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18,001 | Separation of the charge density wave and superconducting states by an intermediate semimetal phase in pressurized TaTe2 | In layered transition metal dichalcogenides (LTMDCs) that display both charge
density waves (CDWs) and superconductivity, the superconducting state generally
emerges directly on suppression of the CDW state. Here, however, we report a
different observation for pressurized TaTe2, a non-superconducting CDW-bearing
LTMDC at ambient pressure. We find that a superconducting state does not occur
in TaTe2 after the full suppression of its CDW state, which we observe at about
3 GPa, but, rather, a non-superconducting semimetal state is observed. At a
higher pressure, ~21 GPa, where both the semimetal state and the corresponding
positive magnetoresistance effect are destroyed, superconductivity finally
emerges and remains present up to ~50 GPa, the high pressure limit of our
measurements. Our pressure-temperature phase diagram for TaTe2 demonstrates
that the CDW and the superconducting phases in TaTe2 do not directly transform
one to the other, but rather are separated by a semimetal state, - the first
experimental case where the CDW and superconducting states are separated by an
intermediate phase in LTMDC systems.
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18,002 | Zero distribution for Angelesco Hermite--Padé polynomials | We consider the problem of zero distribution of the first kind
Hermite--Padé polynomials associated with a vector function $\vec f = (f_1,
\dots, f_s)$ whose components $f_k$ are functions with a finite number of
branch points in plane. We assume that branch sets of component functions are
well enough separated (which constitute the Angelesco case). Under this
condition we prove a theorem on limit zero distribution for such polynomials.
The limit measures are defined in terms of a known vector equilibrium problem.
Proof of the theorem is based on the methods developed by H.~Stahl,
A.~A.~Gonchar and the author. These methods obtained some further
generalization in the paper in application to systems of polynomials defined by
systems of complex orthogonality relations.
Together with the characterization of the limit zero distributions of
Hermite--Padé polynomials by a vector equilibrium problem we consider an
alternative characterization using a Riemann surface $\mathcal R(\vec f)$
associated with $\vec f$. In this terms we present a more general (without
Angelesco condition) conjecture on the zero distribution of Hermite--Padé
polynomials.
Bibliography: 72 items.
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18,003 | Atomic-scale identification of novel planar defect phases in heteroepitaxial YBa$_2$Cu$_3$O$_{7-δ}$ thin films | We have discovered two novel types of planar defects that appear in
heteroepitaxial YBa$_2$Cu$_3$O$_{7-\delta}$ (YBCO123) thin films, grown by
pulsed-laser deposition (PLD) either with or without a
La$_{2/3}$Ca$_{1/3}$MnO$_3$ (LCMO) overlayer, using the combination of
high-angle annular dark-field scanning transmission electron microscopy
(HAADF-STEM) imaging and electron energy loss spectroscopy (EELS) mapping for
unambiguous identification. These planar lattice defects are based on the
intergrowth of either a BaO plane between two CuO chains or multiple Y-O layers
between two CuO$_2$ planes, resulting in non-stoichiometric layer sequences
that could directly impact the high-$T_c$ superconductivity.
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18,004 | Self-consistent assessment of Englert-Schwinger model on atomic properties | Our manuscript investigates a self-consistent solution of the statistical
atom model proposed by Berthold-Georg Englert and Julian Schwinger (the ES
model) and benchmarks it against atomic Kohn-Sham and two orbital-free models
of the Thomas-Fermi-Dirac (TFD)-$\lambda$vW family. Results show that the ES
model generally offers the same accuracy as the well-known TFD-$\frac{1}{5}$vW
model; however, the ES model corrects the failure in Pauli potential
near-nucleus region. We also point to the inability of describing low-$Z$ atoms
as the foremost concern in improving the present model.
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18,005 | Target volatility option pricing in lognormal fractional SABR model | We examine in this article the pricing of target volatility options in the
lognormal fractional SABR model. A decomposition formula by Ito's calculus
yields a theoretical replicating strategy for the target volatility option,
assuming the accessibilities of all variance swaps and swaptions. The same
formula also suggests an approximation formula for the price of target
volatility option in small time by the technique of freezing the coefficient.
Alternatively, we also derive closed formed expressions for a small volatility
of volatility expansion of the price of target volatility option. Numerical
experiments show accuracy of the approximations in a reasonably wide range of
parameters.
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18,006 | Robust Blind Deconvolution via Mirror Descent | We revisit the Blind Deconvolution problem with a focus on understanding its
robustness and convergence properties. Provable robustness to noise and other
perturbations is receiving recent interest in vision, from obtaining immunity
to adversarial attacks to assessing and describing failure modes of algorithms
in mission critical applications. Further, many blind deconvolution methods
based on deep architectures internally make use of or optimize the basic
formulation, so a clearer understanding of how this sub-module behaves, when it
can be solved, and what noise injection it can tolerate is a first order
requirement. We derive new insights into the theoretical underpinnings of blind
deconvolution. The algorithm that emerges has nice convergence guarantees and
is provably robust in a sense we formalize in the paper. Interestingly, these
technical results play out very well in practice, where on standard datasets
our algorithm yields results competitive with or superior to the state of the
art. Keywords: blind deconvolution, robust continuous optimization
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18,007 | One-Dimensional Symmetry Protected Topological Phases and their Transitions | We present a unified perspective on symmetry protected topological (SPT)
phases in one dimension and address the open question of what characterizes
their phase transitions. In the first part of this work we use symmetry as a
guide to map various well-known fermionic and spin SPTs to a Kitaev chain with
coupling of range $\alpha \in \mathbb Z$. This unified picture uncovers new
properties of old models --such as how the cluster state is the fixed point
limit of the Affleck-Kennedy-Lieb-Tasaki state in disguise-- and elucidates the
connection between fermionic and bosonic phases --with the Hubbard chain
interpolating between four Kitaev chains and a spin chain in the Haldane phase.
In the second part, we study the topological phase transitions between these
models in the presence of interactions. This leads us to conjecture that the
critical point between any SPT with $d$-dimensional edge modes and the trivial
phase has a central charge $c \geq \log_2 d$. We analytically verify this for
many known transitions. This agrees with the intuitive notion that the phase
transition is described by a delocalized edge mode, and that the central charge
of a conformal field theory is a measure of the gapless degrees of freedom.
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18,008 | Proceedings Eighth International Symposium on Games, Automata, Logics and Formal Verification | This volume contains the proceedings of the Eighth International Symposium on
Games, Automata, Logic and Formal Verification (GandALF 2017). The symposium
took place in Roma, Italy, from the 20th to the 22nd of September 2017. The
GandALF symposium was established by a group of Italian computer scientists
interested in mathematical logic, automata theory, game theory, and their
applications to the specification, design, and verification of complex systems.
Its aim is to provide a forum where people from different areas, and possibly
with different backgrounds, can fruitfully interact. GandALF has a truly
international spirit, as witnessed by the composition of the program and
steering committee and by the country distribution of the submitted papers.
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18,009 | Valence Bonds in Random Quantum Magnets: Theory and Application to YbMgGaO4 | We analyze the effect of quenched disorder on spin-1/2 quantum magnets in
which magnetic frustration promotes the formation of local singlets. Our
results include a theory for 2d valence-bond solids subject to weak bond
randomness, as well as extensions to stronger disorder regimes where we make
connections with quantum spin liquids. We find, on various lattices, that the
destruction of a valence-bond solid phase by weak quenched disorder leads
inevitably to the nucleation of topological defects carrying spin-1/2 moments.
This renormalizes the lattice into a strongly random spin network with
interesting low-energy excitations. Similarly when short-ranged valence bonds
would be pinned by stronger disorder, we find that this putative glass is
unstable to defects that carry spin-1/2 magnetic moments, and whose residual
interactions decide the ultimate low energy fate. Motivated by these results we
conjecture Lieb-Schultz-Mattis-like restrictions on ground states for
disordered magnets with spin-1/2 per statistical unit cell. These conjectures
are supported by an argument for 1d spin chains. We apply insights from this
study to the phenomenology of YbMgGaO$_4$, a recently discovered triangular
lattice spin-1/2 insulator which was proposed to be a quantum spin liquid. We
instead explore a description based on the present theory. Experimental
signatures, including unusual specific heat, thermal conductivity, and
dynamical structure factor, and their behavior in a magnetic field, are
predicted from the theory, and compare favorably with existing measurements on
YbMgGaO$_4$ and related materials.
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18,010 | TuckER: Tensor Factorization for Knowledge Graph Completion | Knowledge graphs are structured representations of real world facts. However,
they typically contain only a small subset of all possible facts. Link
prediction is a task of inferring missing facts based on existing ones. We
propose TuckER, a relatively simple but powerful linear model based on Tucker
decomposition of the binary tensor representation of knowledge graph triples.
TuckER outperforms all previous state-of-the-art models across standard link
prediction datasets. We prove that TuckER is a fully expressive model, deriving
the bound on its entity and relation embedding dimensionality for full
expressiveness which is several orders of magnitude smaller than the bound of
previous state-of-the-art models ComplEx and SimplE. We further show that
several previously introduced linear models can be viewed as special cases of
TuckER.
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18,011 | Taming Non-stationary Bandits: A Bayesian Approach | We consider the multi armed bandit problem in non-stationary environments.
Based on the Bayesian method, we propose a variant of Thompson Sampling which
can be used in both rested and restless bandit scenarios. Applying discounting
to the parameters of prior distribution, we describe a way to systematically
reduce the effect of past observations. Further, we derive the exact expression
for the probability of picking sub-optimal arms. By increasing the exploitative
value of Bayes' samples, we also provide an optimistic version of the
algorithm. Extensive empirical analysis is conducted under various scenarios to
validate the utility of proposed algorithms. A comparison study with various
state-of-the-arm algorithms is also included.
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18,012 | Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization | We propose a new randomized coordinate descent method for a convex
optimization template with broad applications. Our analysis relies on a novel
combination of four ideas applied to the primal-dual gap function: smoothing,
acceleration, homotopy, and coordinate descent with non-uniform sampling. As a
result, our method features the first convergence rate guarantees among the
coordinate descent methods, that are the best-known under a variety of common
structure assumptions on the template. We provide numerical evidence to support
the theoretical results with a comparison to state-of-the-art algorithms.
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18,013 | Guarantees for Greedy Maximization of Non-submodular Functions with Applications | We investigate the performance of the standard Greedy algorithm for
cardinality constrained maximization of non-submodular nondecreasing set
functions. While there are strong theoretical guarantees on the performance of
Greedy for maximizing submodular functions, there are few guarantees for
non-submodular ones. However, Greedy enjoys strong empirical performance for
many important non-submodular functions, e.g., the Bayesian A-optimality
objective in experimental design. We prove theoretical guarantees supporting
the empirical performance. Our guarantees are characterized by a combination of
the (generalized) curvature $\alpha$ and the submodularity ratio $\gamma$. In
particular, we prove that Greedy enjoys a tight approximation guarantee of
$\frac{1}{\alpha}(1- e^{-\gamma\alpha})$ for cardinality constrained
maximization. In addition, we bound the submodularity ratio and curvature for
several important real-world objectives, including the Bayesian A-optimality
objective, the determinantal function of a square submatrix and certain linear
programs with combinatorial constraints. We experimentally validate our
theoretical findings for both synthetic and real-world applications.
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18,014 | Affine Metrics and Associated Algebroid Structures: Application to General Relativity | In this paper, algebroid bundle associated to affine metrics provide an
structure for unification of gravity and electromagnetism and, geometrization
of matter.
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18,015 | Controllability of temporal networks: An analysis using higher-order networks | The control of complex networks is a significant challenge, especially when
the network topology of the system to be controlled is dynamic. Addressing this
challenge, here we introduce a novel approach which allows exploring the
controllability of temporal networks. Studying six empirical data sets, we
particularly show that order correlations in the sequence of interactions can
both increase or decrease the time needed to achieve full controllability.
Counter-intuitively, we find that this effect can be opposite than the effect
of order correlations on other dynamical processes. Specifically, we show that
order correlations that speed up a diffusion process in a given system can slow
down the control of the same system, and vice-versa. Building on the
higher-order graphical modeling framework introduced in recent works, we
further demonstrate that spectral properties of higher-order network topologies
can be used to analytically explain this phenomenon.
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18,016 | Mapping momentum-dependent electron-phonon coupling and non-equilibrium phonon dynamics with ultrafast electron diffuse scattering | Despite their fundamental role in determining material properties, detailed
momentum-dependent information on the strength of electron-phonon and
phonon-phonon coupling (EPC and PPC, respectively) across the entire Brillouin
zone (BZ) has proved difficult to obtain. Here we demonstrate that ultrafast
electron diffuse scattering (UEDS) directly provides such information. By
exploiting symmetry-based selection rules and time-resolution, scattering from
different phonon branches can be distinguished even without energy resolution.
Using graphite as a model system, we show that UEDS patterns map the relative
EPC and PPC strength through their profound sensitivity to photoinduced changes
in phonon populations. We measure strong EPC to the $K$-point transverse
optical phonon of $A_1'$ symmetry ($K-A_1'$) and along the entire longitudinal
optical branch between $\Gamma-K$, not only to the $\Gamma-E_{2g}$ phonon as
previously emphasized. We also determine that the subsequent phonon relaxation
pathway involves three stages; decay via several identifiable channels to
transverse acoustic (TA) and longitudinal acoustic (LA) phonons (1-2 ps),
intraband thermalization of the non-equilibrium TA/LA phonon populations (30-40
ps) and interband relaxation of the LA/TA modes (115 ps). Combining UEDS with
ultrafast angle-resolved photoelectron spectroscopy will yield a complete
picture of the dynamics within and between electron and phonon subsystems,
helping to unravel complex phases in which the intertwined nature of these
systems have a strong influence on emergent properties.
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18,017 | Meridional Circulation Dynamics in a Cyclic Convective Dynamo | Surface observations indicate that the speed of the solar meridional
circulation in the photosphere varies in anti-phase with the solar cycle. The
current explanation for the source of this variation is that inflows into
active regions alter the global surface pattern of the meridional circulation.
When these localized inflows are integrated over a full hemisphere, they
contribute to the slow down of the axisymmetric poleward horizontal component.
The behavior of this large scale flow deep inside the convection zone remains
largely unknown. Present helioseismic techniques are not sensitive enough to
capture the dynamics of this weak large scale flow. Moreover, the large time of
integration needed to map the meridional circulation inside the convection
zone, also masks some of the possible dynamics on shorter timescales. In this
work we examine the dynamics of the meridional circulation that emerges from a
3D MHD global simulation of the solar convection zone. Our aim is to assess and
quantify the behavior of meridional circulation deep inside the convection
zone, where the cyclic large-scale magnetic field can reach considerable
strength. Our analyses indicate that the meridional circulation morphology and
amplitude are both highly influenced by the magnetic field, via the impact of
magnetic torques on the global angular momentum distribution. A dynamic feature
induced by these magnetic torques is the development of a prominent upward flow
at mid latitudes in the lower convection zone that occurs near the equatorward
edge of the toroidal bands and that peaks during cycle maximum. Globally, the
dynamo-generated large-scale magnetic field drives variations in the meridional
flow, in stark contrast to the conventional kinematic flux transport view of
the magnetic field being advected passively by the flow.
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18,018 | Bayesian analysis of 210Pb dating | In many studies of environmental change of the past few centuries, 210Pb
dating is used to obtain chronologies for sedimentary sequences. One of the
most commonly used approaches to estimate the ages of depths in a sequence is
to assume a constant rate of supply (CRS) or influx of `unsupported' 210Pb from
the atmosphere, together with a constant or varying amount of `supported'
210Pb. Current 210Pb dating models do not use a proper statistical framework
and thus provide poor estimates of errors. Here we develop a new model for
210Pb dating, where both ages and values of supported and unsupported 210Pb
form part of the parameters. We apply our model to a case study from Canada as
well as to some simulated examples. Our model can extend beyond the current CRS
approach, deal with asymmetric errors and mix 210Pb with other types of dating,
thus obtaining more robust, realistic and statistically better defined
estimates.
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18,019 | Analytic continuation with Padé decomposition | The ill-posed analytic continuation problem for Green's functions or
self-energies can be done using the Padé rational polynomial approximation.
However, to extract accurate results from this approximation, high precision
input data of the Matsubara Green's function are needed. The calculation of the
Matsubara Green's function generally involves a Matsubara frequency summation
which cannot be evaluated analytically. Numerical summation is requisite but it
converges slowly with the increase of the Matsubara frequency. Here we show
that this slow convergence problem can be significantly improved by utilizing
the Padé decomposition approach to replace the Matsubara frequency summation
by a Padé frequency summation, and high precision input data can be obtained
to successfully perform the Padé analytic continuation.
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18,020 | Imperative Functional Programs that Explain their Work | Program slicing provides explanations that illustrate how program outputs
were produced from inputs. We build on an approach introduced in prior work by
Perera et al., where dynamic slicing was defined for pure higher-order
functional programs as a Galois connection between lattices of partial inputs
and partial outputs. We extend this approach to imperative functional programs
that combine higher-order programming with references and exceptions. We
present proofs of correctness and optimality of our approach and a
proof-of-concept implementation and experimental evaluation.
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18,021 | Synthetic dimensions in ultracold molecules: quantum strings and membranes | Synthetic dimensions alter one of the most fundamental properties in nature,
the dimension of space. They allow, for example, a real three-dimensional
system to act as effectively four-dimensional. Driven by such possibilities,
synthetic dimensions have been engineered in ongoing experiments with ultracold
matter. We show that rotational states of ultracold molecules can be used as
synthetic dimensions extending to many - potentially hundreds of - synthetic
lattice sites. Microwaves coupling rotational states drive fully controllable
synthetic inter-site tunnelings, enabling, for example, topological band
structures. Interactions leads to even richer behavior: when molecules are
frozen in a real space lattice with uniform synthetic tunnelings, dipole
interactions cause the molecules to aggregate to a narrow strip in the
synthetic direction beyond a critical interaction strength, resulting in a
quantum string or a membrane, with an emergent condensate that lives on this
string or membrane. All these phases can be detected using measurements of
rotational state populations.
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18,022 | Self-Organization and The Origins of Life: The Managed-Metabolism Hypothesis | The managed-metabolism hypothesis suggests that a cooperation barrier must be
overcome if self-producing chemical organizations are to transition from
non-life to life. This barrier prevents un-managed, self-organizing,
autocatalytic networks of molecular species from individuating into complex,
cooperative organizations. The barrier arises because molecular species that
could otherwise make significant cooperative contributions to the success of an
organization will often not be supported within the organization, and because
side reactions and other free-riding processes will undermine cooperation. As a
result, the barrier seriously limits the possibility space that can be explored
by un-managed organizations, impeding individuation, complex functionality and
the transition to life. The barrier can be overcome comprehensively by
appropriate management which implements a system of evolvable constraints. The
constraints support beneficial co-operators and suppress free riders. In this
way management can manipulate the chemical processes of an autocatalytic
organization, producing novel processes that serve the interests of the
organization as a whole and that could not arise and persist spontaneously in
an un-managed chemical organization. Management self-organizes because it is
able to capture some of the benefits that are produced when its management of
an autocatalytic organization promotes beneficial cooperation. Selection
therefore favours the emergence of managers that take over and manage chemical
organizations so as to overcome the cooperation barrier. The managed-metabolism
hypothesis shows that if management is to overcome the cooperation barrier
comprehensively, its interventions must be digitally coded. In this way, the
hypothesis accounts for the two-tiered structure of all living cells in which a
digitally-coded genetic apparatus manages an analogically-informed metabolism.
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18,023 | Pile-up Reduction, Bayesian Decomposition and Applications of Silicon Drift Detectors at LCLS | Silicon drift detectors (SDDs) revolutionized spectroscopy in fields as
diverse as geology and dentistry. For a subset of experiments at ultra-fast,
x-ray free-electron lasers (FELs), SDDs can make substantial contributions.
Often the unknown spectrum is interesting, carrying science data, or the
background measurement is useful to identify unexpected signals. Many
measurements involve only several discrete photon energies known a priori. We
designed a pulse function (a combination of gradual step and exponential decay
function) and demonstrated that for individual pulses the signal amplitude,
peaking time, and pulse amplitude are interrelated and the signal amplitude and
peaking time are obtained for each pulse by fitting. Avoiding pulse shaping
reduced peaking times to tens of nanoseconds, resulting in reduced pulse
pile-up and allowing decomposition of remaining pulse pile-up at photon
separation times down to 100~ns while yielding time-of-arrival information with
precision of 10~nanoseconds. At pulsed sources or high photon rates, photon
pile-up still occurs. We showed that the area of one photon peaks is not
suitable for estimating high photon rates while pile-up spectrum fitting is
relatively simple and preferable to pile-up spectrum deconvolution. We
developed a photon pile-up model for constant intensity sources, extended it to
variable intensity sources (typical for FELs) and used it to fit a complex
pile-up spectrum, demonstrating its accuracy. Based on the pile-up model, we
developed a Bayesian pile-up decomposition method that allows decomposing
pile-up of single events with up to 6 photons from 6 monochromatic lines with
99% accuracy. The usefulness of SDDs will continue into the x-ray FEL era of
science. Their successors, the ePixS hybrid pixel detectors, already offer
hundreds of pixels, each with similar performance to an SDD, in a compact,
robust and affordable package.
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18,024 | Online Learning to Rank in Stochastic Click Models | Online learning to rank is a core problem in information retrieval and
machine learning. Many provably efficient algorithms have been recently
proposed for this problem in specific click models. The click model is a model
of how the user interacts with a list of documents. Though these results are
significant, their impact on practice is limited, because all proposed
algorithms are designed for specific click models and lack convergence
guarantees in other models. In this work, we propose BatchRank, the first
online learning to rank algorithm for a broad class of click models. The class
encompasses two most fundamental click models, the cascade and position-based
models. We derive a gap-dependent upper bound on the $T$-step regret of
BatchRank and evaluate it on a range of web search queries. We observe that
BatchRank outperforms ranked bandits and is more robust than CascadeKL-UCB, an
existing algorithm for the cascade model.
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18,025 | Transforming Coroutining Logic Programs into Equivalent CHR Programs | We extend a technique called Compiling Control. The technique transforms
coroutining logic programs into logic programs that, when executed under the
standard left-to-right selection rule (and not using any delay features) have
the same computational behavior as the coroutining program. In recent work, we
revised Compiling Control and reformulated it as an instance of Abstract
Conjunctive Partial Deduction. This work was mostly focused on the program
analysis performed in Compiling Control. In the current paper, we focus on the
synthesis of the transformed program. Instead of synthesizing a new logic
program, we synthesize a CHR(Prolog) program which mimics the coroutining
program. The synthesis to CHR yields programs containing only simplification
rules, which are particularly amenable to certain static analysis techniques.
The programs are also more concise and readable and can be ported to CHR
implementations embedded in other languages than Prolog.
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18,026 | Gap and rings carved by vortices in protoplanetary dust | Large-scale vortices in protoplanetary disks are thought to form and survive
for long periods of time. Hence, they can significantly change the global disk
evolution and particularly the distribution of the solid particles embedded in
the gas, possibly explaining asymmetries and dust concentrations recently
observed at sub-millimeter and millimeter wavelengths. We investigate the
spatial distribution of dust grains using a simple model of protoplanetary disk
hosted by a giant gaseous vortex. We explore the dependence of the results on
grain size and deduce possible consequences and predictions for observations of
the dust thermal emission at sub-millimeter and millimeter wavelengths. Global
2D simulations with a bi-fluid code are used to follow the evolution of a
single population of solid particles aerodynamically coupled to the gas.
Possible observational signatures of the dust thermal emission are obtained
using simulators of ALMA and ngVLA observations. We find that a giant vortex
not only captures dust grains with Stokes number St < 1 but can also affect the
distribution of larger grains (with St '~' 1) carving a gap associated to a
ring composed of incompletely trapped particles. The results are presented for
different particle size and associated to their possible signatures in disk
observations. Gap clearing in the dust spatial distribution could be due to the
interaction with a giant gaseous vortex and their associated spiral waves,
without the gravitational assistance of a planet. Hence, strong dust
concentrations at short sub-mm wavelengths associated with a gap and an
irregular ring at longer mm and cm wavelengths could indicate the presence of
an unseen gaseous vortex.
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18,027 | Wasserstein Dictionary Learning: Optimal Transport-based unsupervised non-linear dictionary learning | This paper introduces a new nonlinear dictionary learning method for
histograms in the probability simplex. The method leverages optimal transport
theory, in the sense that our aim is to reconstruct histograms using so-called
displacement interpolations (a.k.a. Wasserstein barycenters) between dictionary
atoms; such atoms are themselves synthetic histograms in the probability
simplex. Our method simultaneously estimates such atoms, and, for each
datapoint, the vector of weights that can optimally reconstruct it as an
optimal transport barycenter of such atoms. Our method is computationally
tractable thanks to the addition of an entropic regularization to the usual
optimal transportation problem, leading to an approximation scheme that is
efficient, parallel and simple to differentiate. Both atoms and weights are
learned using a gradient-based descent method. Gradients are obtained by
automatic differentiation of the generalized Sinkhorn iterations that yield
barycenters with entropic smoothing. Because of its formulation relying on
Wasserstein barycenters instead of the usual matrix product between dictionary
and codes, our method allows for nonlinear relationships between atoms and the
reconstruction of input data. We illustrate its application in several
different image processing settings.
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18,028 | A Generalization of Smillie's Theorem on Strongly Cooperative Tridiagonal Systems | Smillie (1984) proved an interesting result on the stability of nonlinear,
time-invariant, strongly cooperative, and tridiagonal dynamical systems. This
result has found many applications in models from various fields including
biology, ecology, and chemistry. Smith (1991) has extended Smillie's result and
proved entrainment in the case where the vector field is time-varying and
periodic. We use the theory of linear totally nonnegative differential systems
developed by Schwarz (1970) to give a generalization of these two results. This
is based on weakening the requirement for strong cooperativity to
cooperativity, and adding an additional observability-type condition.
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18,029 | Geospatial Semantics | Geospatial semantics is a broad field that involves a variety of research
areas. The term semantics refers to the meaning of things, and is in contrast
with the term syntactics. Accordingly, studies on geospatial semantics usually
focus on understanding the meaning of geographic entities as well as their
counterparts in the cognitive and digital world, such as cognitive geographic
concepts and digital gazetteers. Geospatial semantics can also facilitate the
design of geographic information systems (GIS) by enhancing the
interoperability of distributed systems and developing more intelligent
interfaces for user interactions. During the past years, a lot of research has
been conducted, approaching geospatial semantics from different perspectives,
using a variety of methods, and targeting different problems. Meanwhile, the
arrival of big geo data, especially the large amount of unstructured text data
on the Web, and the fast development of natural language processing methods
enable new research directions in geospatial semantics. This chapter,
therefore, provides a systematic review on the existing geospatial semantic
research. Six major research areas are identified and discussed, including
semantic interoperability, digital gazetteers, geographic information
retrieval, geospatial Semantic Web, place semantics, and cognitive geographic
concepts.
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18,030 | Stability of the sum of two solitary waves for (gDNLS) in the energy space | In this paper, we continue the study in \cite{MiaoTX:DNLS:Stab}. We use the
perturbation argument, modulational analysis and the energy argument in
\cite{MartelMT:Stab:gKdV, MartelMT:Stab:NLS} to show the stability of the sum
of two solitary waves with weak interactions for the generalized derivative
Schrödinger equation (gDNLS) in the energy space. Here (gDNLS) hasn't the
Galilean transformation invariance, the pseudo-conformal invariance and the
gauge transformation invariance, and the case $\sigma>1$ we considered
corresponds to the $L^2$-supercritical case.
| 0 | 0 | 1 | 0 | 0 | 0 |
18,031 | A BERT Baseline for the Natural Questions | This technical note describes a new baseline for the Natural Questions. Our
model is based on BERT and reduces the gap between the model F1 scores reported
in the original dataset paper and the human upper bound by 30% and 50% relative
for the long and short answer tasks respectively. This baseline has been
submitted to the official NQ leaderboard at
ai.google.com/research/NaturalQuestions and we plan to opensource the code for
it in the near future.
| 1 | 0 | 0 | 0 | 0 | 0 |
18,032 | Knowledge Evolution in Physics Research: An Analysis of Bibliographic Coupling Networks | Even as we advance the frontiers of physics knowledge, our understanding of
how this knowledge evolves remains at the descriptive levels of Popper and
Kuhn. Using the APS publications data sets, we ask in this letter how new
knowledge is built upon old knowledge. We do so by constructing year-to-year
bibliographic coupling networks, and identify in them validated communities
that represent different research fields. We then visualize their evolutionary
relationships in the form of alluvial diagrams, and show how they remain intact
through APS journal splits. Quantitatively, we see that most fields undergo
weak Popperian mixing, and it is rare for a field to remain isolated/undergo
strong mixing. The sizes of fields obey a simple linear growth with
recombination. We can also reliably predict the merging between two fields, but
not for the considerably more complex splitting. Finally, we report a case
study of two fields that underwent repeated merging and splitting around 1995,
and how these Kuhnian events are correlated with breakthroughs on BEC, quantum
teleportation, and slow light. This impact showed up quantitatively in the
citations of the BEC field as a larger proportion of references from during and
shortly after these events.
| 1 | 1 | 0 | 0 | 0 | 0 |
18,033 | On a lower bound for the energy functional on a family of Hamiltonian minimal Lagrangian tori in $\mathbb{C}P^2$ | We study the energy functional on the set of Lagrangian tori in
$\mathbb{C}P^2$ . We prove that the value of the energy functional on a certain
family of Hamiltonian minimal Lagrangian tori in $\mathbb{C}P^2$ is strictly
larger than energy of the Clifford torus.
| 0 | 0 | 1 | 0 | 0 | 0 |
18,034 | Dynamic Mortality Risk Predictions in Pediatric Critical Care Using Recurrent Neural Networks | Viewing the trajectory of a patient as a dynamical system, a recurrent neural
network was developed to learn the course of patient encounters in the
Pediatric Intensive Care Unit (PICU) of a major tertiary care center. Data
extracted from Electronic Medical Records (EMR) of about 12000 patients who
were admitted to the PICU over a period of more than 10 years were leveraged.
The RNN model ingests a sequence of measurements which include physiologic
observations, laboratory results, administered drugs and interventions, and
generates temporally dynamic predictions for in-ICU mortality at user-specified
times. The RNN's ICU mortality predictions offer significant improvements over
those from two clinically-used scores and static machine learning algorithms.
| 1 | 0 | 1 | 1 | 0 | 0 |
18,035 | Global Strichartz estimates for the Schrödinger equation with non zero boundary conditions and applications | We consider the Schrödinger equation on a half space in any dimension with
a class of nonhomogeneous boundary conditions including Dirichlet, Neuman and
the so-called transparent boundary conditions. Building upon recent local in
time Strichartz estimates (for Dirichlet boundary conditions), we obtain global
Strichartz estimates for initial data in $H^s,\ 0\leq s\leq 2$ and boundary
data in a natural space $\mathcal{H}^s$. For $s\geq 1/2$, the issue of
compatibility conditions requires a thorough analysis of the $\mathcal{H}^s$
space. As an application we solve nonlinear Schrödinger equations and
construct global asymptotically linear solutions for small data. A discussion
is included on the appropriate notion of scattering in this framework, and the
optimality of the $\mathcal{H}^s$ space.
| 0 | 0 | 1 | 0 | 0 | 0 |
18,036 | Structure of $^{20}$Ne states in the resonance $^{16}$O+$α$ elastic scattering | Background
The nuclear structure of the cluster bands in $^{20}$Ne presents a challenge
for different theoretical approaches. It is especially difficult to explain the
broad 0$^+$, 2$^+$ states at 9 MeV excitation energy. Simultaneously, it is
important to obtain more reliable experimental data for these levels in order
to quantitatively assess the theoretical framework.
Purpose
To obtain new data on $^{20}$Ne $\alpha$ cluster structure. Method Thick
target inverse kinematics technique was used to study the $^{16}$O+$\alpha$
resonance elastic scattering and the data were analyzed using an \textit{R}
matrix approach. The $^{20}$Ne spectrum, the cluster and nucleon spectroscopic
factors were calculated using cluster-nucleon configuration interaction model
(CNCIM).
Results
We determined the parameters of the broad resonances in
\textsuperscript{20}Ne: 0$^+$ level at 8.77 $\pm$ 0.150 MeV with a width of 750
(+500/-220) keV; 2$^+$ level at 8.75 $\pm$ 0.100 MeV with the width of 695
$\pm$ 120 keV; the width of 9.48 MeV level of 65 $\pm$ 20 keV and showed that
9.19 MeV, 2$^+$ level (if exists) should have width $\leq$ 10 keV. The detailed
comparison of the theoretical CNCIM predictions with the experimental data on
cluster states was made.
Conclusions
Our experimental results by the TTIK method generally confirm the adopted
data on $\alpha$ cluster levels in $^{20}$Ne. The CNCIM gives a good
description of the $^{20}$Ne positive parity states up to an excitation energy
of $\sim$ 7 MeV, predicting reasonably well the excitation energy of the states
and their cluster and single particle properties. At higher excitations, the
qualitative disagreement with the experimentally observed structure is evident,
especially for broad resonances.
| 0 | 1 | 0 | 0 | 0 | 0 |
18,037 | Terminal-Pairability in Complete Bipartite Graphs | We investigate the terminal-pairibility problem in the case when the base
graph is a complete bipartite graph, and the demand graph is also bipartite
with the same color classes. We improve the lower bound on maximum value of
$\Delta(D)$ which still guarantees that the demand graph $D$ is
terminal-pairable in this setting. We also prove a sharp theorem on the maximum
number of edges such a demand graph can have.
| 0 | 0 | 1 | 0 | 0 | 0 |
18,038 | Rate-optimal Meta Learning of Classification Error | Meta learning of optimal classifier error rates allows an experimenter to
empirically estimate the intrinsic ability of any estimator to discriminate
between two populations, circumventing the difficult problem of estimating the
optimal Bayes classifier. To this end we propose a weighted nearest neighbor
(WNN) graph estimator for a tight bound on the Bayes classification error; the
Henze-Penrose (HP) divergence. Similar to recently proposed HP estimators
[berisha2016], the proposed estimator is non-parametric and does not require
density estimation. However, unlike previous approaches the proposed estimator
is rate-optimal, i.e., its mean squared estimation error (MSEE) decays to zero
at the fastest possible rate of $O(1/M+1/N)$ where $M,N$ are the sample sizes
of the respective populations. We illustrate the proposed WNN meta estimator
for several simulated and real data sets.
| 0 | 0 | 0 | 1 | 0 | 0 |
18,039 | Fast, Robust, and Versatile Event Detection through HMM Belief State Gradient Measures | Event detection is a critical feature in data-driven systems as it assists
with the identification of nominal and anomalous behavior. Event detection is
increasingly relevant in robotics as robots operate with greater autonomy in
increasingly unstructured environments. In this work, we present an accurate,
robust, fast, and versatile measure for skill and anomaly identification. A
theoretical proof establishes the link between the derivative of the
log-likelihood of the HMM filtered belief state and the latest emission
probabilities. The key insight is the inverse relationship in which gradient
analysis is used for skill and anomaly identification. Our measure showed
better performance across all metrics than related state-of-the art works. The
result is broadly applicable to domains that use HMMs for event detection.
| 1 | 0 | 0 | 0 | 0 | 0 |
18,040 | Geometric features of Vessiot--Guldberg Lie algebras of conformal and Killing vector fields on $\mathbb{R}^2$ | This paper locally classifies finite-dimensional Lie algebras of conformal
and Killing vector fields on $\mathbb{R}^2$ relative to an arbitrary
pseudo-Riemannian metric. Several results about their geometric properties are
detailed, e.g. their invariant distributions and induced symplectic structures.
Findings are illustrated with two examples of physical nature: the
Milne--Pinney equation and the projective Schrödinger equation on the Riemann
sphere.
| 0 | 1 | 0 | 0 | 0 | 0 |
18,041 | Fermion inter-particle potentials in 5D and a dimensional restriction prescription to 4D | This work sets out to compute and discuss effects of spin, velocity and
dimensionality on inter-particle potentials systematically derived from gauge
field-theoretic models. We investigate the interaction of fermionic particles
by the exchange of a vector field in a parity-preserving description in
five-dimensional $(5D)$ space-time. A particular dimensional reduction
prescription is adopted $-$ reduction by dimensional restriction $-$ and
special effects, like a pseudo-spin dependence, show up in four dimensions
$(4D)$. What we refer to as pseudo-spin shall be duly explained. The main idea
we try to convey is that the calculation of the potentials in $5D$ and the
consequent reduction to $4D$ exhibits new effects that are not present if the
potential is calculated in $4D$ after the action has been reduced.
| 0 | 1 | 0 | 0 | 0 | 0 |
18,042 | Characterizations of idempotent discrete uninorms | In this paper we provide an axiomatic characterization of the idempotent
discrete uninorms by means of three conditions only: conservativeness,
symmetry, and nondecreasing monotonicity. We also provide an alternative
characterization involving the bisymmetry property. Finally, we provide a
graphical characterization of these operations in terms of their contour plots,
and we mention a few open questions for further research.
| 1 | 0 | 1 | 0 | 0 | 0 |
18,043 | Constraints on the sum of neutrino masses using cosmological data including the latest extended Baryon Oscillation Spectroscopic Survey DR14 quasar sample | We investigate the constraints on the sum of neutrino masses ($\Sigma m_\nu$)
using the most recent cosmological data, which combines the distance
measurement from baryonic acoustic oscillation in the extended Baryon
Oscillation Spectroscopic Survey DR14 quasar sample with the power spectra of
temperature and polarization anisotropies in the cosmic microwave background
from the Planck 2015 data release. We also use other low-redshift observations
including the baryonic acoustic oscillation at relatively low redshifts, the
supernovae of type Ia and the local measurement of Hubble constant. In the
standard cosmological constant $\Lambda$ cold dark matter plus massive neutrino
model, we obtain the $95\%$ \acl{CL} upper limit to be $\Sigma
m_\nu<0.129~\mathrm{eV}$ for the degenerate mass hierarchy, $\Sigma
m_{\nu}<0.159~\mathrm{eV}$ for the normal mass hierarchy, and $\Sigma
m_{\nu}<0.189~\mathrm{eV}$ for the inverted mass hierarchy. Based on Bayesian
evidence, we find that the degenerate hierarchy is positively supported, and
the current data combination can not distinguish normal and inverted
hierarchies. Assuming the degenerate mass hierarchy, we extend our study to
non-standard cosmological models including the generic dark energy, the spatial
curvature, and the extra relativistic degrees of freedom, respectively, but
find these models not favored by the data.
| 0 | 1 | 0 | 0 | 0 | 0 |
18,044 | The effect of the choice of neural network depth and breadth on the size of its hypothesis space | We show that the number of unique function mappings in a neural network
hypothesis space is inversely proportional to $\prod_lU_l!$, where $U_{l}$ is
the number of neurons in the hidden layer $l$.
| 0 | 0 | 0 | 1 | 0 | 0 |
18,045 | Fairwashing: the risk of rationalization | Black-box explanation is the problem of explaining how a machine learning
model -- whose internal logic is hidden to the auditor and generally complex --
produces its outcomes. Current approaches for solving this problem include
model explanation, outcome explanation as well as model inspection. While these
techniques can be beneficial by providing interpretability, they can be used in
a negative manner to perform fairwashing, which we define as promoting the
perception that a machine learning model respects some ethical values while it
might not be the case. In particular, we demonstrate that it is possible to
systematically rationalize decisions taken by an unfair black-box model using
the model explanation as well as the outcome explanation approaches with a
given fairness metric. Our solution, LaundryML, is based on a regularized rule
list enumeration algorithm whose objective is to search for fair rule lists
approximating an unfair black-box model. We empirically evaluate our
rationalization technique on black-box models trained on real-world datasets
and show that one can obtain rule lists with high fidelity to the black-box
model while being considerably less unfair at the same time.
| 1 | 0 | 0 | 1 | 0 | 0 |
18,046 | Volume Dependence of N-Body Bound States | We derive the finite-volume correction to the binding energy of an N-particle
quantum bound state in a cubic periodic volume. Our results are applicable to
bound states with arbitrary composition and total angular momentum, and in any
number of spatial dimensions. The only assumptions are that the interactions
have finite range. The finite-volume correction is a sum of contributions from
all possible breakup channels. In the case where the separation is into two
bound clusters, our result gives the leading volume dependence up to
exponentially small corrections. If the separation is into three or more
clusters, there is a power-law factor that is beyond the scope of this work,
however our result again determines the leading exponential dependence. We also
present two independent methods that use finite-volume data to determine
asymptotic normalization coefficients. The coefficients are useful to determine
low-energy capture reactions into weakly bound states relevant for nuclear
astrophysics. Using the techniques introduced here, one can even extract the
infinite-volume energy limit using data from a single-volume calculation. The
derived relations are tested using several exactly solvable systems and
numerical examples. We anticipate immediate applications to lattice
calculations of hadronic, nuclear, and cold atomic systems.
| 0 | 1 | 1 | 0 | 0 | 0 |
18,047 | Tree-Structured Boosting: Connections Between Gradient Boosted Stumps and Full Decision Trees | Additive models, such as produced by gradient boosting, and full interaction
models, such as classification and regression trees (CART), are widely used
algorithms that have been investigated largely in isolation. We show that these
models exist along a spectrum, revealing never-before-known connections between
these two approaches. This paper introduces a novel technique called
tree-structured boosting for creating a single decision tree, and shows that
this method can produce models equivalent to CART or gradient boosted stumps at
the extremes by varying a single parameter. Although tree-structured boosting
is designed primarily to provide both the model interpretability and predictive
performance needed for high-stake applications like medicine, it also can
produce decision trees represented by hybrid models between CART and boosted
stumps that can outperform either of these approaches.
| 1 | 0 | 0 | 1 | 0 | 0 |
18,048 | Magnetic field influenced electron-impurity scattering and magnetotransport | We formulate a quasiclassical theory ($\omega_c\tau \lesssim 1$ with
$\omega_c$ as the cyclotron frequency and $\tau$ as the relaxation time) to
study the influence of magnetic field on electron-impurity scattering process
in the two-dimensional electron gas. We introduce a general recipe based on an
abstraction of the detailed impurity scattering process to define the
scattering parameter such as the incoming and outgoing momentum and coordinate
jump. In this picture, we can conveniently describe the skew scattering and
coordinate jump, which will eventually modify the Boltzmann equation. We find
an anomalous Hall resistivity different from the conventional Boltzmann-Drude
result and a negative magnetoresistivity parabolic in magnetic field. The
origin of these results has been analyzed. The relevance between our theory and
recent simulation and experimental works is also discussed. Our theory
dominates in dilute impurity system where the correlation effect is negligible.
| 0 | 1 | 0 | 0 | 0 | 0 |
18,049 | Evidence for depletion of heavy silicon isotopes at comet 67P/Churyumov-Gerasimenko | Context. The Rosetta Orbiter Spectrometer for Ion and Neutral Analysis
(ROSINA) was designed to measure the composition of the gas in the coma of
comet 67P/Churyumov-Gerasimenko, the target of the European Space Agency's
Rosetta mission. In addition to the volatiles, ROSINA measured refractories
sputtered off the comet by the interaction of solar wind protons with the
surface of the comet.
Aims. The origin of different solar system materials is still heavily
debated. Isotopic ratios can be used to distinguish between different
reservoirs and investigate processes occurring during the formation of the
solar system.
Methods. ROSINA consisted of two mass spectrometers and a pressure sensor. In
the ROSINA Double Focusing Mass Spectrometer (DFMS), the neutral gas of
cometary origin was ionized and then deflected in an electric and a magnetic
field that separated the ions based on their mass-to-charge ratio. The DFMS had
a high mass resolution, dynamic range, and sensitivity that allowed detection
of rare species and the known major volatiles.
Results. We measured the relative abundance of all three stable silicon
isotopes with the ROSINA instrument on board the Rosetta spacecraft.
Furthermore, we measured $^{13}$C/$^{12}$C in C$_2$H$_4$, C$_2$H$_5$, and CO.
The DFMS in situ measurements indicate that the average silicon isotopic
composition shows depletion in the heavy isotopes $^{29}$Si and $^{30}$Si with
respect to $^{28}$Si and solar abundances, while $^{13}$C to $^{12}$C is
analytically indistinguishable from bulk planetary and meteorite compositions.
Although the origin of the deficiency of the heavy silicon isotopes cannot be
explained unambiguously, we discuss mechanisms that could have contributed to
the measured depletion of the isotopes $^{29}$Si and $^{30}$Si.
| 0 | 1 | 0 | 0 | 0 | 0 |
18,050 | Design, Simulation, and Testing of a Flexible Actuated Spine for Quadruped Robots | Walking quadruped robots face challenges in positioning their feet and
lifting their legs during gait cycles over uneven terrain. The robot Laika is
under development as a quadruped with a flexible, actuated spine designed to
assist with foot movement and balance during these gaits. This paper presents
the first set of hardware designs for the spine of Laika, a physical prototype
of those designs, and tests in both hardware and simulations that show the
prototype's capabilities. Laika's spine is a tensegrity structure, used for its
advantages with weight and force distribution, and represents the first working
prototype of a tensegrity spine for a quadruped robot. The spine bends by
adjusting the lengths of the cables that separate its vertebrae, and twists
using an actuated rotating vertebra at its center. The current prototype of
Laika has stiff legs attached to the spine, and is used as a test setup for
evaluation of the spine itself. This work shows the advantages of Laika's spine
by demonstrating the spine lifting each of the robot's four feet, both as a
form of balancing and as a precursor for a walking gait. These foot motions,
using specific combinations of bending and rotation movements of the spine, are
measured in both simulation and hardware experiments. Hardware data are used to
calibrate the simulations, such that the simulations can be used for control of
balancing or gait cycles in the future. Future work will attach actuated legs
to Laika's spine, and examine balancing and gait cycles when combined with leg
movements.
| 1 | 0 | 0 | 0 | 0 | 0 |
18,051 | The landscape of NeuroImage-ing research | As the field of neuroimaging grows, it can be difficult for scientists within
the field to gain and maintain a detailed understanding of its ever-changing
landscape. While collaboration and citation networks highlight important
contributions within the field, the roles of and relations among specific areas
of study can remain quite opaque. Here, we apply techniques from network
science to map the landscape of neuroimaging research documented in the journal
NeuroImage over the past decade. We create a network in which nodes represent
research topics, and edges give the degree to which these topics tend to be
covered in tandem. The network displays small-world architecture, with
communities characterized by common imaging modalities and medical
applications, and with bridges that integrate these distinct subfields. Using
node-level analysis, we quantify the structural roles of individual topics
within the neuroimaging landscape, and find high levels of clustering within
the structural MRI subfield as well as increasing participation among topics
related to psychiatry. The overall prevalence of a topic is unrelated to the
prevalence of its neighbors, but the degree to which a topic becomes more or
less popular over time is strongly related to changes in the prevalence of its
neighbors. Broadly, this work presents a cohesive model for understanding the
landscape of neuroimaging research across the field, in broad subfields, and
within specific topic areas.
| 1 | 0 | 0 | 1 | 0 | 0 |
18,052 | To tune or not to tune the number of trees in random forest? | The number of trees T in the random forest (RF) algorithm for supervised
learning has to be set by the user. It is controversial whether T should simply
be set to the largest computationally manageable value or whether a smaller T
may in some cases be better. While the principle underlying bagging is that
"more trees are better", in practice the classification error rate sometimes
reaches a minimum before increasing again for increasing number of trees. The
goal of this paper is four-fold: (i) providing theoretical results showing that
the expected error rate may be a non-monotonous function of the number of trees
and explaining under which circumstances this happens; (ii) providing
theoretical results showing that such non-monotonous patterns cannot be
observed for other performance measures such as the Brier score and the
logarithmic loss (for classification) and the mean squared error (for
regression); (iii) illustrating the extent of the problem through an
application to a large number (n = 306) of datasets from the public database
OpenML; (iv) finally arguing in favor of setting it to a computationally
feasible large number, depending on convergence properties of the desired
performance measure.
| 1 | 0 | 0 | 1 | 0 | 0 |
18,053 | SPH Modeling of Short-crested Waves | This study investigates short-crested wave breaking over a planar beach by
using the mesh-free Smoothed Particle Hydrodynamics model, GPUSPH. The
short-crested waves are created by generating intersecting wave trains in a
numerical wave basin. We examine the influence of beach slope, incident wave
height, and incident wave angle on the generated short-crested waves.
Short-crested wave breaking over a steeper beach generates stronger rip
currents, and larger circulation cells in front of the beach. Intersecting wave
trains with a larger incident wave height drive a more complicated
short-crested wave field including isolated breakers and wave amplitude
diffraction. Nearshore circulation induced by short-crested wave breaking is
greatly influenced by the incident wave angle (or the rip current spacing).
There is no secondary circulation cell between the nodal line and the antinodal
line if the rip current spacing is narrow. However, there are multiple
secondary circulation cells observed when the rip current spacing is relatively
large.
| 0 | 1 | 0 | 0 | 0 | 0 |
18,054 | Tunneling Field-Effect Junctions with WS$_2$ barrier | Transition metal dichalcogenides (TMDCs), with their two-dimensional
structures and sizable bandgaps, are good candidates for barrier materials in
tunneling field-effect transistor (TFET) formed from atomic precision vertical
stacks of graphene and insulating crystals of a few atomic layers in thickness.
We report first-principles study of the electronic properties of the
Graphene/WS$_2$/Graphene sandwich structure revealing strong interface effects
on dielectric properties and predicting a high ON/OFF ratio with an appropriate
WS$_2$ thickness and a suitable range of the gate voltage. Both the band
spin-orbit coupling splitting and the dielectric constant of the WS$_2$ layer
depend on its thickness when in contact with the graphene electrodes,
indicating strong influence from graphene across the interfaces. The dielectric
constant is significantly reduced from the bulk WS$_2$ value. The effective
barrier height varies with WS$_2$ thickness and can be tuned by a gate voltage.
These results are critical for future nanoelectronic device designs.
| 0 | 1 | 0 | 0 | 0 | 0 |
18,055 | Topological Larkin-Ovchinnikov phase and Majorana zero mode chain in bilayer superconducting topological insulator films | We theoretically study bilayer superconducting topological insulator film, in
which superconductivity exists for both top and bottom surface states. We show
that an in-plane magnetic field can drive the system into Larkin-Ovchinnikov
(LO) phase, where electrons are paired with finite momenta. The LO phase is
topologically non-trivial and characterized by a Z 2 topological invariant,
leading to a Majorana zero mode chain along the edge perpendicular to in-plane
magnetic fields.
| 0 | 1 | 0 | 0 | 0 | 0 |
18,056 | Optimization and Analysis of Wireless Powered Multi-antenna Cooperative Systems | In this paper, we consider a three-node cooperative wireless powered
communication system consisting of a multi-antenna hybrid access point (H-AP)
and a single-antenna relay and a single-antenna user. The energy constrained
relay and user first harvest energy in the downlink and then the relay assists
the user using the harvested power for information transmission in the uplink.
The optimal energy beamforming vector and the time split between harvest and
cooperation are investigated. To reduce the computational complexity,
suboptimal designs are also studied, where closed-form expressions are derived
for the energy beamforming vector and the time split. For comparison purposes,
we also present a detailed performance analysis in terms of the achievable
outage probability and the average throughput of an intuitive energy
beamforming scheme, where the H-AP directs all the energy towards the user. The
findings of the paper suggest that implementing multiple antennas at the H-AP
can significantly improve the system performance, and the closed-form
suboptimal energy beamforming vector and time split yields near optimal
performance. Also, for the intuitive beamforming scheme, a diversity order of
(N+1)/2 can be achieved, where N is the number of antennas at the H-AP.
| 1 | 0 | 0 | 0 | 0 | 0 |
18,057 | Asai cube L-functions and the local Langlands conjecture | Let $F$ be a non-archimedean locally compact field. We study a class of
Langlands-Shahidi pairs $({\bf H},{\bf L})$, consisting of a quasi-split
connected reductive group $\bf H$ over $F$ and a Levi subgroup $\bf L$ which is
closely related to a product of restriction of scalars of ${\rm GL}_1$'s or
${\rm GL}_2$'s. We prove the compatibility of the resulting local factors with
the Langlands correspondence. In particular, let $E$ be a cubic separable
extension of $F$. We consider a simply connected quasi-split semisimple group
$\bf H$ over $F$ of type $D_4$, with triality corresponding to $E$, and let
$\bf L$ be its Levi subgroup with derived group ${\rm Res}_{E/F} {\rm SL}_2$.
In this way we obtain Asai cube local factors attached to irreducible smooth
representations of ${\rm GL}_2(E)$; we prove that they are Weil-Deligne factors
obtained via the local Langlands correspondence for ${\rm GL}_2(E)$ and tensor
induction from $E$ to $F$. A consequence is that Asai cube $\gamma$- and
$\varepsilon$-factors become stable under twists by highly ramified characters.
| 0 | 0 | 1 | 0 | 0 | 0 |
18,058 | What's in a game? A theory of game models | Game semantics is a rich and successful class of denotational models for
programming languages. Most game models feature a rather intuitive setup, yet
surprisingly difficult proofs of such basic results as associativity of
composition of strategies. We set out to unify these models into a basic
abstract framework for game semantics, game settings. Our main contribution is
the generic construction, for any game setting, of a category of games and
strategies. Furthermore, we extend the framework to deal with innocence, and
prove that innocent strategies form a subcategory. We finally show that our
constructions cover many concrete cases, mainly among the early models and the
very recent sheaf-based ones.
| 1 | 0 | 0 | 0 | 0 | 0 |
18,059 | Proposal for the Detection of Magnetic Monopoles in Spin Ice via Nanoscale Magnetometry | We present a proposal for applying nanoscale magnetometry to the search for
magnetic monopoles in the spin ice materials holmium and dysprosium titanate.
Employing Monte Carlo simulations of the dipolar spin ice model, we find that
when cooled to below $1.5\,$K these materials exhibit a sufficiently low
monopole density to enable the direct observation of magnetic fields from
individual monopoles. At these temperatures we demonstrate that noise
spectroscopy can capture the intrinsic fluctuations associated with monopole
dynamics, allowing one to isolate the qualitative effects associated with both
the Coulomb interaction between monopoles and the topological constraints
implied by Dirac strings. We describe in detail three different nanoscale
magnetometry platforms (muon spin rotation, nitrogen vacancy defects, and
nanoSQUID arrays) that can be used to detect monopoles in these experiments,
and analyze the advantages of each.
| 0 | 1 | 0 | 0 | 0 | 0 |
18,060 | Rule-Based Spanish Morphological Analyzer Built From Spell Checking Lexicon | Preprocessing tools for automated text analysis have become more widely
available in major languages, but non-English tools are often still limited in
their functionality. When working with Spanish-language text, researchers can
easily find tools for tokenization and stemming, but may not have the means to
extract more complex word features like verb tense or mood. Yet Spanish is a
morphologically rich language in which such features are often identifiable
from word form. Conjugation rules are consistent, but many special verbs and
nouns take on different rules. While building a complete dictionary of known
words and their morphological rules would be labor intensive, resources to do
so already exist, in spell checkers designed to generate valid forms of known
words. This paper introduces a set of tools for Spanish-language morphological
analysis, built using the COES spell checking tools, to label person, mood,
tense, gender and number, derive a word's root noun or verb infinitive, and
convert verbs to their nominal form.
| 1 | 0 | 0 | 0 | 0 | 0 |
18,061 | Learning Edge Representations via Low-Rank Asymmetric Projections | We propose a new method for embedding graphs while preserving directed edge
information. Learning such continuous-space vector representations (or
embeddings) of nodes in a graph is an important first step for using network
information (from social networks, user-item graphs, knowledge bases, etc.) in
many machine learning tasks.
Unlike previous work, we (1) explicitly model an edge as a function of node
embeddings, and we (2) propose a novel objective, the "graph likelihood", which
contrasts information from sampled random walks with non-existent edges.
Individually, both of these contributions improve the learned representations,
especially when there are memory constraints on the total size of the
embeddings. When combined, our contributions enable us to significantly improve
the state-of-the-art by learning more concise representations that better
preserve the graph structure.
We evaluate our method on a variety of link-prediction task including social
networks, collaboration networks, and protein interactions, showing that our
proposed method learn representations with error reductions of up to 76% and
55%, on directed and undirected graphs. In addition, we show that the
representations learned by our method are quite space efficient, producing
embeddings which have higher structure-preserving accuracy but are 10 times
smaller.
| 1 | 0 | 0 | 1 | 0 | 0 |
18,062 | Inference, Prediction, and Control of Networked Epidemics | We develop a feedback control method for networked epidemic spreading
processes. In contrast to most prior works which consider mean field, open-loop
control schemes, the present work develops a novel framework for feedback
control of epidemic processes which leverages incomplete observations of the
stochastic epidemic process in order to control the exact dynamics of the
epidemic outbreak. We develop an observation model for the epidemic process,
and demonstrate that if the set of observed nodes is sufficiently well
structured, then the random variables which denote the process' infections are
conditionally independent given the observations. We then leverage the attained
conditional independence property to construct tractable mechanisms for the
inference and prediction of the process state, avoiding the need to use mean
field approximations or combinatorial representations. We conclude by
formulating a one-step lookahead controller for the discrete-time
Susceptible-Infected-Susceptible (SIS) epidemic process which leverages the
developed Bayesian inference and prediction mechanisms, and causes the epidemic
to die out at a chosen rate.
| 0 | 0 | 1 | 0 | 0 | 0 |
18,063 | The Weisfeiler-Leman algorithm and the diameter of Schreier graphs | We prove that the number of iterations taken by the Weisfeiler-Leman
algorithm for configurations coming from Schreier graphs is closely linked to
the diameter of the graphs themselves: an upper bound is found for general
Schreier graphs, and a lower bound holds for particular cases, such as for
Schreier graphs with $G=\mbox{SL}_{n}({\mathbb F}_{q})$ ($q>2$) acting on
$k$-tuples of vectors in ${\mathbb F}_{q}^{n}$; moreover, an exact expression
is found in the case of Cayley graphs.
| 0 | 0 | 1 | 0 | 0 | 0 |
18,064 | Correlations between primes in short intervals on curves over finite fields | We prove an analogue of the Hardy-Littlewood conjecture on the asymptotic
distribution of prime constellations in the setting of short intervals in
function fields of smooth projective curves over finite fields.
| 0 | 0 | 1 | 0 | 0 | 0 |
18,065 | Extracting and Exploiting Inherent Sparsity for Efficient IoT Support in 5G: Challenges and Potential Solutions | Besides enabling an enhanced mobile broadband, next generation of mobile
networks (5G) are envisioned for the support of massive connectivity of
heterogeneous Internet of Things (IoT)s. These IoTs are envisioned for a large
number of use-cases including smart cities, environment monitoring, smart
vehicles, etc. Unfortunately, most IoTs have very limited computing and storage
capabilities and need cloud services. Hence, connecting these devices through
5G systems requires huge spectrum resources in addition to handling the massive
connectivity and improved security. This article discusses the challenges
facing the support of IoTs through 5G systems. The focus is devoted to
discussing physical layer limitations in terms of spectrum resources and radio
access channel connectivity. We show how sparsity can be exploited for
addressing these challenges especially in terms of enabling wideband spectrum
management and handling the connectivity by exploiting device-to-device
communications and edge-cloud. Moreover, we identify major open problems and
research directions that need to be explored towards enabling the support of
massive heterogeneous IoTs through 5G systems.
| 1 | 0 | 0 | 0 | 0 | 0 |
18,066 | Existence and convexity of local solutions to degenerate hessian equations | In this work, we prove the existence of local convex solution to the
degenerate Hessian equation
| 0 | 0 | 1 | 0 | 0 | 0 |
18,067 | Superexponential estimates and weighted lower bounds for the square function | We prove the following superexponential distribution inequality: for any
integrable $g$ on $[0,1)^{d}$ with zero average, and any $\lambda>0$ \[ |\{ x
\in [0,1)^{d} \; :\; g \geq\lambda \}| \leq e^{-
\lambda^{2}/(2^{d}\|S(g)\|_{\infty}^{2})}, \] where $S(g)$ denotes the
classical dyadic square function in $[0,1)^{d}$. The estimate is sharp when
dimension $d$ tends to infinity in the sense that the constant $2^{d}$ in the
denominator cannot be replaced by $C2^{d}$ with $0<C<1$ independent of $d$ when
$d \to \infty$.
For $d=1$ this is a classical result of Chang--Wilson--Wolff [4]; however, in
the case $d>1$ they work with a special square function $S_\infty$, and their
result does not imply the estimates for the classical square function.
Using good $\lambda$ inequalities technique we then obtain unweighted and
weighted $L^p$ lower bounds for $S$; to get the corresponding good $\lambda$
inequalities we need to modify the classical construction.
We also show how to obtain our superexponential distribution inequality
(although with worse constants) from the weighted $L^2$ lower bounds for $S$,
obtained in [5].
| 0 | 0 | 1 | 0 | 0 | 0 |
18,068 | Reframing the S\&P500 Network of Stocks along the \nth{21} Century | Since the beginning of the new millennium, stock markets went through every
state from long-time troughs, trade suspensions to all-time highs. The
literature on asset pricing hence assumes random processes to be underlying the
movement of stock returns. Observed procyclicality and time-varying correlation
of stock returns tried to give the apparently random behavior some sort of
structure. However, common misperceptions about the co-movement of asset prices
in the years preceding the \emph{Great Recession} and the \emph{Global
Commodity Crisis}, is said to have even fueled the crisis' economic impact.
Here we show how a varying macroeconomic environment influences stocks'
clustering into communities. From a sample of 296 stocks of the S\&P 500 index,
distinct periods in between 2004 and 2011 are used to develop networks of
stocks. The Minimal Spanning Tree analysis of those time-varying networks of
stocks demonstrates that the crises of 2007-2008 and 2010-2011 drove the market
to clustered community structures in both periods, helping to restore the stock
market's ceased order of the pre-crises era. However, a comparison of the
emergent clusters with the \textit{General Industry Classification Standard}
conveys the impression that industry sectors do not play a major role in that
order.
| 0 | 0 | 0 | 0 | 0 | 1 |
18,069 | Point-contact spectroscopy of superconducting energy gap in $\rm DyNi_2B_2C$ | The superconducting energy gap in $\rm DyNi_2B_2C$ has been investigated
using a point-contact technique based on the Andreev reflection from a normal
(N)-superconductor (S) boundary, where N is Ag. The observed differential
resistance $dV/dI$ is well described by the Blonder-Tinkham-Klapwijk (BTK)
theory based on the BSC density of states with zero broadening parameter.
Typically, the intensity of the gap structure amounts to several percentage of
the normal state resistance, which is an order of magnitude less than predicted
by the theory. For $\rm DyNi_2B_2C$ with $T_c<T_N$ (the Neel temperature), we
found gap values satisfying the ratio of $2\Delta_0/k_BT_c=3.63\pm 0.05$
similar to other superconducting nickel-borocarbides, both nonmagnetic and
magnetic with $T_c\geq T_N$. The superconducting gap nonlinearity is
superimposed on the antiferromagnetic structure in $dV/dI(V)$ which is
suppressed at the magnetic field of the order of 3T applied nominally in the
$ab$-plane and temperature $\geq 11~K$. The observed superconducting properties
depend on the exact composition and structure at the surface of the crystal.
| 0 | 1 | 0 | 0 | 0 | 0 |
18,070 | Parameterized Complexity of Safe Set | In this paper we study the problem of finding a small safe set $S$ in a graph
$G$, i.e. a non-empty set of vertices such that no connected component of
$G[S]$ is adjacent to a larger component in $G - S$. We enhance our
understanding of the problem from the viewpoint of parameterized complexity by
showing that (1) the problem is W[2]-hard when parameterized by the pathwidth
$pw$ and cannot be solved in time $n^{o(pw)}$ unless the ETH is false, (2) it
admits no polynomial kernel parameterized by the vertex cover number $vc$
unless $\mathrm{PH} = \Sigma^{\mathrm{p}}_{3}$, but (3) it is fixed-parameter
tractable (FPT) when parameterized by the neighborhood diversity $nd$, and (4)
it can be solved in time $n^{f(cw)}$ for some double exponential function $f$
where $cw$ is the clique-width. We also present (5) a faster FPT algorithm when
parameterized by solution size.
| 1 | 0 | 0 | 0 | 0 | 0 |
18,071 | A Matrix Variate Skew-t Distribution | Although there is ample work in the literature dealing with skewness in the
multivariate setting, there is a relative paucity of work in the matrix variate
paradigm. Such work is, for example, useful for modelling three-way data. A
matrix variate skew-t distribution is derived based on a mean-variance matrix
normal mixture. An expectation-conditional maximization algorithm is developed
for parameter estimation. Simulated data are used for illustration.
| 0 | 0 | 1 | 1 | 0 | 0 |
18,072 | Status Updates Through Multicast Networks | Using age of information as the freshness metric, we examine a multicast
network in which real-time status updates are generated by the source and sent
to a group of $n$ interested receivers. We show that in order to keep the
information freshness at each receiver, the source should terminate the
transmission of the current update and start sending a new update packet as
soon as it receives the acknowledgements back from any $k$ out of $n$ nodes. As
the source stopping threshold $k$ increases, a node is more likely to get the
latest generated update, but the age of the most recent update is more likely
to become outdated. We derive the age minimized stopping threshold $k$ that
balances the likelihood of getting the latest update and the freshness of the
latest update for shifted exponential link delay. Through numerical evaluations
for different stopping strategies, we find that waiting for the
acknowledgements from the earliest $k$ out of $n$ nodes leads to lower average
age than waiting for a pre-selected group of $k$ nodes. We also observe that a
properly chosen threshold $k$ can prevent information staleness for increasing
number of nodes $n$ in the multicast network.
| 1 | 0 | 0 | 0 | 0 | 0 |
18,073 | Extension of Convolutional Neural Network with General Image Processing Kernels | We applied pre-defined kernels also known as filters or masks developed for
image processing to convolution neural network. Instead of letting neural
networks find its own kernels, we used 41 different general-purpose kernels of
blurring, edge detecting, sharpening, discrete cosine transformation, etc. for
the first layer of the convolution neural networks. This architecture, thus
named as general filter convolutional neural network (GFNN), can reduce
training time by 30% with a better accuracy compared to the regular
convolutional neural network (CNN). GFNN also can be trained to achieve 90%
accuracy with only 500 samples. Furthermore, even though these kernels are not
specialized for the MNIST dataset, we achieved 99.56% accuracy without ensemble
nor any other special algorithms.
| 1 | 0 | 0 | 1 | 0 | 0 |
18,074 | Sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network | This paper proposes a neural network architecture and training scheme to
learn the start and end time of sound events (strong labels) in an audio
recording given just the list of sound events existing in the audio without
time information (weak labels). We achieve this by using a stacked
convolutional and recurrent neural network with two prediction layers in
sequence one for the strong followed by the weak label. The network is trained
using frame-wise log mel-band energy as the input audio feature, and weak
labels provided in the dataset as labels for the weak label prediction layer.
Strong labels are generated by replicating the weak labels as many number of
times as the frames in the input audio feature, and used for strong label layer
during training. We propose to control what the network learns from the weak
and strong labels by different weighting for the loss computed in the two
prediction layers. The proposed method is evaluated on a publicly available
dataset of 155 hours with 17 sound event classes. The method achieves the best
error rate of 0.84 for strong labels and F-score of 43.3% for weak labels on
the unseen test split.
| 1 | 0 | 0 | 0 | 0 | 0 |
18,075 | Categoricity and Universal Classes | Let $(\mathcal{K} ,\subseteq )$ be a universal class with
$LS(\mathcal{K})=\lambda$ categorical in regular $\kappa >\lambda^+$ with
arbitrarily large models, and let $\mathcal{K}^*$ be the class of all
$\mathcal{A}\in\mathcal{K}_{>\lambda}$ for which there is $\mathcal{B} \in
\mathcal{K}_{\ge\kappa}$ such that $\mathcal{A}\subseteq\mathcal{B}$. We prove
that $\mathcal{K}^*$ is categorical in every $\xi >\lambda^+$,
$\mathcal{K}_{\ge\beth_{(2^{\lambda^+})^+}} \subseteq \mathcal{K}^{*}$, and the
models of $\mathcal{K}^*_{>\lambda^+}$ are essentially vector spaces (or
trivial i.e. disintegrated).
| 0 | 0 | 1 | 0 | 0 | 0 |
18,076 | Water, High-Altitude Condensates, and Possible Methane Depletion in the Atmosphere of the Warm Super-Neptune WASP-107b | The super-Neptune exoplanet WASP-107b is an exciting target for atmosphere
characterization. It has an unusually large atmospheric scale height and a
small, bright host star, raising the possibility of precise constraints on its
current nature and formation history. We report the first atmospheric study of
WASP-107b, a Hubble Space Telescope measurement of its near-infrared
transmission spectrum. We determined the planet's composition with two
techniques: atmospheric retrieval based on the transmission spectrum and
interior structure modeling based on the observed mass and radius. The interior
structure models set a $3\,\sigma$ upper limit on the atmospheric metallicity
of $30\times$ solar. The transmission spectrum shows strong evidence for water
absorption ($6.5\,\sigma$ confidence), and the retrieved water abundance is
consistent with expectations for a solar abundance pattern. The inferred
carbon-to-oxygen ratio is subsolar at $2.7\,\sigma$ confidence, which we
attribute to possible methane depletion in the atmosphere. The spectral
features are smaller than predicted for a cloud-free composition, crossing less
than one scale height. A thick condensate layer at high altitudes (0.1 - 3
mbar) is needed to match the observations. We find that physically motivated
cloud models with moderate sedimentation efficiency ($f_\mathrm{sed} = 0.3$) or
hazes with a particle size of 0.3 $\mu$m reproduce the observed spectral
feature amplitude. Taken together, these findings serve as an illustration of
the diversity and complexity of exoplanet atmospheres. The community can look
forward to more such results with the high precision and wide spectral coverage
afforded by future observing facilities.
| 0 | 1 | 0 | 0 | 0 | 0 |
18,077 | Partial Order on the set of Boolean Regulatory Functions | Logical models have been successfully used to describe regulatory and
signaling networks without requiring quantitative data. However, existing data
is insufficient to adequately define a unique model, rendering the
parametrization of a given model a difficult task.
Here, we focus on the characterization of the set of Boolean functions
compatible with a given regulatory structure, i.e. the set of all monotone
nondegenerate Boolean functions. We then propose an original set of rules to
locally explore the direct neighboring functions of any function in this set,
without explicitly generating the whole set. Also, we provide relationships
between the regulatory functions and their corresponding dynamics.
Finally, we illustrate the usefulness of this approach by revisiting
Probabilistic Boolean Networks with the model of T helper cell differentiation
from Mendoza & Xenarios.
| 1 | 0 | 0 | 0 | 1 | 0 |
18,078 | Scalable solvers for complex electromagnetics problems | In this work, we present scalable balancing domain decomposition by
constraints methods for linear systems arising from arbitrary order edge finite
element discretizations of multi-material and heterogeneous 3D problems. In
order to enforce the continuity across subdomains of the method, we use a
partition of the interface objects (edges and faces) into sub-objects
determined by the variation of the physical coefficients of the problem. For
multi-material problems, a constant coefficient condition is enough to define
this sub-partition of the objects. For arbitrarily heterogeneous problems, a
relaxed version of the method is defined, where we only require that the
maximal contrast of the physical coefficient in each object is smaller than a
predefined threshold. Besides, the addition of perturbation terms to the
preconditioner is empirically shown to be effective in order to deal with the
case where the two coefficients of the model problem jump simultaneously across
the interface. The new method, in contrast to existing approaches for problems
in curl-conforming spaces, preserves the simplicity of the original
preconditioner, i.e., no spectral information is required, whilst providing
robustness with regard to coefficient jumps and heterogeneous materials. A
detailed set of numerical experiments, which includes the application of the
preconditioner to 3D realistic cases, shows excellent weak scalability
properties of the implementation of the proposed algorithms.
| 1 | 0 | 0 | 0 | 0 | 0 |
18,079 | A flux-splitting method for hyperbolic-equation system of magnetized electron fluids in quasi-neutral plasmas | A flux-splitting method is proposed for the hyperbolic-equation system (HES)
of magnetized electron fluids in quasi-neutral plasmas. The numerical fluxes
are split into four categories, which are computed by using an upwind method
which incorporates a flux-vector splitting (FVS) and advection upstream
splitting method (AUSM). The method is applied to a test calculation condition
of uniformly distributed and angled magnetic lines of force. All of the
pseudo-time advancement terms converge monotonically and the conservation laws
are strictly satisfied in the steady state. The calculation results are
compared with those computed by using the elliptic-parabolic-equation system
(EPES) approach using a magnetic-field-aligned mesh (MFAM). Both qualitative
and quantitative comparisons yield good agreements of results, indicating that
the HES approach with the flux-splitting method attains a high computational
accuracy.
| 0 | 1 | 0 | 0 | 0 | 0 |
18,080 | TransFlow: Unsupervised Motion Flow by Joint Geometric and Pixel-level Estimation | We address unsupervised optical flow estimation for ego-centric motion. We
argue that optical flow can be cast as a geometrical warping between two
successive video frames and devise a deep architecture to estimate such
transformation in two stages. First, a dense pixel-level flow is computed with
a geometric prior imposing strong spatial constraints. Such prior is typical of
driving scenes, where the point of view is coherent with the vehicle motion. We
show how such global transformation can be approximated with an homography and
how spatial transformer layers can be employed to compute the flow field
implied by such transformation. The second stage then refines the prediction
feeding a second deeper network. A final reconstruction loss compares the
warping of frame X(t) with the subsequent frame X(t+1) and guides both
estimates. The model, which we named TransFlow, performs favorably compared to
other unsupervised algorithms, and shows better generalization compared to
supervised methods with a 3x reduction in error on unseen data.
| 1 | 0 | 0 | 0 | 0 | 0 |
18,081 | A General Pipeline for 3D Detection of Vehicles | Autonomous driving requires 3D perception of vehicles and other objects in
the in environment. Much of the current methods support 2D vehicle detection.
This paper proposes a flexible pipeline to adopt any 2D detection network and
fuse it with a 3D point cloud to generate 3D information with minimum changes
of the 2D detection networks. To identify the 3D box, an effective model
fitting algorithm is developed based on generalised car models and score maps.
A two-stage convolutional neural network (CNN) is proposed to refine the
detected 3D box. This pipeline is tested on the KITTI dataset using two
different 2D detection networks. The 3D detection results based on these two
networks are similar, demonstrating the flexibility of the proposed pipeline.
The results rank second among the 3D detection algorithms, indicating its
competencies in 3D detection.
| 0 | 0 | 0 | 1 | 0 | 0 |
18,082 | Search for CII Emission on Cosmological Scales at Redshift Z~2.6 | We present a search for CII emission over cosmological scales at
high-redshifts. The CII line is a prime candidate to be a tracer of star
formation over large-scale structure since it is one of the brightest emission
lines from galaxies. Redshifted CII emission appears in the submillimeter
regime, meaning it could potentially be present in the higher frequency
intensity data from the Planck satellite used to measure the cosmic infrared
background (CIB). We search for CII emission over redshifts z=2-3.2 in the
Planck 545 GHz intensity map by cross-correlating the 3 highest frequency
Planck maps with spectroscopic quasars and CMASS galaxies from the Sloan
Digital Sky Survey III (SDSS-III), which we then use to jointly fit for CII
intensity, CIB parameters, and thermal Sunyaev-Zeldovich (SZ) emission. We
report a measurement of an anomalous emission
$\mathrm{I_\nu}=6.6^{+5.0}_{-4.8}\times10^4$ Jy/sr at 95% confidence, which
could be explained by CII emission, favoring collisional excitation models of
CII emission that tend to be more optimistic than models based on CII
luminosity scaling relations from local measurements; however, a comparison of
Bayesian information criteria reveal that this model and the CIB & SZ only
model are equally plausible. Thus, more sensitive measurements will be needed
to confirm the existence of large-scale CII emission at high redshifts.
Finally, we forecast that intensity maps from Planck cross-correlated with
quasars from the Dark Energy Spectroscopic Instrument (DESI) would increase our
sensitivity to CII emission by a factor of 5, while the proposed Primordial
Inflation Explorer (PIXIE) could increase the sensitivity further.
| 0 | 1 | 0 | 0 | 0 | 0 |
18,083 | Ricci flow and diffeomorphism groups of 3-manifolds | We complete the proof of the Generalized Smale Conjecture, apart from the
case of $RP^3$, and give a new proof of Gabai's theorem for hyperbolic
3-manifolds. We use an approach based on Ricci flow through singularities,
which applies uniformly to spherical space forms other than $S^3$ and $RP^3$
and hyperbolic manifolds, to prove that the moduli space of metrics of constant
sectional curvature is contractible. As a corollary, for such a 3-manifold $X$,
the inclusion $\text{Isom} (X,g)\to \text{Diff}(X)$ is a homotopy equivalence
for any Riemannian metric $g$ of constant sectional curvature.
| 0 | 0 | 1 | 0 | 0 | 0 |
18,084 | RLlib: Abstractions for Distributed Reinforcement Learning | Reinforcement learning (RL) algorithms involve the deep nesting of highly
irregular computation patterns, each of which typically exhibits opportunities
for distributed computation. We argue for distributing RL components in a
composable way by adapting algorithms for top-down hierarchical control,
thereby encapsulating parallelism and resource requirements within
short-running compute tasks. We demonstrate the benefits of this principle
through RLlib: a library that provides scalable software primitives for RL.
These primitives enable a broad range of algorithms to be implemented with high
performance, scalability, and substantial code reuse. RLlib is available at
this https URL.
| 1 | 0 | 0 | 0 | 0 | 0 |
18,085 | A Multiscale-Analysis of Stochastic Bistable Reaction-Diffusion Equations | A multiscale analysis of 1D stochastic bistable reaction-diffusion equations
with additive noise is carried out w.r.t. travelling waves within the
variational approach to stochastic partial differential equations. It is shown
with explicit error estimates on appropriate function spaces that up to lower
order w.r.t. the noise amplitude, the solution can be decomposed into the
orthogonal sum of a travelling wave moving with random speed and into Gaussian
fluctuations. A stochastic differential equation describing the speed of the
travelling wave and a linear stochastic partial differential equation
describing the fluctuations are derived in terms of the coefficients. Our
results extend corresponding results obtained for stochastic neural field
equations to the present class of stochastic dynamics.
| 0 | 0 | 1 | 0 | 0 | 0 |
18,086 | MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis | Interpretability has emerged as a crucial aspect of machine learning, aimed
at providing insights into the working of complex neural networks. However,
existing solutions vary vastly based on the nature of the interpretability
task, with each use case requiring substantial time and effort. This paper
introduces MARGIN, a simple yet general approach to address a large set of
interpretability tasks ranging from identifying prototypes to explaining image
predictions. MARGIN exploits ideas rooted in graph signal analysis to determine
influential nodes in a graph, which are defined as those nodes that maximally
describe a function defined on the graph. By carefully defining task-specific
graphs and functions, we demonstrate that MARGIN outperforms existing
approaches in a number of disparate interpretability challenges.
| 1 | 0 | 0 | 1 | 0 | 0 |
18,087 | SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability | We propose a new technique, Singular Vector Canonical Correlation Analysis
(SVCCA), a tool for quickly comparing two representations in a way that is both
invariant to affine transform (allowing comparison between different layers and
networks) and fast to compute (allowing more comparisons to be calculated than
with previous methods). We deploy this tool to measure the intrinsic
dimensionality of layers, showing in some cases needless over-parameterization;
to probe learning dynamics throughout training, finding that networks converge
to final representations from the bottom up; to show where class-specific
information in networks is formed; and to suggest new training regimes that
simultaneously save computation and overfit less. Code:
this https URL
| 1 | 0 | 0 | 1 | 0 | 0 |
18,088 | Discrete Choice and Rational Inattention: a General Equivalence Result | This paper establishes a general equivalence between discrete choice and
rational inattention models. Matejka and McKay (2015, AER) showed that when
information costs are modelled using the Shannon entropy function, the
resulting choice probabilities in the rational inattention model take the
multinomial logit form. By exploiting convex-analytic properties of the
discrete choice model, we show that when information costs are modelled using a
class of generalized entropy functions, the choice probabilities in any
rational inattention model are observationally equivalent to some additive
random utility discrete choice model and vice versa. Thus any additive random
utility model can be given an interpretation in terms of boundedly rational
behavior. This includes empirically relevant specifications such as the probit
and nested logit models.
| 0 | 0 | 0 | 1 | 0 | 0 |
18,089 | A Hardy inequality for ultraspherical expansions with an application to the sphere | We prove a Hardy inequality for ultraspherical expansions by using a proper
ground state representation. From this result we deduce some uncertainty
principles for this kind of expansions. Our result also implies a Hardy
inequality on spheres with a potential having a double singularity.
| 0 | 0 | 1 | 0 | 0 | 0 |
18,090 | Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors | Bayesian Neural Networks (BNNs) have recently received increasing attention
for their ability to provide well-calibrated posterior uncertainties. However,
model selection---even choosing the number of nodes---remains an open question.
Recent work has proposed the use of a horseshoe prior over node pre-activations
of a Bayesian neural network, which effectively turns off nodes that do not
help explain the data. In this work, we propose several modeling and inference
advances that consistently improve the compactness of the model learned while
maintaining predictive performance, especially in smaller-sample settings
including reinforcement learning.
| 0 | 0 | 0 | 1 | 0 | 0 |
18,091 | Efficient Reinforcement Learning via Initial Pure Exploration | In several realistic situations, an interactive learning agent can practice
and refine its strategy before going on to be evaluated. For instance, consider
a student preparing for a series of tests. She would typically take a few
practice tests to know which areas she needs to improve upon. Based of the
scores she obtains in these practice tests, she would formulate a strategy for
maximizing her scores in the actual tests. We treat this scenario in the
context of an agent exploring a fixed-horizon episodic Markov Decision Process
(MDP), where the agent can practice on the MDP for some number of episodes (not
necessarily known in advance) before starting to incur regret for its actions.
During practice, the agent's goal must be to maximize the probability of
following an optimal policy. This is akin to the problem of Pure Exploration
(PE). We extend the PE problem of Multi Armed Bandits (MAB) to MDPs and propose
a Bayesian algorithm called Posterior Sampling for Pure Exploration (PSPE),
which is similar to its bandit counterpart. We show that the Bayesian simple
regret converges at an optimal exponential rate when using PSPE.
When the agent starts being evaluated, its goal would be to minimize the
cumulative regret incurred. This is akin to the problem of Reinforcement
Learning (RL). The agent uses the Posterior Sampling for Reinforcement Learning
algorithm (PSRL) initialized with the posteriors of the practice phase. We
hypothesize that this PSPE + PSRL combination is an optimal strategy for
minimizing regret in RL problems with an initial practice phase. We show
empirical results which prove that having a lower simple regret at the end of
the practice phase results in having lower cumulative regret during evaluation.
| 1 | 0 | 0 | 1 | 0 | 0 |
18,092 | Stochastic Evolution of Augmented Born--Infeld Equations | This paper compares the results of applying a recently developed method of
stochastic uncertainty quantification designed for fluid dynamics to the
Born-Infeld model of nonlinear electromagnetism. The similarities in the
results are striking. Namely, the introduction of Stratonovich cylindrical
noise into each of their Hamiltonian formulations introduces stochastic Lie
transport into their dynamics in the same form for both theories. Moreover, the
resulting stochastic partial differential equations (SPDE) retain their
unperturbed form, except for an additional term representing induced Lie
transport by the set of divergence-free vector fields associated with the
spatial correlations of the cylindrical noise. The explanation for this
remarkable similarity lies in the method of construction of the Hamiltonian for
the Stratonovich stochastic contribution to the motion in both cases; which is
done via pairing spatial correlation eigenvectors for cylindrical noise with
the momentum map for the deterministic motion. This momentum map is responsible
for the well-known analogy between hydrodynamics and electromagnetism. The
momentum map for the Maxwell and Born-Infeld theories of electromagnetism
treated here is the 1-form density known as the Poynting vector. Two Appendices
treat the Hamiltonian structures underlying these results.
| 0 | 1 | 1 | 0 | 0 | 0 |
18,093 | T-matrix evaluation of acoustic radiation forces on nonspherical objects in Bessel beams | Acoustical radiation force (ARF) induced by a single Bessel beam with
arbitrary order and location on a nonspherical shape is studied with the
emphasis on the physical mechanism and parameter conditions of negative
(pulling) forces. Numerical experiments are conducted to verify the T-matrix
method (TMM) for axial ARFs. This study may guide the experimental set-up to
find negative axial ARF quickly and effectively based on the predicted
parameters with TMM, and could be extended for lateral forces. The present work
could help to design acoustic tweezers numerical toolbox, which provides an
alternate to the optic tweezers.
| 0 | 1 | 0 | 0 | 0 | 0 |
18,094 | On the commutative center of Moufang loops | We construct two infinite series of Moufang loops of exponent $3$ whose
commutative center (i.e. the set of elements that commute with all elements of
the loop) is not a normal subloop. In particular, we obtain examples of such
loops of orders $3^8$ and $3^{11}$ one of which can be defined as the Moufang
triplication of the free Burnside group $B(3,3)$.
| 0 | 0 | 1 | 0 | 0 | 0 |
18,095 | The Kontsevich tetrahedral flow in 2D: a toy model | In the paper "Formality conjecture" (1996) Kontsevich designed a universal
flow $\dot{\mathcal{P}}=\mathcal{Q}_{a:b}(\mathcal{P})=a\Gamma_{1}+b\Gamma_{2}$
on the spaces of Poisson structures $\mathcal{P}$ on all affine manifolds of
dimension $n \geqslant 2$. We prove a claim from $\textit{loc. cit.}$ stating
that if $n=2$, the flow $\mathcal{Q}_{1:0}=\Gamma_{1}(\mathcal{P})$ is
Poisson-cohomology trivial: $\Gamma_{1}(\mathcal{P})$ is the Schouten bracket
of $\mathcal{P}$ with $\mathcal{X}$, for some vector field $\mathcal{X}$; we
examine the structure of the space of solutions $\mathcal{X}$. Both the
construction of differential polynomials $\Gamma_{1}(\mathcal{P})$ and
$\Gamma_{2}(\mathcal{P})$ and the technique to study them remain valid in
higher dimensions $n \geqslant 3$, but neither the trivializing vector field
$\mathcal{X}$ nor the setting $b:=0$ survive at $n\geqslant 3$, where the
balance is $a:b=1:6$.
| 0 | 0 | 1 | 0 | 0 | 0 |
18,096 | Twin Learning for Similarity and Clustering: A Unified Kernel Approach | Many similarity-based clustering methods work in two separate steps including
similarity matrix computation and subsequent spectral clustering. However,
similarity measurement is challenging because it is usually impacted by many
factors, e.g., the choice of similarity metric, neighborhood size, scale of
data, noise and outliers. Thus the learned similarity matrix is often not
suitable, let alone optimal, for the subsequent clustering. In addition,
nonlinear similarity often exists in many real world data which, however, has
not been effectively considered by most existing methods. To tackle these two
challenges, we propose a model to simultaneously learn cluster indicator matrix
and similarity information in kernel spaces in a principled way. We show
theoretical relationships to kernel k-means, k-means, and spectral clustering
methods. Then, to address the practical issue of how to select the most
suitable kernel for a particular clustering task, we further extend our model
with a multiple kernel learning ability. With this joint model, we can
automatically accomplish three subtasks of finding the best cluster indicator
matrix, the most accurate similarity relations and the optimal combination of
multiple kernels. By leveraging the interactions between these three subtasks
in a joint framework, each subtask can be iteratively boosted by using the
results of the others towards an overall optimal solution. Extensive
experiments are performed to demonstrate the effectiveness of our method.
| 1 | 0 | 0 | 1 | 0 | 0 |
18,097 | Big Data Analysis Using Shrinkage Strategies | In this paper, we apply shrinkage strategies to estimate regression
coefficients efficiently for the high-dimensional multiple regression model,
where the number of samples is smaller than the number of predictors. We assume
in the sparse linear model some of the predictors have very weak influence on
the response of interest. We propose to shrink estimators more than usual.
Specifically, we use integrated estimation strategies in sub and full models
and shrink the integrated estimators by incorporating a bounded measurable
function of some weights. The exhibited double shrunken estimators improve the
prediction performance of sub models significantly selected from existing
Lasso-type variable selection methods. Monte Carlo simulation studies as well
as real examples of eye data and Riboavin data confirm the superior performance
of the estimators in the high-dimensional regression model.
| 0 | 0 | 0 | 1 | 0 | 0 |
18,098 | Interpreter fr topologists | Let M be a transitive model of set theory. There is a canonical
interpretation functor between the category of regular Hausdorff, continuous
open images of Cech-complete spaces of M and the same category in V, preserving
many concepts of topology, functional analysis, and dynamics. The functor can
be further canonically extended to the category of Borel subspaces. This
greatly simplifies and extends similar results of Fremlin.
| 0 | 0 | 1 | 0 | 0 | 0 |
18,099 | Parallel-Data-Free Voice Conversion Using Cycle-Consistent Adversarial Networks | We propose a parallel-data-free voice-conversion (VC) method that can learn a
mapping from source to target speech without relying on parallel data. The
proposed method is general purpose, high quality, and parallel-data free and
works without any extra data, modules, or alignment procedure. It also avoids
over-smoothing, which occurs in many conventional statistical model-based VC
methods. Our method, called CycleGAN-VC, uses a cycle-consistent adversarial
network (CycleGAN) with gated convolutional neural networks (CNNs) and an
identity-mapping loss. A CycleGAN learns forward and inverse mappings
simultaneously using adversarial and cycle-consistency losses. This makes it
possible to find an optimal pseudo pair from unpaired data. Furthermore, the
adversarial loss contributes to reducing over-smoothing of the converted
feature sequence. We configure a CycleGAN with gated CNNs and train it with an
identity-mapping loss. This allows the mapping function to capture sequential
and hierarchical structures while preserving linguistic information. We
evaluated our method on a parallel-data-free VC task. An objective evaluation
showed that the converted feature sequence was near natural in terms of global
variance and modulation spectra. A subjective evaluation showed that the
quality of the converted speech was comparable to that obtained with a Gaussian
mixture model-based method under advantageous conditions with parallel and
twice the amount of data.
| 1 | 0 | 0 | 1 | 0 | 0 |
18,100 | Cross-validation in high-dimensional spaces: a lifeline for least-squares models and multi-class LDA | Least-squares models such as linear regression and Linear Discriminant
Analysis (LDA) are amongst the most popular statistical learning techniques.
However, since their computation time increases cubically with the number of
features, they are inefficient in high-dimensional neuroimaging datasets.
Fortunately, for k-fold cross-validation, an analytical approach has been
developed that yields the exact cross-validated predictions in least-squares
models without explicitly training the model. Its computation time grows with
the number of test samples. Here, this approach is systematically investigated
in the context of cross-validation and permutation testing. LDA is used
exemplarily but results hold for all other least-squares methods. Furthermore,
a non-trivial extension to multi-class LDA is formally derived. The analytical
approach is evaluated using complexity calculations, simulations, and
permutation testing of an EEG/MEG dataset. Depending on the ratio between
features and samples, the analytical approach is up to 10,000x faster than the
standard approach (retraining the model on each training set). This allows for
a fast cross-validation of least-squares models and multi-class LDA in
high-dimensional data, with obvious applications in multi-dimensional datasets,
Representational Similarity Analysis, and permutation testing.
| 0 | 0 | 0 | 1 | 0 | 0 |
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