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Pill reminder

A pill reminder is any device that reminds users to take medications. Traditional pill reminders are pill containers with electric timers attached, which can be preset for certain times of the day to set off an alarm. More sophisticated pill reminders can also detect when they have been opened, and therefore when the user is away during the time they were supposed to take their medication, they will be reminded of it when they return. This reminder can be in the form of a light, which also helps for deaf or hearing-impaired users. == Mobile app == A newer type of pill reminder is a mobile app that reminds the owner to take the medication. Some of these applications might effectively support adherence to taking medications.
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Myrinet

Myrinet, ANSI/VITA 26-1998, is a high-speed local area networking system designed by the company Myricom to be used as an interconnect between multiple machines to form computer clusters. == Description == Myrinet was promoted as having lower protocol overhead than standards such as Ethernet, and therefore better throughput, less interference, and lower latency while using the host CPU. Although it can be used as a traditional networking system, Myrinet is often used directly by programs that "know" about it, thereby bypassing a call into the operating system. Earlier versions of Myrinet used a variety of media and connectors: Generation 2 used copper media with DC-37 (Myrinet-LAN, M2L- controllers and switches) or microribbon (Myrinet-SAN, M2M-) connectors. Generation 3 used copper media with HSSDC (Myrinet-Serial, M3S-) or microribbon (Myrinet-SAN, M3M-) connectors, or fiber with LC-connectors (Myrinet-Fiber, M3F-). The later versions of Myrinet physically consist of two fibre optic cables, upstream and downstream, connected to the host computers with a single connector. Machines are connected via low-overhead routers and switches, as opposed to connecting one machine directly to another. Myrinet includes a number of fault-tolerance features, mostly backed by the switches. These include flow control, error control, and "heartbeat" monitoring on every link. The "fourth-generation" Myrinet, called Myri-10G, supported a 10 Gbit/s data rate and can use 10 Gigabit Ethernet on PHY, the physical layer (cables, connectors, distances, signaling). Myri-10G started shipping at the end of 2005. Myrinet was approved in 1998 by the American National Standards Institute for use on the VMEbus as ANSI/VITA 26-1998. One of the earliest publications on Myrinet is a 1995 IEEE article. === Performance === Myrinet is a lightweight protocol with little overhead that allows it to operate with throughput close to the basic signaling speed of the physical layer. For supercomputing, the low latency of Myrinet is even more important than its throughput performance, since, according to Amdahl's law, a high-performance parallel system tends to be bottlenecked by its slowest sequential process, which in all but the most embarrassingly parallel supercomputer workloads is often the latency of message transmission across the network. === Deployment === According to Myricom, 141 (28.2%) of the June 2005 TOP500 supercomputers used Myrinet technology. In the November 2005 TOP500, the number of supercomputers using Myrinet was down to 101 computers, or 20.2%, in November 2006, 79 (15.8%), and by November 2007, 18 (3.6%), a long way behind gigabit Ethernet at 54% and InfiniBand at 24.2%. In the June 2014 TOP500 list, the number of supercomputers using Myrinet interconnect was 1 (0.2%). In November 2013, the assets of Myricom (including the Myrinet technology) were acquired by CSP Inc. In 2016, it was reported that Google had also offered to buy the company.
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Shorty Awards

The Shorty Awards (also known as "The Shortys") are awards for outstanding and innovative work in digital and social media content by brands, advertising agencies, and creators. The awards, which generally focus on short-term content, honor achievements in content creation on Twitter, Facebook, YouTube, Instagram, TikTok, Twitch, and other social networking sites. The Shorty Awards began in 2008 and initially recognized achievements by independent creators on Twitter, with the first formal awards ceremony occurring in February 2009. Since then, the awards, which are now awarded each spring, have shifted their focus to recognize content across numerous platforms. Entrant work is judged on the merits of excellence in creativity, strategy, and engagement by the Real Time Academy, a group of industry professionals selected by the Shorty Awards on the basis of their professional reputations, industry knowledge, and personal achievements (which may include previous Shorty wins). An additional public voting component, known as Audience Honor Voting, is also used to select Shorty Awards contenders. Notable Shorty Award winners include Malala Yousafzai, Trevor Noah, Michelle Obama, Conan O’Brien, Lady Gaga, Bill Nye, Jacob Reed, and Lizzo. Brands and organizations such as Chipotle, Duolingo, Marvel Studios, HBO, Red Bull, Airbnb, Nestle, BMW, UNICEF and the Human Rights Campaign have also been awarded. The Shorty Awards also produces an annual award program called The Shorty Impact Awards, a competition dedicated to showcasing digital and social media-based projects by brands, agencies, and organizations that seek to make the world a better place. == List of ceremonies == == 1st Shorty Awards == The awards were created in 2008 by tech entrepreneurs Greg Galant, Adam Varga, and Lee Semel of Sawhorse Media. They invited Twitter account holders to nominate the best Twitter users in general categories such as humor, news, food, and design. Winners were chosen by more than 30,000 Twitter users during the voting period. The founders of Twitter first heard about the awards after the contest had gotten underway and expressed support for it. The first Shorty Awards ceremony was held on February 11, 2009, at the Galapagos Art Space in Brooklyn, New York. Approximately 300 people attended the event. The event was hosted by CNN anchor Rick Sanchez and featured appearances by prominent Twitter users MC Hammer and Gary Vaynerchuk and a video appearance by Shaquille O'Neal. The awards, in 26 categories, were voted on by Twitter users. == 2nd Shorty Awards == Voting for the second Shorty Awards opened in January 2010 in 26 official categories. A Real-Time Photo of the Year category was added to the list of official categories for the first time, recognizing the best photo posted to services such as Twitpic, Yfrog, or Facebook. The second Shorty Awards competition introduced a panel of judges called the Real-Time Academy of Short Form Arts & Sciences whose members were Craig Newmark, David Pogue, Kurt Andersen, Caterina Fake, Joi Ito, Frank Moss, Alberto Ibargüen, Sreenath Sreenivasan, MC Hammer, Alyssa Milano and Jimmy Wales. After public nominations determined the finalists, the academy decided on the winners. Winners were announced at a ceremony held in the Times Center in The New York Times building in Manhattan that was also streamed online. The ceremony was hosted by CNN anchor Rick Sanchez, who presented awards in the official categories as well as the newly added Real-Time Photo of the Year and a special humanitarian award. == 3rd Shorty Awards == The nomination period for the third annual Shorty Awards opened in January 2011 and ran through February 11, 2011, except for new categories that had extended nomination deadlines. There were 30 official categories and five special categories. In addition to Real-Time Photo of the Year, for the first time the awards accepted nominations for Foursquare Mayor of the Year, Foursquare Location of the Year, Microblog of the Year on Tumblr, and a Connecting People award. The awards also introduced new Shorty Industry Awards to recognize the best uses of social media by brands and agencies. Winners were announced at a ceremony on March 28, 2011, hosted by Aasif Mandvi in the Times Center. Other Shorty Awards presenters were scheduled to include Kiefer Sutherland, Jerry Stiller, Anne Meara, Stephen Wallem, Miss USA Rima Fakih, and Miss Teen USA Kamie Crawford. == 4th Shorty Awards == The 4th Annual Shorty Awards featured Ricky Gervais and Tiffani Thiessen. 1.6 million tweeted nominations were made across all the categories to honor the top users on Twitter, Facebook, Tumblr, Foursquare, YouTube and other internet platforms. == 5th Shorty Awards == The 5th Annual Shorty Awards ceremony featured Felicia Day, James Urbaniak, Kristian Nairn, Hannibal Buress, Carrie Keagan, Chris Hardwick, David Karp and Coco Rocha. 2.4 million tweeted nominations were made across all the categories to honor the top users on Twitter, Facebook, Tumblr, Foursquare, YouTube and other internet sites. == 6th Shorty Awards == The ceremony took place on April 7, 2014, at the New York TimesCenter and was hosted by Comedian Natasha Leggero. The show included appearances by Patton Oswalt, Jamie Oliver, Kristen Bell, Jerry Seinfeld, Moshe Kasher, Julie Klausner, Erin Brady, Guy Kawasaki, Matt Walsh, Retta, Us the Duo, Big Boi, Gilbert Gottfried, Thomas Middleditch, Billie Jean King and Leandra Medine. Winners included Jerry Seinfeld and Will Ferrell. == 7th Shorty Awards == The Seventh Annual Shorty Awards was hosted by comedian Rachel Dratch and took place on April 20, 2015, at The Times Center in NYC. The Real-Time Academy, the judging body of the Shortys, tripled in size for the 7th annual Awards and included Alton Brown, Mamrie Hart, Nikki Glaser, OK Go, The Fine Bros, Debbie Sterling, Dan Savage, Deena Varshavskaya and Palmer Luckey. Panic! at the Disco was the musical guest at the ceremony. On-stage presenters included Kevin Jonas, Bill Nye, Bella Thorne, Wyclef Jean, Emily Kinney and Tyler Oakley. == 8th Shorty Awards == The Eighth Annual Shorty Awards were held in NYC at the TimesCenter on April 11, 2016. They were hosted by YouTuber, Writer and Comedian Mamrie Hart with musical performances from Nico & Vinz. Winners of the night included Bill Wurtz, DJ Khaled, Misty Copeland, Casey Neistat, Dwayne Johnson, Hannah Hart, Troye Sivan, Baddie Winkle, Kevin Hart, Taraji P. Henson, King Bach, and Zach King. == 9th Shorty Awards == The Ninth Annual Shorty Awards were held in NYC at the PlayStation Theater on April 23, 2017. They were hosted by two-time Emmy Award winner Tony Hale with a musical performance by Lizzo. Winners of the night included Bill Nye, Shay Mitchell, Doug the Pug, Gigi Gorgeous, Simone Biles, Mara Wilson, Gaten Matarazzo and Chrissy Teigen. == 10th Shorty Awards == The 10th Annual Shorty Awards, took place on April 15, 2018, at the PlayStation Theater, New York City. The ceremony was hosted by actress, singer, and songwriter Keke Palmer with a musical performance by Betty Who. == 11th Shorty Awards == The 11th Annual Shorty Awards were held on May 5, 2019, in New York City at the PlayStation Theater. The ceremony was hosted by American actress and comedian Kathy Griffin, with a musical performance by Tank and the Bangas. == 12th Shorty Awards == The 12th Annual Shorty Awards were held on May 3, 2020. Due to the COVID-19 pandemic, the ceremony took place online for the first time, with presenters and award winners filming from their own homes. The ceremony was hosted by actor J.B. Smoove and featured a remixed performance of Trap Queen by Fetty Wap. Award winners included Jack Stauber, Supercar Blondie, Rose and Rosie, and Greta Thunberg. == 13th Shorty Awards == The 13th Annual Shorty Awards took place from April 26 to May 14, 2021. The ceremony was hosted on different social media platforms, such as Instagram and Clubhouse, to create a more tailored experience. Winners were announced from May 11 to May 14, with 10 winners being revealed each hour from 1 to 4 p.m. EST on the Shorty Awards Instagram account. == 14th Shorty Awards == The 14th Annual Shorty Awards were held virtually on May 15, 2022, honoring the best in social media and digital content. Hosted by Jay Shetty, the event recognized influencers, brands, and organizations across various categories, celebrating excellence in digital storytelling and innovative online campaigns. Notable winners included Tabitha Brown for her food content and the D'Amelio Family for their contributions to family and parenting content. The event highlighted the role of digital media in connecting and inspiring audiences during challenging times. == 15th Shorty Awards == The 15th Annual Shorty Awards celebrated the best in social media and digital content on May 24, 2023, at Tribeca 360° in New York City. Hosted by Jay Pharoah, the event honored creators, brands, and organizations ac
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Perfectly Imperfect (platform)

Perfectly Imperfect is an online newsletter and social media platform. It was initially founded in 2020 as a biweekly email newsletter that focused on recommendations. In January 2024, Perfectly Imperfect launched PI.FYI, a social media platform. The platform is based around sharing recommendations. Its main feed is presented in reverse chronological order and is not algorithmically curated. == History == Perfectly Imperfect was started during the COVID-19 pandemic by Tyler Bainbridge, alongside college friends Alex Cushing and Serey Morm, whom he met at UMass Lowell; Morm later departed. Motivated by a dissatisfaction with algorithm-driven recommendation culture, they launched on Substack in September 2020. Its early newsletter format, PI, published brief recommendation lists and personal notes from contributors. Contributors have included a mix of underground artists and more established creative figures, such as Charli XCX, Chloe Cherry, Chloe Wise, and Meetka Otto. In October 2024, PI announced it was leaving Substack to launch its own site. == Overview == The current platform, PI.FYI, features both editorial content (guest columns, long-form essays, staff picks) and user-generated recommendations. The platform also supports "Ask" posts, where users can solicit recommendations from the community, and allows commenting, liking, and profile customization. In August 2025, it launched an events feature. In 2022, Perfectly Imperfect hosted their first offline event at Baby's All Right in Brooklyn, with a performance by The Dare. They have since expanded their event promotion/sponsorship to markets such as Los Angeles, San Francisco, and even Auckland.
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IMazing

iMazing is mobile device management software that allows users to transfer files and data between iOS devices (iPhone, iPad and iPod Touch) and macOS or Windows computers, in addition to many other features beyond the scope of what Apple's own tools enable. == History == Developed by DigiDNA, iMazing was initially released in 2008 as DiskAid, enabling users to transfer data and files from the iPhone or iPod Touch to Mac or Windows computers. DiskAid was renamed iMazing in 2014. Version 2.0 was released on September 13, 2016. In August 2021, version 2.14 of iMazing added a spyware detection feature. The feature is based on Amnesty International’s Mobile Verification Toolkit to detect Pegasus Spyware following the publication of Pegasus Project. == Description == With iMazing, an iPhone or iPad can be used similarly to an external hard drive. It performs tasks that iTunes doesn’t offer, including incremental backups of iOS devices, browsing and exporting text and voicemail messages, managing apps, encryption, and migrating data from an old phone to a new one. The menu bar app iMazing Mini enables automatic, wireless and encrypted backups of iPhones. The iMazing HEIC Converter is a free desktop app for Mac and PC that lets users convert photos from HEIC format to JPG or PNG.
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Instagram egg

The Instagram egg is a photo of an egg posted by the account @world_record_egg on the social media platform Instagram. It became a global phenomenon and an internet meme within days of its publication on 4 January 2019. It is the second most-liked Instagram post and was the most-liked Instagram post from 14 January 2019 until 20 December 2022, when it was overtaken by Lionel Messi's post showing him and his teammates celebrating after Argentina won the 2022 FIFA World Cup. The owner of the account was revealed to be Chris Godfrey, a British advertising creative, who later worked with his two friends Alissa Khan-Whelan and CJ Brown on a Hulu commercial featuring the egg, intended to raise mental health awareness. == Background == The photo was originally taken by Serghei Platanov, who then posted it to Shutterstock on 23 June 2015 with the title "eggs isolated on white background". == History == On 4 January 2019, the @world_record_egg account was created, and posted an image of a bird egg with the caption, "Let's set a world record together and get the most liked post on Instagram. Beating the current world record held by Kylie Jenner (18 million)! We got this." Jenner's previous record, the first photo of her daughter Stormi, had garnered a total of 18.4 million likes. The post quickly reached 18.4 million likes in just under 10 days, becoming the most-liked Instagram post at the time. It then continued to rise over 45 million likes in the next 48 hours, surpassing the "Despacito" music video and taking the world record for the most-liked online post (on any media platform) in history. After the account became verified on 14 January 2019, the post rose in popularity and likes, which snowballed into coverage in various media outlets. By 18 March 2019, the post had accumulated over 53.3 million likes, nearly three times the previous record of 18.4 million. It posted frequent updates for a few days in the form of Instagram Stories. Alongside the like tally, as of January 2023 the post has 3.8 million comments. Several individuals tried to claim that they were the account's creator, the claims being dismissed by "the egg" on Instagram direct messages. On 3 February 2019, the creator of the Instagram egg was revealed by Hulu and The New York Times to be Chris Godfrey, a British advertising creative. Alissa Khan-Whelan, his colleague, was also outed. On 18 January 2019, the account posted a second picture of an egg, almost identical to the first one apart from a small crack at the top left. As of 25 February 2019, the post accumulated 11.8 million likes. On 22 January 2019, the account posted a third picture of an egg, this time having two larger cracks. In less than 25 minutes, the post accumulated 1 million likes, and by 25 February 2019, it had accumulated 9.5 million likes. On 29 January 2019, a fourth picture of an egg was posted to the account which has another large crack on the right hand side, attracting 7.6 million likes by 25 February 2019. On 1 February 2019, a fifth picture of an egg was posted with stitching like that of a football, referencing the upcoming Super Bowl. That post had accumulated 6.5 million likes by 25 February 2019. The account promised that it would reveal what was inside the egg on 3 February, on the subscription video on demand service Hulu. The Hulu Instagram egg reveal was used to promote an animation about a mental health campaign. A caption from the clip read, "Recently I've started to crack, the pressure of social media is getting to me. If you're struggling too, talk to someone." The video was later posted on the @world_record_egg Instagram account, and this post received over 33 million views by May 2019. As of May 2020, it had received over 41 million views. On 16 July 2019, Chris Godfrey (the creator of the account) was listed as one of the top 25 most influential people on the internet. On 20 December 2022, the record for the most-liked Instagram post was surpassed by a post from Argentine footballer Lionel Messi, showing him and his teammates celebrating after winning the 2022 FIFA World Cup with their national team. The world record egg responded to being overtaken in likes by Messi with "Today [Lionel Messi] has taken the crown, for now. But I'm still left with one question… Who is the greatest of all time – Cristiano Ronaldo or Leo Messi?" The account sold to Dubai-based investor Mustafa El Fishawy in April 2024 for an undisclosed seven-figure sum. Reed Smith, who advised Godfrey, Brown, and Khan-Whelan in the transaction, stated they opted to sell it to "focus on new ventures." On 3 June, @world_record_egg posted an egg with the flag of Palestine in support of the country during the Gaza war; the post's caption described it as an "Egg for Peace" and hoped to "set a new world record together and get the most liked post on Instagram for a good cause." == Reception == In response to breaking the world record for the most-liked Instagram post, the account's owner wrote "This is madness. What a time to be alive." Hours later, Jenner posted a video on Instagram of her cracking open an egg and pouring its yolk onto the ground, with the caption: "Take that little egg." Pundits pontificated on the meaning of the egg picture's dominance over social media's "first family". As Vogue observed, tapping a heart pictogram is easy, and eggs are "lovable". More pointedly: [T]he attention economy is a scam based on requiring little to no labor from both producer and consumer despite commanding the most space, and therefore value, in our digital lives... but it very well could be: As a metaphor for the fragility of the influencer ecosystem, the egg has broken the Internet. The significance of the event and its massive republishing are a topic of discussion. A University of Westminster researcher of internet memes compared it to the movement to name a scientific research vessel in the United Kingdom as Boaty McBoatface. The Instagrammer's success is a rare victory for the unpaid viral campaign on social media. "There is a bit of an anti-celebrity revolt here – 'look what we can do with a simple egg'" The researcher suggests that the accomplishment of becoming such a widely heralded unpaid viral post may become increasingly rare, as social networks rely more on paid and business promotion. The post's spread has been characterized as a populist backlash against "consumerism" and is seen by some as a triumph of community over celebrity. However, propelled by their popular success, the creators promised to release 'egg-centric' memorabilia. Hundreds of games based on the Instagram egg have appeared on Apple's App Store. The creators of the Instagram egg also reached a deal to promote Hulu.
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Data validation and reconciliation

Industrial process data validation and reconciliation, or more briefly, process data reconciliation (PDR), is a technology that uses process information and mathematical methods in order to automatically ensure data validation and reconciliation by correcting measurements in industrial processes. The use of PDR allows for extracting accurate and reliable information about the state of industry processes from raw measurement data and produces a single consistent set of data representing the most likely process operation. == Models, data and measurement errors == Industrial processes, for example chemical or thermodynamic processes in chemical plants, refineries, oil or gas production sites, or power plants, are often represented by two fundamental means: Models that express the general structure of the processes, Data that reflects the state of the processes at a given point in time. Models can have different levels of detail, for example one can incorporate simple mass or compound conservation balances, or more advanced thermodynamic models including energy conservation laws. Mathematically the model can be expressed by a nonlinear system of equations F ( y ) = 0 {\displaystyle F(y)=0\,} in the variables y = ( y 1 , … , y n ) {\displaystyle y=(y_{1},\ldots ,y_{n})} , which incorporates all the above-mentioned system constraints (for example the mass or heat balances around a unit). A variable could be the temperature or the pressure at a certain place in the plant. === Error types === Data originates typically from measurements taken at different places throughout the industrial site, for example temperature, pressure, volumetric flow rate measurements etc. To understand the basic principles of PDR, it is important to first recognize that plant measurements are never 100% correct, i.e. raw measurement y {\displaystyle y\,} is not a solution of the nonlinear system F ( y ) = 0 {\displaystyle F(y)=0\,\!} . When using measurements without correction to generate plant balances, it is common to have incoherencies. Measurement errors can be categorized into two basic types: random errors due to intrinsic sensor accuracy and systematic errors (or gross errors) due to sensor calibration or faulty data transmission. Random errors means that the measurement y {\displaystyle y\,\!} is a random variable with mean y ∗ {\displaystyle y^{}\,\!} , where y ∗ {\displaystyle y^{}\,\!} is the true value that is typically not known. A systematic error on the other hand is characterized by a measurement y {\displaystyle y\,\!} which is a random variable with mean y ¯ {\displaystyle {\bar {y}}\,\!} , which is not equal to the true value y ∗ {\displaystyle y^{}\,} . For ease in deriving and implementing an optimal estimation solution, and based on arguments that errors are the sum of many factors (so that the Central limit theorem has some effect), data reconciliation assumes these errors are normally distributed. Other sources of errors when calculating plant balances include process faults such as leaks, unmodeled heat losses, incorrect physical properties or other physical parameters used in equations, and incorrect structure such as unmodeled bypass lines. Other errors include unmodeled plant dynamics such as holdup changes, and other instabilities in plant operations that violate steady state (algebraic) models. Additional dynamic errors arise when measurements and samples are not taken at the same time, especially lab analyses. The normal practice of using time averages for the data input partly reduces the dynamic problems. However, that does not completely resolve timing inconsistencies for infrequently-sampled data like lab analyses. This use of average values, like a moving average, acts as a low-pass filter, so high frequency noise is mostly eliminated. The result is that, in practice, data reconciliation is mainly making adjustments to correct systematic errors like biases. === Necessity of removing measurement errors === ISA-95 is the international standard for the integration of enterprise and control systems It asserts that: Data reconciliation is a serious issue for enterprise-control integration. The data have to be valid to be useful for the enterprise system. The data must often be determined from physical measurements that have associated error factors. This must usually be converted into exact values for the enterprise system. This conversion may require manual, or intelligent reconciliation of the converted values [...]. Systems must be set up to ensure that accurate data are sent to production and from production. Inadvertent operator or clerical errors may result in too much production, too little production, the wrong production, incorrect inventory, or missing inventory. == History == PDR has become more and more important due to industrial processes that are becoming more and more complex. PDR started in the early 1960s with applications aiming at closing material balances in production processes where raw measurements were available for all variables. At the same time the problem of gross error identification and elimination has been presented. In the late 1960s and 1970s unmeasured variables were taken into account in the data reconciliation process., PDR also became more mature by considering general nonlinear equation systems coming from thermodynamic models., , Quasi steady state dynamics for filtering and simultaneous parameter estimation over time were introduced in 1977 by Stanley and Mah. Dynamic PDR was formulated as a nonlinear optimization problem by Liebman et al. in 1992. == Data reconciliation == Data reconciliation is a technique that targets at correcting measurement errors that are due to measurement noise, i.e. random errors. From a statistical point of view the main assumption is that no systematic errors exist in the set of measurements, since they may bias the reconciliation results and reduce the robustness of the reconciliation. Given n {\displaystyle n} measurements y i {\displaystyle y_{i}} , data reconciliation can mathematically be expressed as an optimization problem of the following form: min x , y ∗ ∑ i = 1 n ( y i ∗ − y i σ i ) 2 subject to F ( x , y ∗ ) = 0 y min ≤ y ∗ ≤ y max x min ≤ x ≤ x max , {\displaystyle {\begin{aligned}\min _{x,y^{}}&\sum _{i=1}^{n}\left({\frac {y_{i}^{}-y_{i}}{\sigma _{i}}}\right)^{2}\\{\text{subject to }}&F(x,y^{})=0\\&y_{\min }\leq y^{}\leq y_{\max }\\&x_{\min }\leq x\leq x_{\max },\end{aligned}}\,\!} where y i ∗ {\displaystyle y_{i}^{}\,\!} is the reconciled value of the i {\displaystyle i} -th measurement ( i = 1 , … , n {\displaystyle i=1,\ldots ,n\,\!} ), y i {\displaystyle y_{i}\,\!} is the measured value of the i {\displaystyle i} -th measurement ( i = 1 , … , n {\displaystyle i=1,\ldots ,n\,\!} ), x j {\displaystyle x_{j}\,\!} is the j {\displaystyle j} -th unmeasured variable ( j = 1 , … , m {\displaystyle j=1,\ldots ,m\,\!} ), and σ i {\displaystyle \sigma _{i}\,\!} is the standard deviation of the i {\displaystyle i} -th measurement ( i = 1 , … , n {\displaystyle i=1,\ldots ,n\,\!} ), F ( x , y ∗ ) = 0 {\displaystyle F(x,y^{})=0\,\!} are the p {\displaystyle p\,\!} process equality constraints and x min , x max , y min , y max {\displaystyle x_{\min },x_{\max },y_{\min },y_{\max }\,\!} are the bounds on the measured and unmeasured variables. The term ( y i ∗ − y i σ i ) 2 {\displaystyle \left({\frac {y_{i}^{}-y_{i}}{\sigma _{i}}}\right)^{2}\,\!} is called the penalty of measurement i. The objective function is the sum of the penalties, which will be denoted in the following by f ( y ∗ ) = ∑ i = 1 n ( y i ∗ − y i σ i ) 2 {\displaystyle f(y^{})=\sum _{i=1}^{n}\left({\frac {y_{i}^{}-y_{i}}{\sigma _{i}}}\right)^{2}} . In other words, one wants to minimize the overall correction (measured in the least squares term) that is needed in order to satisfy the system constraints. Additionally, each least squares term is weighted by the standard deviation of the corresponding measurement. The standard deviation is related to the accuracy of the measurement. For example, at a 95% confidence level, the standard deviation is about half the accuracy. === Redundancy === Data reconciliation relies strongly on the concept of redundancy to correct the measurements as little as possible in order to satisfy the process constraints. Here, redundancy is defined differently from redundancy in information theory. Instead, redundancy arises from combining sensor data with the model (algebraic constraints), sometimes more specifically called "spatial redundancy", "analytical redundancy", or "topological redundancy". Redundancy can be due to sensor redundancy, where sensors are duplicated in order to have more than one measurement of the same quantity. Redundancy also arises when a single variable can be estimated in several independent ways from separate sets of measurements at a given time or time averaging period, using the algebraic constraints. Redundancy is linked to the concept
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Social media use in hiring

Social media use in hiring refers to the examination by employers of job applicants' (public) social media profiles as part of the hiring assessment. For example, the vast majority of Fortune 500 companies use social media as a tool to screen prospective employees and as a tool for talent acquisition. This practice raises ethical questions. Employers and recruiters note that they have access only to information that applicants choose to make public. Many Western-European countries restrict employer's use of social media in the workplace. States including Arkansas, California, Colorado, Illinois, Maryland, Michigan, Nevada, New Jersey, New Mexico, Utah, Washington, and Wisconsin protect applicants and employees from surrendering usernames and passwords for social media accounts. Use of social media has caused significant problems for some applicants who are active on social media. A 2013 survey of 17,000 young people in six countries found that one in ten people aged 16 to 34 claimed to have been rejected for a job because of social media activity. Social media services have been reported to affect deception in resumes. While these services do not affect deception frequency, it does increase deception about interests and hobbies. == Ethical implications == This issue raises many ethical questions that some consider an employer's right and others consider discrimination. As of 2016, except in the states of California, Maryland, and Illinois, there are no laws that prohibit employers from using social media profiles as a basis of whether or not someone should be hired. Title VII also prohibits discrimination during any aspect of employment including hiring or firing, recruitment, or testing. Social media has been integrating into the workplace, and this has led to conflicts within employees and employers.[107] Particularly, Facebook has been seen as a popular platform for employers to investigate in order to learn more about potential employees. This conflict first started in Maryland when an employer requested and received an employee's Facebook username and password. State lawmakers first introduced legislation in 2012 to prohibit employers from requesting passwords to personal social accounts in order to get a job or to keep a job. This led to Canada, Germany, the U.S. Congress and 11 U.S. states to pass or propose legislation that prevents employers' access to private social accounts of employees.[108] Many Western European countries have already implemented laws that restrict the regulation of social media in the workplace. States including Arkansas, California, Colorado, Illinois, Maryland, Michigan, Nevada, New Jersey, New Mexico, Utah, Washington, and Wisconsin have passed legislation that protects potential employees and current employees from employers that demand them to give forth their username or password for a social media account. Laws that forbid employers from disciplining an employee based on activity off the job on social media sites have also been put into act in states including California, Colorado, Connecticut, North Dakota, and New York. Several states have similar laws that protect students in colleges and universities from having to grant access to their social media accounts. Eight states have passed the law that prohibits post secondary institutions from demanding social media login information from any prospective or current students and privacy legislation has been introduced or is pending in at least 36 states as of July 2013. As of May 2014, legislation has been introduced and is in the process of pending in at least 28 states and has been enacted in Maine and Wisconsin. In addition, the National Labor Relations Board has been devoting a lot of their attention to attacking employer policies regarding social media that can discipline employees who seek to speak and post freely on social media sites. Use of social media by young people has caused significant problems for some applicants who are active on social media when they try to enter the job market. A survey of 17,000 young people in six countries in 2013 found that 1 in 10 people aged 16 to 34 have been rejected for a job because of online comments they made on social media websites. A 2014 survey of recruiters found that 93% of them check candidates' social media postings. Moreover, professor Stijn Baert of Ghent University conducted a field experiment in which fictitious job candidates applied for real job vacancies in Belgium. They were identical except in one respect: their Facebook profile photos. It was found that candidates with the most wholesome photos were a lot more likely to receive invitations for job interviews than those with the more controversial photos. In addition, Facebook profile photos had a greater impact on hiring decisions when candidates were highly educated. These cases have created some privacy implications as to whether or not companies should have the right to look at employee's Facebook profiles. In March 2012, Facebook decided they might take legal action against employers for gaining access to employee's profiles through their passwords. According to Facebook Chief Privacy Officer for policy, Erin Egan, the company has worked hard to give its users the tools to control who sees their information. He also said users shouldn't be forced to share private information and communications just to get a job. According to the network's Statement of Rights and Responsibilities, sharing or soliciting a password is a violation of Facebook policy. Employees may still give their password information out to get a job, but according to Erin Egan, Facebook will continue to do their part to protect the privacy and security of their users. == Impacts == Use of social media by young people has caused significant problems for some applicants who are active on social media when they try to enter the job market. A survey of 17,000 young people in six countries in 2013 found that 1 in 10 people aged 16 to 34 have been rejected for a job because of online comments they made on social media websites. A 2014 survey of recruiters found that 93% of them check candidates' social media postings. Moreover, in 2015 professor Stijn Baert of Ghent University conducted a field experiment in which fictitious job candidates applied for real job vacancies in Belgium. They were identical except in one respect: their Facebook profile photos. It was found that candidates with the most wholesome photos were a lot more likely to receive invitations for job interviews than those with the more controversial photos. In addition, Facebook profile photos had a greater impact on hiring decisions when candidates were highly educated. These cases have created some privacy implications as to whether or not companies should have the right to look at employee's Facebook profiles. In March 2012, Facebook decided they might take legal action against employers for gaining access to employee's profiles through their passwords. According to Facebook Chief Privacy Officer for policy, Erin Egan, the company has worked hard to give its users the tools to control who sees their information. He also said users shouldn't be forced to share private information and communications just to get a job. According to the network's Statement of Rights and Responsibilities, sharing or soliciting a password is a violation of Facebook policy. Employees may still give their password information out to get a job, but according to Erin Egan, Facebook will continue to do their part to protect the privacy and security of their users. == Policy Responses == 26 US states now have laws against an employer requiring a current or potential employee to give the employer their username and password.
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Timeline of artificial intelligence risks in global finance

The following article is a broad timeline of the course of events related to artificial intelligence risks in global finance. The AI boom has led to concerns including the existential risk from artificial intelligence, as the uptake on applications of artificial intelligence increases. By late 2025, global finance and artificial intelligence were "deeply intertwined". A June 2025 Menlo Ventures report raised concerns about the sustainability of future revenue and long-term profitability of AI, given the relatively low rate of consumer monetization. == 2017 == 30 NovemberThe New York Times said that new AI reports by McKinsey & Company, the National Bureau of Economic Research, and an AI Index created by university researchers, indicated an early AI boom. The Index built on a project—"The One Hundred Year Study on Artificial Intelligence" launched in 2014. == 2018 == 2018 was a year of incremental AI growth in finance. == 2022 == The release of ChatGPT by OpenAI became the catalyst for an artificial intelligence boom that continues to remake the global economy. According to a European Central Bank report, public interest in AI increased rapidly as evidenced with rising Google searches, AI jobs, models, patents, and innovations since late 2022. At that time Europe led the US in the size of its AI workforce. == 2023 == The regulatory body, the International Monetary Fund (IMF), published their report, "Generative Artificial Intelligence in Finance: Risk Considerations", drawing attention to oversight gaps and the need for regulations. The report explores the risks posed by using generative artificial intelligence (GenAI) systems in the financial sector including "broader risks to financial stability." == 2024 == January 12 In January 2024 Bloomberg's published its list of the "Magnificent Seven" Big Tech companies on the stock market based on their strength, size and market capitalization:Apple, Microsoft, Alphabet (Google), Amazon, Meta Platforms (Facebook), Nvidia, and Tesla. 21 June During the AI boom, Nvidia became the world's most valuable company, surpassing Microsoft, as its value increased to over US$4 trillion. In 2023 and 2024, the "Magnificent Seven" stocks were the primary drivers behind the increase in equity indexes, according to Reuters. == 2025 == === January === 23 January President Donald Trump's AI policy was announced calling for United States global leadership in artificial intelligence. The Economist noted that this politic shift in which the United States seeks "global dominance" in AI includes trimming regulations and assisting in expansion of infrastructure and increase in number of AI workers. Governments of Gulf nations were also investing trillions of dollars in AI. 27 January Against the backdrop of a tech war between China and the United States over AI dominance, within days of the launch of China's free DeepSeek App, it was the most downloaded app in the United States, rising to the first place in the Apple app store. President Trump responded immediately, saying this "sudden rise" should be a "wake-up" call to the United States, and called on US companies to be more competitive. === June === 26 June In their June 2025 report, Menlo Ventures estimated that only about 3% of consumers paid for artificial intelligence-related services, representing about $USD12 billion in annual spending. This is relatively low in contrast to the massive capital expenditure by AI infrastructure companies, which raises concerns about revenue sustainability and long-term profitability. === July === 23 July The Trump administration launched the US AI Action Plan, positioning the United States in a high-stakes technological race with China for global dominance in artificial intelligence, emphasizing that neither nation can afford to fall behind due to the exponential nature of AI advancement. The plan, a new government website and policy speech called for accelerated AI adoption across federal agencies, and a number of initiatives to make is easier for AI infrastructure expansion, and other measures to ensure American leadership in AI standards. Some leading experts warned that the administration failed to provide sufficient regulations and safeguards for AI safety. Concerns were raised about the negative impacts of cuts to research funding and tightened visa policies for scientists, potentially undermining public trust and America's ability to compete internationally. === September === 7 September The Economist cautioned that AI revenues are relatively modest compared to the high cost and investments in the creation of new data centers. Even Sam Altman, OpenAI CEO and one of the leading figures of the AI boom,, raised concerns about investors' outsized hopes for financial returns. At the same time, history has shown that new technologies, like railways and electricity, endured and spread after the initial hype faded. 12 September Economists warn that U.S. households' direct and indirect investments—mutual funds or retirement plans—in the stock market reached an unprecedented historically high level, now representing 45% of all financial assets, or about $USD51.2 trillion. Compared to the Dot-com bubble this represents a sharp increase in exposure. This makes U.S. households vulnerable to market downturns which in turn would result in decreasing consumer spending. U.S. household net worth rose to a record $176.3 trillion in the second quarter, an increase of $7.3 trillion since early 2025 and about $46 trillion higher than before the pandemic. Federal Reserve data attribute the surge primarily to gains in stock markets and housing values. However, the rise in wealth on paper coincided with increased household borrowing and growing government debt. 18 September Questions were being raised about how quickly the data centers, chips, servers, and GPUs assets of major AI companies will depreciate in value. Comparisons have been made to the Railway Mania in the aftermath of the stock market bubble where a valuable physical infrastructure remained standing, and the telecoms crash after the dot-com bubble which left fiber networks. 28 September There were warnings that record-high American stock ownership during the AI-fueled market boom is a red flag for systemic risk, as the current concentration in equities exceeds levels seen before the dot-com bubble burst in 2000, and could amplify the impact of any future stock market correction. === October === 3 October In 2025 alone, venture capitalists invested almost $USD200 billion in the artificial intelligence sector. 29 October Nvidia was the first company in the world to be valued at US$5 trillion, largely due to AI demand and strategic partnerships with leading technology and AI firms. Nvidia's increase in value was "meteoric". === November === 2 November Forbes reported that, since April, the 'Magnificent Seven' tech giants together contributed over 40% of the S&P 500's return, highlighting their outsized influence and the growing impact of AI on market valuations. CNN warned that while there is a current benefit to investors, with such a high concentration in the S&P 500, they are highly exposed to the fate of the Mag Seven. 2 November Globally there are 11,000 datacentres—huge campuses for AI infrastructure, including thousands of chips, GPUS, and servers. This represents a 500% increase over the last two decades. It is anticipated that $3USDtn more will be spent on increasing that number over the next two or three years. 5 November Concerns about the potential for a market bubble were raised as six of the AI-related Big Tech "Magnificent Seven"—that contribute to the AI boom—reported losing ground in the stock market. Global markets and artificial intelligence have become "deeply intertwined", according to a Reuters report. As of November 2025, more than 50% of the 20 largest S&P firms were deeply exposed to AI. In contrast, in 2000, the 20 S&P 500 firms represented 39% of its total value only 11 of these companies were exposed to the internet. If AI fails to deliver strong returns on their investments, these top S&P firms would be significantly impacted, according to the Economist. Analysts suggest that the AI market in 2025 may not behave like a traditional one, as investors are simultaneously aware of the risks and driven by the potential for outsized rewards. Leading AI labs may believe that the first company to achieve artificial general intelligence (AGI), when an AI system surpasses all human cognitive abilities and becomes capable of self-improvement—could dominate the future of technology and finance. While some have estimated that the potential value of such a breakthrough could be as high as $1.46 quadrillion, this figure is speculative and widely debated. 5 November Bloomberg described Nvidia's H100 Hopper-Blackwell AI chips as the "King of AI chips". Nvidia dominates the AI chip market with over 78% of the market share because of both speed and cost. According to B
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Content engineering

Content engineering is a term applied to an engineering specialty dealing with the complexities around the use of content in computer-facilitated environments. Content authoring and production, content management, content modeling, content conversion, and content use and repurposing are all areas involving this practice. It is not a specialty with wide industry recognition and is often performed on an ad hoc basis by members of software development or content production or marketing staff, but is beginning to be recognized as a necessary function in any complex content-centric project involving both content production as well as software system development mainly involving content management systems (CMS) or digital experience platforms (DXP). Content engineering tends to bridge the gap between groups involved in the production of content (publishing and editorial staff, marketing, sales, human resources) and more technologically oriented departments such as software development, or IT that put this content to use in web or other software-based environments, and requires an understanding of the issues and processes of both sides. Typically, content engineering involves extensive use of embedded XML technologies, XML being the most widespread language for representing structured content. Content management systems are a key technology often used in the practice of content engineering. == Definition == Content engineering is the practice of organizing the shape and structure of content by deploying content and metadata models, in authoring and publishing processes in a manner that meets the requirements of an organization's Content Strategy, and its implementation through the use of technology such as CMS, XML, schema markup, artificial intelligence, APIs and others. == Purpose and goal == In very general terms, content engineering practices aim to maximize the ROI of content through content reuse and improving efficiency of content marketing, content operations, content strategy. Content engineering can help address content challenges that fairly typical organizations face: Siloed content supply chains Duplicate content in a myriad of formats Inefficient content authoring workflows Chunky, unstructured content Outdated technology Technology in place does not match needs Inability to reuse content across channels (multi-channel content) Metadata and schema are not used Lack of standards for metadata Lack of findability of content for internal and external use Poor SEO performance Inability to implement personalization == Key skills == Content engineering draws on a combination of technical, strategic, and editorial competencies. Practitioners typically require proficiency across several domains: === Content modeling and information architecture === Content engineers design structured content models that define how content is created, stored, and distributed. This includes building taxonomies, ontologies, and metadata schemas that enable content reuse across channels and platforms. === Structured content and markup languages === Proficiency in XML, JSON, HTML, and schema.org markup is fundamental. Content engineers use these languages to structure content for machine readability, search engine optimization, and interoperability between systems. === Content management systems and platforms === Content engineers require working knowledge of content management systems (CMS), digital experience platforms (DXP), and headless CMS architectures. This includes configuring content types, workflows, and publishing pipelines within these systems. === Workflow design and automation === Designing and implementing content workflows - from authoring through review, approval, and distribution - is a core function. Increasingly, this involves configuring AI-assisted and agentic workflows that automate research, drafting, repurposing, and distribution tasks at scale. === Content strategy and editorial understanding === Unlike purely technical roles, content engineering requires a working understanding of content strategy, brand management, editorial standards, and audience analysis. Content engineers must translate strategic objectives into technical content structures and system configurations. === API integration and data interoperability === Content engineers work with APIs to connect content systems, analytics platforms, distribution channels, and third-party services. Understanding how content flows between systems is essential for enabling multi-channel publishing and content personalization. === Analytics and performance measurement === Measuring content effectiveness through web analytics, SEO performance data, and engagement metrics informs how content engineers refine structures, metadata, and distribution workflows. == The role of a content engineer == Content engineers bridge the divide between content strategists and producers and the developers and content managers who publish and distribute content. But rather than simply wedging themselves between these players, content engineers help define and facilitate the content structure during the entire content strategy, production and distribution cycle from beginning to end. As the role has evolved, content engineers are increasingly expected to build and manage AI-powered content systems, moving beyond traditional CMS configuration into agentic workflows that automate content research, production, and distribution. By integrating skills in business and technology, content engineers do not see content as static or finished. Rather, they look at the value of the content and how it can best be adapted and personalized to serve customers and emerging content platforms, technologies, and opportunities. === Create customer experience === Content marketing suffers from two fundamental limitations that constrain the true power and potential that a great content marketing plan can bring to a business' bottom line: Content relevance: how to make content more relevant and personalized to their audiences. The marketer and content strategist direct the customer experience itself, and the content engineer makes it happen with content structure, schema, metadata, microdata, taxonomy, and CMS topology. Content agility: Marketers who are burdened with one-size-fits-all content remain stuck managing their content rather than their customers' experience. Content engineers give marketers the "super powers" to move content-powered experiences across interfaces and personalization variants. === Break down barriers === Empower content strategists: Content engineers work with content strategists by helping them connect content not as a fixed message, but as a modular construct which can be channeled and manipulated. Enable content producers: A content engineer will work with a content producer by helping to find new sources of content and ways the content can be combined and presented. Guide and free developers: The content engineer helps translate marketing strategy into clear technical needs and functions developers can build into content management systems Enhance content management: Develop content structures that make it easier for content writers and content managers to author to a single, very usable, interface for even complex content types that might contain dozens of elements. Engineer content for success: Content engineers help all members of a marketing team work more smoothly, with the support and structures needed to get the most out of the content they produce. === Salary benchmarks === Content engineering roles command significantly higher salaries than traditional content marketing positions. In the United States, IC-level content engineers earn between $120,000 and $165,000 annually, while senior roles reach $160,000 to $220,000. Head of content engineering positions range from $200,000 to $280,000, and VP-level roles can exceed $375,000. The emergence of dedicated content engineer job postings from companies such as Exit Five reflects the growing recognition of the role as a distinct function within marketing organizations.
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Code (cryptography)

In cryptology, a code is a method used to encrypt a message that operates at the level of meaning; that is, words or phrases are converted into something else. A code might transform "change" into "CVGDK" or "cocktail lounge". The U.S. National Security Agency defined a code as "A substitution cryptosystem in which the plaintext elements are primarily words, phrases, or sentences, and the code equivalents (called "code groups") typically consist of letters or digits (or both) in otherwise meaningless combinations of identical length." A codebook is needed to encrypt, and decrypt the phrases or words. By contrast, ciphers encrypt messages at the level of individual letters, or small groups of letters, or even, in modern ciphers, individual bits. Messages can be transformed first by a code, and then by a cipher. Such multiple encryption, or "superencryption" aims to make cryptanalysis more difficult. Another comparison between codes and ciphers is that a code typically represents a letter or groups of letters directly without the use of mathematics. As such the numbers are configured to represent these three values: 1001 = A, 1002 = B, 1003 = C, ... . The resulting message, then would be 1001 1002 1003 to communicate ABC. Ciphers, however, utilize a mathematical formula to represent letters or groups of letters. For example, A = 1, B = 2, C = 3, ... . Thus the message ABC results by multiplying each letter's value by 13. The message ABC, then would be 13 26 39. Codes have a variety of drawbacks, including susceptibility to cryptanalysis and the difficulty of managing the cumbersome codebooks, so ciphers are now the dominant technique in modern cryptography. In contrast, because codes are representational, they are not susceptible to mathematical analysis of the individual codebook elements. In the example, the message 13 26 39 can be cracked by dividing each number by 13 and then ranking them alphabetically. However, the focus of codebook cryptanalysis is the comparative frequency of the individual code elements matching the same frequency of letters within the plaintext messages using frequency analysis. In the above example, the code group, 1001, 1002, 1003, might occur more than once and that frequency might match the number of times that ABC occurs in plain text messages. (In the past, or in non-technical contexts, code and cipher are often used to refer to any form of encryption). == One- and two-part codes == Codes are defined by "codebooks" (physical or notional), which are dictionaries of codegroups listed with their corresponding plaintext. Codes originally had the codegroups assigned in 'plaintext order' for convenience of the code designed, or the encoder. For example, in a code using numeric code groups, a plaintext word starting with "a" would have a low-value group, while one starting with "z" would have a high-value group. The same codebook could be used to "encode" a plaintext message into a coded message or "codetext", and "decode" a codetext back into plaintext message. In order to make life more difficult for codebreakers, codemakers designed codes with no predictable relationship between the codegroups and the ordering of the matching plaintext. In practice, this meant that two codebooks were now required, one to find codegroups for encoding, the other to look up codegroups to find plaintext for decoding. Such "two-part" codes required more effort to develop, and twice as much effort to distribute (and discard safely when replaced), but they were harder to break. The Zimmermann Telegram in January 1917 used the German diplomatic "0075" two-part code system which contained upwards of 10,000 phrases and individual words. == One-time code == A one-time code is a prearranged word, phrase or symbol that is intended to be used only once to convey a simple message, often the signal to execute or abort some plan or confirm that it has succeeded or failed. One-time codes are often designed to be included in what would appear to be an innocent conversation. Done properly they are almost impossible to detect, though a trained analyst monitoring the communications of someone who has already aroused suspicion might be able to recognize a comment like "Aunt Bertha has gone into labor" as having an ominous meaning. Famous example of one time codes include: In the Bible, Jonathan prearranges a code with David, who is going into hiding from Jonathan's father, King Saul. If, during archery practice, Jonathan tells the servant retrieving arrows "the arrows are on this side of you," David may safely return to court; if the command is "the arrows are beyond you," David must flee. "One if by land; two if by sea" in "Paul Revere's Ride" made famous in the poem by Henry Wadsworth Longfellow "Climb Mount Niitaka" - the signal to Japanese planes to begin the attack on Pearl Harbor During World War II the British Broadcasting Corporation's overseas service frequently included "personal messages" as part of its regular broadcast schedule. The seemingly nonsensical stream of messages read out by announcers were actually one time codes intended for Special Operations Executive (SOE) agents operating behind enemy lines. An example might be "The princess wears red shoes" or "Mimi's cat is asleep under the table". Each code message was read out twice. By such means, the French Resistance were instructed to start sabotaging rail and other transport links the night before D-day. "Over all of Spain, the sky is clear" was a signal (broadcast on radio) to start the nationalist military revolt in Spain on July 17, 1936. Sometimes messages are not prearranged and rely on shared knowledge hopefully known only to the recipients. An example is the telegram sent to U.S. President Harry Truman, then at the Potsdam Conference to meet with Soviet premier Joseph Stalin, informing Truman of the first successful test of an atomic bomb. "Operated on this morning. Diagnosis not yet complete but results seem satisfactory and already exceed expectations. Local press release necessary as interest extends great distance. Dr. Groves pleased. He returns tomorrow. I will keep you posted." == Idiot code == An idiot code is a code that is created by the parties using it. This type of communication is akin to the hand signals used by armies in the field. Example: Any sentence where 'day' and 'night' are used means 'attack'. The location mentioned in the following sentence specifies the location to be attacked. Plaintext: Attack X. Codetext: We walked day and night through the streets but couldn't find it! Tomorrow we'll head into X. An early use of the term appears to be by George Perrault, a character in the science fiction book Friday by Robert A. Heinlein: The simplest sort [of code] and thereby impossible to break. The first ad told the person or persons concerned to carry out number seven or expect number seven or it said something about something designated as seven. This one says the same with respect to code item number ten. But the meaning of the numbers cannot be deduced through statistical analysis because the code can be changed long before a useful statistical universe can be reached. It's an idiot code... and an idiot code can never be broken if the user has the good sense not to go too often to the well. Terrorism expert Magnus Ranstorp said that the men who carried out the September 11 attacks on the United States used basic e-mail and what he calls "idiot code" to discuss their plans. == Cryptanalysis of codes == While solving a monoalphabetic substitution cipher is easy, solving even a simple code is difficult. Decrypting a coded message is a little like trying to translate a document written in a foreign language, with the task basically amounting to building up a "dictionary" of the codegroups and the plaintext words they represent. One fingerhold on a simple code is the fact that some words are more common than others, such as "the" or "a" in English. In telegraphic messages, the codegroup for "STOP" (i.e., end of sentence or paragraph) is usually very common. This helps define the structure of the message in terms of sentences, if not their meaning, and this is cryptanalytically useful. Further progress can be made against a code by collecting many codetexts encrypted with the same code and then using information from other sources spies newspapers diplomatic cocktail party chat the location from where a message was sent where it was being sent to (i.e., traffic analysis) the time the message was sent, events occurring before and after the message was sent the normal habits of the people sending the coded messages etc. For example, a particular codegroup found almost exclusively in messages from a particular army and nowhere else might very well indicate the commander of that army. A codegroup that appears in messages preceding an attack on a particular location may very well stand for that location. Cribs can be an immediate giveaway to the definiti
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Conjugate coding

Conjugate coding is a cryptographic tool, introduced by Stephen Wiesner in the late 1960s. It is part of the two applications Wiesner described for quantum coding, along with a method for creating fraud-proof banking notes. The application that the concept was based on was a method of transmitting multiple messages in such a way that reading one destroys the others. This is called quantum multiplexing and it uses photons polarized in conjugate bases as "qubits" to pass information. Conjugate coding also is a simple extension of a random number generator. At the behest of Charles Bennett, Wiesner published the manuscript explaining the basic idea of conjugate coding with a number of examples but it was not embraced because it was significantly ahead of its time. Because its publication has been rejected, it was developed to the world of public-key cryptography in the 1980s as oblivious transfer, first by Michael Rabin and then by Shimon Even. It is used in the field of quantum computing. The initial concept of quantum cryptography developed by Bennett and Gilles Brassard was also based on this concept.
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Log shipping

Log shipping is the process of automating the backup of transaction log files on a primary (production) database server, and then restoring them onto a standby server. This technique is supported by Microsoft SQL Server, 4D Server, MySQL, and PostgreSQL. Similar to replication, the primary purpose of log shipping is to increase database availability by maintaining a backup server that can replace a production server quickly. Other databases such as Adaptive Server Enterprise and Oracle Database support the technique but require the Database Administrator to write code or scripts to perform the work. Although the actual failover mechanism in log shipping is manual, this implementation is often chosen due to its low cost in human and server resources, and ease of implementation. In comparison, SQL server clusters enable automatic failover, but at the expense of much higher storage costs. Compared to database replication, log shipping does not provide as much in terms of reporting capabilities, but backs up system tables along with data tables, and locks the standby server from users' modifications. A replicated server can be modified (e.g. views) and is therefore unsuitable for failover purposes.
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Chaos Communication Congress

The Chaos Communication Congress is an annual hacker conference organized by the Chaos Computer Club. The congress features a variety of lectures and workshops on technical and political issues related to security, cryptography, privacy and online freedom of speech. It has taken place regularly at the end of the year since 1984, with the current date and duration (27–30 December) established in 2005. It is considered one of the largest events of its kind, alongside DEF CON in Las Vegas. == History == The congress is held in Germany. It started in 1984 in Hamburg, moved to Berlin in 1998, and back to Hamburg in 2012, having exceeded the capacity of the Berlin venue with more than 4500 attendees. Since then, it attracts an increasing number of people: around 6600 attendees in 2012, over 13000 in 2015, and more than 15000 in 2017. From 2017 to 2019, it took place at the Trade Fair Grounds in Leipzig, since the Hamburg venue (CCH) was closed for renovation in 2017 and the existing space was not enough for the growing congress. The congress moved back to Hamburg in 2023, after the renovation of CCH was finished. A large range of speakers are featured. The event is organized by volunteers called Chaos Angels. The non-members entry fee for four days was €100 in 2016, and was raised to €120 in 2018 to include a public transport ticket for the Leipzig area. An important part of the congress are the assemblies, semi-open spaces with clusters of tables and internet connections for groups and individuals to collaborate and socialize in projects, workshops and hands-on talks. These assembly spaces, introduced at the 2012 meeting, combine the hack center project space and distributed group spaces of former years. From 1997 to 2004 the congress also hosted the annual German Lockpicking Championships. 2005 was the first year the Congress lasted four days instead of three and lacked the German Lockpicking Championships. 2020 was the first year where the Congress did not take place at a physical location due to the COVID-19 pandemic, giving way to the first Remote Chaos Experience (rC3). The Chaos Computer Club announced to return to the now newly renovated Congress Center Hamburg for the 37th edition of the Chaos Communication Congress. The announcement confirms the usual date of 27-30 December, notably omitting the year it will be held. On 18 October 2022, they confirmed that the congress will indeed not be held in 2022. On 6 October 2023, the CCC announced that 37C3 will take place again on the usual dates in 2023. === Timeline ===
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List of network buses

List of electrical characteristics of single collision domain segment "slow speed" network buses: The number of nodes can be limited by either number of available addresses or bus capacitance. None of the above use any analog domain modulation techniques like MLT-3 encoding, PAM-5 etc. PSI5 designed with automation applications in mind is a bit unusual in that it uses Manchester code.
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