Systems and Control · Cross-listed from cs.SE
A Scalability Analysis of Quantitative Confidence Assessment Methods for Assurance Cases
Simon Diemert and Jens H. Weber
This paper proposes a model to estimate the decision complexity and effort required to apply quantitative confidence assessment methods to assurance cases. The model considers both the worst and average case for these measures and characterizes how these quantities scale with argument size. Prior work has indicated that the additional effort required to apply these methods is a barrier to their adoption by assurance case practitioners. Researchers developing new methods, or improving existing methods, can use this model to estimate the effort required to apply their method. The proposed model is parameterized using data from published case studies and is applied to three existing quantitative confidence assessment methods: the
Bayesian Belief Network method, the Dempster-Shafer Theory method, and the Certus method. The results show that, while Certus has the highest worst-case decision complexity, its average-case effort is lower than the BBN and DST methods.
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Signal Processing
Universal adaptive beamforming: A Bayesian approach
Diego A. Cuji, Andrew C. Singer and John R. Buck
We present a
Bayesian universal beamforming framework for adaptive array processing in dynamic
underwater acoustic environments with unknown and time-varying propagation geometry. Motivated by ideas from universal prediction and estimation, the proposed approach discretizes the angular domain into a finite set of steering hypotheses and recursively computes posterior probabilities over competing spatial models using observation-dependent likelihood functions. For Gaussian observation models, the posterior update reduces to an exponential-weights recursion driven by hypothesis-dependent beamformer evidence metrics. The resulting framework performs soft spatial inference and adaptive beamforming by continuously redistributing posterior probability across competing steering hypotheses while forming posterior-weighted combinations of branch outputs. The formulation naturally connects to classical adaptive beamformers including matched filtering and minimum mean-square error (MMSE) beamforming. In addition, the framework is extended toward broadband
underwater acoustic communication receivers through frequency-domain beamformer synthesis and adaptive equalization. Posterior probabilities are updated according to branch-specific equalization errors, enabling joint spatial-temporal adaptation under multipath propagation, Doppler-induced distortions, and time-varying channel conditions. Experimental results using MACE data demonstrate reliable communication performance with low overhead, low data detection mean-squared error, and zero observed bit errors.
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Audio and Speech Processing
Phonetically Explainable Speech Deepfake Detection
Manasi Chhibber, Jagabandhu Mishra and Tomi H. Kinnunen
Speech deepfake detection is predominantly treated as an opaque classification task where all temporal frames are aggregated equally. This ignores that different phonetic categories carry vastly different amounts of discriminative information. To address this, we propose a phoneme-guided cross-
attention framework that transforms detection into an interpretable, phonetically grounded process. We factorize the spoofing posterior $P(\text{spoofed}\mid X, W)$, conditioned on the acoustic representation $X$ and the phonetic posteriorgram $W$. The resulting factorization can be written as $P(\text{spoofed} \mid X, W) = \sum_{i=1}^{M} w_i \cdot P(\text{spoofed} \mid X, Z = z_i)$, where $M$ denotes the number of phonetic classes, $P(\text{spoofed} \mid X, Z = z_i)$ is the spoofing probability for the $i$-th phonetic class $z_i$ conditioned on $X$, and each $w_i$ is the prevalence of phonetic class $z_i$ in the utterance. Our
transformer-based architecture instantiates this through a cross-
attention block in which phonetic queries selectively probe information in acoustic keys and values, with softmax-normalized pooling supplying explicit phone-presence weights. Unlike prior approaches that rely heavily on post-hoc explainability methods, our framework offers phonetic-explainability-by-design. We evaluate the framework on an LJSpeech-derived corpus, ASVspoof 2019 LA, and ASVspoof 5 Track 1. Per-phone importance rankings reveal that discriminative power concentrates on articulatory categories that generative models struggle to reproduce faithfully. Stops, fricatives, affricates, nasals, and silence-boundary closures rank most discriminative, while periodic vowels and semivowels rank lower. Beyond competitive performance, our model provides structural interpretability, yielding an inspectable per-articulatory category breakdown of the final verdict.
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Signal Processing · Cross-listed from stat.ME
Kernel Density Estimation by Spectral Decomposition: Data-Driven Tapering and Superposition
Mitchell A. Thornton
Kernel density estimation depends largely on one choice, the smoothing bandwidth. We treat bandwidth selection and density estimation in the characteristic-function domain, where the cyclic group-averaged covariance of the binned data has the squared empirical characteristic function as its spectrum: the true characteristic function sits over a sampling-noise floor of $1/n$, and the bandwidth is the spectral cutoff where the two meet. Several methods follow. An automatic selector strips the floor and minimizes a frequency-domain error criterion, matching the rule of thumb on smooth densities and approaching the best fixed bandwidth on
multimodal ones. An adaptive
estimator generalizes the fixed kernel to the per-frequency optimal Wiener taper, matching or surpassing the best fixed bandwidth on most standard densities, including sharply peaked and comb-like cases where fixed bandwidths fail; deconvolution under known measurement error follows in the same domain. Because the Wiener
estimator resolves sharp structure but does not fit smooth bases as economically as a mixture, a Gaussian mixture is combined with it two ways, a piecewise partition and a superposition of a smooth base and a band-limited residual, the default. A data-driven floor read from the spectrum replaces the assumed $1/n$ floor and stays robust on heaped and rounded data. On the Marron-Wand
benchmark scored by exact integrated squared error, the advantage emerges with sample size, a bias-variance tradeoff: the spectral
estimators carry low bias but pay in variance, so a corrected Botev plug-in leads at $n=100$ while the Wiener filter and superposition take the top two ranks at $n=5000$. The methods are validated on six real
datasets (CRSP returns, NHANES self-reports, CMS dimuon and SDSS spectra, a random-beacon stream, and UNSW-NB15 traffic) and on a synthetic-data quality check. All experiments are reproducible.
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Audio and Speech Processing · Cross-listed from cs.LG
Beyond Classification: A Cough Regression Benchmark for Respiratory Acoustic Foundation Models
Mayur Sanap, Prasanna Desikan and Edgar Lobaton
Respiratory acoustic foundation models (FMs) excel at cough classification, yet their ability to predict continuous health quantities from cough audio remains largely unexplored, despite the clinical value of passive age, BMI, and disease probability estimation in settings where physical measurements are unavailable. We introduce the multi-model, multi-target cough regression
benchmark evaluating five FMs (OPERA-CT, OPERA-CE, OPERA-GT, HeAR, M2D+Resp) across six targets on three
datasets under subject-disjoint protocols, comparing linear, MLP-small, and full MLP regression heads. MLP-small beats the mean-predictor baseline on all tasks and linear probing in 23 of 30 model x task cases, with full MLP overfitting on small clinical data but recovering on larger sets, revealing a
dataset size x head-capacity trade-off. HeAR leads within-
dataset age regression on Coswara (9.12 yr MAE); its CIDRZ result is excluded from headline claims owing to possible HeAR-CIDRZ pretraining overlap. OPERA-GT is favored over OPERA-CT on age in all three
datasets, with the CIDRZ margin within seed variance, extending a generative-pretraining advantage from breath to cough. HeAR and M2D+Resp reach near-full performance at N = 50 samples while OPERA models require N = 400. Cross-
dataset transfer is strongly asymmetric as large diverse data generalises to small clinical populations (CoughVID to CIDRZ: -0.17 yr) but not vice versa (CIDRZ to Coswara: +2.43 yr, +26.6%).
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Systems and Control · Cross-listed from cs.GT
On Type Deception in Linear-Quadratic Differential Games
Jesse Milzman and Dipankar Maity
We consider two-player linear-quadratic differential games of incomplete information, in which one player has a private type initially unknown to the other. The typed player has incentive to conceal their type, while the uninformed player has the potential to infer it during play. Any ex-ante
equilibrium in this setting will decompose into a deceptive, pooling phase, and a complete-information, revelatory phase. We demonstrate how to solve both phases via nested Riccati equations. Candidate
equilibria are then found by maximizing the game value over a scalar revelation time, for which we provide a gradient in the case of time-homogeneous system
matrices. We conclude by demonstrating our framework in a pursuit-evasion game with time-varying control advantages, finding interior optimal revelation times that confirm deception has quantifiable ex-ante value.
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Systems and Control · Cross-listed from cs.RO
A Bilateral Teleoperation Framework for Dexterous Manipulation
Stefano Dalla Gasperina, Dong Ho Kang, Haiyun Zhang et al. (+10 more)
Dexterous teleoperation requires precise arm-hand coordination, low-latency feedback, and robust interaction in real-world contact-rich environments. This paper presents a modular bilateral teleoperation framework that integrates operator-side input interfaces with a
robot-side dexterous hand and compliant robotic arm in a unified control architecture. The system supports position-based hand retargeting, differential arm control, multi-scale haptic feedback, and shared control for stable manipulation. We validate the framework through a real-world dexterous manipulation task, highlighting coordinated arm-hand control and contact-aware interaction. Beyond feasibility, we identify key design insights related to cross-embodiment mismatch, haptic feedback granularity, and shared control. The proposed platform provides a practical teleoperation system and a foundation for collecting high-quality demonstrations for future learning-from-demonstration research.
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All authors Stefano Dalla Gasperina, Dong Ho Kang, Haiyun Zhang, Aldo Galvan, Job D. Ramirez, Aaron Kim, Mark Helwig, Kazuto Yokoyama, Takahisa Ueno, Tetsuya Narita, Ann Majewicz-Fey, Ashish D. Deshpande, Luis Sentis
Systems and Control
Minimum settling-time PI control of pure delay processes under a hard non-overshoot constraint: exact boundary-contact characterization and the role of the MID point
Senol Gulgonul
We solve, exactly, the problem of minimum settling-time PI control of a pure delay process K e^{-Ls} under the hard time-domain constraint of zero overshoot, y(t) <= 1 for all t. The closed loop is a neutral delay system whose step response is piecewise
polynomial on the delay segments, with geometrically decaying jump discontinuities at the segment boundaries t = kL. The constrained optimum is characterized by an equioscillation-type contact structure whose active contacts sit at echo boundaries: kink maxima grazing the setpoint, jumps landing on the settling-band edge, and boundary troughs anchored to it. The number of contact equations equals the number of gains, so the optimum is exactly computable for every band delta. In a closed-form regime, delta in [(3-2 sqrt2)/4, (3-2 sqrt2)/2] approx [4.29%, 8.58%], the optimal gains are independent of delta: K Kp = 1 - sqrt2/2, K Ki L = sqrt2/2, and the optimal settling time is Ts*(delta) = (4 - sqrt2 - 2 sqrt(delta)) L. Outside this window the optimum solves an explicit two-equation
polynomial system per regime, and Ts*(delta) is a staircase with exact flats at integer multiples of L from jump-landing pinning. As delta -> 0 the optimal gains converge to K Kp = e^{-2}, K Ki L = 4 e^{-2}, the generic multiplicity-induced-dominancy (GMID) point of the neutral quasipolynomial. The GMID response satisfies the hard constraint and uniquely maximizes the decay rate; yet at every finite delta the delta-adapted optimum strictly beats the fixed GMID tuning, by about 40% at delta = 2%. The MID point is thus the limit of the optimal gains without ever being the optimal tuning. A numerical extension to first-order-plus-time-delay plants quantifies the speed/robustness trade across Ms in [1.39, 1.76].
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Systems and Control
Data Center Life Cycle Co-Design Optimization
Shrenik Jadhav, Vidhyashree Nagaraju and Zheng Liu
Liquid cooled supercomputers dissipate tens of megawatts of waste heat through cooling plants organized as parallel subloops that serve coolant distribution units. The number of subloops and the assignment of units to them are design decisions fixed at construction, yet they have not been systematically optimized at this scale. We present a framework that integrates operational energy from a validated control optimizer,
embodied carbon and capital cost from a bill of materials, maintenance over the service life, and expected unplanned downtime from a component level reliability model. All 611 ways of partitioning the 25 coolant distribution units of the Frontier supercomputer into two through six subloops are evaluated. When redundancy is not costed, the optimum is two subloops holding 14 and 11 units, at 3,320.7 tonnes of carbon dioxide equivalent and 3,987k dollars over a 7 year horizon, saving 35.7 tonnes and 63k dollars compared to the documented as built configuration of three duty subloops holding 14, 6 and 5 units. The difference is driven by piping rather than by operational energy, and the identity of the optimum is unchanged across 15 sensitivity scenarios and both extremes of physical unit grouping, although its margin narrows under compact grouping. When the N+1 standby train that the plant actually carries is priced, the optimum moves to four or five duty subloops, adjacent to the three the plant runs and far from the unconstrained answer, and the semi-analytical decision rule reproduces this shift across four leadership class systems. Redundancy policy, not cost or carbon, is what sets the subloop count. A conversion of the built plant is shown not to pay back, so the framework is a greenfield design tool.
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Systems and Control
Robust Conformal CBF and CLF Controllers via Iterative Policy Updates
Omid Mirzaeedodangeh, Eliot Shekhtman, Nikolai Matni et al. (+1 more)
Conformal prediction (CP) has been used to obtain probabilistic bounds on the error between a learned dynamics model and the true but unknown system. Such CP bounds can then be embedded into robust control Lyapunov function (CLF) and control barrier function (CBF) frameworks. However, such an approach does not retain stability/safety guarantees because of the distribution shift between the closed-loop trajectory distribution under the deployed CLF/CBF policy and the trajectory distribution from which the CP bound and its guarantees were derived. To address this issue, we propose an episodic framework that iteratively updates the robust conformal CLF/CBF policy while maintaining stability/safety guarantees across episodes. We achieve this by (1) using adversarially robust conformal prediction, and (2) quantifying a distribution shift budget that allows us to control how much the model error can increase across policy updates. This distribution shift budget is derived via a closed-loop trajectory sensitivity analysis, yielding an implicit and an explicit update rule for the CP bound. We analyze convergence of our
algorithm, which we demonstrate on three case studies. To the best of our knowledge, these are the first results that provide stability/safety guarantees for robust conformal CBF/CLF policies.
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All authors Omid Mirzaeedodangeh, Eliot Shekhtman, Nikolai Matni, Lars Lindemann
Image and Video Processing
Chroma-gated, differentiable OKLCH interpolation: Continuous Oklab fallback for color-cast reduction
Naoyuki Uchida
OKLCH – the cylindrical (lightness, chroma, hue) form of Ottosson's Oklab color space – is the interpolation space recommended by CSS Color 4 for gradients and color-mix(), and it is now broadly deployed. Its polar parameterization, however, casts color near the neutral axis in two ways: (1) an inter-hue detour between two chromatic endpoints that sweeps through an unintended hue (blue to yellow visibly passing through green), and (2) an off-line bow when one endpoint is achromatic. Existing remedies are uniformly two-valued – a threshold switch that fires only at an achromatic endpoint – so they address only (2); on chromatic pairs every one of them reduces to raw OKLCH, leaving the (1) inter-hue cast untreated. We introduce Continuous Oklab fallback (COFb), a one-parameter, differentiable chroma gate $w(C)=C^n/(C^n+\sigma^n)$ that continuously blends the OKLCH path toward the linear Oklab path as chroma falls. A single gate reduces the (1) cast that the two-valued family leaves untreated and unifies the handling of (1) and (2) without any endpoint test. We characterize a cast-hue trade-off frontier, adopt a default ($n=1$, the rational Michaelis-Menten form; $\sigma\approx0.19$ for a typical sRGB palette, from a normalization-independent cast-half criterion), and verify the gate's properties symbolically. At the default, COFb halves the inter-hue path detour (mean lateral deviation -49.5%, chroma-weighted hue excursion -35.5%). We also state the method's limits: on (2) alone the two-valued switch remains better, and like any Cartesian blend COFb does not preserve chroma. In deployment, COFb runs entirely in plain Oklab (a,b) to sRGB, so it serves as a fallback that delivers the same cast-reduced gradients where modern CSS color interpolation (color-mix(in oklch) and the like) is unavailable – older engines, image and
video pipelines, or GPU shaders.
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Signal Processing
Generalized likelihood ratio test for magnetic anomaly detection: a geometrical approach
C. Chenevas-Paule, S. Zozor, L.-L. Rouve et al. (+3 more)
State-of-the-art approaches to
magnetic anomaly detection rely on the generalized likelihood ratio test (GLRT). These approaches are based on the formulation of a parametric model of the source to be detected, expressed in a suitable functional basis. One of the primary objectives of this study is to demonstrate that, for a given measurement configuration, the signal is constrained to evolve within a restricted subset of the space generated by these functional bases. The parametric representation of the signal is identified as a semi-algebraic space which, for the dipole model used in this article, turns out to be a cone outside of which the estimated signal does not satisfy the physical equations. Thus, a second objective is to exploit this property to constrain the signal parameters in the GLRT to belong to the semi-algebraic space, in order to improve detection performance. The performance gain of the proposed
algorithm is compared to the one of conventional approaches; numerical simulations show that the proposed approach not only outperforms state-of-the-art methods but can even provide results close to those of the clear-seeing (optimal) receiver.
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All authors C. Chenevas-Paule, S. Zozor, L.-L. Rouve, O. J. J. Michel, O. Pinaud, R. Kukla
Audio and Speech Processing
DDPO-VC: Speaker De-Identification via Diffusion Denoising Policy Optimization
Liming Wang, Cody Karjadi, Rhoda Au et al. (+1 more)
A key challenge of speaker de-identification is the balance between
privacy and utility. Many utility variables, such as the cognitive health status of the speaker, are correlated with the
privacy variable, such as the speaker identity, violating the independence assumption held by the disentanglement-based approaches, causing leakage of private information and the loss of useful information for downstream tasks. To tackle this challenge, we propose a general framework, DDPO-VC, for speaker de-identification through reinforcement learning-based post-training with
diffusion models. Learning from reward signals combining knowledge from
privacy-focused and utility-focused teachers, our method outperforms various strong \deid/ methods in both
privacy preservation and cognitive utility on two commonly used dementia
speech benchmarks. Please check out our code\footnote{\href{https://github.com/cactuswiththoughts/DDPO-VC}{https://github.com/cactuswiththoughts/DDPO-VC}} and demo\footnote{\href{https://cactuswiththoughts.github.io/SpeakerDeID-Demo/}{https://cactuswiththoughts.github.io/SpeakerDeID-Demo/}}.
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All authors Liming Wang, Cody Karjadi, Rhoda Au, James Glass
Systems and Control
Hamilton-Jacobi Reachability-Based Safe Reinforcement Learning for Emergency Collision Avoidance
Yuhong Jiang, Shiyue Zhao, Junzhi Zhang et al. (+4 more)
Emergency collision avoidance under extreme driving conditions demands safety-critical control that accounts for both obstacle proximity and vehicle dynamic stability over a future time horizon, yet existing methods often rely on instantaneous or local safety evaluations. This paper proposes a safe reinforcement learning framework guided by a Hamilton-Jacobi (HJ) reachability based motion safety set that provides forward-looking safety supervision for constrained policy optimization. Specifically, a unified signed safety function is formulated by combining
geometric collision margins and chassis stability limits, and is then extended through reachability analysis into a finite-horizon motion safety set that characterizes whether safety can be maintained under future vehicle state
evolution. To enable practical computation, the motion safety set is approximated from offline extreme driving data, mitigating the computational burden of grid-based HJ solvers. The learned motion safety set is then embedded as a continuous safety cost into a constrained Markov decision process, and a PID-Lagrangian policy optimization scheme is employed to adaptively regulate the Lagrange multiplier for safety constraint enforcement. Simulation and real-vehicle experiments on low-adhesion obstacle-avoidance scenarios demonstrate that the proposed method achieves higher goal-reaching rates, produces smoother avoidance maneuvers, and maintains larger unified safety margins than baseline methods.
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All authors Yuhong Jiang, Shiyue Zhao, Junzhi Zhang, Junfeng Zhang, Xinhan Li, Shijie Zhao, Chengkun He
Signal Processing
CAP: Towards PPG Universal Representation Learning with Patient-level Supervision
Chenyang He, Xinyi Shao, Shun Huang et al. (+4 more)
Photoplethysmography (PPG) plays a central role in wearable health monitoring and clinical decision support. Yet existing approaches to universal PPG representation learning largely focus on signal-level objectives and often overlook patient-level health context, which limits generalization to complex clinical tasks and heterogeneous cohorts. To address this gap, we construct a large-scale paired PPG-EHR
multimodal dataset by distilling fragmented medical histories and clinical records into cohesive, patient-level electronic health records (EHR). Building on this resource, we propose Clinical Anchored Pretraining for PPG (CAP). During pretraining, CAP performs cross-modal contrastive
alignment that anchors PPG representations to patient-level clinical semantics, guiding the encoder beyond waveform fitting toward modeling consistency in a patient's overall physiological state. During downstream adaptation, the pretrained PPG encoder provides clinically grounded representations that strengthen inductive bias and improve robustness and transferability. Experiments demonstrate that CAP consistently outperforms strong baselines on four diverse downstream tasks. CAP achieves a particularly large gain on respiratory rate prediction (up to +87.6% relative improvement over the state-of-the-art baseline) and delivers an average relative +26.7% across all tasks. We further enhance the interpretability of our approach through comprehensive analyses, including ablations and multiple complementary visualizations of the learned representations. The code for our experiments is available at: https://github.com/gody123gody/CAP .
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All authors Chenyang He, Xinyi Shao, Shun Huang, Bosong Huang, Daoqiang Zhang, Ming Jing, Cheng Ding
Audio and Speech Processing
Dynamic Prosody Prediction in LLM-based TTS for Improving Speaker Similarity
Zhenwei Mou, Liping Chen, Yajun Hu et al. (+3 more)
Personalized text-to-
speech (TTS) aims to clone the target speaker in the synthesized
speech, imitating both the voice and speaking style. Current large
language model (LLM)-based TTS methods ignore the style-specific prosodic patterns in generated
speech, resulting in deficient style learning and thus limiting speaker similarity in synthesized
speech. To this end, we investigate the prosody learning conditioned on the synthesized
speech, and propose to predict the prosody of the current syllable based on previously predicted
speech. Experimental results obtained on three
datasets demonstrated the efficacy of the proposed dynamic prosody prediction method in enhancing the prosody learning capability, thereby improving the speaker similarity of the generated
speech. Audio samples are available at https://muzw.github.io/dynapros/.
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All authors Zhenwei Mou, Liping Chen, Yajun Hu, Zhen-Hua Ling, Xin Fang, Jianqing Gao
Audio and Speech Processing
DuraMark: Duration-Embedded Watermarking in LLM-based TTS
Zhenwei Mou, Weili Jiang, Liping Chen et al. (+4 more)
Large
language model (LLM)-based text-to-
speech (TTS) models have achieved remarkable voice cloning capabilities, raising concerns about potential deepfake misuse.
Speech watermarking mitigates this by
embedding traceable information into generated
speech. Mainstream watermarking methods operate at the signal level (waveform or spectrogram), rendering the watermark vulnerable to generative attacks (e.g., neural codec and vocoder). To address this, we propose DuraMark, a robust information-level watermarking framework. It utilizes syllable duration editing to achieve watermark
embedding. Specifically, DuraMark integrates a duration-controllable LLM-based TTS model to edit syllable durations during synthesis, coupled with a duration extractor to extract these durations for detection. Experiments demonstrate DuraMark's superior robustness against generative attacks, significantly outperforming signal-level baselines. Audio samples are available at https://muzw.github.io/duramark_demo/.
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All authors Zhenwei Mou, Weili Jiang, Liping Chen, Zhen-Hua Ling, Kong Aik Lee, Kai Gao, Boyu Zhao
Signal Processing
Surrogate-Assisted Framework for SI-Compliant Interconnect Design Optimization Using the Earth Mover's Distance
Emre Ecik, Werner John, Julian Withöft et al. (+2 more)
This work presents a deterministic, machine-assisted framework for SI-compliant PCB design based on the Earth Mover's Distance (EMD). In contrast to conventional surrogate-based optimization methods that rely on iterative black-box search procedures, the proposed approach follows an interpretable, sequential evaluation strategy. Neural surrogate models are first used to efficiently predict waveform describing features from
topology-dependent design parameters. A decision tree then acts as a physically motivated quality gate that identifies SI-compliant waveforms according to predefined SI criteria. Within the resulting valid solution space, the Earth Mover's Distance is employed as a similarity metric to rank candidate designs according to their proximity to an ideal reference signal. This enables not only the deterministic identification of admissible parameter regions but also a transparent prioritization of physically superior solutions without inverse modeling or
stochastic search procedures. The methodology is demonstrated using a large-scale set of simulated DDR3 fly-by waveforms. By combining surrogate prediction, interpretable classification, and EMD-based waveform evaluation, the framework provides an explainable and computationally efficient alternative to conventional optimization strategies for supporting PCB development with AI-based methods.
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All authors Emre Ecik, Werner John, Julian Withöft, Ralf Brüning, Jürgen Götze
Audio and Speech Processing
VoxWatermark: A Large-Scale Benchmark for Audio Watermark Detection under Perturbations
Farnaz Sedaghati, Yuxi Wang, Zicheng Weng et al. (+1 more)
With the rapid deployment of
speech generation systems in open environments, providing verifiable source attribution and copyright accountability for audio content has become critical. A gap in current research is the lack of a unified
benchmark that systematically compares different watermark injection methods under realistic distribution shifts. To address this, we build VoxWatermark by applying 10 watermarking methods (4 neural and 6 traditional) with unified injection and annotation on
multilingual, multi-source corpora, and introducing no-box, black-box, and white-box perturbations to simulate real recording and transmission conditions. Based on this
benchmark, we propose AudioWMD as a robust baseline detector for large-scale, multi-method, cross-distribution settings. Results show that injection-method diversity and distribution shifts affect detection stability, while validating the effectiveness and scalability of AudioWMD.
Dataset and code are publicly available.
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All authors Farnaz Sedaghati, Yuxi Wang, Zicheng Weng, Wei Rao
Audio and Speech Processing · Cross-listed from cs.SD
FreeSonic: Training-Free Temporal-Aware Decoupled Attention for Precise Audio Editing
Yuxuan Jiang, Mingyang Han, Yusheng Dai et al. (+12 more)
Text-to-audio (TTA) generation has made significant strides, yet achieving precise and consistent audio editing remains a major challenge. However, existing methods struggle to balance temporal consistency with background preservation. In this paper, we propose FreeSonic, a training-free framework leveraging the state-of-the-art Rectified Flow-based TangoFlux model. FreeSonic utilizes an optimized inversion-reverse process and joint text-audio
attention maps for precise target segment extraction. For content editing, a novel scheduled
attention decoupling confines modifications to target regions while preserving original acoustic context. Furthermore, task-oriented noise injection enhances versatility for tasks such as audio removal and non-rigid replacement. Extensive experimental results demonstrate that FreeSonic achieves a superior balance by providing a high-fidelity and efficient solution for precise and consistent audio editing. Project and demos: https://free-sonic.github.io/
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All authors Yuxuan Jiang, Mingyang Han, Yusheng Dai, Andong Wang, Tianhong Zhou, Jiaxin Ye, Dongxiao Wang, Haoxiang Shi, Boyu Li, Jun Song, Cheng Yu, Bo Zheng, Weibei Dou, Zehua Chen, Jun Zhu
Systems and Control · Cross-listed from math.OC
Revisiting The PBH Test: Fast Uncontrollability Certificates via Krylov Methods
Ahmad F. Taha, Mohamad H. Kazma and Abdallah A. Albustami
This letter revisits the classical PBH test through the lens of finite-horizon reachability. By casting state transfer as a minimum energy, primal optimization problem, we show that unreachable state-space maneuvers admit dual infeasibility certificates. These certificates are computable without forming the controllability
matrix meaning that uncontrollability can be efficiently certified. We prove that any such certificate is a linear combination of uncontrollable generalized eigenvectors, thereby providing a spectral interpretation without a global eigendecomposition. We also devise
algorithms based on Krylov sub-space methods that extract some of the uncontrollable PBH modes from a certificate and demonstrate favorable scaling on large dynamic networks with thousands of nodes.
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Audio and Speech Processing · Cross-listed from cs.SD
EchoEdit: Stabilizing Inversion-Free Audio Editing via Optimal Transport Geometry
Zhongyuan Fu, Yuhang Jia, Hui Wang et al. (+6 more)
Text-guided audio editing with pretrained generative models is commonly implemented through inversion or noising. This
topology induces a structural trade-off, as stronger edits require deeper corruption of the very rhythm, transients, timbre, and long-range form that should remain unchanged. Here, we introduce EchoEdit, a training-free and inversion-free framework for real-audio editing that directly constructs an editing field by differencing the drifts conditioned on the source and target
prompts. This construction avoids explicit source inversion, paired edit data, and test-time optimization, but its
stochastic source marginals introduce uncertainty drift, where small random deviations accumulate along the editing trajectory and can move the edited latent away from the audio data
manifold. To address this limitation, we further propose EchoEdit+, an optimal-transport-regularized extension that stabilizes the direct editing path by minimizing the transportation cost between edited variables and the source-conditioned audio
manifold. The resulting OT coupling contracts the
stochastic displacement at noisy states, keeps model queries closer to the training distribution, and preserves structural information while allowing semantic change. Experiments on sound-effect and music editing demonstrate that EchoEdit+ improves target-
prompt alignment and source preservation over inversion-based baselines and the unregularized direct editor. Code and
dataset will be released.
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All authors Zhongyuan Fu, Yuhang Jia, Hui Wang, Pengjun Chen, Jian Gao, Cun Liu, Wenjia Zeng, Yong Chen, Yong Qin
Signal Processing
On the Feasibility of Human Presence Detection Using Ceiling-Mounted Sub-THz Channel Sounding: Conference Room Measurement
Ali Al-Ameri, Xuesong Cai, Aleksei Fedorov et al. (+1 more)
This paper presents a measurement-based investigation on the feasibility of human presence detection using a ceiling-mounted sub-THz channel sounder operating from 134 to 146~GHz. Wideband channel measurements were conducted in an indoor conference room under empty-room, human-present, and water-filled mannequin scenarios across five spatial positions. The measurements were performed using a vector network analyzer combined with sub-THz frequency extenders. Two
antenna beamwidth configurations were used, one with a highly directive horn
antenna on the transmitter side and one with a less directive, open-waveguide transmitter. The measured channel responses were transformed into calibrated power delay profiles and analyzed using normalized channel variation metrics in the delay domain. The results show that human detection is strongly dependent on target position relative to the ceiling-mounted transmitter and receiver as well as on
antenna beamwidth. Furthermore, repeated empty-room measurements reveal that small environmental changes, such as slight furniture displacement, introduce non-negligible channel variations that must be considered when evaluating detection performance. In the wide-beam open-waveguide configuration, the human-present measurements produced lower values of the delay-domain variation metric than the repeated empty-room baseline, whereas the water-filled mannequin produced values at or above this baseline across all positions. With the directive transmitter, the human response exceeded the baseline significantly but only at favorable positions, especially P1 and P2, showing that the sensing response remains spatially selective. These findings provide experimental insight into the capabilities and limitations of ceiling-mounted sub-THz sensing for future integrated sensing and communication systems.
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All authors Ali Al-Ameri, Xuesong Cai, Aleksei Fedorov, Fredrik Tufvesson
Audio and Speech Processing
EChO-Agent: Evidence Chain Orchestration Agent for Audio Reasoning
Siyuan Zhang, Jian Zong, Junyu Wang et al. (+7 more)
While LALMs show promise on audio question answering, they fail to focus on question-relevant segments of audio and provide a clear, checkable
reasoning process when dealing with complex audio
reasoning. Reinforcement learning and tool-augmented prompting can help models better relate questions to audio but lack a reliable way to understand, integrate, and self-verify audio segments. To address this gap, we present EChO-
Agent, a modular
agent framework that reformulates complex audio QA as a planning, tool execution, evidence integration, and answer verification workflow. Experiments on MMAR
benchmark show EChO-
Agent improves both accuracy and rubric scores over baseline and ablation studies show evidence integration is the key factor.
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All authors Siyuan Zhang, Jian Zong, Junyu Wang, Peiyuan Jiang, Jiahao Yan, Jingyu Zhang, Tianrui Wang, Xiaobao Wang, Longbiao Wang, Jianwu Dang
Systems and Control
Differentially Private Consensus for Time-Delay Multi-agent Systems
Mingyu Wang, Xiaofeng Zong, Jimin Wang et al. (+1 more)
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.
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All authors Mingyu Wang, Xiaofeng Zong, Jimin Wang, Ji-Feng Zhang
Signal Processing
Vertical Sub-THz Channel Characterization: Sounder Implementation and Initial Measurements
Ali Al-Ameri, Xuesong Cai, Juan Sanchez et al. (+4 more)
We present a measurement-based characterization of indoor vertical ceiling-to-ground sub-THz channels in the 136-144 GHz band, motivated by ceiling-mounted
radio-unit deployments for future distributed indoor networks. The measurements are performed using a vector network analyzer (VNA)-based channel sounder with a mechanically scanned planar virtual
antenna array (VAA) at the receiver, enabling single-input single-output (SISO), small-array single-input multiple-output (SIMO), and large-array SIMO measurements in three indoor environments: an office, a laboratory, and a ventilation room. The small-array and large-array SIMO measurements synthesize 2 X 2 cm and 30 X 1 cm uniform rectangular arrays (URAs), respectively. The results show that the vertical links are generally dominated by a strong Line-of-Sight (LOS) component close to the ceiling-to-ground direction, but with clear environmental differences. The office and laboratory exhibit relatively limited delay dispersion, whereas the ventilation room shows stronger delayed multipath due to its corrugated metallic ceiling and surrounding metallic structures. The measured root mean square (RMS) delay spreads are 0.55-1.74 ns for the small-array measurements and 0.44-2.57 ns for the large-array measurements, smaller than those reported in several horizontal indoor sub-THz measurement campaigns at similar frequencies. However, the channel is not purely free-space. Repeatable second-order reflections involving the receiver table, ceiling, transmitter structure, and ceiling-mounted objects are observed in all environments. The large-array measurements further reveal spatial non-stationarity along the 30 cm aperture, with several multipath components visible only over limited parts of the array. These results show that ceiling materials, obstructions, and aperture-dependent variations matter in vertical sub-THz channel modeling.
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All authors Ali Al-Ameri, Xuesong Cai, Juan Sanchez, Aleksei Fedorov, Buon Kiong Lau, Ove Edfors, Fredrik Tufvesson
Systems and Control
Optimal Ground-to-Air Interception with Time-Varying Acceleration Bounds
Or Nahum and Vitaly Shaferman
This paper proposes novel optimal-control-based guidance laws for ground-to-air missiles with time-varying acceleration bounds. In such engagements, as the missile climbs in altitude, its acceleration bound decreases, which may lead to acceleration saturation and significant miss distances if not explicitly accounted for. The proposed guidance laws incorporate hard acceleration command constraints directly into a linear-quadratic optimal-control framework, in contrast to conventional unbounded or softly constrained approaches. Analytically based guidance laws are developed for linear zero-order and first-order strictly proper missile dynamics with arbitrary-order linear target dynamics. Unlike the constant hard-bound case with minimum-phase missile dynamics, time-varying acceleration command bounds permit an initial unsaturated interval in which the proposed guidance laws can anticipate future saturation and reshape the acceleration profile accordingly. This enables earlier maneuvers when the missile possesses greater low-altitude maneuverability, fundamentally altering the structure of the optimal solution. The proposed approach is evaluated in nonlinear simulations and compared with equivalent unbounded and softly constrained optimal guidance laws. The results demonstrate substantially improved interception performance under saturation, reduced tuning requirements compared to softly constrained guidance laws, and enhanced capability in challenging engagement scenarios.
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Systems and Control
Adaptive Deep Koopman Operator for Vehicle Dynamics Modeling: A Physics-Informed and Tire-Force-Driven Approach
Wenjie Wang, Hao Chen, Ran Shu et al. (+3 more)
Accurate and adaptive modeling of vehicle dynamics is paramount for the safety of autonomous driving systems, particularly under extreme maneuvers and time-varying parameters. While Deep Koopman operator theory offers a promising global linearization framework, its online application faces a theoretical bottleneck: the high-dimensional lifted state space inherently induces a rank-deficient problem, rendering traditional recursive least squares based updates numerically unstable. To address this, we propose a novel tire-force-driven modeling framework with guaranteed online stability. First, an offline Deep Koopman model is constructed by
embedding 7DOF dynamic
equilibrium constraints into the learning objective, ensuring the structural fidelity and physical interpretability of the lifted
manifold. Second, we theoretically reformulate the operator update in the rank-deficient space as a minimum-norm solution problem. A Physics-Informed Variable Step-Size Normalized Least Mean Squares (PI-VSS-NLMS)
algorithm is proposed, which leverages the projection property of NLMS to act as a stable pseudo-inverse solver while incorporating an anchoring mechanism to suppress parameter drift. Extensive simulations on CarSim and Hardware-in-the-Loop validation on dSPACE MicroAutobox III confirm the superiority of the proposed
algorithm. It achieves robust prediction accuracy under unseen excitations while guaranteeing real-time feasibility with an average execution time of 0.421 ms, thus bridging the gap between theoretical models and practical deployment.
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All authors Wenjie Wang, Hao Chen, Ran Shu, Solyeon Kwon, Kyoungseok Han, Hongyu Shu
Audio and Speech Processing · Cross-listed from cs.SD
When the Same Musical Knowledge Forgets Differently: A Clean Probe of Pathway-Dependent Forgetting
Yu Liu, Zhiwei Yang, Wenxiao Zhang et al. (+8 more)
A model can learn that the piano piece Für Elise is calm and reflective by listening to the audio or by reading a text description, but does it matter which route that knowledge took when it is later at risk of being forgotten? Forgetting research in
multimodal models measures what knowledge is lost under adaptation, yet has not asked whether acquisition route affects how easily that knowledge is forgotten. We call this untested premise the Pathway-Invariant Assumption. Music understanding enables a clean test because a music clip and a canonical text description can be aligned to the same perceptual content, allowing the same knowledge unit to enter a model through listening or reading while the target remains fixed. Across multiple architecturally distinct audio-
language models, we observe a consistent asymmetry: text-pathway knowledge is forgotten more than matched audio-pathway knowledge under identical adaptation pressure. To attribute this effect to route rather than confounds, we introduce the Paired Pathway Controlled Protocol (PPCP), a three-phase design that establishes matched pathway baselines, activates both pathways under symmetric supervision on the same knowledge pool, and applies identical forgetting pressure to both pathways. The gap is stable across models and gain-controlled analyses, persists when contradictory overwrite is replaced by correct-label cross-domain learning, remains under single-modality pressure, and is not removed by lightweight replay. Two independent routing-depth controls confirm that the effect is not explained by architectural depth, pointing to input representation as the dominant factor. Under PPCP, our results demonstrate that forgetting is highly route-dependent, establishing acquisition route as a new analytical dimension for forgetting research and
multimodal system design.
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All authors Yu Liu, Zhiwei Yang, Wenxiao Zhang, Cong Cao, Fangfang Yuan, Kun Peng, Haimei Qin, Lei Jiang, Jin B. Hong, Hao Peng, Yanbing Liu
Systems and Control · Cross-listed from quant-ph
REGRID-QAOA: A Resource-Efficient Hybrid QAOA Framework for Physics-Constrained Power System Islanding
Yuqi Jiang, Yuqi Zhang, Zhiding Liang et al. (+3 more)
Quantum computing has rapidly emerged as a powerful paradigm for tackling computationally demanding problems. In particular,
quantum optimization shows strong promise for hard combinatorial problems in power systems, where increasing distributed energy penetration heightens the need for intentional islanding to maintain grid reliability and resilience. However, power system islanding is an NP-hard combinatorial optimization problem that becomes computationally prohibitive for classical solvers as network size grows, motivating the use of
quantum computing as a promising alternative pipeline. This study develops a resource-efficient hybrid QAOA islanding framework that brings physics-constrained power-system partitioning into the
quantum optimization workflow. The framework combines coherency-informed graph reduction, physics-aware constraint modeling, and structured post-processing to efficiently convert shallow-
circuit QAOA samples into high-quality feasible islanding decisions without deep
circuits or large shot budgets. The proposed framework is validated on the standard IEEE
benchmark systems (9-, 14-, 24-, 30-, 39-, and 57-bus), demonstrating that the hybrid workflow achieves Gurobi-optimal solution quality with a clear
quantum resource advantage over vanilla QAOA, while the resulting islanding solutions satisfy all physical feasibility requirements after network separation. This study establishes QAOA-based islanding as a viable
quantum approach for critical infrastructure, with structured post-processing as the key enabler of
quantum resource efficiency.
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All authors Yuqi Jiang, Yuqi Zhang, Zhiding Liang, Qiang Guan, Yan Li, Ganesh Kumar Venayagamoorthy