Results 91 to 100 of about 219,753 (266)

Diffusion‐Based Generative Model With Scaffold‐Hopping Strategy Yields Highly Potent Bioactive Molecules

open access: yesAdvanced Science, EarlyView.
SMarT‐Diff introduces a multi‐objective generative paradigm that integrates scaffold hopping with structure‐aware scoring to enable controlled exploration beyond the training distribution. The framework consistently balances drug‐likeness, synthesizes accessibility and bioactivity, yielding chemically diverse candidates with enhanced properties.
Yuwei Yang   +8 more
wiley   +1 more source

From Label‐Free Multiphoton Imaging to Pathological Reports: A Vision‐Language Breast Cancer Margin Pathological Diagnosis System

open access: yesAdvanced Science, EarlyView.
MarginPath is a novel vision‐language system that automates breast cancer margin assessment using a single label‐free multiphoton microscopy image. By integrating tumor‐associated collagen signatures with virtual H&E imaging, it generates accurate margin heatmaps and comprehensive diagnostic reports.
Shu Wang   +15 more
wiley   +1 more source

On Adversarial Robust Generalization of DNNs for Remote Sensing Image Classification

open access: yesIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Deep neural networks (DNNs)-based deep learning is an important technical support in the task of remote sensing image classification. But DNNs are susceptible to adversarial attacks.
Wei Xue   +4 more
doaj   +1 more source

PlantGFM: A Genomic Foundation Model for Discovery and Creation of Plant Genes

open access: yesAdvanced Science, EarlyView.
A plant genomic foundation model pre‐trained on 12 species enables both accurate gene prediction and de novo gene design. Through AI‐human knowledge screening, seven designed sequences showed transcriptional activity in plants, with two expressing stable proteins—demonstrating the first DNA‐RNA‐protein expression of LLM‐generated genes in plants and ...
Changhao Li   +10 more
wiley   +1 more source

Improving model adversarial robustness in Extractive Question Answering via Wasserstein-Guided feature Representations

open access: yesAlexandria Engineering Journal
Extractive Question Answering (EQA) models aim to locate accurate answers from passages given a question but are highly susceptible to adversarial attacks.
Gang Huang, Lu Zhang, Hailun Wang
doaj   +1 more source

Interpretable Adversarial Training for Text

open access: yesCoRR, 2019
Generating high-quality and interpretable adversarial examples in the text domain is a much more daunting task than it is in the image domain. This is due partly to the discrete nature of text, partly to the problem of ensuring that the adversarial examples are still probable and interpretable, and partly to the problem of maintaining label invariance ...
Samuel Barham, Soheil Feizi
openaire   +2 more sources

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

open access: yesAdvanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park   +19 more
wiley   +1 more source

Benchmarking the adversarial resilience of machine learning models for DDoS detection

open access: yesArray
Distributed Denial of Service (DDoS) attacks continue to grow in scale and sophistication, making timely and reliable detection increasingly challenging.
Harsh Dadhwal   +3 more
doaj   +1 more source

On the combination of data augmentation method and gated convolution model for building effective and robust intrusion detection

open access: yesCybersecurity, 2020
Deep learning (DL) has exhibited its exceptional performance in fields like intrusion detection. Various augmentation methods have been proposed to improve data quality and eventually to enhance the performance of DL models.
Yixiang Wang   +4 more
doaj   +1 more source

Domain‐Aware Implicit Network for Arbitrary‐Scale Remote Sensing Image Super‐Resolution

open access: yesAdvanced Intelligent Discovery, EarlyView.
Although existing arbitrary‐scale image super‐resolution methods are flexible to reconstruct images with arbitrary scales, the characteristic of training distribution is neglected that there exists domain shift between samples of various scales. In this work, a Domain‐Aware Implicit Network (DAIN) is proposed to handle it from the perspective of domain
Xiaoxuan Ren   +6 more
wiley   +1 more source

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