arXiv subjects

Jie Xu

Publications and source records attributed to Jie Xu.

At least 163 records · Page 9Linked to original sources

arXiv2025

Towards Secure ISAC Beamforming: How Many Dedicated Sensing Beams Are Required?

In this paper, sensing-assisted secure communication in a multi-user multi-eavesdropper integrated sensing and communication (ISAC) system is investigated. Confidential communication signals and dedicated sensing signals are jointly transmitted by a base station (BS) to simultaneously serve users and sense aerial eavesdroppers (AEs). A sum rate maximization problem is formulated under AEs' Signal-to-Interference-plus-Noise Ratio (SINR) and sensing Signal-to-Clutter-plus-Noise Ratio (SCNR) constraints. A fractional-programming-based alternating optimization algorithm is developed to solve this problem for fully digital arrays, where successive convex approximation (SCA) and semidefinite relaxation (SDR) are leveraged to handle non-convex constraints. Furthermore, the minimum number of dedicated sensing beams is analyzed via a worst-case rank bound, upon which the proposed beamforming design is further extended to the hybrid analog-digital (HAD) array architecture, where the unit-modulus constraint is addressed by manifold optimization. Simulation results demonstrate that only a small number of sensing beams are sufficient for both sensing and jamming AEs, and the proposed designs consistently outperform strong baselines while also revealing the communication-sensing trade-off.

eess.SP↗

arXiv2025

Tuning LLM-based Code Optimization via Meta-Prompting: An Industrial Perspective

There is a growing interest in leveraging multiple large language models (LLMs) for automated code optimization. However, industrial platforms deploying multiple LLMs face a critical challenge: prompts optimized for one LLM often fail with others, requiring expensive model-specific prompt engineering. This cross-model prompt engineering bottleneck severely limits the practical deployment of multi-LLM systems in production environments. We introduce Meta-Prompted Code Optimization (MPCO), a framework that automatically generates high-quality, task-specific prompts across diverse LLMs while maintaining industrial efficiency requirements. MPCO leverages metaprompting to dynamically synthesize context-aware optimization prompts by integrating project metadata, task requirements, and LLM-specific contexts. It is an essential part of the ARTEMIS code optimization platform for automated validation and scaling. Our comprehensive evaluation on five real-world codebases with 366 hours of runtime benchmarking demonstrates MPCO's effectiveness: it achieves overall performance improvements up to 19.06% with the best statistical rank across all systems compared to baseline methods. Analysis shows that 96% of the top-performing optimizations stem from meaningful edits. Through systematic ablation studies and meta-prompter sensitivity analysis, we identify that comprehensive context integration is essential for effective meta-prompting and that major LLMs can serve effectively as meta-prompters, providing actionable insights for industrial practitioners.

cs.SE↗

arXiv2025

Rotatable Antenna-Enabled Spectrum Sharing in Cognitive Radio Systems

Non-fixed flexible antenna architectures, such as fluid antenna system (FAS), movable antenna (MA), and pinching antenna, have garnered significant interest in recent years. Among them, rotatable antenna (RA) technology has recently drawn significant attention in wireless systems owing to its unique ability to exploit additional spatial degrees-of-freedom (DoFs) by dynamically adjusting the three-dimensional (3D) boresight direction of each antenna. In this letter, we propose a new RA-assisted cognitive radio (CR) system designed to achieve efficient spectrum sharing while mitigating interference between primary and secondary communication links. Specifically, we formulate an optimization problem for the joint design of the transmit beamforming and the boresight directions of RAs at the secondary transmitter (ST), aimed at maximizing the received signal-to-interference-plus-noise ratio (SINR) at the secondary receiver (SR), while satisfying both interference constraint at the primary receiver (PR) and the maximum transmit power constraint at the ST. Although the formulated problem is challenging to solve due to its non-convexity and coupled variables, we develop an efficient algorithm by leveraging alternating optimization (AO) and successive convex approximation (SCA) techniques to acquire high-quality solutions. Numerical results demonstrate that the proposed RA-assisted system substantially outperforms conventional benchmark schemes in spectrum-sharing CR systems, validating RA's capability to simultaneously enhance the communication quality at the SR and mitigate interference at the PR.

eess.SP↗

arXiv2025

H+: An Efficient Similarity-Aware Aggregation for Byzantine Resilient Federated Learning

Federated Learning (FL) enables decentralized model training without sharing raw data. However, it remains vulnerable to Byzantine attacks, which can compromise the aggregation of locally updated parameters at the central server. Similarity-aware aggregation has emerged as an effective strategy to mitigate such attacks by identifying and filtering out malicious clients based on similarity between client model parameters and those derived from clean data, i.e., data that is uncorrupted and trustworthy. However, existing methods adopt this strategy only in FL systems with clean data, making them inapplicable to settings where such data is unavailable. In this paper, we propose H+, a novel similarity-aware aggregation approach that not only outperforms existing methods in scenarios with clean data, but also extends applicability to FL systems without any clean data. Specifically, H+ randomly selects $r$-dimensional segments from the $p$-dimensional parameter vectors uploaded to the server and applies a similarity check function $H$ to compare each segment against a reference vector, preserving the most similar client vectors for aggregation. The reference vector is derived either from existing robust algorithms when clean data is unavailable or directly from clean data. Repeating this process $K$ times enables effective identification of honest clients. Moreover, H+ maintains low computational complexity, with an analytical time complexity of $\mathcal{O}(KMr)$, where $M$ is the number of clients and $Kr \ll p$. Comprehensive experiments validate H+ as a state-of-the-art (SOTA) method, demonstrating substantial robustness improvements over existing approaches under varying Byzantine attack ratios and multiple types of traditional Byzantine attacks, across all evaluated scenarios and benchmark datasets.

cs.LG↗

arXiv2025

Existence of Positive Scalar Curvature and Positive Yamabe constant on Hypersurfaces of Noncompact Cylinders

Let $ X $ be an oriented, closed manifold with $ \dim X \geqslant 2 $. Let $ (Z, \partial Z) $ be an oriented, compact manifold with (possibly empty) smooth boundary and $ \dim Z \geqslant 2 $. In this article, we show that if the noncompact cylinder $ X \times \mathbb{R} $ admits a complete Riemannian metric $ g $ with positive injectivity radius and uniformly positive scalar curvature, and that is of bounded geometry or bounded curvature, then $ X $ admits a positive scalar curvature metric within the same conformal class provided that some $ g $-angle condition is satisfied. This partially answers a conjecture of Rosenberg and Stolz \cite{RosSto} without topological assumptions. With the $ g $-angle condition, we can also show that if $ (Z \times \mathbb{R}, \partial Z \times \mathbb{R}) $ admits a complete metric $ g $ that has positive Yamabe constant and positive injectivity radius, and is of bounded geometry or bounded curvature, then $ (Z, \partial Z) $ has positive Yamabe constant for the conformal class $ [\imath^{*}g] $ with the natural inclusion $ \imath : (Z, \partial Z) \hookrightarrow (Z \times \mathbb{R}, \partial Z \times \mathbb{R}) $.

math.DG↗

arXiv2025

FedEL: Federated Elastic Learning for Heterogeneous Devices

Federated learning (FL) enables distributed devices to collaboratively train machine learning models while maintaining data privacy. However, the heterogeneous hardware capabilities of devices often result in significant training delays, as straggler clients with limited resources prolong the aggregation process. Existing solutions such as client selection, asynchronous FL, and partial training partially address these challenges but encounter issues such as reduced accuracy, stale updates, and compromised model performance due to inconsistent training contributions. To overcome these limitations, we propose FedEL, a federated elastic learning framework that enhances training efficiency while maintaining model accuracy. FedEL introduces a novel window-based training process, sliding the window to locate the training part of the model and dynamically selecting important tensors for training within a coordinated runtime budget. This approach ensures progressive and balanced training across all clients, including stragglers. Additionally, FedEL employs a tensor importance adjustment module, harmonizing local and global tensor importance to mitigate biases caused by data heterogeneity. The experiment results show that FedEL achieves up to 3.87x improvement in time-to-accuracy compared to baselines while maintaining or exceeding final test accuracy.

cs.LG↗

arXiv2025

Modeling Temporal Dependencies within the Target for Long-Term Time Series Forecasting

Long-term time series forecasting (LTSF) is a critical task across diverse domains. Despite significant advancements in LTSF research, we identify a performance bottleneck in existing LTSF methods caused by the inadequate modeling of Temporal Dependencies within the Target (TDT). To address this issue, we propose a novel and generic temporal modeling framework, Temporal Dependency Alignment (TDAlign), that equips existing LTSF methods with TDT learning capabilities. TDAlign introduces two key innovations: 1) a loss function that aligns the change values between adjacent time steps in the predictions with those in the target, ensuring consistency with variation patterns, and 2) an adaptive loss balancing strategy that seamlessly integrates the new loss function with existing LTSF methods without introducing additional learnable parameters. As a plug-and-play framework, TDAlign enhances existing methods with minimal computational overhead, featuring only linear time complexity and constant space complexity relative to the prediction length. Extensive experiments on six strong LTSF baselines across seven real-world datasets demonstrate the effectiveness and flexibility of TDAlign. On average, TDAlign reduces baseline prediction errors by \textbf{1.47\%} to \textbf{9.19\%} and change value errors by \textbf{4.57\%} to \textbf{15.78\%}, highlighting its substantial performance improvements.

cs.LG↗

arXiv2025

Generative AI Meets Wireless Sensing: Towards Wireless Foundation Model

Generative Artificial Intelligence (GenAI) has made significant advancements in fields such as computer vision (CV) and natural language processing (NLP), demonstrating its capability to synthesize high-fidelity data and improve generalization. Recently, there has been growing interest in integrating GenAI into wireless sensing systems. By leveraging generative techniques such as data augmentation, domain adaptation, and denoising, wireless sensing applications, including device localization, human activity recognition, and environmental monitoring, can be significantly improved. This survey investigates the convergence of GenAI and wireless sensing from two complementary perspectives. First, we explore how GenAI can be integrated into wireless sensing pipelines, focusing on two modes of integration: as a plugin to augment task-specific models and as a solver to directly address sensing tasks. Second, we analyze the characteristics of mainstream generative models, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models, and discuss their applicability and unique advantages across various wireless sensing tasks. We further identify key challenges in applying GenAI to wireless sensing and outline a future direction toward a wireless foundation model: a unified, pre-trained design capable of scalable, adaptable, and efficient signal understanding across diverse sensing tasks.

cs.LG↗

arXiv2025

AI Agentic Programming: A Survey of Techniques, Challenges, and Opportunities

AI agentic programming is an emerging paradigm where large language model (LLM)-based coding agents autonomously plan, execute, and interact with tools such as compilers, debuggers, and version control systems. Unlike conventional code generation, these agents decompose goals, coordinate multi-step processes, and adapt based on feedback, reshaping software development practices. This survey provides a timely review of the field, introducing a taxonomy of agent behaviors and system architectures and examining relevant techniques for planning, context management, tool integration, execution monitoring, and benchmarking datasets. We highlight challenges of this fast-moving field and discuss opportunities for building reliable, transparent, and collaborative coding agents.

cs.SE↗

arXiv2025

Stochastic Periodic Solutions for Newtonian Systems via Lyapunov Function

This paper establishes an existence theory for distributed periodic solutions to Newton's equation with stochastic time-periodic forcing, where the friction matrix is the Hessian of a twice continuously differentiable friction function. Employing the Khasminskii criterion and Lyapunov functions, we prove its existence under the assumptions that both the friction and potential functions tend to positive infinity at infinity, and their gradient inner product grows at least like an even-power polynomial. Provided the potential grows sufficiently fast at infinity, this result persists under bounded friction matrix perturbations. This largely confirms the stochastic Levinson conjecture proposed in [8] and resolves the open problem and the interesting problem posed in [8, P. 342]. Distributed periodic solutions exist in two key cases: 1. Polynomial Case: Both functions are polynomials with positive definite leading terms (a minimal condition for dissipation). 2. Plasma Physics Case: The potential force is bounded, while the friction function has sufficiently high growth (as in many plasma physics models).

math.DS↗

arXiv2025

Environment-Aware IRS Deployment via Channel Knowledge Map: Joint Sensing-Communications Coverage Optimization

This paper studies the intelligent reflecting surface (IRS) deployment optimization problem for IRS-enabled integrated sensing and communications (ISAC) systems, in which multiple IRSs are strategically deployed at candidate locations to assist a base station (BS) to enhance the coverage of both sensing and communications. We present an environment-aware IRS deployment design via exploiting the channel knowledge map (CKM), which provides the channel state information (CSI) between each candidate IRS location and BS or targeted sensing/communication points. Based on the obtained CSI from CKM, we optimize the deployment of IRSs, jointly with the BS's transmit beamforming and IRSs' reflective beamforming during operation, with the objective of minimizing the system cost, while guaranteeing the minimum illumination power requirements at sensing areas and the minimum signal-to-noise ratio (SNR) requirements at communication areas. In particular, we consider two cases when the IRSs' reflective beamforming optimization can be implemented dynamically in real time and quasi-stationarily over the whole operation period, respectively. For both cases, the joint IRS deployment and transmit/reflective beamforming designs are formulated as mixed-integer non-convex optimization problems, which are solved via the successive convex approximation (SCA)-based relax-and-bound method. Specifically, we first relax the binary IRS deployment indicators into continuous variables, then find converged solutions via SCA, and finally round relaxed indicators back to binary values. Numerical results demonstrate the effectiveness of our proposed algorithms in reducing the system cost while meeting the sensing and communication requirements.

eess.SP↗

arXiv2025

Integrated photonic ultrawideband real-time spectrum sensing for 6G wireless networks

The sixth generation (6G) wireless networks require dynamic spectrum management to optimize the utilization of scarce spectral resources and support emerging integrated sensing and communication (ISAC) applications. This necessitates real-time spectrum sensing (RT-SS) capability with ultrawide measurement range, compact size, and low latency. Conventional electronic RT-SS solutions face critical challenges in operating across the millimeter-wave and sub-terahertz bands, which are essential spectra for 6G wireless. While photonic RT-SS has the potential to surpass this limitation, the current implementations feature limited bandwidths below 50 GHz and mostly rely on bulky dispersive fibers with high latency. Here, we address these challenges by developing an integrated photonic RT-SS system capable of ultrabroadband measurement from microwave to sub-terahertz bands, covering the full spectrum for 6G wireless. The photonic RT-SS circuit integrates a broadband electro-optic (EO) modulator for unknown signal loading, an EO tunable microring filter bank for high-speed parallel frequency-to-time mapping, as well as an EO comb for precise channel frequency referencing, all realized on a single thin-film lithium niobate chip. We achieve an unprecedented spectral measurement range beyond 120 GHz at a low latency of less than 100 ns. To validate the effectiveness of our photonic RT-SS system in future 6G scenes, we further propose a heuristic spectro-temporal resource allocation algorithm and conduct a proof-of-concept ISAC demonstration, where a radar adaptively access RT-SS-informed spectrally underutilized regions for high-quality target sensing under dynamic communication interferences. Our work presents a compact and cost-effective solution for efficient spectrum sharing and dynamic management in future 6G ISAC networks.

physics.optics↗

arXiv2025

Compact All optical Reservoir Computing via Luminescence Dynamics in Rare-earth Ions-doped Nanocrystals

Optical neuromorphic computing offers a promising route to high speed, energy efficient information processing. However, photonic neurons, as the critical components for enhancing computational expressivity, still face significant bottlenecks in nonlinear mapping and memory capacity. Here, we demonstrate an all optical reservoir computing system based on rare earth ions doped nanocrystals for the first time, leveraging their intrinsic nonlinear luminescence dynamics and multitimescale memory. Unlike traditional schemes that require bulky optical delays or intricate resonant structures, our platform exploits the material's inherent properties: nonlinear cross-relaxation processes enable nonlinear mapping while millisecond-scale metastable energy levels provide fading memory. As a proof of concept, we achieve 90.7% accuracy in MNIST digit classification and low-error chaotic time-series prediction (NRMSE < 0.1) using the rare-earth ions based system. Our work significantly reduce system footprint and complexity, offering a scalable, fully optical solution for edge computing and real-time neuromorphic applications.

physics.optics↗

arXiv2025

Integrated Sensing, Communication, and Computation for Over-the-Air Federated Edge Learning

This paper studies an over-the-air federated edge learning (Air-FEEL) system with integrated sensing, communication, and computation (ISCC), in which one edge server coordinates multiple edge devices to wirelessly sense the objects and use the sensing data to collaboratively train a machine learning model for recognition tasks. In this system, over-the-air computation (AirComp) is employed to enable one-shot model aggregation from edge devices. Under this setup, we analyze the convergence behavior of the ISCC-enabled Air-FEEL in terms of the loss function degradation, by particularly taking into account the wireless sensing noise during the training data acquisition and the AirComp distortions during the over-the-air model aggregation. The result theoretically shows that sensing, communication, and computation compete for network resources to jointly decide the convergence rate. Based on the analysis, we design the ISCC parameters under the target of maximizing the loss function degradation while ensuring the latency and energy budgets in each round. The challenge lies on the tightly coupled processes of sensing, communication, and computation among different devices. To tackle the challenge, we derive a low-complexity ISCC algorithm by alternately optimizing the batch size control and the network resource allocation. It is found that for each device, less sensing power should be consumed if a larger batch of data samples is obtained and vice versa. Besides, with a given batch size, the optimal computation speed of one device is the minimum one that satisfies the latency constraint. Numerical results based on a human motion recognition task verify the theoretical convergence analysis and show that the proposed ISCC algorithm well coordinates the batch size control and resource allocation among sensing, communication, and computation to enhance the learning performance.

cs.IT↗

arXiv2025

Neural Robot Dynamics

Accurate and efficient simulation of modern robots remains challenging due to their high degrees of freedom and intricate mechanisms. Neural simulators have emerged as a promising alternative to traditional analytical simulators, capable of efficiently predicting complex dynamics and adapting to real-world data; however, existing neural simulators typically require application-specific training and fail to generalize to novel tasks and/or environments, primarily due to inadequate representations of the global state. In this work, we address the problem of learning generalizable neural simulators for robots that are structured as articulated rigid bodies. We propose NeRD (Neural Robot Dynamics), learned robot-specific dynamics models for predicting future states for articulated rigid bodies under contact constraints. NeRD uniquely replaces the low-level dynamics and contact solvers in an analytical simulator and employs a robot-centric and spatially-invariant simulation state representation. We integrate the learned NeRD models as an interchangeable backend solver within a state-of-the-art robotics simulator. We conduct extensive experiments to show that the NeRD simulators are stable and accurate over a thousand simulation steps; generalize across tasks and environment configurations; enable policy learning exclusively in a neural engine; and, unlike most classical simulators, can be fine-tuned from real-world data to bridge the gap between simulation and reality.

cs.RO↗

arXiv2025

$V_{cb}$ puzzle in semi-leptonic $B\to D^*$ decays revisited

Inspired by the newly reported $B\to D^*(\to Dπ)\ell\barν_\ell$ differential decay rates by the Belle and Belle II Collaborations, we revisit the $V_{cb}$ puzzle in semi-leptonic $B\to D^*$ decays, considering the latest lattice QCD~(LQCD) simulations and light-cone sum rule~(LCSR) results. We examine the commonly used Caprini-Lellouch-Neubert~(CLN), Boyd-Grinstein-Lebed~(BGL), and heavy quark effective theory~(HQET) parameterizations. We demonstrate that these three parameterizations yield consistent results and reconfirm the $V_{cb}$ puzzle. Then, we use a state-of-the-art Bayesian method to estimate the impact of higher-order terms beyond the present HQET expansion on the uncertainty of $V_{cb}$. We show that higher-order effects cannot eliminate the deviation between the exclusive and inclusive determinations of $V_{cb}$. Finally, utilizing the best-fit results obtained in the HQET parameterization via fitting LQCD and LCSR data only as inputs, we predict the relevant observables, i.e., $R_{D^*}$, $F_L^{D^*}$, and $P_τ^{D^*}$, sensitive to new physics in the $B\to D^*\ell\barν_\ell$ decays. We conclude that lepton-flavour universality violations still exist in the $b\to cτν$.

hep-ph↗

arXiv2025

Cooperative Sensing-Assisted Predictive Beam Tracking for MIMO-OFDM Networked ISAC Systems

This paper studies a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) networked integrated sensing and communication (ISAC) system, in which multiple base stations (BSs) perform beam tracking to communicate with a mobile device. In particular, we focus on the beam tracking over a number of tracking time slots (TTSs) and suppose that these BSs operate at non-overlapping frequency bands to avoid the severe inter-cell interference. Under this setup, we propose a new cooperative sensing-assisted predictive beam tracking design. In each TTS, the BSs use echo signals to cooperatively track the mobile device as a sensing target, and continuously adjust the beam directions to follow the device for enhancing the performance for both communication and sensing. First, we propose a cooperative sensing design to track the device, in which the BSs first employ the two-dimensional discrete Fourier transform (2D-DFT) technique to perform local target estimation, and then use the extended Kalman filter (EKF) method to fuse their individual measurement results for predicting the target parameters. Next, based on the predicted results, we obtain the achievable rate for communication and the predicted conditional Cramér-Rao lower bound (PC-CRLB) for target parameters estimation in the next TTS, as a function of the beamforming vectors. Accordingly, we formulate the predictive beamforming design problem, with the objective of maximizing the achievable communication rate in the following TTS, while satisfying the PC-CRLB requirement for sensing. To address the resulting non-convex problem, we first propose a semi-definite relaxation (SDR)-based algorithm to obtain the optimal solution, and then develop an alternative penalty-based algorithm to get a high-quality low-complexity solution.

cs.NI↗

arXiv2025

Robust Online Calibration for UWB-Aided Visual-Inertial Navigation with Bias Correction

This paper presents a novel robust online calibration framework for Ultra-Wideband (UWB) anchors in UWB-aided Visual-Inertial Navigation Systems (VINS). Accurate anchor positioning, a process known as calibration, is crucial for integrating UWB ranging measurements into state estimation. While several prior works have demonstrated satisfactory results by using robot-aided systems to autonomously calibrate UWB systems, there are still some limitations: 1) these approaches assume accurate robot localization during the initialization step, ignoring localization errors that can compromise calibration robustness, and 2) the calibration results are highly sensitive to the initial guess of the UWB anchors' positions, reducing the practical applicability of these methods in real-world scenarios. Our approach addresses these challenges by explicitly incorporating the impact of robot localization uncertainties into the calibration process, ensuring robust initialization. To further enhance the robustness of the calibration results against initialization errors, we propose a tightly-coupled Schmidt Kalman Filter (SKF)-based online refinement method, making the system suitable for practical applications. Simulations and real-world experiments validate the improved accuracy and robustness of our approach.

cs.RO↗