Results 61 to 70 of about 2,268,403 (339)

Active Learning‐Guided Accelerated Discovery of Ultra‐Efficient High‐Entropy Thermoelectrics

Advanced Materials, EarlyView.
An active learning framework is introduced for the accelerated discovery of high‐entropy chalcogenides with superior thermoelectric performance. Only 80 targeted syntheses, selected from 16206 possible combinations, led to three high‐performance compositions, demonstrating the remarkable efficiency of data‐driven guidance in experimental materials ...
Hanhwi Jang, Wooseok Lee, Hwa‐Jung Kim, Sohyang Cha, Hosun Shin, Won Bo Lee, Min‐Wook Oh, Yeon Sik Jung, YongJoo Kim   +8 more
wiley   +1 more source

Exploring Robust Features for Improving Adversarial Robustness

IEEE Transactions on Cybernetics
While deep neural networks (DNNs) have revolutionized many fields, their fragility to carefully designed adversarial attacks impedes the usage of DNNs in safety-critical applications. In this paper, we strive to explore the robust features which are not affected by the adversarial perturbations, i.e., invariant to the clean image and its adversarial ...
Hong Wang, Yuefan Deng, Shinjae Yoo, Yuewei Lin   +3 more
openaire   +3 more sources

Organic Electrochemical Transistors for Neuromorphic Devices and Applications

Advanced Materials, EarlyView.
Organic electrochemical transistors are emerging as promising platforms for neuromorphic devices that emulate neuronal and synaptic activities and can seamlessly integrate with biological systems. This review focuses on resultant organic artificial neurons, synapses, and integrated devices, with an emphasis on their ability to perform neuromorphic ...
Kexin Xiang, Jiajun Song, Hong Liu, Junxin Chen, Feng Yan   +4 more
wiley   +1 more source

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

Materials and System Design for Self‐Decision Bioelectronic Systems

Advanced Materials, EarlyView.
This review highlights how self‐decision bioelectronic systems integrate sensing, computation, and therapy into autonomous, closed‐loop platforms that continuously monitor and treat diseases, marking a major step toward intelligent, self‐regulating healthcare technologies.
Qiankun Zeng, Hong Liu, Yongheng Zhang, Youbin Zheng, Xuyin Ding, Mengni Zhu, Guoyue Shi, Yan Wang, Hossam Haick, Min Zhang   +9 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

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