We propose and study a data-driven framework for identifying traffic
congestion functions (numerical relationships between observations of
macroscopic traffic variables) at global scale and segment-level granularity.
In contrast to methods that estimate a separate set of parameters for each
roadway, ours learns a single black-box function over all roadways in a
metropolitan area. First, we pool traffic data from all segments into one
dataset, combining static attributes with dynamic time-dependent features.
Second, we train a feed-forward neural network on this dataset, which we can
then use on any segment in the area. We evaluate how well our framework
identifies congestion functions on observed segments and how it generalizes to
unobserved segments and predicts segment attributes on a large dataset covering
multiple cities worldwide. For identification error on observed segments, our
single data-driven congestion function compares favorably to segment-specific
model-based functions on highway roads, but has room to improve on arterial
roads. For generalization, our approach shows strong performance across cities
and road types: both on unobserved segments in the same city and on zero-shot
transfer learning between cities. Finally, for predicting segment attributes,
we find that our approach can approximate critical densities for individual
segments using their static properties.

提出并研究了一个数据驱动框架，用于在全球范围和分段细粒度上识别交通堵塞函数（宏观交通变量观测之间的数值关系）。与为每个道路估计单独的参数集的方法相比，我们的方法可以在都市区域的所有道路上学习一个单一的黑盒函数。

可扩展的段级交通拥堵函数学习

Scalable Learning of Segment-Level Traffic Congestion Functions

The users of endangered languages struggle to thrive in a digitally-mediated
world. We have developed an automated method for assessing how well every
language recognized by ISO 639 is faring in terms of digital language support.
The assessment is based on scraping the names of supported languages from the
websites of 143 digital tools selected to represent a full range of ways that
digital technology can support languages. The method uses Mokken scale analysis
to produce an explainable model for quantifying digital language support and
monitoring it on a global scale.

该研究开发了一种基于 Mokken 比例分析的自动化方法，用于评估 ISO 639 认可的每种语言在数字语言支持方面的表现，并在全球范围内监测。

全球范围内数字语言支持评估

Assessing Digital Language Support on a Global Scale

High resolution datasets of population density which accurately map
sparsely-distributed human populations do not exist at a global scale.
Typically, population data is obtained using censuses and statistical modeling.
More recently, methods using remotely-sensed data have emerged, capable of
effectively identifying urbanized areas. Obtaining high accuracy in estimation
of population distribution in rural areas remains a very challenging task due
to the simultaneous requirements of sufficient sensitivity and resolution to
detect very sparse populations through remote sensing as well as reliable
performance at a global scale. Here, we present a computer vision method based
on machine learning to create population maps from satellite imagery at a
global scale, with a spatial sensitivity corresponding to individual buildings
and suitable for global deployment. By combining this settlement data with
census data, we create population maps with ~30 meter resolution for 18
countries. We validate our method, and find that the building identification
has an average precision and recall of 0.95 and 0.91, respectively and that the
population estimates have a standard error of a factor ~2 or less. Based on our
data, we analyze 29 percent of the world population, and show that 99 percent
lives within 36 km of the nearest urban cluster. The resulting high-resolution
population datasets have applications in infrastructure planning, vaccination
campaign planning, disaster response efforts and risk analysis such as high
accuracy flood risk analysis.

利用机器学习的计算机视觉方法可从卫星图像中创建全球范围内的人口地图，该方法精度高，适用于全球部署，能够检测城市化地区，有助于基础设施规划、疫苗接种计划、灾难响应和高精度洪水风险分析等。

逐幢建筑绘制全球人口地图

Mapping the world population one building at a time

Computer vision methods that quantify the perception of urban environment are
increasingly being used to study the relationship between a city's physical
appearance and the behavior and health of its residents. Yet, the throughput of
current methods is too limited to quantify the perception of cities across the
world. To tackle this challenge, we introduce a new crowdsourced dataset
containing 110,988 images from 56 cities, and 1,170,000 pairwise comparisons
provided by 81,630 online volunteers along six perceptual attributes: safe,
lively, boring, wealthy, depressing, and beautiful. Using this data, we train a
Siamese-like convolutional neural architecture, which learns from a joint
classification and ranking loss, to predict human judgments of pairwise image
comparisons. Our results show that crowdsourcing combined with neural networks
can produce urban perception data at the global scale.

利用卷积神经网络从大规模的人类认知感知数据集中学习，在全球范围内量化城市环境感知现象的关系。