Zobrazeno 1 - 10
of 8 078
pro vyhledávání: '"A, Arguello"'
We perform a numerical analysis of the massive Schwinger model in the presence of a background electric field. Using the Density Matrix Renormalization Group (DMRG) approach, we efficiently compute the spectrum of the Schwinger model on a staggered l
Externí odkaz:
http://arxiv.org/abs/2412.01902
The global demand for unconventional energy sources such as geothermal energy and white hydrogen requires new exploration techniques for precise subsurface structure characterization and potential reservoir identification. Magnetotelluric (MT) invers
Externí odkaz:
http://arxiv.org/abs/2410.15274
Autor:
Sanchez, Karen, Hinojosa, Carlos, Mieles, Olinto, Zhao, Chen, Ghanem, Bernard, Arguello, Henry
Chronic wounds pose an ongoing health concern globally, largely due to the prevalence of conditions such as diabetes and leprosy's disease. The standard method of monitoring these wounds involves visual inspection by healthcare professionals, a pract
Externí odkaz:
http://arxiv.org/abs/2408.10827
Seismic data frequently exhibits missing traces, substantially affecting subsequent seismic processing and interpretation. Deep learning-based approaches have demonstrated significant advancements in reconstructing irregularly missing seismic data th
Externí odkaz:
http://arxiv.org/abs/2407.17402
Computational optical imaging (COI) systems have enabled the acquisition of high-dimensional signals through optical coding elements (OCEs). OCEs encode the high-dimensional signal in one or more snapshots, which are subsequently decoded using comput
Externí odkaz:
http://arxiv.org/abs/2406.17970
Explainable AI (XAI) algorithms aim to help users understand how a machine learning model makes predictions. To this end, many approaches explain which input features are most predictive of a target label. However, such explanations can still be puzz
Externí odkaz:
http://arxiv.org/abs/2406.03594
Deep-learning (DL)-based image deconvolution (ID) has exhibited remarkable recovery performance, surpassing traditional linear methods. However, unlike traditional ID approaches that rely on analytical properties of the point spread function (PSF) to
Externí odkaz:
http://arxiv.org/abs/2405.16343
Binary Neural Networks emerged as a cost-effective and energy-efficient solution for computer vision tasks by binarizing either network weights or activations. However, common binary activations, such as the Sign activation function, abruptly binariz
Externí odkaz:
http://arxiv.org/abs/2405.02220
In the era of cloud computing and data-driven applications, it is crucial to protect sensitive information to maintain data privacy, ensuring truly reliable systems. As a result, preserving privacy in deep learning systems has become a critical conce
Externí odkaz:
http://arxiv.org/abs/2404.05828
Quantized neural networks employ reduced precision representations for both weights and activations. This quantization process significantly reduces the memory requirements and computational complexity of the network. Binary Neural Networks (BNNs) ar
Externí odkaz:
http://arxiv.org/abs/2404.01278