Systems and Control
Data-Driven Stochastic Distribution System Hardening Based on Bayesian Online Learning
Wenlong Shi, Hongyi Li and Zhaoyu Wang
Extreme weather frequently cause widespread outages in distribution systems (DSs), demonstrating the importance of hardening strategies for resilience enhancement. However, the well-utilization of real-world outage data with associated weather conditions to make informed hardening decisions in DSs is still an open issue. To bridge this research gap, this paper proposes a data-driven
stochastic distribution line (DL) hardening strategy. First, a deep neural network (DNN) regression model is developed to predict the probabilistic
evolution of outage scenarios under various hardening decisions. Based on the DNN predictions, the problem is formulated as a decision-dependent distributionally robust optimization (DRO) model, accounting for uncertainties in outage scenario distributions using a data-driven ambiguity set. To address decision-dependent uncertainty, a
Bayesian online learning
algorithm is proposed. This
algorithm decomposes the original problem into inner and outer problems. Then, it iteratively refines hardening decisions by sequentially incorporating outage data and dynamically updating decision-specific ambiguity sets by using Bayes'
theorem and
Bayesian Inference. Also, the convergence of the
algorithm is proven through dynamic regret analysis. Finally, case studies are implemented on a real-world DS in Redfield, Iowa, USA. A
dataset spanning 24 years (2001-2024) is constructed based on the utility outage records. The simulation results validates the effectiveness of the proposed strategy.
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Systems and Control
Game-theoretic Social Distancing in Competitive Bi-Virus SIS Epidemics
Benjamin Catalano, Keith Paarporn and Sebin Gracy
Numerous elements drive the spread of infectious diseases in complex real-world networks. Of particular interest is social behaviors that evolve in tandem with the spread of disease. Moreover, recent studies highlight the importance of understanding how multiple strains spread simultaneously through a population (e.g. Delta and Omicron variants of SARS-CoV-2). In this paper, we propose a bi-virus SIS epidemic model coupled with a game-theoretic social distancing behavior model. The behaviors are governed by replicator equations from evolutionary game theory. The prevalence of each strain impacts the choice of an individual to social distance, and, in turn, their behavior affects the spread of each virus in the SIS model. Our analysis identifies
equilibria of the system and their local stability properties, which reveal several isolated fixed points with varying levels of social distancing. We find that endemic co-existence is possible only when the reproduction numbers of both strains are equal. Assuming the reproduction number for each virus is the same, we identify suitable parameter regimes that give rise to lines of coexistence
equilibria. Moreover, we also identify conditions for local exponential stability of said lines of
equilibria. We illustrate our findings with several numerical simulations.
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Signal Processing · Cross-listed from cs.LG
How to Combat Reactive and Dynamic Jamming Attacks with Reinforcement Learning
Yalin E. Sagduyu, Tugba Erpek, Kemal Davaslioglu et al. (+1 more)
This paper studies the problem of mitigating reactive jamming, where a jammer adopts a dynamic policy of selecting channels and sensing thresholds to detect and jam ongoing transmissions. The transmitter-receiver pair learns to avoid jamming and optimize throughput over time (without prior knowledge of channel conditions or jamming strategies) by using reinforcement learning (RL) to adapt transmit power, modulation, and channel selection. Q-learning is employed for discrete jamming-event states, while Deep Q-Networks (DQN) are employed for continuous states based on received power. Through different reward functions and action sets, the results show that RL can adapt rapidly to spectrum dynamics and sustain high rates as channels and jamming policies change over time.
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All authors Yalin E. Sagduyu, Tugba Erpek, Kemal Davaslioglu, Sastry Kompella
Systems and Control
Computing Control Lyapunov-Barrier Functions: Softmax Relaxation and Smooth Patching with Formal Guarantees
Jun Liu and Maxwell Fitzsimmons
We present a computational framework for synthesizing a single smooth Lyapunov function that certifies both asymptotic stability and safety. We show that the existence of a strictly compatible pair of control barrier and control Lyapunov functions (CBF-CLF) guarantees the existence of such a function on the exact safe set certified by the barrier. To maximize the certifiable safe domain while retaining differentiability, we employ a log-sum-exp (softmax) relaxation of the nonsmooth maximum barrier, together with a counterexample-guided refinement that inserts half-space cuts until a strict barrier condition is verifiable. We then patch the softmax barrier with a CLF via an explicit smooth bump construction, which is always feasible under the strict compatibility condition. All conditions are formally verified using a satisfiability modulo theories (SMT) solver, enabled by a reformulation of Farkas' lemma for encoding strict compatibility. On
benchmark systems, including a power converter, we show that the certified safe stabilization regions obtained with the proposed approach are often less conservative than those achieved by state-of-the-art sum-of-squares (SOS) compatible CBF-CLF designs.
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Signal Processing · Cross-listed from cs.IT
Collaborative Edge Inference via Semantic Grouping under Wireless Channel Constraints
Mateus P. Mota, Mattia Merluzzi and Emilio Calvanese Strinati
In this paper, we study the framework of collaborative inference, or edge ensembles. This framework enables multiple edge devices to improve classification accuracy by exchanging intermediate features rather than raw observations. However, efficient communication strategies are essential to balance accuracy and bandwidth limitations. Building upon a key-query mechanism for selective information exchange, this work extends collaborative inference by studying the impact of channel noise in feature communication, the choice of intermediate collaboration points, and the communication-accuracy trade-off across tasks. By analyzing how different collaboration points affect performance and exploring communication
pruning, we show that it is possible to optimize accuracy while minimizing resource usage. We show that the intermediate collaboration approach is robust to channel errors and that the query transmission needs a higher degree of reliability than the data transmission itself.
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Image and Video Processing
MACS: Measurement-Aware Consistency Sampling for Inverse Problems
Amirreza Tanevardi, Pooria Abbas Rad Moghadam, Seyed Mohammad Eshtehardian et al. (+2 more)
Diffusion models have emerged as powerful generative priors for solving inverse imaging problems. However, their practical deployment is hindered by the substantial computational cost of slow, multi-step sampling. Although Consistency Models (CMs) address this limitation by enabling high-quality generation in only one or a few steps, their direct application to inverse problems has remained largely unexplored. This paper introduces a modified consistency sampling framework specifically designed for inverse problems. The proposed approach regulates the sampler's stochasticity through a measurement-consistency mechanism that leverages the degradation operator, thereby enforcing fidelity to the observed data while preserving the computational efficiency of consistency-based generation. Comprehensive experiments on the Fashion-MNIST and LSUN Bedroom
datasets demonstrate consistent improvements across both perceptual and pixel-level metrics, including the Fréchet Inception Distance (FID), Kernel Inception Distance (KID), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM), compared with baseline consistency and
diffusion-based sampling methods. The proposed method achieves competitive or superior reconstruction quality with only a small number of sampling steps.
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All authors Amirreza Tanevardi, Pooria Abbas Rad Moghadam, Seyed Mohammad Eshtehardian, Sajjad Amini, Babak Khalaj
Signal Processing · Cross-listed from cs.CR
Spoofing Missed-Detection Bounds for PRF GNSS Ranging Authentication Under AWGN Models
Jason Anderson
Pseudorandom-function (PRF) ranging codes, such as those used in Galileo's encrypted E6-C under the Signal Authentication Service (SAS), enable a receiver to authenticate pseudoranges once the PRF secret is revealed. This work bounds how much authentication security the receiver obtains under Additive White Gaussian Noise (AWGN) assumptions. Against a spoofer that does not estimate the code before submitting its forgery, PRF security makes the forged correlation zero-mean up to the security of the underlying PRF, allowing integration time and C/N$_0$ to mostly determine probability of missed detection (PMD) and probability of false alarm (PFA). Against such a spoofer at a conservative 30 dB-Hz, 400 ms of E6-C aggregation certifies a PMD below $2^{-128}$ (plus any PRF advantage). For a spoofer that estimates chips before submitting a forgery, I derive the receiving-
antenna gain at which authentication security breaks, which is about 12 dB for E6-C for the adversaries modeled. This work can be used to design a PRF GNSS ranging code protocol and a receiver capable of correctly asserting PRF ranging security assuming an AWGN model.
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Signal Processing · Cross-listed from cs.IT
Joint Channel and Semantic-aware Grouping for Effective Collaborative Edge Inference
Mateus P. Mota, Mattia Merluzzi and Emilio Calvanese Strinati
We focus on collaborative edge inference over wireless, which enables multiple devices to cooperate to improve inference performance in the presence of corrupted data. Exploiting a key-query mechanism for selective information exchange (or, group formation for collaboration), we recall the effect of wireless channel impairments in feature communication. We argue and show that a disjoint approach, which only considers either the semantic relevance or channel state between devices, performs poorly, especially in harsh propagation conditions. Based on these findings, we propose a joint approach that takes into account semantic information relevance and channel states when grouping devices for collaboration, by making the general
attention weights dependent of the channel information. Numerical simulations show the superiority of the joint approach against local inference on corrupted data, as well as compared to collaborative inference with disjoint decisions that either consider application or physical layer parameters when forming groups.
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Audio and Speech Processing · Cross-listed from cs.SD
High-Fidelity Speech Enhancement via Discrete Audio Tokens
Luca A. Lanzendörfer, Frédéric Berdoz, Antonis Asonitis et al. (+1 more)
Recent autoregressive
transformer-based
speech enhancement (SE) methods have shown promising results by leveraging advanced semantic understanding and contextual modeling of
speech. However, these approaches often rely on complex multi-stage pipelines and low sampling rate codecs, limiting them to narrow and task-specific
speech enhancement. In this work, we introduce DAC-SE1, a simplified
language model-based SE framework leveraging discrete high-resolution audio representations; DAC-SE1 preserves fine-grained acoustic details while maintaining semantic coherence. Our experiments show that DAC-SE1 surpasses state-of-the-art autoregressive SE methods on both objective perceptual metrics and in a MUSHRA human evaluation. We release our codebase and model checkpoints to support further research in scalable, unified, and high-quality
speech enhancement.
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All authors Luca A. Lanzendörfer, Frédéric Berdoz, Antonis Asonitis, Roger Wattenhofer
Systems and Control
Detection and Identification of Sensor Attacks Using Partially Attack-Free Data
Takumi Shinohara, Karl H. Johansson and Henrik Sandberg
In this paper, we investigate data-driven attack detection and identification in a model-free setting. We consider a practically motivated scenario in which the available
dataset may be compromised by malicious sensor attacks, but contains an unknown, contiguous, partially attack-free interval. The control input is assumed to include a small
stochastic watermarking signal. Under these assumptions, we establish sufficient conditions for attack detection and identification from partially attack-free data. We also develop data-driven detection and identification procedures and characterize their computational complexity. Notably, the proposed framework does not impose a limit on the number of compromised sensors; thus, it can detect and identify attacks even when all sensor outputs are compromised outside the attack-free interval, provided that the attack-free interval is sufficiently long. Finally, we demonstrate the effectiveness of the proposed framework via numerical simulations.
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Audio and Speech Processing · Cross-listed from cs.HC
EvolveCaptions: Empowering DHH Users Through Real-Time Collaborative Captioning
Liang-Yuan Wu and Dhruv Jain
Automatic
Speech Recognition (ASR) systems often struggle to accurately transcribe
speech from Deaf and Hard of Hearing (DHH) speakers. Existing personalization approaches typically require substantial pre-recorded datasets and burden DHH users. We present EvolveCaptions, a collaborative, error-driven personalization system that connects caption correction with speaker-specific ASR adaptation. When transcribing a DHH speaker's
speech, hearing partners repair transcription errors, and the system generates short, targeted clauses for the DHH speaker to record. These samples are used to incrementally fine-tune the model. In a study with 12 DHH and six hearing participants, EvolveCaptions reduced Word Error Rate (WER) across five scripted reading rounds, requiring an average of five minutes of additional
speech recording. Participants found the interfaces usable but considered recording and correction effortful; acceptability depended on context and anticipated long-term benefit. These findings show the potential of collaborative, cumulative adaptation while exposing the labor and conditions needed to sustain it.
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Audio and Speech Processing · Cross-listed from cs.SD
Go witheFlow: Real-time Emotion Driven Audio Effects Modulation
Edmund Dervakos, Spyridon Kantarelis, Vassilis Lyberatos et al. (+2 more)
Music performance is a distinctly human activity, intrinsically linked to the performer's ability to convey, evoke, or express
emotion. Machines cannot perform music in the human sense; they can produce, reproduce, execute, or synthesize music, but they lack the capacity for affective or emotional experience. As such, music performance is an ideal candidate through which to explore aspects of collaboration between humans and machines. In this paper, we introduce the witheFlow system, designed to enhance real-time music performance by automatically modulating audio effects based on features extracted from both biosignals and the audio itself. The system, currently in a proof-of-concept phase, is designed to be lightweight, able to run locally on a laptop, and is open-source given the availability of a compatible Digital Audio Workstation and sensors.
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All authors Edmund Dervakos, Spyridon Kantarelis, Vassilis Lyberatos, Jason Liartis, Giorgos Stamou
Systems and Control · Cross-listed from cs.RO
Product Digital Twin Supporting End-of-life Phase of Electric Vehicle Batteries Utilizing Product-Process-Resource Asset Network
Sara Strakosova, Petr Novak and Petr Kadera
In a circular economy, products in their end-of-life phase should be either remanufactured or recycled. Both of these processes are crucial for sustainability and environmental conservation. However, manufacturers frequently do not support these processes enough in terms of not sharing relevant data about the products nor their (re-)manufacturing processes. This paper proposes to accompany each product with a digital twin technology, specifically the Product Digital Twin (PDT), which can carry information for facilitating and optimizing production and remanufacturing processes. This paper introduces a knowledge representation called Bi-Flow Product-Process-Resource Asset Network (Bi-PAN). Bi-PAN extends a well-proven Product-Process-Resource Asset Network (PAN) paradigm by integrating both assembly and disassembly workflows into a single information model. Such networks enable capturing relevant relationships across products, production resources, manufacturing processes, and specific production operations that have to be done in the manufacturing phase of a product. The proposed approach is demonstrated in a use-case of disassembling electric vehicle (EV) batteries. By utilizing PDTs with Bi-PAN knowledge models, challenges associated with disassembling of EV batteries can be solved flexibly and efficiently for various battery types, enhancing the sustainability of the EV battery life-cycle management.
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Systems and Control · Cross-listed from math.OC
On the (almost) Global Exponential Convergence of the Overparameterized Policy Optimization for the LQR Problem
Moh Kamalul Wafi, Arthur Castello B. de Oliveira and Eduardo D. Sontag
In this work we study the convergence of gradient methods for nonconvex optimization problems – specifically the effect of the problem formulation to the convergence behavior of the solution of a gradient flow. We show through a simple example that, surprisingly, the gradient flow solution can be exponentially or asymptotically convergent, depending on how the problem is formulated. We then deepen the analysis and show that a policy optimization strategy for the continuous-time linear quadratic regulator (LQR) (which is known to present only asymptotic convergence globally) presents almost global exponential convergence if the problem is overparameterized through a linear feed-forward neural network (LFFNN). We prove this qualitative improvement always happens for a simplified version of the LQR problem and derive explicit convergence rates for the gradient flow. Finally, we show that both the qualitative improvement and the quantitative rate gains persist in the general LQR through numerical simulations.
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Systems and Control
Recurrent Control Barrier Functions: A Path Towards Nonparametric Safety Verification
Jixian Liu and Enrique Mallada
Ensuring the safety of complex dynamical systems often relies on Hamilton-Jacobi (HJ) Reachability Analysis or Control Barrier Functions (CBFs). Both methods require computing a function that characterizes a safe set that can be made (control) invariant. However, the computational burden of solving high-dimensional partial differential equations (for HJ Reachability) or large-scale semidefinite programs (for CBFs) makes finding such functions challenging. In this paper, we introduce the notion of Recurrent Control Barrier Functions (RCBFs), a novel class of CBFs that leverages a recurrent property of the trajectories, i.e., coming back to a safe set, for safety verification. Under mild assumptions, we show that the RCBF condition holds for the signed-distance function, turning function design into set identification. Notably, the resulting set need not be invariant to certify safety. We further propose a data-driven nonparametric method to compute safe sets that is massively parallelizable and trades off conservativeness against computational cost.
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Audio and Speech Processing · Cross-listed from cs.SD
SoundReactor: Frame-level Online Video-to-Audio Generation
Koichi Saito, Julian Tanke, Christian Simon et al. (+7 more)
Prevailing
Video-to-Audio (V2A) generation models operate offline, assuming an entire
video sequence or chunks of frames are available beforehand. This critically limits their use in interactive applications such as live content creation and emerging generative world models. To address this gap, we introduce the novel task of frame-level online V2A generation, where a model autoregressively generates audio from
video without access to future
video frames. Furthermore, we propose SoundReactor, which, to the best of our knowledge, is the first simple yet effective framework explicitly tailored for this task. Our design enforces end-to-end causality and targets low per-frame latency with audio-visual synchronization. Our model's backbone is a decoder-only causal
transformer over continuous audio latents. For
vision conditioning, it leverages grid (patch) features extracted from the smallest variant of the DINOv2
vision encoder, which are aggregated into a single
token per frame to maintain end-to-end causality and efficiency. The model is trained through a
diffusion pre-training followed by consistency fine-tuning to accelerate the
diffusion head decoding. On a
benchmark of diverse gameplay
videos from AAA titles, our model successfully generates semantically and temporally aligned, high-quality full-band stereo audio, validated by both objective and human evaluations. Furthermore, our model achieves low per-frame waveform-level latency (26.3ms with the head NFE=1, 31.5ms with NFE=4) on 30FPS, 480p
videos using a single H100. Demo samples are available at https://koichi-saito-sony.github.io/soundreactor/.
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All authors Koichi Saito, Julian Tanke, Christian Simon, Masato Ishii, Kazuki Shimada, Zachary Novack, Zhi Zhong, Akio Hayakawa, Takashi Shibuya, Yuki Mitsufuji
Image and Video Processing
SpurBreast: A Curated Dataset for Investigating Spurious Correlations in Real-world Breast MRI Classification
Jong Bum Won, Wesley De Neve, Joris Vankerschaver et al. (+1 more)
Deep neural networks (DNNs) have demonstrated remarkable success in medical imaging, yet their real-world deployment remains challenging due to spurious correlations, where models can learn non-clinical features instead of meaningful medical patterns. Existing medical imaging
datasets are not designed to systematically study this issue, largely due to restrictive licensing and limited supplementary patient data. To address this gap, we introduce SpurBreast, a curated breast MRI
dataset that intentionally incorporates spurious correlations to evaluate their impact on model performance. Analyzing over 100 features involving patient, device, and imaging protocol, we identify two dominant spurious signals:
magnetic field strength (a global feature influencing the entire image) and image orientation (a local feature affecting spatial
alignment). Through controlled
dataset splits, we demonstrate that DNNs can exploit these non-clinical signals, achieving high validation accuracy while failing to generalize to unbiased test data. Alongside these two
datasets containing spurious correlations, we also provide
benchmark datasets without spurious correlations, allowing researchers to systematically investigate clinically relevant and irrelevant features, uncertainty estimation,
adversarial robustness, and generalization strategies. Models and
datasets are available at https://github.com/utkuozbulak/spurbreast.
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All authors Jong Bum Won, Wesley De Neve, Joris Vankerschaver, Utku Ozbulak
Signal Processing
Unlocking Symbol-Level Precoding Efficiency Through Tensor Equivariant Neural Network
Jinshuo Zhang, Yafei Wang, Xinping Yi et al. (+4 more)
Although symbol-level precoding (SLP) based on constructive interference (CI) exploitation offers performance gains, its high complexity remains a bottleneck. This paper addresses this challenge with an end-to-end deep learning (DL) framework with low inference complexity that leverages the structure of the optimal SLP solution in the closed-form and its inherent tensor equivariance (TE), where TE denotes that a permutation of the input induces the corresponding permutation of the output. Building upon the computationally efficient model-based formulations, as well as their known closed-form solutions, we analyze their relationship with linear precoding (LP) and investigate the corresponding optimality condition. We then construct a mapping from the problem formulation to the solution and prove its TE, based on which the designed networks reveal a specific parameter-sharing pattern that delivers low computational complexity and strong generalization. Leveraging these, we propose the backbone of the framework with an
attention-based TE module, achieving linear computational complexity. Furthermore, we demonstrate that such a framework is also applicable to imperfect CSI scenarios, where we design a TE-based network to map the CSI, statistics, and symbols to auxiliary variables. Simulation results show that the proposed framework captures substantial performance gains of optimal SLP, while achieving an approximately 80-times speedup over conventional methods and maintaining strong generalization across user numbers and symbol block lengths.
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All authors Jinshuo Zhang, Yafei Wang, Xinping Yi, Wenjin Wang, Shi Jin, Symeon Chatzinotas, Björn Ottersten
Signal Processing
Sensing-Secure ISAC: Ambiguity Function Engineering for Impairing Unauthorized Sensing
Kawon Han, Kaitao Meng and Christos Masouros
The deployment of integrated sensing and communication (
ISAC) brings along unprecedented vulnerabilities to authorized sensing, necessitating the development of secure solutions. Sensing parameters are embedded within the target-reflected signal leaked to unauthorized passive radar sensing eavesdroppers (Eve), implying that they can silently extract sensory information without prior knowledge of the information data. To overcome this limitation, we propose a sensing-secure
ISAC framework that ensures secure target detection and estimation for the legitimate system, while obfuscating unauthorized sensing without requiring any prior knowledge of Eve. By introducing artificial imperfections into the ambiguity function (AF) of
ISAC signals, we introduce artificial targets into Eve's range profile which increase its range estimation ambiguity. In contrast, the legitimate sensing receiver (Alice) can suppress these AF artifacts using mismatched filtering, albeit at the expense of signal-to-noise ratio (SNR) loss. Employing an OFDM signal, a structured subcarrier power allocation scheme is designed to shape the secure autocorrelation function (ACF), inserting periodic peaks to mislead Eve's range estimation and degrade target detection performance. To quantify the sensing security, we introduce peak sidelobe level (PSL) and integrated sidelobe level (ISL) as key performance metrics. Then, we analyze the three-way trade-offs between communication, legitimate sensing, and sensing security, highlighting the impact of the proposed sensing-secure
ISAC signaling on system performance. We formulate a convex optimization problem to maximize
ISAC performance while guaranteeing a certain sensing security level. Numerical results validate the effectiveness of the proposed sensing-secure
ISAC signaling, demonstrating its ability to degrade Eve's target estimation while preserving Alice's performance.
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Systems and Control
Cooperative Guidance for Aerial Defense in Multiagent Systems
Shivam Bajpai, Abhinav Sinha and Shashi Ranjan Kumar
This paper addresses a critical aerial defense challenge in contested airspace, involving three autonomous aerial vehicles – a hostile drone (the pursuer), a high-value drone (the evader), and a protective drone (the defender). We present a cooperative guidance framework for the evader-defender team that guarantees interception of the pursuer before it can capture the evader, even under highly dynamic and uncertain engagement conditions. Unlike traditional heuristic, optimal control, or differential game-based methods, we approach the problem within a time-constrained guidance framework, leveraging true proportional
navigation based approach that ensures robust and guaranteed solutions to the aerial defense problem. The proposed strategy is computationally lightweight, scalable to a large number of
agent configurations, and does not require knowledge of the pursuer's strategy or control laws. From arbitrary initial geometries, our method guarantees that key engagement errors are driven to zero within a fixed time, leading to a successful mission. Extensive simulations across diverse and
adversarial scenarios confirm the effectiveness of the proposed strategy and its relevance for real-time autonomous defense in contested airspace environments.
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Systems and Control
Event-triggered control and communication for single-master multi-slave teleoperation systems with Try-Once-Discard protocol
Yuling Li, Chenxi Li, Kun Liu et al. (+2 more)
Single-master multi-slave (SMMS) teleoperation systems can perform multiple tasks remotely in a shorter time, cover large-scale areas, and adapt more easily to single-point failures, thereby effectively encompassing a broader range of applications. As the number of slave manipulators sharing a communication network increases, the limitation of communication bandwidth becomes critical. To alleviate bandwidth usage, the Try-Once-Discard (TOD) scheduling protocol and event-triggered mechanisms are often employed separately. In this paper, we combine both strategies to optimize network bandwidth and energy consumption for SMMS teleoperation systems. Specifically, we propose event-triggered control and communication schemes for a class of SMMS teleoperation systems using the TOD scheduling protocol. Considering dynamic uncertainties, the unavailability of relative velocities, and time-varying delays, we develop adaptive controllers with virtual observers based on event-triggered schemes to achieve master-slave synchronization. Stability criteria for the SMMS teleoperation systems under these event-triggered control and communication schemes are established, demonstrating that Zeno behavior is excluded. Finally, experiments are conducted to validate the effectiveness of the proposed
algorithms.
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All authors Yuling Li, Chenxi Li, Kun Liu, Jie Dong, Rolf Johansson
Audio and Speech Processing · Cross-listed from cs.CL
Chain-of-Thought Reasoning in Streaming Full-Duplex End-to-End Spoken Dialogue Systems
Siddhant Arora, Jinchuan Tian, Hayato Futami et al. (+4 more)
Most end-to-end (E2E) spoken dialogue systems (SDS) rely on voice activity detection (VAD) for turn-taking, but VAD fails to distinguish between pauses and turn completions. Duplex SDS models address this by predicting output continuously, including silence
tokens, thus removing the need for explicit VAD. However, they often have complex dual-channel architecture and lag behind cascaded models in semantic
reasoning. To overcome these challenges, we propose SCoT: a Streaming Chain-of-Thought (CoT) framework for Duplex SDS, alternating between processing fixed-duration user input and generating responses in a blockwise manner. Using frame-level
alignments, we create intermediate targets-aligned user transcripts and system responses for each block. Experiments show that our approach produces more coherent and interpretable responses than existing duplex methods while supporting lower-latency and overlapping interactions compared to turn-by-turn systems.
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All authors Siddhant Arora, Jinchuan Tian, Hayato Futami, Jiatong Shi, Yosuke Kashiwagi, Emiru Tsunoo, Shinji Watanabe
Image and Video Processing
MSRepaint: Multiple Sclerosis Repaint with Conditional Denoising Diffusion Implicit Model for Bidirectional Lesion Filling and Synthesis
Jinwei Zhang, Lianrui Zuo, Yihao Liu et al. (+10 more)
In multiple sclerosis, lesions interfere with automated
magnetic resonance imaging analyses such as
brain parcellation and deformable registration, while lesion
segmentation models are hindered by the limited availability of annotated training data. To address both issues, we propose MSRepaint, a unified
diffusion-based generative model for bidirectional lesion filling and synthesis that restores anatomical continuity for downstream analyses and augments
segmentation through realistic data generation. MSRepaint conditions on spatial lesion masks for voxel-level control, incorporates contrast dropout to handle missing inputs, integrates a repainting mechanism to preserve surrounding anatomy during lesion filling and synthesis, and employs a multi-view DDIM inversion and fusion pipeline for 3D consistency with fast inference. Extensive evaluations demonstrate the effectiveness of MSRepaint across multiple tasks. For lesion filling, we evaluate both the accuracy within the filled regions and the impact on downstream tasks including
brain parcellation and deformable registration. MSRepaint outperforms the traditional lesion filling methods FSL and NiftySeg, and achieves accuracy on par with FastSurfer-LIT, a recent
diffusion model-based inpainting method, while offering over 20 times faster inference. For lesion synthesis, state-of-the-art MS lesion
segmentation models trained on MSRepaint-synthesized data outperform those trained on CarveMix-synthesized data or real ISBI challenge training data across multiple
benchmarks, including the MICCAI 2016 and UMCL
datasets. Additionally, we demonstrate that MSRepaint's unified bidirectional filling and synthesis capability, with full spatial control over lesion appearance, enables high-fidelity simulation of lesion
evolution in longitudinal MS progression.
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All authors Jinwei Zhang, Lianrui Zuo, Yihao Liu, Hang Zhang, Samuel W. Remedios, Bennett A. Landman, Peter A. Calabresi, Shiv Saidha, Scott D. Newsome, Dzung L. Pham, Jerry L. Prince, Ellen M. Mowry, Aaron Carass
Signal Processing · Cross-listed from cs.IT
Variational Secret Common Randomness Extraction
Xinyang Li, Vlad C. Andrei, Peter J. Gu et al. (+3 more)
This paper studies the problem of extracting common randomness (CR) or secret keys from correlated random sources observed by two legitimate parties, Alice and Bob, through public discussion in the presence of an eavesdropper, Eve. We propose a practical two-stage CR extraction framework. In the first stage, the variational probabilistic quantization (VPQ) step is introduced, where Alice and Bob employ probabilistic neural network (NN) encoders to map their observations into discrete, nearly uniform random variables (RVs) with high agreement probability while minimizing information leakage to Eve. This is realized through a variational learning objective combined with
adversarial training. In the second stage, a secure sketch using code-offset construction reconciles the encoder outputs into identical secret keys, whose secrecy is guaranteed by the VPQ objective. As a representative application, we study physical layer key (PLK) generation. Beyond the traditional methods, which rely on the channel reciprocity principle and require two-way channel probing, thus suffering from large protocol overhead and being unsuitable in high mobility scenarios, we propose a sensing-based PLK generation method for integrated sensing and communications (
ISAC) systems, where paired range-angle (RA) maps measured at Alice and Bob serve as correlated sources. The idea is verified through both end-to-end simulations and real-world software-defined
radio (SDR) measurements, including scenarios where Eve has partial knowledge about Bob's position. The results demonstrate the feasibility and convincing performance of both the proposed CR extraction framework and sensing-based PLK generation method.
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All authors Xinyang Li, Vlad C. Andrei, Peter J. Gu, Yiqi Chen, Ullrich J. Mönich, Holger Boche
Systems and Control · Cross-listed from math.OC
LLM-Enhanced, Data-Driven Personalized and Equitable Clinician Scheduling: A Predict-then-Optimize Approach
Anjali Jha, Wanqing Chen, Maxim Eckmann et al. (+3 more)
Clinician scheduling remains a persistent challenge due to limited clinical resources and fluctuating demands. This complexity is especially acute in large academic anesthesiology departments as physicians balance responsibilities across multiple clinical sites with conflicting priorities. Further, scheduling must account for individual clinical and lifestyle preferences to ensure job satisfaction and well-being. Traditional approaches, often based on statistical or rule-based optimization models, rely on structured data and explicit domain knowledge. However, these methods often overlook unstructured information, e.g., free-text notes from routinely administered clinician well-being surveys and scheduling platforms. These notes may reveal implicit and underutilized clinical resources. Neglecting such information can lead to misaligned schedules, increased burnout, overlooked staffing flexibility, and suboptimal utilization of available resources. To address this gap, we propose a predict-then-optimize framework that integrates classification-based clinician availability predictions with a mixed-integer programming schedule optimization model. Large
language models (LLMs) are employed to extract actionable preferences and implicit constraints from unstructured schedule notes, enhancing the reliability of availability predictions. These predictions then inform the schedule optimization considering four objectives: first, ensuring clinical full-time equivalent compliance, second, reducing workload imbalances by enforcing equitable proportions of shift types, third, maximizing clinician availability for assigned shifts, and fourth, schedule consistency. By combining the interpretive power of LLMs with the rigor of mathematical optimization, our framework provides a robust, data-driven solution that enhances operational efficiency while supporting equity and clinician well-being.
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All authors Anjali Jha, Wanqing Chen, Maxim Eckmann, Ian Stockwell, Jianwu Wang, Kai Sun
Audio and Speech Processing · Cross-listed from cs.CL
Stream RAG: Instant and Accurate Spoken Dialogue Systems with Streaming Tool Usage
Siddhant Arora, Haidar Khan, Kai Sun et al. (+14 more)
End-to-end
speech-in
speech-out dialogue systems are emerging as a powerful alternative to traditional ASR-LLM-TTS pipelines, generating more natural, expressive responses with significantly lower latency. However, these systems remain prone to
hallucinations due to limited factual grounding. While text-based dialogue systems address this challenge by integrating tools such as web search and knowledge graph APIs, we introduce the first approach to extend tool use directly into
speech-in
speech-out systems. A key challenge is that tool integration substantially increases response latency, disrupting conversational flow. To mitigate this, we propose Streaming
Retrieval-Augmented Generation (Streaming RAG), a novel framework that reduces user-perceived latency by predicting tool queries in parallel with user
speech, even before the user finishes speaking. Specifically, we develop a post-training pipeline that teaches the model when to issue tool calls during ongoing
speech and how to generate spoken summaries that fuse audio queries with retrieved text results, thereby improving both accuracy and responsiveness. To evaluate our approach, we construct AudioCRAG, a
benchmark created by converting queries from the publicly available CRAG
dataset into
speech form. Experimental results demonstrate that our streaming RAG approach increases QA accuracy by up to 200% relative (from 11.1% to 34.2% absolute) and further enhances user experience by reducing tool use latency by 20%. Importantly, our streaming RAG approach is modality-agnostic and can be applied equally to typed input, paving the way for more agentic, real-time AI assistants.
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All authors Siddhant Arora, Haidar Khan, Kai Sun, Xin Luna Dong, Sajal Choudhary, Seungwhan Moon, Xinyuan Zhang, Adithya Sagar, Surya Teja Appini, Kaushik Patnaik, Sanat Sharma, Shinji Watanabe, Anuj Kumar, Ahmed Aly, Yue Liu, Florian Metze, Zhaojiang Lin
Image and Video Processing · Cross-listed from q-bio.QM
A Multicentric Dataset for Training and Benchmarking Breast Cancer Segmentation in H&E Slides
Carlijn Lems, Leslie Tessier, John-Melle Bokhorst et al. (+18 more)
Automated semantic
segmentation of whole-slide images (WSIs) stained with hematoxylin and eosin (H&E) is essential for large-scale artificial intelligence-based biomarker analysis in breast cancer. However, existing public
datasets for breast cancer
segmentation lack the morphological diversity needed to support model generalizability and robust biomarker validation across heterogeneous patient cohorts. We introduce BrEast cancEr hisTopathoLogy
sEgmentation (BEETLE), a
dataset for multiclass semantic
segmentation of H&E-stained breast cancer WSIs. It consists of 587 biopsies and resections from three collaborating clinical centers and two public
datasets, digitized using seven scanners, and covers all molecular subtypes and histological grades. Using diverse annotation strategies, we collected annotations across four classes - invasive epithelium, non-invasive epithelium, necrosis, and other - with particular focus on morphologies underrepresented in existing
datasets, such as ductal carcinoma in situ and dispersed lobular tumor
cells. The
dataset's diversity and relevance to the rapidly growing field of automated biomarker quantification in breast cancer ensure its high potential for reuse. Finally, we provide a well-curated, multicentric external evaluation set to enable standardized benchmarking of breast cancer
segmentation models.
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All authors Carlijn Lems, Leslie Tessier, John-Melle Bokhorst, Mart van Rijthoven, Witali Aswolinskiy, Matteo Pozzi, Natalie Klubickova, Suzanne Dintzis, Michela Campora, Maschenka Balkenhol, Peter Bult, Joey Spronck, Thomas Detone, Mattia Barbareschi, Enrico Munari, Giuseppe Bogina, Jelle Wesseling, Esther H. Lips, Francesco Ciompi, Frédérique Meeuwsen, Jeroen van der Laak
Signal Processing
Joint DOA and Attitude Sensing Based on Tri-Polarized Continuous Aperture Array
Haonan Si, Zhaolin Wang, Xiansheng Guo et al. (+2 more)
This paper investigates joint direction-of-arrival (DOA) and attitude sensing using tri-polarized continuous aperture arrays (CAPAs). By employing electromagnetic (EM) information theory, the spatially continuous received signals in tri-polarized CAPA are modeled, thereby enabling accurate DOA and attitude estimation. To facilitate subspace decomposition for continuous operators, an equivalent continuous-discrete transformation technique is developed. Moreover, both self- and cross-covariances of tri-polarized signals are exploited to construct a tri-polarized spectrum, significantly enhancing DOA estimation performance. Theoretical analyses reveal that the identifiability of attitude information fundamentally depends on the availability of prior target snapshots. Accordingly, two attitude estimation
algorithms are proposed: one capable of estimating partial attitude information without prior knowledge, and the other achieving full attitude estimation when such knowledge is available. Numerical results demonstrate the feasibility and superiority of the proposed framework.
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All authors Haonan Si, Zhaolin Wang, Xiansheng Guo, Jin Zhang, Yuanwei Liu
Signal Processing
A Secure Affine Frequency Division Multiplexing System for Next-Generation Wireless Communications
Ping Wang, Zulin Wang, Yuanhan Ni et al. (+4 more)
Affine frequency division multiplexing (AFDM) has garnered significant
attention due to its superior performance in high-mobility scenarios, coupled with multiple waveform parameters that provide greater degrees of freedom for system design. This paper introduces a novel secure affine frequency division multiplexing (SE-AFDM) system, which advances prior designs by dynamically varying an AFDM pre-chirp parameter to enhance physical-layer security. In the SE-AFDM system, the pre-chirp parameter is dynamically generated from a codebook controlled by a long-period pseudo-noise (LPPN) sequence. Instead of applying spreading in the data domain, our parameter-domain spreading approach provides additional security while maintaining reliability and high spectrum efficiency. We also propose a synchronization framework to solve the problem of reliably and rapidly synchronizing the time-varying parameter in fast time-varying channels. The theoretical derivations prove that unsynchronized eavesdroppers cannot eliminate the nonlinear impact of the time-varying parameter and further provide useful guidance for codebook design. Simulation results demonstrate the security advantages of the proposed SE-AFDM system in high-mobility scenarios, while our hardware prototype validates the effectiveness of the proposed synchronization framework.
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All authors Ping Wang, Zulin Wang, Yuanhan Ni, Qu Luo, Yuanfang Ma, Xiaosi Tian, Pei Xiao
Signal Processing
Joint Jammer Mitigation and Data Detection
Gian Marti and Christoph Studer
Multi-
antenna (or
MIMO) processing is a promising solution to the problem of jammer mitigation. Existing methods mitigate the jammer based on an estimate of its spatial signature that is acquired through a dedicated training phase. This strategy has two main drawbacks: (i) it reduces the communication rate since no data can be transmitted during the training phase and (ii) it can be evaded by smart or multi-
antenna jammers that do not transmit during the training phase or that dynamically change their subspace through time-varying beamforming. To address these drawbacks, we propose Joint jammer Mitigation and data Detection (JMD), a novel paradigm for
MIMO jammer mitigation. The core idea of JMD is to estimate and remove the jammer interference subspace jointly with detecting the legitimate transmit data over multiple time slots. Doing so removes the need for a dedicated and rate-reducing training period while being able to mitigate smart and dynamic multi-
antenna jammers. We provide two JMD-type
algorithms, SANDMAN and MAED, that differ in the way they estimate the channels of the legitimate transmitters and achieve different complexity-performance tradeoffs. Extensive simulations demonstrate the efficacy of JMD for jammer mitigation.
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