Results 61 to 70 of about 27,717 (260)

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

Adversarial Examples: Opportunities and Challenges [PDF]

IEEE Transactions on Neural Networks and Learning Systems, 2019
16 pages, 13 figures, 5 ...
Jiliang Zhang 0002, Chen Li
openaire   +3 more sources

On‐Chip Photonic Neural Network Architectures

Advanced Optical Materials, EarlyView.
This review presents a comprehensive overview of on‐chip photonic neural network architectures, covering key photonic building blocks, representative network types, and emerging applications. Recent advances, implementation challenges, and future directions are examined, highlighting the potential of integrated photonics to enable ultrafast, energy ...
Seokjin Hong, Berkay Neseli, Seungyeon Cho, Seungsoo Lee, Yeonjun Kim, Seungyoon Choi, Hyo‐Hoon Park, Hamza Kurt   +7 more
wiley   +1 more source

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

Selective Untargeted Evasion Attack: An Adversarial Example That Will Not Be Classified as Certain Avoided Classes

IEEE Access, 2019
Deep neural networks (DNNs) have useful applications in machine learning tasks involving recognition and pattern analysis. Despite the favorable applications of DNNs, these systems can be exploited by adversarial examples.
Hyun Kwon, Yongchul Kim, Hyunsoo Yoon, Daeseon Choi   +3 more
doaj   +1 more source

DualFlow: Generating imperceptible adversarial examples by flow field and normalize flow-based model

Frontiers in Neurorobotics, 2023
Recent adversarial attack research reveals the vulnerability of learning-based deep learning models (DNN) against well-designed perturbations. However, most existing attack methods have inherent limitations in image quality as they rely on a relatively ...
Renyang Liu, Renyang Liu, Xin Jin, Xin Jin, Dongting Hu, Jinhong Zhang, Jinhong Zhang, Yuanyu Wang, Jin Zhang, Wei Zhou, Wei Zhou   +10 more
doaj   +1 more source

Information Transmission Strategies for Self‐Organized Robotic Aggregation

Advanced Robotics Research, EarlyView.
In this review, we discuss how information transmission influences the neighbor‐based self‐organized aggregation of swarm robots. We focus specifically on local interactions regarding information transfer and categorize previous studies based on the functions of the information exchanged.
Shu Leng, Zihan Li, Yifeng Shao, Jinhao Liu, Li Yu, Shuguang Li   +5 more
wiley   +1 more source

Electronic music assassin: towards imperceptible physical adversarial attacks against black-box automatic speech recognitions

Cybersecurity
Speech recognition technology has brought revolutionary changes to our lives, but existing work has demonstrated the feasibility of using adversarial examples (AEs) to mislead speech recognition systems. Most existing adversarial attacks are designed for
Ruiyuan Li
doaj   +1 more source

Contrastive Learning with Adversarial Examples

CoRR, 2020
NeurIPS ...
Chih-Hui Ho, Nuno Vasconcelos
openaire   +3 more sources

Continual Learning for Multimodal Data Fusion of a Soft Gripper

Advanced Robotics Research, EarlyView.
Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley   +1 more source