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A spectral approach to transit timing variations | The high planetary multiplicity revealed by Kepler implies that Transit Time
Variations (TTVs) are intrinsically common. The usual procedure for detecting
these TTVs is biased to long-period, deep transit planets whereas most
transiting planets have short periods and shallow transits. Here we introduce
the Spectral Approach to TTVs technique that allows expanding the TTVs catalog
towards lower TTV amplitude, shorter orbital period, and shallower transit
depth. In the Spectral Approach we assume that a sinusoidal TTV exists in the
data and then calculate the improvement to $\chi^2$ this model allows over that
of linear ephemeris model. This enables detection of TTVs even in cases where
the transits are too shallow so individual transits cannot be timed. The
Spectral Approach is more sensitive due to the reduced number of free
parameters in its model. Using the Spectral Approach, we: (a) detect 131 new
periodic TTVs in Kepler data (an increase of ~2/3 over a previous TTV catalog);
(b) Constrain the TTV periods of 34 long-period TTVs and reduce amplitude
errors of known TTVs; (c) Identify cases of multi-periodic TTVs, for which
absolute planetary mass determination may be possible. We further extend our
analysis by using perturbation theory assuming small TTV amplitude at the
detection stage, which greatly speeds up our detection (to a level of few
seconds per star). Our extended TTVs sample shows no deficit of short period or
low amplitude transits, in contrast to previous surveys in which the detection
schemes were significantly biased against such systems.
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Distinct dynamical behavior in random and all-to-all neuronal networks | Neuronal network dynamics depends on network structure. It is often assumed
that neurons are connected at random when their actual connectivity structure
is unknown. Such models are then often approximated by replacing the random
network by an all-to-all network, where every neuron is connected to all other
neurons. This mean-field approximation is a common approach in statistical
physics. In this paper we show that such approximation can be invalid. We solve
analytically a neuronal network model with binary-state neurons in both random
and all-to-all networks. We find strikingly different phase diagrams
corresponding to each network structure. Neuronal network dynamics is not only
different within certain parameter ranges, but it also undergoes different
bifurcations. Our results therefore suggest cautiousness when using mean-field
models based on all-to-all network topologies to represent random networks.
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A Note on Band-limited Minorants of an Euclidean Ball | We study the Beurling-Selberg problem of finding band-limited $L^1$-functions
that lie below the indicator function of an Euclidean ball. We compute the
critical radius of the support of the Fourier transform for which such
construction can have a positive integral.
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From Distance Correlation to Multiscale Graph Correlation | Understanding and developing a correlation measure that can detect general
dependencies is not only imperative to statistics and machine learning, but
also crucial to general scientific discovery in the big data age. In this
paper, we establish a new framework that generalizes distance correlation --- a
correlation measure that was recently proposed and shown to be universally
consistent for dependence testing against all joint distributions of finite
moments --- to the Multiscale Graph Correlation (MGC). By utilizing the
characteristic functions and incorporating the nearest neighbor machinery, we
formalize the population version of local distance correlations, define the
optimal scale in a given dependency, and name the optimal local correlation as
MGC. The new theoretical framework motivates a theoretically sound Sample MGC
and allows a number of desirable properties to be proved, including the
universal consistency, convergence and almost unbiasedness of the sample
version. The advantages of MGC are illustrated via a comprehensive set of
simulations with linear, nonlinear, univariate, multivariate, and noisy
dependencies, where it loses almost no power in monotone dependencies while
achieving better performance in general dependencies, compared to distance
correlation and other popular methods.
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Out-of-time-order Operators and the Butterfly Effect | Out-of-time-order (OTO) operators have recently become popular diagnostics of
quantum chaos in many-body systems. The usual way they are introduced is via a
quantization of classical Lyapunov growth, which measures the divergence of
classical trajectories in phase space due to the butterfly effect. However, it
is not obvious how exactly they capture the sensitivity of a quantum system to
its initial conditions beyond the classical limit. In this paper, we analyze
sensitivity to initial conditions in the quantum regime by recasting OTO
operators for many-body systems using various formulations of quantum
mechanics. Notably, we utilize the Wigner phase space formulation to derive an
$\hbar$-expansion of the OTO operator for spatial degrees of freedom, and a
large spin $1/s$-expansion for spin degrees of freedom. We find in each case
that the leading term is the Lyapunov growth for the classical limit of the
system and argue that quantum corrections become dominant at around the
scrambling time, which is also when we expect the OTO operator to saturate. We
also express the OTO operator in terms of propagators and see from a different
point of view how it is a quantum generalization of the divergence of classical
trajectories.
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Science and Facebook: the same popularity law! | The distribution of scientific citations for publications selected with
different rules (author, topic, institution, country, journal, etc.) collapse
on a single curve if one plots the citations relative to their mean value. We
find that the distribution of shares for the Facebook posts re-scale in the
same manner to the very same curve with scientific citations. This finding
suggests that citations are subjected to the same growth mechanism with
Facebook popularity measures, being influenced by a statistically similar
social environment and selection mechanism. In a simple master-equation
approach the exponential growth of the number of publications and a
preferential selection mechanism leads to a Tsallis-Pareto distribution
offering an excellent description for the observed statistics. Based on our
model and on the data derived from PubMed we predict that according to the
present trend the average citations per scientific publications exponentially
relaxes to about 4.
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Monocular Vision-based Vehicle Localization Aided by Fine-grained Classification | Monocular camera systems are prevailing in intelligent transportation
systems, but by far they have rarely been used for dimensional purposes such as
to accurately estimate the localization information of a vehicle. In this
paper, we show that this capability can be realized. By integrating a series of
advanced computer vision techniques including foreground extraction, edge and
line detection, etc., and by utilizing deep learning networks for fine-grained
vehicle model classification, we developed an algorithm which can estimate
vehicles location (position, orientation and boundaries) within the environment
down to 3.79 percent position accuracy and 2.5 degrees orientation accuracy.
With this enhancement, current massive surveillance camera systems can
potentially play the role of e-traffic police and trigger many new intelligent
transportation applications, for example, to guide vehicles for parking or even
for autonomous driving.
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Verifying Patterns of Dynamic Architectures using Model Checking | Architecture patterns capture architectural design experience and provide
abstract solutions to recurring architectural design problems. They consist of
a description of component types and restrict component connection and
activation. Therefore, they guarantee some desired properties for architectures
employing the pattern. Unfortunately, most documented patterns do not provide a
formal guarantee of whether their specification indeed leads to the desired
guarantee. Failure in doing so, however, might lead to wrong architectures,
i.e., architectures wrongly supposed to show certain desired properties. Since
architectures, in general, have a high impact on the quality of the resulting
system and architectural flaws are only difficult, if not to say impossible, to
repair, this may lead to badly reparable quality issues in the resulting
system. To address this problem, we propose an approach based on model checking
to verify pattern specifications w.r.t. their guarantees. In the following we
apply the approach to three well-known patterns for dynamic architectures: the
Singleton, the Model-View-Controller, and the Broker pattern. Thereby, we
discovered ambiguities and missing constraints for all three specifications.
Thus, we conclude that verifying patterns of dynamic architectures using model
checking is feasible and useful to discover ambiguities and flaws in pattern
specifications.
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Some integrals of hypergeometric functions | We consider a certain definite integral involving the product of two
classical hypergeometric functions having complicated arguments. We show in
this paper the surprising fact that this integral does not depend on the
parameters of the hypergeometric functions.
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On the second Feng-Rao distance of Algebraic Geometry codes related to Arf semigroups | We describe the second (generalized) Feng-Rao distance for elements in an Arf
numerical semigroup that are greater than or equal to the conductor of the
semigroup. This provides a lower bound for the second Hamming weight for one
point AG codes. In particular, we can obtain the second Feng-Rao distance for
the codes defined by asymptotically good towers of function fields whose
Weierstrass semigroups are inductive. In addition, we compute the second
Feng-Rao number, and provide some examples and comparisons with previous
results on this topic. These calculations rely on Apéry sets, and thus
several results concerning Apéry sets of Arf semigroups are presented.
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Posterior contraction rates for support boundary recovery | Given a sample of a Poisson point process with intensity $\lambda_f(x,y) = n
\mathbf{1}(f(x) \leq y),$ we study recovery of the boundary function $f$ from a
nonparametric Bayes perspective. Because of the irregularity of this model, the
analysis is non-standard. We establish a general result for the posterior
contraction rate with respect to the $L^1$-norm based on entropy and one-sided
small probability bounds. From this, specific posterior contraction results are
derived for Gaussian process priors and priors based on random wavelet series.
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A Fast Image Simulation Algorithm for Scanning Transmission Electron Microscopy | Image simulation for scanning transmission electron microscopy at atomic
resolution for samples with realistic dimensions can require very large
computation times using existing simulation algorithms. We present a new
algorithm named PRISM that combines features of the two most commonly used
algorithms, the Bloch wave and multislice methods. PRISM uses a Fourier
interpolation factor $f$ that has typical values of 4-20 for atomic resolution
simulations. We show that in many cases PRISM can provide a speedup that scales
with $f^4$ compared to multislice simulations, with a negligible loss of
accuracy. We demonstrate the usefulness of this method with large-scale
scanning transmission electron microscopy image simulations of a crystalline
nanoparticle on an amorphous carbon substrate.
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X-ray Astronomical Point Sources Recognition Using Granular Binary-tree SVM | The study on point sources in astronomical images is of special importance,
since most energetic celestial objects in the Universe exhibit a point-like
appearance. An approach to recognize the point sources (PS) in the X-ray
astronomical images using our newly designed granular binary-tree support
vector machine (GBT-SVM) classifier is proposed. First, all potential point
sources are located by peak detection on the image. The image and spectral
features of these potential point sources are then extracted. Finally, a
classifier to recognize the true point sources is build through the extracted
features. Experiments and applications of our approach on real X-ray
astronomical images are demonstrated. comparisons between our approach and
other SVM-based classifiers are also carried out by evaluating the precision
and recall rates, which prove that our approach is better and achieves a higher
accuracy of around 89%.
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Sample-Efficient Learning of Mixtures | We consider PAC learning of probability distributions (a.k.a. density
estimation), where we are given an i.i.d. sample generated from an unknown
target distribution, and want to output a distribution that is close to the
target in total variation distance. Let $\mathcal F$ be an arbitrary class of
probability distributions, and let $\mathcal{F}^k$ denote the class of
$k$-mixtures of elements of $\mathcal F$. Assuming the existence of a method
for learning $\mathcal F$ with sample complexity $m_{\mathcal{F}}(\epsilon)$,
we provide a method for learning $\mathcal F^k$ with sample complexity
$O({k\log k \cdot m_{\mathcal F}(\epsilon) }/{\epsilon^{2}})$. Our mixture
learning algorithm has the property that, if the $\mathcal F$-learner is
proper/agnostic, then the $\mathcal F^k$-learner would be proper/agnostic as
well.
This general result enables us to improve the best known sample complexity
upper bounds for a variety of important mixture classes. First, we show that
the class of mixtures of $k$ axis-aligned Gaussians in $\mathbb{R}^d$ is
PAC-learnable in the agnostic setting with $\widetilde{O}({kd}/{\epsilon ^ 4})$
samples, which is tight in $k$ and $d$ up to logarithmic factors. Second, we
show that the class of mixtures of $k$ Gaussians in $\mathbb{R}^d$ is
PAC-learnable in the agnostic setting with sample complexity
$\widetilde{O}({kd^2}/{\epsilon ^ 4})$, which improves the previous known
bounds of $\widetilde{O}({k^3d^2}/{\epsilon ^ 4})$ and
$\widetilde{O}(k^4d^4/\epsilon ^ 2)$ in its dependence on $k$ and $d$. Finally,
we show that the class of mixtures of $k$ log-concave distributions over
$\mathbb{R}^d$ is PAC-learnable using
$\widetilde{O}(d^{(d+5)/2}\epsilon^{-(d+9)/2}k)$ samples.
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The Bag Semantics of Ontology-Based Data Access | Ontology-based data access (OBDA) is a popular approach for integrating and
querying multiple data sources by means of a shared ontology. The ontology is
linked to the sources using mappings, which assign views over the data to
ontology predicates. Motivated by the need for OBDA systems supporting
database-style aggregate queries, we propose a bag semantics for OBDA, where
duplicate tuples in the views defined by the mappings are retained, as is the
case in standard databases. We show that bag semantics makes conjunctive query
answering in OBDA coNP-hard in data complexity. To regain tractability, we
consider a rather general class of queries and show its rewritability to a
generalisation of the relational calculus to bags.
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Multilink Communities of Multiplex Networks | Multiplex networks describe a large number of complex social, biological and
transportation networks where a set of nodes is connected by links of different
nature and connotation. Here we uncover the rich community structure of
multiplex networks by associating a community to each multilink where the
multilinks characterize the connections existing between any two nodes of the
multiplex network. Our community detection method reveals the rich interplay
between the mesoscale structure of the multiplex networks and their
multiplexity. For instance some nodes can belong to many layers and few
communities while others can belong to few layers but many communities.
Moreover the multilink communities can be formed by a different number of
relevant layers. These results point out that mesoscopically there can be large
differences in the compressibility of multiplex networks.
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P-wave superfluidity of atomic lattice fermions | We discuss the emergence of p-wave superfluidity of identical atomic fermions
in a two-dimensional optical lattice. The optical lattice potential manifests
itself in an interplay between an increase in the density of states on the
Fermi surface and the modification of the fermion-fermion interaction
(scattering) amplitude. The density of states is enhanced due to an increase of
the effective mass of atoms. In deep lattices the scattering amplitude is
strongly reduced compared to free space due to a small overlap of wavefunctions
of fermion sitting in the neighboring lattice sites, which suppresses the
p-wave superfluidity. However, for moderate lattice depths the enhancement of
the density of states can compensate the decrease of the scattering amplitude.
Moreover, the lattice setup significantly reduces inelastic collisional losses,
which allows one to get closer to a p-wave Feshbach resonance. This opens
possibilities to obtain the topological $p_x+ip_y$ superfluid phase, especially
in the recently proposed subwavelength lattices. We demonstrate this for the
two-dimensional version of the Kronig-Penney model allowing a transparent
physical analysis.
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Deep generative models of genetic variation capture mutation effects | The functions of proteins and RNAs are determined by a myriad of interactions
between their constituent residues, but most quantitative models of how
molecular phenotype depends on genotype must approximate this by simple
additive effects. While recent models have relaxed this constraint to also
account for pairwise interactions, these approaches do not provide a tractable
path towards modeling higher-order dependencies. Here, we show how latent
variable models with nonlinear dependencies can be applied to capture
beyond-pairwise constraints in biomolecules. We present a new probabilistic
model for sequence families, DeepSequence, that can predict the effects of
mutations across a variety of deep mutational scanning experiments
significantly better than site independent or pairwise models that are based on
the same evolutionary data. The model, learned in an unsupervised manner solely
from sequence information, is grounded with biologically motivated priors,
reveals latent organization of sequence families, and can be used to
extrapolate to new parts of sequence space
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Consistent Rank Logits for Ordinal Regression with Convolutional Neural Networks | While extraordinary progress has been made towards developing neural network
architectures for classification tasks, commonly used loss functions such as
the multi-category cross entropy loss are inadequate for ranking and ordinal
regression problems. To address this issue, approaches have been developed that
transform ordinal target variables series of binary classification tasks,
resulting in robust ranking algorithms with good generalization performance.
However, to model ordinal information appropriately, ideally, a rank-monotonic
prediction function is required such that confidence scores are ordered and
consistent. We propose a new framework (Consistent Rank Logits, CORAL) with
theoretical guarantees for rank-monotonicity and consistent confidence scores.
Through parameter sharing, our framework benefits from low training complexity
and can easily be implemented to extend common convolutional neural network
classifiers for ordinal regression tasks. Furthermore, our empirical results
support the proposed theory and show a substantial improvement compared to the
current state-of-the-art ordinal regression method for age prediction from face
images.
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High-Pressure Synthesis and Characterization of $β$-GeSe - A Semiconductor with Six-Rings in an Uncommon Boat Conformation | Two-dimensional materials have significant potential for the development of
new devices. Here we report the electronic and structural properties of
$\beta$-GeSe, a previously unreported polymorph of GeSe, with a unique crystal
structure that displays strong two-dimensional structural features.
$\beta$-GeSe is made at high pressure and temperature and is stable under
ambient conditions. We compare it to its structural and electronic relatives
$\alpha$-GeSe and black phosphorus. The $\beta$ form of GeSe displays a boat
conformation for its Ge-Se six-ring, while the previously known $\alpha$ form,
and black phosphorus, display the more common chair conformation for their
six-rings. Electronic structure calculations indicate that $\beta$-GeSe is a
semiconductor, with an approximate bulk band gap of $\Delta~\approx$ 0.5 eV,
and, in its monolayer form, $\Delta~\approx$ 0.9 eV. These values fall between
those of $\alpha$-GeSe and black phosphorus, making $\beta$-GeSe a promising
candidate for future applications. The resistivity of our $\beta$-GeSe crystals
measured in-plane is on the order of $\rho \approx$ 1 $\Omega$cm, while being
essentially temperature independent.
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Mining Significant Microblogs for Misinformation Identification: An Attention-based Approach | With the rapid growth of social media, massive misinformation is also
spreading widely on social media, such as microblog, and bring negative effects
to human life. Nowadays, automatic misinformation identification has drawn
attention from academic and industrial communities. For an event on social
media usually consists of multiple microblogs, current methods are mainly based
on global statistical features. However, information on social media is full of
noisy and outliers, which should be alleviated. Moreover, most of microblogs
about an event have little contribution to the identification of
misinformation, where useful information can be easily overwhelmed by useless
information. Thus, it is important to mine significant microblogs for a
reliable misinformation identification method. In this paper, we propose an
Attention-based approach for Identification of Misinformation (AIM). Based on
the attention mechanism, AIM can select microblogs with largest attention
values for misinformation identification. The attention mechanism in AIM
contains two parts: content attention and dynamic attention. Content attention
is calculated based textual features of each microblog. Dynamic attention is
related to the time interval between the posting time of a microblog and the
beginning of the event. To evaluate AIM, we conduct a series of experiments on
the Weibo dataset and the Twitter dataset, and the experimental results show
that the proposed AIM model outperforms the state-of-the-art methods.
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Machine Learning for Set-Identified Linear Models | Set-identified models often restrict the number of covariates leading to wide
identified sets in practice. This paper provides estimation and inference
methods for set-identified linear models with high-dimensional covariates where
the model selection is based on modern machine learning tools. I characterize
the boundary (i.e, support function) of the identified set using a
semiparametric moment condition. Combining Neyman-orthogonality and sample
splitting ideas, I construct a root-N consistent, the uniformly asymptotically
Gaussian estimator of the support function. I also prove the validity of the
Bayesian bootstrap procedure to conduct inference about the identified set. I
provide a general method to construct a Neyman-orthogonal moment condition for
the support function. I apply this result to estimate sharp nonparametric
bounds on the average treatment effect in Lee (2008)'s model of endogenous
selection and substantially tighten the bounds on this parameter in Angrist et
al. (2006)'s empirical setting. I also apply this result to estimate sharp
identified sets for two other parameters - a new parameter, called a partially
linear predictor, and the average partial derivative when the outcome variable
is recorded in intervals.
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An efficient algorithm to decide periodicity of b-recognisable sets using MSDF convention | Given an integer base $b>1$, a set of integers is represented in base $b$ by
a language over $\{0,1,...,b-1\}$. The set is said to be $b$-recognisable if
its representation is a regular language. It is known that eventually periodic
sets are $b$-recognisable in every base $b$, and Cobham's theorem implies the
converse: no other set is $b$-recognisable in every base $b$.
We are interested in deciding whether a $b$-recognisable set of integers
(given as a finite automaton) is eventually periodic. Honkala showed that this
problem decidable in 1986 and recent developments give efficient decision
algorithms. However, they only work when the integers are written with the
least significant digit first.
In this work, we consider the natural order of digits (Most Significant Digit
First) and give a quasi-linear algorithm to solve the problem in this case.
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Electrical Tuning of Polarizaion-state Using Graphene-Integrated Metasurfaces | Plasmonic metasurfaces have been employed for tuning and controlling light
enabling various novel applications. Their appeal is enhanced with the
incorporation of an active element with the metasurfaces paving the way for
dynamic control. In this letter, we realize a dynamic polarization state
generator using graphene-integrated anisotropic metasurface (GIAM), where a
linear incidence polarization is controllably converted into an elliptical one.
The anisotropic metasurface leads to an intrinsic polarization conversion when
illuminated with non-orthogonal incident polarization. Additionally, the
single-layer graphene allows us to tune the phase and intensity of the
reflected light on the application of a gate voltage, enabling dynamic
polarization control. The stokes polarization parameters of the reflected light
are measured using rotating polarizer method and it is demonstrated that a
large change in the ellipticity as well as orientation angle can be induced by
this device. We also provide experimental evidence that the titl angle can
change independent of the ellipticity going from positive values to nearly zero
to negative values while ellipticity is constant.
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An efficient distribution method for nonlinear two-phase flow in highly heterogeneous multidimensional stochastic porous media | In the context of stochastic two-phase flow in porous media, we introduce a
novel and efficient method to estimate the probability distribution of the
wetting saturation field under uncertain rock properties in highly
heterogeneous porous systems, where streamline patterns are dominated by
permeability heterogeneity, and for slow displacement processes (viscosity
ratio close to unity). Our method, referred to as the frozen streamline
distribution method (FROST), is based on a physical understanding of the
stochastic problem. Indeed, we identify key random fields that guide the
wetting saturation variability, namely fluid particle times of flight and
injection times. By comparing saturation statistics against full-physics Monte
Carlo simulations, we illustrate how this simple, yet accurate FROST method
performs under the preliminary approximation of frozen streamlines. Further, we
inspect the performance of an accelerated FROST variant that relies on a
simplification about injection time statistics. Finally, we introduce how
quantiles of saturation can be efficiently computed within the FROST framework,
hence leading to robust uncertainty assessment.
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Multi-Kernel LS-SVM Based Bio-Clinical Data Integration: Applications to Ovarian Cancer | The medical research facilitates to acquire a diverse type of data from the
same individual for particular cancer. Recent studies show that utilizing such
diverse data results in more accurate predictions. The major challenge faced is
how to utilize such diverse data sets in an effective way. In this paper, we
introduce a multiple kernel based pipeline for integrative analysis of
high-throughput molecular data (somatic mutation, copy number alteration, DNA
methylation and mRNA) and clinical data. We apply the pipeline on Ovarian
cancer data from TCGA. After multiple kernels have been generated from the
weighted sum of individual kernels, it is used to stratify patients and predict
clinical outcomes. We examine the survival time, vital status, and neoplasm
cancer status of each subtype to verify how well they cluster. We have also
examined the power of molecular and clinical data in predicting dichotomized
overall survival data and to classify the tumor grade for the cancer samples.
It was observed that the integration of various data types yields higher
log-rank statistics value. We were also able to predict clinical status with
higher accuracy as compared to using individual data types.
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The universal property of derived geometry | Derived geometry can be defined as the universal way to adjoin finite
homotopical limits to a given category of manifolds compatibly with products
and glueing. The point of this paper is to show that a construction closely
resembling existing approaches to derived geometry in fact produces a geometry
with this universal property.
I also investigate consequences of this definition in particular in the
differentiable setting, and compare the theory so obtained to D. Spivak's
axioms for derived C-infinity geometry.
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Feature Model-to-Ontology for SPL Application Realisation | Feature model are widely used to capture commonalities and variabilities of
artefacts in Software Product Line (SPL). Several studies have discussed the
formal representation of feature diagram using ontologies with different styles
of mapping. However, they still focused on the ontology approach for problem
space and keep the solution space aside. In this paper, we present the
modelling of feature model using OWL ontology and produce an application based
on the ontology. Firstly, we map the features in a running example feature
diagram to OWL classes and properties. Secondly, we verify the consistency of
the OWL ontology by using reasoning engines. Finally, we use the ontology as an
input of Zotonic framework for application realisation.
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Rethinking Reprojection: Closing the Loop for Pose-aware ShapeReconstruction from a Single Image | An emerging problem in computer vision is the reconstruction of 3D shape and
pose of an object from a single image. Hitherto, the problem has been addressed
through the application of canonical deep learning methods to regress from the
image directly to the 3D shape and pose labels. These approaches, however, are
problematic from two perspectives. First, they are minimizing the error between
3D shapes and pose labels - with little thought about the nature of this label
error when reprojecting the shape back onto the image. Second, they rely on the
onerous and ill-posed task of hand labeling natural images with respect to 3D
shape and pose. In this paper we define the new task of pose-aware shape
reconstruction from a single image, and we advocate that cheaper 2D annotations
of objects silhouettes in natural images can be utilized. We design
architectures of pose-aware shape reconstruction which re-project the predicted
shape back on to the image using the predicted pose. Our evaluation on several
object categories demonstrates the superiority of our method for predicting
pose-aware 3D shapes from natural images.
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Rigid local systems and alternating groups | In earlier work, Katz exhibited some very simple one parameter families of
exponential sums which gave rigid local systems on the affine line in
characteristic p whose geometric (and usually, arithmetic) monodromy groups
were SL(2,q), and he exhibited other such very simple families giving SU(3,q).
[Here q is a power of the characteristic p with p odd]. In this paper, we
exhibit equally simple families whose geometric monodromy groups are the
alternating groups Alt(2q). $. We also determine their arithmetic monodromy
groups. By Raynaud's solution of the Abhyankar Conjecture, any finite simple
group whose order is divisible by p will occur as the geometric monodromy group
of some local system on the affine line in characteristic p; the interest here
is that it occurs in our particularly simple local systems.
In the earlier work of Katz, he used a theorem to Kubert to know that the
monodromy groups in question were finite, then work of Gross to determine which
finite groups they were. Here we do not have, at present, any direct way of
showing this finiteness. Rather, the situation is more complicated and more
interesting. Using some basic information about these local systems, a
fundamental dichotomy is proved:
The geometric monodromy group is either Alt(2q) or it is the special
orthogonal group SO(2q-1). An elementary polynomial identity is used to show
that the third moment is 1. This rules out the SO(2q-1) case. This roundabout
method establishes the theorem. It would be interesting to find a "direct"
proof that these local systems have integer (rather than rational) traces; this
integrality is in fact equivalent to their monodromy groups being finite, Even
if one had such a direct proof, it would still require serious group theory to
show that their geometric monodromy groups are the alternating groups.
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Interference effects of deleterious and beneficial mutations in large asexual populations | Linked beneficial and deleterious mutations are known to decrease the
fixation probability of a favorable mutation in large asexual populations.
While the hindering effect of strongly deleterious mutations on adaptive
evolution has been well studied, how weak deleterious mutations, either in
isolation or with superior beneficial mutations, influence the fixation of a
beneficial mutation has not been fully explored. Here, using a multitype
branching process, we obtain an accurate analytical expression for the fixation
probability when deleterious effects are weak, and exploit this result along
with the clonal interference theory to investigate the joint effect of linked
beneficial and deleterious mutations on the rate of adaptation. We find that
when the mutation rate is increased beyond the beneficial fitness effect, the
fixation probability of the beneficial mutant decreases from Haldane's
classical result towards zero. This has the consequence that above a critical
mutation rate that may depend on the population size, the adaptation rate
decreases exponentially with the mutation rate and is independent of the
population size. In addition, we find that for a range of mutation rates, both
beneficial and deleterious mutations interfere and impede the adaptation
process in large populations. We also study the evolution of mutation rates in
adapting asexual populations, and conclude that linked beneficial mutations
have a stronger influence on mutator fixation than the deleterious mutations.
| 0 | 0 | 0 | 0 | 1 | 0 |
Grassmanians and Pseudosphere Arrangements | We extend vector configurations to more general objects that have nicer
combinatorial and topological properties, called weighted pseudosphere
arrangements. These are defined as a weighted variant of arrangements of
pseudospheres, as in the Topological Representation Theorem for oriented
matroids. We show that in rank 3, the real Stiefel manifold, Grassmanian, and
oriented Grassmanian are homotopy equivalent to the analagously defined spaces
of weighted pseudosphere arrangements. We also show for all rank 3 oriented
matroids, that the space of realizations by weighted pseudosphere arrangements
is contractible. This is a sharp contrast with vector configurations, where the
space of realizations can have the homotopy type of any primary semialgebraic
set.
| 0 | 0 | 1 | 0 | 0 | 0 |
Rational homotopy theory via Sullivan models: a survey | This survey contains the main results in rational homotopy, from the
beginning to the most recent ones. It makes the status of the art, gives a
short presentation of some areas where rational homotopy has been used, and
contains a lot of important open problems
| 0 | 0 | 1 | 0 | 0 | 0 |
A Spectroscopic Orbit for the late-type Be star $β$ CMi | The late-type Be star $\beta$ CMi is remarkably stable compared to other Be
stars that have been studied. This has led to a realistic model of the
outflowing Be disk by Klement et al. These results showed that the disk is
likely truncated at a finite radius from the star, which Klement et al.~suggest
is evidence for an unseen binary companion in orbit. Here we report on an
analysis of the Ritter Observatory spectroscopic archive of $\beta$ CMi to
search for evidence of the elusive companion. We detect periodic Doppler shifts
in the wings of the H$\alpha$ line with a period of 170 d and an amplitude of
2.25 km s$^{-1}$, consistent with a low-mass binary companion ($M\approx 0.42
M_\odot$). We then compared the small changes in the violet-to-red peak height
changes ($V/R$) with the orbital motion. We find weak evidence that it does
follow the orbital motion, as suggested by recent Be binary models by Panoglou
et al. Our results, which are similar to those for several other Be stars,
suggest that $\beta$ CMi may be a product of binary evolution where Roche lobe
overflow has spun up the current Be star, likely leaving a hot subdwarf or
white dwarf in orbit around the star. Unfortunately, no direct sign of this
companion star is found in the very limited archive of {\it International
Ultraviolet Explorer} spectra.
| 0 | 1 | 0 | 0 | 0 | 0 |
Variations on known and recent cardinality bounds | Sapirovskii [18] proved that $|X|\leq\pi\chi(X)^{c(X)\psi(X)}$, for a regular
space $X$. We introduce the $\theta$-pseudocharacter of a Urysohn space $X$,
denoted by $\psi_\theta (X)$, and prove that the previous inequality holds for
Urysohn spaces replacing the bounds on celluarity $c(X)\leq\kappa$ and on
pseudocharacter $\psi(X)\leq\kappa$ with a bound on Urysohn cellularity
$Uc(X)\leq\kappa$ (which is a weaker conditon because $Uc(X)\leq c(X)$) and on
$\theta$-pseudocharacter $\psi_\theta (X)\leq\kappa$ respectivly (note that in
general $\psi(\cdot)\leq\psi_\theta (\cdot)$ and in the class of regular spaces
$\psi(\cdot)=\psi_\theta(\cdot)$). Further, in [6] the authors generalized the
Dissanayake and Willard's inequality: $|X|\leq 2^{aL_{c}(X)\chi(X)}$, for
Hausdorff spaces $X$ [25], in the class of $n$-Hausdorff spaces and de Groot's
result: $|X|\leq 2^{hL(X)}$, for Hausdorff spaces [11], in the class of $T_1$
spaces (see Theorems 2.22 and 2.23 in [6]). In this paper we restate Theorem
2.22 in [6] in the class of $n$-Urysohn spaces and give a variation of Theorem
2.23 in [6] using new cardinal functions, denoted by $UW(X)$, $\psi
w_\theta(X)$, $\theta\hbox{-}aL(X)$, $h\theta\hbox{-}aL(X)$,
$\theta\hbox{-}aL_c(X)$ and $\theta\hbox{-}aL_{\theta}(X)$. In [5] the authors
introduced the Hausdorff point separating weight of a space $X$ denoted by
$Hpsw(X)$ and proved a Hausdorff version of Charlesworth's inequality $|X|\leq
psw(X)^{L(X)\psi(X)}$ [7]. In this paper, we introduce the Urysohn point
separating weight of a space $X$, denoted by $Upsw(X)$, and prove that $|X|\leq
Upsw(X)^{\theta\hbox{-}aL_{c}(X)\psi(X)}$, for a Urysohn space $X$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Spatio-temporal intermittency of the turbulent energy cascade | In incompressible and periodic statistically stationary turbulence, exchanges
of turbulent energy across scales and space are characterised by very intense
and intermittent spatio-temporal fluctuations around zero of the
time-derivative term, the spatial turbulent transport of fluctuating energy,
and the pressure-velocity term. These fluctuations are correlated with each
other and with the intense intermittent fluctuations of the interscale energy
transfer rate. These correlations are caused by the sweeping effect, the link
between non-linearity and non-locality, and also relate to geometrical
alignments between the two-point fluctuating pressure force difference and the
two-point fluctuating velocity difference in the case of the correlation
between the interscale transfer rate and the pressure-velocity term. All these
processes are absent from the spatio-temporal average picture of the turbulence
cascade in statistically stationary and homogeneous turbulence.
| 0 | 1 | 0 | 0 | 0 | 0 |
Voltage Analytics for Power Distribution Network Topology Verification | Distribution grids constitute complex networks of lines often times
reconfigured to minimize losses, balance loads, alleviate faults, or for
maintenance purposes. Topology monitoring becomes a critical task for optimal
grid scheduling. While synchrophasor installations are limited in low-voltage
grids, utilities have an abundance of smart meter data at their disposal. In
this context, a statistical learning framework is put forth for verifying
single-phase grid structures using non-synchronized voltage data. The related
maximum likelihood task boils down to minimizing a non-convex function over a
non-convex set. The function involves the sample voltage covariance matrix and
the feasible set is relaxed to its convex hull. Asymptotically in the number of
data, the actual topology yields the global minimizer of the original and the
relaxed problems. Under a simplified data model, the function turns out to be
convex, thus offering optimality guarantees. Prior information on line statuses
is also incorporated via a maximum a-posteriori approach. The formulated tasks
are tackled using solvers having complementary strengths. Numerical tests using
real data on benchmark feeders demonstrate that reliable topology estimates can
be acquired even with a few smart meter data, while the non-convex schemes
exhibit superior line verification performance at the expense of additional
computational time.
| 0 | 0 | 1 | 0 | 0 | 0 |
The comprehension construction | In this paper we construct an analogue of Lurie's "unstraightening"
construction that we refer to as the "comprehension construction". Its input is
a cocartesian fibration $p \colon E \to B$ between $\infty$-categories together
with a third $\infty$-category $A$. The comprehension construction then defines
a map from the quasi-category of functors from $A$ to $B$ to the large
quasi-category of cocartesian fibrations over $A$ that acts on $f \colon A \to
B$ by forming the pullback of $p$ along $f$. To illustrate the versatility of
this construction, we define the covariant and contravariant Yoneda embeddings
as special cases of the comprehension functor. We then prove that the hom-wise
action of the comprehension functor coincides with an "external action" of the
hom-spaces of $B$ on the fibres of $p$ and use this to prove that the Yoneda
embedding is fully faithful, providing an explicit equivalence between a
quasi-category and the homotopy coherent nerve of a Kan-complex enriched
category.
| 0 | 0 | 1 | 0 | 0 | 0 |
Approximate String Matching: Theory and Applications (La Recherche Approchée de Motifs : Théorie et Applications) | The approximate string matching is a fundamental and recurrent problem that
arises in most computer science fields. This problem can be defined as follows:
Let $D=\{x_1,x_2,\ldots x_d\}$ be a set of $d$ words defined on an alphabet
$\Sigma$, let $q$ be a query defined also on $\Sigma$, and let $k$ be a
positive integer. We want to build a data structure on $D$ capable of answering
the following query: find all words in $D$ that are at most different from the
query word $q$ with $k$ errors.
In this thesis, we study the approximate string matching methods in
dictionaries, texts, and indexes, to propose practical methods that solve this
problem efficiently. We explore this problem in three complementary directions:
1) The approximate string matching in the dictionary. We propose two
solutions to this problem, the first one uses hash tables for $k \geq 2$, the
second uses the Trie and reverse Trie, and it is restricted to (k = 1). The two
solutions are adaptable, without loss of performance, to the approximate string
matching in a text.
2) The approximate string matching for \textit{autocompletion}, which is,
find all suffixes of a given prefix that may contain errors. We give a new
solution better in practice than all the previous proposed solutions.
3) The problem of the alignment of biological sequences can be interpreted as
an approximate string matching problem. We propose a solution for peers and
multiple sequences alignment.
\medskip All the results obtained showed that our algorithms, give the best
performance on sets of practical data (benchmark from the real world). All our
methods are proposed as libraries, and they are published online.
| 1 | 0 | 0 | 0 | 0 | 0 |
Imaging anomalous nematic order and strain in optimally doped BaFe$_2$(As,P)$_2$ | We present the strain and temperature dependence of an anomalous nematic
phase in optimally doped BaFe$_2$(As,P)$_2$. Polarized ultrafast optical
measurements reveal broken 4-fold rotational symmetry in a temperature range
above $T_c$ in which bulk probes do not detect a phase transition. Using
ultrafast microscopy, we find that the magnitude and sign of this nematicity
vary on a ${50{-}100}~\mu$m length scale, and the temperature at which it
onsets ranges from 40 K near a domain boundary to 60 K deep within a domain.
Scanning Laue microdiffraction maps of local strain at room temperature
indicate that the nematic order appears most strongly in regions of weak,
isotropic strain. These results indicate that nematic order arises in a genuine
phase transition rather than by enhancement of local anisotropy by a strong
nematic susceptibility. We interpret our results in the context of a proposed
surface nematic phase.
| 0 | 1 | 0 | 0 | 0 | 0 |
Deception Detection in Videos | We present a system for covert automated deception detection in real-life
courtroom trial videos. We study the importance of different modalities like
vision, audio and text for this task. On the vision side, our system uses
classifiers trained on low level video features which predict human
micro-expressions. We show that predictions of high-level micro-expressions can
be used as features for deception prediction. Surprisingly, IDT (Improved Dense
Trajectory) features which have been widely used for action recognition, are
also very good at predicting deception in videos. We fuse the score of
classifiers trained on IDT features and high-level micro-expressions to improve
performance. MFCC (Mel-frequency Cepstral Coefficients) features from the audio
domain also provide a significant boost in performance, while information from
transcripts is not very beneficial for our system. Using various classifiers,
our automated system obtains an AUC of 0.877 (10-fold cross-validation) when
evaluated on subjects which were not part of the training set. Even though
state-of-the-art methods use human annotations of micro-expressions for
deception detection, our fully automated approach outperforms them by 5%. When
combined with human annotations of micro-expressions, our AUC improves to
0.922. We also present results of a user-study to analyze how well do average
humans perform on this task, what modalities they use for deception detection
and how they perform if only one modality is accessible. Our project page can
be found at \url{this https URL}.
| 1 | 0 | 0 | 0 | 0 | 0 |
Extended degenerate Stirling numbers of the second kind and extended degenerate Bell polynomials | In a recent work, the degenerate Stirling polynomials of the second kind were
studied by T. Kim. In this paper, we investigate the extended degenerate
Stirling numbers of the second kind and the extended degenerate Bell
polynomials associated with them. As results, we give some expressions,
identities and properties about the extended degener- ate Stirling numbers of
the second kind and the extended degenerate Bell polynomials.
| 0 | 0 | 1 | 0 | 0 | 0 |
On time and consistency in multi-level agent-based simulations | The integration of multiple viewpoints became an increasingly popular
approach to deal with agent-based simulations. Despite their disparities,
recent approaches successfully manage to run such multi-level simulations. Yet,
are they doing it appropriately?
This paper tries to answer that question, with an analysis based on a generic
model of the temporal dynamics of multi-level simulations. This generic model
is then used to build an orthogonal approach to multi-level simulation called
SIMILAR. In this approach, most time-related issues are explicitly modeled,
owing to an implementation-oriented approach based on the influence/reaction
principle.
| 1 | 0 | 0 | 0 | 0 | 0 |
On The Inductive Bias of Words in Acoustics-to-Word Models | Acoustics-to-word models are end-to-end speech recognizers that use words as
targets without relying on pronunciation dictionaries or graphemes. These
models are notoriously difficult to train due to the lack of linguistic
knowledge. It is also unclear how the amount of training data impacts the
optimization and generalization of such models. In this work, we study the
optimization and generalization of acoustics-to-word models under different
amounts of training data. In addition, we study three types of inductive bias,
leveraging a pronunciation dictionary, word boundary annotations, and
constraints on word durations. We find that constraining word durations leads
to the most improvement. Finally, we analyze the word embedding space learned
by the model, and find that the space has a structure dominated by the
pronunciation of words. This suggests that the contexts of words, instead of
their phonetic structure, should be the future focus of inductive bias in
acoustics-to-word models.
| 1 | 0 | 0 | 0 | 0 | 0 |
A multi-layered energy consumption model for smart wireless acoustic sensor networks | Smart sensing is expected to become a pervasive technology in smart cities
and environments of the near future. These services are improving their
capabilities due to integrated devices shrinking in size while maintaining
their computational power, which can run diverse Machine Learning algorithms
and achieve high performance in various data-processing tasks. One attractive
sensor modality to be used for smart sensing are acoustic sensors, which can
convey highly informative data while keeping a moderate energy consumption.
Unfortunately, the energy budget of current wireless sensor networks is usually
not enough to support the requirements of standard microphones. Therefore,
energy efficiency needs to be increased at all layers --- sensing, signal
processing and communication --- in order to bring wireless smart acoustic
sensors into the market. To help to attain this goal, this paper introduces
WASN-EM: an energy consumption model for wireless acoustic sensors networks
(WASN), whose aim is to aid in the development of novel techniques to increase
the energy-efficient of smart wireless acoustic sensors. This model provides a
first step of exploration prior to custom design of a smart wireless acoustic
sensor, and also can be used to compare the energy consumption of different
protocols.
| 1 | 0 | 0 | 0 | 0 | 0 |
BRAVO - Biased Locking for Reader-Writer Locks | Designers of modern reader-writer locks confront a difficult trade-off
related to reader scalability. Locks that have a compact memory representation
for active readers will typically suffer under high intensity read-dominated
workloads when the "reader indicator"' state is updated frequently by a diverse
set of threads, causing cache invalidation and coherence traffic. Other
designs, such as cohort reader-writer locks, use distributed reader indicators,
one per NUMA node. This improves reader-reader scalability, but also increases
the size of each lock instance.
We propose a simple transformation BRAVO, that augments any existing
reader-writer lock, adding just two integer fields to the lock instance.
Readers make their presence known to writers by hashing their thread's identity
with the lock address, forming an index into a visible readers table. Readers
attempt to install the lock address into that element in the table, making
their existence known to potential writers. All locks and threads in an address
space can share the visible readers table. Updates by readers tend to be
diffused over the table, resulting in a NUMA-friendly design. Crucially,
readers of the same lock tend to write to different locations in the array,
reducing coherence traffic.
Specifically, BRAVO allows a simple compact lock to be augmented so as to
provide scalable concurrent reading but with only a modest increase in
footprint.
| 1 | 0 | 0 | 0 | 0 | 0 |
Internal DLA on Sierpinski gasket graphs | Internal diffusion-limited aggregation (IDLA) is a stochastic growth model on
a graph $G$ which describes the formation of a random set of vertices growing
from the origin (some fixed vertex) of $G$. Particles start at the origin and
perform simple random walks; each particle moves until it lands on a site which
was not previously visited by other particles. This random set of occupied
sites in $G$ is called the IDLA cluster.
In this paper we consider IDLA on Sierpinski gasket graphs, and show that the
IDLA cluster fills balls (in the graph metric) with probability 1.
| 0 | 1 | 1 | 0 | 0 | 0 |
A PCA-based approach for subtracting thermal background emission in high-contrast imaging data | Ground-based observations at thermal infrared wavelengths suffer from large
background radiation due to the sky, telescope and warm surfaces in the
instrument. This significantly limits the sensitivity of ground-based
observations at wavelengths longer than 3 microns. We analyzed this background
emission in infrared high contrast imaging data, show how it can be modelled
and subtracted and demonstrate that it can improve the detection of faint
sources, such as exoplanets. We applied principal component analysis to model
and subtract the thermal background emission in three archival high contrast
angular differential imaging datasets in the M and L filter. We describe how
the algorithm works and explain how it can be applied. The results of the
background subtraction are compared to the results from a conventional mean
background subtraction scheme. Finally, both methods for background subtraction
are also compared by performing complete data reductions. We analyze the
results from the M dataset of HD100546 qualitatively. For the M band dataset of
beta Pic and the L band dataset of HD169142, which was obtained with an annular
groove phase mask vortex vector coronagraph, we also calculate and analyze the
achieved signal to noise (S/N). We show that applying PCA is an effective way
to remove spatially and temporarily varying thermal background emission down to
close to the background limit. The procedure also proves to be very successful
at reconstructing the background that is hidden behind the PSF. In the complete
data reductions, we find at least qualitative improvements for HD100546 and
HD169142, however, we fail to find a significant increase in S/N of beta Pic b.
We discuss these findings and argue that in particular datasets with strongly
varying observing conditions or infrequently sampled sky background will
benefit from the new approach.
| 0 | 1 | 0 | 0 | 0 | 0 |
A weak law of large numbers for estimating the correlation in bivariate Brownian semistationary processes | This article presents various weak laws of large numbers for the so-called
realised covariation of a bivariate stationary stochastic process which is not
a semimartingale. More precisely, we consider two cases: Bivariate moving
average processes with stochastic correlation and bivariate Brownian
semistationary processes with stochastic correlation. In both cases, we can
show that the (possibly scaled) realised covariation converges to the
integrated (possibly volatility modulated) stochastic correlation process.
| 0 | 0 | 1 | 1 | 0 | 0 |
Synthesis of Spatial Charging/Discharging Patterns of In-Vehicle Batteries for Provision of Ancillary Service and Mitigation of Voltage Impact | We develop an algorithm for synthesizing a spatial pattern of
charging/discharging operations of in-vehicle batteries for provision of
Ancillary Service (AS) in power distribution grids. The algorithm is based on
the ODE (Ordinary Differential Equation) model of distribution voltage that has
been recently introduced. In this paper, firstly, we derive analytical
solutions of the ODE model for a single straight-line feeder through a partial
linearization, thereby providing a physical insight to the impact of spatial EV
charging/discharging to the distribution voltage. Second, based on the
analytical solutions, we propose an algorithm for determining the values of
charging/discharging power (active and reactive) by in-vehicle batteries in the
single feeder grid, so that the power demanded as AS (e.g. a regulation signal
to distribution system operator for primary frequency control reserve) is
provided by EVs, and the deviation of distribution voltage from a nominal value
is reduced in the grid. Effectiveness of the algorithm is established with
numerical simulations on the single feeder grid and on a realistic feeder grid
with multiple bifurcations.
| 1 | 0 | 0 | 0 | 0 | 0 |
Matter fields interacting with photons | We have extended the biquaternionic Dirac's equation to include interactions
with photons. The electric field is found to be perpendicular to the matter
magnetic field, and the magnetic field is perpendicular to the matter inertial
field. Inertial and magnetic masses are found to be conserved separately. The
magnetic mass density is a consequence of the coupling between the vector
potential and the matter inertial field. The presence of the vector and scalar
potentials, and the matter inertial and magnetic fields are found to modify the
standard form of the derived Maxwell's equations. The resulting interacting
electrodynamics equations are found to generalize those of axion-like fields of
Frank Wilczek or Chern-Simons equations. The axion field satisfies massive
Klein-Gordon equation if Lorenz gauge condition is violated. Therefore, axion
could be our massive photon. The electromagnetic field vector,
$\vec{F}=\vec{E}+ic\vec{B}$, is found to satisfy massive Dirac's equation in
addition to the fact that $\vec{\nabla}\cdot\vec{F}=0$, where $\vec{E}$ and
$\vec{B}$ are the electric and magnetic fields, respectively.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Unified Approach to Adaptive Regularization in Online and Stochastic Optimization | We describe a framework for deriving and analyzing online optimization
algorithms that incorporate adaptive, data-dependent regularization, also
termed preconditioning. Such algorithms have been proven useful in stochastic
optimization by reshaping the gradients according to the geometry of the data.
Our framework captures and unifies much of the existing literature on adaptive
online methods, including the AdaGrad and Online Newton Step algorithms as well
as their diagonal versions. As a result, we obtain new convergence proofs for
these algorithms that are substantially simpler than previous analyses. Our
framework also exposes the rationale for the different preconditioned updates
used in common stochastic optimization methods.
| 1 | 0 | 1 | 1 | 0 | 0 |
Discretization error cancellation in electronic structure calculation: a quantitative study | It is often claimed that error cancellation plays an essential role in
quantum chemistry and first-principle simulation for condensed matter physics
and materials science. Indeed, while the energy of a large, or even
medium-size, molecular system cannot be estimated numerically within chemical
accuracy (typically 1 kcal/mol or 1 mHa), it is considered that the energy
difference between two configurations of the same system can be computed in
practice within the desired accuracy.
The purpose of this paper is to provide a quantitative study of
discretization error cancellation. The latter is the error component due to the
fact that the model used in the calculation (e.g. Kohn-Sham LDA) must be
discretized in a finite basis set to be solved by a computer. We first report
comprehensive numerical simulations performed with Abinit on two simple
chemical systems, the hydrogen molecule on the one hand, and a system
consisting of two oxygen atoms and four hydrogen atoms on the other hand. We
observe that errors on energy differences are indeed significantly smaller than
errors on energies, but that these two quantities asymptotically converge at
the same rate when the energy cut-off goes to infinity. We then analyze a
simple one-dimensional periodic Schrödinger equation with Dirac potentials,
for which analytic solutions are available. This allows us to explain the
discretization error cancellation phenomenon on this test case with
quantitative mathematical arguments.
| 0 | 1 | 1 | 0 | 0 | 0 |
Analysis of spectral clustering algorithms for community detection: the general bipartite setting | We consider spectral clustering algorithms for community detection under a
general bipartite stochastic block model (SBM). A modern spectral clustering
algorithm consists of three steps: (1) regularization of an appropriate
adjacency or Laplacian matrix (2) a form of spectral truncation and (3) a
k-means type algorithm in the reduced spectral domain. We focus on the
adjacency-based spectral clustering and for the first step, propose a new
data-driven regularization that can restore the concentration of the adjacency
matrix even for the sparse networks. This result is based on recent work on
regularization of random binary matrices, but avoids using unknown population
level parameters, and instead estimates the necessary quantities from the data.
We also propose and study a novel variation of the spectral truncation step and
show how this variation changes the nature of the misclassification rate in a
general SBM. We then show how the consistency results can be extended to models
beyond SBMs, such as inhomogeneous random graph models with approximate
clusters, including a graphon clustering problem, as well as general
sub-Gaussian biclustering. A theme of the paper is providing a better
understanding of the analysis of spectral methods for community detection and
establishing consistency results, under fairly general clustering models and
for a wide regime of degree growths, including sparse cases where the average
expected degree grows arbitrarily slowly.
| 1 | 0 | 0 | 1 | 0 | 0 |
A computational approach to calculate the heat of transport of aqueous solutions | Thermal gradients induce concentration gradients in alkali halide solutions,
and the salt migrates towards hot or cold regions depending on the average
temperature of the solution. This effect has been interpreted using the heat of
transport, which provides a route to rationalize thermophoretic phenomena.
Early theories provide estimates of the heat of transport at infinite dilution.
These values are used to interpret thermodiffusion (Soret) and thermoelectric
(Seebeck) effects. However, accessing heats of transport of individual ions at
finite concentration remains an outstanding question both theoretically and
experimentally. Here we discuss a computational approach to calculate heats of
transport of aqueous solutions at finite concentrations, and apply our method
to study lithium chloride solutions at concentrations $>0.5$~M. The heats of
transport are significantly different for Li$^+$ and Cl$^-$ ions, unlike what
is expected at infinite dilution. We find theoretical evidence for the
existence of minima in the Soret coefficient of LiCl, where the magnitude of
the heat of transport is maximized. The Seebeck coefficient obtained from the
ionic heats of transport varies significantly with temperature and
concentration. We identify thermodynamic conditions leading to a maximization
of the thermoelectric response of aqueous solutions.
| 0 | 1 | 0 | 0 | 0 | 0 |
Dark Matter Annihilation in the Circumgalactic Medium at High Redshifts | Annihilating dark matter (DM) models offer promising avenues for future DM
detection, in particular via modification of astrophysical signals. However
when modelling such potential signals at high redshift the emergence of both
dark matter and baryonic structure, as well as the complexities of the energy
transfer process, need to be taken into account. In the following paper we
present a detailed energy deposition code and use this to examine the energy
transfer efficiency of annihilating dark matter at high redshift, including the
effects on baryonic structure. We employ the PYTHIA code to model
neutralino-like DM candidates and their subsequent annihilation products for a
range of masses and annihilation channels. We also compare different density
profiles and mass-concentration relations for 10^5-10^7 M_sun haloes at
redshifts 20 and 40. For these DM halo and particle models, we show radially
dependent ionisation and heating curves and compare the deposited energy to the
haloes' gravitational binding energy. We use the "filtered" annihilation
spectra escaping the halo to calculate the heating of the circumgalactic medium
and show that the mass of the minimal star forming object is increased by a
factor of 2-3 at redshift 20 and 4-5 at redshift 40 for some DM models.
| 0 | 1 | 0 | 0 | 0 | 0 |
The p-convolution forest: a method for solving graphical models with additive probabilistic equations | Convolution trees, loopy belief propagation, and fast numerical p-convolution
are combined for the first time to efficiently solve networks with several
additive constraints between random variables. An implementation of this
"convolution forest" approach is constructed from scratch, including an
improved trimmed convolution tree algorithm and engineering details that permit
fast inference in practice, and improve the ability of scientists to prototype
models with additive relationships between discrete variables. The utility of
this approach is demonstrated using several examples: these include
illustrations on special cases of some classic NP-complete problems (subset sum
and knapsack), identification of GC-rich genomic regions with a large hidden
Markov model, inference of molecular composition from summary statistics of the
intact molecule, and estimation of elemental abundance in the presence of
overlapping isotope peaks.
| 0 | 0 | 0 | 1 | 0 | 0 |
Statistical Timing Analysis for Latch-Controlled Circuits with Reduced Iterations and Graph Transformations | Level-sensitive latches are widely used in high- performance designs. For
such circuits efficient statistical timing analysis algorithms are needed to
take increasing process vari- ations into account. But existing methods solving
this problem are still computationally expensive and can only provide the yield
at a given clock period. In this paper we propose a method combining reduced
iterations and graph transformations. The reduced iterations extract setup time
constraints and identify a subgraph for the following graph transformations
handling the constraints from nonpositive loops. The combined algorithms are
very efficient, more than 10 times faster than other existing methods, and
result in a parametric minimum clock period, which together with the hold time
constraints can be used to compute the yield at any given clock period very
easily.
| 1 | 0 | 0 | 0 | 0 | 0 |
A moment-angle manifold whose cohomology is not torsion free | In this paper we give a method to construct moment-angle manifolds whose
cohomologies are not torsion free. We also give method to describe the
corresponding simplicial sphere by its non-faces.
| 0 | 0 | 1 | 0 | 0 | 0 |
The committee machine: Computational to statistical gaps in learning a two-layers neural network | Heuristic tools from statistical physics have been used in the past to locate
the phase transitions and compute the optimal learning and generalization
errors in the teacher-student scenario in multi-layer neural networks. In this
contribution, we provide a rigorous justification of these approaches for a
two-layers neural network model called the committee machine. We also introduce
a version of the approximate message passing (AMP) algorithm for the committee
machine that allows to perform optimal learning in polynomial time for a large
set of parameters. We find that there are regimes in which a low generalization
error is information-theoretically achievable while the AMP algorithm fails to
deliver it, strongly suggesting that no efficient algorithm exists for those
cases, and unveiling a large computational gap.
| 0 | 0 | 0 | 1 | 0 | 0 |
On the anomalous {changes of seismicity and} geomagnetic field prior to the 2011 $M_w$ 9.0 Tohoku earthquake | Xu et al. [J. Asian Earth Sci. {\bf 77}, 59-65 (2013)] It has just been
reported that approximately 2 months prior to the $M_w$9.0 Tohoku earthquake
that occurred in Japan on 11 March 2011 anomalous variations of the geomagnetic
field have been observed in the vertical component at a measuring station about
135 km from the epicenter for about 10 days (4 to 14 January 2011). Here, we
show that this observation is in striking agreement with independent recent
results obtained from natural time analysis of seismicity in Japan. In
particular, this analysis has revealed that an unprecedented minimum of the
order parameter fluctuations of seismicity was observed around 5 January 2011,
thus pointing to the initiation at that date of a strong precursory Seismic
Electric Signals activity accompanied by the anomalous geomagnetic field
variations. Starting from this date, natural time analysis of the subsequent
seismicity indicates that a strong mainshock was expected in a few days to one
week after 08:40 LT on 10 March 2011.
| 0 | 1 | 0 | 0 | 0 | 0 |
Exponentially small splitting of separatrices near a period-doubling bifurcation in area-preserving maps | We consider the conservative Hénon family at the period-doubling
bifurcation of its fixed point and demonstrate that the separatrices of the
fixed saddle point nearing the bifurcation split exponentially: given that
$\lambda_+$ is the smaller of the eigenvalues of the saddle point, the angle
between the separatrices along the homoclinic orbit satisfies $$\sin \alpha =
O(e^{-{\pi^2 \over \log |\lambda_+|}})+ O\left( e^{-2 (1-\kappa) {\pi^2 \over
\log |\lambda_+|}} \right),$$ for any positive $\kappa<1$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Learning Dynamics and the Co-Evolution of Competing Sexual Species | We analyze a stylized model of co-evolution between any two purely competing
species (e.g., host and parasite), both sexually reproducing. Similarly to a
recent model of Livnat \etal~\cite{evolfocs14} the fitness of an individual
depends on whether the truth assignments on $n$ variables that reproduce
through recombination satisfy a particular Boolean function. Whereas in the
original model a satisfying assignment always confers a small evolutionary
advantage, in our model the two species are in an evolutionary race with the
parasite enjoying the advantage if the value of its Boolean function matches
its host, and the host wishing to mismatch its parasite. Surprisingly, this
model makes a simple and robust behavioral prediction. The typical system
behavior is \textit{periodic}. These cycles stay bounded away from the boundary
and thus, \textit{learning-dynamics competition between sexual species can
provide an explanation for genetic diversity.} This explanation is due solely
to the natural selection process. No mutations, environmental changes, etc.,
need be invoked.
The game played at the gene level may have many Nash equilibria with widely
diverse fitness levels. Nevertheless, sexual evolution leads to gene
coordination that implements an optimal strategy, i.e., an optimal population
mixture, at the species level. Namely, the play of the many "selfish genes"
implements a time-averaged correlated equilibrium where the average fitness of
each species is exactly equal to its value in the two species zero-sum
competition.
Our analysis combines tools from game theory, dynamical systems and Boolean
functions to establish a novel class of conservative dynamical systems.
| 1 | 0 | 0 | 0 | 0 | 0 |
Deep Neural Networks for Multiple Speaker Detection and Localization | We propose to use neural networks for simultaneous detection and localization
of multiple sound sources in human-robot interaction. In contrast to
conventional signal processing techniques, neural network-based sound source
localization methods require fewer strong assumptions about the environment.
Previous neural network-based methods have been focusing on localizing a single
sound source, which do not extend to multiple sources in terms of detection and
localization. In this paper, we thus propose a likelihood-based encoding of the
network output, which naturally allows the detection of an arbitrary number of
sources. In addition, we investigate the use of sub-band cross-correlation
information as features for better localization in sound mixtures, as well as
three different network architectures based on different motivations.
Experiments on real data recorded from a robot show that our proposed methods
significantly outperform the popular spatial spectrum-based approaches.
| 1 | 0 | 0 | 0 | 0 | 0 |
Active Tolerant Testing | In this work, we give the first algorithms for tolerant testing of nontrivial
classes in the active model: estimating the distance of a target function to a
hypothesis class C with respect to some arbitrary distribution D, using only a
small number of label queries to a polynomial-sized pool of unlabeled examples
drawn from D. Specifically, we show that for the class D of unions of d
intervals on the line, we can estimate the error rate of the best hypothesis in
the class to an additive error epsilon from only $O(\frac{1}{\epsilon^6}\log
\frac{1}{\epsilon})$ label queries to an unlabeled pool of size
$O(\frac{d}{\epsilon^2}\log \frac{1}{\epsilon})$. The key point here is the
number of labels needed is independent of the VC-dimension of the class. This
extends the work of Balcan et al. [2012] who solved the non-tolerant testing
problem for this class (distinguishing the zero-error case from the case that
the best hypothesis in the class has error greater than epsilon).
We also consider the related problem of estimating the performance of a given
learning algorithm A in this setting. That is, given a large pool of unlabeled
examples drawn from distribution D, can we, from only a few label queries,
estimate how well A would perform if the entire dataset were labeled? We focus
on k-Nearest Neighbor style algorithms, and also show how our results can be
applied to the problem of hyperparameter tuning (selecting the best value of k
for the given learning problem).
| 1 | 0 | 0 | 1 | 0 | 0 |
Temperature inside tumor as time function in RF hyperthermia | A simplified 2-D model which is an example of regional RF hyperthermia is
presented. Human body is inside the wire with exciting current and the
electromagnetic energy is concentrated within the tumor. The analyzed model is
therefore a coupling of the electromagnetic field and the temperature field.
Exciting current density in human body has been calculated using the finite
element method, and then bioheat equation in timedepended nonstationary case
has been resolved. At the and obtained results are presented.
| 0 | 1 | 0 | 0 | 0 | 0 |
Dirac Line-nodes and Effect of Spin-orbit Coupling in Non-symmorphic Critical Semimetal MSiS (M=Hf, Zr) | Topological Dirac semimetals (TDSs) represent a new state of quantum matter
recently discovered that offers a platform for realizing many exotic physical
phenomena. A TDS is characterized by the linear touching of bulk (conduction
and valance) bands at discrete points in the momentum space (i.e. 3D Dirac
points), such as in Na3Bi and Cd3As2. More recently, new types of Dirac
semimetals with robust Dirac line-nodes (with non-trivial topology or near the
critical point between topological phase transitions) have been proposed that
extends the bulk linear touching from discrete points to 1D lines. In this
work, using angle-resolved photoemission spectroscopy (ARPES), we explored the
electronic structure of the non-symmorphic crystals MSiS (M=Hf, Zr).
Remarkably, by mapping out the band structure in the full 3D Brillouin Zone
(BZ), we observed two sets of Dirac line-nodes in parallel with the kz-axis and
their dispersions. Interestingly, along directions other than the line-nodes in
the 3D BZ, the bulk degeneracy is lifted by spin-orbit coupling (SOC) in both
compounds with larger magnitude in HfSiS. Our work not only experimentally
confirms a new Dirac line-node semimetal family protected by non-symmorphic
symmetry, but also helps understanding and further exploring the exotic
properties as well as practical applications of the MSiS family of compounds.
| 0 | 1 | 0 | 0 | 0 | 0 |
An Arcsine Law for Markov Random Walks | The classic arcsine law for the number
$N_{n}^{>}:=n^{-1}\sum_{k=1}^{n}\mathbf{1}_{\{S_{k}>0\}}$ of positive terms, as
$n\to\infty$, in an ordinary random walk $(S_{n})_{n\ge 0}$ is extended to the
case when this random walk is governed by a positive recurrent Markov chain
$(M_{n})_{n\ge 0}$ on a countable state space $\mathcal{S}$, that is, for a
Markov random walk $(M_{n},S_{n})_{n\ge 0}$ with positive recurrent discrete
driving chain. More precisely, it is shown that $n^{-1}N_{n}^{>}$ converges in
distribution to a generalized arcsine law with parameter $\rho\in [0,1]$ (the
classic arcsine law if $\rho=1/2$) iff the Spitzer condition $$
\lim_{n\to\infty}\frac{1}{n}\sum_{k=1}^{n}\mathbb{P}_{i}(S_{n}>0)\ =\ \rho $$
holds true for some and then all $i\in\mathcal{S}$, where
$\mathbb{P}_{i}:=\mathbb{P}(\cdot|M_{0}=i)$ for $i\in\mathcal{S}$. It is also
proved, under an extra assumption on the driving chain if $0<\rho<1$, that this
condition is equivalent to the stronger variant $$
\lim_{n\to\infty}\mathbb{P}_{i}(S_{n}>0)\ =\ \rho. $$ For an ordinary random
walk, this was shown by Doney for $0<\rho<1$ and by Bertoin and Doney for
$\rho\in\{0,1\}$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Linear Estimation of Treatment Effects in Demand Response: An Experimental Design Approach | Demand response aims to stimulate electricity consumers to modify their loads
at critical time periods. In this paper, we consider signals in demand response
programs as a binary treatment to the customers and estimate the average
treatment effect, which is the average change in consumption under the demand
response signals. More specifically, we propose to estimate this effect by
linear regression models and derive several estimators based on the different
models. From both synthetic and real data, we show that including more
information about the customers does not always improve estimation accuracy:
the interaction between the side information and the demand response signal
must be carefully modeled. In addition, we compare the traditional linear
regression model with the modified covariate method which models the
interaction between treatment effect and covariates. We analyze the variances
of these estimators and discuss different cases where each respective estimator
works the best. The purpose of these comparisons is not to claim the
superiority of the different methods, rather we aim to provide practical
guidance on the most suitable estimator to use under different settings. Our
results are validated using data collected by Pecan Street and EnergyPlus.
| 1 | 0 | 1 | 0 | 0 | 0 |
Detection principle of gravitational wave detectors | With the first two detections in late 2015, astrophysics has officially
entered into the new era of gravitational wave observations. Since then, much
has been going on in the field with a lot of work focussing on the observations
and implications for astrophysics and tests of general relativity in the strong
regime. However much less is understood about how gravitational detectors
really work at their fundamental level. For decades, the response to incoming
signals has been customarily calculated using the very same physical principle,
which has proved so successful in the first detections. In this paper we review
the physical principle that is behind such a detection at the very fundamental
level, and we try to highlight the peculiar subtleties that make it so hard in
practice. We will then mention how detectors are built starting from this
fundamental measurement element.
| 0 | 1 | 0 | 0 | 0 | 0 |
Private Learning on Networks: Part II | This paper considers a distributed multi-agent optimization problem, with the
global objective consisting of the sum of local objective functions of the
agents. The agents solve the optimization problem using local computation and
communication between adjacent agents in the network. We present two randomized
iterative algorithms for distributed optimization. To improve privacy, our
algorithms add "structured" randomization to the information exchanged between
the agents. We prove deterministic correctness (in every execution) of the
proposed algorithms despite the information being perturbed by noise with
non-zero mean. We prove that a special case of a proposed algorithm (called
function sharing) preserves privacy of individual polynomial objective
functions under a suitable connectivity condition on the network topology.
| 1 | 0 | 1 | 0 | 0 | 0 |
auDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks | auDeep is a Python toolkit for deep unsupervised representation learning from
acoustic data. It is based on a recurrent sequence to sequence autoencoder
approach which can learn representations of time series data by taking into
account their temporal dynamics. We provide an extensive command line interface
in addition to a Python API for users and developers, both of which are
comprehensively documented and publicly available at
this https URL. Experimental results indicate that auDeep
features are competitive with state-of-the art audio classification.
| 1 | 0 | 0 | 0 | 0 | 0 |
The Use of Unlabeled Data versus Labeled Data for Stopping Active Learning for Text Classification | Annotation of training data is the major bottleneck in the creation of text
classification systems. Active learning is a commonly used technique to reduce
the amount of training data one needs to label. A crucial aspect of active
learning is determining when to stop labeling data. Three potential sources for
informing when to stop active learning are an additional labeled set of data,
an unlabeled set of data, and the training data that is labeled during the
process of active learning. To date, no one has compared and contrasted the
advantages and disadvantages of stopping methods based on these three
information sources. We find that stopping methods that use unlabeled data are
more effective than methods that use labeled data.
| 1 | 0 | 0 | 1 | 0 | 0 |
RAFP-Pred: Robust Prediction of Antifreeze Proteins using Localized Analysis of n-Peptide Compositions | In extreme cold weather, living organisms produce Antifreeze Proteins (AFPs)
to counter the otherwise lethal intracellular formation of ice. Structures and
sequences of various AFPs exhibit a high degree of heterogeneity, consequently
the prediction of the AFPs is considered to be a challenging task. In this
research, we propose to handle this arduous manifold learning task using the
notion of localized processing. In particular an AFP sequence is segmented into
two sub-segments each of which is analyzed for amino acid and di-peptide
compositions. We propose to use only the most significant features using the
concept of information gain (IG) followed by a random forest classification
approach. The proposed RAFP-Pred achieved an excellent performance on a number
of standard datasets. We report a high Youden's index
(sensitivity+specificity-1) value of 0.75 on the standard independent test data
set outperforming the AFP-PseAAC, AFP\_PSSM, AFP-Pred and iAFP by a margin of
0.05, 0.06, 0.14 and 0.68 respectively. The verification rate on the UniProKB
dataset is found to be 83.19\% which is substantially superior to the 57.18\%
reported for the iAFP method.
| 0 | 0 | 0 | 0 | 1 | 0 |
Second descent and rational points on Kummer varieties | A powerful method pioneered by Swinnerton-Dyer allows one to study rational
points on pencils of curves of genus 1 by combining the fibration method with a
sophisticated form of descent. A variant of this method, first used by
Skorobogatov and Swinnerton-Dyer in 2005, can be applied to study rational
points on Kummer varieties. In this paper we extend the method to include an
additional step of second descent. Assuming finiteness of the relevant
Tate-Shafarevich groups, we use the extended method to show that the
Brauer-Manin obstruction is the only obstruction to the Hasse principle on
Kummer varieties associated to abelian varieties with all rational 2-torsion,
under mild additional hypotheses.
| 0 | 0 | 1 | 0 | 0 | 0 |
When Can Neural Networks Learn Connected Decision Regions? | Previous work has questioned the conditions under which the decision regions
of a neural network are connected and further showed the implications of the
corresponding theory to the problem of adversarial manipulation of classifiers.
It has been proven that for a class of activation functions including leaky
ReLU, neural networks having a pyramidal structure, that is no layer has more
hidden units than the input dimension, produce necessarily connected decision
regions. In this paper, we advance this important result by further developing
the sufficient and necessary conditions under which the decision regions of a
neural network are connected. We then apply our framework to overcome the
limits of existing work and further study the capacity to learn connected
regions of neural networks for a much wider class of activation functions
including those widely used, namely ReLU, sigmoid, tanh, softlus, and
exponential linear function.
| 1 | 0 | 0 | 1 | 0 | 0 |
Note on the backwards uniqueness of mean curvature flow | In this note, we will show a backwards uniqueness theorem of the mean
curvature flow with bounded second fundamental form in arbitrary codimension.
| 0 | 0 | 1 | 0 | 0 | 0 |
A Volcanic Hydrogen Habitable Zone | The classical habitable zone is the circular region around a star in which
liquid water could exist on the surface of a rocky planet. The outer edge of
the traditional N2-CO2-H2O habitable zone (HZ) extends out to nearly 1.7 AU in
our Solar System, beyond which condensation and scattering by CO2 outstrips its
greenhouse capacity. Here, we show that volcanic outgassing of atmospheric H2
on a planet near the outer edge can extend the habitable zone out to ~2.4 AU in
our solar system. This wider volcanic hydrogen habitable zone (N2-CO2-H2O-H2)
can be sustained as long as volcanic H2 output offsets its escape from the top
of the atmosphere. We use a single-column radiative-convective climate model to
compute the HZ limits of this volcanic hydrogen habitable zone for hydrogen
concentrations between 1% and 50%, assuming diffusion-limited atmospheric
escape. At a hydrogen concentration of 50%, the effective stellar flux required
to support the outer edge decreases by ~35% to 60% for M to A stars. The
corresponding orbital distances increase by ~30% to 60%. The inner edge of this
HZ only moves out by ~0.1 to 4% relative to the classical HZ because H2 warming
is reduced in dense H2O atmospheres. The atmospheric scale heights of such
volcanic H2 atmospheres near the outer edge of the HZ also increase,
facilitating remote detection of atmospheric signatures.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Penrose type inequaltiy for graphs over Reissner-Nordström-anti-deSitter manifold | In this paper, we use the inverse mean curvature flow to establish an optimal
Minkowski type inquality, weighted Alexandrov-Fenchel inequality for the mean
convex star shaped hypersurfaces in Reissner-Nordström-anti-deSitter manifold
and Penrose type inequality for asymptotically locally hyperbolic manifolds in
which can be realized as graphs over Reissner-Nordström-anti-deSitter
manifold.
| 0 | 0 | 1 | 0 | 0 | 0 |
A Novel Approach for Fast and Accurate Mean Error Distance Computation in Approximate Adders | In error-tolerant applications, approximate adders have been exploited
extensively to achieve energy efficient system designs. Mean error distance is
one of the important error metrics used as a performance measure of approximate
adders. In this work, a fast and efficient methodology is proposed to determine
the exact mean error distance in approximate lower significant bit adders. A
detailed description of the proposed algorithm along with an example has been
demonstrated in this paper. Experimental analysis shows that the proposed
method performs better than existing Monte Carlo simulation approach both in
terms of accuracy and execution time.
| 1 | 0 | 0 | 0 | 0 | 0 |
Fluorescent Troffer-powered Internet of Things: An Experimental Study of Electric-field Energy Harvesting | A totally new energy harvesting architecture that exploits ambient
electric-field (E-field) emitting from fluorescent light fixtures is presented.
A copper plate, 50 x 50 cm in size, is placed in between the ambient field to
extract energy by capacitive coupling. A low voltage prototype is designed,
structured and tested on a conventional ceiling-type 4-light fluorescent
troffer operating in 50 Hz 220 V AC power grid. It is examined that the
harvester is able to collect roughly 1.25 J of energy in 25 min when a 0.1 F of
super-capacitor is employed. The equivalent circuit and the physical model of
the proposed harvesting paradigm are provided, and the attainable power is
evaluated in both theoretical and experimental manner. The scavenged energy is
planned to be utilized for building battery-less Internet of Things (IoT)
networks that are obliged to sense environmental parameters, analyze the
gathered data, and remotely inform a higher authority within predefined
periods.
| 1 | 1 | 0 | 0 | 0 | 0 |
Parallel Simultaneous Perturbation Optimization | Stochastic computer simulations enable users to gain new insights into
complex physical systems. Optimization is a common problem in this context:
users seek to find model inputs that maximize the expected value of an
objective function. The objective function, however, is time-intensive to
evaluate, and cannot be directly measured. Instead, the stochastic nature of
the model means that individual realizations are corrupted by noise. More
formally, we consider the problem of optimizing the expected value of an
expensive black-box function with continuously-differentiable mean, from which
observations are corrupted by Gaussian noise. We present Parallel Simultaneous
Perturbation Optimization (PSPO), which extends a well-known stochastic
optimization algorithm, simultaneous perturbation stochastic approximation, in
several important ways. Our modifications allow the algorithm to fully take
advantage of parallel computing resources, like high-performance cloud
computing. The resulting PSPO algorithm takes fewer time-consuming iterations
to converge, automatically chooses the step size, and can vary the error
tolerance by step. Theoretical results are supported by a numerical example. To
demonstrate the performance of the algorithm, we implemented the algorithm to
maximize the pseudo-likelihood of a stochastic epidemiological model to data of
a measles outbreak.
| 0 | 0 | 1 | 0 | 0 | 0 |
Achievable Rate Region of Non-Orthogonal Multiple Access Systems with Wireless Powered Decoder | Non-orthogonal multiple access (NOMA) is a candidate multiple access scheme
in 5G systems for the simultaneous access of tremendous number of wireless
nodes. On the other hand, RF-enabled wireless energy harvesting is a promising
technology for self-sustainable wireless nodes. In this paper, we consider a
NOMA system where the near user harvests energy from the strong radio signal to
power-on the information decoder. A generalized energy harvesting scheme is
proposed by combining the conventional time switching and power splitting
scheme. The achievable rate region of the proposed scheme is characterized
under both constant and dynamic decoding power consumption models. If the
decoding power is constant, the achievable rate region can be found by solving
two convex optimization subproblems, and the regions for two special cases:
time switching and power splitting, are characterized in closed-form. If the
decoding power is proportional to data rate, the achievable rate region can be
found by exhaustive search algorithm. Numerical results show that the
achievable rate region of the proposed generalized scheme is larger than those
of time switching scheme and power splitting scheme, and rate-dependent decoder
design helps to enlarge the achievable rate region.
| 1 | 0 | 0 | 0 | 0 | 0 |
Agent-based model for the origins of scaling in human language | Background/Introduction: The Zipf's law establishes that if the words of a
(large) text are ordered by decreasing frequency, the frequency versus the rank
decreases as a power law with exponent close to -1. Previous work has stressed
that this pattern arises from a conflict of interests of the participants of
communication: speakers and hearers. Methods: The challenge here is to define a
computational language game on a population of agents, playing games mainly
based on a parameter that measures the relative participant's interests.
Results: Numerical simulations suggest that at critical values of the parameter
a human-like vocabulary, exhibiting scaling properties, seems to appear.
Conclusions: The appearance of an intermediate distribution of frequencies at
some critical values of the parameter suggests that on a population of
artificial agents the emergence of scaling partly arises as a self-organized
process only from local interactions between agents.
| 1 | 1 | 0 | 0 | 0 | 0 |
Credible Review Detection with Limited Information using Consistency Analysis | Online reviews provide viewpoints on the strengths and shortcomings of
products/services, influencing potential customers' purchasing decisions.
However, the proliferation of non-credible reviews -- either fake (promoting/
demoting an item), incompetent (involving irrelevant aspects), or biased --
entails the problem of identifying credible reviews. Prior works involve
classifiers harnessing rich information about items/users -- which might not be
readily available in several domains -- that provide only limited
interpretability as to why a review is deemed non-credible. This paper presents
a novel approach to address the above issues. We utilize latent topic models
leveraging review texts, item ratings, and timestamps to derive consistency
features without relying on item/user histories, unavailable for "long-tail"
items/users. We develop models, for computing review credibility scores to
provide interpretable evidence for non-credible reviews, that are also
transferable to other domains -- addressing the scarcity of labeled data.
Experiments on real-world datasets demonstrate improvements over
state-of-the-art baselines.
| 1 | 0 | 0 | 1 | 0 | 0 |
The Asymptotically Self-Similar Regime for the Einstein Vacuum Equations | We develop a local theory for the construction of singular spacetimes in all
spacetime dimensions which become asymptotically self-similar as the
singularity is approached. The techniques developed also allow us to construct
and classify exact self-similar solutions which correspond to the formal
asymptotic expansions of Fefferman and Graham's ambient metric.
| 0 | 0 | 1 | 0 | 0 | 0 |
Kinematics and dynamics of an egg-shaped robot with a gyro driven inertia actuator | The manuscript discusses still preliminary considerations with regard to the
dynamics and kinematics of an egg shaped robot with an gyro driven inertia
actuator. The method of calculation follows the idea that we would like to
express the entire dynamic equations in terms of moments instead of forces.
Also we avoid to derive the equations from a Lagrange function with
constraints. The result of the calculations is meant to be applicable to two
robot prototypes that have been build at the AES\&R Laboratory at the National
Chung Cheng University in Taiwan.
| 1 | 0 | 0 | 0 | 0 | 0 |
Cloaking and anamorphism for light and mass diffusion | We first review classical results on cloaking and mirage effects for
electromagnetic waves. We then show that transformation optics allows the
masking of objects or produces mirages in diffusive regimes. In order to
achieve this, we consider the equation for diffusive photon density in
transformed coordinates, which is valid for diffusive light in scattering
media. More precisely, generalizing transformations for star domains introduced
in [Diatta and Guenneau, J. Opt. 13, 024012, 2011] for matter waves, we
numerically demonstrate that infinite conducting objects of different shapes
scatter diffusive light in exactly the same way. We also propose a design of
external light-diffusion cloak with spatially varying sign-shifting parameters
that hides a finite size scatterer outside the cloak. We next analyse
non-physical parameter in the transformed Fick's equation derived in [Guenneau
and Puvirajesinghe, R. Soc. Interface 10, 20130106, 2013], and propose to use a
non-linear transform that overcomes this problem. We finally investigate other
form invariant transformed diffusion-like equations in the time domain, and
touch upon conformal mappings and non-Euclidean cloaking applied to diffusion
processes.
| 0 | 1 | 1 | 0 | 0 | 0 |
Super Rogers-Szegö polynomials associated with $BC_N$ type of Polychronakos spin chains | As is well known, multivariate Rogers-Szegö polynomials are closely
connected with the partition functions of the $A_{N-1}$ type of Polychronakos
spin chains having long-range interactions. Applying the `freezing trick', here
we derive the partition functions for a class of $BC_N$ type of Polychronakos
spin chains containing supersymmetric analogues of polarized spin reversal
operators and subsequently use those partition functions to obtain novel
multivariate super Rogers-Szegö (SRS) polynomials depending on four types of
variables. We construct the generating functions for such SRS polynomials and
show that these polynomials can be written as some bilinear combinations of the
$A_{N-1}$ type of SRS polynomials. We also use the above mentioned generating
functions to derive a set of recursion relations for the partition functions of
the $BC_N$ type of Polychronakos spin chains involving different numbers of
lattice sites and internal degrees of freedom.
| 0 | 1 | 0 | 0 | 0 | 0 |
A statistical physics approach to learning curves for the Inverse Ising problem | Using methods of statistical physics, we analyse the error of learning
couplings in large Ising models from independent data (the inverse Ising
problem). We concentrate on learning based on local cost functions, such as the
pseudo-likelihood method for which the couplings are inferred independently for
each spin. Assuming that the data are generated from a true Ising model, we
compute the reconstruction error of the couplings using a combination of the
replica method with the cavity approach for densely connected systems. We show
that an explicit estimator based on a quadratic cost function achieves minimal
reconstruction error, but requires the length of the true coupling vector as
prior knowledge. A simple mean field estimator of the couplings which does not
need such knowledge is asymptotically optimal, i.e. when the number of
observations is much large than the number of spins. Comparison of the theory
with numerical simulations shows excellent agreement for data generated from
two models with random couplings in the high temperature region: a model with
independent couplings (Sherrington-Kirkpatrick model), and a model where the
matrix of couplings has a Wishart distribution.
| 0 | 1 | 0 | 1 | 0 | 0 |
Fast Kinetic Scheme : efficient MPI parallelization strategy for 3D Boltzmann equation | In this paper we present a parallelization strategy on distributed memory
systems for the Fast Kinetic Scheme --- a semi-Lagrangian scheme developed in
[J. Comput. Phys., Vol. 255, 2013, pp 680-698] for solving kinetic equations.
The original algorithm was proposed for the BGK approximation of the collision
kernel. In this work we deal with its extension to the full Boltzmann equation
in six dimensions, where the collision operator is resolved by means of fast
spectral method. We present close to ideal scalability of the proposed
algorithm on tera- and peta-scale systems.
| 0 | 1 | 1 | 0 | 0 | 0 |
Finding a Feasible Initial Solution for Flatness-Based Multi-Link Manipulator Motion Planning under State and Control Constraints | In this paper, we present a method to initialize at a feasible point and
unfailingly solve a non-convex optimization problem in which a set-point motion
is planned for a multi-link manipulator under state and control constraints. We
construct an initial feasible solution by analyzing the final time effect for
feasibility problems of flatness based motion planning problems. More
specifically, we first find a feasible time-optimal trajectory under state
constraints without a control constraint by solving a linear programming
problem. Then, we find a feasible trajectory under control constraints by
scaling the trajectory. To evaluate the practical applicability of the proposed
method, we did numerical experiments to solve a multi-link manipulator motion
planning problem by combining the method with recursive inverse dynamics
algorithms.
| 1 | 0 | 1 | 0 | 0 | 0 |
Techniques for visualizing LSTMs applied to electrocardiograms | This paper explores four different visualization techniques for long
short-term memory (LSTM) networks applied to continuous-valued time series. On
the datasets analysed, we find that the best visualization technique is to
learn an input deletion mask that optimally reduces the true class score. With
a specific focus on single-lead electrocardiograms from the MIT-BIH arrhythmia
dataset, we show that salient input features for the LSTM classifier align well
with medical theory.
| 1 | 0 | 0 | 1 | 0 | 0 |
Obtaining Accurate Probabilistic Causal Inference by Post-Processing Calibration | Discovery of an accurate causal Bayesian network structure from observational
data can be useful in many areas of science. Often the discoveries are made
under uncertainty, which can be expressed as probabilities. To guide the use of
such discoveries, including directing further investigation, it is important
that those probabilities be well-calibrated. In this paper, we introduce a
novel framework to derive calibrated probabilities of causal relationships from
observational data. The framework consists of three components: (1) an
approximate method for generating initial probability estimates of the edge
types for each pair of variables, (2) the availability of a relatively small
number of the causal relationships in the network for which the truth status is
known, which we call a calibration training set, and (3) a calibration method
for using the approximate probability estimates and the calibration training
set to generate calibrated probabilities for the many remaining pairs of
variables. We also introduce a new calibration method based on a shallow neural
network. Our experiments on simulated data support that the proposed approach
improves the calibration of causal edge predictions. The results also support
that the approach often improves the precision and recall of predictions.
| 1 | 0 | 0 | 1 | 0 | 0 |
Faster Bounding Box Annotation for Object Detection in Indoor Scenes | This paper proposes an approach for rapid bounding box annotation for object
detection datasets. The procedure consists of two stages: The first step is to
annotate a part of the dataset manually, and the second step proposes
annotations for the remaining samples using a model trained with the first
stage annotations. We experimentally study which first/second stage split
minimizes to total workload. In addition, we introduce a new fully labeled
object detection dataset collected from indoor scenes. Compared to other indoor
datasets, our collection has more class categories, different backgrounds,
lighting conditions, occlusion and high intra-class differences. We train deep
learning based object detectors with a number of state-of-the-art models and
compare them in terms of speed and accuracy. The fully annotated dataset is
released freely available for the research community.
| 0 | 0 | 0 | 1 | 0 | 0 |
Junk News on Military Affairs and National Security: Social Media Disinformation Campaigns Against US Military Personnel and Veterans | Social media provides political news and information for both active duty
military personnel and veterans. We analyze the subgroups of Twitter and
Facebook users who spend time consuming junk news from websites that target US
military personnel and veterans with conspiracy theories, misinformation, and
other forms of junk news about military affairs and national security issues.
(1) Over Twitter we find that there are significant and persistent interactions
between current and former military personnel and a broad network of extremist,
Russia-focused, and international conspiracy subgroups. (2) Over Facebook, we
find significant and persistent interactions between public pages for military
and veterans and subgroups dedicated to political conspiracy, and both sides of
the political spectrum. (3) Over Facebook, the users who are most interested in
conspiracy theories and the political right seem to be distributing the most
junk news, whereas users who are either in the military or are veterans are
among the most sophisticated news consumers, and share very little junk news
through the network.
| 1 | 0 | 0 | 0 | 0 | 0 |
The SeaQuest Spectrometer at Fermilab | The SeaQuest spectrometer at Fermilab was designed to detect
oppositely-charged pairs of muons (dimuons) produced by interactions between a
120 GeV proton beam and liquid hydrogen, liquid deuterium and solid nuclear
targets. The primary physics program uses the Drell-Yan process to probe
antiquark distributions in the target nucleon. The spectrometer consists of a
target system, two dipole magnets and four detector stations. The upstream
magnet is a closed-aperture solid iron magnet which also serves as the beam
dump, while the second magnet is an open aperture magnet. Each of the detector
stations consists of scintillator hodoscopes and a high-resolution tracking
device. The FPGA-based trigger compares the hodoscope signals to a set of
pre-programmed roads to determine if the event contains oppositely-signed,
high-mass muon pairs.
| 0 | 1 | 0 | 0 | 0 | 0 |
Direct-Manipulation Visualization of Deep Networks | The recent successes of deep learning have led to a wave of interest from
non-experts. Gaining an understanding of this technology, however, is
difficult. While the theory is important, it is also helpful for novices to
develop an intuitive feel for the effect of different hyperparameters and
structural variations. We describe TensorFlow Playground, an interactive, open
sourced visualization that allows users to experiment via direct manipulation
rather than coding, enabling them to quickly build an intuition about neural
nets.
| 1 | 0 | 0 | 1 | 0 | 0 |
How close are the eigenvectors and eigenvalues of the sample and actual covariance matrices? | How many samples are sufficient to guarantee that the eigenvectors and
eigenvalues of the sample covariance matrix are close to those of the actual
covariance matrix? For a wide family of distributions, including distributions
with finite second moment and distributions supported in a centered Euclidean
ball, we prove that the inner product between eigenvectors of the sample and
actual covariance matrices decreases proportionally to the respective
eigenvalue distance. Our findings imply non-asymptotic concentration bounds for
eigenvectors, eigenspaces, and eigenvalues. They also provide conditions for
distinguishing principal components based on a constant number of samples.
| 0 | 0 | 1 | 1 | 0 | 0 |
Wavefronts for a nonlinear nonlocal bistable reaction-diffusion equation in population dynamics | The wavefronts of a nonlinear nonlocal bistable reaction-diffusion equation,
\begin{align*} \frac{\partial u}{\partial t}=\frac{\partial^2u}{\partial
x^2}+u^2(1-J_\sigma*u)-du,\quad(t,x)\in(0,\infty)\times\mathbb R, \end{align*}
with $J_\sigma(x)=(1/\sigma)= J(x/\sigma)$ and $ \int_{\mathbb R} J(x)dx=1 $
are investigated in this article. It is proven that there exists a
$c_*(\sigma)$ such that for all $c\geq c_*(\sigma)$, a monotone wavefront
$(c,\omega)$ can be connected by the two positive equilibrium points. On the
other hand, there exists a $c^*(\sigma)$ such that the model admits a
semi-wavefront $(c^*(\sigma),\omega)$ with $\omega(-\infty)=0$. Furthermore, it
is shown that for sufficiently small $\sigma$, the semi-wavefronts are in fact
wavefronts connecting $0$ to the largest equilibrium. In addition, the
wavefronts converge to those of the local problem as $\sigma\to0$.
| 0 | 0 | 1 | 0 | 0 | 0 |
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