Results 91 to 100 of about 215,342 (268)

Multimodal Wearable Biosensing Meets Multidomain AI: A Pathway to Decentralized Healthcare

Advanced Science, EarlyView.
Multimodal biosensing meets multidomain AI. Wearable biosensors capture complementary biochemical and physiological signals, while cross‐device, population‐aware learning aligns noisy, heterogeneous streams. This Review distills key sensing modalities, fusion and calibration strategies, and privacy‐preserving deployment pathways that transform ...
Chenshu Liu, Haolin Fan, Minwoo Kim, Tong Zhou, Pinyi Yang, Lingdi Zhao, Yiran Wang, Ziyuan Che, Chia‐Wei Liu, Bingbing Li, Yangzhi Zhu   +10 more
wiley   +1 more source

Phase space sampling and operator confidence with generative adversarial networks

, 2017
We demonstrate that a generative adversarial network can be trained to produce Ising model configurations in distinct regions of phase space. In training a generative adversarial network, the discriminator neural network becomes very good a discerning ...
Mills, Kyle, Tamblyn, Isaac
core  

FAT: Federated Adversarial Training

, 2020
Federated learning (FL) is one of the most important paradigms addressing privacy and data governance issues in machine learning (ML). Adversarial training has emerged, so far, as the most promising approach against evasion threats on ML models. In this paper, we take the first known steps towards federated adversarial training (FAT) combining both ...
Zizzo, Giulio, Rawat, Ambrish, Sinn, Mathieu, Buesser, Beat   +3 more
openaire   +2 more sources

Probabilistic Modeling for Prediction Errors to Enhance Balancing Market Participation of Photovoltaic Systems: Error Threshold Estimation, Multisite Aggregation, and Overloading Effects

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, Xue Fang, Takashi Oozeki, Yuzuru Ueda   +3 more
wiley   +1 more source

Prostate MR Image Segmentation With Self-Attention Adversarial Training Based on Wasserstein Distance

IEEE Access, 2019
Prostate diseases are very common in men. Accurate segmentation of the prostate plays a significant role in further clinical treatment and diagnosis. There have been some methods that combine the segmentation network and generative adversarial network ...
Chengwei Su, Renxiang Huang, Chang Liu, Tailang Yin, Bo Du   +4 more
doaj   +1 more source

Generative Artificial Intelligence Shaping the Future of Agri‐Food Innovation

AgriFood: Journal of Agricultural Products for Food, EarlyView.
Emerging use cases of generative artificial intelligence in agri‐food innovation. ABSTRACT The recent surge in generative artificial intelligence (AI), typified by models such as GPT, diffusion models, and large vision‐language architectures, has begun to influence the agri‐food sector.
Jun‐Li Xu, Dimitrios Argyropoulos, Tamíris da Costa   +2 more
wiley   +1 more source

Fortify the Guardian, Not the Treasure: Resilient Adversarial Detectors

Mathematics
Adaptive adversarial attacks, where adversaries tailor their strategies with full knowledge of defense mechanisms, pose significant challenges to the robustness of adversarial detectors. In this paper, we introduce RADAR (Robust Adversarial Detection via
Raz Lapid, Almog Dubin, Moshe Sipper
doaj   +1 more source

Domain‐Aware Implicit Network for Arbitrary‐Scale Remote Sensing Image Super‐Resolution

Advanced Intelligent Discovery, EarlyView.
Although existing arbitrary‐scale image super‐resolution methods are flexible to reconstruct images with arbitrary scales, the characteristic of training distribution is neglected that there exists domain shift between samples of various scales. In this work, a Domain‐Aware Implicit Network (DAIN) is proposed to handle it from the perspective of domain
Xiaoxuan Ren, Qian Jiang, Xin Jin, Puming Wang, Xing Chu, Shaowen Yao, Shengfa Miao   +6 more
wiley   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +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