arXiv subjects

Jimin Wang

Publications and source records attributed to Jimin Wang.

At least 19 recordsLinked to original sources

arXiv2026

Differentially Private Consensus for Time-Delay Multi-agent Systems

This paper is concerned with the differentially private consensus problem for discrete-time multi-agent systems with communication delays. The purpose of the paper is to achieve differentially private consensus for such systems while protecting the entire delayed initial histories of all agents. A novel adjacency relation for delayed histories is introduced, and a Laplace-noise-based privacy mechanism is developed, where the noise variance is allowed to vary with time and even increase. By using the difference resolvent function method, decay estimates for the fundamental solutions of the delayed difference equations are derived. Based on these estimates and a backstepping technique, mean square weak consensus, mean square strong consensus, and almost sure strong consensus are established. The estimates for the fundamental solutions are also used to derive an explicit sensitivity bound. Furthermore, a constructive parameter design is provided to achieve a prescribed infinite-horizon $\epsilon^\star$-differential privacy level. Numerical simulations illustrate the theoretical results.

eess.SY↗

arXiv2026

EGTR-Review: Efficient Evidence-Grounded Scientific Peer Review Generation via Multi-Agent Teacher Distillation

Scientific peer review generation has attracted increasing attention for reducing reviewing burdens and providing timely feedback. However, existing Large Language Model (LLM)-based methods often produce generic comments with insufficient evidence support and weak source traceability, while complex multi-agent systems incur high inference costs. To address these challenges, we propose EGTR-Review, an Evidence-Grounded and Traceable Review Generation framework via Multi-Agent Teacher Distillation. EGTR-Review first constructs a multi-agent teacher that performs structure-aware paper decomposition, key-element extraction, external scholarly evidence retrieval, evidence-state labeling, verification reasoning, and review synthesis. It then distills both intermediate reasoning trajectories and final review comments into a lightweight student model through task-prefix-driven multi-task learning. An evidence-weighted objective further reduces the influence of weak, missing, or non-verifiable supervision. Experiments on public peer-review datasets show that EGTR-Review (Student) outperforms strong prompt-based, fine-tuned, and structured/agentic baselines across automatic metrics, LLM-as-Judge evaluation, and human evaluation, while maintaining strong factual grounding and source traceability with substantially lower token consumption and inference time. Our code, prompts, configurations, and sample data are available on GitHub.

cs.CL↗

arXiv2026

Single-shot in situ pulse-duration measurement using plasma grating

Accurate measurement of the pulse duration of ultrashort, ultra-intense laser pulses at focus is essential for strong-field science. Most existing diagnostics, however, cannot allow direct in situ measurement in the focal region because of damage-threshold limits and unavoidable spatial averaging. We present a direct single-shot far-field diagnostic based on a plasma grating. In this method, the pulse duration is encoded in the axial length of an interference-written plasma grating and retrieved from the corresponding Bragg-diffraction signal. Comparison with near-field (pre-focus) autocorrelator measurements and far-field (at-focus) scanning measurements confirms single-shot pulse-duration retrieval in the focal region over 35-130 fs, and the method remains effective at a peak intensity of $\sim 10^{16}{\rm W/cm^2}$. In principle, the measurable range can be extended to 15-300 fs and to higher peak intensities. The method is insensitive to the laser central wavelength and offers a practical approach to far-field diagnostics in high-power laser systems.

physics.optics↗

arXiv2026

Recursive Binary Identification with Differential Privacy and Data Tampering Attacks

In this paper, we consider the parameter estimation in a bandwidth-constrained sensor network communicating through an insecure medium. The sensor performs a local quantization, and transmits a 1-bit message to an estimation center through a wireless medium where the transmission of information is vulnerable to attackers. Both eavesdroppers and data tampering attackers are considered in our setting. A differential privacy method is used to protect the sensitive information against eavesdroppers. Then, a recursive projection algorithm is proposed such that the estimation center achieves the almost sure convergence and mean-square convergence when quantized measurements, differential privacy, and data tampering attacks are considered in a uniform framework. A privacy analysis including the convergence rate with privacy or without privacy is given. Further, we extend the problem to multi-agent systems. For this case, a distributed recursive projection algorithm is proposed with guaranteed almost sure and mean square convergence. A simulation example is provided to illustrate the effectiveness of the proposed algorithms.

eess.SY↗

arXiv2025

Distributed Fusion Estimation with Protecting Exogenous Inputs

In the context of distributed fusion estimation, directly transmitting local estimates to the fusion center may cause a privacy leakage concerning exogenous inputs. Thus, it is crucial to protect exogenous inputs against full eavesdropping while achieving distributed fusion estimation. To address this issue, a noise injection strategy is provided by injecting mutually independent noises into the local estimates transmitted to the fusion center. To determine the covariance matrices of the injected noises, a constrained minimization problem is constructed by minimizing the sum of mean square errors of the local estimates while ensuring ({\epsilon}, {\delta})-differential privacy. Suffering from the non-convexity of the minimization problem, an approach of relaxation is proposed, which efficiently solves the minimization problem without sacrificing differential privacy level. Then, a differentially private distributed fusion estimation algorithm based on the covariance intersection approach is developed. Further, by introducing a feedback mechanism, the fusion estimation accuracy is enhanced on the premise of the same ({\epsilon}, {\delta})-differential privacy. Finally, an illustrative example is provided to demonstrate the effectiveness of the proposed algorithms, and the trade-off between differential privacy level and fusion estimation accuracy.

eess.SY↗

arXiv2025

Observer-based Differentially Private Consensus for Linear Multi-agent Systems

This paper investigates the differentially private consensus problem for general linear multi-agent systems (MASs) based on output feedback protocols. To protect the output information, which is considered private data and may be at high risk of exposure, Laplace noise is added to the information exchange. The conditions for achieving mean square and almost sure consensus in observer-based MASs are established using the backstepping method and the convergence theory for nonnegative almost supermartingales. It is shown that the separation principle remains valid for the consensus problem of linear MASs with decaying Laplace noise. Furthermore, the convergence rate is provided. Then, a joint design framework is developed for state estimation gain, feedback control gain, and noise to ensure the preservation of \epsilon-differential privacy. The output information of each agent is shown to be protected at every time step. Finally, sufficient conditions are established for simultaneously achieving consensus and preserving differential privacy for linear MASs utilizing both full-order and reduced-order observers. Meanwhile, an \epsilon*-differentially private consensus is achieved to meet the desired privacy level. Two simulation examples are provided to validate the theoretical results.

eess.SY↗

arXiv2025

CKKS Cryptosystem-Based Secure Parameter Identification for Multi-Participant ARX Systems

This paper investigates parameter identification for multi-participant autoregressive systems with exogenous input (ARX systems) while protecting the system input and output. A novel Cheon-Kim-Kim-Song (CKKS) cryptosystem-based secure parameter identification algorithm is proposed. By combining a modified CKKS cryptosystem and a proxy re-encryption scheme, the algorithm enables the fusion center to perform homomorphic operations on the ciphertexts which are encrypted using different secret keys. A sufficient condition on the truncation value of the truncated discrete Gaussian noise is derived to ensure the indistinguishability under chosen-plaintext attack (IND-CPA) security of the algorithm under collusion and quantum attacks. For convergence analysis, an auxiliary plaintext sequence is constructed to characterize the encryption noise and quantization error in the encrypted estimate. Using this sequence, a criterion for avoiding plaintext overflow is given, based on which the mean square convergence and convergence rate of the algorithm are given. A numerical example demonstrates the effectiveness and superior performance of the algorithm.

eess.SY↗

arXiv2025

Privacy-Preserving Cram\'er-Rao Lower Bound

This paper establishes the privacy-preserving Cramer-Rao lower bound (CRLB) theory, characterizing the fundamental limit of identification accuracy under privacy constraint for general stochastic obfuscation mechanisms. An identifiability criterion under privacy constraint is derived by using Fisher information matrix as the privacy metric. In the identifiable case, a precise privacy-preserving CRLB is established with an explicit expression, which quantifies the privacy cost without unspecified constant factors. Considering computational efficiency, recursive formulas are developed to compute the privacy-preserving CRLB for multi-measurement systems, reducing the computational burden caused by direct high-dimensional matrix inversion. To demonstrate the tightness of the lower bound, a Gaussian-mechanism-based privacy-preserving RLS algorithm is shown to exactly attain the bound under Gaussian measurement noises, and a maximum-likelihood-based privacy-preserving identification algorithm is proposed to attain the bound in the sense of convergence rates under non-Gaussian measurement noises. For applicability, the proposed theory can be extended to dynamic model state estimation, distributed estimation, and average consensus. Experimental results are provided to demonstrate the privacy-preserving CRLB and show the effectiveness of the proposed algorithms.

eess.SY↗

arXiv2025

Privacy-Preserving Distributed Estimation with Limited Data Rate

This paper focuses on the privacy-preserving distributed estimation problem with a limited data rate, where the observations are the sensitive information. Specifically, a binary-valued quantizer-based privacy-preserving distributed estimation algorithm is developed, which improves the algorithm's privacy-preserving capability and simultaneously reduces the communication costs. The algorithm's privacy-preserving capability, measured by the Fisher information matrix, is dynamically enhanced over time. Notably, the Fisher information matrix of the output signals with respect to the sensitive information converges to zero at a polynomial rate, and the improvement in privacy brought by the quantizers is quantitatively characterized as a multiplicative effect. Regarding the communication costs, each sensor transmits only 1 bit of information to its neighbours at each time step. Additionally, the assumption on the negligible quantization error for real-valued messages is not required. While achieving the requirements of privacy preservation and reducing communication costs, the algorithm ensures that its estimates converge almost surely to the true value of the unknown parameter by establishing a co-design guideline for the time-varying privacy noises and step-sizes. A polynomial almost sure convergence rate is obtained, and then the trade-off between privacy and convergence rate is established. Numerical examples demonstrate the main results.

eess.SY↗

arXiv2025

Differentially Private Recursive Least Squares Estimation for ARX Systems with Multi-Participants

This paper proposes a differentially private recursive least squares algorithm to estimate the parameter of autoregressive systems with exogenous inputs and multi-participants (MP-ARX systems) and protect each participant's sensitive information from potential attackers. We first give a rigorous differential privacy analysis of the algorithm, and establish the quantitative relationship between the added noises and the privacy-preserving level when the system is asymptotically stable. The asymptotic stability of the system is necessary for ensuring the differential privacy of the algorithm. We then give an estimation error analysis of the algorithm under the general and possible weakest excitation condition without requiring the boundedness, independence and stationarity on the regression vectors. Particularly, when there is no regression term in the system output and the differential privacy only on the system output is considered, $\varepsilon$-differential privacy and almost sure convergence of the algorithm can be established simultaneously. To minimize the estimation error of the algorithm with $\varepsilon$-differential privacy, the existence of the noise intensity is proved. Finally, two examples are given to show the efficiency of the algorithm.

eess.SY↗

arXiv2025

Differentially Private Gradient-Tracking-Based Distributed Stochastic Optimization over Directed Graphs

This paper proposes a differentially private gradient-tracking-based distributed stochastic optimization algorithm over directed graphs. In particular, privacy noises are incorporated into each agent's state and tracking variable to mitigate information leakage, after which the perturbed states and tracking variables are transmitted to neighbors. We design two novel schemes for the step-sizes and the sampling number within the algorithm. The sampling parameter-controlled subsampling method employed by both schemes enhances the differential privacy level, and ensures a finite cumulative privacy budget even over infinite iterations. The algorithm achieves both almost sure and mean square convergence for nonconvex objectives. Furthermore, when nonconvex objectives satisfy the Polyak-Lojasiewicz condition, Scheme (S1) achieves a polynomial mean square convergence rate, and Scheme (S2) achieves an exponential mean square convergence rate. The trade-off between privacy and convergence is presented. The effectiveness of the algorithm and its superior performance compared to existing works are illustrated through numerical examples of distributed training on the benchmark datasets "MNIST" and "CIFAR-10".

eess.SY↗

arXiv2024

State Estimation with Protecting Exogenous Inputs via Cram\'er-Rao Lower Bound Approach

This paper addresses the real-time state estimation problem for dynamic systems while protecting exogenous inputs against adversaries, who may be honest-but-curious third parties or external eavesdroppers. The Cram\'er-Rao lower bound (CRLB) is employed to constrain the mean square error (MSE) of the adversary's estimate for the exogenous inputs above a specified threshold. By minimizing the MSE of the state estimate while ensuring a certain privacy level measured by CRLB, the problem is formulated as a constrained optimization. To solve the optimization problem, an explicit expression for CRLB is first provided. As the computational complexity of the CRLB increases with the time step, a low-complexity approach is proposed to make the complexity independent of time. Then, a relaxation approach is proposed to efficiently solve the optimization problem. Finally, a privacy-preserving state estimation algorithm with low complexity is developed, which also ensures $(\epsilon, \delta)$-differential privacy. Two illustrative examples, including a practical scenario for protecting building occupancy, demonstrate the effectiveness of the proposed algorithm.

eess.SY↗

arXiv2024

Baryogenesis via QCD preheating with nonadiabatic baryon chemical potential

The chiral phase transition in QCD can be supercooled in the thermal history of the universe to be instantaneously out-of equilibrium, if QCD is coupled to a dark QCD sector exhibiting the dark chiral phase transition of the first order. In that case the QCD sigma meson field (as the chiral order parameter, or the light quark condensate) starts to roll in a nonadiabatic way down to the true QCD vacuum. Meanwhile a dynamic baryonic chemical potential can be generated solely within QCD, which is governed by the dynamic motion of the QCD sigma meson field, analogously to the spontaneous baryogenesis or the leptogenesis via the Higgs or axionlike relaxation scenario. When QCD is further allowed to communicate with a dark fermion with mass of order of 1 GeV and the baryon number violating coupling to neutron, the nonadiabatic QCD sigma motion along with the nonadiabatic baryon chemical potential can trigger the preheating and produce the baryon number asymmetry. We discuss this scenario in details to find that the QCD-induced dynamic baryon chemical potential plays a significant role for the QCD preheating and the baryogenesis, which yields the desired amount of the asymmetry today consistently with current astrophysical, cosmological, and terrestrial experimental constraints. Cosmological and phenomenological consequences characteristic to the present scenario are also addressed.

hep-ph↗

arXiv2024

Differentially Private Distributed Nonconvex Stochastic Optimization with Quantized Communication

This paper proposes a new distributed nonconvex stochastic optimization algorithm that can achieve privacy protection, communication efficiency and convergence simultaneously. Specifically, each node adds general privacy noises to its local state to avoid information leakage, and then quantizes its noise-perturbed state before transmitting to improve communication efficiency. By using a subsampling method controlled through the sample-size parameter, the proposed algorithm reduces cumulative differential privacy parameters {\epsilon}, {\delta}, and thus enhances the differential privacy level, which is significantly different from the existing works. By using a two-time-scale step-sizes method, the mean square convergence for nonconvex cost functions is given. Furthermore, when the global cost function satisfies the Polyak-Lojasiewicz condition, the convergence rate and the oracle complexity of the proposed algorithm are given. In addition, the proposed algorithm achieves both the mean square convergence and finite cumulative differential privacy parameters {\epsilon}, {\delta} over infinite iterations as the sample-size goes to infinity. A numerical example of the distributed training on the "MNIST" dataset is given to show the effectiveness of the algorithm.

eess.SY↗

arXiv2024

Differentially Private Distributed Stochastic Optimization with Time-Varying Sample Sizes

Differentially private distributed stochastic optimization has become a hot topic due to the urgent need of privacy protection in distributed stochastic optimization. In this paper, two-time scale stochastic approximation-type algorithms for differentially private distributed stochastic optimization with time-varying sample sizes are proposed using gradient- and output-perturbation methods. For both gradient- and output-perturbation cases, the convergence of the algorithm and differential privacy with a finite cumulative privacy budget $\varepsilon$ for an infinite number of iterations are simultaneously established, which is substantially different from the existing works. By a time-varying sample sizes method, the privacy level is enhanced, and differential privacy with a finite cumulative privacy budget $\varepsilon$ for an infinite number of iterations is established. By properly choosing a Lyapunov function, the algorithm achieves almost-sure and mean-square convergence even when the added privacy noises have an increasing variance. Furthermore, we rigorously provide the mean-square convergence rates of the algorithm and show how the added privacy noise affects the convergence rate of the algorithm. Finally, numerical examples including distributed training on a benchmark machine learning dataset are presented to demonstrate the efficiency and advantages of the algorithms.

eess.SY↗

arXiv2023

Consensus-Based Distributed Nonlinear Filtering with Kernel Mean Embedding

This paper proposes a consensus-based distributed nonlinear filter with kernel mean embedding (KME). This fills with gap of posterior density approximation with KME for distributed nonlinear dynamic systems. To approximate the posterior density, the system state is embedded into a higher-dimensional reproducing kernel Hilbert space (RKHS), and then the nonlinear measurement function is linearly converted. As a result, an update rule of KME of posterior distribution is established in the RKHS. To show the proposed distributed filter being capable of achieving the centralized estimation accuracy, a centralized filter, serving as an extension of the standard Kalman filter in the state space to the RKHS, is developed first. Benefited from the KME, the proposed distributed filter converges to the centralized one while maintaining the distributed pattern. Two examples are introduced to demonstrate the effectiveness of the developed filters in target tracking scenarios including nearly constantly moving target and turning target, respectively, with bearing-only, range and bearing measurements.

eess.SY↗

arXiv2023

Differentially Private Bipartite Consensus over Signed Networks with Time-Varying Noises

This paper investigates the differentially private bipartite consensus algorithm over signed networks. The proposed algorithm protects each agent's sensitive information by adding noise with time-varying variances to the cooperative-competitive interactive information. In order to achieve privacy protection, the variance of the added noise is allowed to be increased, and substantially different from the existing works. In addition, the variance of the added noise can be either decaying or constant. By using time-varying step-sizes based on the stochastic approximation method, we show that the algorithm converges in mean-square and almost-surely even with an increasing privacy noise. We further develop a method to design the step-size and the noise parameter, affording the algorithm to achieve asymptotically unbiased bipartite consensus with the desired accuracy and the predefined differential privacy level. Moreover, we give the mean-square and almost-sure convergence rate of the algorithm, and the privacy level with different forms of the privacy noises. We also reveal the algorithm's trade-off between the convergence rate and the privacy level. Finally, a numerical example verifies the theoretical results and demonstrates the algorithm's superiority against existing methods.

eess.SY↗

arXiv2020

2D-Dirac surface states and bulk gap probed via quantum capacitance in a 3D topological insulator

BiSbTeSe$_2$ is a 3D topological insulator (3D-TI) with Dirac type surface states and low bulk carrier density, as donors and acceptors compensate each other. Dominating low temperature surface transport in this material is heralded by Shubnikov-de Haas oscillations and the quantum Hall effect. Here, we experimentally probe and model the electronic density of states (DOS) in thin layers of BiSbTeSe$_2$ by capacitance experiments both without and in quantizing magnetic fields. By probing the lowest Landau levels, we show that a large fraction of the electrons filled via field effect into the system ends up in (localized) bulk states and appears as a background DOS. The surprisingly strong temperature dependence of such background DOS can be traced back to Coulomb interactions. Our results point at the coexistence and intimate coupling of Dirac surface states with a bulk many-body phase (a Coulomb glass) in 3D-TIs.

cond-mat.mes-hall↗