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Xing He

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5 papers

1 author row

EAAI Journal 2026 Journal Article

Fixed-time neurodynamic algorithms with element-wise normalization for sparse signal recovery

Hongsong Wen
Xing He
You Zhao
Junjian Huang
Tingwen Huang

Neurodynamic algorithms represent one of the important approaches for sparse recovery, owing to their fast convergence, real-time processing capability, and parallel implementation advantages, and have been widely applied across various fields. However, for high-dimensional sparse recovery problems with complex data distributions, existing neurodynamic algorithms still suffer from slow convergence and convergence time bounds that depend on the initial values. This paper develops two fixed-time neurodynamic algorithms based on classical locally competitive algorithm (LCA) to deal with the least absolute shrinkage and selection operator (Lasso) problem, which are applied to sparse signal recovery (SSR) and image recovery. Firstly, a generalized LCA (GenLCA) is designed based on the classical LCA, incorporating element-wise normalization method (EwNM) and sliding mode control technique. Then, in the framework of the GenLCA, time-varying coefficients are introduced to design a time-varying GenLCA (TGenLCA). In contrast to the classical LCA, the proposed neurodynamic algorithms are able to adaptively scale the learning rates of each dimension. Furthermore, the fixed-time convergence of the proposed neurodynamic algorithms to the optimal solution of the Lasso problem is established, with settling-time functions independent of the initial values. Finally, the effectiveness and superior convergence of the proposed neurodynamic algorithms are verified by SSR and image recovery experiments.

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AAAI Conference 2026 Conference Paper

FreqCycle: A Multi-Scale Time-Frequency Analysis Method for Time Series Forecasting

Boya Zhang
Shuaijie Yin
Huiwen Zhu
Xing He

Mining time-frequency features is critical for time series forecasting. Existing research has predominantly focused on modeling low-frequency patterns, where most time series energy is concentrated. The overlooking of mid to high frequency continues to limit further performance gains in deep learning models. We propose FreqCycle, a novel framework integrating: (i) a Filter-Enhanced Cycle Forecasting (FECF) module to extract low-frequency features by explicitly learning shared periodic patterns in the time domain, and (ii) a Segmented Frequency-domain Pattern Learning (SFPL) module to enhance mid to high frequency energy proportion via learnable filters and adaptive weighting. Furthermore, time series data often exhibit coupled multi-periodicity, such as intertwined weekly and daily cycles. To address coupled multi-periodicity as well as long lookback window challenges, we extend FreqCycle hierarchically into MFreqCycle, which decouples nested periodic features through cross-scale interactions. Extensive experiments on seven diverse domain benchmarks demonstrate that FreqCycle achieves state-of-the-art accuracy while maintaining faster inference speeds, striking an optimal balance between performance and efficiency.

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AAAI Conference 2025 Conference Paper

CAMH: Advancing Model Hijacking Attack in Machine Learning

Xing He
Jiahao Chen
Yuwen Pu
Qingming Li
Chunyi Zhou
Yingcai Wu
Jinbao Li
Shouling Ji

In the burgeoning domain of machine learning, the reliance on third-party services for model training and the adoption of pre-trained models have surged. However, this reliance introduces vulnerabilities to model hijacking attacks, where adversaries manipulate models to perform unintended tasks, leading to significant security and ethical concerns, like turning an ordinary image classifier into a tool for detecting faces in pornographic content, all without the model owner’s knowledge. This paper introduces Category-Agnostic Model Hijacking (CAMH), a novel model hijacking attack method capable of addressing the challenges of class number mismatch, data distribution divergence, and performance balance between the original and hijacking tasks. CAMH incorporates synchronized training layers, random noise optimization, and a dual-loop optimization approach to ensure minimal impact on the original task’s performance while effectively executing the hijacking task. We evaluate CAMH across multiple benchmark datasets and network architectures, demonstrating its potent attack effectiveness while ensuring minimal degradation in the performance of the original task.

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JMLR Journal 2023 Journal Article

Accelerated Primal-Dual Mirror Dynamics for Centralized and Distributed Constrained Convex Optimization Problems

You Zhao
Xiaofeng Liao
Xing He
Mingliang Zhou
Chaojie Li

This paper investigates two accelerated primal-dual mirror dynamical approaches for smooth and nonsmooth convex optimization problems with affine and closed, convex set constraints. In the smooth case, an accelerated primal-dual mirror dynamical approach (APDMD) based on accelerated mirror descent and primal-dual framework is proposed and accelerated convergence properties of primal-dual gap, feasibility measure and the objective function value along with trajectories of APDMD are derived by the Lyapunov analysis method. Then, we extend APDMD into two distributed dynamical approaches to deal with two types of distributed smooth optimization problems, i.e., distributed constrained consensus problem (DCCP) and distributed extended monotropic optimization (DEMO) with accelerated convergence guarantees. Moreover, in the nonsmooth case, we propose a smoothing accelerated primal-dual mirror dynamical approach (SAPDMD) with the help of smoothing approximation technique and the above APDMD. We further also prove that primal-dual gap, objective function value and feasibility measure along with trajectories of SAPDMD have the same accelerated convergence properties as APDMD by choosing the appropriate smooth approximation parameters. Later, we propose two smoothing accelerated distributed dynamical approaches to deal with nonsmooth DEMO and DCCP to obtain accelerated and efficient solutions. Finally, numerical and comparative experiments are given to demonstrate the effectiveness and superiority of the proposed accelerated mirror dynamical approaches. [abs] [ pdf ][ bib ] &copy JMLR 2023. ( edit, beta )

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TCS Journal 2015 Journal Article

Inefficiency of equilibria for scheduling game with machine activation costs

Ling Lin
Xiaochen Xian
Yujie Yan
Xing He
Zhiyi Tan

In this paper, we study the scheduling game with machine activation costs. A set of jobs is to be processed on parallel identical machines. The number of machines available is unlimited, and an activation cost is needed whenever a machine is activated in order to process jobs. Each job chooses a machine on which it wants to be processed. The cost of a job is the sum of the load of the machine it chooses and its shared activated cost. The social cost is the total cost of all jobs. Representing the Price of Anarchy (PoA) and Price of Stability (PoS) as functions of the number of jobs, we get the tight bounds of PoA and PoS. Representing PoA and PoS as functions of the smallest processing time of jobs, asymptotically tight bound of PoA and improved lower and upper bounds of PoS are also given.

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