153 papers
This paper proposes a novel multi-task subset selection framework that achieves localized distributional robustness by introducing a relative-entropy regularization term, which is proven equivalent to maximizing a monotone composition of submodular functions and can be efficiently solved via greedy algorithms, as validated by experiments on satellite sensor selection and image summarization.
This paper proposes and analyzes stochastic greedy algorithms (MRG, DRG, and Random-WSSA) to efficiently solve budget- and performance-constrained weak submodular sensor selection problems with robustness guarantees, demonstrating their effectiveness in Earth-observing satellite constellation applications.
This paper proposes a scalable interference graph learning framework that combines an evolution strategy with deep hashing to efficiently optimize RTWT slot assignments for low-latency Wi-Fi 7 networks, significantly improving slot efficiency, reducing packet loss, and accelerating training and inference times in dense environments.
This paper demonstrates that while centralized precoding in cell-free massive MIMO is theoretically superior, its performance advantage vanishes under realistic per-AP power constraints, rendering distributed precoding a more robust and practical alternative.
This paper derives an exact analytical three-dimensional channel impulse response for a fully absorbing spherical receiver under uniform drift by utilizing joint first-hitting time-location statistics and a Girsanov-based measure change to overcome previous symmetry-breaking limitations.
This paper proposes SBMA, a novel multiple access scheme that synergistically combines SCMA and BIA to overcome their individual limitations, achieving superior diversity and multiplexing gains alongside enhanced privacy and reduced decoding complexity through the design of advanced two-stage and joint message-passing decoders.
This paper proposes the Meta-Adaptive Unscented Kalman Filter (MA-UKF), a novel framework that leverages recurrent meta-learning to dynamically optimize sigma-point weights based on measurement history, thereby significantly enhancing estimation robustness against non-Gaussian noise and out-of-distribution dynamics compared to traditional static-weight approaches.
FedCova is a dependency-free federated learning framework that enhances model robustness against noisy labels by leveraging feature covariances to unify feature encoding, classifier construction, and label correction without relying on external clean data.
This paper benchmarks eight ECG foundation models across 26 clinical tasks, revealing that architectural choices like ECG-CPC's compact state-space design often outweigh massive scale in performance and label efficiency, while highlighting significant remaining gaps in cardiac structure and outcome prediction.
This survey provides a comprehensive review and unified framework for Graph Neural Networks in EEG-based emotion recognition, categorizing existing methods by graph construction stages to offer guidance on their unique physiological foundations while outlining open challenges and future directions.
Inspired by biological neural mechanisms, the paper proposes Uni-NTFM, a unified foundation model that integrates heterogeneous feature projection, topological embeddings, and a Mixture-of-Experts Transformer to achieve superior generalization across diverse EEG tasks through alignment with the brain's sparse coding and geometric topology.
This study investigates an anomalous back discharge phenomenon in dielectric barrier corona discharges under unipolar half-sine voltage excitation, utilizing an ionic wind model and extensive experiments to demonstrate how voltage polarity, dielectric thickness, and geometric factors influence the timing and amplitude of the resulting partial discharge patterns.
This letter proposes an obstruction-unaware, Pearcey-inspired quartic wavefront shaping strategy that enhances radiative near-field multi-user communications by generating structurally stable wave packets, achieving up to 8.5 dB SINR gain over conventional focusing by improving channel conditioning and mitigating noise amplification under partial blockage.