Aggregating building heating, ventilation, and air-conditioning (HVAC) fleets provides substantial real-time flexibility to power system operations. However, real-time aggregation of multi-zone HVAC fleets faces two key challenges: (i) strong coupling across zones and time makes flexibility characterization high-dimensional and computationally demanding, and (ii) the sequential revelation of temperature states and exogenous conditions requires that decisions made at each period preserve feasibility over the remaining horizon using only currently realized information. To address these challenges, this paper proposes a reachable-set decomposition framework comprising an offline decomposition stage and a real-time policy. In the offline stage, backward reachable sets are formulated to encode remaining-horizon feasibility into per-period state constraints, so that any state within the current reachable set is guaranteed to sustain feasible operation over the entire remaining horizon. A tailored inner approximation is then developed for tractable calculation in multi-zone-coupled HVAC settings. In the real-time stage, aggregate flexibility is computed efficiently via building-level parallel linear programs followed by closed-form Minkowski summation of power intervals, and any regulation signal within the reported flexibility interval admits a recursively feasible disaggregation. Case studies demonstrate the effectiveness of the proposed framework in aggregate flexibility characterization, disaggregation feasibility, and scalable computation.

This paper considers fixed-wing unmanned aerial vehicle (UAV) corridors comprising a main lane, a circular loiter lane for managing traffic congestion, and transit lanes connecting the two. In particular, we address the problem of conflict-free reinsertion of UAVs from the loiter lane back into the main lane. The loiter lane contains a fixed number of equidistant virtual slots that UAVs can occupy. Reinsertion of loiter UAVs into the main lane becomes essential either due to reduced traffic in the main lane or due to a loiter UAV needing to reach its destination urgently. Given the total number of loiter slots, UAV speed limits, and the minimum safety distance, a guidance algorithm is developed to compute the required speed of a loiter UAV in the transit lane to ensure safe reinsertion. The proposed guidance and automation strategies are validated through numerical simulations.

Establishing trustworthy safety assurance for autonomous driving systems (ADSs) requires evidence that failures arise from avoidable system deficiencies rather than unavoidable traffic conflicts. Current adversarial simulation methods can efficiently expose collisions, but generally lack mechanisms to distinguish these fundamentally different failure modes. Here we present CARS (Context-Aware, Responsibility-attributed Scenario generation), a framework that integrates responsibility attribution directly into adversarial scenario generation. CARS combines context-aware adversary selection with a generative adversarial policy optimized in closed-loop simulation to construct collision scenarios that are both physically feasible and diagnostically attributable. Across benchmark datasets spanning heterogeneous national traffic environments, CARS consistently discovers feasible collision scenarios with high attribution rates under multiple regulation-prescribed careful and competent driver models. By coupling adversarial generation with normative responsibility assessment, CARS moves simulation testing beyond collision discovery toward the construction of interpretable, regulation-aligned safety evidence for scalable ADS validation.

Underwater images suffer from wavelength-dependent light absorption and scattering, which reduces visual quality. This phenomenon could limit the operational reliability of autonomous underwater vehicles, marine surveys, and offshore inspection systems. Purely classical methods often achieve suboptimal performance in real-world datasets, while purely data-driven methods lack physical interpretability. In this letter, we propose UDehaze-iT, a deep network for underwater image enhancement that estimates scene depth implicitly and derives per-channel transmission through the Beer-Lambert law with learnable attenuation coefficients. We estimate atmospheric light as a semi-classical per-channel scalar, and a zero-initialized residual refiner corrects remaining artefacts after dehazing. To effectively train our method, we apply a composite loss function consisting of five key terms: a L1 loss, a multi-scale patchwise DCT loss, a forward model reconstruction loss, and two regularization terms. With ~0.9M parameters, UDehaze-iT achieves competitive performance on UIEB and UFO-120 datasets.

Volumetric Modulated Arc Therapy (VMAT) is a cornerstone of modern radiation therapy, enabling highly conformal tumor irradiation and healthy-tissue sparing. Yet, its planning solves inverse and nested optimization for multi-leaf collimators, monitor units and dose parameters, while enforcing their consistency to ensure mechanical deliverability. Nevertheless, this process often requires repeated re-optimization when treatment configurations change, resulting in substantial planning time per patient. To address these problems, we present a diffusion-driven Learning-to-Optimize (L2O) method for end-to-end VMAT planning. A distribution-matching distilled diffusion model learns a clinically feasible manifold of fluence maps, enabling their one-shot generation. On top of this, an LSTM-based L2O module learns gradient update dynamics to swiftly refine fluence maps toward prescribed dose objectives during inference. Experimental results on clinical and public prostate cancer cohorts demonstrate improved planning efficiency, flexibility, and machine deliverability over currently available end-to-end VMAT planners.

This paper proposes a nonlinear guidance strategy capable of intercepting a constant-velocity, non-maneuvering target while strictly satisfying the prescribed bounds on the control input (commanded acceleration). Unlike conventional strategies that estimate time-to-go using linearization or small-angle approximations, the proposed strategy employs true proportional-navigation guidance (TPNG) as a baseline, which utilizes an exact time-to-go formulation and is applicable over a wide range of target motions. In contrast to most existing strategies, which do not incorporate control input bounds into the guidance design, the proposed approach explicitly accounts for these limits by modeling the interceptor acceleration as a dynamic variable. Based on the sliding mode control technique, an effective guidance law that achieves time-constrained interception while accounting for bounded input is then derived. The performance of the proposed strategy is evaluated for various engagement scenarios.

Air-sea surface interactions play a critical role in underwater-to-air optical wireless communication (OWC) links, particularly in vertical autonomous underwater vehicle (AUV) to unmanned aerial vehicle (UAV) scenarios, where the stochastic nature of the sea surface introduces optical distortions that impair link reliability. This work investigates the impact of air-sea surface roughness on AUV-to-UAV OWC systems using two experimentally validated models: the classical Cox-Munk and the Elfouhaily-Chapron-Katsaros-Vandemark (ECKV). A tractable analytical representation of the ECKV model is derived and validated against measured sea-state data. Using both analytical and Monte Carlo approaches, the link ergodic capacity is evaluated with particular emphasis on operating range, pointing errors, receiver field-of-view, and solar noise level, providing practical system design insights.

This article analyzes the achievable sum-rate of multiuser uplink segmented waveguide-enabled pinching-antenna systems (SWANs). To unveil system-design insights, an upper bound on the achievable sum-rate is derived, based on which the existence of an optimal segment activation level is theoretically established. Motivated by this result, hybrid segment selection and aggregation (HSS/A) schemes are proposed to jointly optimize segment activation and pinching-antenna (PA) placement. Correspondingly, low-complexity greedy algorithms are developed for the considered optimization problem. Numerical results validate the theoretical analysis and demonstrate that the proposed HSS/A schemes outperform conventional full-segment aggregation.

This paper proposes a decentralized method for regional pole placement, or $\mathcal{D}$-stability, in linearized networked systems. Existing LMI-based methods are hindered by confidentiality concerns regarding proprietary subsystem models and the absence of communication infrastructures. To overcome these barriers, we map the target region $\mathcal{D}$ of pole placement to an auxiliary left-half plane and introduce positive functions to handle the resulting complex-coefficient dynamics. We prove that $\mathcal{D}$-stability is guaranteed via local frequency-domain criteria without requiring shared subsystem models or inter-subsystem communication. This method is then tailored to DC microgrids, where a loop transformation is utilized to reallocate the burden of stability certification, deriving a broadcastable grid code for decentralized parameter synthesis. Numerical examples verify the efficacy of the proposed method.

We prove a regret lower bound for Gaussian-process bandits on a smooth compact Riemannian manifold $\M$ of dimension $d$ with intrinsic Matérn-$ν$ kernel ($ν>d/2$) that exposes how the geometry of the arm space enters the constant. For any algorithm and time horizon $T$ exceeding an explicit threshold, the worst-case expected regret over the RKHS-ball $\|f\|_{\Hil_{k_ν}}\!\le\!B$ satisfies \begin{multline*} \E[R_T(f)]\;\ge\;c_*(d,ν)\,B^{d/(2ν+d)}\,σ_n^{2ν/(2ν+d)} \\ \cdot\,\vol_g(\M)^{ν/(2ν+d)}\,T^{(ν+d)/(2ν+d)}(\log T)^{ν/(2ν+d)}. \end{multline*} The exponent matches the Vakili--Khezeli--Picheny upper bound \cite{vakili2021information}; the $\vol_g(\M)^{ν/(2ν+d)}$ factor is, to our knowledge, the first explicit volume-dependent geometric constant in a manifold GP-bandit lower bound. We extend the analysis in five directions: (i)~a companion Assouad-style proof gives a different lower bound with a strictly smaller $T$-exponent $(2ν+3d)/(4(ν+d))$ but with a polylog factor of the form $1/(\log\log T)^{(2ν+d)/(4(ν+d))}$, sharpening the $(\log T)^{ν/(2ν+d)}$ Fano polylog of Theorem~\ref{thm:main}; (ii)~we prove a $|G|^{1/2}$ upper bound on the regret of an extrinsic-kernel GP-UCB algorithm on a quotient space $\M=\Mt/G$, plus a bracketing theorem (Theorem~\ref{thm:gauge-bracket}); the precise constant is conjectured to take the modulated form $(1+(|G|-1)h(\rinj/κ))^{1/2}$ (Conjecture~\ref{conj:gauge-modulated}), validated numerically on $\SO(3)$; (iii)~we write the leading constant $c_*(d,ν)$ out fully; (iv)~we extract a curvature dependence $1+O(K\eps_T^2)$ via Bishop--Gromov; (v)~we transfer the bound to the Bayesian regret framework via the Yang--Barron / Castillo et al.\ Bayesian-Fano transfer.

In this paper, a sensing-assisted non-line-of-sight (NLoS) identification method for dynamic uncrewed aerial vehicle (UAV) positioning is proposed for the first time. For urban UAV-to-ground scenarios, a new multi-modal sensing-communication integrated dataset is constructed to support line-of-sight (LoS)/NLoS identification, covering two typical urban scenarios and a wide range of flight altitudes. Based on the constructed dataset, a novel dual-input feature fusion network is proposed, which addresses the challenge of heterogeneous representations between RGB images and channel impulse response (CIR) data to enable the joint extraction and fusion of sensing and communication features for LoS/NLoS identification. Simulation results show that the identification accuracy can reach up to 97.69%, while achieving an improvement of at least 3.59% compared to traditional CIR-only and RGB-only methods. Moreover, strong few-shot generalization is observed, as the proposed method stabilizes and approaches full-sample performance with fewer than 200 target samples and exceeds traditional CIR-only and RGB-only methods with fewer than 100 target samples in all cross-scenario and cross-altitude experiments. Even under Gaussian noise with a variance of 0.35 applied to RGB images, the accuracy degradation remains approximately 0.5%. By utilizing the proposed LoS/NLoS identification method, the error of trilateration positioning can be reduced by approximately 70% in a crossroad scenario, verifying the utility of the proposed method.

This paper proposes a novel UAV-to-Vehicle (U2V) channel model for sixth-generation (6G) intelligent sensing-communication integration, based on three-dimensional (3D) scatterer prediction. To explore the mapping relationship between physical environment and electromagnetic space, a new high-fidelity mixed sensing-communication integration U2V simulation dataset under wide-lane scenarios with different vehicular traffic densities (VTDs) and UAV heights is constructed. Based on the constructed dataset, a novel 3D Scatterer Prediction and Distribution Estimation (3D-SPADE) algorithm is proposed, which leverages LiDAR point clouds to accurately predict the spatial distribution of scatterers. Furthermore, the clustering of scatterers and the subsequent classification into dynamic and static types are meticulously designed for highly dynamic U2V scenarios, while reducing computational complexity and improving modeling accuracy. As LiDAR point clouds vary over time, dynamic and static clusters evolve via 3D-SPADE, enabling precise modeling of channel non-stationarity and consistency. Simulation results demonstrate that, in the wide-lane scenario with varying VTDs and UAV heights, the proposed 3D-SPADE consistently achieves high scatterer occupancy detection performance within the voxel grid. In particular, under favorable configurations, recall reaches 93.26%, and precision reaches 95.74%, highlighting the reliability of 3D-SPADE. Key channel statistical characteristics are simulated and analyzed. These characteristics from the simulation experiments are highly consistent with ray-tracing results and exhibit better agreement than with the standardized model and inconsistent model, validating the necessity of exploring the mapping relationship and the effectiveness of the proposed model.

The Kazantzis-Kravaris-Luenberger (KKL) observer provides a general framework for nonlinear state estimation by immersing the system dynamics into a stable linear or nonlinear latent dynamics. However, the performance of KKL observers relies heavily on the specific choice of these latent dynamics, which is often heuristic. This paper proposes a methodology to learn a KKL observer that combines global stability guarantees with local optimality. We derive a condition on the latent dynamics such that the observer locally mimics the behavior of a Minimum Energy Estimator (Mortensen observer). We then employ Deep Learning to approximate the KKL transformation and the latent dynamics, using neural network architectures that structurally enforce the contraction property. The proposed strategy is validated through numerical simulations on nonlinear benchmarks, demonstrating a good performance in the presence of state and measurement noise.

Uniform rectangular arrays (URA), structured non-uniform rectangular arrays (NURA), and parallelogram shaped (UPgA and NUPgA) arrays admit steering vectors that can be expressed as the Kronecker product of azimuth and elevation steering vectors. Accordingly, the full steering matrix can be represented as the Khatri Rao product of the corresponding azimuth and elevation steering matrices. This paper exploits this structure to develop an economical subspace decoupling framework for two dimensional angle of arrival (AoA) estimation. The proposed method first extracts the joint signal subspace from the spatial covariance matrix. Then it applies a low complexity decoupling scheme to recover the column spaces of the azimuth and elevation steering matrices. With the estimated decoupled subspaces, conventional one dimensional algorithms such as MUSIC, root MUSIC, and ESPRIT can be applied independently along each dimension, followed by pairing through a two dimensional spectral function. Monte Carlo simulations show that the proposed approach achieves higher accuracy than state of the art methods, i.e., two dimensional MUSIC, reduced-dimension MUSIC, and two-dimensional ESPRIT, for medium- and large scale arrays while requiring fewer snapshots, consequently with improved spectral efficiency.

Pseudo-measurements are the dominant source of uncertainty in distribution system state estimation (DSSE), yet their distributional assumptions are treated as fixed inputs by existing uncertainty quantification methods. This paper investigates whether the uncertainty bounds assumed by weighted least squares (WLS)-based DSSE are sensitive to these distributional assumptions, and whether this sensitivity is quantifiable using the Fisher Information Matrix (FIM). We propose a diagnostic framework that compares the true Cramér-Rao Bound (CRB) against the WLS-assumed CRB via a per-bus, per-scenario ratio, computed directly from the converged WLS solution. Pseudo-measurement distributions are varied across five types in 22 variants matched at equal spread to isolate shape effects from variance. Experiments on the CIGRE MV network across 100 operating scenarios yield three findings. First, heavy-tailed and skewed distributions show consistently that WLS systematically overstates its uncertainty bounds. Second, the degree of miscalibration varies across buses and operating scenarios, confirming that distributional sensitivity is not uniform. Third, the CRB ratio is structurally blind to mean-shift bias, exposing a fundamental limitation of variance-based uncertainty diagnostics. Together, these results confirm the hypothesis and show that the choice of pseudo-measurement distribution directly distorts the confidence limits under WLS-based assumptions, which must be explicitly accounted for in any uncertainty-aware DSSE method.

The large-scale replacement of synchronous machines with inverter-based generation (IBG) introduces critical challenges to both voltage and frequency stability. This work builds on a mixed-integer second-order cone programming (MISOCP) framework that co-optimizes unit commitment (UC) model which embeds frequency-nadir constraints through synthetic inertia (SI) dispatch and an SOC voltage stability boundary for IBG buses. The formulation extends by modeling a STATCOM as a reactive-power decision variable in the same MISOCP model. A modified IEEE 30-bus system is used to assess three scheduling strategies: (i) baseline UC with SI only, (ii) voltage-stability-constrained (VSC) UC with SI, and (iii) the joint UC with SI and reactive power support from IBGs. The impact of incorporating a 30~MVAr STATCOM at a weak grid location near the IBG buses is investigated. Simulation results show that the proposed framework enhances voltage security, maintains frequency-nadir compliance, and reduces operating cost, while STATCOM integration further improves dispatch feasibility under high IBG.

State and parameter estimation, along with fault detection, are three crucial estimation problems within the control systems community. Although different approaches have been proposed for each type of problem, the modulating function method proposes a more unified approach to all three problem classes, being used for state and parameter estimation of lumped systems, fault detection, and estimation of distributed and fractional systems. At the core of the method is the modulating function: a function that evaluates to 0 at the left or right boundaries up to a certain order of derivatives. By selecting the modulating functions, one directly determines the filter characteristics, and, for that reason, different function families have been proposed over the years. Nevertheless, many families of modulating functions are given in a rather similar mathematical structure. In light of these structures, this paper formally discusses the algebraic properties of modulating functions, and, after formalizing the closedness and group properties of modulating functions, a simple algorithm to construct new modulating functions is proposed, discussed, and illustrated with the construction of the newly introduced logarithmic modulating function families and 3 non-analytic modulating function families. Moreover, the fact that total modulating functions form a vector space and an algebra is exploited to construct orthonormal modulating functions, which are then used for the parameter estimation of a boat's roll dynamics, effectively avoiding matrix inversion issues.

Underwater environments impose severe constraints on conventional imaging systems and demand solutions that balance high-quality sensing with strict resource efficiency. While emerging event cameras offer a promising alternative, their potential in aquatic scenarios remains largely unexplored. Through the lens of neuromorphic vision, this work pioneers the investigation of motion fields that serve as key media for agile underwater perception. Built upon spiking neural networks, we introduce a self-supervised framework to estimate per-pixel optical flow from asynchronous event streams, elegantly bypassing the long-standing bottleneck of underwater data scarcity. Extensive evaluations demonstrate that our method achieves competitive visual and quantitative results against leading techniques while operating with superior computational efficiency. By bridging neuromorphic sensing and aquatic intelligence, this work opens new frontiers for lightweight, real-time, and low-cost perception on resource-constrained underwater edge platforms.

The escalating data rate demands of future wireless communications necessitate the deployment of extremely large aperture arrays (ELAAs) in communication systems. Acquiring accurate channel state information is crucial to execute effective precoding for such systems, in which the near-field curvature effects on the channel must be considered. Current channel estimation algorithms are generally restricted to the spherical wavefront channel (SWC), which is appropriate for isotropic scatterers, point sources, and planar reflecting surfaces. However, in practical scenarios involving curved reflecting surfaces, the reflected waves exhibit anisotropic rather than spherical wavefronts, significantly degrading the accuracy of conventional SWC-based algorithms. To tackle this challenge, we first derive a parameterized model for the anisotropic wavefront channel (AWC). Based on this model, we then propose the matching-free acquisition of channels with anisotropic wavefronts (MACAW) algorithm. Unlike conventional dictionary-based matching pursuit techniques, MACAW recovers channel parameters through fast-Fourier-transform-based frequency analysis. This approach enables precise channel estimation in AWC scenarios while maintaining a significantly lower computational complexity than existing methods. Simulation results illustrate how physical characteristics of the propagation environment influence the degree of wavefront anisotropy, and demonstrate the effectiveness of the proposed algorithm.

Reconfigurable intelligent surfaces (RISs) have emerged as a promising solution for enabling energy-efficient and flexible spectrum usage in wireless communication, particularly in the context of sixth-generation (6G) networks. While passive RIS architectures are widely regarded as virtually noiseless due to the lack of active components, this idealized assumption can lead to misleading performance evaluations. In this paper, we revisit this assumption and demonstrate that the thermal noise generated by passive RIS elements, though often neglected, can significantly affect system performance. We propose a tractable approximated analytical framework that incorporates RIS-induced thermal noise into the system and derive closed-form expressions for key performance metrics, such as outage probability and throughput. Simulation results validate our approximated analysis and highlight the substantial performance discrepancies that arise when RIS thermal noise is ignored. Our results offer valuable insights into the trade-offs between receiver and RIS noise, guiding the development of robust and efficient 6G communication systems.

This paper analyzes the implications of simplified pipeline gas flow models for integrated energy system planning. A case study of an integrated power-hydrogen expansion planning problem shows that simplifying pressure-flow relationships and gas dynamics can lead to expansion plans that incur substantial regret when evaluated under a more realistic dynamic gas flow model -- due to suboptimal system expansion, operation, and non-supplied hydrogen. Numerical experiments show that planning under the highly simplified transport and transport-linepack models -- commonly used in expansion studies -- can result in regret exceeding several thousand percent and yield expansion plans that lack robustness across demand levels. Planning under steady-state conditions partially mitigates these effects, but still leaves significant cost-reduction potential untapped compared to dynamic planning due to neglected linepack flexibility. Developing efficient solution algorithms for the dynamic model is a promising direction for future research.

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.

During human motor skill training and physical rehabilitation, there is an inherent trade-off between task difficulty and user performance. Characterizing this trade-off is crucial for evaluating user performance, designing assist-as-needed (AAN) protocols, and assessing the efficacy of training protocols. In this study, we propose a novel human-in-the-loop (HiL) Pareto optimization approach to characterize the trade-off between task performance and the perceived challenge level of motor learning or rehabilitation tasks. We adapt Bayesian multi-criteria optimization to systematically and efficiently perform HiL Pareto characterizations. Our HiL optimization employs a hybrid model that measures performance with a quantitative metric, while the perceived challenge level is captured with a qualitative metric. We demonstrate the feasibility of the proposed HiL Pareto characterization through a user study. Furthermore, we present the utility of the framework through three use cases in the context of a manual skill training task with haptic feedback. First, we demonstrate how the characterized trade-off can be used to design a sample AAN training protocol for a motor learning task and to evaluate the group-level efficacy of the proposed AAN protocol relative to a baseline adaptive assistance protocol. Second, we demonstrate that individual-level comparisons of the trade-offs characterized before and after the training session enable fair evaluation of training progress under different assistance levels. This evaluation method is more general than standard performance evaluations, as it can provide insights even when users cannot perform the task without assistance. Third, we show that the characterized trade-offs also enable fair performance comparisons among different users, as they capture the best possible performance of each user under all feasible assistance levels.

We introduce TiCo, a time-controllable spoken dialogue model (SDM) that follows time-constrained instructions (e.g., "Please generate a response lasting about 15 seconds") and generates spoken responses with controllable duration. This capability is valuable for real-world spoken language systems such as voice assistants and interactive agents, where controlling response duration can improve interaction quality. However, despite their strong ability to generate natural spoken responses, existing models lack time awareness and struggle to follow duration-related instructions. To systematically evaluate this, we introduce TiCo-Bench, the first benchmark for time-controllable instruction following in SDMs, on which existing open-source and commercial models frequently fail to satisfy explicit time constraints. TiCo addresses this limitation by enabling an SDM to estimate elapsed speaking time during generation through Spoken Time Markers (STM) (e.g., <10.6 seconds>). These markers help the model maintain awareness of time and adjust the remaining content to meet the target duration. TiCo is post-trained efficiently without question-answer paired data, relying on self-generation and reinforcement learning with verifiable reward. Experimental results show that TiCo reduces duration error by 2.7x over its backbone and 1.6x over the strongest baseline, while preserving response quality.

Deadline misses are more common in real-world systems than one may expect. The weakly-hard task model has become a standard abstraction to describe and analyze how often these misses occur, and has been especially used in control applications. Most existing control approaches check whether a controller manages to stabilize the system it controls when its implementation occasionally misses deadlines. However, they usually do not incorporate deadline-overrun knowledge during the controller synthesis process. In this paper, we present a framework that explicitly integrates weakly-hard constraints into the control design. Our method supports various overrun handling strategies and guarantees stability and performance under weakly-hard constraints. We validate the synthesized controllers on a Furuta pendulum, a representative control benchmark. The results show that constraint-aware controllers significantly outperform traditional designs, demonstrating the benefits of proactive and informed synthesis for overrun-aware real-time control.

Decoding infant cry causes remains challenging for healthcare monitoring due to short nonstationary signals, limited annotations, and strong domain shifts across infants and datasets. We propose a compact acoustic framework that fuses mel-frequency cepstral coefficients (MFCCs), short-time Fourier transform (STFT) features, and fundamental-frequency (F0) contours within a multi-branch convolutional neural network (CNN) encoder, and models temporal dynamics using an enhanced Legendre Memory Unit (LMU). Compared to LSTMs, the LMU backbone provides stable sequence modeling with substantially fewer recurrent parameters, supporting efficient deployment. To improve cross-dataset generalization, we introduce calibrated posterior ensemble fusion with entropy-gated weighting to preserve domain-specific expertise while mitigating dataset bias. Experiments on Baby2020 and Baby Crying demonstrate improved macro-F1 under cross-domain evaluation, along with leakage aware splits and real-time feasibility for on-device monitoring.

Anomalous sound detection (ASD) benchmarks typically assume that the identity of the monitored machine is known at test time and that recordings are evaluated in a machine-wise manner. However, in realistic monitoring scenarios with multiple known machines operating concurrently, test recordings may not be reliably attributable to a specific machine, and requiring machine identity imposes deployment constraints such as dedicated sensors per machine. To reveal performance degradations and method-specific differences in robustness that are hidden under standard machine-wise evaluation, we consider a minimal modification of the ASD evaluation protocol in which test recordings from multiple machines are merged and evaluated jointly without access to machine identity at inference time. Training data and evaluation metrics remain unchanged, and machine identity labels are used only for post hoc evaluation. Experiments with representative ASD methods show that relaxing this assumption reveals performance degradations and method-specific differences in robustness that are hidden under standard machine-wise evaluation, and that these degradations are strongly related to implicit machine identification accuracy.

This work introduces the first closed-loop adaptive optics (AO) system capable of optically correcting aberrations in real-time without a guidestar or a wavefront sensor. Nearly 40 years ago, Cederquist et al. demonstrated that asymmetric apertures enable phase retrieval (PR) algorithms to perform fully computational wavefront sensing, albeit at a high computational cost. More recently, Chimitt et al. extended this approach with machine learning and demonstrated real-time wavefront sensing using only a single (guidestar-based) point-spread-function (PSF) measurement. Inspired by these works, we introduce a guidestar-free AO framework built around asymmetric apertures and machine learning. Our approach combines three key elements: (1) an asymmetric aperture placed at the system's pupil plane that enables PR-based wavefront sensing, (2) a pair of machine learning algorithms that estimate the PSF from natural scene measurements and reconstruct phase aberrations, and (3) a spatial light modulator that performs optical correction. We experimentally validate this framework on dense natural scenes imaged through unknown obscurants. Our method outperforms state-of-the-art guidestar-free wavefront shaping methods, using an order of magnitude fewer measurements and three orders of magnitude less computation.

Rigorous uncertainty quantification is essential for the safe deployment of autonomous systems in unconstrained environments. Conformal Prediction (CP) provides a distribution-free framework for this task, yet its standard formulations rely on exchangeability assumptions that are violated by the distribution shifts inherent in real-world robotics. Existing online CP methods maintain target coverage by adaptively scaling the conformal threshold, but typically employ a static nonconformity score function. We show that this fixed geometry leads to highly conservative, volume-inefficient prediction regions when environments undergo structural shifts. To address this, we propose $\textbf{AdaptNC}$, a framework for the joint online adaptation of both the nonconformity score parameters and the conformal threshold. AdaptNC leverages an adaptive reweighting scheme to optimize score functions, and introduces a replay buffer mechanism to mitigate the coverage instability that occurs during score transitions. We evaluate AdaptNC on diverse robotic benchmarks involving multi-agent policy changes, environmental changes and sensor degradation. Our results demonstrate that AdaptNC significantly reduces prediction region volume compared to state-of-the-art threshold-only baselines while maintaining target coverage levels.

In neural audio synthesis, neural vocoders and codecs are models that reconstruct waveforms from acoustic and latent representations, which are essential to the resulting audio quality. While current models are capable of generating perceptually natural speech, they still struggle with high-fidelity music and singing voice synthesis, as severe aliasing artifacts are introduced by non-linear activation functions and upsampling layers in existing architectures. Although various anti-aliasing techniques have been proposed in digital signal processing, their integration into neural vocoders and codecs remains under-explored. This paper incorporates differentiable anti-aliasing techniques into the activation and upsampling modules to bridge this gap, and thus presents Pupu-Vocoder and Pupu-Codec. We build a test signal benchmark to evaluate the anti-aliased modules, and validate our proposed models on speech, singing voice, music, and audio. Experimental results show that Pupu-Vocoder and Pupu-Codec outperform existing systems on singing voice, music, and audio, while achieving comparable performance on speech. Demos, codes, and checkpoints are available at VocodexElysium.github.io/AliasingFreeNeuralAudioSynthesis/.