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Title: Training Deep AutoEncoders for Collaborative Filtering,
Abstract: This paper proposes a novel model for the rating prediction task in
recommender systems which significantly outperforms previous state-of-the art
models on a time-split Netflix data set. Our model is based on deep autoencoder
with 6 layers and is trained end-to-end without any layer-wise pre-training. We
empirically demonstrate that: a) deep autoencoder models generalize much better
than the shallow ones, b) non-linear activation functions with negative parts
are crucial for training deep models, and c) heavy use of regularization
techniques such as dropout is necessary to prevent over-fiting. We also propose
a new training algorithm based on iterative output re-feeding to overcome
natural sparseness of collaborate filtering. The new algorithm significantly
speeds up training and improves model performance. Our code is available at
this https URL | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Unit circle rectification of the MVDR beamformer,
Abstract: The sample matrix inversion (SMI) beamformer implements Capon's minimum
variance distortionless (MVDR) beamforming using the sample covariance matrix
(SCM). In a snapshot limited environment, the SCM is poorly conditioned
resulting in a suboptimal performance from the SMI beamformer. Imposing
structural constraints on the SCM estimate to satisfy known theoretical
properties of the ensemble MVDR beamformer mitigates the impact of limited
snapshots on the SMI beamformer performance. Toeplitz rectification and
bounding the norm of weight vector are common approaches for such constrains.
This paper proposes the unit circle rectification technique which constraints
the SMI beamformer to satisfy a property of the ensemble MVDR beamformer: for
narrowband planewave beamforming on a uniform linear array, the zeros of the
MVDR weight array polynomial must fall on the unit circle. Numerical
simulations show that the resulting unit circle MVDR (UC MVDR) beamformer
frequently improves the suppression of both discrete interferers and white
background noise compared to the classic SMI beamformer. Moreover, the UC MVDR
beamformer is shown to suppress discrete interferers better than the MVDR
beamformer diagonally loaded to maximize the SINR. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Jackknife Empirical Likelihood-based inference for S-Gini indices,
Abstract: Widely used income inequality measure, Gini index is extended to form a
family of income inequality measures known as Single-Series Gini (S-Gini)
indices. In this study, we develop empirical likelihood (EL) and jackknife
empirical likelihood (JEL) based inference for S-Gini indices. We prove that
the limiting distribution of both EL and JEL ratio statistics are Chi-square
distribution with one degree of freedom. Using the asymptotic distribution we
construct EL and JEL based confidence intervals for realtive S-Gini indices. We
also give bootstrap-t and bootstrap calibrated empirical likelihood confidence
intervals for S-Gini indices. A numerical study is carried out to compare the
performances of the proposed confidence interval with the bootstrap methods. A
test for S-Gini indices based on jackknife empirical likelihood ratio is also
proposed. Finally we illustrate the proposed method using an income data. | [
0,
0,
0,
1,
0,
0
] | [
"Statistics",
"Quantitative Finance"
] |
Title: A maximum principle for free boundary minimal varieties of arbitrary codimension,
Abstract: We establish a boundary maximum principle for free boundary minimal
submanifolds in a Riemannian manifold with boundary, in any dimension and
codimension. Our result holds more generally in the context of varifolds. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Optimal paths on the road network as directed polymers,
Abstract: We analyze the statistics of the shortest and fastest paths on the road
network between randomly sampled end points. To a good approximation, these
optimal paths are found to be directed in that their lengths (at large scales)
are linearly proportional to the absolute distance between them. This motivates
comparisons to universal features of directed polymers in random media. There
are similarities in scalings of fluctuations in length/time and transverse
wanderings, but also important distinctions in the scaling exponents, likely
due to long-range correlations in geographic and man-made features. At short
scales the optimal paths are not directed due to circuitous excursions governed
by a fat-tailed (power-law) probability distribution. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Mathematics"
] |
Title: Optimal proportional reinsurance and investment for stochastic factor models,
Abstract: In this work we investigate the optimal proportional reinsurance-investment
strategy of an insurance company which wishes to maximize the expected
exponential utility of its terminal wealth in a finite time horizon. Our goal
is to extend the classical Cramer-Lundberg model introducing a stochastic
factor which affects the intensity of the claims arrival process, described by
a Cox process, as well as the insurance and reinsurance premia. Using the
classical stochastic control approach based on the Hamilton-Jacobi-Bellman
equation we characterize the optimal strategy and provide a verification result
for the value function via classical solutions of two backward partial
differential equations. Existence and uniqueness of these solutions are
discussed. Results under various premium calculation principles are illustrated
and a new premium calculation rule is proposed in order to get more realistic
strategies and to better fit our stochastic factor model. Finally, numerical
simulations are performed to obtain sensitivity analyses. | [
0,
0,
0,
0,
0,
1
] | [
"Quantitative Finance",
"Mathematics",
"Statistics"
] |
Title: Formal Black-Box Analysis of Routing Protocol Implementations,
Abstract: The Internet infrastructure relies entirely on open standards for its routing
protocols. However, the majority of routers on the Internet are closed-source.
Hence, there is no straightforward way to analyze them. Specifically, one
cannot easily identify deviations of a router's routing functionality from the
routing protocol's standard. Such deviations (either deliberate or inadvertent)
are particularly important to identify since they may degrade the security or
resiliency of the network.
A model-based testing procedure is a technique that allows to systematically
generate tests based on a model of the system to be tested; thereby finding
deviations in the system compared to the model. However, applying such an
approach to a complex multi-party routing protocol requires a prohibitively
high number of tests to cover the desired functionality. We propose efficient
and practical optimizations to the model-based testing procedure that are
tailored to the analysis of routing protocols. These optimizations allow to
devise a formal black-box method to unearth deviations in closed-source routing
protocols' implementations. The method relies only on the ability to test the
targeted protocol implementation and observe its output. Identification of the
deviations is fully automatic.
We evaluate our method against one of the complex and widely used routing
protocols on the Internet -- OSPF. We search for deviations in the OSPF
implementation of Cisco. Our evaluation identified numerous significant
deviations that can be abused to compromise the security of a network. The
deviations were confirmed by Cisco. We further employed our method to analyze
the OSPF implementation of the Quagga Routing Suite. The analysis revealed one
significant deviation. Subsequent to the disclosure of the deviations some of
them were also identified by IBM, Lenovo and Huawei in their own products. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: Active Decision Boundary Annotation with Deep Generative Models,
Abstract: This paper is on active learning where the goal is to reduce the data
annotation burden by interacting with a (human) oracle during training.
Standard active learning methods ask the oracle to annotate data samples.
Instead, we take a profoundly different approach: we ask for annotations of the
decision boundary. We achieve this using a deep generative model to create
novel instances along a 1d line. A point on the decision boundary is revealed
where the instances change class. Experimentally we show on three data sets
that our method can be plugged-in to other active learning schemes, that human
oracles can effectively annotate points on the decision boundary, that our
method is robust to annotation noise, and that decision boundary annotations
improve over annotating data samples. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Extended Kitaev chain with longer-range hopping and pairing,
Abstract: We consider the Kitaev chain model with finite and infinite range in the
hopping and pairing parameters, looking in particular at the appearance of
Majorana zero energy modes and massive edge modes. We study the system both in
the presence and in the absence of time reversal symmetry, by means of
topological invariants and exact diagonalization, disclosing very rich phase
diagrams. In particular, for extended hopping and pairing terms, we can get as
many Majorana modes at each end of the chain as the neighbors involved in the
couplings. Finally we generalize the transfer matrix approach useful to
calculate the zero-energy Majorana modes at the edges for a generic number of
coupled neighbors. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Effects of atrial fibrillation on the arterial fluid dynamics: a modelling perspective,
Abstract: Atrial fibrillation (AF) is the most common form of arrhythmia with
accelerated and irregular heart rate (HR), leading to both heart failure and
stroke and being responsible for an increase in cardiovascular morbidity and
mortality. In spite of its importance, the direct effects of AF on the arterial
hemodynamic patterns are not completely known to date. Based on a multiscale
modelling approach, the proposed work investigates the effects of AF on the
local arterial fluid dynamics. AF and normal sinus rhythm (NSR) conditions are
simulated extracting 2000 $\mathrm{RR}$ heartbeats and comparing the most
relevant cardiac and vascular parameters at the same HR (75 bpm). Present
outcomes evidence that the arterial system is not able to completely absorb the
AF-induced variability, which can be even amplified towards the peripheral
circulation. AF is also able to locally alter the wave dynamics, by modifying
the interplay between forward and backward signals. The sole heart rhythm
variation (i.e., from NSR to AF) promotes an alteration of the regular dynamics
at the arterial level which, in terms of pressure and peripheral perfusion,
suggests a modification of the physiological phenomena ruled by periodicity
(e.g., regular organ perfusion)and a possible vascular dysfunction due to the
prolonged exposure to irregular and extreme values. The present study
represents a first modeling approach to characterize the variability of
arterial hemodynamics in presence of AF, which surely deserves further clinical
investigation. | [
0,
0,
0,
0,
1,
0
] | [
"Quantitative Biology",
"Physics"
] |
Title: Controllability and optimal control of the transport equation with a localized vector field,
Abstract: We study controllability of a Partial Differential Equation of transport
type, that arises in crowd models. We are interested in controlling such system
with a control being a Lipschitz vector field on a fixed control set $\omega$.
We prove that, for each initial and final configuration, one can steer one to
another with such class of controls only if the uncontrolled dynamics allows to
cross the control set $\omega$. We also prove a minimal time result for such
systems. We show that the minimal time to steer one initial configuration to
another is related to the condition of having enough mass in $\omega$ to feed
the desired final configuration. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: The Value of Sharing Intermittent Spectrum,
Abstract: Recent initiatives by regulatory agencies to increase spectrum resources
available for broadband access include rules for sharing spectrum with
high-priority incumbents. We study a model in which wireless Service Providers
(SPs) charge for access to their own exclusive-use (licensed) band along with
access to an additional shared band. The total, or delivered price in each band
is the announced price plus a congestion cost, which depends on the load, or
total users normalized by the bandwidth. The shared band is intermittently
available with some probability, due to incumbent activity, and when
unavailable, any traffic carried on that band must be shifted to licensed
bands. The SPs then compete for quantity of users. We show that the value of
the shared band depends on the relative sizes of the SPs: large SPs with more
bandwidth are better able to absorb the variability caused by intermittency
than smaller SPs. However, as the amount of shared spectrum increases, the
large SPs may not make use of it. In that scenario shared spectrum creates more
value than splitting it among the SPs for exclusive use. We also show that
fixing the average amount of available shared bandwidth, increasing the
reliability of the band is preferable to increasing the bandwidth. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Quantitative Finance"
] |
Title: Origin of layer dependence in band structures of two-dimensional materials,
Abstract: We study the origin of layer dependence in band structures of two-dimensional
materials. We find that the layer dependence, at the density functional theory
(DFT) level, is a result of quantum confinement and the non-linearity of the
exchange-correlation functional. We use this to develop an efficient scheme for
performing DFT and GW calculations of multilayer systems. We show that the DFT
and quasiparticle band structures of a multilayer system can be derived from a
single calculation on a monolayer of the material. We test this scheme on
multilayers of MoS$_2$, graphene and phosphorene. This new scheme yields
results in excellent agreement with the standard methods at a fraction of the
computation cost. This helps overcome the challenge of performing fully
converged GW calculations on multilayers of 2D materials, particularly in the
case of transition metal dichalcogenides which involve very stringent
convergence parameters. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Computer Science"
] |
Title: Sharp measure contraction property for generalized H-type Carnot groups,
Abstract: We prove that H-type Carnot groups of rank $k$ and dimension $n$ satisfy the
$\mathrm{MCP}(K,N)$ if and only if $K\leq 0$ and $N \geq k+3(n-k)$. The latter
integer coincides with the geodesic dimension of the Carnot group. The same
result holds true for the larger class of generalized H-type Carnot groups
introduced in this paper, and for which we compute explicitly the optimal
synthesis. This constitutes the largest class of Carnot groups for which the
curvature exponent coincides with the geodesic dimension. We stress that
generalized H-type Carnot groups have step 2, include all corank 1 groups and,
in general, admit abnormal minimizing curves.
As a corollary, we prove the absolute continuity of the Wasserstein geodesics
for the quadratic cost on all generalized H-type Carnot groups. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Twisted Quantum Double Model of Topological Orders with Boundaries,
Abstract: We generalize the twisted quantum double model of topological orders in two
dimensions to the case with boundaries by systematically constructing the
boundary Hamiltonians. Given the bulk Hamiltonian defined by a gauge group $G$
and a three-cocycle in the third cohomology group of $G$ over $U(1)$, a
boundary Hamiltonian can be defined by a subgroup $K$ of $G$ and a two-cochain
in the second cochain group of $K$ over $U(1)$. The consistency between the
bulk and boundary Hamiltonians is dictated by what we call the Frobenius
condition that constrains the two-cochain given the three-cocyle. We offer a
closed-form formula computing the ground state degeneracy of the model on a
cylinder in terms of the input data only, which can be naturally generalized to
surfaces with more boundaries. We also explicitly write down the ground-state
wavefunction of the model on a disk also in terms of the input data only. | [
0,
1,
1,
0,
0,
0
] | [
"Physics"
] |
Title: Nearly resolution V plans on blocks of small size,
Abstract: In Bagchi (2010) main effect plans "orthogonal through the block factor"
(POTB) have been constructed. The main advantages of a POTB are that (a) it may
exist in a set up where an "usual" orthogonal main effect plan (OMEP) cannot
exist and (b) the data analysis is nearly as simple as an OMEP. In the present
paper we extend this idea and define the concept of orthogonality between a
pair of factorial effects ( main effects or interactions) "through the block
factor" in the context of a symmetrical experiment. We consider plans generated
from an initial plan by adding runs. For such a plan we have derived necessary
and sufficient conditions for a pair of effects to be orthogonal through the
block factor in terms of the generators. We have also derived a sufficient
condition on the generators so as to turn a pair of effects aliased in the
initial plan separated in the final plan. The theory developed is illustrated
with plans for experiments with three-level factors in situations where
interactions between three or more factors are absent. We have constructed
plans with blocks of size four and fewer runs than a resolution $V$ plan
estimating all main effects and all but at most one two-factor interactions. | [
0,
0,
1,
1,
0,
0
] | [
"Statistics",
"Mathematics"
] |
Title: In situ accretion of gaseous envelopes on to planetary cores embedded in evolving protoplanetary discs,
Abstract: The core accretion hypothesis posits that planets with significant gaseous
envelopes accreted them from their protoplanetary discs after the formation of
rocky/icy cores. Observations indicate that such exoplanets exist at a broad
range of orbital radii, but it is not known whether they accreted their
envelopes in situ, or originated elsewhere and migrated to their current
locations. We consider the evolution of solid cores embedded in evolving
viscous discs that undergo gaseous envelope accretion in situ with orbital
radii in the range $0.1-10\rm au$. Additionally, we determine the long-term
evolution of the planets that had no runaway gas accretion phase after disc
dispersal. We find: (i) Planets with $5 \rm M_{\oplus}$ cores never undergo
runaway accretion. The most massive envelope contained $2.8 \rm M_{\oplus}$
with the planet orbiting at $10 \rm au$. (ii) Accretion is more efficient onto
$10 \rm M_{\oplus}$ and $15 \rm M_{\oplus}$ cores. For orbital radii $a_{\rm p}
\ge 0.5 \rm au$, $15 \rm M_{\oplus}$ cores always experienced runaway gas
accretion. For $a_{\rm p} \ge 5 \rm au$, all but one of the $10 \rm M_{\oplus}$
cores experienced runaway gas accretion. No planets experienced runaway growth
at $a_{\rm p} = 0.1 \rm au$. (iii) We find that, after disc dispersal, planets
with significant gaseous envelopes cool and contract on Gyr time-scales, the
contraction time being sensitive to the opacity assumed. Our results indicate
that Hot Jupiters with core masses $\lesssim 15 \rm M_{\oplus}$ at $\lesssim
0.1 \rm au$ likely accreted their gaseous envelopes at larger distances and
migrated inwards. Consistently with the known exoplanet population,
Super-Earths and mini-Neptunes at small radii during the disc lifetime, accrete
only modest gaseous envelopes. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: New Two Step Laplace Adam-Bashforth Method for Integer an Non integer Order Partial Differential Equations,
Abstract: This paper presents a novel method that allows to generalise the use of the
Adam-Bashforth to Partial Differential Equations with local and non local
operator. The Method derives a two step Adam-Bashforth numerical scheme in
Laplace space and the solution is taken back into the real space via inverse
Laplace transform. The method yields a powerful numerical algorithm for
fractional order derivative where the usually very difficult to manage
summation in the numerical scheme disappears. Error Analysis of the method is
also presented. Applications of the method and numerical simulations are
presented on a wave-equation like, and on a fractional order diffusion
equation. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics",
"Physics",
"Computer Science"
] |
Title: The Impact of Alternation,
Abstract: Alternating automata have been widely used to model and verify systems that
handle data from finite domains, such as communication protocols or hardware.
The main advantage of the alternating model of computation is that
complementation is possible in linear time, thus allowing to concisely encode
trace inclusion problems that occur often in verification. In this paper we
consider alternating automata over infinite alphabets, whose transition rules
are formulae in a combined theory of booleans and some infinite data domain,
that relate past and current values of the data variables. The data theory is
not fixed, but rather it is a parameter of the class. We show that union,
intersection and complementation are possible in linear time in this model and,
though the emptiness problem is undecidable, we provide two efficient
semi-algorithms, inspired by two state-of-the-art abstraction refinement model
checking methods: lazy predicate abstraction \cite{HJMS02} and the \impact~
semi-algorithm \cite{mcmillan06}. We have implemented both methods and report
the results of an experimental comparison. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness,
Abstract: Causal effect estimation from observational data is an important and much
studied research topic. The instrumental variable (IV) and local causal
discovery (LCD) patterns are canonical examples of settings where a closed-form
expression exists for the causal effect of one variable on another, given the
presence of a third variable. Both rely on faithfulness to infer that the
latter only influences the target effect via the cause variable. In reality, it
is likely that this assumption only holds approximately and that there will be
at least some form of weak interaction. This brings about the paradoxical
situation that, in the large-sample limit, no predictions are made, as
detecting the weak edge invalidates the setting. We introduce an alternative
approach by replacing strict faithfulness with a prior that reflects the
existence of many 'weak' (irrelevant) and 'strong' interactions. We obtain a
posterior distribution over the target causal effect estimator which shows
that, in many cases, we can still make good estimates. We demonstrate the
approach in an application on a simple linear-Gaussian setting, using the
MultiNest sampling algorithm, and compare it with established techniques to
show our method is robust even when strict faithfulness is violated. | [
1,
0,
0,
1,
0,
0
] | [
"Statistics",
"Mathematics"
] |
Title: Understanding looping kinetics of a long polymer molecule in solution. Exact solution for delocalized sink model,
Abstract: The fundamental understanding of loop formation of long polymer chains in
solution has been an important thread of research for several theoretical and
experimental studies. Loop formations are important phenomenological parameters
in many important biological processes. Here we give a general method for
finding an exact analytical solution for the occurrence of looping of a long
polymer chains in solution modeled by using a Smoluchowski-like equation with a
delocalized sink. The average rate constant for the delocalized sink is
explicitly expressed in terms of the corresponding rate constants for localized
sinks with different initial conditions. Simple analytical expressions are
provided for average rate constant. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Mathematics",
"Quantitative Biology"
] |
Title: High-Level Concepts for Affective Understanding of Images,
Abstract: This paper aims to bridge the affective gap between image content and the
emotional response of the viewer it elicits by using High-Level Concepts
(HLCs). In contrast to previous work that relied solely on low-level features
or used convolutional neural network (CNN) as a black-box, we use HLCs
generated by pretrained CNNs in an explicit way to investigate the
relations/associations between these HLCs and a (small) set of Ekman's
emotional classes. As a proof-of-concept, we first propose a linear admixture
model for modeling these relations, and the resulting computational framework
allows us to determine the associations between each emotion class and certain
HLCs (objects and places). This linear model is further extended to a nonlinear
model using support vector regression (SVR) that aims to predict the viewer's
emotional response using both low-level image features and HLCs extracted from
images. These class-specific regressors are then assembled into a regressor
ensemble that provide a flexible and effective predictor for predicting
viewer's emotional responses from images. Experimental results have
demonstrated that our results are comparable to existing methods, with a clear
view of the association between HLCs and emotional classes that is ostensibly
missing in most existing work. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: A Nonparametric Method for Producing Isolines of Bivariate Exceedance Probabilities,
Abstract: We present a method for drawing isolines indicating regions of equal joint
exceedance probability for bivariate data. The method relies on bivariate
regular variation, a dependence framework widely used for extremes. This
framework enables drawing isolines corresponding to very low exceedance
probabilities and these lines may lie beyond the range of the data. The method
we utilize for characterizing dependence in the tail is largely nonparametric.
Furthermore, we extend this method to the case of asymptotic independence and
propose a procedure which smooths the transition from asymptotic independence
in the interior to the first-order behavior on the axes. We propose a
diagnostic plot for assessing isoline estimate and choice of smoothing, and a
bootstrap procedure to visually assess uncertainty. | [
0,
0,
0,
1,
0,
0
] | [
"Statistics",
"Mathematics"
] |
Title: Finite element error analysis for measure-valued optimal control problems governed by a 1D wave equation with variable coefficients,
Abstract: This work is concerned with the optimal control problems governed by a 1D
wave equation with variable coefficients and the control spaces $\mathcal M_T$
of either measure-valued functions $L_{w^*}^2(I,\mathcal M(\Omega))$ or vector
measures $\mathcal M(\Omega,L^2(I))$. The cost functional involves the standard
quadratic tracking terms and the regularization term $\alpha\|u\|_{\mathcal
M_T}$ with $\alpha>0$. We construct and study three-level in time bilinear
finite element discretizations for this class of problems. The main focus lies
on the derivation of error estimates for the optimal state variable and the
error measured in the cost functional. The analysis is mainly based on some
previous results of the authors. The numerical results are included. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics",
"Computer Science"
] |
Title: Real eigenvalues of a non-self-adjoint perturbation of the self-adjoint Zakharov-Shabat operator,
Abstract: We study the eigenvalues of the self-adjoint Zakharov-Shabat operator
corresponding to the defocusing nonlinear Schrodinger equation in the inverse
scattering method. Real eigenvalues exist when the square of the potential has
a simple well. We derive two types of quantization condition for the
eigenvalues by using the exact WKB method, and show that the eigenvalues stay
real for a sufficiently small non-self-adjoint perturbation when the potential
has some PT-like symmetry. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics",
"Physics"
] |
Title: Fourier-like multipliers and applications for integral operators,
Abstract: Timelimited functions and bandlimited functions play a fundamental role in
signal and image processing. But by the uncertainty principles, a signal cannot
be simultaneously time and bandlimited. A natural assumption is thus that a
signal is almost time and almost bandlimited. The aim of this paper is to prove
that the set of almost time and almost bandlimited signals is not excluded from
the uncertainty principles. The transforms under consideration are integral
operators with bounded kernels for which there is a Parseval Theorem. Then we
define the wavelet multipliers for this class of operators, and study their
boundedness and Schatten class properties. We show that the wavelet multiplier
is unitary equivalent to a scalar multiple of the phase space restriction
operator. Moreover we prove that a signal which is almost time and almost
bandlimited can be approximated by its projection on the span of the first
eigenfunctions of the phase space restriction operator, corresponding to the
largest eigenvalues which are close to one. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics",
"Computer Science"
] |
Title: Inferring Properties of the ISM from Supernova Remnant Size Distributions,
Abstract: We model the size distribution of supernova remnants to infer the surrounding
ISM density. Using simple, yet standard SNR evolution models, we find that the
distribution of ambient densities is remarkably narrow; either the standard
assumptions about SNR evolution are wrong, or observable SNRs are biased to a
narrow range of ambient densities. We show that the size distributions are
consistent with log-normal, which severely limits the number of model
parameters in any SNR population synthesis model. Simple Monte Carlo
simulations demonstrate that the size distribution is indistinguishable from
log-normal when the SNR sample size is less than 600. This implies that these
SNR distributions provide only information on the mean and variance, yielding
additional information only when the sample size grows larger than $\sim{600}$
SNRs. To infer the parameters of the ambient density, we use Bayesian
statistical inference under the assumption that SNR evolution is dominated by
the Sedov phase. In particular, we use the SNR sizes and explosion energies to
estimate the mean and variance of the ambient medium surrounding SNR
progenitors. We find that the mean ISM particle density around our sample of
SNRs is $\mu_{\log{n}} = -1.33$, in $\log_{10}$ of particles per cubic
centimeter, with variance $\sigma^2_{\log{n}} = 0.49$. If interpreted at face
value, this implies that most SNRs result from supernovae propagating in the
warm, ionized medium. However, it is also likely that either SNR evolution is
not dominated by the simple Sedov evolution or SNR samples are biased to the
warm, ionized medium (WIM). | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Statistics"
] |
Title: Mining Target Attribute Subspace and Set of Target Communities in Large Attributed Networks,
Abstract: Community detection provides invaluable help for various applications, such
as marketing and product recommendation. Traditional community detection
methods designed for plain networks may not be able to detect communities with
homogeneous attributes inside on attributed networks with attribute
information. Most of recent attribute community detection methods may fail to
capture the requirements of a specific application and not be able to mine the
set of required communities for a specific application. In this paper, we aim
to detect the set of target communities in the target subspace which has some
focus attributes with large importance weights satisfying the requirements of a
specific application. In order to improve the university of the problem, we
address the problem in an extreme case where only two sample nodes in any
potential target community are provided. A Target Subspace and Communities
Mining (TSCM) method is proposed. In TSCM, a sample information extension
method is designed to extend the two sample nodes to a set of exemplar nodes
from which the target subspace is inferred. Then the set of target communities
are located and mined based on the target subspace. Experiments on synthetic
datasets demonstrate the effectiveness and efficiency of our method and
applications on real-world datasets show its application values. | [
1,
1,
0,
0,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Cascaded Coded Distributed Computing on Heterogeneous Networks,
Abstract: Coded distributed computing (CDC) introduced by Li et al. in 2015 offers an
efficient approach to trade computing power to reduce the communication load in
general distributed computing frameworks such as MapReduce. For the more
general cascaded CDC, Map computations are repeated at $r$ nodes to
significantly reduce the communication load among nodes tasked with computing
$Q$ Reduce functions $s$ times. While an achievable cascaded CDC scheme was
proposed, it only operates on homogeneous networks, where the storage,
computation load and communication load of each computing node is the same. In
this paper, we address this limitation by proposing a novel combinatorial
design which operates on heterogeneous networks where nodes have varying
storage and computing capabilities. We provide an analytical characterization
of the computation-communication trade-off and show that it is optimal within a
constant factor and could outperform the state-of-the-art homogeneous schemes. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: Thermal and non-thermal emission from the cocoon of a gamma-ray burst jet,
Abstract: We present hydrodynamic simulations of the hot cocoon produced when a
relativistic jet passes through the gamma-ray burst (GRB) progenitor star and
its environment, and we compute the lightcurve and spectrum of the radiation
emitted by the cocoon. The radiation from the cocoon has a nearly thermal
spectrum with a peak in the X-ray band, and it lasts for a few minutes in the
observer frame; the cocoon radiation starts at roughly the same time as when
$\gamma$-rays from a burst trigger detectors aboard GRB satellites. The
isotropic cocoon luminosity ($\sim 10^{47}$ erg s$^{-1}$) is of the same order
of magnitude as the X-ray luminosity of a typical long-GRB afterglow during the
plateau phase. This radiation should be identifiable in the Swift data because
of its nearly thermal spectrum which is distinct from the somewhat brighter
power-law component. The detection of this thermal component would provide
information regarding the size and density stratification of the GRB progenitor
star. Photons from the cocoon are also inverse-Compton (IC) scattered by
electrons in the relativistic jet. We present the IC lightcurve and spectrum,
by post-processing the results of the numerical simulations. The IC spectrum
lies in 10 keV--MeV band for typical GRB parameters. The detection of this IC
component would provide an independent measurement of GRB jet Lorentz factor
and it would also help to determine the jet magnetisation parameter. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Astrophysics"
] |
Title: Higher Tetrahedral Algebras,
Abstract: We introduce and study the higher tetrahedral algebras, an exotic family of
finite-dimensional tame symmetric algebras over an algebraically closed field.
The Gabriel quiver of such an algebra is the triangulation quiver associated to
the coherent orientation of the tetrahedron. Surprisingly, these algebras
occurred in the classification of all algebras of generalised quaternion type,
but are not weighted surface algebras. We prove that a higher tetrahedral
algebra is periodic if and only if it is non-singular. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Adaptive multi-penalty regularization based on a generalized Lasso path,
Abstract: For many algorithms, parameter tuning remains a challenging and critical
task, which becomes tedious and infeasible in a multi-parameter setting.
Multi-penalty regularization, successfully used for solving undetermined sparse
regression of problems of unmixing type where signal and noise are additively
mixed, is one of such examples. In this paper, we propose a novel algorithmic
framework for an adaptive parameter choice in multi-penalty regularization with
a focus on the correct support recovery. Building upon the theory of
regularization paths and algorithms for single-penalty functionals, we extend
these ideas to a multi-penalty framework by providing an efficient procedure
for the construction of regions containing structurally similar solutions,
i.e., solutions with the same sparsity and sign pattern, over the whole range
of parameters. Combining this with a model selection criterion, we can choose
regularization parameters in a data-adaptive manner. Another advantage of our
algorithm is that it provides an overview on the solution stability over the
whole range of parameters. This can be further exploited to obtain additional
insights into the problem of interest. We provide a numerical analysis of our
method and compare it to the state-of-the-art single-penalty algorithms for
compressed sensing problems in order to demonstrate the robustness and power of
the proposed algorithm. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Mathematics",
"Statistics"
] |
Title: Shape Generation using Spatially Partitioned Point Clouds,
Abstract: We propose a method to generate 3D shapes using point clouds. Given a
point-cloud representation of a 3D shape, our method builds a kd-tree to
spatially partition the points. This orders them consistently across all
shapes, resulting in reasonably good correspondences across all shapes. We then
use PCA analysis to derive a linear shape basis across the spatially
partitioned points, and optimize the point ordering by iteratively minimizing
the PCA reconstruction error. Even with the spatial sorting, the point clouds
are inherently noisy and the resulting distribution over the shape coefficients
can be highly multi-modal. We propose to use the expressive power of neural
networks to learn a distribution over the shape coefficients in a
generative-adversarial framework. Compared to 3D shape generative models
trained on voxel-representations, our point-based method is considerably more
light-weight and scalable, with little loss of quality. It also outperforms
simpler linear factor models such as Probabilistic PCA, both qualitatively and
quantitatively, on a number of categories from the ShapeNet dataset.
Furthermore, our method can easily incorporate other point attributes such as
normal and color information, an additional advantage over voxel-based
representations. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: Parameter Estimation in Mean Reversion Processes with Periodic Functional Tendency,
Abstract: This paper describes the procedure to estimate the parameters in mean
reversion processes with functional tendency defined by a periodic continuous
deterministic function, expressed as a series of truncated Fourier. Two phases
of estimation are defined, in the first phase through Gaussian techniques using
the Euler-Maruyama discretization, we obtain the maximum likelihood function,
that will allow us to find estimators of the external parameters and an
estimation of the expected value of the process. In the second phase, a
reestimate of the periodic functional tendency with it's parameters of phase
and amplitude is carried out, this will allow, improve the initial estimation.
Some experimental result using simulated data sets are graphically illustrated. | [
0,
0,
0,
1,
0,
0
] | [
"Statistics",
"Mathematics"
] |
Title: User Interface (UI) Design Issues for the Multilingual Users: A Case Study,
Abstract: A multitude of web and desktop applications are now widely available in
diverse human languages. This paper explores the design issues that are
specifically relevant for multilingual users. It reports on the continued
studies of Information System (IS) issues and users' behaviour across
cross-cultural and transnational boundaries. Taking the BBC website as a model
that is internationally recognised, usability tests were conducted to compare
different versions of the website. The dependant variables derived from the
questionnaire were analysed (via descriptive statistics) to elucidate the
multilingual UI design issues. Using Principal Component Analysis (PCA), five
de-correlated variables were identified which were then used for hypotheses
tests. A modified version of Herzberg's Hygiene-motivational Theory about the
Workplace was applied to assess the components used in the website. Overall, it
was concluded that the English versions of the website gave superior usability
results and this implies the need for deeper study of the problems in usability
of the translated versions. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: NeuroRule: A Connectionist Approach to Data Mining,
Abstract: Classification, which involves finding rules that partition a given data set
into disjoint groups, is one class of data mining problems. Approaches proposed
so far for mining classification rules for large databases are mainly decision
tree based symbolic learning methods. The connectionist approach based on
neural networks has been thought not well suited for data mining. One of the
major reasons cited is that knowledge generated by neural networks is not
explicitly represented in the form of rules suitable for verification or
interpretation by humans. This paper examines this issue. With our newly
developed algorithms, rules which are similar to, or more concise than those
generated by the symbolic methods can be extracted from the neural networks.
The data mining process using neural networks with the emphasis on rule
extraction is described. Experimental results and comparison with previously
published works are presented. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Joint Trajectory and Communication Design for UAV-Enabled Multiple Access,
Abstract: Unmanned aerial vehicles (UAVs) have attracted significant interest recently
in wireless communication due to their high maneuverability, flexible
deployment, and low cost. This paper studies a UAV-enabled wireless network
where the UAV is employed as an aerial mobile base station (BS) to serve a
group of users on the ground. To achieve fair performance among users, we
maximize the minimum throughput over all ground users by jointly optimizing the
multiuser communication scheduling and UAV trajectory over a finite horizon.
The formulated problem is shown to be a mixed integer non-convex optimization
problem that is difficult to solve in general. We thus propose an efficient
iterative algorithm by applying the block coordinate descent and successive
convex optimization techniques, which is guaranteed to converge to at least a
locally optimal solution. To achieve fast convergence and stable throughput, we
further propose a low-complexity initialization scheme for the UAV trajectory
design based on the simple circular trajectory. Extensive simulation results
are provided which show significant throughput gains of the proposed design as
compared to other benchmark schemes. | [
1,
0,
1,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: Partial dust obscuration in active galactic nuclei as a cause of broad-line profile and lag variability, and apparent accretion disc inhomogeneities,
Abstract: The profiles of the broad emission lines of active galactic nuclei (AGNs) and
the time delays in their response to changes in the ionizing continuum ("lags")
give information about the structure and kinematics of the inner regions of
AGNs. Line profiles are also our main way of estimating the masses of the
supermassive black holes (SMBHs). However, the profiles often show
ill-understood, asymmetric structure and velocity-dependent lags vary with
time. Here we show that partial obscuration of the broad-line region (BLR) by
outflowing, compact, dusty clumps produces asymmetries and velocity-dependent
lags similar to those observed. Our model explains previously inexplicable
changes in the ratios of the hydrogen lines with time and velocity, the lack of
correlation of changes in line profiles with variability of the central engine,
the velocity dependence of lags, and the change of lags with time. We propose
that changes on timescales longer than the light-crossing time do not come from
dynamical changes in the BLR, but are a natural result of the effect of
outflowing dusty clumps driven by radiation pressure acting on the dust. The
motion of these clumps offers an explanation of long-term changes in
polarization. The effects of the dust complicate the study of the structure and
kinematics of the BLR and the search for sub-parsec SMBH binaries. Partial
obscuration of the accretion disc can also provide the local fluctuations in
luminosity that can explain sizes deduced from microlensing. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: From Plants to Landmarks: Time-invariant Plant Localization that uses Deep Pose Regression in Agricultural Fields,
Abstract: Agricultural robots are expected to increase yields in a sustainable way and
automate precision tasks, such as weeding and plant monitoring. At the same
time, they move in a continuously changing, semi-structured field environment,
in which features can hardly be found and reproduced at a later time.
Challenges for Lidar and visual detection systems stem from the fact that
plants can be very small, overlapping and have a steadily changing appearance.
Therefore, a popular way to localize vehicles with high accuracy is based on
ex- pensive global navigation satellite systems and not on natural landmarks.
The contribution of this work is a novel image- based plant localization
technique that uses the time-invariant stem emerging point as a reference. Our
approach is based on a fully convolutional neural network that learns landmark
localization from RGB and NIR image input in an end-to-end manner. The network
performs pose regression to generate a plant location likelihood map. Our
approach allows us to cope with visual variances of plants both for different
species and different growth stages. We achieve high localization accuracies as
shown in detailed evaluations of a sugar beet cultivation phase. In experiments
with our BoniRob we demonstrate that detections can be robustly reproduced with
centimeter accuracy. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Quantitative Biology"
] |
Title: Higher Theory and the Three Problems of Physics,
Abstract: According to the Butterfield--Isham proposal, to understand quantum gravity
we must revise the way we view the universe of mathematics. However, this paper
demonstrates that the current elaborations of this programme neglect quantum
interactions. The paper then introduces the Faddeev--Mickelsson anomaly which
obstructs the renormalization of Yang--Mills theory, suggesting that to
theorise on many-particle systems requires a many-topos view of mathematics
itself: higher theory. As our main contribution, the topos theoretic framework
is used to conceptualise the fact that there are principally three different
quantisation problems, the differences of which have been ignored not just by
topos physicists but by most philosophers of science. We further argue that if
higher theory proves out to be necessary for understanding quantum gravity, its
implications to philosophy will be foundational: higher theory challenges the
propositional concept of truth and thus the very meaning of theorising in
science. | [
0,
1,
1,
0,
0,
0
] | [
"Physics",
"Mathematics"
] |
Title: Bayesian Approaches to Distribution Regression,
Abstract: Distribution regression has recently attracted much interest as a generic
solution to the problem of supervised learning where labels are available at
the group level, rather than at the individual level. Current approaches,
however, do not propagate the uncertainty in observations due to sampling
variability in the groups. This effectively assumes that small and large groups
are estimated equally well, and should have equal weight in the final
regression. We account for this uncertainty with a Bayesian distribution
regression formalism, improving the robustness and performance of the model
when group sizes vary. We frame our models in a neural network style, allowing
for simple MAP inference using backpropagation to learn the parameters, as well
as MCMC-based inference which can fully propagate uncertainty. We demonstrate
our approach on illustrative toy datasets, as well as on a challenging problem
of predicting age from images. | [
1,
0,
0,
1,
0,
0
] | [
"Statistics",
"Computer Science"
] |
Title: Atomic-Scale Structure Relaxation, Chemistry and Charge Distribution of Dislocation Cores in SrTiO3,
Abstract: By using the state-of-the-art microscopy and spectroscopy in
aberration-corrected scanning transmission electron microscopes, we determine
the atomic arrangements, occupancy, elemental distribution, and the electronic
structures of dislocation cores in the 10°tilted SrTiO3 bicrystal. We
identify that there are two different types of oxygen deficient dislocation
cores, i.e., the SrO plane terminated Sr0.82Ti0.85O3-x (Ti3.67+, 0.48<x<0.91)
and TiO2 plane terminated Sr0.63Ti0.90O3-y (Ti3.60+, 0.57<y<1). They have the
same Burgers vector of a[100] but different atomic arrangements and chemical
properties. Besides the oxygen vacancies, Sr vacancies and rocksalt-like
titanium oxide reconstruction are also identified in the dislocation core with
TiO2 plane termination. Our atomic-scale study reveals the true atomic
structures and chemistry of individual dislocation cores, providing useful
insights into understanding the properties of dislocations and grain
boundaries. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Self-similar solutions of fragmentation equations revisited,
Abstract: We study the large time behaviour of the mass (size) of particles described
by the fragmentation equation with homogeneous breakup kernel. We give
necessary and sufficient conditions for the convergence of solutions to the
unique self-similar solution. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Ultracold atoms in multiple-radiofrequency dressed adiabatic potentials,
Abstract: We present the first experimental demonstration of a multiple-radiofrequency
dressed potential for the configurable magnetic confinement of ultracold atoms.
We load cold $^{87}$Rb atoms into a double well potential with an adjustable
barrier height, formed by three radiofrequencies applied to atoms in a static
quadrupole magnetic field. Our multiple-radiofrequency approach gives precise
control over the double well characteristics, including the depth of individual
wells and the height of the barrier, and enables reliable transfer of atoms
between the available trapping geometries. We have characterised the
multiple-radiofrequency dressed system using radiofrequency spectroscopy,
finding good agreement with the eigenvalues numerically calculated using
Floquet theory. This method creates trapping potentials that can be
reconfigured by changing the amplitudes, polarizations and frequencies of the
applied dressing fields, and easily extended with additional dressing
frequencies. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Distribution Matching in Variational Inference,
Abstract: We show that Variational Autoencoders consistently fail to learn marginal
distributions in latent and visible space. We ask whether this is a consequence
of matching conditional distributions, or a limitation of explicit model and
posterior distributions. We explore alternatives provided by marginal
distribution matching and implicit distributions through the use of Generative
Adversarial Networks in variational inference. We perform a large-scale
evaluation of several VAE-GAN hybrids and explore the implications of class
probability estimation for learning distributions. We conclude that at present
VAE-GAN hybrids have limited applicability: they are harder to scale, evaluate,
and use for inference compared to VAEs; and they do not improve over the
generation quality of GANs. | [
0,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Orthogonal groups in characteristic 2 acting on polytopes of high rank,
Abstract: We show that for all integers $m\geq 2$, and all integers $k\geq 2$, the
orthogonal groups $\Orth^{\pm}(2m,\Fk)$ act on abstract regular polytopes of
rank $2m$, and the symplectic groups $\Sp(2m,\Fk)$ act on abstract regular
polytopes of rank $2m+1$. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Free LSD: Prior-Free Visual Landing Site Detection for Autonomous Planes,
Abstract: Full autonomy for fixed-wing unmanned aerial vehicles (UAVs) requires the
capability to autonomously detect potential landing sites in unknown and
unstructured terrain, allowing for self-governed mission completion or handling
of emergency situations. In this work, we propose a perception system
addressing this challenge by detecting landing sites based on their texture and
geometric shape without using any prior knowledge about the environment. The
proposed method considers hazards within the landing region such as terrain
roughness and slope, surrounding obstacles that obscure the landing approach
path, and the local wind field that is estimated by the on-board EKF. The
latter enables applicability of the proposed method on small-scale autonomous
planes without landing gear. A safe approach path is computed based on the UAV
dynamics, expected state estimation and actuator uncertainty, and the on-board
computed elevation map. The proposed framework has been successfully tested on
photo-realistic synthetic datasets and in challenging real-world environments. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Physics"
] |
Title: Reconstruction formulas for Photoacoustic Imaging in Attenuating Media,
Abstract: In this paper we study the problem of photoacoustic inversion in a weakly
attenuating medium. We present explicit reconstruction formulas in such media
and show that the inversion based on such formulas is moderately ill--posed.
Moreover, we present a numerical algorithm for imaging and demonstrate in
numerical experiments the feasibility of this approach. | [
0,
0,
1,
0,
0,
0
] | [
"Physics",
"Mathematics"
] |
Title: Rank Determination for Low-Rank Data Completion,
Abstract: Recently, fundamental conditions on the sampling patterns have been obtained
for finite completability of low-rank matrices or tensors given the
corresponding ranks. In this paper, we consider the scenario where the rank is
not given and we aim to approximate the unknown rank based on the location of
sampled entries and some given completion. We consider a number of data models,
including single-view matrix, multi-view matrix, CP tensor, tensor-train tensor
and Tucker tensor. For each of these data models, we provide an upper bound on
the rank when an arbitrary low-rank completion is given. We characterize these
bounds both deterministically, i.e., with probability one given that the
sampling pattern satisfies certain combinatorial properties, and
probabilistically, i.e., with high probability given that the sampling
probability is above some threshold. Moreover, for both single-view matrix and
CP tensor, we are able to show that the obtained upper bound is exactly equal
to the unknown rank if the lowest-rank completion is given. Furthermore, we
provide numerical experiments for the case of single-view matrix, where we use
nuclear norm minimization to find a low-rank completion of the sampled data and
we observe that in most of the cases the proposed upper bound on the rank is
equal to the true rank. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: Network structure from rich but noisy data,
Abstract: Driven by growing interest in the sciences, industry, and among the broader
public, a large number of empirical studies have been conducted in recent years
of the structure of networks ranging from the internet and the world wide web
to biological networks and social networks. The data produced by these
experiments are often rich and multimodal, yet at the same time they may
contain substantial measurement error. In practice, this means that the true
network structure can differ greatly from naive estimates made from the raw
data, and hence that conclusions drawn from those naive estimates may be
significantly in error. In this paper we describe a technique that circumvents
this problem and allows us to make optimal estimates of the true structure of
networks in the presence of both richly textured data and significant
measurement uncertainty. We give example applications to two different social
networks, one derived from face-to-face interactions and one from self-reported
friendships. | [
1,
1,
0,
0,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Algebraic Foundations of Proof Refinement,
Abstract: We contribute a general apparatus for dependent tactic-based proof refinement
in the LCF tradition, in which the statements of subgoals may express a
dependency on the proofs of other subgoals; this form of dependency is
extremely useful and can serve as an algorithmic alternative to extensions of
LCF based on non-local instantiation of schematic variables. Additionally, we
introduce a novel behavioral distinction between refinement rules and tactics
based on naturality. Our framework, called Dependent LCF, is already deployed
in the nascent RedPRL proof assistant for computational cubical type theory. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: iCorr : Complex correlation method to detect origin of replication in prokaryotic and eukaryotic genomes,
Abstract: Computational prediction of origin of replication (ORI) has been of great
interest in bioinformatics and several methods including GC Skew, Z curve,
auto-correlation etc. have been explored in the past. In this paper, we have
extended the auto-correlation method to predict ORI location with much higher
resolution for prokaryotes. The proposed complex correlation method (iCorr)
converts the genome sequence into a sequence of complex numbers by mapping the
nucleotides to {+1,-1,+i,-i} instead of {+1,-1} used in the auto-correlation
method (here, 'i' is square root of -1). Thus, the iCorr method uses
information about the positions of all the four nucleotides unlike the earlier
auto-correlation method which uses the positional information of only one
nucleotide. Also, this earlier method required visual inspection of the
obtained graphs to identify the location of origin of replication. The proposed
iCorr method does away with this need and is able to identify the origin
location simply by picking the peak in the iCorr graph. The iCorr method also
works for a much smaller segment size compared to the earlier auto-correlation
method, which can be very helpful in experimental validation of the
computational predictions. We have also developed a variant of the iCorr method
to predict ORI location in eukaryotes and have tested it with the
experimentally known origin locations of S. cerevisiae with an average accuracy
of 71.76%. | [
0,
1,
0,
0,
0,
0
] | [
"Quantitative Biology",
"Computer Science"
] |
Title: Simons' type formula for slant submanifolds of complex space form,
Abstract: In this paper, we study a slant submanifold of a complex space form. We also
obtain an integral formula of Simons' type for a Kaehlerian slant submanifold
in a complex space form and apply it to prove our main result. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: An Agile Software Engineering Method to Design Blockchain Applications,
Abstract: Cryptocurrencies and their foundation technology, the Blockchain, are
reshaping finance and economics, allowing a decentralized approach enabling
trusted applications with no trusted counterpart. More recently, the Blockchain
and the programs running on it, called Smart Contracts, are also finding more
and more applications in all fields requiring trust and sound certifications.
Some people have come to the point of saying that the "Blockchain revolution"
can be compared to that of the Internet and the Web in their early days. As a
result, all the software development revolving around the Blockchain technology
is growing at a staggering rate. The feeling of many software engineers about
such huge interest in Blockchain technologies is that of unruled and hurried
software development, a sort of competition on a first-come-first-served basis
which does not assure neither software quality, nor that the basic concepts of
software engineering are taken into account. This paper tries to cope with this
issue, proposing a software development process to gather the requirement,
analyze, design, develop, test and deploy Blockchain applications. The process
is based on several Agile practices, such as User Stories and iterative and
incremental development based on them. However, it makes also use of more
formal notations, such as some UML diagrams describing the design of the
system, with additions to represent specific concepts found in Blockchain
development. The method is described in good detail, and an example is given to
show how it works. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: Optimizing Prediction Intervals by Tuning Random Forest via Meta-Validation,
Abstract: Recent studies have shown that tuning prediction models increases prediction
accuracy and that Random Forest can be used to construct prediction intervals.
However, to our best knowledge, no study has investigated the need to, and the
manner in which one can, tune Random Forest for optimizing prediction intervals
{ this paper aims to fill this gap. We explore a tuning approach that combines
an effectively exhaustive search with a validation technique on a single Random
Forest parameter. This paper investigates which, out of eight validation
techniques, are beneficial for tuning, i.e., which automatically choose a
Random Forest configuration constructing prediction intervals that are reliable
and with a smaller width than the default configuration. Additionally, we
present and validate three meta-validation techniques to determine which are
beneficial, i.e., those which automatically chose a beneficial validation
technique. This study uses data from our industrial partner (Keymind Inc.) and
the Tukutuku Research Project, related to post-release defect prediction and
Web application effort estimation, respectively. Results from our study
indicate that: i) the default configuration is frequently unreliable, ii) most
of the validation techniques, including previously successfully adopted ones
such as 50/50 holdout and bootstrap, are counterproductive in most of the
cases, and iii) the 75/25 holdout meta-validation technique is always
beneficial; i.e., it avoids the likely counterproductive effects of validation
techniques. | [
0,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: The CMS HGCAL detector for HL-LHC upgrade,
Abstract: The High Luminosity LHC (HL-LHC) will integrate 10 times more luminosity than
the LHC, posing significant challenges for radiation tolerance and event pileup
on detectors, especially for forward calorimetry, and hallmarks the issue for
future colliders. As part of its HL-LHC upgrade program, the CMS collaboration
is designing a High Granularity Calorimeter to replace the existing endcap
calorimeters. It features unprecedented transverse and longitudinal
segmentation for both electromagnetic (ECAL) and hadronic (HCAL) compartments.
This will facilitate particle-flow calorimetry, where the fine structure of
showers can be measured and used to enhance pileup rejection and particle
identification, whilst still achieving good energy resolution. The ECAL and a
large fraction of HCAL will be based on hexagonal silicon sensors of
0.5-1cm$^{2}$ cell size, with the remainder of the HCAL based on
highly-segmented scintillators with SiPM readout. The intrinsic high-precision
timing capabilities of the silicon sensors will add an extra dimension to event
reconstruction, especially in terms of pileup rejection. An overview of the
HGCAL project is presented, covering motivation, engineering design, readout
and trigger concepts, and performance (simulated and from beam tests). | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Targeted Damage to Interdependent Networks,
Abstract: The giant mutually connected component (GMCC) of an interdependent or
multiplex network collapses with a discontinuous hybrid transition under random
damage to the network. If the nodes to be damaged are selected in a targeted
way, the collapse of the GMCC may occur significantly sooner. Finding the
minimal damage set which destroys the largest mutually connected component of a
given interdependent network is a computationally prohibitive simultaneous
optimization problem. We introduce a simple heuristic strategy -- Effective
Multiplex Degree -- for targeted attack on interdependent networks that
leverages the indirect damage inherent in multiplex networks to achieve a
damage set smaller than that found by any other non computationally intensive
algorithm. We show that the intuition from single layer networks that decycling
(damage of the $2$-core) is the most effective way to destroy the giant
component, does not carry over to interdependent networks, and in fact such
approaches are worse than simply removing the highest degree nodes. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Physics",
"Mathematics"
] |
Title: High-accuracy phase-field models for brittle fracture based on a new family of degradation functions,
Abstract: Phase-field approaches to fracture based on energy minimization principles
have been rapidly gaining popularity in recent years, and are particularly
well-suited for simulating crack initiation and growth in complex fracture
networks. In the phase-field framework, the surface energy associated with
crack formation is calculated by evaluating a functional defined in terms of a
scalar order parameter and its gradients, which in turn describe the fractures
in a diffuse sense following a prescribed regularization length scale. Imposing
stationarity of the total energy leads to a coupled system of partial
differential equations, one enforcing stress equilibrium and another governing
phase-field evolution. The two equations are coupled through an energy
degradation function that models the loss of stiffness in the bulk material as
it undergoes damage. In the present work, we introduce a new parametric family
of degradation functions aimed at increasing the accuracy of phase-field models
in predicting critical loads associated with crack nucleation as well as the
propagation of existing fractures. An additional goal is the preservation of
linear elastic response in the bulk material prior to fracture. Through the
analysis of several numerical examples, we demonstrate the superiority of the
proposed family of functions to the classical quadratic degradation function
that is used most often in the literature. | [
0,
1,
1,
0,
0,
0
] | [
"Physics",
"Mathematics"
] |
Title: Straggler Mitigation in Distributed Optimization Through Data Encoding,
Abstract: Slow running or straggler tasks can significantly reduce computation speed in
distributed computation. Recently, coding-theory-inspired approaches have been
applied to mitigate the effect of straggling, through embedding redundancy in
certain linear computational steps of the optimization algorithm, thus
completing the computation without waiting for the stragglers. In this paper,
we propose an alternate approach where we embed the redundancy directly in the
data itself, and allow the computation to proceed completely oblivious to
encoding. We propose several encoding schemes, and demonstrate that popular
batch algorithms, such as gradient descent and L-BFGS, applied in a
coding-oblivious manner, deterministically achieve sample path linear
convergence to an approximate solution of the original problem, using an
arbitrarily varying subset of the nodes at each iteration. Moreover, this
approximation can be controlled by the amount of redundancy and the number of
nodes used in each iteration. We provide experimental results demonstrating the
advantage of the approach over uncoded and data replication strategies. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: Inference Trees: Adaptive Inference with Exploration,
Abstract: We introduce inference trees (ITs), a new class of inference methods that
build on ideas from Monte Carlo tree search to perform adaptive sampling in a
manner that balances exploration with exploitation, ensures consistency, and
alleviates pathologies in existing adaptive methods. ITs adaptively sample from
hierarchical partitions of the parameter space, while simultaneously learning
these partitions in an online manner. This enables ITs to not only identify
regions of high posterior mass, but also maintain uncertainty estimates to
track regions where significant posterior mass may have been missed. ITs can be
based on any inference method that provides a consistent estimate of the
marginal likelihood. They are particularly effective when combined with
sequential Monte Carlo, where they capture long-range dependencies and yield
improvements beyond proposal adaptation alone. | [
0,
0,
0,
1,
0,
0
] | [
"Statistics",
"Computer Science"
] |
Title: Faster Fuzzing: Reinitialization with Deep Neural Models,
Abstract: We improve the performance of the American Fuzzy Lop (AFL) fuzz testing
framework by using Generative Adversarial Network (GAN) models to reinitialize
the system with novel seed files. We assess performance based on the temporal
rate at which we produce novel and unseen code paths. We compare this approach
to seed file generation from a random draw of bytes observed in the training
seed files. The code path lengths and variations were not sufficiently diverse
to fully replace AFL input generation. However, augmenting native AFL with
these additional code paths demonstrated improvements over AFL alone.
Specifically, experiments showed the GAN was faster and more effective than the
LSTM and out-performed a random augmentation strategy, as measured by the
number of unique code paths discovered. GAN helps AFL discover 14.23% more code
paths than the random strategy in the same amount of CPU time, finds 6.16% more
unique code paths, and finds paths that are on average 13.84% longer. Using GAN
shows promise as a reinitialization strategy for AFL to help the fuzzer
exercise deep paths in software. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: Second Order Analysis for Joint Source-Channel Coding with Markovian Source,
Abstract: We derive the second order rates of joint source-channel coding, whose source
obeys an irreducible and ergodic Markov process when the channel is a discrete
memoryless, while a previous study solved it only in a special case. We also
compare the joint source-channel scheme with the separation scheme in the
second order regime while a previous study made a notable comparison only with
numerical calculation. To make these two notable progress, we introduce two
kinds of new distribution families, switched Gaussian convolution distribution
and *-product distribution, which are defined by modifying the Gaussian
distribution. | [
1,
0,
1,
0,
0,
0
] | [
"Computer Science",
"Mathematics",
"Statistics"
] |
Title: Implicit Weight Uncertainty in Neural Networks,
Abstract: Modern neural networks tend to be overconfident on unseen, noisy or
incorrectly labelled data and do not produce meaningful uncertainty measures.
Bayesian deep learning aims to address this shortcoming with variational
approximations (such as Bayes by Backprop or Multiplicative Normalising Flows).
However, current approaches have limitations regarding flexibility and
scalability. We introduce Bayes by Hypernet (BbH), a new method of variational
approximation that interprets hypernetworks as implicit distributions. It
naturally uses neural networks to model arbitrarily complex distributions and
scales to modern deep learning architectures. In our experiments, we
demonstrate that our method achieves competitive accuracies and predictive
uncertainties on MNIST and a CIFAR5 task, while being the most robust against
adversarial attacks. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: A systematic analysis of the XMM-Newton background: III. Impact of the magnetospheric environment,
Abstract: A detailed characterization of the particle induced background is fundamental
for many of the scientific objectives of the Athena X-ray telescope, thus an
adequate knowledge of the background that will be encountered by Athena is
desirable. Current X-ray telescopes have shown that the intensity of the
particle induced background can be highly variable. Different regions of the
magnetosphere can have very different environmental conditions, which can, in
principle, differently affect the particle induced background detected by the
instruments. We present results concerning the influence of the magnetospheric
environment on the background detected by EPIC instrument onboard XMM-Newton
through the estimate of the variation of the in-Field-of-View background excess
along the XMM-Newton orbit. An important contribution to the XMM background,
which may affect the Athena background as well, comes from soft proton flares.
Along with the flaring component a low-intensity component is also present. We
find that both show modest variations in the different magnetozones and that
the soft proton component shows a strong trend with the distance from Earth. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: DeepPermNet: Visual Permutation Learning,
Abstract: We present a principled approach to uncover the structure of visual data by
solving a novel deep learning task coined visual permutation learning. The goal
of this task is to find the permutation that recovers the structure of data
from shuffled versions of it. In the case of natural images, this task boils
down to recovering the original image from patches shuffled by an unknown
permutation matrix. Unfortunately, permutation matrices are discrete, thereby
posing difficulties for gradient-based methods. To this end, we resort to a
continuous approximation of these matrices using doubly-stochastic matrices
which we generate from standard CNN predictions using Sinkhorn iterations.
Unrolling these iterations in a Sinkhorn network layer, we propose DeepPermNet,
an end-to-end CNN model for this task. The utility of DeepPermNet is
demonstrated on two challenging computer vision problems, namely, (i) relative
attributes learning and (ii) self-supervised representation learning. Our
results show state-of-the-art performance on the Public Figures and OSR
benchmarks for (i) and on the classification and segmentation tasks on the
PASCAL VOC dataset for (ii). | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: ADE String Chains and Mirror Symmetry,
Abstract: 6d superconformal field theories (SCFTs) are the SCFTs in the highest
possible dimension. They can be geometrically engineered in F-theory by
compactifying on non-compact elliptic Calabi-Yau manifolds. In this paper we
focus on the class of SCFTs whose base geometry is determined by $-2$ curves
intersecting according to ADE Dynkin diagrams and derive the corresponding
mirror Calabi-Yau manifold. The mirror geometry is uniquely determined in terms
of the mirror curve which has also an interpretation in terms of the
Seiberg-Witten curve of the four-dimensional theory arising from torus
compactification. Adding the affine node of the ADE quiver to the base
geometry, we connect to recent results on SYZ mirror symmetry for the $A$ case
and provide a physical interpretation in terms of little string theory. Our
results, however, go beyond this case as our construction naturally covers the
$D$ and $E$ cases as well. | [
0,
0,
1,
0,
0,
0
] | [
"Physics",
"Mathematics"
] |
Title: Asymptotic efficiency of the proportional compensation scheme for a large number of producers,
Abstract: We consider a manager, who allocates some fixed total payment amount between
$N$ rational agents in order to maximize the aggregate production. The profit
of $i$-th agent is the difference between the compensation (reward) obtained
from the manager and the production cost. We compare (i) the \emph{normative}
compensation scheme, where the manager enforces the agents to follow an optimal
cooperative strategy; (ii) the \emph{linear piece rates} compensation scheme,
where the manager announces an optimal reward per unit good; (iii) the
\emph{proportional} compensation scheme, where agent's reward is proportional
to his contribution to the total output. Denoting the correspondent total
production levels by $s^*$, $\hat s$ and $\overline s$ respectively, where the
last one is related to the unique Nash equilibrium, we examine the limits of
the prices of anarchy $\mathscr A_N=s^*/\overline s$, $\mathscr A_N'=\hat
s/\overline s$ as $N\to\infty$. These limits are calculated for the cases of
identical convex costs with power asymptotics at the origin, and for power
costs, corresponding to the Coob-Douglas and generalized CES production
functions with decreasing returns to scale. Our results show that
asymptotically no performance is lost in terms of $\mathscr A'_N$, and in terms
of $\mathscr A_N$ the loss does not exceed $31\%$. | [
1,
0,
0,
0,
0,
0
] | [
"Mathematics",
"Quantitative Finance"
] |
Title: Non-equilibrium statistical mechanics of continuous attractors,
Abstract: Continuous attractors have been used to understand recent neuroscience
experiments where persistent activity patterns encode internal representations
of external attributes like head direction or spatial location. However, the
conditions under which the emergent bump of neural activity in such networks
can be manipulated by space and time-dependent external sensory or motor
signals are not understood. Here, we find fundamental limits on how rapidly
internal representations encoded along continuous attractors can be updated by
an external signal. We apply these results to place cell networks to derive a
velocity-dependent non-equilibrium memory capacity in neural networks. | [
0,
0,
0,
0,
1,
0
] | [
"Physics",
"Quantitative Biology"
] |
Title: Exponentiated Generalized Pareto Distribution: Properties and applications towards Extreme Value Theory,
Abstract: The Generalized Pareto Distribution (GPD) plays a central role in modelling
heavy tail phenomena in many applications. Applying the GPD to actual datasets
however is a non-trivial task. One common way suggested in the literature to
investigate the tail behaviour is to take logarithm to the original dataset in
order to reduce the sample variability. Inspired by this, we propose and study
the Exponentiated Generalized Pareto Distribution (exGPD), which is created via
log-transform of the GPD variable. After introducing the exGPD we derive
various distributional quantities, including the moment generating function,
tail risk measures. As an application we also develop a plot as an alternative
to the Hill plot to identify the tail index of heavy tailed datasets, based on
the moment matching for the exGPD. Various numerical analyses with both
simulated and actual datasets show that the proposed plot works well. | [
0,
0,
1,
1,
0,
0
] | [
"Statistics",
"Quantitative Finance"
] |
Title: Reflexive polytopes arising from perfect graphs,
Abstract: Reflexive polytopes form one of the distinguished classes of lattice
polytopes. Especially reflexive polytopes which possess the integer
decomposition property are of interest. In the present paper, by virtue of the
algebraic technique on Grönbner bases, a new class of reflexive polytopes
which possess the integer decomposition property and which arise from perfect
graphs will be presented. Furthermore, the Ehrhart $\delta$-polynomials of
these polytopes will be studied. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics",
"Computer Science"
] |
Title: Meta Networks,
Abstract: Neural networks have been successfully applied in applications with a large
amount of labeled data. However, the task of rapid generalization on new
concepts with small training data while preserving performances on previously
learned ones still presents a significant challenge to neural network models.
In this work, we introduce a novel meta learning method, Meta Networks
(MetaNet), that learns a meta-level knowledge across tasks and shifts its
inductive biases via fast parameterization for rapid generalization. When
evaluated on Omniglot and Mini-ImageNet benchmarks, our MetaNet models achieve
a near human-level performance and outperform the baseline approaches by up to
6% accuracy. We demonstrate several appealing properties of MetaNet relating to
generalization and continual learning. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Analysing Magnetism Using Scanning SQUID Microscopy,
Abstract: Scanning superconducting quantum interference device microscopy (SSM) is a
scanning probe technique that images local magnetic flux, which allows for
mapping of magnetic fields with high field and spatial accuracy. Many studies
involving SSM have been published in the last decades, using SSM to make
qualitative statements about magnetism. However, quantitative analysis using
SSM has received less attention. In this work, we discuss several aspects of
interpreting SSM images and methods to improve quantitative analysis. First, we
analyse the spatial resolution and how it depends on several factors. Second,
we discuss the analysis of SSM scans and the information obtained from the SSM
data. Using simulations, we show how signals evolve as a function of changing
scan height, SQUID loop size, magnetization strength and orientation. We also
investigated 2-dimensional autocorrelation analysis to extract information
about the size, shape and symmetry of magnetic features. Finally, we provide an
outlook on possible future applications and improvements. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Algorithms for solving optimization problems arising from deep neural net models: nonsmooth problems,
Abstract: Machine Learning models incorporating multiple layered learning networks have
been seen to provide effective models for various classification problems. The
resulting optimization problem to solve for the optimal vector minimizing the
empirical risk is, however, highly nonconvex. This alone presents a challenge
to application and development of appropriate optimization algorithms for
solving the problem. However, in addition, there are a number of interesting
problems for which the objective function is non- smooth and nonseparable. In
this paper, we summarize the primary challenges involved, the state of the art,
and present some numerical results on an interesting and representative class
of problems. | [
0,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: Are Saddles Good Enough for Deep Learning?,
Abstract: Recent years have seen a growing interest in understanding deep neural
networks from an optimization perspective. It is understood now that converging
to low-cost local minima is sufficient for such models to become effective in
practice. However, in this work, we propose a new hypothesis based on recent
theoretical findings and empirical studies that deep neural network models
actually converge to saddle points with high degeneracy. Our findings from this
work are new, and can have a significant impact on the development of gradient
descent based methods for training deep networks. We validated our hypotheses
using an extensive experimental evaluation on standard datasets such as MNIST
and CIFAR-10, and also showed that recent efforts that attempt to escape
saddles finally converge to saddles with high degeneracy, which we define as
`good saddles'. We also verified the famous Wigner's Semicircle Law in our
experimental results. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Monotonicity and enclosure methods for the p-Laplace equation,
Abstract: We show that the convex hull of a monotone perturbation of a homogeneous
background conductivity in the $p$-conductivity equation is determined by
knowledge of the nonlinear Dirichlet-Neumann operator. We give two independent
proofs, one of which is based on the monotonicity method and the other on the
enclosure method. Our results are constructive and require no jump or
smoothness properties on the conductivity perturbation or its support. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Warm dark matter and the ionization history of the Universe,
Abstract: In warm dark matter scenarios structure formation is suppressed on small
scales with respect to the cold dark matter case, reducing the number of
low-mass halos and the fraction of ionized gas at high redshifts and thus,
delaying reionization. This has an impact on the ionization history of the
Universe and measurements of the optical depth to reionization, of the
evolution of the global fraction of ionized gas and of the thermal history of
the intergalactic medium, can be used to set constraints on the mass of the
dark matter particle. However, the suppression of the fraction of ionized
medium in these scenarios can be partly compensated by varying other
parameters, as the ionization efficiency or the minimum mass for which halos
can host star-forming galaxies. Here we use different data sets regarding the
ionization and thermal histories of the Universe and, taking into account the
degeneracies from several astrophysical parameters, we obtain a lower bound on
the mass of thermal warm dark matter candidates of $m_X > 1.3$ keV, or $m_s >
5.5$ keV for the case of sterile neutrinos non-resonantly produced in the early
Universe, both at 90\% confidence level. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Astrophysics"
] |
Title: Tetramer Bound States in Heteronuclear Systems,
Abstract: We calculate the universal spectrum of trimer and tetramer states in
heteronuclear mixtures of ultracold atoms with different masses in the vicinity
of the heavy-light dimer threshold. To extract the energies, we solve the
three- and four-body problem for simple two- and three-body potentials tuned to
the universal region using the Gaussian expansion method. We focus on the case
of one light particle of mass $m$ and two or three heavy bosons of mass $M$
with resonant heavy-light interactions. We find that trimer and tetramer cross
into the heavy-light dimer threshold at almost the same point and that as the
mass ratio $M/m$ decreases, the distance between the thresholds for trimer and
tetramer states becomes smaller. We also comment on the possibility of
observing exotic three-body states consisting of a dimer and two atoms in this
region and compare with previous work. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Mutual Kernel Matrix Completion,
Abstract: With the huge influx of various data nowadays, extracting knowledge from them
has become an interesting but tedious task among data scientists, particularly
when the data come in heterogeneous form and have missing information. Many
data completion techniques had been introduced, especially in the advent of
kernel methods. However, among the many data completion techniques available in
the literature, studies about mutually completing several incomplete kernel
matrices have not been given much attention yet. In this paper, we present a
new method, called Mutual Kernel Matrix Completion (MKMC) algorithm, that
tackles this problem of mutually inferring the missing entries of multiple
kernel matrices by combining the notions of data fusion and kernel matrix
completion, applied on biological data sets to be used for classification task.
We first introduced an objective function that will be minimized by exploiting
the EM algorithm, which in turn results to an estimate of the missing entries
of the kernel matrices involved. The completed kernel matrices are then
combined to produce a model matrix that can be used to further improve the
obtained estimates. An interesting result of our study is that the E-step and
the M-step are given in closed form, which makes our algorithm efficient in
terms of time and memory. After completion, the (completed) kernel matrices are
then used to train an SVM classifier to test how well the relationships among
the entries are preserved. Our empirical results show that the proposed
algorithm bested the traditional completion techniques in preserving the
relationships among the data points, and in accurately recovering the missing
kernel matrix entries. By far, MKMC offers a promising solution to the problem
of mutual estimation of a number of relevant incomplete kernel matrices. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics",
"Quantitative Biology"
] |
Title: Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks,
Abstract: Data diversity is critical to success when training deep learning models.
Medical imaging data sets are often imbalanced as pathologic findings are
generally rare, which introduces significant challenges when training deep
learning models. In this work, we propose a method to generate synthetic
abnormal MRI images with brain tumors by training a generative adversarial
network using two publicly available data sets of brain MRI. We demonstrate two
unique benefits that the synthetic images provide. First, we illustrate
improved performance on tumor segmentation by leveraging the synthetic images
as a form of data augmentation. Second, we demonstrate the value of generative
models as an anonymization tool, achieving comparable tumor segmentation
results when trained on the synthetic data versus when trained on real subject
data. Together, these results offer a potential solution to two of the largest
challenges facing machine learning in medical imaging, namely the small
incidence of pathological findings, and the restrictions around sharing of
patient data. | [
0,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Quantitative Biology"
] |
Title: Adaptive Feature Representation for Visual Tracking,
Abstract: Robust feature representation plays significant role in visual tracking.
However, it remains a challenging issue, since many factors may affect the
experimental performance. The existing method which combine different features
by setting them equally with the fixed weight could hardly solve the issues,
due to the different statistical properties of different features across
various of scenarios and attributes. In this paper, by exploiting the internal
relationship among these features, we develop a robust method to construct a
more stable feature representation. More specifically, we utilize a co-training
paradigm to formulate the intrinsic complementary information of multi-feature
template into the efficient correlation filter framework. We test our approach
on challenging se- quences with illumination variation, scale variation,
deformation etc. Experimental results demonstrate that the proposed method
outperforms state-of-the-art methods favorably. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: The Remarkable Similarity of Massive Galaxy Clusters From z~0 to z~1.9,
Abstract: We present the results of a Chandra X-ray survey of the 8 most massive galaxy
clusters at z>1.2 in the South Pole Telescope 2500 deg^2 survey. We combine
this sample with previously-published Chandra observations of 49 massive
X-ray-selected clusters at 0<z<0.1 and 90 SZ-selected clusters at 0.25<z<1.2 to
constrain the evolution of the intracluster medium (ICM) over the past ~10 Gyr.
We find that the bulk of the ICM has evolved self similarly over the full
redshift range probed here, with the ICM density at r>0.2R500 scaling like
E(z)^2. In the centers of clusters (r<0.1R500), we find significant deviations
from self similarity (n_e ~ E(z)^{0.1+/-0.5}), consistent with no redshift
dependence. When we isolate clusters with over-dense cores (i.e., cool cores),
we find that the average over-density profile has not evolved with redshift --
that is, cool cores have not changed in size, density, or total mass over the
past ~9-10 Gyr. We show that the evolving "cuspiness" of clusters in the X-ray,
reported by several previous studies, can be understood in the context of a
cool core with fixed properties embedded in a self similarly-evolving cluster.
We find no measurable evolution in the X-ray morphology of massive clusters,
seemingly in tension with the rapidly-rising (with redshift) rate of major
mergers predicted by cosmological simulations. We show that these two results
can be brought into agreement if we assume that the relaxation time after a
merger is proportional to the crossing time, since the latter is proportional
to H(z)^(-1). | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Adaptive Similar Triangles Method: a Stable Alternative to Sinkhorn's Algorithm for Regularized Optimal Transport,
Abstract: In this paper, we are motivated by two important applications:
entropy-regularized optimal transport problem and road or IP traffic demand
matrix estimation by entropy model. Both of them include solving a special type
of optimization problem with linear equality constraints and objective given as
a sum of an entropy regularizer and a linear function. It is known that the
state-of-the-art solvers for this problem, which are based on Sinkhorn's method
(also known as RSA or balancing method), can fail to work, when the
entropy-regularization parameter is small. We consider the above optimization
problem as a particular instance of a general strongly convex optimization
problem with linear constraints. We propose a new algorithm to solve this
general class of problems. Our approach is based on the transition to the dual
problem. First, we introduce a new accelerated gradient method with adaptive
choice of gradient's Lipschitz constant. Then, we apply this method to the dual
problem and show, how to reconstruct an approximate solution to the primal
problem with provable convergence rate. We prove the rate $O(1/k^2)$, $k$ being
the iteration counter, both for the absolute value of the primal objective
residual and constraints infeasibility. Our method has similar to Sinkhorn's
method complexity of each iteration, but is faster and more stable numerically,
when the regularization parameter is small. We illustrate the advantage of our
method by numerical experiments for the two mentioned applications. We show
that there exists a threshold, such that, when the regularization parameter is
smaller than this threshold, our method outperforms the Sinkhorn's method in
terms of computation time. | [
0,
0,
1,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: Maximum genus of the Jenga like configurations,
Abstract: We treat the boundary of the union of blocks in the Jenga game as a surface
with a polyhedral structure and consider its genus. We generalize the game and
determine the maximum genus of the generalized game. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: A Decidable Very Expressive Description Logic for Databases (Extended Version),
Abstract: We introduce $\mathcal{DLR}^+$, an extension of the n-ary propositionally
closed description logic $\mathcal{DLR}$ to deal with attribute-labelled tuples
(generalising the positional notation), projections of relations, and global
and local objectification of relations, able to express inclusion, functional,
key, and external uniqueness dependencies. The logic is equipped with both TBox
and ABox axioms. We show how a simple syntactic restriction on the appearance
of projections sharing common attributes in a $\mathcal{DLR}^+$ knowledge base
makes reasoning in the language decidable with the same computational
complexity as $\mathcal{DLR}$. The obtained $\mathcal{DLR}^\pm$ n-ary
description logic is able to encode more thoroughly conceptual data models such
as EER, UML, and ORM. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: Centrality measures for graphons: Accounting for uncertainty in networks,
Abstract: As relational datasets modeled as graphs keep increasing in size and their
data-acquisition is permeated by uncertainty, graph-based analysis techniques
can become computationally and conceptually challenging. In particular, node
centrality measures rely on the assumption that the graph is perfectly known --
a premise not necessarily fulfilled for large, uncertain networks. Accordingly,
centrality measures may fail to faithfully extract the importance of nodes in
the presence of uncertainty. To mitigate these problems, we suggest a
statistical approach based on graphon theory: we introduce formal definitions
of centrality measures for graphons and establish their connections to
classical graph centrality measures. A key advantage of this approach is that
centrality measures defined at the modeling level of graphons are inherently
robust to stochastic variations of specific graph realizations. Using the
theory of linear integral operators, we define degree, eigenvector, Katz and
PageRank centrality functions for graphons and establish concentration
inequalities demonstrating that graphon centrality functions arise naturally as
limits of their counterparts defined on sequences of graphs of increasing size.
The same concentration inequalities also provide high-probability bounds
between the graphon centrality functions and the centrality measures on any
sampled graph, thereby establishing a measure of uncertainty of the measured
centrality score. The same concentration inequalities also provide
high-probability bounds between the graphon centrality functions and the
centrality measures on any sampled graph, thereby establishing a measure of
uncertainty of the measured centrality score. | [
1,
0,
1,
1,
0,
0
] | [
"Computer Science",
"Statistics",
"Mathematics"
] |
Title: A time-periodic mechanical analog of the quantum harmonic oscillator,
Abstract: We theoretically investigate the stability and linear oscillatory behavior of
a naturally unstable particle whose potential energy is harmonically modulated.
We find this fundamental dynamical system is analogous in time to a quantum
harmonic oscillator. In a certain modulation limit, a.k.a. the Kapitza regime,
the modulated oscillator can behave like an effective classic harmonic
oscillator. But in the overlooked opposite limit, the stable modes of
vibrations are quantized in the modulation parameter space. By analogy with the
statistical interpretation of quantum physics, those modes can be characterized
by the time-energy uncertainty relation of a quantum harmonic oscillator.
Reducing the almost-periodic vibrational modes of the particle to their
periodic eigenfunctions, one can transform the original equation of motion to a
dimensionless Schrödinger stationary wave equation with a harmonic potential.
This reduction process introduces two features reminiscent of the quantum
realm: a wave-particle duality and a loss of causality that could legitimate a
statistical interpretation of the computed eigenfunctions. These results shed
new light on periodically time-varying linear dynamical systems and open an
original path in the recently revived field of quantum mechanical analogs. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Time-resolved polarimetry of the superluminous SN 2015bn with the Nordic Optical Telescope,
Abstract: We present imaging polarimetry of the superluminous supernova SN 2015bn,
obtained over nine epochs between $-$20 and $+$46 days with the Nordic Optical
Telescope. This was a nearby, slowly-evolving Type I superluminous supernova
that has been studied extensively and for which two epochs of
spectropolarimetry are also available. Based on field stars, we determine the
interstellar polarisation in the Galaxy to be negligible. The polarisation of
SN 2015bn shows a statistically significant increase during the last epochs,
confirming previous findings. Our well-sampled imaging polarimetry series
allows us to determine that this increase (from $\sim 0.54\%$ to $\gtrsim
1.10\%$) coincides in time with rapid changes that took place in the optical
spectrum. We conclude that the supernova underwent a `phase transition' at
around $+$20 days, when the photospheric emission shifted from an outer layer,
dominated by natal C and O, to a more aspherical inner core, dominated by
freshly nucleosynthesized material. This two-layered model might account for
the characteristic appearance and properties of Type I superluminous
supernovae. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Solving Non-parametric Inverse Problem in Continuous Markov Random Field using Loopy Belief Propagation,
Abstract: In this paper, we address the inverse problem, or the statistical machine
learning problem, in Markov random fields with a non-parametric pair-wise
energy function with continuous variables. The inverse problem is formulated by
maximum likelihood estimation. The exact treatment of maximum likelihood
estimation is intractable because of two problems: (1) it includes the
evaluation of the partition function and (2) it is formulated in the form of
functional optimization. We avoid Problem (1) by using Bethe approximation.
Bethe approximation is an approximation technique equivalent to the loopy
belief propagation. Problem (2) can be solved by using orthonormal function
expansion. Orthonormal function expansion can reduce a functional optimization
problem to a function optimization problem. Our method can provide an analytic
form of the solution of the inverse problem within the framework of Bethe
approximation. | [
1,
1,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics",
"Mathematics"
] |
Title: Suspensions of finite-size neutrally-buoyant spheres in turbulent duct flow,
Abstract: We study the turbulent square duct flow of dense suspensions of
neutrally-buoyant spherical particles. Direct numerical simulations (DNS) are
performed in the range of volume fractions $\phi=0-0.2$, using the immersed
boundary method (IBM) to account for the dispersed phase. Based on the
hydraulic diameter a Reynolds number of $5600$ is considered. We report flow
features and particle statistics specific to this geometry, and compare the
results to the case of two-dimensional channel flows. In particular, we observe
that for $\phi=0.05$ and $0.1$, particles preferentially accumulate on the
corner bisectors, close to the duct corners as also observed for laminar square
duct flows of same duct-to-particle size ratios. At the highest volume
fraction, particles preferentially accumulate in the core region. For channel
flows, in the absence of lateral confinement particles are found instead to be
uniformily distributed across the channel. We also observe that the intensity
of the cross-stream secondary flows increases (with respect to the unladen
case) with the volume fraction up to $\phi=0.1$, as a consequence of the high
concentration of particles along the corner bisector. For $\phi=0.2$ the
turbulence activity is strongly reduced and the intensity of the secondary
flows reduces below that of the unladen case. The friction Reynolds number
increases with $\phi$ in dilute conditions, as observed for channel flows.
However, for $\phi=0.2$ the mean friction Reynolds number decreases below the
value for $\phi=0.1$. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Neutron Star Planets: Atmospheric processes and habitability,
Abstract: Of the roughly 3000 neutron stars known, only a handful have sub-stellar
companions. The most famous of these are the low-mass planets around the
millisecond pulsar B1257+12. New evidence indicates that observational biases
could still hide a wide variety of planetary systems around most neutron stars.
We consider the environment and physical processes relevant to neutron star
planets, in particular the effect of X-ray irradiation and the relativistic
pulsar wind on the planetary atmosphere. We discuss the survival time of planet
atmospheres and the planetary surface conditions around different classes of
neutron stars, and define a neutron star habitable zone. Depending on as-yet
poorly constrained aspects of the pulsar wind, both Super-Earths around
B1257+12 could lie within its habitable zone. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Quantitative Biology"
] |
Title: On the number of solutions of some transcendental equations,
Abstract: We give upper and lower bounds for the number of solutions of the equation
$p(z)\log|z|+q(z)=0$ with polynomials $p$ and $q$. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Simultaneous shot inversion for nonuniform geometries using fast data interpolation,
Abstract: Stochastic optimization is key to efficient inversion in PDE-constrained
optimization. Using 'simultaneous shots', or random superposition of source
terms, works very well in simple acquisition geometries where all sources see
all receivers, but this rarely occurs in practice. We develop an approach that
interpolates data to an ideal acquisition geometry while solving the inverse
problem using simultaneous shots. The approach is formulated as a joint inverse
problem, combining ideas from low-rank interpolation with full-waveform
inversion. Results using synthetic experiments illustrate the flexibility and
efficiency of the approach. | [
0,
0,
0,
1,
0,
0
] | [
"Physics",
"Mathematics"
] |
Title: Critical neural networks with short and long term plasticity,
Abstract: In recent years self organised critical neuronal models have provided
insights regarding the origin of the experimentally observed avalanching
behaviour of neuronal systems. It has been shown that dynamical synapses, as a
form of short-term plasticity, can cause critical neuronal dynamics. Whereas
long-term plasticity, such as hebbian or activity dependent plasticity, have a
crucial role in shaping the network structure and endowing neural systems with
learning abilities. In this work we provide a model which combines both
plasticity mechanisms, acting on two different time-scales. The measured
avalanche statistics are compatible with experimental results for both the
avalanche size and duration distribution with biologically observed percentages
of inhibitory neurons. The time-series of neuronal activity exhibits temporal
bursts leading to 1/f decay in the power spectrum. The presence of long-term
plasticity gives the system the ability to learn binary rules such as XOR,
providing the foundation of future research on more complicated tasks such as
pattern recognition. | [
0,
1,
0,
0,
0,
0
] | [
"Quantitative Biology",
"Physics"
] |
Title: Harnessing functional segregation across brain rhythms as a means to detect EEG oscillatory multiplexing during music listening,
Abstract: Music, being a multifaceted stimulus evolving at multiple timescales,
modulates brain function in a manifold way that encompasses not only the
distinct stages of auditory perception but also higher cognitive processes like
memory and appraisal. Network theory is apparently a promising approach to
describe the functional reorganization of brain oscillatory dynamics during
music listening. However, the music induced changes have so far been examined
within the functional boundaries of isolated brain rhythms. Using naturalistic
music, we detected the functional segregation patterns associated with
different cortical rhythms, as these were reflected in the surface EEG
measurements. The emerged structure was compared across frequency bands to
quantify the interplay among rhythms. It was also contrasted against the
structure from the rest and noise listening conditions to reveal the specific
components stemming from music listening. Our methodology includes an efficient
graph-partitioning algorithm, which is further utilized for mining prototypical
modular patterns, and a novel algorithmic procedure for identifying switching
nodes that consistently change module during music listening. Our results
suggest the multiplex character of the music-induced functional reorganization
and particularly indicate the dependence between the networks reconstructed
from the {\delta} and {\beta}H rhythms. This dependence is further justified
within the framework of nested neural oscillations and fits perfectly within
the context of recently introduced cortical entrainment to music. Considering
its computational efficiency, and in conjunction with the flexibility of in
situ electroencephalography, it may lead to novel assistive tools for real-life
applications. | [
0,
0,
0,
0,
1,
0
] | [
"Quantitative Biology",
"Computer Science"
] |
Title: Some characterizations of the preimage of $A_{\infty}$ for the Hardy-Littlewood maximal operator and consequences,
Abstract: The purpose of this paper is to give some characterizations of the weight
functions $w$ such that $Mw$ is in $A_{\infty}$. We show that for those weights
to be in $A_{\infty}$ ensures to be in $A_{1}$. We give a criterion in terms of
the local maximal functions $m_{\lambda}$ and we present a pair of
applications, among them someone similar to the Coifman-Rochberg
characterization of $A_{1}$ but using functions of the form $(f^{\#})^{\delta}$
and $(m_{\lambda}u)^{\delta}$ instead of $(Mf)^{\delta}$. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Coqatoo: Generating Natural Language Versions of Coq Proofs,
Abstract: Due to their numerous advantages, formal proofs and proof assistants, such as
Coq, are becoming increasingly popular. However, one disadvantage of using
proof assistants is that the resulting proofs can sometimes be hard to read and
understand, particularly for less-experienced users. To address this issue, we
have implemented a tool capable of generating natural language versions of Coq
proofs called Coqatoo, which we present in this paper. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: Breakdown of the Chiral Anomaly in Weyl Semimetals in a Strong Magnetic Field,
Abstract: The low-energy quasiparticles of Weyl semimetals are a condensed-matter
realization of the Weyl fermions introduced in relativistic field theory.
Chiral anomaly, the nonconservation of the chiral charge under parallel
electric and magnetic fields, is arguably the most important phenomenon of Weyl
semimetals and has been explained as an imbalance between the occupancies of
the gapless, zeroth Landau levels with opposite chiralities. This widely
accepted picture has served as the basis for subsequent studies. Here we report
the breakdown of the chiral anomaly in Weyl semimetals in a strong magnetic
field based on ab initio calculations. A sizable energy gap that depends
sensitively on the direction of the magnetic field may open up due to the
mixing of the zeroth Landau levels associated with the opposite-chirality Weyl
points that are away from each other in the Brillouin zone. Our study provides
a theoretical framework for understanding a wide range of phenomena closely
related to the chiral anomaly in topological semimetals, such as
magnetotransport, thermoelectric responses, and plasmons, to name a few. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Predicting Auction Price of Vehicle License Plate with Deep Recurrent Neural Network,
Abstract: In Chinese societies, superstition is of paramount importance, and vehicle
license plates with desirable numbers can fetch very high prices in auctions.
Unlike other valuable items, license plates are not allocated an estimated
price before auction. I propose that the task of predicting plate prices can be
viewed as a natural language processing (NLP) task, as the value depends on the
meaning of each individual character on the plate and its semantics. I
construct a deep recurrent neural network (RNN) to predict the prices of
vehicle license plates in Hong Kong, based on the characters on a plate. I
demonstrate the importance of having a deep network and of retraining.
Evaluated on 13 years of historical auction prices, the deep RNN outperforms
previous models by a significant margin. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Quantitative Finance"
] |
Title: An Adaptive, Multivariate Partitioning Algorithm for Global Optimization of Nonconvex Programs,
Abstract: In this work, we develop an adaptive, multivariate partitioning algorithm for
solving mixed-integer nonlinear programs (MINLP) with multi-linear terms to
global optimality. This iterative algorithm primarily exploits the advantages
of piecewise polyhedral relaxation approaches via disjunctive formulations to
solve MINLPs to global optimality in contrast to the conventional spatial
branch-and-bound approaches. In order to maintain relatively small-scale
mixed-integer linear programs at every iteration of the algorithm, we
adaptively partition the variable domains appearing in the multi-linear terms.
We also provide proofs on convergence guarantees of the proposed algorithm to a
global solution. Further, we discuss a few algorithmic enhancements based on
the sequential bound-tightening procedure as a presolve step, where we observe
the importance of solving piecewise relaxations compared to basic convex
relaxations to speed-up the convergence of the algorithm to global optimality.
We demonstrate the effectiveness of our disjunctive formulations and the
algorithm on well-known benchmark problems (including Pooling and Blending
instances) from MINLPLib and compare with state-of-the-art global optimization
solvers. With this novel approach, we solve several large-scale instances which
are, in some cases, intractable by the global optimization solver. We also
shrink the best known optimality gap for one of the hard, generalized pooling
problem instance. | [
1,
0,
1,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: Distributions and Statistical Power of Optimal Signal-Detection Methods In Finite Cases,
Abstract: In big data analysis for detecting rare and weak signals among $n$ features,
some grouping-test methods such as Higher Criticism test (HC), Berk-Jones test
(B-J), and $\phi$-divergence test share the similar asymptotical optimality
when $n \rightarrow \infty$. However, in practical data analysis $n$ is
frequently small and moderately large at most. In order to properly apply these
optimal tests and wisely choose them for practical studies, it is important to
know how to get the p-values and statistical power of them. To address this
problem in an even broader context, this paper provides analytical solutions
for a general family of goodness-of-fit (GOF) tests, which covers these optimal
tests. For any given i.i.d. and continuous distributions of the input test
statistics of the $n$ features, both p-value and statistical power of such a
GOF test can be calculated. By calculation we compared the finite-sample
performances of asymptotically optimal tests under the normal mixture
alternative. Results show that HC is the best choice when signals are rare,
while B-J is more robust over various signal patterns. In the application to a
real genome-wide association study, results illustrate that the p-value
calculation works well, and the optimal tests have potentials for detecting
novel disease genes with weak genetic effects. The calculations have been
implemented in an R package SetTest and published on the CRAN. | [
0,
0,
1,
1,
0,
0
] | [
"Statistics"
] |
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