Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: mPyPl: Python Monadic Pipeline Library for Complex Functional Data
  Processing   In this paper, we present a new Python library called mPyPl, which is
intended to simplify complex data processing tasks using functional approach.
This library defines operations on lazy data streams of named dictionaries
represented as generators (so-called multi-field datastreams), and allows
enriching those data streams with more 'fields' in the process of data
preparation and feature extraction. Thus, most data preparation tasks can be
expressed in the form of neat linear 'pipeline', similar in syntax to UNIX
pipes, or |> functional composition operator in F#.
  We define basic operations on multi-field data streams, which resemble
classical monadic operations, and show similarity of the proposed approach to
monads in functional programming. We also show how the library was used in
complex deep learning tasks of event detection in video, and discuss different
evaluation strategies that allow for different compromises in terms of memory
and performance.

30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
15
The categories of this paper are: nucl-ex
16
The categories of this paper are: stat
10
The categories of this paper are: q-bio
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat