Results 41 to 50 of about 85,147 (260)

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +1 more source

Outlier Robust Adversarial Training

, 2023
Supervised learning models are challenged by the intrinsic complexities of training data such as outliers and minority subpopulations and intentional attacks at inference time with adversarial samples. While traditional robust learning methods and the recent adversarial training approaches are designed to handle each of the two challenges, to date, no ...
Hu, Shu, Yang, Zhenhuan, Wang, Xin, Ying, Yiming, Lyu, Siwei   +4 more
openaire   +2 more sources

All‐Optical Reconfigurable Physical Unclonable Function for Sustainable Security

Advanced Materials, EarlyView.
An all‐optical reconfigurable physical unclonable function (PUF) is demonstrated using plasmonic coupling–induced sintering of optically trapped gold nanoparticles, where Brownian motion serves as a robust entropy source. The resulting optical PUF exhibits high encoding density, strong resistance to modeling attacks, and practical authentication ...
Jang‐Kyun Kwak, Changgyun Moon, Hyun‐Bin Yu, Sunkook Kim, Dong‐Hwan Kim   +4 more
wiley   +1 more source

Adversarially Robust Topological Inference

, 2022
The distance function to a compact set plays a crucial role in the paradigm of topological data analysis. In particular, the sublevel sets of the distance function are used in the computation of persistent homology -- a backbone of the topological data analysis pipeline.
Vishwanath, Siddharth, Sriperumbudur, Bharath K., Fukumizu, Kenji, Kuriki, Satoshi   +3 more
openaire   +2 more sources

From the Discovery of the Giant Magnetocaloric Effect to the Development of High‐Power‐Density Systems

Advanced Materials Technologies, EarlyView.
The article overviews past and current efforts on caloric materials and systems, highlighting the contributions of Ames National Laboratory to the field. Solid‐state caloric heat pumping is an innovative method that can be implemented in a wide range of cooling and heating applications.
Agata Czernuszewicz, Yaroslav Mudryk, Jun Cui, Lucas Griffith, Duane D. Johnson, Julie Slaughter   +5 more
wiley   +1 more source

Avoiding catastrophic overfitting in fast adversarial training with adaptive similarity step size.

PLoS ONE
Adversarial training has become a primary method for enhancing the robustness of deep learning models. In recent years, fast adversarial training methods have gained widespread attention due to their lower computational cost.
Jie-Chao Zhao, Jin Ding, Yong-Zhi Sun, Ping Tan, Ji-En Ma, You-Tong Fang   +5 more
doaj   +1 more source

PEGAT: Prediction Error-Guided Adversarial Training to Enhance Robustness of Deep Learning Models in Autonomous Vehicles

IEEE Access
Adversarial training is a widely used method to improve the robustness of deep learning models in various applications. Although adversarial training enhances the robustness of the target model, it also suffers from an accuracy versus robustness trade ...
Manzoor Hussain, Zhengyu Shang, Ahmed Dawod Mohammed Ibrahum, Jang-Eui Hong   +3 more
doaj   +1 more source

Improving Adversarial Robustness via Distillation-Based Purification

Applied Sciences, 2023
Despite the impressive performance of deep neural networks on many different vision tasks, they have been known to be vulnerable to intentionally added noise to input images.
Inhwa Koo, Dong-Kyu Chae, Sang-Chul Lee
doaj   +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, Zhi-Quan Luo   +4 more
openaire   +2 more sources

Learning Highly Dynamic Skills Transition for Quadruped Jumping Through Constrained Space

Advanced Robotics Research, EarlyView.
A quadruped robot masters dynamic jumps through constrained spaces with animal‐inspired moves and intelligent vision control. This hierarchical learning approach combines imitation of biological agility with real‐time trajectory planning. Although legged animals are capable of performing explosive motions while traversing confined spaces, replicating ...
Zeren Luo, Jiahui Zhang, Yimin Han, Ji Ma, Minghao Lu, Ioannis Havoutis, Peng Lu   +6 more
wiley   +1 more source