Advances in machine learning methods provide tools that have broad
applicability in scientific research. These techniques are being applied across
the diversity of nuclear physics research topics, leading to advances that will
facilitate scientific discoveries and societal applications.
This Review gives a snapshot of nuclear physics research which has been
transformed by machine learning techniques.

该论文综述了机器学习方法在核物理研究中的广泛应用，推动了科学发现和社会应用的进步。

核物理中的机器学习

Machine Learning in Nuclear Physics

Online and in the real world, communities are bonded together by emotional
consensus around core issues. Emotional responses to scientific findings often
play a pivotal role in these core issues. When there is too much diversity of
opinion on topics of science, emotions flare up and give rise to conflict. This
conflict threatens positive outcomes for research. Emotions have the power to
shape how people process new information. They can color the public's
understanding of science, motivate policy positions, even change lives. And yet
little work has been done to evaluate the public's emotional response to
science using quantitative methods. In this paper, we use a dataset of
responses to scholarly articles on Facebook to analyze the dynamics of
emotional valence, intensity, and diversity. We present a novel way of
weighting click-based reactions that increases their comprehensibility, and use
these weighted reactions to develop new metrics of aggregate emotional
responses. We use our metrics along with LDA topic models and statistical
testing to investigate how users' emotional responses differ from one
scientific topic to another. We find that research articles related to gender,
genetics, or agricultural/environmental sciences elicit significantly different
emotional responses from users than other research topics. We also find that
there is generally a positive response to scientific research on Facebook, and
that articles generating a positive emotional response are more likely to be
widely shared---a conclusion that contradicts previous studies of other social
media platforms.

通过分析 Facebook 上对学术文章的回应，本研究使用一种新颖的方法来评估公众的情感反应，基于 LDA 主题模型和统计测试，研究发现性别、遗传学以及农业 / 环境科学等主题引发用户的情感反应显著不同，Facebook 上具有积极情感反应的文章更容易被广泛分享，与其他社交媒体平台的先前研究得出的结论相反。

测量 Facebook 对研究的点赞反应的多样性

Measuring the Diversity of Facebook Reactions to Research

The Web has made it possible to harness human cognition en masse to achieve
new capabilities. Some of these successes are well known; for example Wikipedia
has become the go-to place for basic information on all things; Duolingo
engages millions of people in real-life translation of text, while
simultaneously teaching them to speak foreign languages; and fold.it has
enabled public-driven scientific discoveries by recasting complex biomedical
challenges into popular online puzzle games. These and other early successes
hint at the tremendous potential for future crowd-powered capabilities for the
benefit of health, education, science, and society. In the process, a new field
called Human Computation has emerged to better understand, replicate, and
improve upon these successes through scientific research. Human Computation
refers to the science that underlies online crowd-powered systems and was the
topic of a recent visioning activity in which a representative cross-section of
researchers, industry practitioners, visionaries, funding agency
representatives, and policy makers came together to understand what makes
crowd-powered systems successful. Teams of experts considered past, present,
and future human computation systems to explore which kinds of crowd-powered
systems have the greatest potential for societal impact and which kinds of
research will best enable the efficient development of new crowd-powered
systems to achieve this impact. This report summarize the products and findings
of those activities as well as the unconventional process and activities
employed by the workshop, which were informed by human computation research.

该研究总结了一个名为 ' 人类计算 ' 的新领域，介绍了网络系统中人力驱动的成功案例以及如何通过科学研究改进在线群体驱动系统，以实现其潜力最大化，推进社会进步。