Video transition effects are widely used in video editing to connect shots
for creating cohesive and visually appealing videos. However, it is challenging
for non-professionals to choose best transitions due to the lack of
cinematographic knowledge and design skills. In this paper, we present the
premier work on performing automatic video transitions recommendation (VTR):
given a sequence of raw video shots and companion audio, recommend video
transitions for each pair of neighboring shots. To solve this task, we collect
a large-scale video transition dataset using publicly available video templates
on editing softwares. Then we formulate VTR as a multi-modal retrieval problem
from vision/audio to video transitions and propose a novel multi-modal matching
framework which consists of two parts. First we learn the embedding of video
transitions through a video transition classification task. Then we propose a
model to learn the matching correspondence from vision/audio inputs to video
transitions. Specifically, the proposed model employs a multi-modal transformer
to fuse vision and audio information, as well as capture the context cues in
sequential transition outputs. Through both quantitative and qualitative
experiments, we clearly demonstrate the effectiveness of our method. Notably,
in the comprehensive user study, our method receives comparable scores compared
with professional editors while improving the video editing efficiency by
\textbf{300\scalebox{1.25}{$\times$}}. We hope our work serves to inspire other
researchers to work on this new task. The dataset and codes are public at
https://github.com/acherstyx/AutoTransition.

本文介绍了一种基于多模态匹配的自动视频转场推荐方法，通过一个视频转场分类任务和一个匹配模型，该方法可以为每一组相邻的视频镜头推荐最佳的视频转场，得到了与专业编辑相当的用户评分，且可以提高视频编辑效率 300 倍。

自动过渡：学习推荐视频过渡效果

AutoTransition: Learning to Recommend Video Transition Effects

Medication recommendation targets to provide a proper set of medicines
according to patients' diagnoses, which is a critical task in clinics.
Currently, the recommendation is manually conducted by doctors. However, for
complicated cases, like patients with multiple diseases at the same time, it's
difficult to propose a considerate recommendation even for experienced doctors.
This urges the emergence of automatic medication recommendation which can help
treat the diagnosed diseases without causing harmful drug-drug interactions.Due
to the clinical value, medication recommendation has attracted growing research
interests.Existing works mainly formulate medication recommendation as a
multi-label classification task to predict the set of medicines. In this paper,
we propose the Conditional Generation Net (COGNet) which introduces a novel
copy-or-predict mechanism to generate the set of medicines. Given a patient,
the proposed model first retrieves his or her historical diagnoses and
medication recommendations and mines their relationship with current diagnoses.
Then in predicting each medicine, the proposed model decides whether to copy a
medicine from previous recommendations or to predict a new one. This process is
quite similar to the decision process of human doctors. We validate the
proposed model on the public MIMIC data set, and the experimental results show
that the proposed model can outperform state-of-the-art approaches.

本文介绍了一种基于 COGNet 的自动化药物推荐模型，通过引入新的复制或预测机制，有效减少药物推荐错误率，并在实验中验证了该模型的有效性。

用于药物推荐的条件生成网络

Conditional Generation Net for Medication Recommendation

Many feature subset selection (FSS) algorithms have been proposed, but not
all of them are appropriate for a given feature selection problem. At the same
time, so far there is rarely a good way to choose appropriate FSS algorithms
for the problem at hand. Thus, FSS algorithm automatic recommendation is very
important and practically useful. In this paper, a meta learning based FSS
algorithm automatic recommendation method is presented. The proposed method
first identifies the data sets that are most similar to the one at hand by the
k-nearest neighbor classification algorithm, and the distances among these data
sets are calculated based on the commonly-used data set characteristics. Then,
it ranks all the candidate FSS algorithms according to their performance on
these similar data sets, and chooses the algorithms with best performance as
the appropriate ones. The performance of the candidate FSS algorithms is
evaluated by a multi-criteria metric that takes into account not only the
classification accuracy over the selected features, but also the runtime of
feature selection and the number of selected features. The proposed
recommendation method is extensively tested on 115 real world data sets with 22
well-known and frequently-used different FSS algorithms for five representative
classifiers. The results show the effectiveness of our proposed FSS algorithm
recommendation method.

本文提出了一种基于元学习的特征子集选择算法自动推荐方法，通过多个性能指标综合评估候选 FSS 算法的性能，利用 k 近邻分类算法识别相似数据集，从中筛选出表现最佳的 FSS 算法，出色地解决了选择合适 FSS 算法的问题。