Article
Oncology
Camille L. Stewart, Susanne G. Warner, James De Andrade, Andrew Nguyen, Martin Heslin
Summary: The majority of speakers preferred to be introduced with a professional form of address (such as Doctor/Professor), with this preference being consistent across all demographic groups evaluated. The distribution of guidelines to meeting moderators led to a significant increase in the use of professional forms of address during speaker introductions at the 2021 SSO annual meeting.
ANNALS OF SURGICAL ONCOLOGY
(2022)
Article
Social Sciences, Interdisciplinary
Sun-Ha Hong
Summary: Post-truth explores the public's irrationality influenced by platforms and data-driven systems. However, the commitment to facts and Reason dominates this collapse of epistemic consensus. This article reveals the reappropriation and mythologisation through performative invocations of facts, weaponising them against rivals, and the cultivation of a nostalgic past when facts were unquestioned, hindering normative debates around data-driven publics.
BIG DATA & SOCIETY
(2023)
Article
Computer Science, Artificial Intelligence
Qiang Zhang, Yaming Zheng, Qiangqiang Yuan, Meiping Song, Haoyang Yu, Yi Xiao
Summary: This technical review examines the problem of mixed noise pollution in hyperspectral imaging (HSI), providing analysis of noise in different noisy HSIs and discussing crucial points for programming HSI denoising algorithms. It presents a general HSI restoration model for optimization and comprehensively reviews existing HSI denoising methods, including model-driven, data-driven, and model-data-driven strategies. The advantages and disadvantages of each strategy are summarized and contrasted, and evaluation of denoising methods is provided using simulated and real experiments. The review also presents prospects for future HSI denoising methods.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Communication
Ashlin Lee
Summary: This paper introduces a theoretical framework called Informatic Personhood to better understand the relationships between subjects in the context of data and data technologies. The framework consists of two parts: The Informatic Context and The Informatic Person, which address structural developments around data and the relationships of individuals occurring across and through the Informatic Context. This framework aims to address the scale of data-mediated relationships and place subjects firmly in the foreground of data understanding.
INFORMATION COMMUNICATION & SOCIETY
(2021)
Article
Biodiversity Conservation
Fan Liu, Cui Wang, Muchen Luo, Shuling Zhou, Conghu Liu
Summary: This study evaluates the relationship between the agriculture economy, environment, and society using system theory and provides targeted suggestions for the sustainable development of Anhui's agriculture in China. By collecting relevant data and objective weighting of indicators, the comprehensive development level of regional agriculture is measured using the entropy weight method. The coupling coordination degree model is utilized to evaluate the coupling coordination level among the three subsystems of regional agriculture. The obstacle degree model is employed to identify key obstacle factors. The study presents recommendations such as reducing pesticide and agricultural plastic film usage, improving social support for agricultural science, technology, and education, and enhancing the competitiveness of animal husbandry in Anhui Province. This research provides theoretical and methodological support for the coupled and coordinated development of regional agricultural economy, ecology, and society (EES).
ECOLOGICAL INDICATORS
(2022)
Article
Green & Sustainable Science & Technology
Zia Mehrabi, Mollie J. McDowell, Vincent Ricciardi, Christian Levers, Juan Diego Martinez, Natascha Mehrabi, Hannah Wittman, Navin Ramankutty, Andy Jarvis
Summary: Access to big data and mobile technology is limited for small-scale farmers globally, particularly in developing countries where many farmers are unable to access these technologies. It is recommended that governments, development communities, and the private sector focus their efforts on promoting a digital inclusion agenda to provide all farmers with the opportunity for data-driven agriculture.
NATURE SUSTAINABILITY
(2021)
Article
Computer Science, Artificial Intelligence
Giuseppe Patane
Summary: In this article, the limitations of the Fuzzy transform are mentioned as its application is mainly limited to 1-D signals and 2-D regular grid data. The data-driven F-transform is then introduced along with its properties, construction methods, and efficient computation. The evaluation of the data-driven F-transform on large and arbitrary data is also discussed.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Mathematics, Applied
Michele Pavon, Esteban G. Tabak, Giulio Trigila
Summary: Erwin Schrodinger posed and partially solved the problem of finding the most likely random evolution between two continuous probability distributions in 1931/32. This article considers the situation when only samples of the two distributions are available, and proposes a novel iterative procedure inspired by Fortet-IPF-Sinkhorn algorithms. The new approach features constrained maximum likelihood estimation and importance sampling, mitigating the curse of dimensionality in high dimensions.
COMMUNICATIONS ON PURE AND APPLIED MATHEMATICS
(2021)
Editorial Material
Geosciences, Multidisciplinary
Wengang Zhang, Zhongqiang Liu, Mohammad Rezania
Summary: The field of geotechnical engineering has undergone a significant transformation in recent years due to the integration of data-driven models and advanced statistical techniques. These innovative approaches have the potential to address critical challenges and revolutionize geotechnical analyses.
Article
Engineering, Multidisciplinary
Aleksandr Dekhovich, O. Taylan Turan, Jiaxiang Yi, Miguel A. Bessa
Summary: Data-driven modeling in mechanics is advancing rapidly, but cooperation is hindered by the forgetting problem of artificial neural networks. The authors developed a continual learning method applied to solid mechanics for predicting history-dependent plasticity behavior. This work aims to foster cooperative strategies in the mechanics community.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2023)
Article
Automation & Control Systems
Amr Alanwar, Anne Koch, Frank Allgoewer, Karl Henrik Johansson
Summary: This paper discusses how to compute reachable sets directly from noisy data without a given system model. Several reachability algorithms are presented for different types of systems generating the data. For linear systems, an algorithm based on matrix zonotopes is proposed, which computes over-approximated reachable sets. Constrained matrix zonotopes are introduced to provide less conservative reachable sets at the cost of increased computational expenses and incorporate prior knowledge about the unknown system model. The approach is also extended to polynomial and nonlinear systems with theoretical guarantees of proper over-approximation.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Mechanics
Benjamin Herrmann, Peter J. Baddoo, Richard Semaan, Steven L. Brunton, Beverley J. McKeon
Summary: Resolvent analysis identifies the most responsive forcings and receptive states of a dynamic system based on its governing equations. A purely data-driven algorithm has been developed in this work to perform Resolvent analysis without recourse to the governing equations, demonstrating its applicability and data requirements. This method has potential to lower the barrier of entry to Resolvent research and applications by providing a completely equation-free and adjoint-free approach.
JOURNAL OF FLUID MECHANICS
(2021)
Article
Astronomy & Astrophysics
Gregory M. Green, Hans-Walter Rix, Leon Tschesche, Douglas Finkbeiner, Catherine Zucker, Edward F. Schlafly, Jan Rybizki, Morgan Fouesneau, Rene Andrae, Joshua Speagle
Summary: This study presents a data-driven model that accurately maps stellar parameters to broadband stellar photometry, demonstrating its effectiveness through training and testing on various datasets. The model rigorously links spectroscopic and photometric surveys, providing a simple and accurate method for predicting photometry in stellar evolutionary models.
ASTROPHYSICAL JOURNAL
(2021)
Article
Multidisciplinary Sciences
Peter W. Hatfield, Jim A. Gaffney, Gemma J. Anderson, Suzanne Ali, Luca Antonelli, Suzan Basegmez du Pree, Jonathan Citrin, Marta Fajardo, Patrick Knapp, Brendan Kettle, Bogdan Kustowski, Michael J. MacDonald, Derek Mariscal, Madison E. Martin, Taisuke Nagayama, Charlotte A. J. Palmer, J. Luc Peterson, Steven Rose, J. J. Ruby, Carl Shneider, Matt J. Streeter, Will Trickey, Ben Williams
Summary: High-energy-density physics studies matter at extremely high temperatures and densities, producing highly nonlinear plasmas. Understanding these extreme systems is challenging, but machine learning models and data-driven methods are reshaping exploration in this field.
Review
Chemistry, Multidisciplinary
Zhuo Wang, Zhehao Sun, Hang Yin, Xinghui Liu, Jinlan Wang, Haitao Zhao, Cheng Heng Pang, Tao Wu, Shuzhou Li, Zongyou Yin, Xue-Feng Yu
Summary: This article discusses the latest developments in data-driven scientific research in the field of materials science, focusing on frameworks, algorithms, databases, descriptors, and their applications in various areas. It emphasizes the opportunities and challenges in data-driven material innovation.
ADVANCED MATERIALS
(2022)