Theory In, Theory Out: The Uses of Social Theory in Machine Learning for Social Science [0.03%]
从理论到实践：社会学理论在社会科学机器学习中的应用与展望
Jason Radford,Kenneth Joseph
Jason Radford
Research at the intersection of machine learning and the social sciences has provided critical new insights into social behavior. At the same time, a variety of issues have been identified with the machine learning models used to analyze so...
Deep Learning Optimizes Data-Driven Representation of Soil Organic Carbon in Earth System Model Over the Conterminous United States [0.03%]
基于深度学习的美国连土地区土壤有机碳数据驱动表征的优化及在地球系统模型中的应用
Feng Tao,Zhenghu Zhou,Yuanyuan Huang et al.
Feng Tao et al.
Soil organic carbon (SOC) is a key component of the global carbon cycle, yet it is not well-represented in Earth system models to accurately predict global carbon dynamics in response to climate change. This novel study integrated deep lear...
Variational Autoencoder Modular Bayesian Networks for Simulation of Heterogeneous Clinical Study Data [0.03%]
异质性临床研究数据的变分自编码模块贝叶斯网络模拟方法
Luise Gootjes-Dreesbach,Meemansa Sood,Akrishta Sahay et al.
Luise Gootjes-Dreesbach et al.
In the area of Big Data, one of the major obstacles for the progress of biomedical research is the existence of data "silos" because legal and ethical constraints often do not allow for sharing sensitive patient data from clinical studies a...
Improving Diagnosis of Depression With XGBOOST Machine Learning Model and a Large Biomarkers Dutch Dataset (n = 11,081) [0.03%]
基于XGBOOST机器学习模型和大规模生物标志物荷兰数据集改进抑郁症诊断(n=11,081)
Amita Sharma,Willem J M I Verbeke
Amita Sharma
Machine Learning has been on the rise and healthcare is no exception to that. In healthcare, mental health is gaining more and more space. The diagnosis of mental disorders is based upon standardized patient interviews with defined set of q...
Leveraging Big Data and Analytics to Improve Food, Energy, and Water System Sustainability [0.03%]
利用大数据和分析改进食品、能源和水系统的可持续性
Joshua Pitts,Sucharita Gopal,Yaxiong Ma et al.
Joshua Pitts et al.
With the world population projected to grow significantly over the next few decades, and in the presence of additional stress caused by climate change and urbanization, securing the essential resources of food, energy, and water is one of t...
FoodKG: A Tool to Enrich Knowledge Graphs Using Machine Learning Techniques [0.03%]
FoodKG：使用机器学习技术丰富知识图谱的工具
Mohamed Gharibi,Arun Zachariah,Praveen Rao
Mohamed Gharibi
While there exist a plethora of datasets on the Internet related to Food, Energy, and Water (FEW), there is a real lack of reliable methods and tools that can consume these resources. This hinders the development of novel decision-making ap...
Quantitatively Measuring Privacy in Interactive Query Settings Within RDBMS Framework [0.03%]
在关系型数据库管理系统框架下的交互查询环境中定量测量隐私属性
Muhammad Imran Khan,Simon N Foley,Barry O&#x;Sullivan
Muhammad Imran Khan
Little attention has been paid to the measurement of risk to privacy in Database Management Systems, despite their prevalence as a modality of data access. This paper proposes PriDe, a quantitative privacy metric that provides a measure (pr...
Agricultural Drought Monitoring via the Assimilation of SMAP Soil Moisture Retrievals Into a Global Soil Water Balance Model [0.03%]
通过SMAP土壤湿度同化进行全球农业干旱监测
Iliana E Mladenova,John D Bolten,Wade Crow et al.
Iliana E Mladenova et al.
From an agricultural perspective, drought refers to an unusual deficiency of plant available water in the root-zone of the soil profile. This paper focuses on evaluating the benefit of assimilating soil moisture retrievals from the Soil Moi...
Unsupervised Word Embedding Learning by Incorporating Local and Global Contexts [0.03%]
结合局部和全局上下文的无监督词嵌入学习方法
Yu Meng,Jiaxin Huang,Guangyuan Wang et al.
Yu Meng et al.
Word embedding has benefited a broad spectrum of text analysis tasks by learning distributed word representations to encode word semantics. Word representations are typically learned by modeling local contexts of words, assuming that words ...
Commentary: A robust data-driven approach identifies four personality types across four large data sets [0.03%]
评论：一种稳健的数据驱动方法在四大数据集中识别出四种人格类型
Kentaro Katahira,Yoshihiko Kunisato,Yuichi Yamashita et al.
Kentaro Katahira et al.