Image and Video Processing
Temporal Diffusion Bridge Model for Vessel Extraction Using Physics-informed Synthetic Data
Jinkui Hao, Donald R. Cantrell, Ramez Abdalla et al. (+2 more)
Accurate artery extraction from X-ray coronary angiography (XCA) is essential for coronary artery disease diagnosis and intervention. However, low vessel visibility, respiratory and cardiac motion, and limited annotations challenge existing methods. Moreover, most deep learning approaches produce binary vessel masks without preserving clinically relevant intra-arterial contrast information. We propose a Temporal
Diffusion Bridge Model (TDBM) that formulates vessel extraction as image translation and learns from physics-informed synthetic data without manual vessel annotations. A physics-based XCA synthesis engine generates diverse, perfectly aligned pairs of contrast-enhanced and contrast-free images. Using these pairs, TDBM learns cross-domain mappings to recover high-fidelity vessel information. Temporal conditioning incorporates adjacent frames to improve consistency, while a vessel-aware contrastive loss exploits background-invariant synthetic pairs to enhance vascular discrimination. We evaluate TDBM on two XCA vessel segmentation
datasets and conduct a reader study using two real-patient XCA sequence
datasets. Zero-shot segmentation is further assessed on retinal imaging and fluorescence microscopy
datasets. Results demonstrate that TDBM consistently outperforms existing methods and generalizes effectively across imaging modalities. Code and trained weights will be available at: https://github.com/Advanced-AI-in-Medicine-and-Physics-Lab/TDBM
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All authors Jinkui Hao, Donald R. Cantrell, Ramez Abdalla, Sameer A. Ansari, Bo Zhou
Image and Video Processing
RePrior: Measurement-Reconciled Prior Correction for Sparse-View CBCT Reconstruction
Mengrui Yin, Tianxingjian Ding, Wufan Zhao et al. (+2 more)
Sparse-view cone-beam computed
tomography (CBCT) is severely under-constrained by limited X-ray projections. Learned reconstruction can provide complementary anatomical information, but using it as a prior in patient-specific Gaussian reconstruction introduces two challenges. First, the discrepancy between the learned prior and current reconstruction is spatially non-uniform: some local differences contain useful missing 3D information, whereas others reflect prior bias. Second, even a volumetrically beneficial correction can conflict with the observed X-ray measurements. Since X-ray measurements are line integrals, spatially separated corrections may affect overlapping rays, with their signed projection responses reinforcing or partially canceling in the measurement residual, so their feasibility cannot always be assessed independently. In this paper, we introduce RePrior, a multi-stage progressive prior-correction framework that addresses correction actionability, measurement feasibility, and representation expressivity. Selective Correction Proposal (SCP) first identifies actionable signed local corrections from prior–reconstruction discrepancies. Measurement-Constrained Reconciliation (MCR) then jointly enforces their consistency with patient-specific X-ray measurements, while Hybrid Residual Enhancement (HRE) recovers additional correction capacity through complementary voxel- and Gaussian-domain degrees of freedom around the reconciled anchor. RePrior requires neither test-time ground truth nor case-specific network fine-tuning. RePrior gains approximately 2.6~dB in 3D PSNR over R$^2$-Gaussian across LUNA16, PANORAMA, and PENGWIN. Beyond prior-assisted reconstruction, it delivers additional gains in volumetric quality and observed-view projection fidelity, with consistent gains in held-out-view fidelity in the backbone-matched evaluation.
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All authors Mengrui Yin, Tianxingjian Ding, Wufan Zhao, Ziyang Yan, Yu Tian
Systems and Control · Cross-listed from q-bio.NC
Energy, Information, and the Limits of Neuropsychiatric Classification
Farbod Khodadadi, Mina Kheirkhah, Bita Shariatpanahi et al. (+15 more)
Importance: The search for reliable neuroimaging biomarkers of mental disorders has remained elusive despite decades of research. Why clinically consequential disorders yield weak neuroimaging separability remains unresolved. Drawing on statistical thermodynamics and the metabolic costs of neural coding, we propose that psychiatric disorders produce constrained deformations of a largely shared repertoire of
brain activity patterns, limiting the class-specific information available for classification. Observations: We develop a framework linking physical resource constraints, landscape deformation, distributional separation, and attainable classification, and examine its components using human neuroimaging, synthetic data, and an exactly specified physical model. In resting-state fMRI from 1952 FOR2107 participants (823 with major depressive disorder; 1129 healthy controls), held-out energy-landscape estimates and nonlinear classifiers agreed across 13 comparisons (r = 0.996). Similar results emerged at both FOR2107 centers and in 900 SRPBS participants spanning multiple psychiatric diagnoses. In synthetic data, classification improved with distributional separation and approached the optimum. In an
equilibrium three-well model, statistical energy equaled physical potential energy scaled by inverse temperature. Modest deformation redistributed state occupancy while optimal snapshot classification remained weak. A literature map found insufficiently comparable evidence to test the human metabolic constraint. Conclusions: Clinically consequential differences may arise within a largely shared neural repertoire. The thermodynamic framework connects physical deformation to classification under specified assumptions; its metabolic implementation in humans remains untested. Testing this mechanism requires joint measurements of
brain activity, bodily physiology, and metabolism.
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All authors Farbod Khodadadi, Mina Kheirkhah, Bita Shariatpanahi, Sarah Alizadeh, Madleen Stenger, Amaia Zurinaga Gutierrez, Shakiba Bakhshian, José C. García Alanis, Nils R. Winter, FOR2107 Consortium, Frank Bremmer, Stefan G. Hofmann, Udo Dannlowski, Tilo Kircher, Sharmili Edwin Thanarajah, Igor Nenadić, Tim Hahn, Hamidreza Jamalabadi
Image and Video Processing · Cross-listed from cs.RO
When pose graphs are not enough: a failure analysis of pose-graph-based camera estimation for Gaussian splatting
Senthil Palanisamy, Azwad Sabik, Alan Dolhasz et al. (+1 more)
Exterior Gaussian-splat twins for real-estate listings are captured to a fixed recipe: a consumer drone orbits a single property in several overlapping passes at stepped altitudes, with occasional vertical ascents, so the imagery is dense, sequential, GPS- and time-tagged, and revisits each facade from many angles. This structure is tantalizing: it suggests camera poses could be recovered by a dense pose-graph optimization (PGO) whose only unknowns are the poses, far fewer than the joint pose-and-point problem bundle adjustment (BA) solves. PGO is mature in robotics, but almost always as post-processing: it redistributes the drift of an already-decent trajectory once loop closures tie it together. Splatting captures push that regime to its limit, every frame closing loops with dozens of others, raising a clean question: does pose-only optimization reach BA accuracy? We built a system specialized to this capture structure, not a general-purpose SfM tool, to test it. It beat COLMAP on speed but never on quality; this paper is the candid post-mortem. The shortfall arises from several interacting causes, hand-set weights, greedy merging and a noisy GPS ruler, together with two limits inherent to the formulation: SE(3) edges carry no scale degree of freedom, so independently scaled sub-maps drift when merged, and a two-view edge stops short of the coupling one 3D point imposes on every frame that sees it. A viable design must add back Sim(3) constraints, absolute GPS priors, retained structure, and uncertainty-aware weighting
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All authors Senthil Palanisamy, Azwad Sabik, Alan Dolhasz, Mira Slavcheva
Audio and Speech Processing · Cross-listed from cs.SD
Two-Stage Multi-modal Learning for Dynamic Audio-Visual Navigation: From Physical Alignment to Scene Aggregation
Jia Li and Yinfeng Yu
Embodied Audio-Visual Navigation (AVN) requires an
agent to locate sound targets by jointly leveraging visual and auditory cues. While prior work has achieved notable success with static sound sources, real-world scenarios often involve moving targets, giving rise to the more challenging problem of Dynamic Audio-Visual Navigation (D-AVN). Existing approaches largely rely on semantic auditory features, which limits generalization to unheard sound categories and hinders fine-grained spatial
reasoning. To address these challenges, we propose a novel Two-Stage Multi-modal Learning framework for D-AVN. In the Physical
Alignment stage, the proposed Intensity-Calibrated Geometry Encoder (ICGE) performs physics-informed
alignment of sound intensity with visual geometry to capture category-agnostic spatial correspondence. In the Scene Aggregation stage, a
Transformer-based Cross-Modal Scene Aggregator (CMSA) dynamically integrates multi-modal features to construct a robust state representation for policy learning. Extensive experiments on Replica and Matterport3D demonstrate that our method achieves state-of-the-art success rates, while significantly improving generalization and navigation efficiency in complex environments and under unheard sound scenarios.
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Systems and Control · Cross-listed from cs.LG
Deploying RDMA for Distributed Inference and Training At Scale
Yibo Huang, Yi Cui, Yukai Huang et al. (+12 more)
RDMA can reduce communication overhead in distributed machine learning systems, but realizing these benefits in production requires more than replacing TCP. Variable-size messages, concurrent connections, bursty incasts, and service failures introduce challenges that hardware offload alone cannot address. This paper reports our experience deploying ByteExpress, a software communication layer over hardware RDMA, in ByteDance's parameter-server-based inference and training systems. We present three lessons from the deployment: efficient RDMA requires buffer management across application and transport layers; scalable communication requires coordinated management of connections, receive capacity, and outstanding data; and service availability requires recovery above the transport layer. ByteExpress combines an RDMA-aware allocator, zero-copy transfers, QP multiplexing, receive-credit flow control, a software send window, and RPC-assisted recovery. In production inference, ByteExpress reduces mean and P99 model parameter fetch latency by 35.7% and 43.7%, respectively, and total CPU usage by 10.69% compared with kernel TCP. In training, ByteExpress reduces P99 model parameter push latency by up to 74.0% compared with TCP, with no PFC pauses observed at runtime.
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All authors Yibo Huang, Yi Cui, Yukai Huang, Huaping Zhou, Jiancong Tong, Zhaoyu Wu, Gen Miao, Haoran Wei, Boyan Ding, Zhuo Jiang, Jianxi Ye, Yibo Zhu, Chuanxiong Guo, Yang Xu, Jie Wu
Audio and Speech Processing · Cross-listed from cs.CV
Spend Where It Matters: Particle Search with MH Refinement for Budget-Constrained Audio-Visual Alignment
Shivanshu Shekhar, Raghavendra Addanki, Tong Zhang et al. (+1 more)
Reward-guided inference-time
alignment can improve joint audio-visual generation, but optimizing multiple
alignment objectives requires many expensive model evaluations, making existing methods costly under limited compute. Particle-based search enables global exploration but incurs estimation error with a finite population, while Metropolis-Hastings (MH) can refine candidates locally but benefits from a strong initialization. We introduce Spend Where It Matters (\modelname), a two-stage, training-free, reward-agnostic framework that combines particle search with MH refinement under a fixed compute budget. In the first stage, we adaptively allocate particles across denoising checkpoints using online variance estimates, minimizing estimation variance while concentrating compute where it is most useful. This search provides a promising candidate for MH refinement, which uses local noise-and-denoise proposals on completed outputs to further improve reward. By starting from a strong candidate, MH can recover additional reward without requiring a larger particle population or costly global exploration. We show that, at matched compute, combining particle search with MH refinement achieves higher rewards than either approach alone. On JavisBench-Mini, at closely matched compute, \modelname achieves mean relative gains across seven metrics of $30.7\%$ over BoN and $14.1\%$ over EvoSearch with JavisDiT++, and $9.2\%$ over BoN and $17.0\%$ over EvoSearch with LTX-2. Beyond joint audio-visual generation, we also demonstrate the benefits of adaptive particle allocation on SD1.5 image generation, improving AestheticScore by $2.2\%$ and ImageReward by $6.0\%$ over uniform allocation.
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All authors Shivanshu Shekhar, Raghavendra Addanki, Tong Zhang, Uttaran Bhattacharya
Audio and Speech Processing · Cross-listed from cs.SD
Efficient Audio-Visual Speech Enhancement via Reinforcement Learning-Based Neural Architecture Search
Khubaib Ahmed, Ahsan Adeel, Nasir Saleem et al. (+3 more)
Audio-visual
speech enhancement (AVSE) improves
speech intelligibility in noisy environments but often relies on computationally expensive models unsuitable for resource-constrained edge devices. This paper introduces a reinforcement learning-driven neural architecture search (RL-NAS) framework to discover compact, efficient AVSE architectures. The search space preserves temporal
alignment between audio and visual streams, while a novel reward function jointly optimises
speech enhancement performance, measured by SI-SNR improvement, and model compactness. Using proximal policy optimisation with constraint-aware action masking, the framework evaluates only 35 candidate architectures and identifies a model with 2.9 million parameters, compared with 22 million in the baseline. The proposed architecture achieves a 14.5 dB SI-SNR improvement, only 0.7 dB below the baseline, while reducing model size by approximately nine-fold. Evaluation on commercial mobile CPUs demonstrates a 4.3-fold reduction in real-time factor, supporting practical on-device deployment. Compared with random search, RL-NAS achieves 92% higher search efficiency. Architectural analysis reveals that shallower but wider temporal convolutional blocks and aggressive visual pathway
pruning can maintain enhancement performance while substantially reducing computational requirements. These findings demonstrate the potential of reinforcement learning-guided architecture search for developing lightweight, efficient and deployable
multimodal speech enhancement systems.
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All authors Khubaib Ahmed, Ahsan Adeel, Nasir Saleem, Kia Dashtipour, Ahsan Ulhaq, Amir Hussain
Systems and Control · Cross-listed from cs.GT
Game Petri Nets: Compositional Modeling and Analysis of Large-Scale Strategic Systems
Quanyan Zhu
Large-scale strategic systems, such as maintenance workflows, cyber campaigns, and multi-
agent AI pipelines, consist of many interactions whose outcomes determine which later interactions become possible, who takes part, and with what resources. We introduce game Petri nets, formalized as outcome-driven compositional game Petri nets (OCGPNs), to model and analyze such systems. Local games are attached to places of a colored Petri net, and a game-to-
token interface converts each resolved game into typed
tokens that enable, synchronize, or block subsequent games. For general, concurrent nets, we give an operational semantics and a structural theory based on invariants and Farkas certificates, which shows that guards can certify unreachability whereas incentive instruments can only change values. For sequential nets, the net induces a
stochastic game on its game places. We prove that global
equilibria are exactly collections of
equilibria of local games augmented by continuation values, characterize through successor-level pivotality when downstream games change upstream behavior, and compute values place by place via a decomposition into strongly connected components, at a cost additive in local game sizes. A composition calculus based on place fusion glues certificates across subnets and summarizes downstream subnets by their entry values. Two running examples, a maintenance workflow and an intrusion campaign derived from multi-resolution cyber-defense games, illustrate the theory, including the composition of a newly discovered attack scenario with an existing campaign.
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Systems and Control
Distributionally Robust Adaptive Iterative Covariance Steering for Small-Body Proximity Operations
Vivek Khatana, Aditya Gahlawat, Naira Hovakimyan et al. (+1 more)
Autonomous small-body proximity operations require continuous-thrust guidance under uncertain
gravity, solar-radiation pressure (SRP), and
stochastic disturbances. We combine iterative covariance steering (iCS) with $L_1$ distributionally robust adaptive control ($L_1$-DRAC) to provide risk-aware collision avoidance despite mismatch between the nominal planning model and the true dynamics. Iterative covariance steering plans a nominal feedback policy satisfying distributionally robust conditional value-at-risk keep-out constraints, while $L_1$-DRAC compensates for dynamics uncertainty and bounds the residual distributional mismatch through a Wasserstein radius used by the planner. In $\mathbf{1000}$ paired Monte Carlo trials with
gravity, SRP, and
diffusion mismatch, $L_1$-DRAC adaptive control reduces the keep-out violation rate from $\mathbf{32.7\%}$ to $\mathbf{0.2\%}$ and the physical collision rate from $\mathbf{16.4\%}$ to zero, without actuator saturation.
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All authors Vivek Khatana, Aditya Gahlawat, Naira Hovakimyan, Petros Voulgaris
Signal Processing
Generative Control of Time-Modulated Intelligent Reflecting Surfaces for Scalable Directional Modulation
Kailong Wang, Sida Xiang and Athina Petropulu
Time-modulated intelligent reflecting surfaces (TM-IRS) achieve directional modulation by switching their elements on and off at designed instants and durations, so that an undistorted signal reaches the intended user while appearing scrambled elsewhere. We propose a Flow Matching (FM)-based generative framework that designs these parameters for strong scrambling and reliable reception simultaneously. For orthogonal frequency-division multiplexing (OFDM) signals, we introduce a physics-informed reward that maximizes the time-modulation harmonic energy radiated in the null space of the legitimate-user response, subject to a minimum per-subcarrier signal-to-interference-plus-noise ratio (SINR), without requiring knowledge of adversary directions. Unlike prior generative flow network (GFlowNet) approaches, which assign discretized parameters sequentially, the FM model generates complete continuous configurations jointly, decoupling the generation depth from the number of elements and accelerating training. It also learns a distribution over configurations that can be resampled to vary the scrambling pattern. Numerical results show that, compared with a GFlowNet baseline, the proposed approach reduces the time to training loss near-saturation by $99.4\%$ on average for $3{\times}3$ to $12{\times}12$ surfaces, attains higher final rewards, scales to $80{\times}80$, and confines the low-symbol-error region more tightly around the legitimate user.
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Systems and Control · Cross-listed from cs.RO
Verifier-Mediated Multi-LLM Coordination for Multi-Robot Path Planning
Vivek Khatana, Zijian Song, Naira Hovakimyan et al. (+1 more)
Foundation models are increasingly used for high-level multi-
robot task planning, yet independently generated symbolic plans can become jointly infeasible after
geometric grounding because of shared spatial and temporal constraints. We study execution-aware coordination of large-
language-model (LLM) planners for multi-
robot navigation. We compare a centralized architecture, in which one LLM jointly reasons over the
robot team, with a decentralized multi-LLM architecture in which each
robot independently proposes a task plan. A
geometric verifier grounds the candidate plans into
robot trajectories, detects predicted inter-
robot conflicts, and converts conflict information into structured feedback for targeted replanning. This verifier-mediated architecture separates semantic task
reasoning from execution-level feasibility while enabling iterative coordination among LLM planners. Experiments in CARLA and on Crazyflie aerial
robots evaluate coordination performance as task complexity increases and characterize the trade-offs between centralized
reasoning, decentralized verifier-mediated coordination, and physical executability.
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All authors Vivek Khatana, Zijian Song, Naira Hovakimyan, Petros Voulgaris
Systems and Control
ROAM: Reinforcement Learning Optimized Adaptive Moving Target Defense for Smart Grid
Mohammad Zakaria Haider, Prabin Mali, Muhammad Nadeem et al. (+3 more)
Smart grids rely on distributed phasor measurement units (PMUs) and wide-area communication networks whose data-acquisition pipelines are vulnerable to stealthy false data injection (FDI) attacks that evade conventional bad-data detection (BDD). Moving target defense (MTD) counters this exposure by continuously remapping sensor identities, but existing schemes apply static or pre-scheduled randomization and are each constrained by a single failure mode: seed-based methods are fast yet fragile to key compromise, while machine learning (ML) based prediction methods are resilient yet incur prohibitive latency. Neither adapts its randomization to live threat evidence. This paper presents ROAM, a deep reinforcement learning (DRL) driven MTD framework in which a deep Q-network (DQN)
agent continuously re-optimizes the sensor-to-ID mapping at the aggregation layer. At each decision step, the
agent selects the deception coverage, flags the specific sensors to remap, and sets the split of the remapped sensors between seed-based and ML-prediction-based recovery, without modifying PMU hardware and while preserving IEEE C37.118 compatibility. On held-out BDD-compliant FDI scenarios across the IEEE 14-, 57-, and 118- bus systems, ROAM keeps the percentage of stealthy attacks below 3% while sustaining detection rates above 93%. Against an adaptive soft actor-critic (SAC) attacker that observes the defender's configuration, ROAM keeps stealthy attacks below 2.6% with detection above 92%. Hardware-in-the-loop testing on an OPAL-RT platform confirms real-time feasibility, with sub-18ms average shuffle execution and an average reshuffle blind window of 2.6% of the 3-s decision cycle.
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All authors Mohammad Zakaria Haider, Prabin Mali, Muhammad Nadeem, Sumit Paudyal, Fei Ding, Mohammad Ashiqur Rahman