Catastrophic interference, also known as catastrophic forgetting, is the tendency of an artificial neural network to abruptly and drastically forget previously learned information upon learning new information. Neural networks are an important part of the connectionist approach to cognitive science. The issue of catastrophic interference when modeling human memory with connectionist models was originally brought to the attention of the scientific community by research from McCloskey and Cohen (1989), and Ratcliff (1990). It is a radical manifestation of the 'sensitivity-stability' dilemma or the 'stability-plasticity' dilemma. Specifically, these problems refer to the challenge of making an artificial neural network that is sensitive to, but not disrupted by, new information. Lookup tables and connectionist networks lie on the opposite sides of the stability plasticity spectrum. The former remains completely stable in the presence of new information but lacks the ability to generalize, i.e. infer general principles, from new inputs. On the other hand, connectionist networks like the standard backpropagation network can generalize to unseen inputs, but they are sensitive to new information. Backpropagation models can be analogized to human memory insofar as they have a similar ability to generalize, but these networks often exhibit less stability than human memory. Notably, these backpropagation networks are susceptible to catastrophic interference. This is an issue when modelling human memory, because unlike these networks, humans typically do not show catastrophic forgetting. == Discovery == The term catastrophic interference was originally coined by McCloskey and Cohen (1989) but was also brought to the attention of the scientific community by research from Ratcliff (1990). === The Sequential Learning Problem: McCloskey and Cohen (1989) === McCloskey and Cohen (1989) noted the problem of catastrophic interference during two different experiments with backpropagation neural network modelling. Experiment 1: Learning the ones and twos addition facts In their first experiment they trained a standard backpropagation neural network on a single training set consisting of 17 single-digit ones problems (i.e., 1 + 1 through 9 + 1, and 1 + 2 through 1 + 9) until the network could represent and respond properly to all of them. The error between the actual output and the desired output steadily declined across training sessions, which reflected that the network learned to represent the target outputs better across trials. Next, they trained the network on a single training set consisting of 17 single-digit twos problems (i.e., 2 + 1 through 2 + 9, and 1 + 2 through 9 + 2) until the network could represent, respond properly to all of them. They noted that their procedure was similar to how a child would learn their addition facts. Following each learning trial on the twos facts, the network was tested for its knowledge on both the ones and twos addition facts. Like the ones facts, the twos facts were readily learned by the network. However, McCloskey and Cohen noted the network was no longer able to properly answer the ones addition problems even after one learning trial of the twos addition problems. The output pattern produced in response to the ones facts often resembled an output pattern for an incorrect number more closely than the output pattern for a correct number. This is considered to be a drastic amount of error. Furthermore, the problems 2+1 and 1+2, which were included in both training sets, even showed dramatic disruption during the first learning trials of the twos facts. Experiment 2: Replication of Barnes and Underwood (1959) study In their second connectionist model, McCloskey and Cohen attempted to replicate the study on retroactive interference in humans by Barnes and Underwood (1959). They trained the model on A-B and A-C lists and used a context pattern in the input vector (input pattern), to differentiate between the lists. Specifically the network was trained to respond with the right B response when shown the A stimulus and A-B context pattern and to respond with the correct C response when shown the A stimulus and the A-C context pattern. When the model was trained concurrently on the A-B and A-C items then the network readily learned all of the associations correctly. In sequential training the A-B list was trained first, followed by the A-C list. After each presentation of the A-C list, performance was measured for both the A-B and A-C lists. They found that the amount of training on the A-C list in Barnes and Underwood study that lead to 50% correct responses, lead to nearly 0% correct responses by the backpropagation network. Furthermore, they found that the network tended to show responses that looked like the C response pattern when the network was prompted to give the B response pattern. This indicated that the A-C list apparently had overwritten the A-B list. This could be likened to learning the word dog, followed by learning the word stool and then finding that you think of the word stool when presented with the word dog. McCloskey and Cohen tried to reduce interference through a number of manipulations including changing the number of hidden units, changing the value of the learning rate parameter, overtraining on the A-B list, freezing certain connection weights, changing target values 0 and 1 instead 0.1 and 0.9. However, none of these manipulations satisfactorily reduced the catastrophic interference exhibited by the networks. Overall, McCloskey and Cohen (1989) concluded that: at least some interference will occur whenever new learning alters the weights involved in representing old learning the greater the amount of new learning, the greater the disruption in old knowledge interference was catastrophic in the backpropagation networks when learning was sequential but not concurrent === Constraints Imposed by Learning and Forgetting Functions: Ratcliff (1990) === Ratcliff (1990) used multiple sets of backpropagation models applied to standard recognition memory procedures, in which the items were sequentially learned. After inspecting the recognition performance models he found two major problems: Well-learned information was catastrophically forgotten as new information was learned in both small and large backpropagation networks. Even one learning trial with new information resulted in a significant loss of the old information, paralleling the findings of McCloskey and Cohen (1989). Ratcliff also found that the resulting outputs were often a blend of the previous input and the new input. In larger networks, items learned in groups (e.g. AB then CD) were more resistant to forgetting than were items learned singly (e.g. A then B then C...). However, the forgetting for items learned in groups was still large. Adding new hidden units to the network did not reduce interference. Discrimination between the studied items and previously unseen items decreased as the network learned more. This finding contradicts studies on human memory, which indicated that discrimination increases with learning. Ratcliff attempted to alleviate this problem by adding 'response nodes' that would selectively respond to old and new inputs. However, this method did not work as these response nodes would become active for all inputs. A model which used a context pattern also failed to increase discrimination between new and old items. == Proposed solutions == The main cause of catastrophic interference seems to be overlap in the representations at the hidden layer of distributed neural networks. In a distributed representation, each input tends to create changes in the weights of many of the nodes. Catastrophic forgetting occurs because when many of the weights where "knowledge is stored" are changed, it is unlikely for prior knowledge to be kept intact. During sequential learning, the inputs become mixed, with the new inputs being superimposed on top of the old ones. Another way to conceptualize this is by visualizing learning as a movement through a weight space. This weight space can be likened to a spatial representation of all of the possible combinations of weights that the network could possess. When a network first learns to represent a set of patterns, it finds a point in the weight space that allows it to recognize all of those patterns. However, when the network then learns a new set of patterns, it will move to a place in the weight space for which the only concern is the recognition of the new patterns. To recognize both sets of patterns, the network must find a place in the weight space suitable for recognizing both the new and the old patterns. Below are a number of techniques which have empirical support in successfully reducing catastrophic interference in backpropagation neural networks: === Orthogonality === Many of the early techniques in reducing representational overlap involved making either the input vecto
Insider threat
An insider threat is a perceived threat to an organization that comes from people within the organization, such as employees, former employees, contractors or business associates, who have inside information concerning the organization's security practices, data and computer systems. The threat may involve fraud, the theft of confidential or commercially valuable information, the theft of intellectual property, or the sabotage of computer systems. == Overview == Insiders may have accounts giving them legitimate access to computer systems, with this access originally having been given to them to serve in the performance of their duties; these permissions could be abused to harm the organization. Insiders are often familiar with the organization's data and intellectual property as well as the methods that are in place to protect them. This makes it easier for the insider to circumvent any security controls of which they are aware. Physical proximity to data means that the insider does not need to hack into the organizational network through the outer perimeter by traversing firewalls; rather they are in the building already, often with direct access to the organization's internal network. Insider threats are harder to defend against than attacks from outsiders, since the insider already has legitimate access to the organization's information and assets. An insider may attempt to steal property or information for personal gain or to benefit another organization or country. The threat to the organization could also be through malicious software left running on its computer systems by former employees, a so-called logic bomb. == Research == Insider threat is an active area of research in academia and government. The CERT Coordination Center at Carnegie-Mellon University maintains the CERT Insider Threat Center, which includes a database of more than 850 cases of insider threats, including instances of fraud, theft and sabotage; the database is used for research and analysis. CERT's Insider Threat Team also maintains an informational blog to help organizations and businesses defend themselves against insider crime. The Threat Lab and Defense Personnel and Security Research Center (DOD PERSEREC) has also recently emerged as a national resource within the United States of America. The Threat Lab hosts an annual conference, the SBS Summit. They also maintain a website that contains resources from this conference. Complimenting these efforts, a companion podcast was created, Voices from the SBS Summit. In 2022, the Threat Lab created an interdisciplinary journal, Counter Insider Threat Research and Practice (CITRAP) which publishes research on insider threat detection. === Findings === In the 2022 Data Breach Investigations Report (DBIR), Verizon found that 82% of breaches involved the human element, noting that employees continue to play a leading role in cybersecurity incidents and breaches. According to the UK Information Commissioners Office, 90% of all breaches reported to them in 2019 were the result of mistakes made by end users. This was up from 61% and 87% over the previous two years. A 2018 whitepaper reported that 53% of companies surveyed had confirmed insider attacks against their organization in the previous 12 months, with 27% saying insider attacks have become more frequent. A report published in July 2012 on the insider threat in the U.S. financial sector gives some statistics on insider threat incidents: 80% of the malicious acts were committed at work during working hours; 81% of the perpetrators planned their actions beforehand; 33% of the perpetrators were described as "difficult" and 17% as being "disgruntled". The insider was identified in 74% of cases. Financial gain was a motive in 81% of cases, revenge in 23% of cases, and 27% of the people carrying out malicious acts were in financial difficulties at the time. The US Department of Defense Personnel Security Research Center published a report that describes approaches for detecting insider threats. Earlier it published ten case studies of insider attacks by information technology professionals. Cybersecurity experts believe that 38% of negligent insiders are victims of a phishing attack, whereby they receive an email that appears to come from a legitimate source such as a company. These emails normally contain malware in the form of hyperlinks. == Typologies and ontologies == Multiple classification systems and ontologies have been proposed to classify insider threats. Traditional models of insider threat identify three broad categories: Malicious insiders, which are people who take advantage of their access to inflict harm on an organization; Negligent insiders, which are people who make errors and disregard policies, which place their organizations at risk; and Infiltrators, who are external actors that obtain legitimate access credentials without authorization. == Criticisms == Insider threat research has been criticized. Critics have argued that insider threat is a poorly defined concept. Forensically investigating insider data theft is notoriously difficult, and requires novel techniques such as stochastic forensics. Data supporting insider threat is generally proprietary (i.e., encrypted data). Theoretical/conceptual models of insider threat are often based on loose interpretations of research in the behavioral and social sciences, using "deductive principles and intuitions of subject matter expert." Adopting sociotechnical approaches, researchers have also argued for the need to consider insider threat from the perspective of social systems. Jordan Schoenherr said that "surveillance requires an understanding of how sanctioning systems are framed, how employees will respond to surveillance, what workplace norms are deemed relevant, and what ‘deviance’ means, e.g., deviation for a justified organization norm or failure to conform to an organizational norm that conflicts with general social values." By treating all employees as potential insider threats, organizations might create conditions that lead to insider threats. == Sector-specific concerns == === Healthcare === The healthcare industry faces particularly acute insider threat risks due to the large number of workforce members who require access to sensitive patient records for legitimate clinical purposes. The U.S. Department of Health and Human Services has identified unauthorized access by insiders, including workforce snooping on patient records and theft of protected health information for identity fraud, as a persistent enforcement concern. The Health Insurance Portability and Accountability Act (HIPAA) Security Rule addresses insider threats through several administrative safeguards, including workforce security procedures requiring covered entities to implement policies for authorizing and supervising workforce members who work with electronic protected health information, as well as termination procedures to revoke access when employment ends (45 CFR 164.308(a)(3)). The rule also requires audit controls to record and examine information system activity (45 CFR 164.312(b)), enabling detection of unauthorized access by insiders. The December 2024 Notice of proposed rulemaking (NPRM) to overhaul the HIPAA Security Rule would strengthen insider threat defenses by mandating role-based access controls, requiring notification of relevant workforce members within 24 hours of any changes to access privileges, and requiring regular review of audit logs to detect anomalous access patterns.
NewSQL
NewSQL is a class of relational database management systems that seek to provide the scalability of NoSQL systems for online transaction processing (OLTP) workloads while maintaining the ACID guarantees of a traditional database system. Many enterprise systems that handle high-profile data (e.g., financial and order processing systems) are too large for conventional relational databases, but have transactional and consistency requirements that are not practical for NoSQL systems. The only options previously available for these organizations were to either purchase more powerful computers or to develop custom middleware that distributes requests over conventional DBMS. Both approaches feature high infrastructure costs and/or development costs. NewSQL systems attempt to reconcile the conflicts. == History == The term was first used by 451 Group analyst Matthew Aslett in a 2011 research paper discussing the rise of a new generation of database management systems. One of the first NewSQL systems was the H-Store parallel database system. == Applications == Typical applications are characterized by heavy OLTP transaction volumes. OLTP transactions; are short-lived (i.e., no user stalls) touch small amounts of data per transaction use indexed lookups (no table scans) have a small number of forms (a small number of queries with different arguments). However, some support hybrid transactional/analytical processing (HTAP) applications. Such systems improve performance and scalability by omitting heavyweight recovery or concurrency control. == List of NewSQL-databases == Apache Trafodion Clustrix CockroachDB Couchbase CrateDB Google Spanner MySQL Cluster NuoDB OceanBase Pivotal GemFire XD SequoiaDB SingleStore was formerly known as MemSQL. TIBCO Active Spaces TiDB TokuDB TransLattice Elastic Database VoltDB YDB YugabyteDB == Features == The two common distinguishing features of NewSQL database solutions are that they support online scalability of NoSQL databases and the relational data model (including ACID consistency) using SQL as their primary interface. NewSQL systems can be loosely grouped into three categories: === New architectures === NewSQL systems adopt various internal architectures. Some systems employ a cluster of shared-nothing nodes, in which each node manages a subset of the data. They include components such as distributed concurrency control, flow control, and distributed query processing. === SQL engines === The second category are optimized storage engines for SQL. These systems provide the same programming interface as SQL, but scale better than built-in engines. === Transparent sharding === These systems automatically split databases across multiple nodes using Raft or Paxos consensus algorithm.
Metadata management
Metadata management involves managing metadata about other data, whereby this "other data" is generally referred to as content data. The term is used most often in relation to digital media, but older forms of metadata are catalogs, dictionaries, and taxonomies. For example, the Dewey Decimal Classification is a metadata management system developed in 1876 for libraries. == Metadata schema == Metadata management goes by the end-to-end process and governance framework for creating, controlling, enhancing, attributing, defining and managing a metadata schema, model or other structured aggregation system, either independently or within a repository and the associated supporting processes (often to enable the management of content). For web-based systems, URLs, images, video etc. may be referenced from a triples table of object, attribute and value. == Scope == With specific knowledge domains, the boundaries of the metadata for each must be managed, since a general ontology is not useful to experts in one field whose language is knowledge-domain specific. == Metadata Manager == In the process of developing a knowledge management solution, creating a metadata schema, and a system in which metadata is managed, a dedicated resource may be appointed to maintain adherence to metadata standards as defined by data owners as well as general best practice. This person is responsible for curation of the business and technical layers of the metadata schema, and commonly involved with strategy and implementation. A metadata manager is not required to master all aspects, or be involved with everything concerning the solution, but an understanding of as much of the process as possible to ensure a relevant schema is developed. == Metadata management over time == Managing the metadata in a knowledge management solution is an important step in a metadata strategy. It is part of the strategy to make sure that the metadata are complete, current and correct at any given time. Managing a metadata project is also about making sure that users of the system are aware of the possibilities allowed by a well-designed metadata system and how to maximize the benefits of metadata. Regularly monitoring the metadata to ensure that the schema remains relevant is advised. === Wikipedia metadata === Wikipedia is a project that actively manages metadata for its articles and files. For example, volunteer editors carefully curate new biographical articles based on the notability (claim to fame), name, birth, and/or death dates. Similarly, volunteer editors carefully curate new architectural articles based on name, municipality, or geo coordinates. When new articles with a valid alternate spelling are added to Wikipedia that match up to existing articles based on metadata, these are then manually checked and if needed, tagged for merging. When new articles are added that are considered out of scope or otherwise unfit for Wikipedia, these are nominated for deletion. To help keep track of metadata on Wikipedia, the new Wikimedia project Wikidata was established in 2012. Click on the pictures to view more metadata about these images:
Holographic algorithm
In computer science, a holographic algorithm is an algorithm that uses a holographic reduction. A holographic reduction is a constant-time reduction that maps solution fragments many-to-many such that the sum of the solution fragments remains unchanged. These concepts were introduced by Leslie Valiant, who called them holographic because "their effect can be viewed as that of producing interference patterns among the solution fragments". The algorithms are unrelated to laser holography, except metaphorically. Their power comes from the mutual cancellation of many contributions to a sum, analogous to the interference patterns in a hologram. Holographic algorithms have been used to find polynomial-time solutions to problems without such previously known solutions for special cases of satisfiability, vertex cover, and other graph problems. They have received notable coverage due to speculation that they are relevant to the P versus NP problem and their impact on computational complexity theory. Although some of the general problems are #P-hard problems, the special cases solved are not themselves #P-hard, and thus do not prove FP = #P. Holographic algorithms have some similarities with quantum computation, but are completely classical. == Holant problems == Holographic algorithms exist in the context of Holant problems, which generalize counting constraint satisfaction problems (#CSP). A #CSP instance is a hypergraph G=(V,E) called the constraint graph. Each hyperedge represents a variable and each vertex v {\displaystyle v} is assigned a constraint f v . {\displaystyle f_{v}.} A vertex is connected to an hyperedge if the constraint on the vertex involves the variable on the hyperedge. The counting problem is to compute ∑ σ : E → { 0 , 1 } ∏ v ∈ V f v ( σ | E ( v ) ) , ( 1 ) {\displaystyle \sum _{\sigma :E\to \{0,1\}}\prod _{v\in V}f_{v}(\sigma |_{E(v)}),~~~~~~~~~~(1)} which is a sum over all variable assignments, the product of every constraint, where the inputs to the constraint f v {\displaystyle f_{v}} are the variables on the incident hyperedges of v {\displaystyle v} . A Holant problem is like a #CSP except the input must be a graph, not a hypergraph. Restricting the class of input graphs in this way is indeed a generalization. Given a #CSP instance, replace each hyperedge e of size s with a vertex v of degree s with edges incident to the vertices contained in e. The constraint on v is the equality function of arity s. This identifies all of the variables on the edges incident to v, which is the same effect as the single variable on the hyperedge e. In the context of Holant problems, the expression in (1) is called the Holant after a related exponential sum introduced by Valiant. == Holographic reduction == A standard technique in complexity theory is a many-one reduction, where an instance of one problem is reduced to an instance of another (hopefully simpler) problem. However, holographic reductions between two computational problems preserve the sum of solutions without necessarily preserving correspondences between solutions. For instance, the total number of solutions in both sets can be preserved, even though individual problems do not have matching solutions. The sum can also be weighted, rather than simply counting the number of solutions, using linear basis vectors. === General example === It is convenient to consider holographic reductions on bipartite graphs. A general graph can always be transformed it into a bipartite graph while preserving the Holant value. This is done by replacing each edge in the graph by a path of length 2, which is also known as the 2-stretch of the graph. To keep the same Holant value, each new vertex is assigned the binary equality constraint. Consider a bipartite graph G=(U,V,E) where the constraint assigned to every vertex u ∈ U {\displaystyle u\in U} is f u {\displaystyle f_{u}} and the constraint assigned to every vertex v ∈ V {\displaystyle v\in V} is f v {\displaystyle f_{v}} . Denote this counting problem by Holant ( G , f u , f v ) . {\displaystyle {\text{Holant}}(G,f_{u},f_{v}).} If the vertices in U are viewed as one large vertex of degree |E|, then the constraint of this vertex is the tensor product of f u {\displaystyle f_{u}} with itself |U| times, which is denoted by f u ⊗ | U | . {\displaystyle f_{u}^{\otimes |U|}.} Likewise, if the vertices in V are viewed as one large vertex of degree |E|, then the constraint of this vertex is f v ⊗ | V | . {\displaystyle f_{v}^{\otimes |V|}.} Let the constraint f u {\displaystyle f_{u}} be represented by its weighted truth table as a row vector and the constraint f v {\displaystyle f_{v}} be represented by its weighted truth table as a column vector. Then the Holant of this constraint graph is simply f u ⊗ | U | f v ⊗ | V | . {\displaystyle f_{u}^{\otimes |U|}f_{v}^{\otimes |V|}.} Now for any complex 2-by-2 invertible matrix T (the columns of which are the linear basis vectors mentioned above), there is a holographic reduction between Holant ( G , f u , f v ) {\displaystyle {\text{Holant}}(G,f_{u},f_{v})} and Holant ( G , f u T ⊗ ( deg u ) , ( T − 1 ) ⊗ ( deg v ) f v ) . {\displaystyle {\text{Holant}}(G,f_{u}T^{\otimes (\deg u)},(T^{-1})^{\otimes (\deg v)}f_{v}).} To see this, insert the identity matrix T ⊗ | E | ( T − 1 ) ⊗ | E | {\displaystyle T^{\otimes |E|}(T^{-1})^{\otimes |E|}} in between f u ⊗ | U | f v ⊗ | V | {\displaystyle f_{u}^{\otimes |U|}f_{v}^{\otimes |V|}} to get f u ⊗ | U | f v ⊗ | V | {\displaystyle f_{u}^{\otimes |U|}f_{v}^{\otimes |V|}} = f u ⊗ | U | T ⊗ | E | ( T − 1 ) ⊗ | E | f v ⊗ | V | {\displaystyle =f_{u}^{\otimes |U|}T^{\otimes |E|}(T^{-1})^{\otimes |E|}f_{v}^{\otimes |V|}} = ( f u T ⊗ ( deg u ) ) ⊗ | U | ( f v ( T − 1 ) ⊗ ( deg v ) ) ⊗ | V | . {\displaystyle =\left(f_{u}T^{\otimes (\deg u)}\right)^{\otimes |U|}\left(f_{v}(T^{-1})^{\otimes (\deg v)}\right)^{\otimes |V|}.} Thus, Holant ( G , f u , f v ) {\displaystyle {\text{Holant}}(G,f_{u},f_{v})} and Holant ( G , f u T ⊗ ( deg u ) , ( T − 1 ) ⊗ ( deg v ) f v ) {\displaystyle {\text{Holant}}(G,f_{u}T^{\otimes (\deg u)},(T^{-1})^{\otimes (\deg v)}f_{v})} have exactly the same Holant value for every constraint graph. They essentially define the same counting problem. === Specific examples === ==== Vertex covers and independent sets ==== Let G be a graph. There is a 1-to-1 correspondence between the vertex covers of G and the independent sets of G. For any set S of vertices of G, S is a vertex cover in G if and only if the complement of S is an independent set in G. Thus, the number of vertex covers in G is exactly the same as the number of independent sets in G. The equivalence of these two counting problems can also be proved using a holographic reduction. For simplicity, let G be a 3-regular graph. The 2-stretch of G gives a bipartite graph H=(U,V,E), where U corresponds to the edges in G and V corresponds to the vertices in G. The Holant problem that naturally corresponds to counting the number of vertex covers in G is Holant ( H , OR 2 , EQUAL 3 ) . {\displaystyle {\text{Holant}}(H,{\text{OR}}_{2},{\text{EQUAL}}_{3}).} The truth table of OR2 as a row vector is (0,1,1,1). The truth table of EQUAL3 as a column vector is ( 1 , 0 , 0 , 0 , 0 , 0 , 0 , 1 ) T = [ 1 0 ] ⊗ 3 + [ 0 1 ] ⊗ 3 {\displaystyle (1,0,0,0,0,0,0,1)^{T}={\begin{bmatrix}1\\0\end{bmatrix}}^{\otimes 3}+{\begin{bmatrix}0\\1\end{bmatrix}}^{\otimes 3}} . Then under a holographic transformation by [ 0 1 1 0 ] , {\displaystyle {\begin{bmatrix}0&1\\1&0\end{bmatrix}},} OR 2 ⊗ | U | EQUAL 3 ⊗ | V | {\displaystyle {\text{OR}}_{2}^{\otimes |U|}{\text{EQUAL}}_{3}^{\otimes |V|}} = ( 0 , 1 , 1 , 1 ) ⊗ | U | ( [ 1 0 ] ⊗ 3 + [ 0 1 ] ⊗ 3 ) ⊗ | V | {\displaystyle =(0,1,1,1)^{\otimes |U|}\left({\begin{bmatrix}1\\0\end{bmatrix}}^{\otimes 3}+{\begin{bmatrix}0\\1\end{bmatrix}}^{\otimes 3}\right)^{\otimes |V|}} = ( 0 , 1 , 1 , 1 ) ⊗ | U | [ 0 1 1 0 ] ⊗ | E | [ 0 1 1 0 ] ⊗ | E | ( [ 1 0 ] ⊗ 3 + [ 0 1 ] ⊗ 3 ) ⊗ | V | {\displaystyle =(0,1,1,1)^{\otimes |U|}{\begin{bmatrix}0&1\\1&0\end{bmatrix}}^{\otimes |E|}{\begin{bmatrix}0&1\\1&0\end{bmatrix}}^{\otimes |E|}\left({\begin{bmatrix}1\\0\end{bmatrix}}^{\otimes 3}+{\begin{bmatrix}0\\1\end{bmatrix}}^{\otimes 3}\right)^{\otimes |V|}} = ( ( 0 , 1 , 1 , 1 ) [ 0 1 1 0 ] ⊗ 2 ) ⊗ | U | ( ( [ 0 1 1 0 ] [ 1 0 ] ) ⊗ 3 + ( [ 0 1 1 0 ] [ 0 1 ] ) ⊗ 3 ) ⊗ | V | {\displaystyle =\left((0,1,1,1){\begin{bmatrix}0&1\\1&0\end{bmatrix}}^{\otimes 2}\right)^{\otimes |U|}\left(\left({\begin{bmatrix}0&1\\1&0\end{bmatrix}}{\begin{bmatrix}1\\0\end{bmatrix}}\right)^{\otimes 3}+\left({\begin{bmatrix}0&1\\1&0\end{bmatrix}}{\begin{bmatrix}0\\1\end{bmatrix}}\right)^{\otimes 3}\right)^{\otimes |V|}} = ( 1 , 1 , 1 , 0 ) ⊗ | U | ( [ 0 1 ] ⊗ 3 + [ 1 0 ] ⊗ 3 ) ⊗ | V | {\displaystyle =(1,1,1,0)^{\otimes |U|}\left({\begin{bmatrix}0\\1\end{bmatrix}}^{\otimes 3}+{\begin{bmatrix}1\\0\end{bmatrix}}^{\otimes 3}\right)^{\otimes |V|}} = NAND 2 ⊗ | U | EQUAL 3 ⊗ | V | , {\displaystyle ={\text{NAND}}_{2}^{\otim
Computational photography
Computational photography refers to digital image capture and processing techniques that use digital computation instead of optical processes. Computational photography can improve the capabilities of a camera, or introduce features that were not possible at all with film-based photography, or reduce the cost or size of camera elements. Examples of computational photography include in-camera computation of digital panoramas, high-dynamic-range images, and light field cameras. Light field cameras use novel optical elements to capture three-dimensional scene information, which can then be used to produce 3D images, enhanced depth-of-field, and selective de-focusing (or "post focus"). Enhanced depth-of-field reduces the need for mechanical focusing systems. All of these features use computational imaging techniques. The definition of computational photography has evolved to cover a number of subject areas in computer graphics, computer vision, and applied optics. These areas are given below, organized according to a taxonomy proposed by Shree K. Nayar. Within each area is a list of techniques, and for each technique, one or two representative papers or books are cited. Deliberately omitted from the taxonomy are image processing (see also digital image processing) techniques applied to traditionally captured images to produce better images. Examples of such techniques are image scaling, dynamic range compression (i.e. tone mapping), color management, image completion (a.k.a. inpainting or hole filling), image compression, digital watermarking, and artistic image effects. Also omitted are techniques that produce range data, volume data, 3D models, 4D light fields, 4D, 6D, or 8D BRDFs, or other high-dimensional image-based representations. Epsilon photography is a sub-field of computational photography. == Effect on photography == Photos taken using computational photography can allow amateurs to produce photographs rivalling the quality of professional photographers, but as of 2019 do not outperform the use of professional-level equipment. == Computational illumination == This is controlling photographic illumination in a structured fashion, then processing the captured images, to create new images. The applications include image-based relighting, image enhancement, image deblurring, geometry/material recovery and so forth. High-dynamic-range imaging uses differently exposed pictures of the same scene to extend dynamic range. Other examples include processing and merging differently illuminated images of the same subject matter ("lightspace"). == Computational optics == This is a capture of optically coded images, followed by computational decoding to produce new images. Coded aperture imaging was mainly applied in astronomy and X-ray imaging to boost the image quality. Instead of a single pin-hole, a pinhole pattern is applied in imaging, and deconvolution is performed to recover the image. In coded exposure imaging, the on/off state of the shutter is coded to modify the kernel of motion blur. In this way, motion deblurring becomes a well-conditioned problem. Similarly, in a lens based coded aperture, the aperture can be modified by inserting a broadband mask. Thus, out of focus deblurring becomes a well-conditioned problem. The coded aperture can also improve the quality in light field acquisition using Hadamard transform optics. Coded aperture patterns can also be designed using color filters, in order to apply different codes at different wavelengths. This allows for increase the amount of light that reaches the camera sensor, compared to binary masks. == Computational imaging == Computational imaging is a set of imaging techniques that combine data acquisition and data processing to create the image of an object through indirect means to yield enhanced resolution, additional information such as optical phase or 3D reconstruction. The information is often recorded without using a conventional optical microscope configuration or with limited datasets. Computational imaging allows going beyond physical limitations of optical systems, such as numerical aperture, or even obliterates the need for optical elements. For parts of the optical spectrum where imaging elements such as objectives are difficult to manufacture or image sensors cannot be miniaturized, computational imaging provides useful alternatives, in fields such as X-ray and THz radiations. === Common techniques === Among common computational imaging techniques are lensless imaging, computational speckle imaging , ptychography and Fourier ptychography. Computational imaging technique often draws on compressive sensing or phase retrieval techniques, where the angular spectrum of the object is reconstructed. Other techniques are related to the field of computational imaging, such as digital holography, computer vision and inverse problems such as tomography. == Computational processing == This is the processing of non-optically-coded images to produce new images. == Computational sensors == These are detectors that combine sensing and processing, typically in hardware, like the oversampled binary image sensor. == Early work in computer vision == Although computational photography is a currently popular buzzword in computer graphics, many of its techniques first appeared in the computer vision literature, either under other names or within papers aimed at 3D shape analysis. == Art history == Computational photography, as an art form, has been practiced by capturing differently exposed pictures of the same subject matter and combining them. This was the inspiration for the development of the wearable computer in the 1970s and early 1980s. Computational photography was inspired by the work of Charles Wyckoff, and thus computational photography datasets (e.g. differently exposed pictures of the same subject matter that are taken in order to make a single composite image) are sometimes referred to as Wyckoff Sets, in his honor. Early work in this area (joint estimation of image projection and exposure value) was undertaken by Mann and Candoccia. Charles Wyckoff devoted much of his life to creating special kinds of 3-layer photographic films that captured different exposures of the same subject matter. A picture of a nuclear explosion, taken on Wyckoff's film, appeared on the cover of Life Magazine and showed the dynamic range from the dark outer areas to the inner core.
Automatic image annotation
Automatic image annotation (also known as automatic image tagging or linguistic indexing) is the process by which a computer system automatically assigns metadata in the form of captioning or keywords to a digital image. This application of computer vision techniques is used in image retrieval systems to organize and locate images of interest from a database. This method can be regarded as a type of multi-class image classification with a very large number of classes - as large as the vocabulary size. Typically, image analysis in the form of extracted feature vectors and the training annotation words are used by machine learning techniques to attempt to automatically apply annotations to new images. The first methods learned the correlations between image features and training annotations. Subsequently, techniques were developed using machine translation to attempt to translate the textual vocabulary into the 'visual vocabulary,' represented by clustered regions known as blobs. Subsequent work has included classification approaches, relevance models, and other related methods. The advantages of automatic image annotation versus content-based image retrieval (CBIR) are that queries can be more naturally specified by the user. At present, Content-Based Image Retrieval (CBIR) generally requires users to search by image concepts such as color and texture or by finding example queries. However, certain image features in example images may override the concept that the user is truly focusing on. Traditional methods of image retrieval, such as those used by libraries, have relied on manually annotated images, which is expensive and time-consuming, especially given the large and constantly growing image databases in existence.