Papers

Despite the recent development of control theory for multi-agent systems (MASs), the highly desirable separation principle is difficult to establish even for linear MASs, let alone for nonlinear ones that rely solely on output measurements under denial-of-service (DoS) attacks. This paper establishes a separation principle for distributed leader-following control of this class of nonlinear MASs, allowing the observer and the controller to be designed independently. For each agent, two parametric Lyapunov equations (PLEs) are employed to generate two symmetric positive-definite matrices, which respectively support the independent design of the controller gain and the observer gain. To ensure that these two parameters do not affect each other, we adopt a matrix pencil formulation to decouple the relevant coupled terms and exploit time-varying feedback to handle potential impacts arising from nonlinearities. Furthermore, we design a hybrid observer that consists of a local state observer for reconstructing unmeasurable follower states and a distributed leader state observer for estimating the inaccessible leader state. Notably, we find that as long as the nonlinearity of all agents satisfies a linear-growth-type condition and the nonlinear model of the leader is available for followers, the separation principle can be established regardless of the presence of event-triggered control and/or admissible DoS attacks. In our method, the selection of design parameters for each agent is elegantly simple, involving only three parameters: one for the prescribed convergence time $t_f$, and the other two for the controller and the hybrid observer, respectively. Moreover, the latter two parameters can be chosen independently from explicit admissible ranges once the system order is specified. Numerical simulations verify the effectiveness of the proposed method.

We ask: given a retrieved source audio $S$ and a separate reference audio $R$, can we synthesize novel audio $Y$ out of this pair $(S,R)$ such that $Y$ remains acoustically consistent with $S$, while not persistently copying segments of $S$ or $R$? The first clause is a well-known goal in Foley audio production, and the second is a well-known issue in neural RAG when $S$ and $R$ are naively injected into neural generators. We show that both clauses can be addressed simultaneously using a method we coin relational synthesis, a variation of concatenative synthesis where target cost is replaced by a relational Gromov-like structural cost. Rather than imitating the content of $R$, relational synthesis exploits it from the "other side of the hill": it transfers the temporal structure and directed amplitude motion of $R$ to reorganize and concatenate the grains of $S$ in a novel manner that protects $S$'s acoustic information. Our experiments show that relational synthesis integrates naturally with neural RAG and produces Foley audio that performs well on metrics measuring temporal agreement, acoustic fidelity, and leakage persistence, while maintaining distribution-level quality and text alignment.

Diffusion and VLA policies for manipulation are often deployed through downstream impedance controllers. The stiffness and damping gains of these controllers affect task success, yet are commonly inherited from data collection rather than selected for the deployed policy. Although empirical gain sweeps can improve performance, they require repeated evaluation rollouts. We introduce PHRetune, an offline method that derives controller gains for a frozen policy without evaluation rollouts or gain search. Our approach learns a port-Hamiltonian model from demonstrations to estimate the effort and energy associated with the policy's predicted actions. The policy is applied to recorded demonstration observations, and its predictions are assessed against demonstration-derived effort and energy budgets. From this comparison, we derive a single gain scale in closed form, adjusting the downstream controller while preserving the policy and its action representation. The gains are fixed before evaluation, without requiring a prior manipulator model, task rewards, policy retraining, or additional runtime computation. Across LIBERO suites, PHRetune improves Diffusion Policy success by up to 9.4 percentage points, with the derived gains achieving the highest observed success rates in empirical gain sweeps. On all four real-world manipulation tasks, PHRetuned Diffusion Policy outperforms the nominal policy, alternative gain-tuning methods, and a policy-retraining baseline. The same procedure improves success with SmolVLA and OpenVLA-OFT on every task, while reducing acceleration and jerk for both VLA backbones.

Plane-wave ultrasound enables ultrafast imaging but remains constrained by a receive-aperture-dependent resolution- contrast trade-off. Fine structures benefit from high spatial resolution, whereas diffuse tissue and lesions often require improved contrast and clutter suppression. These objectives favor different receive-aperture configurations, yet conventional beamformers operate at a fixed imaging point. We propose a physics-grounded, objective-conditioned deep beamformer that enables controllable image formation within a single network. From single-plane-wave channel data, the network predicts spatially varying receive-apodization weights, conditioned on the desired imaging objective through feature-wise linear modulation. The conditioning variable is explicitly tied to a physically defined receive-aperture configuration and supervised using the correspondingcoherent-plane-wave-compounding target. Differentiable, region-of-interest-aware, physics-guided quality losses further promote objective-specific resolution and contrast during end-to-end training. The model is trained exclusively on simulated channel data spanning diverse target morphologies and echogenicities, and evaluated zero-shot on the PICMUS and CUBDL datasets, including unseen probe geometries. The proposed framework generally improves upon single-plane-wave delay-and-sum reconstruction while providing objective-dependent control. On PICMUS, the proposed method improves gCNR by up to 16.8% and axial resolution by 22% compared with the reference beamforming method. Moreover, switching between the contrast- and resolution-oriented objectives enables controllable changes of up to 4.9% in lesion contrast and 11.4% in lateral resolution. Compared with the best-performing learning-based baselines, the proposed method reduces lateral full-width-at-half-maximum by 5.9% and improves generalized contrast-to-noise ratio by 6.6%.

We propose a learning-to-optimize framework for solving parametric mixed-integer nonlinear programs arising in model predictive control and trajectory optimization. The proposed method combines neural network prediction with a sequential quadratic programming (SQP) refinement layer. First, neural networks are trained using a combination of supervised and self-supervised losses to predict integer solutions together with compatible continuous warm starts. To account for prediction inaccuracies, the trained predictor generates multiple candidate integer solutions, each paired with a continuous initialization. The integer solutions are then fixed, and a differentiable SQP layer refines the associated continuous variables over a prescribed number of iterations. The SQP-layer parameters, including the curvature regularization and step size, are trained to improve both feasibility and objective value after every iteration. By separating integer prediction from continuous optimization-based refinement, the proposed framework improves robustness to imperfect neural predictions while maintaining a predictable computational budget. We demonstrate the proposed framework on contact and trajectory optimization for a planar box-pushing example.

This paper revisits frame-wise saliency for audio moment retrieval (AMR). We show that the frame-wise saliency sequence, conventionally used only as an auxiliary output in DETR-based AMR models, can itself serve as an effective source of moment predictions. We convert the saliency sequence into ranked moments using a simple SED-inspired segmentation rule with no learned parameters, enabling moment retrieval directly from frame-wise temporal information. On the CASTELLA dataset, saliency-based prediction consistently outperforms decoder-based prediction from the same model across all 18 runs of QD-DETR and CG-DETR. For QD-DETR, simply replacing the inference output improves R1@0.7 from 21.0 to 36.1. The advantage remains 7-15 points when the two outputs are evaluated at their independently selected best epochs. The same tendency extends to TaskWeave and UVCOM, whereas TR-DETR shows the opposite behavior, suggesting that how saliency construction may matter. The performance gap is especially pronounced for short moments: for queries whose annotated moments average at most 2 s, R1@0.7 improves from 6.3 to 27.8 with QD-DETR. Decoder supervision nevertheless benefits saliency-based prediction, indicating that its role during training differs from the utility of its inference output.

Mean-field games provide a continuum framework for modeling the dynamics of large, interacting populations of non-cooperative agents. This paper studies symmetry-breaking in a finite-horizon, two-choice min-LQG mean-field game in which identical agents with linear stochastic dynamics choose one of two equally desirable terminal destinations, while trading off control effort against social pressure to conform. The model has an odd symmetry between the two destinations and therefore always admits a symmetric, dynamic Nash equilibrium in which the population splits evenly between them, producing a deadlock collective state at final time. Numerical studies have suggested that, as the penalty for social nonconformity increases, this symmetric equilibrium loses stability as a fixed point of an associated scalar, self-consistency map, and asymmetric consensus Nash equilibria emerge, where most agents select the same destination. By analyzing the linearized forward-backward PDE system through the scalar map representation, we provide a proof of this loss of stability of the symmetric equilibrium. Together with the odd symmetry of the map, this implies the existence of symmetry-broken mean-field game equilibria corresponding to consensus on either destination.

This paper studies data-driven stabilization of unknown discrete-time linear systems using only quantized state measurements that take values in finite sets. The proposed approach consists of two stages. In the controller design stage, we collect quantized data while maintaining a prescribed quantization error bound and use them to formulate a semidefinite program (SDP). We establish a verifiable condition under which any feasible solution to the SDP yields a stabilizing feedback gain, and show that a stabilizing gain can always be obtained when the quantization error is sufficiently small. In the stabilization stage, a Lyapunov-based quantizer update rule is developed to guarantee exponential convergence under quantized state feedback. As a key feature, the number of quantization cells remains finite in both stages and constant during stabilization. Simulation results illustrate the effectiveness of the proposed approach.

Virtual Model Control (VMC) is an approach to design a controller for force-controlled robots in complex uncertain environments. While this method was primarily investigated for legged robot locomotion in the past, it can be more generally applicable to other types of robotic systems. This paper investigates the VMC framework for reaching tasks in a force-controlled robotic arm. We propose six different approaches to designing virtual models in order to achieve reaching tasks in environments with obstacles and uncertainties. A force-controlled 8 degree-of-freedom humanoid robot was used to validate the proposed approach in the real world. We conducted three experiments to test the performance of VMC controllers in terms of predictability, sensitivity to external force, and adaptability against known and unknown obstacles. Experimental analyses show that, even though the proposed approach needs to sacrifice accuracy and trajectory optimality, it enables us to design complex reaching motions under uncertainties, in an intuitive and extendable manner.

As mobile communications cross the threshold from 5G to 6G, the 3GPP has entered a decisive phase. Following the Technical Specification Group (TSG) plenary meetings of June 2026 and the associated 6G workshop in Singapore, the Release-20 study phase is now well underway across all three TSGs: Radio Access Network (RAN), Service and System Aspects (SA), and Core Network and Terminals (CT). Concurrently, the timeline for the first normative 6G specifications in Release 21 has been established. Rather than offering another technology vision, or a chronological account of committee progress, this article asks what the first body of agreed 6G material tells us. We review service requirements, system architecture, the emerging radio interface, native AI, ISAC, non-terrestrial networks (NTN), and core-network protocols, tracing how each moves from study item toward specification. Read across the three TSGs, this material also supports seven cross-cutting lessons: AI has moved from a feature to a primitive spanning every stage, non-terrestrial access has crossed from overlay to substrate, and connectivity is now one objective among three alongside computing and sensing; the industry favors deployability over redesign, with spectrum access and site reuse, rather than waveform innovation, appearing to be the tighter constraint; and standardization targets AI workflows rather than AI models, with testability across vendors recurring as a decisive criterion for which techniques survive. Because the material spans very different stages of maturity, every item cited is tagged with its status at a single snapshot date (approved, draft, individually proposed, or vendor-demonstrated), and the lessons are offered as the authors' synthesis rather than as 3GPP positions. We close with the road toward trials and commercial launch around 2030, and with the open questions these lessons help prioritize.

Speaker tracking is the task of separating and following multiple speakers over time. It must address overlapped speech, speech onset and offset, talker identity, and time-varying speaker count. We propose a segment-online, modular framework for single- and multi-channel speaker tracking. The proposed system first performs speaker separation in each segment and computes speech activity via voice activity detection (VAD). To generate speaker tracks over time, we introduce a two-stage sequential organization strategy: Overlap-based stitching for continuous grouping and memory-based speaker verification for discontinuous grouping. For speaker separation, we employ complex spectral mapping to estimate the real and imaginary spectrograms of underlying speakers. The proposed system achieves state-of-the-art segment-online tracking performance on the LibriCSS and AMI datasets. Our framework significantly reduces diarization error rate (DER) and concatenated minimum-permutation word error rate (cpWER) compared to other methods.

Learning-based wireless research increasingly combines simulation, external model training, and benchmarking. Rebuilding these stages for every experiment is time-consuming, while differences introduced between them can silently change the conditions of a comparison. We present NOEMA, an open-source toolkit that prepares model development and baseline evaluation from a shared, machine-checkable wireless scenario. From the same scenario, NOEMA can prepare benchmark execution, capture aligned training data, and export differentiable components for model development. Researchers can therefore focus on training and selecting their models, then return the selected component to the same controlled experiment for evaluation. NOEMA checks the returned model and verifies that declared comparison conditions, including available information, random conditions, and failure accounting, remain consistent through benchmarking and reporting. An included agentic-control example applies these contracts to a language-model supervisor that selects allocation policies and power budgets from historical link feedback. All 43 regression tests of the workflow checks produced their expected outcomes, including rejection of all 31 cases containing known inconsistencies. Separate fidelity tests showed that captured data and returned-model behavior were preserved. In a 20-replicate quadrature phase-shift keying phase-tracking study, the mean bit-error-rate difference between the learned receiver and five- pilot smoothing was -0.01526 (95% confidence interval: -0.01547 to -0.01506). NOEMA therefore provides both a reusable path for developing learned wireless components and a way to keep their final comparisons inspectable and checkable.

In real-time optimization of simultaneous transmission and reflection reconfigurable intelligent surfaces (STAR-RIS), limitations arise due to Doppler effects and reduced adaptation speed. Existing RIS memoryless surface models use quasi-static optimization and incur high latency, which limits their application in cognitive autonomous networks. To overcome this limitation, we propose a stateful RIS model that includes an element-wise memory, which locally stores and updates its phase history across coherence intervals. Hence, the metasurface becomes a stateful surface with element-level memory that enables the exploitation of temporal channel correlations. This element-wise memory is complemented with a meta-parameter, yielding faster convergence and giving rise to hierarchical adaptation in gradient and episodic timescales. The proposed framework supports autonomous and experience-driven learning, which enables the STAR-RIS architecture and memory to adapt according to the Doppler effect, as seen in cognitive communication systems. Results demonstrate an improvement in spectral efficiency and adaptation speed across various mobility patterns, fading models, interference levels, and array sizes. An analytical field- programmable gate array (FPGA) latency estimate indicates that the critical path fits within the coherence window.

The Offline AI Modules workstream enables practical, low-power, and community-accessible deployment of voice-first AI systems that operate fully offline. Designed for African language communities where speech is the dominant mode of interaction and internet connectivity is unreliable or absent, the workstream delivers three reinforcing components: a modular voice-first offline architecture, a low-cost hardware reference bill of materials, and a reproducible quantization and a reproducible quantization and benchmarking pipeline for instruction-tuned language models in the 2-5B parameter class. This paper presents the first end-to-end benchmark evaluation of the stack across two hardware tiers: an NVIDIA Jetson Orin NX (TierB) and a Raspberry Pi5 (TierA). Three instruction-tuned models are evaluated across four quantization formats, assessed for deployment metrics (decode throughput, chat latency, memory, power) and multilingual quality (topic classification accuracy on MasakhaNEWS across English, Hausa, Igbo, Nigerian Pidgin, and Yoruba; per-language perplexity drift). Speech recognition is evaluated using Ethio-ASR on Amharic and Oromo across both tiers. The principal finding is that Q4_K_M quantization represents the best size-to-quality trade-off for deployment on both tiers: gemma-4-E2B-it achieves 28.8t/s decode throughput and 89.2% topic classification accuracy at Q4_K_M on TierB, while all three models run within the 16GB memory budget on TierA.

This paper investigates the dielectric breakdown behavior of Litz wire insulation subjected to repetitive fast-edge bipolar square-wave voltages representative of wide-bandgap (WBG) power converter stress. Two conductor sizes, 14 AWG and 16 AWG, were tested in a twisted-pair configuration over frequencies ranging from 1 kHz to 30 kHz using a controlled high-voltage pulse generator with 200 ns rise time. The breakdown voltages obtained at each frequency were statistically analyzed using the weighted two-parameter Weibull method in accordance with IEEE Std 930-2004. Between 1 and 30 kHz, the mean breakdown voltage decreases by approximately 10.7% for the 14 AWG construction and 20.0% for the 16 AWG construction. The 14 AWG specimens exhibit a modest increase between 1 and 5 kHz followed by a decline, whereas the 16 AWG mean decreases across the tested frequencies. The Weibull shape parameter varies nonmonotonically with frequency, characterizing differences in the dispersion of the measured breakdown voltages. These findings provide construction-specific breakdown data relevant to Litz-wire insulation assessment in high-frequency power electronic applications. Possible contributions from charge accumulation, localized discharge activity, and dielectric heating are discussed, but were not directly verified in this study.

Lyapunov functions have been a cornerstone of dynamical systems theory ever since the early 1900s. Moreover and akin to Morse theory, Lyapunov functions have been the key in linking topology to dynamical systems theory. In this note we continue along these lines. Suppose that some vector field on Euclidean space has a unique equilibrium point that is globally asymptotically stable (GAS). Under these conditions, it is known that there is always a smooth Lyapunov function to certify stability. One may wonder if this function can always be chosen to be Morse. We show that if the equilibrium point is hyperbolic, this is indeed true. Moreover, we show that if this would be true in general, then the generalized 4-dimensional smooth Poincaré conjecture (SPC4) must be true. Even so, we provide an example that is GAS, but does not admit a Morse Lyapunov function, closing this route of proving SPC4.

Emergency obstacle avoidance requires a mobile robot to brake or change its heading within the available distance. This paper investigates the dependence of maneuver performance and odometric error on approach speed for a differential-drive robot. A footprint-clearance analysis and a normalized wheel-motion index provide a kinematic description of five braking and turning maneuvers. The principal experiment comprises 540 block-randomized trials on tile, of which 539 are retained, at commanded approach speeds of 45, 55, and 65 cm/s. An overhead camera provides an independent pose reference. When encoder, gyro, and camera heading changes are evaluated over complete motion records, the mean encoder discrepancy increases by 4.7-5.1 degrees for the two reverse-spin maneuvers between the lowest and highest speeds; the arc and brake-assisted pivot change by less than 0.4 degrees. Larger encoder discrepancy is associated with lower avoidance success after adjustment for distance, speed, maneuver, day, and turn direction. Gyro discrepancies remain approximately 3 degrees for the reverse spins. Estimated reaction distances for 90% success and their uncertainty quantify the maneuver trade-offs. The results support speed-specific empirical characterization of emergency maneuvers and distinguish encoder error from recovery motion and measurement-window mismatch.

In safety-critical domains such as autonomous driving, systems must be evaluated across a large number of environment conditions, often represented as composite scenarios built from primitive scenarios. Existing statistical model checking (SMC) approaches analyze each composite scenario independently, requiring many expensive simulations and resulting in substantial redundant computation when scenarios share common structure. This work introduces a scenario-based compositional SMC framework for safety and co-safety specifications, enabling efficient analysis of composite scenarios. Our approach decomposes scenarios into primitives and specifications into sub-specifications, verifies each primitive independently, and composes the resulting statistical estimates using importance sampling and kernel density estimation. Our empirical evaluation shows that the proposed framework can accurately answer verification queries for previously unseen composite scenarios while reducing simulation cost through parallelization and trace reuse.

We formulate joint estimation of common-slope decay rates and amplitudes from room impulse responses (RIRs) as parameterized nonnegative matrix factorization with the Itakura–Saito divergence as the loss function (IS-NMF). Estimation at each short-time Fourier transform frequency bin produces detailed reverberation time (RT) curves directly from RIR powers with no backward integration needed. Standard space-alternating generalized expectation-maximization (SAGE) algorithm yields closed-form amplitude updates and a convex subproblem for each decay rate update. To accelerate estimation, we introduce contribution-weighted SAGE, which emphasizes observations where each component contributes strongly to the modeled power. Experiments with synthetic data show accurate recovery of well-separated decays and faster loss reduction than standard SAGE. Application to measured coupled-room RIRs yields frequency-dependent RT curves and reveals complementary space-time contributions of the shared decay components.

This paper derives analytical expressions for the asymptotic variance of the Hermite-Domain Predictor-Based Subspace Identification (HD-PBSID) method. Unlike continuous-time subspace approaches that rely on intermediate-domain transformations, HD-PBSID identifies continuous-time state-space matrices directly. The derivation first establishes the uncertainty of the estimated state-space matrices and then exploits these results to obtain asymptotic variance expressions. These expressions provide analytical uncertainty bounds for system eigenvalues, which are invariant under similarity transformations and therefore uniquely characterize the identified dynamics. The proposed formulas are first validated in an ideal Hermite-domain setting, where the conditions underlying the derivation are satisfied, and then assessed on noisy time-domain data through Monte Carlo simulations. The predicted bounds show very good agreement with the eigenvalue distributions in the ideal setting and acceptable agreement in the time-domain case when the innovation sequence is enforced to be white.

Tracking angle-of-arrival (AoA) under mobility enables proactive beam alignment in multi-antenna wireless systems. However, learning this evolution directly from raw I/Q data is challenging because multipath, channel-gain variations, hardware impairments, and noise mix with angular information. This paper proposes AngularWM, a self-supervised wireless world-modeling approach that learns a predictive latent angular state from ordered multi-antenna I/Q observations. A joint-embedding predictive architecture is combined with a label-free trajectory-level geometric regularizer that encourages an approximately affine, non-collapsed latent evolution motivated by the antenna-array response. No AoA, motion, or displacement labels are used during pretraining. The resulting structured world state supports direct multi-step encoder-space extrapolation and generalizes to angular step sizes not seen during training. On large-scale over-the-air measurements, AngularWM reduces the mean predictor-free AoA error over eight steps from $29.97^\circ$ to $0.49^\circ$. After pretraining, only three or four labeled reference angles are required to calibrate the complete 225-point AoA grid, achieving $0.48^\circ$ and $0.43^\circ$ MAE, respectively. AngularWM also maintains robust beam alignment and spectral efficiency under increasing angular displacement, while reducing the deployed model size from 1.42M to 0.30M parameters and forecast latency.

We formulate a novel optimal control problem for data-driven dynamic resource allocation in multicore real-time systems. The proposed formulation accounts for hard deadline constraints and resource bounds. Under mild assumptions, we prove several results on the feasibility and existence of optimal solution for the proposed formulation. Building on these analyses, we design an algorithm to solve the multi-core dynamic resource allocation problem. We demonstrate the practical use of this new algorithm by experimentally evaluating it on a real multicore platform using four benchmarks from the literature.

We investigate the problem of data payload uncertainty (DPU) in bistatic integrated sensing and communication (ISAC) systems, which refers to imperfect knowledge of transmit data symbols at the sensing receiver. DPU may result from limited fronthaul capacity in base station (BS)-to-BS bistatic sensing or data demodulation errors in BS-to-user equipment (UE) bistatic sensing, and can impair target detection through elevated sidelobe levels in the delay-Doppler domain. To characterize this effect, we derive closed-form expressions for the peak-to-sidelobe level ratio (PSLR) of two common OFDM receivers, namely reciprocal filtering (RF) and matched filtering (MF), under DPU. The analysis shows that the DPU-induced sidelobe degradation of RF depends on the inverse second-, fourth-, and sixth-order constellation moments, whereas that of MF depends only on the direct fourth-order moment, making RF considerably more vulnerable to DPU. To mitigate the resulting sidelobes, we further propose a novel, DPU-aware minimum mean-squared-error (MMSE) receiver. Simulation results validate the PSLR analysis and demonstrate superior detection performance of MMSE over RF and MF while revealing the extent to which it can recover DPU-induced performance loss relative to a genie-aided receiver with perfect knowledge of data payload.

This letter develops a physics-informed one-shot beam tracking method for stacked intelligent metasurface (SIM)-enabled low-Earth-orbit (LEO) links with $K$ users and an $L$-layer SIM with $N$ meta-atoms per layer. The predictable orbital motion is propagated through the line-of-sight (LoS) geometry to forecast the finite-interval variation of the user signal-to-noise ratio (SNR) profile, and an analytical multilayer Jacobian relates this variation to the $LN$ SIM phase coefficients. Beam tracking is formulated as normalized SNR-profile preservation and solved through a single regularized Gauss–Newton correction that requires one Jacobian evaluation and a $K\times K$ linear solve, thereby avoiding iterative reoptimization. A tracking-error bound separates the regularization residual from the nonlinear Taylor remainder and establishes a second-order local error under a full-row-rank Jacobian. Numerical results show that the proposed method retains approximately $94\%$ and $92\%$ of the iterative-reoptimization rate for $L=2$ and $L=4$, respectively, while running about $350\times$ and $260\times$ faster at $N=225$, and confirm the predicted second-order residual error behavior.

We study the problem of computing the tightest \(\alpha\)-potential approximation of a general-sum game over continuous action spaces, within a prescribed class of potential functions and when each player has access only to its own utility function. The difficulty is twofold: the approximation error involves a worst-case search over an infinite set of unilateral deviations, and the required utility information is distributed across players. For a linear-in-parameters potential class, we use an exact finite-tuple reformulation that separates the problem into a global outer search over deviation tuples and distributed convex inner problems. We develop a primal–dual inner oracle tailored to this structure and establish a uniform one-sided accuracy guarantee. This oracle can be combined with global outer search to obtain an end-to-end guarantee on the outer optimization error. We also develop a projected zeroth-order outer method as a computationally lighter alternative for higher-dimensional problems. Numerical experiments illustrate the accuracy–computation tradeoff between the two outer-search methods and show that the proposed optimization framework can improve upon analytical \(\alpha\)-potential constructions.

The polynomial maximization method estimates signal parameters in non-Gaussian noise from a finite description of the noise by its moments and cumulants, without a density. Known accuracy results assume that these noise characteristics are known, or use a design in which estimating them jointly costs no accuracy. In practice they are estimated from the residuals of a preliminary fit, and the effect of this substitution on accuracy is unknown. We treat the feasible scheme as a two-step estimator and, for a general smooth signal model in a fixed design, find the increment of its asymptotic covariance in closed form: $\Sigma_{\rm fe}-\Sigma_{\rm or}=c_2(1-g_{\rm or}) Q^{-1}\bar{d}\bar{d}^{\top}Q^{-1}$, a positive semidefinite matrix of rank at most one. Here $c_2$ is the noise variance, $g_{\rm or}\le 1$ the oracle variance reduction factor, and $Q$ and $\bar{d}$ the second-moment matrix and mean of the signal gradient over the design. Estimating the fractional moments that form the weights costs no first-order accuracy, because that block is orthogonal; the whole increment comes from estimating the sign-preserving centering means. The increment lies along the single direction $Q^{-1}\bar{d}$, so linear combinations of the parameters orthogonal to it keep oracle accuracy. In the trace metric the variance reduction factor becomes $g_{\rm fe}=g_{\rm or}+(1-g_{\rm or})R_D$, where $R_D=\|Q^{-1}\bar{d}\|^2/{\rm tr} Q^{-1}$ depends on the design alone. We give four sufficient conditions for first-order oracle equivalence; noise symmetry is not among them. Monte Carlo experiments with a harmonic signal confirm the direction and size of the increment. A covariance estimate that ignores the increment undercovers where non-Gaussianity gives the largest gain, symmetric noise included.

A new analytical technique is proposed for calculating adequacy metrics for generation planning. The proposed technique is simple, direct, and provides higher accuracy than traditional discretized approaches. Additionally, a new analytical methodology is introduced for calculating loss of load days (LOL Days) as distinct from loss of load events or frequency of load loss. This paper emphasizes that, where feasible, analytical methods are preferable to Monte Carlo simulations because they yield exact closed-form solutions rather than statistical estimates.

Large-scale EEG foundation models have demonstrated promising transferability across neurological disorders, but often require millions of parameters and substantial computational resources. In this paper, we present the Universal Semantic EEG Foundation Model (USE-FM), a lightweight EEG foundation model that learns transferable neural representations through self-supervised signal reconstruction on the Temple University Hospital EEG Corpus (TUEG). After pretraining, the encoder is frozen and evaluated on two clinically distinct downstream tasks, abnormal EEG detection (TUAB) and epileptic seizure recognition (TUEP), using a unified frozen-transfer protocol against recent EEG foundation models, including LUNA-Base and CBraMod. With only 1.46 million parameters, approximately one-fifth the size of existing models, USE-FM achieves competitive overall performance, including strong sensitivity and F1-score on TUEP (SEN $75.00 \pm 14.14$, F1 $70.37 \pm 4.01$), while maintaining competitive performance on TUAB (AUC $85.24 \pm 5.61$). Beyond downstream classification, latent representation analysis using $k$-means clustering together with PCA and t-SNE demonstrates that USE-FM learns organized semantic EEG representations comparable to substantially larger foundation models. These results suggest that large-scale self-supervised pretraining enables lightweight architectures to learn transferable semantic EEG representations, providing a computationally efficient foundation for cross-disorder analysis and future clinical decision support in neurological disorders.

The rapid growth of AI data centers is creating concentrated electricity demands that can exceed available distribution network headroom and delay interconnection. This paper investigates whether capacity can be used effectively by coordinating flexibility on both sides of the interconnection. A day-ahead mixed integer second order cone programming framework maximizes feasible AI data center IT capacity using utility side conservation voltage reduction and network topology reconfiguration, together with data center workload shifting and reserve constrained uninterruptible power supply storage. Background feeder demand uses voltage dependent ZIP models, while the data center is modeled as constant power demand using Training, Inference, and Mixed workload profiles. A two-stage solution first maximizes interconnection capacity and then minimizes feeder losses for the retained capacity, with numerical relaxation screening. Studies on a 116-bus model derived from the IEEE 123-node feeder show that, at the constrained bus-60 connection point, grid side flexibility, driven almost entirely by network reconfiguration, increases feasible IT capacity by approximately 42.5-45.0%, while data center flexibility alone provides approximately 2.1-5.4% gains. Coordinated operation increases feasible IT capacity from approximately 10.5-10.6 MW under BASE operation to 15.3-16.0 MW, corresponding to gains of approximately 45.9-50.8%; the 16.0 MW Inference result is limited by the adopted UPS reserve requirement. Benefits are strongly location dependent and influenced by workload flexibility, deferral duration, PUE, UPS energy and reserve requirements, and feeder loading. Under a combined adverse operating condition, FULL capacity decreases by only 3.4% relative to the nominal Mixed case while retaining substantial additional capacity over BASE.

Active radio-frequency (RF) sensing must acquire evidence under receiver limits while following confirmation, recovery, and stopping instructions. \newhl{We propose RF-Agent, a closed-loop architecture combining language supervision, episode memory, deterministic RF execution, perception feedback, and evidence-based reporting.} An independent auditor checks trajectory compliance. We derive acquisition and supervisory-request bounds, characterize full-history token complexity, and bound joint success by output validity and evidence capacity. \newhl{ActiveRF-AD tests instruction changes at fixed states and evaluates joint success, requiring compliance and RF correctness in the same episode.} \newhl{Training on different instructions at shared sensing states raises counterfactual-pair accuracy from 24.03% to 96.22% and Core joint success from 65.13% to 72.12%. Of the 6.99-point joint-success gain, 6.73 points reflect a decrease in the fraction of all episodes that are RF-correct but noncompliant.} \newhl{At Core budget three, paired-training hierarchical control achieves 66.60% joint success versus 9.17% for direct-action control.} \newhl{In a separate comparison at the same budget, RF-Agent improves joint success by 2.31 points and reduces RF acquisitions by 12.3% relative to an initial-state planner, at increased inference cost.} Cross-backbone diagnostics cover three model families. These results show how instruction-responsive control improves task completion beyond what RF accuracy alone reveals.