Local coordinates are the ones used in a local coordinate system or a local coordinate space. Simple examples: Houses. In order to work in a house construction, the measurements are referred to a control arbitrary point that will allow to check it: stick/sticks on the ground, steel bar, nails... Addresses. Using house numbers to locate a house on a street; the street is a local coordinate system within a larger system composed of city townships, states, countries, postal codes, etc. Local systems exist for convenience. On ancient times, every work was made on relative bases as there was no conception of global systems. Practically, it is better to use local systems for small works as houses, buildings... For most of the applications, it is desired the position of one element relative to one building or location, and in a more local way, relative to one furniture or person. In a regular way, you will not give your position by geographical coordinates rather than "I am 15 meters away of the entry to the building". So it is a pretty common way to locate things. It is possible to bring latitude and longitude for all terrestrial locations, but unless one has a highly precise GPS device or you make astronomical observations, this is impractical. It is much simpler to use a tape, a rope, a chain... The position information (global) should be transformed into a location. Position refers to a numeric or symbolic description within a spatial reference system, whereas location refers to information about surrounding objects and their interrelationships. (Topological space) == Use == In computer graphics and computer animation, local coordinate spaces are also useful for their ability to model independently transformable aspects of geometrical scene graphs. When modeling a car, for example, it is desirable to describe the center of each wheel with respect to the car's coordinate system, but then specify the shape of each wheel in separate local spaces centered about these points. This way, the information describing each wheel can be simply duplicated four times, and independent transformations (e.g., steering rotation) can be similarly effected. Bounding volumes of objects may be described more accurately using extents in the local coordinates, (i.e. an object oriented bounding box, contrasted with the simpler axis aligned bounding box). The trade-off for this flexibility is additional computational cost: the rendering system must access the higher-level coordinate system of the car and combine it with the space of each wheel in order to draw everything in its proper place. Local coordinates also afford digital designers a means around the finite limits of numerical representation. The tread marks on a tire, for example, can be described using millimeters by allowing the whole tire to occupy the entire range of numeric precision available. The larger aspects of the car, such as its frame, might be described in centimeters, and the terrain that the car travels on could be specified in meters. In differential topology, local coordinates on a manifold are defined by means of an atlas of charts. The basic idea behind coordinate charts is that each small patch of a manifold can be endowed with a set of local coordinates. These are collected together into an atlas, and stitched together in such a way that they are self-consistent on the manifold. In Cartography and Maps, the traditional way of works are local datum. With a local datum the land can be mapped on relative small areas as a country. With the need of global systems, the transformations on between datum became a problem, so geodetic datum have been created. More than 150 local datum have been used in the world.
Private cloud computing infrastructure
Private cloud computing infrastructure is a category of cloud computing that provides comparable benefits to public cloud systems, such as self-service and scalability, but it does so via a proprietary framework. In contrast to public clouds, which cater to multiple entities, a private cloud is specifically designed for the requirements and objectives of one organization. == Definition == A private cloud computing infrastructure constitutes a distinctive model of cloud computing that facilitates a secure and distinct cloud environment where only the intended client can function. It can either be physically housed in the organization's in-house data center or be managed by a third-party provider. In a private cloud, the infrastructure and services are always sustained on a private network, and both the hardware and software are devoted exclusively to a single organization. == History == The concept of private cloud infrastructure started to take shape around the mid-2000s, coinciding with the rise of other cloud computing forms. It came into existence as a solution to the shortcomings of public clouds, particularly concerns over data control, security, and network performance. IT departments began to mirror the automation and self-service features of the public cloud in their data centers. Over time, these services became more advanced, and private cloud technology has been refined to address businesses and organizations' diverse needs. == Architecture == Private cloud computing infrastructure generally involves a mix of hardware, network infrastructure, and virtualization software. The hardware, often referred to as a cloud server or cloud array, consists of a server rack or a collection of server racks containing the storage and processors that constitute the cloud. The virtualization software, such as Hyper-V, OpenStack, or VMWare, establishes and oversees virtual machines with which users interact. The network infrastructure connects the private cloud to users and may facilitate connectivity with other on-premises data centers or clouds. == Applications == Private cloud infrastructures are usually utilized by medium to large businesses and organizations that need robust control over their data, have extensive computing needs, or have specific regulatory or compliance obligations. This includes healthcare organizations, government agencies, financial institutions, and any business that needs to process and store large data volumes.
Cortica
Headquartered in Tel Aviv Cortica utilizes unsupervised learning methods to recognize and analyze digital images and video. The technology developed by the Cortica team is based on research of the function of the human brain. == Company Founding == Cortica was founded in 2007 by Igal Raichelgauz, Karina Odinaev and Yehoshua Zeevi. Together, the founders developed the company’s core technology while at Technion – Israel Institute of Technology. By combining discoveries in neuroscience with developments in computer programming, the team created technology that possesses the ability to interpret large amounts of visual data with increased accuracy. This technology, called Image2Text, is based on the founders’ work in digitally replicating cortical neural networks’ ability to identify complex patterns within massive quantities of ambiguous and noisy data. Cortica’s offerings have application in the automotive industry, media industries, as well as the smart city and medical industries. Industry experts suggest that the self-driving automotive industry alone will be worth upwards of $7 trillion while each connected car is expected to generate 4,000 GB of data per day. Beyond that, industry analysts expect the proliferation of surveillance cameras to continue leading to an expected 2,500 Petabytes of data being generated daily by new surveillance cameras. Cortica operates in these high scale industries. The company currently employs professionals from many domains including AI researchers as well as veterans of intelligence units within the Israeli Defense Forces. == Research and Technology == In 2006, Founders Raichelgauz, Odinaev, and Zeevi shared their findings with the 28th IEEE EMBS Annual International Conference in New York in a paper titled, “Natural Signal Classification by Neural Cliques and Phase-Locked Attractors”. That same year, the team also published “Cliques in Neural Ensembles as Perception Carriers" CB Insights recently identified Cortica as the number one patent holder among AI companies. Cortica is researching to develop a machine-learning driving system which can identify objects and pedestrians. Connecting to it, Elon Musk has been rumored to partner with Cortica for his electric car company, Tesla. However, Tesla denies it stating that Musk did not discuss a collaboration with artificial intelligence firm Cortica. == Funding == Cortica raised $7 million in its Series A funding round, announced in August 2012. Investors included Horizons Ventures (the investment firm of Hong Kong billionaire Li Ka-Shing), and Ynon Kreiz, the former chairman and CEO of the Endemol Group. In May 2013, it was announced that Cortica had raised $1.5 million from Russian firm Mail.ru Group. It later transpired that this was a part of Cortica's Series B funding round for $6.4 million, announced in June 2013. The round was led by Horizons Ventures, with participation from the Russian firm Mail.ru Group and other angel investors. In its fourth funding round, Cortica has raised $20 million, bringing the total investments to $38 million. According to a report from The Israeli lead Daily economic newspaper, TheMarker, the fourth round was led by a strategic Chinese investor who will probably help the company expand into the Asian market. == Media coverage == GigaOm listed Cortica as one of the top deep learning startups in a November 2013 article surveying the field, along with AlchemyAPI, Ersatz, and Semantria. Business Insider ranked Cortica as one of the coolest tech companies in Israel. CB Insights has identified Cortica as the top patent holding AI company. In 2017 several leading automotive media outlets covered the launch of Cortica's automotive business unit
Preferential entailment
Preferential entailment is a non-monotonic logic based on selecting only models that are considered the most plausible. The plausibility of models is expressed by an ordering among models called a preference relation, hence the name preference entailment. Formally, given a propositional formula F {\displaystyle F} and an ordering over propositional models ≤ {\displaystyle \leq } , preferential entailment selects only the models of F {\displaystyle F} that are minimal according to ≤ {\displaystyle \leq } . This selection leads to a non-monotonic inference relation: F ⊨ pref G {\displaystyle F\models _{\text{pref}}G} holds if and only if all minimal models of F {\displaystyle F} according to ≤ {\displaystyle \leq } are also models of G {\displaystyle G} . Circumscription can be seen as the particular case of preferential entailment when the ordering is based on containment of the sets of variables assigned to true (in the propositional case) or containment of the extensions of predicates (in the first-order logic case).
Eimear Kenny
Eimear E. Kenny is a researcher in population genetics and translation genomics, and is the Founding Director of the Institute for Genomic Health, and Endowed Chair and Professor of Genomic Health at the Icahn School of Medicine at Mount Sinai. She is known for novel approaches in computational genomics, advancing the study of human genetic variation and its connection to disease risk and diagnosis. Her research has laid the foundation for integrating artificial intelligence (AI) and genomics into precision medicine and routine clinical care. By combining genomics, computer science, and medicine, her work leverages genomic sequencing technologies and machine learning algorithms to uncover insights that improve patient care, accelerate genomic data analysis, and enable the future of AI-driven healthcare. She has led multiple genomics-based clinical trials, applying computational biology and AI in clinical settings to advance genomic medicine and precision healthcare. == Research == A recipient of the Early-Career Award from the American Society of Human Genetics (USA), Kenny, as of 2024, leads a team in genetics, computer science, and medicine, focusing on genetic ancestry, large-scale genomics, clinical trials, and genomic medicine at the Institute for Genomic Health. The lab works to advance understanding of genetic ancestry and its impact on health in order to inform better clinical medicine models. She is recognized for her work to leverage biobanks for translational genomics and her development of new genetic tests an strategies for health care management. In one study, she and her colleagues investigated genetic disorders that might be under-diagnosed due to insufficient data, and found a variant in a collagen gene associated with Steel syndrome. This syndrome caused short stature and bone and joint issues and was thought to be rare. However, the study revealed it is common in individuals with Puerto Rican ancestry. Three of Kenny's genomic medicine clinical trials assessed how to bring new technology, such as digital apps, or information, such as polygenic risk scores, into routine clinical care. In the 2010s, Kenny was instrumental in several large-scale sequencing studies, including the 1000 Genomes Project, the Exome Sequencing Project, the Genome Sequencing Project, and the Trans-Omics for Precision Medicine. In 2012, she led work that discovered the variant responsible for blond hair in Melanesia, work that was featured in the Smithsonian NHGRI Human Genome Exhibit in Washington, D.C. In 2017, her group was one of the first to demonstrate that polygenic risk scores derived in predominantly European populations have reduced accuracy when applied in populations now widely acknowledged as a major challenge in the field of genomic risk prediction. As of 2024, she is Principal Investigator in many NIH-funded international consortium focused on computational genomics and genomic medicine, including Electronic Medical Records and Genomics, Polygenic Risk Methods in Diverse Populations, and the Human Pangenome Reference Consortium. In 2023, Kenny played a key role in a groundbreaking advancement in genomics research by helping to map a diverse human pangenome—a major shift from reliance on a single reference genome. Unlike the earlier genetic map, based on one man of mixed European and African ancestry in Buffalo, this new pangenome project captures far greater human genetic diversity. As reported by The Washington Post, Kenny's work demonstrates how a more inclusive human genome can drive discoveries in rare genetic diseases, improve genomic medicine, and accelerate the future of precision healthcare. Kenny was co-developer and current license holder for Random Forest adMIXture (RFMix), a patented software for inferring continental and sub-continental ancestry at genomic loci. == Education and career == Kenny graduated from Trinity College Dublin with a BA in Biochemistry in 1999 and did a masters in Bioinformatics at Leeds University. She received her PhD in Computational Genomics at Rockefeller University, and did her post-doctoral work in the lab of Dr. Carlos D. Bustamante at Stanford University. === Academic appointments === As of 2024, at Mount Sinai, she serves as the Endowed Chair and Professor of Genomic Health, Professor at the Department of Medicine and Professor at the Department of Genetics and Genomic Sciences. Since 2018 she has served as the Founding Director of the Institute for Genomic Health, and since 2022, she also serves as the Founding Director of the Center for Translational Genomics. She is also the Director of Translational Research, Division for Genomic Medicine. Former appointments include Assistant Professor at the Department of Genetics and Genomic Sciences and Member at The Charles Bronfman Institute of Personalized Medicine, both at Mount Sinai. She was also Bioinformatics Programmer at the California Institute of Technology, and research assistant at the Massachusetts Institute of Technology. == Publications == As of 2024, Kenny is an advisor to Cell Genomics. Google Scholar reports 50,623 citations, an h-index of 66 and an i10-index of 130. The five most-cited articles she contributed to are: Auton, A; Brooks, LD; Durbin, RM; Garrison, EP; Kang, HM; Korbel, JO; Marchini, JL; McCarthy, S; McVean, GA; Abecasis, GR (2015). "A global reference for human genetic variation". Nature. 526 (7571): 68–74. Bibcode:2015Natur.526...68T. doi:10.1038/nature15393. PMC 4750478. PMID 26432245.. Cited by 14847 Abecasis, GR; Auton, A; Brooks, LD; DePristo, MA; Durbin, RM; Handsaker, RE; Kang, HM; Marth, GT; McVean, GA (2012). "An integrated map of genetic variation from 1,092 human genomes". Nature. 491 (7422): 56–65. Bibcode:2012Natur.491...56T. doi:10.1038/nature11632. PMC 3498066. PMID 23128226.. Cited by 8287 Jacob A. Tennessen et al. Evolution and Functional Impact of Rare Coding Variation from Deep Sequencing of Human Exomes.Science337,64–69(2012).DOI:10.1126/science.1219240 Cited by 1886 Taliun, D.; Harris, D.N.; Kessler, M.D.; et al. (2021). "Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program". Nature. 590 (7845): 290–299. Bibcode:2021Natur.590..290T. doi:10.1038/s41586-021-03205-y. PMC 7875770. PMID 33568819.. Cited by 1369 Vilhjálmsson, BJ; et al. (2015). "Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores". Am J Hum Genet. 97 (4): 576–92. doi:10.1016/j.ajhg.2015.09.001. PMC 4596916. PMID 26430803.. Cited by 1327
Vector database
A vector database, vector store or vector search engine is a database that stores and retrieves embeddings of data in vector space. Vector databases typically implement approximate nearest neighbor algorithms so users can search for records semantically similar to a given input, unlike traditional databases which primarily look up records by exact match. Use-cases for vector databases include similarity search, semantic search, multi-modal search, recommendations engines, object detection, and retrieval-augmented generation (RAG). Vector embeddings are mathematical representations of data in a high-dimensional space. In this space, each dimension corresponds to a feature of the data, with the number of dimensions ranging from a few hundred to tens of thousands, depending on the complexity of the data being represented. Each data item is represented by one vector in this space. Words, phrases, or entire documents, as well as images, audio, and other types of data, can all be vectorized. These feature vectors may be computed from the raw data using machine learning methods such as feature extraction algorithms, word embeddings or deep learning networks. The goal is that semantically similar data items receive feature vectors close to each other. Vector retrieval can be combined with metadata filtering or lexical search to support filtered and hybrid retrieval workflows. == Techniques == Common techniques for similarity search on high-dimensional vectors include: Hierarchical Navigable Small World (HNSW) graphs Locality-sensitive hashing (LSH) and sketching Product quantization (PQ) Inverted files These techniques may also be combined in vector search systems. In recent benchmarks, HNSW-based implementations have been among the best performers. Conferences such as the International Conference on Similarity Search and Applications (SISAP) and the Conference on Neural Information Processing Systems (NeurIPS) have hosted competitions on vector search in large databases. == Applications == Vector databases are used in a wide range of machine learning applications including similarity search, semantic search, multi-modal search, recommendations engines, object detection, and retrieval-augmented generation. === Retrieval-augmented generation === An especially common use-case for vector databases is in retrieval-augmented generation (RAG), a method to improve domain-specific responses of large language models. The retrieval component of a RAG can be any search system, but is most often implemented as a vector database. Text documents describing the domain of interest are collected, and for each document or document section, a feature vector (known as an "embedding") is computed, typically using a deep learning network, and stored in a vector database along with a link to the document. Given a user prompt, the feature vector of the prompt is computed, and the database is queried to retrieve the most relevant documents. These are then automatically added into the context window of the large language model, and the large language model proceeds to create a response to the prompt given this context. == Implementations ==
Semantic parameterization
Semantic parameterization is a conceptual modeling process for expressing natural language descriptions of a domain in first-order predicate logic. The process yields a formalization of natural language sentences in Description Logic to answer the who, what and where questions in the Inquiry-Cycle Model (ICM) developed by Colin Potts and his colleagues at the Georgia Institute of Technology. The parameterization process complements the Knowledge Acquisition and autOmated Specification (KAOS) method, which formalizes answers to the when, why and how ICM questions in Temporal Logic, to complete the ICM formalization. The artifacts used in the parameterization process include a dictionary that aligns the domain lexicon with unique concepts, distinguishing between synonyms and polysemes, and several natural language patterns that aid in mapping common domain descriptions to formal specifications. == Relationship to other theories == Semantic Parameterization defines a meta-model consisting of eight roles that are domain-independent and reusable. Seven of these roles correspond to Jeffrey Gruber's thematic relations and case roles in Charles Fillmore's case grammar: The Inquiry-Cycle Model (ICM) was introduced to drive elicitation between engineers and stakeholders in requirements engineering. The ICM consists of who, what, where, why, how and when questions. All but the when questions, which require a Temporal Logic to represent such phenomena, have been aligned with the meta-model in semantic parameterization using Description Logic (DL). == Introduction with Example == The semantic parameterization process is based on Description Logic, wherein the TBox is composed of words in a dictionary, including nouns, verbs, and adjectives, and the ABox is partitioned into two sets of assertions: 1) those assertions that come from words in the natural language statement, called the grounding, and 2) those assertions that are inferred by the (human) modeler, called the meta-model. Consider the following unstructured natural language statement (UNLS) (see Breaux et al. for an extended discussion): UNLS1.0 The customer1,1 must not share2,2 the access-code3,3 of the customer1,1 with someone4,4 who is not the provider5,4. The modeler first identifies intensional and extensional polysemes and synonyms, denoted by the subscripts: the first subscript uniquely refers to the intensional index, i.e., the same first index in two or more words refer to the same concept in the TBox; the second subscript uniquely refers to the extensional index, i.e., two same second index in two or more words refer to the same individual in the ABox. This indexing step aligns words in the statement and concepts in the dictionary. Next, the modeler identifies concepts from the dictionary to compose the meta-model. The following table illustrates the complete DL expression that results from applying semantic parameterization.