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  LRE Map

  The LRE Map (Language Resources and Evaluation) is a freely accessible large database on resources dedicated to Natural language processing. The original feature of LRE Map is that the records are collected during the submission of different major Natural language processing conferences. The records are then cleaned and gathered into a global database called "LRE Map". The LRE Map is intended to be an instrument for collecting information about language resources and to become, at the same time, a community for users, a place to share and discover resources, discuss opinions, provide feedback, discover new trends, etc. It is an instrument for discovering, searching and documenting language resources, here intended in a broad sense, as both data and tools. The large amount of information contained in the Map can be analyzed in many different ways. For instance, the LRE Map can provide information about the most frequent type of resource, the most represented language, the applications for which resources are used or are being developed, the proportion of new resources vs. already existing ones, or the way in which resources are distributed to the community. == Context == Several institutions worldwide maintain catalogues of language resources (ELRA, LDC, NICT Universal Catalogue, ACL Data and Code Repository, OLAC, LT World, etc.) However, it has been estimated that only 10% of existing resources are known, either through distribution catalogues or via direct publicity by providers (web sites and the like). The rest remains hidden, the only occasions where it briefly emerges being when a resource is presented in the context of a research paper or report at some conference. Even in this case, nevertheless, it might be that a resource remains in the background simply because the focus of the research is not on the resource per se. == History == The LRE Map originated under the name "LREC Map" during the preparation of LREC 2010 conference. More specifically, the idea was discussed within the FlaReNet project, and in collaboration with ELRA and the Institute of Computational Linguistics of CNR in Pisa, the Map was put in place at LREC 2010. The LREC organizers asked the authors to provide some basic information about all the resources (in a broad sense, i.e. including tools, standards and evaluation packages), either used or created, described in their papers. All these descriptors were then gathered in a global matrix called the LREC Map. The same methodology and requirements from the authors has been then applied and extended to other conferences, namely COLING-2010, EMNLP-2010, RANLP-2011, LREC 2012, LREC 2014 and LREC 2016. After this generalization to other conferences, the LREC Map has been renamed as the LRE Map. == Size and content == The size of the database increases over time. The data collected amount to 4776 entries. Each resource is described according to the following attributes: Resource type, e.g. lexicon, annotation tool, tagger/parser. Resource production status, e.g. newly created finished, existing-updated. Resource availability, e.g. freely available, from data center. Resource modality, e.g. speech, written, sign language. Resource use, e.g. named entity recognition, language identification, machine translation. Resource language, e.g. English, 23 European Union languages, official languages of India. == Uses == The LRE map is a very important tool to chart the NLP field. Compared to other studied based on subjective scorings, the LRE map is made of real facts. The map has a great potential for many uses, in addition to being an information gathering tool: It is a great instrument for monitoring the evolution of the field (useful for funders), if applied in different contexts and times. It can be seen as a huge joint effort, the beginning of an even larger cooperative action not just among few leaders but among all the researchers. It is also an "educational" means towards the broad acknowledgment of the need of meta-research activities with the active involvement of many. It is also instrumental in introducing the new notion of "citation of resources" that could provide an award and a means of scholarly recognition for researchers engaged in resource creation. It is used to help the organization of the conferences of the field like LREC. == Derived matrices == The data were then cleaned and sorted by Joseph Mariani (CNRS-LIMSI IMMI) and Gil Francopoulo (CNRS-LIMSI IMMI + Tagmatica) in order to compute the various matrices of the final FLaReNet reports. One of them, the matrix for written data at LREC 2010 is as follows: English is the most studied language. Secondly, come French and German languages and then Italian and Spanish. == Future == The LRE Map has been extended to Language Resources and Evaluation Journal and other conferences.

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  Berlekamp–Rabin algorithm

  In number theory, Berlekamp's root finding algorithm, also called the Berlekamp–Rabin algorithm, is the probabilistic method of finding roots of polynomials over the field F p {\displaystyle \mathbb {F} _{p}} with p {\displaystyle p} elements. The method was discovered by Elwyn Berlekamp in 1970 as an auxiliary to the algorithm for polynomial factorization over finite fields. The algorithm was later modified by Rabin for arbitrary finite fields in 1979. The method was also independently discovered before Berlekamp by other researchers. == History == The method was proposed by Elwyn Berlekamp in his 1970 work on polynomial factorization over finite fields. His original work lacked a formal correctness proof and was later refined and modified for arbitrary finite fields by Michael Rabin. In 1986 René Peralta proposed a similar algorithm for finding square roots in F p {\displaystyle \mathbb {F} _{p}} . In 2000 Peralta's method was generalized for cubic equations. == Statement of problem == Let p {\displaystyle p} be an odd prime number. Consider the polynomial f ( x ) = a 0 + a 1 x + ⋯ + a n x n {\textstyle f(x)=a_{0}+a_{1}x+\cdots +a_{n}x^{n}} over the field F p ≃ Z / p Z {\displaystyle \mathbb {F} _{p}\simeq \mathbb {Z} /p\mathbb {Z} } of remainders modulo p {\displaystyle p} . The algorithm should find all λ {\displaystyle \lambda } in F p {\displaystyle \mathbb {F} _{p}} such that f ( λ ) = 0 {\textstyle f(\lambda )=0} in F p {\displaystyle \mathbb {F} _{p}} . == Algorithm == === Randomization === Let f ( x ) = ( x − λ 1 ) ( x − λ 2 ) ⋯ ( x − λ n ) {\textstyle f(x)=(x-\lambda _{1})(x-\lambda _{2})\cdots (x-\lambda _{n})} . Finding all roots of this polynomial is equivalent to finding its factorization into linear factors. To find such factorization it is sufficient to split the polynomial into any two non-trivial divisors and factorize them recursively. To do this, consider the polynomial f z ( x ) = f ( x − z ) = ( x − λ 1 − z ) ( x − λ 2 − z ) ⋯ ( x − λ n − z ) {\textstyle f_{z}(x)=f(x-z)=(x-\lambda _{1}-z)(x-\lambda _{2}-z)\cdots (x-\lambda _{n}-z)} where z {\displaystyle z} is some element of F p {\displaystyle \mathbb {F} _{p}} . If one can represent this polynomial as the product f z ( x ) = p 0 ( x ) p 1 ( x ) {\displaystyle f_{z}(x)=p_{0}(x)p_{1}(x)} then in terms of the initial polynomial it means that f ( x ) = p 0 ( x + z ) p 1 ( x + z ) {\displaystyle f(x)=p_{0}(x+z)p_{1}(x+z)} , which provides needed factorization of f ( x ) {\displaystyle f(x)} . === Classification of === F p {\displaystyle \mathbb {F} _{p}} elements Due to Euler's criterion, for every monomial ( x − λ ) {\displaystyle (x-\lambda )} exactly one of following properties holds: The monomial is equal to x {\displaystyle x} if λ = 0 {\displaystyle \lambda =0} , The monomial divides g 0 ( x ) = ( x ( p − 1 ) / 2 − 1 ) {\textstyle g_{0}(x)=(x^{(p-1)/2}-1)} if λ {\displaystyle \lambda } is quadratic residue modulo p {\displaystyle p} , The monomial divides g 1 ( x ) = ( x ( p − 1 ) / 2 + 1 ) {\textstyle g_{1}(x)=(x^{(p-1)/2}+1)} if λ {\displaystyle \lambda } is quadratic non-residual modulo p {\displaystyle p} . Thus if f z ( x ) {\displaystyle f_{z}(x)} is not divisible by x {\displaystyle x} , which may be checked separately, then f z ( x ) {\displaystyle f_{z}(x)} is equal to the product of greatest common divisors gcd ( f z ( x ) ; g 0 ( x ) ) {\displaystyle \gcd(f_{z}(x);g_{0}(x))} and gcd ( f z ( x ) ; g 1 ( x ) ) {\displaystyle \gcd(f_{z}(x);g_{1}(x))} . === Berlekamp's method === The property above leads to the following algorithm: Explicitly calculate coefficients of f z ( x ) = f ( x − z ) {\displaystyle f_{z}(x)=f(x-z)} , Calculate remainders of x , x 2 , x 2 2 , x 2 3 , x 2 4 , … , x 2 ⌊ log 2 ⁡ p ⌋ {\textstyle x,x^{2},x^{2^{2}},x^{2^{3}},x^{2^{4}},\ldots ,x^{2^{\lfloor \log _{2}p\rfloor }}} modulo f z ( x ) {\displaystyle f_{z}(x)} by squaring the current polynomial and taking remainder modulo f z ( x ) {\displaystyle f_{z}(x)} , Using exponentiation by squaring and polynomials calculated on the previous steps calculate the remainder of x ( p − 1 ) / 2 {\textstyle x^{(p-1)/2}} modulo f z ( x ) {\textstyle f_{z}(x)} , If x ( p − 1 ) / 2 ≢ ± 1 ( mod f z ( x ) ) {\textstyle x^{(p-1)/2}\not \equiv \pm 1{\pmod {f_{z}(x)}}} then gcd {\displaystyle \gcd } mentioned below provide a non-trivial factorization of f z ( x ) {\displaystyle f_{z}(x)} , Otherwise all roots of f z ( x ) {\displaystyle f_{z}(x)} are either residues or non-residues simultaneously and one has to choose another z {\displaystyle z} . If f ( x ) {\displaystyle f(x)} is divisible by some non-linear primitive polynomial g ( x ) {\displaystyle g(x)} over F p {\displaystyle \mathbb {F} _{p}} then when calculating gcd {\displaystyle \gcd } with g 0 ( x ) {\displaystyle g_{0}(x)} and g 1 ( x ) {\displaystyle g_{1}(x)} one will obtain a non-trivial factorization of f z ( x ) / g z ( x ) {\displaystyle f_{z}(x)/g_{z}(x)} , thus algorithm allows to find all roots of arbitrary polynomials over F p {\displaystyle \mathbb {F} _{p}} . === Modular square root === Consider equation x 2 ≡ a ( mod p ) {\textstyle x^{2}\equiv a{\pmod {p}}} having elements β {\displaystyle \beta } and − β {\displaystyle -\beta } as its roots. Solution of this equation is equivalent to factorization of polynomial f ( x ) = x 2 − a = ( x − β ) ( x + β ) {\textstyle f(x)=x^{2}-a=(x-\beta )(x+\beta )} over F p {\displaystyle \mathbb {F} _{p}} . In this particular case problem it is sufficient to calculate only gcd ( f z ( x ) ; g 0 ( x ) ) {\displaystyle \gcd(f_{z}(x);g_{0}(x))} . For this polynomial exactly one of the following properties will hold: GCD is equal to 1 {\displaystyle 1} which means that z + β {\displaystyle z+\beta } and z − β {\displaystyle z-\beta } are both quadratic non-residues, GCD is equal to f z ( x ) {\displaystyle f_{z}(x)} which means that both numbers are quadratic residues, GCD is equal to ( x − t ) {\displaystyle (x-t)} which means that exactly one of these numbers is quadratic residue. In the third case GCD is equal to either ( x − z − β ) {\displaystyle (x-z-\beta )} or ( x − z + β ) {\displaystyle (x-z+\beta )} . It allows to write the solution as β = ( t − z ) ( mod p ) {\textstyle \beta =(t-z){\pmod {p}}} . === Example === Assume we need to solve the equation x 2 ≡ 5 ( mod 11 ) {\textstyle x^{2}\equiv 5{\pmod {11}}} . For this we need to factorize f ( x ) = x 2 − 5 = ( x − β ) ( x + β ) {\displaystyle f(x)=x^{2}-5=(x-\beta )(x+\beta )} . Consider some possible values of z {\displaystyle z} : Let z = 3 {\displaystyle z=3} . Then f z ( x ) = ( x − 3 ) 2 − 5 = x 2 − 6 x + 4 {\displaystyle f_{z}(x)=(x-3)^{2}-5=x^{2}-6x+4} , thus gcd ( x 2 − 6 x + 4 ; x 5 − 1 ) = 1 {\displaystyle \gcd(x^{2}-6x+4;x^{5}-1)=1} . Both numbers 3 ± β {\displaystyle 3\pm \beta } are quadratic non-residues, so we need to take some other z {\displaystyle z} . Let z = 2 {\displaystyle z=2} . Then f z ( x ) = ( x − 2 ) 2 − 5 = x 2 − 4 x − 1 {\displaystyle f_{z}(x)=(x-2)^{2}-5=x^{2}-4x-1} , thus gcd ( x 2 − 4 x − 1 ; x 5 − 1 ) ≡ x − 9 ( mod 11 ) {\textstyle \gcd(x^{2}-4x-1;x^{5}-1)\equiv x-9{\pmod {11}}} . From this follows x − 9 = x − 2 − β {\textstyle x-9=x-2-\beta } , so β ≡ 7 ( mod 11 ) {\displaystyle \beta \equiv 7{\pmod {11}}} and − β ≡ − 7 ≡ 4 ( mod 11 ) {\textstyle -\beta \equiv -7\equiv 4{\pmod {11}}} . A manual check shows that, indeed, 7 2 ≡ 49 ≡ 5 ( mod 11 ) {\textstyle 7^{2}\equiv 49\equiv 5{\pmod {11}}} and 4 2 ≡ 16 ≡ 5 ( mod 11 ) {\textstyle 4^{2}\equiv 16\equiv 5{\pmod {11}}} . == Correctness proof == The algorithm finds factorization of f z ( x ) {\displaystyle f_{z}(x)} in all cases except for ones when all numbers z + λ 1 , z + λ 2 , … , z + λ n {\displaystyle z+\lambda _{1},z+\lambda _{2},\ldots ,z+\lambda _{n}} are quadratic residues or non-residues simultaneously. According to theory of cyclotomy, the probability of such an event for the case when λ 1 , … , λ n {\displaystyle \lambda _{1},\ldots ,\lambda _{n}} are all residues or non-residues simultaneously (that is, when z = 0 {\displaystyle z=0} would fail) may be estimated as 2 − k {\displaystyle 2^{-k}} where k {\displaystyle k} is the number of distinct values in λ 1 , … , λ n {\displaystyle \lambda _{1},\ldots ,\lambda _{n}} . In this way even for the worst case of k = 1 {\displaystyle k=1} and f ( x ) = ( x − λ ) n {\displaystyle f(x)=(x-\lambda )^{n}} , the probability of error may be estimated as 1 / 2 {\displaystyle 1/2} and for modular square root case error probability is at most 1 / 4 {\displaystyle 1/4} . == Complexity == Let a polynomial have degree n {\displaystyle n} . We derive the algorithm's complexity as follows: Due to the binomial theorem ( x − z ) k = ∑ i = 0 k ( k i ) ( − z ) k − i x i {\textstyle (x-z)^{k}=\sum \limits _{i=0}^{k}{\binom {k}{i}}(-z)^{k-i}x^{i}} , we may transition from f ( x ) {\displaystyle f(x)} to f ( x − z ) {\displaystyle f(x-z)} in O ( n 2 ) {\displaystyle O(n^{2})} time. Polynomial multiplication a

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  Collaborative diffusion

  Collaborative Diffusion is a type of pathfinding algorithm which uses the concept of antiobjects, objects within a computer program that function opposite to what would be conventionally expected. Collaborative Diffusion is typically used in video games, when multiple agents must path towards a single target agent. For example, the ghosts in Pac-Man. In this case, the background tiles serve as antiobjects, carrying out the necessary calculations for creating a path and having the foreground objects react accordingly, whereas having foreground objects be responsible for their own pathing would be conventionally expected. Collaborative Diffusion is favored for its efficiency over other pathfinding algorithms, such as A, when handling multiple agents. Also, this method allows elements of competition and teamwork to easily be incorporated between tracking agents. Notably, the time taken to calculate paths remains constant as the number of agents increases.

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  Artificial intelligence in Indonesia

  Artificial intelligence in Indonesia refers to development, use and governance of artificial intelligence in Indonesia. Indonesia has treated AI as a national policy area through the Strategi Nasional Kecerdasan Artifisial or National Artificial Intelligence Strategy for 2020–2045. Public discussion has focused on the role of AI in sectors such as health, agriculture, education, mobile technology and e-commerce. Recent developments include AI ethics guidance issued by the communications ministry. Proposals for a national AI roadmap and sovereign AI fund, investment in cloud and AI infrastructure, and local-language AI initiatives for Bahasa Indonesia and regional Indonesian languages. == National strategy == Indonesia's National Artificial Intelligence Strategy is known in Indonesian as Strategi Nasional Kecerdasan Artifisial or Stranas KA. The strategy was published as a long-term framework for the development and use of AI between 2020 and 2045. It is intended to guide ministries, government agencies, regional governments and other stakeholders. The strategy identifies five priority sectors: health services, bureaucratic reform, education and research, food security, and mobility and smart cities. OECD lists the Ministry of Research and Technology and the National Research and Innovation Agency as organisations associated with the strategy. The strategy was developed through consultation with public and private stakeholders. == Institutions == The Indonesian Artificial Intelligence Industry Research and Innovation Collaboration, known as KORIKA is the nodal agency for the national AI strategy. KORIKA describes its vision as creating a collaborative ecosystem to accelerate implementation of the national AI strategy towards Vision Indonesia 2045. The Ministry of Communication and Digital Affairs has also been involved in AI governance, digital policy and public communication. In 2025, Reuters reported that the ministry was preparing a national AI roadmap to give investors and developers a clearer view of Indonesia's market, infrastructure and computing capacity. == AI Governance == Indonesia has introduced policy guidance on the ethical use of artificial intelligence. The policy sets out ethical values for the development and use of AI. These include humanity, security, transparency, credibility and accountability, personal data protection, sustainable development and intellectual property protection. A UNESCO country profile on Indonesia noted that Indonesia had adopted a national AI strategy and had policy frameworks. It also identified gaps in internet access, gender inclusion, language datasets, digital talent and cybersecurity. UNESCO recommended that Indonesia update its AI standards, invest in ethical AI, strengthen research coordination and consider establishing a national agency for artificial intelligence. In May 2026, Antara News reported comments by Deputy Minister of Communication and Digital Affairs Nezar Patria. Who said that AI safety requires partnerships, shared standards and continuing dialogue. == Sectors == AI policy discussions in Indonesia have identified health, agriculture, education, government services, mobility and smart cities as areas where AI could be applied. Mobile technology and e-commerce have been discussed as important areas of AI adoption in Indonesia. Research on AI adoption in Indonesia by Siddhartha Paul Tiwari and Adi Fahrudin has also examined mobile and e-commerce sectors. UNESCO has also noted that Indonesia's large digital economy and startup ecosystem have supported AI adoption, while also pointing to challenges in talent, research capacity and cybersecurity. Indonesia is one of the developing-country markets attracting AI infrastructure investment, including data centres. == Challenges == Indonesia faces several challenges in developing and governing AI. These include gaps in computing infrastructure, uneven connectivity outside major cities, shortages of skilled workers, limited research funding, cybersecurity risks, misinformation, data leaks and the underrepresentation of Indonesian and indigenous languages in AI datasets. UNESCO noted that Bahasa is spoken by around 200 million people but remains underrepresented in AI. It also noted that Indonesia has more than 700 indigenous languages, many of which face the risk of extinction. UNESCO recommended stronger coordination in AI research and a more unified strategy for using AI in language preservation.

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  Systems development life cycle

  The systems development life cycle (SDLC) describes the typical phases and progression between phases during the development of a computer-based system. These phases progress from inception to retirement. At base, there is just one life cycle, but the taxonomy used to describe it may vary; the cycle may be classified into different numbers of phases and various names may be used for those phases. The SDLC is analogous to the life cycle of a living organism from its birth to its death. In particular, the SDLC varies by system in much the same way that each living organism has a unique path through its life. The SDLC does not prescribe how engineers should go about their work to move the system through its life cycle. Prescriptive techniques are referred to using various terms such as methodology, model, framework, and formal process. Other terms are used for the same concept as SDLC, including software development life cycle (also SDLC), application development life cycle (ADLC), and system design life cycle (also SDLC). These other terms focus on a different scope of development and are associated with different prescriptive techniques, but are about the same essential life cycle. The term "life cycle" is often written without a space, as "lifecycle", with the former more popular in the past and in non-engineering contexts. The acronym SDLC was coined when the longer form was more popular and has remained associated with the expansion, even though the shorter form is popular in engineering. Also, SDLC is relatively unique as opposed to the TLA SDL, which is highly overloaded. == Phases == Depending on the source, the SDLC is described as having different phases and using different terms. Even so, there are common aspects. The following attempts to describe notable phases using notable terminology. The phases are somewhat ordered by the natural sequence of development, although they can be overlapping and iterative. === Conceptualization === During conceptualization (a.k.a. conceptual design, system investigation, feasibility), options and priorities are considered. A feasibility study can determine whether the development effort is worthwhile via activities such as understanding user needs, cost estimation, benefit analysis, and resource analysis. A study should address operational, financial, technical, human factors, and legal/political concerns. === Requirements analysis === Requirements analysis (a.k.a. preliminary design) involves understanding the problem and determining what is needed. Often this involves engaging users to define the requirements and recording them in a document known as a requirements specification. === Design === During the design phase (a.k.a. detail design), a solution is planned. The plan can include relatively high-level information such as describing the major components of the system. The plan can include relatively low-level information such as describing functions, screen layout, business rules, and process flow. The design phase is informed by the requirements of the system. The design must satisfy each requirement. The design may be recorded in textual documents as well as functional hierarchy diagrams, example screen images, business rules, process diagrams, pseudo-code, and data models. === Construction === During construction (a.k.a. implementation, production), the system is realized. Based on the design, hardware and software components are created and integrated. This phase includes testing sub-components, components and the integration of some components, but typically does not include testing at the complete system level. This phase may include the development of training materials, including user manuals and help files. === Acceptance === The acceptance phase (a.k.a. system testing) is about testing the complete system to ensure that it meets customer expectations (requirements). === Deployment === The deployment phase (a.k.a. implementation) involves the logistics of delivery to the customer. Some systems are deployed as a single instance (i.e. in the cloud), and deployment may be ad hoc and manual. Some systems are built in quantity and are associated with manufacturing process and commissioning. This phase may include training users to use the system. It may include transitioning future development to support staff. === Maintenance === During the maintenance phase (a.k.a. operation, utilization, support) development is largely inactive, although this phase does include customer support for resolving user issues and recording suggestions for improvement. Fixes and enhancements are handled by returning to the first phase, conceptualization. For minor changes, the cycle may be significantly abbreviated compared to initial development. === Decommission === Decommission (a.k.a. disposition, retirement, phase-out) is when the system is removed from use, i.e., when it reaches end-of-life. == Practices == === Management and control === SDLC phase objectives are described in this section with key deliverables, a description of recommended tasks, and a summary of related control objectives for effective management. It is critical for the project manager to establish and monitor control objectives while executing projects. Control objectives are clear statements of the desired result or purpose and should be defined and monitored throughout a project. Control objectives can be grouped into major categories (domains), and relate to the SDLC phases as shown in the figure. To manage and control a substantial SDLC initiative, a work breakdown structure (WBS) captures and schedules the work. The WBS and all programmatic material should be kept in the "project description" section of the project notebook. The project manager chooses a WBS format that best describes the project. The diagram shows that coverage spans numerous phases of the SDLC, but the associated MCD (Management Control Domains) shows mappings to SDLC phases. For example, Analysis and Design is primarily performed as part of the Acquisition and Implementation Domain, and System Build and Prototype is primarily performed as part of delivery and support. === Work breakdown structured organization === The upper section of the WBS provides an overview of the project scope and timeline. It should also summarize the major phases and milestones. The middle section is based on the SDLC phases. WBS elements consist of milestones and tasks to be completed rather than activities to be undertaken, and have a deadline. Each task has a measurable output (e.g., an analysis document). A WBS task may rely on one or more activities (e.g., coding). Parts of the project needing support from contractors should have a statement of work (SOW). The development of an SOW does not occur during a specific phase of SDLC but is developed to include the work from the SDLC process that may be conducted by contractors. === Baselines === Baselines are established after four of the five phases of the SDLC, and are critical to the iterative nature of the model. Baselines become milestones. functional baseline: established after the conceptual design phase. allocated baseline: established after the preliminary design phase. product baseline: established after the detailed design and development phase. updated product baseline: established after the production construction phase. In the following diagram, these stages are divided into ten steps, from definition to creation and modification of IT work products:

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  Evidence-based library and information practice

  Evidence-based library and information practice (EBLIP) or evidence-based librarianship (EBL) is the use of evidence-based practices (EBP) in the field of library and information science (LIS). This means that all practical decisions made within LIS should 1) be based on research studies and 2) that these research studies are selected and interpreted according to some specific norms characteristic for EBP. Typically such norms disregard theoretical studies and qualitative studies and consider quantitative studies according to a narrow set of criteria of what counts as evidence. If such a narrow set of methodological criteria are not applied, it is better instead to speak of research based library and information practice. == Characteristics == Evidence-based practice in general has been characterised as a positivist approach; EBLIP is therefore also a positivist approach to LIS. As such, EBLIP is an approach in contrast to other approaches to LIS. The use of statistical approaches known as meta-analysis to conclude what evidence has been reported in the literature is one among other methods which is typical for the evidence-based approach. In 2002, Booth noted the three schools of EBILP had some commonalities, including the context of day-to-day decision-making, an emphasis on improving the quality of professional practice, a pragmatic focus on the 'best available evidence', incorporation of the user perspective, the acceptance of a broad range of quantitative and qualitative research designs, and access, either first-hand or second-hand, to the (process of) evidence-based practice and its products. He added one more, that EBILP is concerned with getting the best value for money. == The role of library and information science in EBP == Evidence-based practice in general is based on a very thorough search of the scientific literature and a very thorough selection and analysis of the retrieved literature. A close familiarity with database searching is needed, and library and information professionals have important roles to play in this respect. Therefore LIS professionals should be well suited to help professionals in other disciplines doing EBP. EBLIP is the application of this approach on LIS itself. It should be mentioned, however, that EBP started in medicine as evidence-based medicine (EBM) from which it spread to other fields. Only slowly and to a limited extent has EBP moved on to LIS. The EBLIP process can be applied to a variety of scenarios in LIS, including customer service, collection development, library management and information literacy instruction. In general, quantitative methods are used in LIS research. A 2010 study revealed five categories that capture the different ways library and information professionals experience evidence-based practice: Evidence-based practice is experienced as irrelevant; Evidence-based practice is experienced as learning from published research; Evidence-based practice is experienced as service improvement; Evidence-based practice is experienced as a way of being; Evidence-based practice is experienced as a weapon.

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  Tertiary source

  A tertiary source is an index or textual consolidation of already published primary and secondary sources that does not provide additional interpretations or analysis of the sources. Some tertiary sources can be used as an aid to find key (seminal) sources, key terms, general common knowledge and established mainstream science on a topic. The exact definition of tertiary varies by academic field. Academic research standards generally do not accept tertiary sources such as encyclopedias as citations, although survey articles are frequently cited rather than the original publication. == Overlap with secondary sources == As is also the case with distinguishing primary and secondary sources in some disciplines, there is not always a clear distinguishing line between secondary and tertiary sources. Depending on the topic of research, a scholar may use a bibliography, dictionary, or encyclopedia as either a tertiary or a secondary source. This causes some difficulty in defining many sources as either one type or the other. In some academic disciplines, the differentiation between a secondary and tertiary source is relative. In the United Nations International Scientific Information System (UNISIST) model, a secondary source is a bibliography, whereas a tertiary source is a synthesis of primary sources. == Types of tertiary sources == Tertiary sources can come in book form or as an online resource. Tertiary sources in book form are frequently organised in alphabetical order, whereas an online tertiary source may be searchable by keyword. Examples of tertiary sources include; reference books, encyclopedias, dictionaries, some textbooks, abstracts, directories, factbooks, handbooks, manuals and compendia. Indexes, bibliographies, concordances, and databases are aggregates of primary and secondary sources and therefore often considered tertiary sources. They may also serve as a point of access to the full or partial text of primary and secondary sources. Almanacs, travel guides, field guides, and timelines are also examples of tertiary sources. Tertiary sources attempt to summarize, collect, and consolidate the source materials into an overview without adding analysis and synthesis of new conclusions. Wikipedia is a tertiary source.

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  Affectiva

  Affectiva is an artificial intelligence software development company. In 2021, the company was acquired by SmartEye. The company claimed its AI understood human emotions, cognitive states, activities and the objects people use, by analyzing facial and vocal expressions. The offshoot of MIT Media Lab, Affectiva created a new technological category of artificial emotional intelligence, namely, Emotion AI. == History == Affectiva was co-founded by Rana el Kaliouby, who became chief executive officer as of May 25, 2016, and Rosalind W. Picard, who worked as chairman and Chief Scientist until 2013. Both of Affectiva's early products grew out of collaborative research at the MIT's Media Lab to help people on the autism spectrum. Affectiva was acquired for a mostly-stock deal of $73.5m by Swedish SmartEye, a former competitor. == Technology == The company has expanded its Emotion AI technology to detect more than facial expressions, reactions and emotions. Affectiva's software detects complex and nuanced emotions, cognitive states, such as drowsiness and distraction, certain activities and the objects people use. It does that by analyzing the human face, vocal intonations and body posture. Affectiva's AI is built with deep learning, computer vision, and large amounts of data that has been collected in real-world scenarios. The AI uses an optical sensor like a webcam or smartphone camera to identify a human face in real-time. Then, computer vision algorithms identify key features on the face, which are analyzed by deep learning algorithms to classify facial expressions. These facial expressions are then mapped back to emotions. One journal paper found the Affectiva iMotions Facial Expression Analysis Software results are comparable to results using facial Electromyography. Affectiva also uses computer vision to detect objects like a cellphone and car seat, as well as body key points, which track body joints to determine movement and location. Affectiva has collected massive amounts of data that are used to train and test the company's deep learning algorithms, and provide insight into human emotional reactions and engagement. The company has analyzed more than 10 million face videos from 90 countries, making it one of the largest data repositories of its kind. Affectiva has also collected more than 19,000 hours of automotive in-cabin data from 4,000 unique individuals. This automotive data is used to adapt its algorithms to varying camera angles, lighting and other environmental conditions in a vehicle. === Applications === Affectiva's AI had many applications, but the company's primary focus is on Media Analytics. Other uses of Affectiva's AI includes applications in automotive, healthcare and mental health, robotics, conversational interfaces, education, gaming, and more. ==== Media analytics ==== Affectiva's technology was first deployed in media analytics, for market research purposes. The company had since then tested more than 53,000 ads in 90 countries. Brands, advertising agencies and insights firms used the company's Emotion AI to measure the unfiltered and unbiased emotional responses consumers have when viewing video ads and movie trailers. These insights helped improve brand and media content, and predict key metrics in advertising such as sales lift, purchase intent and virality. Affectiva's technology was also used in qualitative research. Affectiva had partnered with leading insights firms such as Kantar, LRW, Added Value and Unruly. Through these collaborations, 28 percent of the Fortune Global 500 companies, and 70 percent of the world's largest advertisers, used Affectiva's Emotion AI. On September 5, 2019, Affectiva announced the appointment of Graham Page, a seasoned Kantar executive, as Global Managing Director of Media Analytics to expand on the company's existing footprint in the media analytics space. ==== Automotive ==== On March 21, 2018, Affectiva launched Affectiva Automotive AI, the first multi-modal in-cabin sensing solution to understand what is happening with people in a vehicle. It used cameras in the car to measure in real time, the state of the driver, the state of the occupants and the state of the vehicle interior (i.e. cabin). This insight helped car manufacturers, fleet management companies and rideshare providers improve road safety and build better driver monitoring systems, by understanding dangerous driver behavior such as drowsiness, distraction and anger. It was also used to create more comfortable and enjoyable transportation experiences, by understanding how passengers react to the environment, such as content they can consume in the back of the car. In addition to understanding driver and occupant emotional and cognitive states, Affectiva Automotive AI could also detect contextual cabin information such as the number of passengers, where they are sitting and if an object is present. Affectiva worked with a number of leading car manufacturers and transportation technology companies, including Aptiv, Cerence, Hyundai Kia, Faurecia, Porsche, BMW, GreenRoad Technologies, and Veoneer. == Acquisition == In June 2021 Smart Eye acquired Affectiva.

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  Video Super Resolution

  RTX Video Super Resolution (RTX VSR) is a video scaling feature by Nvidia. It was released on February 28, 2023. == History == The feature was first unveiled during CES 2023 as RTX Video Super Resolution. It uses the on-board Tensor Cores to upscale browser video content in real time. Video Super Resolution was initially only available on RTX 30 and 40 series GPUs, while support for 20 series GPUs was added afterwards; it is now available on all Nvidia RTX-branded GPUs. The feature supports input resolutions from 360p to 1440p and a max output of 4K and comes without support for HDR content although that could be likely added in the future. Nvidia released RTX Video Super Resolution 1.5 with improved video quality and RTX 20 series support on October 17, 2023. == Reception == According to ComputerBase, although "the algorithm is not yet working flawlessly", the feature is "overall recommendable".

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  Token-based replay

  Token-based replay technique is a conformance checking algorithm that checks how well a process conforms with its model by replaying each trace on the model (in Petri net notation ). Using the four counters produced tokens, consumed tokens, missing tokens, and remaining tokens, it records the situations where a transition is forced to fire and the remaining tokens after the replay ends. Based on the count at each counter, we can compute the fitness value between the trace and the model. == The algorithm == Source: The token-replay technique uses four counters to keep track of a trace during the replaying: p: Produced tokens c: Consumed tokens m: Missing tokens (consumed while not there) r: Remaining tokens (produced but not consumed) Invariants: At any time: p + m ≥ c ≥ m {\displaystyle p+m\geq c\geq m} At the end: r = p + m − c {\displaystyle r=p+m-c} At the beginning, a token is produced for the source place (p = 1) and at the end, a token is consumed from the sink place (c' = c + 1). When the replay ends, the fitness value can be computed as follows: 1 2 ( 1 − m c ) + 1 2 ( 1 − r p ) {\displaystyle {\frac {1}{2}}(1-{\frac {m}{c}})+{\frac {1}{2}}(1-{\frac {r}{p}})} == Example == Suppose there is a process model in Petri net notation as follows: === Example 1: Replay the trace (a, b, c, d) on the model M === Step 1: A token is initiated. There is one produced token ( p = 1 {\displaystyle p=1} ). Step 2: The activity a {\displaystyle \mathbf {a} } consumes 1 token to be fired and produces 2 tokens ( p = 1 + 2 = 3 {\displaystyle p=1+2=3} and c = 1 {\displaystyle c=1} ). Step 3: The activity b {\displaystyle \mathbf {b} } consumes 1 token and produces 1 token ( p = 3 + 1 = 4 {\displaystyle p=3+1=4} and c = 1 + 1 = 2 {\displaystyle c=1+1=2} ). Step 4: The activity c {\displaystyle \mathbf {c} } consumes 1 token and produces 1 token ( p = 4 + 1 = 5 {\displaystyle p=4+1=5} and c = 2 + 1 = 3 {\displaystyle c=2+1=3} ). Step 5: The activity d {\displaystyle \mathbf {d} } consumes 2 tokens and produces 1 token ( p = 5 + 1 = 6 {\displaystyle p=5+1=6} , c = 3 + 2 = 5 {\displaystyle c=3+2=5} ). Step 6: The token at the end place is consumed ( c = 5 + 1 = 6 {\displaystyle c=5+1=6} ). The trace is complete. The fitness of the trace ( a , b , c , d {\displaystyle \mathbf {a,b,c,d} } ) on the model M {\displaystyle \mathbf {M} } is: 1 2 ( 1 − m c ) + 1 2 ( 1 − r p ) = 1 2 ( 1 − 0 6 ) + 1 2 ( 1 − 0 6 ) = 1 {\displaystyle {\frac {1}{2}}(1-{\frac {m}{c}})+{\frac {1}{2}}(1-{\frac {r}{p}})={\frac {1}{2}}(1-{\frac {0}{6}})+{\frac {1}{2}}(1-{\frac {0}{6}})=1} === Example 2: Replay the trace (a, b, d) on the model M === Step 1: A token is initiated. There is one produced token ( p = 1 {\displaystyle p=1} ). Step 2: The activity a {\displaystyle \mathbf {a} } consumes 1 token to be fired and produces 2 tokens ( p = 1 + 2 = 3 {\displaystyle p=1+2=3} and c = 1 {\displaystyle c=1} ). Step 3: The activity b {\displaystyle \mathbf {b} } consumes 1 token and produces 1 token ( p = 3 + 1 = 4 {\displaystyle p=3+1=4} and c = 1 + 1 = 2 {\displaystyle c=1+1=2} ). Step 4: The activity d {\displaystyle \mathbf {d} } needs to be fired but there are not enough tokens. One artificial token was produced and the missing token counter is increased by one ( m = 1 {\displaystyle m=1} ). The artificial token and the token at place [ b , d ] {\displaystyle [\mathbf {b,d} ]} are consumed ( c = 2 + 2 = 4 {\displaystyle c=2+2=4} ) and one token is produced at place end ( p = 4 + 1 = 5 {\displaystyle p=4+1=5} ). Step 5: The token in the end place is consumed ( c = 4 + 1 = 5 {\displaystyle c=4+1=5} ). The trace is complete. There is one remaining token at place [ a , c ] {\displaystyle [\mathbf {a,c} ]} ( r = 1 {\displaystyle r=1} ). The fitness of the trace ( a , b , d {\displaystyle \mathbf {a,b,d} } ) on the model M {\displaystyle \mathbf {M} } is: 1 2 ( 1 − m c ) + 1 2 ( 1 − r p ) = 1 2 ( 1 − 1 5 ) + 1 2 ( 1 − 1 5 ) = 0.8 {\displaystyle {\frac {1}{2}}(1-{\frac {m}{c}})+{\frac {1}{2}}(1-{\frac {r}{p}})={\frac {1}{2}}(1-{\frac {1}{5}})+{\frac {1}{2}}(1-{\frac {1}{5}})=0.8}

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  Regulation of algorithms

  Regulation of algorithms, or algorithmic regulation, is the creation of laws, rules and public sector policies for promotion and regulation of algorithms, particularly in artificial intelligence and machine learning. For the subset of AI algorithms, the term regulation of artificial intelligence is used. The regulatory and policy landscape for artificial intelligence (AI) is an emerging issue in jurisdictions globally, including in the European Union. Regulation of AI is considered necessary to both encourage AI and manage associated risks, but challenging. Another emerging topic is the regulation of blockchain algorithms (Use of the smart contracts must be regulated) and is mentioned along with regulation of AI algorithms. Many countries have enacted regulations of high frequency trades, which is shifting due to technological progress into the realm of AI algorithms. The motivation for regulation of algorithms is the apprehension of losing control over the algorithms, whose impact on human life increases. Multiple countries have already introduced regulations in case of automated credit score calculation—right to explanation is mandatory for those algorithms. For example, The IEEE has begun developing a new standard to explicitly address ethical issues and the values of potential future users. Bias, transparency, and ethics concerns have emerged with respect to the use of algorithms in diverse domains ranging from criminal justice to healthcare—many fear that artificial intelligence could replicate existing social inequalities along race, class, gender, and sexuality lines. == Regulation of artificial intelligence == === Public discussion === In 2016, Joy Buolamwini founded Algorithmic Justice League after a personal experience with biased facial detection software in order to raise awareness of the social implications of artificial intelligence through art and research. In 2017 Elon Musk advocated regulation of algorithms in the context of the existential risk from artificial general intelligence. According to NPR, the Tesla CEO was "clearly not thrilled" to be advocating for government scrutiny that could impact his own industry, but believed the risks of going completely without oversight are too high: "Normally the way regulations are set up is when a bunch of bad things happen, there's a public outcry, and after many years a regulatory agency is set up to regulate that industry. It takes forever. That, in the past, has been bad but not something which represented a fundamental risk to the existence of civilisation." In response, some politicians expressed skepticism about the wisdom of regulating a technology that is still in development. Responding both to Musk and to February 2017 proposals by European Union lawmakers to regulate AI and robotics, Intel CEO Brian Krzanich has argued that artificial intelligence is in its infancy and that it is too early to regulate the technology. Instead of trying to regulate the technology itself, some scholars suggest to rather develop common norms including requirements for the testing and transparency of algorithms, possibly in combination with some form of warranty. One suggestion has been for the development of a global governance board to regulate AI development. In 2020, the European Union published its draft strategy paper for promoting and regulating AI. Algorithmic tacit collusion is a legally dubious antitrust practise committed by means of algorithms, which the courts are not able to prosecute. This danger concerns scientists and regulators in EU, US and beyond. European Commissioner Margrethe Vestager mentioned an early example of algorithmic tacit collusion in her speech on "Algorithms and Collusion" on March 16, 2017, described as follows: "A few years ago, two companies were selling a textbook called The Making of a Fly. One of those sellers used an algorithm which essentially matched its rival’s price. That rival had an algorithm which always set a price 27% higher than the first. The result was that prices kept spiralling upwards, until finally someone noticed what was going on, and adjusted the price manually. By that time, the book was selling – or rather, not selling – for 23 million dollars a copy." In 2018, the Netherlands employed an algorithmic system SyRI (Systeem Risico Indicatie) to detect citizens perceived being high risk for committing welfare fraud, which quietly flagged thousands of people to investigators. This caused a public protest. The district court of Hague shut down SyRI referencing Article 8 of the European Convention on Human Rights (ECHR). In 2020, algorithms assigning exam grades to students in the UK sparked open protest under the banner "Fuck the algorithm." This protest was successful and the grades were taken back. In 2024, the Munich Convention on AI, Data and Human Rights was introduced as part of growing international efforts to regulate artificial intelligence through a human rights lens. Developed through a collaborative drafting process involving scholars from the Technical University of Munich, Stellenbosch University, Ulster University, and KNUST, the initiative calls for an international conversation on a binding treaty to safeguard human rights and the principles enshrined in the UN Charter in the age of AI. === Implementation === AI law and regulations can be divided into three main topics, namely governance of autonomous intelligence systems, responsibility and accountability for the systems, and privacy and safety issues. The development of public sector strategies for management and regulation of AI has been increasingly deemed necessary at the local, national, and international levels and in fields from public service management to law enforcement, the financial sector, robotics, the military, and international law. There are many concerns that there is not enough visibility and monitoring of AI in these sectors. In the United States financial sector, for example, there have been calls for the Consumer Financial Protection Bureau to more closely examine source code and algorithms when conducting audits of financial institutions' non-public data. In the United States, on January 7, 2019, following an Executive Order on 'Maintaining American Leadership in Artificial Intelligence', the White House's Office of Science and Technology Policy released a draft Guidance for Regulation of Artificial Intelligence Applications, which includes ten principles for United States agencies when deciding whether and how to regulate AI. In response, the National Institute of Standards and Technology has released a position paper, the National Security Commission on Artificial Intelligence has published an interim report, and the Defense Innovation Board has issued recommendations on the ethical use of AI. In April 2016, for the first time in more than two decades, the European Parliament adopted a set of comprehensive regulations for the collection, storage, and use of personal information, the General Data Protection Regulation (GDPR)1 (European Union, Parliament and Council 2016). The GDPR's policy on the right of citizens to receive an explanation for algorithmic decisions highlights the pressing importance of human interpretability in algorithm design. In 2016, China published a position paper questioning the adequacy of existing international law to address the eventuality of fully autonomous weapons, becoming the first permanent member of the U.N. Security Council to broach the issue, and leading to proposals for global regulation. In the United States, steering on regulating security-related AI is provided by the National Security Commission on Artificial Intelligence. In 2017, the U.K. Vehicle Technology and Aviation Bill imposes liability on the owner of an uninsured automated vehicle when driving itself and makes provisions for cases where the owner has made "unauthorized alterations" to the vehicle or failed to update its software. Further ethical issues arise when, e.g., a self-driving car swerves to avoid a pedestrian and causes a fatal accident. In 2021, the European Commission proposed the Artificial Intelligence Act. == Algorithm certification == There is a concept of algorithm certification emerging as a method of regulating algorithms. Algorithm certification involves auditing whether the algorithm used during the life cycle 1) conforms to the protocoled requirements (e.g., for correctness, completeness, consistency, and accuracy); 2) satisfies the standards, practices, and conventions; and 3) solves the right problem (e.g., correctly model physical laws), and satisfies the intended use and user needs in the operational environment. == Regulation of blockchain algorithms == Blockchain systems provide transparent and fixed records of transactions and hereby contradict the goal of the European GDPR, which is to give individuals full control of their private data. By implementing the Decree on Development of Digital Economy, Bel

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  Schema crosswalk

  A schema crosswalk is a table that shows equivalent elements (or "fields") in more than one database schema. It maps the elements in one schema to the equivalent elements in another. Crosswalk tables are often employed within or in parallel to enterprise systems, especially when multiple systems are interfaced or when the system includes legacy system data. In the context of Interfaces, they function as an internal extract, transform, load (ETL) mechanism. For example, this is a metadata crosswalk from MARC standards to Dublin Core: Crosswalks show people where to put the data from one scheme into a different scheme. They are often used by libraries, archives, museums, and other cultural institutions to translate data to or from MARC standards, Dublin Core, Text Encoding Initiative (TEI), and other metadata schemes. For example, an archive has a MARC record in its catalog describing a manuscript. Suppose the archive makes a digital copy of that manuscript and wants to display it on the web along with the information from the catalog. In that case, it will have to translate the data from the MARC catalog record into a different format, such as Metadata Object Description Schema, that is viewable on a webpage. Because MARC has various fields than MODS, decisions must be made about where to put the data into MODS. This type of "translating" from one format to another is often called "metadata mapping" or "field mapping," and is related to "data mapping", and "semantic mapping". Crosswalks also have several technical capabilities. They help databases using different metadata schemes to share information. They help metadata harvesters create union catalogs. They enable search engines to search multiple databases simultaneously with a single query. == Challenges for crosswalks == One of the biggest challenges for crosswalks is that no two metadata schemes are 100% equivalent. One scheme may have a field that doesn't exist in another scheme or a field that is split into two different fields in another scheme; this is why data is often lost when mapping from a complex scheme to a simpler one. For example, when mapping from MARC to Simple Dublin Core, the distinction between types of titles is lost: Simple Dublin Core only has one "Title" element, so all of the different types of MARC titles get lumped together without further distinctions. A future attempt to convert the metadata back into MARC would enter the information in the basic MARC 245 Title Statement field, with none of the original distinctions. This is why crosswalks are said to be "lateral" (one-way) mappings from one scheme to another. Separate crosswalks would be required to map from scheme A to scheme B and from scheme B to scheme A. === Difficulties in mapping === Other mapping problems arise when: One scheme has one element that needs to be split up with different parts of it placed in multiple other elements in the second scheme ("one-to-many" mapping) One scheme allows an element to be repeated more than once while another only allows that element to appear once with multiple terms in it Schemes have different data formats (e.g. John Doe or Doe, John) An element in one scheme is indexed, but the equivalent element in the other scheme is not Schemes may use different controlled vocabularies Schemes change their standards over time Some of these problems are not fixable. As Karen Coyle says in "Crosswalking Citation Metadata: The University of California's Experience," "The more metadata experience we have, the more it becomes clear that metadata perfection is not attainable, and anyone who attempts it will be sorely disappointed. When metadata is crosswalked between two or more unrelated sources, there will be data elements that cannot be reconciled in an ideal manner. The key to a successful metadata crosswalk is intelligent flexibility. It is essential to focus on the important goals and be willing to compromise to reach a practical conclusion to projects."

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  Supper (Spotify)

  Supper is a web-based application on the Spotify digital music streaming platform. The Supper app was born from a group of friends who had backgrounds in the music and gastronomy industries. Digital music solutions company Artisan Council later executed it. The app now sits in the top 40 applications on Spotify. == About == The Supper Spotify application matches recipes for all occasions and skill levels with a playlist for both preparation and presentation, as envisioned by the chefs themselves. Supper is credited with being one of the first apps to pair music with food. Playing on the social nature of music and food culture, users can seamlessly experience both for the first time with real time music streaming. == Supper.mx == In May 2014 Supper was launched outside of the Spotify streaming platform. Though still in partnership with Spotify, supper.mx allows users to view Supper's music + food collaborations on mobile, tablet and desktop, without the need to download Spotify directly. == Curators == All of the recipes and playlists featured on the Supper app come straight from a growing network of tastemakers, including chefs, musicians and institutions around the world. Each month the recipes and playlists are updated in conjunction with current holidays, events and seasons. === Launch === Launching in October 2013 the first edition of Supper featured content from a range of eating institutions and culture makers from the US and Australia. Brooklyn Bowl (Brooklyn) Roberta's Pizza (Brooklyn) Fancy Hanks (Melbourne) The Foresters/Queenies Upstairs (Sydney) Hipstamatic Panama House (Bondi) Sweetwater Inn (Melbourne) Soul Clap (Syd record label) Yellow Birds (Melbourne) === November 2013 === Yardbird (Hong Kong) Sonoma Bakery (Sydney) Do or Dine (Brooklyn) Cameo Gallery (Brooklyn) Hypertrak (Blog) Blue Smoke (NYC) The Crepes of Wrath (Blog) Willin Low // Wild Rocket - Wild Oats - Relish === December 2013 === The Copper Mill (Sydney) Thug Kitchen Mamak (Sydney) Tutu's (Brooklyn) Chin Chin (Melbourne) Flat Iron Steak (London) Greasy Spoon (Copenhagen) === January 2014 === Mexicali Taco & Co. (LA) Church & State (LA) Salts Cure (LA) Nopa (SF) L & E Oyster (LA) 4100 bar (LA) Golden Gopher (LA) The Pie Hole (LA) State Bird Provisions (SF) === Momofuku === In February 2014 Supper teamed up with restaurant heavy weights Momofuku. The recipes featured came from their iconic New York, Toronto and Sydney restaurants. Head office also got involved with an instructional from Brand Director Sue Chan on how to paint Momofuku vibes on to any party. === SXSW === March sees the Supper team migrate to Austin, Texas for SXSW, bringing together the best eateries the city has to offer as well as the music that has influenced them. Restaurants and eateries on board in 2014 included: The Backspace Kelis Swifts Attic Uchi Jackalope Paul Qui/East Side King Thai Kun Wonderland Hole in the Wall Justine's Brasserie The Liberty === Kelis === In April 2014 Kelis presented 5 of her recipes paired with a personal playlist for Supper. Kelis shared her recipes for apple farro, jerk ribs, New York vanilla bean cheesecake and Jerk Ribs. The Kelis/Supper collaboration coincided with the release of Kelis' 2014 album titled 'Food'. === Roberta's Pizza === In May 2014 Bushwick's Roberta's Pizza was guest curator on the Supper app and website. Included in their selections were restaurants and bars from across New York including Bun-ker Vietnamese, Old Stanley's Bar, St. Anselm, Chuko, Frank's Cocktail Lounge, Junior's Cheesecake, Xi'an Famous Foods, Xe Lua, 124 Old Rabbit and Yuji Ramen.

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  Car–Parrinello molecular dynamics

  Car–Parrinello molecular dynamics (CPMD) refers to either a method used in molecular dynamics (also known as the Car–Parrinello method) or the computational chemistry software package used to implement this method. The CPMD method is one of the major methods for calculating ab initio molecular dynamics (ab initio MD or AIMD). Ab initio molecular dynamics (AIMD) is a computational method that uses first principles through quantum mechanics to simulate the motion of atoms in a system. It is a type of molecular dynamics (MD) simulation that does not rely on empirical potentials or force fields to describe the interactions between atoms, but rather calculates these interactions entirely from the electronic structure of the system using quantum mechanics. In an ab initio MD simulation, the total energy of the system is calculated at each time step using density functional theory (DFT), Hartree-Fock (HF), or other electronic structure calculation methods. The forces acting on each atom are then determined from the gradient of the energy with respect to the atomic coordinates, and the equations of motion are solved to predict the trajectory of the atoms. AIMD permits chemical bond breaking and forming events to occur and accounts for electronic polarization effect. Therefore, Ab initio MD simulations can be used to study a wide range of phenomena, including the structural, thermodynamic, and dynamic properties of materials and chemical reactions. They are particularly useful for systems that are not well described by empirical potentials or force fields, such as systems with strong electronic correlation or systems with many degrees of freedom. However, ab initio MD simulations are computationally demanding and require significant computational resources. The CPMD method is related to the more common Born–Oppenheimer molecular dynamics (BOMD) method in that the quantum mechanical effect of the electrons is included in the calculation of energy and forces for the classical motion of the nuclei. CPMD and BOMD are different types of AIMD. However, whereas BOMD treats the electronic structure problem within the time-independent Schrödinger equation, CPMD explicitly includes the electrons as active degrees of freedom, via (fictitious) dynamical variables. The software is a parallelized plane wave / pseudopotential implementation of density functional theory, particularly designed for ab initio molecular dynamics. == Car–Parrinello method == The Car–Parrinello method is a type of molecular dynamics, usually employing periodic boundary conditions, planewave basis sets, and density functional theory, proposed by Roberto Car and Michele Parrinello in 1985 while working at SISSA, who were subsequently awarded the Dirac Medal by ICTP in 2009. In contrast to Born–Oppenheimer molecular dynamics wherein the nuclear (ions) degree of freedom are propagated using ionic forces which are calculated at each iteration by approximately solving the electronic problem with conventional matrix diagonalization methods, the Car–Parrinello method explicitly introduces the electronic degrees of freedom as (fictitious) dynamical variables, writing an extended Lagrangian for the system which leads to a system of coupled equations of motion for both ions and electrons. In this way, an explicit electronic minimization at each time step, as done in Born–Oppenheimer MD, is not needed: after an initial standard electronic minimization, the fictitious dynamics of the electrons keeps them on the electronic ground state corresponding to each new ionic configuration visited along the dynamics, thus yielding accurate ionic forces. In order to maintain this adiabaticity condition, it is necessary that the fictitious mass of the electrons is chosen small enough to avoid a significant energy transfer from the ionic to the electronic degrees of freedom. This small fictitious mass in turn requires that the equations of motion are integrated using a smaller time step than the one (1–10 fs) commonly used in Born–Oppenheimer molecular dynamics. Currently, the CPMD method can be applied to systems that consist of a few tens or hundreds of atoms and access timescales on the order of tens of picoseconds. == General approach == In CPMD the core electrons are usually described by a pseudopotential and the wavefunction of the valence electrons are approximated by a plane wave basis set. The ground state electronic density (for fixed nuclei) is calculated self-consistently, usually using the density functional theory method. Kohn-Sham equations are often used to calculate the electronic structure, where electronic orbitals are expanded in a plane-wave basis set. Then, using that density, forces on the nuclei can be computed, to update the trajectories (using, e.g. the Verlet integration algorithm). In addition, however, the coefficients used to obtain the electronic orbital functions can be treated as a set of extra spatial dimensions, and trajectories for the orbitals can be calculated in this context. == Fictitious dynamics == CPMD is an approximation of the Born–Oppenheimer MD (BOMD) method. In BOMD, the electrons' wave function must be minimized via matrix diagonalization at every step in the trajectory. CPMD uses fictitious dynamics to keep the electrons close to the ground state, preventing the need for a costly self-consistent iterative minimization at each time step. The fictitious dynamics relies on the use of a fictitious electron mass (usually in the range of 400 – 800 a.u.) to ensure that there is very little energy transfer from nuclei to electrons, i.e. to ensure adiabaticity. Any increase in the fictitious electron mass resulting in energy transfer would cause the system to leave the ground-state BOMD surface. === Lagrangian === L = 1 2 ( ∑ I n u c l e i M I R ˙ I 2 + μ ∑ i o r b i t a l s ∫ d r | ψ ˙ i ( r , t ) | 2 ) − E [ { ψ i } , { R I } ] + ∑ i j Λ i j ( ∫ d r ψ i ψ j − δ i j ) , {\displaystyle {\mathcal {L}}={\frac {1}{2}}\left(\sum _{I}^{\mathrm {nuclei} }\ M_{I}{\dot {\mathbf {R} }}_{I}^{2}+\mu \sum _{i}^{\mathrm {orbitals} }\int d\mathbf {r} \ |{\dot {\psi }}_{i}(\mathbf {r} ,t)|^{2}\right)-E\left[\{\psi _{i}\},\{\mathbf {R} _{I}\}\right]+\sum _{ij}\Lambda _{ij}\left(\int d\mathbf {r} \ \psi _{i}\psi _{j}-\delta _{ij}\right),} where μ {\displaystyle \mu } is the fictitious mass parameter; E[{ψi},{RI}] is the Kohn–Sham energy density functional, which outputs energy values when given Kohn–Sham orbitals and nuclear positions. === Orthogonality constraint === ∫ d r ψ i ∗ ( r , t ) ψ j ( r , t ) = δ i j , {\displaystyle \int d\mathbf {r} \ \psi _{i}^{}(\mathbf {r} ,t)\psi _{j}(\mathbf {r} ,t)=\delta _{ij},} where δij is the Kronecker delta. === Equations of motion === The equations of motion are obtained by finding the stationary point of the Lagrangian under variations of ψi and RI, with the orthogonality constraint. M I R ¨ I = − ∇ I E [ { ψ i } , { R I } ] {\displaystyle M_{I}{\ddot {\mathbf {R} }}_{I}=-\nabla _{I}\,E\left[\{\psi _{i}\},\{\mathbf {R} _{I}\}\right]} μ ψ ¨ i ( r , t ) = − δ E δ ψ i ∗ ( r , t ) + ∑ j Λ i j ψ j ( r , t ) , {\displaystyle \mu {\ddot {\psi }}_{i}(\mathbf {r} ,t)=-{\frac {\delta E}{\delta \psi _{i}^{}(\mathbf {r} ,t)}}+\sum _{j}\Lambda _{ij}\psi _{j}(\mathbf {r} ,t),} where Λij is a Lagrangian multiplier matrix to comply with the orthonormality constraint. === Born–Oppenheimer limit === In the formal limit where μ → 0, the equations of motion approach Born–Oppenheimer molecular dynamics. == Software packages == There are a number of software packages available for performing AIMD simulations. Some of the most widely used packages include: CP2K: an open-source software package for AIMD. Quantum Espresso: an open-source package for performing DFT calculations. It includes a module for AIMD. VASP: a commercial software package for performing DFT calculations. It includes a module for AIMD. Gaussian: a commercial software package that can perform AIMD. NWChem: an open-source software package for AIMD. LAMMPS: an open-source software package for performing classical and ab initio MD simulations. SIESTA: an open-source software package for AIMD. ORCA: a general-purpose quantum chemistry package. == Applications == Studying the behavior of water across different environments, such as near a hydrophobic graphene sheet. Investigating the structure and dynamics of liquid water at ambient temperature. Solving the heat transfer problems (heat conduction and thermal radiation), such as in Si/Ge superlattices. Probing the proton transfer along hydrogen-bonds in different environments, such as in 1D water chains inside carbon nanotubes. Evaluating the critical point of crystals, composites, and solid-state materials, such as aluminum. Predicting and modelling different phases and phase transitions, such as in the amorphous phase of the phase-change memory material GeSbTe. Studying the combustion of combustibles, such as lignite-water systems. Measuring th

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  Information scientist

  The term information scientist developed in the latter part of the twentieth century by Wm. Hovey Smith to describe an individual, usually with a relevant subject degree (such as one in Information and Computer Science - CIS) or high level of subject knowledge, providing focused information to scientific and technical research staff in industry. It is a role quite distinct from and complementary to that of a librarian. Developments in end-user searching, together with some convergence between the roles of librarian and information scientist, have led to a diminution in its use in this context, and the term information officer or information professional (information specialist) are also now used. The term was, and is, also used for an individual carrying out research in information science. Brian C. Vickery mentions that the Institute of Information Scientists (IIS) was established in London during 1958 and lists the criteria put forward by this institute "Criteria for Information Science" (appendix 1) as well as his own "Areas of study in information science" (appendix 2). The IIS merged with the Library Association in 2002 to form the Chartered Institute of Library and Information Professionals (CILIP). == Notable Information Scientists == See also Award of Merit - Association for Information Science and Technology Marcia Bates David Blair (information technologist) Samuel C. Bradford Michael Buckland John M. Carroll Blaise Cronin Emilia Currás Brenda Dervin Eugene Garfield Paul B. Kantor Frederick Wilfrid Lancaster Calvin Mooers Tefko Saracevic Linda C. Smith Robert Saxton Taylor Brian Campbell Vickery Thomas D. Wilson == Additional reading == Ellis, David and Merete Haugan. (1997) "Modelling the information seeking patterns of engineers and research scientists in an industrial environment" (Journal of Documentation, Volume 53(4): pp. 384–403) Poole, Alex H. (2024). "'There's a big difference between going through life with the wind at your back, and going through life leaning into the wind': Feminism in Post-World War II Information Science". Proceedings of the Association for Information Science and Technology. 61: 300–313. doi:10.1002/pra2.1029. Vickery, Brian Campbell (1988) "Essays presented to B. C. Vickery" (Journal of Documentation, Volume 44, pp. 199–283). Vickery, B. & Vickery, A. (1987) Information Science in theory and practice (London: Bowker-Saur, pp. 361–369)

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