arXiv subjects

Chengcheng Zhao

Publications and source records attributed to Chengcheng Zhao.

At least 19 recordsLinked to original sources

arXiv2026

Rethinking Bregman Divergences in Kronecker-Factored Optimizers

Shampoo-style optimizers approximate gradient covariance matrices using Kronecker-factored structures. Recent work~\cite{lin2026understanding} showed that such approximations can be viewed as projections under Bregman matrix divergences, leading to different Kronecker-factored preconditioners. However, it remains unclear what role the choice of divergence plays when the covariance is not exactly Kronecker-factored. We study this question through the spectrum of the covariance matrix. We show that Frobenius, von Neumann, and LogDet divergences distribute the unavoidable Kronecker approximation error differently across the covariance spectrum. We further show that their Kronecker factors are governed by divergence-weighted residuals rather than the raw approximation error, explaining how these spectral preferences are realized in the resulting preconditioners. Empirically, we observe that the top covariance eigenspace is substantially better aligned with the Hessian matrix, while the tail spectrum is much noisier and unreliable. Motivated by these findings, we propose a subspace-aware Kronecker optimizer that applies eigenvalue-based preconditioning in the top subspace and uses an adaptive isotropic acceleration constant in the bottom subspace.

cs.LG↗

arXiv2026

LOONG: Online Time-Optimal Autonomous Flight for MAVs in Cluttered Environments

Autonomous flight of micro air vehicles (MAVs) in unknown, cluttered environments remains challenging for time-critical missions due to conservative maneuvering strategies. This article presents an integrated planning and control framework for high-speed, time-optimal autonomous flight of MAVs in cluttered environments. In each replanning cycle (100 Hz), a time-optimal trajectory under polynomial presentation is generated as a reference, with the time-allocation process accelerated by imitation learning. Subsequently, a time-optimal model predictive contouring control (MPCC) incorporates safe flight corridor (SFC) constraints at variable horizon steps to enable aggressive yet safe maneuvering, while fully exploiting the MAV's dynamics. We validate the proposed framework extensively on a custom-built LiDAR-based MAV platform. Simulation results demonstrate superior aggressiveness compared to the state of the art, while real-world experiments achieve a peak speed of 18 m/s in a cluttered environment and succeed in 10 consecutive trials from diverse start points. The video is available at the following link: https://youtu.be/vexXXhv99oQ.

cs.RO↗

arXiv2025

Row-Stochastic Matrices Can Provably Outperform Doubly Stochastic Matrices in Decentralized Learning

Decentralized learning often involves a weighted global loss with heterogeneous node weights $\lambda$. We revisit two natural strategies for incorporating these weights: (i) embedding them into the local losses to retain a uniform weight (and thus a doubly stochastic matrix), and (ii) keeping the original losses while employing a $\lambda$-induced row-stochastic matrix. Although prior work shows that both strategies target the same $\lambda$-weighted global loss, it remains unclear whether the Euclidean-space guarantees are tight and what fundamentally differentiates their behaviors. To clarify this, we develop a weighted Hilbert-space framework $L^2(\lambda;\mathbb{R}^d)$ and obtain convergence rates that are strictly tighter than those from standard Euclidean analysis. In this geometry, the row-stochastic matrix becomes \emph{self-adjoint} whereas the doubly stochastic one does not, creating additional \emph{penalty terms} that amplify consensus error, thereby slowing convergence. Consequently, the difference in convergence arises not only from spectral gaps but also from these penalty terms. We then derive sufficient conditions under which the row-stochastic design converges faster even with a smaller spectral gap. Finally, by using a Rayleigh-quotient and Loewner-order eigenvalue comparison, we further obtain topology conditions that guarantee this advantage and yield practical topology-design guidelines.

cs.LG↗

arXiv2025

Fully Distributed State Estimation for Multi-agent Systems and its Application in Cooperative Localization

In this paper, we investigate the distributed state estimation problem for a continuous-time linear multi-agent system (MAS) composed of $\mathit{m}$ agents and monitored by the agents themselves. To address this problem, we propose a distributed observer that enables each agent to reconstruct the state of the MAS. The main idea is to let each agent $\mathit{i}$ recover the state of agent $\mathit{j}$ by using leader-follower consensus rules to track agent $\mathit{j}$'s state estimate, which is generated by agent $\mathit{j}$ itself using a Luenberger-like estimation rule. Under the assumptions of node-level observability and topological ordering consistency, we show that the estimation error dynamics are stabilizable if and only if the communication graph is strongly connected. Moreover, we discuss the fully distributed design of the proposed observer, assuming that the agents only know basic MAS configuration information, such as the homogeneity and the maximum number of allowable agents. This design ensures that the proposed observer functions correctly when agents are added or removed. Building on this, we consider cooperative localization as a distributed estimation problem and develop two fully distributed localization algorithms that allow agents to track their own and other agents' positions (and velocities) within the MAS. Finally, we conduct simulations to demonstrate the effectiveness of our proposed theoretical results.

eess.SY↗

arXiv2025

ImprovDML: Improved Trade-off in Private Byzantine-Resilient Distributed Machine Learning

Jointly addressing Byzantine attacks and privacy leakage in distributed machine learning (DML) has become an important issue. A common strategy involves integrating Byzantine-resilient aggregation rules with differential privacy mechanisms. However, the incorporation of these techniques often results in a significant degradation in model accuracy. To address this issue, we propose a decentralized DML framework, named ImprovDML, that achieves high model accuracy while simultaneously ensuring privacy preservation and resilience to Byzantine attacks. The framework leverages a kind of resilient vector consensus algorithms that can compute a point within the normal (non-Byzantine) agents' convex hull for resilient aggregation at each iteration. Then, multivariate Gaussian noises are introduced to the gradients for privacy preservation. We provide convergence guarantees and derive asymptotic learning error bounds under non-convex settings, which are tighter than those reported in existing works. For the privacy analysis, we adopt the notion of concentrated geo-privacy, which quantifies privacy preservation based on the Euclidean distance between inputs. We demonstrate that it enables an improved trade-off between privacy preservation and model accuracy compared to differential privacy. Finally, numerical simulations validate our theoretical results.

cs.LG↗

arXiv2025

Geometric Reachability for Attitude Control Systems via Contraction Theory

In this paper, we present a geometric framework for the reachability analysis of attitude control systems. We model the attitude dynamics on the product manifold $\mathrm{SO}(3) \times \mathbb{R}^3$ and introduce a novel parametrized family of Riemannian metrics on this space. Using contraction theory on manifolds, we establish reliable upper bounds on the Riemannian distance between nearby trajectories of the attitude control systems. By combining these trajectory bounds with numerical simulations, we provide a simulation-based algorithm to over-approximate the reachable sets of attitude systems. We show that the search for optimal metrics for distance bounds can be efficiently performed using semidefinite programming. Additionally, we introduce a practical and effective representation of these over-approximations on manifolds, enabling their integration with existing Euclidean tools and software. Numerical experiments validate the effectiveness of the proposed approach.

eess.SY↗

arXiv2024

Evidence for multiband gapless superconductivity in the topological superconductor candidate 4Hb-TaS2

We present the ultralow-temperature thermal conductivity measurements on single crystals of transition-metal dichalcogenide material 4Hb-TaS$_{2}$, which has recently been proposed as a topological superconductor candidate. In zero field, a small residual linear term $\kappa_{0}/T$ is observed, indicating the existence of a residual density of states in the superconducting state. The slow field dependence of $\kappa_{0}/T$ at low fields rules out the presence of nodes in the superconducting gap, and the S-shaped field dependence across the full field range suggests multiple superconducting gaps in 4Hb-TaS$_{2}$. Our results provide evidence for multiband gapless superconductivity in 4Hb-TaS$_{2}$, and the residual density of states come from certain gapless Fermi surfaces.

cond-mat.supr-con↗

arXiv2024

Near-Optimal Reinforcement Learning with Shuffle Differential Privacy

Reinforcement learning (RL) is a powerful tool for sequential decision-making, but its application is often hindered by privacy concerns arising from its interaction data. This challenge is particularly acute in advanced networked systems, where learning from operational and user data can expose systems to privacy inference attacks. Existing differential privacy (DP) models for RL are often inadequate: the centralized model requires a fully trusted server, creating a single point of failure risk, while the local model incurs significant performance degradation that is unsuitable for many networked applications. This paper addresses this gap by leveraging the emerging shuffle model of privacy, an intermediate trust model that provides strong privacy guarantees without a centralized trust assumption. We present Shuffle Differentially Private Policy Elimination (SDP-PE), the first generic policy elimination-based algorithm for episodic RL under the shuffle model. Our method introduces a novel exponential batching schedule and a ``forgetting'' mechanism to balance the competing demands of privacy and learning performance. Our analysis shows that SDP-PE achieves a near-optimal regret bound, demonstrating a superior privacy-regret trade-off with utility comparable to the centralized model while significantly outperforming the local model. The numerical experiments also corroborate our theoretical results and demonstrate the effectiveness of SDP-PE. This work establishes the viability of the shuffle model for secure data-driven decision-making in networked systems.

cs.LG↗

arXiv2024

Privacy-Preserving Resilient Vector Consensus

This paper studies privacy-preserving resilient vector consensus in multi-agent systems against faulty agents, where normal agents can achieve consensus within the convex hull of their initial states while protecting state vectors from being disclosed. Specifically, we consider a modification of an existing algorithm known as Approximate Distributed Robust Convergence Using Centerpoints (ADRC), i.e., Privacy-Preserving ADRC (PP-ADRC). Under PP-ADRC, each normal agent introduces multivariate Gaussian noise to its state during each iteration. We first provide sufficient conditions to ensure that all normal agents' states can achieve mean square convergence under PP-ADRC. Then, we analyze convergence accuracy from two perspectives, i.e., the Mahalanobis distance of the final value from its expectation and the Hausdorff distance-based alteration of the convex hull caused by noise when only partial dimensions are added with noise. Then, we employ concentrated geo-privacy to characterize privacy preservation and conduct a thorough comparison with differential privacy. Finally, numerical simulations demonstrate the theoretical results.

eess.SY↗

arXiv2024

BreakNet: Discontinuity-Resilient Multi-Scale Transformer Segmentation of Retinal Layers

Visible light optical coherence tomography (vis-OCT) is gaining traction for retinal imaging due to its high resolution and functional capabilities. However, the significant absorption of hemoglobin in the visible light range leads to pronounced shadow artifacts from retinal blood vessels, posing challenges for accurate layer segmentation. In this study, we present BreakNet, a multi-scale Transformer-based segmentation model designed to address boundary discontinuities caused by these shadow artifacts. BreakNet utilizes hierarchical Transformer and convolutional blocks to extract multi-scale global and local feature maps, capturing essential contextual, textural, and edge characteristics. The model incorporates decoder blocks that expand pathwaproys to enhance the extraction of fine details and semantic information, ensuring precise segmentation. Evaluated on rodent retinal images acquired with prototype vis-OCT, BreakNet demonstrated superior performance over state-of-the-art segmentation models, such as TCCT-BP and U-Net, even when faced with limited-quality ground truth data. Our findings indicate that BreakNet has the potential to significantly improve retinal quantification and analysis.

eess.IV↗

arXiv2024

Fully Automated OCT-based Tissue Screening System

This study introduces a groundbreaking optical coherence tomography (OCT) imaging system dedicated for high-throughput screening applications using ex vivo tissue culture. Leveraging OCT's non-invasive, high-resolution capabilities, the system is equipped with a custom-designed motorized platform and tissue detection ability for automated, successive imaging across samples. Transformer-based deep learning segmentation algorithms further ensure robust, consistent, and efficient readouts meeting the standards for screening assays. Validated using retinal explant cultures from a mouse model of retinal degeneration, the system provides robust, rapid, reliable, unbiased, and comprehensive readouts of tissue response to treatments. This fully automated OCT-based system marks a significant advancement in tissue screening, promising to transform drug discovery, as well as other relevant research fields.

physics.med-ph↗

arXiv2023

Heuristic Learning for Co-Design Scheme of Optimal Sequential Attack

This paper considers a novel co-design problem of the optimal \textit{sequential} attack, whose attack strategy changes with the time series, and in which the \textit{sequential} attack selection strategy and \textit{sequential} attack signal are simultaneously designed. Different from the existing attack design works that separately focus on attack subsets or attack signals, the joint design of the attack strategy poses a huge challenge due to the deep coupling relation between the \textit{sequential} attack selection strategy and \textit{sequential} attack signal. In this manuscript, we decompose the sequential co-design problem into two equivalent sub-problems. Specifically, we first derive an analytical closed-form expression between the optimal attack signal and the sequential attack selection strategy. Furthermore, we prove the finite-time inverse convergence of the critical parameters in the injected optimal attack signal by discrete-time Lyapunov analysis, which enables the efficient off-line design of the attack signal and saves computing resources. Finally, we exploit its relationship to design a heuristic two-stage learning-based joint attack algorithm (HTL-JA), which can accelerate realization of the attack target compared to the one-stage proximal-policy-optimization-based (PPO) algorithm. Extensive simulations are conducted to show the effectiveness of the injected optimal sequential attack.

math.OC↗

arXiv2023

Multigap nodeless superconductivity in the topological semimetal PdTe

Recently PdTe was identified as a spin-orbit coupled topological Dirac semimetal and was claimed to exhibit both bulk-nodal and surface-nodeless superconducting gaps. Here we report the ultralow-temperature thermal conductivity measurements on PdTe single crystals with $T_c$ = 4.5 K to investigate its superconducting gap structure. It is found that the residual linear term $κ_0/T$ is negligible in zero magnetic field. Furthermore, the field dependence of $κ_0(H)/T$ exhibits an $\sf S$-shaped curve. These results suggest that PdTe has multiple nodeless superconducting gaps, which is at odds with the claimed bulk-nodal gap. The reason for the discrepancy is likely that previous angle-resolved photoemission spectroscopy measurements were only performed down to 2 K and cannot observe the smaller nodeless gap. The fully gapped superconducting state in PdTe is compatible with it being a topological superconductor candidate.

cond-mat.supr-con↗

arXiv2023

Model-free False Data Injection Attack in Networked Control Systems: A Feedback Optimization Approach

Security issues have gathered growing interest within the control systems community, as physical components and communication networks are increasingly vulnerable to cyber attacks. In this context, recent literature has studied increasingly sophisticated \emph{false data injection} attacks, with the aim to design mitigative measures that improve the systems' security. Notably, data-driven attack strategies -- whereby the system dynamics is oblivious to the adversary -- have received increasing attention. However, many of the existing works on the topic rely on the implicit assumption of linear system dynamics, significantly limiting their scope. Contrary to that, in this work we design and analyze \emph{truly} model-free false data injection attack that applies to general linear and nonlinear systems. More specifically, we aim at designing an injected signal that steers the output of the system toward a (maliciously chosen) trajectory. We do so by designing a zeroth-order feedback optimization policy and jointly use probing signals for real-time measurements. We then characterize the quality of the proposed model-free attack through its optimality gap, which is affected by the dimensions of the attack signal, the number of iterations performed, and the convergence rate of the system. Finally, we extend the proposed attack scheme to the systems with internal noise. Extensive simulations show the effectiveness of the proposed attack scheme.

math.OC↗

arXiv2023

Evidence for gapless quantum spin liquid in a honeycomb lattice

One main theme in current condensed matter physics is the search of quantum spin liquid (QSL), an exotic magnetic state with strongly-fluctuating and highly-entangled spins down to zero temperature without static order. However, there is no consensus on the existence of a QSL ground state in any real material so far. The disorders and competing exchange interactions may prevent the formation of an ideal QSL state on frustrated spin lattices. Here we report systematic heat transport measurements on a honeycomb-lattice compound BaCo2(AsO4)2, which manifests magnetic order in zero field. In a narrow field range after the magnetic order is nearly suppressed by an in-plane field, in both perpendicular and parallel to the zigzag direction, a finite residual linear term of thermal conductivity is clearly observed, which is attributed to the mobile fractionalized spinon excitations. This provides smoking-gun evidence for a gapless QSL state in BaCo2(AsO4)2. We discuss the underlying physics to form this exotic gapless QSL state in Co2+ honeycomb lattice.

cond-mat.str-el↗

arXiv2022

Submodularity-based False Data Injection Attack Scheme in Multi-agent Dynamical Systems

Consensus in multi-agent dynamical systems is prone to be sabotaged by the adversary, which has attracted much attention due to its key role in broad applications. In this paper, we study a new false data injection (FDI) attack design problem, where the adversary with limited capability aims to select a subset of agents and manipulate their local multi-dimensional states to maximize the consensus convergence error. We first formulate the FDI attack design problem as a combinatorial optimization problem and prove it is NP-hard. Then, based on the submodularity optimization theory, we show the convergence error is a submodular function of the set of the compromised agents, which satisfies the property of diminishing marginal returns. In other words, the benefit of adding an extra agent to the compromised set decreases as that set becomes larger. With this property, we exploit the greedy scheme to find the optimal compromised agent set that can produce the maximum convergence error when adding one extra agent to that set each time. Thus, the FDI attack set selection algorithms are developed to obtain the near-optimal subset of the compromised agents. Furthermore, we derive the analytical suboptimality bounds and the worst-case running time under the proposed algorithms. Extensive simulation results are conducted to show the effectiveness of the proposed algorithm.

math.DS↗

arXiv2022

Resilient Average Consensus: A Detection and Compensation Approach

We study the problem of resilient average consensus for multi-agent systems with misbehaving nodes. To protect consensus valuefrom being influenced by misbehaving nodes, we address this problem by detecting misbehaviors, mitigating the corresponding adverse impact and achieving the resilient average consensus. In this paper, general types of misbehaviors are considered,including deception attacks, accidental faults and link failures. We characterize the adverse impact of misbehaving nodes in a distributed manner via two-hop communication information and develop a deterministic detection-compensation-based consensus (D-DCC) algorithm with a decaying fault-tolerant error bound. Considering scenarios where information sets are intermittently available due to link failures, a stochastic extension named stochastic detection-compensation-based consensus(S-DCC) algorithm is proposed. We prove that D-DCC and S-DCC allow nodes to asymptotically achieve resilient averageconsensus exactly and in expectation, respectively. Then, the Wasserstein distance is introduced to analyze the accuracy ofS-DCC. Finally, extensive simulations are conducted to verify the effectiveness of the proposed algorithm

eess.SY↗

arXiv2022

System Identification with Variance Minimization via Input Design

The subspace method is one of the mainstream system identification method of linear systems, and its basic idea is to estimate the system parameter matrices by projecting them into a subspace related to input and output. However, most of the existing subspace methods cannot have the statistic performance guaranteed since the lack of closed-form expression of the estimation. Meanwhile, traditional subspace methods cannot deal with the uncertainty of the noise, and thus stable identification results cannot be obtained. In this paper, we propose a novel improved subspace method from the perspective of input design, which guarantees the consistent and stable identification results with the minimum variance. Specifically, we first obtain a closed-form estimation of the system matrix, then analyze the statistic performance by deriving the maximum identification deviation. This identification deviation maximization problem is non-convex, and is solved by splitting it into two sub-problems with the optimal solution guaranteed. Next, an input design method is proposed to deal with the uncertainty and obtain stable identification results by minimizing the variance. This problem is formulated as a constrained min-max optimization problem. The optimal solution is obtained from transforming the cost function into a convex function while ensuring the safety constraints through the method of predictive control. We prove the consistency and the convergence of the proposed method. Simulation demonstrates the effectiveness of our method.

eess.SY↗