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17,501 | Vector valued maximal Carleson type operators on the weighted Lorentz spaces | In this paper, by using the idea of linearizing maximal op-erators originated
by Charles Fefferman and the TT* method of Stein-Wainger, we establish a
weighted inequality for vector valued maximal Carleson type operators with
singular kernels proposed by Andersen and John on the weighted Lorentz spaces
with vector-valued functions.
| 0 | 0 | 1 | 0 | 0 | 0 |
17,502 | Rigidity of square-tiled interval exchange transformations | We look at interval exchange transformations defined as first return maps on
the set of diagonals of a flow of direction $\theta$ on a square-tiled surface:
using a combinatorial approach, we show that, when the surface has at least one
true singularity both the flow and the interval exchange are rigid if and only
if tan $\theta$ has bounded partial quotients. Moreover, if all vertices of the
squares are singularities of the flat metric, and tan $\theta$ has bounded
partial quotients, the square-tiled interval exchange transformation T is not
of rank one. Finally, for another class of surfaces, those defined by the
unfolding of billiards in Veech triangles, we build an uncountable set of rigid
directional flows and an uncountable set of rigid interval exchange
transformations.
| 0 | 0 | 1 | 0 | 0 | 0 |
17,503 | Global Marcinkiewicz estimates for nonlinear parabolic equations with nonsmooth coefficients | Consider the parabolic equation with measure data \begin{equation*} \left\{
\begin{aligned} &u_t-{\rm div} \mathbf{a}(D u,x,t)=\mu&\text{in}& \quad
\Omega_T, &u=0 \quad &\text{on}& \quad \partial_p\Omega_T, \end{aligned}\right.
\end{equation*} where $\Omega$ is a bounded domain in $\mathbb{R}^n$,
$\Omega_T=\Omega\times (0,T)$, $\partial_p\Omega_T=(\partial\Omega\times
(0,T))\cup (\Omega\times\{0\})$, and $\mu$ is a signed Borel measure with
finite total mass. Assume that the nonlinearity ${\bf a}$ satisfies a small
BMO-seminorm condition, and $\Omega$ is a Reifenberg flat domain. This paper
proves a global Marcinkiewicz estimate for the SOLA (Solution Obtained as
Limits of Approximation) to the parabolic equation.
| 0 | 0 | 1 | 0 | 0 | 0 |
17,504 | Can Who-Edits-What Predict Edit Survival? | As the number of contributors to online peer-production systems grows, it
becomes increasingly important to predict whether the edits that users make
will eventually be beneficial to the project. Existing solutions either rely on
a user reputation system or consist of a highly specialized predictor that is
tailored to a specific peer-production system. In this work, we explore a
different point in the solution space that goes beyond user reputation but does
not involve any content-based feature of the edits. We view each edit as a game
between the editor and the component of the project. We posit that the
probability that an edit is accepted is a function of the editor's skill, of
the difficulty of editing the component and of a user-component interaction
term. Our model is broadly applicable, as it only requires observing data about
who makes an edit, what the edit affects and whether the edit survives or not.
We apply our model on Wikipedia and the Linux kernel, two examples of
large-scale peer-production systems, and we seek to understand whether it can
effectively predict edit survival: in both cases, we provide a positive answer.
Our approach significantly outperforms those based solely on user reputation
and bridges the gap with specialized predictors that use content-based
features. It is simple to implement, computationally inexpensive, and in
addition it enables us to discover interesting structure in the data.
| 1 | 0 | 0 | 1 | 0 | 0 |
17,505 | Introduction to intelligent computing unit 1 | This brief note highlights some basic concepts required toward understanding
the evolution of machine learning and deep learning models. The note starts
with an overview of artificial intelligence and its relationship to biological
neuron that ultimately led to the evolution of todays intelligent models.
| 1 | 0 | 0 | 1 | 0 | 0 |
17,506 | Spatial heterogeneities shape collective behavior of signaling amoeboid cells | We present novel experimental results on pattern formation of signaling
Dictyostelium discoideum amoeba in the presence of a periodic array of
millimeter-sized pillars. We observe concentric cAMP waves that initiate almost
synchronously at the pillars and propagate outwards. These waves have higher
frequency than the other firing centers and dominate the system dynamics. The
cells respond chemotactically to these circular waves and stream towards the
pillars, forming periodic Voronoi domains that reflect the periodicity of the
underlying lattice. We performed comprehensive numerical simulations of a
reaction-diffusion model to study the characteristics of the boundary
conditions given by the obstacles. Our simulations show that, the obstacles can
act as the wave source depending on the imposed boundary condition.
Interestingly, a critical minimum accumulation of cAMP around the obstacles is
needed for the pillars to act as the wave source. This critical value is lower
at smaller production rates of the intracellular cAMP which can be controlled
in our experiments using caffeine. Experiments and simulations also show that
in the presence of caffeine the number of firing centers is reduced which is
crucial in our system for circular waves emitted from the pillars to
successfully take over the dynamics. These results are crucial to understand
the signaling mechanism of Dictyostelium cells that experience spatial
heterogeneities in its natural habitat.
| 0 | 0 | 0 | 0 | 1 | 0 |
17,507 | Yamabe Solitons on three-dimensional normal almost paracontact metric manifolds | The purpose of the paper is to study Yamabe solitons on three-dimensional
para-Sasakian, paracosymplectic and para-Kenmotsu manifolds. Mainly, we proved
that *If the semi-Riemannian metric of a three-dimensional para-Sasakian
manifold is a Yamabe soliton, then it is of constant scalar curvature, and the
flow vector field V is Killing. In the next step, we proved that either
manifold has constant curvature -1 and reduces to an Einstein manifold, or V is
an infinitesimal automorphism of the paracontact metric structure on the
manifold. *If the semi-Riemannian metric of a three-dimensional
paracosymplectic manifold is a Yamabe soliton, then it has constant scalar
curvature. Furthermore either manifold is $\eta$-Einstein, or Ricci flat. *If
the semi-Riemannian metric on a three-dimensional para-Kenmotsu manifold is a
Yamabe soliton, then the manifold is of constant sectional curvature -1,
reduces to an Einstein manifold. Furthermore, Yamabe soliton is expanding with
$\lambda$=-6 and the vector field V is Killing. Finally, we construct examples
to illustrate the results obtained in previous sections.
| 0 | 0 | 1 | 0 | 0 | 0 |
17,508 | Clustering and Hitting Times of Threshold Exceedances and Applications | We investigate exceedances of the process over a sufficiently high threshold.
The exceedances determine the risk of hazardous events like climate
catastrophes, huge insurance claims, the loss and delay in telecommunication
networks.
Due to dependence such exceedances tend to occur in clusters. The cluster
structure of social networks is caused by dependence (social relationships and
interests) between nodes and possibly heavy-tailed distributions of the node
degrees. A minimal time to reach a large node determines the first hitting
time. We derive an asymptotically equivalent distribution and a limit
expectation of the first hitting time to exceed the threshold $u_n$ as the
sample size $n$ tends to infinity. The results can be extended to the second
and, generally, to the $k$th ($k> 2$) hitting times. Applications in
large-scale networks such as social, telecommunication and recommender systems
are discussed.
| 0 | 0 | 1 | 1 | 0 | 0 |
17,509 | Classification of Local Field Potentials using Gaussian Sequence Model | A problem of classification of local field potentials (LFPs), recorded from
the prefrontal cortex of a macaque monkey, is considered. An adult macaque
monkey is trained to perform a memory-based saccade. The objective is to decode
the eye movement goals from the LFP collected during a memory period. The LFP
classification problem is modeled as that of classification of smooth functions
embedded in Gaussian noise. It is then argued that using minimax function
estimators as features would lead to consistent LFP classifiers. The theory of
Gaussian sequence models allows us to represent minimax estimators as finite
dimensional objects. The LFP classifier resulting from this mathematical
endeavor is a spectrum based technique, where Fourier series coefficients of
the LFP data, followed by appropriate shrinkage and thresholding, are used as
features in a linear discriminant classifier. The classifier is then applied to
the LFP data to achieve high decoding accuracy. The function classification
approach taken in the paper also provides a systematic justification for using
Fourier series, with shrinkage and thresholding, as features for the problem,
as opposed to using the power spectrum. It also suggests that phase information
is crucial to the decision making.
| 0 | 0 | 0 | 1 | 0 | 0 |
17,510 | A few explicit examples of complex dynamics of inertia groups on surfaces - a question of Professor Igor Dolgachev | We give a few explicit examples which answer an open minded question of
Professor Igor Dolgachev on complex dynamics of the inertia group of a smooth
rational curve on a projective K3 surface and its variants for a rational
surface and a non-projective K3 surface.
| 0 | 0 | 1 | 0 | 0 | 0 |
17,511 | Ancestral inference from haplotypes and mutations | We consider inference about the history of a sample of DNA sequences,
conditional upon the haplotype counts and the number of segregating sites
observed at the present time. After deriving some theoretical results in the
coalescent setting, we implement rejection sampling and importance sampling
schemes to perform the inference. The importance sampling scheme addresses an
extension of the Ewens Sampling Formula for a configuration of haplotypes and
the number of segregating sites in the sample. The implementations include both
constant and variable population size models. The methods are illustrated by
two human Y chromosome data sets.
| 0 | 0 | 1 | 1 | 0 | 0 |
17,512 | Ellipsoid Method for Linear Programming made simple | In this paper, ellipsoid method for linear programming is derived using only
minimal knowledge of algebra and matrices. Unfortunately, most authors first
describe the algorithm, then later prove its correctness, which requires a good
knowledge of linear algebra.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,513 | Collective strong coupling of cold atoms to an all-fiber ring cavity | We experimentally demonstrate a ring geometry all-fiber cavity system for
cavity quantum electrodynamics with an ensemble of cold atoms. The fiber cavity
contains a nanofiber section which mediates atom-light interactions through an
evanescent field. We observe well-resolved, vacuum Rabi splitting of the cavity
transmission spectrum in the weak driving limit due to a collective enhancement
of the coupling rate by the ensemble of atoms within the evanescent field, and
we present a simple theoretical model to describe this. In addition, we
demonstrate a method to control and stabilize the resonant frequency of the
cavity by utilizing the thermal properties of the nanofiber.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,514 | Robust Model-Based Clustering of Voting Records | We explore the possibility of discovering extreme voting patterns in the U.S.
Congressional voting records by drawing ideas from the mixture of contaminated
normal distributions. A mixture of latent trait models via contaminated normal
distributions is proposed. We assume that the low dimensional continuous latent
variable comes from a contaminated normal distribution and, therefore, picks up
extreme patterns in the observed binary data while clustering. We consider in
particular such model for the analysis of voting records. The model is applied
to a U.S. Congressional Voting data set on 16 issues. Note this approach is the
first instance within the literature of a mixture model handling binary data
with possible extreme patterns.
| 0 | 0 | 0 | 1 | 0 | 0 |
17,515 | The Quest for Solvable Multistate Landau-Zener Models | Recently, integrability conditions (ICs) in mutistate Landau-Zener (MLZ)
theory were proposed [1]. They describe common properties of all known solved
systems with linearly time-dependent Hamiltonians. Here we show that ICs enable
efficient computer assisted search for new solvable MLZ models that span
complexity range from several interacting states to mesoscopic systems with
many-body dynamics and combinatorially large phase space. This diversity
suggests that nontrivial solvable MLZ models are numerous. In addition, we
refine the formulation of ICs and extend the class of solvable systems to
models with points of multiple diabatic level crossing.
| 0 | 1 | 1 | 0 | 0 | 0 |
17,516 | Bayesian Inference of the Multi-Period Optimal Portfolio for an Exponential Utility | We consider the estimation of the multi-period optimal portfolio obtained by
maximizing an exponential utility. Employing Jeffreys' non-informative prior
and the conjugate informative prior, we derive stochastic representations for
the optimal portfolio weights at each time point of portfolio reallocation.
This provides a direct access not only to the posterior distribution of the
portfolio weights but also to their point estimates together with uncertainties
and their asymptotic distributions. Furthermore, we present the posterior
predictive distribution for the investor's wealth at each time point of the
investment period in terms of a stochastic representation for the future wealth
realization. This in turn makes it possible to use quantile-based risk measures
or to calculate the probability of default. We apply the suggested Bayesian
approach to assess the uncertainty in the multi-period optimal portfolio by
considering assets from the FTSE 100 in the weeks after the British referendum
to leave the European Union. The behaviour of the novel portfolio estimation
method in a precarious market situation is illustrated by calculating the
predictive wealth, the risk associated with the holding portfolio, and the
default probability in each period.
| 0 | 0 | 1 | 1 | 0 | 0 |
17,517 | Reconstructing the gravitational field of the local universe | Tests of gravity at the galaxy scale are in their infancy. As a first step to
systematically uncovering the gravitational significance of galaxies, we map
three fundamental gravitational variables -- the Newtonian potential,
acceleration and curvature -- over the galaxy environments of the local
universe to a distance of approximately 200 Mpc. Our method combines the
contributions from galaxies in an all-sky redshift survey, halos from an N-body
simulation hosting low-luminosity objects, and linear and quasi-linear modes of
the density field. We use the ranges of these variables to determine the extent
to which galaxies expand the scope of generic tests of gravity and are capable
of constraining specific classes of model for which they have special
significance. Finally, we investigate the improvements afforded by upcoming
galaxy surveys.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,518 | Hierarchical organization of H. Eugene Stanley scientific collaboration community in weighted network representation | By mapping the most advanced elements of the contemporary social
interactions, the world scientific collaboration network develops an extremely
involved and heterogeneous organization. Selected characteristics of this
heterogeneity are studied here and identified by focusing on the scientific
collaboration community of H. Eugene Stanley - one of the most prolific world
scholars at the present time. Based on the Web of Science records as of March
28, 2016, several variants of networks are constructed. It is found that the
Stanley #1 network - this in analogy to the Erdős # - develops a largely
consistent hierarchical organization and Stanley himself obeys rules of the
same hierarchy. However, this is seen exclusively in the weighted network
representation. When such a weighted network is evolving, an existing relevant
model indicates that the spread of weight gets stimulation to the
multiplicative bursts over the neighbouring nodes, which leads to a balanced
growth of interconnections among them. While not exclusive to Stanley, such a
behaviour is not a rule, however. Networks of other outstanding scholars
studied here more often develop a star-like form and the central hubs
constitute the outliers. This study is complemented by a spectral analysis of
the normalised Laplacian matrices derived from the weighted variants of the
corresponding networks and, among others, it points to the efficiency of such a
procedure for identifying the component communities and relations among them in
the complex weighted networks.
| 1 | 1 | 0 | 0 | 0 | 0 |
17,519 | Rational links and DT invariants of quivers | We prove that the generating functions for the colored HOMFLY-PT polynomials
of rational links are specializations of the generating functions of the
motivic Donaldson-Thomas invariants of appropriate quivers that we naturally
associate with these links. This shows that the conjectural links-quivers
correspondence of Kucharski-Reineke-Stošić-Su{\l}kowski as well as the
LMOV conjecture hold for rational links. Along the way, we extend the
links-quivers correspondence to tangles and, thus, explore elements of a skein
theory for motivic Donaldson-Thomas invariants.
| 0 | 0 | 1 | 0 | 0 | 0 |
17,520 | G2-structures for N=1 supersymmetric AdS4 solutions of M-theory | We study the N=1 supersymmetric solutions of D=11 supergravity obtained as a
warped product of four-dimensional anti-de-Sitter space with a
seven-dimensional Riemannian manifold M. Using the octonion bundle structure on
M we reformulate the Killing spinor equations in terms of sections of the
octonion bundle on M. The solutions then define a single complexified
G2-structure on M or equivalently two real G2-structures. We then study the
torsion of these G2-structures and the relationships between them.
| 0 | 0 | 1 | 0 | 0 | 0 |
17,521 | A Neural Stochastic Volatility Model | In this paper, we show that the recent integration of statistical models with
deep recurrent neural networks provides a new way of formulating volatility
(the degree of variation of time series) models that have been widely used in
time series analysis and prediction in finance. The model comprises a pair of
complementary stochastic recurrent neural networks: the generative network
models the joint distribution of the stochastic volatility process; the
inference network approximates the conditional distribution of the latent
variables given the observables. Our focus here is on the formulation of
temporal dynamics of volatility over time under a stochastic recurrent neural
network framework. Experiments on real-world stock price datasets demonstrate
that the proposed model generates a better volatility estimation and prediction
that outperforms mainstream methods, e.g., deterministic models such as GARCH
and its variants, and stochastic models namely the MCMC-based model
\emph{stochvol} as well as the Gaussian process volatility model \emph{GPVol},
on average negative log-likelihood.
| 1 | 0 | 0 | 1 | 0 | 0 |
17,522 | A Minimalist Approach to Type-Agnostic Detection of Quadrics in Point Clouds | This paper proposes a segmentation-free, automatic and efficient procedure to
detect general geometric quadric forms in point clouds, where clutter and
occlusions are inevitable. Our everyday world is dominated by man-made objects
which are designed using 3D primitives (such as planes, cones, spheres,
cylinders, etc.). These objects are also omnipresent in industrial
environments. This gives rise to the possibility of abstracting 3D scenes
through primitives, thereby positions these geometric forms as an integral part
of perception and high level 3D scene understanding.
As opposed to state-of-the-art, where a tailored algorithm treats each
primitive type separately, we propose to encapsulate all types in a single
robust detection procedure. At the center of our approach lies a closed form 3D
quadric fit, operating in both primal & dual spaces and requiring as low as 4
oriented-points. Around this fit, we design a novel, local null-space voting
strategy to reduce the 4-point case to 3. Voting is coupled with the famous
RANSAC and makes our algorithm orders of magnitude faster than its conventional
counterparts. This is the first method capable of performing a generic
cross-type multi-object primitive detection in difficult scenes. Results on
synthetic and real datasets support the validity of our method.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,523 | Mass-to-Light versus Color Relations for Dwarf Irregular Galaxies | We have determined new relations between $UBV$ colors and mass-to-light
ratios ($M/L$) for dwarf irregular (dIrr) galaxies, as well as for transformed
$g^\prime - r^\prime$. These $M/L$ to color relations (MLCRs) are based on
stellar mass density profiles determined for 34 LITTLE THINGS dwarfs from
spectral energy distribution fitting to multi-wavelength surface photometry in
passbands from the FUV to the NIR. These relations can be used to determine
stellar masses in dIrr galaxies for situations where other determinations of
stellar mass are not possible. Our MLCRs are shallower than comparable MLCRs in
the literature determined for spiral galaxies. We divided our dwarf data into
four metallicity bins and found indications of a steepening of the MLCR with
increased oxygen abundance, perhaps due to more line blanketing occurring at
higher metallicity.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,524 | Insight into High-order Harmonic Generation from Solids: The Contributions of the Bloch Wave-packets Moving on the Group and Phase Velocities | We study numerically the Bloch electron wavepacket dynamics in periodic
potentials to simulate laser-solid interactions. We introduce a new perspective
in the coordinate space combined with the motion of the Bloch electron
wavepackets moving at group and phase velocities under the laser fields. This
model interprets the origins of the two contributions (intra- and interband
transitions) of the high-order harmonic generation (HHG) by investigating the
local and global behavior of the wavepackets. It also elucidates the underlying
physical picture of the HHG intensity enhancement by means of carrier-envelope
phase (CEP), chirp and inhomogeneous fields. It provides a deep insight into
the emission of high-order harmonics from solids. This model is instructive for
experimental measurements and provides a new avenue to distinguish mechanisms
of the HHG from solids in diffrent laser fields.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,525 | Using deterministic approximations to accelerate SMC for posterior sampling | Sequential Monte Carlo has become a standard tool for Bayesian Inference of
complex models. This approach can be computationally demanding, especially when
initialized from the prior distribution. On the other hand, deter-ministic
approximations of the posterior distribution are often available with no
theoretical guaranties. We propose a bridge sampling scheme starting from such
a deterministic approximation of the posterior distribution and targeting the
true one. The resulting Shortened Bridge Sampler (SBS) relies on a sequence of
distributions that is determined in an adaptive way. We illustrate the
robustness and the efficiency of the methodology on a large simulation study.
When applied to network datasets, SBS inference leads to different statistical
conclusions from the one supplied by the standard variational Bayes
approximation.
| 0 | 0 | 0 | 1 | 0 | 0 |
17,526 | Evaluating (and improving) the correspondence between deep neural networks and human representations | Decades of psychological research have been aimed at modeling how people
learn features and categories. The empirical validation of these theories is
often based on artificial stimuli with simple representations. Recently, deep
neural networks have reached or surpassed human accuracy on tasks such as
identifying objects in natural images. These networks learn representations of
real-world stimuli that can potentially be leveraged to capture psychological
representations. We find that state-of-the-art object classification networks
provide surprisingly accurate predictions of human similarity judgments for
natural images, but fail to capture some of the structure represented by
people. We show that a simple transformation that corrects these discrepancies
can be obtained through convex optimization. We use the resulting
representations to predict the difficulty of learning novel categories of
natural images. Our results extend the scope of psychological experiments and
computational modeling by enabling tractable use of large natural stimulus
sets.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,527 | An approach to nonsolvable base change and descent | We present a collection of conjectural trace identities and explain why they
are equivalent to base change and descent of automorphic representations of
$\mathrm{GL}_n(\mathbb{A}_F)$ along nonsolvable extensions (under some
simplifying hypotheses). The case $n=2$ is treated in more detail and
applications towards the Artin conjecture for icosahedral Galois
representations are given.
| 0 | 0 | 1 | 0 | 0 | 0 |
17,528 | Towards Attack-Tolerant Networks: Concurrent Multipath Routing and the Butterfly Network | Targeted attacks against network infrastructure are notoriously difficult to
guard against. In the case of communication networks, such attacks can leave
users vulnerable to censorship and surveillance, even when cryptography is
used. Much of the existing work on network fault-tolerance focuses on random
faults and does not apply to adversarial faults (attacks). Centralized networks
have single points of failure by definition, leading to a growing popularity in
decentralized architectures and protocols for greater fault-tolerance. However,
centralized network structure can arise even when protocols are decentralized.
Despite their decentralized protocols, the Internet and World-Wide Web have
been shown both theoretically and historically to be highly susceptible to
attack, in part due to emergent structural centralization. When single points
of failure exist, they are potentially vulnerable to non-technological (i.e.,
coercive) attacks, suggesting the importance of a structural approach to
attack-tolerance. We show how the assumption of partial trust transitivity,
while more realistic than the assumption underlying webs of trust, can be used
to quantify the effective redundancy of a network as a function of trust
transitivity. We also prove that the effective redundancy of the wrap-around
butterfly topology increases exponentially with trust transitivity and describe
a novel concurrent multipath routing algorithm for constructing paths to
utilize that redundancy. When portions of network structure can be dictated our
results can be used to create scalable, attack-tolerant infrastructures. More
generally, our results provide a theoretical formalism for evaluating the
effects of network structure on adversarial fault-tolerance.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,529 | PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making | Reinforcement learning and symbolic planning have both been used to build
intelligent autonomous agents. Reinforcement learning relies on learning from
interactions with real world, which often requires an unfeasibly large amount
of experience. Symbolic planning relies on manually crafted symbolic knowledge,
which may not be robust to domain uncertainties and changes. In this paper we
present a unified framework {\em PEORL} that integrates symbolic planning with
hierarchical reinforcement learning (HRL) to cope with decision-making in a
dynamic environment with uncertainties.
Symbolic plans are used to guide the agent's task execution and learning, and
the learned experience is fed back to symbolic knowledge to improve planning.
This method leads to rapid policy search and robust symbolic plans in complex
domains. The framework is tested on benchmark domains of HRL.
| 0 | 0 | 0 | 1 | 0 | 0 |
17,530 | Structure of Native Two-dimensional Oxides on III--Nitride Surfaces | When pristine material surfaces are exposed to air, highly reactive broken
bonds can promote the formation of surface oxides with structures and
properties differing greatly from bulk. Determination of the oxide structure,
however, is often elusive through the use of indirect diffraction methods or
techniques that probe only the outer most layer. As a result, surface oxides
forming on widely used materials, such as group III-nitrides, have not been
unambiguously resolved, even though critical properties can depend sensitively
on their presence. In this work, aberration corrected scanning transmission
electron microscopy reveals directly, and with depth dependence, the structure
of native two--dimensional oxides that form on AlN and GaN surfaces. Through
atomic resolution imaging and spectroscopy, we show that the oxide layers are
comprised of tetrahedra--octahedra cation--oxygen units, similar to bulk
$\theta$--Al$_2$O$_3$ and $\beta$--Ga$_2$O$_3$. By applying density functional
theory, we show that the observed structures are more stable than previously
proposed surface oxide models. We place the impact of these observations in the
context of key III-nitride growth, device issues, and the recent discovery of
two-dimnesional nitrides.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,531 | Hydrodynamic charge and heat transport on inhomogeneous curved spaces | We develop the theory of hydrodynamic charge and heat transport in strongly
interacting quasi-relativistic systems on manifolds with inhomogeneous spatial
curvature. In solid-state physics, this is analogous to strain disorder in the
underlying lattice. In the hydrodynamic limit, we find that the thermal and
electrical conductivities are dominated by viscous effects, and that the
thermal conductivity is most sensitive to this disorder. We compare the effects
of inhomogeneity in the spatial metric to inhomogeneity in the chemical
potential, and discuss the extent to which our hydrodynamic theory is relevant
for experimentally realizable condensed matter systems, including suspended
graphene at the Dirac point.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,532 | Simulation assisted machine learning | Predicting how a proposed cancer treatment will affect a given tumor can be
cast as a machine learning problem, but the complexity of biological systems,
the number of potentially relevant genomic and clinical features, and the lack
of very large scale patient data repositories make this a unique challenge.
"Pure data" approaches to this problem are underpowered to detect
combinatorially complex interactions and are bound to uncover false
correlations despite statistical precautions taken (1). To investigate this
setting, we propose a method to integrate simulations, a strong form of prior
knowledge, into machine learning, a combination which to date has been largely
unexplored. The results of multiple simulations (under various uncertainty
scenarios) are used to compute similarity measures between every pair of
samples: sample pairs are given a high similarity score if they behave
similarly under a wide range of simulation parameters. These similarity values,
rather than the original high dimensional feature data, are used to train
kernelized machine learning algorithms such as support vector machines, thus
handling the curse-of-dimensionality that typically affects genomic machine
learning. Using four synthetic datasets of complex systems--three biological
models and one network flow optimization model--we demonstrate that when the
number of training samples is small compared to the number of features, the
simulation kernel approach dominates over no-prior-knowledge methods. In
addition to biology and medicine, this approach should be applicable to other
disciplines, such as weather forecasting, financial markets, and agricultural
management, where predictive models are sought and informative yet approximate
simulations are available. The Python SimKern software, the models (in MATLAB,
Octave, and R), and the datasets are made freely available at
this https URL .
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17,533 | Rechargeable redox flow batteries: Maximum current density with electrolyte flow reactant penetration in a serpentine flow structure | Rechargeable redox flow batteries with serpentine flow field designs have
been demonstrated to deliver higher current density and power density in medium
and large-scale stationary energy storage applications. Nevertheless, the
fundamental mechanisms involved with improved current density in flow batteries
with flow field designs have not been understood. Here we report a maximum
current density concept associated with stoichiometric availability of
electrolyte reactant flow penetration through the porous electrode that can be
achieved in a flow battery system with a "zero-gap"serpentine flow field
architecture. This concept can explain a higher current density achieved within
allowing reactions of all species soluble in the electrolyte. Further
validations with experimental data are confirmed by an example of a vanadium
flow battery with a serpentine flow structure over carbon paper electrode.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,534 | Open problems in mathematical physics | We present a list of open questions in mathematical physics. After a
historical introduction, a number of problems in a variety of different fields
are discussed, with the intention of giving an overall impression of the
current status of mathematical physics, particularly in the topical fields of
classical general relativity, cosmology and the quantum realm. This list is
motivated by the recent article proposing 42 fundamental questions (in physics)
which must be answered on the road to full enlightenment. But paraphrasing a
famous quote by the British football manager Bill Shankly, in response to the
question of whether mathematics can answer the Ultimate Question of Life, the
Universe, and Everything, mathematics is, of course, much more important than
that.
| 0 | 1 | 1 | 0 | 0 | 0 |
17,535 | Stochastic Bandit Models for Delayed Conversions | Online advertising and product recommendation are important domains of
applications for multi-armed bandit methods. In these fields, the reward that
is immediately available is most often only a proxy for the actual outcome of
interest, which we refer to as a conversion. For instance, in web advertising,
clicks can be observed within a few seconds after an ad display but the
corresponding sale --if any-- will take hours, if not days to happen. This
paper proposes and investigates a new stochas-tic multi-armed bandit model in
the framework proposed by Chapelle (2014) --based on empirical studies in the
field of web advertising-- in which each action may trigger a future reward
that will then happen with a stochas-tic delay. We assume that the probability
of conversion associated with each action is unknown while the distribution of
the conversion delay is known, distinguishing between the (idealized) case
where the conversion events may be observed whatever their delay and the more
realistic setting in which late conversions are censored. We provide
performance lower bounds as well as two simple but efficient algorithms based
on the UCB and KLUCB frameworks. The latter algorithm, which is preferable when
conversion rates are low, is based on a Poissonization argument, of independent
interest in other settings where aggregation of Bernoulli observations with
different success probabilities is required.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,536 | Fitting Analysis using Differential Evolution Optimization (FADO): Spectral population synthesis through genetic optimization under self-consistency boundary conditions | The goal of population spectral synthesis (PSS) is to decipher from the
spectrum of a galaxy the mass, age and metallicity of its constituent stellar
populations. This technique has been established as a fundamental tool in
extragalactic research. It has been extensively applied to large spectroscopic
data sets, notably the SDSS, leading to important insights into the galaxy
assembly history. However, despite significant improvements over the past
decade, all current PSS codes suffer from two major deficiencies that inhibit
us from gaining sharp insights into the star-formation history (SFH) of
galaxies and potentially introduce substantial biases in studies of their
physical properties (e.g., stellar mass, mass-weighted stellar age and specific
star formation rate). These are i) the neglect of nebular emission in spectral
fits, consequently, ii) the lack of a mechanism that ensures consistency
between the best-fitting SFH and the observed nebular emission characteristics
of a star-forming (SF) galaxy. In this article, we present FADO (Fitting
Analysis using Differential evolution Optimization): a conceptually novel,
publicly available PSS tool with the distinctive capability of permitting
identification of the SFH that reproduces the observed nebular characteristics
of a SF galaxy. This so-far unique self-consistency concept allows us to
significantly alleviate degeneracies in current spectral synthesis. The
innovative character of FADO is further augmented by its mathematical
foundation: FADO is the first PSS code employing genetic differential evolution
optimization. This, in conjunction with other unique elements in its
mathematical concept (e.g., optimization of the spectral library using
artificial intelligence, convergence test, quasi-parallelization) results in
key improvements with respect to computational efficiency and uniqueness of the
best-fitting SFHs.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,537 | A Combinatoric Shortcut to Evaluate CHY-forms | In \cite{Chen:2016fgi} we proposed a differential operator for the evaluation
of the multi-dimensional residues on isolated (zero-dimensional) poles.In this
paper we discuss some new insight on evaluating the (generalized)
Cachazo-He-Yuan (CHY) forms of the scattering amplitudes using this
differential operator. We introduce a tableau representation for the
coefficients appearing in the proposed differential operator. Combining the
tableaux with the polynomial forms of the scattering equations, the evaluation
of the generalized CHY form becomes a simple combinatoric problem. It is thus
possible to obtain the coefficients arising in the differential operator in a
straightforward way. We present the procedure for a complete solution of the
$n$-gon amplitudes at one-loop level in a generalized CHY form. We also apply
our method to fully evaluate the one-loop five-point amplitude in the maximally
supersymmetric Yang-Mills theory; the final result is identical to the one
obtained by Q-Cut.
| 0 | 0 | 1 | 0 | 0 | 0 |
17,538 | ART: adaptive residual--time restarting for Krylov subspace matrix exponential evaluations | In this paper a new restarting method for Krylov subspace matrix exponential
evaluations is proposed. Since our restarting technique essentially employs the
residual, some convergence results for the residual are given. We also discuss
how the restart length can be adjusted after each restart cycle, which leads to
an adaptive restarting procedure. Numerical tests are presented to compare our
restarting with three other restarting methods. Some of the algorithms
described in this paper are a part of the Octave/Matlab package expmARPACK
available at this http URL.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,539 | Nil extensions of simple regular ordered semigroup | In this paper, nil extensions of some special type of ordered semigroups,
such as, simple regular ordered semigroups, left simple and right regular
ordered semigroup. Moreover, we have characterized complete semilattice
decomposition of all ordered semigroups which are nil extension of ordered
semigroup.
| 0 | 0 | 1 | 0 | 0 | 0 |
17,540 | The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings | We examine a class of embeddings based on structured random matrices with
orthogonal rows which can be applied in many machine learning applications
including dimensionality reduction and kernel approximation. For both the
Johnson-Lindenstrauss transform and the angular kernel, we show that we can
select matrices yielding guaranteed improved performance in accuracy and/or
speed compared to earlier methods. We introduce matrices with complex entries
which give significant further accuracy improvement. We provide geometric and
Markov chain-based perspectives to help understand the benefits, and empirical
results which suggest that the approach is helpful in a wider range of
applications.
| 0 | 0 | 0 | 1 | 0 | 0 |
17,541 | The Bayesian optimist's guide to adaptive immune receptor repertoire analysis | Probabilistic modeling is fundamental to the statistical analysis of complex
data. In addition to forming a coherent description of the data-generating
process, probabilistic models enable parameter inference about given data sets.
This procedure is well-developed in the Bayesian perspective, in which one
infers probability distributions describing to what extent various possible
parameters agree with the data. In this paper we motivate and review
probabilistic modeling for adaptive immune receptor repertoire data then
describe progress and prospects for future work, from germline haplotyping to
adaptive immune system deployment across tissues. The relevant quantities in
immune sequence analysis include not only continuous parameters such as gene
use frequency, but also discrete objects such as B cell clusters and lineages.
Throughout this review, we unravel the many opportunities for probabilistic
modeling in adaptive immune receptor analysis, including settings for which the
Bayesian approach holds substantial promise (especially if one is optimistic
about new computational methods). From our perspective the greatest prospects
for progress in probabilistic modeling for repertoires concern ancestral
sequence estimation for B cell receptor lineages, including uncertainty from
germline genotype, rearrangement, and lineage development.
| 0 | 0 | 0 | 0 | 1 | 0 |
17,542 | Predictive Indexing | There has been considerable research on automated index tuning in database
management systems (DBMSs). But the majority of these solutions tune the index
configuration by retrospectively making computationally expensive physical
design changes all at once. Such changes degrade the DBMS's performance during
the process, and have reduced utility during subsequent query processing due to
the delay between a workload shift and the associated change. A better approach
is to generate small changes that tune the physical design over time, forecast
the utility of these changes, and apply them ahead of time to maximize their
impact.
This paper presents predictive indexing that continuously improves a
database's physical design using lightweight physical design changes. It uses a
machine learning model to forecast the utility of these changes, and
continuously refines the index configuration of the database to handle evolving
workloads. We introduce a lightweight hybrid scan operator with which a DBMS
can make use of partially-built indexes for query processing. Our evaluation
shows that predictive indexing improves the throughput of a DBMS by 3.5--5.2x
compared to other state-of-the-art indexing approaches. We demonstrate that
predictive indexing works seamlessly with other lightweight automated physical
design tuning methods.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,543 | Making Deep Q-learning methods robust to time discretization | Despite remarkable successes, Deep Reinforcement Learning (DRL) is not robust
to hyperparameterization, implementation details, or small environment changes
(Henderson et al. 2017, Zhang et al. 2018). Overcoming such sensitivity is key
to making DRL applicable to real world problems. In this paper, we identify
sensitivity to time discretization in near continuous-time environments as a
critical factor; this covers, e.g., changing the number of frames per second,
or the action frequency of the controller. Empirically, we find that
Q-learning-based approaches such as Deep Q- learning (Mnih et al., 2015) and
Deep Deterministic Policy Gradient (Lillicrap et al., 2015) collapse with small
time steps. Formally, we prove that Q-learning does not exist in continuous
time. We detail a principled way to build an off-policy RL algorithm that
yields similar performances over a wide range of time discretizations, and
confirm this robustness empirically.
| 1 | 0 | 0 | 1 | 0 | 0 |
17,544 | Anomalous current in diffusive ferromagnetic Josephson junctions | We demonstrate that in diffusive superconductor/ferromagnet/superconductor
(S/F/S) junctions a finite, {\it anomalous}, Josephson current can flow even at
zero phase difference between the S electrodes. The conditions for the
observation of this effect are non-coplanar magnetization distribution and a
broken magnetization inversion symmetry of the superconducting current. The
latter symmetry is intrinsic for the widely used quasiclassical approximation
and prevent previous works, based on this approximation, from obtaining the
Josephson anomalous current. We show that this symmetry can be removed by
introducing spin-dependent boundary conditions for the quasiclassical equations
at the superconducting/ferromagnet interfaces in diffusive systems. Using this
recipe we considered generic multilayer magnetic systems and determine the
ideal experimental conditions in order to maximize the anomalous current.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,545 | Rate Optimal Estimation and Confidence Intervals for High-dimensional Regression with Missing Covariates | Although a majority of the theoretical literature in high-dimensional
statistics has focused on settings which involve fully-observed data, settings
with missing values and corruptions are common in practice. We consider the
problems of estimation and of constructing component-wise confidence intervals
in a sparse high-dimensional linear regression model when some covariates of
the design matrix are missing completely at random. We analyze a variant of the
Dantzig selector [9] for estimating the regression model and we use a
de-biasing argument to construct component-wise confidence intervals. Our first
main result is to establish upper bounds on the estimation error as a function
of the model parameters (the sparsity level s, the expected fraction of
observed covariates $\rho_*$, and a measure of the signal strength
$\|\beta^*\|_2$). We find that even in an idealized setting where the
covariates are assumed to be missing completely at random, somewhat
surprisingly and in contrast to the fully-observed setting, there is a
dichotomy in the dependence on model parameters and much faster rates are
obtained if the covariance matrix of the random design is known. To study this
issue further, our second main contribution is to provide lower bounds on the
estimation error showing that this discrepancy in rates is unavoidable in a
minimax sense. We then consider the problem of high-dimensional inference in
the presence of missing data. We construct and analyze confidence intervals
using a de-biased estimator. In the presence of missing data, inference is
complicated by the fact that the de-biasing matrix is correlated with the pilot
estimator and this necessitates the design of a new estimator and a novel
analysis. We also complement our mathematical study with extensive simulations
on synthetic and semi-synthetic data that show the accuracy of our asymptotic
predictions for finite sample sizes.
| 0 | 0 | 0 | 1 | 0 | 0 |
17,546 | Applications of an algorithm for solving Fredholm equations of the first kind | In this paper we use an iterative algorithm for solving Fredholm equations of
the first kind. The basic algorithm is known and is based on an EM algorithm
when involved functions are non-negative and integrable. With this algorithm we
demonstrate two examples involving the estimation of a mixing density and a
first passage time density function involving Brownian motion. We also develop
the basic algorithm to include functions which are not necessarily non-negative
and again present illustrations under this scenario. A self contained proof of
convergence of all the algorithms employed is presented.
| 0 | 0 | 1 | 1 | 0 | 0 |
17,547 | Fully symmetric kernel quadrature | Kernel quadratures and other kernel-based approximation methods typically
suffer from prohibitive cubic time and quadratic space complexity in the number
of function evaluations. The problem arises because a system of linear
equations needs to be solved. In this article we show that the weights of a
kernel quadrature rule can be computed efficiently and exactly for up to tens
of millions of nodes if the kernel, integration domain, and measure are fully
symmetric and the node set is a union of fully symmetric sets. This is based on
the observations that in such a setting there are only as many distinct weights
as there are fully symmetric sets and that these weights can be solved from a
linear system of equations constructed out of row sums of certain submatrices
of the full kernel matrix. We present several numerical examples that show
feasibility, both for a large number of nodes and in high dimensions, of the
developed fully symmetric kernel quadrature rules. Most prominent of the fully
symmetric kernel quadrature rules we propose are those that use sparse grids.
| 1 | 0 | 1 | 1 | 0 | 0 |
17,548 | Conditional Accelerated Lazy Stochastic Gradient Descent | In this work we introduce a conditional accelerated lazy stochastic gradient
descent algorithm with optimal number of calls to a stochastic first-order
oracle and convergence rate $O\left(\frac{1}{\varepsilon^2}\right)$ improving
over the projection-free, Online Frank-Wolfe based stochastic gradient descent
of Hazan and Kale [2012] with convergence rate
$O\left(\frac{1}{\varepsilon^4}\right)$.
| 1 | 0 | 0 | 1 | 0 | 0 |
17,549 | MMD GAN: Towards Deeper Understanding of Moment Matching Network | Generative moment matching network (GMMN) is a deep generative model that
differs from Generative Adversarial Network (GAN) by replacing the
discriminator in GAN with a two-sample test based on kernel maximum mean
discrepancy (MMD). Although some theoretical guarantees of MMD have been
studied, the empirical performance of GMMN is still not as competitive as that
of GAN on challenging and large benchmark datasets. The computational
efficiency of GMMN is also less desirable in comparison with GAN, partially due
to its requirement for a rather large batch size during the training. In this
paper, we propose to improve both the model expressiveness of GMMN and its
computational efficiency by introducing adversarial kernel learning techniques,
as the replacement of a fixed Gaussian kernel in the original GMMN. The new
approach combines the key ideas in both GMMN and GAN, hence we name it MMD GAN.
The new distance measure in MMD GAN is a meaningful loss that enjoys the
advantage of weak topology and can be optimized via gradient descent with
relatively small batch sizes. In our evaluation on multiple benchmark datasets,
including MNIST, CIFAR- 10, CelebA and LSUN, the performance of MMD-GAN
significantly outperforms GMMN, and is competitive with other representative
GAN works.
| 1 | 0 | 0 | 1 | 0 | 0 |
17,550 | Multipartite entanglement after a quantum quench | We study the multipartite entanglement of a quantum many-body system
undergoing a quantum quench. We quantify multipartite entanglement through the
quantum Fisher information (QFI) density and we are able to express it after a
quench in terms of a generalized response function. For pure state initial
conditions and in the thermodynamic limit, we can express the QFI as the
fluctuations of an observable computed in the so-called diagonal ensemble. We
apply the formalism to the dynamics of a quantum Ising chain after a quench in
the transverse field. In this model the asymptotic state is, in almost all
cases, more than two-partite entangled. Moreover, starting from the
ferromagnetic phase, we find a divergence of multipartite entanglement for
small quenches closely connected to a corresponding divergence of the
correlation length.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,551 | Thermal properties of graphene from path-integral simulations | Thermal properties of graphene monolayers are studied by path-integral
molecular dynamics (PIMD) simulations, which take into account the quantization
of vibrational modes in the crystalline membrane, and allow one to consider
anharmonic effects in these properties. This system was studied at temperatures
in the range from 12 to 2000~K and zero external stress, by describing the
interatomic interactions through the LCBOPII effective potential. We analyze
the internal energy and specific heat and compare the results derived from the
simulations with those yielded by a harmonic approximation for the vibrational
modes. This approximation turns out to be rather precise up to temperatures of
about 400~K. At higher temperatures, we observe an influence of the elastic
energy, due to the thermal expansion of the graphene sheet. Zero-point and
thermal effects on the in-plane and "real" surface of graphene are discussed.
The thermal expansion coefficient $\alpha$ of the real area is found to be
positive at all temperatures, in contrast to the expansion coefficient
$\alpha_p$ of the in-plane area, which is negative at low temperatures, and
becomes positive for $T \gtrsim$ 1000~K.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,552 | Measuring Affectiveness and Effectiveness in Software Systems | The summary presented in this paper highlights the results obtained in a
four-years project aiming at analyzing the development process of software
artifacts from two points of view: Effectiveness and Affectiveness. The first
attribute is meant to analyze the productivity of the Open Source Communities
by measuring the time required to resolve an issue, while the latter provides a
novel approach for studying the development process by analyzing the
affectiveness ex-pressed by developers in their comments posted during the
issue resolution phase. Affectivenes is obtained by measuring Sentiment,
Politeness and Emotions. All the study presented in this summary are based on
Jira, one of the most used software repositories.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,553 | Intertangled stochastic motifs in networks of excitatory-inhibitory units | A stochastic model of excitatory and inhibitory interactions which bears
universality traits is introduced and studied. The endogenous component of
noise, stemming from finite size corrections, drives robust inter-nodes
correlations, that persist at large large distances. Anti-phase synchrony at
small frequencies is resolved on adjacent nodes and found to promote the
spontaneous generation of long-ranged stochastic patterns, that invade the
network as a whole. These patterns are lacking under the idealized
deterministic scenario, and could provide novel hints on how living systems
implement and handle a large gallery of delicate computational tasks.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,554 | Accurate Computation of the Distribution of Sums of Dependent Log-Normals with Applications to the Black-Scholes Model | We present a new Monte Carlo methodology for the accurate estimation of the
distribution of the sum of dependent log-normal random variables. The
methodology delivers statistically unbiased estimators for three distributional
quantities of significant interest in finance and risk management: the left
tail, or cumulative distribution function, the probability density function,
and the right tail, or complementary distribution function of the sum of
dependent log-normal factors. In all of these three cases our methodology
delivers fast and highly accurate estimators in settings for which existing
methodology delivers estimators with large variance that tend to underestimate
the true quantity of interest. We provide insight into the computational
challenges using theory and numerical experiments, and explain their much wider
implications for Monte Carlo statistical estimators of rare-event
probabilities. In particular, we find that theoretically strongly-efficient
estimators should be used with great caution in practice, because they may
yield inaccurate results in the pre-limit. Further, this inaccuracy may not be
detectable from the output of the Monte Carlo simulation, because the
simulation output may severely underestimate the true variance of the
estimator.
| 0 | 0 | 0 | 1 | 0 | 0 |
17,555 | The complete unitary dual of non-compact Lie superalgebra su(p,q|m) via the generalised oscillator formalism, and non-compact Young diagrams | We study the unitary representations of the non-compact real forms of the
complex Lie superalgebra sl(n|m). Among them, only the real form su(p,q|m)
(p+q=n) admits nontrivial unitary representations, and all such representations
are of the highest-weight type (or the lowest-weight type). We extend the
standard oscillator construction of the unitary representations of non-compact
Lie superalgebras over standard Fock spaces to generalised Fock spaces which
allows us to define the action of oscillator determinants raised to non-integer
powers. We prove that the proposed construction yields all the unitary
representations including those with continuous labels. The unitary
representations can be diagrammatically represented by non-compact Young
diagrams. We apply our general results to the physically important case of
four-dimensional conformal superalgebra su(2,2|4) and show how it yields
readily its unitary representations including those corresponding to
supermultiplets of conformal fields with continuous (anomalous) scaling
dimensions.
| 0 | 0 | 1 | 0 | 0 | 0 |
17,556 | DeepPainter: Painter Classification Using Deep Convolutional Autoencoders | In this paper we describe the problem of painter classification, and propose
a novel approach based on deep convolutional autoencoder neural networks. While
previous approaches relied on image processing and manual feature extraction
from paintings, our approach operates on the raw pixel level, without any
preprocessing or manual feature extraction. We first train a deep convolutional
autoencoder on a dataset of paintings, and subsequently use it to initialize a
supervised convolutional neural network for the classification phase.
The proposed approach substantially outperforms previous methods, improving
the previous state-of-the-art for the 3-painter classification problem from
90.44% accuracy (previous state-of-the-art) to 96.52% accuracy, i.e., a 63%
reduction in error rate.
| 1 | 0 | 0 | 1 | 0 | 0 |
17,557 | Sharp gradient estimate for heat kernels on $RCD^*(K,N)$ metric measure spaces | In this paper, we will establish an elliptic local Li-Yau gradient estimate
for weak solutions of the heat equation on metric measure spaces with
generalized Ricci curvature bounded from below. One of its main applications is
a sharp gradient estimate for the logarithm of heat kernels. These results seem
new even for smooth Riemannian manifolds.
| 0 | 0 | 1 | 0 | 0 | 0 |
17,558 | Thermodynamic properties of diatomic molecules systems under anharmonic Eckart potential | Due to one of the most representative contributions to the energy in diatomic
molecules being the vibrational, we consider the generalized Morse potential
(GMP) as one of the typical potential of interaction for one-dimensional
microscopic systems, which describes local anharmonic effects. From Eckart
potential (EP) model, it is possible to find a connection with the GMP model,
as well as obtain the analytical expression for the energy spectrum because it
is based on $S\,O\left(2,1\right)$ algebras. In this work we find the
macroscopic properties such as vibrational mean energy $U$, specific heat $C$,
Helmholtz free energy $F$ and entropy $S$ for a heteronuclear diatomic system,
along with the exact partition function and its approximation for the high
temperature region. Finally, we make a comparison between the graphs of some
thermodynamic functions obtained with the GMP and the Morse potential (MP) for
$H\,Cl$ molecules.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,559 | Effects of ultrasound waves intensity on the removal of Congo red color from the textile industry wastewater by Fe3O4@TiO2 core-shell nanospheres | Environmental pollutants, such as colors from the textile industry, affect
water quality indicators like color, smell, and taste. These substances in the
water cause the obstruction of filters and membranes and thereby reduce the
efficiency of advanced water treatment processes. In addition, they are harmful
to human health because of reaction with disinfectants and production of
by-products. Iron oxide nanoparticles are considered effective absorbents for
the removal of pollutants from aqueous environments. In order to increase the
stability and dispersion, nanospheres with iron oxide core and titanium dioxide
coating were used in this research and their ability to absorb Congo red color
was evaluated. Iron oxide-titanium oxide nanospheres were prepared based on the
coprecipitation method and then their physical properties were determined using
a tunneling electron microscope (TEM) and an X-ray diffraction device.
Morphological investigation of the absorbent surface showed that iron
oxide-titanium oxide nanospheres sized about 5 to 10 nm. X-ray dispersion
survey also suggested the high purity of the sample. In addition, the
absorption rate was measured in the presence of ultrasound waves and the
results indicated that the capacity of the synthesized sample to absorb Congo
red is greatly dependent on the intensity power of ultrasound waves, as the
absorption rate reaches 100% at powers above 30 watts.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,560 | Enumeration of Tree-like Maps with Arbitrary Number of Vertices | This paper provides the generating series for the embedding of tree-like
graphs of arbitrary number of vertices, accourding to their genus. It applies
and extends the techniques of Chan, where it was used to give an alternate
proof of the Goulden and Slofstra formula. Furthermore, this greatly
generalizes the famous Harer-Zagier formula, which computes the Euler
characteristic of the moduli space of curves, and is equivalent to the
computation of one vertex maps.
| 0 | 0 | 1 | 0 | 0 | 0 |
17,561 | Depth resolved chemical speciation of a superlattice structure | We report results of simultaneous x-ray reflectivity and grazing incidence
x-ray fluorescence measurements in combination with x-ray standing wave
assisted depth resolved near edge x-ray absorption measurements to reveal new
insights on chemical speciation of W in a W-B4C superlattice structure.
Interestingly, our results show existence of various unusual electronic states
for the W atoms especially those sitting at the surface and interface boundary
of a thin film medium as compared to that of the bulk. These observations are
found to be consistent with the results obtained using first principles
calculations. Unlike the conventional x-ray absorption measurements the present
approach has an advantage that it permits the determination of depth resolved
chemical nature of an element in the thin layered materials at atomic length
scale resolutions.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,562 | Optospintronics in graphene via proximity coupling | The observation of micron size spin relaxation makes graphene a promising
material for applications in spintronics requiring long distance spin
communication. However, spin dependent scatterings at the contact/graphene
interfaces affect the spin injection efficiencies and hence prevent the
material from achieving its full potential. While this major issue could be
eliminated by nondestructive direct optical spin injection schemes, graphenes
intrinsically low spin orbit coupling strength and optical absorption place an
obstacle in their realization. We overcome this challenge by creating sharp
artificial interfaces between graphene and WSe2 monolayers. Application of a
circularly polarized light activates the spin polarized charge carriers in the
WSe2 layer due to its spin coupled valley selective absorption. These carriers
diffuse into the superjacent graphene layer, transport over a 3.5 um distance,
and are finally detected electrically using BN/Co contacts in a non local
geometry. Polarization dependent measurements confirm the spin origin of the
non local signal.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,563 | Control strategy to limit duty cycle impact of earthquakes on the LIGO gravitational-wave detectors | Advanced gravitational-wave detectors such as the Laser Interferometer
Gravitational-Wave Observatories (LIGO) require an unprecedented level of
isolation from the ground. When in operation, they are expected to observe
changes in the space-time continuum of less than one thousandth of the diameter
of a proton. Strong teleseismic events like earthquakes disrupt the proper
functioning of the detectors, and result in a loss of data until the detectors
can be returned to their operating states. An earthquake early-warning system,
as well as a prediction model have been developed to help understand the impact
of earthquakes on LIGO. This paper describes a control strategy to use this
early-warning system to reduce the LIGO downtime by 30%. It also presents a
plan to implement this new earthquake configuration in the LIGO automation
system.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,564 | Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO | The design of spacecraft trajectories for missions visiting multiple
celestial bodies is here framed as a multi-objective bilevel optimization
problem. A comparative study is performed to assess the performance of
different Beam Search algorithms at tackling the combinatorial problem of
finding the ideal sequence of bodies. Special focus is placed on the
development of a new hybridization between Beam Search and the Population-based
Ant Colony Optimization algorithm. An experimental evaluation shows all
algorithms achieving exceptional performance on a hard benchmark problem. It is
found that a properly tuned deterministic Beam Search always outperforms the
remaining variants. Beam P-ACO, however, demonstrates lower parameter
sensitivity, while offering superior worst-case performance. Being an anytime
algorithm, it is then found to be the preferable choice for certain practical
applications.
| 1 | 1 | 0 | 0 | 0 | 0 |
17,565 | Types and unitary representations of reductive p-adic groups | We prove that for every Bushnell-Kutzko type that satisfies a certain
rigidity assumption, the equivalence of categories between the corresponding
Bernstein component and the category of modules for the Hecke algebra of the
type induces a bijection between irreducible unitary representations in the two
categories. This is a generalization of the unitarity criterion of Barbasch and
Moy for representations with Iwahori fixed vectors.
| 0 | 0 | 1 | 0 | 0 | 0 |
17,566 | Average values of L-functions in even characteristic | Let $k = \mathbb{F}_{q}(T)$ be the rational function field over a finite
field $\mathbb{F}_{q}$, where $q$ is a power of $2$. In this paper we solve the
problem of averaging the quadratic $L$-functions $L(s, \chi_{u})$ over
fundamental discriminants. Any separable quadratic extension $K$ of $k$ is of
the form $K = k(x_{u})$, where $x_{u}$ is a zero of $X^2+X+u=0$ for some $u\in
k$. We characterize the family $\mathcal I$ (resp. $\mathcal F$, $\mathcal F'$)
of rational functions $u\in k$ such that any separable quadratic extension $K$
of $k$ in which the infinite prime $\infty = (1/T)$ of $k$ ramifies (resp.
splits, is inert) can be written as $K = k(x_{u})$ with a unique $u\in\mathcal
I$ (resp. $u\in\mathcal F$, $u\in\mathcal F'$). For almost all $s\in\mathbb C$
with ${\rm Re}(s)\ge \frac{1}2$, we obtain the asymptotic formulas for the
summation of $L(s,\chi_{u})$ over all $k(x_{u})$ with $u\in \mathcal I$, all
$k(x_{u})$ with $u\in \mathcal F$ or all $k(x_{u})$ with $u\in \mathcal F'$ of
given genus. As applications, we obtain the asymptotic mean value formulas of
$L$-functions at $s=\frac{1}2$ and $s=1$ and the asymptotic mean value formulas
of the class number $h_{u}$ or the class number times regulator $h_{u} R_{u}$.
| 0 | 0 | 1 | 0 | 0 | 0 |
17,567 | Decoupled molecules with binding polynomials of bidegree (n,2) | We present a result on the number of decoupled molecules for systems binding
two different types of ligands. In the case of $n$ and $2$ binding sites
respectively, we show that, generically, there are $2(n!)^{2}$ decoupled
molecules with the same binding polynomial. For molecules with more binding
sites for the second ligand, we provide computational results.
| 1 | 1 | 0 | 0 | 0 | 0 |
17,568 | Learning to update Auto-associative Memory in Recurrent Neural Networks for Improving Sequence Memorization | Learning to remember long sequences remains a challenging task for recurrent
neural networks. Register memory and attention mechanisms were both proposed to
resolve the issue with either high computational cost to retain memory
differentiability, or by discounting the RNN representation learning towards
encoding shorter local contexts than encouraging long sequence encoding.
Associative memory, which studies the compression of multiple patterns in a
fixed size memory, were rarely considered in recent years. Although some recent
work tries to introduce associative memory in RNN and mimic the energy decay
process in Hopfield nets, it inherits the shortcoming of rule-based memory
updates, and the memory capacity is limited. This paper proposes a method to
learn the memory update rule jointly with task objective to improve memory
capacity for remembering long sequences. Also, we propose an architecture that
uses multiple such associative memory for more complex input encoding. We
observed some interesting facts when compared to other RNN architectures on
some well-studied sequence learning tasks.
| 1 | 0 | 0 | 1 | 0 | 0 |
17,569 | McDiarmid Drift Detection Methods for Evolving Data Streams | Increasingly, Internet of Things (IoT) domains, such as sensor networks,
smart cities, and social networks, generate vast amounts of data. Such data are
not only unbounded and rapidly evolving. Rather, the content thereof
dynamically evolves over time, often in unforeseen ways. These variations are
due to so-called concept drifts, caused by changes in the underlying data
generation mechanisms. In a classification setting, concept drift causes the
previously learned models to become inaccurate, unsafe and even unusable.
Accordingly, concept drifts need to be detected, and handled, as soon as
possible. In medical applications and emergency response settings, for example,
change in behaviours should be detected in near real-time, to avoid potential
loss of life. To this end, we introduce the McDiarmid Drift Detection Method
(MDDM), which utilizes McDiarmid's inequality in order to detect concept drift.
The MDDM approach proceeds by sliding a window over prediction results, and
associate window entries with weights. Higher weights are assigned to the most
recent entries, in order to emphasize their importance. As instances are
processed, the detection algorithm compares a weighted mean of elements inside
the sliding window with the maximum weighted mean observed so far. A
significant difference between the two weighted means, upper-bounded by the
McDiarmid inequality, implies a concept drift. Our extensive experimentation
against synthetic and real-world data streams show that our novel method
outperforms the state-of-the-art. Specifically, MDDM yields shorter detection
delays as well as lower false negative rates, while maintaining high
classification accuracies.
| 1 | 0 | 0 | 1 | 0 | 0 |
17,570 | Yield in Amorphous Solids: The Ant in the Energy Landscape Labyrinth | It has recently been shown that yield in amorphous solids under oscillatory
shear is a dynamical transition from asymptotically periodic to asymptotically
chaotic, diffusive dynamics. However, the type and universality class of this
transition are still undecided. Here we show that the diffusive behavior of the
vector of coordinates of the particles comprising an amorphous solid when
subject to oscillatory shear, is analogous to that of a particle diffusing in a
percolating lattice, the so-called "ant in the labyrinth" problem, and that
yield corresponds to a percolation transition in the lattice. We explain this
as a transition in the connectivity of the energy landscape, which affects the
phase-space regions accessible to the coordinate vector for a given maximal
strain amplitude. This transition provides a natural explanation to the
observed limit-cycles, periods larger than one and diverging time-scales at
yield.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,571 | Statistical methods in astronomy | We present a review of data types and statistical methods often encountered
in astronomy. The aim is to provide an introduction to statistical applications
in astronomy for statisticians and computer scientists. We highlight the
complex, often hierarchical, nature of many astronomy inference problems and
advocate for cross-disciplinary collaborations to address these challenges.
| 0 | 1 | 0 | 1 | 0 | 0 |
17,572 | A note on some algebraic trapdoors for block ciphers | We provide sufficient conditions to guarantee that a translation based cipher
is not vulnerable with respect to the partition-based trapdoor. This trapdoor
has been introduced, recently, by Bannier et al. (2016) and it generalizes that
introduced by Paterson in 1999. Moreover, we discuss the fact that studying the
group generated by the round functions of a block cipher may not be sufficient
to guarantee security against these trapdoors for the cipher.
| 1 | 0 | 1 | 0 | 0 | 0 |
17,573 | Bi-Demographic Changes and Current Account using SVAR Modeling | The paper, as a new contribution, aims to explore the impacts of
bi-demographic structure on the current account and growth. By using a SVAR
modeling, we track the dynamic impacts between the underlying variables of the
Saudi economy. New insights have been developed to study the interrelations
between population growth, current account and economic growth inside the
neoclassical theory of population. The long-run net impact on economic growth
of the bi-population growth is negative, due to the typically lower skill sets
of the immigrant labor population. Besides, the negative long-run contribution
of immigrant workers to the current account growth largely exceeds that of
contributions from the native population, because of the increasing levels of
remittance outflows from the country. We find that a positive shock in
immigration leads to a negative impact on native active age ratio. Thus, the
immigrants appear to be more substitutes than complements for native workers.
| 0 | 0 | 0 | 0 | 0 | 1 |
17,574 | Supervised Machine Learning for Signals Having RRC Shaped Pulses | Classification performances of the supervised machine learning techniques
such as support vector machines, neural networks and logistic regression are
compared for modulation recognition purposes. The simple and robust features
are used to distinguish continuous-phase FSK from QAM-PSK signals. Signals
having root-raised-cosine shaped pulses are simulated in extreme noisy
conditions having joint impurities of block fading, lack of symbol and sampling
synchronization, carrier offset, and additive white Gaussian noise. The
features are based on sample mean and sample variance of the imaginary part of
the product of two consecutive complex signal values.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,575 | End-to-End Optimized Transmission over Dispersive Intensity-Modulated Channels Using Bidirectional Recurrent Neural Networks | We propose an autoencoding sequence-based transceiver for communication over
dispersive channels with intensity modulation and direct detection (IM/DD),
designed as a bidirectional deep recurrent neural network (BRNN). The receiver
uses a sliding window technique to allow for efficient data stream estimation.
We find that this sliding window BRNN (SBRNN), based on end-to-end deep
learning of the communication system, achieves a significant bit-error-rate
reduction at all examined distances in comparison to previous block-based
autoencoders implemented as feed-forward neural networks (FFNNs), leading to an
increase of the transmission distance. We also compare the end-to-end SBRNN
with a state-of-the-art IM/DD solution based on two level pulse amplitude
modulation with an FFNN receiver, simultaneously processing multiple received
symbols and approximating nonlinear Volterra equalization. Our results show
that the SBRNN outperforms such systems at both 42 and 84\,Gb/s, while training
fewer parameters. Our novel SBRNN design aims at tailoring the end-to-end deep
learning-based systems for communication over nonlinear channels with memory,
such as the optical IM/DD fiber channel.
| 1 | 0 | 0 | 1 | 0 | 0 |
17,576 | Effective difference elimination and Nullstellensatz | We prove effective Nullstellensatz and elimination theorems for difference
equations in sequence rings. More precisely, we compute an explicit function of
geometric quantities associated to a system of difference equations (and these
geometric quantities may themselves be bounded by a function of the number of
variables, the order of the equations, and the degrees of the equations) so
that for any system of difference equations in variables $\mathbf{x} = (x_1,
\ldots, x_m)$ and $\mathbf{u} = (u_1, \ldots, u_r)$, if these equations have
any nontrivial consequences in the $\mathbf{x}$ variables, then such a
consequence may be seen algebraically considering transforms up to the order of
our bound. Specializing to the case of $m = 0$, we obtain an effective method
to test whether a given system of difference equations is consistent.
| 0 | 0 | 1 | 0 | 0 | 0 |
17,577 | A note on primitive $1-$normal elements over finite fields | Let $q$ be a prime power of a prime $p$, $n$ a positive integer and $\mathbb
F_{q^n}$ the finite field with $q^n$ elements. The $k-$normal elements over
finite fields were introduced and characterized by Huczynska et al (2013).
Under the condition that $n$ is not divisible by $p$, they obtained an
existence result on primitive $1-$normal elements of $\mathbb F_{q^n}$ over
$\mathbb F_q$ for $q>2$. In this note, we extend their result to the excluded
case $q=2$.
| 0 | 0 | 1 | 0 | 0 | 0 |
17,578 | Sparse Coding Predicts Optic Flow Specificities of Zebrafish Pretectal Neurons | Zebrafish pretectal neurons exhibit specificities for large-field optic flow
patterns associated with rotatory or translatory body motion. We investigate
the hypothesis that these specificities reflect the input statistics of natural
optic flow. Realistic motion sequences were generated using computer graphics
simulating self-motion in an underwater scene. Local retinal motion was
estimated with a motion detector and encoded in four populations of
directionally tuned retinal ganglion cells, represented as two signed input
variables. This activity was then used as input into one of two learning
networks: a sparse coding network (competitive learning) and backpropagation
network (supervised learning). Both simulations develop specificities for optic
flow which are comparable to those found in a neurophysiological study (Kubo et
al. 2014), and relative frequencies of the various neuronal responses are best
modeled by the sparse coding approach. We conclude that the optic flow neurons
in the zebrafish pretectum do reflect the optic flow statistics. The predicted
vectorial receptive fields show typical optic flow fields but also "Gabor" and
dipole-shaped patterns that likely reflect difference fields needed for
reconstruction by linear superposition.
| 0 | 0 | 0 | 0 | 1 | 0 |
17,579 | Revisiting the problem of audio-based hit song prediction using convolutional neural networks | Being able to predict whether a song can be a hit has impor- tant
applications in the music industry. Although it is true that the popularity of
a song can be greatly affected by exter- nal factors such as social and
commercial influences, to which degree audio features computed from musical
signals (whom we regard as internal factors) can predict song popularity is an
interesting research question on its own. Motivated by the recent success of
deep learning techniques, we attempt to ex- tend previous work on hit song
prediction by jointly learning the audio features and prediction models using
deep learning. Specifically, we experiment with a convolutional neural net-
work model that takes the primitive mel-spectrogram as the input for feature
learning, a more advanced JYnet model that uses an external song dataset for
supervised pre-training and auto-tagging, and the combination of these two
models. We also consider the inception model to characterize audio infor-
mation in different scales. Our experiments suggest that deep structures are
indeed more accurate than shallow structures in predicting the popularity of
either Chinese or Western Pop songs in Taiwan. We also use the tags predicted
by JYnet to gain insights into the result of different models.
| 1 | 0 | 0 | 1 | 0 | 0 |
17,580 | Natural Language Multitasking: Analyzing and Improving Syntactic Saliency of Hidden Representations | We train multi-task autoencoders on linguistic tasks and analyze the learned
hidden sentence representations. The representations change significantly when
translation and part-of-speech decoders are added. The more decoders a model
employs, the better it clusters sentences according to their syntactic
similarity, as the representation space becomes less entangled. We explore the
structure of the representation space by interpolating between sentences, which
yields interesting pseudo-English sentences, many of which have recognizable
syntactic structure. Lastly, we point out an interesting property of our
models: The difference-vector between two sentences can be added to change a
third sentence with similar features in a meaningful way.
| 0 | 0 | 0 | 1 | 0 | 0 |
17,581 | Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data | Time series forecasting is a crucial component of many important
applications, ranging from forecasting the stock markets to energy load
prediction. The high-dimensionality, velocity and variety of the data collected
in these applications pose significant and unique challenges that must be
carefully addressed for each of them. In this work, a novel Temporal Logistic
Neural Bag-of-Features approach, that can be used to tackle these challenges,
is proposed. The proposed method can be effectively combined with deep neural
networks, leading to powerful deep learning models for time series analysis.
However, combining existing BoF formulations with deep feature extractors pose
significant challenges: the distribution of the input features is not
stationary, tuning the hyper-parameters of the model can be especially
difficult and the normalizations involved in the BoF model can cause
significant instabilities during the training process. The proposed method is
capable of overcoming these limitations by a employing a novel adaptive scaling
mechanism and replacing the classical Gaussian-based density estimation
involved in the regular BoF model with a logistic kernel. The effectiveness of
the proposed approach is demonstrated using extensive experiments on a
large-scale financial time series dataset that consists of more than 4 million
limit orders.
| 1 | 0 | 0 | 1 | 0 | 1 |
17,582 | Extended Bose Hubbard model for two leg ladder systems in artificial magnetic fields | We investigate the ground state properties of ultracold atoms with long range
interactions trapped in a two leg ladder configuration in the presence of an
artificial magnetic field. Using a Gross-Pitaevskii approach and a mean field
Gutzwiller variational method, we explore both the weakly interacting and
strongly interacting regime, respectively. We calculate the boundaries between
the density-wave/supersolid and the Mott-insulator/superfluid phases as a
function of magnetic flux and uncover regions of supersolidity. The mean-field
results are confirmed by numerical simulations using a cluster mean field
approach.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,583 | Insensitivity of The Distance Ladder Hubble Constant Determination to Cepheid Calibration Modeling Choices | Recent determination of the Hubble constant via Cepheid-calibrated supernovae
by \citet{riess_2.4_2016} (R16) find $\sim 3\sigma$ tension with inferences
based on cosmic microwave background temperature and polarization measurements
from $Planck$. This tension could be an indication of inadequacies in the
concordance $\Lambda$CDM model. Here we investigate the possibility that the
discrepancy could instead be due to systematic bias or uncertainty in the
Cepheid calibration step of the distance ladder measurement by R16. We consider
variations in total-to-selective extinction of Cepheid flux as a function of
line-of-sight, hidden structure in the period-luminosity relationship, and
potentially different intrinsic color distributions of Cepheids as a function
of host galaxy. Considering all potential sources of error, our final
determination of $H_0 = 73.3 \pm 1.7~{\rm km/s/Mpc}$ (not including systematic
errors from the treatment of geometric distances or Type Ia Supernovae) shows
remarkable robustness and agreement with R16. We conclude systematics from the
modeling of Cepheid photometry, including Cepheid selection criteria, cannot
explain the observed tension between Cepheid-variable and CMB-based inferences
of the Hubble constant. Considering a `model-independent' approach to relating
Cepheids in galaxies with known distances to Cepheids in galaxies hosting a
Type Ia supernova and finding agreement with the R16 result, we conclude no
generalization of the model relating anchor and host Cepheid magnitude
measurements can introduce significant bias in the $H_0$ inference.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,584 | Phrase-based Image Captioning with Hierarchical LSTM Model | Automatic generation of caption to describe the content of an image has been
gaining a lot of research interests recently, where most of the existing works
treat the image caption as pure sequential data. Natural language, however
possess a temporal hierarchy structure, with complex dependencies between each
subsequence. In this paper, we propose a phrase-based hierarchical Long
Short-Term Memory (phi-LSTM) model to generate image description. In contrast
to the conventional solutions that generate caption in a pure sequential
manner, our proposed model decodes image caption from phrase to sentence. It
consists of a phrase decoder at the bottom hierarchy to decode noun phrases of
variable length, and an abbreviated sentence decoder at the upper hierarchy to
decode an abbreviated form of the image description. A complete image caption
is formed by combining the generated phrases with sentence during the inference
stage. Empirically, our proposed model shows a better or competitive result on
the Flickr8k, Flickr30k and MS-COCO datasets in comparison to the state-of-the
art models. We also show that our proposed model is able to generate more novel
captions (not seen in the training data) which are richer in word contents in
all these three datasets.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,585 | The three-dimensional structure of swirl-switching in bent pipe flow | Swirl-switching is a low-frequency oscillatory phenomenon which affects the
Dean vortices in bent pipes and may cause fatigue in piping systems. Despite
thirty years worth of research, the mechanism that causes these oscillations
and the frequencies that characterise them remain unclear. Here we show that a
three-dimensional wave-like structure is responsible for the low-frequency
switching of the dominant Dean vortex. The present study, performed via direct
numerical simulation, focuses on the turbulent flow through a 90 \degree pipe
bend preceded and followed by straight pipe segments. A pipe with curvature 0.3
(defined as ratio between pipe radius and bend radius) is studied for a bulk
Reynolds number Re = 11 700, corresponding to a friction Reynolds number
Re_\tau \approx 360. Synthetic turbulence is generated at the inflow section
and used instead of the classical recycling method in order to avoid the
interference between recycling and swirl-switching frequencies. The flow field
is analysed by three-dimensional proper orthogonal decomposition (POD) which
for the first time allows the identification of the source of swirl-switching:
a wave-like structure that originates in the pipe bend. Contrary to some
previous studies, the flow in the upstream pipe does not show any direct
influence on the swirl-switching modes. Our analysis further shows that a
three- dimensional characterisation of the modes is crucial to understand the
mechanism, and that reconstructions based on 2D POD modes are incomplete.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,586 | InfoVAE: Information Maximizing Variational Autoencoders | A key advance in learning generative models is the use of amortized inference
distributions that are jointly trained with the models. We find that existing
training objectives for variational autoencoders can lead to inaccurate
amortized inference distributions and, in some cases, improving the objective
provably degrades the inference quality. In addition, it has been observed that
variational autoencoders tend to ignore the latent variables when combined with
a decoding distribution that is too flexible. We again identify the cause in
existing training criteria and propose a new class of objectives (InfoVAE) that
mitigate these problems. We show that our model can significantly improve the
quality of the variational posterior and can make effective use of the latent
features regardless of the flexibility of the decoding distribution. Through
extensive qualitative and quantitative analyses, we demonstrate that our models
outperform competing approaches on multiple performance metrics.
| 1 | 0 | 0 | 1 | 0 | 0 |
17,587 | Between-class Learning for Image Classification | In this paper, we propose a novel learning method for image classification
called Between-Class learning (BC learning). We generate between-class images
by mixing two images belonging to different classes with a random ratio. We
then input the mixed image to the model and train the model to output the
mixing ratio. BC learning has the ability to impose constraints on the shape of
the feature distributions, and thus the generalization ability is improved. BC
learning is originally a method developed for sounds, which can be digitally
mixed. Mixing two image data does not appear to make sense; however, we argue
that because convolutional neural networks have an aspect of treating input
data as waveforms, what works on sounds must also work on images. First, we
propose a simple mixing method using internal divisions, which surprisingly
proves to significantly improve performance. Second, we propose a mixing method
that treats the images as waveforms, which leads to a further improvement in
performance. As a result, we achieved 19.4% and 2.26% top-1 errors on
ImageNet-1K and CIFAR-10, respectively.
| 1 | 0 | 0 | 1 | 0 | 0 |
17,588 | Girsanov reweighting for path ensembles and Markov state models | The sensitivity of molecular dynamics on changes in the potential energy
function plays an important role in understanding the dynamics and function of
complex molecules.We present a method to obtain path ensemble averages of a
perturbed dynamics from a set of paths generated by a reference dynamics. It is
based on the concept of path probability measure and the Girsanov theorem, a
result from stochastic analysis to estimate a change of measure of a path
ensemble. Since Markov state models (MSM) of the molecular dynamics can be
formulated as a combined phase-space and path ensemble average, the method can
be extended toreweight MSMs by combining it with a reweighting of the Boltzmann
distribution. We demonstrate how to efficiently implement the Girsanov
reweighting in a molecular dynamics simulation program by calculating parts of
the reweighting factor "on the fly" during the simulation, and we benchmark the
method on test systems ranging from a two-dimensional diffusion process to an
artificial many-body system and alanine dipeptide and valine dipeptide in
implicit and explicit water. The method can be used to study the sensitivity of
molecular dynamics on external perturbations as well as to reweight
trajectories generated by enhanced sampling schemes to the original dynamics.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,589 | Coordination of multi-agent systems via asynchronous cloud communication | In this work we study a multi-agent coordination problem in which agents are
only able to communicate with each other intermittently through a cloud server.
To reduce the amount of required communication, we develop a self-triggered
algorithm that allows agents to communicate with the cloud only when necessary
rather than at some fixed period. Unlike the vast majority of similar works
that propose distributed event- and/or self-triggered control laws, this work
doesn't assume agents can be "listening" continuously. In other words, when an
event is triggered by one agent, neighboring agents will not be aware of this
until the next time they establish communication with the cloud themselves.
Using a notion of "promises" about future control inputs, agents are able to
keep track of higher quality estimates about their neighbors allowing them to
stay disconnected from the cloud for longer periods of time while still
guaranteeing a positive contribution to the global task. We prove that our
self-triggered coordination algorithm guarantees that the system asymptotically
reaches the set of desired states. Simulations illustrate our results.
| 1 | 0 | 1 | 0 | 0 | 0 |
17,590 | PatternListener: Cracking Android Pattern Lock Using Acoustic Signals | Pattern lock has been widely used for authentication to protect user privacy
on mobile devices (e.g., smartphones and tablets). Given its pervasive usage,
the compromise of pattern lock could lead to serious consequences. Several
attacks have been constructed to crack the lock. However, these approaches
require the attackers to either be physically close to the target device or be
able to manipulate the network facilities (e.g., WiFi hotspots) used by the
victims. Therefore, the effectiveness of the attacks is significantly impacted
by the environment of mobile devices. Also, these attacks are not scalable
since they cannot easily infer unlock patterns of a large number of devices.
Motivated by an observation that fingertip motions on the screen of a mobile
device can be captured by analyzing surrounding acoustic signals on it, we
propose PatternListener, a novel acoustic attack that cracks pattern lock by
analyzing imperceptible acoustic signals reflected by the fingertip. It
leverages speakers and microphones of the victim's device to play imperceptible
audio and record the acoustic signals reflected by the fingertip. In
particular, it infers each unlock pattern by analyzing individual lines that
compose the pattern and are the trajectories of the fingertip. We propose
several algorithms to construct signal segments according to the captured
signals for each line and infer possible candidates of each individual line
according to the signal segments. Finally, we map all line candidates into grid
patterns and thereby obtain the candidates of the entire unlock pattern. We
implement a PatternListener prototype by using off-the-shelf smartphones and
thoroughly evaluate it using 130 unique patterns. The real experimental results
demonstrate that PatternListener can successfully exploit over 90% patterns
within five attempts.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,591 | Marginal Release Under Local Differential Privacy | Many analysis and machine learning tasks require the availability of marginal
statistics on multidimensional datasets while providing strong privacy
guarantees for the data subjects. Applications for these statistics range from
finding correlations in the data to fitting sophisticated prediction models. In
this paper, we provide a set of algorithms for materializing marginal
statistics under the strong model of local differential privacy. We prove the
first tight theoretical bounds on the accuracy of marginals compiled under each
approach, perform empirical evaluation to confirm these bounds, and evaluate
them for tasks such as modeling and correlation testing. Our results show that
releasing information based on (local) Fourier transformations of the input is
preferable to alternatives based directly on (local) marginals.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,592 | A possible flyby anomaly for Juno at Jupiter | In the last decades there have been an increasing interest in improving the
accuracy of spacecraft navigation and trajectory data. In the course of this
plan some anomalies have been found that cannot, in principle, be explained in
the context of the most accurate orbital models including all known effects
from classical dynamics and general relativity. Of particular interest for its
puzzling nature, and the lack of any accepted explanation for the moment, is
the flyby anomaly discovered in some spacecraft flybys of the Earth over the
course of twenty years. This anomaly manifest itself as the impossibility of
matching the pre and post-encounter Doppler tracking and ranging data within a
single orbit but, on the contrary, a difference of a few mm$/$s in the
asymptotic velocities is required to perform the fitting.
Nevertheless, no dedicated missions have been carried out to elucidate the
origin of this phenomenon with the objective either of revising our
understanding of gravity or to improve the accuracy of spacecraft Doppler
tracking by revealing a conventional origin.
With the occasion of the Juno mission arrival at Jupiter and the close flybys
of this planet, that are currently been performed, we have developed an orbital
model suited to the time window close to the perijove. This model shows that an
anomalous acceleration of a few mm$/$s$^2$ is also present in this case. The
chance for overlooked conventional or possible unconventional explanations is
discussed.
| 0 | 1 | 0 | 0 | 0 | 0 |
17,593 | Crossover between various initial conditions in KPZ growth: flat to stationary | We conjecture the universal probability distribution at large time for the
one-point height in the 1D Kardar-Parisi-Zhang (KPZ) stochastic growth
universality class, with initial conditions interpolating from any one of the
three main classes (droplet, flat, stationary) on the left, to another on the
right, allowing for drifts and also for a step near the origin. The result is
obtained from a replica Bethe ansatz calculation starting from the KPZ
continuum equation, together with a "decoupling assumption" in the large time
limit. Some cases are checked to be equivalent to previously known results from
other models in the same class, which provides a test of the method, others
appear to be new. In particular we obtain the crossover distribution between
flat and stationary initial conditions (crossover from Airy$_1$ to Airy$_{\rm
stat}$) in a simple compact form.
| 0 | 1 | 1 | 0 | 0 | 0 |
17,594 | Multimodel Response Assessment for Monthly Rainfall Distribution in Some Selected Indian Cities Using Best Fit Probability as a Tool | We carry out a study of the statistical distribution of rainfall
precipitation data for 20 cites in India. We have determined the best-fit
probability distribution for these cities from the monthly precipitation data
spanning 100 years of observations from 1901 to 2002. To fit the observed data,
we considered 10 different distributions. The efficacy of the fits for these
distributions was evaluated using four empirical non-parametric goodness-of-fit
tests namely Kolmogorov-Smirnov, Anderson-Darling, Chi-Square, Akaike
information criterion, and Bayesian Information criterion. Finally, the
best-fit distribution using each of these tests were reported, by combining the
results from the model comparison tests. We then find that for most of the
cities, Generalized Extreme-Value Distribution or Inverse Gaussian Distribution
most adequately fits the observed data.
| 0 | 1 | 0 | 1 | 0 | 0 |
17,595 | Small Boxes Big Data: A Deep Learning Approach to Optimize Variable Sized Bin Packing | Bin Packing problems have been widely studied because of their broad
applications in different domains. Known as a set of NP-hard problems, they
have different vari- ations and many heuristics have been proposed for
obtaining approximate solutions. Specifically, for the 1D variable sized bin
packing problem, the two key sets of optimization heuristics are the bin
assignment and the bin allocation. Usually the performance of a single static
optimization heuristic can not beat that of a dynamic one which is tailored for
each bin packing instance. Building such an adaptive system requires modeling
the relationship between bin features and packing perform profiles. The primary
drawbacks of traditional AI machine learnings for this task are the natural
limitations of feature engineering, such as the curse of dimensionality and
feature selection quality. We introduce a deep learning approach to overcome
the drawbacks by applying a large training data set, auto feature selection and
fast, accurate labeling. We show in this paper how to build such a system by
both theoretical formulation and engineering practices. Our prediction system
achieves up to 89% training accuracy and 72% validation accuracy to select the
best heuristic that can generate a better quality bin packing solution.
| 1 | 0 | 0 | 1 | 0 | 0 |
17,596 | Surges of collective human activity emerge from simple pairwise correlations | Human populations exhibit complex behaviors---characterized by long-range
correlations and surges in activity---across a range of social, political, and
technological contexts. Yet it remains unclear where these collective behaviors
come from, or if there even exists a set of unifying principles. Indeed,
existing explanations typically rely on context-specific mechanisms, such as
traffic jams driven by work schedules or spikes in online traffic induced by
significant events. However, analogies with statistical mechanics suggest a
more general mechanism: that collective patterns can emerge organically from
fine-scale interactions within a population. Here, across four different modes
of human activity, we show that the simplest correlations in a
population---those between pairs of individuals---can yield accurate
quantitative predictions for the large-scale behavior of the entire population.
To quantify the minimal consequences of pairwise correlations, we employ the
principle of maximum entropy, making our description equivalent to an Ising
model whose interactions and external fields are notably calculated from past
observations of population activity. In addition to providing accurate
quantitative predictions, we show that the topology of learned Ising
interactions resembles the network of inter-human communication within a
population. Together, these results demonstrate that fine-scale correlations
can be used to predict large-scale social behaviors, a perspective that has
critical implications for modeling and resource allocation in human
populations.
| 1 | 0 | 0 | 0 | 0 | 0 |
17,597 | Semantically Enhanced Dynamic Bayesian Network for Detecting Sepsis Mortality Risk in ICU Patients with Infection | Although timely sepsis diagnosis and prompt interventions in Intensive Care
Unit (ICU) patients are associated with reduced mortality, early clinical
recognition is frequently impeded by non-specific signs of infection and
failure to detect signs of sepsis-induced organ dysfunction in a constellation
of dynamically changing physiological data. The goal of this work is to
identify patient at risk of life-threatening sepsis utilizing a data-centered
and machine learning-driven approach. We derive a mortality risk predictive
dynamic Bayesian network (DBN) guided by a customized sepsis knowledgebase and
compare the predictive accuracy of the derived DBN with the Sepsis-related
Organ Failure Assessment (SOFA) score, the Quick SOFA (qSOFA) score, the
Simplified Acute Physiological Score (SAPS-II) and the Modified Early Warning
Score (MEWS) tools.
A customized sepsis ontology was used to derive the DBN node structure and
semantically characterize temporal features derived from both structured
physiological data and unstructured clinical notes. We assessed the performance
in predicting mortality risk of the DBN predictive model and compared
performance to other models using Receiver Operating Characteristic (ROC)
curves, area under curve (AUROC), calibration curves, and risk distributions.
The derived dataset consists of 24,506 ICU stays from 19,623 patients with
evidence of suspected infection, with 2,829 patients deceased at discharge. The
DBN AUROC was found to be 0.91, which outperformed the SOFA (0.843), qSOFA
(0.66), MEWS (0.73), and SAPS-II (0.77) scoring tools. Continuous Net
Reclassification Index and Integrated Discrimination Improvement analysis
supported the superiority DBN. Compared with conventional rule-based risk
scoring tools, the sepsis knowledgebase-driven DBN algorithm offers improved
performance for predicting mortality of infected patients in ICUs.
| 0 | 0 | 0 | 1 | 0 | 0 |
17,598 | Hölder regularity of viscosity solutions of some fully nonlinear equations in the Heisenberg group | In this paper we prove the Hölder regularity of bounded, uniformly
continuous, viscosity solutions of some degenerate fully nonlinear equations in
the Heisenberg group.
| 0 | 0 | 1 | 0 | 0 | 0 |
17,599 | Normal form for transverse instability of the line soliton with a nearly critical speed of propagation | There exists a critical speed of propagation of the line solitons in the
Zakharov-Kuznetsov (ZK) equation such that small transversely periodic
perturbations are unstable for line solitons with larger-than-critical speeds
and orbitally stable for those with smaller-than-critical speeds. The normal
form for transverse instability of the line soliton with a nearly critical
speed of propagation is derived by means of symplectic projections and
near-identity transformations. Justification of this normal form is provided
with the energy method. The normal form predicts a transformation of the
unstable line solitons with larger-than-critical speeds to the orbitally stable
transversely modulated solitary waves.
| 0 | 1 | 1 | 0 | 0 | 0 |
17,600 | The CLaC Discourse Parser at CoNLL-2016 | This paper describes our submission "CLaC" to the CoNLL-2016 shared task on
shallow discourse parsing. We used two complementary approaches for the task. A
standard machine learning approach for the parsing of explicit relations, and a
deep learning approach for non-explicit relations. Overall, our parser achieves
an F1-score of 0.2106 on the identification of discourse relations (0.3110 for
explicit relations and 0.1219 for non-explicit relations) on the blind
CoNLL-2016 test set.
| 1 | 0 | 0 | 0 | 0 | 0 |
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