Results 131 to 140 of about 5,389,393 (319)

Machine Learning‐Driven Prediction of Microplastic Aging Processes and Environmental Risk Assessment Across Multi‐Media Systems

Advanced Science, EarlyView.
This perspective proposes a cohesive machine learning strategy to decode microplastic aging. It advocates for Federated Learning to dismantle global data silos and introduces the TRACE framework (TRansport, Aging, Corona, Ecotoxicity). By integrating physics‐informed modeling with causal discovery, this approach bridges the laboratory‐field gap to ...
Yaping Lyu, Xinran Qiu, Xing Li, Tianhuan Yang, Xuetao Guo, Hao Qiu, Peng Zhang   +6 more
wiley   +1 more source

AI‐Physics‐Experiment Trinity for Integrated Protein Dynamics Modeling

Advanced Science, EarlyView.
This review unites experiments, physics‐based simulations, and AI as a synergistic triad for protein dynamics modeling. It highlights integrative strategies, resolves sampling and forcefield bottlenecks, and outlines challenges and future directions for accurate, interpretable conformational ensemble prediction.
Chen Shi, Minying Low, Peng Xiu, Kresten Lindorff‐Larsen, Yong Wang   +4 more
wiley   +1 more source

A New Kind of Adversarial Example

CoRR, 2022
Almost all adversarial attacks are formulated to add an imperceptible perturbation to an image in order to fool a model. Here, we consider the opposite which is adversarial examples that can fool a human but not a model. A large enough and perceptible perturbation is added to an image such that a model maintains its original decision, whereas a human ...
openaire   +2 more sources

A Unified Framework for Adversarial Attack and Defense in Constrained Feature Space

, 2022
The generation of feasible adversarial examples is necessary for properly assessing models that work in constrained feature space. However, it remains a challenging task to enforce constraints into attacks that were designed for computer vision.
Simonetto, Thibault, Thibault Simonetto, SIMONETTO, Thibault Jean Angel, Maxime Cordy, Traon, Yves Le, Salijona Dyrmishi, Yves Le Traon, Dyrmishi, Salijona, Ghamizi, Salah, LE TRAON, Yves, Salah Ghamizi, Cordy, Maxime   +11 more
core   +1 more source

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

Advanced 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, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Understanding adversarial robustness against on-manifold adversarial examples

Pattern Recognition
Deep neural networks (DNNs) are shown to be vulnerable to adversarial examples. A well-trained model can be easily attacked by adding small perturbations to the original data. One of the hypotheses of the existence of the adversarial examples is the off-manifold assumption: adversarial examples lie off the data manifold. However, recent research showed
Jiancong Xiao, Liusha Yang, Yanbo Fan, Jue Wang 0001, Zhi-Quan Luo   +4 more
openaire   +2 more sources

GenDroid: A query-efficient black-box android adversarial attack framework

, 2023
The security problems of Android applications have been gradually exposed with the increasing popularity of the Android OS. Machine learning (ML) and deep learning (DL) based Android malware detection is still suffering from adversarial attacks, although
Hongfei Shao, Jiliang Li, Meng, W, Guangdong Bai, Li, J, Shaoying Liu, Cui, J, Liu, S, Bai, H, Bai, G, Guangquan Xu, Xi Zheng, Shao, H, Hongpeng Bai, Xu, G, Zheng, X, Weizhi Meng, Jingyi Cui   +17 more
core   +1 more source

A Solution for Exosome‐Based Analysis: Surface‐Enhanced Raman Spectroscopy and Artificial Intelligence

Advanced Intelligent Discovery, EarlyView.
Exosomes are emerging as powerful biomarkers for disease diagnosis and monitoring. This review highlights the integration of surface‐enhanced Raman spectroscopy with artificial intelligence to enhance molecular fingerprinting of exosomes. Machine learning and deep learning techniques improve spectral interpretation, enabling accurate classification of ...
Munevver Akdeniz, Zakarya Al‐Shaebi, Omer Aydin   +2 more
wiley   +1 more source

High-frequency Feature Masking-based Adversarial Attack Algorithm [PDF]

Jisuanji kexue
Deep neural networks have achieved widespread application in the field of imagerecognition,however,their complex structures make them vulnerable to adversarial attacks.Constructing adversarial examples that are imperceptible to the human eye is crucial ...
WANG Liuyi, ZHOU Chun, ZENG Wenqiang, HE Xingxing, MENG Hua
doaj   +1 more source

Restricted Evasion Attack: Generation of Restricted-Area Adversarial Example

, 2019
Deep neural networks (DNNs) show superior performance in image and speech recognition. However, adversarial examples created by adding a little noise to an original sample can lead to misclassification by a DNN.
Hyun Kwon, Yoon, Hyunsoo, Hyunsoo Yoon, Daeseon Choi, Kwon, Hyun, Choi, Daeseon   +5 more
core   +1 more source