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Title: Kinetically constrained lattice gases: tagged particle diffusion,
Abstract: Kinetically constrained lattice gases (KCLG) are interacting particle systems
on the integer lattice $\mathbb Z^d$ with hard core exclusion and
Kawasaki type dynamics. Their peculiarity is that jumps are allowed only if
the configuration satisfies a constraint which asks for enough empty sites in a
certain local neighborhood. KCLG have been introduced and extensively studied
in physics literature as models of glassy dynamics. We focus on the most
studied class of KCLG, the Kob Andersen (KA) models. We analyze the behavior of
a tracer (i.e. a tagged particle) at equilibrium. We prove that for all
dimensions $d\geq 2$ and for any equilibrium particle density, under diffusive
rescaling the motion of the tracer converges to a $d$-dimensional Brownian
motion with non-degenerate diffusion matrix. Therefore we disprove the
occurrence of a diffusive/non diffusive transition which had been conjectured
in physics literature. Our technique is flexible enough and can be extended to
analyse the tracer behavior for other choices of constraints. | [
0,
1,
1,
0,
0,
0
] |
Title: An Atomistic Fingerprint Algorithm for Learning Ab Initio Molecular Force Fields,
Abstract: Molecular fingerprints, i.e. feature vectors describing atomistic
neighborhood configurations, is an important abstraction and a key ingredient
for data-driven modeling of potential energy surface and interatomic force. In
this paper, we present the Density-Encoded Canonically Aligned Fingerprint
(DECAF) fingerprint algorithm, which is robust and efficient, for fitting
per-atom scalar and vector quantities. The fingerprint is essentially a
continuous density field formed through the superimposition of smoothing
kernels centered on the atoms. Rotational invariance of the fingerprint is
achieved by aligning, for each fingerprint instance, the neighboring atoms onto
a local canonical coordinate frame computed from a kernel minisum optimization
procedure. We show that this approach is superior over PCA-based methods
especially when the atomistic neighborhood is sparse and/or contains symmetry.
We propose that the `distance' between the density fields be measured using a
volume integral of their pointwise difference. This can be efficiently computed
using optimal quadrature rules, which only require discrete sampling at a small
number of grid points. We also experiment on the choice of weight functions for
constructing the density fields, and characterize their performance for fitting
interatomic potentials. The applicability of the fingerprint is demonstrated
through a set of benchmark problems. | [
1,
1,
0,
0,
0,
0
] |
Title: Generalized 4 $\times$ 4 Matrix Formalism for Light Propagation in Anisotropic Stratified Media: Study of Surface Phonon Polaritons in Polar Dielectric Heterostructures,
Abstract: We present a generalized 4 $\times$ 4 matrix formalism for the description of
light propagation in birefringent stratified media. In contrast to previous
work, our algorithm is capable of treating arbitrarily anisotropic or
isotropic, absorbing or non-absorbing materials and is free of discontinous
solutions. We calculate the reflection and transmission coefficients and derive
equations for the electric field distribution for any number of layers. The
algorithm is easily comprehensible and can be straight forwardly implemented in
a computer program. To demonstrate the capabilities of the approach, we
calculate the reflectivities, electric field distributions, and dispersion
curves for surface phonon polaritons excited in the Otto geometry for selected
model systems, where we observe several distinct phenomena ranging from
critical coupling to mode splitting, and surface phonon polaritons in
hyperbolic media. | [
0,
1,
0,
0,
0,
0
] |
Title: Localization of hidden Chua attractors by the describing function method,
Abstract: In this paper the Chua circuit with five linear elements and saturation
non-linearity is studied. Numerical localization of self-excited attractor in
the Chua circuit model can be done by computation of trajectory with initial
data in a vicinity of an unstable equilibrium. For a hidden attractor its basin
of attraction does not overlap with a small vicinity of equilibria, so it is
difficult to find the corresponding initial data for localization. This survey
is devoted to the application of describing function method for localization of
hidden periodic and chaotic attractors in the Chua model. We use a rigorous
justification of the describing function method, based on the method of small
parameter, to get the initial data for the visualization of the hidden
attractors. A new configuration of hidden Chua attractors is presented. | [
0,
1,
0,
0,
0,
0
] |
Title: A New Unbiased and Efficient Class of LSH-Based Samplers and Estimators for Partition Function Computation in Log-Linear Models,
Abstract: Log-linear models are arguably the most successful class of graphical models
for large-scale applications because of their simplicity and tractability.
Learning and inference with these models require calculating the partition
function, which is a major bottleneck and intractable for large state spaces.
Importance Sampling (IS) and MCMC-based approaches are lucrative. However, the
condition of having a "good" proposal distribution is often not satisfied in
practice.
In this paper, we add a new dimension to efficient estimation via sampling.
We propose a new sampling scheme and an unbiased estimator that estimates the
partition function accurately in sub-linear time. Our samples are generated in
near-constant time using locality sensitive hashing (LSH), and so are
correlated and unnormalized. We demonstrate the effectiveness of our proposed
approach by comparing the accuracy and speed of estimating the partition
function against other state-of-the-art estimation techniques including IS and
the efficient variant of Gumbel-Max sampling. With our efficient sampling
scheme, we accurately train real-world language models using only 1-2% of
computations. | [
1,
0,
0,
1,
0,
0
] |
Title: Effects of interaction strength, doping, and frustration on the antiferromagnetic phase of the two-dimensional Hubbard model,
Abstract: Recent quantum-gas microscopy of ultracold atoms and scanning tunneling
microscopy of the cuprates reveal new detailed information about doped Mott
antiferromagnets, which can be compared with calculations. Using cellular
dynamical mean-field theory, we map out the antiferromagnetic (AF) phase of the
two-dimensional Hubbard model as a function of interaction strength $U$, hole
doping $\delta$ and temperature $T$. The Néel phase boundary is non-monotonic
as a function of $U$ and $\delta$. Frustration induced by second-neighbor
hopping reduces Néel order more effectively at small $U$. The doped AF is
stabilized at large $U$ by kinetic energy and at small $U$ by potential energy.
The transition between the AF insulator and the doped metallic AF is
continuous. At large $U$, we find in-gap states similar to those observed in
scanning tunneling microscopy. We predict that, contrary to the Hubbard bands,
these states are only slightly spin polarized. | [
0,
1,
0,
0,
0,
0
] |
Title: A Coupled Lattice Boltzmann Method and Discrete Element Method for Discrete Particle Simulations of Particulate Flows,
Abstract: Discrete particle simulations are widely used to study large-scale
particulate flows in complex geometries where particle-particle and
particle-fluid interactions require an adequate representation but the
computational cost has to be kept low. In this work, we present a novel
coupling approach for such simulations. A lattice Boltzmann formulation of the
generalized Navier-Stokes equations is used to describe the fluid motion. This
promises efficient simulations suitable for high performance computing and,
since volume displacement effects by the solid phase are considered, our
approach is also applicable to non-dilute particulate systems. The discrete
element method is combined with an explicit evaluation of interparticle
lubrication forces to simulate the motion of individual submerged particles.
Drag, pressure and added mass forces determine the momentum transfer by
fluid-particle interactions. A stable coupling algorithm is presented and
discussed in detail. We demonstrate the validity of our approach for dilute as
well as dense systems by predicting the settling velocity of spheres over a
broad range of solid volume fractions in good agreement with semi-empirical
correlations. Additionally, the accuracy of particle-wall interactions in a
viscous fluid is thoroughly tested and established. Our approach can thus be
readily used for various particulate systems and can be extended
straightforward to e.g. non-spherical particles. | [
1,
1,
0,
0,
0,
0
] |
Title: The MERger-event Gamma-Ray (MERGR) Telescope,
Abstract: We describe the MERger-event Gamma-Ray (MERGR) Telescope intended for
deployment by ~2021. MERGR will cover from 20 keV to 2 MeV with a wide field of
view (6 sr) using nineteen gamma-ray detectors arranged on a section of a
sphere. The telescope will work as a standalone system or as part of a network
of sensors, to increase by ~50% the current sky coverage to detect short
Gamma-Ray Burst (SGRB) counterparts to neutron-star binary mergers within the
~200 Mpc range of gravitational wave detectors in the early 2020's. Inflight
software will provide realtime burst detections with mean localization
uncertainties of 6 deg for a photon fluence of 5 ph cm^-2 (the mean fluence of
Fermi-GBM SGRBs) and <3 deg for the brightest ~5% of SGRBs to enable rapid
multi-wavelength follow-up to identify a host galaxy and its redshift. To
minimize cost and time to first light, MERGR is directly derived from
demonstrators designed and built at NRL for the DoD Space Test Program (STP).
We argue that the deployment of a network that provides all-sky coverage for
SGRB detection is of immediate urgency to the multi-messenger astrophysics
community. | [
0,
1,
0,
0,
0,
0
] |
Title: Graham-Witten's conformal invariant for closed four dimensional submanifolds,
Abstract: It was proved by Graham and Witten in 1999 that conformal invariants of
submanifolds can be obtained via volume renormalization of minimal surfaces in
conformally compact Einstein manifolds. The conformal invariant of a
submanifold $\Sigma$ is contained in the volume expansion of the minimal
surface which is asymptotic to $\Sigma$ when the minimal surface approaches the
conformaly infinity. In the paper we give the explicit expression of
Graham-Witten's conformal invariant for closed four dimensional submanifolds
and find critical points of the conformal invariant in the case of Euclidean
ambient spaces. | [
0,
0,
1,
0,
0,
0
] |
Title: Computationally Inferred Genealogical Networks Uncover Long-Term Trends in Assortative Mating,
Abstract: Genealogical networks, also known as family trees or population pedigrees,
are commonly studied by genealogists wanting to know about their ancestry, but
they also provide a valuable resource for disciplines such as digital
demography, genetics, and computational social science. These networks are
typically constructed by hand through a very time-consuming process, which
requires comparing large numbers of historical records manually. We develop
computational methods for automatically inferring large-scale genealogical
networks. A comparison with human-constructed networks attests to the accuracy
of the proposed methods. To demonstrate the applicability of the inferred
large-scale genealogical networks, we present a longitudinal analysis on the
mating patterns observed in a network. This analysis shows a consistent
tendency of people choosing a spouse with a similar socioeconomic status, a
phenomenon known as assortative mating. Interestingly, we do not observe this
tendency to consistently decrease (nor increase) over our study period of 150
years. | [
1,
0,
0,
0,
1,
0
] |
Title: Nonlinear stability for the Maxwell--Born--Infeld system on a Schwarzschild background,
Abstract: In this paper we prove small data global existence for solutions to the
Maxwell--Born--Infeld (MBI) system on a fixed Schwarzschild background. This
system has appeared in the context of string theory and can be seen as a
nonlinear model problem for the stability of the background metric itself, due
to its tensorial and quasilinear nature. The MBI system models nonlinear
electromagnetism and does not display birefringence. The key element in our
proof lies in the observation that there exists a first-order differential
transformation which brings solutions of the spin $\pm 1$ Teukolsky equations,
satisfied by the extreme components of the field, into solutions of a "good"
equation (the Fackerell--Ipser Equation). This strategy was established in [F.
Pasqualotto, The spin $\pm 1$ Teukolsky equations and the Maxwell system on
Schwarzschild, Preprint 2016, arXiv:1612.07244] for the linear Maxwell field on
Schwarzschild. We show that analogous Fackerell--Ipser equations hold for the
MBI system on a fixed Schwarzschild background, which are however nonlinearly
coupled. To essentially decouple these right hand sides, we setup a bootstrap
argument. We use the $r^p$ method of Dafermos and Rodnianski in [M. Dafermos
and I. Rodnianski, A new physical-space approach to decay for the wave equation
with applications to black hole spacetimes, in XVIth International Congress on
Mathematical Physics, Pavel Exner ed., Prague 2009 pp. 421-433, 2009,
arXiv:0910.4957] in order to deduce decay of some null components, and we infer
decay for the remaining quantities by integrating the MBI system as transport
equations. | [
0,
0,
1,
0,
0,
0
] |
Title: Numerical simulation of BOD5 dynamics in Igapó I lake, Londrina, Paraná, Brazil: Experimental measurement and mathematical modeling,
Abstract: The concentration of biochemical oxygen demand, BOD5, was studied in order to
evaluate the water quality of the Igapó I Lake, in Londrina, Paraná State,
Brazil. The simulation was conducted by means of the discretization in
curvilinear coordinates of the geometry of Igapó I Lake, together with finite
difference and finite element methods. The evaluation of the proposed numerical
model for water quality was performed by comparing the experimental values of
BOD5 with the numerical results. The evaluation of the model showed
quantitative results compatible with the actual behavior of Igapó I Lake in
relation to the simulated parameter. The qualitative analysis of the numerical
simulations provided a better understanding of the dynamics of the BOD5
concentration at Igapó I Lake, showing that such concentrations in the
central regions of the lake have values above those allowed by Brazilian law.
The results can help to guide choices by public officials, as: (i) improve the
identification mechanisms of pollutant emitters on Lake Igapó I, (ii)
contribute to the optimal treatment of the recovery of the polluted environment
and (iii) provide a better quality of life for the regulars of the lake as well
as for the residents living on the lakeside. | [
0,
0,
0,
0,
1,
0
] |
Title: Sensitivity Analysis for matched pair analysis of binary data: From worst case to average case analysis,
Abstract: In matched observational studies where treatment assignment is not
randomized, sensitivity analysis helps investigators determine how sensitive
their estimated treatment effect is to some unmeasured con- founder. The
standard approach calibrates the sensitivity analysis according to the worst
case bias in a pair. This approach will result in a conservative sensitivity
analysis if the worst case bias does not hold in every pair. In this paper, we
show that for binary data, the standard approach can be calibrated in terms of
the average bias in a pair rather than worst case bias. When the worst case
bias and average bias differ, the average bias interpretation results in a less
conservative sensitivity analysis and more power. In many studies, the average
case calibration may also carry a more natural interpretation than the worst
case calibration and may also allow researchers to incorporate additional data
to establish an empirical basis with which to calibrate a sensitivity analysis.
We illustrate this with a study of the effects of cellphone use on the
incidence of automobile accidents. Finally, we extend the average case
calibration to the sensitivity analysis of confidence intervals for
attributable effects. | [
0,
0,
0,
1,
0,
0
] |
Title: Closed Sets and Operators thereon: Representations, Computability and Complexity,
Abstract: The TTE approach to Computable Analysis is the study of so-called
representations (encodings for continuous objects such as reals, functions, and
sets) with respect to the notions of computability they induce. A rich variety
of such representations had been devised over the past decades, particularly
regarding closed subsets of Euclidean space plus subclasses thereof (like
compact subsets). In addition, they had been compared and classified with
respect to both non-uniform computability of single sets and uniform
computability of operators on sets. In this paper we refine these
investigations from the point of view of computational complexity. Benefiting
from the concept of second-order representations and complexity recently
devised by Kawamura & Cook (2012), we determine parameterized complexity bounds
for operators such as union, intersection, projection, and more generally
function image and inversion. By indicating natural parameters in addition to
the output precision, we get a uniform view on results by Ko (1991-2013),
Braverman (2004/05) and Zhao & Müller (2008), relating these problems to the
P/UP/NP question in discrete complexity theory. | [
1,
0,
1,
0,
0,
0
] |
Title: Propensity score estimation using classification and regression trees in the presence of missing covariate data,
Abstract: Data mining and machine learning techniques such as classification and
regression trees (CART) represent a promising alternative to conventional
logistic regression for propensity score estimation. Whereas incomplete data
preclude the fitting of a logistic regression on all subjects, CART is
appealing in part because some implementations allow for incomplete records to
be incorporated in the tree fitting and provide propensity score estimates for
all subjects. Based on theoretical considerations, we argue that the automatic
handling of missing data by CART may however not be appropriate. Using a series
of simulation experiments, we examined the performance of different approaches
to handling missing covariate data; (i) applying the CART algorithm directly to
the (partially) incomplete data, (ii) complete case analysis, and (iii)
multiple imputation. Performance was assessed in terms of bias in estimating
exposure-outcome effects \add{among the exposed}, standard error, mean squared
error and coverage. Applying the CART algorithm directly to incomplete data
resulted in bias, even in scenarios where data were missing completely at
random. Overall, multiple imputation followed by CART resulted in the best
performance. Our study showed that automatic handling of missing data in CART
can cause serious bias and does not outperform multiple imputation as a means
to account for missing data. | [
0,
0,
0,
1,
0,
0
] |
Title: Deciding some Maltsev conditions in finite idempotent algebras,
Abstract: In this paper we investigate the computational complexity of deciding if a
given finite algebraic structure satisfies a fixed (strong) Maltsev condition
$\Sigma$. Our goal in this paper is to show that $\Sigma$-testing can be
accomplished in polynomial time when the algebras tested are idempotent and the
Maltsev condition $\Sigma$ can be described using paths. Examples of such path
conditions are having a Maltsev term, having a majority operation, and having a
chain of Jónsson (or Gumm) terms of fixed length. | [
1,
0,
1,
0,
0,
0
] |
Title: MF traces and the Cuntz semigroup,
Abstract: A trace $\tau$ on a separable C*-algebra $A$ is called matricial field (MF)
if there is a trace-preserving morphism from $A$ to $Q_\omega$, where
$Q_\omega$ denotes the norm ultrapower of the universal UHF-algebra $Q$. In
general, the trace $\tau$ induces a state on the Cuntz semigroup $Cu(A)$. We
show there is always a state-preserving morphism from $Cu(A)$ to
$Cu(Q_\omega)$.
As an application, if $A$ is an AI-algebra and $F$ is a free group acting on
$A$, then every trace on the reduced crossed product $A \rtimes F$ is MF. This
further implies the same result when $A$ is an AH-algebra with the ideal
property such that $K_1(A)$ is a torsion group. We also use this to
characterize when $A \rtimes F$ is MF (i.e. admits an isometric morphism into
$Q_\omega$) for many simple, nuclear C*-algebras $A$. | [
0,
0,
1,
0,
0,
0
] |
Title: Matrix product states for topological phases with parafermions,
Abstract: In the Fock representation, we propose a framework to construct the
generalized matrix product states (MPS) for topological phases with $\mathbb{
Z}_{p}$ parafermions. Unlike the $\mathbb{Z}_{2}$ Majorana fermions, the $%
\mathbb{Z}_{p}$ parafermions form intrinsically interacting systems. Here we
explicitly construct two topologically distinct classes of irreducible $%
\mathbb{Z}_{3}$ parafermionic MPS wave functions, characterized by one or two
parafermionic zero modes at each end of an open chain. Their corresponding
parent Hamiltonians are found as the fixed point models of the single
$\mathbb{Z}_{3}$ parafermion chain and two-coupled parafermion chains with
$\mathbb{Z}_{3}\times \mathbb{Z}_{3}$ symmetry. Our results thus pave the road
to investigate all possible topological phases with $\mathbb{Z}_{p}$
parafermions within the matrix product representation in one dimension. | [
0,
1,
0,
0,
0,
0
] |
Title: F-TRIDYN: A Binary Collision Approximation Code for Simulating Ion Interactions with Rough Surfaces,
Abstract: Fractal TRIDYN (F-TRIDYN) is a modified version of the widely used Monte
Carlo, Binary Collision Approximation code TRIDYN that includes an explicit
model of surface roughness and additional output modes for coupling to plasma
edge and material codes. Surface roughness plays an important role in ion
irradiation processes such as sputtering; roughness can significantly increase
the angle of maximum sputtering and change the maximum observed sputtering
yield by a factor of 2 or more. The complete effect of surface roughness on
sputtering and other ion irradiation phenomena is not completely understood.
Many rough surfaces can be consistently and realistically modeled by fractals,
using the fractal dimension and fractal length scale as the sole input
parameters. F-TRIDYN includes a robust fractal surface algorithm that is more
computationally efficient than those in previous fractal codes and which
reproduces available experimental sputtering data from rough surfaces. Fractals
provide a compelling path toward a complete and concise understanding of the
effect that surface geometry plays on the behavior of plasma-facing materials.
F-TRIDYN is a flexible code for simulating ion-solid interactions and coupling
to plasma and material codes for multiscale modeling. | [
0,
1,
0,
0,
0,
0
] |
Title: Measurement of Radon Concentration in Super-Kamiokande's Buffer Gas,
Abstract: To precisely measure radon concentrations in purified air supplied to the
Super-Kamiokande detector as a buffer gas, we have developed a highly sensitive
radon detector with an intrinsic background as low as 0.33$\pm$0.07
mBq/m$^{3}$. In this article, we discuss the construction and calibration of
this detector as well as results of its application to the measurement and
monitoring of the buffer gas layer above Super-Kamiokande. In March 2013, the
chilled activated charcoal system used to remove radon in the input buffer gas
was upgraded. After this improvement, a dramatic reduction in the radon
concentration of the supply gas down to 0.08 $\pm$ 0.07 mBq/m$^{3}$.
Additionally, the Rn concentration of the in-situ buffer gas has been measured
28.8$\pm$1.7 mBq/m$^{3}$ using the new radon detector. Based on these
measurements we have determined that the dominant source of Rn in the buffer
gas arises from contamination from the Super-Kamiokande tank itself. | [
0,
1,
0,
0,
0,
0
] |
Title: On The Robustness of a Neural Network,
Abstract: With the development of neural networks based machine learning and their
usage in mission critical applications, voices are rising against the
\textit{black box} aspect of neural networks as it becomes crucial to
understand their limits and capabilities. With the rise of neuromorphic
hardware, it is even more critical to understand how a neural network, as a
distributed system, tolerates the failures of its computing nodes, neurons, and
its communication channels, synapses. Experimentally assessing the robustness
of neural networks involves the quixotic venture of testing all the possible
failures, on all the possible inputs, which ultimately hits a combinatorial
explosion for the first, and the impossibility to gather all the possible
inputs for the second.
In this paper, we prove an upper bound on the expected error of the output
when a subset of neurons crashes. This bound involves dependencies on the
network parameters that can be seen as being too pessimistic in the average
case. It involves a polynomial dependency on the Lipschitz coefficient of the
neurons activation function, and an exponential dependency on the depth of the
layer where a failure occurs. We back up our theoretical results with
experiments illustrating the extent to which our prediction matches the
dependencies between the network parameters and robustness. Our results show
that the robustness of neural networks to the average crash can be estimated
without the need to neither test the network on all failure configurations, nor
access the training set used to train the network, both of which are
practically impossible requirements. | [
1,
0,
0,
1,
0,
0
] |
Title: The Cut Elimination and the Nonlengthening Property for the Sequent Calculus with Equality,
Abstract: We show how Leibnitz.s indiscernibility principle and Gentzen's original work
lead to extensions of the sequent calculus to first order logic with equality
and investigate the cut elimination property. Furthermore we discuss and
improve the nonlengthening property of Lifshitz and Orevkov. | [
1,
0,
1,
0,
0,
0
] |
Title: On the Whittaker Plancherel Theorem for Real Reductive Groups,
Abstract: The main purpose of this article is to fix several aspects aspects of the
proof of the Whittaker Plancherel Theorem in Real Reductive Groups II that are
affected by recently observed errors or gaps . In the process of completing the
proof of the theorem the paper also gives an exposition of its structure, and
adds some clarifying new results. It also outlines the steps in the proof of
the Harish-Chandra Plancherel theorem as they are needed in our proof of the
Whittaker version. | [
0,
0,
1,
0,
0,
0
] |
Title: Poisson distribution for gaps between sums of two squares and level spacings for toral point scatterers,
Abstract: We investigate the level spacing distribution for the quantum spectrum of the
square billiard. Extending work of Connors--Keating, and Smilansky, we
formulate an analog of the Hardy--Littlewood prime $k$-tuple conjecture for
sums of two squares, and show that it implies that the spectral gaps, after
removing degeneracies and rescaling, are Poisson distributed. Consequently, by
work of Rudnick and Ueberschär, the level spacings of arithmetic toral point
scatterers, in the weak coupling limit, are also Poisson distributed. We also
give numerical evidence for the conjecture and its implications. | [
0,
1,
1,
0,
0,
0
] |
Title: Maria Krawczyk: friend and physicist,
Abstract: With this note, we remember our friend Maria Krawczyk, who passed away this
year, on May 24th. We briefly outline some of her physics interests and main
accomplishments, and her great human and moral qualities. | [
0,
1,
0,
0,
0,
0
] |
Title: Neural Networks Regularization Through Class-wise Invariant Representation Learning,
Abstract: Training deep neural networks is known to require a large number of training
samples. However, in many applications only few training samples are available.
In this work, we tackle the issue of training neural networks for
classification task when few training samples are available. We attempt to
solve this issue by proposing a new regularization term that constrains the
hidden layers of a network to learn class-wise invariant representations. In
our regularization framework, learning invariant representations is generalized
to the class membership where samples with the same class should have the same
representation. Numerical experiments over MNIST and its variants showed that
our proposal helps improving the generalization of neural network particularly
when trained with few samples. We provide the source code of our framework
this https URL . | [
1,
0,
0,
1,
0,
0
] |
Title: Incorporating Global Visual Features into Attention-Based Neural Machine Translation,
Abstract: We introduce multi-modal, attention-based neural machine translation (NMT)
models which incorporate visual features into different parts of both the
encoder and the decoder. We utilise global image features extracted using a
pre-trained convolutional neural network and incorporate them (i) as words in
the source sentence, (ii) to initialise the encoder hidden state, and (iii) as
additional data to initialise the decoder hidden state. In our experiments, we
evaluate how these different strategies to incorporate global image features
compare and which ones perform best. We also study the impact that adding
synthetic multi-modal, multilingual data brings and find that the additional
data have a positive impact on multi-modal models. We report new
state-of-the-art results and our best models also significantly improve on a
comparable phrase-based Statistical MT (PBSMT) model trained on the Multi30k
data set according to all metrics evaluated. To the best of our knowledge, it
is the first time a purely neural model significantly improves over a PBSMT
model on all metrics evaluated on this data set. | [
1,
0,
0,
0,
0,
0
] |
Title: Complete Subgraphs of the Coprime Hypergraph of Integers III: Construction,
Abstract: The coprime hypergraph of integers on $n$ vertices $CHI_k(n)$ is defined via
vertex set $\{1,2,\dots,n\}$ and hyperedge set
$\{\{v_1,v_2,\dots,v_{k+1}\}\subseteq\{1,2,\dots,n\}:\gcd(v_1,v_2,\dots,v_{k+1})=1\}$.
In this article we present ideas on how to construct maximal subgraphs in
$CHI_k(n)$. This continues the author's earlier work, which dealt with bounds
on the size and structural properties of these subgraphs. We succeed in the
cases $k\in\{1,2,3\}$ and give promising ideas for $k\geq 4$. | [
0,
0,
1,
0,
0,
0
] |
Title: Involvement of Surfactant Protein D in Ebola Virus Infection Enhancement via Glycoprotein Interaction,
Abstract: Since the largest 2014-2016 Ebola virus disease outbreak in West Africa,
understanding of Ebola virus infection has improved, notably the involvement of
innate immune mediators. Amongst them, collectins are important players in the
antiviral innate immune defense. A screening of Ebola glycoprotein
(GP)-collectins interactions revealed the specific interaction of human
surfactant protein D (hSP-D), a lectin expressed in lung and liver, two
compartments where Ebola was found in vivo. Further analyses have demonstrated
an involvement of hSP-D in the enhancement of virus infection in several in
vitro models. Similar effects were observed for porcine SP-D (pSP-D). In
addition, both hSP-D and pSP-D interacted with Reston virus (RESTV) GP and
enhanced pseudoviral infection in pulmonary cells. Thus, our study reveals a
novel partner of Ebola GP that may participate to enhance viral spread. | [
0,
0,
0,
0,
1,
0
] |
Title: Deep Approximately Orthogonal Nonnegative Matrix Factorization for Clustering,
Abstract: Nonnegative Matrix Factorization (NMF) is a widely used technique for data
representation. Inspired by the expressive power of deep learning, several NMF
variants equipped with deep architectures have been proposed. However, these
methods mostly use the only nonnegativity while ignoring task-specific features
of data. In this paper, we propose a novel deep approximately orthogonal
nonnegative matrix factorization method where both nonnegativity and
orthogonality are imposed with the aim to perform a hierarchical clustering by
using different level of abstractions of data. Experiment on two face image
datasets showed that the proposed method achieved better clustering performance
than other deep matrix factorization methods and state-of-the-art single layer
NMF variants. | [
1,
0,
0,
0,
0,
0
] |
Title: Separation-Free Super-Resolution from Compressed Measurements is Possible: an Orthonormal Atomic Norm Minimization Approach,
Abstract: We consider the problem of recovering the superposition of $R$ distinct
complex exponential functions from compressed non-uniform time-domain samples.
Total Variation (TV) minimization or atomic norm minimization was proposed in
the literature to recover the $R$ frequencies or the missing data. However, it
is known that in order for TV minimization and atomic norm minimization to
recover the missing data or the frequencies, the underlying $R$ frequencies are
required to be well-separated, even when the measurements are noiseless. This
paper shows that the Hankel matrix recovery approach can super-resolve the $R$
complex exponentials and their frequencies from compressed non-uniform
measurements, regardless of how close their frequencies are to each other. We
propose a new concept of orthonormal atomic norm minimization (OANM), and
demonstrate that the success of Hankel matrix recovery in separation-free
super-resolution comes from the fact that the nuclear norm of a Hankel matrix
is an orthonormal atomic norm. More specifically, we show that, in traditional
atomic norm minimization, the underlying parameter values $\textbf{must}$ be
well separated to achieve successful signal recovery, if the atoms are changing
continuously with respect to the continuously-valued parameter. In contrast,
for the OANM, it is possible the OANM is successful even though the original
atoms can be arbitrarily close.
As a byproduct of this research, we provide one matrix-theoretic inequality
of nuclear norm, and give its proof from the theory of compressed sensing. | [
1,
0,
0,
0,
0,
0
] |
Title: A note on MCMC for nested multilevel regression models via belief propagation,
Abstract: In the quest for scalable Bayesian computational algorithms we need to
exploit the full potential of existing methodologies. In this note we point out
that message passing algorithms, which are very well developed for inference in
graphical models, appear to be largely unexplored for scalable inference in
Bayesian multilevel regression models. We show that nested multilevel
regression models with Gaussian errors lend themselves very naturally to the
combined use of belief propagation and MCMC. Specifically, the posterior
distribution of the regression parameters conditionally on covariance
hyperparameters is a high-dimensional Gaussian that can be sampled exactly (as
well as marginalized) using belief propagation at a cost that scales linearly
in the number of parameters and data. We derive an algorithm that works
efficiently even for conditionally singular Gaussian distributions, e.g., when
there are linear constraints between the parameters at different levels. We
show that allowing for such non-invertible Gaussians is critical for belief
propagation to be applicable to a large class of nested multilevel models. From
a different perspective, the methodology proposed can be seen as a
generalization of forward-backward algorithms for sampling to multilevel
regressions with tree-structure graphical models, as opposed to single-branch
trees used in classical Kalman filter contexts. | [
0,
0,
0,
1,
0,
0
] |
Title: InGaN Metal-IN Solar Cell: optimized efficiency and fabrication tolerance,
Abstract: Choosing the Indium Gallium Nitride (InGaN) ternary alloy for thin films
solar cells might yield high benefits concerning efficiency and reliability,
because its bandgap can be tuned through the Indium composition and radiations
have little destructive effect on it. It may also reveal challenges because
good quality p-doped InGaN layers are difficult to elaborate. In this letter, a
new design for an InGaN thin film solar cell is optimized, where the player of
a PIN structure is replaced by a Schottky contact, leading to a Metal-IN (MIN)
structure. With a simulated efficiency of 19.8%, the MIN structure performs
better than the previously studied Schottky structure, while increasing its
fabrication tolerance and thus functional reliability a. Owing to its good
tolerance to radiations [1], its high light absorption [2, 3] and its
Indium-composition-tuned bandgap [4, 5], the Indium Gallium Nitride (InGaN)
ternary alloy is a good candidate for high-efficiency-high-reliability solar
cells able to operate in harsh environments. Unfortunately, InGaN p-doping is
still a challenge, owing to InGaN residual n-doping [6], the lack of dedicated
ac-ceptors [7] and the complex fabrication process itself [8, 9]. To these
drawbacks can be added the uneasy fabrication of ohmic contacts [4] and the
difficulty to grow the high-quality-high-Indium-content thin films [10] which
would be needed to cover the whole solar spectrum. These drawbacks still
prevent InGaN solar cells to be competitive with other well established III-V
and silicon technologies [11]. In this letter, is proposed a new Metal-IN (MIN)
InGaN solar cell structure where the InGaN p-doped layer is removed and
replaced by a Schottky contact, lifting one of the above mentioned drawbacks. A
set of realistic physical models based on actual measurements is used to
simulate and optimize its behavior and performance using mathematically
rigorous multi-criteria optimization methods, aiming to show that both
efficiency and fabrication tolerances are better than the previously described
simple InGaN Schottky solar cell [12]. | [
0,
1,
0,
0,
0,
0
] |
Title: Proceedings of the IJCAI 2017 Workshop on Learning in the Presence of Class Imbalance and Concept Drift (LPCICD'17),
Abstract: With the wide application of machine learning algorithms to the real world,
class imbalance and concept drift have become crucial learning issues. Class
imbalance happens when the data categories are not equally represented, i.e.,
at least one category is minority compared to other categories. It can cause
learning bias towards the majority class and poor generalization. Concept drift
is a change in the underlying distribution of the problem, and is a significant
issue specially when learning from data streams. It requires learners to be
adaptive to dynamic changes.
Class imbalance and concept drift can significantly hinder predictive
performance, and the problem becomes particularly challenging when they occur
simultaneously. This challenge arises from the fact that one problem can affect
the treatment of the other. For example, drift detection algorithms based on
the traditional classification error may be sensitive to the imbalanced degree
and become less effective; and class imbalance techniques need to be adaptive
to changing imbalance rates, otherwise the class receiving the preferential
treatment may not be the correct minority class at the current moment.
Therefore, the mutual effect of class imbalance and concept drift should be
considered during algorithm design.
The aim of this workshop is to bring together researchers from the areas of
class imbalance learning and concept drift in order to encourage discussions
and new collaborations on solving the combined issue of class imbalance and
concept drift. It provides a forum for international researchers and
practitioners to share and discuss their original work on addressing new
challenges and research issues in class imbalance learning, concept drift, and
the combined issues of class imbalance and concept drift. The proceedings
include 8 papers on these topics. | [
1,
0,
0,
0,
0,
0
] |
Title: Nonlinear dynamics on branched structures and networks,
Abstract: Nonlinear dynamics on graphs has rapidly become a topical issue with many
physical applications, ranging from nonlinear optics to Bose-Einstein
condensation. Whenever in a physical experiment a ramified structure is
involved, it can prove useful to approximate such a structure by a metric
graph, or network. For the Schroedinger equation it turns out that the sixth
power in the nonlinear term of the energy is critical in the sense that below
that power the constrained energy is lower bounded irrespectively of the value
of the mass (subcritical case). On the other hand, if the nonlinearity power
equals six, then the lower boundedness depends on the value of the mass: below
a critical mass, the constrained energy is lower bounded, beyond it, it is not.
For powers larger than six the constrained energy functional is never lower
bounded, so that it is meaningless to speak about ground states (supercritical
case). These results are the same as in the case of the nonlinear Schrodinger
equation on the real line. In fact, as regards the existence of ground states,
the results for systems on graphs differ, in general, from the ones for systems
on the line even in the subcritical case: in the latter case, whenever the
constrained energy is lower bounded there always exist ground states (the
solitons, whose shape is explicitly known), whereas for graphs the existence of
a ground state is not guaranteed. For the critical case, our results show a
phenomenology much richer than the analogous on the line. | [
0,
0,
1,
0,
0,
0
] |
Title: Rank modulation codes for DNA storage,
Abstract: Synthesis of DNA molecules offers unprecedented advances in storage
technology. Yet, the microscopic world in which these molecules reside induces
error patterns that are fundamentally different from their digital
counterparts. Hence, to maintain reliability in reading and writing, new coding
schemes must be developed. In a reading technique called shotgun sequencing, a
long DNA string is read in a sliding window fashion, and a profile vector is
produced. It was recently suggested by Kiah et al. that such a vector can
represent the permutation which is induced by its entries, and hence a
rank-modulation scheme arises. Although this interpretation suggests high error
tolerance, it is unclear which permutations are feasible, and how to produce a
DNA string whose profile vector induces a given permutation. In this paper, by
observing some necessary conditions, an upper bound for the number of feasible
permutations is given. Further, a technique for deciding the feasibility of a
permutation is devised. By using insights from this technique, an algorithm for
producing a considerable number of feasible permutations is given, which
applies to any alphabet size and any window length. | [
1,
0,
0,
0,
0,
0
] |
Title: Effect of ion motion on relativistic electron beam driven wakefield in a cold plasma,
Abstract: Excitation of relativistic electron beam driven wakefield in a cold plasma is
studied using 1-D fluid simulation techniques where the effect of ion motion is
included. We have excited the wakefield using a ultra-relativistic,
homogeneous, rigid electron beam with different beam densities and mass-ratios
(ratio of electron's to ion's mass). We have shown that the numerically excited
wakefield is in a good agreement with the analytical results of Rosenzweig et
al. \textcolor{blue}{[Physical Review A. 40, 9, (1989)]} for several plasma
periods. It is shown here that the excited wake wave is equivalent to the
corresponding "Khachatryan mode" \textcolor{blue}{[Physical Review E. 58, 6,
(1998)]}. After several plasma periods, it is found that the excited wake wave
gradually modifies and finally breaks, exhibiting sharp spikes in density and
sawtooth like structure in electric field profile. It is shown here that the
excited wake wave breaks much below the Khachatryan's wave breaking limit. | [
0,
1,
0,
0,
0,
0
] |
Title: Memory-efficient Kernel PCA via Partial Matrix Sampling and Nonconvex Optimization: a Model-free Analysis of Local Minima,
Abstract: Kernel PCA is a widely used nonlinear dimension reduction technique in
machine learning, but storing the kernel matrix is notoriously challenging when
the sample size is large. Inspired by Yi et al. [2016], where the idea of
partial matrix sampling followed by nonconvex optimization is proposed for
matrix completion and robust PCA, we apply a similar approach to
memory-efficient Kernel PCA. In theory, with no assumptions on the kernel
matrix in terms of eigenvalues or eigenvectors, we established a model-free
theory for the low-rank approximation based on any local minimum of the
proposed objective function. As interesting byproducts, when the underlying
positive semidefinite matrix is assumed to be low-rank and highly structured,
corollaries of our main theorem improve the state-of-the-art results of Ge et
al. [2016, 2017] for nonconvex matrix completion with no spurious local minima.
Numerical experiments also show that our approach is competitive in terms of
approximation accuracy compared to the well-known Nyström algorithm for
Kernel PCA. | [
1,
0,
0,
1,
0,
0
] |
Title: Scalable Graph Learning for Anti-Money Laundering: A First Look,
Abstract: Organized crime inflicts human suffering on a genocidal scale: the Mexican
drug cartels have murdered 150,000 people since 2006, upwards of 700,000 people
per year are "exported" in a human trafficking industry enslaving an estimated
40 million people. These nefarious industries rely on sophisticated money
laundering schemes to operate. Despite tremendous resources dedicated to
anti-money laundering (AML) only a tiny fraction of illicit activity is
prevented. The research community can help. In this brief paper, we map the
structural and behavioral dynamics driving the technical challenge. We review
AML methods, current and emergent. We provide a first look at scalable graph
convolutional neural networks for forensic analysis of financial data, which is
massive, dense, and dynamic. We report preliminary experimental results using a
large synthetic graph (1M nodes, 9M edges) generated by a data simulator we
created called AMLSim. We consider opportunities for high performance
efficiency, in terms of computation and memory, and we share results from a
simple graph compression experiment. Our results support our working hypothesis
that graph deep learning for AML bears great promise in the fight against
criminal financial activity. | [
1,
0,
0,
0,
0,
0
] |
Title: Complete event-by-event $α$/$γ(β)$ separation in a full-size TeO$_2$ CUORE bolometer by Neganov-Luke-magnified light detection,
Abstract: In the present work, we describe the results obtained with a large ($\approx
133$ cm$^3$) TeO$_2$ bolometer, with a view to a search for neutrinoless
double-beta decay ($0\nu\beta\beta$) of $^{130}$Te. We demonstrate an efficient
$\alpha$ particle discrimination (99.9\%) with a high acceptance of the
$0\nu\beta\beta$ signal (about 96\%), expected at $\approx 2.5$ MeV. This
unprecedented result was possible thanks to the superior performance (10 eV rms
baseline noise) of a Neganov-Luke-assisted germanium bolometer used to detect a
tiny (70 eV) light signal from the TeO$_2$ detector, dominated by
$\gamma$($\beta$)-induced Cherenkov radiation but exhibiting also a clear
scintillation component. The obtained results represent a major breakthrough
towards the TeO$_2$-based version of CUORE Upgrade with Particle IDentification
(CUPID), a ton-scale cryogenic $0\nu\beta\beta$ experiment proposed as a
follow-up to the CUORE project with particle identification. The CUORE
experiment began recently a search for neutrinoless double-beta decay of
$^{130}$Te with an array of 988 125-cm$^3$ TeO$_2$ bolometers. The lack of
$\alpha$ discrimination in CUORE makes $\alpha$ decays at the detector surface
the dominant background component, at the level of $\approx 0.01$ counts/(keV
kg y) in the region of interest. We show here, for the first time with a
CUORE-size bolometer and using the same technology as CUORE for the readout of
both heat and light signals, that surface $\alpha$ background can be fully
rejected. | [
0,
1,
0,
0,
0,
0
] |
Title: A Novel Bayesian Multiple Testing Approach to Deregulated miRNA Discovery Harnessing Positional Clustering,
Abstract: MicroRNAs (miRNAs) are small non-coding RNAs that function as regulators of
gene expression. In recent years, there has been a tremendous and growing
interest among researchers to investigate the role of miRNAs in normal cellular
as well as in disease processes. Thus to investigate the role of miRNAs in oral
cancer, we analyse the expression levels of miRNAs to identify miRNAs with
statistically significant differential expression in cancer tissues.
In this article, we propose a novel Bayesian hierarchical model of miRNA
expression data. Compelling evidences have demonstrated that the transcription
process of miRNAs in human genome is a latent process instrumental for the
observed expression levels. We take into account positional clustering of the
miRNAs in the analysis and model the latent transcription phenomenon
nonparametrically by an appropriate Gaussian process.
For the testing purpose we employ a novel Bayesian multiple testing method
where we mainly focus on utilizing the dependence structure between the
hypotheses for better results, while also ensuring optimality in many respects.
Indeed, our non-marginal method yielded results in accordance with the
underlying scientific knowledge which are found to be missed by the very
popular Benjamini-Hochberg method. | [
0,
0,
0,
1,
0,
0
] |
Title: PCN: Point Completion Network,
Abstract: Shape completion, the problem of estimating the complete geometry of objects
from partial observations, lies at the core of many vision and robotics
applications. In this work, we propose Point Completion Network (PCN), a novel
learning-based approach for shape completion. Unlike existing shape completion
methods, PCN directly operates on raw point clouds without any structural
assumption (e.g. symmetry) or annotation (e.g. semantic class) about the
underlying shape. It features a decoder design that enables the generation of
fine-grained completions while maintaining a small number of parameters. Our
experiments show that PCN produces dense, complete point clouds with realistic
structures in the missing regions on inputs with various levels of
incompleteness and noise, including cars from LiDAR scans in the KITTI dataset. | [
1,
0,
0,
0,
0,
0
] |
Title: Deep Relaxation: partial differential equations for optimizing deep neural networks,
Abstract: In this paper we establish a connection between non-convex optimization
methods for training deep neural networks and nonlinear partial differential
equations (PDEs). Relaxation techniques arising in statistical physics which
have already been used successfully in this context are reinterpreted as
solutions of a viscous Hamilton-Jacobi PDE. Using a stochastic control
interpretation allows we prove that the modified algorithm performs better in
expectation that stochastic gradient descent. Well-known PDE regularity results
allow us to analyze the geometry of the relaxed energy landscape, confirming
empirical evidence. The PDE is derived from a stochastic homogenization
problem, which arises in the implementation of the algorithm. The algorithms
scale well in practice and can effectively tackle the high dimensionality of
modern neural networks. | [
1,
0,
1,
0,
0,
0
] |
Title: Forbidden Substrings In Circular K-Successions,
Abstract: In this note we define circular k-successions in permutations in one-line
notation and count permutations that avoid substrings j(j+k) and j(j+k) (mod
n). We also count circular permutations that avoid such substrings, and show
that for substrings j(j+k) (mod n), the number of permutations depends on
whether n is prime, and more generally, on whether n and k are relatively
prime. | [
0,
0,
1,
0,
0,
0
] |
Title: Asymptotic genealogies of interacting particle systems with an application to sequential Monte Carlo,
Abstract: We study weighted particle systems in which new generations are resampled
from current particles with probabilities proportional to their weights. This
covers a broad class of sequential Monte Carlo (SMC) methods, widely-used in
applied statistics and cognate disciplines. We consider the genealogical tree
embedded into such particle systems, and identify conditions, as well as an
appropriate time-scaling, under which they converge to the Kingman n-coalescent
in the infinite system size limit in the sense of finite-dimensional
distributions. Thus, the tractable n-coalescent can be used to predict the
shape and size of SMC genealogies, as we illustrate by characterising the
limiting mean and variance of the tree height. SMC genealogies are known to be
connected to algorithm performance, so that our results are likely to have
applications in the design of new methods as well. Our conditions for
convergence are strong, but we show by simulation that they do not appear to be
necessary. | [
0,
0,
0,
1,
1,
0
] |
Title: Symmetry Enforced Stability of Interacting Weyl and Dirac Semimetals,
Abstract: The nodal and effectively relativistic dispersion featuring in a range of
novel materials including two- dimensional graphene and three-dimensional Dirac
and Weyl semimetals has attracted enormous interest during the past decade.
Here, by studying the structure and symmetry of the diagrammatic expansion, we
show that these nodal touching points are in fact perturbatively stable to all
orders with respect to generic two-body interactions. For effective low-energy
theories relevant for single and multilayer graphene, type-I and type-II Weyl
and Dirac semimetals as well as Weyl points with higher topological charge,
this stability is shown to be a direct consequence of a spatial symmetry that
anti-commutes with the effective Hamiltonian while leaving the interaction
invariant. A more refined argument is applied to the honeycomb lattice model of
graphene showing that its Dirac points are also perturbatively stable to all
orders. We also give examples of nodal Hamiltonians that acquire a gap from
interactions as a consequence of symmetries different from those of Weyl and
Dirac materials. | [
0,
1,
0,
0,
0,
0
] |
Title: Luck is Hard to Beat: The Difficulty of Sports Prediction,
Abstract: Predicting the outcome of sports events is a hard task. We quantify this
difficulty with a coefficient that measures the distance between the observed
final results of sports leagues and idealized perfectly balanced competitions
in terms of skill. This indicates the relative presence of luck and skill. We
collected and analyzed all games from 198 sports leagues comprising 1503
seasons from 84 countries of 4 different sports: basketball, soccer, volleyball
and handball. We measured the competitiveness by countries and sports. We also
identify in each season which teams, if removed from its league, result in a
completely random tournament. Surprisingly, not many of them are needed. As
another contribution of this paper, we propose a probabilistic graphical model
to learn about the teams' skills and to decompose the relative weights of luck
and skill in each game. We break down the skill component into factors
associated with the teams' characteristics. The model also allows to estimate
as 0.36 the probability that an underdog team wins in the NBA league, with a
home advantage adding 0.09 to this probability. As shown in the first part of
the paper, luck is substantially present even in the most competitive
championships, which partially explains why sophisticated and complex
feature-based models hardly beat simple models in the task of forecasting
sports' outcomes. | [
1,
0,
0,
1,
0,
0
] |
Title: Multiscale sequence modeling with a learned dictionary,
Abstract: We propose a generalization of neural network sequence models. Instead of
predicting one symbol at a time, our multi-scale model makes predictions over
multiple, potentially overlapping multi-symbol tokens. A variation of the
byte-pair encoding (BPE) compression algorithm is used to learn the dictionary
of tokens that the model is trained with. When applied to language modelling,
our model has the flexibility of character-level models while maintaining many
of the performance benefits of word-level models. Our experiments show that
this model performs better than a regular LSTM on language modeling tasks,
especially for smaller models. | [
1,
0,
0,
1,
0,
0
] |
Title: Infinite Matrix Product States vs Infinite Projected Entangled-Pair States on the Cylinder: a comparative study,
Abstract: In spite of their intrinsic one-dimensional nature matrix product states have
been systematically used to obtain remarkably accurate results for
two-dimensional systems. Motivated by basic entropic arguments favoring
projected entangled-pair states as the method of choice, we assess the relative
performance of infinite matrix product states and infinite projected
entangled-pair states on cylindrical geometries. By considering the Heisenberg
and half-filled Hubbard models on the square lattice as our benchmark cases, we
evaluate their variational energies as a function of both bond dimension as
well as cylinder width. In both examples we find crossovers at moderate
cylinder widths, i.e. for the largest bond dimensions considered we find an
improvement on the variational energies for the Heisenberg model by using
projected entangled-pair states at a width of about 11 sites, whereas for the
half-filled Hubbard model this crossover occurs at about 7 sites. | [
0,
1,
0,
0,
0,
0
] |
Title: Dialectometric analysis of language variation in Twitter,
Abstract: In the last few years, microblogging platforms such as Twitter have given
rise to a deluge of textual data that can be used for the analysis of informal
communication between millions of individuals. In this work, we propose an
information-theoretic approach to geographic language variation using a corpus
based on Twitter. We test our models with tens of concepts and their associated
keywords detected in Spanish tweets geolocated in Spain. We employ
dialectometric measures (cosine similarity and Jensen-Shannon divergence) to
quantify the linguistic distance on the lexical level between cells created in
a uniform grid over the map. This can be done for a single concept or in the
general case taking into account an average of the considered variants. The
latter permits an analysis of the dialects that naturally emerge from the data.
Interestingly, our results reveal the existence of two dialect macrovarieties.
The first group includes a region-specific speech spoken in small towns and
rural areas whereas the second cluster encompasses cities that tend to use a
more uniform variety. Since the results obtained with the two different metrics
qualitatively agree, our work suggests that social media corpora can be
efficiently used for dialectometric analyses. | [
1,
1,
0,
0,
0,
0
] |
Title: Single-Pass, Adaptive Natural Language Filtering: Measuring Value in User Generated Comments on Large-Scale, Social Media News Forums,
Abstract: There are large amounts of insight and social discovery potential in mining
crowd-sourced comments left on popular news forums like Reddit.com, Tumblr.com,
Facebook.com and Hacker News. Unfortunately, due the overwhelming amount of
participation with its varying quality of commentary, extracting value out of
such data isn't always obvious nor timely. By designing efficient, single-pass
and adaptive natural language filters to quickly prune spam, noise, copy-cats,
marketing diversions, and out-of-context posts, we can remove over a third of
entries and return the comments with a higher probability of relatedness to the
original article in question. The approach presented here uses an adaptive,
two-step filtering process. It first leverages the original article posted in
the thread as a starting corpus to parse comments by matching intersecting
words and term-ratio balance per sentence then grows the corpus by adding new
words harvested from high-matching comments to increase filtering accuracy over
time. | [
1,
0,
0,
0,
0,
0
] |
Title: How Do Elements Really Factor in $\mathbb{Z}[\sqrt{-5}]$?,
Abstract: Most undergraduate level abstract algebra texts use $\mathbb{Z}[\sqrt{-5}]$
as an example of an integral domain which is not a unique factorization domain
(or UFD) by exhibiting two distinct irreducible factorizations of a nonzero
element. But such a brief example, which requires merely an understanding of
basic norms, only scratches the surface of how elements actually factor in this
ring of algebraic integers. We offer here an interactive framework which shows
that while $\mathbb{Z}[\sqrt{-5}]$ is not a UFD, it does satisfy a slightly
weaker factorization condition, known as half-factoriality. The arguments
involved revolve around the Fundamental Theorem of Ideal Theory. | [
0,
0,
1,
0,
0,
0
] |
Title: Improvements on lower bounds for the blow-up time under local nonlinear Neumann conditions,
Abstract: This paper studies the heat equation $u_t=\Delta u$ in a bounded domain
$\Omega\subset\mathbb{R}^{n}(n\geq 2)$ with positive initial data and a local
nonlinear Neumann boundary condition: the normal derivative $\partial
u/\partial n=u^{q}$ on partial boundary $\Gamma_1\subseteq \partial\Omega$ for
some $q>1$, while $\partial u/\partial n=0$ on the other part. We investigate
the lower bound of the blow-up time $T^{*}$ of $u$ in several aspects. First,
$T^{*}$ is proved to be at least of order $(q-1)^{-1}$ as $q\rightarrow 1^{+}$.
Since the existing upper bound is of order $(q-1)^{-1}$, this result is sharp.
Secondly, if $\Omega$ is convex and $|\Gamma_{1}|$ denotes the surface area of
$\Gamma_{1}$, then $T^{*}$ is shown to be at least of order
$|\Gamma_{1}|^{-\frac{1}{n-1}}$ for $n\geq 3$ and
$|\Gamma_{1}|^{-1}\big/\ln\big(|\Gamma_{1}|^{-1}\big)$ for $n=2$ as
$|\Gamma_{1}|\rightarrow 0$, while the previous result is
$|\Gamma_{1}|^{-\alpha}$ for any $\alpha<\frac{1}{n-1}$. Finally, we generalize
the results for convex domains to the domains with only local convexity near
$\Gamma_{1}$. | [
0,
0,
1,
0,
0,
0
] |
Title: Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation,
Abstract: This paper addresses the challenge of humanoid robot teleoperation in a
natural indoor environment via a Brain-Computer Interface (BCI). We leverage
deep Convolutional Neural Network (CNN) based image and signal understanding to
facilitate both real-time object detection and dry-Electroencephalography (EEG)
based human cortical brain bio-signal decoding. We employ recent advances in
dry-EEG technology to stream and collect the cortical waveforms from subjects
while the subjects fixate on variable Steady-State Visual Evoked Potential
(SSVEP) stimuli generated directly from the environment the robot is
navigating. To these ends, we propose the use of novel variable BCI stimuli by
utilising the real-time video streamed via the on-board robot camera as visual
input for SSVEP where the CNN detected natural scene objects are altered and
flickered with differing frequencies (10Hz, 12Hz and 15Hz). These stimuli are
not akin to traditional stimuli - as both the dimensions of the flicker regions
and their on-screen position changes depending on the scene objects detected in
the scene. On-screen object selection via dry-EEG enabled SSVEP in this way,
facilitates the on-line decoding of human cortical brain signals via a
secondary CNN approach into teleoperation robot commands (approach object, move
in a specific direction: right, left or back). This SSVEP decoding model is
trained via a priori offline experimental data in which very similar visual
input is present for all subjects. The resulting offline classification
demonstrates extremely high performance and with mean accuracies of 96% and 90%
for the real-time robot navigation experiment across multiple test subjects. | [
1,
0,
0,
0,
0,
0
] |
Title: Tunable Ampere phase plate for low dose imaging of biomolecular complexes,
Abstract: A novel device that can be used as a tunable support-free phase plate for
transmission electron microscopy of weakly scattering specimens is described.
The device relies on the generation of a controlled phase shift by the magnetic
field of a segment of current-carrying wire that is oriented parallel or
antiparallel to the electron beam. The validity of the concept is established
using both experimental electron holographic measurements and a theoretical
model based on Ampere's law. Computer simulations are used to illustrate the
resulting contrast enhancement for studies of biological cells and
macromolecules. | [
0,
1,
0,
0,
0,
0
] |
Title: An efficient model-free setting for longitudinal and lateral vehicle control. Validation through the interconnected pro-SiVIC/RTMaps prototyping platform,
Abstract: In this paper, the problem of tracking desired longitudinal and lateral
motions for a vehicle is addressed. Let us point out that a "good" modeling is
often quite difficult or even impossible to obtain. It is due for example to
parametric uncertainties, for the vehicle mass, inertia or for the interaction
forces between the wheels and the road pavement. To overcome this type of
difficulties, we consider a model-free control approach leading to
"intelligent" controllers. The longitudinal and the lateral motions, on one
hand, and the driving/braking torques and the steering wheel angle, on the
other hand, are respectively the output and the input variables. An important
part of this work is dedicated to present simulation results with actual data.
Actual data, used in Matlab as reference trajectories, have been previously
recorded with an instrumented Peugeot 406 experimental car. The simulation
results show the efficiency of our approach. Some comparisons with a nonlinear
flatness-based control in one hand, and with a classical PID control in another
hand confirm this analysis. Other virtual data have been generated through the
interconnected platform SiVIC/RTMaps, which is a virtual simulation platform
for prototyping and validation of advanced driving assistance systems. | [
1,
0,
1,
0,
0,
0
] |
Title: Genetic fitting techniques for precision ultracold spectroscopy,
Abstract: We present development of a genetic algorithm for fitting potential energy
curves of diatomic molecules to experimental data. Our approach does not
involve any functional form for fitting, which makes it a general fitting
procedure. In particular, it takes in a guess potential, perhaps from an $ab \
initio$ calculation, along with experimental measurements of vibrational
binding energies, rotational constants, and their experimental uncertainties.
The fitting procedure is able to modify the guess potential until it converges
to better than 1% uncertainty, as measured by $\bar{\chi}^2$. We present the
details of this technique along with a comparison of potentials calculated by
our genetic algorithm and the state of the art fitting techniques based on
inverted perturbation approach for the $X \ ^1\Sigma^+$ and $C \ ^1\Sigma^+$
potentials of lithium-rubidium. | [
0,
1,
0,
0,
0,
0
] |
Title: Robust And Scalable Learning Of Complex Dataset Topologies Via Elpigraph,
Abstract: Large datasets represented by multidimensional data point clouds often
possess non-trivial distributions with branching trajectories and excluded
regions, with the recent single-cell transcriptomic studies of developing
embryo being notable examples. Reducing the complexity and producing compact
and interpretable representations of such data remains a challenging task. Most
of the existing computational methods are based on exploring the local data
point neighbourhood relations, a step that can perform poorly in the case of
multidimensional and noisy data. Here we present ElPiGraph, a scalable and
robust method for approximation of datasets with complex structures which does
not require computing the complete data distance matrix or the data point
neighbourhood graph. This method is able to withstand high levels of noise and
is capable of approximating complex topologies via principal graph ensembles
that can be combined into a consensus principal graph. ElPiGraph deals
efficiently with large and complex datasets in various fields from biology,
where it can be used to infer gene dynamics from single-cell RNA-Seq, to
astronomy, where it can be used to explore complex structures in the
distribution of galaxies. | [
0,
0,
0,
1,
1,
0
] |
Title: Epidemic dynamics in open quantum spin systems,
Abstract: We explore the non-equilibrium evolution and stationary states of an open
many-body system which displays epidemic spreading dynamics in a classical and
a quantum regime. Our study is motivated by recent experiments conducted in
strongly interacting gases of highly excited Rydberg atoms where the
facilitated excitation of Rydberg states competes with radiative decay. These
systems approximately implement open quantum versions of models for population
dynamics or disease spreading where species can be in a healthy, infected or
immune state. We show that in a two-dimensional lattice, depending on the
dominance of either classical or quantum effects, the system may display a
different kind of non-equilibrium phase transition. We moreover discuss the
observability of our findings in laser driven Rydberg gases with particular
focus on the role of long-range interactions. | [
0,
1,
0,
0,
0,
0
] |
Title: In-Hand Object Stabilization by Independent Finger Control,
Abstract: Grip control during robotic in-hand manipulation is usually modeled as part
of a monolithic task, relying on complex controllers specialized for specific
situations. Such approaches do not generalize well and are difficult to apply
to novel manipulation tasks. Here, we propose a modular object stabilization
method based on a proposition that explains how humans achieve grasp stability.
In this bio-mimetic approach, independent tactile grip stabilization
controllers ensure that slip does not occur locally at the engaged robot
fingers. Such local slip is predicted from the tactile signals of each
fingertip sensor i.e., BioTac and BioTac SP by Syntouch. We show that stable
grasps emerge without any form of central communication when such independent
controllers are engaged in the control of multi-digit robotic hands. These
grasps are resistant to external perturbations while being capable of
stabilizing a large variety of objects. | [
1,
0,
0,
0,
0,
0
] |
Title: The X-ray and Mid-Infrared luminosities in Luminous Type 1 Quasars,
Abstract: Several recent studies have reported different intrinsic correlations between
the AGN mid-IR luminosity ($L_{MIR}$) and the rest-frame 2-10 keV luminosity
($L_{X}$) for luminous quasars. To understand the origin of the difference in
the observed $L_{X}-L_{MIR}$ relations, we study a sample of 3,247
spectroscopically confirmed type 1 AGNs collected from Boötes, XMM-COSMOS,
XMM-XXL-North, and the SDSS quasars in the Swift/XRT footprint spanning over
four orders of magnitude in luminosity. We carefully examine how different
observational constraints impact the observed $L_{X}-L_{MIR}$ relations,
including the inclusion of X-ray non-detected objects, possible X-ray
absorption in type 1 AGNs, X-ray flux limits, and star formation contamination.
We find that the primary factor driving the different $L_{X}-L_{MIR}$ relations
reported in the literature is the X-ray flux limits for different studies. When
taking these effects into account, we find that the X-ray luminosity and mid-IR
luminosity (measured at rest-frame $6\mu m$, or $L_{6\mu m}$) of our sample of
type 1 AGNs follow a bilinear relation in the log-log plane: $\log L_X
=(0.84\pm0.03)\times\log L_{6\mu m}/10^{45}{\rm erg\;s^{-1}} + (44.60\pm0.01)$
for $L_{6\mu m} < 10^{44.79}{\rm erg\;s^{-1}} $, and $\log L_X =
(0.40\pm0.03)\times\log L_{6\mu m}/10^{45}{\rm erg\;s^{-1}} +(44.51\pm0.01)$
for $L_{6\mu m} \geq 10^{44.79}{\rm erg\;s^{-1}} $. This suggests that the
luminous type 1 quasars have a shallower $L_{X}-L_{MIR}$ correlation than the
approximately linear relations found in local Seyfert galaxies. This result is
consistent with previous studies reporting a luminosity-dependent
$L_{X}-L_{MIR}$ relation, and implies that assuming a linear $L_{X}-L_{MIR}$
relation to infer the neutral gas column density for X-ray absorption might
overestimate the column densities in luminous quasars. | [
0,
1,
0,
0,
0,
0
] |
Title: Approximation of general facets by regular facets with respect to anisotropic total variation energies and its application to the crystalline mean curvature flow,
Abstract: We show that every bounded subset of an Euclidean space can be approximated
by a set that admits a certain vector field, the so-called Cahn-Hoffman vector
field, that is subordinate to a given anisotropic metric and has a
square-integrable divergence. More generally, we introduce a concept of facets
as a kind of directed sets, and show that they can be approximated in a similar
manner.
We use this approximation to construct test functions necessary to prove the
comparison principle for viscosity solutions of the level set formulation of
the crystalline mean curvature flow that were recently introduced by the
authors. As a consequence, we obtain the well-posedness of the viscosity
solutions in an arbitrary dimension, which extends the validity of the result
in the previous paper. | [
0,
0,
1,
0,
0,
0
] |
Title: Calibrated Fairness in Bandits,
Abstract: We study fairness within the stochastic, \emph{multi-armed bandit} (MAB)
decision making framework. We adapt the fairness framework of "treating similar
individuals similarly" to this setting. Here, an `individual' corresponds to an
arm and two arms are `similar' if they have a similar quality distribution.
First, we adopt a {\em smoothness constraint} that if two arms have a similar
quality distribution then the probability of selecting each arm should be
similar. In addition, we define the {\em fairness regret}, which corresponds to
the degree to which an algorithm is not calibrated, where perfect calibration
requires that the probability of selecting an arm is equal to the probability
with which the arm has the best quality realization. We show that a variation
on Thompson sampling satisfies smooth fairness for total variation distance,
and give an $\tilde{O}((kT)^{2/3})$ bound on fairness regret. This complements
prior work, which protects an on-average better arm from being less favored. We
also explain how to extend our algorithm to the dueling bandit setting. | [
1,
0,
0,
0,
0,
0
] |
Title: HTC Vive MeVisLab integration via OpenVR for medical applications,
Abstract: Virtual Reality, an immersive technology that replicates an environment via
computer-simulated reality, gets a lot of attention in the entertainment
industry. However, VR has also great potential in other areas, like the medical
domain, Examples are intervention planning, training and simulation. This is
especially of use in medical operations, where an aesthetic outcome is
important, like for facial surgeries. Alas, importing medical data into Virtual
Reality devices is not necessarily trivial, in particular, when a direct
connection to a proprietary application is desired. Moreover, most researcher
do not build their medical applications from scratch, but rather leverage
platforms like MeVisLab, MITK, OsiriX or 3D Slicer. These platforms have in
common that they use libraries like ITK and VTK, and provide a convenient
graphical interface. However, ITK and VTK do not support Virtual Reality
directly. In this study, the usage of a Virtual Reality device for medical data
under the MeVisLab platform is presented. The OpenVR library is integrated into
the MeVisLab platform, allowing a direct and uncomplicated usage of the head
mounted display HTC Vive inside the MeVisLab platform. Medical data coming from
other MeVisLab modules can directly be connected per drag-and-drop to the
Virtual Reality module, rendering the data inside the HTC Vive for immersive
virtual reality inspection. | [
1,
0,
0,
0,
0,
0
] |
Title: Explanation of a Polynomial Identity,
Abstract: In this note, we provide a conceptual explanation of a well-known polynomial
identity used in algebraic number theory. | [
0,
0,
1,
0,
0,
0
] |
Title: Quantum Stress Tensor Fluctuations and Primordial Gravity Waves,
Abstract: We examine the effect of the stress tensor of a quantum matter field, such as
the electromagnetic field, on the spectrum of primordial gravity waves expected
in inflationary cosmology. We find that the net effect is a small reduction in
the power spectrum, especially at higher frequencies, but which has a different
form from that described by the usual spectral index. Thus this effect has a
characteristic signature, and is in principle observable. The net effect is a
sum of two contributions, one of which is due to quantum fluctuations of the
matter field stress tensor. The other is a quantum correction to the graviton
field due to coupling to the expectation value of this stress tensor. Both
contributions are sensitive to initial conditions in the very early universe,
so this effect has the potential to act as a probe of these initial conditions. | [
0,
1,
0,
0,
0,
0
] |
Title: Hyperbolic Pascal simplex,
Abstract: In this article we introduce a new geometric object called hyperbolic Pascal
simplex. This new object is presented by the regular hypercube mosaic in the
4-dimensional hyperbolic space. The definition of the hyperbolic Pascal
simplex, whose hyperfaces are hyperbolic Pascal pyramids and faces are
hyperbolic Pascals triangles, is a natural generalization of the definition of
the hyperbolic Pascal triangle and pyramid. We describe the growing of the
hyperbolic Pascal simplex considering the numbers and the values of the
elements. Further figures illustrate the stepping from a level to the next one. | [
0,
0,
1,
0,
0,
0
] |
Title: Small-loss bounds for online learning with partial information,
Abstract: We consider the problem of adversarial (non-stochastic) online learning with
partial information feedback, where at each round, a decision maker selects an
action from a finite set of alternatives. We develop a black-box approach for
such problems where the learner observes as feedback only losses of a subset of
the actions that includes the selected action. When losses of actions are
non-negative, under the graph-based feedback model introduced by Mannor and
Shamir, we offer algorithms that attain the so called "small-loss" $o(\alpha
L^{\star})$ regret bounds with high probability, where $\alpha$ is the
independence number of the graph, and $L^{\star}$ is the loss of the best
action. Prior to our work, there was no data-dependent guarantee for general
feedback graphs even for pseudo-regret (without dependence on the number of
actions, i.e. utilizing the increased information feedback). Taking advantage
of the black-box nature of our technique, we extend our results to many other
applications such as semi-bandits (including routing in networks), contextual
bandits (even with an infinite comparator class), as well as learning with
slowly changing (shifting) comparators.
In the special case of classical bandit and semi-bandit problems, we provide
optimal small-loss, high-probability guarantees of
$\tilde{O}(\sqrt{dL^{\star}})$ for actual regret, where $d$ is the number of
actions, answering open questions of Neu. Previous bounds for bandits and
semi-bandits were known only for pseudo-regret and only in expectation. We also
offer an optimal $\tilde{O}(\sqrt{\kappa L^{\star}})$ regret guarantee for
fixed feedback graphs with clique-partition number at most $\kappa$. | [
1,
0,
0,
0,
0,
0
] |
Title: Fine-Grained Parameterized Complexity Analysis of Graph Coloring Problems,
Abstract: The $q$-Coloring problem asks whether the vertices of a graph can be properly
colored with $q$ colors. Lokshtanov et al. [SODA 2011] showed that $q$-Coloring
on graphs with a feedback vertex set of size $k$ cannot be solved in time
$\mathcal{O}^*((q-\varepsilon)^k)$, for any $\varepsilon > 0$, unless the
Strong Exponential-Time Hypothesis (SETH) fails. In this paper we perform a
fine-grained analysis of the complexity of $q$-Coloring with respect to a
hierarchy of parameters. We show that even when parameterized by the vertex
cover number, $q$ must appear in the base of the exponent: Unless ETH fails,
there is no universal constant $\theta$ such that $q$-Coloring parameterized by
vertex cover can be solved in time $\mathcal{O}^*(\theta^k)$ for all fixed $q$.
We apply a method due to Jansen and Kratsch [Inform. & Comput. 2013] to prove
that there are $\mathcal{O}^*((q - \varepsilon)^k)$ time algorithms where $k$
is the vertex deletion distance to several graph classes $\mathcal{F}$ for
which $q$-Coloring is known to be solvable in polynomial time. We generalize
earlier ad-hoc results by showing that if $\mathcal{F}$ is a class of graphs
whose $(q+1)$-colorable members have bounded treedepth, then there exists some
$\varepsilon > 0$ such that $q$-Coloring can be solved in time
$\mathcal{O}^*((q-\varepsilon)^k)$ when parameterized by the size of a given
modulator to $\mathcal{F}$. In contrast, we prove that if $\mathcal{F}$ is the
class of paths - some of the simplest graphs of unbounded treedepth - then no
such algorithm can exist unless SETH fails. | [
1,
0,
0,
0,
0,
0
] |
Title: Planar Object Tracking in the Wild: A Benchmark,
Abstract: Planar object tracking is an actively studied problem in vision-based robotic
applications. While several benchmarks have been constructed for evaluating
state-of-the-art algorithms, there is a lack of video sequences captured in the
wild rather than in constrained laboratory environment. In this paper, we
present a carefully designed planar object tracking benchmark containing 210
videos of 30 planar objects sampled in the natural environment. In particular,
for each object, we shoot seven videos involving various challenging factors,
namely scale change, rotation, perspective distortion, motion blur, occlusion,
out-of-view, and unconstrained. The ground truth is carefully annotated
semi-manually to ensure the quality. Moreover, eleven state-of-the-art
algorithms are evaluated on the benchmark using two evaluation metrics, with
detailed analysis provided for the evaluation results. We expect the proposed
benchmark to benefit future studies on planar object tracking. | [
1,
0,
0,
0,
0,
0
] |
Title: Experimental demonstration of an atomtronic battery,
Abstract: Operation of an atomtronic battery is demonstrated where a finite-temperature
Bose-Einstein condensate stored in one half of a double-well potential is
coupled to an initially empty load well that is impedance matched by a resonant
terminator beam. The atom number and temperature of the condensate are
monitored during the discharge cycle, and are used to calculate fundamental
properties of the battery. The discharge behavior is analyzed according to a
Thévenin equivalent circuit that contains a finite internal resistance to
account for dissipation in the battery. Battery performance at multiple
discharge rates is characterized by the peak power output, and the current and
energy capacities of the system. | [
0,
1,
0,
0,
0,
0
] |
Title: Sensor Selection and Random Field Reconstruction for Robust and Cost-effective Heterogeneous Weather Sensor Networks for the Developing World,
Abstract: We address the two fundamental problems of spatial field reconstruction and
sensor selection in heterogeneous sensor networks: (i) how to efficiently
perform spatial field reconstruction based on measurements obtained
simultaneously from networks with both high and low quality sensors; and (ii)
how to perform query based sensor set selection with predictive MSE performance
guarantee. For the first problem, we developed a low complexity algorithm based
on the spatial best linear unbiased estimator (S-BLUE). Next, building on the
S-BLUE, we address the second problem, and develop an efficient algorithm for
query based sensor set selection with performance guarantee. Our algorithm is
based on the Cross Entropy method which solves the combinatorial optimization
problem in an efficient manner. | [
0,
0,
0,
1,
0,
0
] |
Title: A characterization of ordinary abelian varieties by the Frobenius push-forward of the structure sheaf II,
Abstract: In this paper, we prove that a smooth projective variety $X$ of
characteristic $p>0$ is an ordinary abelian variety if and only if $K_X$ is
pseudo-effective and $F^e_*\mathcal O_X$ splits into a direct sum of line
bundles for an integer $e$ with $p^e>2$. | [
0,
0,
1,
0,
0,
0
] |
Title: On Minimax Optimality of Sparse Bayes Predictive Density Estimates,
Abstract: We study predictive density estimation under Kullback-Leibler loss in
$\ell_0$-sparse Gaussian sequence models. We propose proper Bayes predictive
density estimates and establish asymptotic minimaxity in sparse models. A
surprise is the existence of a phase transition in the future-to-past variance
ratio $r$. For $r < r_0 = (\surd 5 - 1)/4$, the natural discrete prior ceases
to be asymptotically optimal. Instead, for subcritical $r$, a `bi-grid' prior
with a central region of reduced grid spacing recovers asymptotic minimaxity.
This phenomenon seems to have no analog in the otherwise parallel theory of
point estimation of a multivariate normal mean under quadratic loss. For
spike-and-slab priors to have any prospect of minimaxity, we show that the
sparse parameter space needs also to be magnitude constrained. Within a
substantial range of magnitudes, spike-and-slab priors can attain asymptotic
minimaxity. | [
0,
0,
1,
1,
0,
0
] |
Title: Two forms of minimality in ASPIC+,
Abstract: Many systems of structured argumentation explicitly require that the facts
and rules that make up the argument for a conclusion be the minimal set
required to derive the conclusion. ASPIC+ does not place such a requirement on
arguments, instead requiring that every rule and fact that are part of an
argument be used in its construction. Thus ASPIC+ arguments are minimal in the
sense that removing any element of the argument would lead to a structure that
is not an argument. In this brief note we discuss these two types of minimality
and show how the first kind of minimality can, if desired, be recovered in
ASPIC+. | [
1,
0,
0,
0,
0,
0
] |
Title: Perspectives on constraining a cosmological constant-type parameter with pulsar timing in the Galactic Center,
Abstract: Independent tests aiming to constrain the value of the cosmological constant
$\Lambda$ are usually difficult because of its extreme smallness $\left(\Lambda
\simeq 1\times 10^{-52}~\textrm{m}^{-2},~\textrm{or}~2.89\times
10^{-122}~\textrm{in Planck units}\right)$. Bounds on it from Solar System
orbital motions determined with spacecraft tracking are currently at the
$\simeq 10^{-43}-10^{-44}~\textrm{m}^{-2}~\left(5-1\times 10^{-113}~\textrm{in
Planck units}\right)$ level, but they may turn out to be somewhat optimistic
since $\Lambda$ has not yet been explicitly modeled in the planetary data
reductions. Accurate $\left(\sigma_{\tau_\textrm{p}}\simeq
1-10~\mu\textrm{s}\right)$ timing of expected pulsars orbiting the Black Hole
at the Galactic Center, preferably along highly eccentric and wide orbits,
might, at least in principle, improve the planetary constraints by several
orders of magnitude. By looking at the average time shift per orbit
$\overline{\Delta\delta\tau}^\Lambda_\textrm{p}$, a S2-like orbital
configuration with $e=0.8839,~P_\textrm{b}=16~\textrm{yr}$ would allow to
obtain preliminarily an upper bound of the order of
$\left|\Lambda\right|\lesssim 9\times 10^{-47}~\textrm{m}^{-2}~\left(\lesssim
2\times 10^{-116}~\textrm{in Planck units}\right)$ if only
$\sigma_{\tau_\textrm{p}}$ were to be considered. Our results can be easily
extended to modified models of gravity using $\Lambda-$type parameters. | [
0,
1,
0,
0,
0,
0
] |
Title: Flat bundles over some compact complex manifolds,
Abstract: We construct examples of flat fiber bundles over the Hopf surface such that
the total spaces have no pseudoconvex neighborhood basis, admit a complete
Kähler metric, or are hyperconvex but have no nonconstant holomorphic
functions. For any compact Riemannian surface of positive genus, we construct a
flat $\mathbb P^1$ bundle over it and a Stein domain with real analytic bundary
in it whose closure does not have pseudoconvex neighborhood basis. For a
compact complex manifold with positive first Betti number, we construct a flat
disc bundle over it such that the total space is hyperconvex but admits no
nonconstant holomorphic functions. | [
0,
0,
1,
0,
0,
0
] |
Title: EXONEST: The Bayesian Exoplanetary Explorer,
Abstract: The fields of astronomy and astrophysics are currently engaged in an
unprecedented era of discovery as recent missions have revealed thousands of
exoplanets orbiting other stars. While the Kepler Space Telescope mission has
enabled most of these exoplanets to be detected by identifying transiting
events, exoplanets often exhibit additional photometric effects that can be
used to improve the characterization of exoplanets. The EXONEST Exoplanetary
Explorer is a Bayesian exoplanet inference engine based on nested sampling and
originally designed to analyze archived Kepler Space Telescope and CoRoT
(Convection Rotation et Transits planétaires) exoplanet mission data. We
discuss the EXONEST software package and describe how it accommodates
plug-and-play models of exoplanet-associated photometric effects for the
purpose of exoplanet detection, characterization and scientific hypothesis
testing. The current suite of models allows for both circular and eccentric
orbits in conjunction with photometric effects, such as the primary transit and
secondary eclipse, reflected light, thermal emissions, ellipsoidal variations,
Doppler beaming and superrotation. We discuss our new efforts to expand the
capabilities of the software to include more subtle photometric effects
involving reflected and refracted light. We discuss the EXONEST inference
engine design and introduce our plans to port the current MATLAB-based EXONEST
software package over to the next generation Exoplanetary Explorer, which will
be a Python-based open source project with the capability to employ third-party
plug-and-play models of exoplanet-related photometric effects. | [
0,
1,
0,
1,
0,
0
] |
Title: Intelligence of agents produces a structural phase transition in collective behaviour,
Abstract: Living organisms process information to interact and adapt to their changing
environment with the goal of finding food, mates or averting hazards. The
structure of their niche has profound repercussions by both selecting their
internal architecture and also inducing adaptive responses to environmental
cues and stimuli. Adaptive, collective behaviour underpinned by specialized
optimization strategies is ubiquitously found in the natural world. This
exceptional success originates from the processes of fitness and selection.
Here we prove that a universal physical mechanism of a nonequilibrium
transition underlies the collective organization of information-processing
organisms. As cognitive agents build and update an internal, cognitive
representation of the causal structure of their environment, complex patterns
emerge in the system, where the onset of pattern formation relates to the
spatial overlap of cognitive maps. Studying the exchange of information among
the agents reveals a continuous, order-disorder transition. As a result of the
spontaneous breaking of translational symmetry, a Goldstone mode emerges, which
points at a collective mechanism of information transfer among cognitive
organisms. Taken together, the characteristics of this phase transition
consolidate different results in cognitive and biological sciences in a
universal manner. These finding are generally applicable to the design of
artificial intelligent swarm systems that do not rely on centralized control
schemes. | [
0,
1,
0,
0,
0,
0
] |
Title: Quantifying Filter Bubbles: Analyzing Surprise in Elections,
Abstract: This work analyses surprising elections, and attempts to quantify the notion
of surprise in elections. A voter is surprised if their estimate of the winner
(assumed to be based on a combination of the preferences of their social
connections and popular media predictions) is different from the true winner. A
voter's social connections are assumed to consist of contacts on social media
and geographically proximate people. We propose a simple mathematical model for
combining the global information (traditional media) as well as the local
information (local neighbourhood) of a voter in the case of a two-candidate
election. We show that an unbiased, influential media can nullify the effect of
filter bubbles and result in a less surprised populace. Surprisingly, an
influential media source biased towards the winners of the election also
results in a less surprised populace. Our model shows that elections will be
unsurprising for all of the voters with a high probability under certain
assumptions on the social connection model in the presence of an influential,
unbiased traditional media source. Our experiments with the UK-EU referendum
(popularly known as Brexit) dataset support our theoretical predictions. Since
surprising elections can lead to significant economic movements, it is a
worthwhile endeavour to figure out the causes of surprising elections. | [
1,
0,
0,
0,
0,
0
] |
Title: Energy transfer, pressure tensor and heating of kinetic plasma,
Abstract: Kinetic plasma turbulence cascade spans multiple scales ranging from
macroscopic fluid flow to sub-electron scales. Mechanisms that dissipate large
scale energy, terminate the inertial range cascade and convert kinetic energy
into heat are hotly debated. Here we revisit these puzzles using fully kinetic
simulation. By performing scale-dependent spatial filtering on the Vlasov
equation, we extract information at prescribed scales and introduce several
energy transfer functions. This approach allows highly inhomogeneous energy
cascade to be quantified as it proceeds down to kinetic scales. The pressure
work, $-\left( \boldsymbol{P} \cdot \nabla \right) \cdot \boldsymbol{u}$, can
trigger a channel of the energy conversion between fluid flow and random
motions, which is a collision-free generalization of the viscous dissipation in
collisional fluid. Both the energy transfer and the pressure work are strongly
correlated with velocity gradients. | [
0,
1,
0,
0,
0,
0
] |
Title: The relation between migration and FDI in the OECD from a complex network perspective,
Abstract: We explore the relationship between human migration and OECD's foreign direct
investment (FDI) using a gravity equation enriched with variables that account
for complex-network effects. Based on a panel data analysis, we find a strong
positive correlation between the migration network and the FDI network, which
can be mostly explained by countries' economic/demographic sizes and
geographical distance. We highlight the existence of a stronger positive FDI
relationship in pairs of countries that are more central in the migration
network. Both intensive and extensive forms of centrality are FDI enhancing.
Illuminating this result, we show that bilateral FDI between any two countries
is further affected positively by the complex web of "third party"
corridors/migration stocks of the international migration network (IMN). Our
findings are consistent whether we consider bilateral FDI and bilateral
migration figures, or we focus on the outward FDI and the respective inward
migration of the OECD countries. | [
0,
1,
0,
0,
0,
0
] |
Title: Thermopower and thermal conductivity in the Weyl semimetal NbP,
Abstract: The Weyl semimetal NbP exhibits an extremely large magnetoresistance (MR) and
an ultra-high mobility. The large MR originates from a combination of the
nearly perfect compensation between electron- and hole-type charge carriers and
the high mobility, which is relevant to the topological band structure. In this
work we report on temperature- and field-dependent thermopower and thermal
conductivity experiments on NbP. Additionally, we carried out complementary
heat capacity, magnetization, and electrical resistivity measurements. We found
a giant adiabatic magnetothermopower with a maximum of 800 $\mu$V/K at 50 K in
a field of 9 T. Such large effects have been observed rarely in bulk materials.
We suggest that the origin of this effect might be related to the high
charge-carrier mobility. We further observe pronounced quantum oscillations in
both thermal conductivity and thermopower. The obtained frequencies compare
well with our heat capacity and magnetization data. | [
0,
1,
0,
0,
0,
0
] |
Title: Maximum entropy and population heterogeneity in continuous cell cultures,
Abstract: Continuous cultures of mammalian cells are complex systems displaying
hallmark phenomena of nonlinear dynamics, such as multi-stability, hysteresis,
as well as sharp transitions between different metabolic states. In this
context mathematical models may suggest control strategies to steer the system
towards desired states. Although even clonal populations are known to exhibit
cell-to-cell variability, most of the currently studied models assume that the
population is homogeneous. To overcome this limitation, we use the maximum
entropy principle to model the phenotypic distribution of cells in a chemostat
as a function of the dilution rate. We consider the coupling between cell
metabolism and extracellular variables describing the state of the bioreactor
and take into account the impact of toxic byproduct accumulation on cell
viability. We present a formal solution for the stationary state of the
chemostat and show how to apply it in two examples. First, a simplified model
of cell metabolism where the exact solution is tractable, and then a
genome-scale metabolic network of the Chinese hamster ovary (CHO) cell line.
Along the way we discuss several consequences of heterogeneity, such as:
qualitative changes in the dynamical landscape of the system, increasing
concentrations of byproducts that vanish in the homogeneous case, and larger
population sizes. | [
0,
0,
0,
0,
1,
0
] |
Title: Spin wave propagation and spin polarized electron transport in single crystal iron films,
Abstract: The technique of propagating spin wave spectroscopy is applied to a 20 nm
thick Fe/MgO (001) film. The magnetic parameters extracted from the position of
the resonance peaks are very close to those tabulated for bulk iron. From the
propagating waveforms, a group velocity of 4 km/s and an attenuation length of
about 6 micrometers are extracted for 1.6 micrometers-wavelength spin-wave at
18 GHz. From the measured current-induced spin-wave Doppler shift, we also
extract a surprisingly high degree of spin-polarization of the current of 83%.
This set of results makes single-crystalline iron a promising candidate for
building devices utilizing high frequency spin-waves and spin-polarized
currents. | [
0,
1,
0,
0,
0,
0
] |
Title: Expert-Driven Genetic Algorithms for Simulating Evaluation Functions,
Abstract: In this paper we demonstrate how genetic algorithms can be used to reverse
engineer an evaluation function's parameters for computer chess. Our results
show that using an appropriate expert (or mentor), we can evolve a program that
is on par with top tournament-playing chess programs, outperforming a two-time
World Computer Chess Champion. This performance gain is achieved by evolving a
program that mimics the behavior of a superior expert. The resulting evaluation
function of the evolved program consists of a much smaller number of parameters
than the expert's. The extended experimental results provided in this paper
include a report of our successful participation in the 2008 World Computer
Chess Championship. In principle, our expert-driven approach could be used in a
wide range of problems for which appropriate experts are available. | [
1,
0,
0,
1,
0,
0
] |
Title: A gentle introduction to the minimal Naming Game,
Abstract: Social conventions govern countless behaviors all of us engage in every day,
from how we greet each other to the languages we speak. But how can shared
conventions emerge spontaneously in the absence of a central coordinating
authority? The Naming Game model shows that networks of locally interacting
individuals can spontaneously self-organize to produce global coordination.
Here, we provide a gentle introduction to the main features of the model, from
the dynamics observed in homogeneously mixing populations to the role played by
more complex social networks, and to how slight modifications of the basic
interaction rules give origin to a richer phenomenology in which more
conventions can co-exist indefinitely. | [
1,
1,
0,
0,
0,
0
] |
Title: Modelling hidden structure of signals in group data analysis with modified (Lr, 1) and block-term decompositions,
Abstract: This work is devoted to elaboration on the idea to use block term
decomposition for group data analysis and to raise the possibility of modelling
group activity with (Lr, 1) and Tucker blocks. A new generalization of block
tensor decomposition was considered in application to group data analysis.
Suggested approach was evaluated on multilabel classification task for a set of
images. This contribution also reports results of investigation on clustering
with proposed tensor models in comparison with known matrix models, namely
common orthogonal basis extraction and group independent component analysis. | [
1,
0,
0,
1,
0,
0
] |
Title: ChaLearn Looking at People: A Review of Events and Resources,
Abstract: This paper reviews the historic of ChaLearn Looking at People (LAP) events.
We started in 2011 (with the release of the first Kinect device) to run
challenges related to human action/activity and gesture recognition. Since then
we have regularly organized events in a series of competitions covering all
aspects of visual analysis of humans. So far we have organized more than 10
international challenges and events in this field. This paper reviews
associated events, and introduces the ChaLearn LAP platform where public
resources (including code, data and preprints of papers) related to the
organized events are available. We also provide a discussion on perspectives of
ChaLearn LAP activities. | [
1,
0,
0,
0,
0,
0
] |
Title: Reinterpreting the Origin of Bifurcation and Chaos by Urbanization Dynamics,
Abstract: Chaos associated with bifurcation makes a new science, but the origin and
essence of chaos are not yet clear. Based on the well-known logistic map, chaos
used to be regarded as intrinsic randomicity of determinate dynamics systems.
However, urbanization dynamics indicates new explanation about it. Using
mathematical derivation, numerical computation, and empirical analysis, we can
explore chaotic dynamics of urbanization. The key is the formula of
urbanization level. The urbanization curve can be described with the logistic
function, which can be transformed into 1-dimensional map and thus produce
bifurcation and chaos. On the other hand, the logistic model of urbanization
curve can be derived from the rural-urban population interaction model, and the
rural-urban interaction model can be discretized to a 2-dimensional map. An
interesting finding is that the 2-dimensional rural-urban coupling map can
create the same bifurcation and chaos patterns as those from the 1-dimensional
logistic map. This suggests that the urban bifurcation and chaos come from
spatial interaction between rural and urban population rather than pure
intrinsic randomicity of determinate models. This discovery provides a new way
of looking at origin and essence of bifurcation and chaos. By analogy with
urbanization models, the classical predator-prey interaction model can be
developed to interpret the complex dynamics of the logistic map in physical and
social sciences. | [
0,
1,
0,
0,
0,
0
] |
Title: Biochemical Coupling Through Emergent Conservation Laws,
Abstract: Bazhin has analyzed ATP coupling in terms of quasiequilibrium states where
fast reactions have reached an approximate steady state while slow reactions
have not yet reached equilibrium. After an expository introduction to the
relevant aspects of reaction network theory, we review his work and explain the
role of emergent conserved quantities in coupling. These are quantities, left
unchanged by fast reactions, whose conservation forces exergonic processes such
as ATP hydrolysis to drive desired endergonic processes. | [
0,
0,
0,
0,
1,
0
] |
Title: Artificial Intelligence as an Enabler for Cognitive Self-Organizing Future Networks,
Abstract: The explosive increase in number of smart devices hosting sophisticated
applications is rapidly affecting the landscape of information communication
technology industry. Mobile subscriptions, expected to reach 8.9 billion by
2022, would drastically increase the demand of extra capacity with aggregate
throughput anticipated to be enhanced by a factor of 1000. In an already
crowded radio spectrum, it becomes increasingly difficult to meet ever growing
application demands of wireless bandwidth. It has been shown that the allocated
spectrum is seldom utilized by the primary users and hence contains spectrum
holes that may be exploited by the unlicensed users for their communication. As
we enter the Internet Of Things (IoT) era in which appliances of common use
will become smart digital devices with rigid performance requirements (such as
low latency, energy efficiency, etc.), current networks face the vexing problem
of how to create sufficient capacity for such applications. The fifth
generation of cellular networks (5G) envisioned to address these challenges are
thus required to incorporate cognition and intelligence to resolve the
aforementioned issues. | [
1,
0,
0,
0,
0,
0
] |
Title: Localized Thermal States,
Abstract: It is believed that thermalization in closed systems of interacting particles
can occur only when the eigenstates are fully delocalized and chaotic in the
preferential (unperturbed) basis of the total Hamiltonian. Here we demonstrate
that at variance with this common belief the typical situation in the systems
with two-body inter-particle interaction is much more complicated and allows to
treat as thermal even eigenstates that are not fully delocalized. Using a
semi-analytical approach we establish the conditions for the emergence of such
thermal states in a model of randomly interacting bosons. Our numerical data
show an excellent correspondence with the predicted properties of {\it
localized thermal eigenstates}. | [
0,
1,
0,
0,
0,
0
] |
Title: Interface Phonon Modes in the [AlN/GaN]20 and [Al0.35Ga0.65N/Al0.55Ga0.45N]20 2D Multi Quantum Well Structures,
Abstract: Interface phonon (IF) modes of c-plane oriented [AlN/GaN]20 and
Al0.35Ga0.65N/Al0.55Ga0.45N]20 multi quantum well (MQW) structures grown via
plasma assisted molecular beam epitaxy are reported. The effect of variation in
dielectric constant of barrier layers to the IF optical phonon modes of well
layers periodically arranged in the MQWs investigated. | [
0,
1,
0,
0,
0,
0
] |
Title: Predictability of escape for a stochastic saddle-node bifurcation: when rare events are typical,
Abstract: Transitions between multiple stable states of nonlinear systems are
ubiquitous in physics, chemistry, and beyond. Two types of behaviors are
usually seen as mutually exclusive: unpredictable noise-induced transitions and
predictable bifurcations of the underlying vector field. Here, we report a new
situation, corresponding to a fluctuating system approaching a bifurcation,
where both effects collaborate. We show that the problem can be reduced to a
single control parameter governing the competition between deterministic and
stochastic effects. Two asymptotic regimes are identified: when the control
parameter is small (e.g. small noise), deviations from the deterministic case
are well described by the Freidlin-Wentzell theory. In particular, escapes over
the potential barrier are very rare events. When the parameter is large (e.g.
large noise), such events become typical. Unlike pure noise-induced
transitions, the distribution of the escape time is peaked around a value which
is asymptotically predicted by an adiabatic approximation. We show that the two
regimes are characterized by qualitatively different reacting trajectories,
with algebraic and exponential divergence, respectively. | [
0,
1,
0,
0,
0,
0
] |
Title: Sieving rational points on varieties,
Abstract: A sieve for rational points on suitable varieties is developed, together with
applications to counting rational points in thin sets, the number of varieties
in a family which are everywhere locally soluble, and to the notion of friable
rational points with respect to divisors. In the special case of quadrics,
sharper estimates are obtained by developing a version of the Selberg sieve for
rational points. | [
0,
0,
1,
0,
0,
0
] |
Title: Sampling-based probabilistic inference emerges from learning in neural circuits with a cost on reliability,
Abstract: Neural responses in the cortex change over time both systematically, due to
ongoing plasticity and learning, and seemingly randomly, due to various sources
of noise and variability. Most previous work considered each of these
processes, learning and variability, in isolation -- here we study neural
networks exhibiting both and show that their interaction leads to the emergence
of powerful computational properties. We trained neural networks on classical
unsupervised learning tasks, in which the objective was to represent their
inputs in an efficient, easily decodable form, with an additional cost for
neural reliability which we derived from basic biophysical considerations. This
cost on reliability introduced a tradeoff between energetically cheap but
inaccurate representations and energetically costly but accurate ones. Despite
the learning tasks being non-probabilistic, the networks solved this tradeoff
by developing a probabilistic representation: neural variability represented
samples from statistically appropriate posterior distributions that would
result from performing probabilistic inference over their inputs. We provide an
analytical understanding of this result by revealing a connection between the
cost of reliability, and the objective for a state-of-the-art Bayesian
inference strategy: variational autoencoders. We show that the same cost leads
to the emergence of increasingly accurate probabilistic representations as
networks become more complex, from single-layer feed-forward, through
multi-layer feed-forward, to recurrent architectures. Our results provide
insights into why neural responses in sensory areas show signatures of
sampling-based probabilistic representations, and may inform future deep
learning algorithms and their implementation in stochastic low-precision
computing systems. | [
0,
0,
0,
0,
1,
0
] |
Title: Microscopic Description of Electric and Magnetic Toroidal Multipoles in Hybrid Orbitals,
Abstract: We present a general formalism of multipole descriptions under the space-time
inversion group. We elucidate that two types of atomic toroidal multipoles,
i.e., electric and magnetic, are fundamental pieces to express electronic order
parameters in addition to ordinary electric and magnetic multipoles. By
deriving quantum-mechanical operators for both toroidal multipoles, we show
that electric (magnetic) toroidal multipole higher than dipole (monopole) can
become a primary order parameter in a hybridized-orbital system. We also
demonstrate emergent cross-correlated couplings between electric, magnetic, and
elastic degrees of freedom, such as magneto-electric and
magneto(electro)-elastic couplings, under toroidal multipole orders. | [
0,
1,
0,
0,
0,
0
] |
Title: Algorithms to Approximate Column-Sparse Packing Problems,
Abstract: Column-sparse packing problems arise in several contexts in both
deterministic and stochastic discrete optimization. We present two unifying
ideas, (non-uniform) attenuation and multiple-chance algorithms, to obtain
improved approximation algorithms for some well-known families of such
problems. As three main examples, we attain the integrality gap, up to
lower-order terms, for known LP relaxations for k-column sparse packing integer
programs (Bansal et al., Theory of Computing, 2012) and stochastic k-set
packing (Bansal et al., Algorithmica, 2012), and go "half the remaining
distance" to optimal for a major integrality-gap conjecture of Furedi, Kahn and
Seymour on hypergraph matching (Combinatorica, 1993). | [
1,
0,
0,
0,
0,
0
] |
Title: Teaching the Doppler Effect in Astrophysics,
Abstract: The Doppler effect is a shift in the frequency of waves emitted from an
object moving relative to the observer. By observing and analysing the Doppler
shift in electromagnetic waves from astronomical objects, astronomers gain
greater insight into the structure and operation of our universe. In this
paper, a simple technique is described for teaching the basics of the Doppler
effect to undergraduate astrophysics students using acoustic waves. An
advantage of the technique is that it produces a visual representation of the
acoustic Doppler shift. The equipment comprises a 40 kHz acoustic transmitter
and a microphone. The sound is bounced off a computer fan and the signal
collected by a DrDAQ ADC and processed by a spectrum analyser. Widening of the
spectrum is observed as the fan power supply potential is increased from 4 to
12 V. | [
0,
1,
0,
0,
0,
0
] |
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