AI Headshot Linkedin Generator

AI Headshot Linkedin Generator — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • No Thanks (app)

    No Thanks (app)

    No Thanks is a Palestinian boycott-awareness mobile application developed by Palestinian software engineer Ahmed Bashbash, created to assist consumers in identifying and boycotting products associated with companies linked to Israel. Launched in 13 November 2023, the app gained significant attention amid the Gaza–Israel conflict. == History == No Thanks is a mobile application developed by Ahmed Bashbash, a Palestinian software engineer from Gaza residing in Hungary. The app was conceived in October 2023 following the death of Bashbash's brother in an Israeli airstrike on October 31, 2023. His sister had previously died in 2020 due to delayed medical treatment. The app was officially launched on November 13, 2023, and quickly gained traction, got over 100,000 downloads within its first month of release. On November 30, 2023, Google removed the app from its Play Store due to a violation of its content policies. The app's home page included a description: "Welcome to No Thanks, here you can see if the product in your hand supports killing children in Palestine or not," which was deemed to contravene Google's guidelines on hate speech and sensitive content. On December 3, 2023, following changes to the app's description, Google reinstated the app.

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  • MarkLogic Server

    MarkLogic Server

    MarkLogic Server is a document-oriented database developed by MarkLogic. It is a NoSQL multi-model database that evolved from an XML database to natively store JSON documents and RDF triples, the data model for semantics. MarkLogic is designed to be a data hub for operational and analytical data. == History == MarkLogic Server was built to address shortcomings with existing search and data products. The product first focused on using XML as the document markup standard and XQuery as the query standard for accessing collections of documents up to hundreds of terabytes in size. Currently the MarkLogic platform is widely used in publishing, government, finance and other sectors. MarkLogic's customers are mostly Global 2000 companies. == Technology == MarkLogic uses documents without upfront schemas to maintain a flexible data model. In addition to having a flexible data model, MarkLogic uses a distributed, scale-out architecture that can handle hundreds of billions of documents and hundreds of terabytes of data. It has received Common Criteria certification, and has high availability and disaster recovery. MarkLogic is designed to run on-premises and within public or private cloud environments like Amazon Web Services. == Features == Indexing MarkLogic indexes the content and structure of documents including words, phrases, relationships, and values in over 200 languages with tokenization, collation, and stemming for core languages. Functionality includes the ability to toggle range indexes, geospatial indexes, the RDF triple index, and reverse indexes on or off based on your data, the kinds of queries that you will run, and your desired performance. Full-text search MarkLogic supports search across its data and metadata using a word or phrase and incorporates Boolean logic, stemming, wildcards, case sensitivity, punctuation sensitivity, diacritic sensitivity, and search term weighting. Data can be searched using JavaScript, XQuery, SPARQL, and SQL. Semantics MarkLogic uses RDF triples to provide semantics for ease of storing metadata and querying. ACID Unlike other NoSQL databases, MarkLogic maintains ACID consistency for transactions. Replication MarkLogic provides high availability with replica sets. Scalability MarkLogic scales horizontally using sharding. MarkLogic can run over multiple servers, balancing the load or replicating data to keep the system up and running in the event of hardware failure. Security MarkLogic has built in security features such as element-level permissions and data redaction. Optic API for Relational Operations An API that lets developers view their data as documents, graphs or rows. Security MarkLogic provides redaction, encryption, and element-level security (allowing for control on read and write rights on parts of a document). == Applications == Banking Big Data Fraud prevention Insurance Claims Management and Underwriting Master data management Recommendation engines == Licensing == MarkLogic is available under various licensing and delivery models, namely a free Developer or an Essential Enterprise license.[3] Licenses are available from MarkLogic or directly from cloud marketplaces such as Amazon Web Services and Microsoft Azure. == Releases == 2001 – Cerisent XQE 1: ACID transactions, Full-text search, XML Storage, XQuery, Role-based security 2004 – Cerisent XQE 2: Scale-out architecture, Enhanced search (stemming, thesaurus, wildcard), Backup and restore 2005 – MarkLogic Server 3: Continuing search improvements, Content Processing Framework (including PDF, Word, Excel, PPT), Failover 2008 – MarkLogic Server 4: Geospatial search, entity extraction, advanced XQuery, performance, scalability enhancements, auditing 2011 – MarkLogic Server 5: Flexible replication / DDIL, real-time indexing, advanced search, improved analytics, concurrency enhancements 2012 – MarkLogic Server 6: REST and Java APIs, App Builder, enhanced UI, improved search 2013 – MarkLogic Server 7: Semantic graph, bitemporal data, tiered storage, improved search, better management 2015 – MarkLogic Server 8: A Native JSON storage, Server-side JavaScript, Bitemporal, Node.js client API, Incremental backup, Flexible replication[16] 2017 – MarkLogic Server 9: Data integration across Relational and Non-Relational data, Advanced Encryption, Element Level Security, Redaction 2019 – MarkLogic Server 10: Enhanced Data Hub, improved SQL, security, analytics performance, cloud support 2022 – MarkLogic Server 11: MarkLogic Ops Director (Monitoring and Administration Improvements), expanded PKI 2025 – MarkLogic Server 12: Generative AI and Native Vector Search, Graph Algorithm Support, Virtual TDEs (relational views on the fly)

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  • Retention period

    Retention period

    A retention period (associated with a retention schedule or retention program) is an aspect of records and information management (RIM) and the records life cycle that identifies the duration of time for which the information should be maintained or "retained", irrespective of format (paper, electronic, or other). Retention periods vary with different types of information, based on content and a variety of other factors, including internal organizational need, regulatory requirements for inspection or audit, legal statutes of limitation, involvement in litigation, and taxation and financial reporting needs, as well as other factors as defined by local, regional, state, national, and/or international governing entities. Once an applicable retention period has elapsed for a given type or series of information, and all holds/moratoriums have been released, the information is typically destroyed using an approved and effective destruction method, which renders the information completely and irreversibly unusable via any means. Alternatively, it may be converted from one form to another (e.g. from paper to electronic), depending on the defined retention period per format. Information with historical value beyond its "usable value" may be accessioned to the custody of an archive organization for permanent or extended long-term preservation. == Defensible disposition == Defensible disposition refers to the ability of an identified and applied retention period to effectively provide for the defense of the record, and its eventual destruction or accessioning when scrutinized within a court of law or by other review. It is commonly advised by records and information management (RIM) professionals that any and all retention periods applied to organizational information should be reviewed and approved for use by competent legal counsel, which represents the organization, and is familiar with the specific business needs and legal and regulatory requirements of the organization. Additionally, a practical approach to information assessment/classification, proper documentation of the disposition program, strategic review of disposition policy over time for efficacy are required for proper defensible disposition. == Guidance and education organizations == ARMA International Information and Records Management Society filerskeepers records retention FAQ

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  • Discoverability

    Discoverability

    Discoverability is the degree to which something, especially a piece of content or information, can be found in a search of a file, database, or other information system. Discoverability is a concern in library and information science, many aspects of digital media, software and web development, and in marketing, since products and services cannot be used if people cannot find it or do not understand what it can be used for. In human-computer interaction the term is further used to describe the discoverability of interactions, features and interactive systems overall . Metadata, or "information about information", such as a book's title, a product's description, or a website's keywords, affects how discoverable something is on a database or online. Adding metadata to a product that is available online can make it easier for end users to find the product. For example, if a song file is made available online, making the title, band name, genre, year of release, and other pertinent information available in connection with this song means the file can be retrieved more easily. The organization of information through the implementation of alphabetical structures or the integration of content into search engines exemplifies strategies employed to enhance the discoverability of information. The concept of discoverability, while related to but distinct from accessibility and usability, which are other qualities that affect the usefulness of a piece of information, is a critical aspect of information retrieval. == Etymology == The concept of "discoverability" in an information science and online context is a loose borrowing from the concept of the similar name in the legal profession. In law, "discovery" is a pre-trial procedure in a lawsuit in which each party, through the law of civil procedure, can obtain evidence from the other party or parties by means of discovery devices such as a request for answers to interrogatories, request for production of documents, request for admissions and depositions. Discovery can be obtained from non-parties using subpoenas. When a discovery request is objected to, the requesting party may seek the assistance of the court by filing a motion to compel discovery. == Purpose == The usability of any piece of information directly relates to how discoverable it is, either in a "walled garden" database or on the open Internet. The quality of information available on this database or on the Internet depends upon the quality of the meta-information about each item, product, or service. In the case of a service, because of the emphasis placed on service reusability, opportunities should exist for reuse of this service. However, reuse is only possible if information is discoverable in the first place. To make items, products, and services discoverable, the process is as follows: Document the information about the item, product or service (the metadata) in a consistent manner. Store the documented information (metadata) in a searchable repository. while technically a human-searchable repository, such as a printed paper list would qualify, "searchable repository" is usually taken to mean a computer-searchable repository, such as a database that a human user can search using some type of search engine or "find" feature. Enable search for the documented information in an efficient manner. supports number 2, because while reading through a printed paper list by hand might be feasible in a theoretical sense, it is not time and cost-efficient in comparison with computer-based searching. Apart from increasing the reuse potential of the services, discoverability is also required to avoid development of solution logic that is already contained in an existing service. To design services that are not only discoverable but also provide interpretable information about their capabilities, the service discoverability principle provides guidelines that could be applied during the service-oriented analysis phase of the service delivery process. === Specific to digital media === In relation to audiovisual content, according to the meaning given by the Canadian Radio-television and Telecommunications Commission (CRTC) for the purpose of its 2016 Discoverability Summit, discoverability can be summed up to the intrinsic ability of given content to "stand out of the lot", or to position itself so as to be easily found and discovered. A piece of audiovisual content can be a movie, a TV series, music, a book (eBook), an audio book or podcast. When audiovisual content such as a digital file for a TV show, movie, or song, is made available online, if the content is "tagged" with identifying information such as the names of the key artists (e.g., actors, directors and screenwriters for TV shows and movies; singers, musicians and record producers for songs) and the genres (for movies genres, music genres, etc.). When users interact with online content, algorithms typically determine what types of content the user is interested in, and then a computer program suggests "more like this", which is other content that the user may be interested in. Different websites and systems have different algorithms, but one approach, used by Amazon (company) for its online store, is to indicate to a user: "customers who bought x also bought y" (affinity analysis, collaborative filtering). This example is oriented around online purchasing behaviour, but an algorithm could also be programmed to provide suggestions based on other factors (e.g., searching, viewing, etc.). Discoverability is typically referred to in connection with search engines. A highly "discoverable" piece of content would appear at the top, or near the top of a user's search results. A related concept is the role of "recommendation engines", which give a user recommendations based on his/her previous online activity. Discoverability applies to computers and devices that can access the Internet, including various console video game systems and mobile devices such as tablets and smartphones. When producers make an effort to promote content (e.g., a TV show, film, song, or video game), they can use traditional marketing (billboards, TV ads, radio ads) and digital ads (pop-up ads, pre-roll ads, etc.), or a mix of traditional and digital marketing. Even before the user's intervention by searching for a certain content or type of content, discoverability is the prime factor which contributes to whether a piece of audiovisual content will be likely to be found in the various digital modes of content consumption. As of 2017, modes of searching include looking on Netflix for movies, Spotify for music, Audible for audio books, etc., although the concept can also more generally be applied to content found on Twitter, Tumblr, Instagram, and other websites. It involves more than a content's mere presence on a given platform; it can involve associating this content with "keywords" (tags), search algorithms, positioning within different categories, metadata, etc. Thus, discoverability enables as much as it promotes. For audiovisual content broadcast or streamed on digital media using the Internet, discoverability includes the underlying concepts of information science and programming architecture, which are at the very foundation of the search for a specific product, information or content. === Human-Computer Interaction === In human–computer interaction (HCI), discoverability refers to the ability of users to perceive and comprehend a system, function, or input method upon encountering it, despite a lack of prior awareness or knowledge, whether through intentional effort or serendipitously . The concept was popularised by Don Norman, who framed it around whether users can determine what actions are possible and how to perform them . Discoverability is considered a precondition for learnability, though the two concepts are frequently conflated in the literature . == Applications == === Within a webpage === Within a specific webpage or software application ("app"), the discoverability of a feature, content or link depends on a range of factors, including the size, colour, highlighting features, and position within the page. When colour is used to communicate the importance of a feature or link, designers typically use other elements as well, such as shadows or bolding, for individuals, who cannot see certain colours. Just as traditional paper printing created other physical locations that stood out, such as being "above the fold" of a newspaper versus "below the fold", a web page or app's screenview may have certain locations that give features additional visibility to users, such as being right at the bottom of the web page or screen. The positional advantages or disadvantages of various locations depend on different cultures and languages (e.g., left to right vs. right to left). Some locations have become established, such as having toolbars at the top of a screen or webpage. Some designers have argued t

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  • PagedAttention

    PagedAttention

    PagedAttention is an attention algorithm for efficient serving of large language models (LLMs). It was introduced in 2023 by Woosuk Kwon and colleagues in the paper Efficient Memory Management for Large Language Model Serving with PagedAttention, alongside the vLLM serving engine. The method stores the key–value cache used during autoregressive decoding in fixed-size blocks that can be mapped to non-contiguous physical memory, borrowing ideas from virtual memory, paging, and operating system design. == Background == In transformer inference, the key–value cache grows with sequence length and the number of concurrent requests. Kwon et al. argued that earlier serving systems typically reserved contiguous cache regions in advance, which caused reserved space, internal fragmentation, and external fragmentation. In their experiments, the paper reported that the effective memory utilization of previous systems could fall as low as 20.4%. == Description == PagedAttention partitions the cache of each sequence into fixed-size KV blocks. A request's cache is represented as a sequence of logical blocks, while a block table maps those logical blocks to physical GPU-memory blocks. As a result, neighboring logical blocks do not need to be contiguous in physical memory, and new blocks can be allocated on demand as generation proceeds. The design also makes it easier to share cache state across related decoding paths. In vLLM, physical blocks can be reference-counted and shared among requests or branches, with block-granularity copy-on-write used when a shared block must be modified. The original paper applied this design to parallel sampling, beam search, and prompts with shared prefixes. == Mathematical formulation == For a query token i {\displaystyle i} in causal self-attention, the standard attention output can be written as a i j = exp ⁡ ( q i ⊤ k j / d ) ∑ t = 1 i exp ⁡ ( q i ⊤ k t / d ) , o i = ∑ j = 1 i a i j v j {\displaystyle a_{ij}={\frac {\exp(\mathbf {q} _{i}^{\top }\mathbf {k} _{j}/{\sqrt {d}})}{\sum _{t=1}^{i}\exp(\mathbf {q} _{i}^{\top }\mathbf {k} _{t}/{\sqrt {d}})}},\;\mathbf {o} _{i}=\sum _{j=1}^{i}a_{ij}\mathbf {v} _{j}} where q i {\displaystyle \mathbf {q} _{i}} , k j {\displaystyle \mathbf {k} _{j}} , and v j {\displaystyle \mathbf {v} _{j}} are the query, key, and value vectors, and d {\displaystyle d} is the attention dimension. If the cache is partitioned into blocks of size B {\displaystyle B} , the key and value blocks may be written as K j = ( k ( j − 1 ) B + 1 , … , k j B ) , V j = ( v ( j − 1 ) B + 1 , … , v j B ) {\displaystyle \mathbf {K} _{j}=(\mathbf {k} _{(j-1)B+1},\ldots ,\mathbf {k} _{jB}),\;\mathbf {V} _{j}=(\mathbf {v} _{(j-1)B+1},\ldots ,\mathbf {v} _{jB})} PagedAttention then performs the computation blockwise: A i j = exp ⁡ ( q i ⊤ K j / d ) ∑ t = 1 ⌈ i / B ⌉ exp ⁡ ( q i ⊤ K t / d ) , o i = ∑ j = 1 ⌈ i / B ⌉ V j A i j ⊤ {\displaystyle \mathbf {A} _{ij}={\frac {\exp(\mathbf {q} _{i}^{\top }\mathbf {K} _{j}/{\sqrt {d}})}{\sum _{t=1}^{\lceil i/B\rceil }\exp(\mathbf {q} _{i}^{\top }\mathbf {K} _{t}/{\sqrt {d}})}},\;\mathbf {o} _{i}=\sum _{j=1}^{\lceil i/B\rceil }\mathbf {V} _{j}\mathbf {A} _{ij}^{\top }} where A i j {\displaystyle \mathbf {A} _{ij}} is the vector of attention scores for the j {\displaystyle j} -th KV block. In the formulation given by Kwon et al., this preserves the causal attention calculation while allowing the key and value blocks to reside in non-contiguous physical memory. == Performance and use == The vLLM paper reported that, on its evaluated workloads, the use of PagedAttention and the associated memory-management design improved serving throughput by 2–4× over the compared baselines, including FasterTransformer and Orca, while preserving model outputs. In experiments on OPT-13B with the Alpaca trace, the paper also reported memory savings of 6.1–9.8% for parallel sampling and 37.6–55.2% for beam search through KV-block sharing. A 2024 survey of LLM serving systems described PagedAttention as having become an industry norm in LLM serving frameworks, citing support in TGI, vLLM, and TensorRT-LLM. == Limitations and alternatives == Subsequent work has described trade-offs in the approach. The 2025 vAttention paper argued that PagedAttention requires attention kernels to be rewritten to support paging and increases software complexity, portability issues, redundancy, and execution overhead, proposing instead a memory manager that keeps the cache contiguous in virtual memory while relying on demand paging for physical allocation. === vAttention === Unlike PagedAttention, vAttention does not introduce a different attention rule; it retains the standard attention computation Attention ⁡ ( q i , K , V ) = softmax ⁡ ( q i K ⊤ s c a l e ) V . {\displaystyle \operatorname {Attention} (q_{i},K,V)=\operatorname {softmax} \left({\frac {q_{i}K^{\top }}{\mathrm {scale} }}\right)V.} In the notation of Prabhu et al., the key and value tensors for a request seen so far are K , V ∈ R L ′ × ( H × D ) {\displaystyle K,V\in \mathbb {R} ^{L'\times (H\times D)}} , where L ′ {\displaystyle L'} is the context length seen so far, H {\displaystyle H} is the number of KV heads on a worker, and D {\displaystyle D} is the dimension of each KV head. In systems prior to PagedAttention, the K cache (or V cache) at each layer of a worker is typically allocated as a 4D tensor of shape [ B , L , H , D ] , {\displaystyle [B,L,H,D],} where B {\displaystyle B} is batch size and L {\displaystyle L} is the maximum context length supported by the model. vAttention preserves this contiguous virtual-memory view while deferring physical-memory allocation to runtime. A serving framework maintains separate K and V tensors for each layer, so vAttention reserves 2 N {\displaystyle 2N} virtual-memory buffers on a worker, where N {\displaystyle N} is the number of layers managed by that worker. The maximum size of one virtual-memory buffer is B S = B × S , {\displaystyle BS=B\times S,} where S {\displaystyle S} is the maximum size of a single request's per-layer K cache (or V cache) on a worker. The paper defines S = L × H × D × P , {\displaystyle S=L\times H\times D\times P,} where P {\displaystyle P} is the number of bytes needed to store one element. In this formulation, vAttention keeps the KV cache contiguous in virtual memory and relies on demand paging for physical allocation, rather than modifying the attention kernel to operate over non-contiguous KV-cache blocks.

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  • Tuple

    Tuple

    In mathematics, a tuple is a finite sequence (or ordered list) of numbers. More generally, it is a sequence of mathematical objects, called the elements of the tuple. An n-tuple is a tuple of n elements, where n is a non-negative integer. There is only one 0-tuple, called the empty tuple. A 1-tuple and a 2-tuple are commonly called a singleton and an ordered pair, respectively. The term "infinite tuple" is occasionally used for "infinite sequences". Tuples are usually written by listing the elements within parentheses "( )" and separated by commas; for example, (2, 7, 4, 1, 7) denotes a 5-tuple. Other types of brackets are sometimes used, although they may have a different meaning. An n-tuple can be formally defined as the image of a function that has the set of the first n natural numbers as its domain (1, 2, ..., n). Tuples may be also defined from ordered pairs by a recurrence starting from an ordered pair; indeed, an n-tuple can be identified with the ordered pair of its (n − 1) first elements and its nth element, for example, ( ( ( 1 , 2 ) , 3 ) , 4 ) = ( 1 , 2 , 3 , 4 ) {\displaystyle \left(\left(\left(1,2\right),3\right),4\right)=\left(1,2,3,4\right)} . In computer science, tuples come in many forms. Most typed functional programming languages implement tuples directly as product types, tightly associated with algebraic data types, pattern matching, and destructuring assignment. Many programming languages offer an alternative to tuples, known as record types, featuring unordered elements accessed by label. A few programming languages combine ordered tuple product types and unordered record types into a single construct, as in C structs and Haskell records. Relational databases may formally identify their rows (records) as tuples. Tuples also occur in relational algebra; when programming the semantic web with the Resource Description Framework (RDF); in linguistics; and in philosophy. == Etymology == The term originated as an abstraction of the sequence: single, couple/double, triple, quadruple, quintuple, sextuple, septuple, octuple, ..., n‑tuple, ..., where the prefixes are taken from the Latin names of the numerals. The unique 0-tuple is called the null tuple or empty tuple. A 1‑tuple is called a single (or singleton), a 2‑tuple is called an ordered pair or couple, and a 3‑tuple is called a triple (or triplet). The number n can be any nonnegative integer. For example, a complex number can be represented as a 2‑tuple of reals, a quaternion can be represented as a 4‑tuple, an octonion can be represented as an 8‑tuple, and a sedenion can be represented as a 16‑tuple. Although these uses treat ‑tuple as the suffix, the original suffix was ‑ple as in "triple" (three-fold) or "decuple" (ten‑fold). This originates from medieval Latin plus (meaning "more") related to Greek ‑πλοῦς, which replaced the classical and late antique ‑plex (meaning "folded"), as in "duplex". == Properties == The general rule for the identity of two n-tuples is ( a 1 , a 2 , … , a n ) = ( b 1 , b 2 , … , b n ) {\displaystyle (a_{1},a_{2},\ldots ,a_{n})=(b_{1},b_{2},\ldots ,b_{n})} if and only if a 1 = b 1 , a 2 = b 2 , … , a n = b n {\displaystyle a_{1}=b_{1},{\text{ }}a_{2}=b_{2},{\text{ }}\ldots ,{\text{ }}a_{n}=b_{n}} . Thus a tuple has properties that distinguish it from a set: A tuple may contain multiple instances of the same element, so tuple ( 1 , 2 , 2 , 3 ) ≠ ( 1 , 2 , 3 ) {\displaystyle (1,2,2,3)\neq (1,2,3)} ; but set { 1 , 2 , 2 , 3 } = { 1 , 2 , 3 } {\displaystyle \{1,2,2,3\}=\{1,2,3\}} . Tuple elements are ordered: tuple ( 1 , 2 , 3 ) ≠ ( 3 , 2 , 1 ) {\displaystyle (1,2,3)\neq (3,2,1)} , but set { 1 , 2 , 3 } = { 3 , 2 , 1 } {\displaystyle \{1,2,3\}=\{3,2,1\}} . A tuple has a finite number of elements, while a set or a multiset may have an infinite number of elements. == Definitions == There are several definitions of tuples that give them the properties described in the previous section. === Tuples as functions === The 0 {\displaystyle 0} -tuple may be identified as the empty function. For n ≥ 1 , {\displaystyle n\geq 1,} the n {\displaystyle n} -tuple ( a 1 , … , a n ) {\displaystyle \left(a_{1},\ldots ,a_{n}\right)} may be identified with the surjective function F : { 1 , … , n } → { a 1 , … , a n } {\displaystyle F~:~\left\{1,\ldots ,n\right\}~\to ~\left\{a_{1},\ldots ,a_{n}\right\}} with domain domain ⁡ F = { 1 , … , n } = { i ∈ N : 1 ≤ i ≤ n } {\displaystyle \operatorname {domain} F=\left\{1,\ldots ,n\right\}=\left\{i\in \mathbb {N} :1\leq i\leq n\right\}} and with codomain codomain ⁡ F = { a 1 , … , a n } , {\displaystyle \operatorname {codomain} F=\left\{a_{1},\ldots ,a_{n}\right\},} that is defined at i ∈ domain ⁡ F = { 1 , … , n } {\displaystyle i\in \operatorname {domain} F=\left\{1,\ldots ,n\right\}} by F ( i ) := a i . {\displaystyle F(i):=a_{i}.} That is, F {\displaystyle F} is the function defined by 1 ↦ a 1 ⋮ n ↦ a n {\displaystyle {\begin{alignedat}{3}1\;&\mapsto &&\;a_{1}\\\;&\;\;\vdots &&\;\\n\;&\mapsto &&\;a_{n}\\\end{alignedat}}} in which case the equality ( a 1 , a 2 , … , a n ) = ( F ( 1 ) , F ( 2 ) , … , F ( n ) ) {\displaystyle \left(a_{1},a_{2},\dots ,a_{n}\right)=\left(F(1),F(2),\dots ,F(n)\right)} necessarily holds. Tuples as sets of ordered pairs Functions are commonly identified with their graphs, which is a certain set of ordered pairs. Indeed, many authors use graphs as the definition of a function. Using this definition of "function", the above function F {\displaystyle F} can be defined as: F := { ( 1 , a 1 ) , … , ( n , a n ) } . {\displaystyle F~:=~\left\{\left(1,a_{1}\right),\ldots ,\left(n,a_{n}\right)\right\}.} === Tuples as nested ordered pairs === Another way of modeling tuples in set theory is as nested ordered pairs. This approach assumes that the notion of ordered pair has already been defined. The 0-tuple (i.e. the empty tuple) is represented by the empty set ∅ {\displaystyle \emptyset } . An n-tuple, with n > 0, can be defined as an ordered pair of its first entry and an (n − 1)-tuple (which contains the remaining entries when n > 1): ( a 1 , a 2 , a 3 , … , a n ) = ( a 1 , ( a 2 , a 3 , … , a n ) ) {\displaystyle (a_{1},a_{2},a_{3},\ldots ,a_{n})=(a_{1},(a_{2},a_{3},\ldots ,a_{n}))} This definition can be applied recursively to the (n − 1)-tuple: ( a 1 , a 2 , a 3 , … , a n ) = ( a 1 , ( a 2 , ( a 3 , ( … , ( a n , ∅ ) … ) ) ) ) {\displaystyle (a_{1},a_{2},a_{3},\ldots ,a_{n})=(a_{1},(a_{2},(a_{3},(\ldots ,(a_{n},\emptyset )\ldots ))))} Thus, for example: ( 1 , 2 , 3 ) = ( 1 , ( 2 , ( 3 , ∅ ) ) ) ( 1 , 2 , 3 , 4 ) = ( 1 , ( 2 , ( 3 , ( 4 , ∅ ) ) ) ) {\displaystyle {\begin{aligned}(1,2,3)&=(1,(2,(3,\emptyset )))\\(1,2,3,4)&=(1,(2,(3,(4,\emptyset ))))\\\end{aligned}}} A variant of this definition starts "peeling off" elements from the other end: The 0-tuple is the empty set ∅ {\displaystyle \emptyset } . For n > 0: ( a 1 , a 2 , a 3 , … , a n ) = ( ( a 1 , a 2 , a 3 , … , a n − 1 ) , a n ) {\displaystyle (a_{1},a_{2},a_{3},\ldots ,a_{n})=((a_{1},a_{2},a_{3},\ldots ,a_{n-1}),a_{n})} This definition can be applied recursively: ( a 1 , a 2 , a 3 , … , a n ) = ( ( … ( ( ( ∅ , a 1 ) , a 2 ) , a 3 ) , … ) , a n ) {\displaystyle (a_{1},a_{2},a_{3},\ldots ,a_{n})=((\ldots (((\emptyset ,a_{1}),a_{2}),a_{3}),\ldots ),a_{n})} Thus, for example: ( 1 , 2 , 3 ) = ( ( ( ∅ , 1 ) , 2 ) , 3 ) ( 1 , 2 , 3 , 4 ) = ( ( ( ( ∅ , 1 ) , 2 ) , 3 ) , 4 ) {\displaystyle {\begin{aligned}(1,2,3)&=(((\emptyset ,1),2),3)\\(1,2,3,4)&=((((\emptyset ,1),2),3),4)\\\end{aligned}}} === Tuples as nested sets === Using Kuratowski's representation for an ordered pair, the second definition above can be reformulated in terms of pure set theory: The 0-tuple (i.e. the empty tuple) is represented by the empty set ∅ {\displaystyle \emptyset } ; Let x {\displaystyle x} be an n-tuple ( a 1 , a 2 , … , a n ) {\displaystyle (a_{1},a_{2},\ldots ,a_{n})} , and let x → b ≡ ( a 1 , a 2 , … , a n , b ) {\displaystyle x\rightarrow b\equiv (a_{1},a_{2},\ldots ,a_{n},b)} . Then, x → b ≡ { { x } , { x , b } } {\displaystyle x\rightarrow b\equiv \{\{x\},\{x,b\}\}} . (The right arrow, → {\displaystyle \rightarrow } , could be read as "adjoined with".) In this formulation: ( ) = ∅ ( 1 ) = ( ) → 1 = { { ( ) } , { ( ) , 1 } } = { { ∅ } , { ∅ , 1 } } ( 1 , 2 ) = ( 1 ) → 2 = { { ( 1 ) } , { ( 1 ) , 2 } } = { { { { ∅ } , { ∅ , 1 } } } , { { { ∅ } , { ∅ , 1 } } , 2 } } ( 1 , 2 , 3 ) = ( 1 , 2 ) → 3 = { { ( 1 , 2 ) } , { ( 1 , 2 ) , 3 } } = { { { { { { ∅ } , { ∅ , 1 } } } , { { { ∅ } , { ∅ , 1 } } , 2 } } } , { { { { { ∅ } , { ∅ , 1 } } } , { { { ∅ } , { ∅ , 1 } } , 2 } } , 3 } } {\displaystyle {\begin{array}{lclcl}()&&&=&\emptyset \\&&&&\\(1)&=&()\rightarrow 1&=&\{\{()\},\{(),1\}\}\\&&&=&\{\{\emptyset \},\{\emptyset ,1\}\}\\&&&&\\(1,2)&=&(1)\rightarrow 2&=&\{\{(1)\},\{(1),2\}\}\\&&&=&\{\{\{\{\emptyset \},\{\emptyset ,1\}\}\},\\&&&&\{\{\{\emptyset \},\{\emptyset ,1\}\},2\}\}\\&&&&\\(1,2,3)&=&(1,2)\rightarrow 3&=&\{\{(1,2)\},\{(1,2),3\}\}\\&&&=&\{\{\{\{\{\{\empty

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  • Applications of artificial intelligence

    Applications of artificial intelligence

    Artificial intelligence is the capability of computational systems to perform tasks that are typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. Artificial intelligence has been used in applications throughout industry and academia. Within the field of Artificial Intelligence, there are multiple subfields. The subfield of machine learning has been used for various scientific and commercial purposes, including language translation, image recognition, decision-making, credit scoring, and e-commerce. In recent years, massive advancements have been made in the field of generative artificial intelligence, which uses generative models to generate text, images, videos, and other forms of data. This article describes applications of AI in different sectors. == Agriculture == In agriculture, AI has been proposed as a way for farmers to identify areas that need irrigation, fertilization, or pesticide treatments to increase yields, thereby improving efficiency. AI has been used to attempt to classify livestock pig call emotions, automate greenhouses, detect diseases and pests, and optimize irrigation. == AI-assisted software develoment == == Architecture and design == == Business == A 2023 study found that generative AI increased productivity by 15% in contact centers. Another 2023 study found it increased productivity by up to 40% in writing tasks. An August 2025 review by MIT found that of surveyed companies, 95% did not report any improvement in revenue from the use of AI. A September 2025 article by the Harvard Business Review describes how increased use of AI does not automatically lead to increases in revenue or actual productivity. Referring to "AI generated work content that masquerades as good work, but lacks the substance to meaningfully advance a given task" the article coins the term workslop. Per studies done in collaboration with the Stanford Social Media Lab, workslop does not improve productivity and undermines trust and collaboration among colleagues. In telehealth, agentic AI is reportedly facilitating the creation of large business models (millions in annual profit) with 1-2 employees, such as MEDVi, which as of August 2025 only had 2 employees and ~$75M in annual profit for GLP-1 weight-loss telehealth services. == Chatbots == == Computer science == === Programming assistance === ==== AI-assisted software development ==== AI can be used for real-time code completion, chat, and automated test generation. These tools are typically integrated with editors and IDEs as plugins. AI-assisted software development systems differ in functionality, quality, speed, and approach to privacy. Creating software primarily via AI is known as "vibe coding". Code created or suggested by AI can be incorrect or inefficient. The use of AI-assisted coding can potentially speed-up software development, but can also slow-down the process by creating more work when debugging and testing. The rush to prematurely adopt AI technology can also incur additional technical debt. AI also requires additional consideration and careful review for cybersecurity, since AI coding software is trained on a wide range of code of inconsistent quality and often replicates poor practices. ==== Neural network design ==== AI can be used to create other AIs. For example, around November 2017, Google's AutoML project to evolve new neural net topologies created NASNet, a system optimized for ImageNet and POCO F1. NASNet's performance exceeded all previously published performance on ImageNet. ==== Quantum computing ==== Research and development of quantum computers has been performed with machine learning algorithms. For example, there is a prototype, photonic, quantum memristive device for neuromorphic computers (NC)/artificial neural networks and NC-using quantum materials with some variety of potential neuromorphic computing-related applications. The use of quantum machine learning for quantum simulators has been proposed for solving physics and chemistry problems. === Historical contributions === AI researchers have created many tools to solve the most difficult problems in computer science. Many of their inventions have been adopted by mainstream computer science and are no longer considered AI. All of the following were originally developed in AI laboratories: Time sharing Interactive interpreters Graphical user interfaces and the computer mouse Rapid application development environments The linked list data structure Automatic storage management Symbolic programming Functional programming Dynamic programming Object-oriented programming Optical character recognition Constraint satisfaction == Customer service == === Human resources === AI programs have been used in hiring processes to screen resumes and rank candidates based on their qualifications, predict a candidate's likelihood of success in a given role, and automate repetitive communication tasks using chatbots. Studies on these programs have identified tendencies for gender bias, favoring male names and male-coded characteristics, as well as bias against disabled candidates and racial minorities. === Online and telephone customer service === AI underlies avatars (automated online assistants) on web pages. It can reduce operation and training costs. Pypestream automated customer service for its mobile application to streamline communication with customers. A Google app analyzes language and converts speech into text. The platform can identify angry customers through their language and respond appropriately. Amazon uses a chatbot for customer service that can perform tasks like checking the status of an order, cancelling orders, offering refunds and connecting the customer with a human representative. Generative AI (GenAI), such as ChatGPT, is increasingly used in business to automate tasks and enhance decision-making. === Hospitality === In the hospitality industry, AI is used to reduce repetitive tasks, analyze trends, interact with guests, and predict customer needs. AI hotel services come in the form of a chatbot, application, virtual voice assistant and service robots. == Education == In educational institutions, AI has been used to automate routine tasks such as attendance tracking, grading, and marking. AI tools have also been used to monitor student progress and analyze learning behaviors, with the goal of facilitating timely interventions for students facing academic challenges. == Energy and environment == === Energy system === The U.S. Department of Energy wrote in an April 2024 report that AI may have applications in modeling power grids, reviewing federal permits with large language models, predicting levels of renewable energy production, and improving the planning process for electrical vehicle charging networks. Other studies have suggested that machine learning can be used for energy consumption prediction and scheduling, e.g. to help with renewable energy intermittency management (see also: smart grid and climate change mitigation in the power grid). === Environmental monitoring === Autonomous ships that monitor the ocean, AI-driven satellite data analysis, passive acoustics or remote sensing and other applications of environmental monitoring make use of machine learning. For example, "Global Plastic Watch" is an AI-based satellite monitoring-platform for analysis/tracking of plastic waste sites to help prevention of plastic pollution – primarily ocean pollution – by helping identify who and where mismanages plastic waste, dumping it into oceans. === Early-warning systems === Machine learning can be used to spot early-warning signs of disasters and environmental issues, possibly including natural pandemics, earthquakes, landslides, heavy rainfall, long-term water supply vulnerability, tipping-points of ecosystem collapse, cyanobacterial bloom outbreaks, and droughts. === Economic and social challenges === The University of Southern California launched the Center for Artificial Intelligence in Society, with the goal of using AI to address problems such as homelessness. Stanford researchers use AI to analyze satellite images to identify high poverty areas. == Entertainment and media == === Media === AI applications analyze media content such as movies, TV programs, advertisement videos or user-generated content. The solutions often involve computer vision. Typical scenarios include the analysis of images using object recognition or face recognition techniques, or the analysis of video for scene recognizing scenes, objects or faces. AI-based media analysis can facilitate media search, the creation of descriptive keywords for content, content policy monitoring (such as verifying the suitability of content for a particular TV viewing time), speech to text for archival or other purposes, and the detection of logos, products or celebrity faces for ad placement. Motion interpolation Pixel-art scaling algorithms Image scaling Imag

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  • Webometrics

    Webometrics

    The science of webometrics (also referred to as cybermetrics) aims to quantify the World Wide Web to get knowledge about the number and types of hyperlinks, the structure of the World Wide Web, and using patterns. According to Björneborn and Ingwersen, the definition of webometrics is "the study of the quantitative aspects of the construction and use of information resources, structures and technologies on the Web drawing on bibliometric and informetric approaches." The term webometrics was coined by Almind and Ingwersen (1997). A second definition of webometrics has also been introduced, "the study of web-based content with primarily quantitative methods for social science research goals using techniques that are not specific to one field of study", which emphasizes the development of applied methods for use in the wider social sciences. The purpose of this alternative definition was to help publicize appropriate methods outside the information-science discipline rather than to replace the original definition within information science. Similar scientific fields are: bibliometrics, informetrics, scientometrics, virtual ethnography, and web mining. One relatively straightforward measure is the "web impact factor" (WIF) introduced by Ingwersen (1998). The WIF measure may be defined as the number of web pages in a web site receiving links from other web sites, divided by the number of web pages published in the site that are accessible to the crawler. However, the use of WIF has been disregarded due to the mathematical artifacts derived from power law distributions of these variables. Other similar indicators using size of the institution instead of number of webpages have been proved more useful.

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  • Prosthesis

    Prosthesis

    In medicine, a prosthesis (pl.: prostheses; from Ancient Greek: πρόσθεσις, romanized: prósthesis, lit. 'addition, application, attachment'), or a prosthetic implant, is an artificial device that replaces a missing body part, which may be lost through physical trauma, disease, or a condition present at birth (congenital disorder). Prostheses may restore the normal functions of the missing body part, or may perform a cosmetic function. A person who has undergone an amputation is sometimes referred to as an amputee, Rehabilitation for someone with an amputation is primarily coordinated by a physiatrist as part of an inter-disciplinary team consisting of physiatrists, prosthetists, nurses, physical therapists, and occupational therapists. Prostheses can be created by hand or with computer-aided design (CAD), a software interface that helps creators design and analyze the creation with computer-generated 2-D and 3-D graphics as well as analysis and optimization tools. == Types == A person's prosthetic device should be designed and assembled to meet their individual appearance and functional needs. Depending on personal circumstances, co-morbidities, budget or health insurance coverage, and access to medical care, decisions may need to balance aesthetics and function. In addition, for some individuals, a myoelectric device, a body-powered device, or an activity-specific device may be appropriate options. The person's future goals and vocational aspirations and potential capabilities may help them choose between one or more devices. Craniofacial prostheses include intra-oral and extra-oral prostheses. Extra-oral prostheses are further divided into hemifacial, auricular (ear), nasal, orbital and ocular. Intra-oral prostheses include dental prostheses, such as dentures, obturators, and dental implants. Prostheses of the neck include larynx substitutes, trachea and upper esophageal replacements, Some prostheses of the torso include breast prostheses which may be either single or bilateral, full breast devices or nipple prostheses. Penile prostheses are used to treat erectile dysfunction, perform phalloplasty procedures in men, and to build a new penis in female-to-male gender reassignment surgeries. === Limb prostheses === Limb prostheses include both upper- and lower-extremity prostheses. Upper-extremity prostheses are used at varying levels of amputation: forequarter, shoulder disarticulation, transhumeral prosthesis, elbow disarticulation, transradial prosthesis, wrist disarticulation, full hand, partial hand, finger, partial finger. A transradial prosthesis is an artificial limb that replaces an arm missing below the elbow. Upper limb prostheses can be categorized in three main categories: Passive devices, Body Powered devices, and Externally Powered (myoelectric) devices. Passive devices can either be passive hands, mainly used for cosmetic purposes, or passive tools, mainly used for specific activities (e.g. leisure or vocational). An extensive overview and classification of passive devices can be found in a literature review by Maat et.al. A passive device can be static, meaning the device has no movable parts, or it can be adjustable, meaning its configuration can be adjusted (e.g. adjustable hand opening). Despite the absence of active grasping, passive devices are very useful in bimanual tasks that require fixation or support of an object, or for gesticulation in social interaction. According to scientific data a third of the upper limb amputees worldwide use a passive prosthetic hand. Body Powered or cable-operated limbs work by attaching a harness and cable around the opposite shoulder of the damaged arm. A recent body-powered approach has explored the utilization of the user's breathing to power and control the prosthetic hand to help eliminate actuation cable and harness. The third category of available prosthetic devices comprises myoelectric arms. This particular class of devices distinguishes itself from the previous ones due to the inclusion of a battery system. This battery serves the dual purpose of providing energy for both actuation and sensing components. While actuation predominantly relies on motor or pneumatic systems, a variety of solutions have been explored for capturing muscle activity, including techniques such as Electromyography, Sonomyography, Myokinetic, and others. These methods function by detecting the minute electrical currents generated by contracted muscles during upper arm movement, typically employing electrodes or other suitable tools. Subsequently, these acquired signals are converted into gripping patterns or postures that the artificial hand will then execute. In the prosthetics industry, a trans-radial prosthetic arm is often referred to as a "BE" or below elbow prosthesis. Lower-extremity prostheses provide replacements at varying levels of amputation. These include hip disarticulation, transfemoral prosthesis, knee disarticulation, transtibial prosthesis, Syme's amputation, foot, partial foot, and toe. The two main subcategories of lower extremity prosthetic devices are trans-tibial (any amputation transecting the tibia bone or a congenital anomaly resulting in a tibial deficiency) and trans-femoral (any amputation transecting the femur bone or a congenital anomaly resulting in a femoral deficiency). A transfemoral prosthesis is an artificial limb that replaces a leg missing above the knee. Transfemoral amputees can have a very difficult time regaining normal movement. In general, a transfemoral amputee must use approximately 80% more energy to walk than a person with two whole legs. This is due to the complexities in movement associated with the knee. In newer and more improved designs, hydraulics, carbon fiber, mechanical linkages, motors, computer microprocessors, and innovative combinations of these technologies are employed to give more control to the user. In the prosthetics industry, a trans-femoral prosthetic leg is often referred to as an "AK" or above the knee prosthesis. A transtibial prosthesis is an artificial limb that replaces a leg missing below the knee. A transtibial amputee is usually able to regain normal movement more readily than someone with a transfemoral amputation, due in large part to retaining the knee, which allows for easier movement. Lower extremity prosthetics describe artificially replaced limbs located at the hip level or lower. In the prosthetics industry, a transtibial prosthetic leg is often referred to as a "BK" or below the knee prosthesis. Prostheses are manufactured and fit by clinical prosthetists. Prosthetists are healthcare professionals responsible for making, fitting, and adjusting prostheses and for lower limb prostheses will assess both gait and prosthetic alignment. Once a prosthesis has been fit and adjusted by a prosthetist, a rehabilitation physiotherapist (called physical therapist in America) will help teach a new prosthetic user to walk with a leg prosthesis. To do so, the physical therapist may provide verbal instructions and may also help guide the person using touch or tactile cues. This may be done in a clinic or home. There is some research suggesting that such training in the home may be more successful if the treatment includes the use of a treadmill. Using a treadmill, along with the physical therapy treatment, helps the person to experience many of the challenges of walking with a prosthesis. In the United Kingdom, 75% of lower limb amputations are performed due to inadequate circulation (dysvascularity). This condition is often associated with many other medical conditions (co-morbidities) including diabetes and heart disease that may make it a challenge to recover and use a prosthetic limb to regain mobility and independence. For people who have inadequate circulation and have lost a lower limb, there is insufficient evidence due to a lack of research, to inform them regarding their choice of prosthetic rehabilitation approaches. Lower extremity prostheses are often categorized by the level of amputation or after the name of a surgeon: Transfemoral (Above-knee) Transtibial (Below-knee) Ankle disarticulation (more commonly known as Syme's amputation) Knee disarticulation (also see knee replacement) Hip disarticulation, (also see hip replacement) Hemi-pelvictomy Partial foot amputations (Pirogoff, Talo-Navicular and Calcaneo-cuboid (Chopart), Tarso-metatarsal (Lisfranc), Trans-metatarsal, Metatarsal-phalangeal, Ray amputations, toe amputations). Van Nes rotationplasty ==== Prosthetic raw materials ==== Prosthetic are made lightweight for better convenience for the amputee. Some of these materials include: Plastics: Polyethylene Polypropylene Acrylics Polyurethane Wood (early prosthetics) Rubber (early prosthetics) Lightweight metals: Aluminum Composites: Carbon fiber reinforced polymers Wheeled prostheses have also been used extensively in the rehabilitation of injured domestic animals, including dogs, cats, pigs, rabbits, and

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  • Kinodynamic planning

    Kinodynamic planning

    In robotics and motion planning, kinodynamic planning is a class of problems for which velocity, acceleration, and force/torque bounds must be satisfied, together with kinematic constraints such as avoiding obstacles. The term was coined by Bruce Donald, Pat Xavier, John Canny, and John Reif. Donald et al. developed the first polynomial-time approximation schemes (PTAS) for the problem. By providing a provably polynomial-time ε-approximation algorithm, they resolved a long-standing open problem in optimal control. Their first paper considered time-optimal control ("fastest path") of a point mass under Newtonian dynamics, amidst polygonal (2D) or polyhedral (3D) obstacles, subject to state bounds on position, velocity, and acceleration. Later they extended the technique to many other cases, for example, to 3D open-chain kinematic robots under full Lagrangian dynamics. == Modern approaches == Since the foundational theoretical work of the 1990s, the field has evolved significantly with new algorithmic approaches that address the computational and practical limitations of early methods. === Sampling-based methods === Many practical heuristic algorithms based on stochastic optimization and iterative sampling have been developed by a wide range of authors to address the kinodynamic planning problem. Popular approaches include extensions of RRT algorithms such as RRT for kinodynamic systems, and sampling-based methods like Model Predictive Path Integral (MPPI) control. These stochastic techniques have been shown to work well in practice and can handle complex, high-dimensional state spaces more efficiently than deterministic methods. However, all motion planning methods are subject to the PSPACE-hardnesss of classical motion planning even without dynamics, which means (assuming the usual structural complexity conjectures) they all can be worst-case exponential-time in the state-space dimension (the number of degrees of freedom). On the other hand, the deterministic methods have provable guarantees of completeness, accuracy, and complexity (for fixed dimension, they are polynomial-time not only in the geometric complexity, but also in ( 1 / ε ) {\displaystyle (1/\varepsilon )} , the closeness of the desired approximation), whereas most of the recent heuristic/stochastic methods sacrifice at least one of these criteria. === Mixed-integer optimization approaches === Recent advances in mixed-integer programming have enabled new deterministic approaches to kinodynamic planning. These methods formulate the planning problem as an optimization task that simultaneously determines the spatial path and control sequence while respecting all kinodynamic constraints. By using techniques such as McCormick envelopes to handle bilinear constraints, these approaches can provide globally optimal solutions with mathematical guarantees while achieving significant computational speedups over traditional methods. === Genetic algorithm approaches === Genetic algorithms have also been adapted for kinodynamic planning, particularly for gradient-free optimization in challenging terrain. These methods use evolutionary computation to optimize trajectories over receding horizons, with specialized mutation operators that ensure vehicle controls remain within operational limits. This approach is particularly useful when dealing with non-differentiable cost functions or when gradient information is unavailable or unreliable. === Three-dimensional terrain planning === The foundational theoretical work of the 1990s was extended to higher degrees of freedom, and even to n {\displaystyle n} -link, 3D open-chain kinematic robots under full Lagrangian dynamics. However, many of the subsequent heuristic techniques (typically employing stochastic optimization) were confined to planar environments. More recent kinodynamic planning has extended beyond these planar environments to handle complex 3D terrains represented as simplicial complexes or triangular meshes. This advancement is particularly important for applications such as autonomous vehicle navigation in off-road environments, where elevation changes and terrain geometry significantly impact vehicle dynamics. These methods must account for pitch angles, surface curvature, and the coupling between terrain geometry and vehicle kinodynamic constraints. == Performance and guarantees == The landscape of performance guarantees in kinodynamic planning has evolved considerably. While early heuristic methods could not guarantee optimality, recent mixed-integer approaches have demonstrated the ability to find globally optimal solutions with proven constraint satisfaction. Experimental comparisons have shown that modern optimization-based planners can achieve execution times several orders of magnitude faster than sampling-based methods while maintaining strict adherence to kinodynamic constraints. However, the choice of method often depends on the specific application requirements. Sampling-based methods remain valuable for their ability to quickly find feasible solutions in high-dimensional spaces and their robustness to modeling uncertainties. Optimization-based methods excel when optimality guarantees and constraint compliance are critical, particularly in safety-critical applications. == Applications == Kinodynamic planning finds applications across numerous domains including: Autonomous vehicles: Path planning for cars, trucks, and other ground vehicles that must respect acceleration, steering, and velocity limits Aerial robotics: Trajectory planning for quadrotors and other unmanned aerial vehicles with dynamic constraints Manipulation: Planning for robotic arms where joint velocities, accelerations, and torques are limited Legged locomotion: Footstep and trajectory planning for walking and running robots Space robotics: Planning under thrust and fuel constraints for spacecraft and rovers

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  • Predictor–corrector method

    Predictor–corrector method

    In numerical analysis, predictor–corrector methods belong to a class of algorithms designed to integrate ordinary differential equations – to find an unknown function that satisfies a given differential equation. All such algorithms proceed in two steps: The initial, "prediction" step, starts from a function fitted to the function-values and derivative-values at a preceding set of points to extrapolate ("anticipate") this function's value at a subsequent, new point. The next, "corrector" step refines the initial approximation by using the predicted value of the function and another method to interpolate that unknown function's value at the same subsequent point. == Predictor–corrector methods for solving ODEs == When considering the numerical solution of ordinary differential equations (ODEs), a predictor–corrector method typically uses an explicit method for the predictor step and an implicit method for the corrector step. === Example: Euler method with the trapezoidal rule === A simple predictor–corrector method (known as Heun's method) can be constructed from the Euler method (an explicit method) and the trapezoidal rule (an implicit method). Consider the differential equation y ′ = f ( t , y ) , y ( t 0 ) = y 0 , {\displaystyle y'=f(t,y),\quad y(t_{0})=y_{0},} and denote the step size by h {\displaystyle h} . First, the predictor step: starting from the current value y i {\displaystyle y_{i}} , calculate an initial guess value y ~ i + 1 {\displaystyle {\tilde {y}}_{i+1}} via the Euler method, y ~ i + 1 = y i + h f ( t i , y i ) . {\displaystyle {\tilde {y}}_{i+1}=y_{i}+hf(t_{i},y_{i}).} Next, the corrector step: improve the initial guess using trapezoidal rule, y i + 1 = y i + 1 2 h ( f ( t i , y i ) + f ( t i + 1 , y ~ i + 1 ) ) . {\displaystyle y_{i+1}=y_{i}+{\tfrac {1}{2}}h{\bigl (}f(t_{i},y_{i})+f(t_{i+1},{\tilde {y}}_{i+1}){\bigr )}.} That value is used as the next step. === PEC mode and PECE mode === There are different variants of a predictor–corrector method, depending on how often the corrector method is applied. The Predict–Evaluate–Correct–Evaluate (PECE) mode refers to the variant in the above example: y ~ i + 1 = y i + h f ( t i , y i ) , y i + 1 = y i + 1 2 h ( f ( t i , y i ) + f ( t i + 1 , y ~ i + 1 ) ) . {\displaystyle {\begin{aligned}{\tilde {y}}_{i+1}&=y_{i}+hf(t_{i},y_{i}),\\y_{i+1}&=y_{i}+{\tfrac {1}{2}}h{\bigl (}f(t_{i},y_{i})+f(t_{i+1},{\tilde {y}}_{i+1}){\bigr )}.\end{aligned}}} It is also possible to evaluate the function f only once per step by using the method in Predict–Evaluate–Correct (PEC) mode: y ~ i + 1 = y i + h f ( t i , y ~ i ) , y i + 1 = y i + 1 2 h ( f ( t i , y ~ i ) + f ( t i + 1 , y ~ i + 1 ) ) . {\displaystyle {\begin{aligned}{\tilde {y}}_{i+1}&=y_{i}+hf(t_{i},{\tilde {y}}_{i}),\\y_{i+1}&=y_{i}+{\tfrac {1}{2}}h{\bigl (}f(t_{i},{\tilde {y}}_{i})+f(t_{i+1},{\tilde {y}}_{i+1}){\bigr )}.\end{aligned}}} Additionally, the corrector step can be repeated in the hope that this achieves an even better approximation to the true solution. If the corrector method is run twice, this yields the PECECE mode: y ~ i + 1 = y i + h f ( t i , y i ) , y ^ i + 1 = y i + 1 2 h ( f ( t i , y i ) + f ( t i + 1 , y ~ i + 1 ) ) , y i + 1 = y i + 1 2 h ( f ( t i , y i ) + f ( t i + 1 , y ^ i + 1 ) ) . {\displaystyle {\begin{aligned}{\tilde {y}}_{i+1}&=y_{i}+hf(t_{i},y_{i}),\\{\hat {y}}_{i+1}&=y_{i}+{\tfrac {1}{2}}h{\bigl (}f(t_{i},y_{i})+f(t_{i+1},{\tilde {y}}_{i+1}){\bigr )},\\y_{i+1}&=y_{i}+{\tfrac {1}{2}}h{\bigl (}f(t_{i},y_{i})+f(t_{i+1},{\hat {y}}_{i+1}){\bigr )}.\end{aligned}}} The PECEC mode has one fewer function evaluation than PECECE mode. More generally, if the corrector is run k times, the method is in P(EC)k or P(EC)kE mode. If the corrector method is iterated until it converges, this could be called PE(CE)∞.

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  • Emotion recognition

    Emotion recognition

    Emotion recognition is the process of identifying human emotion. People vary widely in their accuracy at recognizing the emotions of others. Use of technology to help people with emotion recognition is a relatively nascent research area. Generally, the technology works best if it uses multiple modalities in context. To date, the most work has been conducted on automating the recognition of facial expressions from video, spoken expressions from audio, written expressions from text, and physiology as measured by wearables. == Human == Humans show a great deal of variability in their abilities to recognize emotion. A key point to keep in mind when learning about automated emotion recognition is that there are several sources of "ground truth", or truth about what the real emotion is. Suppose we are trying to recognize the emotions of Alex. One source is "what would most people say that Alex is feeling?" In this case, the 'truth' may not correspond to what Alex feels, but may correspond to what most people would say it looks like Alex feels. For example, Alex may actually feel sad, but he puts on a big smile and then most people say he looks happy. If an automated method achieves the same results as a group of observers it may be considered accurate, even if it does not actually measure what Alex truly feels. Another source of 'truth' is to ask Alex what he truly feels. This works if Alex has a good sense of his internal state, and wants to tell you what it is, and is capable of putting it accurately into words or a number. However, some people are alexithymic and do not have a good sense of their internal feelings, or they are not able to communicate them accurately with words and numbers. In general, getting to the truth of what emotion is actually present can take some work, can vary depending on the criteria that are selected, and will usually involve maintaining some level of uncertainty. == Automatic == Decades of scientific research have been conducted developing and evaluating methods for automated emotion recognition. There is now an extensive literature proposing and evaluating hundreds of different kinds of methods, leveraging techniques from multiple areas, such as signal processing, machine learning, computer vision, and speech processing. Different methodologies and techniques may be employed to interpret emotion such as Bayesian networks. , Gaussian Mixture models and Hidden Markov Models and deep neural networks. === Approaches === The accuracy of emotion recognition is usually improved when it combines the analysis of human expressions from multimodal forms such as texts, physiology, audio, or video. Different emotion types are detected through the integration of information from facial expressions, body movement and gestures, and speech. The technology is said to contribute in the emergence of the so-called emotional or emotive Internet. The existing approaches in emotion recognition to classify certain emotion types can be generally classified into three main categories: knowledge-based techniques, statistical methods, and hybrid approaches. ==== Knowledge-based techniques ==== Knowledge-based techniques (sometimes referred to as lexicon-based techniques), utilize domain knowledge and the semantic and syntactic characteristics of text and potentially spoken language in order to detect certain emotion types. In this approach, it is common to use knowledge-based resources during the emotion classification process such as WordNet, SenticNet, ConceptNet, and EmotiNet, to name a few. One of the advantages of this approach is the accessibility and economy brought about by the large availability of such knowledge-based resources. A limitation of this technique on the other hand, is its inability to handle concept nuances and complex linguistic rules. Knowledge-based techniques can be mainly classified into two categories: dictionary-based and corpus-based approaches. Dictionary-based approaches find opinion or emotion seed words in a dictionary and search for their synonyms and antonyms to expand the initial list of opinions or emotions. Corpus-based approaches on the other hand, start with a seed list of opinion or emotion words, and expand the database by finding other words with context-specific characteristics in a large corpus. While corpus-based approaches take into account context, their performance still vary in different domains since a word in one domain can have a different orientation in another domain. ==== Statistical methods ==== Statistical methods commonly involve the use of different supervised machine learning algorithms in which a large set of annotated data is fed into the algorithms for the system to learn and predict the appropriate emotion types. Machine learning algorithms generally provide more reasonable classification accuracy compared to other approaches, but one of the challenges in achieving good results in the classification process, is the need to have a sufficiently large training set. Some of the most commonly used machine learning algorithms include Support Vector Machines (SVM), Naive Bayes, and Maximum Entropy. Deep learning, which is under the unsupervised family of machine learning, is also widely employed in emotion recognition. Well-known deep learning algorithms include different architectures of Artificial Neural Network (ANN) such as Convolutional Neural Network (CNN), Long Short-term Memory (LSTM), and Extreme Learning Machine (ELM). The popularity of deep learning approaches in the domain of emotion recognition may be mainly attributed to its success in related applications such as in computer vision, speech recognition, and Natural Language Processing (NLP). ==== Hybrid approaches ==== Hybrid approaches in emotion recognition are essentially a combination of knowledge-based techniques and statistical methods, which exploit complementary characteristics from both techniques. Some of the works that have applied an ensemble of knowledge-driven linguistic elements and statistical methods include sentic computing and iFeel, both of which have adopted the concept-level knowledge-based resource SenticNet. The role of such knowledge-based resources in the implementation of hybrid approaches is highly important in the emotion classification process. Since hybrid techniques gain from the benefits offered by both knowledge-based and statistical approaches, they tend to have better classification performance as opposed to employing knowledge-based or statistical methods independently. A downside of using hybrid techniques however, is the computational complexity during the classification process. === Datasets === Data is an integral part of the existing approaches in emotion recognition and in most cases it is a challenge to obtain annotated data that is necessary to train machine learning algorithms. For the task of classifying different emotion types from multimodal sources in the form of texts, audio, videos or physiological signals, the following datasets are available: HUMAINE: provides natural clips with emotion words and context labels in multiple modalities Belfast database: provides clips with a wide range of emotions from TV programs and interview recordings SEMAINE: provides audiovisual recordings between a person and a virtual agent and contains emotion annotations such as angry, happy, fear, disgust, sadness, contempt, and amusement IEMOCAP: provides recordings of dyadic sessions between actors and contains emotion annotations such as happiness, anger, sadness, frustration, and neutral state eNTERFACE: provides audiovisual recordings of subjects from seven nationalities and contains emotion annotations such as happiness, anger, sadness, surprise, disgust, and fear DEAP: provides electroencephalography (EEG), electrocardiography (ECG), and face video recordings, as well as emotion annotations in terms of valence, arousal, and dominance of people watching film clips DREAMER: provides electroencephalography (EEG) and electrocardiography (ECG) recordings, as well as emotion annotations in terms of valence, dominance of people watching film clips MELD: is a multiparty conversational dataset where each utterance is labeled with emotion and sentiment. MELD provides conversations in video format and hence suitable for multimodal emotion recognition and sentiment analysis. MELD is useful for multimodal sentiment analysis and emotion recognition, dialogue systems and emotion recognition in conversations. MuSe: provides audiovisual recordings of natural interactions between a person and an object. It has discrete and continuous emotion annotations in terms of valence, arousal and trustworthiness as well as speech topics useful for multimodal sentiment analysis and emotion recognition. UIT-VSMEC: is a standard Vietnamese Social Media Emotion Corpus (UIT-VSMEC) with about 6,927 human-annotated sentences with six emotion labels, contributing to emotion recognition research in Vietnamese

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  • Onshape

    Onshape

    Onshape is a computer-aided design (CAD) software system, delivered over the Internet via a software as a service (SaaS) model. It makes extensive use of cloud computing, with compute-intensive processing and rendering performed on Internet-based servers, and users are able to interact with the system via a web browser or the iOS and Android apps. As a SaaS system, Onshape upgrades are released directly to the web interface, and the software does not require maintenance by the user. Onshape allows teams to collaborate on a single shared design, the same way multiple writers can work together editing a shared document via cloud services. It is primarily focused on mechanical CAD (MCAD) and is used for product and machinery design across many industries, including consumer electronics, mechanical machinery, medical devices, 3D printing, machine parts, and industrial equipment. As of 2025, Onshape is popularly used as a CAD suite for the FIRST Robotics Competition (FRC) alongside the MKCad application available in the Onshape App Store. == Company history == Onshape was developed by a company with the same name. Founded in 2012, Onshape was based in Cambridge, Massachusetts (USA), with offices in Singapore and Pune, India. Its leadership team includes several engineers and executives who originated from SolidWorks, a popular 3D CAD program that runs on Microsoft Windows. Onshape’s co-founders include two former SolidWorks CEOs, Jon Hirschtick and John McEleney. In November 2012, former SolidWorks CEOs Jon Hirschtick and John McEleney led six co-founders launching Belmont Technology, a placeholder name that was later changed to Onshape. The company’s first round of funding was $9 million from North Bridge Venture Partners and Commonwealth Capital. In March 2015, Onshape released the public beta version of its cloud CAD software, after pre-production testing with more than a thousand CAD professionals in 52 countries. Included in the beta launch was Onshape for iPhone. In August 2015, the company released its Onshape for Android app. In December 2015, Onshape launched its full commercial release. The company also launched the Onshape App Store, offering CAM, simulation, rendering and other cloud-based engineering tools. The Onshape App Store was launched with 24 developer partners. In April 2016, Onshape introduced its Education Plan, with a free version of Onshape Professional geared for college students and educators. In May 2016, Onshape released FeatureScript, a new open source (MIT licensed) programming language for creating and customizing CAD features. In October 2019, Onshape agreed to be acquired by PTC. The acquisition closed in November 2019 for $470 million. In February 2024, Onshape released iOS support for the Apple Vision Pro, allowing for real world applications of CAD models and prototypes. In January 2025, Onshape released the CAM studio, allowing users to generate G-code for up to 5-axis Simultaneous milling. == Funding == Onshape was a venture-backed company with investments from firms including Andreessen Horowitz, Commonwealth Capital Ventures, New Enterprise Associates (NEA) and North Bridge Venture Partners. Total venture funding amounted to $169 million. == Supported file formats == === Modelling === ==== Importing ==== As of May 2025, Onshape supported importing (opening) the following common CAD file formats: Parasolid X_T (Preferred) STEP (ISO 10303) ISO JT (ISO 14306) ACIS IGES CATIA v4, v5, v6 Autodesk Inventor Part (.IPT) Assembly (.IAM) Presentation (.IPN) Drawing (.IDW) Pro/ENGINEER, Creo Rhinoceros 3D: .3dm .STL .OBJ SolidWorks file formats Siemens NX file formats Drawings (.DXF/.DWG) ==== Exporting ==== Onshape supports exporting to the following formats: STEP (ISO 10303) Parasolid XT ACIS IGES SolidWorks file formats .STL Rhinoceros 3D: .3dm Collada XML-spec based textual file === Drawing === Ordinary engineering or technical drawing can be exported as .PDF file. === Other Formats === In addition to CAD file formats, Onshape supports importing some Non-CAD file formats for viewing and referencing. === Assembly === Assemblies can be imported and exported to: STEP (ISO 10303) Parasolid XT ACIS Pro/ENGINEER, Creo ISO JT Rhinoceros 3D: .3dm Siemens NX file formats SolidWorks Pack and Go zip file File formats that assemblies can be only-exported to, are: IGES .STL Collada XML-spec based textual file

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  • Rendezvous hashing

    Rendezvous hashing

    Rendezvous or highest random weight (HRW) hashing is an algorithm that allows clients to achieve distributed agreement on a set of k {\displaystyle k} options out of a possible set of n {\displaystyle n} options. A typical application is when clients need to agree on which sites (or proxies) objects are assigned to. Consistent hashing addresses the special case k = 1 {\displaystyle k=1} using a different method. Rendezvous hashing is both much simpler and more general than consistent hashing (see below). == History == Rendezvous hashing was invented by David Thaler and Chinya Ravishankar at the University of Michigan in 1996. Consistent hashing appeared a year later in the literature. Given its simplicity and generality, rendezvous hashing is now being preferred to consistent hashing in real-world applications. Rendezvous hashing was used very early on in many applications including mobile caching, router design, secure key establishment, and sharding and distributed databases. Other examples of real-world systems that use Rendezvous Hashing include the GitHub load balancer, the Apache Ignite distributed database, the Tahoe-LAFS file store, the CoBlitz large-file distribution service, Apache Druid, IBM's Cloud Object Store, the Arvados Data Management System, Apache Kafka, and the Twitter EventBus pub/sub platform. One of the first applications of rendezvous hashing was to enable multicast clients on the Internet (in contexts such as the MBONE) to identify multicast rendezvous points in a distributed fashion. It was used in 1998 by Microsoft's Cache Array Routing Protocol (CARP) for distributed cache coordination and routing. Some Protocol Independent Multicast routing protocols use rendezvous hashing to pick a rendezvous point. == Problem definition and approach == === Algorithm === Rendezvous hashing solves a general version of the distributed hash table problem: We are given a set of n {\displaystyle n} sites (servers or proxies, say). How can any set of clients, given an object O {\displaystyle O} , agree on a k-subset of sites to assign to O {\displaystyle O} ? The standard version of the problem uses k = 1. Each client is to make its selection independently, but all clients must end up picking the same subset of sites. This is non-trivial if we add a minimal disruption constraint, and require that when a site fails or is removed, only objects mapping to that site need be reassigned to other sites. The basic idea is to give each site S j {\displaystyle S_{j}} a score (a weight) for each object O i {\displaystyle O_{i}} , and assign the object to the highest scoring site. All clients first agree on a hash function h ( ⋅ ) {\displaystyle h(\cdot )} . For object O i {\displaystyle O_{i}} , the site S j {\displaystyle S_{j}} is defined to have weight w i , j = h ( O i , S j ) {\displaystyle w_{i,j}=h(O_{i},S_{j})} . Each client independently computes these weights w i , 1 , w i , 2 … w i , n {\displaystyle w_{i,1},w_{i,2}\dots w_{i,n}} and picks the k sites that yield the k largest hash values. The clients have thereby achieved distributed k {\displaystyle k} -agreement. If a site S {\displaystyle S} is added or removed, only the objects mapping to S {\displaystyle S} are remapped to different sites, satisfying the minimal disruption constraint above. The HRW assignment can be computed independently by any client, since it depends only on the identifiers for the set of sites S 1 , S 2 … S n {\displaystyle S_{1},S_{2}\dots S_{n}} and the object being assigned. HRW easily accommodates different capacities among sites. If site S k {\displaystyle S_{k}} has twice the capacity of the other sites, we simply represent S k {\displaystyle S_{k}} twice in the list, say, as S k , 1 , S k , 2 {\displaystyle S_{k,1},S_{k,2}} . Clearly, twice as many objects will now map to S k {\displaystyle S_{k}} as to the other sites. === Properties === Consider the simple version of the problem, with k = 1, where all clients are to agree on a single site for an object O. Approaching the problem naively, it might appear sufficient to treat the n sites as buckets in a hash table and hash the object name O into this table. Unfortunately, if any of the sites fails or is unreachable, the hash table size changes, forcing all objects to be remapped. This massive disruption makes such direct hashing unworkable. Under rendezvous hashing, however, clients handle site failures by picking the site that yields the next largest weight. Remapping is required only for objects currently mapped to the failed site, and disruption is minimal. Rendezvous hashing has the following properties: Low overhead: The hash function used is efficient, so overhead at the clients is very low. Load balancing: Since the hash function is randomizing, each of the n sites is equally likely to receive the object O. Loads are uniform across the sites. Site capacity: Sites with different capacities can be represented in the site list with multiplicity in proportion to capacity. A site with twice the capacity of the other sites will be represented twice in the list, while every other site is represented once. High hit rate: Since all clients agree on placing an object O into the same site SO, each fetch or placement of O into SO yields the maximum utility in terms of hit rate. The object O will always be found unless it is evicted by some replacement algorithm at SO. Minimal disruption: When a site fails, only the objects mapped to that site need to be remapped. Disruption is at the minimal possible level. Distributed k-agreement: Clients can reach distributed agreement on k sites simply by selecting the top k sites in the ordering. == O(log n) running time via skeleton-based hierarchical rendezvous hashing == The standard version of Rendezvous Hashing described above works quite well for moderate n, but when n {\displaystyle n} is extremely large, the hierarchical use of Rendezvous Hashing achieves O ( log ⁡ n ) {\displaystyle O(\log n)} running time. This approach creates a virtual hierarchical structure (called a "skeleton"), and achieves O ( log ⁡ n ) {\displaystyle O(\log n)} running time by applying HRW at each level while descending the hierarchy. The idea is to first choose some constant m {\displaystyle m} and organize the n {\displaystyle n} sites into c = ⌈ n / m ⌉ {\displaystyle c=\lceil n/m\rceil } clusters C 1 = { S 1 , S 2 … S m } , C 2 = { S m + 1 , S m + 2 … S 2 m } … {\displaystyle C_{1}=\left\{S_{1},S_{2}\dots S_{m}\right\},C_{2}=\left\{S_{m+1},S_{m+2}\dots S_{2m}\right\}\dots } Next, build a virtual hierarchy by choosing a constant f {\displaystyle f} and imagining these c {\displaystyle c} clusters placed at the leaves of a tree T {\displaystyle T} of virtual nodes, each with fanout f {\displaystyle f} . In the accompanying diagram, the cluster size is m = 4 {\displaystyle m=4} , and the skeleton fanout is f = 3 {\displaystyle f=3} . Assuming 108 sites (real nodes) for convenience, we get a three-tier virtual hierarchy. Since f = 3 {\displaystyle f=3} , each virtual node has a natural numbering in octal. Thus, the 27 virtual nodes at the lowest tier would be numbered 000 , 001 , 002 , . . . , 221 , 222 {\displaystyle 000,001,002,...,221,222} in octal (we can, of course, vary the fanout at each level - in that case, each node will be identified with the corresponding mixed-radix number). The easiest way to understand the virtual hierarchy is by starting at the top, and descending the virtual hierarchy. We successively apply Rendezvous Hashing to the set of virtual nodes at each level of the hierarchy, and descend the branch defined by the winning virtual node. We can in fact start at any level in the virtual hierarchy. Starting lower in the hierarchy requires more hashes, but may improve load distribution in the case of failures. For example, instead of applying HRW to all 108 real nodes in the diagram, we can first apply HRW to the 27 lowest-tier virtual nodes, selecting one. We then apply HRW to the four real nodes in its cluster, and choose the winning site. We only need 27 + 4 = 31 {\displaystyle 27+4=31} hashes, rather than 108. If we apply this method starting one level higher in the hierarchy, we would need 9 + 3 + 4 = 16 {\displaystyle 9+3+4=16} hashes to get to the winning site. The figure shows how, if we proceed starting from the root of the skeleton, we may successively choose the virtual nodes ( 2 ) 3 {\displaystyle (2)_{3}} , ( 20 ) 3 {\displaystyle (20)_{3}} , and ( 200 ) 3 {\displaystyle (200)_{3}} , and finally end up with site 74. The virtual hierarchy need not be stored, but can be created on demand, since the virtual nodes names are simply prefixes of base- f {\displaystyle f} (or mixed-radix) representations. We can easily create appropriately sorted strings from the digits, as required. In the example, we would be working with the strings 0 , 1 , 2 {\displaystyle 0,1,2} (at tier 1), 20 , 21 , 22 {\displaystyle 20,21,22} (at tier 2), and 200 , 201 , 202

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  • Tuple

    Tuple

    In mathematics, a tuple is a finite sequence (or ordered list) of numbers. More generally, it is a sequence of mathematical objects, called the elements of the tuple. An n-tuple is a tuple of n elements, where n is a non-negative integer. There is only one 0-tuple, called the empty tuple. A 1-tuple and a 2-tuple are commonly called a singleton and an ordered pair, respectively. The term "infinite tuple" is occasionally used for "infinite sequences". Tuples are usually written by listing the elements within parentheses "( )" and separated by commas; for example, (2, 7, 4, 1, 7) denotes a 5-tuple. Other types of brackets are sometimes used, although they may have a different meaning. An n-tuple can be formally defined as the image of a function that has the set of the first n natural numbers as its domain (1, 2, ..., n). Tuples may be also defined from ordered pairs by a recurrence starting from an ordered pair; indeed, an n-tuple can be identified with the ordered pair of its (n − 1) first elements and its nth element, for example, ( ( ( 1 , 2 ) , 3 ) , 4 ) = ( 1 , 2 , 3 , 4 ) {\displaystyle \left(\left(\left(1,2\right),3\right),4\right)=\left(1,2,3,4\right)} . In computer science, tuples come in many forms. Most typed functional programming languages implement tuples directly as product types, tightly associated with algebraic data types, pattern matching, and destructuring assignment. Many programming languages offer an alternative to tuples, known as record types, featuring unordered elements accessed by label. A few programming languages combine ordered tuple product types and unordered record types into a single construct, as in C structs and Haskell records. Relational databases may formally identify their rows (records) as tuples. Tuples also occur in relational algebra; when programming the semantic web with the Resource Description Framework (RDF); in linguistics; and in philosophy. == Etymology == The term originated as an abstraction of the sequence: single, couple/double, triple, quadruple, quintuple, sextuple, septuple, octuple, ..., n‑tuple, ..., where the prefixes are taken from the Latin names of the numerals. The unique 0-tuple is called the null tuple or empty tuple. A 1‑tuple is called a single (or singleton), a 2‑tuple is called an ordered pair or couple, and a 3‑tuple is called a triple (or triplet). The number n can be any nonnegative integer. For example, a complex number can be represented as a 2‑tuple of reals, a quaternion can be represented as a 4‑tuple, an octonion can be represented as an 8‑tuple, and a sedenion can be represented as a 16‑tuple. Although these uses treat ‑tuple as the suffix, the original suffix was ‑ple as in "triple" (three-fold) or "decuple" (ten‑fold). This originates from medieval Latin plus (meaning "more") related to Greek ‑πλοῦς, which replaced the classical and late antique ‑plex (meaning "folded"), as in "duplex". == Properties == The general rule for the identity of two n-tuples is ( a 1 , a 2 , … , a n ) = ( b 1 , b 2 , … , b n ) {\displaystyle (a_{1},a_{2},\ldots ,a_{n})=(b_{1},b_{2},\ldots ,b_{n})} if and only if a 1 = b 1 , a 2 = b 2 , … , a n = b n {\displaystyle a_{1}=b_{1},{\text{ }}a_{2}=b_{2},{\text{ }}\ldots ,{\text{ }}a_{n}=b_{n}} . Thus a tuple has properties that distinguish it from a set: A tuple may contain multiple instances of the same element, so tuple ( 1 , 2 , 2 , 3 ) ≠ ( 1 , 2 , 3 ) {\displaystyle (1,2,2,3)\neq (1,2,3)} ; but set { 1 , 2 , 2 , 3 } = { 1 , 2 , 3 } {\displaystyle \{1,2,2,3\}=\{1,2,3\}} . Tuple elements are ordered: tuple ( 1 , 2 , 3 ) ≠ ( 3 , 2 , 1 ) {\displaystyle (1,2,3)\neq (3,2,1)} , but set { 1 , 2 , 3 } = { 3 , 2 , 1 } {\displaystyle \{1,2,3\}=\{3,2,1\}} . A tuple has a finite number of elements, while a set or a multiset may have an infinite number of elements. == Definitions == There are several definitions of tuples that give them the properties described in the previous section. === Tuples as functions === The 0 {\displaystyle 0} -tuple may be identified as the empty function. For n ≥ 1 , {\displaystyle n\geq 1,} the n {\displaystyle n} -tuple ( a 1 , … , a n ) {\displaystyle \left(a_{1},\ldots ,a_{n}\right)} may be identified with the surjective function F : { 1 , … , n } → { a 1 , … , a n } {\displaystyle F~:~\left\{1,\ldots ,n\right\}~\to ~\left\{a_{1},\ldots ,a_{n}\right\}} with domain domain ⁡ F = { 1 , … , n } = { i ∈ N : 1 ≤ i ≤ n } {\displaystyle \operatorname {domain} F=\left\{1,\ldots ,n\right\}=\left\{i\in \mathbb {N} :1\leq i\leq n\right\}} and with codomain codomain ⁡ F = { a 1 , … , a n } , {\displaystyle \operatorname {codomain} F=\left\{a_{1},\ldots ,a_{n}\right\},} that is defined at i ∈ domain ⁡ F = { 1 , … , n } {\displaystyle i\in \operatorname {domain} F=\left\{1,\ldots ,n\right\}} by F ( i ) := a i . {\displaystyle F(i):=a_{i}.} That is, F {\displaystyle F} is the function defined by 1 ↦ a 1 ⋮ n ↦ a n {\displaystyle {\begin{alignedat}{3}1\;&\mapsto &&\;a_{1}\\\;&\;\;\vdots &&\;\\n\;&\mapsto &&\;a_{n}\\\end{alignedat}}} in which case the equality ( a 1 , a 2 , … , a n ) = ( F ( 1 ) , F ( 2 ) , … , F ( n ) ) {\displaystyle \left(a_{1},a_{2},\dots ,a_{n}\right)=\left(F(1),F(2),\dots ,F(n)\right)} necessarily holds. Tuples as sets of ordered pairs Functions are commonly identified with their graphs, which is a certain set of ordered pairs. Indeed, many authors use graphs as the definition of a function. Using this definition of "function", the above function F {\displaystyle F} can be defined as: F := { ( 1 , a 1 ) , … , ( n , a n ) } . {\displaystyle F~:=~\left\{\left(1,a_{1}\right),\ldots ,\left(n,a_{n}\right)\right\}.} === Tuples as nested ordered pairs === Another way of modeling tuples in set theory is as nested ordered pairs. This approach assumes that the notion of ordered pair has already been defined. The 0-tuple (i.e. the empty tuple) is represented by the empty set ∅ {\displaystyle \emptyset } . An n-tuple, with n > 0, can be defined as an ordered pair of its first entry and an (n − 1)-tuple (which contains the remaining entries when n > 1): ( a 1 , a 2 , a 3 , … , a n ) = ( a 1 , ( a 2 , a 3 , … , a n ) ) {\displaystyle (a_{1},a_{2},a_{3},\ldots ,a_{n})=(a_{1},(a_{2},a_{3},\ldots ,a_{n}))} This definition can be applied recursively to the (n − 1)-tuple: ( a 1 , a 2 , a 3 , … , a n ) = ( a 1 , ( a 2 , ( a 3 , ( … , ( a n , ∅ ) … ) ) ) ) {\displaystyle (a_{1},a_{2},a_{3},\ldots ,a_{n})=(a_{1},(a_{2},(a_{3},(\ldots ,(a_{n},\emptyset )\ldots ))))} Thus, for example: ( 1 , 2 , 3 ) = ( 1 , ( 2 , ( 3 , ∅ ) ) ) ( 1 , 2 , 3 , 4 ) = ( 1 , ( 2 , ( 3 , ( 4 , ∅ ) ) ) ) {\displaystyle {\begin{aligned}(1,2,3)&=(1,(2,(3,\emptyset )))\\(1,2,3,4)&=(1,(2,(3,(4,\emptyset ))))\\\end{aligned}}} A variant of this definition starts "peeling off" elements from the other end: The 0-tuple is the empty set ∅ {\displaystyle \emptyset } . For n > 0: ( a 1 , a 2 , a 3 , … , a n ) = ( ( a 1 , a 2 , a 3 , … , a n − 1 ) , a n ) {\displaystyle (a_{1},a_{2},a_{3},\ldots ,a_{n})=((a_{1},a_{2},a_{3},\ldots ,a_{n-1}),a_{n})} This definition can be applied recursively: ( a 1 , a 2 , a 3 , … , a n ) = ( ( … ( ( ( ∅ , a 1 ) , a 2 ) , a 3 ) , … ) , a n ) {\displaystyle (a_{1},a_{2},a_{3},\ldots ,a_{n})=((\ldots (((\emptyset ,a_{1}),a_{2}),a_{3}),\ldots ),a_{n})} Thus, for example: ( 1 , 2 , 3 ) = ( ( ( ∅ , 1 ) , 2 ) , 3 ) ( 1 , 2 , 3 , 4 ) = ( ( ( ( ∅ , 1 ) , 2 ) , 3 ) , 4 ) {\displaystyle {\begin{aligned}(1,2,3)&=(((\emptyset ,1),2),3)\\(1,2,3,4)&=((((\emptyset ,1),2),3),4)\\\end{aligned}}} === Tuples as nested sets === Using Kuratowski's representation for an ordered pair, the second definition above can be reformulated in terms of pure set theory: The 0-tuple (i.e. the empty tuple) is represented by the empty set ∅ {\displaystyle \emptyset } ; Let x {\displaystyle x} be an n-tuple ( a 1 , a 2 , … , a n ) {\displaystyle (a_{1},a_{2},\ldots ,a_{n})} , and let x → b ≡ ( a 1 , a 2 , … , a n , b ) {\displaystyle x\rightarrow b\equiv (a_{1},a_{2},\ldots ,a_{n},b)} . Then, x → b ≡ { { x } , { x , b } } {\displaystyle x\rightarrow b\equiv \{\{x\},\{x,b\}\}} . (The right arrow, → {\displaystyle \rightarrow } , could be read as "adjoined with".) In this formulation: ( ) = ∅ ( 1 ) = ( ) → 1 = { { ( ) } , { ( ) , 1 } } = { { ∅ } , { ∅ , 1 } } ( 1 , 2 ) = ( 1 ) → 2 = { { ( 1 ) } , { ( 1 ) , 2 } } = { { { { ∅ } , { ∅ , 1 } } } , { { { ∅ } , { ∅ , 1 } } , 2 } } ( 1 , 2 , 3 ) = ( 1 , 2 ) → 3 = { { ( 1 , 2 ) } , { ( 1 , 2 ) , 3 } } = { { { { { { ∅ } , { ∅ , 1 } } } , { { { ∅ } , { ∅ , 1 } } , 2 } } } , { { { { { ∅ } , { ∅ , 1 } } } , { { { ∅ } , { ∅ , 1 } } , 2 } } , 3 } } {\displaystyle {\begin{array}{lclcl}()&&&=&\emptyset \\&&&&\\(1)&=&()\rightarrow 1&=&\{\{()\},\{(),1\}\}\\&&&=&\{\{\emptyset \},\{\emptyset ,1\}\}\\&&&&\\(1,2)&=&(1)\rightarrow 2&=&\{\{(1)\},\{(1),2\}\}\\&&&=&\{\{\{\{\emptyset \},\{\emptyset ,1\}\}\},\\&&&&\{\{\{\emptyset \},\{\emptyset ,1\}\},2\}\}\\&&&&\\(1,2,3)&=&(1,2)\rightarrow 3&=&\{\{(1,2)\},\{(1,2),3\}\}\\&&&=&\{\{\{\{\{\{\empty

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