Papers

Neural audio representation learning has enabled a range of content-oriented applications, but the resulting features remain limited for tasks involving audio processing. Furthermore, it is not obvious what processing-aware representations should capture: the processing itself, abstracted away from source content, or the processed audio that retains it. Existing approaches implicitly commit to one or the other and also differ in their models, data, and evaluation, obscuring which design choices drive their behavior. We address both questions within a unified framework of three objectives: processing consistency, description alignment, and equivariance via forward prediction. We compare all combinations of the objectives under a controlled setup and reveal their relative strengths and interactions. Our framework produces both a transformation embedding and a processed-audio embedding, and we find that the two play complementary roles: distance-based tasks favor the former, while probe-based tasks favor the latter. Combined with improvements in network architecture and training pipeline, our representations outperform prior baselines across retrieval, probe-based evaluation, and style transfer.

Small HAWT is a technology characterized by non-trivial critical points, basically because it is targeted for domestic use and therefore cheap manufacturing and control must conjugate with good efficiency under possibly complex flow conditions (especially in urban environment). Therefore, dynamical control optimization and noise and vibration mitigation are pressing issues for this kind of technology. Despite it is peculiar of small HAWTs that the generator constitutes a non-negligible fraction of the total mass and therefore the electromechanical coupling is relevant, condition monitoring of small HAWT generators is an overlooked topic. The present work is a test case study of damage diagnosis on a permanent magnet generator of a HAWT having 3 kW of maximum power and 2 meters of rotor diameter. The experimental analysis is conducted through wind tunnel tests and on a generator test rig where a damaged and an undamaged generators have been driven at different rotational speeds. Vibration measurements are collected in the wind tunnel through radial accelerometers near the rear bearing of the shaft and in the test rig through uni-axial accelerometers (fixed in radial positions, in order to be aligned with front and rear bearings). The test rig data result being particularly useful for studying the low-frequency tail of the vibration spectrum, where the characteristic frequencies of the bearing are located. The experimental data are analyzed in the time and frequency domain for feature extraction: a fault in the cage of the bearing supporting the generator is diagnosed using in particular the spectral coherence analysis.

Certain defense systems are, by nature, deployed in a civilian environment in order to serve a defensive function for that environment. However, the risk involved with their deployment and operation poses a challenge for the public acceptance of these systems. Compared to safety engineering for civilian systems, the risk constellation is quite different. While reducing risk of safety-critical systems is generally desirable, defense systems are required to produce intended mission effects, which may include harm to an attacking target. In both cases, not all risk can be eliminated. The complex risk constellation of defense systems has implications for the systems' designs. We compare the concepts of risk in existing standards from both the civilian and the military domains. An extended constellation of risks needs to be considered, including risk caused by external threats to security. By including this risk constellation in public communication about defense systems, we aim to stimulate a productive debate.

Interpreting a CT scan means comparing structures on either side, judging how far apart organs sit, and knowing where each one belongs. Medical vision encoders are evaluated on diagnostic accuracy, or through assembled multimodal systems where a failure is hard to attribute, so it remains unclear whether their representations support any of this. We construct SPAR-Bench, eight probes over multi-organ abdominal CT that separate coordinate localization, relational reasoning, and spatial queries, and apply them to five architectural configurations and three medical foundation models, frozen and finetuned. Probes that ask for a comparison within the slice stay at chance, and neither pretraining scale, finetuning, nor architecture closes the gap. Probes that appear solved in domain fall to chance under zero-shot transfer, indicating that their accuracy reflects recall of canonical anatomy rather than computation over the image. Reading the same frozen features with a pooled head rather than the full set of tokens moves relational recovery from 0.7% to 67.8%, so pooled probing understates what a representation holds. Questions the encoders answer well are answered at chance by four open-weight MLLMs. Our results suggest these encoders carry a map of where organs usually lie, and little of the machinery for comparing structures within a particular patient. Code and data will be available at https://spar-bench.github.io.

Token Communications (TokenCom) has recently emerged as a new paradigm in which tokens serve as unified units for communication and computation, enabling efficient multimodal semantic and goal-oriented transmission. In this paper, we develop Ada-TokenCom, a rate-adaptive TokenCom framework based on large autoregressive models, which integrates next-token prediction with arithmetic coding to achieve ultra-low bitrate semantic communication at the token level. We propose a mixed reconstruction/generation scheme, where the transmitter encodes and transmits the highly informative tokens at the beginning of the token sequence leveraging a pre-trained autoregressive large model, while the receiver uses an identical model to predict the rest. Moreover, we design a Lyapunov-based algorithm to dynamically optimize both the source compression rate and the modulation and coding scheme, adapting to time-varying network conditions. Simulation results demonstrate that our proposed Ada-TokenCom framework outperforms both digital and deep joint source-channel coding-based semantic communication baselines.

This paper investigates the design of the on-off division multiple access (ODMA) transmission scheme for multiple-input multiple-output (MIMO) massive unsourced random access (URA) systems with soft-output (SO) polar codes. First, a three-segment pilot-uncoupled coding scheme is introduced under the ODMA framework, which reduces the coding rate of the data segment without increasing the transmission overhead, improving the overall system performance. Building upon this architecture, a hierarchical pattern detection framework is developed. Specifically, a coarse-grained candidate set of transmission patterns is first identified through correlation operations. Based on this, a message-passing (MP)-based pattern detection algorithm is developed to iteratively estimate the posterior probabilities of transmission patterns, followed by the maximum a posteriori (MAP) estimation to obtain the precise pattern detection result. Furthermore, a joint pattern detection and data decoding algorithm based on the bit-wise SO information of polar decoder is investigated, where the posterior probability information provided by the polar decoder is exploited to refine the pattern detection and contribute to an improved accuracy. In addition, by leveraging bit-wise SO information of the successive cancellation list polar decoder, an MP-based iterative decoding algorithm is developed to significantly enhance the decoding performance. The proposed scheme simultaneously exploits the transmission gain of uncoupled-ODMA framework, the coding gain of polar codes in the short-blocklength regime, and the iterative decoding gain enabled by SO information, while the computational complexity is significantly reduced through the hierarchical detection framework. Simulation results demonstrate that the proposed scheme achieves strong robustness ...

Adjustable-set robust model predictive control (MPC) characterizes a state-dependent maximum disturbance radius, which can be interpreted as a certified robustness reserve. Existing methods primarily focus on optimizing and propagating this reserve under in-set disturbances. This letter investigates how much reserve remains after a finite out-of-set disturbance without immediately re-solving the full optimization problem. To this end, we propose a two-sided reserve-depletion envelope formed by independent primal and dual correction hierarchies, which supply monotone lower and upper bounds, respectively. The envelope remains valid across active-set changes, has an online-computable width, and contracts monotonically under subspace expansion. Leveraging its finite-step exactness, we present a basis-first adaptive algorithm that operates in an anytime manner: every completed reduced solve returns a valid certificate, enabling early termination once a prescribed tolerance is reached. Across the tested cases, numerical studies report no certificate violations and median speedups of up to 4.97x over warm-started full re-optimization at a 2% certificate-width tolerance.

In integrated sensing and communication (ISAC) enabled 6G unmanned aerial vehicle (UAV) swarm networks, the widely adopted imitation-based conformity cooperation mechanism can be exploited by Byzantine attackers to fabricate false consensus, causing the effective error probability of normal UAVs to evolve dynamically and far exceed their inherent sensing errors, which invalidates conventional fusion methods built on the independence assumption. This paper proposes a conformity-aware Byzantine-resilient fusion framework that couples evolutionary game theory with maximum a posteriori (MAP) estimation. First, the strategy updates of normal UAVs are characterized by bounded-rational opinion dynamics, and the evolution dynamics of the misinformation ratio together with its evolutionarily stable state (ESS) are derived under death birth updating. Three theoretical results are then established: under heterogeneous per-node sensing errors, the zeroth-order ESS depends on the error distribution only through its mean; a closed-form first-order weak-selection correction to the ESS is obtained, together with an exact mean-field fixed point valid for arbitrary selection intensity; and it is revealed that swarm level misinformation can overwhelm the majority if and only if the attack probability exceeds one half, with this threshold independent of both the sensing error and the malicious ratio. Embedding the predicted error dynamics into a per-node MAP rule, the resulting fusion mechanism achieves nearly 100% situation-inference accuracy under different network topologies, attack intensities, network scales, and sensing-error distributions, and maintains accuracy above 99% under +-20% parameter mismatch. In contrast, majority voting, reputation weighting, and independent fusion collapse completely once the majority-flip threshold is crossed.

For cooperative perception in the internet of vehicles, this paper proposes a generative adversarial network-based semantic communication framework to address the efficiency and fidelity bottlenecks of traditional communication systems in visual data transmission under limited bandwidth and dynamic channel conditions. At the transmitter, the framework adopts a pyramid attention network to extract semantic label maps and introduces a semantic priority preservation mechanism. It assigns differentiated weights to distinct semantic categories based on driving safety, guiding bit allocation and loss function design. At the receiver, an image reconstruction module integrating a coarse to-fine multi-resolution generator and multi-scale discriminator is designed. Combined with the temporal consistency branch, spatial pyramid pooling and class-aware convolutional layers, it achieves high-fidelity reconstruction of high-quality images from corrupted semantic labels. The model is trained with combined adversarial, feature matching and perceptual losses, effectively improving semantic consistency and visual realism of generated images. Experimental results on the Cityscapes dataset show that the proposed method outperforms existing counterparts in both semantic segmentation accuracy and reconstructed image quality, and maintains stable reconstruction performance under AWGN and Rayleigh channels.

Battery health prognostics is a core function in battery management systems (BMSs), yet long-horizon health forecasting from BMS signals remains challenging due to operating-condition dependency and sensor noise. In this paper, we propose PhyMamba, a two-stage physics-modulated Mamba framework that integrates electrochemical aging into sequence modelling. PhyMamba does not require explicit identification of internal aging parameters, which often relies on intrusive measurements. In stage-1, a lightweight Mamba encoder first processes BMS signals and produces a latent representation that is transformed via an aging parameterization module, into physics-informed aging features. In stage-2, a customized Mamba forecasting backbone performs multi-cycle prediction, where physics is tightly integrated to regulate the model's internal temporal updates toward degradation-consistent evolution. Experiments on three public datasets under multiple forecast horizons show that PhyMamba achieves the best aggregated performance, with an overall mean error reduction of 31.8% compared with a diverse range of baselines. PhyMamba also offers an optimized accuracy-efficiency trade-off, which supports practical deployment for robust battery health prognostics.

Space-air-ground integrated networks (SAGINs) can provide ubiquitous and reliable connectivity for unmanned aerial vehicles (UAVs). However, air-to-ground links, which are typically dominated by line-of-sight (LoS) propagation, are vulnerable to passive eavesdropping due to the broadcast nature of wireless channels. To enhance physical-layer security, we investigate a SAGIN-enabled secure downlink communication system in which UAVs select service links among satellite, aerial, and terrestrial networks while adjusting the positions of the movable antenna (MA) array to fully exploit connectivity and spatial degrees of freedom for improved secrecy communication performance. Specifically, we maximize the secrecy energy efficiency (SEE) of a UAV swarm by jointly optimizing the MA positions, UAV trajectories, and link selections, subject to UAV mobility, MA movement, and link connectivity constraints. To reduce the real-time channel state information (CSI) acquisition overhead, we propose a channel knowledge map (CKM)-assisted multi-agent reinforcement learning framework. Specifically, the CKM is first constructed from sparse channel measurements via Kriging interpolation and is then leveraged together with satellite ephemeris information to enable efficient storage and retrieval of CSI. To reduce the action-space dimensionality and computational complexity, we model the MA array using rigid-body kinematics and adjust its position through global rigid-body translation, thereby constructing a low-dimensional hybrid action space for the joint optimization decisions. To align local decisions with system-wide performance under system constraints, we design an individual-team collaborative reward mechanism and introduce action masks to enforce constraints on UAV mobility, collision avoidance, MA regions, and connectivity capacity.

Backup control barrier functions (bCBFs) enforce safety for input-constrained nonlinear systems using a pre-certified backup set and controller, but their performance depends strongly on this prescribed pair. This letter develops a constructive method for synthesizing a less conservative backup pair via finite horizon sum-of-squares (SOS) backward reachability. Starting from an initial backup set, we compute an SOS-certified finite horizon backward reachable set and controller that satisfy safety and input constraints while steering trajectories to the original backup set. We then provide conditions under which this certified set becomes a valid backup set for a piecewise backup controller. The resulting backup pair is integrated into the bCBF framework to certify larger safe sets.

Design-rule integration (DRI) remains a major bottleneck for scalable (Constraint Programming with SAT) CP-SAT-based standard cell synthesis and rapid technology enablement at advanced nodes. It still depends heavily on manual effort and domain expertise. Moreover, existing low-level rule encodings are not expressive enough for emerging constraints such as cut-based rules under multi-patterning technology. This paper presents AutoDRI, a multi-agent framework for automated design-rule integration in standard cell synthesis. AutoDRI combines a geometric semantic library, a standardized conflict-set encoding, a constructive multicolor-cut modeling method, and a feedback-driven multi-agent flow to bridge the semantic gap between natural-language design rules and executable CP-SAT constraints. In the reported experiments, AutoDRI achieves near-perfect rule-integration correctness across 41 cell benchmarks under 10+ complex rules, including colored cut-mask spacing rules, reaching 33/33 correct integrations with Gemini-3-pro and 32/33 with GPT-5.4, while maintaining runtime comparable to manual hard-coding and passing KLayout DRC and Cadence LVS.

Coordination is a desirable feature in multi-agent systems, ranging from robotic swarms to socioeconomic networks. This paper is concerned with promoting coordination among heterogeneous agents, e.g., machines and humans, interacting in a stag-hunt game. In our model the agents exhibit bounded rationality at different levels, which leads to uncertainty and a propensity for errors during learning and decision-making processes. This paper addresses the problem of designing a network topology that maximizes a global metric of coordination under such constraints. While optimizing over the discrete space of finite graphs is generally computationally intractable, we employ a mean-field approach to lift the problem into the space of graphons. Within this framework, we analyze agents following a logit learning dynamics. Using calculus of variations, we show that for systems with a bimodal rationality profile, it suffices to search for optimal graphons in the ensemble of stochastic block models. We then propose a water-filling algorithm to find a locally optimal graphon. Finite graphs can then be sampled from the optimized graphon, bypassing the inherent combinatorial complexities of discrete graph optimization.

Prosody plays an important role in speech translation, conveying information such as emphasis, emotion, and intent beyond lexical content. However, despite recent progress in expressive speech-to-speech translation (S2ST), little is known about how prosodic patterns are similar/different across languages. Understanding these cross-lingual similarities and differences is crucial for effectively incorporating prosody into expressive S2ST systems. In this work, we present the first fine-grained cross-lingual analysis of prosody using multilingual dubbing data across English-German, English-Spanish, and English-French language pairs. We analyze the similarity of pitch, energy, and temporal feature patterns between source and target speech and investigate the linguistic and alignment-related factors affecting this similarity. Our analysis reveals inherent cross-lingual correlations in prosodic structure between certain languages. The findings provide important insights into the transferability of prosody across languages and offer empirical guidance for future expressive speech-to-speech translation systems.

This paper studies multistatic measurement fusion when a target lies within the Tx–Rx (Transmitter-Receiver) baseline ambiguity zone, with particular emphasis on configurations involving two closely spaced stationary Tx–Rx pairs. Such configurations provide overlapping detectable regions and extend the effective detection range compared with sparsely spaced multistatic systems. However, in this region, the accuracy of range and bearing measurements degrades rapidly, and Doppler measurements often remain the only reliable information source. As a result, target trajectory estimation becomes highly challenging, with observability being marginal or even completely lost. To address this problem, the observability of target trajectories is analyzed under various conditions, enabling system designers to assess system performance in advance. A Doppler-only measurement fusion approach is then developed, employing a multiple-initial-point Maximum Likelihood (ML) nonlinear estimator for initial state estimation, followed by dynamic state updates using an Extended Kalman Filter (EKF). Simulation results are presented and shown to be consistent with the observability analysis.

This paper investigates secure downlink transmission assisted by a fluid active reconfigurable intelligent surface (FARIS), which enables both active reflection and dynamic port selection, offering enhanced flexibility for physical-layer security. We formulate a secrecy rate maximization problem that jointly optimizes the transmit beamformer, active reflection coefficients, and fluid port configuration under practical power constraints. To efficiently handle the resulting highly nonconvex problem, we develop a tailored alternating optimization (AO) framework that decomposes the original joint design into tractable subproblems, where each admits an efficient solution while preserving the system constraints, enabling an effective joint optimization of beamforming and FARIS reconfiguration. Numerical results demonstrate that the proposed FARIS-assisted design consistently outperforms the benchmarks. The results further highlight the robustness of FARIS against unfavorable eavesdropping geometries, confirming its potential as a powerful enabler for secure communications in challenging environments.

In this paper, we propose a robust transmission design for multi-user systems assisted by a fluid active reconfigurable intelligent surface (FARIS), which enables both active reflection and dynamic port selection and thereby offers enhanced flexibility, under imperfect channel state information (CSI). We formulate a robust minimum sum-rate maximization problem by jointly optimizing the base station beamformer, the utilized FARIS coefficients, and the active element selection, while explicitly accounting for CSI errors and practical power constraints. The resulting problem is inherently nonconvex due to the coupled optimization variables and discrete port-selection structure. To tackle this challenge, we first reformulate the original problem via a weighted minimum mean square error (WMMSE) approach and then devise an alternating optimization (AO) framework, where each resulting subproblem admits efficient solutions and the overall algorithm converges to a stationary point. Simulation results demonstrate that the proposed robust FARIS scheme consistently outperforms conventional designs, highlighting the effectiveness of jointly leveraging degree-of-freedom (DoF) enhancement and active signal amplification under CSI uncertainty.

Speech and audio LLMs are evaluated by comparing waveform predictions with predictions from an automatic speech recognition (ASR) transcript. For fixed closed-set tasks, this conflates acoustic evidence with the need to invoke a generative audio model. We estimate incremental call value with matched selectors sharing pre-call evidence. Each policy may retain the transcript label, use a local encoder, or invoke a generative model; matched control removes generative actions but preserves pre-call evidence and development selection. On VocalSound, transcript-only accuracy is 0.296, while supervised CLAP and WavLM controls reach 0.850 and 0.854 without calls. Full selector reaches 0.925 at 12.5% calls versus 0.921 for matched No-call selector (difference 0.004; 95% CI [-0.025, 0.033]). Thus, results do not show a call gain after transcript and encoder evidence are available. Relevant quantity is incremental accuracy from allowing calls, not the waveform-transcript gap.

Short-term load forecasting (STLF) provides essential information for numerous applications in modern power systems. However, accurate STLF often relies on fine-grained smart-meter data from distributed users, raising increasing concerns about data privacy. Federated learning (FL) has therefore emerged as a promising privacy-preserving paradigm for STLF. Nevertheless, this paper reveals structured heterogeneity in clients' load data. Specifically, clients exhibit different responses to exogenous factors and distinct temporal load profiles, which can degrade forecasting performance in FL. To mitigate these issues, this paper studies the role of model initialization in federated STLF, and proposes two initialization strategies from global and local perspectives. For global model initialization, when auxiliary public load data are available, a pretrained initialization strategy is developed to initialize the global model before federated training, thereby reducing client drift during the training process. For local model initialization, we propose SLIAvg, a sequential local initialization strategy that promotes a more consistent training process by allowing participating clients to start from progressively adapted models within each communication round. Since the proposed strategies only modify the initialization process, they are compatible with most existing FL frameworks and privacy-enhancing techniques. Experiments on real smart-meter data with two representative forecasting architectures demonstrate that the proposed strategies effectively improve forecasting performance, as evidenced by reduced client drift, improved convergence behavior, and lower forecasting errors.

Multi-Threshold (MT) sampling fixes voltage levels and records when a waveform crosses them. The resulting signal-generated event stream is governed not by a prescribed clock but by the nonuniform geometry of the realized crossing set. Within classical nonuniform sampling, we develop a retained-event MT theory for static, uniformly spaced thresholds. For a realized retained MT time set, Beurling's weak-limit theorem gives the exact necessary-and-sufficient condition for stable sampling of $PW_\Omega$. For a fixed signal class, the same principle gives the a priori criterion: uniform stable MT sampling is equivalent to Bernstein uniqueness for every set in the class weak-limit hull. This criterion reduces to computable forms under structured priors. Periodic templates give finite fiber/rank tests, periodic-gap streams give a Shannon-type complete-interpolation boundary and a stable-sampling density rule, and finite-state gap priors reduce noncritical certification to a maximum-cycle-mean condition. Kadec-type local certificates, retained-density bounds, and finite-matrix tests provide practical sufficient conditions for finite active records, while conditional perturbation bounds quantify how jitter, front-end noise, and threshold error consume a clean sampling margin under event correspondence. The theory also identifies the unavoidable boundary of this model: unrestricted finite-energy retained MT records have zero full-line lower Beurling density and therefore cannot provide a universal prior-free sampling theorem for $PW_\Omega$. Within retained-event point sampling, the MT recovery question is resolved precisely: exact recovery is governed by event-set geometry, computable guarantees require structural priors or finite-dimensional/tail assumptions, and the unrestricted static finite-energy problem is impossible.

Stress detection using physiological signals has gained significant attention due to its impact on both physical and mental health. While existing approaches based on machine learning and deep learning achieve strong predictive performance, they often rely on black-box models and fail to capture individual variability in physiological responses. In this work, we propose an interpretable and personalized framework for stress detection using electrocardiogram (ECG) signals based on motif discovery and Matrix Profile analysis. Instead of training a classifier, the method learns subject-specific baseline cardiac behavior by extracting recurring heartbeat patterns (motifs) from ECG signals. Stress is then detected as a deviation from these baseline patterns using a distance-based anomaly score. Experiments are conducted on the WESAD dataset using a carefully designed train-validation-test protocol to ensure reliable evaluation. The results show that the proposed approach can effectively detect stress for several subjects while providing clear interpretability through direct comparison of ECG patterns. However, the performance varies across individuals due to differences in physiological responses, with some subjects exhibiting minimal morphological changes under stress. Additional analysis incorporating heart rate variability (HRV) features reveals that while HRV can improve performance in certain cases, its contribution is not consistent across all subjects. These findings highlight the importance of personalization and interpretability in physiological stress detection and demonstrate that motif-based approaches provide a meaningful alternative to black-box models, while also revealing inherent limitations due to inter-subject variability.

This paper presents two interpretable machine-learning frameworks for quality screening of e-transaxle assemblies in electric vehicles: a Stagewise Wavelet Isolation Forest (SWIF) framework and a short-time Fourier transform (STFT)-based diagnostic framework. High-dimensional vibration signals acquired from front and back accelerometers are analyzed across multiple operating stages to capture stage-dependent vibration behavior. In the SWIF framework, signals are decomposed using a five-level Daubechies-4 discrete wavelet transform, and blockwise mean-squared coefficients are extracted from the selected wavelet detail level to obtain compact multiscale features. In the STFT-based framework, dominant-frequency trends are extracted from time-frequency representations and summarized through regression coefficients with respect to instantaneous motor speed. Anomaly detection models are trained using accepted production units under the assumption that only a small fraction of accepted assemblies contain latent defects, and their performance is evaluated using road-tested units with validated quality outcomes. Experiments on production and road-tested e-transaxle units show that both approaches provide interpretable diagnostic information, while SWIF achieves the most favorable balance between defect detection and false-positive control. Compared with the STFT-based method and alternative anomaly detectors, SWIF combined with Isolation Forest yields lower anomaly rates within the Accept population while identifying high-risk units from the Reject population. The stagewise structure further localizes anomalous behavior to specific operating conditions, supporting root-cause analysis and targeted process improvement.

Accurate boundary delineation of brain tumors in Magnetic Resonance Imaging (MRI) is a critical yet formidable challenge in neuro-oncology due to inherent scanner noise, complex anatomical structures, and uneven illumination. Traditional edge detection algorithms, while computationally lightweight and mathematically interpretable, frequently fail to capture the diffuse, localized boundaries of edema when relying solely on global preprocessing and manual parameter tuning. To overcome these limitations, we propose a hybrid automated edge detection pipeline. Our approach integrates an optimally configured Contrast-Limited Adaptive Histogram Equalization (CLAHE) layer into a comprehensive morphological preprocessing framework, followed by a deterministic sequential parameter sweep to fully automate threshold selection. The proposed hybrid model demonstrated enhancement in detecting critical anatomical structures in a publicly available benchmark database from Kaggle. By intelligently amplifying localized gradients without overwhelming the image with background noise, our method achieved higher Recall (Sensitivity). Consequently, the overall F1-Score elevated, and the Structural Similarity Index (SSIM) improved, all while maintaining a highly efficient execution. This establishes our optimized pipeline as a highly practical and near real-time operational model for clinical diagnostics, offering a compelling alternative to computationally heavy deep learning approaches.

Dose accumulation is increasingly important in adaptive radiation therapy and reirradiation, but its clinical validity depends on the performance of deformable image registration (DIR). Reirradiation of brain metastases (BMs) with stereotactic radiosurgery (SRS) provides a controlled but clinically meaningful DIR test case: intra-subject brain deformation is usually limited after rigid alignment, yet recurrent lesions can undergo substantial local shape and volume changes that rigid registration cannot capture and can affect dose accumulation. We benchmarked a wide range of learning-based and optimization-based DIR methods on 87 manually screened longitudinal contrast-enhanced T1-weighted MRI lesion pairs from an institutional BM SRS retreatment cohort. Learning-based methods pretrained on healthy-brain MRI were evaluated zero-shot and after instance-specific optimization (ISO) or tumor-proximity target-specific optimization (TSO). Registration was assessed using lesion overlap (Dice), surface distance metrics (HD95 and sASD), target-volume recovery, and runtime and memory. Pretrained learning-based methods showed variable zero-shot performance, while ISO/TSO improved all tested learning-based families. However, optimization-based methods remained the best-performing approach while maintaining reasonable runtime. These findings suggest that even state-of-the-art DIR methods do not yet provide sufficiently accurate and consistent registration for unmonitored use in brain metastasis reirradiation dose accumulation. Because accurate registration is a prerequisite for deformable dose accumulation, clinical application will require case-level quality control and direct assessment of how registration uncertainty affects downstream dose metrics.

Speech language models (speech LLMs) can generate plausible outputs from audio that contains no usable speech evidence. We study this failure as a pre-generation support-estimation problem and present SURE-Voice, a training-free front end that decides whether an audio prompt contains intelligible speech evidence before calling a speech LLM. We build SURE-Challenge with a 640-example SURE-Core split and a 1,920-example SURE-Extended split derived from 120 LibriSpeech source utterances. Using one fixed operating point, an energy screen plus Whisper token confidence raises unsupported accuracy on the held-out Extended test from 0.000–0.133 to 0.919 for six non-degenerate speech LLM backbones, while supported accuracy remains 0.919–0.970 and downstream calls fall from 480 to 287. A 500-clip ESC-50 sanity set shows the same pattern on real environmental audio, with vocal non-speech as a residual failure mode. An overlap diagnostic shows that source attribution remains separate from speech-evidence filtering. The evidence supports a controlled benchmark baseline and a deployment-oriented analysis; it does not establish universal robustness to semantic answerability, gain variation or natural conversations.

We describe the design of a custom crowbar circuit that quickly removes power when a programmable current threshold is exceeded. This protects devices from permanent damage during single event latchup/burnout tests.

Tri-hybrid multiple-input multiple-output architectures have recently emerged as a promising enabler for next-generation wireless systems, as they potentially provide enhanced design flexibility without a proportional increase in hardware cost or power consumption. However, the resulting triple-domain coupling renders beamforming optimization challenging and computationally demanding. This paper develops a fast tri-hybrid beamforming framework for multiuser downlink systems employing dynamic metasurface antennas (DMAs). Based on the equivalence between weighted sum-rate maximization and weighted sum-minimum mean square error minimization, an iterative algorithm is first derived under per-DMA input power constraints, with all update equations available in closed form, but convergence inherently remains slow. To enable real-time operation, the algorithm is further unfolded into a trainable finite-iteration architecture using graph neural networks that ensure permutation equivariance and support varying numbers of users. Trained on ray-tracing channel data, the unfolded method achieves comparable or higher system sum-rates while reducing runtime by more than an order of magnitude. The method also demonstrates strong scalability, robustness, and generalization across various environments.

Parrilo's cones $\Kc{n}{r}$ form a nested sequence of semidefinite-representable inner approximations of the copositive cone $\COP_n$. For $n=5$ their union is all of $\COP_5$, yet no single level attains it, and whether consecutive levels actually differ had remained open beyond the classical first step. No explicit matrix in $\Kc{n}{t}\setminus\Kc{n}{t-1}$ had, to our knowledge, been published for any $t\ge2$ and $n\ge5$. We settle the first three cases. Explicit rational matrices, obtained from diagonal scalings of the Horn matrix shifted along a positive interior direction, lie in $\Kc{5}{2}\setminus\Kc{5}{1}$, in $\Kc{5}{3}\setminus\Kc{5}{2}$, and in $\Kc{5}{4}\setminus\Kc{5}{3}$, giving three consecutive strict inclusions $\Kc{5}{1}\subsetneq\Kc{5}{2}\subsetneq\Kc{5}{3}\subsetneq\Kc{5}{4}$. Each is certified by an exact rational Gram matrix and an exact rational dual moment functional, re-verified by a standalone program in integer arithmetic. The separations are robust. One fixed certificate pair covers an interval of shifts of width exceeding $3\cdot10^{-3}$, and $\Kc{5}{2}\setminus\Kc{5}{1}$ has nonempty interior. Combining a scaling theorem of Dickinson, Dür, Gijben and Hildebrand with the completeness theorem of Schweighofer and Vargas shows further that strict adjacent inclusions recur at arbitrarily large levels. All separators were located by one threshold device: the least shift $\eps_r(M)$ carrying $M$ into $\Kc{5}{r}$ along an interior direction is nonincreasing in $r$, and each strict drop between levels marks a window of separators.

Integrated sensing and communication (ISAC) is regarded as a key technology for sixth-generation wireless networks, allowing sensing and communication operations to jointly utilize the same spectrum and hardware infrastructure. However, in practical uplink ISAC systems, timing offset (TO) and carrier-frequency offset (CFO) introduce phase distortions across subcarriers and OFDM symbols, which can severely degrade both data detection and sensing-parameter estimation. In this paper, we propose a data-aided variational Bayesian (VB) framework for asynchronous uplink OFDM-ISAC systems. Specifically, the received signal is modeled as a sparse multipath superposition, where the transmitted data symbols, complex path gains, spatial frequencies, delay-Doppler parameters, and synchronization parameters are jointly inferred. To enable tractable inference, we develop a mean-field VB algorithm in which von Mises distributions are used for gridless updates of the angular and delay-Doppler phase parameters, while a Gamma-Gaussian prior is adopted to promote path sparsity. A key feature of the proposed framework is its data-aided sensing capability: after initial pilot-based estimation, the detected data symbols are exploited as additional observations to refine the channel and sensing parameters. This substantially increases the effective sensing resources without requiring extra pilot overhead. The simulation results demonstrate that the proposed approach achieves superior performance compared with SAGE, SBL, AB2FM, and pilot-only VB baselines in terms of symbol error rate, channel reconstruction accuracy, path-parameter estimation, TO/CFO estimation, and 3D localization accuracy. The results also demonstrate that ignoring TO and CFO leads to severe sensing degradation, highlighting the importance of synchronization-aware and data-aided receiver design for ISAC systems.