Audio and Speech Processing
Indic-CLAP: Text–Audio Understanding in Indic Languages via Cross-Lingual Distillation
Sanat Kumar Agrawal and Srikanth Raj Chetupalli
Contrastive
Language–Audio Pretraining (CLAP) learns a joint text–audio representation space, which enables zero-shot audio understanding from natural-
language descriptions. However, its English-only text encoder restricts CLAP to tasks and
datasets expressed in English. We present Indic-CLAP, a multilingual text–audio model that extends CLAP to nine Indic
languages spoken by over a billion people. Exploiting CLAP's dual-encoder structure, we propose to train only an Indic text encoder by
distillation while keeping the audio encoder frozen, using machine-translated AudioCaps captions. We compare
distillation from CLAP's text encoder, audio encoder, and a novel hybrid objective combining both. Our evaluation shows that the Indic-CLAP encoder inherits the cross-modal
alignment from the CLAP teacher, as evidenced by the performance on cross-modal
retrieval and zero-shot classification tasks. Further, we study the cross-modal
alignment using modality gap analysis, which explains the performance trends.
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Audio and Speech Processing
Unified Shared Encoder in Spoof-Aware Speaker Verification with Hybrid Wavelet Prompt Tuning
Aref Farhadipour, Srikanth Madikeri, Teodora Vukovic et al. (+2 more)
Spoofing-aware speaker verification (SASV) must confirm who is speaking and that the
speech is genuine, but current systems gain one at the expense of the other. Modular cascades are accurate yet run two large self-supervised encoders, whereas end-to-end models are efficient but lose accuracy when a single
embedding has to serve two conflicting objectives. We show that this trade-off between efficiency and specialization is avoidable. A single frozen W2V-BERT~2.0 backbone is adapted per task by deep hybrid Wavelet
Prompt Tuning (WPT), so each branch obtains its own view of the shared encoder through dedicated
prompts and a task head, and the scores are combined only at inference. Training only about 7M parameters, a small fraction of those a strong two-encoder cascade requires, the system surpasses it on SpoofCeleb evaluation with 0.03% CM-EER and 0.038 min a-DCF against 0.16% and 0.047. Because the backbone is shared and frozen, new branches attach without retraining the heads and
prompts already in place. On ASVspoof5, with
adversarial attacks and codec distortions, it reaches 0.092 min a-DCF and 3.60% CM-EER using only the provided data, indicating that the design holds under realistic in-the-wild threats.
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All authors Aref Farhadipour, Srikanth Madikeri, Teodora Vukovic, Volker Dellwo, Petr Motlicek
Audio and Speech Processing
UZH-CL at ArA-DF 2026: Prompt-Tuned Foundation Models and Track-Adaptive Score Fusion for Arabic Speech Deepfake Detection
Aref Farhadipour, Teodora Vukovic and Petr Motlicek
Detecting synthetic and voice-converted
speech remains difficult for low-resource
languages with dialectal diversity, where systems must generalize across regional dialects and unseen acoustic channels. We present the UZH-CL submission to the ArA-DF 2026 Shared Task on Arabic
speech deepfake detection, covering Track~1 (dialect generalization) and Track~2 (acoustic robustness). We freeze a W2V-BERT-2.0 backbone and adapt it with Wavelet
Prompt Tuning, updating under 1% of parameters, and aggregate multi-layer representations with cross-layer
attention and a general attentive-statistics pooling head rather than a specialized graph backend. Complementary detectors are obtained by varying adaptation strategy, training data, augmentation, and encoder family. We find that the two shift types require different fusion regimes: a broad multi-window ensemble for dialect generalization, and a compact, channel-matched, center-crop ensemble for acoustic robustness. Official evaluation yields 1.96% EER on Track~1 (6th place) and 1.04% EER on Track~2 (3rd place), corresponding to 87% and 96% relative reductions over the XLS-R+AASIST baselines.
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Image and Video Processing
Image-Based Breast Implant Detection for Mammography Dataset Curation and Near-Real-Time Deployment: Comparing Foundation Models and Task-Specific Convolutional Models
Vasisht Ishwar, Hari Trivedi, Young Seok Jeon et al. (+5 more)
Purpose: To evaluate the performance-feasibility tradeoffs of foundation models (FMs) and task-specific convolutional neural networks (CNNs) trained from scratch for breast implant classification in 2D mammography, with emphasis on suitability for near real-time clinical deployment. Methods: We evaluated four models: two FMs (RAD-DINO and MammoCLIP) and two CNNs trained from scratch for implant prediction (ResNet18 and our lightweight ResNetLite). Using the Emory Breast Imaging
Dataset, 5,000 unilateral screening mammograms were used for training/validation and 1,000 manually reviewed unilateral images were held out for testing. For the FMs, global image
embeddings from the pretrained encoder were classified using a support vector machine (SVM). The CNNs were trained end-to-end on 2D mammograms, with ResNetLite optimized via grid search over depth and width to balance accuracy and efficiency. Performance was evaluated using AUROC, sensitivity, specificity, accuracy,
embedding visualization, and inference-latency. Results: All models demonstrated strong performance on held-out test data (n = 1,000). MammoCLIP achieved the highest AUROC (0.999) with the quickest training time of 493 seconds. RAD-DINO achieved the highest sensitivity (0.980; accuracy 0.989) but had the slowest inference and training times. ResNet18 and MammoCLIP achieved comparable accuracy (0.985). ResNetLite showed no statistically significant difference from ResNet18 (AUROC 0.993; accuracy 0.976) despite using only 1.4% of ResNet18's parameters, and had the fastest inference time. Conclusion: FMs and task-specific CNN models reliably detect breast implants on 2D mammography. Model selection is best guided by deployment context: MammoCLIP for GPU-equipped hospital settings requiring scalable integration, and lightweight CNNs such as ResNetLite for resource-constrained or edge deployments.
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All authors Vasisht Ishwar, Hari Trivedi, Young Seok Jeon, Beatrice Brown-Mulry, Frank Li, Rohan Satya Isaac, Mohammadreza Chavoshi, Judy Wawira Gichoya
Audio and Speech Processing
Acoustic gap placement in second-language read speech production
Peyman Jahanbin
Speakers can differ not only in how much silence they produce but also in where interruptions fall, information that global pause counts obscure. We analyzed 115 publicly available
Speech Accent Archive recordings of the same read passage: 57 speakers whose archive metadata listed Mandarin as their first
language and a mainland-China birthplace, and 58 American English first-
language speakers born in the U.S. Midwest. Word-level forced
alignment was used to extract interword acoustic gaps and classify each position as punctuation-marked or unpunctuated. At a 250 ms threshold, punctuation-marked gaps were similar for the Mandarin and English groups (means 5.56 and 5.47), whereas unpunctuated-position gaps were more frequent in the Mandarin group (means 3.67 and 0.43; adjusted rate ratio 11.98, 95% confidence interval 6.53-22.00). A crossed-effects logistic model with random intercepts for speakers and passage positions confirmed that the contrast was disproportionately concentrated at unpunctuated positions (interaction odds ratio 9.44, 95% confidence interval 5.23-17.02). The pattern persisted at a 500 ms threshold, after removing positions adjacent to punctuation, after excluding severe transcript-deviation cases, and when analysis was restricted to acoustically confirmed silence. Nearly half of the Mandarin group's unpunctuated gaps occurred at positions classified as within-phrase in an exploratory single-coder annotation. In this fixed-passage corpus, acoustic gap placement relative to textual and syntactic structure distinguished the groups more clearly than punctuation-marked pausing. The results support placement-sensitive measurement as a reproducible dimension of breakdown fluency while not establishing a causal mechanism, proficiency difference, or perceptual consequence.
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Signal Processing
Cross-Site Transfer and Spatial Forgetting in OTA-Trained Neural Receivers
Riku Luostari, Dani Korpi, Harri Holma et al. (+1 more)
Most published neural-receiver studies train on synthetic channel models, although real over-the-air measurements can improve performance. We examine how much of this benefit transfers from other sites to a distinctly different deployment site and what adaptation to that site costs elsewhere. The study uses a
dataset from a 5.88 GHz measurement campaign covering eight sites and four environment types. An industrial site measured entirely from a vehicle is withheld as a deliberate transfer stress test. One receiver architecture is trained under four regimes: simulation alone; seven other sites; all sites jointly; and other-site training followed by deployment-site finetuning. The last two use identical measurements through different optimization protocols. Across five independently trained seeds, sensitivity gains are evaluated at uncoded bit error rate (BER) targets of $10^{-1}$ and $10^{-2}$, representative of moderate- and higher-rate coded links, respectively. On the deployment site, training with other-site data rather than simulation improves sensitivity by 0.3 dB at $10^{-1}$ and 0.1 dB at $10^{-2}$. Adding deployment-site data contributes less than 0.1 dB at $10^{-1}$; at $10^{-2}$, total gains over simulation reach 0.4 dB with joint training and 0.6 dB with finetuning. Joint training shows no measurable aggregate loss on the other-site test set, whereas finetuning yields the larger deployment-site gain but reduces other-site sensitivity by about 0.1 dB at $10^{-2}$, indicating spatial forgetting.
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All authors Riku Luostari, Dani Korpi, Harri Holma, Olav Tirkkonen
Systems and Control · Cross-listed from math.OC
Control of Markov Jump Linear Systems with Uncertain Lumpable Cluster Observations
Carlos A. F. Persiani, Ram Padmanabhan, Melkior Ornik et al. (+1 more)
In this paper, we consider the control of Markov jump linear systems when the active system mode is not exactly known. We rather assume that this true mode is only known to belong to an observed cluster of modes, making both the dynamics and the associated transition probabilities uncertain. We construct a min-max optimal control problem in this setting, with the objective of robust regulation. Using results in regularized least mean-square optimization, we describe how the solution of this problem can be reduced to a Riccati recursion, such that the controller depends only on knowledge of which cluster the mode lies in. We subsequently establish convergence of the recursion and stability of the resulting closed-loop system. A numerical example illustrates the effectiveness of the proposed methodology.
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All authors Carlos A. F. Persiani, Ram Padmanabhan, Melkior Ornik, Marco H. Terra
Systems and Control
Modular Model Order Reduction for Inverter-Based Power Systems with Stability and Accuracy Guarantees
Hanqing Zhang, Pudong Ge, Mohammad Fahim Shakib et al. (+5 more)
This paper proposes a modular model order reduction framework for large-scale power grids integrated with renewable energy sources. The proposed framework has the advantage of reducing individual sub-areas independently while retaining the power grid interconnection structure. Moreover, given a user-defined accuracy specification on the overall reduced-order interconnected power grid, corresponding target specifications for the individual reduced sub-areas are determined: satisfaction of such individual specifications guarantees the overall user-defined accuracy specification, while also preserving the stability of the overall reduced-order interconnected power grid. The proposed framework is validated on a modified IEEE 118-bus system (where synchronous generators have been replaced by inverter-based resources) by assessing the accuracy specification satisfaction of the overall reduced-order interconnected power grid, in terms of frequency response, stability, active power, and sub-synchronous behaviour when injected with disturbances.
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All authors Hanqing Zhang, Pudong Ge, Mohammad Fahim Shakib, Adrià Junyent-Ferré, Balarko Chaudhuri, Luuk Poort, Lars A. L. Janssen, Giordano Scarciotti
Systems and Control
SD-DPC: Sparse Dictionary Differentiable Predictive Control
Ali Reza Daneshvar Garmroodi and Jan Drgoňa
We present sparse dictionary differentiable predictive control (SD-DPC), a framework for learning sparse, interpretable feedback policies for nonlinear systems from data. A prediction model is first identified by rollout-based sparse identification of nonlinear dynamics (SINDy), building on gradient-based and multistep formulations. The policy is then parameterized as a sparse combination of dictionary functions and trained by differentiating a constrained finite-horizon predictive-control objective through this model, so that its terms are selected by closed-loop performance rather than by imitating a previously trained controller. The result is an explicit feedback law with only a handful of terms. Across three
benchmark control problems, SD-DPC satisfies the constraints in all test scenarios, outperforms a policy distilled onto the same terms by up to an order of magnitude, and requires orders of magnitude less
memory and online computation than an optimization
benchmark, while admitting explicit sensitivity bounds.
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Systems and Control · Cross-listed from math.OC
Accelerated Safe Gradient Flow
Xuting Gao and Sonia Martínez
This letter introduces an accelerated safe gradient flow (A-SGF) for strongly convex constrained optimization problems. The A-SGF filters a nominal second-order accelerated flow through a second-order control barrier function quadratic program. We establish conditions for strict feasibility and local Lipschitz continuity of the resulting vector field, prove forward invariance of a lifted safe set, and show that its unique
equilibrium is the constrained minimizer. A variational Lyapunov analysis isolates the effect of the safety correction and gives explicit conditions on the barrier gains under which A-SGF retains the same convergence rate as the unconstrained Nesterov accelerated method. A nonlinear 2D example demonstrate anytime feasibility and faster convergence of the
algorithm.
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Systems and Control
Towards a Theory of Quantitative Vulnerability Analysis for Engineering Systems
Daniel A. Eisenberg and David L. Alderson
Since the 1980s, risk analysis for engineering systems has been defined based on a tuple of scenarios, $\mathcal{S}$, likelihoods, $L$, and consequences, $C$, where $Risk = (\mathcal{S},L,C)$. We argue that equating this triplet with risk alone is insufficient and potentially misleading. Instead, we define this tuple as a vulnerability space $\mathcal{V} = (\mathcal{S},L,C)$, which becomes risk only when one makes specific interpretations of likelihood and consequence. Here, an interpretation has mathematical meaning, and defines a map that assigns data elements about system vulnerability to a perspective. Formally, we define a vulnerability perspective as a specific choice of interpretations of likelihood and consequence data that allows vulnerability analysis measures to be defined and analyzed. We argue that risk is only one of four common perspectives on $\mathcal{V}$ and demonstrate mathematically why each perspective should remain a distinct concept. First, we show how interpretation of $C$ as a set-valued function produces reliability measures, not risk measures. Then, we show how interpretation of $L$ via possibility theory will produce
adversarial and safety measures distinct from risk. This new theoretical framework clarifies the conceptual, mathematical, and practical differences between risk, reliability,
adversarial, and safety perspectives—four common engineering system analyses often conflated in the literature. A consequence of our theory is that no single perspective will reveal all vulnerabilities of an engineering system, suggesting decision-making via risk analysis may incur unavoidable and underappreciated tradeoffs from other vulnerability perspectives.
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Signal Processing
UrbanEMF: City-Scale EMF Mapping over a Continuous Urban Area with Real-World Base-Station Deployment
Shuangning Li, Chenxin Luo, Shanshan Wang et al. (+3 more)
Electromagnetic Fields (EMF) mapping is essential for wireless propagation modeling and are fundamental to a wide range of applications, including spectrum awareness, network planning, and integrated sensing and communication (
ISAC). However, existing
datasets are often limited to spatially separated 2D scenes without real base station (BS) information. To address this gap, we present UrbanEMF, a city-scale EMF mapping
dataset constructed from real world urban geometry and physical BS deployments using ray tracing. Unlike conventional single scene-based
datasets, UrbanEMF preserves continuous city-scale urban coverage and realistic transmitter location information. It further extends traditional 2D maps by incorporating multiple receiver heights. In addition, both aggregated received signal strength (RSS) maps and path loss maps are provided to characterize complementary aspects of the
radio environment. This work provides a realistic and flexible
benchmark for developing and evaluating learning-based methods in large-scale urban wireless environments. The code for this work is available at: https://github.com/lemonstudy/UrbanEMF
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All authors Shuangning Li, Chenxin Luo, Shanshan Wang, Yarui Zhang, Paul Lagouanelle, Joe Wiart
Audio and Speech Processing · Cross-listed from cs.LG
Effects of interpulse-interval variation on deep-learning classification of bat vocalizations
Welmoed R. Eversteijn, Burooj Ghani, A. Leonie Baier et al. (+1 more)
Temporal context may aid automated bat-species classification, but the contribution of specific features remains unclear. We investigated whether variation in the interpulse interval (IPI)-the time between consecutive call onsets-provides species-discriminative information and whether
transformer-based models are more sensitive to this information than convolutional neural networks. We created two matched
datasets from European bat recordings: a natural-IPI condition retaining the original call timing and a normalized-IPI condition in which call onsets were spaced at 50-ms intervals. EfficientNet-B0 and PaSST were fine-tuned and evaluated within each condition. In an additional experiment, each architecture was trained separately on natural-IPI and normalized-IPI recordings, and evaluated on the same natural-IPI test set. Finally, the pretrained classifiers BatDetect2 and BAT were evaluated on both conditions. Within-condition IPI normalization had model-dependent effects. PaSST accuracy differed little between the natural-IPI ($71 \pm 2.3\%$) and normalized-IPI ($70 \pm 6.3\%$) conditions, whereas EfficientNet accuracy increased from $47 \pm 4.7\%$ to $57 \pm 3.9\%$. PaSST exceeded EfficientNet under both conditions. In the cross-condition evaluation, models trained on natural-IPI recordings outperformed those trained on normalized-IPI recordings on the natural-IPI test set: accuracy decreased from 54% to 50% for EfficientNet and from 65% to 57% for PaSST. BatDetect2 and BAT differed little between IPI conditions. Overall, we found limited support for the hypotheses that natural IPI variation contributes substantially to bat-species classification and that it is used more effectively by
transformer-based than CNN-based models. Nevertheless, the cross-condition performance decrease shows that results obtained under normalized conditions may not transfer fully to natural recordings.
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All authors Welmoed R. Eversteijn, Burooj Ghani, A. Leonie Baier, Dan Stowell
Image and Video Processing
Reliability Stress Tests and Decision-Time Routing for Chest X-ray Vision-Language Models
Xinye Yang, Zhusi Zhong, Scott Collins et al. (+3 more)
Medical
vision-
language model (VLM) evaluation is sensitive to workflow design, prompting strategy, and
benchmark construction, yet most studies treat these factors in isolation. We introduce a reliability stress test for chest X-ray interpretation built on two balanced
datasets (a private report-backed set and a curated MIMIC subset). Three medical VLMs (CheXagent, MedGemma-4B, and MedGemma-27B) are evaluated across three
prompt styles and two workflows (single-VLM and multi-
agent), producing 36 configurations. We show that exact-match accuracy alone can overstate the effectiveness of conservative models that default to "Normal" predictions. Diagnostic reliability also depends heavily on model family and scale: multi-
agent reasoning helps some configurations but hurts others. Building on these observations, we propose a decision-time routing framework that selectively escalates to multi-
agent inference only when beneficial, improving the cost-quality trade-off over fixed workflows. Our results highlight the need for evaluation protocols that jointly consider
prompt sensitivity, failure-mode diversity, and workflow choice before clinical deployment.
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All authors Xinye Yang, Zhusi Zhong, Scott Collins, Grayson Baird, Xuyu Wang, Zhicheng Jiao
Image and Video Processing
Confidence-Gated Cloud-Edge Cascade Triage via Variational Risk Minimization for Medical Imaging
Xinye Yang, Zhusi Zhong, Scott Collins et al. (+7 more)
Emergency chest X-ray (CXR) triage has a structural modality gap: reports arrive after triage decisions, yet
multimodal foundation models require image-text inputs. We present Variational Risk Minimization (VRM), a
distillation framework that treats LVLM-generated report variants as Monte Carlo samples of latent clinical interpretations. Rather than distilling from a single teacher target, VRM learns from a variationally marginalized teacher distribution, enabling uncertainty-aware supervision under missing-modality constraints. Under matched encoder families, VRM outperforms direct fine-tuning baselines and improves calibration with strong recovery from hallucinated supervision. Marginalized supervision reduces report-selection instability. In our compact edge-student instantiation, a confidence-gated cascade reaches AUC 0.941 at 103ms average latency with 20.3% cloud escalation, yielding an explicit reliability-latency operating point for cloud-edge clinical workflows.
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All authors Xinye Yang, Zhusi Zhong, Scott Collins, Michael Bernstein, Grayson Baird, Terrence Healey, Michael Atalay, Mahesh Jayaraman, Xuyu Wang, Zhicheng Jiao
Image and Video Processing
Event-guided Neural Video Compression
Jiyun Kong, Jungwoo Kim, Enes Eray Demirtas et al. (+2 more)
Neural
video codecs derive motion and temporal contexts mainly from RGB frames, leaving room for cross-modal guidance from complementary temporal observations. Event streams can provide such observations by recording brightness changes between frames. In this work, we propose an Event-guided Neural
Video Codec (ENVC) that uses events shared by the encoder and decoder to improve RGB compression efficiency. For motion coding, ENVC forms an event-guided motion prior and codes the remaining motion residual. For frame coding, an event-conditioned predictor supplies multi-scale features for gated temporal context refinement. To support training and evaluation on standard
video datasets, we synthesize paired RGB-event data and assess its predictive utility through comparisons with real events. Across six
benchmarks, ENVC achieves average BD-rate savings of 39.13% using PSNR-RGB and 67.63% using LPIPS relative to DCMVC. Further analyses show that our gains persist on large-motion sequences and that ENVC effectively learns to integrate event information. These results demonstrate the potential of events as a complementary modality for reducing the RGB coding rate. Our model and code are available at https://github.com/kjungwoo03/ENVC.
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All authors Jiyun Kong, Jungwoo Kim, Enes Eray Demirtas, Touradj Ebrahimi, Jong-Seok Lee
Image and Video Processing
ZAGNet: Zone-Aware Graph Aggregation Network for Patient-Level Lung Ultrasound Diagnosis
Li Chen, Shubham Patil, Rashid Al Mukaddim et al. (+3 more)
Patient-level lung ultrasound (LUS) diagnosis requires integrating findings acquired across multiple anatomical zones, yet clinical examinations frequently involve variable and incomplete scanning protocols with missing zones. Existing diagnostic AI methods primarily analyze individual frames or
video loops, relying on heuristic aggregation strategies such as max or mean pooling that ignore inter-zone relationships for patient-level inference. This paper presents ZAGNet, a Zone- Aware Graph Neural Network that represents temporally tracked pathology findings as graph nodes connected by anatomical zone adjacency. A graph
transformer network propagates contextual information across neighboring lung regions, while a virtual global node aggregates graph-level features to predict patientlevel consolidation and pleural effusion using only patient-level supervision. ZAGNet accommodates missing zones by computing on a graph structure without fixed input format or size. We evaluate ZAGNet on a multicenter
dataset of 714 subjects (20,256 LUS
video loops) with exams varying from 4 to 16 zones across anterior, posterior, and lateral thoracic regions. For consolidation diagnosis, ZAGNet achieved an AUC of 0.803 compared to 0.677 (max pooling) and 0.674 (mean pooling). For pleural effusion, AUC increased to 0.893 from 0.804 (max pooling) and 0.815 (mean pooling). These represent improvements of up to 19% and 11% for consolidation and pleural effusion respectively. The results demonstrate that graph-based inter-zone
reasoning provides an effective and clinically consistent framework for automated patient-level LUS assessment.
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All authors Li Chen, Shubham Patil, Rashid Al Mukaddim, Jochen Kruecker, Balasundar Raju, Alvin Chen
Systems and Control · Cross-listed from cs.RO
Reconstruct, Practice, Go Real: Guided Self-Improvement for Embodied Agents
Yen-Jen Wang, Haozhe Jiang, Shuying Deng et al. (+7 more)
Building reliable
robot capabilities across diverse tasks requires substantial human effort to develop and maintain skills, design rewards, and integrate perception with control. We present Reconstruct, Practice, Go Real (RPG), a framework for autonomous improvement of
robot execution systems without updating model weights. RPG identifies manipulation capabilities in an offline
dataset and constructs related practice tasks in simulation. During practice, RPG uses execution feedback, privileged simulator state, and available
dataset videos to diagnose failures. It develops new reusable symbolic skills, refines existing skills, and revises the system
prompt based on these diagnoses. Cross-task evaluation tests individual candidate changes and merged revisions before they are retained for reuse. At test time, a
multimodal LLM uses the resulting system
prompt and skill library to coordinate perception and
robot control. On held-out initializations of 22 manipulation tasks, RPG improves task success from 28.6% after the first practice round to 95.0% after 15 rounds, outperforming all evaluated baselines, including ASPIRE (75.5%) and CaP-
Agent0 powered by GPT-6 Astra Pro (60.0%). After a common calibration and hardware-adaptation procedure, the frozen system succeeds in all 30 physical trials, with ten trials on each of three tasks. Project Website: https://rpg-
robot.github.io/
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All authors Yen-Jen Wang, Haozhe Jiang, Shuying Deng, Haoru Xue, Weirui Ye, Rocky Duan, Nika Haghtalab, S. Shankar Sastry, Pieter Abbeel, Haozhi Qi
Systems and Control
Minimal Experiments for Robust Stabilization: Information, Spectral Geometry, and Duration
Alexey Peregudin and Ngoc Tuan Dinh
On broad classes of linear systems, the shortest experiments are almost as good as the best possible ones. For $n$ states and $m$ inputs, the shortest input sequences that support robust data-driven stabilization of every controllable plant have $mn+1$ steps with exact states and $m(n+1)$ with noisy states. We show that, when the spectral radius is bounded and the spectrum is well separated near the unit circle, these sequences tolerate a fixed fraction of the error level achievable by any experiment, even one designed with full plant knowledge and allowed to use any finite duration. This constant-factor comparison can fail for slowly actuated systems. For $A=I+hG$ with controllability depth $\nu\ge2$, short experiments lose a factor of order $h^{\nu-1}$, and duration of order $1/h$ is both necessary and sufficient to recover a fixed fraction of the optimal tolerance.
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Systems and Control
Feasibility of Simultaneous Input-Output Constraints for Tracking in a Class of LTI Systems: Part I
Junhyeok Yoon, Heather Hussain and Anuradha M. Annaswamy
This paper addresses simultaneous input and output constraint satisfaction for a class of multi-input linear time-invariant systems with state feedback and integral action. A control barrier function (CBF)-based governor modifies the reference command while preserving the nominal feedback controller. Necessary and sufficient conditions are derived under which the governor is feasible at every state of a prescribed operating set and ensures simultaneous input and output constraint satisfaction, forward invariance, and bounded closed-loop solutions. The paper also shows that these conditions need not be met for certain choices of the CBF governor parameters due to conflicts between input and output constraints. A systematic design procedure is proposed that first searches for feasible free parameters and, if necessary, proposes a relaxation in the input constraint that restores feasibility. A companion paper provides numerical examples and counterexamples illustrating these results.
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Image and Video Processing · Cross-listed from cs.CV
MIRTO: a registration-gated, multiverse-tested evaluation protocol for unsupervised anomaly segmentation in brain MRI
Negin Kafee Hernashki and Soumick Chatterjee
Unsupervised anomaly detection (UAD) methods for
brain MRI are ranked by a single score, yet that score rests on choices that are rarely reported: how each anomaly map is aligned with the reference, how and on which data the threshold is set, and which false-positive budget, metric, aggregation and lesion definition are used. We present MIRTO, an evaluation protocol that makes these choices explicit and measures their effect. It gates the geometry of every comparison with a registration check and label-free diagnostics of known power, sets thresholds on validation data alone and reports the false-positive volume actually realised on test, repeats each comparison over 15,552 defensible evaluation pipelines, and attaches paired subject-bootstrap intervals with multiplicity control. Applied to four UAD methods trained on the same healthy data and tested on 312 BraTS 2020 subjects, MIRTO showed that an axis-order mismatch between stored maps and the reference lowered a
diffusion model's voxel AUROC from 0.873 to 0.583 whilst barely moving its slice-level AUROC. Within each metric, the method explained at least 0.95 of the variance in voxel AUROC and AUPRC and 0.77 in Dice, but only 0.14 in lesion sensitivity, where the lesion definition and hit criterion dominated. A Dice advantage that was significant at validation thresholds vanished at equal realised false-positive burden, and an exact identity attributes it to threshold transfer. A training-free change to REFLECT's latent aggregation raised Dice at equal burden by 0.052. Nine hypotheses were tested against explicit criteria; because the same cohort served to develop the protocol, all inference is exploratory.
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Systems and Control
Decentralized Power-Optimal Coordination for Spacecraft Swarms Using Time-Varying Magnetorquer Actuation
Yuta Takahashi and Shin-ichiro Sakai
This paper presents a decentralized power-optimal coordination framework for magnetically actuated spacecraft swarms. Swarms that form large space structures overcome the aperture limit set by the launch vehicle and hold their shape on solar-generated power alone.
Magnetic actuation is propellant-free and generated by a magnetorquer, which is commonly used for attitude control. However, every spacecraft interacts with every other within range, and its effect depends on the actuation power and a carrier frequency. We therefore design a decentralized power-optimal framework to jointly derive the interaction graph, frequency grouping, and controller gains. Our decentralized controller preserves angular momentum, which is a nonholonomic constraint. Then, this framework for connected groups whose memberships overlap across carriers guarantees that the relative position errors, the absolute attitude errors, and the imbalance of the reaction-wheel momenta converge to the desired states under the decentralized power-optimal allocation. A closed-loop simulation of a thousand spacecraft with the complete alternating-current interaction confirms the framework. A fast approximate integration with a proven error bound extends the framework to a long-horizon orbital reconfiguration held with high precision.
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Image and Video Processing
Binary Phase Retrieval of Cosine Transforms via Local Curvature Minimization
Ronald Ogden, Shwetadwip Chowdhury, Takashi Tanaka et al. (+1 more)
Cosine transforms see frequent use in image and
video compression due to their ease of computation and high energy compaction. This has sparked interest within the optics community in computing cosine transforms optically for compression. When imaging in the
Fourier plane to capture a cosine transform, one encounters a phase
retrieval problem: optical fields have both magnitude and phase, but cameras only capture the magnitude of the field. Cosine transforms restrict the phase
retrieval problem to a binary domain as opposed to the unit circle, but general phase
retrieval solutions do not leverage this. In this work, we demonstrate that sign errors in phase
retrieval for cosine transforms result in a significant increase in the magnitude of the Hessian of the transform at the error. Motivated by this, we develop an
algorithm to solve this binary phase
retrieval problem by minimizing the
curvature of the reconstructed cosine transform. We demonstrate via computational experiments that given the magnitude of the cosine transform of an image, we can consistently reconstruct the original image within a multiscale structural similarity index of 0.95. We show that the solve time grows approximately linearly with the number of pixels solved.
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All authors Ronald Ogden, Shwetadwip Chowdhury, Takashi Tanaka, David Fridovich-Keil
Systems and Control
Local Consistency Does Not Guarantee Global Conservation: Auditing Zero-Shot Composition of Airway Flow Operators
Nichula Sathmith Wasalathilaka, Navodya Heshan Samarasinghe Dhanujaya Suraweera, Kevin Dawson et al. (+2 more)
Neural operators approximate PDE solutions within a geometry family, but independently learned local operators need not form a consistent global simulator. We study frozen, single-pass composition for steady incompressible flow in idealized two-dimensional airway trees. Separate Tube, bifurcation, and trifurcation DeepONets are trained on 4,872 primitive CFD cases using field supervision and auxiliary divergence, port-flux, component-balance, and port-pressure penalties. The validation-selected deployment is frozen before whole-tree CFD fields are inspected and assembled without tree training, iterative coupling, flux correction, or CFD-informed adjustment. It retains major flow patterns and controlled pathology responses with 0.204-0.215 s CPU inference, but has a 22.68% prescribed-inlet-normalized external residual. A post-hoc sensitivity protocol, frozen before new training and evaluation, repeats Data, Div, and Full models across three seeds with Tube fixed. Relative to Data, Full reduces primitive composite scores by 26.7% for Y2 and 26.4% for Y3 and reduces assembled component-residual and interface-mismatch RMS by 7.2% and 16.6%, respectively. Nevertheless, mean tree velocity error increases by 7.5%, while external residual increases from 17.49 +/- 4.38% to 26.77 +/- 3.65%. Local regularization can therefore improve primitive and assembled local diagnostics without ensuring accurate global fields or conservation.
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All authors Nichula Sathmith Wasalathilaka, Navodya Heshan Samarasinghe Dhanujaya Suraweera, Kevin Dawson, Chinthaka Jacob Mervyn Parakrama Bandara Ekanayake, Roshan Godaliyadda
Systems and Control
A Dynamic Generalized Kalman Consensus Filter for Switching Sensor Networks
Tirthankar Chakraborty and Arnab Maity
Distributed state estimation is critical for applications such as surveillance, autonomous navigation, and wide-area monitoring, where sensor
agents must cooperatively track targets using only local measurements and neighbor-to-neighbor communication. Existing distributed filters have been shown to achieve accurate estimation even under sparse inter-
agent communication and limited sensing ranges. However, many of these methods rely on consensus parameters that depend on global properties of the communication graph, such as the maximum degree of the graph, and are therefore sensitive to changes in network
topology. This limitation is particularly significant in sensor networks with mobile
agents, where communication links change over time. This paper presents a Dynamic Generalized Kalman Consensus Filter for target tracking in sensor networks with switching communication
topologies. The proposed
algorithm computes information-based consensus weights using only locally available quantities, eliminating the need for global network parameters. Numerical simulations demonstrate that the proposed
algorithm maintains estimation accuracy under switching network
topologies and outperforms existing distributed filters in the given tracking problem.
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Audio and Speech Processing
Multi-sample Synthetic Supervision for Accent Conversion
Yangyang Qu, Michele Panariello, Massimiliano Todisco et al. (+1 more)
Accent conversion (AC) requires changing accent while preserving speaker identity and linguistic content, yet parallel recordings are scarce.
Speech synthesis provides an alternative source of supervision, but generated targets vary in accent realization and source preservation. We propose a multi-sample synthetic supervision framework that constructs conversion targets by jointly assessing these properties across candidate waveforms for each source–accent condition. Selected candidates provide associated discrete
speech codes, representing linguistic content, prosody, and speaking style, as targets for accent-conditioned autoregressive adaptation. Compared with using one generated target per training example, our method improves target-accent classification accuracy by 2.49 percentage points, while speaker similarity remains nearly unchanged, and word error rate increases by 0.29 percentage points. Across six target accents, our method achieves 7.81% WER and the highest mean listening ratings among evaluated systems.
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All authors Yangyang Qu, Michele Panariello, Massimiliano Todisco, Nicholas Evans
Audio and Speech Processing
Shared-State Local Translations for Training-Free Voice Conversion
Yangyang Qu, Michele Panariello, Massimiliano Todisco et al. (+1 more)
In one-shot training-free voice conversion (VC), the source and reference utterances may contain different linguistic content, so reliable frame-level correspondence between them cannot be assumed. We propose StateVC, which jointly defines a common set of local regions from pooled frame-level WavLM representations of the source and reference utterances; we refer to these regions as states. These shared states are obtained by fitting a pair-specific Gaussian mixture model to the pooled representations, without explicit source–reference frame matching. Within each state, StateVC estimates a source-to-reference mean shift in the original WavLM space. Source-frame posterior probabilities then combine the state-specific shifts so that different frames can receive different local updates. For the LibriSpeech one-shot protocol, StateVC achieves the lowest word error rate (WER) and character error rate (CER) among the evaluated systems, at 8.01% and 3.22%, respectively, with a speaker similarity (SIM) of 0.9512. It also achieves the highest mean perceived speaker similarity among the evaluated systems and the highest mean naturalness among the evaluated training-free systems.
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All authors Yangyang Qu, Michele Panariello, Massimiliano Todisco, Nicholas Evans
Audio and Speech Processing · Cross-listed from cs.SD
LAST: Looped Audio Spectrogram Transformer
Haider Al-Tahan, Sean O'Brien, Anastasia Razdaibiedina et al. (+1 more)
Increasing depth of
transformer models improves recognition, but it comes at a substantial cost. Each additional layer requires more parameters, which makes the process computationally inefficient. We ask whether additional processing can focus on integrating features already computed. Looped Audio Spectrogram
Transformer (LAST) first processes all
tokens, then reuses the same blocks to refine only the class
token over fixed audio features, thereby making later passes inexpensive. On AudioSet, ten-pass LAST achieves 0.345 mean average precision, exceeding a twelve-layer sequential
transformer by 2.1% relative with 49.4% fewer parameters, 42% fewer multiply-accumulate operations, and 9.8% higher measured throughput. Across separately trained models, increasing the pass count from two to ten improves accuracy while adding only 1.2% computation. Further evaluations show improved robustness to temporal masking and various other auditory augmentations, with better generalization on classification tasks with music, environmental, and event sounds.
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All authors Haider Al-Tahan, Sean O'Brien, Anastasia Razdaibiedina, N. Apurva Ratan Murty
Systems and Control
Interactive Power Flow in the Browser
Samuel Talkington, Frederik Geth, Qian Zhang et al. (+2 more)
This paper introduces tellegen, an open source framework for interactive power flow (PF) and optimal power flow (OPF) studies that run in the browser. This provides intuitive and democratized access to power system analysis tools compiled to WebAssembly. A user can drag and drop a case file, click and drag to change a nodal demand or line rating, preview the impacts via sensitivity analysis, and obtain an exact re-solve on release. User case files and results stay entirely on the device: tellegen transmits zero Critical Energy/Electric Infrastructure Information (CEII). The framework comprises a core numerical engine for PF and OPF, reusable browser components, saved studies, and structured WebMCP tools for agentic interaction. We evaluate the transmission OPF solver by comparing objectives with PGLib baselines; the distribution PF solver by comparing voltages and currents with OpenDSS; and the WebAssembly execution times by comparing with PowerModels.jl. On realistic synthetic grids, WebAssembly OPF solves take only 25-43% longer than native binary solves. The implementation shows how an engineer can distribute an executable numerical study as a URL, reducing installation and hosting requirements while keeping case data local.
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All authors Samuel Talkington, Frederik Geth, Qian Zhang, Le Xie, Skyler Liu
Signal Processing
On the Impact of Coordinate Descent for Multi-Angle QAOA in the Independent Set Graph Problem
Daeyeun Kim, Seungcheol Oh and Joongheon Kim
Constrained combinatorial optimisation provides a mathematical framework for modelling a wide range of practical decision making problems, including energy network operation, financial portfolio optimisation, logistics, routing, and resource allocation. This paper studies parameter optimisation for the multi angle
quantum approximate optimisation
algorithm (maQAOA) applied to the maximum independent set problem, a canonical constrained graph based optimisation task. We propose a coordinate wise training framework for maQAOA that updates one variational parameter at a time by solving a sequence of one dimensional optimisation subproblems. For each selected coordinate, the method reconstructs the objective dependence on that parameter and moves directly to the parameter value that minimises the training objective, or equivalently maximises the corresponding expected solution quality. While demonstrated specifically on maQAOA, the underlying theoretical principle, that the expectation value with respect to a given parameter can be analytically expressed as a finite
Fourier series, is broadly applicable to general tasks solved via parameterised
quantum circuits. Empirical evaluations on connected ErdHos Renyi graph
benchmarks demonstrate that this analytic approach significantly reduces the computational training cost required to reach target approximation ratios and yields superior convergence reliability compared to standard gradient based,
stochastic, and derivative free baselines.
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