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Gradient descent GAN optimization is locally stable | Despite the growing prominence of generative adversarial networks (GANs),
optimization in GANs is still a poorly understood topic. In this paper, we
analyze the "gradient descent" form of GAN optimization i.e., the natural
setting where we simultaneously take small gradient steps in both generator and
discriminator parameters. We show that even though GAN optimization does not
correspond to a convex-concave game (even for simple parameterizations), under
proper conditions, equilibrium points of this optimization procedure are still
\emph{locally asymptotically stable} for the traditional GAN formulation. On
the other hand, we show that the recently proposed Wasserstein GAN can have
non-convergent limit cycles near equilibrium. Motivated by this stability
analysis, we propose an additional regularization term for gradient descent GAN
updates, which \emph{is} able to guarantee local stability for both the WGAN
and the traditional GAN, and also shows practical promise in speeding up
convergence and addressing mode collapse.
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Transition rates and radiative lifetimes of Ca I | We tabulate spontaneous emission rates for all possible 811
electric-dipole-allowed transitions between the 75 lowest-energy states of Ca
I. These involve the $4sns$ ($n=4-8$), $4snp$ ($n=4-7$), $4snd$ ($n=3-6$),
$4snf$ ($n=4-6$), $4p^2$, and $3d4p$ electronic configurations. We compile the
transition rates by carrying out ab initio relativistic calculations using the
combined method of configuration interaction and many-body perturbation theory.
The results are compared to the available literature values. The tabulated
rates can be useful in various applications, such as optimizing laser cooling
in magneto-optical traps, estimating various systematic effects in optical
clocks and evaluating static or dynamic polarizabilities and long-range
atom-atom interaction coefficients and related atomic properties.
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Defect entropies and enthalpies in Barium Fluoride | Various experimental techniques, have revealed that the predominant intrinsic
point defects in BaF$_2$ are anion Frenkel defects. Their formation enthalpy
and entropy as well as the corresponding parameters for the fluorine vacancy
and fluorine interstitial motion have been determined. In addition, low
temperature dielectric relaxation measurements in BaF$_2$ doped with uranium
leads to the parameters {\tau}$_0$, E in the Arrhenius relation
{\tau}={\tau}$_0$exp(E/kBT) for the relaxation time {\tau}. For the relaxation
peak associated with a single tetravalent uranium, the migration entropy
deduced from the pre-exponential factor {\tau}$_0$, is smaller than the anion
Frenkel defect formation entropy by almost two orders of magnitude. We show
that, despite their great variation, the defect entropies and enthalpies are
interconnected through a model based on anharmonic properties of the bulk
material that have been recently studied by employing density-functional theory
and density-functional perturbation theory.
| 0 | 1 | 0 | 0 | 0 | 0 |
Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning | Exploration in complex domains is a key challenge in reinforcement learning,
especially for tasks with very sparse rewards. Recent successes in deep
reinforcement learning have been achieved mostly using simple heuristic
exploration strategies such as $\epsilon$-greedy action selection or Gaussian
control noise, but there are many tasks where these methods are insufficient to
make any learning progress. Here, we consider more complex heuristics:
efficient and scalable exploration strategies that maximize a notion of an
agent's surprise about its experiences via intrinsic motivation. We propose to
learn a model of the MDP transition probabilities concurrently with the policy,
and to form intrinsic rewards that approximate the KL-divergence of the true
transition probabilities from the learned model. One of our approximations
results in using surprisal as intrinsic motivation, while the other gives the
$k$-step learning progress. We show that our incentives enable agents to
succeed in a wide range of environments with high-dimensional state spaces and
very sparse rewards, including continuous control tasks and games in the Atari
RAM domain, outperforming several other heuristic exploration techniques.
| 1 | 0 | 0 | 0 | 0 | 0 |
Local and collective magnetism of EuFe$_2$As$_2$ | We present an experimental study of the local and collective magnetism of
$\mathrm{EuFe_2As_2}$, that is isostructural with the high temperature
superconductor parent compound $\mathrm{BaFe_2As_2}$. In contrast to
$\mathrm{BaFe_2As_2}$, where only Fe spins order, $\mathrm{EuFe_2As_2}$ has an
additional magnetic transition below 20 K due to the ordering of the Eu$^{2+}$
spins ($J =7/2$, with $L=0$ and $S=7/2$) in an A-type antiferromagnetic texture
(ferromagnetic layers stacked antiferromagnetically). This may potentially
affect the FeAs layer and its local and correlated magnetism. Fe-K$_\beta$
x-ray emission experiments on $\mathrm{EuFe_2As_2}$ single crystals reveal a
local magnetic moment of 1.3$\pm0.15~\mu_B$ at 15 K that slightly increases to
1.45$\pm0.15~\mu_B$ at 300 K. Resonant inelastic x-ray scattering (RIXS)
experiments performed on the same crystals show dispersive broad (in energy)
magnetic excitations along $\mathrm{(0, 0)\rightarrow(1, 0)}$ and $\mathrm{(0,
0)\rightarrow(1, 1)}$ with a bandwidth on the order of 170-180 meV. These
results on local and collective magnetism are in line with other parent
compounds of the $\mathrm{AFe_2As_2}$ series ($A=$ Ba, Ca, and Sr), especially
the well characterized $\mathrm{BaFe_2As_2}$. Thus, our experiments lead us to
the conclusion that the effect of the high magnetic moment of Eu on the
magnitude of both Fe local magnetic moment and spin excitations is small and
confined to low energy excitations.
| 0 | 1 | 0 | 0 | 0 | 0 |
Newton slopes for twisted Artin--Schreier--Witt Towers | We fix a monic polynomial $f(x) \in \mathbb F_q[x]$ over a finite field of
characteristic $p$ of degree relatively prime to $p$. Let $a\mapsto \omega(a)$
be the Teichmüller lift of $\mathbb F_q$, and let $\chi:\mathbb{Z}\to \mathbb
C_p^\times$ be a finite character of $\mathbb Z_p$. The $L$-function associated
to the polynomial $f$ and the so-called twisted character $\omega^u\times \chi$
is denoted by $L_f(\omega^u,\chi,s)$. We prove that, when the conductor of the
character is large enough, the $p$-adic Newton slopes of this $L$-function form
arithmetic progressions.
| 0 | 0 | 1 | 0 | 0 | 0 |
Active model learning and diverse action sampling for task and motion planning | The objective of this work is to augment the basic abilities of a robot by
learning to use new sensorimotor primitives to enable the solution of complex
long-horizon problems. Solving long-horizon problems in complex domains
requires flexible generative planning that can combine primitive abilities in
novel combinations to solve problems as they arise in the world. In order to
plan to combine primitive actions, we must have models of the preconditions and
effects of those actions: under what circumstances will executing this
primitive achieve some particular effect in the world?
We use, and develop novel improvements on, state-of-the-art methods for
active learning and sampling. We use Gaussian process methods for learning the
conditions of operator effectiveness from small numbers of expensive training
examples collected by experimentation on a robot. We develop adaptive sampling
methods for generating diverse elements of continuous sets (such as robot
configurations and object poses) during planning for solving a new task, so
that planning is as efficient as possible. We demonstrate these methods in an
integrated system, combining newly learned models with an efficient
continuous-space robot task and motion planner to learn to solve long horizon
problems more efficiently than was previously possible.
| 1 | 0 | 0 | 1 | 0 | 0 |
End-to-end distance and contour length distribution functions of DNA helices | We present a computational method to evaluate the end-to-end and the contour
length distribution functions of short DNA molecules described by a mesoscopic
Hamiltonian. The method generates a large statistical ensemble of possible
configurations for each dimer in the sequence, selects the global equilibrium
twist conformation for the molecule and determines the average base pair
distances along the molecule backbone. Integrating over the base pair radial
and angular fluctuations, we derive the room temperature distribution functions
as a function of the sequence length. The obtained values for the most probable
end-to-end distance and contour length distance, providing a measure of the
global molecule size, are used to examine the DNA flexibility at short length
scales. It is found that, also in molecules with less than $\sim 60$ base
pairs, coiled configurations maintain a large statistical weight and,
consistently, the persistence lengths may be much smaller than in kilo-base
DNA.
| 0 | 0 | 0 | 0 | 1 | 0 |
Online Estimation of Multiple Dynamic Graphs in Pattern Sequences | Many time-series data including text, movies, and biological signals can be
represented as sequences of correlated binary patterns. These patterns may be
described by weighted combinations of a few dominant structures that underpin
specific interactions among the binary elements. To extract the dominant
correlation structures and their contributions to generating data in a
time-dependent manner, we model the dynamics of binary patterns using the
state-space model of an Ising-type network that is composed of multiple
undirected graphs. We provide a sequential Bayes algorithm to estimate the
dynamics of weights on the graphs while gaining the graph structures online.
This model can uncover overlapping graphs underlying the data better than a
traditional orthogonal decomposition method, and outperforms an original
time-dependent full Ising model. We assess the performance of the method by
simulated data, and demonstrate that spontaneous activity of cultured
hippocampal neurons is represented by dynamics of multiple graphs.
| 1 | 0 | 0 | 1 | 1 | 0 |
Fluid Communities: A Competitive, Scalable and Diverse Community Detection Algorithm | We introduce a community detection algorithm (Fluid Communities) based on the
idea of fluids interacting in an environment, expanding and contracting as a
result of that interaction. Fluid Communities is based on the propagation
methodology, which represents the state-of-the-art in terms of computational
cost and scalability. While being highly efficient, Fluid Communities is able
to find communities in synthetic graphs with an accuracy close to the current
best alternatives. Additionally, Fluid Communities is the first
propagation-based algorithm capable of identifying a variable number of
communities in network. To illustrate the relevance of the algorithm, we
evaluate the diversity of the communities found by Fluid Communities, and find
them to be significantly different from the ones found by alternative methods.
| 1 | 1 | 0 | 0 | 0 | 0 |
Identification of Dynamic Systems with Interval Arithmetic | This paper aims to identify three electrical systems: a series RLC circuit, a
motor/generator coupled system, and the Duffing-Ueda oscillator. In order to
obtain the system's models was used the error reduction ratio and the Akaike
information criterion. Our approach to handle the numerical errors was the
interval arithmetic by means of the resolution of the least squares estimation.
The routines was implemented in Intlab, a Matlab toolbox devoted to arithmetic
interval. Finally, the interval RMSE was calculated to verify the quality of
the obtained models. The applied methodology was satisfactory, since the
obtained intervals encompass the system's data and allow to demonstrate how the
numerical errors affect the answers.
| 1 | 0 | 0 | 0 | 0 | 0 |
Four Fundamental Questions in Probability Theory and Statistics | This study has the purpose of addressing four questions that lie at the base
of the probability theory and statistics, and includes two main steps. As
first, we conduct the textual analysis of the most significant works written by
eminent probability theorists. The textual analysis turns out to be a rather
innovative method of study in this domain, and shows how the sampled writers,
no matter he is a frequentist or a subjectivist, share a similar approach. Each
author argues on the multifold aspects of probability then he establishes the
mathematical theory on the basis of his intellectual conclusions. It may be
said that mathematics ranks second. Hilbert foresees an approach far different
from that used by the sampled authors. He proposes to axiomatize the
probability calculus notably to describe the probability concepts using purely
mathematical criteria. In the second stage of the present research we address
the four issues of the probability theory and statistics following the
recommendations of Hilbert. Specifically, we use two theorems that prove how
the frequentist and the subjectivist models are not incompatible as many
believe. Probability has distinct meanings under different hypotheses, and in
turn classical statistics and Bayesian statistics are available for adoption in
different circumstances. Subsequently, these results are commented upon,
followed by our conclusions
| 0 | 0 | 0 | 1 | 0 | 0 |
Connections on parahoric torsors over curves | We define parahoric $\cG$--torsors for certain Bruhat--Tits group scheme
$\cG$ on a smooth complex projective curve $X$ when the weights are real, and
also define connections on them. We prove that a $\cG$--torsor is given by a
homomorphism from $\pi_1(X\setminus D)$ to a maximal compact subgroup of $G$,
where $D\, \subset\, X$ is the parabolic divisor, if and only if the torsor is
polystable.
| 0 | 0 | 1 | 0 | 0 | 0 |
On Reduced Input-Output Dynamic Mode Decomposition | The identification of reduced-order models from high-dimensional data is a
challenging task, and even more so if the identified system should not only be
suitable for a certain data set, but generally approximate the input-output
behavior of the data source. In this work, we consider the input-output dynamic
mode decomposition method for system identification. We compare excitation
approaches for the data-driven identification process and describe an
optimization-based stabilization strategy for the identified systems.
| 1 | 0 | 0 | 0 | 0 | 0 |
Safety-Aware Apprenticeship Learning | Apprenticeship learning (AL) is a kind of Learning from Demonstration
techniques where the reward function of a Markov Decision Process (MDP) is
unknown to the learning agent and the agent has to derive a good policy by
observing an expert's demonstrations. In this paper, we study the problem of
how to make AL algorithms inherently safe while still meeting its learning
objective. We consider a setting where the unknown reward function is assumed
to be a linear combination of a set of state features, and the safety property
is specified in Probabilistic Computation Tree Logic (PCTL). By embedding
probabilistic model checking inside AL, we propose a novel
counterexample-guided approach that can ensure safety while retaining
performance of the learnt policy. We demonstrate the effectiveness of our
approach on several challenging AL scenarios where safety is essential.
| 1 | 0 | 0 | 0 | 0 | 0 |
Solitary wave solutions and their interactions for fully nonlinear water waves with surface tension in the generalized Serre equations | Some effects of surface tension on fully-nonlinear, long, surface water waves
are studied by numerical means. The differences between various solitary waves
and their interactions in subcritical and supercritical surface tension regimes
are presented. Analytical expressions for new peaked travelling wave solutions
are presented in the case of critical surface tension. The numerical
experiments were performed using a high-accurate finite element method based on
smooth cubic splines and the four-stage, classical, explicit Runge-Kutta method
of order four.
| 0 | 1 | 1 | 0 | 0 | 0 |
Relative weak mixing of W*-dynamical systems via joinings | A characterization of relative weak mixing in W*-dynamical systems in terms
of a relatively independent joining is proven.
| 0 | 0 | 1 | 0 | 0 | 0 |
An assessment of Fe XX - Fe XXII emission lines in SDO/EVE data as diagnostics for high density solar flare plasmas using EUVE stellar observations | The Extreme Ultraviolet Variability Experiment (EVE) on the Solar Dynamics
Observatory obtains extreme-ultraviolet (EUV) spectra of the full-disk Sun at a
spectral resolution of ~1 A and cadence of 10 s. Such a spectral resolution
would normally be considered to be too low for the reliable determination of
electron density (N_e) sensitive emission line intensity ratios, due to
blending. However, previous work has shown that a limited number of Fe XXI
features in the 90-60 A wavelength region of EVE do provide useful
N_e-diagnostics at relatively low flare densities (N_e ~ 10^11-10^12 cm^-3).
Here we investigate if additional highly ionised Fe line ratios in the EVE
90-160 A range may be reliably employed as N_e-diagnostics. In particular, the
potential for such diagnostics to provide density estimates for high N_e
(~10^13 cm^-3) flare plasmas is assessed. Our study employs EVE spectra for
X-class flares, combined with observations of highly active late-type stars
from the Extreme Ultraviolet Explorer (EUVE) satellite plus experimental data
for well-diagnosed tokamak plasmas, both of which are similar in wavelength
coverage and spectral resolution to those from EVE. Several ratios are
identified in EVE data which yield consistent values of electron density,
including Fe XX 113.35/121.85 and Fe XXII 114.41/135.79, with confidence in
their reliability as N_e-diagnostics provided by the EUVE and tokamak results.
These ratios also allow the determination of density in solar flare plasmas up
to values of ~10^13 cm^-3.
| 0 | 1 | 0 | 0 | 0 | 0 |
Multi-Scale Spatially Weighted Local Histograms in O(1) | Weighting pixel contribution considering its location is a key feature in
many fundamental image processing tasks including filtering, object modeling
and distance matching. Several techniques have been proposed that incorporate
Spatial information to increase the accuracy and boost the performance of
detection, tracking and recognition systems at the cost of speed. But, it is
still not clear how to efficiently ex- tract weighted local histograms in
constant time using integral histogram. This paper presents a novel algorithm
to compute accurately multi-scale Spatially weighted local histograms in
constant time using Weighted Integral Histogram (SWIH) for fast search. We
applied our spatially weighted integral histogram approach for fast tracking
and obtained more accurate and robust target localization result in comparison
with using plain histogram.
| 1 | 0 | 0 | 0 | 0 | 0 |
A note on minimal dispersion of point sets in the unit cube | We study the dispersion of a point set, a notion closely related to the
discrepancy. Given a real $r\in (0,1)$ and an integer $d\geq 2$, let $N(r,d)$
denote the minimum number of points inside the $d$-dimensional unit cube
$[0,1]^d$ such that they intersect every axis-aligned box inside $[0,1]^d$ of
volume greater than $r$. We prove an upper bound on $N(r,d)$, matching a lower
bound of Aistleitner et al. up to a multiplicative constant depending only on
$r$. This fully determines the rate of growth of $N(r,d)$ if $r\in(0,1)$ is
fixed.
| 1 | 0 | 0 | 0 | 0 | 0 |
Limit theorems in bi-free probability theory | In this paper additive bi-free convolution is defined for general Borel
probability measures, and the limiting distributions for sums of bi-free pairs
of selfadjoint commuting random variables in an infinitesimal triangular array
are determined. These distributions are characterized by their bi-freely
infinite divisibility, and moreover, a transfer principle is established for
limit theorems in classical probability theory and Voiculescu's bi-free
probability theory. Complete descriptions of bi-free stability and fullness of
planar probability distributions are also set down. All these results reveal
one important feature about the theory of bi-free probability that it parallels
the classical theory perfectly well. The emphasis in the whole work is not on
the tool of bi-free combinatorics but only on the analytic machinery.
| 0 | 0 | 1 | 0 | 0 | 0 |
Axiomatisability and hardness for universal Horn classes of hypergraphs | We characterise finite axiomatisability and intractability of deciding
membership for universal Horn classes generated by finite loop-free
hypergraphs.
| 1 | 0 | 1 | 0 | 0 | 0 |
Snyder Like Modified Gravity in Newton's Spacetime | This work is focused on searching a geodesic interpretation of the dynamics
of a particle under the effects of a Snyder like deformation in the background
of the Kepler problem. In order to accomplish that task, a newtonian spacetime
is used. Newtonian spacetime is not a metric manifold, but allows to introduce
a torsion free connection in order to interpret the dynamic equations of the
deformed Kepler problem as geodesics in a curved spacetime. These geodesics and
the curvature terms of the Riemann and Ricci tensors show a mass and a
fundamental length dependence as expected, but are velocity independent. In
this sense, the effect of introducing a deformed algebra is examinated and the
corresponding curvature terms calculated, as well as the modifications of the
integrals of motion.
| 0 | 1 | 0 | 0 | 0 | 0 |
Dissipatively Coupled Waveguide Networks for Coherent Diffusive Photonics | A photonic circuit is generally described as a structure in which light
propagates by unitary exchange and transfers reversibly between channels. In
contrast, the term `diffusive' is more akin to a chaotic propagation in
scattering media, where light is driven out of coherence towards a thermal
mixture. Based on the dynamics of open quantum systems, the combination of
these two opposites can result in novel techniques for coherent light control.
The crucial feature of these photonic structures is dissipative coupling
between modes, via an interaction with a common reservoir. Here, we demonstrate
experimentally that such systems can perform optical equalisation to smooth
multimode light, or act as a distributor, guiding it into selected channels.
Quantum thermodynamically, these systems can act as catalytic coherent
reservoirs by performing perfect non-Landauer erasure. For lattice structures,
localised stationary states can be supported in the continuum, similar to
compacton-like states in conventional flat band lattices.
| 0 | 1 | 0 | 0 | 0 | 0 |
Inadequate Risk Analysis Might Jeopardize The Functional Safety of Modern Systems | In the early 90s, researchers began to focus on security as an important
property to address in combination with safety. Over the years, researchers
have proposed approaches to harmonize activities within the safety and security
disciplines. Despite the academic efforts to identify interdependencies and to
propose combined approaches for safety and security, there is still a lack of
integration between safety and security practices in the industrial context, as
they have separate standards and independent processes often addressed and
assessed by different organizational teams and authorities. Specifically,
security concerns are generally not covered in any detail in safety standards
potentially resulting in successfully safety-certified systems that still are
open for security threats from e.g., malicious intents from internal and
external personnel and hackers that may jeopardize safety. In recent years
security has again received an increasing attention of being an important issue
also in safety assurance, as the open interconnected nature of emerging systems
makes them susceptible to security threats at a much higher degree than
existing more confined products.This article presents initial ideas on how to
extend safety work to include aspects of security during the context
establishment and initial risk assessment procedures. The ambition of our
proposal is to improve safety and increase efficiency and effectiveness of the
safety work within the frames of the current safety standards, i.e., raised
security awareness in compliance with the current safety standards. We believe
that our proposal is useful to raise the security awareness in industrial
contexts, although it is not a complete harmonization of safety and security
disciplines, as it merely provides applicable guidance to increase security
awareness in a safety context.
| 1 | 0 | 0 | 0 | 0 | 0 |
Simultaneous determination of the drift and diffusion coefficients in stochastic differential equations | In this work, we consider a one-dimensional It{ô} diffusion process X t
with possibly nonlinear drift and diffusion coefficients. We show that, when
the diffusion coefficient is known, the drift coefficient is uniquely
determined by an observation of the expectation of the process during a small
time interval, and starting from values X 0 in a given subset of R. With the
same type of observation, and given the drift coefficient, we also show that
the diffusion coefficient is uniquely determined. When both coefficients are
unknown, we show that they are simultaneously uniquely determined by the
observation of the expectation and variance of the process, during a small time
interval, and starting again from values X 0 in a given subset of R. To derive
these results, we apply the Feynman-Kac theorem which leads to a linear
parabolic equation with unknown coefficients in front of the first and second
order terms. We then solve the corresponding inverse problem with PDE technics
which are mainly based on the strong parabolic maximum principle.
| 0 | 0 | 1 | 0 | 0 | 0 |
A compactness theorem for four-dimensional shrinking gradient Ricci solitons | Haslhofer and Müller proved a compactness Theorem for four-dimensional
shrinking gradient Ricci solitons, with the only assumption being that the
entropy is uniformly bounded from below. However, the limit in their result
could possibly be an orbifold Ricci shrinker. In this paper we prove a
compactness theorem for noncompact four-dimensional shrinking gradient Ricci
solitons with a topological restriction and a noncollapsing assumption, that
is, we consider Ricci shrinkers that can be embedded in a closed four-manifold
with vanishing second homology group over every field and are strongly
$\kappa$-noncollapsed with respect to a universal $\kappa$. In particular, we
do not need any curvature assumption and the limit is still a smooth nonflat
shrinking gradient Ricci soliton.
| 0 | 0 | 1 | 0 | 0 | 0 |
Demand-Independent Optimal Tolls | Wardrop equilibria in nonatomic congestion games are in general inefficient
as they do not induce an optimal flow that minimizes the total travel time.
Network tolls are a prominent and popular way to induce an optimum flow in
equilibrium. The classical approach to find such tolls is marginal cost pricing
which requires the exact knowledge of the demand on the network. In this paper,
we investigate under which conditions demand-independent optimum tolls exist
that induce the system optimum flow for any travel demand in the network. We
give several characterizations for the existence of such tolls both in terms of
the cost structure and the network structure of the game. Specifically we show
that demand-independent optimum tolls exist if and only if the edge cost
functions are shifted monomials as used by the Bureau of Public Roads.
Moreover, non-negative demand-independent optimum tolls exist when the network
is a directed acyclic multi-graph. Finally, we show that any network with a
single origin-destination pair admits demand-independent optimum tolls that,
although not necessarily non-negative, satisfy a budget constraint.
| 1 | 0 | 0 | 0 | 0 | 0 |
An analytic formulation for positive-unlabeled learning via weighted integral probability metric | We consider the problem of learning a binary classifier from only positive
and unlabeled observations (PU learning). Although recent research in PU
learning has succeeded in showing theoretical and empirical performance, most
existing algorithms need to solve either a convex or a non-convex optimization
problem and thus are not suitable for large-scale datasets. In this paper, we
propose a simple yet theoretically grounded PU learning algorithm by extending
the previous work proposed for supervised binary classification (Sriperumbudur
et al., 2012). The proposed PU learning algorithm produces a closed-form
classifier when the hypothesis space is a closed ball in reproducing kernel
Hilbert space. In addition, we establish upper bounds of the estimation error
and the excess risk. The obtained estimation error bound is sharper than
existing results and the excess risk bound does not rely on an approximation
error term. To the best of our knowledge, we are the first to explicitly derive
the excess risk bound in the field of PU learning. Finally, we conduct
extensive numerical experiments using both synthetic and real datasets,
demonstrating improved accuracy, scalability, and robustness of the proposed
algorithm.
| 1 | 0 | 0 | 1 | 0 | 0 |
Benchmarking Data Analysis and Machine Learning Applications on the Intel KNL Many-Core Processor | Knights Landing (KNL) is the code name for the second-generation Intel Xeon
Phi product family. KNL has generated significant interest in the data analysis
and machine learning communities because its new many-core architecture targets
both of these workloads. The KNL many-core vector processor design enables it
to exploit much higher levels of parallelism. At the Lincoln Laboratory
Supercomputing Center (LLSC), the majority of users are running data analysis
applications such as MATLAB and Octave. More recently, machine learning
applications, such as the UC Berkeley Caffe deep learning framework, have
become increasingly important to LLSC users. Thus, the performance of these
applications on KNL systems is of high interest to LLSC users and the broader
data analysis and machine learning communities. Our data analysis benchmarks of
these application on the Intel KNL processor indicate that single-core
double-precision generalized matrix multiply (DGEMM) performance on KNL systems
has improved by ~3.5x compared to prior Intel Xeon technologies. Our data
analysis applications also achieved ~60% of the theoretical peak performance.
Also a performance comparison of a machine learning application, Caffe, between
the two different Intel CPUs, Xeon E5 v3 and Xeon Phi 7210, demonstrated a 2.7x
improvement on a KNL node.
| 1 | 1 | 0 | 0 | 0 | 0 |
A Proof of the Conjecture of Lehmer and of the Conjecture of Schinzel-Zassenhaus | The conjecture of Lehmer is proved to be true. The proof mainly relies upon:
(i) the properties of the Parry Upper functions $f_{house(\alpha)}(z)$
associated with the dynamical zeta functions $\zeta_{house(\alpha)}(z)$ of the
Rényi--Parry arithmetical dynamical systems, for $\alpha$ an algebraic
integer $\alpha$ of house "$house(\alpha)$" greater than 1, (ii) the discovery
of lenticuli of poles of $\zeta_{house(\alpha)}(z)$ which uniformly
equidistribute at the limit on a limit "lenticular" arc of the unit circle,
when $house(\alpha)$ tends to $1^+$, giving rise to a continuous lenticular
minorant ${\rm M}_{r}(house(\alpha))$ of the Mahler measure ${\rm M}(\alpha)$,
(iii) the Poincaré asymptotic expansions of these poles and of this minorant
${\rm M}_{r}(house(\alpha))$ as a function of the dynamical degree. With the
same arguments the conjecture of Schinzel-Zassenhaus is proved to be true. An
inequality improving those of Dobrowolski and Voutier ones is obtained. The set
of Salem numbers is shown to be bounded from below by the Perron number
$\theta_{31}^{-1} = 1.08545\ldots$, dominant root of the trinomial $-1 - z^{30}
+ z^{31}$. Whether Lehmer's number is the smallest Salem number remains open. A
lower bound for the Weil height of nonzero totally real algebraic numbers,
$\neq \pm 1$, is obtained (Bogomolov property). For sequences of algebraic
integers of Mahler measure smaller than the smallest Pisot number, whose houses
have a dynamical degree tending to infinity, the Galois orbit measures of
conjugates are proved to converge towards the Haar measure on $|z|=1$ (limit
equidistribution).
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Pseudo asymptotically periodic solutions for fractional integro-differential neutral equations | In this paper, we study the existence and uniqueness of pseudo
$S$-asymptotically $\omega$-periodic mild solutions of class $r$ for fractional
integro-differential neutral equations. An example is presented to illustrate
the application of the abstract results.
| 0 | 0 | 1 | 0 | 0 | 0 |
Updating the silent speech challenge benchmark with deep learning | The 2010 Silent Speech Challenge benchmark is updated with new results
obtained in a Deep Learning strategy, using the same input features and
decoding strategy as in the original article. A Word Error Rate of 6.4% is
obtained, compared to the published value of 17.4%. Additional results
comparing new auto-encoder-based features with the original features at reduced
dimensionality, as well as decoding scenarios on two different language models,
are also presented. The Silent Speech Challenge archive has been updated to
contain both the original and the new auto-encoder features, in addition to the
original raw data.
| 1 | 0 | 0 | 0 | 0 | 0 |
Magnetic field control of cycloidal domains and electric polarization in multiferroic BiFeO$_3$ | The magnetic field induced rearrangement of the cycloidal spin structure in
ferroelectric mono-domain single crystals of the room-temperature multiferroic
BiFeO$_3$ is studied using small-angle neutron scattering (SANS). The cycloid
propagation vectors are observed to rotate when magnetic fields applied
perpendicular to the rhombohedral (polar) axis exceed a pinning threshold value
of $\sim$5\,T. In light of these experimental results, a phenomenological model
is proposed that captures the rearrangement of the cycloidal domains, and we
revisit the microscopic origin of the magnetoelectric effect. A new coupling
between the magnetic anisotropy and the polarization is proposed that explains
the recently discovered magnetoelectric polarization to the rhombohedral axis.
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DepQBF 6.0: A Search-Based QBF Solver Beyond Traditional QCDCL | We present the latest major release version 6.0 of the quantified Boolean
formula (QBF) solver DepQBF, which is based on QCDCL. QCDCL is an extension of
the conflict-driven clause learning (CDCL) paradigm implemented in state of the
art propositional satisfiability (SAT) solvers. The Q-resolution calculus
(QRES) is a QBF proof system which underlies QCDCL. QCDCL solvers can produce
QRES proofs of QBFs in prenex conjunctive normal form (PCNF) as a byproduct of
the solving process. In contrast to traditional QCDCL based on QRES, DepQBF 6.0
implements a variant of QCDCL which is based on a generalization of QRES. This
generalization is due to a set of additional axioms and leaves the original
Q-resolution rules unchanged. The generalization of QRES enables QCDCL to
potentially produce exponentially shorter proofs than the traditional variant.
We present an overview of the features implemented in DepQBF and report on
experimental results which demonstrate the effectiveness of generalized QRES in
QCDCL.
| 1 | 0 | 0 | 0 | 0 | 0 |
On Optimization of Radiative Dipole Body Array Coils for 7 Tesla MRI | In this contribution we present numerical and experimental results of a
parametric study of radiative dipole antennas in a phased array configuration
for efficient body magnetic resonance imaging at 7T via parallel transmit. For
magnetic resonance imaging (MRI) at ultrahigh fields (7T and higher) dipole
antennas are commonly used in phased arrays, particularly for body imaging
targets. This study reveals the effects of dipole positioning in the array
(elevation of dipoles above the subject and inter-dipole spacing) on their
mutual coupling, $B_1^{+}$ per unit power and $B_1^{+}$ per maximum local SAR
efficiencies as well as the RF-shimming capability. The results demonstrate the
trade-off between low maximum local SAR and sensitivity to the subject
variation and provide the working parameter range for practical body arrays
composed of recently suggested fractionated dipoles.
| 0 | 1 | 0 | 0 | 0 | 0 |
Speaking Style Authentication Using Suprasegmental Hidden Markov Models | The importance of speaking style authentication from human speech is gaining
an increasing attention and concern from the engineering community. The
importance comes from the demand to enhance both the naturalness and efficiency
of spoken language human-machine interface. Our work in this research focuses
on proposing, implementing, and testing speaker-dependent and text-dependent
speaking style authentication (verification) systems that accept or reject the
identity claim of a speaking style based on suprasegmental hidden Markov models
(SPHMMs). Based on using SPHMMs, our results show that the average speaking
style authentication performance is: 99%, 37%, 85%, 60%, 61%, 59%, 41%, 61%,
and 57% belonging respectively to the speaking styles: neutral, shouted, slow,
loud, soft, fast, angry, happy, and fearful.
| 1 | 0 | 0 | 0 | 0 | 0 |
A fixed point formula and Harish-Chandra's character formula | The main result in this paper is a fixed point formula for equivariant
indices of elliptic differential operators, for proper actions by connected
semisimple Lie groups on possibly noncompact manifolds, with compact quotients.
For compact groups and manifolds, this reduces to the Atiyah-Segal-Singer fixed
point formula. Other special cases include an index theorem by Connes and
Moscovici for homogeneous spaces, and an earlier index theorem by the second
author, both in cases where the group acting is connected and semisimple. As an
application of this fixed point formula, we give a new proof of
Harish-Chandra's character formula for discrete series representations.
| 0 | 0 | 1 | 0 | 0 | 0 |
The phonon softening due to melting of the ferromagnetic order in elemental iron | We study the fundamental question of the lattice dynamics of a metallic
ferromagnet in the regime where the static long range magnetic order is
replaced by the fluctuating local moments embedded in a metallic host. We use
the \textit{ab initio} Density Functional Theory(DFT)+embedded Dynamical
Mean-Field Theory(eDMFT) functional approach to address the dynamic stability
of iron polymorphs and the phonon softening with increased temperature. We show
that the non-harmonic and inhomogeneous phonon softening measured in iron is a
result of the melting of the long range ferromagnetic order, and is unrelated
to the first order structural transition from the BCC to the FCC phase, as is
usually assumed. We predict that the BCC structure is dynamically stable at all
temperatures at normal pressure, and is only thermodynamically unstable between
the BCC-$\alpha$ and the BCC-$\delta$ phase of iron.
| 0 | 1 | 0 | 0 | 0 | 0 |
Decomposing manifolds into Cartesian products | The decomposability of a Cartesian product of two nondecomposable manifolds
into products of lower dimensional manifolds is studied. For 3-manifolds we
obtain an analog of a result due to Borsuk for surfaces, and in higher
dimensions we show that similar analogs do not exist unless one imposes further
restrictions such as simple connectivity.
| 0 | 0 | 1 | 0 | 0 | 0 |
Performance of the MAGIC telescopes under moonlight | MAGIC, a system of two imaging atmospheric Cherenkov telescopes, achieves its
best performance under dark conditions, i.e. in absence of moonlight or
twilight. Since operating the telescopes only during dark time would severely
limit the duty cycle, observations are also performed when the Moon is present
in the sky. Here we present a dedicated Moon-adapted analysis and characterize
the performance of MAGIC under moonlight. We evaluate energy threshold, angular
resolution and sensitivity of MAGIC under different background light levels,
based on Crab Nebula observations and tuned Monte Carlo simulations. This study
includes observations taken under non-standard hardware configurations, such as
reducing the camera photomultiplier tubes gain by a factor $\sim$1.7 (reduced
HV settings) with respect to standard settings (nominal HV) or using UV-pass
filters to strongly reduce the amount of moonlight reaching the telescopes
cameras. The Crab Nebula spectrum is correctly reconstructed in all the studied
illumination levels, that reach up to 30 times brighter than under dark
conditions. The main effect of moonlight is an increase in the analysis energy
threshold and in the systematic uncertainties on the flux normalization. The
sensitivity degradation is constrained to be below 10\%, within 15-30\% and
between 60 and 80\% for nominal HV, reduced HV and UV-pass filter observations,
respectively. No worsening of the angular resolution was found. Thanks to
observations during moonlight, the duty cycle can be doubled, suppressing the
need to stop observations around full Moon.
| 0 | 1 | 0 | 0 | 0 | 0 |
Mastering Heterogeneous Behavioural Models | Heterogeneity is one important feature of complex systems, leading to the
complexity of their construction and analysis. Moving the heterogeneity at
model level helps in mastering the difficulty of composing heterogeneous models
which constitute a large system. We propose a method made of an algebra and
structure morphisms to deal with the interaction of behavioural models,
provided that they are compatible. We prove that heterogeneous models can
interact in a safe way, and therefore complex heterogeneous systems can be
built and analysed incrementally. The Uppaal tool is targeted for
experimentations.
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Approximation and Convergence Properties of Generative Adversarial Learning | Generative adversarial networks (GAN) approximate a target data distribution
by jointly optimizing an objective function through a "two-player game" between
a generator and a discriminator. Despite their empirical success, however, two
very basic questions on how well they can approximate the target distribution
remain unanswered. First, it is not known how restricting the discriminator
family affects the approximation quality. Second, while a number of different
objective functions have been proposed, we do not understand when convergence
to the global minima of the objective function leads to convergence to the
target distribution under various notions of distributional convergence.
In this paper, we address these questions in a broad and unified setting by
defining a notion of adversarial divergences that includes a number of recently
proposed objective functions. We show that if the objective function is an
adversarial divergence with some additional conditions, then using a restricted
discriminator family has a moment-matching effect. Additionally, we show that
for objective functions that are strict adversarial divergences, convergence in
the objective function implies weak convergence, thus generalizing previous
results.
| 1 | 0 | 0 | 1 | 0 | 0 |
Driver Distraction Identification with an Ensemble of Convolutional Neural Networks | The World Health Organization (WHO) reported 1.25 million deaths yearly due
to road traffic accidents worldwide and the number has been continuously
increasing over the last few years. Nearly fifth of these accidents are caused
by distracted drivers. Existing work of distracted driver detection is
concerned with a small set of distractions (mostly, cell phone usage).
Unreliable ad-hoc methods are often used.In this paper, we present the first
publicly available dataset for driver distraction identification with more
distraction postures than existing alternatives. In addition, we propose a
reliable deep learning-based solution that achieves a 90% accuracy. The system
consists of a genetically-weighted ensemble of convolutional neural networks,
we show that a weighted ensemble of classifiers using a genetic algorithm
yields in a better classification confidence. We also study the effect of
different visual elements in distraction detection by means of face and hand
localizations, and skin segmentation. Finally, we present a thinned version of
our ensemble that could achieve 84.64% classification accuracy and operate in a
real-time environment.
| 1 | 0 | 0 | 1 | 0 | 0 |
Driver Action Prediction Using Deep (Bidirectional) Recurrent Neural Network | Advanced driver assistance systems (ADAS) can be significantly improved with
effective driver action prediction (DAP). Predicting driver actions early and
accurately can help mitigate the effects of potentially unsafe driving
behaviors and avoid possible accidents. In this paper, we formulate driver
action prediction as a timeseries anomaly prediction problem. While the anomaly
(driver actions of interest) detection might be trivial in this context,
finding patterns that consistently precede an anomaly requires searching for or
extracting features across multi-modal sensory inputs. We present such a driver
action prediction system, including a real-time data acquisition, processing
and learning framework for predicting future or impending driver action. The
proposed system incorporates camera-based knowledge of the driving environment
and the driver themselves, in addition to traditional vehicle dynamics. It then
uses a deep bidirectional recurrent neural network (DBRNN) to learn the
correlation between sensory inputs and impending driver behavior achieving
accurate and high horizon action prediction. The proposed system performs
better than other existing systems on driver action prediction tasks and can
accurately predict key driver actions including acceleration, braking, lane
change and turning at durations of 5sec before the action is executed by the
driver.
| 1 | 0 | 0 | 1 | 0 | 0 |
A simple neural network module for relational reasoning | Relational reasoning is a central component of generally intelligent
behavior, but has proven difficult for neural networks to learn. In this paper
we describe how to use Relation Networks (RNs) as a simple plug-and-play module
to solve problems that fundamentally hinge on relational reasoning. We tested
RN-augmented networks on three tasks: visual question answering using a
challenging dataset called CLEVR, on which we achieve state-of-the-art,
super-human performance; text-based question answering using the bAbI suite of
tasks; and complex reasoning about dynamic physical systems. Then, using a
curated dataset called Sort-of-CLEVR we show that powerful convolutional
networks do not have a general capacity to solve relational questions, but can
gain this capacity when augmented with RNs. Our work shows how a deep learning
architecture equipped with an RN module can implicitly discover and learn to
reason about entities and their relations.
| 1 | 0 | 0 | 0 | 0 | 0 |
Computing low-rank approximations of large-scale matrices with the Tensor Network randomized SVD | We propose a new algorithm for the computation of a singular value
decomposition (SVD) low-rank approximation of a matrix in the Matrix Product
Operator (MPO) format, also called the Tensor Train Matrix format. Our tensor
network randomized SVD (TNrSVD) algorithm is an MPO implementation of the
randomized SVD algorithm that is able to compute dominant singular values and
their corresponding singular vectors. In contrast to the state-of-the-art
tensor-based alternating least squares SVD (ALS-SVD) and modified alternating
least squares SVD (MALS-SVD) matrix approximation methods, TNrSVD can be up to
17 times faster while achieving the same accuracy. In addition, our TNrSVD
algorithm also produces accurate approximations in particular cases where both
ALS-SVD and MALS-SVD fail to converge. We also propose a new algorithm for the
fast conversion of a sparse matrix into its corresponding MPO form, which is up
to 509 times faster than the standard Tensor Train SVD (TT-SVD) method while
achieving machine precision accuracy. The efficiency and accuracy of both
algorithms are demonstrated in numerical experiments.
| 1 | 0 | 0 | 0 | 0 | 0 |
Path Cover and Path Pack Inequalities for the Capacitated Fixed-Charge Network Flow Problem | Capacitated fixed-charge network flows are used to model a variety of
problems in telecommunication, facility location, production planning and
supply chain management. In this paper, we investigate capacitated path
substructures and derive strong and easy-to-compute \emph{path cover and path
pack inequalities}. These inequalities are based on an explicit
characterization of the submodular inequalities through a fast computation of
parametric minimum cuts on a path, and they generalize the well-known flow
cover and flow pack inequalities for the single-node relaxations of
fixed-charge flow models. We provide necessary and sufficient facet conditions.
Computational results demonstrate the effectiveness of the inequalities when
used as cuts in a branch-and-cut algorithm.
| 1 | 0 | 1 | 0 | 0 | 0 |
Bridging the Gap Between Value and Policy Based Reinforcement Learning | We establish a new connection between value and policy based reinforcement
learning (RL) based on a relationship between softmax temporal value
consistency and policy optimality under entropy regularization. Specifically,
we show that softmax consistent action values correspond to optimal entropy
regularized policy probabilities along any action sequence, regardless of
provenance. From this observation, we develop a new RL algorithm, Path
Consistency Learning (PCL), that minimizes a notion of soft consistency error
along multi-step action sequences extracted from both on- and off-policy
traces. We examine the behavior of PCL in different scenarios and show that PCL
can be interpreted as generalizing both actor-critic and Q-learning algorithms.
We subsequently deepen the relationship by showing how a single model can be
used to represent both a policy and the corresponding softmax state values,
eliminating the need for a separate critic. The experimental evaluation
demonstrates that PCL significantly outperforms strong actor-critic and
Q-learning baselines across several benchmarks.
| 1 | 0 | 0 | 1 | 0 | 0 |
Chalcogenide Glass-on-Graphene Photonics | Two-dimensional (2-D) materials are of tremendous interest to integrated
photonics given their singular optical characteristics spanning light emission,
modulation, saturable absorption, and nonlinear optics. To harness their
optical properties, these atomically thin materials are usually attached onto
prefabricated devices via a transfer process. In this paper, we present a new
route for 2-D material integration with planar photonics. Central to this
approach is the use of chalcogenide glass, a multifunctional material which can
be directly deposited and patterned on a wide variety of 2-D materials and can
simultaneously function as the light guiding medium, a gate dielectric, and a
passivation layer for 2-D materials. Besides claiming improved fabrication
yield and throughput compared to the traditional transfer process, our
technique also enables unconventional multilayer device geometries optimally
designed for enhancing light-matter interactions in the 2-D layers.
Capitalizing on this facile integration method, we demonstrate a series of
high-performance glass-on-graphene devices including ultra-broadband on-chip
polarizers, energy-efficient thermo-optic switches, as well as graphene-based
mid-infrared (mid-IR) waveguide-integrated photodetectors and modulators.
| 0 | 1 | 0 | 0 | 0 | 0 |
Speaker verification using end-to-end adversarial language adaptation | In this paper we investigate the use of adversarial domain adaptation for
addressing the problem of language mismatch between speaker recognition
corpora. In the context of speaker verification, adversarial domain adaptation
methods aim at minimizing certain divergences between the distribution that the
utterance-level features follow (i.e. speaker embeddings) when drawn from
source and target domains (i.e. languages), while preserving their capacity in
recognizing speakers. Neural architectures for extracting utterance-level
representations enable us to apply adversarial adaptation methods in an
end-to-end fashion and train the network jointly with the standard
cross-entropy loss. We examine several configurations, such as the use of
(pseudo-)labels on the target domain as well as domain labels in the feature
extractor, and we demonstrate the effectiveness of our method on the
challenging NIST SRE16 and SRE18 benchmarks.
| 1 | 0 | 0 | 0 | 0 | 0 |
Real elliptic curves and cevian geometry | We study the elliptic curve $E_a: (ax+1)y^2+(ax+1)(x-1)y+x^2-x=0$, which we
call the geometric normal form of an elliptic curve. We show that any elliptic
curve whose $j$-invariant is real is isomorphic to a curve $E_a$ in geometric
normal form, and show that for $a \notin \{0, -1, -9\}$, the points on $E_a$,
minus a set of $6$ points, can be characterized in terms of the cevian geometry
of a triangle.
| 0 | 0 | 1 | 0 | 0 | 0 |
Omni $n$-Lie algebras and linearization of higher analogues of Courant algebroids | In this paper, we introduce the notion of an omni $n$-Lie algebra and show
that they are linearization of higher analogues of standard Courant algebroids.
We also introduce the notion of a nonabelian omni $n$-Lie algebra and show that
they are linearization of higher analogues of Courant algebroids associated to
Nambu-Poisson manifolds.
| 0 | 0 | 1 | 0 | 0 | 0 |
Musical Instrument Recognition Using Their Distinctive Characteristics in Artificial Neural Networks | In this study an Artificial Neural Network was trained to classify musical
instruments, using audio samples transformed to the frequency domain. Different
features of the sound, in both time and frequency domain, were analyzed and
compared in relation to how much information that could be derived from that
limited data. The study concluded that in comparison with the base experiment,
that had an accuracy of 93.5%, using the attack only resulted in 80.2% and the
initial 100 Hz in 64.2%.
| 1 | 0 | 0 | 1 | 0 | 0 |
Revealing structure components of the retina by deep learning networks | Deep convolutional neural networks (CNNs) have demonstrated impressive
performance on visual object classification tasks. In addition, it is a useful
model for predication of neuronal responses recorded in visual system. However,
there is still no clear understanding of what CNNs learn in terms of visual
neuronal circuits. Visualizing CNN's features to obtain possible connections to
neuronscience underpinnings is not easy due to highly complex circuits from the
retina to higher visual cortex. Here we address this issue by focusing on
single retinal ganglion cells with a simple model and electrophysiological
recordings from salamanders. By training CNNs with white noise images to
predicate neural responses, we found that convolutional filters learned in the
end are resembling to biological components of the retinal circuit. Features
represented by these filters tile the space of conventional receptive field of
retinal ganglion cells. These results suggest that CNN could be used to reveal
structure components of neuronal circuits.
| 0 | 0 | 0 | 1 | 0 | 0 |
Generalized Yangians and their Poisson counterparts | By a generalized Yangian we mean a Yangian-like algebra of one of two
classes. One of these classes consists of the so-called braided Yangians,
introduced in our previous paper. The braided Yangians are in a sense similar
to the reflection equation algebra. The generalized Yangians of second class,
called the Yangians of RTT type, are defined by the same formulae as the usual
Yangians are but with other quantum $R$-matrices. If such an $R$-matrix is the
simplest trigonometrical $R$-matrix, the corresponding Yangian of RTT type is
the so-called q-Yangian. We claim that each generalized Yangian is a
deformation of the commutative algebra ${\rm Sym}(gl(m)[t^{-1}])$ provided that
the corresponding $R$-matrix is a deformation of the flip. Also, we exhibit the
corresponding Poisson brackets.
| 0 | 0 | 1 | 0 | 0 | 0 |
Asymptotics of Hankel determinants with a one-cut regular potential and Fisher-Hartwig singularities | We obtain asymptotics of large Hankel determinants whose weight depends on a
one-cut regular potential and any number of Fisher-Hartwig singularities. This
generalises two results: 1) a result of Berestycki, Webb and Wong [5] for
root-type singularities, and 2) a result of Its and Krasovsky [37] for a
Gaussian weight with a single jump-type singularity. We show that when we apply
a piecewise constant thinning on the eigenvalues of a random Hermitian matrix
drawn from a one-cut regular ensemble, the gap probability in the thinned
spectrum, as well as correlations of the characteristic polynomial of the
associated conditional point process, can be expressed in terms of these
determinants.
| 0 | 0 | 1 | 0 | 0 | 0 |
Beyond linear galaxy alignments | Galaxy intrinsic alignments (IA) are a critical uncertainty for current and
future weak lensing measurements. We describe a perturbative expansion of IA,
analogous to the treatment of galaxy biasing. From an astrophysical
perspective, this model includes the expected large-scale alignment mechanisms
for galaxies that are pressure-supported (tidal alignment) and
rotation-supported (tidal torquing) as well as the cross-correlation between
the two. Alternatively, this expansion can be viewed as an effective model
capturing all relevant effects up to the given order. We include terms up to
second order in the density and tidal fields and calculate the resulting IA
contributions to two-point statistics at one-loop order. For fiducial
amplitudes of the IA parameters, we find the quadratic alignment and
linear-quadratic cross terms can contribute order-unity corrections to the
total intrinsic alignment signal at $k\sim0.1\,h^{-1}{\rm Mpc}$, depending on
the source redshift distribution. These contributions can lead to significant
biases on inferred cosmological parameters in Stage IV photometric weak lensing
surveys. We perform forecasts for an LSST-like survey, finding that use of the
standard "NLA" model for intrinsic alignments cannot remove these large
parameter biases, even when allowing for a more general redshift dependence.
The model presented here will allow for more accurate and flexible IA treatment
in weak lensing and combined probes analyses, and an implementation is made
available as part of the public FAST-PT code. The model also provides a more
advanced framework for understanding the underlying IA processes and their
relationship to fundamental physics.
| 0 | 1 | 0 | 0 | 0 | 0 |
Modelling thermo-electro-mechanical effects in orthotropic cardiac tissue | In this paper we introduce a new mathematical model for the active
contraction of cardiac muscle, featuring different thermo-electric and
nonlinear conductivity properties. The passive hyperelastic response of the
tissue is described by an orthotropic exponential model, whereas the ionic
activity dictates active contraction incorporated through the concept of
orthotropic active strain. We use a fully incompressible formulation, and the
generated strain modifies directly the conductivity mechanisms in the medium
through the pull-back transformation. We also investigate the influence of
thermo-electric effects in the onset of multiphysics emergent spatiotemporal
dynamics, using nonlinear diffusion. It turns out that these ingredients have a
key role in reproducing pathological chaotic dynamics such as ventricular
fibrillation during inflammatory events, for instance. The specific structure
of the governing equations suggests to cast the problem in mixed-primal form
and we write it in terms of Kirchhoff stress, displacements, solid pressure,
electric potential, activation generation, and ionic variables. We also propose
a new mixed-primal finite element method for its numerical approximation, and
we use it to explore the properties of the model and to assess the importance
of coupling terms, by means of a few computational experiments in 3D.
| 0 | 0 | 0 | 0 | 1 | 0 |
Debt-Prone Bugs: Technical Debt in Software Maintenance | Fixing bugs is an important phase in software development and maintenance. In
practice, the process of bug fixing may conflict with the release schedule.
Such confliction leads to a trade-off between software quality and release
schedule, which is known as the technical debt metaphor. In this article, we
propose the concept of debt-prone bugs to model the technical debt in software
maintenance. We identify three types of debt-prone bugs, namely tag bugs,
reopened bugs, and duplicate bugs. A case study on Mozilla is conducted to
examine the impact of debt-prone bugs in software products. We investigate the
correlation between debt-prone bugs and the product quality. For a product
under development, we build prediction models based on historical products to
predict the time cost of fixing bugs. The result shows that identifying
debt-prone bugs can assist in monitoring and improving software quality.
| 1 | 0 | 0 | 0 | 0 | 0 |
Khovanov-Rozansky homology and higher Catalan sequences | We give a simple recursion which computes the triply graded Khovanov-Rozansky
homology of several infinite families of knots and links, including the
$(n,nm\pm 1)$ and $(n,nm)$ torus links for $n,m\geq 1$. We interpret our
results in terms of Catalan combinatorics, proving a conjecture of Gorsky's.
Our computations agree with predictions coming from Hilbert schemes and
rational DAHA, which also proves the Gorsky-Oblomkov-Rasmussen-Shende
conjectures in these cases. Additionally, our results suggest a topological
interpretation of the symmetric functions which appear in the context of the
$m$-shuffle conjecture of Haglund-Haiman-Loehr-Remmel-Ulyanov.
| 0 | 0 | 1 | 0 | 0 | 0 |
New face of multifractality: Multi-branched left-sidedness and phase transitions in multifractality of interevent times | We develop an extended multifractal analysis based on the Legendre-Fenchel
transform rather than the routinely used Legendre transform. We apply this
analysis to studying time series consisting of inter-event times. As a result,
we discern the non-monotonic behavior of the generalized Hurst exponent - the
fundamental exponent studied by us - and hence a multi-branched left-sided
spectrum of dimensions. This kind of multifractality is a direct result of the
non-monotonic behavior of the generalized Hurst exponent and is not caused by
non-analytic behavior as has been previously suggested. We examine the main
thermodynamic consequences of the existence of this type of multifractality
related to the thermal stable, metastable, and unstable phases within a
hierarchy of fluctuations, and also to the first and second order phase
transitions between them.
| 0 | 0 | 0 | 0 | 0 | 1 |
Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions | By building up on the recent theory that established the connection between
implicit generative modeling and optimal transport, in this study, we propose a
novel parameter-free algorithm for learning the underlying distributions of
complicated datasets and sampling from them. The proposed algorithm is based on
a functional optimization problem, which aims at finding a measure that is
close to the data distribution as much as possible and also expressive enough
for generative modeling purposes. We formulate the problem as a gradient flow
in the space of probability measures. The connections between gradient flows
and stochastic differential equations let us develop a computationally
efficient algorithm for solving the optimization problem, where the resulting
algorithm resembles the recent dynamics-based Markov Chain Monte Carlo
algorithms. We provide formal theoretical analysis where we prove finite-time
error guarantees for the proposed algorithm. Our experimental results support
our theory and shows that our algorithm is able to capture the structure of
challenging distributions.
| 0 | 0 | 0 | 1 | 0 | 0 |
Dual Iterative Hard Thresholding: From Non-convex Sparse Minimization to Non-smooth Concave Maximization | Iterative Hard Thresholding (IHT) is a class of projected gradient descent
methods for optimizing sparsity-constrained minimization models, with the best
known efficiency and scalability in practice. As far as we know, the existing
IHT-style methods are designed for sparse minimization in primal form. It
remains open to explore duality theory and algorithms in such a non-convex and
NP-hard problem setting. In this paper, we bridge this gap by establishing a
duality theory for sparsity-constrained minimization with $\ell_2$-regularized
loss function and proposing an IHT-style algorithm for dual maximization. Our
sparse duality theory provides a set of sufficient and necessary conditions
under which the original NP-hard/non-convex problem can be equivalently solved
in a dual formulation. The proposed dual IHT algorithm is a super-gradient
method for maximizing the non-smooth dual objective. An interesting finding is
that the sparse recovery performance of dual IHT is invariant to the Restricted
Isometry Property (RIP), which is required by virtually all the existing primal
IHT algorithms without sparsity relaxation. Moreover, a stochastic variant of
dual IHT is proposed for large-scale stochastic optimization. Numerical results
demonstrate the superiority of dual IHT algorithms to the state-of-the-art
primal IHT-style algorithms in model estimation accuracy and computational
efficiency.
| 1 | 0 | 0 | 1 | 0 | 0 |
Binary Ensemble Neural Network: More Bits per Network or More Networks per Bit? | Binary neural networks (BNN) have been studied extensively since they run
dramatically faster at lower memory and power consumption than floating-point
networks, thanks to the efficiency of bit operations. However, contemporary
BNNs whose weights and activations are both single bits suffer from severe
accuracy degradation. To understand why, we investigate the representation
ability, speed and bias/variance of BNNs through extensive experiments. We
conclude that the error of BNNs is predominantly caused by the intrinsic
instability (training time) and non-robustness (train & test time). Inspired by
this investigation, we propose the Binary Ensemble Neural Network (BENN) which
leverages ensemble methods to improve the performance of BNNs with limited
efficiency cost. While ensemble techniques have been broadly believed to be
only marginally helpful for strong classifiers such as deep neural networks,
our analyses and experiments show that they are naturally a perfect fit to
boost BNNs. We find that our BENN, which is faster and much more robust than
state-of-the-art binary networks, can even surpass the accuracy of the
full-precision floating number network with the same architecture.
| 0 | 0 | 0 | 1 | 0 | 0 |
Generalized connected sum formula for the Arnold invariants of generic plane curves | We define the generalized connected sum for generic closed plane curves,
generalizing the strange sum defined by Arnold, and completely describe how the
Arnold invariants $J^{\pm}$ and $\mathit{St}$ behave under the generalized
connected sums.
| 0 | 0 | 1 | 0 | 0 | 0 |
Smart patterned surfaces with programmable thermal emissivity and their design through combinatorial strategies | The emissivity of common materials remains constant with temperature
variations, and cannot drastically change. However, it is possible to design
its entire behaviour as a function of temperature, and to significantly modify
the thermal emissivity of a surface through the combination of different
materials and patterns. Here, we show that smart patterned surfaces consisting
of smaller structures (motifs) may be designed to respond uniquely through
combinatorial design strategies by transforming themselves from 2D to 3D
complex structures with a two-way shape memory effect. The smart surfaces can
passively manipulate thermal radiation without-the use of controllers and power
supplies-because their modus operandi has already been programmed and
integrated into their intrinsic characteristics; the environment provides the
energy required for their activation. Each motif emits thermal radiation in a
certain manner, as it changes its geometry; however, the spatial distribution
of these motifs causes them to interact with each other. Therefore, their
combination and interaction determine the global behaviour of the surfaces,
thus enabling their a priori design. The emissivity behaviour is not random; it
is determined by two fundamental parameters, namely the combination of
orientations in which the motifs open (n-fold rotational symmetry (rn)) and the
combination of materials (colours) on the motifs; these generate functions
which fully determine the dependency of the emissivity on the temperature.
| 0 | 1 | 0 | 0 | 0 | 0 |
STAR: Spatio-Temporal Altimeter Waveform Retracking using Sparse Representation and Conditional Random Fields | Satellite radar altimetry is one of the most powerful techniques for
measuring sea surface height variations, with applications ranging from
operational oceanography to climate research. Over open oceans, altimeter
return waveforms generally correspond to the Brown model, and by inversion,
estimated shape parameters provide mean surface height and wind speed. However,
in coastal areas or over inland waters, the waveform shape is often distorted
by land influence, resulting in peaks or fast decaying trailing edges. As a
result, derived sea surface heights are then less accurate and waveforms need
to be reprocessed by sophisticated algorithms. To this end, this work suggests
a novel Spatio-Temporal Altimetry Retracking (STAR) technique. We show that
STAR enables the derivation of sea surface heights over the open ocean as well
as over coastal regions of at least the same quality as compared to existing
retracking methods, but for a larger number of cycles and thus retaining more
useful data. Novel elements of our method are (a) integrating information from
spatially and temporally neighboring waveforms through a conditional random
field approach, (b) sub-waveform detection, where relevant sub-waveforms are
separated from corrupted or non-relevant parts through a sparse representation
approach, and (c) identifying the final best set of sea surfaces heights from
multiple likely heights using Dijkstra's algorithm. We apply STAR to data from
the Jason-1, Jason-2 and Envisat missions for study sites in the Gulf of
Trieste, Italy and in the coastal region of the Ganges-Brahmaputra-Meghna
estuary, Bangladesh. We compare to several established and recent retracking
methods, as well as to tide gauge data. Our experiments suggest that the
obtained sea surface heights are significantly less affected by outliers when
compared to results obtained by other approaches.
| 0 | 1 | 0 | 0 | 0 | 0 |
Magnetization jump in one dimensional $J-Q_{2}$ model with anisotropic exchange | We investigate the adiabatic magnetization process of the one-dimensional
$J-Q_{2}$ model with XXZ anisotropy $g$ in an external magnetic field $h$ by
using density matrix renormalization group (DMRG) method. According to the
characteristic of the magnetization curves, we draw a magnetization phase
diagram consisting of four phases. For a fixed nonzero pair coupling $Q$, i)
when $g<-1$, the ground state is always ferromagnetic in spite of $h$; ii) when
$g>-1$ but still small, the whole magnetization curve is continuous and smooth;
iii) if further increasing $g$, there is a macroscopic magnetization jump from
partially- to fully-polarized state; iv) for a sufficiently large $g$, the
magnetization jump is from non- to fully-polarized state. By examining the
energy per magnon and the correlation function, we find that the origin of the
magnetization jump is the condensation of magnons and the formation of magnetic
domains. We also demonstrate that while the experienced states are
Heisenberg-like without long-range order, all the \textit{jumped-over} states
have antiferromagnetic or Néel long-range orders, or their mixing.
| 0 | 1 | 0 | 0 | 0 | 0 |
CoMID: Context-based Multi-Invariant Detection for Monitoring Cyber-Physical Software | Cyber-physical software continually interacts with its physical environment
for adaptation in order to deliver smart services. However, the interactions
can be subject to various errors when the software's assumption on its
environment no longer holds, thus leading to unexpected misbehavior or even
failure. To address this problem, one promising way is to conduct runtime
monitoring of invariants, so as to prevent cyber-physical software from
entering such errors (a.k.a. abnormal states). To effectively detect abnormal
states, we in this article present an approach, named Context-based
Multi-Invariant Detection (CoMID), which consists of two techniques:
context-based trace grouping and multi-invariant detection. The former infers
contexts to distinguish different effective scopes for CoMID's derived
invariants, and the latter conducts ensemble evaluation of multiple invariants
to detect abnormal states. We experimentally evaluate CoMID on real-world
cyber-physical software. The results show that CoMID achieves a 5.7-28.2%
higher true-positive rate and a 6.8-37.6% lower false-positive rate in
detecting abnormal states, as compared with state-of-the-art approaches (i.e.,
Daikon and ZoomIn). When deployed in field tests, CoMID's runtime monitoring
improves the success rate of cyber-physical software in its task executions by
15.3-31.7%.
| 1 | 0 | 0 | 0 | 0 | 0 |
Asymptotics of multivariate contingency tables with fixed marginals | We consider the asymptotic distribution of a cell in a 2 x ... x 2
contingency table as the fixed marginal totals tend to infinity. The asymptotic
order of the cell variance is derived and a useful diagnostic is given for
determining whether the cell has a Poisson limit or a Gaussian limit. There are
three forms of Poisson convergence. The exact form is shown to be determined by
the growth rates of the two smallest marginal totals. The results are
generalized to contingency tables with arbitrary sizes and are further
complemented with concrete examples.
| 0 | 0 | 1 | 1 | 0 | 0 |
On the Faithfulness of 1-dimensional Topological Quantum Field Theories | This paper explores 1-dimensional topological quantum field theories. We
separately deal with strict and strong 1-dimensional topological quantum field
theories. The strict one is regarded as a symmetric monoidal functor between
the category of 1-cobordisms and the category of matrices, and the strong one
is a symmetric monoidal functor between the category of 1-cobordisms and the
category of finite dimensional vector spaces. It has been proved that both
strict and strong 1-dimensional topological quantum field theories are
faithful.
| 0 | 0 | 1 | 0 | 0 | 0 |
Distribution of the periodic points of the Farey map | We expand the cross section of the geodesic flow in the tangent bundle of the
modular surface given by Series to produce another section whose return map
under the geodesic flow is a double cover of the natural extension of the Farey
map. We use this cross section to extend the correspondence between the closed
geodesics on the modular surface and the periodic points of the Gauss map to
include the periodic points of the Farey map. Then, analogous to the work of
Pollicott, we prove an equidistribution result for the periodic points of the
Farey map when they are ordered according to the length of their corresponding
closed geodesics.
| 0 | 0 | 1 | 0 | 0 | 0 |
Marangoni effects on a thin liquid film coating a sphere with axial or radial thermal gradients | We study the time evolution of a thin liquid film coating the outer surface
of a sphere in the presence of gravity, surface tension and thermal gradients.
We derive the fourth-order nonlinear partial differential equation that models
the thin film dynamics, including Marangoni terms arising from the dependence
of surface tension on temperature. We consider two different imposed
temperature distributions with axial or radial thermal gradients. We analyze
the stability of a uniform coating under small perturbations and carry out
numerical simulations in COMSOL for a range of parameter values. In the case of
an axial temperature gradient, we find steady states with either uniform film
thickness, or with the fluid accumulating at the bottom or near the top of the
sphere, depending on the total volume of liquid in the film, dictating whether
gravity or Marangoni effects dominate. In the case of a radial temperature
gradient, a stability analysis reveals the most unstable non-axisymmetric modes
on an initially uniform coating film.
| 0 | 1 | 0 | 0 | 0 | 0 |
Fooling the classifier: Ligand antagonism and adversarial examples | Machine learning algorithms are sensitive to so-called adversarial
perturbations. This is reminiscent of cellular decision-making where antagonist
ligands may prevent correct signaling, like during the early immune response.
We draw a formal analogy between neural networks used in machine learning and
the general class of adaptive proofreading networks. We then apply simple
adversarial strategies from machine learning to models of ligand
discrimination. We show how kinetic proofreading leads to "boundary tilting"
and identify three types of perturbation (adversarial, non adversarial and
ambiguous). We then use a gradient-descent approach to compare different
adaptive proofreading models, and we reveal the existence of two qualitatively
different regimes characterized by the presence or absence of a critical point.
These regimes are reminiscent of the "feature-to-prototype" transition
identified in machine learning, corresponding to two strategies in ligand
antagonism (broad vs. specialized). Overall, our work connects evolved cellular
decision-making to classification in machine learning, showing that behaviours
close to the decision boundary can be understood through the same mechanisms.
| 0 | 0 | 0 | 1 | 1 | 0 |
A Simplified Approach to Analyze Complementary Sensitivity Trade-offs in Continuous-Time and Discrete-Time Systems | A simplified approach is proposed to investigate the continuous-time and
discrete-time complementary sensitivity Bode integrals (CSBIs) in this note.
For continuous-time feedback systems with unbounded frequency domain, the CSBI
weighted by $1/\omega^2$ is considered, where this simplified method reveals a
more explicit relationship between the value of CSBI and the structure of the
open-loop transfer function. With a minor modification of this method, the CSBI
of discrete-time system is derived, and illustrative examples are provided.
Compared with the existing results on CSBI, neither Cauchy integral theorem nor
Poisson integral formula are used throughout the analysis, and the analytic
constraint on the integrand is removed.
| 1 | 0 | 0 | 0 | 0 | 0 |
Comparing Graph Clusterings: Set partition measures vs. Graph-aware measures | In this paper, we propose a family of graph partition similarity measures
that take the topology of the graph into account. These graph-aware measures
are alternatives to using set partition similarity measures that are not
specifically designed for graph partitions. The two types of measures,
graph-aware and set partition measures, are shown to have opposite behaviors
with respect to resolution issues and provide complementary information
necessary to assess that two graph partitions are similar.
| 0 | 0 | 0 | 1 | 0 | 0 |
Competitive Resource Allocation in HetNets: the Impact of Small-cell Spectrum Constraints and Investment Costs | Heterogeneous wireless networks with small-cell deployments in licensed and
unlicensed spectrum bands are a promising approach for expanding wireless
connectivity and service. As a result, wireless service providers (SPs) are
adding small-cells to augment their existing macro-cell deployments. This added
flexibility complicates network management, in particular, service pricing and
spectrum allocations across macro- and small-cells. Further, these decisions
depend on the degree of competition among SPs. Restrictions on shared spectrum
access imposed by regulators, such as low power constraints that lead to
small-cell deployments, along with the investment cost needed to add small
cells to an existing network, also impact strategic decisions and market
efficiency. If the revenue generated by small-cells does not cover the
investment cost, then there will be no deployment even if it increases social
welfare. We study the implications of such spectrum constraints and investment
costs on resource allocation and pricing decisions by competitive SPs, along
with the associated social welfare. Our results show that while the optimal
resource allocation taking constraints and investment into account can be
uniquely determined, adding those features with strategic SPs can have a
substantial effect on the equilibrium market structure.
| 1 | 0 | 0 | 0 | 0 | 0 |
Gradient Reversal Against Discrimination | No methods currently exist for making arbitrary neural networks fair. In this
work we introduce GRAD, a new and simplified method to producing fair neural
networks that can be used for auto-encoding fair representations or directly
with predictive networks. It is easy to implement and add to existing
architectures, has only one (insensitive) hyper-parameter, and provides
improved individual and group fairness. We use the flexibility of GRAD to
demonstrate multi-attribute protection.
| 0 | 0 | 0 | 1 | 0 | 0 |
Non-commutative crepant resolutions for some toric singularities I | We give a criterion for the existence of non-commutative crepant resolutions
(NCCR's) for certain toric singularities. In particular we recover Broomhead's
result that a 3-dimensional toric Gorenstein singularity has a NCCR. Our result
also yields the existence of a NCCR for a 4-dimensional toric Gorenstein
singularity which is known to have no toric NCCR.
| 0 | 0 | 1 | 0 | 0 | 0 |
High temperature pairing in a strongly interacting two-dimensional Fermi gas | We observe many-body pairing in a two-dimensional gas of ultracold fermionic
atoms at temperatures far above the critical temperature for superfluidity. For
this, we use spatially resolved radio-frequency spectroscopy to measure pairing
energies spanning a wide range of temperatures and interaction strengths. In
the strongly interacting regime where the scattering length between fermions is
on the same order as the inter-particle spacing, the pairing energy in the
normal phase significantly exceeds the intrinsic two-body binding energy of the
system and shows a clear dependence on local density. This implies that pairing
in this regime is driven by many-body correlations, rather than two-body
physics. We find this effect to persist at temperatures close to the Fermi
temperature which demonstrates that pairing correlations in strongly
interacting two-dimensional fermionic systems are remarkably robust against
thermal fluctuations.
| 0 | 1 | 0 | 0 | 0 | 0 |
Untangling the hairball: fitness based asymptotic reduction of biological networks | Complex mathematical models of interaction networks are routinely used for
prediction in systems biology. However, it is difficult to reconcile network
complexities with a formal understanding of their behavior. Here, we propose a
simple procedure (called $\bar \varphi$) to reduce biological models to
functional submodules, using statistical mechanics of complex systems combined
with a fitness-based approach inspired by $\textit{in silico}$ evolution. $\bar
\varphi$ works by putting parameters or combination of parameters to some
asymptotic limit, while keeping (or slightly improving) the model performance,
and requires parameter symmetry breaking for more complex models. We illustrate
$\bar \varphi$ on biochemical adaptation and on different models of immune
recognition by T cells. An intractable model of immune recognition with close
to a hundred individual transition rates is reduced to a simple two-parameter
model. $\bar \varphi$ extracts three different mechanisms for early immune
recognition, and automatically discovers similar functional modules in
different models of the same process, allowing for model classification and
comparison. Our procedure can be applied to biological networks based on rate
equations using a fitness function that quantifies phenotypic performance.
| 0 | 1 | 0 | 0 | 0 | 0 |
Exotica and the status of the strong cosmic censor conjecture in four dimensions | An immense class of physical counterexamples to the four dimensional strong
cosmic censor conjecture---in its usual broad formulation---is exhibited. More
precisely, out of any closed and simply connected 4-manifold an open Ricci-flat
Lorentzian 4-manifold is constructed which is not globally hyperbolic and no
perturbation of it, in any sense, can be globally hyperbolic. This very stable
non-global-hyperbolicity is the consequence of our open spaces having a
"creased end" i.e., an end diffeomorphic to an exotic ${\mathbb R}^4$. Open
manifolds having an end like this is a typical phenomenon in four dimensions.
The construction is based on a collection of results of Gompf and Taubes on
exotic and self-dual spaces, respectively, as well as applying Penrose'
non-linear graviton construction (i.e., twistor theory) to solve the Riemannian
Einstein's equation. These solutions then are converted into stably
non-globally-hyperbolic Lorentzian vacuum solutions. It follows that the
plethora of vacuum solutions we found cannot be obtained via the initial value
formulation of the Einstein's equation because they are "too long" in a certain
sense (explained in the text). This different (i.e., not based on the initial
value formulation but twistorial) technical background might partially explain
why the existence of vacuum solutions of this kind have not been realized so
far in spite of the fact that, apparently, their superabundance compared to the
well-known globally hyperbolic vacuum solutions is overwhelming.
| 0 | 0 | 1 | 0 | 0 | 0 |
Centralities in Simplicial Complexes | Complex networks can be used to represent complex systems which originate in
the real world. Here we study a transformation of these complex networks into
simplicial complexes, where cliques represent the simplices of the complex. We
extend the concept of node centrality to that of simplicial centrality and
study several mathematical properties of degree, closeness, betweenness,
eigenvector, Katz, and subgraph centrality for simplicial complexes. We study
the degree distributions of these centralities at the different levels. We also
compare and describe the differences between the centralities at the different
levels. Using these centralities we study a method for detecting essential
proteins in PPI networks of cells and explain the varying abilities of the
centrality measures at the different levels in identifying these essential
proteins. The paper is written in a self-contained way, such that it can be
used by practitioners of network theory as a basis for further developments.
| 1 | 1 | 0 | 0 | 0 | 0 |
The Samuel realcompactification | For a uniform space (X, $\mu$), we introduce a realcompactification of X by
means of the family $U_{\mu}(X)$ of all the real-valued uniformly continuous
functions, in the same way that the known Samuel compactification is given by
$U^{*}_{\mu}(X)$ the set of all the bounded functions in $U_{\mu}(X)$. We will
call it "the Samuel realcompactification" by several resemblances to the Samuel
compactification. In this note, we present different ways to construct such
realcompactification as well as we study the corresponding problem of knowing
when a uniform space is Samuel realcompact, that is, it coincides with its
Samuel realcompactification. At this respect we obtain as main result a theorem
of Katětov-Shirota type, by means of a new property of completeness
recently introduced by the authors, called Bourbaki-completeness.
| 0 | 0 | 1 | 0 | 0 | 0 |
Segmented Terahertz Electron Accelerator and Manipulator (STEAM) | Acceleration and manipulation of ultrashort electron bunches are the basis
behind electron and X-ray devices used for ultrafast, atomic-scale imaging and
spectroscopy. Using laser-generated THz drivers enables intrinsic
synchronization as well as dramatic gains in field strengths, field gradients
and component compactness, leading to shorter electron bunches, higher
spatio-temporal resolution and smaller infrastructures. We present a segmented
THz electron accelerator and manipulator (STEAM) with extended interaction
lengths capable of performing multiple high-field operations on the energy and
phase-space of ultrashort bunches with moderate charge. With this single
device, powered by few-microjoule, single-cycle, 0.3 THz pulses, we demonstrate
record THz-device acceleration of >30 keV, streaking with <10 fs resolution,
focusing with >2 kT/m strengths, compression to ~100 fs as well as real-time
switching between these modes of operation. The STEAM device demonstrates the
feasibility of future THz-based compact electron guns, accelerators, ultrafast
electron diffractometers and Free-Electron Lasers with transformative impact.
| 0 | 1 | 0 | 0 | 0 | 0 |
The $2$-nd Hessian type equation on almost Hermitian manifolds | In this paper, we derive the second order estimate to the $2$-nd Hessian type
equation on a compact almost Hermitian manifold.
| 0 | 0 | 1 | 0 | 0 | 0 |
Learning graphs from data: A signal representation perspective | The construction of a meaningful graph topology plays a crucial role in the
effective representation, processing, analysis and visualization of structured
data. When a natural choice of the graph is not readily available from the data
sets, it is thus desirable to infer or learn a graph topology from the data. In
this tutorial overview, we survey solutions to the problem of graph learning,
including classical viewpoints from statistics and physics, and more recent
approaches that adopt a graph signal processing (GSP) perspective. We further
emphasize the conceptual similarities and differences between classical and
GSP-based graph inference methods, and highlight the potential advantage of the
latter in a number of theoretical and practical scenarios. We conclude with
several open issues and challenges that are keys to the design of future signal
processing and machine learning algorithms for learning graphs from data.
| 1 | 0 | 0 | 1 | 0 | 0 |
Universal edge transport in interacting Hall systems | We study the edge transport properties of $2d$ interacting Hall systems,
displaying single-mode chiral edge currents. For this class of many-body
lattice models, including for instance the interacting Haldane model, we prove
the quantization of the edge charge conductance and the bulk-edge
correspondence. Instead, the edge Drude weight and the edge susceptibility are
interaction-dependent; nevertheless, they satisfy exact universal scaling
relations, in agreement with the chiral Luttinger liquid theory. Moreover,
charge and spin excitations differ in their velocities, giving rise to the
spin-charge separation phenomenon. The analysis is based on exact
renormalization group methods, and on a combination of lattice and emergent
Ward identities. The invariance of the emergent chiral anomaly under the
renormalization group flow plays a crucial role in the proof.
| 0 | 1 | 0 | 0 | 0 | 0 |
Brownian dynamics of elongated particles in a quasi-2D isotropic liquid | We demonstrate experimentally that the long-range hydrodynamic interactions
in an incompressible quasi 2D isotropic fluid result in an anisotropic viscous
drag acting on elongated particles. The anisotropy of the drag is increasing
with increasing ratio of the particle length to the hydrodynamic scale given by
the Saffman-Delbrück length. The micro-rheology data for translational and
rotational drags collected over three orders of magnitude of the effective
particle length demonstrate the validity of the current theoretical approaches
to the hydrodynamics in restricted geometry. The results also demonstrate
crossovers between the hydrodynamical regimes determined by the characteristic
length scales.
| 0 | 1 | 0 | 0 | 0 | 0 |
Photonic Band Structure of Two-dimensional Atomic Lattices | Two-dimensional atomic arrays exhibit a number of intriguing quantum optical
phenomena, including subradiance, nearly perfect reflection of radiation and
long-lived topological edge states. Studies of emission and scattering of
photons in such lattices require complete treatment of the radiation pattern
from individual atoms, including long-range interactions. We describe a
systematic approach to perform the calculations of collective energy shifts and
decay rates in the presence of such long-range interactions for arbitrary
two-dimensional atomic lattices. As applications of our method, we investigate
the topological properties of atomic lattices both in free-space and near
plasmonic surfaces.
| 0 | 1 | 0 | 0 | 0 | 0 |
Multifractal analysis of the time series of daily means of wind speed in complex regions | In this paper, we applied the multifractal detrended fluctuation analysis to
the daily means of wind speed measured by 119 weather stations distributed over
the territory of Switzerland. The analysis was focused on the inner time
fluctuations of wind speed, which could be more linked with the local
conditions of the highly varying topography of Switzerland. Our findings point
out to a persistent behaviour of all the measured wind speed series (indicated
by a Hurst exponent significantly larger than 0.5), and to a high
multifractality degree indicating a relative dominance of the large
fluctuations in the dynamics of wind speed, especially in the Swiss plateau,
which is comprised between the Jura and Alp mountain ranges. The study
represents a contribution to the understanding of the dynamical mechanisms of
wind speed variability in mountainous regions.
| 0 | 0 | 0 | 1 | 0 | 0 |
Deep Episodic Value Iteration for Model-based Meta-Reinforcement Learning | We present a new deep meta reinforcement learner, which we call Deep Episodic
Value Iteration (DEVI). DEVI uses a deep neural network to learn a similarity
metric for a non-parametric model-based reinforcement learning algorithm. Our
model is trained end-to-end via back-propagation. Despite being trained using
the model-free Q-learning objective, we show that DEVI's model-based internal
structure provides `one-shot' transfer to changes in reward and transition
structure, even for tasks with very high-dimensional state spaces.
| 1 | 0 | 0 | 1 | 0 | 0 |
Disproval of the validated planets K2-78b, K2-82b, and K2-92b | Transiting super-Earths orbiting bright stars in short orbital periods are
interesting targets for the study of planetary atmospheres. While selecting
super-Earths suitable for further characterization from the ground among a list
of confirmed and validated exoplanets detected by K2, we found some suspicious
cases that led to us re-assessing the nature of the detected transiting signal.
We did a photometric analysis of the K2 light curves and centroid motions of
the photometric barycenters. Our study shows that the validated planets K2-78b,
K2-82b, and K2-92b are actually not planets but background eclipsing binaries.
The eclipsing binaries are inside the Kepler photometric aperture, but outside
the ground-based high resolution images used for validation. We advise extreme
care on the validation of candidate planets discovered by space missions. It is
important that all the assumptions in the validation process are carefully
checked. An independent confirmation is mandatory in order to avoid wasting
valuable resources on further characterization of non-existent targets.
| 0 | 1 | 0 | 0 | 0 | 0 |
Long-lived mesoscopic entanglement between two damped infinite harmonic chains | We consider two chains, each made of $N$ independent oscillators, immersed in
a common thermal bath and study the dynamics of their mutual quantum
correlations in the thermodynamic, large-$N$ limit. We show that dissipation
and noise due to the presence of the external environment are able to generate
collective quantum correlations between the two chains at the mesoscopic level.
The created collective quantum entanglement between the two many-body systems
turns out to be rather robust, surviving for asymptotically long times even for
non vanishing bath temperatures.
| 0 | 1 | 0 | 0 | 0 | 0 |
Measured Multiseries and Integration | A paper by Bruno Salvy and the author introduced measured multiseries and
gave an algorithm to compute these for a large class of elementary functions,
modulo a zero-equivalence method for constants. This gave a theoretical
background for the implementation that Salvy was developing at that time. The
main result of the present article is an algorithm to calculate measured
multiseries for integrals of functions of the form h*sin G, where h and G
belong to a Hardy field. The process can reiterated with the resulting algebra,
and also applied to solutions of a second order differential equation of a
particular form.
| 1 | 0 | 0 | 0 | 0 | 0 |
Spectral Approach to Verifying Non-linear Arithmetic Circuits | This paper presents a fast and effective computer algebraic method for
analyzing and verifying non-linear integer arithmetic circuits using a novel
algebraic spectral model. It introduces a concept of algebraic spectrum, a
numerical form of polynomial expression; it uses the distribution of
coefficients of the monomials to determine the type of arithmetic function
under verification. In contrast to previous works, the proof of functional
correctness is achieved by computing an algebraic spectrum combined with a
local rewriting of word-level polynomials. The speedup is achieved by
propagating coefficients through the circuit using And-Inverter Graph (AIG)
datastructure. The effectiveness of the method is demonstrated with experiments
including standard and Booth multipliers, and other synthesized non-linear
arithmetic circuits up to 1024 bits containing over 12 million gates.
| 1 | 0 | 0 | 0 | 0 | 0 |
Signal-based Bayesian Seismic Monitoring | Detecting weak seismic events from noisy sensors is a difficult perceptual
task. We formulate this task as Bayesian inference and propose a generative
model of seismic events and signals across a network of spatially distributed
stations. Our system, SIGVISA, is the first to directly model seismic
waveforms, allowing it to incorporate a rich representation of the physics
underlying the signal generation process. We use Gaussian processes over
wavelet parameters to predict detailed waveform fluctuations based on
historical events, while degrading smoothly to simple parametric envelopes in
regions with no historical seismicity. Evaluating on data from the western US,
we recover three times as many events as previous work, and reduce mean
location errors by a factor of four while greatly increasing sensitivity to
low-magnitude events.
| 1 | 1 | 0 | 0 | 0 | 0 |
Multi-Label Learning with Global and Local Label Correlation | It is well-known that exploiting label correlations is important to
multi-label learning. Existing approaches either assume that the label
correlations are global and shared by all instances; or that the label
correlations are local and shared only by a data subset. In fact, in the
real-world applications, both cases may occur that some label correlations are
globally applicable and some are shared only in a local group of instances.
Moreover, it is also a usual case that only partial labels are observed, which
makes the exploitation of the label correlations much more difficult. That is,
it is hard to estimate the label correlations when many labels are absent. In
this paper, we propose a new multi-label approach GLOCAL dealing with both the
full-label and the missing-label cases, exploiting global and local label
correlations simultaneously, through learning a latent label representation and
optimizing label manifolds. The extensive experimental studies validate the
effectiveness of our approach on both full-label and missing-label data.
| 1 | 0 | 0 | 0 | 0 | 0 |
Complexity of Verifying Nonblockingness in Modular Supervisory Control | Complexity analysis becomes a common task in supervisory control. However,
many results of interest are spread across different topics. The aim of this
paper is to bring several interesting results from complexity theory and to
illustrate their relevance to supervisory control by proving new nontrivial
results concerning nonblockingness in modular supervisory control of discrete
event systems modeled by finite automata.
| 1 | 0 | 0 | 0 | 0 | 0 |
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