Zobrazeno 1 - 10
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pro vyhledávání: '"Cheng, Qiang"'
The classification of histopathological images is crucial for the early and precise detection of breast cancer. This study investigates the efficiency of deep learning models in distinguishing between Invasive Ductal Carcinoma (IDC) and non-IDC in hi
Externí odkaz:
http://arxiv.org/abs/2408.16859
Publikováno v:
Physical Review B 110, 014518 (2024)
The field-free and highly efficient diodes with the nonreciprocity of supercurrent are believed to be the core block of the superconducting computing devices without dissipation. In this paper, we propose a Josephson diode based upon altermagnets wit
Externí odkaz:
http://arxiv.org/abs/2408.01901
Autor:
Ahamed, Md Atik, Cheng, Qiang
Time series classification (TSC) on multivariate time series is a critical problem. We propose a novel multi-view approach integrating frequency-domain and time-domain features to provide complementary contexts for TSC. Our method fuses continuous wa
Externí odkaz:
http://arxiv.org/abs/2406.04419
Autor:
Wang, Shu-Ze, Yu, Xue-Qing, Wei, Li-Xuan, Wang, Li, Cheng, Qiang-Jun, Peng, Kun, Cheng, Fang-Jun, Liu, Yu, Li, Fang-Sen, Ma, Xu-Cun, Xue, Qi-Kun, Song, Can-Li
Publikováno v:
Science Bulletin 69, 1392 (2024)
Magnetic impurities in superconductors are of increasing interest due to emergent Yu-Shiba-Rusinov (YSR) states and Majorana zero modes for fault-tolerant quantum computation. However, a direct relationship between the YSR multiple states and magneti
Externí odkaz:
http://arxiv.org/abs/2403.14970
Autor:
Ahamed, Md Atik, Cheng, Qiang
Long-term time-series forecasting remains challenging due to the difficulty in capturing long-term dependencies, achieving linear scalability, and maintaining computational efficiency. We introduce TimeMachine, an innovative model that leverages Mamb
Externí odkaz:
http://arxiv.org/abs/2403.09898
Large Language Models (LLMs) have demonstrated significant potential and effectiveness across multiple application domains. To assess the performance of mainstream LLMs in public security tasks, this study aims to construct a specialized evaluation b
Externí odkaz:
http://arxiv.org/abs/2402.07234
Autor:
Cheng, Qiang, Sun, Qing-Feng
Publikováno v:
Physical Review B 109, 024517 (2024)
We study the Josephson effect in the spin-singlet superconductor/altermagnet/spin-triplet superconductor junctions using the Green's function method. The current-phase difference relationships in the junctions strongly depend on the orientation of al
Externí odkaz:
http://arxiv.org/abs/2402.02810
Autor:
Ahamed, Md Atik, Cheng, Qiang
Despite the prevalence of images and texts in machine learning, tabular data remains widely used across various domains. Existing deep learning models, such as convolutional neural networks and transformers, perform well however demand extensive prep
Externí odkaz:
http://arxiv.org/abs/2401.08867
Publikováno v:
Physical Review B 108, 134511(2023)
We study the crossed Andreev reflection and the nonlocal transport in the proximitized graphene/supercondcutor/proximitized graphene junctions with the pseudospin staggered potential and the intrinsic spin-orbit coupling. The crossed Andreev reflecti
Externí odkaz:
http://arxiv.org/abs/2310.17301
We study the Andreev reflections and the quantum transport in the proximitized graphene/superconductor junction. The proximitized graphene possesses the pseudospin staggered potential and the intrinsic spin-orbit coupling induced by substrate, which
Externí odkaz:
http://arxiv.org/abs/2307.09833