Traditional approaches to classify the political bias of news articles have
failed to generate accurate, generalizable results. Existing networks premised
on CNNs and DNNs lack a model to identify and extrapolate subtle indicators of
bias like word choice, context, and presentation. In this paper, we propose a
network architecture that achieves human-level accuracy in assigning bias
classifications to articles. The underlying model is based on a novel Mesh
Neural Network (MNN),this structure enables feedback and feedforward synaptic
connections between any two neurons in the mesh. The MNN ontains six network
configurations that utilize Bernoulli based random sampling, pre-trained DNNs,
and a network modelled after the C. Elegans nematode. The model is trained on
over ten-thousand articles scraped from this http URL which are labelled to
indicate political bias. The parameters of the network are then evolved using a
genetic algorithm suited to the feedback neural structure. Finally, the best
performing model is applied to five popular news sources in the United States
over a fifty-day trial to quantify political biases in the articles they
display. We hope our project can spur research into biological solutions for
NLP tasks and provide accurate tools for citizens to understand subtle biases
in the articles they consume.

本文讨论了一种基于 Mesh Neural Network、Bernoulli 以及 genetic algorithm 的网络结构，能够更准确地对新闻文章进行政治偏见分类。

一种基于秀丽隐杆线虫启发的递归反馈网络架构的自动化新闻偏见分类器

An Automated News Bias Classifier Using Caenorhabditis Elegans Inspired Recursive Feedback Network Architecture

Mesh is an important and powerful type of data for 3D shapes and widely
studied in the field of computer vision and computer graphics. Regarding the
task of 3D shape representation, there have been extensive research efforts
concentrating on how to represent 3D shapes well using volumetric grid,
multi-view and point cloud. However, there is little effort on using mesh data
in recent years, due to the complexity and irregularity of mesh data. In this
paper, we propose a mesh neural network, named MeshNet, to learn 3D shape
representation from mesh data. In this method, face-unit and feature splitting
are introduced, and a general architecture with available and effective blocks
are proposed. In this way, MeshNet is able to solve the complexity and
irregularity problem of mesh and conduct 3D shape representation well. We have
applied the proposed MeshNet method in the applications of 3D shape
classification and retrieval. Experimental results and comparisons with the
state-of-the-art methods demonstrate that the proposed MeshNet can achieve
satisfying 3D shape classification and retrieval performance, which indicates
the effectiveness of the proposed method on 3D shape representation.

本研究提出了一种名为 MeshNet 的 Mesh 神经网络，用于从 Mesh 数据中学习 3D 形状表达，并应用于 3D 形状分类和检索任务，并通过与其他方法的比较，证明了该方法的有效性。