The aim of this primer is to introduce the subject of knowledge engineering
in a concise but synthetic way to develop the reader's intuition about the
area.

本文旨在从简洁且综合的方式介绍知识工程学科，以发展读者对该领域的直觉。

知识工程入门

A Knowledge Engineering Primer

Optical flow is the motion of a pixel between at least two consecutive video
frames and can be estimated through an end-to-end trainable convolutional
neural network. To this end, large training datasets are required to improve
the accuracy of optical flow estimation. Our paper presents OmniFlow: a new
synthetic omnidirectional human optical flow dataset. Based on a rendering
engine we create a naturalistic 3D indoor environment with textured rooms,
characters, actions, objects, illumination and motion blur where all components
of the environment are shuffled during the data capturing process. The
simulation has as output rendered images of household activities and the
corresponding forward and backward optical flow. To verify the data for
training volumetric correspondence networks for optical flow estimation we
train different subsets of the data and test on OmniFlow with and without
Test-Time-Augmentation. As a result we have generated 23,653 image pairs and
corresponding forward and backward optical flow. Our dataset can be downloaded
from: this https URL

本文介绍了 OmniFlow，这是一个新的合成全向人类光流数据集，通过渲染引擎创建自然的 3D 室内环境，用于训练光流估计的体积对应网络。

OmniFlow：人类全方向光流

OmniFlow: Human Omnidirectional Optical Flow

We propose generative neural network methods to generate DNA sequences and
tune them to have desired properties. We present three approaches: creating
synthetic DNA sequences using a generative adversarial network; a DNA-based
variant of the activation maximization ("deep dream") design method; and a
joint procedure which combines these two approaches together. We show that
these tools capture important structures of the data and, when applied to
designing probes for protein binding microarrays, allow us to generate new
sequences whose properties are estimated to be superior to those found in the
training data. We believe that these results open the door for applying deep
generative models to advance genomics research.

本文提出了三种生成神经网络方法，用于生成 DNA 序列，并调整其具有期望的性质。这些工具捕捉到数据的重要结构，并在设计蛋白质结合微阵列探针时，生成具有超越训练数据的性能的新序列。我们相信这些结果为将深层生成模型应用于推进基因组学研究开辟了新的空间。