Zobrazeno 1 - 10
of 242 751
pro vyhledávání: '"Gan BY"'
Autor:
Liu, Hao, He, Yutong, Xiong, Bing, Sun, Changzheng, Hao, Zhibiao, Wang, Lai, Wang, Jian, Han, Yanjun, Li, Hongtao, Gan, Lin, Luo, Yi
High-performance electro-optic modulators play a critical role in modern telecommunication networks and intra-datacenter interconnects. Low driving voltage, large electro-optic bandwidth, compact device size, and multi-band operation ability are esse
Externí odkaz:
http://arxiv.org/abs/2411.15037
We introduce Quantum Hamiltonian Descent as a novel approach to solve the graph partition problem. By reformulating graph partition as a Quadratic Unconstrained Binary Optimization (QUBO) problem, we leverage QHD's quantum-inspired dynamics to identi
Externí odkaz:
http://arxiv.org/abs/2411.14696
Autor:
Fini, Enrico, Shukor, Mustafa, Li, Xiujun, Dufter, Philipp, Klein, Michal, Haldimann, David, Aitharaju, Sai, da Costa, Victor Guilherme Turrisi, Béthune, Louis, Gan, Zhe, Toshev, Alexander T, Eichner, Marcin, Nabi, Moin, Yang, Yinfei, Susskind, Joshua M., El-Nouby, Alaaeldin
We introduce a novel method for pre-training of large-scale vision encoders. Building on recent advancements in autoregressive pre-training of vision models, we extend this framework to a multimodal setting, i.e., images and text. In this paper, we p
Externí odkaz:
http://arxiv.org/abs/2411.14402
Autor:
Sun, Qinghui, Wang, Sharon Xuesong, Gan, Tianjun, Ji, Chenyang, Lin, Zitao, Ting, Yuan-Sen, Teske, Johanna, Li, Haining, Liu, Fan, Hua, Xinyan, Tang, Jiaxin, Yu, Jie, Zhang, Jiayue, Badenas-Agusti, Mariona, Vanderburg, Andrew, Ricker, George R., Vanderspek, Roland, Latham, David W., Seager, Sara, Jenkins, Jon M., Schwarz, Richard P., Guillot, Tristan, Tan, Thiam-Guan, Conti, Dennis M., Collins, Kevin I., Srdoc, Gregor, Stockdale, Chris, Suarez, Olga, Zambelli, Roberto, Radford, Don, Barkaoui, Khalid, Evans, Phil, Bieryla, Allyson
The Sun is depleted in refractory elements compared to nearby solar twins, which may be linked to the formation of giant or terrestrial planets. Here we present high-resolution, high signal-to-noise spectroscopic data for 17 solar-like stars hosting
Externí odkaz:
http://arxiv.org/abs/2411.13825
We study structural clustering on graphs in dynamic scenarios, where the graphs can be updated by arbitrary insertions or deletions of edges/vertices. The goal is to efficiently compute structural clustering results for any clustering parameters $\ep
Externí odkaz:
http://arxiv.org/abs/2411.13817
Quantum Convolutional Neural Networks (QCNNs) have emerged as promising models for quantum machine learning tasks, including classification and data compression. This paper investigates the performance of QCNNs in comparison to the hardware-efficient
Externí odkaz:
http://arxiv.org/abs/2411.13468
Autor:
Xu, Chen, Wang, Jie, Jing, Yingjie, Li, Fujia, Gan, Hengqian, Liu, Ziming, Liang, Tiantian, Chen, Qingze, Liu, Zerui, Hou, Zhipeng, Hu, Hao, Hu, Huijie, Huang, Shijie, Jiang, Peng, Zhang, Chuan-Peng, Zhu, Yan
The standing waves existed in radio telescope data are primarily due to reflections among the instruments, which significantly impact the spectrum quality of the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Eliminating these standing
Externí odkaz:
http://arxiv.org/abs/2411.13016
Autor:
Lin, Chunru, Fan, Jugang, Wang, Yian, Yang, Zeyuan, Chen, Zhehuan, Fang, Lixing, Wang, Tsun-Hsuan, Xian, Zhou, Gan, Chuang
It is desired to equip robots with the capability of interacting with various soft materials as they are ubiquitous in the real world. While physics simulations are one of the predominant methods for data collection and robot training, simulating sof
Externí odkaz:
http://arxiv.org/abs/2411.12711
Cross-view geo-localization (CVGL), which involves matching and retrieving satellite images to determine the geographic location of a ground image, is crucial in GNSS-constrained scenarios. However, this task faces significant challenges due to subst
Externí odkaz:
http://arxiv.org/abs/2411.12431
Autor:
Gong, Zheng, Deng, Zhuo, Gan, Run, Niu, Zhiyuan, Chen, Lu, Huang, Canfeng, Liang, Jia, Gao, Weihao, Li, Fang, Zhang, Shaochong, Ma, Lan
The retinal fundus images are utilized extensively in the diagnosis, and their quality can directly affect the diagnosis results. However, due to the insufficient dataset and algorithm application, current fundus image quality assessment (FIQA) metho
Externí odkaz:
http://arxiv.org/abs/2411.12273