Knitting Ripples [0.03%]
涟漪编织法
Karen E Daniels,Mary Williard Elting
Karen E Daniels
Ripples are common in both biological systems and human clothes. Knitters have long exploited the ability of fabric to curl out of plane, by either removing or adding stitches to the material as it is created. Here, we present two knitting ...
Lots of Lovely Numbers, but Why Does Everyone Make It So Difficult? [0.03%]
数据很多，那么问题来了，为何大家把事情搞的这么复杂？
John Pullinger
John Pullinger
Based on keynote at the 4th International Conference on Administrative Data, Cardiff, UK, December 10, 2019. © 2020 The Author.
From Ethnomethodologist to Data Lover: How I Became a Keen Advocate for Using Administrative Data [0.03%]
从民族志学家到数据爱好者：我是如何成为一个热衷于使用行政数据的人的
Betsy Stanko
Betsy Stanko
This article is a personal reflection on a professional journey from ethnographer to keen analyst of administrative data. Using a knitter's analogy, I describe using the thread of administrative information, armed with one stick (social sci...
Electric Dreams of Ukiyo: A Series of Japanese Artworks Created by an Artificial Intelligence [0.03%]
浮世电梦：人工智能创作的日本艺术品系列
Gauthier Vernier,Hugo Caselles-Dupré,Peirre Fautrel
Gauthier Vernier
This opinion piece provides insight on the creation of artistic visuals using artificial intelligence. It describes how 3 artists evolving in this field decided to use technology in a derived way, with the help of researchers and enthusiast...
Avoid Oversimplifications in Machine Learning: Going beyond the Class-Prediction Accuracy [0.03%]
避免机器学习中的过度简化：超越类别预测准确率
Sung Yang Ho,Limsoon Wong,Wilson Wen Bin Goh
Sung Yang Ho
Class-prediction accuracy provides a quick but superficial way of determining classifier performance. It does not inform on the reproducibility of the findings or whether the selected or constructed features used are meaningful and specific...
Global and Local Tensor Factorization for Multi-criteria Recommender System [0.03%]
全局和局部张量分解在多准则推荐系统中的应用
Shuliang Wang,Jingting Yang,Zhengyu Chen et al.
Shuliang Wang et al.
In multi-criteria recommender systems, matrix factorization characterizes users and items via latent factor vectors inferred from user-item rating patterns. However, two-dimensional matrix factorization models may not be able to cope with t...
Michaël Trazzi,Roman V Yampolskiy
Michaël Trazzi
AI must understand human limitations to provide good service and safe interactions. Standardized data on human limits would be valuable in many domains but is not available. The data science community has to work on collecting and aggregati...
Gopal P Sarma,Erik Reinertsen;MLCVD Group
Gopal P Sarma
The intersection of medicine and machine learning (ML) has the potential to transform healthcare. We describe how physiology, a foundational discipline of medical training and practice with a rich quantitative history, could serve as a star...
Aaron M Ellison,Emery R Boose,Barbara S Lerner et al.
Aaron M Ellison et al.
Data provenance is a machine-readable summary of the collection and computational history of a dataset. Data provenance confers or adds value to a dataset, helps reproduce computational analyses, or validates scientific conclusions. The peo...
Random Forest Models for Accurate Identification of Coordination Environments from X-Ray Absorption Near-Edge Structure [0.03%]
基于X射线吸收近边结构的准确配位环境识别的随机森林模型
Chen Zheng,Chi Chen,Yiming Chen et al.
Chen Zheng et al.
Analyzing coordination environments using X-ray absorption spectroscopy has broad applications in solid-state physics and material chemistry. Here, we show that random forest models trained on 190,000 K-edge X-ray absorption near-edge struc...