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Electrical Engineering and Systems Science

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Friday 9 October

Systems and Control · Cross-listed from cs.AI

ConicFlow: constrained Inference-time optimal control for flow matching models

Shervin Khalafi, Kasra Fallah, Sung Cho et al. (+3 more)

We propose ConicFlow, which frames constrained sampling from a flow matching model at inference time as an optimal control problem that balances deviation from the pretrained trajectory against reward, subject to hard terminal constraints. We discretize this problem and adapt receding-horizon control, optimizing only a short window of the trajectory at each step for computational tractability. We address the resulting sequence of receding-horizon subproblems with a receding-horizon primal-dual algorithm. Its dual multipliers capture the difficulty of satisfying each constraint and are refined throughout sampling, steering generation toward feasible final samples. We further propose a terminal-estimate variant that avoids accumulating local truncation errors by evaluating the reward and constraints at the predicted terminal point rather than at the end of the current horizon. Under standard regularity assumptions, we establish finite-horizon guarantees on suboptimality. Under convex-affine structure, we derive a sharper bound that scales linearly with omitted horizon length, and we show that the terminal-estimate variant is provably tighter when the flow dynamics change slowly. Experiments in both low- and high-dimensional settings with a recent large-scale flow matching model show that ConicFlow achieves high reward and strong constraint satisfaction while deviating less from the pretrained flow than recent baselines.
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All authors Shervin Khalafi, Kasra Fallah, Sung Cho, Dongsheng Ding, James Anderson, Alejandro Ribeiro

Audio and Speech Processing

RUBATO: Decoupled Clocks Allow Full-Duplex Models to Think as Fast as They Can

Yi-Jen Shih, Puyuan Peng, Haohe Liu et al. (+2 more)

Recent Speech Full-Duplex (FDX) models are capable of engaging in naturalistic conversation by listening and speaking at the same time. Building on this foundation, recent architectures introduce internal thinking streams to enhance their intelligence. To guarantee these streams process successfully in real time, existing methods enforce a fixed decoding rate, which assumes a hardware profile capable of a specific latency. We argue this static approach is severely suboptimal. Because internal thinking is intrinsically asynchronous, an ideal FDX model should sustain real-time conversational flow while allowing each stream to decode at its own hardware-adaptive rate. Thinking streams must process as fast as the hardware allows to deliver accurate answers faster. To solve this, we propose RUBATO, a novel multistream positional encoding that assigns independent clocks to entirely decouple the streams. This structural isolation empowers the model to zero-shot interpolate across any inference decoding speed. It drastically simplifies dataset construction by requiring training on only two extreme decoding speeds while smoothly adapting to any intermediate rate during deployment. RUBATO demonstrates consistent robustness across five spoken reasoning tasks. On Spoken GSM8K, a baseline model collapses to 1.7% accuracy at interpolated speeds. Conversely, RUBATO-Full sustains 48.7% to 51.1% accuracy across all rates, leveraging faster hardware to achieve a 6.5x shorter time-to-answer with a constant time-to-first-response. This advantage holds on BIG-Bench Audio and amplifies significantly during complex full-duplex interruptions. Specifically, when a user interjects while the model is actively speaking and reasoning, RUBATO-Full achieves 40.0% to 45.1% accuracy, whereas the baseline fails completely.
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All authors Yi-Jen Shih, Puyuan Peng, Haohe Liu, David Harwath, Abdelrahman Mohamed

Systems and Control

Zero-Crossing Time Lower Bounds for the Homogeneous Super-Twisting Algorithm

Diego Gutiérrez-Oribio and Ioannis Stefanou

High-frequency switching and chattering are well-known problems in sliding-mode control. Although the super-twisting algorithm produces a continuous control input, the sliding variable may still cross zero repeatedly near the sliding manifold. This paper studies how fast these crossings can occur and whether they can accumulate in finite time for the scalar homogeneous super-twisting algorithm. Two lower bounds on the time required to return to zero are derived, together with a bound on the time needed to complete a full excursion. These results give minimum separation times and upper bounds on the number of crossings associated with excursions above a prescribed amplitude. Although zero crossings may accumulate in finite time, the amplitudes of the complete excursions considered here must vanish and satisfy a summability condition. Thus, crossings associated with excursions above any prescribed nonzero amplitude cannot accumulate in finite time. When the perturbation-rate bound is zero, the estimates recover the scaling imposed by homogeneity. For the classical super-twisting algorithm, the square-root scaling also holds for nonzero perturbation-rate bounds. Numerical simulations illustrate the results.
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Systems and Control

Large-Scale Data Center Integration in Power Systems: Uncertainty and Multi-Scale Variation

Le Xie, Dongjoo Kim, Qian Zhang et al. (+2 more)

Hyperscale artificial intelligence (AI) data centers are emerging as a structurally new class of electricity demand. Unlike conventional loads, they materialize as spatially concentrated, transmission-level point loads with rapid deployment timelines and converter-dominated dynamics. Their integration is governed by two defining properties: forecast uncertainty in load realization and multi-scale temporal variation spanning sub-seconds to planning horizons. We develop a unified framework that links reliability and affordability to these dimensions through a four-quadrant structure: (reliability, affordability) $\times$ (uncertainty, variation), which integrates probabilistic resource adequacy, generation and transmission expansion, operational security, and market design. Measured traces from a public generative-AI workload dataset show strongly tonal GPU power spectra rather than smooth broadband behavior: harmonic combs at the training-step cadence, and a distinct sub-second periodicity in inference. Across these traces the dominant variance band shifts with workload type (seconds for training and offline inference; tens of seconds and longer for online inference). We report this as a characterization of the measured workloads rather than a universal signature, and take it to indicate that such loads warrant a spectral description rather than a peak-demand one. These signatures directly affect reserve requirements, ramping products, voltage stability, ride-through performance, and harmonic interactions in converter-rich grids. Taken together, the analytical framework and empirical results establish a system-level basis for assessing the reliability and cost implications of large AI data center integration across planning and operational horizons.
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All authors Le Xie, Dongjoo Kim, Qian Zhang, Grant Ruan, Paul L. Joskow

Signal Processing · Cross-listed from cs.IT

Sequential Change Point Detection for PSK Modulation Switching

Gopal Kataria, Davin Sequeira and Arti Yardi

Transmitters in adaptive communication systems may switch their modulation scheme at an unknown time, and a receiver must detect this switch quickly without raising frequent false alarms. Existing modulation classification methods label a finite block of received data and do not provide stopping rules with controlled false-alarm and delay performance. We study the sequential detection of a switch from one phase shift keying (PSK) scheme to another over an additive white Gaussian noise channel. We build cumulative-sum (CUSUM) and moving-sum (MOSUM) detectors from the log-likelihood ratio and its low-SNR approximation. For a switch from quadrature PSK to binary PSK, we derive the moment-generating function and probability density function of the approximate statistic. We use them to relate the CUSUM threshold to the average run length to false alarm and the worst-case detection delay. For a switch between any two PSK orders, we approximate the vector of overlapping MOSUM statistics as multivariate Gaussian and obtain bounds on both measures. Monte Carlo simulations show that our derived expressions for ARL2FA and WADD closely match the empirical results across thresholds, validating the accuracy of the proposed analytical approximations and theoretical bounds.
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Signal Processing · Cross-listed from cs.NI

Semantic Edge-Cloud Communication for Real-Time Urban Traffic Digital Twins

Murat Arda Onsu, Arman Dogru, Poonam Lohan et al. (+2 more)

Urban traffic systems are becoming increasingly complex, demanding real-time traffic perception, proactive collision prediction, and intelligent management to improve safety and reduce congestion. Although AI-driven traffic management has shown strong promise, its success relies on diverse, high-quality data spanning critical scenarios such as collisions, near-collisions, and traffic rule violations. Digital twins offer a powerful solution by enabling synchronized virtual replicas of physical traffic environments for real-time monitoring, collision prediction, traffic-flow optimization, and diverse realistic scenario generation. However, continuously synchronizing a high-fidelity urban traffic digital twin through edge surveillance cameras requires substantial edge-cloud communication, and transmitting raw video streams leads to severe bandwidth and latency constraints. To overcome this challenge, this paper proposes a semantic communication-based edge-cloud framework for real-time urban traffic digital twin applications. By leveraging edge intelligence to extract and transmit only task-relevant semantic traffic information instead of full video feeds, the proposed framework significantly reduces communication overhead while preserving the essential dynamics needed for accurate real-time digital twin reconstruction. Experimental results demonstrate more than 99% transmission savings and an average trajectory reconstruction error of only 0.12 m, confirming the proposed framework as an effective, scalable, and low-latency solution for AI-enabled urban traffic digital twin applications.
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All authors Murat Arda Onsu, Arman Dogru, Poonam Lohan, Burak Kantarci, Aisha Syed

Systems and Control · Cross-listed from math.OC

Intrinsic Contraction Theory and Stabilization of Probability Measures on the Wasserstein Space

Min Kim, Joshua D. Ibrahim and Soon-Jo Chung

Deterministic flows can contract Wasserstein distances between probability laws without being contractive in point-state space. We develop a differential contraction theory for pushforward flows on forward-invariant subsets of the 2-Wasserstein space that quotients out the gauge freedom of the continuity equation. Weighted Helmholtz-Hodge decomposition and the Wasserstein contingent cone define the weighted horizontal tangent cone (whtc). Requiring the contraction quadratic form to be nonpositive only on the whtc yields the Intrinsic Wasserstein Contraction Condition (IWCC), which generalizes the classical contraction LMI and recovers it on Dirac measures. An almost-everywhere Euler approximation based on the superposition principle and Banach-valued Lebesgue differentiation theorem proves that IWCC is sufficient for intrinsic Wasserstein contraction. Law contraction without point-state contraction is illustrated by a rotationally symmetric shear example. Finally, a new comparison lemma yields an incremental Wasserstein ISS theorem; for Brockett's integrator, we prove exponential stabilization of a class of axisymmetric laws to the uniform law on a circle via a smooth feedback.
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Image and Video Processing

Beyond Gate Weights: Decision-Level Interpretation of Radiomics-Image Feature Fusion for RCC Classification

Yuan Liang, Sourav Bhattacharjee and Abraham Campbell

Gate coefficients describe how a fusion layer weights intermediate features, but they do not necessarily reflect the magnitude of branch contributions at the classifier output. We investigate this distinction in a fixed 3D MedVAE–radiomics model for clear cell versus non-clear cell renal cell carcinoma classification from contrast-enhanced CT. Because the model uses a linear classification head, the binary decision logit can be decomposed exactly into a gated image term, a direct radiomics term, and a bias. We define effective radiomics dominance as the direct radiomics term's share of the summed absolute non-bias branch contributions and compare it with structural radiomics weighting. Internally, mean structural radiomics weighting was 0.823, whereas mean effective radiomics dominance was 0.148, with a modest case-wise association (h̊o} = 0.290). The gated image term retained substantially stronger discriminative ranking information than the direct radiomics term. The same structural-to-effective discrepancy was observed in exploratory external evaluation. Branch-targeted permutation, integrated-gradient, and Shapley-style analyses further identified radiomics descriptors influencing the direct radiomics term. Because radiomics also generates the gate, the gated image term remains radiomics-dependent, so additive branch contribution should not be interpreted as total input influence. These findings show that structural gate weighting and decision-level branch contribution represent distinct aspects of gated feature fusion and should be interpreted separately.
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Image and Video Processing

Beyond Discrimination: Evaluating Probability Calibration in Radiomics-Image Fusion for Renal Cell Carcinoma Classification

Yuan Liang, Morgan May, Abraham Campbell et al. (+3 more)

Reliable probability estimates are important for the clinical interpretation of machine-learning classifiers, yet radiomics–image fusion models for renal cell carcinoma (RCC) are commonly evaluated primarily by discrimination. Using 396 patients from the KiTS23 dataset, we evaluated whether adding radiomics to four image-model families improved probabilistic performance in addition to discrimination. Seventeen individual models and four probability-average ensembles were assessed using discrimination, probability calibration, and proper scoring measures. Across 12 matched image-only versus fusion comparisons, AUC and AP improved in all 12, while Brier score and negative log likelihood (NLL) improved in 11 of 12 and expected calibration error (ECE) in eight of 12. The 3D MedVAE gated-fusion model achieved the strongest overall individual-model profile. Probability averaging across concatenation, cross-attention, and gated fusion produced favourable point estimates across all five primary metrics relative to image-only models in all four families, although paired-bootstrap support was stronger for Brier score and NLL than for ECE. These findings show that radiomics–image fusion can improve probabilistic prediction quality beyond discrimination, while calibration behaviour varies across image architectures and fusion strategies. We show that our proposed probability-average ensemble models improve discrimination and calibration simultaneously across all architectures in the study.
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All authors Yuan Liang, Morgan May, Abraham Campbell, Sourav Bhattacharjee, Simon Caton, Pierpaolo Dondio

Image and Video Processing

C$^2$LUE: Clinically Aligned Patient-Level Ultrasound Diagnosis via Evidence-Aware Multi-Instance Learning

Qin-Yi Zhang, Linying Cao, De-Xing Huang et al. (+7 more)

Patient-level ultrasound diagnosis demands not only high predictive accuracy but also clinically aligned mitigation of asymmetric diagnostic risks. While Multi-Instance Learning (MIL) effectively models routine multi-view exams, standard aggregators often dilute heterogeneous diagnostic cues and optimize solely for global accuracy. This frequently yields undesirable sensitivity-specificity trade-offs. To bridge this gap, we propose C$^2$LUE, a novel MIL framework coupling evidence aware aggregation with risk-aligned regularization. To preserve evidence heterogeneity and prevent unimodal attention collapse, C$^2$LUE employs a multi-query cross-attention mechanism built upon a LoRA-adapted DINOv3 backbone. Multiple latent queries explicitly extract and disentangle diverse morphological patterns from the ultrasound sequence. Furthermore, to improve clinical risk alignment, we introduce a dynamic trade-off regularization objective that penalizes sensitivity-specificity imbalance during training. Rather than leaving the clinical trade-off entirely to post-hoc threshold adjustment, this objective encourages the learned score distribution toward a more balanced operating region. Extensive evaluations on patient-level, biopsy-proven thyroid and breast ultrasound datasets demonstrate that C$^2$LUE consistently outperforms prior alternatives. By uniting strong discrimination with a balanced error profile, C$^2$LUE provides a promising solution for real-world ultrasound screening and diagnosis.
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All authors Qin-Yi Zhang, Linying Cao, De-Xing Huang, Haichuan Lin, Yi Yang, Manzhi Wang, Xiao-Hu Zhou, Chen Chen, Shuangyi Wang, Zeng-Guang Hou

Image and Video Processing

Frozen Site-Probe Accuracy Is Not a Standalone Harmonization Score: An Audit of Evaluation Practice for Multi-Site MRI

Muhammad Muqsit Islam, Gloria García Cuenco, José M. Martínez et al. (+1 more)

Image-level MRI harmonization is often evaluated with a frozen site probe, a site classifier trained on raw scans and applied unchanged to harmonized ones: a drop in its accuracy is read as removal of site effects. We audit this practice on ABIDE I (1,112 T1-weighted scans, 17 sites) with ten output sets from seven harmonization methods and controls for chance, head shape, intensity, preprocessing and target alignment. We find that this verdict depends on how the probe is trained, even among probes that classify raw scans well: across random seeds, its balanced accuracy on the same CycleGAN outputs ranges from 0.13 to 0.59 (chance: 0.06). Low frozen-probe accuracy can also arise without harmonization: a Gaussian blur, which increases the intensity distance to the target site, lowers this accuracy to chance. Moreover, where the frozen probe fails, probes trained on the harmonized scans still recover the site, even from intensity histograms alone: on HACA3 outputs, the frozen probe scores 0.25, a probe trained on those outputs 0.75, and one using only their intensity histograms 0.71. Low frozen-probe accuracy therefore does not show that site information has been removed. We recommend reporting several probes trained to convergence, probes trained on harmonized scans, and target alignment.
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All authors Muhammad Muqsit Islam, Gloria García Cuenco, José M. Martínez, Pierrick Coupé

2610.14140 · 9 Oct · 4 pages, 2 figures, 1 table. Submitted to IEEE ISBI 2027. Code and results: https://github.com/muqsitamir/HarmonIt. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

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Image and Video Processing

Exploring the Latent Structure of AMD Severity Using Retinal Foundation Models

Niccolò Fineschi, Jovana Panic, Atsushi Watanabe et al. (+7 more)

Retinal foundation models have demonstrated strong performance across a wide range of ophthalmic tasks, yet relatively little is known about how disease-related information is represented within their learned embeddings and whether this distribution influences downstream prediction performance. In this work, we use Age-related Macular Degeneration (AMD) as a case study to investigate how disease severity is represented within the embedding space of RET-CLIP, a retinal vision-language foundation model, using bilateral fundus images from the AREDS dataset, benchmarked against non-retinal encoders. We first characterize the distribution of AMD severity within the learned representation space and then evaluate how this structure influences classification performance across three clinically relevant task formulations of increasing granularity. Finally, we analyze the most influential latent dimensions combining feature attribution, statistical phenotype association, and qualitative image analyses. Our results show that RET-CLIP represents AMD severity as a continuous spectrum rather than a set of discrete categories, with healthy and advanced disease stages exhibiting clearer separation than intermediate ones. This organization was reflected in downstream performance, which decreased from a balanced accuracy of 0.844 and 0.814 for binary AMD and Late-AMD detection respectively to 0.446 for six-class grading. Furthermore, the most influential latent dimensions were statistically associated to distinct retinal phenotypes, including drusen accumulation, pigmentary alterations, and advanced retinal lesions. These findings suggest that the structure of retinal foundation-model representations influences the difficulty of clinically relevant prediction tasks and demonstrate the potential of representation-level analysis for understanding disease organization and improving interpretability.
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All authors Niccolò Fineschi, Jovana Panic, Atsushi Watanabe, Hideaki Hayashi, Kazuya Nishimura, Yuki Maeno, Samanta Rosati, Gabriella Balestra, Kohji Nishida, Hajime Nagahara

Systems and Control · Cross-listed from cs.NI

Goal-Oriented Bundle Management and Flow Control for Deep-Space Communications via Reinforcement Learning

Sajjad Baghaee, Aimin Li, Shanmugarajan Balasubramanian et al. (+1 more)

Deep-space communications operate under intermittent connectivity, long propagation delays, and relay storage constraints. In such networks, the Bundle Protocol enables store-carry-forward communication, requiring relay nodes to manage limited storage and scarce contact opportunities. Relay-side scheduling and dropping decisions are inherently goal-oriented: transmission resources should be allocated to bundles most valuable to downstream mission objectives. Conventional schedulers, such as FIFO, do not explicitly optimize task utility and may provide unfair service across heterogeneous planetary sources. This paper presents a closed-loop modeling and learning framework for relay-side bundle and flow management in a contact-driven deep-space network, where lunar and Martian updates are forwarded through a shared-buffer relay to Earth. We quantify destination-side information freshness using excess age $\Delta_i^e(t)=[\Delta_i(t)-d_i(t)]^+$, where $\Delta_i(t)$ denotes the Age of Information of source $i$ and $d_i(t)$ is a source-dependent delay baseline. Based on this metric, we define a goal-oriented loss that supports source-dependent nonlinear penalties, allowing prioritization according to mission relevance. We formulate relay control as a constrained semi-Markov decision process (CSMDP) and solve it using masked Lagrangian Proximal Policy Optimization (PPO). Feasibility-aware action masking restricts transmissions to bundles that can complete within the remaining contact window. DTNSim simulations indicate improved age-based utility and inter-flow fairness compared with baseline schedulers. The proposed framework jointly optimizes relay-side bundle scheduling, dropping, and flow control under contact-driven dynamics with explicit fairness considerations across heterogeneous sources.
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All authors Sajjad Baghaee, Aimin Li, Shanmugarajan Balasubramanian, Elif Uysal

Systems and Control

Information-Weighted Distributed Kalman Filter for Multi-Target Tracking over Switching Sensor Networks

Tirthankar Chakraborty and Arnab Maity

Target tracking over wide areas using a network of sensor agents is a challenging problem when agents have limited sensing coverage and communication topologies vary over time. Existing distributed filtering approaches commonly assume that each agent, or at least one of its neighbors, can directly observe the target, and further rely on fixed network topologies, global network parameters, or multiple consensus rounds per time step. These requirements limit their applicability in wide-area network deployments. In this paper, we propose an Information-Weighted Distributed Kalman Filter (IWDKF) that relaxes these assumptions through a weighted-consensus mechanism applicable to arbitrary switching topologies satisfying joint connectivity. The proposed approach requires no prior knowledge of global network properties and limits communication overhead to a single consensus exchange per time step, while enabling information fusion even when some agents lack direct target measurements. The weighted-consensus covariance matrix is shown to remain positive definite, and the resulting consensus update is proven to maintain estimate consistency. It is also established that the proposed weighting strategy preserves the asymptotic agreement property of the underlying consensus protocol under joint connectivity. Numerical simulations of a multi-target tracking scenario involving a network of sensor agents demonstrate the improved estimation accuracy of the proposed IWDKF under both fixed and switching network topologies. The performance of the proposed filter is close to that of the centralized Kalman filter, and is compared with three existing distributed filters.
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Audio and Speech Processing

OpenPA: Forced-alignment-free Multi-granular Pronunciation Assessment with Mixed-transcript Adaptation

Fu-An Chao, Bi-Cheng Yan and Berlin Chen

Recent advances in automatic pronunciation assessment (APA) have driven a shift from scripted to free-form assessment. However, existing methods often rely on multi-stage pipelines with forced alignment and separate feature extractors, limiting their practicality across response settings. In light of this, we propose OpenPA, a novel APA framework that leverages Whisper's cross-attention mechanism to jointly predict word- and utterance-level pronunciation scores without forced alignment. Through mixed-transcript adaptation and adaptive substitution weighting, OpenPA accommodates heterogeneous transcripts across closed- and open-response settings within a single model. Trained solely on speechocean762, OpenPA achieves the strongest overall performance among evaluated systems under in-domain closed and simulated open-response conditions, while maintaining its performance zero-shot on MultiPA. An additional cross-task evaluation on oral-cancer speech suggests its potential applicability beyond non-native speech.
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Image and Video Processing

A Miniaturized Wi-Fi HaLow-Based Wireless Imaging Transfer System for Capsule Endoscopy

Michele Bertozzi, Lorenzo Amelio, Sebastiano Strangio et al. (+5 more)

Wireless capsule endoscopy is constrained by severe limitations in size, available power, and reliable data transmission through biological tissues. Although recent advances in miniature imaging sensors have improved image quality, the wireless transmission of color images at practical frame rates remains a challenging task under the tight capsule constraints. We present the design and the experimental validation of a miniaturized Wireless Imaging Transfer (WIT) system for capsule endoscopy based on a sub-GHz IEEE 802.11ah (Wi-Fi HaLow) wireless link. In addition, a preliminary in-vivo validation of Wi-Fi HaLow communication is reported, showing that a stable through-body wireless link can be established across biological tissues in a clinically relevant porcine model. These in-vivo results provide direct experimental support for the adoption of sub-GHz Wi-Fi HaLow for capsule endoscopy applications. Experimental results demonstrate stable wireless image transmission at a resolution of 320x320 pixels, with measured end-to-end data rates above 12 Mbps and a transmission energy efficiency of 20.7 nJ/bit. We also report a detailed power consumption analysis, with a breakdown of the contributions from the imaging sensor, illumination, digital processing, and RF transmission. When all active subsystem contributions are considered, including the camera, digital processing, RF transmission, and illumination at 60% LED brightness, the total power consumption is approximately 477 mW, corresponding to a complete-chain energy consumption of approximately 39.6 nJ/bit. Overall, the measured performance confirms that sub-GHz Wi-Fi HaLow provides a promising trade-off among throughput, robustness, and energy efficiency for wireless imaging in capsule endoscopy.
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All authors Michele Bertozzi, Lorenzo Amelio, Sebastiano Strangio, Nikita J. Greenidge, Benjamin Calmé, Pietro Valdastri, Giuseppe Iannaccone Fellow, Alessandro Catania

Audio and Speech Processing

OpenSVBench: Diagnosing Speaker Verification Generalization Across Deployment Scenarios

Qituan Shangguan, Bosen Xu and Shuai Wang

Speaker verification (SV) systems now achieve very low error rates on standard public protocols, but those results do not fully describe deployment generalization. VoxCeleb-style evaluations remain useful anchors for English in-the-wild media speech. Scenario-specific evaluations and challenges are also valuable. However, neither setting directly shows whether the same system remains reliable across diverse deployment scenarios. We introduce OpenSVBench, a public benchmark for cross-scenario SV evaluation. Its fixed public core contains 26 trial sets organized into 11 scenario groups, with 9.15M verification trials, frozen target/non-target keys, and metadata for scenario-wise reporting. Around this core, the online leaderboard acts as a diagnostic evaluation interface rather than only a ranked list. It accepts score packages from new systems, evaluates them with frozen keys and common scoring, and reports trial-set breakdowns, scenario-group metrics, macro EER/minDCF, model-wise profiles, and scenario-wise comparisons. We evaluate 18 public checkpoints spanning x-vector, ECAPA-TDNN, CAMPPlus, ResNet and ERes variants, ReDimNet2, SAM-ResNet, and pretrained Transformer speaker recipes. The results show clear cross-scenario gaps: systems with similar macro EER can have different scenario profiles, strong performance in one scenario does not guarantee strong performance in another, and same-scenario trial sets can still expose source and protocol gaps.
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Systems and Control

Learning-Based Power Flow Estimation with Sparse PMU Deployment in Power Systems

Van-Hai Bui, Quang Manh Hoang, Akhtar Hussain et al. (+1 more)

Accurate monitoring of power flow is fundamental to the secure and reliable operation of modern power systems. Existing data-driven power flow estimation methods typically assume that synchronized measurements are available at every bus, requiring extensive deployment of phasor measurement units (PMUs). Such dense sensing infrastructures substantially increase installation, communication, and maintenance costs. This paper proposes a learning-based framework that minimizes the number of installed PMUs while maintaining high-accuracy systemwide power flow estimation. Instead of relying on measurements from all buses, the proposed method identifies an informative subset of buses whose synchronized measurements are sufficient to accurately reconstruct branch power flow across the network. To this end, a neural network is developed to learn the nonlinear relationship between PMU measurements and system-wide power flows, and multiple PMU deployment scenarios are evaluated to quantify the trade-off between measurement availability and estimation accuracy. The results enable utilities to identify cost-effective PMU placement strategies that balance deployment cost and prediction performance. Case studies on the IEEE 30-bus test system demonstrate that the proposed framework significantly reduces the number of required PMUs while maintaining accurate power flow estimation.
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All authors Van-Hai Bui, Quang Manh Hoang, Akhtar Hussain, Wencong Su

Image and Video Processing

Can Multimodal Decision Models Classify Breast Ultrasound Images? Benchmarking CLEF and CLEF-Flash Across Prompts and Datasets

Tran Cao Minh, Hai Tran and Ha Minh Tan

Multimodal decision models return probabilities over predefined alternatives, offering a structured interface for image classification. This study benchmarks inference with CLEF and CLEF-Flash on 2818 breast ultrasound images from Dataset B, BUSI, and BUS-BRA. Dataset B and BUS-BRA use binary benign-malignant classification, whereas BUSI includes normal, benign, and malignant categories. Model parameters remain unchanged, and original images are submitted without user-defined preprocessing. Three prompts provide minimal labels, an explicit classification task, or sonographic criteria. We report accuracy, macro F1, balanced accuracy, macro precision, throughput, and latency. CLEF-Flash with the clinical prompt achieves the highest accuracy on all three datasets: 74.85% on Dataset B, 66.28% on BUSI, and 51.63% on BUS-BRA. On BUSI, however, CLEF with the task-guided prompt achieves the highest macro F1 (64.51%) and balanced accuracy (68.72%). CLEF-Flash has lower latency in every matched comparison. Clinical instructions improve CLEF-Flash accuracy relative to the minimal prompt but reduce CLEF accuracy on all datasets. Confusion matrices reveal frequent benign-to-malignant errors, particularly on BUS-BRA, where all configurations fall below the 67.63% accuracy of a majority-class predictor. These findings demonstrate dataset-and prompt-dependent performance and motivate class-specific evaluation before applying multimodal decision models to breast ultrasound analysis.
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Audio and Speech Processing · Cross-listed from cs.SD

Enhancing Speech Recognition through Text-to-Speech and Voice Conversion Augmentation

Yunus Emre Ozkose and Ali Haznedaroglu

Given the challenges associated with obtaining large volumes of real-world audio, the advancement of Automatic Speech Recognition (ASR) systems increasingly depends on synthetic data. This study focuses on the call center-banking domain, providing a targeted analysis of ASR models in this context. We evaluate real and synthetic datasets, using Word Error Rate (WER) and Character Error Rate (CER) as metrics, and employ a speech quality model to assess the impact on ASR performance. Our research compares Text-to-Speech (TTS) and advanced voice conversion methods, including KNNVC, Seed-VC, and Vec2Wav2, revealing significant improvements in speech quality and ASR accuracy, particularly with Seed-VC. Additionally, our domain-specific experimentation provides insight into the unique challenges and opportunities that arise when applying ASR technologies to industry-relevant settings. This highlights the potential of voice conversion technologies to enhance ASR systems, guiding future research in diverse linguistic scenarios, and paving the way for the broader application of ASR innovations across various fields.
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Image and Video Processing

Model-agnostic Knowledge Augmented Machine Learning for Weakly Annotated Bioimaging Data

Jayanie Bogahawatte, Sachith Seneviratne, Simon Maksour et al. (+6 more)

Machine learning models are increasingly used for automated feature extraction from weakly annotated bioimaging data, such as microscopy and cytomorphology images. Due to the sole dependency of automated feature extraction, existing methods yield inconsistent classification performance across weakly annotated datasets. Here, we propose a model-agnostic knowledge augmentation method that enables ML models to exploit domain knowledge beyond what automated feature extraction can capture. The proposed approach improves classification performance across seven ML architectures. When integrated with masked autoencoder-based self-supervised learning, our method achieves the strongest overall performance, improving precision by 2.9% over the next-best method on three in-house microscopy datasets and by an average of 6.8% across three public cytomorphology benchmarks. We perform nucleus-level classification based on mislocalization of TAR DNA-binding protein 43 (TDP-43) from the nucleus to the cytoplasm, a phenotype linked to Amyotrophic Lateral Sclerosis (ALS), using three independent microscopy datasets of motor neurons derived from human induced pluripotent stem cells. Accurate quantification of this phenotype at scale remains challenging and may contribute to inconsistencies across studies. Application of the best-performing model to these microscopy datasets reveals biological interpretations related to ALS, including dose-dependent effects of the proteasomal stressor, MG132, and the oxidative stressor, sodium arsenite (SA), on TDP-43 mislocalization, variable pathology in motor neurons from sporadic and familial ALS donors, and a lack of protection of the pharmacological compounds, edaravone and niclosamide, against SA-induced TDP-43 mislocalization. Together, our pipeline enables in-vitro cellular image analysis to advance understanding of ALS-associated pathology and potential treatments.
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All authors Jayanie Bogahawatte, Sachith Seneviratne, Simon Maksour, Dzung Do-Ha, Elizabeth Qian, Samuel Brennan, Matthew Keon, Lezanne Ooi, Saman Halgamuge

Signal Processing

OpenH-RF: A Large-Scale Ultrasound Raw Channel Dataset Initiative

T.S.W. Stevens, S.W. Penninga, W.L. van Nierop et al. (+183 more)

Medical ultrasound is a widely accessible, real-time imaging modality, yet progress in data-driven ultrasound research is constrained by the scarcity of large, open collections of raw channel data. Existing public datasets are predominantly based on reconstructed images, while the smaller number of open raw channel datasets is generally specialized to individual reconstruction or imaging tasks, limiting the development of general-purpose methods and foundation models that operate directly on ultrasound signals. While interpretive tasks can be performed on reconstructed images, many of the key technical challenges in ultrasound imaging, such as aberration correction and reverberation reduction, require the raw channel data itself. We introduce OpenH-RF, a community-driven open dataset initiative comprising approximately 40 TB of medical ultrasound raw channel data in 19,332 acquisitions, contributed by 38 institutions across academia and industry, spanning in-vivo, phantom, and simulated data. Each dataset is annotated with a number of supported tasks, ranging from generalized reconstruction, speed-of-sound estimation, and aberration correction to blood flow imaging, motion estimation, quantitative ultrasound, and image interpretation. OpenH-RF provides a unified, self-contained data format that captures raw channel data together with the acquisition, probe, metadata, and reconstruction information required for reproducible processing across scanners and acquisition schemes. The corpus is publicly hosted under a permissive CC BY 4.0 license and can be streamed programmatically and selectively at scale. By bringing heterogeneous ultrasound data into a common representation, backed by modern data infrastructure, OpenH-RF lowers the barrier to working with raw channel data and aims to open medical ultrasound research to new communities. See https://open-h.github.io/OpenH-RF.
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All authors T.S.W. Stevens, S.W. Penninga, W.L. van Nierop, O. Nolan, F. Duelmer, B.N. Frey, B. Federici, V. van Hal, L.D. van Harten, H. de Hoop, R. Lopata, B. Luijten, M. van Sambeek, V. van der Schaft, H.-M. Schwab, M.F. Azampour, N. Navab, R. Ali, A. Andrzejek, J. Baek, B. Boitnott, T. Brevett, H. Hashemi, D. Hyun, J.H. Park, A.V. Telichko, C. Thomas, L. Zhuang, Y. Aklilu, H. Arora, J. Barker, A.K. Mackanic, P. McDowall, L. McHugh, I. Pirozzi, S. Simon-Trench, A.M. Bronstein, O. Senouf, S. Vedula, D. Zadok, H. Rivaz, M. Sharifzadeh, S. Chen, G.P. Luke, S. Wang, Yujia Wu, J. Verde, R. Göbl, O. Zettinig, E. Boctor, Yixuan Wu, H.-M. Bae, M.-G. Kim, Y.-M. Kim, H. Lee, S.-H. Oh, P. Pinto, M. Berrada-Sounni, M. Carley, R.J. Fedewa, S. Gavin, N. Goldsmith, R. Kassayan, C. Low, R. Mehranfar, G. Bardoscia, M. Boccardo, A. Botter, M. Carbonaro, G.L. Cerone, E. Cesti, K. Meiburger, S. Seoni, F. Truscello, B.J. Raiteri, L. Caron, M. Gasse, N. Ghigo, O. Gulenko, A. Leconte, S.A. Lee, A. Malescot, E. Martineau, J. Porée, J. Provost, G. Ramos-Palacios, B. Rauby, R.L. Rungta, A.F. Sadikot, A. Wu, P. Xing, A. Chavignon, C. Ferlay, V. Hingot, M. Reberol, A. Waelbroeck, H.-S. Kwon, J. Bucholz, I. Guracar, R. Loftman, C. Williams, T. Grutman, T. Ilovitsh, T.A. Aleef, P.B. Chen, W. Chen, Y. Chen, M. Frew, M. Honarvar, Y.C. Lee, Z. Li, H. Moradi, R. Rohling, S.E. Salcudean, Z. Wu, Q. Zeng, P.C. Cattin, S. El Hadramy, N. Bottenus, A.S. Perez, S. Feng, Y. Feng, P. Jin, Y. Lin, O. Ostras, G. Pinton, F. Santibanez, M. Sode, B. Turkbey, H. Wang, B.J. Wood, L. Zhang, S. Luo, J. Wiskin, A. Austeng, Y. Karabiyik, S.P. Näsholm, O.M.H. Rindal, D. Bustamante, M. Mehrmohammadi, H. Raeis, A. Hameddine, G. Lajoinie, H. de Roo, M. Versluis, R. Zorgdrager, J.S. Au, A.J.Y. Chee, J. Cohen, T. Gu, J.Y.-H. Hsu, S. Ling, M. Mourtzakis, H. Nahas, P. de la Torre, D. Xiao, A.C.H. Yu, E. Zafiris, P. Jarosik, P. Karwat, Z. Klimonda, M. Lewandowski, B. Byram, C. Khan, Z. Marshall, Y.-C. Pan, S. Liu, T. Sultan, C. Hoerig, J. Mamou, D. Auerbach, Y.C. Eldar, A. Wegerhoff, G.S. Fischer, S. Katayose, X. Ma, R. Murakami, Y. Tang, R. Tsumura, Y. Wang, H.K. Zhang, M. Azizian, S. Huver, J.J. Dahl, R.J.G. van Sloun, W. Simson

Audio and Speech Processing

Vocal Intensity Conversion with Continuous SPL Control via Adversarial Training

Quentin Le Tellier, Albert Rilliard, Olivier Perrotin et al. (+1 more)

Vocal intensity conversion renders a recorded utterance at a different vocal intensity, rather than simply scaling its amplitude, while preserving its content and speaker identity. Existing systems control intensity through a reference utterance or a few discrete categories. Instead, we control the conversion with any target sound pressure level (SPL) on a continuous dB scale. A lightweight converter, trained as a conditional cycle-consistent GAN without parallel data, transforms the features of a frozen speech encoder, from which a pretrained vocoder resynthesizes the speech. We compare three representations, log-mel spectrograms, WavLM, and SpeechTokenizer, and introduce an objective protocol that measures the achieved intensity change against the target one on a calibrated corpus. With WavLM, the converter achieves a measured 83% of the target change in intensity and also shifts the fundamental frequency. A perceptual evaluation confirms that perceived vocal intensity increases with the target level on amplitude-normalized stimuli.
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All authors Quentin Le Tellier, Albert Rilliard, Olivier Perrotin, Marc Evrard

2610.13608 · 9 Oct · 5 pages, 2 figures, 1 table. Submitted to IEEE ICASSP 2027. Code: https://github.com/vers-project/vocal-intensity-conversion. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

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

AudioTETRIS: Token Elimination via Transitional Importance Signals for Efficient Audio LLMs

Shreya Sajid, Anna Vettoruzzo and Joaquin Vanschoren

Large Audio Language Models (LALMs) achieve strong performance on speech-text tasks, but standard audio encoders produce hundreds of tokens for even short audio clips. Because self-attention scales quadratically in sequence length, this inflates inference-time compute substantially. Current training-free token compression methods prune or merge tokens based solely on coarse attention or feature similarity. However, treating audio tokens as generic, isotropic features smooths over spectral transitions, destroying fine-grained phonetic transitions essential for decoding speech. In contrast, speech signals exhibit highly structured temporal patterns where information density peaks at acoustic transitions. We propose AudioTETRIS, a training-free, two-stage framework that uses the audio encoder's representation geometry as an acoustic prior for token importance before applying attention-based compression. Instead of discarding encoder structure after the encoding stage, AudioTETRIS propagates this prior directly into the LLM decoder to compute a per-token fused score. This score drives progressive top-$k$ token selection across decoder layers without requiring additional training or auxiliary models. Evaluated on Qwen2-Audio-7B, Phi-4-Multimodal, and Audio Flamingo 3, AudioTETRIS retains near-uncompressed performance on several speech tasks using only 25% of audio tokens, matches or surpasses full-token accuracy at moderate compression rates, and consistently outperforms existing training-free baselines.
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Systems and Control

Direct Vector Field Guidance for Path Following

Amit Shivam, Nikhil Kumar Singh, Luís Tiago Paiva et al. (+1 more)

This paper presents a path-following guidance framework for a turn-rate-constrained unmanned aerial vehicle (UAVs). The proposed guidance strategy uses commanded course angle as a tanh-hyperbolic shaping function of the lateral deviation from the desired path. Stability characteristics illustrating asymptotic convergence to the desired path are established using Lyapunov analysis. Analytical expressions for the turn-rate and corresponding maximum turn-rate bound are derived, enabling direct gain selection for a turn-rate-limited vehicle. Numerical simulations are performed for straight-line following scenarios under both ideal and non-ideal initial heading conditions. Furthermore, a comparative study against representative arctan-, arcsine-, and arccos-based vector-field guidance laws is conducted using analytically feasible gains selected from their respective turn-rate bounds. Comparative numerical results demonstrate the proposed formulation achieves reliable boundary convergence while maintaining smooth steering behavior and a compact analytical gain-design framework.
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All authors Amit Shivam, Nikhil Kumar Singh, Luís Tiago Paiva, Fernando A.C.C. Fontes

Signal Processing

HF Single-Station Localization via Multimode Ray Tracing and Time-Frequency Information Fusion

Qinglin Li, Wen Liu, Yun Ning et al. (+4 more)

High-frequency (HF) single-station localization estimates an emitter location from measured directions of arrival (DOAs) and an ionospheric propagation model. When the propagation mode composition cannot be identified from conventional direction-finding (DF) measurements, the same DF observation may produce multiple physically valid ground landing points under different propagation modes, making the location inversion ambiguous. This paper proposes a multimode time-frequency information fusion localization method (MTFIF-L).Rather than assuming a propagation mode a priori, MTFIF-L retains valid landing points generated under different propagation modes and uses repeated observations across time windows and, when available, multiple operating frequencies to progressively constrain the resulting candidate locations. Experiments on four measured HF links examine the respective roles of multimode candidate retention, cross-window processing, and cross-frequency processing. All four links yield unique location decisions. Links A–C achieve relative errors of $1.40\%$, $0.78\%$, and $3.41\%$, respectively.Link D, the only single-frequency case, also yields a unique decision from repeated temporal observations, although its post- localization error is substantially larger. Propagation-path analysis further reveals link-dependent differences in reflection layer, hop count, magnetoionic mode, and elevation angle among the ray-tracing paths associated with the measured links.Overall, the results show that repeated temporal observations can support localization within an individual frequency, while multifrequency information provides additional robustness against unreliable frequency-specific outcomes, enabling HF single-station localization under unknown and potentially coexisting propagation modes while retaining interpretable propagation information for post-localization analysis.
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All authors Qinglin Li, Wen Liu, Yun Ning, Zhongxin Deng, Junkang He, Mingshuo Liu, Chuangjian Xia

Signal Processing · Cross-listed from math.PR

Zeros of the Spectrogram of White Noise with Hermite Windows

Charaf Bellourak, Sylvain Meignen, Kévin Polisano et al. (+1 more)

We study the zeros of the spectrogram of complex white noise analyzed with Hermite windows of order n, extending the classical Gaussian-window correspondence with Gaussian Analytic Functions to the polyanalytic setting. Using the polyanalytic Bargmann transform, these zeros split into two families according to the sign of the local Jacobian, giving rise to a natural notion of charge, and we show that these zeros are almost surely simple, isolated, and form a translation and rotation-invariant point process. We derive closed-form expressions for the one-point intensity, decomposed by charge, and also for the unsigned, signed, and hybrid two-point intensities. Besides, we prove that unlike the total repulsion observed with a Gaussian window, the Hermite window of order n generates a pair correlation function that converges at the origin to a strictly positive constant depending on n. Finally, we propose a numerical method for detecting spectrogram zeros via the winding number around candidate local minima.
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All authors Charaf Bellourak, Sylvain Meignen, Kévin Polisano, Mickaël Nahon

Systems and Control · Cross-listed from math.DS

Asymptotic Analysis of Discrete-Time Self-Attention Dynamics in Transformers

Claudio Altafini

In machine learning, a self-attention dynamics describes the attention mechanism of transformers, and is obtained treating the layer index of a transformer as time index. Even though naturally discrete-time, this dynamical system is most often studied in continuous-time. Scope of this paper is to provide a thorough investigation of the discrete-time version of the self-attention dynamics: a complete classification of its equilibria is given, and their stability properties are studied, in both the single-head and the multihead case. We show that in both cases the main attractor of the dynamics is a consensus equilibrium aligned with the principal eigenvector of the value matrix (in the single-head case, a corresponding matrix of weights in the multihead case), even though other locally asymptotically stable bipartite equilibria can be show to exist at least in the single-head situation. The behavior is confirmed by looking at the learned weight matrices of a real transformer.
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Audio and Speech Processing · Cross-listed from cs.SD

Lossless Inference Acceleration for ASR via EAGLE-Style Speculative Decoding

Sen Zhang, Xianghu Yue, Yiming Chen et al. (+3 more)

Autoregressive Transformer and Conformer models have achieved state-of-the-art performance in automatic speech recognition (ASR). However, their sequential token generation mechanism imposes a significant bottleneck during decoding, resulting in substantial inference latency. While speculative decoding has emerged as a promising lossless acceleration technique, existing ASR-oriented approaches either suffer from low draft acceptance rates due to suboptimal draft quality or rely on specially designed external draft models, which limit their generality and practicality. To overcome these limitations, we propose EAGLE-ASR, a novel framework that accelerates ASR inference via EAGLE-style speculative decoding while strictly preserving lossless output quality. Specifically, we introduce Speech-Draft, a lightweight autoregressive module that serves as a universal draft model by jointly leveraging encoder acoustic features and hierarchical decoder representations from the target ASR model. Furthermore, we adopt a multi-step generation strategy with self-distilled data to train the Speech-Draft, enabling it to better approximate the target model's output distribution and maintain causal consistency during multi-token generation. Extensive experiments on representative Whisper and Conformer AED models demonstrate that EAGLE-ASR achieves significant lossless speedups of up to $1.9\times$ and $2.4\times$, respectively, consistently outperforming existing speculative decoding baselines for ASR. Our proposed framework further generalizes effectively to multilingual ASR and speech translation tasks, establishing EAGLE-ASR as a practical and universally applicable solution for accelerating modern ASR systems without compromising recognition accuracy.
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All authors Sen Zhang, Xianghu Yue, Yiming Chen, Wenhuan Lu, Jianguo Wei, Haizhou Li

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

Phonology-Aware Speaker Extraction Frontend for Audio LLMs

Yanxiang Chen, Haoyuan Deng, Jinyu Wang et al. (+4 more)

Target speaker extraction (TSE) is a key frontend for enabling audio large language models (LLMs) to recognize and understand a designated speaker in overlapping speech. However, multilingual mixtures exhibit heterogeneous extraction demands, causing fixed-depth systems to either under-process challenging inputs or waste computation on inputs that have already converged. Existing TSE frontends generally apply uniform computation, while Audio-LLM adaptation methods neither explicitly model language-conditioned extraction demands nor preserve plug-and-play compatibility with frozen downstream models. We therefore propose LATTE, a lightweight 5.2M-parameter frontend that combines an acoustic Difficulty Estimator, a task-adaptive phonology-informed prior learned from multi-depth TSE gains, and an Adaptive Hourglass Extractor to route each mixture without requiring language identity at inference. On PolyExtract, LATTE achieves a macro SI-SNRi of 15.07 dB at 0.30 GFLOPs, retaining performance within 0.09 dB of a 0.53-GFLOP fixed-depth model while reducing computation by 43.4%, and lowers the average WER from 16.8% to 14.6% relative to TF-GridNet. By jointly modeling sample-level extraction demands and phonological structure, LATTE provides a practical framework for quality-aware, compute-efficient multilingual TSE and offers a scalable path toward robust Audio-LLM deployment.
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All authors Yanxiang Chen, Haoyuan Deng, Jinyu Wang, Hongyi Chen, Dingshan Li, Runlin Huang, Weifeng Su