Results 241 to 250 of about 439,560 (345)

Polynomial time learning of simple deterministic languages via queries and a representative sample

open access: bronze, 2004
Yasuhiro Tajima   +3 more
openalex   +1 more source

Dynamic Neural Deactivation Bridges Direct and Competitive Inhibition Processes

open access: yesAdvanced Science, EarlyView.
Dynamic neural deactivation bridges traditionally distinct inhibitory mechanisms—direct inhibition and competition‐induced inhibition—revealing a common neural signature across modalities. Multimodal neuroimaging and behavioral experiments demonstrate a temporal dynamic characterized by progressive frontoparietal activation decay and enhanced sensory ...
Zhenhong He   +6 more
wiley   +1 more source

Digital twins as self-models for intelligent structures. [PDF]

open access: yesSci Rep
Shen X, Wagg DJ, Tipuric M, Bonney MS.
europepmc   +1 more source

The Potential of Cognitive‐Inspired Neural Network Modeling Framework for Computer Vision

open access: yesAdvanced Science, EarlyView.
In article number 202507730, Guorun Li, Lei Liu, Yuefeng Du, and co‐workers present a cognitive modeling framework (CMF) to bridge the ‘representation gap’ and ‘conceptual gap’ between cognitive theory and vision deep neural networks (VDNNs). The research findings provide new insights and solid theoretical support for VDNN modeling inspired by ...
Guorun Li   +5 more
wiley   +1 more source

Semantic Matching of Natural Language Web Queries

open access: bronze, 2004
Naouel Karam   +3 more
openalex   +1 more source

Next‐Generation Piezoelectric Materials in Wearable and Implantable Devices for Continuous Physiological Monitoring

open access: yesAdvanced Science, EarlyView.
An analysis of literature trends and a historical overview of organic and inorganic piezoelectric materials, focusing on their structural diversity, functional mechanisms, and inherent characteristics. It then explores cutting‐edge developments in material synthesis, fabrication processes, and performance optimization, highlighting their applicability ...
Bangul Khan   +7 more
wiley   +1 more source

Interpretable PROTAC Degradation Prediction With Structure‐Informed Deep Ternary Attention Framework

open access: yesAdvanced Science, EarlyView.
PROTAC‐STAN, a structure‐informed deep learning framework is presented for interpretable PROTAC degradation prediction. By modeling molecular hierarchies and protein structures, and simulating ternary interactions via a novel attention network, PROTAC‐STAN achieves significant performance gains over baselines.
Zhenglu Chen   +11 more
wiley   +1 more source

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