Papers

This paper proposes a system-aware adaptive channel state information (CSI) feedback framework for massive multiple-input multiple-output (mMIMO) systems, aiming to dynamically optimize the trade-off between reconstruction fidelity and signaling overhead. While deep learning-based autoencoders (AEs) have enabled significant CSI compression, conventional fixed-ratio schemes fail to adapt effectively to non-stationary channel conditions. To address this limitation, we develop a reinforcement learning (RL)-driven control framework that operates over a bank of pretrained multi-rate AEs, each corresponding to a distinct compression ratio (CR). At each time step, a centralized RL agent selects the most suitable CR for each user based on observed channel conditions and system performance indicators. Distinct from conventional mean squared error (MSE)-centric designs, we introduce a system-aware reward formulation that jointly accounts for spectral efficiency via signal-to-interference-plus-noise ratio (SINR), feedback overhead constraints, and the computational cost of model adaptation. Simulation results on high-dimensional delay-domain CSI datasets demonstrate that the proposed RL-guided framework effectively balances the overhead-accuracy tradeoff and adapts to dynamic channel environments. The proposed method improves spectral efficiency and feedback efficiency compared with fixed compression schemes and adaptive baselines, while maintaining a modest computational and memory footprint. Averaged over different numbers of users and across all considered baselines, the proposed RL framework reduces the CSI feedback cost by more than 53.4%, improves the average downlink sum rate by 53.64%, and reduces the NMSE by 22.38%. These results demonstrate its ability to achieve a more efficient rate-accuracy-feedback tradeoff under dynamic wireless conditions.

In this paper, we introduce a minimal dynamical model for the diffusion of a new social norm, in which individuals transition among three states: non-supporters, silent supporters, and vocal advocates. Despite its simplicity and its close relation to the classical SI-type and SIS-type spreading dynamics, this model exhibits a nontrivial latent-outbreak dynamic pattern: an initial small adoption wave is followed by a long quiescent period and then an abrupt, endogenous explosion of support. Through a complete analytical characterization of equilibria, stability, convergence, and phase-transition conditions, we identify the nonlinear mechanism that generates this incubation phenomenon and derive estimates of the latent period. Our results reveal a dynamical route to sudden social change driven purely by internal interactions rather than external shocks.

Recent advances in 3D Gaussian Splatting (3DGS)-based wireless radiance field (WRF) reconstruction provide an efficient solution for wireless channel modeling. However, existing WRF reconstruction methods rely on pre-collected observations and offline optimization, and thus struggle to provide real-time channel knowledge. To bridge this gap, we propose CORF-GS, a real-time WRF reconstruction framework that processes sequential optical and radio frequency (RF) keyframes. Specifically, CORF-GS constructs a unified Gaussian representation for optical and RF with shared geometry and modality-specific appearance, allowing high-resolution optical images to provide structural priors for WRF reconstruction. When a new keyframe arrives, CORF-GS first employs optical-guided Gaussian sampling to densify the WRF in under-represented regions. Since light and radio waves may respond differently to the same object surfaces due to wavelength mismatch, relying solely on optical guidance may neglect RF-informative areas. Therefore, CORF-GS performs coupled optical-RF optimization to jointly refine the shared Gaussians. Compared with the existing two-stage training pipelines, this prevents WRF from passively adapting to a frozen optical geometry and encourages the shared Gaussians to adapt to both optical structures and RF power distributions. Simulations show that CORF-GS achieves state-of-the-art RF spectrum synthesis quality and reduces the reconstruction time by $6.4\times$ compared with existing WRF methods.

This paper proposes a differentially private distributed cooperative control scheme for multi-agent systems (MAS). Unlike conventional approaches that actively inject artificial noise for privacy protection, this work investigates whether inherent communication noise can itself serve as a natural privacy mechanism. A physically motivated communication-noise model is developed for mobile MAS by incorporating transmitter perturbation, receiver noise, path-loss attenuation, and log-normal shadowing. The resulting effective noise variance depends on inter-agent state differences, thereby capturing the distance-dependent signal perturbation arising in practice. Based on this model, a distributed finite-horizon Linear Quadratic Regulator (LQR) mechanism is designed to achieve formation tracking while protecting agents' private control preferences. Rather than protecting the full local cost function, the proposed privacy formulation focuses on the ratio of the LQR weighting matrices, which captures the trade-off between tracking accuracy and control effort when the quadratic cost structure is publicly known. A set-theoretic sensitivity analysis shows that this weighting-ratio adjacency formulation yields less conservative privacy bounds than gradient-based protection under the considered addition/removal adjacency relation. Theoretical analysis demonstrates that, under suitable design conditions, the proposed mechanism provides bounded cumulative (\epsilon,\delta)-differential privacy guarantees for the weighting ratios over an infinite horizon without artificial noise injection. Meanwhile, the cooperative tracking error is shown to converge almost surely and in mean square to a finite random limit, with its expectation remaining bounded. Numerical examples validate the theoretical results and illustrate the resulting privacy-performance trade-off.

Cell-free networks formed by multiple low Earth orbit (LEO) satellites offer a promising architecture for ubiquitous connectivity, but their cooperative reception is challenged by link-dependent residual delays and Doppler shifts. This paper investigates joint channel estimation and data detection (JCEDD) for multi-LEO-satellite cell-free orthogonal time frequency space (OTFS) uplinks. The JCEDD problem is formulated as a structured bilinear inference problem involving link-specific sparse beam–delay–Doppler channels and a multiuser data vector. We develop a low-complexity hierarchical JCEDD receiver in which all satellites first perform local JCEDD, and their observations and local estimates are then aggregated at a central satellite for cooperative refinement. Computational complexity is reduced by restricting channel estimation to coarse-information-aided local beam–delay–Doppler regions and evaluating the required forward and adjoint operations in a matrix-free manner. Simulation results validate the channel-estimation accuracy and data-detection reliability of the proposed JCEDD receiver.

In public healthcare systems, elective surgery waiting lists are a persistent management challenge, as hospitals must balance limited operating theatre capacity with uncertain and evolving demand. Long-term planning for these lists requires more than assigning operating theatre (OT) time to medical specialities, as isolated allocation decisions may fail to stabilise waiting lists over time. Such planning must account for uncertainty in patient arrivals in the queue and surgery durations, which are often neglected in OT scheduling models. In this context, we propose a novel integrated framework for elective surgery management under uncertainty, linking strategic queue-control decisions with tactical OT scheduling. The framework combines three components: (i) a closed-loop control policy that determines the number of patients to schedule in each planning cycle; (ii) a model to account for overtime-related cancellation risks; and (iii) a mixed-integer linear programming formulation, informed by the previous components, to solve an OT Scheduling Problem. The resulting tactical schedules are therefore guided by the long-term evolution of speciality-specific queues and by cancellation-risk considerations. We evaluate the proposed framework through a case study at University Hospital Unicamp, a large Brazilian public referral hospital. The results show that integrating strategic and tactical decisions stabilises waiting lists, controls cancellation risks, and supports more transparent theatre-allocation decisions, while remaining computationally efficient for practical implementation.

Cartographic generalization is essential for generating multiscale map representations by balancing information preservation and cartographic readability. However, automated generalization remains challenging because existing approaches often treat spatial similarity evaluation, cartographic constraints, and parameter optimization as separate processes, limiting adaptive and interpretable control across scales. This study formulates cartographic generalization as a constrained multiscale similarity optimization problem and proposes a similarity-driven framework for adaptive generalization control. The framework integrates multiscale spatial similarity as an optimization objective to quantify representation consistency between original and generalized data, while incorporating cartographic constraints to regulate readability, smoothness, and geometric validity. A unified objective function is optimized to automatically identify scale-dependent parameter configurations for different generalization algorithms. Experiments using multiple line simplification algorithms, target scales, and similarity measures, including geometric, structural, and learning-based metrics, demonstrate that the proposed framework achieves an effective balance between similarity preservation and cartographic abstraction. The results further show that combining similarity optimization with cartographic constraints provides more consistent and interpretable parameter control than relying on similarity evaluation alone. This study provides a unified optimization perspective that connects similarity assessment, constraint modeling, and algorithm control, contributing to adaptive and automated cartographic generalization.

There is no general method for designing proper parameters to achieve faster convergence in distributed optimization algorithms. In this paper, we consider the distributed inexact gradient tracking (DIGing) algorithm with the objective function being the unweighted sum of squares. By representing the iteration algorithm as a dynamical linear system, we decompose it into different graph frequencies and obtain a set of decoupled subsystems, on which we can easily analyze the convergence rate. By using Routh stability criterion from control theory, we derive the explicit formula of the optimal worst-case convergence rate and the corresponding parameters. We can see that the convergence rate of DIGing is slow even for the simplest objective functions, thus acceleration is necessary for general application. The proposed method can be viewed as the first step toward optimal parameter design of DIGing algorithm in solving general objective functions.

This study introduces a unified control mechanism for integrated charging and service allocation of Mobility-on-Demand Electric Vehicles (MoD-EVs) operating under a flexible A-to-B rental model. The charging operation of MoD-EVs is influenced by stochastic customer rental requests and time-varying electricity prices. The framework captures the nonlinear battery dynamics and optimizes the charging operations against dynamic electricity prices and to meet stochastic customer demand while prioritizing battery health and cost efficiency. To address the computational challenges posed by the nonlinear battery dynamics, the framework employs a piece-wise linear model integrated into a multi-objective, chance-constrained mixed-integer linear programming (MILP) Model Predictive Control (MPC) formulation. A mixed logical switching mechanism is utilized to determine optimal charging sequences. Furthermore, a distributed approach is implemented to ensure computational scalability compared to centralized alternatives. Evaluation of this approach using a state-of-the-art commercial solver with stochastic EV rental requests under different confidence levels and time-varying electricity prices demonstrates significant benefits of the integrated mechanism design, including a reduction in charging costs and battery capacity degradation compared to the prevailing business-as-usual (BAU) approach and state of the art Laxity-based charging (LC) approach.

We develop a novel forecasting scheme to anticipate controller induced loss of control (LOC) in quadrotors and evaluate it on real LOC flight data from four different quadrotors. For this, early warning signals of LOC are derived using critical slowing down (CSD), a generic phenomenon shown to precede critical transitions across various complex ecological and biological systems. As such, our early warning indicators are generic in the sense that no system models are needed to facilitate forecasts of LOC. The approach is evaluated on real quadrotor flight data wherein LOC occurs due to unstable controller behavior arising from input-output delays. Our approach achieves a time-to-LOC forecast of up to 0.9 seconds before LOC occurs, outperforming state-of-the-art recurrent neural network quadrotor LOC forecasters in terms of detection accuracy and LOC data reliance. In particular, we leverage insights from CSD to accurately predict LOC without using data of the LOC event itself. Going further, we apply our forecasters without any re-parameterization to anticipate a different LOC scenario, quadrotor flyways, that occur on other quadrotors flying both indoors and outdoors. Despite these differences, our approach successfully detects LOC, demonstrating that it can generalize across controller architectures, quadrotors, and LOC scenarios.

Semantic integrated sensing and communication (ISAC) is envisioned as a promising paradigm for efficient and intelligent connectivity in future wireless networks. However, the open wireless channel exposes the dual-functional waveform to detection, which challenges the joint guarantee of covertness, sensing fidelity, and semantic accuracy. To address the challenge, we propose CoSMIC, a novel covertness-oriented semantic ISAC framework, where the sensing output is embedded into a dual-functional ISAC waveform through semantic modulation. Specifically, a semantic rotation coding scheme is established to map semantic latents onto the pairwise rotation and scaling of Gaussian reference sequences, which satisfies a derived closed-form covertness constraint by a differentiable budget projection. Moreover, the radar performance is analyzed to confirm an invariant matched-filter mainlobe response and a bounded output signal-to-interference-plus-noise ratio (SINR) under the semantic embedding. Subsequently, a reliability-guided rectified flow (RFlow) refiner is designed to effectively reconstruct high-fidelity semantic representations from coarse observations. Simulation results demonstrate that CoSMIC improves the semantic reconstruction quality by 18% over diffusion-based baseline schemes with substantially reduced inference latency under strict covertness constraints, which validates the applicability to practical ISAC scenarios. The source code and video demonstrations are available at https://github.com/LanceAnlan/CoSMIC-covertness-oriented-semantic-ISAC-framework.

Some text-to-speech systems ship a synthesis model and preset style vectors but withhold the reference encoder that turns a recording into a style vector, so a user cannot obtain a style for a new voice. We recover that vector without the encoder by inverting the released pipeline with gradient descent: all weights stay frozen and only the style vector is optimized, against time-pooled WavLM statistics of one recording of the target. The objective discards the time axis, so no transcript is needed. Two constraints from the release make the result a drop-in style: every row of the timbre style stays on the unit sphere where the presets lie, and the small duration style, which a time-pooled loss cannot see, is fitted to the recording's speaking rate. On SupertonicTTS, over 147 speakers and 100 held-out sentences each, ECAPA-TDNN similarity rises from 0.129 to 0.419, every recovered style starts from a preset and ends closer to its target than that preset was, and the pooled word error rate stays below that of the presets. A verifier at its equal-error point accepts 52% of the recovered voices as the target speaker, against 1% of the presets.

This is an implementation and measurement study of what it costs to run a streaming speech enhancer on a CPU. We port FastEnhancer-Medium at 48 kHz to faster-enhancer.c, a C runtime with six int8 GEMM tiers selected at initialization, leaving architecture and weights untouched. One Apple M2 core reaches 0.069 real-time factor, against 0.230 for the fp32 ONNX Runtime graph on the same machine, a 3.3x speedup. A Galaxy S23+ (Snapdragon 8 Gen 2) reaches 0.096. The speedup comes from specializing every layer of the runtime around one fixed model. Activation ranges are recomputed per frame, so no calibration set is needed; the k=3 convolutions use Winograd F(2,3); cross-stage state is fp16; the GRU and the dequantization epilogues are fused; and nothing is allocated after startup. Over 824 VoiceBank-DEMAND utterances the engine tracks fp32 to within -0.006 PESQ and -0.08 dB SNR. Speed alone does not settle deployment cost. The enhancer holds a fraction of a core for as long as the microphone is open, so its real-time factor is a duty cycle. A benchmark races through a file; an audio callback does not. Pacing to the 6.67 ms deadline costs 4.2x per frame, saves 49% of the energy, and leaves the cheapest core placement missing 96% of its deadlines. All SIMD tiers within an architecture family emit byte-identical output. The runtime is released as a dependency-free library.

As the integration of renewables accelerates the decarbonization process in the energy sector, power systems are becoming increasingly dominated by converter-interfaced generation. However, this results in the emergence of multiple oscillations, which can trigger cascading disconnections and potentially damage generators. To address these critical issues, modern power systems commonly employ classic participation factor (PF) analyses, which relate oscillatory modes (eigenvalues) to controller state variables. While these methods are effective, a critical yet previously unexplored challenge involves capturing the interaction reconfigurations observed after a perturbation occurs. Because PF analyses are limited to fixed operating points, they are structurally unable to analyze these event-dependent mode–state interactions under perturbations. Here, we establish an interaction sensitivity framework that is formulated in perturbation space, uncovering the mechanisms by which modes and states respond to perturbations. This framework provides analytic expressions and physically interpretable mechanisms that explain why specific control adjustments enhance or reduce stability, revealing causal relationships beyond conventional stability analysis. More broadly, this formulation provides a general analytical basis for understanding perturbation-driven interaction reconfigurations in high-dimensional dynamical networks described by state-space equations, and supports advanced controller design in complex systems.

As the share of distributed energy resources (DERs) increases in the distribution network, maintaining compliance with network constraints has become a challenge. In this context, dynamic operating envelopes (DOEs) have emerged as a promising solution, where the distribution network operator (DNO) computes and imposes a dynamically varying import and export limit on power exchange between prosumers and the distribution network. Existing DOE approaches often require the prosumers to report their desired power exchange to the DNO, which then computes DOE limits that typically do not exceed the reported values. However, due to uncertain renewable generation and load demand, such DOE limits can potentially result in unnecessary curtailment of generation and load during real-time operation. This work addresses this limitation by computing flexible DOEs using a flexible optimization framework that trades off optimality with flexibility. The proposed approach computes both upper and lower limits on the active and reactive power exchange between each prosumer and the grid, and tries to contain the reported values between the upper and lower limits. As long as the power exchange resides within the DOE limits, network constraints are satisfied. The proposed flexible DOE is validated on a modified Australian low-voltage distribution network. Compared to non-flexible DOE, the proposed framework demonstrates superior performance in reducing curtailment and total operational costs while consistently maintaining voltage magnitudes within desired limits.

Collective intent prediction in multi-agent systems focuses on predicting the shared objectives and future behaviours of groups of interacting agents. The problem is particularly challenging because collective intent emerges from complex interactions, evolving cooperation structures, and long-term behavioural dependencies among heterogeneous agents operating in dynamic and partially observable environments. Furthermore, functional roles adopted by agents are often latent, may change over time, and are rarely available as explicit annotations, making the learning of coordinated group behaviours significantly more difficult. To address these challenges, this paper proposes a Meta-Role Temporal Graph Network (MR-TGN) framework for collective intent prediction in multi-agent systems. The proposed framework models agents as dynamically evolving graph entities and employs temporal memory mechanisms to encode historical interactions and coordination behaviours. To capture higher-level behavioural knowledge, MR-TGN introduces a memory-enhanced meta-role learning mechanism that derives latent role representations from agent-centric behavioral representations without requiring explicit role labels. An evaluation methodology for early collective intent prediction is proposed to assess prediction accuracy and timeliness, enabling realistic evaluation of the model's ability to anticipate collective objectives during the early stages of mission execution. Experimental results on representative multi-agent scenarios demonstrate that the proposed framework consistently outperforms competitive baselines and achieves effective early prediction of collective intents in dynamic and adversarial environments.

Hyperspectral imaging (HSI) is useful for material discrimination, but operational mine screening also depends on how many false alarms must be inspected before targets are found. This paper studies PFM-1 landmine detection in unmanned aerial vehicle (UAV) visible and near-infrared (VNIR) HSI using spectral angle mapper (SAM), matched filter (MF), adaptive coherence estimator (ACE), and constrained energy minimization (CEM). We compare a ground-measured SVC signature, a fully informed in-scene core-pixel signature, and a simulated human-in-the-loop signature bootstrap. Besides receiver operating characteristic area under the curve and average precision, we report target-discovery curves and spatial candidate-review counts. Full-review bootstrapping reaches the fully informed in-scene signature case after all seven target regions are verified, but the required inspection effort varies strongly: ACE confirms all regions in two rounds and nine candidate inspections, whereas the SAM variants need thousands of candidate reviews for their final target locations. Code is available at https://github.com/SagarLekhak/IEEE_WHISPERS_2026_UAV_HSI_PFM1.

Accurate diagnosis of pediatric asthma in emergency departments remains challenging due to overlapping respiratory symptoms, time constraints, and the limited feasibility of pulmonary function testing in young children. This study investigates the feasibility of pediatric asthma detection in the emergency department using breath sound recordings and machine learning. Thirty-second breath sounds were collected from six chest locations in 31 pediatric patients (10 asthmatic, 21 non-asthmatic) and analyzed using pretrained self-supervised speech representation models (HuBERT, WavLM, and Wav2Vec 2.0) for feature extraction, with patient age and sex incorporated into the feature representations. Conventional machine learning classifiers were trained and evaluated using patient-level stratified group 5-fold cross-validation and leave-one-patient-out validation to ensure the generalizability of the findings. Among the evaluated approaches, Wav2Vec 2.0 combined with histogram-based gradient boosting achieved the strongest and most consistent performance, yielding an accuracy of 0.84, sensitivity of 0.80, specificity of 0.86, and F1-score of 0.76 under both evaluation protocols. The consistency of performance across validation strategies suggests promising generalization to unseen patients. These findings suggest that pretrained self-supervised audio representations offer a promising, non-invasive approach for pediatric asthma detection in real-world emergency department settings, where objective respiratory assessment is often limited.

Amyotrophic lateral sclerosis (ALS) progressively impairs speech motor control, making acoustic analysis a promising biomarker for severity and progression estimation. We propose a subject-level graph framework that aggregates multiple phonation recordings into a unique k-nearest-neighbor graph built from pretrained SSL embeddings of 2s segments. We compare four SSL front-ends (wav2vec 2.0, HuBERT, data2vec-audio, and UniSpeech-SAT) and five graph neural networks (GCN, residual GCN, GAT, GraphSAGE, and GIN) on the SAND dataset tasks (339 participants: 205 ALS, 134 control): 5-class dysarthria severity and 4-class ALSFRS-R progression prediction. On the official validation set, the best configuration (HuBERT+GIN) achieves macro-F$_1$ of 0.73 for Task 1 and 0.69 for Task 2, outperforming SAND validation baselines (0.61 and 0.58). These results highlight the potential of combining GNNs with pretrained cross-lingual speech representations for low-resource ALS detection and progression monitoring.

The early satellites of the third-generation BeiDou Navigation Satellite System (BDS-3) are approaching the end of their design lifetime, making progressive constellation replacement an inevitable engineering task. Meanwhile, laser inter-satellite links (LISLs) provide high-precision time transfer, sub-millimeter-level ranging, and high-rate data forwarding capabilities, offering a promising upgrade path for future BeiDou satellites. This paper investigates the replacement-sequence planning problem for progressively replacing legacy microwave satellites with laser-enabled satellites from a networking perspective. To address this problem, three main contributions are made: (i) BeiDou progressive replacement is formulated as a network-aware launch sequence planning problem over evolving laser–microwave hybrid constellation states; (ii) an integer linear programming (ILP) model is developed to evaluate the networking gain of each candidate launch action under practical engineering constraints; and (iii) a priority-aware search heuristic is introduced to reduce the action evaluation space for efficient round-wise decision making. Simulations based on the actual BDS-3 constellation demonstrate that the proposed replacement sequence achieves a more favorable hybrid-network evolution than the historical BDS-3 launch order, including earlier formation of a ground-accessible laser-enhanced network structure, better utilization of laser-link resources, fewer residual non-anchor satellites, and lower microwave-layer waiting delay. The proposed framework can serve as a network-aware decision-support tool for future BeiDou evolution, and the resulting replacement sequence can provide direct engineering guidance for BeiDou replacement planning.

Stereo depth estimation for driving, robotics and augmented reality must run at high resolution under tight latency budgets, yet in transformer-based matchers the global self-attention that aggregates scene context grows quadratically with the number of pixels and comes to dominate runtime. We show that the joint self-attention stage of a stereo transformer, whose role is to spread context across both views, can be replaced by a data-independent Walsh-Hadamard token mixer that mixes tokens globally in the transform domain at log-linear cost, while the data-dependent cross-attention that performs left-right correspondence is retained. On synthetic driving data the mixer matches the attention baseline in end-point error while reducing model compute by a factor of 2.46 and single-image inference latency by a factor of 2.65. A complexity analysis shows the benefit is governed by the ratio of sequence length to channel width, which explains why high-resolution stereo matching is a particularly favorable setting and why classification transformers are not; we confirm this token-to-channel scaling on non-stereo long-sequence benchmarks. Furthermore, we introduce a hybrid log-disparity loss function designed to up-weight small-disparity pixels corresponding to long-range objects. This approach reduces the error on distant objects without incurring any additional computational overhead.

This paper introduces a discrete-time event-triggered extremum seeking framework for real-time optimization of multivariable nonlinear systems. In contrast to conventional discrete-time extremum seeking implementations that rely on periodic input updates, the proposed scheme updates the control action only when a state-dependent triggering condition is met, enabling aperiodic execution and substantial reduction of actuation and communication effort. The resulting closed-loop architecture reconciles two structurally different paradigms: the periodic excitation required for gradient recovery in extremum seeking and the aperiodic philosophy of event-triggered control. By combining discrete-time averaging arguments with Lyapunov-based analysis, we prove practical convergence of the system trajectories to a neighborhood of the unknown extremum and exponential stability of the corresponding average dynamics despite the loss of uniform sampling. The results show that appropriately designed triggering rules preserve the essential optimization mechanisms of classical extremum seeking while drastically reducing the number of input updates. Numerical simulations demonstrate that comparable optimization performance can be achieved with significantly fewer actuation events, highlighting the suitability of the method for resource-aware digital and networked control implementations.

Although a large amount of work has been devoted to detailed electromagnetic transient simulation in analyzing transient synchronization stability (TSS) of hybrid systems containing renewable energy equipment and synchronous generator (SG), the underlying mechanism considering complete low-voltage ride-through (LVRT) processes of renewable energy equipment remains to be studied. Taking the SG and doubly fed induction generator (SG-DFIG) parallel system as an objective, this work divides its transient processes into four different stages: pre-fault (stage 1), during-fault (stage 2), early post-fault (stage 3), and late post-fault (stage 4), based on the LVRT of the DFIG, and establishes a transient model to describe the complete 4-stage LVRT processes. By studying the condition for entering the LVRT, it is found that vast majority of faults can cause the DFIG to enter the LVRT and make the parallel system exhibit the sequential switching characteristics. Similar to the SG-SG parallel system, which can be reduced to a single SG and described by a second-order swing equation, a unified generalized swing equation (GSE) under different parameters for different stages 1, 2, and 3 is derived. Therefore, the transient stability of the parallel system can be dealt with easily, and further, an improved equal area criterion method considering two additional effects of frequency jump and nonlinear damping is proposed to evaluate the TSS. These GSE-based theoretical analysis results are all supported by extensive hardware-in-the-loop experiments and simulations. Obviously, this work provides a clearer physical picture for the TSS mechanism of the hybrid system considering complete LVRT processes, and makes a closer connection with the transient stability of traditional power systems dominated by SG.

Distributed optimization becomes particularly challenging when nonconvex objectives are combined with nonlinear equality constraints: feasibility is network coupled, while most existing exponentially convergent methods rely on convexity or affine constraints. This paper introduces a feedback-linearization and singular-perturbation framework for two representative problem classes involving, respectively, distributed local nonlinear equality constraints and nonlinear aggregate equality constraints. The framework separates the regulation of consensus and feasibility residuals from optimization along the feasible manifold. Specifically, an ideal feedback-linearized dynamics is constructed whose output behavior can be explicitly assigned, while its zero-output dynamics coincides with the projected gradient flow of the aggregate objective on the feasible manifold. The ideal feedback-linearizing input, however, is determined by a state-dependent, network-coupled algebraic equation and is therefore not directly implementable in a distributed manner. To overcome this obstruction, we replace the nonlocal algebraic solution with a fast residual-tracking dynamics, yielding a singular-perturbation realization that uses only local and neighboring information. Under a local quadratic-growth condition imposed only on the feasible manifold, we establish local exponential convergence of both the ideal and distributed dynamics. For sufficiently strong time-scale separation, the convergence rate of the distributed realization can be chosen arbitrarily close to that of the ideal dynamics. Explicit Euler discretizations are also proved to preserve local exponential convergence. Under stronger manifold regularity conditions, the region of attraction extends to a tubular neighborhood of the feasible manifold and, for affine local equality constraints, to the whole admissible state space.

Language models have demonstrated remarkable success in solving a wide range of tasks. However, answering complex scientific questions about the power flow often requires solving the distribution optimal power flow (D-OPF) problem. These questions call for numerical solvers and simulators, as linguistic reasoning from parametric knowledge often gives incorrect answers. In this work, we present VeraGrid-Agent, a tool-augmented LLM that autonomously writes the simulator input, executes the open-source VeraGrid solver, and reads the solver output before answering. To evaluate performance, we introduce VeraGrid-MCQ-150, a set of deterministic, expert template driven, $150$ multiple-choice questions. We evaluate the performance under two regimes: (i) no-tool reasoning and (ii) agent (LLM with simulator access). Without tools, every model performs with an accuracy of $42.7\%$–$49.3\%$. However, with VeraGrid-Agent, accuracy increases to $97.3\%$–$100.0\%$. We also do a failure-mode analysis to show that the few remaining errors arise from wrong interpretations during multi-step reasoning, rather than any failure in the simulators execution.

Wireless multiport sensing aims to remotely retrieve the scattering matrix of a multiport device under test (DUT) connected to not-directly-accessible (NDA) antennas, based on scattering parameters measured with remotely located accessible antennas that couple over the air (OTA) to the NDA antennas. A central bottleneck is that the required number of accessible antennas grows with the number of unknowns in the DUT's scattering matrix. Here, we address this bottleneck by using a reconfigurable intelligent surface (RIS) to generate measurement diversity during the DUT characterization. We interpret the setup as measuring the DUT via an RIS-programmable OTA fixture. We first characterize the RIS-programmable OTA fixture using a specific known tunable load network to terminate the NDA antennas. Then, we reconstruct the DUT's scattering matrix by multiplexed de-embedding of the RIS-programmable OTA fixture, using measurements of the DUT acquired across multiple RIS configurations. Based on our system model formulated in terms of multiport-network theory, we quantify and maximize the diversity of our measurement sequence by optimizing the deployed ensemble of RIS configurations. We validate our approach experimentally at 2.45 GHz in a rich-scattering radio environment. We systematically examine the influence of the number of accessible antennas, the number of RIS configurations, and the optimization of the RIS configurations. For a SISO link (two accessible antennas), optimized RIS configurations reduce the median reconstruction mean-squared error for 100 DUT measurements by 45% from $1.57{\times}10^{-3}$ to $8.59{\times}10^{-4}$. These results demonstrate that RIS diversity can compensate for a limited number of accessible antennas, paving the way toward low-cost wireless multiport sensing for industrial, biomedical, and smart-environment RFID systems.

Recent advances in speech language models leverage discrete speech representations from pretrained codecs to enable scalable training and generation. However, existing codecs are primarily optimized for compression without accounting for the autoregressive nature of language model training, resulting in suboptimal performance when modeling compressed speech tokens. In this work, we revisit speech discretization from a generative modeling perspective and propose a novel framework that explicitly aligns speech tokenization with autoregressive training. Our approach introduces autoregressive-compatible constraints during codec training, encouraging token sequences that exhibit temporal consistency and predictability. In addition, we propose a heterogeneous downsampling strategy for different layers of speech tokens, distinguishing semantic from acoustic layers, to improve the alignment between semantic tokens and corresponding textual content. Extensive experiments across multiple benchmarks demonstrate that our method bridges the gap between speech compression and generative modeling, enabling more effective continued pretraining of existing language models on speech data. The approach consistently improves performance across multiple codecs, validating its generality and applicability to diverse speech modeling scenarios.

Speech emotion recognition (SER) is a crucial component of human-computer interaction, attracting extensive attention from both industry and academia. However, existing SER systems typically assume alignment between vocal tone and lexical semantics, overlooking the real-world scenarios that involve tone-word conflict-where the emotion conveyed by speech contradicts the literal meaning of the words. To bridge this gap, we introduce TWIN-SER (Tone-Word Incongruent SER), a benchmark for systematic evaluation under acoustic-semantic incongruence, and show that state-of-the-art models degrade severely under such incongruence. To address this, we propose DAS (Disentangled Acoustic-Semantic fusion), a framework that mitigates tone-word conflict by explicitly disentangling acoustic and semantic pathways, selecting informative high-energy embeddings, and adaptively fusing them via a lightweight query-based attention mechanism. Specifically, DAS comprises three crucial modules: i) a heterogeneous feature extraction module that separately captures complementary acoustic and semantic representations from raw input; ii) a high-energy embedding selection module that identifies and retains the most discriminative embeddings; and iii) a Q-Former combination module that bridges the two pathways through cross-attention, enabling robust emotion prediction under incongruent conditions. Extensive experiments demonstrate that DAS consistently outperforms existing methods in tone-word conflict scenarios, as well as in standard in-domain and zero-shot settings. Our code and datasets are available at https://github.com/24DavidHuang/FAS

Inertial navigation systems are specialized navigation apparatuses that equip almost all autonomous underwater and surface vehicles. They require precise initial alignment, i.e., determination of their initial attitude, which is typically achieved: (a) in quasi-static conditions (whenever possible); and (b) in two stages: Coarse Alignment (CA), using methods like TRI-axis Attitude Determination (TRIAD), and Fine Alignment (FA), via Zero Velocity Update (ZVU)-based Extended Kalman Filtering (EKF). However, conventional methods suffer from slow convergence and limited bias estimability. In response, this paper introduces: (a) an optimized version of a recently proposed CA method, namely, TRIAD with Coarse Bias Estimation (TRIAD-CBE); and (b) a novel FA EKF observation model that incorporates Non-Orthonormality (NON) error constraints derived from TRIAD, directly linking these errors to the inertial sensor biases. As validated through extensive Monte Carlo (MC) simulations, as well as real-world experiments using two Inertial Measurement Units (IMUs) of different grades, our approaches substantially accelerate the convergence of misalignment and bias estimates (from minutes to seconds), while maintaining accuracy/precision comparable to traditional techniques.

Wolff-Parkinson-White (WPW) syndrome is a congenital cardiac pre-excitation, clinically important and often missed on the resting 12-lead ECG. Detection is hard: the signature is subtle and the condition rare. We pool two public 12-lead corpora, PTB-XL and Chapman-Shaoxing-Ningbo: 66,951 recordings, 142 of them WPW, a prevalence of 0.21% (about 471:1). Under one pre-specified, leakage-controlled protocol, with a held-out fold contacted exactly once, we compare seven representations of the signal, holding the split and the evaluation fixed. Within these corpora and under a modest compute budget, added diversity and capacity do not raise the ceiling: the most orthogonal detector significantly hurts, a feature-union model matches a two-member vote, a convolutional network reaches the wavelet detector without exceeding it, and self-supervised pretraining fails a pre-specified gate. A leak-free learning curve, re-selecting features at every size, still rises at the full 115 positives for the strongest deployed detector (paired 90-to-100% difference +0.027, 95% CI [0.019, 0.033]), so it is not shown to have saturated. An error analysis tested against independent evidence finds that the missed cases have a narrower QRS, confirmed by an on-machine measurement outside our pipeline after we show the sign of this effect depends on which delineator measures it; that uncertain labels show no enrichment among the misses; and that some apparent false positives are recordings the corpus itself codes as pre-excited, placing part of the label problem in the negative class. We measure the optimism of non-nested selection at 0.11 to 0.13 average precision. The deployed output is a percentile rank in a frozen reference distribution, not a probability. On the held-out fold, on 14 positives, it reaches an average precision of 0.595 and an ROC area of 0.950. It is a screening pre-filter, not a diagnostic tool.