19,286 research outputs found
Geometric deep learning: going beyond Euclidean data
Many scientific fields study data with an underlying structure that is a
non-Euclidean space. Some examples include social networks in computational
social sciences, sensor networks in communications, functional networks in
brain imaging, regulatory networks in genetics, and meshed surfaces in computer
graphics. In many applications, such geometric data are large and complex (in
the case of social networks, on the scale of billions), and are natural targets
for machine learning techniques. In particular, we would like to use deep
neural networks, which have recently proven to be powerful tools for a broad
range of problems from computer vision, natural language processing, and audio
analysis. However, these tools have been most successful on data with an
underlying Euclidean or grid-like structure, and in cases where the invariances
of these structures are built into networks used to model them. Geometric deep
learning is an umbrella term for emerging techniques attempting to generalize
(structured) deep neural models to non-Euclidean domains such as graphs and
manifolds. The purpose of this paper is to overview different examples of
geometric deep learning problems and present available solutions, key
difficulties, applications, and future research directions in this nascent
field
Space-charge distortion of transverse profiles measured by electron-based Ionization Profile Monitors and correction methods
Measurements of transverse profiles using Ionization Profile Monitors (IPMs)
for high brightness beams are affected by the electromagnetic field of the
beam. This interaction may cause a distortion of the measured profile shape
despite strong external magnetic field applied to impose limits on the
transverse movement of electrons. The mechanisms leading to this distortion are
discussed in detail. The distortion itself is described by means of analytic
calculations for simplified beam distributions and a full simulation model for
realistic distributions. Simple relation for minimum magnetic field scaling
with beam parameters for avoiding profile distortions is presented. Further,
application of machine learning algorithms to the problem of reconstructing the
actual beam profile from distorted measured profile is presented. The obtained
results show good agreement for tests on simulation data. The performance of
these algorithms indicate that they could be very useful for operations of IPMs
on high brightness beams or IPMs with weak magnetic field
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
This paper takes a problem-oriented perspective and presents a comprehensive
review of transfer learning methods, both shallow and deep, for cross-dataset
visual recognition. Specifically, it categorises the cross-dataset recognition
into seventeen problems based on a set of carefully chosen data and label
attributes. Such a problem-oriented taxonomy has allowed us to examine how
different transfer learning approaches tackle each problem and how well each
problem has been researched to date. The comprehensive problem-oriented review
of the advances in transfer learning with respect to the problem has not only
revealed the challenges in transfer learning for visual recognition, but also
the problems (e.g. eight of the seventeen problems) that have been scarcely
studied. This survey not only presents an up-to-date technical review for
researchers, but also a systematic approach and a reference for a machine
learning practitioner to categorise a real problem and to look up for a
possible solution accordingly
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