2639 papers
This paper presents a brain-like self-organizing hardware architecture that utilizes electrochemical mechanisms to enable in-materia learning and memory through the co-evolution of structural dynamics and functional connectivity, thereby achieving embodied intelligence via adaptive dendritic memristive networks.
This paper presents a framework for EEG-based imagined handwriting recognition that combines a frozen cross-subject neural encoder with a lightweight target-specific classifier and a confidence-aware trie decoder to achieve rapid personalization, high word-reconstruction accuracy, and low-latency energy-efficient performance on edge devices.
This reproducible multi-dataset evaluation demonstrates that adaptive feature-group masking does not significantly outperform simpler, non-adaptive corruption policies in improving robustness against feature-set shift, suggesting that matched controls and dataset-level inference are more critical than complex adaptation mechanisms for this task.
This paper proposes a closed-loop, data-driven framework that integrates process mining, complex network analysis, explainable machine learning, and digital twin simulation to automate Value Stream Mapping and quantitatively optimize Future-State designs, demonstrating significant reductions in lead time, work-in-process, and defects across real-world manufacturing scenarios.
This paper proposes a robust framework for discovering governing equations of fractional-order dynamical systems from noisy data by integrating a Deep Operator Network (DeepONet) for accurate fractional derivative estimation with ensemble Sparse Identification of Nonlinear Dynamics (SINDy) for sparse regression, demonstrating superior accuracy and generalization compared to classical finite difference methods and standard neural networks.
This study demonstrates that using a small validation budget to select a compact text representation (specifically a TF-IDF and SVD combination) for e-commerce recommendation tasks can reduce computational costs by up to 90% while maintaining test performance comparable to full-budget selection, provided the candidate pool is appropriately constrained.
This study demonstrates that enforcing explicit evidence-admission requirements in clinical question answering significantly alters route rankings and latency metrics while reducing material evidence-validity errors, highlighting the need for standardized admission rules in clinical latency benchmarks.
This paper proposes a two-layer framework featuring candidate evidence reranking (CAPE) and risk-aware answer selection (CTA/EBC) to optimize evidence allocation and answer choice in multi-hop retrieval-augmented generation, significantly improving recall and F1 scores over existing baselines like SAG, RRF, and majority voting.
This pilot study demonstrates that small, open-weight language models applied to psychiatric knowledge questions exhibit poor confidence calibration, domain-dependent accuracy rankings, and that prompting them to abstain from answering often degrades rather than improves performance when evaluated with rigorous paired statistics.
This study introduces the Collective Epistemic Network Framework (CENF), a computational model integrating adaptive field control and trust plasticity that demonstrates significant improvements in consensus accuracy and stability against adversarial attacks in synthetic multi-agent networks, while establishing formal convergence guarantees under specific design conditions.