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1807.02525 | Floquet-Theoretical Formulation and Analysis of High-Harmonic Generation
in Solids | By using the Floquet eigenstates, we derive a formula to calculate the
high-harmonic components of the electric current (HHC) in the setup where a
monochromatic laser field is turned on at some time. On the basis of this
formulation, we study the HHC spectrum of electrons on a one-dimensional chain
with the staggered potential to study the effect of multiple sites in the unit
cell such as the systems with charge density wave (CDW) order. With the help of
the solution for the Floquet eigenstates, we analytically show that two
plateaus of different origins emerge in the HHC spectrum. The widths of these
plateaus are both proportional to the field amplitude, but inversely
proportional to the laser frequency and its square, respectively. We also show
numerically that multi-step plateaus appear when both the field amplitude and
the staggered potential are strong.
| cond-mat.other physics.optics quant-ph |
1807.02526 | The Structural and Kinematic Evolution of Central Star Clusters in Dwarf
Galaxies and Their Dependence on Dark Matter Halo Profiles | Through a suite of direct N-body simulations, we explore how the structural
and kinematic evolution of a star cluster located at the center of a dwarf
galaxy is affected by the shape of its host's dark matter density profile. The
stronger central tidal fields of cuspier halos minimize the cluster's ability
to expand in response to mass loss due to stellar evolution during its early
evolutionary stages and during its subsequent long-term evolution driven by
two-body relaxation. Hence clusters evolving in cuspier dark matter halos are
characterized by more compact sizes, higher velocity dispersions and remain
approximately isotropic at all clustercentric distances. Conversely, clusters
in cored halos can expand more and develop a velocity distribution profile that
becomes increasingly radially anisotropic at larger clustercentric distances.
Finally, the larger velocity dispersion of clusters evolving in cuspier dark
matter profiles results in them having longer relaxation times. Hence clusters
in cuspy galaxies relax at a slower rate and, consequently, they are both less
mass segregated and farther from complete energy equipartition than cluster's
in cored galaxies. Application of this work to observations allows for star
clusters to be used as tools to measure the distribution of dark matter in
dwarf galaxies and to distinguish isolated star clusters from ultra-faint dwarf
galaxies.
| astro-ph.GA |
1807.02527 | Cuckoo's Eggs in Neutron Stars: Can LIGO Hear Chirps from the Dark
Sector? | We explore in detail the possibility that gravitational wave signals from
binary inspirals are affected by a new force that couples only to dark matter
particles. We discuss the impact of both the new force acting between the
binary partners as well as radiation of the force carrier. We identify numerous
constraints on any such scenario, ultimately concluding that observable effects
on the dynamics of binary inspirals due to such a force are not possible if the
dark matter is accrued during ordinary stellar evolution. Constraints arise
from the requirement that the astronomical body be able to collect and bind at
small enough radius an adequate number of dark matter particles, from the
requirement that the particles thus collected remain bound to neutron stars in
the presence of another neutron star, and from the requirement that the theory
allows old neutron stars to exist and retain their charge. Thus, we show that
any deviation from the predictions of general relativity observed in binary
inspirals must be due either to the material properties of the inspiraling
objects themselves, such as a tidal deformability, to a true fifth force
coupled to baryons, or to a non-standard production mechanism for the dark
matter cores of neutron stars. Viable scenarios of the latter type include
production of dark matter in exotic neutron decays, or the formation of compact
dark matter objects in the early Universe that later seed star formation or are
captured by stars.
| hep-ph astro-ph.CO gr-qc hep-ex |
1807.02528 | Counterroating incommensurate magnetic order and strong quantum
fluctuations in the honeycomb layers of $\rm NaNi_2BiO_6$ | We report the magnetic structure and electronic properties of the honeycomb
antiferromagnet $\rm NaNi_2BiO_{5.66}$. We find magnetic order with moments
along the $c$ axis for temperatures below $T_{c1}=6.3(1)\>{\rm K}$ and then in
the honeycomb plane for $T < T_{c2}=4.8(1)\>{\rm K}$ with a counterrotating
pattern and an ordering wave vector ${\bf q}=(\frac{1}{3},\> \frac{1}{3},\>
0.15(1))$. Density functional theory and electron spin resonance indicate this
is high-spin Ni$^{3+}$ magnetism near a high to low spin transition. The
ordering wave vector, in-plane magnetic correlations, missing entropy, spin
state, and superexchange pathways are all consistent with bond-dependent
Kitaev-$\Gamma$-Heisenberg exchange interactions in $\rm NaNi_2BiO_{6-\delta}$.
| cond-mat.str-el |
1807.02529 | NNLO QCD Corrections to Jet Production in Charged Current Deep Inelastic
Scattering | The production of jets in charged current deep inelastic scattering (CC DIS)
constitutes a class of observables that can be used to simultaneously test
perturbative predictions for the strong and the electroweak sectors of the
Standard Model. We compute both single jet and di-jet production in CC DIS for
the first time at next-to-next-to-leading order (NNLO) in the strong coupling.
Our computation is fully differential in the jet and lepton kinematics, and we
observe a substantial reduction of scale variation uncertainties in the NNLO
predictions compared to next-to-leading order (NLO). Our calculation will prove
essential for full exploitation of data at a possible future LHeC collider.
| hep-ph |
1807.02530 | The Supersymmetric R-Parity Violating Dine-Fischler-Srednicki-Zhitnitsky
Axion Model | We propose a complete R-parity violating supersymmetric model with baryon
triality that contains a Dine-Fischler-Srednicki-Zhitnitsky axion chiral
superfield. We parametrize supersymmetry breaking with soft terms, and
determine under which conditions the model is cosmologically viable. As
expected we always find a region of parameter space in which the axion is a
cold dark matter candidate. The mass of the axino, the fermionic partner of the
axion, is controlled by a Yukawa coupling. When heavy [${\mathcal O}$(TeV)],
the axino decays early and poses no cosmological problems. When light
[$\mathcal{O}$(keV)] it can be long lived and a warm dark matter candidate. We
concentrate on the latter case and study in detail the decay modes of the
axino. We find that constraints from astrophysical X- and gamma rays on the
decay into photon and neutrino can set new bounds on some trilinear
supersymmetric R-parity violating Yukawa couplings. In some corners of the
parameter space the decays of a relic axino can also explain a putative 3.5 keV
line.
| hep-ph |
1807.02531 | A powerful radio-loud quasar at the end of cosmic reionization | We present the discovery of the radio-loud quasar PSO J352.4034-15.3373 at
z=5.84 pm 0.02. This quasar is the radio brightest source known, by an order of
magnitude, at z~6 with a flux density in the range of 8-100 mJy from 3GHz to
230MHz and a radio loudness parameter R>~1000. This source provides an
unprecedented opportunity to study powerful jets and radio-mode feedback at the
highest redshifts, and presents the first real chance to probe deep into the
neutral intergalactic medium by detecting 21 cm absorption at the end of cosmic
reionization.
| astro-ph.GA |
1807.02532 | Biautomatic structures in systolic Artin groups | We examine the construction of Huang and Osajda that was used in their proof
of the biautomaticity of Artin groups of almost large type. We describe a
slightly simpler variant of that biautomatic structure, with explicit
descriptions of a few small examples, and we examine some of the properties of
the structure. We explain how the construction can be programmed within the GAP
system.
| math.GR |
1807.02533 | Stinespring's construction as an adjunction | Given a representation of a unital $C^*$-algebra $\mathcal{A}$ on a Hilbert
space $\mathcal{H}$, together with a bounded linear map
$V:\mathcal{K}\to\mathcal{H}$ from some other Hilbert space, one obtains a
completely positive map on $\mathcal{A}$ via restriction using the adjoint
action associated to $V$. We show this restriction forms a natural
transformation from a functor of $C^*$-algebra representations to a functor of
completely positive maps. We exhibit Stinespring's construction as a left
adjoint of this restriction. Our Stinespring adjunction provides a universal
property associated to minimal Stinespring dilations and morphisms of
Stinespring dilations. We use these results to prove the purification postulate
for all finite-dimensional $C^*$-algebras.
| math.OA math-ph math.CT math.MP |
1807.02534 | Galaxy and Mass Assembly (GAMA): Accurate number densities &
environments of massive ultracompact galaxies at 0.02 < z < 0.3 | Massive Ultracompact Galaxies (MUGs) are common at z=2-3, but very rare in
the nearby Universe. Simulations predict that the few surviving MUGs should
reside in galaxy clusters, whose large relative velocities prevent them from
merging, thus maintaining their original properties (namely stellar
populations, masses, sizes and dynamical state). We take advantage of the
high-completeness, large-area spectroscopic GAMA survey, complementing it with
deeper imaging from the KiDS and VIKING surveys. We find a set of 22 bona-fide
MUGs, defined as having high stellar mass (>8x10^10 M_Sun) and compact size
(R_e<2 Kpc) at 0.02 < z < 0.3. An additional set of 7 lower-mass objects
(6x10^10 < M_star/M_Sun < 8x10^10) are also potential candidates according to
typical mass uncertainties. The comoving number density of MUGs at low redshift
(z < 0.3) is constrained at $(1.0\pm 0.4)x 10^-6 Mpc^-3, consistent with galaxy
evolution models. However, we find a mixed distribution of old and young
galaxies, with a quarter of the sample representing (old) relics. MUGs have a
predominantly early/swollen disk morphology (Sersic index 1<n<2.5) with high
stellar surface densities (<Sigma_e> ~ 10^10 M_Sun Kpc^-2). Interestingly, a
large fraction feature close companions -- at least in projection -- suggesting
that many (but not all) reside in the central regions of groups. Halo masses
show these galaxies inhabit average-mass groups. As MUGs are found to be almost
equally distributed among environments of different masses, their relative
fraction is higher in more massive overdensities, matching the expectations
that some of these galaxies fell in these regions at early times. However,
there must be another channel leading some of these galaxies to an abnormally
low merger history because our sample shows a number of objects that do not
inhabit particularly dense environments. (abridged)
| astro-ph.GA |
1807.02535 | Invertible Particle Flow-based Sequential MCMC with extension to
Gaussian Mixture noise models | Sequential state estimation in non-linear and non-Gaussian state spaces has a
wide range of applications in statistics and signal processing. One of the most
effective non-linear filtering approaches, particle filtering, suffers from
weight degeneracy in high-dimensional filtering scenarios. Several avenues have
been pursued to address high-dimensionality. Among these, particle flow
particle filters construct effective proposal distributions by using invertible
flow to migrate particles continuously from the prior distribution to the
posterior, and sequential Markov chain Monte Carlo (SMCMC) methods use a
Metropolis-Hastings (MH) accept-reject approach to improve filtering
performance. In this paper, we propose to combine the strengths of invertible
particle flow and SMCMC by constructing a composite Metropolis-Hastings (MH)
kernel within the SMCMC framework using invertible particle flow. In addition,
we propose a Gaussian mixture model (GMM)-based particle flow algorithm to
construct effective MH kernels for multi-modal distributions. Simulation
results show that for high-dimensional state estimation example problems the
proposed kernels significantly increase the acceptance rate with minimal
additional computational overhead and improve estimation accuracy compared with
state-of-the-art filtering algorithms.
| stat.ME |
1807.02536 | VLASE: Vehicle Localization by Aggregating Semantic Edges | In this paper, we propose VLASE, a framework to use semantic edge features
from images to achieve on-road localization. Semantic edge features denote edge
contours that separate pairs of distinct objects such as building-sky, road-
sidewalk, and building-ground. While prior work has shown promising results by
utilizing the boundary between prominent classes such as sky and building using
skylines, we generalize this approach to consider semantic edge features that
arise from 19 different classes. Our localization algorithm is simple, yet very
powerful. We extract semantic edge features using a recently introduced CASENet
architecture and utilize VLAD framework to perform image retrieval. Our
experiments show that we achieve improvement over some of the state-of-the-art
localization algorithms such as SIFT-VLAD and its deep variant NetVLAD. We use
ablation study to study the importance of different semantic classes and show
that our unified approach achieves better performance compared to individual
prominent features such as skylines.
| cs.CV cs.RO |
1807.02537 | Fully Scalable Gaussian Processes using Subspace Inducing Inputs | We introduce fully scalable Gaussian processes, an implementation scheme that
tackles the problem of treating a high number of training instances together
with high dimensional input data. Our key idea is a representation trick over
the inducing variables called subspace inducing inputs. This is combined with
certain matrix-preconditioning based parametrizations of the variational
distributions that lead to simplified and numerically stable variational lower
bounds. Our illustrative applications are based on challenging extreme
multi-label classification problems with the extra burden of the very large
number of class labels. We demonstrate the usefulness of our approach by
presenting predictive performances together with low computational times in
datasets with extremely large number of instances and input dimensions.
| stat.ML cs.LG |
1807.02538 | Inverse Structure Problem for Neutron-Star Binaries | Gravitational wave detectors in the LIGO/Virgo frequency band are able to
measure the individual masses and the composite tidal deformabilities of
neutron-star binary systems. This paper demonstrates that high accuracy
measurements of these quantities from an ensemble of binary systems can in
principle be used to determine the high density neutron-star equation of state
exactly. This analysis assumes that all neutron stars have the same
thermodynamically stable equation of state, but does not use simplifying
approximations for the composite tidal deformability or make additional
assumptions about the high density equation of state.
| astro-ph.HE gr-qc |
1807.02539 | Substitutional mechanism for growth of hexagonal boron nitride on
epitaxial graphene | Monolayer-thick hexagonal boron nitride (h-BN) is grown on graphene on
SiC(0001), by exposure of the graphene to borazine, (BH)3(NH)3, at 1100 C. The
h-BN films form ~2-micrometer size grains with a preferred orientation of 30
degrees relative to the surface graphene. Low-energy electron microscopy is
employed to provide definitive signatures of the number and composition of
two-dimensional (2D) planes across the surface. These grains are found to form
by substitution for the surface graphene, with the C atoms produced by this
substitution then being incorporated below the h-BN (at the interface between
the existing graphene and the SiC) to form a new graphene plane.
| cond-mat.mes-hall |
1807.02540 | Fine properties of fractional Brownian motions on Wiener space | We study several important fine properties for the family of fractional
Brownian motions with Hurst parameter $H$ under the $(p,r)$-capacity on
classical Wiener space introduced by Malliavin. We regard fractional Brownian
motions as Wiener functionals via the integral representation discovered by
Decreusefond and \"{U}st\"{u}nel, and show non differentiability, modulus of
continuity, law of iterated Logarithm(LIL) and self-avoiding properties of
fractional Brownian motion sample paths using Malliavin calculus as well as the
tools developed in the previous work by Fukushima, Takeda and etc. for Brownian
motion case.
| math.PR |
1807.02541 | Collisionless Dynamics in Two-Dimensional Bosonic Gases | We study the dynamics of dilute and ultracold bosonic gases in a quasi
two-dimensional (2D) configuration and in the collisionless regime. We adopt
the 2D Landau-Vlasov equation to describe a three-dimensional gas under very
strong harmonic confinement along one direction. We use this effective equation
to investigate the speed of sound in quasi 2D bosonic gases, i.e. the sound
propagation around a Bose-Einstein distribution in collisionless 2D gases. We
derive coupled algebraic equations for the real and imaginary parts of the
sound velocity, which are then solved taking also into account the equation of
state of the 2D bosonic system. Above the Berezinskii-Kosterlitz-Thouless
critical temperature we find that there is rapid growth of the imaginary
component of the sound velocity which implies a strong Landau damping. Quite
remarkably, our theoretical results are in good agreement with very recent
experimental data obtained with a uniform 2D Bose gas of $^{87}$Rb atoms.
| cond-mat.quant-gas |
1807.02542 | Analytic continuation of the kite family | We consider results for the master integrals of the kite family, given in
terms of ELi-functions which are power series in the nome $q$ of an elliptic
curve. The analytic continuation of these results beyond the Euclidean region
is reduced to the analytic continuation of the two period integrals which
define $q.$ We discuss the solution to the latter problem from the perspective
of the Picard-Lefschetz formula.
| hep-th hep-ph |
1807.02543 | Relatively Uniformly Continuous Semigroups on Vector Lattices | In this paper we study continuous semigroups of positive operators on general
vector lattices equipped with the relative uniform topology $\tau_{ru}$. We
introduce the notions of strong continuity with respect to $\tau_{ru}$ and
relative uniform continuity for semigroups. These notions allow us to study
semigroups on non-locally convex spaces such as $L^p(\mathbb{R})$ for $0<p<1$
and non-complete spaces such as $Lip(\mathbb{R})$, $UC(\mathbb{R})$, and
$C_c(\mathbb{R})$. We show that the (left) translation semigroup on the real
line, the heat semigroup and some Koopman semigroups are relatively uniformly
continuous on a variety of spaces.
| math.FA |
1807.02544 | Localization and Mirror Symmetry | These notes were born out of a five-hour lecture series for graduate students
at the May 2018 Snowbird workshop Crossing the Walls in Enumerative Geometry.
After a short primer on equivariant cohomology and localization, we provide
proofs of the genus-zero mirror theorems for the quintic threefold, first in
Fan-Jarvis-Ruan-Witten theory and then in Gromov-Witten theory. We make no
claim to originality, except in exposition, where special emphasis is placed on
peeling away the standard technical machinery and viewing the mirror theorems
as closed-formula manifestations of elementary localization recursions.
| math.AG math-ph math.MP |
1807.02545 | A Study of the Lexicography of Hand Gestures During Eating | This paper considers the lexicographical challenge of defining actions a
person takes while eating. The goal is to establish objective and repeatable
gesture definitions based on discernible intent. Such a standard would support
the sharing of data and results between researchers working on the problem of
automatic monitoring of dietary intake. We define five gestures: taking a bite
of food (bite), sipping a drink of liquid (drink), manipulating food for
preparation of intake (utensiling), not moving (rest) and a non-eating category
(other). To test this lexicography, we used our definitions to label a large
data set and tested for inter-rater reliability. The data set consists of a
total of 276 participants eating a single meal while wearing a watch-like
device to track wrist motion. Video was simultaneously recorded and
subsequently reviewed to label gestures. A total of 18 raters manually labeled
51,614 gestures. Every meal was labeled by at least 1 rater, with 95 meals
labeled by 2 raters. Inter-rater reliability was calculated in terms of
agreement, boundary ambiguity, and mistakes. Results were 92.5% agreement (75%
exact agreement, 17.5% boundary ambiguity). Mistakes of intake gestures (0.6%
bite and 1.9% drink) occur much less frequently than non-intake gestures (16.5%
utensiling and 8.7% rest). Similar rates were found across all 18 raters.
Finally, a comparison of gesture segments against single index labels of bites
and drinks from a previous effort showed an agreement of 95.8% with 0.6%
ambiguity and 3.6% mistakes. Overall, these findings take a step towards
developing a consensus lexicography of eating gestures for the research
community.
| eess.SP |
1807.02546 | Non-trivial topology in a layered Dirac nodal-line semimetal candidate
SrZnSb$_2$ with distorted Sb square nets | Dirac states hosted by Sb/Bi square nets are known to exist in the layered
antiferromagnetic AMnX$_2$ (A = Ca/Sr/Ba/Eu/Yb, X=Sb/Bi) material family the
space group to be P4/nmm or I4/mmm. In this paper, we present a comprehensive
study of quantum transport behaviors, angle-resolved photoemission spectroscopy
(ARPES) and first-principles calculations on SrZnSb2, a nonmagnetic analogue to
AMnX2, which crystallizes in the pnma space group with distorted square nets.
From the quantum oscillation measurements up to 35 T, three major frequencies
including F$_1$ = 103 T, F$_2$ = 127 T and F$_3$ = 160 T, are identified. The
effective masses of the quasiparticles associated with these frequencies are
extracted, namely, m*$_1$ = 0.1 m$_e$, m*$_2$ = 0.1 m$_e$ and m*$_3$ =
0.09m$_e$, where m$_e$ is the free electron mass. From the three-band
Lifshitz-Kosevich fit, the Berry phases accumulated along the cyclotron orbit
of the quasiparticles are 0.06$\pi$, 1.2$\pi$ and 0.74$\pi$ for F$_1$, F$_2$
and F$_3$, respectively. Combined with the ARPES data and the first-principles
calculations, we reveal that F2 and F3 are associated with the two nontrivial
Fermi pockets at the Brillouin zone edge while F1 is associated with the
trivial Fermi pocket at the zone center. In addition, the first-principles
calculations further suggest the existence of Dirac nodal line in the band
structure of SrZnSb$_2$.
| cond-mat.mtrl-sci cond-mat.mes-hall cond-mat.str-el |
1807.02547 | 3D Steerable CNNs: Learning Rotationally Equivariant Features in
Volumetric Data | We present a convolutional network that is equivariant to rigid body motions.
The model uses scalar-, vector-, and tensor fields over 3D Euclidean space to
represent data, and equivariant convolutions to map between such
representations. These SE(3)-equivariant convolutions utilize kernels which are
parameterized as a linear combination of a complete steerable kernel basis,
which is derived analytically in this paper. We prove that equivariant
convolutions are the most general equivariant linear maps between fields over
R^3. Our experimental results confirm the effectiveness of 3D Steerable CNNs
for the problem of amino acid propensity prediction and protein structure
classification, both of which have inherent SE(3) symmetry.
| cs.LG stat.ML |
1807.02548 | Delay-Aware Coded Caching for Mobile Users | In this work, we study the trade-off between the cache capacity and the user
delay for a cooperative Small Base Station (SBS) coded caching system with
mobile users. First, a delay-aware coded caching policy, which takes into
account the popularity of the files and the maximum re-buffering delay to
minimize the average rebuffering delay of a mobile user under a given cache
capacity constraint is introduced. Subsequently, we address a scenario where
some files are served by the macro-cell base station (MBS) when the cache
capacity of the SBSs is not sufficient to store all the files in the library.
For this scenario, we develop a coded caching policy that minimizes the average
amount of data served by the MBS under an average re-buffering delay
constraint.
| cs.IT cs.MM eess.IV math.IT |
1807.02549 | Supersymmetric Dirac-Born-Infeld Axionic Inflation and Non-Gaussianity | An analysis is given of inflation based on a supersymmetric Dirac-Born-Infeld
(DBI) action in an axionic landscape. The DBI model we discuss involves a
landscape of chiral superfields with one $U(1)$ shift symmetry which is broken
by instanton type non-perturbative terms in the superpotential. Breaking of the
shift symmetry leads to one pseudo-Nambu-Goldstone-boson which acts as the
inflaton while the remaining normalized phases of the chiral fields generically
labeled axions are invariant under the $U(1)$ shift symmetry. The analysis is
carried out in the vacuum with stabilized saxions, which are the magnitudes of
the chiral fields. Regions of the parameter space where slow-roll inflation
occurs are exhibited and the spectral indices as well as the ratio of the
tensor to the scalar power spectrum are computed. An interesting aspect of
supersymmetric DBI models analyzed is that in most of the parameter space
tensor to scalar ratio and scalar spectral index are consistent with Planck
data if slow roll occurs and is not eternal. Also interesting is that the ratio
of the tensor to the scalar power spectrum can be large and can lie close to
the experimental upper limit and thus testable in improved experiment.
Non-Gaussianity in this class of models is explored.
| hep-ph astro-ph.CO gr-qc hep-th |
1807.02550 | Method for finding the exact effective Hamiltonian of time driven
quantum systems | Time-driven quantum systems are important in many different fields of physics
like cold atoms, solid state, optics, etc. Many of their properties are encoded
in the time evolution operator which is calculated by using a time-ordered
product of actions. The solution to this problem is equivalent to find an
effective Hamiltonian. This task is usually very complex and either requires
approximations, or in very particular and rare cases, a system-dependent method
can be found. Here we provide a general scheme that allows to find such
effective Hamiltonian. The method is based in using the structure of the
associated Lie group and a decomposition of the evolution on each group
generator. The time evolution is thus always transformed in a system of
ordinary non-linear differential equations for a set of coefficients. In many
cases this system can be solved by symbolic computational algorithms. As an
example, an exact solution to three well known problems is provided. For two of
them, the modulated optical lattice and Kapitza pendulum, the exact solutions,
which were already known, are reproduced. For the other example, the Paul trap,
no exact solutions were known. Here we find such exact solution, and as
expected, contain the approximate solutions found by other authors.
| quant-ph physics.atom-ph |
1807.02551 | New Limits of Treewidth-based tractability in Optimization | Sparse structures are frequently sought when pursuing tractability in
optimization problems. They are exploited from both theoretical and
computational perspectives to handle complex problems that become manageable
when sparsity is present. An example of this type of structure is given by
treewidth: a graph theoretical parameter that measures how "tree-like" a graph
is. This parameter has been used for decades for analyzing the complexity of
various optimization problems and for obtaining tractable algorithms for
problems where this parameter is bounded. The goal of this work is to
contribute to the understanding of the limits of the treewidth-based
tractability in optimization. Our results are as follows. First, we prove that,
in a certain sense, the already known positive results on extension complexity
based on low treewidth are the best possible. Secondly, under mild assumptions,
we prove that treewidth is the only graph-theoretical parameter that yields
tractability a wide class of optimization problems, a fact well known in
Graphical Models in Machine Learning and in Constraint Satisfaction Problems,
which here we extend to an approximation setting in Optimization.
| cs.DM cs.CC |
1807.02552 | M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning | Unsupervised domain adaptation techniques have been successful for a wide
range of problems where supervised labels are limited. The task is to classify
an unlabeled `target' dataset by leveraging a labeled `source' dataset that
comes from a slightly similar distribution. We propose metric-based adversarial
discriminative domain adaptation (M-ADDA) which performs two main steps. First,
it uses a metric learning approach to train the source model on the source
dataset by optimizing the triplet loss function. This results in clusters where
embeddings of the same label are close to each other and those with different
labels are far from one another. Next, it uses the adversarial approach (as
that used in ADDA \cite{2017arXiv170205464T}) to make the extracted features
from the source and target datasets indistinguishable. Simultaneously, we
optimize a novel loss function that encourages the target dataset's embeddings
to form clusters. While ADDA and M-ADDA use similar architectures, we show that
M-ADDA performs significantly better on the digits adaptation datasets of MNIST
and USPS. This suggests that using metric-learning for domain adaptation can
lead to large improvements in classification accuracy for the domain adaptation
task. The code is available at \url{https://github.com/IssamLaradji/M-ADDA}.
| cs.LG stat.ML |
1807.02553 | Flow-time Optimization For Concurrent Open-Shop and Precedence
Constrained Scheduling Models | Scheduling a set of jobs over a collection of machines is a fundamental
problem that needs to be solved millions of times a day in various computing
platforms: in operating systems, in large data clusters, and in data centers.
Along with makespan, flow-time, which measures the length of time a job spends
in a system before it completes, is arguably the most important metric to
measure the performance of a scheduling algorithm. In recent years, there has
been a remarkable progress in understanding flow-time based objective functions
in diverse settings such as unrelated machines scheduling, broadcast
scheduling, multi-dimensional scheduling, to name a few.
Yet, our understanding of the flow-time objective is limited mostly to the
scenarios where jobs have simple structures; in particular, each job is a
single self contained entity. On the other hand, in almost all real world
applications, think of MapReduce settings for example, jobs have more complex
structures. In this paper, we consider two classical scheduling models that
capture complex job structures: 1) concurrent open-shop scheduling and 2)
precedence constrained scheduling. Our main motivation to study these problems
specifically comes from their relevance to two scheduling problems that have
gained importance in the context of data centers: co-flow scheduling and DAG
scheduling. We design almost optimal approximation algorithms for open-shop
scheduling and precedence constrained scheduling, and show hardness results.
| cs.DS |
1807.02554 | Methodic of joint using the tools of automation of lexical and parsing
analysis in the process of teaching the programming theory of future
informatics teachers | The place and role of parsing analysis in formation of professional
informatics competences of future informatics teachers is determined. Separated
automation tools for lexical (lex) and syntax (yacc) analysis invariant to the
programming language used. The expediency of using functional programming
languages Scheme and SML is shown for learning how to develop compilers in the
course of programming theory. The example of the MosML dialect illustrates the
main components of the methodic of joint using the tools of automation of
lexical and parsing analysis in the process of teaching the programming theory
of future informatics teachers. The main conclusions and recommendations: 1)
the considered example of the expanded calculator can be refined by changing
the grammar, in particular - for the introduction of conditional and cyclic
constructions; 2) the proposed scheme can be used to implement the interpreter
of any formal language with an arbitrary typing method - the appropriate
examples of study will be subsets of procedural languages Basic and C and
functional languages Scheme and SML: provided the addition of the machine code
generation phase, this provides an opportunity to demonstrate the full
development cycle for programming language compiler.
| cs.PL cs.CY cs.FL |
1807.02555 | Spectrally improved controllable frequency comb quantum memory | We propose a scheme of a universal block of broadband quantum memory
consisting of three ring microresonators forming a controllable frequency comb
and interacting with each other and with a common waveguide. We find the
optimal parameters of the microresonators showing the possibility of highly
efficient storage of light fields on this memory block and we demonstrate the
procedure for gluing several memory blocks for increasing spectral range of the
composite quantum memory while maintaining high efficiency.
| quant-ph |
1807.02556 | X-ray Properties of SPT Selected Galaxy Clusters at 0.2<z<1.5 Observed
with XMM-Newton | We present measurements of the X-ray observables of the intra-cluster medium
(ICM), including luminosity $L_X$, ICM mass $M_{ICM}$, emission-weighted mean
temperature $T_X$, and integrated pressure $Y_X$, that are derived from
XMM-Newton X-ray observations of a Sunyaev-Zel'dovich Effect (SZE) selected
sample of 59 galaxy clusters from the South Pole Telescope SPT-SZ survey that
span the redshift range of $0.20 < z < 1.5$. We constrain the best-fit power
law scaling relations between X-ray observables, redshift, and halo mass. The
halo masses are estimated based on previously published SZE observable to mass
scaling relations, calibrated using information that includes the halo mass
function. Employing SZE-based masses in this sample enables us to constrain
these scaling relations for massive galaxy clusters ($M_{500}\geq 3
\times10^{14}$ $M_\odot$) to the highest redshifts where these clusters exist
without concern for X-ray selection biases. We find that the mass trends are
steeper than self-similarity in all cases, and with $\geq 2.5{\sigma}$
significance in the case of $L_X$ and $M_{ICM}$. The redshift trends are
consistent with the self-similar expectation, but the uncertainties remain
large. Core-included scaling relations tend to have steeper mass trends for
$L_X$. There is no convincing evidence for a redshift-dependent mass trend in
any observable. The constraints on the amplitudes of the fitted scaling
relations are currently limited by the systematic uncertainties on the
SZE-based halo masses, however the redshift and mass trends are limited by the
X-ray sample size and the measurement uncertainties of the X-ray observables.
| astro-ph.CO |
1807.02557 | The Elasticity of Nuclear Pasta | The elastic properties of neutron star crusts are relevant for a variety of
currently observable or near-future electromagnetic and gravitational wave
phenomena. These phenomena may depend on the elastic properties of nuclear
pasta found in the inner crust. We present large scale classical molecular
dynamics simulations where we deform nuclear pasta. We simulate idealized
samples of nuclear pasta and describe their breaking mechanism. We also deform
nuclear pasta that is arranged into many domains, similar to what is known for
the ions in neutron star crusts. Our results show that nuclear pasta may be the
strongest known material, perhaps with a shear modulus of
$10^{30}\,\text{ergs/cm}^3$ and breaking strain greater than 0.1.
| nucl-th |
1807.02558 | Energy Efficient Resource Allocation in EH-enabled CR Networks for IoT | With the rapid growth of Internet of Things (IoT) devices, the next
generation mobile networks demand for more operating frequency bands. By
leveraging the underutilized radio spectrum, the cognitive radio (CR)
technology is considered as a promising solution for spectrum scarcity problem
of IoT applications. In parallel with the development of CR techniques,
Wireless Energy Harvesting (WEH) is considered as one of the emerging
technologies to eliminate the need of recharging or replacing the batteries for
IoT and CR networks. To this end, we propose to utilize WEH for CR networks in
which the CR devices are not only capable of sensing the available radio
frequencies in a collaborative manner but also harvesting the wireless energy
transferred by an Access Point (AP). More importantly, we design an
optimization framework that captures a fundamental tradeoff between energy
efficiency (EE) and spectral efficiency (SE) of the network. In particular, we
formulate a Mixed Integer Nonlinear Programming (MINLP) problem that maximizes
EE while taking into consideration of users' buffer occupancy, data rate
fairness, energy causality constraints and interference constraints. We further
prove that the proposed optimization framework is an NP-Hard problem. Thus, we
propose a low complex heuristic algorithm, called INSTANT, to solve the
resource allocation and energy harvesting optimization problem. The proposed
algorithm is shown to be capable of achieving near optimal solution with high
accuracy while having polynomial complexity. The efficiency of our proposal is
validated through well designed simulations.
| cs.NI |
1807.02559 | A White Dwarf catalogue from Gaia-DR2 and the Virtual Observatory | We present a catalogue of 73,221 white dwarf candidates extracted from the
astrometric and photometric data of the recently published Gaia DR2 catalogue.
White dwarfs were selected from the Gaia Hertzsprung-Russell diagram with the
aid of the most updated population synthesis simulator. Our analysis shows that
Gaia has virtually identified all white dwarfs within 100 pc from the Sun.
Hence, our sub-population of 8,555 white dwarfs within this distance limit and
the colour range considered, $-\,0.52<(G_{\rm BP}-G_{\rm RP})<0.80$, is the
largest and most complete volume-limited sample of such objects to date. From
this sub-sample we identified 8,343 CO-core and 212 ONe-core white dwarf
candidates and derived a white dwarf space density of
$4.9\pm0.4\times10^{-3}\,{\rm pc^{-3}}$. A bifurcation in the
Hertzsprung-Russell diagram for these sources, which our models do not predict,
is clearly visible. We used the Virtual Observatory tool VOSA to derive
effective temperatures and luminosities for our sources by fitting their
spectral energy distributions, that we built from the UV to the NIR using
publicly available photometry through the Virtual Observatory. From these
parameters, we derived the white dwarf radii. Interpolating the radii and
effective temperatures in hydrogen-rich white dwarf cooling sequences, we
derived the surface gravities and masses. The Gaia 100 pc white dwarf
population is clearly dominated by cool ($\sim$ 8,000 K) objects and reveals a
significant population of massive ($M \sim 0.8 M_{\odot}$) white dwarfs, of
which no more than $\sim$ $30-40 \%$ can be attributed to hydrogen-deficient
atmospheres, and whose origin remains uncertain.
| astro-ph.SR |
1807.02560 | Resolving the Powerful Radio-loud Quasar at z~6 | We present high angular resolution imaging ($23.9 \times 11.3$ mas, $138.6
\times 65.5$ pc) of the radio-loud quasar PSO~J352.4034$-$15.3373 at $z=5.84$
with the Very Long Baseline Array (VLBA) at 1.54 GHz. This quasar has the
highest radio-to-optical flux density ratio at such a redshift, making it the
radio-loudest source known to date at $z \sim 6$. The VLBA observations
presented here resolve this quasar into multiple components with an overall
linear extent of 1.62 kpc ($0\rlap{.}{''}28$) and with a total flux density of
$6.57 \pm 0.38$ mJy, which is about half of the emission measured at a much
lower angular resolution. The morphology of the source is comparable with
either a radio core with a one-sided jet, or a compact or a medium-size
Symmetric Object (CSO/MSO). If the source is a CSO/MSO, and assuming an advance
speed of $0.2c$, then the estimated kinematic age is $\sim 10^4$ yr.
| astro-ph.GA |
1807.02561 | Superconductivity in the Superhard Boride WB$_{4.2}$ | We show that the superhard boride WB$_{4.2}$ is a superconductor with a T$_c$
of 2.05(5) K. Temperature-dependent magnetic susceptibility, electrical
resistivity, and specific heat measurements were used to characterize the
superconducting transition. The Sommerfeld constant {\gamma} for WB$_{4.2}$ is
2.07(3) mJ mol$^{-1}$ K$^{-2}$ and the {\Delta}C/{\gamma}T$_c$ = 1.56, which is
somewhat higher than what is expected for weakly coupled BCS type
superconductors. The H$_{c2}$ vs T plot is linear over a wide temperature range
but does show signs of flattening by the lowest temperatures studied and
therefore the zero-temperature upper critical field ({\mu}$_0$H$_{c2}$(0)) for
WB$_{4.2}$ lies somewhere between the linear extrapolation of
{\mu}$_0$H$_{c2}$(T) to 0 K and expectations based on the WHH model.
| cond-mat.supr-con |
1807.02562 | Exploring Scientific Application Performance Using Large Scale Object
Storage | One of the major performance and scalability bottlenecks in large scientific
applications is parallel reading and writing to supercomputer I/O systems. The
usage of parallel file systems and consistency requirements of POSIX, that all
the traditional HPC parallel I/O interfaces adhere to, pose limitations to the
scalability of scientific applications. Object storage is a widely used storage
technology in cloud computing and is more frequently proposed for HPC workload
to address and improve the current scalability and performance of I/O in
scientific applications. While object storage is a promising technology, it is
still unclear how scientific applications will use object storage and what the
main performance benefits will be. This work addresses these questions, by
emulating an object storage used by a traditional scientific application and
evaluating potential performance benefits. We show that scientific applications
can benefit from the usage of object storage on large scales.
| cs.DC |
1807.02563 | Invariant domain preserving discretization-independent schemes and
convex limiting for hyperbolic systems | We introduce an approximation technique for nonlinear hyperbolic systems with
sources that is invariant domain preserving. The method is
discretization-independent provided elementary symmetry and skew-symmetry
properties are satisfied by the scheme. The method is formally first-order
accurate in space. A series of higher-order methods is also introduced. When
these methods violate the invariant domain properties, they are corrected by a
limiting technique that we call convex limiting. After limiting, the resulting
methods satisfy all the invariant domain properties that are imposed by the
user (see Theorem~7.24). A key novelty is that the bounds that are enforced on
the solution at each time step are necessarily satisfied by the low-order
approximation.
| math.NA |
1807.02564 | Applications of Data Mining Techniques for Vehicular Ad hoc Networks | Due to the recent advances in vehicular ad hoc networks (VANETs), smart
applications have been incorporating the data generated from these networks to
provide quality of life services. In this paper, we have proposed taxonomy of
data mining techniques that have been applied in this domain in addition to a
classification of these techniques. Our contribution is to highlight the
research methodologies in the literature and allow for comparing among them
using different characteristics. The proposed taxonomy covers elementary data
mining techniques such as: preprocessing, outlier detection, clustering, and
classification of data. In addition, it covers centralized, distributed,
offline, and online techniques from the literature.
| cs.NI cs.NE |
1807.02565 | Handover Rate Characterization in 3D Ultra-Dense Heterogeneous Networks | Ultra-dense networks (UDNs) envision the massive deployment of heterogenous
base stations (BSs) to meet the desired traffic demands. Furthermore, UDNs are
expected to support the diverse devices e.g., personal mobile devices and
unmanned ariel vehicles. User mobility and the resulting excessive changes in
user to BS associations in such highly dense networks may however nullify the
capacity gains foreseen through BS densification. Thus there exists a need to
quantify the effect of user mobility in UDNs. In this article, we consider a
three-dimensional N-tier downlink network and determine the association
probabilities and inter/intra tier handover rates using tools from stochastic
geometry. In particular, we incorporate user and BSs' antenna heights into the
mathematical analysis and study the impact of user height on the association
and handover rate. The numerical trends show that the intra-tier handovers are
dominant for the tiers with shortest relative elevation w.r.t. the user and
this dominance is more prominent when there exists a high discrepancy among the
tiers' heights. However, biasing can be employed to balance the handover load
among the network tiers.
| cs.NI cs.IT math.IT |
1807.02566 | Updating Probabilistic Knowledge on Condition/Event Nets using Bayesian
Networks | The paper extends Bayesian networks (BNs) by a mechanism for dynamic changes
to the probability distributions represented by BNs. One application scenario
is the process of knowledge acquisition of an observer interacting with a
system. In particular, the paper considers condition/event nets where the
observer's knowledge about the current marking is a probability distribution
over markings. The observer can interact with the net to deduce information
about the marking by requesting certain transitions to fire and observing their
success or failure.
Aiming for an efficient implementation of dynamic changes to probability
distributions of BNs, we consider a modular form of networks that form the
arrows of a free PROP with a commutative comonoid structure, also known as term
graphs. The algebraic structure of such PROPs supplies us with a compositional
semantics that functorially maps BNs to their underlying probability
distribution and, in particular, it provides a convenient means to describe
structural updates of networks.
| cs.LO cs.SI |
1807.02567 | Deep Learning for Launching and Mitigating Wireless Jamming Attacks | An adversarial machine learning approach is introduced to launch jamming
attacks on wireless communications and a defense strategy is presented. A
cognitive transmitter uses a pre-trained classifier to predict the current
channel status based on recent sensing results and decides whether to transmit
or not, whereas a jammer collects channel status and ACKs to build a deep
learning classifier that reliably predicts the next successful transmissions
and effectively jams them. This jamming approach is shown to reduce the
transmitter's performance much more severely compared with random or
sensing-based jamming. The deep learning classification scores are used by the
jammer for power control subject to an average power constraint. Next, a
generative adversarial network (GAN) is developed for the jammer to reduce the
time to collect the training dataset by augmenting it with synthetic samples.
As a defense scheme, the transmitter deliberately takes a small number of wrong
actions in spectrum access (in form of a causative attack against the jammer)
and therefore prevents the jammer from building a reliable classifier. The
transmitter systematically selects when to take wrong actions and adapts the
level of defense to mislead the jammer into making prediction errors and
consequently increase its throughput.
| cs.NI cs.LG stat.ML |
1807.02568 | Interrelated Main-Sequence Mass-Luminosity, Mass-Radius and
Mass-Effective Temperature Relations | Absolute parameters of 509 main-sequence stars selected from the components
of detached-eclipsing spectroscopic binaries in the Solar neighbourhood are
used to study mass-luminosity, mass-radius and mass-effective temperature
relations (MLR, MRR and MTR). The MLR function is found better if expressed by
a six-piece classical MLR ($L \propto M^{\alpha}$) rather than a fifth or a
sixth degree polynomial within the mass range of $0.179\leq M/M_{\odot}\leq
31$. The break points separating the mass-ranges with classical MLR do not
appear to us to be arbitrary. Instead, the data indicate abrupt changes along
the mass axis in the mean energy generation per unit of stellar mass. Unlike
the MLR function, the MRR and MTR functions cannot be determined over the full
range of masses. A single piece MRR function is calibrated from the radii of
stars with $M\leq1.5M_{\odot}$, while a second single piece MTR function is
found for stars with $M>1.5M_{\odot}$. The missing part of the MRR is computed
from the MLR and MTR, while the missing part of the MTR is computed from the
MLR and MRR. As a result, we have interrelated MLR, MRR and MTR, which are
useful in determining the typical absolute physical parameters of main-sequence
stars of given masses. These functions are also useful to estimate typical
absolute physical parameters from typical $T_{eff}$ values. Thus, we were able
to estimate the typical absolute physical parameters of main-sequence stars
observed in the Sejong Open Cluster survey, based on that survey's published
values for $T_{eff}$. Since typical absolute physical parameters of main
sequence stars cannot normally be determined in such photometric surveys, the
interrelated functions are shown to be useful to compute such missing
parameters from similar surveys.
| astro-ph.SR |
1807.02569 | Automated and Interpretable Patient ECG Profiles for Disease Detection,
Tracking, and Discovery | The electrocardiogram or ECG has been in use for over 100 years and remains
the most widely performed diagnostic test to characterize cardiac structure and
electrical activity. We hypothesized that parallel advances in computing power,
innovations in machine learning algorithms, and availability of large-scale
digitized ECG data would enable extending the utility of the ECG beyond its
current limitations, while at the same time preserving interpretability, which
is fundamental to medical decision-making. We identified 36,186 ECGs from the
UCSF database that were 1) in normal sinus rhythm and 2) would enable training
of specific models for estimation of cardiac structure or function or detection
of disease. We derived a novel model for ECG segmentation using convolutional
neural networks (CNN) and Hidden Markov Models (HMM) and evaluated its output
by comparing electrical interval estimates to 141,864 measurements from the
clinical workflow. We built a 725-element patient-level ECG profile using
downsampled segmentation data and trained machine learning models to estimate
left ventricular mass, left atrial volume, mitral annulus e' and to detect and
track four diseases: pulmonary arterial hypertension (PAH), hypertrophic
cardiomyopathy (HCM), cardiac amyloid (CA), and mitral valve prolapse (MVP).
CNN-HMM derived ECG segmentation agreed with clinical estimates, with median
absolute deviations (MAD) as a fraction of observed value of 0.6% for heart
rate and 4% for QT interval. Patient-level ECG profiles enabled quantitative
estimates of left ventricular and mitral annulus e' velocity with good
discrimination in binary classification models of left ventricular hypertrophy
and diastolic function. Models for disease detection ranged from AUROC of 0.94
to 0.77 for MVP. Top-ranked variables for all models included known ECG
characteristics along with novel predictors of these traits/diseases.
| cs.CV |
1807.02570 | Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for
Monocular Direct Sparse Odometry | Monocular visual odometry approaches that purely rely on geometric cues are
prone to scale drift and require sufficient motion parallax in successive
frames for motion estimation and 3D reconstruction. In this paper, we propose
to leverage deep monocular depth prediction to overcome limitations of
geometry-based monocular visual odometry. To this end, we incorporate deep
depth predictions into Direct Sparse Odometry (DSO) as direct virtual stereo
measurements. For depth prediction, we design a novel deep network that refines
predicted depth from a single image in a two-stage process. We train our
network in a semi-supervised way on photoconsistency in stereo images and on
consistency with accurate sparse depth reconstructions from Stereo DSO. Our
deep predictions excel state-of-the-art approaches for monocular depth on the
KITTI benchmark. Moreover, our Deep Virtual Stereo Odometry clearly exceeds
previous monocular and deep learning based methods in accuracy. It even
achieves comparable performance to the state-of-the-art stereo methods, while
only relying on a single camera.
| cs.CV |
1807.02571 | Leveraging Well-Conditioned Bases: Streaming \& Distributed Summaries in
Minkowski $p$-Norms | Work on approximate linear algebra has led to efficient distributed and
streaming algorithms for problems such as approximate matrix multiplication,
low rank approximation, and regression, primarily for the Euclidean norm
$\ell_2$. We study other $\ell_p$ norms, which are more robust for $p < 2$, and
can be used to find outliers for $p > 2$. Unlike previous algorithms for such
norms, we give algorithms that are (1) deterministic, (2) work simultaneously
for every $p \geq 1$, including $p = \infty$, and (3) can be implemented in
both distributed and streaming environments. We apply our results to
$\ell_p$-regression, entrywise $\ell_1$-low rank approximation, and approximate
matrix multiplication.
| cs.DS |
1807.02572 | Quasi-Dilemmas for Artificial Moral Agents | In this paper we describe moral quasi-dilemmas (MQDs): situations similar to
moral dilemmas, but in which an agent is unsure whether exploring the plan
space or the world may reveal a course of action that satisfies all moral
requirements. We argue that artificial moral agents (AMAs) should be built to
handle MQDs (in particular, by exploring the plan space rather than immediately
accepting the inevitability of the moral dilemma), and that MQDs may be useful
for evaluating AMA architectures.
| cs.AI cs.CY |
1807.02573 | High degree $b$-Niven numbers | Let $b$ be a numeration base. A $b$-Niven number is one that is divisible by
the sum of its base $b$ digits. We introduce high degree $b$-Niven numbers.
These are $b$-Niven numbers that have a power greater than $1$ that is
$b$-Niven number. Our main result shows that for each degree there exists an
infinite set of bases $b$ for which $b$-Niven numbers of that degree exist. The
high degree $b$-Niven numbers are given by explicit formulas and have all
digits different from zero.
| math.NT |
1807.02574 | Linear Temporal Logic for Hybrid Dynamical Systems: Characterizations
and Sufficient Conditions | This paper introduces operators, semantics, characterizations, and
solution-independent conditions to guarantee temporal logic specifications for
hybrid dynamical systems. Hybrid dynamical systems are given in terms of
differential inclusions -- capturing the continuous dynamics -- and difference
inclusions -- capturing the discrete dynamics or events -- with constraints.
State trajectories (or solutions) to such systems are parameterized by a hybrid
notion of time. For such broad class of solutions, the operators and semantics
needed to reason about temporal logic are introduced. Characterizations of
temporal logic formulas in terms of dynamical properties of hybrid systems are
presented -- in particular, forward invariance and finite time attractivity.
These characterizations are exploited to formulate sufficient conditions
assuring the satisfaction of temporal logic formulas -- when possible, these
conditions do not involve solution information. Combining the results for
formulas with a single operator, ways to certify more complex formulas are
pointed out, in particular, via a decomposition using a finite state automaton.
Academic examples illustrate the results throughout the paper.
| eess.SY cs.SY math.DS math.OC |
1807.02575 | A gradient flow formulation for the stochastic Amari neural field model | We study stochastic Amari-type neural field equations, which are mean-field
models for neural activity in the cortex. We prove that under certain
assumptions on the coupling kernel, the neural field model can be viewed as a
gradient flow in a nonlocal Hilbert space. This makes all gradient flow methods
available for the analysis, which could previously not be used, as it was not
known, whether a rigorous gradient flow formulation exists. We show that the
equation is well-posed in the nonlocal Hilbert space in the sense that
solutions starting in this space also remain in it for all times and space-time
regularity results hold for the case of spatially correlated noise. Uniqueness
of invariant measures, ergodic properties for the associated Feller semigroups,
and several examples of kernels are also discussed.
| math.AP math.DS math.FA math.PR q-bio.NC |
1807.02576 | Inverse problem of Travel time difference functions on compact
Riemannian manifold with boundary | We show that the travel time difference functions, measured on the boundary,
determine a compact Riemannian manifold with smooth boundary up to Riemannian
isometry, if boundary satisfies a certain visibility condition. This
corresponds with the inverse microseismicity problem. The novelty of our paper
is a new type of a proof and a weaker assumption for the boundary than it has
been presented in the literature before. We also construct an explicit smooth
atlas from the travel time difference functions.
| math.DG |
1807.02577 | Light-induced coherence in an atom-cavity system | We demonstrate light-induced formation of coherence in a cold atomic gas
system that utilizes the suppression of a competing density wave (DW) order.
The condensed atoms are placed in an optical cavity and pumped by an external
optical standing wave, which induces a long-range interaction mediated by
photon scattering and a resulting DW order above a critical pump strength. We
show that light-induced temporal modulation of the pump wave can suppress this
DW order and restore coherence. This establishes a foundational principle of
dynamical control of competing orders analogous to a hypothesized mechanism for
light-induced superconductivity in high-$T_c$ cuprates.
| cond-mat.quant-gas |
1807.02578 | Guided Proceduralization: Optimizing Geometry Processing and Grammar
Extraction for Architectural Models | We describe a guided proceduralization framework that optimizes geometry
processing on architectural input models to extract target grammars. We aim to
provide efficient artistic workflows by creating procedural representations
from existing 3D models, where the procedural expressiveness is controlled by
the user. Architectural reconstruction and modeling tasks have been handled as
either time consuming manual processes or procedural generation with difficult
control and artistic influence. We bridge the gap between creation and
generation by converting existing manually modeled architecture to procedurally
editable parametrized models, and carrying the guidance to procedural domain by
letting the user define the target procedural representation. Additionally, we
propose various applications of such procedural representations, including
guided completion of point cloud models, controllable 3D city modeling, and
other benefits of procedural modeling.
| cs.GR cs.CV |
1807.02579 | Recognizing Galois representations of K3 surfaces | Under the assumption of the Hodge, Tate and Fontaine-Mazur conjectures we
give a criterion for a compatible system of l-adic representations to be
isomorphic to the second cohomology of a K3 surface.
| math.NT |
1807.02580 | Spin wave modes in a cylindrical nanowire in crossover dipolar-exchange
regime | Nanoscale magnetic systems have been studied extensively in various
geometries, such as wires of different cross-sections, arrays of wires, dots,
rings, etc. Such systems have interesting physical properties and promising
applications in advanced magnetic devices. Uniform magnetic nanowires are the
basic structures which were broadly investigated. However, some of their
dynamical properties, like: (anti)crossing between the spin wave modes and
impact of the magnetic field on spin wave spectrum, still need to be exploited.
We continue this research by investigation of the spin wave dynamics in solid
Ni nanowire of the circular cross-section. We use two approaches:
semi-analytical calculations and numerical computations based on finite element
method. We solve coupled Landau-Lifshitz and Maxwell equations and consider
both magnetostatic and exchange interactions. We identify the dispersion
brunches and its (anti)crossing by plotting the spatial profiles of spin wave
amplitudes and magnetostatic potential. We also check how we can tune the
spectrum of the modes by application of the external magnetic field and how it
affects the modes and their dominating type of interaction.
| cond-mat.mes-hall |
1807.02581 | The Goldilocks zone: Towards better understanding of neural network loss
landscapes | We explore the loss landscape of fully-connected and convolutional neural
networks using random, low-dimensional hyperplanes and hyperspheres. Evaluating
the Hessian, $H$, of the loss function on these hypersurfaces, we observe 1) an
unusual excess of the number of positive eigenvalues of $H$, and 2) a large
value of $\mathrm{Tr}(H) / ||H||$ at a well defined range of configuration
space radii, corresponding to a thick, hollow, spherical shell we refer to as
the \textit{Goldilocks zone}. We observe this effect for fully-connected neural
networks over a range of network widths and depths on MNIST and CIFAR-10
datasets with the $\mathrm{ReLU}$ and $\tanh$ non-linearities, and a similar
effect for convolutional networks. Using our observations, we demonstrate a
close connection between the Goldilocks zone, measures of local
convexity/prevalence of positive curvature, and the suitability of a network
initialization. We show that the high and stable accuracy reached when
optimizing on random, low-dimensional hypersurfaces is directly related to the
overlap between the hypersurface and the Goldilocks zone, and as a corollary
demonstrate that the notion of intrinsic dimension is initialization-dependent.
We note that common initialization techniques initialize neural networks in
this particular region of unusually high convexity/prevalence of positive
curvature, and offer a geometric intuition for their success. Furthermore, we
demonstrate that initializing a neural network at a number of points and
selecting for high measures of local convexity such as $\mathrm{Tr}(H) /
||H||$, number of positive eigenvalues of $H$, or low initial loss, leads to
statistically significantly faster training on MNIST. Based on our
observations, we hypothesize that the Goldilocks zone contains an unusually
high density of suitable initialization configurations.
| cs.LG cs.NE stat.ML |
1807.02582 | Gaussian Processes and Kernel Methods: A Review on Connections and
Equivalences | This paper is an attempt to bridge the conceptual gaps between researchers
working on the two widely used approaches based on positive definite kernels:
Bayesian learning or inference using Gaussian processes on the one side, and
frequentist kernel methods based on reproducing kernel Hilbert spaces on the
other. It is widely known in machine learning that these two formalisms are
closely related; for instance, the estimator of kernel ridge regression is
identical to the posterior mean of Gaussian process regression. However, they
have been studied and developed almost independently by two essentially
separate communities, and this makes it difficult to seamlessly transfer
results between them. Our aim is to overcome this potential difficulty. To this
end, we review several old and new results and concepts from either side, and
juxtapose algorithmic quantities from each framework to highlight close
similarities. We also provide discussions on subtle philosophical and
theoretical differences between the two approaches.
| stat.ML cs.LG |
1807.02583 | The Rhombi-Chain Bose-Hubbard Model: geometric frustration and
interactions | We explore the effects of geometric frustration within a one-dimensional
Bose-Hubbard model using a chain of rhombi subject to a magnetic flux. The
competition of tunnelling, self-interaction and magnetic flux gives rise to the
emergence of a pair-superfluid (pair-Luttinger liquid) phase besides the more
conventional Mott-insulator and superfluid (Luttinger liquid) phases. We
compute the complete phase diagram of the model by identifying characteristic
properties of the pair-Luttinger liquid phase such as pair correlation
functions and structure factors and find that the pair-Luttinger liquid phase
is very sensitive to changes away from perfect frustration (half-flux). We
provide some proposals to make the model more resilient to variants away from
perfect frustration. We also study the bipartite entanglement properties of the
chain. We discover that, while the scaling of the block entropy pair-superfluid
and of the single-particle superfluid leads to the same central charge, the
properties of the low-lying entanglement spectrum levels reveal their
fundamental difference.
| cond-mat.quant-gas cond-mat.str-el quant-ph |
1807.02584 | Effect of gravitational lensing on the distribution of gravitational
waves from distant binary black hole mergers | The detailed observation of the distribution of redshifts and chirp masses of
binary black hole mergers is expected to provide a clue to their origin. In
this paper, we develop a hybrid model of the probability distribution function
of gravitational lensing magnification taking account of both strong and weak
gravitational lensing, and use it to study the effect of gravitational lensing
magnification on the distribution of gravitational waves from distant binary
black hole mergers detected in ongoing and future gravitational wave
observations. We find that the effect of gravitational lensing magnification is
significant at high ends of observed chirp mass and redshift distributions.
While a high mass tail in the observed chirp mass distribution is produced by
highly magnified gravitational lensing events, we find that highly demagnified
images of strong lensing events produce a high redshift ($z_{\rm obs}> 15$)
tail in the observed redshift distribution, which can easily be observed in the
third-generation gravitational wave observatories. Such a demagnified,
apparently high redshift event is expected to be accompanied by a magnified
image that is observed typically $10-100$ days before the demagnified image.
For highly magnified events that produce apparently very high chirp masses, we
expect pairs of events with similar magnifications with time delays typically
less than a day. This work suggests the critical importance of gravitational
lensing (de-)magnification on the interpretation of apparently very high mass
or redshift gravitational wave events.
| astro-ph.CO |
1807.02585 | Subgraphs and motifs in a dynamic airline network | How does the small-scale topological structure of an airline network behave
as the network evolves? To address this question, we study the dynamic
properties of small undirected subgraphs using 15 years of data on Southwest
Airlines' domestic route service. We use exact enumeration formulae to identify
statistically over- and under-represented subgraphs, known as motifs and
anti-motifs. We discover substantial topology transitions in Southwest's
network and provide evidence for time-varying power-law scaling between
subgraph counts and the number of edges in the network. We also suggest a
node-ranking measure that can identify important nodes relative to specific
local topologies. Our results extend the toolkit of subgraph-based methods and
provide new insight into transportation networks and the strategic behaviour of
firms.
| cs.SI physics.soc-ph |
1807.02586 | Lagrangian Description in the context of Emergent spacetime | We make use of the Lagrangian description of fluid motion to highlight
certain features in the context of spacetime geometry as emergent phenomena in
fluid systems. We find by using Lagrangian Perturbation Theory (LPT), that not
all kind of perturbations on a steady state flow can produce analogue spacetime
effect. We also explore the manifold structure of emergent spacetime by using
the Lagrangian description of fluid motion. We restrict ourselves to
nonrelativistic flows.
| gr-qc |
1807.02587 | Fast and Accurate Point Cloud Registration using Trees of Gaussian
Mixtures | Point cloud registration sits at the core of many important and challenging
3D perception problems including autonomous navigation, SLAM, object/scene
recognition, and augmented reality. In this paper, we present a new
registration algorithm that is able to achieve state-of-the-art speed and
accuracy through its use of a hierarchical Gaussian Mixture Model (GMM)
representation. Our method constructs a top-down multi-scale representation of
point cloud data by recursively running many small-scale data likelihood
segmentations in parallel on a GPU. We leverage the resulting representation
using a novel PCA-based optimization criterion that adaptively finds the best
scale to perform data association between spatial subsets of point cloud data.
Compared to previous Iterative Closest Point and GMM-based techniques, our
tree-based point association algorithm performs data association in
logarithmic-time while dynamically adjusting the level of detail to best match
the complexity and spatial distribution characteristics of local scene
geometry. In addition, unlike other GMM methods that restrict covariances to be
isotropic, our new PCA-based optimization criterion well-approximates the true
MLE solution even when fully anisotropic Gaussian covariances are used.
Efficient data association, multi-scale adaptability, and a robust MLE
approximation produce an algorithm that is up to an order of magnitude both
faster and more accurate than current state-of-the-art on a wide variety of 3D
datasets captured from LiDAR to structured light.
| cs.CV |
1807.02588 | Generative Probabilistic Novelty Detection with Adversarial Autoencoders | Novelty detection is the problem of identifying whether a new data point is
considered to be an inlier or an outlier. We assume that training data is
available to describe only the inlier distribution. Recent approaches primarily
leverage deep encoder-decoder network architectures to compute a reconstruction
error that is used to either compute a novelty score or to train a one-class
classifier. While we too leverage a novel network of that kind, we take a
probabilistic approach and effectively compute how likely is that a sample was
generated by the inlier distribution. We achieve this with two main
contributions. First, we make the computation of the novelty probability
feasible because we linearize the parameterized manifold capturing the
underlying structure of the inlier distribution, and show how the probability
factorizes and can be computed with respect to local coordinates of the
manifold tangent space. Second, we improved the training of the autoencoder
network. An extensive set of results show that the approach achieves
state-of-the-art results on several benchmark datasets.
| cs.CV cs.LG |
1807.02589 | A note on computing the Smallest Conic Singular Value | The goal of this note is to study the smallest conic singular value of a
matrix from a Lagrangian duality viewpoint and provide an efficient method for
its computation.
| math.NA |
1807.02590 | Resample-smoothing of Voronoi intensity estimators | Voronoi intensity estimators, which are non-parametric estimators for
intensity functions of point processes, are both parameter-free and adaptive;
the intensity estimate at a given location is given by the reciprocal size of
the Voronoi/Dirichlet cell containing that location. Their major drawback,
however, is that they tend to under-smooth the data in regions where the point
density of the observed point pattern is high and over-smooth in regions where
the point density is low. To remedy this problem, i.e. to find some
middle-ground between over- and under-smoothing, we propose an additional
smoothing technique for Voronoi intensity estimators for point processes in
arbitrary metric spaces, which is based on repeated independent thinnings of
the point process/pattern. Through a simulation study we show that our
resample-smoothing technique improves the estimation significantly. In
addition, we study statistical properties such as unbiasedness and variance,
and propose a rule-of-thumb and a data-driven cross-validation approach to
choose the amount of thinning/smoothing to apply. We finally apply our proposed
intensity estimation scheme to two datasets: locations of pine saplings (planar
point pattern) and motor vehicle traffic accidents (linear network point
pattern).
| stat.ME |
1807.02591 | Counterexamples in Scale Calculus | We construct counterexamples to classical calculus facts such as the Inverse
and Implicit Function Theorems in Scale Calculus -- a generalization of
Multivariable Calculus to infinite dimensional vector spaces in which the
reparameterization maps relevant to Symplectic Geometry are smooth. Scale
Calculus is a cornerstone of Polyfold Theory, which was introduced by
Hofer-Wysocki-Zehnder as a broadly applicable tool for regularizing moduli
spaces of pseudoholomorphic curves. We show how the novel nonlinear
scale-Fredholm notion in Polyfold Theory overcomes the lack of Implicit
Function Theorems, by formally establishing an often implicitly used fact: The
differentials of basic germs -- the local models for scale-Fredholm maps --
vary continuously in the space of bounded operators when the base point
changes. We moreover demonstrate that this continuity holds only in specific
coordinates, by constructing an example of a scale-diffeomorphism and
scale-Fredholm map with discontinuous differentials. This justifies the high
technical complexity in the foundations of Polyfold Theory.
| math.SG math.CA |
1807.02592 | Identifying correlations between LIGO's astronomical range and auxiliary
sensors using lasso regression | The range to which the Laser Interferometer Gravitational-Wave Observatory
(LIGO) can observe astrophysical systems varies over time, limited by noise in
the instruments and their environments. Identifying and removing the sources of
noise that limit LIGO's range enables higher signal-to-noise observations and
increases the number of observations. The LIGO observatories are continuously
monitored by hundreds of thousands of auxiliary channels that may contain
information about these noise sources. This paper describes an algorithm that
uses linear regression, namely lasso (least absolute shrinkage and selection
operator) regression, to analyze all of these channels and identify a small
subset of them that can be used to reconstruct variations in LIGO's
astrophysical range. Exemplary results of the application of this method to
three different periods of LIGO Livingston data are presented, along with
computational performance and current limitations.
| astro-ph.IM |
1807.02593 | Gargoyle: A Network-based Insider Attack Resilient Framework for
Organizations | `Anytime, Anywhere' data access model has become a widespread IT policy in
organizations making insider attacks even more complicated to model, predict
and deter. Here, we propose Gargoyle, a network-based insider attack resilient
framework against the most complex insider threats within a pervasive computing
context. Compared to existing solutions, Gargoyle evaluates the trustworthiness
of an access request context through a new set of contextual attributes called
Network Context Attribute (NCA). NCAs are extracted from the network traffic
and include information such as the user's device capabilities, security-level,
current and prior interactions with other devices, network connection status,
and suspicious online activities. Retrieving such information from the user's
device and its integrated sensors are challenging in terms of device
performance overheads, sensor costs, availability, reliability and
trustworthiness. To address these issues, Gargoyle leverages the capabilities
of Software-Defined Network (SDN) for both policy enforcement and
implementation. In fact, Gargoyle's SDN App can interact with the network
controller to create a `defence-in-depth' protection system. For instance,
Gargoyle can automatically quarantine a suspicious data requestor in the
enterprise network for further investigation or filter out an access request
before engaging a data provider. Finally, instead of employing simplistic
binary rules in access authorizations, Gargoyle incorporates Function-based
Access Control (FBAC) and supports the customization of access policies into a
set of functions (e.g., disabling copy, allowing print) depending on the
perceived trustworthiness of the context.
| cs.CR cs.NI |
1807.02594 | Time-Delay Interferometry and Clock-Noise Calibration | The Laser Interferometer Space Antenna is a joint ESA-NASA space-mission to
detect and study mHz cosmic gravitational waves. The trajectories followed by
its three spacecraft result in unequal- and time-varying arms, requiring use of
the Time-Delay Interferometry (TDI) post- processing technique to cancel the
laser phase noises affecting the heterodyne one-way Doppler measurements.
Although the second-generation formulation of TDI cancels the laser phase
noises when the array is both rotating and "flexing", second-generation TDI
combinations for which the phase fluctuations of the onboard ultra stable
oscillators (USOs) can be calibrated out have not appeared yet in the
literature. In this article we present the solution of this problem by
generalizing to the realistic LISA trajectory the USO calibration algorithm
derived by Armstrong, Estabrook and Tinto for a static configuration.
| gr-qc |
1807.02595 | Ergodic Theorems for the Transfer Operators of Noisy Dynamical Systems | We consider stationary stochastic dynamical systems evolving on a compact
metric space, by perturbing a deterministic dynamics with a random noise, added
according to an arbitrary probabilistic distribution. We prove the maximal and
pointwise ergodic theorems for the transfer operators associated to such
systems. The results are extensions to noisy systems of some of the fundamental
ergodic theorems for deterministic systems.
| math.DS |
1807.02596 | Validation of Geant4's G4NRF module against nuclear resonance
fluorescence data from $^{238}$U and $^{27}$Al | G4NRF is a simulation module for modeling nuclear resonance fluorescence
(NRF) interactions in the Geant4 framework. In this work, we validate G4NRF
against both absolute and relative measurements of three NRF interactions near
2.2 MeV in $^{238}$U and $^{27}$Al using the transmission NRF data from the
experiments described in arXiv:1712.02904. Agreement between the absolute NRF
count rates observed in the data and predicted by extensive Geant4+G4NRF
modeling validate the combined Geant4+G4NRF to within $15$--$20\%$ in the
$^{238}$U NRF transitions and $8\%$ in $^{27}$Al, for an average $13\%$
discrepancy across the entire study. The difference between simulation and
experiment in relative NRF rates, as expressed as ratios of count rates in
various NRF lines, is found at the level of ${\lesssim}4\%$, and is
statistically identical to zero. Inverting the analysis, approximate values of
the absolute level widths and branching ratios for $^{238}$U and $^{27}$Al are
also obtained.
| nucl-ex physics.ins-det |
1807.02597 | Trimer Bonding States on the Surface of Transition-metal Dichalcogenide
TaTe2 | We report a comprehensive study on the surface structural and electronic
properties of TaTe2 at room temperature. The surface structure was investigated
using both low energy electron diffraction intensity versus voltage and density
functional theory calculations. The relaxed structures obtained from the two
methods are in good agreement, which is very similar to the bulk, maintaining
double zigzag trimer chains. The calculated density of states indicates that
such structure originates from the trimer bonding states of the Ta dxz and dxy
orbitals. This work will further provide new insights towards the understanding
of the charge density wave phase transition in TaTe2 at low temperature.
| cond-mat.mtrl-sci |
1807.02598 | Axial Quasi-Normal Modes of Scalarized Neutron Stars with Realistic
Equations of State | We compute the axial quasi-normal modes of static neutron stars in scalar
tensor theory. In particular, we employ various realistic equations of state
including nuclear, hyperonic and hybrid matter. We investigate the fundamental
curvature mode and compare the results with those of General Relativity. We
find that the frequency of the modes and the damping time are reduced for the
scalarized neutron stars. In addition, we confirm and extend the universal
relations for quasi-normal modes known in General Relativity to this wide range
of realistic equations of state for scalarized neutron stars and confirm the
universality of the scaled frequency and damping time in terms of the scaled
moment of inertia as well as compactness for neutron stars with and without
scalarization.
| gr-qc |
1807.02599 | From Text to Topics in Healthcare Records: An Unsupervised Graph
Partitioning Methodology | Electronic Healthcare Records contain large volumes of unstructured data,
including extensive free text. Yet this source of detailed information often
remains under-used because of a lack of methodologies to extract interpretable
content in a timely manner. Here we apply network-theoretical tools to analyse
free text in Hospital Patient Incident reports from the National Health
Service, to find clusters of documents with similar content in an unsupervised
manner at different levels of resolution. We combine deep neural network
paragraph vector text-embedding with multiscale Markov Stability community
detection applied to a sparsified similarity graph of document vectors, and
showcase the approach on incident reports from Imperial College Healthcare NHS
Trust, London. The multiscale community structure reveals different levels of
meaning in the topics of the dataset, as shown by descriptive terms extracted
from the clusters of records. We also compare a posteriori against hand-coded
categories assigned by healthcare personnel, and show that our approach
outperforms LDA-based models. Our content clusters exhibit good correspondence
with two levels of hand-coded categories, yet they also provide further medical
detail in certain areas and reveal complementary descriptors of incidents
beyond the external classification taxonomy.
| cs.CL cs.IR cs.LG cs.SI math.SP |
1807.02600 | Some extensive discussions of Liouville's theorem and Cauchy's integral
theorem on structural holomorphic | Classic complex analysis is built on structural function $K=1$ only
associated with Cauchy-Riemann equations, subsequently various generalizations
of Cauchy-Riemann equations start to break this situation. The goal of this
article is to show that only structural function $K=Const$ such that
Liouville's theorem is held, otherwise, it's not valid any more on complex
domain based on structural holomorphic, the correction should be $w=\Phi
{{e}^{-K}}$, where $\Phi =Const$. Those theories in complex analysis which keep
constant are unable to be held as constant in the framework of structural
holomorphic. Synchronously, it deals with the generalization of Cauchy's
integral theorem by using the new perspective of structural holomorphic, it is
also shown that some of theories in the complex analysis are special cases at
$K=Const$, which are narrow to be applied such as maximum modulus principle.
| math.CV |
1807.02601 | RC-positivity, vanishing theorems and rigidity of holomorphic maps | Let $M$ and $N$ be two compact complex manifolds. We show that if the
tautological line bundle $\mathscr{O}_{T_M^*}(1)$ is not pseudo-effective and
$\mathscr{O}_{T_N^*}(1)$ is nef, then there is no non-constant holomorphic map
from $M$ to $N$. In particular, we prove that any holomorphic map from a
compact complex manifold $M$ with RC-positive tangent bundle to a compact
complex manifold $N$ with nef cotangent bundle must be a constant map. As an
application, we obtain that there is no non-constant holomorphic map from a
compact Hermitian manifold with positive holomorphic sectional curvature to a
Hermitian manifold with non-positive holomorphic bisectional curvature.
| math.DG math.AG math.CV |
1807.02602 | Robust Estimation for Two-Dimensional Autoregressive Processes Based on
Bounded Innovation Propagation Representations | Robust methods have been a successful approach to deal with contaminations
and noises in image processing. In this paper, we introduce a new robust method
for two-dimensional autoregressive models. Our method, called BMM-2D, relies on
representing a two-dimensional autoregressive process with an auxiliary model
to attenuate the effect of contamination (outliers). We compare the performance
of our method with existing robust estimators and the least squares estimator
via a comprehensive Monte Carlo simulation study which considers different
levels of replacement contamination and window sizes. The results show that the
new estimator is superior to the other estimators, both in accuracy and
precision. An application to image filtering highlights the findings and
illustrates how the estimator works in practical applications.
| stat.ME |
1807.02603 | A Note on the Shannon Entropy of Short Sequences | For source sequences of length L symbols we proposed to use a more realistic
value to the usual benchmark of number of code letters by source letters. Our
idea is based on a quantifier of information fluctuation of a source, F(U),
which corresponds to the second central moment of the random variable that
measures the information content of a source symbol. An alternative
interpretation of typical sequences is additionally provided through this
approach.
| cs.IT math.IT stat.ME |
1807.02604 | NGC 6744 - A nearby Milky Way twin with a very low-luminosity AGN | NGC 6744 is the nearest and brightest south-hemisphere galaxy with a
morphological type similar to that of the Milky Way. Using data obtained with
the Integral Field Unit of the Gemini South Multi-Object Spectrograph, we found
that this galaxy has a nucleus with LINER (Low Ionization Nuclear Emission Line
Region) surrounded by three line emitting regions. The analysis of the Hubble
Space Telescope archival images revealed that the nucleus is associated with a
blue compact source, probably corresponding to the active galactic nucleus
(AGN). The circumnuclear emission seems to be part of the extended narrow line
region of the AGN. One of these regions, located $\sim$1" southeast of the
nucleus, seems to be associated with the ionization cone of the AGN. The other
two regions are located $\sim$1" south and $\sim$0.6" northeast of the nucleus
and are not aligned with the gaseous rotating disk. Spectral synthesis shows
evidence that this galaxy may have gone through a merger about one billion
years ago. On the basis of the kinematic behavior, we found a gaseous rotating
disk, not co-aligned with the stellar disk. Given the relative degree of
ionization and luminosities of the nuclear and circumnuclear regions, we
suggest that the AGN was more luminous in the past and that the current
circumnuclear emissions are echoes of that phase.
| astro-ph.GA |
1807.02605 | Numerical computation of endomorphism rings | We give practical numerical methods to compute the period matrix of a plane
algebraic curve (not necessarily smooth). We show how automorphisms and
isomorphisms of such curves, as well as the decomposition of their Jacobians up
to isogeny, can be calculated heuristically. Particular applications include
the determination of (generically) non-Galois morphisms between curves and the
identification of Prym varieties.
| math.NT math.AG |
1807.02606 | SmartSeed: Smart Seed Generation for Efficient Fuzzing | Fuzzing is an automated application vulnerability detection method. For
genetic algorithm-based fuzzing, it can mutate the seed files provided by users
to obtain a number of inputs, which are then used to test the objective
application in order to trigger potential crashes. As shown in existing
literature, the seed file selection is crucial for the efficiency of fuzzing.
However, current seed selection strategies do not seem to be better than
randomly picking seed files. Therefore, in this paper, we propose a novel and
generic system, named SmartSeed, to generate seed files towards efficient
fuzzing. Specifically, SmartSeed is designed based on a machine learning model
to learn and generate high-value binary seeds. We evaluate SmartSeed along with
American Fuzzy Lop (AFL) on 12 open-source applications with the input formats
of mp3, bmp or flv. We also combine SmartSeed with different fuzzing tools to
examine its compatibility. From extensive experiments, we find that SmartSeed
has the following advantages: First, it only requires tens of seconds to
generate sufficient high-value seeds. Second, it can generate seeds with
multiple kinds of input formats and significantly improves the fuzzing
performance for most applications with the same input format. Third, SmartSeed
is compatible to different fuzzing tools. In total, our system discovers more
than twice unique crashes and 5,040 extra unique paths than the existing best
seed selection strategy for the evaluated 12 applications. From the crashes
found by SmartSeed, we discover 16 new vulnerabilities and have received their
CVE IDs.
| cs.CR |
1807.02607 | Simultaneous optimization of isocenter locations and sector duration in
radiosurgery | Stereotactic radiosurgery is an effective technique to treat brain tumors for
which several inverse planning methods may be appropriate. We propose an
integer programming model to simultaneous sector duration and isocenter
optimization (SDIO) problem for Leksell Gamma Knife{\textregistered}
Icon{\texttrademark} (Elekta, Stockholm, Sweden) to tractably incorporate
treatment time. We devise a Benders decomposition scheme to solve the SDIO
problem to optimality. The performances of our approaches are assessed using
anonymized data from eight previously treated cases, and obtained treatment
plans are compared against each other and against the clinical plans. The plans
generated by our SDIO model all meet or exceed clinical guidelines while
demonstrating high conformity.
| physics.med-ph |
1807.02608 | Synthetic Sampling for Multi-Class Malignancy Prediction | We explore several oversampling techniques for an imbalanced multi-label
classification problem, a setting often encountered when developing models for
Computer-Aided Diagnosis (CADx) systems. While most CADx systems aim to
optimize classifiers for overall accuracy without considering the relative
distribution of each class, we look into using synthetic sampling to increase
per-class performance when predicting the degree of malignancy. Using low-level
image features and a random forest classifier, we show that using synthetic
oversampling techniques increases the sensitivity of the minority classes by an
average of 7.22% points, with as much as a 19.88% point increase in sensitivity
for a particular minority class. Furthermore, the analysis of low-level image
feature distributions for the synthetic nodules reveals that these nodules can
provide insights on how to preprocess image data for better classification
performance or how to supplement the original datasets when more data
acquisition is feasible.
| cs.LG cs.CV stat.ML |
1807.02609 | Anytime Neural Prediction via Slicing Networks Vertically | The pioneer deep neural networks (DNNs) have emerged to be deeper or wider
for improving their accuracy in various applications of artificial
intelligence. However, DNNs are often too heavy to deploy in practice, and it
is often required to control their architectures dynamically given computing
resource budget, i.e., anytime prediction. While most existing approaches have
focused on training multiple shallow sub-networks jointly, we study training
thin sub-networks instead. To this end, we first build many inclusive thin
sub-networks (of the same depth) under a minor modification of existing
multi-branch DNNs, and found that they can significantly outperform the
state-of-art dense architecture for anytime prediction. This is remarkable due
to their simplicity and effectiveness, but training many thin sub-networks
jointly faces a new challenge on training complexity. To address the issue, we
also propose a novel DNN architecture by forcing a certain sparsity pattern on
multi-branch network parameters, making them train efficiently for the purpose
of anytime prediction. In our experiments on the ImageNet dataset, its
sub-networks have up to $43.3\%$ smaller sizes (FLOPs) compared to those of the
state-of-art anytime model with respect to the same accuracy. Finally, we also
propose an alternative task under the proposed architecture using a
hierarchical taxonomy, which brings a new angle for anytime prediction.
| cs.LG stat.ML |
1807.02610 | White Dwarf Pollution by Asteroids from Secular Resonances | In the past few decades, observations have revealed signatures of metals
polluting the atmospheres of white dwarfs. The diffusion timescale for metals
to sink from the atmosphere of a white dwarf is of the order of days for a
hydrogen-dominated atmosphere. Thus, there must be a continuous supply of
metal-rich material accreting onto these white dwarfs. We investigate the role
of secular resonances that excite the eccentricity of asteroids allowing them
to reach star-grazing orbits leading them to tidal disruption and the formation
of a debris disc. Changes in the planetary system during the evolution of the
star lead to a change in the location of secular resonances. In our Solar
System, the engulfment of the Earth will cause the $\nu_6$ resonance to shift
outwards which will force previously stable asteroids to undergo secular
resonant perturbations. With analytic models and $N$--body simulations we show
that secular resonances driven by two outer companions can provide a source of
continuous pollution. Secular resonances are a viable mechanism for the
pollution of white dwarfs in a variety of exoplanetary system architectures.
| astro-ph.EP |
1807.02611 | New Algorithms for Subset Sum Problem | Given a set (or multiset) S of n numbers and a target number t, the subset
sum problem is to decide if there is a subset of S that sums up to t. There are
several methods for solving this problem, including exhaustive search,
divide-and-conquer method, and Bellman's dynamic programming method. However,
none of them could generate universal and light code. In this paper, we present
a new deterministic algorithm based on a novel data arrangement, which could
generate such code and return all solutions. If n is small enough, it is
efficient for usual purpose. We also present a probabilistic version with
one-sided error and a greedy algorithm which could generate a solution with
minimized variance.
| cs.DS |
1807.02612 | Gradient Hyperalignment for multi-subject fMRI data alignment | Multi-subject fMRI data analysis is an interesting and challenging problem in
human brain decoding studies. The inherent anatomical and functional
variability across subjects make it necessary to do both anatomical and
functional alignment before classification analysis. Besides, when it comes to
big data, time complexity becomes a problem that cannot be ignored. This paper
proposes Gradient Hyperalignment (Gradient-HA) as a gradient-based functional
alignment method that is suitable for multi-subject fMRI datasets with large
amounts of samples and voxels. The advantage of Gradient-HA is that it can
solve independence and high dimension problems by using Independent Component
Analysis (ICA) and Stochastic Gradient Ascent (SGA). Validation using
multi-classification tasks on big data demonstrates that Gradient-HA method has
less time complexity and better or comparable performance compared with other
state-of-the-art functional alignment methods.
| cs.LG q-bio.NC stat.ML |
1807.02613 | The homotopy groups of a homotopy group completion | Let $M$ be a topological monoid with homotopy group completion $\Omega BM$.
Under a strong homotopy commutativity hypothesis on $M$, we show that $\pi_k
(\Omega BM)$ is the quotient of the monoid of free homotopy classes $[S^k, M]$
by its submonoid of nullhomotopic maps.
We give two applications. First, this result gives a concrete description of
the Lawson homology of a complex projective variety in terms of point-wise
addition of spherical families of effective algebraic cycles. Second, we apply
this result to monoids built from the unitary, or general linear,
representation spaces of discrete groups, leading to results about lifting
continuous families of characters to continuous families of representations.
| math.AT |
1807.02614 | On the convergence time of some non-reversible Markov chain Monte Carlo
methods | It is commonly admitted that non-reversible Markov chain Monte Carlo (MCMC)
algorithms usually yield more accurate MCMC estimators than their reversible
counterparts. In this note, we show that in addition to their variance
reduction effect, some non-reversible MCMC algorithms have also the undesirable
property to slow down the convergence of the Markov chain. This point, which
has been overlooked by the literature, has obvious practical implications. We
illustrate this phenomenon for different non-reversible versions of the
Metropolis-Hastings algorithm on several discrete state space examples and
discuss ways to mitigate the risk of a small asymptotic variance/slow
convergence scenario.
| stat.CO |
1807.02615 | Little Boxes: A Dynamic Optimization Approach for Enhanced Cloud
Infrastructures | The increasing demand for diverse, mobile applications with various degrees
of Quality of Service requirements meets the increasing elasticity of on-demand
resource provisioning in virtualized cloud computing infrastructures. This
paper provides a dynamic optimization approach for enhanced cloud
infrastructures, based on the concept of cloudlets, which are located at
hotspot areas throughout a metropolitan area. In conjunction, we consider
classical remote data centers that are rigid with respect to QoS but provide
nearly abundant computation resources. Given fluctuating user demands, we
optimize the cloudlet placement over a finite time horizon from a cloud
infrastructure provider's perspective. By the means of a custom tailed
heuristic approach, we are able to reduce the computational effort compared to
the exact approach by at least three orders of magnitude, while maintaining a
high solution quality with a moderate cost increase of 5.8% or less.
| cs.NI |
1807.02616 | Effects of Predictive Real-Time Traffic Signal Information | This paper analyzes the impact of providing car drivers with predictive
information on traffic signal timing in real-time, including time-to-green and
green-wave speed recommendations. Over a period of six months, the behavior of
these 121 drivers in everyday urban driving was analyzed with and without
access to live traffic signal information. In a first period, drivers had the
information providing service disabled in order to establish a baseline
behavior; after that initial phase, the service was activated. In both cases,
data from smartphone and vehicle sensors was collected, including speed,
acceleration, fuel rate, acceleration and brake pedal positions. We estimated
the changes in the driving behavior which result from drivers' receiving the
traffic signal timing information by carefully comparing distributions of
acceleration/deceleration patterns through statistical analysis. Our analysis
demonstrates that there is a positive effect of providing traffic signal
information timing to the drivers.
| stat.AP |
1807.02617 | Predicting Infant Motor Development Status using Day Long Movement Data
from Wearable Sensors | Infants with a variety of complications at or before birth are classified as
being at risk for developmental delays (AR). As they grow older, they are
followed by healthcare providers in an effort to discern whether they are on a
typical or impaired developmental trajectory. Often, it is difficult to make an
accurate determination early in infancy as infants with typical development
(TD) display high variability in their developmental trajectories both in
content and timing. Studies have shown that spontaneous movements have the
potential to differentiate typical and atypical trajectories early in life
using sensors and kinematic analysis systems. In this study, machine learning
classification algorithms are used to take inertial movement from wearable
sensors placed on an infant for a day and predict if the infant is AR or TD,
thus further establishing the connection between early spontaneous movement and
developmental trajectory.
| cs.LG stat.ML |
1807.02618 | Results on the Spectral Schwartz Distribution | The resolvent of an operator in a Banach space is defined on an open subset
of the complex plane and is holomorphic. It obeys the resolvent equation. A
generalization of this equation to Schwartz distributions is defined and a
Schwartz distribution, which satisfies that equation is called a resolvent
distribution. Its restriction to the subset, where it is continuous, is the
usual resolvent function. Its complex conjugate derivative is,but a factor, the
spectral Schwartz distribution, which is carried by a subset of the spectral
set of the operator. The spectral distribution yields a spectral decomposition.
The spectral distribution of a matrix and a unitary operator are given. If the
the operator is a self-adjoint operator on a Hilbert space, the spectral
distribution is the derivative of the spectral family. We calculate the
spectral distribution of the multiplication operator and some rank one
perturbations. These operators are not necessarily self adjoint and may have
discrete real or imaginary eigenvalues or a nontrivial Jordan decomposition.
| math.FA math.OA |
1807.02619 | On syndetic Riesz sequences | Applying the solution to the Kadison-Singer problem, we show that every
subset $\mathcal{S}$ of the torus of positive Lebesgue measure admits a Riesz
sequence of exponentials $\left\{ e^{i\lambda x}\right\} _{\lambda \in
\Lambda}$ such that $\Lambda\subset\mathbb{Z}$ is a set with gaps between
consecutive elements bounded by ${\displaystyle
\frac{C}{\left|\mathcal{S}\right|}}$. In the case when $\mathcal{S}$ is an open
set we demonstrate, using quasicrystals, how such $\Lambda$ can be
deterministically constructed.
| math.CA |
1807.02620 | Topological solid phase in a quantum dimer model | We present an example for the phase transition between a topological
non-trivial solid phase and a trivial solid phase in the quantum dimer
model(QDM) on triangular lattice. Such a transition is beyond the Landau's
paradigm of phase transition. We have characterized such a transition with the
topological entanglement entropy(TEE) of the system, which is found to change
from $\gamma=\ln 2$ in the topological solid phase to zero in the trivial solid
phase, through a pronounced peak around the transition point. We also
calculated the correlation function of the vison excitation in the QDM and find
that the vison condensate develops right at the topological transition point.
These results imply that the topological order and the related fractionalized
excitation can coexist with conventional symmetry breaking order.
| cond-mat.str-el |
1807.02621 | Reservoir Computing Universality With Stochastic Inputs | The universal approximation properties with respect to $L ^p $-type criteria
of three important families of reservoir computers with stochastic
discrete-time semi-infinite inputs is shown. First, it is proved that linear
reservoir systems with either polynomial or neural network readout maps are
universal. More importantly, it is proved that the same property holds for two
families with linear readouts, namely, trigonometric state-affine systems and
echo state networks, which are the most widely used reservoir systems in
applications. The linearity in the readouts is a key feature in supervised
machine learning applications. It guarantees that these systems can be used in
high-dimensional situations and in the presence of large datasets. The $L ^p $
criteria used in this paper allow the formulation of universality results that
do not necessarily impose almost sure uniform boundedness in the inputs or the
fading memory property in the filter that needs to be approximated.
| cs.ET cs.NE math.DS math.PR |
1807.02622 | R\'enyi Entropy Power Inequalities via Normal Transport and Rotation | Following a recent proof of Shannon's entropy power inequality (EPI), a
comprehensive framework for deriving various EPIs for the R\'enyi entropy is
presented that uses transport arguments from normal densities and a change of
variable by rotation. Simple arguments are given to recover the previously
known R\'enyi EPIs and derive new ones, by unifying a multiplicative form with
constant c and a modification with exponent {\alpha} of previous works. In
particular, for log-concave densities, we obtain a simple transportation proof
of a sharp varentropy bound.
| cs.IT math.IT |
1807.02623 | Core2Vec: A core-preserving feature learning framework for networks | Recent advances in the field of network representation learning are mostly
attributed to the application of the skip-gram model in the context of graphs.
State-of-the-art analogues of skip-gram model in graphs define a notion of
neighbourhood and aim to find the vector representation for a node, which
maximizes the likelihood of preserving this neighborhood.
In this paper, we take a drastic departure from the existing notion of
neighbourhood of a node by utilizing the idea of coreness. More specifically,
we utilize the well-established idea that nodes with similar core numbers play
equivalent roles in the network and hence induce a novel and an organic notion
of neighbourhood. Based on this idea, we propose core2vec, a new algorithmic
framework for learning low dimensional continuous feature mapping for a node.
Consequently, the nodes having similar core numbers are relatively closer in
the vector space that we learn.
We further demonstrate the effectiveness of core2vec by comparing word
similarity scores obtained by our method where the node representations are
drawn from standard word association graphs against scores computed by other
state-of-the-art network representation techniques like node2vec, DeepWalk and
LINE. Our results always outperform these existing methods
| cs.SI physics.soc-ph |
1807.02624 | Structure-preserving model reduction for dynamical systems with a first
integral | Since the expense of the numerical integration of large scale dynamical
systems is often computationally prohibitive, model reduction methods, which
approximate such systems by simpler and much lower order ones, are often
employed to reduce the computational effort. In this paper, for dynamical
systems with a first integral, new structure-preserving model reduction
approaches are presented that yield reduced-order systems while preserving the
first integral. We apply energy-preserving integrators to the reduced-order
systems and show some numerical experiments that demonstrate the favourable
behaviour of the proposed approaches.
| math.NA |