arXiv subjects

Xiaoyu Wang

Publications and source records attributed to Xiaoyu Wang.

At least 127 records · Page 7Linked to original sources

arXiv2025

Decentralized Federated Learning with Model Caching on Mobile Agents

Federated Learning (FL) trains a shared model using data and computation power on distributed agents coordinated by a central server. Decentralized FL (DFL) utilizes local model exchange and aggregation between agents to reduce the communication and computation overheads on the central server. However, when agents are mobile, the communication opportunity between agents can be sporadic, largely hindering the convergence and accuracy of DFL. In this paper, we propose Cached Decentralized Federated Learning (Cached-DFL) to investigate delay-tolerant model spreading and aggregation enabled by model caching on mobile agents. Each agent stores not only its own model, but also models of agents encountered in the recent past. When two agents meet, they exchange their own models as well as the cached models. Local model aggregation utilizes all models stored in the cache. We theoretically analyze the convergence of Cached-DFL, explicitly taking into account the model staleness introduced by caching. We design and compare different model caching algorithms for different DFL and mobility scenarios. We conduct detailed case studies in a vehicular network to systematically investigate the interplay between agent mobility, cache staleness, and model convergence. In our experiments, Cached-DFL converges quickly, and significantly outperforms DFL without caching.

cs.LG↗

arXiv2025

Quantitative Error Bounds for Scaling Limits of Stochastic Iterative Algorithms

Stochastic iterative algorithms, including stochastic gradient descent (SGD) and stochastic gradient Langevin dynamics (SGLD), are widely utilized for optimization and sampling in large-scale and high-dimensional problems in machine learning, statistics, and engineering. Numerous works have bounded the parameter error in, and characterized the uncertainty of, these approximations. One common approach has been to use scaling limit analyses to relate the distribution of algorithm sample paths to a continuous-time stochastic process approximation, particularly in asymptotic setups. Focusing on the univariate setting, in this paper, we build on previous work to derive non-asymptotic functional approximation error bounds between the algorithm sample paths and the Ornstein-Uhlenbeck approximation using an infinite-dimensional version of Stein's method of exchangeable pairs. We show that this bound implies weak convergence under modest additional assumptions and leads to a bound on the error of the variance of the iterate averages of the algorithm. Furthermore, we use our main result to construct error bounds in terms of two common metrics: the Lévy-Prokhorov and bounded Wasserstein distances. Our results provide a foundation for developing similar error bounds for the multivariate setting and for more sophisticated stochastic approximation algorithms.

stat.ML↗

arXiv2025

Generalized Multi-hop Traffic Pressure for Heterogeneous Traffic Perimeter Control

Perimeter control (PC) prevents loss of traffic network capacity due to congestion in urban areas. Homogeneous PC allows all access points to a protected region to have identical permitted inflow. However, homogeneous PC performs poorly when the congestion in the protected region is heterogeneous (e.g., imbalanced demand) since the homogeneous PC does not consider specific traffic conditions around each perimeter intersection. When the protected region has spatially heterogeneous congestion, one needs to modulate the perimeter inflow rate to be higher near low-density regions and vice versa for high-density regions. A naïve approach is to leverage 1-hop traffic pressure to measure traffic condition around perimeter intersections, but such metric is too spatially myopic for PC. To address this issue, we formulate multi-hop downstream pressure grounded on Markov chain theory, which ``looks deeper'' into the protected region beyond perimeter intersections. In addition, we formulate a two-stage hierarchical control scheme that can leverage this novel multi-hop pressure to redistribute the total permitted inflow provided by a pre-trained deep reinforcement learning homogeneous control policy. Experimental results show that our heterogeneous PC approaches leveraging multi-hop pressure significantly outperform homogeneous PC in scenarios where the origin-destination flows are highly imbalanced with high spatial heterogeneity. Moveover, our approach is shown to be robust against turning ratio uncertainties by a sensitivity analysis.

cs.LG↗

arXiv2025

Multi-hop Upstream Anticipatory Traffic Signal Control with Deep Reinforcement Learning

Coordination in traffic signal control is crucial for managing congestion in urban networks. Existing pressure-based control methods focus only on immediate upstream links, leading to suboptimal green time allocation and increased network delays. However, effective signal control inherently requires coordination across a broader spatial scope, as the effect of upstream traffic should influence signal control decisions at downstream intersections, impacting a large area in the traffic network. Although agent communication using neural network-based feature extraction can implicitly enhance spatial awareness, it significantly increases the learning complexity, adding an additional layer of difficulty to the challenging task of control in deep reinforcement learning. To address the issue of learning complexity and myopic traffic pressure definition, our work introduces a novel concept based on Markov chain theory, namely \textit{multi-hop upstream pressure}, which generalizes the conventional pressure to account for traffic conditions beyond the immediate upstream links. This farsighted and compact metric informs the deep reinforcement learning agent to preemptively clear the multi-hop upstream queues, guiding the agent to optimize signal timings with a broader spatial awareness. Simulations on synthetic and realistic (Toronto) scenarios demonstrate controllers utilizing multi-hop upstream pressure significantly reduce overall network delay by prioritizing traffic movements based on a broader understanding of upstream congestion.

cs.LG↗

arXiv2025

Bias voltage controlled inversions of tunneling magnetoresistance in van der Waals heterostructures Fe3GaTe2/hBN/Fe3GaTe2

We report the bias voltage controlled inversions of tunneling magnetoresistance (TMR) in magnetic tunnel junctions composed of Fe3GaTe2 electrodes and hBN tunneling barrier, observed at room temperature. The polarity reversal of TMR occurs consistently at around 0.625 V across multiple devices and temperatures, highlighting the robustness of the effect. To understand this behavior, we developed a theoretical model incorporating spin-resolved density of states (DOS) at high energy levels. By adjusting the DOS weighting at different k points to account for misalignment between the crystal structure of electrodes in experimental devices, we improved agreement between experimental and theoretical inversion voltages. Our results provide valuable insight into the voltage-controlled spin injection and detection in two-dimensional magnetic tunnel junctions, with implications for the development of energy-efficient spintronic devices.

cond-mat.mes-hall↗

arXiv2025

CoPRA: Bridging Cross-domain Pretrained Sequence Models with Complex Structures for Protein-RNA Binding Affinity Prediction

Accurately measuring protein-RNA binding affinity is crucial in many biological processes and drug design. Previous computational methods for protein-RNA binding affinity prediction rely on either sequence or structure features, unable to capture the binding mechanisms comprehensively. The recent emerging pre-trained language models trained on massive unsupervised sequences of protein and RNA have shown strong representation ability for various in-domain downstream tasks, including binding site prediction. However, applying different-domain language models collaboratively for complex-level tasks remains unexplored. In this paper, we propose CoPRA to bridge pre-trained language models from different biological domains via Complex structure for Protein-RNA binding Affinity prediction. We demonstrate for the first time that cross-biological modal language models can collaborate to improve binding affinity prediction. We propose a Co-Former to combine the cross-modal sequence and structure information and a bi-scope pre-training strategy for improving Co-Former's interaction understanding. Meanwhile, we build the largest protein-RNA binding affinity dataset PRA310 for performance evaluation. We also test our model on a public dataset for mutation effect prediction. CoPRA reaches state-of-the-art performance on all the datasets. We provide extensive analyses and verify that CoPRA can (1) accurately predict the protein-RNA binding affinity; (2) understand the binding affinity change caused by mutations; and (3) benefit from scaling data and model size.

q-bio.BM↗

arXiv2024

Stability and convergence analysis of AdaGrad for non-convex optimization via novel stopping time-based techniques

Adaptive gradient optimizers (AdaGrad), which dynamically adjust the learning rate based on iterative gradients, have emerged as powerful tools in deep learning. These adaptive methods have significantly succeeded in various deep learning tasks, outperforming stochastic gradient descent. However, despite AdaGrad's status as a cornerstone of adaptive optimization, its theoretical analysis has not adequately addressed key aspects such as asymptotic convergence and non-asymptotic convergence rates in non-convex optimization scenarios. This study aims to provide a comprehensive analysis of AdaGrad and bridge the existing gaps in the literature. We introduce a new stopping time technique from probability theory, which allows us to establish the stability of AdaGrad under mild conditions. We further derive the asymptotically almost sure and mean-square convergence for AdaGrad. In addition, we demonstrate the near-optimal non-asymptotic convergence rate measured by the average-squared gradients in expectation, which is stronger than the existing high-probability results. The techniques developed in this work are potentially of independent interest for future research on other adaptive stochastic algorithms.

math.OC↗

arXiv2024

SolarDesign: An Online Photovoltaic Device Simulation and Design Platform

SolarDesign (https://solardesign.cn/) is an online photovoltaic device simulation and design platform that provides engineering modeling analysis for crystalline silicon solar cells, as well as emerging high-efficiency solar cells such as organic, perovskite, and tandem cells. The platform offers user-updatable libraries of basic photovoltaic materials and devices, device-level multi-physics simulations involving optical-electrical-thermal interactions, and circuit-level compact model simulations based on detailed balance theory. Employing internationally advanced numerical methods, the platform accurately, rapidly, and efficiently solves optical absorption, electrical transport, and compact circuit models. It achieves multi-level photovoltaic simulation technology from ``materials to devices to circuits'' with fully independent intellectual property rights. Compared to commercial software, the platform achieves high accuracy and improves speed by more than an order of magnitude. Additionally, it can simulate unique electrical transport processes in emerging solar cells, such as quantum tunneling, exciton dissociation, and ion migration.

physics.optics↗

arXiv2024

Nonlinear valley Hall effect in a bilayer transition metal dichalcogenide

Valley-contrasting Hall transport conventionally relies on the inversion symmetry breaking in two-dimensional systems, which greatly limits the selection range of valley materials. In particular, while monolayer transition metal dichalcogenides have been widely utilized as a well-known class of valley materials in valleytronics, the centrosymmetric nature hinders the realization of valley-contrasting properties in the bilayer counterparts. Here, taking MoS$_{2}$ as an example, we discover valley-contrasting transport in bilayer transition metal dichalcogenides by exploring nonlinear transport regime. Using effective models and first-principles calculations, our work demonstrates that nonvanishing nonlinear valley Hall conductivities emerge in a uniaxially strained MoS$_{2}$ bilayer, owing to strain-induced band tilts of Dirac fermions. With the aid of small spin-orbit-coupling induced band splittings, the conduction bands generate much remarkable nonlinear valley Hall conductivity. Moreover, the nonlinear conductivities are highly tunable through modulating the strength and the direction of the strain, chemical potential, and interlayer gap. Our findings not only expands material choices for valleytronic applications, but also provides opportunities for designing advanced electronic devices that leverage nonlinear valley transports.

cond-mat.mes-hall↗

arXiv2024

Bias Voltage Driven Tunneling Magnetoresistance Polarity Reversal in 2D Stripy Antiferromagnet CrOCl

Atomically thin materials with coupled magnetic and electric polarization are critical for developing energy-efficient and high-density spintronic devices, yet they remain scarce due to often conflicting requirements of stabilizing both magnetic and electric orders. The recent discovery of the magnetoelectric effect in the 2D stripy antiferromagnet CrOCl highlights this semiconductor as a promising platform to explore electric field effects on magnetoresistance. In this study, we systematically investigate the magnetoresistance in tunneling junctions of bilayer and monolayer CrOCl. We observe that the transition from antiferromagnetic to ferrimagnetic phases in both cases induces a positive magnetoresistance at low bias voltages, which reverses to a negative value at higher bias voltages. This polarity reversal is attributed to the additional electric dipoles present in the antiferromagnetic state, as supported by our theoretical calculations. These findings suggest a pathway for the electric control of spintronic devices and underscore the potential of 2D magnets like CrOCl in advancing energy-efficient spintronic applications.

cond-mat.mes-hall↗

arXiv2024

Magnetoresistance oscillations in vertical junctions of 2D antiferromagnetic semiconductor CrPS$_4$

Magnetoresistance (MR) oscillations serve as a hallmark of intrinsic quantum behavior, traditionally observed only in conducting systems. Here we report the discovery of MR oscillations in an insulating system, the vertical junctions of CrPS$_4$ which is a two dimensional (2D) A-type antiferromagnetic semiconductor. Systematic investigations of MR peaks under varying conditions, including electrode materials, magnetic field direction, temperature, voltage bias and layer number, elucidate a correlation between MR oscillations and spin-canted states in CrPS$_4$. Experimental data and analysis point out the important role of the in-gap electronic states in generating MR oscillations, and we proposed that spin selected interlayer hopping of localized defect states may be responsible for it. Our findings not only illuminate the unusual electronic transport in CrPS$_4$ but also underscore the potential of van der Waals magnets for exploring interesting phenomena.

cond-mat.mes-hall↗

arXiv2024

Effective Bilevel Optimization via Minimax Reformulation

Bilevel optimization has found successful applications in various machine learning problems, including hyper-parameter optimization, data cleaning, and meta-learning. However, its huge computational cost presents a significant challenge for its utilization in large-scale problems. This challenge arises due to the nested structure of the bilevel formulation, where each hyper-gradient computation necessitates a costly inner optimization procedure. To address this issue, we propose a reformulation of bilevel optimization as a minimax problem, effectively decoupling the outer-inner dependency. Under mild conditions, we show these two problems are equivalent. Furthermore, we introduce a multi-stage gradient descent and ascent (GDA) algorithm to solve the resulting minimax problem with convergence guarantees. Extensive experimental results demonstrate that our method outperforms state-of-the-art bilevel methods while significantly reducing the computational cost.

cs.LG↗

arXiv2024

GestureGPT: Toward Zero-Shot Free-Form Hand Gesture Understanding with Large Language Model Agents

Existing gesture interfaces only work with a fixed set of gestures defined either by interface designers or by users themselves, which introduces learning or demonstration efforts that diminish their naturalness. Humans, on the other hand, understand free-form gestures by synthesizing the gesture, context, experience, and common sense. In this way, the user does not need to learn, demonstrate, or associate gestures. We introduce GestureGPT, a free-form hand gesture understanding framework that mimics human gesture understanding procedures to enable a natural free-form gestural interface. Our framework leverages multiple Large Language Model agents to manage and synthesize gesture and context information, then infers the interaction intent by associating the gesture with an interface function. More specifically, our triple-agent framework includes a Gesture Description Agent that automatically segments and formulates natural language descriptions of hand poses and movements based on hand landmark coordinates. The description is deciphered by a Gesture Inference Agent through self-reasoning and querying about the interaction context (e.g., interaction history, gaze data), which is managed by a Context Management Agent. Following iterative exchanges, the Gesture Inference Agent discerns the user's intent by grounding it to an interactive function. We validated our framework offline under two real-world scenarios: smart home control and online video streaming. The average zero-shot Top-1/Top-5 grounding accuracies are 44.79%/83.59% for smart home tasks and 37.50%/73.44% for video streaming tasks. We also provide an extensive discussion that includes rationale for model selection, generalizability, and future research directions for a practical system etc.

cs.CL↗

arXiv2024

Reactor neutrino liquid xenon coherent elastic scattering experiment

Coherent elastic neutrino-nucleus scattering (CEvNS) provides a unique probe for neutrino properties Beyond the Standard Model (BSM) physics. REactor neutrino LIquid xenon Coherent Scattering experiment (RELICS), a proposed reactor neutrino program using liquid xenon time projection chamber (LXeTPC) technology, aims to investigate the CEvNS process of antineutrinos off xenon atomic nuclei. In this work, the design of the experiment is studied and optimized based on Monte Carlo (MC) simulations. To achieve a sufficiently low energy threshold for CEvNS detection, an ionization-only analysis channel is adopted for RELICS. A high emission rate of delayed electrons after a big ionization signal is the major background, leading to an analysis threshold of 120 photo-electrons in the CEvNS search. The second largest background, nuclear recoils induced by cosmic-ray neutrons, is suppressed via a passive water shield. The physics potential of RELICS is explored with a 32 kg*yr exposure at a baseline of 25 m from a reactor core with a 3 GW thermal power. In an energy range of 120 to 300 PE, corresponding to an average nuclear recoil from 0.63 to 1.36 keV considering the liquid xenon response and detector-related effect, we expect 4639.7 CEvNS and 1687.8 background events. The sensitivity of RELICS to the weak mixing angle is investigated at a low momentum transfer. Our study shows that RELICS can further improve the constraints on the non-standard neutrino interaction (NSI) compared to the current best results.

hep-ex↗

arXiv2024

A Comprehensive Guide to Simulation-based Inference in Computational Biology

Computational models are invaluable in capturing the complexities of real-world biological processes. Yet, the selection of appropriate algorithms for inference tasks, especially when dealing with real-world observational data, remains a challenging and underexplored area. This gap has spurred the development of various parameter estimation algorithms, particularly within the realm of Simulation-Based Inference (SBI), such as neural and statistical SBI methods. Limited research exists on how to make informed choices on SBI methods when faced with real-world data, which often results in some form of model misspecification. In this paper, we provide comprehensive guidelines for deciding between SBI approaches for complex biological models. We apply the guidelines to two agent-based models that describe cellular dynamics using real-world data. Our study unveils a critical insight: while neural SBI methods demand significantly fewer simulations for inference results, they tend to yield biased estimations, a trend persistent even with robust variants of these algorithms. On the other hand, the accuracy of statistical SBI methods enhances substantially as the number of simulations increases. This finding suggests that, given a sufficient computational budget, statistical SBI can surpass neural SBI in performance. Our results not only shed light on the efficacy of different SBI methodologies in real-world scenarios but also suggest potential avenues for enhancing neural SBI approaches. This study is poised to be a useful resource for computational biologists navigating the intricate landscape of SBI in biological modeling.

stat.AP↗

arXiv2024

Mitigating Partial Observability in Adaptive Traffic Signal Control with Transformers

Efficient traffic signal control is essential for managing urban transportation, minimizing congestion, and improving safety and sustainability. Reinforcement Learning (RL) has emerged as a promising approach to enhancing adaptive traffic signal control (ATSC) systems, allowing controllers to learn optimal policies through interaction with the environment. However, challenges arise due to partial observability (PO) in traffic networks, where agents have limited visibility, hindering effectiveness. This paper presents the integration of Transformer-based controllers into ATSC systems to address PO effectively. We propose strategies to enhance training efficiency and effectiveness, demonstrating improved coordination capabilities in real-world scenarios. The results showcase the Transformer-based model's ability to capture significant information from historical observations, leading to better control policies and improved traffic flow. This study highlights the potential of leveraging the advanced Transformer architecture to enhance urban transportation management.

cs.LG↗

arXiv2024

A lifted Bregman strategy for training unfolded proximal neural network Gaussian denoisers

Unfolded proximal neural networks (PNNs) form a family of methods that combines deep learning and proximal optimization approaches. They consist in designing a neural network for a specific task by unrolling a proximal algorithm for a fixed number of iterations, where linearities can be learned from prior training procedure. PNNs have shown to be more robust than traditional deep learning approaches while reaching at least as good performances, in particular in computational imaging. However, training PNNs still depends on the efficiency of available training algorithms. In this work, we propose a lifted training formulation based on Bregman distances for unfolded PNNs. Leveraging the deterministic mini-batch block-coordinate forward-backward method, we design a bespoke computational strategy beyond traditional back-propagation methods for solving the resulting learning problem efficiently. We assess the behaviour of the proposed training approach for PNNs through numerical simulations on image denoising, considering a denoising PNN whose structure is based on dual proximal-gradient iterations.

math.OC↗

arXiv2024

State Estimator Design: Addressing General Delay Structures with Dissipative Constraints

Dissipative estimator (observer) design for continuous time-delay systems poses a significant challenge when an unlimited number of pointwise and general distributed delays (DDs) are concerned. We propose an effective solution to this semi-open problem using the Krasovski\uı functional (KF) framework in conjunction with a quadratic supply rate function, where both the plant and the estimator can accommodate an unlimited number of pointwise and general distributed delays with an unlimited number of square-integrable kernels. A key contribution is the introduction of a control concept called Kronecker-Seuret Decomposition (KSD) for matrix-valued functions, which allows for the factorizations or approximations of any DD integral kernel without introducing conservatism. Moreover, using KSD facilitates the construction of complete-type KFs with integral kernels that can contain any number of weakly differentiable and linearly independent functions. Our proposed solution is formulated as sequential convex SDP problems and is set out in two theorems along with an off-line iterative algorithm, which eliminates the need for nonlinear numerical solvers. We show the effectiveness of our method using two challenging numerical experiments, including a system stabilized by a non-smooth controller.

math.OC↗