arXiv subjects

Penghui Hu

Publications and source records attributed to Penghui Hu.

3 recordsLinked to original sources

arXiv2026

Interplay between Quantum Metric and Hybridized Collective Mode in Flat-Band Superfluids

We investigate collective excitations in flat-band superfluids by incorporating the coupled dynamics of pairing (phase and amplitude) and density fluctuations. We demonstrate that for any time-reversal symmetric superfluid system with an isolated flat band, only a single low-energy collective mode emerges in the long-wavelength limit. In contrast to the linearly dispersive Goldstone mode in conventional superfluids, this hybridized mode is gapless at zero momentum but exhibits a quadratic dispersion ($\omega \propto q^2$) at small momenta. We show that the dispersion coefficients of this collective mode are governed by the normal-state quantum metric of the flat band. These analytical predictions are in excellent agreement with numerical calculations. Our results are applicable to any generic $s$-wave flat-band superfluid, provided the flat band is energetically well separated from other dispersive bands.

cond-mat.supr-con↗

arXiv2024

Make Your Home Safe: Time-aware Unsupervised User Behavior Anomaly Detection in Smart Homes via Loss-guided Mask

Smart homes, powered by the Internet of Things, offer great convenience but also pose security concerns due to abnormal behaviors, such as improper operations of users and potential attacks from malicious attackers. Several behavior modeling methods have been proposed to identify abnormal behaviors and mitigate potential risks. However, their performance often falls short because they do not effectively learn less frequent behaviors, consider temporal context, or account for the impact of noise in human behaviors. In this paper, we propose SmartGuard, an autoencoder-based unsupervised user behavior anomaly detection framework. First, we design a Loss-guided Dynamic Mask Strategy (LDMS) to encourage the model to learn less frequent behaviors, which are often overlooked during learning. Second, we propose a Three-level Time-aware Position Embedding (TTPE) to incorporate temporal information into positional embedding to detect temporal context anomaly. Third, we propose a Noise-aware Weighted Reconstruction Loss (NWRL) that assigns different weights for routine behaviors and noise behaviors to mitigate the interference of noise behaviors during inference. Comprehensive experiments on three datasets with ten types of anomaly behaviors demonstrates that SmartGuard consistently outperforms state-of-the-art baselines and also offers highly interpretable results.

cs.CR↗

arXiv2023

Split Federated Learning: Speed up Model Training in Resource-Limited Wireless Networks

In this paper, we propose a novel distributed learning scheme, named group-based split federated learning (GSFL), to speed up artificial intelligence (AI) model training. Specifically, the GSFL operates in a split-then-federated manner, which consists of three steps: 1) Model distribution, in which the access point (AP) splits the AI models and distributes the client-side models to clients; 2) Model training, in which each client executes forward propagation and transmit the smashed data to the edge server. The edge server executes forward and backward propagation and then returns the gradient to the clients for updating local client-side models; and 3) Model aggregation, in which edge servers aggregate the server-side and client-side models. Simulation results show that the GSFL outperforms vanilla split learning and federated learning schemes in terms of overall training latency while achieving satisfactory accuracy.

cs.DC↗