arXiv subjects

Weihang Gao

Publications and source records attributed to Weihang Gao.

5 recordsLinked to original sources

arXiv2026

AM-FM: A Foundation Model for Ambient Intelligence Through WiFi

Ambient intelligence, continuously understanding human presence, activity, and physiology in physical spaces, is fundamental to smart environments, health monitoring, and human-computer interaction. WiFi infrastructure provides a ubiquitous, always-on, privacy-preserving substrate for this capability across billions of IoT devices. Yet this potential remains largely untapped, as wireless sensing has typically relied on task-specific models that require substantial labeled data and limit practical deployment. We present AM-FM, the first foundation model for ambient intelligence and sensing through WiFi. AM-FM is pre-trained on 9.2 million unlabeled Channel State Information (CSI) samples collected over 439 days from 20 commercial device types deployed worldwide, learning general-purpose representations via contrastive learning, masked reconstruction, and physics-informed objectives tailored to wireless signals. Evaluated on public benchmarks spanning nine downstream tasks, AM-FM shows strong cross-task performance with improved data efficiency, demonstrating that foundation models can enable scalable ambient intelligence using existing wireless infrastructure.

cs.LG↗

arXiv2025

Symmetry-preserving random batch Ewald method for constant-potential simulation of electrochemical systems

Constant potential molecular dynamics simulation plays important role for applications of electrochemical systems, yet the calculation of charge fluctuation on electrodes remains a computational bottleneck. We propose a highly scalable, symmetry-preserving random batch Ewald (SRBE) algorithm to address this challenge. The SRBE algorithm deterministically computes the low-frequency components along the direction perpendicular to electrodes, while efficiently approximating the remaining components using random batch sampling. This approach simultaneously reduces charge and force fluctuations while satisfying the symmetry-preserving mean field condition in anisotropic systems with large aspect ratios. Numerical experiments on electrode/ionic liquid systems validate the high accuracy of the SRBE method in capturing dynamic charging processes and equilibrium electric double layer structures. The SRBE method achieves parallel efficiency improvements of up to two orders of magnitude compared with conventional FFT-based algorithms. These findings highlight its strong potential for enabling large-scale electrochemical simulations and its broad applicability to practical problems in the field.

physics.comp-ph↗

arXiv2025

CSI-Bench: A Large-Scale In-the-Wild Dataset for Multi-task WiFi Sensing

WiFi sensing has emerged as a compelling contactless modality for human activity monitoring by capturing fine-grained variations in Channel State Information (CSI). Its ability to operate continuously and non-intrusively while preserving user privacy makes it particularly suitable for health monitoring. However, existing WiFi sensing systems struggle to generalize in real-world settings, largely due to datasets collected in controlled environments with homogeneous hardware and fragmented, session-based recordings that fail to reflect continuous daily activity. We present CSI-Bench, a large-scale, in-the-wild benchmark dataset collected using commercial WiFi edge devices across 26 diverse indoor environments with 35 real users. Spanning over 461 hours of effective data, CSI-Bench captures realistic signal variability under natural conditions. It includes task-specific datasets for fall detection, breathing monitoring, localization, and motion source recognition, as well as a co-labeled multitask dataset with joint annotations for user identity, activity, and proximity. To support the development of robust and generalizable models, CSI-Bench provides standardized evaluation splits and baseline results for both single-task and multi-task learning. CSI-Bench offers a foundation for scalable, privacy-preserving WiFi sensing systems in health and broader human-centric applications.

eess.SP↗

arXiv2024

RBMD: A molecular dynamics package enabling to simulate 10 million all-atom particles in a single graphics processing unit

This paper introduces a random-batch molecular dynamics (RBMD) package for fast simulations of particle systems at the nano/micro scale. Different from existing packages, the RBMD uses random batch methods for nonbonded interactions of particle systems. The long-range part of Coulomb interactions is calculated in Fourier space by the random batch Ewald algorithm, which achieves linear complexity and superscalability, surpassing classical lattice-based Ewald methods. For the short-range part, the random batch list algorithm is used to construct neighbor lists, significantly reducing both computational and memory costs. The RBMD is implemented on GPU-CPU heterogeneous architectures, with classical force fields for all-atom systems. Benchmark systems are used to validate accuracy and performance of the package. Comparison with the particle-particle particle-mesh method and the Verlet list method in the LAMMPS package is performed on three different NVIDIA GPUs, demonstrating high efficiency of the RBMD on heterogeneous architectures. Our results also show that the RBMD enables simulations on a single GPU with a CPU core up to 10 million particles. Typically, for systems of one million particles, the RBMD allows simulating all-atom systems with a high efficiency of 8.20 ms per step, demonstrating the attractive feature for running large-scale simulations of practical applications on a desktop machine.

physics.comp-ph↗

arXiv2023

Microscopic Energy Storage Mechanism of Dielectric Polymer-Coated Supercapacitors

Supercapacitors have been attracting significant attention as promising energy storage devices. However, the voltage window limitation associated with electrolyte solutions has hindered the improvement of their capacitance. To address this issue and enhance the energy storage capabilities of general traditional supercapacitors, we put forward the dipole induced effects observed in the theoretical framework of the electric double-layer structure. The molecular dynamics results demonstrate that, compared to traditional systems, an improvement of over 50% in integral capacitance at low voltages is achieved. Moreover, a new material-based experimental results obtained from a dielectric supercapacitor employing a hydrated electrolyte solution corroborated the effectiveness of our proposed model, yielding consistent outcomes. We attribute the large capacitance variation to the reorientation of the dipoles, which induces the neutral-to-bilayer transition and the overscreening-to-steric transition, consistent with the polarization process of the polymer in the experiment. We further investigate the capacitance variations under different dipole parameters, such as varying the number of layers, different number densities and different spacings, thereby enriching the experimental results with additional conclusions not previously obtained. This work presents a novel approach that exploits dipole-induced capacitance effects, paving the way for further advances in the field of energy storage technology.

physics.chem-ph↗