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  Drop shadow

  In graphic design and computer graphics, a drop shadow is a visual effect consisting of a drawing element which looks like the shadow of an object, giving the impression that the object is raised above the objects behind it. The drop shadow is often used for elements of a graphical user interface such as windows or menus, and for simple text. The text label for icons on desktops in many desktop environments has a drop shadow, as this effect effectively distinguishes the text from any colored background it may be in front of. A simple way of drawing a drop shadow of a rectangular object is to draw a gray or black area underneath and offset from the object. In general, a drop shadow is a copy in black or gray of the object, drawn in a slightly different position. Realism may be increased by: Darkening the colors of the pixels where the shadow casts instead of making them gray. This can be done with alpha blending the shadow with the area it is cast on. Softening the edges of the shadow. This can be done by adding Gaussian blur to the shadow's alpha channel before blending. Inset drop shadows are a type which draws the shadows inside the element. This allows the interface element to appear as if it is sunken into the interface. == Photo editing == In photo editing or photography post-production, a drop shadow may be added right beneath a model or product in the image. It is used to create contrast between the background and the subject. To add a drop shadow, retouchers use graphic editing tools like Adobe Photoshop. Drop shadows are often used as a visual effect in e-commerce. This is done to improve the presentation of product images and create depth in the image. == Use == Generally, window managers which are capable of compositing allow drop shadow effects, whereas incapable window managers do not. In some operating systems like macOS, drop shadow is used to differentiate between active and inactive windows. Websites are able to use drop shadow effects through the CSS properties box-shadow, text-shadow, and drop-shadow() filter function in filter. The first two are used for elements and text respectively, while the filter applies to the element's content, letting it support oddly shaped elements or transparent images.

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  Operational database

  Operational database management systems (also referred to as OLTP databases or online transaction processing databases), are used to update data in real-time. These types of databases allow users to do more than simply view archived data. Operational databases allow you to modify that data (add, change or delete data), doing it in real-time. OLTP databases provide transactions as main abstraction to guarantee data consistency that guarantee the so-called ACID properties. Basically, the consistency of the data is guaranteed in the case of failures and/or concurrent access to the data. == History == Since the early 1990s, the operational database software market has been largely taken over by SQL engines. In 2014, the operational DBMS market (formerly OLTP) was evolving dramatically, with new, innovative entrants and incumbents supporting the growing use of unstructured data and NoSQL DBMS engines, as well as XML databases and NewSQL databases. NoSQL databases typically have focused on scalability and have renounced to data consistency by not providing transactions as OLTP system do. Operational databases are increasingly supporting distributed database architecture that can leverage distribution to provide high availability and fault tolerance through replication and scale out ability. The growing role of operational databases in the IT industry is moving fast from legacy databases to real-time operational databases capable to handle distributed web and mobile demand and to address Big data challenges. Recognizing this, Gartner started to publish the Magic Quadrant for Operational Database Management Systems in October 2013. == List of operational databases == Notable operational databases include: == Use in business == Operational databases are used to store, manage and track real-time business information. For example, a company might have an operational database used to track warehouse/stock quantities. As customers order products from an online web store, an operational database can be used to keep track of how many items have been sold and when the company will need to reorder stock. An operational database stores information about the activities of an organization, for example customer relationship management transactions or financial operations, in a computer database. Operational databases allow a business to enter, gather, and retrieve large quantities of specific information, such as company legal data, financial data, call data records, personal employee information, sales data, customer data, data on assets and many other information. An important feature of storing information in an operational database is the ability to share information across the company and over the Internet. Operational databases can be used to manage mission-critical business data, to monitor activities, to audit suspicious transactions, or to review the history of dealings with a particular customer. They can also be part of the actual process of making and fulfilling a purchase, for example in e-commerce. == Data warehouse terminology == In data warehousing, the term is even more specific: the operational database is the one which is accessed by an operational system (for example a customer-facing website or the application used by the customer service department) to carry out regular operations of an organization. Operational databases usually use an online transaction processing database which is optimized for faster transaction processing (create, read, update and delete operations). An operational database is the source for a data warehouse. Data from an operational database can be loaded into an operational data store at a data warehouse before the data is processed into the data warehouse.

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  Enterprise data planning

  Enterprise data planning is the starting point for enterprise wide change. It states the destination and describes how you will get there. It defines benefits, costs and potential risks. It provides measures to be used along the way to judge progress and adjust the journey according to changing circumstances. Data is fundamental to investment enterprises. Effective, economic management of data underpins operations and enables transformations needed to satisfy customer demands, competition and regulation. Data warehouse(s) and other aspects of the overall data architecture are critical to the enterprise. EDMworks has created a strategic data planning approach for the Investment Sector. It consists of a planning process, planning intranets, templates and training materials. EDMworks planning process is based on the belief that extensive domain knowledge significantly shortens planning iterations and enables progressively higher quality plans to be produced and implemented. This approach drives the development of an effective and economic enterprise data architecture. Enterprise data planning is based on proven business disciplines. Key architectural layers for data and applications are then added in order to provide an enterprise wide understanding of the uses and interdependencies of data. This enables the definition of the core components of the EDM plan: Industry structure and business objectives Assessment of systems and services Target architecture for applications, data and infrastructure Target organization structures Systems, database, infrastructure and organizational plans Business case, costs, benefits, results and risks. EDMworks uses several components from the Open Systems Group TOGAF enterprise systems planning process. TOGAF acts as an extension to good business planning methods to provide a framework for the development of the systems and data architectural components. == History == James Martin was one of the pathfinders in data planning methodologies. He was one of the first to identify data as being an enterprise wide asset that required management. He developed a series of tools and methods to support that process. Most of the large consulting firms developed their own methods to address the same basic issue. Frequently, their approaches were incorporated into their own branded system development methodologies that encompassed the complete systems development life-cycle. Others, such as Ed Tozer, developed more focused offerings that dealt with the complexities of extracting key business needs from senior management and then defining relevant architectural visions for the specific enterprise. From these various sources, the concepts of Business, Data, Applications and Technology Architectures emerged. The Open Group Architectural Framework (TOGAF) has taken this work forward and has established a sound method in TOGAF version 9. EDMworks approach is to adopt these planning and architectural practices as a basis and then add two additional dimensions to the planning and implementation focus: Domain knowledge of the Investments sector. Investments is a complex global industry with a common set of characteristics about clients, information vendors, competition and regulation. Domain knowledge significantly improves the quality of the planning and implementation processes Development of people and teams. Change is a major feature of in any Enterprise Data Management program and people and teams both need development in order to make EDM effective throughout an organization.

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  Archetype (information science)

  In the field of informatics, an archetype is a formal re-usable model of a domain concept. Traditionally, the term archetype is used in psychology to mean an idealized model of a person, personality or behaviour (see Archetype). The usage of the term in informatics is derived from this traditional meaning, but applied to domain modelling instead. An archetype is defined by the OpenEHR Foundation (for health informatics) as follows: An archetype is a computable expression of a domain content model in the form of structured constraint statements, based on some reference model. openEHR archetypes are based on the openEHR reference model. Archetypes are all expressed in the same formalism. In general, they are defined for wide re-use, however, they can be specialized to include local particularities. They can accommodate any number of natural languages and terminologies. == Formal specifications == The modern archetype formalism is specified and maintained by the openEHR Foundation, and although originally developed for the health IT domain, is completely domain-independent, and has been used in geospatial modelling, telecommunications, and defence. The archetype formalism consists of a number of specifications including: 'ADL 1.4': original release of Archetype Definition Language (ADL) and Archetype Object Model (AOM); widely implemented in health IT domain; 'ADL 2': modern release of Archetype Definition Language (ADL), Archetype Object Model (AOM), Archetype Identification specification and Archetype Technology Overview. The Archetype Technology Overview provides a short technical overview of the archetype formalism useful for new users. The ADL/AOM 1.4 specifications were provided to ISO TC 215 in 2008 by the openEHR Foundation and became the ISO 13606-2 standard, extant until 2019. ISO TC 215 accepted the AOM 2 specification as the basis for a revision of this standard, which was issued in 2019. In late 2015, the Object Management Group (OMG) accepted an RfP entitled 'Archetype Modeling Language (AML)' as a new candidate standard. This specification is a form of ADL re-engineered as a UML profile so as to enable archetype modelling to be supported within UML tools. == Tools == A number of tools area available for working with archetypes. Most are listed on the openEHR modelling tools page. They include: ADL Designer, a modern AOM2-based web editing application Archetype Editor, an original desktop clinical modelling tool Template Designer, an original desktop clinical templating tool LinkEHR, an archetype and data integration tool ADL Workbench, reference compiler and visualiser tool == Example ==

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  Outline of deep learning

  The following outline is provided as an overview of, and topical guide to, deep learning: Deep learning is a subfield of machine learning and artificial intelligence based on artificial neural networks with multiple processing layers. It emphasizes representation learning and is widely used in areas such as computer vision, natural language processing, speech recognition, recommender systems, robotics, and generative artificial intelligence. == Ways to categorize deep learning == A field of study A branch of artificial intelligence A subfield of machine learning A subfield of computer science A form of representation learning A class of methods based on artificial neural networks An approach used in computational statistics == History == === Precursors === Cybernetics Perceptron Connectionism Neocognitron Backpropagation === Milestones === LeNet Long short-term memory Deep belief network AlexNet Sequence to sequence learning Generative adversarial network Residual neural network Transformer BERT Generative pre-trained transformer Diffusion model === Related histories === History of artificial intelligence History of machine learning Timeline of machine learning == Core concepts == == Learning settings == Supervised learning Unsupervised learning Self-supervised learning Semi-supervised learning Reinforcement learning Transfer learning Multitask learning Multimodal learning Online machine learning Continual learning == Common tasks == Image classification Object detection Image segmentation Automatic speech recognition Neural machine translation Question answering Automatic summarization Text-to-image model Protein structure prediction == Architectures == === Feedforward and convolutional architectures === Feedforward neural network Multilayer perceptron Convolutional neural network Radial basis function network Residual neural network U-Net === Recurrent and sequence architectures === Recurrent neural network Long short-term memory Gated recurrent unit Sequence to sequence learning Recursive neural network === Representation-learning architectures === Autoencoder Denoising autoencoder Sparse autoencoder Variational autoencoder Restricted Boltzmann machine Deep belief network === Attention and transformer architectures === Attention (machine learning) Transformer BERT Generative pre-trained transformer Vision transformer === Generative and probabilistic architectures === Autoregressive model Diffusion model Energy-based model Generative adversarial network Mixture of experts === Graph and memory architectures === Graph neural network Graph convolutional network Siamese network Neural Turing machine Memory network Echo state network Capsule neural network == Neural network components and techniques == Artificial neuron Activation function Rectified linear unit Sigmoid function Softmax function Embedding Convolution Pooling layer Attention Batch normalization Layer normalization Residual connections == Training and optimization == Backpropagation Gradient descent Stochastic gradient descent Adam optimization Learning rate Loss function Cross-entropy Mean squared error Regularization Dropout Early stopping Batch normalization Data augmentation Transfer learning Knowledge distillation Ensemble learning Curriculum learning == Datasets and benchmarks == CIFAR-10 ImageNet MNIST database Common Objects in Context (COCO) General Language Understanding Evaluation (GLUE) benchmark LibriSpeech SQuAD == Applications == === Computer vision === Computer vision Facial recognition system Image classification Image segmentation Medical imaging Object detection Optical character recognition === Natural language processing === Automatic summarization Chatbot Information retrieval Large language model Natural language processing Neural machine translation Question answering Sentiment analysis === Speech and audio === Automatic speech recognition Music information retrieval Speaker recognition Speech synthesis === Science and medicine === Bioinformatics Computational biology Drug discovery Medical diagnosis Protein structure prediction === Robotics and control === Autonomous car Computer game bot Control theory Robotics === Recommendation, search, and forecasting === Anomaly detection Forecasting Fraud detection Recommender system Search engine === Generative artificial intelligence === Deepfake Generative artificial intelligence Large language model Speech synthesis Text-to-image model === Computer graphics and video games === Deep Learning Anti-Aliasing (DLAA) Deep Learning Super Sampling (DLSS) == Hardware == AMD Instinct AMD XDNA Application-specific integrated circuit Deep learning processor, Neural processing unit (NPU), or Neural Engine Field-programmable gate array General-purpose computing on graphics processing units (GPGPU) Graphics processing unit NVIDIA Deep Learning Accelerator (NVDLA) Tensor processing unit Vision processing unit Wafer-scale integration === Supporting software platforms === CUDA Metal ROCm == Software == === Open-source frameworks and libraries === === Neural network software === EDLUT Emergent Encog JOONE Neuroph NeuroSolutions OpenNN Peltarion Synapse SNNS === Platforms, tools, and deployment === Amazon SageMaker Google Colab Hugging Face Kaggle Kubeflow MLflow ONNX OpenVINO TensorFlow Hub == Algorithms for deep learning and neural networks == Backpropagation Conjugate gradient method Generalized Hebbian algorithm Gradient descent Levenberg–Marquardt algorithm Perceptron Quasi-Newton method Wake-sleep algorithm == Methods and related topics == === Representation and metric learning === Contrastive learning Embedding Feature learning Manifold learning Metric learning === Generative modeling === Autoregressive model Diffusion model Generative adversarial network Generative model Variational inference === Efficient and scalable deep learning === Knowledge distillation Low-rank approximation Mixture of experts Quantization Sparsity === Reliability, safety, and interpretability === Adversarial machine learning AI alignment Algorithmic bias Catastrophic forgetting Differential privacy Explainable artificial intelligence Federated learning Hallucination (artificial intelligence) == Conferences and workshops == Annual Meeting of the Association for Computational Linguistics Conference on Computer Vision and Pattern Recognition Conference on Neural Information Processing Systems International Conference on Computer Vision International Conference on Learning Representations International Conference on Machine Learning == Organizations == === Research laboratories and institutions === Allen Institute for AI Alberta Machine Intelligence Institute European Laboratory for Learning and Intelligent Systems Google DeepMind Meta AI Mila Microsoft Research Vector Institute === Companies === Anthropic Cerebras Cohere DeepSeek Mistral AI OpenAI Stability AI xAI == Publications == === Books === Deep Learning – Ian Goodfellow and Yoshua Bengio Neural Networks and Deep Learning – Michael Nielsen Perceptrons – Marvin Minsky and Seymour Papert === Journals === IEEE Transactions on Neural Networks and Learning Systems Neural Networks Neural Computation == Influential persons ==

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  Behavior selection algorithm

  In artificial intelligence, a behavior selection algorithm, or action selection algorithm, is an algorithm that selects appropriate behaviors or actions for one or more intelligent agents. In game artificial intelligence, it selects behaviors or actions for one or more non-player characters. Common behavior selection algorithms include: Finite-state machines Hierarchical finite-state machines Decision trees Behavior trees Hierarchical task networks Hierarchical control systems Utility systems Dialogue tree (for selecting what to say) == Related concepts == In application programming, run-time selection of the behavior of a specific method is referred to as the strategy design pattern.

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  Algorithm IMED

  In multi-armed bandit problems, IMED (for Indexed Minimum Empirical Divergence) is an algorithm developed in 2015 by Junya Honda and Akimichi Takemura. It is the first algorithm proved to be asymptotically optimal respect to the problem-dependant Lai–Robbins lower bound for distributions in ( − ∞ , 1 ] {\displaystyle (-\infty ,1]} . == Multi-armed bandit problem == The Multi-armed bandit problem is a sequential game where one player has to choose at each turn between K {\displaystyle K} actions (arms). Behind every arm a {\displaystyle a} there is an unknown distribution ν a {\displaystyle \nu _{a}} that lies in a set D {\displaystyle {\mathcal {D}}} known by the player (for example, D {\displaystyle {\mathcal {D}}} can be the set of Gaussian distributions or Bernoulli distributions). At each turn t {\displaystyle t} the player chooses (pulls) an arm a t {\displaystyle a_{t}} , he then gets an observation X t {\displaystyle X_{t}} of the distribution ν a t {\displaystyle \nu _{a_{t}}} . === Regret minimization === The goal is to minimize the regret at time T {\displaystyle T} that is defined as R T := ∑ a = 1 K Δ a E [ N a ( T ) ] {\displaystyle R_{T}:=\sum _{a=1}^{K}\Delta _{a}\mathbb {E} [N_{a}(T)]} where μ a := E [ ν a ] {\displaystyle \mu _{a}:=\mathbb {E} [\nu _{a}]} is the mean of arm a {\displaystyle a} μ ∗ := max a μ a {\displaystyle \mu ^{}:=\max _{a}\mu _{a}} is the highest mean Δ a := μ ∗ − μ a {\displaystyle \Delta _{a}:=\mu ^{}-\mu _{a}} N a ( t ) {\displaystyle N_{a}(t)} is the number of pulls of arm a {\displaystyle a} up to turn t {\displaystyle t} The player has to find an algorithm that chooses at each turn t {\displaystyle t} which arm to pull based on the previous actions and observations ( a s , X s ) s < t {\displaystyle (a_{s},X_{s})_{s μ } {\displaystyle {\mathcal {K}}_{inf}(\nu ,\mu ,{\mathcal {D}}):=\inf \left\{\mathrm {KL} (\nu ,{\tilde {\nu }})\ |\ {\tilde {\nu }}\in {\mathcal {P}}([-\infty ,1]),\ \mathbb {E} [{\tilde {\nu }}]>\mu \right\}} K L {\displaystyle \mathrm {KL} } is the Kullback–Leibler divergence P ( [ − ∞ , 1 ] ) {\displaystyle {\mathcal {P}}([-\infty ,1])} is the set of distribution in [ − ∞ , 1 ] {\displaystyle [-\infty ,1]} ν ^ a ( t ) {\displaystyle {\hat {\nu }}_{a}(t)} is the empirical distribution of arm a {\displaystyle a} at turn t {\displaystyle t} μ ^ ∗ ( t ) {\displaystyle {\hat {\mu }}^{}(t)} is the highest empirical mean of turn t {\displaystyle t} Remark : For arms a {\displaystyle a} that verify μ ^ a ( t ) = μ ^ ∗ ( t ) {\displaystyle {\hat {\mu }}_{a}(t)={\hat {\mu }}^{}(t)} we have K i n f ( ν ^ a ( t ) , μ ^ ∗ ( t ) ) = 0 {\displaystyle K_{inf}({\hat {\nu }}_{a}(t),{\hat {\mu }}^{}(t))=0} . Then there index is equal to ln ⁡ ( N a ( t ) ) {\displaystyle \ln(N_{a}(t))} === Pseudocode === for each arm i do: n[i] ← 1; nu[i] ← None; mu[i] ← None for t from 1 to K do: select arm t observe reward r n[t] ← n[t] + 1 nu[t] ← update empirical distribution mu[t] ← update empirical mean for t from K+1 to T do: mu ← highest mu for each arm i do: scoreK[i] ← n[i] K_inf(nu[i],mu) scoreN[i] ← ln(n[i]) index[i] ← scoreK[i] + scoreN[i] select arm a with smallest index[a] observe reward r n[a] ← n[a] + 1 nu[a] ← update empirical distribution mu[a] ← update empirical mean == Theoretical results == In the multi-armed bandit problem we have the asymptotic Lai–Robbins lower bound asymptotic lower bound on regret. The algorithm IMED is the first algorithm that matches this lower bound for distribution in ( − ∞ , 1 ] {\displaystyle (-\infty ,1]} in the first order. If the distribution are also bounded then it also match the second order. It is the first algorithm that match the second under of this lower bound. === Lai–Robbins lower bound === In 1985 Lai and Robbins proved an asymptotic, problem-dependent lower bound on regret. In 2018, Aurelien Garivier, Pierre Menard and Gilles Stoltz proved a refined lower bound that gives the second order It states that for every consistent algorithm on the set P ( [ − ∞ , 1 ] ) {\displaystyle {\mathcal {P}}([-\infty ,1])} — that is, an algorithm for which, for every ( ν 1 , … , ν K ) ∈ P ( [ − ∞ , 1 ] ) K {\displaystyle (\nu _{1},\dots ,\nu _{K})\in {\mathcal {P}}([-\infty ,1])^{K}} , the regret R T {\displaystyle R_{T}} is subpolynomial (i.e. R T = o T → + ∞ ( T α ) {\displaystyle R_{T}=o_{T\to +\infty }(T^{\alpha })} for all α > 0 {\displaystyle \alpha >0} ) — we have: R T ≥ ( ∑ a : μ a < μ ∗ Δ a K inf ( ν a , μ ∗ ) ) ln ⁡ T − Ω T → + ∞ ( ln ⁡ ln ⁡ T ) . {\displaystyle R_{T}\geq \left(\sum _{a:\mu _{a}<\mu ^{}}{\frac {\Delta _{a}}{{\mathcal {K}}_{\inf }(\nu _{a},\mu ^{})}}\right)\ln T-\Omega _{T\to +\infty }(\ln \ln T).} This bound is asymptotic (as T → + ∞ {\displaystyle T\to +\infty } ) and gives a first-order lower bound of order ln ⁡ T {\displaystyle \ln T} with the optimal constant in front of it and the second order in − Ω ( ln ⁡ ln ⁡ T ) {\displaystyle -\Omega (\ln \ln T)} . === Regret bound for IMED === If the distribution of every arm a {\displaystyle a} is ( − ∞ , 1 ] {\displaystyle (-\infty ,1]} ( i.e. ν a ∈ P ( [ − ∞ , 1 ] ) ) {\displaystyle \nu _{a}\in {\mathcal {P}}([-\infty ,1]))} then the regret of the algorithm IMED verify R T ≤ ( ∑ a : μ a < μ ∗ Δ a K inf ( ν a , μ ∗ ) ) ln ⁡ T + O ( 1 ) {\displaystyle R_{T}\leq \left(\sum _{a:\mu _{a}<\mu ^{}}{\frac {\Delta _{a}}{{\mathcal {K}}_{\inf }(\nu _{a},\mu ^{})}}\right)\ln T+O(1)} If all the distribution ν a {\displaystyle \nu _{a}} are bounded then it exists a constant C > 0 {\displaystyle C>0} such that for T {\displaystyle T} large enough the regret of IMED is upper bounded by R T ≤ ( ∑ a : μ a < μ ∗ Δ a K inf ( ν a , μ ∗ ) ) ln ⁡ T − C ln ⁡ ln ⁡ T {\displaystyle R_{T}\leq \left(\sum _{a:\mu _{a}<\mu ^{}}{\frac {\Delta _{a}}{{\mathcal {K}}_{\inf }(\nu _{a},\mu ^{})}}\right)\ln T-C\ln \ln T} == Computation time == The algorithm only requiere to compute the K i n f {\displaystyle K_{inf}} for suboptimal arms who are pulled O ( ln ⁡ T ) {\displaystyle O(\ln T)} times, which make it a lot faster than KL-UCB. A faster version of IMED was developed in 2023 to make it even faster, using a Taylor development of the K i n f {\displaystyle K_{inf}} in the first order .

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  Enumeration algorithm

  In computer science, an enumeration algorithm is an algorithm that enumerates the answers to a computational problem. Formally, such an algorithm applies to problems that take an input and produce a list of solutions, similarly to function problems. For each input, the enumeration algorithm must produce the list of all solutions, without duplicates, and then halt. The performance of an enumeration algorithm is measured in terms of the time required to produce the solutions, either in terms of the total time required to produce all solutions, or in terms of the maximal delay between two consecutive solutions and in terms of a preprocessing time, counted as the time before outputting the first solution. This complexity can be expressed in terms of the size of the input, the size of each individual output, or the total size of the set of all outputs, similarly to what is done with output-sensitive algorithms. == Formal definitions == An enumeration problem P {\displaystyle P} is defined as a relation R {\displaystyle R} over strings of an arbitrary alphabet Σ {\displaystyle \Sigma } : R ⊆ Σ ∗ × Σ ∗ {\displaystyle R\subseteq \Sigma ^{}\times \Sigma ^{}} An algorithm solves P {\displaystyle P} if for every input x {\displaystyle x} the algorithm produces the (possibly infinite) sequence y {\displaystyle y} such that y {\displaystyle y} has no duplicate and z ∈ y {\displaystyle z\in y} if and only if ( x , z ) ∈ R {\displaystyle (x,z)\in R} . The algorithm should halt if the sequence y {\displaystyle y} is finite. == Common complexity classes == Enumeration problems have been studied in the context of computational complexity theory, and several complexity classes have been introduced for such problems. A very general such class is EnumP, the class of problems for which the correctness of a possible output can be checked in polynomial time in the input and output. Formally, for such a problem, there must exist an algorithm A which takes as input the problem input x, the candidate output y, and solves the decision problem of whether y is a correct output for the input x, in polynomial time in x and y. For instance, this class contains all problems that amount to enumerating the witnesses of a problem in the class NP. Other classes that have been defined include the following. In the case of problems that are also in EnumP, these problems are ordered from least to most specific: Output polynomial, the class of problems whose complete output can be computed in polynomial time. Incremental polynomial time, the class of problems where, for all i, the i-th output can be produced in polynomial time in the input size and in the number i. Polynomial delay, the class of problems where the delay between two consecutive outputs is polynomial in the input (and independent from the output). Strongly polynomial delay, the class of problems where the delay before each output is polynomial in the size of this specific output (and independent from the input or from the other outputs). The preprocessing is generally assumed to be polynomial. Constant delay, the class of problems where the delay before each output is constant, i.e., independent from the input and output. The preprocessing phase is generally assumed to be polynomial in the input. == Common techniques == Backtracking: The simplest way to enumerate all solutions is by systematically exploring the space of possible results (partitioning it at each successive step). However, performing this may not give good guarantees on the delay, i.e., a backtracking algorithm may spend a long time exploring parts of the space of possible results that do not give rise to a full solution. Flashlight search: This technique improves on backtracking by exploring the space of all possible solutions but solving at each step the problem of whether the current partial solution can be extended to a partial solution. If the answer is no, then the algorithm can immediately backtrack and avoid wasting time, which makes it easier to show guarantees on the delay between any two complete solutions. In particular, this technique applies well to self-reducible problems. Closure under set operations: If we wish to enumerate the disjoint union of two sets, then we can solve the problem by enumerating the first set and then the second set. If the union is non disjoint but the sets can be enumerated in sorted order, then the enumeration can be performed in parallel on both sets while eliminating duplicates on the fly. If the union is not disjoint and both sets are not sorted then duplicates can be eliminated at the expense of a higher memory usage, e.g., using a hash table. Likewise, the cartesian product of two sets can be enumerated efficiently by enumerating one set and joining each result with all results obtained when enumerating the second step. == Examples of enumeration problems == The vertex enumeration problem, where we are given a polytope described as a system of linear inequalities and we must enumerate the vertices of the polytope. Enumerating the minimal transversals of a hypergraph. This problem is related to monotone dualization and is connected to many applications in database theory and graph theory. Enumerating the answers to a database query, for instance a conjunctive query or a query expressed in monadic second-order. There have been characterizations in database theory of which conjunctive queries could be enumerated with linear preprocessing and constant delay. The problem of enumerating maximal cliques in an input graph, e.g., with the Bron–Kerbosch algorithm Listing all elements of structures such as matroids and greedoids Several problems on graphs, e.g., enumerating independent sets, paths, cuts, etc. Enumerating the satisfying assignments of representations of Boolean functions, e.g., a Boolean formula written in conjunctive normal form or disjunctive normal form, a binary decision diagram such as an OBDD, or a Boolean circuit in restricted classes studied in knowledge compilation, e.g., NNF. == Connection to computability theory == The notion of enumeration algorithms is also used in the field of computability theory to define some high complexity classes such as RE, the class of all recursively enumerable problems. This is the class of sets for which there exist an enumeration algorithm that will produce all elements of the set: the algorithm may run forever if the set is infinite, but each solution must be produced by the algorithm after a finite time.

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  Oculus Medium

  Oculus Medium is a digital sculpting software that works with virtual reality headsets and 6DoF motion controllers. It is used to create and paint digital sculptures. Medium works only on Oculus Rift. It was released on December 5, 2016, following with a major update in 2018 introducing new features and a revamped UI. On December 9, 2019, Oculus Medium was acquired by Adobe and re-named to "Medium by Adobe".

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  Information Rules

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

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  StoredIQ

  StoredIQ was a company founded for information lifecycle management (ILM) of unstructured data. Founded in 2001 as Deepfile in Austin, Texas by Jeff Erramouspe, Jeff Bone, Russell Turpin, Rudy Rouhana, Laura Arbilla and Brett Funderburg, the company changed its name in 2005 to StoredIQ. It continued to operate successfully for over a decade until it was acquired in 2012 by IBM. It now serves as a platform for IBM's information life cycle governance, big data governance and enterprise content management technologies. StoredIQ was awarded five patents by the USPTO. The first, originally filed in 2003, enabled unstructured data in file systems to be manipulated in a similar way to information stored in databases. Subsequent patents built upon the patented actionable file system with further enhancements specific to Enterprise Policy Management and expanding the reach of StoredIQ's management capability all the way to individual desktops. In 2008 StoredIQ was recognized as "Best in Compliance" by Network Products Guide. At the same time, StoredIQ was being recognized as a "Top 5 Provider" by the prestigious Socha-Gelbmann eDiscovery survey. There were takeover negotiations with EMC Corporation, initially a strategic investor in StoredIQ, however, the company rejected the approach, leaving EMC to acquire a competitor. The company published a whitepaper titled The Truth About Big Data. This promotion combined with StoredIQ's patented technology led to IBM selecting StoredIQ as the basis for some products.

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  Bisection (software engineering)

  Bisection is a method used in software development to identify change sets that result in a specific behavior change. It is mostly employed for finding the patch that introduced a bug. Another application area is finding the patch that indirectly fixed a bug. == Overview == The process of locating the changeset that introduced a specific regression was described as "source change isolation" in 1997 by Brian Ness and Viet Ngo of Cray Research. Regression testing was performed on Cray's compilers in editions comprising one or more changesets. Editions with known regressions could not be validated until developers addressed the problem. Source change isolation narrowed the cause to a single changeset that could then be excluded from editions, unblocking them with respect to this problem, while the author of the change worked on a fix. Ness and Ngo outlined linear search and binary search methods of performing this isolation. Code bisection has the goal of minimizing the effort to find a specific change set. It employs a divide and conquer algorithm that depends on having access to the code history which is usually preserved by revision control in a code repository. == Bisection method == === Code bisection algorithm === Code history has the structure of a directed acyclic graph which can be topologically sorted. This makes it possible to use a divide and conquer search algorithm which: splits up the search space of candidate revisions tests for the behavior in question reduces the search space depending on the test result re-iterates the steps above until a range with at most one bisectable patch candidate remains === Algorithmic complexity === Bisection is in LSPACE having an algorithmic complexity of O ( log ⁡ N ) {\displaystyle O(\log N)} with N {\displaystyle N} denoting the number of revisions in the search space, and is similar to a binary search. === Desirable repository properties === For code bisection it is desirable that each revision in the search space can be built and tested independently. === Monotonicity === For the bisection algorithm to identify a single changeset which caused the behavior being tested to change, the behavior must change monotonically across the search space. For a Boolean function such as a pass/fail test, this means that it only changes once across all changesets between the start and end of the search space. If there are multiple changesets across the search space where the behavior being tested changes between false and true, then the bisection algorithm will find one of them, but it will not necessarily be the root cause of the change in behavior between the start and the end of the search space. The root cause could be a different changeset, or a combination of two or more changesets across the search space. To help deal with this problem, automated tools allow specific changesets to be ignored during a bisection search. == Automation support == Although the bisection method can be completed manually, one of its main advantages is that it can be easily automated. It can thus fit into existing test automation processes: failures in exhaustive automated regression tests can trigger automated bisection to localize faults. Ness and Ngo focused on its potential in Cray's continuous delivery-style environment in which the automatically isolated bad changeset could be automatically excluded from builds. The revision control systems Fossil, Git and Mercurial have built-in functionality for code bisection. The user can start a bisection session with a specified range of revisions from which the revision control system proposes a revision to test, the user tells the system whether the revision tested as "good" or "bad", and the process repeats until the specific "bad" revision has been identified. Other revision control systems, such as Bazaar or Subversion, support bisection through plugins or external scripts. Phoronix Test Suite can do bisection automatically to find performance regressions.

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  Abdul Majid Bhurgri Institute of Language Engineering

  Abdul Majid Bhurgri Institute of Language Engineering (Sindhi: عبدالماجد ڀرڳڙي انسٽيٽيوٽ آف لئنگئيج انجنيئرنگ) is an autonomous body under the administrative control of the Culture, Tourism and Antiquities Department, Government of Sindh established for bringing Sindhi language at par with national and international languages in all computational process and Natural language processing. == Establishment == In recognition to services of Abdul-Majid Bhurgri, who is the founder of Sindhi computing, Government of Sindh has established the institute after his name. The institute was primarily initiated on the concept given by a language engineer and linguist Amar Fayaz Buriro in briefing to the Minister, Culture, Tourism and Antiquities, Government of Sindh, Syed Sardar Ali Shah on 21 February 2017 on celebration of International Mother Language Day in Sindhi Language Authority, Hyderabad, Sindh. After the presentation and concept given by Amar Fayaz Buriro, the minister Syed Sardar Ali Shah had announced the Institute. Then, Government of Sindh added the development scheme in the Budget of fiscal year 2017-2018. == Projects == The Institute has developed several projects aimed at advancing the Sindhi language and promoting linguistic research. Notable initiatives include the AMBILE Hamiz Ali Sindhi Optical character recognition, which allows for the accurate digitization of Sindhi text, and the ongoing Sindhi WordNet System, a project to build a comprehensive lexical database for Natural language processing. The institute has also created the Font, which integrates symbols from the Indus script, Khudabadi script, and modern Perso-Arabic Script Code for Information Interchange into a single resource for researchers]. Additionally, institute has developed online converter tools that automatically transliterate between the Arabic-Perso script and Devanagari script, improving linguistic accessibility. Another key project is Bhittaipedia, a digital platform dedicated to the preservation and dissemination of the poetry of Shah Abdul Latif Bhittai, one of Sindh's most renowned poet. == Location == The institute is established behind Sindh Museum and Sindhi Language Authority, N-5 National Highway, Qasimabad, Hyderabad, Sindh.

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  In-place algorithm

  In computer science, an in-place algorithm is an algorithm that operates directly on the input data structure without requiring extra space proportional to the input size. In other words, it modifies the input in place, without creating a separate copy of the data structure. An algorithm which is not in-place is sometimes called not-in-place or out-of-place. In-place can have slightly different meanings. In its strictest form, the algorithm can only have a constant amount of extra space, counting everything including function calls and pointers. However, this form is very limited as simply having an index to a length n array requires O(log n) bits. More broadly, in-place means that the algorithm does not use extra space for manipulating the input but may require a small though non-constant extra space for its operation. Usually, this space is O(log n), though sometimes anything in o(n) is allowed. Note that space complexity also has varied choices in whether or not to count the index lengths as part of the space used. Often, the space complexity is given in terms of the number of indices or pointers needed, ignoring their length. In this article, we refer to total space complexity (DSPACE), counting pointer lengths. Therefore, the space requirements here have an extra log n factor compared to an analysis that ignores the lengths of indices and pointers. An algorithm may or may not count the output as part of its space usage. Since in-place algorithms usually overwrite their input with output, no additional space is needed. When writing the output to write-only memory or a stream, it may be more appropriate to only consider the working space of the algorithm. In theoretical applications such as log-space reductions, it is more typical to always ignore output space (in these cases it is more essential that the output is write-only). == Examples == Given an array a of n items, suppose we want an array that holds the same elements in reversed order and to dispose of the original. One seemingly simple way to do this is to create a new array of equal size, fill it with copies from a in the appropriate order and then delete a. function reverse(a[0..n - 1]) allocate b[0..n - 1] for i from 0 to n - 1 b[n − 1 − i] := a[i] return b Unfortunately, this requires O(n) extra space for having the arrays a and b available simultaneously. Also, allocation and deallocation are often slow operations. Since we no longer need a, we can instead overwrite it with its own reversal using this in-place algorithm which will only need constant number (2) of integers for the auxiliary variables i and tmp, no matter how large the array is. function reverse_in_place(a[0..n-1]) for i from 0 to floor((n-2)/2) tmp := a[i] a[i] := a[n − 1 − i] a[n − 1 − i] := tmp As another example, many sorting algorithms rearrange arrays into sorted order in-place, including: bubble sort, comb sort, selection sort, insertion sort, heapsort, and Shell sort. These algorithms require only a few pointers, so their space complexity is O(log n). Quicksort operates in-place on the data to be sorted. However, quicksort requires O(log n) stack space pointers to keep track of the subarrays in its divide and conquer strategy. Consequently, quicksort needs O(log2 n) additional space. Although this non-constant space technically takes quicksort out of the in-place category, quicksort and other algorithms needing only O(log n) additional pointers are usually considered in-place algorithms. Most selection algorithms are also in-place, although some considerably rearrange the input array in the process of finding the final, constant-sized result. Some text manipulation algorithms such as trim and reverse may be done in-place. == In computational complexity == In computational complexity theory, the strict definition of in-place algorithms includes all algorithms with O(1) space complexity, the class DSPACE(1). This class is very limited; it equals the regular languages. In fact, it does not even include any of the examples listed above. Algorithms are usually considered in L, the class of problems requiring O(log n) additional space, to be in-place. This class is more in line with the practical definition, as it allows numbers of size n as pointers or indices. This expanded definition still excludes quicksort, however, because of its recursive calls. Identifying the in-place algorithms with L has some interesting implications; for example, it means that there is a (rather complex) in-place algorithm to determine whether a path exists between two nodes in an undirected graph, a problem that requires O(n) extra space using typical algorithms such as depth-first search (a visited bit for each node). This in turn yields in-place algorithms for problems such as determining if a graph is bipartite or testing whether two graphs have the same number of connected components. == Role of randomness == In many cases, the space requirements of an algorithm can be drastically cut by using a randomized algorithm. For example, if one wishes to know if two vertices in a graph of n vertices are in the same connected component of the graph, there is no known simple, deterministic, in-place algorithm to determine this. However, if we simply start at one vertex and perform a random walk of about 20n3 steps, the chance that we will stumble across the other vertex provided that it is in the same component is very high. Similarly, there are simple randomized in-place algorithms for primality testing such as the Miller–Rabin primality test, and there are also simple in-place randomized factoring algorithms such as Pollard's rho algorithm. == In functional programming == Functional programming languages often discourage or do not support explicit in-place algorithms that overwrite data, since this is a type of side effect; instead, they only allow new data to be constructed. However, good functional language compilers will often recognize when an object very similar to an existing one is created and then the old one is thrown away, and will optimize this into a simple mutation "under the hood". Note that it is possible in principle to carefully construct in-place algorithms that do not modify data (unless the data is no longer being used), but this is rarely done in practice.

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  Ontology (information science)

  In information science, an ontology encompasses a representation, formal naming, and definitions of the categories, properties, and relations between the concepts, data, or entities that pertain to one, many, or all domains of discourse. More simply, an ontology is a way of showing the properties of a subject area and how they are related, by defining a set of terms and relational expressions that represent the entities in that subject area. The field which studies ontologies so conceived is sometimes referred to as applied ontology. Every academic discipline or field, in creating its terminology, thereby lays the groundwork for an ontology. Each uses ontological assumptions to frame explicit theories, research and applications. Improved ontologies may improve problem solving within that domain, interoperability of data systems, and discoverability of data. Translating research papers within every field is a problem made easier when experts from different countries maintain a controlled vocabulary of jargon between each of their languages. For instance, the definition and ontology of economics is a primary concern in Marxist economics, but also in other subfields of economics. An example of economics relying on information science occurs in cases where a simulation or model is intended to enable economic decisions, such as determining what capital assets are at risk and by how much (see risk management). What ontologies in both information science and philosophy have in common is the attempt to represent entities, including both objects and events, with all their interdependent properties and relations, according to a system of categories. In both fields, there is considerable work on problems of ontology engineering (e.g., Quine and Kripke in philosophy, Sowa and Guarino in information science), and debates concerning to what extent normative ontology is possible (e.g., foundationalism and coherentism in philosophy, BFO and Cyc in artificial intelligence). Applied ontology is considered by some as a successor to prior work in philosophy. However many current efforts are more concerned with establishing controlled vocabularies of narrow domains than with philosophical first principles, or with questions such as the mode of existence of fixed essences or whether enduring objects (e.g., perdurantism and endurantism) may be ontologically more primary than processes. Artificial intelligence has retained considerable attention regarding applied ontology in subfields like natural language processing within machine translation and knowledge representation, but ontology editors are being used often in a range of fields, including biomedical informatics and industry. Such efforts often use ontology editing tools such as Protégé. == Ontology in philosophy == Ontology is a branch of philosophy and intersects areas such as metaphysics, epistemology, and philosophy of language, as it considers how knowledge, language, and perception relate to the nature of reality. Metaphysics deals with questions like "what exists?" and "what is the nature of reality?". One of five traditional branches of philosophy, metaphysics is concerned with exploring existence through properties, entities and relations such as those between particulars and universals, intrinsic and extrinsic properties, or essence and existence. Metaphysics has been an ongoing topic of discussion since recorded history. == Etymology == The compound word ontology combines onto-, from the Greek ὄν, on (gen. ὄντος, ontos), i.e. "being; that which is", which is the present participle of the verb εἰμί, eimí, i.e. "to be, I am", and -λογία, -logia, i.e. "logical discourse", see classical compounds for this type of word formation. While the etymology is Greek, the oldest extant record of the word itself, the Neo-Latin form ontologia, appeared in 1606 in the work Ogdoas Scholastica by Jacob Lorhard (Lorhardus) and in 1613 in the Lexicon philosophicum by Rudolf Göckel (Goclenius). The first occurrence in English of ontology as recorded by the OED (Oxford English Dictionary, online edition, 2008) came in Archeologia Philosophica Nova or New Principles of Philosophy (1663) by Gideon Harvey. == Formal ontology == Since the mid-1970s, researchers in the field of artificial intelligence (AI) have recognized that knowledge engineering is the key to building large and powerful AI systems. AI researchers argued that they could create new ontologies as computational models that enable certain kinds of automated reasoning, which was only marginally successful. In the 1980s, the AI community began to use the term ontology to refer to both a theory of a modeled world and a component of knowledge-based systems. In particular, David Powers introduced the word ontology to AI to refer to real world or robotic grounding, publishing in 1990 literature reviews emphasizing grounded ontology in association with the call for papers for a AAAI Summer Symposium Machine Learning of Natural Language and Ontology, with an expanded version published in SIGART Bulletin and included as a preface to the proceedings. Some researchers, drawing inspiration from philosophical ontologies, viewed computational ontology as a kind of applied philosophy. In 1993, the widely cited web page and paper "Toward Principles for the Design of Ontologies Used for Knowledge Sharing" by Tom Gruber used ontology as a technical term in computer science closely related to earlier idea of semantic networks and taxonomies. Gruber introduced the term as a specification of a conceptualization: An ontology is a description (like a formal specification of a program) of the concepts and relationships that can formally exist for an agent or a community of agents. This definition is consistent with the usage of ontology as set of concept definitions, but more general. And it is a different sense of the word than its use in philosophy. Attempting to distance ontologies from taxonomies and similar efforts in knowledge modeling that rely on classes and inheritance, Gruber stated (1993): Ontologies are often equated with taxonomic hierarchies of classes, class definitions, and the subsumption relation, but ontologies need not be limited to these forms. Ontologies are also not limited to conservative definitions, that is, definitions in the traditional logic sense that only introduce terminology and do not add any knowledge about the world (Enderton, 1972). To specify a conceptualization, one needs to state axioms that do constrain the possible interpretations for the defined terms. Recent experimental ontology frameworks have also explored resonance-based AI-human co-evolution structures, such as IAMF (Illumination AI Matrix Framework), OntoMotoOS (a meta-operating system concept for ethical and ontological AI–human co-evolution), and PSRT (Phase-Structural Reality Theory across multi-scale ontological layers). Though not yet widely adopted in academic discourse, such models propose phased approaches to ethical harmonization and structural emergence. As refinement of Gruber's definition Feilmayr and Wöß (2016) stated: "An ontology is a formal, explicit specification of a shared conceptualization that is characterized by high semantic expressiveness required for increased complexity." == Formal ontology components == Contemporary ontologies share many structural similarities, regardless of the language in which they are expressed. Most ontologies describe individuals (instances), classes (concepts), attributes and relations. === Types === ==== Domain ontology ==== A domain ontology (or domain-specific ontology) represents concepts which belong to a realm of the world, such as biology or politics. Each domain ontology typically models domain-specific definitions of terms. For example, the word card has many different meanings. An ontology about the domain of poker would model the "playing card" meaning of the word, while an ontology about the domain of computer hardware would model the "punched card" and "video card" meanings. Since domain ontologies are written by different people, they represent concepts in very specific and unique ways, and are often incompatible within the same project. As systems that rely on domain ontologies expand, they often need to merge domain ontologies by hand-tuning each entity or using a combination of software merging and hand-tuning. This presents a challenge to the ontology designer. Different ontologies in the same domain arise due to different languages, different intended usage of the ontologies, and different perceptions of the domain (based on cultural background, education, ideology, etc.). At present, merging ontologies that are not developed from a common upper ontology is a largely manual process and therefore time-consuming and expensive. Domain ontologies that use the same upper ontology to provide a set of basic elements with which to specify the meanings of the domain ontology entities can be merged with less effo

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