In dynamic wireless environments, accurate channel state information (CSI) prediction remains challenging due to non-stationary fading, mobility. This paper proposes an Uncertainty-Weighted Experience Replay (UW-ER) framework that integrates model uncertainty into the replay sampling process to improve robustness in online CSI prediction. A lightweight LSTM architecture with Monte-Carlo dropout is employed to estimate predictive variance, which is then used to adaptively weight the reconstruction loss for each training sample. The proposed method is evaluated on a UMi-Dense MIMO channel dataset generated using a stochastic fading model consistent with 3GPP standards. Results show that UW-ER achieves stable generalization, with validation NMSE centered near 0 dB and a strong correlation (r = 0.93) between predicted uncertainty and reconstruction error, indicating well-calibrated confidence estimates. Ablation studies demonstrate that the LARS-based replay policy achieves competitive performance with smaller memory budgets compared to conventional reservoir replay. Overall, the UW-ER approach improves continual channel learning stability without increasing computational complexity, offering a scalable solution for future 6G adaptive communication systems.

This work presents the application of the Complex Orthogonal Decomposition (C.O.D.) to a simple spatio-temporal signal. C.O.D. has been introduced rst in the article of B. Feeny, entitled "A Complex Orthogonal Decomposition for Wave Motion Analysis" [1], published in the Journal of Sound and Vibration. The purpose of this signal analysis method is to extract spatial and temporal modes out of a signal. This approach is especially suited to deal with oscillatory signals where phase information is important and where spatial forms are unknown. We provide two theoretical chapters presenting the main mathematical concepts behind C.O.D. and a series of example (with associated Python scripts) to demonstrate the e ciency of the method and some characteristical features.

Robust control barrier functions (CBFs) provide a principled mechanism for smooth safety enforcement under worst-case disturbances. However, existing approaches typically rely on explicit, closed-form structure in the dynamics (e.g., control-affine) and uncertainty models. This has led to limited scalability and generality, with most robust CBFs certifying only conservative subsets of the maximal robust safe set. In this paper, we introduce a new robust CBF framework for general nonlinear systems under bounded uncertainty. We first show that the safety value function solving the dynamic programming Isaacs equation is a valid robust discrete-time CBF that enforces safety on the maximal robust safe set. We then adopt the key reinforcement learning (RL) notion of quality function (or Q-function), which removes the need for explicit dynamics by lifting the barrier certificate into state-action space and yields a novel robust Q-CBF constraint for safety filtering. Combined with adversarial RL, this enables the synthesis and deployment of robust Q-CBFs on general nonlinear systems with black-box dynamics and unknown uncertainty structure. We validate the framework on a canonical inverted pendulum benchmark and a 36-D quadruped simulator, achieving substantially less conservative safe sets than barrier-based baselines on the pendulum and reliable safety enforcement even under adversarial uncertainty realizations on the quadruped.

This work explores methods to identify energy system designs for infeasible control co-design optimization problems. Control co-design, or CCD, has been recognized as a powerful tool to maximize energy system capabilities through simultaneous determination of plant and controller parameters. However, due to the inherent nonlinearities, complexity, and conflicting criteria of energy systems, CCD optimization problems are susceptible to infeasibility and can lack potential solutions. While transforming the optimization problem by relaxing constraints has been developed for optimal control infeasibility challenges, solution feasibility for CCD is relatively unexplored. This paper proposes a framework to convert infeasible optimization problems into solvable forms for a class of CCD problems. The framework introduces a procedure to rank metric bounds from least likely to most likely to cause infeasibility. This provides guidance to algorithmically relax a limited number of constraints, leaving others intact. The proposed framework is applied to a CCD problem for designing a battery within a microgrid. Comparison against a baseline approach for relaxing optimization problems shows the framework requires only a reduced number of iterations to determine a solution.

Speech deepfake detection (SDD) systems perform well on standard benchmarks datasets but often fail to generalize to expressive and emotional spoofing attacks. Many methods rely on spoof-heavy training data, learning dataset-specific artifacts rather than transferable cues of natural speech. In contrast, humans internalize variability in real speech and detect fakes as deviations from it. We introduce ProSDD, a two-stage framework that enriches model embeddings through supervised masked prediction of speaker-conditioned prosodic variation based on pitch, voice activity, and energy. Stage I learns prosodic variability from real speech, and Stage II jointly optimizes this objective with spoof classification. ProSDD consistently outperforms baselines under both ASVspoof 2019 and 2024 training, reducing ASVspoof 2024 EER from 25.43% to 16.14% (2019-trained) and from 39.62% to 7.38% (2024-trained), while achieving 50% relative reductions on EmoFake and EmoSpoof-TTS.

We study sequential decision-making in time-varying Markov decision processes (TVMDPs) under limited update rates, where the decision-maker observes the system and updates its model only intermittently. Such settings arise in applications with sensing, communication, or computational constraints that preclude continuous adaptation. Our goal is to understand how the performance of an agent, which learns and plans using receding-horizon control under these information constraints, degrades as a function of the update rate. We propose a skip-update learning and planning framework that combines likelihood-based estimation of time-varying transition kernels with finite-horizon planning and executes policies between updates using stale information. We analyze its performance via dynamic regret relative to an oracle policy with full knowledge of the dynamics and continuous observations. Our main result establishes a dynamic regret bound that explicitly quantifies the impact of intermittent updates, decomposing regret into contributions from update times and skip intervals and revealing its dependence on temporal variation, estimation uncertainty, and the duration of intervals without updates. In particular, the dominant contribution from skip intervals admits a linear dependence on the interval length and the rate of temporal variation, while its effect is mitigated by mixing-induced contraction.

This paper investigates the problem of data-driven modeling of port-Hamiltonian systems while preserving their intrinsic Hamiltonian structure and stability properties. We propose a novel neural-network-based port-Hamiltonian modeling technique that relaxes the convexity constraint commonly imposed by neural network-based Hamiltonian approximations, thereby improving the expressiveness and generalization capability of the model. By removing this restriction, the proposed approach enables the use of more general non-convex Hamiltonian representations to enhance modeling flexibility and accuracy. Furthermore, the proposed method incorporates information about stable equilibria into the learning process, allowing the learned model to preserve the stability of multiple isolated equilibria rather than being restricted to a single equilibrium as in conventional methods. Two numerical experiments are conducted to validate the effectiveness of the proposed approach and demonstrate its ability to achieve more accurate structure- and stability-preserving learning of port-Hamiltonian systems compared with a baseline method.

This paper extends path integral control (PIC) to partially observed systems by formulating the problem in Gaussian belief space. PIC relies on the diffusion being proportional to the control channel -- the so-called matching condition -- to linearize the Hamilton-Jacobi-Bellman equation via the Cole-Hopf transform; we show that this condition fails in infinite-dimensional belief space under non-affine observations. Restricting to Gaussian beliefs yields a finite-dimensional approximation with deterministic covariance evolution, reducing the problem to stochastic control of the belief mean. We derive necessary and sufficient conditions for matching in this reduced space, obtain an exact Cole-Hopf linearization with a Feynman-Kac representation, and develop the MPPI-Belief algorithm. Numerical experiments on a navigation task with state-dependent observation noise demonstrate the effectiveness of MPPI-Belief relative to certainty-equivalent and particle-filter-based baselines.

The increasing penetration of distributed energy resources (DERs) is transforming distribution networks into actively managed systems, introducing challenges related to voltage regulation, thermal loading limits, and operational security. This paper presents the development and implementation of a real-time Digital Twin (DT) for security assessment and coordinated flexibility activation in active distribution networks, demonstrated on the Bornholm Island system using real measurement data. The implemented DT integrates network topology and smart meter measurements to perform security assessment under normal operation and N-1 contingencies, and to determine corrective and preventive flexibility actions using an optimization-based approach. Results show that load variation and contingency scenarios introduce operational limit violations, primarily driven by voltage magnitude constraints. The implemented flexibility strategy effectively mitigates these violations through coordinated active and reactive power control, enhancing system security and operational efficiency. The findings highlight the potential of DT-based approaches for reliable and flexible operation of future distribution networks.

We study the asymptotic optimality of abstraction-based control synthesis algorithms. Specifically, we consider uncertain MDP (UMDP) abstraction, and investigate whether refinement leads to optimal results, i.e., an optimal controller and zero error bound. Additionally, we study completeness of abstraction-refinement algorithms, i.e., that the algorithm produces near-optimal results in finite time. The focus is on nonlinear stochastic systems with general vector fields and temporal logic specifications. We present an algorithm that abstracts the system into a UMDP and synthesizes a controller with performance guarantees via robust dynamic programming. Then, the algorithm iteratively refines the abstraction until a near-optimality criterion is met. A thorough theoretical analysis reveals a sufficient condition, which we denote vanishing ambiguity, guaranteeing asymptotic optimality of the abstraction process and completeness of the algorithm. We show that set-valued MDP abstractions satisfy this criterion, whereas interval MDP abstractions lack such a guarantee.

This paper addresses the problem of fixed-time cooperative output regulation for linear multi-agent systems over directed graphs under denial-of-service attacks. A novel distributed resilient fixed-time controller is developed that comprises a distributed resilient fixed-time observer taking general directed graphs into consideration, and a distributed resilient fixed-time control law for each agent. The proposed controller neither depends on Laplacian symmetry nor requires strong connectivity and detail-balanced condition, in contrast to existing distributed resilient fixed-time controllers. Under the proposed controller, the regulated outputs converge to zero in a fixed time with its upper bound independent of the initial states of the multi-agent system. Ultimately, the efficacy of the proposed controller is demonstrated via a simulation example.

Cross-facility knowledge transfer in Controlled Environment Agriculture (CEA) can reduce HVAC energy consumption by 30-38% and accelerate new facility commissioning from months to days. However, facility operators refuse to share raw operational data because it encodes commercially sensitive grow recipes. We present HierFedCEA, a hierarchical federated learning framework that enables privacy-preserving climate control optimization across heterogeneous CEA facilities. HierFedCEA decomposes the neural network PID auto-tuning model into three tiers aligned with the physical structure of the control problem: (1) a global physics tier capturing universal thermodynamic relationships; (2) a crop-cluster tier encoding cultivar-specific VPD-to-gain mappings; and (3) a local personalization tier adapting to facility-specific equipment dynamics. The framework applies tier-specific differential privacy budgets and leverages the extreme compactness of the 36-parameter PID model to achieve privacy essentially for free (excess risk < 0.15%). Simulation experiments calibrated from 7+ years of production deployment across 30+ commercial facilities in 8 U.S. climate zones demonstrate that HierFedCEA achieves 94% of centralized training performance while reducing total communication cost to under 1 MB. To the best of our knowledge, this is the first federated learning framework designed for CEA climate control.

Deep learning has enabled highly realistic synthetic speech, raising concerns about fraud, impersonation, and disinformation. Despite rapid progress in neural detectors, transparent baselines are needed to reveal which acoustic cues reliably separate real from synthetic speech. This paper presents an interpretable classical machine learning baseline for deepfake audio detection using the Fake-or-Real (FoR) dataset. We extract prosodic, voice-quality, and spectral features from two-second clips at 44.1 kHz (high-fidelity) and 16 kHz (telephone-quality) sampling rates. Statistical analysis (ANOVA, correlation heatmaps) identifies features that differ significantly between real and fake speech. We then train multiple classifiers -- Logistic Regression, LDA, QDA, Gaussian Naive Bayes, SVMs, and GMMs -- and evaluate performance using accuracy, ROC-AUC, EER, and DET curves. Pairwise McNemar's tests confirm statistically significant differences between models. The best model, an RBF SVM, achieves ~93% test accuracy and ~7% EER on both sampling rates, while linear models reach ~75% accuracy. Feature analysis reveals that pitch variability and spectral richness (spectral centroid, bandwidth) are key discriminative cues. These results provide a strong, interpretable baseline for future deepfake audio detectors.

Peer-to-peer (P2P) energy management facilitates decentralized resource allocation among prosumers, improving local hosting capacity for renewables and minimizing energy expenditures while ensuring data privacy through distributed coordination. However, conventional P2P energy management methods are confined to synchronous scheduling paradigms, creating synchronization bottlenecks that fundamentally conflict with the dynamic and decentralized nature of P2P energy management tasks. To bridge this gap, this paper focuses on resolving a class of dynamic energy management problems over asynchronous P2P (Asyn-P2P) transactive networks. We first recast the dynamic energy management problems into a saddle-point problem, and then propose a synchronous decentralized dynamic energy management algorithm, dubbed Syn-DYNA,based on operator splitting theory. To eliminate the global synchronization clock in Syn-DYNA, we introduce a random activation scheme, together with local buffers for latest state tracking, to develop an asynchronous variant of Syn-DYNA, namely Asyn-DYNA. Based on monotone operator theory, theoretical analysis proves a non-asymptotic linear convergence rate for Syn-DYNA and establishes the almost sure convergence ofAsyn-DYNA. Numerical experiments validate effectiveness of Syn-DYNA and Asyn-DYNA algorithms by tackling a dynamic energy management task over P2P transactive networks.

The rapid development of cyber-physical systems is driving a transition toward mixed traffic environments comprising both human-driven and connected and automated vehicles (CAVs). This shift presents a unique opportunity to leverage the efficient operation of CAVs to improve overall network throughput. This paper introduces a hierarchical framework designed to bridge macroscopic routing optimization at the network level with microscopic vicinity control at signalized intersections. The upper layer utilizes aggregated traffic information to provide proactive routing guidance for CAVs, aiming to minimize total travel time. The lower layer leverages local vehicle states to jointly optimize traffic light phases and individual CAV trajectories, aiming to reduce intersection crossing delays and optimize energy consumption, respectively. The effectiveness of the proposed framework is validated through SUMO on the Sioux Falls benchmark network. Results demonstrate that the integration of these macroscopic and microscopic layers yields significantly better performance compared to applying either layer in isolation, significantly improving network throughput and reducing congestion.

Conventional climate control in Controlled Environment Agriculture (CEA) uses independent PID loops for temperature and humidity, creating cross-coupling conflicts that waste 20-40% of HVAC energy. We propose a cascading architecture that elevates Vapor Pressure Deficit (VPD) from a monitored metric to the primary outer-loop control variable. A 7-3-3 neural network optimizer selects energy-minimal temperature-humidity setpoints along the VPD constraint surface, feeding inner PID loops that drive HVAC actuators. Lyapunov stability analysis guarantees bounded PID gains. Deployment across 30+ commercial facilities in 8 U.S. climate zones over 7+ years demonstrates 30-38% HVAC energy reduction, 68-73% improvement in VPD stability, and 60-67% faster disturbance recovery compared to independent PID baselines.

Semantic segmentation of histopathology images under class imbalance is typically addressed through frequency-based loss reweighting, which implicitly assumes that rare classes are difficult. However, true difficulty also arises from morphological variability, boundary ambiguity, and contextual similarity-factors that frequency cannot capture. We propose Dynamic Focal Attention (DFA), a simple and efficient mechanism that learns class-specific difficulty directly within the cross-attention of query-based mask decoders. DFA introduces a learnable per-class bias to attention logits, enabling representation-level reweighting prior to prediction rather than gradient-level reweighting after prediction. Initialised from a log-frequency prior to prevent gradient starvation, the bias is optimised end-to-end, allowing the model to adaptively capture difficulty signals through training, effectively unifying frequency-based and difficulty-aware approaches under a common attention-bias framework. On three histopathology benchmarks (BDSA, BCSS, CRAG), DFA consistently improves Dice and IoU, matching or exceeding a difficulty-aware baseline without a separate estimator or additional training stage. These results demonstrate that encoding class difficulty at the representation level provides a principled alternative to conventional loss reweighting for imbalanced segmentation.

This work presents a cascaded hybrid control framework for quadrotor trajectory tracking under nonlinear dynamics and external disturbances. In quadrotor systems, the altitude and attitude channels exhibit fast, structured dynamics that are well suited to reliable regulation, whereas horizontal-position control is more strongly affected by coupling effects, uncertainty, and disturbances, so that neither pure feedback control nor purely learning-based control alone is equally well suited to all channels. Accordingly, the proposed framework augments conventional proportional-integral-derivative (PID) stabilization for altitude and attitude control with an enhanced Twin Delayed Deep Deterministic Policy Gradient (TD3) agent incorporating a multi-Q-network structure, thereby improving horizontal-position control under severe disturbances. To further strengthen disturbance rejection in altitude and attitude control, a hybrid disturbance observer (HDOB) using low-pass and exponential moving average filtering is embedded in the control loops. The proposed TD3 enhancements are verified through ablation studies, and both numerical simulations and real-world flight tests on the quadrotor platform demonstrate that the proposed method achieves more accurate and robust trajectory tracking under wind disturbances than baseline approaches.

Even when providing long-run, worst-case guarantees to competing flows of unit-sized tasks, a slot-timed, constant-capacity server's scheduler may retain significant, short-run, scheduling flexibility. Existing worst-case scheduling frameworks offer only limited opportunities to characterize and exploit this flexibility. We introduce a state-based framework that overcomes these limitations. Each flow's guarantee is modeled as a worst-case service that can be updated as tasks arrive and are served. Taking all flows' worst-case services as a collective state, a state-based scheduler ensures, from slot to slot, transitions between schedulable states. This constrains its scheduling flexibility to a polytope consisting of all feasible schedules that preserve schedulability. We fully characterize this polytope, enabling scheduling flexibility to be fully exploited. But, as our framework is general, full exploitation is computationally complex. To reduce complexity, we show: that when feasible schedules exist, at least one can be efficiently identified by simply maximizing the server's capacity slack; that a special class of worst-case services, min-plus services, can be efficiently specified and updated using the min-plus algebra; and that efficiency can be further improved by restricting attention to a min-plus service subclass, dual-curve services. This last specialization turns out to be a dynamic extension of service curves that approaches near practical viability while maintaining all features essential to our framework.

In this paper, we introduce GatherMOS, a novel framework that leverages large language models (LLM) as meta-evaluators to aggregate diverse signals into quality predictions. GatherMOS integrates lightweight acoustic descriptors with pseudo-labels from DNSMOS and VQScore, enabling the LLM to reason over heterogeneous inputs and infer perceptual mean opinion scores (MOS). We further explore both zero-shot and few-shot in-context learning setups, showing that zero-shot GatherMOS maintains stable performance across diverse conditions, while few-shot guidance yields large gains when support samples match the test conditions. Experiments on the VoiceBank-DEMAND dataset demonstrate that GatherMOS consistently outperforms DNSMOS, VQScore, naive score averaging, and even learning-based models such as CNN-BLSTM and MOS-SSL when trained under limited labeled-data conditions. These results highlight the potential of LLM-based aggregation as a practical strategy for non-intrusive speech quality evaluation.

The increasing deployment of agentic artificial intelligence (AI) systems has intensified the demand for efficient agent to agent communication, particularly over bandwidth limited wireless links. In embodied AI applications, agents must exchange task related information under strict latency and reliability constraints. Existing agent communication methods primarily focus on connectivity and protocol efficiency, but lack effective mechanisms to reduce physical layer transmission overhead while preserving task this http URL address this challenge, this paper proposes a semantic agent communication framework that reduces communication overhead while maintaining task performance and shared understanding among agents. An LLM based semantic processor is first introduced to reorganize and condense agent generated messages by extracting task relevant semantic content. To cope with information loss introduced by aggressive message reduction, an importance-aware semantic transmission strategy is developed, which adaptively protects semantic components according to their task importance. Furthermore, a task specific knowledge base is incorporated as long term semantic memory to support recurring tasks and further reduce bandwidth consumption with minimal performance degradation. Experimental results and ablation studies demonstrate that the proposed framework achieves nearly 50% bandwidth reduction with negligible loss in task completion performance compared to conventional transmission schemes.

Beyond-diagonal reconfigurable intelligent surfaces (BD-RISs), originally in the passive form, have attracted attention due to their benefits in enhanced wave manipulating through flexible inter-element connections and element arrangements. To mitigate the severe multiplicative fading, the concept of active BD-RISs with signal amplification capability has recently been proposed. Inspired by this, we investigate the hybrid transmitting and reflecting mode of active BD-RISs to achieve full-space coverage. We start by deriving a physics compliant communication model applying active BD-RIS with hybrid mode. We further propose novel architectures including reciprocal and non-reciprocal implementations with cell-wise single, group, and fully connections. We also develop a unified optimization framework for the joint transmit precoding and hybrid mode active BD-RIS design to maximize the sum rate of multi-user communication systems, which is applicable to all considered architectures. Numerical results demonstrate that, under the same total power budget, the proposed active BD-RIS with hybrid mode substantially outperforms active and passive simultaneous transmitting and reflecting RISs as well as passive BD-RISs with hybrid mode. This shows the synergy gain from inter-element connection, element arrangements, and active amplification.

High-precision direction-of-arrival (DOA) estimation, as a key sensing capability for 6G-enabled applications such as autonomous driving and extended reality, is increasingly dependent on the effective exploitation of spatial degrees of freedom (DOFs). This paper integrates two frontier DOFs-oriented paradigms and proposes a fluid antenna-enabled hybrid analog-digital (FA-HAD) architecture, which features an extremely lightweight front-end configuration mechanism and efficient spatial DOFs exploitation. Within this architecture, a collaborative spatial-phase sampling strategy is first developed to enable real-time 2-D DOA estimation under compressive observations, and a single-source CRLB analysis is provided to quantify the achievable performance limit, offering quantitative guidance for accuracy-overhead trade-offs. Furthermore, an efficient virtual-array spatial covariance matrix reconstruction method is proposed to recover a physically meaningful covariance representation, thereby providing a covariance-domain interface that is directly reusable by a broad class of existing covariance-based array processing and array design techniques, which strengthens the scalability and transferability of the proposed architecture. Building upon the reconstructed SCM, a Jacobi-Anger expansion based dimension-reduced MUSIC estimator is further derived for arbitrary planar arrays with a favorable computational cost. Simulation results demonstrate that the proposed FA-HAD framework attains DOA accuracy close to fully digital systems while substantially reducing RF hardware complexity and training overhead.

This paper proposes an improved approach for open-set speaker identification based on pretrained speaker foundation models. Building upon the previous Speaker Reciprocal Points Learning framework (V1), we first introduce an enhanced open-set learning objective by integrating reciprocal points learning with logit normalization (LogitNorm) and incorporating adaptive anchor learning to better constrain target speaker representations and improve robustness. Second, we propose a model fusion strategy to stabilize and enhance the few-shot tuning process, effectively reducing result randomness and improving generalization. Furthermore, we introduce a model selection method to ensure optimal performance in model fusion. Experimental evaluations on the VoxCeleb, ESD and 3D-Speaker datasets demonstrate the effectiveness and robustness of the proposed method under diverse conditions. On a newly proposed Vox1-O-like test set, our method reduces the EER from 1.28% to 0.09%, achieving a relative reduction of approximately 93%.

We address the problem of interaction topology identification in open multi-agent systems (OMAS) with dynamic node sets and fast switching interactions. In such systems, new agents join and interactions change rapidly, resulting in intervals with short dwell time and rendering conventional segment-wise estimation and clustering methods unreliable. To overcome this, we propose a projection-based dissimilarity measure derived from a consistency property of local least-squares operators, enabling robust mode clustering. Aggregating intervals within each cluster yields accurate topology estimates. The proposed framework offers a systematic solution for reconstructing the interaction topology of OMAS subject to fast switching. Finally, we illustrate our theoretical results via numerical simulations.

Solving optimal control problems to determine a stabilizing controller involves a significant computational effort. Time-varying optimal control provides a remedy by designing a tracking system, given as an ordinary differential equation, to track the solution of the optimal control problem. To improve the applicability of the method, measurement errors are considered in this paper and it is described how these errors influence a control Lyapunov function-based decay condition. As a result of these investigations, input-affine constraints that meet the standard formulation and that describe the set of admissible controls are obtained. The paper also derives a requirement on the necessary measurement accuracy as well as a triggering condition for taking a new measurement. The main theorem combines these results into a robustly stabilizing control algorithm, meaning that all closed-loop trajectories starting in a vicinity around the true state converge to zero. Additionally, the tracking system ensures that the optimal control is tracked at the end of each sampling period. The effectiveness of this approach is demonstrated using a train acceleration model and the well-known predator-prey model.

The abundance of process operating data in modern industries, along with the rapid advancement of learning techniques, has led to a paradigm shift towards data-centric analysis and control. However, integrating machine learning with control theory for big data-driven control of nonlinear systems remains a challenging open problem. This is because the state-based, model-centric, and causal framework of classical control theory fundamentally contradicts the trajectory-based, set-theoretic, and causality-absent rationale of big data-based learning approaches. Using the behavioral framework, we show that dynamical systems possess an intrinsic state variable that encodes the system behavior in a bijective and causality-free manner, and control design can be carried out entirely within the state space. This approach not only resolves the aforementioned conflict but also complements machine learning techniques well, leading to a neural network architecture that is capable of learning the behavior representation well-suited for control design.

In process operations, it is desirable to manage the sensitivity of the system output against external disturbance in the form of finite $\mathcal{L}_2$-gain stabilization. This matter is, however, nonsensical for stochastic systems because the stochastic uncertainties in the control input almost always lead to an unbounded $\mathcal{L}_2$ gain from the disturbance to the output. To address this issue, this article develops a novel concept that characterizes the $\mathcal{L}_2$ gain of stochastic systems in a probabilistic way. Combined with a large data set, we formulate a data-driven probabilistic finite $\mathcal{L}_2$-gain stabilization design using noisy trajectory measurements and the disturbance forecast that does not necessarily agree with the actual future disturbance. The design approach consists of a data-driven trajectory estimation algorithm, whose resulting estimation error covariance is nicely integrated into the feasibility conditions for controller synthesis, leading to a convex offline design in the form of linear matrix inequalities. The effectiveness of the proposed design, along with the additional insights provided by the approach, is illustrated via a numerical example.

We address the multi-agent motion planning problem where interactions, collisions, and congestion co-exist. Conventional game-theoretic planners capture interactions among agents but often converge to conservative, congested equilibria. Homotopy planners, on the other hand, can explore topologically distinct paths, but lack mechanisms to account for the interdependence of agents' future actions. We propose a unified framework that leverages homotopy classes as structured strategy sets within a receding-horizon setup. At each planning stage, a deterministic homotopy planner generates topologically distinct paths for each agent, conditioned on the joint configuration. To avoid intractable growth of candidate paths, we propose a simple heuristic filtering step that selects a top-$K$ subset of the most suitable congestion-free joint strategies to ensure computational tractability. These serve as initializations for a potential game that enforces homotopy-consistent constraints and yields a generalized open-loop Nash equilibrium (OLNE), with penalties discouraging abrupt strategy shifts in a receding-horizon setting. Simulations with three agents demonstrate improved efficiency (faster completion) and enhanced safety (greater inter-agent clearance, leading to reduced congestion) compared to a local baseline and NH-ORCA that do not reason about homotopies. Hardware trials with two robots and one human demonstrate robustness to irrational behaviors, where our method adapts by switching to alternative feasible equilibria while the baseline game fails.

Indoor wireless communication environments are strongly influenced by dynamic conditions, which affect channel state information (CSI) and, consequently, the precoding strategy and the selection of the access point (AP). Device-free sensing and localization functionalities can provide information about these conditions, including, for example, the user's position and the position of mobile blocking objects. To model the statistical relationship between the CSI and the provided conditions, we employ a conditional variational autoencoder (cVAE). We treat the user and object positions - referred to as context information - as conditional inputs to the cVAE. The proposed model does not rely on ground-truth CSI and is trained directly on noisy data. Once trained, the framework can infer channel statistics solely from user and blocking object positions, enabling proactive AP selection based on inferred statistical CSI without requiring continuous CSI estimation. Extensive simulations with the state-of-the-art ray-tracing tool Sionna validate the proposed method.

Network-controlled repeaters (NCRs) are a low-cost means to extend coverage and strengthen macro diversity in wireless networks. They operate in real time by amplifying and re-transmitting the incoming signal with only hardware-level delays, without requiring any channel state information (CSI) at the repeater itself. However, their power amplifiers (PAs) generate non-linear distortion that is jointly forwarded with the desired signal and can undermine multiuser performance unless the distortion statistics are exploited. This paper develops a distortion-aware (DA) uplink framework for repeater-assisted massive MIMO (RA-MIMO) under PA non-linearities. We adopt a memoryless third-order polynomial model for the repeater PA and characterize the achievable spectral efficiency (SE) using the Bussgang decomposition. Closed-form expressions are derived for the Bussgang gain matrix and the distortion covariance. We also design a DA combining vector that maximizes the effective signal-to-interference-plus-distortion ratio.

To date, various paradigms of soft-Computing have been used to solve many modern problems. Among them, a self organizing combination of fuzzy systems and neural networks can make a powerful decision making system. Here, a Dynamic Growing Fuzzy Neural Controller (DGFNC) is combined with an adaptive strategy and applied to a 3PSP parallel robot position control problem. Specifically, the dynamic growing mechanism is considered in more detail. In contrast to other self-organizing methods, DGFNC adds new rules more conservatively; hence the pruning mechanism is omitted. Instead, the adaptive strategy 'adapts' the control system to parameter variation. Furthermore, a sliding mode-based nonlinear controller ensures system stability. The resulting general control strategy aims to achieve faster response with less computation while maintaining overall stability. Finally, the 3PSP is chosen due to its complex dynamics and the utility of such approaches in modern industrial systems. Several simulations support the merits of the proposed DGFNC strategy as applied to the 3PSP robot.

This letter derives the noncoherent (NC) maximum likelihood (ML) detection rule for LoRa signals under Rician multi-path fading channel. The proposed NC-ML detection only requires the channel statistic, not the actual instantaneous channel state information (CSI), which eliminates the overhead associated with channel estimation. Simulation results show that despite the low-complexity, the proposed detection scheme significantly improves the performance of LoRa detection over multipath channel. Notably, in time-invariant channel, the NCML receiver can achieve an equivalently good performance as compared to existing coherent schemes, and even surpasses them when Doppler shift is present, while not relying on the channel estimation nor reference signal extracted from the preamble.

Data-driven reachability analysis using matrix zonotopes faces a fundamental challenge: the number of generators in the reachable set grows exponentially during propagation, while current order reduction yields overly conservative approximations in data-driven settings. This paper introduces an orthogonal matrix-based framework that appropriately transfers the coordinate system before reducing the generators of the reachable set, dramatically reducing reachable set volumes. By exploiting the factorized structure of data-driven matrix zonotope generators, we develop several efficient algorithms to solve the problem. Numerical experiments demonstrate order-of-magnitude volume reductions compared to traditional methods, while maintaining comparable generator numbers. Our method provides a practical solution to improve precision in data-driven safety verification.

This paper investigates the uplink capacity of single-input single-output (SISO) systems assisted by a swarm of network-controlled repeaters (NCRs). We develop a rigorous wideband formulation based on OFDM signaling. Starting from the continuous-time passband model, we derive the capacity expression for the repeater-assisted OFDM channel, accounting for amplified noise contributions from multiple repeaters. Numerical results demonstrate that NCRs can substantially enhance system capacity even with simple activation strategies, and that activating only the closest repeater yields nearly the same performance as activating all repeaters, thereby offering significant energy-saving opportunities. These findings highlight the potential of NCR swarms as a cost-effective and scalable solution for coverage extension and capacity enhancement in wideband wireless networks.

We consider uplink frugal simultaneous localization and mapping (SLAM) in phase-coherent distributed MIMO (D-MIMO) systems, where a network of spatially separated single-antenna access points (APs) coherently receives narrowband, single-snapshot pilot signals from a single-antenna user equipment (UE). In contrast to existing phase-coherent localization and SLAM methods that rely on wideband measurements and/or multi-antenna APs, the proposed frugal setting operates with the minimum possible localization resources: a single subcarrier and a single snapshot at each single-antenna AP. In this paper, we formulate phase-coherent frugal SLAM as a coherent imaging problem, constructing a spatial image over a region of interest by treating the distributed AP observations as coming from a large synthetic aperture. Based on the coherent image, we develop a detection and localization framework that jointly identifies the UE, reflective surfaces, and scatterers. Simulation results validate the proposed framework and provide insights into the impact of grid resolution and off-grid error on detection and localization performance.

Heart rate variability (HRV) analysis is important for the assessment of autonomic cardiovascular regulation. The inverse Gaussian process (IGP) has been widely used for beat-to-beat HRV modeling, as it gives a physiological relevant interpretation of heart depolarization process. A key challenge in IGP-based heartbeat modeling is the accurate estimation of time-varying parameters. In this study, we investigated whether recurrent neural networks (RNNs) can be used for IGP parameter identification and thereby enhance probabilistic modeling of R-R dynamics. Specifically, four representative RNN architectures, namely, GRU, LSTM, Structured State Space sequence model (S4), and Mamba, were evaluated using the Kolmogorov-Smirnov statistics. The results demonstrate the possibility of combining neural sequence models with the IGP framework for beat-wise R-R series modeling. This approach provides a flexible basis for probabilistic HRV modeling and for future incorporation of more complex physiological mechanisms and dynamic conditions.

The invariance principle, through which the steady-state behavior of nonlinear systems was introduced by Isidori and Byrnes, is leveraged in this article to bring forth a unifying characterization of the frequency response of nonlinear systems. We show that, for systems under nonlinear periodic excitations, the frequency response can still be defined as a complex-valued function in a phasor form. However, together with suitable notions of gain and phase functions, we show the existence of another function that completes the frequency response and allows quantifying the distortion introduced by the system in the steady-state output. This nonlinear characterization enabled the representation over input frequency and amplitude of the gain, phase, and distortion produced by the system, via a nonlinear enhancement of the Bode diagrams. This graphical representation of the frequency response is well-suited to performance analysis of a nonlinear system and, furthermore, allows for the formulation of the loop-shaping problem for nonlinear systems.

We propose a matrix zonotope perturbation framework that leverages matrix perturbation theory to characterize how noise-induced distortions alter the dynamics within sets of models. The framework derives interpretable Cai-Zhang bounds for matrix zonotopes (MZs) and extends them to constrained matrix zonotopes (CMZs). Motivated by this analysis and the computational burden of CMZ-based reachable-set propagation, we introduce a coefficient-space approximation in which the constrained coefficient space of the CMZ is over-approximated by an unconstrained zonotope. Replacing CMZ-constrained-zonotope (CZ) products with unconstrained MZ-zonotope multiplication yields a simpler and more scalable reachable-set update. Experimental results demonstrate that the proposed method is substantially faster than the standard CMZ approach while producing reachable sets that are less conservative than those obtained with existing MZ-based methods, advancing practical, accurate, and real-time data-driven reachability analysis.

Odrzywolek (2026) recently introduced the Exp-Minus-Log (EML) operator eml (x, y) = exp(x) - ln(y) and proved constructively that, paired with the constant 1, it generates the entire scientific-calculator basis of elementary functions; in this sense EML is to continuous mathematics what NAND is to Boolean logic. We investigate whether such a uniform single-operator representation can accelerate either the forward simulation or the parameter identification of a six-branch RC equivalent-circuit model (6rc ECM) of a lithium-ion battery cell. We give the analytical EML rewrite of the discretized state-space recursion, derive an exact operation count, and quantify the depth penalty of the master-formula construction used for gradient-based symbolic regression. Our analysis shows that direct EML simulation is slower than the classical exponential-Euler scheme (a ~ 25x instruction overhead per RC branch), but EML-based parametrization offers a structurally complete, gradient-differentiable basis that competes favourably with non-parametric DRT deconvolution and metaheuristic optimisation when the cardinality of RC branches is unknown a priori. We conclude with a concrete recommendation: use EML only on the parametrization side of the 6rc workflow, keeping the classical recursion at runtime.

The growing proliferation of unmanned aerial vehicles (UAVs) poses major challenges for reliable airspace surveillance, as drones are typically small, have low radar cross-sections, and often move slowly in cluttered environments. These characteristics make the joint tasks of detecting, localizing, and tracking multiple objects difficult for conventional detect-then-track (DTT) approaches, which rely on pre-processed measurements and may discard informative low-signal-to-noise ratio (SNR) signal components. To overcome these limitations, we propose a variational message passing (VMP)-based direct multiobject tracking (MOT) method that operates directly on raw radar signals and explicitly accounts for an unknown and time-varying number of objects. The proposed method is formulated for MIMO multi-radar systems and performs data fusion by jointly processing the signals of all radar sensors using a probabilistic model. A superimposed signal model is employed to capture correlations in the raw sensor data caused by closely spaced objects, and a hierarchical Bernoulli-Gamma model is introduced to jointly model object existence, reflectivities, and the reliability of individual radar-object links. Using a mean-field approximation, we derive message updates, yielding a computationally efficient VMP algorithm that simultaneously performs object detection, track formation, state estimation, and nuisance parameter learning directly from the radar signal. Simulation results in synthetic scenarios with weak and closely-spaced objects show that the proposed direct-MOT method outperforms a conventional pipeline based on super-resolution estimation followed by belief propagation (BP)-based tracking, particularly in low-SNR and clutter-rich conditions, demonstrating the advantages of direct signal-level inference and coherent multi-radar fusion.

Synchronized radio telescope-based experiments conducted since 2017, together with subsequent interferometer experiments, provide evidence of an anomalous source of 3.7 Hz bandwidth pulses, sourced from near the direction of the star Rigel. The current experiment, reported here, uses a two-element phase-measuring interferometer to monitor the hypothetical pulse source across azimuths within the beam-widths of the elements of a south-facing interferometer. 123 days of phase measurements of 3.7 Hz bandwidth pulse pairs, adds to the prior evidence that the pulsing signal source has celestial origin. Associated measurements of noise power in 954 Hz and 50 MHz bandwidths, made simultaneous with the 0.27 second duration pulse pair measurements, are presented. Measurement results are presented to aid in the development of independent experimental replication, and alternate and auxiliary explanatory hypotheses.

Future mobile networks must achieve substantial improvements in energy efficiency to offset the anticipated traffic growth. Despite this requirement, many discussions regarding physical layer design remain primarily focused on peak data rates and spectral efficiency, even though typical network operation is dominated by low-data-rate regimes. To address this mismatch, the Gearbox-PHY was proposed as an energy-efficient physical layer architecture that dynamically switches between modulation schemes and their associated analog front ends in order to adapt to varying operating requirements. This paper quantifies the achievable energy savings by jointly modeling front end power consumption and hardware-aware spectral efficiency to formulate an energy-per-bit minimization problem. To move beyond idealized assumptions, non-ideal hardware effects, including oscillator phase noise and limited quantizer resolution, are incorporated. These impairments simultaneously affect power consumption and achievable spectral efficiency, thereby introducing trade-offs between front end complexity, hardware non-linearities, spectral efficiency, and energy efficiency. Numerical results demonstrate that the Gearbox-PHY enables significant energy savings, particularly at low data rates. Evaluations with spatially distributed users confirm that gains of up to two orders of magnitude persist in a cellular deployment scenario.

The increasing penetration of Distributed Energy Resources (DERs), particularly electric vehicles, heat pumps, and photovoltaic systems, is fundamentally changing power flows in Low-Voltage (LV) distribution networks. Despite this transition, Distribution System Operators (DSOs) often lack reliable and up-to-date knowledge of the DER capacity connected downstream of LV substations. Limited observability, incomplete topology information, and restricted access to customer-level data make it difficult to maintain accurate DER registries, creating uncertainty in both operational and planning processes. This paper presents aggregated DER installed capacity, estimated at LV aggregation points, as a practical and scalable approach to improving DER awareness without requiring customer-level monitoring. We define the problem of estimating DER installed capacities from commonly available substation and feeder measurements. By linking these estimates to operational and planning needs, we discuss how knowledge of aggregated DER installed capacity enhances DER-aware forecasting, congestion management, flexibility quantification, hosting capacity assessment, and monitoring of DER adoption.

Crack segmentation on edge devices can support continuous infrastructure monitoring and maintenance and thereby help to preserve public safety. Furthermore, autonomous infrastructure monitoring by using Unmanned Aerial Vehicles (UAVs) can reduce inspection risks, as human operators no longer need to enter hazardous areas. Edge processing reduces the cost of inspection by eliminating the need for high resolution image storage for offline processing and mitigates the security risks and bandwidth requirements of streaming to cloud servers. Edge inference is difficult due to the limited memory and computational capabilities of edge devices, which can affect both accuracy and latency. Furthermore, battery-powered devices are subject to strict power and energy constraints. Together, these limitations impose restrictions on the model size and computational complexity that can be deployed close to the sensor. In recent years, Transformers have achieved state-of-the-art accuracy in a variety of applications, including semantic segmentation. However, Transformer-based models are typically large and computationally intensive, making efficient edge deployment difficult. To address this, we first apply knowledge distillation to enhance the performance of the base models. We then use PTQ to compress the models further. Additionally, we consider the deployment of these models across multiple edge platforms. To maximize energy efficiency, we design and implement a custom hardware architecture for the models on an FPGA. Our results show that Knowledge Distillation (KD) improves all tested U-Net variants. Among the evaluated platforms, the selected FPGA implementation achieves 398 FPS at 204.99 Frames/J while maintaining a mean IoU of 69.42%. In addition, our best model reaches 71.92% mean IoU, which is 8.82 percentage points (pps) higher than the previously reported result on the CrackVision12K dataset.

The rise of Artificial Intelligence (AI)-driven services, machine-type communications, and massive Internet of Things (IoT) deployments is reshaping wireless traffic toward dense, uplink-oriented, bursty, and latency-critical patterns. In these regimes, Multi-User Multiple-Input Multiple-Output (MU-MIMO) is essential to support massive concurrent connectivity through spatial multiplexing. However, the need for frequent, low-latency scheduling decisions exposes fundamental scalability barriers in existing user selection approaches. The inherently combinatorial nature of MU-MIMO user selection leads computational complexity to grow rapidly with both the number of candidate users and spatial layers, rendering existing near-optimal heuristic methods impractical in dense and highly dynamic scenarios. This paper introduces the Space Splitting-based User Selection (SS-US) algorithm, a complexity barrier-breaking, massively parallelizable method that departs from subset-based selection by constructing orthonormal spatial bases and independently matching users to spatial directions. Simulation results across diverse MIMO configurations, channel conditions, and user densities show that SS-US reduces computational complexity by over three orders of magnitude while achieving spectral efficiency comparable to state-of-the-art practical baselines.

Oil spills represent a severe threat, making early-stage thickness estimation crucial for guiding remediation efforts. Unmanned Aerial Vehicles (UAVs) are an attractive platform for environmental monitoring. However, due to their limited computation and power budgets, real-time onboard processing requires optimized algorithms or lightweight machine learning models. While the standard U-Net architecture is often too large for constrained UAV hardware, the compressed Tiny U-Net variant fits on FPGA platforms and achieves competitive estimation performance (0.79 in the metric Intersection over Union, or IoU). Despite this success, Tiny U-Net processes every radar image through the complete inference pipeline, resulting in unnecessary computation for simple cases. To address this inefficiency, we integrate an early exit feature into the Tiny U-Net architecture. We introduce an early exit branch that returns an early prediction when a compact confidence score exceeds a tunable threshold, bypassing deeper layers for high-confidence evaluations. Our experiments demonstrate that this design achieves comparable IoU to the full baseline model. Crucially, the technique is shown to reduce the average number of multiplications by up to 42% for an aggressive threshold, reducing the dynamic power consumption. Choosing a threshold that ensures extreme confidence reduces the complexity-reduction gains for an improved IoU. This early exit approach substantially improves computational efficiency in Tiny U-Net, enabling more practical deployment in UAV-based environmental monitoring systems.

Designing robust architectures that can mitigate sophisticated attacks is now a key priority for modern wireless systems. This paper investigates a single-cell bistatic integrated sensing and communication (ISAC) network facing simultaneous coordinated active jamming and malicious detection. These threats aim to disrupt the downlink communication and detect the presence of the ISAC target, respectively. To counter these attacks, we propose the SAFE-ISAC framework, which utilizes a simultaneous transmit and reflect reconfigurable intelligent surface (STAR-RIS) to jointly suppress jamming power and reduce the malicious detector's Signal-to-Interference-plus-Noise Ratio (SINR). We formulate a joint minimization problem for jamming gain and detection probability by optimizing the STAR-RIS reflection and transmission responses. This non-convex problem is decoupled into two subproblems: i) malicious detection mitigation in the transmission subspace, solved using the Dinkelbach method and Semidefinite Programming (SDP) relaxation, and ii) jamming suppression in the reflection subspace, addressed via Polak-Reibére Riemannian conjugate gradient algorithm. Numerical results validate that the proposed scheme effectively achieves jamming mitigation and target concealment while meeting all communication and sensing Quality-of-Service (QoS) requirements, compared to existing benchmarks.

In this paper, we propose a novel framework for the joint identification of system dynamics and noise covariance in linear systems, under general noise distributions beyond Gaussian. Specifically, we would like to simultaneously estimate the dynamical matrix $A$ and the noise covariance matrix $\varSigma$ using state transition data. The formulation builds upon a novel parameterization of the state-transition distribution, which enables more effective use of distributional "shape" information for improved identification accuracy. We introduce two practical estimators, namely the maximum likelihood estimator (MLE) and the score-matching estimator (SME), to solve the joint dynamics-covariance identification problem, and provide rigorous analysis of their statistical properties and sample complexity. Simulation results show that the proposed estimators outperform the ordinary least squares (OLS) baseline.

Real-world physics can only be analytically modeled with a certain level of precision for modern intricate robotic systems. As a result, tracking aggressive trajectories accurately could be challenging due to the existence of residual physics during controller synthesis. This paper presents a self-supervised residual learning and trajectory optimization framework to address the aforementioned challenges. At first, unknown dynamic effects on the closed-loop model are learned and treated as residuals of the nominal dynamics, jointly forming a hybrid model. We show that learning with analytic gradients can be achieved using only trajectory-level data while enjoying accurate long-horizon prediction with an arbitrary integration step size. Subsequently, a trajectory optimizer is developed to compute the optimal reference trajectory with the residual physics along it minimized. It ends up with trajectories that are friendly to the following control level. The agile flight of quadrotors illustrates that by utilizing the hybrid dynamics, the proposed optimizer outputs aggressive motions that can be precisely tracked.

Precise control in modern robotic applications is always an open issue due to unknown time-varying disturbances. Existing meta-learning-based approaches require a shared representation of environmental structures, which lack flexibility for realistic non-structural disturbances. Besides, representation error and the distribution shifts can lead to heavy degradation in prediction accuracy. This work presents a generalizable disturbance estimation framework that builds on meta-learning and feedback-calibrated online adaptation. By extracting features from a finite time window of past observations, a unified representation that effectively captures general non-structural disturbances can be learned without predefined structural assumptions. The online adaptation process is subsequently calibrated by a state-feedback mechanism to attenuate the learning residual originating from the representation and generalizability limitations. Theoretical analysis shows that simultaneous convergence of both the online learning error and the disturbance estimation error can be achieved. Through the unified meta-representation, our framework effectively estimates multiple rapidly changing disturbances, as demonstrated by quadrotor flight experiments. See the project page for video, supplementary material and code: this https URL.

Covert quantum communication is usually analyzed under idealized assumptions that channel parameters, such as transmissivity and background noise, are perfectly known and constant. In realistic optical links, including satellite, fiber, and free-space systems, these parameters vary because of environmental fluctuations, calibration noise, and estimation errors. We study covert quantum communication over compound quantum optical channels with bounded uncertainty in both transmissivity and thermal noise, and derive guarantees that hold for all admissible channel realizations. We develop a robust framework for certifying both covertness and reliability under uncertainty. A central finding is that robustness cannot be obtained by simply inserting worst-case parameter values into known-channel bounds: the channel realizations that are most adverse for covertness and reliability generally occur at different corners of the uncertainty set. This creates a fundamental trade-off in secure system design. We derive a closed-form lower bound on the worst-case guaranteed number of covert qubits that can be transmitted reliably, identify a sharp feasibility boundary beyond which the guaranteed payload drops to zero, and quantify the security penalty caused by uncertainty. We validate the covertness term with QuTiP simulations of a four-mode bosonic model and combine it with an analytical reliability bound to evaluate the robust payload. Our results move covert quantum communication from nominal perfect-knowledge analysis to certified worst-case operation under uncertainty.

Covert wireless communication aims to establish a reliable link while hiding the transmission from an adversary. In wireless settings, uncertainty plays a central role in this tradeoff: it can help mask the signal from a warden, but it also complicates robust system design. This raises a basic question: under bounded uncertainty, are reliability and covertness governed by the same adverse conditions? If not, robust covert design cannot be reduced to a single worst-case environment. In this paper, we study this question in a covert wireless model with quasi-static fading, outage-based reliability at Bob and radiometric detection at Willie. Uncertainty is represented through bounded intervals for Bob's average channel strength and Willie's noise power. To obtain a tractable characterization, we adopt a conditional large-N midpoint-threshold surrogate for Willie's detector, parameterized by a Willie-side fading realization. Within this framework, we show that the reliability constraint is governed by Bob's smallest admissible channel parameter, whereas the covertness constraint is governed by Willie's smallest admissible noise level. This establishes a conflict-aware robust-design principle: the adverse realizations for reliability and covertness differ. Based on this result, we derive closed-form expressions for the robustly feasible transmit power and the corresponding robust optimal rate. Numerical results show that bounded uncertainty contracts the feasible region, monotonically reduces the robust optimal rate, and can cause substantial loss relative to the nominal design. Monte Carlo results further show that the conditional surrogate closely tracks the midpoint-threshold radiometer in the intended low-effective-SNR regime. Overall, the paper shows that even in a streamlined wireless setting, robust covert design requires different adverse-case reasoning for reliability and covertness.

Human behavior in interactive settings is shaped not only by individual objectives but also by shared constraints with others, such as safety. Understanding how people allocate responsibility, i.e., how much one deviates from their desired policy to accommodate others, can inform the design of socially compliant and trustworthy autonomous systems. In this work, we introduce a method for learning a probabilistic responsibility allocation model that captures the multimodal uncertainty inherent in multi-agent interactions. Specifically, our approach leverages the latent space of a conditional variational autoencoder, combined with techniques from multi-agent trajectory forecasting, to learn a distribution over responsibility allocations conditioned on scene and agent context. Although ground-truth responsibility labels are unavailable, the model remains tractable by incorporating a differentiable optimization layer that maps responsibility allocations to induced controls, which are available. We evaluate our method on the INTERACTION driving dataset and demonstrate that it not only achieves strong predictive performance but also provides interpretable insights, through the lens of responsibility, into patterns of multi-agent interaction.

This paper presents HUANet, a constrained deep neural network architecture that unrolls the iterations of the Alternating Direction Method of Multipliers (ADMM) into a trainable neural network for solving constrained convex optimization problems. Existing end-to-end learning methods operate as black-box mappings from parameters to solutions, often lacking explicit optimality principles and failing to enforce constraints. To address this limitation, we unroll ADMM and embed a hard-constrained neural network at each iteration to accelerate the algorithm, where equality constraints are enforced via a differentiable correction stage at the network output. Furthermore, we incorporate first-order optimality conditions as soft constraints during training to promote the convergence of the proposed unrolled algorithm. Extensive numerical experiments are conducted to validate the effectiveness of the proposed architecture for constrained optimization problems.

Semiconductor failure analysis (FA) requires engineers to examine inspection images, correlate equipment telemetry, consult historical defect records, and write structured reports, a process that can consume several hours of expert time per case. We present SemiFA, an agentic multi-modal framework that autonomously generates structured FA reports from semiconductor inspection images in under one minute. SemiFA decomposes FA into a four-agent LangGraph pipeline: a DefectDescriber that classifies and narrates defect morphology using DINOv2 and LLaVA-1.6, a RootCauseAnalyzer that fuses SECS/GEM equipment telemetry with historically similar defects retrieved from a Qdrant vector database, a SeverityClassifier that assigns severity and estimates yield impact, and a RecipeAdvisor that proposes corrective process adjustments. A fifth node assembles a PDF report. We introduce SemiFA-930, a dataset of 930 annotated semiconductor defect images paired with structured FA narratives across nine defect classes, drawn from procedural synthesis, WM-811K, and MixedWM38. Our DINOv2-based classifier achieves 92.1% accuracy on 140 validation images (macro F1 = 0.917), and the full pipeline produces complete FA reports in 48 seconds on an NVIDIA A100-SXM4-40 GB GPU. A GPT-4o judge ablation across four modality conditions demonstrates that multi-modal fusion improves root cause reasoning by +0.86 composite points (1-5 scale) over an image-only baseline, with equipment telemetry as the more load-bearing modality. To our knowledge, SemiFA is the first system to integrate SECS/GEM equipment telemetry into a vision-language model pipeline for autonomous FA report generation.

Safe navigation for multi-robot systems requires enforcing safety without sacrificing task efficiency under decentralized decision-making. Existing decentralized methods often assume robot homogeneity, making shared safety requirements non-uniformly interpreted across heterogeneous agents with structurally different dynamics, which could lead to avoidance obligations not physically realizable for some robots and thus cause safety violations or deadlock. In this paper, we propose Capability-Aware Heterogeneous Control Barrier Function (CA-HCBF), a decentralized framework for consistent safety enforcement and capability-aware coordination in heterogeneous robot teams. We derive a canonical second-order control-affine representation that unifies holonomic and nonholonomic robots under acceleration-level control via canonical transformation and backstepping, preserving forward invariance of the safe set while avoiding relative-degree mismatch across heterogeneous dynamics. We further introduce a support-function-based directional capability metric that quantifies each robot's ability to follow its motion intent, deriving a pairwise responsibility allocation that distributes the safety burden proportionally to each robot's motion capability. A feasibility-aware clipping mechanism further constrains the allocation to each agent's physically achievable range, mitigating infeasible constraint assignments common in dense decentralized CBF settings. Simulations with up to 30 heterogeneous robots and a physical multi-robot demonstration show improved safety and task efficiency over baselines, validating real-world applicability across robots with distinct kinematic constraints.

This paper studies a stochastic algorithm for linearly constrained nonconvex optimization, where the objective function is smooth but only unbiased stochastic gradients with bounded variance are available. We propose a momentum-based augmented Lagrangian method that employs a Polyak-type gradient estimator and requires only one stochastic gradient evaluation per iteration. Under the standard stochastic oracle model and the smoothness condition of the expected objective, we establish a convergence guarantee in terms of the first-order KKT residual of the original constrained problem. In particular, the proposed method computes an $\epsilon$-stationary solution in expectation within $O(\epsilon^{-4})$ stochastic gradient evaluations. Numerical experiments further show that the proposed method achieves competitive iteration complexity and improved wall-clock efficiency compared with representative recursive-momentum baselines.

Aerial object detection in UAV imagery presents unique challenges due to the high prevalence of tiny objects, adverse environmental conditions, and strict computational constraints. Standard YOLO-based detectors fail to address these jointly: their minimum detection stride of 8 pixels renders sub-32px objects nearly undetectable, their CIoU loss produces zero gradients for non-overlapping tiny boxes, and their architectures contain significant filter redundancy. We propose DroneScan-YOLO, a holistic system contribution that addresses these limitations through four coordinated design choices: (1) increased input resolution of 1280x1280 to maximize spatial detail for tiny objects, (2) RPA-Block, a dynamic filter pruning mechanism based on lazy cosine-similarity updates with a 10-epoch warm-up period, (3) MSFD, a lightweight P2 detection branch at stride 4 adding only 114,592 parameters (+1.1%), and (4) SAL-NWD, a hybrid loss combining Normalized Wasserstein Distance with size-adaptive CIoU weighting, integrated into YOLOv8's TaskAligned assignment pipeline. Evaluated on VisDrone2019-DET, DroneScan-YOLO achieves 55.3% mAP@50 and 35.6% mAP@50-95, outperforming the YOLOv8s baseline by +16.6 and +12.3 points respectively, improving recall from 0.374 to 0.518, and maintaining 96.7 FPS inference speed with only +4.1% parameters. Gains are most pronounced on tiny object classes: bicycle AP@50 improves from 0.114 to 0.328 (+187%), and awning-tricycle from 0.156 to 0.237 (+52%).

The United States designates Food and Agriculture as one of sixteen critical infrastructure sectors, yet no mandatory cybersecurity requirements exist for agricultural operations and no formal threat model has been published for Controlled Environment Agriculture (CEA) systems. This paper presents the first comprehensive threat model for IoT-enabled CEA, applying STRIDE analysis, MITRE ATT&CK for ICS mapping, and IEC 62443 zone-and-conduit decomposition to a production platform deployed across 30+ commercial facilities in 8 U.S. climate zones. We enumerate 123 unique threats across 25 data-flow-diagram elements spanning 15 communication protocols, 10 of which operate with zero authentication or encryption by design. We identify five novel attack classes unique to AI-driven CEA: stealth destabilization of neural-network-tuned PID controllers, baseline drift poisoning of anomaly detectors, cross-facility propagation via federated transfer learning, adversarial agronomic schedules that exploit crop biology rather than computational models, and reward poisoning of reinforcement-learning energy optimizers. Physical impact analysis quantifies crop loss timelines from minutes (aeroponics) to days, including worker safety hazards from CO2 injection manipulation. A survey of 10 commercial CEA vendors reveals only one CVE ever issued, zero bug bounty programs, and zero IEC 62443 certifications. We propose a defense-in-depth countermeasure framework and recommend Security Level 2 as a minimum baseline.

Safety filters provide a practical approach for enforcing safety constraints in autonomous systems. While learning-based tools scale to high-dimensional systems, their performance depends on informative data that includes states likely to lead to constraint violation, which can be difficult to efficiently sample in complex, high-dimensional systems. In this work, we characterize trajectories that barely avoid safety violations using the Pontryagin Maximum Principle. These boundary trajectories are used to guide data collection for learned Hamilton-Jacobi Reachability, concentrating learning efforts near safety-critical states to improve efficiency. The learned Control Barrier Value Function is then used directly for safety filtering. Simulations and experimental validation on a shared-control automotive racing application demonstrate PMP sampling improves learning efficiency, yielding faster convergence, reduced failure rates, and improved safe set reconstruction, with wall times around 3ms.

Non-pharmaceutical interventions are critical for epidemic suppression but impose substantial societal costs, motivating feedback control policies that adapt to time-varying transmission. We formulate an infinite-horizon optimal control problem for a mobility-coupled networked SIQR epidemic model that minimizes isolation burden while enforcing epidemic suppression through a spectral decay condition. From this formulation, we derive a safety-critical Model Predictive Control (MPC) framework in which the spectral certificate is imposed as a hard stage-wise constraint, yielding a tunable exponential decay rate for infections. Exploiting the monotone depletion of susceptible populations, we construct a robust terminal set and safe backup policy. This structure ensures recursive feasibility and finite-horizon closed-loop exponential decay, and it certifies the existence of a globally stabilizing feasible continuation under bounded worst-case transmission rates. Numerical simulations on a 14-county Massachusetts network under a variant-induced surge show that, with administrative rate limits, reactive myopic control fails whereas MPC anticipates the shock and maintains exponential decay with lower isolation burden.

Biosignals exhibit substantial cross-subject and cross-session variability, inducing severe domain shifts that degrade post-deployment performance for small, edge-oriented AI models. On-device adaptation is therefore essential to both preserve user privacy and ensure system reliability. However, existing sub-100 mW MCU-based wearable platforms can only support shallow or sparse adaptation schemes due to the prohibitive memory footprint and computational cost of full backpropagation (BP). In this paper, we propose BioTrain, a framework enabling full-network fine-tuning of state-of-the-art biosignal models under milliwatt-scale power and sub-megabyte memory constraints. We validate BioTrain using both offline and on-device benchmarks on EEG and EOG datasets, covering Day-1 new-subject calibration and longitudinal adaptation to signal drift. Experimental results show that full-network fine-tuning achieves accuracy improvements of up to 35% over non-adapted baselines and outperforms last-layer updates by approximately 7% during new-subject calibration. On the GAP9 MCU platform, BioTrain enables efficient on-device training throughput of 17 samples/s for EEG and 85 samples/s for EOG models within a power envelope below 50 mW. In addition, BioTrain's efficient memory allocator and network topology optimization enable the use of a large batch size, reducing peak memory usage. For fully on-chip BP on GAP9, BioTrain reduces the memory footprint by 8.1x, from 5.4 MB to 0.67 MB, compared to conventional full-network fine-tuning using batch normalization with batch size 8.

Accurate modeling of robot dynamics is essential for model-based control, yet remains challenging under distributional shifts and real-time constraints. In this work, we formulate system identification as an in-context meta-learning problem and compare deterministic and generative sequence models for forward dynamics prediction. We take a Transformer-based meta-model, as a strong deterministic baseline, and introduce to this setting two complementary diffusion-based approaches: (i) inpainting diffusion (Diffuser), which learns the joint input-observation distribution, and (ii) conditioned diffusion models (CNN and Transformer), which generate future observations conditioned on control inputs. Through large-scale randomized simulations, we analyze performance across in-distribution and out-of-distribution regimes, as well as computational trade-offs relevant for control. We show that diffusion models significantly improve robustness under distribution shift, with inpainting diffusion achieving the best performance in our experiments. Finally, we demonstrate that warm-started sampling enables diffusion models to operate within real-time constraints, making them viable for control applications. These results highlight generative meta-models as a promising direction for robust system identification in robotics.

Accurate Global Horizontal Irradiance (GHI) forecasting is critical for grid stability, particularly in arid regions characterized by rapid aerosol fluctuations. While recent trends favor computationally expensive Transformer-based architectures, this paper challenges the prevailing "complexity-first" paradigm. We propose a lightweight, Physics-Informed Hybrid CNN-BiLSTM framework that prioritizes domain knowledge over architectural depth. The model integrates a Convolutional Neural Network (CNN) for spatial feature extraction with a Bi-Directional LSTM for capturing temporal dependencies. Unlike standard data-driven approaches, our model is explicitly guided by a vector of 15 engineered features including Clear-Sky indices and Solar Zenith Angle - rather than relying solely on raw historical data. Hyperparameters are rigorously tuned using Bayesian Optimization to ensure global optimality. Experimental validation using NASA POWER data in Sudan demonstrates that our physics-guided approach achieves a Root Mean Square Error (RMSE) of 19.53 W/m^2, significantly outperforming complex attention-based baselines (RMSE 30.64 W/m^2). These results confirm a "Complexity Paradox": in high-noise meteorological tasks, explicit physical constraints offer a more efficient and accurate alternative to self-attention mechanisms. The findings advocate for a shift towards hybrid, physics-aware AI for real-time renewable energy management.

Turbofan engine degradation under sustained operational stress necessitates robust prognostic systems capable of accurately estimating the Remaining Useful Life (RUL) of critical components. Existing deep learning approaches frequently fail to simultaneously capture multi-sensor spatial correlations and long-range temporal dependencies, while standard symmetric loss functions inadequately penalize the safety-critical error of over-estimating residual life. This study proposes a hybrid architecture integrating Twin-Stage One-Dimensional Convolutional Neural Networks (1D-CNN), a Bidirectional Long Short-Term Memory (BiLSTM) network, and a custom Bahdanau Additive Attention mechanism. The model was trained and evaluated on the NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) FD001 sub-dataset employing a zero-leakage preprocessing pipeline, piecewise-linear RUL labeling capped at 130 cycles, and the NASA-specified asymmetric exponential loss function that disproportionately penalizes over-estimation to enforce industrial safety constraints. Experiments on 100 test engines achieved a Root Mean Squared Error (RMSE) of 17.52 cycles and a NASA S-Score of 922.06. Furthermore, extracted attention weight heatmaps provide interpretable, per-engine insights into the temporal progression of degradation, supporting informed maintenance decision-making. The proposed framework demonstrates competitive performance against established baselines and offers a principled approach to safe, interpretable prognostics in industrial settings.

Ultrasonic metal welding (UMW) is widely used in industrial applications but is sensitive to tool wear, surface contamination, and material variability, which can lead to unexpected process faults and unsatisfactory weld quality. Conventional monitoring systems typically rely on supervised learning models that assume all fault types are known in advance, limiting their ability to handle previously unseen process faults. To address this challenge, this paper proposes an adaptive condition monitoring approach that enables unknown fault detection and few-shot continual learning for UMW. Unknown faults are detected by analyzing hidden-layer representations of a multilayer perceptron and leveraging a statistical thresholding strategy. Once detected, the samples from unknown fault types are incorporated into the existing model through a continual learning procedure that selectively updates only the final layers of the network, which enables the model to recognize new fault types while preserving knowledge of existing classes. To accelerate the labeling process, cosine similarity transformation combined with a clustering algorithm groups similar unknown samples, thereby reducing manual labeling effort. Experimental results using a multi-sensor UMW dataset demonstrate that the proposed method achieves 96% accuracy in detecting unseen fault conditions while maintaining reliable classification of known classes. After incorporating a new fault type using only five labeled samples, the updated model achieves 98% testing classification accuracy. These results demonstrate that the proposed approach enables adaptive monitoring with minimal retraining cost and time. The proposed approach provides a scalable solution for continual learning in condition monitoring where new process conditions may constantly emerge over time and is extensible to other manufacturing processes.

Electricity is typically traded in day-ahead auctions because many power system decisions, such as unit commitment, must be made in advance. However, when wind and solar generators sell power one day ahead, they face uncertainty about their actual production. In current day-ahead auctions, this uncertainty cannot be directly communicated, leading to inefficient use of renewable energy and suboptimal system decisions. We show how this problem can be addressed using the concept of equilibrium under uncertainty from microeconomic theory. In particular, we demonstrate that electricity contracts should be conditioned not only on the time and location of delivery, but also on the state of the world (e.g., whether it will be windy or calm). This requires a precise definition of the state of the world. Since there are infinitely many possible definitions, criteria are needed to select among them. We develop such criteria and show that the resulting states correspond to solutions of an optimal partitioning problem. Finally, we illustrate how these states can be computed and interpreted using a case study of offshore wind farms in the European North Sea.

During the first superior conjunction of the Tianwen-1 Mars probe in October 2021, its downlink signal received by the Wuqing 70-m radio telescope passed within 4.53 solar radii of the Sun. The signal was significantly perturbed by the solar wind, providing a mechanism to probe coronal activity. We analyze the Doppler frequency scintillation spectrum of the solar wind within 10 solar radii to derive a characteristic frequency scintillation parameter. Statistical analysis indicates this parameter increases as the signal path approaches the Sun, with notable anomalies observed on October 5, 13, and 15. Comparisons with SOHO and SDO data reveal strong spatio-temporal correlations between these scintillation anomalies and coronal activity. We demonstrate that this parameter effectively identifies solar phenomena, including coronal streamers, high-speed solar wind, and coronal mass ejections (CMEs). Quantitative analysis confirms a distinct temporal correlation and delay between frequency scintillation and solar wind speed changes, validating the feasibility of spatially localizing solar activity.

Mobile robots joining public spaces like sidewalks must care for pedestrian comfort. Many studies consider pedestrians' objective safety, for example, by developing collision avoidance algorithms, but not enough studies take the pedestrian's subjective safety or comfort into consideration. Quantifying comfort is a major challenge that hinders mobile robots from understanding and responding to human emotions. We empirically look into the relationship between the mobile robot-pedestrian interaction kinematics and subjective comfort. We perform one-on-one experimental trials, each involving a mobile robot and a volunteer. Statistical analysis of pedestrians' reported comfort versus the kinematic variables shows moderate but significant correlations for most variables. Based on these empirical findings, we design three comfort estimators/predictors derived from the minimum distance, the minimum projected time-to-collision, and a composite estimator. The composite estimator employs all studied kinematic variables and reaches the highest prediction rate and classifying performance among the predictors. The composite predictor has an odds ratio of 3.67. In simple terms, when it identifies a pedestrian as comfortable, it is almost 4 times more likely that the pedestrian is comfortable rather than uncomfortable. The study provides a comfort quantifier for incorporating pedestrian feelings into path planners for more socially compliant robots.

Deep neural networks (DNNs) deliver state-of-the-art accuracy on regression and classification tasks, yet two structural deficits persistently obstruct their deployment in safety-critical, resource-constrained settings: (i) opacity of the learned function, which precludes formal verification, and (ii) reliance on heterogeneous, library-bound activation functions that inflate latency and silicon area on edge hardware. The recently introduced Exp-Minus-Log (EML) Sheffer operator, eml(x, y) = exp(x) - ln(y), was shown by Odrzywolek (2026) to be sufficient - together with the constant 1 - to express every standard elementary function as a binary tree of identical nodes. We propose to embed EML primitives inside conventional DNN architectures, yielding a hybrid DNN-EML model in which the trunk learns distributed representations and the head is a depth-bounded, weight-sparse EML tree whose snapped weights collapse to closed-form symbolic sub-expressions. We derive the forward equations, prove computational-cost bounds, analyse inference and training acceleration relative to multilayer perceptrons (MLPs) and physics-informed neural networks (PINNs), and quantify the trade-offs for FPGA/analog deployment. We argue that the DNN-EML pairing closes a literature gap: prior neuro-symbolic and equation-learner approaches (EQL, KAN, AI-Feynman) work with heterogeneous primitive sets and do not exploit a single hardware-realisable Sheffer element. A balanced assessment shows that EML is unlikely to accelerate training, and on commodity CPU/GPU it is also unlikely to accelerate inference; however, on a custom EML cell (FPGA logic block or analog circuit) the asymptotic latency advantage can reach an order of magnitude with simultaneous gain in interpretability and formal-verification tractability.

Automated driving at unsignalized intersections is challenging due to complex multi-vehicle interactions and the need to balance safety and efficiency. Model Predictive Control (MPC) offers structured constraint handling through optimization but relies on hand-crafted rules that often produce overly conservative behavior. Deep Reinforcement Learning (RL) learns adaptive behaviors from experience but often struggles with safety assurance and generalization to unseen environments. In this study, we present an integrated MPC-RL framework to improve navigation performance in multi-agent scenarios. Experiments show that MPC-RL outperforms standalone MPC and end-to-end RL across three traffic-density levels. Collectively, MPC-RL reduces the collision rate by 21% and improves the success rate by 6.5% compared to pure MPC. We further evaluate zero-shot transfer to a highway merging scenario without retraining. Both MPC-based methods transfer substantially better than end-to-end PPO, which highlights the role of the MPC backbone in cross-scenario robustness. The framework also shows faster loss stabilization than end-to-end RL during training, which indicates a reduced learning burden. These results suggest that the integrated approach can improve the balance between safety performance and efficiency in multi-agent intersection scenarios, while the MPC component provides a strong foundation for generalization across driving environments. The implementation code is available open-source.

A rigid motion in $\mathbb{R}^d$ consists of a proper rotation and a translation, and it can be represented as a matrix in $\mathbb{R}^{(d+1)\times (d+1)}$. The problem of rigid motion synchronization aims to estimate a collection of rigid motions $G^*_1, \dots, G^*_n$ from noisy observations of their comparisons ${G^*_i}^{-1} G^*_j$. Such problems naturally arise in diverse applications across signal processing, robotics, and computer vision, and have thus attracted intense research attention in recent years. Motivated by geometric considerations, this paper develops a novel spectral approach for rigid motion synchronization, called the anchored spectral estimator (ASE). Theoretically, we establish uniform estimation error bounds for the estimators produced by ASE. Empirically, we show that ASE outperforms the widely used two-stage approach, which first estimates the rotations and then the translations. Further numerical experiments on the multiple point-set registration problem are presented to demonstrate the superiority of ASE over state-of-the-art methods.

We promote in this paper the processing of radar data in the frequency domain to achieve higher robustness against noise and structural errors, especially in comparison to feature-based methods. This holds also for high dynamics in the scene, i.e., ego-motion of the vehicle with the sensor plus the presence of an unknown number of other moving objects. In addition to the high robustness, the processing in the frequency domain has the so far neglected advantage that the underlying correlation based methods used for, e.g., registration, provide information about all moving structures in the scene. A typical automotive application case is overtaking maneuvers, which in the context of autonomous racing are used here as a motivating example. Initial experiments and results with Fourier SOFT in 2D (FS2D) are presented that use the Boreas dataset to demonstrate radar-only-odometry, i.e., radar-odometry without sensor-fusion, to support our arguments.

A mode sorter separates a set of M orthogonal spatial modes in a shared input channel into M different output channels. Here we present an analytic derivation and experimental validation of a single plane device for sorting spatial modes from a diverse variety of mode families, including Hermite-Gaussian (HG), Laguerre-Gaussian (LG), Bessel-Gaussian (BG), with almost no cross-talk. This sorting capability is required for a wide range of applications that employ classical or quantum light. We also show that applying this design in order to sort a set of Orbital Angular Momentum (OAM) modes with zero radial index reproduces the well-known Fork grating configuration. Furthermore, by taking the limit of M -> inf, we present an analytical expression for sorting all the modes of a given family. By operating this device in reverse, it can be used to generate arbitrary modes, by illuminating it with a Gaussian beam. The power transmission coefficient for this sorter goes as 1/M and we provide a mathematical proof that this is optimal for any typical arrangement of the detector positions. We further study the sorter sensitivity to wavelength and random phase noise.

Satellite dynamics and tracking remain important challenges in the context of space exploration and communication systems. Accurate state estimation is essential to maintain reliable orbital motion and system performance. This paper presents a mathematical framework for satellite state estimation based on a linearized model described by radial and angular states. The model incorporates two types of measurement noise corresponding to range and scaled angular deviations, which are assumed to be mutually independent with known covariance structures. The estimation problem is formulated using the Kalman filter, together with the associated Algebraic Riccati Equation (ARE), leading to both time-varying and steady-state solutions. In addition, a micro-Kalman filter ($\mu$KF) formulation is considered and compared with the classical Kalman filter, as well as with the extended Kalman filter (EKF), unscented Kalman filter (UKF), and an adaptive Kalman filter under a unified simulation setup. The results demonstrate that the proposed $\mu$KF achieves estimation performance nearly identical to that of the classical Kalman filter and its variants, with small and bounded estimation errors. The mean square estimation error (MSEE) remains low for all state variables under both noise configurations, confirming the effectiveness of the proposed approach for linear Gaussian systems.

Positron emission tomography (PET) provides molecular biomarkers for Alzheimer's disease and related dementias (ADRD) and is increasingly used for diagnosis, staging, and clinical trial enrichment. However, its use is limited by cost, regulatory restrictions, and the invasiveness of radiotracer injection. Although current frameworks emphasize multimodal biomarker assessment, including the amyloid/tau/neurodegeneration (A/T/N) scheme, these barriers constrain access to PET imaging. Cross-modal image synthesis may help address this gap by reconstructing unavailable modalities from routine scans. Because PET is clinically valuable for regional uptake patterns rather than exact voxel-wise intensities, perceptual losses that capture higher-level semantic features are well suited to PET synthesis. Existing 2D, 3D, and 2.5D perceptual losses for 3D synthesis each have limitations, including restricted volumetric context, scarcity of pretrained 3D models, and difficulty balancing optimization across anatomical planes. In this study, we synthesize tau PET from structural MRI by generating 3D pseudo-[18F]flortaucipir standardized uptake value ratio (SUVR) maps from 3D T1-weighted MR images. We propose a cyclic 2.5D perceptual loss that alternates optimization across axial, coronal, and sagittal planes during training to improve volumetric consistency. We also standardize PET SUVRs by scanner manufacturer, reducing inter-manufacturer variability and better preserving high-uptake regions. Using cohorts spanning the ADRD spectrum from the ADNI and the SCAN cohort, we show that the method generalizes across U-Net, UNETR, SwinUNETR, CycleGAN, and Pix2Pix, with strong performance. Notably, it improves agreement between synthesized SUVRs and measured PET in brain regions relevant to Alzheimer-type tau pathology. Code is publicly available at this https URL.

In this paper, we perform asymptotic analyses of the widely used ESPRIT direction-of-arrival (DoA) estimator for large arrays, where the array size $N$ and the number of snapshots $T$ grow to infinity at the same pace. In this large-dimensional regime, the sample covariance matrix (SCM) is known to be a poor eigenspectral estimator of the population covariance. We show that the classical ESPRIT algorithm, that relies on the SCM, and as a consequence of the large-dimensional inconsistency of the SCM, produces inconsistent DoA estimates as $N,T \to \infty$ with $N/T \to c \in (0,\infty)$, for both widely-~and~closely-spaced DoAs. Leveraging tools from random matrix theory (RMT), we propose an improved G-ESPRIT method and prove its consistency in the same large-dimensional setting. From a technical perspective, we derive a novel bound on the eigenvalue differences between two potentially non-Hermitian matrices, which may be of independent interest. Numerical simulations are provided to corroborate our theoretical findings.

We consider the problem of sampling from a product-of-experts-type model that encompasses many standard prior and posterior distributions commonly found in Bayesian imaging. We show that this model can be easily lifted into a novel latent variable model, which we refer to as a Gaussian latent machine. This leads to a general sampling approach that unifies and generalizes many existing sampling algorithms in the literature. Most notably, it yields a highly efficient and effective two-block Gibbs sampling approach in the general case, while also specializing to direct sampling algorithms in particular cases. Finally, we present detailed numerical experiments that demonstrate the efficiency and effectiveness of our proposed sampling approach across a wide range of prior and posterior sampling problems from Bayesian imaging.

This paper proposes a neural stochastic optimization method for efficiently solving the two-stage stochastic unit commitment (2S-SUC) problem under high-dimensional uncertainty scenarios. The proposed method approximates the second-stage recourse problem using a deep neural network trained to map commitment decisions and uncertainty features to recourse costs. The trained network is subsequently embedded into the first-stage UC problem as a mixed-integer linear program (MILP), allowing for explicit enforcement of operational constraints while preserving the key uncertainty characteristics. A scenario-embedding network is employed to enable dimensionality reduction and feature aggregation across arbitrary scenario sets, serving as a data-driven scenario reduction mechanism. Numerical experiments on IEEE 5-bus, 30-bus, and 118-bus systems demonstrate that the proposed neural two-stage stochastic optimization method achieves solutions with an optimality gap of less than 1%, while enabling orders-of-magnitude speedup compared to conventional MILP solvers and decomposition-based methods. Moreover, the model's size remains constant regardless of the number of scenarios, offering significant scalability for large-scale stochastic unit commitment problems.

Two-stage stochastic unit commitment (2S-SUC) problems have been widely adopted to manage the uncertainties introduced by high penetrations of intermittent renewable energy resources. While decomposition-based algorithms such as column-and-constraint generation has been proposed to solve these problems, they remain computationally prohibitive for large-scale, real-time applications. In this paper, we introduce a Neural Column-and-Constraint Generation (Neural CCG) method to significantly accelerate the solution of 2S-SUC problems. The proposed approach integrates a neural network that approximates the second-stage recourse problem by learning from high-level features of operational scenarios and the first-stage commitment decisions. This neural estimator is embedded within the CCG framework, replacing repeated subproblem solving with rapid neural evaluations. We validate the effectiveness of the proposed method on the IEEE 118-bus system. Compared to the original CCG and a state-of-the-art commercial solver, Neural CCG achieves up to 130.1$\times$ speedup while maintaining a mean optimality gap below 0.096\%, demonstrating its strong potential for scalable stochastic optimization in power system.

The rapid development of the low-altitude economy has imposed unprecedented demands on wireless infrastructure to accommodate large-scale drone deployments and facilitate intelligent services in dynamic airspace environments. However, unlocking its full potential in practical applications presents significant challenges. Traditional aerial systems predominantly focus on air-ground communication services, often neglecting the integration of sensing, computation, control, and energy-delivering functions, which hinders the ability to meet diverse mission-critical demands. Besides, the absence of systematic low-altitude airspace planning and management exacerbates issues regarding dynamic interference in three-dimensional space, coverage instability, and scalability. To overcome these challenges, a comprehensive framework, termed low-altitude wireless network (LAWN), has emerged to seamlessly integrate communication, sensing, computation, control, and air traffic management into a unified design. This article provides a comprehensive overview of LAWN systems, introducing LAWN system fundamentals and the evolution of functional designs. Subsequently, we delve into performance evaluation metrics and review critical concerns surrounding privacy and security in the open-air network environment. Finally, we present the cutting-edge developments in airspace structuring and air traffic management, providing insights to facilitate the practical deployment of LAWNs.

This paper presents a general framework for solving the control allocation problem (CAP) in thrust-vector controlled rigid-bodies with an arbitrary number of thrusters. Two novel solutions are proposed: a closed-form, Lipschitz continuous mapping that ensures smooth actuator orientation references, and a convex optimization formulation capable of handling practical actuator constraints such as thrust saturation and angular rate limits. Both methods leverage the nullspace structure of the allocation mapping to perform singularity avoidance while generating sub-optimal yet practical solutions. The effectiveness and generality of the proposed framework are demonstrated through numerical examples on a marine vessel and an aerial quadcopter.

The increasing integration of intermittent distributed energy resources (DERs) has introduced significant variability in distribution networks, posing challenges to voltage regulation and reactive power management. This paper presents a novel neural two-stage stochastic Volt-VAR optimization (2S-VVO) method for three-phase unbalanced distribution systems considering network reconfiguration under uncertainty. To address the computational intractability associated with solving large-scale scenario-based 2S-VVO problems, a learning-based acceleration strategy is introduced, wherein the second-stage recourse model is approximated by a neural network. This neural approximation is embedded into the optimization model as a mixed-integer linear program (MILP), enabling effective enforcement of operational constraints related to the first-stage decisions. Numerical simulations on a 123-bus unbalanced distribution system demonstrate that the proposed approach achieves over 50 times speedup compared to conventional solvers and decomposition methods, while maintaining a typical optimality gap below 0.30%. These results underscore the method's efficacy and scalability in addressing large-scale stochastic VVO problems under practical operating conditions.

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.

Standard model-based control design deteriorates when the system dynamics change during operation. To overcome this challenge, online and adaptive methods have been proposed in the literature. In this work, we consider the class of discrete-time linear systems with unknown time-varying parameters. We propose a simple, modular, and computationally tractable approach by combining two classical and well-known building blocks from estimation and control: the least mean square filter and the certainty-equivalent linear quadratic regulator. Despite both building blocks being simple and off-the-shelf, our analysis shows that they can be seamlessly combined to a powerful pipeline with stability guarantees. Namely, finite-gain $\ell^2$-stability of the closed-loop interconnection of the unknown system, the parameter estimator, and the controller is proven, despite the presence of unknown disturbances and time-varying parametric uncertainties. Real-world applicability of the proposed algorithm is showcased by simulations carried out on a nonlinear planar quadrotor.

Actuator amplitude and rate saturation (A\&RSat), together with their consequent windup problem, have long been recognised as challenges in control systems. Anti-windup (AW) solutions have been developed over the past decades, which can generally be categorised into two main groups: classical and modern anti-windup (CAW and MAW) approaches. Classical methods have provided simple and effective results, mainly addressing amplitude saturation. In contrast, modern approaches offer powerful and theoretically sound solutions capable of handling both amplitude and rate saturations. However, MAW's derivation process often imposes restrictive conditions and can be complex to apply in practical engineering problems. Nevertheless, the literature has paid limited attention (if not entirely ignored) to the potential of simple yet effective CAW schemes that can operate in the presence of both A\&RSat elements. This paper revisits this issue and proposes modifications to two well-known controllers: PID and LQI. The obtained results, benchmarked on the REMUS AUV yaw control problem and compared with constrained MPC, indicate that these classical techniques can still provide simple yet effective solutions with comparable performance, at least for SISO systems. These findings may stimulate further research into solutions that achieve comparable performance with only one (or a limited number of) additional tuning parameters and straightforward implementation.

We study distributed hypothesis testing under a covertness constraint in the non-alert situation, which requires that under the null-hypothesis an external warden be unable to detect whether communication between the sensor and the decision center is taking place. We characterize the achievable Stein exponent of this setup when the channel from the sensor to the decision center is a partially-connected discrete memoryless channel (DMC), i.e., when certain output symbols can only be induced by some of the inputs. The Stein-exponent in this case, does not depend on the specific transition law of the DMC and equals Shalaby and Papamarcou's exponent without a warden but where the sensor can send $k$ noise-free bits to the decision center, for $k$ a function that is sublinear in the observation length $n$. For fully-connected DMCs, we propose an achievable Stein-exponent and show that it can improve over the local exponent at the decision center. All our coding schemes do not require that the sensor and decision center share a common secret key, as commonly assumed in covert communication. Moreover, in our schemes the divergence covertness constraint vanishes (almost) exponentially fast in the obervation length $n$, again, an atypical behaviour for covert communication.

Audio Foundation Models (AFMs), a specialized category of Generative AI (GenAI), have the potential to transform signal processing (SP) education by integrating core applications such as speech and audio enhancement, denoising, source separation, feature extraction, automatic classification, and real-time signal analysis into learning and research. This paper introduces SPEduAFM, a conceptual AFM tailored for SP education, bridging traditional SP principles with GenAI-driven innovations. Through an envisioned case study, we outline how AFMs can enable a range of applications, including automated lecture transcription, interactive demonstrations, and inclusive learning tools, showcasing their potential to transform abstract concepts into engaging, practical experiences. This paper also addresses challenges such as ethics, explainability, and customization by highlighting dynamic, real-time auditory interactions that foster experiential and authentic learning. By presenting SPEduAFM as a forward-looking vision, we aim to inspire broader adoption of GenAI in engineering education, enhancing accessibility, engagement, and innovation in the classroom and beyond.

The increasing demand for ubiquitous, highcapacity mobile connectivity has driven cellular systems to explore beyond-terrestrial deployments. In this paper, we present a system-level performance evaluation of fifth-generation (5G) non-terrestrial network (NTN) enabled by high-altitude platform station (HAPS)-based base stations (BSs) equipped with tri-sectoral reflector antennas against fourth-generation (4G) terrestrial network (TN) and 5G TN deployments in a multicell dense urban environment. Using the simulation results comprising the average effective downlink signal-to-interference-plus-noise ratio (SINR) and the average user throughput, along with the subsequent interference analysis, we demonstrate that the reflector-based HAPS architecture is primarily constrained by inter-cell interference, while the combination of reflector configuration and deployment altitude represents a key design parameter.

Arc-fault circuit interrupters (AFCIs) are essential for mitigating fire hazards in residential photovoltaic (PV) systems, yet achieving reliable DC arc-fault detection under real-world conditions remains challenging. Spectral interference from inverter switching, hardware heterogeneity, operating-condition drift, and environmental noise collectively compromise conventional AFCI solutions. This paper proposes a lightweight, transferable, and self-adaptive learning-driven framework (LD-framework) for intelligent DC arc-fault detection. At the device level, LD-Spec learns compact spectral representations enabling efficient on-device inference and near-perfect arc discrimination. Across heterogeneous inverter platforms, LD-Align performs cross-hardware representation alignment to ensure robust detection despite hardware-induced distribution shifts. To address long-term evolution, LD-Adapt introduces a cloud-edge collaborative self-adaptive updating mechanism that detects unseen operating regimes and performs controlled model evolution. Extensive experiments involving over 53,000 labeled samples demonstrate near-perfect detection, achieving 0.9999 accuracy and 0.9996 F1-score. Across diverse nuisance-trip-prone conditions, including inverter start-up, grid transitions, load switching, and harmonic disturbances, the method achieves a 0% false-trip rate. Cross-hardware transfer shows reliable adaptation using only 0.5%-1% labeled target data while preserving source performance. Field adaptation experiments demonstrate recovery of detection precision from 21% to 95% under previously unseen conditions. These results indicate that the LD-framework enables a scalable, deployment-oriented AFCI solution maintaining highly reliable detection across heterogeneous devices and long-term operation.

This paper presents the design and implementation of an asynchronous delta modulator as a spike encoder for event-driven neural recording in a 65nm CMOS process. The proposed neuromorphic front-end converts analog signals into discrete, asynchronous ON and OFF spikes, effectively compressing continuous biopotentials into spike trains compatible with spiking neural networks (SNNs). Its asynchronous operation enables seamless integration with neuromorphic architectures for real-time decoding in closed-loop brain-machine interfaces (BMIs). Measurement results from silicon demonstrate an energy consumption of 60.73 nJ/spike, an F1-score of 80% compared to a behavioral model of the asynchronous delta modulator, and a compact pixel area of 73.45 um $\times$ 73.64 um.

Accurate material recognition is a fundamental capability for intelligent perception systems to interact safely and effectively with the physical world. For instance, distinguishing visually similar objects like glass and plastic cups is critical for safety but challenging for vision-based methods due to specular reflections, transparency, and visual deception. While millimeter-wave (mmWave) radar offers robust material sensing regardless of lighting, existing camera-radar fusion methods are limited to closed-set categories and lack semantic interpretability. In this paper, we introduce VLMaterial, a training-free framework that fuses vision-language models (VLMs) with domain-specific radar knowledge for physics-grounded material identification. First, we propose a dual-pipeline architecture: an optical pipeline uses the segment anything model and VLM for material candidate proposals, while an electromagnetic characterization pipeline extracts the intrinsic dielectric constant from radar signals via an effective peak reflection cell area (PRCA) method and weighted vector synthesis. Second, we employ a context-augmented generation (CAG) strategy to equip the VLM with radar-specific physical knowledge, enabling it to interpret electromagnetic parameters as stable references. Third, an adaptive fusion mechanism is introduced to intelligently integrate outputs from both sensors by resolving cross-modal conflicts based on uncertainty estimation. We evaluated VLMaterial in over 120 real-world experiments involving 41 diverse everyday objects and 4 typical visually deceptive counterfeits across varying environments. Experimental results demonstrate that VLMaterial achieves a recognition accuracy of 96.08%, delivering performance on par with state-of-the-art closed-set benchmarks while eliminating the need for extensive task-specific data collection and training.

We study the convex hulls of reachable sets of nonlinear systems with bounded disturbances and uncertain initial conditions. Reachable sets play a critical role in control, but remain notoriously challenging to compute, and existing over-approximation tools tend to be conservative or computationally expensive. In this work, we characterize the convex hulls of reachable sets as the convex hulls of solutions of an ordinary differential equation with initial conditions on the sphere. This finite-dimensional characterization unlocks an efficient sampling-based estimation algorithm to accurately over-approximate reachable sets. We also study the structure of the boundary of the reachable convex hulls and derive error bounds for the estimation algorithm. We give applications to neural feedback loop analysis and robust MPC.

The Koopman operator provides a powerful framework for representing the dynamics of general nonlinear dynamical systems. However, existing data-driven approaches to learning the Koopman operator rely on batch data. In this work, we present a sparse online learning algorithm that learns the Koopman operator iteratively via stochastic approximation, with explicit control over model complexity and provable convergence guarantees. Specifically, we study the Koopman operator via its action on the reproducing kernel Hilbert space (RKHS), and address the mis-specified scenario where the dynamics may escape the chosen RKHS. In this mis-specified setting, we relate the Koopman operator to the conditional mean embeddings (CME) operator. We further establish both asymptotic and finite-time convergence guarantees for our learning algorithm in mis-specified setting, with trajectory-based sampling where the data arrive sequentially over time. Numerical experiments demonstrate the algorithm's capability to learn unknown nonlinear dynamics.

The availability of continuous glucose monitors as over-the-counter commodities have created a unique opportunity to monitor a person's blood glucose levels, forecast blood glucose trajectories and provide automated interventions to prevent devastating chronic complications that arise from poor glucose control. However, forecasting blood glucose levels is challenging because blood glucose changes consistently in response to food intake, medication intake, physical activity, sleep, and stress. It is particularly difficult to accurately predict BGL from multimodal and irregularly sampled data and over long prediction horizons. Furthermore, these forecasting models must operate in real-time on edge devices to provide in-the-moment interventions. To address these challenges, we propose GlucoNet, an AI-powered sensor system for continuously monitoring behavioral and physiological health and robust forecasting of blood glucose patterns. GlucoNet devises a feature decomposition-based transformer model that incorporates patients' behavioral and physiological data and transforms sparse and irregular patient data (e.g., diet and medication intake data) into continuous features using a mathematical model, facilitating better integration with the BGL data. Given the non-linear and non-stationary nature of BG signals, we propose a decomposition method to extract both low and high-frequency components from the BGL signals, thus providing accurate forecasting. To reduce the computational complexity, we also propose to employ knowledge distillation to compress the transformer model. GlucoNet achieves a 60% improvement in RMSE and a 21% reduction in the number of parameters, improving RMSE and MAE by 51% and 57%, using data obtained involving 12 participants with T1-Diabetes. These results underscore GlucoNet's potential as a compact and reliable tool for real-world diabetes prevention and management.

Federated learning (FL) has emerged as a promising framework for distributed learning, enabling collaborative model training without sharing private data. Existing wireless FL works primarily adopt two communication strategies: (1) over-the-air (OTA) computation, which exploits wireless signal superposition for simultaneous gradient aggregation, and (2) digital communication, which allocates orthogonal resources for gradient uploads. Prior works on both schemes typically assume \emph{homogeneous} wireless conditions (equal path loss across devices) to enforce zero-bias updates or permit uncontrolled bias, resulting in suboptimal performance and high-variance model updates in \emph{heterogeneous} environments, where devices with poor channel conditions slow down convergence. This paper addresses FL over heterogeneous wireless networks by proposing novel OTA and digital FL updates that allow a structured, time-invariant model bias, thereby reducing variance in FL updates. We analyze their convergence under a unified framework and derive an upper bound on the model ``optimality error", which explicitly quantifies the effect of bias and variance in terms of design parameters. Next, to optimize this trade-off, we study a non-convex optimization problem and develop a successive convex approximation (SCA)-based framework to jointly optimize the design parameters. We perform extensive numerical evaluations with several related design variants and state-of-the-art OTA and digital FL schemes. Our results confirm that minimizing the bias-variance trade-off while allowing a structured bias provides better FL convergence performance than existing schemes.

Audio and music generation based on flexible multimodal control signals is a widely applicable topic, with the following key challenges: 1) a unified multimodal modeling framework, and 2) large-scale, high-quality training data. As such, we propose AudioX, a unified framework for anything-to-audio generation that integrates varied multimodal conditions (i.e., text, video, and audio signals) in this work. The core design in this framework is a Multimodal Adaptive Fusion module, which enables the effective fusion of diverse multimodal inputs, enhancing cross-modal alignment and improving overall generation quality. To train this unified model, we construct a large-scale, high-quality dataset, IF-caps, comprising over 7 million samples curated through a structured data annotation pipeline. This dataset provides comprehensive supervision for multimodal-conditioned audio generation. We benchmark AudioX against state-of-the-art methods across a wide range of tasks, finding that our model achieves superior performance, especially in text-to-audio and text-to-music generation. These results demonstrate our method is capable of audio generation under multimodal control signals, showing powerful instruction-following potential. The code and datasets will be available at this https URL.

We consider the problem of estimating cross-spectral quantities in the low-frequency regime, where long observation times limit averaging over large ensembles of periodograms, thereby preventing the use of approximate Gaussian statistics. This case is relevant for precision low-frequency gravitational experiments such as LISA and LISA Pathfinder. We present a Bayesian method for estimating spectral quantities in multivariate Gaussian time series. The approach, based on periodograms and Wishart statistics, yields closed-form expressions at any given frequency for the marginal posterior distributions of the individual power spectral densities, the pairwise coherence, and the multiple coherence, as well as for the joint posterior distribution of the full cross-spectral density matrix. In the context of noise projection -- where one series is modeled as a linear combination of filtered versions of the others, plus a background component -- the method also provides closed-form posteriors for both the susceptibilities, i.e., the filter transfer functions, and the power spectral density of the background. We apply the method to data from the LISA Pathfinder mission, showing effective decorrelation of temperature-induced acceleration noise and reliable estimation of its coupling coefficient.

Random walk (RW)-based algorithms have long been popular in distributed systems due to low overheads and scalability, with recent growing applications in decentralized learning. However, their reliance on local interactions makes them inherently vulnerable to malicious behavior. In this work, we investigate an adversarial threat that we term the ``Pac-Man'' attack, in which a malicious node probabilistically terminates any RW that visits it. This stealthy behavior gradually eliminates active RWs from the network, effectively halting the learning process without triggering failure alarms. To counter this threat, we propose the Average Crossing (AC) algorithm--a fully decentralized mechanism for duplicating RWs to prevent RW extinction in the presence of Pac-Man. Our theoretical analysis establishes that (i) the RW population remains almost surely bounded under AC and (ii) RW-based stochastic gradient descent remains convergent under AC, even in the presence of Pac-Man, with a quantifiable deviation from the true optimum. Our extensive empirical results on both synthetic and real-world datasets corroborate our theoretical findings. Furthermore, they uncover a phase transition in the extinction probability as a function of the duplication threshold. We offer theoretical insights by analyzing a simplified variant of the AC, which sheds light on the observed phase transition.

Autonomous vehicles must navigate dynamically uncertain environments while balancing safety and efficiency. This challenge is exacerbated by unpredictable human-driven vehicle (HV) behaviors and perception inaccuracies, necessitating planners that adapt to evolving uncertainties while maintaining safe trajectories. Overly conservative planning degrades driving efficiency, while deterministic methods risk failure in unexpected scenarios. To address these issues, we propose a real-time contingency trajectory optimization framework. Our method employs event-triggered online learning of HV control-intent sets to dynamically quantify multimodal HV uncertainties and incrementally refine their forward reachable sets (FRSs). Crucially, we enforce invariant safety through FRS-based barrier constraints that ensure safety without reliance on accurate trajectory prediction. These constraints are seamlessly embedded in contingency trajectory optimization and solved efficiently through consensus alternating direction method of multipliers (ADMM). The system continuously adapts to HV behavioral uncertainties, preserving feasibility and safety without excessive conservatism. High-fidelity simulations on highway and urban scenarios, along with a series of real-world experiments, demonstrate significant improvements in driving efficiency and passenger comfort while maintaining safety under uncertainty. The project page is available at this https URL.

Advanced feature extraction methods have significantly contributed to enhancing the task of person re-identification. In addition, modifications to objective functions have been developed to further improve performance. Nonetheless, selecting better class representatives is an underexplored area of research that can also lead to advancements in re-identification performance. Although past works have experimented with using the centroid of a gallery image class during training, only a few have investigated alternative representations during the retrieval stage. In this paper, we demonstrate that these prior techniques yield suboptimal results in terms of re-identification metrics. To address the re-identification problem, we propose a generalized selection method that involves choosing representations that are not limited to class centroids. Our approach strikes a balance between accuracy and mean average precision, leading to improvements beyond the state of the art. For example, the actual number of representations per class can be adjusted to meet specific application requirements. We apply our methodology on top of multiple re-identification embeddings, and in all cases it substantially improves upon contemporary results.

Embedding non-restrictive prior knowledge, such as energy conservation laws, into learning methods is a key motive to construct physically consistent dynamics models from limited data, relevant for, e.g., model-based control. Recent work incorporates Hamiltonian dynamics into Gaussian Processes (GPs) to obtain uncertainty-quantifying, energy-consistent models, but these methods rely on -- rarely available -- velocity or momentum data. In this paper, we study dynamics learning using Hamiltonian GPs and focus on learning solely from input-output data, without relying on velocity or momentum measurements. Adopting a non-conservative formulation, energy exchange with the environment, e.g., through external forces or dissipation, can be captured. We provide a fully Bayesian scheme for estimating probability densities of unknown hidden states, GP hyperparameters, as well as structural hyperparameters, such as damping coefficients. The proposed method is evaluated in a nonlinear simulation case study and compared to a state-of-the-art approach that relies on momentum measurements.

As perception-based controllers for autonomous systems become increasingly popular in the real world, it is important that we can formally verify their safety and performance despite perceptual uncertainty. Unfortunately, the verification of such systems remains challenging, largely due to the complexity of the controllers, which are often nonlinear, nonconvex, learning-based, and/or black-box. Prior works propose verification algorithms that are based on approximate reachability methods, but they often restrict the class of controllers and systems that can be handled or result in overly conservative analyses. Hamilton-Jacobi (HJ) reachability analysis is a popular formal verification tool for general nonlinear systems that can compute optimal reachable sets under worst-case system uncertainties; however, its application to perception-based systems is currently underexplored. In this work, we propose RoVer-CoRe, a framework for the Robust Verification of Controllers via HJ Reachability. To the best of our knowledge, RoVer-CoRe is the first HJ reachability-based framework for the verification of perception-based systems under perceptual uncertainty. Our key insight is to concatenate the system controller, observation function, and the state estimation modules to obtain an equivalent closed-loop system that is readily compatible with existing reachability frameworks. Within RoVer-CoRe, we propose novel methods for formal safety verification and robust controller design. We demonstrate the efficacy of the framework in case studies involving aircraft taxiing and NN-based rover navigation. Code is available at the link in the footnote.

Spatially inhomogeneous magnetic fields offer a valuable, non-visual information source for positioning. Among systems leveraging this, magnetic field-based simultaneous localization and mapping (SLAM) systems are particularly attractive. These systems execute positioning and magnetic field mapping tasks simultaneously, and they have bounded positioning error within previously visited regions. However, state-of-the-art magnetic-field SLAM methods typically require low-drift odometry data provided by visual odometry, a wheel encoder, or pedestrian dead-reckoning technology. To address this limitation, this work proposes loosely coupled and tightly coupled inertial magnetic SLAM (IM-SLAM) systems, which use only low-cost sensors: an inertial measurement unit (IMU), 30 magnetometers, and a barometer. Both systems are based on a magnetic-field-aided inertial navigation system (INS) and use error-state Kalman filters for state estimation. The key difference between the two systems is whether the navigation state estimation is done in one or two steps. These systems are evaluated in real-world indoor environments with multi-floor structures. The results of the experiment show that the tightly coupled IM-SLAM system achieves lower positioning errors than the loosely coupled system in most scenarios, with typical errors on the order of meters per 100 meters traveled. These results demonstrate the feasibility of developing a full 3D IM-SLAM system using low-cost sensors. A potential application of the proposed systems is for the positioning of emergency response officers.

As demand-side flexibility becomes increasingly necessary to integrate variable renewable energy, understanding electricity demand composition across different grid levels is essential. However, at regional and national scales, visibility into the relative contributions of different consumer categories remains limited due to the complexity and cost of collecting end-use consumption data. To address this challenge, we propose a blind source separation framework to disaggregate open-access high-voltage grid load measurements into sectoral contributions. The approach relies on a constrained variant of non-negative matrix factorization, termed linearly-constrained non-negative matrix factorization (LCNMF), which allows prior information to be incorporated as linear constraints on the factor matrices, thereby providing weak supervision of the separation process. The framework is evaluated using Italian national load data from 2021 to 2023. Results demonstrate the identifiability of residential, services, and industrial load components and provide monthly sectoral consumption estimates consistent with reported statistics. The proposed method is generalizable and applicable to load disaggregation problems across multiple grid scales where disaggregated measurements are unavailable.

Scientists face significant visualization challenges as time-varying datasets grow in speed and volume, often requiring specialized infrastructure and expertise to handle massive datasets. Petascale climate models generated in NASA laboratories require a dedicated group of graphics and media experts and access to high-performance computing resources. Scientists may need to share scientific results with the community iteratively and quickly. However, the time-consuming trial-and-error process incurs significant data transfer overhead and far exceeds the time and resources allocated for typical post-analysis visualization tasks, disrupting the production workflow. Our paper introduces a user-friendly framework for creating 3D animations of petascale, time-varying data on a commodity workstation. Our contributions: (i) Generalized Animation Descriptor (GAD) with a keyframe-based adaptable abstraction for animation, (ii) efficient data access from cloud-hosted repositories to reduce data management overhead, (iii) tailored rendering system, and (iv) an LLM-assisted conversational interface as a scripting module to allow domain scientists with no visualization expertise to create animations of their region of interest. We demonstrate the framework's effectiveness with two case studies: first, by generating animations in which sampling criteria are specified based on prior knowledge, and second, by generating AI-assisted animations in which sampling parameters are derived from natural-language user prompts. In all cases, we use large-scale NASA climate-oceanographic datasets that exceed 1PB in size yet achieve a fast turnaround time of 1 minute to 2 hours. Users can generate a rough draft of the animation within minutes, then seamlessly incorporate as much high-resolution data as needed for the final version.

Wireless bioelectronic interfaces are increasingly used to control tissue-engineered biohybrid robotic systems. However, a unifying engineering framework linking device design to system-level control remains underdeveloped. Here, we propose that wireless control in biohybrid robotics can be formulated as a coupled co-design problem of integrating signal delivery, spatial selectivity, scalability, and interface stability. We analyze three representative control strategies, wireless electrical stimulation, wireless optoelectronic stimulation, and neuromuscular integration, which operates within a distinct regime with characteristic trade-offs. Across these modalities, the tissue-device interface emerges as a key constraint, governing the interplay between electromagnetic coupling, circuit performance, and biomechanical response. Based on this framework, we outline practical design principles spanning electromagnetic field distribution, circuit architecture, and actuator mechanics. We further propose a transition from open-loop stimulation to closed-loop biohybrid autonomy enabled by organoid-integrated bioelectronics and bidirectional microelectrode interfaces. This work establishes a system-level perspective on wireless bioelectronic control and provides design guidelines for developing stable, scalable, and autonomous biohybrid robotic systems.

Large language models (LLMs) have been proposed as supervisory agents for spacecraft operations, but existing approaches rely on static prompting and do not improve across repeated executions. We introduce \textsc{GUIDE}, a non-parametric policy improvement framework that enables cross-episode adaptation without weight updates by evolving a structured, state-conditioned playbook of natural-language decision rules. A lightweight acting model performs real-time control, while offline reflection updates the playbook from prior trajectories. Evaluated on an adversarial orbital interception task in the Kerbal Space Program Differential Games environment, GUIDE's evolution consistently outperforms static baselines. Results indicate that context evolution in LLM agents functions as policy search over structured decision rules in real-time closed-loop spacecraft interaction.

In this work, we first prove that the separation principle holds for communication-constrained LQR problems under i.i.d. zero-mean disturbances with a symmetric distribution. We then solve the dynamic programming problem and show that the optimal scheduling policy is a symmetric threshold rule on the accumulated disturbance since the most recent update, while the optimal controller is a discounted linear feedback law independent of the scheduling policy.

Prime Video regularly conducts load tests to simulate the viewer traffic spikes seen during live events such as Thursday Night Football as well as video-on-demand (VOD) events such as Rings of Power. While these stress tests validate system capacity, they can sometimes miss service behaviors unique to real event traffic. We present a graph-based anomaly detection system that identifies under-represented services using unsupervised node-level graph embeddings. Built on a GCN-GAE, our approach learns structural representations from directed, weighted service graphs at minute-level resolution and flags anomalies based on cosine similarity between load test and event embeddings. The system identifies incident-related services that are documented and demonstrates early detection capability. We also introduce a preliminary synthetic anomaly injection framework for controlled evaluation that show promising precision (96%) and low false positive rate (0.08%), though recall (58%) remains limited under conservative propagation assumptions. This framework demonstrates practical utility within Prime Video while also surfacing methodological lessons and directions, providing a foundation for broader application across microservice ecosystems.