A5100399975- Black Holes and Theoretical Physics
- Atmospheric Ozone and Climate
- Spectroscopy and Laser Applications
- Model Reduction and Neural Networks
- Opinion Dynamics and Social Influence
- Cosmology and Gravitation Theories
- Complex Network Analysis Techniques
- Social Media and Politics
- Mathematical functions and polynomials
- Physics of Superconductivity and Magnetism
- Astrophysics and Cosmic Phenomena
- Gambling Behavior and Treatments
- Sports Analytics and Performance
- Artificial Intelligence in Games
- Mathematical Analysis and Transform Methods
- CCD and CMOS Imaging Sensors
- Inorganic Fluorides and Related Compounds
- Approximation Theory and Sequence Spaces
- Superconductivity in MgB2 and Alloys
- Gamma-ray bursts and supernovae
Institute of High Energy Physics
2023
Chinese Academy of Sciences
2023
Nanjing University
2023
University of Chinese Academy of Sciences
2022-2023
University of Science and Technology of China
2018
Capital Normal University
2010
Heads-up no-limit Texas hold’em (HUNL) is the quintessential game with imperfect information. Representative priorworks like DeepStack and Libratus heavily rely on counter-factual regret minimization (CFR) its variants to tackleHUNL. However, prohibitive computation cost of CFRiteration makes it difficult for subsequent researchers learnthe CFR model in HUNL apply other practical applications. In this work, we present AlphaHoldem, a high-performance lightweight AI obtained an end-to-end...
A bstract We construct a holographic model to study the striped superconductor on ionic lattices. This features phase diagram with three distinct phases, namely charge density wave (CDW) phase, ordinary superconducting (SC) and (SSC). The effect of lattices is investigated in detail. First, due periodic nature background, different types CDW solutions can be found below critical temperature. Furthermore, increase lattice amplitude these are locked commensurate states. Second, we find that...
We construct a neural network to learn the Reissner-Nordstr\"om-anti--de Sitter black hole metric based on data of optical conductivity by holography. The linear perturbative equation for Maxwell field is rewritten in terms such that constructed discretization this differential equation. In contrast all previous models anti--de Sitter/deep learning duality, derivative function appears motion and we propose distinct finite difference methods discretize function. notion reduced also proposed...
The charge response of the detector is very important for data reconstruction and energy spectrum analysis. In this paper, LHAASO large size PMT studied in depth, including difference detection efficiency between MCP Dynode structure PMTs, a calibration nonlinear method based on continuous optical attenuator proposed. After correction from calibration, experimental can reach good agreement with simulation results.
We construct a neural network to learn the RN-AdS black hole metric based on data of optical conductivity by holography. The linear perturbative equation for Maxwell field is rewritten in terms such that constructed discretization this differential equation. In contrast all previous models AdS/DL (deep learning) duality, derivative function appears motion and we propose distinct finite difference methods discretize function. notion reduced also proposed avoid divergence near horizon.The...