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Lose It!

Lose It! is an American health and wellness mobile app developed by FitNow, Inc. The app generates calorie budgets for users by tracking weight, exercise, food and calorie intake, and personal goals, primarily to assist them in achieving weight loss. == History == Lose It! was developed in Boston and debuted in 2008. The app and its associated company were founded by J.J. Allaire, Charles Teague and Paul Dicristina. Prior to founding Lose It!, Teague and Allaire had founded the online research tool Onfolio, which was acquired by Microsoft in 2006. The Lose It! app was originally released as an iOS app before being released as a website in 2010 and an Android app in 2011. In 2015, Lose It! announced plans to release the app internationally. Lose It! was also available as an app for Apple Watch at its launch in 2015. The app’s “Snap It” feature, which allows users to approximate calorie counts by taking pictures of their daily meals and snacks, was released in beta in 2016. Snap It was named an Innovation Awards Honoree at the 2017 Consumer Electronics Show in Las Vegas. In 2020, Patrick Wetherille, one of the company’s earliest employees, was appointed chief executive officer. == App == Lose It! is weight loss app. The app allows users to set goals such as increasing strength, overall health/maintenance, and weight loss. It provides users recommended calorie budgets based on data such as their current weight and their desired weight. Lose It! also tracks data such as exercise/activity level and food consumption and allows users to track calories consumed by scanning barcodes for food products then retrieving calorie information for products. The app can also estimate the amount of calories in a food products. Lose It! has integration features connecting it to other apps such as Fitbit and Runkeeper. It also has social features such as joining groups and sharing progress with friends. The Premium version of the app allows users to track foods according to specific diets like keto, heart healthy or Mediterranean.
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Account verification

Account verification is the process of verifying that a new or existing account is owned and operated by a specified real individual or organization. A number of websites, for example social media websites, offer account verification services. Verified accounts are often visually distinguished by check mark icons or badges next to the names of individuals or organizations. Account verification can enhance the quality of online services, mitigating sockpuppetry, bots, trolling, spam, vandalism, fake news, disinformation and election interference. == History == Account verification was introduced by Twitter in June 2009, initially as a feature for public figures and accounts of interest, individuals in "music, acting, fashion, government, politics, religion, journalism, media, sports, business and other key interest areas". A similar verification system was adopted by Google+ in 2011, Facebook page in October 2015 (Available in United States, Canada, United Kingdom, Australia and New Zealand) Facebook profile and Facebook page in 2018 (Available in Worldwide) Instagram in 2014, and Pinterest in 2015. On YouTube, users are able to submit a request for a verification badge once they obtain 100,000 or more subscribers. It also has an "official artist" badge for musicians and bands. In July 2016, Twitter announced that, beyond public figures, any individual would be able to apply for account verification. This was temporarily suspended in February 2018, following a backlash over the verification of one of the organisers of the far-right Unite the Right rally due to a perception that verification conveys "credibility" or "importance". In March 2018, during a live-stream on Periscope, Jack Dorsey, co-founder and CEO of Twitter, discussed the idea of allowing any individual to get a verified account. Twitter reopened account verification applications in May 2021 after revamping their account verification criteria. This time offering notability criteria for the account categories of government, companies, brands, and organizations, news organizations and journalists, entertainment, sports and activists, organizers, and other influential individuals. Instagram began allowing users to request verification in August 2018. In April 2018, Mark Zuckerberg, co-founder and CEO of Facebook, announced that purchasers of political or issue-based advertisements would be required to verify their identities and locations. He also indicated that Facebook would require individuals who manage large pages to be verified. In May 2018, Kent Walker, senior vice president of Google, announced that, in the United States, purchasers of political-leaning advertisements would need to verify their identities. In November 2022, Elon Musk included a blue verification check mark with a paid Twitter Blue monthly membership. Prior to Musk's acquisition of Twitter, Twitter offered this check mark at no charge to confirmed high profile users. On December 19, 2022, Twitter introduced two new check mark colors: gold for accounts from official businesses and organizations, and grey for accounts from governments or multilateral organizations. The type of check mark can be confirmed by visiting the profile page, then clicking or tapping on the check mark. == Techniques == === Identity verification services === Identity verification services are third-party solutions which can be used to ensure that a person provides information which is associated with the identity of a real person. Such services may verify the authenticity of identity documents such as drivers licenses or passports, called documentary verification, or may verify identity information against authoritative sources such as credit bureaus or government data, called nondocumentary verification. === Identity documents verification === The uploading of scanned or photographed identity documents is a practice in use, for example, at Facebook. According to Facebook, there are two reasons that a person would be asked to send a scan of or photograph of an ID to Facebook: to show account ownership and to confirm their name. In January 2018, Facebook purchased Confirm.io, a startup that was advancing technologies to verify the authenticity of identification documentation. === Biometric verification === === Behavioral verification === Behavioral verification is the computer-aided and automated detection and analysis of behaviors and patterns of behavior to verify accounts. Behaviors to detect include those of sockpuppets, bots, cyborgs, trolls, spammers, vandals, and sources and spreaders of fake news, disinformation and election interference. Behavioral verification processes can flag accounts as suspicious, exclude accounts from suspicion, or offer corroborating evidence for processes of account verification. === Bank account verification === Identity verification is required to establish bank accounts and other financial accounts in many jurisdictions. Verifying identity in the financial sector is often required by regulation such as Know Your Customer or Customer Identification Program. Accordingly, bank accounts can be of use as corroborating evidence when performing account verification. Bank account information can be provided when creating or verifying an account or when making a purchase. === Postal address verification === Postal address information can be provided when creating or verifying an account or when making and subsequently shipping a purchase. A hyperlink or code can be sent to a user by mail, recipients entering it on a website verifying their postal address. === Telephone number verification === A telephone number can be provided when creating or verifying an account or added to an account to obtain a set of features. During the process of verifying a telephone number, a confirmation code is sent to a phone number specified by a user, for example in an SMS message sent to a mobile phone. As the user receives the code sent, they can enter it on the website to confirm their receipt. === Email verification === An email account is often required to create an account. During this process, a confirmation hyperlink is sent in an email message to an email address specified by a person. The email recipient is instructed in the email message to navigate to the provided confirmation hyperlink if and only if they are the person creating an account. The act of navigating to the hyperlink confirms receipt of the email by the person. The added value of an email account for purposes of account verification depends upon the process of account verification performed by the specific email service provider. === Multi-factor verification === Multi-factor account verification is account verification which simultaneously utilizes a number of techniques. === Multi-party verification === The processes of account verification utilized by multiple service providers can corroborate one another. OpenID Connect includes a user information protocol which can be used to link multiple accounts, corroborating user information. == Account verification and good standing == On some services, account verification is synonymous with good standing. Twitter reserves the right to remove account verification from users' accounts at any time without notice. Reasons for removal may reflect behaviors on and off Twitter and include: promoting hate and/or violence against, or directly attacking or threatening other people on the basis of race, ethnicity, national origin, sexual orientation, gender, gender identity, religious affiliation, age, disability, or disease; supporting organizations or individuals that promote the above; inciting or engaging in the harassment of others; violence and dangerous behavior; directly or indirectly threatening or encouraging any form of physical violence against an individual or any group of people, including threatening or promoting terrorism; violent, gruesome, shocking, or disturbing imagery; self-harm, suicide; and engaging in other activity on Twitter that violates the Twitter Rules. In April 2023, Blue ticks were removed from all Twitter accounts that had not subscribed to Twitter Blue.
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DiscoVision

DiscoVision is the name of several things related to the video LaserDisc format. It was the original name of the "Reflective Optical Videodisc System" format later known as "LaserVision" or LaserDisc. == Description == MCA DiscoVision, Inc. was a division of entertainment giant MCA (Music Corporation of America), established in 1969 to develop and sell an optical videodisc system. MCA released discs pressed in Carson and Costa Mesa, California on the DiscoVision label from the format's Atlanta, Georgia launch in 1978 to 1982 and the release of the film The Four Seasons. DiscoVision titles included films from Universal Pictures, Paramount Pictures, Warner Bros. Pictures, and Disney content. Agreements were made with Columbia Pictures and United Artists, though no discs were released on the DiscoVision label from either studio. Most of these companies later established their own labels for the format, the first being Paramount with a dozen movies released on the Paramount Home Video label in the summer of 1981. The successor to MCA DiscoVision, DiscoVision Associates (DVA), was the result of a partnership between IBM and MCA. It was hoped that the merger would provide the basis for improvement of the quality of DiscoVision pressings, but no appreciable improvement ever took hold. In 1981, responsibility for the laser videodisc was sold to Pioneer Electronic Corporation, after MCA Discovision had previously started a partnership in 1977 with Pioneer, Universal Pioneer, to produce the Pioneer PR-7820 player (the first industrial model of DiscoVision player from 1978), as well as establishing disc pressing plants in Japan. As part of the partnership, Pioneer, in association with MCA, had a disc replication facility in Kofu, Japan that produced discs. Some of the last DiscoVision label discs were manufactured by Pioneer in Japan. In the same year, MCA discontinued their DiscoVision branding, due to the sale of the technology to Pioneer (who then rebranded the format as LaserDisc) and in turn rebranded their laserdisc releases, now fabricated by Pioneer, under the MCA Videodisc banner; this was changed to the "MCA Home Video" name for both its VHS and videodisc releases. Some of DiscoVision's technical staff went on to form MCA Video Games, in an effort to produce video game cartridges. DiscoVision Associates later evolved into a patent holding company which manages and licenses intellectual property related to LaserDisc, Compact Disc, and optical disc technologies, as well as other non-disc related fields. In 1989, Pioneer acquired DiscoVision Associates where it continues to license its technologies independently. As the portfolio of patent expired, the presence of DiscoVision became less visible. However, it established the success of a patent holding company, which other companies are stimulated to generate royalty income from their own patent portfolio.
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Conversion path

A conversion path is a description of the steps taken by a user of a website towards a desired end from the standpoint of the website operator or marketer. The typical conversion path begins with a user arriving at a landing page or a product page and proceeding through a series of page transitions until reaching a final state, either positive (e.g. purchase) or negative (e.g. abandoned session). In practice, the study of the dynamics of this process by the interested party has evolved into a sophisticated field, where various statistical methods are being applied to the optimization of outcomes. This includes real-time adjustment of presented content, in which a website operator tries to provide deliberate incentives to increase the odds of conversion based on various sources of information, including demographic traits, search history, and browsing events. In practice, this reflects in different content presented to users arriving from online advertising versus search engines, and similarly, different content is presented depending on their demographic segments. The fundamental metric describing this process in the aggregate is known as conversion rate.
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Patch management

Patch management (or patch management policy or patch policy or patch management process) is concerned with the identification, acquisition, distribution, testing and installation of patches to systems. Proper patch management can be a net productivity boost for an organization. Patches can be used to defend against and eliminate potential vulnerabilities of a system, so that no threats may exploit them. Problems can arise during patch management, including buggy patches that either fail to fix their problem or introduce new issues. Patch management tools help orchestrate all of the procedures involved in patch management. == Description == Patch management is defined as a sub-practice of various disciplines including vulnerability management (part of security management), lifecycle management (with further possible sub-classification into application lifecycle management and release management), change management, and systems management. The practice is broadly concerned with the identification, acquisition, distribution, and installation of patches to systems. Some definitions of patch management are as a software-level practice, while others are as a systems-level process: software, drivers, and firmware. == Cost–benefit analysis == While reserving time for patching takes up enterprise resources, there are balancing factors which can make proper patch management into a net productivity boost for an organization. Up-to-date systems often perform more efficiently, less costly, with less errors, less security risks, and better user workflow. Additionally, compliance with changing local and federal regulations are more likely to be satisfied. Patching security vulnerabilities has been one among many competing priorities for organizations, leading to longer periods before patching for some organizations. Equifax was too slow to implement its 2015 patch management plan to be able to mitigate or prevent the 2017 Equifax data breach, leading to scrutiny from regulators. == Relation to security management == Patches can be used to defend against and eliminate potential vulnerabilities of a system, so that no threats may exploit them; therefore, patch management can be considered a sub-discipline of vulnerability management. Every patchable device in a system presents an attack surface that must be secured. === Time plan === Automatic updates are where the patch is applied automatically with little to know actions or planning required. This approach is recommended for many individuals and organizations. Some organizations also have to prioritize which patches to prioritize given limited resources. Patch Tuesday is the most common process when major companies like Microsoft and Adobe release patches on a known date so that companies can plan resources around implementing the patches more quickly. Linux is open-sourced and patches can be released at any time, leading some to rely on mailing lists or other ways to be alerted to updates. === Inventory === Taking an inventory of software and hardware, including versions can make it easier to correlate with bugs or patches as they become known. Taking stock of how much education and support others in an organization need to install their patches can also help for planning how to implement the patch or design systems to begin with. Streamlining the process by using tools that can communicate with each other can also help to reduce the time of exposure to known vulnerabilities. == Challenges == There are a multitude of problems that can arise during patch management. A common issue is buggy patches, which either fail to fix their problem or introduce new issues. Another issue is deployment synchronization, since various subsystems may receive instructions to update at different times. Similarly, the difficulty of patch management across many devices may grow at an uncontrollable rate depending on organizational size. One prominent demonstration of the challenges facing proper patch management was the buggy Falcon Sensor patch by CrowdStrike which caused one of the worst IT outages of all time. == Implementations == A patch management tool (alternatively patch manager, patch management system, patch management software, or centralized patch management) help orchestrate all of the procedures involved in patch management. Tools can be in-house (applied locally by local administrators), or external, as with managed service providers (applied externally by a provider). === Patch management software === Windows Update for Business, System Center Configuration Manager, and Windows Server Update Services offer control over patch deployment, with features enabling testing, scheduling updates, and setting custom configurations on Windows platforms. === Managed service providers === == Regulatory requirements (United States) == Timely patching of software vulnerabilities is a requirement under multiple regulatory frameworks in the United States. The Health Insurance Portability and Accountability Act (HIPAA) Security Rule requires covered entities to protect electronic protected health information by implementing security measures sufficient to reduce risks to a reasonable and appropriate level, which industry guidance has long interpreted to include timely patch management. A proposed new HIPAA Security Rule would make patch management requirements explicit, mandating that covered entities and business associates deploy security patches and updates within a defined risk-based timeline and maintain written procedures for prioritizing, testing, and applying patches to systems that store, process, or transmit ePHI. The 2025 proposal continues to receive industry pushback as of December 2025. HIPAA was last updated in 2013. The Payment Card Industry Data Security Standard (PCI DSS) requires organizations to protect system components from known vulnerabilities by installing applicable security patches within one month of release for critical patches. The Cybersecurity and Infrastructure Security Agency (CISA) maintains a Known Exploited Vulnerabilities (KEV) catalog that compels U.S. federal agencies to remediate listed vulnerabilities within specified timelines. Agencies are typically required to patch within 3 weeks, though some vulnerabilities must be fixed within 24 hours.
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WEA Manufacturing

WEA Manufacturing was the record, tape, and compact disc manufacturing arm of WEA International Inc. from 1978 to 2003, when it was sold and merged into Cinram International, a previous competitor. The last owner when the plant closed was Technicolor. == History == WEA Manufacturing Inc. was created in 1978–1979 when Warner Communications Inc. purchased two of its longtime suppliers: the record pressing plants Specialty Records Corporation (Olyphant, Pennsylvania) and Allied Record Company (Los Angeles). The company was headquartered in Olyphant, where the original plant was replaced in late 1981 by a new facility which retained the name Specialty Records Corporation. The Specialty Records Corporation name was dropped in 1996 in favor of WEA Manufacturing. The company invested in CD manufacturing in 1986, matching a $247,000 contribution by economic development corporation Ben Franklin Technology Partners to develop and implement new processes of manufacturing audio CDs and CD-ROMs. BFTP assembled a team of experts in physics, electrical engineering, and thin film technology from the University of Scranton and Lehigh University to carry out the research and development. The Olyphant plant and another plant in Alsdorf, Germany, were expanded to support CD pressing that year, with the Olyphant facility's production commencing first in September 1986. WEA Manufacturing grew to become one of the largest manufacturers of recorded media in the world. The company began manufacturing Laserdiscs in July 1991. The company's DVD division, Warner Advanced Media Operations (WAMO), helped design the high-density format used in DVDs, and manufactured some of the first DVDs in the late 1990s. The company was sold to Cinram International in October 2003 and no longer exists under the name WEA Manufacturing, but the Olyphant plant continued to operate under its new ownership. In 2005, the company was Lackawanna County's largest employer, with over 2,300 people working at the Olyphant plant. Cinram closed the former Allied plant in 2006, while Technicolor (which purchased Cinram's assets in 2015) closed the Olyphant plant in 2018. == Patents == WEA Manufacturing held U.S. patents related to compact disc manufacture: Print scanner, (1993). Interference of converging spherical waves with application to the design of light-readable information-recording media and systems for reading such media, (2004). Method of manufacturing a composite disc structure and apparatus for performing the method, (2005). Methods and apparatus for reducing the shrinkage of an optical disc's clamp area and the resulting optical disc, (2005). == Litigation == In 1990, WEA Manufacturing was sued by a Canadian firm, Optical Recording Co. (ORC), for alleged infringement of two 1971 patents related to glass mastering equipment which was used by Time Warner and WEA Manufacturing in the manufacture of approximately 450 million CDs. ORC contended that unlike five other major CD manufacturers in the U.S., Time Warner had refused to license the technology from ORC. In 1992, a jury assessed damages of 6 cents per disc, plus $4–5 million in interest.
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Amplified conference

An amplified conference is a conference or similar event in which the talks and discussions at the conference are 'amplified' through use of networked technologies in order to extend the reach of the conference deliberations. The term was originally coined by Lorcan Dempsey in a blog post. The term is now widely used within the academic and research community with Wankel proposing the following definition: The extension of a physical event (or a series of events) through the use of social media tools for expanding access to (aspects of) the event beyond physical and temporal bounds. Such amplification takes place in the context of intent to make the most of the intellectual content, discussion, networking, and discovery initiated by the event through the process of sharing with co-attendees, colleagues, friends and wider informed publics. A paper by Haider and others illustrates how amplified conferences are becoming mainstream in a discussion on "how social media have been employed as part of the project, particularly around event amplification". As described by Guy in the Ariadne ejournal the term is not a prescriptive one, but rather describes a pattern of behaviors which initially took place at IT and Web-oriented conferences once WiFi networks started to become available at conference venues and delegates started to bring with them networked devices such as laptops and, more recently, PDAs and mobile phones. == Different Approaches to 'Amplification' of Conferences == There are a number of ways in which conferences can be amplified through use of networked technologies: Amplification of the audiences' voice: Prior to the availability of real time chat technologies at events (whether use of IRC, Twitter, instant messaging clients, etc.) it was only feasible to discuss talks with immediate neighbours, and even then this may be considered rude. Amplification of the speaker's talk: The availability of video and audio-conferencing technologies make it possible for a speaker to be heard by an audience which isn't physically present at the conference. Although use of video technologies has been available to support conferences for some time, this has normally been expensive and require use of dedicated video-conferencing technologies. However the availability of lightweight desktop tools make it much easier to deploy such technologies, without even, requiring the involvement of conference organisers. Amplification across time: Video and audio technologies can also be used to allow a speaker's talk to be made available after the event, with use of podcasting or videocasting technologies allowing the talks to be easily syndicated to mobile devices as well as accessed on desktop computers. Amplification of the speaker's slides: The popularity of global repository services for slides, such as SlideShare, enable the slides used by a speaker to be more easily found, embedded on other Web sites and commented upon, in ways that were not possible when the slides, if made available at all, were only available on a conference Web site. Amplification of feedback to the speaker: Micro-blogging technologies, such as Twitter, are being used not only as a discussion channel for conference participants but also as a way of providing real-time feedback to a speaker during a talk. We are also now seeing dedicated microblogging technologies, such as Coveritlive and Scribblelive, being developed which aim to provide more sophisticated 'back channels' for use at conferences. Amplification of a conference's collective memory: The popularity of digital cameras and the photographic capabilities of many mobile phones is leading to many photographs being taken at conferences. With such photographs often being uploaded to popular photographic sharing services, such as Flickr, and such collections being made more easy to discover through agreed use of tags, we are seeing amplification of the memories of an event though the sharing of such resources. The ability of such photographic resources to be 'mashed up' with, say, accompanying music, can similarly help to enrich such collective experiences. Amplification of the learning: The ability to be able to follow links to resources and discuss the points made by a speaker during a talk can enrich the learning which takes place at an event, as described by Shabajee's article on "'Hot' or Not? Welcome to real-time peer review" published in the Times Higher Education Supplement in May 2003. Long term amplification of conference outputs: The availability in a digital format of conference resources, including 'official' resources such as slides, video and audio recordings, etc. which have been made by the conference organisers with the approval of speakers, together with more nebulous resources such as archives of conference back channels, and photographs and unofficial recordings taken at the event may help to provide a more authentic record of an event, which could potentially provide a valuable historical record. The amplification of conferences can be viewed as an example of how new technologies are altering standard practice. By using these techniques a different type of interaction is created at the conference itself, but also the boundaries around the conference can be seen as permeable, with remote participants engaging in discussion. An amplified conference also provides a considerably altered archive compared with a 'traditional' one. For the latter, the printed proceedings will be the main record, but for an amplified event this record is distributed across many media and takes in a wider range of content types, including the papers, videos of the presentations (for example on YouTube), the slides (e.g. on Slideshare), photos of the event (Flickr), interaction between participants (Twitter), reflections and comments (blogs), etc. The amplified conference represents an example of changing practice in digital scholarship.
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Data plan

A data plan is a subscription plan from a cellular or other mobile service provider to provide internet data and connectivity. == Formatting == Data plans are usually created by a contract between the telecommunications carrier and the user of their service. This contract outlines a maximum amount of usable data, usually highlighted in either megabytes or gigabytes, allotted per month for the user. In most cases companies will allow a user to surpass the amount of data allowed in the contract, however, will have to pay a per-gigabyte fee, ranging anywhere from five to fifteen U.S. dollars. === Popularization of unlimited plans === Unlimited data plans have seen a large increase in usage by consumers since their initial introduction by U.S. network T-Mobile. These plans, instead of setting an overall maximum for the user, have an amount set-up that, when surpassed, will slow the speed of the network for that user. Unlimited plans typically cost significantly more than the traditional shared data plans, which is a major reason that carriers have set large boundaries and fees. The limits imposed on unlimited plans are designed to fight against attempts to misuse the network, such as a DDoS attack, but are more commonly reasoned as a method to increase the number of people that can use one tower simultaneously. === Data speed changes === When a network is near reaching peak capacity data speeds may be slowed down by carriers as part of most major telecom contracts. This, as stated previously, allows for more people to be utilizing one tower, reducing needed capital for the company. Since speed changes are allowed at the company's will, the user has no official guarantee of speed on most major networks. === Costs brought upon by additional data === In many cases both the user and carrier have to incur additional costs when a user utilizes more of a given data package, which has helped in the proliferation of data caps and other forms of shared data plans. Most of the charges that the carrier has to incur for additional data usage is partially or fully given to the user of the network. ==== Users ==== Users are required to pay flat-rate additional fees that occur when they go above the amount of data given to them in their contract, utility, or prepaid plan. The cost per gigabyte of this fee is usually higher than what the contract itself offers, which discourages users from over-utilizing data and incurring a charge for the carrier. Certain contracts, which do not offer paying additional fees for an increase in data, may result in a shutdown of service, or in extremely rare cases, termination of the service as a whole. ==== Carriers ==== Carriers incur costs for additional data usage, as it limits the number of customers, and associated contracts, that they can handle on one network. Creating more cell phone towers in a given area would be costly, and largely useless until particular spikes in traffic. When the peak usable amount of one tower is reached, it may cause negative public relations towards the reliability of the corporation as a whole.
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Transfer learning

Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related task. For example, for image classification, knowledge gained while learning to recognize cars could be applied when trying to recognize trucks. This topic is related to the psychological literature on transfer of learning, although practical ties between the two fields are limited. Reusing or transferring information from previously learned tasks to new tasks has the potential to significantly improve learning efficiency. Since transfer learning makes use of training with multiple objective functions it is related to cost-sensitive machine learning and multi-objective optimization. == History == In 1976, Bozinovski and Fulgosi published a paper addressing transfer learning in neural network training. The paper gives a mathematical and geometrical model of the topic. In 1981, a report considered the application of transfer learning to a dataset of images representing letters of computer terminals, experimentally demonstrating positive and negative transfer learning. In 1992, Lorien Pratt formulated the discriminability-based transfer (DBT) algorithm. By 1998, the field had advanced to include multi-task learning, along with more formal theoretical foundations. Influential publications on transfer learning include the book Learning to Learn in 1998, a 2009 survey and a 2019 survey. Ng said in his NIPS 2016 tutorial that TL would become the next driver of machine learning commercial success after supervised learning. In the 2020 paper, "Rethinking Pre-Training and self-training", Zoph et al. reported that pre-training can hurt accuracy, and advocate self-training instead. == Definition == The definition of transfer learning is given in terms of domains and tasks. A domain D {\displaystyle {\mathcal {D}}} consists of: a feature space X {\displaystyle {\mathcal {X}}} and a marginal probability distribution P ( X ) {\displaystyle P(X)} , where X = { x 1 , . . . , x n } ∈ X {\displaystyle X=\{x_{1},...,x_{n}\}\in {\mathcal {X}}} . Given a specific domain, D = { X , P ( X ) } {\displaystyle {\mathcal {D}}=\{{\mathcal {X}},P(X)\}} , a task consists of two components: a label space Y {\displaystyle {\mathcal {Y}}} and an objective predictive function f : X → Y {\displaystyle f:{\mathcal {X}}\rightarrow {\mathcal {Y}}} . The function f {\displaystyle f} is used to predict the corresponding label f ( x ) {\displaystyle f(x)} of a new instance x {\displaystyle x} . This task, denoted by T = { Y , f ( x ) } {\displaystyle {\mathcal {T}}=\{{\mathcal {Y}},f(x)\}} , is learned from the training data consisting of pairs { x i , y i } {\displaystyle \{x_{i},y_{i}\}} , where x i ∈ X {\displaystyle x_{i}\in {\mathcal {X}}} and y i ∈ Y {\displaystyle y_{i}\in {\mathcal {Y}}} . Given a source domain D S {\displaystyle {\mathcal {D}}_{S}} and learning task T S {\displaystyle {\mathcal {T}}_{S}} , a target domain D T {\displaystyle {\mathcal {D}}_{T}} and learning task T T {\displaystyle {\mathcal {T}}_{T}} , where D S ≠ D T {\displaystyle {\mathcal {D}}_{S}\neq {\mathcal {D}}_{T}} , or T S ≠ T T {\displaystyle {\mathcal {T}}_{S}\neq {\mathcal {T}}_{T}} , transfer learning aims to help improve the learning of the target predictive function f T ( ⋅ ) {\displaystyle f_{T}(\cdot )} in D T {\displaystyle {\mathcal {D}}_{T}} using the knowledge in D S {\displaystyle {\mathcal {D}}_{S}} and T S {\displaystyle {\mathcal {T}}_{S}} . == Applications == Algorithms for transfer learning are available in Markov logic networks and Bayesian networks. Transfer learning has been applied to cancer subtype discovery, building utilization, general game playing, text classification, digit recognition, medical imaging and spam filtering. In 2020, it was discovered that, due to their similar physical natures, transfer learning is possible between electromyographic (EMG) signals from the muscles and classifying the behaviors of electroencephalographic (EEG) brainwaves, from the gesture recognition domain to the mental state recognition domain. It was noted that this relationship worked in both directions, showing that electroencephalographic can likewise be used to classify EMG. The experiments noted that the accuracy of neural networks and convolutional neural networks were improved through transfer learning both prior to any learning (compared to standard random weight distribution) and at the end of the learning process (asymptote). That is, results are improved by exposure to another domain. Moreover, the end-user of a pre-trained model can change the structure of fully-connected layers to improve performance.
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Homeboyz Interactive

Homeboyz Interactive (HBI) was a faith-based recruitment, training and job placement non-profit business in Milwaukee, Wisconsin, United States, founded by a Jesuit brother in 1996 to transform gang members into productive workers. == History == James Holub, a former Jesuit brother affiliated with Wheeling Jesuit University, asked gang members in the Southside of Milwaukee, WI how they could be helped, to break the cycle of poverty and violence. The youth suggested that they be trained for work they found exciting. To attract interest, the training must lead to jobs that paid at least a living wage, and computer skills seemed the most attractive. The non-profit Homeboyz Interactive was established to prepare professionals in web design, application development, and PC/network support. This non-profit outfit spawned the for-profit web design firm HBI Consulting, which provided trainees with work experience. It turned out more than 20 teachers yearly for computer and computer network programs for high schools and other clients, as well as for computer service providers. Some graduates of the program continued their education, some founded their own business, and others continued working at HBI. The Economist described this effort as "turning thugs into programmers" on Milwaukee's South Side, which has proportionally twice as many murders as New York. Holub had "buried his 28th gang member" before he implemented the Homeboyz plan, with the understanding that "nothing stops a bullet like a job." The programs would pass through about 80 prospects a year who successfully completed training and provide them with a job while studying for their high school equivalency test, before they were asked to decide in which direction to go. Most accepted a job or went on to community college but about 25 entered the Homeboyz training for computer programmers. Of first 150 graduates of this program none lost their job; their average pay after two years was US$63,000. Some preferred to return to full-time work at HBI. By 2002, a total of 142 people had graduated from HBI training and moved into full-time IT careers. The training curriculum as of 2000 included JavaScript and Photoshop, among other web-development tools. In 2000, HBI received a 14% ownership stake in reEmploy.com, a payrolling company, in exchange for the development of an electronic time sheet created by the organization. As of 2001, HBI Consulting, the for profit web design firm, had 72 clients. Among those clients were GE Medical, Toyota Forklift, Northwestern Mutual Life, Verizon Wireless, BP; and Marquette University. Companies that graduates of HBI's training programs secured positions have included Northwestern Mutual and Manpower Inc., United Community Center in Milwaukee and EKI Consulting. A pair of graduates also started their own company in 2002, Innovative Source, a web design firm, which itself has had clients such as the University of Wisconsin-Milwaukee and the Milwaukee Women's Center. This was a common path forward, graduates starting their own consulting firms. In 2004, HBI received a grant for General Support from the Vine and Branches Foundation in the amount of US$120,000. The product Project Foundry found its start in the difficulty of managing project-based learning across dozens of students with widely varying levels of skill, a problem encountered by Shane Krukowski, who developed the software while teaching at HBI. Krukowski subsequently an eponymous company to commercialize the software through a subscription-based business model. Some came to Homeboyz through the criminal courts or Department of Corrections. A Jesuit Volunteer (JV) was assigned to work with the program, and to add a spiritual dimension through regular reflection together. Gradually the market began prioritizing graphic design and flash images more than site construction. After 2006 Homeboyz HBI morphed into several spinoffs and ceased to exist as a separate entity.
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Macroelectronics

Macroelectronics are flexible electronics that cover a large area. The most visible example of macroelectronics is flat-panel displays. Other emerging applications include rollable display, printable thin film solar cell and electronic skin. Flat-panel displays fabricated on glass substrates are fragile so fabricating directly on flexible substrates, such as polymers is being explored. Displays made on thin polymer substrates can be more rugged than glass. In September 2005, Philips Polymer Vision revealed the world's first prototype of a rollable electronic reader, which can unfold to a 5-inch display and roll back into a pocket-size (100×60×20 mm) device. Thin-film devices on flexible polymer substrates can lend themselves to low-cost fabrication processes (i.e., roll-to-roll printing), resulting in lightweight, rugged and flexible macroelectronic products.
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Gnowit

Gnowit (pronounced "know it") is a Canadian software company that provides automated, near-real-time monitoring of legislative, regulatory, and political activity across Canada. Its platform aggregates and analyzes information from government publications, parliamentary debates, committee, and proceedings to provide searchable alerts and reports for organizations monitoring public policy and regulatory developments. The system uses natural-language processing and machine learning techniques to organize and filter large volumes of public information.; the company reports that new publication documents are captured and millions of items are added to its repository daily. == History, Founders and Leadership == Gnowit was co-founded in Ottawa in 2010 by Shahzad Khan and Mohammad Al-Azzouni; Khan serves as chief executive officer. Khan holds a PhD in Computer Science from the University of Cambridge, has more than two decades of experience in AI/ML and computational linguistics, and has authored or co-authored 37 peer-reviewed publications and five patents. Traditionally, companies performed this analysis manually; Gnowit has delivered efficiencies achieved through AI innovations. The company has participated in several Canadian startup and accelerator programs, including Carleton University's Lead To Win initiative, the University of Ottawa's Startup Garage, the Invest Ottawa incubator, and the League of Innovators' BOOST program. === Kubernetes validation (2019–2020) === As part of a Canada's Centre of Excellence in Next Generation Networks (CENGN) project, Gnowit validated a containerized version of its web-intelligence software on Kubernetes. Between 2019 and 2020, Gnowit participated in a project with Canada’s Centre of Excellence in Next Generation Networks (CENGN) to test and scale its platform using containerized infrastructure based on Kubernetes. The initiative focused on improving scalability and supporting the company’s transition from a monolithic software architecture to a cloud-native deployment model. == Products and services == Gnowit markets several modules for public-affairs, compliance, and market-intelligence teams. Legislative & Regulatory Monitoring (vAnalyst). vAnalyst is a monitoring platform that tracks legislative and regulatory activity across Canadian federal, provincial, and territorial jurisdictions. The system aggregates parliamentary debates, bills, committee proceedings, and regulatory publications and provides searchable alerts and reporting tools. The product monitors more than two million web sources to surface relevant items quickly. Parliamentary Live (vAnalyst). Monitors live video feeds from parliamentary sessions and committees with same-day transcripts, AI-generated summaries, witness summaries, and motion detection; municipal coverage is offered as an option. Gnowit can avail transcripts up to two weeks before official releases. These transcripts enable users to navigate and review lengthy parliamentary sittings and committee discussions through searchable text. Municipal Monitoring (vAnalyst). The platform also tracks council meetings, agendas, bylaws, and other municipal government publications from hundreds of Canadian municipalities. The platform aggregates these sources into a single searchable interface for reviewing local government decisions. Curation Edge (analyst service). Curation Edge is an add-on service in which expert analysts work and collaborate with clients to develop a tailored curation guide and deliver daily newsletters or briefs on legislation and media. These reports provide concise summaries, relevant links, and optional metadata, prioritizing key updates with additional context and analysis. The service is customizable, including branding and formatting for executive audiences, and is intended to reduce information overload, support decision-making, and streamline the synthesis and distribution of information. === Coverage and sources === Gnowit monitors sources span Canadian government materials across federal, provincial, and territorial jurisdictions Hansard transcripts (All Jurisdictions, including committees), order papers, committee transcripts, gazettes, bills, acts and regulations, consultations, regulatory-agency publications, and global news media as well as press releases and council-meeting materials from hundreds of municipalities. == Partnerships and support == Gnowit reports collaborations with Canadian academic and ecosystem partners, including: Algonquin College Carleton University McGill University University of Ottawa Université du Québec en Outaouais (UQO) Queen's University The company also participated in the accelerator program at Invest Ottawa and has received support from Canadian research and innovation programs, including: NRC Industrial Research Assistance Program (NRC-IRAP) Mitacs Ontario Centre of Innovation (OCI) (formerly OCE) Gnowit has also referenced membership in the Southern Ontario Smart Computing Innovation Platform (Government of Canada profile: FedDev Ontario – SOSCIP overview). == Technology == Gnowit develops technology intended to support timely decision-making by delivering updates from monitored web sources as they are published. The platform applies artificial intelligence (AI) and machine learning (ML) techniques to monitor, capture, clean, analyze, filter, and organize text, and to generate concise briefs. Its technical approach combines Boolean queries, shallow language processing techniques, and machine learning classifiers within a self-service interface. The company has described its longer-term development framework in relation to a belief–desire–intention (BDI) model of intelligent agents on the web. Gnowit and its founder are listed as inventors/assignees on patents concerning multi-document clustering, salient-content extraction, and sentiment analysis methods that are consistent with these features: US 9,600,470 – Method and system relating to re-labelling multi-document clusters (assignee: Whyz Technologies Ltd.). US 9,336,202 – Method and system relating to salient content extraction for information retrieval (assignee: Whyz Technologies Ltd.). CA 2,865,184 C – Method and system relating to re-labelling multi-document clusters. CA 2,865,186 C – Procédé et système concernant l'analyse de sentiment d'un contenu (sentiment analysis; French record). CA 2,865,187 C – Method and system relating to salient content extraction for information retrieval. == Research and community == In January 2025, Gnowit personnel contributed to regulatory NLP by co-authoring a peer-reviewed paper at the 1st Regulatory NLP Workshop (RegNLP 2025), co-located with COLING in Abu Dhabi. Titled Unifying Large Language Models and Knowledge Graphs for Efficient Regulatory Information Retrieval and Answer Generation, the work introduces PolicyInsight, a framework that joins a dynamic policy data model and knowledge graph with LLMs to monitor policy texts, detect changes, and support retrieval and answer generation; the author list includes Shahzad Khan (CEO, Gnowit Inc.). (ACL Anthology, aclweb.org). Similar information-retrieval technologies are widely used for competitive intelligence, policy monitoring, and media analysis. == White paper == Gnowit has published a practical guide, Automated Government Information Monitoring, which outlines how GR and regulatory teams can design a monitoring and briefing workflow and describes Gnowit's automation features and export options (PDF, email, dashboards, CSV/JSON/XML/API).
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Pixel binning

Pixel binning, also known as binning, is a process image sensors of digital cameras use to combine adjacent pixels throughout an image, by summing or averaging their values, during or after readout. It improves low-light performance while still allowing for highly detailed photographs in good light. Charge from adjacent pixels in CCD or charge-coupled device image sensors and some other image sensors can be combined during readout, increasing the line rate or frame rate. In the context of image processing, binning is the procedure of combining clusters of adjacent pixels, throughout an image, into single pixels. For example, in 2×2 binning, an array of 4 pixels becomes a single larger pixel, reducing the number of pixels to 1/4 and halving the image resolution in each dimension. The result can be the sum, average, median, minimum, or maximum value of the cluster. Some systems use more advanced algorithms such as considering the values of nearby pixels, edge detection, self-claimed "AI", etc. to increase the perceived visual quality of the final downsized image. This aggregation, although associated with loss of information, reduces the amount of data to be processed, facilitating analysis. The binned image has lower resolution, but the relative noise level in each pixel is generally reduced. == History == Normally, an increase in megapixel count on a constant image sensor size would lead to a sacrifice of the surface size of the individual pixels, which would result in each pixel being able to catch less light in the same time, thus leading to a darker and/or noisier image in low light (given the same exposure time). In the past, camera manufacturers had to compromise between low-light performance and the amount of detail in good light, by dropping the megapixel count like HTC did in 2013 with their four-megapixel "UltraPixel" camera. However, this results in less detailed images in daylight where enough light is available. With pixel binning, the camera has "the best of both worlds", meaning both the benefit of high detail in good light and the benefit of high brightness in low light. In low light, the surfaces of four or more pixels can act as one large pixel that catches far more light. For example, some smartphones such as the Samsung Galaxy A15 are able to capture photographs with up to fifty megapixels in daylight. However, in low light, the individual pixels would be too small to capture the light needed for a bright image with the short exposure time available for handheld shooting. Therefore, with pixel binning activated, the 50-megapixel image sensor acts as a 12.5-megapixel image sensor, a quarter of its original resolution, with an accordingly larger surface area per pixel.
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Scalable Video Coding

Scalable Video Coding (SVC) is a video compression standard developed jointly by the ITU-T and the ISO/IEC. The two organizations formed the Joint Video Team (JVT) to create the H.264/MPEG-4 AVC standard (ITU-T Rec. H.264 | ISO/IEC 14496-10 AVC). SVC aims to provide adaptable or scalable content, allowing a single encoded video stream to be decoded at various bitrates, resolutions, and quality levels, thus catering to diverse devices and network conditions. == History == In October 2003, the Moving Picture Experts Group (MPEG) issued a Call for Proposals on SVC Technology. Fourteen proposals were submitted, twelve of which utilized wavelet compression, while the remaining two were extensions of H.264/MPEG-4 AVC. The proposal from the Heinrich-Hertz-Institut (HHI) was selected by MPEG as the foundation for the SVC standardization project. In January 2005, MPEG and the Video Coding Experts Group (VCEG) agreed to finalize SVC as an amendment to the H.264/MPEG-4 AVC standard. In November 2008, Google launched Gmail Video Chat, which employed an H.264/SVC codec, marking the first consumer application of the standard. This service was succeeded by Google+ Hangouts in 2012. In 2011, Google Code highlighted SVC as the successor to the open-source RVC video chat engine, noting its prominence in 2010. == Principles of scalability == === Overview === Scalability refers to the ability to represent a video signal at multiple levels of detail within a single encoded bitstream. This enables decoding of a base layer for basic quality and additional enhancement layers for progressively higher quality. SVC defines three types of scalability: Spatial scalability: Supports multiple resolution levels. Temporal scalability: Enables varying frame rates. Quality scalability: Provides different image quality levels. === Spatial scalability === Spatial scalability allows the reconstruction of video at different resolutions, such as QCIF, CIF, or SD. This is achieved through a pyramidal decomposition into multiple spatial layers. === Temporal scalability === Temporal scalability adjusts the frame rate of the decoded video stream. Various frame rates are supported using a hierarchical structure of video frames. === Quality scalability === Quality scalability, or Signal-to-Noise Ratio (SNR) scalability, improves the signal-to-noise ratio of a layer, reducing quantization distortion between the original and reconstructed images. SVC supports two approaches: Fine Grain Scalability (FGS) and Coarse Grain Scalability (CGS). ==== Coarse Grain Scalability (CGS) ==== CGS incorporates quality scalability across spatial resolutions. Each spatial resolution is encoded as a separate layer, refining texture and motion data. For a given resolution, quality scalability is achieved by encoding multiple quality layers with progressively finer quantization steps, starting from a base layer with minimal quality. ==== Fine Grain Scalability (FGS) ==== FGS enables progressive refinement of transformed coefficients within a single spatial layer. The base quality layer is encoded using the AVC standard with an initial quantization parameter (QP) ensuring minimal acceptable quality. Subsequent refinement layers reduce the QP by six, halving the quantization step. The refinement data stream can be truncated at any point, allowing fine-grained quality scalability.
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Frictionless sharing

Frictionless sharing refers to the transparent or automatic dissemination of user activity across social media platforms, typically without requiring explicit action from the user each time content is shared. The concept gained prominence in 2011 after Mark Zuckerberg announced a series of new features for Facebook at the F8 developers conference, framing the changes as enabling “real-time serendipity in a friction-less experience.” == History and concept == Before 2011, the term “frictionless sharing” was occasionally used in academic and technical contexts to describe sharing of resources with minimal effort, such as through social bookmarking or Creative Commons licensing to reduce barriers to reuse of research data. The concept took on a broader cultural meaning when Facebook introduced its Timeline interface and new “social apps” in 2011. These features enabled third-party applications to automatically publish user activity to the platform—effectively shifting sharing from a deliberate act to a passive process. For example, integrating music streaming service Spotify meant that any song a user listened to could automatically appear in a Facebook “Ticker,” allowing friends to see the activity and click through to play the song themselves. == Zuckerberg’s vision == Zuckerberg articulated a vision of a Web in which sharing occurs by default rather than by choice: “You read, you watch, you listen, you buy—and everyone you know will hear all about it on Facebook.” This “frictionless” model assumes ongoing consent after an initial opt-in. Once users connect an app to their profile, any future activity with that app may be automatically shared. This shift from intentional posting to ambient sharing represented a significant evolution in how personal data is distributed online. == Criticism and debate == Many commentators and users have raised concerns about frictionless sharing. While some criticism centers on online privacy, others focus on how automatic updates can flood news feeds and erode the social value of sharing. Critics argue that when sharing becomes automatic, it dilutes the personal curation that makes social media exchanges meaningful. According to Slate, this approach risks “killing taste,” because users typically choose to share only select content they find worth highlighting, rather than everything they consume. AL.com similarly observed that the frictionless model encourages over-sharing, overwhelming both users and their networks with minor or trivial activities. For example, integrating multiple platforms—such as Twitter, Foursquare, Pinterest, Spotify, and others—can create an incessant stream of updates that some users may find intrusive or irritating. This can lead to what critics describe as “narcissistic” or noisy timelines, potentially undermining the “social” nature of social media. == Business model and data implications == For Facebook, frictionless sharing offers clear business advantages. More frequent and detailed sharing provides valuable data that can be used to refine targeted advertising and personalize content delivery. The model also encourages users to spend more time on the platform, reinforcing its position as a central hub of online social activity. Other technology companies have experimented with similar approaches. Google has introduced forms of cross-platform integration that facilitate automatic activity sharing, though with a more explicit opt-in structure compared to Facebook. This approach has been described as “friction with consent,” allowing users to manually enable or disable integrations on a per-service basis.
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