Zobrazeno 1 - 10
of 189 818
pro vyhledávání: '"Michael, G."'
Autor:
Ma, Wenlin, Guo, Hong, Xu, Haojie, Jones, Michael G., Zhang, Chuan-Peng, Zhu, Ming, Wang, Jing, Wang, Jie, Jiang, Peng
We present the first HI mass function (HIMF) measurement for the recent FAST All Sky HI (FASHI) survey and the most complete measurements of HIMF in the local universe so far by combining the HI catalogues from HI Parkes All Sky Survey (HIPASS), Arec
Externí odkaz:
http://arxiv.org/abs/2411.09903
Autor:
Zhang, Hongyun, Li, Qian, Scheer, Michael G., Wang, Renqi, Tuo, Chuyi, Zou, Nianlong, Chen, Wanying, Li, Jiaheng, Cai, Xuanxi, Bao, Changhua, Li, Ming-Rui, Deng, Ke, Watanabe, Kenji, Taniguchi, Takashi, Ye, Mao, Tang, Peizhe, Xu, Yong, Yu, Pu, Avila, Jose, Dudin, Pavel, Denlinger, Jonathan D., Yao, Hong, Lian, Biao, Duan, Wenhui, Zhou, Shuyun
Publikováno v:
PNAS 121, (43) e2410714121 (2024)
Flat bands and nontrivial topological physics are two important topics of condensed matter physics. With a unique stacking configuration analogous to the Su-Schrieffer-Heeger (SSH) model, rhombohedral graphite (RG) is a potential candidate for realiz
Externí odkaz:
http://arxiv.org/abs/2411.07906
Diabetic retinopathy is the leading cause of vision loss in working-age adults worldwide, yet under-resourced regions lack ophthalmologists. Current state-of-the-art deep learning systems struggle at these institutions due to limited generalizability
Externí odkaz:
http://arxiv.org/abs/2411.00869
Autor:
Pan, Hongyi, Durak, Gorkem, Zhang, Zheyuan, Taktak, Yavuz, Keles, Elif, Aktas, Halil Ertugrul, Medetalibeyoglu, Alpay, Velichko, Yury, Spampinato, Concetto, Schoots, Ivo, Bruno, Marco J., Keswani, Rajesh N., Tiwari, Pallavi, Bolan, Candice, Gonda, Tamas, Goggins, Michael G., Wallace, Michael B., Xu, Ziyue, Bagci, Ulas
Federated learning (FL) enables collaborative model training across institutions without sharing sensitive data, making it an attractive solution for medical imaging tasks. However, traditional FL methods, such as Federated Averaging (FedAvg), face d
Externí odkaz:
http://arxiv.org/abs/2410.22530
Autor:
Wang, Shuyuan, Duan, Jingliang, Lawrence, Nathan P., Loewen, Philip D., Forbes, Michael G., Gopaluni, R. Bhushan, Zhang, Lixian
Model-free reinforcement learning (RL) is inherently a reactive method, operating under the assumption that it starts with no prior knowledge of the system and entirely depends on trial-and-error for learning. This approach faces several challenges,
Externí odkaz:
http://arxiv.org/abs/2410.16821
Planning and performing interactive tasks, such as conducting experiments to determine the melting point of an unknown substance, is straightforward for humans but poses significant challenges for autonomous agents. We introduce ReasonPlanner, a nove
Externí odkaz:
http://arxiv.org/abs/2410.09252
Autor:
Huang, Chi Z., Ching-Roa, Vincent D., Heckman, Connor M., Ibrahim, Sherrif F., Giacomelli, Michael G.
High-speed multiplex imaging of fluorescent probes is limited by a combination of spectral resolution, sensitivity, high cost and low light throughput of detectors, and filters. In this work, we present a hyperspectral detection system based on a sil
Externí odkaz:
http://arxiv.org/abs/2410.08936
Autor:
Scheer, Michael G.
We introduce a semidefinite relaxation method called Hamiltonian bootstrap which finds lower bounds to the ground state energy of a quantum Hamiltonian subject to Hermitian linear constraints, along with approximations of the corresponding ground sta
Externí odkaz:
http://arxiv.org/abs/2410.00810
Inferring treatment effects on a survival time outcome based on data from an observational study is challenging due to the presence of censoring and possible confounding. An additional challenge occurs when a unit's treatment affects the outcome of o
Externí odkaz:
http://arxiv.org/abs/2409.13190
Aims. To investigate the influence of distance to filaments and dark matter halos on galaxy cold gas content in the empirical model NeutralUniverseMachine (NUM) and the hydrodynamical simulation IllustrisTNG. Methods. We use DisPerSE to identify cosm
Externí odkaz:
http://arxiv.org/abs/2409.08539