Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A method to challenge symmetries in data with self-supervised learning   Symmetries are key properties of physical models and experimental designs,
but may or may not be realized in nature. We propose a practical and general
method to test such suspect symmetries in data. Self-supervision, which derives
learning objectives from data, is used to train models to predict 'which is
real?' between real and symmetrically transformed data; if a model predicts
successfully in independent tests, then its target symmetry is challenged.
Crucially, our method handles filtered data, which often arise from
inefficiencies or deliberate selections, and which can give the illusion of
asymmetry if mistreated. Examples are used to demonstrate how the method works
and how the models' predictions can be interpreted.

30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
16
The categories of this paper are: stat
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
2
The categories of this paper are: math