Signal Processing
High-Temporal-Resolution Motion Correction in Magnetic Resonance Fingerprinting Using a Quantitative Scout and Compact Spiral Navigators
Aizada Nurdinova, Xiaozhi Cao, Daniel R. Abraham et al. (+6 more)
Motion correction in
magnetic resonance fingerprinting (MRF) helps preserve the accuracy of quantitative maps; however, existing approaches provide motion updates only every 7-8 seconds. We propose a navigation framework that integrates compact k-space navigators throughout the MRF acquisition, enabling sub-second motion estimation at minimal sequence overhead. A 3D spiral-projection MRF sequence was augmented with three orthogonal spiral navigators inserted every 0.5 seconds, enabling motion estimation by comparing navigator signals with quantitative scout (Q-Scout) data, i.e., motion-free low-resolution k-space with matching contrast
evolution. The Q-Scout is obtained via a rapid calibration during the dummy preparation period, incurring no additional scan time. Motion estimation is formulated as dictionary matching in a discriminant subspace with optimization refinement. The method was evaluated in simulation and in vivo for 1 mm isotropic
brain 3D MRF at 3 T. Across 35 motion-corrupted acquisitions with motion-free references available, the proposed motion correction reduced MRF reconstruction normalized root-mean-square error (NRMSE) by 7.1% and increased the structural similarity index measure (SSIM) by 0.085. Motion estimates aligned with 8 second temporal-rate image-based navigation (mean absolute difference of 0.15 mm and 0.23 degrees), while the proposed method provided higher temporal resolution and improved motion correction. The proposed framework enables robust motion navigation in MRF at 0.5 second temporal resolution with minimal sequence overhead. By using contrast-consistent modeling and efficient inference, it improves the reliability of quantitative MRI under rapid, unpredictable motion.
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All authors Aizada Nurdinova, Xiaozhi Cao, Daniel R. Abraham, Daniel Polak, Nan Wang, Xuetong Zhou, Yimeng Lin, Brian A. Hargreaves, Kawin Setsompop
Image and Video Processing
Entropy-map SSIM analysis of Salt and Pepper Noise Removal via Recursive Median Filterring
Petr Boriskov and Andrei Velichko
This paper studies the removal of salt-and-pepper (SP) noise from grayscale images using a median filter (MF) within a recursive thresholding
algorithm. Denoising performance is assessed using two complementary metrics: SSIM-Img and SSIM-Map.SSIM-Img is standard Image Quality Assessment (IQA), the conventional Structural Similarity Index (SSIM) computed between the restored and clean images. SSIM-Map is novel IQA, an SSIM-based evaluation computed between
entropy maps of these images, where the maps are obtained using singular value-decomposition
entropy in sliding windows. We show that SSIM-Map is more sensitive to residual impulse artifacts, blur, and local intensity transitions, and therefore complements the conventional SSIM-Img metric.
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Systems and Control
Higher-Order Approximation of Exit Functionals in Sampling-Based Stochastic Model Predictive Control
Sashank Modali and Takashi Tanaka
Safety evaluation in sampling-based
stochastic model predictive control often requires numerical estimation of exit functionals. The approximation of first-exit times and exit indicators is therefore a key numerical bottleneck, and discretization error in these quantities directly affects the resulting controller. This paper studies how existing higher-order methods for strong approximation of exit times can be brought into safe control. Two cases are highlighted. For general noncommutative dynamics, an adaptive order-1 Milstein discretization is used together with Lévy-area simulation via Wiktorsson's method. For commutative dynamics, an adaptive order-1.5 construction achieves a stronger exit-time rate. Under a local anti-concentration condition on the exit-time law, we show that strong exit-time approximation transfers to strong approximation of the failure indicator. The methods are then studied in the context of chance-constrained path integral control, which provides an exact continuous-time representation of safety through exit events. Numerical experiments compare the two cases in terms of strong exit-time error, failure-indicator error, and closed-loop constraint satisfaction, showing improvement over Euler-Maruyama and thereby enabling existing and future techniques whose applicability depends on improved strong approximation.
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Audio and Speech Processing
SPADE: A Multilingual Dataset for Speech Partial Deepfake Detection and Localization
Yuan Tseng, Aishwarya Fursule, Andrew Zijun Ma et al. (+4 more)
Recent improvements in voice-cloning
speech generation systems raise concerns about misuse by malicious actors to impersonate others and spread misinformation. Detecting such tampering is difficult, since deepfakes in the wild may be created by different generative models in a wide range of
languages. Furthermore, the
speech audio may also only be partially modified, presenting a different and potentially more challenging task than detecting fully-synthetic
speech waveforms. To enable further research in this direction, we propose a multilingual
dataset for detection and localization of partially edited
speech samples. Our
dataset includes
speech in 12
languages, generated by up to five systems per
language, and includes both a training set as well as an evaluation
benchmark. To showcase the utility of our proposed
dataset, we train localization models of existing architectures and study generalization across three axes: across different
languages, across different
speech synthesis and editing systems, and across different acoustic environments. Our results show that localization models almost always generalize poorly to
speech edited by systems not seen during training. On the other hand, generalization to edited
speech in unseen
languages still degrades performance but to a lesser extent. We also augment our testing sets with noise to evaluate generalization across acoustic environments, and find that performance of localization models degrade significantly when tested on different acoustic conditions. All together, our results imply that existing deepfake
speech detection methods are insufficient for reliably detecting edit-based
speech deepfakes in various scenarios unseen during training. SPADE is publicly available on HuggingFace.
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All authors Yuan Tseng, Aishwarya Fursule, Andrew Zijun Ma, Vamshi Nallaguntla, Anderson Avila, Shruti Kshirsagar, David Harwath
Audio and Speech Processing
Qwen-Audio-Agent Technical Report
Chong Deng, Yunjie Ji, Yuxiang Kong et al. (+5 more)
We present Qwen-Audio-
Agent, a harness that combines full-duplex voice interaction with asynchronous task execution through a foreground-background architecture. A Frontend
Agent manages dialogue and selects between direct tool use and delegation, while a Backend
Agent carries out delegated tasks in a separate context. An Orchestration Runtime maintains task state, coordinates requests for user input and authorization, and schedules the return of results to the conversation. The runtime separates
speech interruption from task cancellation and execution completion from result delivery, allowing conversation to continue while delegated work proceeds. Environmental events and persistent
memory provide context within and across sessions. Independent adapters support integration with different frontend models, backend
agents, and clients. We instantiate the architecture in desktop assistance, intelligent cockpits, and voice customer service. On an in-house cockpit
benchmark of 134 cases, mixed execution achieves a task success rate of 91.04%, compared with 72.39% and 80.60% for the direct and all delegated configurations, respectively. In a separate latency evaluation on matched successful turns, mixed execution reduces mean task execution latency by 26.73% and 30.91% relative to these baselines, respectively. These results support the complementary use of direct tool calls for immediate operations and backend delegation for multi-step tasks.
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All authors Chong Deng, Yunjie Ji, Yuxiang Kong, Xiangang Li, Xu Li, Binbin Zhang, Haina Zhu, Jianheng Zhuo
Systems and Control · Cross-listed from cs.MA
Indirect tipping: a social attack surface in AI agent populations
Ariel Flint, Luca Maria Aiello, Sara M. Constantino et al. (+2 more)
As generative AI
agents are deployed at scale, safety will depend not only on technical safeguards and individual model design, but also on collective
equilibria that determine how
agent populations process information, prioritize actions, and respond to uncertainty. Yet the same
equilibria that enable
agents to coordinate also create a social attack surface. The standard framework to assess this vulnerability is critical mass dynamics: the minimum fraction of
adversarial agents required to overturn an
equilibrium through direct competition. Here, we show that this approach risks underestimating system vulnerability by reducing the problem to the identification of singular tipping points, and ignoring indirect but potentially more efficient routes through which collective behavior can be redirected. Through experiments with populations of LLM
agents and an analytic framework that captures their collective dynamics at scale, we map critical-mass thresholds that define a directed, weighted
topology over the space of coordination
equilibria, and treat this
topology as a navigable landscape. We show that indirect tipping through intermediate stepping-stone
equilibria can reduce the committed minority required to reach an alternative state, bypass majority requirements, and make possible transitions inaccessible through direct challenges. The diversity of available alternatives and timing of the attack further reshape this landscape, creating opportunities for control as well as risks of unintended destabilization. These results show that an
equilibrium's resistance to committed intervention is not an intrinsic property but a structural feature of its competitive relations with alternative states. Securing populations of interacting AI
agents therefore requires mapping this social landscape alongside individual
agent capabilities and the technical channels through which they interact.
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All authors Ariel Flint, Luca Maria Aiello, Sara M. Constantino, Romualdo Pastor-Satorras, Andrea Baronchelli
Audio and Speech Processing
Qwen-Audio-3.1-Realtime: Towards Reliable Agentic Voice Interaction
Lujia Bao, Qian Chen, Luyao Cheng et al. (+15 more)
Real-time voice assistants must reason over evolving requests, execute actions, and follow conversational rules. Qwen-Audio-3.1-Realtime brings these requirements together through Think, Act, and Speak and Coordinate. Think combines Core-Cocktail supervised fine-tuning with Multimodality and Multi-Teacher On-Policy
Distillation (M$^{2}$-OPD) to transfer
language capabilities and develop native audio skills. Act uses self-evolving executable environments and multi-granularity rollouts for Group Relative Policy Optimization (GRPO), teaching the model to use tools, interpret feedback, and complete tasks. Speak and Coordinate aligns whether, when, and how the assistant speaks or acts. We evaluate audio
reasoning, multilingual understanding, tool use, conversational behavior, full-duplex interaction, and safety. Compared with Qwen-Audio-3.0-Realtime, 3.1 raises overall task success from 78.4% to 82.0% on our half-duplex
speech-to-text adaptation of $\tau$-Voice. On
speech-to-
speech Full-Duplex-Bench v1.5, the response rate to background
speech falls from 73.0% to 13.0%. We also present a separate Voice Harness prototype, using Qwen-Audio-3.0-Realtime as its foreground, that extends spoken interaction to persistent tasks through foreground–background coordination and
memory.
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All authors Lujia Bao, Qian Chen, Luyao Cheng, Chong Deng, Yuxiang Kong, Xiangang Li, Xu Li, Jiaqing Liu, Chao-Hong Tan, Haoyu Wang, Wen Wang, Xilou Wang, Haoxiang Xu, Junhao Xu, Liang Yi, Binbin Zhang, Qinglin Zhang, Qiquan Zhang
Systems and Control
Efficient physiological control of an integrated system architecture for continuous-flow ventricular assist devices: in-silico study
Bruno J. Santos and Idagene A. Cestari
This study presents the development and in silico evaluation of an Integrated System Architecture (ISA) for the physiological control of continuous-flow ventricular assist devices (VADs). The system employs an automatic controller based on pressure measurements at the VAD inflow cannula to estimate heart rate and ventricular filling pressure, enabling dynamic speed adjustments that optimize VAD-patient interaction. The evaluation encompassed 27 simulation scenarios assessing four aspects: operating speed regulation, responsiveness to variable demand, adverse event mitigation, and physiological impact. The controller continuously adapted to preload, afterload, and heart rate, accommodating flow demands with speed adjustments ranging from -10% (-600 rpm) to +43% (+2,600 rpm) relative to the baseline (6,000 rpm). Furthermore, dynamic regulation eliminated ventricular suction and backflow events, which were previously observed in 12 of the 27 uncontrolled scenarios. Hemodynamic and metabolic outcomes demonstrated improved left ventricular ejection fraction (+6.23% to +62.31%), reduced ejection work (-1,658.64 to -206.37 mmHg*mL), decreased pressure-volume loop area (-9,485.17 to -1,056.05 mmHg*mL), and reduced myocardial oxygen consumption (-20.5 to -1.14 mL/min). Concurrently, total oxygen delivery increased (+56.9 to +626.23 mL/min) along with cardiac power (+100 to +3,070 mW). These findings demonstrate that the ISA successfully maintains physiological regulation and mitigates adverse events, establishing a solid foundation for future in vitro validation.
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Systems and Control
Control Synthesis against LTL Specifications with Long-Run Visit Proportion Objectives
Zhiyuan Huang, Zhao Tong, Jiakai Li et al. (+2 more)
This paper investigates the path-planning problem for systems required to satisfy a linear temporal logic (LTL) specification while achieving a desired long-run visit proportion. For a path represented in prefix-suffix structure, the long-run visit proportion quantifies the asymptotic occurrence proportion of an atomic proposition (AP) sequence of interest in the suffix trace. Such a quantitative requirement generally cannot be expressed by standard LTL specifications. Furthermore, we develop a planning approach that synthesizes an LTL-satisfying path whose long-run visit proportion remains within a prescribed tolerance of a desired value while satisfying an overall cost constraint. By adjusting the desired proportion, the synthesized path can allocate more or less long-run
attention to the atomic proposition sequence of interest, thereby improving the flexibility and efficiency of the task execution. Finally, experiments on a quadruped
robot demonstrate the practical significance of the proposed long-run visit proportion and the effectiveness of the proposed planning approach.
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All authors Zhiyuan Huang, Zhao Tong, Jiakai Li, Chenrui Xiang, Bingzhuo Zhong
Signal Processing · Cross-listed from cs.LG
G-NAC: Graph Neural Automata Clustering via Emergent Domain Formation
Keith Miller and Tristan Crawford
We introduce Graph Neural Automata Clustering (G-NAC), an unsupervised clustering method in which observations interact as
cells on a fixed neighborhood graph. A shared recurrent graph-neural cellular rule evolves latent domain states through local interactions, which are converted into a rank-based spectral affinity for partitioning. Across 73 clustering tasks from 57
benchmark datasets, G-NAC achieved a mean adjusted Rand index (ARI) of 0.7951, comparable to Genie at 0.7941 and higher than the other evaluated baselines. Empirical training time and GPU
memory scaled approximately linearly from 5,000 to 100,000 nodes. Learned transition rules also transferred from smaller source graphs to independent 100,000-node samples generated under matched conditions. These results demonstrate a recurrent graph-clustering formulation while identifying dependencies on graph quality, readout design, and source-target similarity.
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Image and Video Processing
When is a closed-form RGB->S/P ratio adequate? A hyperspectral characterization on natural scenes for mesopic display
Naoyuki Uchida
Mesopic and low-light display transforms require, as their driving signal, a per-pixel scotopic-to-photopic luminance ratio (S/P); the exact spectral S/P is unavailable for ordinary RGB content, so a low-cost closed form that estimates S/P from a linear-RGB triplet is used in its place. Such closed forms exist but have been characterized only on narrowband / LED sources, i.e. spectrally sparse spectra, where a relative error of ~41% has been reported for a three-channel projection. Display content, however, is natural and broadband. We ask whether the same closed form is adequate there, using per-pixel spectral S/P from hyperspectral imagery as ground truth. On a daylight radiance time-series, a six-scalar closed form (three photopic and three scotopic channel weights) reproduces spectral S/P with a median error of ~0.07 that is time-invariant once the RGB input is chromatically adapted to D65; evaluated in un-adapted sRGB the error instead carries a color-temperature tilt across illuminants (~0.19), so adaptation is the enabling step for this use case. The result generalizes to an independent fifty-scene set (pooled median 0.024; 45/50 scenes within a pre-registered 0.10 band), with the few exceedances concentrated in saturated, spectrally-peaky surfaces that approach the narrowband regime (floral close-ups in this set). The scotopic weight vector is shown to be primary-model dependent, but the value used here is corroborated by a primary-free XYZ projection, and the median error stays within the band across all principled coefficient choices. We do not claim observer-validated appearance fidelity or adequacy on narrowband sources; both are out of scope. Both outcomes follow from the same three-channel projection: it is overwhelmed by spectrally sparse inputs and adequate on spectrally smooth ones.
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Audio and Speech Processing · Cross-listed from cs.SD
Fast Time-Varying Exponentiated Convolution Methods for Generative Direction Dependent Reverberation
Yuancheng Luo
Spherical harmonic encoded acoustic sound-fields capture directional characteristics of room impulse responses that are useful for accurate spatial audio reproduction. However, high costs of multi-microphone measurements and numerical simulations motivate alternative data-set augmentation and synthetic data generation methods that supplement small collections. This paper introduces time-varying exponentiated convolution methods that transform both Gaussian noise and impulse responses into reverberation and modified spectral-decay fields respectively. We derive two recursive and fast convolution
algorithms that extend into the spherical harmonic domain, model smooth reverberation time distributions with non-stationary Gaussian processes, and realize an optimal filter design. Experiments evaluate computational performance, and validate out-of-distribution generated impulse responses.
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Audio and Speech Processing
XSQ-AST: An Explainable Audio Spectrogram Transformer Framework for Localising Synthetic Speech Artifacts
Ben Heritage, Luca Resti, Mónica Villanueva Aylagas et al. (+3 more)
Localising artifacts in synthetic
speech remains challenging, as most evaluation methods yield only global quality scores. This paper presents XSQ-AST, a framework that combines the SQ-AST
speech quality model with WhisperX phoneme
alignment and multiple saliency methods to produce temporally localised artifact diagnostics without model retraining. Saliency maps are projected onto continuous distributions via kernel density estimation and onto phoneme boundaries via phoneme-discretised saliency maps. A 40-participant listening test validated the framework across five perceptual dimensions.
Attention Rollout,
Attention Flow and an adapted GradCAM produced temporal distributions that correlated with listener highlights, with different methods best suited to different artifact types. An AUC-ROC analysis confirmed discrimination above chance.
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All authors Ben Heritage, Luca Resti, Mónica Villanueva Aylagas, Timothy Mehlenbacher, Konrad Tollmar, James Alfred Walker
Signal Processing
Neural Kalman Filtering for Unknown Dynamics: Task-Aware Learning with a Koopman Backbone
Mintaek Oh, Jeonghun Park, Nir Shlezinger et al. (+2 more)
Recent years have witnessed a growing interest in AI-aided Kalman filters. While emerging methodologies, such as KalmanNet, were shown to facilitate tracking in partially known state-space models, they are not directly applicable when the underlying dynamics is unknown. To overcome this limitation, we extend the KalmanNet philosophy to the unknown-dynamics regime by developing blind Kalman filtering frameworks that learn both the predictor and the correction gain from data, assuming that the state-
evolution function and the noise statistics are both unavailable. To this end, we first introduce a task-aware neural Kalman filtering framework, Blind-KalmanNet, which carries the learning principle of the Kalman gain into the prediction step through a two-head neural architecture that jointly learns a state-dependent linear surrogate and the Kalman gain from data. Building on this formulation, we then develop our main framework, Koopman-aided Blind-KalmanNet, which incorporates Koopman operator theory to lift the unknown dynamics into a latent space where the state evolves linearly. The lifted linear predictor integrates seamlessly into the Blind-KalmanNet structure: the pre-trained deep Koopman network serves as a globally structured predictor, augmented by the task-aware residual surrogate and the learned Kalman gain inherited from Blind-KalmanNet. Extensive experiments demonstrate that the proposed frameworks achieve competitive performance against baselines, with Koopman-aided Blind-KalmanNet attaining the best accuracy across all considered settings.
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All authors Mintaek Oh, Jeonghun Park, Nir Shlezinger, Yonina C. Eldar, Jinseok Choi
Image and Video Processing
Disparity Estimation of Planar Reflective Surfaces Using Specular Reflections From a Single Light Source
Katja Kossira, Frank Sippel, Jürgen Seiler et al. (+1 more)
Multi-camera imaging and camera arrays have become ubiquitous in many applications, such as autonomous driving,
robot control, or virtual reality, and accurate disparity maps of objects and their environment are essential for reliable operation. Despite recent advances in neural networks, correctly estimating the disparity of flat and textureless objects remains challenging. In particular, we consider a scenario defined by flat, textureless surfaces illuminated by a single fixed light source, resulting in one dominant specular reflection visible on the object surface. Under these conditions, reliable
geometric and photometric cues are missing, and the specular reflection often causes mispredictions, especially when using conventional methods that rely on texture information to match corresponding pixels. To address this issue, the novel Specular Reflection Disparity Estimation SRDE
algorithm is introduced, which is specifically designed for the constrained scenario of planar, textureless objects and single-source illumination. Unlike conventional stereo matching methods, SRDE ignores texture and instead leverages the
geometric properties of specular reflections by incorporating the position information of the reflective region, the light source, and the camera setup. We show that SRDE outperforms existing methods by a notable margin, achieving more than a 52% improvement in End Point Error on synthetic images. Further tests demonstrate superior performance on real-world data. Furthermore, we integrate SRDE into existing neural disparity estimation pipelines by selectively replacing predictions in specular regions without modifying the backbone model. This hybrid strategy enables additional performance gains without requiring network retraining.
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All authors Katja Kossira, Frank Sippel, Jürgen Seiler, André Kaup
Image and Video Processing · Cross-listed from cs.CV
GraphSVR: q-Space–Aware Graph-Based Slice-to-Volume Registration for Diffusion MRI
Noga Kertes, Daphna Link Sourani, Alex M. Bronstein et al. (+1 more)
Diffusion-weighted imaging (DWI) remains highly vulnerable to subject motion, particularly in time-efficient protocols and in motion-prone populations. While slice-to-volume registration (SVR) can mitigate inter-slice and inter-stack misalignment,
diffusion MRI introduces additional complexity due to
diffusion-direction-dependent contrast and the requirement to align dozens of measurements within a common reference frame, effectively yielding a 4D registration problem. Existing approaches rely primarily on sequential modeling or pairwise similarity and often degrade under sparse gradient sampling or severe motion. We introduce GraphSVR, a q-space-aware graph-based framework for 4D SVR registration in DWI. GraphSVR represents slice groups as nodes in an acquisition-structured graph, with edges encoding temporal proximity, spatial slice geometry and
diffusion encoding relationships. A graph neural network predicts globally consistent stack-wise rigid motion, optimized in a self-supervised, zero-shot manner using only an anatomical reference image, without requiring paired ground-truth motion. We evaluate GraphSVR using both fully synthetic
diffusion simulations and realistic recombination-based simulations from real acquisitions with controllable motion severity and gradient sparsity. Performance is quantified using grid error (mm) and rotation error relative to known ground-truth transforms. Under severe motion, GraphSVR reduces grid error and rotation error by 73% compared to FSL eddy, the standard DWI motion-correction method, with the largest gains observed in sparse-direction regimes. These results demonstrate that explicitly modeling acquisition structure through graph-based
reasoning improves robustness and global consistency in 4D DWI motion estimation. Code is available at https://github.com/nogakertes/GraphSVR.git.
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All authors Noga Kertes, Daphna Link Sourani, Alex M. Bronstein, Moti Freiman
Systems and Control
Explicit Second-Order Bounds on the Domain of Validity for Lyapunov-Schmidt Reduction
Pranav Gupta, Ravi Banavar and Anastasia Bizyaeva
Lyapunov-Schmidt reduction is a widely used dimensionality reduction technique for bifurcation analysis in high-dimensional systems. While classical formulations guarantee the local existence of reduced-order equations, they typically lack explicit quantitative estimates on the size of the neighbourhoods in which these representations faithfully capture the full bifurcation structure. In recent work, we have addressed this limitation by deriving bounds on the domain of validity of this reduction using first-order conditions on the vector field together with a quantitative result for the implicit function
theorem. In this article we explore beyond, and adopt second-order conditions that incorporate the Hessians of the vector field to develop these bounds, and obtain a new set of results. We then investigate the applicability of these newly derived bounds to Hopfield-like networked dynamical systems, evaluate these bounds for pitchfork bifurcations on connected regular graphs, and finally examine the relationship between the two certified domains for this class of systems.
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Systems and Control
Offline Reinforcement Learning for Distribution-Grid Protection
Julian Oelhaf, Alexander Luce, Christian Bergler et al. (+2 more)
Data-driven protection may complement conventional relays in distribution grids whose operating conditions vary with distributed generation, switching events, and changing short-
circuit levels. We study line-selective tripping from static trajectories of a realistically simulated CIGRE medium-voltage network using offline reinforcement learning. A convolutional Q-network receives causal voltage-current phasor and apparent-impedance features, optionally together with raw waveforms, and is trained with conservative Q-learning (CQL). A controlled sensitivity study evaluates two observation windows, reward variants, and three CQL weights under a common split and training protocol; one exploratory post-hoc run additionally increases the discount factor from $\gamma$=0.95 to 0.99. On 225 held-out episodes, the best per-timestep result is obtained with combined input and CQL weight $\alpha$=0.9, reaching precision 0.9993, recall 0.9496, and F1-score 0.9738. Because dense per-timestep scores do not encode the terminal semantics of relay operation, we also evaluate the first non-wait action in each episode. The default combined-input
agent selects the correct line-trip action first in 98.13% of 214 fault episodes, but trips in 72.73% of the 11 non-fault episodes. In the post-hoc run, the corresponding rates are 98.60% and 54.55%, respectively. The results show that dense predictive performance and terminal protection behavior can lead to different model rankings. Offline CQL therefore demonstrates strong faulted-line selection on the simulated fault episodes, while the static trajectories, small non-fault set, and single-seed post-hoc design preclude conclusions about practical relay security or deployment readiness.
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All authors Julian Oelhaf, Alexander Luce, Christian Bergler, Andreas Maier, Siming Bayer
Systems and Control
Adaptive sliding mode formation control for space interferometer missions
Mauro Mancini, Giulia Alessandra Tataru, Satoshi Satoh et al. (+1 more)
This paper addresses high-precision formation control for spacecraft operating in low Earth orbit, motivated by the requirements of future space interferometry missions such as SILVIA. The proposed approach formulates the relative dynamics within a port-
Hamiltonian framework and introduces an Adaptive Boundary-layer Sliding Mode Control (AB-SMC) law to overcome the limitations of conventional SMC with constant gains. The key innovation lies in a dynamic, error-dependent adjustment of the sliding
manifold, enhancing transient performance while guaranteeing high-precision trajectory tracking. Rigorous Lyapunov-based analysis establishes explicit ultimate bounds on the tracking error and ensures closed-loop stability, while extensive Monte Carlo simulations further validate the proposed AB-SMC compared to standard control approaches. Results show that AB-SMC achieves faster convergence, lower control effort, and sub-millimeter tracking accuracy, demonstrating its practical robustness and implementation feasibility in realistic, uncertain orbital environments while respecting low-thrust constraints.
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All authors Mauro Mancini, Giulia Alessandra Tataru, Satoshi Satoh, Elisa Capello
Image and Video Processing
Recurrent Convolutional Neural Networks for LiDAR-Based Attitude Initialization of Rotating Spacecraft
Luca Bechis, Jean-Luc Sarvadon, Petre Ricioppo et al. (+1 more)
Accurate attitude estimation is essential for autonomous in-orbit servicing and proximity operations. This work proposes a Recurrent Convolutional Neural Network (RCNN) used in coarse attitude initialization of known, possibly tumbling spacecraft using
LiDAR-derived depth images. By processing temporal sequences of 2D point-cloud projections, the RCNN effectively handles
symmetries, occlusions, and degraded sensing. Simulations across various spacecraft geometries, angular velocities, and ranges show that the RCNN yields lower initialization errors and higher convergence rates than conventional CNN baseline within the adopted experimental framework, with performance varying across angular velocity conditions.
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All authors Luca Bechis, Jean-Luc Sarvadon, Petre Ricioppo, Mauro Mancini
Systems and Control
Agentic AI Enabling Autonomous, Self-Organizing, and Evolving UAV Networks
Zhaoyang Li, Xingzhi Jin, Zijiu Yang et al. (+2 more)
As low-altitude applications expand across emergency response, intelligent transportation, and autonomous operations, they demand communication networks that can deliver flexible, resilient, and rapidly deployable connectivity. Heterogeneous
UAV networks are a promising solution, as they can dynamically provide sensing, access, relay, and backhaul functions. Yet, most existing approaches assume predefined missions, prior knowledge of user distributions, and manually configured infrastructure, making them ill suited to dynamic and initially unknown environments. Addressing this limitation requires a shift from mission-oriented
UAV deployment to autonomous network formation, in which
UAVs continuously perceive their surroundings, infer evolving service demands, and self-organize network resources. Agentic AI, empowered by large
language models (LLMs), offers a new foundation for this shift by integrating closed-loop perception,
reasoning, planning, and execution across heterogeneous information sources. Unlike conventional optimization and learning methods designed for individual networking tasks, agentic AI can coordinate these capabilities to support sustained, network-level autonomy. In this article, we explore agentic AI for autonomous and self-organizing heterogeneous
UAV networks in low-altitude environments. Our key contribution is an LLM-assisted architecture in which a base-station-hosted
agent conducts global network
reasoning and autonomously reconfigures access and backhaul infrastructure. The proposed system explores unknown environments, discovers users, and deploys
UAVs on demand to provide access and establish end-to-end backhaul connectivity. A case study illustrates how this agentic-AI-driven approach can transform
UAVs from task-specific platforms into a continuously evolving communication network.
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All authors Zhaoyang Li, Xingzhi Jin, Zijiu Yang, Qianqian Yang, Zhiguo Shi
Systems and Control · Cross-listed from cs.DC
Analytical Power-Aware Provisioning for Prefill-Decode Disaggregated AI Inference
Mingyuan Yan, Haiyu Wang, Linxuan Biao et al. (+3 more)
Power availability increasingly constrains the operation of AI inference fleets, creating a need for provisioning methods that jointly consider serving capacity and power consumption. Prefill–decode (PD) disaggregation has emerged as a prevalent architecture for large-scale inference serving. However, determining the appropriate numbers of prefill and decode instances is challenging because serving capacity depends jointly on workload characteristics, hardware constraints, queueing, and KV-cache reservations. Existing approaches largely rely on profiling and simulation, providing limited analytical insight into how provisioning decisions shape the tradeoff between serving capacity and power consumption. This paper develops an analytical framework for power-aware provisioning of PD-disaggregated AI inference. Given an inference workload and hardware, the framework models the serving capacity and average power consumption of a provisioned deployment. The serving-capacity model is derived from the joint distribution of input–output lengths and hardware compute and
memory limits. In particular, it explicitly captures the coupling between prefill and decode induced by KV-cache reservations, as well as the impact of request queueing. On this basis, the power model determines per-instance power consumption as a function of normalized serving throughput. Together, the models determine the serving capacity–power Pareto front among candidate provisioned deployments, enabling the service provider to choose a provisioned deployment as the workload or available power changes.
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All authors Mingyuan Yan, Haiyu Wang, Linxuan Biao, H. Jonathan Chao, Sai Qian Zhang, Wenqi Cui
Systems and Control
Multi-Period Repetitive Control Design in a Time Delay Framework with Application to an Active Vibration Isolation System
Xiaoran Han, Sil Spanjer and Wouter B.J. Hakvoort
Multi-period Repetitive Control (MPRC) can lead to amplification at non-repetitive frequencies due to the multiplication of multi-period repetitive control in their closed-loop sensitivity functions, which leads to closed-to imaginary poles. We propose a MPRC structure that eliminates such interaction. The scheme can be equivalently converted into a disturbance observer to improve robustness to frequency uncertainties. We derive an upper bound on the sensitivity function that can be optimized at either repetitive or non-repetitive frequencies. Experimental results on an active vibration isolation system show superior performance over existing robust MPRC schemes, and the capability to suppress unknown time-varying periodic disturbances.
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Signal Processing · Cross-listed from cs.IT
Deterministic Smoothed MAP Detection for High-Dimensional MIMO Systems
Vitor Tucci Ramos, Stephane Senecal and Sheng Yang
We propose a deterministic smoothed maximum a posteriori (SMAP) framework for high-dimensional multiple-input multiple-output (
MIMO) detection. The discrete constellation prior is approximated by an i.i.d. Gaussian mixture, yielding a differentiable MAP objective that retains the constellation structure while enabling continuous optimization. Unlike sampling-based approaches, SMAP formulates detection as a deterministic optimization problem and can be initialized by the output of an arbitrary detector, allowing it to operate either as a standalone detector or as a refinement stage. For hard detection, we introduce SMAP-KR, which complements the continuous solution with a local $K_R$-neighbor search evaluated according to the original maximum-likelihood metric. Annealed continuation improves robustness for higher-order constellations, while a two-neighbor approximation reduces the cost of evaluating the mixture prior. We further develop a soft-output SMAP method in which a local Gaussian approximation based on the
curvature of the smoothed posterior provides symbol probabilities and bit log-likelihood ratios without posterior sampling or explicit counterhypothesis lists. Numerical results for critically loaded MU-
MIMO systems show that SMAP-KR provides an increasingly favorable performance-complexity tradeoff as the system dimension grows and can also effectively refine the output of existing detectors. For coded transmission, soft SMAP provides substantial block-error-rate gains over both LMMSE- and K-best-based soft detection.
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Systems and Control
Optimal Allocation of Grid-Forming Frequency Shaping Control
Zhaomin Lyu, Ding Zhang and Yan Jiang
Various inverter-based control strategies have been proposed to improve frequency security for power systems with high renewable penetration. Among them, the grid-forming frequency shaping control is particularly promising due to its ability to shape the post-contingency aggregate system frequency dynamics into first-order with prescribed rate of change of frequency (RoCoF) and steady-state frequency deviation. Moreover, the shaped aggregate dynamics depends on the harmonic sum of all inverter transfer functions rather than on their distribution. In light of this, here we explore how to allocate shaping control resources to minimize the transient control effort needed to achieve ideal coherent dynamics. We formulate it as a constrained optimization problem by tackling two main difficulties. First, we make the squared $\mathcal{H}_2$ norm legitimate quantification of transient control cost under step power imbalances through a system transformation. Second, we simplify the $s$-domain constraint for Nadir elimination into a standard constraint that is easy to implement in optimization. The resulting non-convex optimization problem can be solved by existing successive convexification method. The effectiveness of the allocation has been verified on the modified Icelandic Power Network test case.
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Signal Processing
Advanced 400G-SerDes Modulations and Equalizers for GPU Cluster in AI Era
Sheng Chen, Zhengxiang Cheng, Ji Zhou et al. (+5 more)
High-speed GPU cluster interconnects are evolving from 200 to 400 Gbps to meet the growing computing demands of artificial intelligence. We investigate high-order modulation and advanced equalization for 400 Gbps interconnects. Theoretical bit error rate (BER) expressions for 4/8-ary pulse amplitude modulation (PAM) and approximate expressions for six PAM6 formats generated from 32-ary quadrature amplitude modulation (32QAM) are derived. In additive white Gaussian noise, PAM6 generated by framed-cross 32QAM achieves the best BER performance among the PAM6 formats. PAM6 and PAM8 incur peak signal-to-noise ratio (PSNR) penalties of approximately 4.2 and 7.2 dB, respectively, relative to PAM4 at the KP4 forward error correction (KP4-FEC) limit. Simulations compare PAM4, PAM6, and PAM8 at a line rate of 440 Gbps (net rate of 400G) under different bandwidth limitations. As bandwidth becomes more constrained, bandwidth requirements increasingly dominate performance over noise tolerance. PAM4 requires the lowest PSNR to reach the KP4-FEC limit in mildly bandwidth-limited channels, whereas PAM6 performs best under moderate limitations. PAM8 offers a significant advantage under severe limitations because of its lower bandwidth requirement and reduced susceptibility to high-frequency attenuation. With maximum likelihood sequence estimation, PAM6 and PAM8 reach the KP4-FEC limit under all evaluated channel conditions, while PAM6 requires a lower PSNR in most channels, highlighting its potential for 400G-SerDes systems.
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All authors Sheng Chen, Zhengxiang Cheng, Ji Zhou, Haide Wang, Mengqi Guo, Weiping Liu, Liangchuan Li, Xiangjun Xin
Systems and Control · Cross-listed from cs.RO
FoldQuantVLA: Native Low-Bit Quantization of Vision-Language-Action Models via Consistent Folding
Hung T. Ho, Khanh D. Nguyen, Quang D. Nguyen et al. (+5 more)
Low-bit
vision-
language-action inference must reduce observation-to-action latency while preserving
robot behavior. We present FoldQuantVLA, a post-training quantization framework that carries a consistent activation representation through calibration, weight rounding, and native integer execution. It combines channel scaling and block Hadamard transforms with dynamic per-
token quantization, without policy retraining. Custom TensorRT plugins execute projections in both the
language backbone and iterative action expert with four-bit weights and activations (W4A4) on Ada GPUs and Jetson AGX Orin. Evaluation spans LIBERO, SimplerEnv, and two
robot platforms. Across three GR00T checkpoints and $\pi_{0.5}$, W4A4 achieves $1.20$ to $1.33\times$ speedups over floating-point TensorRT on Orin and $1.25$ to $1.52\times$ on desktop. Retaining
language attention-output and feed-forward down projections at eight bits (W8A8) improves held-out action fidelity on all four checkpoints. Across four real-
robot tasks, this configuration raises observed GR00T N1.7 success from $80.0\%$ with uniform W4A4 to $92.5\%$ over 80 trials per configuration, with a measured additional Orin latency of 1 ms.
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All authors Hung T. Ho, Khanh D. Nguyen, Quang D. Nguyen, Thanh Q. Duong, Ngan Le, Meng Guo, Vien A. Ngo, An T. Le
Systems and Control · Cross-listed from math.OC
Benign Geometry and Distributional Robustness of $\mathcal{H}_2$ Synthesis
Arman Sharifi Kolarijani, Peyman Mohajerin Esfahani, Tamás Keviczky et al. (+1 more)
In this paper, we study standard and distributionally robust $\mathcal{H}_2$ synthesis problem of a stabilizing state-feedback controller for discrete-time linear time-invariant systems. Without requiring a nonsingular disturbance controllability Gramian or a positive-definite control penalty, we establish that stationarity of a stabilizing gain in standard $\mathcal{H}_2$ synthesis is equivalent to global optimality and derive an exact Pythagorean identity for the performance difference. We then generalize the $\mathcal{H}_2$ synthesis problem by considering i.i.d. disturbances with zero mean and uniformly bounded second moments, and extend the stationarity-optimality equivalence to the corresponding distributionally robust $\mathcal{H}_2$ synthesis. Moreover, we show that every standard $\mathcal{H}_2$-optimal gain is simultaneously optimal for every such distributionally robust problem. Finally, we specialize the framework to Frobenius, Kullback–Leibler, and Wasserstein-2 ambiguity sets and obtain explicit characterizations of their worst-case covariances, which, in turn, provide performance certificates for a common optimal controller.
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All authors Arman Sharifi Kolarijani, Peyman Mohajerin Esfahani, Tamás Keviczky, Mohamad Amin Sharifi Kolarijani
Image and Video Processing
JEVQA - Video Quality from Metadata, Bitstream, and Pixel Features with a General-Purpose Decision Model
Werner Robitza
Instrumental quality models for
video quality prediction are usually trained for a fixed set of codecs or other input features, and every new input variable requires retraining. Novel, general-purpose decision models can answer questions without task-specific training, but it is unclear whether they can judge
video quality. We evaluate Jev, a commercial “System One” model that returns probability distributions over a provided answer scale, as a zero-shot
video quality model. We call the resulting method JEVQA. In a first study on 1,936 AV1, H.264, HEVC, and VP9 encodes of 22 sources, scored against VMAF as ground truth, using encoding metadata only, JEVQA reached a Pearson correlation of 0.737, on par with the standardized ITU-T P.1204.1 model (0.733). Giving the model bitstream data raised the accuracy to 0.797, and combined pixel-based and bitstream features raised it to 0.824. A pixel-only variant failed in our tests. In a second study, using H.264, HEVC, and VP9 encodes in the AVT-VQDB-UHD-1 database, the metadata-only model reached a correlation of 0.879 with MOS, close to P.1204.1 (0.898). Bitstream statistics did not help there. Our results show that trained models on the same features remain clearly ahead in both studies, but that zero-shot classifiers are promising.
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Systems and Control · Cross-listed from cs.RO
Multi-Agent Transportation of Free-Flyers in Microgravity Via Pushing Interaction Under Human-in-the-Loop Control
Gregorio Marchesini, Nicola De Carli, Sihyun Cho et al. (+5 more)
We propose a safety-critical framework for the cooperative transportation of passive targets in microgravity, where a team of chaser
robots acts through unilateral pushing contacts to track a human-provided desired twist while ensuring safe target motion. The pushing-only nature of the interaction introduces sparse, configuration-dependent actuation constraints requiring chasers to physically relocate on the target body when the desired pushing allocation changes. To address these challenges, we formulate a delay-aware feedback control architecture leveraging Control Lyapunov Function (CLF) and Control Barrier Function (CBF) constraints within a mixed-integer thrust allocation program to enforce stability and safety of the target, respectively. The proposed framework enables reference tracking while guaranteeing obstacle avoidance with a circular obstacle despite intermittent control authority, providing a foundation for human-supervised cooperative transportation of free-flyers in space environments. The proposed framework is validated through Gazebo simulations.
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All authors Gregorio Marchesini, Nicola De Carli, Sihyun Cho, Youngkyoung Kong, Elias Krantz, Mani Hemanth Dhullipalla, Dimos V. Dimarogonas, H. Jin Kim