Zobrazeno 1 - 10
of 21 167
pro vyhledávání: '"zhang, Ya"'
We revisit the challenging problem of identifying the quantum spin liquid candidate in the spin-1/2 $J_1$-$J_2$ Heisenberg antiferromagnet on the square lattice. By integrating the Gutzwiller-guided density matrix renormalization group method with an
Externí odkaz:
http://arxiv.org/abs/2411.14114
Training a generalizable agent to continually learn a sequence of tasks from offline trajectories is a natural requirement for long-lived agents, yet remains a significant challenge for current offline reinforcement learning (RL) algorithms. Specific
Externí odkaz:
http://arxiv.org/abs/2411.11364
Autor:
Wang, Pingjie, Zhao, Zihan, Zhao, Liudan, He, Miao, Sun, Xin, Zhang, Ya, Sun, Kun, Wang, Yanfeng, Wang, Yu
Auscultation of internal body sounds is essential for diagnosing a range of health conditions, yet its effectiveness is often limited by clinicians' expertise and the acoustic constraints of human hearing, restricting its use across various clinical
Externí odkaz:
http://arxiv.org/abs/2411.07547
In this work, we demonstrate a kinetic superconductor in a simple lattice model with only one term: the nearest neighbor hopping $t$. The hopping is projected into a constrained Hilbert space, in the same spirit as the usual t-J model with $J=0$, whe
Externí odkaz:
http://arxiv.org/abs/2411.07292
Autor:
Zhou, Boran, Zhang, Ya-Hui
The recent experimental observation of quantum anomalous Hall (QAH) effects in the rhombohedrally stacked pentalayer graphene has motivated theoretical discussions on the possibility of quantum anomalous Hall crystal (QAHC), a topological version of
Externí odkaz:
http://arxiv.org/abs/2411.04174
Autor:
Yang, Hui, Zhang, Ya-Hui
We performed a random phase approximation (RPA) calculation for a spin-valley polarized model of the rhombohedral tetra-layer graphene to study the possibility of chiral superconductor from the Kohn-Luttinger mechanism. We included the realistic band
Externí odkaz:
http://arxiv.org/abs/2411.02503
Offline reinforcement learning (RL) methods harness previous experiences to derive an optimal policy, forming the foundation for pre-trained large-scale models (PLMs). When encountering tasks not seen before, PLMs often utilize several expert traject
Externí odkaz:
http://arxiv.org/abs/2411.01168
The purpose of offline multi-task reinforcement learning (MTRL) is to develop a unified policy applicable to diverse tasks without the need for online environmental interaction. Recent advancements approach this through sequence modeling, leveraging
Externí odkaz:
http://arxiv.org/abs/2411.01146
In numerous artificial intelligence applications, the collaborative efforts of multiple intelligent agents are imperative for the successful attainment of target objectives. To enhance coordination among these agents, a distributed communication fram
Externí odkaz:
http://arxiv.org/abs/2411.00382
Autor:
Ouyang, Zetian, Qiu, Yishuai, Wang, Linlin, de Melo, Gerard, Zhang, Ya, Wang, Yanfeng, He, Liang
With the proliferation of Large Language Models (LLMs) in diverse domains, there is a particular need for unified evaluation standards in clinical medical scenarios, where models need to be examined very thoroughly. We present CliMedBench, a comprehe
Externí odkaz:
http://arxiv.org/abs/2410.03502