Accern API
Authentication
curl "http://feed.accern.com/v3/alphas?token=TOKEN"
require 'uri'
require 'net/http'
url = URI("http://feed.accern.com/v3/alphas?token=TOKEN")
http = Net::HTTP.new(url.host, url.port)
request = Net::HTTP::Get.new(url)
response = http.request(request)
puts response.read_body
import requests
url = "http://feed.accern.com/v3/alphas?token=TOKEN"
req = requests.get(url)
text_response = req.text # read response as Text
print(text_response)
json_response = req.json() # read response as JSON
print(json_response)
if (!require("jsonlite")) install.packages("jsonlite")
url <- "http://feed.accern.com/v3/alphas?token=TOKEN"
response <- fromJSON(url)
print(response)
Make sure to replace TOKEN above
To authenticate provide your authentication token in the url. We provide the authentication token in your welcome email.
Feed
curl "http://feed.accern.com/v3/alphas?token=TOKEN"
url = URI("http://feed.accern.com/v3/alphas?token=TOKEN")
url = "http://feed.accern.com/v3/alphas?token=TOKEN"
url <- "http://feed.accern.com/v3/alphas?token=TOKEN"
The above url returns the most recent 100 documents.
[
{
"id": 1774184,
"article_id": {
"$oid": "589b44a569fe9f7f77024f4a"
},
"article_sentiment": 0.098,
"article_traffic": null,
"article_type": "blog",
"article_url": "http://feedproxy.google.com/~r/RedmondPie/~3/mF7K04DF1y4/",
"author_id": null,
"correlations": null,
"entities": [
{
"name": "Apple Inc.",
"type": "Public",
"index": "S&P 500, Russell 1000, Russell 3000, Wilshire 5000, BARRON'S 400, NASDAQ 100",
"region": "North America",
"sector": "Technology",
"ticker": "AAPL",
"country": "United States",
"exchange": "NASDAQ",
"industry": "Computer Manufacturing",
"entity_id": "EQ0010169500001000",
"global_id": "BBG000B9XRY4",
"competitors": [
"GOOG",
"HPQ"
]
},
{
"name": "Amazon.com, Inc.",
"type": "Public",
"index": "S&P 500, Russell 1000, Russell 3000, Wilshire 5000, NASDAQ 100",
"region": "North America",
"sector": "Consumer Services",
"ticker": "AMZN",
"country": "United States",
"exchange": "NASDAQ",
"industry": "Catalog/Specialty Distribution",
"entity_id": "EQ0021695200001000",
"global_id": "BBG000BVPV84",
"competitors": [
"AAPL",
"BKS"
]
}
],
"event_author_rank": [
{
"author_rank": 4,
"event_group": "Employment Actions"
},
{
"author_rank": 4,
"event_group": "Employment Actions"
}
],
"event_groups": [
{
"type": "Recruitment",
"group": "Employment Actions"
},
{
"type": "Layoff",
"group": "Employment Actions"
}
],
"event_impact_score": {
"overall": 40.88471673254282,
"on_entities": [
{
"entity": "AAPL",
"on_entity": 31
},
{
"entity": "AMZN",
"on_entity": 32
}
]
},
"event_source_rank": [
{
"event_group": "Employment Actions",
"source_rank": 6
},
{
"event_group": "Employment Actions",
"source_rank": 6
}
],
"event_summary": {
"group": "",
"theme": "",
"topic": "",
"action": "",
"sub-theme": "",
"acting_party": ""
},
"first_mention": false,
"harvested_at": "2017-02-08 16:17:39 UTC",
"overall_author_rank": 5,
"overall_source_rank": 6,
"source_id": null,
"story_id": {
"$oid": "589a6b6469fe9f7f70ac1df6"
},
"story_saturation": "high",
"story_sentiment": 0.072,
"story_shares": null,
"story_volume": 58
}
]
GET http://feed.accern.com/v3/alphas?token=TOKEN
By default this request will return the most recent 100 documents.
Filtering
Filter by last_id
curl "http://feed.accern.com/v3/alphas?last_id=1774184&token=TOKEN"
url = URI("http://feed.accern.com/v3/alphas?last_id=1774184&token=TOKEN")
url = "http://feed.accern.com/v3/alphas?last_id=1774184&token=TOKEN"
url <- "http://feed.accern.com/v3/alphas?last_id=1774184&token=TOKEN"
Filter by index
curl "http://feed.accern.com/v3/alphas?index=sp500&token=TOKEN"
url = URI("http://feed.accern.com/v3/alphas?index=sp500&token=TOKEN")
url = "http://feed.accern.com/v3/alphas?index=sp500&token=TOKEN"
url <- "http://feed.accern.com/v3/alphas?index=sp500&token=TOKEN"
Filter by multiple indexes
curl "http://feed.accern.com/v3/alphas?index=sp500,dow30&token=TOKEN"
url = URI("http://feed.accern.com/v3/alphas?index=sp500,dow30&token=TOKEN")
url = "http://feed.accern.com/v3/alphas?index=sp500,dow30&token=TOKEN"
url <- "http://feed.accern.com/v3/alphas?index=sp500,dow30&token=TOKEN"
Filter by ticker
curl "http://feed.accern.com/v3/alphas?ticker=amzn&token=TOKEN"
url = URI("http://feed.accern.com/v3/alphas?ticker=amzn&token=TOKEN")
url = "http://feed.accern.com/v3/alphas?ticker=amzn&token=TOKEN"
url <- "http://feed.accern.com/v3/alphas?ticker=amzn&token=TOKEN"
Filter by multiple tickers
curl "http://feed.accern.com/v3/alphas?ticker=aapl,amzn&token=TOKEN"
url = URI("http://feed.accern.com/v3/alphas?ticker=aapl,amzn&token=TOKEN")
url = "http://feed.accern.com/v3/alphas?ticker=aapl,amzn&token=TOKEN"
url <- "http://feed.accern.com/v3/alphas?ticker=aapl,amzn&token=TOKEN"
| Parameter | Description |
|---|---|
| last_id | Returns the latest 100 documents that came after the provided id. Used to prevent duplicates while keeping in sync (see streaming section). |
| index | Filters documents by the index, see below table for supported indexes. To filter by multiple indexes pass a comma separated list of indexes. |
| ticker | Filters documents by ticker. To filter by multiple tickers pass a comma separated list of tickers. |
Allowed index values
| index | expected query string value |
|---|---|
| S&P 500 | sp500 |
| Russell 1000 | russell1000 |
| Russell 3000 | russell3000 |
| Wilshire 5000 | wilshire5000 |
| Barron's 400 | barrons400 |
| DOW 30 | dow30 |
File Format
curl "http://feed.accern.com/v3/alphas.csv?token=TOKEN"
url = URI("http://feed.accern.com/v3/alphas.csv?token=TOKEN")
url = "http://feed.accern.com/v3/alphas.csv?token=TOKEN"
url <- "http://feed.accern.com/v3/alphas.csv?token=TOKEN"
By default the response from the API feed is in JSON format. But if you append .csv you will get the data in CSV format.
| CSV Columns |
|---|
| id |
| article_id |
| story_id |
| harvested_at |
| entities_name_1 |
| entities_ticker_1 |
| entities_global_id_1 |
| entities_entity_id_1 |
| entities_type_1 |
| entities_exchange_1 |
| entities_sector_1 |
| entities_industry_1 |
| entities_country_1 |
| entities_region_1 |
| entities_index_1 |
| entities_competitors_1 |
| entities_name_2 |
| entities_ticker_2 |
| entities_global_id_2 |
| entities_entity_id_2 |
| entities_type_2 |
| entities_exchange_2 |
| entities_sector_2 |
| entities_industry_2 |
| entities_country_2 |
| entities_region_2 |
| entities_index_2 |
| entities_competitors_2 |
| event_groups_group_1 |
| event_groups_type_1 |
| event_groups_group_2 |
| event_groups_type_2 |
| story_sentiment |
| story_saturation |
| story_volume |
| first_mention |
| article_type |
| article_sentiment |
| overall_source_rank |
| event_source_rank_1 |
| event_source_rank_2 |
| overall_author_rank |
| event_author_rank_1 |
| event_author_rank_2 |
| event_impact_score_overall |
| event_impact_score_entity_1 |
| event_impact_score_entity_2 |
| event_summary_group |
| event_summary_theme |
| event_summary_topic |
| event_summary_action |
| event_summary_sub-theme |
| event_summary_acting_party |
| article_url |
Streaming
To stay in sync with the API you make a request with last_id=[lastest document id]
then grab the id of the latest document that comes back and repeat. We packaged this logic up in our Accern gem, to install follow the instructions on the repo.
Backfill
The API allows you to access data going back 30 days, anything older we provide via other means. To start from 30 days ago and move forward you have to provide last_id=0
. Then continue to hit the API while setting the last_id
query string parameter.
Accern Overview
The Accern API provides a comprehensive, REST-based interface for accessing all financial-related articles processed by our platform within the last 30 days.
Each article is processed through our data pipeline, extracted for entities like equities and financial events which are made available through the API.
We also include numerous, insightful analytics like sentiment, impact score, story saturation, etc.
NOTE: Further details in Accern Analytics Section.
Data Attributes
Sample object (article)
Table illustrates all the attributes in a single object (article) of the Accern API response
{
"id": 1774184,
"article_id": {
"$oid": "589b44a569fe9f7f77024f4a"
},
"article_sentiment": 0.098,
"article_traffic": null,
"article_type": "blog",
"article_url": "http://feedproxy.google.com/~r/RedmondPie/~3/mF7K04DF1y4/",
"author_id": null,
"correlations": null,
"entities": [
{
"name": "Apple Inc.",
"type": "Public",
"index": "S&P 500, Russell 1000, Russell 3000, Wilshire 5000, BARRON'S 400, NASDAQ 100",
"region": "North America",
"sector": "Technology",
"ticker": "AAPL",
"country": "United States",
"exchange": "NASDAQ",
"industry": "Computer Manufacturing",
"entity_id": "EQ0010169500001000",
"global_id": "BBG000B9XRY4",
"competitors": [
"GOOG",
"HPQ"
]
},
{
"name": "Amazon.com, Inc.",
"type": "Public",
"index": "S&P 500, Russell 1000, Russell 3000, Wilshire 5000, NASDAQ 100",
"region": "North America",
"sector": "Consumer Services",
"ticker": "AMZN",
"country": "United States",
"exchange": "NASDAQ",
"industry": "Catalog/Specialty Distribution",
"entity_id": "EQ0021695200001000",
"global_id": "BBG000BVPV84",
"competitors": [
"AAPL",
"BKS"
]
}
],
"event_author_rank": [
{
"author_rank": 4,
"event_group": "Employment Actions"
},
{
"author_rank": 4,
"event_group": "Employment Actions"
}
],
"event_groups": [
{
"type": "Recruitment",
"group": "Employment Actions"
},
{
"type": "Layoff",
"group": "Employment Actions"
}
],
"event_impact_score": {
"overall": 40.88471673254282,
"on_entities": [
{
"entity": "AAPL",
"on_entity": 31
},
{
"entity": "AMZN",
"on_entity": 32
}
]
},
"event_source_rank": [
{
"event_group": "Employment Actions",
"source_rank": 6
},
{
"event_group": "Employment Actions",
"source_rank": 6
}
],
"event_summary": {
"group": "",
"theme": "",
"topic": "",
"action": "",
"sub-theme": "",
"acting_party": ""
},
"first_mention": false,
"harvested_at": "2017-02-08 16:17:39 UTC",
"overall_author_rank": 5,
"overall_source_rank": 6,
"source_id": null,
"story_id": {
"$oid": "589a6b6469fe9f7f70ac1df6"
},
"story_saturation": "high",
"story_sentiment": 0.072,
"story_shares": null,
"story_volume": 58
}
| Attributes | Type | Description |
|---|---|---|
| id | integer | unique id for feed (1 or greater) |
| article_id.$oid | string | unique id per article |
| article_sentiment | decimal | determines if article was written positively/negatively (-1.000 - 1.000) |
| article_type | string | determines the source of an information (ex. blog, article, tweet) |
| article_url | url string | original link to article |
| entities | list | List of associated equities objects that are identified for this article |
| entities_name | string | name of the company (8,000+ U.S. public equities) |
| entities_type | string | Classifying if it's publicly traded (ex. public) |
| entities_index | string | Comma-separated string of indices company is listed on |
| entities_region | string | Region of the company's headquarters |
| entities_sector | string | Sector of the company |
| entities_ticker | string | Ticker of the company |
| entities_country | string | Country of company's headquarters |
| entities_exchange | string | Exchange the company is traded on |
| entities_industry | string | Industry of the company |
| entities_entity_id | string | Entity level ID of the company, derived from Bloomberg Open Symbology |
| entities_global_id | string | Unique global ID of the company, derived from Bloomberg Open Symbology |
| entities_competitors | list | List of top three competitors associated with the company |
| event_author_rank | list | Each object indicates the author's reliability in reporting on specific events |
| event_groups | list | Each object has a major event group and a subsection of that group |
| event_groups_type | string | A subsection of an event group for more detail |
| event_groups_group | string | A major event i.e. event group |
| event_impact_score | object | Calculates the article's impact i.e. chance of affecting the associated company's stock price |
| event_impact_score_overall | decimal | Determines chance of event affecting stock prices in general by end of trading day |
| event_impact_score_on_entities | list | Determines chance of event affecting associated company's stock price by end of trading day |
| event_source_rank | list | Each object indicates the source's reliability in reporting on specific events |
| event_summary_topic | string | Level 1 event category |
| event_summary_group | string | Level 2 event category |
| event_summary_theme | string | Level 3 event category |
| event_summary_sub_theme | string | Level 4 event category |
| event_summary_action | string | action of an event |
| event_summary_acting_party | string | parties associated with the event |
| first_mention | boolean | If this article is the first one to break this new story |
| harvested_at | datetime | UTC formatted time when Accern received article |
| overall_author_rank | integer | rank (1-10) of how reliable author is at releasing articles in general |
| overall_source_rank | integer | rank (1-10) of how reliable source is at releasing articles in general |
| story_saturation | string | how much exposure this story has currently. ex. high, mid, low |
| story_sentiment | decimal | +ve/-ve sentiment score of the story by averaging related articles' sentiment published so far |
| story_volume | integer | number of articles associated with this story until now |
Data Coverage
Brief elaboration on what kind of data and where it comes from. Table of average daily statistics regarding the data processing pipeline.
Types of Data -
Accern acquires information from many types of online sources.
- Public News Websites
- Public Blogs
- Press Releases
- Financial Documents ex. SEC Filings
- Other Social Media ex. Tumblr
How we Acquire the Data -
Accern has multiple avenues for financial information. They include our own, in-house web scrapers and data obtained via partnered data providers.
Data Providers: Majority of our data comes through our data providers. Currently monitoring around 300million+ sources (websites). NOTE: Twitter counts as a single source.
Proprietary Scrapers: Accern has proprietary crawlers that monitor around 0.5million+ public, high-alpha sources. NOTE: These important sources break market-moving news the fastest.
Quick Info on Data Pipeline -
| Numbers | Numbers |
|---|---|
| Total Websites Monitored | 300 million-plus |
| Number of Articles Processed Each Day | 5 million-plus |
| Number of Articles Delivered Each Day | 20,000-plus |
| Format of Data Delivered | JSON, CSV |
| Real-time Data Delivery Method | REST API, Web Portal |
| Processing Time Per Article Published | 40 milliseconds |
| Trading Analytics Derived Per Article | 10-plus |
| Archive Date Range Available | 08/25/2012 to 08/19/2016 (4 years) |
| Number of Archive Articles | 15 million-plus |
| Archive Delivery Method | FTP, Dropbox |
| Data Financial Asset Mapping | Tickers, Bloomberg ID |
| Financial Assets Coverage | 8,000-plus U.S. public equities |
| Financial Events Coverage | 1,000-plus financial events |
Story Classification
THOUSANDS of sources/authors post about the SAME news stories resulting in MILLIONS of articles/day.
Accern groups together articles talking about similar information (i.e. certain equities A, B, & C are involved in some financial event D) into UNIQUE STORIES. Accern is the pioneer of this story classification process, allowing you to track how information flows online.
- You will know the current exposure of a financial news headline
- You will know the overall sentiment of this market event
- You will know if the source that just posted a rumor is reliable based on authenticity of previous rumor-related stories published
- Many more analytics
In Depth: Story classification model is agnostic of article sentiment and it only groups based on the extracted entities and events. Semantic structure of the article is taken as input. The model identifies important themes and checks in the last 2 weeks for similar themes. If similar theme was found, it groups along with this theme. A combination of machine learning models used to extract entities and then linked back amongst the 8k+ U.S equities and 1k+ financial events.
Accern has compiled multiple entity dictionaries for linking names mentioned in media to the correct entities.
Proprietary Entities Dictionary Accern has developed a proprietary dictionary that maps all ways financial entities are mentioned in the media to the 8,000-plus U.S. public equities.
The dictionary consists of over 150,000 company name variations and includes Bloomberg IDs, ticker symbols, etc. mapped back to these company names. How? - Looking at historical data, we identify the different ways companies have been mentioned to build and update this dictionary.
Proprietary Financial Events Dictionary Accern has developed a proprietary dictionary that maps over 30,000 financial event variations mentioned in media to an aggregated list of 1,000-plus financial events. How? - We worked with financial analysts/equity researchers to figure out an initial list of important financial events. Then, starting with this list and looking at historical data, we figured out different variations of event names.
Noise Cancellation
Accern processes millions of articles/day of which a significant amount ends up as spam/ads/coupons/etc. To tackle this issue, Accern has optimized it's noise detection algorithms over the years. This noise cancellation mechanism takes around 150M+ articles (tweets, articles, blogs, etc.) every day and gives out around 25k articles at the end.
Proprietary Pattern Recognition Spam Detector
Our proprietary noise cancellation mechanism identifies if the input article qualifies as spam and does not contain any insightful information.
How? - A compiled list of regex-expression based phrases are used to classify the articles as spam or not. Using machine learning models and pattern recognition, Accern is able to automatically figure out which articles can be classified as spam based on the way their titles are written.
Example: Save Up to 50% on Your Purchases with Coupons at Amazon.com!
Accern will use pattern recognition to identify future articles talking about coupon codes from Amazon and automatically remove it.
Proprietary Blacklist of Spam Websites Accern has compiled a proprietary list of websites that are known to release spam, irrelevant content to financial markets investors.
How? - Looking in the historical archive of articles posted by different news sources, we calculate the probability that a newly published article from this source will turn out as spam. If the probability is above a threshold, we include it in our blacklist which is constantly kept up to date.
Accern Analytics
Unique Stories (story_id)
What is it? Financial news stories are published on the web and social media in many forms. For ex. articles, tweets, & SEC filings. We scour millions of these articles talking about financial events and group them into similar stories. Each story specifies 2 important details. The equities that are being talked about (currently amongst 8000+ US public equities) and a description of the associated financial events (currently 1000+ financial event distinctions available)
Quick Definition: a story is an event that involves a company.
How is it created? Every day we scrape a million+ articles of various types such as blogs, tweets, SEC filings and news articles. These mentions go through the story classification model to identify the associated equities (from the existing 8000+ US public equities) and the associated financial event (1000+ distinctions available). All mentions that are similar in the above entities are grouped together into their own stories. Each story which is a combination of companies and events is given unique ids - (story_id).
Examples
An example of a big, viral story regarding Google and Legal Actions consists of articles below that are all talking about similar information.
'Google alleges Uber stole its self-driving secrets' by Livemint.com
'Google accuses Uber of stealing self-drive technology' by Business-standard.com
'Lawsuit: Google self-driving car spinout Waymo claims Uber using stolen laser-mapping technology' by geekwire.com
'Waymo: Uber stole our self-driving car tech' by cnet.com
An example of a story reporting on a Rumour about Apple can be seen to have been initially posted on a blog before finally showing up on other sources wih bigger reach.
"Apple reportedly plans to 'significantly' expand Seattle office after Turi acquisition" by bizjournals.com
"Apple plans expansion of artificial intelligence efforts in Seattle" by forums.imore.com
First Mention (first_mention)
What is it? Whenever a financial news story breaks out, many articles (mentions) get published about the same story. First Mention tells us if the article is the FIRST to break that new story.
Quick Definition: A story that has not been mentioned on the internet for at least 2 weeks.
How is it created? The story classification model extracts a theme(combination of event and companies) from input article. It then searches for highly similar themes in the last 2 weeks.
If it finds one, it groups this article with the existing story (theme) and sets it's first_mention to FALSE. Otherwise, it creates a new story and this article's first_mention attribute is set to TRUE.
Examples
In brief, the power of knowing an article is the first to break a new story ->
| date | headline | first_mention |
|---|---|---|
| 08:50 AM 28 Feb | Xbox launches Netflix-like service for gamers | TRUE |
| 11:35 AM 28 Feb | GameStop stock price tanks after Microsoft announces new digital-gaming service | FALSE |
Sentiment (article_sentiment)
What is it? Sentiment score (-1 - +1) of the article based on title and content.
Quick Definition: determines if the article was written positively or negatively by the author/editor.
How is it created? Sentiment Analysis of articles involves 3 parallel models. (bag of words + n-grams + deep-learning)
Bag of Words involves a proprietary list of 300,000-plus positive and negative words, differently weighed, which are used to gauge a base sentiment of an article.
N-grams involves a proprietary list of positive and negative two- to three-word phrases which are used to gauge a more accurate sentiment in articles. These lists are compiled by financial analysts.
Next, a Deep Learning model predicts how much the article is positive or negative based on the vector representation of its text.
Finally, a meta model (ensemble learning) uses the output of all these 3 models to generate a final score.
Examples
Snippets of articles with a negative sentiment about a publicly traded equity - Tesco Inc
Tesco strike to escalate - "Over 2-thousand staff in 22 Tesco stores will be on strike by the middle of next week. Another 24 stores were balloted for industrial action by Mandate over the past three nights - 6 agreed to join the 16 stores currently on the picket line. The retailer says the results of the ballot mean there's an onus on the union to call-off the strike...."
Striking Tesco workers will not have Family Income Supplement suspended - "The ongoing strike at a number of Tesco stores has been suspended from this morning after both sides in the dispute agreed to attend discussions at the invitation of the Labour Court. Tesco has confirmed that it will not make any changes to pre-1996 terms and conditions whilst this process is ongoing. The Mandate trade union said all pickets will be suspended and the talks are expected to get under way this weekend...."
Tesco workers shouldn’t lose Family Income Supplement - "This would be a completely unfair and mean spirited move. I believe that there is scope under the current legislation for the Minister to direct that no such decision is made. 'By engaging in strike action, the workers are already seeing a reduction in their take home pay; a cut to their FIS payment will devastate families....'"
Sentiment (story_sentiment)
What is it? An average of all the sentiment scores of articles that fall into this story until now. NOTE: If there is only one article in a story so far, then its first_mention=TRUE , and story_sentiment equals article_sentiment.
Quick Definition: aggregates article sentiment and calculate the average sentiment for each story
How is it created? Article sentiments calculated via the procedure explained in the above Article Sentiment Section are all aggregated and the average is calculated. As the story grows, the overall sentiment keeps changing and a trend can be captured with time.
Examples
Looking at articles reporting on company earnings of Baidu (BIDU), there exist mixed reviews from different sources. Interestingly, the overall story sentiment saturated to end up as positive as more and more articles were posted.
Initial Negative articles -
- Baidu's quarterly revenue falls 2.6 percent - "Baidu Inc reported a second straight drop in quarterly revenue as regulatory scrutiny into healthcare and related advertisements continued to take a toll on the Chinese internet search giant. The drop, however, was within the 17.84-18.38 billion yuan range the company had previously forecast. Analysts estimate that healthcare accounts for about 20-30 percent of Baidu's search revenue, which represents more than 80 percent of the company's total sales...."
Dispersed Positive articles -
Baidu reports stable 2016 revenue growth - "Chinese Internet giant Baidu reported stable revenue growth in 2016, helped in part by artificial intelligence (AI) upgrades to its various products. Baidu continued to see stable user growth for its search and map services, with its mobile payment business Baidu Wallet attracting 100 million users by the end of 2016, surging 88 percent compared with 2015...."
Baidu posts bleak fourth quarter, but sees business reshuffle driving 2017 growth - "Baidu Inc's revenue fell for a second straight quarter, hurt by a government crackdown on healthcare advertising, but the internet search giant expects a rebound this year as it retools to find growth outside its core ad business. The company has stumbled over the past few years - firstly from a cash-burning subsidy war with rivals such as Alibaba in businesses like food delivery, movie tickets and taxi hailing...."
Accern Rank(overall_source_rank)
What is it? Accern Rank identifies if the information from a source is posted promptly and if that information will go viral (similar articles published by others). Rank 1 is lowest and Rank 10 is highest. In other words, it lets you know which sources usually are among the first to publish articles on a new story and also informs if they have a knack at posting on stories that become wide spread.
Quick Definition:
overall_source_rank - determines if the SOURCE is reliable at releasing trending stories. i.e. reliable indicating ability to post early and trending indicating potential of wide spread stories.
overall_author_rank - determines if the AUTHOR is reliable at releasing trending stories.
How is it created? A graphical model takes into account historical data (past articles), how certain news appeared in the past and how the distribution of articles within a story looked like. It checks in the past, which sources posted faster in comparison to other sources which then posted contextually similar articles.
Examples
Overall Source Rank (High) - StreetInsider releases stories first, and their stories get republished by many other sources.
Overall Author Rank (Low-Mid) - John Paul* releases stories on StreetInsider first, but his stories don’t get republished by any other authors.
*
Accern Rank(event_source_rank)
What is it? Ranks are based on the same Accern Rank model which tries to predict promptness and ability to post republished stories. Rank 1 is lowest and Rank 10 is highest. event_source_rank/event_author_rank is more precise. For ex. Tumblr posts rumors faster than others. Bloomberg posts financial docs faster than others. It would be prudent to the client to notice that sources will have varied ranks for different events.
Quick Definition:
event_source_rank - determines if the SOURCE is reliable at releasing articles associated with a financial event.
event_author_rank - determines if the AUTHOR is reliable at releasing articles associated with a financial event..
How is it created? The ranking model is the same. Ranks are calculated by filtering based on financial events.
Examples
Event Source Rank (High) - StreetInsider releases lawsuit stories first, and their lawsuit stories get republished by many other sources.
Event Author Rank (Low-Mid) - John Paul* releases lawsuit stories on StreetInsider late, but his stories are republished by some authors.
Saturation (story_saturation)
What is it? The online exposure of the story i.e. have a lot of people seen this story/information already? This is one of the useful metrics possible thanks to story classification. Note: Twitter tweets takes into account the number of followers, retweets, and other metrics that twitter user has.
Quick Definition: gauge the current potential exposure of a story
How is it created? Based on web traffic information (provided by Alexa Rank) of related articles and previous historical data, we predict the exposure of the story into different levels - low, mid, & high.
3-Step Process for Computing Saturation
Step 1: accumulate web traffic per story We accumulate total web traffic based on all related articles per story
Step 2: average web traffic per story We look back at all similar stories’ total web traffic and take an average per story
Step 3: segment average story web traffic We segment the average story web traffic into low, mid, and high saturation
Examples
Saturation (High) - story published on 100+ websites
Saturation (High) - story published on two major newswires
Saturation (Low) - story published on one medium-traffic website
Saturation (Low) - story published on five small websites
Impact (_overall)
What is it? When a certain type of story appears in media, calculate the probability that the stock price of the company moves up/down by more than 1% by EOD. Overall Impact score checks how an event like Company Earnings generally has high impact compared to other events. Overall Impact Score is an average across all the entity_impact_scores for different companies.
Quick Definition (event_impact_score_overall): determines if an event has a chance of affecting stock prices of companies in general by more than 1% at the end of the trading day.
How is it created? An example of a retrospective metric. Looking back in history archive, how market behaved for a past similar event is evaluated. In brief, by overlaying 3+ years of financial events data with stock prices market data, we determine if the event has a chance of moving the stock price of companies in general by more than 1% by the end of the day.
Examples
- Event Impact Score Overall (High) - In the past 3 years, whenever a lawsuit happened, it affected the stock prices of companies in general by 1% or more EOD.
Impact (_on_entities)
What is it? Entity Impact Score is more precise as it it returns a probability that particular event will affect SPECIFIC equities. For example, event_impact_score_on_entities of 90 for Apple and 'Mergers & Acquisition' event conveys this sort of story/theme moved the market before in the past and there is a high likelihood now as well. Also, this event can vary in impact score for different companies.
Quick Definition (event_impact_score_on_entities:) determines if an event has a chance of affecting the stock price of the mentioned company by more than 1% at the end of the trading day.
How is it created? Using the same procedure to calculate overall impact score of an event, this process filters based on every entity and calculates respective probabilities.
Examples
Sample Snippet
{
"....": "...",
"event_groups": [
{
"type": "Financial Results",
"group": "Company Earnings"
}
],
"event_impact_score": {
"overall": 48.55540720961282,
"on_entities": [
{
"entity": "EBAY",
"on_entity": 26
},
{
"entity": "AMZN",
"on_entity": 36
}
]
}
}
Considering the right-side snippet, we see that the results of Company Earnings reports have a lower impact on EBay and Amazon compared to all the companies overall.
Similarly, an event involving Criminal Actions/Fraud may have a high overall impact (event_impact_score.overall), but certain entities like Google are impacted 50% less. (event_impact_score.on_entities.on_entity)


About The director
The Ssembatya Shafic of recent
The road to where i am now has been rough and thats why maybe some people have to tell their friends that they wish them a rough road, and by saying that they are really talking about a fact of life, i have met a series of people good and others mean, and for this have always stuck on the useful personalities and this is one reason as to why i deiver postively where ever am involved in any activity, i have always made concrete research, going deep into books, discovering this and that, and for any one's information one cant know everything but we jsut have to stick on what we wish to perfect so that we dont confuse our selves, and by sparing time for a course of our intersts trust sucess awaits at the apex.
It's never too late for a succesfull person to redeem, pick interest now be inspired and you will see things work out and wonder, i know something, it's that human beings are capable of doing everything but interest, inspiration, dedication, love and care for a particular activity lets us off topic, wake up and plan.
Building my information technology carrier started from 2010 at mother janet primary school - mukoko where i had my primary leaving ward, we had a few working computers that per the time used cathode ray tube monitors the so called big behind simply because they had the cathode ray tube monitors, they had some wierd font color i just recall that now but its really here that i picked confidence, love and counrage to take on commputer studies particularly for i was confident about what i thought i would become if at all i held on with that objective, and with my secondary life at st.mary's secondary schoool nkozi i found a woman Madam Nakiyingi Rosette, i treasure this lady lots , she opened my eyes in reletion to information technology, having payed attension to her it's why we still touch, and now 2020 at 19 in uganda martyrs university nkozi - bachelor of science in informaion tehcnology.
More about Shafic
I have made sites before and one of them is my resume, it has all the detailed information about who i am Click here to visit site


Insert Astarte AppEngine API base URL here.
Insert the realm name here.
The authentication token is needed to access your data in Astarte.


Admin
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Password :
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Thank you for contacting us, we will reply as soon as possible.


This intention simplifies a conditional operator ?: when one of the branches is a constant true or
false.
So a ? b : false
gets turned into a && b
and a ? true : b
gets turned into a || b
.
While you would normally not write such code, you may end up with it as a result of other refactorings.


| Overview | Module | Use | Devguide | Index |
| Services' Summary | Interfaces' Summary | Properties' Summary | Services' Details | Interfaces' Details | Properties' Details |
| service GenericDrawingDocument |
|
| Included Services - Summary | |
| ::com::sun::star::document::OfficeDocument | this base service describes storing and printing |
| Exported Interfaces - Summary | |
| ::com::sun::star::lang::XMultiServiceFactory | this factory lets you create various services that are used inside a drawing document (details) |
| ::com::sun::star::beans::XPropertySet | lets you access the properties of this service. |
| XDrawPageDuplicator | lets you duplicate pages inside this document. |
| XDrawPagesSupplier | provides an object that is implementing the service DrawPages. With this service you have access to the DrawPages inside a document. |
| XMasterPagesSupplier | returns an object that is implementing the service MasterPages. With this service you have access to the MasterPages inside a document. |
| XLayerSupplier | returns an object that is implementing the service LayerManager. This service lets you manipulate the Layers of this document. |
| ::com::sun::star::style::XStyleFamiliesSupplier | This interface lets you access the styles contained in this document |
| Properties' Summary | |
| long TabStop |
This property specifies the length between the default tab stops inside text in this document in 1/100th mm. |
| ::com::sun::star::lang::Locale CharLocale |
contains the identifier of the default locale of the document. |
| ::com::sun::star::awt::Rectangle VisibleArea |
if this document is an ole client, this is the current visible area in 100th mm |
| [ readonly ] ::com::sun::star::i18n::XForbiddenCharacters ForbiddenCharacters |
This property gives the XForbiddenCharacters. |
| Included Services - Details | |
::com::sun::star::document::OfficeDocument
|
| Exported Interfaces - Details | |
::com::sun::star::lang::XMultiServiceFactory
|
|
::com::sun::star::beans::XPropertySet
|
|
XDrawPageDuplicator
|
|
| XDrawPagesSupplier |
|
XMasterPagesSupplier
|
|
XLayerSupplier
|
|
::com::sun::star::style::XStyleFamiliesSupplier
|
| Properties' Details | |
TabStop
|
|
CharLocale
|
|
VisibleArea
|
|
ForbiddenCharacters
|
Copyright © 2013, The Apache Software Foundation, Licensed under the Apache License, Version 2.0. Apache, the Apache feather logo, Apache OpenOffice and OpenOffice.org are trademarks of The Apache Software Foundation. Other names may be trademarks of their respective owners.


Workout
dehaze
Upper body with wieghts
Abs
Complete Workout
Complete Workout
Upper body
Abs
Complete Body Workout
00:00:00
Jumping jacks


--- layout: post title: The Mac Was Hacked? Really? date: 2007-04-24 23:28:00.000000000 -07:00 type: post published: true status: publish categories: [] tags: [] meta: _publicize_pending: '1' author: login: phoneboy email<EMAIL_ADDRESS>display_name: Dameon Welch first_name: '' last_name: '' ---
Everyone blew this supposed "Mac" security issue out of the water, it seems. The Mac was "hacked," but it wasn't exactly specific to the Mac as the issue could be replicated in any browser on any system. It was a local exploit, at best, and it involved cross-site scripting, something that is inherently dangerous on all computers.
Please let me know when the Mac can be remotely rooted, though. That will be some serious news.


@* * Copyright 2023 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import config.AppConfig @import models.request.AuthenticatedRequest @import views.html.components.input_accessible_auto_complete @import views.html.includes.main @import views.html.partials.{back_link, error_summary} @import views.html.helper.CSPNonce @this( govukLayouWrapper: main, backLink: back_link ) @(allKeywords: Seq[Keyword], newKeywordForm: Form[String])(implicit request: AuthenticatedRequest[_], messages: Messages, appConfig: AppConfig) @additionalCSS = { ) } @additionalJS = { } @govukLayouWrapper( pageTitle = messages("management.create-keyword-title"), backLink = Some(backLink()), additionalJS = Some(additionalJS), additionalCSS = Some(additionalCSS) ) { @error_summary(newKeywordForm.errors)


To create your playlist, Login to the Spotify.
To create your playlist, login to Spotify.
Log in with Spotify


AMP Pan Zoom
Note that for testing purposes, I've added a black border to all amp-pan-zooms. These are not actually present as part of the default UI. The show/hide and seat picking state between components is shared here for simplicity.
AMP Pan Zoom Basic Example - Layout Responsive
This is amp pan zoom with a basic image
AMP Pan-Zoom Advanced Example - Layout Responsive
Double tap or pinch to zoom in and out. This tests all initial configurations. Note that the max scale has been set to 5 in this example, which is different from the default of 3.
Current scale: 1
AMP Pan-Zoom - Layout Fill
This just tests basic features under layout fill
Current scale: 1
AMP Pan-Zoom - Layout Fixed Height
This just tests basic features under layout fixed height
Current scale: 1
AMP Pan-Zoom - Layout Fixed
This just tests basic features under layout fixed height. Double tap is disabled in this example. There should be no tap latency.
Current scale: 1
AMP Pan-Zoom Toggle Visibility Example
This tests whether or not the gestures reactivate after amp-pan-zoom's visibility has been toggled. Click on each button to toggle visibilty or display. Verify that amp-pan-zoom's zooming and panning via all gestures still work after toggling.
Current scale: 1
AMP Pan-Zoom Hide Controls Example
The controls buttons is hidden. Double tap zoom and pinch zoom should work in this case.
AMP Pan Zoom Disable Doubletap Example
Double tap is disabled in this example.


Ryan Conrardy
From outside Chicago, Illinois.
Biggest G in the computer Science program.
Double major in biology and computer, already graduated with a bachlor in biology so I've pristeged back to a freshman.
Dank memes and crippling depression.
NOTES:
Hierarchy of Abstractions
- Application: Message
- Transport: Segment
- Network: Datagrams
- Link: Frames
- Physical
Class Notes
October 5th
- Packets: Different names in different layers
- How big? (how many bits?)
- What kinds of info does packet carry?
- How is packet divided/organized? (Which group of bits fot which info)
- Naming/Addressing
- IP Addresses
- Ports
- Mac Addresses
- Routing (Describe and classify algorithms)
- Diykstra's Shortest path algorithm
- Reliable Transport--Finite State Machine
October 6th
Definitions
- Communication link: connection that allows transfer of information between hosts and end systems
- Packet switches: groups all transmitted data into suitably sized blocks, called packets, which are then transmitted.
- Transmission rate: speed in bits/second that data is passed over a link
- Packet: package of information sent over a link. May be the whole set, or a part of the total data
- Link-layer switch:
- Router switch:
- Route/Path: sequence of communication links and packet switches that a packet goes from end system to end system.
- Internet Service Provider: a network of packet switches and communication links. Provide connection to end systems


'Don't Compare Me To Ronaldo Yet'
by SL InternationalReal Madrid's latest wonderkid has called for calm from the media over recent comparisons to Los Blancos legend Cristiano Ronaldo.
Read: Ronaldo Facing Two-Year Ban?
Eighteen-year-old starlet Rodrygo has been making headlines in recent weeks after breaking into the first team at the Santiago Bernabeu.
The former Santos prodigy has already scored five goals in six appearances for the LaLiga giants, including a hat-trick in the UEFA Champions League against Galatasaray last week, and earned a call-up to the Brazil national team.
His form for Real has earned him comparisons to Ronaldo, with some even claiming he is the player to fill the void left behind by the club's all-time record goalscorer.
Rodrygo has since revealed that he would have loved to have played alongside the Portugal star, who moved to Juventus at the beginning of the 2018/19 campaign, but aaded that it is too soon to see him as the 34-year-old's replacement in Madrid.
Choose the left midfielder for SA’s Real Madrid Dream Team & win a replica shirt!
"Being able to play alongside Cristiano Ronaldo at Real Madrid was a dream of mine and it's a shame that he left because now I think it would be very difficult to play with him," Rodrygo said, as reported by Marca.
"I am still a very young player to talk about filling the place left by someone like Cristiano Ronaldo, who is one of the greatest players in the history of the club – if not the greatest.
"I don't want to weigh myself down with the pressure of replacing Cristiano Ronaldo."
Click through the gallery below to see the most expensive teenagers in world football:
Rodrygo joined Real from Santos at the beginning of this season for a reported transfer fee of €45 million (741 million).
Do you think Rodrygo can reach the same heights as Ronaldo? Share your thoughts by commenting in the section below.


The lick occurs at {{lick_start_string}}
{{notes}}
Reported on {{date_reported}}{{#reporter_exists}} by {{reporter_name}}{{/reporter_exists}}


Not Found
`/blog/how-to-promises/' not found.
WEBrick/1.3.1 (Ruby/2.0.0/2014-05-08) at localhost:4000


WBRT plus TMZ WBRT Study or Subgrow Events Total Events Total_Weight_M-i Chua D (2010) 3 47 & 48 3.6% Dau-Dow Li¢2018) ? 18 8 18 3.6% Fei Teng (2017) 18 28 168 25 10.9% Geng Lif2017) 15 39g 14 38 «10.0% Hassler MR( 2013) 13 22 3 13 45% J-Yong Peng (2008) 4 19 4 21 27% Lei Shi (2044) a 43 & 41 5.8% Shi-Jie Zhan (2016) 43 30 12 30 8.6% Tiar-Lu Gu (2015) 42 52 $ S50 6.5% Wei Cheng (2013) 16 30 14 28 10.7% Xia Deng (2017) 55 129 43° 109 33.2% Total (95% Ci) 455 420 100.0%
Total events 165 134 Heterogeneity: ChiF= 2.07, df= 10 (P= 4.00), /7= 0% Test for overall effect: Z= 1.13 P = 0.28)
Risk ratio
Fixed, 95% Cl 0.61 (0.16, 2.42] 1.40 (0.54, 3.60] 1.45 [0.77, 1.74] 1.07 [0.60, 7.91] 1.54 (0.74, 2.32] 1.11 (0.32, 3.82] 1.07 (0.46, 2.51] 1.08 [0.59, 4.97] 1.28 (0.59, 2.77] 0.99 (0.64, 1.61] 1.08 (0.80, 1.47]
1.11 [0.93, 1.32]
Risk ratio 95% Cl
—}—
oo » > +> —_|_|_e- 0.05 0.2 1 5 20 Favours fexperimental] Favours [control]


Budgetbowl
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{% extends 'base.html' %} {% block title %}Dashboard{% endblock %} {% block content %} {% include 'bases/validation-error-base.html' %}
Orders in {{ fiscal_year }}
Spent this season
Vendors
Fiscal Year


GAS-X ULTRA STRENGTH ANTIGAS SOFTGELS- dimethicone capsule, liquid filled
Novartis Consumer Health Inc.
Disclaimer: Most OTC drugs are not reviewed and approved by FDA, however they may be marketed if they comply with applicable regulations and policies. FDA has not evaluated whether this product complies.
Active ingredient
Simethicone
Uses
for the relief of
- pressure and bloating commonly referred to as gas
Warnings
Keep out of Reach of Children
Directions
- adults: swallow with water 1 or 2 softgels as needed after meals and at bedtime
- do not exceed 2 softgels in 24 hours except under the advice and supervision of a physician
Other Information
- store at a controlled room temperature 20-25°C (68-77°F )
- protect from heat and moisture
Inactive ingredients
FD&C yellow 6, gelatin, glycerin, hypromellose, light mineral oil, mannitol, peppermint oil, purified water, sorbitol, soy lecithin, titanium dioxide
Questions or Comments?
call 1-800-452-0051
Additional information listed on other panels
Tamper Evident Feature: Gas-X Ultra Strength Softgels are sealed in blister packets. Use only if the individual seal is unbroken.
Distributed by: Novartis Consumer Health, Inc. Parsippany, NJ 07054-0622
©200#
Novartis Consumer Health Inc.


Chennai power disruption: Thursday, November 21
Power will be suspended in parts of Chennai for maintenance work.
by Express Web DeskThe Tamil Nadu Generation and Distribution Corporation (Tangedco) has announced that power will be suspended in parts of Chennai on Thursday for carrying out day-long maintenance work in the city.
The city will face a power shutdown from 9 am to 5 pm, with supply expected to resume before 5 pm if the work is completed.
As per a press release given by the company, the following neighbourhoods in Chennai will be affected:
KK Nagar: KK Nagar, Ashok Nagar, MGR Nagar, Ekkattuthangal, Kalaimagal Nagar, Balaji Nagar, Visalakshi Nagar, Parts of West Mambalam, Brindavan Nagar, Nakkeran Street, Parts of Guindy, Jafferkhanpet, West KK Nagar, Parts of Nesapakkam and Parts of Vadapalani.
Madhavaram: Kanagachatiram, Ganapathy Thottam, Vedachallam Street, K K R Nagar, Thattankulam Road, G N T Road, St. Annis College and School.
Puzhal: Jawaharlal Nagar, Kamaraj Nagar, Bypass Salai, Mondiyamman Koil Street, Indira Gandhi Nagar, PWD Salai and M A Nagar.


{% extends 'html/errors/error.html' %} {%- block status_code -%}{{ response_code }}{%- endblock -%} {%- block error_message -%}{{ response_message }}{%- endblock -%} {%- block error_body -%} {%- if error_message is defined -%}
{{ error_message }}
{%- endif -%}If you typed the web address, check it is correct.
If you pasted the web address, check you copied the entire address.
If the web address is correct or you selected a link or button, please contact us to report the issue.
{%- endblock -%}


This helper offers a method to measure the execution time of a block in a template.
Allows you to measure the execution time of a block in a template and records the result to the log. Wrap this block around expensive operations or possible bottlenecks to get a time reading for the operation. For example, let‘s say you thought your file processing method was taking too long; you could wrap it in a benchmark block.
<% benchmark "Process data files" do %> <%= expensive_files_operation %> <% end %>
That would add something like "Process data files (345.2ms)" to the log, which you can then use to compare timings when optimizing your code.
You may give an optional logger level as the :level option. (:debug, :info, :warn, :error); the default value is :info.
<% benchmark "Low-level files", :level => :debug do %> <%= lowlevel_files_operation %> <% end %>
Finally, you can pass true as the third argument to silence all log activity inside the block. This is great for boiling down a noisy block to just a single statement:
<% benchmark "Process data files", :level => :info, :silence => true do %> <%= expensive_and_chatty_files_operation %> <% end %>
[ show source ]
# File vendor/rails/actionpack/lib/action_view/helpers/benchmark_helper.rb, line 34 34: def benchmark(message = "Benchmarking", options = {}) 35: if controller.logger 36: if options.is_a?(Symbol) 37: ActiveSupport::Deprecation.warn("use benchmark('#{message}', :level => :#{options}) instead", caller) 38: options = { :level => options, :silence => false } 39: else 40: options.assert_valid_keys(:level, :silence) 41: options[:level] ||= :info 42: end 43: 44: result = nil 45: ms = Benchmark.ms { result = options[:silence] ? controller.logger.silence { yield } : yield } 46: controller.logger.send(options[:level], '%s (%.1fms)' % [ message, ms ]) 47: result 48: else 49: yield 50: end 51: end


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from __future__ import unicode_literals
from pepper import config, logger
from google.cloud import speech, translate_v2
import numpy as np
from typing import List, Tuple, Iterable, Union
[docs]class UtteranceHypothesis(object):
"""
Automatic Speech Recognition (ASR) Hypothesis
Parameters
----------
transcript: str
Utterance Hypothesis Transcript
confidence: float
Utterance Hypothesis Confidence
"""
def __init__(self, transcript, confidence):
# type: (str, float) -> None
self._transcript = transcript
self._confidence = confidence
@property
def transcript(self):
# type: () -> str
"""
Automatic Speech Recognition Hypothesis Transcript
Returns
-------
transcript: str
"""
return self._transcript
@transcript.setter
def transcript(self, value):
# type: (str) -> None
self._transcript = value
@property
def confidence(self):
# type: () -> float
"""
Automatic Speech Recognition Hypothesis Confidence
Returns
-------
confidence: float
"""
return self._confidence
@confidence.setter
def confidence(self, value):
# type: (float) -> None
self._confidence = value
def __repr__(self):
return "<'{}' [{:3.2%}]>".format(self.transcript, self.confidence)
[docs]class AbstractTranslator(object):
"""
Abstract Translator
Parameters
----------
source: str
Two Character Source Language Code
target: str
Two Character Target Language Code
"""
def __init__(self, source, target):
# type: (str, str) -> None
self._source = source
self._target = target
@property
def source(self):
# type: () -> str
"""
Source Language
Returns
-------
source: str
Two Character Source Language Code
"""
return self._source
@property
def target(self):
# type: () -> str
"""
Target Language
Returns
-------
target: str
Two Character Target Language Code
"""
return self._target
[docs] def translate(self, text):
# type: (str) -> str
"""
Translate Text from Source to Target Language
Parameters
----------
text: str
Returns
-------
translated_text: str
Translated Text
"""
raise NotImplementedError()
[docs]class GoogleTranslator(AbstractTranslator):
"""
Google Translator
Parameters
----------
source: str
Two Character Source Language Code
target: str
Two Character Target Language Code
"""
def __init__(self, source, target):
# type: (str, str) -> None
super(GoogleTranslator, self).__init__(source, target)
if source != target:
self._translate = lambda text: translate_v2.Client(target_language=self.target).translate(
text, source_language=self.source, target_language=self.target)['translatedText']
else:
# String Identity Function
self._translate = lambda text: text
[docs] def translate(self, text):
# type: (str) -> str
"""
Translate Text from Source to Target Language
Parameters
----------
text: str
Returns
-------
translated_text: str
Translated Text
"""
return self._translate(text)
[docs]class AbstractASR(object):
"""
Abstract Automatic Speech Recognition (ASR)
Parameters
----------
language: str
Language Code <LC> & Region Code <RC> -> "LC-RC"
"""
MAX_ALTERNATIVES = 10
def __init__(self, language=config.APPLICATION_LANGUAGE):
# type: (str) -> None
self._language = language
self._log = logger.getChild("{} ({})".format(self.__class__.__name__, self.language))
@property
def language(self):
# type: () -> str
"""
Automatic Speech Recognition Language
Returns
-------
language: str
Language Code <LC> & Region Code <RC> -> "LC-RC"
"""
return self._language
[docs] def transcribe(self, audio):
# type: (np.ndarray) -> List[UtteranceHypothesis]
"""
Transcribe Speech in Audio
Parameters
----------
audio: numpy.ndarray
Audio Samples (Containing Speech)
Returns
-------
transcript: List[UtteranceHypothesis]
"""
raise NotImplementedError()
[docs]class BaseGoogleASR(AbstractASR, GoogleTranslator):
"""
Abstract Base Google Automatic Speech Recognition (ASR)
Handles common parameters for SynchronousGoogleASR and StreamedGoogleASR
Parameters
----------
language: str
Language Code <LC> & Region Code <RC> -> "LC-RC"
sample_rate: int
Number of Audio Samples per second that will be handled to ASR transcription (16k is nice!)
hints: Tuple[str]
Words or Phrases that ASR should be extra sensitive to
"""
def __init__(self, language=config.APPLICATION_LANGUAGE, sample_rate=config.MICROPHONE_SAMPLE_RATE, hints=()):
# type: (str, int, Iterable[str]) -> None
AbstractASR.__init__(self, language)
GoogleTranslator.__init__(self, config.APPLICATION_LANGUAGE[:2], config.INTERNAL_LANGUAGE[:2])
self._client = speech.SpeechClient()
self._config = speech.types.RecognitionConfig(
# Each Sample is of dtype int16
encoding=speech.enums.RecognitionConfig.AudioEncoding.LINEAR16,
# The are 'sample_rate' samples per second
sample_rate_hertz=sample_rate,
# The Language & Region
# Tip: use en-GB for better understanding of 'academic English'
language_code=language,
# The maximum number of hypotheses to generate per speech recognition
max_alternatives=self.MAX_ALTERNATIVES,
# Particular words or phrases the Speech Recognition should be extra sensitive to
speech_contexts=[speech.types.SpeechContext(phrases=hints)])
self._log = logger.getChild(self.__class__.__name__)
self._log.debug("Booted ({} -> {})".format(self.source, self.target))
[docs] def transcribe(self, audio):
# type: (np.ndarray) -> List[UtteranceHypothesis]
"""
Transcribe Speech in Audio
Parameters
----------
audio: numpy.ndarray
Audio Samples (Containing Speech)
Returns
-------
transcript: List[UtteranceHypothesis]
"""
raise NotImplementedError()
[docs]class StreamedGoogleASR(BaseGoogleASR):
"""
Streamed Google Automatic Speech Recognition (ASR)
Recognises Speech 'live' as it is spoken. Should be faster than Synchronous ASR
Parameters
----------
language: str
Language Code <LC> & Region Code <RC> -> "LC-RC"
sample_rate: int
Number of Audio Samples per second that will be handled to ASR transcription (16k is nice!)
hints: Tuple[str]
Words or Phrases that ASR should be extra sensitive to
"""
def __init__(self, language=config.APPLICATION_LANGUAGE, sample_rate=config.MICROPHONE_SAMPLE_RATE, hints=()):
# type: (str, int, Iterable[str]) -> None
super(StreamedGoogleASR, self).__init__(language, sample_rate, hints)
# String Containing Live Hypothesis -> used for debugging
# Don't use the contents of this string for any important logic, because:
# - Contents change all the time as Utterances progress
# - Contents get cleared as soon as a (possible) new Utterance starts
# - The actual Hypotheses may differ from the live string due to Google Smarts...
# Use the transcribe method instead!
self._live = ""
self._streaming_config = speech.types.StreamingRecognitionConfig(
# The Config from BaseGoogleASR
config=self._config,
# Finalize Transcription after a single Utterance! (Keep it True)
single_utterance=True,
# Provide 'live' transcription: see self._live docs
interim_results=True
)
@property
def live(self):
# type: () -> Union[str, unicode]
"""
Live Speech Transcript String (Debug/Visual purposes only)
Returns
-------
live: str
Live Speech Transcript String
"""
return self._live
[docs] def transcribe(self, audio):
# type: (Iterable[np.ndarray]) -> List[UtteranceHypothesis]
"""
Transcribe Speech in Audio (Streamed)
Instead of a single Block of Audio, this function takes an Iterable of Audio frames.
One frame is processed at a time while the audio is being generated by the speaker.
This provides a faster ASR than the synchronous version, and a live representation of the utterance.
TODO: this breaks the abstract specification, is there a neater way to do this?
Parameters
----------
audio: Iterable[numpy.ndarray]
Iterable of Audio Samples (Containing Speech)
Returns
-------
transcript: List[UtteranceHypothesis]
"""
# TODO: Sometimes this fails! (network issues?) Current Solution: Retry a few times!
for i in range(3):
try:
return self._transcribe(audio)
except:
self._log.error("ASR Transcription Error (try {})".format(i+1))
return [] # Return an empty list if ASR transcription fails
def _transcribe(self, audio):
# type: (Iterable[np.ndarray]) -> List[UtteranceHypothesis]
"""
(Private) Transcribe Speech in Audio (Streamed)
Instead of a single Block of Audio, this function takes an Iterable of Audio frames.
One frame is processed at a time while the audio is being generated by the speaker.
This provides a faster ASR than the synchronous version, and a live representation of the utterance.
TODO: this breaks the abstract specification, is there a neater way to do this?
Parameters
----------
audio: Iterable[numpy.ndarray]
Iterable of Audio Samples (Containing Speech)
Returns
-------
transcript: List[UtteranceHypothesis]
"""
hypotheses = []
# Iterate over API Responses generated by each chunk of speech
for response in self._client.streaming_recognize(self._streaming_config, self._request(audio)):
live = "" # Reset Live Speech
for result in response.results:
# If ASR is done processing a whole Utterance
if result.is_final:
# Iterate over alternatives (a.k.a. hypotheses)
for alternative in result.alternatives:
# Add current alternative to hypotheses
hypotheses.append(UtteranceHypothesis(
# (Possibly translate) Speech transcript
self.translate(alternative.transcript),
# Add Hypothesis Confidence
alternative.confidence
))
# If not Final (a.k.a. speech is still going on)
elif result.alternatives:
# Add Transcript of first hypothesis to live string
live += result.alternatives[0].transcript
# Update current live string (to be accessible through StreamedGoogleASR.live)
self._live = live
# When a single utterance is done, return UtteranceHypotheses (sorted by confidence)
return sorted(hypotheses, key=lambda hypothesis: hypothesis.confidence, reverse=True)
@staticmethod
def _request(audio):
"""
Wrap audio chunks into StreamingRecognizeRequest objects
Parameters
----------
audio: Iterable[np.ndarray]
Yields
------
request: speech.types.StreamingRecognizeRequest
"""
for frame in audio:
yield speech.types.StreamingRecognizeRequest(audio_content=frame.tobytes())
[docs]class SynchronousGoogleASR(BaseGoogleASR):
"""
Synchronous Google Automatic Speech Recognition (ASR)
Recognises Speech 'live' as it is spoken. Should be faster than Synchronous ASR
Parameters
----------
language: str
Language Code <LC> & Region Code <RC> -> "LC-RC"
sample_rate: int
Number of Audio Samples per second that will be handled to ASR transcription (16k is nice!)
hints: Tuple[str]
Words or Phrases that ASR should be extra sensitive to
"""
def __init__(self, language=config.APPLICATION_LANGUAGE, sample_rate=config.MICROPHONE_SAMPLE_RATE, hints=()):
# type: (str, int, Iterable[str]) -> None
super(SynchronousGoogleASR, self).__init__(language, sample_rate, hints)
[docs] def transcribe(self, audio):
# type: (np.ndarray) -> List[UtteranceHypothesis]
"""
Transcribe Speech in Audio
Parameters
----------
audio: numpy.ndarray
Returns
-------
hypotheses: List[UtteranceHypothesis]
"""
hypotheses = []
for result in self._client.recognize(self._config, self._request(audio)).results:
for alternative in result.alternatives:
hypotheses.append(UtteranceHypothesis(self.translate(alternative.transcript), alternative.confidence))
return sorted(hypotheses, key=lambda hypothesis: hypothesis.confidence, reverse=True)
@staticmethod
def _request(audio):
"""
Wrap Audio in RecognitionAudio
Parameters
----------
audio: np.ndarray
Returns
-------
request: speech.types.RecognitionAudio
"""
return speech.types.RecognitionAudio(content=audio.tobytes())


--- layout: null section-type: ltdh19 title: LTDH19 --- ## Le Tour du Hack 2019 Coming back for the third time, #LTDH19 takes place in Edinburgh on the 23rd & 24th of March 2019. Find out more by visiting #LTDH19's website here!


The type matching rules for communications in MPI are not changed: the datatype specification for each item sent should match, in type signature, the datatype specification used to receive this item (unless one of the types is MPI_PACKED). Also, the type of a message item should match the type declaration for the corresponding communication buffer location, unless the type is MPI_BYTE or MPI_PACKED. Interlanguage communication is allowed if it complies with these rules.
Example
In the example below, a Fortran array is sent from Fortran and received in
C.
! FORTRAN CODE REAL R(5) INTEGER TYPE, IERR, MYRANK, AOBLEN(1), AOTYPE(1) INTEGER (KIND=MPI_ADDRESS_KIND) AODISP(1) ! create an absolute datatype for array R AOBLEN(1) = 5 CALL MPI_GET_ADDRESS( R, AODISP(1), IERR) AOTYPE(1) = MPI_REAL CALL MPI_TYPE_CREATE_STRUCT(1, AOBLEN,AODISP,AOTYPE, TYPE, IERR) CALL MPI_TYPE_COMMIT(TYPE, IERR) CALL MPI_COMM_RANK( MPI_COMM_WORLD, MYRANK, IERR) IF (MYRANK.EQ.0) THEN CALL MPI_SEND( MPI_BOTTOM, 1, TYPE, 1, 0, MPI_COMM_WORLD, IERR) ELSE CALL C_ROUTINE(TYPE) END IF
/* C code */ void C_ROUTINE(MPI_Fint *fhandle) { MPI_Datatype type; MPI_Status status; type = MPI_Type_f2c(*fhandle); MPI_Recv( MPI_BOTTOM, 1, type, 0, 0, MPI_COMM_WORLD, &status); }
MPI implementors may weaken these type matching rules, and allow messages to be sent with Fortran types and received with C types, and vice versa, when those types match. I.e., if the Fortran type INTEGER is identical to the C type int, then an MPI implementation may allow data to be sent with datatype MPI_INTEGER and be received with datatype MPI_INT. However, such code is not portable.


class SparkConf extends Cloneable with Logging with Serializable
Configuration for a Spark application. Used to set various Spark parameters as key-value pairs.
Most of the time, you would create a SparkConf object with new SparkConf()
, which will load
values from any spark.*
Java system properties set in your application as well. In this case,
parameters you set directly on the SparkConf
object take priority over system properties.
For unit tests, you can also call new SparkConf(false)
to skip loading external settings and
get the same configuration no matter what the system properties are.
All setter methods in this class support chaining. For example, you can write
new SparkConf().setMaster("local").setAppName("My app")
.
- Source
- SparkConf.scala
- Note
Once a SparkConf object is passed to Spark, it is cloned and can no longer be modified by the user. Spark does not support modifying the configuration at runtime.
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- SparkConf
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
- Definition Classes
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final
def
==(arg0: Any): Boolean
- Definition Classes
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final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
-
def
clone(): SparkConf
Copy this object
Copy this object
- Definition Classes
- SparkConf → AnyRef
-
def
contains(key: String): Boolean
Does the configuration contain a given parameter?
-
final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
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-
def
equals(arg0: Any): Boolean
- Definition Classes
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def
finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
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- Annotations
- @throws( classOf[java.lang.Throwable] )
-
def
get(key: String, defaultValue: String): String
Get a parameter, falling back to a default if not set
-
def
get(key: String): String
Get a parameter; throws a NoSuchElementException if it's not set
-
def
getAll: Array[(String, String)]
Get all parameters as a list of pairs
-
def
getAllWithPrefix(prefix: String): Array[(String, String)]
Get all parameters that start with
prefix
-
def
getAppId: String
Returns the Spark application id, valid in the Driver after TaskScheduler registration and from the start in the Executor.
-
def
getAvroSchema: Map[Long, String]
Gets all the avro schemas in the configuration used in the generic Avro record serializer
-
def
getBoolean(key: String, defaultValue: Boolean): Boolean
Get a parameter as a boolean, falling back to a default if not set
Get a parameter as a boolean, falling back to a default if not set
- Exceptions thrown
IllegalArgumentException
If the value cannot be interpreted as a boolean
-
final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
def
getDouble(key: String, defaultValue: Double): Double
Get a parameter as a double, falling back to a default if not ste
Get a parameter as a double, falling back to a default if not ste
- Exceptions thrown
NumberFormatException
If the value cannot be interpreted as a double
-
def
getExecutorEnv: Seq[(String, String)]
Get all executor environment variables set on this SparkConf
-
def
getInt(key: String, defaultValue: Int): Int
Get a parameter as an integer, falling back to a default if not set
Get a parameter as an integer, falling back to a default if not set
- Exceptions thrown
NumberFormatException
If the value cannot be interpreted as an integer
-
def
getLong(key: String, defaultValue: Long): Long
Get a parameter as a long, falling back to a default if not set
Get a parameter as a long, falling back to a default if not set
- Exceptions thrown
NumberFormatException
If the value cannot be interpreted as a long
-
def
getOption(key: String): Option[String]
Get a parameter as an Option
-
def
getSizeAsBytes(key: String, defaultValue: Long): Long
Get a size parameter as bytes, falling back to a default if not set.
Get a size parameter as bytes, falling back to a default if not set.
- Exceptions thrown
NumberFormatException
If the value cannot be interpreted as bytes
-
def
getSizeAsBytes(key: String, defaultValue: String): Long
Get a size parameter as bytes, falling back to a default if not set.
Get a size parameter as bytes, falling back to a default if not set. If no suffix is provided then bytes are assumed.
- Exceptions thrown
NumberFormatException
If the value cannot be interpreted as bytes
-
def
getSizeAsBytes(key: String): Long
Get a size parameter as bytes; throws a NoSuchElementException if it's not set.
Get a size parameter as bytes; throws a NoSuchElementException if it's not set. If no suffix is provided then bytes are assumed.
- Exceptions thrown
NumberFormatException
If the value cannot be interpreted as bytesjava.util.NoSuchElementException
If the size parameter is not set
-
def
getSizeAsGb(key: String, defaultValue: String): Long
Get a size parameter as Gibibytes, falling back to a default if not set.
Get a size parameter as Gibibytes, falling back to a default if not set. If no suffix is provided then Gibibytes are assumed.
- Exceptions thrown
NumberFormatException
If the value cannot be interpreted as Gibibytes
-
def
getSizeAsGb(key: String): Long
Get a size parameter as Gibibytes; throws a NoSuchElementException if it's not set.
Get a size parameter as Gibibytes; throws a NoSuchElementException if it's not set. If no suffix is provided then Gibibytes are assumed.
- Exceptions thrown
NumberFormatException
If the value cannot be interpreted as Gibibytesjava.util.NoSuchElementException
If the size parameter is not set
-
def
getSizeAsKb(key: String, defaultValue: String): Long
Get a size parameter as Kibibytes, falling back to a default if not set.
Get a size parameter as Kibibytes, falling back to a default if not set. If no suffix is provided then Kibibytes are assumed.
- Exceptions thrown
NumberFormatException
If the value cannot be interpreted as Kibibytes
-
def
getSizeAsKb(key: String): Long
Get a size parameter as Kibibytes; throws a NoSuchElementException if it's not set.
Get a size parameter as Kibibytes; throws a NoSuchElementException if it's not set. If no suffix is provided then Kibibytes are assumed.
- Exceptions thrown
NumberFormatException
If the value cannot be interpreted as Kibibytesjava.util.NoSuchElementException
If the size parameter is not set
-
def
getSizeAsMb(key: String, defaultValue: String): Long
Get a size parameter as Mebibytes, falling back to a default if not set.
Get a size parameter as Mebibytes, falling back to a default if not set. If no suffix is provided then Mebibytes are assumed.
- Exceptions thrown
NumberFormatException
If the value cannot be interpreted as Mebibytes
-
def
getSizeAsMb(key: String): Long
Get a size parameter as Mebibytes; throws a NoSuchElementException if it's not set.
Get a size parameter as Mebibytes; throws a NoSuchElementException if it's not set. If no suffix is provided then Mebibytes are assumed.
- Exceptions thrown
NumberFormatException
If the value cannot be interpreted as Mebibytesjava.util.NoSuchElementException
If the size parameter is not set
-
def
getTimeAsMs(key: String, defaultValue: String): Long
Get a time parameter as milliseconds, falling back to a default if not set.
Get a time parameter as milliseconds, falling back to a default if not set. If no suffix is provided then milliseconds are assumed.
- Exceptions thrown
NumberFormatException
If the value cannot be interpreted as milliseconds
-
def
getTimeAsMs(key: String): Long
Get a time parameter as milliseconds; throws a NoSuchElementException if it's not set.
Get a time parameter as milliseconds; throws a NoSuchElementException if it's not set. If no suffix is provided then milliseconds are assumed.
- Exceptions thrown
NumberFormatException
If the value cannot be interpreted as millisecondsjava.util.NoSuchElementException
If the time parameter is not set
-
def
getTimeAsSeconds(key: String, defaultValue: String): Long
Get a time parameter as seconds, falling back to a default if not set.
Get a time parameter as seconds, falling back to a default if not set. If no suffix is provided then seconds are assumed.
- Exceptions thrown
NumberFormatException
If the value cannot be interpreted as seconds
-
def
getTimeAsSeconds(key: String): Long
Get a time parameter as seconds; throws a NoSuchElementException if it's not set.
Get a time parameter as seconds; throws a NoSuchElementException if it's not set. If no suffix is provided then seconds are assumed.
- Exceptions thrown
NumberFormatException
If the value cannot be interpreted as secondsjava.util.NoSuchElementException
If the time parameter is not set
-
def
hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
def
initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
- Attributes
- protected
- Definition Classes
- Logging
-
def
initializeLogIfNecessary(isInterpreter: Boolean): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
-
def
isTraceEnabled(): Boolean
- Attributes
- protected
- Definition Classes
- Logging
-
def
log: Logger
- Attributes
- protected
- Definition Classes
- Logging
-
def
logDebug(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logDebug(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logError(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logError(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logInfo(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logInfo(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logName: String
- Attributes
- protected
- Definition Classes
- Logging
-
def
logTrace(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logTrace(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logWarning(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logWarning(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
final
def
notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
def
registerAvroSchemas(schemas: Schema*): SparkConf
Use Kryo serialization and register the given set of Avro schemas so that the generic record serializer can decrease network IO
-
def
registerKryoClasses(classes: Array[Class[_]]): SparkConf
Use Kryo serialization and register the given set of classes with Kryo.
Use Kryo serialization and register the given set of classes with Kryo. If called multiple times, this will append the classes from all calls together.
-
def
remove(key: String): SparkConf
Remove a parameter from the configuration
-
def
set(key: String, value: String): SparkConf
Set a configuration variable.
-
def
setAll(settings: Iterable[(String, String)]): SparkConf
Set multiple parameters together
-
def
setAppName(name: String): SparkConf
Set a name for your application.
Set a name for your application. Shown in the Spark web UI.
-
def
setExecutorEnv(variables: Array[(String, String)]): SparkConf
Set multiple environment variables to be used when launching executors.
Set multiple environment variables to be used when launching executors. (Java-friendly version.)
-
def
setExecutorEnv(variables: Seq[(String, String)]): SparkConf
Set multiple environment variables to be used when launching executors.
Set multiple environment variables to be used when launching executors. These variables are stored as properties of the form spark.executorEnv.VAR_NAME (for example spark.executorEnv.PATH) but this method makes them easier to set.
-
def
setExecutorEnv(variable: String, value: String): SparkConf
Set an environment variable to be used when launching executors for this application.
Set an environment variable to be used when launching executors for this application. These variables are stored as properties of the form spark.executorEnv.VAR_NAME (for example spark.executorEnv.PATH) but this method makes them easier to set.
-
def
setIfMissing(key: String, value: String): SparkConf
Set a parameter if it isn't already configured
-
def
setJars(jars: Array[String]): SparkConf
Set JAR files to distribute to the cluster.
Set JAR files to distribute to the cluster. (Java-friendly version.)
-
def
setJars(jars: Seq[String]): SparkConf
Set JAR files to distribute to the cluster.
-
def
setMaster(master: String): SparkConf
The master URL to connect to, such as "local" to run locally with one thread, "local[4]" to run locally with 4 cores, or "spark://master:7077" to run on a Spark standalone cluster.
-
def
setSparkHome(home: String): SparkConf
Set the location where Spark is installed on worker nodes.
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
toDebugString: String
Return a string listing all keys and values, one per line.
Return a string listing all keys and values, one per line. This is useful to print the configuration out for debugging.
-
def
toString(): String
- Definition Classes
- AnyRef → Any
-
final
def
wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native()


|
Crazy
For This Girl
(Evan And Jaron) |
|
she
rolls the window down
and she talks over the sound of the cars that pass us by and I don't know why but she's changed my mind would you look at her as she looks at me she's got me thinkin about her constantly but she don't know how I feel and as she carries on without a doubt I wonder if she's figured out I'm crazy for this girl she was the one to hold me the night the sky fell down and what was I thinkin when the world didn't end why didn't I know what I know now and right now face to face all my fears pushed aside and right now I'm ready to spend the rest of my life with you |


Magister Mirien:
What was your first impression of Dion?  I suspect it is the influence of the Giants' relics in the northern town. Their legacy is still felt here...
Learn skills.
Ask about Skill Enchanting.
Quest


One of my current projects is analyzing the relationship between p53 expression and cellular senescence. This project is based on mouse models created by first-author of this Cell paper. This paper described the mouse models with different genotypes of a tumor suppressor gene that promote the expression of p53. It is very interesting how mouse models of various degree of severeness of the geneotype was able to be engineered to study how the expression level pf p53 will effect cell senescence. Some things to note is that low p53 expression level will lead to tumorigenesis and high p53 expression level will lead to premature cell aging. It is very exciting to see how the p53 interact with cellular development since this may be the key to unlock the secrets behind aging.
Machine learning is a rapidly growing field.
No matter what discipline you are in, it is always good to know some basics of machine learning.
This website gives some details about what machine learning is and why it is interesting.
To summarize, machine learning allows the computer to compute numerous possible outcomes and predict the most likely one based on the information available to it.
This task would be difficult for humans to complete since we are not as good as picking up subtle relationships between events.
If you are even slightly intrigued, I would definitely encourage you to check out the article above.
I am aspired to work at Illumina like many other bioinformaticians. Illumina is a
Biotechnology company that provides sequencing service. It generates over 90% of the
sequencing data we see today. I am very eager for an internship at Illumina because
I believe I will be able to learn so much from a company that's literally the Google
of genetics. There is a video about the purpose of Illumina on the website linked above.
Illumina believes in the possibility in science and in its people. It is exciting to
hear Illumina employees to discuss all the cutting edge technology Illumina is working on.
Artificial intelligence and machine learning technology has been utilized by many fields, and
health field is no exception. There is currently an AI program developed by Google Health and
Imperial College London that is being developed to aid in screening mammography. In many cases,
manual routine screening has a very high error rate, and with the help of AI programs, we
may be able to more accurately detect early stage breast cancer from routine X-ray screening.
In fact Google’s DeepMind system does experimentally better than physicians in diagnosing
cancer from X-ray.
Starting a little over 2 decades ago, the first sequencing was done. Ever since that, there's been
an explosion of new data about human genomes (along with other closely related species).
The amount of information still waiting to be analyze and discover is enormous. This is why there is a
great demand of bioinformaticians, as there are seemingly endless data to be processed. Being a bioinformatician
is not just about running pipelines, it is more about making sense of the data with biology.
Creating predictive model is also part of bioinformatics and it might be able to help combat recent global outbreak of novel coronavirus.
Because in many cases, it is difficult to see how the situation will play out until you are from the future. It
is useful to make model that can predict how things will unfold based on the information we have currently.
Not only can we make models that predict how many people will be infected in a particular city, we can also
make prediction based on policies to figure out what is the best solution.


* Your open license to the
datasets
* Giving City credit
* Future changes to
datasets/Terms of Use
* Compliance with law -
Your responsibility
* No warranty with datasets
* Exclusion of liability
* Liability for not
complying with Terms of Use
* Cancellation for non-compliance
* No endorsement
* No association
*
Governing law and jurisdiction from which datasets are published
Introduction
By accessing the data sets, you agree to these Terms of Use, which are intended
to protect and promote the City's commitments to open data and ensure that the
recipients of these data sets give back to the community the benefits they
derive from these data sets. If you have any questions or comments about these
Terms of Use, please feel free to contact us
.
Your open license to the data
sets
The City of London (City) now grants you a world-wide, royalty-free,
non-exclusive license to use, modify, and distribute the data sets in all
current and future media and formats for any lawful purpose. You now acknowledge
that this license does not give you a copyright or other proprietary interest in
the data sets. If you distribute or provide access to these data sets to any
other person, whether in original or modified form, you agree to include a copy
of, or this Uniform Resource Locator (URL) for, these Terms of Use and to ensure
they agree to and are bound by them but without introducing any further
restrictions of any kind.
Giving City credit
Although you are not required to credit the City for each use or reproduction of
the data sets, you are entitled to do so and encouraged to conspicuously
announce that these data sets are publicly available from the City under these
Terms of Use. Upon the request of the City, you may be required to remove a
credit from future uses or reproductions should the City decide that such credit
is not in the public interest.
Future changes to data
sets/Terms of Use
The City may at any time and from time to time add, delete, or change the data
sets or these Terms of Use. Notice of changes may be posted on the home page for
these data sets or this page. Any change is effective immediately upon posting,
unless otherwise stated.
Compliance with law -
Your responsibility
You assume sole responsibility for your use and reproduction of the data sets
and for complying with all applicable laws and industry standards.
No warranty with data sets
You get NO WARRANTIES, none of any kind. By this, we mean, for example (but
without limiting the total intended scope of the preceding sentence), (1) that
while reasonable efforts have been made in preparing these data sets for use by
you, the City cannot give any promises as to the completeness, currency, or
accuracy of the data sets nor that access will be continuous, (2) the City
cannot make any promise that the data sets are free and clear of any possible
third party copyright, moral rights, or other claim, (3) the data sets have been
modified from their original source, as data initially generated by the City for
its internal uses, and (4) all data visualizations on maps are approximate and
include only records that can be mapped.
You accept these data sets on an "as-is, where is" basis and agree to use them
at your own risk.
Exclusion of liability
You agree that you will not and cannot sue the City for anything, which the City
does or does not do (even if intentional or negligent) in connection with the
data sets and your use or inability to use them. Without limiting the general
scope of the preceding sentence, this means that the City and its agents are not
liable on any legal theory or basis for any direct, incidental, indirect,
special, punitive, exemplary, or consequential damages or losses, including
without limitation, loss of revenue or anticipated profits, loss of goodwill,
loss of business, loss of data, computer failure or malfunction, or any other
damages or losses.
Liability for not
complying with Terms of Use
If, as a result of your breach of these Terms of Use, the City gets sued or is
required to pay someone money, you agree to protect the City and reimburse the
City for everything which you cause the City to suffer. This means that you
agree to defend, indemnify, and hold harmless the City and all of its agents
from any and all liabilities incurred in connection with any claim arising from
any breach by you of these Terms of Use, including reasonable legal fees and
costs. You agree to cooperate fully in the defense of any such claim. The City
reserves the right to assume, at its own expense, the exclusive defense and
control of any matter otherwise subject to indemnification by you. You agree not
to settle any matter without the written consent of the City.
Cancellation for non-compliance
The City may, in its sole discretion, cancel or suspend your access to the data
sets without notice and for any reason, including anything which the City, in
its sole discretion, believes is a breach of these Terms of Use or is otherwise
unlawful or harmful to others. In the event of cancellation or suspension, you
will no longer be authorized to use or reproduce these data sets, and the City
may use any means possible to enforce its decision. Such cancellation or
suspension will not affect any person who has received the data sets from you
and who is otherwise in compliance with these Terms of Use.
No endorsement
You may not publicly represent or imply that the City is participating in, or
has sponsored, approved, or endorsed the manner or purpose of, your use or
reproduction of these data sets.
No association
You may not use any trademark, official mark, official emblem or logo, of the
City, or any of its other references or means of promotion or publicity without
the City’s prior written consent nor in any event to represent or imply an
association or affiliation with the City.
Governing law and jurisdiction from which data sets are published
These data sets are published from within the province of Ontario, Canada. These
Terms of Use are governed by Ontario law and the City and you now irrevocably
submit to the exclusive jurisdiction of Ontario courts with respect to any and
all matters arising under these Terms of Use or these data sets.