Story Writing AI

Story Writing AI — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • CPU modes

    CPU modes

    CPU modes (also called processor modes, CPU states, CPU privilege levels and other names) are operating modes for the central processing unit of most computer architectures that place restrictions on the type and scope of operations that can be performed by instructions being executed by the CPU. For example, this design allows an operating system to run with more privileges than application software by running the operating systems and applications in different modes. Ideally, only highly trusted kernel code is allowed to execute in the unrestricted mode; everything else (including non-supervisory portions of the operating system) runs in a restricted mode and must use a system call (via interrupt) to request the kernel perform on its behalf any operation that could damage or compromise the system, making it impossible for untrusted programs to alter or damage other programs (or the computing system itself). Device drivers are designed to be part of the kernel due to the need for frequent I/O access. Multiple modes can be implemented, e.g. allowing a hypervisor to run multiple operating system supervisors beneath it, which is the basic design of many virtual machine systems available today. == Mode types == The unrestricted mode is often called kernel mode, but many other designations exist (master mode, supervisor mode, privileged mode, etc.). Restricted modes are usually referred to as user modes, but are also known by many other names (slave mode, problem state, etc.). Hypervisor Hypervisor mode is used to support virtualization, allowing the simultaneous operation of multiple operating systems. Kernel and user In kernel mode, the CPU may perform any operation allowed by its architecture; any instruction may be executed, any I/O operation initiated, any area of memory accessed, and so on. In the other CPU modes, certain restrictions on CPU operations are enforced by the hardware. Typically, certain instructions are not permitted (especially those—including I/O operations—that could alter the global state of the machine), some memory areas cannot be accessed, etc. User-mode capabilities of the CPU are typically a subset of those available in kernel mode, but in some cases, such as hardware emulation of non-native architectures, they may be significantly different from those available in standard kernel mode. Some CPU architectures support more modes than those, often with a hierarchy of privileges. These architectures are often said to have ring-based security, wherein the hierarchy of privileges resembles a set of concentric rings, with the kernel mode in the center. Multics hardware was the first significant implementation of ring security, but many other hardware platforms have been designed along similar lines, including the Intel 80286 protected mode, and the IA-64 as well, though it is referred to by a different name in these cases. Mode protection may extend to resources beyond the CPU hardware itself. Hardware registers track the current operating mode of the CPU, but additional virtual-memory registers, page-table entries, and other data may track mode identifiers for other resources. For example, a CPU may be operating in Ring 0 as indicated by a status word in the CPU itself, but every access to memory may additionally be validated against a separate ring number for the virtual-memory segment targeted by the access, and/or against a ring number for the physical page (if any) being targeted. This has been demonstrated with the PSP handheld system. Hardware that meets the Popek and Goldberg virtualization requirements makes writing software to efficiently support a virtual machine much simpler. Such a system can run software that "believes" it is running in supervisor mode, but is actually running in user mode. == Architectures == Several computer systems introduced in the 1960s, such as the IBM System/360, DEC PDP-6/PDP-10, the GE-600/Honeywell 6000 series, and the Burroughs B5000 series and B6500 series, support two CPU modes; a mode that grants full privileges to code running in that mode, and a mode that prevents direct access to input/output devices and some other hardware facilities to code running in that mode. The first mode is referred to by names such as supervisor state (System/360), executive mode (PDP-6/PDP-10), master mode (GE-600 series), control mode (B5000 series), and control state (B6500 series). The second mode is referred to by names such as problem state (System/360), user mode (PDP-6/PDP-10), slave mode (GE-600 series), and normal state (B6500 series); there are multiple non-control modes in the B5000 series. === RISC-V === RISC-V has three main CPU modes: User Mode (U), Supervisor Mode (S), and Machine Mode (M). Virtualization is supported via an orthogonal CSR setting instead of a fourth mode.

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  • Mathematical knowledge management

    Mathematical knowledge management

    Mathematical knowledge management (MKM) is the study of how society can effectively make use of the vast and growing literature on mathematics. It studies approaches such as databases of mathematical knowledge, automated processing of formulae and the use of semantic information, and artificial intelligence. Mathematics is particularly suited to a systematic study of automated knowledge processing due to the high degree of interconnectedness between different areas of mathematics.

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  • Information Rules

    Information Rules

    Information Rules is a 1999 book by Carl Shapiro and Hal Varian applying traditional economic theories to modern information-based technologies. The book examines commercial strategies appropriate to companies that deal in information, given the high "first copy" and low "subsequent copy" costs of information commodities, such as music CDs or original texts. == Content == The book examines competing standards, and how a company might influence widespread consumer acceptance of one over another, such as VHS versus Betamax, or HD DVD versus Blu-ray. The book mentions possible business strategies of such publishers as Encyclopædia Britannica who have to confront how to stay viable as technology changes the value and availability of information.

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  • Open Compute Project

    Open Compute Project

    The Open Compute Project (OCP) is an organization that facilitates the sharing of data center product designs and industry best practices among companies. Founded in 2011, OCP has significantly influenced the design and operation of large-scale computing facilities worldwide. As of February 2025, over 400 companies across the world are members of OCP, including Arm, Meta, IBM, Wiwynn, Intel, Nokia, Google, Microsoft, Seagate Technology, Dell, Rackspace, Hewlett Packard Enterprise, NVIDIA, Cisco, Goldman Sachs, Fidelity, Lenovo, Accton Technology Corporation and Alibaba Group. == Structure == The Open Compute Project Foundation is a 501(c)(6) non-profit incorporated in the state of Delaware, United States. OCP has multiple committees, including the board of directors, advisory board and steering committee to govern its operations. As of July 2020, there are seven members who serve on the board of directors which is made up of one individual member and six organizational members. Mark Roenigk (Facebook) is the Foundation's president and chairman. Andy Bechtolsheim is the individual member. In addition to Mark Roenigk who represents Facebook, other organizations on the Open Compute board of directors include Intel (Rebecca Weekly), Microsoft (Kushagra Vaid), Google (Partha Ranganathan), and Rackspace (Jim Hawkins). A list of members can be found on the OCP website. == History == The Open Compute Project began at Facebook (now Meta) in 2009 as an internal project called "Project Freedom". The hardware designs and engineering teams were led by Amir Michael (Manager, Hardware Design) and sponsored by Jonathan Heiliger (VP, Technical Operations) and Frank Frankovsky (Director, Hardware Design and Infrastructure). The three would later open source the designs of Project Freedom and co-found the Open Compute Project. The project was announced at a press event at Facebook's headquarters in Palo Alto on April 7, 2011. == OCP projects == The Open Compute Project Foundation maintains a number of OCP projects, such as: === Server designs === In 2013, two years after the Open Compute Project had started, it was noted that the goal of a more modular server design was "still a long way from live data centers". However, by then some aspects published had been used in Facebook's Prineville data center to improve energy efficiency, as measured by the power usage effectiveness index defined by The Green Grid. Efforts to advance server compute node designs included one for Intel processors and one for AMD processors. Also in 2013, Calxeda contributed a design with ARM architecture processors. Since then, several generations of OCP server designs have been deployed: Wildcat (Intel), Spitfire (AMD), Windmill (Intel E5-2600), Watermark (AMD), Winterfell (Intel E5-2600 v2) and Leopard (Intel E5-2600 v3). === OCP Accelerator Module === OCP Accelerator Module (OAM) is a design specification for hardware architectures that implement artificial intelligence systems that require high module-to-module bandwidth. OAM is used in some of AMD's Instinct accelerator modules. === Rack and power designs === Designs for a mechanical mounting system to replace standard 19-inch racks have been published, with a cabinet the same outside width (600 mm) and depth as existing racks, but with an interior space allowing for wider equipment chassis with a 537 mm width (21 inches). This allows more equipment to fit in the same volume and improves air flow. Compute chassis sizes are defined in multiples of an OpenU or OU, which is 48 mm, slightly taller than the 44 mm rack unit defined for 19-inch racks. As of March 2026, the most current base mechanical definition is the Open Rack V3.1 Specification. At the time the base specification was released, Meta also defined in greater depth the specifications for the rectifiers and power shelf. Specifications for the power monitoring interface (PMI), a communications interface enabling upstream communications between the rectifiers and battery backup unit(BBU) were published by Meta that same year, with Delta Electronics as the main technical contributor to the BBU spec. However, since 2022 the AI boom in the data center has created higher power requirements in order to satisfy the demands of AI accelerators that have been released. As of September 2024, Meta is in the process of updating its Open Rack v3 rectifier, power shelf, battery backup and power management interface specifications to accommodate this increased energy demand. In May 2024, at an Open Compute regional summit, Meta and Rittal outlined their plans for development of their High Power Rack (HPR) ecosystem in conjunction with rack, power and cable partners, increasing power capacity in the rack to 92 kilowatts or more. At the same meeting, Delta Electronics and Advanced Energy reported on their progress in developing new Open Compute standard specifications for power shelf and rectifier designs for HPR applications. Rittal also outlined their collaboration with Meta in designing airflow containment, busbar designs and grounding schemes for the new HPR requirements. === Data storage === Open Vault storage building blocks (also called "Knox") offer high disk densities, with 30 drives in a 2 OU Open Rack chassis designed for easy disk drive replacement. The 3.5 inch disks are stored in two drawers, five across and three deep in each drawer, with connections via serial attached SCSI. There is a "cold storage" variant where idle disks power down to reduce energy consumption. Another design concept was contributed by Hyve Solutions, a division of Synnex, in 2012. At the OCP Summit 2016 Facebook, together with Taiwanese ODM Wistron's spin-off Wiwynn, introduced "Lightning", a flexible NVMe JBOF (just a bunch of flash), based on the existing Open Vault (Knox) design. === Energy efficient data centers === The OCP has published data center designs for energy efficiency. These include power distribution at three-phase 277/480 VAC, which eliminates one transformer stage in typical North American data centers, a single voltage (12.5 VDC) power supply designed to work with 277/480 VAC input, and 48 VDC battery backup. For European (and other 230V countries) datacenters, there is a specification for 230/400 VAC power distribution and its conversion to 12.5 VDC. === Open networking switches === On May 8, 2013, an effort to define an open network switch was announced. The plan was to allow Facebook to load its own operating system software onto its top-of-rack switches. Press reports predicted that more expensive and higher-performance switches would continue to be popular, while less expensive products treated more like a commodity. The first attempt at an open networking switch by Facebook was designed together with Taiwanese ODM Accton using Broadcom Trident II chip and is called "Wedge"; the Linux OS that it runs is called "FBOSS". Later switch contributions include "6-pack" and Wedge-100, based on Broadcom Tomahawk chips. Similar switch hardware designs have been contributed by: Accton Technology Corporation (and its Edgecore Networks subsidiary), Mellanox Technologies, Interface Masters Technologies, Agema Systems. Capable of running Open Network Install Environment (ONIE)-compatible network operating systems such as Cumulus Linux, Switch Light OS by Big Switch Networks, or PICOS by Pica8. A similar project for a custom switch for the Google platform had been rumored, and evolved to use the OpenFlow protocol. === Servers === A sub-project for Mezzanine (NIC) OCP NIC 3.0 specification 1v00 was released in late 2019 establishing three form factors: SFF, TSFF, and LFF. == Litigation == In March, 2015, BladeRoom Group Limited and Bripco (UK) Limited sued Facebook, Emerson Electric Co. and others alleging that Facebook has disclosed BladeRoom and Bripco's trade secrets for prefabricated data centers in the Open Compute Project. Facebook petitioned for the lawsuit to be dismissed, but this was rejected in 2017. A confidential mid-trial settlement was agreed in April 2018.

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  • Rabbit r1

    Rabbit r1

    The Rabbit r1 is an artificial intelligence personal assistant device developed by the American technology startup Rabbit Inc and co-designed by Teenage Engineering. It was announced at the 2024 Consumer Electronics Show as a handheld device intended to perform digital tasks through voice commands, touch interaction, and web-based AI agents. The r1 was marketed around Rabbit's concept of a "large action model" (LAM), which the company described as software able to operate websites and services on behalf of users. The device runs rabbitOS, an operating system based on the Android Open Source Project. Its services have included AI search, image recognition, voice interaction, music playback, rideshare and food-ordering integrations, and later experimental web-agent features such as LAM Playground and teach mode. Initial reviews were largely negative, with reviewers criticizing the device's limited functionality, bugs, and unclear advantages over a smartphone. Critics also questioned Rabbit's claims after the r1 software was shown to run on an Android phone. Rabbit continued to issue software updates after launch, including rabbitOS 2 in September 2025, which introduced a redesigned card-based interface, gesture navigation, and a "creations" feature for generating small software tools and experiences on the device. Rabbit Inc was founded by Jesse Lyu Cheng. == Hardware == Display: A 2.88-inch touchscreen for interactive user input. Input: push-to-talk button to activate voice commands; scroll wheel; Gyroscope; Magnetometer; Accelerometer; GPS. Camera: 8 MP single camera, with a resolution of 3264x2448, allowing for the connected external AI to use computer vision. Audio: Equipped with a speaker and dual microphones for audio interaction. Connectivity: Supports Wi-Fi and cellular connections via a SIM card slot to access internet services. Processor: Runs on a 2.3GHz MediaTek Helio P35 processor. Memory: Contains 4GB of RAM for operational tasks. Storage: Offers 128GB of internal storage for data. Ports: Utilizes a USB-C port for charging and data connections. == Software == The Rabbit r1 runs rabbitOS, which is based on the Android Open Source Project (AOSP), specifically Android 13. Rabbit founder Jesse Lyu described rabbitOS as a "very bespoke AOSP" after reports that the r1's software could be run on a conventional Android phone. Rabbit described the r1 as using a large action model (LAM), a type of AI agent intended to perform tasks across software interfaces rather than only answer questions. At launch, the device supported a limited set of services, including AI search, vision features, music playback, and some third-party integrations. Perplexity.ai was one of the AI services used to answer user queries. In 2024, Rabbit released several software updates that added features and attempted to address early criticism of the device. In July 2024, the company launched "beta rabbit", an advanced search and conversation mode for more complex queries. In October 2024, it released LAM Playground, a web-based agent feature intended to let the r1 operate websites on behalf of users. Reviewers found the feature experimental; Android Authority reported that it could perform some navigation tasks but struggled with CAPTCHAs, loops, and unintended behavior. In November 2024, Rabbit introduced a beta "teach mode", which allowed users to demonstrate web-based tasks in the Rabbithole web portal and later ask the r1 to repeat them. The company described teach mode as experimental, and The Verge noted that Rabbit warned users that results could be unpredictable and that CAPTCHA-protected sites could cause problems. Rabbit released rabbitOS 2 in September 2025. The update redesigned the interface around a card-based layout, added additional touchscreen gestures, and introduced "creations", a feature that lets users generate simple software tools, games, and interfaces through natural-language prompts. Coverage of the update described it as a major software overhaul rather than new hardware. == Reception == === Funding === Rabbit raised $20 million in funding from Khosla Ventures, Synergis Capital and Kakao Investment in October 2023. The company announced an additional $10 million in funding in December 2023. === Sales === Following its announcement at the 2024 Consumer Electronics Show, 130,000 units were sold. On August 13, 2024, Rabbit announced that sales of r1 had expanded to the entire European Union (except Malta) and United Kingdom. On August 21, 2024, sales of r1 expanded to Singapore. === Reviews === The r1 was met with strong criticism immediately after Rabbit began shipping the device. Some reviews questioned what the device was able to do that a smartphone could not, while comparing it to the similar Humane Ai Pin. YouTuber Marques Brownlee called the device "barely reviewable". Android Authority's Mishaal Rahman managed to install Rabbit r1's software on a Pixel 6a smartphone, after a tipster shared an APK file. The Verge echoed the claims made by Rahman. In response, Lyu published statements confirming its use of Android, but denying that the r1 is an Android app. Mashable called its Vision features impressive, but said that "these praise-worthy features are overshadowed by buggy performance". Ars Technica wrote a blog post claiming "the company is blocking access from bootleg APKs". TechCrunch gave a slightly more positive review, calling the device a "fun peep at a possible future", but could not "advise anyone to buy one now." Shortly after the launch of r1, Rabbit began a weekly cadence of software updates to address much of the criticism from the early reviews, including "battery and GPS performance, time zone selection, and more". Digital Trends said the Magic Camera feature "takes the most mundane, ordinary, and badly composed photos and makes something fun and eye-catching from them." Mashable said the "beta rabbit" feature "makes Rabbit R1 more conversational and intelligent". Later coverage noted that Rabbit continued to update the r1 after its poorly received launch. The Verge reported in September 2024 that about 5,000 of roughly 100,000 purchasers were using the device at any given moment, citing Lyu, and described the product as having launched before it was ready. In 2025, coverage of rabbitOS 2 described the update as an attempt to reset the device's software experience after the criticism of its original release. == Controversies == === GAMA project === Rabbit Inc has garnered attention due to allegations surrounding its funding and the company's past projects. The company came under scrutiny when Stephen Findeisen, known as Coffeezilla on YouTube, published a video in May 2024, alleging that Rabbit Incorporation was "built on a scam". Rabbit Incorporation, initially named Cyber Manufacturing Co, rebranded just two months before launching the Rabbit R1. The company, under its former name, raised $6 million in November 2021 for a project called GAMA, described as a "Next Generation NFT Project." Jesse Lyu, the CEO of Rabbit Incorporation, referred to GAMA as a "fun little project." Coffeezilla, who investigates influencer scams, highlighted old Clubhouse recordings of Jesse Lyu discussing the GAMA project. In these recordings, Lyu emphasized the substantial funding behind GAMA and its potential to be a revolutionary, carbon-negative cryptocurrency. Coffeezilla questioned the whereabouts of the funds raised for GAMA, estimating that approximately $1 million in refunds to investors remained unresolved. He suggested that the rebranding to Rabbit Incorporation and the shift to developing the Rabbit R1 were attempts to divert from the GAMA project's issues. In response to Coffeezilla's inquiries, Rabbit Incorporation stated that the $6 million raised was used for the GAMA project. The company said that NFTs cannot be refunded unless the owner agrees to "burn" them on the blockchain. Rabbit Incorporation also said that the GAMA project was open-sourced and returned to the community, aligning with community feedback. They also mentioned that efforts to buy back NFTs were made to counteract malicious trading and maintain market stability. === Security === In June 2024, Engadget reported that the Rabbitude team, a community reverse engineering project, had gained access to the r1's codebase revealing that r1's software contained several hardcoded API keys in its code for ElevenLabs, Microsoft Azure, Yelp, and Google Maps, potentially allowing unauthorized access to r1 responses, including those containing the users' personal information. For a short time, Rabbit immediately began revoking and rotating those secrets and confirmed that the code was leaked by an employee who had "been terminated and remains under investigation". In July 2024, the company revealed that all user chats and device pairing data were logged on the r1 with no ability to delete them. This meant that lost or stolen devices could be used to extract user

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  • MaPS S.A.

    MaPS S.A.

    MaPS S.A. is a software editor founded in 2011 by Thierry Muller. The company is headquartered in Luxembourg. Its platform, called MaPS System, provides Data Management software for Multichannel Marketing. == History and funding == The first version of MaPS System was released under the agency Prem1um S.A. in 2005 in the partnership with Pingroom agency. In combination with MaPS System, Prem1um also provided various consulting services in Marketing, Publishing and Sales. This is where MaPS System takes its names (M stands for Marketing, P for Publishing and S for Sales). In 2011, after being successful, Prem1um S.A. decided to enable the software MaPS System to operate independently under MaPS S.A., as a separate company and editor of the software. The first financial supports were provided by Malta ICI, a Venture Capital firm, and the local partner Chameleon Invest, a seed-capital fund led by Business Angels, who invested €900,000. In a second investment round in 2014 led by Newion Investments, a Venture Capital firm, €1.4 Million were raised, thus amounting to total assets of €2.2 Million. In 2016, the company was taken over by three private investors. In 2018, after two years of continuous growth and European expansion in Belgium, Germany and Switzerland, MaPS S.A acquired Awevo, an e-commerce web agency. == Products == The services included in MaPS System range from the data centralization, Data Governance to an optimized Multichannel Marketing. The software currently includes more than 35 modules for Master Data Management, Product Information Management, Digital Asset Management, Business Process Management including catalogue Publishing features. == Certifications == In 2019, MaPS System and Awevo received "Made in Luxembourg" label, given to the companies whose services are entirely designed in Luxembourg, without any production or development offshoring. MaPS System is a member of ICT Cluster by Luxinnovation.

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  • Small data

    Small data

    Small data is data that is 'small' enough for human comprehension. It is data in a volume and format that makes it accessible, informative and actionable. The term "big data" is about machines and "small data" is about people. This is to say that eyewitness observations or five pieces of related data could be small data. Small data is what we used to think of as data. The only way to comprehend Big data is to reduce the data into small, visually-appealing objects representing various aspects of large data sets (such as histogram, charts, and scatter plots). Big Data is all about finding correlations, but Small Data is all about finding the causation, the reason why. A formal definition of small data has been proposed by Allen Bonde, former vice-president of Innovation at Actuate - now part of OpenText: "Small data connects people with timely, meaningful insights (derived from big data and/or “local” sources), organized and packaged – often visually – to be accessible, understandable, and actionable for everyday tasks." Another definition of small data is: The small set of specific attributes produced by the Internet of Things. These are typically a small set of sensor data such as temperature, wind speed, vibration and status. It was estimated (2016) that “If one takes the top 100 biggest innovations of our time, perhaps around 60% to 65% percent are really based on Small Data.” as Martin Lindstrom puts it. Small data includes everything from Snapchat to simple objects such as the post-it note. Lindstrom believes we become so focused on Big-Data that we tend to forget about more basic concepts and creativity. Lindstrom defines Small Data "as seemingly insignificant observations you identify in consumers’ homes, is everything from how you place your shoes on how you hang your paintings". He thus considers that one should perfectly master the basic (Small Data) in order to mine and find correlations. == Academic Recognition and Methodology == The growing significance of "small data" as a distinct field of inquiry was highlighted by the 2024 Thematic Einstein Semester (TES) on Small Data Analysis, hosted by the Berlin Mathematics Research Center MATH+. A central focus of this semester was the transition from theoretical analysis to practical decision-making. Because small data sets are primarily used to drive specific actions, the presentation of results becomes an essential methodological step. The semester’s findings emphasized that while small data may lack volume, it often contains a high density of "many possible interpretations." Consequently, the final conference of the TES was structured around the pillars of interpretation, explanation, and knowledge gain. Participants sought to develop new mathematical and methodical representations that could accurately depict this wealth of interpretative possibilities. This work underscores that analyzing small data is not purely a computational task; it requires a robust interface between mathematics and diverse disciplines to ensure that insights are both contextually grounded and scientifically rigorous. == Uses in business == === Marketing === Bonde has written about the topic for Forbes, Direct Marketing News, CMO.com and other publications. According to Martin Lindstrom, in his book, Small Data: "{In customer research, small data is} Seemingly insignificant behavioural observations containing very specific attributes pointing towards an unmet customer need. Small data is the foundation for breakthrough ideas or completely new ways to turnaround brands." His approach is based on the combination of the observation of small samples with intuition. Marketers can obtain market insights from gathering Small Data by engaging with and observing people in their own environments. In comparison to Big Data, Small Data has the power to trigger emotions and to provide insights into the reasons behind the behaviours of customers. It may uncover detailed information on a person's extroversion or introversion, self-confidence, whether one is having problems in his/her relationship, etc. According to Lindstrom, relationships among people and customer segments are organized around four criteria: Climate: It reveals for example how a person's environment affects their diet. Rulership: The power or government in charge Religion: The prevalence of religion in a country, depending on its influence, indicates whether a person's decision making process is impacted by their belief system. Tradition: Cultural norms influence people's behaviors and interactions. Many companies underestimate the power of Small Data, using samples of millions of consumers instead of recognizing the value of closely observing small samples in their market research. In his book, Lindstrom defines "7Cs", which companies should consider in the attempt to derive meaningful customer insights and market trends through small data from their customers: Collecting: Understanding the manner in which observations are translated inside a home. Clues: Uncovering other distinctive emotional reflections that can be observed. Connecting: Identifying the consequences of emotional behaviour. Causation: Understanding what emotions are being evoked. Correlation: Identifying the initial date of appearance of the behaviour or emotion. Compensation: Identifying the unmet or unfulfilled desire. Concept: Defining the “big idea” compensation for the identified consumer need. Some of Lindstrom's clients such as Lowes Foods looked at data in a different way and actually chose to live with the customer. “As you enter their store, they have now created an amazing community where every staff member acts in a character mood, based on Small Data”. The supermarket made everything it can to make the customer feel at home. All the behaviours of employees are inspired by customer feedbacks gathered from interviews directly done at customer’s home. === Healthcare === Researchers at Cornell University started developing applications to monitor health problems in patients, based on small data. This is an initiative of Cornell's Small Data Lab, in close cooperation with Weill Cornell Medicine College, led by Deborah Estrin. The Small Data Lab developed a series of apps, focusing not only on gathering data from patients' pain but also tracking habits in areas such as grocery shopping. In the case of patients with rheumatoid arthritis for example, which has flares and remissions that do not follow a particular cycle, the app gathers information passively, thus allowing to forecast when a flare might be coming up based on small changes in behaviour. Other apps developed also include monitoring online grocery shopping, to use this information from every user to adapt their groceries to the recommendations of nutritionists, or monitoring email language to identify patterns that might indicate "fluctuations in cognitive performance, fatigue, side effects of medication or poor sleep, and other conditions and treatments that are typically self-reported and self-medicated". === Postal Service === The United States Postal Service (USPS) used optical character recognition (OCR) to automatically read and process 98% of all hand-addressed mail and 99.5% of machine-printed mail. By combining this technology with its small data sample of US zip codes, the USPS can now process more than 36,000 pieces of mail per hour. === Aerospace === In 2015, Boeing established the analytics lab for aerospace data in cooperation with the Carnegie Mellon University to leverage the university's leadership in machine learning, language technologies and data analytics. One of the initiatives projects aims to by standardize maintenance logs using AI to dramatically reduce costs. Currently, there is no standardized procedure to document maintenance logs leading to small but highly unstructured data sets. As a result, it becomes highly difficult for maintenance workers to translate these variations in maintenance logs within a short period of time. However, with AI and a narrow data set of common aircraft maintenance terminology, it becomes possible to dynamically translate these logs in real time. By using AI to enhance the speed and accuracy of the airline maintenance workflow, airlines stand to save billions according to the Harvard Business Review.

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  • Umbrella review

    Umbrella review

    In medical research, an umbrella review is a review of systematic reviews or meta-analyses. They may also be called overviews of reviews, reviews of reviews, summaries of systematic reviews, or syntheses of reviews. Umbrella reviews are among the highest levels of evidence currently available in medicine. By summarizing information from multiple overview articles, umbrella reviews make it easier to review the evidence and allow for comparison of results between each of the individual reviews. Umbrella reviews may address a broader question than a typical review, such as discussing multiple different treatment comparisons instead of only one. They are especially useful for developing guidelines and clinical practice, and when comparing competing interventions.

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  • Web Dynpro

    Web Dynpro

    Web Dynpro (WD) is a web application technology developed by SAP SE that focuses on the development of server-side business applications. For modern releases (for instance as of NetWeaver 750, software layer SAP_UI) the user interface is rendered according to the HTML5 web standard. Since Netweaver 754 (software layer SAP_UI, ABAP Platform 1909) a touch enabled user interface is available. The newly released versions usually follow the SAP Fiori design principles. One of its main design features is that the user interface is defined in an entirely declarative manner. Web Dynpro applications can be developed using either the Java (Web Dynpro for Java, WDJ or WD4J) or ABAP (Web Dynpro ABAP, WDA or WD4A) development infrastructure. == Overview == The earliest version of Web Dynpro appeared in 2003 and was based on Java. This variant was released approximately 18 months before the ABAP variant. As of 2010, the Java variant of Web Dynpro was put into maintenance mode. WD follows a design architecture based on an interpretation of the MVC design pattern and uses a model driven development approach ("minimize coding, maximize design"). The Web Dynpro Framework is a server-side runtime environment into which many dedicated "hook methods" are available. The developer then places their own custom coding within these hook methods in order to implement the desired business functionality. These hook methods belong to one of the broad categories of either "life-cycle" and "round-trip"; that is, those methods that are concerned with the life-cycle of a software component (i.e. processing that takes place at start up and shut down etc.), and those methods that are concerned with processing the fixed sequence of events that take place during a client-initiated round trip to the server. Web Dynpro is aimed at the development of business applications that follow standardized UI principles, applications that connect to backend systems and which are scalable. Key Capabilities Declarative way of development: Web Dynpro offers a graphical and declarative means of UI development. UI controls, building blocks, views and windows are modeled, and the business logic can be coded separately. Separation of user interface and business logic: One advantage of Web Dynpro over SAP GUI is the separation between business logic and user interface, and the structured development process with less implementation effort. Support of stateful application: The state of the application is kept in the back-end. This leads to a reduced data transfer from ABAP server to browser and vice versa. Regarding Web Dynpro ABAP there is only one programming language (ABAP) and only one system necessary. Therefore, development can be easier and cost efficient.

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  • Explore-then-commit algorithm

    Explore-then-commit algorithm

    Explore Then Commit (ETC) is an algorithm for the multi-armed bandit problem foc,used on finding the best trade-off between exploration and exploitation. == Multi-armed bandit problem == The multi-armed bandit problem is a sequential game where one player has to choose at each turn between K {\displaystyle K} actions (arms). Behind every arm a {\displaystyle a} is an unknown distribution ν a {\displaystyle \nu _{a}} that lies in a set D {\displaystyle {\mathcal {D}}} known by the player (for example, D {\displaystyle {\mathcal {D}}} can be the set of Gaussian distributions or Bernoulli distributions). At each turn t {\displaystyle t} the player chooses (pulls) an arm a t {\displaystyle a_{t}} , they then get an observation X t {\displaystyle X_{t}} of the distribution ν a t {\displaystyle \nu _{a_{t}}} . === Regret minimization === The goal is to minimize the regret at time T {\displaystyle T} that is defined as R T := ∑ a = 1 K Δ a E [ N a ( T ) ] {\displaystyle R_{T}:=\sum _{a=1}^{K}\Delta _{a}\mathbb {E} [N_{a}(T)]} where μ a := E [ ν a ] {\displaystyle \mu _{a}:=\mathbb {E} [\nu _{a}]} is the mean of arm a {\displaystyle a} μ ∗ := max a μ a {\displaystyle \mu ^{}:=\max _{a}\mu _{a}} is the highest mean Δ a := μ ∗ − μ a {\displaystyle \Delta _{a}:=\mu ^{}-\mu _{a}} N a ( t ) {\displaystyle N_{a}(t)} is the number of pulls of arm a {\displaystyle a} up to turn t {\displaystyle t} The player has to find an algorithm that chooses at each turn t {\displaystyle t} which arm to pull based on the previous actions and observations ( a s , X s ) s < t {\displaystyle (a_{s},X_{s})_{s Read more →

  • Mobile content management system

    Mobile content management system

    A mobile content management system (MCMs) is a type of content management system (CMS) capable of storing and delivering content and services to mobile devices, such as mobile phones, smart phones, and PDAs. Mobile content management systems may be discrete systems, or may exist as features, modules or add-ons of larger content management systems capable of multi-channel content delivery. Mobile content delivery has unique, specific constraints including widely variable device capacities, small screen size, limitations on wireless bandwidth, sometimes small storage capacity, and (for some devices) comparatively weak device processors. Demand for mobile content management increased as mobile devices became increasingly ubiquitous and sophisticated. MCMS technology initially focused on the business to consumer (B2C) mobile market place with ringtones, games, text-messaging, news, and other related content. Since, mobile content management systems have also taken root in business-to-business (B2B) and business-to-employee (B2E) situations, allowing companies to provide more timely information and functionality to business partners and mobile workforces in an increasingly efficient manner. A 2008 estimate put global revenue for mobile content management at US$8 billion. == Key features == === Multi-channel content delivery === Multi-channel content delivery capabilities allow users not to manage a central content repository while simultaneously delivering that content to mobile devices such as mobile phones, smartphones, tablets and other mobile devices. Content can be stored in a raw format (such as Microsoft Word, Excel, PowerPoint, PDF, Text, HTML etc.) to which device-specific presentation styles can be applied. === Content access control === Access control includes authorization, authentication, access approval to each content. In many cases the access control also includes download control, wipe-out for specific user, time specific access. For the authentication, MCM shall have basic authentication which has user ID and password. For higher security many MCM supports IP authentication and mobile device authentication. === Specialized templating system === While traditional web content management systems handle templates for only a handful of web browsers, mobile CMS templates must be adapted to the very wide range of target devices with different capacities and limitations. There are two approaches to adapting templates: multi-client and multi-site. The multi-client approach makes it possible to see all versions of a site at the same domain (e.g. sitename.com), and templates are presented based on the device client used for viewing. The multi-site approach displays the mobile site on a targeted sub-domain (e.g. mobile.sitename.com). === Location-based content delivery === Location-based content delivery provides targeted content, such as information, advertisements, maps, directions, and news, to mobile devices based on current physical location. Currently, GPS (global positioning system) navigation systems offer the most popular location-based services. Navigation systems are specialized systems, but incorporating mobile phone functionality makes greater exploitation of location-aware content delivery possible.

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  • Library and information scientist

    Library and information scientist

    A library and information scientist, also known as a library scholar, is a researcher or academic who specializes in the field of library and information science and often participates in scholarly writing about and related to library and information science. A library and information scientist is neither limited to any one subfield of library and information science nor any one particular type of library. These scientists come from all information-related sectors including library and book history. == University of Chicago Graduate Library School == The University of Chicago Graduate Library School was established in 1928 to grant a graduate degree in librarianship with an emphasis on research. The program expanded the concept of librarianship, focused on scientific inquiry and established it as a domain for scientific study. In The Spirit of Inquiry: The Graduate Library School at Chicago, 1921-51 Richardson reviewed the history of the School and its impact on the discipline. == Bibliometric mappings == Bibliometric methods have been used to create maps of library and information science, thus identifying the most important researchers as well as their relative connections (or distances) and identifying emerging trends related to LIS publications within the field. White and McCain (1998) made a map of information science and Åström (2002), Chen, Ibekwe-SanJuan, and Hou (2010), Janssens, Leta, Glanzel, and De Moor (2006), and Zhao and Strotmann (2008) constructed some later maps of library and information science. Jabeen, Yun, Rafiq, and Jabeen (2015) mapped the growth and trends of LIS publications. == Notable library and information scientists == See also Beta Phi Mu Award, Award of Merit - Association for Information Science and Technology, Justin Winsor Prize (library)

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  • Space partitioning

    Space partitioning

    In geometry, space partitioning is the process of dividing an entire space (usually a Euclidean space) into two or more disjoint subsets (see also partition of a set). In other words, space partitioning divides a space into non-overlapping regions. Any point in the space can then be identified to lie in exactly one of the regions. == Overview == Space-partitioning systems are often hierarchical, meaning that a space (or a region of space) is divided into several regions, and then the same space-partitioning system is recursively applied to each of the regions thus created. The regions can be organized into a tree, called a space-partitioning tree. Most space-partitioning systems use planes (or, in higher dimensions, hyperplanes) to divide space: points on one side of the plane form one region, and points on the other side form another. Points exactly on the plane are usually arbitrarily assigned to one or the other side. Recursively partitioning space using planes in this way produces a BSP tree, one of the most common forms of space partitioning. == Uses == === In computer graphics === Space partitioning is particularly important in computer graphics, especially heavily used in ray tracing, where it is frequently used to organize the objects in a virtual scene. A typical scene may contain millions of polygons. Performing a ray/polygon intersection test with each would be a very computationally expensive task. Storing objects in a space-partitioning data structure (k-d tree or BSP tree for example) makes it easy and fast to perform certain kinds of geometry queries—for example in determining whether a ray intersects an object, space partitioning can reduce the number of intersection test to just a few per primary ray, yielding a logarithmic time complexity with respect to the number of polygons. Space partitioning is also often used in scanline algorithms to eliminate the polygons out of the camera's viewing frustum, limiting the number of polygons processed by the pipeline. There is also a usage in collision detection: determining whether two objects are close to each other can be much faster using space partitioning. === In integrated circuit design === In integrated circuit design, an important step is design rule check. This step ensures that the completed design is manufacturable. The check involves rules that specify widths and spacings and other geometry patterns. A modern design can have billions of polygons that represent wires and transistors. Efficient checking relies heavily on geometry query. For example, a rule may specify that any polygon must be at least n nanometers from any other polygon. This is converted into a geometry query by enlarging a polygon by n/2 at all sides and query to find all intersecting polygons. === In probability and statistical learning theory === The number of components in a space partition plays a central role in some results in probability theory. See Growth function for more details. === In geography and GIS === There are many studies and applications where Geographical Spatial Reality is partitioned by hydrological criteria, administrative criteria, mathematical criteria or many others. In the context of cartography and GIS - Geographic Information System, is common to identify cells of the partition by standard codes. For example the for HUC code identifying hydrographical basins and sub-basins, ISO 3166-2 codes identifying countries and its subdivisions, or arbitrary DGGs - discrete global grids identifying quadrants or locations. == Data structures == Common space-partitioning systems include: BSP trees Quadtrees Octrees k-d trees Bins == Number of components == Suppose the n-dimensional Euclidean space is partitioned by r {\displaystyle r} hyperplanes that are ( n − 1 ) {\displaystyle (n-1)} -dimensional. What is the number of components in the partition? The largest number of components is attained when the hyperplanes are in general position, i.e, no two are parallel and no three have the same intersection. Denote this maximum number of components by C o m p ( n , r ) {\displaystyle Comp(n,r)} . Then, the following recurrence relation holds: C o m p ( n , r ) = C o m p ( n , r − 1 ) + C o m p ( n − 1 , r − 1 ) {\displaystyle Comp(n,r)=Comp(n,r-1)+Comp(n-1,r-1)} C o m p ( 0 , r ) = 1 {\displaystyle Comp(0,r)=1} - when there are no dimensions, there is a single point. C o m p ( n , 0 ) = 1 {\displaystyle Comp(n,0)=1} - when there are no hyperplanes, all the space is a single component. And its solution is: C o m p ( n , r ) = ∑ k = 0 n ( r k ) {\displaystyle Comp(n,r)=\sum _{k=0}^{n}{r \choose k}} if r ≥ n {\displaystyle r\geq n} C o m p ( n , r ) = 2 r {\displaystyle Comp(n,r)=2^{r}} if r ≤ n {\displaystyle r\leq n} (consider e.g. r {\displaystyle r} perpendicular hyperplanes; each additional hyperplane divides each existing component to 2). which is upper-bounded as: C o m p ( n , r ) ≤ r n + 1 {\displaystyle Comp(n,r)\leq r^{n}+1}

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  • Principles for a Data Economy

    Principles for a Data Economy

    The Principles for a Data Economy – Data Rights and Transactions is a transatlantic legal project carried out jointly by the American Law Institute (ALI) and the European Law Institute (ELI). The Principles for a Data Economy deals with a range of different legal questions that arise in the data economy. Since data is different from other tradeable items, the Principles draw up legal rules for data transactions and data rights that take into account the interests of different stakeholders involved in the data economy. The Principles are designed to facilitate contractual relations as well as the drafting of model agreements and can guide courts and legislators worldwide. The project proposes a set of principles that can be implemented in any legal system and is designed to work in conjunction with any kind of data privacy/data protection law, intellectual property law or trade secret law. The Principles do not address or seek to change any of the substantive rules of these bodies of law. The Project Team consists of Neil B Cohen and Christiane Wendehorst (as Project Reporters) and Lord John Thomas as well as Steven O. Weise (as Project Chairs). == Characteristics of data == The law governing trades in commerce has historically focused on trade in items that are tangible like goods or on intangible assets, such as shares or licenses. However, data does not fit into any of these traditional categories, nor does it qualify as a service. It is often unclear how traditional legal rules and doctrines can apply to data, as data is different from other assets in many ways. For example, data can be multiplied at basically no cost and can be used in parallel for a variety of different purposes by many different people at the same time (data is a “non-rivalrous” resource). Uncertainty regarding the applicable rules to govern the data economy may inhibit innovation and growth and trouble stakeholders like data-driven industries, start-ups, and consumers. == Stakeholders in the data economy == The Principles have taken the basic types of players and relations which can be found in data ecosystems as a starting point to provide guidance in different situations. The central actors in the data economy are data controllers (also called “data holders”). They are in a position to access the data and decide for which purposes and means this data should be processed. A controller may exercise control all by itself or share it with co-controllers, such as under a data pooling arrangement. Data processors provide the processing of data on a controller’s behalf as a service. Another important group of stakeholders includes those that contribute to the generation of data (e.g. data subjects). Other players in the data economy include data assemblers or data intermediaries (e.g. data trusts). == History of the project and timeline == Before the official adoption of the project by ALI and ELI bodies in 2018, the project team carried out a Feasibility Study from October 2016 to February 2018. In the following years, the project team produced a number of drafts (e.g. “Preliminary Drafts” No. 1 to 4, “Tentative Draft No. 1”) and project progress were regularly discussed with advisory bodies and members of both the ALI and the ELI. The project reporters also included feedback and insights from industry stakeholders and experts that was gained after several meetings and workshops, hosted, inter alia by UNCITRAL, UNIDROIT and several national governmental institutions. Tentative Draft No. 2 was presented at the ALI Annual Meeting in May 2021 and approved by ALI membership. The latest draft ("Final Council Draft") was also approved by the ELI Council and ELI Membership. The Principles for a Data Economy were presented at an international conference with representatives from institutions such as the Uniform Law Commission (ULC), the European Commission, UNIDROIT, the OECD, the International Chamber of Commerce (ICC) and the World Economic Forum (WEF) in October 2021. == Project structure == The current draft (“Tentative Draft No. 2”) of the Principles consists of five Parts that each governs different aspects of the data economy: General Provisions, Data Contracts, Data Rights, Third Party Aspects of Data Activities, and Multi-State Issues. === General Provisions === Part I includes general provisions that apply to all other Parts of the Principles for a Data Economy. This Part sets out the purpose of the Principles: they aim to make existing law in the field of the data economy more coherent and support the development of the law in this field by courts and legislators worldwide. It is also clarified that the Principles have a wide scope of application and can be used in a variety of ways by stakeholders in the data economy. The Principles may, for example, serve private parties as a basis for contract formation, guide the deliberations of arbitral tribunals or inspire national legislation. Part I then defines several key terms, such as ‘digital data’ and ‘data right’. The scope of the Principles is limited to matters where information is recorded as an asset, resource or tradeable commodity and where large amounts of data, rather than single pieces of information, are concerned. This Part also clarifies that remedies with respect to data contracts and data rights are left to the applicable national law. === Data Contracts === Part II lists different types of contracts that often occur in the data economy and establishes two broad categories, namely contracts for the supply and sharing of data and contracts for services with regard to data. Contracts for the supply and sharing of data include, e.g. data transfer contracts or data pooling arrangements, while contracts for services with regard to data cover contracts for the processing of data or data intermediary contracts. The Principles provide default terms for each contract type, on issues such as the manner in which data should supply or which characteristics the data supplied should meet. These default terms 'automatically' become part of the contract unless the parties agree otherwise. === Data Rights === Part III governs legally protected interests of players in the data economy that stem from the characteristics of data as a resource (e.g. its non-rivalrous nature) or from public interest considerations. Such data rights may include the right to data access, the right to require the controller to desist from data activities or to correct incorrect/incomplete data, or even to receive an economic share in profits derived from the use of data. For example, the Principles deal with data rights of stakeholders that had a share in the co-generation of data and identify different factors to be considered in determining whether to afford a party a data right. The underlying idea that parties who have contributed to the generation of data should have some rights in the utilization of the data is also recognized by governmental institutions, such as by the Japanese Ministry of Economy, Trade and Industry (METI), and the term co-generated data, which was coined by the Principles for a Data Economy, has been adopted, inter alia by the European Commission, the German Data Ethics Commission and the Global Partnership on Artificial Intelligence (GPAI). This Part also deals with data rights for the public interest, such as data sharing rights in the field of innovation. === Third Party Aspects === Part IV governs different situations in which data transactions interfere with the rights of third parties. Such rights include intellectual property rights or rights derived from data privacy or data protection law. This Part sets out under which circumstances data activities should be considered wrongful vis à vis another party. For example, a data activity (like data processing or the onward supply of data) could be considered wrongful, if a controller interferes with the rights of data subjects that are protected by data-protection law. A data activity could also be wrongful if the controller is non-compliant with contractual limitations on data activities, enforceable by the protected party (e.g. a controller may only process data for a certain purpose). If someone obtained access to data by unauthorized means (i.e. data “theft”) this could also be considered wrongful. The Part on Third-Party Aspects also takes a detailed look at the effects of the onward supply of data can have on third parties, while balancing the protection of third parties on the one hand, with the interests of data recipients and the desire to encourage data sharing on the other. === Multi-State Issues === As transactions in the data economy are international by nature and hardly occur within one legal system alone, the Part V of the Principles also briefly touches upon the applicability of the rules and doctrines of private international law to such transactions. == Links == Website of the “Principles for a Data Economy – Data Rights and Transaction

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  • International Philosophical Bibliography

    International Philosophical Bibliography

    The International Philosophical Bibliography (IPB), also known in French as Répertoire bibliographique de la philosophie (RBP), is a bibliographic database covering publications on the history of philosophy and continental philosophy. The database comprises records of publications in over 30 languages. Annually, about 12,000 records are added. The indexes include, among other elements, over 84,000 names of authors, editors, translators, reviewers, and collaborators, as well as more than 3,000 commentaries on philosophical works, making it the world's most complete index in Philosophy. Since 1934, the IPB has been developed by the Higher Institute of Philosophy at the University of Louvain (UCLouvain), first in Leuven and since 1978 in Louvain-la-Neuve. The online version was launched by Peeters Publishers in 1997 and continues to be updated quarterly.

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