Results 91 to 100 of about 8,744,617 (299)

High‐Entropy Alloy Interlayers Toward Advanced Joining for High‐Performance Structural Applications: Current Progress and Emerging Challenges

Advanced Engineering Materials, EarlyView.
HEA interlayers offer a versatile route for joining high‐performance structural materials. Their compositional and structural design regulates interfacial reactions, suppresses brittle IMCs, and improves metallurgical bonding. Sandwich interlayers further integrate defect healing with precipitation strengthening, enabling improved strength–ductility ...
Lin Yuan, Jiafen Song, Jinglong Li, Jiangtao Xiong, Tao Yang   +4 more
wiley   +1 more source

Let’s Talk about Bias and Diversity in Data, Software, and Institutions

, 2020
Presented online on November 20, 2020 at 12:00 p.m.Tiffany Deng leads the Responsible AI Program Management Team at Google where she is focused on helping people build products that work for everyone.
Gontijo Lopes, Raphael, Deng, Tiffany, Isbell, Charles L., Desai, Deven   +3 more
core   +1 more source

3D‐Printed Titanium Gyroid Scaffold Structure Integrated With Tough Hybrid Materials for Cartilage Replacement

Advanced Engineering Materials, EarlyView.
This study proposes a potential device design for joint cartilage replacement. Silica‐polytetrahydrofuran (SiO2‐PolyTHF) hybrids with customizable mechanical properties were developed to mimic the characteristics of a natural meniscus. These were synthesized through a two‐pot sol–gel hybrid process.
Yu‐Chien Lin, Gloria R. Young, Francesca Tallia, Reece N. Oosterbeek, Shaaz Ghouse, Edward Spiers, Nupur Kohli, Tom Briggs, Marc Masen, Siwei Li, Ren‐Jei Chung, Jonathan R. T. Jeffers, Julian R. Jones   +12 more
wiley   +1 more source

Security in Machine Learning (ML) Workflows

International Journal of Computing and Engineering
Purpose: This paper addresses the comprehensive security challenges inherent in the lifecycle of machine learning (ML) systems, including data collection, processing, model training, evaluation, and deployment. The imperative for robust security mechanisms within ML workflows has become increasingly paramount in the rapidly advancing field of ML, as ...
Dinesh Reddy Chittibala, Srujan Reddy Jabbireddy   +1 more
openaire   +1 more source

Machine learning and its applications in reliability analysis systems [PDF]

, 1994
In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs).
Hong, Hui-ling
core  

Artificial Intelligence and Machine Learning in Cybersecurity: Opportunities and Challenges

, 2023
Artificial intelligence (AI) and machine learning (ML) are transforming the field of cybersecurity by providing new tools and methods to detect, analyze, and respond to cyber threats.
M.Ruthvik Mohan
core   +1 more source

Investigation of Resonance Characteristics of More Than 10 000 Metal‐Coated Silicon MEMS Cantilevers Using Machine Learning

Advanced Engineering Materials, EarlyView.
More than 10 000 metal‐coated silicon MEMS cantilevers are fabricated and characterized to build a large‐scale experimental dataset spanning multiple vibrational modes. Machine learning algorithms are benchmarked to predict resonance frequency and quality factor, with ensemble based models achieving accuracy approaching experimental uncertainty.
Sina Zare Pakzad, Saurabh Thorat, Andre Loch Gesing, Dominik Huber, Christopher Zellner, Patrick Egger, Georg Pfusterschmied, Ulrich Schmid, Michael Schneider   +8 more
wiley   +1 more source

Machine Learning (ML) library in Linux kernel

CoRR
Linux kernel is a huge code base with enormous number of subsystems and possible configuration options that results in unmanageable complexity of elaborating an efficient configuration. Machine Learning (ML) is approach/area of learning from data, finding patterns, and making predictions without implementing algorithms by developers that can introduce ...
openaire   +3 more sources

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

ML-Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies

CoRR, 2018
Poster, selected for the 2nd Reproducibility in Machine Learning Workshop at ICML 2018, Stockholm ...
Correa Publio, Gustavo, Esteves, Diego, Ławrynowicz, Agnieszka, Panov, Panče, Soldatova, Larisa, Soru, Tommaso, Vanschoren, J., Zafar, Hamid   +7 more
openaire   +3 more sources