Papers

Fresh from arXiv, typeset properly. Highlighted words trade on the market.

Electrical Engineering and Systems Science

eess Watch Electrical Engineering and Systems Science

Sunday 13 September

Audio and Speech Processing

Modeling, Scaling, and Decoding: Optimizing Controllable Speech Generation with Nonverbal Vocalizations

Ziyu Zhang, Yun Chen, Taihui Wang et al. (+5 more)

Controllable synthesis of nonverbal vocalizations (NVVs) is es- sential for natural and expressive speech, but remains challeng- ing due to their acoustic diversity and imbalanced distribution in existing corpora. To address these challenges, we develop an NVV-aware DiTAR system that models continuous speech latents, encodes the 16 target NVV categories as dedicated to- kens, and adapts stop prediction to distinguish mid-utterance vocalizations from utterance boundaries. Training begins with large-scale bilingual pre-training on diverse NVV speech, fol- lowed by continued supervised fine-tuning on a corpus en- hanced through targeted synthetic augmentation and frequency- aware rebalancing. At inference time, we select the acoustic prompt, tune the LM-guidance and noise-injection scales, and apply Best-of-N sampling with multi-metric selection to re- duce generation failures. The final system achieves an official weighted bilingual score of 62.786, ranking first in Mandarin, second in English, and first overall among participating systems in Track 2 of the ISCSLP 2026 NVVSpeech Challenge. Ab- lation studies show that targeted augmentation benefits under- represented NVV categories the most, while robust candidate selection requires balancing NVV correctness, lexical fidelity, and perceptual quality.

Read the full abstract

All authors Ziyu Zhang, Yun Chen, Taihui Wang, Hanzhao Li, Qicong Xie, Rilin Chen, Zhixian Zhao, Lei Xie

Systems and Control · Cross-listed from cs.RO

Novel Ex-vivo Calf Brain Model with Integrated Sub-Skull Force Sensors to Access Simulated Neurosurgical Procedures

Hamad Binhammad, Matheus Ballestero, Mohammed Babgi et al. (+9 more)

Surgical tissue manipulation demands precision; however, tool-tissue manipulation force magnitudes under realistic conditions are rarely quantified. To address this gap, we proposed and validated a portable ex-vivo force-sensing platform that measures tool-tissue interaction forces across the skull-brain interface during simulated neurosurgery. The system involves fresh calf brain tissue, used as a biological surrogate for brain parenchyma, placed in a 3D-printed human skull model equipped with a 6 degree-of-freedom force/torque sensor and a real-time data acquisition system. Five validation protocols assessed the accuracy and dynamic fidelity of the platform against ground-truth measurement, static accuracy and linearity using calibrated weights (0.5-50 g), minimum detectable force, spatial consistency across different anatomical regions, effect of surgical draping, and long-duration stability. Across protocols, measured forces showed excellent agreement with reference loads (correlation R = 0.9997), with RMSE < 0.005 N and mean relative error under 2%. The platform reliably detected low-magnitude forces down to 1 g (9.8 mN), while surgical drapes introduced no meaningful signal distortion and prolonged recordings exhibited minimal drift. Overall, the proposed framework provides objective, high-fidelity force quantification for skill training and performance assessment using fresh calf brain tissue and may serve as a foundation for force-based evaluation across other surgical procedures. Future work will integrate clinically used surgical instruments to increase procedural realism and will progress toward clinical trials to evaluate usability, educational impact, and translational relevance in practice-adjacent settings.

Read the full abstract

All authors Hamad Binhammad, Matheus Ballestero, Mohammed Babgi, Seana Shaka, Nima Hemati, Rothaina Saeedi, Aiden Mazidi, Bianca Giglio, Rukun Dou, Houssem-Eddine Gueziri, Amir Hooshiar, Rolando F. Del Maestro

Saturday 12 September

Systems and Control · Cross-listed from cs.AR

SiliconBench: Speed, Memory, and Fidelity for LLM Serving on Unified-Memory Desktops

Ranran Haoran Zhang, Aysa Xuemo Fan, David Munhá Correia et al. (+2 more)

Concurrent local LLM serving on unified-memory desktops must preserve memory headroom and output fidelity, which speed-only rankings overlook. We introduce SiliconBench, which evaluates nine Apple Silicon serving engines through three lenses: speed, memory, and fidelity. We evaluate chat and agent serving on Qwen3, Qwen3.5, and Gemma 4. We use a classification task to check for quality regressions against an NVIDIA reference. DGX Spark provides a complementary serving-performance reference. Three desiderata guide interpretation: serving architecture readiness, memory discipline, and multi-node scaling. On Qwen3-0.6B, vllm-metal alone more than doubles throughput on both workloads from concurrency 1 to 16. CUDA vLLM and SGLang show stronger concurrency scaling on the same prompts. Explicit memory budgets do not guarantee memory headroom: two stacks complete every request while memory use approaches physical capacity and throughput declines. The newer model architectures have narrower engine support. Their evaluated implementations match the fidelity reference. Only three stacks satisfy the completion, fidelity, and model-coverage gates. Comparisons on larger dense and MoE models reinforce the importance of scheduling prompt processing alongside ongoing generation: vllm-metal's packed prefill-decode path maintains lower first-token latency than omlx under concurrent load. In the tested two-machine configurations, tensor parallelism over Thunderbolt RDMA scales while pipeline parallelism over TCP regresses. We release benchmark code, per-run results, and maintenance journals, supported by a workflow combining bounded agent fixes with human review.

Read the full abstract

All authors Ranran Haoran Zhang, Aysa Xuemo Fan, David Munhá Correia, Alex Cheema, Rui Zhang

Systems and Control · Cross-listed from math.OC

Resilient Extremum Seeking Control for Cyber-Physical Systems Under Denial-of-Service Attacks

Filipe da Silva Bastos Teixeira, Pedro Henrique Silva Coutinho, Tiago Roux Oliveira et al. (+1 more)

Extremum seeking control (ESC) relies on deliberately injected excitation to extract optimization information from measured outputs, making its networked implementation particularly vulnerable to denial-of-service (DoS) attacks. This paper develops a resilient discrete-time ESC architecture for cyber-physical systems subject to DoS attacks. By holding the most recently successfully transmitted signals during DoS intervals, the proposed mechanism yields averaged error dynamics with an exponentially contracting mode under successful communication and a neutral mode under attack. For deterministic DoS attacks, practical exponential convergence to the extremum is established under an average bound on the attack duration, with an explicit convergence rate depending on the fraction of time under attack. For probabilistic DoS attacks, almost-sure and in-probability convergence properties are established via stochastic averaging, with the attack success probability explicitly entering the convergence rate. A key finding is that a seemingly natural zero-input strategy can fundamentally compromise ESC: attacks synchronized with the dither generate an $\mathcal{O}(1/a)$ bias in the averaged dynamics, where $a$ is the dither amplitude, and can displace the equilibrium from the true optimizer. Thus, under the proposed hold-based architecture, increasingly severe DoS attacks primarily slow the optimization process rather than destroy its stability, provided that communication is not permanently blocked. Numerical simulations illustrate the theoretical guarantees and the failure mechanism induced by dither-synchronized attacks.

Read the full abstract

All authors Filipe da Silva Bastos Teixeira, Pedro Henrique Silva Coutinho, Tiago Roux Oliveira, Miroslav Krstic

Signal Processing

Inverse Maxwell-Based Wall-Aware OFDM-ISAC for Slow-Moving Target Sensing

Tri Nhu Do, Yosefine Triwidyastuti and Gunes Karabulut Kurt

In this paper, we study integrated sensing and communication (ISAC) for short-range indoor orthogonal frequency-division multiplexing (OFDM) systems in which the sensing path crosses a building wall. The target is a slow-moving user equipment behind the wall, and its echo is embedded in the wall reflection and static indoor clutter. Because the wall adds excess propagation length, attenuation, and internal reflections, a conventional delay transform reports an apparent range. We therefore formulate physical-range estimation as an inverse Maxwell problem for the known wall: the range operator is the distorted-Born Jacobian of the discretized one-dimensional Helmholtz equation, assembled offline from the calibrated wall and applied online as a per-snapshot range inversion, so that the range-Doppler map is indexed by physical rather than apparent range. We then prove that this operator carries a structural limitation: when the two-way wall factor has constant magnitude and linear phase, the wall-aware range image is identically the free-space image on a translated grid, for every ridge level, taper, and noise realization. A free-space branch translated by the same excess length is therefore a required comparison. Sensing-only numerical results for a representative layered wall, with every branch on one range grid and a common post-FFT CSI model, show that the proposed inverse removes the range bias that wall-unaware processing cannot, and that a scalar-corrected free-space inverse tracks it closely, the two differing only marginally in empirical range RMSE and detection probability across the tested sweep.

Read the full abstract

Signal Processing

Fourth-Order Co-Channel Interference-Aware OFDM-ISAC Sensing in Cluttered Environments

Tri Nhu Do, Yosefine Triwidyastuti and Gunes Karabulut Kurt

In this paper, we develop a sensing-centric receiver for monostatic orthogonal frequency-division multiplexing integrated sensing and communication (OFDM-ISAC) in the simultaneous presence of passive clutter and a non-cooperative co-channel OFDM transmitter. We show that removing the known ISAC symbols maps target and clutter echoes to deterministic delay-Doppler components, while the non-cooperative waveform remains a random, non-Gaussian, generically full-rank residual. Guided by this distinction, we combine moving-target indication for zero-Doppler clutter with a centered diagonal fourth-order statistic for interference identification. To obtain an unbiased fourth-order estimate from phase-aligned proper snapshots, we derive additive and multiplicative finite-sample bias corrections and pair the corrected statistic with a second-order moment to separate interference from Gaussian noise. Fourth order provides identification and presence testing, whereas second order provides the diagonal disturbance-power proxy used for weighting. We further introduce diagonally loaded weights that enforce a range-Doppler aperture-efficiency floor. Simulations show that the interference estimate approaches its theoretical profile as coherent support grows and remains insensitive to deterministic echoes, that the presence test resolves interference well below the level at which weighting matters, and that loaded processing improves target detection while controlling point-spread-function degradation.

Read the full abstract

Signal Processing · Cross-listed from stat.ME

Finite-Sample FDR Control for Greedy Aggregation over Networks

Mehrdad Pournaderi

Distributed multiple testing asks $N$ sites, each holding p-values for its own hypotheses, to control the false discovery rate (FDR) of the discoveries made across the whole network while communicating only a small number of bits. Greedy interval aggregation (Pournaderi and Xiang, IEEE TSIPN, 2023) meets the communication budget but controls FDR only asymptotically, and its FDR can exceed the target at finite sample sizes; the same interval counts both select the rejection regions and calibrate the stopping rule, which biases the selected interval densities upward. We propose budgeted BONuS-GA: each site mixes synthetic uniform p-values into its data, the center ranks candidate p-value intervals across sites using the pooled counts, and each site's false discoveries are estimated from its own synthetic counts under a per-site share of the level. The procedure needs no knowledge of the null proportions, uses every p-value for both selection and inference, and controls $FDR\le\alpha$ at every sample size for any fixed assignment of hypotheses to sites, assuming only that the null p-values are independent uniforms, independent of the non-null p-values. It keeps the original $O(\sqrt m\log m)$ communication, $m$ being the total number of p-values, when $N=O(\sqrt m)$. We also give a sample-splitting alternative, cross-fit greedy aggregation, with finite-sample control under a random-site model, and we quantify the selection bias behind the original procedure's failure. In a monitoring-network simulation the budgeted procedure retains most of the power of its heuristic counterpart at moderate-to-large site sizes.

Read the full abstract

Audio and Speech Processing · Cross-listed from cs.SD

A New Transformer-Based Approach for Audio-Based Kinship Verification and a New Uncontrolled Mandarin Kinship Speech Dataset

Qiyang Sun, Langqing Zhang, Yupei Li et al. (+1 more)

Kinship verification is a task involving determining whether two individuals share a first-order kin relation. To tackle this task, we propose CONVTRAP-TN, a new architecture for audio-based kinship verification, and conduct an ablation study on the proposed model. To the best of our knowledge, we are the first to apply the successful transformer architecture to the task of audio-based kinship verification. Furthermore, we also collect a custom speech dataset, ARKIN, which accurately reflects everyday recording conditions. We do this because only a few speech datasets with kinship labels currently exist, all of which either source extremely noisy in-the-wild data from the internet, or instruct speakers to record in specific environments. These settings fail to reflect real-world scenarios where users record on personal devices under unrestrained conditions. Additionally, we perform a series of preliminary baseline experiments on the collected dataset, including speaker verification and recognition, speech recognition, age estimation, and kinship verification, as well as cross-dataset kinship verification experiments to show that existing methods are not robust across datasets.

Read the full abstract

All authors Qiyang Sun, Langqing Zhang, Yupei Li, Björn Schuller

Audio and Speech Processing · Cross-listed from cs.SD

Robust Cross-Domain Speech-Based Alzheimer's Disease Detection via Iterative Adversarial Self-Training

Luqi Sun, Shreeram Suresh Chandra, Aurosweta Mahapatra et al. (+3 more)

As Alzheimer's disease (AD) has increasingly become a major global public health issue, speech-based AD detection has attracted widespread attention. However, most existing methods are trained and evaluated on a single dataset, often leading to severe cross-domain performance degradation due to reliance on dataset-specific artifacts rather than disease-related speech cues. In real-world applications, reliable Alzheimer's disease detection requires models that are robust to variations in recording environments, speakers and data collection conditions. To address this challenge, this paper adopts unsupervised domain adaptation to learn robust, domain-invariant feature representations in the absence of target-domain diagnosis labels. On this basis, a novel unsupervised domain adaptation method, Iterative Adversarial Self-Training (IAST), is proposed. Results demonstrate that IAST significantly improves the generalization ability and robustness under various cross-domain settings.

Read the full abstract

All authors Luqi Sun, Shreeram Suresh Chandra, Aurosweta Mahapatra, Emily Mower Provost, Brian MacWhinney, Berrak Sisman

Systems and Control

Sparse Generalized-Admittance AC Power Flow for Fast Contingency Analysis and Remedial-Action Assessment

Pierre Artoisenet and Noémie Verstraete

Repeated AC power-flow calculations are common tasks in transmission system operations, in particular for contingency analysis and remedial-action assessment. For large networks, their cumulative runtime can become a computational bottleneck in operational processes. This paper develops a sparse reformulation of the generalized bus-admittance power-flow method known as PFPD. Loads and generators are represented by fixed shunt admittance terms on the diagonal of the admittance matrix, while corrective nodal currents are iteratively computed to match the constant-power and regulated-voltage constraints. In contrast to the original formulation, the slack bus is treated as a fixed-voltage boundary and removed from the admittance block decomposition. The resulting iteration reuses sparse LU factorizations of the initial generalized non-slack matrix and its PQ block, without explicitly forming dense inverse matrices. The same formulation admits two initialization modes: an unsolved base case using the same flat-start shunt heuristic as in the primary PFPD method, and repeated post-action calculations initialized from a solved operating point. Localized topology and parameter changes are expressed as low-rank admittance updates and the resulting admittance matrices are inverted by means of the Woodbury identity. An optional local-network formulation can provide an warm start before full-network refinement. N-1 experiments on the 1354-bus and 9241-bus PEGASE systems and the 6717-bus synthetic Texas system yield average per-contingency speedups between 14.4 and 18.0 relative to pandapower. On transformer tap-position actions, the method is 2.8–2.9 times faster than pandapower and 7.2–29.9 times faster than PowSyBl OpenLoadFlow.

Read the full abstract

Image and Video Processing

Deformable 2D Gaussian Splatting for Efficient 4K Video Compression

Chenhao Zhang and Fengqing Zhu

Ultra-High-Definition (UHD) video presents significant challenges for efficient storage and real-time decoding. Learning-based methods, such as Neural Video Compression (NVC) and Implicit Neural Representations (INR), achieve competitive rate-distortion performance but suffer from high decoding latency and excessive memory usage. Meanwhile, Gaussian Splatting has recently attracted attention in the computer graphics community due to its ultra-fast rendering and high-fidelity visual quality. Despite these advantages, its application in video compression remains largely unexplored. To bridge this gap, we propose a real-time video compression framework that represents and compresses a Group of Pictures (GOP) using a coarse-to-fine multi-scale 2D Gaussian Splatting (2DGS) structure coupled with a lightweight deformation network. Experiments demonstrate that our method delivers rate-distortion performance in LPIPS that surpasses H.265 and other state-of-the-art learning-based video compression methods. Our work demonstrates the potential of Gaussian Splatting as a practical solution for efficient high-resolution video compression.

Read the full abstract

Audio and Speech Processing

HARP: Agentic Hybrid Retrieval and Analysis for Long-Form Audio

Chin-Jou Li, Masao Someki, Woojeong Jin et al. (+4 more)

Long-form audio analysis requires systems to localize and integrate evidence distributed across extended recordings. While existing work primarily retrieves semantic content through structured textual representations, many real-world queries depend on acoustic evidence that is better preserved in continuous representations or raw audio. We introduce HARP (Hybrid Audio Retrieval Pipeline), an agentic framework and benchmark for systematically studying retrieval and evidence representations in long-audio analysis. Hybrid retrieval combining keyword and vector search shows the most robust performance. When paired with both metadata and retrieved audio as evidence, average answer accuracy improves by around 10% and rationale accuracy by around 6% over single-modality retrieval and evidence. Fine-grained evaluation shows that answer accuracy alone overestimates system capability and that HARP mostly follows human performance trends across query types. These results highlight the importance of combining structured retrieval with flexible access to audio evidence and evaluating long-audio systems beyond answer accuracy.

Read the full abstract

All authors Chin-Jou Li, Masao Someki, Woojeong Jin, Yashish M. Siriwardena, Tanmay Laud, Shanil Puri, Shinji Watanabe

Systems and Control

Real-Time Synthesis of Robust Controlled Invariant Sets for Monotone Systems

Yasin Sonmez, Mahmoud Khaled, Majid Zamani et al. (+1 more)

Safety-critical control of autonomous systems requires formal safety certificates, such as controlled invariant sets, that must be computed online as conditions change. Although standard synthesis algorithms scale poorly with state dimension, monotone dynamical systems with lower-closed safety specifications allow for accelerated computation of controlled invariant sets. In particular, lazy fixed-point algorithms exploit monotonicity and track only the antichain basis of the set. However, membership tests and redundancy checks against an evolving basis remain major bottlenecks. We introduce a threshold-function reformulation in which a lower-closed set on a d-dimensional grid is represented by its column heights along a designated axis. This reformulates the greatest-fixed-point iteration as independent one-dimensional binary searches, one per grid column, yielding an embarrassingly parallel iteration with asymptotically lower computational complexity than the lazy fixed-point algorithm. Experiments synthesize invariant sets on 3D grids with 10^9 cells in under 50 ms and 10^14 cells in under two minutes. We further demonstrate online re-synthesis in a safety-informed model predictive controller example.

Read the full abstract

All authors Yasin Sonmez, Mahmoud Khaled, Majid Zamani, Murat Arcak

Systems and Control

Parallel Policy-Gradient Methods for Parameter Optimization of Nonlinear Feedback Controllers

An Nguyen and Leilei Cui

Structured feedback controllers provide rigorous stability guarantees, but often require manual parameter tuning to achieve good closed-loop performance. Policy-gradient methods offer a systematic approach to parameter optimization; however, conventional gradient evaluation requires sequential forward state rollout and backward costate propagation. This letter develops a time-parallel policy-gradient framework for discrete-time nonlinear control-affine systems. We derive the policy-gradient expression where the state and costate rollouts required for policy-gradient evaluation are formulated as residual-minimization problems and solved using Gauss-Newton (GN) iterations with parallel associative scans. For closed-loop systems that are globally asymptotically stable and locally exponentially stable, we show that the residual-minimization problems satisfy a local Polyak-Lojasiewicz (PL) inequality and that the GN iterates converge locally at a quadratic rate. Moreover, the PL constant, the size of the convergence neighborhood, and the quadratic convergence bound are independent of the rollout horizon T. We also prove that, for any finite horizon T, the state solver recovers the exact trajectory from any initialization in at most T iterations. Finally, an inertia-wheel pendulum example with interconnection and damping assignment passivity-based control (IDA-PBC) demonstrates improved closed-loop performance and the computational benefits of the proposed parallel policy-gradient framework.

Read the full abstract

Systems and Control · Cross-listed from cs.NI

Enhancing SDVN Performance via Policy-Driven Lightweight Control-Plane Resizing Strategies

Muhammad Zain Ul Abideen, Prathapasinghe Dharmawansa, Nurul Huda Mahmood et al. (+1 more)

Software-defined vehicular networks (SDVNs) under high mobility and fluctuating traffic demand offer programmable, centralized control for latency-sensitive intelligent transportation systems. However, data-plane Quality of Service (QoS) is often degraded by control-plane overload due to frequent handovers and dense vehicle-to-infrastructure (V2I) contacts. To address this, we propose two lightweight mechanisms for low-latency control-plane resizing in multi-controller SDVNs. The first - Control-plane Centric Control-plane Resizing Mechanism - proactively offloads roadside units from overloaded controllers to underloaded or idle ones when a predefined load threshold is exceeded, preventing prolonged overload with minimal decision latency. The second - Data-plane Centric Control-plane Resizing Mechanism - triggers resizing based on observable data-plane QoS degradation, such as average round-trip time exceeding a QoS threshold, aligning control-plane adaptation with V2I service experience. Both mechanisms are implemented and evaluated on Mininet-WiFi emulation testbeds with realistic worst-case vehicles mobility. Compared to fixed single-controller and static multi-controller benchmarks, the proposed algorithms significantly reduce end-to-end delay and packet loss while improving load balancing rate.

Read the full abstract

All authors Muhammad Zain Ul Abideen, Prathapasinghe Dharmawansa, Nurul Huda Mahmood, Chafika Benzaïd

Systems and Control

Functional Observers for Output-Feedback Safety with High-Order Control Barrier Functions

Shima Sadat Mousavi, Pol Mestres and Aaron D. Ames

This paper studies output-feedback safety filtering for linear systems using high-order control barrier functions (HOCBFs) when the full state is unavailable. Since the HOCBF conditions depend only on specific state functionals, full-state reconstruction is unnecessary; instead, their implementation requires only functional observability or detectability of these quantities. We show that the HOCBF gains determine the required functionals and hence their observability properties, coupling safety and estimation design. We exploit this structure using scalar functional observers and develop a polynomial co-design framework that characterizes HOCBF observability and detectability, observer existence and order, and convergence-rate limitations. A certified one-sided estimation-error bound then enables safe output-feedback implementation.

Read the full abstract

Systems and Control · Cross-listed from cs.RO

Gradient-Free Neural Hamilton-Jacobi Reachability for Scalable Safety-Critical Control

Zeyuan Feng, Ali Fuat Sahin, Santiago Thorup et al. (+1 more)

Hamilton-Jacobi (HJ) reachability provides a principled framework for synthesizing safety certificates and robust controllers for safety-critical robotic systems. However, applying reachability analysis to high-dimensional nonlinear systems remains challenging: classical grid-based solvers suffer from the curse of dimensionality, continuous-time neural solvers require accurate spatial value gradients, and reinforcement-learning-based approaches often suffer from weak boundary anchoring and non-stationary adversarial policy optimization. We propose a discrete-time neural reachability framework for control-disturbance-affine systems that learns backward reachable tubes (BRTs) and backward reach-avoid tubes (BRATs) through Bellman-Isaacs value propagation. Our key idea is to combine equation-driven self-supervision with structured policy learning: rather than computing explicit PDE-gradients, we exploit the bang-bang structure of optimal safety interventions to construct approximate teacher actions from gradient-free value probes, converting adversarial actor learning into supervised policy learning. To stabilize long-horizon value propagation, we leverage the learned actor to train the value function backward from the terminal boundary using a windowed temporal curriculum, where each window is used as the boundary condition for the next window. Across benchmark problems up to 80 dimensions, our method learns accurate reachability value functions while improving stability over existing learning-based solvers. We further demonstrate observation-space scalability on F1-tenth racing with over 16,000-dimensional egocentric inputs. The learned safety filter generalizes zero-shot to unseen tracks and transfers to a physical RC car, achieving real-time robust collision avoidance.

Read the full abstract

All authors Zeyuan Feng, Ali Fuat Sahin, Santiago Thorup, Somil Bansal

Systems and Control

Behavior of Nonlinear Opinion Dynamics over Large Networks

Yu Xing, Anastasia Bizyaeva and Karl H. Johansson

Opinion dynamics have been studied for decades across disciplines, with much of the theoretical literature focusing on behaviors such as consensus, polarization, and clustering. Although classic models can exhibit more complex opinion patterns in simulations, quantifying such distributions is not fully understood. To address this question, in this paper, we study the behavior of nonlinear opinion dynamics over large-scale networks using graphons, which capture the underlying network structure. In the model, agents update their opinions according to a nonlinear rule that includes saturation effects in interactions. The network is represented by random graphs generated from a graphon, and a corresponding nonlinear dynamical model is introduced over the graphon. We show that the graphon dynamics approximate the finite-dimensional system, when the network size is large. Leveraging spectral approximation results for random graphs, we further show that the equilibria of the nonlinear model can also be approximated by those of the continuum limit. This result enables a quantitative characterization of the opinion distribution based on the underlying graphon structure. The theoretical results are illustrated by numerical simulation.

Read the full abstract

Systems and Control

SafePG: Safe and Globally Optimal Reinforcement Learning with Hard Constraints

Vipul K. Sharma, Wesley A. Suttle and S. Sivaranjani

We present an optimal and convergent model-free policy gradient (PG) reinforcement learning (RL) framework for controlling nonlinear dynamical systems under hard safety constraints. We first construct a class of stochastic wrapper policies centered around a deterministic controller, thereby enabling exploration in unknown environments while preserving the underlying deterministic control structure. We then define a class of parameterized safe-by-construction control policies by truncating these stochastic policies onto hard safety constraints. We next establish, via measure-theoretic arguments, that the potentially nonconvex RL objective under the truncated policy class, as well as its policy gradients, are well-defined. We then develop a model-free PG algorithm based on stochastic gradient ascent that directly searches over these truncated policies and leverage gradient dominance to establish convergence and optimality guarantees. Finally, we validate this framework through simulations on a safe quadrotor navigation problem.

Read the full abstract

Systems and Control

Predefined-Time Integral Reinforcement Learning for Saturated Unknown Nonlinear Multi-Agent Systems Under FDI Attacks and Disturbances

Tien Dat Vu and Minh Doan

This paper addresses secure leader-follower formation of unknown nonlinear multi-agent systems under actuator constraints, external disturbances, and false-data-injection (FDI) attacks. The graph-coupled coordination-error dynamics are formulated as local zero-sum differential games, where a nonquadratic input utility yields saturation-compatible secure policies and actuator-channel FDI and disturbances act as adversarial inputs. To eliminate explicit dependence on the unknown nonlinear drift, an integral Bellman-Isaacs identity enables critic-only learning from finite trajectory data. A two-power state-cost structure and a deadline-parameterized critic update connect optimal learning with predefined-time stabilization. Unlike fixed-time methods whose settling-time bound is determined by preselected gains, the proposed framework assigns the overall deadline first and allocates it among data informativity, critic learning, the reinforcement window, and formation convergence. Practical predefined-time convergence of the critic and formation errors to bounded residual sets is established independently of initial conditions, while secure actuator constraints are satisfied by construction. Simulations validate the framework under FDI attacks, disturbances, input constraints, and different initial conditions.

Read the full abstract

Systems and Control

Fixed-Time Resilient Integral Reinforcement Learning for Input-Constrained Unknown Nonlinear Systems Under FDI Attacks and Disturbances: A Data-Driven Admissible Warm Start

Tien Dat Vu and Minh Doan

This paper develops a resilient learning controller for unknown nonlinear systems operating under actuator limits, false-data-injection attacks, and external disturbances. The key idea is to learn a saturated secure policy directly from finite trajectory data while guaranteeing that both the learning error and the closed-loop state converge to compact neighborhoods within a uniform fixed time independent of initial conditions. An integral formulation removes the unknown drift from the implementable learning law, while stored informative data sustain learning after online excitation fades. To mitigate the closed-loop sensitivity to arbitrary critic initialization, pre-deployment data, which may also be reused from the replay stack, are lifted through a finite-dimensional Koopman representation to construct a stabilizing initial policy, whose inverse saturated-policy map provides a data-driven critic-weight warm start. The resulting controller preserves input constraints by construction and guarantees practical fixed-time robustness under persistent attacks and disturbances. The proposed learning and initialization architecture is further verified through a two-link robot stabilization example, where the results demonstrate rapid state recovery, bounded critic learning, reliable actuator-constraint satisfaction, and improved closed-loop behavior under informed critic initialization.

Read the full abstract

Systems and Control

Characterizing Identifiability and Generalization for Inverse Receding-Horizon Linear-Quadratic Regulator Problems

Zhiyuan Jin, Jingqi Li and David Fridovich-Keil

We consider the problem of objective inference in the context of receding-horizon linear-quadratic regulator (LQR). In this setting, we are given sequential state-action observations, where each observed action is the first control of a newly solved finite-horizon LQR problem. We characterize when the objective of that problem is uniquely identifiable from these observations and when additional observations provide no new information about the objective. We then analyze action prediction at unseen states and show that all objectives reproducing the observed actions yield identical actions throughout the affine hull of the observed states; outside this hull, we derive an upper bound on the prediction error. % and deriving a prediction-error bound outside this hull. Additionally, we show that, when only the linear objective terms are unknown, exact prediction holds at every state. Finally, numerical results show that, even under stochastic observation noise, re-optimizing an inferred objective enables accurate action prediction at unseen states across different planning horizons.

Read the full abstract

Signal Processing

Impact of PA Nonlinearities on AFDM Sensing: A Matched Filtering Perspective

Eya Gourar, Yahia Medjahdi, Laurent Clavier et al. (+2 more)

Affine Frequency Division Multiplexing (AFDM) is a promising waveform for integrated sensing and communications (ISAC) due to its Doppler resilience. However, such waveforms exhibit a high peak-to-average power ratio (PAPR), and they require highly linear Power Amplifiers (PAs), which conflicts with ISAC requirements, where high transmit power for sensing is always beneficial. This paper investigates the impact of PA nonlinearities on the sensing performance of AFDM. Using the Bussgang decomposition of the nonlinearly amplified AFDM signal, we analyze the delay-Doppler ambiguity function for the sensing matched filtering and show that AFDM's sidelobes remain nearly unaffected. Simulation results confirm that AFDM outperforms OFDM in target detection under high Doppler and nonlinear amplification, highlighting its suitability for the sensing task.

Read the full abstract

All authors Eya Gourar, Yahia Medjahdi, Laurent Clavier, Abdul Karim Gizzini, Patrick Sondi

Signal Processing · Cross-listed from cs.NI

Towards a Connected Heterogeneous All-Medium Integrated Network (CHAIN) for Converged Connectivity Across Land, Sea, Air, and Space

Paul Anthony Haigh, Sujan Rajbhandari, Kyle Bottrill et al. (+12 more)

Next-generation connectivity depends on data traversing multiple physical media within a single end-to-end path, yet research in optical fibre, free-space optical and radio wireless, non-terrestrial networks, and underwater communications has advanced largely in isolation. This fragmentation has a wider adverse impact on communication performance, deployment and adaption as the most demanding open problems in next-generation connectivity, cross-medium channel characterisation, transport-layer protocol design across heterogeneous latency regimes, and cross-domain orchestration, sit at the boundaries between domains and can only be addressed by bringing these challenges together in cross-domain systems. Space-air-ground integrated network research has begun to treat three domains analytically, and its recent extension to the sea surface represents the most ambitious multi-domain framework proposed to date, yet neither has produced experimental results, a deployed system, or a standards engagement. The subsurface and maritime domain remains absent from both. This paper proposes the connected heterogeneous all-medium integrated network (CHAIN), a framework that treats the undersea, maritime, terrestrial, aerial and space domains as a single design space. We identify an all-photonic network backbone as the unifying physical infrastructure, Artificial intelligence (AI)-driven orchestration as the cross-domain control layer, and the joints between domains as a unification challenge. We survey the state of the art across all domains and the existing cross-domain literature, develop the CHAIN framework and its technical pillars, characterise the five core open problems the field must resolve, and set out a research roadmap from near-term measurement campaigns and testbed validation through cross-medium field trials to global-scale deployment.

Read the full abstract

All authors Paul Anthony Haigh, Sujan Rajbhandari, Kyle Bottrill, Abderrahmen Trichili, Scott Watson, Hanaa Abumarshoud, David Benton, Sinan Sinanovic, Johannes Herrnsdorf, Peter Christopher, Mohsen Khalily, Rahim Tafazolli, Harad Haas, Martin Lavery, Wasiu Popoola

Audio and Speech Processing · Cross-listed from cs.SD

StepAudio 3 Realtime Technical Report

Bin Lin, Bo Zhao, Boyang Zhang et al. (+87 more)

Realtime spoken interaction demands deep reasoning, prompt responses, and fluid turn-taking. We present StepAudio 3 Realtime, an audio-language foundation model organized around a continuous listen-converse-think-act loop. Deep Perception captures rich acoustic cues to interpret user intent, while Seamless Duplex models synchronized audio streams to handle pauses, backchannels, and interruptions naturally. Crucially, we resolve the tension between deep deliberation and latency via Think-While-Speaking, executing private reasoning in parallel with spoken delivery. In reasoning mode, StepAudio 3 reaches a 73.0 macro average on StepAudioChat. With Think-While-Speaking, it achieves dialogue and reasoning performance comparable to dedicated reasoning models while speaking in real time. Furthermore, an integrated Voice Agent handles asynchronous tool execution without disrupting the dialogue flow. StepAudio 3 Realtime achieves top-tier performance across key dimensions: an exceptional 90.6 on the MMSU benchmark, 98.9 Overall on the Artificial Analysis Full-Duplex Bench, and a 56.0% macro task-success rate on $\tau$-Voice.

Read the full abstract

All authors Bin Lin, Bo Zhao, Boyang Zhang, Boyong Wu, Chao Yan, Chen Geng, Chen Wu, Cheng Yi, Chengli Feng, Chenglin Zhu, Chengting Feng, Chengyuan Yao, Daijiao Liu, DanNi Wan, Daxin Jiang, Dongjian Li, Dongqing Pang, Fei Tian, Feng Tian, Future Li, Gang Yu, Guanglong Yang, Haoyang Zhang, Hongyuan Wang, Jia Peng, Jiahao Song, Jialong Xue, Jiamin Fan, Jiangjie Zhen, Jianzheng Gao, Jincheng Wen, Jinghua Liang, Jinglan Gong, Jun Chen, Li Xie, Liang Zhao, Lifang Zhang, Lingli Ji, Lun Cai, Min Xu, Peilin Li, Peng Yang, Pengfei Tan, Qingjian Lin, Qinxin Du, Ruijie Xiong, Runze Li, Shenghua Hu, Shengqian Qin, Shi Qiu, Siqi Tu, Siyi Zhou, Tianjiao Deng, Wanying Lu, Weiming Niu, Wen Sun, WenWen Qu, Xiangyu Zhang, Xianwei Zhang, Xiaosu Su, Xing Chen, Xinyu Liu, Xuerui Yang, Yan Wu, Yang Li, Yang Yang, Yechang Huang, Yibo Zhu, Yifan Zhang, Yinuo Yan, Youjun Chen, Yu Fu, Yu Luo, Yu Zhou, Yujie Chen, Yumang Wang, Yunzhou Ju, Yuxiang Yang, Yuxin Li, Yuxin Zhang, Zekai Liu, Zengwei Yao, Zhaoxin Yuan, Zhenwei Mou, Zhiquan Zhang, Zhiyue Wu, Zichao Li, Zichao Zhou, Ziqi Ren, Zixuan Wang

Systems and Control · Cross-listed from math.OC

Distributed Convex Second-Order Optimization for AC/DC Hybrid Distribution Systems within PROcess Orchestration Framework

Xinliang Dai, Yuning Jiang, Junyi Zhai et al. (+5 more)

Existing operation formulations for AC/DC distribution grid often oversimplify the interplay of AC/DC interconnections and VSC characteristics. Moreover, current distributed algorithms depend on gradient-based methods, which achieve only linear convergence rates and struggle to scale efficiently for large systems. This paper proposes a convex ALADIN variant that exploits second-order derivatives of convex subproblems, achieving locally quadratic convergence for distributed nonlinear but convex optimization problems. Then, this convex ALADIN is applied to a separable AC/DC OPF model for AC/DC hybrid distribution grid, explicitly incorporating complicated AC/DC interconnections and multiple droop-controlled VSCs while retaining computational traceability. To bridge theory with practice, we implement the distributed convex second-order optimization framework using the PROcess Orchestration Framework, a co-simulation platform that automates workflow design, model integration, and distributed execution via a user-friendly graphical interface, eliminating manual coding. Numerical results illustrate effectiveness of the proposed model.

Read the full abstract

All authors Xinliang Dai, Yuning Jiang, Junyi Zhai, Jianlei Liu, Thorsten Schlachter, Colin N. Jones, Xiao-Ping Zhang, Veit Hagenmeyer

Systems and Control

Diffusion-Based Multiple-Shooting Indirect Optimal Control for Fuel-Optimal Spacecraft Trajectory Generation

Saeid Tafazzol, Ehsan Taheri and Ryne Beeson

Diffusion-based generative models (DMs) have found applications in control problems, and in particular robotics, where the DMs enable exploration of possible control solutions. A critical shortcoming of these applications is that they have lacked optimality guarantees. This is a problem for their potential use in fuel-optimal spacecraft trajectories that are characterized with long time-horizons and bang-bang profiles. Alternatively, indirect optimal control methods ensure explicit satisfaction of necessary conditions, but are highly sensitive to the initial costate estimation needed to solve the resulting Hamiltonian boundary-value problems (HBVPs). To alleviate this sensitivity and enlarge the convergence domain of HBVPs, advanced indirect methods have been developed that use smoothing approaches and continuation. We propose a diffusion-based multiple shooting indirect control method that combines the exploration capability of DMs with indirect method to generate fuel-optimal spacecraft trajectories. We benchmark our method against an advanced indirect method on a fuel-optimal Earth-Mars low-thrust transfer problem, showing higher convergence robustness than the advanced indirect method that is based on random costate initialization. Code and visualizations are available at https://saeidtafazzol.github.io/Diffusion_Indirect_Control/.

Read the full abstract

Signal Processing · Cross-listed from math.ST

Can one regularizer reproduce posterior means at two noise levels?

Kang Liu

Consider Gaussian denoising with a fixed prior and varying noise variance. When the prior has a density, maximum a posteriori estimation uses its negative logarithm as a fixed regularizer; only the weight of the quadratic data term changes with the noise variance. We ask whether a fixed regularizer can also recover the posterior mean. Although a suitable regularizer exists at each variance, we prove that no single regularizer works at two distinct positive variances for any compactly supported prior other than a point mass. This holds even for nonconvex and nonsmooth regularizers. A residual computed from two denoiser evaluations gives positive lower bounds on the uniform approximation error and excess mean squared error of estimators using a common regularizer. Numerical examples illustrate these bounds and the cost of reusing a regularizer across noise levels.

Read the full abstract

Systems and Control · Cross-listed from cs.AI

LoRA Fine-Tuned Models for Control Systems Course Q&A: A Multidimensional Evaluation of Model Scale and Rank Effects

Shaowen Lu, Chengxu Liu, Ping Zhou et al. (+1 more)

Large language models (LLMs) are increasingly used in specialized university courses, but control-systems questions require coordinated terminology, notation, derivations, and stepwise explanations. Direct general-purpose responses may be inconsistently structured and hard to verify. Using exercises and reference solutions from a Linear Control Systems course, we built a supervised fine-tuning dataset of 360 system-user-assistant conversations. We applied LoRA to Qwen2.5-3B-Instruct and Qwen2.5-7B-Instruct. With identical data splits, inference settings, and evaluation protocols, we compared base and fine-tuned models and tested LoRA ranks r=4, 8, and 16. Evaluation used ROUGE, BERTScore, and structured-output features to measure reference-answer similarity and stability of the Solution-Method-Teaching Points format. LoRA improved both similarity and structured-output stability at both sizes. On the current test set, 7B-r16 achieved the highest ROUGE-L (0.4093) and BERTScore-F1 (0.8643), while r=8 offered a better balance between performance and parameter efficiency. Bootstrap resampling showed ROUGE-L gains of 0.0764 [0.0613, 0.0915] for 3B-r16 and 0.0874 [0.0687, 0.1042] for 7B-r16; both intervals exceeded zero, indicating stable textual-similarity improvements on the current test set. These results suggest LoRA can align open-source instruction-tuned models more closely with the language and pedagogical organization of course reference answers. However, the metrics mainly capture textual similarity and formatting consistency, not domain-specific reasoning or mathematical correctness, which require expert assessment and task-specific rubrics.

Read the full abstract

All authors Shaowen Lu, Chengxu Liu, Ping Zhou, Tao Yang

Audio and Speech Processing

DualSpecSE: A Dual-Path Speech Enhancement Network Integrating Mel and Complex Spectrograms

Xingchen Li, Ziqian Wang, Zikai Liu et al. (+4 more)

In this paper, we propose DualSpecSE, a speech enhancement framework that jointly models Mel-spectrogram and complex spectrogram in a dual-path architecture for improved ASR performance and higher-quality speech reconstruction. The Mel branch learns coarse-grained acoustic representations and produces enhanced Mel-spectrograms for direct ASR usage, while the complex branch refines fine-grained spectral details for high-fidelity waveform reconstruction. Built upon the cross-band and narrow-band blocks from CleanMel, DualSpecSE introduces an interaction module and a fusion module to enable effective information exchange between the two branches. The model simultaneously outputs enhanced Mel and complex spectrogram without requiring a pretrained vocoder. Experimental results demonstrate consistent improvements in speech fidelity, perceptual quality, and ASR performance. Codes and audio samples are available.

Read the full abstract

All authors Xingchen Li, Ziqian Wang, Zikai Liu, Yike Zhu, Zihan Zhang, Longshuai Xiao, Lei Xie