As an important clean and renewable kind of energy, wind power plays an
important role in coping with energy crisis and environmental pollution.
However, the volatility and intermittency of wind speed restrict the
development of wind power. To improve the utilization of wind power, this study
proposes a new wind speed prediction model based on data noise reduction
technology, temporal convolutional network (TCN), and gated recurrent unit
(GRU). Firstly, an adaptive data noise reduction algorithm P-SSA is proposed
based on singular spectrum analysis (SSA) and Pearson correlation coefficient.
The original wind speed is decomposed into multiple subsequences by SSA and
then reconstructed. When the Pearson correlation coefficient between the
reconstructed sequence and the original sequence is greater than 0.99, other
noise subsequences are deleted to complete the data denoising. Then, the
receptive field of the samples is expanded through the causal convolution and
dilated convolution of TCN, and the characteristics of wind speed change are
extracted. Then, the time feature information of the sequence is extracted by
GRU, and then the wind speed is predicted to form the wind speed sequence
prediction model of P-SSA-TCN-GRU. The proposed model was validated on three
wind farms in Shandong Province. The experimental results show that the
prediction performance of the proposed model is better than that of the
traditional model and other models based on TCN, and the wind speed prediction
of wind farms with high precision and strong stability is realized. The wind
speed predictions of this model have the potential to become the data that
support the operation and management of wind farms. The code is available at
link.

提出了一种基于数据降噪技术、时序卷积网络（TCN）和门控循环单元（GRU）的新型风速预测模型，通过数据降噪、特征提取和序列预测实现了具有高精度和强稳定性的风速预测，为风电场的运行和管理提供了支持。

基于自适应降噪技术和时域卷积网络的风电场超短期多步风速预测

Ultra-short-term multi-step wind speed prediction for wind farms based  on adaptive noise reduction technology and temporal convolutional network

The journey of reducing noise from distant supervision (DS) generated
training data has been started since the DS was first introduced into the
relation extraction (RE) task. For the past decade, researchers apply the
multi-instance learning (MIL) framework to find the most reliable feature from
a bag of sentences. Although the pattern of MIL bags can greatly reduce DS
noise, it fails to represent many other useful sentence features in the
datasets. In many cases, these sentence features can only be acquired by extra
sentence-level human annotation with heavy costs. Therefore, the performance of
distantly supervised RE models is bounded. In this paper, we go beyond typical
MIL framework and propose a novel contrastive instance learning (CIL)
framework. Specifically, we regard the initial MIL as the relational triple
encoder and constraint positive pairs against negative pairs for each instance.
Experiments demonstrate the effectiveness of our proposed framework, with
significant improvements over the previous methods on NYT10, GDS and KBP.

本文提出了一种新的对比实例学习框架，用于减少远程监督生成的训练数据中的噪声。与传统的多实例学习框架不同，该框架将初步多实例学习作为关系三元组编码器，并针对每个实例的正负对进行限制，实验证明该方法在 NYT10、GDS 和 KBP 上较以前的方法有显著提高。

面向远程监督关系抽取的对比实例学习框架

CIL: Contrastive Instance Learning Framework for Distantly Supervised  Relation Extraction

Automatic relation extraction (RE) for types of interest is of great
importance for interpreting massive text corpora in an efficient manner.
Traditional RE models have heavily relied on human-annotated corpus for
training, which can be costly in generating labeled data and become obstacles
when dealing with more relation types. Thus, more RE extraction systems have
shifted to be built upon training data automatically acquired by linking to
knowledge bases (distant supervision). However, due to the incompleteness of
knowledge bases and the context-agnostic labeling, the training data collected
via distant supervision (DS) can be very noisy. In recent years, as increasing
attention has been brought to tackling question-answering (QA) tasks, user
feedback or datasets of such tasks become more accessible. In this paper, we
propose a novel framework, ReQuest, to leverage question-answer pairs as an
indirect source of supervision for relation extraction, and study how to use
such supervision to reduce noise induced from DS. Our model jointly embeds
relation mentions, types, QA entity mention pairs and text features in two
low-dimensional spaces (RE and QA), where objects with same relation types or
semantically similar question-answer pairs have similar representations. Shared
features connect these two spaces, carrying clearer semantic knowledge from
both sources. ReQuest, then use these learned embeddings to estimate the types
of test relation mentions. We formulate a global objective function and adopt a
novel margin-based QA loss to reduce noise in DS by exploiting semantic
evidence from the QA dataset. Our experimental results achieve an average of
11% improvement in F1 score on two public RE datasets combined with TREC QA
dataset.

本篇论文介绍了一种基于 QA 数据集的间接标注来降低 DS 数据产生噪声的新方法 ReQuest，并使用学习到的嵌入估计测试关系提及的类型。实验结果在两个公共关系提取数据集上相结合以 TREC QA 数据集，F1 得分平均提高了 11％。