arXiv subjects

Yong Xie

Publications and source records attributed to Yong Xie.

34 records · Page 2Linked to original sources

arXiv2025

Rethinking Federated Learning Over the Air: The Blessing of Scaling Up

Federated learning facilitates collaborative model training across multiple clients while preserving data privacy. However, its performance is often constrained by limited communication resources, particularly in systems supporting a large number of clients. To address this challenge, integrating over-the-air computations into the training process has emerged as a promising solution to alleviate communication bottlenecks. The system significantly increases the number of clients it can support in each communication round by transmitting intermediate parameters via analog signals rather than digital ones. This improvement, however, comes at the cost of channel-induced distortions, such as fading and noise, which affect the aggregated global parameters. To elucidate these effects, this paper develops a theoretical framework to analyze the performance of over-the-air federated learning in large-scale client scenarios. Our analysis reveals three key advantages of scaling up the number of participating clients: (1) Enhanced Privacy: The mutual information between a client's local gradient and the server's aggregated gradient diminishes, effectively reducing privacy leakage. (2) Mitigation of Channel Fading: The channel hardening effect eliminates the impact of small-scale fading in the noisy global gradient. (3) Improved Convergence: Reduced thermal noise and gradient estimation errors benefit the convergence rate. These findings solidify over-the-air model training as a viable approach for federated learning in networks with a large number of clients. The theoretical insights are further substantiated through extensive experimental evaluations.

cs.LG↗

arXiv2025

Enhanced Charging in Multi-Battery Systems by Nonreciprocity

Quantum batteries (QBs), harnessing quantum systems to transfer and store energy, have garnered substantial attention recently, enabling potentials in enhanced charging capacity, increased charging power, and device miniaturization. However, constrained by the weak interaction between the quantum nodes, the implementations of QB networks exhibit limited charging performance. In this work, we propose an efficient approach to improving charging in multi-battery systems by capitalizing on nonreciprocity. By constructing non-Hermitian Aharonov-Bohm triangles to establish unidirectional energy transfer in both cascaded and parallel configurations, we can achieve a significant enhancement of the stored energy in QBs especially in the weak interaction regime. Remarkably, the nonreciprocal cascaded setups display an exponentially increasing gain in the battery energy as the charging distance lengthens compared to the reciprocal counterparts. Furthermore, we demonstrate that nonreciprocity can also lead to the same enhancement in the charging power of QBs, accelerating the charging processes. Our findings provide a practical pathway for enhancing the charging performance of QBs and exhibit the potentials for constructing efficient QB networks.

quant-ph↗

arXiv2025

ReCoM: Realistic Co-Speech Motion Generation with Recurrent Embedded Transformer

We present ReCoM, an efficient framework for generating high-fidelity and generalizable human body motions synchronized with speech. The core innovation lies in the Recurrent Embedded Transformer (RET), which integrates Dynamic Embedding Regularization (DER) into a Vision Transformer (ViT) core architecture to explicitly model co-speech motion dynamics. This architecture enables joint spatial-temporal dependency modeling, thereby enhancing gesture naturalness and fidelity through coherent motion synthesis. To enhance model robustness, we incorporate the proposed DER strategy, which equips the model with dual capabilities of noise resistance and cross-domain generalization, thereby improving the naturalness and fluency of zero-shot motion generation for unseen speech inputs. To mitigate inherent limitations of autoregressive inference, including error accumulation and limited self-correction, we propose an iterative reconstruction inference (IRI) strategy. IRI refines motion sequences via cyclic pose reconstruction, driven by two key components: (1) classifier-free guidance improves distribution alignment between generated and real gestures without auxiliary supervision, and (2) a temporal smoothing process eliminates abrupt inter-frame transitions while ensuring kinematic continuity. Extensive experiments on benchmark datasets validate ReCoM's effectiveness, achieving state-of-the-art performance across metrics. Notably, it reduces the Fréchet Gesture Distance (FGD) from 18.70 to 2.48, demonstrating an 86.7% improvement in motion realism. Our project page is https://yong-xie-xy.github.io/ReCoM/.

cs.GR↗

arXiv2025

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks

As deep learning models are increasingly deployed in safety-critical applications, evaluating their vulnerabilities to adversarial perturbations is essential for ensuring their reliability and trustworthiness. Over the past decade, a large number of white-box adversarial robustness evaluation methods (i.e., attacks) have been proposed, ranging from single-step to multi-step methods and from individual to ensemble methods. Despite these advances, challenges remain in conducting meaningful and comprehensive robustness evaluations, particularly when it comes to large-scale testing and ensuring evaluations reflect real-world adversarial risks. In this work, we focus on image classification models and propose a novel individual attack method, Probability Margin Attack (PMA), which defines the adversarial margin in the probability space rather than the logits space. We analyze the relationship between PMA and existing cross-entropy or logits-margin-based attacks, and show that PMA can outperform the current state-of-the-art individual methods. Building on PMA, we propose two types of ensemble attacks that balance effectiveness and efficiency. Furthermore, we create a million-scale dataset, CC1M, derived from the existing CC3M dataset, and use it to conduct the first million-scale white-box adversarial robustness evaluation of adversarially-trained ImageNet models. Our findings provide valuable insights into the robustness gaps between individual versus ensemble attacks and small-scale versus million-scale evaluations.

cs.LG↗

arXiv2024

Adaptive extended Kalman filter and point ahead angle prediction in the detection of gravitational waves in space

In the detection of gravitational waves in space, during the science phase of the mission, the point ahead angle mechanism (PAAM) serves to steer a laser beam to compensate for the angle generated by the relative motion of the two spacecrafts (SCs) during the approximately 10 seconds of flight time a laser beam will take from one SC to reach a distant SC of three million kilometers away. The common practice for pointing stability control of a laser beam is to first do a coarse tracking by the PAAM to steer a laser beam to compensate for the relative motion between two SCs, to be followed by a fine pointing stability control. In the present work, by exploiting the near-circular orbit structure of individual SC in the triangular constellation, the feasibility of inserting an adaptive Kalman filter (AEKF) into the PAAM control loop is investigated. By adopting a colored measurement noise model that closely resembles the prospective on orbit situation, numerical simulation suggests that the dynamic range of the PAAM may be reduced to the level of nano-radians using the prediction of the pointing head angle (PAA) by the AEKF. This will cut down on the TTL coupling noise and the position noise budget allocated to the PAAM. This in turn reduces the dynamic range of the fine pointing control and leaves room to improve its accuracy, thereby offers the prospect of reduction of the position noise budget allocated to the laser pointing instability as a whole.

astro-ph.IM↗

arXiv2024

Across-Platform Detection of Malicious Cryptocurrency Transactions via Account Interaction Learning

With the rapid evolution of Web3.0, cryptocurrency has become a cornerstone of decentralized finance. While these digital assets enable efficient and borderless financial transactions, their pseudonymous nature has also attracted malicious activities such as money laundering, fraud, and other financial crimes. Effective detection of malicious transactions is crucial to maintaining the security and integrity of the Web 3.0 ecosystem. Existing malicious transaction detection methods rely on large amounts of labeled data and suffer from low generalization. Label-efficient and generalizable malicious transaction detection remains a challenging task. In this paper, we propose ShadowEyes, a novel malicious transaction detection method. Specifically, we first propose a generalized graph structure named TxGraph as a representation of malicious transaction, which captures the interaction features of each malicious account and its neighbors. Then we carefully design a data augmentation method tailored to simulate the evolution of malicious transactions to generate positive pairs. To alleviate account label scarcity, we further design a graph contrastive mechanism, which enables ShadowEyes to learn discriminative features effectively from unlabeled data, thereby enhancing its detection capabilities in real-world scenarios. We conduct extensive experiments using public datasets to evaluate the performance of ShadowEyes. The results demonstrate that it outperforms state-of-the-art (SOTA) methods in four typical scenarios. Specifically, in the zero-shot learning scenario, it can achieve an F1 score of 76.98% for identifying gambling transactions, surpassing the SOTA method by12.05%. In the scenario of across-platform malicious transaction detection, ShadowEyes maintains an F1 score of around 90%, which is 10% higher than the SOTA method.

cs.CR↗

arXiv2024

Approaching the Intrinsic Properties of Moiré Structures Using Atomic Force Microscopy Ironing

Stacking monolayers of transition metal dichalcogenides (TMDs) has led to the discovery of a plethora of new exotic phenomena, resulting from moiré pattern formation. Due to the atomic thickness and high surface-to-volume ratio of heterostructures, the interfaces play a crucial role. Fluctuations in the interlayer distance affect interlayer coupling and moiré effects. Therefore, to access the intrinsic properties of the TMD stack, it is essential to obtain a clean and uniform interface between the layers. Here, we show that this is achieved by ironing with the tip of an atomic force microscope. This post-stacking procedure dramatically improves the homogeneity of the interfaces, which is reflected in the optical response of the interlayer exciton. We demonstrate that ironing improves the layer coupling, enhancing moiré effects and reducing disorder. This is crucial for the investigation of TMD heterostructure physics, which currently suffers from low reproducibility.

cond-mat.mes-hall↗

arXiv2024

Learning from Contrastive Prompts: Automated Optimization and Adaptation

As LLMs evolve, significant effort is spent on manually crafting prompts. While existing prompt optimization methods automate this process, they rely solely on learning from incorrect samples, leading to a sub-optimal performance. Additionally, an unexplored challenge in the literature is prompts effective for prior models may not perform well on newer versions or different languages. We propose the Learning from Contrastive Prompts (LCP) framework to address these gaps, enhancing both prompt optimization and adaptation. LCP employs contrastive learning to generate effective prompts by analyzing patterns in good and bad prompt examples. Our evaluation on the Big-Bench Hard dataset shows that LCP has a win rate of over 76% over existing methods in prompt optimization and demonstrates strong adaptability across different model versions, families, and languages. LCP offers a systematic approach to prompt engineering, reducing manual effort in deploying LLMs across varied contexts.

cs.CL↗

arXiv2024

Mutual Information Guided Optimal Transport for Unsupervised Visible-Infrared Person Re-identification

Unsupervised visible infrared person re-identification (USVI-ReID) is a challenging retrieval task that aims to retrieve cross-modality pedestrian images without using any label information. In this task, the large cross-modality variance makes it difficult to generate reliable cross-modality labels, and the lack of annotations also provides additional difficulties for learning modality-invariant features. In this paper, we first deduce an optimization objective for unsupervised VI-ReID based on the mutual information between the model's cross-modality input and output. With equivalent derivation, three learning principles, i.e., "Sharpness" (entropy minimization), "Fairness" (uniform label distribution), and "Fitness" (reliable cross-modality matching) are obtained. Under their guidance, we design a loop iterative training strategy alternating between model training and cross-modality matching. In the matching stage, a uniform prior guided optimal transport assignment ("Fitness", "Fairness") is proposed to select matched visible and infrared prototypes. In the training stage, we utilize this matching information to introduce prototype-based contrastive learning for minimizing the intra- and cross-modality entropy ("Sharpness"). Extensive experimental results on benchmarks demonstrate the effectiveness of our method, e.g., 60.6% and 90.3% of Rank-1 accuracy on SYSU-MM01 and RegDB without any annotations.

cs.CV↗

arXiv2024

Orangutan: A Multiscale Brain Emulation-Based Artificial Intelligence Framework for Dynamic Environments

Achieving General Artificial Intelligence (AGI) has long been a grand challenge in the field of AI, and brain-inspired computing is widely acknowledged as one of the most promising approaches to realize this goal. This paper introduces a novel brain-inspired AI framework, Orangutan. It simulates the structure and computational mechanisms of biological brains on multiple scales, encompassing multi-compartment neuron architectures, diverse synaptic connection modalities, neural microcircuits, cortical columns, and brain regions, as well as biochemical processes including facilitation, feedforward inhibition, short-term potentiation, and short-term depression, all grounded in solid neuroscience. Building upon these highly integrated brain-like mechanisms, I have developed a sensorimotor model that simulates human saccadic eye movements during object observation. The model's algorithmic efficacy was validated through testing with the observation of handwritten digit images.

q-bio.NC↗

arXiv2022

An automated system for strain engineering and straintronics of 2D materials

This work presents an automated three-point bending apparatus that can be used to study strain engineering and straintronics in two-dimensional materials. We benchmark the system by reporting reproducible strain tuned micro-reflectance, Raman, and photoluminescence spectra for monolayer molybdenum disulfide (MoS2). These results are in good agreement with reported literature using conventional bending apparatus. We further utilize the system to automate strain investigations of straintronic devices by measuring the piezoresistive effect and the strain effect on photoresponse in an MoS2 electrical device. The details of the construction of the straightforward system are given and we anticipate it can be easily implemented for study of strain engineering and straintronics in a wide variety of 2D material systems.

physics.app-ph↗

arXiv2022

A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Predictions

More and more investors and machine learning models rely on social media (e.g., Twitter and Reddit) to gather real-time information and sentiment to predict stock price movements. Although text-based models are known to be vulnerable to adversarial attacks, whether stock prediction models have similar vulnerability is underexplored. In this paper, we experiment with a variety of adversarial attack configurations to fool three stock prediction victim models. We address the task of adversarial generation by solving combinatorial optimization problems with semantics and budget constraints. Our results show that the proposed attack method can achieve consistent success rates and cause significant monetary loss in trading simulation by simply concatenating a perturbed but semantically similar tweet.

cs.CR↗

arXiv2022

Investigation of Deformation and Fracture Mechanisms in Two-dimensional Gallium Telluride Multilayers Using Nanoindentation

Two-dimensional (2D) materials possess great potential for flexible devices, ascribing to their outstanding electrical, optical, and mechanical properties. However, their mechanical deformation property and fracture mechanism, which are inescapable in many applications like flexible optoelectronics, are still unclear or not thoroughly investigated due methodology limitations. In light of this, such mechanical properties and mechanisms are explored on example of gallium telluride (GaTe), a promising optoelectronic candidate with an ultrahigh photo-responsibility and a high plasticity within 2D family. Considering the driving force insufficient in atomic force microscopy (AFM)-based nanoindentation method, here the mechanical properties of both substrate-supported and suspended GaTe multilayers were systematically investigated through full-scale Berkovich-tip nanoindentation, micro-Raman spectroscopy, AFM, and scanning electron microscopy. An unusual concurrence of multiple pop-in and load-drop events in loading curve was observed. By further correlating to molecular dynamics calculations, this concurrence was unveiled originating from the interlayer sliding mediated layers-by-layers fracture mechanism within GaTe multilayers. The van der Waals force between GaTe multilayers and substrates was revealed much stronger than that between GaTe interlayers, resulting in the easy sliding and fracture of multilayers within GaTe. This work provides new insights into the deformation and fracture mechanisms of GaTe and other similar 2D multilayers in flexible applications.

cond-mat.mtrl-sci↗

arXiv2020

Exploring the $e^+e^-\to Λ_c^+Λ_c^-$ cross sections

A simultaneous fit is performed to the $e^+e^-\to Λ_c^+Λ_c^-$ cross section data measured by Belle and BESIII from threshold up to 5.4 GeV. In order to accommodate both the BESIII measurement near threshold and the Belle observation of a resonance $Y(4630)$, we build a composite PDF with a Breit-Wigner resonance and a continuum contribution to model the full cross section line shape of $e^+e^-\to Λ_c^+Λ_c^-$. The fit gives a mass of $M=[4636.1_{-7.2}^{+9.8} ($stat$)\pm 8.0($syst$)]$~MeV/$c^2$, a width of $Γ_{\rm tot}=[34.5_{-16.2}^{+21.0} ($stat$)\pm 5.6($syst$)]$~MeV, and $Γ_{e^+ e^-}\mathcal{B}[Y(4630)\toΛ_c^+Λ_c^-]=[18.3_{-6.1}^{+8.8} ($stat$)\pm 1.1($syst$)]$~eV/$c^2$ for the resonance. The width of $Y(4630)$ from our study is narrower than the previous Belle fit. The mass and width of $Y(4630)$ also show good agreement with a vector resonance $Y(4626)$ recently observed in $D_s^+D_{s1}(2536)^-$ by Belle.

hep-ex↗

arXiv2020

Hexagonal Boron Nitride Phononic Crystal Waveguides

Hexagonal boron nitride (h-BN), one of the hallmark van der Waals (vdW) layered crystals with an ensemble of attractive physical properties, is playing increasingly important roles in exploring two-dimensional (2D) electronics, photonics, mechanics, and emerging quantum engineering. Here, we report on the demonstration of h-BN phononic crystal waveguides with designed pass and stop bands in the radio frequency (RF) range and controllable wave propagation and transmission, by harnessing arrays of coupled h-BN nanomechanical resonators with engineerable coupling strength. Experimental measurements validate that these phononic crystal waveguides confine and support 15 to 24 megahertz (MHz) wave propagation over 1.2 millimeters. Analogous to solid-state atomic crystal lattices, phononic bandgaps and dispersive behaviors have been observed and systematically investigated in the h-BN phononic waveguides. Guiding and manipulating acoustic waves on such additively integratable h-BN platform may facilitate multiphysical coupling and information transduction, and open up new opportunities for coherent on-chip signal processing and communication via emerging h-BN photonic and phononic devices.

cond-mat.mes-hall↗

arXiv2017

Liquid crystal self-assembly of upconversion nanorods enriched by depletion forces for mesostructured material preparation

Monodisperse rod-like colloidal particles are known for spontaneously forming both nematic and smectic liquid crystal phases, but their self-assembly was typically exploited from the fundamental soft condensed matter physics perspective. Here we demonstrate that depletion interactions, driven by non-adsorbing polymers like dextran and surfactants, can be used to enrich self-organization of photon-upconversion nanorods into orientationally ordered nematic and smectic-like membrane colloidal superstructures. We study thermodynamic phase diagrams and demonstrate polarization-dependent photon upconversion exhibited by the ensuing composites, which arises from the superposition of unique properties of the solid nanostructures and the long-range ordering enabled by liquid crystalline self-organization. Finally, we discuss how our method of utilizing self-assembly due to the steric and electrostatic interactions, along with attractive depletion forces, can enable technological uses of lyotropic colloidal liquid crystals and mesostructured composite materials enabled by them, even when they are formed by anisotropic nanoparticles with relatively small aspect ratios.

cond-mat.soft↗