Zobrazeno 1 - 10
of 503
pro vyhledávání: '"Wang, HanChen"'
Full Waveform Inversion (FWI) is a vital technique for reconstructing high-resolution subsurface velocity maps from seismic waveform data, governed by partial differential equations (PDEs) that model wave propagation. Traditional machine learning app
Externí odkaz:
http://arxiv.org/abs/2410.09002
Autor:
Cohn, Clayton, Davalos, Eduardo, Vatral, Caleb, Fonteles, Joyce Horn, Wang, Hanchen David, Ma, Meiyi, Biswas, Gautam
Recent technological advancements have enhanced our ability to collect and analyze rich multimodal data (e.g., speech, video, and eye gaze) to better inform learning and training experiences. While previous reviews have focused on parts of the multim
Externí odkaz:
http://arxiv.org/abs/2408.14491
Publikováno v:
Nano Letters 2024 24 (33), 10251-10257
Charge-spin interconversion processes underpin the generation of spin-orbit torques in magnetic/nonmagnetic bilayers. However, efficient sources of spin currents such as 5d metals are also efficient spin sinks, resulting in a large increase of magnet
Externí odkaz:
http://arxiv.org/abs/2408.12165
Autor:
Wang, Hanchen David, Khan, Nibraas, Chen, Anna, Sarkar, Nilanjan, Wisniewski, Pamela, Ma, Meiyi
Recent global estimates suggest that as many as 2.41 billion individuals have health conditions that would benefit from rehabilitation services. Home-based Physical Therapy (PT) faces significant challenges in providing interactive feedback and meani
Externí odkaz:
http://arxiv.org/abs/2408.11837
Exceptional points with coalescence of eigenvalues and eigenvectors are spectral singularities in the parameter space, achieving which often needs fine-tuning of parameters in quantum systems. We predict a persistent realization of nodal magnon-photo
Externí odkaz:
http://arxiv.org/abs/2407.21597
Graph Neural Networks (GNNs) are vital in data science but are increasingly susceptible to adversarial attacks. To help researchers develop more robust GNN models, it's essential to focus on designing strong attack models as foundational benchmarks a
Externí odkaz:
http://arxiv.org/abs/2407.18170
Ultrasound computed tomography (USCT) is a promising technique that achieves superior medical imaging reconstruction resolution by fully leveraging waveform information, outperforming conventional ultrasound methods. Despite its advantages, high-qual
Externí odkaz:
http://arxiv.org/abs/2407.14564
Autor:
Legrand, William, Kemna, Yana, Schären, Stefan, Wang, Hanchen, Petrosyan, Davit, Siegl, Luise, Schlitz, Richard, Lammel, Michaela, Gambardella, Pietro
The magnetic resonance of iron garnets is at the heart of rich physics and provides a crucial technology used inside microwave components. A barrier still stands in the way of integrated cryogenic devices with iron garnets, because their epitaxy requ
Externí odkaz:
http://arxiv.org/abs/2407.06850
Spatial-Temporal Graph (STG) data is characterized as dynamic, heterogenous, and non-stationary, leading to the continuous challenge of spatial-temporal graph learning. In the past few years, various GNN-based methods have been proposed to solely foc
Externí odkaz:
http://arxiv.org/abs/2403.12418
Autor:
Liu, Shengchao, Wang, Chengpeng, Lu, Jiarui, Nie, Weili, Wang, Hanchen, Li, Zhuoxinran, Zhou, Bolei, Tang, Jian
Deep generative models (DGMs) have been widely developed for graph data. However, much less investigation has been carried out on understanding the latent space of such pretrained graph DGMs. These understandings possess the potential to provide cons
Externí odkaz:
http://arxiv.org/abs/2401.17123