Data is a critical element in any discovery process. In the last decades, we
observed exponential growth in the volume of available data and the technology
to manipulate it. However, data is only practical when one can structure it for
a well-defined task. For instance, we need a corpus of text broken into
sentences to train a natural language machine-learning model. In this work, we
will use the token \textit{dataset} to designate a structured set of data built
to perform a well-defined task. Moreover, the dataset will be used in most
cases as a blueprint of an entity that at any moment can be stored as a table.
Specifically, in science, each area has unique forms to organize, gather and
handle its datasets. We believe that datasets must be a first-class entity in
any knowledge-intensive process, and all workflows should have exceptional
attention to datasets' lifecycle, from their gathering to uses and evolution.
We advocate that science and engineering discovery processes are extreme
instances of the need for such organization on datasets, claiming for new
approaches and tooling. Furthermore, these requirements are more evident when
the discovery workflow uses artificial intelligence methods to empower the
subject-matter expert. In this work, we discuss an approach to bringing
datasets as a critical entity in the discovery process in science. We
illustrate some concepts using material discovery as a use case. We chose this
domain because it leverages many significant problems that can be generalized
to other science fields.

数据集在科学发现过程中的组织和应用至关重要，也应该成为任何知识密集型过程中的一等公民，并且应该引起人们对数据集生命周期的重视和关注，尤其是在使用人工智能方法来探索科学知识时。

加速科学的数据集工程白皮书

Position Paper on Dataset Engineering to Accelerate Science

In this paper, we argue that the way we have been training and evaluating ML
models has largely forgotten the fact that they are applied in an organization
or societal context as they provide value to people. We show that with this
perspective we fundamentally change how we evaluate, select and deploy ML
models - and to some extent even what it means to learn. Specifically, we
stress that the notion of value plays a central role in learning and
evaluating, and different models may require different learning practices and
provide different values based on the application context they are applied. We
also show that this concretely impacts how we select and embed models into
human workflows based on experimental datasets. Nothing of what is presented
here is hard: to a large extent is a series of fairly trivial observations with
massive practical implications.

本文指出机器学习模型在组织或社会环境中的应用为人们提供价值，因此应该重新评估和选择模型。同时，强调了学习和评估中的价值观念应该起到至关重要的作用，并且不同的模型需要在不同的应用环境中学习和提供不同的价值。最后，提出了这种新的视角和方法对实验数据和人类工作流嵌入模型的影响。

重新思考和重新计算机器学习模型的价值

Rethinking and Recomputing the Value of ML Models

This work describes an automatic news chatbot that draws content from a
diverse set of news articles and creates conversations with a user about the
news. Key components of the system include the automatic organization of news
articles into topical chatrooms, integration of automatically generated
questions into the conversation, and a novel method for choosing which
questions to present which avoids repetitive suggestions. We describe the
algorithmic framework and present the results of a usability study that shows
that news readers using the system successfully engage in multi-turn
conversations about specific news stories.

本论文介绍了一个从多样化的新闻文章中提取主题并自动生成对话的聊天机器人，其关键组成部分包括新闻文章的主题分组、自动生成的问题集成到对话中以及通过一种新颖的方法选择问题，以避免重复性建议。我们描述了算法框架，并呈现了一项可用性研究的结果，表明使用该系统的新闻读者成功地参与了关于特定新闻故事的多轮对话。

What's The Latest? A Question-driven News Chatbot

Networks are a fundamental tool for understanding and modeling complex
systems in physics, biology, neuroscience, engineering, and social science.
Many networks are known to exhibit rich, lower-order connectivity patterns that
can be captured at the level of individual nodes and edges. However,
higher-order organization of complex networks---at the level of small network
subgraphs---remains largely unknown. Here we develop a generalized framework
for clustering networks based on higher-order connectivity patterns. This
framework provides mathematical guarantees on the optimality of obtained
clusters and scales to networks with billions of edges. The framework reveals
higher-order organization in a number of networks including information
propagation units in neuronal networks and hub structure in transportation
networks. Results show that networks exhibit rich higher-order organizational
structures that are exposed by clustering based on higher-order connectivity
patterns.

基于高阶连接模式的聚类可以揭示网络的高阶组织结构，此框架对于大规模网络的优化性能具有数学保证。