Variational autoencoders provide a principled framework for learning deep
latent-variable models and corresponding inference models. In this work, we
provide an introduction to variational autoencoders and some important
extensions.

该论文介绍了变分自编码器及其重要扩展，它们提供了学习深度潜变量模型及对应推理模型的原则框架。

变分自编码器简介

An Introduction to Variational Autoencoders

Consensus clustering fuses diverse basic partitions (i.e., clustering results
obtained from conventional clustering methods) into an integrated one, which
has attracted increasing attention in both academic and industrial areas due to
its robust and effective performance. Tremendous research efforts have been
made to thrive this domain in terms of algorithms and applications. Although
there are some survey papers to summarize the existing literature, they neglect
to explore the underlying connection among different categories. Differently,
in this paper we aim to provide an embedding prospective to illustrate the
consensus mechanism, which transfers categorical basic partitions to other
representations (e.g., binary coding, spectral embedding, etc) for the
clustering purpose. To this end, we not only unify two major categories of
consensus clustering, but also build an intuitive connection between consensus
clustering and graph embedding. Moreover, we elaborate several extensions of
classical consensus clustering from different settings and problems. Beyond
this, we demonstrate how to leverage consensus clustering to address other
tasks, such as constrained clustering, domain adaptation, feature selection,
and outlier detection. Finally, we conclude this survey with future work in
terms of interpretability, learnability and theoretical analysis.

本文综述了共识聚类的现有文献，探索不同类别之间的关系，并提供了转换基本分区到其他表示形式的共识机制，以及在不同设置和问题上的几种扩展方法和应用，包括约束聚类、域自适应、特征选择和异常检测等。

共识聚类：嵌入视角、拓展与超越

Consensus Clustering: An Embedding Perspective, Extension and Beyond

The Point Pair Feature (Drost et al. 2010) has been one of the most
successful 6D pose estimation method among model-based approaches as an
efficient, integrated and compromise alternative to the traditional local and
global pipelines. During the last years, several variations of the algorithm
have been proposed. Among these extensions, the solution introduced by
Hinterstoisser et al. (2016) is a major contribution. This work presents a
variation of this PPF method applied to the SIXD Challenge datasets presented
at the 3rd International Workshop on Recovering 6D Object Pose held at the ICCV
2017. We report an average recall of 0.77 for all datasets and overall recall
of 0.82, 0.67, 0.85, 0.37, 0.97 and 0.96 for hinterstoisser, tless, tudlight,
rutgers, tejani and doumanoglou datasets, respectively.

本文介绍了一种基于改进的点对特征算法的 6D 姿态估计方法，应用于 ICCV 2017 举办的 SIXD Challenge 数据集，并报告了所有数据集的平均召回率为 0.77，总召回率分别为 0.82、0.67、0.85、0.37、0.97 和 0.96。

基于点对特征的 6D 姿态估计改进方法

6D Pose Estimation using an Improved Method based on Point Pair Features

In this paper, we address the problem of change in an abstract argumentation
system. We focus on a particular change: the addition of a new argument which
interacts with previous arguments. We study the impact of such an addition on
the outcome of the argumentation system, more particularly on the set of its
extensions. Several properties for this change operation are defined by
comparing the new set of extensions to the initial one, these properties are
called structural when the comparisons are based on set-cardinality or
set-inclusion relations. Several other properties are proposed where
comparisons are based on the status of some particular arguments: the accepted
arguments; these properties refer to the evolution of this status during the
change, e.g., Monotony and Priority to Recency. All these properties may be
more or less desirable according to specific applications. They are studied
under two particular semantics: the grounded and preferred semantics.

本文研究了抽象论证系统中一种特定的添加新论点的变化，探讨了这种变化对于该论证系统中扩展集合的影响，基于集合基数和包含关系定义了几个结构属性，还提出了一些基于特定论点状态变化的属性，研究了这些属性在两种特定语义下的可行性和优越性。

抽象参数化框架的变化：添加一个参数

Change in Abstract Argumentation Frameworks: Adding an Argument

Extensions and variants are given for the well-known comparison principle for
Gaussian processes based on ordering by pairwise distance.

介绍了基于成对距离排序的高斯过程比较原理的扩展和变体。