3,380 research outputs found
Entanglement Entropy of ABJM Theory and Entropy of Topological Black Hole
In this paper we discuss the supersymmetric localization of the 4D
off-shell gauged supergravity in the background of the
neutral topological black hole, which is the gravity dual of
the ABJM theory defined on the boundary . We
compute the large- expansion of the supergravity partition function. The
result gives the black hole entropy with the logarithmic correction, which
matches the previous result of the entanglement entropy of the ABJM theory up
to some stringy effects. Our result is consistent with the previous on-shell
one-loop computation of the logarithmic correction to black hole entropy. It
provides an explicit example of the identification of the entanglement entropy
of the boundary conformal field theory with the bulk black hole entropy beyond
the leading order given by the classical Bekenstein-Hawking formula, which
consequently tests the AdS/CFT correspondence at the subleading order.Comment: 34 pages, 1 figure; minor changes in v2; references added in v3,
published version in JHE
catena-Poly[[[diaquaÂ(2-fluoroÂbenzoato-κ2 O,O′)strontium]-μ3-2-fluoroÂbenzoato-κ5 O:O,O′:O′,F] monohydrate]
In the title compound, {[Sr(C7H4FO2)2(H2O)2]·H2O}n, the SrII atom is coordinated by six O atoms and one F atom from four 2-fluoroÂbenzoate ligands and two water molÂecules, resulting in an irregular SrFO8 coordination environment. The μ3-2-fluoroÂbenzoate ligand bridges three symmetry-related SrII atoms, giving rise to a chain structure extending along [010]. The polymeric chains are connected via O—H⋯O hydrogen bonds into a two-dimensional supraÂmolecular structure parallel to (100)
Interpretable Convolutional Neural Networks
This paper proposes a method to modify traditional convolutional neural
networks (CNNs) into interpretable CNNs, in order to clarify knowledge
representations in high conv-layers of CNNs. In an interpretable CNN, each
filter in a high conv-layer represents a certain object part. We do not need
any annotations of object parts or textures to supervise the learning process.
Instead, the interpretable CNN automatically assigns each filter in a high
conv-layer with an object part during the learning process. Our method can be
applied to different types of CNNs with different structures. The clear
knowledge representation in an interpretable CNN can help people understand the
logics inside a CNN, i.e., based on which patterns the CNN makes the decision.
Experiments showed that filters in an interpretable CNN were more semantically
meaningful than those in traditional CNNs.Comment: In this version, we release the website of the code. Compared to the
previous version, we have corrected all values of location instability in
Table 3--6 by dividing the values by sqrt(2), i.e., a=a/sqrt(2). Such
revisions do NOT decrease the significance of the superior performance of our
method, because we make the same correction to location-instability values of
all baseline
Heavy Quark Energy Loss in Nuclear Medium
Multiple scattering, modified fragmentation functions and radiative energy
loss of a heavy quark propagating in a nuclear medium are investigated in
perturbative QCD. Because of the quark mass dependence of the gluon formation
time, the medium size dependence of heavy quark energy loss is found to change
from a linear to a quadratic form when the initial energy and momentum scale
are increased relative to the quark mass. The radiative energy loss is also
significantly suppressed relative to a light quark due to the suppression of
collinear gluon emission by a heavy quark.Comment: 4 pages in Revtex, 3 figure
- …