Systems and Control
A Decoupled Approach for Modeling N-k Interdiction with Induced Failures in Electric Power Grids
Andrew Chio, Kaarthik Sundar, Russell Bent et al. (+1 more)
The electric power grid is a critical infrastructure system subject to different types of failures which can have devastating impacts on society. One framework to study the resilience of the grid is the N-k interdiction model, which introduces an attacker and defender model to identify grid vulnerabilities. However, traditional N-k interdiction models characterize the power grid with steady-state assumptions. One key aspect missing from this analysis is the behavior of the grid as it evolves from the pre-failure grid state and post-failure grid state. During such a transition period, automated protection systems, transient instabilities, and other phenomena can trigger cascading failures that can occur before grid operators can respond. To bridge this gap, this paper first introduces the generalized induced failure N-k (N-k.k') problem, where an initial set of k failures induces an additional set of k' failures. In this context, we propose a disjoint optimization approach that decouples the computation of cascading failures from the interdiction model. This system response model, furthermore, is treated as a black box module without compromising the underlying performance guarantees of the interdiction model. Extensive experiments on several
benchmark power grid case studies showcase the efficacy and scalability of the approach.
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All authors Andrew Chio, Kaarthik Sundar, Russell Bent, Nalini Venkatasubramanian
Image and Video Processing
Deep Learning for Atmospheric Turbulence Mitigation: A Comparison Between GANs and Transformers
Juan S. Carrillo and Jérôme Gilles
Atmospheric
turbulence creates complex spatial and temporal distortions that significantly reduce image quality. This work compares two deep learning methods for
turbulence reduction: Generative
Adversarial Networks (GANs) and
Transformer-based models. Both methods are trained and tested on the SOTIS
dataset, which includes multi-frame sequences under different
turbulence conditions with ground truth references. Our results show an important finding: GAN-based models perform better in severely degraded, low-quality situations, while
Transformer-based models achieve better results in moderate to high-quality settings, especially in correcting
geometric distortions. We also enhance the
Turbulence Mitigation
Transformer (TMT) by introducing a refined training strategy, an augmented input design that includes a temporally averaged frame as prior structural information, and a modified loss function that incorporates the Structural Similarity Index (SSIM). These improvements lead to consistent performance gains across various
turbulence conditions. Overall, this study provides an in-depth evaluation of deep learning methods for
turbulence reduction and offers practical guidance for selecting models in different imaging environments.
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Signal Processing
FFDT: Sim-to-Real Domain Transfer Method for Far-Field Ground Penetrating Radar
Chi Wang and Hai-Han Sun
The scarcity of real-world ground penetrating radar (RealGPR) data is a major barrier to the development of deep learning (DL) methods for GPR data processing. Although simulated GPR (SimGPR)
datasets can be used to train DL models, differences in hardware configuration and system parameters between SimGPR and RealGPR create a sim-to-real domain gap that leads to poor generalization of DL models to RealGPR measurements. To address this gap, we present a physics-based far-field GPR domain transfer (FFDT) method to transfer far-field SimGPR data to the corresponding RealGPR-domain data in the same scenario. In FFDT, SimGPR and RealGPR are modeled using angle-dependent transfer functions that capture three-dimensional
antenna radiation characteristics. These transfer functions are then combined with ray tracing to enable the sim-to-real data transfer. Extensive studies across diverse objects and soil conditions demonstrate that FFDT-transferred data closely match RealGPR ground truth. To further demonstrate its value for RealGPR-domain
dataset generation, FFDT is implemented to build a RealGPR-domain
dataset for training a DL model for clutter removal. The model achieves robust performance on RealGPR measurements. With one-time SimGPR and RealGPR system calibration, FFDT enables RealGPR-domain
dataset generation without requiring task-specific RealGPR field measurements for DL model training or fine-tuning, thereby addressing the RealGPR data scarcity bottleneck and supporting reliable development and deployment of DL methods for real-world GPR applications.
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Systems and Control · Cross-listed from cs.RO
Full-Order Safety for Nonlinear Reduced-Order MPC
Luis Pabon, Johannes Köhler, Revant Kasichainula et al. (+1 more)
Reduced-order models (ROMs) make model predictive control (MPC) of high-dimensional nonlinear systems tractable, but the resulting modeling error can lead to constraint violations on the real system. We introduce CoRE-MPC, a robust MPC formulation that plans with a nonlinear ROM and guarantees recursive feasibility and constraint satisfaction for the full-order system. It can be applied with a broad class of nonlinear model reduction techniques and the resulting MPC does not require evaluating the full-order model online, thus ensuring computational efficiency. The key insight is in how the ROM affects the dynamics of the prediction error. We account for this with two error radii that are propagated alongside the ROM and provide a guaranteed bound on the prediction error. The approach is validated in simulation on an 80-state oscillator chain, a Burgers equation, and a 9,768-state finite-element soft
robot, where a two-state ROM with data-driven bounds keeps all 1,628 nodes clear of a wall at a median solve time below 1 ms for a 20 ms control period. Across these examples, CoRE-MPC is 13 to 61 times faster than full-order robust MPC at only 27 to 35% higher closed-loop cost, whereas existing certified schemes are either infeasible or at least twice as conservative. Code: [released upon publication].
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All authors Luis Pabon, Johannes Köhler, Revant Kasichainula, Marco Pavone
Image and Video Processing · Cross-listed from physics.optics
High-resolution retinal matrix imaging via computational motion and aberration correction
Aymeric Melt, Léo Puyo, Victor Barolle et al. (+3 more)
High-resolution optical imaging of the human retina is essential for diagnosing ophthalmic and neurodegenerative diseases. While adaptive optics combined with coherence gating can resolve retinal structures at the cellular scale, it requires complex, costly, and cumbersome hardware to physically correct for ocular aberrations and motion.
Matrix imaging offers a powerful digital alternative by leveraging wave linearity to undo aberrations and multiple scattering; however, its in vivo deployment has been hindered by continuous, involuntary eye movements that destroy phase coherence during acquisition. Here, we overcome this fundamental barrier and demonstrate the first in vivo reflection
matrix imaging of the human retina using an ultra-simplified optical setup without physical correctors. We introduce a fully computational framework that tracks and compensates for multi-axial ocular motion directly from raw interferometric data, thereby restoring the phase coherence of a multi-spectral distortion
matrix. This enables local, high-order aberration correction to achieve diffraction-limited, volumetric imaging of retinal tissues. By shifting the hardware burden to physics-guided numerical computation, this paradigm opens the way for accessible, non-invasive, and high-resolution digital ophthalmology.
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All authors Aymeric Melt, Léo Puyo, Victor Barolle, Claude Boccara, Gereon Hüttmann, Alexandre Aubry
Image and Video Processing
Per-Bundle, Pre-Reconstruction Inversion for Temporal CT: Flux-Compensation Design and Multi-Patient Generalization
Guy M Besson
Paper 1 established the per-bundle statistical limits of Temporal CT (TCT) and benchmarked a learned prior on a single patient at a uniform incident count $N_0$. This paper lifts both restrictions. SNN2, a Gauss-Newton
estimator, converges to the per-bundle Poisson maximum-likelihood solution and is the prior-free baseline. N$_0$FC, a bow-tie and tube-current-modulation co-design, compresses within-bundle
photon-flux dynamic range. V8c is a neural-network residual corrector trained on a 19-patient corpus stratified by water-equivalent diameter (WED). T2bp, a per-patient empirical-Bayes post-processor, combines per-bundle Poisson likelihoods with a prior built from the patient's own bundles; it requires no training, so it cannot inherit a mismatched training prior. Results use Paper 1's bias-charged Dose Inflation Factor (DIF), extended to per-ray budgets. V8c beats SNN2 on in-distribution patients under baseline acquisition but fails out of distribution. Composed with SNN2, T2bp matches or beats T2bp$\circ$V8c on three of four N$_0$FC test
cells (WED 207-342 mm), with the largest gains on the high-WED and out-of-distribution patients, and trails by 3% on the fourth. On 23 non-test corpus volumes, N$_0$FC with closed-loop tube-current modulation lowers the per-volume median middle-path Cramer-Rao floor, as a DIF, from 23-48 under baseline acquisition to 3.3-6.3, near the equal-attenuation value 13/3; T2bp$\circ$SNN2 reaches this floor without training, its mean squared error 0.74-1.26 times the floor (median 0.89) over the bulk of each volume. Acquisition co-design removes most of the attenuation-contrast penalty, and a training-free
estimator attains the resulting limit. The remaining three- to six-fold gap to dose parity with single-source CT, and the high-attenuation tail, are the target of inter-bundle priors in later work.
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Image and Video Processing
Perceptual Characterization of Scan-induced Artifacts in Dynamic 3D Meshes
Huong Hoang, Truong Nguyen and Pamela Cosman
Dynamic meshes reconstructed from real-world scenes often exhibit scan-induced artifacts such as surface jitter and missing geometry, which strongly impact perceived visual quality. Objective assessment remains challenging due to the absence of artifact-free ground truth and limited perceptual understanding of artifact interactions. This paper presents a systematic and controlled perceptual study of scan-induced artifacts in dynamic meshes, focusing on surface jitter and holes. To enable systematic analysis, we introduce a physically grounded artifact synthesis framework in which holes are synthesized using a reconstruction uncertainty model derived from multi-view geometry. The study reveals asymmetric cross-artifact interactions, non-additive integration, and motion-dependent temporal masking. Based on these findings, we propose a compact, perceptually grounded no-reference quality assessment framework that encodes the observed perceptual mechanisms.
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Signal Processing
Carbon-Aware Joint Resource and Energy Scheduling for Cellular Base Stations
Jaebum Park, Yutao Xie, Amitava Ghosh et al. (+2 more)
Decarbonizing cellular networks requires coordinating communication-resource and battery operation with time-varying electricity prices and carbon intensity. We jointly optimize base station and battery operation under a carbon-adjusted electricity price and traffic-demand constraints, and solve the mixed-integer nonconvex problem through a two-stage decomposition exact under a minimum-power rule. Using California price and carbon-intensity data, we show that carbon-aware scheduling reduces emissions most when low-price and low-carbon periods are misaligned. Above a carbon price of roughly 100-300 USD/ton, battery operation shifts from cost-driven to carbon-driven, cutting emissions by up to 57.8% relative to a cost-only schedule.
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All authors Jaebum Park, Yutao Xie, Amitava Ghosh, Yuanyuan Shi, Robert W. Heath Jr
Systems and Control
Measurements and Analysis of Distortion in Inertial Force Exciters for Flat-Panel Loudspeakers
Stephen F. Roessner and Michael C. Heilemann
Flat-panel loudspeakers are typically driven by dynamic exciters that employ a voice coil coupled to a reaction mass to exert force on the panel. Unlike conventional loudspeakers, where the cone is relatively light weight, heavy panels may require significant exertion from the driver to achieve sound pressure levels required of consumer grade loudspeakers. Overexertion can cause harmonic distortion and reduced sound quality, especially in drivers where the voice coil is not centered in the
magnetic field. There are many different exciters available commercially with different power ratings and price points. The frequency response, linearity, total-harmonic distortion (THD), intermodulation distortion (IMD), and force vs. displacement were measured for three types of exciters to determine the consistency of manufacturing and use in panel loudspeakers. These exciters were found to have high variability in all metrics among units of the same type. Distortion metrics such as IMD may be reduced by running impedance sweeps to match exciters with similar response characteristics in multi-actuator panels.
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Systems and Control · Cross-listed from cs.RO
Learning in Orbit: Onboard Training and Closed-Loop Attitude Control with a Thermal Segmentation Model on a Spacecraft
Elena Sorina Lupu, Hannah Grauer, Phaedrus Leeds et al. (+9 more)
Deep space missions increasingly require spacecraft capable of autonomously operating in environments with communication delays and bandwidth limitations. A key enabler of such autonomy is the ability to run learning-based
algorithms directly onboard. In this work, we present a flight deployment on a 6U CubeSat that demonstrates (1) onboard training and inference of a thermal segmentation model on an NVIDIA Jetson TX2 NX GPU in space and (2) closed-loop attitude control using the segmentation model on a real-time, thermal image feed to track the Moon. In addition to this demonstration, we implement ROS 2 Foxy as a data transport middleware and integrate an open-source file system that provides strong data integrity guarantees and self-healing features. Over the course of approximately one year, we performed 12 experiments on the spacecraft, advancing the state of the art in autonomous spacecraft systems by showing how modern learning-based pipelines, commercially available hardware, and open-source software can operate reliably under the compute and environmental constraints of Low Earth Orbit (LEO).
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All authors Elena Sorina Lupu, Hannah Grauer, Phaedrus Leeds, Nikhil Ranganathan, Connor Lee, Sulekha Kishore, Cailyn Smith, Shon Cortes, Brian Hardy, Benjamen Bycroft, Darren W. Rowen, Soon-Jo Chung
Image and Video Processing
DoReMe: Dual optimization of Reconstruction and Motion estimation for self-navigated free-breathing abdominal MRI using Gaussian Splatting
Majd Helo, Dominik Nickel and Thomas Kuestner
Purpose: Free-breathing abdominal MRI is limited by respiratory motion, which introduces image artifacts and k-space inconsistencies. Existing reconstruction approaches often depend on pre-estimated motion fields or external respiratory surrogate signals. We propose a coupled reconstruction-motion estimation
algorithm for self-navigated free-breathing acquisition, in which motion is represented by compact Gaussian primitives and jointly refined with the image estimates during iterative reconstruction. Methods: An iterative reconstruction that alternates between data consistency updates and motion modelling was proposed. Respiratory motion was represented using Gaussian primitives. A Gaussian splatting was integrated into the reconstruction
algorithm. The method was evaluated on self-navigated free-breathing Cartesian gradient-echo (GRE) acquisition in four healthy volunteers, including retrospective scan shortening experiments, and was compared with regularized compressed sensing (CS) reconstruction. Results: Respiratory motion states were reconstructed in approximately 1.5 min. Compared with CS, the proposed method improved visual image quality, particularly in coronal liver views, and better preserved anatomical structures in retrospectively shortened acquisitions. These findings provide proof of concept for the proposed method. Conclusion: The proposed coupled reconstruction-motion estimation
algorithm improved visual image quality compared with CS alone. Gaussian primitives provided a compact and sufficiently flexible representation of nonrigid respiratory motion with practical runtime, supporting further scanner integration and validation in larger cohorts.
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Signal Processing
Sequential RF Fingerprinting for AIS: Rejecting Unknown Vessels and Insider MMSI Spoofs Days After Enrollment
Fawaz Abdul Razak, Bora Turgut, Mason Clark et al. (+1 more)
Any AIS transmitter can claim any vessel's identity (MMSI), because nothing in the protocol authenticates it; only the transmitter's analog hardware reveals who is really sending. We treat AIS traffic as streams of bursts keyed by claimed MMSI and accumulate physical-layer evidence with a sequential test. Each burst is scored by its k-nearest-neighbor distance to enrolled fingerprints in the feature space of a classifier whose weights are fixed after training, calibrated into a tail probability, and accumulated by a
geometric-
entropy-minimization cumulative-sum (GEM-CUSUM) statistic. The same test rejects unknown transmitters when scored against all enrolled vessels, and exposes insider spoofs when scored against the claimed vessel. A threshold refreshed from genuine traffic alone keeps it at a stated false-alarm rate as fingerprints drift. On AIS in-phase/quadrature (I/Q) recordings captured over 13 days at the Istanbul Strait (Bosphorus), the detector rejects unseen vessels at least 10 days after enrollment. Evaluated over 50-burst trials with every vessel weighted equally, it reaches an area under the ROC curve (AUROC) of 0.956 (95% confidence interval 0.913-0.989), where the best score computed from one burst at a time reaches 0.899. A threshold refreshed from the first quarter of the enrolled vessels' traffic holds 5.3% false alarms (3.0-9.2% across five independently trained encoders) at 88.8% detection. Scored against the claimed MMSI, enrolled transmitters spoofing another vessel are rejected at 0.991, a threat that open-set tests cannot see (AUROC 0.500, chance level). The lightweight
Transformer GLFormer, reimplemented from its paper (no code is public) and retrained on the same data, reaches 0.652 in the same detector with its published in-phase input and 0.849 when given the same I/Q input as our encoder.
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All authors Fawaz Abdul Razak, Bora Turgut, Mason Clark, Yasin Yilmaz
Systems and Control
Global Parameter Identification and Uncertainty Quantification of reduced-order ADM1 using Maximum Likelihood Estimation and Bayesian Inference
Simon Hellmann, Leander Lerch, Terrance Wilms et al. (+6 more)
Anaerobic digestion (AD) models can be challenging to calibrate against real data due to limited practical identifiability, data scarcity, and model complexity. Weighted least squares objective functions and gradient-based solvers using finite differences are often used in literature, but result in suboptimal parameter estimates and large uncertainty bands. This paper identifies the parameters of a reduced-order ADM1 by comparing the frameworks of maximum likelihood estimation (MLE) and
Bayesian inference (BI), and quantifies uncertainties of parameters and outputs. Multirate experimental data are taken from a lab-scale AD reactor fed with agricultural substrates and operated at organic loading rates of 3$\,$g VS $\mathrm{m^{-3}d^{-1}}$. Global optima are found via global and gradient-based local search
algorithms using forward sensitivities. Enhanced scatter search provides the best global search results. These are refined by the Gauss-Newton
algorithm as the best local search, which delivers $R^2$ values of 0.95 for acetic acid and 0.77 for biogas production. BI yields substantially narrower uncertainty bands than MLE and avoids the need for a global search by incorporating prior knowledge about parameter distributions. The search space is captured efficiently by log10 scaling of parameters. Algorithmic details on its implications are provided. Despite a high-resolution lab-scale
dataset, the ADM1-specific inhibition parameter $K_\mathrm{I,nh3}$ is practically not identifiable with MLE, and shows a low output sensitivity, whereas a parameter for the effective ion concentration shows superb practical identifiabilty at high confidence with both MLE and BI. The present work lays a scalable base for calibration of nonlinear AD models by providing reliable simulations. It thus advances model-based monitoring and control applications of multirate processes such as agricultural AD plants.
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All authors Simon Hellmann, Leander Lerch, Terrance Wilms, Moritz Reumschüssel, Alberto Meola, Félix Delory, Steffi Knorn, Stefan Streif, Sören Weinrich
Systems and Control · Cross-listed from cs.GR
DiffGIPC: Differentiable Mixed-Coordinate Contact Dynamics on GPU
Kemeng Huang, Bowen Ren, Xinyu Lu et al. (+2 more)
Contact-rich inverse problems require efficient gradients and reliable enforcement of non-penetration. Existing differentiable methods provide fast simulation and rigid-deformable coupling, but contact treatments based on penalties or velocity-level constraints can permit penetration or positional drift. Incremental Potential Contact (IPC) maintains non-penetration through
geometric barriers and {continuous collision detection (CCD)}. However, repeated forward and backward evaluations remain costly in full-space finite-element (FEM) implementations, particularly when nearly rigid components retain many vertex {degrees of freedom (DoFs)}. We present DiffGIPC, a GPU-accelerated differentiable IPC framework for optimizing interacting deformable and nearly rigid bodies. Our affine-body derivatives enable {rest-shape} design, initial-state optimization, and articulated control while retaining only twelve motion DoFs per affine body. These gradients account for how changes in geometry, placement, and actuation affect other objects through collision and friction. We preserve these interactions in one coupled FEM-affine adjoint system per frame, reducing the backward-system size without discarding the dependencies that drive the inverse problem. GPU-optimized backward computation further reduces the cost of repeated gradient evaluations. A reusable PyTorch interface connects supported physical parameters to user-defined parameter maps and losses across design, identification, motion, placement, and articulated control. Experiments demonstrate peak speedups over DiffIPC of approximately 150.0x with full-space FEM and 4133.3x with affine bodies for combined forward simulation and backward differentiation.
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All authors Kemeng Huang, Bowen Ren, Xinyu Lu, Minchen Li, Taku komura
Image and Video Processing
Focus, Align, and Diffuse: Time-Series-Aware Keyframe Diffusion for Cardiac Dynamic Synthesis from Sparse Observations
Junkai Liu, Haofan Wu, Nay Aung et al. (+3 more)
Dense cardiac dynamics are essential for assessing cardiac function, yet in practice clinicians often observe only extremely sparse measurements. However, recovering full dynamics from such sparsity is a highly ill-posed problem due to the lack of temporal cues. While auxiliary time-series signals like ECG provide physiological guidance, effectively exploiting them is challenging due to the cross-modal representation gap and phase misalignment. In this work, we present CardioFAD, a Focus–Align–Diffuse paradigm that reformulates sparse cardiac dynamic synthesis as a recoverability–alignability anchor discovery problem. Our core insight is that resolving extreme temporal ambiguity requires a compact set of anchor states that are both sufficient to recover the visual trajectory and reliable for ECG–visual
alignment. We therefore learn motion-salient keyframes under two complementary principles: trajectory sufficiency, which preserves trajectory-critical intra-modal motion content, and anchor
alignment, which promotes consistent inter-modal correspondence at these anchors. We implement the Focus and Align modules optimized by the two aspects. Diffuse then performs two-stage generation by first synthesizing keyframes and subsequently interpolating the remaining frames. Extensive experiments on cardiac MRI and echocardiography
videos demonstrate superior visual quality, temporal coherence, and improved functional consistency under the extreme one-shot regime.
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All authors Junkai Liu, Haofan Wu, Nay Aung, Joao A. C. Lima, Steffen E. Petersen, Le Zhang
Signal Processing
A Wideband STAR-RIS Employing Shared-Aperture Polarization Conversion and Diagonal Switching Topology for 6G Applications
Ruiqi Wang and Atif Shamim
Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (
STAR-RIS) enable programmable wave control on both sides of a surface, extending wireless coverage beyond single-sided operation. However, practical
STAR-RIS demonstrations in literature have narrow bandwidths, because it is challenging to achieve wideband reflection and transmission simultaneously from the same aperture. To overcome this limitation, a wideband
STAR-RIS architecture is proposed based on shared-aperture polarization conversion and diagonal switching
topology. Here, multiple resonances enable wideband magnitude responses and mirror-symmetric current reversal maintains $180^{\circ}$ phase difference in both reflection and transmission modes. Accordingly, a diagonal four-branch unit
cell configuration is developed, where the tailored top patch directs the received wave either through a pair of blind vias connected by a buried strip to the shared top aperture for reflection, or to the bottom ring patch using a through via for transmission. Based on the proposed design, a 10 by 10
STAR-RIS prototype is fabricated and experimentally characterized. The measured 3-dB bandwidths reach 19.8% in reflection mode and 21.9% in transmission mode. Near-field measurements show average signal enhancements of 19.8 and 22.6 dB for reflection and transmission, respectively, over the wide frequency range of 6.8-8.4 GHz. Far-field measurements further demonstrate average received-power enhancements of 11.2 dB in reflection and 11.9 dB in transmission. To the best of the authors' knowledge, this work presents the first experimentally demonstrated wideband
STAR-RIS covering the 7.125-8.4 GHz band within the emerging 6G FR3 spectrum.
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Systems and Control
A Performative Control Approach to Day-to-Day Traffic Signal Retiming
Shuchang Jiang, Xinwu Qian, Santiago Segarra et al. (+1 more)
Conventional fixed-time traffic signal optimization and recent day-to-day signal retiming generally model route adaptation under prescribed route-choice behavior and origin-destination (OD) demand, while neglecting the fact that travelers' behavior can be performative to the signal control policy. A signal timing policy learned from historical traffic observations must therefore account for how its own deployment changes the distribution on which it will be evaluated in the future. We formulate this problem as performative signal control under observed traveler responses and characterize performative stability of the coupled policy-demand system. We propose incremental day-to-day retiming using the local policy gradient at the current operating point, estimated offline through simulation-based zeroth-order optimization without an explicit day-to-day demand-response model. This gradual approach provides a feasible path for policy and demand to co-evolve toward stability. Experiments on a downtown Denver network show that our approach improves congestion, throughput, and stability, generalizes across different demand levels, and remains effective when the offline simulator contains mismatched travel demand and routing behaviors.
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All authors Shuchang Jiang, Xinwu Qian, Santiago Segarra, Zhongyuan Zhao
Image and Video Processing
Personalising Deformable Image Registration with Clinical Context and Deformation History for Adaptive Radiotherapy
Caiwen Jiang, Songlin Zhao, Yuzhen Ding et al. (+23 more)
Adaptive radiotherapy must account for anatomical changes during treatment, but doing so depends on reliably mapping targets, organs at risk and dose across serial images. Existing registration methods treat each image pair independently, without using radiotherapy-specific context or the patient's preceding anatomical trajectory. We developed Clinical-context-Aware and Patient-adaptive Deformable Image Registration (CAP-DIR), integrating source contours, dose, clinical text and prior-registration features. We evaluated 16,361 CT examinations from 3,473 patients across four institutional cohorts and five public
benchmarks. CAP-DIR increased internal-test organ-at-risk-region normalized cross-correlation from 84.76% to 96.92% relative to the strongest learning-based comparator, with low deformation folding, and remained superior across seven external cohorts. In a blinded review of 20 longitudinal cases, clinically acceptable contours increased from 45.0% to 82.5% for clinical target volumes and from 22.5% to 62.5% for organs at risk, while estimated editing time decreased. In a controlled
benchmark, CAP-DIR reduced global dose-mapping error by 34.5% relative to the strongest comparator. These findings show that clinically informed, longitudinal registration can improve contour usability and dose mapping for adaptive radiotherapy.
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All authors Caiwen Jiang, Songlin Zhao, Yuzhen Ding, Jia Mi, Samir H. Patel, Alexandra Bennett, Keldon K. Lin, Terence T. Sio, Jonathan B. Ashman, Lisa A. McGee, Jean-Claude M. Rwigema, William G. Rule, Sameer R. Keole, Sujay A. Vora, William W. Wong, Nathan Y. Yu, Michele Y. Halyard, Steven E. Schild, Safia K. Ahmed, Daniel J. Ma, Robert L. Foote, Michelle A. Neben Wittich, Thomas J. Whitaker, Haibo Lin, Dinggang Shen, Wei Liu
Signal Processing
RetinaSC: Retinal Semantic Communication Framework for Diabetic Retinopathy Detection
Avani Shinod, Rangineni Rushitha, Asqeem Nas et al. (+2 more)
This paper presents a lightweight semantic com- munication framework for diabetic retinopathy (DR) grading from fundus images pertaining specifically to the APTOS2019
dataset. The framework includes semantic encoder designed using ResNet34, U-Net, MobileNetV2, and EfficientNet-B0 architectures. Further, the trained models are used to train the same framework for IDRiD
dataset using transfer learning approach. Such a framework will enable the telemedicine sector in the early detection of DR and prevent total blindness. The results reveal that for APTOS, task-oriented semantic systems can achieve satisfactory robust performance (more than 0.8 weighted F1 score for ResNet34 and EfficientNet-B0) across different channels. In particular, considering the trade-off between complexity, latency, throughput, and F1 score, MobileNetV2 is the best model with weighted F1 score of 0.76-0.78 for both task and reconstruction- oriented modules and end-to-end latency of only 8.162 ms.
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All authors Avani Shinod, Rangineni Rushitha, Asqeem Nas, Anirban Bhowal, Shikha Baghel
Systems and Control · Cross-listed from cs.MA
Optimizing Team Behavior via Extremum-seeking Control in Multi-agent Reinforcement Learning
Svar Rajankumar Patel and Kshitij Jerath
In cooperative Multi-
Agent Reinforcement Learning (MARL),
agents must learn to coordinate their actions to achieve shared objectives. A key indicator of effective coordination is behavioral diversity, i.e., the
agents' potential to enact distinct yet complementary control policies given the same set of observations. Although behavioral diversity can enable role specialization and richer joint behaviors, its appropriate level is highly context- and task-dependent and often costly to tune. We propose Adaptive Diversity Control (ADiCo), a two-timescale framework that adapts behavioral diversity online during training. On a fast timescale, Diversity Control (DiCo) regulates system diversity toward a target value. On a slower timescale, an Extremum Seeking Controller (ESC) adjusts this target using only mean episodic returns, without requiring an explicit model or analytical gradient of the diversity-performance landscape. We evaluate ADiCo on six configurations across two cooperative MARL tasks, where it maintains task-appropriate diversity levels: low diversity when homogeneous policies are beneficial and higher diversity when heterogeneous policies are required. Through this adaptation, ADiCo matches or exceeds the mean performance of DiCo (best grid-searched diversity target) and Unconstrained DiCo (diversity allowed to emerge without target regulation). These results demonstrate that incorporating classical feedback control into MARL training enables effective coordination by providing a systematic alternative to task-specific manual selection of the diversity target.
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Systems and Control · Cross-listed from math.OC
Compositional Sensitivity Analysis for Multiobjective System Co-Design
Alan Bonomi, Antonio Terpin, Yubo Cai et al. (+3 more)
Sensitivity analysis of coupled systems typically propagates derivatives of single-valued maps, whereas multiobjective optimization generally treats sensitivity at the system level rather than through interconnected subsystems. We develop a mathematical framework for compositional sensitivity analysis in multiobjective system design, building on monotone co-design. Subsystems are represented as design problems (DPs), interconnected through series, parallel, union, intersection, and feedback composition. We introduce tangential DPs (TDPs) to describe first-order variations around an operating point, show that they preserve the structure of DPs, and characterize their queries through graphical derivatives. To account for multiple internal realizations of a system-level operating point, tangential co-design problems compose families of local TDPs by taking a union over these realizations while retaining the original interconnection structure. We show that these compositions yield families of TDPs and establish conditions under which sensitivity analysis commutes with individual composition operations. Beyond infinitesimal sensitivity, we show that bounded tangential models of convex DPs provide tighter outer approximations of feasible finite changes, which can be propagated through co-design problems without union compositions. We conclude with several convex and nonconvex numerical examples that showcase the framework's accuracy and computational benefits.
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All authors Alan Bonomi, Antonio Terpin, Yubo Cai, Yujun Huang, Meshal Alharbi, Gioele Zardini
Systems and Control · Cross-listed from cs.IT
Dynamic Attack Surfaces: A Queue of Branching Processes
Ismail Cosandal, Abdullah Y. Etcibasi, C. Emre Koksal et al. (+1 more)
Modern attack surfaces are inherently dynamic. Vulnerabilities emerge over time and they remain exposed to exploit until defenders discover and patch them. Artificial intelligence (AI) amplifies these dynamics by accelerating both offensive and defensive processes, making modern cybersecurity increasingly a competition among interacting time scales and resources. We capture this competition using a queue of branching processes that couples vulnerability discovery and resource-constrained patching with the evolving attacker population targeting each vulnerability. Our primary risk metric is the cumulative number of exploits a vulnerability experiences before it is patched. We derive the expected cumulative number of exploits under birth-death-immigration (BDI) and rise-decay (RD) models of attacker activity. Under a BDI model of attacker activity, we identify a phase transition: the expected cumulative number of exploits is finite only when attacker growth remains below both the vulnerability discovery rate and the effective patching rate. Across both attacker models, increasing the exploit rate does not necessarily increase the expected number of exploits because faster attacker departure can sufficiently limit the active attacker population. Finally, we show that equal proportional AI amplification of offensive and defensive capabilities need not preserve security: accelerating both sides can shift the defensive bottleneck and move the system into an unbounded-risk regime.
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All authors Ismail Cosandal, Abdullah Y. Etcibasi, C. Emre Koksal, Sennur Ulukus
Systems and Control
Rethinking electrified transportation: The case for dynamic wireless charging
Dipanjan Ghose, Junjie Qin and S. Sivaranjani
Long-term infrastructure investment often requires choosing between an established technology and a capital-intensive alternative that gains value as demand matures. For electric vehicle (EV) charging, this trade-off is between incumbent stationary charging technology and emerging dynamic wireless charging (DWC). DWC could fundamentally alter transportation electrification by enabling high-throughput charging in motion and reducing reliance on large onboard batteries; however, its high upfront cost makes the timing and location of deployment critical. Overcommitting to stationary charging stations (SCS) risks technological lock-in and forgoing the benefits of DWC, whereas premature DWC deployment can strand capital in underutilized corridors. Through a joint investment assessment framework coupling DWC, SCS, and supporting grid infrastructure across nearly 49,000 miles of the US Interstate freight network, we find that higher utilization and longer project lifetimes rapidly shift investment toward DWC, whose share rises sharply from 48% at a project lifetime of 30 years to 100% at 32 years. Contiguous DWC segments can share grid connections, enabling them to provide over six times the rated charging capacity of SCS while requiring 46% less investment in new distribution-grid infrastructure. We also find that a combined DWC and SCS portfolio reaches a maximum lifetime value of USD 60 billion, 63% above the case with only SCS. We further identify specific traffic demand, construction, and grid access conditions that shape where each technology becomes advantageous, yielding actionable thresholds for SCS and DWC deployment that directly inform long-term EV charging infrastructure investment and policy.
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Systems and Control · Cross-listed from cs.LG
Model-Based Online Decision Making via Generative Trajectory Planning
Haldun Balim, Yilun Du and Na Li
Online decision-making faces a tension between efficient inference and long-horizon
reasoning. Value-based methods rely on short-horizon bootstrapping that limits the propagation of long-term information, while trajectory optimization methods plan explicitly but must solve a high-dimensional optimization problem at every decision step. We propose Generative Trajectory Planning (GTP), a model-based framework that addresses this tension by learning a generative model as a proposal distribution over action sequences. Instead of optimizing sequences from scratch, GTP samples candidate sequences from a
diffusion prior and refines them using learned dynamics, reward, and value models, enabling efficient trajectory-level
reasoning. Across continuous control and long-horizon manipulation tasks, GTP matches or exceeds state-of-the-art model-free,
diffusion-policy, and model-based baselines, and remains stable in regimes where strong baselines degrade. Our results suggest that generative models are best understood not as policies, but as structured proposal distributions for planning, offering a new perspective on how to integrate generative modeling with model-based decision-making.
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Systems and Control · Cross-listed from math.OC
Optimal Decay Matrix Control Barrier Functions: Soundness and Completeness for Matrix Safety Constraints
Samuel G. Gessow, Albert Wu, Aaron D. Ames et al. (+1 more)
Many safety requirements, such as keeping a manipulator away from kinematic singularities, a network connected, or a system observable, amount to keeping a state-dependent
matrix positive definite.
Matrix control barrier functions (MCBFs) enforce such requirements with a semidefinite program that minimally modifies a nominal control input. The MCBF constraint bounds how fast the safety margin, the smallest
eigenvalue of the
matrix, may decay. Yet a fixed decay rule is conservative, rejecting safe controls that drive the state quickly toward, but never across, the boundary of the safe set. Scalar optimal decay (SOD) addresses this issue by scaling the decay term by a scalar weight that is optimized together with the control. For scalar barrier functions, SOD then rejects a control at the boundary only if the control would decrease the margin. For MCBFs, however, SOD can reject controls that leave the margin unchanged, and near the safety boundary SOD accepts such controls only at a high cost. We introduce
matrix optimal decay (MOD), which replaces the scalar weight with a
matrix multiplier while keeping the semidefinite program convex. MOD is provably sound and complete, certifying at the boundary exactly the controls that do not decrease the margin. If some control can strictly increase the margin at every boundary point, the MOD-MCBF filtered controller is continuous and renders the safe set forward invariant. We also add an optional recovery condition that drives the system toward the safe set after a violation without affecting soundness or completeness. On a planar two-link arm avoiding singularities, MOD-MCBF certifies safe motions that both a fixed decay rule and SOD reject, and tracks an end-effector target more closely than SOD.
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All authors Samuel G. Gessow, Albert Wu, Aaron D. Ames, Brett T. Lopez