Papers

Recent Speech-to-Speech Translation (S2ST) systems achieve strong semantic accuracy yet consistently strip away non-verbal vocalizations (NVs), such as laughter and crying that convey pragmatic intent, which severely limits real-world utility. We address this via three contributions. First, we propose a synthesis pipeline for building scalable expressive datasets to overcome the data scarcity limitation. Second, we propose MoVE, a Mixture-of-LoRA-Experts architecture with expressive-specialized adapters and a soft-weighting router that blends experts for capturing hybrid expressive states. Third, we show pretrained AudioLLMs enable striking data efficiency: 30 minutes of curated data is enough for strong performance. On English-Chinese S2ST, while comparing with strong baselines, MoVE reproduces target NVs in 76% of cases and achieves the highest human-rated naturalness and emotional fidelity among all compared systems, where existing S2ST systems preserve at most 14% of NVs.

This paper provides a comprehensive framework for designing functional observers for linear systems subject to delayed output measurements. Moving beyond traditional methodologies, the proposed observer generates an estimate $\hat{z}(t)$ that predicts the current state functional $z(t)=Fx(t)$ using delayed data. By neutralizing sensing latency, the observer serves as a potent time-delay compensator, effectively expanding the practical utility of functional observer theory. The proposed observer architecture offers greater robustness and versatility than traditional Luenberger-type observers by leveraging multiple delayed components to preserve accuracy despite latency. A key contribution of this work is a novel method for extending the maximum allowable measurement delay while maintaining the asymptotic stability of the estimation-error system. Existence conditions are established together with constructive synthesis procedures. Extensive numerical examples are given to illustrate the proposed theory.

Forecasting the cost evolution of emerging clean technologies is crucial for informed policy, investment, and decarbonization decisions, yet it remains deeply uncertain. Learning curves, which link cost declines to cumulative deployment, are widely used for technological cost forecasting. However, applying them to emerging technologies is challenging due to parametric uncertainty in learning rates, which are scarce and highly uncertain, and structural uncertainty stemming from multiple plausible learning frameworks. Using water electrolysis as a case study, we evaluate how different learning structures, from shared to fragmented learning across technology variants and regions, alter expected cost paths. We interrogate model assumptions that represent contrasting industrial realities, including competition among electrolyzer variants and supply chain fragmentation associated with protectionism and industrial policy. We find that plausible modeling choices generate widely different trajectories, with materially different implications for policy design and technology strategy. We argue for routinely applying multiple learning frameworks to explore decision spaces and stress-test conclusions for scale-up planning, national industrial strategy, and energy-systems modeling.

Engineering projects are the result of the combined effort of their members. Yet, it has been documented that labor division withing projects is unevenly distributed: some project members are specialists undertaking only few tasks, whereas other are generalists and are responsible for the success of many tasks. Moreover, the latter are often facilitators of project integration. Such a workload distribution prompts one question: how resilient is a project to key personnel loss? Far from being a theoretical problem, the reliance of a project on a few key people can lead to severe economic losses and delays. We argue that current methods to estimate such a risk are unsatisfactory: some methods offer a best-case estimate and are, therefore, too optimistic; other methods fail to capture project fragmentation leading to biased estimates and unrealistic consequences in many settings. In this paper, we develop a novel method to assess project vulnerability by looking at it from the lens of network robustness. We compare our method against existing alternatives and show that it offers better and more consistent estimates of project resilience to personnel loss.

Transmitter-resolved radio map estimation (RME) from sparse measurements is essential for obtaining source-specific received-power information in wireless networks. This paper proposes SeaGAT, a Structured Edge-Aware Graph Attention Network for transmitter-resolved pointwise RME. For each target–transmitter query, SeaGAT constructs a target-centered graph from a bounded transmitter-specific reference set. Reference representations generate messages and provide measurement context to attention scoring, whereas structured edge representations encode explicit query–reference relations and enter only the scoring procedure. Within a fixed sampled support, we derive a differential identity that decomposes local variation of the evidence aggregate into attention-weighted message changes and score-induced weight redistribution; edge-relation perturbations act only through the latter, whereas a reference-RSS perturbation can affect both pathways. The bounded star graph yields graph-side computation linear in the selected reference count $K$, while support-truncation analysis bounds deviation from the same-parameter full-support aggregate by the product of omitted attention mass and cross-support message diameter. Extensive computer simulations evaluated with ray-tracing dataset show that SeaGAT achieves the lowest mean RMSE compared with baselines; replacing learned attention with uniform weights increases RMSE by $1.58$–$2.14$~dB. It also maintains stable transfer across the tested urban-layout and carrier-frequency shifts.

In today's day and age, we face a challenge in detecting deepfake images because of the fast evolution of modern generative models and the poor generalization capability of existing methods. In this paper, we use an ensemble of fine-tuned vision transformers like DINOv2, AIMv2 and OpenCLIP's ViT-L/14 to create generalizable method to detect deepfakes. We use the DF-Wild dataset released as part of the IEEE SP Cup 2025, because it uses a challenging and diverse set of manipulations and generation techniques. We started our experiments with CNN classifiers trained on spatial features. Experimental results show that our ensemble outperforms individual models and strong CNN baselines, achieving an AUC of 96.77% and an Equal Error Rate (EER) of just 9% on the DF-Wild test set, beating the state-of-the-art deepfake detection algorithm Effort by 7.05% and 8% in AUC and EER respectively. This was the winning solution for SP Cup, presented at ICASSP 2025.

This paper investigates communication-efficient neural network transmission by exploiting structured symmetry constraints in convolutional kernels. Instead of transmitting all model parameters, we propose a degrees-of-freedom (DoF) based codec that sends only the unique coefficients implied by a chosen symmetry group, enabling deterministic reconstruction of the full weight tensor at the receiver. The proposed framework is evaluated under quantization and noisy channel conditions across multiple symmetry patterns, signal-to-noise ratios, and bit-widths. To improve robustness against transmission impairments, a projection step is further applied at the receiver to enforce consistency with the symmetry-invariant subspace, effectively denoising corrupted parameters. Experimental results on MNIST and CIFAR-10 using a DeepCNN architecture demonstrate that DoF-based transmission achieves substantial bandwidth reduction while preserving significantly higher accuracy than pruning-based baselines, which often suffer catastrophic degradation. Among the tested symmetries, central-skew symmetry consistently provides the best accuracy-compression tradeoff, confirming that structured redundancy can be leveraged for reliable and efficient neural model delivery over constrained links.

This paper presents a stochastic delayed differential model for rumor propagation during infodemic that incorporates human behavioral response, public skepticism and fact-checking mechanisms. A discrete time delay is introduced to model natural lags in information processing and institutional response. Additionally, we adopt additive stochastic perturbations to model random fluctuations in social interaction and exposure. We present a rigorous stability analysis of the proposed rumor transmission model and derive convergence guarantees under reproduction number conditions. We also validate the model by numerical simulations and analyze the outbreak severity and quantify uncertainty under variable information processing delays. The results highlight the importance of timely awareness and fact-checking interventions for mitigating misinformation spread during pandemics

Precise aerial radio environment characterization is vital for low-altitude airspace planning. However, existing datasets and construction methods lack the high-resolution granularity required for complex aerial spaces, particularly failing to capture spatial variations across both horizontal and vertical dimensions. To address these gaps, this paper introduces FARM, a pioneering foundation model for unified aerial radio map (ARM) construction. FARM is supported by our newly curated, high-granularity full-domain ARM dataset, which features multi-band and multi-antenna configurations, effectively filling a critical void in comprehensive low-altitude radio data. Structurally, FARM leverages a masked autoencoder to extract deep latent representations of the aerial radio environment, which subsequently guide a diffusion-based decoder to synthesize high-fidelity signal distributions through only a few iterative refinement steps. Benefiting from this design, the architecture seamlessly accommodates both condition-based and condition-free ARM construction, providing robust support for diverse signal and environmental priors. Extensive experiments demonstrate that FARM significantly outperforms state-of-the-art benchmarks while exhibiting strong cross-scenario generalization. Crucially, we validate the transferability of FARM on a real-world dataset collected from field tests, proving its robust deployment capability. Ultimately, FARM serves as a foundational infrastructure for the low-altitude economy by enabling autonomous aerial logistics and intelligent urban networking.

Time series forecasting is traditionally dominated by sequence-based architectures such as recurrent neural networks and attention mechanisms, which process all time steps uniformly and often incur substantial computational cost. However, real-world temporal signals typically exhibit heterogeneous structure, where informative patterns are sparsely distributed and interspersed with redundant observations. This work introduces SPaRSe-TIME, a structured and computationally efficient framework that models time series through a decomposition into three complementary components: saliency, memory, and trend. The proposed approach reformulates temporal modeling as a projection onto informative subspaces, where saliency acts as a data-dependent sparsification operator, memory captures dominant low-rank temporal patterns, and trend encodes low-frequency dynamics. These components are integrated through a lightweight, adaptive mapping that enables simplified, selective, and interpretable temporal reasoning. Extensive experiments on diverse real-world datasets demonstrate that SPaRSe-TIME achieves competitive predictive performance compared to recurrent and attention-based architectures, while significantly reducing computational complexity. The model is particularly effective in structured time series with clear temporal components and provides explicit interpretability through component-wise contributions. Furthermore, analysis reveals both the strengths and limitations of decomposition-based modeling, highlighting challenges in highly stochastic and complex multivariate settings. Overall, SPaRSe-TIME offers a principled alternative to monolithic sequence models, bridging efficiency, interpretability, and performance, and providing a scalable framework for time series learning.

The ensemble Kalman filter (EnKF) is widely used for nonlinear and high-dimensional state estimation because it replaces explicit covariance propagation with ensemble statistics. However, conventional EnKFs can become overconfident under nonlinear measurements, while covariance inflation is only an indirect remedy. This paper proposes a covariance-adaptive and recalibrated EnKF (CAR-EnKF) that combines (i) recalibration of the covariance effect of the selected Kalman gain after the ensemble-mean update and (ii) a positive semidefinite covariance compensation driven by measurement nonlinearity. A normalized-innovation-squared feedback law adapts the compensation magnitude online. The recalibration and conditional back-out mechanisms are inherited from our prior nonlinear Kalman filter framework. This paper develops covariance-matching realizations for the stochastic EnKF and Ensemble Transform Kalman Filter (ETKF), together with EnKF-specific covariance compensation and NIS-based adaptation. The resulting corrections recover the conventional update for linear measurements. Experiments on feature-based SLAM and Lorenz–96 show lower RMSE than conventional EnKF baselines, especially at low measurement noise. The related codes are available at \href{https://github.com/Shida-Jiang/CAR-EnKF-A-Covariance-Adaptive-and-Recalibrated-Ensemble-Kalman-Filter-Framework}

Various distributed gradient descent algorithms for multi-agent optimization have incorporated the Nesterov accelerated gradient method, where the use of momentum enhances convergence rates. These algorithms have found broad applications in large-scale machine learning and optimization owing to their simplicity and low communication complexity. In this paper, we establish a continuous-time approximation of distributed Nesterov gradient descent. The convergence properties and convergence rate of the resulting distributed Nesterov flow are analyzed using Lyapunov methods. Building on these insights, we design new parameter choices within the flow, from which we derive flow-inspired discrete-time algorithms for multi-agent optimization. Surprisingly, the resulting algorithms achieve faster convergence compared to existing distributed gradient descent methods: they require fewer iterations to reach the same accuracy for strongly convex functions and exhibit an improved convergence rate for general convex functions without incurring additional communication rounds. Furthermore, we investigate the influence of the network topology on algorithm performance and derive an explicit relationship between the convergence rate and the graph condition number. Numerical simulations are presented to validate the effectiveness of the proposed approach.

The scarcity of labeled clinical data in oncology makes Few-Shot Learning (FSL) a critical framework for Computer Aided Diagnostics, but we observed that standard Prototypical Networks often struggle with the "prototype instability" caused by morphological noise and high intra-class variance in brain tumor scans. Our work attempts to minimize this by integrating a non-linear Logistic Chaos Module into a fine-tuned ResNet-18 backbone creating the Chaos-Enhanced ProtoNet(CE-ProtoNet). Using the deterministic ergodicity of the logistic chaos map we inject controlled perturbations into support features during episodic training-essentially for "stress testing" the embedding space. This process makes the model to converge on noise-invariant representations without increasing computational overhead. Testing this on a 4-way 5-shot brain tumor classification task, we found that a 15% chaotic injection level worked efficiently to stabilize high-dimensional clusters and reduce class dispersion. Our method achieved a peak test accuracy of 84.52%, outperforming standard ProtoNet. Our results suggest the idea of using chaotic perturbation as an efficient, low-overhead regularization tool, for the data-scarce regimes.

Large Audio-Language Models (LALMs) are increasingly integrated into daily applications, yet their generative biases remain underexplored. Existing speech fairness benchmarks rely on synthetic speech and Multiple-Choice Questions (MCQs), both offering a fragmented view of fairness. We propose VIBE, a framework that evaluates generative bias through open-ended tasks such as personalized recommendations, using human-recorded speech. Unlike MCQs, our method allows stereotypical associations to manifest organically without predefined options, making it easily extensible to new tasks. Evaluating 12 state-of-the-art LALMs reveals systematic biases in realistic scenarios. Both gender and accent cues trigger statistically significant distributional shifts, and bias magnitude is strongly task-dependent.

Breast cancer diagnosis demands rapid and precise tools, yet traditional histopathological methods often fall short in intra-operative settings. Deep Ultraviolet (DUV) fluorescence imaging emerges as a transformative approach, offering high-contrast, label-free visualization of whole-slide images (WSIs) with unprecedented detail, surpassing conventional hematoxylin and eosin (H&E) staining in speed and resolution. However, existing deep learning methods for breast cancer classification, predominantly patch-based, fragment spatial context and incur significant preprocessing overhead, limiting their clinical utility. Moreover, standard attention mechanisms, such as Spatial, Squeeze-and-Excitation, Global Context and Guided Context Gating, fail to fully exploit the rich, multi-scale regional relationships inherent in DUV-WSI data, often prioritizing generic feature recalibration over diagnostic specificity. This study introduces a novel Region-Affinity Attention mechanism tailored for DUV-WSI breast cancer classification, processing entire slides without patching to preserve spatial integrity. By modeling local neighbor distances and constructing a full affinity matrix, our method dynamically highlights diagnostically relevant regions, augmented by a contrastive loss to enhance feature discriminability. Evaluated on a dataset of 136 DUV-WSI samples, our approach achieves an accuracy of 92.67 +/- 0.73% and an AUC of 95.97%, outperforming existing attention methods.

Selective State Space Models (SSMs), notably Mamba, employ diagonal state transitions that limit both memory retention and bilinear computational capacity. We propose a factorized bilinear input modulation that augments the SSM with a state-input product, interpretable as a finite-dimensional Koopman bilinear form. After introducing a shared state across channels (Coupled SSM), the modulation admits three implementations. Coupled Bilinear Input Modulation (seq-BIM) retains the full bilinear product on the input side at the cost of sequential computation, Coupled Gated Modulation (GM) linearizes it into a gate modulation that is compatible with the parallel scan, and Parallel Bilinear Input Modulation (p-BIM) places the same bilinear product on the state transition while remaining parallel-scannable. Experiments on a multiple input-delay pendulum (memory retention) and NARMA-10 (bilinear computation) reveal a clear dissociation. GM substantially improves memory retention but not bilinear computation, while both seq-BIM and p-BIM improve both. A pathway ablation confirms that the two downstream routes of the bilinear signal serve complementary roles. The improvement is statistically robust, with the bilinear variants consistently outperforming the other variants on bilinear computation. Furthermore, only the bilinear variants benefit from increasing the SSM state dimension, while coupling or gate modulation alone show no improvement, establishing the bilinear mechanism as uniquely capable of exploiting larger state spaces.

Inductive bias refers to restrictions on the hypothesis class that enable a learning method to generalize effectively from limited data. A canonical example in control is linearity, which underpins low sample-complexity guarantees for stabilization and optimal control. For general nonlinear dynamics, by contrast, guarantees often rely on smoothness assumptions (e.g., Lipschitz continuity) which, when combined with covering arguments, can lead to data requirements that grow exponentially with the ambient dimension. In this paper we argue that data-efficient nonlinear control demands exploiting inductive bias embedded in nature itself, namely, structure imposed by physical laws. Focusing on Hamiltonian systems, we leverage symplectic geometry and intrinsic recurrence on energy level sets to solve target reachability problems. Our approach combines the recurrence property with a recently proposed class of policies, called chain policies, which composes locally certified trajectory segments extracted from demonstrations to achieve target reachability. We provide sufficient conditions for reachability under this construction and show that the resulting data requirements depend on explicit geometric and recurrence properties of the Hamiltonian rather than the state dimension.

Automating medical reports for retinal images requires a sophisticated blend of visual pattern recognition and deep clinical knowledge. Current Large Vision-Language Models (LVLMs) often struggle in specialized medical fields where data is scarce, leading to models that overfit and miss subtle but critical pathologies. To address this, we introduce DREAM (Dynamic Retinal Enhancement with Adaptive Multi-modal Fusion), a novel framework for high-fidelity medical report generation that excels even with limited data. DREAM employs a unique two-stage fusion mechanism that intelligently integrates visual data with clinical keywords curated by ophthalmologists. First, the Abstractor module maps image and keyword features into a shared space, enhancing visual data with pathology-relevant insights. Next, the Adaptor performs adaptive multi-modal fusion, dynamically weighting the importance of each modality using learnable parameters to create a unified representation. To ensure the model's outputs are semantically grounded in clinical reality, a Contrastive Alignment module aligns these fused representations with ground-truth medical reports during training. By combining medical expertise with an efficient fusion strategy, DREAM sets a new state-of-the-art on the DeepEyeNet benchmark, achieving a BLEU-4 score of 0.241, and further demonstrates strong generalization to the ROCO dataset.

This paper establishes a sufficient condition for guaranteeing power flow solvability in distribution grids with inverter-based resources (IBRs) operating under IEEE 1547 compliant Volt-Var control. While designed to improve voltage profiles, reactive power injection can drive the system toward its operational limits. Under these stressed conditions, any further incremental reactive power injection can trigger voltage collapse, the point at which a power flow solution ceases to exist. In this paper, by leveraging a phasor-based voltage representation, the power flow equations with Volt-Var control are developed in the complex fixed point form, enabling a compact formulation and the rigorous application of fixed-point theorems. Addressing the challenges posed by the non-holomorphicity of the complex power flow equations due to the Volt-Var function's dependence on voltage magnitude, the solvability conditions are then developed using the Brouwer fixed-point theorem. The proposed conditions are validated through simulations on distribution test feeders, with a primary focus on their application to real-time decision-making for voltage regulation services.

This paper addresses the critical issue of spectrum scarcity and the need to support diverse services, including communication and learning tasks, by presenting a reconfigurable intelligent surface (RIS)-aided wireless network framework that integrates non-orthogonal multiple access (NOMA) with over-the-air federated learning (AirFL). The proposed system leverages the ability of RIS to adaptively shape wireless channels, aiming to enhance overall network performance for both communication and learning through concurrent uplink transmissions. To tackle critical challenges such as co-channel interference, imperfect channel state information (CSI), and successive interference cancellation (SIC), we develop an optimization framework that focuses on minimizing the optimality gap. This joint optimization is formulated as a non-convex problem, complicated by the intricate interactions between NOMA and AirFL users as well as the impact of imperfect CSI and SIC. To overcome these challenges and reduce the optimality gap, we reformulate the optimization problem as a Markov decision process and solve it using a long short-term memory deep deterministic policy gradient (LSTM-DDPG) algorithm, a memory-based approach within deep reinforcement learning (DRL). Simulation results demonstrate that the proposed approach achieves faster convergence, lower variance, and improved robustness under channel uncertainty, outperforming baseline DRL algorithms such as DDPG, soft actor-critic (SAC), and advantage actor-critic (A2C).

Dielectric elastomer actuators (DEAs) have garnered extensive attention especially in soft robotic applications over the past few decades owing to the advantages of lightweight, large strain, fast response and high energy density. However, because the DEAs suffer from nonlinear elasticity, inherent viscoelastic creep, hysteresis and vibrational dynamics, the modeling, control and self-sensing of DEAs are challenging, thereby hindering the practical applications of DEAs. In order to address these challenges, numerous studies have been conducted. In this review, various physics-based modeling methods and phenomenological modeling methods for predicting the electromechanical response of DEAs are presented and discussed. Different control methods for DEAs are reviewed, which are classified into open-loop feedforward control, feedback control, feedforward-feedback control and adaptive feedforward control. Physics-based self-sensing methods and data-driven self-sensing methods for reconstructing the DEA displacement without the need for additional sensors are discussed. Finally, the existing problems and new opportunities for the further studies are summarized.

Near Field Communication (NFC) cards are widely used for identification, but their passive nature often limits the ability to incorporate additional security mechanisms. As a result, anyone holding the card may be incorrectly recognized as an authenticated user. To overcome this limitation, this paper presents a secure manual password input framework using a virtual PIN pad for passive NFC cards. Users input passwords by pressing designated regions on the card, which induces measurable impedance variations in the NFC antenna. These variations change the RF signals subtly, and a deep learning model is used to infer the intended password from the resulting signal patterns. A key challenge is that identical press interactions can produce significantly different responses across NFC cards, which yields unreliable recognition. To address this, we introduce a lightweight recognition approach that operates directly within the RF feature space at the penultimate layer of a temporal neural encoder. An adversarial domain-alignment module reshapes virtual PIN pad press-response embeddings into compact, card-invariant clusters, which enables stable and consistent recognition across heterogeneous cards. To support model training and evaluation, a reconfigurable software-defined radio (SDR) testbed is developed, and PIN pad press-response data are collected from commercially available ISO/IEC 15693 cards. Recognition is performed using a Mahalanobis distance metric derived from a calibration-based covariance model that captures feature correlations. Experimental results show that the proposed system achieves a 98.20% recognition acceptance rate and remains robust under substantial noise degradation. The framework is fully card-agnostic and can be seamlessly integrated into existing NFC infrastructures.

Future spacecraft operations require autonomy that can interpret high-level mission intent while preserving safety. However, existing trajectory optimization still relies heavily on expert-crafted formulations and does not support intent-conditioned decision-making. This paper proposes an intent-aligned spacecraft guidance framework that links high-level reasoning and safe trajectory optimization through explicit intermediate abstractions, based on behavior sequences and waypoint constraints. A foundation model first predicts an intent-aligned behavior plan, a waypoint generation model then converts it into waypoint constraints, and the safe trajectory is computed via optimization. This decomposition enables scalable supervision without sacrificing safety. Numerical experiments in close-proximity operation scenarios demonstrate that the proposed pipeline achieves over 90% SCP convergence and yields a $1.5\times$ higher rate of generating trajectories that satisfy the top intent-prioritized performance criteria than heuristic decision-making. These results support the use of intermediate behavior abstraction as a practical interface between foundation-model reasoning and safety-critical onboard spacecraft autonomy.

Time Series Foundation Models (TSFMs) have demonstrated strong generalization capability and data efficiency in time series forecasting through large-scale pretraining. However, adapting TSFMs to downstream forecasting tasks remains challenging due to temporal distribution shifts and varying data availability. Specifically, the non-stationary and uncertain nature of time series data leads to discrepancies between historical training and future forecasting distributions, making existing Supervised FineTuning (SFT)-based adaptation vulnerable to overfitting and limited generalization. Moreover, forecasting tasks often operate under varying data regimes, requiring TSFMs to extract generalizable temporal patterns from limited training samples. To address these challenges, we propose Time series Reinforcement FineTuning (TimeRFT), a reinforcement learning-based adaptation paradigm for TSFMs. TimeRFT introduces two forecasting-oriented training recipes: (i) A quality-aware temporal reward mechanism providing fine-grained credit assignment by holistically evaluating the contribution of each prediction step to overall forecasting performance. (ii) A difficulty-aware data selection strategy prioritizing informative time series samples with generalizable forecasting patterns. Extensive experiments on diverse real-world forecasting benchmarks demonstrate that TimeRFT consistently surpasses SFT-based adaptation methods across various real-world forecasting tasks with different data regimes, achieving improved prediction accuracy and enhanced generalization against unforeseen distribution shifts.

In sixth-generation (6G) cellular networks and beyond, aerial platforms, such as uncrewed aerial vehicles (UAVs) and high-altitude platform stations (HAPS), are anticipated to play a crucial role in enhancing connectivity, expanding network coverage, and supporting advanced communication services. However, the deployment of energy-efficient onboard communication systems is essential for their widespread adoption and effectiveness. The integration of energy harvesting (EH) into aerial platforms is envisioned to be pivotal in promoting both energy and cost efficiency. In this paper, we propose a new paradigm for aerial platforms in which they can collect energy from the transmitted signals of nearby aerial platforms. The paper employs a two-tier architecture with HAPS super-macro base stations (HAPS-SMBS) system: regular HAPS-SMBS nodes serve as base stations, while a "mother" HAPS-SMBS node acts as a manager to coordinate communications between regular HAPS-SMBS and the ground station, thus enabling wireless energy transfer. Specifically, we analyze the characteristics of EH-enabled HAPS-SMBS and compare their performance with those without EH. Additionally, we derive the optimal regular HAPS-SMBS positioning to mitigate signal attenuation and power loss. Subsequently, we formulate a joint optimization problem for regular HAPS-SMBS positioning and the EH factor. We solve the problem using the iterative distance and EH factor algorithm (IDFA); however, we employ $Q$-learning to verify its effectiveness. Our findings indicate that, compared to conventional EH systems, IDFA and $Q$-learning exhibit higher data rate performance. In contrast, $Q$-learning outperforms IDFA systems in linear modelswith intensive training in approximating optimal values. Furthermore, maximizing transmit power achieves higher gains than systems without EH.

Controllers for motor drives typically require a current reference which will satisfy the requested torque subject to system constraints. This work generalizes existing current reference theory to the case of the Wound Rotor Synchronous Machine (WRSM). By incorporating the additional rotor-current degree-of-freedom, along with magnetic saturation, cross-coupling, and speed-dependent core losses, the problem of finding an optimal current reference is formulated within affine flux regions as a quadratically constrained quadratic program using a piecewise-affine approximation derived from finite-element data. The solution is characterized according to the active constraint regime, yielding closed-form or low-dimensional polynomial solutions in several cases, and a small semidefinite program in the voltage constrained regime. The proposed framework extends unified optimal current reference theory beyond the permanent-magnet setting to three degree-of-freedom WRSMs while remaining computationally tractable. Results on a physical WRSM prototype illustrate the effectiveness of the approach across the torque-speed operating envelope.

Accurate segmentation of gastrointestinal (GI) organs in magnetic resonance enterography (MRE) is critical for diagnosing inflammatory bowel disease (IBD). However, anatomical variability, class imbalance, and low tissue contrast hinder reliable automation. This study proposes a dual-stage deep learning framework for organ-specific segmentation of GI structures from coronal MRE images to address these challenges. A publicly available MRE dataset of 3,195 coronal T2-weighted HASTE slices from 114 IBD patients was used. Initially, a DenseNet201-UNet++ model generated coarse masks for ROI extraction. A DenseNet121-SelfONN-UNet model was then trained on organ-specific patches. Extensive data augmentation, normalization, five-fold cross-validation, and class-specific weighting were applied to mitigate severe class imbalance, particularly for the appendix. The initial stage achieved strong organ localization but underperformed for the appendix; class weighting improved its DSC from 6.76% to 85.76%. The second-stage DenseNet121-SelfONN-UNet significantly enhanced segmentation across all GI structures, with notable DSC gains (cecum +23.62%, sigmoid +18.57%, rectum +17.99%, small intestine +16.06%). Overall, the framework achieved mDSC of 88.99%, mIoU of 84.76%, and mHD95 of 6.94 mm, outperforming all baselines. This framework demonstrates the effectiveness of a coarse-to-fine, organ-aware segmentation strategy for intestinal MRE. Despite higher computational cost, it shows strong potential for clinical translation and enables anatomically informed diagnostic tools in gastroenterology.

Ising machines—special-purpose hardware for heuristically solving Ising optimization problems—based on probabilistic bits (p-bits) have been established as a promising alternative to heuristic optimization algorithms run on conventional computers. However, it has—until now—been thought that Ising spins that are connected in probabilistic Ising machines (PIMs) cannot be updated in parallel without ruining the machine's solving ability. This has presented a major challenge to realizing the potential for probabilistic Ising machines to act as fast solvers for densely connected Ising problems. In this paper, we show that it is possible to circumvent this conventional wisdom. We introduce a modified form of Ising spin dynamics for PIMs, adding an inertia term, and verify in algorithm simulations, field-programmable gate array (FPGA) emulation, and in FPGA experiments that the modified dynamics enables fully parallel, synchronous updates and at the same time improves the achieved success probability. Our evaluations were performed with various types of abstract (Max-Cut and Sherrington-Kirkpatrick model) and application-derived (multiple-input and multiple-output, MIMO detection) dense Ising benchmark instances. Performing fully parallel updates results in a speed advantage that grows superlinearly with the number of spins, giving rise to large time-to-solution reductions for practical problem sizes. For both MC and the SK model at a problem size of 200, our approach achieved an average speedup of ~34x, with the best single-instance speedup reaching 150x. As an example of the practical utility of our approach in an application where speed is critical, we co-design the algorithm dynamics and hardware implementation for MIMO detection, achieving improved detection accuracy relative to the standard linear detector and higher throughput than the conventional sequential-update PIM.

To meet the growing data traffic demand in future wireless systems, novel transmission architectures capable of adapting to complex propagation environments are required. Movable antenna (MA) systems have recently emerged as a promising approach, enabling the physical repositioning of antenna elements to exploit spatial degrees of freedom. However, existing studies largely rely on idealized or simplistic channel models, leaving open the question of whether the performance gains of MA systems persist under realistic propagation conditions. This paper investigates the performance of downlink multi-user MIMO systems with movable antennas using deterministic ray-traced channel models. A simulation framework combining three-dimensional ray tracing and field-response channel modeling is developed, and antenna positions are optimized using particle swarm optimization and genetic algorithms. Simulation results reveal that while simplified distance-based channel models predict large performance disparities between competing array configurations, realistic ray-traced channels significantly compress these differences, indicating that propagation effects dominate over pure array geometry optimization. Nevertheless, movable antenna systems retain strong effectiveness over conventional fixed arrays across different user distributions, array sizes, and multipath conditions, even in geometry-constrained propagation environments.

Dynamic operating envelopes (DOEs) provide a systematic framework to integrate the flexibility of distribution grid resources while safeguarding network limits such as line ratings and voltage bounds. However, the flexibility derived from individual DOEs is often restricted and conservative, especially when some resources can coordinate via communication with an aggregator. This paper presents a convex, geometry-aware framework for constructing DOE for distribution grid customers under partial coordination, with coordinated customers modeled through polytopal flexibility sets and non-coordinated customers through hyperrectangles. The framework additionally incorporates fairness constraints for export and import headroom allocated to the customers within the DOE design. To account for forecast uncertainty in inelastic injections, the DOE design is extended to a robust formulation for bounded uncertainty sets. Case studies on two European three-phase low-voltage feeders, a widely used test feeder and a large-scale (3589)-bus system, show that the proposed DOE construction expands aggregate flexibility while maintaining network feasibility, fairness, and robustness to forecast uncertainty. Coordinating 30% of customers increases the aggregate active-power range by approximately 25% on the test feeder, while coordinating 20 customers on the large-scale feeder increases it by approximately 43%.