Papers

This paper investigates how to recover parameters of a linear time invariant system from values and derivatives of its transfer function matrix, along several particular directions at a prescribed set of points in the complex plane, in which system matrices depend on these parameters through a linear fractional transformation. A necessary and sufficient condition is derived for a unique determination of these system parameters, which is expressed by a vector inequality. Under some particular situations, this condition reduces to a full column rank requirement on a constant matrix. Moreover, a method is given to recover system parameters from these values and derivatives, which is expressed by a vector linear equation with some rank constraints, for which various methods exist for finding its solutions. Robustness of the suggested recovery method is also clarified. A numerical example is given to illustrate characteristics of the suggested method, as well as effectiveness of derivative information introduction in parameter recovery, in which natural frequency and damping ratio are to be recovered for a transfer function.

Near-field localization is expected to play a crucial role in enabling a plethora of applications under the paradigm of 6G networks. The conventional localization methods rely on complex infrastructure for providing cooperative anchor nodes that often contribute to higher network overload and energy consumption. To address this, the passive reconfigurable intelligent surfaces (RISs) can be leveraged as perfectly synced reference nodes for developing anchor-free near-field localization. First, we obtain the optimal RIS configurations that maximizes the block-wise averaged trace of Fisher information matrix so that localization error variance can be minimized across the area-of-interest (AoI). Next, we present a two-stage anchor-free localization framework wherein first a coarse estimate is obtained using cosine similarity between the coarse grid and the signal received under pre-defined optimal RIS configurations. In second stage, we refine solution similarly using a finer grid constructed around the coarse estimate. The numerical results show that the proposed RIS-aided anchor-free localization provides small root mean square error for practical values of signal-to-noise ratio (SNR), RIS dimension, and number of antennas at user.

In recent years, the music research community has examined risks of AI models for music, with generative AI models in particular, raised concerns about copyright, deepfakes, and transparency. In our work, we raise concerns about cultural and genre biases in AI for music systems (music-AI systems) which affect stakeholders including creators, distributors, and listeners shaping representation in AI for music. These biases can misrepresent marginalized traditions, especially from the Global South, producing inauthentic outputs (e.g., distorted ragas) that reduces creators' trust on these systems. Such harms risk reinforcing biases, limiting creativity, and contributing to cultural erasure. To address this, we offer recommendations at dataset, model and interface level in music-AI systems.

Deterministic electromagnetic (EM) simulators provide accurate radio propagation modeling but often require expert configuration and lack interactive flexibility. We present RadioSim Agent, an agentic framework that integrates large language models (LLMs) with physics-based EM solvers and vision-enabled reasoning to enable interactive and explainable radio map generation. The framework encapsulates ray-tracing models as callable simulation tools, orchestrated by an LLM capable of interpreting natural language objectives, managing simulation workflows, and visually analyzing resulting radio maps. Demonstrations in urban UAV communication scenarios show that the agent autonomously selects appropriate propagation mechanisms, executes deterministic simulations, and provides semantic and visual summaries of pathloss behavior. The results indicate that RadioSim Agent provides multimodal interpretability and intuitive user interaction, paving the way for intelligent EM simulation assistants in next-generation wireless system design.

We address the problem of fast time-varying channel estimation in millimeter-wave (mmWave) MIMO systems with imperfect channel state information (CSI) and facilitate efficient channel reconstruction. Specifically, leveraging the low-rank and sparse characteristics of the mmWave channel matrix, a two-phase rank-aware compressed sensing framework is proposed for efficient channel estimation and reconstruction. In the first phase, a robust rank-one matrix completion (R1MC) algorithm is used to reconstruct part of the observed channel matrix through low-rank matrix completion (LRMC). To address abrupt rank changes caused by user mobility, a discrete-time autoregressive (AR) model is established that leverages temporal rank correlations across consecutive time instances to enable adaptive observation matrix completion, thereby improving estimation accuracy under dynamic conditions. In the second phase, a rank-aware block orthogonal matching pursuit (RA-BOMP) algorithm is developed for sparse channel recovery with low computational complexity. Furthermore, a rank-aware measurement matrix design is introduced to improve angle estimation accuracy. Simulation results demonstrate that, compared with existing benchmark algorithms, the proposed approach achieves superior channel estimation performance while significantly reducing computational complexity and training overhead.

This paper develops a general framework with convergence guarantees for multi-agent control synthesis, which applies to a wide range of problems, including those with time-varying objective functions. The proposed framework achieves decentralization without inducing entangled dynamics among agents, and it naturally supports multi-objective robotics and real-time implementation. To demonstrate its generality and effectiveness, the framework is applied to three representative problems, namely time-varying leader-follower formation control, decentralized coverage control for time-varying density functions, which is a long-standing open problem, and safe formation navigation in a dense environment.

Endoscopic images often suffer from diverse and co-occurring degradations such as low lighting, smoke, and bleeding, which obscure critical clinical details. Existing restoration methods are typically task-specific and often require prior knowledge of the degradation type, limiting their robustness in real-world clinical use. We propose EndoIR, an all-in-one, degradation-agnostic diffusion-based framework that restores multiple degradation types using a single model. EndoIR introduces a Dual-Domain Prompter that extracts joint spatial-frequency features, coupled with an adaptive embedding that encodes both shared and task-specific cues as conditioning for denoising. To mitigate feature confusion in conventional concatenation-based conditioning, we design a Dual-Stream Diffusion architecture that processes clean and degraded inputs separately, with a Rectified Fusion Block integrating them in a structured, degradation-aware manner. Furthermore, Noise-Aware Routing Block improves efficiency by dynamically selecting only noise-relevant features during denoising. Experiments on SegSTRONG-C and CEC datasets demonstrate that EndoIR achieves state-of-the-art performance across multiple degradation scenarios while using fewer parameters than strong baselines, and downstream segmentation experiments confirm its clinical utility.

Post-training quantization offers an efficient pathway to deploy super-resolution models, yet existing methods treat weight and activation quantization independently, missing their critical interplay. Through controlled experiments on SwinIR, we uncover a striking asymmetry: weight quantization primarily degrades structural similarity, while activation quantization disproportionately affects pixel-level accuracy. This stems from their distinct roles–weights encode learned restoration priors for textures and edges, whereas activations carry input-specific intensity information. Building on this insight, we propose HarmoQ, a unified framework that harmonizes quantization across components through three synergistic steps: structural residual calibration proactively adjusts weights to compensate for activation-induced detail loss, harmonized scale optimization analytically balances quantization difficulty via closed-form solutions, and adaptive boundary refinement iteratively maintains this balance during optimization. Experiments show HarmoQ achieves substantial gains under aggressive compression, outperforming prior art by 0.46 dB on Set5 at 2-bit while delivering 3.2x speedup and 4x memory reduction on A100 GPUs. This work provides the first systematic analysis of weight-activation coupling in super-resolution quantization and establishes a principled solution for efficient high-quality image restoration.

Classifier-guided diffusion models have emerged as a powerful approach for conditional image generation, but they suffer from overconfident predictions during early denoising steps, causing the guidance gradient to vanish. This paper introduces two complementary contributions to address this issue. First, we propose a differentiable calibration objective based on the Smooth Expected Calibration Error (Smooth ECE), which improves classifier calibration with minimal fine-tuning and yields measurable improvements in Frechet Inception Distance (FID). Second, we develop enhanced sampling guidance methods that operate on off-the-shelf classifiers without requiring retraining. These include tilted sampling with batch-level reweighting, adaptive entropy-regularized sampling to preserve diversity, and a novel f-divergence-based sampling strategy that strengthens class-consistent guidance while maintaining mode coverage. Experiments on ImageNet 128x128 demonstrate that our divergence-regularized guidance achieves an FID of 2.13 using a ResNet-101 classifier, improving upon existing classifier-guided diffusion methods while requiring no diffusion model retraining. The results show that principled calibration and divergence-aware sampling provide practical and effective improvements for classifier-guided diffusion.

Image Coding for Machines (ICM) has become increasingly important with the rapid integration of computer vision technology into real-world applications. However, most neural network-based ICM frameworks operate at a fixed rate, thus requiring individual training for each target bitrate. This limitation may restrict their practical usage. Existing variable rate image compression approaches mitigate this issue but often rely on additional training, which increases computational costs and complicates deployment. Moreover, variable rate control has not been thoroughly explored for ICM. To address these challenges, we propose a training-free framework for quantization strength control which enables flexible bitrate adjustment. By exploiting the scale parameter predicted by the hyperprior network, the proposed method adaptively modulates quantization step sizes across both channel and spatial dimensions. This allows the model to preserve semantically important regions while coarsely quantizing less critical areas. Our architectural design further enables continuous bitrate control through a single parameter. Experimental results demonstrate the effectiveness of our proposed method, achieving up to 11.07% BD-rate savings over the non-adaptive variable rate baseline. The code is available at https://github.com/qwert-top/AQVR-ICM.

Missiles pose a major threat to aircraft in modern air combat. Advances in technology make them increasingly difficult to detect until they are close to the target and highly resistant to jamming. The evasion maneuver is the last line of defense for an aircraft. However, conventional rule-based evasion strategies are limited by computational demands and aerodynamic constraints, and existing learning-based approaches remain unconvincing for manned aircraft against modern missiles. To enhance aircraft survivability, this study investigates missile evasion inspired by the pursuit-evasion game between a gazelle and a cheetah and proposes a multi-stage reinforcement learning-based evasion strategy. The strategy learns a large azimuth policy to turn to evade, a small azimuth policy to keep moving away, and a short distance policy to perform agile aggressive maneuvers to avoid. One of the three policies is activated at each stage based on distance and azimuth. To evaluate performance, a high-fidelity simulation environment modeling an F-16 aircraft and missile under various conditions is used to compare the proposed approach with baseline strategies. Experimental results show that the proposed method achieves superior performance, enabling the F-16 aircraft to successfully avoid missiles with a probability of 80.89 percent for velocities ranging from 800 m/s to 1400 m/s, maximum overloads from 40 g to 50 g, detection distances from 5000 m to 15000 m, and random azimuths. When the missile is detected beyond 8000 m, the success ratio increases to 85.06 percent.

This paper explores the development of learning-based tunable control gains using EMT-in-the-loop simulation framework (e.g., PSCAD interfaced with Python-based learning modules) to address critical sub-synchronous oscillations. Since sub-synchronous control interactions (SSCI) arise from the mis-tuning of control gains under specific grid configurations, effective mitigation strategies require adaptive re-tuning of these gains. Such adaptiveness can be achieved by employing a closed-loop, learning-based framework that considers the grid conditions responsible for such sub-synchronous oscillations. This paper addresses this need by adopting methodologies inspired by Markov decision process (MDP) based reinforcement learning (RL), with a particular emphasis on simpler deep policy gradient methods with additional SSCI-specific signal processing modules such as down-sampling, bandpass filtering, and oscillation energy dependent reward computations. Our experimentation in a real-world event setting demonstrates that the deep policy gradient based trained policy can adaptively compute gain settings in response to varying grid conditions and optimally suppress control interaction-induced oscillations.

Agentic language models compose multi step reasoning chains, yet intermediate steps can be corrupted by inconsistent context, retrieval errors, or adversarial inputs, which makes post hoc evaluation too late because errors propagate before detection. We introduce a diagnostic that requires no additional training and uses only the forward pass to emit a binary accept or reject signal during agent execution. The method analyzes token graphs induced by attention and computes two spectral statistics in early layers, namely the high frequency energy ratio and spectral entropy. We formalize these signals, establish invariances, and provide finite sample estimators with uncertainty quantification. Under a two regime mixture assumption with a monotone likelihood ratio property, we show that a single threshold on the high frequency energy ratio is optimal in the Bayes sense for detecting context inconsistency. Empirically, the high frequency energy ratio exhibits robust bimodality during context verification across multiple model families, which enables gating decisions with overhead below one millisecond on our hardware and configurations. We demonstrate integration into retrieval augmented agent pipelines and discuss deployment as an inline safety monitor. The approach detects contamination while the model is still processing the text, before errors commit to the reasoning chain.

The transition towards clean energy and the introduction of Inverter-Based Resources (IBRs) are leading to the formation of Microgrids (MGs) and Networks of MGs (NMGs). MGs and NMGs can operate autonomously in islanded mode, which requires Grid-Forming (GFM) IBRs that can perform black start, synchronization, restoration and regulation. However, such IBRs can face synchronization instability issues, which might be worsened by inadequate secondary level frequency and voltage regulation. Accordingly, we propose an autonomous and distributed synchronization and restoration scheme using Distributed-Averaging Proportional-Integral (DAPI) control. To validate the proposed method, we model and simulate a high-fidelity islanded and modified IEEE 123 bus system, modeled as an NMG consisting of 7 MGs. The MATLAB/Simulink simulation results demonstrate an effective autonomous soft-start, synchronization, connection and regulation procedure using DAPI control and distributed breaker operation logic.

Sparse signal recovery has been a cornerstone of advancements in data processing and imaging. Recently, the squared ratio of $\ell_1$ to $\ell_2$ norms, $(\ell_1/\ell_2)^2$, has been introduced as a sparsity-prompting function, showing superior performance compared to traditional $\ell_1$ minimization, particularly in challenging scenarios with high coherence and dynamic range. This paper explores the integration of the proximity operator of $(\ell_1/\ell_2)^2$ and $\ell_1/\ell_2$ into efficient optimization frameworks, including the Accelerated Proximal Gradient (APG) and Alternating Direction Method of Multipliers (ADMM). We rigorously analyze the convergence properties of these algorithms and demonstrate their effectiveness in compressed sensing and image restoration applications. Numerical experiments highlight the advantages of our proposed methods in terms of recovery accuracy and computational efficiency, particularly under noise and high-coherence conditions.

Most of the rigid-body systems which evolve on nonlinear Lie groups where Euclidean control designs lose geometric meaning. In this paper, we introduce a log-linear backstepping control law on SE2(3) that preserves full rotational-translational coupling. Leveraging a class of mixed-invariant system, which is a group-affine dynamic model, we derive exact logarithmic error dynamics that are linear in the Lie algebra. The closed-form expressions for the left- and right-Jacobian inverses of SE2(3) are expressed in the paper, which provides us the exact error dynamics without local approximations. A log-linear backstepping control design ensures exponential stability for our error dynamics; since our error dynamics is a block-triangular structure, this allows us to use Linear Matrix Inequality (LMI) formulation or $H_\infty$ gain performance design. This work establishes the exact backstepping framework for a class of mixed-invariant system, providing a geometrically consistent foundation for future Unmanned Aerial Vehicle (UAV) and spacecraft control design.

Accurate multiple-input multiple-output (MIMO) channel estimation is critical for next-generation wireless systems, enabling enhanced communication and sensing performance. Traditional model-based channel estimation methods suffer, however, from performance degradation in complex environments with a limited number of pilots, while purely data-driven approaches lack physical interpretability, require extensive data collection, and are usually site-specific. This paper presents a novel physics-informed neural network (PINN) framework that combines model-based channel estimation with a deep network to exploit prior information about the propagation environment and achieve superior performance under pilot-constrained scenarios. The proposed approach employs an enhanced U-Net architecture with cross-attention mechanisms to fuse initial channel estimates with received signal strength (RSS) maps to provide refined channel estimates. Comprehensive evaluation using realistic ray-tracing data from urban environments demonstrates significant performance improvements, achieving over 5 dB gain in normalized mean squared error (NMSE) compared to state-of-the-art methods, with particularly strong performance in pilot-limited scenarios and robustness across different frequencies and environments with only minimal fine-tuning. The proposed framework maintains practical computational complexity, making it viable for massive MIMO systems in upper mid-band frequencies.

This paper proposes an energy-workload coupled migration optimization strategy for virtual power plants (VPPs) with data centers (DCs) to enhance resource scheduling flexibility and achieve precise demand response (DR) curve tracking. A game-based coupled migration framework characterized by antisymmetric matrices is first established to facilitate the coordination of cross-regional resource allocation between VPPs. To address the challenge posed to conventional probabilistic modeling by the inherent data sparsity of DC workloads, deterministic equivalent transformations of fuzzy chance constraints are derived based on fuzzy set theory, and non-convex stochastic problems are transformed into a solvable second-order cone program. To address the multi-player interest coordination problem in cooperative games, an improved Shapley value profit allocation method with the VPP operator as intermediary is proposed to achieve a balance between theoretical fairness and computational feasibility. In addition, the alternating direction method of multipliers with consensus-based variable splitting is introduced to solve the high-dimensional non-convex optimization problem, transforming coupled antisymmetric constraints into separable subproblems with analytical solutions. Simulations based on real data from Google's multiple DCs demonstrate the effectiveness of the proposed method in improving DR curve tracking precision and reducing operational costs.

We introduce a differentiable framework for zero-shot adaptive control over parametric families of nonlinear dynamical systems. Our approach integrates a function encoder-based neural ODE (FE-NODE) for modeling system dynamics with a differentiable predictive control (DPC) for offline self-supervised learning of explicit control policies. The FE-NODE captures nonlinear behaviors in state transitions and enables zero-shot adaptation to new systems without retraining, while the DPC efficiently learns control policies across system parameterizations, thus eliminating costly online optimization common in classical model predictive control. We demonstrate the efficiency, accuracy, and online adaptability of the proposed method across a range of nonlinear systems with varying parametric scenarios, highlighting its potential as a general-purpose tool for fast zero-shot adaptive control.

Conditionally automated driving requires drivers to resume vehicle control promptly when automation reaches its operational limits. Ensuring smooth vehicle control transitions is critical for the safety and efficiency of mixed-traffic transportation systems, where complex interactions and variable traffic behaviors pose additional challenges. This study addresses this challenge by introducing an adaptive time budget framework that provides drivers with sufficient time to complete takeovers both safely and comfortably across diverse scenarios. We focus in particular on the takeover buffer, that is, the extra time available after drivers consciously resume control to complete evasive maneuvers. A driving simulator experiment is conducted to evaluate the influence of different takeover buffer lengths on safety-related indicators (minimum time-to-collision, maximum deceleration, and steering wheel angle) and subjective assessments (perceived time sufficiency, perceived risk, and performance satisfaction). Results show that (i) takeover buffers of about 5-6 seconds consistently lead to optimal safety and comfort; and (ii) drivers prefer relatively stable takeover buffers across varying traffic densities and n-back tasks. This study introduces an adaptive time budget framework that dynamically allocates transition time by incorporating a predicted takeover time and a preferred takeover buffer (piecewise function). This can serve as an important first step toward providing drivers with sufficient time to resume vehicle control across diverse scenarios, which needs to be validated in more diverse and real-world driving contexts. By aligning the provided time budget with driver needs under specific circumstances, the adaptive framework can improve reliability of control transitions, facilitate human-centered automated driving, reduce crash risk, and maintain overall traffic efficiency.

Object: Modern computational MRI denoising approaches are often designed assuming fixed k-space coverage. This contrasts with earlier acquisition-design literature that leveraged k-space coverage modifications (e.g., reducing spatial resolution) to improve SNR. This work investigates whether the performance of modern computational denoising methods can be further enhanced by k-space coverage modifications. Materials and Methods: Using realistic simulations of noisy data, k-space coverage and averaging patterns were optimized for two advanced image denoising/reconstruction approaches: parallel imaging with total variation regularization and a U-Net neural network. For reference, comparisons against classical linear filtering/apodization methods were also performed. Performance was quantified using normalized root-mean-squared error (NRMSE) and structural similarity (SSIM) metrics. Results: Advanced computational denoising methods can be substantially enhanced, both quantitatively and qualitatively, by reducing the spatial resolution of the acquisition to improve SNR. Indeed, even simple linear filtering/apodization with optimized k-space coverage can rival advanced methods using naive higher-resolution coverage. Discussion: Classical acquisition design principles that allow spatial resolution to be traded for SNR enhancement are still very relevant for modern computational denoising techniques. However, the optimization of k-space coverage and denoising/reconstruction methods can also be somewhat confounded because the NRMSE and SSIM metrics have low sensitivity to spatial resolution.

In this paper, a learning framework is introduced which incorporates principles of probabilistic inference, variational optimization, and geometry-preserving operations inspired by quantum transformations. The central innovation of this quantum-inspired variational convolution (QiVC) lies in its quantum-inspired rotated ensemble (QiRE) mechanism. QiRE performs differentiable low-dimensional subspace rotations of convolutional weights. By drawing a mathematical analogy from unitary evolution, this approach enables structured uncertainty modeling that respects the intrinsic geometry of the parameter space. To demonstrate its practical potential, the concept is instantiated in a QiVC-based convolutional network (QiVC-Net) and evaluated in the context of biosignal classification, focusing on phonocardiogram (PCG) recordings. The proposed QiVC-Net integrates an architecture in which the QiVC layer does not introduce additional parameters, instead performing an ensemble rotation of the convolutional weights through a structured mechanism ensuring robustness without added highly computational burden. Experiments on two benchmark datasets, PhysioNet CinC 2016 and PhysioNet CirCor DigiScope 2022, show that QiVC-Net achieves state-of-the-art performance, reaching accuracies of 97.84% and 97.89%, respectively. These findings highlight the versatility of the QiVC framework and its promise for advancing uncertainty-aware modeling in real-world biomedical signal analysis. The implementation of the QiVConv layer is available in GitHub for public use.

Resource-intensive builds are often executed directly on the controller by conventional Jenkins installations, which can lower reliability and overload system resources. Jenkins functions as a containerized controller with persistent volumes in the controller-light CI/CD framework presented in this paper, delegating difficult build and packaging tasks to a remote Docker host. The controller container maintains secure SSH connections to remote compute nodes while focusing solely on orchestration and reporting. Atomic deployments with time-stamped backups, containerized build environments, immutable artifact packaging, and automated notifications are all included in the system. Faster build throughput, reduced CPU and RAM consumption on the controller, and reduced artifact delivery latency are all revealed by experimental evaluation. For small and medium-sized DevOps businesses looking for scalable automation without adding orchestration complexity, this method offers a repeatable, low-maintenance solution.

Automated task-assignment systems often serve stochastic tasks subject to finite deadlines. In these settings, conventional backlog-based stability can be misleading: finite task lifetimes may keep the number of outstanding tasks bounded even as deadline failures continue indefinitely, while average failure-rate criteria can still permit recurrent failures. We introduce average cost stability, a criterion which is particularly useful for time-sensitive tasks, combining two observable quantities: the number of outstanding tasks and the cumulative number of irrecoverable deadline failures. Under bounded arrivals and uniformly bounded service windows, we show that the outstanding-task count is uniformly bounded independently of the assignment policy and prove that our average cost stability is equivalent to bounded expected cumulative failures. We further characterize degenerate backlog stability, in which backlog remains bounded despite unbounded cumulative failures. We instantiate the framework in an adversarial pickup-and-delivery system where internal fleet agents spoof reported locations to attract assignments and leave requests unserviced. We develop deadline-aware assignment procedures and adversarial models with varying knowledge and coordination capabilities. Experiments using real mobility-on-demand request data and our proposed adversarial models demonstrate that backlog can remain bounded while cancellations persist, whereas our proposed average cost stability correctly identifies such behavior as unstable.

The deployment of artificial intelligence models at the edge is increasingly critical for autonomous robots operating in GPS-denied environments where local, resource-efficient reasoning is essential. This work demonstrates the feasibility of deploying small Vision-Language Models (VLMs) on mobile robots to achieve real-time scene understanding and reasoning under strict computational constraints. Unlike prior approaches that separate perception from mobility, the proposed framework enables simultaneous movement and reasoning in dynamic environments using only on-board hardware. The system integrates a compact VLM with multimodal perception to perform contextual interpretation directly on embedded hardware, eliminating reliance on cloud connectivity. Experimental validation highlights the balance between computational efficiency, task accuracy, and system responsiveness. Implementation on a mobile robot confirms one of the first successful deployments of small VLMs for concurrent reasoning and mobility at the edge. This work establishes a foundation for scalable, assured autonomy in applications such as service robotics, disaster response, and defense operations.

This paper proposes a fault-tolerant control strategy based on a tilt-rotor quadcopter prototype, utilizing nonlinear model predictive control to maintain both attitude and position stability in the event of rotor failure. The control strategy employs an extended state observer to predict model deviations following a fault and adjusts the original model in the subsequent time step, thereby achieving active fault-tolerant control. The proposed method is evaluated through simulations and compared to both traditional quadcopter and tilt-rotor quadcopter without observer under identical conditions. The results demonstrate that the tilt-rotor quadcopter can maintain position control without sacrificing yaw stability, unlike traditional quadcopters.

We study adversarially robust multitask adaptive linear quadratic control; a setting where multiple systems collaboratively learn control policies under model uncertainty and adversarial corruption. We propose a clustered multitask approach that integrates clustering and system identification with resilient aggregation to mitigate corrupted model updates. Our analysis characterizes how clustering accuracy, intra-cluster heterogeneity, and adversarial behavior affect the expected regret of certainty-equivalent (CE) control across LQR tasks. We establish non-asymptotic bounds demonstrating that the regret decreases inversely with the number of honest systems per cluster and that this reduction is preserved under a bounded fraction of adversarial systems within each cluster.

This paper proposes a coherency control strategy for Inverter-Based Resources (IBRs) to establish coherence among power system devices. Using the equivalence of the Complex Frequency (CF) of the injected currents as the definition for coherency among devices, the control enforces an output current with a proportional magnitude and a constant phase shift relative to a reference. This formulation makes the control technology-agnostic, enabling coherency with any type of resource. Case studies based on the two-area and IEEE 39-bus systems demonstrate the controller's potential to improve damping and overall dynamic behavior. The paper further evaluates practical implementation aspects including delay/noise sensitivity and the trade-off between oscillation mitigation and disturbance propagation. This work establishes coherency as a viable direct control objective for IBRs in modern power systems.

We present ETHOS (Encountered-Type Haptics for On-demand Social Interaction), a dynamic encountered-type haptic display (ETHD) that enables natural physical contact in virtual reality (VR) during social interactions such as handovers, fist bumps, and high-fives. The system integrates a torque-controlled robotic manipulator with interchangeable passive props (silicone hand replicas and a baton), marker-based physical-virtual registration via a ChArUco board, and a safety monitor that gates motion based on the user's head and hand pose. We introduce two control strategies: (i) a static mode that presents a stationary prop aligned with its virtual counterpart, consistent with prior ETHD baselines, and (ii) a dynamic mode that continuously updates prop position by exponentially blending an initial mid-point trajectory with real-time hand tracking, generating a unique contact point for each interaction. Bench tests show static colocation accuracy of 5.09 +/- 0.94 mm, while user interactions achieved temporal alignment with an average contact latency of 28.53 +/- 31.21 ms across all interaction and control conditions. These results demonstrate the feasibility of recreating socially meaningful haptics in VR. By incorporating essential safety and control mechanisms, ETHOS establishes a practical foundation for high-fidelity, dynamic interpersonal interactions in virtual environments.

Flow matching (FM) has shown promising results in data-driven planning. However, it inherently lacks formal guarantees for ensuring state and action constraints, whose satisfaction is a fundamental and crucial requirement for the safety and admissibility of planned trajectories on various systems. Moreover, existing FM planners do not ensure the dynamical consistency, which potentially renders trajectories inexecutable. We address these shortcomings by proposing SAD-Flower, a novel framework for generating Safe, Admissible, and Dynamically consistent trajectories. Our approach relies on an augmentation of the flow with a virtual control input. Thereby, principled guidance can be derived using techniques from nonlinear control theory, providing formal guarantees for state constraints, action constraints, and dynamic consistency. Crucially, SAD-Flower operates without retraining, enabling test-time satisfaction of unseen constraints. Through extensive experiments across several tasks, we demonstrate that SAD-Flower outperforms various generative-model-based baselines in ensuring constraint satisfaction.