Results 121 to 130 of about 357,414 (246)

TAPAS: Datasets for Learning the Learning with Errors Problem

AI-powered attacks on Learning with Errors (LWE), an important hard math problem in post-quantum cryptography, rival or outperform "classical" attacks on LWE under certain parameter settings. Despite the promise of this approach, a dearth of accessible data limits AI practitioners' ability to study and improve these attacks.
Saxena, Eshika, Alfarano, Alberto, Charton, François, Wenger, Emily, Lauter, Kristin   +4 more
openaire   +2 more sources

Accelerated Discovery of High Performance Ni3S4/Ni3Mo HER Catalysts via Bayesian Optimization

Advanced Functional Materials, EarlyView.
Integrated workflow accelerates the catalyst discovery of hydrogen evolution reaction via Bayesian optimization. An experiment‐trained surrogate model proposes synthesis conditions, guiding iterative refinement using electrochemical performance metrics.
Namuersaihan Namuersaihan, Zhiqiang Zhao, Oliver J. Conquest, Ying Shu, Haoyue Sun, Chunjing Su, Qi Cheng, Aloysius Soon, Catherine Stampfl, Jun Huang   +9 more
wiley   +1 more source

CIDER: Cyber‐Security in Industrial IoT Using Deep Learning and Ring Learning with Errors

IET Cyber-Physical Systems
Traditional security measures such as access control and authentication need to be more effective against ever‐evolving threats. Moreover, security concerns increase as more industries shift towards adopting the industrial Internet of things (IIoT ...
Siu Ting Tsoi, Anish Jindal
doaj   +1 more source

Frontier Advances of Emerging High‐Entropy Anodes in Alkali Metal‐Ion Batteries

Advanced Functional Materials, EarlyView.
Recent advances in microscopic morphology control of high‐entropy anode materials for alkali metal‐ion batteries. Abstract With the growing demand for sustainable energy, portable energy storage systems have become increasingly critical. Among them, the development of rechargeable batteries is primarily driven by breakthroughs in electrode materials ...
Liang Du, Kaidi Zhang, Liang Wang, Di Zhao, Mengqing Hu, Mingli Li, Ming Zhou, Zhenzhen Wu, Mengyang Dong, Bo Fu, Porun Liu, Yun Wang, Hongxia Wang, Yuantong Gu, Lei Zhang   +14 more
wiley   +1 more source

Electrocatalytic Reduction of CO2 to Ethylene: Catalyst Design and Synchrotron‐Based Characterizations

Advanced Functional Materials, EarlyView.
This review evaluates strategies for electrochemical CO2 reduction to ethylene, focusing on copper‐based catalyst design and microenvironment modulation to achieve industrial‐grade performance. By leveraging operando synchrotron‐based characterizations, we provide a multiscale understanding of dynamic structural transformations and key reaction ...
Meng Zhang, Zuolong Chen, Yimin A. Wu
wiley   +1 more source

Predictive reward-prediction errors of climbing fiber inputs integrate modular reinforcement learning with supervised learning.

PLoS Computational Biology
Although the cerebellum is typically associated with supervised learning algorithms, it also exhibits extensive involvement in reward processing. In this study, we investigated the cerebellum's role in executing reinforcement learning algorithms, with a ...
Huu Hoang, Shinichiro Tsutsumi, Masanori Matsuzaki, Masanobu Kano, Keisuke Toyama, Kazuo Kitamura, Mitsuo Kawato   +6 more
doaj   +1 more source

Artificial Intelligence as the Next Visionary in Liquid Crystal Research

Advanced Functional Materials, EarlyView.
The functions of AI in the research laboratory are becoming increasingly sophisticated, allowing the entire process of hypothesis formulation, material design, synthesis, experimental design, and reiterative testing to be automated. In our work, we conceive how the incorporation of AI in the laboratory environment will transform the role and ...
Mert O. Astam, Ouassim L'Karkouri, Danqing Liu   +2 more
wiley   +1 more source

Optoelectronic Synaptic Devices Using Molecular Telluride Phase‐Change Inks for Three‐Factor Learning

Advanced Functional Materials, EarlyView.
Optoelectronic synaptic devices based on solution‐processed molecular telluride GST‐225 phase‐change inks are demonstrated for three‐factor learning. A global optical signal broadcast through a silicon waveguide induces non‐volatile conductance updates exclusively in locally electrically flagged memristors.
Kevin Portner, Dhananjeya Kumaar, Giacomo Torti, Killian Keller, Till Zellweger, Florian M. Schenk, Christoph Weilenmann, Daniel Chelladurai, Vara Prasad Jonnalagadda, Ghazi Sarwat Syed, Vanessa Wood, Jürg Leuthold, Alexandros Emboras, Maksym Yarema, Mathieu Luisier   +14 more
wiley   +1 more source

Learning from errors: unnecessary intensive care unit admissions. [PDF]

BMJ Case Rep, 2017
Rambarat CA, Winchester DE.
europepmc   +1 more source

From Bug to Feature: Harnessing Cross‐Sensitivity for Multiparametric Luminescence Sensing

Advanced Functional Materials, EarlyView.
Cross‐sensitivity in luminescence sensing is reframed from a limitation into a resource for multiparametric detection. Using ruby microspheres as a model system, cross‐sensitivity is quantitatively assessed and exploited through linear discriminant analysis, enabling simultaneous, correction‐free pressure and temperature sensing with a single ...
Nikita Panov, Liyan Ming, Stanislav Sinogeikin, Erving Ximendes, Daniel Jaque, Riccardo Marin   +5 more
wiley   +1 more source