Papers

Conventional bandwidth-limited ranging resolves delay on the scale of \(c_0/(2B)\), yet a large distributed aperture can support substantially finer localization through near-field carrier-phase diversity. This letter characterizes the coherent main-lobe width of the localization likelihood for pinching-antenna systems and shows that it is governed by the spread of the direction cosines subtended by the aperture at the target. The resulting expression recovers classical far-field angular and Fresnel-range scalings and reduces to a wavelength-scale cell when the aperture subtends a large solid angle. Guided by this characterization, a hierarchical maximum-likelihood search is developed, combining noncoherent coarse localization, coherent lobe selection, and local refinement. Numerical results under scattering demonstrate submillimeter localization above a threshold signal-to-noise ratio without localization failures in the tested ensemble.

Many signal processing tasks require intensive computations. Quantum computing promises to accelerate certain tasks, but algorithms must be designed around the limitations of quantum mechanics. This paper provides a quantum implementation of finite impulse response (FIR) filters, which are a widely used tool in classical signal processing. The filter can be parallelized and composed as part of larger quantum algorithms, with potential for speedups using quantum amplitude estimation and future fault-tolerant quantum hardware. To accomplish these properties, we introduce a state-space framework wherein sample-based signal processing is performed with unitary quantum circuits. We present the quantum delay gate as the quantum analog of the delay line in discrete-time signal processing. Unitary circuits intrinsically describe lossless systems, but we also implement lowpass and other bounded filters by emulating a projection operator. The FIR filter implementation is tested and demonstrated with a quantum circuit simulator.

Image quality assessment (IQA) datasets use different subjective protocols and rating scales, so their scores are not directly comparable. The lack of a common perceptual scale hinders multi-dataset training and precludes direct inter-dataset evaluation. We address this by conducting a new subjective experiment and using its ratings as perceptual anchors to fit monotonic mappings that place the existing scores of 23 IQA datasets on a common quality scale while preserving within-dataset rankings. The resulting dataset, MOSAIQ-500K, contains over 500,000 images and is, to our knowledge, the largest IQA dataset with perceptually aligned subjective scores. We also propose MOSAIQ-Bench, an inter-dataset benchmark, and use it to evaluate 31 IQA methods, revealing substantial gaps between intra- and inter-dataset performance, particularly on authentic distortions. The aligned scores offer a key advantage: replacing specialized multi-dataset training mechanisms with standard regression losses yields comparable intra-dataset accuracy and better inter-dataset performance. MOSAIQ thus provides a practical foundation for combining heterogeneous IQA datasets to train more generalizable IQA models. Code and dataset will be made public at ivc.uwaterloo.ca/projects/unifying_iqa_datasets/ upon acceptance.

In this paper, we propose an alignment-path distillation framework for streaming automatic speech recognition (ASR) with large language models (LLMs). Interleaved streaming ASR-LLMs use forced alignments (FA) from alignment models, such as those trained with connectionist temporal classification (CTC), to construct speech-text training sequences. However, alignments obtained from a separate acoustic model may be inconsistent with those learned by LLM-based ASR. This motivates us to transfer alignment information from a non-streaming ASR-LLM to improve streaming recognition. Specifically, we extract monotonic alignment paths from a non-streaming teacher's soft text-audio attention and use them to construct interleaved training sequences. The framework also includes logit and hidden-state distillation to learn from the teacher's output distributions and internal representations. Experimental results show that, without logit or hidden-state distillation, training with teacher-derived alignment paths achieves a 5.2% relative error rate reduction compared with training using forced alignments. When both models use logit and hidden-state distillation, teacher-derived alignments yield a 3.9% relative error rate reduction, with similar mean emission latency but higher flicker. The complete framework achieves a 16.6% relative error rate reduction compared with training using forced alignments without logit or hidden-state distillation.

This paper investigates multi-time scale commu- nication schemes in synchronization of heterogeneous Lienard oscillator systems. Existing results rely on global exchange of information for all networked systems at discrete time instants, providing limited flexibility in adjusting the trade-off between communication rate and synchronization performance. A scheme to investigate how global updates are complemented by more frequent local interactions within a sub-network is thus proposed in this paper. For a prescribed bound on the synchronization error, conditions are derived under which the global and local update rates can be selected as design parameters. A numerical example illustrates the beneficial effect of the local interactions.

3D sequences provide an alternative to 2D or multiband sequences for BOLD fMRI. The impact of acquisition time differences between slices is well understood for 2D or multiband sequences. For 3D sequences, k-space is partitioned into multiple segments and the final image depends on samples taken over an extended period of time. Any precise timing information is lost during reconstruction. A theoretical description of how sequence timing impacts 3D BOLD fMRI data properties is lacking in the literature. We present a formal framework that models sequence timing effects and draw connections to existing literature. The framework rests on the statistical description of a spin system that allows incorporation of BOLD signal changes and is completed by a general description of spatial encoding and image reconstruction. Using this formulation, we define three key images: the actual image reconstructed from a segmented 3D acquisition, an optimal reference image unaffected by sequence timing, and an error image representing their difference. Two sets of operators are defined that describe the generation of these images and allow us to analyse the effects of sequence timing independent of the object. All operators take the form of spatio-temporal filters. They mix signal content spatially and based on its temporal waveform. High-frequency BOLD signal content is affected more strongly compared to low-frequency content. Simulations demonstrate that while the total variance of 3D image time series can be reduced, spatio-temporal fidelity is increasingly lost. Furthermore, we hypothesize that the heightened sensitivity of 3D sequences to physiological noise (compared to 2D) is caused by sequence timing damping thermal noise more strongly than physiological fluctuations. The impact of motion and system instabilities on image time series needs to be studied further.

SpeechLLMs have shown strong potential for emotion recognition, yet they read the predicted emotion off a generative decoder not suited for classification: it can emit labels outside the target set and favors frequent classes. We propose a discriminative adaptation that reads the final prompt token's hidden state through a classification head, producing a label in one forward pass without modifying the backbone. Because this readout starts from the hidden state the model would otherwise decode, it gives a controlled comparison of generative and discriminative inference in an otherwise identical speechLLM. We keep the head a single linear layer, trading little accuracy for interpretability: each emotion becomes one direction in the LLM output token space, revealing associated tokens. On IEMOCAP, across two speechLLM architectures, it improves Macro F1 and removes hallucinations, with largest gains on realistic ASR transcripts. Our analysis reveals that these emotion directions encode indirect associations mirroring biases in web-scale text.

This paper presents dynamics-relaxed model predictive control (DR-MPC), a novel MPC formulation for legged locomotion, and a tailored interior-point method (IPM) solver. The formulation combines online optimization feasibility by construction with a contact-aware input parameterization. DR-MPC moves the dynamics equality and affine input constraints into quadratic penalties and retains only nonempty box constraints. The resulting box-constrained quadratic program (QP) has a block-arrow Hessian that enables the state and affine-output directions to be eliminated through a Schur complement. The solver factors only the reduced control system after swing-force elimination and contact-aligned move blocking. For the evaluated implementations using the same DR-MPC formulation, our method achieves median end-to-end MPC speedups of $16.0\times$ over HPIPM and $4.4\times$ over OSQP, with comparable locomotion performance in simulation. DR-MPC achieves a median onboard MPC end-to-end time of $4.4$ ms and is validated on a Unitree Go1 quadruped. Open-source code will be made available after publication.

Space-air-ground integrated networks (SAGINs) integrate heterogeneous platforms with different coverage, mobility, and payload constraints, which creates strong demands for flexible antenna architectures. Foldable antenna arrays (FAAs) provide a promising solution by reconfiguring array geometry, antenna positions, and orientations through controllable joints or hinges. However, existing studies on FAAs have mainly considered simple folding configurations and limited deployment benefits, while FAAs' architectures and broader potential for SAGIN remain largely unexplored. In this article, we present the fundamentals and architectures of FAA, including folding mechanisms, foldability levels, and functional capabilities. We then examine FAA applications for ground base stations, low-altitude platforms, high-altitude platforms, and satellites, together with their potential challenges and solutions. Finally, we highlight open issues and future research directions, and provide a case study to demonstrate the FAA-enabled communication performance improvement in SAGIN.

Networked low-altitude unmanned aerial vehicles (UAVs) need reliable and adaptive decision-making capabilities to operate under uncertain observations, dynamic environments, and intermittent connectivity, while many existing agentic systems remain limited by hallucination risks, data dependence, and weak generalization. This article investigates neuro-symbolic agentic AI (NSAAI) as a framework for combining neural grounding, symbolic reasoning, and closed-loop agentic interaction to support more reliable and adaptive UAV autonomy. We first examine its capability foundations in data efficiency, compositional generalization, continual learning, and zero-shot transfer, and then develop a reference architecture integrating task and goal management, neuro-symbolic planning, verification and metacognition, skill execution and network interaction, and shared knowledge and memory. An urban fire-inspection case implemented in LAESim illustrates how a UAV can coordinate sensing and cloud access under intermittent connectivity, reuse a verified image-delivery skill, and satisfy explicit evidence conditions before completing the mission. The results illustrate the potential of NSAAI to support reusable skills, evidence-grounded decision-making, and adaptive mission execution in networked UAV systems. We further discuss key research directions in uncertainty-aware reasoning, knowledge and skill expansion, adaptive self-monitoring, and standardized evaluation.

Barrier Lyapunov functions (BLFs) have been a popular choice when dealing with constrained control problems. In the current article, we present a unified asymmetric barrier Lyapunov function that generalizes the existing logarithmic symmetric Lyapunov function. We show that the proposed function is smooth and does not require the discontinuous switching function that is ubiquitous in the asymmetric barrier Lyapunov functions existing in the literature. Based on the proposed barrier Lyapunov function, a control law, guaranteeing exponentially fast output tracking, is designed for a class of single-input single-output nonlinear systems with output constraints. We show that the proposed control law unifies the control design and structure for a system with either symmetric or asymmetric output constraints. Furthermore, we constructively show that the proposed function can be bounded from above and below by symmetric class $\mathcal{K}$ functions which help in establishing local exponential convergence together with the proposed control law. Lastly, we provide numerical examples to illustrate the performance of the proposed control and compare the results with the existing logarithmic BLFs.

Question answering has advanced rapidly with large language models, but predominantly for high-resource languages, in both text and spoken settings. Spoken question answering (SQA) benchmark for Telugu remains unexplored, and the reliability of automatic evaluation in this setting remains unquantified. We introduce VākQA, a Telugu SQA benchmark of 2,001 factoid question-answer pairs across six domains, with 2.53 hours of speech audio, bilingual transcriptions, and human-verified reference answers. We first validate evaluation methods against human judgements: Gemini-as-a-judge best approximates human ratings but is non-uniformly strict, while open-weight judges systematically penalize correct Telugu answers that differ in surface form from the reference. Using this validated setup, we benchmark proprietary and open-weight models across input modality, language, and domain. We observe that Telugu phrasing retains cultural specificity that is lost in translation, speech input introduces phonetic confusions that alter question meaning, and cascaded ASR-MT errors compound progressively. VākQA is publicly released.

Voice activity detection (VAD) fronts most voice-agent pipelines, yet production detectors treat all human speech, background talkers included, as valid activity; in crowded settings this floods recognition, stalls turn-taking, and triggers false barge-in. We formalize Foreground VAD (FVAD): a frame-synchronous, enrollment-free task in which only the dominant speaker, defined by sustained presence rather than instantaneous loudness, is positive, and which reduces to conventional VAD when a single speaker is present. We show that foreground selectivity is largely governed by training supervision: the crucial ingredient is an augmentation recipe pairing foreground-only labels with competing-speaker mixing, generated fully automatically without human annotation. To quantify selectivity we introduce the Background False-Alarm Rate (BG-FAR), gated by foreground F1, and build a controlled benchmark, Mix-Interference, complemented by an adapted VOiCES for real-world far-field evaluation. Across equal-size backbones, Mamba and LSTM perform on par while a longer-context attention model is no better, suggesting that training supervision plays a substantially larger role than temporal modeling capacity in achieving foreground selectivity. The resulting lightweight streaming model, Mamba-FVAD, outperforms commercial VADs and enrollment-based speaker-aware systems in foreground selectivity while staying competitive on conventional VAD, at 1-2 ms per-frame CPU latency.

Speech emotion recognition (SER) benefits from heterogeneous acoustic representations, but views derived from the same utterance contain both overlapping emotional evidence and representation-dependent cues. Direct fusion may therefore propagate redundant information or obscure complementary details. To address this issue, we propose Tri-view Consensus-Guided Fusion (TriCGF) for jointly modeling spectrogram, Mel-frequency cepstral coefficients, and HuBERT representations. TriCGF organizes each view into common and view-specific components before fusion. Cross-view Consensus Learning aggregates the common components into a global reference, while View-wise Gated Integration adaptively combines this reference with each view-specific component. A soft difference regularizer further discourages excessive information overlap. Under speaker-independent evaluation, TriCGF achieves 74.19% weighted accuracy (WA) and 75.17% unweighted accuracy (UA) on IEMOCAP, and 94.36% WA and 94.28% UA on EmoDB, outperforming representative SER methods on both datasets.

Battery energy storage systems (BESSs) are widely integrated into smart grids. For an isolated BESS network, however, capacity degradation and power loss of battery units increase operating costs. To alleviate this problem, two distributed economic dispatch (ED) schemes with discrete-time dynamics are developed in this paper, thus obtaining the optimal output power vector and ensuring supply-demand balance while considering dynamic line loss and capacity constraints. It is worth mentioning that proportional-integral protocols with reset mechanisms (PI+R protocols) are introduced into these schemes which can greatly improve the consensus rate and control accuracy. Specifically, a marginal cost (MC) consensus controller and an average power mismatch estimator are the core components of each scheme, where these two are coupled to each other. In this regard, the difference between the two schemes is that for the MC consensus controllers, one involves incorporating the estimated average power mismatch feedback term into the closed-loop system error to design the PI+R protocol, while the other does not. In addition, regarding to each PI+R protocol, the integral term is reset to 0 when the proportional term experiences zero crossing, in order to accelerate the convergence rate and reduce overshoot. The effectiveness, conditions under which the reset mechanism works, and stability of these schemes are all well analyzed. Finally, some simulation cases are designed and compared with an existing solution. From the simulation results, it can be seen that the designed second scheme greatly improves the performance of the previous scheme in consensus rate, convergence rate, BESS/agent plug and play, load switching, and wide area system application.

Automated video-based surgical skill assessment has advanced rapidly, yet rigorous evaluation of continuous standardized score prediction under participant-level generalization to unseen trainees remains limited. We introduce VBA-Net+, a video-only framework for Fundamentals of Laparoscopic Surgery (FLS) score regression and pass-fail classification using pretrained video foundation models as frozen feature extractors. We evaluate two FLS datasets, suturing and pattern cutting, using VideoPrism, V-JEPA2, and VideoMAE v2, with a frame-level SimCLR baseline. A lightweight fully convolutional head is trained on embeddings offline and evaluated using participant-level leave-one-user-out (LOUO) cross-validation within the standardized assessment protocol. For continuous score prediction, the best representation achieves $R^2$=0.6367 for suturing and 0.9261 for pattern cutting. For pass-fail classification at official FLS thresholds, area under the receiver operating characteristic curve (AUC) reaches 0.9073 and 0.9906, respectively. Frozen video-encoder pipelines generally outperformed the frame-level SimCLR pipeline, particularly for suturing, providing a benchmark for video-only FLS assessment.

Conversational context provides semantic and acoustic cues across turns for automatic speech recognition (ASR), but relying on historical transcripts can propagate recognition errors and discard pronunciation and speaker information. We present a multimodal conversational-context framework for LLM-based ASR that integrates a scenario-controlled data pipeline, scalable multimodal context training, and systematic evaluation. We construct dialogues around entities and their confusable forms and interleave historical user speech with assistant text responses for supervised fine-tuning. We also introduce MM-ContextASR Bench, which evaluates contextual understanding and entity error correction across five scenarios. Experiments with Qwen3-Omni and Step-Audio-2-mini reveal limitations in handling irrelevant and erroneous history and show that our data construction and training improve context utilization, with multimodal context achieving the highest overall entity recall on both models. Further experiments on accent, dialect, and target-speaker ASR demonstrate the value of historical speech. The benchmark data and evaluation code are publicly available at https://github.com/llh666521/MM-ContextASR.

Accurate classification of brain disorders from neuroimaging data remains challenging because of substantial inter-subject heterogeneity and the complex multi-scale patterns present in functional connectivity and morphological representations. To address these challenges, we propose HyperAMS-Net, a deep learning framework for brain disorder classification using neuroimaging representations derived from resting-state functional MRI or structural MRI. HyperAMS-Net integrates adaptive multi-scale convolution, hypergraph attention, spatial-channel attention, and adaptive feature fusion. Specifically, adaptive multi-scale convolution learns data-driven weights over multiple receptive fields to capture complementary patterns at different scales. Hypergraph attention models higher-order dependencies among learned feature representations through node–hyperedge–node message passing, while spatial-channel attention enhances discriminative feature learning. Adaptive feature fusion further aggregates complementary information across parallel network branches. HyperAMS-Net is evaluated on three benchmark datasets spanning distinct brain disorders: ABIDE for autism spectrum disorder, REST-meta-MDD for major depressive disorder, and ADNI for Alzheimer's disease, using 5-fold stratified cross-validation. HyperAMS-Net achieves state-of-the-art performance across all evaluated datasets, attaining the highest accuracy and AUC among the compared methods. Ablation studies further demonstrate the contribution of each proposed component, with the largest performance degradation observed when hypergraph attention is removed.

Flow state, characterized by deep engagement and immersion during challenging activities, represents a valuable mental state with significant implications for learning, performance, and rehabilitation outcomes. While flow has been extensively studied behaviorally, objective neurophysiological detection methods suitable for real-world deployment remain limited. Electroencephalography (EEG) offers a promising avenue for flow detection due to its accessibility, portability, and superior temporal resolution; however, the utility of consumer-grade EEG devices for robust, and subject independent flow classification has been insufficiently explored. This study validates entropy-based biomarkers for flow state detection using two wearable EEG headsets, Muse-S and Emotiv Insight, across 45 participants performing adaptive Tetris gameplay. After denoising, we applied the Discrete Wavelet Transform (DWT) to decompose the signals into multiple frequency sub-bands. From each sub-band, entropy-based features, combining channel-wise measures (Slope Entropy, Distribution Entropy, Spectral Entropy) with cross-channel descriptors (Cross Distribution Entropy, Cross Spectral Entropy) were extracted. These features were then used as input to a Random Forest classifier (RF), evaluated with two validation schemes: Random Sampling (RS) and Leave-One-Subject-Out (LOSO). Under random sampling cross-validation, Random Forest classifiers achieved 97% mean accuracy; under the more rigorous leave-one-subject-out (LOSO) scheme, average accuracy reached 75%, demonstrating genuine cross-subject generalizability. Comprehensive multi-classifier validation (SVM-RBF, GentleBoost, k-NN, Fitted Discriminant, Naive Bayes) confirmed that entropy biomarkers are robust across diverse modeling frameworks.

Accurate estimation of left ventricular ejection fraction (EF) from echocardiography is central to cardiovascular care, and deep learning enables automated EF prediction from echocardiographic video. Because EF is clinically derived from left-ventricular (LV) volumes, a widely held intuition is that explicit LV segmentation should improve prediction. We introduce a quantitative criterion, the segmentation ceiling, that makes this testable: from EF as a normalized difference of end-diastolic and end-systolic volumes, we derive in closed form how per-frame segmentation area error propagates into EF error, and thus the accuracy a mask must reach before it can improve on direct regression. Using EchoNet-Dynamic, a UniFormer-S backbone, and the empirically measured within-patient error correlation, the criterion places the break-even near 10% per-frame area error, whereas a representative segmenter operates at roughly 14%, above the ceiling. Consistent with this, four strategies for injecting segmentation or area information (a predicted-mask channel, end-diastolic/end-systolic clip sampling, and per-bin and amplitude area-consistency objectives) fail to beat a raw-video baseline; ground-truth masks help only through label leakage. Input representation thus not being the limit, we identify generalization as the practical lever: weight averaging with strong augmentation attains a test R^2 of 0.806 (MAE 4.08) under a matched dense-clip protocol, comparable to an R(2+1)D baseline (0.811) while tightening the validation-to-test gap. Finally, a heteroscedastic beta-NLL formulation yields informative, well-calibrated per-prediction uncertainty, larger for clinically harder low-EF cases, where Monte-Carlo dropout does not. The segmentation ceiling gives a concrete design criterion for when mask-guided EF estimation is worthwhile, plus a simple, uncertainty-aware recipe for EF regression.

For second-order systems under continuous sliding mode control (SMC), the literature has long relied, largely through phase-portrait illustrations, on the implicit convention that the phase-plane trajectory approaches the equilibrium along a direction tangential to the designed sliding surface. This paper investigates this tangential convergence assumption through a rigorous phase-plane analysis of the double-integrator system subject to standard linear SMC. We reveal that the asymptotic state ratio is not unique but instead exhibits a bifurcation that depends on the control gains, the sliding surface parameter, and the initial conditions. The key finding is that the system may converge along an implicit secondary manifold rather than aligning with the designed sliding surface, implying that smooth entry is not globally guaranteed. We derive closed-form expressions for the convergence ratios and establish a classification framework that precisely characterizes when the smooth-entry assumption holds and when it fails. The analysis is further extended to classical PD control, and we show that the bifurcation threshold coincides with the critical damping boundary that separates the two convergence regimes. These findings bridge terminal geometry and convergence smoothness, providing a predictive framework for high-performance motion control design. Simulation results validate the proposed classification of convergence regimes.

Quantum computers are technologically novel and unusual, but at system scale they should be engineered using many of the same principles that govern classical heterogeneous accelerators. This paper argues that utility-scale quantum architecture is primarily a cost-performance problem across a coupled quantum-classical system, leading to a blueprint for scalable quantum processing unit (QPU) design. Our architecture blueprint is organized around clean logical abstractions, hiding details and complexity of physical qubit modalities below the instruction set architecture (ISA) boundary, and specializes recurring functions aggressively to minimize the cost for utility scale quantum computations. Its low-level implementation through specialized local hardware for control, readout, and quantum error correction (QEC) closely resembles the architecture of high-performance network stacks. One of our main insights is that the design principles and the resulting architecture closely follow established practice from classical computing and networking.

This paper presents a scalable framework for high-precision near-field multipath channel parameter estimation in extremely large antenna array (ELAA) systems, enabling joint recovery of path number, path gains, angles, and ranges from a single noisy observation. The key idea is to interpret the near-field multipath channel as a superposition of spatial chirp components with spatially varying frequencies and exploit this structure through a partitioned ELAA architecture. Specifically, we establish a Chirp-coupled Subarray Far-field (CSF) model, where each near-field path is locally represented as a far-field sinusoid with a constant spatial frequency within each subarray, while these local spatial frequencies are coupled across subarrays through a linear relationship induced by the underlying spatial chirp, forming a path-specific chirp trajectory. Based on this model, we propose the CHirp-coupled Angular-Range estiMation (CHARM) algorithm, which performs gridless local frequency estimation followed by cross-subarray trajectory recovery. To mitigate the potential modeling mismatch of the CSF model, we further propose the enhanced CHARM (E-CHARM) algorithm, which refines the CHARM estimate under the near-field channel model through maximum likelihood. The computational complexity of the proposed algorithms scales linearly with the array size. Moreover, simulation results show that the proposed algorithms achieve reliable path-number detection, high-precision angle-range estimation, and accurate channel reconstruction.

In this work we present an experimental demonstration of one-dimensional Fourier-domain imaging using a fully-digital wirelessly coordinated coherent distributed antenna array (CDA) receiver. The nodes consist of two software-defined radios (SDRs) operating with independent system clocks performing wireless time, frequency, and phase coordination without a shared reference such as the global navigation satellite system (GNSS). Two spatially separated noise sources transmit independent noise waveforms with a bandwidth of 25 MHz and a carrier frequency of 915 MHz. Two sources were positioned at angles of $-6.28{\deg}$ and $12.95{\deg}$ off broadside and imaging was performed. The two sources are clearly resolved to within $3{\deg}$ of their expected locations in both the individual and combined source measurements and demonstrating the Fourier-domain image reconstruction principle using a fully-digitally coordinated distributed antenna array.

Clinical B-mode images are widely available as potential data sources for quantitative ultrasound (QUS) analysis for tissue characterization. However, standard clinical ultrasound devices apply unknown log-compression to RF envelope data before display and storage. Previous work has demonstrated estimation of the underlying RF envelope statistics in the presence of an unknown compression law. Using Fisher information analysis, we show that finite-offset log compression causes severe information loss when estimating the Rayleigh scale $\sigma$, which controls diffuse speckle. For a single image window, unknown compression raises the minimum achievable variance for unbiased estimation of $\sigma$ by a compression-independent factor of approximately 34.98. When $M$ equal-sized windows share the same unknown compression settings, the excess variance decays as $1/M$; even in the most favorable regime, reducing the variance inflation factor below 1.1 requires 340 windows. Our analysis treats the contrast parameter $a$ as unknown and the boundary offset $b$ as known; estimating $b$ experimentally shows even larger variance. We validate this theory using synthetic estimation experiments and demonstrate RF-scale recovery on real RF-envelope windows from the OASBUD dataset. Together, these results clarify the limitations of using routine B-mode images for QUS.

Traditional dynamical system models, including Koopman operator representations, are fundamentally equality-based, whereas many analysis and control tools rely on inequalities. This mismatch motivates representations that are intrinsically aligned with certification tasks involved in the analysis and control synthesis problems. In this paper, we propose a data-to-certificates (D2C) paradigm that bypasses explicit model construction and directly learns certificates from data. We introduce supereigenfunctions of the Koopman operator as an inequality-based generalization of eigenfunctions that define exponential growth envelopes encoding stability, safety, and uncertainty propagation, thereby serving as certificates for a range of control objectives. We establish their theoretical foundations and show that the associated rates recover intrinsic dynamical quantities such as Lyapunov exponents. Two complementary constructions are developed: a geometric approach based on the multiplicative ergodic theorem (MET), and a resolvent/Gramian formulation that enables computation directly from trajectory data. The resulting framework yields certificates that can be used for stability and contraction analysis, as well as stabilizing and safety-critical control synthesis via convex quadratic programming-based optimization program. Numerical examples demonstrate the effectiveness of the proposed data-driven certification approach for stabilization, contraction, and safe control design.

Natural turn-taking in full-duplex voice interaction requires determining from partial speech whether a pause reflects hesitation or a completed conversational intent. Acoustic voice activity detection lacks this semantic information, while cascaded ASR-based endpointing introduces transcription dependence and additional processing stages. We formulate semantic endpoint detection as a causal audio-language reasoning task and introduce FD-VAD, an ASR-free streaming endpointer that maps bounded causal audio windows directly to Continue/Stop decisions. FD-VAD combines a frozen speech encoder with a lightweight modality adapter and a parameter-efficiently adapted language model, using a last-chunk training objective for streaming inference. We further introduce confidence-gated endpoint commitment to control interruption versus delay and boundary-focused hard-negative sampling to improve decisions around ambiguous turn boundaries. Across in-domain and conversational evaluations, FD-VAD outperforms strong streaming and non-streaming semantic turn classifiers, and achieves the highest EOT recall among qualifying systems on TurnBench dev set $0.853$ (at FP<=0.10) in a zero-shot setting. These results show that semantic endpointing can be performed directly from streaming audio without intermediate ASR or dialogue state tracking.

Agentic speech systems must exchange more than text, including speaker identity, timing, phonetic information, and behavioral annotations. Yet these signals are often produced in incompatible tool-specific formats, making agent-to-agent handoff fragile. We present OneVoice, a lightweight, JSON-native intermediate representation that provides a shared semantic structure for speech pipelines. OneVoice organizes heterogeneous speech evidence into validated session records with stable identifiers, layered transcripts, explicit timing relationships, and provenance information. We evaluate OneVoice in two complementary multi-agent workflows covering event aggregation and acoustic-phonetic temporal linking across three language models. Compared with implicit agent-defined handoffs, OneVoice substantially improves aggregation reliability and the preservation of temporal relationships, demonstrating the value of an explicit speech-specific representation for agent communication.

Multi-source remote sensing image classification has attracted increasing attention due to the complementary spectral, structural, and geometric information. However, existing methods still suffer from two limitations: insufficient semantic contextual modeling and unreliable feature fusion caused by slight spatial misalignment. To address these issues, we propose a Semantic-Guided Fusion Network (SGFNet) for multi-source remote sensing image classification. Specifically, the Semantic Mixing Convolution Block (SMCB) is designed to dynamically generate semantic-aware convolution kernels according to contextual relationships among feature representations. In addition, the Frequency Modulated Fusion Block (FMFB) is introduced to perform cross-modal interaction in the frequency domain, which effectively alleviates the influence of slight spatial misalignment and improves complementary information fusion. Extensive experiments conducted on the Augsburg and Houston 2018 datasets demonstrate that the proposed SGFNet consistently outperforms several state-of-the-art methods. The codes are publicly available at https://github.com/oucailab/SGFNet .

This tutorial provides a unified view of the emerging area of bilinear learning and control. Using linear systems as a benchmark, it explains what fundamentally changes in the bilinear settings, how recent theory addresses finite-sample learning and control, and how these ideas connect to broader themes in nonlinear control, representation learning, and data-driven decision making. For learning, we emphasize tools that are particularly useful in the bilinear settings, such as one-sided Bernstein's inequality for dependent and heavy-tailed covariates, blocking arguments, and martingale concentration for input-dependent noise. We then apply these tools to obtain finite-sample learning guarantees for fully observed bilinear systems, partially observed bilinear systems, and linear systems with bilinear observations. For control, we discuss quadratic control from bilinear observations, where the classical separation principle fails, and review tractable approaches based on belief-space receding horizon control. We also cover stabilization of bilinear dynamics under state feedback using semi-definite programming, LMI relaxations, sum-of-squares methods, and Koopman-based lifting. We conclude by discussing connections to reinforcement learning and machine learning, and some open problems in combined learning and control of bilinear systems.