{% extends "layout.html" %} {% block title %}Help - {% endblock %} {% block content %}
Welcome to the intelligent™ search engine for the Lojban dictionary!
Using me is easy, just enter a search word up there! ➚
There are however some neat tricks you might be interested in.
Prefix a query with the name of a field followed by a colon and that query will be limited to that field. This restricts the results of the rest of the query to entries that match that field-specific query.
Example: number type:cmavo
.
The above gives you a list of all particles matching “number”. The fields you can search are: word, gloss, affix, class, type, definition and notes.
For the fields affix, type and word, some characters have special meanings.
A *
matches anything at that position, a ?
matches any single character.
Also, [abc]
matches any of “a”, “b” and “c” whereas [!abc]
matches anything except those.
Example: word:[bd]a?
.
This lists all words that begin with a “b” or a “d”, followed by an “a” and any other one character.
For text fields I compare the stem of words, so that “amused” finds “amusing” and “amusement”.
Example: amused
.
If you need a space in a query and don't mean two separate restrictions, you need to quote the query.
Example: type:"experimental gismu"
.
I have an OpenSearch description, which means you can add me as a search engine in Firefox.
If you're using Chrome or Chromium I'm already set up for you, just type {{ website }}
followed by your search in the location bar.
If you right click on the location bar you can edit search engines and set up a more convinient keyword for me.


Want to contribute? Shoot over a message! See something wrong? Make sure to make it known! Here's a form link. Fill it out if you think we're missing a keyset or want to add your own. You will also be able to ask questions and get in contact with us through the form.
Want updates? You can fill out the form at this link and we'll set you up with some emails. Due to the automated nature of the list, we can't use a traditional subscription service. You won't receive a confirmation email but you'll be put on the lists you select. If you realize you haven't been receiving emails, get in touch with us at<EMAIL_ADDRESS>and we'll check it out. If you wish to unsubscribe from the email lists, there will be a link inside each email that will allow you to fill out an unsubscribe form.
This site started as a method to keep track of keysets internally without the need to remember to visit a bunch of pages. It evolved into a full fledged tracker after the sudden influx of group buys over the past month. It was hashed out in a few days so it's pretty rough around the edges. The tracker will be updated frequently as group buys come and go. Only 'current' group buys will be on the tracker. Once an item has mostly been recieved by buyers, it will be removed.


--- title: Naomi Klein The Shock Doctrine date: '2009-01-02 19:25:21 +0100' date_gmt: '2009-01-02 17:25:21 +0100' categories: - General ---
Finished reading Naomi Klein's The Shock Doctrine: The Rise of Disaster Capitalism. War is economics === continuous growth for rich people.


Thank you. We hope your experience will awesome and we can’t wait to get your feedback and see what we can do better.


--- layout: page title: "Hi, I'm Dean" subtitle: R-Shiny Expert / Open-Source Developer / Tireless Traveller css: "/assets/css/index.css" share-img: /assets/img/deanimg.jpeg share-title: "Dean Attali - R-Shiny consultant" share-description: "R-Shiny developer and consultant with a MSc in Bioinformatics and a Bachelor of Computer Science. Previously a software engineer at Google, IBM, and Wish.com." support_promo_box: true cover-img: - "/assets/img/big-imgs/costa-rica-house.jpeg" : "Montezuma, Costa Rica (2011)" - "/assets/img/big-imgs/grouse-grind.jpeg" : "Vancouver, Canada (2014)" - "/assets/img/big-imgs/hawaii1.jpeg" : "Kauai, HI, USA (2014)" - "/assets/img/big-imgs/hawaii2.jpeg" : "Kauai, HI, USA (2014)" - "/assets/img/big-imgs/hongkong-cliff-dive.jpeg" : "Hong Kong (2014)" - "/assets/img/big-imgs/hongkong-infinity-pool.jpeg" : "Hong Kong (2014)" - "/assets/img/big-imgs/israel-dive1.jpeg" : "Eilat, Israel (2013)" - "/assets/img/big-imgs/israel-dive2.jpeg" : "Eilat, Israel (2013)" - "/assets/img/big-imgs/israel-dive3.jpeg" : "Eilat, Israel (2013)" - "/assets/img/big-imgs/laos-pond2.jpeg" : "Luang Prabang, Laos (2014)" - "/assets/img/big-imgs/laos-pond1.jpeg" : "Luang Prabang, Laos (2014)" - "/assets/img/big-imgs/laos-pond3.jpeg" : "Luang Prabang, Laos (2014)" - "/assets/img/big-imgs/st-martin.jpeg" : "St Maarten (2014)" - "/assets/img/big-imgs/tanzania.jpeg" : "Mt Kilimanjaro, Tanzania (2012)" - "/assets/img/big-imgs/vietnam-beach.jpg" : "Mui Ne, Vietnam (2013)" - "/assets/img/big-imgs/vietnam-climb.jpeg" : "Cat Ba, Vietnam (2013)" - "/assets/img/big-imgs/vietnam-climb2.jpeg" : "Cat Ba, Vietnam (2013)" - "/assets/img/big-imgs/vietnam-dunes.jpeg" : "Mui Ne, Vietnam (2013)" - "/assets/img/big-imgs/vietnam-dunes2.jpeg" : "Mui Ne, Vietnam (2013)" - "/assets/img/big-imgs/vietnam-fruits.jpeg" : "Nha Trang, Vietnam (2013)" - "/assets/img/big-imgs/vietnam-hat.jpeg" : "Hoi An, Vietnam (2013)" - "/assets/img/big-imgs/vietnam-jump.jpeg" : "Mui Ne, Vietnam (2013)" - "/assets/img/big-imgs/vietnam-ricefield.jpeg" : "Sapa, Vietnam (2013)" - "/assets/img/big-imgs/vietnam-scooter.jpeg" : "Da Nang, Vietnam (2013)" - "/assets/img/big-imgs/vietnam-walk.jpeg" : "Sapa, Vietnam (2013)" - "/assets/img/big-imgs/california-skydive.jpeg" : "Davis, CA, USA (2008)" - "/assets/img/big-imgs/california-surf.jpeg" : "Los Angeles, CA, USA (2008)" - "/assets/img/big-imgs/california-surf2.jpeg" : "Los Angeles, CA, USA (2008)" - "/assets/img/big-imgs/california-surf3.jpeg" : "Santa Cruz, CA, USA (2009)" - "/assets/img/big-imgs/costa-rica.jpeg" : "Arenal, Costa Rica (2011)" - "/assets/img/big-imgs/rio-hanggliding.JPG" : "Rio de Janeiro, Brazil (2015)" - "/assets/img/big-imgs/rio-steps.jpg" : "Rio de Janeiro, Brazil (2015)" - "/assets/img/big-imgs/hawaii-turtles.jpg" : "Oahu, HI, USA (2016)" - "/assets/img/big-imgs/banff-mountains.jpg" : "Banff, Canada (2017)" - "/assets/img/big-imgs/delft-canal.jpg" : "Delft, Netherlands (2017)" - "/assets/img/big-imgs/delft-duck.jpg" : "Delft, Netherlands (2017)" - "/assets/img/big-imgs/israel-camel.jpg" : "Arad, Israel (2018)" - "/assets/img/big-imgs/israel-haifa.jpg" : "Haifa, Israel (2018)" - "/assets/img/big-imgs/maui-volcano.jpg" : "Maui, HI, USA (2018)" ---
{% assign posts = paginator.posts | default: site.posts %}


{% extends "layout.html" %} {% block page_title %} Hackney - Maintain My Home {% endblock %} {% block content %} Maintain My Home Request a repair What is your address? Search for your address by postcode Find my address Flat 1, 8 Hoxton Square, N1 6NU Flat 2, 8 Hoxton Square, N1 6NU Flat 4, 8 Hoxton Square, N1 6NU Flat 5, 8 Hoxton Square, N1 6NU My address isn't here Back to add repair details {% endblock %} {% block scripts %} {% include "includes/scripts.html" %} {% block page_scripts %} {% endblock %} {% endblock %}
Back to add repair details


After bypoll defeat, Siddaramaiah resigns as CLP leader
by Akram MohammedFollowing the lacklustre performance of the Congress in the Karnataka bypolls, Congress leader Siddaramaiah resigned as the Congress Legislature Party leader here on Monday.
Speaking at a press conference, Siddaramaiah said that, as CLP leader, he had the responsibility to uphold democratic ideals.
"In the interests of the party, I have resigned as Congress Legislature Party and will be sending my resignation letter to AICC president Sonia Gandhi," he said.
A copy of the letter will be sent to AICC general secretary K C Venugopal and KPCC president Dinesh Gundu Rao, he said.
"I had expected voters to reject the disqualified MLAs who had indulged in horsetrading. My expectation was that voters, in order to preserve democratic ideals will punish the disqualified MLAs and teach them a lesson. However, that was not to be," he said.
"I respect the mandate given by the electorate in the #KarnatakaBypolls. I expected people to teach Karnataka BJP leaders a great lesson for orchestrating Operation Kamala. I am taking the moral responsibility & resigning as Leader of CLP & Leader of Opposition," tweeted Siddaramaiah.
"I sincerely thank INC President Smt. Sonia Gandhi for having entrusted me with the responsibilities and I will continue my efforts as a loyal Congressman to strengthen the party," he said in his tweet.


{% extends "developers/base_impala.html" %} {% set title = _('Testing Apps') %} {% block title %}{{ hub_page_title(title) }}{% endblock %} {% block content %} {{ hub_breadcrumbs(items=[(url('mkt.developers.apps.testing'), _('Testing')), (None, title)]) }} {{ title }} {{ _('Use these tools for testing your apps prior to submission to the Firefox Marketplace.') }} {% include 'developers/includes/testing_nav.html' %} {% endblock %}
{{ _('Use these tools for testing your apps prior to submission to the Firefox Marketplace.') }}


{% extends "base.html" %} {% block title %}Password reset completed{% endblock %} {% block content %}
Your password has been reset. You may go ahead and log in now.


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What is Bitcoin? A Step-By-Step Guide For Beginners
Details
March 16, 2018
The Lakes Golf Course 400 S. Sepulveda Boulevard, El Segundo,
Angelina Johnson
speaker
Is your business ready for a production blockchain rollout?
Details
March 16, 2018
Juarez & Associates, 12139 National Boulevard, Los Angeles, CA, U.S.
Peter Spenser
speaker
What is Bitcoin? A Step-By-Step Guide For Beginners
Frank Godman
speaker
Coming Soon
Apply them in Your Own Routines


The Works of Édouard Manet:
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As always, everyone at square gallery thanks you
for your support. We couldn’t exist without you. |
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Prime Minister Lee Hsien Loong to visit South Korea on Friday
SINGAPORE: Prime Minister Lee Hsien Loong will be in South Korea from Friday (Nov 22) to Nov 27, the Prime Minister’s Office (PMO) said on Friday.
He will first travel to Seoul where he will meet President Moon Jae-in for a bilateral visit, before going to Busan to attend the Association of Southeast Asian Nations (ASEAN)-Republic of Korea (ROK) Commemorative Summit.
In Seoul, President Moon will host an official lunch for Mr Lee. Both leaders will also witness the signing and exchanges of Memoranda of Understanding in areas such as manufacturing of pharmaceuticals, smart cities and cyber-cooperation.
READ: South Korean President Moon Jae-in meets President Halimah Yacob at Istana
In Busan, Mr Lee and other ASEAN leaders will join President Moon in commemorating the 30th anniversary of dialogue relations between the grouping and South Korea.
At the Summit, the leaders will take stock of the ASEAN-ROK relationship and discuss ways to strengthen linkages, the PMO said.
“They will also exchange views on regional and international issues,” a spokesperson said.
According to the ASEAN website, the 10-member grouping and South Korea initiated sectoral dialogue relations in November 1989. South Korea was accorded a full Dialogue Partner status at the 24th ASEAN Ministerial Meeting in July 1991. The partnership was elevated to a Summit level in 1997.
Mr Lee will be accompanied by his wife Ho Ching, Minister for Foreign Affairs Vivian Balakrishnan, Senior Parliamentary Secretary for Foreign Affairs Tan Wu Meng, and other Government officials.
During Mr Lee’s absence, Deputy Prime Minister Heng Swee Keat will be Acting Prime Minister.


iSEE
(Rue-Albrecht, Marini, Soneson and Lun, F1000Research 2018) is an R
package for interactive exploratory analysis and visualization of data provided
in SummarizedExperiment objects (or any class extending SummarizedExperiment).
Here, we show how to prepare the output generated by alevin in a form suitable
for exploration with iSEE
. We illustrate the procedure using the example data
provided in the alevinQC
R package
(https://github.com/csoneson/alevinQC),
which consists of a mix of mouse and human cells, and was quantified using a
combined mouse and human reference transcriptome.
suppressPackageStartupMessages({
library(tximeta)
library(iSEE)
library(shiny)
library(scater)
library(scran)
library(SC3)
library(cowplot)
})
Alevin output files can be imported into R using the tximport
or tximeta
packages. While tximport
returns a count matrix, tximeta
generates a
SummarizedExperiment object, and attempts to automatically retrieve information
about the transcriptome that was used for the alevin quantification. Note that
you need tximport
>= 1.11.5 or tximeta
>= 1.1.10 to read alevin output.
Alternatively, you can load the alevin count matrix with the code provided in
the alevin-monocle tutorial
and create the SummarizedExperiment object manually.
## Path to alevin quant matrix
alevinquant <- system.file("extdata/alevin_example/alevin/quants_mat.gz",
package = "alevinQC")
## Read alevin output with tximeta and generate a SummarizedExperiment object
alevin <- tximeta::tximeta(coldata = data.frame(files = alevinquant,
names = "sample1"),
type = "alevin")
## importing quantifications
## reading in alevin gene-level counts across cells
## couldn't find matching transcriptome, returning un-ranged SummarizedExperiment
The code above generates a SummarizedExperiment object, with the UMI counts from
alevin contained in the counts
assay.
alevin
## class: SummarizedExperiment
## dim: 107450 298
## metadata(3): tximetaInfo quantInfo countsFromAbundance
## assays(1): counts
## rownames(107450): ENSG00000223972.5 ENSG00000227232.5 ...
## ENSMUSG00000094855.1 ENSMUSG00000095041.7
## rowData names(0):
## colnames(298): CGTAGCGCACACATGT CTGGTCTTCGGTTAAC ...
## TGGGCGTAGCGTCAAG CGAGCACTCCCAGGTG
## colData names(0):
dim(assay(alevin, "counts"))
## [1] 107450 298
The SummarizedExperiment object can be directly imported and explored with
iSEE
.
app <- iSEE(alevin)
shiny::runApp(app)
At this stage, any additional row (gene) and/or column (cell) annotations, as
well as reduced dimension representations, can be added to the object for
visualization with iSEE
. As an example, we will first flag the cells that are
included in the final whitelist from alevin, and subset the SummarizedExperiment
object to only those cells. Then, we will use the scater
package to calculate
QC metrics and perform dimension reduction using PCA and t-SNE (after
normalization and log-transformation of the counts using the scran
package),
and finally cluster the cells using the SC3
package. All the output of these
operations are stored in the object created above (which is converted to a
SingleCellExperiment to allow the inclusion of the reduced dimension
representations). Note that the code below is just an example, and there are
many other ways of performing these analyses. For more detailed information
about each step, we refer to the documentation and publications corresponding to
the respective packages.
## Add information about whitelist membership
whitelist <- read.delim(system.file("extdata/alevin_example/alevin/whitelist.txt",
package = "alevinQC"),
header = FALSE, as.is = TRUE)$V1
alevin$whitelist <- colnames(alevin) %in% whitelist
## Subset to only cells in whitelist
alevin <- alevin[, alevin$whitelist]
## Convert to SingleCellExperiment
alevin <- as(alevin, "SingleCellExperiment")
## Normalize using scran/scater
alevin <- scran::computeSumFactors(alevin, min.mean = 0.25)
alevin <- scater::normalize(alevin)
## Calculate QC metrics using scater
alevin <- scater::calculateQCMetrics(alevin)
## Dimension reduction
set.seed(42)
alevin <- scater::runPCA(alevin)
alevin <- scater::runTSNE(alevin)
## t-SNE plots, coloured by the expression of a human gene and a mouse gene,
## respectively
cowplot::plot_grid(
scater::plotTSNE(alevin, colour_by = "ENSG00000140988.15"),
scater::plotTSNE(alevin, colour_by = "ENSMUSG00000091957.3"),
nrow = 1
)
## Clustering with SC3
rowData(alevin)$feature_symbol <- rownames(alevin)
alevin <- sc3(alevin, n_cores = 1, ks = 2:5)
## Setting SC3 parameters...
## Calculating distances between the cells...
## Performing transformations and calculating eigenvectors...
## Performing k-means clustering...


{% extends "base.html" %} {% block title %}
Medieval catalogues > UNIVERSITY AND COLLEGE LIBRARIES OF CAMBRIDGE > Peterhouse > Catalogue, 24 Dec. 1418
{% endif %}1091 identified entries found.
All data was derived from the List of Identifications by Professor Richard Sharpe.
{% if not printing %}
A key to codes used in the List is available (opens in new tab).{% endif %}


---
title: Tree Toppers
keywords: youth, camp, week long
description: Camp for ages 8-13 with many opportunities to swim, hike, learn archery and study the Bible. Tree Topper campers also have the ability to cook out several of their meals!
---
{% block MainText %}
{# macro copied and edited #}
{% for camp in summer.camp_names %}
{%if (camp.info.name == resource.meta.name) or (camp.info.name == resource.meta.title) %}
{{camp.info.name}}
{% filter markdown %}
**_Youth Grades 4th-6th and one 7th-9th week_**
{%endfilter%}
{% include "galleryInsert.j2" %}
{% filter markdown %}
{%for d_w in summer.dates %}
{%for no, week in camp.info.weeks %}
{% if d_w.week.index == week.index %}
{% for p_no, price in camp.info.prices %}
{% if no == p_no %}
**_{{camp.info.name}} {{no}} {%if no == "4" %}*(grades 7-9){%else%}(grades 4-6){%endif%}: {{week.date}} - {{price}}_**
{%endif%}
{%endfor%}
{%endif%}
{%endfor%}
{%endfor%}
{%endfilter%}
{%endif%}
{%endfor%} {# ends for camp in summer.camp_names #}
{# end of modified macro #}
{% filter markdown %}
Sleep under the stars in our tree houses, learn how to cook your meals over a campfire and enjoy a unique camp experience. Our older Tree Toppers will enjoy adding high ropes to their experience.
**Limit 20 campers per session.**
Stay two weeks using [Connections][connectionsLink] options (or use Connections for a Saturday or Sunday pickup time.)
[connectionsLink]: /program/summer/connections.html
{%endfilter%}


SIX DAYS, SEVEN NIGHTS A film review by Steve Rhodes Copyright 1998 Steve Rhodes
RATING (0 TO ****): ***
Although Director Ivan Reitman occasionally misses, as he did with FATHER'S DAY, he generally hits the mark dead-on, as he did with GHOSTBUSTERS and DAVE. His latest motion picture, SIX DAYS, SEVEN NIGHTS, is a big-hearted movie filled with comedy and romance. Best described as a SWISS FAMILY ROBINSON-style love story, the movie stars Harrison Ford and Anne Heche as two people who go from hatred to love as they struggle with dire circumstances. With a marvelous script by Michael Browning, the story provides ample laughs as well as a few beautifully tender moments.
It all starts when Robin Monroe (Heche), the assistant editor of "Dazzle," is given a ticket to a remote tropical island by her would-be husband, Frank Martin (David Schwimmer). He is going to take her away from her killer work schedule in a frozen Manhattan so that he can pop the question on a warm beach under a swaying palm tree. "I want this to be the most unforgettable vacation of our lives," he tells her in the movie's most prophetic line.
They ride the last leg of their flight in a jury-rigged plane flown by a graying pilot named Quinn Harris (Ford). Quinn's busty companion, Angelica (Jacqueline Obradors), looking like one of those girls that your mother warned you about, comes along for the ride.
After they arrive, Robin's office demands that she fly to Tahiti for a day to supervise a photo shoot, so she hires a reluctant Quinn to transport her. The plane crashes on an uninhabited island, and most of the remaining movie is about how Robin and Quinn cope. Facing everything from scorpions to pirates, they survive, but just barely.
 she taunts him. "You send them into the wilderness with a Q-tip and a knife, and they build you a shopping mall." Finally after one calamity after another, she allows as how, "I've had just about as much vacation as I can stand."
The movie is as good with the physical comedy as the verbal. In one delicious scene, Robin freezes in the water when a snake crawls into her shorts. The look on both of their eyes as Quinn reaches deep into her crotch area to retrieve the snake is a sweet mixture of shock, fright and ecstasy.
Starting out despising and ridiculing each other, they end up romantically inclined even if not exactly lovers.  In an age in which actors quickly throw off their clothes and jump into bed, the intimacy of simple kissing is rarely bothered with and certainly never discussed.
There is excellent, albeit subtle, chemistry between Robin and Quinn. She's the smart professional with the sexy outfits. And he's her opposite, a boozing pilot who is past his prime. Nevertheless, they find that circumstances make each attracted to the other.
 Acting like something they're not is what actors are paid to do in every film they are in.)
The movie cuts between Robin and Quinn's hardships and the luxury that Frank and Angelica are enjoying back at the resort. Angelica is a generous sort - "It's like after a funeral, everybody has sex," she says - so she invites Frank to share his grief with her.
With Michael Chapman's breathtaking cinematography - the picture was filmed on Kauai - and Randy Edelman's dreamy music, the movie is easy to fall for.
 No matter how long they remain stranded on the desert island, she never has a bad hair day. She even awakens with fresh lipstick. Life in the wilderness with a sexy woman has never been better.
SIX NIGHTS, SEVEN NIGHTS runs 1:41. It is rated PG-13 for adult themes, mild profanity and cartoonish violence and would be fine for kids around eleven and up.
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


Appendix:
House of Turps
Activity Sheet
Cut-out diagrams for
Build Your Own House of Turps
.
One
Two


Front Line Tank: Transforms into a stone statue to protect himself and regain energy, then awakens to strike the enemy at the critical moment
| Health | 80 | Health | ? | |
| Physical Attack | 6.3 | Physical Attack | ? | |
| Magic Attack | 6.7 | Magic Attack | ? | |
| Physical Armor | 3.3 | Physical Armor | ? | |
| Magic Armor | 3.7 | Magic Armor | ? |
None
This Ultimate causes Kasar to switch between two modes (statue and flying), and does not use up Energy. As he transforms into flying mode, enemies are terrified, causing them to have reduced Movement and Attack Speed. While in flying mode, he has increased Magic Attack is able to Attack the enemy, but all Attacks as well as the act of flying expend Energy. As he transforms into statue mode, nearby enemies take Magic Damage and are Stunned. While in statue mode, he is unable to actively attack, but he gradually gains Health and Energy and is immune to 40% of the Damage he receives
When Kasar transforms into statue mode, he gets a shield that can resist Damage. When he transforms into flying mode, he immediately ambushes the weakest enemy
While in flying mode, Kasar picks up an enemy and carries them away. A few seconds later, the enemy falls from the sky, their impact with the ground dealing AoE Magic Damage and Stunning them, as well as Stunning surrounding enemies
While in flying mode, Kasar attacks an enemy who is currently affected by a Control Ability. A swarm of bats surrounds the enemy, dealing Magic Damage and extending the time that they are affected by the Control Ability
When Kasar is in statue mode, whenever allied or enemy non-summoned units take Magic Damage, Kasar will gain some Energy. The amount of Energy recovered is proportional to the amount of Damage taken. As Kasar gains Energy, his Attacks gradually become less affected by enemy Magic Armor. His attacks become more effective in this way up to 2500 Energy
*Key items required to fuse up to Orange+2 and fully equip. Not all items required for fusions listed. Signature Item not included.


By: Aditya Jain
Here I am leveraging Generative Adversarial Networks (GANs), and more specifically Deep Convolutional Generative Adversarial Networks (DCGANs) to generate new faces which were never seen before.
I have trained a GAN on 100k celebrities image dataset. Here is the training progress.


Design ideas
We want to play on the "retro" name, but keep things readable. The color palette is roughly inspired from the original NES colors.
Fonts
The logo font is Press Start 2P, we don't use it anywhere else because it gets tiring quickly.
The rest of the app uses whatever sans-serif
gives us, although one could probably spend time finding something better.
Color palette
#262524
#F2E6D8
#F20505
#F23535
#F2F2F2


Interface IDeviceListener
Inherited Members
System.IDisposable.Dispose()
Assembly: Device.Net.dll
Syntax
public interface IDeviceListener : IDisposable
Methods
|
Improve this Doc
View Source
CheckForDevicesAsync(CancellationToken)
Declaration
Task CheckForDevicesAsync(CancellationToken cancellationToken = default(CancellationToken))
Parameters
| Type |
Name |
Description |
| System.Threading.CancellationToken |
cancellationToken |
|
Returns
| Type |
Description |
| System.Threading.Tasks.Task |
|
|
Improve this Doc
View Source
Start()
Declaration
|
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Stop()
Declaration
Events
|
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View Source
DeviceDisconnected
Declaration
event EventHandler<DeviceEventArgs> DeviceDisconnected
Event Type
|
Improve this Doc
View Source
DeviceInitialized
Declaration
event EventHandler<DeviceEventArgs> DeviceInitialized
Event Type


Overrated or Nah
Overrated or Nah is a site dedicated to answering a simple question: Is this overrated, or nah? Places to eat, the latest tech, fashion trends, cities around the world, we want to know how you really feel. Simply click one of the three options below to cast your votes on whether you feel something is overrated or underrated. To see the most up-to-date power rankings, click the trophy icon to the left.
Overrated or Nah was founded by Aaron Tian, Matia Mearns, Juan Pablo Quintero, Brette Connolly, and Chahat Chadha and built with huge help from William Holley IV. We are currently based out of New York City, though we’re looking to represent the whole world. If you have any tips, comments, or suggestions on how we can improve the site, we’d love to hear it! Click the tab "Contact" above.
FAQ:
How do I play?
Just click 1 of the 3 options, based on how you feel about the presented topic.
What if I feel something is rated just right?
Simply click "IDK" if you feel something is just right, or if you're not familiar with it.
Can I add my own topic to be rated?
Send it over to<EMAIL_ADDRESS>and we'll take a look at it!
Is this a new site?
Yes, we are pretty new! Be gentle.


11.6. Minimal Cost Spanning Trees (WORK IN PROGRESS)¶
11.6.1. Minimal Cost Spanning Trees¶
The minimal-cost spanning tree (MCST) problem takes as input a connected, undirected graph \(\mathbf{G}\), where each edge has a distance or weight measure attached. The MCST is the graph containing the vertices of \(\mathbf{G}\) along with the subset of \(\mathbf{G}\) ‘s edges that (1) has minimum total cost as measured by summing the values for all of the edges in the subset, and (2) keeps the vertices connected. Applications where a solution to this problem is useful include soldering the shortest set of wires needed to connect a set of terminals on a circuit board, and connecting a set of cities by telephone lines in such a way as to require the least amount of cable.
The MCST contains no cycles. If a proposed MCST did have a cycle, a cheaper MCST could be had by removing any one of the edges in the cycle. Thus, the MCST is a free tree with \(|\mathbf{V}| - 1\) edges. The name “minimum-cost spanning tree” comes from the fact that the required set of edges forms a tree, it spans the vertices (i.e., it connects them together), and it has minimum cost. Figure 11.7.1 shows the MCST for an example graph.
Todo
- type: Slideshow
Replace the previous diagram with a slideshow illustrating the concept of MCST.
<IP_ADDRESS>. Prim’s Algorithm¶
The first of our two algorithms for finding MCSTs is commonly referred to as Prim’s algorithm. Prim’s algorithm is very simple. Start with any Vertex \(N\) in the graph, setting the MCST to be \(N\) initially. Pick the least-cost edge connected to \(N\). This edge connects \(N\) to another vertex; call this \(M\). Add Vertex \(M\) and Edge \((N, M)\) to the MCST. Next, pick the least-cost edge coming from either \(N\) or \(M\) to any other vertex in the graph. Add this edge and the new vertex it reaches to the MCST. This process continues, at each step expanding the MCST by selecting the least-cost edge from a vertex currently in the MCST to a vertex not currently in the MCST.
Prim’s algorithm is quite similar to Dijkstra’s algorithm for finding the single-source shortest paths. The primary difference is that we are seeking not the next closest vertex to the start vertex, but rather the next closest vertex to any vertex currently in the MCST. Thus we replace the lines:
if (D[w] > (D[v] + G.weight(v, w)))
D[w] = D[v] + G.weight(v, w);
in Djikstra’s algorithm with the lines:
if (D[w] > G.weight(v, w))
D[w] = G.weight(v, w);
in Prim’s algorithm.
The following code shows an implementation for Prim’s algorithm that searches the distance matrix for the next closest vertex.
// Compute shortest distances to the MCST, store them in D.
// V[i] will hold the index for the vertex that is i's parent in the MCST
void Prim(Graph G, int s, int[] D, int[] V) {
for (int i=0; i<G.nodeCount(); i++) { // Initialize
D[i] = INFINITY;
}
D[s] = 0;
for (int i=0; i<G.nodeCount(); i++) { // Process the vertices
int v = minVertex(G, D); // Find next-closest vertex
G.setValue(v, VISITED);
if (D[v] == INFINITY) { return; } // Unreachable
if (v != s) { AddEdgetoMST(V[v], v); }
int[] nList = G.neighbors(v);
for (int j=0; j<nList.length; j++) {
int w = nList[j];
if (D[w] > G.weight(v, w)) {
D[w] = G.weight(v, w);
V[w] = v;
}
}
}
}
# Compute shortest distances to the MCST, store them in D.
# V[i] will hold the index for the vertex that is i's parent in the MCST
def Prim(G, s, D, V):
for i in range(G.nodeCount()): # Initialize
D[i] = math.inf
D[s] = 0
for i in range(G.nodeCount()): # Process the vertices
v = minVertex(G, D) # Find next-closest vertex
G.setValue(v, VISITED)
if D[v] == INFINITY:
return # Unreachable
if v != s:
AddEdgetoMST(V[v], v)
for w in G.neighbors(v):
if D[w] > G.weight(v, w):
D[w] = G.weight(v, w)
V[w] = v
For each vertex \(I\), when \(I\) is processed by Prim’s
algorithm, an edge going to \(I\) is added to the MCST that we are
building.
Array V[I]
stores the previously visited vertex that is
closest to Vertex I.
This information lets us know which edge goes into the MCST when
Vertex \(I\) is processed.
The implementation above also contains calls to
AddEdgetoMST
to indicate which edges are actually added to the
MCST.
<IP_ADDRESS>. Prim’s Algorithm Alternative Implementation¶
Alternatively, we can implement Prim’s algorithm using a
priority queue to find the next closest vertex, as
shown next.
As with the priority queue version of Dijkstra’s algorithm,
the heap stores DijkElem
objects.
// Prims MCST algorithm: priority queue version
void PrimPQ(Graph G, int s, int[] D, int[] V) {
int v; // The current vertex
KVPair[] E = new KVPair[G.edgeCount()]; // Heap for edges
E[0] = new KVPair(0, s); // Initial vertex
MinHeap H = new MinHeap(E, 1, G.edgeCount());
for (int i=0; i<G.nodeCount(); i++) { // Initialize distance
D[i] = INFINITY;
}
D[s] = 0;
for (int i=0; i<G.nodeCount(); i++) { // For each vertex
KVPair temp = H.removemin();
if (temp == null) { return; } // Unreachable nodes exist
v = (Integer)temp.value();
while (G.getValue(v) == VISITED) {
KVPair temp = H.removemin();
if (temp == null) { return; } // Unreachable nodes exist
v = (Integer)temp.value();
}
G.setValue(v, VISITED);
if (D[v] == INFINITY) { return; } // Unreachable
if (v != s) { AddEdgetoMST(V[v], v); } // Add edge to MST
int[] nList = G.neighbors(v);
for (int j=0; j<nList.length; j++) {
int w = nList[j];
if (D[w] > G.weight(v, w)) { // Update D
D[w] = G.weight(v, w);
V[w] = v; // Where it came from
H.insert(D[w], w);
}
}
}
}
# Prim's MCST algorithm: priority queue version
def PrimPQ(G, s, D, V):
E = [None] * G.edgeCount() # Heap for edges
E[0] = (0, s) # Initial vertex
H = MinHeap(E, 1, G.edgeCount())
for i in range(G.nodeCount()): # Initialize distance
D[i] = math.inf
D[s] = 0
for i in range(G.nodeCount()): # For each vertex
temp = H.removemin()
if temp is None:
return # Unreachable nodes exist
v = temp.value()
while G.getValue(v) == VISITED:
temp = H.removemin()
if temp is None:
return # Unreachable nodes exist
v = temp.value()
G.setValue(v, VISITED)
if D[v] == math.inf
return # Unreachable
if v != s:
AddEdgetoMST(V[v], v) # Add edge to MST
for w in G.neighbors(v):
if D[w] > G.weight(v, w):
D[w] = G.weight(v, w)
V[w] = v # Where it came from
H.insert(D[w], w)
Todo
- type: Slideshow
Implement a slideshow demonstrating the Priority Queue version of Prim’s algorithm
Prim’s algorithm is an example of a greedy
algorithm.
At each step in the for
loop, we select the least-cost edge that
connects some marked vertex to some unmarked vertex.
The algorithm does not otherwise check that the MCST really should
include this least-cost edge.
This leads to an important question:
Does Prim’s algorithm work correctly?
Clearly it generates a spanning tree (because each pass through the
for
loop adds one edge and one unmarked vertex to the spanning tree
until all vertices have been added), but does this tree have minimum
cost?
Theorem: Prim’s algorithm produces a minimum-cost spanning tree.
Proof: We will use a proof by contradiction. Let \(\mathbf{G} = (\mathbf{V}, \mathbf{E})\) be a graph for which Prim’s algorithm does not generate an MCST. Define an ordering on the vertices according to the order in which they were added by Prim’s algorithm to the MCST: \(v_0, v_1, ..., v_{n-1}\). Let edge \(e_i\) connect \((v_x, v_i)\) for some \(x < i\) and \(i \leq 1\). Let \(e_j\) be the lowest numbered (first) edge added by Prim’s algorithm such that the set of edges selected so far cannot be extended to form an MCST for \(\mathbf{G}\). In other words, \(e_j\) is the first edge where Prim’s algorithm “went wrong.” Let \(\mathbf{T}\) be the “true” MCST. Call \(v_p (p<j)\) the vertex connected by edge \(e_j\), that is, \(e_j = (v_p, v_j)\).
Because \(\mathbf{T}\) is a tree, there exists some path in \(\mathbf{T}\) connecting \(v_p\) and \(v_j\). There must be some edge \(e'\) in this path connecting vertices \(v_u\) and \(v_w\), with \(u < j\) and \(w \geq j\). Because \(e_j\) is not part of \(\mathbf{T}\), adding edge \(e_j\) to \(\mathbf{T}\) forms a cycle. Edge \(e'\) must be of lower cost than edge \(e_j\), because Prim’s algorithm did not generate an MCST. This situation is illustrated in Figure 11.7.2. However, Prim’s algorithm would have selected the least-cost edge available. It would have selected \(e'\), not \(e_j\). Thus, it is a contradiction that Prim’s algorithm would have selected the wrong edge, and thus, Prim’s algorithm must be correct. BOX HERE
Todo
- type: Exercise
Proficiency exercise for Prim’s algorithm.


The first Re multiplier is multiplied by the basic R0 to give the new Re after the first intervention. The second Re multiplier is multiplied by the basic R0 and the first Re multiplier to give the new Re after the second intervention.
Re after third intervention = R0 * intervention1.multiplier * intervention2.multiplier * intervention3.multiplier
You can have any number of interventions. The template Excel input comes with 3. If you want fewer, delete one or more rows. If you want more, just add rows using the same format.
You can decrease iter
on the Internal sheet to 500 or less. The results will be less reliable and you will probably get some warnings from Stan, but if you just want to test something out, this may work. You should increase iter
back to at least 1000 for final projections.
If there are a small number of divergent transitions (less than ~10), there is usually little impact on the projections ((but this is not guaranteed). If there are a larger number, increase adapt_delta
to 0.9 or 0.95 or 0.99 on the Internal sheet. Run time will be slower. We don’t recommnd using pairs()
. If increasing adapt_delta
does not help, there may be a problem with your parameter distributions or data (e.g. hospital census data is not compatible with cumulative hospitals).


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>>
<<
Usr
Pri
JfC
LJ
Phr
Dic
Voc
!:
Help
Dictionary
| Real / Imaginary |
+. 0 0 0 |
GCD (Or)
|
+.y yields a two-element list of the real and imaginary parts of
its argument. For example, +.3j5 is 3 5,
and +.3 is 3 0 .
|
|
x+.y is the greatest common divisor of x
and y . If the arguments are boolean (0 or 1),
the functions +. and *. are equivalent to logical or
and and. The function -. similarly restricted
is not.
|
]y=: i+2*j. i=: i.4
0 1j2 2j4 3j6
+. y
0 0
1 2
2 4
3 6
The greatest common divisor divides both of its
arguments x and y to produce results that have
no common factor, that is, the GCD of the quotients is 1.
Moreover, these quotients represent the fraction x%y in lowest form.
For example:
x=: 24 [ y=: 60
x;y;(x +. y);((x , y) % (x +. y))
+--+--+--+---+
|24|60|12|2 5|
+--+--+--+---+
lff=: , % +. Gives lowest form of fraction
x;y;(x lff y);(%/x lff y);(%/x,y);(+./x lff y)
+--+--+---+---+---+-+
|24|60|2 5|0.4|0.4|1|
+--+--+---+---+---+-+
Since the functions =| and =<.
(tests for non-negative and for integer) produce boolean results,
the phrase (=|)+.(=<.) is a test for non-negative or
integer:
(test=: (=|) +. (=<.)) _2 _2.4 3 3.5
1 0 1 1
The duality of or and and may be shown as follows:
d (+./ ; *.&.-./ ; *./ ; +.&.-./) d=: 0 1
+---+---+---+---+
|0 1|0 1|0 0|0 0|
|1 1|1 1|0 1|0 1|
+---+---+---+---+
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Published on Jan 21, 2019
Here's the timeline of her life:
Mother noun. A person who has the ability to detect a lie, hear the smallest noises, and see out the back of their head.
- 1974 Born on 11th day of Febraury 1974 in the Philippines
- 1980 Start her elementary education at Palsong Elementary School
- 1986
- 1990
- 1990 Started her college education in Camarines Sur State Agricultural College — Main Campus taking Bachelor of Science in Agriculture
- 1995 Graduated in college.
- 2011
- 2014
- January 26 Take Licensure Examination for Teacher
- April 4 Pass the Licensure Examination for Teacher
- 2015
- March 3 Resigned as volunteer teacher in Bolo Norte High School
- 2016 Start her professional teaching at Ragay Agricultural and Fisheries School
- 2018 Start her master's in Agricultural Studies at Central Bicol State University of Agriculture — Main Campus
Trainings
Seminars / Conferences / Workshops / Short Courses
Bread and Pastry Production NC II
02 Feb, 2016 — 26 Feb, 2016
K to 12 Mass Training For Grade 8 ...
27 Apr, 2013 — 31 Apr, 2013
Regional Forum on Barangay ...
19 Oct, 2009 — 21 Oct, 2009
Seminar Workshop on Gender ...
30 Aug, 2008 — 30 Aug, 2008
Seminar on Barangay Budget ...
07 Nov, 2008 — 07 Nov, 2008
Seminar on the Procurement ...
25 July, 2008 — 26 July, 2008
Gov. L-Ray Barangay ...
23 Apr, 2008 — 23 Apr, 2008
Enhancing Proficiency of Barangay ...
3 Apr, 2008 — 5 Apr, 2008
Seminar Workshop on the Updates ...
30 Mar, 2008 — 31 Mar, 2008
Seminar Workshop for Barangay ...
30 Aug, 2005 — 30 Aug, 2005
Copyright © 2019. Crafted with ❤ and lots of .
Challege at freeCodeCamp.


Due to a wider range of multi arch, tools WILL return different results based off of not only the Windows arch, but the CURRENT process arch (ie, the reverse shell you're running in). Be sure to enumerate both and attempt the exploit in both (unless otherwise noted)
Determine Current Process Arch (AMD64 = a 32 bit process under WOW64 mode):
If %PROCESSOR_ARCHITEW6432% == AMD64 ECHO 32 bit process
Or in PowerShell:
if ($env:PROCESSOR_ARCHITEW6432 -eq "AMD64") {"32 bit process"}
If it's not set!: you're either in a 32-bit proces on a 32-bit windows. OR you're in a 64-bit process on a 64-bit windows.
Check methods for determining Windows Arch.
System Folders
64 bit versions of Windows have dynamic system folders C:\Windows\sys* to support both 64 and 32 bit programs.
PowerShell.exe, CMD.exe and many other programs are available as both 32 bit or 64 bit.
The dynamic sys folders will appear differently to a 32 bit session and a 64 bit session:
32 bit folder 64 bit folder
32 bit session C:\Windows\system32\ C:\Windows\sysNative\
64 bit session C:\Windows\sysWOW64\ C:\Windows\system32\
By default a 32 bit session will launch 32bit executables from C:\Windows\System32\ but you can still choose to launch 64 bit executables by specifying SysNative
By default a 64 bit session will launch 64bit executables also from C:\Windows\System32\ but you can still choose to launch 32 bit executables by specifying sysWOW64.
The sysNative folder is not visible to 64 bit processes or programs and cannot been seen in Windows Explorer.
The PROCESSOR_ARCHITEW6432 environment variable. If it’s set, then you’re in a 32-bit process on 64-bit Windows, and must use SysNative. If it’s not set, then you’re either in a 32-bit process on 32-bit Windows, or in a 64-bit process on 64-bit Windows. In that case, you can use System32.
Detect 64 bit processor hardware
In Vista and greater, you can use WMIC os get osarchitecture, or in WMI Win32_ComputerSystem/OSArchitecture
There are a number of promising looking options in WMI and Systeminfo, but they all pertain to the OS not the CPU.
Detect a 64 bit Operating System
:: Installed OS
Set _os_bitness=64
IF %PROCESSOR_ARCHITECTURE% == x86 (
IF NOT DEFINED PROCESSOR_ARCHITEW6432 Set _os_bitness=32
)
Echo Operating System is %_os_bitness% bit
Via David Wang’s blog post: Detect OS Bitness
Windows 10 on ARM includes an x86-on-ARM64 emulation, so the possible values for PROCESSOR_ARCHITECTURE are: AMD64 or IA64 or ARM64 or (for 32 bit) x86
Another method to detect 64 bit process (powershell only):
[Environment]::Is64BitProcess


The Open Targets Platform allows you to prioritise targets based on the strength of their association with a disease. We assess the key factors that relate to the confidence in the target-disease association. We provide association scores to help you answering these questions:
The association score is a numerical value varying from 0 to 1, which indicates the strength of the association between a target and a disease. A score of 1 refers to the strongest association, whereas a score of 0 corresponds to no evidence supporting an association. In our Platform, we represent the different scores with varying shades of blue: the darker the blue, the stronger the association.
We start by generating a score for each evidence from different data sources (e.g. GWAS catalog, European Variation Archive) within a data type (e.g. Genetic associations). We define the evidence score as:
s = F * S * C
where
s = score
F = frequency, the relative occurrence of a target-disease evidence
S = severity, the magnitude or strength of the effect described by the evidence
C = confidence, overall confidence for the observation that generates the target-disease evidence
The evidence score summarises the strength of the evidence and depends on factors that affect its relative strength. These factors are specific to the different data sources in the Platform:
| Data type | Data sources and factors that affect the relative strength of the evidence scores |
|---|---|
| Genetic associations | GWAS and PheWASCatalog (functional consequence score of variants, normalised p-value and normalised sample size); European Variation Archive (functional consequence score of variants e.g. germline variants that cause transcrip ablation will have a score of 1, whereas variants that are intronic will have a score of 0.5); UniProt (curator inference score based on how strong the evidence for the gene's involvement in the disease is. If the evidence is strong, the score will be 1. For evidence deemed not to be strong by the curator, the score will be 0.5); Gene2Phenotype (variants are inferred by curators and will have a score of 1, the highest functional consequence score); Genomics England PanelAPP (gene-disease associations are curated and crowdsourced by experts and will have the highest score of 1) |
| Somatic mutations | Cancer Gene Census (functional consequence score of variants); European Variation Archive (functional consequence score of variants); IntOgen (binned score based on tumour type categories. If the gene has several signals of positive selections in the tumour, the score will be 0.25. If the gene is already described as a cancer gene and exhibits a signal of positive selection in a tumor type, the score will be 0.5. If in addition to a signal of positive selection, the gene is functionally connected to other genes in the same tumor type, the score will be 0.75) |
| Drugs | ChEMBL (Clinical trials phase binned score. Scores will be 0.09 for phase 0, 0.1 for phase I, 0.2 for Phase II, 0.7 for Phase III, and 1 for Phase IV drugs) |
| Affected pathways | Reactome (functional consequence of 1 for a pathaway inferred by a curator) |
| RNA Expression | Expression Atlas score (normalised p-value, normalised expression fold change and normalised percentile rank) |
| Text mining | Europe PMC (weighting document sections, sentence locations and title for full text articles and abstracts (Kafkas et al 2016) |
| Animal models | Phenodigm (similarity score between a mouse model and a human disease described by Smedley et al 2013)) |
Once we have the scores for each evidence, we calculate an overall score for a data type (e.g. Genetic associations). In this step, we take into account that although multiple occurrences of evidence can suggest a strong association, the inclusion of further new evidence should not have a great impact on the overall score. For this reason, we calculate the sum of the harmonic progression of each score and adjust the contribution of each of them using a heuristic weighting. Throughout this process, the value of the score is always capped at 1, the most confident association.
We have implemented this framework for the current version of the Platform (please refer to "Open Targets: a platform for therapeutic target identification and validation" for more details. We will continue to explore and work on alternative statistical models to keep providing robust scoring systems for target-disease associations.


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The Premier League Merlin Ball integrates 3D-printed texture to provide
optimal touch and control in wet
and dry conditions. Four-panel construction gives the ball a bigger sweet spot, and Nike Aerow Trac
grooves allow consistent spin.
Four-panel construction enlarges the striking surface for a bigger sweet spot.
3D ink creates variations in texture for targeted control in wet and dry conditions.
Nike Aerow Trac grooves allow consistency in ball spin.
Latex bladder maintains air pressure more consistently than traditional rubber bladders.
2019/20 2019> will be the 20th Premier League season for which Nike has supplied the match ball.
Starting with the Nike Geo Merlin in 2000/01, through to the Premier League Merlin for 2019/20,
Nike's match balls bring pinnacle performance innovation to the pitch, season after season.


Technologies
-
Data Resources
- .csv files: Approx. 2000 * 15 records.
- geoJSON file: Contains polygon cordinates for countries with properties.
-
Data Cleaning and Munging
- Jupyter file: Used pandas, numpy, JSON, SQLAlchemy libraries from python to Extract, Clean, Tranform and Load the data.
-
Database
- PostgreSQL: Considered to use SQL as data was suitable for RDBMS.
-
API
- Flask App: Developed app for managing HTTP request and rendering data based on requirements.
-
Frontend
- HTML, CSS, Bootstrap, Javascript for web pages.
-
Visualizations
- Leaflet Map - Choropleth: To visualize the countries colored or patterned based on the behavior and attitude of entrepreneurs.
- Plotly - Time Series: To analyze the trend for the parameters across years for different countries.
- Chart.js - Doughnut Chart: To visualize weightage of all given Indicators for selected country.
- D3 - Clustered Column Chart: To understand comparison between selected Indicators over the period of time.


Ali Agha-Mohammadi
Qualcomm
Antonio Agudo
Universidad de Zaragoza
Ho Seok Ahn
The University of Auckland, Auckland
Arash Ajoudani
Italian Institute of Technology, Italy
Baris Akgun
Georgia Tech
Riad Akrour
Technical University Darmstadt
Eren Aksoy
Karlsruher Institut für Technologie (KIT), Germany
Jordi Albo-Canals
Tufts University
Javier Alonso Mora
MIT
Ron Alterovitz
University of North Carolina at Chapel Hill
Chris Amato
University of New Hampshire
Francesco Amigoni
Politecnico of Milan, Italy
Anelia Angelova
California Institute of Technology
Alex Ansari
Northwestern University
Brenna Argall
Northwestern University
Miguel Arias Estrada
NAOE, Mexico
Paulo Arratia
University of Pennsylvania
Tamim Asfour
KIT (Karlsruhe Institute of Technology)
Kalle Åström
Lund Institute of Technology/Lund University
Nikolay Atanasov
University of Pennsylvania
Daniel Aukes
Arizona State University
Nora Ayanian
University of Southern California
Ko Ayusawa
AIST
Hossein Azizpour
KTH Stockholm, Sweden (Kungliga Tekniska högskolan)
Drew Bagnell
Carnegie Mellon University
Haoyu Bai
National University of Singapore
Swee Balachandran
University of Michigan
Devin Balkcom
Dartmouth
Tim Barfoot
University of Toronto
Christoph Bartneck
University of Caterbury
Nicola Basilico
University of Milan, Italy
Aaron Becker
Univeristy of Houston
Jens Behley
University of Bonn, Germany
Yasemin Bekiroglu
KTH Stockholm, Sweden (Kungliga Tekniska högskolan)
Heni Ben Amor
Arizona State University, Georgia Institute of Technology
Mehdi Benallegue
LAAS, LPPA, Université Montpellier 2
Dmitry Berenson
Worcester Polytechnic Institute
Soulaimane Berkane
University of Western Ontario
Alexandre Bernardino
University of Lisbon
Sourabh Bhattacharya
Iowa State University, UIUC
Subhrajit Bhattacharya
University of Pennsylvania
Bastian Bischoff
Robert Bosch GmbH
Joydeep Biswas
U Mass
Mårten Björkman
KTH Stockholm, Sweden (Kungliga Tekniska högskolan)
Jan Olaf Blech
RMIT
Jeannette Bohg
Max Planck Institute for Intelligent Systems
Vincent Bonnet
Advanced Mechatronics Lab
Byron Boots
Georgia Tech
Abdeslam Boularias
Rutgers University
Karim Bouyarmane
CNRS - University of Montpellier LIRMM
Chris Bowen
University of North Carolina at Chapel Hill
Sean Bowman
University of Pennsylvania
David Braun
Singapore University of Technology and Design
Dieter Büchler
Max Planck
Jonas Buchli
ETH Zurich
Wolfram Burgard
University of Freiburg
Michael Burri
ETH Zurich
Stephen Butler
Rice University
Arunkumar Byravan
University of Washington
Cesar Cadena
ETH Zürich
Maya Cakmak
University of Washington, Willow Garage, Inc.
Roberto Calandra
TU Darmstadt, Max Planck
Sylvain Calinon
Idiap Research Institute, EPFL, Italian Institute of Technology (IIT)
Marcello Calisti
Scuola Superiore Sant'Anna, Pisa, Italy
Berk Calli
Yale
Luca Carlone
Georgia Institute of Technology, MIT
Stefano Carpin
University of California, Merced
Ashwin Carvalho
Berkeley
M. Cenk Cavusoglu
Case Western University
Andrea Censi
MIT, Caltech
Suman Chakravorty
Texas A&M University
Tiffany Chen
Toyota, Georgia Institute of Technology
Greg Chirikjian
John Hopkins University
Hamid Chitsaz
Colorado State University
Margarita Chli
ETH Zurich, University of Edinburgh
Han-Lim Choi
KAIST
Youngjin Choi
Hanyang University
Howie Choset
Carnegie Mellon University
Siddharth Choudhary
Georgia Institute of Technology
Girish Chowdhary
Oklahoma State University, MIT
Jonathan Clark
Florida State University
Nikolaus Correll
University of Colorado, Boulder
Rui Cortesao
University of Coimbra
Francesco Corucci
Scuola Superiore Sant'Anna, Pisa, Italy
David Crandall
Indiana University
Raffaello D'Andrea
ETH Zurich
Houman Dallali
Michigan Tech
Steven Damer
University of Minnesota
Philip Dames
University of Pennsylvania
Hao Dang
Columbia University
Christian Daniel
TU Darmstadt, Max Planck
Kostas Daniilidis
University of Pennsylvania
Neil Dantam
Rice University
Andrew Davison
Imperial College London
Marcio de Queiroz
Louisiana State University
Guilaume Dechambrier
EPFL
Marc Deisenroth
Imperial College London
Robin Deits
MIT
Babette Dellen
Hochschule Koblenz
Jory Denny
Texas A&M University
Yancy Diaz-Mercado
Georgia Tech University
Alexander Dietrich
DLR
Eric Diller
University of Toronto
Jerry Ding
Berkeley
Mehmet R Dogar
MIT, Carnegie Mellon University
Andreas Dörr
Robert Bosch GmbH
Anca Dragan
UC Berkeley, Carnegie Mellon University
Katie Driggs-Campbell
Berkeley
Evan Drumwright
George Washington University
Tom Duckett
University of Lincoln
Matthew Dunbabin
Queensland University of Technology
Felix Duvallet
Carnegie Mellon University, EPFL
Nick Eckenstein
University of Pennsylvania
Deepak Edakkattil Gopinath
Georgia Tech
Magnus Egerstedt
Georgia Tech
Chinwe Ekenna
Texas A&M University
Brendan Englot
Stevens Institute of Technology
Johannes Englsberger
DLR
Ozgur Erkent
University of Innsbruck
Adrien Escande
Carnegie Mellon University
Marco Ewerton
TU Darmstadt, Max Planck
Jan Faigl
Czech Technical University in Prague
Maurice Fallon
University of Edinburgh
Egidio Falotico
Scuola Superiore Sant'Anna, Pisa, Italy
Pooyan Fazli
Cleveland State University
Wyatt Felt
University of Michigan
Lu Feng
Univ. Pennsylvania
Francois Ferland
ENSTA (École Nationale Supérieure de Techniques Avancées)-Paris Tech
Fabrizio Ferrandi
Politechnico di Milano
Nadia Figueroa
EPFL (the Swiss Federal Institute of Technology at Lausanne)
Ross Finman
MIT
Chelsea Finn
Berkeley
Paolo Fiorini
University of Verona
John Folkesson
KTH Stockholm, Sweden (Kungliga Tekniska högskolan)
Arturo Forner-Cordero
University of São Paulo, Brazil
Christian Forster
University of Zürich
Simone Frintrop
University of Bonn, Germany
Jie Fu
Worchester Polytechnic Institute
Bruno Gabrich
University of Pennsylvania
Jürgen Gall
University of Bonn, Germany
Andrej Gams
Jozef Stefan Institute
Gowtham Garimella
Johns Hopkins University
Tim Genewein
Max Planck
Mukulika Ghosh
Texas A&M University
Igor Gilitschenski
ETH Zurich
Giuseppina Gini
Politechnico Milano
Julio Godoy
University of Minnesota
Matthew Gombolay
MIT
Sebastian Gomez
Max Planck
Nakul Gopalan
Brown University
Alex Gorodetsky
MIT
Dan Grollman
University of Maryland, Baltimore County, University of Washington
Jacopo Guanetti
Berkeley
Abhinav Gupta
Carnegie Mellon University
Joakim Gustafson
KTH Stockholm, Sweden (Kungliga Tekniska högskolan)
Stephen Guy
University of Minnesota, University of North Carolina at Chapel Hill
Dylan Hadfield-Menell
Berkeley
Dan Halperin
Tel Aviv University
Ankur Handa
University of Cambridge
Marc Hanheide
University of Lincoln
Justin Hart
UBC(University of British Columbia)
Kenji Hashimoto
Waseda University, Tokyo, Japan
Ross Hatton
Oregon State University, Carnegie Mellon University
Kris Hauser
Duke University
Bradley Hayes
MIT
David Held
Stanford University
Lindsey Hines
Max Planck Institute for Intelligent Systems
Sandra Hirche
Technische Universität München (TUM)
Guy Hoffman
Cornell University
Geoffrey Hollinger
Oregon State University
Baisravan Homchaudhuri
University of New Mexico
Matthew Howard
Kings College London
Thomas Howard
Jet populson Lab NASA
David Hsu
Singapore University of Technology and Design
Guoquan Huang
University of Delaware
Shoudong Huang
University of Technology Sydney
Vadim Indelman
Technion – Israel Institute of Technology
Serena Ivaldi
Kookmin
Sam Jacobs
ABB
Amir Jafari
University of Texas San Antonio, USA
Lorenzo Jamone
Instituto Superior Técnico,
Lucas Janson
Stanford University
Elizabeth Jensen
University of New Mexico
Edward Johns
Imperial College
Aaron Johnson
Carnegie Mellon University
Matthew Johnson-Roberson
University of Michigan
Vlad Joukov
University of Waterloo
Leslie Kaelbling
MIT
Michael Kaess
Carnegie Mellon University
Sinan Kalkan
Middle East Technical University
Marcelo Kallmann
University of California at Merced
Oren Kanner
Yale
Eva Kanso
USC
Mubbasir Kapadia
Rutgers University, University of Pennsylvania, Disney Research
Ioannis Karamouzas
University of Minnesota
Bilal Kartal
University of New Mexico
Navab Kashiri
Italian Institute of Technology
Lydia Kavraki
Rice University
Carl Kershaw
University of Michigan
Rebecca Khurshid
MIT
Hyounjin Kim
Seoul National University
Jin Seob Kim
Johns Hopkins, JHU
Sangbae Kim
MIT
Seungsu Kim
Center of Excellence Cognitive Interaction Technology (CITEC), Bielefeld, Swiss Federal Institute of Technology in Lausanne (EPFL)
Alexandra Kirsch
TU Munich
Franziska Klügl
Orebro Universitet
Ross Knepper
Cornell University, MIT
Jens Kober
TU Delft, Max Planck Institute for Intelligent Systems
Marin Kobilarov
John Hopkins University
Okan Koc
Max Planck
Mangal Kothari
University of Leicester
Rigas Kouskouridas
Imperial College London
Oliver Kroemer
USC, Technical University of Darmstadt
Klas Kronander
EPFL
Michael Kuhlman
Naval Research Laboratory
Tomas Kulvicius
University of Southern Denmark
Alan Kuntz
University of North Carolina at Chapel Hill
Andras Kupcsik
National University of Singapore
Hanna Kurniawati
University of Queensland
Benoit Landry
MIT
Pem Lasota
MIT
Kevin Leahy
Boston University
Alex Lee
Northwestern University
Roland Lenain
Irstea
Sio-Hoi Leng
Sorbonne Universites
Sebastien Lengagne
Karlsruhe Institute of Technology
Ian Lenz
Cornell
Stefan Leutenegger
Imperial College London, ETH Zurich
Sergey Levine
INRIA
Alberto Quattrini Li
University of South Carolina
Binbin Li
Texas A&M University
Jinglin Li
General Motors, U. North Carolina
Miao Li
EPFL
Tsai-Yen Li
National Chengchi University
Yin Li
Georgia Tech
Zhibin Li
University of Edinburgh
Maxim Likhachev
Carnegie Mellon University, University of Pennsylvania
Sejoon Lim
Kookmin University
Zhan Wei Lim
National University of Singapore
Chao Liu
Northwestern University
Guilin Liu
George Mason University
Huaping Liu
Tsinghua University, China
Lantao Liu
University of Southern California
Ming Liu
City University of Hong Kong, Hong Kong University of Science and Technology (HKUST).
Giuseppe Loianno
University of Pennsylvania
Amy Loutfi
Örebro University, Sweden
Tomas Lozano-Perez
MIT
Yan Lu
Texas A&M University
James MacGlashan
Brown University
Guilherme Maeda
TU Darmstadt, Max Planck
Nina Mahmoudian
Michigan Technological University, Virginia Tech
Nicolas Mansard
LAAS
Simon Manschitz
TU Darmstadt, Max Planck
Satyanarayana Manyam
AFRL, Air Force Research Lab
Matthew Marge
Carnegie Mellon University
Zoltan-Csaba Marton
DLR
Lorenzo Masia
Nanyang Technological University
Matteo Matteucci
Politecnico of Milan, Italy
Cynthia Matuszek
University of Maryland, Baltimore County, University of Washington
Walterio Mayol
University of Bristol
Mladen Mazuran
Universitat Freiburg
Harris Mcclamroch
University of Michigan
Marissa McCoy
Yale
Troy Mcmahon
Texas A&M University
Jose Ramon Medina Hernandez
Technische Universität München (TUM)
Elena Messina
National Institute of Standards and Technology (NIST)
Nathan Michael
Carnegie Mellon University, University of Pennsylvania
Michael Milford
Queensland University of Technology
Stefano Mintchev
EPFL
Eduardo Montijano
Centro Universitario de la Defensa., University of Zaragoza
Ajung Moon
University of British Columbia
Hyungpil Moon
Carnegie Mellon University
Marco Morales
Instituto Tecnologico Autonomo de Mexico
Kristi Morgansen
University of Washington
Jun Morimoto
ATR
Gregory Morse
University of Central Florida
Akshay Morye
University of Oxford
Yasamin Mostofi
University of California, Santa Barbara
Anastasios Mourikis
University of California at Riverside
Caio Mucchiani
University of Pennsylvania
Raul Mur-Artal
Universidad de Zaragoza
Rafael Murrieta
Centro de Investigación en Matemáticas (CIMAT), UIUC
Bilge Mutlu
U Wisconsin Madison
Jun Nakanishi
Technische Universität München
Duy Nguyen-Tuong
TU Darmstadt, Max Planck
Chris Nielsen
University of Waterloo
Stefanos Nikolaidis
Carnegie Mellon University
Andreas Nuechter
Universitat Wuerzberg
Ernesto Nunes
University of New Mexico
Jason O'Kane
University of South Carolina
John Oberlin
Brown University
Meeko Oishi
University of New Mexico
Lauro Ojeda
University of Michigan
Kenn Oldham
Universiry of Michigan
Andrej Olenšek
University Rehabilitation Institute, Ljubljana
Edwin Olson
University of Michigan
Gabor Orosz
University of Michigan
Takayuki Osa
TU Darmstadt, Max Planck
Christian Ott
Caltech
Lionel Ott
University of Sydney
Michael Otte
University of Colorado at Boulder
Taskin Padir
Northeastern
Lucia Pais
EPFL
Joni Pajarinen
Aalto University
Gianluca Palli
University of Bologna
Jia Pan
City University of Hong Kong, UC Berkeley
Dimitra Panagou
University of Michigan
Simone Parisi
Technical University Darmstadt
Chonhyon Park
University of North Carolina at Chapel Hill
Jae Sung Park
University of North Carolina at Chapel Hill
Jaeheung Park
Seoul National University
Andrea Parri
The BioRobotics Institute, Scuola Superiore Sant'Anna
Shashank Pathak
Instituto Italiano do Tecnologia
Liam Paull
MIT
James Paulos
University of Pennsylvania
Marco Pavone
Stanford
Lina Maria Paz
University of Oxford
Ivan Petrovic
University of Zagreb
Matthew Piccoli
University of Pennsylvania
Sudeep Pillai
MIT
Carlo Pinciroli
École Polytechnique de Montréal
Giulia Piovan
University of California at Santa Barbara
Erion Plaku
The Catholic University of America
Florian Pokorny
University of Southern California, Berkeley
Paul Pounds
University of Queensland
Andrzej Pronobis
University of Washington
Andreaa Radulescu
University of Edinburgh
Irene Rae
University of Washington-Madison, Microsoft Research
Subramanian Ramamoorthy
University of Edinburgh, UK
Vasumathi Raman
United Technologies Research Center
Fabio Ramos
University of Sydney
Ananth Ranganathan
Google, Honda
Sivakumar Rathinam
Texas A&M University
Andreagiovanni Reina
University of Sheffield
Ioannis Rekleitis
University of South Carolina
C. David Remy
University of Michigan
Hongliang Ren
National University of Singapore
Charles Richter
MIT
Barry Ridge
Jozef Stefan Institute
Laurel D. Riek
University of Southern California
Ludovic Righetti
Max-Planck Institute for Intelligent Systems, USC
Paolo Robuffo Giordano
IRISA, Max Planck Institute for Biological Cybernetics
Samuel Rodriguez
Texas A&M University
Javier Romero
Max Plank
David Rosen
MIT
Ugo Rosolia
Berkeley
Stergios Roumeliotis
University of Minnesota
Nicholas Roy
MIT
Mike Rubenstein
Harvard, Northwestern University
Wheeler Ruml
University of New Hampshire
Sadra Sadraddini
Boston University
Tim Salcudean
University of British Columbia
Gionata Salvietti
Istituto Italiano Tecnologia
Oren Salzman
Tel Aviv University
Read Sandstrom
Texas A&M University
José Santos-Victor
University of Lisbon, Portugal
Junaed Sattar
University of Minnesota
Davide Scaramuzza
University of Zurich
Connor Schenck
University of Washington
Edward Schmerling
Stanford
Angela Schoellig
University of Toronto
John Schulman
UC Berkeley
Luis Sentis
University of Texas at Austin
Matthew Sheckells
Johns Hopkins University
Dylan Shell
Texas A&M University
Shaojie Shen
HKUST
Zvi Shiller
Ariel University
Jivko Sinapov
University of Texas at Austin
Sebastjan Šlajpah
University of Ljubljana
Stephen Smith
University of Waterloo
Marion Sobotka
Technische Universität München
Nicolas Sommer
EPFL
Hyun Oh Song
Stanford
Andrew Specian
University of Pennsylvania
Markus Spies
Robert Bosch GmbH
Aaron St. Clair
Georgia Tech
Edward Steager
University of Pennsylvania
Bastian Steder
University of Freiburg
Bener Suay
Worcester Polytechnic Institute
Niko Suenderhauf
Technische Universitat Chemnitz
Wael Suleiman
Université de Sherbrooke
Wen Sun
Carnegie Mellon University
Chris Sweeney
University of California at Santa Barbara
Dan Szafir
University of Colorado
Jun Takamatsu
Nara Institute of Science and Technology, Japan
Leila Takayama
Google
James Tangorra
Drexel
Juan D. Tardos
Universidad de Zaragoza
Yuval Tassa
University of Washington
Pratap Tokekar
Virginia Tech, University of Pennsylvania
Tarik Tosun
University of Pennsylvania
Marc Toussaint
University of Stuttgart
Peter Trautman
Galois
Rudolph Triebel
Technische Universität München, University of Oxford
Roberto Tron
University of Pennsylvania
Nikos Tsagarakis
Italian Institute of Technology
Jana Tumova
KTH Royal Institute of Technology
Terry Taewoong Um
University of Waterloo
Paul Umbanhower
Northwestern University
Vaibhav Vasant Unhelkar
MIT
Ben Upcroft
Queensland University of Technology
Diane Uwacu
Texas A&M University
Kyriakos Vamvoudakis
University of California at Santa Barbara
Herke van Hoof
Technische Universitat Darmstadt
Bram Vanderborght
Vrije Universiteit Brussel, Belgium
Lorenzo Vannucci
Scuola Superiore Sant'Anna
Jacob Varley
Columbia University
Cristian-Ioan Vasile
Boston University
Ram Vasudevan
University of Michigan
Will Vega-Brown
MIT
Markus Vincze
TU Vienna, Austria
Julia Vinogradska
TU Darmstadt, Max Planck
Christopher Vo
George Mason University
Jenny Walter
Vassar University
Matthew Walter
Toyota Technological Institute at Chicago
Zheng Wang
The University of Hong Kong
Zhikun Wang
Google, TU Darmstadt, Max Planck
Yorai Wardi
Georgia Tech University
Steven Waslander
University of Waterloo
Astrid Weiss
Vienna University of Technology
Tim Wheeler
Stanford University
Tom Whelan
Imperial College London
David Wilkie
University of North Carolina at Chapel Hill
Ryan Williams
University of Southern California
Stefan Williams
The University of Sydney
David Wolinski
INRIA
Lawson L.S. Wong
MIT
Tichakorn Wongpiromsarn
Singapore-MIT Alliance for Research and Technology
Chih-Peng Wu
Texas A&M University
Emily Wu
Brown University
Kanzhi Wu
University of Technology Sydney
Liao Wu
National University of Singapore
Manuel Wuthrich
Max Plank
Yiliang Xu
Apple, Kitware
Yuji Yamakawa
University of Tokyo, Japan
Ko Yamamoto
University of Tokyo, Japan
Yezhou Yang
University of Maryland
Hsin-Yi Yeh
Texas A&M University
Shu-Hao Yeh
Texas A&M University
Dmitry Yershov
MIT
Hak Yi
University of California at Los Angeles
Jingang Yi
Rutgers
Sung-Eui Yoon
KAIST
Fisher Yu
Princeton University
Jingjin Yu
Rutgers University
Hao Zhang
Colorado School of Mines
Weixuan Zhang
ETH Zurich
Zijian Zhao
Shandong University
Yu Zheng
University of Michigan
Zeeshan Zia
NEC Labs
Brian Ziebart
University of Illinois


Almost 600 patients waiting for beds in Irish hospitals
by By Digital Desk staffThere are 582 patients waiting for beds in Irish hopitals this morning.
406 are waiting in the emergency department, while 176 are in wards elsewhere in the hospital.
Limerick and Cork have two of the worst hit hospitals today.
The INMO revealed the figures as part of their Trolley Watch update.
The worst-hit hospitals are:
- University Hospital Limerick – 55
- Cork University Hospital – 42
- University Hospital Galway – 41
- Tallaght University Hospital – 35
- St Vincent’s University Hospital – 33
READ MORE
Michael Healy-Rae treated for smoke inhalation after fire in office


Start mastering the Korean language today! Soyoon's Korean Language Service will help you achieve your future communication goals.
Find Out MoreSoyoon's Korean Language Service provides everything you need to master the Korean language.
Soyoon was born in Seoul and is avid music lover. She is a good listener that is addicted to travel. She has an aptitude for learning languages and is eager to sharing her language learning abilities with you.
Get Started!Often the key to learning is having a caring eye review your written work. Every week you will be assigned an essay on a topic of your choosing. This way the assignment has immediate use and high relevance to the challanges you face presently in Korea.
Soyoon puts you to work immediately with small translation projects. These tasks are great confidence boosters! The immediate feedback accelerates learning and lets you know that you can handle sharing information with your friends and colleagues in Korea.
Korean grammar is very challenging. Soyoon constantly works with you on grammar. Her specialized training program has a plethora of exercises to keep your grammer fresh and appropriate for all situations.
It is easy to converse with Soyoon on just about any topic. Soyoon's conversation sessions are the key to re-inforcing all other exercises and helping you dominate the language so you can start speaking with confidence in the shortest possible time!
Ready to start learning Korean with us? That's great! Give us a call or send us an email and we will get back to you as soon as possible!
010-3707-6539


We are planning make IRIS open source in summer 2017. Then you can get the IRIS Service up and running, set up the IRIS Administration Frontend and adapt the IRIS Frontend to your needs.


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REMOVE_NTH()
REMOVE_NTH(anyArray, position) → newArray
Remove the element at position from the anyArray.
- anyArray (array): array with elements of arbitrary type
- position (number): the position of the element to remove. Positions start
at 0. Negative positions are supported, with -1 being the last array element.
If position is out of bounds, the array is returned unmodified.
- returns newArray (array): anyArray without the element at position
Examples
RETURN REMOVE_NTH( [ "a", "b", "c", "d", "e" ], 1 )
RETURN REMOVE_NTH( [ "a", "b", "c", "d", "e" ], -2 )


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{$Think.session.admin_name}
{if $Think.session.admin_level}
{$Think.session.school}
超级管理员
{else /}
{$Think.session.school}
普通管理员
{/if}


Links to some favorite book related websites
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Bookshelf Porn
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on Brain Pickings
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| TA_ID | Name | Enrollment Number | Course Alloted |
|---|---|---|---|
| S1924 | Roshan Kumar choudhary | 18115087 | EEN 104 Electrical Measurement and Measuring Instruments |
| S1925 | Jonti Kumawat | 18115047 | EEN 104 Electrical Measurement and Measuring Instruments |
| S1926 | Harsh Kesarwani | 18113052 | EEN 102 Network Theory |
| S1927 | Shivang Bhawsar | 18115096 | EEN 102 Network Theory |
| S1928 | Raman Walia | 18115079 | EEN 106 Analog Electronics |
| S1929 | Aadarsh Mishra | 18115001 | EEN 106 Analog Electronics |
| S1950 | Inzamam | 18115042 | MIN 106 Engineering Thermodynamics |
| S1966 | Aryan Raj | 18115022 | MAN 002 Mathematical Methods |
| S1967 | Rahul Jaiswal | 18115076 | MAN 002 Mathematical Methods |
| S1968 | Pranay Jain | 18112053 | MAN 002 Mathematical Methods |
| TA_ID | Name | Enrollment Number | Course Alloted |
|---|---|---|---|
| S1937 | Karan Sharma | 18112033 | CHN 102 Material and Energy Balances |
| S1938 | Himanshu Seervi | 18112027 | CHN 104 Fluid Dynamics |
| S1939 | Vinay Agarwal | 18118083 | CHN 106 Thermodynamics and Chemical Kinetics |
| S1940 | Muskan | 18112042 | CHN 106 Thermodynamics and Chemical Kinetics |
| S1941 | Narpat Singh | 18112044 | CHN 102 Material and Energy Balances |
| S1942 | Vivekanand Verma | 18112089 | CHN 102 Material and Energy Balances |
| S1943 | Anurag Jaiswal | 18112013 | CHN 102 Material and Energy Balances |
| S1945 | Prateek Gupta | 18112056 | CYN 002 Organic and Inorganic Chemistry |
| S1946 | Vatsal Agarwal | 18112086 | CYN 002 Organic and Inorganic Chemistry |
| S1949 | Shuvam Samadder | 17112076 | EEN 112 Electrical Science |
| S1969 | Shikhar saxena | 18112074 | MAN 002 Mathematical Methods |
| S1970 | Swapnil Vatsal | 18118074 | MAN 002 Mathematical Methods |
| S1971 | Arjun Aryan | 18112015 | MAN 002 Mathematical Methods |
| S1978 | Ritik Gupta | 18112067 | CHN 104 Fluid Dynamics |
| TA_ID | Name | Enrollment Number | Course Alloted |
|---|---|---|---|
| S1951 | Noor Nisha | 18117065 | MIN 104 Manufacturing Technology |
| S1952 | Vedansh Jain | 18115118 | MIN 106 Engineering Thermodynamics |
| S1955 | Mahek Raina | 18117051 | MIN 108 Engineering Drawing |
| S1963 | Abhilash Mittal | 18410002 | MAN 004 Numerical Method |
| S1964 | Udit Kalani | 16312029 | MAN 106 Data Structures |
| S1965 | Saakshi Choudhary | 18117087 | PHN 008 Electromagnetic Theory |
| S1976 | Yash Kute | 18410027 | MAN 004 Numerical Method |
| TA_ID | Name | Enrollment Number | Course Alloted |
|---|---|---|---|
| S1905 | Suteekshn Mahajan | 18119040 | MIN 110 Fluid Mechanics |
| S1956 | Omkar Bhalerao | 18119023 | MIN 108 Engineering Drawing |
| S1957 | VIPUL BANSAL | 18119043 | MAN 006 Probability and Statistics |
| S1951 | Noor Nisha | 18117065 | MIN 104 Manufacturing Technology |
| TA_ID | Name | Enrollment Number | Course Alloted |
|---|---|---|---|
| S1934 | Harshal Mittal | 16116020 | CSN 102 Data Structures |
| S1935 | Leshna Balara | 18114044 | CSN 102 Data Structures |
| S1936 | Aditya Doneria | 17116005 | ECN 104 Digital Logic Design |
| S1948 | Arham Habib | 18112014 | EEN 112 Electrical Science |
| TA_ID | Name | Enrollment Number | Course Alloted |
|---|---|---|---|
| S1930 | Anmol Khemuka | 18114007 | MAN 010 Optimization Techniques |
| S1931 | Kavya Barnwal | 18114039 | CSN 106 Discrete Structures |
| S1932 | Masih Ahmed | 18117056 | CSN 106 Discrete Structures |
| S1933 | Ankit Dhankhar | 16114014 | CSN 102 Data Structures |
| S1936 | Aditya Doneria | 17116005 | ECN 104 Digital Logic Design |
| S1960 | Vidhit jain | 18312033 | ECN 102 Fundamentals of Electronics |
| S1974 | Dhruv Kumravat | 18312012 | ECN 102 Fundamentals of Electronics |
| TA_ID | Name | Enrollment Number | Course Alloted |
|---|---|---|---|
| S1906 | Parth Singh Parihar | 18118047 | CYN 006 General Chemistry II |
| S1907 | Deep Behal | 17118019 | MTN 104 Structural Metallurgy |
| S1908 | VATSAL KHANDELWAL | 18118081 | MTN 102 Metallurgical Thermodynamics |
| S1909 | Om Pawaskar | 18118046 | MTN 102 Metallurgical Thermodynamics |
| TA_ID | Name | Enrollment Number | Course Alloted |
|---|---|---|---|
| S1914 | Kamini Kumari | 18113068 | CYN 008 General Chemistry |
| S1921 | Kanishk Singal | 18410009 | ESN 102 Physical Geology |
| S1922 | Jashan Grover | 18410008 | ESN 104 Crystallography and Mineralogy |
| S1952 | Vedansh Jain | 18115118 | MIN 106 Engineering Thermodynamics |
| S1963 | Abhilash Mittal | 18410002 | MAN 004 Numerical Method |
| TA_ID | Name | Enrollment Number | Course Alloted |
|---|---|---|---|
| S1920 | Piyush | 18411021 | HYN 102 Hydrology |
| S1921 | Kanishk Singal | 18410009 | ESN 102 Physical Geology |
| S1923 | Ashutosh Pal | 18411008 | ESN 104 Crystallography and Mineralogy |
| S1958 | Kaaira Gupta | 18411015 | MAN 006 Probability and Statistics |
| S1975 | AKASH KURMI | 18312005 | PHN 008 Electromagnetic Theory |
| TA_ID | Name | Enrollment Number | Course Alloted |
|---|---|---|---|
| S1910 | Shreyansh Kothari | 18113128 | CYN 008 General Chemistry |
| S1911 | Yugant Kumar | 18113150 | CYN 008 General Chemistry |
| S1912 | Sanskar Nigoti | 18113119 | CEN 108 Fluid Mechanics |
| S1913 | Akash Kunal | 18113006 | CEN 108 Fluid Mechanics |
| S1914 | Kamini Kumari | 18113068 | CYN 008 General Chemistry |
| S1915 | Yashasvi Budhwar | 18113148 | CEN 102 Solid Mechanics |
| S1916 | Shubham Goyal | 18113130 | CEN 102 Solid Mechanics |
| S1958 | Kaaira Gupta | 18411015 | MAN 006 Probability and Statistics |
| S1959 | Rizul bansal | 18113112 | MAN 006 Probability and Statistics |
| S1977 | Chidella Vaibhavi | 18113037 | CEN 104 Water Supply Engineering |
| TA_ID | Name | Enrollment Number | Course Alloted |
|---|---|---|---|
| S1953 | Sawarnik Kaushal | 18312027 | MAN 102 Linear Algebra |
| S1960 | Vidhit jain | 18312033 | ECN 102 Fundamentals of Electronics |
| S1961 | Nikunj Goyal | 18312020 | MAN 004 Numerical Method |
| S1962 | Shubham jindal | 18312031 | MAN 004 Numerical Method |
| S1974 | Dhruv Kumravat | 18312012 | ECN 102 Fundamentals of Electronics |
| S1975 | AKASH KURMI | 18312005 | PHN 008 Electromagnetic Theory |
| TA_ID | Name | Enrollment Number | Course Alloted |
|---|---|---|---|
| S1901 | Pradum Kumar | 18111023 | BTN 106 Biochemistry |
| S1902 | Sanchit verma | 18111025 | CYN 002 Organic and Inorganic Chemistry |
| S1903 | Ujjwal Kumar | 18111030 | BTN 102 Process Calculations |
| S1904 | Wagh Vishal Natha | 18111032 | BTN 104 Cell Biology |
| S1968 | Pranay Jain | 18112053 | MAN 002 Mathematical Methods |
| TA_ID | Name | Enrollment Number | Course Alloted |
|---|---|---|---|
| S1917 | Shivam MIshra | 18310016 | CYN 102 Physical Chemistry |
| S1918 | Ghanshyam Rathod | 18310006 | CYN 104 General Organic and Inorganic Chemistry |
| S1919 | Ghanshyam Rathod | 18310006 | CYN 106 Basic Analytical Chemistry |
| S1962 | Shubham jindal | 18312031 | MAN 004 Numerical Method |
| TA_ID | Name | Enrollment Number | Course Alloted |
|---|---|---|---|
| S1944 | Anuj Singh | 18112012 | CYN 002 Organic and Inorganic Chemistry |
| S1947 | Rakshit Kumar | 18121019 | EEN 112 Electrical Science |
| S1953 | Sawarnik Kaushal | 18312027 | MAN 102 Linear Algebra |


|
marking
1.0
A marking script for 1st year programming
|
Holds the grading rubric. More...
Public Member Functions | |
| def | __init__ (self) |
| def | make (self, rubric) |
| Performs a deep copy of the provided rubric. More... | |
| def | addMarks (self) |
| Computes the total mark for the student. More... | |
Public Attributes | |
| studentName | |
| attributes | |
| maxVals | |
| total | |
| comments | |
Holds the grading rubric.
studentName: The name of the current student.
The elements of the marking rubric. maxVals: The maximum values of each element of the marking rubric. total: The total mark for the student. comments: The instructor's comments for the student.
| def utils.Rubric.addMarks | ( | self | ) |
| def utils.Rubric.make | ( | self, | |
| rubric | |||
| ) |


I spent quite a bit of time coming up with an idea for the project. I was struggling until I started thinking about things that can be helpful to me.
I am in afternoon art right now, and a lot of my art supplies were scattered in different places in my room. There's too many things like crayons, different type of erasers, different type of pencil (2B,4B,8B), colors...
So here comes my idea, an art supply box! Something that's colorful and can carry all of my stuff. (it's like a toolbox but with art)
So finally... here we go, start working!
I made most part of the box in wood, while there would be smaller and prettier containers inside. Then I also used fabric on the top as cover of the box.
First, I 3D printed the smaller containers. I used Fusion 360 to design a Lego-like box (with lid). It's basically just a box with cylinders on its sides and top/bottom (one positive, one negative) so it can be pluged into each other.
Here's a fun fact, the container is suppose to be a square box, but then I think about it... I can't hold much with that (not even a crayon) So last second, I changed it's diameters in Makerbot app to make it a long rectangle.
Then I finally printed the box, I set it to be auto-fill supports.
There are four total containers with two lids, you can stack them on top of each other or besides each other.
Second, I started the wood working to make the structure of my box. I used very thin wood for the outer wall so the box would not be too heavy. But those thin layer can't connect with each other just by themselves.
So I used thicker wood sticks to make a hollow rectangle. I nailed it on the base which made nailing the walls so much easier.
Since I need to make a style of tool box, so I cut another thin wood into trapizium shape to make a higher handle on top of the box.
Finally, I added the trapizium on the sides, and then nailed in the handles. I got my wooden structure now :)
Third, I laser cut a piece of 3/8 wood with dimension of half of the wodden box. It is cut with lots of tiny holes in them so my different pencils and pens would fit through.
It is place inside the box, with the thick wooden square in the bottom, it is several cm above the bottom of the wooden box.
To vector a piece of 3/8 wood, the setting of the machine is power 100, speed 8. It's dimension is 5 by 8 inches (on Print setting).
Lastly, I sewed 2 piece of similar themed, different pattern fabric together to make the cover of the box. I had to do a french sew like the one in the pillow case, so that it hides the sewing line on my fabric (in the back).
Also, I cut another green fabric into six small pieces to stick them in the bottom of the box for decoration.
Finally, I painted all of the sides and the pieces of the big wooden box with spray paint. The color is combined in a random way (expressing my art style) mostly black, white and red.
After combining all of the pieces together, puting stuff in the right place, the art toolbox is finished!!! Now I can put my art supplies in there :P
I spent around a class and a half to think of an idea. Then, I started working on 3D and laser printing in around one more class.
Most of my time is used on spray painting and wood working. I spend three afternoons after school working on that plus sewing.
I spent so much time on my project, I am the kind of person where if I don't have an idea, I don't spent time on it. But once I got a cool idea, I want to make it the best I could (same as my art :P)
In total, I spent around 6-7 hours (plus Sunday afternoon, excluding time of thinking and doing this webpage). I think it's so worth it since I can finally clear up my stuff and have a clean inventory of art supplies.
In general, I learned so much in this class, especially in this final project. Most important thing is time management!!! We are desginers here, no one is telling us when to do what, but we have to finish it in time.
It's important to know how to plan. Plus I learned how to use the pressurized nail gun, it really save my time by a lot.
Also, I learned how to use the other sewing machine (kinda different~). Finally, I learned laser cutting and how to use lots of the wood cutting machines back in WIC~
In conclusion, I'm happy to be in this class to learned all about designing and making useful or fun stuff :D


Pig Dice
Play Dice for Your Life!
Player 1
Score:
Your Roll
Player 2
Score:
Your Roll
Roll The Dice
Hold
Start a New Game
Play against AI
Roll
Hold


Search results with keywords: {{searchKeywords}}. Click here to clear keywords.
| Name | ID | Version | Publisher | |
|---|---|---|---|---|
| Results below are filtered from local cache. Click here to search on server. | ||||
{{ interface.version }} |
{{ interface.publisherName }} | |||
| Name | ID | Version | Publisher | |
|---|---|---|---|---|
| Results below are filtered from local cache. Click here to search on server. | ||||
{{ capability.version }} |
{{ capability.publisherName }} | |||


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{% extends 'layouts/layout-email.html' %} {% block pageTitle %} Change your phone number {% endblock %} {% block content %}
Your phone number has been changed to {{ data['new-number'] }}
Go back to the service you were trying to use and log back in.


Please download and read the class guide carefully, then record your session.
{{classData?.demoClass?.description}}
Please download the attached contract file, sign it and upload that signed contract.
{{contract?.guideLines}}


AVIB
Alvis B
Hey! I am AVIB , a python hobbyist.
My interests are mainly in discord bots and python gui. Want to see some of my projects? Just scroll down.
Projects:
Alvi Bot
Alvi Bot is a multi-purpose discord bot with many useful commands.
This Website
A portfolio.


GStreamer
Section: Misc. Reference Manual Pages (1)Updated: May 2007
Index Return to Main Contents
NAME
gst-launch - build and run a GStreamer pipelineSYNOPSIS
gst-launch [OPTION...] PIPELINE-DESCRIPTIONDESCRIPTION
gst-launch is a tool that builds and runs basic GStreamer pipelines.
In simple form, a PIPELINE-DESCRIPTION is a list of elements separated by exclamation marks (!). Properties may be appended to elements, in the form property=value.
For a complete description of possible PIPELINE-DESCRIPTIONS see the section pipeline description below or consult the GStreamer documentation.
Please note that gst-launch is primarily a debugging tool for developers and users. You should not build applications on top of it. For applications, use the gst_parse_launch() function of the GStreamer API as an easy way to construct pipelines from pipeline descriptions.
OPTIONS
gst-launch accepts the following options:- --help
- Print help synopsis and available FLAGS
- -v, --verbose
- Output status information and property notifications
- -q, --quiet
- Do not print any progress information
- -m, --messages
- Output messages posted on the pipeline's bus
- -t, --tags
- Output tags (also known as metadata)
- -o FILE, --output=FILE
- Save XML representation of pipeline to FILE and exit
- -f, --no_fault
- Do not install a fault handler
- -T, --trace
- Print memory allocation traces. The feature must be enabled at compile time to work.
GSTREAMER OPTIONS
gst-launch also accepts the following options that are common to all GStreamer applications:- --gst-version
- Prints the version string of the GStreamer core library.
- --gst-fatal-warnings
- Causes GStreamer to abort if a warning message occurs. This is equivalent to setting the environment variable G_DEBUG to 'fatal_warnings' (see the section environment variables below for further information).
- --gst-debug=STRING
-
A comma separated list of category_name:level pairs to specify debugging levels
for each category. Level is in the range 0-5 where 0 will show no messages, and
5 will show all messages. The wildcard * can be used to match category names.
Use --gst-debug-help to show category names
Example: GST_CAT:5,GST_ELEMENT_*:3,oggdemux:5
- --gst-debug-level=LEVEL
- Sets the threshold for printing debugging messages. A higher level will print more messages. The useful range is 0-5, with the default being 0.
- --gst-debug-no-color
- GStreamer normally prints debugging messages so that the messages are color-coded when printed to a terminal that handles ANSI escape sequences. Using this option causes GStreamer to print messages without color. Setting the GST_DEBUG_NO_COLOR environment variable will achieve the same thing.
- --gst-debug-disable
- Disables debugging.
- --gst-debug-help
- Prints a list of available debug categories and their default debugging level.
- --gst-plugin-spew
- GStreamer info flags to set Enable printout of errors while loading GStreamer plugins
- --gst-plugin-path=PATH
- Add directories separated with ':' to the plugin search path
- --gst-plugin-load=PLUGINS
-
Preload plugins specified in a comma-separated list. Another way to specify
plugins to preload is to use the environment variable GST_PLUGIN_PATH
PIPELINE DESCRIPTION
A pipeline consists elements and links. Elements can be put into bins of different sorts. Elements, links and bins can be specified in a pipeline description in any order.
Elements
ELEMENTTYPE [PROPERTY1 ...]
Creates an element of type ELEMENTTYPE and sets the PROPERTIES.
Properties
PROPERTY=VALUE ...
Sets the property to the specified value. You can use gst-inspect(1) to
find out about properties and allowed values of different elements.
Enumeration properties can be set by name, nick or value.
Bins
[BINTYPE.] ( [PROPERTY1 ...] PIPELINE-DESCRIPTION )
Specifies that a bin of type BINTYPE is created and the given properties are set. Every element between the braces is put into the bin. Please note the dot that has to be used after the BINTYPE. You will almost never need this functionality, it is only really useful for applications using the gst_launch_parse() API with 'bin' as bintype. That way it is possible to build partial pipelines instead of a full-fledged top-level pipeline.
Links
[[SRCELEMENT].[PAD1,...]] ! [[SINKELEMENT].[PAD1,...]] [[SRCELEMENT].[PAD1,...]] ! CAPS ! [[SINKELEMENT].[PAD1,...]]
Links the element with name SRCELEMENT to the element with name SINKELEMENT,
using the caps specified in CAPS as a filter.
Names can be set on elements with the name property. If the name is omitted, the
element that was specified directly in front of or after the link is used. This
works across bins. If a padname is given, the link is done with these pads. If
no pad names are given all possibilities are tried and a matching pad is used.
If multiple padnames are given, both sides must have the same number of pads
specified and multiple links are done in the given order.
So the simplest link is a simple exclamation mark, that links the element to
the left of it to the element right of it.
Caps
MIMETYPE [, PROPERTY[, PROPERTY ...]]] [; CAPS[; CAPS ...]]
Creates a capability with the given mimetype and optionally with given properties. The mimetype can be escaped using " or '. If you want to chain caps, you can add more caps in the same format afterwards.
Properties
NAME=[(TYPE)]VALUE
in lists and ranges: [(TYPE)]VALUE
Sets the requested property in capabilities. The name is an alphanumeric value
and the type can have the following case-insensitive values:
- i or int for integer values or ranges
- f or float for float values or ranges
- 4 or fourcc for FOURCC values
- b, bool or boolean for boolean values
- s, str or string for strings
- fraction for fractions (framerate, pixel-aspect-ratio)
- l or list for lists
If no type was given, the following order is tried: integer, float, boolean,
string.
Integer values must be parsable by strtol(), floats by strtod(). FOURCC values may
either be integers or strings. Boolean values are (case insensitive) yes,
no, true or false and may like strings be escaped with " or '.
Ranges are in this format: [ VALUE, VALUE ]
Lists use this format: ( VALUE [, VALUE ...] )
PIPELINE CONTROL
A pipeline can be controlled by signals. SIGUSR2 will stop the pipeline
(GST_STATE_NULL); SIGUSR1 will put it back to play (GST_STATE_PLAYING).
By default, the pipeline will start in the playing state.
There are currently no signals defined to go into the ready or pause
(GST_STATE_READY and GST_STATE_PAUSED) state explicitely.
PIPELINE EXAMPLES
The examples below assume that you have the correct plug-ins available. In general, "osssink" can be substituted with another audio output plug-in such as "esdsink", "alsasink", "osxaudiosink", or "artsdsink". Likewise, "xvimagesink" can be substituted with "ximagesink", "sdlvideosink", "osxvideosink", or "aasink". Keep in mind though that different sinks might accept different formats and even the same sink might accept different formats on different machines, so you might need to add converter elements like audioconvert and audioresample (for audio) or ffmpegcolorspace (for video) in front of the sink to make things work.

 Threads are used
automatically. To make this even easier, you can use the playbin element:
gst-launch playbin uri=file:///home/joe/foo.avi
Filtered connections
These examples show you how to use filtered caps.
gst-launch videotestsrc ! 'video/x-raw-yuv,format=(fourcc)YUY2;video/x-raw-yuv,format=(fourcc)YV12' ! xvimagesink
Show a test image and use the YUY2 or YV12 video format for this.
gst-launch osssrc ! 'audio/x-raw-int,rate=[32000,64000],width=[16,32],depth={16,24,32},signed=(boolean)true' ! wavenc ! filesink location=recording.wav
record audio and write it to a .wav file. Force usage of signed 16 to 32 bit
samples and a sample rate between 32kHz and 64KHz.
ENVIRONMENT VARIABLES
- GST_DEBUG
- Comma-separated list of debug categories and levels, e.g. GST_DEBUG=totem:4,typefind:5
- GST_DEBUG_NO_COLOR
- When this environment variable is set, coloured debug output is disabled.
- GST_DEBUG_DUMP_DOT_DIR
- When set to a filesystem path, store dot files of pipeline graphs there.
- GST_REGISTRY
- Path of the plugin registry file. Default is ~/.gstreamer-0.10/registry-CPU.xml where CPU is the machine/cpu type GStreamer was compiled for, e.g. 'i486', 'i686', 'x86-64', 'ppc', etc. (check the output of "uname -i" and "uname -m" for details).
- GST_REGISTRY_UPDATE
- Set to "no" to force GStreamer to assume that no plugins have changed, been added or been removed. This will make GStreamer skip the initial check whether a rebuild of the registry cache is required or not. This may be useful in embedded environments where the installed plugins never change. Do not use this option in any other setup.
- GST_PLUGIN_PATH
- Specifies a list of directories to scan for additional plugins. These take precedence over the system plugins.
- GST_PLUGIN_SYSTEM_PATH
- Specifies a list of plugins that are always loaded by default. If not set, this defaults to the system-installed path, and the plugins installed in the user's home directory
- OIL_CPU_FLAGS
- Useful liboil environment variable. Set OIL_CPU_FLAGS=0 when valgrind or other debugging tools trip over liboil's CPU detection (quite a few important GStreamer plugins like videotestsrc, audioconvert or audioresample use liboil).
- G_DEBUG
- Useful GLib environment variable. Set G_DEBUG=fatal_warnings to make GStreamer programs abort when a critical warning such as an assertion failure occurs. This is useful if you want to find out which part of the code caused that warning to be triggered and under what circumstances. Simply set G_DEBUG as mentioned above and run the program in gdb (or let it core dump). Then get a stack trace in the usual way.
FILES
- ~/.gstreamer-0.10/registry-*.xml
- The xml plugin database; can be deleted at any time, will be re-created automatically when it does not exist yet or plugins change.
SEE ALSO
gst-feedback(1), gst-inspect(1), gst-typefind(1)AUTHOR
The GStreamer team at http://gstreamer.freedesktop.org/
Index
- NAME
- SYNOPSIS
- DESCRIPTION
- OPTIONS
- GSTREAMER OPTIONS
- PIPELINE DESCRIPTION
- PIPELINE CONTROL
- PIPELINE EXAMPLES
- ENVIRONMENT VARIABLES
- FILES
- SEE ALSO
- AUTHOR
This document was created by man2html, using the manual pages.
Time: 05:29:04 GMT, December 24, 2015


There are several ways to change atom type:
1. Select atom type button on toolbar, then click atoms to change their type
2. Mouse-hover an atom, then press keyboard key to change its type: C, N, S, O, l (Cl), b (Br), F, I, A, X
3. Select text tool on toolbar, then click an atom
4. Mouse-hover an atom, then press Enter key to show text edit box