Gas (sometimes stylized in all caps), formerly known as Melt as well as Crush, was an American anonymous social media app. Launched in August 2022, the app is oriented towards high schoolers. The app was developed by Nikita Bier, Isaiah Turner, and former Facebook engineer Dave Schatz. Gas was largely based upon the prior tbh app developed by co-founder Nikita Bier, along with Erik Hazzard, Kyle Zaragoza, and Nicolas Ducdodon in September 2017. tbh was acquired by Facebook inc. (now Meta Platforms) on October 16, 2017, and nearly a year later in July 2018 was dissolved, owing to low usage. Gas follows a similar purpose to tbh in being a social media app oriented towards high schoolers. In the app, users participate in anonymous polls regarding pre-written complimentary statements to their peers, such as "I'd say yes if (blank) asked me out on a date," "I think (blank) is the coolest kid in school," or "would make an ugly face and still look pretty." Winners of said polls receive a "flame." The name of the app is derived from this, with "gassing someone up" being Gen Z slang for complimenting someone. Users can pay a $6.99 subscription that enables "God Mode," which shows hints regarding who voted for them in a poll. Gas overtook TikTok and BeReal as the most downloaded app on the Apple App Store in October 2022 (the app is currently not available for Android). The app has over 5.1 million downloads as of early November 2022, over a million active users and 300 thousand daily downloads as of October 2022. Currently, the app is available in Canada and the majority of the United States. On January 17, 2023, Gas was acquired by Discord, however it would remain a standalone app and its developers became Discord staff members. On October 18, 2023, Discord announced that service for Gas would be permanently ending effective November 7, 2023, due to a steep decline in users. Effective November 7, the app became completely unusable. == Controversy regarding human-trafficking == Beginning in October 2022, rumors spread largely throughout TikTok and Snapchat alleged that the app was linked to human trafficking (in particular sex trafficking). According to Bier, the rumor originated with a single user review from China on October 5, and then was disseminated through TikTok accounts with "few to no US teen followers." Although largely dismissed as a hoax by experts, who cite how the app doesn't log user locations and general anonymity, the hoax became pervasive to the extent that various police departments, school systems, and local news outlets began issuing warnings regarding the app. For instance, on October 31, 2022, the police department of Piedmont, Oklahoma issued a warning to parents, encouraging them to check their children's phones, while on November 3, the Oklahoma Oktaha Public School system stated in a Facebook post that "Children are being kidnapped in other towns and this new app is thought to be the source of predators finding their location." (both statements have since been retracted by Police Chief Scott Singer and Superintendent Jerry Needham respectively). Additionally, local medial outlets such as KOCO in Oklahoma City ran stories making similar statements. The rumor had a negative impact on the app, with downloads plateauing for a two-week period in late October and with 3% of users in a single day reportedly uninstalling the app. Revenue and ratings have also reportedly dropped and the company's social media accounts have been bombarded with comments labeling them as sex-traffickers. Additionally, the four-person development team has reportedly been bombarded with various death threats as a result.
Computer audition
Computer audition (CA) or machine listening is the general field of study of algorithms and systems for audio interpretation by machines. Since the notion of what it means for a machine to "hear" is very broad and somewhat vague, computer audition attempts to bring together several disciplines that originally dealt with specific problems or had a concrete application in mind. The engineer Paris Smaragdis, interviewed in Technology Review, talks about these systems — "software that uses sound to locate people moving through rooms, monitor machinery for impending breakdowns, or activate traffic cameras to record accidents." Inspired by models of human audition, CA deals with questions of representation, transduction, grouping, use of musical knowledge and general sound semantics for the purpose of performing intelligent operations on audio and music signals by the computer. Technically this requires a combination of methods from the fields of signal processing, auditory modelling, music perception and cognition, pattern recognition, and machine learning, as well as more traditional methods of artificial intelligence for musical knowledge representation. == Applications == Like computer vision versus image processing, computer audition versus audio engineering deals with understanding of audio rather than processing. It also differs from problems of speech understanding by machine since it deals with general audio signals, such as natural sounds and musical recordings. Applications of computer audition are widely varying, and include search for sounds, genre recognition, acoustic monitoring, music transcription, score following, audio texture, music improvisation, emotion in audio and so on. == Related disciplines == Computer Audition overlaps with the following disciplines: Music information retrieval: methods for search and analysis of similarity between music signals. Auditory scene analysis: understanding and description of audio sources and events. Computational musicology and mathematical music theory: use of algorithms that employ musical knowledge for analysis of music data. Computer music: use of computers in creative musical applications. Machine musicianship: audition driven interactive music systems. == Areas of study == Since audio signals are interpreted by the human ear–brain system, that complex perceptual mechanism should be simulated somehow in software for "machine listening". In other words, to perform on par with humans, the computer should hear and understand audio content much as humans do. Analyzing audio accurately involves several fields: electrical engineering (spectrum analysis, filtering, and audio transforms); artificial intelligence (machine learning and sound classification); psychoacoustics (sound perception); cognitive sciences (neuroscience and artificial intelligence); acoustics (physics of sound production); and music (harmony, rhythm, and timbre). Furthermore, audio transformations such as pitch shifting, time stretching, and sound object filtering, should be perceptually and musically meaningful. For best results, these transformations require perceptual understanding of spectral models, high-level feature extraction, and sound analysis/synthesis. Finally, structuring and coding the content of an audio file (sound and metadata) could benefit from efficient compression schemes, which discard inaudible information in the sound. Computational models of music and sound perception and cognition can lead to a more meaningful representation, a more intuitive digital manipulation and generation of sound and music in musical human-machine interfaces. The study of CA could be roughly divided into the following sub-problems: Representation: signal and symbolic. This aspect deals with time-frequency representations, both in terms of notes and spectral models, including pattern playback and audio texture. Feature extraction: sound descriptors, segmentation, onset, pitch and envelope detection, chroma, and auditory representations. Musical knowledge structures: analysis of tonality, rhythm, and harmonies. Sound similarity: methods for comparison between sounds, sound identification, novelty detection, segmentation, and clustering. Sequence modeling: matching and alignment between signals and note sequences. Source separation: methods of grouping of simultaneous sounds, such as multiple pitch detection and time-frequency clustering methods. Auditory cognition: modeling of emotions, anticipation and familiarity, auditory surprise, and analysis of musical structure. Multi-modal analysis: finding correspondences between textual, visual, and audio signals. === Representation issues === Computer audition deals with audio signals that can be represented in a variety of fashions, from direct encoding of digital audio in two or more channels to symbolically represented synthesis instructions. Audio signals are usually represented in terms of analogue or digital recordings. Digital recordings are samples of acoustic waveform or parameters of audio compression algorithms. One of the unique properties of musical signals is that they often combine different types of representations, such as graphical scores and sequences of performance actions that are encoded as MIDI files. Since audio signals usually comprise multiple sound sources, then unlike speech signals that can be efficiently described in terms of specific models (such as source-filter model), it is hard to devise a parametric representation for general audio. Parametric audio representations usually use filter banks or sinusoidal models to capture multiple sound parameters, sometimes increasing the representation size in order to capture internal structure in the signal. Additional types of data that are relevant for computer audition are textual descriptions of audio contents, such as annotations, reviews, and visual information in the case of audio-visual recordings. === Features === Description of contents of general audio signals usually requires extraction of features that capture specific aspects of the audio signal. Generally speaking, one could divide the features into signal or mathematical descriptors such as energy, description of spectral shape etc., statistical characterization such as change or novelty detection, special representations that are better adapted to the nature of musical signals or the auditory system, such as logarithmic growth of sensitivity (bandwidth) in frequency or octave invariance (chroma). Since parametric models in audio usually require very many parameters, the features are used to summarize properties of multiple parameters in a more compact or salient representation. === Musical knowledge === Finding specific musical structures is possible by using musical knowledge as well as supervised and unsupervised machine learning methods. Examples of this include detection of tonality according to distribution of frequencies that correspond to patterns of occurrence of notes in musical scales, distribution of note onset times for detection of beat structure, distribution of energies in different frequencies to detect musical chords and so on. === Sound similarity and sequence modeling === Comparison of sounds can be done by comparison of features with or without reference to time. In some cases an overall similarity can be assessed by close values of features between two sounds. In other cases when temporal structure is important, methods of dynamic time warping need to be applied to "correct" for different temporal scales of acoustic events. Finding repetitions and similar sub-sequences of sonic events is important for tasks such as texture synthesis and machine improvisation. === Source separation === Since one of the basic characteristics of general audio is that it comprises multiple simultaneously sounding sources, such as multiple musical instruments, people talking, machine noises or animal vocalization, the ability to identify and separate individual sources is very desirable. Unfortunately, there are no methods that can solve this problem in a robust fashion. Existing methods of source separation rely sometimes on correlation between different audio channels in multi-channel recordings. The ability to separate sources from stereo signals requires different techniques than those usually applied in communications where multiple sensors are available. Other source separation methods rely on training or clustering of features in mono recording, such as tracking harmonically related partials for multiple pitch detection. Some methods, before explicit recognition, rely on revealing structures in data without knowing the structures (like recognizing objects in abstract pictures without attributing them meaningful labels) by finding the least complex data representations, for instance describing audio scenes as generated by a few tone patterns and their trajectories (polyphonic voices) and acoustical contours drawn by a tone (c
GeWorkbench
geWorkbench (genomics Workbench) is an open-source software platform for integrated genomic data analysis. It is a desktop application written in the programming language Java. geWorkbench uses a component architecture. As of 2016, there are more than 70 plug-ins available, providing for the visualization and analysis of gene expression, sequence, and structure data. geWorkbench is the Bioinformatics platform of MAGNet, the National Center for the Multi-scale Analysis of Genomic and Cellular Networks, one of the 8 National Centers for Biomedical Computing funded through the NIH Roadmap (NIH Common Fund). Many systems and structure biology tools developed by MAGNet investigators are available as geWorkbench plugins. == Features == Computational analysis tools such as t-test, hierarchical clustering, self-organizing maps, regulatory network reconstruction, BLAST searches, pattern-motif discovery, protein structure prediction, structure-based protein annotation, etc. Visualization of gene expression (heatmaps, volcano plot), molecular interaction networks (through Cytoscape), protein sequence and protein structure data (e.g., MarkUs). Integration of gene and pathway annotation information from curated sources as well as through Gene Ontology enrichment analysis. Component integration through platform management of inputs and outputs. Among data that can be shared between components are expression datasets, interaction networks, sample and marker (gene) sets and sequences. Dataset history tracking - complete record of data sets used and input settings. Integration with 3rd party tools such as GenePattern, Cytoscape, and Genomespace. Demonstrations of each feature described can be found at GeWorkbench-web Tutorials. == Versions == geWorkbench is open-source software that can be downloaded and installed locally. A zip file of the released version Java source is also available. Prepackaged installer versions also exist for Windows, Macintosh, and Linux.
Multiple discriminant analysis
Multiple Discriminant Analysis (MDA) is a multivariate dimensionality reduction technique. It has been used to predict signals as diverse as neural memory traces and corporate failure. MDA is not directly used to perform classification. It merely supports classification by yielding a compressed signal amenable to classification. The method described in Duda et al. (2001) §3.8.3 projects the multivariate signal down to an M−1 dimensional space where M is the number of categories. MDA is useful because most classifiers are strongly affected by the curse of dimensionality. In other words, when signals are represented in very-high-dimensional spaces, the classifier's performance is catastrophically impaired by the overfitting problem. This problem is reduced by compressing the signal down to a lower-dimensional space as MDA does. MDA has been used to reveal neural codes.
Linguamatics
Linguamatics, headquartered in Cambridge, England, with offices in the United States and UK, is a provider of text mining systems through software licensing and services, primarily for pharmaceutical and healthcare applications. Founded in 2001, the company was purchased by IQVIA in January 2019. == Technology == The company develops enterprise search tools for the life sciences sector. The core natural language processing engine (I2E) uses a federated architecture to incorporate data from 3rd party resources. Initially developed to be used interactively through a graphic user interface, the core software also has an application programming interface that can be used to automate searches. LabKey, Penn Medicine, Atrius Health and Mercy all use Linguamatics software to extract electronic health record data into data warehouses. Linguamatics software is used by 17 of the top 20 global pharmaceutical companies, the US Food and Drug Administration, as well as healthcare providers. == Software community == The core software, "I2E", is used by a number of companies to either extend their own software or to publish their data. Copyright Clearance Center uses I2E to produce searchable indexes of material that would otherwise be unsearchable due to copyright. Thomson Reuters produces Cortellis Informatics Clinical Text Analytics, which depends on I2E to make clinical data accessible and searchable. Pipeline Pilot can integrate I2E as part of a workflow. ChemAxon can be used alongside I2E to allow named entity recognition of chemicals within unstructured data. Data sources include MEDLINE, ClinicalTrials.gov, FDA Drug Labels, PubMed Central, and Patent Abstracts.
COVID-19 apps
COVID-19 apps include mobile-software applications for digital contact-tracing—i.e. the process of identifying persons ("contacts") who may have been in contact with an infected individual—deployed during the COVID-19 pandemic. Numerous tracing applications have been developed or proposed, with official government support in some territories and jurisdictions. Several frameworks for building contact-tracing apps have been developed. Privacy concerns have been raised, especially about systems that are based on tracking the geographical location of app users. Less overtly intrusive alternatives include the co-option of Bluetooth signals to log a user's proximity to other cellphones. (Bluetooth technology has form in tracking cell-phones' locations.)) On 10 April 2020, Google and Apple jointly announced that they would integrate functionality to support such Bluetooth-based apps directly into their Android and iOS operating systems. India's COVID-19 tracking app Aarogya Setu became the world's fastest growing application—beating Pokémon Go—with 50 million users in the first 13 days of its release. == Rationale == Contact tracing is an important tool in infectious disease control, but as the number of cases rises time constraints make it more challenging to effectively control transmission. Digital contact tracing, especially if widely deployed, may be more effective than traditional methods of contact tracing. In a March 2020 model by the University of Oxford Big Data Institute's Christophe Fraser's team, a coronavirus outbreak in a city of one million people is halted if 80% of all smartphone users take part in a tracking system; in the model, the elderly are still expected to self-isolate en masse, but individuals who are neither symptomatic nor elderly are exempt from isolation unless they receive an alert that they are at risk of carrying the disease. Some proponents advocate for legislation exempting certain COVID-19 apps from general privacy restrictions. == Issues == === Uptake === Ross Anderson, professor of security engineering at Cambridge University, listed a number of potential practical problems with app-based systems, including false positives and the potential lack of effectiveness if takeup of the app is limited to only a small fraction of the population. In Singapore, only one person in three had downloaded the TraceTogether app by the end of June 2020, despite legal requirements for most workers; the app was also underused, as it required users to keep it open at all times on iOS. A team at the University of Oxford simulated the effect of a contact tracing app on a city of 1 million. They estimated that if the app was used in conjunction with the shielding of over-70s, then 56% of the population would have to be using the app for it to suppress the virus. This would be equivalent to 80% of smartphone users in the United Kingdom. They found that the app could still slow the spread of the virus if fewer people downloaded it, with one infection being prevented for every one or two users. In August 2020, the American Civil Liberties Union (ACLU) argued that there were disparities in smartphone use between demographics and minority groups, and that "even the most comprehensive, all-seeing contact tracing system is of little use without social and medical systems in place to help those who may have the virus — including access to medical care, testing, and support for those who are quarantined." === App store restrictions === Addressing concerns about the spread of misleading or harmful apps, Apple, Google and Amazon set limits on which types of organizations could add coronavirus-related apps to its App Store, limiting them to only "official" or otherwise reputable organizations. === Ethical principles of mass surveillance using COVID-19 contact tracing apps === The advent of COVID-19 contact tracing apps has led to concerns around privacy, the rights of app users, and governmental authority. The European Convention on Human Rights, the International Covenant on Civil and Political Rights (ICCPR) and the United Nations and the Siracusa Principles have outlined 4 principles to consider when looking at the ethical principles of mass surveillance with COVID-19 contact tracing apps. These are necessity, proportionality, scientific validity, and time boundedness. Necessity is defined as the idea that governments should only interfere with a person's rights when deemed essential for public health interests. The potential risks associated with infringements of personal privacy must be outweighed by the possibility of reducing significant harm to others. Potential benefits of contact-tracing apps that may be considered include allowing for blanket population-level quarantine measures to be lifted sooner and the minimization of people under quarantine. Hence, some contend that contact-tracing apps are justified as they may be less intrusive than blanket quarantine measures. Furthermore, the delay of an effective contact-tracing app with significant health and economic benefits may be considered unethical. Proportionality refers to the concept that a contact tracing app's potential negative impact on a person's rights should be justifiable by the severity of the health risks that are being addressed. Apps must use the most privacy-preserving options available to achieve their goals, and the selected option should not only be a logical option for achieving the goal but also an effective one. Scientific validity evaluates whether an app is effective, timely and accurate. Traditional manual contact-tracing procedures are not efficient enough for the COVID-19 pandemic, and do not consider asymptomatic transmission. Contact-tracing apps, on the other hand, can be effective COVID-19 contact-tracing tools that reduce R value to less than 1, leading to sustained epidemic suppression. However, for apps to be effective, there needs to be a minimum 56-60% uptake in the population. Apps should be continually modified to reflect current knowledge on the diseases being monitored. Some argue that contact-tracing apps should be considered societal experimental trials where results and adverse effects are evaluated according to the stringent guidelines of social experiments. Analyses should be conducted by independent research bodies and published for wide dissemination. Despite the current urgency of our pandemic situation, we should still adhere to the standard rigors of scientific evaluation. Time boundedness describe the need for establishing legal and technical sunset clauses so that they are only allowed to operate as long as necessary to address the pandemic situation. Apps should be withdrawn as soon as possible after the end of the pandemic. If the end of the pandemic cannot be predicted, the use of apps should be regularly reviewed and decisions about continued use should be made at each review. Collected data should only be retained by public health authorities for research purposes with clear stipulations on how long the data will be held for and who will be responsible for security, oversight, and ownership. === Privacy, discrimination and marginalisation concerns === The American Civil Liberties Union (ACLU) has published a set of principles for technology-assisted contact tracing and Amnesty International and over 100 other organizations issued a statement calling for limits on this kind of surveillance. The organisations declared eight conditions on governmental projects: surveillance would have to be "lawful, necessary and proportionate"; extensions of monitoring and surveillance would have to have sunset clauses; the use of data would have to be limited to COVID-19 purposes; data security and anonymity would have to be protected and shown to be protected based on evidence; digital surveillance would have to address the risk of exacerbating discrimination and marginalisation; any sharing of data with third parties would have to be defined in law; there would have to be safeguards against abuse and the rights of citizens to respond to abuses; "meaningful participation" by all "relevant stakeholders" would be required, including that of public health experts and marginalised groups. The German Chaos Computer Club (CCC) and Reporters Without Borders also issued checklists. The Exposure Notification service intends to address the problem of persistent surveillance by removing the tracing mechanism from their device operating systems once it is no longer needed. On 20 April 2020, it was reported that over 300 academics had signed a statement favouring decentralised proximity tracing applications over centralised models, given the difficulty in precluding centralised options being used "to enable unwarranted discrimination and surveillance." In a centralised model, a central database records the ID codes of meetings between users. In a decentralised model, this information is recorded on individual phones, with the role of the central
Markov blanket
In statistics and machine learning, a Markov blanket of a random variable is a set of variables that renders the variable conditionally independent of all other variables in the system. This concept is central in probabilistic graphical models and feature selection. If a Markov blanket is minimal—meaning that no variable in it can be removed without losing this conditional independence—it is called a Markov boundary. Identifying a Markov blanket or boundary allows for efficient inference and helps isolate relevant variables for prediction or causal reasoning. The terms Markov blanket and Markov boundary were coined by Judea Pearl in 1988. A Markov blanket may be derived from the structure of a probabilistic graphical model such as a Bayesian network or Markov random field. == Definition == A Markov blanket of a random variable Y {\displaystyle Y} in a random variable set S = { X 1 , … , X n } {\displaystyle {\mathcal {S}}=\{X_{1},\ldots ,X_{n}\}} is any subset S 1 {\displaystyle {\mathcal {S}}_{1}} of S {\displaystyle {\mathcal {S}}} , conditioned on which other variables are independent with Y {\displaystyle Y} : Y ⊥ ⊥ S ∖ S 1 ∣ S 1 {\displaystyle Y\perp \!\!\!\perp {\mathcal {S}}\smallsetminus {\mathcal {S}}_{1}\mid {\mathcal {S}}_{1}} It means that S 1 {\displaystyle {\mathcal {S}}_{1}} contains at least all the information one needs to infer Y {\displaystyle Y} , where the variables in S ∖ S 1 {\displaystyle {\mathcal {S}}\smallsetminus {\mathcal {S}}_{1}} are redundant. In general, a given Markov blanket is not unique. Any set in S {\displaystyle {\mathcal {S}}} that contains a Markov blanket is also a Markov blanket itself. Specifically, S {\displaystyle {\mathcal {S}}} is a Markov blanket of Y {\displaystyle Y} in S {\displaystyle {\mathcal {S}}} . === Example === In a Bayesian network, the Markov blanket of a node consists of its parents, its children, and its children's other parents (i.e., co-parents). Knowing the values of these nodes makes the target node conditionally independent of the rest of the network. In a Markov random field, the Markov blanket of a node is simply its immediate neighbors. == Markov condition == The concept of a Markov blanket is rooted in the Markov condition, which states that in a probabilistic graphical model, each variable is conditionally independent of its non-descendants given its parents. This condition implies the existence of a minimal separating set — the Markov blanket — that shields a variable from the rest of the network. For instance, when a person holds an object stationary against gravity, the object’s acceleration is fully determined by its direct causes—namely, the upward force from the hand and the downward gravitational pull. Other variables such as air pressure or temperature are causally irrelevant. == Markov boundary == A Markov boundary of Y {\displaystyle Y} in S {\displaystyle {\mathcal {S}}} is a subset S 2 {\displaystyle {\mathcal {S}}_{2}} of S {\displaystyle {\mathcal {S}}} , such that S 2 {\displaystyle {\mathcal {S}}_{2}} itself is a Markov blanket of Y {\displaystyle Y} , but any proper subset of S 2 {\displaystyle {\mathcal {S}}_{2}} is not a Markov blanket of Y {\displaystyle Y} . In other words, a Markov boundary is a minimal Markov blanket. The Markov boundary of a node A {\displaystyle A} in a Bayesian network is the set of nodes composed of A {\displaystyle A} 's parents, A {\displaystyle A} 's children, and A {\displaystyle A} 's children's other parents. In a Markov random field, the Markov boundary for a node is the set of its neighboring nodes. In a dependency network, the Markov boundary for a node is the set of its parents. === Uniqueness of Markov boundary === The Markov boundary always exists. Under some mild conditions, the Markov boundary is unique. However, for most practical and theoretical scenarios multiple Markov boundaries may provide alternative solutions. When there are multiple Markov boundaries, quantities measuring causal effect could fail. == In cognitive science == In the study of consciousness, brain function, and complex adaptive systems, Markov blankets are proposed as a mathematical mechanism which delimits the extent of cognitive entities, whether it be physical or causal.