Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Rabbit, toad, and the Moon: Can machine categorize them into one class?   Recent machine learning algorithms such as neural networks can classify
objects and actions in video frames with high accuracy. Here, I discuss a
classification of objects based on basal dynamic patterns referencing one
tradition, the link between rabbit, toad, and the Moon, which can be seen in
several cultures. In order for them to be classified into one class, a basic
pattern of behavior (cyclic appearance and disappearance) works as a feature
point. A static character such as the shape and time scale of the behavior are
not essential for this classification. In cognitive semantics, image schemas
are introduced to describe basal patterns of events. If learning of these image
schemas is attained, a machine may be able to categorize rabbit, toad, and the
Moon as the same class. For learning, video frames that show boundary boxes or
segmentation may be helpful. Although this discussion is preliminary and many
tasks remain to be solved, the classification based on basal behaviors can be
an important topic for cognitive processes and computer science.

0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin