Hasil untuk "Computer Science"

Brian L. Sullivan, Jocelyn L Aycrigg, Jessie Barry et al.

W. Banzhaf, F. Francone, Robert E. Keller et al.

T. Cech, M. Waldrop

D. Watts

Harold Abelson, Gerald J. Sussman

From the Publisher: With an analytical and rigorous approach to problem solving and programming techniques,this book is oriented toward engineering. Structure and Interpretation of Computer Programs emphasizes the central role played by different approaches to dealing with time in computational models. Its unique approach makes it appropriate for an introduction to computer science courses,as well as programming languages and program design.

R. Sawyer

Dd Wang, Js Chen, 王东东

A. French

Franziska Frankfurter

Christina Kluge

Gwo-jen Hwang, Li-Hsueh Yang, Sheng-Yuan Wang

Lisa R. Goldberg

Judea Pearl is on a mission to change the way we interpret data. An eminent professor of computer science, Pearl has documented his research and opinions in scholarly books and papers. Now, he has ...

Lara K. T. Smetana, R. Bell

Jeetendra Kumar, Rashmi Gupta, Suvarna Sharma et al.

Presents corrections to the paper, (Corrections to “IoT-Enabled Advanced Water Quality Monitoring System for Pond Management and Environmental Conservation”).

Mathieu Calvat, Chris Bean, Dhruv Anjaria et al.

Abstract To leverage advancements in machine learning for metallic materials design and property prediction, it is crucial to develop a data-reduced representation of metal microstructures that surpasses the limitations of current physics-based discrete microstructure descriptors. This need is particularly relevant for metallic materials processed through additive manufacturing, which exhibit complex hierarchical microstructures that cannot be adequately described using the conventional metrics typically applied to wrought materials. Furthermore, capturing the spatial heterogeneity of microstructures at the different scales is necessary within such framework to accurately predict their properties. To address these challenges, we propose the physical spatial mapping of metal diffraction latent space features. This approach integrates (i) point diffraction data encoding via variational autoencoders or contrastive learning and (ii) the physical mapping of the encoded values. Together, these steps offer a method to comprehensively describe metal microstructures. We demonstrate this approach on a wrought and additively manufactured alloy, showing that it effectively encodes microstructural information and enables direct identification of microstructural heterogeneity not directly possible by physics-based models. This data-reduced microstructure representation opens the application of machine learning models in accelerating metallic material design and accurately predicting their properties.

Hassan-Roland Nasser, Marianne Cockburn, Marie Schneider

Studying animals’ rhythmicity provides insights into their physiological and psychological states. The degree of functional coupling (DFC) is one of the algorithms available to assess rhythmicity in activity-related time series data, such as accelerometer or GPS data. However, DFC computation is complex, as it includes frequency spectrum analysis and statistical significance testing. This paper introduces digiRhythm, an R package that makes the DFC-based rhythmicity analysis easily accessible. Beyond the DFC, the package includes an additional set of tools, which are crucial for rhythmicity investigations, such as actogram generation, daily activity visualization, and diurnality index computation.