Zobrazeno 1 - 10
of 7 180
pro vyhledávání: '"CHEN, Chong"'
Autor:
Han, Lei, Wang, Qian, Lu, Ying, Tao, Sheng, Zhu, Wenxuan, Feng, Xiaoyu, Liang, Shixuan, Bai, Hua, Chen, Chong, Wang, Kai, Yang, Zhou, Fan, Xiaolong, Song, Cheng, Pan, Feng
Two-dimensional (2D) hybrid organic-inorganic perovskite (HOIP) demonstates great potential for developing flexible and wearable spintronic devices, by serving as spin sources via the bulk Rashba effect (BRE). However, the practical application of BR
Externí odkaz:
http://arxiv.org/abs/2410.18681
We present a comprehensive study of spontaneous vectorization in the Einstein-Maxwell-Vector (EMV) model, uncovering a novel vectorization mechanism driven by the competition between electromagnetic and vector fields. By utilizing a generalized coord
Externí odkaz:
http://arxiv.org/abs/2410.16920
We select and investigate six global solar extreme ultraviolet (EUV) wave events using data from the Solar Dynamics Observatory (SDO) and the Solar and Heliospheric Observatory (SOHO). These eruptions are all on the limb but recorded as halo coronal
Externí odkaz:
http://arxiv.org/abs/2409.15017
The largest geomagnetic storm in two decades occurred in 2024 May with a minimum $D_{\rm st}$ of $-412$ nT. We examine its solar and interplanetary origins by combining multipoint imaging and in situ observations. The source active region, NOAA AR 13
Externí odkaz:
http://arxiv.org/abs/2409.11492
Purpose: To perform image registration and averaging of multiple free-breathing single-shot cardiac images, where the individual images may have a low signal-to-noise ratio (SNR). Methods: To address low SNR encountered in single-shot imaging, especi
Externí odkaz:
http://arxiv.org/abs/2409.02348
Autor:
Long, Qingqing, Yan, Yuchen, Zhang, Peiyan, Fang, Chen, Cui, Wentao, Ning, Zhiyuan, Xiao, Meng, Cao, Ning, Luo, Xiao, Xu, Lingjun, Jiang, Shiyue, Fang, Zheng, Chen, Chong, Hua, Xian-Sheng, Zhou, Yuanchun
Large-scale "pre-train and prompt learning" paradigms have demonstrated remarkable adaptability, enabling broad applications across diverse domains such as question answering, image recognition, and multimodal retrieval. This approach fully leverages
Externí odkaz:
http://arxiv.org/abs/2408.14520
In the information retrieval (IR) area, dense retrieval (DR) models use deep learning techniques to encode queries and passages into embedding space to compute their semantic relations. It is important for DR models to balance both efficiency and eff
Externí odkaz:
http://arxiv.org/abs/2408.08066
Autor:
Chang, Chi-Chih, Lin, Wei-Cheng, Lin, Chien-Yu, Chen, Chong-Yan, Hu, Yu-Fang, Wang, Pei-Shuo, Huang, Ning-Chi, Ceze, Luis, Wu, Kai-Chiang
KV-Cache compression methods generally sample a KV-Cache of effectual tokens or quantize it into lower bits. However, these methods cannot exploit the redundancy of the hidden dimension of KV tensors. This paper investigates a unique hidden dimension
Externí odkaz:
http://arxiv.org/abs/2407.21118
Autor:
Liu, Zheng, Liang, Hao, Huang, Xijie, Xiong, Wentao, Yu, Qinhan, Sun, Linzhuang, Chen, Chong, He, Conghui, Cui, Bin, Zhang, Wentao
Recently, with the rise of web images, managing and understanding large-scale image datasets has become increasingly important. Vision Large Language Models (VLLMs) have recently emerged due to their robust vision-understanding capabilities. However,
Externí odkaz:
http://arxiv.org/abs/2407.20756
Graph classification is a critical task in numerous multimedia applications, where graphs are employed to represent diverse types of multimedia data, including images, videos, and social networks. Nevertheless, in real-world scenarios, labeled graph
Externí odkaz:
http://arxiv.org/abs/2407.14081