Citation recommendation (CR) models may help authors find relevant articles
at various stages of the paper writing process. Most research has dealt with
either global CR, which produces general recommendations suitable for the
initial writing stage, or local CR, which produces specific recommendations
more fitting for the final writing stages. We propose the task of
paragraph-level CR as a middle ground between the two approaches, where the
paragraph's topic sentence is taken as input and recommendations for citing
within the paragraph are produced at the output. We propose a model for this
task, fine-tune it using the quadruplet loss on the dataset of ACL papers, and
show improvements over the baselines.

提出了基于段落级别的引用推荐任务，通过 fine-tune 模型和四元组损失函数在 ACL 论文数据集上的实验，证明了该方法的优越性。

基于主题句作为查询的段落级引文推荐

Paragraph-level Citation Recommendation based on Topic Sentences as  Queries

We tackle the problem of place recognition from point cloud data and
introduce a self-attention and orientation encoding network (SOE-Net) that
fully explores the relationship between points and incorporates long-range
context into point-wise local descriptors. Local information of each point from
eight orientations is captured in a PointOE module, whereas long-range feature
dependencies among local descriptors are captured with a self-attention unit.
Moreover, we propose a novel loss function called Hard Positive Hard Negative
quadruplet loss (HPHN quadruplet), that achieves better performance than the
commonly used metric learning loss. Experiments on various benchmark datasets
demonstrate superior performance of the proposed network over the current
state-of-the-art approaches. Our code is released publicly at
this https URL

介绍了一种基于自注意力和定向编码网络的点云数据识别方法，通过采用 Hard Positive Hard Negative 四元组损失函数和采样方法，取得优于现有方法的性能表现。

SOE-Net：一种用于基于点云的场所识别的自注意力和方向编码网络

SOE-Net: A Self-Attention and Orientation Encoding Network for Point  Cloud based Place Recognition

Person re-identification (ReID) is an important task in wide area video
surveillance which focuses on identifying people across different cameras.
Recently, deep learning networks with a triplet loss become a common framework
for person ReID. However, the triplet loss pays main attentions on obtaining
correct orders on the training set. It still suffers from a weaker
generalization capability from the training set to the testing set, thus
resulting in inferior performance. In this paper, we design a quadruplet loss,
which can lead to the model output with a larger inter-class variation and a
smaller intra-class variation compared to the triplet loss. As a result, our
model has a better generalization ability and can achieve a higher performance
on the testing set. In particular, a quadruplet deep network using a
margin-based online hard negative mining is proposed based on the quadruplet
loss for the person ReID. In extensive experiments, the proposed network
outperforms most of the state-of-the-art algorithms on representative datasets
which clearly demonstrates the effectiveness of our proposed method.

本篇论文提出一种基于四元组损失和在线难负样本挖掘的四重神经网络用于人员再识别，相对于三元组损失，该方法在训练集和测试集中具有更好的泛化性能和更高的性能表现，并在多个代表性数据集上超过了大部分最先进算法，证明该方法的有效性。