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Diffuse Gamma Rays in 3D Galactic Cosmic-ray Propagation Models | The Picard code for the numerical solution of the Galactic cosmic ray
propagation problem allows for high-resolution models that acknowledge the 3D
structure of our Galaxy. Picard was used to determine diffuse gamma-ray
emission of the Galaxy over the energy range from 100 MeV to 100 TeV. We
discuss the impact of a cosmic-ray source distribution aligned with the
Galactic spiral arms for a range of such spiral-arm models. As expected, the
impact on the gamma-ray emission is most distinct in the inverse-Compton
channel, where imprints of the spiral arms are visible and yield predictions
that are no longer symmetric to the rotational axis of the Milkyway. We will
illustrate these differences by a direct comparison to results from previous
axially symmetric Galactic propagation models: we find differences in the
gamma-ray flux both on global scales and on local scales related to the spiral
arm tangents. We compare gamma-ray flux and spectra at on-arm vs. off-arm
projections and characterize the differences to axially symmetric models.
| 0 | 1 | 0 | 0 | 0 | 0 |
Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference | We propose a simple and general variant of the standard reparameterized
gradient estimator for the variational evidence lower bound. Specifically, we
remove a part of the total derivative with respect to the variational
parameters that corresponds to the score function. Removing this term produces
an unbiased gradient estimator whose variance approaches zero as the
approximate posterior approaches the exact posterior. We analyze the behavior
of this gradient estimator theoretically and empirically, and generalize it to
more complex variational distributions such as mixtures and importance-weighted
posteriors.
| 1 | 0 | 0 | 1 | 0 | 0 |
Consistent hydrodynamic theory of chiral electrons in Weyl semimetals | The complete set of Maxwell's and hydrodynamic equations for the chiral
electrons in Weyl semimetals is presented. The formulation of the Euler
equation takes into account the explicit breaking of the Galilean invariance by
the ion lattice. It is shown that the Chern-Simons (or Bardeen-Zumino)
contributions should be added to the electric current and charge densities in
Maxwell's equations that provide the information on the separation of Weyl
nodes in energy and momentum. On the other hand, these topological
contributions do not directly affect the Euler equation and the energy
conservation relation for the electron fluid. By making use of the proposed
consistent hydrodynamic framework, we show that the Chern-Simons contributions
strongly modify the dispersion relations of collective modes in Weyl
semimetals. This is reflected, in particular, in the existence of distinctive
anomalous Hall waves, which are sustained by the local anomalous Hall currents.
| 0 | 1 | 0 | 0 | 0 | 0 |
Design and Processing of Invertible Orientation Scores of 3D Images for Enhancement of Complex Vasculature | The enhancement and detection of elongated structures in noisy image data is
relevant for many biomedical imaging applications. To handle complex crossing
structures in 2D images, 2D orientation scores $U: \mathbb{R} ^ 2\times S ^ 1
\rightarrow \mathbb{C}$ were introduced, which already showed their use in a
variety of applications. Here we extend this work to 3D orientation scores $U:
\mathbb{R} ^ 3 \times S ^ 2\rightarrow \mathbb{C}$. First, we construct the
orientation score from a given dataset, which is achieved by an invertible
coherent state type of transform. For this transformation we introduce 3D
versions of the 2D cake-wavelets, which are complex wavelets that can
simultaneously detect oriented structures and oriented edges. Here we introduce
two types of cake-wavelets, the first uses a discrete Fourier transform, the
second is designed in the 3D generalized Zernike basis, allowing us to
calculate analytical expressions for the spatial filters. Finally, we show two
applications of the orientation score transformation. In the first application
we propose an extension of crossing-preserving coherence enhancing diffusion
via our invertible orientation scores of 3D images which we apply to real
medical image data. In the second one we develop a new tubularity measure using
3D orientation scores and apply the tubularity measure to both artificial and
real medical data.
| 1 | 0 | 0 | 0 | 0 | 0 |
The origin and early evolution of life in chemical complexity space | Life can be viewed as a localized chemical system that sits on, or in the
basin of attraction of, a metastable dynamical attractor state that remains out
of equilibrium with the environment. Such a view of life allows that new living
states can arise through chance changes in local chemical concentration
(=mutations) that move points in space into the basin of attraction of a life
state - the attractor being an autocatalytic sets whose essential (=keystone)
species are produced at a higher rate than they are lost to the environment by
diffusion, such that growth in expected. This conception of life yields several
new insights and conjectures. (1) This framework suggests that the first new
life states to arise are likely at interfaces where the rate of diffusion of
keystone species is tied to a low-diffusion regime, while precursors and waste
products diffuse at a higher rate. (2) There are reasons to expect that once
the first life state arises, most likely on a mineral surface, additional
mutations will generate derived life states with which the original state will
compete. (3) I propose that in the resulting adaptive process there is a
general tendency for higher complexity life states (i.e., ones that are further
from being at equilibrium with the environment) to dominate a given mineral
surface. (4) The framework suggests a simple and predictable path by which
cells evolve and provides pointers on why such cells are likely to acquire
particulate inheritance. Overall, the dynamical systems theoretical framework
developed provides an integrated view of the origin and early evolution of life
and supports novel empirical approaches.
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Principal Component Analysis for Functional Data on Riemannian Manifolds and Spheres | Functional data analysis on nonlinear manifolds has drawn recent interest.
Sphere-valued functional data, which are encountered for example as movement
trajectories on the surface of the earth, are an important special case. We
consider an intrinsic principal component analysis for smooth Riemannian
manifold-valued functional data and study its asymptotic properties. Riemannian
functional principal component analysis (RFPCA) is carried out by first mapping
the manifold-valued data through Riemannian logarithm maps to tangent spaces
around the time-varying Fréchet mean function, and then performing a
classical multivariate functional principal component analysis on the linear
tangent spaces. Representations of the Riemannian manifold-valued functions and
the eigenfunctions on the original manifold are then obtained with exponential
maps. The tangent-space approximation through functional principal component
analysis is shown to be well-behaved in terms of controlling the residual
variation if the Riemannian manifold has nonnegative curvature. Specifically,
we derive a central limit theorem for the mean function, as well as root-$n$
uniform convergence rates for other model components, including the covariance
function, eigenfunctions, and functional principal component scores. Our
applications include a novel framework for the analysis of longitudinal
compositional data, achieved by mapping longitudinal compositional data to
trajectories on the sphere, illustrated with longitudinal fruit fly behavior
patterns. RFPCA is shown to be superior in terms of trajectory recovery in
comparison to an unrestricted functional principal component analysis in
applications and simulations and is also found to produce principal component
scores that are better predictors for classification compared to traditional
functional functional principal component scores.
| 0 | 0 | 1 | 1 | 0 | 0 |
Braids with as many full twists as strands realize the braid index | We characterize the fractional Dehn twist coefficient of a braid in terms of
a slope of the homogenization of the Upsilon function, where Upsilon is the
function-valued concordance homomorphism defined by Ozsváth, Stipsicz, and
Szabó. We use this characterization to prove that $n$-braids with fractional
Dehn twist coefficient larger than $n-1$ realize the braid index of their
closure. As a consequence, we are able to prove a conjecture of Malyutin and
Netsvetaev stating that $n$-times twisted braids realize the braid index of
their closure. We provide examples that address the optimality of our results.
The paper ends with an appendix about the homogenization of knot concordance
homomorphisms.
| 0 | 0 | 1 | 0 | 0 | 0 |
The Formal Semantics of Rascal Light | Rascal is a high-level transformation language that aims to simplify software
language engineering tasks like defining program syntax, analyzing and
transforming programs, and performing code generation. The language provides
several features including built-in collections (lists, sets, maps), algebraic
data-types, powerful pattern matching operations with backtracking, and
high-level traversals supporting multiple strategies. Interaction between
different language features can be difficult to comprehend, since most features
are semantically rich. The report provides a well-defined formal semantics for
a large subset of Rascal, called Rascal Light, suitable for developing formal
techniques, e.g., type systems and static analyses. Additionally, the report
states and proofs a series of interesting properties of the semantics,
including purity of backtracking, strong typing, partial progress and the
existence of a terminating subset.
| 1 | 0 | 0 | 0 | 0 | 0 |
On inverse and right inverse ordered semigroups | A regular ordered semigroup $S$ is called right inverse if every principal
left ideal of $S$ is generated by an $\mathcal{R}$-unique ordered idempotent.
Here we explore the theory of right inverse ordered semigroups. We show that a
regular ordered semigroup is right inverse if and only if any two right
inverses of an element $a\in S$ are $\mathcal{R}$-related. Furthermore,
different characterizations of right Clifford, right group-like, group like
ordered semigroups are done by right inverse ordered semigroups. Thus a
foundation of right inverse semigroups has been developed.
| 0 | 0 | 1 | 0 | 0 | 0 |
Refining the Two-Dimensional Signed Small Ball Inequality | The two-dimensional signed small ball inequality states that for all possible
choices of signs, $$ \left\| \sum_{|R| = 2^{-n}}{ \varepsilon_R h_R}
\right\|_{L^{\infty}} \gtrsim n,$$ where the summation runs over all dyadic
rectangles in the unit square and $h_R$ denotes the associated Haar function.
This inequality first appeared in the work of Talagrand, and alternative proofs
are due to Temlyakov and Bilyk & Feldheim (who showed that the supremum equals
$n+1$ in all cases). We prove that for all integers $0\leq k \leq n+1$ and all
possible choices of signs, $$ \left| \left\{ x \in [0,1)^2: \sum_{|R| =
2^{-n}}{ \varepsilon_R h_R} = n + 1 - 2k\right\} \right| =
\frac{1}{2^{n+1}}\binom{n+1}{k}.$$
| 0 | 0 | 1 | 0 | 0 | 0 |
The structure, capability and the Schur multiplier of generalized Heisenberg Lie algebras | From [Problem 1729, Groups of prime power order, Vol. 3], Berkovich et al.
asked to obtain the Schur multiplier and the representation of a group $G$,
when $G$ is a special $p$-group minimally generated by $d$ elements and
$|G'|=p^{\frac{1}{2}d(d-1)}$. Since there are analogies between groups and Lie
algebras, we intend to give an answer to this question similarly for nilpotent
Lie algebras. Furthermore, we give some results about the tensor square and the
Schur multiplier of some nilpotent Lie algebras of class two.
| 0 | 0 | 1 | 0 | 0 | 0 |
Fractional Brownian markets with time-varying volatility and high-frequency data | Diffusion processes driven by Fractional Brownian motion (FBM) have often
been considered in modeling stock price dynamics in order to capture the long
range dependence of stock price observed in reality. Option prices for such
models had been obtained by Necula (2002) under constant drift and volatility.
We obtain option prices under time varying volatility model. The expression
depends on volatility and the Hurst parameter in a complicated manner. We
derive a central limit theorem for the quadratic variation as an estimator for
volatility for both the cases, constant as well as time varying volatility.
That will help us to find estimators of the option prices and to find their
asymptotic distributions.
| 0 | 0 | 1 | 1 | 0 | 0 |
Experimental GHZ Entanglement beyond Qubits | The Greenberger-Horne-Zeilinger (GHZ) argument provides an all-or-nothing
contradiction between quantum mechanics and local-realistic theories. In its
original formulation, GHZ investigated three and four particles entangled in
two dimensions only. Very recently, higher dimensional contradictions
especially in three dimensions and three particles have been discovered but it
has remained unclear how to produce such states. In this article we
experimentally show how to generate a three-dimensional GHZ state from
two-photon orbital-angular-momentum entanglement. The first suggestion for a
setup which generates three-dimensional GHZ entanglement from these entangled
pairs came from using the computer algorithm Melvin. The procedure employs
novel concepts significantly beyond the qubit case. Our experiment opens up the
possibility of a truly high-dimensional test of the GHZ-contradiction which,
interestingly, employs non-Hermitian operators.
| 0 | 1 | 0 | 0 | 0 | 0 |
On global Okounkov bodies of spherical varieties | We define and study the global Okounkov moment cone of a projective spherical
variety X, generalizing both the global Okounkov body and the moment body of X
defined by Kaveh and Khovanskii. Under mild assumptions on X we show that the
global Okounkov moment cone of X is rational polyhedral. As a consequence, also
the global Okounkov body of X, with respect to a particular valuation, is
rational polyhedral.
| 0 | 0 | 1 | 0 | 0 | 0 |
From the simple reacting sphere kinetic model to the reaction-diffusion system of Maxwell-Stefan type | In this paper we perform a formal asymptotic analysis on a kinetic model for
reactive mixtures in order to derive a reaction-diffusion system of
Maxwell-Stefan type. More specifically, we start from the kinetic model of
simple reacting spheres for a quaternary mixture of monatomic ideal gases that
undergoes a reversible chemical reaction of bimolecular type. Then, we consider
a scaling describing a physical situation in which mechanical collisions play a
dominant role in the evolution process, while chemical reactions are slow, and
compute explicitly the production terms associated to the concentration and
momentum balance equations for each species in the reactive mixture. Finally,
we prove that, under isothermal assumptions, the limit equations for the scaled
kinetic model is the reaction diffusion system of Maxwell-Stefan type.
| 0 | 1 | 0 | 0 | 0 | 0 |
Origin of soft glassy rheology in the cytoskeleton | Dynamically crosslinked semiflexible biopolymers such as the actin
cytoskeleton govern the mechanical behavior of living cells. Semiflexible
biopolymers stiffen nonlinearly in response to mechanical loads, whereas the
crosslinker dynamics allow for stress relaxation over time. Here we show,
through rheology and theoretical modeling, that the combined nonlinearity in
time and stress leads to an unexpectedly slow stress relaxation, similar to the
dynamics of disordered systems close to the glass transition. Our work suggests
that transient crosslinking combined with internal stress is the microscopic
origin for the universal glassy dynamics as frequently observed in cellular
mechanics.
| 0 | 0 | 0 | 0 | 1 | 0 |
Pattern Search Multidimensional Scaling | We present a novel view of nonlinear manifold learning using derivative-free
optimization techniques. Specifically, we propose an extension of the classical
multi-dimensional scaling (MDS) method, where instead of performing gradient
descent, we sample and evaluate possible "moves" in a sphere of fixed radius
for each point in the embedded space. A fixed-point convergence guarantee can
be shown by formulating the proposed algorithm as an instance of General
Pattern Search (GPS) framework. Evaluation on both clean and noisy synthetic
datasets shows that pattern search MDS can accurately infer the intrinsic
geometry of manifolds embedded in high-dimensional spaces. Additionally,
experiments on real data, even under noisy conditions, demonstrate that the
proposed pattern search MDS yields state-of-the-art results.
| 0 | 0 | 0 | 1 | 0 | 0 |
MH370 Burst Frequency Offset Analysis and Implications on Descent Rate at End-of-Flight | Malaysian Airlines flight MH370 veered off course unexpectedly during a
scheduled trip from Kuala Lumpur to Beijing on the 7th of March 2014. MH370 was
tracked via military radar into the Malacca Straits and, after disappearing
from radar, was subsequently believed to have turned south towards the southern
Indian Ocean before crashing approximately 6 hours later. This article
discusses specifically the analysis of burst frequency offset (BFO) metadata
from the SATCOM messages. It is shown that the BFOs corresponding to the last
two SATCOM messages from the plane at 00:19:29Z and 00:19:37Z 8th March 2014
suggest that flight MH370 was rapidly descending and accelerating downwards
when message exchange with the ground station ceased.
| 0 | 0 | 0 | 1 | 0 | 0 |
Classifying Symmetrical Differences and Temporal Change in Mammography Using Deep Neural Networks | We investigate the addition of symmetry and temporal context information to a
deep Convolutional Neural Network (CNN) with the purpose of detecting malignant
soft tissue lesions in mammography. We employ a simple linear mapping that
takes the location of a mass candidate and maps it to either the contra-lateral
or prior mammogram and Regions Of Interest (ROI) are extracted around each
location. We subsequently explore two different architectures (1) a fusion
model employing two datastreams were both ROIs are fed to the network during
training and testing and (2) a stage-wise approach where a single ROI CNN is
trained on the primary image and subsequently used as feature extractor for
both primary and symmetrical or prior ROIs. A 'shallow' Gradient Boosted Tree
(GBT) classifier is then trained on the concatenation of these features and
used to classify the joint representation. Results shown a significant increase
in performance using the first architecture and symmetry information, but only
marginal gains in performance using temporal data and the other setting. We
feel results are promising and can greatly be improved when more temporal data
becomes available.
| 1 | 0 | 0 | 0 | 0 | 0 |
Deep Echo State Network (DeepESN): A Brief Survey | The study of deep recurrent neural networks (RNNs) and, in particular, of
deep Reservoir Computing (RC) is gaining an increasing research attention in
the neural networks community. The recently introduced deep Echo State Network
(deepESN) model opened the way to an extremely efficient approach for designing
deep neural networks for temporal data. At the same time, the study of deepESNs
allowed to shed light on the intrinsic properties of state dynamics developed
by hierarchical compositions of recurrent layers, i.e. on the bias of depth in
RNNs architectural design. In this paper, we summarize the advancements in the
development, analysis and applications of deepESNs.
| 1 | 0 | 0 | 1 | 0 | 0 |
Computational topology of graphs on surfaces | Computational topology is an area that revisits topological problems from an
algorithmic point of view, and develops topological tools for improved
algorithms. We survey results in computational topology that are concerned with
graphs drawn on surfaces. Typical questions include representing surfaces and
graphs embedded on them computationally, deciding whether a graph embeds on a
surface, solving computational problems related to homotopy, optimizing curves
and graphs on surfaces, and solving standard graph algorithm problems more
efficiently in the case of surface-embedded graphs.
| 1 | 0 | 1 | 0 | 0 | 0 |
Identifying hazardousness of sewer pipeline gas mixture using classification methods: a comparative study | In this work, we formulated a real-world problem related to sewer pipeline
gas detection using the classification-based approaches. The primary goal of
this work was to identify the hazardousness of sewer pipeline to offer safe and
non-hazardous access to sewer pipeline workers so that the human fatalities,
which occurs due to the toxic exposure of sewer gas components, can be avoided.
The dataset acquired through laboratory tests, experiments, and various
literature sources was organized to design a predictive model that was able to
identify/classify hazardous and non-hazardous situation of sewer pipeline. To
design such prediction model, several classification algorithms were used and
their performances were evaluated and compared, both empirically and
statistically, over the collected dataset. In addition, the performances of
several ensemble methods were analyzed to understand the extent of improvement
offered by these methods. The result of this comprehensive study showed that
the instance-based learning algorithm performed better than many other
algorithms such as multilayer perceptron, radial basis function network,
support vector machine, reduced pruning tree. Similarly, it was observed that
multi-scheme ensemble approach enhanced the performance of base predictors.
| 1 | 0 | 0 | 0 | 0 | 0 |
The rational points on certain Abelian varieties over function fields | In this paper, we consider Abelian varieties over function fields that arise
as twists of Abelian varieties by cyclic covers of irreducible quasi-projective
varieties. Then, in terms of Prym varieties associated to the cyclic covers, we
prove a structure theorem on their Mordell-Weil group. Our results give an
explicit method for construction of elliptic curves, hyper- and super-elliptic
Jacobians that have large ranks over function fields of certain varieties.
| 0 | 0 | 1 | 0 | 0 | 0 |
Multivariate inhomogeneous diffusion models with covariates and mixed effects | Modeling of longitudinal data often requires diffusion models that
incorporate overall time-dependent, nonlinear dynamics of multiple components
and provide sufficient flexibility for subject-specific modeling. This
complexity challenges parameter inference and approximations are inevitable. We
propose a method for approximate maximum-likelihood parameter estimation in
multivariate time-inhomogeneous diffusions, where subject-specific flexibility
is accounted for by incorporation of multidimensional mixed effects and
covariates. We consider $N$ multidimensional independent diffusions $X^i =
(X^i_t)_{0\leq t\leq T^i}, 1\leq i\leq N$, with common overall model structure
and unknown fixed-effects parameter $\mu$. Their dynamics differ by the
subject-specific random effect $\phi^i$ in the drift and possibly by (known)
covariate information, different initial conditions and observation times and
duration. The distribution of $\phi^i$ is parametrized by an unknown
$\vartheta$ and $\theta = (\mu, \vartheta)$ is the target of statistical
inference. Its maximum likelihood estimator is derived from the continuous-time
likelihood. We prove consistency and asymptotic normality of $\hat{\theta}_N$
when the number $N$ of subjects goes to infinity using standard techniques and
consider the more general concept of local asymptotic normality for less
regular models. The bias induced by time-discretization of sufficient
statistics is investigated. We discuss verification of conditions and
investigate parameter estimation and hypothesis testing in simulations.
| 0 | 0 | 1 | 1 | 0 | 0 |
Stability of patterns in the Abelian sandpile | We show that the patterns in the Abelian sandpile are stable. The proof
combines the structure theory for the patterns with the regularity machinery
for non-divergence form elliptic equations. The stability results allows one to
improve weak-* convergence of the Abelian sandpile to pattern convergence for
certain classes of solutions.
| 0 | 0 | 1 | 0 | 0 | 0 |
Position Heaps for Parameterized Strings | We propose a new indexing structure for parameterized strings, called
parameterized position heap. Parameterized position heap is applicable for
parameterized pattern matching problem, where the pattern matches a substring
of the text if there exists a bijective mapping from the symbols of the pattern
to the symbols of the substring. We propose an online construction algorithm of
parameterized position heap of a text and show that our algorithm runs in
linear time with respect to the text size. We also show that by using
parameterized position heap, we can find all occurrences of a pattern in the
text in linear time with respect to the product of the pattern size and the
alphabet size.
| 1 | 0 | 0 | 0 | 0 | 0 |
Quasi-Steady Model of a Pumping Kite Power System | The traction force of a kite can be used to drive a cyclic motion for
extracting wind energy from the atmosphere. This paper presents a novel
quasi-steady modelling framework for predicting the power generated over a full
pumping cycle. The cycle is divided into traction, retraction and transition
phases, each described by an individual set of analytic equations. The effect
of gravity on the airborne system components is included in the framework. A
trade-off is made between modelling accuracy and computation speed such that
the model is specifically useful for system optimisation and scaling in
economic feasibility studies. Simulation results are compared to experimental
measurements of a 20 kW kite power system operated up to a tether length of 720
m. Simulation and experiment agree reasonably well, both for moderate and for
strong wind conditions, indicating that the effect of gravity has to be taken
into account for a predictive performance simulation.
| 1 | 0 | 1 | 0 | 0 | 0 |
Expansion of pinched hypersurfaces of the Euclidean and hyperbolic space by high powers of curvature | We prove convergence results for expanding curvature flows in the Euclidean
and hyperbolic space. The flow speeds have the form $F^{-p}$, where $p>1$ and
$F$ is a positive, strictly monotone and 1-homogeneous curvature function. In
particular this class includes the mean curvature $F=H$. We prove that a
certain initial pinching condition is preserved and the properly rescaled
hypersurfaces converge smoothly to the unit sphere. We show that an example due
to Andrews-McCoy-Zheng can be used to construct strictly convex initial
hypersurfaces, for which the inverse mean curvature flow to the power $p>1$
loses convexity, justifying the necessity to impose a certain pinching
condition on the initial hypersurface.
| 0 | 0 | 1 | 0 | 0 | 0 |
Sound Mixed-Precision Optimization with Rewriting | Finite-precision arithmetic computations face an inherent tradeoff between
accuracy and efficiency. The points in this tradeoff space are determined,
among other factors, by different data types but also evaluation orders. To put
it simply, the shorter a precision's bit-length, the larger the roundoff error
will be, but the faster the program will run. Similarly, the fewer arithmetic
operations the program performs, the faster it will run; however, the effect on
the roundoff error is less clear-cut. Manually optimizing the efficiency of
finite-precision programs while ensuring that results remain accurate enough is
challenging. The unintuitive and discrete nature of finite-precision makes
estimation of roundoff errors difficult; furthermore the space of possible data
types and evaluation orders is prohibitively large. We present the first fully
automated and sound technique and tool for optimizing the performance of
floating-point and fixed-point arithmetic kernels. Our technique combines
rewriting and mixed-precision tuning. Rewriting searches through different
evaluation orders to find one which minimizes the roundoff error at no
additional runtime cost. Mixed-precision tuning assigns different finite
precisions to different variables and operations and thus provides
finer-grained control than uniform precision. We show that when these two
techniques are designed and applied together, they can provide higher
performance improvements than each alone.
| 1 | 0 | 0 | 0 | 0 | 0 |
Negative thermal expansion and metallophilicity in Cu$_3$[Co(CN)$_6$] | We report the synthesis and structural characterisation of the molecular
framework copper(I) hexacyanocobaltate(III), Cu$_3$[Co(CN)$_6$], which we find
to be isostructural to H$_3$[Co(CN)$_6$] and the colossal negative thermal
expansion material Ag$_3$[Co(CN)$_6$]. Using synchrotron X-ray powder
diffraction measurements, we find strong positive and negative thermal
expansion behaviour respectively perpendicular and parallel to the trigonal
crystal axis: $\alpha_a$ = 25.4(5)\,MK$^{-1}$ and $\alpha_c$ =
$-$43.5(8)\,MK$^{-1}$. These opposing effects collectively result in a volume
expansivity $\alpha_V$ = 7.4(11)\,MK$^{-1}$ that is remarkably small for an
anisotropic molecular framework. This thermal response is discussed in the
context of the behaviour of the analogous H- and Ag-containing systems. We make
use of density-functional theory with many-body dispersion interactions
(DFT+MBD) to demonstrate that Cu$\ldots$Cu metallophilic (`cuprophilic')
interactions are significantly weaker in Cu$_3$[Co(CN)$_6$] than Ag$\ldots$Ag
interactions in Ag$_3$[Co(CN)$_6$], but that this lowering of energy scale
counterintuitively translates to a more moderate---rather than
enhanced---degree of structural flexibility. The same conclusion is drawn from
consideration of a simple lattice dynamical model, which we also present here.
Our results demonstrate that strong interactions can actually be exploited in
the design of ultra-responsive materials if those interactions are set up to
act in tension.
| 0 | 1 | 0 | 0 | 0 | 0 |
Quantum Fluctuations along Symmetry Crossover in Kondo-correlated Quantum Dot | Universal properties of entangled many-body states are controlled by their
symmetry and quantum fluctuations. By magnetic-field tuning of the spin-orbital
degeneracy in a Kondo-correlated quantum dot, we have modified quantum
fluctuations to directly measure their influence on the many-body properties
along the crossover from $SU(4)$ to $SU(2)$ symmetry of the ground state.
High-sensitive current noise measurements combined with the non-equilibrium
Fermi liquid theory clarify that the Kondo resonance and electron correlations
are enhanced as the fluctuations, measured by the Wilson ratio, increase along
the symmetry crossover. Our achievement demonstrates that non-linear noise
constitutes a measure of quantum fluctuations that can be used to tackle
quantum phase transitions.
| 0 | 1 | 0 | 0 | 0 | 0 |
Impossibility results on stability of phylogenetic consensus methods | We answer two questions raised by Bryant, Francis and Steel in their work on
consensus methods in phylogenetics. Consensus methods apply to every practical
instance where it is desired to aggregate a set of given phylogenetic trees
(say, gene evolution trees) into a resulting, "consensus" tree (say, a species
tree). Various stability criteria have been explored in this context, seeking
to model desirable consistency properties of consensus methods as the
experimental data is updated (e.g., more taxa, or more trees, are mapped).
However, such stability conditions can be incompatible with some basic
regularity properties that are widely accepted to be essential in any
meaningful consensus method. Here, we prove that such an incompatibility does
arise in the case of extension stability on binary trees and in the case of
associative stability. Our methods combine general theoretical considerations
with the use of computer programs tailored to the given stability requirements.
| 0 | 0 | 0 | 0 | 1 | 0 |
Improved Distributed Degree Splitting and Edge Coloring | The degree splitting problem requires coloring the edges of a graph red or
blue such that each node has almost the same number of edges in each color, up
to a small additive discrepancy. The directed variant of the problem requires
orienting the edges such that each node has almost the same number of incoming
and outgoing edges, again up to a small additive discrepancy.
We present deterministic distributed algorithms for both variants, which
improve on their counterparts presented by Ghaffari and Su [SODA'17]: our
algorithms are significantly simpler and faster, and have a much smaller
discrepancy. This also leads to a faster and simpler deterministic algorithm
for $(2+o(1))\Delta$-edge-coloring, improving on that of Ghaffari and Su.
| 1 | 0 | 0 | 0 | 0 | 0 |
Global geometry and $C^1$ convex extensions of $1$-jets | Let $E$ be an arbitrary subset of $\mathbb{R}^n$ (not necessarily bounded),
and $f:E\to\mathbb{R}$, $G:E\to\mathbb{R}^n$ be functions. We provide necessary
and sufficient conditions for the $1$-jet $(f,G)$ to have an extension $(F,
\nabla F)$ with $F:\mathbb{R}^n\to\mathbb{R}$ convex and of class $C^{1}$.
Besides, if $G$ is bounded we can take $F$ so that $\textrm{Lip}(F)\lesssim
\|G\|_{\infty}$. As an application we also solve a similar problem about
finding convex hypersurfaces of class $C^1$ with prescribed normals at the
points of an arbitrary subset of $\mathbb{R}^n$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Interpretable Deep Learning applied to Plant Stress Phenotyping | Availability of an explainable deep learning model that can be applied to
practical real world scenarios and in turn, can consistently, rapidly and
accurately identify specific and minute traits in applicable fields of
biological sciences, is scarce. Here we consider one such real world example
viz., accurate identification, classification and quantification of biotic and
abiotic stresses in crop research and production. Up until now, this has been
predominantly done manually by visual inspection and require specialized
training. However, such techniques are hindered by subjectivity resulting from
inter- and intra-rater cognitive variability. Here, we demonstrate the ability
of a machine learning framework to identify and classify a diverse set of
foliar stresses in the soybean plant with remarkable accuracy. We also present
an explanation mechanism using gradient-weighted class activation mapping that
isolates the visual symptoms used by the model to make predictions. This
unsupervised identification of unique visual symptoms for each stress provides
a quantitative measure of stress severity, allowing for identification,
classification and quantification in one framework. The learnt model appears to
be agnostic to species and make good predictions for other (non-soybean)
species, demonstrating an ability of transfer learning.
| 1 | 0 | 0 | 1 | 0 | 0 |
Differentiable Compositional Kernel Learning for Gaussian Processes | The generalization properties of Gaussian processes depend heavily on the
choice of kernel, and this choice remains a dark art. We present the Neural
Kernel Network (NKN), a flexible family of kernels represented by a neural
network. The NKN architecture is based on the composition rules for kernels, so
that each unit of the network corresponds to a valid kernel. It can compactly
approximate compositional kernel structures such as those used by the Automatic
Statistician (Lloyd et al., 2014), but because the architecture is
differentiable, it is end-to-end trainable with gradient-based optimization. We
show that the NKN is universal for the class of stationary kernels. Empirically
we demonstrate pattern discovery and extrapolation abilities of NKN on several
tasks that depend crucially on identifying the underlying structure, including
time series and texture extrapolation, as well as Bayesian optimization.
| 0 | 0 | 0 | 1 | 0 | 0 |
Airy structures and symplectic geometry of topological recursion | We propose a new approach to the topological recursion of Eynard-Orantin
based on the notion of Airy structure, which we introduce in the paper. We
explain why Airy structure is a more fundamental object than the one of the
spectral curve. We explain how the concept of quantization of Airy structure
leads naturally to the formulas of topological recursion as well as their
generalizations. The notion of spectral curve is also considered in a more
general framework of Poisson surfaces endowed with foliation. We explain how
the deformation theory of spectral curves is related to Airy structures. Few
other topics (e.g. the Holomorphic Anomaly Equation) are also discussed from
the general point of view of Airy structures.
| 0 | 0 | 1 | 0 | 0 | 0 |
Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes | Exploiting the theory of state space models, we derive the exact expressions
of the information transfer, as well as redundant and synergistic transfer, for
coupled Gaussian processes observed at multiple temporal scales. All of the
terms, constituting the frameworks known as interaction information
decomposition and partial information decomposition, can thus be analytically
obtained for different time scales from the parameters of the VAR model that
fits the processes. We report the application of the proposed methodology
firstly to benchmark Gaussian systems, showing that this class of systems may
generate patterns of information decomposition characterized by mainly
redundant or synergistic information transfer persisting across multiple time
scales or even by the alternating prevalence of redundant and synergistic
source interaction depending on the time scale. Then, we apply our method to an
important topic in neuroscience, i.e., the detection of causal interactions in
human epilepsy networks, for which we show the relevance of partial information
decomposition to the detection of multiscale information transfer spreading
from the seizure onset zone.
| 0 | 0 | 1 | 1 | 0 | 0 |
Transforming Speed Sequences into Road Rays on the Map with Elastic Pathing | Advances in technology have provided ways to monitor and measure driving
behavior. Recently, this technology has been applied to usage-based automotive
insurance policies that offer reduced insurance premiums to policy holders who
opt-in to automotive monitoring. Several companies claim to measure only speed
data, which they further claim preserves privacy. However, we have developed an
algorithm - elastic pathing - that successfully tracks drivers' locations from
speed data. The algorithm tracks drivers by assuming a start position, such as
the driver's home address (which is typically known to insurance companies),
and then estimates the possible routes by fitting the speed data to map data.
To demonstrate the algorithm's real-world applicability, we evaluated its
performance with driving datasets from central New Jersey and Seattle,
Washington, representing suburban and urban areas. We are able to estimate
destinations with error within 250 meters for 17% of the traces and within 500
meters for 24% of the traces in the New Jersey dataset, and with error within
250 and 500 meters for 15.5% and 27.5% of the traces, respectively, in the
Seattle dataset. Our work shows that these insurance schemes enable a
substantial breach of privacy.
| 1 | 0 | 0 | 0 | 0 | 0 |
Solitons and geometrical structures in a perfect fluid spacetime | Geometrical aspects of a perfect fluid spacetime are described in terms of
different curvature tensors and $\eta$-Ricci and $\eta$-Einstein solitons in a
perfect fluid spacetime are determined. Conditions for the Ricci soliton to be
steady, expanding or shrinking are also given. In a particular case when the
potential vector field $\xi$ of the soliton is of gradient type,
$\xi:=grad(f)$, we derive from the soliton equation a Laplacian equation
satisfied by $f$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Hemodynamics of a Bileaflet Mechanical Heart Valve with Different Levels of Dysfunction | Heart disease is one of leading causes of mortality worldwide. Healthy heart
valves are key for proper heart function. When these valves dysfunction, a
replacement is often necessary in severe cases. The current study presents an
investigation of the pulsatile blood flow through a bileaflet mechanical heart
valve (BMHV) where one leaflet is healthy and can fully open and the other
leaflet cannot fully open with different levels of dysfunction. To better
understand the implications that a dysfunctional leaflet has on the blood flow
through these valves, analysis of flow characteristics such as velocity,
pressure drop, wall shear stress and vorticity profiles was performed. Results
suggested that leaflet dysfunction caused increased local velocities,
separation regions and wall shear stresses. For example, the maximum velocity
increased from 2.53 m/s to 4.9 m/s when dysfunction increased from 0% to 100%.
The pressure drop increased (by up to 300%) with dysfunctionality. Results
suggested that leaflet dysfunction also caused increased wall shear stresses on
the valve frame where higher stresses developed around the hinges (at 75% and
100% dysfunctions). Analysis also showed that increased dysfunctionality of one
leaflet led to higher net shear forces on both the healthy and dysfunctional
leaflets (by up to 200% and 600%, respectively).
| 0 | 1 | 0 | 0 | 0 | 0 |
Nonnegative Hermitian vector bundles and Chern numbers | We show in this article that if a holomorphic vector bundle has a nonnegative
Hermitian metric in the sense of Bott and Chern, which always exists on
globally generated holomorphic vector bundles, then some special linear
combinations of Chern forms are strongly nonnegative. This particularly implies
that all the Chern numbers of such a holomorphic vector bundle are nonnegative
and can be bounded below and above respectively by two special Chern numbers.
As applications, we obtain a family of new results on compact connected complex
manifolds which are homogeneous or can be holomorphically immersed into complex
tori, some of which improve several classical results.
| 0 | 0 | 1 | 0 | 0 | 0 |
The Ubiquity of Large Graphs and Surprising Challenges of Graph Processing | Graph processing is becoming increasingly prevalent across many application
domains. In spite of this prevalence, there is little research about how graphs
are actually used in practice. We conducted an online survey aimed at
understanding: (i) the types of graphs users have; (ii) the graph computations
users run; (iii) the types of graph software users use; and (iv) the major
challenges users face when processing their graphs. We describe the
participants' responses to our questions highlighting common patterns and
challenges. We further reviewed user feedback in the mailing lists, bug
reports, and feature requests in the source repositories of a large suite of
software products for processing graphs. Through our review, we were able to
answer some new questions that were raised by participants' responses and
identify specific challenges that users face when using different classes of
graph software. The participants' responses and data we obtained revealed
surprising facts about graph processing in practice. In particular, real-world
graphs represent a very diverse range of entities and are often very large, and
scalability and visualization are undeniably the most pressing challenges faced
by participants. We hope these findings can guide future research.
| 1 | 0 | 0 | 0 | 0 | 0 |
On the classification of four-dimensional gradient Ricci solitons | In this paper, we prove some classification results for four-dimensional
gradient Ricci solitons. For a four-dimensional gradient shrinking Ricci
soliton with $div^4Rm^\pm=0$, we show that it is either Einstein or a finite
quotient of $\mathbb{R}^4$, $\mathbb{S}^2\times\mathbb{R}^2$ or
$\mathbb{S}^3\times\mathbb{R}$. The same result can be obtained under the
condition of $div^4W^\pm=0$. We also present some classification results of
four-dimensional complete non-compact gradient expanding Ricci soliton with
non-negative Ricci curvature and gradient steady Ricci solitons under certain
curvature conditions.
| 0 | 0 | 1 | 0 | 0 | 0 |
In-gap bound states induced by a single nonmagnetic impurity in sign-preserving s-wave superconductors with incipient bands | We have investigated the in-gap bound states (IGBS) induced by a single
nonmagnetic impurity in multiband superconductors with incipient bands.
Contrary to the naive expectation, we found that even if the superconducting
(SC) order parameter is sign-preserving s-wave on the Fermi surfaces, the
incipient bands may still affect the appearance and locations of the IGBS,
although the gap between the incipient bands and the Fermi level is much larger
than the SC gap. Therefore in scanning tunneling microscopy experiments, the
IGBS induced by a single nonmagnetic impurity are not the definitive evidences
for the sign-changing order parameter on the Fermi surfaces. Our findings have
special implications for the experimental determination of the pairing symmetry
in the FeSe-based superconductors.
| 0 | 1 | 0 | 0 | 0 | 0 |
Gridbot: An autonomous robot controlled by a Spiking Neural Network mimicking the brain's navigational system | It is true that the "best" neural network is not necessarily the one with the
most "brain-like" behavior. Understanding biological intelligence, however, is
a fundamental goal for several distinct disciplines. Translating our
understanding of intelligence to machines is a fundamental problem in robotics.
Propelled by new advancements in Neuroscience, we developed a spiking neural
network (SNN) that draws from mounting experimental evidence that a number of
individual neurons is associated with spatial navigation. By following the
brain's structure, our model assumes no initial all-to-all connectivity, which
could inhibit its translation to a neuromorphic hardware, and learns an
uncharted territory by mapping its identified components into a limited number
of neural representations, through spike-timing dependent plasticity (STDP). In
our ongoing effort to employ a bioinspired SNN-controlled robot to real-world
spatial mapping applications, we demonstrate here how an SNN may robustly
control an autonomous robot in mapping and exploring an unknown environment,
while compensating for its own intrinsic hardware imperfections, such as
partial or total loss of visual input.
| 0 | 0 | 0 | 0 | 1 | 0 |
Calculation of thallium hyperfine anomaly | We suggest a method to calculate hyperfine anomaly for many-electron atoms
and ions. At first, we tested this method by calculating hyperfine anomaly for
hydrogen-like thallium ion and obtained fairly good agreement with analytical
expressions. Then we did calculations for the neutral thallium and tested an
assumption, that the the ratio between the anomalies for $s$ and $p_{1/2}$
states is the same for these two systems. Finally, we come up with
recommendations about preferable atomic states for the precision measurements
of the nuclear $g$ factors.
| 0 | 1 | 0 | 0 | 0 | 0 |
Elliptic supersymmetric integrable model and multivariable elliptic functions | We investigate the elliptic integrable model introduced by Deguchi and
Martin, which is an elliptic extension of the Perk-Schultz model. We introduce
and study a class of partition functions of the elliptic model by using the
Izergin-Korepin analysis. We show that the partition functions are expressed as
a product of elliptic factors and elliptic Schur-type symmetric functions. This
result resembles the recent works by number theorists in which the
correspondence between the partition functions of trigonometric models and the
product of the deformed Vandermonde determinant and Schur functions were
established.
| 0 | 1 | 0 | 0 | 0 | 0 |
Crystalline Electric Field Randomness in the Triangular Lattice Spin-Liquid YbMgGaO$_4$ | We apply moderate-high-energy inelastic neutron scattering (INS) measurements
to investigate Yb$^{3+}$ crystalline electric field (CEF) levels in the
triangular spin-liquid candidate YbMgGaO$_4$. Three CEF excitations from the
ground-state Kramers doublet are centered at the energies $\hbar \omega$ = 39,
61, and 97\,meV in agreement with the effective \mbox{spin-1/2} $g$-factors and
experimental heat capacity, but reveal sizable broadening. We argue that this
broadening originates from the site mixing between Mg$^{2+}$ and Ga$^{3+}$
giving rise to a distribution of Yb--O distances and orientations and, thus, of
CEF parameters that account for the peculiar energy profile of the CEF
excitations. The CEF randomness gives rise to a distribution of the effective
spin-1/2 $g$-factors and explains the unprecedented broadening of low-energy
magnetic excitations in the fully polarized ferromagnetic phase of YbMgGaO$_4$,
although a distribution of magnetic couplings due to the Mg/Ga disorder may be
important as well.
| 0 | 1 | 0 | 0 | 0 | 0 |
Quadrics and Scherk towers | We investigate the relation between quadrics and their Christoffel duals on
the one hand, and certain zero mean curvature surfaces and their Gauss maps on
the other hand. To study the relation between timelike minimal surfaces and the
Christoffel duals of 1-sheeted hyperboloids we introduce para-holomorphic
elliptic functions. The curves of type change for real isothermic surfaces of
mixed causal type turn out to be aligned with the real curvature line net.
| 0 | 0 | 1 | 0 | 0 | 0 |
Scalable Greedy Feature Selection via Weak Submodularity | Greedy algorithms are widely used for problems in machine learning such as
feature selection and set function optimization. Unfortunately, for large
datasets, the running time of even greedy algorithms can be quite high. This is
because for each greedy step we need to refit a model or calculate a function
using the previously selected choices and the new candidate.
Two algorithms that are faster approximations to the greedy forward selection
were introduced recently ([Mirzasoleiman et al. 2013, 2015]). They achieve
better performance by exploiting distributed computation and stochastic
evaluation respectively. Both algorithms have provable performance guarantees
for submodular functions.
In this paper we show that divergent from previously held opinion,
submodularity is not required to obtain approximation guarantees for these two
algorithms. Specifically, we show that a generalized concept of weak
submodularity suffices to give multiplicative approximation guarantees. Our
result extends the applicability of these algorithms to a larger class of
functions. Furthermore, we show that a bounded submodularity ratio can be used
to provide data dependent bounds that can sometimes be tighter also for
submodular functions. We empirically validate our work by showing superior
performance of fast greedy approximations versus several established baselines
on artificial and real datasets.
| 1 | 0 | 0 | 1 | 0 | 0 |
Budget-Constrained Multi-Armed Bandits with Multiple Plays | We study the multi-armed bandit problem with multiple plays and a budget
constraint for both the stochastic and the adversarial setting. At each round,
exactly $K$ out of $N$ possible arms have to be played (with $1\leq K \leq N$).
In addition to observing the individual rewards for each arm played, the player
also learns a vector of costs which has to be covered with an a-priori defined
budget $B$. The game ends when the sum of current costs associated with the
played arms exceeds the remaining budget.
Firstly, we analyze this setting for the stochastic case, for which we assume
each arm to have an underlying cost and reward distribution with support
$[c_{\min}, 1]$ and $[0, 1]$, respectively. We derive an Upper Confidence Bound
(UCB) algorithm which achieves $O(NK^4 \log B)$ regret.
Secondly, for the adversarial case in which the entire sequence of rewards
and costs is fixed in advance, we derive an upper bound on the regret of order
$O(\sqrt{NB\log(N/K)})$ utilizing an extension of the well-known
$\texttt{Exp3}$ algorithm. We also provide upper bounds that hold with high
probability and a lower bound of order $\Omega((1 - K/N)^2 \sqrt{NB/K})$.
| 1 | 0 | 0 | 1 | 0 | 0 |
Thermalized Axion Inflation | We analyze the dynamics of inflationary models with a coupling of the
inflaton $\phi$ to gauge fields of the form $\phi F \tilde{F}/f$, as in the
case of axions. It is known that this leads to an instability, with exponential
amplification of gauge fields, controlled by the parameter $\xi=
\dot{\phi}/(2fH)$, which can strongly affect the generation of cosmological
perturbations and even the background. We show that scattering rates involving
gauge fields can become larger than the expansion rate $H$, due to the very
large occupation numbers, and create a thermal bath of particles of temperature
$T$ during inflation. In the thermal regime, energy is transferred to smaller
scales, radically modifying the predictions of this scenario. We thus argue
that previous constraints on $\xi$ are alleviated. If the gauge fields have
Standard Model interactions, which naturally provides reheating, they
thermalize already at $\xi\gtrsim2.9$, before perturbativity constraints and
also before backreaction takes place. In absence of SM interactions (i.e. for a
dark photon), we find that gauge fields and inflaton perturbations thermalize
if $\xi\gtrsim3.4$; however, observations require $\xi\gtrsim6$, which is above
the perturbativity and backreaction bounds and so a dedicated study is
required. After thermalization, though, the system should evolve non-trivially
due to the competition between the instability and the gauge field thermal
mass. If the thermal mass and the instabilities equilibrate, we expect an
equilibrium temperature of $T_{eq} \simeq \xi H/\bar{g}$ where $\bar{g}$ is the
effective gauge coupling. Finally, we estimate the spectrum of perturbations if
$\phi$ is thermal and find that the tensor to scalar ratio is suppressed by
$H/(2T)$, if tensors do not thermalize.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Data-Driven Approach to Extract Connectivity Structures from Diffusion Tensor Imaging Data | Diffusion Tensor Imaging (DTI) is an effective tool for the analysis of
structural brain connectivity in normal development and in a broad range of
brain disorders. However efforts to derive inherent characteristics of
structural brain networks have been hampered by the very high dimensionality of
the data, relatively small sample sizes, and the lack of widely acceptable
connectivity-based regions of interests (ROIs). Typical approaches have focused
either on regions defined by standard anatomical atlases that do not
incorporate anatomical connectivity, or have been based on voxel-wise analysis,
which results in loss of statistical power relative to structure-wise
connectivity analysis. In this work, we propose a novel, computationally
efficient iterative clustering method to generate connectivity-based
whole-brain parcellations that converge to a stable parcellation in a few
iterations. Our algorithm is based on a sparse representation of the whole
brain connectivity matrix, which reduces the number of edges from around a half
billion to a few million while incorporating the necessary spatial constraints.
We show that the resulting regions in a sense capture the inherent connectivity
information present in the data, and are stable with respect to initialization
and the randomization scheme within the algorithm. These parcellations provide
consistent structural regions across the subjects of population samples that
are homogeneous with respect to anatomic connectivity. Our method also derives
connectivity structures that can be used to distinguish between population
samples with known different structural connectivity. In particular, new
results in structural differences for different population samples such as
Females vs Males, Normal Controls vs Schizophrenia, and different age groups in
Normal Controls are also shown.
| 0 | 0 | 0 | 1 | 1 | 0 |
A survey of location inference techniques on Twitter | The increasing popularity of the social networking service, Twitter, has made
it more involved in day-to-day communications, strengthening social
relationships and information dissemination. Conversations on Twitter are now
being explored as indicators within early warning systems to alert of imminent
natural disasters such earthquakes and aid prompt emergency responses to crime.
Producers are privileged to have limitless access to market perception from
consumer comments on social media and microblogs. Targeted advertising can be
made more effective based on user profile information such as demography,
interests and location. While these applications have proven beneficial, the
ability to effectively infer the location of Twitter users has even more
immense value. However, accurately identifying where a message originated from
or author's location remains a challenge thus essentially driving research in
that regard. In this paper, we survey a range of techniques applied to infer
the location of Twitter users from inception to state-of-the-art. We find
significant improvements over time in the granularity levels and better
accuracy with results driven by refinements to algorithms and inclusion of more
spatial features.
| 1 | 0 | 0 | 0 | 0 | 0 |
Detection and segmentation of the Left Ventricle in Cardiac MRI using Deep Learning | Manual segmentation of the Left Ventricle (LV) is a tedious and meticulous
task that can vary depending on the patient, the Magnetic Resonance Images
(MRI) cuts and the experts. Still today, we consider manual delineation done by
experts as being the ground truth for cardiac diagnosticians. Thus, we are
reviewing the paper - written by Avendi and al. - who presents a combined
approach with Convolutional Neural Networks, Stacked Auto-Encoders and
Deformable Models, to try and automate the segmentation while performing more
accurately. Furthermore, we have implemented parts of the paper (around three
quarts) and experimented both the original method and slightly modified
versions when changing the architecture and the parameters.
| 0 | 0 | 0 | 1 | 0 | 0 |
Curvature-driven stability of defects in nematic textures over spherical disks | Stabilizing defects in liquid-crystal systems is crucial for many physical
processes and applications ranging from functionalizing liquid-crystal textures
to recently reported command of chaotic behaviors of active matters. In this
work, we perform analytical calculations to study the curvature driven
stability mechanism of defects based on the isotropic nematic disk model that
is free of any topological constraint. We show that in a growing spherical disk
covering a sphere the accumulation of curvature effect can prevent typical +1
and +1/2 defects from forming boojum textures where the defects are repelled to
the boundary of the disk. Our calculations reveal that the movement of the
equilibrium position of the +1 defect from the boundary to the center of the
spherical disk occurs in a very narrow window of the disk area, exhibiting the
first-order phase-transition-like behavior. For the pair of +1/2 defects by
splitting a +1 defect, we find the curvature driven alternating repulsive and
attractive interactions between the two defects. With the growth of the
spherical disk these two defects tend to approach and finally recombine towards
a +1 defect texture. The sensitive response of defects to curvature and the
curvature driven stability mechanism demonstrated in this work in nematic disk
systems may have implications towards versatile control and engineering of
liquid crystal textures in various applications.
| 0 | 1 | 0 | 0 | 0 | 0 |
Heterogeneous nucleation of catalyst-free InAs nanowires on silicon | We report on the heterogeneous nucleation of catalyst-free InAs nanowires on
Si (111) substrates by chemical beam epitaxy. We show that nanowire nucleation
is enhanced by sputtering the silicon substrate with energetic particles. We
argue that particle bombardment introduces lattice defects on the silicon
surface that serve as preferential nucleation sites. The formation of these
nucleation sites can be controlled by the sputtering parameters, allowing the
control of nanowire density in a wide range. Nanowire nucleation is accompanied
by unwanted parasitic islands, but by careful choice of annealing and growth
temperature allows to strongly reduce the relative density of these islands and
to realize samples with high nanowire yield.
| 0 | 1 | 0 | 0 | 0 | 0 |
Random Transverse Field Spin-Glass Model on the Cayley tree : phase transition between the two Many-Body-Localized Phases | The quantum Ising model with random couplings and random transverse fields on
the Cayley tree is studied by Real-Space-Renormalization in order to construct
the whole set of eigenstates. The renormalization rules are analyzed via large
deviations. The phase transition between the paramagnetic and the spin-glass
Many-Body-Localized phases involves the activated exponent $\psi=1$ and the
correlation length exponent $\nu=1$. The spin-glass-ordered cluster containing
$N_{SG}$ spins is found to be extremely sparse with respect to the total number
$N$ of spins : its size grows only logarithmically at the critical point
$N_{SG}^{criti} \propto \ln N$, and it is sub-extensive $N_{SG} \propto
N^{\theta}$ in the finite region of the spin-glass phase where the continuously
varying exponent $\theta$ remains in the interval $0<\theta<1$.
| 0 | 1 | 0 | 0 | 0 | 0 |
Femtosecond laser inscription of Bragg grating waveguides in bulk diamond | Femtosecond laser writing is applied to form Bragg grating waveguides in the
diamond bulk. Type II waveguides are integrated with a single pulse
point-by-point periodic laser modification positioned towards the edge of the
waveguide core. These photonic devices, operating in the telecommunications
band, allow for simultaneous optical waveguiding and narrowband reflection from
a 4th order grating. This fabrication technology opens the way towards advanced
3D photonic networks in diamond for a range of applications.
| 0 | 1 | 0 | 0 | 0 | 0 |
FORM version 4.2 | We introduce FORM 4.2, a new minor release of the symbolic manipulation
toolkit. We demonstrate several new features, such as a new pattern matching
option, new output optimization, and automatic expansion of rational functions.
| 1 | 0 | 0 | 0 | 0 | 0 |
An Analysis of Two Common Reference Points for EEGs | Clinical electroencephalographic (EEG) data varies significantly depending on
a number of operational conditions (e.g., the type and placement of electrodes,
the type of electrical grounding used). This investigation explores the
statistical differences present in two different referential montages: Linked
Ear (LE) and Averaged Reference (AR). Each of these accounts for approximately
45% of the data in the TUH EEG Corpus. In this study, we explore the impact
this variability has on machine learning performance. We compare the
statistical properties of features generated using these two montages, and
explore the impact of performance on our standard Hidden Markov Model (HMM)
based classification system. We show that a system trained on LE data
significantly outperforms one trained only on AR data (77.2% vs. 61.4%). We
also demonstrate that performance of a system trained on both data sets is
somewhat compromised (71.4% vs. 77.2%). A statistical analysis of the data
suggests that mean, variance and channel normalization should be considered.
However, cepstral mean subtraction failed to produce an improvement in
performance, suggesting that the impact of these statistical differences is
subtler.
| 0 | 0 | 0 | 1 | 0 | 0 |
A Concurrency-Optimal Binary Search Tree | The paper presents the first \emph{concurrency-optimal} implementation of a
binary search tree (BST). The implementation, based on a standard sequential
implementation of an internal tree, ensures that every \emph{schedule} is
accepted, i.e., interleaving of steps of the sequential code, unless
linearizability is violated. To ensure this property, we use a novel read-write
locking scheme that protects tree \emph{edges} in addition to nodes.
Our implementation outperforms the state-of-the art BSTs on most basic
workloads, which suggests that optimizing the set of accepted schedules of the
sequential code can be an adequate design principle for efficient concurrent
data structures.
| 1 | 0 | 0 | 0 | 0 | 0 |
Controlling seizure propagation in large-scale brain networks | Information transmission in the human brain is a fundamentally dynamic
network process. In partial epilepsy, this process is perturbed and highly
synchronous seizures originate in a local network, the so-called epileptogenic
zone (EZ), before recruiting other close or distant brain regions. We studied
patient-specific brain network models of 15 drug-resistant epilepsy patients
with implanted stereotactic electroencephalography (SEEG) electrodes. Each
personalized brain model was derived from structural data of magnetic resonance
imaging (MRI) and diffusion tensor weighted imaging (DTI), comprising 88 nodes
equipped with region specific neural mass models capable of demonstrating a
range of epileptiform discharges. Each patients virtual brain was further
personalized through the integration of the clinically hypothesized EZ.
Subsequent simulations and connectivity modulations were performed and
uncovered a finite repertoire of seizure propagation patterns. Across patients,
we found that (i) patient-specific network connectivity is predictive for the
subsequent seizure propagation pattern; (ii)seizure propagation is
characterized by a systematic sequence of brain states; (iii) propagation can
be controlled by an optimal intervention on the connectivity matrix; (iv) the
degree of invasiveness can be significantly reduced via the here proposed
seizure control as compared to traditional resective surgery. To stop seizures,
neurosurgeons typically resect the EZ completely. We showed that stability
analysis of the network dynamics using graph theoretical metrics estimates
reliably the spatiotemporal properties of seizure propagation. This suggests
novel less invasive paradigms of surgical interventions to treat and manage
partial epilepsy.
| 0 | 0 | 0 | 0 | 1 | 0 |
DroidStar: Callback Typestates for Android Classes | Event-driven programming frameworks, such as Android, are based on components
with asynchronous interfaces. The protocols for interacting with these
components can often be described by finite-state machines we dub *callback
typestates*. Callback typestates are akin to classical typestates, with the
difference that their outputs (callbacks) are produced asynchronously. While
useful, these specifications are not commonly available, because writing them
is difficult and error-prone.
Our goal is to make the task of producing callback typestates significantly
easier. We present a callback typestate assistant tool, DroidStar, that
requires only limited user interaction to produce a callback typestate. Our
approach is based on an active learning algorithm, L*. We improved the
scalability of equivalence queries (a key component of L*), thus making active
learning tractable on the Android system.
We use DroidStar to learn callback typestates for Android classes both for
cases where one is already provided by the documentation, and for cases where
the documentation is unclear. The results show that DroidStar learns callback
typestates accurately and efficiently. Moreover, in several cases, the
synthesized callback typestates uncovered surprising and undocumented
behaviors.
| 1 | 0 | 0 | 0 | 0 | 0 |
Piezoelectricity for Nondestructive Testing of Crystal Surfaces | A stress is applied at the flat face and the apex of a prismatic
piezoelectric crystal. The voltage generated at these points differs in order
of magnitude. The result may be used to nondestructively test the uniformity of
surfaces of piezoelectric crystals.
| 0 | 1 | 0 | 0 | 0 | 0 |
ALMA Observations of Starless Core Substructure in Ophiuchus | Compact substructure is expected to arise in a starless core as mass becomes
concentrated in the central region likely to form a protostar. Additionally,
multiple peaks may form if fragmentation occurs. We present ALMA Cycle 2
observations of 60 starless and protostellar cores in the Ophiuchus molecular
cloud. We detect eight compact substructures which are >15 arcsec from the
nearest Spitzer YSO. Only one of these has strong evidence for being truly
starless after considering ancillary data, e.g., from Herschel and X-ray
telescopes. An additional extended emission structure has tentative evidence
for starlessness. The number of our detections is consistent with estimates
from a combination of synthetic observations of numerical simulations and
analytical arguments. This result suggests that a similar ALMA study in the
Chamaeleon I cloud, which detected no compact substructure in starless cores,
may be due to the peculiar evolutionary state of cores in that cloud.
| 0 | 1 | 0 | 0 | 0 | 0 |
Neuron-inspired flexible memristive device on silicon (100) | Comprehensive understanding of the world's most energy efficient powerful
computer, the human brain, is an elusive scientific issue. Still, already
gained knowledge indicates memristors can be used as a building block to model
the brain. At the same time, brain cortex is folded allowing trillions of
neurons to be integrated in a compact volume. Therefore, we report flexible
aluminium oxide based memristive devices fabricated and then derived from
widely used bulk mono-crystalline silicon (100). We use complementary metal
oxide semiconductor based processes to layout the foundation for ultra large
scale integration (ULSI) of such memory devices to advance the task of
comprehending a physical model of human brain.
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Detecting laws in power subgroups | A group law is said to be detectable in power subgroups if, for all coprime
$m$ and $n$, a group $G$ satisfies the law if and only if the power subgroups
$G^m$ and $G^n$ both satisfy the law. We prove that for all positive integers
$c$, nilpotency of class at most $c$ is detectable in power subgroups, as is
the $k$-Engel law for $k$ at most 4. In contrast, detectability in power
subgroups fails for solvability of given derived length: we construct a finite
group $W$ such that $W^2$ and $W^3$ are metabelian but $W$ has derived length
$3$. We analyse the complexity of the detectability of commutativity in power
subgroups, in terms of finite presentations that encode a proof of the result.
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On the Reliable Detection of Concept Drift from Streaming Unlabeled Data | Classifiers deployed in the real world operate in a dynamic environment,
where the data distribution can change over time. These changes, referred to as
concept drift, can cause the predictive performance of the classifier to drop
over time, thereby making it obsolete. To be of any real use, these classifiers
need to detect drifts and be able to adapt to them, over time. Detecting drifts
has traditionally been approached as a supervised task, with labeled data
constantly being used for validating the learned model. Although effective in
detecting drifts, these techniques are impractical, as labeling is a difficult,
costly and time consuming activity. On the other hand, unsupervised change
detection techniques are unreliable, as they produce a large number of false
alarms. The inefficacy of the unsupervised techniques stems from the exclusion
of the characteristics of the learned classifier, from the detection process.
In this paper, we propose the Margin Density Drift Detection (MD3) algorithm,
which tracks the number of samples in the uncertainty region of a classifier,
as a metric to detect drift. The MD3 algorithm is a distribution independent,
application independent, model independent, unsupervised and incremental
algorithm for reliably detecting drifts from data streams. Experimental
evaluation on 6 drift induced datasets and 4 additional datasets from the
cybersecurity domain demonstrates that the MD3 approach can reliably detect
drifts, with significantly fewer false alarms compared to unsupervised feature
based drift detectors. The reduced false alarms enables the signaling of drifts
only when they are most likely to affect classification performance. As such,
the MD3 approach leads to a detection scheme which is credible, label efficient
and general in its applicability.
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Exploiting generalization in the subspaces for faster model-based learning | Due to the lack of enough generalization in the state-space, common methods
in Reinforcement Learning (RL) suffer from slow learning speed especially in
the early learning trials. This paper introduces a model-based method in
discrete state-spaces for increasing learning speed in terms of required
experience (but not required computational time) by exploiting generalization
in the experiences of the subspaces. A subspace is formed by choosing a subset
of features in the original state representation (full-space). Generalization
and faster learning in a subspace are due to many-to-one mapping of experiences
from the full-space to each state in the subspace. Nevertheless, due to
inherent perceptual aliasing in the subspaces, the policy suggested by each
subspace does not generally converge to the optimal policy. Our approach,
called Model Based Learning with Subspaces (MoBLeS), calculates confidence
intervals of the estimated Q-values in the full-space and in the subspaces.
These confidence intervals are used in the decision making, such that the agent
benefits the most from the possible generalization while avoiding from
detriment of the perceptual aliasing in the subspaces. Convergence of MoBLeS to
the optimal policy is theoretically investigated. Additionally, we show through
several experiments that MoBLeS improves the learning speed in the early
trials.
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Quasitriangular structure and twisting of the 2+1 bicrossproduct model | We show that the bicrossproduct model
$C[SU_2^*]{\blacktriangleright\!\!\triangleleft} U(su_2)$ quantum Poincare
group in 2+1 dimensions acting on the quantum spacetime $[x_i,t]=\imath\lambda
x_i$ is related by a Drinfeld and module-algebra twist to the quantum double
$U(su_2)\ltimes C[SU_2]$ acting on the quantum spacetime
$[x_\mu,x_\nu]=\imath\lambda\epsilon_{\mu\nu\rho}x_\rho$. We obtain this twist
by taking a scaling limit as $q\to 1$ of the $q$-deformed version of the above
where it corresponds to a previous theory of $q$-deformed Wick rotation from
$q$-Euclidean to $q$-Minkowski space. We also recover the twist result at the
Lie bialgebra level.
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Can simple transmission chains foster collective intelligence in binary-choice tasks? | In many social systems, groups of individuals can find remarkably efficient
solutions to complex cognitive problems, sometimes even outperforming a single
expert. The success of the group, however, crucially depends on how the
judgments of the group members are aggregated to produce the collective answer.
A large variety of such aggregation methods have been described in the
literature, such as averaging the independent judgments, relying on the
majority or setting up a group discussion. In the present work, we introduce a
novel approach for aggregating judgments - the transmission chain - which has
not yet been consistently evaluated in the context of collective intelligence.
In a transmission chain, all group members have access to a unique collective
solution and can improve it sequentially. Over repeated improvements, the
collective solution that emerges reflects the judgments of every group members.
We address the question of whether such a transmission chain can foster
collective intelligence for binary-choice problems. In a series of numerical
simulations, we explore the impact of various factors on the performance of the
transmission chain, such as the group size, the model parameters, and the
structure of the population. The performance of this method is compared to
those of the majority rule and the confidence-weighted majority. Finally, we
rely on two existing datasets of individuals performing a series of binary
decisions to evaluate the expected performances of the three methods
empirically. We find that the parameter space where the transmission chain has
the best performance rarely appears in real datasets. We conclude that the
transmission chain is best suited for other types of problems, such as those
that have cumulative properties.
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A Theoretical Analysis of First Heuristics of Crowdsourced Entity Resolution | Entity resolution (ER) is the task of identifying all records in a database
that refer to the same underlying entity, and are therefore duplicates of each
other. Due to inherent ambiguity of data representation and poor data quality,
ER is a challenging task for any automated process. As a remedy, human-powered
ER via crowdsourcing has become popular in recent years. Using crowd to answer
queries is costly and time consuming. Furthermore, crowd-answers can often be
faulty. Therefore, crowd-based ER methods aim to minimize human participation
without sacrificing the quality and use a computer generated similarity matrix
actively. While, some of these methods perform well in practice, no theoretical
analysis exists for them, and further their worst case performances do not
reflect the experimental findings. This creates a disparity in the
understanding of the popular heuristics for this problem. In this paper, we
make the first attempt to close this gap. We provide a thorough analysis of the
prominent heuristic algorithms for crowd-based ER. We justify experimental
observations with our analysis and information theoretic lower bounds.
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Dependency resolution and semantic mining using Tree Adjoining Grammars for Tamil Language | Tree adjoining grammars (TAGs) provide an ample tool to capture syntax of
many Indian languages. Tamil represents a special challenge to computational
formalisms as it has extensive agglutinative morphology and a comparatively
difficult argument structure. Modelling Tamil syntax and morphology using TAG
is an interesting problem which has not been in focus even though TAGs are over
4 decades old, since its inception. Our research with Tamil TAGs have shown us
that we can not only represent syntax of the language, but to an extent mine
out semantics through dependency resolution of the sentence. But in order to
demonstrate this phenomenal property, we need to parse Tamil language sentences
using TAGs we have built and through parsing obtain a derivation we could use
to resolve dependencies, thus proving the semantic property. We use an in-house
developed pseudo lexical TAG chart parser; algorithm given by Schabes and Joshi
(1988), for generating derivations of sentences. We do not use any statistics
to rank out ambiguous derivations but rather use all of them to understand the
mentioned semantic relation with in TAGs for Tamil. We shall also present a
brief parser analysis for the completeness of our discussions.
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Loop conditions | We discuss such Maltsev conditions that consist of just one linear equation,
we call them loop conditions. To every such condition can be assigned a graph.
We provide a classification of conditions with undirected graphs. It follows
that the Siggers term is the weakest non-trivial loop condition.
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Impact of Feature Selection on Micro-Text Classification | Social media datasets, especially Twitter tweets, are popular in the field of
text classification. Tweets are a valuable source of micro-text (sometimes
referred to as "micro-blogs"), and have been studied in domains such as
sentiment analysis, recommendation systems, spam detection, clustering, among
others. Tweets often include keywords referred to as "Hashtags" that can be
used as labels for the tweet. Using tweets encompassing 50 labels, we studied
the impact of word versus character-level feature selection and extraction on
different learners to solve a multi-class classification task. We show that
feature extraction of simple character-level groups performs better than simple
word groups and pre-processing methods like normalizing using Porter's Stemming
and Part-of-Speech ("POS")-Lemmatization.
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One-Shot Learning of Multi-Step Tasks from Observation via Activity Localization in Auxiliary Video | Due to burdensome data requirements, learning from demonstration often falls
short of its promise to allow users to quickly and naturally program robots.
Demonstrations are inherently ambiguous and incomplete, making correct
generalization to unseen situations difficult without a large number of
demonstrations in varying conditions. By contrast, humans are often able to
learn complex tasks from a single demonstration (typically observations without
action labels) by leveraging context learned over a lifetime. Inspired by this
capability, our goal is to enable robots to perform one-shot learning of
multi-step tasks from observation by leveraging auxiliary video data as
context. Our primary contribution is a novel system that achieves this goal by:
(1) using a single user-segmented demonstration to define the primitive actions
that comprise a task, (2) localizing additional examples of these actions in
unsegmented auxiliary videos via a metalearning-based approach, (3) using these
additional examples to learn a reward function for each action, and (4)
performing reinforcement learning on top of the inferred reward functions to
learn action policies that can be combined to accomplish the task. We
empirically demonstrate that a robot can learn multi-step tasks more
effectively when provided auxiliary video, and that performance greatly
improves when localizing individual actions, compared to learning from
unsegmented videos.
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Larger is Better: The Effect of Learning Rates Enjoyed by Stochastic Optimization with Progressive Variance Reduction | In this paper, we propose a simple variant of the original stochastic
variance reduction gradient (SVRG), where hereafter we refer to as the variance
reduced stochastic gradient descent (VR-SGD). Different from the choices of the
snapshot point and starting point in SVRG and its proximal variant, Prox-SVRG,
the two vectors of each epoch in VR-SGD are set to the average and last iterate
of the previous epoch, respectively. This setting allows us to use much larger
learning rates or step sizes than SVRG, e.g., 3/(7L) for VR-SGD vs 1/(10L) for
SVRG, and also makes our convergence analysis more challenging. In fact, a
larger learning rate enjoyed by VR-SGD means that the variance of its
stochastic gradient estimator asymptotically approaches zero more rapidly.
Unlike common stochastic methods such as SVRG and proximal stochastic methods
such as Prox-SVRG, we design two different update rules for smooth and
non-smooth objective functions, respectively. In other words, VR-SGD can tackle
non-smooth and/or non-strongly convex problems directly without using any
reduction techniques such as quadratic regularizers. Moreover, we analyze the
convergence properties of VR-SGD for strongly convex problems, which show that
VR-SGD attains a linear convergence rate. We also provide the convergence
guarantees of VR-SGD for non-strongly convex problems. Experimental results
show that the performance of VR-SGD is significantly better than its
counterparts, SVRG and Prox-SVRG, and it is also much better than the best
known stochastic method, Katyusha.
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Concerning the Neural Code | The central problem with understanding brain and mind is the neural code
issue: understanding the matter of our brain as basis for the phenomena of our
mind. The richness with which our mind represents our environment, the
parsimony of genetic data, the tremendous efficiency with which the brain
learns from scant sensory input and the creativity with which our mind
constructs mental worlds all speak in favor of mind as an emergent phenomenon.
This raises the further issue of how the neural code supports these processes
of organization. The central point of this communication is that the neural
code has the form of structured net fragments that are formed by network
self-organization, activate and de-activate on the functional time scale, and
spontaneously combine to form larger nets with the same basic structure.
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Community Question Answering Platforms vs. Twitter for Predicting Characteristics of Urban Neighbourhoods | In this paper, we investigate whether text from a Community Question
Answering (QA) platform can be used to predict and describe real-world
attributes. We experiment with predicting a wide range of 62 demographic
attributes for neighbourhoods of London. We use the text from QA platform of
Yahoo! Answers and compare our results to the ones obtained from Twitter
microblogs. Outcomes show that the correlation between the predicted
demographic attributes using text from Yahoo! Answers discussions and the
observed demographic attributes can reach an average Pearson correlation
coefficient of \r{ho} = 0.54, slightly higher than the predictions obtained
using Twitter data. Our qualitative analysis indicates that there is semantic
relatedness between the highest correlated terms extracted from both datasets
and their relative demographic attributes. Furthermore, the correlations
highlight the different natures of the information contained in Yahoo! Answers
and Twitter. While the former seems to offer a more encyclopedic content, the
latter provides information related to the current sociocultural aspects or
phenomena.
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SPASS: Scientific Prominence Active Search System with Deep Image Captioning Network | Planetary exploration missions with Mars rovers are complicated, which
generally require elaborated task planning by human experts, from the path to
take to the images to capture. NASA has been using this process to acquire over
22 million images from the planet Mars. In order to improve the degree of
automation and thus efficiency in this process, we propose a system for
planetary rovers to actively search for prominence of prespecified scientific
features in captured images. Scientists can prespecify such search tasks in
natural language and upload them to a rover, on which the deployed system
constantly captions captured images with a deep image captioning network and
compare the auto-generated captions to the prespecified search tasks by certain
metrics so as to prioritize those images for transmission. As a beneficial side
effect, the proposed system can also be deployed to ground-based planetary data
systems as a content-based search engine.
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Nonlinear Sequential Accepts and Rejects for Identification of Top Arms in Stochastic Bandits | We address the M-best-arm identification problem in multi-armed bandits. A
player has a limited budget to explore K arms (M<K), and once pulled, each arm
yields a reward drawn (independently) from a fixed, unknown distribution. The
goal is to find the top M arms in the sense of expected reward. We develop an
algorithm which proceeds in rounds to deactivate arms iteratively. At each
round, the budget is divided by a nonlinear function of remaining arms, and the
arms are pulled correspondingly. Based on a decision rule, the deactivated arm
at each round may be accepted or rejected. The algorithm outputs the accepted
arms that should ideally be the top M arms. We characterize the decay rate of
the misidentification probability and establish that the nonlinear budget
allocation proves to be useful for different problem environments (described by
the number of competitive arms). We provide comprehensive numerical experiments
showing that our algorithm outperforms the state-of-the-art using suitable
nonlinearity.
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A Machine Learning Framework to Forecast Wave Conditions | A~machine learning framework is developed to estimate ocean-wave conditions.
By supervised training of machine learning models on many thousands of
iterations of a physics-based wave model, accurate representations of
significant wave heights and period can be used to predict ocean conditions. A
model of Monterey Bay was used as the example test site; it was forced by
measured wave conditions, ocean-current nowcasts, and reported winds. These
input data along with model outputs of spatially variable wave heights and
characteristic period were aggregated into supervised learning training and
test data sets, which were supplied to machine learning models. These machine
learning models replicated wave heights with a root-mean-squared error of 9cm
and correctly identify over 90% of the characteristic periods for the test-data
sets. Impressively, transforming model inputs to outputs through matrix
operations requires only a fraction (<1/1,000) of the computation time compared
to forecasting with the physics-based model.
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Unobtrusive Deferred Update Stabilization for Efficient Geo-Replication | In this paper we propose a novel approach to manage the throughput vs latency
tradeoff that emerges when managing updates in geo-replicated systems. Our
approach consists in allowing full concurrency when processing local updates
and using a deferred local serialisation procedure before shipping updates to
remote datacenters. This strategy allows to implement inexpensive mechanisms to
ensure system consistency requirements while avoiding intrusive effects on
update operations, a major performance limitation of previous systems. We have
implemented our approach as a variant of Riak KV. Our extensive evaluation
shows that we outperform sequencer-based approaches by almost an order of
magnitude in the maximum achievable throughput. Furthermore, unlike previous
sequencer-free solutions, our approach reaches nearly optimal remote update
visibility latencies without limiting throughput.
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Exploratory Analysis of Pairwise Interactions in Online Social Networks | In the last few decades sociologists were trying to explain human behaviour
by analysing social networks, which requires access to data about interpersonal
relationships. This represented a big obstacle in this research field until the
emergence of online social networks (OSNs), which vastly facilitated the
process of collecting such data. Nowadays, by crawling public profiles on OSNs,
it is possible to build a social graph where "friends" on OSN become
represented as connected nodes. OSN connection does not necessarily indicate a
close real-life relationship, but using OSN interaction records may reveal
real-life relationship intensities, a topic which inspired a number of recent
researches. Still, published research currently lacks an extensive exploratory
analysis of OSN interaction records, i.e. a comprehensive overview of users'
interaction via different ways of OSN interaction. In this paper we provide
such an overview by leveraging results of conducted extensive social experiment
which managed to collect records for over 3,200 Facebook users interacting with
over 1,400,000 of their friends. Our exploratory analysis focuses on extracting
population distributions and correlation parameters for 13 interaction
parameters, providing valuable insight in online social network interaction for
future researches aimed at this field of study.
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Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation | Across a variety of scientific disciplines, sparse inverse covariance
estimation is a popular tool for capturing the underlying dependency
relationships in multivariate data. Unfortunately, most estimators are not
scalable enough to handle the sizes of modern high-dimensional data sets (often
on the order of terabytes), and assume Gaussian samples. To address these
deficiencies, we introduce HP-CONCORD, a highly scalable optimization method
for estimating a sparse inverse covariance matrix based on a regularized
pseudolikelihood framework, without assuming Gaussianity. Our parallel proximal
gradient method uses a novel communication-avoiding linear algebra algorithm
and runs across a multi-node cluster with up to 1k nodes (24k cores), achieving
parallel scalability on problems with up to ~819 billion parameters (1.28
million dimensions); even on a single node, HP-CONCORD demonstrates
scalability, outperforming a state-of-the-art method. We also use HP-CONCORD to
estimate the underlying dependency structure of the brain from fMRI data, and
use the result to identify functional regions automatically. The results show
good agreement with a clustering from the neuroscience literature.
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Thought Viruses and Asset Prices | We use insights from epidemiology, namely the SIR model, to study how agents
infect each other with "investment ideas." Once an investment idea "goes
viral," equilibrium prices exhibit the typical "fever peak," which is
characteristic for speculative excesses. Using our model, we identify a time
line of symptoms that indicate whether a boom is in its early or later stages.
Regarding the market's top, we find that prices start to decline while the
number of infected agents, who buy the asset, is still rising. Moreover, the
presence of fully rational agents (i) accelerates booms (ii) lowers peak prices
and (iii) produces broad, drawn-out, market tops.
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Walking Through Waypoints | We initiate the study of a fundamental combinatorial problem: Given a
capacitated graph $G=(V,E)$, find a shortest walk ("route") from a source $s\in
V$ to a destination $t\in V$ that includes all vertices specified by a set
$\mathscr{W}\subseteq V$: the \emph{waypoints}. This waypoint routing problem
finds immediate applications in the context of modern networked distributed
systems. Our main contribution is an exact polynomial-time algorithm for graphs
of bounded treewidth. We also show that if the number of waypoints is
logarithmically bounded, exact polynomial-time algorithms exist even for
general graphs. Our two algorithms provide an almost complete characterization
of what can be solved exactly in polynomial-time: we show that more general
problems (e.g., on grid graphs of maximum degree 3, with slightly more
waypoints) are computationally intractable.
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Imaging a Central Ionized Component, a Narrow Ring, and the CO Snowline in the Multi-Gapped Disk of HD 169142 | We report Very Large Array observations at 7 mm, 9 mm, and 3 cm toward the
pre-transitional disk of the Herbig Ae star HD 169142. These observations have
allowed us to study the mm emission of this disk with the highest angular
resolution so far ($0\rlap."12\times0\rlap."09$, or 14 au$\times$11 au, at 7
mm). Our 7 and 9 mm images show a narrow ring of emission at a radius of
$\sim25$ au tracing the outer edge of the inner gap. This ring presents an
asymmetric morphology that could be produced by dynamical interactions between
the disk and forming planets. Additionally, the azimuthally averaged radial
intensity profiles of the 7 and 9 mm images confirm the presence of the
previously reported gap at $\sim45$ au, and reveal a new gap at $\sim85$ au. We
analyzed archival DCO$^+$(3-2) and C$^{18}$O(2-1) ALMA observations, showing
that the CO snowline is located very close to this third outer gap. This
suggests that growth and accumulation of large dust grains close to the CO
snowline could be the mechanism responsible for this proposed outer gap.
Finally, a compact source of emission is detected at 7 mm, 9 mm, and 3 cm
toward the center of the disk. Its flux density and spectral index indicate
that it is dominated by free-free emission from ionized gas, which could be
associated with either the photoionization of the inner disk, an independent
object, or an ionized jet.
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Viconmavlink: A software tool for indoor positioning using a motion capture system | Motion capture is a widely-used technology in robotics research thanks to its
precise posi tional measurements with real-time performance. This paper
presents ViconMAVLink, a cross-platform open-source software tool that provides
indoor positioning services to networked robots. ViconMAVLink converts Vicon
motion capture data into proper pose and motion data formats and send
localization information to robots using the MAVLink protocol. The software is
a convenient tool for mobile robotics researchers to conduct experiments in a
controlled indoor environment.
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On the set of optimal homeomorphisms for the natural pseudo-distance associated with the Lie group S^1 | If $\varphi$ and $\psi$ are two continuous real-valued functions defined on a
compact topological space $X$ and $G$ is a subgroup of the group of all
homeomorphisms of $X$ onto itself, the natural pseudo-distance
$d_G(\varphi,\psi)$ is defined as the infimum of $\mathcal{L}(g)=\|\varphi-\psi
\circ g \|_\infty$, as $g$ varies in $G$. In this paper, we make a first step
towards extending the study of this concept to the case of Lie groups, by
assuming $X=G=S^1$. In particular, we study the set of the optimal
homeomorphisms for $d_G$, i.e. the elements $\rho_\alpha$ of $S^1$ such that
$\mathcal{L}(\rho_\alpha)$ is equal to $d_G(\varphi,\psi)$. As our main
results, we give conditions that a homeomorphism has to meet in order to be
optimal, and we prove that the set of the optimal homeomorphisms is finite
under suitable conditions.
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A generalization of an identity due to Kimura and Ruehr | An identity stated by Kimura and proved by Ruehr, Kimura and others
stipulates that for any function $f$ continuous on $[-\frac{1}{2},
\frac{3}{2}]$ one has $$ \int_{-1/2}^{3/2} f(3x^2 - 2x^3) dx = 2 \int_0^1
f(3x^2 - 2x^3) dx. $$ We prove that this equality is not an isolated example by
providing a family of polynomials, related to the Tchebychev polynomials and of
which $(3x^2 - 2x^3)$ is a particular case, giving rise to similar identities.
| 0 | 0 | 1 | 0 | 0 | 0 |
Towards Scalable Spectral Clustering via Spectrum-Preserving Sparsification | The eigendeomposition of nearest-neighbor (NN) graph Laplacian matrices is
the main computational bottleneck in spectral clustering. In this work, we
introduce a highly-scalable, spectrum-preserving graph sparsification algorithm
that enables to build ultra-sparse NN (u-NN) graphs with guaranteed
preservation of the original graph spectrums, such as the first few
eigenvectors of the original graph Laplacian. Our approach can immediately lead
to scalable spectral clustering of large data networks without sacrificing
solution quality. The proposed method starts from constructing low-stretch
spanning trees (LSSTs) from the original graphs, which is followed by
iteratively recovering small portions of "spectrally critical" off-tree edges
to the LSSTs by leveraging a spectral off-tree embedding scheme. To determine
the suitable amount of off-tree edges to be recovered to the LSSTs, an
eigenvalue stability checking scheme is proposed, which enables to robustly
preserve the first few Laplacian eigenvectors within the sparsified graph.
Additionally, an incremental graph densification scheme is proposed for
identifying extra edges that have been missing in the original NN graphs but
can still play important roles in spectral clustering tasks. Our experimental
results for a variety of well-known data sets show that the proposed method can
dramatically reduce the complexity of NN graphs, leading to significant
speedups in spectral clustering.
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Deep-Learnt Classification of Light Curves | Astronomy light curves are sparse, gappy, and heteroscedastic. As a result
standard time series methods regularly used for financial and similar datasets
are of little help and astronomers are usually left to their own instruments
and techniques to classify light curves. A common approach is to derive
statistical features from the time series and to use machine learning methods,
generally supervised, to separate objects into a few of the standard classes.
In this work, we transform the time series to two-dimensional light curve
representations in order to classify them using modern deep learning
techniques. In particular, we show that convolutional neural networks based
classifiers work well for broad characterization and classification. We use
labeled datasets of periodic variables from CRTS survey and show how this opens
doors for a quick classification of diverse classes with several possible
exciting extensions.
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Bulk crystalline optomechanics | Brillouin processes couple light and sound through optomechanical three-wave
interactions. Within bulk solids, this coupling is mediated by the intrinsic
photo-elastic material response yielding coherent emission of high frequency
(GHz) acoustic phonons. This same interaction produces strong optical
nonlinearities that overtake both Raman or Kerr nonlinearities in practically
all solids. In this paper, we show that the strength and character of Brillouin
interactions are radically altered at low temperatures when the phonon
coherence length surpasses the system size. In this limit, the solid becomes a
coherent optomechanical system with macroscopic (cm-scale) phonon modes
possessing large ($60\ \mu \rm{g}$) motional masses. These phonon modes, which
are formed by shaping the surfaces of the crystal into a confocal phononic
resonator, yield appreciable optomechanical coupling rates (${\sim}100$ Hz),
providing access to ultra-high $Q$-factor ($4.2{\times}10^7$) phonon modes at
high ($12$ GHz) carrier frequencies. The single-pass nonlinear optical
susceptibility is enhanced from its room temperature value by more than four
orders of magnitude. Through use of bulk properties, rather than
nano-structural control, this comparatively simple approach is enticing for the
ability to engineer optomechanical coupling at high frequencies and with high
power handling. In contrast to cavity optomechanics, we show that this system
yields a unique form of dispersive symmetry breaking that enables selective
phonon heating or cooling without an optical cavity (i.e., cavity-less
optomechanics). Extending these results, practically any transparent
crystalline material can be shaped into an optomechanical system as the basis
for materials spectroscopy, new regimes of laser physics, precision metrology,
quantum information processing, and for studies of macroscopic quantum
coherence.
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Swift Linked Data Miner: Mining OWL 2 EL class expressions directly from online RDF datasets | In this study, we present Swift Linked Data Miner, an interruptible algorithm
that can directly mine an online Linked Data source (e.g., a SPARQL endpoint)
for OWL 2 EL class expressions to extend an ontology with new SubClassOf:
axioms. The algorithm works by downloading only a small part of the Linked Data
source at a time, building a smart index in the memory and swiftly iterating
over the index to mine axioms. We propose a transformation function from mined
axioms to RDF Data Shapes. We show, by means of a crowdsourcing experiment,
that most of the axioms mined by Swift Linked Data Miner are correct and can be
added to an ontology. We provide a ready to use Protégé plugin implementing
the algorithm, to support ontology engineers in their daily modeling work.
| 1 | 0 | 0 | 0 | 0 | 0 |
Political Footprints: Political Discourse Analysis using Pre-Trained Word Vectors | In this paper, we discuss how machine learning could be used to produce a
systematic and more objective political discourse analysis. Political
footprints are vector space models (VSMs) applied to political discourse. Each
of their vectors represents a word, and is produced by training the English
lexicon on large text corpora. This paper presents a simple implementation of
political footprints, some heuristics on how to use them, and their application
to four cases: the U.N. Kyoto Protocol and Paris Agreement, and two U.S.
presidential elections. The reader will be offered a number of reasons to
believe that political footprints produce meaningful results, along with some
suggestions on how to improve their implementation.
| 1 | 0 | 0 | 0 | 0 | 0 |
Predicting the Quality of Short Narratives from Social Media | An important and difficult challenge in building computational models for
narratives is the automatic evaluation of narrative quality. Quality evaluation
connects narrative understanding and generation as generation systems need to
evaluate their own products. To circumvent difficulties in acquiring
annotations, we employ upvotes in social media as an approximate measure for
story quality. We collected 54,484 answers from a crowd-powered
question-and-answer website, Quora, and then used active learning to build a
classifier that labeled 28,320 answers as stories. To predict the number of
upvotes without the use of social network features, we create neural networks
that model textual regions and the interdependence among regions, which serve
as strong benchmarks for future research. To our best knowledge, this is the
first large-scale study for automatic evaluation of narrative quality.
| 1 | 0 | 0 | 0 | 0 | 0 |
Fast Approximate Natural Gradient Descent in a Kronecker-factored Eigenbasis | Optimization algorithms that leverage gradient covariance information, such
as variants of natural gradient descent (Amari, 1998), offer the prospect of
yielding more effective descent directions. For models with many parameters,
the covariance matrix they are based on becomes gigantic, making them
inapplicable in their original form. This has motivated research into both
simple diagonal approximations and more sophisticated factored approximations
such as KFAC (Heskes, 2000; Martens & Grosse, 2015; Grosse & Martens, 2016). In
the present work we draw inspiration from both to propose a novel approximation
that is provably better than KFAC and amendable to cheap partial updates. It
consists in tracking a diagonal variance, not in parameter coordinates, but in
a Kronecker-factored eigenbasis, in which the diagonal approximation is likely
to be more effective. Experiments show improvements over KFAC in optimization
speed for several deep network architectures.
| 0 | 0 | 0 | 1 | 0 | 0 |
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