Papers

Reinforcement learning offers the prospect of a reusable sequential decision-making mechanism for spacecraft trajectory design, motivating policy interfaces that connect learned decisions to the underlying maneuver geometry. This paper develops Reachability Analysis-Informed Reinforcement Learning (RARL) for deterministic multi-impulse interplanetary transfers, placing intermediate waypoint selection at the center of the learned decision process. Local first-order reachability maps bounded velocity perturbations into an ellipsoidal set of next-node positions, within which the policy selects its waypoint. Lambert reconstruction then determines the corresponding maneuver to reach this selected waypoint along a dynamically consistent ballistic arc, coupling learned transfer-geometry selection with classical astrodynamics. A terminal two-impulse reconstruction completes the rendezvous, supported by a linear maneuver-demand assessment used for reward shaping. Numerical studies characterize this interface on a two-body Earth-Mars benchmark. Across three independent training runs, RARL achieves a mean maneuver cost of 10.23 km/s, 1.72% above a validated local sequential convex programming reference. Training over dispersed initial states extends policy reuse across a departure family with fixed target state and transfer duration. Each of the three independently trained multi-state policies completes all 10,000 held-out Monte Carlo departures without impulse-cap violations, compared with a mean feasibility rate of 6.49% for single-state policies. This broader sampled feasibility is accompanied by a 0.61% increase in mean nominal maneuver cost, without further training across departures. These results demonstrate that a reachability-informed decision interface supports benchmark-quality trajectory construction and policy reuse across dispersed departure conditions.

Studying brain lateralization and the resulting hemispheric asymmetry has been a topic of interest for many researchers. Very few studies have focused on differences in brain lateralization and hemispheric asymmetry associated with neurodegeneration. Additionally, Quantitative Susceptibility Mapping (QSM) has not been extensively used to investigate these differences. This article aims to study differences in hemispheric asymmetry between healthy and Alzheimer's disease (AD)-affected brains by calculating the Myelin Hemispheric Ratio (MHR) and Oxygen Extraction Fraction Hemispheric Ratio (OEFHR) between the two hemispheres using QSM as an intermediate marker of neurodegeneration. The estimations and corresponding results show marginal yet promising differences between healthy subjects and AD patients. These encouraging initial results emphasize the need to further investigate structural brain asymmetry and its alteration as a potential marker of AD-related neurodegeneration.

This paper presents a control allocation framework with adaptive augmentation for a trailing edge morphing aircraft, providing stability guarantees under uncertainty while exploiting the available morphing degrees of freedom to optimize aerodynamic efficiency. A baseline controller is designed using the nominal aircraft model to establish the desired closed-loop reference dynamics. For the aerodynamics, the wing shape is optimized dependent on the flight state to achieve a target elliptical lift distribution corresponding to minimum induced drag. The adaptive augmentation is tailored to the control allocation problem to account for the uncertain system dynamics and stabilize the aircraft around the reference dynamics. The resulting stabilization condition is formulated as hard constraint in the allocation problem while minimizing deviations from the corresponding state-dependent aerodynamically optimal wing shape. Numerical results demonstrate that the adaptive augmentation compensates for matched uncertainties while the control allocation optimizes the wing shape with respect to the reference elliptical lift distribution.

Data-driven model order reduction methods such as operator inference enable the efficient construction of reduced-order models directly from high-dimensional time-domain data. Standard operator inference typically seeks a Galerkin-type reduced model in a prescribed low-dimensional subspace by identifying its reduced operators from projected snapshot data. The resulting inference problem is formulated as a least-squares problem admitting an efficient closed-form solution. However, it is well known from intrusive model order reduction for linear time-invariant systems that Petrov-Galerkin projections can additionally preserve important system properties such as stability and passivity. To overcome the limitations of standard operator inference, we extend the framework for linear time-invariant systems to incorporate Petrov-Galerkin projections and provide explicit error expressions and bounds between the intrusive and nonintrusive reduced operators, thus generalizing results from the literature. We demonstrate the proposed approach in the context of dissipative and port-Hamiltonian systems. Furthermore, we introduce a novel convex optimization formulation that explicitly enforces the port-Hamiltonian structure on the inferred operators. The effectiveness of the proposed methods is demonstrated on several well-established benchmark problems, including the CD player, an atmospheric model, a mass-spring-damper system, and a poroelasticity system.

Smartphones rely on Global Navigation Satellite System (GNSS)-based positioning for many of the functions they execute everyday. The GNSS receivers embedded in smartphones are susceptible to anthropogenic radio frequency interference attacks in the forms of jamming and spoofing due to the low-power and open-architecture signals they receive from the satellite constellations. While jamming is a practice that denies a GNSS receiver the ability to form a position, velocity, and time (PVT) solution, spoofing represents a more insidious threat by using forged satellite signals that aim at causing the victim receiver to compute a false PVT solution. The ubiquity of smartphones and the sensitive geolocation data they hold make them a primary target for malicious spoofing. However, their hardware constraints and the lack of deep visibility into the GNSS receiver processing chain create significant hurdles for effective countermeasures. Existing surveys comprehensively explore general spoofing countermeasures but fail to address these mobile-specific limitations. This article fills that gap with a novel survey focused on techniques viable within the unique constraints of smartphone architectures. Specifically, we establish a taxonomy for defining GNSS spoofing attack effects and countermeasures, provide a historical review of smartphone vulnerability characterization, and provide an overview of techniques proposed to detect and counteract smartphone spoofing threats, offering a comparative framework to weigh their respective pros and cons on mobile platforms.

Movable antenna (MA) arrays reshape the propagation channel by mechanically changing the element positions, which suits Internet-of-Things access points whose devices cannot adapt on their own. Existing position optimization assumes the channel is known at every candidate configuration. In practice a configuration can be evaluated only after the array has moved there, over several slots limited by the actuator speed, during which the channel changes. We formulate the positioning of M MAs serving K < M devices without channel state information as a nonstationary kernelized bandit over the joint configuration space, driven by a single scalar rate feedback per slot under a reachability constraint and an actuation-energy cost. Since the information gain of a standard kernel grows exponentially with M , and the multiuser sum rate depends on the positions through a sum of per-antenna terms, we design a compositional kernel that retains interactions up to antenna pairs. On this kernel we propose CoMoveUCB, which selects a target configuration over the whole feasible set by coordinate ascent and retains it under a persistence rule. We prove sublinear dynamic regret with a polynomial dependence on M . Simulations show that CoMoveUCB outperforms fixed, measure-then-optimize, and reachability-confined benchmarks across loadings, confirming the value of repositioning an MA array under realistic actuation limits.

Integrated sensing and communication (ISAC) performance analysis commonly employs the Cramér–Rao bound (CRB) as the sensing metric. The CRB lower-bounds estimation-error variance under a target-present model but does not characterize the reliability of the energy detection that receivers often use to validate returns against noise and clutter. Considering the range CRB in a single-user, single-target joint power–bandwidth allocation problem, we establish the conditions under which a rate-optimal allocation at a given CRB requirement fails to achieve a given detection probability. We then develop detection-constrained rate–CRB characterizations at two levels: 1) a detection-aware (DA) formulation that treats the CRB and detection probability as parallel requirements, and 2) a detect–then–estimate (DTE) formulation using a detection-gated CRB defined on frames that cross an energy threshold. We analyze the resulting changes in the optimal rate and feasible power–bandwidth allocations, and for DTE further show how detection probability and conditional estimation accuracy jointly determine downstream tracking performance. Multi-user, multi-target resource allocation is then formulated under CRB-only, DA, and DTE characterizations. The results show that CRB-only allocation can conceal inadequate detection reliability, and that accounting for detection and receiver architecture materially changes the power–bandwidth allocation and the communication–sensing tradeoff.

In modern radar and Integrated Sensing and Communication (ISAC) systems, the conventional point-scatterer assumption becomes increasingly inaccurate due to higher spatial resolution. In this context, Extended Target Tracking (ETT) has grown interest in recent years. However, most existing ETT works are designed for monostatic systems due to the fine time synchronisation or per-node antenna arrays required for ranging and angle estimation in multistatic scenarios. In contrast, this paper proposes a novel approach to extended target tracking in a distributed antenna system, where only bistatic Doppler frequency measurements are available. Doppler-only tracking is particularly attractive as it avoids the two aforementioned issues. To address this Doppler-only extended target tracking problem, we develop a modified Rao-Blackwellised Particle Filter (RBPF) that replaces the standard Kalman update with a particle-specific Extended Kalman Filter update followed by a relinearisation step, handling the non-linearities of both the rigid extended target dynamics and the observation model. Numerical simulations show the effectiveness of the proposed algorithm for accurate extended target tracking under this Doppler-only setting.

Quadrupedal locomotion requires balancing conflicting objectives such as command tracking, stability, and energy efficiency, yet conventional reinforcement learning (RL) hardcodes these priorities into a fixed scalar reward at training time. We present PROMO (Preference-Conditioned Multi-Objective Reinforcement Learning), a semantic multi-objective approach that makes this trade-off an explicit runtime input to a single locomotion policy. PROMO conditions the policy on deployment facing preferences while keeping embodiment-specific locomotion priors fixed, thereby separating operator intent from reward shaping terms required for viable gait generation. Compared with fixed-objective controllers, multi-objective baselines, and independently trained specialists, PROMO achieves objective specialization and robustness from a single deployable policy. Across 100 sampled preferences in simulation, 67 behaviors are non-dominated under exact Pareto dominance, with a mean preference-objective correlation of 0.843, demonstrating broad Pareto coverage and predictable preference response. The same policy transfers zero-shot to a Unitree Go2, where preference changes alone reduce specific energy by up to 30.4%, position error by 38.7%, and peak body-attitude deviation by 59.0% relative to the balanced preference. These results establish preference-conditioned multi-objective RL as a practical runtime interface for adaptive legged locomotion, extending its role beyond offline Pareto-set construction. Open-source code and videos are available at https://amrmousa.com/promo/.

The advancement of deep learning-based speech synthesis has significantly increased the diversity of deepfake speech, posing threats to voice authentication. While centralized training is effective for deepfake speech detection (DSD), it requires considerable computational resources and raises privacy concerns. To address these issues, we propose a Federated DSD (FedDSD) method that enables collaborative model training across decentralized speech datasets without sharing raw audio. Specifically, each client trains a local model using the FedProx algorithm to mitigate the effects of data heterogeneity and uploads model parameters to a central server. To improve global model aggregation, we further propose a layer-wise center-guided weighting aggregation (L-CGWA) strategy that adjusts each client's contribution per layer based on its distance to a reference center, capturing inter-client and inter-layer discrepancies and enhancing the robustness of model aggregation. Experimental results demonstrate that models trained under the proposed FedDSD method achieve equal error rates (EERs) comparable to those obtained via centralized co-training, while significantly out-performing models trained on individual corpora. Furthermore, the proposed FedDSD method demonstrates robust generalization capabilities across diverse cross-domain datasets.

The generalization ability of Fake Speech Detection (FSD) models is crucial for real-world deployment. Existing multi-dataset co-training methods rely on fixed training sets and cannot adapt to emerging spoofing types. Although con-tinual learning has been explored, many approaches overlook limited data storage at individual devices, thereby restricting practical applicability. To address this, we propose FedCFM, a Federated continual domain generalization framework via Conditional Flow Matching (CFM) for collaboration without sharing raw speech data across distributed clients facing diverse and evolving spoofing attacks. Each client trains a CFM-based generator to model spoof-type-specific embedding distributions, and cross-client generator exchange enables synthesis of unseen spoof-type embeddings for continual classifier updating through generative replay and knowledge distillation. With the same training datasets, FedCFM achieves lower EER than the eval-uated centralized and federated domain generalization baselines, demonstrating strong cross-domain generalization. Code will be released on https://github.com/jspycpp/FedCFM.

Optimal experimental design (OED) decides which measurements to acquire for downstream estimation. Classically, this is done by optimizing an information criterion derived from a linear-Gaussian model, for which the estimator is available in closed form. Many modern estimators such as Lasso, elastic net, or total-variation-based estimators, however, are nonsmooth and lack a closed-form solution. To extend OED to these scenarios, we recast the problem as what it implicitly is: a bilevel program whose lower level computes the deployed nonsmooth estimator and whose upper level scores its validation prediction risk. Rather than fixing the number of measurements in advance, we charge each continuous acquisition weight a concave sparsity price. As a result, how many and which measurements to keep are both outcomes of the optimization. A measurement survives only if its estimator-aware value exceeds its price, and progressively increasing the price yields a sequence of designs that explores the trade-off between measurement cardinality and estimation accuracy. Using a value-function penalty reformulation, we develop a single-loop proximal-gradient algorithm that avoids differentiating the nonsmooth solution map and establish its convergence to an (approximate) stationary point. Experiments on synthetic sparse recovery and image reconstruction demonstrate the benefits of the proposed method.

We introduce a novel approach to model predictive control that incorporates multi-step uncertainty prediction for safely controlling systems characterized by uncertainties dependent on both state and control variables. The discrepancy between real-world systems and their control-oriented representations arises from inherent uncertainties, which frequently correlate with state and control variables, a common occurrence in modeling errors. As these uncertainties accumulate and propagate over time, they can produce substantial deviations over extended horizons, potentially compromising the integrity of safety-critical applications. Although existing stochastic control frameworks can maintain system operation within safety boundaries at specified confidence levels, they necessitate accurate prediction of state distributions throughout the control horizon. This prediction represents a significant challenge for systems where uncertainties vary with state and control inputs. Our contribution addresses this challenge through a Model Predictive Controller leveraging multi-step Gaussian Process Regression to capture and anticipate uncertainties that are state- and control-dependent. We further propose an iterative solution to the optimization problem in our MPC framework and discuss the convergence of the algorithm. To demonstrate the method in a practical application, we conduct an in-depth analysis of vehicle lateral control, particularly during lane-changing maneuvers, examining how errors propagate through the system model. The effectiveness of our proposed methodology is validated through comprehensive simulations.

We consider stochastic games with independent controlled chains and unknown transition kernels, where players observe only their local states and realized payoffs. We develop a fully online, decentralized, and uncoordinated mirror-descent algorithm that operates in the dual space of occupancy measures for approximating stationary Nash equilibrium (NE) policies. The algorithm uses a single transition/reward sample at every primitive time step, relies only on local information, and requires neither coverage of the joint state space nor synchronized episodes. Under uniform-ergodicity and finite-coverage assumptions, we show that, with high probability, the time-averaged fixed-comparator regret decays at the canonical $O(T^{-1/2})$ rate, up to logarithmic factors and polynomial dependence on the game parameters. In particular, the complexity depends on the cover times of the individual local state spaces rather than the product state space, avoiding exponential dependence on the number of players and the sizes of the joint state and action spaces. The resulting finite-time regret bound further yields an approximate coarse-correlated-equilibrium guarantee, which is natural for arbitrary reward functions since computing a stationary $\epsilon$-NE is PPAD-hard in this setting. Under an additional global variational-stability condition, we show that the same fully online algorithm converges asymptotically in the last iterate to a stationary $\epsilon$-NE. Our results provide a fully online and scalable learning framework for stochastic games with unknown independent chains. The algorithm can also be viewed as a primal-dual framework for Markov games that exploits the independence and local structure of the players' controlled transition chains.

Electroencephalography (EEG) foundation models are often pretrained with a fixed channel vocabulary or a limited set of montages, making transfer difficult when electrode layouts change. We propose CortexBridge, a lightweight adapter that combines EEG features with electrode and atlas coordinates to map arbitrary montages into a shared cortical latent space. Evaluated with three frozen foundation models on five brain-computer interface (BCI) datasets from the Mother of All BCI Benchmarks (MOABB), CortexBridge improves performance in 13 of 15 evaluations. The gains in balanced accuracy average 0.80% for EEGPT, 0.70% for LaBraM, and 3.26% for CBraMod, with a maximum gain of 13.02% on 12-class steady-state visual evoked potential (SSVEP) classification. Visualizations of the learned atlas representations reveal task-dependent spatial patterns, with SSVEP showing a more concentrated representation in the Yeo Visual network than auditory P300. These results establish cortical alignment as a learnable and anatomically grounded routing mechanism from heterogeneous EEG montages to pretrained foundation models.

Vocal behavior provides important signals for speech-based depression detection, but its interpretation often depends on what is being said and how it functions in context. However, existing methods typically treat acoustic cues as context-independent markers, making it difficult to distinguish vocal form from its context-dependent communicative function. We propose ParaCalib, a framework that semantically calibrates paralinguistic behavior by interpreting vocal patterns relative to utterance-level semantic context and inferred communicative function and representing them in a comparable state space. Concretely, ParaCalib uses an Audio-Language Model (ALM) to generate contextualized vocal descriptions and an LLM-based Paralinguistic State Extractor (PSE) to map these descriptions into a structured Semantically Calibrated Paralinguistic (SC-Para) representation. ParaCalib achieves the highest mean Macro-F1 among the evaluated methods, reaching 71.9% on DAIC-WOZ and 90.5% on MODMA. Our controlled analysis provides direct evidence of semantic calibration at the PSE stage: under a fixed caption-level acoustic description, varying the accompanying semantic context changes the inferred depression-related paralinguistic evidence. Our exploratory analysis further identifies recurring configurations of paralinguistic states associated with depression labels rather than a single uniformly dominant state.

BraTS 2025 Lighthouse Challenge Task 1 (BraTS-GLI 2025) evaluates glioma segmentation in pre-treatment and post-treatment multimodal MRI. The resection cavity (RC) is applicable only to post-treatment cases, creating different target definitions across the two cohorts. We developed RC-aware nnU-Netv2, a framework that combines lesion- and boundary-aware single-cohort training with an RC-aware joint objective and treatment-status-guided routing. During pooled training, the joint objective applies both standard and boundary-weighted binary cross-entropy to all four region channels, while using standard Dice supervision for channels with a non-empty target in the current mini-batch and a controlled false-positive penalty for channels that are empty across the mini-batch. This empty-target handling is particularly relevant to the all-zero RC target in pre-treatment cases. Beyond the standard nnU-Netv2 augmentation pipeline, the submission does not use synthetic tumor generation, on-the-fly GliGAN augmentation, model-level probability averaging, voting, multi-fold fusion, or multi-architecture ensembling. Each case is routed to exactly one specialized model. Our submission ranked second in BraTS-GLI 2025. On the official blind test set, our method achieved mean lesion-wise Dice scores of 0.7878, 0.8709, 0.7923, and 0.8715 and mean NSD@1.0 scores of 0.8309, 0.8712, 0.7980, and 0.8336 for enhancing tumor, RC, tumor core, and whole tumor, respectively. In paired post-treatment analysis, the RC-aware joint model improved mean lesion-wise Dice by 0.042 (95% CI: [0.028, 0.057]) and mean NSD@1.0 by 0.043 (95% CI: [0.028, 0.059]) compared with joint baseline training.

Frequency-domain physical neural networks (PNNs) have emerged as a promising analog computing paradigm, achieving a reduced number of devices, enhanced robustness, and high inference accuracy. However, existing demonstrations across various physical domains, such as optics, acoustics, and electronics, are mostly specific to the target devices or platforms, usually requiring hardware-dependent models and post-fabrication parameter tuning. Here, we demonstrate cross-platform implementations of frequency-domain PNN across different wave-based computing platforms using identical, pre-trained parameters. By exploiting shared second-order nonlinear processes, the PNN model is implemented on optical, microwave, and acoustic-wave platforms without the need of fine tuning the parameters. Evaluated on a unified four-class classification task, the frequency-domain PNN achieves high inference accuracies of 97.6% in optics, 98.4% in electronics, and 98.2% in mechanics. Further, we systematically compare related performance metrics across these three distinct physical domains, paving the way to a universal neural network across different physical platforms.

Sparse view 3D reconstruction is an important and common scenario in multimedia applications, such as augmented reality/virtual reality (AR/VR) content creation, cultural heritage digitization, and certain robotic applications, where only a limited number of randomly captured views may be available. However, sparse views contain only limited 3D information, posing two major challenges:1) too few images are available for matching, making it difficult to build multi-view consistency; 2) insufficient view coverage leads to a lack of information in under-sampled regions, resulting in missing parts of object structure. Existing methods mostly still rely on limited reprojection errors and regularization terms, which are prone to overfitting to a single view and inconsistent appearances across views. In geometrically under-sampled regions, they often rely on heuristic density control, lacking reliable guidance and often resulting in blurring and structural holes.To address these issues, this paper proposes Hierarchical Gaussian Fields (HierGF), which revisits sparse-view reconstruction from a hierarchical geometry-perception perspective and converts limited observations into reliable self-generated supervision beyond fixed priors and heuristic density control. In particular, we transform coarse 3D geometric information and additional 2D generative priors into structured pseudo-supervision through a two-stage geometry-perception backbone network, thereby enhancing multi-view consistency with very few input views. In addition, we introduce a learnable confidence network to guide gradients toward cross-view consistent content, and a geometrically consistent densification module to improve the reconstruction of multi-view alignment and under-sampled regions.

Behavioral models of nonlinear microwave devices – the Cardiff model, X-parameters, the higher-order describing functions of the mechanical-systems literature – all share a functional form. This tutorial derives that form from time invariance alone. It develops the consequences of that derivation using only the harmonic-balance description of a single-tone periodic steady state, in which each port waveform is a finite set of harmonic phasors, and uses them to give the Cardiff model's three exponents physical interpretations. In the monomial rewriting the magnitude exponent $m$ is the order of the device's load-side nonlinearity; the phase exponent $n$ is set by the drive-side harmonic; and the conjugate index $r$ obeys $r_{\max}=\lfloor K/2\rfloor$, where $K$ is the degree of the load-side nonlinearity. The familiar restriction $r\le1$ is therefore a statement about the device, exact whenever $K\le3$. The final sections show that a tailored A-pull measurement displays this decomposition directly: each spectral cluster's half-width is the order of the nonlinearity that produced it. The tutorial is pedagogical and is meant as a gentler introduction to the results treated in the companion paper "Time invariance, circle symmetry, and the completeness of the Cardiff behavioral model", which states and proves the theorems in full.

The ASF is a major source of positioning error in eLoran system and is affected by terrain characteristics along the signal propagation path. This study analyzes the correlations between spatial ASF and path-based terrain features using NASA SRTM 30m and NGII 90m DEMs. Path mean elevation and path mean slope were extracted along the propagation paths from the Gwangju eLoran transmitter to measurement locations in the Incheon and Pyeongtaek port areas. The results show that both terrain features are strongly correlated with spatial ASF, with the higher-resolution NASA SRTM DEM generally exhibiting stronger correlations, particularly for path mean slope. These findings demonstrate the importance of DEM spatial resolution in terrain-based spatial ASF analysis and provide a basis for selecting terrain data for future machine-learning-based ASF prediction

Low Earth orbit (LEO) satellite Doppler measurements extracted from passive software-defined radio (SDR) spectrum data require temporal alignment with predicted satellite trajectories for reliable satellite association. In our previous framework, Doppler slope information was used during both segment generation and clock offset estimation, while the clock offset was estimated at a single coarse receiver position. This study proposes a satellite matching framework based on continuity-based Doppler segmentation and multi-position clock offset estimation to reduce these dependencies. Doppler segments are first generated using only temporal and frequency continuity, thereby separating segment generation from the slope criterion used for clock offset estimation. The clock offset is then estimated using five coarse receiver positions, consisting of one city-level nominal position and four surrounding positions, through a two-stage procedure based on slope compatibility and a combined Doppler cost. The aligned segments are subsequently associated with candidate satellites using a matching score based on the same Doppler slope and bias-removed RMSE metrics, and the resulting associations are evaluated through receiver localization. Experiments using six hours of passive Starlink/OneWeb monitoring data show that continuity-based segmentation alone did not improve receiver localization compared with slope-based segmentation under single-position clock offset estimation. However, applying the proposed multi-position clock offset estimation to the same continuity-based Doppler segments reduced the localization error from 32.99 km to 1.84 km, achieving the lowest error among the evaluated methods. The results for the evaluated dataset show improved spatial consistency of satellite associations when multi-position clock offset estimation is applied to continuity-based Doppler segments.

The use of signals from Earth-orbiting Global Navigation Satellite System (GNSS) constellations for positioning, navigation, and timing on the lunar surface was recently demonstrated by the Lunar GNSS Receiver Experiment (LuGRE) aboard Blue Ghost Mission 1. For receiver clock synchronization using GNSS, Kalman filter performance depends on the satellite-specific measurement uncertainty used to construct the measurement noise covariance. Terrestrial measurement weighting commonly relies on receiver elevation angle, but the elevation angles in the analyzed LuGRE data are confined to 24.2-31.5 degrees, providing limited discrimination of measurement quality. In contrast, satellite nadir angles span 12.1-66.6 degrees, and the measurement variability increases substantially at small nadir angles. Based on this observation, we propose a measurement weighting model that combines satellite nadir angle with C/N0 for lunar surface receiver clock synchronization. The proposed model is evaluated against three weighting methods adopted from the literature using seven LuGRE operation periods (OPs). It achieves the lowest receiver clock bias root mean square error (RMSE) in six of the seven OPs and an overall RMSE of 5.78 m, which is 19.5% lower than that of the best comparison model. The proposed model also provides the lowest clock bias prediction RMSE under simulated measurement outages.

Video-to-speech synthesis aims to generate natural-sounding speech from silent talking-face videos while ensuring phonetic accuracy. A fundamental challenge in this task is the inherent one-to-many mapping problem, where visual dynamics often lack sufficient information to uniquely determine the corresponding utterance. To address this, we propose Watch Your Speech (WYS), a video-to-speech synthesis framework that incorporates textual conditioning as an explicit linguistic cue to mitigate visual ambiguity. Our framework features an attention-based embedding fusion module that synergistically integrates textual context with video sequences, coupled with a conditional flow matching objective for high-fidelity speech generation. Extensive experiments on the LRS2 and LRS3 datasets demonstrate that WYS achieves superior performance, establishing new state-of-the-art results in audio-visual synchronization (LSE-C/D) while maintaining highly competitive textual accuracy (WER). Subjective evaluations further confirm that our model generates speech with near-human naturalness, validating the effectiveness of textual conditioning in content-controlled video-to-speech synthesis. Project page: https://github.com/gunwoo5034/Watch-your-Speech

Learning-based driving planners are usually trained and evaluated in open loop against logged trajectories. In closed loop, a trajectory with small displacement error can still stall the vehicle, steer it into a conflict with surrounding agents, or be executed with abrupt braking. We introduce Closed-Loop Refinement and Execution (CLRE), a hierarchical receding-horizon control framework designed to mitigate these failure modes while leaving the upstream planner frozen and adding no new learned model. The upper layer treats the nominal trajectory as a reference and solves a finite-horizon optimal control problem that trades route progress against interaction with predicted agents. Solving it from several initializations gives a candidate set, and a prediction-conditioned oriented-bounding-box (OBB) feasibility test retains only candidates whose minimum predicted OBB clearance over the horizon meets a threshold. The lower layer executes the lowest-cost survivor, or a route-centerline backup when none remains, through the tracking controller supplied with the planner, augmented by a range-based speed bound and a saturated proportional braking law. In closed-loop simulation on the 220-route Bench2Drive validation set with VAD as the upstream planner, CLRE raises the driving score from 42.26 to 55.89 and route completion from 55.69 to 71.51, and reduces collision events from 117 to 97.

This paper proposes a physics-guided Bayesian optimization for high-dimensional mixed-variable multiple-input and multiple-output (MIMO) base station (BS) design. The considered problem jointly selects a subset of candidate sites for BS deployment and optimizes the azimuth angles, downtilt angles, and transmit power spectral densities of the BSs, while each configuration is evaluated using computationally expensive site-specific ray tracing. To efficiently optimize the system configuration, the proposed method constructs a low-cost physics-based proxy from precomputed propagation information. The proxy-estimated communication coverage is used as the Gaussian process (GP) prior mean, and a residual GP with three-dimensional physical features learns the discrepancy between the proxy and full evaluations. Ray-tracing-based evaluations in two urban scenarios show that the proposed method achieves up to approximately 15 percentage points higher coverage than conventional and high-dimensional optimization baselines under the same evaluation budget.

Traditional guided wave methods for structural health monitoring typically assume uniform material properties, which limit their ability to characterize heterogeneous structures with spatially varying thickness, damage, or material properties. These are challenges commonly encountered in corrosion assessment, composite delamination detection, and structural degradation monitoring. This paper presents a wave physics-informed regression approach that enables spatially resolved characterization of material properties by extracting local dispersion curves across a structure. Our approach focuses on a highly interpretable but flexible physics-informed framework that can be solved using fast algorithms and achieve robust numerical solutions. This paper discusses the mathematical design of the framework, the algorithm, and its interpretation. The framework was applied to a guided wave wavefield imaging dataset from a thin aluminum plate with non-uniform thickness around a hole to validate its practicality. The framework creates an accurate thickness map (correlation coefficient 0.94 with x-ray CT validation) as well as extracts the frequency-dependent velocities of waves within those regions.

Embodied assistants in domestic environments must infer what happened, where and when it occurred, and how to respond. To address this, we introduce RMS-AQA, a spatial audio question answering (SAQA) benchmark for real-world domestic environments. The benchmark features a two-stage question-answering (QA) format to comprehensively assess the ability of audio-language models (ALMs) to first ground audible sound events and subsequently perform complex spatio-temporal reasoning based on that grounding. To maximize acoustic realism, our dataset combines authentic real-world first-order Ambisonics (FOA) recordings with high-fidelity synthetic data generated using measured room impulse responses (RIRs). Furthermore, we provide a lightweight spatial plug-in that injects FOA-format data into frozen audio-language backbones. Experimental results reveal that the primary challenges stem from concurrent sources, far distance, and sim-to-real domain gap between RIR-synthesized and authentic recordings.

This paper studies communication-aware safe control for discrete-time linear multi-agent systems under limited information exchange. The main challenge lies in the coupling between remote-state estimation and safe controller design, since estimation errors affect the state evolution through the controller gains, while the controller design must account for the resulting observer-induced state perturbations to guarantee safety. To address this challenge, a distributed k-hop observer is developed to reconstruct unavailable remote states, and a uniform observer-error bound is derived. The effect of the observer-induced state perturbation on closed-loop safety is accounted for in both the construction of local {\epsilon}i-robust safe invariant (RSI) sets and the enforcement of pairwise relative-state safety constraints. The resulting safety conditions ensure that all agents remain within their local RSI sets while all pairwise relative-state safety constraints are satisfied. A linear matrix inequality (LMI)-based optimization method is developed to jointly synthesize the distributed observers, local controllers, and RSI sets. A case study illustrates the effectiveness of the proposed method.

General-purpose embodied robots should support both navigation toward language-specified destinations and dynamic person tracking under arbitrary initial target azimuths. However, existing methods typically rely on forward-facing observations and address these tasks with separate policies, limiting omnidirectional perception and unified closed-loop control. We present UniTrackPLA, a unified panorama-language-action model for instruction-guided navigation and dynamic person tracking. Its Panoramic-Aware Encoding (PAE) preserves the temporal and azimuthal structure of perspective views projected from each panorama, enabling perspective-pretrained visual encoders to process omnidirectional observations. A shared vision-language backbone grounds instructions in the panoramic context and predicts continuous robot-centric waypoint chunks for both tasks. World-Action Consistency (WAC) further predicts action-conditioned future visual states and verifies waypoint prefixes online, allowing reliable actions to be reused while triggering replanning upon inconsistency. We also introduce OmniTrackNav-Bench, comprising 5,000 simulated tracking trajectories, 10,000 simulated VLN routes, and 96 verified real-world routes, providing 919,978 waypoint-supervision instances. UniTrackPLA improves overall tracking SR from 23.50% to 35.00% and Omni-VLN SR/SPL from 13.00%/12.77% to 19.75%/19.29%. Incorporating 76 real-world routes further improves held-out EP@0.2m from 42.92% to 92.08%. Closed-loop experiments on a Go2-W robot demonstrate unified panoramic tracking and navigation across indoor and outdoor environments. The project page is at https://tw5775.github.io/UniTrackPLA.