arXiv subjects

Xiaomeng Chen

Publications and source records attributed to Xiaomeng Chen.

At least 19 recordsLinked to original sources

arXiv2026

Blow-up suppression for the 2D parabolic-parabolic Patlak-Keller-Segel-Navier-Stokes system

Chemotactic aggregation can overcome diffusion and lead to finite-time blow-up in Patlak-Keller-Segel models. Mixing induced by suitable fluid flows can counteract this aggregation through enhanced dissipation. Earlier results of Zeng-Zhang-Zi (J. Funct. Anal., 2021) for the coupled system impose additional smallness assumptions on the initial chemical gradient. A natural question is whether this restriction can be removed for the coupled system near a stationary Couette flow. We give a positive answer to this question for the two-dimensional parabolic-parabolic Patlak-Keller-Segel-Navier-Stokes system near the Couette flow $(Ay,0)$ in $\mathbb T\times\mathbb R$. It is proved that, when the Couette flow is strong enough ($A$ is large enough), the system admits a global solution that remains uniformly bounded in time for arbitrary fixed initial data. The main difficulties are the transient growth of the chemical gradient and the nonlinear coupling among the density, chemical field and fluid. We develop a two-stage energy method combining short-time estimates in Kelvin coordinates with long-time estimates in physical coordinates. The key is to split the nonzero chemical modes into two parts while accounting for the nonlinear transport. One part carries the initial data, and the other is a forced component with zero initial data. A weighted coupled energy controls the former through a time-integrable Gronwall coefficient and absorbs the feedback from the latter without increasing the initial energy. Enhanced dissipation then provides the bounds needed to enter the long-time regime, while one-dimensional zero-mode estimates and Moser iteration complete the argument without a mass restriction.

math.AP↗

arXiv2026

PhantomTac: Hijacking Robotic Tactile Systems with Deploy-and-forget Tags

Tactile perception is crucial for precise robotic manipulation, providing an important foundation for dexterous, closed-loop force control. Among various tactile sensing techniques, magnetic tactile sensors stand out thanks to their fine-grained, three-dimensional force perception capability and strong robustness against visual occlusion. In this study, we discover a physical loophole called PhantomTac in magnetic tactile sensors. The attacker can alter the magnetic field, thus spoofing the force interpreted by magnetic tactile sensors. The attacker can stealthily deploy such an attack by simply attaching a thin (the thickness is around 0.5 mm), battery-free magnetic tag to the surface of an object. Hence, the attack tag can be easily concealed by a barcode sticker. Such an attack scheme is "deploy-and-forget" -- the attacker can deploy the tag and leave. When a robotic hand is picking up an object, it may severely damage the object. Specifically, spoofing the normal force can crush the object, whereas spoofing the shear force can lead to a loose grip, thus dropping the object. Compared with directly sabotaging the robotic hand, PhantomTac is much more concealed as the attacker does not need access to the device. We performed extensive reverse-engineering efforts to characterize the sensing mechanism of commercial-off-the-shelf tactile sensors and proposed a practical tag design. End-to-end evaluations conducted on real robots and robotic arms demonstrate that our attack can bypass existing anti-interference mechanisms and effectively hijack their closed-loop control processes, achieving an attack success rate exceeding 95% when the grasping-position offset is bounded within 8 mm. Finally, based on the limitations of existing anti-magnetic interference mechanisms, we propose a multimodal-consistency defense mechanism to mitigate such physical-layer security threats.

cs.CR↗

arXiv2026

Rebuilding Startups: An Empirical Study on Remote Work and Skill Complementarity

Remote work is a common practice of workplace flexibility enabled by digital infrastructure, yet its implications for firms' talent composition and organizational structure remain underexplored. This question is especially important for startups, which often operate with constraints in accessing talent through local labor markets. We focus on skill complementarity, a dimension of talent composition that is critical to startups' early-stage development. By integrating LinkedIn job postings, worker skills, and worker mobility data, we study the impact of remote work on skill complementarity in startups. We find that higher remote work intensity increases firm-level skill complementarity, primarily by helping startups recruit new workers whose skills better match the firms' hiring needs and diversify from the skill sets of existing employees. We further show that this shift in workforce composition carries organizational consequences: remote work is associated with greater organizational hierarchy through increased skill complementarity. These findings suggest that, for startups, remote work is not merely a flexibility policy but a hiring mechanism that helps young firms assemble complementary talent despite resource constraints. At the same time, this may push startups toward more hierarchical structures, which serve as formalized coordination mechanisms to effectively achieve the value of a more complementary workforce.

econ.GN↗

arXiv2026

Beyond Hearing: Learning Task-Agnostic ExG Representations from Earphones via Physiology-Informed Tokenization

Electrophysiological (ExG) signals offer valuable insights into human physiology, yet building foundation models that generalize across everyday tasks remains challenging due to two key limitations: (i)~insufficient data diversity, as most ExG recordings are collected in controlled labs with bulky, expensive devices; and (ii)~task-specific model designs that require tailored processing (i.e., targeted frequency filters) and architectures, which limit generalization across tasks. To address these challenges, we introduce an approach for scalable, task-agnostic ExG monitoring in the wild. We collected 50 hours of unobtrusive free-living ExG data with an earphone-based hardware prototype to narrow the data diversity gap. At the core of our approach is Physiology-informed Multi-band Tokenization (PiMT), which decomposes ExG signals into 12 physiology-informed tokens, followed by a reconstruction task to learn robust representations. This enables adaptive feature recognition across the full frequency spectrum while capturing task-relevant information. Experiments on our new DailySense dataset, the first to enable ExG-based analysis across five human senses, together with four public ExG benchmarks, demonstrate that PiMT consistently outperforms state-of-the-art methods across diverse tasks.

eess.AS↗

arXiv2025

Event-triggered Dual Gradient Tracking for Distributed Resource Allocation

High communication costs create a major bottleneck for distributed resource allocation over unbalanced directed networks. Conventional dual gradient tracking methods, while effective for problems on unbalanced digraphs, rely on periodic communication that creates significant overhead in resource-constrained networks. This paper introduces a novel event-triggered dual gradient tracking algorithm to mitigate this limitation, wherein agents communicate only when local state deviations surpass a predefined threshold. We establish comprehensive convergence guarantees for this approach. First, we prove sublinear convergence for non-convex dual objectives and linear convergence under the Polyak-Łojasiewicz condition. Building on this, we demonstrate that the proposed algorithm achieves sublinear convergence for general strongly convex cost functions and linear convergence for those that are also Lipschitz-smooth. Numerical experiments confirm that our event-triggered method significantly reduces communication events compared to periodic schemes while preserving comparable convergence performance.

eess.SY↗

arXiv2025

Error Floor of ML-Decoded Spinal Codes in the Finite Blocklength Regime

Spinal codes is a new family of capacity-achieving rateless codes that has been shown to achieve better rate performance compared to Raptor codes, Strider codes, and rateless Low-Density Parity-Check (LDPC) codes. This correspondence addresses the performance limitations of Spinal codes in the finite block length regime, uncovering an error floor phenomenon at high Signal-to-Noise Ratios (SNRs). We develop an analytical expression to approximate the error floor and devise SNR thresholds at which the error floor initiates. Numerical results across {Additive White Gaussian Noise (AWGN), rayleigh, and nakagami-m fading channels} verify the accuracy of our analysis. The analysis and numerical results also show that transmitting more passes of symbols can lower the error floor but does not affect the SNR threshold, providing insights on the performance target, the working SNR region, and the code design.

cs.IT↗

arXiv2025

Unified Upper Bounds on the ML decoding Error Probability of Spinal Codes over Fading Channels

Performance evaluation of particular channel coding has been a significant topic in coding theory, often involving the use of bounding techniques. This paper focuses on the new family of capacity-achieving codes, Spinal codes, to provide a comprehensive analysis framework to tightly upper bound the block error rate (BLER) of Spinal codes in the finite block length (FBL) regime. First, we resort to a variant of the Gallager random coding bound to upper bound the BLER of Spinal codes over the fading channel. Then, this paper derives a new bound without resorting to the use of Gallager random coding bound, achieving provable tightness over the wide range of signal-to-noise ratios (SNR). The derived BLER upper bounds in this paper are generalized, facilitating the performance evaluations of Spinal codes over different types of fast fading channels. Over the Rayleigh, Nakagami-m, and Rician fading channels, this paper explicitly derived the BLER upper bounds on Spinal codes as case studies. Based on the bounds, we theoretically reveal that the tail transmission pattern (TTP) for ML-decoded Spinal codes remains optimal in terms of reliability performance. Simulations verify the tightness of the bounds and the insights obtained.

cs.IT↗

arXiv2025

A Large Language Model Powered Integrated Circuit Footprint Geometry Understanding

Printed-Circuit-board (PCB) footprint geometry labeling of integrated circuits (IC) is essential in defining the physical interface between components and the PCB layout, requiring exceptional visual perception proficiency. However, due to the unstructured footprint drawing and abstract diagram annotations, automated parsing and accurate footprint geometry modeling remain highly challenging. Despite its importance, no methods currently exist for automated package geometry labeling directly from IC mechanical drawings. In this paper, we first investigate the visual perception performance of Large Multimodal Models (LMMs) when solving IC footprint geometry understanding. Our findings reveal that current LMMs severely suffer from inaccurate geometric perception, which hinders their performance in solving the footprint geometry labeling problem. To address these limitations, we propose LLM4-IC8K, a novel framework that treats IC mechanical drawings as images and leverages LLMs for structured geometric interpretation. To mimic the step-by-step reasoning approach used by human engineers, LLM4-IC8K addresses three sub-tasks: perceiving the number of pins, computing the center coordinates of each pin, and estimating the dimensions of individual pins. We present a two-stage framework that first trains LMMs on synthetically generated IC footprint diagrams to learn fundamental geometric reasoning and then fine-tunes them on real-world datasheet drawings to enhance robustness and accuracy in practical scenarios. To support this, we introduce ICGeo8K, a multi-modal dataset with 8,608 labeled samples, including 4138 hand-crafted IC footprint samples and 4470 synthetically generated samples. Extensive experiments demonstrate that our model outperforms state-of-the-art LMMs on the proposed benchmark.

cs.CV↗

arXiv2025

Differentially Private Dual Gradient Tracking for Distributed Resource Allocation

This paper investigates privacy issues in distributed resource allocation over directed networks, where each agent holds a private cost function and optimizes its decision subject to a global coupling constraint through local interaction with other agents. Conventional methods for resource allocation over directed networks require all agents to transmit their original data to neighbors, which poses the risk of disclosing sensitive and private information. To address this issue, we propose an algorithm called differentially private dual gradient tracking (DP-DGT) for distributed resource allocation, which obfuscates the exchanged messages using independent Laplacian noise. Our algorithm ensures that the agents' decisions converge to a neighborhood of the optimal solution almost surely. Furthermore, without the assumption of bounded gradients, we prove that the cumulative differential privacy loss under the proposed algorithm is finite even when the number of iterations goes to infinity. To the best of our knowledge, we are the first to simultaneously achieve these two goals in distributed resource allocation problems over directed networks. Finally, numerical simulations on economic dispatch problems within the IEEE 14-bus system illustrate the effectiveness of our proposed algorithm.

eess.SY↗

arXiv2025

Harnessing the Power of Vibration Motors to Develop Miniature Untethered Robotic Fishes

Miniature underwater robots play a crucial role in the exploration and development of marine resources, particularly in confined spaces and high-pressure deep-sea environments. This study presents the design, optimization, and performance of a miniature robotic fish, powered by the oscillation of bio-inspired fins. These fins feature a rigid-flexible hybrid structure and use an eccentric rotating mass (ERM) vibration motor as the excitation source to generate high-frequency unidirectional oscillations that induce acoustic streaming for propulsion. The drive mechanism, powered by miniature ERM vibration motors, eliminates the need for complex mechanical drive systems, enabling complete isolation of the entire drive system from the external environment and facilitating the miniaturization of the robotic fish. A compact, untethered robotic fish, measuring 85*60*45 mm^3, is equipped with three bio-inspired fins located at the pectoral and caudal positions. Experimental results demonstrate that the robotic fish achieves a maximum forward swimming speed of 1.36 body lengths (BL) per second powered by all fins and minimum turning radius of 0.6 BL when powered by a single fin. These results underscore the significance of employing the ERM vibration motor in advancing the development of highly maneuverable, miniature untethered underwater robots for various marine exploration tasks.

cs.RO↗

arXiv2024

An Efficient Distributed Nash Equilibrium Seeking with Compressed and Event-triggered Communication

Distributed Nash equilibrium (NE) seeking problems for networked games have been widely investigated in recent years. Despite the increasing attention, communication expenditure is becoming a major bottleneck for scaling up distributed approaches within limited communication bandwidth between agents. To reduce communication cost, an efficient distributed NE seeking (ETC-DNES) algorithm is proposed to obtain an NE for games over directed graphs, where the communication efficiency is improved by event-triggered exchanges of compressed information among neighbors. ETC-DNES saves communication costs in both transmitted bits and rounds of communication. Furthermore, our method only requires the row-stochastic property of the adjacency matrix, unlike previous approaches that hinged on doubly-stochastic communication matrices. We provide convergence guarantees for ETC-DNES on games with restricted strongly monotone mappings and testify its efficiency with no sacrifice on the accuracy. The algorithm and analysis are extended to a compressed algorithm with stochastic event-triggered mechanism (SETC-DNES). In SETC-DNES, we introduce a random variable in the triggering condition to further enhance algorithm efficiency. We demonstrate that SETC-DNES guarantees linear convergence to the NE while achieving even greater reductions in communication costs compared to ETC-DNES. Finally, numerical simulations illustrate the effectiveness of the proposed algorithms.

eess.SY↗

arXiv2024

Optimal Sampling for Uncertainty-of-Information Minimization in a Remote Monitoring System

In this paper, we study a remote monitoring system where a receiver observes a remote binary Markov source and decides whether to sample and transmit the state through a randomly delayed channel. We adopt uncertainty of information (UoI), defined as the entropy conditional on past observations at the receiver, as a metric of value of information, in contrast to the traditional state-agnostic nonlinear age of information (AoI) penalty functions. To address the limitations of prior UoI research that assumes one-time-slot delays, we extend our analysis to scenarios with random delays. We model the problem as a partially observable Markov decision process (POMDP) problem and simplify it to a semi-Markov decision process (SMDP) by introducing the belief state. We propose two algorithms: A globally optimal bisection relative value iteration (bisec-RVI) algorithm and a computationally efficient sub-optimal index-based threshold algorithm to solve the long-term average UoI minimization problem. Numerical simulations demonstrate that our sampling policies surpass traditional zero wait and AoI-optimal policies, particularly under conditions of large delay, with the sub-optimal policy nearly matching the performance of the optimal one.

cs.IT↗

arXiv2024

Communication-efficient and Differentially-private Distributed Nash Equilibrium Seeking with Linear Convergence

The distributed computation of a Nash equilibrium (NE) for non-cooperative games is gaining increased attention recently. Due to the nature of distributed systems, privacy and communication efficiency are two critical concerns. Traditional approaches often address these critical concerns in isolation. This work introduces a unified framework, named CDP-NES, designed to improve communication efficiency in the privacy-preserving NE seeking algorithm for distributed non-cooperative games over directed graphs. Leveraging both general compression operators and the noise adding mechanism, CDP-NES perturbs local states with Laplacian noise and applies difference compression prior to their exchange among neighbors. We prove that CDP-NES not only achieves linear convergence to a neighborhood of the NE in games with restricted monotone mappings but also guarantees $ε$-differential privacy, addressing privacy and communication efficiency simultaneously. Finally, simulations are provided to illustrate the effectiveness of the proposed method.

eess.SY↗

arXiv2024

Compression-based Privacy Preservation for Distributed Nash Equilibrium Seeking in Aggregative Games

This paper explores distributed aggregative games in multi-agent systems. Current methods for finding distributed Nash equilibrium require players to send original messages to their neighbors, leading to communication burden and privacy issues. To jointly address these issues, we propose an algorithm that uses stochastic compression to save communication resources and conceal information through random errors induced by compression. Our theoretical analysis shows that the algorithm guarantees convergence accuracy, even with aggressive compression errors used to protect privacy. We prove that the algorithm achieves differential privacy through a stochastic quantization scheme. Simulation results for energy consumption games support the effectiveness of our approach.

eess.SY↗

arXiv2024

Distributed Average Consensus via Noisy and Non-Coherent Over-the-Air Aggregation

Over-the-air aggregation has attracted widespread attention for its potential advantages in task-oriented applications, such as distributed sensing, learning, and consensus. In this paper, we develop a communication-efficient distributed average consensus protocol by utilizing over-the-air aggregation, which exploits the superposition property of wireless channels rather than combat it. Noisy channels and non-coherent transmission are taken into account, and only half-duplex transceivers are required. We prove that the system can achieve average consensus in mean square and even almost surely under the proposed protocol. Furthermore, we extend the analysis to the scenarios with time-varying topology. Numerical simulation shows the effectiveness of the proposed protocol.

eess.SP↗

arXiv2023

Hausdorff measure for the singularity set of the 3D chemotaxis-Navier-Stokes equations

Suspensions of aerobic bacteria often develop flows from the interplay of chemotaxis and buoyancy, which is so-called the chemotaxis-Navier-Stokes flow. In 2004, Dombrowski et al. observed that Bacterial flow in a sessile drop related to those in the Boycott effect of sedimentation can carry bioconvective plumes, viewed from below through the bottom of a petri dish, and the horizontal "turbulence" white line near the top is the air-water-plastic contact line. In pendant drops such self-concentration occurs at the bottom. On scales much larger than a cell, concentrated regions exhibit transient, reconstituting, high-speed jets straddled by vortex streets. It's interesting to verify these turbulent phenomena mathematically. In this note, we investigate the Hausdorff dimension of these vortices (singular points) by considering partial regularity of weak solutions of the three dimensional chemotaxis-Navier-Stokes equations, and showed that the singular dimension is not larger than $1$, which seems to be consistent with the linear singularity in the experiment.

math.AP↗

arXiv2023

Global existence of suitable weak solutions to the 3D chemotaxis-Navier-Stokes equations

In 2004, Dombrowski et al. showed that suspensions of aerobic bacteria often develop flows from the interplay of chemotaxis and buoyancy, which is described as the chemotaxis-Navier-Stokes model, and they observed self-concentration occurs as a turbulence by exhibiting transient, reconstituting, high-speed jets, which entrains nearby fluid to produce paired, oppositely signed vortices. In order to investigate the properties of these vortices (singular points), one approach is to follow the partial regularity theory of Caffarelli-Kohn-Nirenberg to study the singularity properties of suitable weak solutions. In this paper, we established the existence of suitable weak solution for the three dimensional chemotaxis-Navier-Stokes equations, where the main difficulty is to establish appropriate local energy inequalities.

math.AP↗

arXiv2023

Partial regularity of solutions to the 3D chemotaxis-Navier-Stokes equations at the first blow-up time

In this note, we investigate partial regularity of weak solutions of the three dimensional chemotaxis-Navier-Stokes equations, and obtain the $\frac53$-dimensional Hausdorff measure of the possible singular set is vanishing at the first blow-up time. The new ingredients are to establish certain type of local energy inequality and deal with the non-scaling invariant quantity of $n\ln n$, which seems to be the first description for the singular set of weak solutions of the chemotaxis-fluid model, which is motivated by Caffarelli-Kohn-Nirenberg's partial regularity theory \cite{CKN}.

math.AP↗