Zobrazeno 1 - 10
of 131 805
pro vyhledávání: '"Fink A"'
Publikováno v:
Nature and Science of Sleep, Vol Volume 14, Pp 1877-1886 (2022)
Irina Topchiy,1,2,* Anne M Fink,1,2 Katherine A Maki,2,3 Michael W Calik1,2,* 1Center for Sleep and Health Research, University of Illinois Chicago, Chicago, IL, USA; 2Department of Biobehavioral Nursing Science; University of Illinois Chicag
Externí odkaz:
https://doaj.org/article/0b207fe4de1f4855a444918865f4db6a
The task of open-set domain generalization (OSDG) involves recognizing novel classes within unseen domains, which becomes more challenging with multiple modalities as input. Existing works have only addressed unimodal OSDG within the meta-learning fr
Externí odkaz:
http://arxiv.org/abs/2407.01518
Autor:
Delory, Alexandre, Prada, Claire, Lanoy, Maxime, Eddi, Antonin, Fink, Mathias, Lemoult, Fabrice
The interaction between waves and evolving media challenges traditional conservation laws. We experimentally investigate the behavior of elastic wave packets crossing a moving interface that separates two media with distinct propagation properties, o
Externí odkaz:
http://arxiv.org/abs/2406.15100
Trajectory planning for autonomous driving is challenging because the unknown future motion of traffic participants must be accounted for, yielding large uncertainty. Stochastic Model Predictive Control (SMPC)-based planners provide non-conservative
Externí odkaz:
http://arxiv.org/abs/2406.13396
Damage to road pavement can develop into cracks, potholes, spallings, and other issues posing significant challenges to the integrity, safety, and durability of the road structure. Detecting and monitoring the evolution of these damages is crucial fo
Externí odkaz:
http://arxiv.org/abs/2406.18586
Fault detection is crucial in industrial systems to prevent failures and optimize performance by distinguishing abnormal from normal operating conditions. Data-driven methods have been gaining popularity for fault detection tasks as the amount of con
Externí odkaz:
http://arxiv.org/abs/2406.06607
Graph signal processing represents an important advancement in the field of data analysis, extending conventional signal processing methodologies to complex networks and thereby facilitating the exploration of informative patterns and structures acro
Externí odkaz:
http://arxiv.org/abs/2406.03898
Visual anomaly detection (AD) inherently faces significant challenges due to the scarcity of anomalous data. Although numerous works have been proposed to synthesize anomalous samples, the generated samples often lack authenticity or can only reflect
Externí odkaz:
http://arxiv.org/abs/2406.01078
Regression models often fail to generalize effectively in regions characterized by highly imbalanced label distributions. Previous methods for deep imbalanced regression rely on gradient-based weight updates, which tend to overfit in underrepresented
Externí odkaz:
http://arxiv.org/abs/2405.18202
Detecting out-of-distribution (OOD) samples is important for deploying machine learning models in safety-critical applications such as autonomous driving and robot-assisted surgery. Existing research has mainly focused on unimodal scenarios on image
Externí odkaz:
http://arxiv.org/abs/2405.17419