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26,300 | In protein structure, STRIDE (Structural identification) is an algorithm for the assignment of protein secondary structure elements given the atomic coordinates of the protein, as defined by X-ray crystallography, protein NMR, or another protein structure determination method. In addition to the hydrogen bond criteria ... | STRIDE (algorithm) | 0.807193 |
26,301 | Institute of Power Machinery and Mechanics Institute of Thermal and Nuclear Power Engineering Institute of Energy Efficiency and Hydrogen Technologies Institute of Electrical Engineering Institute of Electrical Power Engineering Institute of Information Technologies and Computer Science Institute of Radio Engineering a... | Moscow Power Engineering Institute | 0.807188 |
26,302 | In some fields of physics, they are distinguished by their origin: Gamma rays are created by nuclear decay while X-rays originate outside the nucleus. In astrophysics, gamma rays are conventionally defined as having photon energies above 100 keV and are the subject of gamma ray astronomy, while radiation below 100 keV ... | Gamma emission | 0.807187 |
26,303 | However, there are other rare natural sources, such as terrestrial gamma-ray flashes, which produce gamma rays from electron action upon the nucleus. Notable artificial sources of gamma rays include fission, such as that which occurs in nuclear reactors, and high energy physics experiments, such as neutral pion decay a... | Gamma emission | 0.807187 |
26,304 | In seven-dimensional geometry, a rectified 7-simplex is a convex uniform 7-polytope, being a rectification of the regular 7-simplex. There are four unique degrees of rectifications, including the zeroth, the 7-simplex itself. Vertices of the rectified 7-simplex are located at the edge-centers of the 7-simplex. Vertices... | Birectified 7-simplex | 0.807185 |
26,305 | Wilhelm Lenz and Ernst Ising created and analyzed the Ising model (1925) which is essentially a non-learning artificial recurrent neural network (RNN) consisting of neuron-like threshold elements. In 1972, Shun'ichi Amari made this architecture adaptive. His learning RNN was popularised by John Hopfield in 1982. | History of artificial neural networks | 0.807184 |
26,306 | With mathematical notation, Rosenblatt described circuitry not in the basic perceptron, such as the exclusive-or circuit that could not be processed by neural networks at the time. In 1959, a biological model proposed by Nobel laureates Hubel and Wiesel was based on their discovery of two types of cells in the primary ... | History of artificial neural networks | 0.807184 |
26,307 | Researchers started applying these ideas to computational models in 1948 with Turing's B-type machines. Farley and Clark (1954) first used computational machines, then called "calculators", to simulate a Hebbian network. Other neural network computational machines were created by Rochester, Holland, Habit and Duda (195... | History of artificial neural networks | 0.807184 |
26,308 | Warren McCulloch and Walter Pitts (1943) also considered a non-learning computational model for neural networks. This model paved the way for research to split into two approaches. One approach focused on biological processes while the other focused on the application of neural networks to artificial intelligence. This... | History of artificial neural networks | 0.807184 |
26,309 | In 2010, Backpropagation training through max-pooling was accelerated by GPUs and shown to perform better than other pooling variants. Behnke (2003) relied only on the sign of the gradient (Rprop) on problems such as image reconstruction and face localization. Rprop is a first-order optimization algorithm created by Ma... | History of artificial neural networks | 0.807184 |
26,310 | Thus, while also using a pyramidal structure as in the neocognitron, it performed a global optimization of the weights instead of a local one.In 1988, Wei Zhang et al. applied backpropagation to a CNN (a simplified Neocognitron with convolutional interconnections between the image feature layers and the last fully conn... | History of artificial neural networks | 0.807184 |
26,311 | In 1969, Kunihiko Fukushima also introduced the ReLU (rectified linear unit) activation function. The rectifier has become the most popular activation function for CNNs and deep neural networks in general.The time delay neural network (TDNN) was introduced in 1987 by Alex Waibel and was one of the first CNNs, as it ach... | History of artificial neural networks | 0.807184 |
26,312 | The first deep learning MLP was published by Alexey Grigorevich Ivakhnenko and Valentin Lapa in 1965, as the Group Method of Data Handling. This method employs incremental layer by layer training based on regression analysis, where useless units in hidden layers are pruned with the help of a validation set. The first d... | History of artificial neural networks | 0.807184 |
26,313 | Earlier adversarial machine learning systems "neither involved unsupervised neural networks nor were about modeling data nor used gradient descent. "In 2014, this adversarial principle was used in a generative adversarial network (GAN) by Ian Goodfellow et al. Here the environmental reaction is 1 or 0 depending on whet... | History of artificial neural networks | 0.807184 |
26,314 | In 1991, Juergen Schmidhuber published adversarial neural networks that contest with each other in the form of a zero-sum game, where one network's gain is the other network's loss. The first network is a generative model that models a probability distribution over output patterns. The second network learns by gradient... | History of artificial neural networks | 0.807184 |
26,315 | Researchers demonstrated (2010) that deep neural networks interfaced to a hidden Markov model with context-dependent states that define the neural network output layer can drastically reduce errors in large-vocabulary speech recognition tasks such as voice search.GPU-based implementations of this approach won many patt... | History of artificial neural networks | 0.807184 |
26,316 | Between 2009 and 2012, recurrent neural networks and deep feedforward neural networks developed in Schmidhuber's research group won eight international competitions in pattern recognition and machine learning. For example, the bi-directional and multi-dimensional long short-term memory (LSTM) of Graves et al. won three... | History of artificial neural networks | 0.807184 |
26,317 | == Linear neural network == The simplest kind of feedforward neural network is a linear network, which consists of a single layer of output nodes; the inputs are fed directly to the outputs via a series of weights. The sum of the products of the weights and the inputs is calculated in each node. The mean squared errors... | History of artificial neural networks | 0.807184 |
26,318 | The development of metal–oxide–semiconductor (MOS) very-large-scale integration (VLSI), combining millions or billions of MOS transistors onto a single chip in the form of complementary MOS (CMOS) technology, enabled the development of practical artificial neural networks in the 1980s.Computational devices were created... | History of artificial neural networks | 0.807184 |
26,319 | Support vector machines, developed at AT&T Bell Laboratories by Vladimir Vapnik with colleagues (Boser et al., 1992, Isabelle Guyon et al., 1993, Corinna Cortes, 1995, Vapnik et al., 1997) and simpler methods such as linear classifiers gradually overtook neural networks. However, neural networks transformed domains suc... | History of artificial neural networks | 0.807184 |
26,320 | LSTM has become the most cited neural network of the 20th century.In 2015, Rupesh Kumar Srivastava, Klaus Greff, and Schmidhuber used LSTM principles to create the Highway network, a feedforward neural network with hundreds of layers, much deeper than previous networks. 7 months later, Kaiming He, Xiangyu Zhang; Shaoqi... | History of artificial neural networks | 0.807184 |
26,321 | This led to the deep learning method called long short-term memory (LSTM), published in 1997. LSTM recurrent neural networks can learn "very deep learning" tasks with long credit assignment paths that require memories of events that happened thousands of discrete time steps before. The "vanilla LSTM" with forget gate w... | History of artificial neural networks | 0.807184 |
26,322 | Sepp Hochreiter's diploma thesis (1991) was called "one of the most important documents in the history of machine learning" by his supervisor Juergen Schmidhuber. Hochreiter not only tested the neural history compressor, but also identified and analyzed the vanishing gradient problem. He proposed recurrent residual con... | History of artificial neural networks | 0.807184 |
26,323 | Self-organizing maps (SOMs) were described by Teuvo Kohonen in 1982. SOMs are neurophysiologically inspired artificial neural networks that learn low-dimensional representations of high-dimensional data while preserving the topological structure of the data. They are trained using competitive learning. SOMs create inte... | History of artificial neural networks | 0.807184 |
26,324 | Here a slow feedforward neural network learns by gradient descent to control the fast weights of another neural network through outer products of self-generated activation patterns called "FROM" and "TO" which in Transformer terminology are called "key" and "value" for "self-attention." This fast weight "attention mapp... | History of artificial neural networks | 0.807184 |
26,325 | Many modern large language models such as ChatGPT, GPT-4, and BERT use a feedforward neural network called Transformer by Ashish Vaswani et. al. in their 2017 paper "Attention Is All You Need." Transformers have increasingly become the model of choice for natural language processing problems, replacing recurrent neural... | History of artificial neural networks | 0.807184 |
26,326 | This RBM is a generative stochastic feedforward neural network that can learn a probability distribution over its set of inputs. Once sufficiently many layers have been learned, the deep architecture may be used as a generative model by reproducing the data when sampling down the model (an "ancestral pass") from the to... | History of artificial neural networks | 0.807184 |
26,327 | The RNN hierarchy can be "collapsed" into a single RNN, by "distilling" a higher level "chunker" network into a lower level "automatizer" network. In 1993, a chunker solved a deep learning task whose CAP depth exceeded 1000. Such history compressors can substantially facilitate downstream supervised deep learning.Geoff... | History of artificial neural networks | 0.807184 |
26,328 | In the 1980s, backpropagation did not work well for deep FNNs and RNNs. Here the word "deep" refers to the number of layers through which the data is transformed. More precisely, deep learning systems have a substantial credit assignment path (CAP) depth. The CAP is the chain of transformations from input to output. | History of artificial neural networks | 0.807184 |
26,329 | In theoretical computer science and coding theory, the long code is an error-correcting code that is locally decodable. Long codes have an extremely poor rate, but play a fundamental role in the theory of hardness of approximation. | Long code (mathematics) | 0.807183 |
26,330 | Thurston's geometrization conjecture states that certain three-dimensional topological spaces each have a unique geometric structure that can be associated with them. It is an analogue of the uniformization theorem for two-dimensional surfaces, which states that every simply-connected Riemann surface can be given one o... | Low dimensional topology | 0.80718 |
26,331 | An exotic R4 is a differentiable manifold that is homeomorphic but not diffeomorphic to the Euclidean space R4. The first examples were found in the early 1980s by Michael Freedman, by using the contrast between Freedman's theorems about topological 4-manifolds, and Simon Donaldson's theorems about smooth 4-manifolds. ... | Low dimensional topology | 0.80718 |
26,332 | A topological space X is a 3-manifold if every point in X has a neighbourhood that is homeomorphic to Euclidean 3-space. The topological, piecewise-linear, and smooth categories are all equivalent in three dimensions, so little distinction is made in whether we are dealing with say, topological 3-manifolds, or smooth 3... | Low dimensional topology | 0.80718 |
26,333 | A 4-manifold is a 4-dimensional topological manifold. A smooth 4-manifold is a 4-manifold with a smooth structure. In dimension four, in marked contrast with lower dimensions, topological and smooth manifolds are quite different. There exist some topological 4-manifolds that admit no smooth structure and even if there ... | Low dimensional topology | 0.80718 |
26,334 | In mathematics, the uniformization theorem says that every simply connected Riemann surface is conformally equivalent to one of the three domains: the open unit disk, the complex plane, or the Riemann sphere. In particular it admits a Riemannian metric of constant curvature. This classifies Riemannian surfaces as ellip... | Low dimensional topology | 0.80718 |
26,335 | Thales' theorem states that if A is any point of the circle with diameter BC (except B or C themselves) ABC is a right triangle where A is the right angle. The converse states that if a right triangle is inscribed in a circle then the hypotenuse will be a diameter of the circle. A corollary is that the length of the hy... | Right angled triangle | 0.80718 |
26,336 | The triangle can be inscribed in a semicircle, with one side coinciding with the entirety of the diameter (Thales' theorem). The circumcenter is the midpoint of the longest side. The longest side is a diameter of the circumcircle ( c = 2 R ) . {\displaystyle \displaystyle (c=2R).} | Right angled triangle | 0.80718 |
26,337 | One specific type of syn-pentane interaction is known as 1,3 allylic strain or (A1,3 strain). For instance in certain aldol adducts with 2,6-disubstituted aryl groups the molecular geometry has the vicinal hydrogen atoms in an antiperiplanar configuration both in a crystal lattice (X-ray diffraction) and in solution pr... | Pentane interference | 0.807179 |
26,338 | Nuclear orientation, in nuclear physics, is the directional ordering of an assembly of nuclear spins with respect to some axis in space. It is one of the nuclear spectroscopy methods. A nuclear level with spin in a magnetic field will divide into magnetic sub-levels with an energy spacing. The populations of these leve... | Nuclear orientation | 0.807179 |
26,339 | With the rise of deep learning, a new family of methods, called deep generative models (DGMs),is formed through the combination of generative models and deep neural networks. An increase in the scale of the neural networks is typically accompanied by an increase in the scale of the training data, both of which are requ... | Generative model | 0.807179 |
26,340 | Standard examples of each, all of which are linear classifiers, are: generative classifiers: naive Bayes classifier and linear discriminant analysis discriminative model: logistic regressionIn application to classification, one wishes to go from an observation x to a label y (or probability distribution on labels). One... | Generative model | 0.807179 |
26,341 | In mathematical analysis, the Schur test, named after German mathematician Issai Schur, is a bound on the L 2 → L 2 {\displaystyle L^{2}\to L^{2}} operator norm of an integral operator in terms of its Schwartz kernel (see Schwartz kernel theorem). Here is one version. Let X , Y {\displaystyle X,\,Y} be two measurable s... | Schur test | 0.807178 |
26,342 | A helioscope is an instrument used in observing the sun and sunspots. The helioscope was first used by Benedetto Castelli (1578-1643) and refined by Galileo Galilei (1564–1642). The method involves projecting an image of the sun onto a white sheet of paper suspended in a darkened room with the use of a telescope.The fi... | Helioscope | 0.807177 |
26,343 | The notion of mass as a property of an object from Newtonian mechanics does not bear a precise relationship to the concept in relativity. Relativistic mass is not referenced in nuclear and particle physics, and a survey of introductory textbooks in 2005 showed that only 5 of 24 texts used the concept, although it is st... | Mass in special relativity | 0.807177 |
26,344 | The word "mass" has two meanings in special relativity: invariant mass (also called rest mass) is an invariant quantity which is the same for all observers in all reference frames, while the relativistic mass is dependent on the velocity of the observer. According to the concept of mass–energy equivalence, invariant ma... | Mass in special relativity | 0.807177 |
26,345 | It explains simply and quantitatively why a body subject to a constant acceleration cannot reach the speed of light, and why the mass of a system emitting a photon decreases. In relativistic quantum chemistry, relativistic mass is used to explain electron orbital contraction in heavy elements. | Mass in special relativity | 0.807177 |
26,346 | Relativistic mass and rest mass are both traditional concepts in physics, but the relativistic mass corresponds to the total energy. The relativistic mass is the mass of the system as it would be measured on a scale, but in some cases (such as the box above) this fact remains true only because the system on average mus... | Mass in special relativity | 0.807177 |
26,347 | Perovskite are mixed metal oxide with a well-defined cubic structure and a general formula of ABO3, where A is an alkaline earth or lanthanide element and B is a transition metal. These materials are attractive for CO2 separation because of the tunability of the metal sites as well as their stabilities at elevated temp... | Membrane method | 0.807177 |
26,348 | It has been shown that the permeability of a membrane is the production of adsorption and diffusion. In low loading conditions, the adsorption can be computed by the Henry coefficient.If the assumption is made that the energy of a particle does not change when moving through this structure, only the entropy of the mole... | Membrane method | 0.807177 |
26,349 | Nanoporous membranes are fundamentally different from polymer-based membranes in that their chemistry is different and that they do not follow the Robeson limit for a variety of reasons. The simplified figure of a nanoporous membrane shows a small portion of an example membrane structure with cavities and windows. The ... | Membrane method | 0.807177 |
26,350 | For diffusion, an increase in entropy will lead to a decrease in free energy which in turn leads to a decrease in the diffusion coefficient. Conversely, changing the window geometry will primarily effect the diffusion of the molecules and not the Henry coefficient. In summary, by using the above simplified analysis, it... | Membrane method | 0.807177 |
26,351 | In mathematics and physics, in particular differential geometry and general relativity, a warped geometry is a Riemannian or Lorentzian manifold whose metric tensor can be written in form d s 2 = g a b ( y ) d y a d y b + f ( y ) g i j ( x ) d x i d x j . {\displaystyle ds^{2}=g_{ab}(y)\,dy^{a}\,dy^{b}+f(y)g_{ij}(x)\,d... | Warped model | 0.807177 |
26,352 | The International Committee on Nomenclature of Viruses (ICNV) was established in 1966, at the International Congress for Microbiology in Moscow, to standardize the naming of viruses. The ICVN published its first report in 1971. For viruses infecting vertebrates, the first report included 19 genera, 2 families, and a fu... | International Committee on Taxonomy of Viruses | 0.807176 |
26,353 | Stochastic scheduling concerns scheduling problems involving random attributes, such as random processing times, random due dates, random weights, and stochastic machine breakdowns. Major applications arise in manufacturing systems, computer systems, communication systems, logistics and transportation, and machine lear... | Stochastic scheduling | 0.807175 |
26,354 | Limit points of a set should also not be confused with boundary points. For example, 0 {\displaystyle 0} is a boundary point (but not a limit point) of the set { 0 } {\displaystyle \{0\}} in R {\displaystyle \mathbb {R} } with standard topology. However, 0.5 {\displaystyle 0.5} is a limit point (though not a boundary p... | Limit points | 0.807174 |
26,355 | In mathematics, a limit point, accumulation point, or cluster point of a set S {\displaystyle S} in a topological space X {\displaystyle X} is a point x {\displaystyle x} that can be "approximated" by points of S {\displaystyle S} in the sense that every neighbourhood of x {\displaystyle x} with respect to the topology... | Limit points | 0.807174 |
26,356 | Furthermore, optimization theory is a well-developed field of mathematics. These two factors make rational choice models tractable compared to other approaches to choice. Most importantly, this approach is strikingly general. | Rational Choice Theory | 0.807174 |
26,357 | Rational choice theory provides a framework to explain why groups of rational individuals can come to collectively irrational decisions. For example, while at the individual level a group of people may have common interests, applying a rational choice framework to their individually rational preferences can explain gro... | Rational Choice Theory | 0.807174 |
26,358 | The center of mass plays an important role in astronomy and astrophysics, where it is commonly referred to as the barycenter. The barycenter is the point between two objects where they balance each other; it is the center of mass where two or more celestial bodies orbit each other. When a moon orbits a planet, or a pla... | Center of Gravity | 0.807174 |
26,359 | Michael J. Flynn views the first RISC system as the IBM 801 design, begun in 1975 by John Cocke and completed in 1980. The 801 developed out of an effort to build a 24-bit high-speed processor to use as the basis for a digital telephone switch. To reach their goal of switching 1 million calls per hour (300 per second) ... | RISC instruction set | 0.807172 |
26,360 | Variable geometry turbocharger can be used to manage boost levels, negating the need for an external boost controller. Also, the wastegate itself has a similar function to a boost controller, in that it is used to manage the turbocharger's boost pressure. == References == | Boost control | 0.807171 |
26,361 | Synthetic data is information that's artificially generated rather than produced by real-world events. Typically created using algorithms, synthetic data can be deployed to validate mathematical models and to train machine learning models.Data generated by a computer simulation can be seen as synthetic data. This encom... | Synthetic data | 0.807171 |
26,362 | Synthetic data is increasingly being used for machine learning applications: a model is trained on a synthetically generated dataset with the intention of transfer learning to real data. Efforts have been made to enable more data science experiments via the construction of general-purpose synthetic data generators, suc... | Synthetic data | 0.807171 |
26,363 | Scientific modelling of physical systems, which allows to run simulations in which one can estimate/compute/generate datapoints that haven't been observed in actual reality, has a long history that runs concurrent with the history of physics itself. For example, research into synthesis of audio and voice can be traced ... | Synthetic data | 0.807171 |
26,364 | For proving his theorem, Vincent proved a result that is useful on its own: For working with real numbers, one may always choose c = d = 1, but, as effective computations are done with rational numbers, it is generally convenient to suppose that a, b, c, d are integers. The "small enough" condition has been quantified ... | Real-root isolation | 0.807168 |
26,365 | Vincent's theorem (1834) provides a method for real-root isolation, which is at the basis of the most efficient real-root-isolation algorithms. It concerns the positive real roots of a square-free polynomial (that is a polynomial without multiple roots). If a 1 , a 2 , … , {\displaystyle a_{1},a_{2},\ldots ,} is a sequ... | Real-root isolation | 0.807168 |
26,366 | The use of continued fractions for real-root isolation has been introduced by Vincent, although he credited Joseph-Louis Lagrange for this idea, without providing a reference. For making an algorithm of Vincent's theorem, one must provide a criterion for choosing the a i {\displaystyle a_{i}} that occur in his theorem.... | Real-root isolation | 0.807168 |
26,367 | The first complete root-isolation procedure results of Sturm's theorem (1829), which expresses the number of real roots in an interval in terms of the number of sign variations of the values of a sequence of polynomials, called Sturm's sequence, at the ends of the interval. Sturm's sequence is the sequence of remainder... | Real-root isolation | 0.807168 |
26,368 | This is a generalization of Descartes' rule of signs, because, for b sufficiently large, there is no sign variation in the coefficients of f(x + b), and all real roots are smaller than b. Budan's may provide a real-root-isolation algorithm for a square-free polynomial (a polynomial without multiple root): from the coef... | Real-root isolation | 0.807168 |
26,369 | Descartes' rule of signs asserts that the difference between the number of sign variations in the sequence of the coefficients of a polynomial and the number of its positive real roots is a nonnegative even integer. It results that if this number of sign variations is zero, then the polynomial does not have any positiv... | Real-root isolation | 0.807168 |
26,370 | Vincent's auxiliary theorem insures the termination of this procedure. Except for the initialization, all these changes of variable consists of the composition of at most two very simple changes of variable which are the scalings by two x → x/2 , the translation x → x + 1, and the inversion x → 1/x , the latter consist... | Real-root isolation | 0.807168 |
26,371 | As the use Descartes' rule of signs and Vincent's auxiliary theorem is much more computationally efficient than the use of Sturm's theorem, only the former is described in this section. The bisection method based on Descartes' rules of signs and Vincent's auxiliary theorem has been introduced in 1976 by Akritas and Col... | Real-root isolation | 0.807168 |
26,372 | (The single changes variable x = (2By – B) may also be used.) The method requires an algorithm for testing whether an interval has zero, one, or possibly several roots, and for warranting termination, this testing algorithm must exclude the possibility of getting infinitely many times the output "possibility of several... | Real-root isolation | 0.807168 |
26,373 | Moreover, it may be difficult to distinguish the real roots from the non-real roots with small imaginary part (see the example of Wilkinson's polynomial in next section). The first complete real-root isolation algorithm results from Sturm's theorem (1829). However, when real-root-isolation algorithms began to be implem... | Real-root isolation | 0.807168 |
26,374 | In mathematics, and, more specifically in numerical analysis and computer algebra, real-root isolation of a polynomial consist of producing disjoint intervals of the real line, which contain each one (and only one) real root of the polynomial, and, together, contain all the real roots of the polynomial. Real-root isola... | Real-root isolation | 0.807168 |
26,375 | Short decay times are important for the measurement of time intervals and for the operation in fast coincidence circuits. High density and fast response time can allow detection of rare events in particle physics. Particle energy deposited in the material of a scintillator is proportional to the scintillator's response... | Scintillating material | 0.807167 |
26,376 | Gaseous scintillators consist of nitrogen and the noble gases helium, argon, krypton, and xenon, with helium and xenon receiving the most attention. The scintillation process is due to the de-excitation of single atoms excited by the passage of an incoming particle. This de-excitation is very rapid (~1 ns), so the dete... | Scintillating material | 0.807167 |
26,377 | Such arrays are often used in medical physics or security applications to detect X-rays or γ rays: high-Z, high density materials (e.g. LYSO, BGO) are typically preferred for this type of applications. Scintillation in inorganic crystals is typically slower than in organic ones, ranging typically from 1.48 ns for ZnO(G... | Scintillating material | 0.807167 |
26,378 | The National Centre for Artificial Intelligence (NCAI) is a government agency in India that works for the advancement and application of artificial intelligence (AI) technologies. It operates under the purview of the Ministry of Electronics and Information Technology, and works to promote AI research, development, and ... | National Centre for Artificial Intelligence | 0.807165 |
26,379 | Of objects: Parity is the property of an integer of whether it is even or oddFor more examples, see Category:Algebraic properties of elements. Of operations: associative property commutative property of binary operations between real and complex numbers distributive propertyFor more examples, see Category:Properties of... | Property (mathematics) | 0.807165 |
26,380 | While the interdisciplinary concept of neuromorphic engineering is relatively new, many of the same ethical considerations apply to neuromorphic systems as apply to human-like machines and artificial intelligence in general. However, the fact that neuromorphic systems are designed to mimic a human brain gives rise to u... | Neuromorphic computing | 0.807165 |
26,381 | The songs were old Belgian and French flute minuets, from which the chip learned the rules at play and then applied them.The Blue Brain Project, led by Henry Markram, aims to build biologically detailed digital reconstructions and simulations of the mouse brain. The Blue Brain Project has created in silico models of ro... | Neuromorphic computing | 0.807165 |
26,382 | In 2022, researchers at MIT have reported the development of brain-inspired artificial synapses, using the ion proton (H+), for 'analog deep learning'.Intel unveiled its neuromorphic research chip, called “Loihi”, in October 2017. The chip uses an asynchronous spiking neural network (SNN) to implement adaptive self-mod... | Neuromorphic computing | 0.807165 |
26,383 | Each NeuroCore's analog circuitry is designed to emulate neural elements for 65536 neurons, maximizing energy efficiency. The emulated neurons are connected using digital circuitry designed to maximize spiking throughput.A research project with implications for neuromorphic engineering is the Human Brain Project that i... | Neuromorphic computing | 0.807165 |
26,384 | The Joint Artificial Intelligence Center, a branch of the U.S. military, is a center dedicated to the procurement and implementation of AI software and neuromorphic hardware for combat use. Specific applications include smart headsets/goggles and robots. JAIC intends to rely heavily on neuromorphic technology to connec... | Neuromorphic computing | 0.807165 |
26,385 | The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors, spintronic memories, threshold switches, transistors, among others. Training software-based neuromorphic systems of spiking neural networks can be achieved using error backpropagation, e.g., using Python based ... | Neuromorphic computing | 0.807165 |
26,386 | Neuromemristive systems is a subclass of neuromorphic computing systems that focuses on the use of memristors to implement neuroplasticity. While neuromorphic engineering focuses on mimicking biological behavior, neuromemristive systems focus on abstraction. For example, a neuromemristive system may replace the details... | Neuromorphic computing | 0.807165 |
26,387 | {\displaystyle \left|\sum _{i\in S_{j}}\langle v_{i},x\rangle ^{2}-{\frac {1}{2}}\right|\leq 5{\sqrt {\alpha }}\ {\text{ for all }}x\in \mathbb {R} ^{d}{\text{ with }}\|x\|=1.} Here the "discrepancy" becomes visible when α is small enough: the quadratic form on the unit sphere can be split into two roughly equal pieces... | Kadison–Singer problem | 0.807162 |
26,388 | Consider the separable Hilbert space ℓ2 and two related C*-algebras: the algebra B {\displaystyle B} of all continuous linear operators from ℓ2 to ℓ2, and the algebra D {\displaystyle D} of all diagonal continuous linear operators from ℓ2 to ℓ2. A state on a C*-algebra A {\displaystyle A} is a continuous linear functio... | Kadison–Singer problem | 0.807162 |
26,389 | In mathematics, the Kadison–Singer problem, posed in 1959, was a problem in functional analysis about whether certain extensions of certain linear functionals on certain C*-algebras were unique. The uniqueness was proved in 2013. The statement arose from work on the foundations of quantum mechanics done by Paul Dirac i... | Kadison–Singer problem | 0.807162 |
26,390 | Some examples of complete theories are: Presburger arithmetic Tarski's axioms for Euclidean geometry The theory of dense linear orders without endpoints The theory of algebraically closed fields of a given characteristic The theory of real closed fields Every uncountably categorical countable theory Every countably cat... | Complete theory | 0.807161 |
26,391 | Gödel's completeness theorem is about this latter kind of completeness. Complete theories are closed under a number of conditions internally modelling the T-schema: For a set of formulas S {\displaystyle S}: A ∧ B ∈ S {\displaystyle A\land B\in S} if and only if A ∈ S {\displaystyle A\in S} and B ∈ S {\displaystyle B\i... | Complete theory | 0.807161 |
26,392 | In mathematical logic, a theory is complete if it is consistent and for every closed formula in the theory's language, either that formula or its negation is provable. That is, for every sentence φ , {\displaystyle \varphi ,} the theory T {\displaystyle T} contains the sentence or its negation but not both (that is, ei... | Complete theory | 0.807161 |
26,393 | A 2018 definition states "Big data is where parallel computing tools are needed to handle data", and notes, "This represents a distinct and clearly defined change in the computer science used, via parallel programming theories, and losses of some of the guarantees and capabilities made by Codd's relational model. "In a... | Big data analysis | 0.80716 |
26,394 | state of Massachusetts announced the Massachusetts Big Data Initiative in May 2012, which provides funding from the state government and private companies to a variety of research institutions. The Massachusetts Institute of Technology hosts the Intel Science and Technology Center for Big Data in the MIT Computer Scien... | Big data analysis | 0.80716 |
26,395 | Encrypted search and cluster formation in big data were demonstrated in March 2014 at the American Society of Engineering Education. Gautam Siwach engaged at Tackling the challenges of Big Data by MIT Computer Science and Artificial Intelligence Laboratory and Amir Esmailpour at the UNH Research Group investigated the ... | Big data analysis | 0.80716 |
26,396 | The company explores selling the "anonymous aggregated genetic data" to other researchers and pharmaceutical companies for research purposes if patients give their consent. Ahmad Hariri, professor of psychology and neuroscience at Duke University who has been using 23andMe in his research since 2009 states that the mos... | Big data analysis | 0.80716 |
26,397 | These fast and exact calculations eliminate any "friction points", or human errors that could be made by one of the numerous science and biology experts working with the DNA. DNAStack, a part of Google Genomics, allows scientists to use the vast sample of resources from Google's search server to scale social experiment... | Big data analysis | 0.80716 |
26,398 | It is considered one of the most ambitious scientific projects ever undertaken. When the Sloan Digital Sky Survey (SDSS) began to collect astronomical data in 2000, it amassed more in its first few weeks than all data collected in the history of astronomy previously. Continuing at a rate of about 200 GB per night, SDSS... | Big data analysis | 0.80716 |
26,399 | The use and adoption of big data within governmental processes allows efficiencies in terms of cost, productivity, and innovation, but does not come without its flaws. Data analysis often requires multiple parts of government (central and local) to work in collaboration and create new and innovative processes to delive... | Big data analysis | 0.80716 |
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