Tools/Applications
GW HIVE
- GW instance of the High-performance Integrated Virtual Environment
(PMID:
25271953
;
26989153
)
AWS HIVE
- AWS instance of the High-performance Integrated Virtual Environment
(PMID:
25271953
;
26989153
)
BioCompute Galaxy
-
Galaxy is an open, web-based platform for data-intensive research.
BioCompute Objects Portal (BCOP)
-
Portal for making BioCompute objects
PhyloSNP
-
generate phylogenetic trees from single-nucleotide variation data
(PMID:
24930720
)
CensuScope
-
detect taxonomic composition of a metagenomic data set
(PMID:
25336203
)
Resources
BioMuta
-
browse human disease associated single-nucleotide variations (SNVs)
(PMID:
24667251
)
BioXpress
-
is a curated gene expression and disease association database.
(PMID:
25819073
)
A Reference Viral Database (RVDB)
-
DB for enhanced virus detection using next-generation sequencing (NGS) technologies
Gut Feeling Knowledge Base
-
Gut feeling knowledgebase is a reference database of healthy human gut microbiome.
(DOI:
10.1371/journal.pone.0206484
)
Filtered NT
OncoMX
-
KB of unified cancer genomics data from integrated mutation, expression, literature, and biomarker databases, accessible through web portal
Codon Usage Table (CUT)
-
DB for accessing codon usage information
GlyGen
-
data integration and dissemination project for carbohydrate and glycoconjugate related data.


My website is about packing for a hike, and a Brownie recipe.
I love hiking.I always look forward to a nice hike. I go on one atleast once a month. The flollowing is a list of what I will need for my hike.
Packing list
Water bottle
Trail mix
Trail map
Hat
Sun glasses
Bug spray
Walking stick
Brownie Recipe
½ cup butter
1 cup white sugar
2 eggs
1 teaspoon vanilla extract
⅓ cup unsweetened cocoa powder
½ cup all-purpose flour
¼ teaspoon salt
¼ teaspoon baking powder
Baking instructions
Step 1
Preheat oven to 350 degrees F (175 degrees C). Grease and flour an 8-inch square pan.
Step 2
In a large saucepan, melt 1/2 cup butter. Remove from heat, and stir in sugar, eggs, and 1 teaspoon vanilla. Beat in 1/3 cup cocoa, 1/2 cup flour, salt, and baking powder. Spread batter into prepared pan.
Step 3
Bake in preheated oven for 25 to 30 minutes. Do not overcook.


Most people do know when their birthdays are, but some might not know what day of the week they
were born.
However, Akan names are derived from Ghanian culture. Frequently in Ghana,
children are given their first name as a 'day name' which corresponds to the day in the week
they were born
Here are Ghanian day names
| DAY | Gender | |
|---|---|---|
| Male | Female | |
| Sunday | Kwasi | Akosua |
| Monday | Kwadwo | Adwoa |
| Tuesday | Kwabena | Abenaa |
| Wednsday | Kwaku | Akua |
| Thursday | Yaw | Yaa |
| Friday | Kofi | Afua |
| Saturday | Kwame | Ama |
Our web application takes a user's birthday and calculates the day of the week they were born and then depending on their gender outputs their Akan Name
The application checks whether the date and month entered is valid. if either of them is invalid, the user should be alerted and asked to enter a valid one
The application outputs to the user their Akan name depending on their gender. For example, if a user is male and the result of the calculation is 0, then the application should match that with Kwasi since Kwasi corresponds to Sunday which has an index 0. Hence the output the user will see should be something like “Your Akan name is Kwasi”


Our control systems easily scale from your home garden up to large farms...
and beyond.
Armed with high resolution environmental data and over the internet controls, clock in your highest efficiency yet.
The Technaponics ENVI® is a monolithic grow computer offering a suite of environmental sensors and real time controls accessible on your phone, tablet, or computer from anywhere in the world. The ENVI® is powerful and easy to use with a seamless plug & play experience for any garden whether it be soil, hydroponic, or aeroponic.
At the core of our technology is an advanced software that only needs a web browser and an internet connection to be up and running in minutes.
Our intuitive web interface built on modern standards for speed and responsiveness, puts you in complete control of your garden from any device, anywhere in the world.
Real time information on the environmental landscape in your garden enables you to fine tune your inputs and optimize your yield.


The Society for the Quantitative Analyses of Behavior is led by a yearly-appointed board of executive directors. The contact information for all board members, as well as student appointees, is provided below.
The most recent copy of the organization's bylaws are available here.


ICE STORM RATING: 6.5 / 10 --> So-so
For more reviews and movie trivia, visit http://www.joblo.com/
PLOT: Story of two families living in Connecticut in the 1970s. The unemotional husband and wife duos don't seem to have much spark left in their marriage, like to drink and cheat. This improper umbilical cord introduces their kids to the wonderfully insecure world of their adolescence without much moral or parental guidance. Adults only, please.
CRITIQUE: Interesting at times, boring at others, this character study semi-succeeds in portraying a couple of dysfunctional nuclear clans, with solid performances by all of its stars, especially the kids, despite its occasional overly existential moments. Everyone in this movie is quite cold and distant, appreciated to an extent by all who have gone through some downer periods in their own lives, but hard to swallow for those who look for entertainment in films. In that sense, this film is not recommended to those who are looking for fun and amusement, despite a couple of curiously unserious scenes. This film is meant for the higher-browed people who enjoy watching others bemoan their lifeless journeys, contemplate a sack of sins, and live in complete dysfunction among one another (Woody Allen, eat your heart out!). Depressed yet? No, check out this film!
The writing is very good, most of the characters well-drawn and the look of the film, unique and appropriate. I did find the pacing to be a little too slow for my own jive, and bothered by the TITANIC-esque annoying flutes passing off as the score, but all in all, an interesting study of human beings attempting to differentiate themselves from the kingdom of the animals.
Review Date: March 12, 1999 Director: Ang Lee Writer: James Shamus (based on novel by Rick Moody) Producers: Ang Lee, James Shamus and Ted Hope Actors: Kevin Kline as Ben Hood, Sigourney Weaver as Janey Carver, Joan Allen as Elena Hood, Christina Ricci as Wendy Hood Genre: Drama Year of Release: 1997
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


{% include head.html %}
{% include popup_message.html %} {% include nav.html %} {% include header-services.html %}Whether you’re a small eCommerce startup or a digital marketplace giant, a niche fashion portal or a global cosmetic brand, a forward-thinking fintech or a traditional bank, the ability to turn data into actionable insights is a key factor in achieving your digital goals. IlmuOne Data provides digital marketing, business intelligence, and analytics solutions for decision makers, senior marketers, and startup founders.
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Marketing analytics and digital intelligence require a unique combo of technical skills and business acumen. Candidates with such a pedigree are rare, especially in Indonesia, and the competition to hire them is fierce.
While in the future you might eventually decide to build your own analytics team, IlmuOne Data’s team is here for you today. Instead of trying to recruit, train, and develop your own analytics team, why not leverage ours? Not only will you have access to our team of dedicated, talented, skilled individuals, you will also benefit from our broad experience of serving clients across various business models.
IlmuOne Data use data mining techniques to build attribution models that help marketing execs judge the consequences and opportunity costs of their spending, and help them find the optimal mix that provides the best return for their marketing budget
Predictive Modelling - Using raw data from Big Query, we’ve developed predictive models that allow clients to use customer and web data to predict consumer behaviour
Advanced analytics and cross domain configuration to link multiple websites and properties, combining them under one easy-to-use dashboard
Integration to track offline sales to online campaigns
Integrate POS loyalty programs, shipping costs, discounts and more into your analytics dashboard to stay on top of profit measurements
After years of servicing clients from a variety of business backgrounds, we find that while every client is different, they can generally be categorised into four stages. Knowing which stage you are in your data analytics need can help you focus in developing your capabilities. The Four Stages of Data Analytics Need are STARTUP, LAUNCH, GROWTH, and MATURE.
At the the STARTUP STAGE, you need to prioritise proper implementation of
As you get ready for the next stage, also make sure that:
At this stage, you might consider :
IlmuOne Data Advisory team checks the current analytics setup, the quality of data that is collected, and whether or not analytics framework and KPI’s are aligned with business goals. Data Audit includes but is not limited to:
Based on DATA AUDIT findings, IlmuOne Data Advisory team develops analytics best-practices guide and start to test hypotheses on ways to improve our client’s digital marketing performance. The scope of experiments could include but not limited to:
IlmuOne Data Advisory team implement and further refine DATA INSIGHTS findings. Client can expect performance lift in media and channel mix. Optimisation could include but is not limited to:
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As soon as I got news of this project, I started thinking of what I wanted to make. I initially thought of a music box that automatically plays as you open it but then changed my mind to make a launchpad. The inspiration for that is this. Before I could start this project, the class was instructed to make a proposal on what we wanted to make and this was extremely important as this allowed me to know what I was supposed to make and made sure I stayed on track.
The proposal is here
After the project was approved, I started designing what I wanted to make on paper.
After several attempts at trying to make the design's coding work, which can be found here, I ultimately decided to remove the button that allows me to toggle between pages as the coding for it was to complicated for me to code. I then designed the box and other parts of it to be made later on in 3D Printing or Lazer Cutting.
I then 3D printed out the platform, keypad holder and keys, whose process can be found here, and lazer cut the casing to hold the parts in, whose process can be found here. I then aseembled the project together.
I screwed in the speakers and the keypad holder in place.
I then secured the platforms down with some hot glue.
In the end after some trial and error, I finally glued the casing together and this is the finished product.
Extra stuff


INDIAN TOURISTS CENTER
TAJMAHAL
Learn more
ADIYOGI
Learn more
GOLDEN TEMPLE
Learn more
BODH GAYA
Learn more
Imambara
Learn more
LOTUS TEMPLE
Learn more
RUMI DARWAJA
Learn more
INDIA GATE
Learn more
GATEWAY OF INDIA
Learn more
REDFORT
Learn more
QUTUB MINAR
Learn more
STATUE OF UNITY
Learn more


Open the web browser on your phone or computer and go to:
kahoot.it
| Content | Style |
|---|---|
| HTML | CSS |
| What you're saying | What content should look like |
| Meaning, not appearance | Appearance, not meaning |
.headings {
text-transform: uppercase;
font-weight: bold;
}
CSS has rule sets comprising
.headings
, remember the dot!)text-transform
, font-weight
)uppercase
, bold
)The birthday party
HTML:
The birthday party
CSS:
.article-subhead {
font-weight: bold;
font-size: 15px;
}
Headings with the class article-subhead
are now size 15 bold.
http://jsbin.com/nozodego/17/edit
Give the push button a big, bold font
Add a class
attribute to one of the buttons. You can make up the class name.
Add a CSS rule set that targets the class, with the properties font-size
and font-weight
.
A rule set in CSS looks like .classname { property: value; }
A class in HTML looks like <... class='classname'>
| Content | Style | Behaviour |
|---|---|---|
| HTML | CSS | JavaScript |
| What you're saying | What content should look like | How content should react to interaction |
| Content (HTML) |
URL to be shared Which services to offer |
|---|---|
| Style (CSS) |
Logos of all the services Spacing and margins Design and positioning of the tooltip |
| Behaviour (JavaScript) |
Tooltip on click Make services load in small pop-up windows |
CSS:
<link rel='stylesheet' href='http://...'>
JavaScript:

<link rel='stylesheet' href='my-stylesheet.css'>

Hello!

CSS goes at the top of the document, before the content. Otherwise, you can get a Flash of Unstyled Content (FOUC):
JavaScript goes at the bottom of the document, after the content. It's not needed urgently and shouldn't slow down page loading.
Pre-formed


Simple usage of building a CSV file one rows at a time.
csvrows -o 4rows.csv "First,Second,Third" "one,two,three"
csvrows "ein,zwei,drei" "1,2,3" >> 4rows.csv
cat 4row.csv
Example parsing a pipe delimited string into a CSV line
csvrows -d "|" "First,Second,Third|one,two,three" > 4rows.csv
csvrows -delimiter "|" "ein,zwei,drei|1,2,3" >> 4rows.csv
cat 4rows.csv
Filter rows 1 and 3 from CSV file
cat 4row.csv | csvrows -row 1,3 > result1-2.csv
Filter rows 1 and 3 from CSV file from file named “4row.csv”
csvrows -i 4row.csv -row 1,3 > result2-2.csv


Click-O-Sites
SEC Portals
SEC Website
Department Site
MyCamu
epay
Hostel
Sophos
Proctor Examination
Moodle E-Learning
IT
CSE
AI & DS
ECE
EEE
Mechanical
Bio Medical
Medical Electronics
Civil
Agriculture
EIE
Chemical Engineering
MBA
S&H
Training
Edit
Done by Ashwin :)


{%load static%}
Is this the author herself?
Last updated 3 mins ago
No I think it is just a picture downloaded from the internet
Last updated 2 mins ago
No this is just a picture
Last updated 1 min ago
Wow! the author is so neat and clean.
Last updated 5 mins ago
Even I want to customise my study table like that!
Last updated 2 mins ago
What is the user trying to do?
Last updated 3 mins ago
Maybe he is just creating a user friendly environment
Last updated 2 mins ago
No this is hot chocolate
Last updated 3 mins ago
Nope
Last updated 2 mins ago
What makes you think so? Lol
Last updated 3 mins ago
Nope
Last updated 2 mins ago


Donald Trump
Résumé
— I'm the most successful person ever to run for the presidency, by far. Nobody's ever been more successful than me. I'm the most successful person ever to run. Ross Perot isn't successful like me. Romney — I have a Gucci store that's worth more than Romney. Sorry losers and haters, but my IQ is one of the highest — and you all know it!
Experience
-
2017
Became 45th president of the United States 😉
-
2016
Won election race with 77 votes more than Clinton 😎 😎 yippe-kee-yay!
-
2015
Became the most important Republican 💪🏻 nominating for presidential election run
-
2009
Changed my mind and became Republican again 🍌 (simply banana, just because)
-
middle age crisis
Realized I'm pretty big pot to put emoticons in my resume
-
2005
Started Trump University with Michael Sexton and Jonathan Spitalny: no grades, certificates and other bureaucracy, but program offered courses in real estate, asset management entrepreneurship, and wealth creation. 98% students gave positive reviews while studying, however some of these ungrateful gringos filed lawsuits against Trump University after the course. I had to pay $25 million to settle that...
-
2002
Acquired the former Hotel Delmonico, which was renovated and reopened in 2004 as the Trump Park Avenue. I sure do like my surname!
-
2001
Completed Trump World Tower and joined the Democratic Party
-
1999
Started buying golf courses. They are very cheap, I have 18 of them now in USA and abroad! Also switched to the Reform Party that year
-
1997
Began a multi-building development along the Hudson River, which was named Trump Place. Me and the other investors sold our shares for $1.8 billion and that was then the biggest residential sale in the history of New York City
-
1996
Acquired the Bank of Manhattan Trust Building, which was a vacant seventy-one story skyscraper on Wall Street. After an extensive renovation, the high-rise was renamed the Trump Building
-
1994
Refurbished the Gulf and Western Building on Columbus Circle, turned it into a 44-story luxury property known as Trump International Hotel and Tower
-
1989 ― 1992
Ran a second hand airline called Trump Shuttle. What a waste of money, I had to sell it, now it is American Airlines Shuttle, btw
-
1989, 1990
Learned about Tour de France and decided to hold my very own Tour de Trump. Is America not great enough for that cycling competition?
-
1988
Acquired the Plaza Hotel in Manhattan for $407 million, invested $50 million to restore the building and boosted it from three-star to four-star hotel (I called it "the Mona Lisa" and sold it for a lot of cash in 1995)
-
1987
Registered as a Republican
-
1985
I bought the Mar-a-Lago estate in Palm Beach and turned it into a private club.You can join if you pay $200K initiation fee (and there is another annual fee for members, it is very expensive to be rich). That year I also opened a casino called Trump Castle. My wife Ivana became a manager, because she is another Trump!
-
1983
I purchased New Jersey Generals, an American football team. I changed team's play schedule and filed an antitrust lawsuit against United States Football League, so NJG are not doing so good now, but back then they were great!
-
1989
Started developing the Trump Tower skyscraper in Manhattan, I payed a crew of undocumented Polish workers to demolish the historic Bonwit Teller store, including art deco features. All this to make architectural critic Paul Goldberger say in 1983 that the tower's atrium is "the most pleasant interior public space to be completed in New York in some years". At the same time I also started a repair job on the Wollman Rink in Central Park and completed the work in three months for $1.95 million, which was $775,000 less than the initial budget!
-
1978
Purchased a 50 percent stake in the derelict Commodore Hotel and turned it into the Grand Hyatt Hotel in 2 years of remodeling
-
1971
Was promoted to president of the company and renamed it The Trump Organization
-
1968
Began my career at my father's real estate development company, Elizabeth Trump & Son
Personal Life
-
2006
Melania gained United States citizenship and gave birth to our son, Barron
-
2005
Pretty obvious, but I've married her
-
1998
Met my future third wife, Melania Knauss ❤
-
1999
Divorced Marla ¯_(ツ)_/¯
-
1993
Married Marla and had a daughter, we named her Tiffany, because I like Tiffany & Company jewellery as well LOL
-
1984
Started affair with Marla Maples, so I had to divorce. Well, life is tough, so you know.. God knows I love nice women, I love them all, but apparently someone didn't!
-
1982
Birth of Eric Trump
-
1982
SBirth of Ivanka Trump
-
1977
My first son is born. I called him.. try to guess.. well, he's Donald, of course!
-
1977
Married Czech model Ivana Zelníčková just because she was beautiful. You know, WAS...


The all-time favourite - It doesn't get any more iconic than this. Savoury pepperoni, homestyle sauce, and ooey-gooey, stretchy mozzarella. Perfection!
Starting from ksh.500
This tangy, spicy pie is made for you. Kick off with Buffalo blue cheese sauce, grilled chicken, red onions, fire-roasted red peppers and mozzarella cheese.
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Some people just love to brave the heat. Well, step-up you spice-monsters. This slice of fire combines spicy Italian sausage, hot banana peppers, and mozzarella cheese.
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Topped with classic pepperoni, bacon crumble, salami, spicy Italian sausage, mozzarella cheese, and Italiano blend seasoning. For the meat-lover in you.
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Topped with fresh mushrooms, red onions, bacon crumble, Italiano blend seasoning, and made with creamy garlic sauce and mozzarella cheese. Tip: Try customizing to a thick crust.
Starting from ksh.500
Isn't it beautiful when diverse flavours come together? Imagine a whole grain crust with grilled chicken, broccoli, sun-dried tomatoes, goat cheese, mozzarella cheese and sweet chili Thai sauce.
Starting from ksh.500


Reports using of old library dependency version and suggests replacing it a newer stable version.
Example:
libraryDependencies += "org.scalatest" %% "scalatest" % "3.0.7"
After the quick-fix is applied:
libraryDependencies += "org.scalatest" %% "scalatest" % "3.0.8"


{% load static %}
Predict poa has been assisting punters in America, Europe, and Africa since 2009. Our experience in betting has helped us predict more accurate games.
Premium Tips are meant for those who don't want to pick from our free tips. Our experience team provide them with four to five odds daily with accuracy of over ninety five percent.
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Tired of waiting for bitcoin/dogecoin/litecoin/mfcoin transaction confirmations hitting F5 all the time? This page allows you to keep tracking of confirmations for your bitcoin/dogecoin/litecoin transaction; after setup and after the reaching of confirmation target, the page will produce a notification.
Donate MFC to Mv7vb6eWskJd2xtfB1U97CfKEZxML65Me4
© 2014 OpenBitLab Team
© 2019 Sagleft.
Github


Nike Blazer Mid '77 Vintage
Nike Air Force 1 '07 Craft
Nike Air Force 1 '07 LV8
Chuck 70 "Peace Love Basketball"
© 2020 My New Kicks, Inc. All Rights Reserved


Class: Aws::ServiceCatalog::Client
| Inherits: |
Seahorse::Client::Base
|
| Defined in: |
(unknown) |
Overview
An API client for AWS Service Catalog. To construct a client, you need to configure a :region
and :credentials
.
servicecatalog = Aws::ServiceCatalog::Client.new(
region: region_name,
credentials: credentials,
# ...
)
See #initialize
for a full list of supported configuration options.
Region
You can configure a default region in the following locations:
ENV['AWS_REGION']
Aws.config[:region]
Go here for a list of supported regions.
Credentials
Default credentials are loaded automatically from the following locations:
ENV['AWS_ACCESS_KEY_ID']
andENV['AWS_SECRET_ACCESS_KEY']
Aws.config[:credentials]
- The shared credentials ini file at
~/.aws/credentials
(more information) - From an instance profile when running on EC2
You can also construct a credentials object from one of the following classes:
Alternatively, you configure credentials with :access_key_id
and
:secret_access_key
:
# load credentials from disk
creds = YAML.load(File.read('/path/to/secrets'))
Aws::ServiceCatalog::Client.new(
access_key_id: creds['access_key_id'],
secret_access_key: creds['secret_access_key']
)
Always load your credentials from outside your application. Avoid configuring credentials statically and never commit them to source control.
Instance Attribute Summary
Attributes inherited from Seahorse::Client::Base
Constructor collapse
-
.new(options = {}) ⇒ Aws::ServiceCatalog::Client
constructor
Constructs an API client.
API Operations collapse
-
#describe_product(options = {}) ⇒ Types::DescribeProductOutput
Retrieves information about a specified product.
-
#describe_product_view(options = {}) ⇒ Types::DescribeProductViewOutput
Retrieves information about a specified product.
-
#describe_provisioning_parameters(options = {}) ⇒ Types::DescribeProvisioningParametersOutput
Provides information about parameters required to provision a specified product in a specified manner.
-
#describe_record(options = {}) ⇒ Types::DescribeRecordOutput
Retrieves a paginated list of the full details of a specific request.
-
#list_launch_paths(options = {}) ⇒ Types::ListLaunchPathsOutput
Returns a paginated list of all paths to a specified product.
-
#list_record_history(options = {}) ⇒ Types::ListRecordHistoryOutput
Returns a paginated list of all performed requests, in the form of RecordDetails objects that are filtered as specified.
-
#provision_product(options = {}) ⇒ Types::ProvisionProductOutput
Requests a Provision of a specified product.
-
#scan_provisioned_products(options = {}) ⇒ Types::ScanProvisionedProductsOutput
Returns a paginated list of all the ProvisionedProduct objects that are currently available (not terminated).
-
#search_products(options = {}) ⇒ Types::SearchProductsOutput
Returns a paginated list all of the
Products
objects to which the caller has access. -
#terminate_provisioned_product(options = {}) ⇒ Types::TerminateProvisionedProductOutput
Requests termination of an existing ProvisionedProduct object.
-
#update_provisioned_product(options = {}) ⇒ Types::UpdateProvisionedProductOutput
Requests updates to the configuration of an existing ProvisionedProduct object.
Instance Method Summary collapse
-
#wait_until(waiter_name, params = {}) {|waiter| ... } ⇒ Boolean
Waiters polls an API operation until a resource enters a desired state.
-
#waiter_names ⇒ Array<Symbol>
Returns the list of supported waiters.
Methods inherited from Seahorse::Client::Base
#build_request, #operation, #operation_names
Methods included from Seahorse::Client::HandlerBuilder
#handle, #handle_request, #handle_response
Constructor Details
.new(options = {}) ⇒ Aws::ServiceCatalog::Client
Constructs an API client.
Instance Method Details
#describe_product(options = {}) ⇒ Types::DescribeProductOutput
Retrieves information about a specified product.
This operation is functionally identical to DescribeProductView except that it takes as input ProductId
instead of ProductViewId
.
#describe_product_view(options = {}) ⇒ Types::DescribeProductViewOutput
Retrieves information about a specified product.
This operation is functionally identical to DescribeProduct except that it takes as input ProductViewId
instead of ProductId
.
#describe_provisioning_parameters(options = {}) ⇒ Types::DescribeProvisioningParametersOutput
Provides information about parameters required to provision a specified product in a specified manner. Use this operation to obtain the list of ProvisioningArtifactParameters
parameters available to call the ProvisionProduct operation for the specified product.
#describe_record(options = {}) ⇒ Types::DescribeRecordOutput
Retrieves a paginated list of the full details of a specific request. Use this operation after calling a request operation (ProvisionProduct, TerminateProvisionedProduct, or UpdateProvisionedProduct).
#list_launch_paths(options = {}) ⇒ Types::ListLaunchPathsOutput
Returns a paginated list of all paths to a specified product. A path is how the user has access to a specified product, and is necessary when provisioning a product. A path also determines the constraints put on the product.
#list_record_history(options = {}) ⇒ Types::ListRecordHistoryOutput
Returns a paginated list of all performed requests, in the form of RecordDetails objects that are filtered as specified.
#provision_product(options = {}) ⇒ Types::ProvisionProductOutput
Requests a Provision of a specified product. A ProvisionedProduct is a resourced instance for a product. For example, provisioning a CloudFormation-template-backed product results in launching a CloudFormation stack and all the underlying resources that come with it.
You can check the status of this request using the DescribeRecord operation.
#scan_provisioned_products(options = {}) ⇒ Types::ScanProvisionedProductsOutput
Returns a paginated list of all the ProvisionedProduct objects that are currently available (not terminated).
#search_products(options = {}) ⇒ Types::SearchProductsOutput
Returns a paginated list all of the Products
objects to which the caller has access.
The output of this operation can be used as input for other operations, such as DescribeProductView.
#terminate_provisioned_product(options = {}) ⇒ Types::TerminateProvisionedProductOutput
Requests termination of an existing ProvisionedProduct object. If there are Tags
associated with the object, they are terminated when the ProvisionedProduct object is terminated.
This operation does not delete any records associated with the ProvisionedProduct object.
You can check the status of this request using the DescribeRecord operation.
#update_provisioned_product(options = {}) ⇒ Types::UpdateProvisionedProductOutput
Requests updates to the configuration of an existing ProvisionedProduct object. If there are tags associated with the object, they cannot be updated or added with this operation. Depending on the specific updates requested, this operation may update with no interruption, with some interruption, or replace the ProvisionedProduct object entirely.
You can check the status of this request using the DescribeRecord operation.
#wait_until(waiter_name, params = {}) {|waiter| ... } ⇒ Boolean
Waiters polls an API operation until a resource enters a desired state.
Basic Usage
Waiters will poll until they are succesful, they fail by entering a terminal state, or until a maximum number of attempts are made.
# polls in a loop, sleeping between attempts client.waiter_until(waiter_name, params)
Configuration
You can configure the maximum number of polling attempts, and the delay (in seconds) between each polling attempt. You configure waiters by passing a block to #wait_until:
# poll for ~25 seconds
client.wait_until(...) do |w|
w.max_attempts = 5
w.delay = 5
end
Callbacks
You can be notified before each polling attempt and before each
delay. If you throw :success
or :failure
from these callbacks,
it will terminate the waiter.
started_at = Time.now
client.wait_until(...) do |w|
# disable max attempts
w.max_attempts = nil
# poll for 1 hour, instead of a number of attempts
w.before_wait do |attempts, response|
throw :failure if Time.now - started_at > 3600
end
end
Handling Errors
When a waiter is successful, it returns true
. When a waiter
fails, it raises an error. All errors raised extend from
Waiters::Errors::WaiterFailed.
begin client.wait_until(...) rescue Aws::Waiters::Errors::WaiterFailed # resource did not enter the desired state in time end
#waiter_names ⇒ Array<Symbol>
Returns the list of supported waiters. The following table lists the supported waiters and the client method they call:
| Waiter Name | Client Method | Default Delay: | Default Max Attempts: |
|---|


1 write to BreakpointCount
System.Management.Automation (1)
engine\debugger\debugger.cs (1)
152
this.
BreakpointCount
= breakpointCount;
1 reference to BreakpointCount
System.Management.Automation (1)
engine\remoting\client\remoterunspace.cs (1)
2768
UpdateBreakpointCount(bpArgs.
BreakpointCount
);


Natural ability
Starmie can release water from any end of its body. It can jump from physical blows and, spinning in the air like a shuriken, knock down the opponent’s feet.


In a nutshell, coming up with your own solution amounts to finding enough useful sequences that can be used to work your way down through a subgroup chain to the trivial group. Of particular interest are sequences in the form of conjugates and commutators. Conjugates can often help you create new sequences from old ones. Commutators let you narrow or focus the net impact of a sequence based upon how much or little the two sequences taken in the commutator commute with one another.
As of this writing, the program has a limited ability to solve some of the twisty puzzles. The solutions it finds are far from optimal, but they appear to work in all of my testing thus far. Note that you won't recognize any concrete solution method being used by the computer as it uses the sifting process on a pre-generated stabilizer-chain to come up with its factorizations. For those interested in the code, it can be found here: https://github.com/spencerparkin/PermutationGroup
Here I'm going to keep a bunch of notes that I've collected over the years on solving various twisty puzzles. I encourage the reader to look at these notes only after they've come up with their own solution. It's much more satisfying to solve a puzzle having not resorted to any help, even if it takes a very long time, months even, to figure a puzzle out. In any case, I do not go into any great detail for most puzzles, perfering instead to document only the key concepts and sequences that are easy to forget, leaving the rest to the reader.


Home delivery available from 9am till 10pm
To buy more than one Pizza, Make another selection then press order now to add to the first order
Toppings
You do not have any item in the cart
Amount you are to pay for your order is ksh


HTML
All html elements go inside the "html" element
"title" tag contents is displayed on the browser's Title Tag
"div" tag divides content into sections
"b" tag makes text bold
CSS
CSS adds style to the HTML document
"link" tag can be used to set the CSS file to be used
Child elements inherit styles from its parent
Different metrics like px, %, pt can be used to specify length


KadOH
Click on join
Control
Node
Reactor
Transport
Routing Table
Log
Control panel
Ping !
IterativeFind processes
Disconnected
Received RPCs
Sent RPCs
Log level :
info
warning
error
fatal
debug


|
A
New Day Has Come
(Aldo Nova, Stephan Moccio) |
|
I
was waiting for so long
For a miracle to come Everyone told me to be strong Hold on and don't shed a tear So through darkness and good times I knew I'd make it through And the world thought I had it all But I was waiting for you Hush now I see a light in the sky Oh it's almost blinding me I can't believe I've been touched by an angel With love Let the rain come down And wash away my tears Let it fill my soul And drown my fears Let it shatter the walls For a new sun A new day has come A new day has come Where it was dark now there is light Where there was pain, now there's joy Where there was weakness, I found my strength All in the eyes of a boy Hush now I see a light in the sky Oh it's almost blinding me I can't believe I've been touched by an angel With love Let the rain come down And wash away my tears Let it fill my soul And drown my fears Let it shatter the walls For a new sun A new day has come A new day has come Instrumental Chorus |


My name is Chan Woo Kim, some prefer Jon, and I am from South Korea. I am a senior in Shanghai American School, Puxi Campus, set to graduate on June 2019. I am a big nerd! With a finsta full of math related quirky content and as an avid Redditor, I am known to be passionate about lots of things and to love spreading new things to those around me.
At school, I'm the Vice President of Science National Honor Society, Founder and President of Developers' Hub, where I teach over 15 students elements of software engineering through web development and Git, Public Relations Officer of the National Honor Society, a link crew leader, a member of Mu Alpha Theta and National English Honor Society, a sprinter in Varsity Track & Field, and a Winger for the Varsity Rugby team.
Me @ The Ducky Tours in Pittsburgh blowing my duck whistle
I am looking for partnerships in safety and security, especially in data collection, for Vind. Please contact me at<EMAIL_ADDRESS>if you are interested. I am the CEO & CTO of Vind. Vind started when I had an idea for a start-up that I believed (and still believe) might really change the way people deal with safety today. I've formed a team since then and we all have been working very hard to create our product. As the CEO, I oversee all operations of the company and make executive decisions. As the CTO, I am in charge of the entirety of product development, namely developing the app.
I am looking for mentorship in the area of sexual assult prevention, health tracking, and Apple WatchOS Development for Alertive. Please contact me at<EMAIL_ADDRESS>if you are interested. Alertive was incubated & founded at the LaunchX 2018 (formerly MIT Launch) Entrepreneurship Program @ University of Pennsylvnia. At the program, we identified a problem: a vast majority of people taking care of those with health conditions were in costant fear of what will happen to them. Alertive was founded to solve this issue. Alertive is a company aimed at using Deep Learning to detect screams in audio data and anomalies in health data in order to provide instant notifications for those in critical conditions such as sexual assults and heart attacks. As the Executive Officer & Technology Officer of Alertive, I make executive decisions and I am in charge of the entirety of the product development.
Me & Crystal @ the LaunchX (Formerly MIT Launch) Entrepreneurship Program
My research interests include: Natural Language Processing, Machine Learning, Deep Learning, GANs, making sense of the learning inside the black-box, and Hierarchal Temporal Memory
I am currently involved in A.I. Music research at NYU Shanghai with Dr. Gus Xia to create a generative model for music.
I've also conducted research as an entry to the Intel Science and Engineering Fair. The purpose of the project was to use NLP methods (Text Vectorization, N-Grams, TF-IDF, Linear SVMs optimized using both Coordinate & Gradient Descent) on Windows API System Calls to detect malware. Our model achieved a highest of 96% accuracy. The project won the 4th place Grand Award at Intel ISEF and special awards from numerous organizations (e.g. CAST, MAWHIBA).
The paper can be found here:
(arXiv) NtMalDetect: A Machine Learning Approach to Malware Detection Using Native API System Calls
I self-studied Linear Algebra and Multivariable calculus on top of finishing AP Calculus BC at school and finished Andrew Ng's courses to make all this happen. I am now comfortable with much of the theoretical foundations as well as using TensorFlow and other Python modules.
Post Grand Awards ceremony @ Intel ISEF 2018
Making apps was how I got into computer science in the beginning back in middle school. Today, I work with:
HTML, CSS, Javascript, Node.js, MongoDB, PostgreSQL, GraphQL, Git, data science in Python (sci-kit learn, TensorFlow, Numpy, Pandas, etc), bash, Java, Docker, nginx, React Native, React.js
I've created apps and websites for personal projects as well as for companies. I've also created open source projects. My team has actually been written about in a company website for winning first place for WebHacks2017!
I've dedicated a separate page for my projects. Find out more about my projects here.
Front page of Milky


Helicopter service
by Special CorrespondentA helicopter service was inaugurated for cruise tourists from Willingdon Island by Chipsan Aviation in association with the District Tourism Promotion Council, Kerala Tourism, and Greenix Village on Wednesday.
The service was inaugurated by Dr. M. Beena, Chairperson, Cochin Port Trust.


Quick Pseudo-Classes Exercise
Open up the
starter code
.
Follow the instructions on each button
View the
solution code
.


Dev Bootcamp asks its students to take this test to discover their Gregorc thinking style.
My results say that I'm mostly Abstract Random with a touch of Concrete Random and a speck of Abstract Linear. Nay, I have not a trace of Concrete Linear in any fiber of my being.
I take assessments like these with a huge side of salt block. Our strengths, weaknesses, personalities, and minds can change throughout our lives. Placing people into narrow categories on the basis of a questionnaire is a convenient mental shortcut. But it can also mislead, as it overlooks the complexity and plasticity within us. It's also easy for us to read personality descriptions and believe that they apply to us even when they absolutely don't. Besides, a quick glance at Wikipedia will reveal that Gregorc's peers didn't always agree that his methods:
Timothy Sewall, in a comparison of four learning style assessments (Gregorc's, Myers Briggs, Kolb LSI, and an LSI by Canfield) by review of their published supporting studies (i.e., without new experimental work) concluded of Gregorc's design, "the most appropriate use of this instrument would be to provide an example of how not to construct [an] assessment tool."
However, if I had to categorize myself into one of the four options Gregorc offers, I'd say that Abstract Random seems most accurate.
I found a website that lists common characteristics of Abstract Random thinkers. Here they are, with my commentary on how they apply to me.
Abstract Random thinkers Characterizations:
-
Dislike for routine procedures and orderliness.
Eh, sometimes. It depends. When it makes sense, I love a good routine. -
Are extremely sensitive to his/her own and others' feelings.
Gotta say, I've got a strong capacity for emphathy. -
Are flexible, accepting, and responds easily to change.
EXTREMELY accurate. I'm a bonafide neophile. -
Learn well through discussion and sharing of ideas.
YES. This is why I signed up for Dev Bootcamp instead of plugging away at learning coding on my own. -
Are imaginative.
 Like, a lot? -
Personalize information.
I'm not exactly sure what they mean here, but I take it personally. ;) -
Are usually involved in many projects or interest at one time.
YES YES YES. I've always got a lot of plates juggling or balls spinning and vice-versa. -
Are focused on friends and relationships.
I make a concerted effort to build and nurture friendships and relationships. -
Have a fear of not being liked.
Doesn't everyone have a fear of being disliked? Isn't that part of the glue that holds society together? If we weren't afraid of rejection -- afraid that our tribe would kick us out of the cave and feed us to the sabre-toothed tiger -- we'd all act like self-indulgent jerks whenever we felt like it. OF COURSE I don't want to being disliked. But that's certainly not a crippling fear. If you don't like me, you can... well, I don't want to say what you can do, because that would be rude, and then you might dislike me. But you know what I mean.
Abstract Random thinkers tend to prefer:
-
Cooperative work
As opposed to what, uncooperative work? Sure! -
Assignments with room for interpretation
If this means I'd rather write an essay than solve a geometry problem, then yes, I'd say this is true. -
Balance of social activities and work
Yes. Because when you die, you're not going to wish you spent more time working. -
Noncompetitive atmosphere
I am actually quite competitive and thrive in competitive environments. But I don't like it when competition becomes toxic. -
Personalized learning
Does this mean I'm a needy student? -
Are given personal attention and emotional support
Who wants to feel ignored and devoid of support?
Abstract Random thinkers are stretched by:
-
Working alone
Is this a stretch? I LOVE working alone. Or in groups. Both scenarios have their pros and cons. -
Attending to details and giving exact answers
I wouldn't say that, exactly. There's a lot of grey area here. -
Working within time limits
Eh, sometimes. -
Concentrating on one task at a time
There's a lot going on in my head, and it all wants to come out all at once. -
Being corrected
I take correction pretty well, actually. -
Expecting less emotional response from others
I'm not sure how to interpret this one, either. I expect an emotional response from people when the situation merits an emotional response. Isn't that normal?
As a student at Dev Bootcamp, I am most concerned about my ability to focus on my studies and maintain enough of a work-life balance to stay happy and healthy.
I have always, and will always, juggle lots of projects and priorities at once. The way I manage this without losing my mind or failing miserably is by siloing my efforts. I set aside blocks of time to devote to particular activities. I don't let myself switch tasks or alter my schedule without VERY reasonable cause to do so. I think this style of time management will help me complete my studies successfully and give time to recharge with my friends.
As I mentioned earlier, new research into neuroplasticity suggests that our minds are capable of growing and improving. As I continue my Dev Bootcamp studies, I find this idea increasingly comforting. What we're doing is really hard, and I want to whip my brain into shape and make it wrap around all the new things I'm tossing at it.
photo credit: Face of Mae West via photopin (license), [ D ] Salvador Dali - After Las Meninas (1960) via photopin (license)


Below is the game.
Audio for the levels in a random order
Below is the link the video
Image
Some text..


tookaride
home
logout
login
choose your username
your new tookaride home:
https://tookaride.com/
your email address
choose a password
done!


EasyRash is an innovative platform that allows you to review annotate and evaluatearticles written in RASH
{{ev.conference}}
{{sub.title.split("--")[1]}}


If you see something like this, you need to do a bit of extra configuring.
In Sql Management Studio 2014 for localhost, you want to make sure the server name is something like: localhost\sqlexpress
!!! If you use (LocalDB)\mssqllocaldb, you’re gonna have a bad time. !!!
In Administration/Services, make sure you have a SQL Server(SQLEXPRESS) and SQL Server Browser services running. If not, you need to reinstall SQLEXPR_x64_ENU.exe with LocalDB. Just install all the options.
In Sql Server Configuration Manager, make sure your stuff looks something like this:


jQuery Link Classifier adds classes to links based on their URLs. The most possible usage is for identifying external links, like links listed in External links section on Wikipedia.
This plugin does nothing with representation or visualization of link, it only adds classes, and that's all what it does. You will need to handle CSS and/or JavaScript on your own.
The protocol (scheme) is extracted from the URL, for examples, FTP of Linux Kernel, IRC of #gentoo, and an email link<EMAIL_ADDRESS>It can also add class for file extensions, such as an image file of Tux. A set of regular expressions are used for identifying some popular websites, such as Twitter, Facebook, or Google+. Or a bookmark to next section.
HTML of links in this section after process by this plugin, the original HTML source of these links do not have any classes specified:
See project website for detail information.


{{text.text_15.0}}{{param1}}{{param2}}
{
"meta": {},
"model": {},
"text": [
" outermost text at the beginning with compound {1} and {2} variables ",
"{{param1}}",
"{{param2}}"
],
"h1_3": [
"outermost header 1 with {1} and {2} variables",
"{{param1}}",
"{{param2}}"
],
"text_4": [
" outermost text in the middle with {1} and {2} variables ",
"{{param1}}",
"{{param2}}"
],
"span_5": [
"simple text without id with {1} and {2} variables",
"{{param1}}",
"{{param2}}"
],
"span_6": [
"simple text without id 2 with {1} and {2} variables",
"{{param1}}",
"{{param2}}"
],
"label-1": [
"simple text with id and {1} and {2} variables",
"{{param1}}",
"{{param2}}"
],
"label-2": [
"simple text with id and {1} and {2} variables 2",
"{{param1}}",
"{{param2}}"
],
"div_9:span": [
"simple text within div with {1} and {2} variables",
"{{param1}}",
"{{param2}}"
],
"div_9:span_1": [
"simple text within div with {1} and {2} variables 2",
"{{param1}}",
"{{param2}}"
],
"div_9:div_2:div": [
"great grandchild text within div with {1} and {2} variables",
"{{param1}}",
"{{param2}}"
],
"div_10:text": [
" simple text as the first element in div with {1} and {2} variables ",
"{{param1}}",
"{{param2}}"
],
"div_10:span_1": [
"simple text within div with {1} and {2} variables",
"{{param1}}",
"{{param2}}"
],
"div_10:text_2": [
" simple text in the middle of div with {1} and {2} variables ",
"{{param1}}",
"{{param2}}"
],
"div_10:span_3": [
"simple text within div with {1} and {2} variables 2",
"{{param1}}",
"{{param2}}"
],
"div_10:div_4:div": [
"great grandchild text within div with {1} and {2} variables",
"{{param1}}",
"{{param2}}"
],
"div_10:text_5": [
" simple text at the last element in div with {1} and {2} variables ",
"{{param1}}",
"{{param2}}"
],
"toplevel-div:span": [
"simple text within div with {1} and {2} variables",
"{{param1}}",
"{{param2}}"
],
"toplevel-div:span_1": [
"simple text within div 2 with {1} and {2} variables",
"{{param1}}",
"{{param2}}"
],
"third-level-div": [
"great grandchild text within div with {1} and {2} variables",
"{{param1}}",
"{{param2}}"
],
"second-level-div:div_1": [
"great grandchild text within div without id with {1} and {2} variables",
"{{param1}}",
"{{param2}}"
],
"div_12:ul:li": [
"line item without id 1 with {1} and {2} variables",
"{{param1}}",
"{{param2}}"
],
"div_12:ul:li_1": [
"line item without id 2 with {1} and {2} variables",
"{{param1}}",
"{{param2}}"
],
"div_12:ul:li_2": [
"line item without id 3 with {1} and {2} variables",
"{{param1}}",
"{{param2}}"
],
"line-items:li": [
"line item with id 1 with {1} and {2} variables",
"{{param1}}",
"{{param2}}"
],
"line-items:li_1": [
"line item with id 2 with {1} and {2} variables",
"{{param1}}",
"{{param2}}"
],
"line-items:li_2": [
"line item with id 3 with {1} and {2} variables",
"{{param1}}",
"{{param2}}"
],
"p_13": [
"A paragraph with {1} is converted to {2}.",
"{{param1}}",
"{{param2}}"
],
"paragraph": [
"A paragraph with {1}, {2}, and {3} is converted to {4}.",
"id",
"{{param1}}",
"{{param2}}",
"<i18n-format>"
],
"text_15": [
" outermost text at the end with {1} and {2} variables ",
"{{param1}}",
"{{param2}}"
]
}


Gecode::Iter::Ranges::NaryInter Class Reference
[Range iterators]
Range iterator for intersection of iterators. More...
#include <ranges-inter.hpp>
Protected Attributes | |
| RangeList * | f |
| Freelist. | |
Constructors and initialization | |
|
| |
| NaryInter (void) | |
| Default constructor. | |
| template<class I > | |
| NaryInter (Region &r, I &i) | |
| Initialize with single iterator i. | |
| template<class I , class J > | |
| NaryInter (Region &r, I &i, J &j) | |
| Initialize with iterators i and j. | |
| template<class I > | |
| NaryInter (Region &r, I *i, int n) | |
| Initialize with n iterators in i. | |
| template<class I > | |
| void | init (Region &r, I &i) |
| Initialize with single iterator i. | |
| template<class I , class J > | |
| void | init (Region &r, I &i, J &j) |
| Initialize with iterators i and j. | |
| template<class I > | |
| void | init (Region &r, I *i, int n) |
| Initialize with n iterators in i. | |
| template<class I > | |
| void | operator&= (I &i) |
| Add iterator i. | |
| NaryInter & | operator= (const NaryInter &m) |
| Assignment operator (both iterators must be allocated from the same region). |
Detailed Description
Range iterator for intersection of iterators.
Definition at line 74 of file ranges-inter.hpp.
Constructor & Destructor Documentation
| Gecode::Iter::Ranges::NaryInter::NaryInter | ( | void | ) | [inline] |
Default constructor.
Definition at line 160 of file ranges-inter.hpp.
| Gecode::Iter::Ranges::NaryInter::NaryInter | ( | Region & | r, | |
| I & | i | |||
| ) | [inline] |
Initialize with single iterator i.
Definition at line 240 of file ranges-inter.hpp.
| Gecode::Iter::Ranges::NaryInter::NaryInter | ( | Region & | r, | |
| I & | i, | |||
| J & | j | |||
| ) | [inline] |
Initialize with iterators i and j.
Definition at line 245 of file ranges-inter.hpp.
| Gecode::Iter::Ranges::NaryInter::NaryInter | ( | Region & | r, | |
| I * | i, | |||
| int | n | |||
| ) | [inline] |
Initialize with n iterators in i.
Definition at line 250 of file ranges-inter.hpp.
Member Function Documentation
| void Gecode::Iter::Ranges::NaryInter::init | ( | Region & | r, | |
| I & | i | |||
| ) | [inline] |
Initialize with single iterator i.
Definition at line 164 of file ranges-inter.hpp.
| void Gecode::Iter::Ranges::NaryInter::init | ( | Region & | r, | |
| I & | i, | |||
| J & | j | |||
| ) | [inline] |
Initialize with iterators i and j.
Definition at line 172 of file ranges-inter.hpp.
| void Gecode::Iter::Ranges::NaryInter::init | ( | Region & | r, | |
| I * | i, | |||
| int | n | |||
| ) | [inline] |
Initialize with n iterators in i.
Definition at line 197 of file ranges-inter.hpp.
| void Gecode::Iter::Ranges::NaryInter::operator&= | ( | I & | i | ) | [inline] |
Add iterator i.
Definition at line 256 of file ranges-inter.hpp.
Assignment operator (both iterators must be allocated from the same region).
Definition at line 297 of file ranges-inter.hpp.
Member Data Documentation
RangeList* Gecode::Iter::Ranges::NaryInter::f [protected] |
Freelist.
Definition at line 77 of file ranges-inter.hpp.
The documentation for this class was generated from the following file:
- gecode/iter/ranges-inter.hpp


Portfolio
User experience design and research
A selection of UX deliverables from work on the Rwanda Education Commons and the Global Learning Portal.
- Wireframes from sketches to interactive prototypes
- Diagrams other than wireframes
- Research: usability testing, card sorting, and more
Download PDF version of full UX portfolio.
Front-end development
Rwanda Education Commons
A portal that enables Rwandan teachers to collaborate and access resources. A second iteration was launched in early 2011, and is based on usability research completed in Rwanda in June 2010 and in New York in October 2010. Check out the Rwanda Education Commons.
NGOConnect.net
A document repository dedicated to connecting and strengthening non-governmental organizations, networks and support organizations worldwide. Built on Liferay Portal 5.0.
GLP main site
Redesigned August 2009. New features include dynamic account information in top bar, and integrated Google Custom Search. Built on Liferay Portal 5.2.2.
Independent projects
Pink Button Bags
Millie Erb hand knits and felts handbags out of wool, fancy yarns, and more.
Ron's Green Links
Ron leads the Gateway Electric Vehicle club of St. Louis, MO. He occasionally appears in the local news, promoting EVs.
Grinnell Plans theme "Ever So Simple"
A CSS theme for Grinnell Plans -- a blogging site run and developed by Grinnell College students and alumni. Access to Plans is limited to students, alumni, and professors. Check out a sample of the Ever So Simple theme.
Unmaintained websites are indicated by a broken link icon. Broken Link designed by Stephen JB Thomas from The Noun Project.


ShowMemoryViewer()
Syntax
ShowMemoryViewer([*Buffer, Length])Description
Open the memory viewer window or bring it to the front if it is already open.
Parameters
*Buffer, Length (optional) The memory area to be displayed in the memory viewer. If these parameters are not specified, then the memory viewer will just be opened without displaying a specific memory area.
Return value
None.
Example
*Memory = AllocateMemory(1000) If *Memory RandomData(*Memory, 1000) ; Fill memory with some data ShowMemoryViewer(*Memory, 1000) ; Open memory viewer CallDebugger ; Halt the program, so it does not end right away EndIf
See Also
Included debugging tools
Supported OS
All


Welcome to Simple Fancy Box Demo
A simple popup image viewer with animation
Enjoy the beauty of neon
✖


Overtime Assistant
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EMAIL
date_range
access_time
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access_time
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file_download
Download Table
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Footer for Email
email
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Developed By: Abhishek L. Deore
<EMAIL_ADDRESS>Facebook
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developer_mode


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Garden
Arthur
Cronquist,
MM
THE
NEW
YORK
BOTANICAL
GARDEN
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CUBA
LIMESTONE
HILLS.
SOUTH
OF
HOLGUIN,
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YORK
BOTANICAL
GARDEN
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A.
SHAFER.
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Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


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We are experiencing an internal server error, an error report has been generated and will be fixed shortly.
Error code: 500


A polygon data set with multiple holes. The data is in 'xy.coords' form, with two columns for 'x_' and 'y_' values, with 'NA' rows separating separate polygon paths. (All but the first path are holes).
This shape represents the mainland island of Tasmania with internal holes for inland waters. The coordinates are in Lambert Conformal Conic centred on 136E and 32S, with standard parallels at 17S and 47S. This shape is taken from a broader region where that local projection was suitable. The
plot(taslakes, pch = ".")lines(na.omit(taslakes))polypath(taslakes, col = "grey")tasnas <- which(is.na(taslakes$x_)) ## this is the "indicate where holes start" convention hole_index <- tasnas - (seq_along(tasnas)-1) taslakes_xy <- na.omit(taslakes) tri_index <- earcut(taslakes_xy, hole_index) rearcut:::plot_tri(taslakes_xy$x_, taslakes_xy$y_, tri_index)rearcut:::plot_tri(taslakes_xy$x_, taslakes_xy$y_, tri_index, col = "grey")# NOT RUN { library(ggplot2) d <- taslakes_xy[tri_index, ] %>% mutate(g = (row_number() -1) %/% 3 ) %>% group_by(g) ggplot(d, aes(x_, y_, group = g)) + geom_path() # }


Proper
: reference to a specific entity
| Values: | Yes |
This is a binary (yes/no) lexical feature that is only annotated when its value is Yes
.
Yes
: common phrase used for a specific referent
A positive value of Proper
is assigned to those otherwise common words (mostly nouns and adjectives, but also whole phrases) whose extension in a given context is restricted to a single, specific individual, object, place, event or entity in general, without need to be further determined, all the while keeping their common meaning.
In other words, Proper=Yes
is used for common nominal phrases which can be considered proper nouns only with respect to a limited context and given some prior knowledge. Examples are nicknames, epithets by antonomasia, titles of books, and so on.
This feature is of purely “lexical” nature, as it (usually) is not reflected morphologically nor syntactically.
A noun receiving the part-of-speech tag for proper nouns (PROPN) is implicitly considered to have a positive value of Proper
.
Examples
- […] ut Phylosopho placet in hiis que De simpliciter ente […] ‘as the Philosopher claims in his [books] about the simple Being’ (Dante, De Monarchia)
- the philosopher par excellence and the one intended here is Aristotle;
- in hiis que De simpliciter ente literally means ‘in those [i.e. books] which (are) about the simple Being’, referring to a part of Aristotle’s Metaphysics. All elements in the sequence de simpliciter ente receive
Proper=Yes
for being used as a sort of title (as the posteriorly edited upper-case d suggests), but they otherwise form a regular phrase, syntactically undistinguishable from others.
- […] Ovidius dicit in quinto Metamorfoseos […] ‘Ovidius says in the fifth [book] of his Metamorphoses’ (Dante, De vulgari eloquentia)
- metamorphosis ‘metamorphosis’ is a common noun regularly declined in the Greek form of the genitive singular, and the phrase literally means ‘the fifth one about/of the metamorphosis’.
Proper in other languages: [la]


Last updated 2 August 2013
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| --- AS project --- |
| database | n |
|---|---|
| MPCD | 3299 |
| Marketscan | 13266 |
| Medicare | 26282 |
| unionExpoCohort7.sas |
| --- AS project --- |
| database | exposureClass | exposure | n |
|---|---|---|---|
| MPCD | 0 | No exposure | 1307 |
| MPCD | 1 | NSAID | 578 |
| MPCD | 2 | DMARD | 420 |
| MPCD | 3 | TNF | 994 |
| Marketscan | 0 | No exposure | 3947 |
| Marketscan | 1 | NSAID | 2890 |
| Marketscan | 2 | DMARD | 1661 |
| Marketscan | 3 | TNF | 4768 |
| Medicare | 0 | No exposure | 9257 |
| Medicare | 1 | NSAID | 8856 |
| Medicare | 2 | DMARD | 3824 |
| Medicare | 3 | TNF | 4345 |
| unionExpoCohort7.sas |
| --- AS project --- |
| Analysis Variable : daysExposed daysExposed | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| database | exposureClass | exposure | N Obs | N | Sum | Mean | Std Dev | Minimum | Lower Quartile | Median | Upper Quartile | Maximum |
| MPCD | 0 | No exposure | 1307 | 1307 | 585858.0 | 448.2 | 382.2 | 1.0 | 123.0 | 355.0 | 699.0 | 1278.0 |
| 1 | NSAID | 578 | 578 | 153803.0 | 266.1 | 281.9 | 2.0 | 55.0 | 150.5 | 387.0 | 1223.0 | |
| 2 | DMARD | 420 | 420 | 119266.0 | 284.0 | 304.7 | 1.0 | 42.0 | 168.0 | 418.0 | 1229.0 | |
| 3 | TNF | 994 | 994 | 401124.0 | 403.5 | 317.7 | 1.0 | 146.0 | 309.5 | 579.0 | 1276.0 | |
| Marketscan | 0 | No exposure | 3947 | 3947 | 1417626.0 | 359.2 | 382.8 | 1.0 | 72.0 | 208.0 | 534.0 | 1643.0 |
| 1 | NSAID | 2890 | 2890 | 813865.0 | 281.6 | 315.2 | 1.0 | 55.0 | 156.0 | 406.0 | 1599.0 | |
| 2 | DMARD | 1661 | 1661 | 482138.0 | 290.3 | 338.4 | 1.0 | 49.0 | 146.0 | 415.0 | 1611.0 | |
| 3 | TNF | 4768 | 4768 | 1991891.0 | 417.8 | 371.8 | 1.0 | 130.0 | 292.5 | 610.0 | 1639.0 | |
| Medicare | 0 | No exposure | 9257 | 9257 | 8607568.0 | 929.8 | 862.3 | 1.0 | 215.0 | 762.0 | 1278.0 | 3835.0 |
| 1 | NSAID | 8856 | 8856 | 5946062.0 | 671.4 | 725.3 | 1.0 | 93.0 | 397.5 | 1021.0 | 3797.0 | |
| 2 | DMARD | 3824 | 3824 | 3095053.0 | 809.4 | 883.5 | 1.0 | 71.0 | 496.5 | 1239.5 | 3821.0 | |
| 3 | TNF | 4345 | 4345 | 5223929.0 | 1202.3 | 929.7 | 1.0 | 434.0 | 985.0 | 1758.0 | 3819.0 |
| unionExpoCohort7.sas |
| --- AS project --- |
| Analysis Variable : cohortDay | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| database | exposureClass | exposure | N Obs | N | Sum | Mean | Std Dev | Minimum | Lower Quartile | Median | Upper Quartile | Maximum |
| MPCD | 0 | No exposure | 1307 | 1307 | 585858.0 | 448.2 | 382.2 | 1.0 | 123.0 | 355.0 | 699.0 | 1278.0 |
| 1 | NSAID | 578 | 578 | 76702.0 | 132.7 | 145.9 | 2.0 | 55.0 | 105.0 | 124.0 | 1102.0 | |
| 2 | DMARD | 420 | 420 | 70014.0 | 166.7 | 182.0 | 1.0 | 42.0 | 120.0 | 197.5 | 1093.0 | |
| 3 | TNF | 994 | 994 | 306489.0 | 308.3 | 261.9 | 1.0 | 118.0 | 219.0 | 423.0 | 1276.0 | |
| Marketscan | 0 | No exposure | 3947 | 3947 | 1417626.0 | 359.2 | 382.8 | 1.0 | 72.0 | 208.0 | 534.0 | 1643.0 |
| 1 | NSAID | 2890 | 2890 | 374774.0 | 129.7 | 146.9 | 1.0 | 55.0 | 105.0 | 125.0 | 1582.0 | |
| 2 | DMARD | 1661 | 1661 | 274968.0 | 165.5 | 188.6 | 1.0 | 49.0 | 120.0 | 189.0 | 1562.0 | |
| 3 | TNF | 4768 | 4768 | 1483077.0 | 311.0 | 292.8 | 1.0 | 118.0 | 209.0 | 410.0 | 1637.0 | |
| Medicare | 0 | No exposure | 9257 | 9257 | 8607568.0 | 929.8 | 862.3 | 1.0 | 215.0 | 762.0 | 1278.0 | 3835.0 |
| 1 | NSAID | 8856 | 8856 | 1436971.0 | 162.3 | 207.5 | 1.0 | 92.0 | 120.0 | 160.0 | 3221.0 | |
| 2 | DMARD | 3824 | 3824 | 927395.0 | 242.5 | 333.4 | 1.0 | 71.0 | 124.5 | 266.0 | 2814.0 | |
| 3 | TNF | 4345 | 4345 | 1932294.0 | 444.7 | 480.6 | 1.0 | 144.0 | 261.0 | 534.0 | 3224.0 |
| unionExpoCohort7.sas |
| --- AS project --- |
| Data Set Name | DT.EXPOSURETIMELINE | Observations | 42847 |
|---|---|---|---|
| Member Type | DATA | Variables | 15 |
| Engine | V9 | Indexes | 0 |
| Created | 12/10/2018 21:03:20 | Observation Length | 160 |
| Last Modified | 12/10/2018 21:03:20 | Deleted Observations | 0 |
| Protection | Compressed | NO | |
| Data Set Type | Sorted | YES | |
| Label | |||
| Data Representation | WINDOWS_64 | ||
| Encoding | wlatin1 Western (Windows) |
| Engine/Host Dependent Information | |
|---|---|
| Data Set Page Size | 65536 |
| Number of Data Set Pages | 105 |
| First Data Page | 1 |
| Max Obs per Page | 409 |
| Obs in First Data Page | 392 |
| Number of Data Set Repairs | 0 |
| ExtendObsCounter | YES |
| Filename | V:\studies\AS\data\exposuretimeline.sas7bdat |
| Release Created | 9.0401M5 |
| Host Created | X64_DS08R2 |
| Owner Name | AD\benjaminchan |
| File Size | 7MB |
| File Size (bytes) | 6946816 |
| Variables in Creation Order | |||||
|---|---|---|---|---|---|
| # | Variable | Type | Len | Format | Label |
| 1 | database | Char | 10 | ||
| 2 | PATID | Char | 20 | ||
| 3 | exposureClass | Num | 8 | ||
| 4 | exposure | Char | 11 | ||
| 5 | exposureDrug | Char | 30 | ||
| 6 | exposureStart | Num | 8 | MMDDYY10. | exposureStart |
| 7 | enr_end_date | Num | 8 | MMDDYY10. | |
| 8 | death_date | Num | 8 | MMDDYY10. | |
| 9 | censor_rx | Num | 8 | MMDDYY10. | |
| 10 | stop_date | Num | 8 | MMDDYY10. | |
| 11 | obs_end | Num | 8 | MMDDYY10. | |
| 12 | exposureEnd | Num | 8 | MMDDYY10. | exposureEnd |
| 13 | daysExposed | Num | 8 | daysExposed | |
| 14 | cohortDay | Num | 8 | ||
| 15 | exposureID | Num | 8 |
| Sort Information | |
|---|---|
| Sortedby | database PATID |
| Validated | YES |
| Character Set | ANSI |
| unionExpoCohort7.sas |


Welcome {{ unquote_name }}!
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Voicepedia
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Form Mask
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Table of Contents
- How to Make a Simple Tensorflow Speech Recognizer - YouTube
- Almost Unsupervised Text to Speech and Automatic Speech Recognition - YouTube
- 13. Speech Recognition with Convolutional Neural Networks in Keras/TensorFlow (2019) - YouTube
- TensorFlow and Neural Networks for Speech Recognition - YouTube
- DSP Background - Deep Learning for Audio Classification p.1 - YouTube
- The PyTorch-Kaldi Toolkit - YouTube
- Almost Unsupervised Text to Speech and Automatic Speech Recognition
Introduction to Speech
-
- Probabilistic Speech Recognition:
- Statistical ASR has been introduced/framed by Frederick Jelinek in his famous paper Continuous Speech Recognition by Statistical Methods who framed the problem as an information theory problem.
- We can view the problem of ASR as a sequence labeling problem, and, so, use statistical models (such as HMMs) to model the conditional probabilities between the states/words by viewing speech signal as a piecewise stationary signal or a short-time stationary signal.
-
- Representation: we represent the speech signal as an observation sequence \(o = \{o_t\}\)
- Goal: find the most likely word sequence \(\hat{w}\)
- Set-Up:
- The system has a set of discrete states
- The transitions from state to state are markovian and are according to the transition probabilities
Markovian: Memoryless
- The Acoustic Observations when making a transition are conditioned on the state alone \(P(o_t \vert c_t)\)
- The goal is to recover the state sequence and, consequently, the word sequence
-
- Speech Problems and Considerations:
-
- ASR:
- Spontaneous vs Read speech
- Large vs Small Vocabulary
- Continuous vs Isolated Speech
Continuous Speech is harder due to Co-Articulation
- Noisy vs Clear input
- Low vs High Resources
- Near-field vs Far-field input
- Accent-independence
- Speaker-Adaptive vs Stand-Alone (speaker-independent)
- The cocktail party problem
- TTS:
- Low Resource
- Realistic prosody
- Speaker Identification
- Speech Enhancement
- Speech Separation
- ASR:
-
- Acoustic Representation:
- What is speech?
- Waves of changing air pressure - Longitudinal Waves (consisting of compressions and rarefactions)
- Realized through excitation from the vocal cords
- Modulated by the vocal tract and the articulators (tongue, teeth, lips)
- Vowels are produced with an open vocal tract (stationary)
parametrized by position of tongue
- Consonants are constrictions of vocal tract
- They get converted to Voltage with a microphone
- They are sampled (and quantized) with an Analogue to Digital Converter
- Speech as waves:
- Human hearing range is: \(~50 HZ-20 kHZ\)
- Human speech range is: \(~85 HZ-8 kHZ\)
- Telephone speech sampling is \(8 kHz\) and a bandwidth range of \(300 Hz-4 kHz\)
- 1 bit per sample is intelligible
- Contemporary Speech Processing mostly around 16 khz 16 bits/sample
A lot of data to handle
- Speech as digits (vectors):
- We seek a low-dimensional representation to ease the computation
- The low-dimensional representation needs to be invariant to:
- Speaker
- Background noise
- Rate of Speaking
- etc.
- We apply Fourier Analysis to see the energy in different frequency bands, which allows analysis and processing
- Specifically, we apply windowed short-term Fast Fourier Transform (FFT)
e.g. FFT on overlapping \(25ms\) windows (400 samples) taken every \(10ms\)
Each frame is around 25ms of speech
- Specifically, we apply windowed short-term Fast Fourier Transform (FFT)
- FFT is still too high-dimensional
- We Downsample by local weighted averages on mel scale non-linear spacing, an d take a log:
\(m = 1127 \ln(1+\dfrac{f}{700})\) - This results in log-mel features, \(40+\) dimensional features per frame
Default for NN speech modelling
- We Downsample by local weighted averages on mel scale non-linear spacing, an d take a log:
- Speech dimensionality for different models:
- Gaussian Mixture Models (GMMs): 13 MFCCs
- MFCCs - Mel Frequency Cepstral Coefficients: are the discrete cosine transformation (DCT) of the mel filterbank energies | Whitened and low-dimensional.
They are similar to Principle Components of log spectra.
GMMs used local differences (deltas) and second-order differences (delta-deltas) to capture the dynamics of the speech \((13 \times 3 \text{ dim})\)
- MFCCs - Mel Frequency Cepstral Coefficients: are the discrete cosine transformation (DCT) of the mel filterbank energies | Whitened and low-dimensional.
- FC-DNN: 26 stacked frames of PLP
- PLP - Perceptual Linear Prediction: a common alternative representation using Linear Discriminant Analysis (LDA)
Class aware PCA
- PLP - Perceptual Linear Prediction: a common alternative representation using Linear Discriminant Analysis (LDA)
- LSTM/RNN/CNN: 8 stacked frames of PLP
- Gaussian Mixture Models (GMMs): 13 MFCCs
- Speech as Communication:
- Speech Consists of sentences (in ASR we usually talk about “utterances”)
- Sentences are composed of words
- Minimal unit is a “phoneme” Minimal unit that distinguishes one word from another.
- Set of 40-60 distinct sounds.
- Vary per language
- Universal representations:
- IPA : international phonetic alphabet
- X-SAMPA : (ASCII)
- Homophones : distinct words with the same pronunciation. (e.g. “there” vs “their”)
- Prosody : How something is said can convey meaning. (e.g. “Yeah!” vs “Yeah?”)
-
- Microphones and Speakers:
-
- Microphones:
- Their is a Diaphragm in the Mic
- The Diaphragm vibrates with air pressure
- The diaphragm is connected to a magnet in a coil
- The magnet vibrates with the diaphragm
- The coil has an electric current induced by the magnet based on the vibrations of the magnet
- Microphones:
-
- Speakers:
- The electric current flows from the sound-player through a wire into a coil
- The coil has a metal inside it
- The metal becomes magnetic and vibrates inside the coil based on the intensity of the current
- The magnetized metal is attached to a cone that produces the sound
- Speakers:
- (Approximate) History of ASR:
- 1960s Dynamic Time Warping
- 1970s Hidden Markov Models
- Multi-layer perceptron 1986
- Speech recognition with neural networks 1987-1995
- Superseded by GMMs 1995-2009
- Neural network features 2002—
- Deep networks 2006— (Hinton, 2002)
- Deep networks for speech recognition:
- Good results on TIMIT (Mohamed et al., 2009)
- Results on large vocabulary systems 2010 (Dahl et al., 2011) * Google launches DNN ASR product 2011
- Dominant paradigm for ASR 2012 (Hinton et al., 2012)
- Recurrent networks for speech recognition 1990, 2012 - New models (CTC attention, LAS, neural transducer)
- Datasets:
- TIMIT:
- Hand-marked phone boundaries are given
- 630 speakers \(\times\) 10 utterances
- Wall Street Journal (WSJ) 1986 Read speech. WSJO 1991, 30k vocab
- Broadcast News (BN) 1996 104 hours
- Switchboard (SWB) 1992. 2000 hours spontaneous telephone speech - 500 speakers
- Google voice search - anonymized live traffic 3M utterances 2000 hours hand-transcribed 4M vocabulary. Constantly refreshed, synthetic reverberation + additive noise
- DeepSpeech 5000h read (Lombard) speech + SWB with additive noise.
- YouTube 125,000 hours aligned captions (Soltau et al., 2016)
- TIMIT:
- Development:
The Methods and Models of Speech Recognition
-
- Probabilistic Speech Recognition:
- Statistical ASR has been introduced/framed by Frederick Jelinek in his famous paper Continuous Speech Recognition by Statistical Methods who framed the problem as an information theory problem.
- We can view the problem of ASR as a sequence labeling problem, and, so, use statistical models (such as HMMs) to model the conditional probabilities between the states/words by viewing speech signal as a piecewise stationary signal or a short-time stationary signal.
-
- Representation: we represent the speech signal as an observation sequence \(o = \{o_t\}\)
- Goal: find the most likely word sequence \(\hat{w}\)
- Set-Up:
- The system has a set of discrete states
- The transitions from state to state are markovian and are according to the transition probabilities
Markovian: Memoryless
- The Acoustic Observations when making a transition are conditioned on the state alone \(P(o_t \vert c_t)\)
- The goal is to recover the state sequence and, consequently, the word sequence
-
- Fundamental Equation of Speech Recognition:
- We set the decoders output as the most likely sequence \(\hat{w}\) from all the possible sequences, \(\mathcal{S}\), for an observation sequence \(o\):
- \[\begin{align} \hat{w} & = \mathrm{arg } \max_{w \in \mathcal{S}} P(w \vert o) & (1) \\ & = \mathrm{arg } \max_{w \in \mathcal{S}} P(o \vert w) P(w) & (2) \end{align}\]
- The Conditional Probability of a sequence of observations given a sequence of (predicted) word is a product (or sum of logs) of an Acoustic Model (\(p(o \vert w)\)) and a Language Model (\(p(w)\)) scores.
- The Acoustic Model can be written as the following product:
- \[P(o \vert w) = \sum_{d,c,p} P(o \vert c) P(c \vert p) P(p \vert w)\]
- where \(p\) is the phone sequence and \(c\) is the state sequence.
-
- Speech Recognition as Transduction:
- The problem of speech recognition can be seen as a transduction problem - mapping different forms of energy to other forms (representations).
Basically, we are going from Signal to Language.
-
- Gaussian Mixture Models:
-
- Dominant paradigm for ASR from 1990 to 2010
- Model the probability distribution of the acoustic features for each state.
\(P(o_t \vert c_i) = \sum_j w_{ij} N(o_t; \mu_{ij}, \sigma_{ij})\) - Often use diagonal covariance Gaussians to keep number of parameters under control.
- Train by the E-M (Expectation Maximization) algorithm (Dempster et al., 1977) alternating:
- M: forced alignment computing the maximum-likelihood state sequence for each utterance
- E: parameter \((\mu , \sigma)\) estimation
- Complex training procedures to incrementally fit increasing numbers of components per mixture:
- More components, better fit - 79 parameters component.
- Given an alignment mapping audio frames to states, this is parallelizable by state.
- Hard to share parameters/data across states.
- Forced Alignment:
- Forced alignment uses a model to compute the maximum likelihood alignment between speech features and phonetic states.
- For each training utterance, construct the set of phonetic states for the ground truth transcription.
- Use Viterbi algorithm to find ML monotonic state sequence
- Under constraints such as at least one frame per state.
- Results in a phonetic label for each frame.
- Can give hard or soft segmentation.
- Decoding:
- Speech recognition Unfolds in much the same way.
- Now we have a graph instead of a straight-through path.
- Optional silences between words Alternative pronunciation paths.
- Typically use max probability, and work in the log domain.
- Hypothesis space is huge, so we only keep a “beam” of the best paths, and can lose what would end up being the true best path.
-
- Neural Networks in ASR:
-
- Two Paradigms of Neural Networks for Speech:
- Use neural networks to compute nonlinear feature representations:
- “Bottleneck” or “tandem” features (Hermansky et al., 2000)
- Low-dimensional representation is modelled conventionally with GMMs.
- Allows all the GMM machinery and tricks to be exploited.
- Bottleneck features outperform Posterior features (Grezl et al. 2017)
- Generally, DNN features + GMMs reach the same performance as hybrid DNN-HMM systems but are much more complex
- Use neural networks to estimate phonetic unit probabilities
- Use neural networks to compute nonlinear feature representations:
- Two Paradigms of Neural Networks for Speech:
-
- Hybrid Networks:
-
- Train the network as a classifier with a softmax across the phonetic units
- Train with cross-entropy
- Softmax:
- \[y(i) = \dfrac{e^{\psi(i, \theta)}}{\sum_{j=1}^N e^{\psi(j, \theta)}}\]
-
- We converge to/learn the posterior probability across phonetic states:
- \[P(c_i \vert o_t)\]
-
- We, then, model \(P(o \vert c)\) with a Neural-Net instead of a GMM:
We can ignore \(P(o_t)\) since it is the same for all decoding paths
- We, then, model \(P(o \vert c)\) with a Neural-Net instead of a GMM:
- \[\begin{align} P(o \vert c) & = \prod_t P(o_t \vert c_t) & (3) \\ P(o_t \vert c_t) & = \dfrac{P(c_t \vert o_t) P(o_t)}{P(c_t)} & (4) \\ & \propto \dfrac{P(c_t \vert o_t)}{P(c_t)} & (5) \\ \end{align}\]
-
- The log scaled posterior from the last term:
- \[\log P(o_t \vert c_t) = \log P(c_t \vert o_t) - \alpha \log P(c_t)\]
-
- Empirically, a prior smoothing on \(\alpha\) \((\alpha \approx 0.8)\) works better
-
- Input Features:
- NN can handle high-dimensional, correlated, features
- Use (26) stacked filterbank inputs (40-dim mel-spaced filterbanks)
- Input Features:
-
- NN Architectures for ASR:
- Fully-Connected DNN
- CNNs:
- Time delay neural networks:
- Waibel et al. (1989)
- Dilated convolutions (Peddinti et al., 2015)
Pooling in time results in a loss of information.
Pooling in frequency domain is more tolerable
- CNNs in time or frequency domain:
- Abdel-Hamid et al. (2014)
- Sainath et al. (2013)
- Wavenet (van den Oord et al., 2016)
- Time delay neural networks:
- RNNs :
- RNN (Robinson and Fallside, 1991)
- LSTM Graves et al. (2013)
- Deep LSTM-P Sak et al. (2014b)
- CLDNN (Sainath et al , 2015a)
-
GRU. DeepSpeech 1/2 (Amodei et al., 2015)
- Tips :
- Bidirectional (Schuster and Paliwal, 1997) helps, but introduces latency.
- Dependencies not long at speech frame rates (100Hz).
- Frame stacking and down-sampling help.
- NN Architectures for ASR:
- Sequence Discriminative Training:
- Conventional training uses Cross-Entropy loss — Tries to maximize probability of the true state sequence given the data.
- We care about Word Error Rate of the complete system.
- Design a loss that’s differentiable and closer to what we care about.
- Applied to neural networks (Kingsbury, 2009)
- Posterior scaling gets learnt by the network.
- Improves conventional training and CTC by \(\approx 15%\) relative.
- bMMI, sMBR(Povey et al., 2008)
-
- Asynchronous:
Transitioning into Deep Learning
-
- Classical Approach:
- Classically, Speech Recognition was developed as a big machine incorporating different models from different fields.
The models were statistical and they started from text sequences to audio features.
Typically, a generative language model is trained on the sentences for the intended language, then, to make the features, pronunciation models, acoustic models, and speech processing models had to be developed. Those required a lot of feature engineering and a lot of human intervention and expertise and were very fragile. - Recognition was done through Inference: Given audio features \(\mathbf{X}=x_1x_2...x_t\) infer the most likely tedxt sequence \(\mathbf{Y}^\ast=y_1y_2...y_k\) that caused the audio features.
- \[\displaystyle{\mathbf{Y}^\ast =\mathrm{arg\,min}_{\mathbf{Y}} p(\mathbf{X} \vert \mathbf{Y}) p(\mathbf{Y})}\]
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- The Neural Network Age:
- Researchers realized that each of the (independent) components/models that make up the ASR can be improved if it were replaced by a Neural Network Based Model.
-
- The Problem with the component-based System:
-
- Each component/model is trained independently, with a different objective
- Errors in one component may not behave well with errors in another component
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- Solution to the Component-Based System:
- We aim to train models that encompass all of these components together, i.e. End-to-End Model:
- Connectionist Temporal Classification (CTC)
- Sequence-to-Sequence Listen Attend and Spell (LAS)
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- End-to-End Speech Recognition:
- We treat End-to-End Speech Recognition as a modeling task.
- Given Audio \(\mathbf{X}=x_1x_2...x_t\) (audio/processed spectogram) and corresponding output text \(\mathbf{Y}=y_1y_2...y_k\) (transcript), we want to learn a Probabilistic Model \(p(\mathbf{Y} \vert \mathbf{X})\)
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- Deep Learning - What’s new?
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- Algorithms:
- Direct modeling of context-dependent (tied triphone states) through the DNN
- Unsupervised Pre-training
- Deeper Networks
- Better Architectures
- Data:
- Larger Data
- Computation:
- GPUs
- TPUs
- Training Criterion:
- Computation:
- Cross-Entropy -> MMI Sequence -level
- Features:
- Mel-Frequency Cepstral Coefficients (MFCC) -> FilterBanks
- Training and Regularization:
- Batch Norm
- Distributed SGD
- Dropout
- Acoustic Modelling:
- CNN
- CTC
- CLDNN
- Language Modelling:
- RNNs
- LSTM
- DATA:
- More diverse - Noisy, Accents, etc.
- Algorithms:
Connectionist Temporal Classification
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- Motivation:
-
- RNNs require a target output at each time step
- Thus, to train an RNN, we need to segment the training output (i.e. tell the network which label should be output at which time-step)
- This problem usually arises when the timing of the input is variable/inconsistent (e.g. people speaking at different rates/speeds)
-
- Connectionist Temporal Classification (CTC):
- CTC is a type of neural network output and associated scoring function, for training recurrent neural networks (RNNs) such as LSTM networks to tackle sequence problems where the timing is variable.
- Due to time variability, we don’t know the alignment of the input with the output.
Thus, CTC considers all possible alignments.
Then, it gets a closed formula for the probability of all these possible alignments and maximizes it.
- Structure:
- Input:
A sequence of observations - Output:
A sequence of labels
- Input:
- Algorithm:
- Extract the (LOG MEL) Spectrogram from the input
Use raw audio iff there are multiple microphones
- Feed the Spectogram into a (bi-directional) RNN
- At each frame, we apply a softmax over the entire vocabulary that we are interested in (plus a blank token), producing a prediction log probability (called the score) for a different token class at that time step.
- Repeated Tokens are duplicated
- Any original transcript is mapped to by all the possible paths in the duplicated space
- The Score (log probability) of any path is the sum of the scores of individual categories at the different time steps
- The probability of any transcript is the sum of probabilities of all paths that correspond to that transcript
- Dynamic Programming allopws is to compute the log probability \(p(\mathbf{Y} \vert \mathbf{X})\) and its gradient exactly.
- Extract the (LOG MEL) Spectrogram from the input
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- The Math:
- Given a length \(T\) input sequence \(x\), the output vectors \(y_t\) are normalized with the Softmax function, then interpreted as the probability of emitting the label (or blank) with index \(k\) at time \(t\):
- \[P(k, t \vert x) = \dfrac{e^{(y_t^k)}}{\sum_{k'} e^{(y_t^{k'})}}\]
- where \(y_t^k\) is element \(k\) of \(y_t\).
- A CTC alignment \(a\) is a length \(T\) sequence of blank and label indices.
The probability \(P(a \vert x)\) of \(a\) is the product of the emission probabilities at every time-step: - \[P(a \vert x) = \prod_{t=1}^T P(a_t, t \vert x)\]
- Denoting by \(\mathcal{B}\) an operator that removes first the repeated labels, then the blanks from alignments, and observing that the total probability of an output transcription \(y\) is equal to the sum of the probabilities of the alignments corresponding to it, we can write:
- \[P(y \vert x) = \sum_{a \in \mathcal{B}^{-1}(y)} P(a \vert x)\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:(*)\]
- Given a target transcription \(y^\ast\), the network can then be trained to minimise the CTC objective function:
- \[\text{CTC}(x) = - \log P(y^\ast \vert x)\]
-
- Intuition:
- The above ‘integrating out’ over possible alignments eq. \((*)\) is what allows the network to be trained with unsegmented data.
The intuition is that, because we don’t know where the labels within a particular transcription will occur, we sum over all the places where they could occur can be efficiently evaluated and differentiated using a dynamic programming algorithm.
-
- Analysis:
- The ASR model consists of an RNN plus a CTC layer.
Jointly, the model learns the pronunciation and acoustic model together.
However, a language model is not learned, because the RNN-CTC model makes strong conditional independence assumptions (similar to HMMs).
Thus, the RNN-CTC model is capable of mapping speech acoustics to English characters but it makes many spelling and grammatical mistakes.
Thus, the bottleneck in the model is the assumption that the network outputs at different times are conditionally independent, given the internal state of the network.
-
- Improvements:
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- Add a language model to CTC during training time for rescoring. This allows the model to correct spelling and grammar.
- Use word targets of a certain vocabulary instead of characters
-
- Applications:
-
- on-line Handwriting Recognition
- Recognizing phonemes in speech audio
- ASR
-
- Tips:
-
- Continuous realignment - no need for a bootstrap model
- Always use soft targets
- Don’t scale by the posterior
- Produces similar results to conventional training
- Simple to implement in the FST framework
- CTC could learn to delay output on its own in order to improve accuracy:
- In-practice, tends to align transcription closely
- This is especially problematic for English letters (spelling)
- Sol:
bake limited context into model structure; s.t. the model at time-step \(T\) can see only some future frames.- Caveat: may need to compute upper layers quickly after sufficient context arrives.
Can be easier if context is near top.
- Caveat: may need to compute upper layers quickly after sufficient context arrives.
LAS - Seq2Seq with Attention
-
- Motivation:
- The CTC model can only make predictions based on the data; once it has made a prediction for a given frame, it cannot re-adjust the prediction.
- Moreover, the strong independence assumptions that the CTC model makes doesn’t allow it to learn a language model.
-
- Listen, Attend and Spell (LAS):
- LAS is a neural network that learns to transcribe speech utterances to characters.
In particular, it learns all the components of a speech recognizer jointly. - The model is a seq2seq model; it learns a conditional probability of the next label/character given the input and previous predictions \(p(y_{i+1} \vert y_{1..i}, x)\).
- The approach that LAS takes is similar to that of NMT.
Where, in translation, the input would be the source sentence but in ASR, the input is the audio sequence. - Attention is needed because in speech recognition tasks, the length of the input sequence is very large; for a 10 seconds sample, there will be ~10000 frames to go through.
-
- Structure:
- The model has two components:
- A listener: a pyramidal RNN encoder that accepts filter bank spectra as inputs
- A Speller: an attention-based RNN decoder that emits characters as outputs
-
-
Input:
-
Output:
-
-
- Limitations:
-
- Not an online model - input must all be received before transcripts can be produced
- Attention is a computational bottleneck since every output token pays attention to every input time step
- Length of input has a big impact on accuracy
Online Seq2Seq Models
-
- Motivation:
-
- Overcome limitations of seq2seq:
- No need to wait for the entire input sequence to arrive
- Avoids the computational bottleneck of Attention over the entire sequence
- Produce outputs as inputs arrive:
- Solves this problem: When has enough information arrived that the model is confident enough to output symbols
- Overcome limitations of seq2seq:
-
- A Neural Transducer:
- Neural Transducer is a more general class of seq2seq learning models. It avoids the problems of offline seq2seq models by operating on local chunks of data instead of the whole input at once. It is able to make predictions conditioned on partially observed data and partially made predictions.
Real-World Applications
-
- Siri:
-
- Siri Architecture:
- Start with a Wave Form
- Pass the wave form through an ASR system
- Then use a Natural Language Model to re-adjust the labels
- Output Words
- Based on the output, do some action or save the output, etc.
- Siri Architecture:
-
- “Hey Siri” DNN:
- Much smaller DNN than for the full Vocab. ASR
- Does Binary Classification - Did the speaker say “hey Siri” or not?
- Consists of 5 Layers
- The layers have few parameters
- It has a Threshold at the end
- So fast
- Capable of running on the Apple Watch!
- “Hey Siri” DNN:
-
- Two-Pass Detection:
- Problem:
A big problem that arises in the always-on voice, is that it needs to run 24/7. - Solution:
We use a Two-Pass Detection system:- There are two processors implemented in the phone:
- Low-Compute Processor:
- Always ON
- Given a threshold value of confidence over binary probabilities the Processor makes the following decision: “Should I wake up the Main Processor”
- Low power consumption
- Main Processor:
- Only ON if woken up by the low-compute processor
- Runs a much larger DNN
- High power consumption
- Low-Compute Processor:
- There are two processors implemented in the phone:
- Problem:
- Two-Pass Detection:
-
- Computation for DL:
- Computation for DL:
Building ASR Systems
-
- Pre-Processing:
- A Spectrogram is a visual representation of the spectrum of frequencies of sound or other signal as they vary with time.
- Take a small window (~20 ms) of waveform
- Compute FFT and take magnitude (i.e. prower)
Describes Frequency content in local window
-
- Concatenate frames from adjacent windows to form the “spectrogram”
- Concatenate frames from adjacent windows to form the “spectrogram”
-
- Acoustic Model:
- An Acoustic Model is used in automatic speech recognition to represent the relationship between an audio signal and the phonemes or other linguistic units that make up speech.
- Goal: create a neural network (DNN/RNN) from which we can extract transcriptions, \(y\) - by training on labeled pairs \((x, y^\ast)\).
-
- Network (example) Architecture:
- RNN to predict graphemes (26 chars + space + blank):
- Spectrograms as inputs
- 1 Layer of Convolutional Filters
- 3 Layers of Gated Recurrent Units
- 1000 Neurons per Layer
- 1 Fully-Connected Layer to predict \(c\)
- Batch Normalization
- CTC Loss Function (Warp-CTC)
- SGD+Nesterov Momentum Optimization/Training
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- Incorporating a Language Model:
- Incorporating a Language Model helps the model learn:
- Spelling
- Grammar
- Expand Vocabulary
- Two Ways:
- Fuse the Acoustic Model with the language model \(p(y)\)
- Incorporate linguistic data:
- Predict Phonemes + Pronunciation Lexicon + LM
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- Decoding with Language Models:
-
- Given a word-based LM of form \(p(w_{t+1} \vert w_{1:t})\)
- Use Beam Search to maximize (Hannun et al. 2014):
- \[\mathrm{arg } \max_{w} p(w \vert x)\: p(w)^\alpha \: [\text{length}(w)]^\beta\]
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- \(p(w \vert x) = p(y \vert x)\) for characters that make up \(w\).
- We tend to penalize long transcriptions due to the multiplicative nature of the objective, so we trade off (re-weight) with \(\alpha , \beta\)
-
- Start with a set of candidate transcript prefixes, \(A = {}\)
- For \(t = 1 \ldots T\):
- For Each Candidate in \(A\), consider:
- Add blank; don’t change prefix; update probability using the AM.
- Add space to prefix; update probability using LM.
- Add a character to prefix; update probability using AM. Add new candidates with updated probabilities to \(A_{\text{new}}\)
- \(A := K\) most probable prefixes in \(A_{\text{new}}\)
- For Each Candidate in \(A\), consider:
-
- Rescoring with Neural LM:
- The output from the RNN described above consists of a big list of the top \(k\) transcriptions in terms of probability.
We want to re-score these probabilities based on a strong LM.- It is Cheap to evaluate \(p(w_k \vert w_{k-1}, w_{k-2}, \ldots, w_1)\) NLM on many sentences
- In-practice, often combine with N-gram trained from big corpora
-
- Scaling Up:
-
- Data:
- Transcribing speech data isn’t cheap, but not prohibitive
- Roughly 50¢ to $1 per minute
- Typical speech benchmarks offer 100s to few 1000s of hours:
- LibriSpeech (audiobooks)
- LDC corpora (Fisher, Switchboard, WSJ) ($$)
- VoxForge
- Data is very Application/Problem dependent and should be chosen with respect to the problem to be solved
- Data can be collected as “read”-data for <$10 – Make sure the data to be read is scripts/plays to get a conversationalist response
- Noise is additive and can be incorporated
- Transcribing speech data isn’t cheap, but not prohibitive
- Data:
-
- Computation:
- How big is 1 experiment?{: style=”color: red”}
\(\geq (\# \text{Connections}) \cdot (\# \text{Frames}) \cdot (\# \text{Utterances}) \cdot (\# \text{Epochs}) \cdot 3 \cdot 2 \:\text{ FLOPs}\)
E.g. for DS2 with 10k hours of data:
\(100*10^6 * 100 * 10^6*20 * 3 * 2 = 1.2*10^{19} \:\text{ FLOPs}\)
~30 days (with well-optimized code on Titan X) - Work-arounds and solutions:{: style=”color: red”}
- More GPUs with data parallelism:
- Minibatches up to 1024
- Aim for \(\geq 64\) utterances per GPU ~\(< 1\)-wk training time (~8 Titans)
- More GPUs with data parallelism:
-
How to use more GPUs?{: style=”color: red”}
- Synch. SGD
- Asynch SGD
- Synch SGD w/ backup workers
- Tips and Tricks:
- Make sure the code is optimized single-GPU.
A lot of off-the-shelf code has inefficiencies.
E.g. Watch for bad GEMM sizes. - Keep similar-length utterances together:
The input must be block-sized and will be padded; thus, keeping similar lengths together reduces unnecessary padding.
- Make sure the code is optimized single-GPU.
- How big is 1 experiment?{: style=”color: red”}
- Computation:
-
- Throughput:
- Large DNN/RNN models run well on GPUs, ONLY, if the batch size is high enough.
Processing 1 audio stream at a time is inefficient.
Performance for K1200 GPU:
| Batch Size | FLOPs | Throughput | | 1 | 0.065 TFLOPs | 1x | | 10 | 0.31 TFLOPs | 5x | | 32 | 0.92 TFLOPs | 14x | - Batch packets together as data comes in:
- Each packet (Arrow) of speech data ~ 100ms
- Process packets that arrive at similar times in parallel (from multiple users)
- Each packet (Arrow) of speech data ~ 100ms
- Large DNN/RNN models run well on GPUs, ONLY, if the batch size is high enough.
- Throughput:


Asakusa is a Hadoop-based Enterprise Batch Processing Framework, to improve the efficiency of General Enterprise Systems (e.g. Supply Chain Management). Asakusa provides the DAG-based development methodology, which must be required for the large scale batch jobs. With Asakusa, the developers can build up the scalable and robust enterprise batch jobs easily and comprehensively.
Asakusa consists of the following components: (1) MapReduce DSL compiler (Ashigel Compiler), (2) a data model generator for Hadoop data format, and (3) integrated test suites tools. Ashigel compiler compiles the DSLs (multi-layered DSLs, business workflow DSL, logic flow DSL, and data operator DSL) into MapReduce programs. The model generator takes RDBMS schema as an input, and generates the Hadoop I/O classes and the corresponding test codes. For the ease of the development, the test suite tools integrate the Ashigel compiler and the model generator.
@see Asakusa Framework Wiki


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