--- layout: post title: SHH, Windows will Support OpenSSH date: 2015-10-27 10:00:21.000000000 -04:00 type: post published: true status: publish categories: - Administration - DevOps - Windows Client - Windows Server tags: - SSH - Windows meta: _edit_last: '1' _oembed_71876d739ec5933258e19e18f196d5d9: '{{unknown}}' _oembed_7fe1264ad7e4076de89975920e057760: '{{unknown}}' _jetpack_related_posts_cache: a:1:{s:32:"8f6677c9d6b0f903e98ad32ec61f8deb";a:2:{s:7:"expires";i:1457857183;s:7:"payload";a:3:{i:0;a:1:{s:2:"id";i:248;}i:1;a:1:{s:2:"id";i:261;}i:2;a:1:{s:2:"id";i:1181;}}}} author: login: adminian email<EMAIL_ADDRESS>display_name: adminian first_name: '' last_name: '' ---
Microsoft loves Linux.
Who is this Microsoft? and what did you do to telnet?.. Gopher must've ate it. Open Source for life!
Those fellows who work on PowerShell must be crazy! Who'd want port 22 open on a Windows box?
Me.
No more nc.exe bro dawg! Captn_Jack and his fellow RedHat wearing brethren can finally manage Windows and Linux over thy administrator controlled port.
Supercilious really...
But why is SSH coming? I mean it's only 30 years in the making...
Well here is what the PowerShell Team has to say:
Finally, I'd like to share some background on today’s announcement, because this is the 3rd time the PowerShell team has attempted to support SSH. The first attempts were during PowerShell V1 and V2 and were rejected. Given our changes in leadership and culture, we decided to give it another try and this time, because we are able to show the clear and compelling customer value, the company is very supportive. So I want to take a minute and thank all of you in the community who have been clearly and articulately making the case for why and how we should support SSH!
Emphasis put on the key point.
Trust me, I'm a professional.


DevConf is one of Poland's premier annual conferences dedicated to software development. It's based on principles we believe are the success factors of an ultimate conference experience.
If you are looking for a technology agnostic software development conference with high-level inspirational talks, deep dives into certain subjects, live coding sessions and plenty of networking time, make sure to book 26-28th of September 2018 and come to Kraków, Poland.
Join 500+ fellow developers in this yearly tech event.
Get deeply into specific subjects during hands-on meetings.
Mingle with open-minded, eager-to-learn poeple just like you!
Organized by developers for developers.
All prices are subject to 23% VAT. Tickets for hands-on workshops available at a later date.
For those that already know they simply can't miss the event. No speakers nor agenda released.
Early bird gets the worm! Some content of the event might be known. Prices still discounted.
Available now!
Regular price of the event. All speakers and agenda released to the public.
Starting: June 2018
Keepers of late hours pay the most. Do not leave this decision until last minute.
Starting: September 2018


Convex.jl
Optimization for Everyone
David Zeng · Karanveer Mohan
What is Convex.jl
- Interface between math and solvers
- Focus on convex optimization
Convex Optimization
- Fast (provable polynomial complexity)
- Global solution
- Sub-routines and relaxations
Applications
- Machine learning
- Finance
- Signal and image processing
- Computer vision
- Robot motion planning
- Quantum science
- Cooking?!
Convex Functions
$f$ is convex if the domain of $f$ is a convex set, and
for all $\theta \in [0,1]$
$$
f(\theta x + (1-\theta)y ) \leq \theta f(x) + (1-\theta) f(y)
$$
Equivalently:
- $f$ has nonnegative (upward) curvature
- The graph of $f$ never lies above its chords
- $f'' \geq 0$ (if $f$ is differentiable)
Other Definitions
- $f$ is concave if and only if $-f$ is convex
- $f$ is affine if and only if $f$ is both convex and concave
Traditional Form
$$
\begin{array}{ll}
\mbox{minimize} & f_0(x) \\
\mbox{subject to} & f_i(x) \leq 0, \quad i=1, \ldots, m_1\\
& h_i(x) = 0, \quad i=1, \ldots, m_2\\
\end{array}
$$
- Variable $x\in \mathbf{R}^n$
- $f_i$ are all convex
- $h_i$ are all affine
Conic Form
$$
\begin{array}{ll}
\mbox{minimize} & c^T x \\
\mbox{subject to} & Ax + s= b\\
& s \in \mathcal K\\
\end{array}
$$
where $\mathcal K$ is a convex cone
$$ s \in \mathcal K \iff rs \in \mathcal K, \quad \forall r>0$$
Some Convex Cones
- Positive orthant: $$\mathcal K_+ = \{x: x\geq 0\}$$
- Second order cone: $$\mathcal K_{\mathrm{SOC}} = \{(x,t): \|x\|_2 \leq t\}$$
- Semidefinite cone: $$\mathcal K_{\mathrm{SDP}} = \{X: X = X^T,~ v^T X v \geq 0,~ \forall v \in \mathbf{R}^n\}$$
Affine Functions
- +, -, /, *
- Indexing: x[1:3, 2]
- k-th diagonal: diag(x, k)
- Transpose: x'
- Dot product: x' * y
- Reshape: vec(x) or reshape(x, 2, 3)
- Stacking: [x y] or [x, y]
Elementwise Functions
- Min, max: min(x, y) or max(x, y)
- Minimum, maximum: minimum(x) or maximum(x)
- Positive: pos(x)
- Negative: neg(x)
- Inverse positive: inv_pos(x)
- Square, square root: x^2 or sqrt(x)
- Absolute value: abs(x)
- Geometric mean: geo_mean(x, y)
- Exponent, log: exp(x) or log(x)
Vector or Matrix Functions
- Norm: norm(x, 1), norm(x, 2), norm(x, :Inf)
- Quadratic form: quad_form(x, P)
- Quadratic over linear: quad_over_lin(x, y)
- Norm_2 squared: sum_squares(x)
- Inverse positive: inv_pos(x)
- Square root: sqrt(x)
- Square: x^2
- Absolute: abs(x)
- Geometric mean: geo_mean(x, y)
- Max/min eigenvalue: lambda_max(X), lambda_min(X)
- Singular values: nuclear_norm(X), operator_norm(X)
Nonconvex optimization
- Mixed integer programs supported
Why Convex.jl?
- Separation of concerns
- 40+ functions supported
- MathProgBase interface to solvers
What's next?
- Other forms of convex optimization problems
- More functions (tell us what you want!)
- More backend solvers
- We value your feedback!
Collaborators
- Convex.jl: Madeleine Udell, Jenny Hong
- cvx: Michael Grant, Stephen Boyd
- cvxpy: Steven Diamond, Eric Chu, Stephen Boyd
- scs: Brendan O’Donoghue, Eric Chu, Neal Parikh, Stephen Boyd
- ecos: Alexander Domahidi, Eric Chu, Stephen Boyd
- The JuliaOpt team + Tony Kelman


Sorting emails
If you access multiple POP email accounts with Outlook,there can be probelms of jumbled inbox. Outlook delivers all new mails to the Inbox folder indiscriminately and makes it hard to guess which email arrived where.
While setting up Outlook, to deliver mail to different inboxes, is dificult but Inbox can be sorted by account (and then by date) easily.
To sort the emails in Outlook Inbox by the account through which they were received:
1.Select View | Current View | Customize Current View... from the menu.
2·Click the Sort button. Make sure "All Mail Fields" is selected under "Select available fields from:" at the bottom of the dialog that comes up.
3·Now choose "To Account" from the "Sort items by" drop-down menu. Optionally, select criteria for further sorting using the "Then by" fields.
4· Click OK.
5· Click OK again.
Searching emails :To find related emails swiftly in Outlook:
1. Open a message in Outlook.
2. In Outlook 2000, choose Actions | Find All | Related Messages.
This brings up a search window with all related messages that Outlook could find.


?HERBARIUM
OTTO
DEGENER
6
7
8
9
10
The
Newark
copyright
reserved
botanical
Garden
det.
CHARLES
BAEHNI
Herbarium
of
Otto
d
EGENER
//7f~
SAPOTA
achras
Mill.
/
Paget.Bermuda.
,"Montrose"
pasture.
(JIUQQ.
July
12,1921.
V;'-.V/”;',
¥
TŽ
*
01442931
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


Artistic style transfer using convolutional neural networks
This work is an extension of the work by Harish Narayanan. The goal is to apply this idea to text generation. How can we generate writing of a specific person?
Github Project here
This work is an extension of the work by Harish Narayanan. The goal is to apply this idea to text generation. How can we generate writing of a specific person?
Github Project here


More Jataka Tales
by Ellen C. Babbitt
“The Tricky Wolf and the Rats”
Additional Information
- Year Published: 1922
- Language: English
- Country of Origin: India
- Source: Babbitt, E.C. (Ed.). (1922). More Jataka Tales. New York, NY: D. Appleton-Century Company.
-
Readability:
- Flesch–Kincaid Level: 2.4
- Word Count: 408
- Genre: Fairy Tale/Folk Tale
- Keywords: peace, revenge
- ✎ Cite This
Downloads
Once upon a time a Big Rat lived in the forest, and many hundreds of other Rats called him their Chief.
A Tricky Wolf saw this troop of Rats, and began to plan how he could catch them. He wanted to eat them, but how was he to get them? At last he thought of a plan. He went to a corner near the home of the Rats and waited until he saw one of them coming. Then he stood up on his hind legs.
The Chief of the Rats said to the Wolf, “Wolf, why do you stand on your hind legs?”
“Because I am lame,” said the Tricky Wolf.  “I keep my mouth open so that I may drink in all the air I can,” said the Wolf. “I live on air; it is my only food day after day. I can not run or walk, so I stay here. I try not to complain.” When the Rats went away the Wolf lay down.
The Chief of the Rats was sorry for the Wolf, and he went each night and morning with all the other Rats to talk with the Wolf, who seemed so poor, and who did not complain.
Each time as the Rats were leaving, the Wolf caught and ate the last one. Then he wiped his lips, and looked as if nothing had happened. Each night there were fewer Rats at bedtime. Then they asked the Chief of the Rats what the trouble was. He could not be sure, but he thought the Wolf was to blame.
So the next day the Chief said to the other Rats, “You go first this time and I will go last.”
They did so, and as the Chief of the Rats went by, the Wolf made a spring at him. But the Wolf was not quick enough, and the Chief of the Rats got away.
“So this is the food you eat. Your legs are not so lame as they were. You have played your last trick, Wolf,” said the Chief of the Rats, springing at the Wolf’s throat. He bit the Wolf, so that he died. And ever after the Rats lived happily in peace and quiet.


--- layout: post date: 2011-02-17 time: "09:00:00" authors: ["Greg Wilson"] title: Reddit on Scientific Programming tags: ["Noticed"] ---
Nature's article on scientific computing has attracted 75 comments on Reddit. Some interesting ideas being kicked around; please go ahead and add your own.


{{ openMail.subject }}
{{ openMail.sender }}
To: {{ openMail.receiver }} {{ openMail.time | date:'short' }}


A cookie is a baked or cooked food that is typically small, flat and sweet. It usually contains flour, sugar and some type of oil or fat. It may include other ingredients such as raisins, oats, chocolate chips, nuts, etc.
In most English-speaking countries except for the United States and Canada, crisp cookies are called biscuits. Chewier biscuits are sometimes called cookies even in the United Kingdom.[3] Some cookies may also be named by their shape, such as date squares or bars.
Cookies or biscuits may be mass-produced in factories, made in small bakeries or homemade. Biscuit or cookie variants include sandwich biscuits, such as custard creams, Jammie Dodgers, Bourbons and Oreos, with marshmallow or jam filling and sometimes dipped in chocolate or another sweet coating. Cookies are often served with beverages such as milk, coffee or tea. Factory-made cookies are sold in grocery stores, convenience stores and vending machines. Fresh-baked cookies are sold at bakeries and coffeehouses, with the latter ranging from small business-sized establishments to multinational corporations such as Starbucks.
The word dates from 1730, Scottish usage, but the word meant "plain bun," rather than thin baked good, and so it is not certain whether it is the same word. From 1808, the word "cookie" is attested "...in the sense of "small, flat, sweet cake" in American English. The American use is derived from Dutch koekje "little cake," which is a diminutive of "koek" ("cake"), which came from the Middle Dutch word "koke".[5] Another claim is that the American name derives from the Dutch word koekje or more precisely its informal, dialect variant koekie[6] which means little cake, and arrived in American English with the Dutch settlement of New Netherland, in the early 1600s.[citation needed]
According to the Scottish National Dictionary, its Scottish name derives from the diminutive form (+ suffix -ie) of the word cook, giving the Middle Scots cookie, cooky or cu(c)kie. There was much trade and cultural contact across the North Sea between the Low Countries and Scotland during the Middle Ages, which can also be seen in the history of curling and, perhaps, golf.[citation needed]
Cookies are most commonly baked until crisp or just long enough that they remain soft, but some kinds of cookies are not baked at all. Cookies are made in a wide variety of styles, using an array of ingredients including sugars, spices, chocolate, butter, peanut butter, nuts, or dried fruits. The softness of the cookie may depend on how long it is baked.
A general theory of cookies may be formulated this way. Despite its descent from cakes and other sweetened breads, the cookie in almost all its forms has abandoned water as a medium for cohesion. Water in cakes serves to make the base (in the case of cakes called "batter"[7]) as thin as possible, which allows the bubbles – responsible for a cake's fluffiness – to better form. In the cookie, the agent of cohesion has become some form of oil. Oils, whether they be in the form of butter, vegetable oils, or lard, are much more viscous than water and evaporate freely at a much higher temperature than water. Thus a cake made with butter or eggs instead of water is far denser after removal from the oven.
Oils in baked cakes do not behave as baking soda tends to in the finished result. Rather than evaporating and thickening the mixture, they remain, saturating the bubbles of escaped gases from what little water there might have been in the eggs, if added, and the carbon dioxide released by heating the baking powder. This saturation produces the most texturally attractive feature of the cookie, and indeed all fried foods: crispness saturated with a moisture (namely oil) that does not sink into it.
Cookie-like hard wafers have existed for as long as baking is documented, in part because they deal with travel very well, but they were usually not sweet enough to be considered cookies by modern standards.[8]
Cookies appear to have their origins in 7th century AD Persia, shortly after the use of sugar became relatively common in the region.[9][1] They spread to Europe through the Muslim conquest of Spain. By the 14th century, they were common in all levels of society throughout Europe, from royal cuisine to street vendors.[citation needed]
With global travel becoming widespread at that time, cookies made a natural travel companion, a modernized equivalent of the travel cakes used throughout history. One of the most popular early cookies, which traveled especially well and became known on every continent by similar names, was the jumble, a relatively hard cookie made largely from nuts, sweetener, and water.
Cookies came to America through the Dutch in New Amsterdam in the late 1620s. The Dutch word "koekje" was Anglicized to "cookie" or cooky. The earliest reference to cookies in America is in 1703, when "The Dutch in New York provided...'in 1703...at a funeral 800 cookies...'"[10]
The most common modern cookie, given its style by the creaming of butter and sugar, was not common until the 18th century.[11] Cookies may be "dunked" into a drink, especially tea, coffee, or milk, an approach which releases more flavour from confections by dissolving the sugars,[12] while also softening their texture. Dunking can be used to melt chocolate on biscuits to create a richer flavour.
There are a number of slang usages of the term "cookie". The slang use of "cookie" to mean a person, "especially an attractive woman" is attested to in print since 1920.[22] The catchphrase "that's the way the cookie crumbles", which means "that's just the way things happen" is attested to in print in 1955.[23] Other slang terms include "smart cookie” and “tough cookie.” According to The Cambridge International Dictionary of Idioms, a smart cookie is “someone who is clever and good at dealing with difficult situations.” [24] The word "cookie" has been vulgar slang for "vagina" since 1970 in the US.[25] The word "cookies" is used to refer to the contents of the stomach, often in reference to vomiting (e.g., "pop your cookies" a 1960s expression, or "toss your cookies", a 1970s expression).[26] The expression "cookie cutter", in addition to referring literally to a culinary device to rolled cookie dough into shapes, is also used metaphorically to refer to items or things "having the same configuration or look as many others" (e.g., a "cookie cutter tract house") or to label something as "stereotyped or formulaic" (e.g., an action movie filled with "generic cookie cutter characters").[27]
Cookie Monster is a Muppet on the long-running children's television show Sesame Street who is best known for his voracious appetite for cookies and his famous eating phrases, such as "Me want cookie!", "Me eat cookie!"


Authors: A. Mackay, B. Sanz-Izquierdo and E.A. Parker
Source: FERMAT, Volume 2, Article 8, Mar.-Apr., 2014
Abstract: This review gives an account of the route through which Frequency Selective Surfaces have developed in form and in application over the past few decades. Applications range from their use in radome structures, and in meta-materials, to their incorporation into buildings for signal propagation control in the built environment. For some of these applications the geometries of the array elements can be very simple, but in cases where surfaces are curved, significantly more complicated configurations are likely to be required. Fabrication cost is an important issue. In applications to multiband radiometry for satellite systems for example, the high cost of precision manufacture is normally considered to be acceptable. In contrast, in the built environment inexpensive larger scale production techniques are important. Inkjet or 3D printing may offer cost effective ways forward.
Index Terms: Frequency Selective Surfaces, Additive manufacturing, 3D printing, Electromagnetic Wave Propagation, Electromagnetic Architecture, Metamaterials, Radar Absorbent Materials, Antennas.
View PDF
Evolution of Frequency Selective Surfaces


Last Updated: {{ updated_at }}
Project whiteboard
People whiteboard
Emoji reactions to project issues on GitHub
Project demand vs team capacity plot
Update the visualisations (slow!)
Download all the visualisations as a zip file.


The web is made up of lots of HTML documents, HTML stands for Hyper Text Markup Languge. So we can difine that a web page is a text document written in a language called HTML.  When a person goes to a web page like www.google.com, their computer sends a HTTP Request to a server. The server finds the appropriate HTML document and sends it back to the user's computer where a web browser interprets the page and displays it on the user's screen.This page is a grate way to start.
✤Text content (what you can see) ✤Markup(What it looks like) ✤Links to other pages ✤References to other documents(images and videos)
There are 6 important tags that "every" programer should know:
✤b tag --> Makes "contents" bold.
✤em tag --> Makes "contents" italic.
✤br tag --> Which is a inline, and makes space betwen lines.
✤p tag --> Whis is a block makes diffrent paragrahs.
✤a tag --> Makes text into a hyperlink(ex.-"www.google.com")
✤image tag --> Links an imaage to your HTML docuemt.
HTML elements are either inline or block. Block elements form an "invisible box" around the content inside of them.
≅Inline Elements-->a, b, em, image. ≅Block Elements-->p
What this menas isn't literally that computers are stupid, simply that if you make a mistake, the computer isn't going to forgive it and run normally, because that is what it was designed for.


Pick a movie:
Star Wars Episode III ($14)
The Wolf of Wall Street ($16)
The Revenant ($19)
Guardians of the Galaxy 2 ($22)
N/A
Selected
Occupied
You have selected
0
seats for a price of $
0


‘Tinkler Will Come Up With A Plan To Stop Chiefs’
by Soccer LadumaMaritzburg United’s Gabriel Nyoni is confident that his side will dig deep in making sure that they are no pushovers against the high-firing Kaizer Chiefs.
The Team of Choice and Amakhosi will lock horns on Sunday afternoon in the semi-finals of the Telkom Knockout competition and despite the unpredictability in cup fixtures, it seems that Chiefs are the favourites to head into the final.
Read more: Transfer Window Countdown: 7 Players Who Are On Sundowns’ Radar…
However, speaking in an exclusive feature with Soccer Laduma this week, Maritzburg’s striker, Nyoni is confident in how his head coach, Eric Tinkler will set-up to try and stop Amakhosi.
“Stopping Chiefs will take some doing, not only for Maritzburg United but for all the teams because if Khama Billiat is not performing, they have Nurkovic doing well. If Lebogang Manyama is not performing, they have Dumisani Zuma firing on all cylinders, so it’s really difficult, but doable,” he said.
“They play as a compact unit, which is why they are winning so many games. The team spirit is very high in their team. When they attack, they put in more bodies in your box. But all we can do is follow the instructions of our coach, who has studied them. He will definitely come up with a plan to stop them,” Nyoni added.
To read more on the Telkom Knockout feature ahead of this weekend’s action, check out this week’s edition of Soccer Laduma, issue number 1150.


Here are detailed, step by step instructions, on how to install Selenium Grid on an Linux machine.
Ensure that the JDK 1.5 or newer is installed on
your machine. To check the version of you JDK,
open a terminal window and run the following command:
java -version
If the JDK is not installed or is older than 1.5, please install a newer JDK following the instructions described at http://java.sun.com/j2se/1.5.0/install-linux.html.
Selenium Grid requires that you install Ant 1.7.
Depending on your environment and Linux flavor you might
already have Ant 1.7 on your machine. To check whether
Ant is installed and which version you have,
execute ant -version
in a terminal:
If you already have the correct Ant version you can skip the
rest of this section and jump to
Step 3: Installing Selenium Grid
. Otherwise you can install
Ant 1.7 manually by following the instructions below:
Once it has been downloaded, unzip the file in a
directory on your local system. For instance under
/usr/local/apache-ant-1.7.1
:
Users might not have root access, let's document an
install in the Home directory. e.g. ~/utils
.
You then need to modify your PATH
so that your
system picks up the right Ant launcher. Just go to a
Terminal window and:
cd
and then the ENTER
key), and open the
.bash_profile
file.
.bash_profile
file so that the i
freshly installed Ant version 1.7 is first in your path:
.bash_profile
file (Menu "File"->"Save")
Apply the .bash_profile
to the current terminal. Then
verify that your PATH
is set accordingly and that you
pick up the right Ant version:
/usr/local/selenium-grid-1.0
).
We will refer to the directory where Selenium Grid is installed as
"Selenium Grid Home". For instance if you unzipped the distribution
within /usr/local/selenium-grid-1.0
, your Selenium Grid Home is
/usr/local/selenium-grid-1.0
.
Verify the Selenium Grid install: from the terminal window
go to the Selenium Grid home directory and run:
ant sanity-check
If the build is successful, you are all set!


What is an ideal software project? What is an ideal delivery cycle? What is an ideal culture? From a client’s perspective do they see the team as a sluggish liability or is the development team proactive and looking for the next strategic or tactical step to take?
Ideally, I see the development team as a group that should be leading a company with technology. If a team isn’t doing that, they’re likely to be running the risk of appearing as a liability and risk. Often these are the types of teams that are often outsourced or off-shored because it seems easier to the clients or management.
I don’t like the idea of getting stuck in a team like that. However, I’d do everything in my power to change that situation. In the past I’ve done just that. It is hard, but it is worth it. It boils down, unfortunately, to a perception and practice problem most of the time.  Once you get them all together… well, you can read the picture. ;)
So what is my ideal team, product, and environment look like? That’s simple to answer.
- Team cohesion through pairing, eating lunch together, having a beer once in a while, easy conversations, hallway troubleshooting, and other social interactions. These interactions should be easy, comfortable, almost as if everybody were friends. Better yet, the ideal situation is simply that people working on a project actually be friends. No reason, in and ideal situation, for everyone not to be.
- Frequent delivery of product. Weekly, maybe every two weeks, but not much longer than that. The customer or client needs to be kept informed. If it is difficult to deliver something every week or two, that should be the top fix it item on the list of things to do. In this ideal environment of mine, I’d like to keep conversation and delivery on a weekly basis. Two weeks, often is a long time between delivery points.
- Communication among all lines of the company. There should be zero resistance to talking to any part of the company, developer directly to whoever is involved in the product. If there is a user, the developers should have access to them.
- Casual work environments are important. Generation Y especially, but X and others also don’t particularly like an environment to be socially stuffy because of forced attire. Dress comfortably, yet respectfully.
Usually with three out of four of these I’m a happy developer.
What other characteristics draw you into a team or a product to work on? What gets you excited about the software you’re going to build or the team you’re going to work with?
- Bobby (@NotMyself) followed up on his blog here.
- Even though she didn’t write it in response to my blog entry, Kate’s blog entries on how to be awesome, reflects a lot of a great team member. Part 1, part 2, Part 3a, Part 3b, and Part 4. All good reads.
If you write up your thoughts or ideas on an ideal dev shop or ideal product, let me know and I’ll provide a link to your blog as well! :)


{% block content %}
Invoice |
Berkeley Model United Nations |
|
BILL TO {{ name }} |
DATE {{ date_registered }} |
PLEASE PAY ${{ amount_due }} |
DUE DATE {{ due_date }} |
| Activity | Quantity | Rate | Amount | ||
|---|---|---|---|---|---|
| Delegate Fee | {{ delegate_total }} | ${{ delegate_fee }} | ${{ delegate_fees }} | ||
| Registration Fees | 1 | ${{ registration_fee }} | ${{ registration_fee }} |
| Thank you for registering for BMUN. Please visit our website to find our deadlines for fees. In order to pay, please send your check to: | Fees Paid | ${{ fees_paid }} | |
| TOTAL DUE | ${{ amount_due }} | ||
|
Berkeley Model United Nations PO Box 4306 Berkeley, CA 94704-0306 United States |
THANK YOU. | ||
| Please email<EMAIL_ADDRESS>if you have any questions. If you wish to pay online via credit card, please email<EMAIL_ADDRESS>|
<EMAIL_ADDRESS>bmun.org |


Advertisement Data Fields¶
This part defines the advertisement data fields used in the btshell
app. For a complete list of all data types and
formats used for Extended Inquiry Response (EIR), Advertising Data (AD), and OOB data blocks, refer to the Supplement
to the Bluetooth Core Specification, CSSv6, available for download
here.
| Name | Definition | Details | btshell Notes |
|---|---|---|---|
| flags | Indicates basic information about the advertiser. | Flags used over the LE physical channel are: * Limited Discoverable Mode * General Discoverable Mode * BR/EDR Not Supported * Simultaneous LE and BR/EDR to Same Device Capable (Controller) * Simultaneous LE and BR/EDR to Same Device Capable (Host) | NimBLE will auto-calculate if set to 0. |
| uuid16 | 16-bit Bluetooth Service UUIDs | Indicates the Service UUID list is incomplete i.e. more 16-bit Service UUIDs available. 16 bit UUIDs shall only be used if they are assigned by the Bluetooth SIG. | Set repeatedly for multiple service UUIDs. |
| uuid16_is_complete | 16-bit Bluetooth Service UUIDs | Indicates the Service UUID list is complete. 16 bit UUIDs shall only be used if they are assigned by the Bluetooth SIG. | |
| uuid32 | 32-bit Bluetooth Service UUIDs | Indicates the Service UUID list is incomplete i.e. more 32-bit Service UUIDs available. 32 bit UUIDs shall only be used if they are assigned by the Bluetooth SIG. | Set repeatedly for multiple service UUIDs. |
| uuid32_is_complete | 32-bit Bluetooth Service UUIDs | Indicates the Service UUID list is complete. 32 bit UUIDs shall only be used if they are assigned by the Bluetooth SIG. | |
| uuid128 | Global 128-bit Service UUIDs | More 128-bit Service UUIDs available. | Set repeatedly for multiple service UUIDs. |
| uuid128_is_complete | Global 128-bit Service UUIDs | Complete list of 128-bit Service UUIDs | |
| tx_power_level | TX Power Level | Indicates the transmitted power level of the packet containing the data type. The TX Power Level data type may be used to calculate path loss on a received packet using the following equation: pathloss = Tx Power Level – RSSI where “RSSI” is the received signal strength, in dBm, of the packet received. | NimBLE will auto-calculate if set to -128. |
| slave_interval_range | Slave Connection Interval Range | Contains the Peripheral’s preferred connection interval range, for all logical connections. Size: 4 Octets . The first 2 octets defines the minimum value for the connection interval in the following manner: connIntervalmin = Conn_Interval_Min * 1.25 ms Conn_Interval_Min range: 0x0006 to 0x0C80 Value of 0xFFFF indicates no specific minimum. The other 2 octets defines the maximum value for the connection interval in the following manner: connIntervalmax = Conn_Interval_Max * 1.25 ms Conn_Interval_Max range: 0x0006 to 0x0C80 Conn_Interval_Max shall be equal to or greater than the Conn_Interval_Min. Value of 0xFFFF indicates no specific maximum. | |
| service_data_uuid16 | Service Data - 16 bit UUID | Size: 2 or more octets The first 2 octets contain the 16 bit Service UUID followed by additional service data | |
| public_target_address | Public Target Address | Defines the address of one or more intended recipients of an advertisement when one or more devices were bonded using a public address. This data type shall exist only once. It may be sent in either the Advertising or Scan Response data, but not both. | |
| appearance | Appearance | Defines the external appearance of the device. The Appearance data type shall exist only once. It may be sent in either the Advertising or Scan Response data, but not both. | |
| advertising_interval | Advertising Interval | Contains the advInterval value as defined in the Core specification, Volume 6, Part B, Section <IP_ADDRESS>. | |
| service_data_uuid32 | Service Data - 32 bit UUID | Size: 4 or more octets The first 4 octets contain the 32 bit Service UUID followed by additional service data | |
| service_data_uuid128 | Service Data - 128 bit UUID | Size: 16 or more octets The first 16 octets contain the 128 bit Service UUID followed by additional service data | |
| uri | Uniform Resource Identifier (URI) | Scheme name string and URI as a UTF-8 string | |
| mfg_data | Manufacturer Specific data | Size: 2 or more octets The first 2 octets contain the Company Identifier Code followed by additional manufacturer specific data | |
| eddystone_url |


Giacomo Bertoldi, Alberto Sartori, Stefano Cozzini
The Plan: The Five Ws and the How
I graduated with a thesis in HEP (phenomenology, hadron spectroscopy) at the University of Bari
Embedded Systems ≈ HPC
knowledge of the hardware, limited resource and relevance of computational time
Embedded Systems ≠ HPC
numerical algorithms, open-source development environment and higher level languages
Funny enough, thanks to a script:
# Sending MADs with Python!
for indx, s in schools.iterrows():
print("Sending to {}, {}".format(s.Istituto, s.Comune))
code = indx.lower()
send_email(code, "pec")
(You can find the whole script here)
More to the point, long interest in OSS and CS
a program that prepares students for careers in the fast-growing field of HPC
an integrated hydrological model that simulates the heat and water budgets at and below the soil surface
Trieste (SISSA and ICTP) and Bolzano (Eurac)
a private research institute based in Bolzano and with a strong focus on the alpine environment
GEOtop, scientifically advanced, but
codebase & documentation need a fix
(not uncommon among scientific software packages)
High Performance Computing
High Performance Computing
High Productivity Computing
So the title of this presentation should really be:
See previous talk...
GEOtop as library?
PyGEOtop? GEOtop.jl?
methods(sin) # ex. sin(x), x = 1, 3.14, 1 + 2im, [1 2; 2 1]...
# 12 methods for generic function "sin":
[1] sin(x::BigFloat) in Base.MPFR at mpfr.jl:743
[2] sin(::Missing) in Base.Math at math.jl:1072
[3] sin(a::Complex{Float16}) in Base.Math at math.jl:1020
[4] sin(a::Float16) in Base.Math at math.jl:1019
[5] sin(z::Complex{T}) where T in Base at complex.jl:796
[6] sin(x::T) where T<:Union{Float32, Float64} in Base.Math at special/trig.jl:30
[7] sin(x::Real) in Base.Math at special/trig.jl:53
[8] sin(A::LinearAlgebra.Hermitian{#s616,S} where S<:(AbstractArray{#s617,2} where #s617<:#s616) where #s616<:Complex) in LinearAlgebra at /build/julia/src/julia-1.2.0/usr/share/julia/stdlib/v1.2/LinearAlgebra/src/symmetric.jl:759
[9] sin(A::Union{LinearAlgebra.Hermitian{#s617,S}, LinearAlgebra.Symmetric{#s617,S}} where S where #s617<:Real) in LinearAlgebra at /build/julia/src/julia-1.2.0/usr/share/julia/stdlib/v1.2/LinearAlgebra/src/symmetric.jl:755
[10] sin(D::LinearAlgebra.Diagonal) in LinearAlgebra at /build/julia/src/julia-1.2.0/usr/share/julia/stdlib/v1.2/LinearAlgebra/src/diagonal.jl:468
[11] sin(A::AbstractArray{#s617,2} where #s617<:Real) in LinearAlgebra at /build/julia/src/julia-1.2.0/usr/share/julia/stdlib/v1.2/LinearAlgebra/src/dense.jl:786
[12] sin(A::AbstractArray{#s617,2} where #s617<:Complex) in LinearAlgebra at /build/julia/src/julia-1.2.0/usr/share/julia/stdlib/v1.2/LinearAlgebra/src/dense.jl:793
The research reported in this work will be supported by OGS and CINECA under HPC-TRES program award number 2019-33
using DifferentialEquations, ParameterizedFunctions, Plots
lorenz = @ode_def begin # define the system
dx = σ * (y - x)
dy = x * (ρ - z) - y
dz = x * y - β*z
end σ ρ β
u0 = [1.0,0.0,0.0] # initial conditions
tspan = (0.0,100.0) # timespan
p = [10.0,28.0,8/3] # parameters
prob = ODEProblem(lorenz, u0, tspan, p) # define the problem
sol = solve(prob) # solve it
plot(sol, vars = (1, 2, 3)) # plot solution
function getenv(var::AbstractString)
val = ccall((:getenv, "libc"),
Cstring, (Cstring,), var)
if val == C_NULL
error("getenv: undefined variable: ", var)
end
unsafe_string(val)
end
getenv("SHELL") # "/bin/bash"
using CuArrays, CUDAnative
xs, ys = CuArray(rand(1024)), CuArray(rand(1024))
zs = CuArray(zeros(1024))
function kernel_vadd(out, a, b)
i = (blockIdx().x-1) * blockDim().x + threadIdx().x
out[i] = a[i] + b[i]
return
end
@cuda (1, length(xs)) kernel_vadd(zs, xs, ys)
macro time(ex)
return quote
local t0 = time()
local val = $ex
local t1 = time()
println("elapsed time: ", t1-t0, " seconds")
val
end
end
@time sin(3.14) # output: elapsed time 0.000002 seconds


Tree Trunks
Tree
Fountain
Ball
Hey, bird!
Dragonfly
Camouflage
Fly fly away
Let's explore
Aim higher
Stoned
Yellow flower/h2>
Fashionable camel
No teeth, no problem
Good horse
A happy frown?
Dandelion
White flower
Red flower
Good weed?
Spider
Fairy dream
Pretty flower
Spongebob
Park
Pizza/h2>
Got a good date?
Swim
Kite 'em
Camel Ride
Badminton
Swing


Intuit Front End Bootcamp
grid
Amit
Arya
*
*
*
*
7 yrs at Intuit
Recently moved to MTV
Photography
this is a sample profile page. dont contact this guy.


Spacing
- src
- utilities
- spacing.scss
Class list
- .m-(level)
- .mt-(level)
- .mb-(level)
- .ml-(level)
- .mr-(level)
- .mx-(level)
- .my-(level)
- .m-(level)-(breakpoint)
- .mt-(level)-(breakpoint)
- .mb-(level)-(breakpoint)
- .ml-(level)-(breakpoint)
- .mr-(level)-(breakpoint)
- .mx-(level)-(breakpoint)
- .my-(level)-(breakpoint)
- .mx-auto
- .p-(level)
- .pt-(level)
- .pb-(level)
- .pl-(level)
- .pr-(level)
- .px-(level)
- .py-(level)
- .p-(level)-(breakpoint)
- .pt-(level)-(breakpoint)
- .pb-(level)-(breakpoint)
- .pl-(level)-(breakpoint)
- .pr-(level)-(breakpoint)
- .px-(level)-(breakpoint)
- .py-(level)-(breakpoint)
Description
In the above classes m stands for margin, and p stands for padding.
t stands for top, b stands for bottom, l stands for left, r stands for right, x stands for the x axis, and y stands for the y axis.
The values that can be plugged into (level) as seen above are determined by the $SPACING_LEVELS global variable, which Sandbox defaults to a value of 4. This means that the values 1, 2, 3, 4 could be plugged into (level). If you set $SPACING_LEVELS to 6 and ran a build, the values 1 - 6 could be plugged in.
A non integer value that can be plugged into (level), regardless of what $SPACING_LEVELS value is used, is half. Each of these values is a multiplier on the global $UNIT_SPACING value which is used.
As always, the values that can be plugged into (breakpoint) are any of the breakpoint suffixes: sm, md, lg.
Example
Reduce the width of your browser to see the example in action.

Margin x auto
Use the .mx-auto class on a block level element with a fixed width to center it along the x axis of its parent.


Thinking about abstract commands...
...does not work
at all
.
Thinking about abstract commands...
...does not work
at all
.
Motor Imagery
EEG setup
Visualization
Visualization
Supplementary materials
Visualization - bonus track
Pre- and post-test exercices


|
||||||||||
| PREV NEXT | FRAMES NO FRAMES |
| Packages that use com.hp.hpl.jena.rdf.model | |
|---|---|
| com.hp.hpl.jena.assembler | |
| com.hp.hpl.jena.db | A general database backend for persistent storage of Jena models. |
| com.hp.hpl.jena.enhanced | This package defines the enhanced node and graph classes; an enhanced node is one embedded in a particular enhanced graph. |
| com.hp.hpl.jena.graph | This package defines the Graph and Node family of classes, which form the underlying datatypes of the Jena system. |
| com.hp.hpl.jena.ontology | Provides a set of abstractions and convenience classes for accessing and manipluating ontologies represented in RDF. |
| com.hp.hpl.jena.rdf.arp | A parser for RDF/XML. |
| com.hp.hpl.jena.rdf.listeners | A package defining some useful implementations of
ModelChangedListener , for listening to
(a) all triples added or removed, exploding composite objects,
(b) all objects added or removed, as themselves,
(c) notification of additions/removals, but no details,
and (d) accepting but ignoring all changes, as a base-class to
be extended. |
| com.hp.hpl.jena.rdf.model | A package for creating and manipulating RDF graphs. |
| com.hp.hpl.jena.rdf.model.impl | This package contains implementations of the interfaces defined in the .model package, eg ModelCom for Model, ResourceImpl for Resource, and so on. |
| com.hp.hpl.jena.reasoner | The Jena2 reasoner subsystem is designed to allow a range of inference engines to be plugged into Jena. |
| com.hp.hpl.jena.reasoner.dig | |
| com.hp.hpl.jena.reasoner.rulesys | Provides a selection of simple rule engines for Jena inference models. |
| com.hp.hpl.jena.reasoner.transitiveReasoner | This package contains a reasoner which supports transitive-reflexive closure of subPropertyOf and subClassOf relations. |
| com.hp.hpl.jena.shared | This package defines some classes common to the Jena API and SPI levels, in particular the JenaException class from which all Jena-specific exceptions hang, and the interface PrefixMapping for translation to and from QNames. |
| com.hp.hpl.jena.util | Miscellaneous collection of utility classes. |
| com.hp.hpl.jena.vocabulary | A package containing constant classes with predefined constant objects for classes and properties defined in well known vocabularies. |
| com.hp.hpl.jena.xmloutput | Writing RDF/XML. |
| Classes in com.hp.hpl.jena.rdf.model used by com.hp.hpl.jena.assembler | |
|---|---|
| Literal
An RDF Literal. |
|
| Model
An RDF Model. |
|
| Property
An RDF Property. |
|
| RDFNode
Interface covering RDF resources and literals. |
|
| Resource
An RDF Resource. |
|
| Statement
An RDF Statement. |
| Classes in com.hp.hpl.jena.rdf.model used by com.hp.hpl.jena.db | |
|---|---|
| Model
An RDF Model. |
|
| ModelCon
Convenience methods which extend the Model interface. |
|
| ModelGraphInterface
ModelGraphInterface - this interface mediates between the API Model level and the SPI Graph level. |
|
| ModelLock
Deprecated. For naming compatibility - use Lock interface instead |
|
| RDFReaderF
An RDFReader factory inferface. |
|
| RDFWriterF
An RDFReader factory inferface. |
| Classes in com.hp.hpl.jena.rdf.model used by com.hp.hpl.jena.enhanced | |
|---|---|
| RDFNode
Interface covering RDF resources and literals. |
| Classes in com.hp.hpl.jena.rdf.model used by com.hp.hpl.jena.graph | |
|---|---|
| AnonId
Create a new id for an anonymous node. |
| Classes in com.hp.hpl.jena.rdf.model used by com.hp.hpl.jena.ontology | |
|---|---|
| InfModel
An extension to the normal Model interface that supports access to any underlying inference capability. |
|
| Literal
An RDF Literal. |
|
| Model
An RDF Model. |
|
| ModelCon
Convenience methods which extend the Model interface. |
|
| ModelGetter
A ModelGetter object can retrieve a Model given a URL for it. |
|
| ModelGraphInterface
ModelGraphInterface - this interface mediates between the API Model level and the SPI Graph level. |
|
| ModelLock
Deprecated. For naming compatibility - use Lock interface instead |
|
| ModelMaker
A ModelMaker contains a collection of named models, methods for creating new models [both named and anonymous] and opening previously-named models, removing models, and accessing a single "default" Model for this Maker. |
|
| NodeIterator
An iterator which returns RDF nodes. |
|
| Property
An RDF Property. |
|
| RDFList
Provides a convenience encapsulation for lists formed from chains of RDF statements arranged to form a head/tail cons-cell structure. |
|
| RDFNode
Interface covering RDF resources and literals. |
|
| RDFReaderF
An RDFReader factory inferface. |
|
| RDFWriterF
An RDFReader factory inferface. |
|
| Resource
An RDF Resource. |
|
| Statement
An RDF Statement. |
| Classes in com.hp.hpl.jena.rdf.model used by com.hp.hpl.jena.rdf.arp | |
|---|---|
| Model
An RDF Model. |
|
| RDFErrorHandler
A generic error handler. |
|
| RDFReader
An RDFReader reads a serialized represenation of RDF,
e.g. |
| Classes in com.hp.hpl.jena.rdf.model used by com.hp.hpl.jena.rdf.listeners | |
|---|---|
| Model
An RDF Model. |
|
| ModelChangedListener
The interface for classes that listen for model-changed events. |
|
| Statement
An RDF Statement. |
|
| StmtIterator
An iterator which returns RDF Statements. |
| Classes in com.hp.hpl.jena.rdf.model used by com.hp.hpl.jena.rdf.model | |
|---|---|
| Alt
An RDF Alternative container. |
|
| AnonId
Create a new id for an anonymous node. |
|
| Bag
An RDF Bag container. |
|
| Container
An RDF Container. |
|
| InfModel
An extension to the normal Model interface that supports access to any underlying inference capability. |
|
| Literal
An RDF Literal. |
|
| Model
An RDF Model. |
|
| ModelChangedListener
The interface for classes that listen for model-changed events. |
|
| ModelCon
Convenience methods which extend the Model interface. |
|
| ModelFactoryBase
Helper functions for ModelFactory - in here to keep from obtruding on the end-users. |
|
| ModelGetter
A ModelGetter object can retrieve a Model given a URL for it. |
|
| ModelGraphInterface
ModelGraphInterface - this interface mediates between the API Model level and the SPI Graph level. |
|
| ModelLock
Deprecated. For naming compatibility - use Lock interface instead |
|
| ModelMaker
A ModelMaker contains a collection of named models, methods for creating new models [both named and anonymous] and opening previously-named models, removing models, and accessing a single "default" Model for this Maker. |
|
| ModelReader
A component interface used by ModelGetter for reading models. |
|
| ModelSource
The revised and soon-to-be-core interface for sources of models, typically generated from RDF descriptions. |
|
| NodeIterator
An iterator which returns RDF nodes. |
|
| NsIterator
An iterator which returns namespace URI's. |
|
| ObjectF
A factory for creating objects from RDF Literal strings. |
|
| Property
An RDF Property. |
|
| RDFErrorHandler
A generic error handler. |
|
| RDFList
Provides a convenience encapsulation for lists formed from chains of RDF statements arranged to form a head/tail cons-cell structure. |
|
| RDFList.ApplyFn
Interface that encapsulates a function to apply to every element in a list. |
|
| RDFList.ReduceFn
Interface that encapsulates a function to apply to each element of a list in turn, and passing the result to an accumulator. |
|
| RDFNode
Interface covering RDF resources and literals. |
|
| RDFReader
An RDFReader reads a serialized represenation of RDF,
e.g. |
|
| RDFReaderF
An RDFReader factory inferface. |
|
| RDFVisitor
|
|
| RDFWriter
RDFWriter is an interface to RDF serializers. |
|
| RDFWriterF
An RDFReader factory inferface. |
|
| ReifiedStatement
A ReifiedStatement represents a Statement as a Resource. |
|
| ResIterator
An iterator which returns RDF Resources. |
|
| Resource
An RDF Resource. |
|
| ResourceF
Create an application specific resource instance. |
|
| ResourceFactory.Interface
the interface to resource factory objects. |
|
| RSIterator
An RSIterator is a ClosableIterator (qv) which delivers only ReifedStatement's and which has a next-method with that return type. |
|
| Selector
A Statement selector. |
|
| Seq
RDF Sequence container. |
|
| Statement
An RDF Statement. |
|
| StatementBoundary
An interface for expressing search boundaries in terms of bounding statements. |
|
| StmtIterator
An iterator which returns RDF Statements. |
| Classes in com.hp.hpl.jena.rdf.model used by com.hp.hpl.jena.rdf.model.impl | |
|---|---|
| Model
An RDF Model. |
|
| ModelCon
Convenience methods which extend the Model interface. |
|
| ModelGraphInterface
ModelGraphInterface - this interface mediates between the API Model level and the SPI Graph level. |
|
| ModelLock
Deprecated. For naming compatibility - use Lock interface instead |
|
| RDFReaderF
An RDFReader factory inferface. |
|
| RDFWriterF
An RDFReader factory inferface. |
| Classes in com.hp.hpl.jena.rdf.model used by com.hp.hpl.jena.reasoner | |
|---|---|
| Model
An RDF Model. |
|
| Property
An RDF Property. |
|
| Resource
An RDF Resource. |
| Classes in com.hp.hpl.jena.rdf.model used by com.hp.hpl.jena.reasoner.dig | |
|---|---|
| Model
An RDF Model. |
|
| Property
An RDF Property. |
|
| Resource
An RDF Resource. |
| Classes in com.hp.hpl.jena.rdf.model used by com.hp.hpl.jena.reasoner.rulesys | |
|---|---|
| Model
An RDF Model. |
|
| Property
An RDF Property. |
|
| Resource
An RDF Resource. |
| Classes in com.hp.hpl.jena.rdf.model used by com.hp.hpl.jena.reasoner.transitiveReasoner | |
|---|---|
| Model
An RDF Model. |
|
| Property
An RDF Property. |
|
| Resource
An RDF Resource. |
| Classes in com.hp.hpl.jena.rdf.model used by com.hp.hpl.jena.shared | |
|---|---|
| Model
An RDF Model. |
|
| Property
An RDF Property. |
|
| Resource
An RDF Resource. |
| Classes in com.hp.hpl.jena.rdf.model used by com.hp.hpl.jena.util | |
|---|---|
| Model
An RDF Model. |
|
| ModelCon
Convenience methods which extend the Model interface. |
|
| ModelGraphInterface
ModelGraphInterface - this interface mediates between the API Model level and the SPI Graph level. |
|
| ModelLock
Deprecated. For naming compatibility - use Lock interface instead |
|
| Property
An RDF Property. |
|
| RDFNode
Interface covering RDF resources and literals. |
|
| RDFReaderF
An RDFReader factory inferface. |
|
| RDFWriterF
An RDFReader factory inferface. |
|
| Resource
An RDF Resource. |
|
| Statement
An RDF Statement. |
| Classes in com.hp.hpl.jena.rdf.model used by com.hp.hpl.jena.vocabulary | |
|---|---|
| Model
An RDF Model. |
|
| Property
An RDF Property. |
|
| RDFNode
Interface covering RDF resources and literals. |
|
| Resource
An RDF Resource. |
| Classes in com.hp.hpl.jena.rdf.model used by com.hp.hpl.jena.xmloutput | |
|---|---|
| RDFWriter
RDFWriter is an interface to RDF serializers. |
|
||||||||||
| PREV NEXT | FRAMES NO FRAMES |


For this personal project, we imagined a dynamic & modular identity based on the theme "Imaginary Bestiary", to question the relation between humans & animals. We wanted to create something traditionnal & modern, based on modern art & anthropology. We based our identity around shapes and we randomly added some elements, animals or humans characteristics. So we can have a playful & animated logo.


--- layout: millenium title: About - Millenium Collection ---
Please refer to the size charts in this page before ordering an item. There's no refund if you buy the wrong size.
We ship to Canada, USA and worldwide.
Shipping is based on weight and per item.
Printed goods are not refundable in any case. Sizes charts are provided on every item, so make sure you take proper measurements before ordering. You can consult the policies of our different suppliers for more details :
Art of Where
If you have any questions about the shop or something else, feel free to write me a message on my social medias below or you can email me.


Or enter purchases manually...


Relations as well as obtaining them is a very special feature. Usually, this functionality not of interest of standard template formatting. This is because of it has been designed especially to be used together with logging libraries. Most of these logging libraries provide a parameter for additional logging information that almost always consist of a list of label–value–pairs. And this is exactly what this feature wants to support.
In detail, Relations means the relation between arguments and their occurrence inside the format string. And as already mentioned, this feature is reserved for a very specific use case, formatting of logging messages. But anyway, users may take advantage of this feature for any other purpose. Furthermore, this feature works for index–based as well as for label–based formatting instructions. But keep in mind, the name "Value" is prepended in case of index–based formatting instructions.
The example below shows how to obtain argument relations as well as how to process them.
IArgumentRelations relations;
String result = Template.Format(out relations, "text {1} text {0} text {2} text {1}", 111, "John Doe", 333.33);
Console.WriteLine(result);
foreach ((String Label, Object Value) relation in relations.ToArray())
{
Console.WriteLine($"* {relation.Label}: {relation.Value}");
}
The result printed out to the Console window would look like as shown here.
text John Doe text 111 text 333.33 text John Doe
* Value1: John Doe
* Value0: 111
* Value2: 333.33
As already stated, each label is prepended by name "Value" and is followed by the index of its occurrence within the format string. But this applies to index–based formatting only. Furthermore, redundant label–value–pairs are avoided.
Something similar is also possible with template parameters. But in such a case the template parameter label is used instead, as the next example demonstrates.
IArgumentRelations relations;
String result = Template.Format(out relations, "text {index} text {user} text {price}", 111, "John Doe", 333.33);
Console.WriteLine(result);
foreach ((String Label, Object Value) relation in relations.ToArray())
{
Console.WriteLine($"* {relation.Label}: {relation.Value}");
}
In this case the result printed out to the Console window would look like as shown here.
text 111 text John Doe text 333.33
* index: 111
* user: John Doe
* price: 333.33
Just a final note about argument processing. The “overhanging” arguments are not returned in the result, if the number of objects in the argument array is greater than the number of elements referenced by the format string.


|
QuantLib: a free/open-source library for quantitative finance
Fully annotated sources - version 1.30
|
Risk neutral terminal probability density for the Heston model. More...
#include <ql/methods/finitedifferences/utilities/hestonrndcalculator.hpp>
Public Member Functions | |
| HestonRNDCalculator (ext::shared_ptr< HestonProcess > hestonProcess, Real integrationEps=1e-6, Size maxIntegrationIterations=10000UL) | |
| Real | pdf (Real x, Time t) const override |
| Real | cdf (Real x, Time t) const override |
| Real | invcdf (Real q, Time t) const override |
| Public Member Functions inherited from RiskNeutralDensityCalculator | |
| virtual Real | pdf (Real x, Time t) const =0 |
| virtual Real | cdf (Real x, Time t) const =0 |
| virtual Real | invcdf (Real p, Time t) const =0 |
| virtual | ~RiskNeutralDensityCalculator ()=default |
Private Member Functions | |
| Real | x_t (Real x, Time t) const |
Private Attributes | |
| const ext::shared_ptr< HestonProcess > | hestonProcess_ |
| const Real | x0_ |
| const Real | integrationEps_ |
| const Size | maxIntegrationIterations_ |
Risk neutral terminal probability density for the Heston model.
References:
The formulas are taken from A. Dragulescu, V. Yakovenko, 2002. Probability distribution of returns in the Heston model with stochastic volatility. http://arxiv.org/pdf/cond-mat/0203046.pdf
Definition at line 45 of file hestonrndcalculator.hpp.
|
explicit |
Definition at line 110 of file hestonrndcalculator.cpp.
Implements RiskNeutralDensityCalculator.
Definition at line 123 of file hestonrndcalculator.cpp.
Implements RiskNeutralDensityCalculator.
Definition at line 130 of file hestonrndcalculator.cpp.
Implements RiskNeutralDensityCalculator.
Definition at line 138 of file hestonrndcalculator.cpp.
|
private |
Definition at line 59 of file hestonrndcalculator.hpp.
|
private |
Definition at line 60 of file hestonrndcalculator.hpp.
|
private |
Definition at line 61 of file hestonrndcalculator.hpp.
|
private |
Definition at line 62 of file hestonrndcalculator.hpp.


class: center, middle, inverse, title-slide # Supervised learning ###
Data Analytics for Psychology and Business
### April 2019 --- layout: true
        cdsbasel.github.io/dataanalytics/      Data Analytics for Psychology and Business | April 2019
 --- class: center, middle
Fitting

Evaluation

Tuning
 --- .pull-left45[ # Fitting

 Models are actually <high>families of models</high>, with every parameter combination specifying a different model. To fit a model means to <high>identify</high> from the family of models <high>the specific model that fits the data best</high>. ] .pull-right45[

 adapted from explainxkcd.com
 ] --- # Which of these models is better? Why?  --- # Which of these models is better? Why?  --- # Loss function .pull-left45[ Possible <high>the most important concept</high> in statistics and machine learning. The loss function defines some <high>summary of the errors committed by the model</high>.
 `$$\Large Loss = f(Error)$$`
 Two purposes

  Purpose 	  Description

  Fitting 	  Find parameters that minimize loss function.

  Evaluation 	  Calculate loss function for fitted model.

 ] .pull-right45[  ] --- class: center, middle <high>
Regression
</high>
Decision Trees

Random Forests
 --- # Regression .pull-left45[ In [regression](https://en.wikipedia.org/wiki/Regression_analysis), the criterion `\(Y\)` is modeled as the <high>sum</high> of <high>features</high> `\(X_1, X_2, ...\)` <high>times weights</high> `\(\beta_1, \beta_2, ...\)` plus `\(\beta_0\)` the so-called the intercept.

 `$$\large \hat{Y} = \beta_{0} + \beta_{1} \times X_1 + \beta_{2} \times X2 + ...$$`

 The weight `\(\beta_{i}\)` indiciates the <high>amount of change</high> in `\(\hat{Y}\)` for a change of 1 in `\(X_{i}\)`. Ceteris paribus, the <high>more extreme</high> `\(\beta_{i}\)`, the <high>more important</high> `\(X_{i}\)` for the prediction of `\(Y\)` (Note: the scale of `\(X_{i}\)` matters too!). If `\(\beta_{i} = 0\)`, then `\(X_{i}\)` <high>does not help</high> predicting `\(Y\)` ] .pull-right45[  ] --- # Regression loss .pull-left45[
  Mean Squared Error (MSE)

<high>Average squared distance</high> between predictions and true values?
 $$ MSE = \frac{1}{n}\sum_{i \in 1,...,n}(Y_{i} - \hat{Y}_{i})^{2}$$
 Mean Absolute Error (MAE)

<high>Average absolute distance</high> between predictions and true values?
 $$ MAE = \frac{1}{n}\sum_{i \in 1,...,n} \lvert Y_{i} - \hat{Y}_{i} \rvert$$
 ] .pull-right45[  ] --- # Fitting .pull-left45[ There are two fundamentally different ways to find the set of parameters that minimizes loss.  Analytically  In rare cases, the parameters can be <high>directly calculated</high>, e.g., using the normal equation: `$$\boldsymbol \beta = (\boldsymbol X^T\boldsymbol X)^{-1}\boldsymbol X^T\boldsymbol y$$`  Numerically  In most cases, parameters need to be found using a <high>directed trial and error</high>, e.g., gradient descent: `$$\boldsymbol \beta_{n+1} = \boldsymbol \beta_{n}+\gamma \nabla F(\boldsymbol \beta_{n})$$` ] .pull-right45[

 adapted from me.me
 ] --- .pull-left45[ # Fitting

 There are two fundamentally different ways to find the set of parameters that minimizes loss.  Analytically  In rare cases, the parameters can be <high>directly calculated</high>, e.g., using the normal equation: `$$\boldsymbol \beta = (\boldsymbol X^T\boldsymbol X)^{-1}\boldsymbol X^T\boldsymbol y$$`  Numerically  In most cases, parameters need to be found using a <high>directed trial and error</high>, e.g., gradient descent: `$$\boldsymbol \beta_{n+1} = \boldsymbol \beta_{n}+\gamma \nabla F(\boldsymbol \beta_{n})$$` ] .pull-right45[
<br2>

 adapted from dunglai.github.io

 adapted from dunglai.github.io
 ] --- # 2 types of supervised problems .pull-left5[ There are two types of supervised learning problems that can often be approached using the same model. Regression Regression problems involve the <high>prediction of a quantitative feature</high>. E.g., predicting the cholesterol level as a function of age. Classification Classification problems involve the <high>prediction of a categorical feature</high>. E.g., predicting the type of chest pain as a function of age. ] .pull-right4[

 ] --- # Logistic regression .pull-left45[ In [logistic regression](https://en.wikipedia.org/wiki/Logistic_regression), the class criterion `\(Y \in (0,1)\)` is modeled also as the <high>sum of feature times weights</high>, but with the prediction being transformed using a <high>logistic link function</high>:

 `$$\large \hat{Y} = Logistic(\beta_{0} + \beta_{1} \times X_1 + ...)$$`

 The logistic function <high>maps predictions to the range of 0 and 1</high>, the two class values.

 $$ Logistic(x) = \frac{1}{1+exp(-x)}$$ ] .pull-right45[  ] --- # Logistic regression .pull-left45[ In [logistic regression](https://en.wikipedia.org/wiki/Logistic_regression), the class criterion `\(Y \in (0,1)\)` is modeled also as the <high>sum of feature times weights</high>, but with the prediction being transformed using a <high>logistic link function</high>:

 `$$\large \hat{Y} = Logistic(\beta_{0} + \beta_{1} \times X_1 + ...)$$`

 The logistic function <high>maps predictions to the range of 0 and 1</high>, the two class values.

 $$ Logistic(x) = \frac{1}{1+exp(-x)}$$ ] .pull-right45[  ] --- # Classification loss - two ways .pull-left45[ Distance Logloss is <high>used to fit the parameters</high>, alternative distance measures are MSE and MAE. `$$\small LogLoss = -\frac{1}{n}\sum_{i}^{n}(log(\hat{y})y+log(1-\hat{y})(1-y))$$` `$$\small MSE = \frac{1}{n}\sum_{i}^{n}(y-\hat{y})^2, \: MAE = \frac{1}{n}\sum_{i}^{n} \lvert y-\hat{y} \rvert$$` Overlap Does the <high>predicted class match the actual class</high>. Often preferred for <high>ease of interpretation</high>. `$$\small Loss_{01}=\frac{1}{n}\sum_i^n I(y \neq \lfloor \hat{y} \rceil)$$` ] .pull-right45[  ] --- # Confusion matrix .pull-left45[ The confusion matrix <high>tabulates prediction matches and mismatches</high> as a function of the true class. The confusion matrix permits specification of a number of <high>helpful performance metrics</high>.
  Confusion matrix

  	  <eq>ŷ = 1</eq> 	  <eq>ŷ = 0</eq>

  <eq>y = 1</eq> 	   True positive (TP) 	   False negative (FN)

  <eq>y = 0</eq> 	   False positive (FP) 	   True negative (TN)

 ] .pull-right45[ Accuracy: Of all cases, what percent of predictions are correct? `$$\small Acc. = \frac{TP + TN}{ TP + TN + FN + FP} = 1-Loss_{01}$$`

 Sensitivity: Of the truly Positive cases, what percent of predictions are correct? `$$\small Sensitivity = \frac{TP}{ TP +FN }$$` Specificity: Of the truly Negative cases, what percent of predictions are correct?

 `$$\small Specificity = \frac{TN}{ TN + FP }$$` ] --- # Confusion matrix .pull-left45[ The confusion matrix <high>tabulates prediction matches and mismatches</high> as a function of the true class. The confusion matrix permits specification of a number of <high>helpful performance metrics</high>.
  Confusion matrix

  	  <eq>"Default"</eq> 	  <eq>"Repay"</eq>

  <eq>Default</eq> 	   TP = 3 	   FN = 1

  <eq>Repay</eq> 	   FP = 1 	   TN = 2

 ] .pull-right45[ Accuracy: Of all cases, what percent of predictions are correct? `$$\small Acc. = \frac{TP + TN}{ TP + TN + FN + FP} = 1-Loss_{01}$$`

 Sensitivity: Of the truly Positive cases, what percent of predictions are correct? `$$\small Sensitivity = \frac{TP}{ TP +FN }$$` Specificity: Of the truly Negative cases, what percent of predictions are correct?

 `$$\small Specificity = \frac{TN}{ TN + FP }$$` ] --- class: center, middle
Fitting

Evaluation

Tuning
 --- # Hold-out data .pull-left45[ Model performance must be evaluated as true prediction on an <high>unseen data set</high>. The unseen data set can be <high>naturally</high> occurring, e.g., using 2019 stock prizes to evaluate a model fit using 2018 stock prizes. More commonly unseen data is created by <high>splitting the available data</high> into a training set and a test set. ] .pull-right45[

 ] --- # Training Training a model means to <high>fit the model</high> to data by finding the parameter combination that <high>minizes some error function</high>, e.g., mean squared error (MSE).

 --- # Test To test a model means to <high>evaluate the prediction error</high> for a fitted model, i.e., for a <high>fixed parameter combination</high>.

 --- .pull-left4[

 # Overfitting Occurs when a model <high>fits data too closely</high> and therefore <high>fails to reliably predict</high> future observations. In other words, overfitting occurs when a model <high>'mistakes' random noise for a predictable signal</high>. More <high>complex models</high> are more <high>prone to overfitting</high>. ] .pull-right5[

 ] --- # Overfitting  --- class: center, middle <high>
Regression
</high>
Decision Trees

Random Forests
 --- # Regularized regression .pull-left45[ Penalizes regression loss for having large `\(\beta\)` values using the <high>lambda λ tuning parameter</high> and one of several penalty functions. $$Regularized \;loss = \sum_i^n (y_i-\hat{y}_i)^2+\lambda \sum_j^p f(\beta_j)) $$

  Name 	  Function 	  Description

  Lasso 	  |βj| 	  Penalize by the <high>absolute</high> regression weights.

  Ridge 	  βj2 	  Penalize by the <high>squared</high> regression weights.

  Elastic net 	  |βj| + βj2 	  Penalize by Lasso and Ridge penalties.

 ] .pull-right45[

 from mallorcazeitung.es
 ] --- .pull-left45[ # Regularized regression

 Despite <high>superficial similarities</high>, Lasso and Ridge show very different behavior.

 Ridge By penalizing the most extreme βs most strongly, Ridge leads to (relatively) more <high>uniform βs</high>.

 Lasso By penalizing all βs equally, irrespective of magnitude, Lasso drives some βs to 0 resulting effectively in <high>automatic feature selection</high>. ] .pull-right45[

 Ridge

 from James et al. (2013) ISLR

 Lasso

 from James et al. (2013) ISLR
 ] --- class: center, middle
Regression
 <high>
Decision Trees
</high>
Random Forests
 --- # CART .pull-left45[ CART is short for <high>Classification and Regression Trees</high>, which are often just called <high>Decision trees</high>. In [decision trees](https://en.wikipedia.org/wiki/Decision_tree), the criterion is modeled as a <high>sequence of logical TRUE or FALSE questions</high>.

 ] .pull-right45[

 ] --- # Classificiation trees .pull-left45[ Classification trees (and regression trees) are created using a relatively simple <high>three-step algorithm</high>. Algorithm 1 - <high>Split</high> nodes to maximize purity gain (e.g., Gini gain). 2 - <high>Repeat</high> until pre-defined threshold (e.g., `minsplit`) splits are no longer possible. 3 - <high>Prune</high> tree to reasonable size. ] .pull-right45[

 ] --- # Node splitting .pull-left45[ Classification trees attempt to <high>minize node impurity</high> using, e.g., the <high>Gini coefficient</high>. `$$\large Gini(S) = 1 - \sum_j^kp_j^2$$` Nodes are <high>split</high> using the variable and split value that <high>maximizes Gini gain</high>. `$$Gini \; gain = Gini(S) - Gini(A,S)$$` with `$$Gini(A, S) = \sum \frac{n_i}{n}Gini(S_i)$$` ] .pull-right45[

 ] --- # Pruning trees .pull-left45[ Classification trees are <high>pruned</high> back such that every split has a purity gain of at least <high><mono>cp</mono></high>, with `cp` typically set to `.01`. Minimize:
 $$ \large `\begin{split} Loss = & Impurity\,+\\ &cp*(n\:terminal\:nodes)\\ \end{split}` $$ ] .pull-right45[

 ] --- # Pruning trees .pull-left45[ Classification trees are <high>pruned</high> back such that every split has a purity gain of at least <high><mono>cp</mono></high>, with `cp` typically set to `.01`. Minimize:
 $$ \large `\begin{split} Loss = & Impurity\,+\\ &cp*(n\:terminal\:nodes)\\ \end{split}` $$ ] .pull-right45[

 ] --- # Regression trees .pull-left45[ Trees can also be used to perform regression tasks. Instead of impurity, regression trees attempt to <high>minimize within-node variance</high> (or maximize node homogeneity): `$$\large SSE = \sum_{i \in S_1}(y_i - \bar{y}_1)^2+\sum_{i \in S_2}(y_i - \bar{y}_2)^2$$` Algorithm 1 - <high>Split</high> nodes to maximize homogeneity gain. 2 - <high>Repeat</high> until pre-defined threshold (e.g., `minsplit`) splits are no longe possible. 3 - <high>Prune</high> tree to reasonable size. ] .pull-right45[

 ] --- class: center, middle
Regression

Decision Trees
 <high>
Random Forests
</high> --- .pull-left45[ # Random Forest

 In [Random Forest](https://en.wikipedia.org/wiki/Random_forest), the criterion is modeled as the <high>aggregate prediction of a large number of decision trees</high> each based on different features.
 Algorithm 1 - <high>Repeat</high> *n* times 1 - <high>Resample</high> data 2 - <high>Grow</high> non-pruned decision tree Each split <high>consider only m features</high> 2 - <high>Average</high> fitted values ] .pull-right45[

 ] --- # Random Forest .pull-left45[

 Random forests make use of important machine learning elements, <high>resampling</high> and <high>averaging</high> that together are also referred to as <high>bagging</high>.
 <col width="30%"> <col width="70%">
  Element 	  Description

  Resampling 	  Creates new data sets that vary in their composition thereby <high>deemphasizing idiosyncracies</high> of the available data.

  Averaging 	  Combining predictions typically <high>evens out idiosyncracies</high> of the models created from single data sets.

 ] .pull-right45[

 ] --- # Random Forest .pull-left45[

 Random forests make use of important machine learning elements, <high>resampling</high> and <high>averaging</high> that together are also referred to as <high>bagging</high>.
 <col width="30%"> <col width="70%">
  Element 	  Description

  Resampling 	  Creates new data sets that vary in their composition thereby <high>deemphasizing idiosyncracies</high> of the available data.

  Averaging 	  Combining predictions typically <high>evens out idiosyncracies</high> of the models created from single data sets.

 ] .pull-right45[

 ] --- class: center, middle
Fitting

Evaluation

Tuning
 --- # Tuning .pull-left45[ All machine learning models are equipped with tuning parameters that <high> control model complexity<high>. These tuning parameters can be identified using a <high>validation set</high> created from the traning data. Logic 1 - Create separate test set. 2 - Fit model using various tuning parameters. 3 - Select tuning leading to best prediction on validation set. 4 - Refit model to entire training set (training + validation). ] .pull-right45[

 ] --- # Resampling methods .pull-left4[ Resampling methods automatize and generalize model tuning.
 <col width="30%"> <col width="70%">
  Method 	  Description

  k-fold cross-validation 	  Splits the data in k-pieces, use <high>each piece once</high> as the validation set, while using the other one for training.

  Bootstrap 	  For B bootstrap rounds <high>sample</high> from the data <high>with replacement</high> and split the data in training and validation set.

 ] .pull-right5[

 ] --- # Resampling methods .pull-left4[ Resampling methods automatize and generalize model tuning.
 <col width="30%"> <col width="70%">
  Method 	  Description

  k-fold cross-validation 	  Splits the data in k-pieces, use <high>each piece once</high> as the validation set, while using the other one for training.

  Bootstrap 	  For B bootstrap rounds <high>sample</high> from the data <high>with replacement</high> and split the data in training and validation set.

 ] .pull-right5[

 ] --- # Resampling methods .pull-left4[ Resampling methods automatize and generalize model tuning.
 <col width="30%"> <col width="70%">
  Method 	  Description

  k-fold cross-validation 	  Splits the data in k-pieces, use <high>each piece once</high> as the validation set, while using the other one for training.

  Bootstrap 	  For B bootstrap rounds <high>sample</high> from the data <high>with replacement</high> and split the data in training and validation set.

 ] .pull-right5[

 ] --- .pull-left45[ # k-fold cross validation for Ridge and Lasso

 Goal Use 10-fold cross-validation to identify <high>optimal regularization parameters</high> for a regression model. Consider `\(\alpha \in 0, .5, 1\)` and `\(\lambda \in 1, 2, ..., 100\)` ] .pull-right45[

 ] --- class: middle, center
Materials


WITNESS LIST
State Affairs Committee
May 21, 2015 - 8:00 AM
For:
Gore, Rex (Self)
Olsen, Brian (Self; Ucjet)
Against:
Bell, Ronda (AFscme)
Capo, Zeph (Self; Houston Federation of Teachers, Houston Community College System)
Cole, John (Self)
Coyle, Steve (Self; Education Austin)
Dewey, Andrew (Self)
Duron, Carmen (Self; Texas State Employees Union)
Hernandez, Sally (Self)
Levy, Rick (Texas aflcio)
Lowry, Lance (AFSCME Texas Correctional Employees - Huntsville)
Lugo, Judy (Texas State Employees Union)
Maxey, Glen (Self)
Patrick, John (Self; Texas AFL CIO)
Perkins, Cindy (Self)
Plock, August (Self)
Siegelin, Cheri (Afscme)
Wilson, Cathe (Self; Gatesville local 3920)
Registering, but not testifying:
For:
Bailey, Brad (Self; Keep Texas Working)
Brooks, Jeffrey (Texas Conservative Coalition)
Chambers, Kimberly (Self)
Cubriel, Beth (Republican party of texas)
Dyer, Don (Self)
Dyer, Don (Self)
Fisher, Jon (Associated Builders and Contractors of Texas)
McDonald, Tony (Self)
Norman, Scott (Texas Association of Builders)
Posey, Jake (International Franchise Association)
Simpson, Stephanie (TX Assn of Manufacturers)
Southwell, Brent (Self)
Spilman, Annie (National Federation of Independent Business/TX)
Sullivan, Michael (Self; Empower Texans / Texans for Fiscal Responsibility)
Tarlton, Patrick (Texas Chemical Council)
Venable, Peggy (Self; Americans for Prosperity-Texas)
Against:
Aguilar, Leonard (Self; Southwest Pipe Trades Association)
Albert, David (Self)
1
WITNESS LIST
Against:
Alvarado, Aidan (Laredo Fire Fighters Association)
Arabie, Joe (Texas afl-cio)
Armentrout, Nathan (Self)
Bailey, Richard (Self; AFSCME 1624)
Barrientos, Alicia (Self)
Beardall, Bill (Self)
Beneski, Amy (Texas Association of School Administrators)
Blanton, Chris (Self; Afscme)
Boggess, Bethany (Self; Workers defense project)
Booker, Denee (Self)
Box, Daniel (Self; Houston city workers)
Bradley, Susan (Texas State Employees Union)
Briggs, Terry (Self; Brotherhood of Locomotive Engineers and Trainmen)
Brown, Caitlin (Self)
Brown, Garry (Self)
Buhler, Gail (Self)
Bunker, Phil (Teamsters)
Callaway, Anthony (Self; Afscme 1624)
Canales Zarate, Maria (Self)
Caraway, TG (Self)
Carlton, Kristin (Self; AFSCME 1624)
Carrillovaldez, Lydia (Self; Eduaction)
Castro, Susan (Self; San Antonio Aliiance of Teachers and Support Personnel)
Cebulski, Mark (Self; AFSCME Texas Retirees)
cervella, Jennifer (Self)
Christian, DeBoss (Self; A.T.U.1338)
Cofer, Rick (Self; AFSCME)
Cole, Cynthia (Self; Afscme 1550)
Coleman, Debra (Self; Texas State Employees Union)
Corbitt, Chris (Self; Afscme 1624)
Cortez, Judy (Self; AFSCME 1624)
Cox, Nancy (Self)
Crews, Andre (Texas Department of Criminal Justice)
Crow, David (Arlington Professional Fire Fighters)
Crow, David (Arlington Professional Fire Fighters)
Cummins, Tom (Self; Bexar County Federation of Teachers)
Cunningham, Michael (Self; Texas State Building and Construction Trades Council)
Dailey, Sean (Self; Houston Professional Firefighters Association)
Das, Saurabh (Self)
Day, Kenneth (Self; Amalgamated transit union 1338)
Deshields, Glenn (Texas State Association of Fire Fighters)
Dillard, Annabelle (Self)
Dominguez, Brenda (Self)
Dominguez, John (Self)
Dubon, Alex (Self)
2
WITNESS LIST
Against:
Dunlap, Traci (Self)
Earnest, Daniel (Self; San Antonio Police Officers Association)
Eaton, Holly (Texas classrrom teachers Assoc)
English, Brian (SMART-TD)
Fetonte, Barbara (Self; Texas state employees union)
Flores, Mando (Self; Afscme local 1624)
Forbes, Lee (Asian Pacific American Labor Alliance)
Foster, David (Self)
Franks, Margie (Self)
Frazier, Frederick (Self; Dallas police association)
Fullerton, Amanda (Workers Defense Project)
Gallardo, Maxie (Workers Defense Project)
Gardner, Andrea (Self; International Longshoremen's Association)
Garibay, Montserrat (Self)
Giarratani, Dominic (Texas Association of School Boards)
Greene, Joshua (Self; Northside American Federation of Teachers)
Gregg, Brock (Self; Association of tx professional educators)
Guerrero, Kenneth (Self)
Guthrie, Carol (Self; AFscme 1624)
Hallford, Currie (CWA/ Texas Political and Legislative Committee)
Hamilton, Bill (Self; Retired state employees association)
Harris, Dwight (Self; Texas American Federation of Teachers)
Hartman, Eric (Self)
Haverlah, Leroy (Self)
Henry, Craig (Self)
Hernandez, Mary esther (Self; Texas State Employees Union)
Hiner, Harrison (Texas State Employees Union)
Hinojosa, Mike (Self)
Hollingsworth, Lonnie (Texas Classroom Teachers Association)
Howell, Gary (AFSCME 1624)
Hunt, Ray (Houston Police Officers' Union)
Hunter, Amy (Self; AFSCME 1624)
Jimenez, Maria (Self)
Johnson, Aaron (Self; Equal Justice Center)
Johnson, Anthony (Self)
Jones, Chris (Self; Combined Law Enforcement Associations of Texas (CLEAT))
Jones, Thomas (AFSCME)
Kennedy, Mary (Self)
Kiluk, Todd (Self; Afscme)
Kirchner, Gabe (Self)
Kirk, Susie (Self)
Klinke, Bernard (Self; Education Austin)
Lara, Rene (Texas AFL-CIO)
Lawrence, Kevin (Tmpa)
Lee, Cheryl (CWA 6132)
3
WITNESS LIST
Against:
Lee, David (Self; Alliance/AFT)
Lee, Virginia (Self; AFSCME)
Lewis, Anne (Self)
Lopez, Carisa (Self; Texas State Employees Union)
Lopez, Carlos (Self)
Lopez, Jason (Self; AFSCME 1624)
Magee, Rachel (Self; International Alliance of Theatrical Stage Employees Local 205)
Mahoney, Tim (Self)
Maldonado, Mark (Self)
Malfaro, Louis (Self; Texas AFT)
Marquardt, Anthony (Atcems association)
Martin, Ed (Texas State Teachers Association)
Marves, Horace (Self; Transport union local 260)
Mashberg, Amy (Self)
McCoy, Brittany (Self)
McCulloch, Mark (Self)
Mckim, Mark (Self)
Melina Raab, Ted (Self; Texas AFT (American Federation of Teachers))
Molina, Raymond (Self; Afscme)
Morales, George (AFSCME 1624)
Moreno, Randy (Self; AustinFirefighters Association)
Musselman, KT (Self)
Nicks, Bob (Austin Firefighters Association)
O'Brien, Krissy (Self; AFSCME 1624)
Oldham, Pamela (Self)
Ortiz, Chrisrina (Self)
Owen, Martha (Self; Texas aft)
Pannell, Lisa (Self; Education Austin)
Peavler, Jason (Communications Workers of America Local 6132)
Pedigo, Gary (Self; Brotherhood of Locomotive Engineers and Trainman)
Perez-Wiseley, Teresa (Self; Texas alliance for Retired Americans)
Perkins, Crystal (Self)
Phillips, Atha (Self; AfscME 1624)
Potter, Shelley (Self; San Antonio Alliance of Teachers and Support Personnel)
Powers, Maura (Self; Afscme tx retirees)
Quinzi, Patty (Self)
Ramon, Andy (Self)
Ramsey, Ronald (Self; Ibew local union 500)
Rodriguez, Jimmy (Self; San Antonio Police Officers Association)
Rotz, Jessica (Self)
Rush, Barbara (Self)
Saunders, Kamron (SMART - TD)
Scallon, Susan (Self; AFSCME 1624)
Scott, Glenn (Self)
Seely, Peggy (Self; AFSCME Texas Retirees Chapter 12)
4
WITNESS LIST
Against:
Shea, Brigid (Self)
Sidney, Cynthia (Self; Houston organization of public employees)
Sigala ramon, Mayra (Self)
Sills, Ed (Self)
Sisson, John (Self)
Smith, Dean (Self; Afscme)
Smith, Stacey (Self; Education Austin)
Smith, Tom "Smitty" (Self; Public citizen)
Smolka, Doug (Self; San Antonio Building Trades Council)
Speight, Calvin (Self; Plumbers Local 68)
Speight, Dennis (Self)
Stalinsky, Jason (Self)
Stewart, Bob (Self)
Studebaker, Jenn (Self)
Suits, Stacy (Self)
Takahashi, Joean (Self; Afscme)
Tarr, Tanya (Self; Texas AFT)
Tedford, Mark (Self)
Terry, Mark (Texas Elementary Principals and Supervisors Association)
Thomas, David (Self)
Torrence, Philesha (Self; AFL CIO)
Turrubiarte, Rebecca (Self; Alfmc)
Umphress, John (Self; Afscme 1624)
Underwood, Trasell (Self; Education austin)
Vigil, Maria (Self; AFScme)
Villarreal, Johnny (Houston fire fighters local 341)
Wagnerr, Christian (Self; International Brotherhood of Electrical Workers Local 520)
Wald, Jerome (Self; Texas State Employees Union)
Washenfelder, Karrie (Self; Fort bend employee federation)
White, Kirk (Self; Education Austin)
Wilkison, Charley (Self; Combined Law Enforcement Associations of Texas)
Wolf, Lacy (Self; Heat and Frost Insulators and Allied Workers Local 22)
WyliE, Jason (Self)
Zarate, Juan (Self)
Zarifis, Ken (Self; Education Austin)
Zavodny, Donald (AFSCME Texas Correction)
5


Lesson 4 - Multi-body Problem: A better solution
Notes
Downloads
Python Libraries
Note: put these in the same directory as your .pynb notebooks.
Data
Galaxy snapshot files - different resolutions
Articles
- Integrators Article - this article describes the fundamental problems to solving the motions of multiple gravitating systems and discusses several solvers (including ones we’ve looked at in this class). (Calculus is involved.)
Links
Other exoplanets
Materials
- Lecture 4, Part 1 - Higher order solvers: (view,
source, present)
- Lecture 4, Part 2 - Onto N-body!: (view,
source, present)
- Notebook Part1_programming_hermite_notes_lesson04:
(view,
download)
- Notebook Part2_hermite_in2d_nbody:
(view,
download)
- Notebook Part3_programming_readWriteData_keplersData_lesson04:
(view,
download)
- Notebook inClass_lesson04:
(view,
download)


--- title: Oh, Woe is We(eds)! date: 2013-07-17 00:00:00 -06:00 categories: - whats-blooming layout: post blog-banner: whats-blooming-now-summer.jpg post-date: July 17, 2013 post-time: 5:18 PM blog-image: wbn-default.jpg ---
Bittersweet Nightshade Solanum dulcamara
This European native has been introduced to most continents and grows from sea level to over 1.25 miles above sea level in the Alps.
Bittersweet Nightshade Fruit Solanum dulcamara
While the fruit of this plant is poisonous, the plant itself was used to treat eczema, rheumatism and whooping cough in Europe for hundreds of years.
Redroot Pigweed Amaranthus retroflexus
This native annual was used by Iroquois witchcraft practitioners to make a person break out in cancer, and by the Navajo people as an antidote to snakebites.
Russian Olive Elaeagnus angustifolia
This native to Europe and Central Asia is considered an invasive, noxious weed through much of the United States due to it aggressively displacing native cottonwoods and willows along waterways.
American Willowherb Epilobium ciliatum
This perennial native has become a favorite food source for the invasive Australian Grapevine Moth's larvae here in North America, and has become invasive in China and Europe after being introduced in those countries.
Common Purslane Portulaca oleracea
While this invasive annual is mostly considered a lawn weed in the U.S., it is grown extensively as a vegetable that is eaten fresh or in soups throughout Europe, Asia and Mexico.
Hoe Nightshade Solanum physalifolium
This invasive annual is known to carry a bacterium that causes brown rot, which can spread when rivers along which the plant are growing are tapped to irrigate potato fields.


Education
State College of Florida
Location: Bradenton, Fl
Degree: Associates of the Arts
GPA: 3.16
Brigham Young University - Idaho
Location: Rexburg, ID
Degree: Bachelor's of Science in Software Engineering
Minor: Computer Information Technology
GPA: 3.2


Trailer Values
Initialise a trailer object
Is the trailer connected to the truck
Current wheel values of the trailer, like rotation , substance, ... for more information
Constant wheel values like Count, Radius, ... for more information
Acceleration values of the trailer for more informaiton
Id for internal use by code. Limited to C-identifier characters and dots
Name of cargo accessory for internal use by code. Limited to C-identifier characters and dots.
States of the Wheels
Positive velocity corresponds to forward movement
Value is from <0.25, 0.25> range in counterclockwise direction when looking from top (e.g. 0.25 corresponds to left and -0.25 corresponds to right)
Set to zero for non-steered wheels
Value is from <0.0, 1.0) range in which value increase corresponds to forward movement
For use with simple lifted/ non-lifted test or logical visualization of the lifting progress.
Value of 0 corresponds to non-lifted axle.
Value of 1 corresponds to fully lifted axle. Set to zero or not provided for non-liftable axles.
Might have non-linear relation to lift ratio. Set to zero or not provided for non-liftable axles.
Substance below the wheel
Vertical displacement of the wheel from its axis in meters
Angular velocity of the wheel in rotations per second
Steering rotation of the wheel in rotations
Rolling rotation of the wheel in rotations
Is true if the wheel contacts the ground
Represents vehicle space linear velocity of the trailer measured in m/s
Represents vehicle space angular velocity of the trailer measured in rotation/s
Represents vehicle space linear acceleration of the trailer measured in m/s^2
Represents vehicle space angular acceleration of the trailer measured in rotations/s^2


Geometric figures
Calculate the area and perimeter of a square
Square side:
Calculate perimeter
Calculate area
Calculate the area and perimeter of a triangle
Triangle side 1:
Triangle side 2:
Triangle side 3 (base):
Triangle height
Calculate perimeter
Calculate area
Calculate the diameter, perimeter and area of a circle
Circle radius:
Calculate diameter
Calculate perimeter
Calculate area


Package gcexportdata
Package gcexportdata provides functions for locating, reading, and
writing export data files containing type information produced by the
gc compiler. This package supports go1.7 export data format and all
later versions.
Although it might seem convenient for this package to live alongside
go/types in the standard library, this would cause version skew
problems for developer tools that use it, since they must be able to
consume the outputs of the gc compiler both before and after a Go
update such as from Go 1.7 to Go 1.8. Because this package lives in
golang.org/x/tools, sites can update their version of this repo some
time before the Go 1.8 release and rebuild and redeploy their
developer tools, which will then be able to consume both Go 1.7 and
Go 1.8 export data files, so they will work before and after the
Go update. (See discussion at https://golang.org/issue/15651..)
- func Find(importPath, srcDir string) (filename, path string)
- func NewImporter(fset *token.FileSet, imports map[string]*types.Package) types.ImporterFrom
- func NewReader(r io.Reader) (io.Reader, error)
- func Read(in io.Reader, fset *token.FileSet, imports map[string]*types.Package, path string) (*types.Package, error)
- func Write(out io.Writer, fset *token.FileSet, pkg *types.Package) error
Package files
gcexportdata.go
importer.go
func Find(importPath, srcDir string) (filename, path string)
Find returns the name of an object (.o) or archive (.a) file
containing type information for the specified import path,
using the workspace layout conventions of go/build.
If no file was found, an empty filename is returned.
A relative srcDir is interpreted relative to the current working directory.
Find also returns the package's resolved (canonical) import path,
reflecting the effects of srcDir and vendoring on importPath.
func NewImporter(fset *token.FileSet, imports map[string]*types.Package) types.ImporterFrom
NewImporter returns a new instance of the types.Importer interface
that reads type information from export data files written by gc.
The Importer also satisfies types.ImporterFrom.
Export data files are located using "go build" workspace conventions
and the build.Default context.
Use this importer instead of go/importer.For("gc", ...) to avoid the
version-skew problems described in the documentation of this package,
or to control the FileSet or access the imports map populated during
package loading.
▾ Example
ExampleNewImporter demonstrates usage of NewImporter to provide type
information for dependencies when type-checking Go source code.
Code:
const src = `package myrpc
// choosing a package that doesn't change across releases
import "net/rpc"
const serverError rpc.ServerError = ""
`
fset := token.NewFileSet()
f, err := parser.ParseFile(fset, "myrpc.go", src, 0)
if err != nil {
log.Fatal(err)
}
packages := make(map[string]*types.Package)
imp := gcexportdata.NewImporter(fset, packages)
conf := types.Config{Importer: imp}
pkg, err := conf.Check("myrpc", fset, []*ast.File{f}, nil)
if err != nil {
log.Fatal(err)
}
pi := packages["net/rpc"].Scope().Lookup("ServerError")
fmt.Printf("type %s.%s %s // %s\n",
pi.Pkg().Path(),
pi.Name(),
pi.Type().Underlying(),
slashify(fset.Position(pi.Pos())),
)
twopi := pkg.Scope().Lookup("serverError")
fmt.Printf("const %s %s = %s // %s\n",
twopi.Name(),
twopi.Type(),
twopi.(*types.Const).Val(),
slashify(fset.Position(twopi.Pos())),
)
Output:
type net/rpc.ServerError string // $GOROOT/src/net/rpc/client.go:20:1
const serverError net/rpc.ServerError = "" // myrpc.go:6:7
func NewReader(r io.Reader) (io.Reader, error)
NewReader returns a reader for the export data section of an object
(.o) or archive (.a) file read from r. The new reader may provide
additional trailing data beyond the end of the export data.
func Read(in io.Reader, fset *token.FileSet, imports map[string]*types.Package, path string) (*types.Package, error)
Read reads export data from in, decodes it, and returns type
information for the package.
The package name is specified by path.
File position information is added to fset.
Read may inspect and add to the imports map to ensure that references
within the export data to other packages are consistent. The caller
must ensure that imports[path] does not exist, or exists but is
incomplete (see types.Package.Complete), and Read inserts the
resulting package into this map entry.
On return, the state of the reader is undefined.
▾ Example
ExampleRead uses gcexportdata.Read to load type information for the
"fmt" package from the fmt.a file produced by the gc compiler.
Code:
filename, path := gcexportdata.Find("fmt", "")
if filename == "" {
log.Fatalf("can't find export data for fmt")
}
fmt.Printf("Package path: %s\n", path)
fmt.Printf("Export data: %s\n", filepath.Base(filename))
f, err := os.Open(filename)
if err != nil {
log.Fatal(err)
}
defer f.Close()
r, err := gcexportdata.NewReader(f)
if err != nil {
log.Fatalf("reading export data %s: %v", filename, err)
}
fset := token.NewFileSet()
imports := make(map[string]*types.Package)
pkg, err := gcexportdata.Read(r, fset, imports, path)
if err != nil {
log.Fatal(err)
}
members := pkg.Scope().Names()
if members[0] == ".inittask" {
members = members[1:]
}
fmt.Printf("Package members: %s...\n", members[:5])
println := pkg.Scope().Lookup("Println")
posn := fset.Position(println.Pos())
posn.Line = 123
fmt.Printf("Println type: %s\n", println.Type())
fmt.Printf("Println location: %s\n", slashify(posn))
Output:
Package path: fmt
Export data: fmt.a
Package members: [Errorf Formatter Fprint Fprintf Fprintln]...
Println type: func(a ...interface{}) (n int, err error)
Println location: $GOROOT/src/fmt/print.go:123:1
func Write(out io.Writer, fset *token.FileSet, pkg *types.Package) error
Write writes encoded type information for the specified package to out.
The FileSet provides file position information for named objects.


GLADIATOR A film review by Christopher Null Copyright 2000 filmcritic.com filmcritic.com
"Joey, do you like movies about gladiators?" Hey, who doesn't!?
The awe-inspiring trailers for Gladiator may have you dreaming of Spartacus and Ben-Hur, but you may be surprised to find this film in reality a less palatable mélange of Braveheart and Mad Max: Beyond Thunderdome. This isn't altogether a bad thing, but those expecting a new Roman epic that will stand the test of time (like Spartacus and Ben-Hur) are in for some surprises.
The currently hot Russell Crowe stars as Maximus, "the general who became a slave" who begins the film as a commander so high in the echelons of the Roman army that the aging Caesar Marcus Aurelius (Richard Harris) hand-picks him to become the next Caesar... instead of his own son Commodus (not pronounced Commode-us, played by Joaquin Phoenix).
Joaquin Phoenix's appearance in a movie always means trouble, and sure enough, dad is soon dead, Commodus is Emperor, and Maximus is off to execution. It's a roundabout road (that lasts 150 minutes) to get from general to slave and slave to gladiator. From there, Maximus ends up in Rome at the Colosseum (you know: THE Colosseum), threatening to overthrow the Emperor and return power to the Republic. And here we are, 1600 years before the American Revolution.
Gladiator's finest hour comes when the gladiating begins (though this is a good 60 minutes into the picture). And we are talking serious carnage. Bloodletting. Decapitations. People severed in two. Dirty fighting. Joey would like this movie.
Shot in an aggressive yet vaguely annoying chop-chop style, Gladiator borrows heavily from the camera style Spielberg used in Saving Private Ryan's battle scenes: Print every other frame of film twice, and run at regular speed. This casts everything in a dreamlike quality, which sometimes works in the film but gets old due to overuse and overall poor lighting.
Then there's the chit-chat of Gladiator's "plot," a threadbare bit of political intrigue, with Commodus worried sick about Maximus's rise to popularity via the mob crowds at the pit, Connie Nielsen's Lucilla (Commodus's sister), also worried sick in fear that her son will be killed... blah blah blah. I zoned out whenever one of these weepy people took the stage, waiting for the fighting to start again. You know, with the chariots! And the tigers!
There's something Jerry Springeresque about Gladiator, but Crowe lends a lot of legitimacy to the proceedings. Less effective is Phoenix, who invites you to believe he is an actual Emperor of Rome. This turns out to be impossible, as Phoenix comes across as, well, River Phoenix's little brother. There's also the little issue that Gladiator stretches the boundaries of "historical fiction" to new and impossible lengths, which almost goes without saying. Almost.
RATING: ***
|------------------------------| \ ***** Perfection \ \ **** Good, memorable film \ \ *** Average, hits and misses \ \ ** Sub-par on many levels \ \ * Unquestionably awful \ |------------------------------|
MPAA Rating: R
Director: Ridley Scott Producer: David H. Franzoni, Steven Spielberg, Douglas Wick Writer: David H. Franzoni, John Logan, William Nicholson Starring: Russell Crowe, Joaquin Phoenix, Richard Harris, Djimon Hounsou, Tomas Arana, Spencer Treat Clark, Derek Jacobi, Ralph Moeller, Connie Nielsen, Oliver Reed
http://www.gladiator-thefilm.com/ --- Christopher Null -<EMAIL_ADDRESS>- http://www.filmcritic.com
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


@import data._ @import helper._ @import play.Environment @import data.viewModel.PageStatus @import helpers.CallHelper.relative @import org.webjars.play.WebJarsUtil @(reportForm: Form[ShortFormatPreSentenceReportData], encrypter: String => String)(implicit pageStatuses: Map[Int, PageStatus], webJarsUtil: WebJarsUtil, environment: Environment) @implicitField = @{ FieldConstructor(govukElements.f) } @analysisHint = {
Your analysis needs to go beyond the facts provided by the CPS.
Consider the offender’s account and attitude and make links to any previous offending behaviour and reports by the probation service.
Do they accept responsibility? Are they in denial? If they are, be clear where lack of remorse differs from any inability to show remorse. Is the offender aware of the impact on the victim?
Outline the emotional or cognitive factors that underpin the crime. Were there sexual motivations, financial gains or addictions? What level of premeditation or planning was involved? Are there any patterns suggesting likelihood of similar offending?
Is there any evidence of discrimination in their attitude or behaviour? Is the offender’s maturity an issue in this offence - are they easily led or influenced by a peer group?
Make suggestions about possible rehabilitative interventions.
} @main("Offence analysis - Short Format Pre Sentence Report") {


--- layout: sidebar-right title: "Local theatre company to tour Suffolk libraries in October" date: 2018-09-03 author: james-powell category: news excerpt: "QuirkHouse Theatre Company will be bringing their flagship play, Talking in the Library, to its natural home for Libraries Week 2018." breadcrumb: news featured-image: /images/featured/featured-talking-in-the-library.jpg featured-alt: "Characters from play Talking in the Library" --- {% include /c/figure.html alt="Characters from play Talking in the Library" src="/images/featured/featured-talking-in-the-library.jpg" caption="Characters from QuirkHouse Theatre Company's Talking in the Library" %} QuirkHouse Theatre Company will be bringing their flagship play, Talking in the Library, to its natural home for Libraries Week 2018.
Written by award-winning playwright and novelist Jackie Carreira, Talking In The Library was the company's inaugural play in 2014. Since then, the play has been performed by companies around the UK and was voted runner-up in the Fringe Guru Audience Choice Awards at the Brighton Fringe Festival. Now, by popular demand, it’s back home in Suffolk and being performed in six libraries from 11-20 October.
This time QuirkHouse are performing to raise funds for local libraries and to promote the importance of literacy and the library service. They are generously donating all funds raised from ticket sales to the libraries.
"Without public libraries," Jackie said, "I know I wouldn't be a writer today. I will always champion them whenever I can."
Actor and co-founder of QuirkHouse Theatre Company, AJ Deane, will be reprising his role as 'Jay' and said: "We are delighted that this tour starts during Libraries Week and are proud to be supporting them and the essential work they do in the community."
Melissa Matthews, Suffolk Libraries arts programmes coordinator, said: “We are thrilled to be welcoming the team at QuirkHouse to libraries and very grateful they are also helping to raise money to support the library service. It’s a perfect way to launch National Libraries Week which is all about supporting libraries and showing people how important they are.”
There will be a special preview show at the Constitutional Club in Bury St Edmunds, before a performance across the border in Thetford on Wednesday 10 October, then the tour gets underway in Suffolk by visiting the following six Suffolk libraries:
{% assign theatre-events = site.events | where: "event-category", "talking-in-the-library" | sort: "event-start-date" %} {% for event in theatre-events %}Tickets are £8 and £5 for concessions (all tickets are £5 at Glemsford) and are available from the library.
Independent theatre reviewer Broadway Baby has described Talking in the Library as: “Full of touching observations and warm humour, with an abundance of clever parallels to famous literary pieces, making it a real treat for theatre and literary fans to unpick.”
Libraries Week 2018 takes place between 8–13 October to celebrate the nation’s libraries. This year there is a focus on wellbeing and how libraries bring communities together, combat loneliness, provide a space for reading and creativity and support people with their mental health.


This password has previously appeared in a data breach and should never be used. If you've ever used it anywhere before, change it!
We are checking against 551,509,767 real world passwords previously exposed in data breaches. This exposure makes them unsuitable for ongoing use as they're at much greater risk of being used to take over other accounts. Read more about how the privacy of searched passwords is protected.
This password has not yet been breached, feel free to use it.
We are checking against 551,509,767 real world passwords previously exposed in data breaches. This exposure makes them unsuitable for ongoing use as they're at much greater risk of being used to take over other accounts. Read more about how the privacy of searched passwords is protected.