConnectionParams
AccessKey :
PipServices3.Aws.Connect.AwsConnectionParams
Account :
PipServices3.Aws.Connect.AwsConnectionParams
Arn :
PipServices3.Aws.Connect.AwsConnectionParams
AwsConnectionParams() :
PipServices3.Aws.Connect.AwsConnectionParams
- c -
CloseAsync() :
PipServices3.Aws.Count.CloudWatchCounters
,
PipServices3.Aws.Log.CloudWatchLogger
CloudWatchCounters() :
PipServices3.Aws.Count.CloudWatchCounters
CloudWatchLogger() :
PipServices3.Aws.Log.CloudWatchLogger
Configure() :
PipServices3.Aws.Connect.AwsConnectionResolver
,
PipServices3.Aws.Count.CloudWatchCounters
,
PipServices3.Aws.Log.CloudWatchLogger
- d -
DefaultAwsFactory() :
PipServices3.Aws.Build.DefaultAwsFactory
- f -
FromConfig() :
PipServices3.Aws.Connect.AwsConnectionParams
FromString() :
PipServices3.Aws.Connect.AwsConnectionParams
- i -
IsOpen() :
PipServices3.Aws.Count.CloudWatchCounters
,
PipServices3.Aws.Log.CloudWatchLogger
- m -
MergeConfigs() :
PipServices3.Aws.Connect.AwsConnectionParams
- o -
OpenAsync() :
PipServices3.Aws.Count.CloudWatchCounters
,
PipServices3.Aws.Log.CloudWatchLogger
- p -
Partition :
PipServices3.Aws.Connect.AwsConnectionParams
- r -
Region :
PipServices3.Aws.Connect.AwsConnectionParams
ResolveAsync() :
PipServices3.Aws.Connect.AwsConnectionResolver
Resource :
PipServices3.Aws.Connect.AwsConnectionParams
ResourceType :
PipServices3.Aws.Connect.AwsConnectionParams
- s -
Save() :
PipServices3.Aws.Count.CloudWatchCounters
,
PipServices3.Aws.Log.CloudWatchLogger
Service :
PipServices3.Aws.Connect.AwsConnectionParams
SetReferences() :
PipServices3.Aws.Connect.AwsConnectionResolver
,
PipServices3.Aws.Count.CloudWatchCounters
,
PipServices3.Aws.Log.CloudWatchLogger
- v -
Validate() :
PipServices3.Aws.Connect.AwsConnectionParams
- w -
Write() :
PipServices3.Aws.Log.CloudWatchLogger
Generated by
1.8.14


Homing Geo-distributed Network Services – Network service providers (NSPs) typically run geo-distributed network infrastructures to
better serve their clients’ needs.
Network services offered by NSPs consist of virtual network functions (VNFs) and policies of how such network functions should be deployed.
In addition to the geo-distributed nature of the infrastructure, the ever evolving requirements impose an additional challenge.
Thus, we need an extensible framework that can (and should) accommodate new requirements on the fly. We design such framework that can flexibly
serve complex homing requests and formulate the homing problem using binary quadratic programming (BQP) and implement an incremental solution to the problem.
Previous Projects
Scalable Search Service for Geo-distributed Systems – Finding nodes which match certain criteria, based on potentially highly dynamic information, is a critical need
in many distributed systems, ranging from cloud management, to network service deployments, to emerging IoT applications.
With the increasing scale, dynamicity, and richness of data, existing systems, which typically implement a custom solution
based around message queues where nodes push status to a central database, are ill-suited for this purpose. To this end,
we present FOCUS, a general and scalable service which easily integrates into existing and emerging systems to provide this
fundamental capability. FOCUS utilizes a gossip-based protocol for nodes to organize into groups based on attributes and
current value. With this approach, nodes need not synchronize with a central database, and instead the FOCUS service only
needs to query the sub-set of nodes which have the potential to positively match a given query. We show FOCUS’s flexibility
through an operational example of complex querying for Virtual Network Functions instantiation over cloud sites, and illustrate
its ease of integration by replacing the push-based approach in OpenStack’s placement service. Our evaluation demonstrates a
5-15x reduction in bandwidth consumption and an ability to scale much better than existing approaches.
Publications: FOCUS (IEEE ICDCS’19) and NodeFinder (Usenix HotCloud’18)
Automated Network Management using Natural Language – In this work, we introduce Natural Language Processing to network management by leveraging the capabilities of
Natural Language Processing tools, such as speech recognition and text parsing, to extract useful information to build network
tasks. We propose an intermediate network-agnostic layer that acts as the medium between natural language input (spoken
or written) and different network implementations. We have leveraged the programmability that Software Defined Networks
(SDN) offers to build a prototype tool that takes natural language text as an input and uses it to build abstract tasks. Such tasks
are then passed to a network controller to be performed in real time. To our knowledge, this is the first work that provides such
interface between network users (in the form of natural language) and different network systems. This work is along the lines of the now trending theme of “intent-based networking” (IBN).
Publications: HeyNet: (IEEE INFOCOM WORKSHOPS’17)
Sateless Network Functions – In this work, we propose Stateless Network Functions, a new architecture for network functions virtualization,
where we decouple the existing design of network functions into a stateless processing component along with
a data store layer. In breaking the tight coupling, we enable a more elastic and resilient network function infrastructure. Our StatelessNF processing instances are
architected around efficient pipelines utilizing DPDK for high performance network I/O, packaged as Docker
containers for easy deployment, and a data store interface optimized based on the expected request patterns to efficiently access a RAMCloud-based data store.
A network-wide orchestrator monitors the instances for load and failure, manages instances to scale and provide
resilience, and leverages an OpenFlow-based network to direct traffic to instances. We implemented three example network functions (network address translator, firewall, and load balancer). Our evaluation shows (i) we are able to reach a throughput of 10Gbit/sec, with an added
latency overhead of between 100µs and 500µs, (ii) we are able to have a failover which does not disrupt ongoing traffic, and (iii) when scaling out and scaling in we
are able to match the ideal performance.
Publications: StatelessNF: (Usenix NSDI’17)


|
| Value |
Description |
Notes |
|
| 01 |
WCO Harmonized System |
World Customs Organization Harmonized Commodity Coding and Description System. Use 6 or 8 digits, without punctuation |
| 02 |
UNSPSC |
UN Standard Product and Service Classification |
| 03 |
HMRC |
UK Revenue and Customs classifications, based on the Harmonized System |
| 04 |
Warenverzeichnis für die Außenhandelsstatistik |
German export trade classification, based on the Harmonised System |
| 05 |
TARIC |
EU TARIC codes, an extended version of the Harmonized System |
| 06 |
Fondsgroep |
Centraal Boekhuis free classification field for publishers |
| 07 |
Sender’s product category |
A product category (not a subject classification) assigned by the sender |
| 08 |
GAPP Product Class |
Product classification maintained by the Chinese General Administration of Press and Publication (http://www.gapp.gov.cn) |
| 09 |
CPA |
Statistical Classification of Products by Activity in the European Economic Community, see http://ec.europa.eu/eurostat/ramon/nomenclatures/index.cfm?TargetUrl=LST_NOM_DTL&StrNom=CPA_2008.
Up to six digits, with one or two periods. For example, printed children’s books are ‘58.11.13’, but the periods are normally
ommited in ONIX
|
| 10 |
NCM |
Mercosur/Mercosul Common Nomenclature, based on the Harmonised System |
| 11 |
CPV |
Common Procurement Vocabulary, uses to describe requirements for tender for public tendering and procurement within the EU.
Code is a nine digit number (including the check digit). See http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=URISERV:l22008
|
| 50 |
Electre genre |
Typologie de marché géré par Electre (Market segment code maintained by Electre) |
|


|
NumCpp
2.11.0
A Templatized Header Only C++ Implementation of the Python NumPy Library
|
Data Structures | |
| class | Poly1d |
Functions | |
| template<typename dtype > | |
| NdArray< double > | chebyshev_t (uint32 n, const NdArray< dtype > &inArrayX) |
| template<typename dtype > | |
| double | chebyshev_t (uint32 n, dtype x) |
| template<typename dtype > | |
| NdArray< double > | chebyshev_u (uint32 n, const NdArray< dtype > &inArrayX) |
| template<typename dtype > | |
| double | chebyshev_u (uint32 n, dtype x) |
| template<typename dtype > | |
| NdArray< double > | hermite (uint32 n, const NdArray< dtype > &inArrayX) |
| template<typename dtype > | |
| double | hermite (uint32 n, dtype x) |
| template<typename dtype > | |
| NdArray< double > | laguerre (uint32 n, const NdArray< dtype > &inArrayX) |
| template<typename dtype > | |
| double | laguerre (uint32 n, dtype x) |
| template<typename dtype > | |
| NdArray< double > | laguerre (uint32 n, uint32 m, const NdArray< dtype > &inArrayX) |
| template<typename dtype > | |
| double | laguerre (uint32 n, uint32 m, dtype x) |
| template<typename dtype > | |
| NdArray< double > | legendre_p (uint32 m, uint32 n, const NdArray< dtype > &inArrayX) |
| template<typename dtype > | |
| double | legendre_p (uint32 m, uint32 n, dtype x) |
| template<typename dtype > | |
| NdArray< double > | legendre_p (uint32 n, const NdArray< dtype > &inArrayX) |
| template<typename dtype > | |
| double | legendre_p (uint32 n, dtype x) |
| template<typename dtype > | |
| NdArray< double > | legendre_q (int32 n, const NdArray< dtype > &inArrayX) |
| template<typename dtype > | |
| double | legendre_q (int32 n, dtype x) |
| template<typename dtype1 , typename dtype2 > | |
| std::complex< double > | spherical_harmonic (uint32 n, int32 m, dtype1 theta, dtype2 phi) |
| template<typename dtype1 , typename dtype2 > | |
| double | spherical_harmonic_i (uint32 n, int32 m, dtype1 theta, dtype2 phi) |
| template<typename dtype1 , typename dtype2 > | |
| double | spherical_harmonic_r (uint32 n, int32 m, dtype1 theta, dtype2 phi) |
| NdArray<double> nc::polynomial::chebyshev_t | ( | uint32 | n, |
| const NdArray< dtype > & | inArrayX | ||
| ) |
Chebyshev Polynomial of the first kind. NOTE: Use of this function requires using the Boost includes.
| n | the order of the chebyshev polynomial |
| inArrayX | the input value |
| double nc::polynomial::chebyshev_t | ( | uint32 | n, |
| dtype | x | ||
| ) |
Chebyshev Polynomial of the first kind. NOTE: Use of this function requires using the Boost includes.
| n | the order of the chebyshev polynomial |
| x | the input value |
| NdArray<double> nc::polynomial::chebyshev_u | ( | uint32 | n, |
| const NdArray< dtype > & | inArrayX | ||
| ) |
Chebyshev Polynomial of the second kind. NOTE: Use of this function requires using the Boost includes.
| n | the order of the chebyshev polynomial |
| inArrayX | the input value |
| double nc::polynomial::chebyshev_u | ( | uint32 | n, |
| dtype | x | ||
| ) |
Chebyshev Polynomial of the second kind. NOTE: Use of this function requires using the Boost includes.
| n | the order of the chebyshev polynomial |
| x | the input value |
| NdArray<double> nc::polynomial::hermite | ( | uint32 | n, |
| const NdArray< dtype > & | inArrayX | ||
| ) |
Hermite Polynomial. NOTE: Use of this function requires either using the Boost includes or a C++17 compliant compiler.
| n | the order of the hermite polynomial |
| inArrayX | the input value |
| double nc::polynomial::hermite | ( | uint32 | n, |
| dtype | x | ||
| ) |
Hermite Polynomial NOTE: Use of this function requires either using the Boost includes or a C++17 compliant compiler.
| n | the order of the hermite polynomial |
| x | the input value |
| NdArray<double> nc::polynomial::laguerre | ( | uint32 | n, |
| const NdArray< dtype > & | inArrayX | ||
| ) |
Laguerre Polynomial. NOTE: Use of this function requires either using the Boost includes or a C++17 compliant compiler.
| n | the order of the leguerre polynomial |
| inArrayX | the input value |
| double nc::polynomial::laguerre | ( | uint32 | n, |
| dtype | x | ||
| ) |
Laguerre Polynomial. NOTE: Use of this function requires either using the Boost includes or a C++17 compliant compiler.
| n | the order of the leguerre polynomial |
| x | the input value |
| NdArray<double> nc::polynomial::laguerre | ( | uint32 | n, |
| uint32 | m, | ||
| const NdArray< dtype > & | inArrayX | ||
| ) |
Associated Laguerre Polynomial. NOTE: Use of this function requires either using the Boost includes or a C++17 compliant compiler.
| n | the order of the leguerre polynomial |
| m | the degree of the legendre polynomial |
| inArrayX | the input value |
Associated Laguerre Polynomial. NOTE: Use of this function requires either using the Boost includes or a C++17 compliant compiler.
| n | the order of the leguerre polynomial |
| m | the degree of the legendre polynomial |
| x | the input value |
| NdArray<double> nc::polynomial::legendre_p | ( | uint32 | m, |
| uint32 | n, | ||
| const NdArray< dtype > & | inArrayX | ||
| ) |
Associated Legendre Polynomial of the first kind. NOTE: Use of this function requires either using the Boost includes or a C++17 compliant compiler.
| m | the order of the legendre polynomial |
| n | the degree of the legendre polynomial |
| inArrayX | the input value. Requires -1 <= x <= 1 |
Associated Legendre Polynomial of the first kind. NOTE: Use of this function requires either using the Boost includes or a C++17 compliant compiler.
| m | the order of the legendre polynomial |
| n | the degree of the legendre polynomial |
| x | the input value. Requires -1 <= x <= 1 |
| NdArray<double> nc::polynomial::legendre_p | ( | uint32 | n, |
| const NdArray< dtype > & | inArrayX | ||
| ) |
Legendre Polynomial of the first kind. NOTE: Use of this function requires either using the Boost includes or a C++17 compliant compiler.
| n | the degree of the legendre polynomial |
| inArrayX | the input value. Requires -1 <= x <= 1 |
| double nc::polynomial::legendre_p | ( | uint32 | n, |
| dtype | x | ||
| ) |
Legendre Polynomial of the first kind. NOTE: Use of this function requires either using the Boost includes or a C++17 compliant compiler.
| n | the degree of the legendre polynomial |
| x | the input value. Requires -1 <= x <= 1 |
| NdArray<double> nc::polynomial::legendre_q | ( | int32 | n, |
| const NdArray< dtype > & | inArrayX | ||
| ) |
Legendre Polynomial of the second kind. NOTE: Use of this function requires using the Boost includes.
| n | the order of the legendre polynomial |
| inArrayX | the input value. Requires -1 <= x <= 1 |
| double nc::polynomial::legendre_q | ( | int32 | n, |
| dtype | x | ||
| ) |
Legendre Polynomial of the second kind. NOTE: Use of this function requires using the Boost includes.
| n | the order of the legendre polynomial |
| x | the input value. Requires -1 <= x <= 1 |
| std::complex<double> nc::polynomial::spherical_harmonic | ( | uint32 | n, |
| int32 | m, | ||
| dtype1 | theta, | ||
| dtype2 | phi | ||
| ) |
Returns the value of the Spherical Harmonic Ynm(theta, phi). The spherical harmonics Ynm(theta, phi) are the angular portion of the solution to Laplace's equation in spherical coordinates where azimuthal symmetry is not present. NOTE: Use of this function requires using the Boost includes.
| n | order of the harmonic |
| m | degree of the harmonic |
| theta | Azimuthal (longitudinal) coordinate; must be in [0, 2*pi]. |
| phi | Polar (colatitudinal) coordinate; must be in [0, pi]. |
| double nc::polynomial::spherical_harmonic_i | ( | uint32 | n, |
| int32 | m, | ||
| dtype1 | theta, | ||
| dtype2 | phi | ||
| ) |
Returns the imaginary part of the Spherical Harmonic Ynm(theta, phi). The spherical harmonics Ynm(theta, phi) are the angular portion of the solution to Laplace's equation in spherical coordinates where azimuthal symmetry is not present. NOTE: Use of this function requires using the Boost includes.
| n | order of the harmonic |
| m | degree of the harmonic |
| theta | Azimuthal (longitudinal) coordinate; must be in [0, 2*pi]. |
| phi | Polar (colatitudinal) coordinate; must be in [0, pi]. |
| double nc::polynomial::spherical_harmonic_r | ( | uint32 | n, |
| int32 | m, | ||
| dtype1 | theta, | ||
| dtype2 | phi | ||
| ) |
Returns the real part of the Spherical Harmonic Ynm(theta, phi). The spherical harmonics Ynm(theta, phi) are the angular portion of the solution to Laplace's equation in spherical coordinates where azimuthal symmetry is not present. NOTE: Use of this function requires using the Boost includes.
| n | order of the harmonic |
| m | degree of the harmonic |
| theta | Azimuthal (longitudinal) coordinate; must be in [0, 2*pi]. |
| phi | Polar (colatitudinal) coordinate; must be in [0, pi]. |


{% extends "layout.html" %} {% block main %}
{{ ctype | upper }}
Share this keycard with the other spymaster(s). Pick a spymster to do this and share resulting page with other spymaster(s). Do this only once per game.
Confirmation code: {{ hash }}


UD for LANGUAGE
This is a work-in-progress overview of the UD annotation for Georgian.
Tokenization and Word Segmentation
- In Modern Georgian, words are delimited regularly by white spaces and punctuation marks. However, in Old Georgian, tokenization was an irregular process, as words were sometimes separated by white spaces and sometimes not. Additionally, depending on the century, words in Old Georgian could also be separated by paragraph separators (჻).
- Punctuation symbols are not separated from the words; that holds even for hyphenated compounds such as siblings “და-ძმა” ‘sister and brother’ (one token) etc. However, the dash is separated from the surrounding characters. They can consist of a sequence of symbols, such as a question mark followed by an exclamation mark (?!), an exclamation mark followed by two full stops (!..) and ellipsis (…) and appear: a) in abbreviations (ა.შ. ‘etc.’, ე.ი. ‘i.e.’, etc.) and b) in numeric expressions (1.2, 0,5, etc.).
- Due to rich agglutinating type of morphology, clitics can be treated as multi-word tokens and segmented to individual syntactic words in the following cases: a) auxiliary verbs (AUX) attached to the nominal paradigm, which add functional and grammatical meaning to the sentence, expressing tense, aspect, mood, etc.: სახლია = სახლი+ა ‘is a house’; b) postpositions represented by a suffix attached to an inflected nominal (noun, adjective, numeral and pronoun): სახლში = სახლ+ში ‘in the house’; c) the indirect speech particle represented by a suffix attached to an inflected nominal or verb: სახლიო = სახლი+ო ‘a house as it was said’, წერსო = წერს+ო ‘he writes as it was said’.
Nominal Multiword expressions (MWEs)
The Georgian Multiword expressions (MWEs) are (continuous or discontinuous) sequences of words with the following compulsory properties:
- They show some degree of orthographic, morphological, syntactic or semantic idiosyncrasy with respect to what is considered general grammar rules of a language (e.g. მეცხრე ცა ‘cloud nine’, ბინძური ფული ‘dirty money’ etc.).
- Their component words include a head word and at least one other syntactically related word. Most often the relation they maintain is a syntactic (direct or indirect) dependence but it can also be e.g. a coordination (ბურთი და მოედანი ‘ball and field, ltr. do what you want’ etc.). Depending on the category of the head word, the whole MWE can be nominal, adjectival, verbal etc. Probably the most salient property of MWEs is semantic non-compositionality. In other words, it is often impossible to deduce the meaning of the whole unit from the meanings of its parts and from its syntactic structure. For instance, while it is easy to interpret phrases like to kick the ball or to spill some water from the words that compose them, it is almost impossible to guess, without knowing it beforehand, that _ჩიტის რძე _ means ‘Bird’s milk, ltr. everything smb. wants’.
ერთგვარი ჯაჭვური რეაქცია მოხდა. \n A kind of nuclear reaction happened
nsubj(რეაქცია, მოხდა)
Modifier Dependents
A nominal head does not take any core arguments but may be associated with different types of modifiers:
- An
nmod
is a nominal phrase modifying the head of another nominal phrase. - An
amod
is an adjective modifying the head of a nominal phrase. - A
nummod
is a numeral modifying the head of a nominal phrase.
ფაიფურის თიხა
nmod(თიხა, ფაიფურის)
არ არსებობს გამოუვალი მდგომარეობა
amod(მდგომარეობა-4, გამოუვალი-3)
მეცხრე ცა
nummod(ცა, მეცხრე)
Function Word Dependents
Nominals may also contain the following typical function word dependents:
- Adpositions attach to the head of the nominal with the
case
relation.
მეცხრე ცაზე
nummod(ცა-2, მეცხრე-1)
case(ცა-2, ზე-3)
Instruction: Describe the general rules for delimiting words (for example, based on whitespace and punctuation) and exceptions to these rules. Specify whether words with spaces and/or multiword tokens occur. Include links to further language-specific documentation if available.
Morphology
Tags
-
UD_Georgian-GLC treebank uses all UPOS tags except of DET and [SYM] (https://universaldependencies.org/u/pos/SYM.html).
- There are two types of particles (PART) in Georgian. Those acting as separate words subdivided into different lexical classes: არ ‘not’, ვერ ‘not’ etc.) and those belonging to clitics and occupying concrete slots in the nominal or verbal paradigm: −ღა, -ცა etc. Thus, the PART tag is assigned to those particles, which act as separate tokens and the verbal particle denoting evidentiality: -მეთქი ‘As I said’, -ო ‘as he/them said’ etc.
- Auxiliaries (AUX) add functional and grammatical meaning to the sentence, expressing tense, aspect, mood, etc., and can be of two types: (a) those which accompany the nominal paradigm and (b) those which accompany the verbal paradigm. If the auxiliaries are represented as clitics: მთავარია = მთავარი + ა ‘it is important’, სახლია = სახლი + ა ‘it is a house’, they are considered as copula verbs and assigned cop in DEPREL; while if they act as separate words: უნდა ‘it should’, შეუძლია ‘it can’ etc., they are considered as auxiliaries and assigned aux in DEPREL.
- Verbal forms indicating verbal nouns or verbal adjectives are assigned a tag VERB with additional information on the features of verbal forms, especially, participles are assigned: VerbForm=Part, while masdars - VerbForm=Vnoun.
Instruction: Specify any unused tags. Explain what words are tagged as PART. Describe how the AUX-VERB and DET-PRON distinctions are drawn, and specify whether there are (de)verbal forms tagged as ADJ, ADV or NOUN. Include links to language-specific tag definitions if any.
Features
Lexical Features
- UD_Georgian-GLC treebank uses all main lexical FEATS tags except of [REflex] (https://universaldependencies.org/u/feat/Reflex.html)
Inflectional Features
Nominal Features
- UD_Georgian-GLC treebank uses all main inflectional FEATS tags except of [Gender] (https://universaldependencies.org/u/feat/Gender.html), [NounClass] (https://universaldependencies.org/u/feat/NounClass.html) and [Definite] (https://universaldependencies.org/u/feat/Definite.html).
- Nouns have inherent Animacy and inflect for Number (singular or plural) and Case (nominative, ergative, dative, genetive, instrumental, essive (adverbial) and vocative).
- Adjectives in the majority of cases agree with nouns (in both attributive and predicative position) with respect to Case. Many adjectives inflect for Degree (diminutive, positive, comparative, superlative), Case and Number.
- Numerals agree with nouns (in both attributive and predicative position) with respect to Case and inflect for Case and very rarely for Number: ორი ‘two in Singular’ -> ორნი ‘two in Plural’.
- Many pronouns agree with nouns with respect to Case and inflect for Case and Number. Some types of pronouns (personal, demonstrative and possessive pronouns) reflect Person (1, 2, 3).
Verbal Features
- UD_Georgian-GLC treebank uses all main inflectional FEATS tags except of [Polarity] (https://universaldependencies.org/u/feat/Polarity.html), [Polite] (https://universaldependencies.org/u/feat/Polite.html) and [Clusivity] (https://universaldependencies.org/u/feat/Clusivity.html).
- Verbs inflect for Tense (present, imperfect, future, aorist, perfect, pluperfect), Aspect (imperfective, perfective) and Mood (indicative, subjunctive, conditional, imperative) so called TAM series, which specifies case-marking and relationships so called agreement between participants such as agent and patient by means of preverbs, version markers and thematic suffixes. The category of voice can be subdivided into the active, autoactive, inactive, passive and mediopassive voices, but the traditional Georgian grammar (Shanidze 1961, 1973) distinguishes only active, middle and passive voices.
- The category of personality covers unipersonal, bipersonal and tripersonal verbs. As a result verbal morphology includes agreement between the verb and its arguments in terms of Person, Case and Number. While in Indo-European languages, the verb generally agrees with the subject of the sentence, in Georgian the verb agrees not only with the subject, but with its objects as well, both direct and/or indirect. The verb in Georgian has core and peripheral arguments. A core argument agrees morphologically with the verb by means of Person (1, 2 or 3 for Person[subj], Person[obj], Person[iobj]) and Number (singular or plural for Number[subj], Number[obj], Number[io]), while a peripheral argument does not. The number of core arguments affects the conjugation system as a whole, subdividing it into subject and object paradigms.
- There are two types of nonfinite forms: participles and masdars.
Instruction: Describe inherent and inflectional features for major word classes (at least NOUN and VERB). Describe other noteworthy features. Include links to language-specific feature definitions if any.
Syntax
- UD_Georgian-GLC treebank uses all main DEPREL tags except of [expl] (https://universaldependencies.org/u/dep/expl.html), [dislocated] (https://universaldependencies.org/u/dep/dislocated.html), [clf] (https://universaldependencies.org/u/dep/clf.html) and [reparandum] (https://universaldependencies.org/u/dep/reparandum.html). Georgian language has a flexible word order, which sometimes follows Subject-Indirect Object-Object-Verb patterns.
- Subjects have the following characteristics:
- Case marking: Subjects occur in nominative, ergative or dative.
- Passivization: Subjects are suppressed when verbs (both intransitive and transitive) are passivized.
- A verb always agrees with first and second-person subjects in number, while third-person inanimate subjects require singular number agreement regardless of their logical number.
- Direct objects have the following characteristics:
- Case marking: Direct objects occur in nominative or dative.
- Indirect objects have the following characteristics:
- Case marking: Indirect objects occur in dative.
- The following scheme reflects the correlation between the case of the core arguments and the conjugation system of a verb:
| v-type | —————— | m-type | —————— |
|---|---|---|---|
| NOM | NOM (v-set) | ||
| NOM | NOM (v-set) + DAT | DAT (m-set) | |
| NOM | ERG (v-set) + DAT | NOM (m-set) | |
| NOM | ERG (v-set) + DAT | NOM (m-set) + DAT | DAT (∅ -a) |
| NOM | ERG (v-set) + DAT | NOM (∅-set) + DAT | DAT (m- -a) |
- The following subtypes are used in Georgian:
- acl: clausal modifier of noun (adnominal clause)
- advcl: adverbial clause modifier
- advmod: adverbial modifier
- advmod:emph: emphasizing word, intensifier
- advmod:lmod: locative adverbial modifier
- amod: adjectival modifier
- appos: appositional modifier
- aux: auxiliary
- case: case marking
- cc: coordinating conjunction
- ccomp: clausal complement
- compound: compound
- conj: conjunct
- cop: copula
- csubj: clausal subject
- csubj:outer: outer clause clausal subject
- csubj:pass: clausal passive subject
- dep: unspecified dependency
- det: determiner
- det:nummod: pronominal quantifier agreeing in case with the noun
- det:poss: possessive determiner
- discourse: discourse element
- fixed: fixed multiword expression
- flat: flat multiword expression
- flat:foreign: foreign words
- flat:name: names
- goeswith: goes with
- iobj: indirect object
- list: list
- mark: marker
- nmod: nominal modifier
- nsubj: nominal subject
- nummod: numeric modifier
- obj: object
- obl: oblique nominal
- orphan: orphan
- parataxis: parataxis
- punct: punctuation
- root: root
- vocative: vocative
- xcomp: open clausal complement
Instruction: Give criteria for identifying core arguments (subjects and objects), and describe the range of copula constructions in nonverbal clauses. List all subtype relations used. Include links to language-specific relations definitions if any.
Treebanks
There are not UD treebanks of Georgian.
Instruction: Treebank-specific pages are generated automatically from the README file in the treebank repository and
from the data in the latest release. Link to the respective *-index.html
page in the treebanks
folder, using the language code
and the treebank code in the file name.


Zend Framework
LICENSE
This source file is subject to the new BSD license that is bundled with this package in the file LICENSE.txt. It is also available through the world-wide-web at this URL: http://framework.zend.com/license/new-bsd If you did not receive a copy of the license and are unable to obtain it through the world-wide-web, please send an email to<EMAIL_ADDRESS>so we can send you a copy immediately.
__construct(
string $filename, string $mode
=
r+b
)
:
void
Class constructor. Open the file.
| Name | Type | Description |
|---|---|---|
| $filename | string | |
| $mode | string |
_fread(
integer $length
=
1
)
:
string
Read a $length bytes from the file and advance the file pointer.
| Name | Type | Description |
|---|---|---|
| $length | integer |
| Type | Description |
|---|---|
| string |
_fwrite(
string $data, integer $length
=
null
)
:
void
Writes $length number of bytes (all, if $length===null) to the end of the file.
| Name | Type | Description |
|---|---|---|
| $data | string | |
| $length | integer |
flush(
)
:
boolean
Flush output.
Returns true on success or false on failure.
| Type | Description |
|---|---|
| boolean |
lock(
integer $lockType, boolean $nonBlockingLock
=
false
)
:
boolean
Lock file
Lock type may be a LOCK_SH (shared lock) or a LOCK_EX (exclusive lock)
| Name | Type | Description |
|---|---|---|
| $lockType | integer | |
| $nonBlockingLock | boolean |
| Type | Description |
|---|---|
| boolean |
readBinary(
)
:
string
Reads binary data from the current position in the file and advances the file pointer.
Inherited from: \Zend_Search_Lucene_Storage_File::readBinary()| Type | Description |
|---|---|
| string |
readByte(
)
:
integer
Reads a byte from the current position in the file and advances the file pointer.
Inherited from: \Zend_Search_Lucene_Storage_File::readByte()| Type | Description |
|---|---|
| integer |
readBytes(
integer $num
)
:
string
Read num bytes from the current position in the file and advances the file pointer.
Inherited from: \Zend_Search_Lucene_Storage_File::readBytes()| Name | Type | Description |
|---|---|---|
| $num | integer |
| Type | Description |
|---|---|
| string |
readInt(
)
:
integer
Reads an integer from the current position in the file and advances the file pointer.
Inherited from: \Zend_Search_Lucene_Storage_File::readInt()| Type | Description |
|---|---|
| integer |
readLong(
)
:
integer|float
Returns a long integer from the current position in the file and advances the file pointer.
Inherited from: \Zend_Search_Lucene_Storage_File::readLong()readLong32Bit(
)
:
integer|float
Returns a long integer from the current position in the file, advances the file pointer and return it as float (for 32-bit platforms).
Inherited from: \Zend_Search_Lucene_Storage_File::readLong32Bit()readString(
)
:
string
Reads a string from the current position in the file and advances the file pointer.
Inherited from: \Zend_Search_Lucene_Storage_File::readString()| Type | Description |
|---|---|
| string |
readVInt(
)
:
integer
Returns a variable-length integer from the current position in the file and advances the file pointer.
Inherited from: \Zend_Search_Lucene_Storage_File::readVInt()| Type | Description |
|---|---|
| integer |
seek(
integer $offset, integer $whence
=
SEEK_SET
)
:
integer
Sets the file position indicator and advances the file pointer.
The new position, measured in bytes from the beginning of the file, is obtained by adding offset to the position specified by whence, whose values are defined as follows: SEEK_SET - Set position equal to offset bytes. SEEK_CUR - Set position to current location plus offset. SEEK_END - Set position to end-of-file plus offset. (To move to a position before the end-of-file, you need to pass a negative value in offset.) SEEK_CUR is the only supported offset type for compound files
Upon success, returns 0; otherwise, returns -1
| Name | Type | Description |
|---|---|---|
| $offset | integer | |
| $whence | integer |
| Type | Description |
|---|---|
| integer |
writeByte(
integer $byte
)
:
void
Writes a byte to the end of the file.
Inherited from: \Zend_Search_Lucene_Storage_File::writeByte()| Name | Type | Description |
|---|---|---|
| $byte | integer |
writeBytes(
string $data, integer $num
=
null
)
:
void
Writes num bytes of data (all, if $num===null) to the end of the string.
Inherited from: \Zend_Search_Lucene_Storage_File::writeBytes()| Name | Type | Description |
|---|---|---|
| $data | string | |
| $num | integer |
writeInt(
integer $value
)
:
void
Writes an integer to the end of file.
Inherited from: \Zend_Search_Lucene_Storage_File::writeInt()| Name | Type | Description |
|---|---|---|
| $value | integer |
writeLong(
integer $value
)
:
void
Writes long integer to the end of file
Inherited from: \Zend_Search_Lucene_Storage_File::writeLong()| Name | Type | Description |
|---|---|---|
| $value | integer |
| Exception | Description |
|---|---|
| \Zend_Search_Lucene_Exception |
writeLong32Bit(
integer|float $value
)
:
void
Writes long integer to the end of file (32-bit platforms implementation)
Inherited from: \Zend_Search_Lucene_Storage_File::writeLong32Bit()| Name | Type | Description |
|---|---|---|
| $value | integer|float |
| Exception | Description |
|---|---|
| \Zend_Search_Lucene_Exception |
writeString(
string $str
)
:
void
Writes a string to the end of file.
Inherited from: \Zend_Search_Lucene_Storage_File::writeString()| Name | Type | Description |
|---|---|---|
| $str | string |
| Exception | Description |
|---|---|
| \Zend_Search_Lucene_Exception |
writeVInt(
integer $value
)
:
void
Writes a variable-length integer to the end of file.
Inherited from: \Zend_Search_Lucene_Storage_File::writeVInt()| Name | Type | Description |
|---|---|---|
| $value | integer |