Search results for " Neural Networks."

showing 10 items of 374 documents

The tensor of interaction of a two-level system with an arbitrary strain field

2007

The interaction between two-level systems (TLS) and strain fields in a solid is contained in the diagonal matrix element of the interaction hamiltonian, $\delta$, which, in general, has the expression $\delta=2[\gamma]:[S]$, with the tensor $[\gamma]$ describing the TLS ``deformability'' and $[S]$ being the symmetric strain tensor. We construct $[\gamma]$ on very general grounds, by associating to the TLS two objects: a direction, $\hat\bt$, and a forth rank tensor of coupling constants, $[[R]]$. Based on the method of construction and on the invariance of the expression of $\delta$ with respect to the symmetry transformation of the solid, we conclude that $[[R]]$ has the same structure as …

Coupling constantPhysicsHistoryCondensed Matter - Materials SciencePhononIsotropyInfinitesimal strain theoryMaterials Science (cond-mat.mtrl-sci)FOS: Physical sciencesDisordered Systems and Neural Networks (cond-mat.dis-nn)Condensed Matter - Disordered Systems and Neural NetworksPolarization (waves)Computer Science ApplicationsEducationsymbols.namesakeQuantum mechanicsDiagonal matrixPerpendicularsymbolsHamiltonian (quantum mechanics)
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Energy landscape properties studied using symbolic sequences

2006

We investigate a classical lattice system with $N$ particles. The potential energy $V$ of the scalar displacements is chosen as a $\phi ^4$ on-site potential plus interactions. Its stationary points are solutions of a coupled set of nonlinear equations. Starting with Aubry's anti-continuum limit it is easy to establish a one-to-one correspondence between the stationary points of $V$ and symbolic sequences $\bm{\sigma} = (\sigma_1,...,\sigma_N)$ with $\sigma_n=+,0,-$. We prove that this correspondence remains valid for interactions with a coupling constant $\epsilon$ below a critical value $\epsilon_c$ and that it allows the use of a ''thermodynamic'' formalism to calculate statistical prope…

Coupling constantStatistical Mechanics (cond-mat.stat-mech)FOS: Physical sciencesEnergy landscapeStatistical and Nonlinear PhysicsGeometryDisordered Systems and Neural Networks (cond-mat.dis-nn)Condensed Matter - Disordered Systems and Neural NetworksCondensed Matter PhysicsPotential energyPower lawStationary pointSingularityGround stateCondensed Matter - Statistical MechanicsSaddleMathematical physicsMathematicsPhysica D: Nonlinear Phenomena
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Modified mode-coupling theory for the collective dynamics of simple liquids

2011

Recently it has been shown that mode-coupling theory, which accounts for the salient features of glassy relaxation near the liquid–glass transition, is also capable of describing the collective excitations of simple liquids away from the glass transition. In order to further improve the agreement between theory and computer simulations on Lennard-Jones argon we modify MCT by taking binary collisions into account. This, in fact, improves the agreement. We also show that multiplying the memory function of the original theory with a reduction factor leads to similar results.

CouplingChemistryFunction (mathematics)Condensed Matter PhysicsCondensed Matter::Disordered Systems and Neural NetworksCondensed Matter::Soft Condensed MatterReduction (complexity)Mode couplingQuasiparticleRelaxation (physics)General Materials ScienceStatistical physicsGlass transitionExcitationJournal of Physics: Condensed Matter
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Deep Learning Architectures for DNA Sequence Classification

2016

DNA sequence classification is a key task in a generic computational framework for biomedical data analysis, and in recent years several machine learning technique have been adopted to successful accomplish with this task. Anyway, the main difficulty behind the problem remains the feature selection process. Sequences do not have explicit features, and the commonly used representations introduce the main drawback of the high dimensionality. For sure, machine learning method devoted to supervised classification tasks are strongly dependent on the feature extraction step, and in order to build a good representation it is necessary to recognize and measure meaningful details of the items to cla…

DNA sequence classificatio Convolutional Neural Networks Recurrent Neural Networks Deep learning networksSettore INF/01 - Informatica
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Hierarchically nested factor model from multivariate data

2005

We show how to achieve a statistical description of the hierarchical structure of a multivariate data set. Specifically we show that the similarity matrix resulting from a hierarchical clustering procedure is the correlation matrix of a factor model, the hierarchically nested factor model. In this model, factors are mutually independent and hierarchically organized. Finally, we use a bootstrap based procedure to reduce the number of factors in the model with the aim of retaining only those factors significantly robust with respect to the statistical uncertainty due to the finite length of data records.

Data recordsStructure (mathematical logic)Multivariate statisticsCovariance matrixFinance commerce hierarchical structureGeneral Physics and AstronomySimilarity matrixFOS: Physical sciencesDisordered Systems and Neural Networks (cond-mat.dis-nn)Condensed Matter - Disordered Systems and Neural Networkscomputer.software_genreHierarchical clusteringCondensed Matter - Other Condensed MatterSet (abstract data type)Factor (programming language)Data miningcomputerMathematicscomputer.programming_languageOther Condensed Matter (cond-mat.other)
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Mašīnmācīšanās uzdevumu risināšanai interaktīvās tekstuālās vidēs

2021

Interaktīvas tekstuālas piedzīvojumu spēles var izmantot, lai pārbaudītu mašīnmācīšanās aģentu spējas tikt galā ar dažādiem izaicinājumiem, kas saistīti ar dabiskās valodas izpratni, problēmu risināšanu un atbilžu meklēšanu, vai tādas darbības izvēles stratēģiju apgūšana, kas vispārinās uz iepriekš nesastaptām vidēm. TextWorld platforma ir šādiem pētījumiem domāts ietvars un palīgrīki, ar kuru palīdzību var darbināt daudzas iepriekšpublicētas teksta piedzīvojumu spēles, vai arī definēt un ģenerēt jaunas spēles, dažādās sarežģītības pakāpēs un gandrīz bezgalīgās variācijās. Šajā darbā aprakstīta tāda algoritmiska orākula (oracle) ieviešana, kas var veiksmīgi atrisināt spēles no 3 dažādām iep…

Datorzinātneinteraktīvas tekstuālas piedzīvojumu spēlesMeta­learningmašīnmācīšanāsArtificial Neural NetworksText Adventure Games
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A sentence based system for measuring syntax complexity using a recurrent deep neural network

2018

In this paper we present a deep neural network model capable of inducing the rules that identify the syntax complexity of an Italian sentence. Our system, beyond the ability of choosing if a sentence needs of simplification, gives a score that represent the confidence of the model during the process of decision making which could be representative of the sentence complexity. Experiments have been carried out on one public corpus created specifically for the problem of text-simplification.

Deep Neural NetworksComputer Science (all)ComputingMethodologies_DOCUMENTANDTEXTPROCESSINGText simplificationDeep neural networkNatural Language Processing
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A recurrent deep neural network model to measure sentence complexity for the Italian Language

2019

Text simplification (TS) is a natural language processing task devoted to the modification of a text in such a way that the grammar and structure of the phrases is greatly simplified, preserving the underlying meaning and information contents. In this paper we give a contribution to the TS field presenting a deep neural network model able to detect the complexity of italian sentences. In particular, the system gives a score to an input text that identifies the confidence level during the decision making process and that could be interpreted as a measure of the sentence complexity. Experiments have been carried out on one public corpus of Italian texts created specifically for the task of TS…

Deep Neural NetworksText Simplification Natural Language Processing Deep Neural NetworksSettore INF/01 - InformaticaComputingMethodologies_DOCUMENTANDTEXTPROCESSINGAutomatic Text Complexity EvaluationNLP
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Mapping and holistic design of natural hydraulic lime mortars

2020

Supplementary data to this article can be found online at https://doi.org/10.1016/j.cemconres.2020.106167.

Design0211 other engineering and technologies02 engineering and technologyengineering.materialCompatibilityFlexural strengthEngenharia e Tecnologia::Engenharia CivilConsistency (statistics)021105 building & constructionGeneral Materials ScienceGeotechnical engineeringMathematicsScience & TechnologyAggregate (composite)Artificial neural networksMonument protectionHydraulic limeExperimental dataBuilding and Construction021001 nanoscience & nanotechnologyCompressive strengthCompatibility (mechanics):Engenharia Civil [Engenharia e Tecnologia]engineeringNatural hydraulic limeMortar0210 nano-technologyMortar characteristicsCement and Concrete Research
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Capabilities of Ultrametric Automata with One, Two, and Three States

2016

Ultrametric automata use p-adic numbers to describe the random branching of the process of computation. Previous research has shown that ultrametric automata can have a significant decrease in computing complexity. In this paper we consider the languages that can be recognized by one-way ultrametric automata with one, two, and three states. We also show an example of a promise problem that can be solved by ultrametric integral automaton with three states.

Discrete mathematicsBinary treeComputationPrime number020206 networking & telecommunications02 engineering and technologyNonlinear Sciences::Cellular Automata and Lattice GasesCondensed Matter::Disordered Systems and Neural NetworksAutomatonTuring machinesymbols.namesakeRegular language0202 electrical engineering electronic engineering information engineeringsymbolsMathematics::Metric Geometry020201 artificial intelligence & image processingPromise problemUltrametric spaceComputer Science::DatabasesComputer Science::Formal Languages and Automata TheoryMathematics
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