Results 91 to 100 of about 1,517,908 (216)

Toward Knowledge‐Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human–AI Synergy

open access: yesAdvanced Intelligent Discovery, EarlyView.
This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee   +3 more
wiley   +1 more source

Insider Trading, Option Exercises and Private Benefits of Control [PDF]

open access: yes
We investigate patterns of abnormal stock performance around insider trades and option exercises on the Dutch market. Listed firms in the Netherlands have a long tradition of employing many anti-shareholder mechanisms limiting shareholders rights.
Prof. Dr. Luc Renneboog   +2 more
core  

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

open access: yesAdvanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source

Infants match auditory and visual speech in schematic point-light displays

open access: yes, 2010
Infants’ sensitivity to visual prosodic motion in infant-directed speech was examined by testing whether 8-month-olds can match an audio-only sentence with its visual-only schematic point-light display.
Kim, Jeesun, Kitamura, Christine (R8951)
core  

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

open access: yesAdvanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi   +2 more
wiley   +1 more source

Full body aero-tactile integration in speech perception

open access: yes, 2010
We follow up on our research demonstrating that aero-tactile information can enhance or interfere with accurate auditory perception, even among uninformed and untrained perceivers [1].
Derrick, Donald (R16935), Gick, Bryan
core  

MolMiner: Toward Controllable, Three‐Dimensional‐Aware, Fragment‐Based Molecular Design

open access: yesAdvanced Intelligent Discovery, EarlyView.
MolMiner is a fragment‐based, geometry‐aware, and order‐agnostic generative model for molecular design with strong inductive biases. Using symmetry‐aware fragment assembly, dynamic three‐dimensional geometry, and multi‐property conditioning, MolMiner enables interpretable and controllable molecular generation.
Raul Ortega‐Ochoa   +2 more
wiley   +1 more source

Accelerating the Discovery of Proton Conducting Electrolytes via Machine Learning‐Enabled Literature Mining

open access: yesAdvanced Intelligent Discovery, EarlyView.
An end‐to‐end knowledge discovery framework is established to automate high‐precision property extraction from small, specialized literature corpora. Utilizing a domain‐specific bidirectional encoder representation from a transformer model and data augmentation, the system accurately extracts and structures electrolyte performance data, ultimately ...
Gaheun Shin   +4 more
wiley   +1 more source

Infant-directed speech enhances temporal rhythmic structure in the envelope

open access: yes, 2014
Infant-directed speech (IDS) supports language learning via mechanisms that are still not well-understood. Here, we adopt a ‘temporal sampling’ perspective to investigate whether rhythmic enhancements in the temporal structure of IDS could support multi ...
Leong, Victoria   +3 more
core  

Data‐Efficient Cycle‐Level Capacity Prediction Using 1D Deep Convolutional Network

open access: yesAdvanced Intelligent Discovery, EarlyView.
We introduce DeepBat, a deep learning framework featuring a 1D convolutional backbone designed to extract latent degradation patterns from a microstructurally diverse electrode dataset. By learning complex formulation–performance relationships, the model accurately predicts long‐term specific discharge capacity using limited early‐cycle data, providing
Tao Huang   +16 more
wiley   +1 more source

Home - About - Disclaimer - Privacy