Papers

This paper addresses distributed state estimation for multi-agent systems with local and relative measurements, motivated by cooperative localization problems in which the global state dimension scales with the size of the network. We consider a Kalman-like observer in information form and introduce a sparsity-preserving prediction step based on an exponential forgetting factor, thereby avoiding the dense Riccati recursion of the standard information filter. The correction step is recast as a strongly convex quadratic program with structure induced by the sensing graph, which enables a distributed solution based on the alternating direction method of multipliers (ADMM). In the resulting scheme, each agent updates local copies of its own correction variable and those of its neighbors using only local communication, thus avoiding centralized matrix inversion and consensus over full global-state quantities. A two-time-scale stability analysis is developed for the interconnected observer: the reduced estimation-error dynamics are shown to be uniformly exponentially stable, the ADMM dynamics define an exponentially stable fast subsystem, and these properties are combined to establish uniform exponential stability of the overall distributed observer. Numerical simulations in a multi-agent cooperative localization scenario illustrate the performance of the proposed distributed observer.

Efficient beam alignment is fundamental to high-throughput and reliable connectivity in Vehicle-to-Everything (V2X) systems. However, conventional beam management in dynamic vehicular topologies incurs prohibitive alignment overhead and struggles to maintain robust links under rapid mobility. To overcome these challenges, this paper proposes a distributed multimodal graph beam alignment (GBA) framework. The core innovation lies in leveraging onboard multimodal sensing data to predict implicit feedback while employing graph neural networks to coordinate multi-user alignment, thereby jointly enhancing scalability and drastically reducing overhead. The architecture adopts a dual-network design with GBA-RSU and GBA-Vehicle units, optimized through a hybrid strategy of centralized learning and federated learning (FL) to balance global performance with local privacy. Furthermore, a dedicated data augmentation (DA) scheme is introduced to address multimodal data imbalance issues in vehicular networks. Negative augmentation applies dominant modality dropout to bolster robustness, while positive augmentation generates underrepresented samples to mitigate label imbalance. Numerical results demonstrate that GBA maintains a competitive sum rate on par with high-resolution codebook-based feedback yet reduces beam alignment overhead by over 90% and scales efficiently in mobile scenarios. Notably, integrating DA enables GBA to consistently outperform state-of-the-art FL-based alignment benchmarks, with particularly pronounced gains under severe label and modality imbalance, establishing a practical solution for V2X beam management.

High-fidelity control of superconducting qubits requires carefully shaped microwave pulses to avoid several different kinds of error at once. This article is a pedagogical bridging text aimed at upper-level undergraduate and early graduate students who have completed an introductory quantum mechanics course and a first course in quantum computing or quantum information, but who have not yet encountered the physical implementation of qubit gates. We integrate physical intuition for pulse design, analytical gate-level descriptions, and practical hardware considerations into a single, derivation-driven narrative, with explicit learning objectives. We begin with simple pulse envelopes and their spectral properties, showing how finite bandwidth produces leakage outside the computational subspace. This motivates the derivative removal by adiabatic gate (DRAG) technique, which we derive explicitly using the Magnus expansion, obtaining a clear, order-by-order account of which physical error channel appears at which order and why DRAG's cancellation is necessarily incomplete. We discuss the practical hardware realities of control pulse generation, focusing on arbitrary waveform generators (AWG), local oscillators (LO), and IQ mixing. Finally, we extend the discussion to two-qubit operation via the cross-resonance gate, and interpret how driving the control qubit at the target qubit's transition frequency necessarily produces several unwanted interaction terms alongside the desired one, and how successive generations of pulse-engineering strategies have been designed to suppress them.

This paper addresses characterizations of Integral Input-to-State Stability (iISS) for hybrid systems with memory. Based on the Krasovskii approach, a novel Lyapunov characterization of iISS is established to extend the hybrid system theory to the time-delay case. In particular, we introduce the notions of dissipativity, detectability and storage functional to describe the iISS property from different perspectives. Under mild regularity and convexity assumptions, the equivalence relations among diverse stability descriptions are established, which lays a solid foundation for the control design. Finally, a numerical example is presented to illustrate the derived results.

The aim of this paper is to demonstrate some interesting and useful approaches for writing a program in the assembly language. In order to demonstrate the possibilities of the assembly language, a project called "Arkanoid" was created. This project is written in assembly language and it presents few interesting algorithms. Assembly language, which is used for designing the game is x86 Assembly language, which produces object code for the x86 class of processors. As a working environment is chosen Visual Studio 2015, because it gives the useful tools for debugging and testing of the created software (game). Execution of the program results in a "Arkanoid" game, placed in Windows OS Console.

Neural representations (NRs), such as neural fields and 3D Gaussians, effectively model volumetric data in computed tomography (CT) but suffer from severe artifacts under sparse-view settings. To address this, we propose DiffNR, a novel framework that enhances NR optimization with diffusion priors. At its core is SliceFixer, a single-step diffusion model designed to correct artifacts in degraded slices. We integrate specialized conditioning layers into the network and develop tailored data curation strategies to support model finetuning. During reconstruction, SliceFixer periodically generates pseudo-reference volumes, providing auxiliary 3D perceptual supervision to fix underconstrained regions. Compared to prior methods that embed CT solvers into time-consuming iterative denoising, our repair-and-augment strategy avoids frequent diffusion model queries, leading to better runtime performance. Extensive experiments show that DiffNR improves PSNR by 3.99 dB on average, generalizes well across domains, and maintains efficient optimization.

Speaker diarization (SD) is the task of answering "who spoke when" in a multi-speaker audio stream. Classically, an SD system clusters segments of speech belonging to an individual speaker's identity. Recent years have seen substantial progress in SD through end-to-end neural diarization (EEND) approaches. DiariZen, a hybrid SD pipeline built upon a structurally pruned WavLM-Large encoder, a Conformer backend with powerset classification, and VBx clustering, represents the leading open-source state of the art at the time of writing across multiple benchmarks. Despite its strong performance, the DiariZen architecture spans several repositories and frameworks, making it difficult for researchers and practitioners to understand, reproduce, or extend the system as a whole. This tutorial paper provides a self-contained, block-by-block explanation of the complete DiariZen pipeline, decomposing it into seven stages: (1) audio loading and sliding window segmentation, (2) WavLM feature extraction with learned layer weighting, (3) Conformer backend and powerset classification, (4) segmentation aggregation via overlap-add, (5) speaker embedding extraction with overlap exclusion, (6) VBx clustering with PLDA scoring, and (7) reconstruction and RTTM output. For each block, we provide the conceptual motivation, source code references, intermediate tensor shapes, and annotated visualizations of the actual outputs on a 30s excerpt from the AMI Meeting Corpus. The implementation is available at https://github.com/nikhilraghav29/diarizen-tutorial, which includes standalone executable scripts for each block and a Jupyter notebook that runs the complete pipeline end-to-end.

Brain-inspired non-Boolean computing and sensing enable energy-efficient, error-tolerant, and highly parallel information processing, yet their deployment remains limited by the lack of compact, scalable spiking hardware. Mott phase-transition materials offer a promising route because their abrupt insulator-to-metal transitions enable neuron-like thresholding and oscillations. Among them, vanadium dioxide (VO$_2$) is particularly attractive owing to its near-room-temperature transition, fast switching, and scalability. However, existing VO$_2$ neuristors rely on discrete components, limiting integration density. Here, we report monolithic back-end-of-the-line (BEOL) integration of one-transistor-one-VO$_2$-memristor (1T-1MR) spiking neurons on a CMOS-compatible platform. VO$_2$ nanosheets are fabricated by pulsed-laser deposition atop dielectrically isolated silicon-on-insulator (SOI) p-type junctionless field-effect transistors (JLFETs) below 430 $^\circ$C. The architecture exhibits gate-tunable oscillations from 40 to 410 kHz in 60 nm-thin VO$_2$ devices with a 6 $\mu$m$^2$ active area, achieving 18 pJ per spike and 8 $\mu$W at room temperature, with potential for sub-3 $\mu$W operation. We uncover a non-monotonic dependence of oscillation frequency on bias current and temperature and analyze bias-dependent stochastic firing, revealing the nonlinear physics of integrated VO$_2$ thin-film memristors. Finally, we demonstrate voltage-controlled oscillator functionality and on-chip resistive coupling between two nano-oscillators mediated by a JLFET. These results establish a pathway toward dense, energy-efficient, monolithically integrated Mott neuromorphic hardware compatible with future computing and spiking sensing systems.

Deep learning-based channel estimation has been recognized as a promising technique for sixth-generation wireless systems. However, most existing approaches rely solely on least-squares estimates obtained from demodulation reference signals, which fail to explicitly exploit channel time-frequency correlation parameters. Inspired by the independent channel parameter estimation enabled by semi-static reference signals in modern wireless systems, this letter presents a parameter-aware deep learning-based channel estimation framework termed HyperCEUNet. Specifically, the proposed hypernetwork generates an adaptive front-end convolutional layer based on estimated channel parameters, serving as a pre-filtering stage before the UNet-based estimator. In addition, the Wiener-filtered channel estimates are adopted to provide a correlation-aware initialization for data resources. Simulation results demonstrate that our proposed HyperCEUNet effectively improves channel estimation accuracy compared with its conventional counterparts.

This study proposes an encrypted visual feedback control algorithm for regulating a one-dimensional stage using Ring Learning With Errors (RLWE) encryption. The proposed algorithm performs both feature extraction and controller computations directly on encrypted images, ensuring that sensitive visual data remain protected throughout the entire control process. Furthermore, an image captured by the camera is encrypted into a single ciphertext leveraging the message packing technique of RLWE encryption, thereby reducing computational cost. The effectiveness of the proposed framework is demonstrated through numerical simulations.

Full-duplex interaction, where speakers and listeners converse simultaneously, is a key element of human communication often missing from traditional spoken dialogue systems. These systems, based on rigid turn-taking paradigms, struggle to respond naturally in dynamic conversations. The Full-Duplex Interaction Track of ICASSP 2026 Human-like Spoken Dialogue Systems Challenge (HumDial Challenge) aims to advance the evaluation of full-duplex systems by offering a framework for handling real-time interruptions, speech overlap, and dynamic turn negotiation. We introduce a comprehensive benchmark for full-duplex spoken dialogue systems, built from the HumDial Challenge. We release a high-quality dual-channel dataset of real human-recorded conversations, capturing interruptions, overlapping speech, and feedback mechanisms. This dataset forms the basis for the HumDial-FDBench benchmark, which assesses a system's ability to handle interruptions while maintaining conversational flow. Additionally, we create a public leaderboard to compare the performance of open-source and proprietary models, promoting transparent, reproducible evaluation. These resources support the development of more responsive, adaptive, and human-like dialogue systems.

A problem of online estimation of unknown parameters is considered for a linear regression equation, which is affected by an additive perturbation that can be caused by measurement noise (that corrupts regressor and regressand), as well as external perturbations. Known approaches to solve this problem typically have one of the following disadvantages: 1) they ensure convergence of a parametric error to a compact set with non-adjustable bound, 2) independence of all system regressor elements from the perturbation/noise is required to annihilate them, 3) an instrumental variable is needed to be selected. On the basis of the novel perturbation annihilation procedure, in the present paper, we propose three new estimation laws, which are free from the above-mentioned drawbacks and ensure exponential convergence of the parametric error to an arbitrarily small neighborhood of zero, particularly, in case more than a half (not all) of the regressor elements are independent from additive perturbation. One of the proposed estimation laws is used for the design of Generalized Parameter Estimation-Based Observer (GPEBO) for nonlinear affine systems to enhance GPEBO performance in case when the measured system output is corrupted by noise. The theoretical results are supported by examples and mathematical modelling.

Distributed stochastic optimization enables multi-agent collaboration in applications such as distributed learning and sensor networks, but also raises critical privacy concerns due to the involvement of sensitive data. While existing privacy-preserving approaches often face limitations in balancing accuracy with efficiency, we propose a novel distributed stochastic gradient descent algorithm that integrates Paillier homomorphic encryption with heterogeneous and time-varying random stepsizes. The proposed algorithm provides inherent privacy protection against both internal honest-but-curious agents and external eavesdroppers, without relying on any trusted neighbors. Furthermore, we incorporate an attenuation factor to effectively mitigate quantization error induced by the encryption process, ensuring almost sure convergence to the optimal solution while maintaining privacy preservation. Numerical simulations demonstrate the effectiveness and efficiency of the proposed approach.

We study sensor scheduling for continuous-discrete Kalman filtering with Poisson measurement arrivals and propose an information-form deterministic surrogate for scalable offline design. Unlike the covariance-form surrogate, the sensing rates enter through sensor-specific additive information increments, eliminating mixed state-input derivatives in the transcribed nonlinear program and thereby yielding a simpler derivative structure. We further show that, together with the covariance-form surrogate, the proposed surrogate provides computable two-sided performance bounds for a given schedule under stochastic measurement arrivals. Numerical experiments demonstrate substantial computational savings, especially in many-sensor settings, while retaining comparable realized Monte Carlo performance and providing computable two-sided performance bounds for the returned schedule.

This study presents two analytical closed-form PI controller tuning solutions for second-order plants with real poles, each achieving monotonic step response and minimum settling time. The first solution employs pole-zero cancellation, placing the controller zero at the slower plant pole and reducing the closed-loop dynamics to a critically damped second-order system. The second solution, applicable when the plant pole ratio is less than two, places all three closed-loop poles at a common location without cancelling any plant pole, yielding a closed-loop transfer function with a triple real pole and a zero. Despite retaining a closed-loop zero, this solution achieves strictly faster settling time than the pole-zero cancellation method in its region of applicability. The two solutions coincide at the boundary pole ratio of two and together form a continuous piecewise-analytical tuning covering the full range of plant pole ratios. This study further establishes that closed-loop transfer functions of the form a^n/(s + a)^n possess a maximum sensitivity Ms together with phase margin and gain margin that are independent of the pole location a and depend solely on the order n, yielding universal robustness constants for each n. A closed-form expression GM(n) = 1 + sec^n(pi/n) is established for the gain margin of the family. Numerical verification confirms the analytical results across multiple plant configurations.

There has been remarkable progress over the past decade in establishing finite-sample, non-asymptotic bounds on recovering unknown system parameters from observed system behavior. Surprisingly, however, we show that the current state-of-the-art bounds do not accurately capture the statistical complexity of system identification, even in the most fundamental setting of estimating a discrete-time linear dynamical system (LDS) via ordinary least-squares regression (OLS). Specifically, we utilize asymptotic normality to identify classes of problem instances for which current bounds overstate the squared parameter error, in both spectral and Frobenius norm, by a factor of the state-dimension of the system. Informed by this discrepancy, we then sharpen the OLS parameter error bounds via a novel second-order decomposition of the parameter error, where crucially the lower-order term is a matrix-valued martingale that we show correctly captures the CLT scaling. From our analysis we obtain finite-sample bounds for both (i) stable systems and (ii) the many-trajectories setting that match the instance-specific optimal rates up to constant factors in Frobenius norm, and polylogarithmic state-dimension factors in spectral norm.

The increasing integration of renewable energy sources exacerbates the spatial and temporal differences in frequency across the power system, posing a serious challenge to the accurate and efficient assessment of system frequency security. To address this issue, a generic effective nodal frequency (ENF) model is first established to concisely characterize nodal frequency dynamics. This model is featured by the effective nodal inertia (ENI), damping, and primary regulation parameters, which retain only the dominant constant component governing nodal frequency dynamic performance. This model enables the tractable analytical formulation of nodal frequency trajectory and the key frequency security indicators. Quantitative analysis under the temporary power disturbance condition reveals that the ENI is the most influential parameter governing frequency security. Consequently, the critical nodal inertia for ensuring nodal frequency security is analytically derived. A system-level frequency security index based on the actual ENI and critical nodal inertia is proposed. On the basis of the proposed index, the system frequency security assessment is carried out with the procedure of “offline calculation and online evaluation”, which is achieved using a lookup table approach and an interpolation method. Simulations on the modified IEEE 39-bus system verify the effectiveness of the proposed assessment method.

This paper proposes a scalable coordination framework with aggregator-side privacy protection for storage-like distributed energy resources (DERs). The framework adopts a two-layer architecture. At the macroscopic layer, building upon an Eulerian modeling perspective, the DER population is represented as a continuum whose density evolution is governed by a partial differential equation (PDE), such that the computational complexity is independent of the population size. To address the bilinear non-convexity in this PDE-constrained optimization problem, we develop a convexification method that combines finite-volume discretization with a flux-lifting technique, reformulating the macroscopic problem into a sparse linear program (LP). The LP solution yields a unified, state-dependent broadcast signal for population coordination. Furthermore, a Wasserstein-based relaxation is introduced to replace rigid cyclic constraints and provide additional operational flexibility for improved economic performance. At the microscopic layer, individual resources autonomously recover local setpoints from the broadcast signal and their local states, while an upstream data-mixing protocol aggregates individual states into a macroscopic density histogram without exposing raw individual states to the aggregator. Numerical studies validate the scalability, feasibility, and economic effectiveness of the proposed framework.

The Euclidean Steiner tree problem, normally posed in two dimensions, seeks to connect a set of prescribed terminal nodes by placing additional nodes, known as Steiner points, with edges connecting such nodes either to another Steiner point or a terminal node, and with the placements minimising the sum of all the edge lengths of the associated tree. We consider a problem in which we start with a known solution to a Steiner tree problem, and the terminal positions are then perturbed. A first-order approximation theorem is established for efficiently updating the Steiner point positions to recover a Steiner tree solution after the perturbations to terminal nodes. Numerical examples illustrate the effectiveness of our approach (including a stepwise application for large perturbations) as well as its limitations.

We propose a generalized dynamic phasor (DP) framework to analyze inverter-based resources (IBRs) connected to multi-machine systems under balanced and unbalanced conditions. It captures subsynchronous oscillations (SSOs) induced by grid-following (GFL) IBRs. The linearizability and time invariance of the framework enables us to perform eigen decomposition, which is a powerful tool for root-cause analysis of the SSO modes and damping controller design. The same framework also enables analysis of excitation of the SSO modes in presence of data center (DC) loads. The GFL IBRs are modeled in their respective $dq$-frame DPs and the detailed model of synchronous generators (SGs) along with dynamic transmission network models are represented in $pnz$-frame DPs. Several case studies are performed on the modified IEEE two-area benchmark system, where $2$ SGs are replaced by GFL IBRs and validated with EMTDC/PSCAD simulations. First, time- and frequency-domain analyses of the SSO mode are presented followed by the design of a robust decentralized $\mathcal{H}_\infty$ damping controller based on local signals of the GFL IBRs. Second, the dynamic behavior of the system following an unbalanced fault is demonstrated that is damped by the proposed damping controller. Finally, excitation of the SSO mode in presence of DC load is exhibited and its locational impact is analytically quantified.

With the evolution of multiple-input multiple-output (MIMO) technology toward extremely large (XL) MIMO systems comprising hundreds of, or more, antennas, this work investigates scalable and fronthaul-efficient reception design for the uplink of cell-free (CF) XL-MIMO systems. In such systems, the uplink signals transmitted by mobile user equipments (UEs) are jointly decoded at a central processing unit (CPU) connected to distributed access points (APs) via finite-capacity fronthaul links. We address the joint optimization of linear transform matrices, used by the APs to reduce the signal dimension and fronthaul load, and fronthaul compression strategies to maximize the uplink sumrate. A fractional programming (FP)-based iterative algorithm is first developed, followed by a reduced-complexity variant, termed accelerated FP (A-FP), along with its decentralized implementation whose fronthaul overhead remains independent of the number of AP antennas. Numerical results show that the proposed A-FP scheme significantly reduces computational complexity compared to FP implemented with general-purpose solvers, while substantially outperforming scalable baseline schemes that rely solely on local channel state information.

We present Seed3D 2.0, an advanced 3D content generation system built on Seed3D 1.0, with substantial improvements across generation fidelity, simulation-ready capabilities, and application coverage. For geometry, a coarse-to-fine two-stage pipeline decouples global structure learning from high-frequency detail recovery, while a locality-aware VAE achieves higher spatial compression and more efficient decoding. For texture and material generation, we replace the cascaded pipeline of Seed3D 1.0 with a unified PBR model that directly generates multi-view albedo and metallic-roughness maps, enhanced by Mixture-of-Experts scaling and VLM-based semantic conditioning for improved material precision and visual fidelity. Beyond single-object generation, Seed3D 2.0 introduces a simulation-ready model suite comprising scene layout planning, part-aware decomposition, and training-free articulation generation, enabling coherent scene construction and part-level physical interaction across physics and graphics engines. A large-scale human preference study against five recent commercial models shows that Seed3D 2.0 achieves consistent win rates of 69.0% to 89.9% in textured 3D asset generation. Seed3D 2.0 is available on https://exp.volcengine.com/ark/vision?_vtm_=0.0.c70961.d701978.0&mode=vision&modelId=doubao-seed3d-2-0-260328&tab=Gen3D

Ultra-wide bandgap (UWBG) Ga$_2$O$_3$ is a promising semiconductor for high-power and high-temperature electronics. Reliable qualification of these devices under extreme operating conditions is essential, yet conventional reliability testing is inherently time-consuming. Autonomous experimentation offers a new paradigm by enabling measurement planning and model refinement to evolve in parallel in real time. We present a Safe Active Learning (SAL) framework for autonomous reliability characterization of Ga$_2$O$_3$-based diode sensors under coupled thermal and hydrogen stress. We first evaluate SAL in simulation, where it safely expands the explored region while learning the evolving rectification surface. Second, we demonstrate SAL experimentally on an automated high-temperature probe-station platform using a Pt/Cr$_2$O$_3$:Mg/$\beta$-Ga$_2$O$_3$ diode sensor of H$_2$ and temperature, spanning 0-800 ppm H$_2$ and 350-550 {\deg}C. Finally, we use the SAL-generated dataset for offline long-horizon forecasting of the diode current at a target voltage with a structured Gaussian-process model. Its condition-dependent Kohlrausch–Williams–Watts mean and residual covariance kernel were engineered with artificial-intelligence assistance using the SAL data and an auxiliary validation dataset spanning 1,000 hours at 400 {\deg}C across multiple H$_2$ concentrations. This dataset guided kernel design and validation, and the resulting model captures its long-time, saturating degradation trends. Although demonstrated here for a rectifying Ga$_2$O$_3$-based diode, SAL is applicable to other device classes whenever a suitable safety observable can be measured in situ.

We tested whether auditory-evoked EEG supports subject-independent five-vowel perception decoding when trial identity, model identity, prediction provenance, and participant-level inference are controlled within a single benchmark. We reconstructed Study 2 event tables from OpenNeuro ds006104 version 1.0.1 and analysed the consonant-vowel pair task. One-to-one marker-stimulus pairing yielded 3,840 independent trials; control-condition selection and artifact rejection retained 1,094 epochs from 16 participants and 61 EEG channels. Thirteen unique implementations were evaluated using leave-one-subject-out testing, with participant metrics reconstructed from 36,102 trial predictions across 33 complete prediction replicas. Random Forest was numerically highest at 21.474% balanced accuracy (95% participant-bootstrap interval, 19.526-23.482%; chance, 20%), but neither its participant-level tests nor any implementation survived correction across the 13-model family. Deep-model performance was close to chance, and several architectures showed substantial seed-dependent variation and low trial-label agreement. An exploratory MDM analysis comprising 9,616 genuine refits across training cohorts of 3-15 participants showed no monotonic performance gain. Within this dataset and protocol, evidence for reliable cross-subject five-vowel decoding is limited. The benchmark provides a reproducible chain from source rows to retained epochs, predictions, participant-level metrics, multiplicity-adjusted inference, and bounded diagnostic analyses.

Traditional glass-based optics are typically optimized for narrow spectral bands, such as the visible (400-700nm) or shortwave infrared (1000-1800nm). While the emergence of VIS-SWIR sensors (400-1700nm) offers transformative potential, refractive optics struggle to focus this entire range simultaneously. Mirrors represent a promising achromatic alternative; however, they are often sidelined by field curvature, and off-axis aberrations. This paper introduces Computational Mirrors, a framework that enables high-resolution, wide-field-of-view imaging across the complete VIS-SWIR spectrum using a single sensor. Our method is built on the observation that distinct regions of the field of view reach focus at varying distances from the mirror. By capturing a minimal focal stack (2-4 images), we utilize a computational backend to recover a sharp, all-in-focus image. A key contribution of this work is SeidelConv, a novel, physics-inspired, spatially-varying point spread function (PSF) model designed to accurately characterize and correct the off-axis aberrations inherent in simple concave mirrors. We demonstrate the efficacy of our approach using a first-of-its-kind 50mm F/1 optical system equipped with a VIS-SWIR sensor. Our system produces sharp images across RGB, NIR, and SWIR wavelengths without requiring refocusing, revealing material details invisible within individual spectral bands. We further validate the scalability of our approach with a 100mm F/2 system optimized for long-range imaging.

Speech neuroprosthesis systems decode intended speech from neural activity in the absence of audible output, offering a path to restoring communication for individuals with speech-impairing conditions. Current approaches decode predominantly from motor cortical areas, discarding others – such as area 44, part of Broca's area – that may encode complementary linguistic information. We introduce MoDAl (Modality Decorrelation and Alignment), a framework that discovers complementary neural modalities through the interplay of two objectives in a shared projection space. A contrastive loss aligns each of several parallel brain encoders with the text embeddings of a pretrained large language model (LLM), while a decorrelation loss prevents the encoders from coalescing to duplicative representations. We prove that these objectives are in productive tension: Contrastive alignment induces transitive modality coalescence, which decorrelation must counteract for the framework to discover diverse neurolinguistic modalities. On the Brain-to-Text Benchmark '24, MoDAl reduces word error rate (WER) from 26.3% to 21.6% compared to the previous best end-to-end method, with the gain from incorporating previously discarded area 44 signals arising entirely from the decorrelation mechanism. Analysis of the discovered modalities reveals functional specialization: Encoders receiving area 44 input capture structural and syntactic properties (sentence length, grammatical voice, wh-words), consistent with the neurolinguistic understanding of Broca's area.

Autonomous fixed-wing flight is becoming a key capability in aerial robotics, enabling sensing, mobility, and contingency operations across both small-scale Uncrewed Aircraft Systems and large-scale Advanced Air Mobility. During unpowered operation in fixed-wing platforms, airspeed is regulated solely through potential-kinetic energy exchange, making airspeed dynamics highly sensitive to guidance commands, particularly under wind. This paper presents a viability-based airspeed protection for ground-referenced guidance in steady wind, where airspeed evolution depends explicitly on the commanded flight path angle. Leveraging Nagumo's tangency condition, we derive a closed-form, wind-dependent characterization of admissible guidance commands that guarantees forward invariance of a safe airspeed envelope. These conditions are embedded within an offline quadratic programming framework to certify airspeed-safe maneuver primitives for non-ascending flight at the guidance level. The approach is validated using a high-fidelity unpowered fixed-wing aircraft model on gliding trajectories formed by concatenating certified maneuver primitives, demonstrating strict airspeed boundedness. Future work will address unsteady wind fields and flight experiments.

Precise positioning is a key enabler for emerging 5G applications, from autonomous transport to industrial automation. Yet the open physical layer (PL) leaves standard positioning reference signals (PRSs) vulnerable to manipulation. This work addresses the security of downlink observed time difference of arrival positioning (DL-OTDOA) through three contributions. First, we introduce VeriLoc, an open-source system-level simulator designed for realistic channel modeling and PL threat injection. Second, we propose three novel security techniques to enhance resilience and threat detection: encrypted PRS to prevent adversarial waveform synthesis, angular-based source authentication (ABSA), and a cross-layer downlink-uplink handshaking protocol to detect attacks that cannot be mitigated by encryption. Third, utilizing VeriLoc, we evaluate the proposed techniques alongside position tracking and a PRS authentication scheme, which extends the original hash-based message authentication code (HMAC) scheme design to support digital signatures. Simulation results demonstrate that while encryption, authentication schemes, and tracking robustly counter selective PRS spoofing and jamming, the proposed spatial and cross-layer mechanisms are essential for detecting meaconing, collectively maintaining attack detection rates in excess of 90% while keeping false alarm rates minimal.

Approximate Message Passing (AMP) is a general framework for iterative algorithms, originally developed for compressed sensing and later extended to a wide range of high-dimensional inference problems. Although recent work has advanced matrix AMP, complex AMP, and AMP for non-separable functions independently, a unified state evolution theory for complex AMP with non-separable denoisers has been lacking. This article fills that gap by establishing state evolution in the setting of complex, non-separable denoising functions. The proposed approach constructs an augmented real-valued system that lifts the problem to a higher-dimensional space, then recovers the complex domain through a many-to-one canonical transformation. Under this construction, the Onsager correction naturally involves Wirtinger derivatives, and the resulting state evolution reduces to scalar complex recursions despite the non-separable structure of the denoisers. The framework extends to the matrix-valued setting, accommodating multiple feature vectors simultaneously. This generalization enables AMP to exploit joint structural constraints, such as simultaneous group and element sparsity, in complex-valued recovery problems. The complex sparse group least absolute shrinkage and selection operator (LASSO) serves as a key instantiation, motivated by preamble detection in Orthogonal Time-Frequency Space (OTFS)-based unsourced random access. Numerical experiments confirm that state evolution accurately predicts performance and show that complex non-separable denoising can produce significant gains over separable and real-valued alternatives.

We study the Suscectible-Infected-Recovered-Susceptible (SIRS) epidemic model on deterministic networks. For connected but otherwise general interaction patterns and heterogeneous recovery and loss-of-immunity rates, we identify a fundamental parameter R_0 (the basic reproduction number), which fully characterizes the qualitative dynamic behavior of the system. This parameter is the dominant eigenvalue of a rescaled version of the interaction matrix, whose rows are normalized by the corresponding recovery rates. We prove that a transcritical bifurcation occurs as R_0 crosses the threshold value 1. Specifically, we show that, if R_0 does not exceed 1, then the disease-free equilibrium is globally asymptotically stable, whereas, if R_0 is larger than 1, then the disease-free equilibrium is unstable and there exists a unique endemic equilibrium, which is asymptotically stable. As a byproduct of our analysis, we also identify key monotonicity properties of the dependence of the endemic equilibrium on the model parameters (the interaction matrix as well as the recovery rates and the loss-of-immunity rates) and obtain a distributed iterative algorithm for its computation, with provable convergence guarantees. Our results extend existing ones available in the literature for network SIRS epidemic models with rank-one interaction matrices and homogeneous recovery rates (including the single homogeneous population SIRS epidemic model).