Search results for "NEA"

showing 10 items of 16082 documents

Twister Tries

2015

Many commonly used data-mining techniques utilized across research fields perform poorly when used for large data sets. Sequential agglomerative hierarchical non-overlapping clustering is one technique for which the algorithms’ scaling properties prohibit clustering of a large amount of items. Besides the unfavorable time complexity of O(n 2 ), these algorithms have a space complexity of O(n 2 ), which can be reduced to O(n) if the time complexity is allowed to rise to O(n 2 log2 n). In this paper, we propose the use of locality-sensitive hashing combined with a novel data structure called twister tries to provide an approximate clustering for average linkage. Our approach requires only lin…

ta113Hierarchical agglomerative clusteringta112Fuzzy clusteringBrown clusteringComputer scienceSingle-linkage clusteringcomputer.software_genreHierarchical clusteringLocality-sensitive hashingData setCURE data clustering algorithmlocality-sensitive hashingaverage linkageData miningHierarchical clustering of networkslinear complexityCluster analysishierarchical clusteringAlgorithmcomputerTime complexityProceedings of the 2015 ACM SIGMOD International Conference on Management of Data
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Reduced Order Models for Pricing European and American Options under Stochastic Volatility and Jump-Diffusion Models

2017

Abstract European options can be priced by solving parabolic partial(-integro) differential equations under stochastic volatility and jump-diffusion models like the Heston, Merton, and Bates models. American option prices can be obtained by solving linear complementary problems (LCPs) with the same operators. A finite difference discretization leads to a so-called full order model (FOM). Reduced order models (ROMs) are derived employing proper orthogonal decomposition (POD). The early exercise constraint of American options is enforced by a penalty on subset of grid points. The presented numerical experiments demonstrate that pricing with ROMs can be orders of magnitude faster within a give…

ta113Mathematical optimizationGeneral Computer ScienceStochastic volatilityDifferential equationEuropean optionMonte Carlo methods for option pricingJump diffusion010103 numerical & computational mathematics01 natural sciencesTheoretical Computer Science010101 applied mathematicsValuation of optionsModeling and Simulationlinear complementary problemRange (statistics)Asian optionreduced order modelFinite difference methods for option pricing0101 mathematicsAmerican optionoption pricingMathematicsJournal of Computational Science
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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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Iterative Methods for Pricing American Options under the Bates Model

2013

We consider the numerical pricing of American options under the Bates model which adds log-normally distributed jumps for the asset value to the Heston stochastic volatility model. A linear complementarity problem (LCP) is formulated where partial derivatives are discretized using finite differences and the integral resulting from the jumps is evaluated using simple quadrature. A rapidly converging fixed point iteration is described for the LCP, where each iterate requires the solution of an LCP. These are easily solved using a projected algebraic multigrid (PAMG) method. The numerical experiments demonstrate the efficiency of the proposed approach. Furthermore, they show that the PAMG meth…

ta113Mathematical optimizationStochastic volatilityDiscretizationIterative methodComputer scienceFinite difference methodLinear complementarity problemIterative methodQuadrature (mathematics)Multigrid methodFixed-point iterationBates modelLinear complementarity problemGeneral Earth and Planetary SciencesPartial derivativeAmerican optionGeneral Environmental ScienceProcedia Computer Science
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Exploiting ongoing EEG with multilinear partial least squares during free-listening to music

2016

During real-world experiences, determining the stimulus-relevant brain activity is excitingly attractive and is very challenging, particularly in electroencephalography. Here, spectrograms of ongoing electroencephalogram (EEG) of one participant constructed a third-order tensor with three factors of time, frequency and space; and the stimulus data consisting of acoustical features derived from the naturalistic and continuous music formulated a matrix with two factors of time and the number of features. Thus, the multilinear partial least squares (PLS) conforming to the canonical polyadic (CP) model was performed on the tensor and the matrix for decomposing the ongoing EEG. Consequently, we …

ta113Multilinear mapmedicine.diagnostic_testBrain activity and meditationSpeech recognition02 engineering and technologyElectroencephalographyta3112Matrix decomposition03 medical and health sciences0302 clinical medicinetensor decompositionFrequency domainPartial least squares regression0202 electrical engineering electronic engineering information engineeringmedicineSpectrogramOngoing EEG020201 artificial intelligence & image processingmusicTime domain030217 neurology & neurosurgerymultilinear partial least squaresMathematics
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IMEX schemes for pricing options under jump–diffusion models

2014

We propose families of IMEX time discretization schemes for the partial integro-differential equation derived for the pricing of options under a jump-diffusion process. The schemes include the families of IMEX-midpoint, IMEX-CNAB and IMEX-BDF2 schemes. Each family is defined by a convex combination parameter [email protected]?[0,1], which divides the zeroth-order term due to the jumps between the implicit and explicit parts in the time discretization. These IMEX schemes lead to tridiagonal systems, which can be solved extremely efficiently. The schemes are studied through Fourier stability analysis and numerical experiments. It is found that, under suitable assumptions and time step restric…

ta113Numerical AnalysisMathematical optimizationTridiagonal matrixDiscretizationApplied MathematicsJump diffusionStability (probability)Term (time)Computational MathematicsValuation of optionsConvex combinationLinear multistep methodMathematicsApplied Numerical Mathematics
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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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Depth perception in tablet-based augmented reality at medium- and far-field distances

2013

Current augmented reality (AR) systems often fail to indicate the distance between the user and points of interest in the environment. Empirical evaluations of human depth perception in AR settings compared to real world settings are needed. Our goal in this study was to understand tablet-based AR depth perception by comparing it with real-world depth perception.

ta113Point of interestComputer sciencebusiness.industrybisectionNear and far fieldDistance perceptionhahmottaminenComputer visionAugmented realityArtificial intelligencebusinessDepth perceptionlisätty todellisuus
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Towards Computer-based Exams in CS1

2017

Even though IDEs are often a central tool when learning to program in CS1, many teachers still lean on paper-based exams. In this study, we examine the “test mode effect” in CS1 exams using the Rainfall problem. The test mode was two-phased. Half of the participants started working on the problem with pen and paper, while the other half had access to an IDE. After submitting their solution, all students could rework their solution on an IDE. The experiment was repeated twice during subsequent course instances. The results were mixed. From the marking perspective, there was no statistically significant difference resulting from the mode. However, the students starting with the paper-based pa…

ta113examinations (education)tietokoneavusteinen opetusMultimediaComputer scienceRainfall problemComputer basedtentitvasta-alkajatcomputer.software_genreprogrammingcomputer-assisted teachingbeginnersComputingMilieux_COMPUTERSANDEDUCATIONta516ohjelmointiCS1computer
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The challenges of analysing blood stains with hyperspectral imaging

2014

Hyperspectral imaging is a potential noninvasive technology for detecting, separating and identifying various substances. In the forensic and military medicine and other CBRNE related use it could be a potential method for analyzing blood and for scanning other human based fluids. For example, it would be valuable to easily detect whether some traces of blood are from one or more persons or if there are some irrelevant substances or anomalies in the blood. This article represents an experiment of separating four persons' blood stains on a white cotton fabric with a SWIR hyperspectral camera and FT-NIR spectrometer. Each tested sample includes standardized 75 _l of 100 % blood. The results s…

ta113ta222SpectrometerComputer sciencebusiness.industrySample (material)Blood StainsNear-infrared spectroscopyHyperspectral imagingPattern recognitionArtificial intelligencebusinessta116
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