Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to Pre-process Laser Induced Breakdown Spectroscopy Signals
  Without Clean Data   This work tests whether deep neural networks can clean laser induced
breakdown spectroscopy (LIBS) signals by using only uncleaned raw measurements.
Our view of this problem considers a disentanglement of the effects of the
target of interest from those of the nuisance factors (with non-zero mean) by
leveraging the vast amounts of redundancies in LIBS data and our proposed
learning formulation. This later aims at promoting consistency between repeated
measurement views of a target while simultaneously removing consistencies with
all other LIBS measurements taken throughout the history of the instrument.
Evaluations on real data from the ChemCam instrument onboard the Martian
Curiosity rover show a superior performance in cleaning LIBS signals compared
to the standard approaches being used by the ChemCam team.

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The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
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The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
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The categories of this paper are: econ
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The categories of this paper are: nlin
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The categories of this paper are: physics
5
The categories of this paper are: gr-qc
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The categories of this paper are: alg-geom
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The categories of this paper are: cond-mat
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The categories of this paper are: acc-phys
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The categories of this paper are: ao-sci
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The categories of this paper are: cmp-lg
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The categories of this paper are: q-fin
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The categories of this paper are: dg-ga