arXiv subjects

Ziang Chen

Publications and source records attributed to Ziang Chen.

At least 19 recordsLinked to original sources

arXiv2026

Fading Suppression in Distributed Acoustic Sensing Using Frequency-Domain Replica Reconstruction and Noise-Whitened Principal Component Combining

Frequency-selective Rayleigh fading in distributed acoustic sensing (DAS) based on phase-sensitive optical time-domain reflectometry (phi-OTDR) calls for effective diversity, but existing rotated-vector-sum (RVS) combining of nonoverlapping subbands incurs a spatial-resolution penalty. In this letter, we propose frequency-domain replica reconstruction and noise-whitened principal component combining (PCC) that combines a full-band replica and overlapping subbands using fixed weights estimated across probing periods after whitening correlated replica noise. This approach retains wider subbands and reduces reliance on noisy first-period alignment, balancing diversity with nominal spatial resolution. Experiments over approximately 7.1 km of fiber with 16-microsecond linear frequency-modulated (LFM) pulses recover 500-Hz vibrations with reduced background phase excursions and a 2.46-2.76-dB improvement in the vibration-to-background ratio (VBR) over the first-period RVS.

eess.SP↗

arXiv2026

Cyclic-Prefix OFDM Probing for Spatial-ISI-Free Distributed Acoustic Sensing via Frequency-Domain Channel Reconstruction

Nonzero sidelobes in matched-filter pulse-compression distributed acoustic sensing (DAS) cause deterministic inter-range-bin leakage, termed spatial inter-symbol interference (ISI), and may produce false Rayleigh-backscatter responses. We propose a data-bearing cyclic-prefix orthogonal frequency-division multiplexing (CP-OFDM) DAS system for phase-sensitive optical time-domain reflectometry (phi-OTDR), using the same waveform for probing and forward communication. To our knowledge, this is the first formulation of distributed Rayleigh backscattering as a finite-memory sensing multipath channel. We rigorously prove that, when the useful OFDM and CP lengths cover the channel memory, the receiver reconstructs range-bin coefficients without deterministic waveform-induced spatial ISI. CP removal, one-tap frequency-domain equalization, and inverse discrete Fourier transform provide the reconstruction for subsequent gauge-differential phase demodulation. In a simulated 5.2-km link with ten simultaneous strong and weak events spaced 5.31-5.83 m within groups, CP-OFDM suppresses off-event spatial-ISI leakage. Phase-trace mean-square error improves by up to 29.55 dB over matched-filter pulse compression. A heterodyne coherent experiment over 5.2 km at a 111.984-MHz occupied bandwidth recovers 500-Hz vibrations near 80 m under 1- and 5-V piezoelectric transducer drives. Under identical probing conditions, CP-OFDM improves the vibration-to-background ratio by approximately 3.2 and 6.2 dB, respectively, over matched-filter linear frequency-modulated pulse compression while preserving local waveforms. The same probe recovers an image with zero measured bit-error rate and a median error vector magnitude of -23.14 dB. These results validate spatial-ISI-free DAS reconstruction and provide an initial demonstration of shared-waveform optical-fiber integrated sensing and communication (ISAC).

eess.SP↗

arXiv2026

Prototyping the Next-Generation Spectrograph for Space Exploration: Digital Micromirror Device-based Multi-Object Spectrograph (DMD-MOS) Calibration and Performance Assessment

Abandoning the traditional approaches to multi-object spectrographs, the Digital Micro-mirror Device-based Multi-Object Spectrograph (DMD-MOS) leverages a robust, elegant method for creating multi-object spectroscopy slit masks through use of a DMD, which is an array of microscopic flat mirrors where each micro-mirror can be addressed to physically flip between two discrete angles. We have designed two optical channels for DMD-MOS - one for each of these two positions: 1) an imaging channel that can be used for multi-object target selection and 2) a spectrograph channel. Using this two-channel approach, DMD-MOS can simultaneously image the telescope field of view (FOV) with the imaging channel and actuate the specific DMD micro-mirrors to send light from chosen targets into the spectrograph channel. In this work, we present the results of our laboratory testing of DMD-MOS including spectral resolution, line spread function, wavelength coverage, and the mapping between the DMD and imaging channel planes, the latter of which enables DMD-MOS and our control software to instantaneously configure the DMD to observe chosen target(s) in the acquisition field.

astro-ph.IM↗

arXiv2026

Fiber-Topology-Assisted Indoor Positioning Under UWB Degradation with Fixed-Lag Factor Graphs

Ultra-wideband (UWB) provides absolute spatial constraints for indoor positioning, but these constraints become insufficient when multiple radio links are blocked or unavailable. A calibrated sensing fiber, in contrast, provides a fixed spatial topology that can be activated by distributed acoustic sensing (DAS) events independently of radio propagation. Motivated by this complementarity, we present FTALoc-FGO, a topology-aware fixed-lag factor-graph framework for fiber-assisted localization under UWB degradation. Along-fiber DAS events are mapped through the calibrated topology into anisotropic spatial observations, where a single fiber layer provides a directional constraint and two orthogonal layers provide complementary two-dimensional information. Fisher-information analysis characterizes this topology-dependent information and motivates the directional and joint fiber factors used in the fixed-lag graph. Experiments combining measured DAS events with simulated UWB ranges under controlled degradation evaluate the method over 30 paired realizations. Under complete UWB loss, FTALoc-FGO reduces the causal outage RMSE by 32.6\% and 66.4\% for single- and dual-layer routes, respectively, relative to robust UWB-only FGO. These results demonstrate that calibrated fiber topology can provide complementary spatial support when radio-based observations become insufficient.

eess.SP↗

arXiv2026

Covariance-Weighted Spectral Delay Fusion With a One-Dimensional Affine Model for High-Precision Distributed Optical-Fiber Sensing

Periodic disturbances can produce ambiguous delay estimates, limiting reliable high-precision localization in distributed optical-fiber sensing. We develop spectral delay fusion for a sensing system using a dual-wavelength bidirectional Mach-Zehnder interferometer, with four phase traces recovered by heterodyne detection and digital demodulation. With calibrated propagation parameters and timing offsets fixed, the six pairwise delay predictions form a one-dimensional affine line segment parameterized by the position of a single dominant disturbance, with sensitivities determined by propagation direction and chromatic dispersion. A generalized least-squares estimator combines unwrapped delays from robust cross-spectral phase slopes with wrapped delays from polarity-invariant phase alignment to jointly estimate position and integer ambiguities under the proposed model, using an effective joint covariance to account for shared-channel and cross-representation dependence. Experiments use a 131.335-km sensing fiber at 1530 and 1550 nm, with periodic phase perturbations applied at five nominal positions from 25 to 125 km. Across the reported groups of 20 records, the proposed method yields sample standard deviations of 1.007-1.685 m at a drive voltage of 500 mV and 0.449-1.324 m at 1 V. The ratio of the smallest single-pair sample standard deviation to that of the proposed method ranges from 2.57 to 19.56 at 500 mV and from 2.12 to 2900 at 1 V. The upper ratio reflects unstable single-pair phase-slope delay estimates for periodic disturbances in the 1-V, nominal 50-km group, where the proposed covariance-weighted fusion maintains meter-scale localization repeatability.

eess.SP↗

arXiv2026

Regularity of Second-Order Elliptic PDEs in Spectral Barron Spaces

We establish a regularity theorem for second-order elliptic PDEs on $\mathbb{R}^{d}$ in spectral Barron spaces. Under mild ellipticity and smallness assumptions, the solution gains two additional orders of Barron regularity. As a corollary, we identify a class of PDEs whose solutions can be approximated by two-layer neural networks with cosine activation functions, where the width of the neural network is independent of the spatial dimension.

math.AP↗

arXiv2026

A Continuous Payload-Bearing Discrete Multitone Modulation Framework for Fiber-Optic Integrated Sensing and Communication

A key challenge in fiber-optic integrated sensing and communication (ISAC) is to make the payload-bearing waveform itself serve both functions without a separate sensing waveform or sensing-only silent interval. We propose a continuous discrete multitone (DMT) framework with two waveform modes, in which the same payload-bearing waveform supports forward intensity-modulation/direct-detection (IM/DD) communication and backward distributed acoustic sensing (DAS). A unified phase-sensitive optical time-domain reflectometry (ϕ-OTDR) model represents distributed Rayleigh backscattering as a finite-memory sensing multipath channel. It shows that conventional pulse-and-wait ϕ-OTDR requires a round-trip-time-scale silent interval to isolate successive returns, while nonzero off-peak samples in practical matched-filter (MF) pulse compression cause spatial intersymbol interference (ISI). Continuous DMT instead retains superposed returns and separates range-cell contributions through known-waveform channel reconstruction. Cyclic-prefix DMT (CP-DMT) uses a full-memory CP and one-tap frequency-domain equalization (FDE); under sufficient-CP and ideal full-bin inversion conditions, it eliminates MF-correlation-induced spatial ISI. Cyclic-prefix-free DMT (NoCP-DMT) applies regularized least squares (LS) to the long-memory linear convolution, avoiding the CP at higher receiver complexity. Experiments over a 10-km fiber link localize a 600-Hz disturbance applied by a piezoelectric transducer (PZT) near 5.071 km and recover gauge-differential phase with correlations of 0.989 and 0.987 for CP-DMT and NoCP-DMT, respectively. At 2- and 1-V PZT drive levels, the ten-record localization standard deviations are 0.16/0.11 m and 0.18/0.38 m, respectively. The corresponding IM/DD error vector magnitude values range from 4.80% to 5.00%, with no bit errors observed.

eess.SP↗

arXiv2026

Exact and Efficient Representation of Totally Anti-Symmetric Functions

This paper concerns the long-standing question of representing (totally) anti-symmetric functions in high dimensions. We propose a new ansatz based on the composition of an odd function with a fixed set of anti-symmetric basis functions. We prove that this ansatz can exactly represent every anti-symmetric and continuous function and the number of basis functions has efficient scaling with respect to dimension (number of particles). The singular locus of the anti-symmetric basis functions is precisely identified.

math.CA↗

arXiv2026

Barron Space Representations for Elliptic PDEs with Homogeneous Boundary Conditions

We study the complexity of approximating high-dimensional second-order elliptic PDEs with homogeneous boundary conditions on the unit hypercube using Barron spaces. Under suitable Barron assumptions on the coefficients and forcing term, we prove that the solutions can be approximated to any prescribed accuracy $\varepsilon>0$ by two-layer neural networks whose widths and relevant parameters are bounded by $\mathcal{O}\bigl(d^{C|\log\varepsilon|}\bigr)$. Consequently, we identify a class of elliptic PDEs that can be approximated by shallow neural networks without suffering from the curse of dimensionality.

math.NA↗

arXiv2026

Cyclic-Prefix-Free OFDM With Tail-Reuse Reconstruction for Distributed Acoustic Sensing

Orthogonal frequency-division multiplexing (OFDM) enables frequency-domain reconstruction of the distributed Rayleigh backscatter channel in coherent distributed acoustic sensing (DAS), but an explicitly transmitted cyclic prefix (CP) lengthens the probing period and reduces the slow-time Nyquist limit of each range cell. We investigate a repeated cyclic-prefix-free OFDM waveform for DAS, in which the tail of the preceding useful block serves as a virtual cyclic extension. A finite-memory range condition for tail-reuse reconstruction is derived, and circular folding is identified when the useful period is shorter than the channel memory. For fixed useful-block length and fiber memory, removing the explicit CP increases the period-limited highest unaliased vibration frequency without changing the occupied-bandwidth-limited spatial resolution. In a 5.2-km numerical configuration, a 75-MSa/s processing rate and a 4096-sample useful block give a 54.61-us probing period and a 9.16-kHz slow-time Nyquist limit. Simulations verify tail reuse, the predicted folding boundary, and recovery of 100 vibration events from 800 Hz to 8800 Hz. Bandwidth-scaling simulations further show that joint processing of fine spatial observations improves differential-phase reliability and reconstruction SNR at a fixed reporting interval. Experiments on a 5.2-km coherent DAS link with 111.984-MHz occupied OFDM bandwidth blindly localize 500-Hz and 3-kHz PZT-induced vibrations at 5063.7 m and 5070.1 m, respectively, and recover their waveforms and spectra. The results demonstrate feasible tail-reuse channel reconstruction and quantify the extension of the unaliased vibration bandwidth.

eess.SP↗

arXiv2026

Carbery's inequality in the Schatten--von Neumann classes

Carbery posed a question of sharpened triangle inequalities for families of operators in the Schatten--von Neumann classes $S_p$, $p\geq 2$. He established a weaker form of the desired estimate for even integer values of $p$. In the commutative setting the corresponding sharp inequality (with optimal exponent $p'=\frac{p}{p-1}$) was recently obtained for all integer $p\geq 2$. In the present work we resolve Carbery's question completely in the non-commutative setting: we prove the sharp inequality $$ \Big\|\sum_{j} T_{j}\Big\|_{S_{p}}\leq \|(α_{ij}^{p'})\|^{1/p'}_{\ell_{2}\to \ell_{2}} \Big( \sum_{j} \|T_{j}\|^{p}_{S_{p}}\Big)^{1/p} $$ for all $p\geq 2$ and all countable sequences of operators $(T_{j}) \subset S_{p}$, where $α_{ij}$ are almost orthogonality coefficients. The proof is based on a block-operator reduction and a complex interpolation of the polar parts of the blocks.

math.FA↗

arXiv2026

Almost-Orthogonality in Lp Spaces: A Case Study with Grok

Carbery proposed the following sharpened form of triangle inequality for many functions: for any $p\ge 2$ and any finite sequence $(f_j)_j\subset L^p$ we have \[ \Big\|\sum_j f_j\Big\|_p \ \le\ \left(\sup_{j} \sum_{k} α_{jk}^{\,c}\right)^{1/p'} \Big(\sum_j \|f_j\|_p^p\Big)^{1/p}, \] where $c=2$, $1/p+1/p'=1$, and $α_{jk}=\sqrt{\frac{\|f_{j}f_{k}\|_{p/2}}{\|f_{j}\|_{p}\|f_{k}\|_{p}}}$. In the first part of this paper we construct a counterexample showing that this inequality fails for every $p>2$. We then prove that if an estimate of the above form holds, the exponent must satisfy $c\le p'$. Finally, at the critical exponent $c=p'$, we establish the inequality for all integer values $p\ge 2$. In the second part of the paper we obtain a sharp three-function bound \[ \Big\|\sum_{j=1}^{3} f_j\Big\|_p \ \le\ \left(1+2Γ^{c(p)}\right)^{1/p'} \Big(\sum_{j=1}^{3} \|f_j\|_p^p\Big)^{1/p}, \] where $p \geq 3$, $c(p) = \frac{2\ln(2)}{(p-2)\ln(3)+2\ln(2)}$ and $Γ=Γ(f_1,f_2,f_3)\in[0,1]$ quantifies the degree of orthogonality among $f_1,f_2,f_3$. The exponent $c(p)$ is optimal, and improves upon the power $r(p) = \frac{6}{5p-4}$ obtained previously by Carlen, Frank, and Lieb. Some intermediate lemmas and inequalities appearing in this work were explored with the assistance of the large language model Grok.

math.CA↗

arXiv2026

Aegis: Automated Error Generation and Attribution for Multi-Agent Systems

Large language model based multi-agent systems (MAS) have unlocked significant advancements in tackling complex problems, but their increasing capability introduces a structural fragility that makes them difficult to debug. A key obstacle to improving their reliability is the severe scarcity of large-scale, diverse datasets for error attribution, as existing resources rely on costly and unscalable manual annotation. To address this bottleneck, we introduce Aegis, a novel framework for Automated error generation and attribution for multi-agent systems. Aegis constructs a large dataset of 9,533 trajectories with annotated faulty agents and error modes, covering diverse MAS architectures and task domains. This is achieved using a LLM-based manipulator that can adaptively inject context-aware errors into successful execution trajectories. Leveraging fine-grained labels and the structured arrangement of positive-negative sample pairs, Aegis supports three different learning paradigms: Supervised Fine-Tuning, Reinforcement Learning, and Contrastive Learning. We develop learning methods for each paradigm. Comprehensive experiments show that trained models consistently achieve substantial improvements in error attribution. Notably, several of our fine-tuned LLMs demonstrate performance competitive with or superior to proprietary models an order of magnitude larger, validating our automated data generation framework as a crucial resource for developing more robust and interpretable multi-agent systems. Our project website is available at https://kfq20.github.io/Aegis-Website/.

cs.RO↗

arXiv2026

Exact Instance Compression for Convex Empirical Risk Minimization via Color Refinement

Empirical risk minimization (ERM) can be computationally expensive, with standard solvers scaling poorly even in the convex setting. We propose a novel lossless compression framework for convex ERM based on color refinement, extending prior work from linear programs and convex quadratic programs to a broad class of differentiable convex optimization problems. We develop concrete algorithms for a range of models, including linear and polynomial regression, binary and multiclass logistic regression, regression with elastic-net regularization, and kernel methods such as kernel ridge regression and kernel logistic regression. Numerical experiments on representative datasets demonstrate the effectiveness of the proposed approach.

math.OC↗

arXiv2025

R-Log: Incentivizing Log Analysis Capability in LLMs via Reasoning-based Reinforcement Learning

The growing complexity of log data in modern software systems has prompted the use of Large Language Models (LLMs) for automated log analysis. Current approaches typically rely on direct supervised fine-tuning (SFT) on log-label pairs. However, this exacerbates the domain discrepancy between general-purpose LLMs and specialized log data, causing overfitting. Furthermore, SFT's imbalanced loss computation often allows lengthy contexts to overwhelm critical, concise details in model answers, leading to hallucinations. To address these limitations, we propose R-Log, a novel reasoning-based paradigm that mirrors the structured, step-by-step analytical process of human engineers. This approach enhances generalizability by learning the underlying rules behind conclusions. We further employ Reinforcement Learning (RL) to optimize the model within a simulated O&M environment, thereby reducing hallucinations by directly rewarding correct outcomes. R-Log is first cold-started on a curated dataset of 2k+ reasoning trajectories, guided by 13 strategies from manual O&M practices, to establish an initial reasoning capability. This ability is then refined via RL using a joint reward function. Empirical evaluations on real-world logs show that R-Log outperforms existing methods across five log analysis tasks, particularly in unseen scenarios (by 228.05%). We also designed R-Log-fast with 5x speedup while keeping 93% of the efficacy.

cs.SE↗

arXiv2025

ValuePilot: A Two-Phase Framework for Value-Driven Decision-Making

Personalized decision-making is essential for human-AI interaction, enabling AI agents to act in alignment with individual users' value preferences. As AI systems expand into real-world applications, adapting to personalized values beyond task completion or collective alignment has become a critical challenge. We address this by proposing a value-driven approach to personalized decision-making. Human values serve as stable, transferable signals that support consistent and generalizable behavior across contexts. Compared to task-oriented paradigms driven by external rewards and incentives, value-driven decision-making enhances interpretability and enables agents to act appropriately even in novel scenarios. We introduce ValuePilot, a two-phase framework consisting of a dataset generation toolkit (DGT) and a decision-making module (DMM). DGT constructs diverse, value-annotated scenarios from a human-LLM collaborative pipeline. DMM learns to evaluate actions based on personal value preferences, enabling context-sensitive, individualized decisions. When evaluated on previously unseen scenarios, DMM outperforms strong LLM baselines, including GPT-5, Claude-Sonnet-4, Gemini-2-flash, and Llama-3.1-70b, in aligning with human action choices. Our results demonstrate that value-driven decision-making is an effective and extensible engineering pathway toward building interpretable, personalized AI agents.

cs.AI↗

arXiv2025

Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia

Large Language Model (LLM) agents have demonstrated impressive capabilities for social interaction and are increasingly being deployed in situations where they might engage with both human and artificial agents. These interactions represent a critical frontier for LLM-based agents, yet existing evaluation methods fail to measure how well these capabilities generalize to novel social situations. In this paper, we introduce a method for evaluating the ability of LLM-based agents to cooperate in zero-shot, mixed-motive environments using Concordia, a natural language multi-agent simulation environment. Our method measures general cooperative intelligence by testing an agent's ability to identify and exploit opportunities for mutual gain across diverse partners and contexts. We present empirical results from the NeurIPS 2024 Concordia Contest, where agents were evaluated on their ability to achieve mutual gains across a suite of diverse scenarios ranging from negotiation to collective action problems. Our findings reveal significant gaps between current agent capabilities and the robust generalization required for reliable cooperation, particularly in scenarios demanding persuasion and norm enforcement.

cs.AI↗

arXiv2025

LogPurge: Log Data Purification for Anomaly Detection via Rule-Enhanced Filtering

Log anomaly detection, which is critical for identifying system failures and preempting security breaches, detects irregular patterns within large volumes of log data, and impacts domains such as service reliability, performance optimization, and database log analysis. Modern log anomaly detection methods rely on training deep learning models on clean, anomaly-free log sequences. However, obtaining such clean log data requires costly and tedious human labeling, and existing automatic cleaning methods fail to fully integrate the specific characteristics and actual semantics of logs in their purification process. In this paper, we propose a cost-aware, rule-enhanced purification framework, LogPurge, that automatically selects a sufficient subset of normal log sequences from contamination log sequences to train a anomaly detection model. Our approach involves a two-stage filtering algorithm: In the first stage, we use a large language model (LLM) to remove clustered anomalous patterns and enhance system rules to improve LLM's understanding of system logs; in the second stage, we utilize a divide-and-conquer strategy that decomposes the remaining contaminated regions into smaller subproblems, allowing each to be effectively purified through the first stage procedure. Our experiments, conducted on two public datasets and one industrial dataset, show that our method significantly removes an average of 98.74% of anomalies while retaining 82.39% of normal samples. Compared to the latest unsupervised log sample selection algorithms, our method achieves F-1 score improvements of 35.7% and 84.11% on the public datasets, and an impressive 149.72% F-1 improvement on the private dataset, demonstrating the effectiveness of our approach.

cs.SE↗