When performing a task alone, humans achieve a certain level of performance.
When humans are assisted by a tool or automation to perform the same task,
performance is enhanced (augmented). Recently developed cognitive systems are
able to perform cognitive processing at or above the level of a human in some
domains. When humans work collaboratively with such cogs in a human/cog
ensemble, we expect augmentation of cognitive processing to be evident and
measurable. This paper shows the degree of cognitive augmentation depends on
the nature of the information the cog contributes to the ensemble. Results of
an experiment are reported showing conceptual information is the most effective
type of information resulting in increases in cognitive accuracy, cognitive
precision, and cognitive power.

研究发现，人类与认知系统进行协作可以增强信息处理能力，但所提供的信息类型对认知精度、准确性和能力的影响不同。通过实验得出，提供概念信息是最有效的。

信息类型对人类认知增强的影响

The Effect of Information Type on Human Cognitive Augmentation

Short text classification is one of important tasks in Natural Language
Processing (NLP). Unlike paragraphs or documents, short texts are more
ambiguous since they have not enough contextual information, which poses a
great challenge for classification. In this paper, we retrieve knowledge from
external knowledge source to enhance the semantic representation of short
texts. We take conceptual information as a kind of knowledge and incorporate it
into deep neural networks. For the purpose of measuring the importance of
knowledge, we introduce attention mechanisms and propose deep Short Text
Classification with Knowledge powered Attention (STCKA). We utilize Concept
towards Short Text (C- ST) attention and Concept towards Concept Set (C-CS)
attention to acquire the weight of concepts from two aspects. And we classify a
short text with the help of conceptual information. Unlike traditional
approaches, our model acts like a human being who has intrinsic ability to make
decisions based on observation (i.e., training data for machines) and pays more
attention to important knowledge. We also conduct extensive experiments on four
public datasets for different tasks. The experimental results and case studies
show that our model outperforms the state-of-the-art methods, justifying the
effectiveness of knowledge powered attention.

本文提出了一种使用外部知识源增强短文本语义表示的方法：包括注意力机制，将知识嵌入深度神经网络。通过实验验证，这种方法在短文本分类任务中优于现有技术。

基于知识引导注意力的深度短文本分类

Deep Short Text Classification with Knowledge Powered Attention

We introduce a simple recurrent variational auto-encoder architecture that
significantly improves image modeling. The system represents the
state-of-the-art in latent variable models for both the ImageNet and Omniglot
datasets. We show that it naturally separates global conceptual information
from lower level details, thus addressing one of the fundamentally desired
properties of unsupervised learning. Furthermore, the possibility of
restricting ourselves to storing only global information about an image allows
us to achieve high quality 'conceptual compression'.

通过引入一种简单的循环变分自编码器结构，我们在图像建模方面取得了显著进展，实现了割裂了关于全局概念信息和底层细节的分离，达到了无监督学习的基石之一。同时，我们通过仅存储关于图像的全局信息，实现了高质量的 ' 概念压缩 '。