Transmission in Multimode fiber with deep learning
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Driven by the need for more efficient and seamless integration of physical models and data, physics -informed neural networks (PINNs) have seen a surge of interest in recent years. However, ensuring the reliability of their convergence and accuracy remains ...
Lausanne2024
Fiber endoscopy plays an important role in the clinical diagnosis and treatment processes involved in modern medicine. Thin fiber probes can relay information from confined places in the human body that are inaccessible for conventional bulky microscopes. ...
Multimode optical fibers are the backbone of telecommunication and medical imaging. When light with high intensity travels through a multimode fiber, photons and matter start to interact and propa-gation becomes nonlinear. The nonlinear propagation of ligh ...
Jean-Louis Cohen was a historian and curator of architecture who inspired and impacted the work of many. His unexpected passing this summer was a loss to the entire expanded field of architecture. A pioneer of transnational and transmedial histories of arc ...
Extensive research has been conducted on fault diagnosis of planetary gearboxes using vibration signals and deep learning (DL) approaches. However, DL-based methods are susceptible to the domain shift problem caused by varying operating conditions of the g ...
Spatiotemporal nonlinear interactions in multimode fibers are of interest for beam shaping and frequency conversion by exploiting the nonlinear interaction of different pump modes from quasi-continuous wave to ultrashort pulses centered around visible to i ...
2020
Theoretical and computational approaches to the study of materials and molecules have, over the last few decades, progressed at an exponential rate. Yet, the possibility of producing numerical predictions that are on par with experimental measurements is t ...
EPFL2021
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Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks. In realistic learning scenarios, the presence of heterogeneity across different clients' local datasets poses an optimization ch ...
JMLR-JOURNAL MACHINE LEARNING RESEARCH2021
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We present an efficient and accurate people detection approach based on deep learning to detect people attacks and intrusion in video surveillance scenarios Unlike other approaches using background segmentation and pre-processing techniques, which are not ...
2020
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In this work we investigate stochastic non-convex optimization problems wherethe objective is an expectation over smooth loss functions, and the goal is to find an approximate stationary point. The most popular approach to handling such problems is varianc ...
