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showing 10 items of 5782 documents

Nestriktās ekvivalences nestriktās klasterizācijas algoritmā

2022

Maģistra darba mērķis ir iepazīstināt lasītāju ar izstrādātu, ERAF projekta ietvaros, klasterizācijas metodi FERC, un parādīt tās priekšrocības salīdzinājumā ar nestrikto C-means. Projekta rezultāti tiks publicēti žurnālā Springer. FERC metodē, mēs ieviešam nestrīktās ekvivalences attiecības dažādiem objekta atribūtiem, un tad mēs agregējam šīs nestriktās ekvivalences attiecības vienā, lai noteiktu objekta piederības pakāpi kādam klasterim. Darbā tiek parādīts, ka klasterizācijas procesā, katram objekta atribūtam var uzdot tā nozīmīgumu, ieviešot atribūtu svarus. Līdz šim klasterizācijas procesā, atribūtu svari netika izmantoti. Kā arī darbā tiek parādīti ilustratīvi piemēri.

t-normaT - ekvivalencenestrīktās ekvivalences attiecībasMatemātikaFERCnestriktā klasteru analīze
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Reduced Order Models for Pricing American Options under Stochastic Volatility and Jump-diffusion Models

2016

American options can be priced by solving linear complementary problems (LCPs) with parabolic partial(-integro) differential operators under stochastic volatility and jump-diffusion models like Heston, Merton, and Bates models. These operators are discretized using finite difference methods leading to a so-called full order model (FOM). Here reduced order models (ROMs) are derived employing proper orthogonal decomposition (POD) and non negative matrix factorization (NNMF) in order to make pricing much faster within a given model parameter variation range. The numerical experiments demonstrate orders of magnitude faster pricing with ROMs. peerReviewed

ta113Mathematical optimizationStochastic volatilityDiscretizationComputer scienceJump diffusionFinite difference method010103 numerical & computational mathematics01 natural sciencesNon-negative matrix factorization010101 applied mathematicsValuation of optionslinear complementary problemRange (statistics)General Earth and Planetary SciencesApplied mathematicsreduced order modelFinite difference methods for option pricing0101 mathematicsAmerican optionoption pricingGeneral Environmental ScienceProcedia Computer Science
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Cluster-Based RF Fingerprint Positioning Using LTE and WLAN Outdoor Signals

2015

In this paper we evaluate user-equipment (UE) positioning performance of three cluster-based RF fingerprinting methods using LTE and WLAN signals. Real-life LTE and WLAN data were collected for the evaluation purpose using consumer cellular-mobile handset utilizing ‘Nemo Handy’ drive test software tool. Test results of cluster-based methods were compared to the conventional grid-based RF fingerprinting. The cluster-based methods do not require grid-cell layout and training signature formation as compared to the gridbased method. They utilize LTE cell-ID searching technique to reduce the search space for clustering operation. Thus UE position estimation is done in short time with less comput…

ta113PercentileK-nearest neighborComputer sciencebusiness.industrycell-IDFingerprint (computing)Real-time computingFingerprint recognitionGridHandsetlaw.inventionminimization of drive testsEuclidean distanceLTElawEmbedded systemgrid-based RF fingerprintingRadio frequencyCluster analysisbusinessfuzzy C-meanshierarchical clustering
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Process and product in non-narrative writing : a teaching material package for the upper secondary school

1997

teaching material packagecontext-based instructionteaching-learning cycleprocess-based instructionnon-narrative writingpeer evaluation
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Il design della Tecno-Natura. Nuovi scenari del design sostenibile nell'epoca delle nanotecnologie.

2011

tecno-naturadesign sostenibilenanotecnologie.Settore ICAR/13 - Disegno Industriale
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The Tucker tensor decomposition for data analysis: capabilities and advantages

2022

Tensors are powerful multi-dimensional mathematical objects, that easily embed various data models such as relational, graph, time series, etc. Furthermore, tensor decomposition operators are of great utility to reveal hidden patterns and complex relationships in data. In this article, we propose to study the analytical capabilities of the Tucker decomposition, as well as the differences brought by its major algorithms. We demonstrate these differences through practical examples on several datasets having a ground truth. It is a preliminary work to add the Tucker decomposition to the Tensor Data Model, a model aiming to make tensors data-centric, and to optimize operators in order to enable…

tensor decompositionTucker[INFO.INFO-NA] Computer Science [cs]/Numerical Analysis [cs.NA]data analysistensor
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CCDC 662715: Experimental Crystal Structure Determination

2008

Related Article: J.Bould, A.Laromaine, N.J.Bullen, C.Vinas, M.Thornton-Pett, R.Sillanpaa, R.Kivekas, J.D.Kennedy, F.Teixidor|2008|Dalton Trans.||1552|doi:10.1039/b715845a

tetra-n-butylammonium 7-(o-cyanopyridine-N)-89:1011-dimuH-nido-undecaborateSpace GroupCrystallographyCrystal SystemCrystal StructureCell ParametersExperimental 3D Coordinates
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CCDC 1958345: Experimental Crystal Structure Determination

2020

Related Article: Raphael C. A. Vaz, Isabela O. Esteves, Willian X. C. Oliveira, João Honorato, Felipe T. Martins, Lippy F. Marques, Guilherme L. dos Santos, Ricardo O. Freire, Larissa T. Jesus, Emerson F. Pedroso, Wallace C. Nunes, Miguel Julve, Cynthia L. M. Pereira|2020|Dalton Trans.|49|16106|doi:10.1039/D0DT02497J

tetra-n-butylammonium tetrakis((4-fluoroanilino)(oxo)acetato)-(dimethyl sulfoxide)-terbium(iii) dihydrateSpace GroupCrystallographyCrystal SystemCrystal StructureCell ParametersExperimental 3D Coordinates
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CCDC 911161: Experimental Crystal Structure Determination

2013

Related Article: Jesús Ferrando-Soria, María Castellano, Rafael Ruiz-García, Joan Cano, Miguel Julve, Francesc Lloret, Catalina Ruiz-Pérez, Jorge Pasán, Laura Cañadillas-Delgado, Donatella Armentano, Yves Journaux , Emilio Pardo|2013|Chem.-Eur.J.|19|12124|doi:10.1002/chem.201204484

tetrakis(Tetra-n-butylammonium) bis(mu2-NN'-p-phenylenebis(oxamato))-di-copper(ii) methanol solvateSpace GroupCrystallographyCrystal SystemCrystal StructureCell ParametersExperimental 3D Coordinates
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CCDC 1982787: Experimental Crystal Structure Determination

2020

Related Article: Souvik Maity, Tanmoy Kumar Ghosh, Carlos J. Gómez-García, Ashutosh Ghosh|2020|Cryst.Growth Des.|20|7300|doi:10.1021/acs.cgd.0c00957

tetrakis(mu-2-{[(3-aminopropyl)imino]methyl}-6-methoxyphenolato)-bis(mu-chloro)-tetrakis(mu-hydroxido)-diaqua-dichloro-di-terbium(iii)-tetra-nickel(ii) bis(chloride) dodecahydrateSpace GroupCrystallographyCrystal SystemCrystal StructureCell ParametersExperimental 3D Coordinates
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