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pro vyhledávání: '"ABDELHAK, M."'
We consider a multiple hypothesis testing problem in a sensor network over the joint spatial-time domain. The sensor network is modeled as a graph, with each vertex representing a sensor and a signal over time associated with each vertex. We assume a
Externí odkaz:
http://arxiv.org/abs/2408.03142
The block diagonal structure of an affinity matrix is a commonly desired property in cluster analysis because it represents clusters of feature vectors by non-zero coefficients that are concentrated in blocks. However, recovering a block diagonal aff
Externí odkaz:
http://arxiv.org/abs/2312.01137
In recent years, Graph neural networks (GNNs) have emerged as a prominent tool for classification tasks in machine learning. However, their application in regression tasks remains underexplored. To tap the potential of GNNs in regression, this paper
Externí odkaz:
http://arxiv.org/abs/2311.16856
Bitcoin as a cryptocurrency has been one of the most important digital coins and the first decentralized digital currency. Deep neural networks, on the other hand, has shown promising results recently; however, we require huge amount of high-quality
Externí odkaz:
http://arxiv.org/abs/2311.06280
To alleviate the bias generated by the l1-norm in the low-rank tensor completion problem, nonconvex surrogates/regularizers have been suggested to replace the tensor nuclear norm, although both can achieve sparsity. However, the thresholding function
Externí odkaz:
http://arxiv.org/abs/2310.06233
This paper presents a novel loss function referred to as hybrid ordinary-Welsch (HOW) and a new sparsity-inducing regularizer associated with HOW. We theoretically show that the regularizer is quasiconvex and that the corresponding Moreau envelope is
Externí odkaz:
http://arxiv.org/abs/2310.04762
Autor:
van Rijnbach, Milou, Berlea, Dumitru Vlad, Dao, Valerio, Gaži, Martin, Allport, Phil, Tortajada, Ignacio Asensi, Behera, Prafulla, Bortoletto, Daniela, Buttar, Craig, Dachs, Florian, Dash, Ganapati, Dobrijević, Dominik, Fasselt, Lucian, de Acedo, Leyre Flores Sanz, Gabrielli, Andrea, González, Vicente, Gustavino, Giuliano, Jana, Pranati, Pernegger, Heinz, Riedler, Petra, Sandaker, Heidi, Sánchez, Carlos Solans, Snoeys, Walter, Suligoj, Tomislav, Núñez, Marcos Vázquez, Vijay, Anusree, Weick, Julian, Worm, Steven, Zoubir, Abdelhak M.
MALTA2 is the latest full-scale prototype of the MALTA family of Depleted Monolithic Active Pixel Sensors (DMAPS) produced in Tower Semiconductor 180 nm CMOS technology. In order to comply with the requirements of High Energy Physics (HEP) experiment
Externí odkaz:
http://arxiv.org/abs/2308.13231
The identification of the dependent components in multiple data sets is a fundamental problem in many practical applications. The challenge in these applications is that often the data sets are high-dimensional with few observations or available samp
Externí odkaz:
http://arxiv.org/abs/2305.19121