62,462 research outputs found
Efficacy of crustal superfluid neutrons in pulsar glitch models
In order to assess the ability of purely crust-driven glitch models to match
the observed glitch activity in the Vela pulsar, we conduct a systematic
analysis of the dependence of the fractional moment of inertia of the inner
crustal neutrons on the stiffness of the nuclear symmetry energy at saturation
density . We take into account both crustal entrainment and the fact that
only a fraction of the core neutrons may couple to the crust on the
glitch-rise timescale. We use a set of consistently-generated crust and core
compositions and equations-of-state which are fit to results of low-density
pure neutron matter calculations. When entrainment is included at the level
suggested by recent microscopic calculations and the core is fully coupled to
the crust, the model is only able to account for the Vela glitch activity for a
1.4 star if the equation of state is particularly stiff MeV.
However, an uncertainty of about 10\% in the crust-core transition density and
pressure allows for the Vela glitch activity to be marginally accounted for in
the range MeV consistent with a range of experimental results.
Alternatively, only a small amount of core neutrons need be involved. If less
than 50\% of the core neutrons are coupled to the crust during the glitch, we
can also account for the Vela glitch activity using crustal neutrons alone for
EOSs consistent with the inferred range of . We also explore the possibility
of Vela being a high-mass neutron star, and of crustal entrainment being
reduced or enhanced relative to its currently predicted values.Comment: 10 pages, 6 figure
Persistent Orbital Degeneracy in Carbon Nanotubes
The quantum-mechanical orbitals in carbon nanotubes are doubly degenerate
over a large number of states in the Coulomb blockade regime. We argue that
this experimental observation indicates that electrons are reflected without
mode mixing at the nanotube-metal contacts. Two electrons occupying a pair of
degenerate orbitals (a ``shell'') are found to form a triplet state starting
from zero magnetic field. Finally, we observe unexpected low-energy excitations
at complete filling of a four-electron shell.Comment: 6 pages, 4 figure
Towards Analyzing Semantic Robustness of Deep Neural Networks
Despite the impressive performance of Deep Neural Networks (DNNs) on various
vision tasks, they still exhibit erroneous high sensitivity toward semantic
primitives (e.g. object pose). We propose a theoretically grounded analysis for
DNN robustness in the semantic space. We qualitatively analyze different DNNs'
semantic robustness by visualizing the DNN global behavior as semantic maps and
observe interesting behavior of some DNNs. Since generating these semantic maps
does not scale well with the dimensionality of the semantic space, we develop a
bottom-up approach to detect robust regions of DNNs. To achieve this, we
formalize the problem of finding robust semantic regions of the network as
optimizing integral bounds and we develop expressions for update directions of
the region bounds. We use our developed formulations to quantitatively evaluate
the semantic robustness of different popular network architectures. We show
through extensive experimentation that several networks, while trained on the
same dataset and enjoying comparable accuracy, do not necessarily perform
similarly in semantic robustness. For example, InceptionV3 is more accurate
despite being less semantically robust than ResNet50. We hope that this tool
will serve as a milestone towards understanding the semantic robustness of
DNNs.Comment: Presented at European conference on computer vision (ECCV 2020)
Workshop on Adversarial Robustness in the Real World (
https://eccv20-adv-workshop.github.io/ ) [best paper award]. The code is
available at https://github.com/ajhamdi/semantic-robustnes
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