Papers

This paper presents a computationally lightweight scaling model for cylindrical lithium-ion battery cells, intended for early-stage battery design-space exploration. The model maps selected geometric and electrode-level design variables, including cell height, cell diameter, cathode active loading, and cathode porosity, to cell-level performance indicators such as capacity, DC internal resistance, mass, volume, and winding length. The scaling model is validated against available cylindrical cell data by comparing predicted capacity, internal resistance, and winding length. The validated model is subsequently used in a single-cell design-space exploration and global sensitivity analysis to evaluate capacity, internal resistance, gravimetric energy density, and volumetric energy density. The results identify the dominant design variables, favourable parameter directions, and key trade-offs between cell geometry, electrode loading, resistance, and energy density. The proposed model provides a basis for future integration into higher-level battery system and vehicle optimization frameworks.

We study trajectory prediction from libraries of stored output templates. Given the past of an unknown trajectory, the goal is to predict its future without identifying the state-space model that generated it. We show that libraries of trajectories generated by one or more dynamical systems define behavioral spaces that can be used as prediction mechanisms. For linear systems, we characterize exact prediction in terms of continuation maps, behavioral containment, and spectral conditions on output-visible eigenvalues. We also analyze robustness to noisy observations and noisy libraries, derive error bounds for out-of-library trajectories, and show how interconnection constraints can compose template libraries into new behavioral spaces with emergent modes. Finally, we extend the framework to nonlinear systems whose output trajectories are contained in, or immersed into, finite-dimensional linear behaviors. These results provide a theory of template-based prediction machines capable of generalizing beyond the stored trajectories and, in some cases, beyond the systems that generated them.

A planar dual-band reconfigurable shared-aperture antenna array is proposed for compact next-generation wireless front ends that require both sub-6-GHz and centimeter-wave (cm-wave) coverage. The array integrates a 2 by 2 sub-6-GHz microstrip dipole array and a 4 by 4 cm-wave stacked patch array within the same aperture, while providing independent beam control in the two bands without conventional T/R modules or beamforming networks. Slot-coupled feeding is employed to separate the radiating aperture from the reconfigurable RF feeding networks and DC bias circuits. PIN-diode-loaded split feeding rings first provide independent 1-bit phase reconfigurability for both bands. A compact reconfigurable $90^{\circ}$ phase shifter is then introduced as an additional phase-control stage, resulting in 2-bit phase control for sub-6 GHz elements and cm-wave subarrays. To reduce cross-band coupling in the compact shared aperture, a double-layer electromagnetic band-gap (EBG) structure is used to suppress cm-wave surface waves and higher-order sub-6-GHz modes excited by the cm-wave elements. A prototype is fabricated and measured. In the sub-6-GHz band, 11 reconfigurable radiation patterns are obtained, including two difference patterns and nine directional beams, with a peak broadside gain of 10.5 dBi. In the cm-wave band, two-dimensional beam scanning up to $\pm40^{\circ}$ is demonstrated with a peak gain of 14.6 dBi in both the E-plane and H-plane. These results show that the proposed architecture can combine dual-band shared-aperture integration and independent reconfigurable beam control in a compact antenna platform.

This paper presents the mAIEnergy dataset, an open-access, multimodal corpus developed to support Large Language Model (LLM) applications in the energy sector. The dataset integrates approximately 50,000 textual documents, 20,000 images, 25 million numerical time series records, and 2 million geospatial and relational data entries. It includes policy and regulatory texts, scientific articles and news articles, satellite and contextual imagery, electricity system measurements, weather observations, statistical indicators, and geospatial representations of energy infrastructure and related entities. All data have been harmonized into structured, ready-to-use formats, accompanied by consistent metadata and reproducible data retrieval and preparation workflows. The dataset can serve as a foundational energy knowledge base, allowing energy stakeholders to integrate additional open-source or proprietary data. The mAIEnergy dataset adheres to Findable, Accessible, Interoperable, and Reusable (FAIR) principles, enhancing its applicability for AI-driven energy research, modeling, and decision-making.

Reliable electrocardiogram (ECG) monitoring in wearable and space environments requires effective denoising of signals corrupted by non-stationary electromyogram (EMG) interference. This paper presents a comparative evaluation of model-based and DL-based denoising techniques for upper-arm ECG recordings acquired under real conditions. The model-based methods include three empirical mode decomposition (EMD) variants and a discrete wavelet transform (DWT) approach, while the deep learning (DL) side is represented by a stacked denoising autoencoder (SDAE) and a physics-informed neural network (PINN). All methods are evaluated on real acquisitions under both relaxed and voluntary muscle contraction conditions, using root mean squared error (RMSE), Pearson correlation, and peak-to-peak signal-to-noise ratio (PPSNR) as performance metrics. Results reveal a fundamental trade-off: DL methods achieve superior morphological reconstruction, while DWT provides the strongest noise suppression, highlighting complementary strengths for wearable cardiac monitoring applications.

Heterogeneous wireless sensor networks (HWSNs) in space and extreme environments must reliably transmit diverse analog physical signals over resource-constrained fading channels, subject to bandwidth limitations, power budgets, and reconstruction quality requirements. This paper addresses two fundamental questions: (i) what is the minimum signal-to-noise ratio (SNR) a sensing link must sustain to reconstruct an analog signal at a prescribed distortion, regardless of the decoder used, and (ii) how can knowledge of the signal's intrinsic structure be exploited to jointly allocate power and bandwidth across an HWSN? Both questions are answered through the Renyi information dimension (RID), which quantifies the intrinsic complexity of an analog source distribution. By combining the RID with rate-distortion theory and Shannon channel capacity, a closed-form SNR lower bound is derived, parameterized solely by the source RID. Building on these foundations, a cross-layer resource allocation framework is introduced that exploits the per-node RID to jointly assign transmit power and bandwidth, achieving strict power saving relative to a Gaussian-assumption baseline while guaranteeing prescribed reconstruction quality and outage constraints at every node.

Distributed wireless spiking neural networks (DWSNNs) are a promising paradigm for energy-efficient edge inference in resource-constrained environments such as wireless sensor networks (WSNs). Yet, two limitations persist: their internal decision process is opaque, and their residual energy footprint remains a limiting factor for ultra-low-power deployments. This paper proposes a systematic methodology to convert a trained DWSNN into an equivalent Spiking Neural P (SN P) system, a biologically-inspired, rule-based computational model drawn from membrane computing, by extracting symbolic firing rules from the hidden-layer spike activity. The resulting SN P system provides direct, human-readable decision explanations while consuming three orders of magnitude less energy than its parent SNN. Experiments on the Neuromorphic MNIST (N-MNIST) dataset with a two-layer fully connected SNN using phase encoding and Leaky Integrate-and-Fire (LIF) neurons show that the SN P system retains approximately 84% of the original classification accuracy (73.77% vs. 87.68%) while the output layer connectivity decreases from 1000 to 120 class-specific connections. This complexity reduction is governed by a parameter related to the number of relevant hidden neurons per class that can be chosen according to a trade-off between computational complexity reduction and output accuracy. These results position SN P systems as lightweight, interpretable surrogates for trained distributed wireless SNNs in neuromorphic edge deployments.

Connected and Automated Vehicles (CAVs) operating on lane-free highways offer substantial gains in traffic efficiency. However, their inherent nonlinear dynamics and the presence of coupled, nonconvex safety constraints present critical challenges to control design. Centralized Model Predictive Control (MPC) ensures safety, but suffers from scalability and communication limitations. To address these challenges, this paper investigates decentralized MPC (DMPC) for CAV coordination, focusing on iterative, non-cooperative algorithms, including Jacobi-type and Gauss-Seidel-type. A novel decoupling method is developed to transform nonconvex safety constraints into convex, locally enforceable constraints, inspired by buffered Voronoi cells. The simulation results show that the proposed DMPC algorithms achieve safe and efficient vehicle trajectories while substantially improving scalability, highlighting their potential for future lane-free CAV traffic systems. Ultimately, the results indicate that the most suitable decentralized control strategy depends on the desired trade-off between safety, performance, and computational efficiency.

Diffusion-weighted imaging (DWI) is acquired as part of bi-parametric prostate MRI, but suffers from artifacts that degrade downstream quantitative and diagnostic performance. While DWI preprocessing is standard in brain imaging, its adoption in prostate imaging remains limited and lacks standardized pipelines. This study investigated the effect of different DWI preprocessing strategies on apparent diffusion coefficient (ADC) estimation and automatic Prostate Imaging Reporting and Data System (PI-RADS) classification. 268 cases were derived from the fastMRI prostate cohort by sequentially applying denoising, Gibbs-ringing correction, and diffeomorphic registration for susceptibility distortion correction. ADC maps were compared using linear least squares (LLS) and iteratively-weighted LLS (IWLLS). A 3-class DenseNet classifier was trained to predict PI-RADS scores from multi-channel MRI inputs. ADC analysis revealed statistically significant differences across preprocessing pipelines, with LLS and IWLLS producing numerically equivalent maps. Linear relationships between ADC values were preserved across most datasets (PCC ~0.99), while distortion correction realigned DWI to T2w anatomy and altered ADC values accordingly (PCC ~0.90). Classification showed the best AUROC and sensitivity for high-risk PI-RADS classes in the fully processed dataset. False-negative analysis revealed this dataset produced the least overconfident incorrect predictions on high-risk classes, which is a desirable property for clinical triage. DWI preprocessing, particularly distortion correction, enhances both ADC map quality and the predictive power of deep learning models for PI-RADS classification, supporting the need for optimized preprocessing pipelines in prostate MRI.

In this work, we consider the identification and control of nonlinear systems with finite action spaces. The unknown dynamics are estimated from finite samples with Koopman operator regression in a reproducing kernel Hilbert space, yielding a linear switching predictive model, the switches governed by the value of the control variable. In order to perform control in closed-loop, the learned dynamics are employed in an infinite-horizon optimal control problem with time-varying stage cost, which is solved by means of model predictive control. In a theoretical analysis, we derive learning rates for the Koopman dynamics approximation. We further quantify, under suitable assumptions, the sub-optimality of the model predictive control strategy, both in the case of exact Koopman dynamics, and in the case of learned ones. Numerical simulations on the Duffing oscillator complement our theoretical findings.

This paper addresses the optimal control of epidemic dynamics under conflicting socio-economic objectives. We propose an economic Model Predictive Control (MPC) framework, applied to an extended SEIR-V (Susceptible-Exposed-Infected-Recovered-Vaccinated) compartmental model to govern the spread of an infectious disease while minimizing economic disruption. The control problem is formulated as a constrained nonlinear optimization problem, in which the controller dynamically adjusts social interaction levels (transmission rate beta) and vaccination efforts to minimize a composite cost function that penalizes fatalities, healthcare capacity violations, and economic losses. We conduct a rigorous sensitivity analysis of the prediction horizon N, demonstrating that the closed loop is robust to the horizon choice and that N = 35 days minimizes the realized cost. Furthermore, both the closed-loop solution and an open-loop turnpike analysis across diverse initial conditions reveal that the celebrated "Hammer and Dance" mitigation strategy emerges naturally as the mathematical optimum: the optimal trajectories anchor to a unique suppression turnpike (maximum lockdown) to drive hospitalizations toward the disease-free equilibrium before progressively reopening the economy. Through a turnpike-based argument we establish practical asymptotic stability of the optimal operating point, providing a mathematically grounded decision-support tool for pandemic policy.

Sampling determines the form of information available to downstream reconstruction systems. Conventional lowrate sampling forms finite-dimensional observations directly from the raw waveform, with the sampling rule mainly guided by bandwidth, sparsity, or fixed signal-level structures. For acoustic signals such as speech, however, reconstruction-relevant information is often expressed through content-related spectral-temporal structures rather than waveform samples alone. This paper proposes semantic sampling via learnable observation front ends, where finite-dimensional observations are generated from learned signal responses instead of directly subsampled waveform points. The proposed front end consists of a semantic feature filterbank, a constrained semantic observation matrix, and a low-rate readout module. The filterbank maps the input waveform into multiple acoustic response channels, the observation matrix combines these responses into a small number of observation channels, and the readout module produces low-rate finite-dimensional samples. A reconstruction network is then used to recover the signal from the resulting observations. Experiments on low-rate speech reconstruction show that, under the same observation budget, the proposed semantic sampling front end provides more informative observations than fixed low-rate sampling and neural restoration methods based on predetermined low-rate waveforms. The improvements in waveform fidelity, spectral consistency, and perceptual quality show that learnable observation front ends preserve more useful information for acoustic signal reconstruction under the same observation budget.

The electrical incompatibility between vehicles and traction network in railway system can result in system instability and oscillation overvoltage issues. To analyze the system stability, impedance-based frequency-domain methods are commonly used. However, the current impedance-based modeling methods face challenges in practical implementation due to the requirement of precise analytical models and detailed internal parameters for all vehicles. Moreover, multiple vehicles operate simultaneously in railway systems, each with different operating conditions and internal parameters, thereby influencing system stability to different extents. Therefore, it is crucial to accurately identify the critical vehicles to prevent resonance accidents. To address these challenges, a component connection-based modeling approach for the railway vehicle-grid system is proposed, which only requires the measured impedance results without the internal information of vehicles. In addition, a multilevel sensitivity analysis method is introduced to quantitatively identify the critical vehicles and internal parameters that influence system stability, which outperforms traditional sensitivity analysis methods in computational complexity. Furthermore, a system-level electrical compatibility test process for the railway vehicle-grid system is provided, incorporating the proposed stability and sensitivity analysis methods. Finally, case studies based on the real-world train schedule of a multivehicle-accessed railway vehicle-grid system are designed to verify the correctness of the proposed method.

Cloud gaming streams cloud-rendered frames under strict motion-to-photon latency, yet its at-scale viability is increasingly constrained by bandwidth cost: in our study of the T cloud gaming platform, bandwidth accounts for 30-60% of total operating expense. This high bandwidth consumption stems from a fidelity-first objective of making the stream perceptually indistinguishable from local gameplay. It drives production systems toward best-effort bitrate allocation that pushes the encoder to the highest rate allowed by congestion control. However, the bitrate-perception relationship saturates: beyond a frame-dependent perceptually lossless threshold, additional bits yield negligible perceptual improvement, creating systematic redundant quality that wastes bandwidth. We present SAIL, a production quality-aware rate control system with the goal of achieving perceptually lossless quality while avoiding unnecessary bandwidth waste. SAIL adopts a post-encoding architecture to enable millisecond-scale feedback at near-zero overhead. It comprises three key designs: (i) an encoder-driven quality assessment model that leverages zero-cost encoder outputs for real-time quality estimation; (ii) a hybrid rate control mechanism that balances steady-state adaptation with dynamic spike absorption; and (iii) a network-aware strategy that coordinates with congestion control to prevent capacity underestimation. SAIL has been fully deployed on the T cloud gaming platform and reduces bandwidth consumption by 44.27% and end-to-end latency by 8.37% without degrading perceived quality, serving tens of millions of users and accumulating billions of hours of total gameplay.

We study recovery control in replicated computing systems. Such systems consist of replicas that collectively provide a service to a client population. This redundancy enables the system to withstand failures provided that failed replicas are recovered faster than new failures occur. We show that the problem of deciding when to initiate recovery of selected replicas can be formulated as a partially observable Markov decision problem (POMDP) with a multiagent structure. We exploit this structure to apply a multiagent rollout method for approximating optimal control policies. Our method uses precomputed signaling information that reduces the need for replica coordination and facilitates parallel computations. Experiments show that our method scales to systems with up to 70 replicas and reduces costs compared to the recovery policies currently used in practice.

Large-scale pretrained ASR models such as Whisper exhibit strong multilingual capabilities. However, fine-tuning on low-resource languages often causes catastrophic forgetting. Although continual learning mitigates this issue, existing methods struggle to regulate cross-task interference in multilingual settings, where dominant languages bias optimization. We propose Unified Gradient Projection (UGP), which constrains parameter updates using reference gradients from language-balanced replay in a unified projection space. By equalizing per-language contributions in the projection, UGP reduces dominant-language bias and improves cross-lingual stability. We further show that combining gradient-level projection with data-level replay yields complementary gains in stability and plasticity. Across diverse low-resource language groups and model scales, UGP enables effective adaptation while substantially mitigating forgetting. On Whisper-large-v3, it achieves near-zero average forgetting.

Reconfigurable distributed antennas and reflecting surface (RDARS) has recently emerged as a promising architecture for integrated sensing and communication (ISAC), owing to its flexible element-wise mode switching between connection and reflection modes. In this paper, to fully reap the benefits of mode configuration, muting elements that can absorb the incident energy are introduced into RDARS-aided ISAC systems to mitigate multi-user interference (MUI) and enhance sensing performance. To draw useful insights, we first investigate the special cases of single-UE communication, single-target sensing, and two-UE communication to reveal the importance of muting elements. Specifically, the maximum communication and sensing signal-to-noise ratio (SNR), and the signal-to-interference-plus-noise ratio (SINR) expressions are respectively derived for the three cases, together with the optimal number of muting elements for explicitly characterizing the tradeoff between reflection gain loss and MUI suppression. Next, we consider the joint waveform and tri-mode switching design for RDARS-aided ISAC systems, where an alternating optimization (AO)-based penalty dual decomposition (APDD) algorithm is proposed to solve the mixed-integer nonlinear programming (MINLP) problem. Furthermore, a model-driven APDD-Net is developed by deeply unfolding the APDD iterations into a layer-wise architecture, where key parameters are learned to reduce the computational complexity and accelerate convergence. Simulation results verify the theoretical findings on the muting gain and demonstrate that the proposed APDD-Net achieves a better tradeoff between communication and sensing performance compared with benchmark schemes.

Speech enhancement (SE) can substantially improve perceptual quality, yet enhanced speech does not necessarily improve automatic speech recognition (ASR). Common explanations such as enhancement artifacts and over-suppression remain qualitative and do not localize which enhancement component affects recognition. We study inference-time polar projection, which transforms an STFT mask $M=Ae^{j\phi}$ into $M_{\alpha,\gamma}=A^\alpha e^{j\gamma\phi}$ and independently varies magnitude strength and estimated phase correction with frozen SE and ASR models. The resulting response curves separate effects that are coupled in ordinary enhanced-vs-noisy comparisons. On VoiceBank+DEMAND with FRCRN, wav2vec2.0 improves from 17.13 to 10.20 WER near full magnitude correction ($\alpha=.85$), while Whisper reaches its best WER under much weaker correction, improving from 6.44 to 5.57 at $\alpha=.25$ and returning to 6.46 at full magnitude. A second enhancer, a robustness-oriented ASR model, and a reverberant WSJ0 mixture set generalize the pattern and show that the preferred magnitude region depends on the recognizer and acoustic condition. Estimated phase correction is not a stable ASR lever in these evaluations: its effect is smaller, setting-dependent, and provides no consistent gain once magnitude strength is fixed.

Implicit neural controllers (INCs) are static feedback laws that are evaluated through an algebraic fixed point {equation}; they include as special cases neural network controllers. We propose a so-called implicit representation of neural networks as a key enabling device that exposes the controller as a trainable linear interconnection closed through a known static activation map, thereby making well-posedness and Lyapunov/IQC analysis mathematically easy to handle. For finite-dimensional LTI plants, we first develop a rigorous analysis theory for a given INC, including Perron–Frobenius and norm conditions for well posedness, LMI/IQC certificates for exponential stability, and LMIs for discounted infinite-horizon quadratic performance. We then formulate synthesis as a certification-compatible heuristic search: training is carried out under explicit well-posedness constraints, implicit-differentiation formulas provide gradients, and the resulting controller is accepted only after independent post-training LMIs or regional admissibility checks are feasible. Finally, we establish constrained-control separation results: for a specific scalar unstable plant with hard actuator bounds, an INC achieves a strictly smaller discounted infinite-horizon cost than any admissible finite-order dynamic linear controller. Additional results cover quadratic state-input costs, comparison with linear static output feedback, and computable upper/lower-bound certificates. Numerical examples illustrate the mechanism and the resulting certified performance.

We address ambivalence and hesitancy (A/H) recognition in the ABAW 2026 BAH Challenge: given a short interview video, predict whether the person shows signs of A/H. Our system combines affect-specialised text, audio, and visual representations with a small set of readable linguistic hesitation cues, fused by a reliability gate we call Affective Marker Fusion (AMF), and finished with a simple AP-weighted ensemble at a fixed decision threshold. We also introduce ASR-erased time: speech recognisers delete fillers and hesitation pauses from the transcript, but the chunk timestamps keep the time those events took, and sixteen features built from these gaps form the strongest and most independent non-verbal channel we measured (AP $0.718$, correlation $0.11$–$0.36$ with all other members). Across controlled experiments we find three things: cross-modal conflict design does not reliably help on BAH; language is by far the strongest channel while affect-specialised audio is a useful second; and calibration matters more than architecture. Fitting ensemble weights and a threshold on the small validation split overfits: it scores $0.741$ macro-F1 on validation but only $0.690$ on the untouched test set. AP-weighting at a fixed threshold instead reaches $\mathbf{0.731}$ on test.

The double-sided power supply railway system increases the simultaneous operation of vehicles on the grid, potentially causing system instability and oscillation overvoltage issues. As vehicles frequently switch operating points during operation, it is essential to analyze system stability across a wide range of conditions. Therefore, accurately identifying the black-box impedance of vehicle converters at multiple operating points is crucial for studying railway vehicle-grid system stability. However, traditional impedance identification methods require extensive data and lack interpretability, leading to significant computational and data burdens. This study introduces an interpretable residual feedforward neural network (ResFNN) combined with SHapley Additive exPlanations for training vehicle impedance models, reducing data requirements while maintaining accuracy. Additionally, a component connection method is proposed for deriving the impedance matrix of a multivehicle railway system under the double-sided feeding mode. This method incorporates the dynamic mobility of vehicles and their positional distribution, and it utilizes the ResFNN to identify impedance for stability analysis. Real operational data from actual railway lines is used as case study to analyze the stability of the double-sided power supply railway system. The results demonstrate that this approach accurately assesses both lowfrequency and high-frequency instability issues.

In mountainous regions where traction loads constitute a significant portion of a long-chain weak power grid (PG) with sustainable energy, the interaction between the traction power supply system and the PG becomes increasingly evident. The integrated power flow calculation (PFC) method and its linearized model are quite important for the PG - traction network (TN) joint planning. However, existing research on the port load characteristics of the EMUs and the connection angle characteristics of traction transformers is insufficient, and there is a lack of effective methods for PFC or linearized PFC in systems that couple the PG with the traction network. To fill this gap, this paper proposes an integrated PFC model for the AT TN - PG coupled system, along with a linearized method. Firstly, according to the relationship of the phases between the PG and the AT traction network, the node admittance matrix of the coupled system has been constructed. Then, the issue of power injection equations being unable to deal with the EMUs port load is resolved by merging the contact line node and the rail node. Subsequently, the integrated PFC equations for the coupling system are established. Next, a hybrid phase linear decoupled power flow model for the coupling system is developed, employing the correspondence between the phases of the PG and the TN, as well as the phase angle differences among various nodes and branches. Numerical simulations conducted in a specific region demonstrate the necessity of an integrated PFC for the coupled system and validate both the accuracy and efficiency of the linearized model.

This paper investigates acoustic intensity estimation using pairs of cardioid microphones based on the cardioid-cardioid (C-C) method. Unlike conventional pressure-difference techniques, the C-C method is intrinsically less sensitive to the relationship between microphone spacing and acoustic wavelength. However, practical microphones inevitably deviate from ideal cardioid directivity, producing direction-dependent estimation errors. To improve robustness against such errors, a measurement framework based on spherical tight-frame microphone configurations is proposed. Directional intensity components measured along multiple axes are combined to reconstruct the three-dimensional acoustic intensity vector. Furthermore, directivity errors are represented using Legendre polynomial and spherical harmonic expansions, and a geometry-dependent leakage metric is introduced to quantify the error-suppression capability of different microphone arrangements. Theoretical analysis and numerical simulations demonstrate that tight-frame configurations effectively suppress direction-dependent errors through geometric averaging. The proposed leakage metric provides a qualitative indication of microphone directivity imperfections on the reconstructed intensity vector. The results further indicate that accurate wide-band acoustic-intensity estimation can be achieved even with relatively large microphone spacings, which are generally impractical in conventional pressure-difference approaches. The proposed framework provides a physically interpretable and practically useful approach for acoustic intensity measurement using directional microphone arrays.

Holographic multiple-input multiple-output (HMIMO) has emerged as a promising technology for future wireless systems by enabling continuous electromagnetic (EM) field control over large apertures. However, user-side EM exposure has become an increasingly important concern in large-scale array systems. This paper addresses this issue by developing a multiuser uplink HMIMO model, where a physically consistent specific absorption rate (SAR) model is established to quantify the EM exposure. On this basis, a spectral efficiency (SE) maximization problem is addressed by developing a modified iterative water-filling algorithm. Simulation results demonstrate that the proposed algorithm effectively improves the system SE while satisfying the SAR constraints.

This paper presents a hierarchical multi-agent architecture in which independent large language model (LLM) planners perform strategic role assignment for attacking and defending robot teams, decoupled from low-level control execution. At each planning cycle, each team's LLM planner observes its own team in full but the opposing team only within its robots' combined field of view, then assigns each robot a tactical role - e.g., hold a perimeter, neutralize an intruder on contact, or converge with teammates for capture - together with a natural-language justification. Each robot independently executes its assigned role through a receding-horizon model predictive control (MPC) controller, followed by a discrete-time control barrier function (CBF) filter for safety and role-dependent engagement constraints. Differentiated capture and neutralization incentives require the defending planner to balance threat resolution against resource allocation under partial observability. We evaluate the framework across variable-sized adversary teams using both state-based tactical reasoning and image-based contact classification. Results show that collective team behavior can be adapted through high-level LLM role assignment while retaining the same underlying low-level control architecture.

In this paper, a multidisciplinary design optimization problem under uncertainty is formulated for wave energy converter array. An array of heaving point absorbers for grid-scale energy production with decision variables and parameters chosen from the coupled disciplines of geometry, hydrodynamics, layout, and trajectory optimization thus resulting in a control co-design formulation of the plant and the control together. We study the benefits of MDO as applied to WEC farm layout optimization. We vary the wave energy converter (WEC) dimensions, array layout, and control gain to minimize the power per volume. Uncertainty in the electrical power is handled using regression based on polynomial chaos expansion (PCE) method at each design iteration. Traditional WEC farm design optimization approaches often neglect the multidisciplinary, coupled nature of WECs and the inherent uncertainty in ocean wave conditions and control responses. This leads to designs that may under perform in real-world environments. In this work, we address this limitation by incorporating uncertainty directly into the design optimization process using the technique of polynomial chaos expansion (PCE) to quantify the variability of the performance due to uncertain wave environment.

A voice agent must decide, moment to moment, whether the user has finished; silence rarely settles it: a caller reading a phone number pauses mid-digits, a one-word "Stop!" ends a turn, a long question carries pauses longer than real turn-gaps. A voice-activity detector plus a silence timeout (the deployed default) cannot separate these, because within-turn pauses routinely exceed between-turn gaps; what distinguishes them is whether the words so far form a complete thought: what a recognizer computes to produce a transcript. We present the first open training recipe and benchmark for turn-aware streaming ASR: a small LoRA adapter on Qwen3-ASR-0.6B, trained in hours on one GPU, that transcribes, detects end-of-turn from meaning and silence, handles dictation, and grounds transcription in context. On a deployment-matched benchmark it reaches 0.97 boundary recall at 0.39 s median latency with 0.3 false fires per speech-minute, replicated on a fresh test set; no silence timeout reaches this point. The recipe rests on one principle: every streaming-decision label must be computable from input up to the decision point. Offline corpora violate it, encoding the future; such clairvoyant labels manufactured oscillation and a phantom recall-versus-precision trade-off, exposed when one appended second of silence raised a "broken" model's end-of-turn recall from 0.10 to 1.00. The same leak recurred with context: an always-matching biasing prefix became a copied shortcut (40% intrusion), and counterfactuals disagreeing with the audio cut this to 0.8% while keeping most of a +28.9 pp entity-recall benefit.

Music source separation (MSS) is increasingly used not to remix music but to measure it: separated drum stems feed studies of microtiming, dynamics, and groove. The field evaluates separators almost exclusively by signal-to-distortion ratio (SDR), yet microrhythm research shows that a sound's perceived temporal location (its p-centre) is co-determined by its attack and envelope, precisely the properties SDR was not designed to protect. We quantify what four open separators spanning four architecture generations (Spleeter, HT-Demucs, BS-Roformer, SCNetXL) do to rhythm-critical signal properties, using the 50-track MUSDB18-HQ test set, where true stems make every claim falsifiable. Three findings emerge. (1) Onset timing is safe: onset F-measure tracks SI-SDR (Spearman rho = 0.62) and is invariant to input length. (2) Transient and dynamic shape are not: their distortion correlates only weakly with SI-SDR (|rho| <= 0.29), and the model ranking inverts - the SDR leader distorts drum attacks twice as much as its capability-matched CNN counterpart, while the SDR-worst model preserves dynamics better than a mid-pack one. Each model imposes a systematic, model-specific bias on the dynamic profile. (3) Input length reshapes the rendered attack of a fixed passage (marginally more for the transformer; paired p = 0.044) while leaving onset locations untouched. For rhythmic studies, separator choice and input conditions are methodological variables to be reported, and SDR alone cannot stand in for them.

Remaining useful life prediction for aircraft centrifugal air compressors in real commercial operations poses challenges that controlled benchmark datasets do not expose. In-flight sensor signals superimpose genuine degradation on continuously varying operating conditions, and no channel can be assumed a priori to carry a reliable degradation signature. Moreover, models trained on one subset of a fleet generalize poorly to unseen aircraft and installation positions, a cross-domain problem of underappreciated practical severity. This paper presents a framework combining physics-guided processing with adaptive temporal encoding. A population-level selection procedure identifies surge margin as the most consistent degradation indicator across the fleet. Operating regime filtering and windowed aggregation recover the health trend from operational noise, and two physically motivated features encoding current health level and cumulative degradation rate enable cross-unit comparison without absolute signal values. Sinusoidal positional encoding provides lifecycle context without data leakage, and a structured cross-domain evaluation identifies encoding period mismatch as the primary mechanism of performance loss under fleet transfer. An adaptive recalibration scheme estimates each target unit's lifecycle scale from early observations alone, requiring no future information or labeled target data. The approach yields substantial, consistent gains in cross-domain accuracy across transfer scenarios of increasing domain distance, and a Gaussian Process Regression model further provides calibrated uncertainty estimates supporting risk-informed maintenance scheduling.

Zero-shot object-goal navigation aims to enable an intelligent agent to explore and navigate to objects of unknown categories in an unfamiliar environment without specific target training. In zero-shot navigation tasks, pre-trained large models are usually employed to leverage their prior knowledge for guiding the agent's navigation. However, existing zero-shot object-goal navigation methods based on large language models (LLMs) merely utilize LLMs as flat reasoning tools to directly associate objects or regions. They lack the hierarchical spatial cognition modeling of human-like room semantics to object localization, which leads to strong blindness in exploration, insufficient accuracy in semantic association, and failure to fully unleash the common-sense reasoning potential of LLMs. This paper proposes an LLM-driven hierarchical room-to-object (HRO) framework for zero-shot object-goal navigation, which guides the agent to explore and navigate to the target object in a coarse-to-fine manner. Experiments on Gibson and HM3D datasets verify that our HRO framework achieves superior success rate and generalization over existing LLM-based methods, underscoring LLMs' strong potential for zero-shot object-goal navigation.