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Researcher profile

Yang Fu

Yang Fu contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computer Visionhep-lathep-phcond-mat.mtrl-scicond-mat.str-elcond-mat.supr-conMachine Learningnucl-thRoboticscond-mat.mes-halleess.SPeess.SYhep-exNetworking and Internet Architecturephysics.app-ph

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Published work

13 published item(s)

preprint2026arXiv

Hybrid RIS-Aided Digital Over-the-Air Computing for Edge AI Inference: Joint Feature Quantization and Active-Passive Beamforming Design

The vision of 6G networks aims to enable edge inference by leveraging ubiquitously deployed artificial intelligence (AI) models, facilitating intelligent environmental perception for a wide range of applications. A critical operation in edge inference is for an edge node (EN) to aggregate multi-view sensory features extracted by distributed agents, thereby boosting perception accuracy. Over-the-air computing (AirComp) emerges as a promising technique for rapid feature aggregation by exploiting the waveform superposition property of analog-modulated signals, which is, however, incompatible with existing digital communication systems. Meanwhile, hybrid reconfigurable intelligent surface (RIS), a novel RIS architecture capable of simultaneous signal amplification and reflection, exhibits potential for enhancing AirComp. Therefore, this paper proposes a Hybrid RIS-aided Digital AirComp (HRD-AirComp) scheme, which employs vector quantization to map high-dimensional features into discrete codewords that are digitally modulated into symbols for wireless transmission. By judiciously adjusting the AirComp transceivers and hybrid RIS reflection to control signal superposition across agents, the EN can estimate the aggregated features from the received signals. To endow HRD-AirComp with a task-oriented design principle, we derive a surrogate function for inference accuracy that characterizes the impact of feature quantization and over-the-air aggregation. Based on this surrogate, we formulate an optimization problem targeting inference accuracy maximization, and develop an efficient algorithm to jointly optimize the quantization bit allocation, agent transmission coefficients, EN receiving beamforming, and hybrid RIS reflection beamforming. Experimental results demonstrate that the proposed HRD-AirComp outperforms baselines in terms of both inference accuracy and uncertainty.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Joint Energy Management and Coordinated AIGC Workload Scheduling for Distributed Data Centers: A Diffusion-Aided Reward Shaping Approach

Artificial intelligence-generated content (AIGC) has emerged as a transformative paradigm for automating the creation of diverse and customized content, giving rise to rapidly growing computational workloads in cloud data centers. It is imperative for AIGC service providers (ASPs) to strategically schedule AIGC workloads to reduce data center energy costs while guaranteeing high-quality content generation. However, the distinctive characteristics of AIGC services pose critical challenges, including model heterogeneity across ASPs, implicit service quality evaluation, and complex inference process control. To tackle these challenges, we propose a joint energy management and coordinated AIGC workload scheduling framework, which introduces an explicit mathematical characterization of service quality to promote both job transfer among ASPs and fine-grained inference process configuration. Moreover, various energy resources within data centers are jointly considered to enhance power usage flexibility. Subsequently, a system utility maximization problem is formulated to balance AIGC service revenue with operational penalties and costs. Nevertheless, the strong coupling among job scheduling decisions induces severe reward sparsity, which limits the effectiveness of existing deep reinforcement learning (DRL) algorithms. To address this issue, we develop a diffusion model-aided reward shaping approach to synthesize complementary reward signals through a multi-step denoising process. This approach is seamlessly integrated with DRL to enable efficient learning of scheduling policies under sparse environmental feedback. Experiments based on real-world models and datasets demonstrate that our scheme effectively accommodates electricity price fluctuations and AIGC model heterogeneity, while achieving superior learning convergence and system utility compared with benchmark methods.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Nematic-fluctuation-mediated superconductivity in CuxTiSe2

The interplay among electronic nematicity, charge density wave, and superconductivity in correlated electronic systems has induced extensive research interest. Here, we discover the existence of nematic fluctuations in TiSe2 single crystal and investigate its evolution with Cu intercalation. It is observed that the elastoresistivity coefficient mEg exhibits a divergent temperature dependence following a Curie-Weiss law at high temperature. Upon Cu intercalation, the characteristic temperature T* of nematic fluctuation is progressively suppressed and becomes near zero when the superconductivity is optimized. Further intercalation of Cu leads to the sign change of T* and the suppression of superconductivity. These results strongly indicate that nematic phase transition may play a vital role in enhancing superconductivity in CuxTiSe2. Therefore, CuxTiSe2 provides a unique material platform to explore the nematic-fluctuation-mediated superconductivity.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Category-Level 6D Object Pose Estimation in the Wild: A Semi-Supervised Learning Approach and A New Dataset

6D object pose estimation is one of the fundamental problems in computer vision and robotics research. While a lot of recent efforts have been made on generalizing pose estimation to novel object instances within the same category, namely category-level 6D pose estimation, it is still restricted in constrained environments given the limited number of annotated data. In this paper, we collect Wild6D, a new unlabeled RGBD object video dataset with diverse instances and backgrounds. We utilize this data to generalize category-level 6D object pose estimation in the wild with semi-supervised learning. We propose a new model, called Rendering for Pose estimation network RePoNet, that is jointly trained using the free ground-truths with the synthetic data, and a silhouette matching objective function on the real-world data. Without using any 3D annotations on real data, our method outperforms state-of-the-art methods on the previous dataset and our Wild6D test set (with manual annotations for evaluation) by a large margin. Project page with Wild6D data: https://oasisyang.github.io/semi-pose .

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

DexMV: Imitation Learning for Dexterous Manipulation from Human Videos

While significant progress has been made on understanding hand-object interactions in computer vision, it is still very challenging for robots to perform complex dexterous manipulation. In this paper, we propose a new platform and pipeline DexMV (Dexterous Manipulation from Videos) for imitation learning. We design a platform with: (i) a simulation system for complex dexterous manipulation tasks with a multi-finger robot hand and (ii) a computer vision system to record large-scale demonstrations of a human hand conducting the same tasks. In our novel pipeline, we extract 3D hand and object poses from videos, and propose a novel demonstration translation method to convert human motion to robot demonstrations. We then apply and benchmark multiple imitation learning algorithms with the demonstrations. We show that the demonstrations can indeed improve robot learning by a large margin and solve the complex tasks which reinforcement learning alone cannot solve. More details can be found in the project page: https://yzqin.github.io/dexmv

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Lattice QCD calculation of the two-photon exchange contribution to the muonic-hydrogen Lamb shift

We develop a method for lattice QCD calculation of the two-photon exchange contribution to the muonic-hydrogen Lamb shift. To demonstrate its feasibility, we present the first lattice calculation with a gauge ensemble at $m_π= 142$ MeV. By adopting the infinite-volume reconstruction method along with an optimized subtraction scheme, we obtain $ΔE_{\text{TPE}} = -28.9(4.9)~μ\text{eV} + 93.72~μ\text{eV}/\text{fm}^2 \cdot\langle r_p^2 \rangle$, or $ΔE_{\text{TPE}} = 37.4(4.9)~μ$eV, which is consistent with the previous theoretical results in a range of 20-50 $μ$eV.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Lattice QCD calculation of the two-photon exchange contribution to the muonic-hydrogen Lamb shift

We develop a method for lattice QCD calculation of the two-photon exchange (TPE) contribution to the muonic-hydrogen Lamb shift. To demonstrate the feasibility of this method, we also present an exploratory study with a gauge ensemble at $m_π= 142$ MeV. By adopting the infinite-volume reconstruction (IVR) method along with an optimized subtraction scheme, we obtain a preliminary result of the TPE contribution which agrees well with previous calculation using other methods and one magnitude smaller compare to the large $\sim300~μ$eV discrepancy for the proton radius puzzle.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Superconductivity and normal-state properties of kagome metal RbV3Sb5 single crystals

We report the discovery of superconductivity and detailed normal-state physical properties of RbV3Sb5 single crystals with V kagome lattice. RbV3Sb5 single crystals show a superconducting transition at Tc ~ 0.92 K. Meanwhile, resistivity, magnetization and heat capacity measurements indicate that it exhibits anomalies of properties at T* ~ 102 - 103 K, possibly related to the formation of charge ordering state. When T is lower than T*, the Hall coefficient RH undergoes a drastic change and sign reversal from negative to positive, which can be partially explained by the enhanced mobility of hole-type carriers. In addition, the results of quantum oscillations show that there are some very small Fermi surfaces with low effective mass, consistent with the existence of multiple highly dispersive Dirac band near the Fermi energy level.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Lattice QCD calculation of the pion charge radius using a model-independent method

We use a method to calculate the hadron's charge radius without model-dependent momentum extrapolations. The method does not require the additional quark propagator inversions on the twisted boundary conditions or the computation of the momentum derivatives of quark propagators and thus is easy to implement. We apply this method to the calculation of pion charge radius $\langle r_π^2\rangle$. For comparison, we also determine $\langle r_π^2\rangle$ with the traditional approach of computing the slope of the form factors. The new method produces results consistent with those from the traditional method and with statistical errors 1.5-1.9 times smaller. For the four gauge ensembles at the physical pion masses, the statistical errors of $\langle r_π^2\rangle$ range from 2.1% to 4.6% by using $\lesssim50$ configurations. For the ensemble at $m_π\approx 340$ MeV, the statistical uncertainty is even reduced to a sub-percent level.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Magnetic topological insulator MnBi6Te10 with zero-field ferromagnetic state and gapped Dirac surface states

Magnetic topological insulators (TIs) with nontrivial topological electronic structure and broken time-reversal symmetry exhibit various exotic topological quantum phenomena. The realization of such exotic phenomena at high temperature is one of central topics in this area. We reveal that MnBi6Te10 is a magnetic TI with an antiferromagnetic ground state below 10.8 K whose nontrivial topology is manifested by Dirac-like surface states. The ferromagnetic axion insulator state with Z4 = 2 emerges once spins polarized at field as low as 0.1 T, accompanied with saturated anomalous Hall resistivity up to 10 K. Such a ferromagnetic state is preserved even external field down to zero at 2 K. Theoretical calculations indicate that the few-layer ferromagnetic MnBi6Te10 is also topologically nontrivial with a non-zero Chern number. Angle-resolved photoemission spectroscopy experiments further reveal three types of Dirac surface states arising from different terminations on the cleavage surfaces, one of which has insulating behavior with an energy gap of ~ 28 meV at the Dirac point. These outstanding features suggest that MnBi6Te10 is a promising system to realize various topological quantum effects at zero field and high temperature.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Observation of the polaronic character of excitons in a two-dimensional semiconducting magnet $\mathrm{CrI_3}$

Exciton dynamics can be strongly affected by lattice vibrations through electron-phonon coupling. This is rarely explored in two-dimensional magnetic semiconductors. Focusing on bilayer CrI3, we first show the presence of strong electron-phonon coupling through temperature-dependent photoluminescence and absorption spectroscopy. We then report the observation of periodic broad modes up to the 8th order in Raman spectra, attributed to the polaronic character of excitons. We establish that this polaronic character is dominated by the coupling between the charge-transfer exciton at 1.96 eV and a longitudinal optical phonon at 120.6 cm-1. We further show that the emergence of long-range magnetic order enhances the electron-phonon coupling strength by about 50$\%$ and that the transition from layered antiferromagnetic to ferromagnetic order tunes the spectral intensity of the periodic broad modes, suggesting a strong coupling among the lattice, charge and spin in two-dimensional CrI3. Our study opens opportunities for tailoring light-matter interactions in two-dimensional magnetic semiconductors.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Investigation upon the performance of piezoelectric energy harvester with elastic extensions

Piezoelectric vibration energy harvesters have attracted much attention due to its potential to replace currently popular batteries and to provide an sustainable power sources. Many researchers have proposed ways to increase the performance of piezoelectric energy harvesters like bandwidth, working frequency and output performance. Here in this contribution, we propose the method of using elastic extensions to tune the performance of a piezoelectric energy harvester. Mathematical model of the proposed device is derived and analyzed. Numerical simulations are done to investigate the influences of the derived parameters, like length ratio λ_l , bending stiffness ratio λ_B , and line density ratio λ_m . Results show that the elastic extension does change the motion of the proposed device and help tune the performance of piezoelectric energy harvesters.

0 citations0 reviewsNo code linkedOpen access
preprint2018arXiv

STA: Spatial-Temporal Attention for Large-Scale Video-based Person Re-Identification

In this work, we propose a novel Spatial-Temporal Attention (STA) approach to tackle the large-scale person re-identification task in videos. Different from the most existing methods, which simply compute representations of video clips using frame-level aggregation (e.g. average pooling), the proposed STA adopts a more effective way for producing robust clip-level feature representation. Concretely, our STA fully exploits those discriminative parts of one target person in both spatial and temporal dimensions, which results in a 2-D attention score matrix via inter-frame regularization to measure the importances of spatial parts across different frames. Thus, a more robust clip-level feature representation can be generated according to a weighted sum operation guided by the mined 2-D attention score matrix. In this way, the challenging cases for video-based person re-identification such as pose variation and partial occlusion can be well tackled by the STA. We conduct extensive experiments on two large-scale benchmarks, i.e. MARS and DukeMTMC-VideoReID. In particular, the mAP reaches 87.7% on MARS, which significantly outperforms the state-of-the-arts with a large margin of more than 11.6%.

0 citations0 reviewsNo code linkedOpen access