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Electrical Engineering and Systems Science

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Saturday 12 September

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

DiTAR+: Dual Optimization for Robust Autoregressive Diffusion Speech Synthesis

Ziyu Zhang, Tianlun Zuo, Hanzhao Li et al. (+2 more)

Continuous-latent Autoregressive Diffusion Transformer (AR-DiT) models have demonstrated immense potential in zero-shot speech generation. However, they still suffer from limited decoding stability when synthesizing long utterances or complex linguistic structures. This instability primarily stems from a restricted historical receptive field and an acoustic inertia dependency within the diffusion decoder, which causes the model to ignore semantic conditions. To address these challenges, we propose DiTAR+, a dual-optimization framework. First, we introduce Dilated Context Sampling to expand the macro-level historical receptive field without violating physical temporal continuity, thereby preventing cumulative error propagation. Second, we propose Hierarchical Acoustic Masking to prevent shallow layers from attending to acoustic pre-context, explicitly decoupling semantic alignment from acoustic detail reconstruction. Extensive experiments show that our framework effectively mitigates pronunciation errors and semantic hallucinations, enhances generation robustness on challenging sentences, and maintains exceptionally high speaker similarity throughout the entirety of long-form utterances. On the linguistically challenging ZH-Hard set, DiTAR+ reduces the word error rate from 12.478% to 9.893%, and on extended utterances of 25 to 35 seconds it improves speaker similarity from 0.741 to 0.759 while simultaneously lowering the word error rate from 2.778% to 2.173%, outperforming both discrete-token and pure flow-matching baselines.
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All authors Ziyu Zhang, Tianlun Zuo, Hanzhao Li, Haoyu Zhang, Lei Xie

Systems and Control

Influence Enhancement in Opinion Dynamics Using Edge Modification: A Kron Reduction-Based Approach

Aashi Shrinate, Aravind Seshadri, Twinkle Tripathy et al. (+3 more)

With the emergence of online social networks as a primary platform for advertising and advocacy, enhancing a user's influence has become of significant interest. In this paper, we investigate this problem under the Friedkin Johnsen opinion dynamics model, wherein stubborn agents influence the opinions of other agents in the network. Unlike most of the existing works, we leverage topological properties of the network to increase the influence of a desired stubborn agent. Specifically, we introduce the notion of edge modification, which mimics the mechanism of recommendations in social networks. First, we present a topology-based condition that identifies edge modifications that always increase the influence of a desired stubborn agent. It is shown that the impact of the chosen edge modifications remains robust to changes in parameters such as stubbornness and the interaction weights. Thereafter, we formulate a discrete optimisation problem to identify a set of edge modifications that maximise the agent's influence centrality. We present a computationally efficient approximate solution to the optimisation problem. Finally, we demonstrate the effectiveness of our approach on the Friedkin-Johnsen opinion dynamics over the Erdos Renyi random graph.
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All authors Aashi Shrinate, Aravind Seshadri, Twinkle Tripathy, Laxmidhar Behera, Lingfei Wang, Karl Henrik Johansson

Systems and Control

Optimal Threshold Type Policies for Partially Observable Restless Bandits

Anu Krishna, Rahul Meshram and Kesav Ram Kaza

We study a finite-state partially observable restless multi-armed bandit (PO-RMAB) motivated by resource-constrained wildlife monitoring. The underlying condition of each location evolves independently, while only a limited number of locations can be actively monitored at each decision epoch. Activation reveals the current state, whereas passive operation provides no observation, yielding a collapsing-bandit belief dynamics. Our main contribution is a structural characterisation of optimal policies for the multidimensional belief-state problem. We establish sufficient conditions under which an activation advantage is monotone with respect to the belief state, and hence the optimal policy has the threshold type structure over the $(M-1)$-dimensional belief simplex. The key result is obtained by bounding the variation of the value function through a model-dependent Lipschitz constant. We further specialise the result to one-step birth-death dynamics and derive explicit bounds.
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Audio and Speech Processing · Cross-listed from cs.SD

Musical Timing in Studio Recordings

Konstantinos Tsioutas and George Xylomenos

Studio recording techniques vary considerably, ranging from live recordings in a shared acoustic space, to isolated booth recording and overdubbing, where musicians record their parts separately, while listening to previously recorded material. These approaches differ in terms of physical co-presence, visual contact, sound leakage, and acoustic isolation, factors that may influence musical coordination. This study investigates whether the recording style affects timing precision; specifically, whether musicians performing together in the same space achieve tighter temporal coordination. To address these questions, two datasets with a total of 391 multi-track recordings were analyzed to measure the timing relationships between musicians. In addition, an automated sound-leakage detection method was employed to infer the recording conditions of each song: pairwise Mel-spectrogram similarities between the tracks were computed, and summary statistics from the resulting similarity matrices were used to identify recordings with shared acoustic content. The results suggest that recordings showing evidence of shared room acoustics and sound leakage tend to exhibit lower timing variability than highly isolated recordings, whereas overdubbing is associated with increased timing variability.
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Signal Processing · Cross-listed from cs.NI

Trustworthy, Explainable, and Sustainable Decentralized Intelligence for 6G Networks

Giovanni Perin, Michele Rossi, Enrique Tomás Martínez Beltrán et al. (+13 more)

As 6G networks transition from theoretical frameworks into operational realities, artificial intelligence (AI) evolves from an add-on optimization tool into a distributed and interconnected structural layer. Unlike previous network generations that mostly relied on centralized cloud analytics platforms, AI-native 6G networks operate across a dynamic, multi-domain edge-cloud continuum where data originates from heterogeneous sources including user devices, radio access networks, sensing infrastructures, and vertical applications. Centralizing this massive volume of data creates severe communication overhead, unacceptable latency bottlenecks, single points of failure, and complex cross-domain governance challenges. Consequently, decentralization becomes a fundamental architectural requirement for future 6G network intelligence and zero-touch operations. Security serves as the primary enabler of this decentralized paradigm. Critical security functions, such as real-time threat detection, physical-layer attack mitigation, slice protection, and intrusion detection, require immediate access to local context and telemetry before operational data loses its value. However, moving intelligence to the edge via collaborative paradigms like federated learning (FL) and decentralized FL (DFL) introduces complex trade-offs. System security cannot be addressed in isolation; it is deeply intertwined with equally important aspects like trustworthiness, explainability, and energy sustainability. Taking these aspects into account, this paper develops a unified perspective on decentralized intelligence for 6G, arguing that decentralization, trustworthiness, explainability, and sustainability must be designed jointly rather than treated as independent requirements
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All authors Giovanni Perin, Michele Rossi, Enrique Tomás Martínez Beltrán, Fernando Torres-Vega, José María Jorquera Valero, Manuel Gil Pérez, Eunjeong Jeong, Nikolaos Pappas, Farah Abed Zadeh, Chamara Sandeepa, Bartlomiej Siniarski, Madhusanka Liyanage, Betül Güvenç Paltun, Leyli Karaçay, Ioannis Pitsiorlas, Marios Kountouris

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

CRAF: Cross-View Residual-Aware Fusion for Deepfake Speech Detection

Minh-Xuan Phan, Khalid Zaman, Candy Olivia Mawalim et al. (+1 more)

Recent advances in speech synthesis and voice conversion have made deepfake speech increasingly realistic, making generalization to unseen spoofing attacks a critical challenge. Pretrained speech and audio models offer a promising direction for improving robustness to such unseen attacks. Self-supervised learning (SSL) models capture fine-grained, low-level acoustic characteristics, whereas Auditory Large Language Models (ALLMs) provide higher-level contextual representations. These complementary views can provide useful cues for improving generalization to unseen attacks. However, direct fusion does not explicitly disentangle information shared across the two views from view-specific complementary information, limiting effective cross-view integration. To address this, we propose CRAF, a cross-view residual-aware fusion framework that uses ALLM-guided cross-view attention to enrich SSL representations and adopts ALLM as a high-level reference to separate ALLM-explainable information from complementary SSL residual information. The residual is selectively refined through adaptive gating and integrated through SSL-primary fusion. Experiments on ASVspoof 5 show that CRAF with Kimi-Audio achieves an EER of 5.96% and a minDCF of 0.1192, demonstrating robustness to unseen spoofing attacks.
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All authors Minh-Xuan Phan, Khalid Zaman, Candy Olivia Mawalim, Masashi Unoki

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

Realtime-Venus: A full-duplex interaction system with asynchronous delegation

Ant Group

Natural interaction in digital and physical environments requires continuous perception and timely responses. Spoken dialogue relies on acoustic and linguistic cues, while video interaction also requires grounding the conversation in evolving visual context. We present Realtime-Venus, a proactive full-duplex interaction system with two separately trained 9B models: Realtime-Venus-Omni for audio-visual interaction and Realtime-Venus-Audio for spoken interaction. Each model serves as a complete conversational frontend, integrating continuous perception, conversational control, and native speech generation through a shared causal timeline for user inputs, model outputs, and delegation events. A dual-loop runtime coordinates live interaction with background reasoning and tool execution. Foreground interaction continues while Realtime-Venus-Harness executes tasks asynchronously and returns results for integration into the ongoing dialogue. Both models follow a common post-training recipe combining offline understanding, proactive full-duplex trajectories, and delegation workflows. Among the evaluated online models, Realtime-Venus-Omni achieves the highest scores on six of eight video benchmarks, including StreamingBench (70.2%), OVO-Bench (64.7%), and Daily-Omni (81.3%). Across eight audio understanding and spoken question answering benchmarks, Realtime-Venus-Audio leads the compared models on MMAU (78.0%), MMAU-Pro (63.2%), Llama Questions (83.8%), and Speech CMMLU (67.8%), while matching the best VoiceBench AlpacaEval score of 4.81. On Full-Duplex-Bench v1.5, Realtime-Venus-Audio responds to 75% of user interruptions and achieves continuation rates of 97%, 88%, and 86% under backchannels, other-directed speech, and background speech, respectively, exceeding Gemini 3.1 Live and GPT-4o on all three continuation metrics.
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Systems and Control · Cross-listed from math.OC

Interior-point proximal methods for nonsmooth optimization in Hilbert spaces with cone-ordered constraints

Behzad Azmi and Alberto De Marchi

We study an inexact interior-point method for nonsmooth, possibly nonconvex optimization in a Hilbert space with inequality constraints ordered by a cone in a Banach lattice, with particular emphasis on infinite-dimensional state-constrained optimal control. The objective function is given by the sum of a smooth, possibly nonconvex term and a convex, possibly nonsmooth term with a computable proximal mapping. The constraints are formulated by means of an order cone in a Banach lattice. This setting covers finite-dimensional nonsmooth nonlinear problems with componentwise constraints as well as infinite-dimensional PDE-constrained optimization problems with pointwise state constraints. The method is based on barrier-regularized subproblems, which are solved inexactly by a proximal-gradient method. We consider logarithmic and power-type barriers and derive the differentiability and curvature estimates needed for the convergence and complexity analysis. Under suitable constraint qualifications and compactness assumptions, we establish approximate KKT conditions for the original problem and convergence of the inexact interior-point sequence. For logarithmic and power-type barriers, we derive complementarity estimates and outer iteration bounds; the corresponding power-barrier rates and total inner-outer complexity bounds are stated under explicit barrier-path and uniform smoothness assumptions. For convex problems, we obtain stronger convergence results. We apply the framework to state-constrained semilinear elliptic optimal control and sparse dictionary learning with nonlinear side constraints. Numerical experiments illustrate the proposed method.
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Audio and Speech Processing · Cross-listed from cs.SD

The VoiceMOS Challenge 2026: Evaluating Speech Enhancement, Emotional TTS and Accented TTS Systems

Wen-Chin Huang, Wei Wang, Marvin Sach et al. (+4 more)

We present the results of the VoiceMOS Challenge 2026, the fifth edition of a scientific challenge on automatic prediction of subjective speech assessments. After expanding the scope to music and general audio in 2025, we refocused the evaluation target on speech and organized three tracks: prediction of absolute and comparative category ratings for enhanced speech, prediction of naturalness and emotion-related tasks for emotional text-to-speech systems, and prediction of speaker and accent similarity for codec-based speech synthesis systems. The challenge attracted a total of 18 teams worldwide, with most teams successfully surpassing the provided baselines. We summarize the challenge results, representative top-performing systems, participant feedback, and directions for future editions.
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All authors Wen-Chin Huang, Wei Wang, Marvin Sach, Xiaoxue Gao, Nicholas Sanders, Erica Cooper, Toda Tomoki

Systems and Control

From Benchmark to Deployment: Shift-Robust Fabric Recognition for Industrial Textile Onboarding

Haochen Li, Chenwei Wang, Felicity S.C. Tang et al. (+3 more)

Automatically recognising a fabric's construction (jersey, twill, satin) is a bottleneck in textile sourcing, where incoming swatches are still typed by hand. Benchmark accuracy suggests the problem is solved, yet rarely survives deployment. On the \numClasses{}-class FabricFlow benchmark we expose three gaps that headline accuracy hides. First, a duplication audit reveals train/test leakage that inflates accuracy; we rebuild leakage-free splits that report the true difficulty. Second, on the clean data the binding failure is acquisition-source shift between catalogues, not the peripheral shortcuts one might fear: on an archive-exclusive hold-out, standard training holds 58.0% Top-1 at a calibration error of 0.158, while a simple, architecture-agnostic central-texture recipe adds 13.5 Top-1 points and restores calibration. Third, because confusing one fabric family for another is costlier than a within-family slip, we optimise a taxonomic-severity cost: a confidence-gated routing policy auto-types confident swatches and refers only the uncertain minority to a human, sharply cutting onboarding cost. Throughout we report honest negatives: hierarchical classification, OCR fusion and zero-shot vision–language models all fail to help, yielding a concrete, calibrated, cost-aware recipe for deployable textile onboarding.
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All authors Haochen Li, Chenwei Wang, Felicity S.C. Tang, Misbah Iqbal, Carman K. M. Lee, Elif Ozden Yenigun

Systems and Control · Cross-listed from math.OC

On the Equivalence of Stochastic Control and Path Space Formulations for Schrödinger Bridges over Compact Connected Lie Groups

Hamza Mahmood, Georgiy A. Bondar, Abhishek Halder et al. (+1 more)

We establish the equivalence between the stochastic optimal control and path space formulations of the Schrödinger bridge problem (SBP) for the kinematic equation on a compact connected Lie group. Using the geometric concepts of horizontal lift and stochastic anti-development, we derive a Girsanov-type change-of-measure result, and show that the expected control energy equals the relative entropy of the controlled path law with respect to the reference Wiener measure. Thus, the SBP is equivalently a path space relative entropy minimization problem subject to prescribed endpoint marginals.Our result has three useful implications. From an analytic viewpoint, the shown equivalence helps prove the existence and uniqueness of the SB. From a probabilistic viewpoint, it helps interpret the SB as the most probable deviation of the uncontrolled stochastic dynamics consistent with the endpoint constraints. From a computational viewpoint, it allows using static Sinkhorn recursions to directly solve the relative entropy minimization problem and compute the optimal path measure. We illustrate the equivalence numerically on the torus $\mathbb{T}^2$. The code is publicly available at: https://github.com/gradslab/LargeDeviationSBP
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All authors Hamza Mahmood, Georgiy A. Bondar, Abhishek Halder, Adeel Akhtar

Systems and Control

Low-Ripple Modulation Strategy for a Photovoltaic-Based Triple-Port Hydrogen Production System

Shiqi Zhang, Ziang Jiao, Jiaxin Su et al. (+4 more)

Among various production methods, hydrogen generation via electrolysis powered by renewable energy plays a key role in achieving large-scale green hydrogen production. The triple active bridge isolated DC-DC conversion system exhibits significant application potential in hydrogen production due to its advantages, such as high energy density, wide step-down ratio, and high reliability. However, the output current ripple at the hydrogen production port critically affects the efficiency of the electrolyzer and the hydrogen production rate. Existing studies have limited optimization effects on current ripple and struggle to achieve dynamic optimization, leading to constrained ripple suppression under dynamic operating conditions. To address this issue, this paper proposes a low-ripple modulation strategy based on coordinated optimization of inner and outer phase-shift angles for multi-port power conversion systems in renewable energy hydrogen production. By establishing an accurate mathematical model, the optimal phase-shift angle combination under minimal current ripple conditions is derived. An improved differential evolution algorithm with adaptive parameter strategy is employed to achieve global optimization under dynamic conditions. Simulation and experimental results demonstrate that the proposed strategy effectively suppresses current ripple, providing an efficient and reliable solution for hydrogen production from fluctuating renewable energy sources.
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All authors Shiqi Zhang, Ziang Jiao, Jiaxin Su, Ning Wang, Zheng Li, Xiaoqiang Guo, Changchun Hua

Signal Processing

Channel Estimation for Movable Antenna Systems: Challenges, Solutions, and Opportunities

Linchu Chen, Zhendong Li, Zile Zou et al. (+5 more)

Movable antenna (MA) has emerged as a promising technology for future wireless networks by exploiting channel variation over local antenna movement regions. However, accurate and efficient channel acquisition in MA systems remains challenging due to the trade-off between estimation accuracy and computational complexity. In this article, the MA channel model and the associated estimation framework are first reviewed, where channel information over the movement region is reconstructed from finite measurements by exploiting shared path parameters. The structured dependence of MA observations across space, time, and frequency naturally motivates the adoption of tensor-based modeling for channel estimation. Subsequently, we discuss the tensor-based signal model and corresponding parameter estimation methods from multidimensional observations. These methods are further compared with conventional channel estimation methods in terms of estimation accuracy, computational complexity, and general applicability. Furthermore, a representative case study is provided to illustrate the performance and characteristics of different algorithms under MA channel estimation settings. Finally, some future research directions for tensor decomposition-based channel estimation in MA systems are outlined.
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All authors Linchu Chen, Zhendong Li, Zile Zou, Zhou Su, Lin Chen, Ruoyu Zhang, Qingqing Wu, Wen Chen

Audio and Speech Processing

Subphonetic Acoustic Modeling via Optimal Transport for Pronunciation Assessment

Haopeng Geng, Jiun-Ting Li, Daisuke Saito et al. (+1 more)

Pronunciation assessment requires acoustic evidence that is temporally precise, diagnostically meaningful, and faithful to the learner's actual production. However, existing acoustic models often struggle to provide recognition and segmentation evidence simultaneously. CTC-based phone recognizers can predict phone sequences flexibly, but their sparse and peaky posteriors often miss phone boundaries and fine-grained pronunciation cues. In contrast, text-dependent forced aligners provide reliable temporal information when transcripts are available, but are not directly applicable to reference-free pronunciation analysis. In this work, we propose a topology-aware frame-wise acoustic model that learns dense ordered state posteriors within each phone. The key idea is to recover phone-internal state structure in a neural acoustic model by combining ordered subphonetic states with optimal temporal transport classification (OTTC). This combination encourages dense monotonic frame-level state discrimination while preserving phone recognition ability. Experiments on read, spontaneous, and L2 speech show improved segmentation over neural baselines with competitive recognition performance. Downstream evaluations further show gains in mispronunciation detection and automatic pronunciation assessment. Probing analysis suggests that the learned states capture phoneme-dependent acoustic structure rather than arbitrary frame-level distributions.
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All authors Haopeng Geng, Jiun-Ting Li, Daisuke Saito, Nobuaki Minematsu

Image and Video Processing

SONAR: A Structure-Consistent Neural Operator for Null-Space-Aware Sparse View CT Reconstruction

Song Ni, Haijun Yu, Haodong Li et al. (+4 more)

Sparse-view computed tomography (CT) reduces radiation dose and acquisition time but remains severely ill-posed because incomplete projections poorly constrain null-space information. Existing learning-based methods often estimate this information in high-dimensional image space, conflate physical measurement errors with prediction errors, and depend on fixed discretizations. We propose SONAR, a Structure-Consistent Neural Operator for Null-Space-Aware Reconstruction. Instead of recovering the full null-space component, SONAR predicts a low-dimensional null-space-aware representation from the acquired projections as pseudo-measurements. It separates measurement and pseudo-measurement residuals, lifts them into the image domain through physics operators, and applies independent neural operators to constrain their structural effects, thereby accommodating admissible errors while suppressing unsupported structures. To support cross-discretization reconstruction, an anisotropic U-shaped neural operator models the periodic angular and nonperiodic detector dimensions using direction-dependent continuous supports, while image-domain neural operators re-discretize continuous kernels on target grids. These components form an optimization-inspired unrolled network. Experiments on simulated AAPM and clinical MARS photon-counting CT data demonstrate consistent improvements across seen and unseen view settings and unseen image resolutions. On AAPM dataset, SONAR improves PSNR by 1.87~dB at 62 views and by 7.63~dB under zero-shot transfer to a $512\times512$ grid over the strongest competing methods. SONAR also achieves the best overall performance in all clinical settings evaluated, demonstrating accurate, structurally reliable, and discretization-robust sparse-view CT reconstruction.
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All authors Song Ni, Haijun Yu, Haodong Li, Changsheng Fang, Shuyi Fan, Yixing Huang, Hengyong Yu

Systems and Control

Data-Driven Spiking Control for Distributed $\varepsilon$-Nash Equilibrium Seeking

Linqi Wang, Wei Xiao, Yuzhou Wei et al. (+3 more)

This paper studies how a feedback law synthesized directly from data can be realized by spiking control while retaining a game-theoretic performance guarantee. We consider distributed $\varepsilon$-Nash equilibrium (NE) seeking in network games played by linear dynamical agents with unknown models and exogenous disturbances. The pseudo-gradient of the game is treated as a regulated error, and local internal models account for signals generated by known exosystems. Robust linear matrix inequalities are then used to compute stabilizing analogue feedback gains directly from noisy local input-state data, without identifying the agent dynamics. To implement these gains using only fixed-weight spikes, we develop two spiking realizations. The first realization uses non-interacting leaky integrate-and-fire units, while the second permits reset coupling among the neuronal units. In both cases, a continuous auxiliary coordinate exposes the impulsive closed loop as the stable analogue system driven by a bounded implementation error. This representation yields forward completeness, Zeno-freeness, and an ultimate bound on the pseudo-gradient, subject to explicit event-processing conditions for the connected architecture. The bound implies that, after a finite transient, the agents' outputs constitute an $\varepsilon$-NE for every $\varepsilon$ above a finite threshold. A spacecraft formation reconfiguration example illustrates the data-driven synthesis, the two spiking realizations, and their practical equilibrium behavior.
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All authors Linqi Wang, Wei Xiao, Yuzhou Wei, Bin Xin, Keyou You, Gang Wang

Systems and Control

Underwater Barrier Sensing Under a Network False-Alarm Budget

Hesam Mosalli, Stephane Blouin and Amir G. Aghdam

We study joint sensor placement and detector operating-point allocation for guarding a region against an adversarial crossing. Each sensor may adjust its detection threshold, while all sensors draw on a shared network-level false-alarm budget. For a Gaussian signal-excess model of a square-law detector, we give a necessary and sufficient condition for the negative-log miss hazard to be concave in a sensor's false-alarm probability. At fixed deployment, this yields a concave max-min allocation whose saddle point equalizes marginal barrier gain per unit alarm against a worst-case occupation measure. A perspective reformulation supplies linear cuts for the mixed-integer joint problem, and a separate certificate lower-bounds the continuum barrier strength independently of the graph used for path search. On a deep-water case study based on the canonical Munk profile, reallocating a fixed alarm budget raises the graph design objective by 14.9% at unchanged sensor count and alarm load, and the certificate establishes the barrier requirement on the interpolated continuum field, which the graph value alone cannot.
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Audio and Speech Processing · Cross-listed from cs.SD

UniqueShip: Mitigating Data Leakage in Acoustic Ship Classification Benchmark Datasets

Connor Hashemi, Trevor Stout, Anthony Hoogs et al. (+1 more)

Underwater Acoustic Target Recognition (UATR) of ships is well-suited for machine learning, yet its progress is hindered by the lack of large, diverse, and publicly available labeled datasets. In this work, we introduce UniqueShip, a machine learning-ready benchmark dataset for UATR applications sourced from the open Ocean Networks Canada (ONC) repository. Unlike previous datasets, we explicitly control for "data leakage" between the training and evaluation sets to ensure more reliable and generalizable model evaluation that does not encourage the model to memorize individual ships. We demonstrate that typical, random data partitioning in two prominent UATR datasets leads to falsely optimistic test performance, increasing accuracy by 10-48 percentage points compared to our more careful partitioning. Ablations on UniqueShip further show that doubling the number of unique vessels improves accuracy by 2.4-2.6 percentage points, while doubling total audio duration improves only by 0.8-1.3 points, indicating that vessel diversity should drive dataset curation more than total hours. We provide baselines with convolutional and transformer backbones, and analyze how ship metadata correlates with classification performance, finding that individual vessel characteristics predict classification difficulty far better than distance to the hydrophone alone. Overall, UniqueShip contains 2,460 hours of ship-radiated audio from 4,218 unique vessels (3,437 hours including background). We publish the dataset, code, and easy-to-download splits at uniqueshipdata.org to foster further UATR research.
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All authors Connor Hashemi, Trevor Stout, Anthony Hoogs, Jason Parham

Friday 11 September

Image and Video Processing

Anatomy-Change-Aware Bidirectional Selective State-Space Memory for Clinically Deployed Thoracic Radiotherapy Auto-Contouring

Galib Ahmed, Istiak Ahmed, Aritra Islam Saswato et al. (+15 more)

We developed DAMM-Net++, a 2.5D architecture for thoracic OAR and target volume segmentation that addresses three persistent challenges in radiotherapy auto-contouring: inter-slice surface incoherence, systematic failure on small low-contrast targets, and the absence of per-case reliability signals. The central component is an anatomy-change-aware bidirectional selective state-space memory that models through-plane anatomical change and selectively propagates context along the axial slice sequence. A boundary-aware decoder sharpens near-surface predictions, and an uncertainty head provides calibrated per-voxel confidence for clinical triage. We evaluated 2,146 patients across four centers, an independent external cohort of 112 patients, and a multicenter reader study involving 17 radiation oncologists on 305 cases. The model achieves a mean Dice of 0.955 and HD95 of 3.78 mm, with the largest gains on low-contrast organs-at-risk (OARs) and target volumes where through-plane context is most critical. The uncertainty head is well-calibrated and supports case-level triage. In the reader study, AI assistance reduced contouring time by 75-80% across experience levels and raised junior-reader IoU from 0.861 to 0.925, matching the unedited model. External validation showed a modest internal-to-external drop (< 5%) with calibrated uncertainty transferring without recalibration. The complete deployment pipeline from DICOM ingestion to TPS-compatible RTSTRUCT export has been integrated into the clinical workflow at a partner hospital, where it is used to assist with contouring. These results suggest that anatomically motivated inter-slice memory, paired with uncertainty-guided review, offers a clinically viable path for thoracic auto-contouring.
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All authors Galib Ahmed, Istiak Ahmed, Aritra Islam Saswato, Asib Mostakim Fony, Kazi Shahriar Sanjid, Md. Tanzim Hossain, Md. Anwarul Islam, Md. Nishan Khan, Md. Misbah Khan, Labiba Faiza Karim, Jobaer Rahman, S M Hasibul Hoque, Rahnuma Shahrin Rista, Kamruzzaman Rumman, Md Arifur Rahman, Syed Md. Akram Hussain, Mohammad Ashrafuzzaman Khan, M. Monir Uddin

Image and Video Processing

Automated Distinction of Intimal and Medial Intracranial Arterial Calcification from CT Head

Benjamin Jin, Maria del C. Valdés Hernández, Richard Bortsov et al. (+3 more)

Intracranial arterial calcifications (IACs) are a common finding on clinical non-contrast enhanced head CT scans and are associated with neurovascular disease. Calcifications can occur in the intimal or medial layer of the arterial wall, subtypes that differ in aetiology and may have distinct clinical relevance. These subtypes can be visually distinguished by radiologists based on the shape of the calcifications. We investigate three automated approaches for subtype classification of IAC from head CT-derived segmentation masks: (1) an automated adaptation of the established radiological visual score, (2) a sphericity-based method, and (3) a method based on shape embeddings extracted by a medical shape foundation model. All approaches use the same lightweight classification pipeline on top of the features they compute and are evaluated using 5-fold cross-validation. The three methods achieved comparable performance, with the embedding-based approach yielding the best overall results with a weighted F1 (mean $\pm$ SD) of up to 71.5 $\pm$ 3.7 for a single artery and 59.8 $\pm$ 1.7 for the joint artery classification. Performance was largely preserved when using automated instead of manual IAC segmentation masks, and we found the difference in weighted F1 not significant. Our results show that fully automated IAC subtype quantification from head CT is feasible and remains robust to the use of manual and automated IAC segmentation masks. Code at https://github.com/bjin96/iac-subtyping.
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All authors Benjamin Jin, Maria del C. Valdés Hernández, Richard Bortsov, Joanna M. Wardlaw, Daniel Bos, Grant Mair

Audio and Speech Processing

StepAudio 3 Music Technical Report

Chengli Feng, Zhiyue Wu, Jiahao Song et al. (+10 more)

We introduce StepAudio 3 Music, a large-scale, long-form music generation model that supports explicit musical planning and open-domain text-controlled generation. The StepAudio Music Tokenizer represents audio as a 50-Hz stream from a 65536-entry single codebook, using semantically informed self-supervised and multi-task training to preserve musical structure and reconstruction-relevant information. A flow-matching diffusion Transformer (DiT) predicts continuous StepAudio VAE latents, which our VAE decoder converts into 48-kHz audio. This discrete-continuous design is guided by comparisons of single-codebook VQ, Semantic and Acoustic RVQ, and different DiT configurations. For explicit planning, a Mixture-of-Experts autoregressive model uses ABC notation to produce an intermediate arrangement plan (ABC-CoT) before predicting music tokens, making harmony, rhythm, and melodic structure part of the generation context. A progressive training curriculum and supervised fine-tuning support song and instrumental generation, accompaniment generation from dry vocals, and cover-song synthesis for up to 5 minutes and 30 seconds. With reinforcement learning via direct preference optimization (DPO), the final model achieves the highest AudioBox Content Enjoyment, Content Usefulness, and Production Quality scores and the highest MuQ-MuLan similarity among the evaluated systems, with competitive SongBench results. On the preliminary Artificial Analysis Music Arena Vocals leaderboard, it obtains a Quality Elo of 1105, behind only Suno V5.5 and Mureka and ahead of Suno V5, MiniMax models, and other systems. Audio demonstrations are available at https://stepaudiollm.github.io/step-audio-3-music.
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All authors Chengli Feng, Zhiyue Wu, Jiahao Song, Zheqi Dai, Boyang Wang, Ruibin Yuan, Junming Gong, Wenxiao Zhao, Jing Guo, Gang Yu, Xiangyu Zhang, Xuerui Yang, Chao Yan

Image and Video Processing

LM-PCVMNet: Pediatric Cervical Vertebral Maturation Analysis with Deep Fusion of Landmarks and Metadata

Peng Wang, Wanzhen Song, Anli Wang et al. (+3 more)

Cervical vertebral maturation (CVM) assessment plays a pivotal role in orthodontic diagnosis and determining the optimal timing of treatment, especially for pediatric patients. In this paper, we propose LM-PCVMNet, a novel deep learning framework for automatic pediatric CVM staging. Specifically, our method integrates vertebral anatomical landmark information, heatmap-guided feature modulation, and metadata-informed similarity modeling into a unified learning framework. We introduce a heatmap-guided feature modulation module that enhances feature extraction by leveraging landmark-centered heatmaps to highlight morphologically relevant vertebral regions. A vertebral landmark-prompting block is designed to incorporate anatomical geometry into the representation learning process. Furthermore, we develop a learnable metadata supervised contrastive loss that adaptively modulates positive-pair similarity based on metadata similarity, enabling the model to learn more biologically consistent and discriminative features. To facilitate further research in pediatric orthodontic treatment, we additionally release PCVM+. It contains 1800 lateral cephalometric radiographs from real-world patients aged 3-15 years, with expert-annotated CVM stages, 13 vertebral anatomical landmarks, and corresponding metadata. We perform comprehensive experiments on two datasets, and the results show that our method achieves state-of-the-art performance, effectively improving landmark localization and classification accuracy over existing models. Code and dataset will be available at github.com/ybupengwang/LM-PCVMNet.
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All authors Peng Wang, Wanzhen Song, Anli Wang, Xueshuo Xie, Xiaohang Guan, Tao Li

Image and Video Processing

Conditioning noise is a free regularizer for LoRA fine-tuning: no pathology encoder required for diffusion-based artifact detection in histopathology

Konstantinos Moutselos and Ilias Maglogiannis

Diffusion-based artifact detectors score whole-slide image patches by reconstruction error under a model fine-tuned on clean tissue. We show that conditioning this fine-tuning on random Gaussian embeddings – resampled at every step from approx. 200 KB of precomputed embedding statistics, with no encoder, no cache, and no change to inference – consistently widens the clean/artifact separation. A four-step ablation chain shows the benefit requires neither content (shuffled real embeddings), provenance (synthetic Gaussians), a tuned intensity (flat across an 8x variance range), nor per-patch identity (fresh per-step noise); a LoRA-dropout control shows the conditioning pathway specifically, not generic weight perturbation, carries the effect. Patch-level gains of +0.25-0.48 Cohen's d replicate across nine trainings; honest leave-one-slide-out evaluation clears a pre-registered bar in 2/2 seeds; and two pre-registered external endpoints on a 281-case set confirm pooled Delta F1 = +0.0073 (95% CI) and +0.0129 (97.5% CI, two-look corrected). We release the full evaluation protocol, including measured seed noise and selection-optimism pricing.
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Image and Video Processing

A deep dictionary network-based foundation model for ultra-low-dose CT denoising

Baoshun Shi, Shuangyi Yang, Ke Jiang et al. (+3 more)

Ultra-low-dose computed tomography (ULDCT) reduces radiation exposure but suffers from severe noise that degrades diagnostic image quality. Existing deep learning-based denoising methods are typically trained in an organ-specific fashion, resulting in limited generalization across heterogeneous multi?organ imaging scenarios. Foundation models present a promising all-in-one paradigm for unified multi-organ denoising. However, their architectures suffer from poor interpretability and rely on heuristic training strategies. To address these limitations, we propose an architecture?interpretable foundation model based on the deep dictionary network (DDN) for unified multi-organ ULDCT denoising. Inspired by multilayer sparse representation theory, DDN cascades convolutional sparse coding layers with iterative soft-thresholding, providing inherent architectural interpretability. Furthermore, a dynamic dictionary module and a threshold generation module are embedded within each layer to enhance representation ability. We conduct DDN pre-training on more than one million multi-organ normal-dose CT images by recovering clean images from Gaussian-noised inputs. Sparse regularization is additionally imposed on latent feature representations, guiding the network to learn compact and noise-robust priors. The complete architecture is jointly fine-tuned on multi-organ ULDCT datasets, enabling a single unified model to perform denoising across diverse anatomical regions. Extensive experiments validate that our proposed method achieves state-of-the?art performance and consistently surpasses competing ULDCT methods across all mul
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All authors Baoshun Shi, Shuangyi Yang, Ke Jiang, Bin Zhu, Zhanli Hu, Huazhu Fu

Signal Processing

Learning-Free Hierarchical Joint Estimation of AoA, Pointing Error, Receiver Jitter, and Turbulence in Multi-Aperture FSO Systems

Mahtab Dashtebozorgi, Meysam Ghanbari, Rula Ammuri et al. (+2 more)

This paper proposes a learning free hierarchical estimator for jointly recovering the angle of arrival (AoA), transmitter pointing error, receiver induced jitter, and per aperture turbulence coefficients in a multi aperture free space optical system. The proposed method exploits the distinct spatial signatures contained in quad photodetector measurements. First, normalized quadrant imbalance ratios provide an approximate closed form AoA estimate. Next, AoA compensated lens powers are transformed into a log linear regression model for non iterative pointing error estimation. Finally, receiver jitter and turbulence coefficients are directly reconstructed after compensating for the estimated angular losses. The method requires neither neural network training nor exhaustive multidimensional search and has computational complexity linear in the number of lenses. Monte Carlo results demonstrate robust AoA estimation under Gamma Gamma turbulence, reveal turbulence induced error floors in OLS based pointing and jitter estimation, and show improved turbulence reconstruction with larger arrays and explicit pointing compensation.
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All authors Mahtab Dashtebozorgi, Meysam Ghanbari, Rula Ammuri, Mazen Hasna, Khalid Qaraqe

Signal Processing

Progressively Attenuated Multi-Branch Reception for Inter-HAPS Optical Links

Meysam Ghanbari, Betul Beidas, Wasiu O. Popoola et al. (+3 more)

Inter HAPS optical links can experience receiver saturation at short separations and become signal to noise ratio (SNR) limited at longer distances. This work proposes a saturation aware spatial multi branch intensity modulation direct detection receiver using nonoverlapping apertures with progressive attenuation and selection of the nonsaturated branch with the highest instantaneous SNR. A Gaussian beam model with finite aperture pointing loss and common two dimensional pointing jitter yields closed form branch level expressions for saturation, insufficient SNR, usability, and outage probabilities. Results show that additional branches suppress short range saturation outage, shift the minimum outage point toward shorter separations, and enable progressive attenuation to control the saturation SNR tradeoff. Monte Carlo simulations closely match the analytical results. Overall, the proposed architecture provides a low complexity approach to extending the usable dynamic range of inter HAPS optical receivers.
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All authors Meysam Ghanbari, Betul Beidas, Wasiu O. Popoola, Mazen O. Hasna, Khalid A. Qaraqe, Mohammed Elamassie

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

Predictive audio representations for early detection and tracking of hidden dynamic objects

Katerina Vinciguerra, Moritz Brandes, Danilo Hollosi et al. (+1 more)

Predicting potential dangers is core to safety. Forecasting the presence of other traffic agents is core to danger prediction. Occluded traffic agents challenge detection systems as they might become visible too late, leaving the autonomous vehicle too little time to identify, plan and act accordingly in a robust and safe way. Previous works proved that auditory perception, being omnidirectional and not constrained by a field-of-view, provides fundamental cues for early spotting of different road users, even when hidden by other vehicles or infrastructures. Yet, those contributions deal with scenarios with only one vehicle present, and they either identify the type of vehicle or estimate its direction of arrival. In this work, we move forward, and propose a multi-task system which, simultaneously, estimates the number of vehicles present, their type, and their direction of arrival. Our methodological contribution is a two-stage pipeline: a self-supervised pre-training stage inspired by the Joint- Embedding Predictive Architecture (JEPA) applied directly to multichannel raw waveforms, followed by supervised multi-task fine-tuning with a bidirectional LSTM and three classification heads. The pre-training stage trains the encoder without labels, pushing it to predict the latent representation of a future audio segment from its past context. To train and test our framework, since no suitable dataset was publicly available, we collected an ad-hoc one covering Non-Line-Of-Sight scenarios with multiple traffic agents simultaneously operating. Experimental evaluation shows that our method outperforms the state of the art; it also proves that our design choice allows the model to learn robust representations, which can be transferred to an unseen driving scenario, maintaining reasonable and stable performance.
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All authors Katerina Vinciguerra, Moritz Brandes, Danilo Hollosi, Letizia Marchegiani

Systems and Control

Predicted-Flow Control Barrier Functions for Non-Control-Affine Systems

Amirsaeid Safari and Jesse B. Hoagg

Control barrier functions (CBFs) enforce safety through conditions imposed pointwise in time without consideration of state evolution over a future horizon. Thus, CBF-based controls are typically myopic. Predicted-flow CBFs (P-CBFs) generalize CBFs from a function of the current state to a functional of the predicted flow under a parametrized control. P-CBFs can certify safety over the entire prediction horizon while simultaneously allowing for performance optimization over the horizon. However, prior work with P-CBFs only applies to control-affine systems, and suffers from a limitation where the prediction horizon can shrink or even vanish. This article addresses both of these shortcomings by introducing a planning control that smoothly transitions from a parametric plan to a backup control (if needed) and a time-shift parameter that determines if transition is needed. The evolution of the control-plan and time-shift parameters is determined from a single guaranteed-feasible convex optimization, which reduces to a quadratic program (QP) if the control limits are a convex polyhedron. The real-time executed control is determined from the instantaneous planning control and time shift. This method simultaneously addresses safety certification and performance optimization over the fixed prediction horizon. The approach is compared to nonlinear model predictive control in simulation of an autonomous car navigating a dense obstacle environment.
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Systems and Control · Cross-listed from cs.RO

Differential Realizability of Static Control Allocation in Multirotors: An Impossibility under Nonredundant Full Actuation and a Pseudoinverse Obstruction under Redundant Actuation

Antonio Franchi and Mirko Mizzoni

Control allocation for multirotors with bidirectional propellers is commonly formulated in signed-thrust variables, where the wrench map is linear. The signed-quadratic map from physical rotor speed to thrust, however, is not a local diffeomorphism at zero speed. This work derives two distinct consequences. Under nonredundant full actuation, a single-propeller reversal removes one instantaneous task direction; hence, no global continuously differentiable exact static allocator exists over the complete task space. Under redundant actuation, the physical task map may remain regular, yet a transverse pseudoinverse zero crossing requires an unbounded rotor-speed derivative. We define differential realizability as regularity of the physical lift of an actuator-output section, derive exact and first-order validity conditions, and distinguish structural rank loss from an allocator- induced rate singularity. A local nullspace deformation repairs isolated pseudoinverse reversals, while a global fixed-orthant construction establishes existence of regular sections at the cost of persistent task-preserving internal actuation.
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Signal Processing

Noise-Domain OTFS: Statistical Delay-Doppler Signaling for Future Wireless Networks

Mahmoud Aldababsa

Orthogonal time frequency space (OTFS) usually conveys information through deterministic constellation symbols placed on a delay–Doppler (DD) grid. This article develops a different viewpoint, termed noise-domain OTFS (ND-OTFS), in which each message selects the covariance of a random DD-domain vector. The resulting statistical codeword can encode information through per-bin variance, equal-energy correlation structure, or separable DD covariance. We explain the transceiver principle, receiver implications, reliability–rate tradeoffs, and practical challenges. Representative results show that a matched-payload coherent OTFS reference retains a clear raw-reliability advantage, whereas ND-OTFS enables information recovery from covariance geometry even when competing states have identical average energy. Additional statistical observations reduce finite-sample overlap at the cost of latency and rate. ND-OTFS therefore complements, rather than replaces, deterministic OTFS by introducing a matrix-valued statistical signaling dimension across DD resources.
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