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  • Discrete skeleton evolution

    Discrete skeleton evolution

    Discrete Skeleton Evolution (DSE) describes an iterative approach to reducing a morphological or topological skeleton. It is a form of pruning in that it removes noisy or redundant branches (spurs) generated by the skeletonization process, while preserving information-rich "trunk" segments. The value assigned to individual branches varies from algorithm to algorithm, with the general goal being to convey the features of interest of the original contour with a few carefully chosen lines. Usually, clarity for human vision (aka. the ability to "read" some features of the original shape from the skeleton) is valued as well. DSE algorithms are distinguished by complex, recursive decision-making processes with high computational requirements. Pruning methods such as by structuring element (SE) convolution and the Hough transform are general purpose algorithms which quickly pass through an image and eliminate all branches shorter than a given threshold. DSE methods are most applicable when detail retention and contour reconstruction are valued. == Methodology == === Pre-processing === Input images will typical contain more data than is necessary to generate an initial skeleton, and thus must be reduced in some way. Reducing the resolution, converting to grayscale, and then binary by masking or thresholding are common first steps. Noise removal may occur before and/or after converting an image to binary. Morphological operations such as closing, opening, and smoothing of the binary image may also be part of pre-processing. Ideally, the binarized contour should be as noise-free as possible before the skeleton is generated. === Skeletonization === DSE techniques may be applied to an existing skeleton or incorporated as part of the skeleton growing algorithm. Suitable skeletons may be obtained using a variety of methods: Thinning algorithms, such as the Grassfire transform Voronoi diagram Medial Axis Transform or Symmetry Axis Transform Distance Mapping === Significance Measures === DSE and related methods remove entire spurious branches while leaving the main trunk intact. The intended result is typically optimized for visual clarity and retention of information, such that the original contour can be reconstructed from the fully pruned skeleton. The value of various properties must be weighted by the application, and improving the efficiency is an ongoing topic of research in computer vision and image processing. Some significance measures include: Discrete Bisector Function Contour length Bending Potential Ratio Discrete Curve Evolution === Iteration === Each branch is evaluated during a pass through the skeletonized image according to the specific algorithm being used. Low value branches are removed and the process is repeated until a desired threshold of simplicity is reached. === Reconstruction === If all points on the output skeleton are the center points of maximal disks of the image and the radius information is retained, a contour image can be reconstructed == Applications == === Handwriting and text parsing === Variability in hand-written text is an ongoing challenge, simplification makes it somewhat easier for computer vision algorithms to make judgements about intended characters. === Soft body classification (animals) === The maximal disks centered on the skeleton imply roughly spherical masses, the features of the extracted skeleton are relatively unchanged even as the soft body deforms or self-occludes. Skeleton information is one facet of determining whether two animals are the "same" some way, though it must usually be paired with another technique to effectively identify a target. === Medical uses === Investigation of organs, tissue damage and deformation caused by disease.

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  • Social media use by the Islamic State

    Social media use by the Islamic State

    The Islamic State is widely known for its posting of disturbing content, such as beheading videos, on the internet. This propaganda is disseminated through websites and many social media platforms such as Twitter, Facebook, Telegram, and YouTube. By utilizing social media, the organization has garnered a strong following and successfully recruited tens of thousands of followers from around the world. In response to its successful use of social media, many websites and social media platforms have banned accounts and removed content promoting the Islamic State from their platforms. == Background == The Islamic State is a Jihadist militant group and a former unrecognised proto-state. The group sophisticatedly utilizes social media as a tool for spreading its message and for international recruitment. == Target audience == IS targets a variety of different groups both in the Middle East and Western Countries. There are a wide variety of motives for why fighters may be prompted to join IS. Researchers from Quantum cite nine attributes characteristic of a fighter looking to join IS: status seeking, identity seeking, revenge, redemption, thrill, ideology, justice, and death. The standard IS recruit, both from the Middle East and Western countries, is relatively young. The average age of IS fighters is around 26 years old, with 86% of recruits being male. Middle Eastern recruits come from economically disadvantaged backgrounds in Northern Iraq. Recent destruction in the Iraq War and Syrian Civil War has created hatred of Western Powers in the region. By 2025, researchers identified a significant shift toward targeting minors and adolescents, a phenomenon dubbed the "Alt-Jihad." This younger demographic is targeted not through theological arguments, but through a "victimhood-revenge" narrative that blends extremist ideology with pop-culture aesthetics in gaming environments like Roblox and Minecraft. In 2024 alone, 42 minors were arrested in Europe for involvement in IS-related plotting or propaganda. Western recruits are often second or third-generation immigrants. Computer scientists Zeeshan ul-hassan Usmani also found that the majority of the Western recruits do not feel "at home" in their home country. As a result, these fighters often have desires to go abroad and escape conditions in their home country. In addition to recruitment, IS's social media presence is also meant to intimidate and spread terror around the world. IS's posting of beheadings and other execution videos primarily target the Western world. == Content and messages == IS produces propaganda videos that range from video executions to full-length documentaries. The videos have a high production quality and incorporate montages, slow motion scenes, and are often accompanied by a short dialogue. IS has a dedicated team of over 100 media insurgents dedicated to recording these videos. While the group previously relied on glossy magazines like Dabiq, post-territorial strategies have shifted focus to the weekly newsletter Al-Naba. Unlike previous publications designed for recruitment, Al-Naba serves as a "central pillar" of the group's media strategy, focusing on bureaucratic reporting and military statistics to project a narrative of endurance and maintain internal cohesion among dispersed fighters. The IS executions typically consist of beheadings or mass shootings in retaliation to western intervention in IS territory. The particular videos that IS often post include executions of "enemies of the Caliphate," which often consist of westerners or Jordanian nationals. Most infamously, an executioner nicknamed Jihadi John was seen in many of these videos prior to his death in 2015. Jihadi John is notorious for executing many US, UK, and Japanese citizens such as Steven Sotloff, David Haines, and Alan Henning. In many of the videos and materials produced by IS, there is the theme of inclusion and brotherhood. Additionally, the videos also focus on three main messages: Convey narrative of global war and ultimate victory Radicalize populations globally Encourage international lone state actor and small cell attacks in support of IS These messages can be seen throughout all content produced by the Islamic State such as war documentaries, execution videos, and Rumiyah (magazine). == Social media usage == From 2013 to 2014, the organization primarily used mainstream platforms such as Twitter, Facebook, and YouTube. In 2014, these large social media platforms removed IS content. Since then, IS has chosen to utilize social media platforms that either protect their content or allow for content to quickly be reposted. These platforms of choice are Telegram, Justpaste.it, and Surespot, until the latter's shutdown in 2022. By 2025, the group had further diversified into decentralized platforms like Rocket.Chat and TamTam to evade moderation. IS also implements marketing initiatives like “Jihadist Follow Friday,” which encourages users to follow new IS-related accounts each Friday. This specific hashtag mirrors commonly used hashtags such as #motivation monday or #throwbackthursday. To augment their online presence and popularity, the organization encourages their followers to use a plethora of Arabic hashtags, which translate to #theFridayofSupportingISIS, and #CalamityWillBefalltheUS. This allows them to gain followers each week while promoting their community and message on a weekly basis. === Twitter === During 2014, there were an estimated 46,000 to 90,000 Twitter accounts that advocated for IS or were run by supporters of the group. In 2015, Twitter reported that it banned 125,000 IS sympathetic accounts. In 2016, it published an update of 325,000 deleted accounts. Though many accounts have been suspended, IS supporters often create new accounts. Twitter defines those who recreate accounts as “resurgents” and explains that these are often difficult accounts to remove completely, since they tend to pop back up in alternate forms. It is estimated that approximately 20% of all IS affiliated Twitter accounts can be traced back to fake accounts created by the same user. Many of these accounts are traced back to the “Baqiya family,” which is an online network of thousands of IS followers. Many of these accounts are active during important IS military victories. During the IS march on Mosul, there were about 42,000 tweets on Twitter supporting the invasion. === Telegram === During 2014, IS became very active on Telegram after many major social media platforms banned IS content and sympathetic accounts. Telegram is an encrypted messaging application. The platform by nature is created as an end-to-end user encryption platform. Further, it also has special features such as the self-destruct timer which erase all evidence and messages. The app has a user data protection policy because violating this policy could potentially damage the app’s brand of customer privacy. Government agencies have been unable to break Telegram's encryption technology. On Telegram, IS often uses the hashtag #KhilafahNews to attract their users. Telegram is used by IS to plan social media campaigns on alternate platforms. The organization also uses Telegram as an anchor platform to connect with their user base when their other accounts are banned on Twitter and Facebook. On 28 February 2016 a video was uploaded threatening to expose the najaasah and shoot the hesitates. Produced by Ibn-Altayb and distributed by Al-Hayat, the video shows footage of Bruxelles attacks and the victims. In July 2017, Telegram came under scrutiny from the media and news media outlets. It has been documented that IS gunmen have used this app to maintain contact with IS leaders in Raqqa days before terror attacks in Turkey, Berlin, and St. Petersburg. Despite concerns from Western media, there has been little to no action taken against IS accounts on Telegram. In April 2019 a video was uploaded in which they urged lone wolves to attempt to attack during the Holy Week in Sevilla and Málaga. In Sevilla, a jihadist who intended to perform a lone wolf attack was arrested. === TikTok === In October 2019, it was reported that IS recruitment content was discovered on TikTok. Approximately two dozen accounts were subsequently shut down in response. By 2025, TikTok had evolved into a "low-threshold" gateway for extremist recruitment, characterized by researchers as part of a "Virtual Caliphate Complex." Nearly 93 unofficial IS support groups, known as "feeder groups," were found to be repackaging official IS content into short-form videos with pink hearts, catchy music, and internet memes to evade detection and appeal to the "TikTok generation." This content often promotes a "victimhood-revenge" narrative rather than complex theology, specifically designed to radicalize minors. === Justpaste.it === Justpaste.it, an anonymous photo and text sharing website, has also been utilized heavily. With the option to lock images, the website allows anonymous

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  • AMiner (database)

    AMiner (database)

    AMiner (formerly ArnetMiner) is a free online service used to index, search, and mine big scientific data. == Overview == AMiner (ArnetMiner) is designed to search and perform data mining operations against academic publications on the Internet, using social network analysis to identify connections between researchers, conferences, and publications. This allows it to provide services such as expert finding, geographic search, trend analysis, reviewer recommendation, association search, course search, academic performance evaluation, and topic modeling. AMiner was created as a research project in social influence analysis, social network ranking, and social network extraction. A number of peer-reviewed papers have been published arising from the development of the system. It has been in operation for more than three years, and has indexed 130,000,000 researchers and more than 265 million publications. The research was funded by the Chinese National High-tech R&D Program and the National Science Foundation of China. AMiner is commonly used in academia to identify relationships between and draw statistical correlations about research and researchers. It has attracted more than 10 million independent IP accesses from 220 countries and regions. The product has been used in Elsevier's SciVerse platform, and academic conferences such as SIGKDD, ICDM, PKDD, WSDM. == Operation == AMiner automatically extracts the researcher profile from the web. It collects and identifies the relevant pages, then uses a unified approach to extract data from the identified documents. It also extracts publications from online digital libraries using heuristic rules. It integrates the extracted researchers’ profiles and the extracted publications. It employs the researcher name as the identifier. A probabilistic framework has been proposed to deal with the name ambiguity problem in the integration. The integrated data is stored into a researcher network knowledge base (RNKB). The principal other product in the area are Google Scholar, Elsevier's Scirus, and the open source project CiteSeer. == History == It was initiated and created by professor Jie Tang from Tsinghua University, China. It was first launched in March 2006. The following provide a list of updates in the past years: March 2006, Version 0.1, Functions include researcher profiling, expert search, conference search, and publication search. The system was developed in Perl; August 2006, Version 1.0, The system was re-implemented in Java; July 2007, Version 2.0, New functions include researcher interest mining, association search, survey paper finding (unavailable now); April 2008, Version 3.0, New functions include query understanding, new GUI, and search log analysis; November 2008, Version 4.0, New functions include graph search, topic modeling, NSF/NSFC funding information extraction; April 2009, Version 5.0, New functions include Profile edition, open API service, Bole search, course search (unavailable now); December 2009, Version 6.0, New functions include academic performance evaluation, user feedback, conference analysis; May 2010, Version 7.0, New functions include name disambiguation, paper-reviewer recommendation, ArnetPage creation; March 2012, Version II, renamed as AMiner, rewrote all the codes and redesign the GUI. New functions include: geographic search, ArnetAPP platform. June 2014, Version II, renamed as AMiner, rewrote all the codes and redesign the GUI. New functions include: geographic search, ArnetAPP platform. December 2015, a completely new version got online. May 2017, professional version got online. April 2018, New functions include Trend Analysis, a deep learning based Name Disambiguation == Resources == AMiner published several datasets for academic research purpose, including Open Academic Graph, DBLP+citation (a data set augmenting citations into the DBLP data from Digital Bibliography & Library Project), Name Disambiguation, Social Tie Analysis. For more available datasets and source codes for research, please refer to.

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  • LTE Advanced

    LTE Advanced

    LTE Advanced, also named or recognized as LTE+, LTE-A or 4G+, is a 4G mobile cellular communication standard developed by 3GPP as a major enhancement of the Long Term Evolution (LTE) standard. Three technologies from the LTE-Advanced tool-kit – carrier aggregation, 4x4 MIMO and 256QAM modulation in the downlink – if used together and with sufficient aggregated bandwidth, can deliver maximum peak downlink speeds approaching, or even exceeding, 1 Gbit/s. This is significantly more than the peak 300 Mbit/s rate offered by the preceding LTE standard. Later developments have resulted in LTE Advanced Pro (or 4.9G) which increases bandwidth even further. The first ever LTE Advanced network was deployed in 2013 by SK Telecom in South Korea. In August 2019, the Global mobile Suppliers Association (GSA) reported that there were 304 commercially launched LTE-Advanced networks in 134 countries. Overall, 335 operators are investing in LTE-Advanced (in the form of tests, trials, deployments or commercial service provision) in 141 countries. == Name == LTE Advanced is also named (indicated as) LTE+, LTE-A, or (on Samsung Galaxy and Xiaomi smartphones) as 4G+. Such networks have also often been described as ‘Gigabit LTE networks’ mirroring a term that is also used in the fixed broadband industry. == History == The mobile communication industry and standards organizations have therefore started work on 4G access technologies, such as LTE Advanced. At a workshop in April 2008 in China, 3GPP agreed the plans for work on Long Term Evolution (LTE). A first set of specifications were approved in June 2008. Besides the peak data rate 1 Gb/s as defined by the ITU-R, it also targets faster switching between power states and improved performance at the cell edge. Detailed proposals are being studied within the working groups. The LTE+ format was first proposed by NTT DoCoMo of Japan and has been adopted as the international standard. It was formally submitted as a candidate 4G to ITU-T in late 2009 as meeting the requirements of the IMT-Advanced standard, and was standardized by the 3rd Generation Partnership Project (3GPP) in March 2011 as 3GPP Release 10. The work by 3GPP to define a 4G candidate radio interface technology started in Release 9 with the study phase for LTE-Advanced. Being described as a 3.9G (beyond 3G but pre-4G), the first release of LTE did not meet the requirements for 4G (also called IMT Advanced as defined by the International Telecommunication Union) such as peak data rates up to 1 Gb/s. The ITU has invited the submission of candidate Radio Interface Technologies (RITs) following their requirements in a circular letter, 3GPP Technical Report (TR) 36.913, "Requirements for Further Advancements for E-UTRA (LTE-Advanced)." These are based on ITU's requirements for 4G and on operators’ own requirements for advanced LTE. Major technical considerations include the following: Continual improvement to the LTE radio technology and architecture Scenarios and performance requirements for working with legacy radio technologies Backward compatibility of LTE-Advanced with LTE. An LTE terminal should be able to work in an LTE-Advanced network and vice versa. Any exceptions will be considered by 3GPP. Consideration of recent World Radiocommunication Conference (WRC-07) decisions regarding frequency bands to ensure that LTE-Advanced accommodates the geographically available spectrum for channels above 20 MHz. Also, specifications must recognize those parts of the world in which wideband channels are not available. Likewise, 'WiMAX 2', 802.16m, has been approved by ITU as the IMT Advanced family. WiMAX 2 is designed to be backward compatible with WiMAX 1 devices. Most vendors now support conversion of 'pre-4G', pre-advanced versions and some support software upgrades of base station equipment from 3G. == Proposals == The target of 3GPP LTE Advanced is to reach and surpass the ITU requirements. LTE Advanced should be compatible with first release LTE equipment, and should share frequency bands with first release LTE. In the feasibility study for LTE Advanced, 3GPP determined that LTE Advanced would meet the ITU-R requirements for 4G. The results of the study are published in 3GPP Technical Report (TR) 36.912. One of the important LTE Advanced benefits is the ability to take advantage of advanced topology networks; optimized heterogeneous networks with a mix of macrocells with low power nodes such as picocells, femtocells and new relay nodes. The next significant performance leap in wireless networks will come from making the most of topology, and brings the network closer to the user by adding many of these low power nodes – LTE Advanced further improves the capacity and coverage, and ensures user fairness. LTE Advanced also introduces multicarrier to be able to use ultra wide bandwidth, up to 100 MHz of spectrum supporting very high data rates. In the research phase many proposals have been studied as candidates for LTE Advanced (LTE-A) technologies. The proposals could roughly be categorized into: Support for relay node base stations Coordinated multipoint (CoMP) transmission and reception UE Dual TX antenna solutions for SU-MIMO and diversity MIMO, commonly referred to as 2x2 MIMO Scalable system bandwidth exceeding 20 MHz, up to 100 MHz Carrier aggregation of contiguous and non-contiguous spectrum allocations Local area optimization of air interface Nomadic / Local Area network and mobility solutions Flexible spectrum usage Cognitive radio Automatic and autonomous network configuration and operation Support of autonomous network and device test, measurement tied to network management and optimization Enhanced precoding and forward error correction Interference management and suppression Asymmetric bandwidth assignment for FDD Hybrid OFDMA and SC-FDMA in uplink UL/DL inter eNB coordinated MIMO SONs, Self Organizing Networks methodologies Within the range of system development, LTE-Advanced and WiMAX 2 can use up to 8x8 MIMO and 128-QAM in downlink direction. Example performance: 100 MHz aggregated bandwidth, LTE-Advanced provides almost 3.3 Gbit peak download rates per sector of the base station under ideal conditions. Advanced network architectures combined with distributed and collaborative smart antenna technologies provide several years road map of commercial enhancements. The 3GPP standards Release 12 added support for 256-QAM. A summary of a study carried out in 3GPP can be found in TR36.912. == Timeframe and introduction of additional features == Original standardization work for LTE-Advanced was done as part of 3GPP Release 10, which was frozen in April 2011. Trials were based on pre-release equipment. Major vendors support software upgrades to later versions and ongoing improvements. In order to improve the quality of service for users in hotspots and on cell edges, heterogeneous networks (HetNets) are formed of a mixture of macro-, pico- and femto base stations serving corresponding-size areas. Frozen in December 2012, 3GPP Release 11 concentrates on better support of HetNet. Coordinated Multi-Point operation (CoMP) is a key feature of Release 11 in order to support such network structures. Whereas users located at a cell edge in homogenous networks suffer from decreasing signal strength compounded by neighbor cell interference, CoMP is designed to enable use of a neighboring cell to also transmit the same signal as the serving cell, enhancing quality of service on the perimeter of a serving cell. In-device Co-existence (IDC) is another topic addressed in Release 11. IDC features are designed to ameliorate disturbances within the user equipment caused between LTE/LTE-A and the various other radio subsystems such as WiFi, Bluetooth, and the GPS receiver. Further enhancements for MIMO such as 4x4 configuration for the uplink were standardized. The higher number of cells in HetNet results in user equipment changing the serving cell more frequently when in motion. The ongoing work on LTE-Advanced in Release 12, amongst other areas, concentrates on addressing issues that come about when users move through HetNet, such as frequent hand-overs between cells. It also included use of 256-QAM. == First technology demonstrations and field trials == This list covers technology demonstrations and field trials up to the year 2014, paving the way for a wider commercial deployment of the VoLTE technology worldwide. From 2014 onwards various further operators trialled and demonstrated the technology for future deployment on their respective networks. These are not covered here. Instead a coverage of commercial deployments can be found in the section below. == LTE Advanced Pro == LTE Advanced Pro (LTE-A Pro, also known as 4.5G, 4.5G Pro, 4.9G, Pre-5G, 5G Project) is a name for 3GPP release 13 and 14. It is an evolution of LTE Advanced (LTE-A) cellular standard supporting data rates in excess of 3 Gbit/s using 32-carrier aggregation. It also introduces th

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  • SAP BTP

    SAP BTP

    SAP Business Technology Platform (SAP BTP) is a platform as a service developed by SAP SE that offers a suite of services including database and data management, AI, analytics, application development, automation and integration all running on one unified platform. == Overview == SAP BTP is made up of four components: Application development and automation: to create applications or extend existing applications. Data and analytics: to access and analyze data across SAP and third-party systems using multi-cloud architecture. Integration: to integrate and connect applications and data. Artificial Intelligence (AI): to access large language models (LLMs) to develop AI. == History == SAP BTP was introduced as part of the SAP strategy to unify its portfolio and cloud offerings under a single platform. The platform was evolved from earlier initiatives such as SAP Cloud Platform and now serves as the central hub for cloud, data, analytics, integration and AI technologies. Initially unveiled as "SAP NetWeaver Cloud" belonging to the SAP HANA Cloud portfolio on October 16, 2012 the cloud platform was reintroduced with the new name "SAP HANA Cloud Platform" on May 13, 2013 as the foundation for SAP cloud products, including the SAP BusinessObjects Cloud. Adoption of the SAP HANA Cloud Platform in 2015 stood at over 4000 customers and 500 partners. In 2016, SAP and Apple Inc. partnered to develop mobile applications on iOS using cloud-based software development kits (SDKs) for the SAP Cloud Platform. On February 27, 2017, SAP HANA Cloud Platform was renamed "SAP Cloud Platform" at the Mobile World Congress. On January 18, 2021, the name "SAP Cloud Platform" was retired from the SAP product portfolio to support SAP BTP. As of October 2024, SAP states that SAP BTP is used by more than 27,000 customers and more than 2,800 partners. Recently, SAP Business One has worked on improving the functionalities of BTP to cater for the demands of digital transformation. The platform offers comprehensive services in AI, application development, automation, integration, data management, and analytics.

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  • Web worker

    Web worker

    A web worker, as defined by the World Wide Web Consortium (W3C) and the Web Hypertext Application Technology Working Group (WHATWG), is a JavaScript script executed from an HTML page that runs in the background, independently of scripts that may also have been executed from the same HTML page. Web workers are often able to utilize multi-core CPUs more effectively. The W3C and WHATWG envision web workers as long-running scripts that are not interrupted by scripts that respond to clicks or other user interactions. Keeping such workers from being interrupted by user activities should allow Web pages to remain responsive at the same time as they are running long tasks in the background. The web worker specification is part of the HTML Living Standard. == Overview == As envisioned by WHATWG, web workers are relatively heavy-weight and are not intended to be used in large numbers. They are expected to be long-lived, with a high start-up performance cost, and a high per-instance memory cost. Web workers run outside the context of an HTML document's scripts. Consequently, while they do not have access to the DOM, they can facilitate concurrent execution of JavaScript programs. == Features == Web workers interact with the main document via message passing. The following code creates a Worker that will execute the JavaScript in the given file. To send a message to the worker, the postMessage method of the worker object is used as shown below. The onmessage property uses an event handler to retrieve information from a worker. Once a worker is terminated, it goes out of scope and the variable referencing it becomes undefined; at this point a new worker has to be created if needed. == Example == The simplest use of web workers is for performing a computationally expensive task without interrupting the user interface. In this example, the main document spawns a web worker to compute prime numbers, and progressively displays the most recently found prime number. The main page is as follows: The Worker() constructor call creates a web worker and returns a worker object representing that web worker, which is used to communicate with the web worker. That object's onmessage event handler allows the code to receive messages from the web worker. The Web Worker itself is as follows: To send a message back to the page, the postMessage() method is used to post a message when a prime is found. == Support == If the browser supports web workers, a Worker property will be available on the global window object. The Worker property will be undefined if the browser does not support it. The following example code checks for web worker support on a browser Web workers are currently supported by Chrome, Opera, Edge, Internet Explorer (version 10), Mozilla Firefox, and Safari. Mobile Safari for iOS has supported web workers since iOS 5. The Android browser first supported web workers in Android 2.1, but support was removed in Android versions 2.2–4.3 before being restored in Android 4.4.

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  • SD-WAN

    SD-WAN

    A Software-Defined Wide Area Network (SD-WAN) is a wide area network that uses software-defined networking technology, such as communicating over the Internet using overlay tunnels which are encrypted when destined for internal organization locations. If standard tunnel setup and configuration messages are supported by all of the network hardware vendors, SD-WAN simplifies the management and operation of a WAN by decoupling the networking hardware from its control mechanism. This concept is similar to how software-defined networking implements virtualization technology to improve data center management and operation. In practice, proprietary protocols are used to set up and manage an SD-WAN, meaning there is no decoupling of the hardware and its control mechanism. A key application of SD-WAN is to allow companies to build higher-performance WANs using lower-cost and commercially available Internet access, enabling businesses to partially or wholly replace more expensive private WANs connection technologies such as MPLS. When SD-WAN traffic is carried over the Internet, there are no end-to-end performance guarantees. Carrier MPLS VPN WAN services are not carried as Internet traffic, but rather over carefully controlled carrier capacity, and do come with an end-to-end performance guarantee. == History == WANs were very important for the development of networking in general and for a long time one of the most important applications of networks both for military and enterprise applications. The ability to communicate data over long distances was one of the main driving factors for the development of data communications, as it made it possible to overcome the distance limitations, as well as shortening the time necessary to exchange messages with other parties. Legacy WANs allowed communication over circuits connecting two or more endpoints. Earlier networking supported point-to-point communication over a slow speed circuit, usually between two fixed locations. As networking progressed, WAN circuits became faster and more flexible. Innovations like circuit and packet switching (in the form of X.25, ATM and later Internet Protocol or Multiprotocol Label Switching) allowed communication to become more dynamic, supporting ever-growing networks. The need for strict control, security and quality of service (QOS) meant that multinational corporations were very conservative in leasing and operating their WANs. National regulations restricted the companies that could provide local service in each country, and complex arrangements were necessary to establish truly global networks. All that changed with the growth of the Internet, which permitted entities around the world to connect to each other. However, over the first years, the uncontrolled nature of the Internet was not considered adequate or safe for private corporate use. Independent of safety concerns, connectivity to the Internet became a necessity to the point where every branch required Internet access. At first, due to safety concerns, private communications were still done via WAN, and communication with other entities (including customers and partners) moved to the Internet. As the Internet grew in reach and maturity, companies started to evaluate how to leverage it for private corporate communications. During the early 2000s, application delivery over the WAN became an important topic of research and commercial innovation. Over the next decade, increasing computing power made it possible to create software-based appliances that were able to analyze traffic and make informed decisions without delays, making it possible to create large-scale overlay networks over the public Internet that could replicate all the functionality of legacy WANs, at a fraction of the cost. SD-WAN combines several networking aspects to create full-fledged private networks, with the ability to dynamically share network bandwidth across the connection points. Additional enhancements include central controllers, zero-touch provisioning, integrated analytics and on-demand circuit provisioning, with some network intelligence based in the cloud, allowing centralized policy management and security. Networking publications started using the term SD-WAN to describe this new networking trend as early as 2014. With the rapid shift to remote work as a result of lockdowns and stay at home orders during the COVID-19 pandemic, SD-WAN grew in popularity as a way of connecting remote workers. == Overview == WANs allow companies to extend their computer networks over large distances, connecting remote branch offices to data centers and to each other, and delivering applications and services required to perform business functions. Due to the physical constraints imposed by the propagation time over large distances, and the need to integrate multiple service providers to cover global geographies (often crossing nation boundaries), WANs face important operational challenges, including network congestion, packet delay variation, packet loss, and even service outages. Modern applications such as VoIP calling, videoconferencing, streaming media, and virtualized applications and desktops require low latency. Bandwidth requirements are also increasing, especially for applications featuring high-definition video. It can be expensive and difficult to expand WAN capability, with corresponding difficulties related to network management and troubleshooting. SD-WAN products are designed to address these network problems. By enhancing or even replacing traditional branch routers with virtualization appliances that can control application-level policies and offer a network overlay, less expensive consumer-grade Internet links can act more like a dedicated circuit. This simplifies the setup process for branch personnel. SD-WAN products can be physical appliances or software based only. === Components === The MEF Forum has defined an SD-WAN architecture consisting of an SD-WAN edge, SD-WAN gateway, SD-WAN controller and SD-WAN orchestrator. ==== SD-WAN edge ==== The SD-WAN edge is a physical or virtual network function that is placed at an organization's branch/regional/central office site, data center, and in public or private cloud platforms. MEF Forum has published the first SD-WAN service standard, MEF 70 which defines the fundamental characteristics of an SD-WAN service plus service requirements and attributes. ==== SD-WAN gateway ==== SD-WAN gateways provide access to the SD-WAN service in order to shorten the distance to cloud-based services or the user, and reduce service interruptions. A distributed network of gateways may be included in an SD-WAN service by the vendor or setup and maintained by the organization using the service. By sitting outside the headquarters in the cloud, the gateway also reduces headquarters traffic. ==== SD-WAN orchestrator ==== The SD-WAN orchestrator is a cloud hosted or on-premises web management tool that allows configuration, provisioning and other functions when operating an SD-WAN. It simplifies application traffic management by allowing central implementation of an organization's business policies. ==== SD-WAN controller ==== The SD-WAN controller functionality, which can be placed in the orchestrator or in an SD-WAN gateway, is used to make forwarding decisions for application flows. Application flows are IP packets that have been classified to determine their user application or grouping of applications to which they are associated. The grouping of application flows based on a common type, e.g., conferencing applications, is referred to as an Application Flow Group in MEF 70. Per MEF 70, the SD-WAN Edge classifies incoming IP packets at the SD-WAN UNI (SD-WAN user network interface), determines, via OSI Layer 2 through Layer 7 classification, which application flow the IP packets belong to, and then applies the policies to block the application flow or allow the application flows to be forwarded based on the availability of a route to the destination SD-WAN UNI on a remote SD-WAN Edge. This helps ensure that application performance meets service level agreements (SLAs). == Required characteristics == The Gartner research firm has defined an SD-WAN as having four required characteristics: The ability to support multiple connection types, such as MPLS, last mile fiber optic network or through high speed cellular networks e.g. 4G LTE and 5G wireless technologies The ability to do dynamic path selection, for load sharing and resiliency purposes A simple interface that is easy to configure and manage The ability to support VPNs, and third party services such as WAN optimization controllers, firewalls and web gateways == Features == Features of SD-WANs include resilience, quality of service (QoS), security, and performance, with flexible deployment options; simplified administration and troubleshooting; and online traffic engineering. === Resilience === A resilient SD-WAN reduces network downtime. To

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  • Open Sound Control

    Open Sound Control

    Open Sound Control (OSC) is a protocol for networking sound synthesizers, computers, and other multimedia devices for purposes such as musical performance or show control. OSC's advantages include interoperability, accuracy, flexibility and enhanced organization and documentation. Its disadvantages include higher bandwidth requirements, increased load on embedded processors, and lack of standardized messages/interoperability. The first specification was released in March 2002. == Motivation == OSC is a content format developed at CNMAT by Adrian Freed and Matt Wright comparable to XML, WDDX, or JSON. It was originally intended for sharing music performance data (gestures, parameters and note sequences) between musical instruments (especially electronic musical instruments such as synthesizers), computers, and other multimedia devices. OSC is sometimes used as an alternative to the 1983 MIDI standard, when higher resolution and a richer parameter space is desired. OSC messages are transported across the internet and within local subnets using UDP/IP and Ethernet. OSC messages between gestural controllers are usually transmitted over serial endpoints of USB wrapped in the SLIP protocol. == Features == OSC's main features, compared to MIDI, include: Open-ended, dynamic, URI-style symbolic naming scheme Symbolic and high-resolution numeric data Pattern matching language to specify multiple recipients of a single message High resolution time tags "Bundles" of messages whose effects must occur simultaneously == Applications == There are dozens of OSC applications, including real-time sound and media processing environments, web interactivity tools, software synthesizers, programming languages and hardware devices. OSC has achieved wide use in fields including musical expression, robotics, video performance interfaces, distributed music systems and inter-process communication. The TUIO community standard for tangible interfaces such as multitouch is built on top of OSC. Similarly the GDIF system for representing gestures integrates OSC. OSC is used extensively in experimental musical controllers, and has been built into several open source and commercial products. The Open Sound World (OSW) music programming language is designed around OSC messaging. OSC is the heart of the DSSI plugin API, an evolution of the LADSPA API, in order to make the eventual GUI interact with the core of the plugin via messaging the plugin host. LADSPA and DSSI are APIs dedicated to audio effects and synthesizers. In 2007, a standardized namespace within OSC called SYN, for communication between controllers, synthesizers and hosts, was proposed. == Design == OSC messages consist of an address pattern (such as /oscillator/4/frequency), a type tag string (such as ,fi for a float32 argument followed by an int32 argument), and the arguments themselves (which may include a time tag). Address patterns form a hierarchical name space, reminiscent of a Unix filesystem path, or a URL, and refer to "Methods" inside the server, which are invoked with the attached arguments. Type tag strings are a compact string representation of the argument types. Arguments are represented in binary form with four-byte alignment. The core types supported are 32-bit two's complement signed integers 32-bit IEEE floating point numbers Null-terminated arrays of eight-bit encoded data (C-style strings) arbitrary sized blob (e.g. audio data, or a video frame) An example message is included in the spec (with null padding bytes represented by ␀): /oscillator/4/frequency␀,f␀␀, Followed by the 4-byte float32 representation of 440.0: 0x43dc0000. Messages may be combined into bundles, which themselves may be combined into bundles, etc. Each bundle contains a timestamp, which determines whether the server should respond immediately or at some point in the future. Applications commonly employ extensions to this core set. More recently some of these extensions such as a compact Boolean type were integrated into the required core types of OSC 1.1. The advantages of OSC over MIDI are primarily internet connectivity; data type resolution; and the comparative ease of specifying a symbolic path, as opposed to specifying all connections as seven-bit numbers with seven-bit or fourteen-bit data types. This human-readability has the disadvantage of being inefficient to transmit and more difficult to parse by embedded firmware, however. The spec does not define any particular OSC Methods or OSC Containers. All messages are implementation-defined and vary from server to server.

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  • Machine unlearning

    Machine unlearning

    Machine unlearning is a branch of machine learning focused on removing specific undesired element, such as private data, wrong or manipulated training data, outdated information, copyrighted material, harmful content, dangerous abilities, or misinformation, without needing to rebuild models from the ground up. Large language models, like the ones powering ChatGPT, may be asked not just to remove specific elements but also to unlearn a "concept," "fact," or "knowledge," which aren't easily linked to specific examples. New terms such as "model editing," "concept editing," and "knowledge unlearning" have emerged to describe this process. == History == Early research efforts were largely motivated by Article 17 of the GDPR, the European Union's privacy regulation commonly known as the "right to be forgotten" (RTBF), introduced in 2014. The GDPR did not anticipate that the development of large language models would make data erasure a complex task. This issue has since led to research on "machine unlearning," with a growing focus on removing copyrighted material, harmful content, dangerous capabilities, and misinformation. Just as early experiences in humans shape later ones, some concepts are more fundamental and harder to unlearn. A piece of knowledge may be so deeply embedded in the model's knowledge graph that unlearning it could cause internal contradictions, requiring adjustments to other parts of the graph to resolve them. Researchers have now also started studying unlearning in the context of removing incorrect or adversarially manipulated training data such as systematically biased labels or poisoning attacks. == Motivations == At present, machine unlearning is motivated by a growing range of concerns that extend well beyond the field's original focus on data privacy. A widely used taxonomy in the literature distinguishes two high-level categories of motivation. Access revocation covers cases where a data subject or rights holder requests the removal of data they own or control. This is most commonly associated with RTBF established by the European Union's General Data Protection Regulation (GDPR) and analogous legislation such as the California Consumer Privacy Act (CCPA). These regulations grant individuals the legal right to request erasure of their personal data from any system that has processed it, including models that were trained on it. Access revocation also encompasses the removal of copyrighted or pay-walled content that was incorporated into training corpora without the necessary licenses, a concern that has become prominent with the widespread use of largely web-scraped pre-training datasets. Model correction covers cases where the model exhibits undesirable behavior arising from the training data, regardless of any individual's request. This includes: Removal of toxic, biased, or unsafe outputs introduced by harmful content in the training set Correction of stale or factually incorrect associations, such as outdated knowledge encoded in a deployed model Removal of dangerous capabilities, such as detailed knowledge of the synthesis of chemical or biological agents Correction of the influence of data poisoning or adversarial attacks that have corrupted model behavior This second category has been formalized as corrective machine unlearning, which frames unlearning as a post-training mechanism for repairing the effects of bad or harmful training data. It is closely related to the AI safety literature, where data filtering alone has been found insufficient to prevent hazardous knowledge from being encoded in model weights, motivating unlearning as a complementary risk mitigation strategy. A further distinction has been drawn in the literature between removal {eliminating the influence of specific training data on model parameters) and suppression (preventing the model from generating specific outputs regardless of how that knowledge is encoded). These two goals are not equivalent: removing training data does not guarantee meaningful output suppression, and suppressing outputs does not constitute removal of the underlying training data's influence. == SISA Training == SISA is a training strategy consisting of four mechanisms designed to make machine unlearning more efficient by structuring how models are trained and updated. Its goal is to allow a system to remove the influence of specific data points without retraining an entire model from scratch. By reorganizing training data and workflows, SISA reduces the computational burden of unlearning requests. Sharding divides the training dataset into multiple disjoint subsets, or shards. Each shard is used to train a separate model instance. This ensures that a single data point affects only one shard, so unlearning it requires updating only the corresponding shard rather than the full model. Isolation refers to training each shard independently, with nothing shared across shards during the training process. This separation prevents cross-contamination between shards, ensuring that forgetting data in one shard does not require adjustments to any others. Slicing breaks the data within each shard into sequential slices and stores model states after each slice is trained on. When an unlearning request targets a piece of data, the system can roll back to the checkpoint before the point was seen and retrain only from that slice forward. This reduces retraining time even within a shard. Aggregation occurs at inference, when the model is queried. It combines the outputs of each shard to determine the output of the overall model. This is often through majority voting or averaging. This allows SISA-trained systems to behave like a single model despite being composed of multiple shard-level models. Together, these mechanisms enable machine learning systems to forget specific data points with far lower computational cost than full retraining. The trade-off is that sharding and slicing can lead to reduced model accuracy, worse generalization, and increased storage requirements for the intermediate checkpoints. This can be tolerable based on the needs of the individual or organization to comply with "right to be forgotten" or efficiently recover from backdoor attacks. == Algorithms == Machine unlearning algorithms are broadly categorized into exact and approximate methods, reflecting a fundamental trade-off between formal guarantees and computational tractability. === Exact Unlearning === Exact unlearning methods produce a model that is statistically indistinguishable from one retrained from scratch on the dataset with the forget data removed. The canonical framework for exact unlearning is SISA Training (Sharded, Isolated, Sliced, and Aggregated), introduced by Bourtoule et al. (2021). SISA partitions the training dataset into disjoint shards and trains a separate sub-model on each. At inference time, predictions are aggregated across sub-models. When an unlearning request is received, only the sub-model corresponding to the shard containing the target data requires retraining, reducing computational overhead proportionally to the number of shards. Exact methods provide the strongest guarantees but become prohibitively expensive for large pre-trained neural networks and are generally limited to settings where training can be structured in advance. === Approximate Unlearning === Approximate unlearning methods seek to produce a model whose behavior is sufficiently close to an exactly unlearned model without the cost of full retraining. These methods dominate practical applications. Common approaches include: Gradient Ascent: The model is fine-tuned by maximizing the loss on the forget set, directly degrading its performance on targeted data. This is the most direct approach but risks destabilizing performance on retained data. Random Labelling: The model is fine-tuned on the forget set using randomly shuffled labels, confusing its associations with the targeted data while producing a less aggressive weight shift than pure gradient ascent. Gradient Difference: Combines gradient ascent on the forget set with simultaneous gradient descent on the retain set, using the retain objective as a regularizer to preserve general model utility. KL Divergence Regularization: Minimizes the KL divergence between the outputs of the unlearned model and the original model on the retain set, anchoring behavior on data the model should remember. Weight Pruning and Fine-tuning: Parameters with the smallest L1-norm are pruned — targeting weights most weakly associated with general knowledge and potentially most associated with the forget set — followed by fine-tuning on the retain set to restore utility. Layer Reset and Fine-tuning: The first or last k layers are re-initialized to random weights and the model is subsequently fine-tuned on the retain set. This is a coarse but computationally simple approach. Selective Synaptic Dampening: Uses influence functions to estimate the effect of individual trainin

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  • Mike Little

    Mike Little

    Mike Little (born 12 May 1962) is an English web developer and writer. He is the co-founder of the free and open source web publishing software WordPress. == Biography == Mike Little was born in Manchester, England in 1962 to a Nigerian father, who was a mathematics lecturer and musician, and an English mother who worked as a primary school teacher. Little was placed into foster care when he was four months of age, and was later adopted by the same family. He grew up on a council estate in Brinnington, Stockport, and was educated at Stockport School. In 2003, Little and Matt Mullenweg started working on a project in which they built on b2/cafelog and later named it WordPress, releasing the first version on 27 May 2003. Little states that, despite not being invited to join his co-founder's for-profit business Automattic, he and Mullenweg remain on good terms. He clarified: "I don’t want it to sound like he cheated me out of something or ripped me off in some way. He didn’t." In June 2013, Little was awarded the SAScon's "Outstanding Contribution to Digital" award for his part in co-founding and developing WordPress. Little has been described as "modest" and living in "virtual anonymity". He has one daughter. He identifies as a follower of Stoicism and a humanist, and in 2021, he became a patron of charity Humanists UK.

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  • Military communications

    Military communications

    Military communications or military signals involve all aspects of communications, or conveyance of information, by armed forces. Examples from Jane's Military Communications include text, audio, facsimile, tactical ground-based communications, naval signalling, terrestrial microwave, tropospheric scatter, satellite communications systems and equipment, surveillance and signal analysis, security, direction finding and jamming. The most urgent purposes are to communicate information to commanders and orders from them. Military communications span from pre-history to the present. The earliest military communications were delivered by runners. Later, communications progressed to visual signals. For example, Naval ships would use flag signaling to communicate from ship to ship. These flags are a uniform set of easily identifiable nautical codes that would convey visual messages and codes between ships and from ship to shore. Then militaries discovered methods to use audible signaling to communicate with each other. This way of communicating was possible because of telegraphs. They are an electronic device that is used by a sender and when the sender presses on the telegraph key, they interrupt the current creating an audible pulse that is heard at the receiving station. The receiver then decodes the pulses to decode the messages. Since then, military communication has evolved and advanced much further. Today, there are many perspectives used to examine how troops around the world communicate. Anthony King states how Military sociologists have attempted to explain how military institutions develop and maintain high levels of social cohesion. == History == In past centuries communicating a message usually required someone to go to the destination, bringing the message. Thus, the term communication often implied the ability to transport people and supplies. A place under siege was one that lost communication in both senses. The association between transport and messaging declined in recent centuries. The first military communications involved the use of runners or the sending and receiving of simple signals (sometimes encoded to be unrecognizable). The first distinctive uses of military communications were called semaphore. Modern units specializing in these tactics are usually designated as signal corps. The Roman system of military communication (cursus publicus or cursus vehicularis) is an early example of this. Later, the terms signals and signaller became words referring to a highly-distinct military occupation dealing with general communications methods (similar to those in civil use) rather than with weapons. Present-day military forces of an informational society conduct intense and complicated communicating activities on a daily basis, using modern telecommunications and computing methods. Only a small portion of these activities are directly related to combat actions. Modern concepts of network-centric warfare (NCW) rely on network-oriented methods of communications and control to make existing forces more effective. == Military communications equipment == Drums, horns, flags, and riders on horseback were some of the early methods the military used to send messages over distances. The advent of distinctive signals led to the formation of the signal corps, a group specialized in the tactics of military communications. The signal corps evolved into a distinctive occupation where the signaller became a highly technical job dealing with all available communications methods including civil ones. In the middle 20th century radio equipment came to dominate the field. Many modern pieces of military communications equipment are built to both encrypt and decode transmissions and survive rough treatment in hostile climates. They use different frequencies to send signals to other radio stations to communicate. Radios have played a major role in military communication. Since they are capable of sending radio waves to transmit voice signals over long distances. This can be helpful for communication on the battlefield since it is a good way to send messages undetected over long distances. Radios are also very reliable because even in harsh weather conditions they are still able to help communicate among the soldiers. Militaries still use radios and continue to improve the technology because of their durability and reliability for military communication. Spelling alphabets such as the NATO phonetic alphabet are used to aid radio communications by reducing ambiguity between letters. Military communications – or "comms" – are activities, equipment, techniques, and tactics used by the military in some of the most hostile areas of the earth and in challenging environments such as battlefields, on land (compare radio in a box), underwater and also in air. Military comms include command, control and communications and intelligence and were known as the C3I model before computers were fully integrated. The U.S. Army expanded the model to C4I when it recognized the vital role played by automated computer equipment to send and receive large, bulky amounts of data. In the modern world, most nations attempt to minimize the risk of war caused by miscommunication or inadequate communication. As a result, military communication is intense and complicated and often motivates the development of advanced technology for remote systems such as satellites. Satellites have been improving and are being used more and more for communication. They are being made to have higher transmission capacity to help with their communication abilities. The military is upgrading satellites to be immune to interference during combat operations. This advancement will establish stable, high-quality information highways for long distance communication. Aircraft are also beneficial for communication, both crewed and uncrewed, as well as computers. Computers and their varied applications have revolutionized military comms. Although military communication is designed for warfare, it also supports intelligence-gathering and communication between adversaries, and thus sometimes prevents war. The six categories of military comms are: alert measurement systems cryptography military radio systems command and control signal corps network-centric warfare The alert measurement systems are various states of alertness or readiness for the armed forces used around the world during a state of war, act of terrorism or a military attack against a state. They are known by different acronyms, such as DEFCON, or defense readiness condition, used by the U.S. Armed Forces. Cryptography is the study of methods of converting messages to a form unreadable except to one who knows how to decrypt them. This ancient military comms art gained new importance with the rise of radio systems whose signals traveled far and were easily intercepted. Cryptographic software is also widely used in civilian commerce. == Commercial refile == In United States military communications systems, commercial refile refers to sending a military message via a commercial communications network. The message may come from a military network, such as a tape relay network, a point-to-point telegraph network, a radio-telegraph network, or the Defense Switched Network. Commercial refiling of a message will usually require a reformatting of the message, particularly the heading.

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  • LTE (telecommunication)

    LTE (telecommunication)

    In telecommunications, Long Term Evolution (LTE) is a standard for wireless broadband communication for cellular mobile devices and data terminals. It is considered to be a "transitional" 4G technology, and is therefore also referred to as 3.95G as a step above 3G. LTE is based on the 2G GSM/EDGE and 3G UMTS/HSPA standards. It improves on those standards' capacity and speed by using a different radio interface and core network improvements. LTE is the upgrade path for carriers with both GSM/UMTS networks and CDMA2000 networks. LTE has been succeeded by LTE Advanced, which is officially defined as a "true" 4G technology and also named "LTE+". == Terminology == The standard is developed by the 3GPP (3rd Generation Partnership Project) and is specified in its Release 8 document series, with minor enhancements described in Release 9. LTE is also called 3.95G and has been marketed as 4G LTE and Advanced 4G; but the original version did not meet the technical criteria of a 4G wireless service, as specified in the 3GPP Release 8 and 9 document series for LTE Advanced. The requirements were set forth by the ITU-R organisation in the IMT Advanced specification; but, because of market pressure and the significant advances that WiMAX, Evolved High Speed Packet Access, and LTE bring to the original 3G technologies, ITU-R later decided that LTE and the aforementioned technologies can be called 4G technologies. The LTE Advanced standard formally satisfies the ITU-R requirements for being considered IMT-Advanced. To differentiate LTE Advanced and WiMAX-Advanced from current 4G technologies, ITU has defined the latter as "True 4G". == Overview == LTE stands for Long Term Evolution and is a registered trademark owned by ETSI (European Telecommunications Standards Institute) for the wireless data communications technology and development of the GSM/UMTS standards. However, other nations and companies do play an active role in the LTE project. The goal of LTE was to increase the capacity and speed of wireless data networks using new DSP (digital signal processing) techniques and modulations that were developed around the turn of the millennium. A further goal was the redesign and simplification of the network architecture to an IP-based system with significantly reduced transfer latency compared with the 3G architecture. The LTE wireless interface is incompatible with 2G and 3G networks, so it must be operated on a separate radio spectrum. The idea of LTE was first proposed in 1998, with the use of the COFDM radio access technique to replace the CDMA and studying its Terrestrial use in the L band at 1428 MHz (TE) In 2004 by Japan's NTT Docomo, with studies on the standard officially commenced in 2005. In May 2007, the LTE/SAE Trial Initiative (LSTI) alliance was founded as a global collaboration between vendors and operators with the goal of verifying and promoting the new standard to ensure the global introduction of the technology as quickly as possible. The LTE standard was finalized in December 2008, and the first publicly available LTE service was launched by TeliaSonera in Oslo and Stockholm on December 14, 2009, as a data connection with a USB modem. The LTE services were launched by major North American carriers as well, with the Samsung SCH-r900 being the world's first LTE Mobile phone starting on September 21, 2010, and Samsung Galaxy Indulge being the world's first LTE smartphone starting on February 10, 2011, both offered by MetroPCS, and the HTC ThunderBolt offered by Verizon starting on March 17 being the second LTE smartphone to be sold commercially. In Canada, Rogers Wireless was the first to launch LTE network on July 7, 2011, offering the Sierra Wireless AirCard 313U USB mobile broadband modem, known as the "LTE Rocket stick" then followed closely by mobile devices from both HTC and Samsung. Initially, CDMA operators planned to upgrade to rival standards called UMB and WiMAX, but major CDMA operators (such as Verizon, Sprint and MetroPCS in the United States, Bell and Telus in Canada, au by KDDI in Japan, SK Telecom in South Korea and China Telecom/China Unicom in China) have announced instead they intend to migrate to LTE. The next version of LTE is LTE Advanced, which was standardized in March 2011. Services commenced in 2013. Additional evolution known as LTE Advanced Pro was approved in 2015. The LTE specification provides downlink peak rates of 300 Mbit/s, uplink peak rates of 75 Mbit/s, and QoS provisions permitting a transfer latency of less than 5 ms in the radio access network. LTE has the ability to manage fast-moving mobiles and supports multicast and broadcast streams. LTE supports scalable carrier bandwidths, from 1.4 MHz to 20 MHz and supports both frequency division duplexing (FDD) and time-division duplexing (TDD). The IP-based network architecture, called the Evolved Packet Core (EPC) designed to replace the GPRS Core Network, supports seamless handovers for both voice and data to cell towers with older network technology such as GSM, UMTS and CDMA2000. The simpler architecture results in lower operating costs (for example, each E-UTRA cell will support up to four times the data and voice capacity supported by HSPA). Because LTE frequencies and bands differ from country to country, only multi-band phones can use LTE in all countries where it is supported. == History == === 3GPP standard development timeline === In 2004, NTT Docomo of Japan proposes LTE as the international standard. In September 2006, Siemens Networks (today Nokia Networks) showed in collaboration with Nomor Research the first live emulation of an LTE network to the media and investors. As live applications, two users streaming an HDTV video in the downlink and playing an interactive game in the uplink have been demonstrated. In February 2007, Ericsson demonstrated for the first time in the world, LTE with bit rates up to 144 Mbit/s In September 2007, NTT Docomo demonstrated LTE data rates of 200 Mbit/s with power level below 100 mW during the test. In November 2007, Infineon presented the world's first RF transceiver named SMARTi LTE, supporting LTE functionality in a single-chip RF silicon processed in CMOS In early 2008, LTE test equipment began shipping from several vendors and at the Mobile World Congress 2008 in Barcelona, Ericsson demonstrated the world's first end-to-end mobile call enabled by LTE on a small handheld device. Motorola demonstrated an LTE RAN (Radio Access Network) standard compliant eNodeB and LTE chipset at the same event. At the February 2008 Mobile World Congress: Motorola demonstrated how LTE can accelerate the delivery of personal media experience with HD video demo streaming, HD video blogging, online gaming, and VoIP over LTE running a RAN standard-compliant LTE network & LTE chipset. Ericsson EMP (later ST-Ericsson) demonstrated the world's first end-to-end LTE call on handheld Ericsson demonstrated LTE FDD and TDD mode on the same base station platform. Freescale Semiconductor demonstrated streaming HD video with peak data rates of 96 Mbit/s downlink and 86 Mbit/s uplink. NXP Semiconductors (later part of ST-Ericsson) demonstrated a multi-mode LTE modem as the basis for a software-defined radio system for use in cellphones. picoChip and Mimoon demonstrated a base station reference design. This runs on a common hardware platform (multi-mode / software-defined radio) with their WiMAX architecture. In April 2008, Motorola demonstrated the first EV-DO to LTE hand-off handling over streaming a video from LTE to a commercial EV-DO network and back to LTE. In April 2008, LG Electronics and Nortel demonstrated LTE data rates of 50 Mbit/s while travelling at 110 km/h (68 mph). In November 2008, Motorola demonstrated industry first over-the-air LTE session in 700 MHz spectrum. Researchers at Nokia Siemens Networks and Heinrich Hertz Institut have demonstrated LTE with 100 Mbit/s Uplink transfer speeds. At the February 2009 Mobile World Congress: Infineon demonstrated a single-chip 65 nm CMOS RF transceiver providing 2G/3G/LTE functionality Launch of ng Connect program, a multi-industry consortium founded by Alcatel-Lucent to identify and develop wireless broadband applications. Motorola provided LTE drive tour on the streets of Barcelona to demonstrate LTE system performance in a real-life metropolitan RF environment In July 2009, Nujira demonstrated efficiencies of more than 60% for an 880 MHz LTE Power Amplifier In August 2009, Nortel and LG Electronics demonstrated the first successful handoff between CDMA and LTE networks in a standards-compliant manner In August 2009, Alcatel-Lucent receives FCC certification for LTE base stations for the 700 MHz spectrum band. In September 2009, Nokia Siemens Networks demonstrated the world's first LTE call on standards-compliant commercial software. In October 2009, Ericsson and Samsung demonstrated interoperability between the first ever commercial LTE device and the live network in

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  • Machine learning

    Machine learning

    Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without being explicitly programmed. Advances in the field of deep learning have allowed neural networks, a class of statistical algorithms, to surpass many previous machine learning approaches in performance. Statistics and mathematical optimisation methods compose the foundations of machine learning. Data mining is a related field of study, focusing on exploratory data analysis (EDA) through unsupervised learning. From a theoretical viewpoint, probably approximately correct learning provides a mathematical and statistical framework for describing machine learning. Most traditional machine learning and deep learning algorithms can be described as empirical risk minimisation under this framework. == History == The term machine learning was coined in 1959 by Arthur Samuel, an IBM employee and pioneer in the field of computer gaming and artificial intelligence. The synonym self-teaching computers was also used during this time period. The earliest machine learning program was introduced in the 1950s, when Samuel invented a computer program that calculated the chance of winning in checkers for each side, but the history of machine learning is rooted in decades of efforts to study human cognitive processes. In 1949, Canadian psychologist Donald Hebb published the book The Organization of Behavior, in which he introduced a theoretical neural structure formed by certain interactions among nerve cells. The Hebbian theory of neuron interaction set the groundwork for how many machine learning algorithms work, with connected artificial neurons changing the strength of their connections based on data. Other researchers who have studied human cognitive systems contributed to the modern machine learning technologies as well, including Walter Pitts and Warren McCulloch, who proposed the first mathematical model of neural networks including algorithms that mirror human thought processes. By the early 1960s, an experimental "learning machine" with punched tape memory, called Cybertron, had been developed by Raytheon Company to analyse sonar signals, electrocardiograms, and speech patterns using rudimentary reinforcement learning. It was repetitively "trained" by a human operator/teacher to recognise patterns and equipped with a "goof" button to cause it to reevaluate incorrect decisions. A representative book on research into machine learning during the 1960s was Nils Nilsson's book "Learning Machines", dealing mostly with machine learning for pattern classification. Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973. In 1981, a report was given on using teaching strategies so that an artificial neural network learns to recognise 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal. Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E." This definition of the tasks in which machine learning is concerned is fundamentally operational rather than defining the field in cognitive terms. This follows Alan Turing's proposal in his paper "Computing Machinery and Intelligence", in which the question, "Can machines think?", is replaced by asking whether machines can convincingly imitate a human in its responses to human-posed questions. In 2014 Ian Goodfellow and others introduced generative adversarial networks (GANs) which could produce realistic synthetic data. By 2016 AlphaGo had won against top human players in Go using reinforcement learning techniques. == Relationships to other fields == === Artificial intelligence === As a scientific endeavour, machine learning grew out of the quest for artificial intelligence (AI). In the early days of AI as an academic discipline, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what were then termed "neural networks"; these were mostly perceptrons and other models that were later found to be reinventions of the generalised linear models of statistics. Probabilistic reasoning was also employed, especially in automated medical diagnosis. However, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation. By 1980, expert systems had come to dominate AI, and statistics was out of favour. Work on symbolic/knowledge-based learning continued within AI, leading to inductive logic programming (ILP), but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval. Neural network research was abandoned by AI and computer science around the same time. This subfield, termed "connectionism", was continued by researchers from other disciplines, including John Hopfield, David Rumelhart, and Geoffrey Hinton. Their main success came in the mid-1980s with the reinvention of backpropagation. Machine learning (ML), reorganised and recognised as its own field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics, fuzzy logic, and probability theory. === Data compression === === Data mining === Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction based on known properties learned from the training data, data mining focuses on the discovery of previously unknown properties in the data (this is the analysis step of knowledge discovery in databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data. Machine learning also has intimate ties to optimization: Many learning problems are formulated as minimisation of some loss function on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the preassigned labels of a set of examples). === Generalization === Characterizing the generalisation of various learning algorithms is an active topic of current research, especially for deep learning algorithms. === Statistics === Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from a sample, while machine learning finds generalisable predictive patterns. Conventional statistical analyses require the a priori selection of a model most suitable for the study data set. In addition, only significant or theoretically relevant variables based on previous experience are included for analysis. In contrast, machine learning is not built on a pre-structured model; rather, the data shape the model by detecting underlying patterns. The more variables (input) used to train the model, the more accurate the ultimate model will be. Leo Breiman distinguished two statistical modelling paradigms: the data model and the algorithmic model, wherein "algorithmic model" means more or less the machine learning algorithms like Random forest. Some statisticians have adopted methods from machine learning, producing the field of statistical learning. === Statistical physics === Analytical and computational techniques derived from deep-rooted physics of disordered systems can be extended to large-scale problems, including machine learning, e.g., to analyse the weight space of deep neural networks. Statistical physics is thus

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  • Digital intermediate

    Digital intermediate

    Digital intermediate (DI) is a motion picture finishing process which classically involves digitizing a motion picture and manipulating the color and other image characteristics. == Definition and overview == A digital intermediate often replaces or augments the photochemical timing process and is usually the final creative adjustment to a movie before distribution in theaters. It is distinguished from the telecine process in which film is scanned and color is manipulated early in the process to facilitate editing. However the lines between telecine and DI are continually blurred and are often executed on the same hardware by colorists of the same background. These two steps are typically part of the overall color management process in a motion picture at different points in time. A digital intermediate is also customarily done at higher resolution and with greater color fidelity than telecine transfers. Although originally used to describe a process that started with film scanning and ended with film recording, digital intermediate is also used to describe color correction and color grading and even final mastering when a digital camera is used as the image source and/or when the final movie is not output to film. This is due to recent advances in digital cinematography and digital projection technologies that strive to match film origination and film projection. In traditional photochemical film finishing, an intermediate is produced by exposing film to the original camera negative. The intermediate is then used to mass-produce the films that get distributed to theaters. Color grading is done by varying the amount of red, green, and blue light used to expose the intermediate. The digital intermediate process uses digital tools to color grade, which allows for much finer control of individual colors and areas of the image, and allows for the adjustment of image structure (grain, sharpness, etc.). The intermediate for film reproduction can then be produced by means of a film recorder. The physical intermediate film that is a result of the recording process is sometimes also called a digital intermediate, and is usually recorded to internegative (IN) stock, which is inherently finer-grain than original camera negative (OCN). One of the key technical achievements that made the transition to DI possible was the use of 3D look-up tables, which could be used to mimic how the digital image would look once it was printed onto release print stock. This removed a large amount of guesswork from the film-making process, and allowed greater freedom in the colour grading process while reducing risk. The digital master is often used as a source for a DCI-compliant distribution of the motion picture for digital projection. For archival purposes, the digital master created during the digital intermediate process can be recorded to very stable high dynamic range yellow-cyan-magenta (YCM) separations on black-and-white film with an expected 100-year or longer life. While still subject to the natural degradation of any analog chemical master, this archival format, long used in the industry prior to the invention of DI, was considered valuable for providing an archival medium that is independent of changes in digital data recording technologies and file formats that might otherwise render digitally archived material unreadable in the long term. A "film intermediate" is an analog variation of a digital intermediate, where a project shot on digital video is printed onto film stock and transferred back to digital video to emulate film. The term was coined after it was used on the Oscar-winning 2012 short film "Curfew". The process was also used on the films Dune (2021) and The Batman (2022). == History == Telecine tools to electronically capture film images are nearly as old as broadcast television, but the resulting images were widely considered unsuitable for exposing back onto film for theatrical distribution. Film scanners and recorders with quality sufficient to produce images that could be inter-cut with regular film began appearing in the 1970s, with significant improvements in the late 1980s and early 1990s. During this time, digitally processing an entire feature-length film was impractical because the scanners and recorders were extremely slow and the image files were too large compared to computing power available. Instead, individual shots or short sequences were processed for visual effects. In 1992, Visual Effects Supervisor/Producer Chris F. Woods broke through several "techno-barriers" in creating a digital studio to produce the visual effects for the 1993 release Super Mario Bros. It was the first feature film project to digitally scan a large number of VFX plates (over 700) at 2K resolution. It was also the first film scanned and recorded at Kodak's just launched Cinesite facility in Hollywood. This project based studio was the first feature film to use Discreet Logic's (now Autodesk) Flame and Inferno systems, which enjoyed early dominance as high resolution / high performance digital compositing systems. Digital film compositing for visual effects was immediately embraced, while optical printer use for VFX declined just as quickly. Chris Watts further revolutionized the process on the 1998 feature film Pleasantville, becoming the first visual effects supervisor for New Line Cinema to scan, process, and record the majority of a feature-length, live-action, Hollywood film digitally. The first Hollywood film to utilize a digital intermediate process from beginning to end was O Brother, Where Art Thou? in 2000 and in Europe it was Chicken Run released that same year. The process rapidly caught on in the mid-2000s. Around 50% of Hollywood films went through a digital intermediate in 2005, increasing to around 70% by mid-2007. This is due not only to the extra creative options the process affords film makers but also the need for high-quality scanning and color adjustments to produce movies for digital cinema. == Milestones == 1990: The Rescuers Down Under – First feature-length film to be entirely recorded to film from digital files; in this case animation assembled on computers using Walt Disney Feature Animation and Pixar's CAPS system. 1992: Visual effects supervisor and producer Chris F. Woods creates a VFX studio to produce the visual effects for the 1993 film Super Mario Bros. It was the first 35mm feature film to digitally scan a large number of VFX plates (over 700) at 2K resolution, as well as to output the finished VFX to 35mm negative at 2K. 1993: Snow White and the Seven Dwarfs – First film to be entirely scanned to digital files, manipulated, and recorded back to film at 4K resolution. The restoration project was done entirely at 4K resolution and 10-bit color depth using the Cineon system to digitally remove dirt and scratches and restore faded colors. 1998: Pleasantville – The first time the majority of a new feature film was scanned, processed, and recorded digitally. The black-and-white meets color world portrayed in the movie was filmed entirely in color and selectively desaturated and contrast adjusted digitally. The work was done in Los Angeles by Cinesite utilizing a Spirit DataCine for scanning at 2K resolution and a MegaDef color correction system from UK Company Pandora International 1998: Zingo - The first feature film to use digital color correction via digital intermediate in its entirety. The work was performed at the Digital Film Lab in Copenhagen, using a Spirit Datacine to transfer the entire film to digital files at 2K resolution. The digital intermediate process was also used to perform a digital blowup of the film's original Super 16 source format to a 35mm output. 1999: Pacific Ocean Post Film, a team led by John McCunn and Greg Kimble used Kodak film scanners & laser film printer, Cineon software as well as proprietary tools to rebuild and repair the first two reels of the 1968 Beatles' film Yellow Submarine for re-release. 1999: Star Wars: Episode I – The Phantom Menace - Industrial Light & Magic (ILM) scanned the entirety of the visual effects-laden film for the purposes of digital enhancement and the integration of thousands of separately filmed elements with computer generated characters and environments. Outside of the approximately 2000 effects shots that were digitally manipulated, the remaining 170 non-effects shots were also scanned for continuity. However, after the digital shots were manipulated at ILM, they were filmed out individually and sent to Deluxe Labs where they were processed and color timed photochemically. 2000: Sorted - The first feature-length, color 35mm motion picture to fully utilize the digital intermediate process in its entirety from inception to completion. The film was produced at Wave Pictures' digital intermediate film facility in London, England. It was scanned at 2K resolution with 8 bits color depth per color / per pixel using a pin registered, liquid gate Oxberry

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  • Radio network

    Radio network

    A radio network is a system that distributes radio signals to multiple receivers or enables two-way communication between stations and mobile units. Worldwide, radio networks include broadcast networks, such as BBC Radio in the United Kingdom and NPR in the United States, which transmit one-to-many signals for news, entertainment, and public information; two-way radio networks, used by police, fire services, taxicabs, and delivery fleets for operational communication; and cellular networks, such as Verizon, Vodafone, and China Mobile, which provide mobile telephony and data services using frequency or time division duplexing. While all rely on radio-frequency technology like transmitters, receivers, and antennas, their network architectures, protocols, and regulatory frameworks differ substantially across applications and regions. The two-way type of radio network shares many of the same technologies and components as the broadcast-type radio network but is generally set up with fixed broadcast points (transmitters) with co-located receivers and mobile receivers/transmitters or transceivers. In this way both the fixed and mobile radio units can communicate with each other over broad geographic regions ranging in size from small single cities to entire states/provinces or countries. There are many ways in which multiple fixed transmit/receive sites can be interconnected to achieve the range of coverage required by the jurisdiction or authority implementing the system: conventional wireless links in numerous frequency bands, fibre-optic links, or microwave links. In all of these cases the signals are typically backhauled to a central switch of some type where the radio message is processed and resent (repeated) to all transmitter sites where it is required to be heard. In contemporary two-way radio systems, a concept called trunking is commonly used to achieve better efficiency of radio spectrum use. It provides a very wide range of coverage, with no switching of channels required by the mobile radio user as it roams throughout the system coverage. Trunking of two-way radio is identical to the concept used for cellular phone systems where each fixed and mobile radio is specifically identified to the system controller and its operation is switched by the controller. == Broadcasting networks == The broadcast type of radio network is a network system which distributes radio programming to multiple stations simultaneously, or slightly delayed, for the purpose of extending total coverage beyond the limits of a single broadcast signal. The resulting expanded audience for radio programming or information essentially applies the benefits of mass-production to the broadcasting enterprise. A radio network has two sales departments, one to package and sell programs to radio stations, and one to sell the audience of those programs to advertisers. Most radio networks also produce much of their programming. Originally, radio networks owned some or all of the stations that broadcast the network's radio format programming. Presently however, there are many networks that do not own any stations and only produce and/or distribute programming. Similarly station ownership does not always indicate network affiliation. A company might own stations in several different markets and purchase programming from a variety of networks. Radio networks rose rapidly with the growth of regular broadcasting of radio to home listeners in the 1920s. This growth took various paths in different places. In Britain the BBC was developed with public funding, in the form of a broadcast receiver license, and a broadcasting monopoly in its early decades. In contrast, in the United States various competing commercial broadcasting networks arose funded by advertising revenue. In that instance, the same corporation that owned or operated the network often manufactured and marketed the listener's radio. Major technical challenges to be overcome when distributing programs over long distances are maintaining signal quality and managing the number of switching/relay points in the signal chain. Early on, programs were sent to remote stations (either owned or affiliated) by various methods, including leased telephone lines, pre-recorded gramophone records and audio tape. The world's first all-radio, non-wireline network was claimed to be the Rural Radio Network, a group of six upstate New York FM stations that began operation in June 1948. Terrestrial microwave relay, a technology later introduced to link stations, has been largely supplanted by coaxial cable, fiber, and satellite, which usually offer superior cost-benefit ratios. Many early radio networks evolved into television networks.

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