Search results for "Tensor"
showing 10 items of 550 documents
Tensor tympani muscle: strange chewing muscle
2007
This work seeks to alert medical and odontological staff to understanding and using interdisciplinary handling for detecting different pathologies’ common otic symptoms. It offers better tools for this shared symptomatology during therapy’s conservative phase. Tensor tympani muscle physiology and function in the middle ear have been veiled, even when their dysfunction and anatomical relationships may explain a group of confused otic symptoms during conventional clinical evaluation. Middle ear muscles share a common embryological and functional origin with chewing and facial muscles. This article emphasizes that these muscles share a functional neurological and anatomical dimension with the …
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 …
Higher-order Nonnegative CANDECOMP/PARAFAC Tensor Decomposition Using Proximal Algorithm
2019
Tensor decomposition is a powerful tool for analyzing multiway data. Nowadays, with the fast development of multisensor technology, more and more data appear in higherorder (order > 4) and nonnegative form. However, the decomposition of higher-order nonnegative tensor suffers from poor convergence and low speed. In this study, we propose a new nonnegative CANDECOM/PARAFAC (NCP) model using proximal algorithm. The block principal pivoting method in alternating nonnegative least squares (ANLS) framework is employed to minimize the objective function. Our method can guarantee the convergence and accelerate the computation. The results of experiments on both synthetic and real data demonstrate …
Deriving electrophysiological brain network connectivity via tensor component analysis during freely listening to music
2020
Recent studies show that the dynamics of electrophysiological functional connectivity is attracting more and more interest since it is considered as a better representation of functional brain networks than static network analysis. It is believed that the dynamic electrophysiological brain networks with specific frequency modes, transiently form and dissolve to support ongoing cognitive function during continuous task performance. Here, we propose a novel method based on tensor component analysis (TCA), to characterize the spatial, temporal, and spectral signatures of dynamic electrophysiological brain networks in electroencephalography (EEG) data recorded during free music-listening. A thr…
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…
Multi-domain Features of the Non-phase-locked Component of Interest Extracted from ERP Data by Tensor Decomposition
2020
The waveform in the time domain, spectrum in the frequency domain, and topography in the space domain of component(s) of interest are the fundamental indices in neuroscience research. Despite the application of time–frequency analysis (TFA) to extract the temporal and spectral characteristics of non-phase-locked component (NPLC) of interest simultaneously, the statistical results are not always expectedly satisfying, in that the spatial information is not considered. Complex Morlet wavelet transform is widely applied to TFA of event-related-potential (ERP) data, and mother wavelet (which should be firstly defined by center frequency and bandwidth (CFBW) before using the method to TFA of ERP…
Sparse nonnegative tensor decomposition using proximal algorithm and inexact block coordinate descent scheme
2021
Nonnegative tensor decomposition is a versatile tool for multiway data analysis, by which the extracted components are nonnegative and usually sparse. Nevertheless, the sparsity is only a side effect and cannot be explicitly controlled without additional regularization. In this paper, we investigated the nonnegative CANDECOMP/PARAFAC (NCP) decomposition with the sparse regularization item using l1-norm (sparse NCP). When high sparsity is imposed, the factor matrices will contain more zero components and will not be of full column rank. Thus, the sparse NCP is prone to rank deficiency, and the algorithms of sparse NCP may not converge. In this paper, we proposed a novel model of sparse NCP w…
Veselības apdrošināšanas atlīdzību pieteikumu attēlu informācijas apstrāde, izmantojot dziļos neironu tīklus
2018
Pieaugot pakalpojumu kvalitātes standartiem, uzņēmumi aizvien biežāk aplūko procesu uzlabošanas iespējas. Dziļo neironu tīklu risinājumu rīki paliek aizvien pieejamāki, bet cik vienkārši ir iekļaut šādas tehnoloģijas konkrēta procesa apstrāde? Lai atbildētu uz šo jautājumu, tika izvirzīts mērķis aplūkot konkrēta procesa automatizācijas iespējas, pieejamos atvērtā koda rīkus un identificēt iespējamos sarežģījumus. Darba ietvaros tika analizēts zaudējumu pieteikumu process un pētītas tā automatizācijas iespējas. Tika izveidotas vairākas datu kopas, aplūkota dokumentu lokalizēšana attēlos un izveidots risinājums, kas veiksmīgi lokalizē populārākos pieteikumu dokumentu veidus, kā arī tika anali…
Transportlīdzekļu apdrošināšanas atlīdzību prognozēšana izmantojot dziļos neironu tīklus
2019
Lai apdrošināšanas produkti darbotos veiksmīgi, viens no būtiskākajiem faktoriem ir apdrošināšana risku izvērtēšana. Viena no risku izvērtēšanas sastāvdaļām ir iespējamo apdrošināšanas atlīdzības apmēra prognozēšana, balstoties gan uz apdrošinātā objekta raksturlielumiem, gan klienta parametriem un vēsturi. Šajā darbā tika apskatītas dziļo neironu tīklu tehnoloģiju pielietojums apdrošināšanas atlīdzību summu novērtēšanā un balstoties uz transportlīdzekļu apdrošināšanas atlīdzību datiem, tika izveidots un apmācīts dziļo neironu tīkla modelis. Tika izveidots risinājums, ar kura palīdzību var prognozēt apdrošināšanas atlīdzību rezervju lielumu nākamajiem periodiem, izmantojot klienta un apdroš…
Sustaining Attention for a Prolonged Duration Affects Dynamic Organizations of Frequency-Specific Functional Connectivity
2020
Sustained attention encompasses a cascade of fundamental functions. The human ability to implement a sustained attention task is supported by brain networks that dynamically formed and dissolved through oscillatory synchronization. The decrement of vigilance induced by prolonged task engagement affects sustained attention. However, little is known about which stage or combinations are affected by vigilance decrement. Here, we applied an analysis framework composed of weighted phase lag index (wPLI) and tensor component analysis (TCA) to an EEG dataset collected during 80 min sustained attention task to examine the electrophysiological basis of such effect. We aimed to characterize the phase…