Systems and Control
Admittance Sensitivity-Informed Modular GP for Scalable Topology-Adaptive Power-Flow Learning
Henrique O. Caetano, Carlos Dias Maciel and Rahul K. Gupta
Data-driven approaches for learning power flow models suffer from weak generalization across varying network
topologies and limited computational scalability. Existing methods typically rely on training over a large set of grid
topologies, which becomes impractical for large networks. This paper proposes a scalable and computationally efficient framework for
topology-adaptive learning of power flow solutions. We propose a modular architecture consisting of bus-level Gaussian Process (GP) models, where each GP collects local features based on bus-level egonet definition. The localized bus-level feature includes first-order power and admittance sensitivities, nodal injections and node degree. In addition to the modular architecture, we propose using Random
Fourier Features (RFF) for feature reduction, which further enhances the computational scalability. We evaluate the effectiveness of the proposed method by simulations across multiple
benchmark networks under N-1, N-2, and N-3 contingencies. Results for the PEGASE 1354 bus system under N-3 contingencies demonstrate high predictive quality, with an $R^2$ score of 0.983 and a voltage-magnitude RMSE of 0.0023 p.u. The framework maintains recall rates exceeding 98% for detecting voltage limit violations across all test cases. Furthermore, the approach exhibits scalability, completing training and testing for the PEGASE 1354 system in 116.47 seconds while outperforming existing
benchmarks in zero-shot generalization without requiring additional training samples.
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Systems and Control
From Well-Posed Inversion to Learning Design: Physics- Informed Neural Estimation for Autonomic Regulation
Sara Nour Sadoun, Giuseppe Alessio D'Inverno, Francois Cottin et al. (+2 more)
Learning-based and physics-informed methods are increasingly used for inverse estimation in controlled nonlinear dynamical systems. However, in many such approaches, the theoretic requirements that make unknown-input reconstruction meaningful, namely well-posedness in the sense of Hadamard, are often disregarded or weakly addressed through generic regularization terms with no explicit guarantees. In this work, we adopt a complementary viewpoint in which these control-theoretic and structural conditions inform the
estimator design and constrain its training. We thus develop a physics-informed input-state neural
estimator for joint unknown-input and state estimation in nonlinear controlled systems with partial measurements. In the present work, this general framework is instantiated on a model of autonomic cardiac regulation, provides a concrete study case. The
estimator is formulated as an inverse neural map conditioned on time and measured outputs, and is trained under data fidelity and dynamical consistency constraints. To ensure it complies with the same structural requirements imposed in robust estimation, we derive left-invertibility conditions by differential-algebraic elimination and embed the resulting constraints directly into the training objective. We further analyze a priori the stability of the inverse mapping to output perturbations and derive a conservative Lipschitz bound that guides the tuning of cost functional hyper-parameters. The framework is evaluated on simulated data, where ground truth data is available, and on two distinct
datasets of real cardiovascular recordings. The results show that incorporating control-theoretic solvability constraints into physics-informed learning improves the reliability of inverse inference beyond forward consistency alone.
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All authors Sara Nour Sadoun, Giuseppe Alessio D'Inverno, Francois Cottin, Arnaud Boutin, Taous-Meriem Laleg-Kirati
Signal Processing
Chasing Lightning: Detecting, Characterizing, and Identifying a Powerful Space-Based GNSS Interference Source
Zachary L. Clements, Argyris Kriezis and Todd E. Humphreys
This paper analyzes and identifies a space-based Global
Navigation Satellite System (GNSS) interference source that has caused scores of powerful transient wide-area interference events over continental Europe, Greenland, and Canada since 2019. While terrestrial or near-terrestrial sources are primarily responsible for the recent uptick in GNSS interference worldwide, space-based interferers are of special concern given their potential for vast geographic reach and their portent of a qualitative escalation in GNSS interference. Based on data collected between 2019 and 2026 from a network of terrestrial GNSS reference stations, this paper (1) develops a received-power-based detection framework; (2) details the spatial, temporal, and spectral patterns of wide-area interference events caused by the source; (3) presents and analyzes identification techniques that blend received-power and time-difference-of-arrival measurements; and (4) applies these techniques to confidently identify the GNSS interference source as a constellation of Russian early warning satellites in Molniya ("lightning") orbits.
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Systems and Control · Cross-listed from math.OC
Optimal Finite-Horizon LQR Control for Traffic Flow via Variable Speed Limits
Brian Block and Stephanie Stockar
This article presents a finite-horizon linear quadratic regulator for the control of the first-order Lighthill-Whitham-Richards traffic model with a triangular fundamental diagram. The in-domain control action is realized through variable speed limits implemented as a source term in the governing
hyperbolic partial differential equation. Unlike prior studies on infinite-horizon formulations, this article develops a finite-horizon LQR framework, deriving a space and time varying state feedback function for
hyperbolic PDEs. The solution to the finite time optimal control problem relies on the solution of another PDE, called the Riccati PDE. The resulting nonlinear Riccati PDE is solved analytically via the parametric method of characteristics. The Riccati PDE solution is a function of both time and space, as well as the traffic regime. A sensitivity analysis demonstrates the effects of the LQR parameters for both the infinite and finite time horizon problem in different traffic situations, while siulations validate the finite-horizon LQR's ability to guarentee finite-time convergence. Comapred to the infinite-horizon LQR, the proposed approach achieves significantly improved control performance across various scenarios, making it particularly suitable for time-sensitive traffic management applications.
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Systems and Control
Enhancing Offshore Wind Simulations: Interpolated Switching via DLL Black-Boxes
Nicolae Darii, Ranjan Sharma, Vladislav Akhmatov et al. (+4 more)
The modern power system, increasingly composed of Inverter-Based Resources (IBR) from multiple manufacturers, requires new study and design techniques that balance accuracy with the need to protect the Intellectual Property (IP) of various stakeholders. One possible method to support detailed electromagnetic transient (EMT) simulations is to convert the original equipment manufacturers (OEM) models into shareable black-box versions using dynamic link libraries (DLLs). This technique prevents IP violations while potentially maintaining simulation accuracy by
embedding the original components within the shareable DLL. Thereby, this work aims explicitly to enhance simulation fidelity by translating full-switching models of offshore wind turbines (OWTs). In this context, the paper offers valuable recommendations, including how to convert interpolation-based elements, preserve simulation speed, recognize limitations, and outline future improvements
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All authors Nicolae Darii, Ranjan Sharma, Vladislav Akhmatov, Kanakesh Vatta kkuni, Chi Su, Oscar Saborio-Romano, Nicolaos A. Cutululis
Systems and Control · Cross-listed from math.DS
Semidefinite Programming Certificates for Synchronization of Kuramoto Oscillators on Arcs
Swapnil Tripathi, Mahmut Kudeyt, Alk{\i}m Gökçen et al. (+2 more)
A class of Kuramoto models with a general coupling function that can be expressed in terms of a finite number of harmonics, each comprising sinusoidal terms, is studied. We propose a novel approach for certifying local phase synchronization in this class for all initial conditions lying on an arc. The trace parametrization property and Gram
matrix representation of a trigonometric
polynomial are utilized along with Putinar's Positivstellensatz to obtain semidefinite programming certificates for the stability of the phase-difference system, which in turn implies synchronization of the original system. The results can be extended to any system of coupled oscillators where the forward-invariance on arcs can be established.
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All authors Swapnil Tripathi, Mahmut Kudeyt, Alk{\i}m Gökçen, Savaş Şahin, Özkan Karabacak
Systems and Control
Systematic Gray-Box Identification Methodology for Voltage Source Converters
Nicolae Darii, Luis A. Garcia-Reyes, Ignasi Ventura Nadal et al. (+4 more)
This paper introduces a systematic gray-box identification framework for voltage-source converter models based solely on terminal time-series data. The proposed approach combines a physically informed white-box standard model with iterative time-domain calibration to estimate controller parameters that mimic the behavior of the black-box model in electromagnetic transient (EMT) simulations. Unlike conventional frequency-domain identification methods, the framework leverages time-domain data more effectively to better constrain the surrogate model across a broader operating range and capture reference-signal dynamics. To evaluate the accuracy of the identified model, the paper presents additional frequency-domain validation metrics based on Nyquist analysis and singular value decomposition, allowing for both quantitative assessment of model divergence and qualitative classification of mismatch types. The methodology is tested on cases with increasing structural uncertainty, from exact parametric recovery to an actual detailed EMT black-box model. Results demonstrate that the proposed framework can accurately recover parameters when the internal structure is known, adjust for moderate structural mismatch with extra degrees of freedom, and offer a reliability measure for small-signal stability analysis of converter models protected by intellectual property
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All authors Nicolae Darii, Luis A. Garcia-Reyes, Ignasi Ventura Nadal, Oscar Saborio Romano, Ranjan Sharma, Oriol Gomis-Bellmunt, Nicolaos A. Cutululis
Systems and Control
Estimation of Equivalent SCR for Offshore Wind
Nicolae Darii, Ranjan Sharma, Germano Rugendo Mugambi et al. (+2 more)
The integration of offshore wind power plants (OW-PPs) into weak grids can pose stability challenges due to the interaction between inverter-based resources (IBRs), Flexible AC Transmission Systems (FACTS) and the grid. In this context, long HVAC transmission systems, relatively common for OWPPs, can exacerbate the stability challenges. Therefore, this paper introduces a novel methodology for estimating the equivalent short-
circuit ratio (ESCR) at the offshore point of connection (PoC), combining analytical two-port network (TPN) modeling with electromagnetic transient (EMT) simulations. The approach derives the Thevenin equivalent impedance for passive and active components, enabling accurate ESCR computations without complex derivations. Limitations of traditional SCR metrics are addressed by incorporating the dynamics of the converters, such as static synchronous compensators (STATCOMs), into a hybrid EMT-TPN method for synthesizing equivalent impedances. The process is then verified on the CIGRE OWPP
benchmark and is found to capture ESCR variations with cable lengths, shunt reactors, and grid strength. Additionally, the results emphasize the correlation between the ESCR and voltage stability, highlighting the role of STATCOMs in supporting voltage stability in weak grids. This modular framework aids in OWPP design and stability analysis for converter-dominated systems.
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All authors Nicolae Darii, Ranjan Sharma, Germano Rugendo Mugambi, Oscar Saborio Romano, Nicolaos A. Cutululis
Systems and Control
Recursive Learning of Feedforward and Compliance Compensation Parameters for Precision Motion Systems
M. Wind, J. Pierssens, R. Beerens et al. (+2 more)
To meet the stringent requirements of future motion systems exhibiting time-varying and/or position-dependent behavior, online data must be leveraged to improve control performance. This paper presents a recursive
algorithm for simultaneous learning of feedforward and compliance compensation parameters. A multivariate regression formulation is proposed that jointly estimates friction, mass, jerk, and compliance compensation parameters while mitigating parameter coupling. Experimental results on a high-tech
semiconductor metrology and inspection system demonstrate an order-of-magnitude improvement in servo performance.
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All authors M. Wind, J. Pierssens, R. Beerens, V. Dolk, T. van Keulen
Signal Processing
Voxel-CKM: Voxelized Radio Frequency Radiance Fields for Fast and Few-Shot CKM Construction
Hanlei Li, Guangyi Zhang, Kequan Zhou et al. (+2 more)
Channel knowledge maps (CKMs) are designed to predict channel state information (CSI) from user locations, thereby enabling low-overhead CSI acquisition. However, existing CKM construction methods often require hours-to-days of training time and dense measurements, resulting in substantial deployment cost. In this paper, we propose Voxel-CKM, a novel voxelized
radio frequency (RF) radiance field framework for fast and few-shot CKM construction. The core idea is to replace implicit neural representations with explicit voxel grids to efficiently capture the spatial variation of wireless channels. Building upon this, we further introduce a compact vector-
matrix (VM) decomposition to parameterize these voxel grids using a small set of
matrices and vectors, which significantly accelerates convergence and facilitates fast CKM construction. To enable few-shot learning, we incorporate a transmitter prior as an inductive bias to guide the learning process under sparse measurements. Additionally, a total-variation (TV) regularization loss is proposed to mitigate overfitting and stabilize optimization. Experiments show that Voxel-CKM substantially accelerates training convergence and improves performance in the few-shot regime.
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All authors Hanlei Li, Guangyi Zhang, Kequan Zhou, Yunlong Cai, Guanding Yu
Systems and Control
Unstable Poles Arising in AC Power Grid Subsystem Representations
Liam Hallinan and Ioannis Lestas
Recent small-signal stability studies of AC grids have shifted towards analysing power systems as interconnections of subsystems and leveraging their input-output properties to derive scalable stability certificates. Two subsystem representations appear frequently in the literature: the PQ model, coupling powers to phase angle and voltage magnitude, and the IV model, coupling currents to voltages. In this paper, we derive both models without simplifying the bus or line dynamics and show that a loop transformation relates the two. One of the main results in the paper is to then show analytically that each representation may exhibit unstable poles depending primarily on the operating point (IV model) or the presence of high-frequency passive dynamics (PQ model). In particular, such unstable poles in the subsystems can occur even when the aggregate interconnection is stable and well-behaved. These effects are validated numerically, including a case study using the full-order dynamics of a synchronous generator with an exciter and
transformer. Our results highlight that care must be taken when choosing a subsystem representation, as neglecting high-frequency dynamics or device operating points may obscure unstable poles that must be stabilised by the network interconnection and must be accounted for in system identification.
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Systems and Control
Enhancing Collective Self-Consumption through Water Storage Heater Flexibility
Pierre-Yves Massé, Maylis Duru, Benoit Couraud et al. (+12 more)
While Renewable Energy Communities (RECs) and Collective Self-Consumption (CSC) schemes have emerged as promising tools to accelerate renewable energy adoption and support the net-zero transition, their full potential can only be realised when complemented by demand-side flexibility that aligns consumption with renewable generation. Water storage heaters can function as distributed thermal storage, absorbing excess renewable energy at the community level. This work quantifies both the benefits of water storage heaters flexibility for energy consumers in a CSC community in France (such as energy bill reduction, increase of self-consumption), and the challenges related to the implementation and user acceptance. At the first stage, an annual simulation analysis is performed on a community of 41 households and a large
solar PV plant, comparing a scenario without a CSC community, a scenario with a standard CSC community, and a scenario with a CSC community with flexibility from water storage heaters, which showed that an average benefit of 70euro/year per household can be achieved due to flexibility and an increase of 6% and 22% of
solar PV community self-consumption and self-production respectively. In the second stage, we present the results of the real-world deployment in the community, analysing its technical performance and user reception, and examine the main factors shaping user engagement and satisfaction.
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All authors Pierre-Yves Massé, Maylis Duru, Benoit Couraud, Haicheng Ling, Solal Bizeul, Hugo Roussel, Cléa Verdot, Mariane Vittoz, Estefania Alvarez, Merlinda Andoni, Yann Rozier, Sonam Norbu, David Flynn, Erwin Franquet, Thibault Rihet
Systems and Control · Cross-listed from cs.IT
Secrecy Sum Rate Maximization for OIRS-Aided Visible Light Communications with Confidential Messages
Trinh K. Nguyen, Hung K. Hoang, Thanh V. Pham et al. (+1 more)
This paper investigates the secrecy sum-rate (SSR) performance of optical intelligent reflecting surface (OIRS)-assisted multi-user visible light communication (VLC) systems under line-of-sight (LoS) blockages. To mitigate physical obstructions and internal eavesdropping, a joint optimization problem is formulated to maximize the SSR through the co-design of the transmission precoder and OIRS units assignment. Due to the binary constraints and coupled variables, the problem is highly non-convex. To solve it efficiently, an alternating optimization (AO) framework integrating the concave-convex procedure (CCCP) and first-order Taylor approximations is developed. Simulation results demonstrate the convergence of the proposed
algorithm and show that increasing the number of OIRS reflecting units yields significant SSR gains.
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All authors Trinh K. Nguyen, Hung K. Hoang, Thanh V. Pham, Chuyen T. Nguyen
Systems and Control
Surrogate Modeling of Interconnector Flows: A Machine Learning Alternative to Full-Scale Power System Simulations with Application to Cross-Border Electricity Exchange
Robert Gaugl, Eloy Insunza, José Portela et al. (+1 more)
Cross-border electricity exchanges are crucial for operating and planning highly renewable power systems. Many studies reduce spatial granularity to keep models tractable and prescribe cross-border exchanges exogenously, often by reusing historical import/export time series. Such assumptions become inconsistent as renewable penetration changes the magnitude and timing of flows. The core contribution of this work is a machine-learning (ML) surrogate framework that maps nodal time-series data (e.g., hourly demand and renewable generation) to synthetic interconnector-level flow time series. The resulting flows serve as fixed boundary conditions in reduced power system optimization models (PSOMs). This addresses studies focused on a subset of an interconnected system, for which repeatedly solving the full model across multiple
climate years or scenarios is computationally expensive. We demonstrate the framework on a pan-European single-node-per-country DC optimal power flow model. We
benchmark k-nearest neighbors, ridge regression, random forests, Extra Trees, gradient boosting, XGBoost, and feedforward neural-network (SQU) surrogates. As an additional contribution, we introduce a novel custom neural-network loss that softly penalizes physically implausible flow patterns. This soft penalty is not a guarantee of full power-flow feasibility. Across
benchmarks, the SQU variants provide the most consistent out-of-sample and downstream optimization performance while substantially outperforming scaled historical profiles. The ML-generated flows closely reproduce country-level results of the full European model while greatly reducing computation time. The proposed framework enables scenario-consistent reduced PSOM studies across
climate years after a single full-model run, eliminating repeated pan-European simulations and supporting more robust
climate-year analyses under fixed system assumptions.
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All authors Robert Gaugl, Eloy Insunza, José Portela, Sonja Wogrin
Image and Video Processing
When BBR Meets Live Streaming
Xu Yan, Tong Li, Bo Wu et al. (+3 more)
Recently, industrial pioneers like Amazon, Tencent, ByteDance, and Huawei have been adopting BBR as their congestion control
algorithm for live-streaming applications, including TikTok Live. However, BBR, originally crafted for bulk data transmission, faces multiple challenges in live-streaming scenarios. In this paper, we first explore two key issues associated with BBR due to inaccurate bandwidth estimation in live-streaming scenarios: (i) BBR cannot easily exit its startup phase, resulting in a fierce self-inflicted loss. (ii) BBR sends data at a lower rate than the available bandwidth during its stable phase. We then propose BBR-Copilot, an auxiliary congestion control component that cooperates with BBR, making BBR better adapt to live-streaming scenarios. BBR-Copilot allows for proactively generating accurate bandwidth measurement samples by smartly creating and sending extra data. We implement the BBR-Copilot prototype upon QUIC and evaluate it via testbed. Experimental evaluation results show that BBR-Copilot effectively enhances BBR's performance in live-streaming scenarios.
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All authors Xu Yan, Tong Li, Bo Wu, Cheng Luo, Jiuxiang Zhu, Laizhong Cui
Audio and Speech Processing
WavTTS: Towards High-Quality Zero-Shot TTS via Direct Raw Waveform Modeling
Wenxi Chen, Dongya Jia, Yushen Chen et al. (+11 more)
Recently,
diffusion models operating on VAE latents or mel-spectrograms have become the dominant paradigm for zero-shot TTS. Although these compressed representations improve generation efficiency, they inevitably suffer from information loss and non-end-to-end training. Theoretically, directly modeling raw waveforms circumvents these issues; however, this direction remains underexplored and is often deemed difficult due to the extremely long sequence length of audio signals. To overcome this, we propose WavTTS, the first raw waveform generative TTS model that substantially narrows the gap with latent-space generative models. Built upon the flow matching with
Diffusion Transformer (DiT), WavTTS directly models
speech waveforms via a simple patchification strategy, while integrating multi-scale mel-spectrogram supervision to provide perceptual guidance during training. Furthermore, we investigate the impact of prediction targets and noise scheduling in waveform
diffusion, and develop an effective schedule design to improve generation quality. Evaluations on open-source
benchmarks demonstrate that WavTTS closely approaches the performance of current state-of-the-art latent generative zero-shot TTS models, while substantially outperforming previous end-to-end
speech generation models. Our findings demonstrate the feasibility of scaling
diffusion-based TTS directly in the waveform space, opening a new direction for end-to-end
speech generation.
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All authors Wenxi Chen, Dongya Jia, Yushen Chen, Zhikang Niu, Yuzhe Liang, Xiquan Li, Ruiqi Yan, Ziyang Ma, Guanrou Yang, Sanyuan Chen, Yue Wang, Zhuo Chen, Kai Yu, Xie Chen
Signal Processing
Instantaneous Risk Minimization for Secure Integrated Sensing and Communication
Chao Ge, Na Zhao and Yuan Shen
To ensure worst-case physical layer security, this paper proposes a robust beamforming framework for secure integrated sensing and communication (
ISAC) systems. Different from conventional designs that focus on maximizing the ergodic secrecy rate, the proposed method aims to minimize instantaneous information leakage risk. We formulate a multi-objective optimization problem that jointly suppresses the worst-case eavesdropper signal-to-interference-plus-noise ratio (SINR), improving sensing accuracy, and ensuring the quality of service (QoS) for legitimate users. To address the resulting non-convex problem, we develop a hierarchical iterative
algorithm, in which the outer loop refines the continuous uncertainty regions based on the updated sensing performance, and the inner loop optimizes beamforming under the refined uncertainty regions. Theoretical analysis and simulation results demonstrate that the proposed method achieves per-transmission security guarantees with practical complexity.
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Image and Video Processing
SMAC: Spatial-Modal Joint Modeling and Adaptive Representation Collapse for Multimodal Object Tracking
Meijing Gao, Qitai Sun, Huanyu Sun et al. (+5 more)
Multimodal multi-object tracking (MOT) under complex illumination remains challenging due to insufficient joint modeling of spatial and modal features and the limited adaptability of fixed fusion strategies. To address these issues, this paper proposes a spatial-modal convolution fusion and
distillation-
prompt-based
multimodal MOT framework. A spatial-modal fusion backbone is first constructed, where a Basic module performs spatial feature extraction and modal interaction via decoupled 3D convolution, while a Mixed module models nonlinear cross-modal correlations through amplitude-phase decomposition. In addition, a representation collapse network is designed for adaptive
multimodal fusion. A
Distillation Prompt Guidance (DPG) module generates dynamic modal weights under teacher supervision, and a Global Modal Difference Aggregation (GMDA) module preserves discriminative information during
multimodal representation collapse. Extensive experiments on the UniRTL
dataset demonstrate the effectiveness of the proposed method. The proposed tracker achieves 63.31 HOTA and 79.21 MOTA on the RNT modality, outperforming several state-of-the-art methods while maintaining favorable inference efficiency. The source code and pretrained models are publicly available at https://github.com/QitaiSun/SMAC.
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All authors Meijing Gao, Qitai Sun, Huanyu Sun, Bingxuan Yang, Bingzhou Sun, Xu Chen, Yonghao Yan, Yuxuan Yang
Systems and Control
Navigating the unknown in large-scale operational transformation programs: The "Sirius Days" framework as a 'pilot-organization' for characterizing emerging issues
Françoise Zink, Chipten Valibhay and Jose Bonet Faus
Large-scale digital transformation programs must simultaneously sustain existing operations and navigate deep unknowns emerging from IT-business-operations interactions -a challenge conventional project governance frameworks inadequately address. Based on a longitudinal case study of a transformation program, we investigate the ”Sirius Days,” a monthly senior management retreat identified as a critical success factor. We show that this framework constitutes a pilot-organization: an organizational 'dispositif' (or apparatus) that deconstructs established knowledge or assumptions, formulates rigorous
conjectures, and tests them in real conditions. It generated five resilience levers -systemic characterization of unknowns, early anomaly discernment, expansion of performance norms, social capital creation through a community of inquiry, and expansion of organizational agility across scales -revealing a model of an organizational 'dispositif' that operationalizes navigating unknowns across cognitive, social, and normative dimensions.
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Signal Processing
Node-Oriented Proactive Spectral Modulation: A Unified Fractional Framework for Graph Signal Denoising
Manjun Cui, Zhichao Zhang and Yangfan He
Graph signal denoising is a fundamental task in graph signal processing. While the node-oriented filtering approach enhances spatial adaptability, it suffers from spectral
rigidity due to its reliance on the graph
Fourier transform. Conversely, emerging fractional-domain transforms provide crucial spectral flexibility but are fundamentally limited by their globally shared filtering paradigm, failing to accommodate localized topological variations. To bridge this gap, this paper proposes a generalized node-oriented fractional filtering (NOFF) framework that seamlessly integrates localized spatial adaptability with proactive spectral modulation across various fractional transforms. However, straightforwardly assigning independent full-rank filters to all vertices incurs a prohibitive parameter space, leading to severe overfitting on random noise. To mitigate this, we introduce the low-rank NOFF (LRNOFF) architecture. By imposing a strict low-rank constraint, LRNOFF inherently acts as a powerful implicit regularizer, preventing noise memorization and ensuring the extraction of robust spectral bases. Furthermore, we develop an efficient computational implementation termed LRNOFF-Fast, which drastically reduces computational and
memory overhead while preserving theoretical optimality. Experiments on real-world
datasets demonstrate that the proposed framework achieves state-of-the-art performance.
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Systems and Control · Cross-listed from cs.LG
Validation-Gated Multi-Agent Governance for Online Adaptation of Thermal-Hydraulic Surrogate Models under Operating-Regime Shift
Doyeong Lim, Seungyoon Lee and In Cheol Bang
Artificial-intelligence surrogates can support second-by-second thermal-hydraulic
forecasting, but models selected and frozen offline may become condition-locked once deployed outside their pretraining envelope. This study develops a guarded continual-adaptation framework for experimental thermal-hydraulic loop data in which role-separated
agents - Monitor, Diagnosis, Adaptation, Safety-Auditor, and Orchestrator - diagnose error signatures, prioritize candidate model families, and review promotions, while deterministic champion-challenger gates and background shadow learning retain final authority over model replacement. Seven surrogate families were screened by blocked three-fold cross-validation, and a temporal
Fourier neural operator was selected as the initial champion for 60-s-history-to-10-s-trajectory
forecasting on two held-out transients, with three seeds per adaptive mode. Static deployment gave a channel-averaged MAE of 7.06 and a 56.8% warning-exceedance ratio; rule-based adaptation reduced MAE to 6.54, whereas shadow refresh alone remained close to Static. The MA-Full mode, in which the role-separated multi-
agent council reviews every evaluated stream step, achieved the lowest mean error, 5.72, and 35.8% exceedance, corresponding to a 19.0% improvement over Static. Paired bootstrap intervals against Static excluded zero, although intervals among adaptive modes overlapped and the six paired units limit broad statistical claims. Validated promotions from the neural operator to
Transformer and graph neural network indicate that logged, gate-controlled adaptation can support auditable surrogate
evolution while deterministic gates retain deployment authority.
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Audio and Speech Processing
SpeakerCard-1M: An Evidence-Grounded Corpus for In-the-Wild Speaker Verification
Junyi Peng, Oldřich Plchot, Xiao Song et al. (+9 more)
Modern speaker verification (SV) systems rely on speaker
embeddings that are effective but difficult to interpret or query in natural
language. Most existing
speech-text corpora target controllable synthesis or utterance-level captioning, offering limited speaker-level supervision for in-the-wild speaker recognition. This paper introduces SpeakerCard-1M, a bilingual speaker resource for evidence-grounded SV, derived from VoxCeleb1/2 and CN-Celeb1/2, where the “-1M” suffix refers to the 1.78M utterance-level captions contained in the release. We adopt a tool-first, LLM-last approach in which ten acoustic probes produce field-level evidence, the evidence is aggregated into speaker profiles under a schema that separates relatively stable traits from utterance-level states, and bilingual Speaker Cards are rendered by a constrained LLM that sees only the structured fields. The release includes 56.7k Speaker Card records over 10.2k speakers, 1.78M utterance-level captions, and speaker-ID-disjoint hard-negative triplets. We further define two SV-oriented cross-modal protocols, bidirectional Speaker-Text
Retrieval (T2S-R / S2T-R) and Attribute-Conditioned Verification (AC-Verify), and compare a dual-encoder baseline against recent audio
language models under a zero-shot forced-choice setting. Joint audio-text training costs only 0.31% absolute EER on VoxCeleb1-O relative to the audio-only baseline. Under a style-symmetric LLM-generated counterfactual protocol, eight recent audio
language models (7B-30B+ parameters, both open- and closed-source) score 49-77% on pitch-level AC-Verify in a 2-way forced-choice setting, compared with 88.66% for our dual encoder.
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All authors Junyi Peng, Oldřich Plchot, Xiao Song, Dading Chong, Lichun Fan, Hang Su, Themos Stafylakis, Junjie Li, Kong Aik Lee, Shuai Wang, Jian Luan, Jan Černocký
Image and Video Processing · Cross-listed from cs.AI
Do Real-World Datasets Contain Natural Experiments? An Empirical Study Using Causal Feature Selection
Gautam Gare, John Galeotti, Michael Mozer et al. (+2 more)
In nature, events that affect some individuals or groups but not others constitute an implicit intervention and are known as natural experiments. For example, the COVID-19 pandemic was an intervention by the coronavirus on the sub-population infected with COVID. We ask, do natural experiments occur in existing real-world
datasets? If yes, how should we treat them? To detect natural experiments in data, we use causal discovery to recover the underlying causal graph and perform feature selection based on causal links. If downstream performance improves by treating the data as interventional rather than observational, we argue that this suggests the
dataset contains natural experiments. We first validate this hypothesis by simulating
datasets with and without natural experiments using synthetic graphs. We then perform a systematic empirical evaluation on a large suite of real-world
datasets. Our results indicate that real-world
datasets do contain natural experiments and we can take advantage of those natural experiments to improve model performance using causal inference. Our work represents the initial foray into this area, offering a preliminary exploration within a limited scope.
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All authors Gautam Gare, John Galeotti, Michael Mozer, Deva Ramanan, Nan Rosemary Ke
Audio and Speech Processing · Cross-listed from cs.CL
Benchmarking Speech-to-Speech Translation Models
Alkis Koudounas, Hayato Futami, Quentin Jodelet et al. (+3 more)
Speech-to-
speech translation (S2ST) has advanced rapidly, but offline evaluation lacks a unified protocol: studies report non-overlapping metric subsets, preventing direct comparisons. We introduce COMPASS, a unified and reproducible benchmarking framework integrating 46 metrics across eight dimensions, and deploy it on 1,248 model-
language configurations from FLEURS and CVSS, spanning cascaded and end-to-end architectures over ten
language pairs. Architectures exhibit complementary strengths: best-vs-worst gaps exceed 30% on naturalness and speaker preservation but remain within a few points on translation quality, so single-metric rankings systematically misrepresent system quality. Correlation filtering reduces 46 metrics to 10 per direction, with three axes requiring different metrics across X$\to$EN and EN$\to$X (e.g., TER/UTMOS vs. ChrF++/NISQA-MOS); these subsets preserve rankings (Spearman's $\rho>0.80$) while cutting evaluation time by $\approx 2.5\times$. Human validation across dubbing, podcasts, and medical domains shows standalone MOS predictors fail to predict listener preference, while top domain-specific metrics correlate with human judgment ($\rho \geq 0.90$). We release COMPASS as a foundation for domain-aware S2ST evaluation.
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All authors Alkis Koudounas, Hayato Futami, Quentin Jodelet, Osamu Take, Shinji Watanabe, Emiru Tsunoo
Signal Processing · Cross-listed from cs.RO
Toward Gripper-Integrated Active Electrosense for Pre-Contact Sensing in Underwater Soft Grippers
Ahsan Tanveer, Muhammad Hamza, Waqar Hussain Afridi et al. (+2 more)
Underwater manipulation often occurs under degraded visibility due to turbidity, glare, and gripper occlusion, limiting the reliability of
vision-based perception during approach and grasping. In such settings, soft grippers are well suited for compliant interaction, but they typically lack an onboard pre-contact cue that can guide approach and closure when
vision is unreliable. This extended abstract explores active electrosense as a lightweight sensing modality that can provide a proximity-like signal prior to contact by measuring perturbations of an applied electric field in conductive media. We instrument an octopus-inspired gripper with a discrete electrode layout and record multi-channel sensing voltages using off-the-shelf hardware. Simulation and tank experiments with a suspended conductive sphere show structured, object-dependent changes in the multi-electrode voltage readout relative to empty-water baselines, with detectability varying across excitation of 5 to 20 V and frequencies from 1 mHz to 1 kHz. These findings motivate systematic investigation of gripper-integrated electrosense as a complementary pre-contact cue for
underwater soft manipulation.
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All authors Ahsan Tanveer, Muhammad Hamza, Waqar Hussain Afridi, Chen Wang, Guangming Xie
Audio and Speech Processing · Cross-listed from cs.MM
Inference-Time Scaling for Joint Audio-Video Generation
Jaemin Jung, Kyeongha Rho, Inkyu Shin et al. (+1 more)
Joint audio-
video generation aims to synthesize realistic audio-
video pairs that are both semantically aligned with text
prompts and precisely synchronized. While existing joint audio-
video generation models often require substantial training resources to improve fidelity, Inference-Time Scaling (ITS) has recently emerged as a promising training-free alternative in single-modality domains. However, extending ITS from a single modality to
multimodal domains is non-trivial, as it requires balancing multiple heterogeneous objectives. In this paper, we present the first comprehensive study of ITS for joint audio-
video generation. We first demonstrate that a multi-verifier framework is essential to address the limitations of single-objective guidance, including asymmetric performance trade-offs and verifier hacking. Through systematic analysis, we then identify an optimal multi-verifier combination that yields balanced improvements across all quality dimensions. Finally, to effectively aggregate diverse reward signals, we propose Adaptive Reward Weighting (ARW), a novel test-time optimization
algorithm. ARW treats reward aggregation as an online optimization problem, utilizing learnable parameters to calibrate reward variances without requiring prior knowledge of reward distributions, thereby ensuring robust multi-objective selection. Experimental results on VGGSound and JavisBench-mini
benchmarks demonstrate that our framework significantly enhances semantic
alignment, perceptual quality, and audio-visual synchronization of generated outputs. Synthesized samples and code are available on the project page: https://jung-jaemin.github.io/ITS-AVGen-Proj.
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All authors Jaemin Jung, Kyeongha Rho, Inkyu Shin, Joon Son Chung
Systems and Control
Equivalent Circuit Model based Electric Vehicle Evacuation with Mobile Charging Stations
Joseph Moyalan, Ricardo de Castro, Shuang Feng et al. (+3 more)
The increasing penetration of electric vehicles (EVs) introduces new challenges for emergency evacuation planning due to limited driving range, long charging times, and constrained charging infrastructure, particularly under disaster induced disruptions. This paper proposes a novel optimization based evacuation framework for EVs using Equivalent
Circuit Models (ECMs) to jointly address routing, charging, and congestion management. By leveraging electrical analogies, traffic flow is modeled as electrical current, travel time as resistance, and driving range as voltage, enabling the use of Kirchhoff laws to enforce flow balance and energy feasibility constraints. The proposed controllable ECM incorporates binary switches to regulate route selection and explicitly models charging delays and range replenishment at both Fixed Charging Stations (FCSs) and Mobile Charging Stations (MCSs). The resulting formulation leads to an integer programming problem that determines optimal evacuation routes, charging durations, and the placement and number of MCSs to minimize evacuation time. The framework is extended to multiple origin destination pairs using the principle of superposition and supports
fairness aware performance metrics, including worst case, average, and variance based evacuation times. Simulation studies on large scale transportation networks in California demonstrate that the proposed approach significantly improves evacuation efficiency and robustness, particularly in scenarios with limited charging access, highlighting the critical role of MCSs in EV based emergency evacuations.
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All authors Joseph Moyalan, Ricardo de Castro, Shuang Feng, Xuchang Tang, Xinfan Lin, Qijian Gan
Systems and Control
Dynamics of the Thermomagnetic Pendulum
Ryan Thompson, Ethan Wang and Nilay Kant
A thermomagnetic pendulum is introduced as a coupled thermo-
magnetic-mechanical system consisting of a
ferromagnetic bob under
gravity and an offset permanent magnet. Heating drives the bob temperature above and below the Curie point, causing
magnetic attraction to vanish and recover as the bob moves and cools. A multiphysics model is developed in which the
magnetic torque depends nonlinearly on the bob temperature field and pendulum configuration. The formulation couples transient three-dimensional heat transfer, a temperature-dependent magnetization law, and pendulum dynamics. Simulations show angular torque asymmetry, rapid force reduction near the Curie point, and sustained oscillations.
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Audio and Speech Processing
AnyAudio-Judge: A Dynamic Rubric-Based Benchmark and Evaluator for Audio Instruction Following
Haitao Li, Tian Tan, Yuguang Yang et al. (+2 more)
The rapid advancement of instruction-guided audio generation has highlighted the critical need for robust
alignment evaluation. Current automated evaluation methods heavily rely on holistic scoring from general-purpose large
language models, which struggle to decouple complex instructions, lack interpretability, and fail to capture fine-grained attribute mismatches. To address this, we introduce a novel dynamic rubric-based evaluation paradigm that adaptively decomposes complex audio captions into a variable number of independent, verifiable binary rubric items. To rigorously
benchmark this capability, we propose the AnyAudio-Judge Bench, a comprehensive, bilingual
benchmark comprising 7,920 meticulously curated samples across four diverse audio domains (
speech, sound, music, and mixed), featuring deliberately constructed hard negatives. Furthermore, we construct a large-scale corpus of 105K samples with explicit Chain-of-Thought (CoT) rationales to train our dedicated evaluator, the AnyAudio-Judge model. By employing a training pipeline that combines Supervised Fine-Tuning (SFT) and Group Relative Policy Optimization (GRPO), our model successfully aligns its
reasoning paths with the rubric-based scoring mechanism. Extensive experiments demonstrate that AnyAudio-Judge not only significantly enhances zero-shot
alignment detection compared to state-of-the-art baselines, but also provides precise and interpretable reward signals that substantially improve instruction
alignment in downstream reinforcement learning for audio generation.
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All authors Haitao Li, Tian Tan, Yuguang Yang, Shan Yang, Xie Chen
Systems and Control
Online Approximate Dynamic Programming within Linear Model Validity Regions Exploiting Impulsive Braking Inputs
Adebayo Olayinka Oke and Nilay Kant
Persistent excitation required for online approximate dynamic programming (ADP) can drive a nonlinear system beyond the region where its local linear model remains valid. For a class of second-order nonlinear systems , this paper develops an impulse-supervised exploration and learning framework that retains the state within a prescribed linear model validity region (LMVR) while permitting any desired persistent excitation for critic learning. The online learning dynamics are formulated as a hybrid system, and positive invariance of the prescribed exploration set within LMVR is established by resetting the state into its interior through impulsive braking, followed by recovery before learning resumes. Simulations on a nonlinear system demonstrate exploration within the LMVR despite high-amplitude probing excitation and convergence of the learned weights to that given by algebraic Riccati equation.
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