This webpage was created in the Voice, Web and Mobile Applications Brandeis Justice Seminar program in the summer of 2015.


Get a configuration value from the config file.
The aws configure get command can be used to print a configuration value in the AWS config file. The get command supports two types of configuration values, unqualified and qualified config values.
Note that aws configure get only looks at values in the AWS configuration file. It does not resolve configuration variables specified anywhere else, including environment variables, command line arguments, etc.
Every value in the AWS configuration file must be placed in a section (denoted by [section-name] in the config file). To retrieve a value from the config file, the section name and the config name must be known.
An unqualified configuration name refers to a name that is not scoped to a specific section in the configuration file. Sections are specified by separating parts with the "." character (section.config-name). An unqualified name will be scoped to the current profile. For example, aws configure get aws_access_key_id will retrieve the aws_access_key_id from the current profile, or the default profile if no profile is specified. You can still provide a --profile argument to the aws configure get command. For example, aws configure get region --profile testing will print the region value for the testing profile.
A qualified name is a name that has at least one "." character in the name. This name provides a way to specify the config section from which to retrieve the config variable. When a qualified name is provided to aws configure get, the currently specified profile is ignored. Section names that have the format [profile profile-name] can be specified by using the profile.profile-name.config-name syntax, and the default profile can be specified using the default.config-name syntax.
aws configure get varname [--profile profile-name]
varname (string) The name of the config value to retrieve.


--- layout: post status: publish published: true title: Movies author: display_name: Tomas Restrepo login: tomasr email<EMAIL_ADDRESS>url: http://winterdom.com/ author_login: tomasr author_email<EMAIL_ADDRESS>author_url: http://winterdom.com/ wordpress_id: 865 wordpress_url: http://winterdom.com/2002/08/movies-2 date: '2002-08-02 19:22:16 +0000' date_gmt: '2002-08-02 19:22:16 +0000' categories: - Personal tags: [] comments: [] ---
I just returned an hour ago from the movies, watching Minority Report. I found the movie pretty interesting, and fun to watch :)


Create a game with a flapping birdThis is going to be cool. Anyone interested?
Anyone need a Drupal Developer?5 years experience in drupal.
EnlinkThis is a awesome product. We already created it, we need fund for launching product.


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First, as you can probably tell, I am not publishing my column weekly as I had previously done. I intend to keep publishing as time and resources permit. Ideally this will be every other week. I appreciate your patience. Next, I would like to hear from people about their favorite or most helpful GeneralBit article. I am creating talks and writing containing collections of GeneralBits articles and want to use the ones that are really helpful, not the ones I think are helpful. Send me your suggestion to me. Your feedback is always appreciated. Finally, I have converted all of the article files to a php format. You may have to fix your bookmarks. In general you can just delete the html suffix.
In a production environment it is critical to maintain control over the structure of your databases. If everyone has the ability to alter tables, change keys, add tables willy-nilly then the integrity of your data structures will deteriorate. If your site involves more than one database with the same schema you can easily get the databases schemas out of sync, causing both software bugs and data problems. Just think of the chaos if you had a thousand or more databases with the same schema to maintain (some people do). (I use the word schema to mean your database structure, not a namespace within your database.) I am going to outline a simple, common sense way to manage database updates. Use it as a template and tune it to the needs of your organization or rewrite it using the languages of your choice. I'll use bash along with psql. Also feel free to change the names of objects, but keep in the spirit of the suggestions. Note: This model is not directly applicable to slony clusters. With slony clusters the DDL is executed via a slonik script and only to one node. The first requirement is that all production database schemas must be in sync. If they are not in sync you need to bring them into sync. Doing that is cruelly difficult most of the time.  (Bringing your databases into sync is not the subject of this article, but may be the subject of a future article if there is need and interest.) The first rule is that all schema change statements (DDL) must be edited into a file, say updateN.sql and applied as a SQL script. They are never issued via a GUI tool or by typing in psql directly. Just forget about those methods. They are not repeatable or scaleable. The SQL script should be written and tested on a development database before considered for application to production databases, of course. You should have a directory updates in your source tree. Everything should be under source code control. In that directory, you will have a few objects. The .../Update Directory
The Update ProcessesPre-Requisites:
For each updateN.sql
Apply change N to each host/machine using applyupdate.sh. Below is the crux of the work by applyupdate.sh. See the actual script for details. We first apply the change using psql and capture STDIN and STDERR into an output file. Then we diff the output file with the known good output file. If they are different, raise an error. Log the update to the updatelog, successful or not. psql -h $host $db < update_$N.sql > update_$N.out 2>&1 diff update_$N.out Canon/update_$N.out if [ $? != 0 ] ; then ERROR updating this host/machine info available here now: host/machine, update_$N.out, timestamp HANDLE ERROR: log, email, handle store lists fi echo `date` $host $db update_$N >> updatelog.out; cat update_N.out >> updatelog.out; echo >> updatelog.out; There are many variations and niceties that could be added to this basic and ordinary process. A script could be written to update the test db to a particular update or you can keep the last one around if you are confident no one has messed with it. A change or wrapper script to applyupdate.sh should allow you to run a particular update on all of your production databases automatically. This model assumes a central machine has access to all other machines. You can change the method of connectivity to meet your network model. With regards to update files: Sometimes, in spite of best efforts you will have to back out a change. Do it with a subsequent update script. Don't "fix" a script you have already applied. That is done and you cannot go back. If you find you are having to backout changes, make it a requirement that each update script have a corresponding undo script. This requirement is a bit excessive, however, it may encourage more care when writing and testing the update script in the first place. Logging can be distributed as you see fit. Perhaps you only want to log the update script output if it is bad. Keep in mind that you need one place to look to see the latest update of all machines. Really. You will want that. It is also possible to have a table in each database recording the timestamp and update number of each update run to validate the update machine by machine. Error handling is important. If you are watching each update, you can keep the exit on error condition. If you are updating many machines, you may want to collect the errors and address them each afterwards. You can automate the error raising, but you will still have to look at each machine with errors. Luckily you have tested well, so there should be no problems :) Don't forget to check in your changes.
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Comments and Corrections are welcome. Suggestions and contributions of items are also welcome. Send them in! Copyright A. Elein Mustain 2003, 2004, 2005, 2006 |


{% extends "marketing-base.html" %} {% block title %}Logged Out - {{ block.super }}{% endblock %} {% block body_id %}logout{% endblock %} {% block body_class %}secondary full{% endblock %} {% block content %}
Heads up! You have been successfully logged out, thanks for using Clutch!


Firstly, i was a nexus 5 user, which I accidentally bricked. Anyway, It’s Better than Nexus 5, Moto g(3), One plus one, Mi4i(minus 4g, which mi4i has) Pros. Brilliant display
Likes: 112     Comments: 43Firstly, i was a nexus 5 user, which I accidentally bricked. Anyway, It’s Better than Nexus 5, Moto g(3), One plus one, Mi4i(minus 4g, which mi4i has) Pros. Brilliant display
Likes: 112     Comments: 43Firstly, i was a nexus 5 user, which I accidentally bricked. Anyway, It’s Better than Nexus 5, Moto g(3), One plus one, Mi4i(minus 4g, which mi4i has) Pros. Brilliant display
Likes: 112     Comments: 43Firstly, i was a nexus 5 user, which I accidentally bricked. Anyway, It’s Better than Nexus 5, Moto g(3), One plus one, Mi4i(minus 4g, which mi4i has) Pros. Brilliant display
Likes: 112     Comments: 43Firstly, i was a nexus 5 user, which I accidentally bricked. Anyway, It’s Better than Nexus 5, Moto g(3), One plus one, Mi4i(minus 4g, which mi4i has) Pros. Brilliant display
Likes: 112     Comments: 43Firstly, i was a nexus 5 user, which I accidentally bricked. Anyway, It’s Better than Nexus 5, Moto g(3), One plus one, Mi4i(minus 4g, which mi4i has) Pros. Brilliant display
Likes: 112     Comments: 43


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BABETTE'S FEAST A film review by Ben Hoffman Copyright 1997 Ben Hoffman
In Denmark, a short story by Isak Dinesen is turned into an enchanting film.
BABETTE'S FEAST is a delight. The story itself is nothing much and doesn't even sound as if it would be anything one would care to make into a film. But the beauty is what's wrapped around the story and the characters in it.
Set in an isolated little. windswept village in Denmark, the residents are kept alive by their unquestioning belief in God. There are 2 elderly spinster sisters ("just past their youthful bloom") who, when their minister father dies, become the center of the spiritual community.
Babette, a Frenchwoman, leaves France where there is civil unrest, for Denmark. She is taken in as a maid and helper by the 2 sisters. For the 100th birthday of their deceased father, the sisters decide to give a nice dinner to about a dozen of their church friends. .and they allow Babette to make the dinner. As she has just come into some money she also insists on paying for the food. How that wonderfully funny feast transforms the town is the story.
You are warned not to go to the movie while hungry.
4 big bytes
4 Bytes = Superb 3 Bytes = Too good to miss 2 Bytes = Average 1 Byte = Save your money
Ben Hoffman
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


Vicky's little Vikings © Flat 4 Gate Keepers Lodge, 83 High Street. London N8 7FD
Website created by Will Thirkettle


No other artist made it bigger or had closer ties to the Backdoor than Tom Waits; aside from being a. 1975 Jimmy Buffett & the. Coral Reefer Band Backdoor Adele's 2011 San Diego appearance was initially scheduled for June 8th at Humphrey's By the Bay but was. 2000 Tim McGraw, Faith Hill, Keith Urban - Cox.
1 0 By Dave Good, June 12, 2013. Facebook · Twitter The science says we can play Tim McGraw. and Justin Timberlake on the same station. We're going to
Apr 20, 2013 Tim McGraw Forever & Always March 27 June June 1 - Salt Lake City, Utah. Our Song June 2 - Denver. August 15 - San Diego, California. His first break in acting happened by chance, when at the age of seven he Following high school, Mark moved with. his family to San Diego and soon He attended London's Central School of Speech and Drama and left in June Tim McGraw Soundtrack, The Blind Side Three-time Grammy Award winner Tim McGraw,
Gregory & The Hawk AKA Meredith. Godreau - For the Best November 13, 2011 San Diego, CA. Tim McGraw - She's my kinda rain June. 25. June 7, 2011. http://www. sandiego org/articles/summer/4th-of-july-fireworks-in-san-diego. aspx. Imagine Dragons, Tim McGraw, John Mayer, 311, The Wailers, Matt & Kim
in San Diego on June 21st; check out audio clips (and order a copy) from the CD. Odds & Ends: country star Tim McGraw names. some his favorite cover tunes; Jun 5, 2013 Lover of Books - Friday, June 7 start time 10:00pm PT Tim McGraw Unfortunately we don't get. much rain here in San Diego I love the May 22, 2012. Kris Kristofferson & Friends Concert Series Schedule: Friday, June 15 San Diego, CA Spreckels Theatre, 8:00 pm. Kris Kristofferson Reply · 1 · Like · June 18 at 7:36am Would be nice if just once he would return to San Diego, CA, where he has not played. Tim Larson · Orion High School.
Fri, 7 Jun 2013
hotel at a. Here are a few. to consider. properties in Boston, Atlanta, San. Diego, New York, Nashville and more Bal Harbour, Fla. Faith Hill spearheaded the Nashville. rising concert with husband Tim McGraw We're
 Published: Monday, June 03, 2013, 9:21am diagnosed with cancer in his upper jaw. bone and will have surgery on June 7. On May 26, Kelly joined country music. artist Tim McGraw on stage and threw. FOX Sports San Diego May 29, 1994 June 2-26: "Studio Visits: Photographs. of California Artists by Jim July 8: Emmylou Harris with San Diego Symphony, Embarcadero. July 20: Tim McGraw, Orange County Fair July 23: Jan & Dean, Orange County Fair A collection of news and information related to San Diego (San Diego, On June 8, he departs from the beach in San Diego and head to Hampton Roads The Saturday evening concerts will be held 5-7 p. m on the lawn behind. the Ted Constant Choose among Tim McGraw, Keith Urban, Toby Keith, Trace Adkins and San Diego Country. Bands > Will travel 500 miles. from San Diego. Tammy Wynette, Rascal Flatts, Tim Mcgraw, Brooks & Dunn, Martina Mcbride, Hal


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RAW A film review by Mark R. Leeper Copyright 1988 Mark R. Leeper
Capsule review: Ultimately a very downbeat and bitter stand-up comic concert film. The problem is not that the humor is tasteless but that it is not particularly clever. RAW is a short film that has occasional laugh-out-loud jokes but not enough to make it worthwhile. Rating: 0.
I am not sure you can measure a concert film by the same standards by which you measure other films. Certainly a lot of the criteria you would apply to the quality of a dramatic film just do not apply to a concert film--considerations like, "Did the plot make sense?" But then, if one says a concert film is not a dramatic film, can you really say that MY DINNER WITH ANDRE is? It is somewhere in the gray area between drama and concert film. Well, one thing that seems to be called a concert film is a filmed comedy act by a stand-up comic.  Eddie Murphy's RAW just barely squeaks by on a technicality.)
And if RAW does just squeak by, that is just about the only place it does squeak. Generally it tends to roar in language that will never make it to commercial television. In fact, part of Murphy's appeal seems to be in the creative ways he finds to use profanity. And his appeal is undeniable and amazing. He has a line of anti-feminist and anti-homosexual patter that would do a Grand Wizard proud, and yet it is considered unobjectionable because it is embedded in profanity, like almonds in a Hershey bar, and hence is funny. It can be a strange world.
RAW begs comparison to other black stand-up comic concert films, particularly those of Richard Pryor and Bill Cosby. Even more than of dramatic films, comparisons of stand-up comics will be a matter of taste, but for my money Murphy comes in a distinct third. Of the three comics Cosby is the funniest.  From Cosby to Pryor to Murphy the jokes become more bitter, less creative, more profane, and generally less funny. Murphy does a good impression of Cosby telling him to clean up his act, but the profanity that is Murphy's trademark is the least of his faults. Murphy's humor is at bottom very downbeat and often, as when he describes his bowel movements, just not all that interesting. Rate RAW a flat 0 on the -4 to +4 scale.
Mark R. Leeper ihnp4!mtgzz!leeper<EMAIL_ADDRESS>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


To assist you with troubleshooting internet connection issues and assist in isolating packet loss on your network. It does this by allowing you to retrieve the top trace route destinations your internet provider uses, and recursively ping each server in series while capturing the results and writing them to a log file. Each iteration it queries the destinations again, before shuffling the sequence of destinations to ensure the analysis is unbiased and consistent across each trace route.
| Version: | 0.1.1 |
| Depends: | R (≥ 3.4.0) |
| Imports: | dplyr, stringr, tibble, tictoc, tidyselect, data.table, lubridate, plotly, reshape2 |
| Published: | 2018-10-26 |
| Author: | Jesse Vent [aut, cre] |
| Maintainer: | Jesse Vent <cryptopackage at icloud.com> |
| BugReports: | https://github.com/JesseVent/pingers/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/JesseVent/pingers |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | pingers results |
| Reference manual: | pingers.pdf |
| Package source: | pingers_0.1.1.tar.gz |
| Windows binaries: | r-devel: pingers_0.1.1.zip, r-release: pingers_0.1.1.zip, r-oldrel: pingers_0.1.1.zip |
| OS X binaries: | r-release: pingers_0.1.1.tgz, r-oldrel: pingers_0.1.1.tgz |
Please use the canonical form https://CRAN.R-project.org/package=pingers to link to this page.


{% extends "base_minimal.html" %} {% from "security/_macros.html" import render_field_with_errors, render_field %} {% include "security/_messages.html" %} {% import "bootstrap/wtf.html" as wtf%} {% block content %}


boa
[source] [example]A simple object-oriented language with eager evaluation, first-class functions, and dynamic types. It is based on a notion of objects as extensible records. There are no classes.
The language has the following features:
- integers with arithmetical operations
*
,+
,-
,/
- booleans with conditional statements and comparison
=
,<
of integers - first-class functions
- mutable objects as extensible records, i.e., an object
obj1
can be extended by objectobj2
to form the combined objectobj1 with obj2
.
An interesting feature of the language is that, since “everything is an object”, a value may behave simultaneously as an integer, boolean, function, and a record. See examples below.
Example
The file example.boa
shows basic examples and implements recursion and lists using
objects.


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aah aims to provide necessary components to build modern Web and API application with secure, high performance, scalable yet lightweight, flexible. aah takes care of infrastructure, boilerplate code, repetitive activities, reusable components, etc. aah is not a micro web framework.
aah aims to provide necessary components to build modern Web and API application with secure, high performance, scalable yet lightweight, flexible. aah takes care of infrastructure, boilerplate code, repetitive activities, reusable components, etc. aah is not a micro web framework.
{{ i18n . "test.text.msg.render" "welcome to aah :)


Tutorial 4
Welcome to the fourth tutorial in the InterMine python tutorial series! In the previous tutorials we learned about the basic backbone of a query - views and constraints. This short tutorial will talk about another feature of an InterMine query - Outer and Inner Joins.
When we add a path to a query, even if it is in the view, then by default there is a constraint involved. Our query will consist of only those records that have information in the fields or attributes that are described by the path.
Here, we’ll try to get genes involved in a biosynthetic process and any publications on them. If a particular gene has publication information available, then we want to view the general information about that gene.
By default, InterMine is designed to give you an Inner Join, which basically means that no partial matches will be part of the result. However, for the example we are discussing here, we would require something known as an Outer Join. An outer Join on Gene.publications would help in solving the problem for us.
Here is a comparison table of Inner Join and Outer Join:
| Inner Join | Outer Join |
|---|---|
| default behavior | optional behavior |
| implicitly constrains the values of that path to be non-null | allows null values in the joined path |
| no data will be returned if any one gene has not matched attributes described by the path | query will continue even if some genes don’t have matched attributes described by the path |
Screenshot of the same query with Inner Join and Outer Join:
With Outer Join:
With Inner Join:
PS: in the first second screenshot, results for 0610009B22Rik takes up three rows, which is the same as the SINGLE row with three publications in the second screenshot.
Let’s start by creating a new query object like we saw earlier. We're selecting the primaryIdentifier and symbol for each gene, as well as the year, first author, and title of any associated publications.
Here we add the constraint like this, which constrains ontology terms to be any term that contains “biosynthetic process”. The *s in the constraint are a wildcard, meaning that any additional text can come before or after the phrase “biosynthetic process”.
Then, we add an outer join using the query.outerjoin method, asserting that all genes should be returned, and if publication information is available we will return it as well.
Here we can see the output.
Another query feature that InterMine has is the ability to define shorter column names. This can be done using the add_path_description method.
This helps us when we want to print our tables into a file and want the column names to be in a readable format.
In this example, we use Ontology Term to represent ontologyAnnotations.ontologyTerm and Pub. to represent publications.
In the next tutorial, we will look at dealing with the results that are returned by our queries.
Thank you for watching!


In the example, we run an election with five candidates (A, B, C, D, E), who are competing to fill two positions, and there are ten voters.
Each voter gets a ballot paper which looks like this:
Voters fill in their preference by ranking the candidates 1 to 5, with 1 being the most preferred candidate. For example, for someone whose favourite candidate is Candidate D:
(On a real paper ballot, the order of the candidates would be randomised, to prevent people who just fill in “1, 2, 3, 4, 5” top-to-bottom from giving an advantage to the earlier candidates.)
In this simple example, all five preferences must be indicated for the vote to be valid.
The idea is: we proceed in rounds. Each round we count the first-choice (#1) votes for each candidate, and all but one of the candidates go through, selected by those having the most votes.
Then, for each ballot paper, the remaining candidates are automatically re-ranked, preserving the preference order.
Here is a quick example of what that means. Suppose someone voted like this:
| A | B | C | D | E |
|---|---|---|---|---|
| 1 | 3 | 5 | 4 | 2 |
and candidates A, B, C and D go through to the next round. Then Candidate E is no longer available, so remaining ballots are adjusted like this:
| A | B | C | D | E |
|---|---|---|---|---|
| 1 | 2 | 4 | 3 |
A is still this voter’s #1 choice; their #2 choice is no longer available, so their new #2 choice is their old #3 choice; their new #3 choice is their old #4 choice; their new #4 choice is their old #5 choice. The remaining four candidates are now ranked 1–4, with the same preference order as before.
We keep going, with one fewer candidate going through each round, until there are the same number of candidates as positions to fill: the remaining candidates have the strongest support overall, and take those positions. In the special case that there is only one position to fill, we can stop when the one candidate has over 50% of the vote.
The ballots are all returned; this is how everybody voted:
| Candidate: | A | B | C | D | E |
|---|---|---|---|---|---|
| Voter 1 | 1 | 3 | 5 | 4 | 2 |
| Voter 2 | 1 | 3 | 4 | 2 | 5 |
| Voter 3 | 5 | 3 | 2 | 4 | 1 |
| Voter 4 | 5 | 1 | 2 | 4 | 3 |
| Voter 5 | 2 | 1 | 5 | 4 | 3 |
| Voter 6 | 1 | 5 | 2 | 4 | 3 |
| Voter 7 | 3 | 4 | 1 | 5 | 2 |
| Voter 8 | 4 | 3 | 2 | 5 | 1 |
| Voter 9 | 2 | 3 | 1 | 5 | 4 |
| Voter 10 | 2 | 5 | 3 | 1 | 4 |
In this table, each row is one voter’s ballot paper, with their numbered preferences, and each column is the votes which each candidate got. The first-choice (#1) votes are highlighted blue.
At the end of Round 1, the #1 vote counts are as follows:
| Candidate: | A | B | C | D | E |
|---|---|---|---|---|---|
| Round 1: #1 votes | 3 | 2 | 2 | 1 | 2 |
These are calculated by just counting #1 votes down each column.
In this round there were five candidates at the start, and the four with the strongest support go through to the next round. In our example, A, B, C and E go forward (highlighted yellow).
At the start of the second round we have one fewer candidates. Candidate D is no longer in the race, so they are removed from the ballot papers and everyone has their preferences automatically re-ranked to preserve the preference order.
This "adjusting the ballot paper" can feel inappropriate, but it perfectly preserves everyone’s ordered preference for the remaining candidates. There are other mathematically equivalent ways to think about it (such as now counting #2 votes from voters whose #1 preference isn’t available), but the way I’m describing it here just keeps things simple for the purposes of counting.
So now the votes for the remaining candidates look like this:
| Candidate: | A | B | C | D | E |
|---|---|---|---|---|---|
| Voter 1 | 1 | 3 | 4 | 2 | |
| Voter 2 | 1 | 2 | 3 | 4 | |
| Voter 3 | 4 | 3 | 2 | 1 | |
| Voter 4 | 4 | 1 | 2 | 3 | |
| Voter 5 | 2 | 1 | 4 | 3 | |
| Voter 6 | 1 | 4 | 2 | 3 | |
| Voter 7 | 3 | 4 | 1 | 2 | |
| Voter 8 | 4 | 3 | 2 | 1 | |
| Voter 9 | 2 | 3 | 1 | 4 | |
| Voter 10 | 1 | 4 | 2 | 3 |
In pink, I’ve highlighted the new #1 vote which has resulted from the re-ranking of remaining candidates.
Now again counting the #1 votes for each candidate we get these counts:
| Candidate: | A | B | C | D | E |
|---|---|---|---|---|---|
| Round 1: #1 votes | 3 | 2 | 2 | 1 | 2 |
| Round 2: #1 votes | 4 | 2 | 2 | 2 |
And now the 3 strongest candidates should go through to the next round.
However, you’ll notice a problem: Candidate A clearly has the most #1 votes and goes through (yellow highlight), but Candidates B, C and E are tied for second place. So to break the tie we count #2 votes for the tied candidates:
| Candidate: | A | B | C | D | E |
|---|---|---|---|---|---|
| Round 1: #1 votes | 3 | 2 | 2 | 1 | 2 |
| Round 2: #1 votes | 4 | 2 | 2 | 2 | |
| Round 2 TIEBREAK: #2 votes | 1 | 5 | 2 |
So now it’s clear that even though Candidates B, C and E were tied for #1 votes, C and E are more voters’ second choice, so they have the strongest overall support and go through alongside Candidate A. (Shown by the yellow highlights.)
In case there are ties for #2 votes too, proceed to counting #3 votes, and so on. If you run out of lower preferences to count and it’s still tied, then there is no difference in voter preference among the tied candidates, so the ones to progress to the next round must be chosen by another method, such as drawing straws (or the digital equivalent).
Again we have one fewer candidates. Like last round, Candidate B is no longer in the race, so like last round we remove them from the ballots and re-rank the remaining candidates:
| Candidate: | A | B | C | D | E |
|---|---|---|---|---|---|
| Voter 1 | 1 | 3 | 2 | ||
| Voter 2 | 1 | 2 | 3 | ||
| Voter 3 | 3 | 2 | 1 | ||
| Voter 4 | 3 | 1 | 2 | ||
| Voter 5 | 1 | 3 | 2 | ||
| Voter 6 | 1 | 2 | 3 | ||
| Voter 7 | 3 | 1 | 2 | ||
| Voter 8 | 3 | 2 | 1 | ||
| Voter 9 | 2 | 1 | 3 | ||
| Voter 10 | 1 | 2 | 3 |
Again I’ve highlighted the existing #1 votes in blue and the new #1 votes for the remaining candidates in pink.
Now again we count the #1 votes:
| Candidate: | A | B | C | D | E |
|---|---|---|---|---|---|
| Round 1: #1 votes | 3 | 2 | 2 | 1 | 2 |
| Round 2: #1 votes | 4 | 2 | 2 | 2 | |
| Round 2 TIEBREAK: #2 votes | 1 | 5 | 2 | ||
| Round 3: #1 votes | 5 | 3 | 2 |
This time there are two clear winners, and no ties. Candidates A and C go through to the next round.
In this round there are two remaining candidates, and two positions to fill, so we stop.
Candidates A and C have the strongest overall support from the voters, and take the positions.
One of the goals of the single-transferable vote system is to allow voters' greater expressive power, and reduce the need for tactical voting: as well as voting for their favourite candidates, they can also express preference about the remaining candidates. However one drawback is that it is more complex than one-preference first-past-the-post voting. There are many variants to single-transferable voting which allow voters’ aggregate preferences to be even more efficiently represented in the election outcome, at the cost of far more complexity in the counting. What is presented here is, I think, a good balance between being representative, and being clear, transparent and accessibly explainable in the counting.
Written by Cai. Dedicated to XR.
The source of singletransferablevote.info is CC0 public domain.
If you spot an error, or want to contribute clear guides to the other flavours of single transferable voting, please submit a pull request what is a pull request?. Or you could email me.
Download a PDF version of this page.
Last updated 2021-03-11.


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{% load staticfiles %} {% load static %}
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Functions
The following functions are available globally.
-
Undocumented
Declaration
Swift
func normalizeVector(_ iv: SCNVector3) -> SCNVector3
-
Adds two SCNVector3 vectors and returns the result as a new SCNVector3.
Declaration
Swift
func + (left: SCNVector3, right: SCNVector3) -> SCNVector3
-
Increments a SCNVector3 with the value of another.
Declaration
Swift
func += ( left: inout SCNVector3, right: SCNVector3)
-
Subtracts two SCNVector3 vectors and returns the result as a new SCNVector3.
Declaration
Swift
func - (left: SCNVector3, right: SCNVector3) -> SCNVector3
-
Decrements a SCNVector3 with the value of another.
Declaration
Swift
func -= ( left: inout SCNVector3, right: SCNVector3)
-
Multiplies two SCNVector3 vectors and returns the result as a new SCNVector3.
Declaration
Swift
func * (left: SCNVector3, right: SCNVector3) -> SCNVector3
-
Multiplies a SCNVector3 with another.
Declaration
Swift
func *= ( left: inout SCNVector3, right: SCNVector3)
-
Multiplies the x, y and z fields of a SCNVector3 with the same scalar value and returns the result as a new SCNVector3.
Declaration
Swift
func * (vector: SCNVector3, scalar: Float) -> SCNVector3
-
Multiplies the x and y fields of a SCNVector3 with the same scalar value.
Declaration
Swift
func *= ( vector: inout SCNVector3, scalar: Float)
-
Divides two SCNVector3 vectors abd returns the result as a new SCNVector3
Declaration
Swift
func / (left: SCNVector3, right: SCNVector3) -> SCNVector3
-
Divides a SCNVector3 by another.
Declaration
Swift
func /= ( left: inout SCNVector3, right: SCNVector3)
-
Divides the x, y and z fields of a SCNVector3 by the same scalar value and returns the result as a new SCNVector3.
Declaration
Swift
func / (vector: SCNVector3, scalar: Float) -> SCNVector3
-
Divides the x, y and z of a SCNVector3 by the same scalar value.
Declaration
Swift
func /= ( vector: inout SCNVector3, scalar: Float)
-
Negate a vector
Declaration
Swift
func SCNVector3Negate(vector: SCNVector3) -> SCNVector3
-
Returns the length (magnitude) of the vector described by the SCNVector3
Declaration
Swift
func SCNVector3Length(vector: SCNVector3) -> Float
-
Returns the distance between two SCNVector3 vectors
Declaration
Swift
func SCNVector3Distance(vectorStart: SCNVector3, vectorEnd: SCNVector3) -> Float
-
Returns the distance between two SCNVector3 vectors
Declaration
Swift
func SCNVector3Normalize(vector: SCNVector3) -> SCNVector3
-
Calculates the dot product between two SCNVector3 vectors
Declaration
Swift
func SCNVector3DotProduct(left: SCNVector3, right: SCNVector3) -> Float
-
Calculates the cross product between two SCNVector3 vectors
Declaration
Swift
func SCNVector3CrossProduct(left: SCNVector3, right: SCNVector3) -> SCNVector3
-
Calculates the SCNVector from lerping between two SCNVector3 vectors
Declaration
Swift
func SCNVector3Lerp(vectorStart: SCNVector3, vectorEnd: SCNVector3, t: Float) -> SCNVector3
-
Project the vector, vectorToProject, onto the vector, projectionVector.
Declaration
Swift
func SCNVector3Project(vectorToProject: SCNVector3, projectionVector: SCNVector3) -> SCNVector3


@ 3:00pm - 7:00pm, November 29th, 2015
@ 1:30pm - 4:30pm, November 22nd, 2015
@ 10:00am - 2:00pm, November 22nd, 2015
@ 1:30pm - 5:30pm, November 15th, 2015
@ 10:00am - 2:00pm, November 15th, 2015"


Welcome to Astronomical Spectroscopy
Spectroscopy provides a window into the physics of our Universe and allows the study of the motions, composition, and characteristics in astronomical phemomenon. The goals of this course are to explore how spectroscopic data is obtained and analyzed to explore the physical processes of distant objects. This will take us on a spectral tour of the Universe, from which we can measure the tempartures, pressures, densities, dynamics, and magnitic fields of the great diversity of astrophysical sources.


A tutorial by Dominik Moritz (@domoritz) and Kanit "Ham" Wongsuphasawat (@kanitw)
Interactive Data Lab, University of Washington
(Based on Vadim Ogievetsky and Scott Murray's work)
D3 uses CSS Selectors
Single selector:
#foo //
foo //
.foo //
[foo=bar] //
foo bar //
foo.bar //
foo#bar //
.select()
.select()
+ .attr()
, .style()
Select and modify element properties
Note: notice chained syntax, object as argument
.select()
only selects one item!
.selectAll()
See also: http://mbostock.github.io/d3/tutorial/circle.html
.data()
.data()
Tell D3 that you want the selected elements to correspond to data!
__data__
property!
.data()
is called, __data__
of each element will contain the data mapped to that element.You should have seen this from last slide!
Data can be objects too!
.data()
Calling .data()
creates three arrays:
enter
(Data without corresponding DOM elements)update
(DOM elements mapped to data)exit
(DOM elements now missing data).data()
returns the update.
.enter()
.enter()
Shorter: append, then update all together!
.enter()
from scratchThis is a common way that people build visualization in D3!
.exit()
.transition()
Note: Transitions in D3 use cubic-in-out as default easing functions. But there are others.
.transition()
& .delay()
The default is index mapping.
.data(...,key)
A key should be a unique string.
http://bost.ocks.org/mike/selection/#key
.data(...,key)
Notice how you can save lines of code with selection.call()
http://bost.ocks.org/mike/selection/#key
New Data?
See even better bars transition.


#include <views.h>
Inheritance diagram for TScrollBar::
Public Methods | |
| TScrollBar (const TRect &bounds) | |
| virtual void | draw () |
| virtual TPalette& | getPalette () const |
| virtual void | handleEvent (TEvent &event) |
| virtual void | scrollDraw () |
| virtual int | scrollStep (int part) |
| void | setParams (int aValue, int aMin, int aMax, int aPgStep, int aArStep) |
| void | setRange (int aMin, int aMax) |
| void | setStep (int aPgStep, int aArStep) |
| void | setValue (int aValue) |
| void | drawPos (int pos) |
| int | getPos () |
| int | getSize () |
Static Public Methods | |
| TStreamable* | build () |
Public Attributes | |
| int | value |
| TScrollChars | chars |
| int | minVal |
| int | maxVal |
| int | pgStep |
| int | arStep |
Static Public Attributes | |
| TScrollChars | vChars |
| TScrollChars | hChars |
| const char* const | name |
Protected Methods | |
| TScrollBar (StreamableInit) | |
| virtual void | write (opstream &os) |
| virtual void* | read (ipstream &is) |
|
|
Creates and initializes a scroll bar with the given bounds by calling the TView constructor. Sets value, maxVal and minVal to zero. Sets pgStep and arStep to 1. The shapes of the scroll bar parts are set to the defaults in chars data member. If `bounds' produces size.x = 1, scroll bar is vertical; otherwise, it is horizontal. Vertical scroll bars have the growMode data member set to gfGrowLoX | gfGrowHiX | gfGrowHiY; horizontal scroll bars have the growMode data member set to gfGrowLoY | gfGrowHiX | gfGrowHiY. |
|
|
Each streamable class needs a "builder" to allocate the correct memory for its objects together with the initialized virtual table pointers. This is achieved by calling this constructor with an argument of type StreamableInit. |
|
|
Called to create an object in certain stream-reading situations. Reimplemented from TView. |
|
|
Draws the scroll bar depending on the current bounds, value, and palette. Reimplemented from TView. |
|
|
Undocumented. |
|
|
Returns the default palette. Reimplemented from TView. |
|
|
Undocumented. |
|
|
Undocumented. |
|
|
Handles scroll bar events by calling TView::handleEvent(). Mouse events are broadcast to the scroll bar's owner, which must handle the implications of the scroll bar changes. handleEvent() also determines which scroll bar part has received a mouse click (or equivalent keystroke). Data member value is adjusted according to the current arStep or pgStep values. The scroll bar indicator is redrawn. Reimplemented from TView. |
|
|
Reads from the input stream `is'. Reimplemented from TView. |
|
|
Is called whenever value data member changes. This virtual member function defaults by sending a cmScrollBarChanged message to the scroll bar's owner:
message(owner, evBroadcast, cmScrollBarChanged, this);
|
|
|
By default, scrollStep() returns a positive or negative step value, depending on the scroll bar part given by `part', and the current values of arStep and pgStep. Parameter `part' should be one of the following constants:
Constant Value Meaning sbLeftArrow 0 Left arrow of horizontal scroll bar sbRightArrow 1 Right arrow of horizontal scroll bar sbPageLeft 2 Left paging area of horizontal scroll bar sbPageRight 3 Right paging area of horizontal scroll bar sbUpArrow 4 Top arrow of vertical scroll bar sbDownArrow 5 Bottom arrow of vertical scroll bar sbPageUp 6 Upper paging area of vertical scroll bar sbPageDown 7 Lower paging area of vertical scroll bar sbIndicator 8 Position indicator on scroll bar These constants define the different areas of a TScrollBar in which the mouse can be clicked. The scrollStep() function converts these constants into actual scroll step values. Although defined, the sbIndicator constant is never passed to scrollStep(). |
|
|
Sets the value, minVal, maxVal, pgStep and arStep with the given argument values. Some adjustments are made if your arguments conflict. The scroll bar is redrawn by calling drawView(). If value is changed, scrollDraw() is also called. |
|
|
Sets the legal range for value by setting minVal and maxVal to the given arguments `aMin' and `aMax'. Calls setParams(), so drawView() and scrollDraw() will be called if the changes require the scroll bar to be redrawn. |
|
|
Sets pgStep and arStep to the given arguments `aPgStep' and `aArStep'. Calls setParams() with the other arguments set to their current values. |
|
|
Sets value to `aValue' by calling setParams() with the other arguments set to their current values. Note: drawView() and scrollDraw() will be called if this call changes value. |
|
|
Writes to the output stream `os'. Reimplemented from TView. |
|
|
Variable arStep is the amount added or subtracted to the scroll bar's value data member when an arrow area is clicked (sbLeftArrow, sbRightArrow, sbUpArrow, or sbDownArrow) or the equivalent keystroke made. The TScrollBar constructor sets arStep to 1 by default. |
|
|
TScrollChars is defined as:
typedef char TScrollChars[5]; Variable chars is set with the five basic character patterns used to draw the scroll bar parts. |
|
|
Undocumented. |
|
|
Variable maxVal represents the maximum value for the value data member. The TScrollBar constructor sets maxVal to zero by default. |
|
|
Variable minVal represents the minimum value for the value data member. The TScrollBar constructor sets minVal to zero by default. |
|
|
Undocumented. Reimplemented from TView. |
|
|
Variable pgStep is the amount added or subtracted to the scroll bar's value data member when a mouse click event occurs in any of the page areas (sbPageLeft, sbPageRight, sbPageUp, or sbPageDown) or an equivalent keystroke is detected (Ctrl-Left, Ctrl-Right, PgUp, or PgDn). The TScrollBar constructor sets pgStep to 1 by default. You can change pgStep using setParams(), setStep() or TScroller::setLimit(). |
|
|
Undocumented. |
|
|
This variable represents the current position of the scroll bar indicator. This marker moves along the scroll bar strip to indicate the relative position of the scrollable text being viewed relative to the total text available for scrolling. The TScrollBar constructor sets value to zero by default. |


r = Vectors.Utilities.roots(p);
This function computes the roots of a polynomial P of x
P = p[1]*x^n + p[2]*x^(n-1) + ... + p[n-1]*x + p[n+1];
with the coefficient vector p. It is assumed that the first element of p is not zero, i.e., that the polynomial is of order size(p,1)-1.
To compute the roots, the eigenvalues of the corresponding companion matrix C
|-p[2]/p[1] -p[3]/p[1] ... -p[n-2]/p[1] -p[n-1]/p[1] -p[n]/p[1] | | 1 0 0 0 0 | | 0 1 ... 0 0 0 | C = | . . ... . . . | | . . ... . . . | | 0 0 ... 0 1 0 |
are calculated. These are the roots of the polynomial.
Since the companion matrix has already Hessenberg form, the
transformation to Hessenberg form has not to be performed. Function
eigenvaluesHessenberg
provides efficient eigenvalue computation for those matrices.
r = roots({1,2,3}); // r = [-1.0, 1.41421356237309; // -1.0, -1.41421356237309] // which corresponds to the roots: -1.0 +/- j*1.41421356237309
encapsulated function roots import Modelica.Math.Matrices; import Modelica; extends Modelica.Icons.Function; input Real p[:] "Vector with polynomial coefficients p[1]*x^n + p[2]*x^(n-1) + p[n]*x +p[n-1]"; output Real roots[max(0, size(p, 1) - 1), 2] = fill(0, max(0, size(p, 1) - 1), 2) "roots[:,1] and roots[:,2] are the real and imaginary parts of the roots of polynomial p"; end roots;


Let $\Delta$ be the Laplacian on a Riemannian symmetric space $X=G/K$ of
the noncompact type and let
$\sigma(\Delta)\subseteq \mathbb{C}$ denote its spectrum. The resolvent
$(\Delta-z)^{-1}$ is a holomorphic function
on $\mathbb{C} \setminus \sigma(\Delta)$, with values in the space of
bounded operators on $L^2(X)$.
If we view it as a function with values in $\Hom(C_c^\infty(X),
C_c^\infty(X)^*)$, then it often
admits a meromorphic continuation beyond $\mathbb{C} \setminus
\sigma(\Delta)$.
We study this meromorphic continuation as a map defined on a Riemann
surface above
$\mathbb{C} \setminus \sigma(\Delta)$. The poles of
the meromorphically extended resolvent are called resonances. The image of
the residue operator
at a resonance is a $G$-module. The main problems are the existence and
the localization of the
resonances as well as the study of the (spherical) representations of $G$
so obtained.
In this talk, based on joint works with Joachim Hilgert and Tomasz Przebinda,
we will describe a variety of different situations occurring in the rank
two case.


‘Madden 20’: How To Download The Greatest Legends Roster Ever
by Brian MaziqueI’m a roster maker for sports video games, so I know greatness when I see it. I was considering creating a legends roster for Madden 20, but I decided to take a look on the shared files before taking on such a daunting task. Thankfully, I found a legends roster that was done even more thoroughly than the project I had planned.
The roster maker’s name is cottonpicker122, and his or her work is up and available on PlayStation 4. The name of the roster is “LEGENDZ.” There is also a “LEGENDZ53MAN” roster from cottonpicker122 which is franchise-mode ready.
All you have to do is head over to the customize icon (the gear on the lower right) from the main menu, select it. Then click on “share & manage files.” Next, select “Download Community Files.” You can click the Triangle to search for “cottonpicker122.” You should see both of the aforementioned rosters on the screen.
Download the one you want, load it from the “share & manage files” screen, and head into a Play Now or franchise mode.
Sorry Xbox One and PC owners. Hopefully there is someone who has done something similar and just as strong on those platforms.
What Makes This Roster So Good?
The depth on every team is startling. There are full depth charts with plausible players at every position. A great all-time/legends roster doesn’t have era biases. Cottonpicker122’s roster has players from every era of the NFL and it accurately captures the stars for each franchise.
Not only are the rosters packed, each of the players that have been created have accurate and era-specific equipment. Take a look at Jim Brown from the Cleveland Browns. I put him in a classic Browns uniform so that you can get the gist of the detail:
It was great just looking through all of the teams.
Where You Might Want to Make a Few Edits
First, Madden doesn’t allow you to add X-Factor or Superstar abilities to created or edited players outside of a franchise mode. It’s a silly limitation, but it’s a reality. You’ll have to start a franchise mode and then edit each player giving them the appropriate abilities, or you can go without that step. However, the active players who are on these rosters will have their abilities while the others will not.
I don’t like that inconsistency, but to each their own.
Secondly, there are a few ratings that I will change before starting a franchise with this roster. For example, Miami Dolphins great Dan Marino was notoriously one of the least mobile quarterbacks of any era, yet he has a 70 speed rating. That’s about 30 points too high for Dan The Man.
 It’s missing the bigger picture.
If you have a desire to play with the best legends roster on Madden 20, go search for cottonpicker122’s work, download it, and hop in the virtual football time machine.


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Annual COMSA conference
Type:
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Start:
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End:
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Venue:
Conference Hall, Lagos
Toke Weds Desmond
Type:
Party
Guests:
600
Start:
08-01-18
End:
08-01-18
Venue:
Aurora Event Center, Osun
CompanyX Board Meeting
Type:
Meeting
Guests:
15
Start:
21-12-17
End:
22-12-17
Venue:
Inner room, Golden hotel, Lagos
Retirement Party
Type:
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Guests:
150
Start:
02-02-18
Start:
04-02-18
Venue:
MKO Banquet Hall,Lagos
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If you have added a facility, and you are wondering where you can find it, don’t worry, you can even edit or delete it. This section explains how you can edit or delete a facility. The following steps can help you out;
- First of all, navigate to “Profile” by tapping it at the bottom of the home screen (Where you can find the list of facilities). It is the last menu to the right in the menus at the bottom of your screen.
- In the list of profile options, look for “Manage” (Manage replaces the “Lift a facility” option which was available when you were adding a new facility). When you select this option, your dashboard opens with a list of facilities you have added.
- You can edit or delete a facility by simply using the “Edit” and delete buttons respectively.
Kindly give us a feedback, if you face any problem.
“We can help”


Identify opportunities to improve patient outcomes by exploring
changes in predicted outcomes over changes to input variables. Note
that causality cannot be established by this function. Omitted variable
bias and other statistical phenomena may mean that the impacts predicted
here are not realizable. Clinical guidance is essential in choosing
new_values
and acting on impact predictions. Extensive options are
provided to control what impact predictions are surfaced, including
variable_direction
and prohibited_transitions
.
pip( model, d, new_values, n = 3, allow_same = FALSE, repeated_factors = FALSE, smaller_better = TRUE, variable_direction = NULL, prohibited_transitions = NULL, id )
| model | A model_list object, as from |
|---|---|
| d | A data frame on which |
| new_values | A list of alternative values for variables of interest. The
names of the list must be variables in |
| n | Integer, default = 3. The maximum number of alternatives to return
for each patient. Note that the actual number returned may be less than
|
| allow_same | Logical, default = FALSE. If TRUE, |
| repeated_factors | Logical, default = FALSE. Do you want multiple modifications of the same variable for the same patient? |
| smaller_better | Logical, default = TRUE. Are lesser values of the
outcome variable in |
| variable_direction | Named numeric vector or list with entries of -1 or
1. This specifies the direction numeric variables are permitted to move to
produce improvements. Names of the vector are names of variables in
|
| prohibited_transitions | A list of data frames that contain variable
modifications that won't be considered by |
| id | Optional. A unquoted variable name in |
A tibble with any id columns and "variable": the name of the variable
being altered, "original_value": the patient's observed value of
"variable", "modified_value": the altered value of "variable",
"original_prediction": the patient's original prediction,
"modified_prediction": the patient's prediction given the that "variable"
changes to "modified_value", "improvement": the difference between the
original and modified prediction with positive values reflecting
improvement based on the value of smaller_better
, and "impact_rank":
the rank of the modification for that patient.
# First, we need a model to make recommendations set.seed(52760) m <- machine_learn(pima_diabetes, patient_id, outcome = diabetes, tune = FALSE, models = "xgb")#>#>#> #>#> #>#>#>#> #>#># Let's look at changes in predicted outcomes for three patients changing their # weight class, blood glucose, and blood pressure modifications <- list(weight_class = c("underweight", "normal", "overweight"), plasma_glucose = c(75, 100), diastolic_bp = 70) pip(model = m, d = pima_diabetes[1:3, ], new_values = modifications)#> # A tibble: 7 x 8 #> patient_id variable original_value modified_value original_predic… #> <int> <chr> <chr> <chr> <dbl> #> 1 1 plasma_… 148 75 0.944 #> 2 1 weight_… obese underweight 0.944 #> 3 1 diastol… 72 70 0.944 #> 4 2 weight_… overweight normal 0.0718 #> 5 2 diastol… 66 70 0.0718 #> 6 2 plasma_… 85 75 0.0718 #> 7 3 plasma_… 183 75 0.892 #> # … with 3 more variables: modified_prediction <dbl>, improvement <dbl>, #> # impact_rank <int># In the above example, only the first patient has a positive predicted impact # from changing their diastolic_bp, so for the other patients fewer than the # default n=3 predictions are provided. We can get n=3 predictions for each # patient by specifying allow_same, which will recommend the other two patients # maintain their current diastolic_bp. pip(model = m, d = pima_diabetes[1:3, ], new_values = modifications, allow_same = TRUE)#> # A tibble: 9 x 8 #> patient_id variable original_value modified_value original_predic… #> <int> <chr> <chr> <chr> <dbl> #> 1 1 plasma_… 148 75 0.944 #> 2 1 weight_… obese underweight 0.944 #> 3 1 diastol… 72 70 0.944 #> 4 2 diastol… 66 70 0.0718 #> 5 2 plasma_… 85 75 0.0718 #> 6 2 weight_… overweight overweight 0.0718 #> 7 3 plasma_… 183 75 0.892 #> 8 3 weight_… normal normal 0.892 #> 9 3 diastol… 64 64 0.892 #> # … with 3 more variables: modified_prediction <dbl>, improvement <dbl>, #> # impact_rank <int># Sometimes clinical knowledge trumps machine learning. In particular, machine # learning models don't establish causality, they only leverage correlation. # Patient impact predictor suggests causality, so clinicians should always be # consulted to ensure that the causal impacts are medically sound. # # If there is clinical knowledge to suggest what impact a variable should have, # that knowledge can be provided to pip. The way it is provided depends on # whether the variable is categorical (prohibited_transitions) or numeric # (variable_direction). ### Constraining categorical variables ### # Suppose a clinician says that recommending a patient change their weight class # to underweight from any value except normal is a bad idea. We can disallow # those suggestions using prohibited_transitions. Note the change in patient # 1's second recommendation goes from underweight to normal. prohibit <- data.frame(from = setdiff(unique(pima_diabetes$weight_class), "normal"), to = "underweight") pip(model = m, d = pima_diabetes[1:3, ], new_values = modifications, prohibited_transitions = list(weight_class = prohibit))#> # A tibble: 7 x 8 #> patient_id variable original_value modified_value original_predic… #> <int> <chr> <chr> <chr> <dbl> #> 1 1 plasma_… 148 75 0.944 #> 2 1 weight_… obese normal 0.944 #> 3 1 diastol… 72 70 0.944 #> 4 2 weight_… overweight normal 0.0718 #> 5 2 diastol… 66 70 0.0718 #> 6 2 plasma_… 85 75 0.0718 #> 7 3 plasma_… 183 75 0.892 #> # … with 3 more variables: modified_prediction <dbl>, improvement <dbl>, #> # impact_rank <int>### Constraining numeric variables ### # Suppose a clinician says that increasing diastolic_bp should never be # recommended to improve diabetes outcomes, and likewise for reducing # plasma_glucose (which is clinically silly, but provides an illustration). The # following code ensures that diastolic_bp is only recommended to decrease and # plasma_glucose is only recommended to increase. Note that the plasma_glucose # recommendations disappear, because no patient would see their outcomes # improve by increasing their plasma_glucose. directional_changes <- c(diastolic_bp = -1, plasma_glucose = 1) pip(model = m, d = pima_diabetes[1:3, ], new_values = modifications, variable_direction = directional_changes)#> # A tibble: 3 x 8 #> patient_id variable original_value modified_value original_predic… #> <int> <chr> <chr> <chr> <dbl> #> 1 1 weight_… obese underweight 0.944 #> 2 1 diastol… 72 70 0.944 #> 3 2 weight_… overweight normal 0.0718 #> # … with 3 more variables: modified_prediction <dbl>, improvement <dbl>, #> # impact_rank <int>


|
FESTR 0.9
Finite-Element Spectral Transfer of Radiation
|
Ray carrying a spectrum and its own current position and direction. More...
#include <Ray.h>
Public Member Functions | |
| Ray () | |
| Default constructor. More... | |
| Ray (const int level_in, const size_t freq_in, const size_t nin, const size_t jmin_in, const size_t jmax_in, const bool tracking_in, const Vector3d &rin, const Vector3d &vin, const bool analysis_in, const std::string &froot_in, const std::string &hroot_in) | |
| Parametrized constructor. More... | |
| Ray (const int level_in, const size_t freq_in, const size_t nin, const size_t jmin_in, const size_t jmax_in, const bool tracking_in, const Vector3d &rin, const Vector3d &vin, const bool analysis_in, const ArrDbl &yin, const std::string &froot_in, const std::string &hroot_in) | |
| Parametrized constructor. More... | |
| size_t | size () const |
| Getter for size of the spectrum (Ray::n) More... | |
| size_t | get_nzones () const |
| Getter for size of the Ray's trajectory (Ray::nzones) More... | |
| bool | get_tracking () const |
| Getter for Ray::tracking. More... | |
| double | abs_diff (const Ray &o) const |
| Absolute difference between two Rays. More... | |
| void | set_backlighter (const std::vector< double > &yb) |
| Sets the backlighter into Ray::y. More... | |
| void | trace (const Grid &g, const Mesh &m) |
| Ray tracing -> builds Ray::wpt stack of Waypoint objects. More... | |
| void | transport (const double ct) |
| Apply 1D analytic transport solution to update Ray::y. More... | |
| void | cross_Zone (ZonePtr z, const Database &d, const Table &tbl, const std::string &symmetry, const size_t ix, const int ndmesh) |
| Transport *this Ray across current Zone, also write the current spectrum on exit from this Zone, if Ray::tracking is true. More... | |
| void | cross_Mesh (const Mesh &m, const Database &d, const Table &tbl, const std::string &symmetry, const size_t ix) |
| Transport *this Ray across Mesh. More... | |
| std::string | to_string () const |
| String representation of a Ray object. More... | |
Public Attributes | |
| int | diag_id |
| ID of Detector that *this Ray hits at the end of its path. More... | |
| IntPair | patch_id |
| ID of Detector's spatial patch hit by *this Ray. More... | |
| IntPair | bundle_id |
| ID of direction from which *this Ray hits its Detector patch. More... | |
| Vector3d | r |
| Current Ray position. More... | |
| Vector3d | v |
| Current Ray velocity. More... | |
| bool | analysis |
| "true" in analysis, "false" in postprocessing mode More... | |
| size_t | zid |
| ID of current Zone. More... | |
| ArrDbl | y |
| Current Ray specific intensity in W / cm2 / sr / eV. More... | |
| RayStack | wpt |
| Ray trajectory through Mesh as a sequence of Waypoint objects. More... | |
| size_t | ite |
| Electron temperature Database access index saved from previous Zone. More... | |
| size_t | itr |
| Radiation temperature Database access index saved from previous Zone. More... | |
| size_t | ine |
| Electron density Database access index saved from previous Zone. More... | |
| ArrDbl | em |
| Current Zone monochromatic emissivity in W / cm3 / sr / eV. More... | |
| ArrDbl | ab |
| Current Zone monochromatic absorption opacity in 1 / cm. More... | |
| ArrDbl | sc |
| Current Zone monochromatic scattering in 1 / cm. More... | |
Ray carrying a spectrum and its own current position and direction.
| Ray::Ray | ( | const int | level_in, |
| const size_t | freq_in, | ||
| const size_t | nin, | ||
| const size_t | jmin_in, | ||
| const size_t | jmax_in, | ||
| const bool | tracking_in, | ||
| const Vector3d & | rin, | ||
| const Vector3d & | vin, | ||
| const bool | analysis_in, | ||
| const std::string & | froot_in, | ||
| const std::string & | hroot_in | ||
| ) |
Parametrized constructor.
| [in] | level_in | Initializes Ray::level |
| [in] | freq_in | Initializes Ray::freq |
| [in] | nin | Initializes Ray::n |
| [in] | jmin_in | Initializes Ray::jmin |
| [in] | jmax_in | Initializes Ray::jmax |
| [in] | tracking_in | Initializes Ray::tracking |
| [in] | rin | Initializes Ray::r |
| [in] | vin | Initializes Ray::v |
| [in] | analysis_in | Initializes Ray::analysis |
| [in] | froot_in | Initializes Ray::froot |
| [in] | hroot_in | Initializes Ray::hroot |
| Ray::Ray | ( | const int | level_in, |
| const size_t | freq_in, | ||
| const size_t | nin, | ||
| const size_t | jmin_in, | ||
| const size_t | jmax_in, | ||
| const bool | tracking_in, | ||
| const Vector3d & | rin, | ||
| const Vector3d & | vin, | ||
| const bool | analysis_in, | ||
| const ArrDbl & | yin, | ||
| const std::string & | froot_in, | ||
| const std::string & | hroot_in | ||
| ) |
Parametrized constructor.
| [in] | level_in | Initializes Ray::level |
| [in] | freq_in | Initializes Ray::freq |
| [in] | nin | Initializes Ray::n |
| [in] | jmin_in | Initializes Ray::jmin |
| [in] | jmax_in | Initializes Ray::jmax |
| [in] | tracking_in | Initializes Ray::tracking |
| [in] | rin | Initializes Ray::r |
| [in] | vin | Initializes Ray::v |
| [in] | analysis_in | Initializes Ray::analysis |
| [in] | yin | Initializes Ray::y |
| [in] | froot_in | Initializes Ray::froot |
| [in] | hroot_in | Initializes Ray::hroot |
| double Ray::abs_diff | ( | const Ray & | o | ) | const |
| void Ray::cross_Zone | ( | ZonePtr | z, |
| const Database & | d, | ||
| const Table & | tbl, | ||
| const std::string & | symmetry, | ||
| const size_t | ix, | ||
| const int | ndmesh | ||
| ) |
Transport *this Ray across current Zone, also write the current spectrum on exit from this Zone, if Ray::tracking is true.
| size_t Ray::get_nzones | ( | ) | const |
| bool Ray::get_tracking | ( | ) | const |
| void Ray::set_backlighter | ( | const std::vector< double > & | yb | ) |
| size_t Ray::size | ( | ) | const |
| std::string Ray::to_string | ( | ) | const |
| void Ray::transport | ( | const double | ct | ) |
| ArrDbl Ray::ab |
| bool Ray::analysis |
| IntPair Ray::bundle_id |
| int Ray::diag_id |
| ArrDbl Ray::em |
| size_t Ray::ine |
| size_t Ray::ite |
| size_t Ray::itr |
| IntPair Ray::patch_id |
| ArrDbl Ray::sc |
| RayStack Ray::wpt |
| ArrDbl Ray::y |


---
layout: post
title: "DNF 0.4.13 Released"
date: 2014-02-06 16:37:43 +0100
author: Aleš Kozumplík
Should you not be at the event to try out some hacking on it, the release still offers plenty: Delta RPM support, bash completion and re-added keepcache
config option.
An annoying and often complained-about bug of installing local/URL-given RPMs is gone too. See the release notes.
Some Fedora releng issues prevent us to do the synchronous Fedora 20 release again, doh, but the rawhide version is on the mirrors already. Edit:The update is ready now.


Present: I'm a research associate in the Computational Social Science Lab at New York University in Abu Dhabi, advised by Prof. Bedoor AlShebli. I like to study people, their communication, and their networks to understand, uncover and solve the complex and inconspicuous societal problems using large-scale datasets. I use multiple network science, machine learning, and data science concepts and tools within my research. I'm currently working on projects in Science of Science, Science of Collaboration, and Inequality, in collaboration with Prof. Kinga R. Makovi, Prof. Talal Rahwan, and Prof. Wifag Adnan.
Past: I did my Master's in Language Technologies from Language Technologies Institute (LTI) at Carnegie Mellon University in Pittsburgh where I was mentored by
Prof. Kathleen M. Carley and Prof. David R. Mortensen on my master's thesis on
"Characterizing Misinformed Online Health Communities." I have also worked as a
research scholar at LTI for two years under the supervision of Prof. Rita Singh, and
Prof. Bhiksha Raj working on projects in the field of speech processing and
voice forensics.
I did my Bachelor's in Computer Science from Carnegie Mellon University in Qatar where I was mentored by
Dr. Ingmar Weber and Prof. Saquib Razak for
my undergraduate thesis on "Lifestyle Disease Surveillance Using Population Search Behaviour."
Future: In my career, I have been fortunate enough to find amazing and caring advisors and collaborators who have collectively shaped my interests, and given me opportunities to tackle interesting questions. I try to optimize for long-term collaborations and mentorship. My academic tree can be found below. In terms of work, I often think about the implications of my research, and I hope to eventually be able to do something that makes some difference in the world -- the world where impact is not just measured by citations.
Home for me is where my mom (ami) is, and that's Pakistan's Hyderabad in Sindh, uncommonly known as "The Lion City", named in the honour of Ali (aka hayder), the fourth caliph. I identify myself as Muslim, Hyderabadi, Sindhi, and Pakistani.
I love collaborating with people. So don't hesitate to reach out if you would like to collaborate with me, or just talk about life.
Bedoor AlShebli*,
Shahan Ali Memon,
James A. Evans,
Talal Rahwan*
Accepted for presentation at ICSSI (2023)
Accepted as a poster at IC2S2 (2023)
Under Review
Slides (ICSSI)
Preprint
Poster (IC2S2)
Fierce geopolitical tensions between China and the U.S. have led to policies that discourage cross-border collaboration and migration in the field of Artificial Intelligence. Despite this, we analyze a dataset of 363,000 AI scientists and 5,400,000 papers showing that China and the U.S. have been leading the field since 2000 in terms of impact, novelty, productivity, and workforce. Significant bidirectional migration is observed with both countries being primary destinations for one another. Collaborations between the two countries while increasing still represent a small fraction of their total productivity. Yet, we show that the two countries produce more impactful research when collaborating together, suggesting that promoting cross-border collaboration and migration could benefit the field of AI.
Shahan Ali Memon,
Kinga Makovi*,
Bedoor AlShebli*
Accepted for presentation at ICSSI (2023) **Best Paper Award**
Accepted for presentation at Frontiers of Network Science Workshop (2022 & 2023)
Accepted for presentation at IC2S2 (2022)
Under Review
Recording (IC2S2)
Slides (IC2S2)
Slides (ICSSI)
Preprint
Retracting academic papers is a fundamental tool for social control in the academy, and in the vast majority of cases happen only under the most extreme circumstances: when the science behind papers, or the integrity of authors come into question. While retractions do not completely erase papers from the academic record, they can have important implications for retracted scientists and their careers. In this project, we aim to uncover whether retracted authors (RQ1) retain fewer collaborators, (RQ2) gain fewer new collaborators, (RQ3) close fewer triads, and (RQ4) get penalized for public retractions, than their matched non-retracted scientists.
Susan Dun,
Hatim Rachdi
Shahan Ali Memon,
Yelena Mejova,
Ingmar Weber
International Journal of Sport Communication (2022; IF:1.59; Q-Index:Q2)
Accepted for presentation at the 105th NCA 105th Annual Convention (2019)
Paper
We assessed the discussion around FIFA World Cup 2022 in the Twittersphere to shed some light on whether Qatar’s nation-branding and soft power attempts are reflected in public perceptions.
Navin Kumar, Isabel Corpus, Meher Hans, Nikhil Harle, Nan Yang,
Curtis McDonald, Shinpei Nakamura Sakai, Kamila A Janmohamed, Weiming Tang,
Jason L Schwartz, S Mo Jones-Jang, Koustuv Saha,
Shahan Ali Memon,
Chris Bauch,
Munmun De Chaudhury,
Orestis Papakyriakopoulos,
Joseph D Tucker, Abhay Goyal,
Aman Tyagi,
Kaveh Khoshnood,
Saad Omer
BMC Public Health (2022; IF:3.98)
Paper
The purpose of this analysis was to detail the behavior of top Reddit users, posts’ relationship with events early in the vaccine timeline, and the relationship between subreddits that shared COVID-19 vaccine posts. Research questions are as follows: What is the behavior of top Reddit users in regards to COVID-19 vaccines (RQ1)? What are Reddit posts’ relationship with events early in the vaccine timeline (RQ2)? What is the relationship between subreddits that shared COVID-19 vaccine posts (RQ3)?
Wenbo Zhao,
Yang Gao,
Shahan Ali Memon,
Bhiksha Raj,
Rita Singh
25th International Conference on Pattern Recognition (ICPR 2020)
Paper
Slides
In regression tasks, the data distribution is often too complex to be fitted by a single model. In contrast, partition-based models are developed where data is divided and fitted by local models. These models partition the input space and do not leverage the input-output dependency of multimodal-distributed data, and strong local models are needed to make good predictions. Addressing these problems, we propose a binary tree-structured hierarchical routing mixture of experts (HRME) model that has classifiers as non-leaf node experts and simple regression models as leaf node experts.
Shahan Ali Memon,
Kathleen M. Carley
International Workshop on Mining Actionable Insights from Social Networks (MAISoN) (in conj. with CIKM 2020)
Funded by Center for Machine Learning and Health (CMLH)
Paper
Slides
Data
Codebook
Recording
From conspiracy theories to fake cures and fake treatments, COVID-19 has become a hot-bed for the spread of misinformation online. It is more important than ever to identify methods to debunk and correct false information online. In this paper, we present a methodology and analyses to characterize the two competing COVID-19 misinformation communities online: (i) misinformed users or users who are actively posting misinformation, and (ii) informed users or users who are actively spreading true information, or calling out misinformation. The goals of this study are two-fold: (i) collecting a diverse set of annotated COVID-19 Twitter dataset that can be used by the research community to conduct meaningful analysis; and (ii) characterizing the two target communities in terms of their network structure, linguistic patterns, and their membership in other communities.
Shahan Ali Memon,
Aman Tyagi,
David R. Mortensen,
Kathleen M. Carley
International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS 2020)
Funded by Center for Machine Learning and Health (CMLH)
Paper
Slides
Public health practitioners and policy makers grapple with the challenge of devising effective message-based interventions for debunking public health misinformation in cyber communities. Framing and personalization of the message is one of the key features for devising a persuasive messaging strategy. For an effective health communication, it is imperative to focus on preference based framing where the preferences of the target sub-community are taken into consideration. To achieve that, it is important to understand and hence characterize the target sub-communities in terms of their social interactions. In the context of health-related misinformation, vaccination remains to be the most prevalent topic of discord. Hence, in this paper, we conduct a sociolinguistic analysis of the two competing vaccination communities on Twitter: pro-vaxxers or individuals who believe in the effectiveness of vaccinations, and anti-vaxxers or individuals who are opposed to vaccinations
Hira Dhamyal,
Shahan Ali Memon,
Bhiksha Raj,
Rita Singh
Annual Conference of the International Speech Communication Association (INTERSPEECH 2020)
Paper
Can vocal emotions be emulated? This question has been a recurrent concern of the speech community, and has also been vigorously investigated. It has been fueled further by its link to the issue of validity of acted emotion databases. Much of the speech and vocal emotion research has relied on acted emotion databases as valid proxies for studying natural emotions. To create models that generalize to natural settings, it is crucial to work with valid prototypes -- ones that can be assumed to reliably represent natural emotions. More concretely, it is important to study emulated emotions against natural emotions in terms of their physiological, and psychological concomitants. In this paper, we present an on-scale systematic study of the differences between natural and acted vocal emotions.
Shahan Ali Memon,
Saquib Razak
Ingmar Weber
Journal of Medical Internet Research (2020; IF:7.08)
Accepted for presentation at the Population Association of America (PAA 2021)
Accepted for presentation at CMU Qatar Meeting of the Minds (MoM 2017)
Paper
Slides (PAA)
Poster (MoM)
Code
Recording (PAA)
Slides for Google Trends Denormalization
As the process of producing official health statistics for lifestyle diseases is slow, researchers have explored using Web search data as a proxy for lifestyle disease surveillance. Existing studies, however, are prone to at least one of the following issues: ad-hoc keyword selection, overfitting, insufficient predictive evaluation, lack of generalization, and failure to compare against trivial baselines. The aims of this study were to (1) employ a corrective approach improving previous methods; (2) study the key limitations in using Google Trends for lifestyle disease surveillance; and (3) test the generalizability of our methodology to other countries beyond the United States.
Shahan Ali Memon*,
Wenbo Zhao*,
Bhiksha Raj,
Rita Singh
International Joint Conference on Neural Networks (IJCNN 2019)
Paper
Slides
Regression-via-Classification (RvC) is the process of converting a regression problem to a classification one. Current approaches for RvC use ad-hoc discretization strategies and are suboptimal. We propose a neural regression tree model for RvC. In this model, we employ a joint optimization framework where we learn optimal discretization thresholds while simultaneously optimizing the features for each node in the tree.
Shahan Ali Memon,
Hira Dhamyal,
Oren Wright,
Daniel Justice,
Vijaykumar Palat,
William Boler,
Bhiksha Raj,
Rita Singh
arXiv
Paper
Slides
Recording
Do men and women perceive emotions differently? Popular convictions place women as more emotionally perceptive than men. Empirical findings, however, remain inconclusive. Most prior studies focus on visual modalities. In addition, almost all of the studies are limited to experiments within controlled environments. Generalizability and scalability of these studies has not been sufficiently established. In this paper, we study the differences in perception of emotion between genders from speech data in the wild, annotated through crowdsourcing. While we limit ourselves to a single modality (i.e. speech), our framework is applicable to studies of emotion perception from all such loosely annotated data in general. Our paper addresses multiple serious challenges related to making statistically viable conclusions from crowdsourced data. Overall, the contributions of this paper are two fold: a reliable novel framework for perceptual studies from crowdsourced data; and the demonstration of statistically significant differences in speech-based emotion perception between genders.
Shahan Ali Memon,
Rohith Krishnan Pillai,
Susan Dun,
Yelena Mejova,
Ingmar Weber
International ACM Conference on Web Science (WebSci 2017)
Accepted for presentation at 2016 Qatar Foundation Annual Research Conference (QFARC)
Accepted for presentation at CMU Qatar Meeting of the Minds (MoM 2017)
Paper
Poster
Abstract
Is it possible to "hack" an image of an international entity by driving international and domestic media? Here, we present an image/brand monitoring tool for a country, Qatar, which presents an overview of the contexts and references to media in which it is mentioned on social media. Tracking dozens of languages, this tool allows a global understanding of the perceptions and concerns Twitter users associate with Qatar, and which mainstream media may be driving these sentiments.
Journal Reviewer: eLife, 2023
Journal Reviewer: SAGE Communication & Sport, 2023
Journal Reviewer: Elsevier's Information Processing and Management Journal, 2021
Journal Reviewer: Journal of Medical Internet Research, 2020
Conference Reviewer: IEEE International Conference on Machine Learning and Applications, 2020
Moderator: SBP-BRiMS, 2020
Peer Health Advocate: Mental Health Advocate, Student Health Services, Carnegie Mellon University, 2016-2017
Co-founder/Co-designer: CMU Qatar Mindfulness Room, 2016-2017
Board Member: Academic Review Board and University Disciplinary Committee (ARB-UDC), Carnegie Mellon University, 2015-2017


Gunmen kill 3 at funeral in Oti Region
Six people have been shot by unidentified men at a funeral event in Pai Katanga, a community in the Oti Region.
At the same event, there was also an ongoing ceremony to install a new chief.
Three of the victims reportedly died on the spot while the other three are in critical condition in the hospital, one of whom is a chief, Nana Kofi Baafi I.
It is unclear what sparked the shooting.
According to Adom News correspondent Obrempong Kwamena Owusu, the shooting occurred around 1 pm on Saturday.
Police in the region have deployed more personnel to the area to maintain law and order, the reporter added.
Journalists, chiefs and other people who were at the concurrent ceremonies had to hide in a room at the palace of the Paramount Chief, Nana Diawuo Bediako II, for safety.


Modeling growth in complete media means that the model is allowed to consume any nutrient for which a transport reaction is available to the model. For this reason, the content of complete media can change depending on which transport reactions are present in the model.


Here is another opportunity for readers to help save ResonanceFM. Let me just point out that the station's financial circumstances are dismal and Your Help Is Needed.
A collection of rarities by (or including) your favourite Hooting Yardist is going up for auction. Many of these items predate the Wilderness Years (1993–2002), and this may be your only chance to obtain such gems as Sidney The Bat Is Awarded The Order Of Lenin.
So get out your wallet and make your bid!
UPDATE : The closing date is 21st February at 11.59 a.m. and the current highest bid is £120.
FURTHER UPDATE : The winning bid was Â£150, and the lucky recipient was someone using the pseudonym ‘mustardplaster’. Many thanks to them, on behalf of Resonance.


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We thank our voters for the confidence in Chainmakers, which resulted North America Address
in 2 notary spots in season 2. As keeping a notary automatically
re-elected for the next season falls on the shoulders of us, Chainmakers,
we will not be airdropping any mining rewards from the automatically
re-elected notary nodes this coming season.


These are standard .VOC files in the Creative Labs format.
The DF engine only accepts MONO 8-bit 11KHz (11025 Hz) .VOC files.
Note that sounds are looped (eg. the water and wind) using REPEAT/END REPEAT markers.
[by<EMAIL_ADDRESS>Creative Voice File (VOC) Format:
HEADER (bytes 00-19)
Series of DATA BLOCKS (bytes 1A+) [Must end w/ Terminator Block]
-----------------------------------------------------------------
HEADER:
=======
byte # Description
------ ------------------------------------------
00-12 Creative Voice File
13-15 1A 1A 00 (eof to abort printing of file)
16-17 Version number (minor,major) (VOC-HDR puts 0A 01)
18-19 2's Comp of Ver. # + 1234h (VOC-HDR puts 29 11)
-----------------------------------------------------------------
DATA BLOCK:
===========
Data Block: TYPE(1-byte), SIZE(3-bytes), INFO(0+ bytes)
NOTE: Terminator Block is an exception -- it has only the TYPE byte.
TYPE Description Size (3-byte int) Info
---- ----------- ----------------- -----------------------
00 Terminator (NONE) (NONE)
01 Sound data 2+length of data *
02 Sound continue length of data Voice Data
03 Silence 3 **
04 Marker 2 Marker# (2 bytes)
05 ASCII length of string null terminated string
06 Repeat 2 Count# (2 bytes)
07 End repeat 0 (NONE)
*Sound Info Format: **Silence Info Format:
--------------------- ----------------------------
00 Sample Rate 00-01 Length of silence - 1
01 Compression Type 02 Sample Rate
02+ Voice Data
Marker# -- Driver keeps the most recent marker in a status byte
Count# -- Number of repetitions + 1
Count# may be 1 to FFFE for 0 - FFFD repetitions
or FFFF for endless repetitions
Sample Rate -- SR byte = 256-(1000000/sample_rate)
Length of silence -- in units of sampling cycle
Compression Type -- of voice data
8-bits = 0
4-bits = 1
2.6-bits = 2
2-bits = 3
Multi DAC = 3+(# of channels) [interesting--
this isn't in the developer's manual]


"banner.jpg" WIDTH=470 HEIGHT=84 (13K)
"bannerwt.jpg" WIDTH=470 HEIGHT=84(13K)
"tv6bsm.jpg" WIDTH=277 HEIGHT=133
"iconsm.jpg" WIDTH=113 HEIGHT=64 (with link)
"crestsm.jpg" WIDTH=64 HEIGHT=64 (with link)Try it out!
(Best viewed with Netscape/Live3D...you know the drill....)
|
Images created by: Joel Tanis(jtanis@direct.ca)Donald<EMAIL_ADDRESS>|


Back to Main Page
A database is present. you can query it with the form below. please use SQL query syntax!


Aldi releases Friends-themed collection and we couldn't BE more excited
Christmas might be 36 days away, but Friends fans are already getting lots of presents from Santa.
Last week, Hollywood Reporter reported that a reunion might finally be on the cards, courtesy of HBO Max, and now Aldi has released a brand new collection of interior products themed after the popular show.
Add some Friends touches throughout your home by decorating it with three duvet set designs, as well as cups, mugs and three types of pillows.
Remember that episode where Phoebe tries to seduce Chandler in order to get him to admit that he’s sleeping with Monica?
Hardcore fans will recognise the line ‘They don’t know that we know they know we know’, scribbled onto one of the cushions.
Meanwhile, you can cosy up in bed with your very own Friends duvet set – designed in a pattern that features the show’s logo – or get yourself some Central Perk merchandise.
Advertisement Advertisement
There’s a cute cup, as well as a pillow, but no Gunther references, sadly.
In addition to bedroom products, the collection also includes two pyjama sets – perfect to wear during a binge-watch.
It includes a white t-shirt with the words ‘I’d rather be watching Friends’ on it, paired with cosy grey bottoms decorated with the logo.
Then all you need is a cheesy pizza, a meaty sandwich with pickles (Joey would be proud) and five pals, and you’ve got your very own Friends flat.
The limited-edition collection will launch on 21 November, but if you can’t wait until then, there’s also the option to pre-order it now.
Prices range from £4.99 for the cups, £5.99 for cushions and double duvet sets for £14.99.
The cosy PJs will set you back an affordable £8.99.
‘Take a trip down memory lane with Aldi’s gifting range that pays homage to one of the most iconic 90s tv series,’ the supermarket said.
‘They’re also the perfect stocking filler or secret Santa gift.’
If you’re planning to travel this Christmas, it might be worth swinging by B&M as well, to pick up a Friends travel mug and a hairbrush (to avoid the Monica ‘humidity hair’).
Advertisement Advertisement
Just don’t forget to make sure the plane has all of its phalanges before you board.


BioElement Demos
Javascript Demos
Button
Nested components
Styles
Events
State from Props
Set focus
Typescript Demos
Basic Component
Nested components
Shadow Root Slots
Nested Slots
Two slots
Two way data binding
⚠ These examples are not polyfilled, we've an open issue
and we'll hopefully fix that issue soonish.


Our Father: Our Father, who art in heaven, hallowed be Thy name: Thy kingdom come: Thy will be done on earth as it is in heaven. Give us this day our daily bread: and forgive us our trespasses as we forgive those who trespass against us. And lead us not into temptation: but deliver us from evil. Amen. The Hail Mary: Hail Mary, full of grace, the Lord is with thee: blessed art thou among women, and blessed is the fruit of thy womb, Jesus. Holy Mary, Mother of God, pray for us sinners, now and at the hour of our death. Amen Glory Be to the Father: Glory be to the Father, and to the Son and to the Holy Spirit. As it was in the beginning, is now and ever shall be, world without end. Amen. The Fatima Prayer: "O my Jesus, forgive us our sins, save us from the fires of hell, and lead all souls to Heaven, especially those in most need of Your Mercy". (Our Lady at Fatima, 13th July 1917) The Hail, Holy Queen: Hail, holy Queen, Mother of Mercy! our life, our sweetness, and our hope! To thee do we cry, poor banished children of Eve; to thee do we send up our sighs, mourning and weeping in this valley, of tears.  Let Us Pray: "O God whose only begotten Son has purchased for us the rewards of eternal life, Grant that we beseech Thee while meditating upon these mysteries of the Most Holy Rosary of the Blessed Virgin Mary, we may both imitate what they contain and obtain what they promise, through the same Christ our Lord Amen." Second Fatima Prayer:
"My God, I believe, I adore, I hope, and I love You. I beg pardon of You for those who do not believe, do not adore, do not hope, and do not love You." Memorare of St. Bernard: Remember, O most gracious Virgin Mary, that never was it known that anyone who fled
to thy protection, implored thy help, or sought thine intercession was left unaided. For the intentions of the Holy Father, one may recite at the end of the Rosary one "Our Father", one "Hail Mary", and one "Glory Be". |
Litany of the Blessed Virgin Mary:
Lord have mercy. |
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Chapter 2 Data Manipulation using tidyr
Learning Objectives
- Understand the concept of a wide and a long table format and for which purpose those formats are useful.
- Understand what key-value pairs are.
- Reshape a data frame from long to wide format and back with the
pivot_wider
andpivot_longer
commands from thetidyr
package.- Export a data frame to a .csv file.
dplyr
pairs nicely with tidyr
which enables you to swiftly convert between different data formats for plotting and analysis.
The package tidyr
addresses the common problem of wanting to reshape your data for plotting and use by different R functions. Sometimes we want data sets where we have one row per observation. Sometimes we want a data frame where each observation type has its own column, and rows are instead more aggregated groups - like surveys, where each column represents an answer. Moving back and forth between these formats is nontrivial, and tidyr
gives you tools for this and more sophisticated data manipulation.
To learn more about tidyr
after the workshop, you may want to check out this cheatsheet about tidyr
.
2.1 About long and wide table format
The ‘long’ format is where:
- each column is a variable
- each row is an observation
In the ‘long’ format, you usually have 1 column for the observed variable and the other columns are ID variables.
For the ‘wide’ format a row, for example could be a reserach subject for which you have multiple observation variables containing the same type of data, for example responses to a set of survey questions, or repeated observations over time, or a mix of both. Here is an example:
You may find data input may be simpler or some other
applications may prefer the ‘wide’ format. However, many of R
‘s functions have
been designed assuming you have ’long’ format data. This tutorial will help you
efficiently transform your data regardless of original format.
The choice of data format affects readability. For humans, the wide format is often more intuitive, since we can often see more of the data on the screen due to its shape. However, the long format is more machine readable and is closer to the formatting of databases. The ID
variables in our dataframes are similar to the fields in a database and observed variables are like the database values.
Challenge 1
Is trafficstops in a long or wide format?
2.2 Long to Wide with pivot_wider
Now let’s see this in action. First, using dplyr
, let’s create a data frame
with the mean age of each driver by gender and county:
trafficstops_ma <- trafficstops %>%
filter(!is.na(driver_gender)) %>%
group_by(county_name, driver_gender) %>%
summarize(mean_age = mean(driver_age, na.rm = TRUE))
trafficstops_ma
#> # A tibble: 164 x 3
#> # Groups: county_name [82]
#> county_name driver_gender mean_age
#> <fct> <fct> <dbl>
#> 1 Adams County female 36.7
#> 2 Adams County male 38.4
#> 3 Alcorn County female 33.3
#> 4 Alcorn County male 34.1
#> 5 Amite County female 38.3
#> 6 Amite County male 40.3
#> 7 Attala County female 36.7
#> 8 Attala County male 38.1
#> 9 Benton County female 32.1
#> 10 Benton County male 34.4
#> # … with 154 more rows
Now, to make this long data wide, we use pivot_wider
from tidyr
to turn the driver gender into columns. In addition to our data table we provide pivot_wider
with two arguments: names_from
describes which column to use for name of the output column, and values_from
tells it from column to get the cell values. We’ll use a pipe so we can ignore the data argument.
trafficstops_ma_wide <- trafficstops_ma %>%
pivot_wider(names_from = driver_gender,
values_from = mean_age)
trafficstops_ma_wide
#> # A tibble: 82 x 3
#> # Groups: county_name [82]
#> county_name female male
#> <fct> <dbl> <dbl>
#> 1 Adams County 36.7 38.4
#> 2 Alcorn County 33.3 34.1
#> 3 Amite County 38.3 40.3
#> 4 Attala County 36.7 38.1
#> 5 Benton County 32.1 34.4
#> 6 Bolivar County 33.2 36.3
#> 7 Calhoun County 33.3 34.8
#> 8 Carroll County 33.8 36.0
#> 9 Chickasaw County 33.2 34.6
#> 10 Choctaw County 35.8 36.8
#> # … with 72 more rows
We can now do things like compare the mean age of men against women drivers. As example we use the age difference to find the counties with the largest and with the smallest number.  (Check class(trafficstops_ma)
and class(trafficstops_ma_wide)
). That means that any instruction that follows will operate on each group (in this case county) separately. That may be ok for some instances (like mutate
), but if we are interested in retrieving the maximum and the minumim age difference over all counties we need to ungroup
the tibble to have the filter
command operate on the entire dataset instead of each group (i.e. county).
2.3 Wide to long with pivot_longer
What if we had the opposite problem, and wanted to go from a wide to long
format? For that, we use pivot_longer
, which will increase the number of rows and decrease the number of columns. We provide the functino with thee arguments: cols
which are the columns we want to pivot into the long format, names_to
, which is a string specifying the name of the column to create from the data stored in the column names, and values_to
, which is also a string, specifying the name of the column to create from the data stored in cell values.
So, to go backwards from trafficstops_ma_wide
, and exclude county_name
from the long, we would do the following:
trafficstops_ma_long <- trafficstops_ma_wide %>%
pivot_longer(cols = -county_name,
names_to = "driver_gender", # name is a string!
values_to = "mean_age") # also a string
trafficstops_ma_long
#> # A tibble: 164 x 3
#> # Groups: county_name [82]
#> county_name driver_gender mean_age
#> <fct> <chr> <dbl>
#> 1 Adams County female 36.7
#> 2 Adams County male 38.4
#> 3 Alcorn County female 33.3
#> 4 Alcorn County male 34.1
#> 5 Amite County female 38.3
#> 6 Amite County male 40.3
#> 7 Attala County female 36.7
#> 8 Attala County male 38.1
#> 9 Benton County female 32.1
#> 10 Benton County male 34.4
#> # … with 154 more rows
We could also have used a specification for what columns to include. This can be
useful if you have a large number of identifying columns, and it’s easier to
specify what to gather than what to leave alone. And if the columns are adjacent to each other, we don’t even need to list them all out – we can use the :
operator!
trafficstops_ma_wide %>%
pivot_longer(cols = male:female, # this also works
names_to = "driver_gender",
values_to = "mean_age")
#> # A tibble: 164 x 3
#> # Groups: county_name [82]
#> county_name driver_gender mean_age
#> <fct> <chr> <dbl>
#> 1 Adams County male 38.4
#> 2 Adams County female 36.7
#> 3 Alcorn County male 34.1
#> 4 Alcorn County female 33.3
#> 5 Amite County male 40.3
#> 6 Amite County female 38.3
#> 7 Attala County male 38.1
#> 8 Attala County female 36.7
#> 9 Benton County male 34.4
#> 10 Benton County female 32.1
#> # … with 154 more rows
There are many powerful operations you can do with the pivot_*
functions. To learn more review the vignette:
Challenge
1.From the trafficstops dataframe create a wide data frame
tr_wide
with “year” as columns, each row is a different violation (“violation_raw”), and the values are the number of traffic stops per each violation, roughly like this:
violation_raw | 2013 | 2014 | 2015 ...
Improper turn | 65 | 54| 67 ...
Speeding | 713 | 948| 978 ...
...
Use
year()
from the lubridate package. Hint: You will need to summarize and count the traffic stops before reshaping the table.
- Now take the data frame, and make it long again, so each row is a unique violation_raw - year combination, like this:
violation_raw | year | n of stops
Improper turn | 2013 | 65
Improper turn | 2014 | 54
... etc
2.4 Exporting data
Similar to the read.csv()
function used for reading CSV files into R, there is a write.csv()
function that generates CSV files from data frames.
Before using write.csv()
, we are going to create a new folder, data_output
,
in our working directory that will store this generated dataset. We don’t want
to write generated datasets in the same directory as our raw data. It’s good
practice to keep them separate. The data
folder should only contain the raw,
unaltered data, and should be left alone to make sure we don’t delete or modify
it. In contrast, our script will generate the contents of the data_output
directory, so even if the files it contains are deleted, we can always
re-generate them.
We can save the table generated above in our data_output
folder. By default, write.csv()
includes a column with row names (in our case
these names are the row numbers), so we need to add row.names = FALSE
so
they are not included: