Audio and Speech Processing · Cross-listed from cs.SD
OmniCustom: Sync Audio-Video Customization Via Joint Audio-Video Generation Model
Maomao Li, Zhen Li, Kaipeng Zhang et al. (+3 more)
Existing mainstream
video customization methods focus on generating identity-consistent
videos based on given reference images and textual
prompts. Benefiting from the rapid advancement of joint audio-
video generation, this paper proposes a more compelling new task: sync audio-
video customization, which aims to synchronously customize both
video identity and audio timbre. Specifically, given a reference image $I^{r}$ and a reference audio $A^{r}$, this novel task requires generating
videos that maintain the identity of the reference image while imitating the timbre of the reference audio, with spoken content freely specifiable through user-provided textual
prompts. To this end, we propose OmniCustom, a powerful DiT-based audio-
video customization framework that can synthesize a
video following reference image identity, audio timbre, and text
prompts all at once in a zero-shot manner. Our framework is built on three key contributions. First, identity and audio timbre control are achieved through separate reference identity and audio LoRA modules that operate through self-
attention layers within the base audio-
video generation model. Second, we introduce a contrastive learning objective alongside the standard flow matching objective. It uses predicted flows conditioned on reference inputs as positive examples and those without reference conditions as negative examples, thereby enhancing the model ability to preserve identity and timbre. Third, we train OmniCustom on our constructed large-scale, high-quality audio-visual human
dataset. Extensive experiments demonstrate that OmniCustom outperforms existing methods in generating audio-
video content with consistent identity and timbre fidelity. Project page: https://omnicustom-project.github.io/page/.
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All authors Maomao Li, Zhen Li, Kaipeng Zhang, Guosheng Yin, Zhifeng Li, Dong Xu
Systems and Control · Cross-listed from cs.RO
Scaling Verification Can Be More Effective than Scaling Policy Learning for Vision-Language-Action Alignment
Jacky Kwok, Xilun Zhang, Mengdi Xu et al. (+4 more)
The long-standing
vision of general-purpose
robots hinges on their ability to understand and act upon natural
language instructions.
Vision-
Language-Action (VLA) models have made remarkable progress toward this goal, yet their generated actions can still misalign with the given instructions. In this paper, we investigate test-time verification as a means to shrink the "intention-action gap." We first characterize the test-time scaling laws for
embodied instruction following and demonstrate that jointly scaling the number of rephrased instructions and generated actions greatly increases test-time sample diversity, often recovering correct actions more efficiently than scaling each dimension independently. To capitalize on these scaling laws, we present CoVer, a contrastive verifier for
vision-
language-action
alignment, and show that our architecture scales gracefully with additional computational resources and data. We then introduce CoVer-VLA, a hierarchical test-time verification pipeline using the trained verifier. At deployment, our framework precomputes a diverse set of rephrased instructions from a
Vision-
Language-Model (VLM), repeatedly generates action candidates for each instruction, and then uses the verifier to select the optimal high-level
prompt and low-level action chunks. Compared to scaling policy pre-training on the same data, our verification approach yields 22% gains in-distribution and 13% out-of-distribution on the SIMPLER
benchmark, with a further 45% improvement in real-world experiments. On the PolaRiS
benchmark, CoVer-VLA achieves 14% gains in task progress and 9% in success rate.
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All authors Jacky Kwok, Xilun Zhang, Mengdi Xu, Yuejiang Liu, Azalia Mirhoseini, Chelsea Finn, Marco Pavone
Signal Processing · Cross-listed from cs.IT
Transmit or Idle: Efficient AoI Optimal Transmission Policy for Gossiping Receivers
Irtiza Hasan and Ahmed Arafa
We study the optimal transmission and scheduling policy for a transmitter (source) communicating with two gossiping receivers aiming at tracking the source's status over time using the age of information (AoI) metric. Gossiping enables local information exchange in a decentralized manner without relying solely on the transmitter's direct communication, which we assume incurs a transmission cost. On the other hand, gossiping may be communicating stale information, necessitating the transmitter's intervention. With communication links having specific success probabilities, we formulate an average-cost Markov Decision Process (MDP) to jointly minimize the sum AoI and transmission cost for such a system in a time-slotted setting. We employ the Relative Value Iteration (RVI)
algorithm to evaluate the optimal policy for the transmitter and then prove several structural properties showing that it has an age-difference threshold structure with minimum age activation in the case where gossiping is relatively more reliable. Specifically, direct transmission is optimal only if the minimum AoI of the receivers is large enough and their age difference is below a certain threshold. Otherwise, the transmitter idles to effectively take advantage of gossiping and reduce direct transmission costs. Numerical evaluations demonstrate the significance of our optimal policy compared to multiple baselines. Our result is a first step towards characterizing optimal freshness and transmission cost trade-offs in gossiping networks.
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Systems and Control
Equivalent Circuit Modeling of Grid-Forming Inverters in (Sub)-Transient Time-Frame
Ambuj Gupta, Balarko Chaudhuri and Mark O'Malley
The widely accepted definition of grid-forming (GFM) inverter states that it should behave as a (nearly) constant voltage source behind an impedance by maintaining a (nearly) constant internal voltage phasor in the sub-transient to transient time frame. Some system operators further mandate permissible ranges for this effective impedance. However, these specifications do not clearly define the location of the internal voltage source, and no systematic method exists to quantify its effective impedance for a black-box GFM model. To address this, we first compare the transient responses of an ideal voltage source and a GFM to show that an idealistic GFM maintains a (nearly) constant voltage across the filter capacitor, rather than at the inverter switches. Then we propose a systematic method to quantify the effective impedance of a GFM from its black-box model using frequency-domain admittance plots. Using standard PSCAD GFM models developed by NLR (formerly NREL), we demonstrate that the GFM's equivalent impedance model captures the sub-transient response and static voltage stability limit accurately. Further, replacing the GFM with the proposed equivalent
circuit model in the modified IEEE-39 bus system is shown to reproduce the small-signal stability characteristics with reasonable accuracy.
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Signal Processing
Continuous and Discrete-Time Filters: A Unified Operational Perspective
Luca Giangrande
Continuous time (CT) and discrete time (DT) linear time invariant (LTI) systems are commonly introduced through distinct mathematical formalisms, which can obscure their underlying dynamical equivalence. This tutorial presents a unified treatment of firstorder CT and DT systems, emphasizing their shared modal structure and stability properties. Beginning with transfer functions and pole zero representations in the Laplace domain, canonical first order low pass and high-pass dynamics are examined from an operational perspective. The discussion then transitions to discrete-time sequences and the Z transform, highlighting
geometric sequences as eigenfunctions of DT systems and establishing the correspondence between the left half of the s plane and the interior of the unit circle in the z plane. Practical discretization and sampled data implementations are analyzed to illustrate how continuous time dynamics are reinterpreted through recursion and accumulation in discrete time realizations. By maintaining structural
symmetry between domains, the manuscript consolidates established concepts into a coherent framework linking mathematical representation, physical realizability, and implementation.
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Signal Processing · Cross-listed from cs.CE
Systematic Analysis of Penalty-Optimised Illumination Design for Tomographic Volumetric Additive Manufacturing via the Extendable Framework TVAM AID Using the Core Imaging Library
Nicole Pellizzon, Richard Huber, Jon Spangenberg et al. (+1 more)
Tomographic Volumetric Additive Manufacturing(TVAM) is a novel manufacturing method that allows for the fast creation of objects of complex geometry in layerless fashion. The process is based on the solidification of photopolymer that occurs when a sufficient threshold dose of light-energy is absorbed. In order to create complex shapes, an illumination plan must be designed to force solidification in some desired areas while leaving other regions
liquid. Determining an illumination plan can be considered as an optimisation problem where a variety of objective functionals (penalties) can be used. This work considers a selection of penalty functions and their impact on selected printing metrics; linking the shape of penalty functions to ranges of light-energy dose levels in in-part regions that should be printed and out-of-part regions that should remain
liquid. Further, the threshold parameters that are typically used to demarcate minimum light-energy for in-part regions and maximum light-energy for out-of-part regions are investigated systematically as design parameters on both existing and new methods. This enables the characterisation of their effects on some selected printing metrics as well as informed selection for default values. This work is underpinned by a reproducible and extensible framework, TVAM Adaptive Illumination Design(TVAM AID), which makes use of the open-source Core Imaging Library(CIL) that is designed for tomographic imaging with an emphasis on reconstruction. The foundation of TVAM AID which is presented here can hence be easily enhanced by existing functionality in CIL thus lowering the barrier to entry and encouraging use of strategies that already exist for reconstruction optimisation.
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All authors Nicole Pellizzon, Richard Huber, Jon Spangenberg, Jakob Sauer Jørgensen
Signal Processing
ViPer NL-COMM: Making Vector Perturbation Precoding Practical
Thomas James Thomas, George N. Katsaros, Chathura Jayawardena et al. (+1 more)
Large
MIMO systems rely on efficient downlink precoding to enhance data rates and improve connectivity through spatial multiplexing. However, currently employed linear precoding techniques, such as MMSE, significantly limit the achievable spectral efficiency. To meet practical error-rate targets, existing linear methods require an excessively high number of access point
antennas relative to the number of supported users, leading to disproportionate increases in power consumption.Efficient non-linear processing frameworks for uplink
MIMO transmissions, such as NL-COMM, have been proposed. However, downlink non-linear precoding methods, such as Vector Perturbation (VP), remain impractical for real-world deployment due to their exponentially increasing computational complexity with the number of supported streams. This work presents ViPer NL-COMM, the first practical algorithmic and implementation framework for VP-based precoding. ViPer NL-COMM extends the core principles of NL-COMM to the precoding problem, enabling scalable parallelization and real-time computational performance while maintaining the substantial spectral-efficiency benefits of VP precoding. ViPer NL-COMM consists of a novel mathematical framework and an FPGA prototype capable of supporting large
MIMO configurations (up to 16x16), high-order modulation (256-QAM), and wide bandwidths (100 MHz) within practical power and resource budgets. System-level evaluations demonstrate that ViPer NL-COMM achieves target error rates using only half the number of transmit
antennas required by linear precoding, yielding net power savings on the order of hundreds of Watts at the RF front end. Moreover, ViPer NL-COMM enables supporting more information streams than available AP
antennas when the streams are of low-rate, paving the way for enhanced massive-connectivity scenarios in next-generation wireless networks.
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All authors Thomas James Thomas, George N. Katsaros, Chathura Jayawardena, Konstantinos Nikitopoulos
Systems and Control · Cross-listed from cs.RO
Safety Beyond the Training Data: Robust Out-of-Distribution MPC via Conformalized System Level Synthesis
Anutam Srinivasan, Antoine Leeman and Glen Chou
We present a novel framework for robust out-of-distribution planning and control using conformal prediction (CP) and system level synthesis (SLS), addressing the challenge of ensuring safety and robustness when using learned dynamics models beyond the training data distribution. We first derive high-confidence model error bounds using weighted CP with a learned, state-control-dependent covariance model. These bounds are integrated into an SLS-based robust nonlinear model predictive control (MPC) formulation, which performs constraint tightening over the prediction horizon via volume-optimized forward reachable sets. We provide theoretical guarantees on coverage and robustness under distributional drift, and analyze the impact of data density and trajectory tube size on prediction coverage. Empirically, we demonstrate our method on nonlinear systems of increasing complexity, including a 4D car and a {12D} quadcopter, improving safety and robustness compared to fixed-bound and non-robust baselines, especially outside of the data distribution.
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Signal Processing
Exploiting Structural Flexibility in SIM-Enabled Communications: From Adaptive Inter-Layer Spacing to Fully Morphable Layers
Ahmed Magbool, Vaibhav Kumar, Marco Di Renzo et al. (+1 more)
Stacked intelligent metasurfaces (SIMs) have recently emerged as a promising metasurface-based physical-layer paradigm for wireless communications, enabling wave-domain signal processing through multiple cascaded metasurface layers. However, conventional SIM designs rely on rigid planar layers with fixed interlayer spacing, which constrain the propagation geometry and can lead to performance saturation as the number of layers increases. This paper investigates the potential of introducing structural flexibility into SIM-enabled communication systems. Specifically, we consider two flexible SIM architectures: distance-adaptive SIM (DSIM), where interlayer distances are optimized, and stacked flexible intelligent metasurface (SFIM), where each metasurface layer is fully morphable. We jointly design the meta-atom positions and responses together with the transmit beamformer to maximize the system sum rate under per-user rate, quantization, morphing, and interlayer distance constraints. An alternating optimization framework combining gradient projection, penalty-based method, and successive convex approximation is developed to address the resulting non-convex problems. Perturbation analysis reveals that the flexibility gains of both DSIM and SFIM scale approximately linearly with the morphing range, with SFIM exhibiting a faster growth rate. Simulation results demonstrate that flexible SIM designs mitigate performance saturation with increasing layers and achieve significant transmit power savings compared to rigid SIMs.
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All authors Ahmed Magbool, Vaibhav Kumar, Marco Di Renzo, Mark F. Flanagan
Systems and Control
A Numerical Investigation of Indirect Adaptive Predictive Control with Structure-Informed Nonlinear Regressors
Tam W. Nguyen
This paper numerically investigates nonlinear model construction from supplied structural information within predictive cost adaptive control. The study varies the retained nonlinear functions, coefficient sharing, and forward reconstruction under a common identification and control architecture. Recursive least squares updates unknown coefficients from direct or integrated relations that are linear in the parameters. The reconstructed nonlinear forward predictor need not be parameter-linear. A local affine approximation, constructed from the predictor value and Jacobian, is frozen over the prediction horizon for constrained quadratic optimization. Scalar full-state studies compare exact parameterizations, Taylor-informed approximations, broader dictionaries, and incorrectly restricted models. The comparisons include parameter variation, structural changes, and selected noisy cases. Common-data tests assess nonlinear prediction, whereas common-query forecasts distinguish nonlinear-model error from affine-freezing error. Under the tested conditions, correct structure improves prediction relative to affine models without increasing parameter count. Integrated physical identification reduces prediction error relative to direct sampled-map fitting after initialization. Additional coefficients and improved nonlinear prediction do not ensure lower tracking error. Forgetting improves readaptation without ensuring parameter recovery, and coefficient activation does not establish structure discovery. Affine freezing can reverse the nonlinear prediction advantage over an affine baseline. The results support evaluating representation, identification, reconstruction, and horizon prediction separately before increasing model complexity.
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Image and Video Processing
UPDA: Unsupervised Progressive Domain Adaptation for No-Reference Point Cloud Quality Assessment
Bingxu Xie, Fang Zhou, Jincan Wu et al. (+3 more)
While no-reference point cloud quality assessment (NR-PCQA) approaches have achieved significant progress over the past decade, their performance often degrades substantially when a distribution gap exists between the training (source domain) and testing (target domain) data. However, to date, limited
attention has been paid to transferring NR-PCQA models across domains. To address this challenge, we propose the first unsupervised progressive domain adaptation (UPDA) framework for NR-PCQA, which introduces a two-stage coarse-to-fine
alignment paradigm to address domain shifts. At the coarse-grained stage, a discrepancy-aware coarse-grained
alignment method is designed to capture relative quality relationships between cross-domain samples through a novel quality-discrepancy-aware hybrid loss, circumventing the challenges of direct absolute feature
alignment. At the fine-grained stage, a perception fusion fine-grained
alignment approach with symmetric feature fusion is developed to identify domain-invariant features, while a conditional discriminator selectively enhances the transfer of quality-relevant features. Extensive experiments demonstrate that the proposed UPDA effectively enhances the performance of NR-PCQA methods in cross-domain scenarios, validating its practical applicability. The code is available at https://github.com/yokeno1/UPDA-main.
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All authors Bingxu Xie, Fang Zhou, Jincan Wu, Yonghui Liu, Weiqing Li, Zhiyong Su
Signal Processing
Radio Map Prediction from Noisy Environment Information and Sparse Observations
Fabian Jaensch, Çağkan Yapar, Giuseppe Caire et al. (+1 more)
Many works have investigated
radio map and path loss prediction in wireless networks using deep learning, in particular using convolutional neural networks. However, most assume perfect environment information, which is unrealistic in practice due to sensor limitations, mapping errors, and temporal changes. We demonstrate that convolutional neural networks trained with task-specific perturbations of geometry, materials, and Tx positions can implicitly compensate for prediction errors caused by inaccurate environment inputs. When tested with noisy inputs on synthetic indoor scenarios, models trained with perturbed environment data reduce error by up to 25% compared to models trained on clean data. We verify our approach on real-world measurements, achieving 2.1 dB RMSE with noisy input data and 1.3 dB with complete information, compared to 2.3-3.1 dB for classical methods such as ray-tracing and radial basis function interpolation. Additionally, we compare different ways of encoding environment information at varying levels of detail and we find that, in the considered single-room indoor scenarios, binary occupancy encoding performs at least as well as detailed material property information, simplifying practical deployment.
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All authors Fabian Jaensch, Çağkan Yapar, Giuseppe Caire, Begüm Demir
Image and Video Processing
Learning Perceptual Representations for Gaming NR-VQA with Multi-Task FR Signals
Yu-Chih Chen, Michael Wang, Chieh-Dun Wen et al. (+4 more)
No-reference
video quality assessment (NR-VQA) for gaming
videos is challenging due to limited human-rated
datasets and unique content characteristics including fast motion, stylized graphics, and compression artifacts. We present MTL-VQA, a multi-task learning framework that uses full-reference (FR) quality metrics as supervisory signals to learn perceptually meaningful features without human labels during pretraining. By jointly optimizing multiple complementary proxy FR objectives with adaptive task weighting, our approach learns shared representations that transfer effectively to downstream NR-VQA. Experiments on gaming
video datasets show that MTL-VQA achieves competitive performance against state-of-the-art methods in both mean opinion score-supervised and label-efficient or self-supervised settings.
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All authors Yu-Chih Chen, Michael Wang, Chieh-Dun Wen, Kai-Siang Ma, Avinab Saha, Li-Heng Chen, Alan Bovik
Systems and Control
Gradient-Based Adaptive Prediction and Control for Nonlinear Dynamical Systems
Yujing Liu, Xin Zheng, Zhixin Liu et al. (+1 more)
This paper investigates gradient-based adaptive prediction and control for nonlinear
stochastic dynamical systems under a weak convexity condition on the prediction-based loss. This condition accommodates a broad range of nonlinear models in control and machine learning such as saturation functions, sigmoid, ReLU and tanh activation functions, and standard classification models. Without requiring any persistent excitation of the data, we establish global convergence of the proposed adaptive predictor and derive explicit rates for its asymptotic performance. Furthermore, under a classical nonlinear minimum-phase condition and with a linear growth bound on the nonlinearities, we establish the convergence rate of the resulting closed-loop control error. Finally, we demonstrate the effectiveness of the proposed adaptive prediction
algorithm on a real-world judicial sentencing
dataset. The adaptive control performance will also be evaluated via a numerical simulation.
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All authors Yujing Liu, Xin Zheng, Zhixin Liu, Lei Guo
Audio and Speech Processing · Cross-listed from cs.SD
Musical Metamerism with Time–Frequency Scattering
Vincent Lostanlen and Han Han
The concept of metamerism originates from colorimetry, where it describes a sensation of visual similarity between two colored lights despite significant differences in spectral content. Likewise, we propose to call “musical metamerism” the sensation of auditory similarity which is elicited by two music fragments which differ in terms of underlying waveforms. In this technical report, we describe a method to generate musical metamers from any audio recording. Our method is based on joint time–frequency scattering in Kymatio, an open-source software in Python which enables GPU computing and automatic differentiation. The advantage of our method is that it does not require any manual preprocessing, such as transcription, beat tracking, or source separation. We provide a mathematical description of JTFS as well as some excerpts from the Kymatio source code. Lastly, we review the prior work on JTFS and draw connections with closely related
algorithms, such as spectrotemporal receptive fields (STRF), modulation power spectra (MPS), and Gabor filterbank (GBFB).
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Signal Processing
Is Downlink Training Necessary for User-Centric Cell-Free RSMA Systems With Mobile Users?
Ravi Kiran Palla, Dheeraj Naidu Amudala and Rohit Budhiraja
We study the spectral efficiency (SE) of a ratesplitting multiple access (RSMA) enabled multi-clustered
cell-free (CF) massive multiple-input multiple-output (mMIMO) system. The access points (APs) in each cluster serve mobile user equipments (UEs) by employing RSMA. The UEs employ successive interference cancellation to decode their data. This work emphasizes the role of downlink (DL) pilots in realizing RSMA benefits in practical CF systems with spatially-correlated Rician channels which observe random phase shifts, pilot contamination, and channel aging due to UE mobility. We numerically show that DL pilots are required for RSMA in user-centric CF mMIMO systems with channel aging to outperform spatial division multiple access. We show that the degraded channel quality due to higher UE velocity and longer resource block lengths significantly reduces the RSMA SE. Increasing the number of clusters can compensate for the SE loss.
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Image and Video Processing · Cross-listed from cs.DB
Data-Driven Trajectory Imputation for Vessel Mobility Analysis
Giannis Spiliopoulos, Alexandros Troupiotis-Kapeliaris, Kostas Patroumpas et al. (+4 more)
Modeling vessel activity at sea is critical for a wide range of applications, including route planning, transportation logistics, maritime safety, and environmental monitoring. Over the past two decades, the Automatic Identification System (AIS) has enabled real-time monitoring of hundreds of thousands of vessels, generating huge amounts of data daily. One major challenge in using AIS data is the presence of large gaps in vessel trajectories, often caused by coverage limitations or intentional transmission interruptions. These gaps can significantly degrade data quality, resulting in inaccurate or incomplete analysis. State-of-the-art imputation approaches have mainly been devised to tackle gaps in vehicle trajectories, even when the underlying road network is not considered. But the motion patterns of sailing vessels differ substantially, e.g., smooth turns, maneuvering near ports, or navigating in adverse weather conditions. In this application paper, we propose HABIT, a lightweight, configurable H3 Aggregation-Based Imputation framework for vessel Trajectories. This data-driven framework provides a valuable means to impute missing trajectory segments by extracting, analyzing, and indexing motion patterns from historical AIS data. Our empirical study over AIS data across various timeframes, densities, and vessel types reveals that HABIT produces maritime trajectory imputations performing comparably to baseline methods in terms of accuracy, while performing better in terms of latency while accounting for vessel characteristics and their motion patterns.
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All authors Giannis Spiliopoulos, Alexandros Troupiotis-Kapeliaris, Kostas Patroumpas, Nikolaos Liapis, Dimitrios Skoutas, Dimitris Zissis, Nikos Bikakis
Image and Video Processing
Time-resolved aortic 3D shape reconstruction from a limited number of cine 2D MRI slices
Gloria Wolkerstorfer, Stefano Buoso, Rabea Schlenker et al. (+3 more)
Background and Objective: To assess the feasibility and accuracy of reconstructing time-resolved, three-dimensional, subject-specific aortic geometries from a limited number of standard cine 2D
magnetic resonance imaging (MRI) acquisitions. This is achieved by coupling a statistical shape model with a differentiable volumetric mesh optimization
algorithm. Methods: Cine 2D MRI slices were manually segmented and used to reconstruct subject-specific aortic geometries via a differentiable mesh optimization
algorithm, constrained by a statistical shape model. Optimal slice positioning was first evaluated on synthetic data, followed by in-vivo acquisition in 30 subjects (19 volunteers and 11 aortic stenosis patients). Time-resolved aortic geometries were reconstructed, from which
geometric descriptors and radial strain were derived. In a subset of 10 subjects, 4D flow MRI data was acquired to provide volumetric reference for peak-systolic shape comparison. Results: Accurate reconstruction was achieved using as few as six cine 2D MRI slices. Agreement with 4D flow MRI reference data yielded a Dice score of (89.9 +/- 1.6) %, Intersection over Union of (81.7 +/- 2.7) %, Hausdorff distance of (7.3 +/- 3.3) mm, and Chamfer distance of (3.7 +/- 0.6) mm. The mean absolute radius error along the aortic arch was (0.8 +/- 0.6) mm. Secondary analysis demonstrated significant differences in
geometric features and radial strain across age groups, with strain decreasing progressively with age at values of (11.00 +/- 3.11) x 10-2 vs. (3.74 +/- 1.25) x 10-2 vs. (2.89 +/- 0.87) x 10-2 for the young, mid-age, and elderly groups, respectively. Conclusion: The proposed framework enables reconstruction of time-resolved, subject-specific aortic geometries from a limited number of standard cine 2D MRI acquisitions, providing a practical basis for downstream computational analysis.
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All authors Gloria Wolkerstorfer, Stefano Buoso, Rabea Schlenker, Jochen von Spiczak, Robert Manka, Sebastian Kozerke
Systems and Control
A day-ahead market model for power systems: benchmarking and security implications
Andrej Stankovski, Blazhe Gjorgiev, James Ciyu Qin et al. (+1 more)
Power system security assessments, e.g. via cascading outage models, often use operational set-points based on optimal power flow (OPF) dispatch. However, driven by cost minimization, OPF provides an ideal, albeit unrealistic, clearing of the generating units that disregards the complex interactions among
market participants. In addition, existing
market modeling tools often utilize economic dispatch and unit commitment to minimize total system costs, often disregarding the profit-driven behavior of
market participants. The security of the system, therefore, may be overestimated. To address this gap, we introduce a social-welfare-based day-ahead
market-clearing model. The security implications are analyzed using Cascades, a model for cascading failure analysis. We apply this model to the IEEE-118 bus system with three independent control zones. The results show that
market dispatch leads to an increase in demand not served (DNS) of up to 80% higher than OPF, highlighting a significant security overestimation. This is especially pronounced in large-scale cascading events with DNS above 100MW. A key driver is the increased dispatch of storage and gas units, which can place the system in critical operating conditions. Operators can use this information to properly estimate the impact of the
market on system security and plan efficient expansion strategies.
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All authors Andrej Stankovski, Blazhe Gjorgiev, James Ciyu Qin, Giovanni Sansavini
Signal Processing
EqDeepRx: Learning a Scalable and Interference Mitigating MIMO Receiver
Mikko Honkala, Dani Korpi, Elias Raninen et al. (+1 more)
While machine learning (ML)-based receiver
algorithms have received a great deal of
attention in the recent literature, they often suffer from poor scaling with increasing spatial multiplexing order and lack of explainability and generalization. This paper presents EqDeepRx, a practical deep-learning-aided multiple-input multiple-output (
MIMO) receiver, which is built by augmenting linear receiver processing with carefully engineered ML blocks. At the core of the receiver model is a shared-weight DetectorNN that operates independently on each spatial stream or layer, enabling near-linear complexity scaling with respect to multiplexing order. To ensure better explainability and generalization, EqDeepRx retains conventional channel estimation and augments it with a lightweight DenoiseNN that learns frequency-domain smoothing. To reduce the dimensionality of the DetectorNN inputs, the receiver utilizes two linear equalizers in parallel: a linear minimum mean-square error (LMMSE) equalizer with interference-plus-noise covariance estimation and a regularized zero-forcing (RZF) equalizer. The parallel equalized streams are jointly consumed by the DetectorNN, after which a compact DemapperNN produces bit log-likelihood ratios for channel decoding. 5G/6G-compliant end-to-end simulations across multiple channel scenarios, pilot patterns, and inter-
cell interference conditions show improved error rate and spectral efficiency over a conventional baseline, while maintaining low-complexity inference and support for different
MIMO configurations without retraining.
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All authors Mikko Honkala, Dani Korpi, Elias Raninen, Janne M. J. Huttunen
Systems and Control
Grounded Laplacians of Directed Signed Matrix-Weighted Networks: Spectral Properties and Applications to Non-Trivial Consensus
Tianmu Niu, Bing Mao, Hao Liao et al. (+2 more)
Grounded
Laplacians provide the spectral link between external information and network convergence. This paper establishes positive-stability results for grounded
Laplacians in directed signed
matrix-weighted networks, where directionality, antagonism, and singular edge weight
matrices coexist. First, under in-degree dominance and positive-negative reachability, we derive explicit local thresholds for the grounding gains. Second, a scaled, kernel-based certificate replaces the unscaled degree condition with a signed
matrix-weighted Dirichlet decomposition and a joint-kernel test for the scaled symmetric part. The computable margin $\gamma_p$ lower-bounds the minimum real part of the spectrum and certifies exponential contraction in the $P$-norm. Under absolute generalized balance, the kernel-intersection test is given; the balanced and definite-edge unbalanced undirected cases follow. As an application, non-trivial consensus (NTC) on signed
matrix-weighted networks is studied. Informed
agents, external signals and coupling terms are designed to steer all
agents to any prescribed nonzero state without requiring structural balance. Switching
topology case retains non-trivial consensus result under certain conditions. Realizing NTC on signed
matrix-weighted networks demonstrates that groups with both cooperative and antagonistic multi-dimensional interactions can achieve consensus, which was previously deemed exclusive to fully cooperative groups.
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All authors Tianmu Niu, Bing Mao, Hao Liao, Xiaoqun Wu, Tingwen Huang
Systems and Control · Cross-listed from cs.CR
Solving the Post-Quantum Control Plane Bottleneck: Energy-Aware Cryptographic Scheduling in Open RAN
Neha Gupta, Hamed Alimohammadi, Mohammad Shojafar et al. (+2 more)
The Open
Radio Access Network (O-RAN) offers flexibility and innovation but introduces unique security vulnerabilities, particularly from cryptographically relevant
quantum computers. While Post-
Quantum Cryptography (PQC) is the primary scalable defence, its computationally intensive handshakes create a significant bottleneck for the RAN control plane, posing sustainability challenges. This paper proposes an energy-aware framework to solve this PQC bottleneck, ensuring
quantum resilience without sacrificing operational energy efficiency. The system employs an O-RAN aligned split: a Crypto Policy rApp residing in the Non-Real-Time (Non-RT) RIC defines the strategic security envelope (including PQC suites), while a Security Operations Scheduling (SOS) xApp in the Near-RT RIC converts these into tactical timing and placement intents. Cryptographic enforcement remains at standards-compliant endpoints: the Open Fronthaul utilizes Media Access Control Security (MACsec) at the O-DU/O-RU, while the xhaul (midhaul and backhaul) utilizes IP Security (IPsec) at tunnel terminators. The SOS xApp reduces PQC overhead by batching non-urgent handshakes, prioritizing session resumption, and selecting parameters that meet slice SLAs while minimizing joules per secure connection. We evaluate the architecture via a Discrete-Event Simulation (DES) using 3GPP-aligned traffic profiles and verified hardware
benchmarks from literature. Results show that intelligent scheduling can reduce per-handshake energy by approximately 60 percent without violating slice latency targets.
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All authors Neha Gupta, Hamed Alimohammadi, Mohammad Shojafar, De Mi, Muhammad N.M. Bhutta
Image and Video Processing · Cross-listed from cs.CV
Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data
Yiming Zhou, Xuenjie Xie, Panfeng Li et al. (+3 more)
Segment Anything Models (SAM) achieve impressive universal
segmentation performance but require massive
datasets (e.g., 11M images) and rely solely on RGB inputs. Recent efficient variants reduce computation but still depend on large-scale training. We propose a lightweight RGB-D fusion framework that augments EfficientViT-SAM with
monocular depth priors. Depth maps are generated with a pretrained
estimator and fused mid-level with RGB features through a dedicated depth encoder. Trained on only 11.2k samples (less than 0.1% of SA-1B), our method achieves higher accuracy than EfficientViT-SAM, showing that depth cues provide strong
geometric priors for
segmentation.
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All authors Yiming Zhou, Xuenjie Xie, Panfeng Li, Albrecht Kunz, Ahmad Osman, Xavier Maldague
Signal Processing
On the Maintainability of Pinching-Antenna Systems: A Failure-Repair Perspective
Chongjun Ouyang, Hao Jiang, Zhaolin Wang et al. (+2 more)
The pinching-
antenna system (PASS) enables wireless channel reconfiguration through optimized placement of pinching
antennas along dielectric waveguides. In this article, a unified analytical framework is proposed to characterize the maintainability of PASS. Within this framework, random waveguide failures and repairs are modeled by treating the waveguide lifetime and repair time as exponentially distributed random variables, which are characterized by the failure rate and the repair rate, respectively. The operational state of the waveguide is described by a two-state continuous-time Markov chain, for which the transition probabilities and steady-state probabilities of the waveguide being working or failed are analyzed. By incorporating the randomness of the waveguide operational state into the transmission rate, system maintainability is characterized using the probability of non-zero rate (PNR) and outage probability (OP). The proposed framework is applied to both a conventional PASS employing a single long waveguide and a segmented waveguide-enabled pinching-
antenna system (SWAN) composed of multiple short waveguide segments under two operational protocols: segment switching (SS) and segment aggregation (SA). Closed-form expressions for the PNR and OP are derived for both architectures, and the corresponding scaling laws are analyzed with respect to the service-region size and the number of segments. It is proven that both SS-based and SA-based SWAN achieve higher PNR and lower OP than conventional PASS, which confirms the maintainability advantage of
segmentation. Numerical results demonstrate that: i) the maintainability gain of SWAN over conventional PASS increases with the number of segments, and ii) SA provides stronger maintainability than SS.
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All authors Chongjun Ouyang, Hao Jiang, Zhaolin Wang, Yuanwei Liu, Zhiguo Ding
Image and Video Processing
U-DAVI: Uncertainty-Aware Diffusion-Prior-Based Amortized Variational Inference for Image Reconstruction
Ayush Varshney, Katherine L. Bouman and Berthy T. Feng
Ill-posed imaging inverse problems remain challenging due to the ambiguity in mapping degraded observations to clean images.
Diffusion-based generative priors have recently shown promise, but typically rely on computationally intensive iterative sampling or per-instance optimization. Amortized variational inference frameworks address this inefficiency by learning a direct mapping from measurements to posteriors, enabling fast posterior sampling without requiring the optimization of a new posterior for every new set of measurements. However, they still struggle to reconstruct fine details and complex textures. To address this, we extend the amortized framework by injecting spatially adaptive perturbations to measurements during training, guided by uncertainty estimates, to emphasize learning in the most uncertain regions. Experiments on deblurring and super-resolution demonstrate that our method achieves superior or competitive performance to previous
diffusion-based approaches, delivering more realistic reconstructions without the computational cost of iterative refinement.
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Image and Video Processing
BSoNet: Deep Learning Solution for Optimizing Image Quality of Portable Backscatter Imaging Systems
Linxuan Li, Wenjia Wei, Yunfei Lu et al. (+3 more)
Portable backscatter imaging systems (PBI) integrate an X-ray source and detector in a single unit, utilizing Compton scattering
photons to rapidly acquire superficial or shallow structural information of an inspected object through single-sided imaging. The application of this technology overcomes the limitations of traditional transmission X-ray detection, offering greater flexibility and portability, making it the preferred tool for the rapid and accurate identification of potential threats in scenarios such as borders, ports, and industrial nondestructive security inspections. However, the image quality is significantly compromised due to the limited number of Compton backscattered
photons. The insufficient
photon counts result primarily from
photon absorption in materials, the pencil-beam scanning design, and short signal sampling times. It therefore yields severe image noise and an extremely low signal-to-noise ratio, greatly reducing the accuracy and reliability of PBI systems. To address these challenges, this paper introduces BSoNet, a novel deep learning-based approach specifically designed to optimize the image quality of PBI systems. The approach significantly enhances image clarity, recognition, and contrast while meeting practical application requirements. It transforms PBI systems into more effective and reliable inspection tools, contributing significantly to strengthening security protection.
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All authors Linxuan Li, Wenjia Wei, Yunfei Lu, Wenwen Zhang, Yanlong Zhang, Wei Zhao
Audio and Speech Processing
Exploring Frequency-Domain Feature Modeling for HRTF Magnitude Upsampling
Xingyu Chen, Hanwen Bi, Fei Ma et al. (+3 more)
Accurate upsampling of Head-Related Transfer Functions (HRTFs) from sparse measurements is crucial for personalized spatial audio rendering. Traditional interpolation methods, such as kernel-based weighting or basis function expansions, rely on measurements from a single subject and are limited by the spatial sampling
theorem, resulting in significant performance degradation under sparse sampling. Recent learning-based methods alleviate this limitation by leveraging cross-subject information, yet most existing neural architectures primarily focus on modeling spatial relationships across directions, while spectral dependencies along the frequency dimension are often modeled implicitly or treated independently. However, HRTF magnitude responses exhibit strong local continuity and long-range structure in the frequency domain, which are not fully exploited. This work investigates frequency-domain feature modeling by examining how different architectural choices, ranging from per-frequency multilayer perceptrons to convolutional, dilated convolutional, and
attention-based models, affect performance under varying sparsity levels, showing that explicit spectral modeling consistently improves reconstruction accuracy, particularly under severe sparsity. Motivated by this observation, a frequency-domain Conformer-based architecture is adopted to jointly capture local spectral continuity and long-range frequency correlations. Experimental results on the SONICOM and HUTUBS
datasets demonstrate that the proposed method achieves state-of-the-art performance in terms of interaural level difference and log-spectral distortion.
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All authors Xingyu Chen, Hanwen Bi, Fei Ma, Sipei Zhao, Eva Cheng, Ian S. Burnett
Signal Processing
Beamforming Gain Maximization for Fluid Reconfigurable Intelligent Surface: A Minkowski Geometry Approach
Hong-Bae Jeon
This paper investigates beamforming-gain maximization for a fluid reconfigurable intelligent surface (FRIS)-assisted downlink system, where each active port applies a finite-resolution unit-modulus phase selected from a discrete codebook. The resulting design couples the multi-
antenna base-station (BS) beamformer with combinatorial FRIS port selection and discrete phase assignment, leading to a highly nonconvex mixed discrete optimization. To address this challenge, we develop an alternating-optimization (AO) framework that alternates between a closed-form maximum-ratio-transmission (MRT) update at the BS and an {optimal} FRIS-configuration update. The key step of the proposed FRIS configuration is a Minkowski-geometry reformulation of the FRIS codebook superposition: by convexifying the feasible reflected-sum set and exploiting support-function identities, we convert the FRIS subproblem into a one-dimensional maximization over a directional parameter. For each direction, the optimal configuration is obtained constructively via per-port directional scoring, Top-$M_o$ port selection, and optimal codeword assignment. For the practically important regular $M_p$-gon phase-shifter codebook, we further derive closed-form score expressions and establish a piecewise-smooth structure of the resulting support function, which leads to a finite critical-angle search that provably identifies the global optimum without exhaustive angular sweeping. Simulation results demonstrate that the proposed framework consistently outperforms
benchmarks, achieves near-optimal beamforming gains in exhaustive-search validations, accurately identifies the optimal direction via support-function maximization, and converges rapidly within a few AO iterations.
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Systems and Control · Cross-listed from q-bio.NC
CL API: Real-Time Closed-Loop Interactions with Biological Neural Networks
David Hogan, Andrew Doherty, Boon Kien Khoo et al. (+6 more)
Biological neural networks (BNNs) are increasingly explored for their rich dynamics, parallelism, and adaptive behavior. Beyond understanding their function as a scientific endeavour, a key focus has been using these biological systems as a novel computing substrate. However, BNNs can only function as reliable information-processing systems if inputs are delivered in a temporally and structurally consistent manner. In practice, this requires stimulation with precisely controlled structure, microsecond-scale timing, multi-channel synchronization, and the ability to observe and respond to neural activity in real-time. Existing approaches to interacting with BNNs face a fundamental trade-off: they either depend on low-level hardware mechanisms, imposing prohibitive complexity for rapid iteration, or they sacrifice temporal and structural control, undermining consistency and reproducibility - particularly in closed-loop experiments. The Cortical Labs Application Programming Interface (CL API) enables real-time, sub-millisecond closed-loop interactions with BNNs. Taking a contract-based API design approach, the CL API provides users with precise stimulation semantics, transactional admission, deterministic ordering, and explicit synchronization guarantees. This contract is presented through a declarative Python interface, enabling non-expert programmers to express complex stimulation and closed-loop behavior without managing low-level scheduling or hardware details. Ultimately, the CL API provides an accessible and reproducible foundation for real-time experimentation with BNNs, supporting both fundamental biological research and emerging neurocomputing applications.
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All authors David Hogan, Andrew Doherty, Boon Kien Khoo, Johnson Zhou, Richard Salib, James Stewart, Kiaran Lawson, Alon Loeffler, Brett Kagan
Signal Processing · Cross-listed from physics.optics
Dynamic Dispersion Accumulation in Fiber Loops for Realizing Record-High Frequency Resolution or Ultra-Low Signal Sampling Rate in Dispersion-Based Photonics-Assisted Wideband Microwave Measurement Systems
Chi Jiang, Taixia Shi, Hang Yang et al. (+4 more)
Dispersion-based photonics-assisted microwave measurement systems provide immense potential for real-time analysis of wideband and dynamic signals. However, they face two critical challenges: a difficulty in achieving high frequency resolution over a wideband analysis bandwidth, and a reliance on large-bandwidth-and-high-sampling-rate oscilloscopes to capture the resulting ultra-narrow pulses. We introduce a dynamic dispersion accumulation technique to overcome these limitations. By circulating the optical signal in fiber loops containing a dispersion-compensating fiber, we achieve a high accumulated dispersion of -215700 ps/nm. This high dispersion relaxes the required chirp rate of the chirped optical signal, enabling two distinct advantages: When the analysis bandwidth is fixed, a lower chirp rate enables a longer temporal period, yielding a record-high frequency resolution of 27.9 MHz; When the temporal period is fixed, a lower chirp rate enables a smaller bandwidth, generating a wider pulse and thus relaxing pulse sampling requirements at the expense of analysis bandwidth. This sacrifice in analysis bandwidth can be compensated by a duty-cycle-enabling technique, which holds the potential to extend the analysis bandwidth beyond 100 GHz. This work breaks the performance and hardware limitations in dispersion-based systems, paving the way for high frequency resolution, wideband microwave measurement systems that are both real-time and cost-effective.
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All authors Chi Jiang, Taixia Shi, Hang Yang, Lei Gao, Xianxin Zhang, Yiqing Liu, Yang Chen