Temporal Knowledge Graph Completion (TKGC) under the extrapolation setting
aims to predict the missing entity from a fact in the future, posing a
challenge that aligns more closely with real-world prediction problems.
Existing research mostly encodes entities and relations using sequential graph
neural networks applied to recent snapshots. However, these approaches tend to
overlook the ability to skip irrelevant snapshots according to entity-related
relations in the query and disregard the importance of explicit temporal
information. To address this, we propose our model, Re-Temp (Relation-Aware
Temporal Representation Learning), which leverages explicit temporal embedding
as input and incorporates skip information flow after each timestamp to skip
unnecessary information for prediction. Additionally, we introduce a two-phase
forward propagation method to prevent information leakage. Through the
evaluation on six TKGC (extrapolation) datasets, we demonstrate that our model
outperforms all eight recent state-of-the-art models by a significant margin.

Re-Temp 是一种新的模型，旨在通过引入显式的时间嵌入和在每个时间戳之后引入跳过信息流来从未来的事实中预测缺失实体，并通过两阶段的前向传播方法防止信息泄漏，通过对六个 TKGC (外推) 数据集的评估，我们证明了我们的模型在性能上优于八个最新的先进模型。

消除时间知识图谱的关系感知时间表示学习

Re-Temp: Relation-Aware Temporal Representation Learning for Temporal  Knowledge Graph Completion

Unsupervised pre-training methods for large vision models have shown to
enhance performance on downstream supervised tasks. Developing similar
techniques for satellite imagery presents significant opportunities as
unlabelled data is plentiful and the inherent temporal and multi-spectral
structure provides avenues to further improve existing pre-training strategies.
In this paper, we present SatMAE, a pre-training framework for temporal or
multi-spectral satellite imagery based on Masked Autoencoder (MAE). To leverage
temporal information, we include a temporal embedding along with independently
masking image patches across time. In addition, we demonstrate that encoding
multi-spectral data as groups of bands with distinct spectral positional
encodings is beneficial. Our approach yields strong improvements over previous
state-of-the-art techniques, both in terms of supervised learning performance
on benchmark datasets (up to $\uparrow$ 7%), and transfer learning performance
on downstream remote sensing tasks, including land cover classification (up to
$\uparrow$ 14%) and semantic segmentation. Code and data are available on the
project website: this https URL

通过使用遮蔽自动编码器（MAE）的卫星图像的预训练框架 SatMAE，我们结合时间嵌入和分组打包多光谱数据以提高预训练能力，进一步提高基准数据集上的监督学习性能（最高可达 7%），并在后续的遥感分类任务中表现出强大的转移学习能力（最高可达 14%）。

SatMAE: 为时间序列和多光谱卫星图像预训练 Transformers

SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery

The prediction of periodical time-series remains challenging due to various
types of data distortions and misalignments. Here, we propose a novel model
called Temporal embedding-enhanced convolutional neural Network (TeNet) to
learn repeatedly-occurring-yet-hidden structural elements in periodical
time-series, called abstract snippets, for predicting future changes. Our model
uses convolutional neural networks and embeds a time-series with its potential
neighbors in the temporal domain for aligning it to the dominant patterns in
the dataset. The model is robust to distortions and misalignments in the
temporal domain and demonstrates strong prediction power for periodical
time-series.
We conduct extensive experiments and discover that the proposed model shows
significant and consistent advantages over existing methods on a variety of
data modalities ranging from human mobility to household power consumption
records. Empirical results indicate that the model is robust to various factors
such as number of samples, variance of data, numerical ranges of data etc. The
experiments also verify that the intuition behind the model can be generalized
to multiple data types and applications and promises significant improvement in
prediction performances across the datasets studied.

本文提出了一种基于时间嵌入的卷积神经网络模型 (TeNet)，通过对周期性时间序列中反复发生但被隐藏的结构元素 —— 抽象片段进行学习来预测未来的变化，该模型能够对时间序列中的数据扭曲和错位具有鲁棒性，并在人类移动性到家庭用电记录等多种数据模态上展示出优异的预测性能。