Zobrazeno 1 - 10
of 1 877
pro vyhledávání: '"Wang, JiaMing"'
Deep learning has excelled in medical image classification, but its clinical application is limited by poor interpretability. Capsule networks, known for encoding hierarchical relationships and spatial features, show potential in addressing this issu
Externí odkaz:
http://arxiv.org/abs/2411.01564
Autor:
Ruffolo, David, Thepthong, Panisara, Pongkitiwanichakul, Peera, Roy, Sohom, Pecora, Francesco, Bandyopadhyay, Riddhi, Chhiber, Rohit, Usmanov, Arcadi V., Stevens, Michael, Badman, Samuel, Romeo, Orlando, Wang, Jiaming, Goodwill, Joshua, Goldstein, Melvyn L., Matthaeus, William H.
Using Parker Solar Probe data from orbits 8 through 17, we examine fluctuation amplitudes throughout the critical region where the solar wind flow speed approaches and then exceeds the Alfv\'en wave speed, taking account of various exigencies of the
Externí odkaz:
http://arxiv.org/abs/2409.02612
Autor:
Wang, Jiaming, Matthaeus, William H., Chhiber, Rohit, Roy, Sohom, Pradata, Rayta A., Pecora, Francesco, Yang, Yan
We present a broad review of 1/f noise observations in the heliosphere, and discuss and complement the theoretical background of generic 1/f models as relevant to NASA's Polarimeter to Unify the Corona and Heliosphere (PUNCH) mission. First observed
Externí odkaz:
http://arxiv.org/abs/2409.02255
Autor:
Chhiber, Rohit, Pecora, Francesco, Usmanov, Arcadi V, Matthaeus, William H, Goldstein, Melvyn L, Roy, Sohom, Wang, Jiaming, Thepthong, Panisara, Ruffolo, David
The transition from subAlfv\'enic to superAlfv\'enic flow in the solar atmosphere is examined by means of Parker Solar Probe (PSP) measurements during solar encounters 8 to 14. Around 220 subAlfv\'enic periods with a duration $\ge$ 10 minutes are ide
Externí odkaz:
http://arxiv.org/abs/2405.10437
The traditional Kalman filter (KF) is widely applied in control systems, but it relies heavily on the accuracy of the system model and noise parameters, leading to potential performance degradation when facing inaccuracies. To address this issue, int
Externí odkaz:
http://arxiv.org/abs/2404.03915
Autor:
Geng, Xinyu, Wang, Jiaming, Gong, Jiawei, Xue, Yuerong, Xu, Jun, Chen, Fanglin, Huang, Xiaolin
Redundancy is a persistent challenge in Capsule Networks (CapsNet),leading to high computational costs and parameter counts. Although previous works have introduced pruning after the initial capsule layer, dynamic routing's fully connected nature and
Externí odkaz:
http://arxiv.org/abs/2403.13351
Autor:
Ma, Ziyang, Yang, Guanrou, Yang, Yifan, Gao, Zhifu, Wang, Jiaming, Du, Zhihao, Yu, Fan, Chen, Qian, Zheng, Siqi, Zhang, Shiliang, Chen, Xie
In this paper, we focus on solving one of the most important tasks in the field of speech processing, i.e., automatic speech recognition (ASR), with speech foundation encoders and large language models (LLM). Recent works have complex designs such as
Externí odkaz:
http://arxiv.org/abs/2402.08846
Autor:
Wang, Jiaming, Chhiber, Rohit, Roy, Sohom, Cuesta, Manuel E., Pecora, Francesco, Yang, Yan, Fu, Xiangrong, Li, Hui, Matthaeus, William H.
A well-known property of solar wind plasma turbulence is the observed anisotropy of the autocorrelations, or equivalently the spectra, of velocity and magnetic field fluctuations. Here we explore the related but apparently not well-studied issue of t
Externí odkaz:
http://arxiv.org/abs/2402.05191
Autor:
Wang, Jiaming, Soh, Harold
To advance the field of autonomous robotics, particularly in object search tasks within unexplored environments, we introduce a novel framework centered around the Probable Object Location (POLo) score. Utilizing a 3D object probability map, the POLo
Externí odkaz:
http://arxiv.org/abs/2311.07992
Autor:
Du, Zhihao, Wang, Jiaming, Chen, Qian, Chu, Yunfei, Gao, Zhifu, Li, Zerui, Hu, Kai, Zhou, Xiaohuan, Xu, Jin, Ma, Ziyang, Wang, Wen, Zheng, Siqi, Zhou, Chang, Yan, Zhijie, Zhang, Shiliang
Generative Pre-trained Transformer (GPT) models have achieved remarkable performance on various natural language processing tasks, and have shown great potential as backbones for audio-and-text large language models (LLMs). Previous mainstream audio-
Externí odkaz:
http://arxiv.org/abs/2310.04673