Results 91 to 100 of about 172,371 (266)
Advanced Science, EarlyView.This review comprehensively summarizes the atomic defects in TMDs for their applications in sustainable energy storage devices, along with the latest progress in ML methodologies for high‐throughput TEM data analysis, offering insights on how ML‐empowered microscopy facilitates bridging structure–property correlation and inspires knowledge for precise ...
Zheng Luo +6 more
wiley +1 more source
Generating Natural Adversarial Examples
, 2017 Due to their complex nature, it is hard to characterize the ways in which machine learning models can misbehave or be exploited when deployed. Recent work on adversarial examples, i.e. inputs with minor perturbations that result in substantially different model predictions, is helpful in evaluating the robustness of these models by exposing the ...
Zhao, Zhengli +2 more
openaire +2 more sources
Adversarial Examples in the Physical World [PDF]
, 2018 Most existing machine learning classifiers are highly vulnerable to adversarial examples. An adversarial example is a sample of input data which has been modified very slightly in a way that is intended to cause a machine learning classifier to misclassify it.
Kurakin, Alexey +2 more
openaire +2 more sources
Engineering Immune Cell to Counteract Aging and Aging‐Associated Diseases
Advanced Science, EarlyView.This review highlights a paradigm shift in which advanced immune cell therapies, initially developed for cancer, are now being harnessed to combat aging. By engineering immune cells to selectively clear senescent cells and remodel pro‐inflammatory tissue microenvironments, these strategies offer a novel and powerful approach to delay age‐related ...
Jianhua Guo +5 more
wiley +1 more source
Synthesizing Robust Adversarial Examples
, 2017 Standard methods for generating adversarial examples for neural networks do not consistently fool neural network classifiers in the physical world due to a combination of viewpoint shifts, camera noise, and other natural transformations, limiting their relevance to real-world systems.
Athalye, Anish +3 more
openaire +2 more sources
Advanced Science, EarlyView.Schematic illustration of SiNDs composite materials synthesis and its internal photophysical process mechanism. And an AI‐assisted dynamic information encryption process. ABSTRACT Persistent luminescence materials typically encounter an intrinsic trade‐off between high phosphorescence quantum yield (PhQY) and ultralong phosphorescence lifetime.
Yulu Liu +9 more
wiley +1 more source
A survey of practical adversarial example attacks
Cybersecurity, 2018 Adversarial examples revealed the weakness of machine learning techniques in terms of robustness, which moreover inspired adversaries to make use of the weakness to attack systems employing machine learning.
Lu Sun, Mingtian Tan, Zhe Zhou
doaj +1 more source
Adversarial Examples and Metrics
, 2020 25 pages, 1 figure, under submission, fixe typos from previous ...
Döttling, Nico +3 more
openaire +2 more sources
Advanced Energy and Sustainability Research, EarlyView.This study proposes a method to increase the value of solar power in balancing markets by managing prediction errors. The approach models prediction uncertainties and quantifies reserve requirements based on a probabilistic model. This enables the more reliable participation of photovoltaic plants in balancing markets across multiple sites, especially ...
Jindan Cui +3 more
wiley +1 more source
Cross-Gen: An Efficient Generator Network for Adversarial Attacks on Cross-Modal Hashing Retrieval
Future InternetResearch on deep neural network (DNN)-based multi-dimensional data visualization has thoroughly explored cross-modal hash retrieval (CMHR) systems, yet their vulnerability to malicious adversarial examples remains evident.
Chao Hu +7 more
doaj +1 more source