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With AlchemyLanguage

12 Semantic Text Analysis APIs Using Natural Language Processing]]></description>
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    <dc:creator>msszczep</dc:creator><description><![CDATA[Until recently, records of police misconduct in Chicago have been kept secret.

In 2014, the court decision Kalven v. Chicago opened those files to the public.]]></description>
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    <dc:creator>msszczep</dc:creator><description><![CDATA[The MPQA Opinion Corpus contains news articles from a wide variety of news sources manually annotated for opinions and other private states (i.e., beliefs, emotions, sentiments, speculations, etc.).]]></description>
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    <link>http://techcrunch.com/2015/09/10/big-data-doesnt-exist/#.iddvsm:x54j</link>
    <dc:creator>msszczep</dc:creator><description><![CDATA[If most large datasets are useless, why talk about them at all? Because they aren’t useless for everyone. Deep-learning models can separate signal from noise, finding patterns that would typically take experts months to codify. But typical deep-learning models only work on massive amounts of labeled data. And labelling a large dataset takes hundreds of thousands of dollars and months of time. That’s a job for a corporate behemoth like Facebook or Google. Too many smaller companies don’t realize this and acquire massive data stores that they can’t afford to use.

These companies have a better option. They can get more value out of the data they already have.

True, most deep-learning algorithms need large datasets. But we can also design them to make inferences from small data, just like humans do. Using transfer learning, we can train an algorithm on a large dataset before sending it to work on a small one. This makes the learning process 100 to 1,000 times more effective.]]></description>
<dc:subject>bigdata datascience datamining data philosophy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:msszczep/b:36ce842034fd/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:philosophy"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.ibm.com/analytics/watson-analytics/">
    <title>Easy analytics | Home | IBM Watson Analytics</title>
    <dc:date>2015-09-07T14:03:03+00:00</dc:date>
    <link>http://www.ibm.com/analytics/watson-analytics/</link>
    <dc:creator>msszczep</dc:creator><description><![CDATA[Predictive analytics and data visualization built for you.
Analyze your data in minutes on your own without downloading software.]]></description>
<dc:subject>ibm datamining analytics analysis watson data bigdata datascience</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:msszczep/b:afbd4c8c64a4/</dc:identifier>
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<item rdf:about="http://www.wired.com/2014/01/how-to-hack-okcupid/2/">
    <title>How a Math Genius Hacked OkCupid to Find True Love | WIRED</title>
    <dc:date>2015-09-02T23:42:22+00:00</dc:date>
    <link>http://www.wired.com/2014/01/how-to-hack-okcupid/2/</link>
    <dc:creator>msszczep</dc:creator><dc:subject>love sex math mathematics bigdata datamining datascience code python software programming</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:msszczep/b:d33f1c5edfaa/</dc:identifier>
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</item>
<item rdf:about="http://fgiasson.com/blog/index.php/2015/04/02/clj-fst-finite-state-transducers-fst-for-clojure/">
    <title>clj-fst: Finite State Transducers (FST) for Clojure at Frederick Giasson’s Weblog</title>
    <dc:date>2015-08-28T21:39:14+00:00</dc:date>
    <link>http://fgiasson.com/blog/index.php/2015/04/02/clj-fst-finite-state-transducers-fst-for-clojure/</link>
    <dc:creator>msszczep</dc:creator><description><![CDATA[Everything is a matter of scale. Using a map, or such generic structures, for efficiently handling millions or billions of values is far from effective, if even possible.

That is why we need some specialized structures like FSTs: to be able to create such huge associative structures that are lightning fast to query and that use a minimum of memory.

There are two general use cases for using FSTs:

When you want to know if an instance A exists in a really huge set X (where the set X is the FST)
When you want to get a list of outputs from a given input from a really huge set.]]></description>
<dc:subject>clojure data datamining code software programming datascience</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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<item rdf:about="http://sunlightfoundation.com/blog/2014/09/02/what-can-we-learn-from-800000-public-comments-on-the-fccs-net-neutrality-plan/">
    <title>What can we learn from 800,000 public comments on the FCC's net neutrality plan? - Sunlight Foundation Blog</title>
    <dc:date>2015-08-16T12:41:35+00:00</dc:date>
    <link>http://sunlightfoundation.com/blog/2014/09/02/what-can-we-learn-from-800000-public-comments-on-the-fccs-net-neutrality-plan/</link>
    <dc:creator>msszczep</dc:creator><dc:subject>netneutrality bigdata datamining code software programming policy politics internet</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:msszczep/b:254ba05d44be/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:politics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:internet"/>
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</item>
<item rdf:about="http://mmds.org/">
    <title>Mining of Massive Datasets</title>
    <dc:date>2015-07-18T19:10:02+00:00</dc:date>
    <link>http://mmds.org/</link>
    <dc:creator>msszczep</dc:creator><description><![CDATA[Big-data is transforming the world. Here you will learn data mining and machine learning techniques to process large datasets and extract valuable knowledge from them.]]></description>
<dc:subject>education course online video lecture lectures code software programming bigdata datascience machinelearning ai statistics datamining book coursera</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:msszczep/b:54d58e30c95d/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:ai"/>
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</item>
<item rdf:about="http://www.washingtonpost.com/news/the-intersect/wp/2015/01/13/is-this-the-single-most-important-trick-to-going-viral/">
    <title>Is this the single most important trick to going viral? - The Washington Post</title>
    <dc:date>2015-01-14T19:49:18+00:00</dc:date>
    <link>http://www.washingtonpost.com/news/the-intersect/wp/2015/01/13/is-this-the-single-most-important-trick-to-going-viral/</link>
    <dc:creator>msszczep</dc:creator><dc:subject>viral popular facebook statistics bigdata datamining datascience</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:msszczep/b:588a12f6cdf7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:viral"/>
	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:popular"/>
	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:facebook"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:bigdata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:datamining"/>
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</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.bbc.co.uk/things/">
    <title>BBC - Things</title>
    <dc:date>2014-09-24T02:46:23+00:00</dc:date>
    <link>http://www.bbc.co.uk/things/</link>
    <dc:creator>msszczep</dc:creator><description><![CDATA[BBC Things provides a single reference for all of the things that matter to the BBC and our audiences.

It uses Semantic Web technologies that allow open access to our data and is built on top of our Linked Data Platform. The types of data we maintain about things can be found in our Ontologies.]]></description>
<dc:subject>datamining data machinelearning ontology catalog ai semanticweb</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:msszczep/b:67d7174b3117/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:datamining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:ontology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:catalog"/>
	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:semanticweb"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.kennybastani.com/2014/09/deep-learning-sentiment-analysis-for.html">
    <title>Deep Learning Sentiment Analysis for Movie Reviews using Neo4j</title>
    <dc:date>2014-09-19T15:43:26+00:00</dc:date>
    <link>http://www.kennybastani.com/2014/09/deep-learning-sentiment-analysis-for.html</link>
    <dc:creator>msszczep</dc:creator><description><![CDATA[Deep Learning Sentiment Analysis for Movie Reviews using Neo4j]]></description>
<dc:subject>datamining algorithms graph deeplearning machinelearning ai nlp code software javascript</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:msszczep/b:dcc86c99b7cd/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:graph"/>
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</item>
<item rdf:about="http://www.datasciencetoolkit.org/">
    <title>Data Science Toolkit</title>
    <dc:date>2014-06-03T11:50:56+00:00</dc:date>
    <link>http://www.datasciencetoolkit.org/</link>
    <dc:creator>msszczep</dc:creator><description><![CDATA[Street Address to Coordinates
Google-style Geocoder
Coordinates to Political Areas
Text to Sentiment
Coordinates to Statistics
Geodict
IP Address to Coordinates
Text to Sentences
HTML to Text
HTML to Story
Text to People
Text to Times
File to Text]]></description>
<dc:subject>datamining tools data api opensource useful statistics code datascience programming software</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:msszczep/b:815ed762083d/</dc:identifier>
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</item>
<item rdf:about="https://import.io/">
    <title>import.io | Structured Web Data Scraping | import•io</title>
    <dc:date>2014-04-26T13:02:47+00:00</dc:date>
    <link>https://import.io/</link>
    <dc:creator>msszczep</dc:creator><description><![CDATA[
Turn the web into data, today
Transform any website into a table of data or an API in minutes without even writing any code.]]></description>
<dc:subject>datamining data api json scraping code web webapp webdev useful</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:msszczep/b:d1f552a20bb9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:datamining"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:api"/>
	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:json"/>
	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:scraping"/>
	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:code"/>
	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:web"/>
	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:webapp"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:useful"/>
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</item>
<item rdf:about="http://www.cs.uvm.edu/~icdm/algorithms/10Algorithms-08.pdf">
    <title>Top 10 Algorithms in Data Mining</title>
    <dc:date>2014-03-29T16:22:12+00:00</dc:date>
    <link>http://www.cs.uvm.edu/~icdm/algorithms/10Algorithms-08.pdf</link>
    <dc:creator>msszczep</dc:creator><dc:subject>datamining algorithms machinelearning algorithm ai bigdata datascience</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:msszczep/b:ceab7ebe17dd/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:datascience"/>
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</item>
<item rdf:about="http://saedsayad.com/">
    <title>Data Mining Map</title>
    <dc:date>2014-01-13T16:07:26+00:00</dc:date>
    <link>http://saedsayad.com/</link>
    <dc:creator>msszczep</dc:creator><dc:subject>datamining data algorithm kaggle bigdata reference flowchart</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:msszczep/b:1bb95dcf2ec1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:datamining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:algorithm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:kaggle"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:reference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:flowchart"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.efytimes.com/e1/fullnews.asp?edid=119694">
    <title>29 Free eBooks On Databases, Data Mining And Information Retrieval</title>
    <dc:date>2013-12-24T05:43:52+00:00</dc:date>
    <link>http://www.efytimes.com/e1/fullnews.asp?edid=119694</link>
    <dc:creator>msszczep</dc:creator><dc:subject>books ebooks online database datamining bigdata information</dc:subject>
<dc:identifier>https://pinboard.in/u:msszczep/b:c9e042689c1e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:books"/>
	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:ebooks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:online"/>
	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:database"/>
	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:datamining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:bigdata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:msszczep/t:information"/>
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</item>
<item rdf:about="http://www.datatau.com/">
    <title>DataTau - Hacker News for datamining</title>
    <dc:date>2013-12-12T22:59:20+00:00</dc:date>
    <link>http://www.datatau.com/</link>
    <dc:creator>msszczep</dc:creator><dc:subject>datamining data news hackernews bigdata datascience code programming</dc:subject>
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<item rdf:about="https://scraperwiki.com/">
    <title>ScraperWiki</title>
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    <link>https://scraperwiki.com/</link>
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    <dc:creator>msszczep</dc:creator><description><![CDATA["Most importantly, if that's what I can do with a limited set of my own data, imagine what the NSA can do with the datasets it has access to. If you don't think determining an anonymous caller's gender is particularly useful, think about the other things you might find out from a better set of data and more precise algorithms, like which callers are likely to be related to one another (I'm going to try that one on myself next), or with location data, where they're likely to be at any given time. Once you start combining these questions and running these algorithms on multiple people's sets of data, you start to see how you can build up a fairly complete picture of just about anyone's life without truly knowing anything about them at all."]]></description>
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    <link>http://www.bdatafest.computationalreporting.com/home</link>
    <dc:creator>msszczep</dc:creator><description><![CDATA[Analyzing money's influence in politics]]></description>
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    <title>A Programmer's Guide to Data Mining | The Ancient Art of the Numerati</title>
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<item rdf:about="http://www-stat.stanford.edu/~tibs/ElemStatLearn/">
    <title>Elements of Statistical Learning: data mining, inference, and prediction. 2nd Edition.</title>
    <dc:date>2013-03-07T19:53:03+00:00</dc:date>
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    <title>Data, Data, Data: Thousands of Public Data Sources | The Official Blog of BigML.com</title>
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<item rdf:about="http://thechangelog.com/the-city-of-chicago-is-on-github/">
    <title>The City of Chicago is on Github - The Changelog</title>
    <dc:date>2013-03-04T16:13:11+00:00</dc:date>
    <link>http://thechangelog.com/the-city-of-chicago-is-on-github/</link>
    <dc:creator>msszczep</dc:creator><dc:subject>chicago hacker github city data datamining database datascience</dc:subject>
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<item rdf:about="http://narrativescience.com/">
    <title>Business Intelligence Reporting Services | Narrative Science</title>
    <dc:date>2013-02-28T17:01:52+00:00</dc:date>
    <link>http://narrativescience.com/</link>
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<item rdf:about="http://occupydatanyc.org/">
    <title>#OccupyData NYC | Next Hackathon: March 1st + 2nd, 2013. James Gallery @ CUNY Graduate Center</title>
    <dc:date>2013-02-21T15:27:54+00:00</dc:date>
    <link>http://occupydatanyc.org/</link>
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<item rdf:about="http://noahveltman.com/crossword/">
    <title>Noah Veltman | New York Times Crossword Analysis</title>
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    <title>School for quants - FT.com</title>
    <dc:date>2013-02-06T21:17:54+00:00</dc:date>
    <link>http://www.ft.com/cms/s/2/0664cd92-6277-11e1-872e-00144feabdc0.html#axzz1oEeYcqi8</link>
    <dc:creator>msszczep</dc:creator><description><![CDATA[High quality global journalism requires investment. Please share this article with others using the link below, do not cut & paste the article. See our Ts&Cs and Copyright Policy for more detail. Email ftsales.support@ft.com to buy additional rights. http://www.ft.com/cms/s/2/0664cd92-6277-11e1-872e-00144feabdc0.html#ixzz2K9juWUc4

Under the direction of the PhD students, the undergraduates were writing computer programs to haul millions of pages of publicly available digital chatter – from Facebook, Twitter, blogs and news stories – into a real-time archive which could be analysed for signs of the public mood, particularly in regard to financial markets. ]]></description>
<dc:subject>computing business economics data algorithm socialmedia datascience datamining</dc:subject>
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    <dc:creator>msszczep</dc:creator><description><![CDATA[Researchers have created software that predicts when and where disease outbreaks might occur based on two decades of New York Times articles and other online data. The research comes from Microsoft and the Technion-Israel Institute of Technology.]]></description>
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Several novice programmers who signed up for a free machine-learning class on Coursera have gone on recently to win predictive-modeling competitions. Maybe it’s not that hard to mint new data scientists after all.
]]></description>
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    <link>https://github.com/clips/pattern</link>
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<item rdf:about="http://gigaom.com/data/forget-your-fancy-data-science-try-overkill-analytics/">
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<item rdf:about="http://www.michaelnielsen.org/ddi/how-to-crawl-a-quarter-billion-webpages-in-40-hours/">
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<item rdf:about="http://i.stanford.edu/~ullman/mmds.html">
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<item rdf:about="http://gigaom.com/cloud/how-indias-favorite-tv-show-uses-data-to-change-the-world/">
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<item rdf:about="http://www.bigfastblog.com/how-to-get-experience-working-with-large-datasets">
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<item rdf:about="http://bid.berkeley.edu/cs294-1-spring12/index.php/Main_Page">
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<item rdf:about="http://www.kaggle.com/">
    <title>Kaggle, we're making data science a sport</title>
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<item rdf:about="http://sunlightfoundation.com/blog/2011/12/12/announcing-the-return-of-capitol-words/">
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