The eco-toll estimation problem quantifies the expected environmental cost
(e.g., energy consumption, exhaust emissions) for a vehicle to travel along a
path. This problem is important for societal applications such as eco-routing,
which aims to find paths with the lowest exhaust emissions or energy need. The
challenges of this problem are three-fold: (1) the dependence of a vehicle's
eco-toll on its physical parameters; (2) the lack of access to data with
eco-toll information; and (3) the influence of contextual information (i.e. the
connections of adjacent segments in the path) on the eco-toll of road segments.
Prior work on eco-toll estimation has mostly relied on pure data-driven
approaches and has high estimation errors given the limited training data. To
address these limitations, we propose a novel Eco-toll estimation
Physics-informed Neural Network framework (Eco-PiNN) using three novel ideas,
namely, (1) a physics-informed decoder that integrates the physical laws of the
vehicle engine into the network, (2) an attention-based contextual information
encoder, and (3) a physics-informed regularization to reduce overfitting.
Experiments on real-world heavy-duty truck data show that the proposed method
can greatly improve the accuracy of eco-toll estimation compared with
state-of-the-art methods.

该论文提出了一种新的 Eco-PiNN 物理信息神经网络框架来解决环保通行费用估算问题，通过采用物理信息解码器、基于注意力的上下文信息编码器和物理信息正则化等三个创新点，成功地提高了环保通行费用估算的准确性。

Eco-PiNN：一种用于生态通行费估计的基于物理知识的神经网络

Eco-PiNN: A Physics-informed Neural Network for Eco-toll Estimation

Language is constantly changing and evolving, leaving language models to
quickly become outdated, both factually and linguistically. Recent research
proposes we continuously update our models using new data. Continuous training
allows us to teach language models about new events and facts and changing
norms. However, continuous training also means continuous costs. We show there
is currently limited evidence for the benefits of continuous training, be it
for the actual downstream performance or the environmental cost. Our results
show continuous training does not significantly improve performance. While it
is clear that, sooner or later, our language models need to be updated, it is
unclear when this effort is worth the cost. We call for a critical reflection
about when and how to use continuous training and for more benchmarks to
support this research direction.

本文提出增量学习语言模型存在环境费用，并呼吁对连续训练的使用进行反思和对增量学习的研究方向进行更多基准测试。