Zobrazeno 1 - 10
of 34 306
pro vyhledávání: '"Chen, Gang"'
Publikováno v:
Comptes Rendus. Chimie, Vol 26, Iss G2, Pp 145-155 (2023)
Currently, researchers have indicated that inorganic minerals in reservoirs, such as clay minerals, carbonates and quartz, can catalyze the evolution of organic matter into oil and gas. Therefore it is reasonable to believe that the minerals in reser
Externí odkaz:
https://doaj.org/article/a4987c21dc744d8abcbd1c7f18ccdb87
Computation of spin-resummed observables in post-Minkowskian dynamics typically involve evaluation of Feynman integrals deformed by an exponential factor, where the exponent is a linear sum of the momenta being integrated. Such integrals can be viewe
Externí odkaz:
http://arxiv.org/abs/2406.17658
Autor:
Xu, Peng, Chen, Gang
The lower bound of estimated accuracy for a parameter unitarily encoded in closed systems has been obtained, and both optimal initial states and detection operators can be designed guided by the lower bound. In this letter, we demonstrate that the lo
Externí odkaz:
http://arxiv.org/abs/2406.11287
Within the evolving landscape of deep learning, the dilemma of data quantity and quality has been a long-standing problem. The recent advent of Large Language Models (LLMs) offers a data-centric solution to alleviate the limitations of real-world dat
Externí odkaz:
http://arxiv.org/abs/2406.15126
Autor:
Chen, Gang, Wang, Tianheng
We consider the covariant proposal for the gravitational Compton amplitude for a Kerr black hole. Employing the covariant three- and four-point Compton amplitudes, we assemble the classical one-loop integrand on the maximal cut at all orders in spin,
Externí odkaz:
http://arxiv.org/abs/2406.09086
Autor:
Chen, Ping, Zhang, Wenjie, He, Shuibing, Gu, Yingjie, Peng, Zhuwei, Huang, Kexin, Zhan, Xuan, Chen, Weijian, Zheng, Yi, Wang, Zhefeng, Yin, Yanlong, Chen, Gang
Large model training has been using recomputation to alleviate the memory pressure and pipelining to exploit the parallelism of data, tensor, and devices. The existing recomputation approaches may incur up to 40% overhead when training real-world mod
Externí odkaz:
http://arxiv.org/abs/2406.08756
Autor:
Reynolds, Richard C., Glen, Daniel R., Chen, Gang, Saad, Ziad S., Cox, Robert W., Taylor, Paul A.
FMRI data are noisy, complicated to acquire, and typically go through many steps of processing before they are used in a study or clinical practice. Being able to visualize and understand the data from the start through the completion of processing,
Externí odkaz:
http://arxiv.org/abs/2406.05248
Many machine learning models are susceptible to adversarial attacks, with decision-based black-box attacks representing the most critical threat in real-world applications. These attacks are extremely stealthy, generating adversarial examples using h
Externí odkaz:
http://arxiv.org/abs/2406.04998
Assembling a slave object into a fixture-free master object represents a critical challenge in flexible manufacturing. Existing deep reinforcement learning-based methods, while benefiting from visual or operational priors, often struggle with small-b
Externí odkaz:
http://arxiv.org/abs/2406.00364
Similarity search is a fundamental but expensive operator in querying trajectory data, due to its quadratic complexity of distance computation. To mitigate the computational burden for long trajectories, neural networks have been widely employed for
Externí odkaz:
http://arxiv.org/abs/2405.19761