Zobrazeno 1 - 10
of 81 393
pro vyhledávání: '"Guoqing AN"'
Autor:
Wang, Weiqiao1 (AUTHOR) wangweiqiao@tongji.edu.cn
Publikováno v:
Religions. Feb2024, Vol. 15 Issue 2, p217. 37p.
Autor:
Weiqiao Wang
Publikováno v:
Religions, Vol 15, Iss 2, p 217 (2024)
Through an exploration of meal regulations, dining rituals, and monastic rules of Han Buddhist and Cistercian monks, this article discusses how food affects space formation, layout organization, and site selection in monastic venues using Guoqing Si
Externí odkaz:
https://doaj.org/article/01779777f5f34f9188f47ecc105a2f9d
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Non-Hermiticity and chirality are two fundamental properties known to give rise to various intriguing phenomena. However, the interplay between these properties has been rarely explored. In this work, we bridge this gap by introducing an off-diagonal
Externí odkaz:
http://arxiv.org/abs/2411.14545
This paper explores the optimal investment problem of a renewal risk model with generalized Erlang distributed interarrival times. We assume that the phases of the interarrival time can be observed. The price of the risky asset is driven by the CEV m
Externí odkaz:
http://arxiv.org/abs/2411.13111
Autor:
Peters, Matthew L., Wang, Guoqing, Spierings, David C., Drucker, Niv, Hu, Beili, Chen, Yu-Ting, Vuletić, Vladan
Using the strong dispersive coupling to a high-cooperativity cavity, we demonstrate fast and non-destructive number-resolved detection of atoms in optical tweezers. We observe individual atom-atom collisions, quantum state jumps, and atom loss events
Externí odkaz:
http://arxiv.org/abs/2411.12622
Autor:
John, Peter St., Lin, Dejun, Binder, Polina, Greaves, Malcolm, Shah, Vega, John, John St., Lange, Adrian, Hsu, Patrick, Illango, Rajesh, Ramanathan, Arvind, Anandkumar, Anima, Brookes, David H, Busia, Akosua, Mahajan, Abhishaike, Malina, Stephen, Prasad, Neha, Sinai, Sam, Edwards, Lindsay, Gaudelet, Thomas, Regep, Cristian, Steinegger, Martin, Rost, Burkhard, Brace, Alexander, Hippe, Kyle, Naef, Luca, Kamata, Keisuke, Armstrong, George, Boyd, Kevin, Cao, Zhonglin, Chou, Han-Yi, Chu, Simon, Costa, Allan dos Santos, Darabi, Sajad, Dawson, Eric, Didi, Kieran, Fu, Cong, Geiger, Mario, Gill, Michelle, Hsu, Darren, Kaushik, Gagan, Korshunova, Maria, Kothen-Hill, Steven, Lee, Youhan, Liu, Meng, Livne, Micha, McClure, Zachary, Mitchell, Jonathan, Moradzadeh, Alireza, Mosafi, Ohad, Nashed, Youssef, Paliwal, Saee, Peng, Yuxing, Rabhi, Sara, Ramezanghorbani, Farhad, Reidenbach, Danny, Ricketts, Camir, Roland, Brian, Shah, Kushal, Shimko, Tyler, Sirelkhatim, Hassan, Srinivasan, Savitha, Stern, Abraham C, Toczydlowska, Dorota, Veccham, Srimukh Prasad, Venanzi, Niccolò Alberto Elia, Vorontsov, Anton, Wilber, Jared, Wilkinson, Isabel, Wong, Wei Jing, Xue, Eva, Ye, Cory, Yu, Xin, Zhang, Yang, Zhou, Guoqing, Zandstein, Becca, Dallago, Christian, Trentini, Bruno, Kucukbenli, Emine, Rvachov, Timur, Calleja, Eddie, Israeli, Johnny, Clifford, Harry, Haukioja, Risto, Haemel, Nicholas, Tretina, Kyle, Tadimeti, Neha, Costa, Anthony B
Artificial Intelligence models encoding biology and chemistry are opening new routes to high-throughput and high-quality in-silico drug development. However, their training increasingly relies on computational scale, with recent protein language mode
Externí odkaz:
http://arxiv.org/abs/2411.10548
Summarizing patient clinical notes is vital for reducing documentation burdens. Current manual summarization makes medical staff struggle. We propose an automatic method using LLMs, but long inputs cause LLMs to lose context, reducing output quality
Externí odkaz:
http://arxiv.org/abs/2411.08586
A Heterogeneous Graph Neural Network Fusing Functional and Structural Connectivity for MCI Diagnosis
Brain connectivity alternations associated with brain disorders have been widely reported in resting-state functional imaging (rs-fMRI) and diffusion tensor imaging (DTI). While many dual-modal fusion methods based on graph neural networks (GNNs) hav
Externí odkaz:
http://arxiv.org/abs/2411.08424
Autor:
Zhou, Chuyu, Li, Tianyu, Lan, Chenxi, Du, Rongyu, Xin, Guoguo, Nan, Pengyu, Yang, Hangzhou, Wang, Guoqing, Liu, Xun, Li, Wei
Soft- and hard-constrained Physics Informed Neural Networks (PINNs) have achieved great success in solving partial differential equations (PDEs). However, these methods still face great challenges when solving the Navier-Stokes equations (NSEs) with
Externí odkaz:
http://arxiv.org/abs/2411.08122