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Title: Two-fermion Bethe-Salpeter Equation in Minkowski Space: the Nakanishi Way,
Abstract: The possibility of solving the Bethe-Salpeter Equation in Minkowski space,
even for fermionic systems, is becoming actual, through the applications of
well-known tools: i) the Nakanishi integral representation of the
Bethe-Salpeter amplitude and ii) the light-front projection onto the
null-plane. The theoretical background and some preliminary calculations are
illustrated, in order to show the potentiality and the wide range of
application of the method. | [
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1,
0,
0,
0,
0
] |
Title: Fractional Laplacians on the sphere, the Minakshisundaram zeta function and semigroups,
Abstract: In this paper we show novel underlying connections between fractional powers
of the Laplacian on the unit sphere and functions from analytic number theory
and differential geometry, like the Hurwitz zeta function and the
Minakshisundaram zeta function. Inspired by Minakshisundaram's ideas, we find a
precise pointwise description of $(-\Delta_{\mathbb{S}^{n-1}})^s u(x)$ in terms
of fractional powers of the Dirichlet-to-Neumann map on the sphere. The Poisson
kernel for the unit ball will be essential for this part of the analysis. On
the other hand, by using the heat semigroup on the sphere, additional pointwise
integro-differential formulas are obtained. Finally, we prove a
characterization with a local extension problem and the interior Harnack
inequality. | [
0,
0,
1,
0,
0,
0
] |
Title: The Ramsey theory of the universal homogeneous triangle-free graph,
Abstract: The universal homogeneous triangle-free graph, constructed by Henson and
denoted $\mathcal{H}_3$, is the triangle-free analogue of the Rado graph. While
the Ramsey theory of the Rado graph has been completely established, beginning
with Erdős-Hajnal-Posá and culminating in work of Sauer and
Laflamme-Sauer-Vuksanovic, the Ramsey theory of $\mathcal{H}_3$ had only
progressed to bounds for vertex colorings (Komjáth-Rödl) and edge
colorings (Sauer). This was due to a lack of broadscale techniques.
We solve this problem in general: For each finite triangle-free graph $G$,
there is a finite number $T(G)$ such that for any coloring of all copies of $G$
in $\mathcal{H}_3$ into finitely many colors, there is a subgraph of
$\mathcal{H}_3$ which is again universal homogeneous triangle-free in which the
coloring takes no more than $T(G)$ colors. This is the first such result for a
homogeneous structure omitting copies of some non-trivial finite structure. The
proof entails developments of new broadscale techniques, including a flexible
method for constructing trees which code $\mathcal{H}_3$ and the development of
their Ramsey theory. | [
0,
0,
1,
0,
0,
0
] |
Title: Five-dimensional Perfect Simplices,
Abstract: Let $Q_n=[0,1]^n$ be the unit cube in ${\mathbb R}^n$, $n \in {\mathbb N}$.
For a nondegenerate simplex $S\subset{\mathbb R}^n$, consider the value
$\xi(S)=\min \{\sigma>0: Q_n\subset \sigma S\}$. Here $\sigma S$ is a
homothetic image of $S$ with homothety center at the center of gravity of $S$
and coefficient of homothety $\sigma$. Let us introduce the value $\xi_n=\min
\{\xi(S): S\subset Q_n\}$. We call $S$ a perfect simplex if $S\subset Q_n$ and
$Q_n$ is inscribed into the simplex $\xi_n S$. It is known that such simplices
exist for $n=1$ and $n=3$. The exact values of $\xi_n$ are known for $n=2$ and
in the case when there exist an Hadamard matrix of order $n+1$, in the latter
situation $\xi_n=n$. In this paper we show that $\xi_5=5$ and $\xi_9=9$. We
also describe infinite families of simplices $S\subset Q_n$ such that
$\xi(S)=\xi_n$ for $n=5,7,9$. The main result of the paper is the existence of
perfect simplices in ${\mathbb R}^5$.
Keywords: simplex, cube, homothety, axial diameter, Hadamard matrix | [
0,
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0
] |
Title: Quantum Fluctuations in Mesoscopic Systems,
Abstract: Recent experimental results point to the existence of coherent quantum
phenomena in systems made of a large number of particles, despite the fact that
for many-body systems the presence of decoherence is hardly negligible and
emerging classicality is expected. This behaviour hinges on collective
observables, named quantum fluctuations, that retain a quantum character even
in the thermodynamic limit: they provide useful tools for studying properties
of many-body systems at the mesoscopic level, in between the quantum
microscopic scale and the classical macroscopic one. We hereby present the
general theory of quantum fluctuations in mesoscopic systems and study their
dynamics in a quantum open system setting, taking into account the unavoidable
effects of dissipation and noise induced by the external environment. As in the
case of microscopic systems, decoherence is not always the only dominating
effect at the mesoscopic scale: certain type of environments can provide means
for entangling collective fluctuations through a purely noisy mechanism. | [
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1,
0,
0,
0,
0
] |
Title: Big Data Model Simulation on a Graph Database for Surveillance in Wireless Multimedia Sensor Networks,
Abstract: Sensors are present in various forms all around the world such as mobile
phones, surveillance cameras, smart televisions, intelligent refrigerators and
blood pressure monitors. Usually, most of the sensors are a part of some other
system with similar sensors that compose a network. One of such networks is
composed of millions of sensors connect to the Internet which is called
Internet of things (IoT). With the advances in wireless communication
technologies, multimedia sensors and their networks are expected to be major
components in IoT. Many studies have already been done on wireless multimedia
sensor networks in diverse domains like fire detection, city surveillance,
early warning systems, etc. All those applications position sensor nodes and
collect their data for a long time period with real-time data flow, which is
considered as big data. Big data may be structured or unstructured and needs to
be stored for further processing and analyzing. Analyzing multimedia big data
is a challenging task requiring a high-level modeling to efficiently extract
valuable information/knowledge from data. In this study, we propose a big
database model based on graph database model for handling data generated by
wireless multimedia sensor networks. We introduce a simulator to generate
synthetic data and store and query big data using graph model as a big
database. For this purpose, we evaluate the well-known graph-based NoSQL
databases, Neo4j and OrientDB, and a relational database, MySQL.We have run a
number of query experiments on our implemented simulator to show that which
database system(s) for surveillance in wireless multimedia sensor networks is
efficient and scalable. | [
1,
0,
0,
0,
0,
0
] |
Title: Solution of linear ill-posed problems by model selection and aggregation,
Abstract: We consider a general statistical linear inverse problem, where the solution
is represented via a known (possibly overcomplete) dictionary that allows its
sparse representation. We propose two different approaches. A model selection
estimator selects a single model by minimizing the penalized empirical risk
over all possible models. By contrast with direct problems, the penalty depends
on the model itself rather than on its size only as for complexity penalties. A
Q-aggregate estimator averages over the entire collection of estimators with
properly chosen weights. Under mild conditions on the dictionary, we establish
oracle inequalities both with high probability and in expectation for the two
estimators. Moreover, for the latter estimator these inequalities are sharp.
The proposed procedures are implemented numerically and their performance is
assessed by a simulation study. | [
0,
0,
1,
0,
0,
0
] |
Title: Long-range dynamical magnetic order and spin tunneling in the cooperative paramagnetic states of the pyrochlore analogous spinel antiferromagnets CdYb2X4 (X = S, Se),
Abstract: Magnetic systems with spins sitting on a lattice of corner sharing regular
tetrahedra have been particularly prolific for the discovery of new magnetic
states for the last two decades. The pyrochlore compounds have offered the
playground for these studies, while little attention has been comparatively
devoted to other compounds where the rare earth R occupies the same
sub-lattice, e.g. the spinel chalcogenides CdR2X4 (X = S, Se). Here we report
measurements performed on powder samples of this series with R = Yb using
specific heat, magnetic susceptibility, neutron diffraction and
muon-spin-relaxation measurements. The two compounds are found to be
magnetically similar. They long-range order into structures described by the
\Gamma_5 irreducible representation. The magnitude of the magnetic moment at
low temperature is 0.77 (1) and 0.62 (1) mu_B for X = S and Se, respectively.
Persistent spin dynamics is present in the ordered states. The spontaneous
field at the muon site is anomalously small, suggesting magnetic moment
fragmentation. A double spin-flip tunneling relaxation mechanism is suggested
in the cooperative paramagnetic state up to 10 K. The magnetic space groups
into which magnetic moments of systems of corner-sharing regular tetrahedra
order are provided for a number of insulating compounds characterized by null
propagation wavevectors. | [
0,
1,
0,
0,
0,
0
] |
Title: Sentiment Analysis by Joint Learning of Word Embeddings and Classifier,
Abstract: Word embeddings are representations of individual words of a text document in
a vector space and they are often use- ful for performing natural language pro-
cessing tasks. Current state of the art al- gorithms for learning word
embeddings learn vector representations from large corpora of text documents in
an unsu- pervised fashion. This paper introduces SWESA (Supervised Word
Embeddings for Sentiment Analysis), an algorithm for sentiment analysis via
word embeddings. SWESA leverages document label infor- mation to learn vector
representations of words from a modest corpus of text doc- uments by solving an
optimization prob- lem that minimizes a cost function with respect to both word
embeddings as well as classification accuracy. Analysis re- veals that SWESA
provides an efficient way of estimating the dimension of the word embeddings
that are to be learned. Experiments on several real world data sets show that
SWESA has superior per- formance when compared to previously suggested
approaches to word embeddings and sentiment analysis tasks. | [
1,
0,
0,
1,
0,
0
] |
Title: Normal-state Properties of a Unitary Bose-Fermi Mixture: A Combined Strong-coupling Approach with Universal Thermodynamics,
Abstract: We theoretically investigate normal-state properties of a unitary Bose-Fermi
mixture. Including strong hetero-pairing fluctuations, we evaluate the Bose and
Fermi chemical potential, internal energy, pressure, entropy, as well as
specific heat at constant volume $C_V$, within the framework of a combined
strong-coupling theory with exact thermodynamic identities. We show that
hetero-pairing fluctuations at the unitarity cause non-monotonic temperature
dependence of $C_V$, being qualitatively different from the monotonic behavior
of this quantity in the weak- and strong-coupling limit. On the other hand,
such an anomalous behavior is not seen in the other quantities. Our results
indicate that the specific heat $C_V$, which has recently become observable in
cold atom physics, is a useful quantity for understanding strong-coupling
aspects of this quantum system. | [
0,
1,
0,
0,
0,
0
] |
Title: Verification Studies for the Noh Problem using Non-ideal Equations of State and Finite Strength Shocks,
Abstract: The Noh verification test problem is extended beyond the commonly studied
ideal gamma-law gas to more realistic equations of state (EOSs) including the
stiff gas, the Noble-Abel gas, and the Carnahan-Starling EOS for hard-sphere
fluids. Self-similarity methods are used to solve the Euler compressible flow
equations, which in combination with the Rankine-Hugoniot jump conditions
provide a tractable general solution. This solution can be applied to fluids
with EOSs that meet criterion such as it being a convex function and having a
corresponding bulk modulus. For the planar case the solution can be applied to
shocks of arbitrary strength, but for cylindrical and spherical geometries it
is required that the analysis be restricted to strong shocks. The exact
solutions are used to perform a variety of quantitative code verification
studies of the Los Alamos National Laboratory Lagrangian hydrocode FLAG. | [
1,
1,
0,
0,
0,
0
] |
Title: Motivic zeta functions and infinite cyclic covers,
Abstract: We associate with an infinite cyclic cover of a punctured neighborhood of a
simple normal crossing divisor on a complex quasi-projective manifold (assuming
certain finiteness conditions are satisfied) a rational function in $K_0({\rm
Var}^{\hat \mu}_{\mathbb{C}})[\mathbb{L}^{-1}]$, which we call {\it motivic
infinite cyclic zeta function}, and show its birational invariance. Our
construction is a natural extension of the notion of {\it motivic infinite
cyclic covers} introduced by the authors, and as such, it generalizes the
Denef-Loeser motivic Milnor zeta function of a complex hypersurface singularity
germ. | [
0,
0,
1,
0,
0,
0
] |
Title: End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks,
Abstract: Recent years have seen a sharp increase in the number of related yet distinct
advances in semantic segmentation. Here, we tackle this problem by leveraging
the respective strengths of these advances. That is, we formulate a conditional
random field over a four-connected graph as end-to-end trainable convolutional
and recurrent networks, and estimate them via an adversarial process.
Importantly, our model learns not only unary potentials but also pairwise
potentials, while aggregating multi-scale contexts and controlling higher-order
inconsistencies. We evaluate our model on two standard benchmark datasets for
semantic face segmentation, achieving state-of-the-art results on both of them. | [
1,
0,
0,
0,
0,
0
] |
Title: Practical Bayesian optimization in the presence of outliers,
Abstract: Inference in the presence of outliers is an important field of research as
outliers are ubiquitous and may arise across a variety of problems and domains.
Bayesian optimization is method that heavily relies on probabilistic inference.
This allows outstanding sample efficiency because the probabilistic machinery
provides a memory of the whole optimization process. However, that virtue
becomes a disadvantage when the memory is populated with outliers, inducing
bias in the estimation. In this paper, we present an empirical evaluation of
Bayesian optimization methods in the presence of outliers. The empirical
evidence shows that Bayesian optimization with robust regression often produces
suboptimal results. We then propose a new algorithm which combines robust
regression (a Gaussian process with Student-t likelihood) with outlier
diagnostics to classify data points as outliers or inliers. By using an
scheduler for the classification of outliers, our method is more efficient and
has better convergence over the standard robust regression. Furthermore, we
show that even in controlled situations with no expected outliers, our method
is able to produce better results. | [
1,
0,
0,
1,
0,
0
] |
Title: Generating Shared Latent Variables for Robots to Imitate Human Movements and Understand their Physical Limitations,
Abstract: Assistive robotics and particularly robot coaches may be very helpful for
rehabilitation healthcare. In this context, we propose a method based on
Gaussian Process Latent Variable Model (GP-LVM) to transfer knowledge between a
physiotherapist, a robot coach and a patient. Our model is able to map visual
human body features to robot data in order to facilitate the robot learning and
imitation. In addition , we propose to extend the model to adapt robots'
understanding to patient's physical limitations during the assessment of
rehabilitation exercises. Experimental evaluation demonstrates promising
results for both robot imitation and model adaptation according to the
patients' limitations. | [
1,
0,
0,
0,
0,
0
] |
Title: Superconvergence analysis of linear FEM based on the polynomial preserving recovery and Richardson extrapolation for Helmholtz equation with high wave number,
Abstract: We study superconvergence property of the linear finite element method with
the polynomial preserving recovery (PPR) and Richardson extrapolation for the
two dimensional Helmholtz equation. The $H^1$-error estimate with explicit
dependence on the wave number $k$ {is} derived.
First, we prove that under the assumption $k(kh)^2\leq C_0$ ($h$ is the mesh
size) and certain mesh condition, the estimate between the finite element
solution and the linear interpolation of the exact solution is superconvergent
under the $H^1$-seminorm, although the pollution error still exists. Second, we
prove a similar result for the recovered gradient by PPR and found that the PPR
can only improve the interpolation error and has no effect on the pollution
error. Furthermore, we estimate the error between the finite element gradient
and recovered gradient and discovered that the pollution error is canceled
between these two quantities. Finally, we apply the Richardson extrapolation to
recovered gradient and demonstrate numerically that PPR combined with the
Richardson extrapolation can reduce the interpolation and pollution errors
simultaneously, and therefore, leads to an asymptotically exact {\it a
posteriori} error estimator. All theoretical findings are verified by numerical
tests. | [
0,
0,
1,
0,
0,
0
] |
Title: Semi-blind source separation with multichannel variational autoencoder,
Abstract: This paper proposes a multichannel source separation technique called the
multichannel variational autoencoder (MVAE) method, which uses a conditional
VAE (CVAE) to model and estimate the power spectrograms of the sources in a
mixture. By training the CVAE using the spectrograms of training examples with
source-class labels, we can use the trained decoder distribution as a universal
generative model capable of generating spectrograms conditioned on a specified
class label. By treating the latent space variables and the class label as the
unknown parameters of this generative model, we can develop a
convergence-guaranteed semi-blind source separation algorithm that consists of
iteratively estimating the power spectrograms of the underlying sources as well
as the separation matrices. In experimental evaluations, our MVAE produced
better separation performance than a baseline method. | [
0,
0,
0,
1,
0,
0
] |
Title: Quantitative characterization of pore structure of several biochars with 3D imaging,
Abstract: Pore space characteristics of biochars may vary depending on the used raw
material and processing technology. Pore structure has significant effects on
the water retention properties of biochar amended soils. In this work, several
biochars were characterized with three-dimensional imaging and image analysis.
X-ray computed microtomography was used to image biochars at resolution of 1.14
$\mu$m and the obtained images were analysed for porosity, pore-size
distribution, specific surface area and structural anisotropy. In addition,
random walk simulations were used to relate structural anisotropy to diffusive
transport. Image analysis showed that considerable part of the biochar volume
consist of pores in size range relevant to hydrological processes and storage
of plant available water. Porosity and pore-size distribution were found to
depend on the biochar type and the structural anisotopy analysis showed that
used raw material considerably affects the pore characteristics at micrometre
scale. Therefore attention should be paid to raw material selection and quality
in applications requiring optimized pore structure. | [
0,
1,
0,
0,
0,
0
] |
Title: Compressed H$_3$S: inter-sublattice Coulomb coupling in a high-$\textit{T}$$_C$ superconductor,
Abstract: Upon thermal annealing at or above room temperature (RT) and high pressure
$\it P$ $\sim$ 155 GPa, H$_3$S exhibits superconductivity at $\it T_C$ $\sim$
200 K. Various theoretical frameworks with strong electron-phonon coupling and
Coulomb repulsion have reproduced this record-level $\it T_C$. Of particular
relevance is that observed H-D isotopic correlations among $\it T_C$, $\it P$,
and annealed order indicate limitations on the H-D isotope effect, leaving open
for consideration unconventional high-$\it T_C$ superconductivity with
electronic-based enhancements. The present work examines Coulombic pairing
arising from interactions between neighboring S and H species on separate
interlaced sublattices constituting H$_3$S in the Im$\overline{3}$m structure.
The optimal transition temperature is calculated from $\it{T}$$_{C0}$ =
$\it{k}$$_B$$^{-1}$$\Lambda$$\it{e}$$^2$/$\ell$$\zeta$, with $\Lambda$ =
0.007465 $\AA$, inter-sublattice S-H separation spacing $\zeta$ =
$\it{a}$$_0$/$\sqrt{2}$, interaction charge linear spacing $\ell$ =
$\it{a}$$_0$(3/$\sigma$)$^{1/2}$, average participating charge fraction
$\sigma$ = 3.43 $\pm$ 0.10 estimated from theory, and lattice parameter
$\it{a}$$_0$ = 3.0823 \AA. The result $\it{T}$$_{C0}$ = 198.5 $\pm$ 3.0 K is in
excellent agreement with transition temperatures determined from resistivity
and susceptibility data. Analysis of mid-infrared reflectivity confirms
correlation between boson energy and $\zeta$$^{-1}$. Suppression of $\it T_C$
with increasing residual resistance for $<$ RT annealing is treated by
scattering-induced pair breaking. Correspondence with layered high-$\it T_C$
superconductor structures are discussed. A model considering Compton scattering
of virtual photons of energies $\leq$ $\it e$$^2$/$\zeta$ by inter-sublattice
electrons is introduced, illustrating $\Lambda$ is proportional to the reduced
electron Compton wavelength. | [
0,
1,
0,
0,
0,
0
] |
Title: Ground state solutions for a nonlinear Choquard equation,
Abstract: We discuss the existence of ground state solutions for the Choquard equation
$$-\Delta u=(I_\alpha*F(u))F'(u)\quad\quad\quad\text{in }\mathbb R^N.$$ We
prove the existence of solutions under general hypotheses, investigating in
particular the case of a homogeneous nonlinearity $F(u)=\frac{|u|^p}p$. The
cases $N=2$ and $N\ge3$ are treated differently in some steps. The solutions
are found through a variational mountain pass strategy. The result presented
are contained in the papers with arXiv ID 1212.2027 and 1604.03294 | [
0,
0,
1,
0,
0,
0
] |
Title: Ultrashort dark solitons interactions and nonlinear tunneling in the modified nonlinear Schrödinger equation with variable coefficients,
Abstract: We present the study of the dark soliton dynamics in an inhomogenous fiber by
means of a variable coefficient modified nonlinear Schrödinger equation
(Vc-MNLSE) with distributed dispersion, self-phase modulation, self-steepening
and linear gain/loss. The ultrashort dark soliton pulse evolution and
interaction is studied by using the Hirota bilinear (HB) method. In particular,
we give much insight into the effect of self-steepening (SS) on the dark
soliton dynamics. The study reveals a shock wave formation, as a major effect
of SS. Numerically, we study the dark soliton propagation in the continuous
wave background, and the stability of the soliton solution is tested in the
presence of photon noise. The elastic collision behaviors of the dark solitons
are discussed by the asymptotic analysis. On the other hand, considering the
nonlinear tunneling of dark soliton through barrier/well, we find that the
tunneling of the dark soliton depends on the height of the barrier and the
amplitude of the soliton. The intensity of the tunneling soliton either forms a
peak or valley and retains its shape after the tunneling. For the case of
exponential background, the soliton tends to compress after tunneling through
the barrier/well. | [
0,
1,
0,
0,
0,
0
] |
Title: Parsec-scale Faraday rotation and polarization of 20 active galactic nuclei jets,
Abstract: We perform polarimetry analysis of 20 active galactic nuclei (AGN) jets using
the Very Long Baseline Array (VLBA) at 1.4, 1.6, 2.2, 2.4, 4.6, 5.0, 8.1, 8.4,
and 15.4 GHz. The study allowed us to investigate linearly polarized properties
of the jets at parsec-scales: distribution of the Faraday rotation measure (RM)
and fractional polarization along the jets, Faraday effects and structure of
Faraday-corrected polarization images. Wavelength-dependence of the fractional
polarization and polarization angle is consistent with external Faraday
rotation, while some sources show internal rotation. The RM changes along the
jets, systematically increasing its value towards synchrotron self-absorbed
cores at shorter wavelengths. The highest core RM reaches 16,900 rad/m^2 in the
source rest frame for the quasar 0952+179, suggesting the presence of highly
magnetized, dense media in these regions. The typical RM of transparent jet
regions has values of an order of a hundred rad/m^2. Significant transverse
rotation measure gradients are observed in seven sources. The magnetic field in
the Faraday screen has no preferred orientation, and is observed to be random
or regular from source to source. Half of the sources show evidence for the
helical magnetic fields in their rotating magnetoionic media. At the same time
jets themselves contain large-scale, ordered magnetic fields and tend to align
its direction with the jet flow. The observed variety of polarized signatures
can be explained by a model of spine-sheath jet structure. | [
0,
1,
0,
0,
0,
0
] |
Title: Optical computing by injection-locked lasers,
Abstract: A programmable optical computer has remained an elusive concept. To construct
a practical computing primitive equivalent to an electronic Boolean logic, one
should find a nonlinear phenomenon that overcomes weaknesses present in many
optical processing schemes. Ideally, the nonlinearity should provide a
functionally complete set of logic operations, enable ultrafast all-optical
programmability, and allow cascaded operations without a change in the
operating wavelength or in the signal encoding format. Here we demonstrate a
programmable logic gate using an injection-locked Vertical-Cavity
Surface-Emitting Laser (VCSEL). The gate program is switched between the AND
and the OR operations at the rate of 1 GHz with Bit Error Ratio (BER) of 10e-6
without changes in the wavelength or in the signal encoding format. The scheme
is based on nonlinearity of normalization operations, which can be used to
construct any continuous complex function or operation, Boolean or otherwise. | [
1,
0,
0,
0,
0,
0
] |
Title: Dynamic anisotropy in MHD turbulence induced by mean magnetic field,
Abstract: In this paper, we study the development of anisotropy in strong MHD
turbulence in the presence of a large scale magnetic field B 0 by analyzing the
results of direct numerical simulations. Our results show that the developed
anisotropy among the different components of the velocity and magnetic field is
a direct outcome of the inverse cascade of energy of the perpendicular velocity
components u? and a forward cascade of the energy of the parallel component u k
. The inverse cascade develops for a strong B0, where the flow exhibits a
strong vortical structure by the suppression of fluctuations along the magnetic
field. Both the inverse and the forward cascade are examined in detail by
investigating the anisotropic energy spectra, the energy fluxes, and the shell
to shell energy transfers among different scales. | [
0,
1,
0,
0,
0,
0
] |
Title: Conformal metrics with prescribed fractional scalar curvature on conformal infinities with positive fractional Yamabe constants,
Abstract: Let $(X, g^+)$ be an asymptotically hyperbolic manifold and $(M, [\hat{h}])$
its conformal infinity. Our primary aim in this paper is to introduce the
prescribed fractional scalar curvature problem on $M$ and provide solutions
under various geometric conditions on $X$ and $M$. We also obtain the existence
results for the fractional Yamabe problem in the endpoint case, e.g., $n = 3$,
$\gamma = 1/2$ and $M$ is non-umbilic, etc. Every solution we find turns out to
be smooth on $M$. | [
0,
0,
1,
0,
0,
0
] |
Title: kd-switch: A Universal Online Predictor with an application to Sequential Two-Sample Testing,
Abstract: We propose a novel online predictor for discrete labels conditioned on
multivariate features in $\mathbb{R}^d$. The predictor is pointwise universal:
it achieves a normalized log loss performance asymptotically as good as the
true conditional entropy of the labels given the features. The predictor is
based on a feature space discretization induced by a full-fledged k-d tree with
randomly picked directions and a switch distribution, requiring no
hyperparameter setting and automatically selecting the most relevant scales in
the feature space. Using recent results, a consistent sequential two-sample
test is built from this predictor. In terms of discrimination power, on
selected challenging datasets, it is comparable to or better than
state-of-the-art non-sequential two-sample tests based on the train-test
paradigm and, a recent sequential test requiring hyperparameters. The time
complexity to process the $n$-th sample point is $O(\log n)$ in probability
(with respect to the distribution generating the data points), in contrast to
the linear complexity of the previous sequential approach. | [
1,
0,
0,
1,
0,
0
] |
Title: A simple alteration of the peridynamics correspondence principle to eliminate zero-energy deformation,
Abstract: We look for an enhancement of the correspondence model of peridynamics with a
view to eliminating the zero-energy deformation modes. Since the non-local
integral definition of the deformation gradient underlies the problem, we
initially look for a remedy by introducing a class of localizing corrections to
the integral. Since the strategy is found to afford only a reduction, and not
complete elimination, of the oscillatory zero-energy deformation, we propose in
the sequel an alternative approach based on the notion of sub-horizons. A most
useful feature of the last proposal is that the setup, whilst providing the
solution with the necessary stability, deviates only marginally from the
original correspondence formulation. We also undertake a set of numerical
simulations that attest to the remarkable efficacy of the sub-horizon based
methodology. | [
0,
1,
0,
0,
0,
0
] |
Title: Binary systems with an RR Lyrae component - progress in 2016,
Abstract: In this contribution, we summarize the progress made in the investigation of
binary candidates with an RR Lyrae component in 2016. We also discuss the
actual status of the RRLyrBinCan database. | [
0,
1,
0,
0,
0,
0
] |
Title: Covering and tiling hypergraphs with tight cycles,
Abstract: Given $3 \leq k \leq s$, we say that a $k$-uniform hypergraph $C^k_s$ is a
tight cycle on $s$ vertices if there is a cyclic ordering of the vertices of
$C^k_s$ such that every $k$ consecutive vertices under this ordering form an
edge. We prove that if $k \ge 3$ and $s \ge 2k^2$, then every $k$-uniform
hypergraph on $n$ vertices with minimum codegree at least $(1/2 + o(1))n$ has
the property that every vertex is covered by a copy of $C^k_s$. Our result is
asymptotically best possible for infinitely many pairs of $s$ and $k$, e.g.
when $s$ and $k$ are coprime.
A perfect $C^k_s$-tiling is a spanning collection of vertex-disjoint copies
of $C^k_s$. When $s$ is divisible by $k$, the problem of determining the
minimum codegree that guarantees a perfect $C^k_s$-tiling was solved by a
result of Mycroft. We prove that if $k \ge 3$ and $s \ge 5k^2$ is not divisible
by $k$ and $s$ divides $n$, then every $k$-uniform hypergraph on $n$ vertices
with minimum codegree at least $(1/2 + 1/(2s) + o(1))n$ has a perfect
$C^k_s$-tiling. Again our result is asymptotically best possible for infinitely
many pairs of $s$ and $k$, e.g. when $s$ and $k$ are coprime with $k$ even. | [
0,
0,
1,
0,
0,
0
] |
Title: Visualizations for an Explainable Planning Agent,
Abstract: In this paper, we report on the visualization capabilities of an Explainable
AI Planning (XAIP) agent that can support human in the loop decision making.
Imposing transparency and explainability requirements on such agents is
especially important in order to establish trust and common ground with the
end-to-end automated planning system. Visualizing the agent's internal
decision-making processes is a crucial step towards achieving this. This may
include externalizing the "brain" of the agent -- starting from its sensory
inputs, to progressively higher order decisions made by it in order to drive
its planning components. We also show how the planner can bootstrap on the
latest techniques in explainable planning to cast plan visualization as a plan
explanation problem, and thus provide concise model-based visualization of its
plans. We demonstrate these functionalities in the context of the automated
planning components of a smart assistant in an instrumented meeting space. | [
1,
0,
0,
0,
0,
0
] |
Title: Generative adversarial interpolative autoencoding: adversarial training on latent space interpolations encourage convex latent distributions,
Abstract: We present a neural network architecture based upon the Autoencoder (AE) and
Generative Adversarial Network (GAN) that promotes a convex latent distribution
by training adversarially on latent space interpolations. By using an AE as
both the generator and discriminator of a GAN, we pass a pixel-wise error
function across the discriminator, yielding an AE which produces non-blurry
samples that match both high- and low-level features of the original images.
Interpolations between images in this space remain within the latent-space
distribution of real images as trained by the discriminator, and therfore
preserve realistic resemblances to the network inputs. | [
0,
0,
0,
1,
0,
0
] |
Title: Attribute-Guided Face Generation Using Conditional CycleGAN,
Abstract: We are interested in attribute-guided face generation: given a low-res face
input image, an attribute vector that can be extracted from a high-res image
(attribute image), our new method generates a high-res face image for the
low-res input that satisfies the given attributes. To address this problem, we
condition the CycleGAN and propose conditional CycleGAN, which is designed to
1) handle unpaired training data because the training low/high-res and high-res
attribute images may not necessarily align with each other, and to 2) allow
easy control of the appearance of the generated face via the input attributes.
We demonstrate impressive results on the attribute-guided conditional CycleGAN,
which can synthesize realistic face images with appearance easily controlled by
user-supplied attributes (e.g., gender, makeup, hair color, eyeglasses). Using
the attribute image as identity to produce the corresponding conditional vector
and by incorporating a face verification network, the attribute-guided network
becomes the identity-guided conditional CycleGAN which produces impressive and
interesting results on identity transfer. We demonstrate three applications on
identity-guided conditional CycleGAN: identity-preserving face superresolution,
face swapping, and frontal face generation, which consistently show the
advantage of our new method. | [
1,
0,
0,
1,
0,
0
] |
Title: Inconsistency of Measure-Theoretic Probability and Random Behavior of Microscopic Systems,
Abstract: We report an inconsistency found in probability theory (also referred to as
measure-theoretic probability). For probability measures induced by real-valued
random variables, we deduce an "equality" such that one side of the "equality"
is a probability, but the other side is not. For probability measures induced
by extended random variables, we deduce an "equality" such that its two sides
are unequal probabilities. The deduced expressions are erroneous only when it
can be proved that measure-theoretic probability is a theory free from
contradiction. However, such a proof does not exist. The inconsistency appears
only in the theory rather than in the physical world, and will not affect
practical applications as long as ideal events in the theory (which will not
occur physically) are not mistaken for observable events in the real world.
Nevertheless, unlike known paradoxes in mathematics, the inconsistency cannot
be explained away and hence must be resolved. The assumption of infinite
additivity in the theory is relevant to the inconsistency, and may cause
confusion of ideal events and real events. As illustrated by an example in this
article, since abstract properties of mathematical entities in theoretical
thinking are not necessarily properties of physical quantities observed in the
real world, mistaking the former for the latter may lead to misinterpreting
random phenomena observed in experiments with microscopic systems. Actually the
inconsistency is due to the notion of "numbers" adopted in conventional
mathematics. A possible way to resolve the inconsistency is to treat "numbers"
from the viewpoint of constructive mathematics. | [
0,
0,
1,
0,
0,
0
] |
Title: Robustness of Quasiparticle Interference Test for Sign-changing Gaps in Multiband Superconductors,
Abstract: Recently, a test for a sign-changing gap function in a candidate multiband
unconventional superconductor involving quasiparticle interference data was
proposed. The test was based on the antisymmetric, Fourier transformed
conductance maps integrated over a range of momenta $\bf q$ corresponding to
interband processes, which was argued to display a particular resonant form,
provided the gaps changed sign between the Fermi surface sheets connected by
$\bf q$. The calculation was performed for a single impurity, however, raising
the question of how robust this measure is as a test of sign-changing pairing
in a realistic system with many impurities. Here we reproduce the results of
the previous work within a model with two distinct Fermi surface sheets, and
show explicitly that the previous result, while exact for a single nonmagnetic
scatterer and also in the limit of a dense set of random impurities, can be
difficult to implement for a few dilute impurities. In this case, however,
appropriate isolation of a single impurity is sufficient to recover the
expected result, allowing a robust statement about the gap signs to be made. | [
0,
1,
0,
0,
0,
0
] |
Title: A distributed primal-dual algorithm for computation of generalized Nash equilibria with shared affine coupling constraints via operator splitting methods,
Abstract: In this paper, we propose a distributed primal-dual algorithm for computation
of a generalized Nash equilibrium (GNE) in noncooperative games over network
systems. In the considered game, not only each player's local objective
function depends on other players' decisions, but also the feasible decision
sets of all the players are coupled together with a globally shared affine
inequality constraint. Adopting the variational GNE, that is the solution of a
variational inequality, as a refinement of GNE, we introduce a primal-dual
algorithm that players can use to seek it in a distributed manner. Each player
only needs to know its local objective function, local feasible set, and a
local block of the affine constraint. Meanwhile, each player only needs to
observe the decisions on which its local objective function explicitly depends
through the interference graph and share information related to multipliers
with its neighbors through a multiplier graph. Through a primal-dual analysis
and an augmentation of variables, we reformulate the problem as finding the
zeros of a sum of monotone operators. Our distributed primal-dual algorithm is
based on forward-backward operator splitting methods. We prove its convergence
to the variational GNE for fixed step-sizes under some mild assumptions. Then a
distributed algorithm with inertia is also introduced and analyzed for
variational GNE seeking. Finally, numerical simulations for network Cournot
competition are given to illustrate the algorithm efficiency and performance. | [
1,
0,
1,
0,
0,
0
] |
Title: Extensions of isomorphisms of subvarieties in flexile varieties,
Abstract: Let $X$ be a quasi-affine algebraic variety isomorphic to the complement of a
closed subvariety of dimension at most $n-3$ in $\C^n$. We find some conditions
under which an isomorphism of two closed subvarieties of $X$ can be extended to
an automorphism of $X$. | [
0,
0,
1,
0,
0,
0
] |
Title: Frequentist coverage and sup-norm convergence rate in Gaussian process regression,
Abstract: Gaussian process (GP) regression is a powerful interpolation technique due to
its flexibility in capturing non-linearity. In this paper, we provide a general
framework for understanding the frequentist coverage of point-wise and
simultaneous Bayesian credible sets in GP regression. As an intermediate
result, we develop a Bernstein von-Mises type result under supremum norm in
random design GP regression. Identifying both the mean and covariance function
of the posterior distribution of the Gaussian process as regularized
$M$-estimators, we show that the sampling distribution of the posterior mean
function and the centered posterior distribution can be respectively
approximated by two population level GPs. By developing a comparison inequality
between two GPs, we provide exact characterization of frequentist coverage
probabilities of Bayesian point-wise credible intervals and simultaneous
credible bands of the regression function. Our results show that inference
based on GP regression tends to be conservative; when the prior is
under-smoothed, the resulting credible intervals and bands have minimax-optimal
sizes, with their frequentist coverage converging to a non-degenerate value
between their nominal level and one. As a byproduct of our theory, we show that
the GP regression also yields minimax-optimal posterior contraction rate
relative to the supremum norm, which provides a positive evidence to the long
standing problem on optimal supremum norm contraction rate in GP regression. | [
0,
0,
1,
1,
0,
0
] |
Title: Most Probable Evolution Trajectories in a Genetic Regulatory System Excited by Stable Lévy Noise,
Abstract: We study the most probable trajectories of the concentration evolution for
the transcription factor activator in a genetic regulation system, with
non-Gaussian stable Lévy noise in the synthesis reaction rate taking into
account. We calculate the most probable trajectory by spatially maximizing the
probability density of the system path, i.e., the solution of the associated
nonlocal Fokker-Planck equation. We especially examine those most probable
trajectories from low concentration state to high concentration state (i.e.,
the likely transcription regime) for certain parameters, in order to gain
insights into the transcription processes and the tipping time for the
transcription likely to occur. This enables us: (i) to visualize the progress
of concentration evolution (i.e., observe whether the system enters the
transcription regime within a given time period); (ii) to predict or avoid
certain transcriptions via selecting specific noise parameters in particular
regions in the parameter space. Moreover, we have found some peculiar or
counter-intuitive phenomena in this gene model system, including (a) a smaller
noise intensity may trigger the transcription process, while a larger noise
intensity can not, under the same asymmetric Lévy noise. This phenomenon does
not occur in the case of symmetric Lévy noise; (b) the symmetric Lévy
motion always induces transition to high concentration, but certain asymmetric
Lévy motions do not trigger the switch to transcription. These findings
provide insights for further experimental research, in order to achieve or to
avoid specific gene transcriptions, with possible relevance for medical
advances. | [
0,
0,
0,
0,
1,
0
] |
Title: Optically Coupled Methods for Microwave Impedance Microscopy,
Abstract: Scanning Microwave Impedance Microscopy (MIM) measurement of
photoconductivity with 50 nm resolution is demonstrated using a modulated
optical source. The use of a modulated source allows for measurement of
photoconductivity in a single scan without a reference region on the sample, as
well as removing most topographical artifacts and enhancing signal to noise as
compared with unmodulated measurement. A broadband light source with tunable
monochrometer is then used to measure energy resolved photoconductivity with
the same methodology. Finally, a pulsed optical source is used to measure local
photo-carrier lifetimes via MIM, using the same 50 nm resolution tip. | [
0,
1,
0,
0,
0,
0
] |
Title: Generalized feedback vertex set problems on bounded-treewidth graphs: chordality is the key to single-exponential parameterized algorithms,
Abstract: It has long been known that Feedback Vertex Set can be solved in time
$2^{\mathcal{O}(w\log w)}n^{\mathcal{O}(1)}$ on $n$-vertex graphs of treewidth
$w$, but it was only recently that this running time was improved to
$2^{\mathcal{O}(w)}n^{\mathcal{O}(1)}$, that is, to single-exponential
parameterized by treewidth. We investigate which generalizations of Feedback
Vertex Set can be solved in a similar running time. Formally, for a class
$\mathcal{P}$ of graphs, the Bounded $\mathcal{P}$-Block Vertex Deletion
problem asks, given a graph~$G$ on $n$ vertices and positive integers~$k$
and~$d$, whether $G$ contains a set~$S$ of at most $k$ vertices such that each
block of $G-S$ has at most $d$ vertices and is in $\mathcal{P}$. Assuming that
$\mathcal{P}$ is recognizable in polynomial time and satisfies a certain
natural hereditary condition, we give a sharp characterization of when
single-exponential parameterized algorithms are possible for fixed values of
$d$: if $\mathcal{P}$ consists only of chordal graphs, then the problem can be
solved in time $2^{\mathcal{O}(wd^2)} n^{\mathcal{O}(1)}$, and if $\mathcal{P}$
contains a graph with an induced cycle of length $\ell\ge 4$, then the problem
is not solvable in time $2^{o(w\log w)} n^{\mathcal{O}(1)}$ even for fixed
$d=\ell$, unless the ETH fails. We also study a similar problem, called Bounded
$\mathcal{P}$-Component Vertex Deletion, where the target graphs have connected
components of small size rather than blocks of small size, and we present
analogous results. For this problem, we also show that if $d$ is part of the
input and $\mathcal{P}$ contains all chordal graphs, then it cannot be solved
in time $f(w)n^{o(w)}$ for some function $f$, unless the ETH fails. | [
1,
0,
0,
0,
0,
0
] |
Title: Laser Wakefield Accelerators,
Abstract: The one-dimensional wakefield generation equations are solved for increasing
levels of non-linearity, to demonstrate how they contribute to the overall
behaviour of a non-linear wakefield in a plasma. The effect of laser guiding is
also studied as a way to increase the interaction length of a laser wakefield
accelerator. | [
0,
1,
0,
0,
0,
0
] |
Title: Permutation complexity of images of Sturmian words by marked morphisms,
Abstract: We show that the permutation complexity of the image of a Sturmian word by a
binary marked morphism is $n+k$ for some constant $k$ and all lengths $n$
sufficiently large. | [
1,
0,
0,
0,
0,
0
] |
Title: Three principles of data science: predictability, computability, and stability (PCS),
Abstract: We propose the predictability, computability, and stability (PCS) framework
to extract reproducible knowledge from data that can guide scientific
hypothesis generation and experimental design. The PCS framework builds on key
ideas in machine learning, using predictability as a reality check and
evaluating computational considerations in data collection, data storage, and
algorithm design. It augments PC with an overarching stability principle, which
largely expands traditional statistical uncertainty considerations. In
particular, stability assesses how results vary with respect to choices (or
perturbations) made across the data science life cycle, including problem
formulation, pre-processing, modeling (data and algorithm perturbations), and
exploratory data analysis (EDA) before and after modeling.
Furthermore, we develop PCS inference to investigate the stability of data
results and identify when models are consistent with relatively simple
phenomena. We compare PCS inference with existing methods, such as selective
inference, in high-dimensional sparse linear model simulations to demonstrate
that our methods consistently outperform others in terms of ROC curves over a
wide range of simulation settings. Finally, we propose a PCS documentation
based on Rmarkdown, iPython, or Jupyter Notebook, with publicly available,
reproducible codes and narratives to back up human choices made throughout an
analysis. The PCS workflow and documentation are demonstrated in a genomics
case study available on Zenodo. | [
1,
0,
0,
1,
0,
0
] |
Title: The Elasticity of Puiseux Monoids,
Abstract: Let $M$ be an atomic monoid and let $x$ be a non-unit element of $M$. The
elasticity of $x$, denoted by $\rho(x)$, is the ratio of its largest
factorization length to its shortest factorization length, and it measures how
far is $x$ from having a unique factorization. The elasticity $\rho(M)$ of $M$
is the supremum of the elasticities of all non-unit elements of $M$. The monoid
$M$ has accepted elasticity if $\rho(M) = \rho(m)$ for some $m \in M$. In this
paper, we study the elasticity of Puiseux monoids (i.e., additive submonoids of
$\mathbb{Q}_{\ge 0}$). First, we characterize the Puiseux monoids $M$ having
finite elasticity and find a formula for $\rho(M)$. Then we classify the
Puiseux monoids having accepted elasticity in terms of their sets of atoms.
When $M$ is a primary Puiseux monoid, we describe the topology of the set of
elasticities of $M$, including a characterization of when $M$ is a bounded
factorization monoid. Lastly, we give an example of a Puiseux monoid that is
bifurcus (that is, every nonzero element has a factorization of length at most
$2$). | [
0,
0,
1,
0,
0,
0
] |
Title: On the extremal extensions of a non-negative Jacobi operator,
Abstract: We consider minimal non-negative Jacobi operator with $p\times p-$matrix
entries. Using the technique of boundary triplets and the corresponding Weyl
functions, we describe the Friedrichs and Krein extensions of the minimal
Jacobi operator. Moreover, we parameterize the set of all non-negative
self-adjoint extensions in terms of boundary conditions. | [
0,
0,
1,
0,
0,
0
] |
Title: A Next-Best-Smell Approach for Remote Gas Detection with a Mobile Robot,
Abstract: The problem of gas detection is relevant to many real-world applications,
such as leak detection in industrial settings and landfill monitoring. Using
mobile robots for gas detection has several advantages and can reduce danger
for humans. In our work, we address the problem of planning a path for a mobile
robotic platform equipped with a remote gas sensor, which minimizes the time to
detect all gas sources in a given environment. We cast this problem as a
coverage planning problem by defining a basic sensing operation -- a scan with
the remote gas sensor -- as the field of "view" of the sensor. Given the
computing effort required by previously proposed offline approaches, in this
paper we suggest a online coverage algorithm, called Next-Best-Smell, adapted
from the Next-Best-View class of exploration algorithms. Our algorithm
evaluates candidate locations with a global utility function, which combines
utility values for travel distance, information gain, and sensing time, using
Multi-Criteria Decision Making. In our experiments, conducted both in
simulation and with a real robot, we found the performance of the
Next-Best-Smell approach to be comparable with that of the state-of-the-art
offline algorithm, at much lower computational cost. | [
1,
0,
0,
0,
0,
0
] |
Title: KZ-Calogero correspondence revisited,
Abstract: We discuss the correspondence between the Knizhnik-Zamolodchikov equations
associated with $GL(N)$ and the $n$-particle quantum Calogero model in the case
when $n$ is not necessarily equal to $N$. This can be viewed as a natural
"quantization" of the quantum-classical correspondence between quantum Gaudin
and classical Calogero models. | [
0,
1,
1,
0,
0,
0
] |
Title: High Rate LDPC Codes from Difference Covering Arrays,
Abstract: This paper presents a combinatorial construction of low-density parity-check
(LDPC) codes from difference covering arrays. While the original construction
by Gallagher was by randomly allocating bits in a sparse parity-check matrix,
over the past 20 years researchers have used a variety of more structured
approaches to construct these codes, with the more recent constructions of
well-structured LDPC coming from balanced incomplete block designs (BIBDs) and
from Latin squares over finite fields. However these constructions have
suffered from the limited orders for which these designs exist. Here we present
a construction of LDPC codes of length $4n^2 - 2n$ for all $n$ using the cyclic
group of order $2n$. These codes achieve high information rate (greater than
0.8) for $n \geq 8$, have girth at least 6 and have minimum distance 6 for $n$
odd. | [
1,
0,
1,
0,
0,
0
] |
Title: Bootstrapping spectral statistics in high dimensions,
Abstract: Statistics derived from the eigenvalues of sample covariance matrices are
called spectral statistics, and they play a central role in multivariate
testing. Although bootstrap methods are an established approach to
approximating the laws of spectral statistics in low-dimensional problems,
these methods are relatively unexplored in the high-dimensional setting. The
aim of this paper is to focus on linear spectral statistics as a class of
prototypes for developing a new bootstrap in high-dimensions --- and we refer
to this method as the Spectral Bootstrap. In essence, the method originates
from the parametric bootstrap, and is motivated by the notion that, in high
dimensions, it is difficult to obtain a non-parametric approximation to the
full data-generating distribution. From a practical standpoint, the method is
easy to use, and allows the user to circumvent the difficulties of complex
asymptotic formulas for linear spectral statistics. In addition to proving the
consistency of the proposed method, we provide encouraging empirical results in
a variety of settings. Lastly, and perhaps most interestingly, we show through
simulations that the method can be applied successfully to statistics outside
the class of linear spectral statistics, such as the largest sample eigenvalue
and others. | [
0,
0,
0,
1,
0,
0
] |
Title: Weakly Supervised Classification in High Energy Physics,
Abstract: As machine learning algorithms become increasingly sophisticated to exploit
subtle features of the data, they often become more dependent on simulations.
This paper presents a new approach called weakly supervised classification in
which class proportions are the only input into the machine learning algorithm.
Using one of the most challenging binary classification tasks in high energy
physics - quark versus gluon tagging - we show that weakly supervised
classification can match the performance of fully supervised algorithms.
Furthermore, by design, the new algorithm is insensitive to any mis-modeling of
discriminating features in the data by the simulation. Weakly supervised
classification is a general procedure that can be applied to a wide variety of
learning problems to boost performance and robustness when detailed simulations
are not reliable or not available. | [
0,
1,
0,
1,
0,
0
] |
Title: Carrier loss mechanisms in textured crystalline Si-based solar cells,
Abstract: A quite general device analysis method that allows the direct evaluation of
optical and recombination losses in crystalline silicon (c-Si)-based solar
cells has been developed. By applying this technique, the optical and physical
limiting factors of the state-of-the-art solar cells with ~20% efficiencies
have been revealed. In the established method, the carrier loss mechanisms are
characterized from the external quantum efficiency (EQE) analysis with very low
computational cost. In particular, the EQE analyses of textured c-Si solar
cells are implemented by employing the experimental reflectance spectra
obtained directly from the actual devices while using flat optical models
without any fitting parameters. We find that the developed method provides
almost perfect fitting to EQE spectra reported for various textured c-Si solar
cells, including c-Si heterojunction solar cells, a dopant-free c-Si solar cell
with a MoOx layer, and an n-type passivated emitter with rear locally diffused
(PERL) solar cell. The modeling of the recombination loss further allows the
extraction of the minority carrier diffusion length and surface recombination
velocity from the EQE analysis. Based on the EQE analysis results, the carrier
loss mechanisms in different types of c-Si solar cells are discussed. | [
0,
1,
0,
0,
0,
0
] |
Title: The generating function for the Airy point process and a system of coupled Painlevé II equations,
Abstract: For a wide class of Hermitian random matrices, the limit distribution of the
eigenvalues close to the largest one is governed by the Airy point process. In
such ensembles, the limit distribution of the k-th largest eigenvalue is given
in terms of the Airy kernel Fredholm determinant or in terms of Tracy-Widom
formulas involving solutions of the Painlevé II equation. Limit distributions
for quantities involving two or more near-extreme eigenvalues, such as the gap
between the k-th and the \ell-th largest eigenvalue or the sum of the k largest
eigenvalues, can be expressed in terms of Fredholm determinants of an Airy
kernel with several discontinuities. We establish simple Tracy-Widom type
expressions for these Fredholm determinants, which involve solutions to systems
of coupled Painlevé II equations, and we investigate the asymptotic behavior
of these solutions. | [
0,
0,
1,
0,
0,
0
] |
Title: Multilayer Network Modeling of Integrated Biological Systems,
Abstract: Biological systems, from a cell to the human brain, are inherently complex. A
powerful representation of such systems, described by an intricate web of
relationships across multiple scales, is provided by complex networks.
Recently, several studies are highlighting how simple networks -- obtained by
aggregating or neglecting temporal or categorical description of biological
data -- are not able to account for the richness of information characterizing
biological systems. More complex models, namely multilayer networks, are needed
to account for interdependencies, often varying across time, of biological
interacting units within a cell, a tissue or parts of an organism. | [
0,
0,
0,
0,
1,
0
] |
Title: Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks,
Abstract: Efforts to reduce the numerical precision of computations in deep learning
training have yielded systems that aggressively quantize weights and
activations, yet employ wide high-precision accumulators for partial sums in
inner-product operations to preserve the quality of convergence. The absence of
any framework to analyze the precision requirements of partial sum
accumulations results in conservative design choices. This imposes an
upper-bound on the reduction of complexity of multiply-accumulate units. We
present a statistical approach to analyze the impact of reduced accumulation
precision on deep learning training. Observing that a bad choice for
accumulation precision results in loss of information that manifests itself as
a reduction in variance in an ensemble of partial sums, we derive a set of
equations that relate this variance to the length of accumulation and the
minimum number of bits needed for accumulation. We apply our analysis to three
benchmark networks: CIFAR-10 ResNet 32, ImageNet ResNet 18 and ImageNet
AlexNet. In each case, with accumulation precision set in accordance with our
proposed equations, the networks successfully converge to the single precision
floating-point baseline. We also show that reducing accumulation precision
further degrades the quality of the trained network, proving that our equations
produce tight bounds. Overall this analysis enables precise tailoring of
computation hardware to the application, yielding area- and power-optimal
systems. | [
1,
0,
0,
1,
0,
0
] |
Title: Rates of estimation for determinantal point processes,
Abstract: Determinantal point processes (DPPs) have wide-ranging applications in
machine learning, where they are used to enforce the notion of diversity in
subset selection problems. Many estimators have been proposed, but surprisingly
the basic properties of the maximum likelihood estimator (MLE) have received
little attention. In this paper, we study the local geometry of the expected
log-likelihood function to prove several rates of convergence for the MLE. We
also give a complete characterization of the case where the MLE converges at a
parametric rate. Even in the latter case, we also exhibit a potential curse of
dimensionality where the asymptotic variance of the MLE is exponentially large
in the dimension of the problem. | [
0,
0,
1,
1,
0,
0
] |
Title: Negative refraction in Weyl semimetals,
Abstract: We theoretically propose that Weyl semimetals may exhibit negative refraction
at some frequencies close to the plasmon frequency, allowing transverse
magnetic (TM) electromagnetic waves with frequencies smaller than the plasmon
frequency to propagate in the Weyl semimetals. The idea is justified by the
calculation of reflection spectra, in which \textit{negative} refractive index
at such frequencies gives physically correct spectra. In this case, a TM
electromagnetic wave incident to the surface of the Weyl semimetal will be bent
with a negative angle of refraction. We argue that the negative refractive
index at the specified frequencies of the electromagnetic wave is required to
conserve the energy of the wave, in which the incident energy should propagate
away from the point of incidence. | [
0,
1,
0,
0,
0,
0
] |
Title: Towards Object Life Cycle-Based Variant Generation of Business Process Models,
Abstract: Variability management of process models is a major challenge for
Process-Aware Information Systems. Process model variants can be attributed to
any of the following reasons: new technologies, governmental rules,
organizational context or adoption of new standards. Current approaches to
manage variants of process models address issues such as reducing the huge
effort of modeling from scratch, preventing redundancy, and controlling
inconsistency in process models. Although the effort to manage process model
variants has been exerted, there are still limitations. Furthermore, existing
approaches do not focus on variants that come from change in organizational
perspective of process models. Organizational-driven variant management is an
important area that still needs more study that we focus on in this paper.
Object Life Cycle (OLC) is an important aspect that may change from an
organization to another. This paper introduces an approach inspired by real
life scenario to generate consistent process model variants that come from
adaptations in the OLC. | [
1,
0,
0,
0,
0,
0
] |
Title: Online Multi-Label Classification: A Label Compression Method,
Abstract: Many modern applications deal with multi-label data, such as functional
categorizations of genes, image labeling and text categorization.
Classification of such data with a large number of labels and latent
dependencies among them is a challenging task, and it becomes even more
challenging when the data is received online and in chunks. Many of the current
multi-label classification methods require a lot of time and memory, which make
them infeasible for practical real-world applications. In this paper, we
propose a fast linear label space dimension reduction method that transforms
the labels into a reduced encoded space and trains models on the obtained
pseudo labels. Additionally, it provides an analytical method to update the
decoding matrix which maps the labels into the original space and is used
during the test phase. Experimental results show the effectiveness of this
approach in terms of running times and the prediction performance over
different measures. | [
0,
0,
0,
1,
0,
0
] |
Title: Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming,
Abstract: The need to efficiently calculate first- and higher-order derivatives of
increasingly complex models expressed in Python has stressed or exceeded the
capabilities of available tools. In this work, we explore techniques from the
field of automatic differentiation (AD) that can give researchers expressive
power, performance and strong usability. These include source-code
transformation (SCT), flexible gradient surgery, efficient in-place array
operations, higher-order derivatives as well as mixing of forward and reverse
mode AD. We implement and demonstrate these ideas in the Tangent software
library for Python, the first AD framework for a dynamic language that uses
SCT. | [
1,
0,
0,
0,
0,
0
] |
Title: Evidence for coherent spicule oscillations by correcting Hinode/SOT Ca II H in the southeast limb of the Sun,
Abstract: Wave theories of heating the chromosphere, corona, and solar wind due to
photospheric fluctuations are strengthened by the existence of observed wave
coherency up to the transition region (TR). The coherency of solar spicules'
intensity oscillations was explored using the Solar Optical Telescope (SOT) on
the Hinode spacecraft with a height increase above the solar limb in active
region (AR). We used time sequences near the southeast region from the
Hinode/SOT in Ca II H line obtained on April 3, 2015 and applied the
de-convolution procedure to the spicule in order to illustrate how effectively
our restoration method works on fine structures such as spicules. Moreover, the
intensity oscillations at different heights above the solar limb were analysed
through wavelet transforms. Afterwards, the phase difference was measured among
oscillations at two certain heights in search of evidence for coherent
oscillations. The results of wavelet transformations revealed dominant period
peaks in 2, 4, 5.5, and 6.5 min at four separate heights. The dominant
frequencies for coherency level higher than 75 percent was found to be around
5.5 and 8.5 mHz. Mean phase speeds of 155-360 km s-1 were measured. We found
that the mean phase speeds increased with height. The results suggest that the
energy flux carried by coherent waves into the corona and heliosphere may be
several times larger than previous estimates that were based solely on constant
velocities. We provide compelling evidence for the existence of upwardly
propagating coherent waves. | [
0,
1,
0,
0,
0,
0
] |
Title: Opinion diversity and community formation in adaptive networks,
Abstract: It is interesting and of significant importance to investigate how network
structures co-evolve with opinions. The existing models of such co-evolution
typically lead to the final states where network nodes either reach a global
consensus or break into separated communities, each of which holding its own
community consensus. Such results, however, can hardly explain the richness of
real-life observations that opinions are always diversified with no global or
even community consensus, and people seldom, if not never, totally cut off
themselves from dissenters. In this article, we show that, a simple model
integrating consensus formation, link rewiring and opinion change allows
complex system dynamics to emerge, driving the system into a dynamic
equilibrium with co-existence of diversified opinions. Specifically, similar
opinion holders may form into communities yet with no strict community
consensus; and rather than being separated into disconnected communities,
different communities remain to be interconnected by non-trivial proportion of
inter-community links. More importantly, we show that the complex dynamics may
lead to different numbers of communities at steady state with a given tolerance
between different opinion holders. We construct a framework for theoretically
analyzing the co-evolution process. Theoretical analysis and extensive
simulation results reveal some useful insights into the complex co-evolution
process, including the formation of dynamic equilibrium, the phase transition
between different steady states with different numbers of communities, and the
dynamics between opinion distribution and network modularity, etc. | [
1,
1,
0,
0,
0,
0
] |
Title: On the local and global comparison of generalized Bajraktarević means,
Abstract: Given two continuous functions $f,g:I\to\mathbb{R}$ such that $g$ is positive
and $f/g$ is strictly monotone, a measurable space $(T,A)$, a measurable family
of $d$-variable means $m: I^d\times T\to I$, and a probability measure $\mu$ on
the measurable sets $A$, the $d$-variable mean $M_{f,g,m;\mu}:I^d\to I$ is
defined by $$
M_{f,g,m;\mu}(\pmb{x})
:=\left(\frac{f}{g}\right)^{-1}\left(
\frac{\int_T f\big(m(x_1,\dots,x_d,t)\big) d\mu(t)}
{\int_T g\big(m(x_1,\dots,x_d,t)\big) d\mu(t)}\right)
\qquad(\pmb{x}=(x_1,\dots,x_d)\in I^d). $$ The aim of this paper is to study
the local and global comparison problem of these means, i.e., to find
conditions for the generating functions $(f,g)$ and $(h,k)$, for the families
of means $m$ and $n$, and for the measures $\mu,\nu$ such that the comparison
inequality $$
M_{f,g,m;\mu}(\pmb{x})\leq M_{h,k,n;\nu}(\pmb{x}) \qquad(\pmb{x}\in I^d) $$
be satisfied. | [
0,
0,
1,
0,
0,
0
] |
Title: Fixed points of diffeomorphisms on nilmanifolds with a free nilpotent fundamental group,
Abstract: Let $M$ be a nilmanifold with a fundamental group which is free $2$-step
nilpotent on at least 4 generators. We will show that for any nonnegative
integer $n$ there exists a self-diffeomorphism $h_n$ of $M$ such that $h_n$ has
exactly $n$ fixed points and any self-map $f$ of $M$ which is homotopic to
$h_n$ has at least $n$ fixed points. We will also shed some light on the
situation for less generators and also for higher nilpotency classes. | [
0,
0,
1,
0,
0,
0
] |
Title: Offloading Execution from Edge to Cloud: a Dynamic Node-RED Based Approach,
Abstract: Fog computing enables use cases where data produced in end devices are
stored, processed, and acted on directly at the edges of the network, yet
computation can be offloaded to more powerful instances through the edge to
cloud continuum. Such offloading mechanism is especially needed in case of
modern multi-purpose IoT gateways, where both demand and operation conditions
can vary largely between deployments. To facilitate the development and
operations of gateways, we implement offloading directly as part of the IoT
rapid prototyping process embedded in the software stack, based on Node-RED. We
evaluate the implemented method using an image processing example, and compare
various offloading strategies based on resource consumption and other system
metrics, highlighting the differences in handling demand and service levels
reached. | [
1,
0,
0,
0,
0,
0
] |
Title: A nested sampling code for targeted searches for continuous gravitational waves from pulsars,
Abstract: This document describes a code to perform parameter estimation and model
selection in targeted searches for continuous gravitational waves from known
pulsars using data from ground-based gravitational wave detectors. We describe
the general workings of the code and characterise it on simulated data
containing both noise and simulated signals. We also show how it performs
compared to a previous MCMC and grid-based approach to signal parameter
estimation. Details how to run the code in a variety of cases are provided in
Appendix A. | [
0,
1,
0,
0,
0,
0
] |
Title: Learning Sparse Adversarial Dictionaries For Multi-Class Audio Classification,
Abstract: Audio events are quite often overlapping in nature, and more prone to noise
than visual signals. There has been increasing evidence for the superior
performance of representations learned using sparse dictionaries for
applications like audio denoising and speech enhancement. This paper
concentrates on modifying the traditional reconstructive dictionary learning
algorithms, by incorporating a discriminative term into the objective function
in order to learn class-specific adversarial dictionaries that are good at
representing samples of their own class at the same time poor at representing
samples belonging to any other class. We quantitatively demonstrate the
effectiveness of our learned dictionaries as a stand-alone solution for both
binary as well as multi-class audio classification problems. | [
1,
0,
0,
0,
0,
0
] |
Title: Matter-wave solutions in the Bose-Einstein condensates with the harmonic and Gaussian potentials,
Abstract: We study exact solutions of the quasi-one-dimensional Gross-Pitaevskii (GP)
equation with the (space, time)-modulated potential and nonlinearity and the
time-dependent gain or loss term in Bose-Einstein condensates. In particular,
based on the similarity transformation, we report several families of exact
solutions of the GP equation in the combination of the harmonic and Gaussian
potentials, in which some physically relevant solutions are described. The
stability of the obtained matter-wave solutions is addressed numerically such
that some stable solutions are found. Moreover, we also analyze the parameter
regimes for the stable solutions. These results may raise the possibility of
relative experiments and potential applications. | [
0,
1,
1,
0,
0,
0
] |
Title: Histogram Transform-based Speaker Identification,
Abstract: A novel text-independent speaker identification (SI) method is proposed. This
method uses the Mel-frequency Cepstral coefficients (MFCCs) and the dynamic
information among adjacent frames as feature sets to capture speaker's
characteristics. In order to utilize dynamic information, we design super-MFCCs
features by cascading three neighboring MFCCs frames together. The probability
density function (PDF) of these super-MFCCs features is estimated by the
recently proposed histogram transform~(HT) method, which generates more
training data by random transforms to realize the histogram PDF estimation and
recedes the commonly occurred discontinuity problem in multivariate histograms
computing. Compared to the conventional PDF estimation methods, such as
Gaussian mixture models, the HT model shows promising improvement in the SI
performance. | [
1,
0,
0,
1,
0,
0
] |
Title: Comment on "Kinetic decoupling of WIMPs: Analytic expressions",
Abstract: Visinelli and Gondolo (2015, hereafter VG15) derived analytic expressions for
the evolution of the dark matter temperature in a generic cosmological model.
They then calculated the dark matter kinetic decoupling temperature
$T_{\mathrm{kd}}$ and compared their results to the Gelmini and Gondolo (2008,
hereafter GG08) calculation of $T_{\mathrm{kd}}$ in an early matter-dominated
era (EMDE), which occurs when the Universe is dominated by either a decaying
oscillating scalar field or a semistable massive particle before Big Bang
nucleosynthesis. VG15 found that dark matter decouples at a lower temperature
in an EMDE than it would in a radiation-dominated era, while GG08 found that
dark matter decouples at a higher temperature in an EMDE than it would in a
radiation-dominated era. VG15 attributed this discrepancy to the presence of a
matching constant that ensures that the dark matter temperature is continuous
during the transition from the EMDE to the subsequent radiation-dominated era
and concluded that the GG08 result is incorrect. We show that the disparity is
due to the fact that VG15 compared $T_\mathrm{kd}$ in an EMDE to the decoupling
temperature in a radiation-dominated universe that would result in the same
dark matter temperature at late times. Since decoupling during an EMDE leaves
the dark matter colder than it would be if it decoupled during radiation
domination, this temperature is much higher than $T_\mathrm{kd}$ in a standard
thermal history, which is indeed lower than $T_{\mathrm{kd}}$ in an EMDE, as
stated by GG08. | [
0,
1,
0,
0,
0,
0
] |
Title: Dimension preserving resolutions of singularities of Poisson structures,
Abstract: Some Poisson structures do admit resolutions by symplectic manifolds of the
same dimension. We give examples and simple conditions under which such
resolutions can not exist. | [
0,
0,
1,
0,
0,
0
] |
Title: Iterative Amortized Inference,
Abstract: Inference models are a key component in scaling variational inference to deep
latent variable models, most notably as encoder networks in variational
auto-encoders (VAEs). By replacing conventional optimization-based inference
with a learned model, inference is amortized over data examples and therefore
more computationally efficient. However, standard inference models are
restricted to direct mappings from data to approximate posterior estimates. The
failure of these models to reach fully optimized approximate posterior
estimates results in an amortization gap. We aim toward closing this gap by
proposing iterative inference models, which learn to perform inference
optimization through repeatedly encoding gradients. Our approach generalizes
standard inference models in VAEs and provides insight into several empirical
findings, including top-down inference techniques. We demonstrate the inference
optimization capabilities of iterative inference models and show that they
outperform standard inference models on several benchmark data sets of images
and text. | [
0,
0,
0,
1,
0,
0
] |
Title: Magnifying the early episodes of star formation: super star clusters at cosmological distances,
Abstract: We study the spectrophotometric properties of a highly magnified (\mu~40-70)
pair of stellar systems identified at z=3.2222 behind the Hubble Frontier Field
galaxy cluster MACS~J0416. Five multiple images (out of six) have been
spectroscopically confirmed by means of VLT/MUSE and VLT/X-Shooter
observations. Each image includes two faint (m_uv~30.6), young (<100 Myr),
low-mass (<10^7 Msun), low-metallicity (12+Log(O/H)~7.7, or 1/10 solar) and
compact (30 pc effective radius) stellar systems separated by ~300pc, after
correcting for lensing amplification. We measured several rest-frame
ultraviolet and optical narrow (\sigma_v <~ 25 km/s) high-ionization lines.
These features may be the signature of very hot (T>50000 K) stars within dense
stellar clusters, whose dynamical mass is likely dominated by the stellar
component. Remarkably, the ultraviolet metal lines are not accompanied by Lya
emission (e.g., CIV / Lya > 15), despite the fact that the Lya line flux is
expected to be 150 times brighter (inferred from the Hbeta flux). A
spatially-offset, strongly-magnified (\mu>50) Lya emission with a spatial
extent <~7.6 kpc^2 is instead identified 2 kpc away from the system. The origin
of such a faint emission can be the result of fluorescent Lya induced by a
transverse leakage of ionizing radiation emerging from the stellar systems
and/or can be associated to an underlying and barely detected object (with m_uv
> 34 de-lensed). This is the first confirmed metal-line emitter at such
low-luminosity and redshift without Lya emission, suggesting that, at least in
some cases, a non-uniform covering factor of the neutral gas might hamper the
Lya detection. | [
0,
1,
0,
0,
0,
0
] |
Title: Ergodicity of a system of interacting random walks with asymmetric interaction,
Abstract: We study N interacting random walks on the positive integers. Each particle
has drift {\delta} towards infinity, a reflection at the origin, and a drift
towards particles with lower positions. This inhomogeneous mean field system is
shown to be ergodic only when the interaction is strong enough. We focus on
this latter regime, and point out the effect of piles of particles, a
phenomenon absent in models of interacting diffusion in continuous space. | [
0,
0,
1,
0,
0,
0
] |
Title: Extreme value statistics for censored data with heavy tails under competing risks,
Abstract: This paper addresses the problem of estimating, in the presence of random
censoring as well as competing risks, the extreme value index of the
(sub)-distribution function associated to one particular cause, in the
heavy-tail case. Asymptotic normality of the proposed estimator (which has the
form of an Aalen-Johansen integral, and is the first estimator proposed in this
context) is established. A small simulation study exhibits its performances for
finite samples. Estimation of extreme quantiles of the cumulative incidence
function is also addressed. | [
0,
0,
1,
1,
0,
0
] |
Title: Projected Variational Integrators for Degenerate Lagrangian Systems,
Abstract: We propose and compare several projection methods applied to variational
integrators for degenerate Lagrangian systems, whose Lagrangian is of the form
$L = \vartheta(q) \cdot \dot{q} - H(q)$ and thus linear in velocities. While
previous methods for such systems only work reliably in the case of $\vartheta$
being a linear function of $q$, our methods are long-time stable also for
systems where $\vartheta$ is a nonlinear function of $q$. We analyse the
properties of the resulting algorithms, in particular with respect to the
conservation of energy, momentum maps and symplecticity. In numerical
experiments, we verify the favourable properties of the projected integrators
and demonstrate their excellent long-time fidelity. In particular, we consider
a two-dimensional Lotka-Volterra system, planar point vortices with
position-dependent circulation and guiding centre dynamics. | [
0,
1,
0,
0,
0,
0
] |
Title: Boosted Generative Models,
Abstract: We propose a novel approach for using unsupervised boosting to create an
ensemble of generative models, where models are trained in sequence to correct
earlier mistakes. Our meta-algorithmic framework can leverage any existing base
learner that permits likelihood evaluation, including recent deep expressive
models. Further, our approach allows the ensemble to include discriminative
models trained to distinguish real data from model-generated data. We show
theoretical conditions under which incorporating a new model in the ensemble
will improve the fit and empirically demonstrate the effectiveness of our
black-box boosting algorithms on density estimation, classification, and sample
generation on benchmark datasets for a wide range of generative models. | [
1,
0,
0,
1,
0,
0
] |
Title: Overlapping community detection using superior seed set selection in social networks,
Abstract: Community discovery in the social network is one of the tremendously
expanding areas which earn interest among researchers for the past one decade.
There are many already existing algorithms. However, new seed-based algorithms
establish an emerging drift in this area. The basic idea behind these
strategies is to identify exceptional nodes in the given network, called seeds,
around which communities can be located. This paper proposes a blended strategy
for locating suitable superior seed set by applying various centrality measures
and using them to find overlapping communities. The examination of the
algorithm has been performed regarding the goodness of the identified
communities with the help of intra-cluster density and inter-cluster density.
Finally, the runtime of the proposed algorithm has been compared with the
existing community detection algorithms showing remarkable improvement. | [
1,
0,
0,
0,
0,
0
] |
Title: How LinkedIn Economic Graph Bonds Information and Product: Applications in LinkedIn Salary,
Abstract: The LinkedIn Salary product was launched in late 2016 with the goal of
providing insights on compensation distribution to job seekers, so that they
can make more informed decisions when discovering and assessing career
opportunities. The compensation insights are provided based on data collected
from LinkedIn members and aggregated in a privacy-preserving manner. Given the
simultaneous desire for computing robust, reliable insights and for having
insights to satisfy as many job seekers as possible, a key challenge is to
reliably infer the insights at the company level when there is limited or no
data at all. We propose a two-step framework that utilizes a novel, semantic
representation of companies (Company2vec) and a Bayesian statistical model to
address this problem. Our approach makes use of the rich information present in
the LinkedIn Economic Graph, and in particular, uses the intuition that two
companies are likely to be similar if employees are very likely to transition
from one company to the other and vice versa. We compute embeddings for
companies by analyzing the LinkedIn members' company transition data using
machine learning algorithms, then compute pairwise similarities between
companies based on these embeddings, and finally incorporate company
similarities in the form of peer company groups as part of the proposed
Bayesian statistical model to predict insights at the company level. We perform
extensive validation using several different evaluation techniques, and show
that we can significantly increase the coverage of insights while, in fact,
even improving the quality of the obtained insights. For example, we were able
to compute salary insights for 35 times as many title-region-company
combinations in the U.S. as compared to previous work, corresponding to 4.9
times as many monthly active users. Finally, we highlight the lessons learned
from deployment of our system. | [
1,
0,
0,
0,
0,
0
] |
Title: Generative Adversarial Network based Autoencoder: Application to fault detection problem for closed loop dynamical systems,
Abstract: Fault detection problem for closed loop uncertain dynamical systems, is
investigated in this paper, using different deep learning based methods.
Traditional classifier based method does not perform well, because of the
inherent difficulty of detecting system level faults for closed loop dynamical
system. Specifically, acting controller in any closed loop dynamical system,
works to reduce the effect of system level faults. A novel Generative
Adversarial based deep Autoencoder is designed to classify datasets under
normal and faulty operating conditions. This proposed network performs
significantly well when compared to any available classifier based methods, and
moreover, does not require labeled fault incorporated datasets for training
purpose. Finally, this aforementioned network's performance is tested on a high
complexity building energy system dataset. | [
0,
0,
0,
1,
0,
0
] |
Title: A space-time finite element method for neural field equations with transmission delays,
Abstract: We present and analyze a new space-time finite element method for the
solution of neural field equations with transmission delays. The numerical
treatment of these systems is rare in the literature and currently has several
restrictions on the spatial domain and the functions involved, such as
connectivity and delay functions. The use of a space-time discretization, with
basis functions that are discontinuous in time and continuous in space
(dGcG-FEM), is a natural way to deal with space-dependent delays, which is
important for many neural field applications. In this article we provide a
detailed description of a space-time dGcG-FEM algorithm for neural delay
equations, including an a-priori error analysis. We demonstrate the application
of the dGcG-FEM algorithm on several neural field models, including problems
with an inhomogeneous kernel. | [
0,
0,
1,
0,
0,
0
] |
Title: Predicting Positive and Negative Links with Noisy Queries: Theory & Practice,
Abstract: Social networks involve both positive and negative relationships, which can
be captured in signed graphs. The {\em edge sign prediction problem} aims to
predict whether an interaction between a pair of nodes will be positive or
negative. We provide theoretical results for this problem that motivate natural
improvements to recent heuristics.
The edge sign prediction problem is related to correlation clustering; a
positive relationship means being in the same cluster. We consider the
following model for two clusters: we are allowed to query any pair of nodes
whether they belong to the same cluster or not, but the answer to the query is
corrupted with some probability $0<q<\frac{1}{2}$. Let $\delta=1-2q$ be the
bias. We provide an algorithm that recovers all signs correctly with high
probability in the presence of noise with $O(\frac{n\log
n}{\delta^2}+\frac{\log^2 n}{\delta^6})$ queries. This is the best known result
for this problem for all but tiny $\delta$, improving on the recent work of
Mazumdar and Saha \cite{mazumdar2017clustering}. We also provide an algorithm
that performs $O(\frac{n\log n}{\delta^4})$ queries, and uses breadth first
search as its main algorithmic primitive. While both the running time and the
number of queries for this algorithm are sub-optimal, our result relies on
novel theoretical techniques, and naturally suggests the use of edge-disjoint
paths as a feature for predicting signs in online social networks.
Correspondingly, we experiment with using edge disjoint $s-t$ paths of short
length as a feature for predicting the sign of edge $(s,t)$ in real-world
signed networks. Empirical findings suggest that the use of such paths improves
the classification accuracy, especially for pairs of nodes with no common
neighbors. | [
1,
0,
0,
0,
0,
0
] |
Title: Simulating a Topological Transition in a Superconducting Phase Qubit by Fast Adiabatic Trajectories,
Abstract: The significance of topological phases has been widely recognized in the
community of condensed matter physics. The well controllable quantum systems
provide an artificial platform to probe and engineer various topological
phases. The adiabatic trajectory of a quantum state describes the change of the
bulk Bloch eigenstates with the momentum, and this adiabatic simulation method
is however practically limited due to quantum dissipation. Here we apply the
`shortcut to adiabaticity' (STA) protocol to realize fast adiabatic evolutions
in the system of a superconducting phase qubit. The resulting fast adiabatic
trajectories illustrate the change of the bulk Bloch eigenstates in the
Su-Schrieffer-Heeger (SSH) model. A sharp transition is experimentally
determined for the topological invariant of a winding number. Our experiment
helps identify the topological Chern number of a two-dimensional toy model,
suggesting the applicability of the fast adiabatic simulation method for
topological systems. | [
0,
1,
0,
0,
0,
0
] |
Title: Theoretical properties of quasi-stationary Monte Carlo methods,
Abstract: This paper gives foundational results for the application of
quasi-stationarity to Monte Carlo inference problems. We prove natural
sufficient conditions for the quasi-limiting distribution of a killed diffusion
to coincide with a target density of interest. We also quantify the rate of
convergence to quasi-stationarity by relating the killed diffusion to an
appropriate Langevin diffusion. As an example, we consider in detail a killed
Ornstein--Uhlenbeck process with Gaussian quasi-stationary distribution. | [
0,
0,
1,
1,
0,
0
] |
Title: Spin controlled atom-ion inelastic collisions,
Abstract: The control of the ultracold collisions between neutral atoms is an extensive
and successful field of study. The tools developed allow for ultracold chemical
reactions to be managed using magnetic fields, light fields and spin-state
manipulation of the colliding particles among other methods. The control of
chemical reactions in ultracold atom-ion collisions is a young and growing
field of research. Recently, the collision energy and the ion electronic state
were used to control atom-ion interactions. Here, we demonstrate
spin-controlled atom-ion inelastic processes. In our experiment, both
spin-exchange and charge-exchange reactions are controlled in an ultracold
Rb-Sr$^+$ mixture by the atomic spin state. We prepare a cloud of atoms in a
single hyperfine spin-state. Spin-exchange collisions between atoms and ion
subsequently polarize the ion spin. Electron transfer is only allowed for
(RbSr)$^+$ colliding in the singlet manifold. Initializing the atoms in various
spin states affects the overlap of the collision wavefunction with the singlet
molecular manifold and therefore also the reaction rate. We experimentally show
that by preparing the atoms in different spin states one can vary the
charge-exchange rate in agreement with theoretical predictions. | [
0,
1,
0,
0,
0,
0
] |
Title: Casper the Friendly Finality Gadget,
Abstract: We introduce Casper, a proof of stake-based finality system which overlays an
existing proof of work blockchain. Casper is a partial consensus mechanism
combining proof of stake algorithm research and Byzantine fault tolerant
consensus theory. We introduce our system, prove some desirable features, and
show defenses against long range revisions and catastrophic crashes. The Casper
overlay provides almost any proof of work chain with additional protections
against block reversions. | [
1,
0,
0,
0,
0,
0
] |
Title: Form factors of local operators in supersymmetric quantum integrable models,
Abstract: We apply the nested algebraic Bethe ansatz to the models with gl(2|1) and
gl}(1|2) supersymmetry. We show that form factors of local operators in these
models can be expressed in terms of the universal form factors. Our derivation
is based on the use of the RTT-algebra only. It does not refer to any specific
representation of this algebra. We obtain thus determinant representations for
form factors of local operators in the cases where an explicit solution of the
quantum inverse scattering problem is not known. | [
0,
0,
1,
0,
0,
0
] |
Title: Goldstone and Higgs Hydrodynamics in the BCS-BEC Crossover,
Abstract: We discuss the derivation of a low-energy effective field theory of phase
(Goldstone) and amplitude (Higgs) modes of the pairing field from a microscopic
theory of attractive fermions. The coupled equations for Goldstone and Higgs
fields are critically analyzed in the Bardeen-Cooper-Schrieffer (BCS) to
Bose-Einstein condensate (BEC) crossover both in three spatial dimensions and
in two spatial dimensions. The crucial role of pair fluctuations is
investigated, and the beyond-mean-field Gaussian theory of the BCS-BEC
crossover is compared with available experimental data of the two-dimensional
ultracold Fermi superfluid. | [
0,
1,
0,
0,
0,
0
] |
Title: A note on knot concordance and involutive knot Floer homology,
Abstract: We prove that if two knots are concordant, their involutive knot Floer
complexes satisfy a certain type of stable equivalence. | [
0,
0,
1,
0,
0,
0
] |
Title: $M$-QAM Precoder Design for MIMO Directional Modulation Transceivers,
Abstract: Spectrally efficient multi-antenna wireless communication systems are a key
challenge as service demands continue to increase. At the same time, powering
up radio access networks is facing environmental and regulation limitations. In
order to achieve more power efficiency, we design a directional modulation
precoder by considering an $M$-QAM constellation, particularly with
$M=4,8,16,32$. First, extended detection regions are defined for desired
constellations using analytical geometry. Then, constellation points are placed
in the optimal positions of these regions while the minimum Euclidean distance
to adjacent constellation points and detection region boundaries is kept as in
the conventional $M$-QAM modulation. For further power efficiency and symbol
error rate similar to that of fixed design in high SNR, relaxed detection
regions are modeled for inner points of $M=16,32$ constellations. The modeled
extended and relaxed detection regions as well as the modulation
characteristics are utilized to formulate symbol-level precoder design problems
for directional modulation to minimize the transmission power while preserving
the minimum required SNR at the destination. In addition, the extended and
relaxed detection regions are used for precoder design to minimize the output
of each power amplifier. We transform the design problems into convex ones and
devise an interior point path-following iterative algorithm to solve the
mentioned problems and provide details on finding the initial values of the
parameters and the starting point. Results show that compared to the benchmark
schemes, the proposed method performs better in terms of power and peak power
reduction as well as symbol error rate reduction for a wide range of SNRs. | [
1,
0,
0,
0,
0,
0
] |
Title: Green function for linearized Navier-Stokes around a boundary layer profile: near critical layers,
Abstract: This is a continuation and completion of the program (initiated in
\cite{GrN1,GrN2}) to derive pointwise estimates on the Green function and sharp
bounds on the semigroup of linearized Navier-Stokes around a generic stationary
boundary layer profile. This is done via a spectral analysis approach and a
careful study of the Orr-Sommerfeld equations, or equivalently the
Navier-Stokes resolvent operator $(\lambda - L)^{-1}$. The earlier work
(\cite{GrN1,GrN2}) treats the Orr-Sommerfeld equations away from critical
layers: this is the case when the phase velocity is away from the range of the
background profile or when $\lambda$ is away from the Euler continuous
spectrum. In this paper, we study the critical case: the Orr-Sommerfeld
equations near critical layers, providing pointwise estimates on the Green
function as well as carefully studying the Dunford's contour integral near the
critical layers.
As an application, we obtain pointwise estimates on the Green function and
sharp bounds on the semigroup of the linearized Navier-Stokes problem near
monotonic boundary layers that are spectrally stable to the Euler equations,
complementing \cite{GrN1,GrN2} where unstable profiles are considered. | [
0,
0,
1,
0,
0,
0
] |
Title: Interacting superradiance samples: modified intensities and timescales, and frequency shifts,
Abstract: We consider the interaction between distinct superradiance (SR) systems and
use the dressed state formalism to solve the case of two interacting two-atom
SR samples at resonance. We show that the ensuing entanglement modifies the
transition rates and intensities of radiation, as well as introduces a
potentially measurable frequency chirp in the SR cascade, the magnitude of
which being a function of the separation between the samples. For the dominant
SR cascade we find a significant reduction in the duration and an increase of
the intensity of the SR pulse relative to the case of a single two-atom SR
sample. | [
0,
1,
0,
0,
0,
0
] |
Title: An optimal XP algorithm for Hamiltonian cycle on graphs of bounded clique-width,
Abstract: In this paper, we prove that, given a clique-width $k$-expression of an
$n$-vertex graph, \textsc{Hamiltonian Cycle} can be solved in time
$n^{\mathcal{O}(k)}$. This improves the naive algorithm that runs in time
$n^{\mathcal{O}(k^2)}$ by Espelage et al. (WG 2001), and it also matches with
the lower bound result by Fomin et al. that, unless the Exponential Time
Hypothesis fails, there is no algorithm running in time $n^{o(k)}$ (SIAM. J.
Computing 2014).
We present a technique of representative sets using two-edge colored
multigraphs on $k$ vertices. The essential idea is that, for a two-edge colored
multigraph, the existence of an Eulerian trail that uses edges with different
colors alternately can be determined by two information: the number of colored
edges incident with each vertex, and the connectedness of the multigraph. With
this idea, we avoid the bottleneck of the naive algorithm, which stores all the
possible multigraphs on $k$ vertices with at most $n$ edges. | [
1,
0,
0,
0,
0,
0
] |
Title: On the lateral instability analysis of MEMS comb-drive electrostatic transducers,
Abstract: This paper investigates the lateral pull-in effect of an in-plane
overlap-varying transducer. The instability is induced by the translational and
rotational displacements. Based on the principle of virtual work, the
equilibrium conditions of force and moment in lateral directions are derived.
The analytical solutions of the critical voltage, at which the pull-in
phenomenon occurs, are developed when considering only the translational
stiffness or only the rotational stiffness of the mechanical spring. The
critical voltage in general case is numerically determined by using nonlinear
optimization techniques, taking into account the combined effect of translation
and rotation. The effects of possible translational offsets and angular
deviations to the critical voltage are modeled and numerically analyzed. The
investigation is then the first time expanded to anti-phase operation mode and
Bennet's doubler configuration of the two transducers. | [
0,
1,
0,
0,
0,
0
] |
Title: How Wrong Am I? - Studying Adversarial Examples and their Impact on Uncertainty in Gaussian Process Machine Learning Models,
Abstract: Machine learning models are vulnerable to Adversarial Examples: minor
perturbations to input samples intended to deliberately cause
misclassification. Current defenses against adversarial examples, especially
for Deep Neural Networks (DNN), are primarily derived from empirical
developments, and their security guarantees are often only justified
retroactively. Many defenses therefore rely on hidden assumptions that are
subsequently subverted by increasingly elaborate attacks. This is not
surprising: deep learning notoriously lacks a comprehensive mathematical
framework to provide meaningful guarantees.
In this paper, we leverage Gaussian Processes to investigate adversarial
examples in the framework of Bayesian inference. Across different models and
datasets, we find deviating levels of uncertainty reflect the perturbation
introduced to benign samples by state-of-the-art attacks, including novel
white-box attacks on Gaussian Processes. Our experiments demonstrate that even
unoptimized uncertainty thresholds already reject adversarial examples in many
scenarios.
Comment: Thresholds can be broken in a modified attack, which was done in
arXiv:1812.02606 (The limitations of model uncertainty in adversarial
settings). | [
1,
0,
0,
1,
0,
0
] |
Title: Energy Efficient Adaptive Network Coding Schemes for Satellite Communications,
Abstract: In this paper, we propose novel energy efficient adaptive network coding and
modulation schemes for time variant channels. We evaluate such schemes under a
realistic channel model for open area environments and Geostationary Earth
Orbit (GEO) satellites. Compared to non-adaptive network coding and adaptive
rate efficient network-coded schemes for time variant channels, we show that
our proposed schemes, through physical layer awareness can be designed to
transmit only if a target quality of service (QoS) is achieved. As a result,
such schemes can provide remarkable energy savings. | [
1,
0,
0,
0,
0,
0
] |
Title: Multitarget search on complex networks: A logarithmic growth of global mean random cover time,
Abstract: We investigate multitarget search on complex networks and derive an exact
expression for the mean random cover time that quantifies the expected time a
walker needs to visit multiple targets. Based on this, we recover and extend
some interesting results of multitarget search on networks. Specifically, we
observe the logarithmic increase of the global mean random cover time with the
target number for a broad range of random search processes, including generic
random walks, biased random walks, and maximal entropy random walks. We show
that the logarithmic growth pattern is a universal feature of multi-target
search on networks by using the annealed network approach and the
Sherman-Morrison formula. Moreover, we find that for biased random walks, the
global mean random cover time can be minimized, and that the corresponding
optimal parameter also minimizes the global mean first passage time, pointing
towards its robustness. Our findings further confirm that the logarithmic
growth pattern is a universal law governing multitarget search in confined
media. | [
1,
1,
0,
0,
0,
0
] |
Title: Hierarchical Clustering with Prior Knowledge,
Abstract: Hierarchical clustering is a class of algorithms that seeks to build a
hierarchy of clusters. It has been the dominant approach to constructing
embedded classification schemes since it outputs dendrograms, which capture the
hierarchical relationship among members at all levels of granularity,
simultaneously. Being greedy in the algorithmic sense, a hierarchical
clustering partitions data at every step solely based on a similarity /
dissimilarity measure. The clustering results oftentimes depend on not only the
distribution of the underlying data, but also the choice of dissimilarity
measure and the clustering algorithm. In this paper, we propose a method to
incorporate prior domain knowledge about entity relationship into the
hierarchical clustering. Specifically, we use a distance function in
ultrametric space to encode the external ontological information. We show that
popular linkage-based algorithms can faithfully recover the encoded structure.
Similar to some regularized machine learning techniques, we add this distance
as a penalty term to the original pairwise distance to regulate the final
structure of the dendrogram. As a case study, we applied this method on real
data in the building of a customer behavior based product taxonomy for an
Amazon service, leveraging the information from a larger Amazon-wide browse
structure. The method is useful when one wants to leverage the relational
information from external sources, or the data used to generate the distance
matrix is noisy and sparse. Our work falls in the category of semi-supervised
or constrained clustering. | [
0,
0,
0,
1,
0,
0
] |
Title: Simple Root Cause Analysis by Separable Likelihoods,
Abstract: Root Cause Analysis for Anomalies is challenging because of the trade-off
between the accuracy and its explanatory friendliness, required for industrial
applications. In this paper we propose a framework for simple and friendly RCA
within the Bayesian regime under certain restrictions (that Hessian at the mode
is diagonal, here referred to as \emph{separability}) imposed on the predictive
posterior. We show that this assumption is satisfied for important base models,
including Multinomal, Dirichlet-Multinomial and Naive Bayes. To demonstrate the
usefulness of the framework, we embed it into the Bayesian Net and validate on
web server error logs (real world data set). | [
0,
0,
0,
1,
0,
0
] |
Title: Computing the quality of the Laplace approximation,
Abstract: Bayesian inference requires approximation methods to become computable, but
for most of them it is impossible to quantify how close the approximation is to
the true posterior. In this work, we present a theorem upper-bounding the KL
divergence between a log-concave target density
$f\left(\boldsymbol{\theta}\right)$ and its Laplace approximation
$g\left(\boldsymbol{\theta}\right)$. The bound we present is computable: on the
classical logistic regression model, we find our bound to be almost exact as
long as the dimensionality of the parameter space is high.
The approach we followed in this work can be extended to other Gaussian
approximations, as we will do in an extended version of this work, to be
submitted to the Annals of Statistics. It will then become a critical tool for
characterizing whether, for a given problem, a given Gaussian approximation is
suitable, or whether a more precise alternative method should be used instead. | [
0,
0,
1,
1,
0,
0
] |
Title: Correlations between thresholds and degrees: An analytic approach to model attacks and failure cascades,
Abstract: Two node variables determine the evolution of cascades in random networks: a
node's degree and threshold. Correlations between both fundamentally change the
robustness of a network, yet, they are disregarded in standard analytic methods
as local tree or heterogeneous mean field approximations because of the bad
tractability of order statistics. We show how they become tractable in the
thermodynamic limit of infinite network size. This enables the analytic
description of node attacks that are characterized by threshold allocations
based on node degree. Using two examples, we discuss possible implications of
irregular phase transitions and different speeds of cascade evolution for the
control of cascades. | [
0,
1,
0,
0,
0,
0
] |
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