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Landweber iteration

The Landweber iteration or Landweber algorithm is an algorithm to solve ill-posed linear inverse problems, and it has been extended to solve non-linear problems that involve constraints. The method was first proposed in the 1950s by Louis Landweber, and it can be now viewed as a special case of many other more general methods. == Basic algorithm == The original Landweber algorithm attempts to recover a signal x from (noisy) measurements y. The linear version assumes that y = A x {\displaystyle y=Ax} for a linear operator A. When the problem is in finite dimensions, A is just a matrix. When A is nonsingular, then an explicit solution is x = A − 1 y {\displaystyle x=A^{-1}y} . However, if A is ill-conditioned, the explicit solution is a poor choice since it is sensitive to any noise in the data y. If A is singular, this explicit solution doesn't even exist. The Landweber algorithm is an attempt to regularize the problem, and is one of the alternatives to Tikhonov regularization. We may view the Landweber algorithm as solving: min x ‖ A x − y ‖ 2 2 / 2 {\displaystyle \min _{x}\|Ax-y\|_{2}^{2}/2} using an iterative method. The algorithm is given by the update x k + 1 = x k − ω A ∗ ( A x k − y ) . {\displaystyle x_{k+1}=x_{k}-\omega A^{}(Ax_{k}-y).} where the relaxation factor ω {\displaystyle \omega } satisfies 0 < ω < 2 / σ 1 2 {\displaystyle 0<\omega <2/\sigma _{1}^{2}} . Here σ 1 {\displaystyle \sigma _{1}} is the largest singular value of A {\displaystyle A} . If we write f ( x ) = ‖ A x − y ‖ 2 2 / 2 {\displaystyle f(x)=\|Ax-y\|_{2}^{2}/2} , then the update can be written in terms of the gradient x k + 1 = x k − ω ∇ f ( x k ) {\displaystyle x_{k+1}=x_{k}-\omega \nabla f(x_{k})} and hence the algorithm is a special case of gradient descent. For ill-posed problems, the iterative method needs to be stopped at a suitable iteration index, because it semi-converges. This means that the iterates approach a regularized solution during the first iterations, but become unstable in further iterations. The reciprocal of the iteration index 1 / k {\displaystyle 1/k} acts as a regularization parameter. A suitable parameter is found, when the mismatch ‖ A x k − y ‖ 2 2 {\displaystyle \|Ax_{k}-y\|_{2}^{2}} approaches the noise level. Using the Landweber iteration as a regularization algorithm has been discussed in the literature. == Nonlinear extension == In general, the updates generated by x k + 1 = x k − τ ∇ f ( x k ) {\displaystyle x_{k+1}=x_{k}-\tau \nabla f(x_{k})} will generate a sequence f ( x k ) {\displaystyle f(x_{k})} that converges to a minimizer of f whenever f is convex and the stepsize τ {\displaystyle \tau } is chosen such that 0 < τ < 2 / ( ‖ ∇ f ‖ 2 ) {\displaystyle 0<\tau <2/(\|\nabla f\|^{2})} where ‖ ⋅ ‖ {\displaystyle \|\cdot \|} is the spectral norm. Since this is special type of gradient descent, there currently is not much benefit to analyzing it on its own as the nonlinear Landweber, but such analysis was performed historically by many communities not aware of unifying frameworks. The nonlinear Landweber problem has been studied in many papers in many communities; see, for example. == Extension to constrained problems == If f is a convex function and C is a convex set, then the problem min x ∈ C f ( x ) {\displaystyle \min _{x\in C}f(x)} can be solved by the constrained, nonlinear Landweber iteration, given by: x k + 1 = P C ( x k − τ ∇ f ( x k ) ) {\displaystyle x_{k+1}={\mathcal {P}}_{C}(x_{k}-\tau \nabla f(x_{k}))} where P {\displaystyle {\mathcal {P}}} is the projection onto the set C. Convergence is guaranteed when 0 < τ < 2 / ( ‖ A ‖ 2 ) {\displaystyle 0<\tau <2/(\|A\|^{2})} . This is again a special case of projected gradient descent (which is a special case of the forward–backward algorithm) as discussed in. == Applications == Since the method has been around since the 1950s, it has been adopted and rediscovered by many scientific communities, especially those studying ill-posed problems. In X-ray computed tomography it is called simultaneous iterative reconstruction technique (SIRT). It has also been used in the computer vision community and the signal restoration community. It is also used in image processing, since many image problems, such as deconvolution, are ill-posed. Variants of this method have been used also in sparse approximation problems and compressed sensing settings.
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International Teletraffic Congress

The International Teletraffic Congress (ITC) is the first international conference in networking science and practice. It was created in 1955 by Arne Jensen to initially cater to the emerging need to understand and model traffic in telephone networks using stochastic methodologies, and to bring together researchers with these considerations as a common theme. Up through World War II, teletraffic research was done mainly by engineers and mathematicians working in telephone companies. Most of their work was published in local or company journals. In 1955, however, the field acquired a formal, international, institutional structure, with the organization of the first International Teletraffic Congress (ITC). Over the years, it has broaden its scope to address a wide spectrum ranging from the mathematical theory of traffic processes, stochastic system modelling and analysis, traffic and performance measurements, network management, traffic engineering to network capacity planning and cost optimization, including network economics and reliability for various types of networks. ITC served as a forum for all theoretical fundamentals and engineering practices for large-scale deployment and operation of telecommunications networks. Since its inception, ITC witnessed the evolution of communications and networking: the influence of computer science on telecommunication, the advent of the Internet and the massive deployment of mobile communications and optics, the appearance of peer-to-peer networking and social networks, the ever increasing speed and flexibility of new communication technologies, networks, user devices, and applications, and the ever changing operation challenges arising from this development. ITC documented this evolution with contemporary measurement studies, performance analyses of new technologies, recommendations for provisioning and configuration, and greatly contributed to the methodological toolbox of network scientists. Today, with its conferences, specialist seminars, regional seminars, training courses and publications, the ITC aims at a worldwide forum for all questions related to network and service performance, management, and assessment, both present and futuristic. The notion of traffic is broadly used to encompass data traffic from the MAC layer all the way to application traffic in the application layer. The scope of ITC is thus ranging all issues embedding operations, design, planning, economics and performance analysis of current and emerging communication networks and services, to be addressed by applying a variety of tools from different fields, such as Stochastic Processes, Information theory, Control theory, Signal and Processing, Game theory and optimization techniques, Statistical methodologies and Artificial Intelligence techniques. The target audience of such issues is experts from research organizations, universities, equipment vendors and suppliers, network operators, service providers, system integrators and international technical organizations, guaranteeing a well-balanced contribution from theory, application, and practice. The general goal remains to bring researchers and practitioners together toward operational understanding of all types of current and future networks. The ITC is ruled by the International Advisory Council (IAC) which gathers a number of technical experts, from universities and the research arms of key corporations in the industry, from countries having a strong tradition in teletraffic development. The IAC responsibilities are to disseminate information on teletraffic which is of interest for the whole community and: to select the locations of Plenary Congresses and to ensure their high-level technical programme to support Specialist Seminars on specific topics of current interest to promote Regional Seminars for the dissemination of teletraffic concepts in developing countries to facilitate the liaison activity with the ITU through participation in the standardization process and in the Development Programme The technical program and the organization of each ITC event remains within the responsibilities of the hosting country, but with significant IAC support to guarantee that the event is consistent with the quality standards established during the previous congresses. The ITC Plenary Congresses were scheduled tri-annually from 1955 until 1995 when the interval became bi-annual to account for the ever-accelerating development of network technologies, products and services and the associated dramatic increases in network demands. Similarly, to better cover the impact of dramatic changes undergoing in the field of computer and communication systems, networks and usage, it has been decided to hold the Plenary Congress on an annual basis from 2009. == Content == Teletraffic science is the traditional term for all theoretical fundamentals and engineering practices to describe data flows in telecommunication networks, the performance of the usage of network resources, procedures for sizing of resources and engineering the networks for given traffic load and quality of service requirements. For more than 50 years of the 20th century, traffic or teletraffic has been identified primarily with telephone networks. With the huge development of computers, stored program control of network nodes and computer communication, the traditional teletraffic science field naturally extended to computer networks, mobile and wireless/optical networks, and for a wide spectrum of new applications. The convergence between the voice network, the Internet, the television and mobility raised new questions that request new models and tools to be developed. In addition, the development of community networks, home networking, multiple access networking technologies, and the advent of pervasive and ambient communications dictates new challenges to be addressed. Today, ITC addresses the emerging paradigms such as an increasing diversity of distributed applications and services over various media like mobile/optical networks, enabling new markets and economy. ITC has steered the evolutions in communications since its creation in 1955 and remains at the forefront of innovation regarding modeling and performance. The scientific roots of communications traffic are based on the theory of probability and stochastic processes, modelling and performance evaluation. Modelling is the key for the mathematical description and quantitative performance analysis. Traffic flows are described by stochastic processes with complex dependencies which have to be validated by traffic measurements. Modelling also includes operational properties of resource control reflected by service strategies such as queueing disciplines, admission control, and routing. The results of such performance analyses are used for resource dimensioning (sizing), resource management, and network optimization while providing targeted Quality of Service. Teletraffic science is closely related to methods of operation research (queueing theory, optimization, forecasting) and computational sciences (simulation technology distributed systems). In this context, ITC represents a wide community of researchers and practitioners and is regularly organizing events like Congresses, Specialist Seminars and Workshops in order to discuss the latest changes in the modelling, design and performance of communication systems, networks and services. === The evolution of technologies of the 20th century === ITC has been witnessing the change of communication and networking technologies which are reflected in the proceedings and programs of the congresses. The specialist seminars and the motto of the congresses thereby reflect the hot topics of that time and the evolution. Selected topics of the 70's, 80's and 90's were 1998: Traffic Issues related to Multimedia and Nomadic Communications 1995: Traffic Modeling and Measurement in Broadband and Mobile Communications 1990: Broadband Technologies: Architectures, Applications, Control and Performance 1986: ISDN Traffic Issues 1984: Fundamentals of Teletraffic Theory 1977: Modeling of SPC Exchanges and Data Networks === Recent topics in the 21st century === With the rise of the Internet, new networking paradigms and technologies but also new challenges emerged: 2020: Teletraffic in the era of beyond-5G and AI 2019: Networked Systems and Services 2018: Teletraffic in the Smart World 2017: Ubiquitous, software-based, and sustainable networks and services 2016: Digital Connected World 2015: Traffic, Performance and Big Data 2014: Towards a Sustainable World 2013: Energy Efficient and Green Networking 2010: Multimedia Applications - Traffic, Performance and QoE 2009: Network Virtualization - Concepts and Performance 2008: Future Internet Design and Experimental Facilities 2008: Quality of Experience 2002: Internet Traffic Engineering and Traffic Management == Arne Jensen Lifetime Achievement Awards == The Arne Jensen Lifetime A
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Infone

Infone was a service launched by Metro One Telecommunications in 2003. The service was discontinued effective December 14, 2005. == How it worked == Infone included directory assistance and other services via a toll-free phone number. A user could call 888-411-1111 to request directory assistance, directions, traffic information, movie times, call completion, dinner reservation assistance and other services. Infone provided a number of innovative 411 'concierge'-like services, including movie listings from a live operator, and offered a feature where they could provide information from a linked Microsoft Outlook calendar when set up in advance. For a period of time they advertised heavily on U.S. television, featuring ads with then Governor of Minnesota Jesse Ventura, emphasizing their use of all U.S. based operators. The price offered was $0.89 per call up to 15 minutes (for use when the operator connects you to the requested number, as well as for additional information requests afterwards), with $0.05 for each additional minute, making Infone also a competitively priced long-distance service. New users received 5–10 free calls. Infone identified a registered user (along with billing information; the service was only payable by credit card) by caller ID (numbers were registered on signing up) and by an advanced voiceprint recognition system (VPRS) from SpeechWorks that identified the user when the user called from an unregistered telephone number (or no caller ID) through the use of a personal phrase spoken by the user (e.g., "Hello Infone!") after the welcome tone.
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Duck face

Duck face or duck lips is a photographic pose that is common on profile pictures in social networks. The lips are pressed together as in a pout and the cheeks are typically also sucked in. The pose is usually seen as an attempt to appear alluring, but it can be ironic or an attempt to hide self-conscious embarrassment. == History == Fashion models frequently use exaggerated pouts, and self-portraits with a pouty face go back to Rembrandt. In the 1994 film Four Weddings and a Funeral, one of the lead characters, Henrietta, played by Anna Chancellor, is nicknamed Duckface for her pouty expressions. Ben Stiller mocked models' pouty expressions in 1996 comedy sketches and the 2001 feature film Zoolander. The silly expressions made by his narcissistic character have retroactively been identified as an example of duck face. As social networks became popular, young women frequently made exaggeratedly pouty expressions. This became a major fad by the 2010s, provoking a strong negative reaction among some viewers. OxfordDictionaries.com added "duck face" as a new word in 2014 to their list of current and modern words, but it has not been added to the Oxford English Dictionary. In an animal communication studies of capuchin monkeys, the "duck face" term has been used synonymously with "protruded lip face", which females exhibit in the proceptive phase before mating.
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Query understanding

Query understanding is the process of inferring the intent of a search engine user by extracting semantic meaning from the searcher’s keywords. Query understanding methods generally take place before the search engine retrieves and ranks results. It is related to natural language processing but specifically focused on the understanding of search queries. == Methods == === Stemming and lemmatization === Many languages inflect words to reflect their role in the utterance they appear in. The variation between various forms of a word is likely to be of little importance for the relatively coarse-grained model of meaning involved in a retrieval system, and for this reason the task of conflating the various forms of a word is a potentially useful technique to increase recall of a retrieval system. Stemming algorithms, also known as stemmers, typically use a collection of simple rules to remove suffixes intended to model the language’s inflection rules. For some languages, there are simple lemmatisation methods to reduce a word in query to its lemma or root form or its stem; for others, this operation involves non-trivial string processing and may require recognizing the word's part of speech or referencing a lexical database. The effectiveness of stemming and lemmatization varies across languages. === Query Segmentation === Query segmentation is a key component of query understanding, aiming to divide a query into meaningful segments. Traditional approaches, such as the bag-of-words model, treat individual words as independent units, which can limit interpretative accuracy. For languages like Chinese, where words are not separated by spaces, segmentation is essential, as individual characters often lack standalone meaning. Even in English, the BOW model may not capture the full meaning, as certain phrases—such as "New York"—carry significance as a whole rather than as isolated terms. By identifying phrases or entities within queries, query segmentation enhances interpretation, enabling search engines to apply proximity and ordering constraints, ultimately improving search accuracy and user satisfaction. === Entity recognition === Entity recognition is the process of locating and classifying entities within a text string. Named-entity recognition specifically focuses on named entities, such as names of people, places, and organizations. In addition, entity recognition includes identifying concepts in queries that may be represented by multi-word phrases. Entity recognition systems typically use grammar-based linguistic techniques or statistical machine learning models. === Query rewriting === Query rewriting is the process of automatically reformulating a search query to more accurately capture its intent. Query expansion adds additional query terms, such as synonyms, in order to retrieve more documents and thereby increase recall. Query relaxation removes query terms to reduce the requirements for a document to match the query, thereby also increasing recall. Other forms of query rewriting, such as automatically converting consecutive query terms into phrases and restricting query terms to specific fields, aim to increase precision. === Spelling Correction === Automatic spelling correction is a critical feature of modern search engines, designed to address common spelling errors in user queries. Such errors are especially frequent as users often search for unfamiliar topics. By correcting misspelled queries, search engines enhance their understanding of user intent, thereby improving the relevance and quality of search results and overall user experience.
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Digital exhibition

Digital Exhibition includes both the projection technologies, such as High Definition, and delivery technologies of a film to a movie theater. Delivery technologies include disk drives, satellite relay, and fiber optics. This can save money in distribution but is usually more expensive overall due to maintenance and standardization of technology. However, there are benefits to digital exhibition, for example it requires less assembly by the exhibitor and can contain the trailers that the distributor wishes.
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Deplatforming

Deplatforming, also known as no-platforming, is a boycott on an individual or group by removing the platforms used to share their information or ideas. The term is commonly associated with social media. == History == === Deplatforming of invited speakers === In the United States, the banning of speakers on university campuses dates back to the 1940s. This was carried out by the policies of the universities themselves. The University of California had a policy known as the Speaker Ban, codified in university regulations under President Robert Gordon Sproul, that mostly, but not exclusively, targeted communists. One rule stated that "the University assumed the right to prevent exploitation of its prestige by unqualified persons or by those who would use it as a platform for propaganda." This rule was used in 1951 to block Max Shachtman, a socialist, from speaking at the University of California at Berkeley. In 1947, former U.S. Vice President Henry A. Wallace was banned from speaking at UCLA because of his views on U.S. Cold War policy, and in 1961, Malcolm X was prohibited from speaking at Berkeley as a religious leader. Controversial speakers invited to appear on college campuses have faced deplatforming attempts to disinvite them or to otherwise prevent them from speaking. The British National Union of Students established its No Platform policy as early as 1973. In the mid-1980s, visits by South African ambassador Glenn Babb to Canadian college campuses faced opposition from students opposed to apartheid. In the United States, recent examples include the March 2017 disruption by protestors of a public speech at Middlebury College by political scientist Charles Murray. In February 2018, students at the University of Central Oklahoma rescinded a speaking invitation to creationist Ken Ham, after pressure from an LGBT student group. In March 2018, a "small group of protesters" at Lewis & Clark Law School attempted to stop a speech by visiting lecturer Christina Hoff Sommers. In the 2019 film No Safe Spaces, Adam Carolla and Dennis Prager documented their own disinvitation along with others. As of February 2020, the Foundation for Individual Rights in Education, a speech advocacy group, documented 469 disinvitation or disruption attempts at American campuses since 2000, including both "unsuccessful disinvitation attempts" and "successful disinvitations"; the group defines the latter category as including three subcategories: formal disinvitation by the sponsor of the speaking engagement; the speaker's withdrawal "in the face of disinvitation demands"; and "heckler's vetoes" (situations when "students or faculty persistently disrupt or entirely prevent the speakers' ability to speak"). === Deplatforming in social media === Beginning in 2015, Reddit banned several communities on the site ("subreddits") for violating the site's anti-harassment policy. A 2017 study published in the journal Proceedings of the ACM on Human-Computer Interaction, examining "the causal effects of the ban on both participating users and affected communities," found that "the ban served a number of useful purposes for Reddit" and that "Users participating in the banned subreddits either left the site or (for those who remained) dramatically reduced their hate speech usage. Communities that inherited the displaced activity of these users did not suffer from an increase in hate speech." In June 2020 and January 2021, Reddit also issued bans to pro-Trump communities over violations of the website's content and harassment policies. On May 2, 2019, Facebook and the Facebook-owned platform Instagram announced a ban of "dangerous individuals and organizations" including Nation of Islam leader Louis Farrakhan, Milo Yiannopoulos, Alex Jones and his organization InfoWars, Paul Joseph Watson, Laura Loomer, and Paul Nehlen. In the wake of the 2021 storming of the US Capitol, Twitter banned then-president Donald Trump, as well as 70,000 other accounts linked to the event and the far-right movement QAnon. Some studies have found that the deplatforming of extremists reduced their audience, although other research has found that some content creators became more toxic following deplatforming and migration to alt-tech platform. ==== Twitter ==== On November 18, 2022, Elon Musk, as newly appointed CEO of Twitter, reopened previously banned Twitter accounts of high-profile users, including Kathy Griffin, Jordan Peterson, and The Babylon Bee as part of the new Twitter policy. As Musk exclaimed, "New Twitter policy is freedom of speech, but not freedom of reach". ==== Alex Jones ==== On August 6, 2018, Facebook, Apple, YouTube and Spotify removed all content by Jones and InfoWars for policy violations. YouTube removed channels associated with InfoWars, including The Alex Jones Channel. On Facebook, four pages associated with InfoWars and Alex Jones were removed over repeated policy violations. Apple removed all podcasts associated with Jones from iTunes. On August 13, 2018, Vimeo removed all of Jones's videos because of "prohibitions on discriminatory and hateful content". Facebook cited instances of dehumanizing immigrants, Muslims and transgender people, as well as glorification of violence, as examples of hate speech. After InfoWars was banned from Facebook, Jones used another of his websites, NewsWars, to circumvent the ban. Jones's accounts were also removed from Pinterest, Mailchimp and LinkedIn. As of early August 2018, Jones retained active accounts on Instagram, Google+ and Twitter. In September, Jones was permanently banned from Twitter and Periscope after berating CNN reporter Oliver Darcy. On September 7, 2018, the InfoWars app was removed from the Apple App Store for "objectionable content". He was banned from using PayPal for business transactions, having violated the company's policies by expressing "hate or discriminatory intolerance against certain communities and religions." After Elon Musk's purchase of Twitter several previously banned accounts were reinstated including Donald Trump, Andrew Tate and Ye resulting in questioning if Alex Jones will be unbanned as well. However Musk denied that Alex Jones will be unbanned criticizing Jones as a person that "would use the deaths of children for gain, politics or fame". InfoWars remained available on Roku devices in January 2019, a year after the channel's removal from multiple streaming services. Roku indicated that they do not "curate or censor based on viewpoint," and that it had policies against content that is "unlawful, incited illegal activities, or violates third-party rights," but that InfoWars was not in violation of these policies. Following a social media backlash, Roku removed InfoWars and stated "After the InfoWars channel became available, we heard from concerned parties and have determined that the channel should be removed from our platform." In March 2019, YouTube terminated the Resistance News channel due to its reuploading of live streams from InfoWars. On May 1, 2019, Jones was barred from using both Facebook and Instagram. Jones briefly moved to Dlive, but was suspended in April 2019 for violating community guidelines. In March 2020, the InfoWars app was removed from the Google Play store due to claims of Jones disseminating COVID-19 misinformation. A Google spokesperson stated that "combating misinformation on the Play Store is a top priority for the team" and apps that violate Play policy by "distributing misleading or harmful information" are removed from the store. ==== Donald Trump ==== On January 6, 2021, in a joint session of the United States Congress, the counting of the votes of the Electoral College was interrupted by a breach of the United States Capitol chambers. The rioters were supporters of President Donald Trump who hoped to delay and overturn the President's loss in the 2020 election. The event resulted in five deaths and at least 400 people being charged with crimes. The certification of the electoral votes was only completed in the early morning hours of January 7, 2021. In the wake of several Tweets by President Trump on January 7, 2021 Facebook, Instagram, YouTube, Reddit, and Twitter all deplatformed Trump to some extent. Twitter deactivated his personal account, which the company said could possibly be used to promote further violence. Trump subsequently tweeted similar messages from the President's official US Government account @POTUS, which resulted in him being permanently banned on January 8. Twitter then announced that Trump's ban from their platform would be permanent. Trump planned to rejoin on social media through the use of a new platform by May or June 2021, according to Jason Miller on a Fox News broadcast. The same week Musk announced Twitter's new freedom of speech policy, he tweeted a poll to ask whether to bring back Trump into the platform. The poll ended with 51.8% in favor of unbanning Trump's account. Twitter has since reinstated Trump's Twitter accou
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Hashtag

A hashtag is a metadata tag operator that is prefaced by the hash symbol, #. On social media, hashtags are used on microblogging and photo-sharing services–especially Twitter and Tumblr–as a form of user-generated tagging that enables cross-referencing of content by topic or theme. For example, a search within Instagram for the hashtag #flowers returns all posts that have been tagged with that term. After the initial hash symbol, a hashtag may include letters, numerals or other punctuation. The use of hashtags was first proposed by American blogger and product consultant Chris Messina in a 2007 tweet. Messina made no attempt to patent the use because he felt that "they were born of the internet, and owned by no one". Hashtags became entrenched in the culture of Twitter and soon emerged across Instagram, Facebook, and YouTube. In June 2014, hashtag was added to the Oxford English Dictionary as "a word or phrase with the symbol # in front of it, used on social media websites and apps so that you can search for all messages with the same subject". == Origin and acceptance == The number sign or hash symbol, #, has long been used in information technology to highlight specific pieces of text. In 1970, the number sign was used to denote immediate address mode in the assembly language of the PDP-11 when placed next to a symbol or a number, and around 1973, '#' was introduced in the C programming language to indicate special keywords that the C preprocessor had to process first. The pound sign was adopted for use within IRC (Internet Relay Chat) networks around 1988 to label groups and topics. Channels or topics that are available across an entire IRC network are prefixed with a hash symbol # (as opposed to those local to a server, which uses an ampersand '&'). The use of the pound sign in IRC inspired Chris Messina to propose a similar system on Twitter to tag topics of interest on the microblogging network. He proposed the usage of hashtags on Twitter: How do you feel about using # (pound) for groups. As in #barcamp [msg]? According to Messina, he suggested use of the hashtag to make it easy for lay users without specialized knowledge of search protocols to find specific relevant content. Therefore, the hashtag "was created organically by Twitter users as a way to categorize messages". The first published use of the term "hash tag" was in a blog post "Hash Tags = Twitter Groupings" by Stowe Boyd, on August 26, 2007, according to lexicographer Ben Zimmer, chair of the American Dialect Society's New Words Committee. Messina's suggestion to use the hashtag was not immediately adopted by Twitter, but the convention gained popular acceptance when hashtags were used in tweets relating to the 2007 San Diego forest fires in Southern California. The hashtag gained international acceptance during the 2009–2010 Iranian election protests; Twitter users used both English- and Persian-language hashtags in communications during the events. Hashtags have since played critical roles in recent social movements such as #jesuischarlie, #BLM, and #MeToo. Beginning July 2, 2009, Twitter began to hyperlink all hashtags in tweets to Twitter search results for the hashtagged word (and for the standard spelling of commonly misspelled words). In 2010, Twitter introduced "Trending Topics" on the Twitter front page, displaying hashtags that are rapidly becoming popular, and the significance of trending hashtags has become so great that the company makes significant efforts to foil attempts to spam the trending list. During the 2010 World Cup, Twitter explicitly encouraged the use of hashtags with the temporary deployment of "hashflags", which replaced hashtags of three-letter country codes with their respective national flags. Other platforms such as YouTube and Gawker Media followed in officially supporting hashtags, and real-time search aggregators such as Google Real-Time Search began supporting hashtags. == Format == A hashtag must begin with a hash (#) character followed by other characters, and is terminated by a space or the end of the line. Some platforms may require the # to be preceded with a space. Most or all platforms that support hashtags permit the inclusion of letters (without diacritics), numerals, and underscores. Other characters may be supported on a platform-by-platform basis. Some characters, such as "&", are generally not supported as they may already serve other search functions. Hashtags are not case sensitive (a search for "#hashtag" will match "#HashTag" as well), but the use of embedded capitals (i.e., CamelCase) increases legibility and improves accessibility. Languages that do not use word dividers handle hashtags differently. In China, microblogs Sina Weibo and Tencent Weibo use a double-hashtag-delimited #HashName# format, since the lack of spacing between Chinese characters necessitates a closing tag. Twitter uses a different syntax for Chinese characters and orthographies with similar spacing conventions: the hashtag contains unspaced characters, separated from preceding and following text by spaces (e.g., '我 #爱你' instead of '我#爱你') or by zero-width non-joiner characters before and after the hashtagged element, to retain a linguistically natural appearance (displaying as unspaced '我‌#爱‌你', but with invisible non-joiners delimiting the hashtag). === Etiquette and regulation === Some communities may limit, officially or unofficially, the number of hashtags permitted on a single post. Misuse of hashtags can lead to account suspensions. Twitter warns that adding hashtags to unrelated tweets, or repeated use of the same hashtag without adding to a conversation can filter an account from search results, or suspend the account. Individual platforms may deactivate certain hashtags either for being too generic to be useful, such as #photography on Instagram, or due to their use to facilitate illegal activities. === Alternate formats === In 2009, StockTwits began using ticker symbols preceded by the dollar sign (e.g., $XRX). In July 2012, Twitter began supporting the tag convention and dubbed it the "cashtag". The convention has extended to national currencies, and Cash App has implemented the cashtag to mark usernames. == Function == Hashtags are particularly useful in unmoderated forums that lack a formal ontological organization. Hashtags help users find content similar interest. Hashtags are neither registered nor controlled by any one user or group of users. They do not contain any set definitions, meaning that a single hashtag can be used for any number of purposes, and that the accepted meaning of a hashtag can change with time. Hashtags intended for discussion of a particular event tend to use an obscure wording to avoid being caught up with generic conversations on similar subjects, such as a cake festival using #cakefestival rather than simply #cake. However, this can also make it difficult for topics to become "trending topics" because people often use different spelling or words to refer to the same topic. For topics to trend, there must be a consensus, whether silent or stated, that the hashtag refers to that specific topic. Hashtags may be used informally to express context around a given message, with no intent to categorize the message for later searching, sharing, or other reasons. Hashtags may thus serve as a reflexive meta-commentary. This can help express contextual cues or offer more depth to the information or message that appears with the hashtag. "My arms are getting darker by the minute. #toomuchfaketan". AnoHashtags can also be used to express personal feelings and emotions. ther function of the hashtag can be used to express personal feelings and emotions. For example, with "It's Monday!! #excited #sarcasm" in which the adjectives are directly indicating the emotions of the speaker. Verbal use of the word hashtag is sometimes used in informal conversations. Use may be humorous, such as "I'm hashtag confused!" By August 2012, use of a hand gesture, sometimes called the "finger hashtag", in which the index and middle finger both hands are extended and arranged perpendicularly to form the hash, was documented. === Co-optation by other industries === Companies, businesses, and advocacy organizations have taken advantage of hashtag-based discussions for promotion of their products, services or campaigns. In the early 2010s, some television broadcasters began to employ hashtags related to programs in digital on-screen graphics, to encourage viewers to participate in a backchannel of discussion via social media prior to, during, or after the program. Television commercials have sometimes contained hashtags for similar purposes. The increased usage of hashtags as brand promotion devices has been compared to the promotion of branded "keywords" by AOL in the late 1990s and early 2000s, as such keywords were also promoted at the end of television commercials and series episodes. Organized real-world events have used hashta
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Spreading activation

Spreading activation is a method for searching associative networks, biological and artificial neural networks, or semantic networks. The search process is initiated by labeling a set of source nodes (e.g. concepts in a semantic network) with weights or "activation" and then iteratively propagating or "spreading" that activation out to other nodes linked to the source nodes. Most often these "weights" are real values that decay as activation propagates through the network. When the weights are discrete this process is often referred to as marker passing. Activation may originate from alternate paths, identified by distinct markers, and terminate when two alternate paths reach the same node. However brain studies show that several different brain areas play an important role in semantic processing. Spreading activation in semantic networks as a model were invented in cognitive psychology to model the fan out effect. Spreading activation can also be applied in information retrieval, by means of a network of nodes representing documents and terms contained in those documents. == Cognitive psychology == As it relates to cognitive psychology, spreading activation is the theory of how the brain iterates through a network of associated ideas to retrieve specific information. The spreading activation theory presents the array of concepts within our memory as cognitive units, each consisting of a node and its associated elements or characteristics, all connected together by edges. A spreading activation network can be represented schematically, in a sort of web diagram with shorter lines between two nodes meaning the ideas are more closely related and will typically be associated more quickly to the original concept. In memory psychology, the spreading activation model holds that people organize their knowledge of the world based on their personal experiences, which in turn form the network of ideas that is the person's knowledge of the world. When a word (the target) is preceded by an associated word (the prime) in word recognition tasks, participants seem to perform better in the amount of time that it takes them to respond. For instance, subjects respond faster to the word "doctor" when it is preceded by "nurse" than when it is preceded by an unrelated word like "carrot". This semantic priming effect with words that are close in meaning within the cognitive network has been seen in a wide range of tasks given by experimenters, ranging from sentence verification to lexical decision and naming. As another example, if the original concept is "red" and the concept "vehicles" is primed, they are much more likely to say "fire engine" instead of something unrelated to vehicles, such as "cherries". If instead "fruits" was primed, they would likely name "cherries" and continue on from there. The activation of pathways in the network has everything to do with how closely linked two concepts are by meaning, as well as how a subject is primed. == Algorithm == A directed graph is populated by Nodes[ 1...N ] each having an associated activation value A [ i ] which is a real number in the range [0.0 ... 1.0]. A Link[ i, j ] connects source node[ i ] with target node[ j ]. Each edge has an associated weight W [ i, j ] usually a real number in the range [0.0 ... 1.0]. Parameters: Firing threshold F, a real number in the range [0.0 ... 1.0] Decay factor D, a real number in the range [0.0 ... 1.0] Steps: Initialize the graph setting all activation values A [ i ] to zero. Set one or more origin nodes to an initial activation value greater than the firing threshold F. A typical initial value is 1.0. For each unfired node [ i ] in the graph having an activation value A [ i ] greater than the node firing threshold F: For each Link [ i, j ] connecting the source node [ i ] with target node [ j ], adjust A [ j ] = A [ j ] + (A [ i ] W [ i, j ] D) where D is the decay factor. If a target node receives an adjustment to its activation value so that it would exceed 1.0, then set its new activation value to 1.0. Likewise maintain 0.0 as a lower bound on the target node's activation value should it receive an adjustment to below 0.0. Once a node has fired it may not fire again, although variations of the basic algorithm permit repeated firings and loops through the graph. Nodes receiving a new activation value that exceeds the firing threshold F are marked for firing on the next spreading activation cycle. If activation originates from more than one node, a variation of the algorithm permits marker passing to distinguish the paths by which activation is spread over the graph The procedure terminates when either there are no more nodes to fire or in the case of marker passing from multiple origins, when a node is reached from more than one path. Variations of the algorithm that permit repeated node firings and activation loops in the graph, terminate after a steady activation state, with respect to some delta, is reached, or when a maximum number of iterations is exceeded. == Examples ==
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Information element

An information element, sometimes informally referred to as a field, is an item in Q.931 and Q.2931 messages, IEEE 802.11 management frames, and cellular network messages sent between a base transceiver station and a mobile phone or similar piece of user equipment. An information element is often a type–length–value item, containing 1) a type (which corresponds to the label of a field), a length indicator, and a value, although any combination of one or more of those parts is possible. A single message may contain multiple information elements. The abbreviation IE is found in many technical specification documents from 3GPP. It is not uncommon for a single specification document to contain thousands of references to IEs.
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Content strategy

Content strategy guides the planning, development, and management of content. It is a recognized field in user experience design, and it also draws from adjacent disciplines such as information architecture, content management, business analysis, digital marketing, and technical communication. == Definitions == Content strategy has been described as planning for "the creation, publication, and governance of useful, usable content." It has also been called "a repeatable system that defines the entire editorial content development process for a website development project." In a 2007 article titled "Content Strategy: The Philosophy of Data," Rachel Lovinger describes the goal of content strategy as using "words and data to create unambiguous content that supports meaningful, interactive experiences." Here, she also provided the analogy that "content strategy is to copywriting as information architecture is to design." She encourages content strategists and collaborators to engage in early discussions about content meaning, models, and tools, to make sure strategy is integrated from the start rather than as an afterthought. The Content Strategy Alliance combines Kevin Nichols' definition with Kristina Halvorson's and defines content strategy as "getting the right content to the right user at the right time through strategic planning of content creation, delivery, and governance." == Practitioners == Content strategists are often familiar with a wide range of approaches, techniques, and tools. The perspectives that content strategists bring also depend heavily on their professional training and education. For instance, some specialize in "front-end strategy," which includes developing personas, journey mapping the user experience, aligning business strategy and user needs, developing a brand strategy, exploring different channels, and creating style guidelines and search engine optimization (SEO) guidelines. Others specialize in "back-end strategy," which includes creating content models, planning taxonomies and metadata, structuring content management systems, and building systems to support content reuse. Both roles involve addressing workflow and governance issues. Many organizations and individuals tend to confuse content strategists with editors. However, content strategy is "about more than just the written word," according to Washington State University associate professor Brett Atwood. For example, Atwood indicates that a practitioner needs to also "consider how content might be re-distributed and/or re-purposed in other channels of delivery." It has also been proposed that the content strategist performs the role of a curator. Just as a museum curator sifts through a collection of content and identifies key pieces that can be juxtaposed against each other to create meaning and spur excitement, a content strategist "must approach a business’s content as a medium that needs to be strategically selected and placed to engage the audience, convey a message, and inspire action."
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Content creation

Content creation is the act of making and sharing media content, particularly in digital contexts. A content creator is the person or studio behind such content. According to Dictionary.com, content refers to "something that is to be expressed through some medium, as speech, writing or any of various arts" for self-expression, distribution, marketing and/or publication. Content creation encompasses various activities, including maintaining and updating web sites, blogging, article writing, photography, videography, online commentary, social media accounts, and editing and distribution of digital media. In a survey conducted by the Pew Research Center, the content thus created was defined as "the material people contribute to the online world". In addition to traditional forms of content creation, digital platforms face growing challenges related to privacy, copyright, misinformation, platform moderation policies, and the repercussions of violating community guidelines. == Content creators == Content creation is the process of producing and sharing various forms of content such as text, images, audio, and video, designed to engage and inform a specific audience. It plays a crucial role in digital marketing, branding, and online communication and brand awareness. Content can be created for a range of platforms, including social media, websites, blogs, and multimedia channels. Whether it's through written articles, compelling photography, or engaging videos, content creation helps businesses build a connection with their audience, increase visibility, and drive traffic. The process typically involves identifying the target audience, brainstorming ideas, creating the content, and distributing it across various channels. Successful content creation combines creativity with strategic planning, considering audience preferences, trends, and platform characteristics to achieve marketing and branding goals. === News organizations === News organizations, especially those with a large and global reach like The New York Times, NPR, and CNN, consistently create some of the most shared content on the Web, especially in relation to current events. In the words of a 2011 report from the Oxford School for the Study of Journalism and the Reuters Institute for the Study of Journalism, "Mainstream media is the lifeblood of topical social media conversations in the UK." While the rise of digital media has disrupted traditional news outlets, many have adapted and have begun to produce content that is designed to function on the web and be shared on social media. The social media site Twitter is a major distributor and aggregator of breaking news from various sources, and the function and value of Twitter in the distribution of news is a frequent topic of discussion and research in journalism. User-generated content, social media blogging and citizen journalism have changed the nature of news content in recent years. The company Narrative Science is now using artificial intelligence to produce news articles and interpret data. === Colleges, universities, and think tanks === Academic institutions, such as colleges and universities, create content in the form of books, journal articles, white papers, and some forms of digital scholarship, such as blogs that are group edited by academics, class wikis, or video lectures that support a massive open online course (MOOC). Through an open data initiative, institutions may make raw data supporting their experiments or conclusions available on the Web. Academic content may be gathered and made accessible to other academics or the public through publications, databases, libraries, and digital libraries. Academic content may be closed source or open access (OA). Closed-source content is only available to authorized users or subscribers. For example, an important journal or a scholarly database may be a closed source, available only to students and faculty through the institution's library. Open-access articles are open to the public, with the publication and distribution costs shouldered by the institution publishing the content. === Companies === Corporate content includes advertising and public relations content, as well as other types of content produced for profit, including white papers and sponsored research. Advertising can also include auto-generated content, with blocks of content generated by programs or bots for search engine optimization. Companies also create annual reports which are part of their company's workings and a detailed review of their financial year. This gives the stakeholders of the company insight into the company's current and future prospects and direction. === Artists and writers === Cultural works, like music, movies, literature, and art, are also major forms of content. Examples include traditionally published books and e-books as well as self-published books, digital art, fanfiction, and fan art. Independent artists, including authors and musicians, have found commercial success by making their work available on the Internet. === Government === Through digitization, sunshine laws, open records laws and data collection, governments may make statistical, legal or regulatory information available on the Internet. National libraries and state archives turn historical documents, public records, and unique relics into online databases and exhibits. This has raised significant privacy issues. In 2012, The Journal News, a New York state paper, sparked an outcry when it published an interactive map of the state's gun owner locations using legally obtained public records. Governments also create online or digital propaganda or misinformation to support domestic and international goals. This can include astroturfing, or using media to create a false impression of mainstream belief or opinion. Governments can also use open content, such as public records and open data, in service of public health, educational and scientific goals, such as crowdsourcing solutions to complex policy problems. In 2013, the National Aeronautics and Space Administration (NASA) joined the asteroid mining company Planetary Resources to crowdsource the hunt for near-Earth objects. Describing NASA's crowdsourcing work in an interview, technology transfer executive David Locke spoke of the "untapped cognitive surplus that exists in the world" which could be used to help develop NASA technology. In addition to making governments more participatory, open records and open data have the potential to make governments more transparent and less corrupt. === Users === The introduction of Web 2.0 made it possible for content consumers to be more involved in the generation and sharing of content. With the advent of digital media, the amount of user generated content, as well as the age and class range of users, has increased. 8% of Internet users are very active in content creation and consumption. Worldwide, about one in four Internet users are significant content creators, and users in emerging markets lead the world in engagement. Research has also found that young adults of a higher socioeconomic background tend to create more content than those from lower socioeconomic backgrounds. 69% of American and European internet users are "spectators", who consume—but do not create—online and digital media. The ratio of content creators to the amount of content they generate is sometimes referred to as the 1% rule, a rule of thumb that suggests that only 1% of a forum's users create nearly all of its content. Motivations for creating new content may include the desire to gain new knowledge, the possibility of publicity, or simple altruism. Users may also create new content in order to bring about social reforms. However, researchers caution that in order to be effective, context must be considered, a diverse array of people must be included, and all users must participate throughout the process. According to a 2011 study, minorities create content in order to connect with their communities online. African-American users have been found to create content as a means of self-expression that was not previously available. Media portrayals of minorities are sometimes inaccurate and stereotypical which affects the general perception of these minorities. African-Americans respond to their portrayals digitally through the use of social media such as Twitter and Tumblr. The creation of Black Twitter has allowed a community to share their problems and ideas. ==== Teens ==== Younger users now have greater access to content, content creating applications, and the ability to publish to different types of media, such as Facebook, Blogger, Instagram, DeviantArt, or Tumblr. As of 2005, around 21 million teens used the internet and 57%, or 12 million teens, consider themselves content creators. This proportion of media creation and sharing is higher than that of adults. With the advent of the Internet, teens have had more access to tools for sharing an
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Spreading activation

Spreading activation is a method for searching associative networks, biological and artificial neural networks, or semantic networks. The search process is initiated by labeling a set of source nodes (e.g. concepts in a semantic network) with weights or "activation" and then iteratively propagating or "spreading" that activation out to other nodes linked to the source nodes. Most often these "weights" are real values that decay as activation propagates through the network. When the weights are discrete this process is often referred to as marker passing. Activation may originate from alternate paths, identified by distinct markers, and terminate when two alternate paths reach the same node. However brain studies show that several different brain areas play an important role in semantic processing. Spreading activation in semantic networks as a model were invented in cognitive psychology to model the fan out effect. Spreading activation can also be applied in information retrieval, by means of a network of nodes representing documents and terms contained in those documents. == Cognitive psychology == As it relates to cognitive psychology, spreading activation is the theory of how the brain iterates through a network of associated ideas to retrieve specific information. The spreading activation theory presents the array of concepts within our memory as cognitive units, each consisting of a node and its associated elements or characteristics, all connected together by edges. A spreading activation network can be represented schematically, in a sort of web diagram with shorter lines between two nodes meaning the ideas are more closely related and will typically be associated more quickly to the original concept. In memory psychology, the spreading activation model holds that people organize their knowledge of the world based on their personal experiences, which in turn form the network of ideas that is the person's knowledge of the world. When a word (the target) is preceded by an associated word (the prime) in word recognition tasks, participants seem to perform better in the amount of time that it takes them to respond. For instance, subjects respond faster to the word "doctor" when it is preceded by "nurse" than when it is preceded by an unrelated word like "carrot". This semantic priming effect with words that are close in meaning within the cognitive network has been seen in a wide range of tasks given by experimenters, ranging from sentence verification to lexical decision and naming. As another example, if the original concept is "red" and the concept "vehicles" is primed, they are much more likely to say "fire engine" instead of something unrelated to vehicles, such as "cherries". If instead "fruits" was primed, they would likely name "cherries" and continue on from there. The activation of pathways in the network has everything to do with how closely linked two concepts are by meaning, as well as how a subject is primed. == Algorithm == A directed graph is populated by Nodes[ 1...N ] each having an associated activation value A [ i ] which is a real number in the range [0.0 ... 1.0]. A Link[ i, j ] connects source node[ i ] with target node[ j ]. Each edge has an associated weight W [ i, j ] usually a real number in the range [0.0 ... 1.0]. Parameters: Firing threshold F, a real number in the range [0.0 ... 1.0] Decay factor D, a real number in the range [0.0 ... 1.0] Steps: Initialize the graph setting all activation values A [ i ] to zero. Set one or more origin nodes to an initial activation value greater than the firing threshold F. A typical initial value is 1.0. For each unfired node [ i ] in the graph having an activation value A [ i ] greater than the node firing threshold F: For each Link [ i, j ] connecting the source node [ i ] with target node [ j ], adjust A [ j ] = A [ j ] + (A [ i ] W [ i, j ] D) where D is the decay factor. If a target node receives an adjustment to its activation value so that it would exceed 1.0, then set its new activation value to 1.0. Likewise maintain 0.0 as a lower bound on the target node's activation value should it receive an adjustment to below 0.0. Once a node has fired it may not fire again, although variations of the basic algorithm permit repeated firings and loops through the graph. Nodes receiving a new activation value that exceeds the firing threshold F are marked for firing on the next spreading activation cycle. If activation originates from more than one node, a variation of the algorithm permits marker passing to distinguish the paths by which activation is spread over the graph The procedure terminates when either there are no more nodes to fire or in the case of marker passing from multiple origins, when a node is reached from more than one path. Variations of the algorithm that permit repeated node firings and activation loops in the graph, terminate after a steady activation state, with respect to some delta, is reached, or when a maximum number of iterations is exceeded. == Examples ==
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GeForce RTX 50 series

The GeForce RTX 50 series of consumer graphics cards is the successor of Nvidia's GeForce 40 series. Announced at CES 2025, it debuted with the release of the RTX 5070, RTX 5080 and RTX 5090 in January 2025. It is based on Nvidia's Blackwell architecture featuring Nvidia RTX's fourth-generation RT cores for hardware-accelerated real-time ray tracing, and fifth-generation deep learning–focused Tensor Cores. The GPUs are manufactured by TSMC on a custom 4N process node. == Background == In March 2024, Nvidia announced the Blackwell architecture for its datacenter products. Like Ampere, the architecture is shared by consumer and datacenter products rather than having distinct architectures released simultaneously like Ada Lovelace for consumers and Hopper for datacenter. At the Game Awards in December 2024, a cinematic trailer for The Witcher IV was shown that had been pre-rendered on an "unannounced Nvidia GeForce RTX GPU". This was assumed to be an upcoming GeForce RTX 50 series GPU. Following the RTX 50 series announcement, Nvidia confirmed that the trailer was "pre-rendered in Unreal Engine 5 on a GeForce RTX 5090". Later in the same month, it was reported that Nvidia had begun stockpiling GeForce RTX 50 series units in U.S. warehouses due to a threatened 10% import tariff and 60% tariff on Chinese imports that Donald Trump promised in his re-election campaign. === Announcement === On January 6, 2025, the GeForce RTX 50 series was officially announced for desktop and mobile devices during Nvidia's CES keynote in Las Vegas. The pricing announcement was met with surprise as the RTX 5080 at $999 was the same price that the RTX 4080 Super released at a year earlier despite the anticipated price increases. Nvidia CEO Jensen Huang falsely claimed that the RTX 5070 could reach "RTX 4090 performance at $549", a figure that relies on the use of DLSS 4 upscaling and Multi Frame generation, and is not an indication of raw performance. == Features == === Blackwell architecture === The GeForce RTX 50 series is powered by the Blackwell microarchitecture, which continues Ada Lovelace's emphasis on high graphics frequencies and large L2 caches. The Blackwell architecture introduces Nvidia RTX's fourth-generation RT cores for hardware-accelerated real-time ray tracing and fifth-generation Tensor Cores for AI compute and performing floating-point calculations. === GDDR7 === RTX 50 series GPUs are the first consumer GPUs to feature GDDR7 video memory for greater memory bandwidth over the same bus width compared to the GDDR6 and GDDR6X memory used in the GeForce 40 series. RTX 50 series desktop GPUs use GDDR7 modules from Samsung due to them being available for validation earlier than modules from SK Hynix and Micron. === 12V-2×6 connector === The GeForce RTX 50 series uses the 16-pin 12V-2×6 connector, which is a revision of the 12VHPWR connector featured on the GeForce 40 series. There were problems with the 12VHPWR connector melting on some RTX 4090 GPUs due to the connector not being fully seated and connector design flaws that did not implement a high enough safety and error tolerance. The 12V-2×6 connector revision, published by PCI-SIG in July 2023, addressed this by shortening the four sense pins so the connector will not push any power if it has not been fully seated. The 12VHPWR design would still draw up to 150W of power even if the sense pins were not making full contact. 12V-2×6 is backwards compatible with existing 12VHPWR cables and adapters. Nvidia has mandated to its AIB partners that the 16-pin 12V-2×6 connector be used on all RTX 50 series designs. With the GeForce 40 series, the 12VHPWR connector was only mandated on higher power cards such as the RTX 4070 Super, RTX 4070 Ti, RTX 4070 Ti Super, RTX 4080, RTX 4080 Super and RTX 4090 while RTX 4060, RTX 4060 Ti and RTX 4070 AIB designs had the option of using 8-pin PCIe connectors. The 600W-capable 12VHPWR connector would not have been necessary on sub-200W cards. === DLSS 4 === The fourth generation of Deep Learning Super Sampling (DLSS) was unveiled alongside the RTX 50 series. DLSS 4 upscaling uses a new vision transformer-based model for enhanced image quality with reduced ghosting and greater image stability in motion compared to the previous convolutional neural network (CNN) model. DLSS 4 also allows a greater number of frames to be generated and interpolated based on a single traditionally rendered frame. This form of frame generation called Multi Frame Generation is exclusive to the RTX 50 series while the GeForce 40 series is limited to one interpolated frame per traditionally rendered frame. Nvidia claims that DLSS 4's frame generation model uses 30% less video memory with the example of Warhammer 40,000: Darktide using 400 MB less memory at 4K resolution with frame generation enabled. Nvidia claims that 75 titles will integrate DLSS 4 Multi Frame Generation at launch, including Alan Wake 2, Cyberpunk 2077, Indiana Jones and the Great Circle, and Star Wars Outlaws. === Media Engine and I/O === The RTX 50 series includes DisplayPort 2.1b UHBR20 (80Gbps) with higher display output data rates to support high resolution and high refresh rate displays. The GeForce 40 series received criticism for only including DisplayPort 1.4a (32Gbps) while the competing Radeon RX 7000 series included DisplayPort 2.1 UHBR13.5 (54Gbps). At CES 2025, VESA announced a collaboration with Nvidia on the new DP80LL ("low loss") UHBR20 active cable standard. DP80LL allows for 80Gbps DisplayPort 2.1 cables up to 3 meters long as passive DP80 cables are limited in length due to signal integrity concerns. The RTX 50 series introduces the ninth-generation NVENC encoder and sixth-generation NVDEC video decoder. For the first time in a consumer GeForce GPU, encoding and decoding video in the 4:2:2 color format for professional-grade higher color depth is supported. == List of GPUs == === Desktop === GeForce RTX 50 series desktop GPUs are the second consumer GPUs to utilize a PCIe 5.0 interface and the first to feature GDDR7 video memory (except for the entry level RTX 5050 that still uses GDDR6). They are fabricated by TSMC using a custom 5 nm process dubbed 4N. === Mobile === Laptops featuring GeForce RTX 50 series laptop GPUs were shown at CES 2025. Laptops with RTX 50 series GPUs were paired with Intel's Arrow Lake-HX and AMD's Strix Point and Fire Range CPUs. Nvidia claims that Blackwell architecture's new Max-Q features can increase battery life by up to 40% over GeForce 40 series laptops. For example, Advanced Power Gating saves power by turning off areas of the GPU that are unused and the paired GDDR7 memory can run in an "ultra" low-voltage state. Initial RTX 50 series laptops will become available in March 2025 starting at $1,299. == Controversies == === 12V-2x6 power connector issue === The 12V-2x6 connector used by multiple 5090 cards faces criticism due to a design flaw that can potentially cause the connector to melt. The flaw primarily affect Nvidia's own RTX 5090 FE and RTX 5080 FE cards and are similar to the failures seen on the RTX 40 series but models by third party OEMs have been affected as well. === Availability and pricing === The releases of the RTX 5090, 5080 and 5070 Ti were marked by severe availability issues and pricing well above MSRP. Pricing became an issue again at the end of 2025 due to an ongoing memory supply shortage. Nvidia has been rumored to cut production of 16GB VRAM cards, affecting the availability of the RTX 5060 Ti 16GB and RTX 5070 Ti SKUs. === 32-bit support removal for CUDA, OpenCL, and GPU PhysX === Support for 32-bit OpenCL, and CUDA applications (and as a result 32-bit GPU-accelerated PhysX), was dropped for the GeForce RTX 50 series, which resulted in several applications encountering performance issues with GPU PhysX options or not being able to run at all, causing negative reactions from numerous gaming communities. On December 4, 2025, with the release of driver version 591.44, 32-bit GPU-accelerated PhysX support was restored for certain games. Support for more games was promised in the future. === Incomplete dies and missing ROPs === The dies of certain RTX 5090/5090D, 5080, and 5070 Ti cards were missing eight render output units (ROPs), resulting in slower graphics while pure compute and AI workloads are unaffected. Nvidia claimed that less than 0.5% of cards are affected and that the "production anomaly" has been rectified. === Black screen issues === Some RTX 5080 and 5090 users reported an issue where the system would boot into a black screen after installing Nvidia drivers. Nvidia confirmed the issue and said that a new driver update would fix it for people who hadn't received a VBIOS update yet. Released on February 27, 2025 Nvidia drivers version 572.60 claim to have fixed the issue. Nvidia has since released multiple hotfix and Game Ready drivers that contain additional fixes for the issue. === Windows driver branch quality and stabilit
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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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