Papers

Transmission switching is an effective approach for restoring operational flexibility and improving the economic performance of AC/multi-terminal DC (MTDC) grids following DC-side faults. Its practical implementation requires that the faulted branch be safely isolated and that the associated switching commands be successfully delivered through the communication network. However, conventional transmission switching models generally represent switching actions using binary variables, without considering the availability of communication resources or whether the corresponding DC circuit breakers (DCCBs) can interrupt the fault current affected by time delay. To address these issues, this paper develops an explicit optimal info–power flow (OIPF) formulation that incorporates information transmission into the post-fault transmission-switching optimization for AC/MTDC grids. The switching status of each DC branch is determined by the availability of its communication path and associated communication resources. In addition, binary switching decisions are supplemented by a DCCB feasibility constraint that limits the estimated delayed-interruption current to the DCCB breaking capability. The resulting problem is formulated as a mixed-integer second-order cone programming (MISOCP) model that can be efficiently solved using off-the-shelf optimization solvers. Numerical results reveal that limited communication capacity and additional routing costs may render electrically beneficial switching actions infeasible or uneconomical, while delay-aware DCCB interruption constraints may favor lower-delay communication routes despite their higher costs.

Intelligent power dispatch is enabling fast and self-adaptive decision-making in microgrids, yet its deployment in real-world industry remains limited due to unresolved safety concerns. Existing safety-preserving approaches typically rely on an accurate characterization of the feasible operating region, which is impossible to obtain under uncertain and heterogeneous system dynamics. Moreover, even a dispatch command verified as feasible at a discrete setpoint may become unsafe during its continuous physical execution. To address these challenges, this paper proposes a safety-aware dispatch adjustment framework that uses performance-equivalent modeling of closed-loop systems (PEM-CLS) and embeds high-order control barrier functions and control Lyapunov functions within a quadratic programming formulation (HOCBF-CLF-QP). PEM-CLS captures EMS-required power-regulation dynamics solely from port-level responses, without requiring internal subsystem models, parameters, or controller information. Its modeling error is analytically characterized and explicitly bounded, enabling requirement-oriented dynamic abstraction for safety-critical control. Based on these PEMs, the HOCBF-CLF-QP safety controller coordinates heterogeneous transient power responses and corrects unsafe EMS commands to maintain dynamic safety during command execution. Four deliberately constructed extreme stress-test scenarios are used to expose conflicts between nominal EMS commands and physical safety. Without intervention, the DC-bus voltage violates its safety limit in all four cases, whereas the proposed framework maintains QP feasibility and limits the voltage deviation. The results demonstrate that the proposed framework provides an algorithm-agnostic safety execution layer for intelligent power dispatch without requiring an exact feasible-region model or detailed internal information of lower-level devices.

This paper considers a distributed estimation system where edge nodes transmit averaged features to a center node for parameter estimation. A key challenge arises when the center node possesses auxiliary side information: how to design local extractors that are universally optimal for any downstream task, while avoiding redundancy with the side information. To tackle this problem, we orthogonally decompose the joint function space and demonstrate in the weak-dependence regime that the modal decomposition of the canonical dependence kernel's conditional-additive component yields optimal, task-independent features. We construct a distributed nested H-score to learn this decomposition and show that, under a limited feature budget, the mean-squared error (MSE) gap to the best task-specific features is determined solely by the task's alignment with the discarded, least informative modes, and vanishes to leading order for every square-integrable task once the budget is sufficient.

The introduction of Reconfigurable Intelligent Surfaces (RIS) technology marks a transformative era in modern communication systems, enhancing coverage and facilitating higher data transmission rates. While recent research has delved into the integration of RIS into satellite networks, a critical and practical gap persists concerning its implementation in the satellite-Very Small Aperture Terminal (VSAT) downlink, specifically amplifying contributions from the VSAT side. This paper addresses this gap by proposing a simple and engineering methodology to optimize RIS position and orientation, incorporating considerations for the antennas' radiation patterns and the system's topology to streamline optimization variables. The primary objective of this paper is to alleviate the double fading constraint in the reflect link without the need for active elements, achieved by leveraging the Fresnel zone of the RIS. The improvement power ratio depends on the frequency and the VSAT 's diameter. The results show that for a VSAT with diameter 1m the gain is up to 9dB at 2 GHz (S band) and up to 14dB at 4 GHz (C band). Additionally, the RIS channel equation is refined, encompassing the tropospheric attenuation and accounting for electromagnetic wave polarization.

Channel estimation for extremely large-scale uniform planar arrays (UPAs) requires exploiting near-field channel structure to reduce pilot overhead. With a specifically devised channel representation matrix, the near-field channel exhibits a 2D sparse structure, which a conventional 1D block partition might divide into multiple groups. This work develops a 2D block orthogonal matching pursuit (2D-BOMP) estimator that exploits this structure through rectangular coefficient grouping. We establish sufficient recovery conditions for noiseless and noisy measurements and compare the sufficient pilot-length requirements of 1D and 2D partitions. Numerical simulations demonstrate the estimation benefits of 2D-BOMP in estimating the near-field channels of large-scale UPAs, compared with the conventional 1D-BOMP as well as the polar-domain based OMP method.

This paper delves into the intricate aspects of Reconfigurable Intelligent Surfaces (RIS) in the antenna design. It investigates the properties of the array factor of RIS, highlighting the significance of phase shifts in establishing of the reflect path. Additionally, the paper investigates spatial positions requiring identical phase shifts, nullification of the array factor, the count of total maxima, second maximum determination (amplitude of the second lobe), and the calculation of the 3dB bandwidth. They are very useful for RIS-assisted radio communication systems.

Wireless propagation modeling is foundational to the evaluation and design of modern communication systems. To achieve accurate wireless propagation prediction, recent learning-based approaches leverage scene geometry and materials, demonstrating promising generalization across transceiver locations and unseen scenes. However, these approaches remain largely confined to a fixed configuration of array size, receiver orientation, and transmit beam. In this work, we introduce RFWeave, a learned wireless propagation model designed to generalize across scenes, transceiver locations, and transceiver configurations. To accommodate varying array structures, RFWeave incorporates variable-length transceiver tokens that represent antenna elements individually. Furthermore, to capture the geometry between transmit and receive antenna elements, RFWeave aggregates their pairwise spatial relations into propagation tokens. A configuration-conditioned decoder predicts the spatial spectrum and received power. RFWeave is trained on a large-scale simulated dataset spanning 10,000 indoor scenes and more than 5 million channel records under diverse transceiver configurations. Extensive evaluations show that RFWeave achieves state-of-the-art performance across all evaluated generalization settings, improving spatial spectrum structural similarity (SSIM) by up to 15.7% and reducing received power mean absolute error (MAE) by up to 35.4% over the strongest baselines on unseen scenes. Additional experiments on measured DICHASUS data and in beam management further demonstrate RFWeave's broader applicability to real-world channels and downstream wireless tasks.

Ultrasound image quality depends on both the probe geometry and the transmit and receive processing, but engineers usually design the two separately. Physics-based simulators (Field II, k-Wave, MUST/SIMUS) compute the radio-frequency channel data but give no gradients, so design studies must use grid or black-box searches that become slow as the number of parameters increases. We present a fully differentiable end-to-end pipeline for plane-wave ultrasound imaging, with synthetic speckle phantoms, an analytical frequency-domain RF simulator, delay-and-sum beamforming and an extensible image-quality objective. The element pitch, the transmit and receive apodization and the global speed of sound are differentiable parameters of one computation graph, so one reverse-mode pass gives their exact joint gradients. Studies of each parameter rank the design parameters: the optimized pitch decreases the grating lobes, and the speed-of-sound autofocus gives clear contrast and resolution gains. Over 15 test phantoms, the optimized pitch increases the cyst gCNR from 0.55 to 0.66 (about 20%), and the speed-of-sound correction adds a further 0.05. The lateral point-spread function agrees with Field II within about 10%, and on 49 real CUBDL plane-wave acquisitions from five institutions, the framework finds the global speed of sound with a mean absolute difference of 16.6 m/s from the published corrections and phantom speed of sound.

Surface-based cortical analysis is useful in many applications as it is a natural way to model the highly folded structure of the cerebral cortex, allowing quantities such as curvature and thickness to be easily calculated. Existing deep learning (DL) methods have focused on 1x1x1 mm3 T1-weighted (T1w) scans, thus replicating classical pipelines. Here we present a neural network that is trained exclusively on synthetic scans with random contrast and resolution. Consequently, it can be applied to almost any kind of magnetic resonance imaging (MRI) scan. In addition, our model provides vertex-wise uncertainty estimates of the predicted surface as well as a spherical registration to fsaverage space. Our model performs on par with other state-of-the-art DL methods on 1x1x1 mm3 T1w scans and outperforms the only other method capable of estimating cortical surfaces from low resolution, non-T1w scans. The predicted uncertainty, moreover, correlates with overall scan quality on low resolution scans and highlights areas of poor surface reconstruction on 1x1x1 mm3 T1w scans, suggesting that it can be used for quality control. Finally, the predicted spherical registrations prove comparable to other DL and classical registration methods. These qualities, when combined with the fast inference time, will provide the ability to analyze large, existing databases of clinical MRI scans, with the potential for finding disease-specific patterns that would otherwise be infeasible in a research setting.

Conventional Gaussian filtering typically approximates nonlinear models or their statistical moments and retains the Kalman update structure. The resulting approximation errors can affect both state estimates and their uncertainty. This paper presents the Natural Gradient Gaussian Approximation (NANO) framework for nonlinear filtering. Both prediction and measurement update are formulated as optimization problems over Gaussian distributions, with the entire Gaussian distribution serving as the optimization variable. The framework integrates observation losses, distributional discrepancies, natural-gradient optimization, and finite-step update rules, providing a unified formulation for constructing different filters. Natural-gradient iterations account for the geometry of the Gaussian family and jointly update the distribution parameters. The framework incorporates the existing NANO-P method using the Kullback-Leibler divergence and introduces NANO-W using the Wasserstein distance. NANO-P maintains covariance positive definiteness through a positive-semidefinite curvature approximation, while NANO-W uses a covariance transport update that accommodates indefinite curvature. Their update rules and sufficient conditions for covariance positive definiteness are established within the common framework. Through these two realizations, the proposed framework achieves the best estimation accuracy among the evaluated methods on each of six benchmarks. Runtime evaluations further assess its computational feasibility for practical applications.

We present a scalable method for identifying nonlinear dynamics from partial measurements, supporting physics-based, neural-network, and gray-box models. We use the Prediction Error Method (PEM) coupled with state estimation performed by a Moving Horizon Estimation (MHE) scheme, which can account directly for general nonlinear dynamics. This results in a bi-level optimization problem that scales poorly with increasing model parameters. To improve computational efficiency, we derive a reformulation that avoids explicit parameter-wide sensitivity matrices and exploits the temporal structure of the underlying optimization problem. Numerical comparisons show up to a $215\times$ speedup in hypergradient evaluation. Racing-vehicle simulations demonstrate accurate prediction and physical-parameter recovery in tire-force identification. Real-world data show that the gray-box model improves prediction accuracy with a neural residual while retaining physical interpretability.

State estimation is widely used in networked dynamical systems, but released estimates may inadvertently reveal sensitive information about private state variables. Existing privacy-preserving estimation frameworks often do not explicitly account for adversarial prior knowledge. Motivated by this gap, we study privacy-preserving state estimation for discrete-time linear stochastic systems under prior information exposure using pointwise maximal leakage (PML). We first reveal a privacy anti-optimality property of the Kalman filter: in the absence of additive release noise, the steady-state Kalman filter maximizes PML privacy leakage among the considered class of stable linear estimators. We further establish a nontrivial lower bound on the steady-state estimation error enforced by $(\varepsilon,\delta)$-PML privacy. We then derive sufficient release-noise conditions that guarantee $(\varepsilon,\delta)$-PML privacy, first for the Kalman filter and then for stable linear estimators. Finally, a convex semidefinite program is formulated for a joint design of the estimator gain and release noise covariance, providing a tractable privacy-preserving estimator with an explicit upper bound on the steady-state estimation error. Simulations on a source–load power system demonstrate the proposed privacy–utility tradeoff.

This work develops a geometric theory of aerodynamic promptness for multirotor platforms whose propeller directions depend on a smooth orientation-mechanism configuration. The actuator state consists of the actual signed propeller speeds and the mechanism configuration, and the nonlinear actuator map retains both the signed-quadratic propeller law and the configuration-dependent thrust directions. Constant direction maps represent fixed propellers, while shared dependence on mechanism coordinates represents mechanically coupled tilts. A Riemannian metric on the product actuator manifold compares propeller accelerations and orientation-mechanism rates through physically interpretable scales. Composing the actuator-map differential, the musical isomorphism induced by this metric, and the dual differential defines a task-rate authority map from wrench covectors to wrench-rate vectors. In fixed task coordinates, the square root of the determinant of its matrix defines aerodynamic promptness. Positive promptness is equivalent to local wrench-rate surjectivity, while the associated spectrum resolves directional authority and conditioning. The same construction yields the metric horizontal inverse, the orthogonal projector onto wrench-fiber directions, and task-preserving internal motions that regulate promptness without changing the instantaneous wrench. The construction is related intrinsically to dynamic feedback linearization and specialized to the independently tilting, radially arranged Holocopter and Voliro architectures, clarifying how their established allocation and rank constructions fit within the same actuator-manifold geometry.

Safe robot navigation in unknown, dynamic environments requires more than geometric collision checking: a robot still far from an obstacle may be unable to avoid it because of its speed, orientation, or the obstacle's motion. Hamilton–Jacobi (HJ) reachability analysis captures this in a value function whose sign separates states that admit a safe continuation from those where collision is unavoidable, but computing it online for every newly observed environment is too expensive. This dissertation trains neural implicit representations offline on reachability solutions to give optimization-based controllers this information in real time. Since the value function never exceeds the constraint, it equals a directly computed constraint, such as a signed distance, minus a nonnegative dynamics correction. Learning only this correction keeps represented obstacles outside the predicted safe set despite approximation errors. The first two of five methods use the learned function as a terminal constraint in model predictive control, for static local environments and predicted obstacle motion. The next two use it as a barrier candidate in control barrier function safety filters, for static environments and, through pairwise barriers in relative coordinates, for a varying number of moving obstacles. Simulations and hardware experiments on ground and aerial robots show better collision avoidance than geometric and hand-designed baselines, especially for short horizons and moving obstacles. The fifth method replaces the grid-based offline solver, which limits the first four to low-dimensional models. For control-affine dynamics with polytopic inputs, the HJ Hamiltonian is maximized at a vertex, so a parallelizable tree search over vertex inputs can generate training labels. With a loss from the HJ variational inequality, these labels yield barrier candidates for systems with up to 13 states.

Neural networks (NNs) have been employed in various ways to accelerate the solution of multi-parametric optimization problems (OPs), which prominently arise in model predictive control (MPC). Early approaches focused on approximating the parametric solution (e.g., the optimal control law) directly by NNs. More recently, learning-based techniques have been used to emulate or enhance solver iterations. For the direct approximation of the parametric solution, it has been demonstrated that exploiting structural properties of the underlying OP facilitates the design of suitable NN architectures. In this paper, we show that structural analysis can also be leveraged to systematically integrate learning into classical optimization algorithms, thereby enhancing their performance. We formalize our concept for the case of multi-parametric quadratic programs (mpQPs), which frequently arise in linear-quadratic MPC, as a representative application. More precisely, we demonstrate that tailored NNs enable a parameter-dependent fine-tuning of the step sizes in a standard dual projected gradient scheme. The design of the NNs is guided by the observation that optimal step sizes, which permit obtaining the exact optimal solution in finitely many iterations, are piecewise constant functions over the parameter space (e.g., the state space in MPC). We establish key theoretical properties of the proposed approach and illustrate its effectiveness in a numerical case study.

Reconfigurable intelligent surfaces (RISs) have emerged as a key enabler for next-generation wireless networks, offering control over the propagation environment through nearly passive reflecting elements. When combined with spatial modulation (SM), an RIS shapes a finite set of noise-free signal points at the receiver, and the error rate performance under maximum-likelihood (ML) detection is governed by the smallest pairwise Euclidean distance among these points. In this paper, we propose a low-complexity RIS phase shift design algorithm for RIS-assisted SM under practical discrete phase shift control. The proposed method targets the minimum Euclidean distance directly through a reduced greedy search over the discrete set of RIS phase shifts, in contrast to existing approaches that align RIS phase shifts to maximize the effective channel gain and therefore do not directly address the dominant error events. The search effort is controlled by a small set of integer parameters, providing a tunable trade-off between bit error rate (BER) performance and computational cost. We evaluate the proposed method against continuous and quantized phase-alignment baselines, including a complexity-matched comparison against the most directly comparable benchmark. Numerical results show that the proposed method achieves a significant SNR gain over quantized phase alignment under the same discrete hardware model, and this advantage persists under imperfect channel state information.

Terrestrial network (TN) failures during disasters can overload surviving base stations (BSs) and disrupt emergency services. A non-terrestrial network (NTN) provides fallback, but its feeder link can become a bottleneck as users migrate. This paper studies post-disaster network resilience through service-priority-aware orchestration of constrained NTN feeder capacity in a post-failure snapshot. A lightweight Cross-Entropy Method (CEM) tunes feeder, offload, and user-ranking parameters offline, not as an online artificial intelligence (AI) controller. A Python system-level simulator sweeps operating regions under abstract TN-NTN link and queueing models. Gains depend on failure severity. At a 180-Mbps feeder under total TN outage, the proposed policy increases the absolute emergency-weighted continuity metric Cemg from 0.399 to 0.666 relative to the matched rule baseline with uniform feeder scaling (paired gain: 0.267; 95% bootstrap CI: [0.264, 0.271]); mild damage can reduce aggregate continuity. Offline CEM feeder-weight tuning further improves severe-failure continuity over fixed service priorities, whereas tuned offload and user ranking provide no detectable incremental gain.

This paper investigates the geometric detection of latent dynamic transitions (where the latent space is defined as a finite-dimensional state space reconstructed via a delay-embedding Hankel matrix) in mutually coupled phase-locked loops (PLLs) exhibiting on-off intermittent chaos. We propose a data-driven approach to identify the transition of the effective dimension in latent dynamics as a precursor to physical bursting events. By applying Singular Value Decomposition (SVD) to a Hankel matrix constructed from high-resolution oversampled data, we extract the effective dimension that reflects the dynamical shift of intrinsic variables - from a synchronized two-dimensional manifold to a higher-dimensional chaotic state - prior to the actual burst. This geometric indicator enables a proactive, event-driven intervention using Pyragas-type delayed feedback control, which is activated only when the detected dimension exceeds a critical threshold. Simulation results demonstrate that this geometric precursor effectively compensates for control latencies and substantially reduces accumulated control energy compared to conventional continuous feedback. The findings establish a methodological strategy for stabilizing infinite-dimensional delayed systems by exploiting early warning signals embedded in dimensional transitions of latent dynamics.

Speech enhancement with wearable and edge devices requires architectures that balance enhancement quality against power and memory resources. Spiking Neural Networks (SNNs) offer a promising path towards this goal through sparse, event-driven computation, but existing SNN-based speech enhancement methods are largely restricted to single-channel processing and therefore cannot exploit the spatial diversity available from microphone arrays. In this work, we propose a fully SNN framework for multichannel speech enhancement based on a fully connected architecture (FC-SNN) with sequential spike encoding, derived from a compact artificial neural network (ANN) baseline via leaky integrate-and-fire neurons. We characterize how enhancement performance scales with model size and spike-train length relative to ANN and classical beamforming baselines, and report the resulting parameter counts and computational complexity. We further compare direct and rate spike encoding, revealing a clear trade-off between event-driven computation and enhancement performance, and use these results to identify favorable configurations for edge devices.

Opinion dynamics models relate interpersonal influence to consensus or persistent disagreement. We study a discrete-time model combining weighted-average and weighted-median updates on a fixed directed network. Individuals update synchronously with possibly different adjustment coefficients. We characterize equilibria through local balance conditions and opinion level sets, and establish convergence to a Lyapunov-stable equilibrium using nonexpansiveness and Krasnosel'skii–Mann theory. We define type-dependent cohesive sets: each weighted-average member assigns all its weight to the set, whereas each weighted-median member assigns at least one half. Consensus is reached from every initial condition if and only if no two such sets are disjoint. Otherwise, an open set of initial conditions leads to persistent disagreement. Replacing weighted-median individuals by weighted-average individuals preserves consensus whenever it is already guaranteed for all initial conditions. Four numerical studies examine the number and locations of weighted-average individuals. The frequency of reaching a prescribed consensus tolerance is nondecreasing with their fraction in the sampled ensembles, but individual realizations need not follow this trend.

In unmanned aerial vehicle (UAV) networks, integrated communication and sensing (ISAC) is emerging as a key enabler to support extended and continuous communication and sensing capabilities for sixth-generation (6G) services among UAVs operating in swarms. In this paper, a comprehensive framework is presented to investigate the ISAC coverage probability of a reference UAV-base station (BS) in a UAV network. A fixed number of UAV user equipment (UAVUEs) and UAV-BSs are deployed in an aerial corridor modeled as a finite cylinder. The three-dimensional (3D) binomial point process (BPP) is used to model both the 3D spatial locations of the UAV-UEs and the UAV-BSs. By adopting realistic 3D third generation partnership project (3GPP) antenna patterns for both the UAV-UEs and UAV-BSs, the UAVs' boresight direction of the maximum antenna gain is modeled as a random unit vector. All UAV-BSs have sensing capabilities, so an arbitrary yet fixed reference UAV-BS acts as a monostatic radar to acquire sensing measurements associated with a target UAV-UE at a given time snapshot while the target UAVUE communicates with the reference UAV-BS. Consequently, considering both clutter and communication interference, exact analytical expressions for ISAC coverage probability are derived. Low complexity baseline analytical expressions are also derived by ignoring clutter interference and directional characteristics of the UAV-UEs. Finally, motivated by the vision for intelligent UAV networks, a DeepSets-based model is developed and trained to estimate the optimal location of the reference UAV-BS that maximizes ISAC performance given a spatial deployment of the UAVs. To the best of our knowledge, this is the first work to rigorously investigate ISAC performance in UAV networks while incorporating the directionality characteristics of the UAV antennas.

Speech timing is fundamental for speech models to recognize words accurately, but how word-timing information is represented in speech models remains underexplored. We study this question using the framework of representational geometry, which investigates whether and how concepts are represented in low-dimensional subspaces of the representation space. Prior work in cognitive neuroscience has shown that when HuBERT feature sequences are averaged across words of the same duration, they form near-circular trajectories from word onset to word offset in a two-dimensional "temporal subspace." Here we show that this circular structure of word timing is a general property of deep speech representations, by demonstrating its emergence in nine additional speech models covering both supervised and self-supervised. We then move beyond averaged word segments and show that in HuBERT, the circular trajectories are consistent enough at the level of individual words to perform word segmentation. Our approach, Rotary Segmentation (RoSeg), achieves 64.3% boundary F-score on Librispeech by detecting word boundaries when cycles complete in the temporal subspace. Although supervision is needed to identify a good subspace, nevertheless this result demonstrates much stronger implicit word knowledge in HuBERT than has previously been shown.

Electromagnetic information theory (EIT) brings electromagnetic physics into wireless channel analysis and offers a physical view of channel structures hidden by conventional models. This paper studies channel instability near electromag- netic singular states, where small environmental perturbations can cause large variations in the end-to-end channel. Using resolvent-based spectral analysis, we develop an electromag- netic multiple-input multiple-output (MIMO) channel model that decomposes the end-to-end channel into a smooth regular component and singularity-associated components. The analysis shows that the singularity-associated components become more sensitive to environmental perturbations near singular states. The proposed model also reveals a limitation of conventional water filling (WF): power allocation based only on instan- taneous channel gains does not account for such instability. We therefore estimate singularity information from broadband MIMO channel state information (CSI) and develop a singularity- aware transmit covariance optimization method. Simulations demonstrate numerical stability of the estimated pole frequencies and illustrate the environmental sensitivity of pole-associated channel components. The proposed allocation reduces weighted singular-channel exposure by 90.32% while retaining 95% of the nominal WF rate.

We revisit the problem of approximating a linear time-invariant LTI model by a reduced-order model (ROM) in an $H_2$-optimal sense. A widely used method for computing such ROMs is the iterative rational Krylov algorithm (IRKA). However, in practice, the stability of the resulting ROM is not generally guaranteed. In this work, we develop a novel framework that can be used to obtain $H_2$-optimal models of single-input single-output LTI systems while enforcing stability of the ROM. We use Malmquist-Takenaka orthogonal expansions in the corresponding $H_2$-spaces and their connection to hyperbolic geometry to develop an equilibrium function. We show that the poles of an $H_2$-optimal ROM transfer function are defined by the zeros of this equilibrium function. The resulting nonlinear root-finding problem is solved using a Newton-type method formulated on the products of Poincaré disk manifolds, where updates are performed along hyperbolic geodesics ensuring that the stability constraint is satisfied at every iteration. We present numerical experiments on benchmark systems for both discrete- and continuous-time LTI MOR problems, including examples where IRKA produces unstable reduced models while the proposed method preserves stability throughout the iteration.

Commercial balancing wheels are closed systems: an internal controller keeps the board upright, and a rider steers by shifting weight and twisting the hips. We augment a one-wheel with a lead-screw weight shift for longitudinal motion and a reaction wheel for yaw. The reaction wheel stores limited momentum, so a turn is a sequence of torque pulses, and whether a pulse rotates the body depends on static friction at the tire contact. From a 24-pulse torque sweep on one surface we fit a two-part model that gives the probability that a pulse rotates the body, treating the breakaway torque as random, and the rotation angle when it does; at maximum torque the posterior engagement probability is 0.54 (90% credible interval 0.24-0.84). The weight shift is modeled as a rate- limited input with a measured speed gain and an assumed first-order response, which predicts stopping distances four times those of a direct-force model. These models drive an event-triggered supervisor that schedules pulses by expected heading improvement, brakes when the predicted braking path would leave the safe set, tightens margins for model uncertainty, and updates the friction estimate online. In simulation on 500 plants drawn from the fitted models, the robust supervisor completed 92.6% of missions with 1.8% safety violations and 1.6% safe aborts, versus 84.0% and 7.2% for a supervisor using mean models and 1.8% and 20.4% for a baseline that ignores friction. A video of the hardware experiments is available online (https://github.com/ASAS-Labs/x-wheel).

Temporal audio grounding requires locating every occurrence of a queried target, making both occurrence counts and interval boundaries essential. We propose RefTAG, a post-training framework for verifiable temporal self-correction. Its Reflection Chain-of-Thought (Re-CoT) protocol combines draft intervals, discrete count and boundary actions, and revised intervals in one response. Supervised fine-tuning on these trajectories (Re-CoT-SFT) teaches the protocol; reinforcement learning (RL) jointly rewards final localization and revision outcomes. Across 8,228 queries, Re-CoT-SFT improves query-weighted mIoU over final-answer SFT by 1.74 percentage points. RL further improves TAG-Bench multi-target mIoU by 0.40 points and R@0.5 by 0.86 points. The final model achieves 38.50% TAG-Bench mIoU and 51.01% mean mIoU across three datasets, outperforming the evaluated open-source MOSS-Audio and FireRedAudio models and the commercial Gemini-3.1-Pro on both measures. These results demonstrate that explicitly coupling count- and boundary-aware revision with joint reward optimization provides an effective and verifiable path to temporal audio grounding.

Spatially encoded speech conveys not only linguistic information but also spatial cues. This enables listeners to distinguish and localize speakers, contributing to more natural and realistic auditory experiences in applications such as remote conferencing and immersive media. Neural speech codecs (NSCs) enable high-quality reconstruction of monaural speech at increasingly low bitrates while preserving linguistic information, but their ability to effectively preserve spatial cues remains limited. We propose FOAdapter, a plug-and-play spatial tokenizer that leverages a pretrained monaural speech codec to efficiently represent both linguistic and spatial information in first-order ambisonic (FOA) speech at low bitrates. Comprehensive evaluation demonstrates that FOAdapter consistently preserves spatial information across all evaluated codecs. Source code and audio examples are available on our demo website.

Automatic Speech Recognition (ASR) systems are commonly designed to transcribe linguistic content independently of speaker identity. However, speaker-specific information can persist within the latent representations of ASR models, raising privacy concerns when training data includes sensitive individuals. While machine unlearning has been studied in several speech tasks, applying speaker unlearning to ASR remains challenging due to the strong entanglement of linguistic and speaker-related information in speech representations. In this work, we conduct a layer-wise analysis of Self-Supervised Learning (SSL) based ASR representations, revealing that speaker identity is primarily encoded in intermediate encoder layers. Based on this observation, we explore a feature-level unlearning strategy that targets these identity structures. Experiments on the VCTK corpus show that the proposed approach removes the influence of specific speakers while maintaining robust transcription performance.

This paper studies near-field massive MIMO channel estimation based on an electromagnetic channel model with limited pilots. Starting from Maxwell's equations, we derive the homogeneous background response and the scattering response in the spatial and wavenumber domains. It is found that a compact scattering cluster produces a localized receive–transmit transverse-wavenumber block across finite apertures, thereby yields a block-sparse NLOS channel coefficient matrix in the wavenumber domain. Considering the spatial non-stationarity of large-scale antenna arrays, we introduce visibility factors at the transmit and receive sides of the channel model. To solve the non-stationary sparse channel estimation problem in the wavenumber domain, we propose a visibility-aware block-sparse Bayesian channel estimation algorithm. Simulation results demonstrate that the proposed method achieves lower channel-estimation NMSE than benchmark schemes.

The upcoming sixth generation (6G) wireless networks are designed to support advanced applications that the current air interface based on orthogonal frequency division multiplexing (OFDM) cannot accommodate, necessitating innovations in waveform design. Orthogonal time frequency space (OTFS) has emerged as a promising alternative, offering enhanced performance particularly in high-mobility channel environments. However, deploying fundamentally different waveforms over the same frequencies fails to exploit the mutual relationships between them. In this paper, a forward-compatible yet backward-compatible coexistence framework is proposed, where various OFDM waveform options are incorporated while seamlessly integrating OTFS within the same network. The proposed coexistence paradigm involves utilizing a specially designed spreading matrix in the delay-Doppler (DD) domain to process OTFS data. The matrix is tailored to establish orthogonality between OTFS and OFDM waveforms, effectively reducing cross-interference when both coexist within the same time-frequency (TF) resources. Our results demonstrate up to a 6 dB improvement in bit error rate (BER) performance for the OTFS waveform compared to other coexistence schemes. The proposed approach also shows greater interference resilience and approaches the upper bounds of achievable rates. By utilizing the full bandwidth and time duration, it enhances range and velocity resolutions, leading to improved sensing performance. These findings are supported by mathematical analysis and extensive simulations for uplink scenarios.