Papers

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

Electrical Engineering and Systems Science

eess Watch Electrical Engineering and Systems Science

Tuesday 3 February

Audio and Speech Processing

Mići Princ – A Little Boy Teaching Speech Technologies the Chakavian Dialect

Nikola Ljubešić, Peter Rupnik and Tea Perinčić

This paper documents our efforts in releasing the printed and audio book of the translation of the famous novel The Little Prince into the Chakavian dialect, as a computer-readable, AI-ready dataset, with the textual and the audio components of the two releases now aligned on the level of each written and spoken word. Our motivation for working on this release is multiple. The first one is our wish to preserve the highly valuable and specific content beyond the small editions of the printed and the audio book. With the dataset published in the CLARIN.SI repository, this content is from now on at the fingertips of any interested individual. The second motivation is to make the data available for various artificial-intelligence-related usage scenarios, such as the one we follow upon inside this paper already – adapting the Whisper-large-v3 open automatic speech recognition model, with decent performance on standard Croatian, to Chakavian dialectal speech. We can happily report that with adapting the model, the word error rate on the selected test data has being reduced to a half, while we managed to remove up to two thirds of the error on character level. We envision many more usages of this dataset beyond the set of experiments we have already performed, both on tasks of artificial intelligence research and application, as well as dialectal research. The third motivation for this release is our hope that this, now highly structured dataset, will be transformed into a digital online edition of this work, allowing individuals beyond the research and technology communities to enjoy the beauty of the message of the little boy in the desert, told through the spectacular prism of the Chakavian dialect.

Read the full abstract

Signal Processing · Cross-listed from cs.LG

Function-Space Empirical Bayes Regularisation with Large Vision-Language Model Priors

Pengcheng Hao, Huaze Tang, Ercan Engin Kuruoglu et al. (+1 more)

Bayesian deep learning (BDL) provides a principled framework for reliable uncertainty quantification by combining deep neural networks with Bayesian inference. A central challenge in BDL lies in the design of informative prior distributions that scale effectively to high-dimensional data. Recent functional variational inference (VI) approaches address this issue by imposing priors directly in function space; however, most existing methods rely on Gaussian process (GP) priors, whose expressiveness and generalisation capabilities become limited in high-dimensional regimes. In this work, we propose VLM-FS-EB, a novel function-space empirical Bayes regularisation framework, leveraging large vision-language models (VLMs) to generates semantically meaningful context points. These synthetic samples are then used VLMs for embeddings to construct expressive functional priors. Furthermore, the proposed method is evaluated against various baselines, and experimental results demonstrate that our method consistently improves predictive performance and yields more reliable uncertainty estimates, particularly in out-of-distribution (OOD) detection tasks and data-scarce regimes.

Read the full abstract

All authors Pengcheng Hao, Huaze Tang, Ercan Engin Kuruoglu, Wenbo Ding

Systems and Control · Cross-listed from cs.LG

Geometry-Preserving Neural Architectures on Manifolds with Boundary

Karthik Elamvazhuthi, Shiba Biswal, Kian Rosenblum et al. (+4 more)

A growing number of neural architectures have been proposed to enforce geometric constraints, including projection-based networks, exponential-map updates, constrained output layers, and manifold neural ODEs. We provide a unified framework for these geometry-preserving architectures by organizing them according to where and how constraints are enforced, either throughout the intermediate layers or only at the final output. This perspective reveals several gaps in the existing theory. To address these gaps, we prove high-level approximation theorems for projected neural ODEs, intermediate augmented architectures, and final augmented architectures on prox-regular constraint sets, including smooth manifolds with boundary. Numerical experiments on synthetic dynamics over S^2, the disk, SO(3), together with real-world protein backbone data on SE(3), demonstrate exact feasibility for analytic updates and show that the final augmentation have simpler architecture and outperform in most tasks considered. When the constraint set is unknown, we learn projections via small-time heat-kernel limits, showing diffusion/flow-matching can be used as data-based projections. Moreover, we also the demonstrate the usefulness of the architectures that enforce non-convex constraints for path planning on manifolds with boundary.

Read the full abstract

All authors Karthik Elamvazhuthi, Shiba Biswal, Kian Rosenblum, Arushi Katyal, Tianli Qu, Grady Ma, Rishi Sonthalia

Systems and Control

ProOPF: Benchmarking and Improving LLMs for Professional-Grade Power Systems Optimization Modeling

Chao Shen, Zihan Guo, Xu Wan et al. (+6 more)

Growing renewable penetration introduces substantial uncertainty into power system operations, necessitating frequent adaptation of dispatch objectives and constraints and challenging expertise-intensive, near-real-time modeling workflows. Large Language Models (LLMs) provide a promising avenue for automating this process by translating natural-language (NL) operational requirements into executable optimization models via semantic reasoning and code synthesis. Yet existing LLM datasets and benchmarks for optimization modeling primarily target coarse-grained cross-domain generalization, offering limited, rigorous evaluation in power-system settings, particularly for Optimal Power Flow (OPF). We therefore introduce ProOPF-D and ProOPF-B, a dataset and benchmark for professional-grade OPF modeling: ProOPF-D contains 12K instances pairing NL requests with parameter adjustments and structural extensions to a canonical OPF, together with executable implementations; ProOPF-B provides 121 expert-annotated test cases with ground-truth code, enabling end-to-end evaluation under both concrete and abstract OPF modeling regimes.

Read the full abstract

All authors Chao Shen, Zihan Guo, Xu Wan, Zhenghao Yang, Yifan Zhang, Wengi Huang, Jie Song, Zongyan Zhang, Mingyang Sun

Signal Processing

Stationarity and Spectral Characterization of Random Signals on Simplicial Complexes

Madeline Navarro, Andrei Buciulea, Santiago Segarra et al. (+1 more)

It is increasingly common for data to possess intricate structure, necessitating new models and analytical tools. Graphs, a prominent type of structure, can encode the relationships between any two entities (nodes). However, graphs neither allow connections that are not dyadic nor permit relationships between sets of nodes. We thus turn to simplicial complexes for connecting more than two nodes as well as modeling relationships between simplices, such as edges and triangles. Our data then consist of signals lying on topological spaces, represented by simplicial complexes. Much recent work explores these topological signals, albeit primarily through deterministic formulations. We propose a probabilistic framework for random signals defined on simplicial complexes. Specifically, we generalize the classical notion of stationarity. By spectral dualities of Hodge and Dirac theory, we define stationary topological signals as the outputs of topological filters given white noise. This definition naturally extends desirable properties of stationarity that hold for both time-series and graph signals. Crucially, we properly define topological power spectral density (PSD) through a clear spectral characterization. We then discuss the advantages of topological stationarity due to spectral properties via the PSD. In addition, we empirically demonstrate the practicality of these benefits through multiple synthetic and real-world simulations.

Read the full abstract

All authors Madeline Navarro, Andrei Buciulea, Santiago Segarra, Antonio Marques

Systems and Control

Fast Diffusion with Physics-Correction for ACOPF

Shashank Shekhar, Abhinav Karn, Kris Keshav et al. (+2 more)

Generating large-scale, physically consistent AC Optimal Power Flow (ACOPF) datasets is essential for modern data-driven power system applications. The central challenge lies in balancing solution accuracy with computational efficiency. Recent diffusion-based generative models produce high-quality samples; however, their slow sampling procedures limit practical scalability. In this work, we argue that exact physical feasibility is ultimately enforced by power flow solvers or projection steps, and therefore the generative model only needs to produce good initializations rather than perfectly feasible solutions. Based on this insight, we propose a fast diffusion framework using Denoising Diffusion Implicit Models (DDIM) combined with physics-guided corrections during sampling. The proposed method replaces slow stochastic refinement with a small number of deterministic steps and explicit constraint guidance. Experiments on IEEE 6-, 24-, and 118-bus systems show that our approach achieves up to 20 times faster sampling than standard diffusion models while maintaining comparable statistical accuracy and physical consistency. This makes the method well suited for scalable OPF dataset generation and practical power system learning tasks. We release the implementation code at https://github.com/PSquare-Lab/DDIM_OPF.

Read the full abstract

All authors Shashank Shekhar, Abhinav Karn, Kris Keshav, Shivam Bansal, Parikshit Pareek

Signal Processing

Tri-Hybrid Holographic Beamforming for Integrated Sensing and Communication

Shupei Zhang, Shuhao Zeng, Boya Di et al. (+1 more)

Integrated sensing and communication (ISAC) can perform both communication and sensing tasks using the same frequency band and hardware, making it a key technology for 6G. As a low-cost implementation for large-scale antenna arrays, reconfigurable holographic surfaces (RHSs) can be integrated into ISAC systems to realize the holographic ISAC paradigm, where enlarged radiation apertures achieve significant beamforming gains. In this paper, we investigate the tri-hybrid holographic ISAC framework, where the beamformer comprises digital, analog, and RHS-based electromagnetic (EM) layers. The analog layer employs a small number of phase shifters (PSs) to provide subarray-level phase control for the amplitude-modulated RHSs. Tri-hybrid beamforming provides a pathway for low-cost large-scale holographic ISAC. However, compared to conventional ISAC systems, it is challenging to achieve joint subarray-level phase control via PSs and element-level radiation amplitude control via RHSs for holographic ISAC. To address this, we present a tri-hybrid holographic ISAC scheme that minimizes sensing waveform error while satisfying the minimum user rate requirement. A joint optimization approach for PS phases and RHS amplitude responses is designed to address inter-layer coupling and distinct feasible regions. Theoretical analyses reveal that the optimized amplitude responses cluster near boundary values, i.e., 1-bit amplitude control, to reduce hardware and algorithmic complexity. Simulation results show that the proposed scheme achieves a controllable performance trade-off between communication and sensing tasks. Measured RHS beam gain validates the enhancement of holographic beamforming through subarray-level phase shifting. Moreover, as the number of RHS elements increases, the proposed approach exceeds the performance of conventional hybrid beamforming while significantly reducing the number of PSs.

Read the full abstract

All authors Shupei Zhang, Shuhao Zeng, Boya Di, Lingyang Song

Systems and Control · Cross-listed from math.OC

Data-driven stabilization of continuous-time systems with noisy input-output data

Masashi Wakaiki

We study data-driven stabilization of continuous-time systems in autoregressive form when only noisy input-output data are available. First, we provide an operator-based characterization of the set of systems consistent with the data. Next, combining this characterization with behavioral theory, we establish a necessary and sufficient condition for the noisy data to be informative for quadratic stabilization. This condition is formulated in terms of linear matrix inequalities, whose solutions yield a stabilizing controller. Finally, we characterize data informativity for system identification in the noise-free setting.

Read the full abstract

Audio and Speech Processing

WaveScat: Wavelet Scattering Front-Ends with Self-Supervised Features for Speech Deepfake Detection

Xi Xuan, Davide Carbone, Wenxin Zhang et al. (+1 more)

Existing front-ends for speech deepfake detection are primarily categorized into two types. Hand-crafted filterbank features are transparent but limited in capturing higher-level information. SSL features, in turn, lack interpretability and may overlook fine-grained spectral anomalies. We propose WaveScat, a novel family of feature extractors that combines the best of both worlds via the wavelet scattering transform (WST), which cascades wavelet convolutions with modulus nonlinearities to produce deformation-stable, multi-scale features. Experiments on the recent Deepfake-Eval-2024 benchmark, together with cross-dataset evaluations on SpoofCeleb, In-the-Wild, and ASVspoof 5, show that WaveScat outperforms existing front-ends by a wide margin. Our analysis reveals that a small averaging scale combined with high-frequency and directional resolutions is critical for capturing subtle artifacts. This underscores the value of stable and translation-invariant features for speech deepfake detection. The code and supplementary materials are available at https://github.com/xxuan-acoustics/WaveScat.

Read the full abstract

All authors Xi Xuan, Davide Carbone, Wenxin Zhang, Tomi H. Kinnunen

Systems and Control · Cross-listed from cs.LG

Human-Centric Traffic Signal Control for Equity: A Multi-Agent Action Branching Deep Reinforcement Learning Approach

Xiaocai Zhang, Neema Nassir, Lok Sang Chan et al. (+1 more)

Coordinating traffic signals along multimodal corridors is challenging because many multi-agent deep reinforcement learning (DRL) approaches remain vehicle-centric and struggle with high-dimensional discrete action spaces. We propose MA2B-DDQN, a human-centric multi-agent action-branching double Deep Q-Network (DQN) framework that explicitly optimizes traveler-level equity. Our key contribution is an action-branching discrete control formulation that decomposes corridor control into (i) local, per-intersection actions that allocate green time between the next two phases and (ii) a single global action that selects the total duration of those phases. This decomposition enables scalable coordination under discrete control while reducing the effective complexity of joint decision-making. We also design a human-centric reward that penalizes the number of delayed individuals in the corridor, accounting for pedestrians, vehicle occupants, and transit passengers. Extensive evaluations across seven realistic traffic scenarios in Melbourne, Australia, demonstrate that our approach significantly reduces the number of impacted travelers, outperforming existing DRL and baseline methods. Experiments confirm the robustness of our model, showing minimal variance across diverse settings. This framework not only advocates for a fairer traffic signal system but also provides a scalable solution adaptable to varied urban traffic conditions.

Read the full abstract

All authors Xiaocai Zhang, Neema Nassir, Lok Sang Chan, Milad Haghani

Systems and Control

Hybrid-Field Channel Estimation for XL-MIMO Systems: Dictionary-based Sparse Signal Recovery

David William Marques Guerra and Taufik Abrao

Extremely large-scale multiple-input multiple-output (XL-MIMO) systems are a key technology for future wireless networks, but the large array aperture naturally creates a hybrid-field (HF) propagation regime in which far-field (FF) planar-wave and near-field (NF) spherical-wave components coexist. This work considers the problem of HF channel estimation (CE) and introduces a unified model that superimposes FF and NF contributions according to the Rayleigh distance boundary. By exploiting the inherent sparsity of the channel in the angular and polar domains, we formulate the estimation task as a sparse recovery problem. Unlike conventional approaches that require prior knowledge of the channel sparsity level, the proposed method operates without requiring knowledge of the sparsity level L and the NF/FF ratio {\gamma}, which are used only for synthetic channel generation in simulations. The channel estimator determines the number of paths adaptively through a residual-based stopping rule. A combined FF/NF dictionary is employed to initialize the support, and each selected atom undergoes continuous parameter refinement to mitigate grid mismatch. Simulation results demonstrate that the proposed estimator achieves accurate HF channel reconstruction under both line-of-sight (LoS) and non-line-of-sight (NLoS) conditions, offering a practical and computationally efficient solution for XL-MIMO systems. Extremely Large-Scale MIMO (XL-MIMO); Channel State Information (CSI); Channel estimation (CE); hybrid-field (HF) wave propagation; near-field (NF) spherical wave model; far-field (FF) planar wave model

Read the full abstract

Monday 2 February

Image and Video Processing

Guidestar-Free Adaptive Optics with Asymmetric Apertures

Weiyun Jiang, Haiyun Guo, Christopher A. Metzler et al. (+1 more)

This work introduces the first closed-loop adaptive optics (AO) system capable of optically correcting aberrations in real-time without a guidestar or a wavefront sensor. Nearly 40 years ago, Cederquist et al. demonstrated that asymmetric apertures enable phase retrieval (PR) algorithms to perform fully computational wavefront sensing, albeit at a high computational cost. More recently, Chimitt et al. extended this approach with machine learning and demonstrated real-time wavefront sensing using only a single (guidestar-based) point-spread-function (PSF) measurement. Inspired by these works, we introduce a guidestar-free AO framework built around asymmetric apertures and machine learning. Our approach combines three key elements: (1) an asymmetric aperture placed at the system's pupil plane that enables PR-based wavefront sensing, (2) a pair of machine learning algorithms that estimate the PSF from natural scene measurements and reconstruct phase aberrations, and (3) a spatial light modulator that performs optical correction. We experimentally validate this framework on dense natural scenes imaged through unknown obscurants. Our method outperforms state-of-the-art guidestar-free wavefront shaping methods, using an order of magnitude fewer measurements and three orders of magnitude less computation.

Read the full abstract

All authors Weiyun Jiang, Haiyun Guo, Christopher A. Metzler, Ashok Veeraraghavan

Image and Video Processing

Condition Errors Refinement in Autoregressive Image Generation with Diffusion Loss

Yucheng Zhou, Hao Li and Jianbing Shen

Recent studies have explored autoregressive models for image generation, with promising results, and have combined diffusion models with autoregressive frameworks to optimize image generation via diffusion losses. In this study, we present a theoretical analysis of diffusion and autoregressive models with diffusion loss, highlighting the latter's advantages. We present a theoretical comparison of conditional diffusion and autoregressive diffusion with diffusion loss, demonstrating that patch denoising optimization in autoregressive models effectively mitigates condition errors and leads to a stable condition distribution. Our analysis also reveals that autoregressive condition generation refines the condition, causing the condition error influence to decay exponentially. In addition, we introduce a novel condition refinement approach based on Optimal Transport (OT) theory to address “condition inconsistency”. We theoretically demonstrate that formulating condition refinement as a Wasserstein Gradient Flow ensures convergence toward the ideal condition distribution, effectively mitigating condition inconsistency. Experiments demonstrate the superiority of our method over diffusion and autoregressive models with diffusion loss methods.

Read the full abstract

Systems and Control · Cross-listed from cs.DC

Computationally Efficient Laplacian CL-colME

Nikola Stankovic

Decentralized collaborative mean estimation (colME) is a fundamental task in heterogeneous networks. Its graph-based variants B-colME and C-colME achieve high scalability of the problem. This paper evaluates the consensus-based C-colME framework, which relies on doubly stochastic averaging matrices to ensure convergence to the oracle solution. We propose CL-colME, a novel variant utilizing Laplacian-based consensus to avoid the computationally expensive normalization processes. Simulation results show that the proposed CL-colME maintains the convergence behavior and accuracy of C-colME while improving computational efficiency.

Read the full abstract

Systems and Control · Cross-listed from cs.LG

How Does the Lagrangian Guide Safe Reinforcement Learning through Diffusion Models?

Xiaoyuan Cheng, Wenxuan Yuan, Boyang Li et al. (+7 more)

Diffusion policy sampling enables reinforcement learning (RL) to represent multimodal action distributions beyond suboptimal unimodal Gaussian policies. However, existing diffusion-based RL methods primarily focus on offline settings for reward maximization, with limited consideration of safety in online settings. To address this gap, we propose Augmented Lagrangian-Guided Diffusion (ALGD), a novel algorithm for off-policy safe RL. By revisiting optimization theory and energy-based model, we show that the instability of primal-dual methods arises from the non-convex Lagrangian landscape. In diffusion-based safe RL, the Lagrangian can be interpreted as an energy function guiding the denoising dynamics. Counterintuitively, direct usage destabilizes both policy generation and training. ALGD resolves this issue by introducing an augmented Lagrangian that locally convexifies the energy landscape, yielding a stabilized policy generation and training process without altering the distribution of the optimal policy. Theoretical analysis and extensive experiments demonstrate that ALGD is both theoretically grounded and empirically effective, achieving strong and stable performance across diverse environments.

Read the full abstract

All authors Xiaoyuan Cheng, Wenxuan Yuan, Boyang Li, Yuanchao Xu, Yiming Yang, Hao Liang, Bei Peng, Robert Loftin, Zhuo Sun, Yukun Hu

Systems and Control · Cross-listed from cs.LG

Spatiotemporal Decision Transformer for Traffic Coordination

Haoran Su, Yandong Sun and Hanxiao Deng

Traffic signal control is a critical challenge in urban transportation, requiring coordination among multiple intersections to optimize network-wide traffic flow. While reinforcement learning has shown promise for adaptive signal control, existing methods struggle with multi-agent coordination and sample efficiency. We introduce MADT (Multi-Agent Decision Transformer), a novel approach that reformulates multi-agent traffic signal control as a sequence modeling problem. MADT extends the Decision Transformer paradigm to multi-agent settings by incorporating: (1) a graph attention mechanism for modeling spatial dependencies between intersections, (2) a|temporal transformer encoder for capturing traffic dynamics, and (3) return-to-go conditioning for target performance specification. Our approach enables offline learning from historical traffic data, with architecture design that facilitates potential online fine-tuning. Experiments on synthetic grid networks and real-world traffic scenarios demonstrate that MADT achieves state-of-the-art performance, reducing average travel time by 5-6% compared to the strongest baseline while exhibiting superior coordination among adjacent intersections.

Read the full abstract

Signal Processing

Gridless Full-Space DOA Estimation for STAR-RIS-Assisted Wireless Systems

Ziming Liu, Tao Chen, Muran Guo et al. (+1 more)

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) enable full-space ($0^\circ$–$360^\circ$) signal coverage, making them a compelling platform for integrated sensing and communication in next-generation wireless networks. In this paper, we investigate gridless direction-of-arrival (DOA) estimation across the full spatial domain in STAR-RIS-assisted systems operating with a single RF sensing chain. We show that the coupled reflection-transmission mechanism of STAR-RIS induces a multichannel finite-rate-of-innovation (FRI) structure in the received signal, which enables casting DOA estimation as a structured low-rank recovery problem without angular grid discretization. Building on this observation, we develop a proximal gradient descent algorithm with alternating projections onto a block-Hankel matrix set, enabling robust angle retrieval from limited measurements. Two practically relevant STAR-RIS configurations are addressed: element-wise uniform and nonuniform energy-splitting designs, each handled through a dedicated lifting strategy that preserves the underlying algebraic structure. A Ziv-Zakai bound is derived for the coupled full-space sensing model as a performance benchmark across the full SNR range. Numerical results show that the proposed methods consistently outperform grid-based baselines, achieving sub-degree accuracy within $\pm 60^\circ$ of boresight at comparable or lower computational cost.

Read the full abstract

All authors Ziming Liu, Tao Chen, Muran Guo, Francesco Verde

Systems and Control

Estimation of Cell-to-Cell Variation and State of Health for Battery Modules with Parallel-Connected Cells

Qinan Zhou and Jing Sun

Estimating cell-to-cell variation (CtCV) and state of health (SoH) for battery modules composed of parallel-connected cells is challenging when only module-level signals are measurable and individual cell behaviors remain unobserved. Although progress has been made in SoH estimation, CtCV estimation remains unresolved in the literature. This paper proposes a unified framework that accurately estimates both CtCV and SoH for modules using only module-level information extracted from incremental capacity analysis (ICA) and differential voltage analysis (DVA). With the proposed framework, CtCV and SoH estimations can be decoupled into two separate tasks, allowing each to be solved with dedicated algorithms without mutual interference and providing greater design flexibility. The framework also exhibits strong versatility in accommodating different CtCV metrics, highlighting its general-purpose nature. Experimental validation on modules with three parallel-connected cells demonstrates that the proposed framework can systematically select optimal module-level features for CtCV and SoH estimations, deliver accurate CtCV and SoH estimates with high confidence and low computational complexity, remain effective across different C-rates, and be suitable for onboard implementation.

Read the full abstract

Systems and Control · Cross-listed from cs.RO

IMAGINE: Intelligent Multi-Agent Godot-based Indoor Networked Exploration

Tiago Leite, Maria Conceição and António Grilo

The exploration of unknown, Global Navigation Satellite System (GNSS) denied environments by an autonomous communication-aware and collaborative group of Unmanned Aerial Vehicles (UAVs) presents significant challenges in coordination, perception, and decentralized decision-making. This paper implements Multi-Agent Reinforcement Learning (MARL) to address these challenges in a 2D indoor environment, using high-fidelity game-engine simulations (Godot) and continuous action spaces. Policy training aims to achieve emergent collaborative behaviours and decision-making under uncertainty using Network-Distributed Partially Observable Markov Decision Processes (ND-POMDPs). Each UAV is equipped with a Light Detection and Ranging (LiDAR) sensor and can share data (sensor measurements and a local occupancy map) with neighbouring agents. Inter-agent communication constraints include limited range, bandwidth and latency. Extensive ablation studies evaluated MARL training paradigms, reward function, communication system, neural network (NN) architecture, memory mechanisms, and POMDP formulations. This work jointly addresses several key limitations in prior research, namely reliance on discrete actions, single-agent or centralized formulations, assumptions of a priori knowledge and permanent connectivity, inability to handle dynamic obstacles, short planning horizons and architectural complexity in Recurrent NNs/Transformers. Results show that the scalable training paradigm, combined with a simplified architecture, enables rapid autonomous exploration of an indoor area. The implementation of Curriculum-Learning (five increasingly complex levels) also enabled faster, more robust training. This combination of high-fidelity simulation, MARL formulation, and computational efficiency establishes a strong foundation for deploying learned cooperative strategies in physical robotic systems.

Read the full abstract

Systems and Control · Cross-listed from math.OC

Sub-optimality bounds for certainty equivalent policies in partially observed systems

Berk Bozkurt, Aditya Mahajan, Ashutosh Nayyar et al. (+1 more)

In this paper, we present a generalization of the certainty equivalence principle of stochastic control. One interpretation of the classical certainty equivalence principle for linear systems with output feedback and quadratic costs is as follows: the optimal action at each time is obtained by evaluating the optimal state-feedback policy of the stochastic linear system at the minimum mean square error (MMSE) estimate of the state. Motivated by this interpretation, we consider certainty equivalent policies for general (non-linear) partially observed stochastic systems that allow for any state estimate rather than restricting to MMSE estimates. In such settings, the certainty equivalent policy is not optimal. For models where the cost and the dynamics are smooth in an appropriate sense, we derive upper bounds on the sub-optimality of certainty equivalent policies. We present several examples to illustrate the results.

Read the full abstract

All authors Berk Bozkurt, Aditya Mahajan, Ashutosh Nayyar, Yi Ouyang

Image and Video Processing

Real-time topology-aware M-mode OCT segmentation for robotic deep anterior lamellar keratoplasty (DALK) guidance

Rosalinda Xiong, Jinglun Yu, Yaning Wang et al. (+2 more)

Robotic deep anterior lamellar keratoplasty (DALK) requires accurate real time depth feedback to approach Descemet's membrane (DM) without perforation. M-mode intraoperative optical coherence tomography (OCT) provides high temporal resolution depth traces, but speckle noise, attenuation, and instrument induced shadowing often result in discontinuous or ambiguous layer interfaces that challenge anatomically consistent segmentation at deployment frame rates. We present a lightweight, topology aware M-mode segmentation pipeline based on UNeXt that incorporates anatomical topology regularization to stabilize boundary continuity and layer ordering under low signal to noise ratio conditions. The proposed system achieves end to end throughput exceeding 80 Hz measured over the complete preprocessing inference overlay pipeline on a single GPU, demonstrating practical real time guidance beyond model only timing. This operating margin provides temporal headroom to reject low quality or dropout frames while maintaining a stable effective depth update rate. Evaluation on a standard rabbit eye M-mode dataset using an established baseline protocol shows improved qualitative boundary stability compared with topology agnostic controls, while preserving deployable real time performance.

Read the full abstract

All authors Rosalinda Xiong, Jinglun Yu, Yaning Wang, Ziyi Huang, Jin U. Kang

Image and Video Processing

Super-Resolution and Denoising of Corneal B-Scan OCT Imaging Using Diffusion Model Plug-and-Play Priors

Yaning Wang, Jinglun Yu, Wenhan Guo et al. (+4 more)

Optical coherence tomography (OCT) is pivotal in corneal imaging for both surgical planning and diagnosis. However, high-speed acquisitions often degrade spatial resolution and increase speckle noise, posing challenges for accurate interpretation. We propose an advanced super-resolution framework leveraging diffusion model plug-and-play (PnP) priors to achieve 4x spatial resolution enhancement alongside effective denoising of OCT Bscan images. Our approach formulates reconstruction as a principled Bayesian inverse problem, combining Markov chain Monte Carlo sampling with pretrained generative priors to enforce anatomical consistency. We comprehensively validate the framework using in vivo fisheye corneal datasets, to assess robustness and scalability under diverse clinical settings. Comparative experiments against bicubic interpolation, conventional supervised U-Net baselines, and alternative diffusion priors demonstrate that our method consistently yields more precise anatomical structures, improved delineation of corneal layers, and superior noise suppression. Quantitative results show state-of-the-art performance in peak signal-to-noise ratio, structural similarity index, and perceptual metrics. This work highlights the potential of diffusion-driven plug-and-play reconstruction to deliver high-fidelity, high-resolution OCT imaging, supporting more reliable clinical assessments and enabling advanced image-guided interventions. Our findings suggest the approach can be extended to other biomedical imaging modalities requiring robust super-resolution and denoising.

Read the full abstract

All authors Yaning Wang, Jinglun Yu, Wenhan Guo, Ziyi Huang, Rosalinda Xiong, Yu Sun, Jin U. Kang

Image and Video Processing

Wide-field high-resolution microscopy via high-speed galvo scanning and real-time mosaicking

Ziyi Huang, Rosalinda Xiong, Yaning Wang et al. (+2 more)

Wide-field high-resolution microscopy requires fast scanning and accurate image mosaicking to cover large fields of view without compromising image quality. However, conventional galvanometric scanning, particularly under sinusoidal driving, can introduce nonuniform spatial sampling, leading to geometric inconsistencies and brightness variations across the scanned field. To address these challenges, we present an image mosaicking framework for wide-field microscopic imaging that is applicable to both linear and sinusoidal galvanometric scanning strategies. The proposed approach combines a translation-based geometric mosaicking model with region-of-interest (ROI) based brightness correction and seam-aware feathering to improve radiometric consistency across large fields of view. The method relies on calibrated scan parameters and synchronized scan–camera control, without requiring image-content-based registration. Using the proposed framework, wide-field mosaicked images were successfully reconstructed under both linear and sinusoidal scanning strategies, achieving a field of view of up to $2.5 \times 2.5~\mathrm{cm}^2$ with a total acquisition time of approximately $6~\mathrm{s}$ per dataset. Quantitative evaluation shows that both scanning strategies demonstrate improved image quality, including enhanced brightness uniformity, increased contrast-to-noise ratio (CNR), and reduced seam-related artifacts after image processing, while preserving a lateral resolution of $7.81~\mu\mathrm{m}$. Overall, the presented framework provides a practical and efficient solution for scan-based wide-field microscopic mosaicking.

Read the full abstract

All authors Ziyi Huang, Rosalinda Xiong, Yaning Wang, Jinglun Yu, Jin U. Kang

Image and Video Processing

Physics-based generation of multilayer corneal OCT data via Gaussian modeling and MCML for AI-driven diagnostic and surgical guidance applications

Jinglun Yu, Yaning Wang, Rosalinda Xiong et al. (+3 more)

Training deep learning models for corneal optical coherence tomography (OCT) imaging is limited by the availability of large, well-annotated datasets. We present a configurable Monte Carlo simulation framework that generates synthetic corneal B-scan optical OCT images with pixel-level five-layer segmentation labels derived directly from the simulation geometry. A five-layer corneal model with Gaussian surfaces captures curvature and thickness variability in healthy and keratoconic eyes. Each layer is assigned optical properties from the literature and light transport is simulated using Monte Carlo modeling of light transport in multi-layered tissues (MCML), while incorporating system features such as the confocal PSF and sensitivity roll-off. This approach produces over 10,000 high-resolution (1024x1024) image-label pairs and supports customization of geometry, photon count, noise, and system parameters. The resulting dataset enables systematic training, validation, and benchmarking of AI models under controlled, ground-truth conditions, providing a reproducible and scalable resource to support the development of diagnostic and surgical guidance applications in image-guided ophthalmology.

Read the full abstract

All authors Jinglun Yu, Yaning Wang, Rosalinda Xiong, Ziyi Huang, Kristina Irsch, Jin U. Kang

Audio and Speech Processing

WAXAL: A Large-Scale Multilingual African Language Speech Corpus

Abdoulaye Diack, Perry Nelson, Kwaku Agbesi et al. (+40 more)

The advancement of speech technology has predominantly favored high-resource languages, creating a significant digital divide for speakers of most Sub-Saharan African languages. To address this gap, we introduce WAXAL, a large-scale, openly accessible speech dataset for 24 languages representing over 100 million speakers. The collection consists of two main components: an Automated Speech Recognition (ASR) dataset containing approximately 1,250 hours of transcribed, natural speech from a diverse range of speakers, and a Text-to-Speech (TTS) dataset with around 235 hours of high-quality, single-speaker recordings reading phonetically balanced scripts. This paper details our methodology for data collection, annotation, and quality control, which involved partnerships with four African academic and community organizations. We provide a detailed statistical overview of the dataset and discuss its potential limitations and ethical considerations. The WAXAL datasets are released at https://huggingface.co/datasets/google/WaxalNLP under the permissive CC-BY-4.0 license to catalyze research, enable the development of inclusive technologies, and serve as a vital resource for the digital preservation of these languages.

Read the full abstract

All authors Abdoulaye Diack, Perry Nelson, Kwaku Agbesi, Angela Nakalembe, MohamedElfatih MohamedKhair, Vusumuzi Dube, Tavonga Siyavora, Subhashini Venugopalan, Jason Hickey, Uche Okonkwo, Abhishek Bapna, Isaac Wiafe, Raynard Dodzi Helegah, Elikem Doe Atsakpo, Charles Nutrokpor, Fiifi Baffoe Payin Winful, Kafui Kwashie Solaga, Jamal-Deen Abdulai, Akon Obu Ekpezu, Audace Niyonkuru, Samuel Rutunda, Boris Ishimwe, Michael Melese, Engineer Bainomugisha, Joyce Nakatumba-Nabende, Andrew Katumba, Claire Babirye, Jonathan Mukiibi, Vincent Kimani, Samuel Kibacia, James Maina, Fridah Emmah, Ahmed Ibrahim Shekarau, Ibrahim Shehu Adamu, Yusuf Abdullahi, Howard Lakougna, Bob MacDonald, Hadar Shemtov, Aisha Walcott-Bryant, Moustapha Cisse, Avinatan Hassidim, Jeff Dean, Yossi Matias

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

Automated Dysphagia Screening Using Noninvasive Neck Acoustic Sensing

Jade Chng, Rong Xing, Yunfei Luo et al. (+4 more)

Pharyngeal health plays a vital role in essential human functions such as breathing, swallowing, and vocalization. Early detection of swallowing abnormalities, also known as dysphagia, is crucial for timely intervention. However, current diagnostic methods often rely on radiographic imaging or invasive procedures. In this study, we propose an automated framework for detecting dysphagia using portable and noninvasive acoustic sensing coupled with applied machine learning. By capturing subtle acoustic signals from the neck during swallowing tasks, we aim to identify patterns associated with abnormal physiological conditions. Our approach achieves promising test-time abnormality detection performance, with an AUC-ROC of 0.904 under 5 independent train-test splits. This work demonstrates the feasibility of using noninvasive acoustic sensing as a practical and scalable tool for pharyngeal health monitoring.

Read the full abstract

All authors Jade Chng, Rong Xing, Yunfei Luo, Kristen Linnemeyer-Risser, Tauhidur Rahman, Andrew Yousef, Philip A Weissbrod

Signal Processing · Cross-listed from cs.LG

Neural Probabilistic Amplitude Shaping for Nonlinear Fiber Channels

Mohammad Taha Askari, Lutz Lampe and Amirhossein Ghazisaeidi

We introduce neural probabilistic amplitude shaping, a joint-distribution learning framework for coherent fiber systems. The proposed scheme provides a 0.5 dB signal-to-noise ratio gain over sequence selection for dual-polarized 64-QAM transmission across a single-span 205 km link.

Read the full abstract

Image and Video Processing · Cross-listed from physics.med-ph

Perfusion Imaging and Single Material Reconstruction in Polychromatic Photon Counting CT

Namhoon Kim, Ashwin Pananjady, Amir Pourmorteza et al. (+1 more)

Background: Perfusion computed tomography (CT) images the dynamics of a contrast agent through the body over time, and is one of the highest X-ray dose scans in medical imaging. Recently, a theoretically justified reconstruction algorithm based on a monotone variational inequality (VI) was proposed for single material polychromatic photon-counting CT, and showed promising early results at low-dose imaging. Purpose: We adapt this reconstruction algorithm for perfusion CT, to reconstruct the concentration map of the contrast agent while the static background tissue is assumed known; we call our method VI-PRISM (VI-based PeRfusion Imaging and Single Material reconstruction). We evaluate its potential for dose-reduced perfusion CT, using a digital phantom with water and iodine of varying concentration. Methods: Simulated iodine concentrations range from 0.05 to 2.5 mg/ml. The simulated X-ray source emits photons up to 100 keV, with average intensity ranging from $10^5$ down to $10^2$ photons per detector element. The number of tomographic projections was varied from 984 down to 8 to characterize the tradeoff in photon allocation between views and intensity. Results: We compare VI-PRISM against filtered back-projection (FBP), and find that VI-PRISM recovers iodine concentration with error below 0.4 mg/ml at all source intensity levels tested. Even with a dose reduction between 10x and 100x compared to FBP, VI-PRISM exhibits reconstruction quality on par with FBP. Conclusion: Across all photon budgets and angular sampling densities tested, VI-PRISM achieved consistently lower RMSE, reduced noise, and higher SNR compared to filtered back-projection. Even in extremely photon-limited and sparsely sampled regimes, VI-PRISM recovered iodine concentrations with errors below 0.4 mg/ml, showing that VI-PRISM can support accurate and dose-efficient perfusion imaging in photon-counting CT.

Read the full abstract

All authors Namhoon Kim, Ashwin Pananjady, Amir Pourmorteza, Sara Fridovich-Keil

Signal Processing

Pilots and Other Predictable Elements of the Starlink Ku-Band Downlink

Wenkai Qin, Mark L. Psiaki, John R. Bowman et al. (+1 more)

We identify and characterize dedicated pilot symbols and other predictable elements embedded within the Starlink Ku-band downlink waveform. Exploitation of these predictable elements enables precise opportunistic positioning, navigation, and timing using compact, low-gain receivers by maximizing the signal processing gain available for signal acquisition and time-of-arrival (TOA) estimation. We develop an acquisition and demodulation framework to decode Starlink frames and disclose the explicit sequences of the edge pilots – bands of 4QAM symbols located at both edges of each Starlink channel that apparently repeat identically across all frames, beams, channels, and satellites. We further reveal that the great majority of QPSK-modulated symbols do not carry high-entropy user data but instead follow a regular tessellated structure superimposed on a constant reference template. We demonstrate that exploiting frame-level predictable elements yields a processing gain of approximately 48 dB, thereby enabling low-cost, compact receivers to extract precise TOA measurements even from low-SNR Starlink side beams.

Read the full abstract

All authors Wenkai Qin, Mark L. Psiaki, John R. Bowman, Todd E. Humphreys

Signal Processing · Cross-listed from cs.LG

Learning Consistent Causal Abstraction Networks

Gabriele D'Acunto, Paolo Di Lorenzo and Sergio Barbarossa

Causal artificial intelligence aims to enhance explainability, trustworthiness, and robustness in AI by leveraging structural causal models (SCMs). In this pursuit, recent advances formalize network sheaves and cosheaves of causal knowledge. Pushing in the same direction, we tackle the learning of consistent causal abstraction network (CAN), a sheaf-theoretic framework where (i) SCMs are Gaussian, (ii) restriction maps are transposes of constructive linear causal abstractions (CAs) adhering to the semantic embedding principle, and (iii) edge stalks correspond–up to permutation–to the node stalks of more detailed SCMs. Our problem formulation separates into edge-specific local Riemannian problems and avoids nonconvex objectives. We propose an efficient search procedure, solving the local problems with SPECTRAL, our iterative method with closed-form updates and suitable for positive definite and semidefinite covariance matrices. Experiments on synthetic data show competitive performance in the CA learning task, and successful recovery of diverse CAN structures.

Read the full abstract