Results 151 to 160 of about 249,409 (250)

Overcoming the Nyquist Limit in Molecular Hyperspectral Imaging by Reinforcement Learning

open access: yesAdvanced Intelligent Discovery, EarlyView.
Explorative spectral acquisition guide automatically selects informative spectral bands to optimize downstream tasks, outperforming full‐spectrum acquisition. The selected hyperspectral data are used for tasks such as unmixing and segmentation. BandOptiNet encodes selection states and outputs optimal bands to guide spectral acquisition. Recent advances
Xiaobin Tang   +4 more
wiley   +1 more source

The Design Space of Lightweight Cryptography

open access: yes, 2015
International audienceFor constrained devices, standard cryptographic algorithms can be too big, too slow or too energy-consuming. The area of lightweight cryptography studies new algorithms to overcome these problems.
Mouha, Nicky
core  

When Biology Meets Medicine: A Perspective on Foundation Models

open access: yesAdvanced Intelligent Discovery, EarlyView.
Artificial intelligence, and foundation models in particular, are transforming life sciences and medicine. This perspective reviews biological and medical foundation models across scales, highlighting key challenges in data availability, model evaluation, and architectural design.
Kunying Niu   +3 more
wiley   +1 more source

A Robust Deep Temporal Causal Discovery Platform for Single‐Cell Gene Regulatory Network Reconstruction

open access: yesAdvanced Intelligent Discovery, EarlyView.
scTIGER2.0 is a deep‐learning framework that infers gene regulatory networks from single‐cell RNA sequencing data. By integrating correlation, pseudotime ordering, deep learning and bootstrap‐based significance testing, it reduces false positives and reveals directional gene interactions.
Nishi Gupta   +3 more
wiley   +1 more source

Generative and Experimental Validation of High Refractive Index Polymers via Domain Knowledge Approach with Small Data

open access: yesAdvanced Intelligent Discovery, EarlyView.
This research demonstrates that the combination of domain knowledge–based multiple regression, multi‐objective Bayesian optimization, and generative models is a suitable prediction tool for candidates of high refractive index polymers, even with the constraints in the model trained on limited data. The experimental validation can reproduce the proposed
Takuya Yokoo   +3 more
wiley   +1 more source

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