Papers

The Fisher information matrix (FIM) determines the accuracy attainable in an estimation problem. For a parametric process with memory, it has a closed form only for narrow classes of models, and it cannot be computed when the model is available only as a simulator. This paper estimates the Fisher information rate of such a process, the per-observation limit of its FIM, from simulator output alone and without knowledge of the memory length. The construction uses the identity between the FIM and the local curvature of the Kullback–Leibler (KL) divergence. Directional divergence rates are estimated by context-tree weighting (CTW), and the matrix is recovered from them by least squares. We establish strong consistency in an iterated limit and a finite-sample bound, uniform over every working depth at least as large as the true memory length, which gives a mean error of order $n^{-1/3}$. The analysis also delivers a finite-sample rate for CTW-based estimation of the KL divergence rate. Experiments confirm the predicted exponents, show that the accuracy does not degrade as the working depth grows, and use the estimate to plan a sampling budget that attains a prescribed precision. The estimator stays accurate on a hidden-state source that has no finite memory length.

The physical model of multi-mode pinching-antenna systems (PASS) is proposed based on the coupled-mode theory. Within the considered model, multiple guided modes are simultaneously excited in a single waveguide and exploited as independent signal-bearing channels, thus providing additional modal degrees of freedom for signal transmission. Under the local electromagnetic perturbations introduced by PAs, the undesired guided-modes coupling is explicitly investigated, and the resulting inter-mode interference (IMI) issue is revealed. The sufficient hardware-design condition for achieving the IMI-free regime is further established, for which the practical feasibility is validated through full-wave electromagnetic simulations. Then, a tractable signal model is derived for multi-mode PASS operating over the IMI-free regime. To demonstrate the benefits of the proposed multi-mode PASS model, the integrated sensing and communications (ISAC) is studied as a representative application scenario, where the base station simultaneously communicates with a communication user and senses a target by using two guided modes. A joint baseband and pinching beamforming optimization problem is formulated for the minimization of the Cramer-Rao bound for target localization, subject to the minimum communication rate requirement, under both continuous and discrete PAs activation cases. An alternating optimization-based algorithm is developed to address the formulated non-convex problem. For the baseband beamforming, a penalty-based successive convex approximation method is invoked. For the pinching beamforming, a particle swarm optimization algorithm and a two-sided matching algorithm are proposed for the continuous and discrete PAs activation cases, respectively. Numerical results obtained in the ISAC application scenario demonstrate the superiority of the proposed multi-mode PASS model over the single-mode PASS.

Neyshekar is presented as an open Persian read-speech corpus designed for coverage of both formal and informal language, named entities, and longer utterances. In version 6, 62,279 validated recordings totalling 99.02 hours are provided from 190 contributors, with 34,541 distinct recorded prompts. The prompt pool was assembled from human-written material, contextualised homographs, and reviewed language-model-generated text. Text entries were normalised with the shekar library, which supports both formal and informal Persian, and every submitted recording was reviewed against a common validation rubric. About 24% of released clips are classified as informal by an automatic classifier; these register labels are not human-validated. Item-level rater labels are provided for reproducible agreement estimation, opaque per-clip contributor identifiers make the speaker-disjoint partitioning auditable and support contributor-clustered uncertainty estimates, and a text-disjoint test subset is included for evaluation beyond previously seen prompts. Per-contributor recording load and reference-free signal quality are characterised for every released clip. Corpus characteristics are compared with Persian Common Voice under shared processing. Utility is assessed through two ASR architectures, three optimisation seeds, WER and CER, and independent evaluation on the public PSRB sample. Against duration-matched Common Voice training at approximately 32 hours, in-domain WER is reduced by 9.5 points for Whisper and 11.6 points for XLS-R, and by approximately eight points for both architectures on the independent PSRB sample. Transfer and mixture benefits are not consistently observed across architectures and training budgets. The corpus is released under CC0; code and data are made available through the project repository at https://github.com/amirivojdan/neyshekar.

3GPP has progressively expanded AI/ML lifecycle management in the radio access network, from one-sided model control to Release 20 support for two-sided CSI-feedback model pairing. Yet a basic control question remains: when monitoring detects degradation, how quickly must a corrective action take effect? To expose this dependency, we stress-test three activation and rollback strategies in a surrogate regime-shift environment using 150 independently trained PPO candidate policies, each evaluated over 20 matched noise realizations. We add 0-40 control-step delay only to corrective lifecycle commands. With no added delay, stability-gated blending reduces mean post-shift cumulative SLA deficit from 47.02 to 7.57 violation-steps relative to hard cutover; at 40 steps, the deficit rises to 44.38, only 5.6% below the hard-cutover baseline. KPI-threshold rollback loses its advantage within only a few control intervals, while blending degrades more gradually. These results do not set a physical 6G latency bound. They show why timing requirements matter for corrective actions: lifecycle performance depends on when the command takes effect. Motivated by Layer-1/Layer-2 Triggered Mobility, we examine a standards split in which Layer 3 retains lifecycle configuration while a compact Layer 2 trigger is considered only for the latency-critical subset, together with pair-consistency, local-fallback, and security/freshness requirements.

Suppressive jamming is a severe challenge to digital receivers in wireless communications, since high-power jamming may cause analog-to-digital converter (ADC) overload or automatic gain control (AGC)-limited quantization blocking of weak desired signals. Analog beamforming in the spatial domain can be employed to combat suppressive jamming signals before they reach the ADC. In this paper, we introduce a comprehensive anti-jamming scheme considering both direction-of-arrival (DoA) acquisition and suppressive jamming elimination based on a Dynamic Agile Reconfigurable Intelligent Surface Antenna Array (DARISAA). DARISAA is a novel type of reconfigurable antenna array composed of many metamaterial elements, which can dynamically adapt its phase responses, enabling suppressive jamming cancellation at the antenna-end. Accurate DoA estimation schemes for jamming and desired signals are proposed based on a subspace approach by utilizing the dynamic agility of the DARISAA. By leveraging the spatial-domain preprocessing capability of DARISAA and the digital beamforming of the multi-channel receiver, an analog (antenna)-digital hybrid anti-jamming scheme is developed to effectively suppress high-power jamming and significantly improve the signal-to-interference-plus-noise ratio (SINR) even with low-resolution ADCs. Simulation results demonstrate that the proposed comprehensive anti-jamming scheme achieves significant SINR enhancement, highlighting the advantages of DARISAA-enabled spatial front-end processing for jamming suppression.

Geophysical data collected from active field sites are often contaminated by complex and heterogeneous noise, obscuring weak seismic events, and complicating automated interpretation. Although deep learning offers promising solutions for seismic processing, its performance is highly sensitive to the nature of training noise, especially under out-of-distribution (OOD) conditions. This study investigates the influence of noise parameters, such as type, scale, and complexity on the performance, generalization and robustness of neural networks in two geophysical tasks: first break picking and denoising. We simulate seismic-while-drilling data and apply controlled input source noise augmentation using stochastic generators to vary the noise characteristics. Different neural networks are trained on fixed noise types and scales, then evaluated across both seen and unseen noise scenarios. We incrementally increase the complexity of the noise by introducing compound noise mixtures and assess the performance of the model under increasingly challenging OOD conditions. This yields a robustness matrix that captures the generalizability of each model relative to its training configuration. Results indicate that larger noise scales boost generalization, and that effective alignment between noise type, task complexity, and architecture is key for maximizing generalization gains. In addition, training with compound noises mitigate weaknesses associated with single-noise training, acting as an additional implicit regularizer to improving robustness. These findings highlight key factors influencing model resilience in noisy geophysical environments and offer guidance for developing deep learning models that generalize effectively across diverse and unpredictable noise conditions.

Uncrewed Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) is a key technology for future 6G networks, providing wide coverage and flexible computing services. However, the limited resources of UAVs and the dynamic changes in the network structure make it difficult to maintain high efficiency. Existing methods often ignore the semantic information of tasks and the complex relationships among UAVs and mobile terminals, resulting in poor coordination. This paper proposes a joint optimization framework for task offloading, task migration, and trajectory control in semantic communication-enabled multi-UAV edge computing systems, aiming to maximize semantic similarity while minimizing task latency and system energy consumption. To tackle the resultant problem, we develop a Heterogeneous Graph Attention Network-based Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (HAN-MATD3) algorithm. Specifically, we use a heterogeneous graph to model the network topology and apply HAN to extract important semantic features. These features are then integrated into the MATD3 framework to support joint optimization.

Accurate 3D localization of unmanned aerial vehicles (UAVs) remains challenging for existing multimodal approaches due to sparse LiDAR geometry, modality-imbalanced fusion, and redundant feature transmission over constrained edge-to-server links. To address these limitations, we propose PRI-Net, an efficient and lightweight multimodal fusion framework for UAV localization that integrates point cloud splatting, residual attention fusion, and an information bottleneck. Specifically, a 3D point cloud splatting (3DPCS) strategy is introduced to transform sparse LiDAR observations into geometrically consistent dense depth maps. A residual attention fusion (RAF) module is then designed to alleviate modal bias by using an image branch for coarse estimation and a gated fusion branch for refinement. In addition, a multimodal information bottleneck (MIB) module compacts features by filtering task-irrelevant redundancy. Experiments show that PRI-Net achieves high localization accuracy with lightweight architectures, while reducing feature dimensionality and improving edge-to-server UAV sensing efficiency and robustness.

Automating clinical documentation from long-form doctor-patient conversations remains challenging for modern audio-language models. While cascaded ASR systems perform well, end-to-end (E2E) models often struggle with information loss and hallucinations on extended audio. For the BeTraC 2026 challenge, the ASLP team presents a fully E2E multimodal system that generates structured SOAP notes directly from audio, bypassing intermediate transcripts. We constructed a 1.41-million-sample multi-task corpus and applied a multi-stage pipeline: domain pre-training, supervised fine-tuning, and reward optimization. Evaluating the architecture under both Lightweight (3B) and Heavyweight (30B) constraints reveals that each training stage progressively enhances performance. Furthermore, scaling to 30B parameters substantially boosts concept extraction and summarization quality. Ultimately, our E2E systems consistently outperform representative cascaded ASR+LLM baselines, proving the efficacy of direct multimodal optimization for clinical documentation.

Although flexible resource aggregators (FRAs), represented by electric vehicle aggregators (EVAs) and load aggregators (LAs), possess inherent flexibility to mitigate three-phase unbalance, the key challenge lies in how to effectively incentivize them to actively participate in unbalance mitigation. To address this, this paper proposes a generalized Nash bargaining (GNB)-based chance-constrained coordinated operation model for the distribution system operator (DSO) and FRAs. In this framework, the DSO and FRAs cooperate to mitigate three-phase unbalance, where FRAs provide flexibility to reduce the DSO's unbalance mitigation costs, and the DSO offers economic incentives to FRAs in return. Bargaining power is tailored according to each participant's contribution to unbalance mitigation to ensure fair profit allocation. Moreover, a scenario-based chance-constrained formulation is integrated to handle photovoltaic (PV) output uncertainties. To solve the model, a distributed proximal decomposition algorithm (PDA) is introduced for the independent model, while the coordinated model is decomposed into a social welfare maximization subproblem and a payment bargaining subproblem, with an improved bilinear Benders decomposition algorithm developed to solve the former. Numerical results validate the effectiveness of the proposed method in mitigating unbalance, ensuring fair profit distribution, and enhancing robustness against uncertainties.

In an integrated sensing and communications (ISAC) system, targets may intercept information. We address this specific security issue in a full-duplex ISAC system with malicious eavesdroppers aiming to intercept uplink (UL) and downlink (DL) communication exchanges between the dual-functional radar and communication base station and legitimate communication users. We formulate an optimization framework to maximize the sum secrecy rate for both DL and UL, considering power budget constraints for sensing and communications. The optimization problem is non-convex, so we introduce an iterative joint Taylor-block cyclic coordinate descent (IJTB) method to approximate it as a convex problem. The IJTB method alternates between sub-problems: one for UL beamformers and another for UL power allocation, artificial noise covariance, and DL beamforming, using Taylor approximations to simplify optimization. Simulations demonstrate the effectiveness of our approach compared to standard benchmarks.

When reinforcement learning (RL) is used for post-training automatic speech recognition (ASR), the reward almost always lives in the text space: it compares a hypothesis with the reference and never checks whether the hypothesis is supported by the audio. On highly regular speech this licenses a shortcut - guessing from a strong language prior rather than listening. Once the acoustics degrade, the shortcut runs unchecked and emits fluent but ungrounded words, i.e., insertion errors. We propose an acoustic-fidelity reward: a GRPO reward augmented with a separately pretrained, permanently frozen, non-autoregressive character-level wav2vec2-CTC acoustic judge, used strictly at training and absent at inference, where a single model decodes greedily. Trained on LibriSpeech and evaluated across a six-tier difficulty gradient including real AMI meeting speech (33,282 utterance-condition instances), the method reduces insertion errors by 28.3% on close-talking AMI-IHM and 22.3% on far-field AMI-SDM, while lowering WER on AMI-SDM from 35.89% to 34.71% and showing no detectable WER difference on the other five tiers, against a schedule-matched WER-GRPO baseline. The insertion reduction holds under a meeting-level clustered bootstrap. Four prespecified analyses support content-conditioned insertion calibration: output collapses 85-90% on unintelligible audio that preserves energy and voice activity; the gain is not recovered by the evaluated 32-best CTC rescoring configuration, yet RL internalizes it into a single greedy decoding run; and policy-only confidence yields lower insertion-AURC in all four evaluated settings. We frame this as a mechanism paper, demonstrated in one instantiation: a 7B speech LLM with a 0.3B CTC judge.

Observed radiation patterns often serve as a primary evidence of antenna's behavior, but translating them into meaningful interpretations is a nontrivial and expertise intensive task. This demand necessitates automated pattern interpretation, a diagnosis problem encountered in applications encompassing Radio Frequency (RF) surveillance, non cooperative emitter characterization and Over The Air (OTA) testing. This work addresses the incorporation of Contrastive Language Image Pre training (CLIP) and other recent vision language models to perform such a diagnosis. These models analyze the multimodal data and extract discriminative features directly from radiation pattern images, to train Machine Learning (ML) approaches for meaningful inferences on geometrical and performance parameters of the antenna. Performance of three such vision language models is demonstrated using RadPat 50K, a synthetic dataset of radiation pattern images generated for uniform linear arrays (ULAs). The radiation pattern images are processed by the vision language models to obtain discriminative features. The CLIP extracted features are processed by either ML classification models to infer antenna parameters number of elements, element spacing, weighting scheme, presence of grating lobe and steering angle, or ML regression models to infer parameters beam width, directivity and main lobe direction. ML models achieve accuracies exceeding 70 percent, highlighting the potential of multimodal Artificial Intelligence (AI) towards intuitive antenna analysis.

A new robustness metric $\nu$ was recently proposed as a substitute for the structured singular value $\mu$ to better capture robustness to sparse diagonal uncertainty, but its computation has remained an open problem. In this paper, we show that computing $\nu$ is equivalent to maximizing the spectral radius of a nonnegative matrix product. This equivalence allows us to transfer existing results on spectral-radius maximization to $\nu$, including a refined upper bound and conditions under which the bounds coincide. We then provide reformulations and structural results that enable an algorithm to solve the nonconvex optimization problem for nontrivial problems using global solvers. This also enables identification of the most fragile parts of the system. Our results do not yet provide a scalable solution for computing $\nu$, but they are an important step toward computability and interpretability.

Speech Large Language Models (Speech LLMs) have shown strong performance across diverse tasks, yet their utility for pathological speech analysis remains underexplored. In this work, we investigate the effectiveness of internal representations from encoder and decoder components of Speech LLMs for Parkinson's Disease (PD) detection across multilingual and cross-lingual settings. Our findings reveal that encoder representations consistently outperform their decoder counterparts in most models and settings and that pathological cues may be progressively attenuated as audio representations are projected into the language model space. We further show that generative outputs are less reliable for clinical tasks compared to internal representations. To leverage information spread across multiple layers, we propose a Squeeze-and-Excitation (SE)-based dynamic layer aggregation framework, which surpasses best-layer selection in multiple experiments, suggesting that PD-relevant acoustic cues are distributed across transformer layers rather than concentrated in one.

A differentiable digital signal processing (DDSP) autoencoder reconstructs a monophonic harmonic sound through three types of synthesis parameters: fundamental frequency, loudness, and timbre features. To handle mixtures of harmonic sounds within the DDSP approach, we have previously proposed a DDSP mixture model (DDSPMM). It represents a mixture as the sum of source signals synthesized by the decoders of pretrained DDSP autoencoders. Although DDSPMM enables direct estimation of synthesis parameters of each source from mixtures, it does not explicitly model temporal variations in the synthesis parameters and can produce excessive temporal fluctuations. In this paper, we propose a method for estimating synthesis parameters with temporally plausible trajectories by incorporating a denoising diffusion probabilistic model (DDPM) into the DDSPMM-based estimation. The DDPM is trained as a generative model of synthesis parameters. During estimation, the proposed method guides the DDPM reverse diffusion process with the reconstruction error between the observed mixture and the mixture synthesized by DDSPMM from the current estimates. Experiments on woodwind and string instrument ensembles showed that the DDPM-based regularization improves synthesis parameter estimation by imposing temporal plausibility on the estimated trajectories.

We present a learning-based approach to kinodynamic online motion planning for an Unmanned Aerial Vehicle (UAV) operating at a fixed altitude in unknown dynamic environments, where real-time avoidance of both static and dynamic obstacles must be achieved under conditions of extreme partial observability. The UAV is controlled with a single degree of freedom (yaw only), resulting in constrained, nonholonomic motion similar to fixed-wing platforms. The proposed framework integrates a behavior grid map representation with Deep Reinforcement Learning (DRL), using Proximal Policy Optimization (PPO) for stable policy learning in continuous control. The key idea is the co-design of a state representation and control policy that enables reliable navigation using only three low-cost directional range sensors, without reliance on dense sensing modalities such as LiDAR or vision-based systems. The behavior grid map dynamically aggregates sparse measurements into a structured local representation that supports real-time decision-making for obstacle avoidance and target reaching. Extensive simulations across environments of varying sizes and obstacle densities demonstrate that the proposed standard and enhanced methods achieve higher success rates than PPO variants and Model Predictive Control (MPC) (94% vs. 79–90% in small-scale high-congestion scenarios, and 83% vs. 62–71% in large-scale high-congestion scenarios), while maintaining real-time performance. Real-world experiments across four scenarios further confirm practical feasibility, with consistent target-reaching behaviour and no collisions under the tested conditions.

Person re-identification (ReID) is essential for multi-camera surveillance and tracking, yet remains difficult due to viewpoint and illumination changes, occlusion, background clutter, and low resolution imagery. We propose a generative AI integrated multimodal ReID framework designed explicitly for robustness under missing cues and low latency deployment. The key idea is a cost aware early-exit cascade that prioritizes inexpensive, high confidence evidence and only triggers expensive modalities for ambiguous cases. Our system integrates (i) global visual embeddings from segmented person regions, (ii) automatically generated fine grained semantic attribute descriptions generated by vision-language models (VLMs), and (iii) optional facial embeddings when face observations are reliable. To optimize the balance between accuracy and latency, we use a cost aware early-exit cascade instead of fusing all modalities. Specifically, we first inspect the top-k retrieval results to determine whether the query is unambiguous. If the best match is clearly separated from the remaining candidates, we stop early and return the result to minimize latency; in ambiguous cases, we keep multiple hypotheses and invoke additional modalities (face/semantic) with adaptive reliability weighting to refine the decision. We report person re-identification performance using mAP and Rank-1 accuracy on the Market-1501 and DukeMTMC-reID benchmarks. The proposed adaptive early-exit cascade resolves 60.7% of DukeMTMC-reID queries and 68.4% of Market-1501 queries without invoking semantic reasoning, reducing computational overhead while maintaining competitive retrieval performance.

This paper addresses the angle-based formation tracking problem for a class of heterogeneous planar underactuated agents subject to disturbances. A representative example is a group of underactuated surface vessels (USVs) operating in the surge-sway-yaw plane, in which each vessel has three degrees of freedom but only two independent control inputs, which are surge force and yaw moment. The desired formation is characterized by a set of longitudinal offset points, referred to as hand points, together with prescribed angular constraints among triplets of these points. The formation tracking problem is studied on a leader-follower interaction graph, assuming the leader moves at constant velocity. Under the assumption that relative velocity measurements are available, the first control law achieves asymptotic formation tracking with internal stability guarantees. Moreover, a second algorithm that does not rely on relative velocity information is introduced, which successfully drives the followers' hand points to their desired configuration. Numerical simulations involving USVs are presented to validate the effectiveness of the proposed approaches.

Soft robots are commonly sought for safety-critical interactions in delicate environments, where accurate position and orientation control is imperative. Model predictive control (MPC) offers a solution, but it requires a model of the robot's infinite-dimensional nonlinear dynamics that is at once accurate and computationally cheap. Recent theory on adiabatic spectral submanifolds (aSSMs) and their applications to soft robots provide data-driven model-reduction methods to construct such models. Here, we extend these methods to identify aSSMs from enlarged observable datasets and upgrade the currently available aSSM-MPC schemes. Evaluated on a high-fidelity finite-element simulation of a pressure-actuated soft arm, our controller reduces position and orientation tracking error by more than 60% compared to existing data-driven baselines.

Dynamic trajectory prediction has become an important paradigm for data-driven transient stability analysis (TSA), yet most existing predictors remain system-specific and require substantial retraining when network configurations, generation mixes, or state-variable sets change. Uni-TSA introduced a general-purpose TSA framework that combines channel-independent modeling with a pretrained large language model (LLM) predictor. Nevertheless, its application to heterogeneous systems is limited by ambiguity in short observations, a mismatch between numerical trajectories and LLM embeddings, neglected coupling among state variables, and the high inference cost of dense backbones. This paper proposes LLaTSA, an LLM-aligned framework for general-purpose trajectory-based TSA. LLaTSA first incorporates operating conditions, disturbance attributes, and state-variable identity through a structured textual prefix. It then aligns normalized temporal patches with a TSA-related vocabulary before processing them with a pretrained sparse decoder-only mixture-of-experts (MoE) backbone. A state-variable coupling module captures coordinated post-fault evolution, while teacher forcing and rollout-based training support iterative long-horizon prediction. Case studies on multiple test systems demonstrate accurate trajectory prediction, reliable stability discrimination, and effective adaptation across unseen scenarios.

This paper addresses both accurate near-field channel acquisition and scalable precoding for wideband extremely large aperture MIMO (XL-MIMO) OFDM systems, with particular focus on reducing complexity. In the considered system, each MIMO multipath component is characterized by five continuous angle-distance-delay parameters, making conventional sparse recovery methods computationally infeasible while suffering from grid mismatch. We propose a decoupled off-grid channel estimation algorithm based on sequential sparse Bayesian learning (SBL). By exploiting the separable structure of the OFDM near-field atom, the five-dimensional joint search is replaced by a sequence of low-dimensional operations and active-set inference, avoiding multiplicative scaling with the per-dimension grid sizes. A continuous-domain Newton refinement is incorporated based on the exact marginal likelihood to mitigate the grid mismatch. Based on the estimated multipath parameters, we then develop a subcarrier-cooperative hybrid precoding framework for multiuser wideband transmission. A large-scale-matrix-inversion-free alternating optimization algorithm is established to maximize the sum user rate, together with a non-iterative low-complexity design that exploits the parameterized channel structure and provides an effective initialization. Simulation results demonstrate the accuracy of the estimation methods compared to several benchmarks and the effectiveness of the precoding algorithm based on estimated channel parameters.

Transmission grids are increasingly stressed by the fluctuating nature of renewable energy sources and by increasing electricity demand. Such grids were mainly built for a different generation fleet and load conditions. Grid topology optimization offers the possibility to redistribute power flows by modifying the busbar topology of substations in the grid. However, the combinatorial explosion of feasible busbar configurations makes topology optimization impractical for system operators. This paper proposes a methodology to identify a small subset of high-value busbar topologies to capture the economic benefit of topology optimization across a range of renewable-demand patterns. The optimization model is based on a LPAC approximation of the optimal power flow formulation, and AC-feasibility checks of the optimal topology applied to the IEEE 118-bus test case. In the test case, we select two distinct pairs of substations (46-49 and 24-69) and we optimize their topology separately over 365 clustered timesteps with different wind and load conditions. For each pair, we identify the four most recurrent optimal topologies and evaluate their performance under standard, and congested conditions, with and without a discrete load growth. Results show that selecting from this reduced set of topologies reduces total generation costs by up to 0.147% compared to a plain AC-OPF. In addition, we show the influence of topology optimization on the hosting capacity of selected busbars under discrete load growth. Our findings provide a practical methodology for system operators to select a subset of optimal busbar topologies to be used in their grid for different wind-load conditions, resulting in decreasing generation costs without the computational and operational risks of real-time switching decisions.

Instruction-guided music editors typically process each request independently, limiting their ability to support workflows in which users progressively refine a track. We introduce AURA, a unified multimodal framework for conversational music editing. AURA uses a multimodal large language model to interpret the complete dialogue history, an optional image, and reference audio, distilling the editing intent into compact concept tokens. A concept-to-audio module injects these tokens and frame-aligned reference features into a frozen MusicGen backbone, enabling precise edits while preserving unaffected content. AURA optimizes only 91M parameters while retaining 1.9B frozen backbone parameters. Experiments on Slakh2100 and MoisesDB demonstrate substantial improvements in edit correctness and content preservation over existing instruction-guided methods, including a 4-5 times reduction in FAD for out-of-domain addition and removal.

Inspecting or servicing a non-cooperative spacecraft is a two-player game: the target can thrust to defeat the inspector's plan. Hamilton-Jacobi-Isaacs reachability answers such games exactly but its cost grows exponentially with state dimension, while learning-based controllers scale yet certify nothing. Convex formulations are the usual escape, typically cast as convex-concave saddle-point problems. That framing fails in the terminal-distance orbital game: with effort regularization, the payoff is convex in both controls, so no pure open-loop saddle point need exist. In its place we derive two exact one-sided guarantees read from the players' terminal reachable sets via support functions: an escape certificate proving the target can hold a guaranteed standoff, and a security strategy bounding the miss distance the inspector can force. Together they bracket the engagement without assuming player rationality, and over two hundred perturbed trials the escape certificate separated capture from escape without error. A projected extragradient method supplies a strategy pair in about 25 ms, certified in place by best-response gaps. The construction carries unchanged to elliptical reference orbits via Yamanaka-Ankersen dynamics, where the orbital phase moves miss distance by nearly a factor of two, reproduced by nonlinear Keplerian propagation. Under receding-horizon play the inspector captures in half of ten representative engagements. Angles-only navigation error, keep-out zones, multiple pursuers, and closed-loop play are treated as extensions.

We study the problem of identifying nontrivial, i.e., nonzero, periodic fixed-points in discrete-time Lur'e feedback systems. Using the circulant matrix constructed from the transfer function of the linear subsystem, whether stable or unstable, we introduce an algebraic framework that allows us to determine when such fixed-points exist. This framework yields a sector bound defined by two vectors, whose slopes correspond to the maximum and minimum positive singular values of the circulant matrix. Assuming that the nonlinear feedback function is memoryless, we show that a necessary condition for the existence of nontrivial $P$-periodic fixed-points is that the intersection of the continuous completion of the nonlinear feedback function with that sector bound contains at least one point other than the origin. Our characterization provides a unified condition valid for all periods $P$, and further enables us to derive upper bounds on the amplitudes of admissible periodic fixed-points with bounded feedback functions. In particular, for relay feedback systems with passive feedback functions, we derive both upper and lower bounds for the amplitudes of such periodic fixed-points.

Kalman filters (KFs) choose the gain as the product of the state-measurement cross-covariance and the inverse of the innovation covariance. In nonlinear systems, the predictive distribution generally changes shape, and these covariances require approximation. Conventional nonlinear KFs approximate them around a single center, the predicted state. They can produce inaccurate gains and overconfident covariance estimates when one local approximation does not represent the measurement geometry across the uncertainty region. To address this issue, we introduce probe sets, small covariance-scaled collections of states at which the filter repeats its local approximation. We then select the gain that minimizes the average covariance reported by these local approximations. The same construction is used in our previously proposed covariance recalibration step to improve covariance consistency. The construction applies to different KF variants without requiring a specific approximation rule. For quadratic measurements, we derive conditions under which probing yields lower normalized true error variance than the corresponding single-center gain when prediction uncertainty is sufficiently understated. We apply the framework to four KF variants and compare the resulting filters with multiple baselines across 300 randomized setups for each of two systems. The results show that probing preserves typical accuracy while substantially reducing covariance inconsistency and the rare large-error trajectories that dominate the root mean square of per-run errors. The code is available at https://github.com/Shida-Jiang/Probe_KF.

This is a position paper. We demonstrate that recursive self-improvement (RSI) large language model (LLM) agents are a natural search engine for dynamic model identification of inverter-based resources (IBRs) whose internal controls are often proprietary and hidden from grid operators. White-box models provide physical transparency but require vendor disclosure; black-box models avoid this requirement but sacrifice interpretability; and existing grey-box approaches, including sparse and symbolic regression, are poorly suited to discovering feedback control architectures or incorporating control-engineering priors. Our position is that this gap can be alleviated by (1) restricting the search space to a typed vocabulary of standard control modules, including PI controllers, phase-locked loops (PLLs), low-pass filters, etc., composed under block-diagram grammar rules, and (2) using an RSI LLM agent to perform program search over candidate block-diagram models, guided by measured frequency-domain admittance data at the point of common coupling (PCC), while fitting the free parameters of each candidate by nonlinear least squares. We instantiate this position by adapting ThetaEvolve, an open-source program-evolution framework supporting in-context evolution and test-time learning, to inverter model discovery. In a proof-of-concept study on a grid-following (GFL) inverter benchmark, the RSI loop reduces the normalized root mean square error (NRMSE) of a naive open-loop model from 0.470 to 0.0435 and identifies a 15-module closed-loop structure that closely resembles the hidden ground-truth GFL controller.

Bird-strike mitigation at wind farms requires identifying distant birds that span only tens of pixels, so the classifier's input resolution N is a design variable, not a fixed specification. We study it with a factorial design over 14 side lengths N (16 to 224), six architectures, two training and evaluation regimes and 30 random seeds – 2,520 checkpoints and 5,040 evaluations – plus latency measured on an NVIDIA Jetson Orin Nano. Four results. (1) The selected N depends on the target: 0.90 is met on validation by ResNet50 at N=112 in an estimated 1.85 ms (0.8980 on test) and 0.95 by DINOv2-L at N=144 in 12.70 ms; changing the model buys more accuracy than raising N (+5.93 versus +2.33 points at N=112). (2) The benefit of lowering N depends on the assumed preprocessing path: N=224 -> 80 saves 13.5% when each individual is decoded from its own file but 46.2% when the detector decodes the 4K frame once; the Pareto set grows from 20 to 23 configurations. (3) Accuracy must be measured on the deployed engine: half precision costs ViT-S/16 alone 4 to 7 points at N>=96 while the CNNs stay within 0.1 points, and with selection held at validation the choice differs at 26 of 176 targets. A broken FP16 engine can run faster than a correct one, undetectable from latency; admitting 14 ViT-S/16 FP32 configurations moves the recommendation over the 0.931-0.938 band and under the 10 ms budget. (4) ViT-L-scale models fit this device, but activations exceed the FP16 range; splitting the graph at transformer-block boundaries confines FP32 to the affected segments, making the deployed DINOv2-L chain 1.85x faster than the single-engine build. We also quantify how the regime-difference sign stabilises with seed count; a sensitivity split removing some forms of group sharing preserves all 14 non-trivial signs at the selection boundary.

This paper proposes a Gaussian function-based trigonometric functional link artificial neural network (GTFLN) filter for linear-in-the-parameters nonlinear filtering. Compared with the adaptive exponential TFLN (AETFLN) filter, the GTFLN filter provides smooth and localized basis functions with reduced computational complexity, where modeling advantages are theoretically established through the smoothness, reproducing kernel Hilbert space, approximation error, and operator theory properties. To maximize the modeling performance, an optimized scaling parameter for the GTFLN filter is derived, yielding the optimized GTFLN (OGTFLN) filter. Least mean square (LMS) adaptation is applied to the GTFLN and OGTFLN filters for nonlinear system identification, resulting in the GTFLMS and OGTFLMS algorithms, respectively. Moreover, the theoretical steady-state excess mean-square error of the GTFLN filter is analyzed. Simulations validate the effectiveness of the theoretical analysis and demonstrate the improved performance of the GTFLN and OGTFLN filters over the linear-in-the-parameters benchmarks in nonlinear system identification and nonlinear acoustic echo cancellation. Based on the GTFLN filter, the filtered-g LMS (FgLMS) algorithm is proposed for nonlinear active noise control. Simulations demonstrate improved stability and noise reduction performance compared to the benchmarks.