The present study explores the intricacies of causal relationship extraction,
a vital component in the pursuit of causality knowledge. Causality is
frequently intertwined with temporal elements, as the progression from cause to
effect is not instantaneous but rather ensconced in a temporal dimension. Thus,
the extraction of temporal causality holds paramount significance in the field.
In light of this, we propose a method for extracting causality from the text
that integrates both temporal and causal relations, with a particular focus on
the time aspect. To this end, we first compile a dataset that encompasses
temporal relationships. Subsequently, we present a novel model, TC-GAT, which
employs a graph attention mechanism to assign weights to the temporal
relationships and leverages a causal knowledge graph to determine the adjacency
matrix. Additionally, we implement an equilibrium mechanism to regulate the
interplay between temporal and causal relations. Our experiments demonstrate
that our proposed method significantly surpasses baseline models in the task of
causality extraction.

本研究通过整合时间和因果关系的方式，提出 TC-GAT 模型并用于因果关系提取任务，在编译了包括时间关系在内的数据集基础上，进行了实验表明其比基线模型更为有效。

TC-GAT：用于时序因果发现的图注意力网络

TC-GAT: Graph Attention Network for Temporal Causality Discovery

When learning a task as a team, some agents in Multi-Agent Reinforcement
Learning (MARL) may fail to understand their true impact in the performance of
the team. Such agents end up learning sub-optimal policies, demonstrating
undesired lazy behaviours. To investigate this problem, we start by formalising
the use of temporal causality applied to MARL problems. We then show how
causality can be used to penalise such lazy agents and improve their
behaviours. By understanding how their local observations are causally related
to the team reward, each agent in the team can adjust their individual credit
based on whether they helped to cause the reward or not. We show empirically
that using causality estimations in MARL improves not only the holistic
performance of the team, but also the individual capabilities of each agent. We
observe that the improvements are consistent in a set of different
environments.

通过引入时间因果关系的惩罚机制，多智能体强化学习问题中的懒惰智能体可以在了解到其本地观察与团队回报的因果关系的基础上，在团队表现和个体能力方面得到改善。

高效多智能体强化学习中的因果检测

Causality Detection for Efficient Multi-Agent Reinforcement Learning

The precipitation nowcasting methods have been elaborated over the centuries
because rain has a crucial impact on human life. Not only quantitative
precipitation forecast (QPF) models and convolutional long short-term memory
(ConvLSTM), but also various sophisticated methods such as the latest MetNet-2
are emerging. In this paper, we propose a paired complementary temporal
cycle-consistent adversarial networks (PCT-CycleGAN) for radar-based
precipitation nowcasting, inspired by cycle-consistent adversarial networks
(CycleGAN), which shows strong performance in image-to-image translation.
PCT-CycleGAN generates temporal causality using two generator networks with
forward and backward temporal dynamics in paired complementary cycles. Each
generator network learns a huge number of one-to-one mappings about
time-dependent radar-based precipitation data to approximate a mapping function
representing the temporal dynamics in each direction. To create robust temporal
causality between paired complementary cycles, novel connection loss is
proposed. The generator network learning forward temporal dynamics in
PCT-CycleGAN generates radar-based precipitation data 10 minutes from the
current time. Also, it provides a reliable prediction of up to 2 hours with
iterative forecasting. The superiority of PCT-CycleGAN is demonstrated through
qualitative and quantitative comparisons with several previous methods.

本文提出一种基于对抗生成网络的降水预测方法，称为配对相补的时序循环一致性对抗网络（PCT-CycleGAN）。该方法在雷达降水数据的时间依赖上生成鲁棒的时间因果关系，可以可靠地预测接下来的 2 小时内的降水情况，并表现出比之前的方法更优异的性能。

PCT-CycleGAN: 基于配对互补时间循环一致性对抗网络的基于雷达的降水预测

PCT-CycleGAN: Paired Complementary Temporal Cycle-Consistent Adversarial Networks for Radar-Based Precipitation Nowcasting

Time-aware encoding of frame sequences in a video is a fundamental problem in
video understanding. While many attempted to model time in videos, an explicit
study on quantifying video time is missing. To fill this lacuna, we aim to
evaluate video time explicitly. We describe three properties of video time,
namely a) temporal asymmetry, b)temporal continuity and c) temporal causality.
Based on each we formulate a task able to quantify the associated property.
This allows assessing the effectiveness of modern video encoders, like C3D and
LSTM, in their ability to model time. Our analysis provides insights about
existing encoders while also leading us to propose a new video time encoder,
which is better suited for the video time recognition tasks than C3D and LSTM.
We believe the proposed meta-analysis can provide a reasonable baseline to
assess video time encoders on equal grounds on a set of temporal-aware tasks.

本研究提出了一个评估视频时间的方法，通过描述视频时间的三个属性并将其量化，为现代视频编码器提供效果评估，同时推荐了一种更适合视频时间识别任务的新型视频时间编码器。