id int32 0 100k | text stringlengths 21 3.54k | source stringlengths 1 124 | similarity float32 0.78 0.88 |
|---|---|---|---|
2,300 | One of the first general purpose computers, ENIAC, was used as a very simple type of physics engine. It was used to design ballistics tables to help the United States military estimate where artillery shells of various mass would land when fired at varying angles and gunpowder charges, also accounting for drift caused by wind. The results were calculated a single time only, and were tabulated into printed tables handed out to the artillery commanders. Physics engines have been commonly used on supercomputers since the 1980s to perform computational fluid dynamics modeling, where particles are assigned force vectors that are combined to show circulation. | Physics engines | 0.834981 |
2,301 | Engines that use bounding boxes or bounding spheres as the final shape for collision detection are considered extremely simple. Generally a bounding box is used for broad phase collision detection to narrow down the number of possible collisions before costly mesh on mesh collision detection is done in the narrow phase of collision detection. Another aspect of precision in discrete collision detection involves the framerate, or the number of moments in time per second when physics is calculated. | Physics engines | 0.834981 |
2,302 | It would thus be impossible to insert a rod or fire a projectile through the handle holes on the vase, because the physics engine model is based on the cylinder and is unaware of the handles. The simplified mesh used for physics processing is often referred to as the collision geometry. This may be a bounding box, sphere, or convex hull. | Physics engines | 0.834981 |
2,303 | Objects in games interact with the player, the environment, and each other. Typically, most 3D objects in games are represented by two separate meshes or shapes. One of these meshes is the highly complex and detailed shape visible to the player in the game, such as a vase with elegant curved and looping handles. For purpose of speed, a second, simplified invisible mesh is used to represent the object to the physics engine so that the physics engine treats the example vase as a simple cylinder. | Physics engines | 0.834981 |
2,304 | An alternative to using bounding box-based rigid body physics systems is to use a finite element-based system. In such a system, a 3-dimensional, volumetric tessellation is created of the 3D object. The tessellation results in a number of finite elements which represent aspects of the object's physical properties such as toughness, plasticity, and volume preservation. Once constructed, the finite elements are used by a solver to model the stress within the 3D object. | Physics engines | 0.834981 |
2,305 | A primary limit of physics engine realism is the approximated result of the constraint resolutions and collision result due to the slow convergence of algorithms. Collision detection computed at a too low frequency can result in objects passing through each other and then being repelled with an abnormal correction force. On the other hand, approximated results of reaction force is due to the slow convergence of typical Projected Gauss Seidel solver resulting in abnormal bouncing. Any type of free-moving compound physics object can demonstrate this problem, but it is especially prone to affecting chain links under high tension, and wheeled objects with actively physical bearing surfaces. Higher precision reduces the positional/force errors, but at the cost of needing greater CPU power for the calculations. | Physics engines | 0.834981 |
2,306 | However, during the last two decades further knowledge has highlighted the great intricacy of dinoflagellate life histories. More than 10% of the approximately 2000 known marine dinoflagellate species produce cysts as part of their life cycle (see diagram on the right). These benthic phases play an important role in the ecology of the species, as part of a planktonic-benthic link in which the cysts remain in the sediment layer during conditions unfavorable for vegetative growth and, from there, reinoculate the water column when favorable conditions are restored.Indeed, during dinoflagellate evolution the need to adapt to fluctuating environments and/or to seasonality is thought to have driven the development of this life cycle stage. | Dinophyte algae | 0.834973 |
2,307 | Many dinoflagellates are photosynthetic, but a large fraction of these are in fact mixotrophic, combining photosynthesis with ingestion of prey (phagotrophy and myzocytosis).In terms of number of species, dinoflagellates are one of the largest groups of marine eukaryotes, although substantially smaller than diatoms. Some species are endosymbionts of marine animals and play an important part in the biology of coral reefs. Other dinoflagellates are unpigmented predators on other protozoa, and a few forms are parasitic (for example, Oodinium and Pfiesteria). | Dinophyte algae | 0.834973 |
2,308 | This can introduce both nonfatal and fatal illnesses. One such poison is saxitoxin, a powerful paralytic neurotoxin.Human inputs of phosphate further encourage these red tides, so strong interest exists in learning more about dinoflagellates, from both medical and economic perspectives. Dinoflagellates are known to be particularly capable of scavenging dissolved organic phosphorus for P-nutrient, several HAS species have been found to be highly versatile and mechanistically diversified in utilizing different types of DOPs. The ecology of harmful algal blooms is extensively studied. | Dinophyte algae | 0.834973 |
2,309 | More modern-looking forms proliferate during the later Jurassic and Cretaceous. This trend continues into the Cenozoic, albeit with some loss of diversity.Molecular phylogenetics show that dinoflagellates are grouped with ciliates and apicomplexans (=Sporozoa) in a well-supported clade, the alveolates. | Dinophyte algae | 0.834973 |
2,310 | He was ordained as a deacon in 1819, a priest in 1822 and appointed Vicar of Wymeswold in Leicestershire in 1826 (until 1835).In 1839 he was appointed Dean of Ely cathedral, Cambridgeshire, a position he held for the rest of his life, some 20 years. Together with the architect George Gilbert Scott he undertook a major restoration of the cathedral building. This included the installation of the boarded ceiling.While holding this position he wrote a text book on algebra, A Treatise on Algebra (1830). Later, a second edition appeared in two volumes, the one called Arithmetical Algebra (1842) and the other On Symbolical Algebra and its Applications to the Geometry of Position (1845). | Arithmetical algebra | 0.834947 |
2,311 | George Peacock FRS (9 April 1791 – 8 November 1858) was an English mathematician and Anglican cleric. He founded what has been called the British algebra of logic. | Arithmetical algebra | 0.834947 |
2,312 | Peacock's principle says that the form on the left side is equivalent to the form on the right side, not only when the said restrictions of being less are removed, but when a {\displaystyle a} , b {\displaystyle b} , c {\displaystyle c} , d {\displaystyle d} denote the most general algebraic symbol. It means that a {\displaystyle a} , b {\displaystyle b} , c {\displaystyle c} , d {\displaystyle d} may be rational fractions, or surds, or imaginary quantities, or indeed operators such as d d x {\displaystyle {\frac {d}{dx}}} . The equivalence is not established by means of the nature of the quantity denoted; the equivalence is assumed to be true, and then it is attempted to find the different interpretations which may be put on the symbol. | Arithmetical algebra | 0.834947 |
2,313 | All the results of arithmetical algebra which are deduced by the application of its rules, and which are general in form though particular in value, are results likewise of symbolical algebra where they are general in value as well as in form; thus the product of a m {\displaystyle a^{m}} and a n {\displaystyle a^{n}} which is a m + n {\displaystyle a^{m+n}} when m {\displaystyle m} and n {\displaystyle n} are whole numbers and therefore general in form though particular in value, will be their product likewise when m {\displaystyle m} and n {\displaystyle n} are general in value as well as in form; the series for ( a + b ) n {\displaystyle (a+b)^{n}} determined by the principles of arithmetical algebra when n {\displaystyle n} is any whole number, if it be exhibited in a general form, without reference to a final term, may be shown upon the same principle to the equivalent series for ( a + b ) n {\displaystyle (a+b)^{n}} when n {\displaystyle n} is general both in form and value." The principle here indicated by means of examples was named by Peacock the "principle of the permanence of equivalent forms," and at page 59 of the Symbolical Algebra it is thus enunciated: "Whatever algebraic forms are equivalent when the symbols are general in form, but specific in value, will be equivalent likewise when the symbols are general in value as well as in form." For example, let a {\displaystyle a} , b {\displaystyle b} , c {\displaystyle c} , d {\displaystyle d} denote any integer numbers, but subject to the restrictions that b {\displaystyle b} is less than a {\displaystyle a} , and d {\displaystyle d} less than c {\displaystyle c} ; it may then be shown arithmetically that ( a − b ) ( c − d ) = a c + b d − a d − b c {\displaystyle (a-b)(c-d)=ac+bd-ad-bc} . | Arithmetical algebra | 0.834947 |
2,314 | When m {\displaystyle m} and / n {\displaystyle /n} are compounded we get the idea of a rational fraction; for in general m / n {\displaystyle m/n} will not reduce to a number nor to the reciprocal of a number. Suppose, however, that we pass over this objection; how does Peacock lay the foundation for general algebra? He calls it symbolical algebra, and he passes from arithmetical algebra to symbolical algebra in the following manner: "Symbolical algebra adopts the rules of arithmetical algebra but removes altogether their restrictions; thus symbolical subtraction differs from the same operation in arithmetical algebra in being possible for all relations of value of the symbols or expressions employed. | Arithmetical algebra | 0.834947 |
2,315 | For instance, in a b {\displaystyle ab} , a {\displaystyle a} can denote only an integer number, but b {\displaystyle b} may denote a rational fraction. Now there is no more fundamental principle in arithmetical algebra than that a b = b a {\displaystyle ab=ba} ; which would be illegitimate on Peacock's principle. | Arithmetical algebra | 0.834947 |
2,316 | Hence the following dilemma: Either a b {\displaystyle {\frac {a}{b}}} must be held to be an impossible expression in general, or else the meaning of the fundamental symbol of algebra must be extended so as to include rational fractions. If the former horn of the dilemma is chosen, arithmetical algebra becomes a mere shadow; if the latter horn is chosen, the operations of algebra cannot be defined on the supposition that the elementary symbol is an integer number. Peacock attempts to get out of the difficulty by supposing that a symbol which is used as a multiplier is always an integer number, but that a symbol in the place of the multiplicand may be a fraction. | Arithmetical algebra | 0.834947 |
2,317 | Peacock's principle may be stated thus: the elementary symbol of arithmetical algebra denotes a digital, i.e., an integer number; and every combination of elementary symbols must reduce to a digital number, otherwise it is impossible or foreign to the science. If a {\displaystyle a} and b {\displaystyle b} are numbers, then a + b {\displaystyle a+b} is always a number; but a − b {\displaystyle a-b} is a number only when b {\displaystyle b} is less than a {\displaystyle a} . Again, under the same conditions, a b {\displaystyle ab} is always a number, but a b {\displaystyle {\frac {a}{b}}} is really a number only when b {\displaystyle b} is an exact divisor of a {\displaystyle a} . | Arithmetical algebra | 0.834947 |
2,318 | Peacock's main contribution to mathematical analysis is his attempt to place algebra on a strictly logical basis. He founded what has been called the British algebra of logic; to which Gregory, De Morgan and Boole belonged. His answer to Maseres and Frend was that the science of algebra consisted of two parts—arithmetical algebra and symbolical algebra—and that they erred in restricting the science to the arithmetical part. His view of arithmetical algebra is as follows: "In arithmetical algebra we consider symbols as representing numbers, and the operations to which they are submitted as included in the same definitions as in common arithmetic; the signs + {\displaystyle +} and − {\displaystyle -} denote the operations of addition and subtraction in their ordinary meaning only, and those operations are considered as impossible in all cases where the symbols subjected to them possess values which would render them so in case they were replaced by digital numbers; thus in expressions such as a + b {\displaystyle a+b} we must suppose a {\displaystyle a} and b {\displaystyle b} to be quantities of the same kind; in others, like a − b {\displaystyle a-b} , we must suppose a {\displaystyle a} greater than b {\displaystyle b} and therefore homogeneous with it; in products and quotients, like a b {\displaystyle ab} and a b {\displaystyle {\frac {a}{b}}} we must suppose the multiplier and divisor to be abstract numbers; all results whatsoever, including negative quantities, which are not strictly deducible as legitimate conclusions from the definitions of the several operations must be rejected as impossible, or as foreign to the science." | Arithmetical algebra | 0.834947 |
2,319 | DnaSP — DNA Sequence Polymorphism, is a software package for the analysis of nucleotide polymorphism from aligned DNA sequence data. MEGA, Molecular Evolutionary Genetics Analysis, is a software package used for estimating rates of molecular evolution, as well as generating phylogenetic trees, and aligning DNA sequences. Available for Windows, Linux and Mac OS X (since ver. 5.x). | Nucleotide diversity | 0.834935 |
2,320 | Nucleotide diversity is a concept in molecular genetics which is used to measure the degree of polymorphism within a population. One commonly used measure of nucleotide diversity was first introduced by Nei and Li in 1979. This measure is defined as the average number of nucleotide differences per site between two DNA sequences in all possible pairs in the sample population, and is denoted by π {\displaystyle \pi } . | Nucleotide diversity | 0.834935 |
2,321 | A p-dimensional submanifold Σ of M is said to be a calibrated submanifold with respect to φ (or simply φ-calibrated) if TΣ lies in G(φ). A famous one line argument shows that calibrated p-submanifolds minimize volume within their homology class. Indeed, suppose that Σ is calibrated, and Σ ′ is a p submanifold in the same homology class. Then ∫ Σ v o l Σ = ∫ Σ φ = ∫ Σ ′ φ ≤ ∫ Σ ′ v o l Σ ′ {\displaystyle \int _{\Sigma }\mathrm {vol} _{\Sigma }=\int _{\Sigma }\varphi =\int _{\Sigma '}\varphi \leq \int _{\Sigma '}\mathrm {vol} _{\Sigma '}} where the first equality holds because Σ is calibrated, the second equality is Stokes' theorem (as φ is closed), and the inequality holds because φ is a calibration. | Calibrated geometry | 0.834927 |
2,322 | In the mathematical field of differential geometry, a calibrated manifold is a Riemannian manifold (M,g) of dimension n equipped with a differential p-form φ (for some 0 ≤ p ≤ n) which is a calibration, meaning that: φ is closed: dφ = 0, where d is the exterior derivative for any x ∈ M and any oriented p-dimensional subspace ξ of TxM, φ|ξ = λ volξ with λ ≤ 1. Here volξ is the volume form of ξ with respect to g.Set Gx(φ) = { ξ as above: φ|ξ = volξ }. (In order for the theory to be nontrivial, we need Gx(φ) to be nonempty.) | Calibrated geometry | 0.834927 |
2,323 | His work heavily influenced computer pioneer John von Neumann, information theorist Claude Shannon, anthropologists Margaret Mead and Gregory Bateson, and others. Norbert Wiener is credited as being one of the first to theorize that all intelligent behavior was the result of feedback mechanisms, that could possibly be simulated by machines and was an important early step towards the development of modern artificial intelligence.Norbert Wiener .. was one of the first to theorize that all intelligent behavior was the result of feedback mechanisms. Mechanisms that could possibly be simulated by machines. | I Am a Mathematician | 0.834912 |
2,324 | We have decided to call the entire field of control and communication theory, whether in the machine or in the animal, by the name Cybernetics, which we form from the Greek κυβερνήτης or steersman. with implications for engineering, systems control, computer science, biology, neuroscience, philosophy, and the organization of society. | I Am a Mathematician | 0.834912 |
2,325 | The MATH domain, in molecular biology, is a binding domain that was defined originally by a region of homology between otherwise functionally unrelated domains, the intracellular TRAF-C domains of TRAF proteins and a C-terminal region of extracellular meprins A and B. Although apparently functionally unrelated, intracellular TRAFs and extracellular meprins share a conserved region of about 180 residues, the meprin and TRAF homology (MATH) |domain. Meprins are mammalian tissue-specific metalloendopeptidases of the astacin family implicated in developmental, normal and pathological processes by hydrolysing a variety of proteins. Various growth factors, cytokines, and extracellular matrix proteins are substrates for meprins. | MATH domain | 0.834893 |
2,326 | Changes in quaternary structure can occur through conformational changes within individual subunits or through reorientation of the subunits relative to each other. It is through such changes, which underlie cooperativity and allostery in "multimeric" enzymes, that many proteins undergo regulation and perform their physiological function. The above definition follows a classical approach to biochemistry, established at times when the distinction between a protein and a functional, proteinaceous unit was difficult to elucidate. More recently, people refer to protein–protein interaction when discussing quaternary structure of proteins and consider all assemblies of proteins as protein complexes. | Protein multimer | 0.834839 |
2,327 | The analysis of the internal dynamics of structurally different, but functionally similar enzymes has highlighted a common relationship between the positioning of the active site and the two principal protein sub-domains. In fact, for several members of the hydrolase superfamily, the catalytic site is located close to the interface separating the two principal quasi-rigid domains. Such positioning appears instrumental for maintaining the precise geometry of the active site, while allowing for an appreciable functionally oriented modulation of the flanking regions resulting from the relative motion of the two sub-domains. | Protein dynamics | 0.834826 |
2,328 | Steering may be supplied by a rider or, under certain circumstances, by the bike itself. This self-stability is generated by a combination of several effects that depend on the geometry, mass distribution, and forward speed of the bike. Tires, suspension, steering damping, and frame flex can also influence it, especially in motorcycles. | Bike physics | 0.834815 |
2,329 | Then they constructed a physical model to validate that prediction. This may require some of the details provided below about steering geometry or stability to be re-evaluated. Bicycle dynamics was named 26 of Discover's 100 top stories of 2011.In 2013, Eddy Merckx Cycles was awarded over €150,000 with Ghent University to examine bicycle stability. | Bike physics | 0.834815 |
2,330 | Thus, by the end of the 19th century, Carlo Bourlet, Emmanuel Carvallo, and Francis Whipple had showed with rigid-body dynamics that some safety bicycles could actually balance themselves if moving at the right speed. Bourlet won the Prix Fourneyron, and Whipple won the Cambridge University Smith Prize. It is not clear to whom should go the credit for tilting the steering axis from the vertical which helps make this possible.In 1970, David E. H. Jones published an article in Physics Today showing that gyroscopic effects are not necessary to balance a bicycle. | Bike physics | 0.834815 |
2,331 | Capsize is the word used to describe a bike falling over without oscillation. During capsize, an uncontrolled front wheel usually steers in the direction of lean, but never enough to stop the increasing lean, until a very high lean angle is reached, at which point the steering may turn in the opposite direction. A capsize can happen very slowly if the bike is moving forward rapidly. Because the capsize instability is so slow, on the order of seconds, it is easy for the rider to control, and is actually used by the rider to initiate the lean necessary for a turn.For most bikes, depending on geometry and mass distribution, capsize is stable at low speeds, and becomes less stable as speed increases until it is no longer stable. However, on many bikes, tire interaction with the pavement is sufficient to prevent capsize from becoming unstable at high speeds. | Bike physics | 0.834815 |
2,332 | This AVI movie shows weave. For most bikes, depending on geometry and mass distribution, weave is unstable at low speeds, and becomes less pronounced as speed increases until it is no longer unstable. While the amplitude may decrease, the frequency actually increases with speed. | Bike physics | 0.834815 |
2,333 | J. Phys. 42, 701–702 The Physics of Everyday Phenomena, W. T. Griffith, McGraw–Hill, New York, 1998, pp. 149–150. The Way Things Work., Macaulay, Houghton-Mifflin, New York, NY, 1989 | Bike physics | 0.834815 |
2,334 | Of the two, lateral dynamics has proven to be the more complicated, requiring three-dimensional, multibody dynamic analysis with at least two generalized coordinates to analyze. At a minimum, two coupled, second-order differential equations are required to capture the principal motions. Exact solutions are not possible, and numerical methods must be used instead. Competing theories of how bikes balance can still be found in print and online. On the other hand, as shown in later sections, much longitudinal dynamic analysis can be accomplished simply with planar kinetics and just one coordinate. | Bike physics | 0.834815 |
2,335 | Conversely, distinct elements of the kernel violate injectivity directly: if there would exist an element g ≠ e G ∈ ker f {\displaystyle g\neq e_{G}\in \ker f} , then f ( g ) = f ( e G ) = e H {\displaystyle f(g)=f(e_{G})=e_{H}} , thus f would not be injective. ker f is a subgroup of G and further it is a normal subgroup. Thus, there is a corresponding quotient group G/(ker f). This is isomorphic to f(G), the image of G under f (which is a subgroup of H also), by the first isomorphism theorem for groups. In the special case of abelian groups, there is no deviation from the previous section. | Kernel of a homomorphism | 0.834805 |
2,336 | In 1992, 1996 and 2001, the following descriptions were used for each of the ratings. These ratings have been applied to "units of assessment", such as French or Chemistry, which often broadly equate to university departments. Various unofficial league tables have been created of university research capability by aggregating the results from units of assessment. Compiling league tables of universities based on the RAE is problematic, as volume and quality are both significant factors. | Research Assessment Exercise | 0.834746 |
2,337 | It was announced in the 2006 Budget that after the 2008 exercise a system of metrics would be developed in order to inform future allocations of QR funding. Following initial consultation with the higher education sector, it is thought that the Higher Education Funding Councils will introduce a metrics based system of assessment for subjects in science, technology, engineering and medicine. A process of peer review is likely to remain for mathematics, statistics, arts, humanities and social studies subjects. HEFCE has developed a new set of arrangements, known as the Research Excellence Framework (REF), which has been introduced as a follow on to the 2008 RAE. | Research Assessment Exercise | 0.834746 |
2,338 | The differential and integral formulations are mathematically equivalent; both are useful. The integral formulation relates fields within a region of space to fields on the boundary and can often be used to simplify and directly calculate fields from symmetric distributions of charges and currents. On the other hand, the differential equations are purely local and are a more natural starting point for calculating the fields in more complicated (less symmetric) situations, for example using finite element analysis. | Maxwell's laws | 0.834745 |
2,339 | In the electric and magnetic field formulation there are four equations that determine the fields for given charge and current distribution. A separate law of nature, the Lorentz force law, describes how, conversely, the electric and magnetic fields act on charged particles and currents. A version of this law was included in the original equations by Maxwell but, by convention, is included no longer. The vector calculus formalism below, the work of Oliver Heaviside, has become standard. | Maxwell's laws | 0.834745 |
2,340 | It is manifestly rotation invariant, and therefore mathematically much more transparent than Maxwell's original 20 equations in x,y,z components. The relativistic formulations are even more symmetric and manifestly Lorentz invariant. For the same equations expressed using tensor calculus or differential forms, see § Alternative formulations. | Maxwell's laws | 0.834745 |
2,341 | The only difference would be the order in which the topics are taught. Supporters of using integrated curricula in the United States believe that students will be able to see the connections between algebra and geometry better in an integrated curriculum. General mathematics is another term for a mathematics course organized around different branches of mathematics, with topics arranged according to the main objective of the course. | Integrated mathematics | 0.834744 |
2,342 | Precalculus is the exception to the rule, as it usually integrates algebra, trigonometry, and geometry topics. Statistics may be integrated into all the courses or presented as a separate course. New York State began using integrated math curricula in the 1980s, but recently returned to a traditional curriculum. | Integrated mathematics | 0.834744 |
2,343 | Integrated mathematics is the term used in the United States to describe the style of mathematics education which integrates many topics or strands of mathematics throughout each year of secondary school. Each math course in secondary school covers topics in algebra, geometry, trigonometry and functions. Nearly all countries throughout the world, except the United States, follow this type of curriculum.In the United States, topics are usually integrated throughout elementary school up to the seventh or sometimes eighth grade. Beginning with high school level courses, topics are usually separated so that one year a student focuses entirely on algebra (if it was not already taken in the eighth grade), the next year entirely on geometry, then another year of algebra (sometimes with trigonometry), and later an optional fourth year of precalculus or calculus. | Integrated mathematics | 0.834744 |
2,344 | In signal processing and electronics, the frequency response of a system is the quantitative measure of the magnitude and phase of the output as a function of input frequency. The frequency response is widely used in the design and analysis of systems, such as audio and control systems, where they simplify mathematical analysis by converting governing differential equations into algebraic equations. In an audio system, it may be used to minimize audible distortion by designing components (such as microphones, amplifiers and loudspeakers) so that the overall response is as flat (uniform) as possible across the system's bandwidth. In control systems, such as a vehicle's cruise control, it may be used to assess system stability, often through the use of Bode plots. | Frequency responses | 0.834719 |
2,345 | Advanced Placement (AP) Physics 1 is a year-long introductory physics course administered by the College Board as part of its Advanced Placement program. It is intended to proxy a one-semester algebra-based university course in mechanics. Along with AP Physics 2, the first AP Physics 1 exam was administered in 2015. In its first five years, AP Physics 1 covered forces and motion, conservation laws, waves, and electricity. As of 2021, AP Physics 1 includes mechanics topics only. | AP Physics 1 | 0.834718 |
2,346 | Multiple Choice and Free Response Sections of the AP® Physics 1 exam are also assessed on scientific practices. Below are tables representing the practices assessed and their weighting for both parts of the exam | AP Physics 1 | 0.834718 |
2,347 | The heavily computational AP Physics B course served for four decades as the College Board's algebra-based offering. As part of the College Board's redesign of science courses, AP Physics B was discontinued; therefore, AP Physics 1 and 2 were created with guidance from the National Research Council and the National Science Foundation. The course covers material of a first-semester university undergraduate physics course offered at American universities that use best practices of physics pedagogy. The first AP Physics 1 classes had begun in the 2014–2015 school year, with the first AP exams administered in May 2015. | AP Physics 1 | 0.834718 |
2,348 | AP Physics 1 is an algebra-based, introductory college-level physics course that includes mechanics topics such as motion, force, momentum, energy, harmonic motion, and rotation; The College Board published a curriculum framework that includes seven big ideas on which the AP Physics 1 and 2 courses are based, along with "enduring understandings" students are expected to acquire within each of the big ideas. :Questions for the exam are constructed with direct reference to items in the curriculum framework. Student understanding of each topic is tested with reference to multiple skills—that is, questions require students to use quantitative, semi-quantitative, qualitative, and experimental reasoning in each content area. | AP Physics 1 | 0.834718 |
2,349 | It is thus common to find local energy minimization methods combined with global energy optimization, to find the global energy minimum (and other low energy states). At finite temperature, the molecule spends most of its time in these low-lying states, which thus dominate the molecular properties. Global optimization can be accomplished using simulated annealing, the Metropolis algorithm and other Monte Carlo methods, or using different deterministic methods of discrete or continuous optimization. While the force field represents only the enthalpic component of free energy (and only this component is included during energy minimization), it is possible to include the entropic component through the use of additional methods, such as normal mode analysis. Molecular mechanics potential energy functions have been used to calculate binding constants, protein folding kinetics, protonation equilibria, active site coordinates, and to design binding sites. | Molecular mechanics | 0.834703 |
2,350 | Another application of molecular mechanics is energy minimization, whereby the force field is used as an optimization criterion. This method uses an appropriate algorithm (e.g. steepest descent) to find the molecular structure of a local energy minimum. These minima correspond to stable conformers of the molecule (in the chosen force field) and molecular motion can be modelled as vibrations around and interconversions between these stable conformers. | Molecular mechanics | 0.834703 |
2,351 | The system is divided into two regions - one of which is treated with quantum mechanics (QM) allowing breaking and formation of bonds and the rest of the protein is modeled using molecular mechanics (MM). MM alone does not allow the study of mechanisms of enzymes, which QM allows. QM also produces more exact energy calculation of the system although it is much more computationally expensive. | Molecular mechanics | 0.834703 |
2,352 | The dihedral or torsional terms typically have multiple minima and thus cannot be modeled as harmonic oscillators, though their specific functional form varies with the implementation. This class of terms may include improper dihedral terms, which function as correction factors for out-of-plane deviations (for example, they can be used to keep benzene rings planar, or correct geometry and chirality of tetrahedral atoms in a united-atom representation). The non-bonded terms are much more computationally costly to calculate in full, since a typical atom is bonded to only a few of its neighbors, but interacts with every other atom in the molecule. | Molecular mechanics | 0.834703 |
2,353 | The following functional abstraction, termed an interatomic potential function or force field in chemistry, calculates the molecular system's potential energy (E) in a given conformation as a sum of individual energy terms. E = E covalent + E noncovalent {\displaystyle \ E=E_{\text{covalent}}+E_{\text{noncovalent}}\,} where the components of the covalent and noncovalent contributions are given by the following summations: E covalent = E bond + E angle + E dihedral {\displaystyle \ E_{\text{covalent}}=E_{\text{bond}}+E_{\text{angle}}+E_{\text{dihedral}}} E noncovalent = E electrostatic + E van der Waals {\displaystyle \ E_{\text{noncovalent}}=E_{\text{electrostatic}}+E_{\text{van der Waals}}} The exact functional form of the potential function, or force field, depends on the particular simulation program being used. Generally the bond and angle terms are modeled as harmonic potentials centered around equilibrium bond-length values derived from experiment or theoretical calculations of electronic structure performed with software which does ab-initio type calculations such as Gaussian. For accurate reproduction of vibrational spectra, the Morse potential can be used instead, at computational cost. | Molecular mechanics | 0.834703 |
2,354 | ISBN 978-0-387-95404-2. Krishnan Namboori; Ramachandran, K. S.; Deepa Gopakumar (2008). Computational Chemistry and Molecular Modeling: Principles and Applications. Berlin: Springer. ISBN 978-3-540-77302-3. | Molecular mechanics | 0.834703 |
2,355 | Becker OM (2001). Computational biochemistry and biophysics. New York, N.Y. | Molecular mechanics | 0.834703 |
2,356 | In molecular mechanics, several ways exist to define the environment surrounding a molecule or molecules of interest. A system can be simulated in vacuum (termed a gas-phase simulation) with no surrounding environment, but this is usually undesirable because it introduces artifacts in the molecular geometry, especially in charged molecules. Surface charges that would ordinarily interact with solvent molecules instead interact with each other, producing molecular conformations that are unlikely to be present in any other environment. | Molecular mechanics | 0.834703 |
2,357 | In mathematics, a polynomial Diophantine equation is an indeterminate polynomial equation for which one seeks solutions restricted to be polynomials in the indeterminate. A Diophantine equation, in general, is one where the solutions are restricted to some algebraic system, typically integers. (In another usage ) Diophantine refers to the Hellenistic mathematician of the 3rd century, Diophantus of Alexandria, who made initial studies of integer Diophantine equations. An important type of polynomial Diophantine equations takes the form: s a + t b = c {\displaystyle sa+tb=c} where a, b, and c are known polynomials, and we wish to solve for s and t. A simple example (and a solution) is: s ( x 2 + 1 ) + t ( x 3 + 1 ) = 2 x {\displaystyle s(x^{2}+1)+t(x^{3}+1)=2x} s = − x 3 − x 2 + x {\displaystyle s=-x^{3}-x^{2}+x} t = x 2 + x . | Polynomial Diophantine equation | 0.834673 |
2,358 | Since NUMA largely influences memory access performance, certain software optimizations are needed to allow scheduling threads and processes close to their in-memory data. Microsoft Windows 7 and Windows Server 2008 R2 added support for NUMA architecture over 64 logical cores. Java 7 added support for NUMA-aware memory allocator and garbage collector. Linux kernel: Version 2.5 provided a basic NUMA support, which was further improved in subsequent kernel releases. | Non-Uniform Memory Access | 0.834668 |
2,359 | However, a competing meeting forced ISMB to change venues at short notice. The conference was held at Stanford University in August 1994 and was organised by Russ Altman, a research scientist at Stanford University School of Medicine. To emphasise the international aspect of the conference, ISMB 1995 was held at Robinson College, Cambridge. ISMB 1995 also marked a shift in the focus of the conference. ISCB Board member and Director of the Spanish National Bioinformatics Institute Alfonso Valencia has stated that, in 1995, "the conference changed from a computer science-based conference to a point where everyone realized that, if you want to make progress, there has to be more focus in biology." | Intelligent Systems for Molecular Biology | 0.834662 |
2,360 | Having successfully applied for grants from AAAI, NIH and the Department of Energy Office of Health and Environmental Research, the first ISMB conference was held in July 1993, at the NLM. The conference was chaired by Hunter, David Searls (research associate professor at University of Pennsylvania School of Medicine) and Jude Shavlik (assistant professor of computer science at University of Wisconsin–Madison) and attracted over 200 attendees from 13 countries, submitting 69 scientific papers.The success of the first conference prompted the announcement of a second ISMB conference at the end of the meeting. ISMB 1994 was initially planned to be held in Seattle. | Intelligent Systems for Molecular Biology | 0.834662 |
2,361 | The origins of the ISMB conference lie in a workshop for artificial intelligence researchers with an interest in molecular biology held in November 1991. The workshop was organised by American researcher Lawrence Hunter, then director of the Machine Learning Project at the United States National Institutes of Health's National Library of Medicine (NLM) in Bethesda, Maryland. A subsequent workshop on the same topic held in 1992, hosted by the NLM and the National Science Foundation, made it clear that a regular international conference for the field was required. Such a conference would be dedicated to molecular biology as a rapidly emerging application of artificial intelligence. | Intelligent Systems for Molecular Biology | 0.834662 |
2,362 | Keynote talks are presented in a single track and generally attract the largest audience. These presentations are chosen to highlight outstanding research in the field of bioinformatics. Notable ISMB keynote speakers have included eight Nobel laureates: Richard J. Roberts (keynote speaker in 1994, 2006), John Sulston (1995), Manfred Eigen (1999), Gerald Edelman (2000), Sydney Brenner (2003), Kurt Wüthrich (2006), Robert Huber (2006) and Michael Levitt (2015).As of 2012, ISMB runs on a budget in excess of $1.5M and, in terms of proceeds, brings in four times that of the other ISCB conferences (ISCB-Latin America, ISCB-Africa, ISCB-Asia, Rocky Mountain Bioinformatics Conference, CSHALS and the Great Lakes Bioinformatics Conference) combined. Standard registration fees (as of 2013) are around $1,000 for academics who are ISCB members ($1,350 for non-members), with lower rates for students and higher rates for corporate delegates respectively. Discounts are provided for early registration. | Intelligent Systems for Molecular Biology | 0.834662 |
2,363 | Since ISMB 2001, proceedings have been published in the journal Bioinformatics. The number of posters presented at ISMB has also increased dramatically. 25 posters were presented at ISMB 1994; at recent ISMB meetings, 500-1,000 posters have been presented in multiple poster sessions. | Intelligent Systems for Molecular Biology | 0.834662 |
2,364 | The introduction of parallel tracks to ISMB was controversial. Christopher Rawlings (head of Computational and Systems Biology at Rothamsted Research and organiser of ISMB 1995) has said: "There were a lot of people who wanted to keep it more strongly in the AI intelligent systems model and have a meeting where everybody would go to everything. But it just grew too big. | Intelligent Systems for Molecular Biology | 0.834662 |
2,365 | In 2004, ISMB was jointly held with the European Conference on Computational Biology for the first time. The conference was also co-located with the Genes, Proteins and Computers conference. This meeting, held in Glasgow, UK, was the largest bioinformatics conference ever held, attended by 2,136 delegates, submitting 496 scientific papers. Alfonso Valencia considers ISMB/ECCB 2004 to be an important milestone in the history of ISMB: "it was the first one where the balance between Europe and the States became an important part of the conference. It was here that we established the rules and the ways and the spirit of collaboration between the Americans and the Europeans." The success of the joint conference paved the way for future European ISMB meetings to be held jointly with ECCB. | Intelligent Systems for Molecular Biology | 0.834662 |
2,366 | ISMB 1997 was held in Halkidiki, Greece and marked the foundation of the International Society for Computational Biology (ISCB). ISCB was formed with a focus on managing all scientific, organizational and financial aspects of the ISMB conference and to provide a forum for scientists to address the emerging role of computers in the biological sciences. ISCB has assisted in organising the ISMB conference series since 1998. The period following the formation of ISCB also marked an expansion in the number of ISMB attendees: ISMB 2000 (held at the University of California, San Diego) was attended by over 1,000 delegates, submitting 141 scientific papers. This meeting was also the last time ISMB would be held at a university, due to size limitations. | Intelligent Systems for Molecular Biology | 0.834662 |
2,367 | Although the return to Vienna was only deemed partially successful due to price increases, Boston (which hosted ISMB 2010 and 2014) is predicted to become a 'safe' site which ISMB can periodically return to.ISMB celebrated its 20th meeting with ISMB 2012, held in Long Beach, California. This event attracted around 1,600 delegates, submitting 268 scientific papers. Richard H. Lathrop and Lawrence Hunter presented a special keynote presentation, looking back at previous ISMB meetings and attempting to predict where the field of bioinformatics may head in the future. ISMB/ECCB 2013 was held in Berlin, Germany and was attended by around 2,000 delegates, submitting 233 scientific papers. | Intelligent Systems for Molecular Biology | 0.834662 |
2,368 | Most presentations are given in multiple parallel tracks; however, keynote talks are presented in a single track and are chosen to reflect outstanding research in bioinformatics. Notable ISMB keynote speakers have included eight Nobel laureates. The recipients of the ISCB Overton Prize and ISCB Accomplishment by a Senior Scientist Award are invited to give keynote talks as part of the programme. The proceedings of the conference are currently published by the journal Bioinformatics. | Intelligent Systems for Molecular Biology | 0.834662 |
2,369 | Since 2004, European meetings have been held jointly with the European Conference on Computational Biology (ECCB). The main ISMB conference is usually held over three days and consists of presentations, poster sessions and keynote talks. | Intelligent Systems for Molecular Biology | 0.834662 |
2,370 | Intelligent Systems for Molecular Biology (ISMB) is an annual academic conference on the subjects of bioinformatics and computational biology organised by the International Society for Computational Biology (ISCB). The principal focus of the conference is on the development and application of advanced computational methods for biological problems. The conference has been held every year since 1993 and has grown to become one of the largest and most prestigious meetings in these fields, hosting over 2,000 delegates in 2004. From the first meeting, ISMB has been held in locations worldwide; since 2007, meetings have been located in Europe and North America in alternating years. | Intelligent Systems for Molecular Biology | 0.834662 |
2,371 | Notable SIG meetings include the Bioinformatics Open Source Conference (BOSC), which has been held annually since 2000 and Bio-Ontologies, which has been held annually since 1998. Satellite meetings are usually two days long and are held in conjunction with ISMB. The 12th CAMDA conference and the 9th 3DSIG meeting were held as satellite meetings of ISMB/ECCB 2013. | Intelligent Systems for Molecular Biology | 0.834662 |
2,372 | Pre-conference tutorials have played an important role in ISMB since the first conference. Tutorials at ISMB 1994 included introductions to genetic algorithms, neural networks, AI for molecular biologists and molecular biology for computer scientists. Tutorials on computational mass spectrometry-based proteomics and ENCODE data access were presented at ISMB/ECCB 2013.As attendance at ISMB grew in the late 1990s, several satellite meetings and special interest group (SIG) meetings formed alongside the main conference. SIG meetings are held over one or two days before the main conference and focus on a specific topic, allowing more detailed discussion than there would be time for in the main conference. | Intelligent Systems for Molecular Biology | 0.834662 |
2,373 | The insights gained from these breakthrough studies led Pace to propose the idea of cloning DNA directly from environmental samples as early as 1985. This led to the first report of isolating and cloning bulk DNA from an environmental sample, published by Pace and colleagues in 1991 while Pace was in the Department of Biology at Indiana University. Considerable efforts ensured that these were not PCR false positives and supported the existence of a complex community of unexplored species. | Metagenomics | 0.834643 |
2,374 | The sequence-driven approach to screening is limited by the breadth and accuracy of gene functions present in public sequence databases. In practice, experiments make use of a combination of both functional and sequence-based approaches based upon the function of interest, the complexity of the sample to be screened, and other factors. An example of success using metagenomics as a biotechnology for drug discovery is illustrated with the malacidin antibiotics. | Metagenomics | 0.834643 |
2,375 | Microbial communities play a key role in preserving human health, but their composition and the mechanism by which they do so remains mysterious. Metagenomic sequencing is being used to characterize the microbial communities from 15–18 body sites from at least 250 individuals. This is part of the Human Microbiome initiative with primary goals to determine if there is a core human microbiome, to understand the changes in the human microbiome that can be correlated with human health, and to develop new technological and bioinformatics tools to support these goals.Another medical study as part of the MetaHit (Metagenomics of the Human Intestinal Tract) project consisted of 124 individuals from Denmark and Spain consisting of healthy, overweight, and irritable bowel disease patients. The study attempted to categorize the depth and phylogenetic diversity of gastrointestinal bacteria. | Metagenomics | 0.834643 |
2,376 | Isotope separation is the process of concentrating specific isotopes of a chemical element by removing other isotopes. The use of the nuclides produced is varied. The largest variety is used in research (e.g. in chemistry where atoms of "marker" nuclide are used to figure out reaction mechanisms). By tonnage, separating natural uranium into enriched uranium and depleted uranium is the largest application. | Electromagnetic separation | 0.834626 |
2,377 | scikit-learn, an open source machine learning library for Python Orange, a free data mining software suite, module Orange.ensemble Weka is a machine learning set of tools that offers variate implementations of boosting algorithms like AdaBoost and LogitBoost R package GBM (Generalized Boosted Regression Models) implements extensions to Freund and Schapire's AdaBoost algorithm and Friedman's gradient boosting machine. jboost; AdaBoost, LogitBoost, RobustBoost, Boostexter and alternating decision trees R package adabag: Applies Multiclass AdaBoost.M1, AdaBoost-SAMME and Bagging R package xgboost: An implementation of gradient boosting for linear and tree-based models. | Boosting (meta-algorithm) | 0.834605 |
2,378 | AdaBoost is very popular and the most significant historically as it was the first algorithm that could adapt to the weak learners. It is often the basis of introductory coverage of boosting in university machine learning courses. There are many more recent algorithms such as LPBoost, TotalBoost, BrownBoost, xgboost, MadaBoost, LogitBoost, and others. Many boosting algorithms fit into the AnyBoost framework, which shows that boosting performs gradient descent in a function space using a convex cost function. | Boosting (meta-algorithm) | 0.834605 |
2,379 | The recognition of object categories in images is a challenging problem in computer vision, especially when the number of categories is large. This is due to high intra class variability and the need for generalization across variations of objects within the same category. Objects within one category may look quite different. Even the same object may appear unalike under different viewpoint, scale, and illumination. | Boosting (meta-algorithm) | 0.834605 |
2,380 | Robert Schapire's affirmative answer in a 1990 paper to the question of Kearns and Valiant has had significant ramifications in machine learning and statistics, most notably leading to the development of boosting.When first introduced, the hypothesis boosting problem simply referred to the process of turning a weak learner into a strong learner. "Informally, problem asks whether an efficient learning algorithm that outputs a hypothesis whose performance is only slightly better than random guessing implies the existence of an efficient algorithm that outputs a hypothesis of arbitrary accuracy ." Algorithms that achieve hypothesis boosting quickly became simply known as "boosting". Freund and Schapire's arcing (Adaptive Resampling and Combining), as a general technique, is more or less synonymous with boosting. | Boosting (meta-algorithm) | 0.834605 |
2,381 | In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones. Boosting is based on the question posed by Kearns and Valiant (1988, 1989): "Can a set of weak learners create a single strong learner?" A weak learner is defined to be a classifier that is only slightly correlated with the true classification (it can label examples better than random guessing). In contrast, a strong learner is a classifier that is arbitrarily well-correlated with the true classification. | Boosting (meta-algorithm) | 0.834605 |
2,382 | Boosting algorithms can be based on convex or non-convex optimization algorithms. Convex algorithms, such as AdaBoost and LogitBoost, can be "defeated" by random noise such that they can't learn basic and learnable combinations of weak hypotheses. This limitation was pointed out by Long & Servedio in 2008. However, by 2009, multiple authors demonstrated that boosting algorithms based on non-convex optimization, such as BrownBoost, can learn from noisy datasets and can specifically learn the underlying classifier of the Long–Servedio dataset. | Boosting (meta-algorithm) | 0.834605 |
2,383 | Object categorization is a typical task of computer vision that involves determining whether or not an image contains some specific category of object. The idea is closely related with recognition, identification, and detection. Appearance based object categorization typically contains feature extraction, learning a classifier, and applying the classifier to new examples. There are many ways to represent a category of objects, e.g. from shape analysis, bag of words models, or local descriptors such as SIFT, etc. Examples of supervised classifiers are Naive Bayes classifiers, support vector machines, mixtures of Gaussians, and neural networks. However, research has shown that object categories and their locations in images can be discovered in an unsupervised manner as well. | Boosting (meta-algorithm) | 0.834605 |
2,384 | The Collegiate Aerial Robotics Demonstration (CARD) is a robotics competition for college and university students inspired by FIRST. The inaugural event was held at the 2011 FIRST Championship in St. Louis, Missouri. | Collegiate Aerial Robotics Demonstration | 0.834604 |
2,385 | Trained models derived from biased or non-evaluated data can result in skewed or undesired predictions. Bias models may result in detrimental outcomes thereby furthering the negative impacts on society or objectives. Algorithmic bias is a potential result of data not being fully prepared for training. Machine learning ethics is becoming a field of study and notably be integrated within machine learning engineering teams. | Applications of machine learning | 0.834588 |
2,386 | Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973. In 1981 a report was given on using teaching strategies so that a neural network learns to recognize 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal.Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E." This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. | Applications of machine learning | 0.834588 |
2,387 | The term machine learning was coined in 1959 by Arthur Samuel, an IBM employee and pioneer in the field of computer gaming and artificial intelligence. The synonym self-teaching computers was also used in this time period.By the early 1960s an experimental "learning machine" with punched tape memory, called Cybertron, had been developed by Raytheon Company to analyze sonar signals, electrocardiograms, and speech patterns using rudimentary reinforcement learning. It was repetitively "trained" by a human operator/teacher to recognize patterns and equipped with a "goof" button to cause it to re-evaluate incorrect decisions. A representative book on research into machine learning during the 1960s was Nilsson's book on Learning Machines, dealing mostly with machine learning for pattern classification. | Applications of machine learning | 0.834588 |
2,388 | Embedded Machine Learning is a sub-field of machine learning, where the machine learning model is run on embedded systems with limited computing resources such as wearable computers, edge devices and microcontrollers. Running machine learning model in embedded devices removes the need for transferring and storing data on cloud servers for further processing, henceforth, reducing data breaches and privacy leaks happening because of transferring data, and also minimizes theft of intellectual properties, personal data and business secrets. Embedded Machine Learning could be applied through several techniques including hardware acceleration, using approximate computing, optimization of machine learning models and many more. | Applications of machine learning | 0.834588 |
2,389 | The self-learning algorithm updates a memory matrix W =||w(a,s)|| such that in each iteration executes the following machine learning routine: in situation s perform action a receive consequence situation s' compute emotion of being in consequence situation v(s') update crossbar memory w'(a,s) = w(a,s) + v(s')It is a system with only one input, situation, and only one output, action (or behavior) a. There is neither a separate reinforcement input nor an advice input from the environment. The backpropagated value (secondary reinforcement) is the emotion toward the consequence situation. The CAA exists in two environments, one is the behavioral environment where it behaves, and the other is the genetic environment, wherefrom it initially and only once receives initial emotions about situations to be encountered in the behavioral environment. After receiving the genome (species) vector from the genetic environment, the CAA learns a goal-seeking behavior, in an environment that contains both desirable and undesirable situations. | Applications of machine learning | 0.834588 |
2,390 | Performing machine learning can involve creating a model, which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems. | Applications of machine learning | 0.834588 |
2,391 | Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data. Machine learning also has intimate ties to optimization: many learning problems are formulated as minimization of some loss function on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples). | Applications of machine learning | 0.834588 |
2,392 | Some systems are so brittle that changing a single adversarial pixel predictably induces misclassification. Machine learning models are often vulnerable to manipulation and/or evasion via adversarial machine learning.Researchers have demonstrated how backdoors can be placed undetectably into classifying (e.g., for categories "spam" and well-visible "not spam" of posts) machine learning models which are often developed and/or trained by third parties. Parties can change the classification of any input, including in cases for which a type of data/software transparency is provided, possibly including white-box access. | Applications of machine learning | 0.834588 |
2,393 | Typically, machine learning models require a high quantity of reliable data in order for the models to perform accurate predictions. When training a machine learning model, machine learning engineers need to target and collect a large and representative sample of data. Data from the training set can be as varied as a corpus of text, a collection of images, sensor data, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. | Applications of machine learning | 0.834588 |
2,394 | The theory of belief functions, also referred to as evidence theory or Dempster–Shafer theory, is a general framework for reasoning with uncertainty, with understood connections to other frameworks such as probability, possibility and imprecise probability theories. These theoretical frameworks can be thought of as a kind of learner and have some analogous properties of how evidence is combined (e.g., Dempster's rule of combination), just like how in a pmf-based Bayesian approach would combine probabilities. However, there are many caveats to these beliefs functions when compared to Bayesian approaches in order to incorporate ignorance and Uncertainty quantification. These belief function approaches that are implemented within the machine learning domain typically leverage a fusion approach of various ensemble methods to better handle the learner's decision boundary, low samples, and ambiguous class issues that standard machine learning approach tend to have difficulty resolving. However, the computational complexity of these algorithms are dependent on the number of propositions (classes), and can lead a much higher computation time when compared to other machine learning approaches. | Applications of machine learning | 0.834588 |
2,395 | ".Modern-day machine learning has two objectives, one is to classify data based on models which have been developed, the other purpose is to make predictions for future outcomes based on these models. A hypothetical algorithm specific to classifying data may use computer vision of moles coupled with supervised learning in order to train it to classify the cancerous moles. A machine learning algorithm for stock trading may inform the trader of future potential predictions. | Applications of machine learning | 0.834588 |
2,396 | Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results. Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision. Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.Machine learning has been used as a strategy to update the evidence related to a systematic review and increased reviewer burden related to the growth of biomedical literature. While it has improved with training sets, it has not yet developed sufficiently to reduce the workload burden without limiting the necessary sensitivity for the findings research themselves. | Applications of machine learning | 0.834588 |
2,397 | Self-learning, as a machine learning paradigm was introduced in 1982 along with a neural network capable of self-learning, named crossbar adaptive array (CAA). It is learning with no external rewards and no external teacher advice. The CAA self-learning algorithm computes, in a crossbar fashion, both decisions about actions and emotions (feelings) about consequence situations. The system is driven by the interaction between cognition and emotion. | Applications of machine learning | 0.834588 |
2,398 | This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is potential for machine learning in health care to provide professionals an additional tool to diagnose, medicate, and plan recovery paths for patients, but this requires these biases to be mitigated. | Applications of machine learning | 0.834588 |
2,399 | Using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants by similarity to previous successful applicants. Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. AI can be well-equipped to make decisions in technical fields, which rely heavily on data and historical information. | Applications of machine learning | 0.834588 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.