activity
20232026
most citedA Pseudo-Fermion Propagator Approach to the Fermion Sign Problem

2 citations · 2 across the 6 of their papers we have counts for

collaborators

6 papers

cs.RO2026

BFA++: Hierarchical Best-Feature-Aware Token Prune for Multi-View Vision Language Action Model

Haosheng Li, Weixin Mao, Zihan Lan +6

Vision-Language-Action (VLA) models have achieved significant breakthroughs by leveraging Large Vision Language Models (VLMs) to jointly interpret instructions and visual inputs. H…

physics.comp-ph2025★ 2 cited

A Pseudo-Fermion Propagator Approach to the Fermion Sign Problem

Yunuo Xiong, Hongwei Xiong

In this work, within the framework of path integral Monte Carlo, we construct a pseudo-fermion propagator by replacing the original fermionic determinant with its absolute value. T…

cond-mat.mtrl-sci2025

Study of the uniform electron gas through parametrized partition functions

Tommaso Morresi, Giovanni Garberoglio, Hongwei Xiong +1

We investigate the energy per particle, static structure factor, and momentum distribution of the uniform electron gas for different conditions defined by the dimensionless tempera…

cond-mat.quant-gas2024

Ab initio simulation of the universal properties of unitary Fermi gas in a harmonic trap

Yunuo Xiong, Hongwei Xiong

Chang and Bertsch [Phys. Rev. A 76, 021603(R) (2007)] proposed a simple formula for the ground state energy of a unitary Fermi gas in a harmonic trap, based on their Green's functi…

cond-mat.quant-gas2024

Ab initio simulations of the thermodynamic properties and phase transition of Fermi systems based on fictitious identical particles and physics-informed neural networks

Yunuo Xiong, Hongwei Xiong

Fictitious identical particle thermodynamics has emerged as a powerful tool to overcome the fermion sign problem, enabling highly accurate simulations of one thousand fermions in w…

cs.CV2023

Noise robust neural network architecture

Xiong Yunuo, Xiong Hongwei

In which we propose neural network architecture (dune neural network) for recognizing general noisy image without adding any artificial noise in the training data. By representing…