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1. Shard Calculation

Query:

Response:

Calculated Shard: 180 (from laksa044)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa180.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, attrs_boilerpipe_text AS attrs_boilerpipe_text, attrs_markdown AS attrs_markdown, attrs_readable_markdown AS attrs_readable_markdown, meta_canonical AS meta_canonical FROM crawler3.page_info_local FINAL PREWHERE (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://news.mit.edu/2014/interactive-error-correcting-code-1002\')), getAhrefsUnparsedNoserviceFromURL(\'https://news.mit.edu/2014/interactive-error-correcting-code-1002\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/news.mit.edu\/2014\/interactive-error-correcting-code-1002","crawl_time":1775266544,"first_indexed_time":1598305522,"http_code":200,"src_unparsed":"edu,mit!news,\/2014\/interactive-error-correcting-code-1002 s443","src_root_hash":"10722954425220430980","history_drop_reason":null,"meta_title":"New frontier in error-correcting codes | MIT News | Massachusetts Institute of Technology","meta_descriptions":["Coding scheme for interactive communication is the first to near optimality on three classical measures."],"attrs_boilerpipe_text":"Error-correcting codes are one of the\nglories\nof the information age: They’re what guarantee the flawless transmission of digital information over the airwaves or through copper wire, even in the presence of the corrupting influences that engineers call “noise.”\nBut classical error-correcting codes work best with large chunks of data: The bigger the chunk, the higher the rate at which it can be transmitted error-free. In the Internet age, however, distributed computing is becoming more and more common, with devices repeatedly exchanging small chunks of data over long periods of time.\nSo for the last 20 years, researchers have been investigating interactive-coding schemes, which address the problem of long sequences of short exchanges. Like classical error-correcting codes, interactive codes are evaluated according to three criteria: How much noise can they tolerate? What’s the maximum transmission rate they afford? And how time-consuming are the encoding and decoding processes?\nAt the IEEE Symposium on Foundations of Computer Science this month, MIT graduate students past and present will describe the first interactive coding scheme to approach the optimum on all three measures.\n“Previous to this work, it was known how to get two out of three of these things to be optimal,” says Mohsen Ghaffari, a graduate student in electrical engineering and computer science and one of the paper’s two co-authors. “This paper achieves all three of them.”\nVicious noise\nMoreover, where Claude Shannon’s groundbreaking 1948 analysis of error-correcting codes considered the case of random noise, in which every bit of transmitted data has the same chance of being corrupted, Ghaffari and his collaborator —\nBernhard Haeupler\n, who did his graduate work at MIT and is now an assistant professor at Carnegie Mellon University — consider the more stringent case of “adversarial noise,” in which an antagonist is trying to interfere with transmission in the most disruptive way possible.\n“We don’t know what type of random noise will be the one that actually captures reality,” Ghaffari explains. “If we knew the best one, we would just use that. But generally, we don’t know. So you try to generate a coding that is as general as possible.” A coding scheme that could thwart an active adversary would also thwart any type of random noise.\nError-correcting codes — both classical and interactive — work by adding some extra information to the message to be transmitted. They might, for instance, tack on some bits that describe arithmetic relationships between the message bits. Both the message bits and the extra bits are liable to corruption, so decoding a message — extracting the true sequence of message bits from the sequence that arrives at the receiver — is usually a process of iterating back and forth between the message bits and the extra bits, trying to iron out discrepancies.\nIn interactive communication, the maximum tolerable error rate is one-fourth: If the adversary can corrupt more than a quarter of the bits sent, perfectly reliable communication is impossible. Some prior interactive-coding schemes, Ghaffari explains, could handle that error rate without requiring too many extra bits. But the decoding process was prohibitively complex.\nMaking a list\nTo keep the complexity down, Ghaffari and Haeupler adopted a technique called list decoding. Rather than iterating back and forth between message bits and extra bits until the single most probable interpretation emerges, their algorithm iterates just long enough to create a list of likely candidates. At the end of their mutual computation, each of the interacting devices may have a list with hundreds of entries.\nBut each device, while it has only imperfect knowledge of the messages sent by the other, has perfect knowledge of the messages it sent. So if, at the computation’s end, the devices simply exchange lists, each has enough additional information to zero in on the optimal decoding.\nThe maximum tolerable error rate for an interactive-coding scheme — one-fourth — is a theoretical result. The minimum length of an encoded message and the minimum decoding complexity, on the other hand, are surmises based on observation.\nBut Ghaffari and Haeupler’s decoding algorithm is nearly linear, meaning that its execution time is roughly proportional to the length of the messages exchanged.\n“It is optimal in the sense that it is linear,” says Mark Braverman, an assistant professor of computer science at Princeton University who has also worked on interactive coding. “That’s an important benchmark.”\nBut linear relationships are still defined by constants: y = x is a linear relationship, but so is y = 1,000,000,000x. A linear algorithm that takes an extra second of computation for each additional bit of data it considers isn’t as good as a linear algorithm that takes an extra microsecond.\n“We still need to worry a little bit about constants,” Braverman says. “But before you can worry about constants, you have to know that there is a constant-rate scheme. This is very nice progress and a prerequisite to asking those next questions.”","attrs_markdown":"[Skip to content ↓](https:\/\/news.mit.edu\/2014\/interactive-error-correcting-code-1002#main)\n\n[Massachusetts Institute of Technology](http:\/\/web.mit.edu\/)\n\n[MIT Top Menu↓](https:\/\/news.mit.edu\/2014\/interactive-error-correcting-code-1002#tle-nav--menu)\n\n- [Education](http:\/\/web.mit.edu\/education)\n- [Research](http:\/\/web.mit.edu\/research)\n- [Innovation](http:\/\/web.mit.edu\/innovation)\n- [Admissions + Aid](http:\/\/web.mit.edu\/admissions-aid)\n- [Campus Life](http:\/\/web.mit.edu\/campus-life)\n- [News](https:\/\/news.mit.edu\/)\n- [Alumni](http:\/\/web.mit.edu\/alumni)\n- [About MIT](http:\/\/web.mit.edu\/about)\n- More ↓\n  - [News](https:\/\/news.mit.edu\/)\n  - [Alumni](http:\/\/web.mit.edu\/alumni)\n  - [About MIT](http:\/\/web.mit.edu\/about)\n\n[Search MIT](http:\/\/web.mit.edu\/search\/)\n\nSee More Results\n\n[Suggestions or feedback?](http:\/\/web.mit.edu\/feedback)\n\n## [MIT News \\| Massachusetts Institute of Technology - On Campus and Around the world](https:\/\/news.mit.edu\/)\n[Subscribe to MIT News newsletter](http:\/\/web.mit.edu\/mit-daily-weekly\/)\n\n[Search news](https:\/\/news.mit.edu\/2014\/interactive-error-correcting-code-1002#site-nav--browse)\n\n## Browse By\n### Topics\n[View All](https:\/\/news.mit.edu\/topics) →\n\nExplore:\n\n- [Machine learning](https:\/\/news.mit.edu\/topic\/machine-learning)\n- [Sustainability](https:\/\/news.mit.edu\/topic\/sustainability)\n- [Startups](https:\/\/news.mit.edu\/topic\/startups)\n- [Black holes](https:\/\/news.mit.edu\/topic\/black-holes)\n- [Classes and programs](https:\/\/news.mit.edu\/topic\/classes-and-programs)\n\n### Departments\n[View All](https:\/\/news.mit.edu\/departments) →\n\nExplore:\n\n- [Aeronautics and Astronautics](https:\/\/news.mit.edu\/department\/aeronautics-and-astronautics)\n- [Brain and Cognitive Sciences](https:\/\/news.mit.edu\/department\/brain-and-cognitive-sciences)\n- [Architecture](https:\/\/news.mit.edu\/department\/architecture)\n- [Political Science](https:\/\/news.mit.edu\/department\/political-science)\n- [Mechanical Engineering](https:\/\/news.mit.edu\/department\/mechanical-engineering)\n\n### Centers, Labs, & Programs\n[View All](https:\/\/news.mit.edu\/centers-labs-programs) →\n\nExplore:\n\n- [Abdul Latif Jameel Poverty Action Lab (J-PAL)](https:\/\/news.mit.edu\/clp\/abdul-latif-jameel-poverty-action-lab-j-pal)\n- [Picower Institute for Learning and Memory](https:\/\/news.mit.edu\/clp\/picower-institute)\n- [Media Lab](https:\/\/news.mit.edu\/clp\/media-lab)\n- [Lincoln Laboratory](https:\/\/news.mit.edu\/clp\/lincoln-laboratory)\n\n### Schools\n- [School of Architecture + Planning](https:\/\/news.mit.edu\/school\/architecture-and-planning)\n- [School of Engineering](https:\/\/news.mit.edu\/school\/engineering)\n- [School of Humanities, Arts, and Social Sciences](https:\/\/news.mit.edu\/school\/humanities-arts-and-social-sciences)\n- [Sloan School of Management](https:\/\/news.mit.edu\/school\/management)\n- [School of Science](https:\/\/news.mit.edu\/school\/science)\n- [MIT Schwarzman College of Computing](https:\/\/news.mit.edu\/school\/mit-schwarzman-college-computing)\n\n[View all news coverage of MIT in the media](https:\/\/news.mit.edu\/in-the-media) →\n\n[Listen to audio content from MIT News](https:\/\/news.mit.edu\/podcasts) →\n\n[Subscribe to MIT newsletter](http:\/\/web.mit.edu\/subscribe\/) →\n\n[Close](https:\/\/news.mit.edu\/2014\/interactive-error-correcting-code-1002)\n\n#### Breadcrumb\n1. [MIT News](https:\/\/news.mit.edu\/)\n2. New frontier in error-correcting codes\n# New frontier in error-correcting codes\nCoding scheme for interactive communication is the first to near optimality on three classical measures.\n\nLarry Hardesty \\| MIT News Office\n\nPublication Date:\n\nOctober 2, 2014\n\n[Press Inquiries](https:\/\/news.mit.edu\/2014\/interactive-error-correcting-code-1002#press-inquiries)\n\n### Press Contact:\nAbby Abazorius\n\nEmail:  [abbya@mit.edu](mailto:abbya@mit.edu)\n\nPhone:  617-253-2709\n\nMIT News Office\n\n### Media Download\n[![](https:\/\/news.mit.edu\/sites\/default\/files\/styles\/news_article__download\/public\/download\/201410\/MIt-Interactive-Coding-01.jpg?itok=ug7QPeUr)](https:\/\/news.mit.edu\/sites\/default\/files\/download\/201410\/MIt-Interactive-Coding-01.jpg)\n\n↓ [Download Image](https:\/\/news.mit.edu\/sites\/default\/files\/download\/201410\/MIt-Interactive-Coding-01.jpg)\n\nCredits: Illustration: Jose-Luis Olivares\/MIT\n\n#### \\*Terms of Use:\nImages for download on the MIT News office website are made available to non-commercial entities, press and the general public under a [Creative Commons Attribution Non-Commercial No Derivatives license](http:\/\/creativecommons.org\/licenses\/by-nc-nd\/3.0\/). You may not alter the images provided, other than to crop them to size. A credit line must be used when reproducing images; if one is not provided below, credit the images to \"MIT.\"\n\n[Close](https:\/\/news.mit.edu\/2014\/interactive-error-correcting-code-1002)\n\n![](https:\/\/news.mit.edu\/sites\/default\/files\/styles\/news_article__image_gallery\/public\/images\/201410\/MIt-Interactive-Coding-01.jpg?itok=oSWfJXxS)\n\nCredits:\n\nIllustration: Jose-Luis Olivares\/MIT\n\nPrevious image Next image\n\nError-correcting codes are one of the [glories](http:\/\/www.technologyreview.com\/article\/420369\/code-quest\/) of the information age: They’re what guarantee the flawless transmission of digital information over the airwaves or through copper wire, even in the presence of the corrupting influences that engineers call “noise.”\n\nBut classical error-correcting codes work best with large chunks of data: The bigger the chunk, the higher the rate at which it can be transmitted error-free. In the Internet age, however, distributed computing is becoming more and more common, with devices repeatedly exchanging small chunks of data over long periods of time.\n\nSo for the last 20 years, researchers have been investigating interactive-coding schemes, which address the problem of long sequences of short exchanges. Like classical error-correcting codes, interactive codes are evaluated according to three criteria: How much noise can they tolerate? What’s the maximum transmission rate they afford? And how time-consuming are the encoding and decoding processes?\n\nAt the IEEE Symposium on Foundations of Computer Science this month, MIT graduate students past and present will describe the first interactive coding scheme to approach the optimum on all three measures.\n\n“Previous to this work, it was known how to get two out of three of these things to be optimal,” says Mohsen Ghaffari, a graduate student in electrical engineering and computer science and one of the paper’s two co-authors. “This paper achieves all three of them.”\n\n**Vicious noise**\n\nMoreover, where Claude Shannon’s groundbreaking 1948 analysis of error-correcting codes considered the case of random noise, in which every bit of transmitted data has the same chance of being corrupted, Ghaffari and his collaborator — [Bernhard Haeupler](http:\/\/newsoffice.mit.edu\/2013\/ad-hoc-networks-0109), who did his graduate work at MIT and is now an assistant professor at Carnegie Mellon University — consider the more stringent case of “adversarial noise,” in which an antagonist is trying to interfere with transmission in the most disruptive way possible.\n\n“We don’t know what type of random noise will be the one that actually captures reality,” Ghaffari explains. “If we knew the best one, we would just use that. But generally, we don’t know. So you try to generate a coding that is as general as possible.” A coding scheme that could thwart an active adversary would also thwart any type of random noise.\n\nError-correcting codes — both classical and interactive — work by adding some extra information to the message to be transmitted. They might, for instance, tack on some bits that describe arithmetic relationships between the message bits. Both the message bits and the extra bits are liable to corruption, so decoding a message — extracting the true sequence of message bits from the sequence that arrives at the receiver — is usually a process of iterating back and forth between the message bits and the extra bits, trying to iron out discrepancies.\n\nIn interactive communication, the maximum tolerable error rate is one-fourth: If the adversary can corrupt more than a quarter of the bits sent, perfectly reliable communication is impossible. Some prior interactive-coding schemes, Ghaffari explains, could handle that error rate without requiring too many extra bits. But the decoding process was prohibitively complex.\n\n**Making a list**\n\nTo keep the complexity down, Ghaffari and Haeupler adopted a technique called list decoding. Rather than iterating back and forth between message bits and extra bits until the single most probable interpretation emerges, their algorithm iterates just long enough to create a list of likely candidates. At the end of their mutual computation, each of the interacting devices may have a list with hundreds of entries.\n\nBut each device, while it has only imperfect knowledge of the messages sent by the other, has perfect knowledge of the messages it sent. So if, at the computation’s end, the devices simply exchange lists, each has enough additional information to zero in on the optimal decoding.\n\nThe maximum tolerable error rate for an interactive-coding scheme — one-fourth — is a theoretical result. The minimum length of an encoded message and the minimum decoding complexity, on the other hand, are surmises based on observation.\n\nBut Ghaffari and Haeupler’s decoding algorithm is nearly linear, meaning that its execution time is roughly proportional to the length of the messages exchanged.\n\n“It is optimal in the sense that it is linear,” says Mark Braverman, an assistant professor of computer science at Princeton University who has also worked on interactive coding. “That’s an important benchmark.”\n\nBut linear relationships are still defined by constants: y = x is a linear relationship, but so is y = 1,000,000,000x. A linear algorithm that takes an extra second of computation for each additional bit of data it considers isn’t as good as a linear algorithm that takes an extra microsecond.\n\n“We still need to worry a little bit about constants,” Braverman says. “But before you can worry about constants, you have to know that there is a constant-rate scheme. This is very nice progress and a prerequisite to asking those next questions.”\n\n### Share this news article on:\n- [X](https:\/\/twitter.com\/share?url=https:\/\/news.mit.edu\/2014\/interactive-error-correcting-code-1002)\n- [Facebook](https:\/\/www.facebook.com\/sharer\/sharer.php?u=https:\/\/news.mit.edu\/2014\/interactive-error-correcting-code-1002)\n- [LinkedIn](https:\/\/www.linkedin.com\/sharing\/share-offsite\/?url=https:\/\/news.mit.edu\/2014\/interactive-error-correcting-code-1002)\n- [Reddit](http:\/\/www.reddit.com\/submit?url=https:\/\/news.mit.edu\/2014\/interactive-error-correcting-code-1002&title=New%20frontier%20in%20error-correcting%20codes)\n- [Print](https:\/\/news.mit.edu\/2014\/interactive-error-correcting-code-1002)\n\n## Related Links\n- [Paper: “Optimal Error Rates for Interactive Coding II: Efficiency and List Decoding.”](http:\/\/people.csail.mit.edu\/ghaffari\/papers\/interactive_protocol2.pdf)\n- [Mohsen Ghaffari](http:\/\/people.csail.mit.edu\/ghaffari\/)\n- [Theory of Distributed Systems group](http:\/\/groups.csail.mit.edu\/tds\/)\n- [Computer Science and Artificial Intelligence Laboratory](http:\/\/csail.mit.edu\/)\n- [Department of Electrical Engineering and Computer Science](http:\/\/www.eecs.mit.edu\/)\n- [School of Engineering](http:\/\/engineering.mit.edu\/)\n\n## Related Topics\n- [Algorithms](https:\/\/news.mit.edu\/topic\/algorithms)\n- [Information theory](https:\/\/news.mit.edu\/topic\/information-theory)\n- [School of Engineering](https:\/\/news.mit.edu\/topic\/school-engineering)\n- [Computer Science and Artificial Intelligence Laboratory (CSAIL)](https:\/\/news.mit.edu\/topic\/computer-science-and-artificial-intelligence-laboratory-csail)\n- [Electrical engineering and computer science (EECS)](https:\/\/news.mit.edu\/topic\/electrical-engineering-computer-science-eecs)\n\n## Related Articles\n[![](https:\/\/news.mit.edu\/sites\/default\/files\/styles\/news_article__archive\/public\/images\/201301\/20130108165805-0.jpg?itok=I57QTVHq)](https:\/\/news.mit.edu\/2013\/ad-hoc-networks-0109)\n\n### [Guaranteed delivery — in ad hoc networks](https:\/\/news.mit.edu\/2013\/ad-hoc-networks-0109)\n\n[![The principle behind network coding is often explained by reference to a so-called butterfly network. When messages A and B reach the same node, they're scrambled together, and their combination (A+B) is passed to the next node. Further downstream, one node uses A to recover B from A+B, while another uses B to recover A from A+B.](https:\/\/news.mit.edu\/sites\/default\/files\/styles\/news_article__archive\/public\/images\/201205\/20120514164930-1.jpg?itok=VveMPIwK)](https:\/\/news.mit.edu\/2012\/search-for-perfect-code-0515)\n\n### [The elusive capacity of networks](https:\/\/news.mit.edu\/2012\/search-for-perfect-code-0515)\n\n[![](https:\/\/news.mit.edu\/sites\/default\/files\/styles\/news_article__archive\/public\/images\/201202\/20120209160519-0.jpg?itok=Lp647JOd)](https:\/\/news.mit.edu\/2012\/error-correcting-codes-0210)\n\n### [The blind codemaker](https:\/\/news.mit.edu\/2012\/error-correcting-codes-0210)\n\n[![Claude Shannon's clever electromechanical mouse, which he called Theseus, was one of the earliest attempts to teach a machine to learn and one of the first experiments in artificial intelligence.](https:\/\/news.mit.edu\/sites\/default\/files\/styles\/news_article__archive\/public\/images\/201001\/20100114141937-1.jpg?itok=aBLXlUmm)](https:\/\/news.mit.edu\/2010\/explained-shannon-0115)\n\n### [Explained: The Shannon limit](https:\/\/news.mit.edu\/2010\/explained-shannon-0115)\n\nPrevious item Next item\n\n## More MIT News\n[![Hundreds of parallel lines comprised of spheres recede into the distance. In the middle ground are seven large red dots and a white 3D arrow pointing up.](https:\/\/news.mit.edu\/sites\/default\/files\/styles\/news_article__recent_news\/public\/images\/202603\/MIT-physics-Moire-Crystal.jpg?itok=RGEkXyNw)](https:\/\/news.mit.edu\/2026\/electrons-in-moire-crystals-explore-higher-dimensional-quantum-worlds-0403)\n\n### [Electrons in moiré crystals explore higher-dimensional quantum worlds](https:\/\/news.mit.edu\/2026\/electrons-in-moire-crystals-explore-higher-dimensional-quantum-worlds-0403)\nMIT physicists have discovered 3D “moiré crystals” that simulate four-dimensional quantum materials to a T.\n\n[Read full story](https:\/\/news.mit.edu\/2026\/electrons-in-moire-crystals-explore-higher-dimensional-quantum-worlds-0403) →\n\n[![Sara Jex takes a selfie with four family members inside a large, abandoned industrial 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[News](https:\/\/news.mit.edu\/)\n- [Alumni](http:\/\/web.mit.edu\/alumni)\n- [About MIT](http:\/\/web.mit.edu\/about)\n- [Join us in building a better world.](http:\/\/web.mit.edu\/building-a-better-world\/)\n\n[Massachusetts Institute of Technology](http:\/\/web.mit.edu\/)  \n77 Massachusetts Avenue, Cambridge, MA, USA\n\nRecommended Links:\n\n- [Visit](http:\/\/web.mit.edu\/visitmit)\n- [Map (opens in new window)](http:\/\/whereis.mit.edu\/)\n- [Events (opens in new window)](http:\/\/calendar.mit.edu\/)\n- [People (opens in new window)](http:\/\/web.mit.edu\/people.html)\n- [Careers (opens in new window)](http:\/\/careers.mit.edu\/)\n- [Contact](http:\/\/web.mit.edu\/contact)\n- [Privacy](http:\/\/web.mit.edu\/privacy)\n- [Accessibility](http:\/\/web.mit.edu\/accessibility)\n- - [Social Media Hub](http:\/\/socialmediahub.mit.edu\/)\n  - [MIT on X](https:\/\/twitter.com\/mit)\n  - [MIT on Facebook](https:\/\/www.facebook.com\/MITnews)\n  - [MIT on YouTube](https:\/\/www.youtube.com\/mit)\n  - [MIT on Instagram](https:\/\/www.instagram.com\/mit)","attrs_readable_markdown":"Error-correcting codes are one of the [glories](http:\/\/www.technologyreview.com\/article\/420369\/code-quest\/) of the information age: They’re what guarantee the flawless transmission of digital information over the airwaves or through copper wire, even in the presence of the corrupting influences that engineers call “noise.”\n\nBut classical error-correcting codes work best with large chunks of data: The bigger the chunk, the higher the rate at which it can be transmitted error-free. In the Internet age, however, distributed computing is becoming more and more common, with devices repeatedly exchanging small chunks of data over long periods of time.\n\nSo for the last 20 years, researchers have been investigating interactive-coding schemes, which address the problem of long sequences of short exchanges. Like classical error-correcting codes, interactive codes are evaluated according to three criteria: How much noise can they tolerate? What’s the maximum transmission rate they afford? And how time-consuming are the encoding and decoding processes?\n\nAt the IEEE Symposium on Foundations of Computer Science this month, MIT graduate students past and present will describe the first interactive coding scheme to approach the optimum on all three measures.\n\n“Previous to this work, it was known how to get two out of three of these things to be optimal,” says Mohsen Ghaffari, a graduate student in electrical engineering and computer science and one of the paper’s two co-authors. “This paper achieves all three of them.”\n\n**Vicious noise**\n\nMoreover, where Claude Shannon’s groundbreaking 1948 analysis of error-correcting codes considered the case of random noise, in which every bit of transmitted data has the same chance of being corrupted, Ghaffari and his collaborator — [Bernhard Haeupler](http:\/\/newsoffice.mit.edu\/2013\/ad-hoc-networks-0109), who did his graduate work at MIT and is now an assistant professor at Carnegie Mellon University — consider the more stringent case of “adversarial noise,” in which an antagonist is trying to interfere with transmission in the most disruptive way possible.\n\n“We don’t know what type of random noise will be the one that actually captures reality,” Ghaffari explains. “If we knew the best one, we would just use that. But generally, we don’t know. So you try to generate a coding that is as general as possible.” A coding scheme that could thwart an active adversary would also thwart any type of random noise.\n\nError-correcting codes — both classical and interactive — work by adding some extra information to the message to be transmitted. They might, for instance, tack on some bits that describe arithmetic relationships between the message bits. Both the message bits and the extra bits are liable to corruption, so decoding a message — extracting the true sequence of message bits from the sequence that arrives at the receiver — is usually a process of iterating back and forth between the message bits and the extra bits, trying to iron out discrepancies.\n\nIn interactive communication, the maximum tolerable error rate is one-fourth: If the adversary can corrupt more than a quarter of the bits sent, perfectly reliable communication is impossible. Some prior interactive-coding schemes, Ghaffari explains, could handle that error rate without requiring too many extra bits. But the decoding process was prohibitively complex.\n\n**Making a list**\n\nTo keep the complexity down, Ghaffari and Haeupler adopted a technique called list decoding. Rather than iterating back and forth between message bits and extra bits until the single most probable interpretation emerges, their algorithm iterates just long enough to create a list of likely candidates. At the end of their mutual computation, each of the interacting devices may have a list with hundreds of entries.\n\nBut each device, while it has only imperfect knowledge of the messages sent by the other, has perfect knowledge of the messages it sent. So if, at the computation’s end, the devices simply exchange lists, each has enough additional information to zero in on the optimal decoding.\n\nThe maximum tolerable error rate for an interactive-coding scheme — one-fourth — is a theoretical result. The minimum length of an encoded message and the minimum decoding complexity, on the other hand, are surmises based on observation.\n\nBut Ghaffari and Haeupler’s decoding algorithm is nearly linear, meaning that its execution time is roughly proportional to the length of the messages exchanged.\n\n“It is optimal in the sense that it is linear,” says Mark Braverman, an assistant professor of computer science at Princeton University who has also worked on interactive coding. “That’s an important benchmark.”\n\nBut linear relationships are still defined by constants: y = x is a linear relationship, but so is y = 1,000,000,000x. A linear algorithm that takes an extra second of computation for each additional bit of data it considers isn’t as good as a linear algorithm that takes an extra microsecond.\n\n“We still need to worry a little bit about constants,” Braverman says. “But before you can worry about constants, you have to know that there is a constant-rate scheme. This is very nice progress and a prerequisite to asking those next questions.”","meta_canonical":null}

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Meta Description	Coding scheme for interactive communication is the first to near optimality on three classical measures.
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Boilerpipe Text	Error-correcting codes are one of the glories of the information age: They’re what guarantee the flawless transmission of digital information over the airwaves or through copper wire, even in the presence of the corrupting influences that engineers call “noise.” But classical error-correcting codes work best with large chunks of data: The bigger the chunk, the higher the rate at which it can be transmitted error-free. In the Internet age, however, distributed computing is becoming more and more common, with devices repeatedly exchanging small chunks of data over long periods of time. So for the last 20 years, researchers have been investigating interactive-coding schemes, which address the problem of long sequences of short exchanges. Like classical error-correcting codes, interactive codes are evaluated according to three criteria: How much noise can they tolerate? What’s the maximum transmission rate they afford? And how time-consuming are the encoding and decoding processes? At the IEEE Symposium on Foundations of Computer Science this month, MIT graduate students past and present will describe the first interactive coding scheme to approach the optimum on all three measures. “Previous to this work, it was known how to get two out of three of these things to be optimal,” says Mohsen Ghaffari, a graduate student in electrical engineering and computer science and one of the paper’s two co-authors. “This paper achieves all three of them.” Vicious noise Moreover, where Claude Shannon’s groundbreaking 1948 analysis of error-correcting codes considered the case of random noise, in which every bit of transmitted data has the same chance of being corrupted, Ghaffari and his collaborator — Bernhard Haeupler , who did his graduate work at MIT and is now an assistant professor at Carnegie Mellon University — consider the more stringent case of “adversarial noise,” in which an antagonist is trying to interfere with transmission in the most disruptive way possible. “We don’t know what type of random noise will be the one that actually captures reality,” Ghaffari explains. “If we knew the best one, we would just use that. But generally, we don’t know. So you try to generate a coding that is as general as possible.” A coding scheme that could thwart an active adversary would also thwart any type of random noise. Error-correcting codes — both classical and interactive — work by adding some extra information to the message to be transmitted. They might, for instance, tack on some bits that describe arithmetic relationships between the message bits. Both the message bits and the extra bits are liable to corruption, so decoding a message — extracting the true sequence of message bits from the sequence that arrives at the receiver — is usually a process of iterating back and forth between the message bits and the extra bits, trying to iron out discrepancies. In interactive communication, the maximum tolerable error rate is one-fourth: If the adversary can corrupt more than a quarter of the bits sent, perfectly reliable communication is impossible. Some prior interactive-coding schemes, Ghaffari explains, could handle that error rate without requiring too many extra bits. But the decoding process was prohibitively complex. Making a list To keep the complexity down, Ghaffari and Haeupler adopted a technique called list decoding. Rather than iterating back and forth between message bits and extra bits until the single most probable interpretation emerges, their algorithm iterates just long enough to create a list of likely candidates. At the end of their mutual computation, each of the interacting devices may have a list with hundreds of entries. But each device, while it has only imperfect knowledge of the messages sent by the other, has perfect knowledge of the messages it sent. So if, at the computation’s end, the devices simply exchange lists, each has enough additional information to zero in on the optimal decoding. The maximum tolerable error rate for an interactive-coding scheme — one-fourth — is a theoretical result. The minimum length of an encoded message and the minimum decoding complexity, on the other hand, are surmises based on observation. But Ghaffari and Haeupler’s decoding algorithm is nearly linear, meaning that its execution time is roughly proportional to the length of the messages exchanged. “It is optimal in the sense that it is linear,” says Mark Braverman, an assistant professor of computer science at Princeton University who has also worked on interactive coding. “That’s an important benchmark.” But linear relationships are still defined by constants: y = x is a linear relationship, but so is y = 1,000,000,000x. A linear algorithm that takes an extra second of computation for each additional bit of data it considers isn’t as good as a linear algorithm that takes an extra microsecond. “We still need to worry a little bit about constants,” Braverman says. “But before you can worry about constants, you have to know that there is a constant-rate scheme. This is very nice progress and a prerequisite to asking those next questions.”
Markdown	[Skip to content ↓](https://news.mit.edu/2014/interactive-error-correcting-code-1002#main) [Massachusetts Institute of Technology](http://web.mit.edu/) [MIT Top Menu↓](https://news.mit.edu/2014/interactive-error-correcting-code-1002#tle-nav--menu) - [Education](http://web.mit.edu/education) - [Research](http://web.mit.edu/research) - [Innovation](http://web.mit.edu/innovation) - [Admissions + Aid](http://web.mit.edu/admissions-aid) - [Campus Life](http://web.mit.edu/campus-life) - [News](https://news.mit.edu/) - [Alumni](http://web.mit.edu/alumni) - [About MIT](http://web.mit.edu/about) - More ↓ - [News](https://news.mit.edu/) - [Alumni](http://web.mit.edu/alumni) - [About MIT](http://web.mit.edu/about) [Search MIT](http://web.mit.edu/search/) See More Results [Suggestions or feedback?](http://web.mit.edu/feedback) ## [MIT News \\| Massachusetts Institute of Technology - On Campus and Around the world](https://news.mit.edu/) [Subscribe to MIT News newsletter](http://web.mit.edu/mit-daily-weekly/) [Search news](https://news.mit.edu/2014/interactive-error-correcting-code-1002#site-nav--browse) ## Browse By ### Topics [View All](https://news.mit.edu/topics) → Explore: - [Machine learning](https://news.mit.edu/topic/machine-learning) - [Sustainability](https://news.mit.edu/topic/sustainability) - [Startups](https://news.mit.edu/topic/startups) - [Black holes](https://news.mit.edu/topic/black-holes) - [Classes and programs](https://news.mit.edu/topic/classes-and-programs) ### Departments [View All](https://news.mit.edu/departments) → Explore: - [Aeronautics and Astronautics](https://news.mit.edu/department/aeronautics-and-astronautics) - [Brain and Cognitive Sciences](https://news.mit.edu/department/brain-and-cognitive-sciences) - [Architecture](https://news.mit.edu/department/architecture) - [Political Science](https://news.mit.edu/department/political-science) - [Mechanical Engineering](https://news.mit.edu/department/mechanical-engineering) ### Centers, Labs, & Programs [View All](https://news.mit.edu/centers-labs-programs) → Explore: - [Abdul Latif Jameel Poverty Action Lab (J-PAL)](https://news.mit.edu/clp/abdul-latif-jameel-poverty-action-lab-j-pal) - [Picower Institute for Learning and Memory](https://news.mit.edu/clp/picower-institute) - [Media Lab](https://news.mit.edu/clp/media-lab) - [Lincoln Laboratory](https://news.mit.edu/clp/lincoln-laboratory) ### Schools - [School of Architecture + Planning](https://news.mit.edu/school/architecture-and-planning) - [School of Engineering](https://news.mit.edu/school/engineering) - [School of Humanities, Arts, and Social Sciences](https://news.mit.edu/school/humanities-arts-and-social-sciences) - [Sloan School of Management](https://news.mit.edu/school/management) - [School of Science](https://news.mit.edu/school/science) - [MIT Schwarzman College of Computing](https://news.mit.edu/school/mit-schwarzman-college-computing) [View all news coverage of MIT in the media](https://news.mit.edu/in-the-media) → [Listen to audio content from MIT News](https://news.mit.edu/podcasts) → [Subscribe to MIT newsletter](http://web.mit.edu/subscribe/) → [Close](https://news.mit.edu/2014/interactive-error-correcting-code-1002) #### Breadcrumb 1. [MIT News](https://news.mit.edu/) 2. New frontier in error-correcting codes # New frontier in error-correcting codes Coding scheme for interactive communication is the first to near optimality on three classical measures. Larry Hardesty \\| MIT News Office Publication Date: October 2, 2014 [Press Inquiries](https://news.mit.edu/2014/interactive-error-correcting-code-1002#press-inquiries) ### Press Contact: Abby Abazorius Email: [abbya@mit.edu](mailto:abbya@mit.edu) Phone: 617-253-2709 MIT News Office ### Media Download [![](https://news.mit.edu/sites/default/files/styles/news_article__download/public/download/201410/MIt-Interactive-Coding-01.jpg?itok=ug7QPeUr)](https://news.mit.edu/sites/default/files/download/201410/MIt-Interactive-Coding-01.jpg) ↓ [Download Image](https://news.mit.edu/sites/default/files/download/201410/MIt-Interactive-Coding-01.jpg) Credits: Illustration: Jose-Luis Olivares/MIT #### \Terms of Use: Images for download on the MIT News office website are made available to non-commercial entities, press and the general public under a [Creative Commons Attribution Non-Commercial No Derivatives license](http://creativecommons.org/licenses/by-nc-nd/3.0/). You may not alter the images provided, other than to crop them to size. A credit line must be used when reproducing images; if one is not provided below, credit the images to "MIT." [Close](https://news.mit.edu/2014/interactive-error-correcting-code-1002) ![](https://news.mit.edu/sites/default/files/styles/news_article__image_gallery/public/images/201410/MIt-Interactive-Coding-01.jpg?itok=oSWfJXxS) Credits: Illustration: Jose-Luis Olivares/MIT Previous image Next image Error-correcting codes are one of the [glories](http://www.technologyreview.com/article/420369/code-quest/) of the information age: They’re what guarantee the flawless transmission of digital information over the airwaves or through copper wire, even in the presence of the corrupting influences that engineers call “noise.” But classical error-correcting codes work best with large chunks of data: The bigger the chunk, the higher the rate at which it can be transmitted error-free. In the Internet age, however, distributed computing is becoming more and more common, with devices repeatedly exchanging small chunks of data over long periods of time. So for the last 20 years, researchers have been investigating interactive-coding schemes, which address the problem of long sequences of short exchanges. Like classical error-correcting codes, interactive codes are evaluated according to three criteria: How much noise can they tolerate? What’s the maximum transmission rate they afford? And how time-consuming are the encoding and decoding processes? At the IEEE Symposium on Foundations of Computer Science this month, MIT graduate students past and present will describe the first interactive coding scheme to approach the optimum on all three measures. “Previous to this work, it was known how to get two out of three of these things to be optimal,” says Mohsen Ghaffari, a graduate student in electrical engineering and computer science and one of the paper’s two co-authors. “This paper achieves all three of them.” Vicious noise* Moreover, where Claude Shannon’s groundbreaking 1948 analysis of error-correcting codes considered the case of random noise, in which every bit of transmitted data has the same chance of being corrupted, Ghaffari and his collaborator — [Bernhard Haeupler](http://newsoffice.mit.edu/2013/ad-hoc-networks-0109), who did his graduate work at MIT and is now an assistant professor at Carnegie Mellon University — consider the more stringent case of “adversarial noise,” in which an antagonist is trying to interfere with transmission in the most disruptive way possible. “We don’t know what type of random noise will be the one that actually captures reality,” Ghaffari explains. “If we knew the best one, we would just use that. But generally, we don’t know. So you try to generate a coding that is as general as possible.” A coding scheme that could thwart an active adversary would also thwart any type of random noise. Error-correcting codes — both classical and interactive — work by adding some extra information to the message to be transmitted. They might, for instance, tack on some bits that describe arithmetic relationships between the message bits. Both the message bits and the extra bits are liable to corruption, so decoding a message — extracting the true sequence of message bits from the sequence that arrives at the receiver — is usually a process of iterating back and forth between the message bits and the extra bits, trying to iron out discrepancies. In interactive communication, the maximum tolerable error rate is one-fourth: If the adversary can corrupt more than a quarter of the bits sent, perfectly reliable communication is impossible. Some prior interactive-coding schemes, Ghaffari explains, could handle that error rate without requiring too many extra bits. But the decoding process was prohibitively complex. Making a list To keep the complexity down, Ghaffari and Haeupler adopted a technique called list decoding. Rather than iterating back and forth between message bits and extra bits until the single most probable interpretation emerges, their algorithm iterates just long enough to create a list of likely candidates. At the end of their mutual computation, each of the interacting devices may have a list with hundreds of entries. But each device, while it has only imperfect knowledge of the messages sent by the other, has perfect knowledge of the messages it sent. So if, at the computation’s end, the devices simply exchange lists, each has enough additional information to zero in on the optimal decoding. The maximum tolerable error rate for an interactive-coding scheme — one-fourth — is a theoretical result. The minimum length of an encoded message and the minimum decoding complexity, on the other hand, are surmises based on observation. But Ghaffari and Haeupler’s decoding algorithm is nearly linear, meaning that its execution time is roughly proportional to the length of the messages exchanged. “It is optimal in the sense that it is linear,” says Mark Braverman, an assistant professor of computer science at Princeton University who has also worked on interactive coding. “That’s an important benchmark.” But linear relationships are still defined by constants: y = x is a linear relationship, but so is y = 1,000,000,000x. A linear algorithm that takes an extra second of computation for each additional bit of data it considers isn’t as good as a linear algorithm that takes an extra microsecond. “We still need to worry a little bit about constants,” Braverman says. “But before you can worry about constants, you have to know that there is a constant-rate scheme. This is very nice progress and a prerequisite to asking those next questions.” ### Share this news article on: - [X](https://twitter.com/share?url=https://news.mit.edu/2014/interactive-error-correcting-code-1002) - [Facebook](https://www.facebook.com/sharer/sharer.php?u=https://news.mit.edu/2014/interactive-error-correcting-code-1002) - [LinkedIn](https://www.linkedin.com/sharing/share-offsite/?url=https://news.mit.edu/2014/interactive-error-correcting-code-1002) - [Reddit](http://www.reddit.com/submit?url=https://news.mit.edu/2014/interactive-error-correcting-code-1002&title=New%20frontier%20in%20error-correcting%20codes) - [Print](https://news.mit.edu/2014/interactive-error-correcting-code-1002) ## Related Links - [Paper: “Optimal Error Rates for Interactive Coding II: Efficiency and List Decoding.”](http://people.csail.mit.edu/ghaffari/papers/interactive_protocol2.pdf) - [Mohsen Ghaffari](http://people.csail.mit.edu/ghaffari/) - [Theory of Distributed Systems group](http://groups.csail.mit.edu/tds/) - [Computer Science and Artificial Intelligence Laboratory](http://csail.mit.edu/) - [Department of Electrical Engineering and Computer Science](http://www.eecs.mit.edu/) - [School of Engineering](http://engineering.mit.edu/) ## Related Topics - [Algorithms](https://news.mit.edu/topic/algorithms) - [Information theory](https://news.mit.edu/topic/information-theory) - [School of Engineering](https://news.mit.edu/topic/school-engineering) - [Computer Science and Artificial Intelligence Laboratory (CSAIL)](https://news.mit.edu/topic/computer-science-and-artificial-intelligence-laboratory-csail) - [Electrical engineering and computer science (EECS)](https://news.mit.edu/topic/electrical-engineering-computer-science-eecs) ## Related Articles [![](https://news.mit.edu/sites/default/files/styles/news_article__archive/public/images/201301/20130108165805-0.jpg?itok=I57QTVHq)](https://news.mit.edu/2013/ad-hoc-networks-0109) ### [Guaranteed delivery — in ad hoc networks](https://news.mit.edu/2013/ad-hoc-networks-0109) [![The principle behind network coding is often explained by reference to a so-called butterfly network. When messages A and B reach the same node, they're scrambled together, and their combination (A+B) is passed to the next node. 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Readable Markdown	Error-correcting codes are one of the [glories](http://www.technologyreview.com/article/420369/code-quest/) of the information age: They’re what guarantee the flawless transmission of digital information over the airwaves or through copper wire, even in the presence of the corrupting influences that engineers call “noise.” But classical error-correcting codes work best with large chunks of data: The bigger the chunk, the higher the rate at which it can be transmitted error-free. In the Internet age, however, distributed computing is becoming more and more common, with devices repeatedly exchanging small chunks of data over long periods of time. So for the last 20 years, researchers have been investigating interactive-coding schemes, which address the problem of long sequences of short exchanges. Like classical error-correcting codes, interactive codes are evaluated according to three criteria: How much noise can they tolerate? What’s the maximum transmission rate they afford? And how time-consuming are the encoding and decoding processes? At the IEEE Symposium on Foundations of Computer Science this month, MIT graduate students past and present will describe the first interactive coding scheme to approach the optimum on all three measures. “Previous to this work, it was known how to get two out of three of these things to be optimal,” says Mohsen Ghaffari, a graduate student in electrical engineering and computer science and one of the paper’s two co-authors. “This paper achieves all three of them.” Vicious noise Moreover, where Claude Shannon’s groundbreaking 1948 analysis of error-correcting codes considered the case of random noise, in which every bit of transmitted data has the same chance of being corrupted, Ghaffari and his collaborator — [Bernhard Haeupler](http://newsoffice.mit.edu/2013/ad-hoc-networks-0109), who did his graduate work at MIT and is now an assistant professor at Carnegie Mellon University — consider the more stringent case of “adversarial noise,” in which an antagonist is trying to interfere with transmission in the most disruptive way possible. “We don’t know what type of random noise will be the one that actually captures reality,” Ghaffari explains. “If we knew the best one, we would just use that. But generally, we don’t know. So you try to generate a coding that is as general as possible.” A coding scheme that could thwart an active adversary would also thwart any type of random noise. Error-correcting codes — both classical and interactive — work by adding some extra information to the message to be transmitted. They might, for instance, tack on some bits that describe arithmetic relationships between the message bits. Both the message bits and the extra bits are liable to corruption, so decoding a message — extracting the true sequence of message bits from the sequence that arrives at the receiver — is usually a process of iterating back and forth between the message bits and the extra bits, trying to iron out discrepancies. In interactive communication, the maximum tolerable error rate is one-fourth: If the adversary can corrupt more than a quarter of the bits sent, perfectly reliable communication is impossible. Some prior interactive-coding schemes, Ghaffari explains, could handle that error rate without requiring too many extra bits. But the decoding process was prohibitively complex. Making a list To keep the complexity down, Ghaffari and Haeupler adopted a technique called list decoding. Rather than iterating back and forth between message bits and extra bits until the single most probable interpretation emerges, their algorithm iterates just long enough to create a list of likely candidates. At the end of their mutual computation, each of the interacting devices may have a list with hundreds of entries. But each device, while it has only imperfect knowledge of the messages sent by the other, has perfect knowledge of the messages it sent. So if, at the computation’s end, the devices simply exchange lists, each has enough additional information to zero in on the optimal decoding. The maximum tolerable error rate for an interactive-coding scheme — one-fourth — is a theoretical result. The minimum length of an encoded message and the minimum decoding complexity, on the other hand, are surmises based on observation. But Ghaffari and Haeupler’s decoding algorithm is nearly linear, meaning that its execution time is roughly proportional to the length of the messages exchanged. “It is optimal in the sense that it is linear,” says Mark Braverman, an assistant professor of computer science at Princeton University who has also worked on interactive coding. “That’s an important benchmark.” But linear relationships are still defined by constants: y = x is a linear relationship, but so is y = 1,000,000,000x. A linear algorithm that takes an extra second of computation for each additional bit of data it considers isn’t as good as a linear algorithm that takes an extra microsecond. “We still need to worry a little bit about constants,” Braverman says. “But before you can worry about constants, you have to know that there is a constant-rate scheme. This is very nice progress and a prerequisite to asking those next questions.”
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