The Gravity Recovery and Climate Experiment (GRACE) satellite mission,
spanning from 2002 to 2017, has provided a valuable dataset for monitoring
variations in Earth's gravity field, enabling diverse applications in
geophysics and hydrology. The mission was followed by GRACE Follow-On in 2018,
continuing data collection efforts. The monthly Earth gravity field, derived
from the integration different instruments onboard satellites, has shown
inconsistencies due to various factors, including gaps in observations for
certain instruments since the beginning of the GRACE mission.
With over two decades of GRACE and GRACE Follow-On data now available, this
paper proposes an approach to fill the data gaps and forecast GRACE
accelerometer data. Specifically, we focus on accelerometer data and employ
Long Short-Term Memory (LSTM) networks to train a model capable of predicting
accelerometer data for all three axes.
In this study, we describe the methodology used to preprocess the
accelerometer data, prepare it for LSTM training, and evaluate the model's
performance. Through experimentation and validation, we assess the model's
accuracy and its ability to predict accelerometer data for the three axes. Our
results demonstrate the effectiveness of the LSTM forecasting model in filling
gaps and forecasting GRACE accelerometer data.

该研究提出了一种填补数据空缺和预测 GRACE 加速度计数据的方法，使用了 Long Short-Term Memory（LSTM）网络来训练一个能够预测三个轴加速度计数据的模型，并通过实验证明了该 LSTM 预测模型在填补数据空缺和预测 GRACE 加速度计数据方面的有效性。

基于 LSTM 的 GRACE 加速度计数据预测模型

LSTM-Based Forecasting Model for GRACE Accelerometer Data

In this paper, we focus on the imbalance issue, which is rarely studied in
aspect term extraction and aspect sentiment classification when regarding them
as sequence labeling tasks. Besides, previous works usually ignore the
interaction between aspect terms when labeling polarities. We propose a
GRadient hArmonized and CascadEd labeling model (GRACE) to solve these
problems. Specifically, a cascaded labeling module is developed to enhance the
interchange between aspect terms and improve the attention of sentiment tokens
when labeling sentiment polarities. The polarities sequence is designed to
depend on the generated aspect terms labels. To alleviate the imbalance issue,
we extend the gradient harmonized mechanism used in object detection to the
aspect-based sentiment analysis by adjusting the weight of each label
dynamically. The proposed GRACE adopts a post-pretraining BERT as its backbone.
Experimental results demonstrate that the proposed model achieves consistency
improvement on multiple benchmark datasets and generates state-of-the-art
results.

本文提出了 GRACE 模型，通过引入层级标签和动态调节标签权重机制，解决了既往研究所忽视的方面术语交互和情感分类不平衡问题。实验结果表明，该模型在多个基准数据集上都取得了最新的、一致的最佳结果。

GRACE：面向方面情感分析的梯度协调和级联标注

GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based  Sentiment Analysis

Despite the recent development in the topic of explainable AI/ML for image
and text data, the majority of current solutions are not suitable to explain
the prediction of neural network models when the datasets are tabular and their
features are in high-dimensional vectorized formats. To mitigate this
limitation, therefore, we borrow two notable ideas (i.e., "explanation by
intervention" from causality and "explanation are contrastive" from philosophy)
and propose a novel solution, named as GRACE, that better explains neural
network models' predictions for tabular datasets. In particular, given a
model's prediction as label X, GRACE intervenes and generates a
minimally-modified contrastive sample to be classified as Y, with an intuitive
textual explanation, answering the question of "Why X rather than Y?" We carry
out comprehensive experiments using eleven public datasets of different scales
and domains (e.g., # of features ranges from 5 to 216) and compare GRACE with
competing baselines on different measures: fidelity, conciseness, info-gain,
and influence. The user-studies show that our generated explanation is not only
more intuitive and easy-to-understand but also facilitates end-users to make as
much as 60% more accurate post-explanation decisions than that of Lime.

提出了一种名为 GRACE 的新的解释深度学习模型在表格数据方面的预测的方法，它基于干预与对比这两个思想，并生成易于理解的文本解释，相较于竞争基线方式，其生成的解释直观易懂且提高了用户决策准确性达 60%。