Record sealing is the process of making public records inaccessible to the public. In many cases, a person with a sealed record gains the legal right to deny or not acknowledge anything to do with the arrest and the legal proceedings from the case itself. Records are commonly sealed in a number of situations: Sealed birth records (typically after adoption or determination of paternity) Juvenile criminal records may be sealed Other types of cases involving juveniles may be sealed, anonymized, or pseudonymized ("impounded"); e.g., child sex offense or custody cases Cases using witness protection information may be partly sealed Cases involving trade secrets Cases involving state secrets == Filing under seal in US court == Normally, records should not be filed under seal without a court permission. However, FRCP 5.2 requires that sensitive text – like Social Security number, Taxpayer Identification Number, birthday, bank accounts, and children’s names – should be redacted off the filings made with the court and accompanying exhibits. A person making a redacted filing can file an unredacted copy under seal, or the Court can choose to order later that an additional filing be made under seal without redaction. Alternately, the filing party may ask the court’s permission to file some exhibits completely under seal. When the document is filed "under seal", it should have a clear indication for the court clerk to file it separately – most often by stamping words "Filed Under Seal" on the bottom of each page. Person making filing should also provide instructions to the court clerk that the document needs to be filed "under seal". Courts often have specific requirements to these filings in their Local Rules. == Difference from expungement == Expungement, which is a physical destruction, namely a complete erasure of one's criminal records, and therefore usually carries a higher standard, differs from record sealing, which is only to restrict the public's access to records, so that only certain law enforcement agencies or courts, under special circumstances, will have access to them. A record seal will greatly improve the chance of employment, as employers will not have access to damning records. There are occasions, like expungement, where one can truthfully state under oath that they have never been convicted before. Most of the time, a record seal has more relaxed requirements than an expungement. If an expungement is not allowed with a case, then sealing a record may be the best bet. Different states have different terms for what constitutes sealing of a record. == Cybersecurity incidents involving sealed records == Several cybersecurity incidents have demonstrated that sealed court documents are not always secure in practice, with vulnerabilities and data breaches exposing sensitive information. In January 2021, following the SolarWinds cyber attack, the U.S. Bankruptcy Court United States District Court for the District of Nevada announced that its Case Management/Electronic Case Files CM/ECF system had been potentially compromised. The judiciary stated that additional safeguards were being implemented to protect filings, and that the review of the incident and its impact was ongoing. Reports noted that the breach raised concerns about exposure of highly sensitive and sealed documents submitted through the CM/ECF system. In 2023, security researcher Jason Parker, following a tip from an activist, identified flaws in online court systems that exposed sealed records including confidential testimony and medical records through publicly accessible portals. In 2024, a cyber intrusion targeting attorneys in a civil case involving Representative Matt Gaetz led to the unauthorized access and leak of sealed depositions and related records. The breach exposed confidential testimony and financial records, some of which were later reported by news outlets, raising concerns about the security of electronically stored legal materials and the handling of sealed filings. In 2025, multiple reports confirmed that the federal judiciary's CM/ECF and PACER (law) filing system was compromised, exposing sealed indictments, confidential informant information, and other sensitive filings. Some courts temporarily reverted to paper-based filing to mitigate the risks of further disclosure. The FBI later confirmed that the breach had exposed sealed records, and investigators suspected foreign state actors were involved. == GAO publications referencing sealed records == Closed Criminal Plea and Sentencing Proceedings (1983) – Reviewed Department of Justice policies on closing plea and sentencing hearings. GAO noted that sealed transcripts should be unsealed once the reasons for closure no longer applied. Information on Plea Agreements and Settlements in Defense Procurement Fraud Cases (1992) – Examined outcomes of procurement fraud prosecutions. GAO observed that in some instances the results were sealed from public access. Military Recruiting: More Needs to Be Done to Better Screen Applicants and Detect Fraud (1999) – Investigated fraudulent enlistments in the armed forces. The report highlighted that sealed juvenile records often prevented recruiters from discovering prior offenses. Social Security Numbers: Governments Could Do More to Reduce Display in Public Records (2004) – Analyzed risks associated with SSN availability in state and local records. GAO pointed out that some categories of records, such as adoption proceedings, were sealed and less likely to expose identifiers. Social Security Numbers: Stronger Safeguards Needed to Protect Privacy (2005 testimony) – Testimony before Congress reiterating concerns over SSN exposure in public records, while noting that sealed categories (e.g., adoption) were exceptions. U.S. Supreme Court: Policies and Perspectives on Video and Audio Coverage of Appellate Court Proceedings (2016) – Surveyed appellate court policies on courtroom media coverage. The report acknowledged distinctions between public filings, confidential submissions, and sealed materials. Evictions: National Data Are Limited and Challenging to Collect (2024) – Examined nationwide eviction data. GAO reported that in some states eviction records may be sealed or expunged, limiting researchers' ability to compile datasets. DOD Fraud Risk Management: Enhanced Data and Collaboration Could Improve Efforts (2024) – Reviewed Department of Defense fraud-risk management. GAO noted that some adjudicative records in its dataset were sealed, restricting completeness of oversight data.
Iterative reconstruction
Iterative reconstruction refers to iterative algorithms used to reconstruct 2D and 3D images in certain imaging techniques. For example, in computed tomography an image must be reconstructed from projections of an object. Here, iterative reconstruction techniques are usually a better, but computationally more expensive alternative to the common filtered back projection (FBP) method, which directly calculates the image in a single reconstruction step. In recent research works, scientists have shown that extremely fast computations and massive parallelism is possible for iterative reconstruction, which makes iterative reconstruction practical for commercialization. == Basic concepts == The reconstruction of an image from the acquired data is an inverse problem. Often, it is not possible to exactly solve the inverse problem directly. In this case, a direct algorithm has to approximate the solution, which might cause visible reconstruction artifacts in the image. Iterative algorithms approach the correct solution using multiple iteration steps, which allows to obtain a better reconstruction at the cost of a higher computation time. There are a large variety of algorithms, but each starts with an assumed image, computes projections from the image, compares the original projection data and updates the image based upon the difference between the calculated and the actual projections. === Algebraic reconstruction === The Algebraic Reconstruction Technique (ART) was the first iterative reconstruction technique used for computed tomography by Hounsfield. === Iterative Sparse Asymptotic Minimum Variance === The iterative sparse asymptotic minimum variance algorithm is an iterative, parameter-free superresolution tomographic reconstruction method inspired by compressed sensing, with applications in synthetic-aperture radar, computed tomography scan, and magnetic resonance imaging (MRI). === Statistical reconstruction === There are typically five components to statistical iterative image reconstruction algorithms, e.g. An object model that expresses the unknown continuous-space function f ( r ) {\displaystyle f(r)} that is to be reconstructed in terms of a finite series with unknown coefficients that must be estimated from the data. A system model that relates the unknown object to the "ideal" measurements that would be recorded in the absence of measurement noise. Often this is a linear model of the form A x + ϵ {\displaystyle \mathbf {A} x+\epsilon } , where ϵ {\displaystyle \epsilon } represents the noise. A statistical model that describes how the noisy measurements vary around their ideal values. Often Gaussian noise or Poisson statistics are assumed. Because Poisson statistics are closer to reality, it is more widely used. A cost function that is to be minimized to estimate the image coefficient vector. Often this cost function includes some form of regularization. Sometimes the regularization is based on Markov random fields. An algorithm, usually iterative, for minimizing the cost function, including some initial estimate of the image and some stopping criterion for terminating the iterations. === Learned Iterative Reconstruction === In learned iterative reconstruction, the updating algorithm is learned from training data using techniques from machine learning such as convolutional neural networks, while still incorporating the image formation model. This typically gives faster and higher quality reconstructions and has been applied to CT and MRI reconstruction. == Advantages == The advantages of the iterative approach include improved insensitivity to noise and capability of reconstructing an optimal image in the case of incomplete data. The method has been applied in emission tomography modalities like SPECT and PET, where there is significant attenuation along ray paths and noise statistics are relatively poor. Statistical, likelihood-based approaches: Statistical, likelihood-based iterative expectation-maximization algorithms are now the preferred method of reconstruction. Such algorithms compute estimates of the likely distribution of annihilation events that led to the measured data, based on statistical principle, often providing better noise profiles and resistance to the streak artifacts common with FBP. Since the density of radioactive tracer is a function in a function space, therefore of extremely high-dimensions, methods which regularize the maximum-likelihood solution turning it towards penalized or maximum a-posteriori methods can have significant advantages for low counts. Examples such as Ulf Grenander's Sieve estimator or Bayes penalty methods, or via I.J. Good's roughness method may yield superior performance to expectation-maximization-based methods which involve a Poisson likelihood function only. As another example, it is considered superior when one does not have a large set of projections available, when the projections are not distributed uniformly in angle, or when the projections are sparse or missing at certain orientations. These scenarios may occur in intraoperative CT, in cardiac CT, or when metal artifacts require the exclusion of some portions of the projection data. In Magnetic Resonance Imaging it can be used to reconstruct images from data acquired with multiple receive coils and with sampling patterns different from the conventional Cartesian grid and allows the use of improved regularization techniques (e.g. total variation) or an extended modeling of physical processes to improve the reconstruction. For example, with iterative algorithms it is possible to reconstruct images from data acquired in a very short time as required for real-time MRI (rt-MRI). In Cryo Electron Tomography, where the limited number of projections are acquired due to the hardware limitations and to avoid the biological specimen damage, it can be used along with compressive sensing techniques or regularization functions (e.g. Huber function) to improve the reconstruction for better interpretation. Here is an example that illustrates the benefits of iterative image reconstruction for cardiac MRI.
For a Breath I Tarry
"For a Breath I Tarry" is a 1966 post-apocalyptic novelette by American writer Roger Zelazny, which was nominated for the Hugo Award for Best Novelette in 1967. Set in a future long after the self-extinction of humanity, the novelette recounts the tale of Frost, a sentient machine. Although humans have caused their own extinction, the sentient machines that they created continue the work of rebuilding a shattered Earth. Along the way, the story explores the differences between humanity and machines, the former experiencing the world qualitatively, while the latter doing so quantitatively. This difference is illustrated through philosophical conversations between Frost and another machine named Mordel. Frost's goal of becoming human, along with literary allusions, drives the plot and sets the tone of the novelette. These allusions include the first chapter of the Book of Job, in both situation and language, since verses are both quoted directly and paraphrased. In addition, the first three chapters of the Book of Genesis are echoed. Finally, Frost and Mordel enter into a Faustian bargain, though with better results than in the original story. The other major character is the Beta Machine, Frost's peer in the Southern Hemisphere. (Frost controls the Northern Hemisphere.) The novelette hints that though being a machine, Beta has a feminine personality. After Frost has succeeded in his millennium-long quest to become human (via recovered DNA), Beta agrees to join him in becoming human—suggesting the possibility of rebirth for the human race. The novelette has appeared in collections of Zelazny's works and in anthologies. The title is from a phrase in the poet A. E. Housman's collection A Shropshire Lad.
TCEC Season 14
The 14th season of the Top Chess Engine Championship took place between 17 November 2018 and 24 February 2019. Stockfish was the defending champion, having defeated Komodo in the previous season's superfinal. The season is notable for two things: the emergence of two strong, new engines, the Komodo variant Komodo Monte Carlo tree search (MCTS) and the neural network engine Leela Chess Zero, and the dramatic superfinal. Komodo MCTS and Leela fought their way from Division 4 and Division 3 respectively to the Premier Division, with Leela further qualifying for the superfinal against Stockfish. The superfinal was a topsy-turvy affair with the lead changing hands several times. It finished as the closest superfinal TCEC has ever seen, with Stockfish winning by a single game, 50.5–49.5 (+10 =81 -9). == Overview == === Structure === The season comprised five divisions: from the lowest Division 4 to the Premier Division. The top two engines of each division promote to the division above, while the bottom two engines relegate. The top two engines of the Premier Division contest a 100-game superfinal. The lengths of the opening books used increases as the divisions progress. The superfinal itself used a custom opening book designed by Jeroen Noomen. === Rules === The TCEC draw and win rules were slightly modified for Season 14. The game is now adjudicated as drawn if, after move 30, both engines have evals ±0.08 for five consecutive moves, and there are neither pawn moves nor a capture. Win adjudication now occurs if both engines have an eval of ±10 for five consecutive moves. Following the controversy over DeusX's participation last season, the uniqueness rule for neural networks was modified such that at least two of the following three hallmarks must be unique: The code for training the neural network The neural network (and weights file) itself The engine that executes this network This change meant DeusX did not meet the uniqueness criteria and therefore did not participate. Aside from this change, the season used the standard rules of the TCEC. == Results == === Division 4 === New entrant Komodo MCTS dominated Division 4, winning by a clear four points, although it did lose a game to second-place finisher rofChade. Fellow new entrant Scorpio NN performed badly and finished last, drawing only one game and losing the rest. === Division 3 === The neural network engine Leela Chess Zero had just missed promotion to Division 2 in the previous season. Since its relatively weak performance last season was partly due to hardware problems, and since it had shown a lot of improvement in strength, it was the hot favourite in this division. Leela lived up to its billing by comprehensively defeating everyone else. In a portent of future divisions however, Leela surprisingly dropped a game to third-place Arasan. Komodo MCTS was also improving quickly, and an updated version finished second behind Leela. The gap between second and third was 6.5 points, illustrating the gulf in class. === Division 2 === Although Division 2 engines are significantly stronger than Division 3, Leela and Komodo MCTS continued to dominate the competition, and again finished first and second. Komodo MCTS only lost one game to Leela, while Leela's tendency to occasionally lose to weaker engines saw her losing a game to 4th-placed Booot. Third place finisher Xiphos gave Leela and Komodo MCTS a run for their money, and was in the running up until the final rounds when it lost a crucial game to Leela. This loss left it one point behind Komodo MCTS in the final standings. === Division 1 === Leela and Komodo MCTS's rampage through the lower divisions continued, and they again finished first and second. In a demonstration of how much it had improved, Leela scored 20/28 in this division, the same score it had achieved in Division 2. This was also a TCEC points record for this division. However, Leela dropped a game against fourth-place finisher Chiron. Komodo MCTS, which had yet to lose a game in the lower divisions except to Leela, also conceded its first loss to third-place Fizbo. At the other end of the table, former champions Jonny and Fritz, which had not been updated, found themselves outclassed and finished second-last and last respectively; however with fellow competitor Ginkgo crashing five times (and therefore being disqualified), Jonny managed to stay in the division. The penultimate game for this division set a new TCEC moves record for a decisive game: 308 moves before Leela defeated Fritz. === Premier division === This was the strongest premier division ever, with multiple-time champions Stockfish, Komodo, and Houdini in the mix. Right from the start it became clear that Stockfish was in a league of its own, and it dominated the division, scoring wins against every other engine without losing a game. Second place however was a hotly-contested affair, with Leela, Komodo and Houdini neck-and-neck for most of the division. Houdini took the early lead, but Komodo gained second after winning two games by forfeit when its sibling Komodo MCTS crashed. This led to murmurs of a "Konspiracy". However, when both Komodo and Houdini failed to score more wins against the lower half of the field, Leela was able to take the lead. Halfway through the division the race was upended again when Leela went through a bad streak, losing three games in a row to Stockfish, Komodo, and Fire. This led to Komodo regaining second place, only for Komodo MCTS to crash yet again. By TCEC rules this meant Komodo MCTS was disqualified and all its scores were zeroed out, which put Leela back in second place. With three games left, Leela missed a win against Andscacs, which would've more or less secured her a place in the superfinal. Meanwhile, Komodo kept the division interesting by winning two of its last three games. Because Komodo had superior tiebreakers to Leela, this meant Komodo would qualify for the superfinal unless Leela managed to hold Stockfish to a draw with Black in the last game of the division. In a tense final game, Stockfish came close to winning, but missed the winning line. Leela managed to draw and qualified for the superfinal. At the other end of the table, it was quickly apparent that Ethereal and Andscacs were the weakest engines and would likely relegate. However, when Komodo MCTS was disqualified (and therefore relegated), it threw both engines a lifeline, since they could now stay in the division by beating the other. Andscacs was able to score a head-to-head win against Ethereal, but was crushed by Stockfish (+0 =2 -4) and Leela (+0 =3 -3). Ethereal didn't manage to score a win in the entire division, but did manage to score more draws than Andscacs, condemning Andscacs to relegation. === Superfinal === Going into the superfinal expectations were high for Leela: she had received a new network and had just won her first major competition when she defeated Houdini in the second TCEC cup. However, she had won the tournament without having played Stockfish (who had been surprisingly eliminated by Houdini in the semifinals). That, plus the fact that Stockfish dominated Premier Division and had never lost a match to Leela, left it unclear which engine was superior, although most spectators favored Stockfish. The superfinal turned out to be a roller-coaster. It began with Stockfish drawing first blood in game 7, and then scoring another win in game 10. Leela hit back with wins in game 11 and 13, but then lost games 20, 21, and 22. This gave Stockfish a 3-point lead. However, in the next 30 games, Leela was the only one to score wins: it first equalized by winning games 25, 27, and 29, and then took the lead by winning games 49 and 53. Stockfish won game 56, but Leela won game 63, maintaining her lead. There followed two dramatic games. In game 65, Leela built up a winning position. Stockfish showed a +153 evaluation, indicating that it had found a forced line leading to an endgame tablebase win; indeed analysis with 7-piece tablebases showed that Leela's position was winning. Under previous seasons' rules, the game would have been adjudicated as a win because Leela's evaluation was above 6.5. However under the new rules, Leela's +8.92 evaluation was not enough to adjudicate. It turned out that Leela could not see the winning line, and shuffled her pieces aimlessly, leading to a 50-move draw. In game 66, Stockfish was given a substantial advantage by the opening, but failed to make the most of it. The evaluations were leveling out to zero when the internet connection to the GPU servers was cut off. By tournament rules, this meant the game was replayed from scratch. After a further internet disconnection and restart, Stockfish handled the opening better and won, leaving Leela with a 1-point lead. In the last third of the superfinal, there followed more drama as Leela often built up strong advantages, but Stockfish showed great resourcefulness in defending inferior positions. Meanwh
Woken Furies
Woken Furies (2005) is a science fiction novel by British writer Richard Morgan. It is the third novel featuring the anti-hero Takeshi Kovacs and is the sequel to Broken Angels. This addition to the series casts light upon Kovacs' early life providing information on his post-envoy activities. Morgan's official website and interviews suggest that Woken Furies could be the last Kovacs novel, although in 2018 (before Netflix cancelled the show) Morgan stated that the Netflix adaptation has "kind of woken it all up again" after all these years, making him possibly reconsider being done with Kovacs. == Plot == Takeshi Kovacs finds himself in a new "sleeve," or human body, back on his home planet of Harlan's World. He is on the run after making numerous attacks against the Knights of the New Revelation, an extremist religious order responsible for the death of his lost love and her daughter. Because she had violated tenets about resleeving, her executioners dropped her and her daughter's cortical stacks in the sea, effectively preventing them from being resleeved (into new bodies). While trying to secure passage after his most recent attack, Kovacs saves a woman named Sylvie from a group of religious zealots. In return, she allows him to take refuge with her mercenary "deCom" crew as they head out to decommission sentient military hardware that has run amok on the island of New Hokkaido (AKA New Hok). Sylvie is the "command head" of her crew, co-ordinating them during missions by using her biologically implanted circuitry and software. During one of these missions, Sylvie collapses, regains consciousness, and Kovacs realizes that her personality seems to have been replaced by that of long-dead revolutionary leader Quellcrist Falconer. Harlan's World is surrounded by automated "orbitals" which target flying objects, such as vehicles, with high-energy beam weapons known as "angelfire"; Falconer is believed to have died without a backup of her cortical stack when her getaway aircraft was destroyed by angelfire 300 years prior. When Sylvie's crew returns from New Hok, they discover a younger version of Kovacs has been illegally duplicated into a different body (AKA "double sleeved") and is hunting them on behalf of the Harlan family that rules the planet. Most of Sylvie's crew is killed and Sylvie/Quellcrist is captured. Kovacs schemes to rescue Sylvie by approaching old criminal associates of his, the Little Blue Bugs. The Little Blue Bugs mount a semi-successful attack on a Harlan fortress and rescue Sylvie/Quellcrist. Hiding from Harlan forces in a floating base, the neo-Quellists are sold out by its owner and recaptured. An assault by Kovacs and a single UN Envoy on the base ends badly when Kovacs is betrayed by the Envoy who was actually embedded with several colleagues. However, Sylvie/Quellcrist has established a connection with the orbitals and calls down angelfire, eliminating their captors. The younger Kovacs is killed in the aftermath. Sylvie explains that angelfire is a destructive recording device. Thus, in destroying Quellcrist and the helicopter carrying her, it copied her. When the technology of the deCom crews advanced far enough, her persona was able to insert itself into Sylvie's implants and co-exist in her body. The novel ends with Kovacs, Virginia Vidaura, and Sylvie/Quellcrist waiting to see if they can use Sylvie/Quellcrist's newfound connection to the orbitals and the expansion of a long-dormant genetic virus to turn the population against the ruling oligarchy.
Information extraction
Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents and other electronically represented sources. Typically, this involves processing human language texts by means of natural language processing (NLP). Recent activities in multimedia document processing like automatic annotation and content extraction out of images/audio/video/documents could be seen as information extraction. Recent advances in NLP techniques have allowed for significantly improved performance compared to previous years. An example is the extraction from newswire reports of corporate mergers, such as denoted by the formal relation: MergerBetween ( c o m p a n y 1 , c o m p a n y 2 , d a t e ) {\displaystyle \operatorname {MergerBetween} (\mathrm {company} _{1},\mathrm {company} _{2},\mathrm {date} )} , from an online news sentence such as: "Yesterday, New York based Foo Inc. announced their acquisition of Bar Corp." A broad goal of IE is to allow computation to be done on the previously unstructured data. A more specific goal is to allow automated reasoning about the logical form of the input data. Structured data is semantically well-defined data from a chosen target domain, interpreted with respect to category and context. Information extraction is the part of a greater puzzle which deals with the problem of devising automatic methods for text management, beyond its transmission, storage and display. The discipline of information retrieval (IR) has developed automatic methods, typically of a statistical flavor, for indexing large document collections and classifying documents. Another complementary approach is that of natural language processing (NLP) which has solved the problem of modelling human language processing with considerable success when taking into account the magnitude of the task. In terms of both difficulty and emphasis, IE deals with tasks in between both IR and NLP. In terms of input, IE assumes the existence of a set of documents in which each document follows a template, i.e. describes one or more entities or events in a manner that is similar to those in other documents but differing in the details. An example, consider a group of newswire articles on Latin American terrorism with each article presumed to be based upon one or more terroristic acts. We also define for any given IE task a template, which is a(or a set of) case frame(s) to hold the information contained in a single document. For the terrorism example, a template would have slots corresponding to the perpetrator, victim, and weapon of the terroristic act, and the date on which the event happened. An IE system for this problem is required to "understand" an attack article only enough to find data corresponding to the slots in this template. == History == Information extraction dates back to the late 1970s in the early days of NLP. An early commercial system from the mid-1980s was JASPER built for Reuters by the Carnegie Group Inc with the aim of providing real-time financial news to financial traders. Beginning in 1987, IE was spurred by a series of Message Understanding Conferences. MUC is a competition-based conference that focused on the following domains: MUC-1 (1987), MUC-3 (1989): Naval operations messages. MUC-3 (1991), MUC-4 (1992): Terrorism in Latin American countries. MUC-5 (1993): Joint ventures and microelectronics domain. MUC-6 (1995): News articles on management changes. MUC-7 (1998): Satellite launch reports. Considerable support came from the U.S. Defense Advanced Research Projects Agency (DARPA), who wished to automate mundane tasks performed by government analysts, such as scanning newspapers for possible links to terrorism. == Present significance == The present significance of IE pertains to the growing amount of information available in unstructured form. Tim Berners-Lee, inventor of the World Wide Web, refers to the existing Internet as the web of documents and advocates that more of the content be made available as a web of data. Until this transpires, the web largely consists of unstructured documents lacking semantic metadata. Knowledge contained within these documents can be made more accessible for machine processing by means of transformation into relational form, or by marking-up with XML tags. An intelligent agent monitoring a news data feed requires IE to transform unstructured data into something that can be reasoned with. A typical application of IE is to scan a set of documents written in a natural language and populate a database with the information extracted. == Tasks and subtasks == Applying information extraction to text is linked to the problem of text simplification in order to create a structured view of the information present in free text. The overall goal being to create a more easily machine-readable text to process the sentences. Typical IE tasks and subtasks include: Template filling: Extracting a fixed set of fields from a document, e.g. extract perpetrators, victims, time, etc. from a newspaper article about a terrorist attack. Event extraction: Given an input document, output zero or more event templates. For instance, a newspaper article might describe multiple terrorist attacks. Knowledge Base Population: Fill a database of facts given a set of documents. Typically the database is in the form of triplets, (entity 1, relation, entity 2), e.g. (Barack Obama, Spouse, Michelle Obama) Named entity recognition: recognition of known entity names (for people and organizations), place names, temporal expressions, and certain types of numerical expressions, by employing existing knowledge of the domain or information extracted from other sentences. Typically the recognition task involves assigning a unique identifier to the extracted entity. A simpler task is named entity detection, which aims at detecting entities without having any existing knowledge about the entity instances. For example, in processing the sentence "M. Smith likes fishing", named entity detection would denote detecting that the phrase "M. Smith" does refer to a person, but without necessarily having (or using) any knowledge about a certain M. Smith who is (or, "might be") the specific person whom that sentence is talking about. Coreference resolution: detection of coreference and anaphoric links between text entities. In IE tasks, this is typically restricted to finding links between previously extracted named entities. For example, "International Business Machines" and "IBM" refer to the same real-world entity. If we take the two sentences "M. Smith likes fishing. But he doesn't like biking", it would be beneficial to detect that "he" is referring to the previously detected person "M. Smith". Relationship extraction: identification of relations between entities, such as: PERSON works for ORGANIZATION (extracted from the sentence "Bill works for IBM.") PERSON located in LOCATION (extracted from the sentence "Bill is in France.") Semi-structured information extraction which may refer to any IE that tries to restore some kind of information structure that has been lost through publication, such as: Table extraction: finding and extracting tables from documents. Table information extraction : extracting information in structured manner from the tables. This task is more complex than table extraction, as table extraction is only the first step, while understanding the roles of the cells, rows, columns, linking the information inside the table and understanding the information presented in the table are additional tasks necessary for table information extraction. Comments extraction : extracting comments from the actual content of articles in order to restore the link between authors of each of the sentences Language and vocabulary analysis Terminology extraction: finding the relevant terms for a given corpus Audio extraction Template-based music extraction: finding relevant characteristic in an audio signal taken from a given repertoire; for instance time indexes of occurrences of percussive sounds can be extracted in order to represent the essential rhythmic component of a music piece. Note that this list is not exhaustive and that the exact meaning of IE activities is not commonly accepted and that many approaches combine multiple sub-tasks of IE in order to achieve a wider goal. Machine learning, statistical analysis and/or natural language processing are often used in IE. IE on non-text documents is becoming an increasingly interesting topic in research, and information extracted from multimedia documents can now be expressed in a high level structure as it is done on text. This naturally leads to the fusion of extracted information from multiple kinds of documents and sources. == World Wide Web applications == IE has been the focus of the MUC conferences. The proliferation of the Web, however, intensified the need for developing IE systems that help people
Painworth
PainWorth is a justice, legal and insurance services application founded by Canadian entrepreneurs Mike Zouhri, Chris Trudel and Ryan Bencic. The application is a "robot lawyer" that uses artificial intelligence to automate personal injury claims for injury victims. It is currently available in Canada and the United States. PainWorth has been featured by several news outlets, including CTV, Global News, CBC, and has also been featured by the American Bar Association and LexisNexis for its role addressing social issues such as access to justice and other systemic issues in the legal and insurance industry. == Application == PainWorth began as a tool for calculating non-pecuniary damages for injury victims but has since expanded beyond a personal injury calculator to include features that help injury victims and business users with pecuniary damages, economic calculations, prescribed rates and providing informational guides to help navigate settlement negotiation, managing claims records and other issues encountered by self-represented litigants or claims managers. The platform makes use of automation to provide free user-guided calculations, steps and processes to successfully settle an injury claim. The application is supported by Microsoft Azure. == Personal Injury Calculator == PainWorth is the first service to use Artificial Intelligence to interpret case law in order to determine the value of pain and suffering incurred by specific injury types and injury severities. The cited case law is used as evidence and presented in statistical models to determine an accurate valuation compliant with the jurisdiction, regulatory rules and case complexities. == General Damages Calculator == PainWorth also offers a personal injury settlement calculator that assesses general damages based on specific case complexities and jurisdiction. The service takes into account medical complications and recovery in order to calculate the fair valuation. == Injury Settlement Platform == PainWorth insurance settlement platform facilitates a direct and automated way resolution center to settle cases for their assessed value without enduring the hardship of litigation. In 2021, Painworth won the title of World's Best Emerging Insurance Product for the development of this platform. == History == In 2019, Mike Zouhri was struck by a drunk driver which left him seriously injured and resulted in a lawsuit. Frustrated by the slow and expensive process, Zouhri went down to the law library and learned how to manage injury claims. After learning the process, he partnered lawyers and legal advisors to create an app to allow users to quickly settle their own injury claims fairly and accurately. Immediately after its launch, PainWorth quickly became widely used by thousands of users and gained significant media coverage. Global News reported that the bot had successfully helped people with more than $10 million in claims in only a few short months, all free of charge. In July 2020, PainWorth began raising concern over injustices and gender bias in the legal system. in Canadian courts.