Urban traffic emissions represent a significant concern due to their
detrimental impacts on both public health and the environment. Consequently,
decision-makers have flagged their reduction as a crucial goal. In this study,
we first analyze the correlation between traffic flux and pollution in the city
of Valencia, Spain. Our results demonstrate that traffic has a significant
impact on the levels of certain pollutants (especially $\text{NO}_\text{x}$).
Secondly, we develop an alarm system to predict if a street is likely to
experience unusually high traffic in the next 30 minutes, using an independent
three-tier level for each street. To make the predictions, we use traffic data
updated every 10 minutes and Long Short-Term Memory (LSTM) neural networks. We
trained the LSTM using traffic data from 2018, and tested it using traffic data
from 2019.

该研究分析了城市交通通量与污染之间的相关性，并开发了一个警报系统来预测未来 30 分钟内某条街道是否会遇到异常高的交通流量，使用每 10 分钟更新的交通数据和长短期记忆（LSTM）神经网络进行预测。

利用深度学习在瓦伦西亚市建立实时交通警报

Establishing a real-time traffic alarm in the city of Valencia with Deep  Learning

It is usually hard for a learning system to predict correctly on rare events
that never occur in the training data, and there is no exception for
segmentation algorithms. Meanwhile, manual inspection of each case to locate
the failures becomes infeasible due to the trend of large data scale and
limited human resource. Therefore, we build an alarm system that will set off
alerts when the segmentation result is possibly unsatisfactory, assuming no
corresponding ground truth mask is provided. One plausible solution is to
project the segmentation results into a low dimensional feature space; then
learn classifiers/regressors to predict their qualities. Motivated by this, in
this paper, we learn a feature space using the shape information which is a
strong prior shared among different datasets and robust to the appearance
variation of input data.The shape feature is captured using a Variational
Auto-Encoder (VAE) network that trained with only the ground truth masks.
During testing, the segmentation results with bad shapes shall not fit the
shape prior well, resulting in large loss values. Thus, the VAE is able to
evaluate the quality of segmentation result on unseen data, without using
ground truth. Finally, we learn a regressor in the one-dimensional feature
space to predict the qualities of segmentation results. Our alarm system is
evaluated on several recent state-of-art segmentation algorithms for 3D medical
segmentation tasks. Compared with other standard quality assessment methods,
our system consistently provides more reliable prediction on the qualities of
segmentation results.

本文通过学习不同数据集共享的先验形状信息构建了一个低维特征空间，然后使用 Variational Auto-Encoder（变分自编码器）来学习该特征空间，并将其用于无标签的分割结果质量评估，进而设计了一个报警系统来提供可靠的分割质量预测。

基于形状模型的分割算法报警系统

An Alarm System For Segmentation Algorithm Based On Shape Model

When securing complex infrastructures or large environments, constant
surveillance of every area is not affordable. To cope with this issue, a common
countermeasure is the usage of cheap but wide-ranged sensors, able to detect
suspicious events that occur in large areas, supporting patrollers to improve
the effectiveness of their strategies. However, such sensors are commonly
affected by uncertainty. In the present paper, we focus on spatially uncertain
alarm signals. That is, the alarm system is able to detect an attack but it is
uncertain on the exact position where the attack is taking place. This is
common when the area to be secured is wide such as in border patrolling and
fair site surveillance. We propose, to the best of our knowledge, the first
Patrolling Security Game model where a Defender is supported by a spatially
uncertain alarm system which non-deterministically generates signals once a
target is under attack. We show that finding the optimal strategy in arbitrary
graphs is APX-hard even in zero-sum games and we provide two (exponential time)
exact algorithms and two (polynomial time) approximation algorithms.
Furthermore, we analyse what happens in environments with special topologies,
showing that in linear and cycle graphs the optimal patrolling strategy can be
found in polynomial time, de facto allowing our algorithms to be used in
real-life scenarios, while in trees the problem is NP-hard. Finally, we show
that without false positives and missed detections, the best patrolling
strategy reduces to stay in a place, wait for a signal, and respond to it at
best. This strategy is optimal even with non-negligible missed detection rates,
which, unfortunately, affect every commercial alarm system. We evaluate our
methods in simulation, assessing both quantitative and qualitative aspects.

研究了在安全性方面，如何应对范围较大，常常有不确定信号的情况下，使用可提高安全策略有效性的巡逻模型，并提供了多种算法来应对不同拓扑结构，实现最佳策略的优化。