The Karhunen--Loève transform (KLT) diagonalizes the covariance of a second-order process and is optimal for mean-square truncation. Which classical transform it reduces to is governed by the symmetry commutant of the covariance: when the kernel commutes with a group action, the KLT eigenfunctions are the irreducible representation functions of that group, recovering the Fourier, cosine, Mellin, and spherical-harmonic systems. We study the inverse question. Given a covariance $R$ and a finite-dimensional space of candidate generators, the generator nearest to commuting with $R$, the minimizer of $\delta(A,R)=\|[R,A]\|_F/(\|R\|_F\|A\|_F)$, is the smallest-eigenvalue solution of a double-commutator eigenvalue problem $\mathrm{ad}_R^2(A^\ast)=\lambda A^\ast$, a Hermitian generalized eigenvalue problem of size the number of generators, independent of dimension. The framework recovers hidden transforms as well as classical ones: a variational characterization turns the existence of a commuting generator into a spectral condition, and a tridiagonal commutant-uniqueness result yields the prolate spheroidal, cosine, and discrete orthogonal-polynomial bases as exact recoveries, with matrix-valued extensions, and produces a continuum of transforms interpolating between and beyond the classical families. When symmetry is approximate, the coding penalty of the symmetry-adapted blockwise transform equals the multi-information among the sectors, an exact threshold between the fixed and data-driven transforms. We further give a graph-automorphism characterization of permutation structure, a sequential deflation for non-Abelian symmetry, and stability bounds under estimation error. As an application, the KLT of a two-paradigm covariance is synthesized from its two known generators, without forming the mixed covariance, reaching the full-data transform's compaction from few observations.

Radio frequency (RF) integrated circuit design using the open-source complementary Metal-Oxide semiconductor (CMOS) ecosystem, such as for phase-locked loops (PLLs), is limited by the absence of reliable passive device models, particularly on-chip spiral inductors. Consequently, prior work relies on ring-oscillator-based voltage-controlled oscillators (VCOs) with degraded phase noise performance. This work presents a 2.4 GHz type-II fractional-N PLL implemented in the IHP SG13G2 130 nm BiCMOS open-source technology. The proposed design employs a cross-coupled differential LC-VCO integrated with a custom-designed spiral inductor, developed using an open-source electromagnetic modelling workflow in OpenEMS. The optimized inductor achieves 4 nH inductance with a quality factor of 16.8 at 2.45 GHz. The LC-VCO sensitivity is approximately 120 MHz/V, while the PLL phase noise is -100.8 dBc/Hz at 1 MHz offset. The complete PLL is realized using a fully open-source electronic design automation (EDA) flow, occupying a total area of 930 um x 666 um (~0.619 mm2) and consuming 12.73 mW, demonstrating the feasibility of RF integrated circuit design in an open-source CMOS IC design ecosystem.

This paper addresses the problem of observer design for a class of linear descriptor systems affected by a certain class of unknown unmatched disturbances. The objective is to estimate the components of the state vector, as well as the unknown parameters of the unmeasured disturbance. To solve this problem, structural assumptions are introduced under which an observer for the dynamic part of the state vector is constructed. Then, based on the obtained state estimate, the disturbance signal is reconstructed, and its unknown parameters are identified. A new parameterization method is proposed for a class of disturbance input signals that depend nonlinearly on unknown parameters, making it possible to obtain a linear regression in the corresponding unknowns. Numerical simulations are presented to demonstrate the effectiveness of the proposed procedures.

A pinching-antenna system (PASS) is considered for over-the-air computation (AirComp). Multiple dielectric waveguides are deployed at the base station, and one pinching antenna (PA) is activated on each waveguide. For practical implementation, each PA is restricted to a finite set of preconfigured locations. The resulting discrete activation problem is formulated to minimize the AirComp mean-squared error (MSE). After the optimal aggregation vector is derived, the minimum MSE is expressed through an inverse Gram matrix. A rank-one recursion is derived to evaluate the exact MSE reduction produced by each candidate. Greedy search and beam search are then developed for fast tree search. To further reduce complexity, coherent aggregation search is proposed from a first-order MSE approximation. It admits a separable closed-form selection rule and is asymptotically optimal in the low signal-to-noise ratio (SNR) regime. Numerical results show that the proposed methods substantially improve the AirComp accuracy of PASS over conventional antenna arrays.

Signal Temporal Logic (STL) is increasingly used to describe interpretable objectives and constraints for optimal control and learning methods, especially when no target time series data is available. In this work, we propose to synthesize parameters for nonlinear systems that robustly satisfy continuous-time STL specifications for uncertain initial conditions. To this end, we use gradient-based optimization along with set-based reachability verification to efficiently learn in high-dimensional parameter spaces while providing provable satisfaction guarantees for the optimized parameters. We demonstrate the effectiveness and scalability of our method on three systems with up to 18 parameter dimensions.

We study Model Predictive Path Integral (MPPI) control for nonlinear systems with additive process disturbances whose covariance is unknown, spatially varying, and slowly time-varying. A mismatched disturbance covariance produces a persistent penalty in closed-loop stability certificates, while online estimation can reduce this penalty as data are collected. We propose a cell-wise recursive covariance estimator with spatial diffusion and prove a finite-horizon error bound that separates stochastic-approximation error, spatial-smoothing bias, and temporal-drift effects. The diffusion kernel is chosen to be reversible with respect to the stationary visitation measure, making the diffusion operator dissipative in the weighted Lyapunov analysis. We then substitute the resulting covariance estimate into the MPPI sampling distribution and derive an adaptive stability certificate with an explicit learning penalty. The main result is a payoff theorem: after a computable crossover time, the adaptive controller achieves a strictly tighter certified stability bound than any fixed covariance choice whose mismatch exceeds the residual smoothing and drift allowance. Numerical experiments illustrate the estimator convergence and the resulting stability-tightening effect.

Fine-tuning language models often requires enforcing constraints on individual inputs without compromising downstream performance. Existing constrained alignment methods impose constraints on average, which can induce undesirable disparities across inputs or users. We propose a novel alignment framework that addresses this gap by enforcing per-sample constraints while still minimizing an average loss. To mitigate the impact of overly restrictive constraints and outliers, we introduce a learned, sample-dependent relaxation that minimally adjusts the constraints, trading off a user-defined relaxation cost with the training objective. To address practical duality and optimization challenges, we develop an augmented Lagrangian approach tailored to this formulation. We demonstrate the flexibility of the framework by instantiating it under distinct small language-model fine-tuning tasks and constraints: safety in instruction following, preferences in function calling and length in re-ranking. Across these settings, our approach reduces tail constraint violations while largely preserving the model's performance.

Phone segmentation and recognition are inherently related tasks, yet modern approaches typically model them separately. We argue that phonetic structure is already latent in the representations of self-supervised speech models (S3Ms), and one only needs to steer them to solve both tasks. We leverage S3M-based Phonological Activation Mapping (SPAM), which maps each S3M representation frame to a vector of phonological feature activations, such as voicing and nasality. On top of SPAM, we introduce two simple but effective lightweight, gradient-descent-free prediction heads: a recognition head and a segmentation head. Our method requires less than a minute of phonetic transcriptions, and generalizes to unseen phones during training. Across a diverse range of datasets, our approach attains strong segmentation and recognition performance.

Target speech extraction (TSE) has largely been dominated by neural network-based approaches trained and evaluated on synthetic fully overlapped data. The Real-TSE Challenge aims to advance performance on real-world far-field noisy and reverberant recordings. This technical report describes MERL's submission to the Real-TSE Challenge. Rather than proposing a novel model architecture, we built upon the baseline model and focused primarily on data preparation and cleaning. Our system was trained in four stages, beginning with pre-training on fully overlapped mixtures and simulated multi-talker conversations with noise and reverberation applied to both the mixture and the enrollment utterances. We then adapted the model to real-world conditions using noisy far-field recordings with pseudo-targets derived from processed close-talk microphone signals. Our submission achieved first place in the second track, demonstrating the critical importance of high-quality data preparation. Furthermore, we observed that DNSMOS and speaker similarity are susceptible to over-optimization, motivating an investigation of their robustness using adversarial attacks. The results show that both metrics can be driven to extreme values without degrading the token error rate or the VAD-based F1 score.

Frontier autonomous-driving models -- especially vision-language-action (VLA) models, whose forward pass approaches $\sim$60~TFLOPs -- are outgrowing economical onboard deployment, since peak hardware sits idle most of the day. Cloud inference can instead share GPUs across active vehicles, but the vehicle must upload through a capacity-limited uplink, reach a GPU without queueing, and return a decision within the closed-loop budget. This paper asks: can the cloud drive? We answer with an analytical framework coupling communication limits, a roofline GPU service model, stochastic latency, and utilization-aware cost across three model classes, three offloading strategies, and three communication generations, applied to New York City. Separating a reactive 100~ms budget from a 300~ms deliberative tier (presuming an onboard reactive fallback), we find three \emph{nested} binding regimes. Communication binds first in dense cells: 5G fails early, 5G-Advanced is the practical threshold for feature-level offloading, and 6G adds headroom. Compute binds next under the reactive budget: near-term VLA is latency-infeasible regardless of bandwidth, because autoregressive FP16 decode is memory-bandwidth-bound (~114 ms on 2025 hardware). Its floor clears 100 ms around 2027; 6G then admits feature-level VLA by ~2028, 5G-Advanced only at light loading and not the dense corridor, and the deliberative tier from 2026. Cost binds last: once admissible, utilization-pooled cloud GPUs undercut onboard hardware for VLA, whose baseline (up to \$8,500 per vehicle-year) is expensive and idle; feature-level offloading (S2) is where the VLA cost crossover concentrates. Latency decides which model is admissible in which year; cost decides whether it is economical.

Reliable fault recovery of grid-forming (GFM) converters under current-limited conditions is increasingly important as inverter-based resources replace synchronous generation. Existing current-limiting strategies primarily focus on current-angle regulation and synchronization trajectory shaping, while the interaction between the current limiter and the voltage control structure remains insufficiently understood. Consequently, post-fault recovery may exhibit converter trapping in current-limited control (CLC) or oscillatory transitions between CLC and constant voltage control (CVC). This paper shows that, under conventional PI-based voltage control, the interaction between the voltage controller and the current limiter creates a moving recovery boundary that contributes to these recovery failures. To address this issue, a post-fault recovery framework is proposed that combines structurally decoupled virtual admittance voltage control with current-angle steering. The proposed framework simultaneously improves synchronization trajectory evolution and stabilizes the recovery boundary during fault recovery. Experimental validation on a 3-kVA GFM inverter prototype confirms reliable post-fault synchronization recovery under both symmetrical and unsymmetrical voltage sag conditions, with trapping and oscillatory CLC-CVC transitions eliminated.

This paper introduces a digital-twin (DT)-assisted cooperative perception framework designed to improve perception accuracy under end-to-end (E2E) latency constraints and to balance perception accuracy and E2E latency under communication resource constraints in autonomous vehicles. We formulate an optimization problem that maximizes perception accuracy subject to latency and communication limitations, and solve it using a newly proposed coarse-to-fine search (CTFS) algorithm. Simulation results show that the proposed CTFS algorithm achieves 96.6% perception accuracy, close to exhaustive search, under latency constraints while reducing computational complexity by approximately 85.78%. The DT-assisted framework further achieves a 50% reduction in the non-DT communication cost through estimated, time-synchronized state updates.

Medical imaging models are often deployed without the demographic, acquisition, and quality metadata needed for subgroup auditing. Once those metadata disappear, clinically critical failure modes can be masked by strong aggregate performance, and many robust-learning methods lose the group structure they rely on. We present CAPRA, a calibrated proxy-axis framework for hidden subgroup analysis under missing metadata. CAPRA predicts image-derived semantic axes, calibrates axis posteriors on a small metadata-labeled split via patient-level cross-fitting, and organizes those posteriors into a calibrated subgroup interface that supports both deployment-time failure analysis and downstream robust learning without requiring subgroup labels at deployment. Across fundus, dermoscopy, and chest radiography, CAPRA reveals disparity patterns missed by metadata-only slicing, remains informative under dataset shift, and produces subgroup partitions that align more closely with explicit failure axes than image-only or latent-slice baselines. The same interface can also be reused by downstream robust learners, although those gains are domain-dependent. Overall, CAPRA turns hidden subgroup analysis under missing metadata into a calibrated, interpretable, and reusable subgroup interface for deployment-time analysis and robust transfer.

We study conditions under which stability of the origin of stochastic differential equations is robust to small perturbations. We express robustness in two ways, firstly in the sense that stochastic stability is maintained under small parametric perturbations not exceeding a state-dependent bound vanishing at the origin but positive elsewhere, and secondly via stochastic input-to-state stability (ISS) which allows non-zero perturbations everywhere. We prove the former property assuming the existence of a Lyapunov function certifying stochastic stability of the nominal system. Under the same assumption, stochastic ISS holds under a suitable state-dependent perturbation scaling. Stochastic exponential stability is maintained under proportionally bounded perturbations and implies exponential ISS even without perturbation scaling. Finally, we propose a novel approach to stochastic integrator backstepping in pure-feedback form that uses the tools from our robustness analysis.

Zak-Orthogonal Time Frequency Space (OTFS) modulation is known to be robust to Doppler spread in high mobility scenarios when compared to Orthogonal Frequency Division Multiplexing (OFDM). This is due to the fact that the channel response to a Zak-OTFS carrier within a frame can be accurately estimated from the channel response to another carrier within the same frame. However, an important open problem and question is whether inter-frame channel prediction is possible with Zak-OTFS, i.e., is it possible to accurately predict the channel response to a Zak-OTFS carrier in a frame based on knowledge of the channel response to some Zak-OTFS carrier in \emph{another} frame (i.e., not the same frame). In this paper we show that indeed inter-frame channel prediction is possible. We show that the effective DD domain channel filter coefficients vary in a deterministic manner as we move from current to future frames in time and frequency. We also show that the subspace spanned by channel filter coefficients of consecutive frames in time/frequency is invariant to discrete shifts in time and frequency. We exploit the deterministic variation and subspace invariance to propose a novel deterministic ESPIRIT-type method which uses the effective DD domain channel filter taps/coefficients estimated in training frames (i.e., current/past frames in time and frequency having both pilot and data carriers) to predict the effective DD domain channel filter for frames which are several tens of frames in future and several tens of frames away in frequency.

Data-driven control methods, like Data-enabled Predictive Control (DeePC), are often formulated for linear systems, where the principle of superposition allows global system behavior to be inferred from locally collected data through Willems' fundamental lemma. This principle does not hold for nonlinear systems, whose dynamics may vary across operating regions. We propose a data-driven predictive control framework for nonlinear systems that incorporates data column preferences according to their proximity to the current operating point through a weighted norm regularization, thereby localizing the predictor without discarding any data. We show how the proposed weighting scheme induces operating point-dependent data prioritization and ensures a well-posed optimization problem. A numerical study on a nonlinear two-tank system demonstrates that the proposed method matches or outperforms hard data-selection schemes while retaining the full data matrix and its rank, thereby guaranteeing feasibility.

Modern control systems increasingly rely on heterogeneous sensors operating at different sampling rates, where intermittently missing outputs pose fundamental challenges for system identification. This paper proposes a non-iterative, control-oriented identification method for multirate systems based on cyclic reformulation. The method transforms multirate data into an expanded time-invariant representation and yields M parameter sets from a single input-output dataset, where M is the least common multiple of the sensor sampling periods. These parameter sets are used in two complementary ways: their centroid serves as a noise-reduced nominal model, while their convex hull gives a polytopic uncertainty model compatible with vertex-based LMI robust control design. Building on the noise-free structural recovery theorem of the authors' preceding work, which is restated here in the notation of the present paper, the present paper newly introduces the centroid and polytopic models derived from the M parameter sets; finite-noise behavior is treated as an empirical observation and is evaluated numerically. Numerical simulations support both models: an illustrative SISO example shows that the centroid attains higher validation FIT than the best individual vertex and substantially outperforms an interpolation-based baseline, while a MIMO multirate sensing example confirms, in line with the LTI counterpart, that the constructed polytope contains models whose validation FIT exceeds 95\% on average even at the highest tested noise level. The polytope is interpreted cautiously, with finite-noise behavior assessed through output-level validation statistics rather than realization-dependent matrix-coordinate distances. The proposed framework therefore links multirate system identification with robust-control-oriented uncertainty modeling without iterative EM-type optimization.

The rapid expansion of solar photovoltaic (PV) systems has increased the need for reliable and scalable fault classification, as manual inspection is impractical at scale. Thermal infrared (IR) imaging provides a non-contact solution for identifying PV faults; however, accurate classification remains challenging due to class imbalance, limited texture information, and subtle thermal differences. In this work, we investigate the applicability of Joint-Embedding Predictive Architecture (JEPA) for thermal IR PV fault classification across various scenarios and propose JEFFNet (JEPA-EFFicientNet), a multibranch architecture that combines JEPA-based self-supervised representation learning with EfficientNetV2-S-based supervised convolutional feature extraction. JEFFNet fuses semantic representations from a JEPA-pretrained Vision Transformer with convolutional features from EfficientNetV2-S, enabling complementary feature learning. JEFFNet is evaluated on two public thermal IR datasets, PVF-10 and InfraredSolarModules (ISM), for both multiclass and derived binary (healthy/faulty) classification. On PVF-10, JEFFNet achieves an F1-score of $93.21$ and an accuracy of $94.33$ in the 10-class task, and an F1-score of $97.53$ and an accuracy of $96.41$ in the derived 2-class task. On ISM, JEFFNet achieves an F1-score of $72.60$ and an accuracy of $83.88$ in the 12-class task, and an F1-score of $94.69$ and an accuracy of $94.78$ in the derived 2-class task. JEFFNet also uses only 108.6M parameters versus 205.91M for GEPFNet, a 47.2\% reduction. These results demonstrate that combining self-supervised semantic and supervised convolutional features provides an effective, parameter-efficient solution for thermal IR PV fault classification. The source code is publicly available at this https URL

Grant-Free (GF) random access is promising for low Earth orbit satellite Internet due to its reduced access latency. However, existing schemes suffer from poor performance in massive connectivity scenarios. To address this challenge, we firstly propose an iterative residual feedback multi-measurement vector approximate message passing algorithm. This algorithm leverages multi-domain synergistic sparsity in the spatial-frequency and angular-delay domains to alternately perform active user terminal detection (AUD) and channel estimation (CE). Additionally, a residual feedback mechanism is incorporated to suppress error accumulation, thereby enhancing AUD performance. Furthermore, conventional data detection (DD) methods significantly degrade when active user terminals are spatially close or outnumber the satellite's receive antennas, making the demodulation problem rank-deficient or underdetermined. To mitigate this, we design a data modulation scheme via joint spatial-frequency multi-domain spreading, which utilizes observations from both spatial and frequency domains to facilitate multi-domain DD. Simulation results demonstrate that the proposed scheme significantly outperforms existing GF methods in terms of AUD accuracy, CE precision, and bit error rate, especially under conditions of low effective pilot length and practical signal-to-noise ratios.

As power systems transition toward inverter-based resource (IBR)-dominated grids, traditional system strength definitions and metrics are becoming increasingly inadequate to characterize upcoming stability challenges. Emerging definitions characterize system strength in terms of "voltage source behind impedance (VSBI)" characteristics. Similarly, Grid-ForMing (GFM) IBRs are expected to contribute voltage stiffness by exhibiting near-constant VSBI characteristics in the (sub-)transient time frame. To quantify VSBI characteristics as a measure of system strength or grid-forming capability, this paper proposes the Jacobian Voltage Stiffness Metric (JVSM), derived from the frequency-domain Jacobian. JVSM provides a measure of both small-signal voltage magnitude and phase-angle stiffness. JVSM is demonstrated to serve as a compliance criterion for evaluating the VSBI characteristics of GFM IBRs. When applied for grid strength assessment, it more effectively identifies small-signal stability problems than state-of-the-art strength metrics. The proposed JVSM is validated through electromagnetic transient simulation case studies using the National Laboratory of the Rockies (NLR, formerly NREL) and WECC-approved industry-standard GFM IBR models and on a modified IEEE 39-bus system.

For solid-state transformers (SSTs) in high-power grid-connected applications, improperly designed control loops can excite strong inherent AC-DC port coupling, leading to low-frequency oscillation issues, especially under weak grid conditions. To address this problem, this article establishes a multiport admittance matrix for the SST, encompassing its AC dq axes and primary DC port, to characterize its inherent dynamics. Subsequently, a multiport dissipativity analysis is conducted to evaluate the robust stability of the SST. By leveraging the decomposition of passivity conditions into distinct self- and coupling-dissipativity indices, the specific root causes of instability are diagnosed. This framework reveals that a severe coupling-dissipativity failure, induced by the internal dynamics of the synchronization loop, is the dominant instability mechanism rather than a localized self-dissipativity issue. Guided by this diagnosis, a stabilizing controller featuring dynamics-free orthogonal signal reconstruction is designed to reshape the admittance characteristics of the SST. This enhancement specifically targets the identified coupling-dissipativity deficiencies, thereby resolving the root cause of the instability. Finally, the stability analysis and the effectiveness of the enhancement strategy are validated on a down-scaled SST prototype. Experimental results demonstrate that the criterion accurately predicts the coupling-induced oscillations and that the enhanced controller guarantees stable operation under challenging weak-grid conditions.

Electrical railway harmonic instability issues are common in the high-frequency range. The effective frequency of the traditional converter's small-signal averaging model is below 1/2 switching frequency since the pulse width modulation (PWM) sideband harmonic components are ignored. In this article, the dynamic propagations of perturbation frequency and the generated PWM sideband components are constructed first. Then the locomotive rectifier's multi-frequency input-admittance model is derived appropriately. Afterward, an admittance conversion approach is used to convert the multi-frequency model into the single-input-single-output (SISO) model whereas retaining the sideband frequency couplings. The proposed SISO model is more accurate than the traditional small-signal averaging model in the frequency range higher than 1 / 2 switching frequency. It is found that PWM sideband harmonics dominate the locomotive rectifier's input-admittance characteristic higher than 1 / 2 switching frequency. Finally, based on the proposed model, the influence of different switching frequencies, control bandwidths, and traction network impedance on system harmonic stability is revealed by the hardware-in-the-loop (HIL) results.

Due to the use of PI-based d q current decoupling in the pulse rectifier of CRH5 high-speed trains, the PI parameters directly affect the traction system's control performance. Linearized control may have issues with reference trajectory changes or model mismatches, leading to a decrease in system performance, while nonlinear control may have problems with jitter and poor steady-state accuracy. This paper proposes a new control strategy that replaces all PI in the d q current decoupling control with a single intelligent agent. This method based on Deep Reinforcement Learning (DRL) can avoid various drawbacks of linearization and nonlinear control and ensure the stability of intermediate DC voltage. However, when EMUs are in different working conditions and switching, the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm used in traction dual rectifiers does not have a good control effect. Focusing on the issue, Reward Shaping (RS) is added to re-design a nonlinear reward function, which can be combined with Prioritized Experience Replay (PER) to increase the convergence speed of the episode reward. The simulation results show that the improved control strategy can be effectively applied to EMUs working in multiple conditions. Finally, the stability analysis is carried out using Lyapunov's second method and the verification results of the hardware-in-the-loop (HIL) simulation platform show that the DRL control has a good effect.

The problem of Optimal Power Flow (OPF) is central to the secure and economic operation of modern power systems. However, increasing renewable energy penetration, and decreasing system inertia pose significant challenges to conventional optimization-based OPF solvers. While machine learning approaches have demonstrated substantial computational speed-ups, purely data-driven methods often suffer from data dependency, limited generalization, and lack of guaranteed physical feasibility. This paper suggests a physics-informed neural network (PINN) framework for solving the OPF problem in renewable energy-dominated, low-inertia power systems. In contrast to conventional OPF formulations, the model explicitly incorporates a location-aware inertia constraint based on the concept of system inertia strength, which accounts for the electrical distance between generation units and disturbance locations. Simulation results on a 6 GW test system demonstrate high accuracy. The mean absolute error (MAE) for both the training and testing datasets is approximately 0.045% of the total system capacity. The findings demonstrate that the proposed PINN framework is capable of producing highly accurate OPF solutions while ensuring compliance with both physical laws and inertia-related constraints. Overall, the findings highlight the potential of physics-informed learning to enable secure, efficient, and computationally scalable OPF for future low-inertia power systems.

Ambient Internet of Things (A-IoT) deployments impose stringent hardware constraints, including low-order modulations and simple transceiver architectures. We propose a tensor-based grant-free random access scheme for the initial access message (Msg1) tailored to these limitations. Each device transmits replicas of its short payload structured as a rank-1 tensor over multiple access occasions. The A-IoT receiver performs joint activity detection, channel estimation, and decoding via tensor decomposition and successive interference cancellation. Results show significant throughput gains over conventional schemes while preserving low-complexity transmitter designs.

Fast ray tracing (RT) has stimulated the Digital Twin (DT) as an emerging technology for environment-aware communications. Since wireless propagation is governed by the interaction between site geometry and electromagnetic (EM) properties of the environment, DT-based approaches can provide site-specific prior information for channel estimation. In this work, we investigate the robustness of DT to aid the channel estimation, where multipath features extracted via RT are used to construct the low-rank (LR) eigenstructure of the channel covariance matrix. This LR structure is used in channel estimation. However, the digital representation of propagation model is inaccurate and thus it affects the LR. We explicitly analyze these model mismatches that arise from user positioning errors, which translate into geometric inconsistencies in the site representation, and EM material calibration errors. We derive a first-order perturbative model that separates geometric perturbations, affecting angles and delays, from EM perturbations, affecting path gains. Based on this perturbed model, we provide a normalized mean-square error (NMSE) analysis that reveals a fundamental difference between geometric and EM perturbations. In particular, we show that LR estimation is inherently robust to EM calibration perturbations, while positioning errors, dominate performance degradation by altering the channel eigenstructure. Numerical results confirm that, in urban, suburban and rural scenarios, positioning errors are the primary limiting factor, whereas EM calibration errors have a comparatively limited impact. Despite these mismatches, DT-empowered estimators provide up to 10dB NMSE improvement, over baseline methods, in the urban low signal-to-noise ratio (SNR) settings, while achieving performance comparable to baseline estimators at high SNR for moderate (< 1 m) positioning errors.

Renewable energy communities can increase local photovoltaic (PV) use, but feeder-level surplus can still cause reverse power flow in low-voltage networks. Existing sharing coefficient methods are mainly used ex-post for surplus allocation and billing, so they do not directly guide demand toward hours and feeders where shared PV can reduce export. This paper proposes a sharing coefficient-based demand response framework that converts dynamic proportional allocation outcomes into household specific day-ahead price signals. The feeder-aware design first shares surplus within each feeder, while the feeder-agnostic design shares surplus through a single community pool. The energy community manager iteratively computes the allocation from submitted demand and PV forecasts, decomposes purchased energy into same-feeder, inter-feeder, and grid-import components, and coordinates household load reshaping through a convex optimization model. Using measured profiles from 15 households and AC power flow analysis, the framework reduces feeder reverse energy by 45.0% and 44.6% on selected high reverse energy days, and by 69.0% and 66.3% over the annual window, for the feeder-aware and feeder-agnostic cases, respectively. These results show that sharing coefficients can be used not only for ex-post billing, but also as operational price signals for demand response, with feeder-aware allocation providing an additional network benefit by accounting for household location in the low-voltage network.

Unmanned Aerial Vehicle (UAV)-based antenna measurement systems provide a flexible and cost-effective alternative to conventional antenna test ranges for characterizing large and installed antennas. However, their accuracy depends on precise UAV positioning and efficient flight-time utilization, both of which are strongly influenced by the selection of drone assemblies, including the airframe, flight controller, propulsion system, positioning modules, and onboard instrumentation. This paper presents a comprehensive study of UAV-based antenna measurements with emphasis on improving positioning accuracy and optimizing flight endurance through systematic drone assembly selection. The acquired near-field measurement data are susceptible to positioning errors, amplitude and phase inconsistencies, and irregular sampling, which degrade the reconstructed far-field pattern. To address these challenges, the recorded near-field data are processed using the Adaptive Sparse Inverse Radiation Estimation (ASPIRE) algorithm. ASPIRE compensates for positioning inaccuracies and reconstructs the far-field pattern from irregularly sampled near-field data using sparse signal recovery, enabling accurate Near-Field to Far-Field (NF-FF) transformation. At 6.7125 GHz, ASPIRE achieves a residual of 1.94% and a beamwidth error of 0.4 degrees relative to a conventional facility measurement while using only 24% of the 17,298-element RWG mesh as active support. The results demonstrate that the combination of optimized drone assembly selection and ASPIRE-based NF-FF transformation significantly improves the accuracy of UAV-based antenna measurements and produces far-field patterns that closely agree with conventional antenna test range measurements.

We propose a low-complexity LLR approximation for SD decoding in IM-DD systems with relative intensity noise. Our approximation results in no BER performance penalty for a concatenated KP4+Hamming FEC scheme, and outperforms mismatched AWGN-based LLRs.

This study investigates orbital angular momentum (OAM)-based holographic multiple-input multiple-output (HMIMO) links enabled by stacked intelligent metasurfaces (SIM) in the radiative near-field. By using multilayer programmable metasurfaces at both link ends, SIMs enable analog electromagnetic domain wave processing for low-complexity and energy-efficient flexible wavefront synthesis. We analyzed OAM mode generation and reception with SIM-based transceivers and quantified their ability to synthesize near-orthogonal modes in practical discrete HMIMO architectures. We further developed a correlation-driven optimization algorithm that maximizes reconstruction accuracy of OAM beams. Numerical evaluations revealed a fundamental decoupling between the required antenna aperture, which limits the supported mode orders, and the SIM layer depth, which governs crosstalk suppression. The results confirm that properly dimensioned SIM architectures provide robust near-field spatial multiplexing, nearly balanced per-mode capacities, and graceful degradation across link distances without requiring continuous phase re-optimization, thereby supporting scalable and low-overhead HMIMO communications.

This paper studies event-triggered parameter estimation in sensor fusion systems where sensors transmit measurements to a gradient based estimator. We introduce a regressor-driven local triggering rule that requires no knowledge of the current parameter estimate and depends solely on the regressor signals. Under a persistent excitation condition on the aggregate regressor, we derive explicit design inequalities on the estimator gain and event thresholds that guarantee global exponential convergence. The analysis is based on a time-varying Lyapunov function. We further provide a sufficient condition on the regressor dynamics that enforces a uniform lower bound on inter-event times, excluding Zeno behavior. Simulations show substantial communication savings while preserving exponential convergence.

Deep reinforcement learning (DRL) has been widely adopted for wireless resource allocation due to its model-free adaptability. However, online exploration is costly, as randomly initialized policies may violate long-term constraints before sufficient data are collected. Future wireless systems must cope with increasingly dynamic traffic, fluctuating channel conditions, and stringent energy efficiency requirements, demanding algorithms that can learn quickly with minimal environment interactions to reduce both energy consumption and signaling overhead. We develop Fused-CPRO, a knowledge-fused constrained policy reuse optimization method addressing these challenges. Fused-CPRO constructs the allocation policy as a mixture of a learnable target policy, source policies from related scenarios, and domain-knowledge (DK) policies from expert rules, jointly optimizing the target policy and reuse probabilities under a constrained Markov decision process (CMDP). This fusion of heterogeneous priors accelerates convergence and enhances robustness. Constrained stochastic successive convex approximation (CSSCA) handles non-convex objectives and constraints, while a critic trained from mixed offline-online data improves sample efficiency by reusing pre-collected experience. We prove almost-sure convergence to a Karush-Kuhn-Tucker (KKT) point. Simulations on delay-constrained multi-user multiple-input multiple-output (MU-MIMO) power control and Cramer-Rao bound (CRB)-constrained multiple-input multiple-output integrated sensing and communication (MIMO-ISAC) beamforming demonstrate that Fused-CPRO improves empirical performance and converges substantially faster than representative baselines.

Continuous-aperture arrays (CAPAs) offer rich electromagnetic degrees of freedom for integrated sensing and communication (ISAC), but optimizing continuous current distributions leads to challenging infinite-dimensional problems. This paper investigates a CAPA-enabled downlink ISAC system with symbol-level precoding and receive polarization combining. The transmit current is optimized to maximize weighted target-illumination power while enforcing constructive-interference constraints for communication users. To address the resulting infinite-dimensional nonconvex problem, we establish that the optimal current distribution lies in a finite-dimensional subspace spanned by the communication and sensing electromagnetic responses. This result yields an exact, structure-preserving reformulation in terms of finite-dimensional coefficients. A penalty projected-gradient algorithm is then developed to jointly optimize the current coefficients and polarization combiners. Simulation results demonstrate that the proposed framework achieves higher sensing utility and improved communication reliability than conventional Fourier-basis CAPA and spatially discrete array baselines.

Sepsis is a leading cause of mortality, yet optimal treatment policies remain contested. Existing reinforcement learning (RL) approaches learn fixed strategies for sepsis treatment, limiting adaptability to changing clinical objectives during inference. We propose EHRMPC, a framework that decouples learning patient dynamics from optimizing treatment by training a patient digital twin in the form of a generative electronic health record (EHR) model. The digital twin predicts clinical trajectories under interventions and enables model predictive control (MPC) to optimize treatments via inference-time planning over simulations. We evaluate EHR-MPC on a multicenter ICU sepsis cohort spanning 8 hospitals in the Mass General Brigham health system using both off-policy importance sampling and on-policy simulation-based evaluation. Relative to RL baselines, EHR-MPC achieves comparable off-policy performance and improved simulation performance. Unlike RL, this work frames sepsis treatment optimization as inference-time control over learned patient dynamics, establishing a general framework for decision making with generative clinical models.

Perfusion imaging guides clinical evaluation of stroke and brain tumors by characterizing tissue-level hemodynamics. Routine quantification relies on manual arterial input function (AIF) selection followed by deconvolution, producing summary maps without an executable temporal model for simulation or mechanistic insight. Tracer-dynamics-based models infer transport or compartmental parameters from perfusion time series, but do not yield clinically actionable perfusion indices (e.g., CBF, CBV, MTT) that inform diagnosis and treatment decisions. In this work, we propose HemoPIC, a physics-informed cerebral hemodynamics digital twin that explains perfusion time series through tracer mass conservation and a lumped parameter hemodynamic model. Specifically, HemoPIC solves a constrained inverse problem that jointly estimates digital twin parameters and latent states from perfusion imaging, eliminating manual AIF selection and deconvolution from routine perfusion quantification while directly producing clinically actionable perfusion summary maps. Experiments demonstrate that HemoPIC reconstructs tracer dynamics, generates physiologically consistent perfusion maps with lesion hypoperfusion patterns, satisfies central volume consistency, and yields a mechanistic hemodynamic digital twin that enables forward simulation and counterfactual intervention analysis. Code is publicly available at this https URL.

The functional organization of the brain relies on coordinated activity across spatially distributed regions, making the analysis of inter-regional dependencies fundamental. Existing connectivity measures address this predominantly through phase synchronization, which is vulnerable to volume conduction artifacts and discards amplitude-domain coupling. This study introduces the Spatial Neighboring Scattering Transform, which extends the wavelet scattering transform to the multichannel setting, yielding two descriptors that jointly capture amplitude-envelope coupling between channels and its modulation across frequency scales. SNST was evaluated on the BCI Competition IV-2a motor imagery dataset using a bias-corrected, false-discovery-rate-controlled statistical pipeline, with the validation criterion defined as spatial consistency of significant coupling across subjects. The first-order descriptor identified statistically significant amplitude coupling within a central-parietal electrode neighborhood, reproduced consistently across all subjects and both imagery conditions. The second-order descriptor revealed that this coupling is periodically gated by slow rhythms, indicating a cross-frequency amplitude-modulation structure absent from single-frequency connectivity measures. Phase lag index and weighted phase lag index, computed under an identical correction procedure and verified robust to volume conduction, identified negligible significant coupling with zero overlap with SNST findings, demonstrating that amplitude envelope coupling constitutes a largely distinct connectivity signal. These results establish SNST as a cross-channel scattering-based connectivity descriptor that recovers amplitude-envelope and cross-frequency coupling structure systematically, applicable to any multichannel EEG analysis where amplitude-domain inter-regional dependence is of interest.

A model predictive controller (MPC) is used to construct a virtual assistant to aid a physician in prescribing cortisol replacement therapy for patients with adrenal insufficiency (AI). AI, also known as hypocortisolism, is a condition that occurs due to a low concentration of cortisol. This hormonal imbalance significantly impacts the individual's ability to regulate stress, metabolism, and immune responses. Thus, it is essential to maintain cortisol levels within a healthy range. The production of cortisol is governed by the hypothalamus-pituitary-adrenal (HPA) axis, a part of the endocrine system. In this paper, a novel mathematical model of the HPA axis is proposed that incorporates the endogenous circadian rhythm. This model simulates two conditions of hypocortisolism: primary and secondary AI. Adrenal insufficiency cannot be cured, but it can be treated with cortisol replacement therapy. The standard practice is to prescribe a therapeutic dose of hydrocortisone (HC). To evaluate the accuracy of the proposed HPA axis model, an open-loop cortisol replacement strategy with a fixed dosage is used to simulate both primary and secondary AI. The simulation results show that, analytically, it is possible to arrive at a fixed working cortisol replacement strategy. However, this strategy, though effective, is not optimal. To obtain optimal cortisol replacement strategies, an MPC is proposed. An important feature of MPC is that constraints on allowable cortisol replacement dosages can be rigorously addressed. This controller can serve as a virtual assistant to physicians in prescribing daily cortisol replacement therapy.

Distributed IoT systems generate multivariate time-series streams for monitoring physical assets, servers, and embedded sensing platforms. Detecting abnormal temporal behavior is critical for fault diagnosis, predictive maintenance, and security. However, practical IoT anomaly detection is hindered by decentralized and non-IID data, limited bandwidth, and the constrained computation and memory of edge devices. This paper proposes FedKAD, a resource-efficient federated Koopman anomaly detection framework for distributed IoT multivariate time series. Unlike deep-learning-based anomaly detectors that require training and communicating large neural models, FedKAD learns normal temporal dynamics through lightweight sliding-window Koopman representations. Federated training is formulated as a low-rank consensus problem, where raw sensor streams and local reduced dynamics remain on device while only compact subspace variables are exchanged with the server. To optimize the shared representation under orthonormality constraints, we develop a federated Stiefel-ADMM algorithm and provide convergence and stationarity analysis under partial client participation. During inference, each client detects anomalies locally by measuring the prediction residual between observed future trajectories and the learned Koopman dynamics. Experiments on four widely used multivariate time-series anomaly detection benchmarks show that FedKAD maintains or improves detection performance compared with federated deep-learning baselines. More importantly for IoT deployment, FedKAD provides up to $2.1\times10^3$ faster training, $80\times$ lower communication, and $79\times$ lower inference latency than neural baselines, confirming its suitability for resource-constrained edge devices.

Large language model (LLM)-based audio-visual speech recognition (LLM-AVSR) has recently demonstrated strong robustness in adverse acoustic environments by leveraging complementary audio and visual information. Existing approaches typically employ independently pretrained acoustic and visual encoders, whose outputs are projected and fused as soft prompts to condition an LLM for speech recognition. However, most methods perform multimodal fusion without explicitly addressing the representational discrepancy between audio, visual and text modalities, potentially limiting the effectiveness of cross-modal integration. In this paper, we propose an optimal transport (OT)-based semantic alignment framework for LLM-AVSR. The proposed method explicitly bridges the modality gap by aligning the acoustic and visual representations with reference to the linguistic embedding space of the LLM before multimodal fusion. Specifically, OT is used to estimate probabilistic coupling matrices that characterize structured correspondences between modality-specific features and linguistic embeddings. The resulting OT couplings are further utilized as soft pseudo-labels to supervise contrastive learning, encouraging the extraction of semantically coherent and cross-modal consistent audio-visual representations. By anchoring multimodal features to the linguistic space of the LLM, the proposed framework facilitates more effective multimodal fusion and decoding. We implement the proposed framework using a Whisper-based acoustic encoder, an AV-HuBERT-based visual encoder, and a LLaMA3.2-3B decoder. Experiments conducted on the LRS3-TED benchmark demonstrate consistent improvements over strong baselines and achieve state-of-the-art performance under both clean and noisy evaluation conditions across a wide range of signal-to-noise ratios (SNRs).

Data-driven methods, including graph neural networks, have been studied for accelerating power flow calculations in recent years, but very little attention has been paid to the solution feasibility, which can be obtained by traditional solvers. This paper presents a Variational Graph Autoencoder (VGAE) that detects the power flow solution feasibility, using the IEEE 118-bus case, to assess the validity of the solutions provided by AI-driven solvers.

Representation alignment (REPA) has been investigated to accelerate diffusion training, but we observe that regularizing intermediate representations in diffusion Transformers (DiT) may implicitly entangle latents and limit generative capacity. To address this issue, we propose ReGen, a hierarchical multi-prompt representation generation framework that jointly estimates multiple vector fields for both representations and data within a single diffusion model. We further introduce generalized flow matching (GFM) to improve the generalization of conditional flow matching (CFM). We validate ReGen on single-stage waveform diffusion models including neural audio codec and Wave-VAE. ReGen significantly improves waveform generation quality from highly compressed latent representations at 12.5 Hz. We also present ReGenVoice, a latent diffusion model (LDM)-based text-to-speech model that achieves strong speech intelligibility (WER) and speaker similarity (SIM) with a small dataset. Moreover, operating the LDM at 6.25 Hz with rich semantic and acoustic latent representation enables efficient training and sampling, requiring only 1 day of training on 4 GPUs and fast inference with an RTF of 0.08. Audio samples are available at this https URL.

We study the optimal transport of optimally controlled agents from a compactly supported absolutely continuous source to a discrete target measure. The ground cost for the transport is induced by the optimal cost of the agents' motion. When this ground cost satisfies the twist condition, the optimal transport map is given almost everywhere in terms of a Laguerre tessellation of the state space. We refer to this control-theoretic generalization of Laguerre tessellation as Control Laguerre Tessellation (CLT), and illustrate it for two ground costs induced by linear controlled agents with minimum energy and minimum time objectives.

Mobile robots are going to share the sidewalks with pedestrians. They must ensure their objective safety and respect the walkers' subjective safety/comfort. Computationally efficient Social Force Models (SFM) present interpretable solutions for real-time robot navigation in dynamic crowds. Recent explorations of Projected Time-to-collision (PTTC) integration into SFM variants, for example, PTTC-based SFM (TSFM), improve safety metrics. But the effect of predictive variants is unclear. We introduce Predictive SFM (PSFM) and Predictive TSFM (PTSFM) by integrating predicted social force vectors over a finite time horizon. The paper implements SFM, TSFM, PSFM, and PTSFM on a nonholonomic mobile robot and performs experimental trials with volunteers attending a facing scenario. We systematically study objective and subjective safety across the variants. Minimum PTTC, average speed, minimum distance, lateral distance, and the maximum trajectory curvature benchmark the objective safety. Likert scale post-interaction surveys assess subjective safety by marking comfort, smoothness, distance appropriateness, and speed suitability. We confirm that PTTC integration improves safety metrics. The prediction contribution is limited and occasionally visible in some of the sub-metrics. Some participants perceive smoother movements and safer speed behavior with predictive methods, but Mann-Whitney tests reveal no significant differences in subjective ratings. Therefore, PTTC-based navigation enhances safety, whereas the formulated prediction offers limited additional benefits in single-pedestrian scenarios.

Aperture masking interferometry is a technique originally designed to alleviate the influence of atmospheric turbulence on images recorded on ground-based telescopes. In this communication, we explore the optimization of the aperture mask by an optical/digital co-design approach in order to obtain diffraction-limited images of relatively bright objects imaged through turbulence. We show that, with a few simplifying assumptions, it is possible to express the Mean Square Error of the restored image as a function of the chosen mask, of the spatial Power Spectral Density of the observed object and of the noise level, without actually computing any image. This allows us to optimize the aperture mask with a reduced computing cost. We also implement a multi-frame myopic algorithm to estimate jointly the observed object, the piston and the tip-tilt in front of each sub-aperture, and check by simulations that the aperture masks obtained indeed allow a satisfactory image reconstruction.

Reliable terrain understanding is a prerequisite for autonomous robot navigation. Yet, the widespread RGB-based perception can fail under low illumination, shadows, and material ambiguities. In this work we propose DRIFT, a lightweight multispectral framework that combines raw spectral bands and illumination-tolerant band-ratio representations through a dual-stream residual architecture and a differential fusion branch. Band ratios attenuate multiplicative acquisition effects (illumination/sensor gains), while the differential fusion explicitly highlights discrepancies between absolute-band and ratio-derived cues, which improves the robustness to noisy or partially unreliable spectral measurements. In the paper (i) we evaluate DRIFT on a new oil-on-soil multispectral dataset acquired using a MicaSense RedEdge-P camera mounted on an Unmanned Aerial Vehicle, and (ii) we provide an additional controlled study on water-on-grass under varying illumination and thermal perturbations (hot/cold water) to analyze NIR-sensitive effects. DRIFT consistently improves over strong baselines, while remaining compatible with edge deployment.

We present the commissioning and operation of the Graph Neural Network Electromagnetic Calorimeter Trigger Module (GNN-ETM) of the Belle II experiment at the SuperKEKB collider. The GNN-ETM processes calorimeter trigger cells as graph nodes to perform clustering and feature extraction. We fully integrate the system with the successive stages of the first-level trigger, develop slow-control drivers, and add online monitoring capabilities. We optimise the existing FPGA-based architecture through hardware-algorithm co-design, achieving an overall system latency of 1.053 us. Our hardware implementation is validated through register-transfer-level simulations, achieving bit-accurate agreement with the offline reference model. Online monitoring enables the measurement of instantaneous trigger rates, providing a quantitative basis for trigger-level performance studies. In summary, we report on the GNN-ETM as a fully operational, low-latency trigger module with online control and monitoring capabilities, compatible with the latency requirements of the Belle II first-level trigger system.

We study the problem of multi-snapshot spike deconvolution, where the goal is to recover the locations of sparse impulses from their noisy convolution with a known point spread function (PSF) across multiple snapshots. We adopt a variable-projection formulation that eliminates the amplitudes in closed form, reducing the task to a nonconvex least-squares problem over the spike locations alone, which we refer to as the variable-projection formulation of spike deconvolution (VarProSD). We provide an explicit characterization of the basin of convexity of the VarProSD objective in terms of key PSF properties, including its power spectral density and smoothness, revealing how sampling bandwidth and spike separation influence the local geometry. Within this basin, we establish that the estimator is consistent in the number of snapshots under stochastic noise, and provide a complementary, sharper error bound under adversarial noise via the local Lipschitz property of the inverse map. We further show local convergence guarantees for gradient descent when initialized within the basin. A central ingredient throughout is the use of Beurling--Selberg extremal approximations, which enable sharp, PSF-agnostic bounds on the conditioning of the structured matrices arising in the optimization landscape. Numerical experiments validate our theoretical findings and demonstrate the effectiveness of modified ESPRIT initialization followed by gradient-based refinement.

As chip complexity increases and time-to-market pressures grow, front-end design has become a critical bottleneck in chip development. Recently, Large Language Models (LLMs) have shown great potential in Electronic Design Automation (EDA). Beyond specification understanding, LLMs show the potential to serve as a unified intelligent interface for hardware description language (HDL) generation, testbench construction, and design space exploration. The rise of agentic AI, represented by pioneering systems such as OpenClaw, offers a strategic roadmap for the next generation EDA. From this perspective, this paper discusses the evolution of EDA from localized assistance to autonomous agentic execution. Then, we review representative advances of LLMs in front-end design, focusing on key tasks such as circuit and testbench generation from a shared specification, as well as design quality improvement in established workflows such as high-level synthesis. Finally, we discuss the key challenges and limitations of integrating LLMs into EDA, and outline future opportunities for advancing LLM-enabled front-end design, offering a systematic perspective for researchers interested in leveraging agentic AI technologies for EDA.

Current electroencephalography (EEG)-based dream detection relies on power spectral density (PSD) and statistical moment features, achieving a state-of-the-art area under the receiver operating characteristic curve (AUC) of approximately 0.70 on the DREAM database (Wong et al., 2025, Nature Communications). We introduce PHINN-EEG (Persistent Homology Inspired Neural Network for EEG), the first topological time-series framework for dream mentation analysis. Using sliding-window Takens delay embeddings and Vietoris-Rips filtrations on multichannel pre-awakening EEG epochs, we extract Dynamic Betti Curves that characterize the geometric architecture of neural activity, not merely its energy. These topological invariants, combined with topology-conditioned flow matching, are analytically projected to outperform existing PSD and catch22 benchmarks, targeting AUC = 0.82-0.90 on the 1,462-awakening open-access subset of the DREAM database (drawn from a full registry of 3,191 total awakenings from 263 participants across 20 independent laboratories). We further introduce a topology-conditioned rectified flow model for dream-state EEG synthesis-with a spectral-conditioned flow model of comparable feature dimensionality as an additional ablation baseline to isolate the value of topological conditioning specifically-and propose a set of candidate Betti transition archetypes linking topology to phenomenological dream report categories, presented as an exploratory hypothesis space pending empirical validation. If validated, this work represents a paradigm shift from spectral energy to phase-space geometry in neural rare-event detection, with potential future implications for wearable BCI dream monitoring.

This paper investigates the impact of loss function design in deep unfolding techniques for sparse signal recovery algorithms. We focus on deep unfolded versions of the fundamental iterative shrinkage thresholding algorithm (ISTA) and the iterative hard thresholding algorithm (IHT). To obtain a guideline for the loss function design, we examine the effect of supervised learning using mean squared error and unsupervised learning using the objective function of the original optimization problem. Our simulation results reveal that the effect of loss function design significantly depends on the convexity of the optimization problem. For convex $\ell_1$-regularized problems, supervised-ISTA achieves better final recovery accuracy but fails to minimize the original objective function, whereas we empirically observe that unsupervised-ISTA converges to a nearly identical solution as conventional ISTA but with accelerated convergence. Conversely, for nonconvex $\ell_0$-regularized problems, both supervised-IHT and unsupervised-IHT converge to better local minima than the original IHT, showing similar performance under the training conditions regardless of the loss function employed. However, when the test conditions differ from the training conditions, our results suggest that unsupervised learning offers better robustness to distribution mismatch. These findings provide valuable insights into the design of effective deep unfolded networks for sparse signal recovery applications.

In this work, we propose an event-triggered moving horizon estimation (ET-MHE) scheme for the remote state estimation of general nonlinear systems. In the presented method, whenever an event is triggered, a single measurement is transmitted and the nonlinear MHE optimization problem is subsequently solved. If no event is triggered, the current state estimate is updated using an open-loop prediction based on the system dynamics. Moreover, we introduce a novel event-triggering rule under which we demonstrate robust global exponential stability of the ET-MHE scheme, assuming a suitable detectability condition is met. In addition, we show that with the adoption of a varying horizon length, a tighter bound on the estimation error can be achieved. Finally, we validate the effectiveness of the proposed method through two illustrative examples.

The concept of a security index quantifies the minimum number of components that must be compromised to carry out a stealth attack. This metric enables system operators to assess the security risk of each component and implement countermeasures accordingly. In this paper, we introduce a data-driven security index that can be computed solely from input/output data when the system model is unknown. We show a sufficient condition under which the data-driven security index coincides with the model-based security index, which implies that the exact risk level of each component can be identified solely from data. We also provide an algorithm for computing the data-driven security index.

This paper presents an autonomous sensing framework for identifying and localizing multiple User Equipments (UEs) in Fifth Generation (5G) cellular networks using a non-serving Unmanned Aerial Vehicle (UAV). A complete onboard processing chain is developed to perform synchronization, multi-UE identification, and localization directly from standard 3GPP-compliant uplink Sounding Reference Signals (SRS). Unlike conventional UAV-assisted approaches relying on serving nodes or infrastructure support, the proposed platform operates as a passive sensing UAV, requiring only limited initial coordination with the network and no mission-time control-plane interaction. The approach exploits the structured and periodic nature of SRS transmissions together with a tailored protocol configuration to ensure robust operation under realistic multi-UE interference. The system operates with narrowband SRS (1.4 MHz), reducing UE power consumption and hardware complexity while enabling high multiplexing through cyclic shifts and frequency resources. Reliable synchronization and multi-UE identification are achieved even when multiple UEs share the same resources. The UAV autonomously collects measurements along its trajectory and estimates UE positions using a trajectory-based localization strategy. The proposed framework is validated through extensive simulations and a full-scale experimental campaign, achieving localization errors below 8 m in urban scenarios and below 3 m in rural conditions, outperforming state-of-the-art Angle of Arrival (AoA)- and Time Difference of Arrival (TDoA)-based methods by about 5-6 m. These results demonstrate the feasibility of infrastructure-independent sensing UAVs for Low-Altitude Wireless Networks (LAWN), enabling scalable and rapidly deployable situational awareness in emergency and connectivity-limited environments.

The Federal Interagency Traumatic Brain Injury Research (FITBIR) database is a centralized data repository for traumatic brain injury (TBI) research. It includes over 45,529 magnetic resonance images (MRI) from 6,211 subjects (9,229 imaging sessions) across 26 studies with heterogeneous organization formats, contrasts, acquisition parameters, and demographics. In this work, we organized and harmonized all available structural and diffusion MRI from FITBIR along with relevant demographic information into the Brain Imaging Data Structure. We analyzed whole-brain mean fractional anisotropy, mean diffusivity, total intracranial volume, and the volumes of 132 regions of interest using UNesT segmentations. There were 4,868 subjects (7,035 sessions) with structural MRI and 2,666 subjects (3,763 sessions) with diffusion MRI following quality assurance and harmonization. We modeled profiles for these metrics across ages with generalized additive models for location, scale, and shape (GAMLSS) and found significant differences in subjects with TBI compared to controls in volumes of 15 regions of the brain (q < 0.05, likelihood ratio test with false discovery rate correction).

Inverse systems for discrete-time linear periodically time-varying (LPTV) plants are fundamental to feedforward control and iterative learning control of multirate and periodic systems. Building on the classical cyclic reformulation, which converts an N-periodic system into an equivalent LTI system at the original sampling rate, this paper derives an explicit closed-form N-periodic state-space realization of the inverse for an arbitrary uniform periodic relative degree r >= 0 (defined through the periodic Markov parameters). The key technical result is a structure-preservation property: after absorbing a phase shift for r >= 1, the LTI inverse of the cycled plant provably retains the cyclic (block-circulant/block-diagonal) structure, so that the periodic inverse matrices can be read off block-by-block. The resulting inverse system is real-valued, causal for r = 0 and r-step-delayed for r >= 1, operates at the original sampling rate, and reconstructs the input exactly under matched initial conditions, with geometric error decay otherwise. Its stability is characterized by the invariant zeros of the cycled plant, generalizing the minimum phase condition of the LTI case. Numerical examples illustrate the construction, the stability characterization, and the implementation as an online periodic filter.

Pinching-antenna systems is a promising architecture for flexible wireless communications, but energy efficiency (EE) maximization remains largely unexplored, as limited existing studies mainly focus on transmit power minimization. This paper investigates EE maximization in a downlink non-orthogonal multiple access (NOMA)-assisted PASS by explicitly modeling the pinching antenna (PA) activation power and jointly optimizing discrete PA activation and power allocation under both quality-of-service and transmit power constraints. To tackle the resulting mixed-integer nonlinear programming problem, a two-layer iterative algorithm is proposed with an EE-oriented matching-based PA activation and a low-complexity Dinkelbach-based power allocation with closed-form updates. Numerical results demonstrate that the proposed solution achieves substantial EE gains over the considered benchmark schemes, while exhibiting fast convergence. The impact of activation power has been analyzed and the significance of accounting it in EE maximization problem is also demonstrated.

Liquid-cooled exascale supercomputers dissipate heat through cooling plants organized as multiple parallel subloops, but how to allocate coolant distribution units (CDUs) across subloops and how to distribute flow among them has not been systematically addressed for facilities at this scale. This paper presents a three-layer optimization framework that jointly determines the integer partition of CDUs across subloops, the continuous flow fraction allocation, and the per-timestep co-design optimization of total flow rate and supply temperature subject to per-subloop thermal safety constraints. The Modelica simulation model is built based on the data of Frontier exascale supercomputer at Oak Ridge National Laboratory. By developing a reduced-order surrogate model, all 611 feasible partitions of 25 CDUs are evaluated across the full year operational dataset of 49,353 timesteps. Three progressively richer operational strategies are compared, ranging from flow control optimization to full three-layer co-design optimization with dynamically adjusted flow fractions. The optimal design within the surrogate optimization problem is a two-subloop plant achieving 35.48% annual cooling energy savings, only 0.18% above the current three-subloop Frontier design at 35.30%. Most of the savings are delivered by supervisory co-optimization of total flow rate and supply temperature; the distinct role of flow fraction optimization is design robustness rather than additional raw savings. Flow fraction optimization compensates for any feasible CDU-to-subloop assignment, reducing the design sensitivity by 93% and providing a low-cost software-only pathway to near-optimal performance on the existing Frontier hardware. The framework is transferable to other liquid-cooled high-performance computing this http URL is transferable to other liquid-cooled high-performance computing plants.

HyperSpectral-MultiSpectral Image (HSI-MSI) fusion aims to recover a high-resolution hyperspectral image from a low-resolution HSI and a high-resolution MSI. Classical methods such as Coupled Nonnegative Matrix Factorization (CNMF) benefit from a strong physical interpretability but suffer from inferior results compared to their deep-learning counterparts. To address this limitation, we propose SCALMU (Synthetically-trained Coupling of Adaptive Learned Multiplicative Updates), a novel blind unrolled neural network architecture that integrates adaptive learnable matrices within the classical framework of CNMF multiplicative updates, improving its results. Due to its architectural proximity with CNMF, the resulting algorithm preserves physical interpretability and nonnegativity constraints. To overcome the scarcity of supervised training data, we generate a synthetic HSI-MSI dataset using the dead leaves model and train SCALMU end-to-end under synthetic supervision. Experiments on several datasets show that SCALMU outperforms state-of-the-art methods and highlights the potential of blind fusion trained with synthetic data. The code is available at this https URL

Stacked intelligent metasurfaces (SIMs) are emerging as a promising architecture for sixth-generation (6G) and beyond wireless systems, enabling richer electromagnetic-wave manipulation than conventional single-layer metasurfaces. However, strong inter-layer coupling and multilinear parameter interactions make accurate, scalable channel estimation challenging. This paper proposes TenSIM, a tensor-based channel-estimation framework for SIM-assisted multiple-input multiple-output (MIMO) systems. By exploiting a structured SIM training protocol, TenSIM derives two parity-dependent observation models: a PARAllel FACtor (PARAFAC) model for odd-layer SIMs and a Tucker model for even-layer SIMs. These formulations decouple the transmitter-SIM and SIM-receiver channel factors while accounting for inter-layer wave coupling. Based on these tensor models, we develop alternating least squares estimators, establish rank-based identifiability conditions using the associated design matrices, and provide practical sufficient conditions for full-column-rank training designs, including scaling ambiguities. Numerical results reveal the main trade-offs. Both TenSIM-PARAFAC and TenSIM-Tucker improve with signal-to-noise ratio and training diversity, outperforming unstructured least-squares baselines by exploiting the tensor structure of the SIM cascade. TenSIM-PARAFAC offers better scalability, lower complexity, and stronger robustness to inter-layer spacing, whereas TenSIM-Tucker can achieve more accurate channel reconstruction when sufficient training and strong layer coupling are available. The framework also remains effective under imperfect or blind SIM training with additional pilot diversity. Overall, TenSIM offers a unified, physically interpretable approach to channel estimation in SIM-assisted MIMO systems, with explicit identifiability, complexity, and performance trade-offs.

We propose PreSPA (Partial-Reference Structural Prediction Approach), a Partial-Reference Image Quality Assessment framework that decomposes perceptual quality into two complementary indices. A structure-aware index, operating in a No-Reference manner, captures structural degradation through Hermite-Gauss prediction of the distorted gradient field and the angular variance of its curvature. A texture-sensitive index estimates local noise through a scalar prior $\mu$, obtained from energy differences between reference and distorted complex gradient maps on strong-edge regions and accumulated over weakly-structured ones, reflecting the perceptual leakage of degraded edges into surrounding textures. Crucially, $\mu$ is the only information extracted from the reference and is computed once per image pair, reducing the reference footprint to a single scalar value. The final score is produced by an affine fusion with only three interpretable parameters, making the method compact, transparent, and computationally efficient, with the viewing distance embedded into the operator scale and no dataset-specific calibration. Extensive evaluations on six standard benchmarks show that PreSPA consistently rivals or exceeds leading No-Reference approaches, while in several cases matching the accuracy of Full-Reference models.

Visual Place Recognition (VPR) in mobile robotics enables robots to localize themselves by recognizing previously visited locations using visual data. While the reliability of VPR methods has been extensively studied under conditions such as changes in illumination, season, weather and viewpoint, the impact of motion blur is relatively unexplored despite its relevance not only in rapid motion scenarios but also in low-light conditions where longer exposure times are necessary. Similarly, the role of image deblurring in enhancing VPR performance under motion blur has received limited attention so far. This paper bridges these gaps by introducing a new benchmark designed to evaluate VPR performance under the influence of motion blur and image deblurring. The benchmark includes three datasets that encompass a wide range of motion blur intensities, providing a comprehensive platform for analysis. Experimental results with several well-established VPR and image deblurring methods provide new insights into the effects of motion blur and the potential improvements achieved through deblurring. Building on these findings, the paper proposes adaptive deblurring strategies for VPR, designed to effectively manage motion blur in dynamic, real-world scenarios.

Hearables are becoming ubiquitous, yet their sound controls remain blunt: users can either enable global noise suppression or focus on a single target sound. Real-world acoustic scenes, however, contain many simultaneous sources that users may want to adjust independently. We introduce Aurchestra, the first system to provide fine-grained, real-time soundscape control on resource-constrained hearables. Our system has two key components: (1) a dynamic interface that surfaces only active sound classes and (2) a real-time, on-device multi-output extraction network that generates separate streams for each selected class, achieving robust performance for upto 5 overlapping target sounds, and letting users mix their environment by customizing per-class volumes, much like an audio engineer mixes tracks. We optimize the model architecture for multiple compute-limited platforms and demonstrate real-time performance on 6 ms streaming audio chunks. Across real-world environments in previously unseen indoor and outdoor scenarios, our system enables expressive per-class sound control and achieves substantial improvements in target-class enhancement and interference suppression. Our results show that the world need not be heard as a single, undifferentiated stream: with Aurchestra, the soundscape becomes truly programmable.