Výsledky vyhledávání - "model matrix"

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Weight-based Channel-model Matrix Framework provides a reasonable solution for EEG-based cross-dataset emotion recognition

Autor: Chen, Huayu, He, Huanhuan, Zhu, Jing, Sun, Shuting, Li, Jianxiu, Shao, Xuexiao, Li, Junxiang, Li, Xiaowei, Hu, Bin

Cross-dataset emotion recognition as an extremely challenging task in the field of EEG-based affective computing is influenced by many factors, which makes the universal models yield unsatisfactory results. Facing the situation that lacks EEG informa

Externí odkaz: http://arxiv.org/abs/2209.05849

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Enhancement of interfacial adhesion to carbon fibers via electrostatic interaction using polyvinyl chloride as a model matrix

Autor: Uematsu, Hideyuki, Doi, Miyuki, Yorikane, Shunya, Yamaguchi, Ayaka, Nishimura, Fumihiro, Sugihara, Shinji, Yamane, Masachika, Ozaki, Yukihiro, Tanoue, Shuichi

Publikováno v: In Surfaces and Interfaces November 2023 42 Part A

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On quenches to the critical point of the three states Potts model -- Matrix Product State simulations and CFT

Autor: Robertson, Niall F., Surace, Jacopo, Tagliacozzo, Luca

Conformal Field Theories (CFTs) have been used extensively to understand the physics of critical lattice models at equilibrium. However, the applicability of CFT calculations to the behavior of the lattice systems in the out-of-equilibrium setting is

Externí odkaz: http://arxiv.org/abs/2110.07078

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A Diagnostic for Bias in Linear Mixed Model Estimators Induced by Dependence Between the Random Effects and the Corresponding Model Matrix

Autor: Karl, Andrew T., Zimmerman, Dale L.

Publikováno v: Journal of Statistical Planning and Inference, 211, March 2021, Pages 107-118

We explore how violations of the often-overlooked standard assumption that the random effects model matrix in a linear mixed model is fixed (and thus independent of the random effects vector) can lead to bias in estimators of estimable functions of t

Externí odkaz: http://arxiv.org/abs/2003.08087

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A fast methodology for large-scale focusing inversion of gravity and magnetic data using the structured model matrix and the $2D$ fast Fourier transform

Autor: Renaut, Rosemary A., Hogue, Jarom D., Vatankhah, Saeed

Publikováno v: Geophysical Journal International, 23, 2, 1378-1397, 2020

Focusing inversion of potential field data for the recovery of sparse subsurface structures from surface measurement data on a uniform grid is discussed. For the uniform grid the model sensitivity matrices exhibit block Toeplitz Toeplitz block struct

Externí odkaz: http://arxiv.org/abs/2004.13904

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