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Excitonic effects in third harmonic generation: the case of carbon nanotubes and nanoribbons | Linear and nonlinear optical properties of low dimensional nanostructures
have attracted a large interest in the scientific community as tools to probe
the strong confinement of the electrons and for possible applications in
optoelectronic devices. In particular it has been shown that the linear optical
response of carbon nanotubes [Science 308, 838 (2005)] and graphene nanoribbons
[Nat. Comm. 5, 4253 (2014)] is dominated by bounded electron-hole pairs, the
excitons. The role of excitons in linear response has been widely studied, but
still little is known on their effect on nonlinear susceptibilities. Using a
recently developed methodology [Phys. Rev. B 88, 235113 (2013)] based on
well-established ab-initio many-body perturbation theory approaches, we find
that quasiparticle shifts and excitonic effects significantly modify the
third-harmonic generation in carbon nanotubes and graphene nanoribbons. For
both systems the net effect of many-body effects is to reduce the intensity of
the main peak in the independent particle spectrum and redistribute the
spectral weight among several excitonic resonances.
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A general family of congruences for Bernoulli numbers | We prove a general family of congruences for Bernoulli numbers whose index is
a polynomial function of a prime, modulo a power of that prime. Our family
generalizes many known results, including the von Staudt--Clausen theorem and
Kummer's congruence.
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The Fourier algebra of a rigid $C^{\ast}$-tensor category | Completely positive and completely bounded mutlipliers on rigid
$C^{\ast}$-tensor categories were introduced by Popa and Vaes. Using these
notions, we define and study the Fourier-Stieltjes algebra, the Fourier algebra
and the algebra of completely bounded multipliers of a rigid $C^{\ast}$-tensor
category. The rich structure that these algebras have in the setting of locally
compact groups is still present in the setting of rigid $C^{\ast}$-tensor
categories. We also prove that Leptin's characterization of amenability still
holds in this setting, and we collect some natural observations on property
(T).
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On lattice path matroid polytopes: integer points and Ehrhart polynomial | In this paper we investigate the number of integer points lying in dilations
of lattice path matroid polytopes. We give a characterization of such points as
polygonal paths in the diagram of the lattice path matroid. Furthermore, we
prove that lattice path matroid polytopes are affinely equivalent to a family
of distributive polytopes. As applications we obtain two new infinite families
of matroids verifying a conjecture of De Loera et.~al. and present an explicit
formula of the Ehrhart polynomial for one of them.
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Quantum effects and magnetism in the spatially distributed DNA molecules | Electronic and magnetic properties of DNA structures doped by simple and
transition d- and f-metal ions (Gd, La, Cu, Zn, Au) are reviewed. Both one- and
two dimensional systems are considered. A particular attention is paid to
gadolinium and copper doped DNA systems, their unusual magnetism being treated.
The problem of classical and quantum transport (including transfer of genetic
information during replication and transcription) and electron localization in
biological systems is discussed.
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A Stochastic Model for Short-Term Probabilistic Forecast of Solar Photo-Voltaic Power | In this paper, a stochastic model with regime switching is developed for
solar photo-voltaic (PV) power in order to provide short-term probabilistic
forecasts. The proposed model for solar PV power is physics inspired and
explicitly incorporates the stochasticity due to clouds using different
parameters addressing the attenuation in power.Based on the statistical
behavior of parameters, a simple regime-switching process between the three
classes of sunny, overcast and partly cloudy is proposed. Then, probabilistic
forecasts of solar PV power are obtained by identifying the present regime
using PV power measurements and assuming persistence in this regime. To
illustrate the technique developed, a set of solar PV power data from a single
rooftop installation in California is analyzed and the effectiveness of the
model in fitting the data and in providing short-term point and probabilistic
forecasts is verified. The proposed forecast method outperforms a variety of
reference models that produce point and probabilistic forecasts and therefore
portrays the merits of employing the proposed approach.
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Stability and elasticity of metastable solid solutions and superlattices in the MoN-TaN system: a first-principles study | Employing ab initio calculations, we discuss chemical, mechanical, and
dynamical stability of MoN-TaN solid solutions together with cubic-like MoN/TaN
superlattices, as another materials design concept. Hexagonal-type structures
based on low-energy modifications of MoN and TaN are the most stable ones over
the whole composition range. Despite being metastable, disordered cubic
polymorphs are energetically significantly preferred over their ordered
counterparts. An in-depth analysis of atomic environments in terms of bond
lengths and angles reveals that the chemical disorder results in (partially)
broken symmetry, i.e., the disordered cubic structure relaxes towards a
hexagonal NiAs-type phase, the ground state of MoN. Surprisingly, also the
superlattice architecture is clearly favored over the ordered cubic solid
solution. We show that the bi-axial coherency stresses in superlattices break
the cubic symmetry beyond simple tetragonal distortions and lead to a new
tetragonal $\zeta$-phase (space group P4/nmm), which exhibits a more negative
formation energy than the symmetry-stabilized cubic structures of MoN and TaN.
Unlike cubic TaN, the $\zeta\text{-TaN}$ is elastically and vibrationally
stable, while $\zeta$-MoN is stabilized only by the superlattice structure. To
map compositional trends in elasticity, we establish mechanical stability of
various Mo$_{1-x}$Ta$_x$N systems and find the closest high-symmetry
approximants of the corresponding elastic tensors. According to the estimated
polycrystalline moduli, the hexagonal polymorphs are predicted to be extremely
hard, however, less ductile than the cubic phases and superlattices. The trends
in stability based on energetics and elasticity are corroborated by density of
electronic states.
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High Accuracy Classification of Parkinson's Disease through Shape Analysis and Surface Fitting in $^{123}$I-Ioflupane SPECT Imaging | Early and accurate identification of parkinsonian syndromes (PS) involving
presynaptic degeneration from non-degenerative variants such as Scans Without
Evidence of Dopaminergic Deficit (SWEDD) and tremor disorders, is important for
effective patient management as the course, therapy and prognosis differ
substantially between the two groups. In this study, we use Single Photon
Emission Computed Tomography (SPECT) images from healthy normal, early PD and
SWEDD subjects, as obtained from the Parkinson's Progression Markers Initiative
(PPMI) database, and process them to compute shape- and surface fitting-based
features for the three groups. We use these features to develop and compare
various classification models that can discriminate between scans showing
dopaminergic deficit, as in PD, from scans without the deficit, as in healthy
normal or SWEDD. Along with it, we also compare these features with Striatal
Binding Ratio (SBR)-based features, which are well-established and clinically
used, by computing a feature importance score using Random forests technique.
We observe that the Support Vector Machine (SVM) classifier gave the best
performance with an accuracy of 97.29%. These features also showed higher
importance than the SBR-based features. We infer from the study that shape
analysis and surface fitting are useful and promising methods for extracting
discriminatory features that can be used to develop diagnostic models that
might have the potential to help clinicians in the diagnostic process.
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End-to-End Learning for Structured Prediction Energy Networks | Structured Prediction Energy Networks (SPENs) are a simple, yet expressive
family of structured prediction models (Belanger and McCallum, 2016). An energy
function over candidate structured outputs is given by a deep network, and
predictions are formed by gradient-based optimization. This paper presents
end-to-end learning for SPENs, where the energy function is discriminatively
trained by back-propagating through gradient-based prediction. In our
experience, the approach is substantially more accurate than the structured SVM
method of Belanger and McCallum (2016), as it allows us to use more
sophisticated non-convex energies. We provide a collection of techniques for
improving the speed, accuracy, and memory requirements of end-to-end SPENs, and
demonstrate the power of our method on 7-Scenes image denoising and CoNLL-2005
semantic role labeling tasks. In both, inexact minimization of non-convex SPEN
energies is superior to baseline methods that use simplistic energy functions
that can be minimized exactly.
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Self-compression of spatially limited laser pulses in a system of coupled light-guides | The self-action features of wave packets propagating in a two-dimensional
system of equidistantly arranged fibers are studied analytically and
numerically on the basis of the discrete nonlinear Schrödinger equation.
Self-consistent equations for the characteristic scales of a Gaussian wave
packet are derived on the basis of the variational approach, which are proved
numerically for powers $\mathcal{P} < 10 \mathcal{P}_\text{cr}$ exceeding
slightly the critical one for self-focusing. At higher powers, the wave beams
become filamented, and their amplitude is limited due to nonlinear breaking of
the interaction between neighbor light-guides. This make impossible to collect
a powerful wave beam into the single light-guide. The variational analysis show
the possibility of adiabatic self-compression of soliton-like laser pulses in
the process of their three-dimensional self-focusing to the central
light-guide. However, the further increase of the field amplitude during
self-compression leads to the longitudinal modulation instability development
and formation of a set of light bullets in the central fiber. In the regime of
hollow wave beams, filamentation instability becomes predominant. As a result,
it becomes possible to form a set of light bullets in optical fibers located on
the ring.
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A Hand-Held Multimedia Translation and Interpretation System with Application to Diet Management | We propose a network independent, hand-held system to translate and
disambiguate foreign restaurant menu items in real-time. The system is based on
the use of a portable multimedia device, such as a smartphones or a PDA. An
accurate and fast translation is obtained using a Machine Translation engine
and a context-specific corpora to which we apply two pre-processing steps,
called translation standardization and $n$-gram consolidation. The phrase-table
generated is orders of magnitude lighter than the ones commonly used in market
applications, thus making translations computationally less expensive, and
decreasing the battery usage. Translation ambiguities are mitigated using
multimedia information including images of dishes and ingredients, along with
ingredient lists. We implemented a prototype of our system on an iPod Touch
Second Generation for English speakers traveling in Spain. Our tests indicate
that our translation method yields higher accuracy than translation engines
such as Google Translate, and does so almost instantaneously. The memory
requirements of the application, including the database of images, are also
well within the limits of the device. By combining it with a database of
nutritional information, our proposed system can be used to help individuals
who follow a medical diet maintain this diet while traveling.
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Adding Neural Network Controllers to Behavior Trees without Destroying Performance Guarantees | In this paper, we show how controllers created using data driven designs,
such as neural networks, can be used together with model based controllers in a
way that combines the performance guarantees of the model based controllers
with the efficiency of the data driven controllers. The considered performance
guarantees include both safety, in terms of avoiding designated unsafe parts of
the state space, and convergence, in terms of reaching a given beneficial part
of the state space. Using the framework Behavior Trees, we are able to show how
this can be done on the top level, concerning just two controllers, as
described above, but also note that the same approach can be used in arbitrary
sub-trees. The price for introducing the new controller is that the upper bound
on the time needed to reach the desired part of the state space increases. The
approach is illustrated with an inverted pendulum example.
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Drop pattern resulting from the breakup of a bidimensional grid of liquid filaments | A rectangular grid formed by liquid filaments on a partially wetting
substrate evolves in a series of breakups leading to arrays of drops with
different shapes distributed in a rather regular bidimensional pattern. Our
study is focused on the configuration produced when two long parallel filaments
of silicone oil, which are placed upon a glass substrate previously coated with
a fluorinated solution, are crossed perpendicularly by another pair of long
parallel filaments. A remarkable feature of this kind of grids is that there
are two qualitatively different types of drops. While one set is formed at the
crossing points, the rest are consequence of the breakup of shorter filaments
formed between the crossings. Here, we analyze the main geometric features of
all types of drops, such as shape of the footprint and contact angle
distribution along the drop periphery. The formation of a series of short
filaments with similar geometric and physical properties allows us to have
simultaneously quasi identical experiments to study the subsequent breakups. We
develop a simple hydrodynamic model to predict the number of drops that results
from a filament of given initial length and width. This model is able to yield
the length intervals corresponding to a small number of drops and its
predictions are successfully compared with the experimental data as well as
with numerical simulations of the full Navier--Stokes equation that provide a
detailed time evolution of the dewetting motion of the filament till the
breakup into drops. Finally, the prediction for finite filaments is contrasted
with the existing theories for infinite ones.
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FIRED: Frequent Inertial Resets with Diversification for Emerging Commodity Cyber-Physical Systems | A Cyber-Physical System (CPS) is defined by its unique characteristics
involving both the cyber and physical domains. Their hybrid nature introduces
new attack vectors, but also provides an opportunity to design new security
defenses. In this paper, we present a new domain-specific security mechanism,
FIRED, that leverages physical properties such as inertia of the CPS to improve
security.
FIRED is simple to describe and implement. It goes through two operations:
Reset and Diversify, as frequently as possible -- typically in the order of
seconds or milliseconds. The combined effect of these operations is that
attackers are unable to gain persistent control of the system. The CPS stays
safe and stable even under frequent resets because of the inertia present.
Further, resets simplify certain diversification mechanisms and makes them
feasible to implement in CPSs with limited computing resources.
We evaluate our idea on two real-world systems: an engine management unit of
a car and a flight controller of a quadcopter. Roughly speaking, these two
systems provide typical and extreme operational requirements for evaluating
FIRED in terms of stability, algorithmic complexity, and safety requirements.
We show that FIRED provides robust security guarantees against hijacking
attacks and persistent CPS threats. We find that our defense is suitable for
emerging CPS such as commodity unmanned vehicles that are currently unregulated
and cost sensitive.
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Modelling of Dictyostelium Discoideum Movement in Linear Gradient of Chemoattractant | Chemotaxis is a ubiquitous biological phenomenon in which cells detect a
spatial gradient of chemoattractant, and then move towards the source. Here we
present a position-dependent advection-diffusion model that quantitatively
describes the statistical features of the chemotactic motion of the social
amoeba {\it Dictyostelium discoideum} in a linear gradient of cAMP (cyclic
adenosine monophosphate). We fit the model to experimental trajectories that
are recorded in a microfluidic setup with stationary cAMP gradients and extract
the diffusion and drift coefficients in the gradient direction. Our analysis
shows that for the majority of gradients, both coefficients decrease in time
and become negative as the cells crawl up the gradient. The extracted model
parameters also show that besides the expected drift in the direction of
chemoattractant gradient, we observe a nonlinear dependency of the
corresponding variance in time, which can be explained by the model.
Furthermore, the results of the model show that the non-linear term in the mean
squared displacement of the cell trajectories can dominate the linear term on
large time scales.
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Cycles of Activity in the Jovian Atmosphere | Jupiter's banded appearance may appear unchanging to the casual observer, but
closer inspection reveals a dynamic, ever-changing system of belts and zones
with distinct cycles of activity. Identification of these long-term cycles
requires access to datasets spanning multiple jovian years, but explaining them
requires multi-spectral characterization of the thermal, chemical, and aerosol
changes associated with visible color variations. The Earth-based support
campaign for Juno's exploration of Jupiter has already characterized two
upheaval events in the equatorial and temperate belts that are part of
long-term jovian cycles, whose underlying sources could be revealed by Juno's
exploration of Jupiter's deep atmosphere.
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Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information | This paper offers a methodological contribution at the intersection of
machine learning and operations research. Namely, we propose a methodology to
quickly predict tactical solutions to a given operational problem. In this
context, the tactical solution is less detailed than the operational one but it
has to be computed in very short time and under imperfect information. The
problem is of importance in various applications where tactical and operational
planning problems are interrelated and information about the operational
problem is revealed over time. This is for instance the case in certain
capacity planning and demand management systems.
We formulate the problem as a two-stage optimal prediction stochastic program
whose solution we predict with a supervised machine learning algorithm. The
training data set consists of a large number of deterministic (second stage)
problems generated by controlled probabilistic sampling. The labels are
computed based on solutions to the deterministic problems (solved independently
and offline) employing appropriate aggregation and subselection methods to
address uncertainty. Results on our motivating application in load planning for
rail transportation show that deep learning algorithms produce highly accurate
predictions in very short computing time (milliseconds or less). The prediction
accuracy is comparable to solutions computed by sample average approximation of
the stochastic program.
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Consistent Approval-Based Multi-Winner Rules | This paper is an axiomatic study of consistent approval-based multi-winner
rules, i.e., voting rules that select a fixed-size group of candidates based on
approval ballots. We introduce the class of counting rules, provide an
axiomatic characterization of this class and, in particular, show that counting
rules are consistent. Building upon this result, we axiomatically characterize
three important consistent multi-winner rules: Proportional Approval Voting,
Multi-Winner Approval Voting and the Approval Chamberlin-Courant rule. Our
results demonstrate the variety of multi-winner rules and illustrate three
different, orthogonal principles that multi-winner voting rules may represent:
individual excellence, diversity, and proportionality.
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Bridge functional for the molecular density functional theory with consistent pressure and surface tension and its importance for solvation in water | We address the problem of predicting the solvation free energy and
equilibrium solvent density profile in fews minutes from the molecular density
functional theory beyond the usual hypernetted-chain approximation. We
introduce a bridge functional of a coarse-grained, weighted solvent density. In
few minutes at most, for solutes of sizes ranging from small compounds to large
proteins, we produce (i) an estimation of the free energy of solvation within 1
kcal/mol of the experimental data for the hydrophobic solutes presented here,
and (ii) the solvent distribution around the solute. Contrary to previous
propositions, this bridge functional is thermodynamically consistent in that it
produces the correct liquid-vapor coexistence and the experimental surface
tension. We show this consistency to be of crucial importance for water at room
temperature and pressure. This bridge functional is designed to be simple,
local, and thus numerically efficient. Finally, we illustrate this new level of
molecular theory of solutions with the study of the hydration shell of a
protein.
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Fourier multiplier theorems for Triebel-Lizorkin spaces | In this paper we study sharp generalizations of $\dot{F}_p^{0,q}$ multiplier
theorem of Mikhlin-Hörmander type. The class of multipliers that we consider
involves Herz spaces $K_u^{s,t}$. Plancherel's theorem proves
$\widehat{L_s^2}=K_2^{s,2}$ and we study the optimal triple $(u,t,s)$ for which
$\sup_{k\in\mathbb{Z}}{\big\Vert \big(
m(2^k\cdot)\varphi\big)^{\vee}\big\Vert_{K_u^{s,t}}}<\infty$ implies
$\dot{F}_p^{0,q}$ boundedness of multiplier operator $T_m$ where $\varphi$ is a
cutoff function. Our result also covers the $BMO$-type space
$\dot{F}_{\infty}^{0,q}$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Individual dynamic predictions using landmarking and joint modelling: validation of estimators and robustness assessment | After the diagnosis of a disease, one major objective is to predict
cumulative probabilities of events such as clinical relapse or death from the
individual information collected up to a prediction time, including usually
biomarker repeated measurements. Several competing estimators have been
proposed to calculate these individual dynamic predictions, mainly from two
approaches: joint modelling and landmarking. These approaches differ by the
information used, the model assumptions and the complexity of the computational
procedures. It is essential to properly validate the estimators derived from
joint models and landmark models, quantify their variability and compare them
in order to provide key elements for the development and use of individual
dynamic predictions in clinical follow-up of patients. Motivated by the
prediction of two competing causes of progression of prostate cancer from the
history of prostate-specific antigen, we conducted an in-depth simulation study
to validate and compare the dynamic predictions derived from these two methods.
Specifically, we formally defined the quantity to estimate and its estimators,
proposed techniques to assess the uncertainty around predictions and validated
them. We also compared the individual dynamic predictions derived from joint
models and landmark models in terms of prediction error, discriminatory power,
efficiency and robustness to model assumptions. We show that these prediction
tools should be handled with care, in particular by properly specifying models
and estimators.
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A partial converse to the Andreotti-Grauert theorem | Let $X$ be a smooth projective manifold with $\dim_\mathbb{C} X=n$. We show
that if a line bundle $L$ is $(n-1)$-ample, then it is $(n-1)$-positive. This
is a partial converse to the Andreotti-Grauert theorem. As an application, we
show that a projective manifold $X$ is uniruled if and only if there exists a
Hermitian metric $\omega$ on $X$ such that its Ricci curvature
$\mathrm{Ric}(\omega)$ has at least one positive eigenvalue everywhere.
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Ab initio study of magnetocrystalline anisotropy, magnetostriction, and Fermi surface of L10 FeNi (tetrataenite) | The ordered L1$_0$ FeNi phase (tetrataenite) is recently considered as a
promising candidate for the rare-earth free permanent magnets applications. In
this work we calculate several characteristics of the L1$_0$ FeNi, where most
of the results come form the fully relativistic full potential FPLO method with
the generalized gradient approximation (GGA). A special attention deserves the
summary of the magnetocrystalline anisotropy energies (MAE's), the full
potential calculations of the anisotropy constant $K_3$, and the combined
analysis of the Fermi surface and three-dimensional $\mathbf{k}$-resolved MAE.
Other calculated parameters presented in this article are the magnetic moments
$m_{s}$ and $m_{l}$, magnetostrictive coefficient $\lambda_{001}$, bulk modulus
B$_0$, and lattice parameters. The MAE's summary shows rather big discrepancies
between the experimental MAE's from literature and also between the calculated
MAE's.
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Modular groups, Hurwitz classes and dynamic portraits of NET maps | An orientation-preserving branched covering $f: S^2 \to S^2$ is a nearly
Euclidean Thurston (NET) map if each critical point is simple and its
postcritical set has exactly four points. Inspired by classical, non-dynamical
notions such as Hurwitz equivalence of branched covers of surfaces, we develop
invariants for such maps. We then apply these notions to the classification and
enumeration of NET maps. As an application, we obtain a complete classification
of the dynamic critical orbit portraits of NET maps.
| 0 | 0 | 1 | 0 | 0 | 0 |
Proximal Planar Shape Signatures. Homology Nerves and Descriptive Proximity | This article introduces planar shape signatures derived from homology nerves,
which are intersecting 1-cycles in a collection of homology groups endowed with
a proximal relator (set of nearness relations) that includes a descriptive
proximity. A 1-cycle is a closed, connected path with a zero boundary in a
simplicial complex covering a finite, bounded planar shape. The signature of a
shape sh A (denoted by sig(sh A)) is a feature vector that describes sh A. A
signature sig(sh A) is derived from the geometry, homology nerves, Betti
number, and descriptive CW topology on the shape sh A. Several main results are
given, namely, (a) every finite, bounded planar shape has a signature derived
from the homology group on the shape, (b) a homology group equipped with a
proximal relator defines a descriptive Leader uniform topology and (c) a
description of a homology nerve and union of the descriptions of the 1-cycles
in the nerve have same homotopy type.
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Asymptotic analysis of a 2D overhead crane with input delays in the boundary control | The paper investigates the asymptotic behavior of a 2D overhead crane with
input delays in the boundary control. A linear boundary control is proposed.
The main feature of such a control lies in the facts that it solely depends on
the velocity but under the presence of time-delays. We end-up with a
closed-loop system where no displacement term is involved. It is shown that the
problem is well-posed in the sense of semigroups theory. LaSalle's invariance
principle is invoked in order to establish the asymptotic convergence for the
solutions of the system to a stationary position which depends on the initial
data. Using a resolvent method it is proved that the convergence is indeed
polynomial.
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Abrupt disappearance and reemergence of the SU(2) and SU(4) Kondo effects due to population inversion | The interplay of almost degenerate levels in quantum dots and molecular
junctions with possibly different couplings to the reservoirs has lead to many
observable phenomena, such as the Fano effect, transmission phase slips and the
SU(4) Kondo effect. Here we predict a dramatic repeated disappearance and
reemergence of the SU(4) and anomalous SU(2) Kondo effects with increasing gate
voltage. This phenomenon is attributed to the level occupation switching which
has been previously invoked to explain the universal transmission phase slips
in the conductance through a quantum dot. We use analytical arguments and
numerical renormalization group calculations to explain the observations and
discuss their experimental relevance and dependence on the physical parameters.
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Stochastic Global Optimization Algorithms: A Systematic Formal Approach | As we know, some global optimization problems cannot be solved using analytic
methods, so numeric/algorithmic approaches are used to find near to the optimal
solutions for them. A stochastic global optimization algorithm (SGoal) is an
iterative algorithm that generates a new population (a set of candidate
solutions) from a previous population using stochastic operations. Although
some research works have formalized SGoals using Markov kernels, such
formalization is not general and sometimes is blurred. In this paper, we
propose a comprehensive and systematic formal approach for studying SGoals.
First, we present the required theory of probability (\sigma-algebras,
measurable functions, kernel, markov chain, products, convergence and so on)
and prove that some algorithmic functions like swapping and projection can be
represented by kernels. Then, we introduce the notion of join-kernel as a way
of characterizing the combination of stochastic methods. Next, we define the
optimization space, a formal structure (a set with a \sigma-algebra that
contains strict \epsilon-optimal states) for studying SGoals, and we develop
kernels, like sort and permutation, on such structure. Finally, we present some
popular SGoals in terms of the developed theory, we introduce sufficient
conditions for convergence of a SGoal, and we prove convergence of some popular
SGoals.
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A complete characterization of optimal dictionaries for least squares representation | Dictionaries are collections of vectors used for representations of elements
in Euclidean spaces. While recent research on optimal dictionaries is focussed
on providing sparse (i.e., $\ell_0$-optimal,) representations, here we consider
the problem of finding optimal dictionaries such that representations of
samples of a random vector are optimal in an $\ell_2$-sense. For us, optimality
of representation is equivalent to minimization of the average $\ell_2$-norm of
the coefficients used to represent the random vector, with the lengths of the
dictionary vectors being specified a priori. With the help of recent results on
rank-$1$ decompositions of symmetric positive semidefinite matrices and the
theory of majorization, we provide a complete characterization of
$\ell_2$-optimal dictionaries. Our results are accompanied by polynomial time
algorithms that construct $\ell_2$-optimal dictionaries from given data.
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Least Square Variational Bayesian Autoencoder with Regularization | In recent years Variation Autoencoders have become one of the most popular
unsupervised learning of complicated distributions.Variational Autoencoder
(VAE) provides more efficient reconstructive performance over a traditional
autoencoder. Variational auto enocders make better approximaiton than MCMC. The
VAE defines a generative process in terms of ancestral sampling through a
cascade of hidden stochastic layers. They are a directed graphic models.
Variational autoencoder is trained to maximise the variational lower bound.
Here we are trying maximise the likelihood and also at the same time we are
trying to make a good approximation of the data. Its basically trading of the
data log-likelihood and the KL divergence from the true posterior. This paper
describes the scenario in which we wish to find a point-estimate to the
parameters $\theta$ of some parametric model in which we generate each
observations by first sampling a local latent variable and then sampling the
associated observation. Here we use least square loss function with
regularization in the the reconstruction of the image, the least square loss
function was found to give better reconstructed images and had a faster
training time.
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Rational points of rationally simply connected varieties over global function fields | A complex projective manifold is rationally connected, resp. rationally
simply connected, if finite subsets are connected by a rational curve, resp.
the spaces parameterizing these connecting rational curves are themselves
rationally connected. We prove that a projective scheme over a global function
field with vanishing "elementary obstruction" has a rational point if it
deforms to a rationally simply connected variety in characteristic 0. This
gives new, uniform proofs over these fields of the Period-Index Theorem, the
quasi-split case of Serre's "Conjecture II", and Lang's $C_2$ property.
| 0 | 0 | 1 | 0 | 0 | 0 |
A Koszul sign map | We define a Koszul sign map encoding the Koszul sign convention. A
cohomological interpretation is given.
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Combining low- to high-resolution transit spectroscopy of HD 189733b. Linking the troposphere and the thermosphere of a hot gas giant | Space-borne low-to medium-resolution (R~10^2-10^3) transmission spectroscopy
of atmospheres detect the broadest spectral features (alkali doublets,
molecular bands, scattering), while high-resolution (R~10^5), ground-based
observations probe the sharpest features (cores of the alkali lines, molecular
lines).The two techniques differ by:(1) The LSF of ground-based observations is
10^3 times narrower than for space-borne observations;(2)Space-borne
transmission spectra probe up to the base of thermosphere, while ground-based
observations can reach pressures down to 10^(-11);(3)Space-borne observations
directly yield the transit depth of the planet, while ground-based observations
measure differences in the radius of the planet at different wavelengths.It is
challenging to combine both techniques.We develop a method to compare
theoretical models with observations at different resolutions.We introduce
PyETA, a line-by-line 1D radiative transfer code to compute transmission
spectra at R~10^6 (0.01 A) over a broad wavelength range.An hybrid forward
modeling/retrieval optimization scheme is devised to deal with the large
computational resources required by modeling a broad wavelength range (0.3-2
$\mu$m) at high resolution.We apply our technique to HD189733b.Here, HST
observations reveal a flattened spectrum due to scattering by aerosols, while
high-resolution ground-based HARPS observations reveal the sharp cores of
sodium lines.We reconcile these results by building models that reproduce
simultaneously both data sets, from the troposphere to the thermosphere. We
confirm:(1)the presence of scattering by tropospheric aerosols;(2)that the
sodium core feature is of thermospheric origin.Accounting for aerosols, the
sodium cores indicate T up to 10000K in the thermosphere.The precise value of
the thermospheric temperature is degenerate with the abundance of sodium and
altitude of the aerosol deck.
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Factorizations in Modules and Splitting Multiplicatively Closed Subsets | We introduce the concept of multiplicatively closed subsets of a commutative
ring $R$ which split an $R$-module $M$ and study factorization properties of
elements of $M$ with respect to such a set. Also we demonstrate how one can
utilize this concept to investigate factorization properties of $R$ and deduce
some Nagata type theorems relating factorization properties of $R$ to those of
its localizations, when $R$ is an integral domain.
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ROPPERI - A TPC readout with GEMs, pads and Timepix | The concept of a hybrid readout of a time projection chamber is presented. It
combines a GEM-based amplification and a pad-based anode plane with a pixel
chip as readout electronics. This way, a high granularity enabling to identify
electron clusters from the primary ionisation is achieved as well as
flexibility and large anode coverage. The benefits of this high granularity, in
particular for dE/dx measurements, are outlined and the current software and
hardware development status towards a proof-of-principle is given.
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A Shared Task on Bandit Learning for Machine Translation | We introduce and describe the results of a novel shared task on bandit
learning for machine translation. The task was organized jointly by Amazon and
Heidelberg University for the first time at the Second Conference on Machine
Translation (WMT 2017). The goal of the task is to encourage research on
learning machine translation from weak user feedback instead of human
references or post-edits. On each of a sequence of rounds, a machine
translation system is required to propose a translation for an input, and
receives a real-valued estimate of the quality of the proposed translation for
learning. This paper describes the shared task's learning and evaluation setup,
using services hosted on Amazon Web Services (AWS), the data and evaluation
metrics, and the results of various machine translation architectures and
learning protocols.
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Double Sparsity Kernel Learning with Automatic Variable Selection and Data Extraction | Learning with Reproducing Kernel Hilbert Spaces (RKHS) has been widely used
in many scientific disciplines. Because a RKHS can be very flexible, it is
common to impose a regularization term in the optimization to prevent
overfitting. Standard RKHS learning employs the squared norm penalty of the
learning function. Despite its success, many challenges remain. In particular,
one cannot directly use the squared norm penalty for variable selection or data
extraction. Therefore, when there exists noise predictors, or the underlying
function has a sparse representation in the dual space, the performance of
standard RKHS learning can be suboptimal. In the literature,work has been
proposed on how to perform variable selection in RKHS learning, and a data
sparsity constraint was considered for data extraction. However, how to learn
in a RKHS with both variable selection and data extraction simultaneously
remains unclear. In this paper, we propose a unified RKHS learning method,
namely, DOuble Sparsity Kernel (DOSK) learning, to overcome this challenge. An
efficient algorithm is provided to solve the corresponding optimization
problem. We prove that under certain conditions, our new method can
asymptotically achieve variable selection consistency. Simulated and real data
results demonstrate that DOSK is highly competitive among existing approaches
for RKHS learning.
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Controlling of blow-up responses by a nonlinear $\cal{PT}$ symmetric coupling | We investigate the dynamics of a coupled waveguide system with competing
linear and nonlinear loss-gain profiles which can facilitate power saturation.
We show the usefulness of the model in achieving unidirectional beam
propagation. In this regard, the considered type of coupled waveguide system
has two drawbacks, (i) difficulty in achieving perfect isolation of light in a
waveguide and (ii) existence of blow-up type behavior for certain input power
situations. We here show a nonlinear $\cal{PT}$ symmetric coupling that helps
to overcome these two drawbacks. Such a nonlinear coupling has close connection
with the phenomenon of stimulated Raman scattering. In particular, we have
elucidated the role of this nonlinear coupling using an integrable $\cal{PT}$
symmetric situation. In particular, using the integrals of motion, we have
reduced this coupled waveguide problem to the problem of dynamics of a particle
in a potential. With the latter picture, we have clearly illustrated the role
of the considered nonlinear coupling. The above $\cal{PT}$ symmetric case
corresponds to a limiting form of a general equation describing the phenomenon
of stimulated Raman scattering. We also point out the ability to transport
light unidirectionally even in this general case.
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Text Extraction From Texture Images Using Masked Signal Decomposition | Text extraction is an important problem in image processing with applications
from optical character recognition to autonomous driving. Most of the
traditional text segmentation algorithms consider separating text from a simple
background (which usually has a different color from texts). In this work we
consider separating texts from a textured background, that has similar color to
texts. We look at this problem from a signal decomposition perspective, and
consider a more realistic scenario where signal components are overlaid on top
of each other (instead of adding together). When the signals are overlaid, to
separate signal components, we need to find a binary mask which shows the
support of each component. Because directly solving the binary mask is
intractable, we relax this problem to the approximated continuous problem, and
solve it by alternating optimization method. We show that the proposed
algorithm achieves significantly better results than other recent works on
several challenging images.
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Deep Echo State Networks with Uncertainty Quantification for Spatio-Temporal Forecasting | Long-lead forecasting for spatio-temporal systems can often entail complex
nonlinear dynamics that are difficult to specify it a priori. Current
statistical methodologies for modeling these processes are often highly
parameterized and thus, challenging to implement from a computational
perspective. One potential parsimonious solution to this problem is a method
from the dynamical systems and engineering literature referred to as an echo
state network (ESN). ESN models use so-called {\it reservoir computing} to
efficiently compute recurrent neural network (RNN) forecasts. Moreover,
so-called "deep" models have recently been shown to be successful at predicting
high-dimensional complex nonlinear processes, particularly those with multiple
spatial and temporal scales of variability (such as we often find in
spatio-temporal environmental data). Here we introduce a deep ensemble ESN
(D-EESN) model. We present two versions of this model for spatio-temporal
processes that both produce forecasts and associated measures of uncertainty.
The first approach utilizes a bootstrap ensemble framework and the second is
developed within a hierarchical Bayesian framework (BD-EESN). This more general
hierarchical Bayesian framework naturally accommodates non-Gaussian data types
and multiple levels of uncertainties. The methodology is first applied to a
data set simulated from a novel non-Gaussian multiscale Lorenz-96 dynamical
system simulation model and then to a long-lead United States (U.S.) soil
moisture forecasting application.
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Using Convex Optimization of Autocorrelation with Constrained Support and Windowing for Improved Phase Retrieval Accuracy | In imaging modalities recording diffraction data, the original image can be
reconstructed assuming known phases. When phases are unknown, oversampling and
a constraint on the support region in the original object can be used to solve
a non-convex optimization problem.
Such schemes are ill-suited to find the optimum solution for sparse data,
since the recorded image does not correspond exactly to the original wave
function. We construct a convex optimization problem using a relaxed support
constraint and a maximum-likelihood treatment of the recorded data as a sample
from the underlying wave function. We also stress the need to use relevant
windowing techniques to account for the sampled pattern being finite.
On simulated data, we demonstrate the benefits of our approach in terms of
visual quality and an improvement in the crystallographic R-factor from .4 to
.1 for highly noisy data.
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Parametrizations, weights, and optimal prediction: Part 1 | We consider the problem of the annual mean temperature prediction. The years
taken into account and the corresponding annual mean temperatures are denoted
by $0,\ldots, n$ and $t_0$, $\ldots$, $t_n$, respectively. We propose to
predict the temperature $t_{n+1}$ using the data $t_0$, $\ldots$, $t_n$. For
each $0\leq l\leq n$ and each parametrization $\Theta^{(l)}$ of the Euclidean
space $\mathbb{R}^{l+1}$ we construct a list of weights for the data
$\{t_0,\ldots, t_l\}$ based on the rows of $\Theta^{(l)}$ which are correlated
with the constant trend. Using these weights we define a list of predictors of
$t_{l+1}$ from the data $t_0$, $\ldots$, $t_l$. We analyse how the
parametrization affects the prediction, and provide three optimality criteria
for the selection of weights and parametrization. We illustrate our results for
the annual mean temperature of France and Morocco.
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Time irreversibility from symplectic non-squeezing | The issue of how time reversible microscopic dynamics gives rise to
macroscopic irreversible processes has been a recurrent issue in Physics since
the time of Boltzmann whose ideas shaped, and essentially resolved, such an
apparent contradiction. Following Boltzmann's spirit and ideas, but employing
Gibbs's approach, we advance the view that macroscopic irreversibility of
Hamiltonian systems of many degrees of freedom can be also seen as a result of
the symplectic non-squeezing theorem.
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On Optimal Weighted-Delay Scheduling in Input-Queued Switches | Motivated by relatively few delay-optimal scheduling results, in comparison
to results on throughput optimality, we investigate an input-queued switch
scheduling problem in which the objective is to minimize a linear function of
the queue-length vector. Theoretical properties of variants of the well-known
MaxWeight scheduling algorithm are established within this context, which
includes showing that these algorithms exhibit optimal heavy-traffic
queue-length scaling. For the case of $2 \times 2$ input-queued switches, we
derive an optimal scheduling policy and establish its theoretical properties,
demonstrating fundamental differences with the variants of MaxWeight
scheduling. Our theoretical results are expected to be of interest more broadly
than input-queued switches. Computational experiments demonstrate and quantify
the benefits of our optimal scheduling policy.
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Hausdorff dimension, projections, intersections, and Besicovitch sets | This is a survey on recent developments on the Hausdorff dimension of
projections and intersections for general subsets of Euclidean spaces, with an
emphasis on estimates of the Hausdorff dimension of exceptional sets and on
restricted projection families. We shall also discuss relations between
projections and Hausdorff dimension of Besicovitch sets.
| 0 | 0 | 1 | 0 | 0 | 0 |
Interpolating between matching and hedonic pricing models | We consider the theoretical properties of a model which encompasses
bi-partite matching under transferable utility on the one hand, and hedonic
pricing on the other. This framework is intimately connected to tripartite
matching problems (known as multi-marginal optimal transport problems in the
mathematical literature). We exploit this relationship in two ways; first, we
show that a known structural result from multi-marginal optimal transport can
be used to establish an upper bound on the dimension of the support of stable
matchings. Next, assuming the distribution of agents on one side of the market
is continuous, we identify a condition on their preferences that ensures purity
and uniqueness of the stable matching; this condition is a variant of a known
condition in the mathematical literature, which guarantees analogous properties
in the multi-marginal optimal transport problem. We exhibit several examples of
surplus functions for which our condition is satisfied, as well as some for
which it fails.
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Modeling and predicting the short term evolution of the Geomagnetic field | The coupled evolution of the magnetic field and the flow at the Earth's core
mantle boundary is modeled within the 1900.0-2014.0 time period. To constraint
the dynamical behavior of the system with a core field model deriving from
direct measurements of the Earth's magnetic field we used an Ensemble Kalman
filter algorithm. By simulating an ensemble of possible states, access to the
complete statistical properties of the considered fields is available.
Furthermore, the method enables to provide predictions and to assess their
reliability. In this study, we could highlight the cohabitation of two distinct
flow regimes. One associated with the large scale part of the eccentric gyre,
which evolves slowly in time and posses a very long memory of its past, and a
faster one associated with the small scale velocity field. We show that the
latter can exhibit rapid variations in localized areas. The combination of the
two regimes can predict quite well the decadal variations in length of day, but
it can also explain the discrepancies between the physically predicted and the
observed trend in these variations. Hindcast tests demonstrate that the model
is well balanced and that it can provide accurate short term predictions of a
mean state and its associated uncertainties. However, magnetic field
predictions are limited by the high randomization rate of the different
velocity field scales, and after approximately 2000 years of forecast, no
reliable information on the core field can be recovered.
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Analysing the Potential of BLE to Support Dynamic Broadcasting Scenarios | In this paper, we present a novel approach for broadcasting information based
on a Bluetooth Low Energy (BLE) ibeacon technology. We propose a dynamic method
that uses a combination of Wi-Fi and BLE technology where every technology
plays a part in a user discovery and broadcasting process. In such system, a
specific ibeacon device broadcasts the information when a user is in proximity.
Using experiments, we conduct a scenario where the system discovers users,
disseminates information, and later we use collected data to examine the system
performance and capability. The results show that our proposed approach has a
promising potential to become a powerful tool in the discovery and broadcasting
concept that can be easily implemented and used in business environments.
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$\texttt{PyTranSpot}$ - A tool for multiband light curve modeling of planetary transits and stellar spots | Several studies have shown that stellar activity features, such as occulted
and non-occulted starspots, can affect the measurement of transit parameters
biasing studies of transit timing variations and transmission spectra. We
present $\texttt{PyTranSpot}$, which we designed to model multiband transit
light curves showing starspot anomalies, inferring both transit and spot
parameters. The code follows a pixellation approach to model the star with its
corresponding limb darkening, spots, and transiting planet on a two dimensional
Cartesian coordinate grid. We combine $\texttt{PyTranSpot}$ with an MCMC
framework to study and derive exoplanet transmission spectra, which provides
statistically robust values for the physical properties and uncertainties of a
transiting star-planet system. We validate $\texttt{PyTranSpot}$'s performance
by analyzing eleven synthetic light curves of four different star-planet
systems and 20 transit light curves of the well-studied WASP-41b system. We
also investigate the impact of starspots on transit parameters and derive
wavelength dependent transit depth values for WASP-41b covering a range of
6200-9200 $\AA$, indicating a flat transmission spectrum.
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Interplay of spatial dynamics and local adaptation shapes species lifetime distributions and species-area relationships | The distributions of species lifetimes and species in space are related,
since species with good local survival chances have more time to colonize new
habitats and species inhabiting large areas have higher chances to survive
local disturbances. Yet, both distributions have been discussed in mostly
separate communities. Here, we study both patterns simultaneously using a
spatially explicit, evolutionary community assembly approach. We present and
investigate a metacommunity model, consisting of a grid of patches, where each
patch contains a local food web. Species survival depends on predation and
competition interactions, which in turn depend on species body masses as the
key traits. The system evolves due to the migration of species to neighboring
patches, the addition of new species as modifications of existing species, and
local extinction events. The structure of each local food web thus emerges in a
self-organized manner as the highly non-trivial outcome of the relative time
scales of these processes. Our model generates a large variety of complex,
multi-trophic networks and therefore serves as a powerful tool to investigate
ecosystems on long temporal and large spatial scales. We find that the observed
lifetime distributions and species-area relations resemble power laws over
appropriately chosen parameter ranges and thus agree qualitatively with
empirical findings. Moreover, we observe strong finite-size effects, and a
dependence of the relationships on the trophic level of the species. By
comparing our results to simple neutral models found in the literature, we
identify the features that are responsible for the values of the exponents.
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Routing Symmetric Demands in Directed Minor-Free Graphs with Constant Congestion | The problem of routing in graphs using node-disjoint paths has received a lot
of attention and a polylogarithmic approximation algorithm with constant
congestion is known for undirected graphs [Chuzhoy and Li 2016] and [Chekuri
and Ene 2013]. However, the problem is hard to approximate within polynomial
factors on directed graphs, for any constant congestion [Chuzhoy, Kim and Li
2016].
Recently, [Chekuri, Ene and Pilipczuk 2016] have obtained a polylogarithmic
approximation with constant congestion on directed planar graphs, for the
special case of symmetric demands. We extend their result by obtaining a
polylogarithmic approximation with constant congestion on arbitrary directed
minor-free graphs, for the case of symmetric demands.
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Fast sampling of parameterised Gaussian random fields | Gaussian random fields are popular models for spatially varying
uncertainties, arising for instance in geotechnical engineering, hydrology or
image processing. A Gaussian random field is fully characterised by its mean
function and covariance operator. In more complex models these can also be
partially unknown. In this case we need to handle a family of Gaussian random
fields indexed with hyperparameters. Sampling for a fixed configuration of
hyperparameters is already very expensive due to the nonlocal nature of many
classical covariance operators. Sampling from multiple configurations increases
the total computational cost severely. In this report we employ parameterised
Karhunen-Loève expansions for sampling. To reduce the cost we construct a
reduced basis surrogate built from snapshots of Karhunen-Loève eigenvectors.
In particular, we consider Matérn-type covariance operators with unknown
correlation length and standard deviation. We suggest a linearisation of the
covariance function and describe the associated online-offline decomposition.
In numerical experiments we investigate the approximation error of the reduced
eigenpairs. As an application we consider forward uncertainty propagation and
Bayesian inversion with an elliptic partial differential equation where the
logarithm of the diffusion coefficient is a parameterised Gaussian random
field. In the Bayesian inverse problem we employ Markov chain Monte Carlo on
the reduced space to generate samples from the posterior measure. All numerical
experiments are conducted in 2D physical space, with non-separable covariance
operators, and finite element grids with $\sim 10^4$ degrees of freedom.
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Universal Scalable Robust Solvers from Computational Information Games and fast eigenspace adapted Multiresolution Analysis | We show how the discovery of robust scalable numerical solvers for arbitrary
bounded linear operators can be automated as a Game Theory problem by
reformulating the process of computing with partial information and limited
resources as that of playing underlying hierarchies of adversarial information
games. When the solution space is a Banach space $B$ endowed with a quadratic
norm $\|\cdot\|$, the optimal measure (mixed strategy) for such games (e.g. the
adversarial recovery of $u\in B$, given partial measurements $[\phi_i, u]$ with
$\phi_i\in B^*$, using relative error in $\|\cdot\|$-norm as a loss) is a
centered Gaussian field $\xi$ solely determined by the norm $\|\cdot\|$, whose
conditioning (on measurements) produces optimal bets. When measurements are
hierarchical, the process of conditioning this Gaussian field produces a
hierarchy of elementary bets (gamblets). These gamblets generalize the notion
of Wavelets and Wannier functions in the sense that they are adapted to the
norm $\|\cdot\|$ and induce a multi-resolution decomposition of $B$ that is
adapted to the eigensubspaces of the operator defining the norm $\|\cdot\|$.
When the operator is localized, we show that the resulting gamblets are
localized both in space and frequency and introduce the Fast Gamblet Transform
(FGT) with rigorous accuracy and (near-linear) complexity estimates. As the FFT
can be used to solve and diagonalize arbitrary PDEs with constant coefficients,
the FGT can be used to decompose a wide range of continuous linear operators
(including arbitrary continuous linear bijections from $H^s_0$ to $H^{-s}$ or
to $L^2$) into a sequence of independent linear systems with uniformly bounded
condition numbers and leads to $\mathcal{O}(N \operatorname{polylog} N)$
solvers and eigenspace adapted Multiresolution Analysis (resulting in near
linear complexity approximation of all eigensubspaces).
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Preliminary Experiments using Subjective Logic for the Polyrepresentation of Information Needs | According to the principle of polyrepresentation, retrieval accuracy may
improve through the combination of multiple and diverse information object
representations about e.g. the context of the user, the information sought, or
the retrieval system. Recently, the principle of polyrepresentation was
mathematically expressed using subjective logic, where the potential
suitability of each representation for improving retrieval performance was
formalised through degrees of belief and uncertainty. No experimental evidence
or practical application has so far validated this model. We extend the work of
Lioma et al. (2010), by providing a practical application and analysis of the
model. We show how to map the abstract notions of belief and uncertainty to
real-life evidence drawn from a retrieval dataset. We also show how to estimate
two different types of polyrepresentation assuming either (a) independence or
(b) dependence between the information objects that are combined. We focus on
the polyrepresentation of different types of context relating to user
information needs (i.e. work task, user background knowledge, ideal answer) and
show that the subjective logic model can predict their optimal combination
prior and independently to the retrieval process.
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General Robust Bayes Pseudo-Posterior: Exponential Convergence results with Applications | Although Bayesian inference is an immensely popular paradigm among a large
segment of scientists including statisticians, most of the applications
consider the objective priors and need critical investigations (Efron, 2013,
Science). And although it has several optimal properties, one major drawback of
Bayesian inference is the lack of robustness against data contamination and
model misspecification, which becomes pernicious in the use of objective
priors. This paper presents the general formulation of a Bayes pseudo-posterior
distribution yielding robust inference. Exponential convergence results related
to the new pseudo-posterior and the corresponding Bayes estimators are
established under the general parametric set-up and illustrations are provided
for the independent stationary models and the independent non-homogenous
models. For the first case, the discrete priors and the corresponding maximum
posterior estimators are discussed with additional details. We further apply
this new pseudo-posterior to propose robust versions of the Bayes predictive
density estimators and the expected Bayes estimator for the fixed-design
(normal) linear regression models; their properties are illustrated both
theoretically as well as empirically.
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The $H_0$ tension in light of vacuum dynamics in the Universe | Despite the outstanding achievements of modern cosmology, the classical
dispute on the precise value of $H_0$, which is the first ever parameter of
modern cosmology and one of the prime parameters in the field, still goes on
and on after over half a century of measurements. Recently the dispute came to
the spotlight with renewed strength owing to the significant tension (at
$>3\sigma$ c.l.) between the latest Planck determination obtained from the CMB
anisotropies and the local (distance ladder) measurement from the Hubble Space
Telescope (HST), based on Cepheids. In this work, we investigate the impact of
the running vacuum model (RVM) and related models on such a controversy. For
the RVM, the vacuum energy density $\rho_{\Lambda}$ carries a mild dependence
on the cosmic expansion rate, i.e. $\rho_{\Lambda}(H)$, which allows to
ameliorate the fit quality to the overall $SNIa+BAO+H(z)+LSS+CMB$ cosmological
observations as compared to the concordance $\Lambda$CDM model. By letting the
RVM to deviate from the vacuum option, the equation of state $w=-1$ continues
to be favored by the overall fit. Vacuum dynamics also predicts the following:
i) the CMB range of values for $H_0$ is more favored than the local ones, and
ii) smaller values for $\sigma_8(0)$. As a result, a better account for the LSS
structure formation data is achieved as compared to the $\Lambda$CDM, which is
based on a rigid (i.e. non-dynamical) $\Lambda$ term.
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On certain type of difference polynomials of meromorphic functions | In this paper, we investigate zeros of difference polynomials of the form
$f(z)^nH(z, f)-s(z)$, where $f(z)$ is a meromorphic function, $H(z, f)$ is a
difference polynomial of $f(z)$ and $s(z)$ is a small function. We first obtain
some inequalities for the relationship of the zero counting function of
$f(z)^nH(z, f)-s(z)$ and the characteristic function and pole counting function
of $f(z)$. Based on these inequalities, we establish some difference analogues
of a classical result of Hayman for meromorphic functions. Some special cases
are also investigated. These results improve previous findings.
| 0 | 0 | 1 | 0 | 0 | 0 |
Teaching methods are erroneous: approaches which lead to erroneous end-user computing | If spreadsheets are not erroneous then who, or what, is? Research has found
that end-users are. If end-users are erroneous then why are they? Research has
found that responsibility lies with human beings' fast and slow thinking modes
and the inappropriate way they use them. If we are aware of this peculiarity of
human thinking, then why do we not teach students how to train their brains?
This is the main problem, this is the weakest link in the process: teaching. We
have to make teachers realize that end-users are erroneous because of the
erroneous teaching approaches to end-user computing. The proportion of fast and
slow thinking modes is not constant, and teachers are mistaken when they apply
the same proportion in both the teaching and end-user roles. Teachers should
believe in the incremental nature of science and have high self-efficacy to
make students understand and appreciate science. This is not currently the case
in ICT and CS, and it is high time fundamental changes were introduced.
| 1 | 0 | 0 | 0 | 0 | 0 |
Modulational Instability in Linearly Coupled Asymmetric Dual-Core Fibers | We investigate modulational instability (MI) in asymmetric dual-core
nonlinear directional couplers incorporating the effects of the differences in
effective mode areas and group velocity dispersions, as well as phase- and
group-velocity mismatches. Using coupled-mode equations for this system, we
identify MI conditions from the linearization with respect to small
perturbations. First, we compare the MI spectra of the asymmetric system and
its symmetric counterpart in the case of the anomalous group-velocity
dispersion (GVD). In particular, it is demonstrated that the increase of the
inter-core linear-coupling coefficient leads to a reduction of the MI gain
spectrum in the asymmetric coupler. The analysis is extended for the asymmetric
system in the normal-GVD regime, where the coupling induces and controls the
MI, as well as for the system with opposite GVD signs in the two cores.
Following the analytical consideration of the MI, numerical simulations are
carried out to explore nonlinear development of the MI, revealing the
generation of periodic chains of localized peaks with growing amplitudes, which
may transform into arrays of solitons.
| 0 | 1 | 0 | 0 | 0 | 0 |
Discovering Visual Concept Structure with Sparse and Incomplete Tags | Discovering automatically the semantic structure of tagged visual data (e.g.
web videos and images) is important for visual data analysis and
interpretation, enabling the machine intelligence for effectively processing
the fast-growing amount of multi-media data. However, this is non-trivial due
to the need for jointly learning underlying correlations between heterogeneous
visual and tag data. The task is made more challenging by inherently sparse and
incomplete tags. In this work, we develop a method for modelling the inherent
visual data concept structures based on a novel Hierarchical-Multi-Label Random
Forest model capable of correlating structured visual and tag information so as
to more accurately interpret the visual semantics, e.g. disclosing meaningful
visual groups with similar high-level concepts, and recovering missing tags for
individual visual data samples. Specifically, our model exploits hierarchically
structured tags of different semantic abstractness and multiple tag statistical
correlations in addition to modelling visual and tag interactions. As a result,
our model is able to discover more accurate semantic correlation between
textual tags and visual features, and finally providing favourable visual
semantics interpretation even with highly sparse and incomplete tags. We
demonstrate the advantages of our proposed approach in two fundamental
applications, visual data clustering and missing tag completion, on
benchmarking video (i.e. TRECVID MED 2011) and image (i.e. NUS-WIDE) datasets.
| 1 | 0 | 0 | 0 | 0 | 0 |
GALILEO: A Generalized Low-Entropy Mixture Model | We present a new method of generating mixture models for data with
categorical attributes. The keys to this approach are an entropy-based density
metric in categorical space and annealing of high-entropy/low-density
components from an initial state with many components. Pruning of low-density
components using the entropy-based density allows GALILEO to consistently find
high-quality clusters and the same optimal number of clusters. GALILEO has
shown promising results on a range of test datasets commonly used for
categorical clustering benchmarks. We demonstrate that the scaling of GALILEO
is linear in the number of records in the dataset, making this method suitable
for very large categorical datasets.
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Approximation by mappings with singular Hessian minors | Let $\Omega\subset\mathbb R^n$ be a Lipschitz domain. Given $1\leq p<k\leq n$
and any $u\in W^{2,p}(\Omega)$ belonging to the little Hölder class
$c^{1,\alpha}$, we construct a sequence $u_j$ in the same space with
$\operatorname{rank}D^2u_j<k$ almost everywhere such that $u_j\to u$ in
$C^{1,\alpha}$ and weakly in $W^{2,p}$. This result is in strong contrast with
known regularity behavior of functions in $W^{2,p}$, $p\geq k$, satisfying the
same rank inequality.
| 0 | 0 | 1 | 0 | 0 | 0 |
Adaptive Cardinality Estimation | In this paper we address cardinality estimation problem which is an important
subproblem in query optimization. Query optimization is a part of every
relational DBMS responsible for finding the best way of the execution for the
given query. These ways are called plans. The execution time of different plans
may differ by several orders, so query optimizer has a great influence on the
whole DBMS performance. We consider cost-based query optimization approach as
the most popular one. It was observed that cost-based optimization quality
depends much on cardinality estimation quality. Cardinality of the plan node is
the number of tuples returned by it.
In the paper we propose a novel cardinality estimation approach with the use
of machine learning methods. The main point of the approach is using query
execution statistics of the previously executed queries to improve cardinality
estimations. We called this approach adaptive cardinality estimation to reflect
this point. The approach is general, flexible, and easy to implement. The
experimental evaluation shows that this approach significantly increases the
quality of cardinality estimation, and therefore increases the DBMS performance
for some queries by several times or even by several dozens of times.
| 1 | 0 | 0 | 1 | 0 | 0 |
Non-stationary Stochastic Optimization under $L_{p,q}$-Variation Measures | We consider a non-stationary sequential stochastic optimization problem, in
which the underlying cost functions change over time under a variation budget
constraint. We propose an $L_{p,q}$-variation functional to quantify the
change, which yields less variation for dynamic function sequences whose
changes are constrained to short time periods or small subsets of input domain.
Under the $L_{p,q}$-variation constraint, we derive both upper and matching
lower regret bounds for smooth and strongly convex function sequences, which
generalize previous results in Besbes et al. (2015). Furthermore, we provide an
upper bound for general convex function sequences with noisy gradient feedback,
which matches the optimal rate as $p\to\infty$. Our results reveal some
surprising phenomena under this general variation functional, such as the curse
of dimensionality of the function domain. The key technical novelties in our
analysis include affinity lemmas that characterize the distance of the
minimizers of two convex functions with bounded Lp difference, and a cubic
spline based construction that attains matching lower bounds.
| 1 | 0 | 0 | 1 | 0 | 0 |
Spin conductance of YIG thin films driven from thermal to subthermal magnons regime by large spin-orbit torque | We report a study on spin conductance in ultra-thin films of Yttrium Iron
Garnet (YIG), where spin transport is provided by propagating spin waves, that
are generated and detected by direct and inverse spin Hall effects in two Pt
wires deposited on top. While at low current the spin conductance is dominated
by transport of thermal magnons, at high current, the spin conductance is
dominated by low-damping non-equilibrium magnons thermalized near the spectral
bottom by magnon-magnon interaction, with consequent a sensitivity to the
applied magnetic field and a longer decay length. This picture is supported by
microfocus Brillouin Light Scattering spectroscopy.
| 0 | 1 | 0 | 0 | 0 | 0 |
Mathematical renormalization in quantum electrodynamics via noncommutative generating series | In this work, we focus on on the approach by noncommutative formal power
series to study the combinatorial aspects of the renormalization at the
singularities in $\{0,1,+\infty\}$ of the solutions of nonlinear differential
equations involved in quantum electrodynamics.
| 0 | 0 | 1 | 0 | 0 | 0 |
Inference in Deep Networks in High Dimensions | Deep generative networks provide a powerful tool for modeling complex data in
a wide range of applications. In inverse problems that use these networks as
generative priors on data, one must often perform inference of the inputs of
the networks from the outputs. Inference is also required for sampling during
stochastic training on these generative models. This paper considers inference
in a deep stochastic neural network where the parameters (e.g., weights, biases
and activation functions) are known and the problem is to estimate the values
of the input and hidden units from the output. While several approximate
algorithms have been proposed for this task, there are few analytic tools that
can provide rigorous guarantees in the reconstruction error. This work presents
a novel and computationally tractable output-to-input inference method called
Multi-Layer Vector Approximate Message Passing (ML-VAMP). The proposed
algorithm, derived from expectation propagation, extends earlier AMP methods
that are known to achieve the replica predictions for optimality in simple
linear inverse problems. Our main contribution shows that the mean-squared
error (MSE) of ML-VAMP can be exactly predicted in a certain large system limit
(LSL) where the numbers of layers is fixed and weight matrices are random and
orthogonally-invariant with dimensions that grow to infinity. ML-VAMP is thus a
principled method for output-to-input inference in deep networks with a
rigorous and precise performance achievability result in high dimensions.
| 1 | 0 | 0 | 1 | 0 | 0 |
Differentially Private Variational Dropout | Deep neural networks with their large number of parameters are highly
flexible learning systems. The high flexibility in such networks brings with
some serious problems such as overfitting, and regularization is used to
address this problem. A currently popular and effective regularization
technique for controlling the overfitting is dropout. Often, large data
collections required for neural networks contain sensitive information such as
the medical histories of patients, and the privacy of the training data should
be protected. In this paper, we modify the recently proposed variational
dropout technique which provided an elegant Bayesian interpretation to dropout,
and show that the intrinsic noise in the variational dropout can be exploited
to obtain a degree of differential privacy. The iterative nature of training
neural networks presents a challenge for privacy-preserving estimation since
multiple iterations increase the amount of noise added. We overcome this by
using a relaxed notion of differential privacy, called concentrated
differential privacy, which provides tighter estimates on the overall privacy
loss. We demonstrate the accuracy of our privacy-preserving variational dropout
algorithm on benchmark datasets.
| 1 | 0 | 0 | 1 | 0 | 0 |
Persistent Currents in Ferromagnetic Condensates | Persistent currents in Bose condensates with a scalar order parameter are
stabilized by the topology of the order parameter manifold. In condensates with
multicomponent order parameters it is topologically possible for supercurrents
to `unwind' without leaving the manifold. We study the energetics of this
process in the case of ferromagnetic condensates using a long wavelength energy
functional that includes both the superfluid and spin stiffnesses. Exploiting
analogies to an elastic rod and rigid body motion, we show that the current
carrying state in a 1D ring geometry transitions between a spin helix in the
energy minima and a soliton-like configuration at the maxima. The relevance to
recent experiments in ultracold atoms is briefly discussed.
| 0 | 1 | 0 | 0 | 0 | 0 |
Parameter Adaptation and Criticality in Particle Swarm Optimization | Generality is one of the main advantages of heuristic algorithms, as such,
multiple parameters are exposed to the user with the objective of allowing them
to shape the algorithms to their specific needs. Parameter selection,
therefore, becomes an intrinsic problem of every heuristic algorithm. Selecting
good parameter values relies not only on knowledge related to the problem at
hand, but to the algorithms themselves. This research explores the usage of
self-organized criticality to reduce user interaction in the process of
selecting suitable parameters for particle swarm optimization (PSO) heuristics.
A particle swarm variant (named Adaptive PSO) with self-organized criticality
is developed and benchmarked against the standard PSO. Criticality is observed
in the dynamic behaviour of this swarm and excellent results are observed in
the long run. In contrast with the standard PSO, the Adaptive PSO does not
stagnate at any point in time, balancing the concepts of exploration and
exploitation better. A software platform for experimenting with particle
swarms, called PSO Laboratory, is also developed. This software is used to test
the standard PSO as well as all other PSO variants developed in the process of
creating the Adaptive PSO. As the software is intended to be of aid to future
and related research, special attention has been put in the development of a
friendly graphical user interface. Particle swarms are executed in real time,
allowing users to experiment by changing parameters on-the-fly.
| 1 | 0 | 0 | 0 | 0 | 0 |
Model Predictive Control meets robust Kalman filtering | Model Predictive Control (MPC) is the principal control technique used in
industrial applications. Although it offers distinguishable qualities that make
it ideal for industrial applications, it can be questioned its robustness
regarding model uncertainties and external noises. In this paper we propose a
robust MPC controller that merges the simplicity in the design of MPC with
added robustness. In particular, our control system stems from the idea of
adding robustness in the prediction phase of the algorithm through a specific
robust Kalman filter recently introduced. Notably, the overall result is an
algorithm very similar to classic MPC but that also provides the user with the
possibility to tune the robustness of the control. To test the ability of the
controller to deal with errors in modeling, we consider a servomechanism system
characterized by nonlinear dynamics.
| 0 | 0 | 1 | 0 | 0 | 0 |
Election forensic analysis of the Turkish Constitutional Referendum 2017 | With a majority of 'Yes' votes in the Constitutional Referendum of 2017,
Turkey continues its transition from democracy to autocracy. By the will of the
Turkish people, this referendum transferred practically all executive power to
president Erdogan. However, the referendum was confronted with a substantial
number of allegations of electoral misconducts and irregularities, ranging from
state coercion of 'No' supporters to the controversial validity of unstamped
ballots. In this note we report the results of an election forensic analysis of
the 2017 referendum to clarify to what extent these voting irregularities were
present and if they were able to influence the outcome of the referendum. We
specifically apply novel statistical forensics tests to further identify the
specific nature of electoral malpractices. In particular, we test whether the
data contains fingerprints for ballot-stuffing (submission of multiple ballots
per person during the vote) and voter rigging (coercion and intimidation of
voters). Additionally, we perform tests to identify numerical anomalies in the
election results. We find systematic and highly significant support for the
presence of both, ballot-stuffing and voter rigging. In 6% of stations we find
signs for ballot-stuffing with an error (probability of ballot-stuffing not
happening) of 0.15% (3 sigma event). The influence of these vote distortions
were large enough to tip the overall balance from 'No' to a majority of 'Yes'
votes.
| 0 | 1 | 0 | 1 | 0 | 0 |
Efficient Bayesian inference for multivariate factor stochastic volatility models with leverage | This paper discusses the efficient Bayesian estimation of a multivariate
factor stochastic volatility (Factor MSV) model with leverage. We propose a
novel approach to construct the sampling schemes that converges to the
posterior distribution of the latent volatilities and the parameters of
interest of the Factor MSV model based on recent advances in Particle Markov
chain Monte Carlo (PMCMC). As opposed to the approach of Chib et al. (2006} and
Omori et al. (2007}, our approach does not require approximating the joint
distribution of outcome and volatility innovations by a mixture of bivariate
normal distributions. To sample the free elements of the loading matrix we
employ the interweaving method used in Kastner et al. (2017} in the Particle
Metropolis within Gibbs (PMwG) step. The proposed method is illustrated
empirically using a simulated dataset and a sample of daily US stock returns.
| 0 | 0 | 0 | 1 | 0 | 0 |
Next Steps for the Colorado Risk-Limiting Audit (CORLA) Program | Colorado conducted risk-limiting tabulation audits (RLAs) across the state in
2017, including both ballot-level comparison audits and ballot-polling audits.
Those audits only covered contests restricted to a single county; methods to
efficiently audit contests that cross county boundaries and combine ballot
polling and ballot-level comparisons have not been available.
Colorado's current audit software (RLATool) needs to be improved to audit
these contests that cross county lines and to audit small contests efficiently.
This paper addresses these needs. It presents extremely simple but
inefficient methods, more efficient methods that combine ballot polling and
ballot-level comparisons using stratified samples, and methods that combine
ballot-level comparison and variable-size batch comparison audits in a way that
does not require stratified sampling.
We conclude with some recommendations, and illustrate our recommended method
using examples that compare them to existing approaches. Exemplar open-source
code and interactive Jupyter notebooks are provided that implement the methods
and allow further exploration.
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HD 202206 : A Circumbinary Brown Dwarf System | With Hubble Space Telescope Fine Guidance Sensor astrometry and previously
published radial velocity measures we explore the exoplanetary system HD
202206. Our modeling results in a parallax, $\pi_{abs} = 21.96\pm0.12$
milliseconds of arc, a mass for HD 202206 B of M$_B = 0.089^{ +0.007}_{-0.006}$
Msun, and a mass for HD 202206 c of M$_c = 17.9 ^{ +2.9}_{-1.8}$ MJup. HD
202206 is a nearly face-on G+M binary orbited by a brown dwarf. The system
architecture we determine supports past assertions that stability requires a
5:1 mean motion resonance (we find a period ratio, $P_c/P_B = 4.92\pm0.04$) and
coplanarity (we find a mutual inclination, Phi = 6 \arcdeg \pm 2 \arcdeg).
| 0 | 1 | 0 | 0 | 0 | 0 |
On Optimal Group Claims at Voting in a Stochastic Environment | There is a paradox in the model of social dynamics determined by voting in a
stochastic environment (the ViSE model) called "pit of losses." It consists in
the fact that a series of democratic decisions may systematically lead the
society to the states unacceptable for all the voters. The paper examines how
this paradox can be neutralized by the presence in society of a group that
votes for its benefit and can regulate the threshold of its claims. We obtain
and analyze analytical results characterizing the welfare of the whole society,
the group, and the other participants as functions of the said claims
threshold.
| 1 | 0 | 1 | 0 | 0 | 0 |
Exploring many body localization and thermalization using semiclassical method | The Discrete Truncated Wigner Approximation (DTWA) is a semi-classical phase
space method useful for the exploration of Many-body quantum dynamics. In this
work we investigate Many-Body Localization (MBL) and thermalization using DTWA
and compare its performance to exact numerical solutions. By taking as a
benchmark case a 1D random field Heisenberg spin chain with short range
interactions, and by comparing to numerically exact techniques, we show that
DTWA is able to reproduce dynamical signatures that characterize both the
thermal and the MBL phases. It exhibits the best quantitative agreement at
short times deep in each of the phases and larger mismatches close to the phase
transition. The DTWA captures the logarithmic growth of entanglement in the MBL
phase, even though a pure classical mean-field analysis would lead to no
dynamics at all. Our results suggest the DTWA can become a useful method to
investigate MBL and thermalization in experimentally relevant settings
intractable with exact numerical techniques, such as systems with long range
interactions and/or systems in higher dimensions.
| 0 | 1 | 0 | 0 | 0 | 0 |
An FPTAS for the Knapsack Problem with Parametric Weights | In this paper, we investigate the parametric weight knapsack problem, in
which the item weights are affine functions of the form $w_i(\lambda) = a_i +
\lambda \cdot b_i$ for $i \in \{1,\ldots,n\}$ depending on a real-valued
parameter $\lambda$. The aim is to provide a solution for all values of the
parameter. It is well-known that any exact algorithm for the problem may need
to output an exponential number of knapsack solutions. We present the first
fully polynomial-time approximation scheme (FPTAS) for the problem that, for
any desired precision $\varepsilon \in (0,1)$, computes
$(1-\varepsilon)$-approximate solutions for all values of the parameter. Our
FPTAS is based on two different approaches and achieves a running time of
$\mathcal{O}(n^3/\varepsilon^2 \cdot \min\{ \log^2 P, n^2 \} \cdot \min\{\log
M, n \log (n/\varepsilon) / \log(n \log (n/\varepsilon) )\})$ where $P$ is an
upper bound on the optimal profit and $M := \max\{W, n \cdot \max\{a_i,b_i: i
\in \{1,\ldots,n\}\}\}$ for a knapsack with capacity $W$.
| 1 | 0 | 1 | 0 | 0 | 0 |
Mellin and Wiener-Hopf operators in a non-classical boundary value problem describing a Lévy process | Markov processes are well understood in the case when they take place in the
whole Euclidean space. However, the situation becomes much more complicated if
a Markov process is restricted to a domain with a boundary, and then a
satisfactory theory only exists for processes with continuous trajectories.
This research, into non-classical boundary value problems, is motivated by the
study of stochastic processes, restricted to a domain, that can have
discontinuous trajectories.
To make this general problem more tractable, we consider a particular
operator, $\mathcal{A}$, which is chosen to be the generator of a certain
stable Lévy process restricted to the positive half-line. We are able to
represent $\mathcal{A}$ as a (hyper-) singular integral and, using this
representation, deduce simple conditions for its boundedness, between Bessel
potential spaces. Moreover, from energy estimates, we prove that, under certain
conditions, $\mathcal{A}$ has a trivial kernel.
A central feature of this research is our use of Mellin operators to deal
with the leading singular terms that combine, and cancel, at the boundary.
Indeed, after considerable analysis, the problem is reformulated in the context
of an algebra of multiplication, Wiener-Hopf and Mellin operators, acting on a
Lebesgue space. The resulting generalised symbol is examined and, it turns out,
that a certain transcendental equation, involving gamma and trigonometric
functions with complex arguments, plays a pivotal role. Following detailed
consideration of this transcendental equation, we are able to determine when
our operator is Fredholm and, in that case, calculate its index. Finally,
combining information on the kernel with the Fredholm index, we establish
precise conditions for the invertibility of $\mathcal{A}$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Long-term photometric behavior of the eclipsing cataclysmic variable V729 Sgr | We present the analysis results of an eclipsing cataclysmic variable (CV)
V729 Sgr, based on our observations and AAVSO data. Some outburst parameters
were determined such as outburst amplitude ($A_{n}$) and recurrence time
($T_{n}$), and then the relationship between $A_{n}$ and $T_{n}$ is discussed.
A cursory examination for the long-term light curves reveals that there are
small-amplitude outbursts and dips present, which is similar to the behaviors
seen in some nova-like CVs (NLs). More detailed inspection suggests that the
outbursts in V729 Sgr may be Type A (outside-in) with a rise time $\sim1.76$ d.
Further analysis also shows that V729 Sgr is an intermediate between dwarf nova
and NLs, and we constrain its mass transfer rate to $1.59\times10^{-9} <
\dot{M}_{2} < 5.8\times10^{-9}M_{\odot}yr^{-1}$ by combining the theory for Z
Cam type stars with observations. Moreover, the rapid oscillations in V729 Sgr
were detected and analyzed for the first time. Our results indicate that the
oscillation at $\sim 25.5$ s is a true DNO, being associated with the accretion
events. The classification of the oscillations at $\sim 136$ and $154$ s as
lpDNOs is based on the relation between $P_{lpDNOs}$ and $P_{DNOs}$. Meanwhile,
the QPOs at the period of hundreds of seconds are also detected.
| 0 | 1 | 0 | 0 | 0 | 0 |
SPUX: Scalable Particle Markov Chain Monte Carlo for uncertainty quantification in stochastic ecological models | Calibration of individual based models (IBMs), successful in modeling complex
ecological dynamical systems, is often performed only ad-hoc. Bayesian
inference can be used for both parameter estimation and uncertainty
quantification, but its successful application to realistic scenarios has been
hindered by the complex stochastic nature of IBMs. Computationally expensive
techniques such as Particle Filter (PF) provide marginal likelihood estimates,
where multiple model simulations (particles) are required to get a sample from
the state distribution conditional on the observed data. Particle ensembles are
re-sampled at each data observation time, requiring particle destruction and
replication, which lead to an increase in algorithmic complexity. We present
SPUX, a Python implementation of parallel Particle Markov Chain Monte Carlo
(PMCMC) algorithm, which mitigates high computational costs by distributing
particles over multiple computational units. Adaptive load re-balancing
techniques are used to mitigate computational work imbalances introduced by
re-sampling. Framework performance is investigated and significant speed-ups
are observed for a simple predator-prey IBM model.
| 1 | 0 | 0 | 1 | 0 | 0 |
A Social Network Analysis Framework for Modeling Health Insurance Claims Data | Health insurance companies in Brazil have their data about claims organized
having the view only for providers. In this way, they loose the physician view
and how they share patients. Partnership between physicians can view as a
fruitful work in most of the cases but sometimes this could be a problem for
health insurance companies and patients, for example a recommendation to visit
another physician only because they work in same clinic. The focus of the work
is to better understand physicians activities and how these activities are
represented in the data. Our approach considers three aspects: the
relationships among physicians, the relationships between physicians and
patients, and the relationships between physicians and health providers. We
present the results of an analysis of a claims database (detailing 18 months of
activity) from a large health insurance company in Brazil. The main
contribution presented in this paper is a set of models to represent: mutual
referral between physicians, patient retention, and physician centrality in the
health insurance network. Our results show the proposed models based on social
network frameworks, extracted surprising insights about physicians from real
health insurance claims data.
| 1 | 0 | 0 | 0 | 0 | 0 |
Congruences for Restricted Plane Overpartitions Modulo 4 and 8 | In 2009, Corteel, Savelief and Vuletić generalized the concept of
overpartitions to a new object called plane overpartitions. In recent work, the
author considered a restricted form of plane overpartitions called $k$-rowed
plane overpartions and proved a method to obtain congruences for these and
other types of combinatorial generating functions. In this paper, we prove
several restricted and unrestricted plane overpartition congruences modulo $4$
and $8$ using other techniques.
| 0 | 0 | 1 | 0 | 0 | 0 |
AndroVault: Constructing Knowledge Graph from Millions of Android Apps for Automated Analysis | Data driven research on Android has gained a great momentum these years. The
abundance of data facilitates knowledge learning, however, also increases the
difficulty of data preprocessing. Therefore, it is non-trivial to prepare a
demanding and accurate set of data for research. In this work, we put forward
AndroVault, a framework for the Android research composing of data collection,
knowledge representation and knowledge extraction. It has started with a
long-running web crawler for data collection (both apps and description) since
2013, which guarantees the timeliness of data; With static analysis and dynamic
analysis of the collected data, we compute a variety of attributes to
characterize Android apps. After that, we employ a knowledge graph to connect
all these apps by computing their correlation in terms of attributes; Last, we
leverage multiple technologies such as logical inference, machine learning, and
correlation analysis to extract facts (more accurate and demanding, either high
level or not, data) that are beneficial for a specific research problem. With
the produced data of high quality, we have successfully conducted many research
works including malware detection, code generation, and Android testing. We
would like to release our data to the research community in an authenticated
manner, and encourage them to conduct productive research.
| 1 | 0 | 0 | 0 | 0 | 0 |
Universal and generalizable restoration strategies for degraded ecological networks | Humans are increasingly stressing ecosystems via habitat destruction, climate
change and global population movements leading to the widespread loss of
biodiversity and the disruption of key ecological services. Ecosystems
characterized primarily by mutualistic relationships between species such as
plant-pollinator interactions may be particularly vulnerable to such
perturbations because the loss of biodiversity can cause extinction cascades
that can compromise the entire network. Here, we develop a general restoration
strategy based on network-science for degraded ecosystems. Specifically, we
show that network topology can be used to identify the optimal sequence of
species reintroductions needed to maximize biodiversity gains following partial
and full ecosystem collapse. This restoration strategy generalizes across
topologically-disparate and geographically-distributed ecosystems.
Additionally, we find that although higher connectance and diversity promote
persistence in pristine ecosystems, these attributes reduce the effectiveness
of restoration efforts in degraded networks. Hence, focusing on restoring the
factors that promote persistence in pristine ecosystems may yield suboptimal
recovery strategies for degraded ecosystems. Overall, our results have
important insights for designing effective ecosystem restoration strategies to
preserve biodiversity and ensure the delivery of critical natural services that
fuel economic development, food security and human health around the globe
| 0 | 0 | 0 | 0 | 1 | 0 |
Information transmission and signal permutation in active flow networks | Recent experiments show that both natural and artificial microswimmers in
narrow channel-like geometries will self-organise to form steady, directed
flows. This suggests that networks of flowing active matter could function as
novel autonomous microfluidic devices. However, little is known about how
information propagates through these far-from-equilibrium systems. Through a
mathematical analogy with spin-ice vertex models, we investigate here the
input-output characteristics of generic incompressible active flow networks
(AFNs). Our analysis shows that information transport through an AFN is
inherently different from conventional pressure or voltage driven networks.
Active flows on hexagonal arrays preserve input information over longer
distances than their passive counterparts and are highly sensitive to bulk
topological defects, whose presence can be inferred from marginal input-output
distributions alone. This sensitivity further allows controlled permutations on
parallel inputs, revealing an unexpected link between active matter and group
theory that can guide new microfluidic mixing strategies facilitated by active
matter and aid the design of generic autonomous information transport networks.
| 1 | 1 | 0 | 0 | 0 | 0 |
Heuristic Framework for Multi-Scale Testing of the Multi-Manifold Hypothesis | When analyzing empirical data, we often find that global linear models
overestimate the number of parameters required. In such cases, we may ask
whether the data lies on or near a manifold or a set of manifolds (a so-called
multi-manifold) of lower dimension than the ambient space. This question can be
phrased as a (multi-) manifold hypothesis. The identification of such intrinsic
multiscale features is a cornerstone of data analysis and representation and
has given rise to a large body of work on manifold learning. In this work, we
review key results on multi-scale data analysis and intrinsic dimension
followed by the introduction of a heuristic, multiscale framework for testing
the multi-manifold hypothesis. Our method implements a hypothesis test on a set
of spline-interpolated manifolds constructed from variance-based intrinsic
dimensions. The workflow is suitable for empirical data analysis as we
demonstrate on two use cases.
| 0 | 0 | 0 | 1 | 0 | 0 |
Kitting in the Wild through Online Domain Adaptation | Technological developments call for increasing perception and action
capabilities of robots. Among other skills, vision systems that can adapt to
any possible change in the working conditions are needed. Since these
conditions are unpredictable, we need benchmarks which allow to assess the
generalization and robustness capabilities of our visual recognition
algorithms. In this work we focus on robotic kitting in unconstrained
scenarios. As a first contribution, we present a new visual dataset for the
kitting task. Differently from standard object recognition datasets, we provide
images of the same objects acquired under various conditions where camera,
illumination and background are changed. This novel dataset allows for testing
the robustness of robot visual recognition algorithms to a series of different
domain shifts both in isolation and unified. Our second contribution is a novel
online adaptation algorithm for deep models, based on batch-normalization
layers, which allows to continuously adapt a model to the current working
conditions. Differently from standard domain adaptation algorithms, it does not
require any image from the target domain at training time. We benchmark the
performance of the algorithm on the proposed dataset, showing its capability to
fill the gap between the performances of a standard architecture and its
counterpart adapted offline to the given target domain.
| 1 | 0 | 0 | 0 | 0 | 0 |
Event Analysis of Pulse-reclosers in Distribution Systems Through Sparse Representation | The pulse-recloser uses pulse testing technology to verify that the line is
clear of faults before initiating a reclose operation, which significantly
reduces stress on the system components (e.g. substation transformers) and
voltage sags on adjacent feeders. Online event analysis of pulse-reclosers are
essential to increases the overall utility of the devices, especially when
there are numerous devices installed throughout the distribution system. In
this paper, field data recorded from several devices were analyzed to identify
specific activity and fault locations. An algorithm is developed to screen the
data to identify the status of each pole and to tag time windows with a
possible pulse event. In the next step, selected time windows are further
analyzed and classified using a sparse representation technique by solving an
l1-regularized least-square problem. This classification is obtained by
comparing the pulse signature with the reference dictionary to find a set that
most closely matches the pulse features. This work also sheds additional light
on the possibility of fault classification based on the pulse signature. Field
data collected from a distribution system are used to verify the effectiveness
and reliability of the proposed method.
| 1 | 0 | 0 | 0 | 0 | 0 |
Projected Power Iteration for Network Alignment | The network alignment problem asks for the best correspondence between two
given graphs, so that the largest possible number of edges are matched. This
problem appears in many scientific problems (like the study of protein-protein
interactions) and it is very closely related to the quadratic assignment
problem which has graph isomorphism, traveling salesman and minimum bisection
problems as particular cases. The graph matching problem is NP-hard in general.
However, under some restrictive models for the graphs, algorithms can
approximate the alignment efficiently. In that spirit the recent work by Feizi
and collaborators introduce EigenAlign, a fast spectral method with convergence
guarantees for Erdős-Renyí graphs. In this work we propose the algorithm
Projected Power Alignment, which is a projected power iteration version of
EigenAlign. We numerically show it improves the recovery rates of EigenAlign
and we describe the theory that may be used to provide performance guarantees
for Projected Power Alignment.
| 1 | 0 | 1 | 1 | 0 | 0 |
3D mean Projective Shape Difference for Face Differentiation from Multiple Digital Camera Images | We give a nonparametric methodology for hypothesis testing for equality of
extrinsic mean objects on a manifold embedded in a numerical spaces. The
results obtained in the general setting are detailed further in the case of 3D
projective shapes represented in a space of symmetric matrices via the
quadratic Veronese-Whitney (VW) embedding. Large sample and nonparametric
bootstrap confidence regions are derived for the common VW-mean of random
projective shapes for finite 3D configurations. As an example, the VW MANOVA
testing methodology is applied to the multi-sample mean problem for independent
projective shapes of $3D$ facial configurations retrieved from digital images,
via Agisoft PhotoScan technology.
| 0 | 0 | 0 | 1 | 0 | 0 |
Video Highlight Prediction Using Audience Chat Reactions | Sports channel video portals offer an exciting domain for research on
multimodal, multilingual analysis. We present methods addressing the problem of
automatic video highlight prediction based on joint visual features and textual
analysis of the real-world audience discourse with complex slang, in both
English and traditional Chinese. We present a novel dataset based on League of
Legends championships recorded from North American and Taiwanese Twitch.tv
channels (will be released for further research), and demonstrate strong
results on these using multimodal, character-level CNN-RNN model architectures.
| 1 | 0 | 0 | 0 | 0 | 0 |
Effects of Interactions on Dynamic Correlations of Hard-Core Bosons at Finite Temperatures | We investigate how dynamic correlations of hard-core bosonic excitation at
finite temperature are affected by additional interactions besides the
hard-core repulsion which prevents them from occupying the same site. We focus
especially on dimerized spin systems, where these additional interactions
between the elementary excitations, triplons, lead to the formation of bound
states, relevant for the correct description of scattering processes. In order
to include these effects quantitatively we extend the previously developed
Brückner approach to include also nearest-neighbor (NN) and next-nearest
neighbor (NNN) interactions correctly in a low-temperature expansion. This
leads to the extension of the scalar Bethe-Salpeter equation to a matrix-valued
equation. Exemplarily, we consider the Heisenberg spin ladder to illustrate the
significance of the additional interactions on the spectral functions at finite
temperature which are proportional to inelastic neutron scattering rates.
| 0 | 1 | 0 | 0 | 0 | 0 |
The Frechet distribution: Estimation and Application an Overview | In this article, we consider the problem of estimating the parameters of the
Fréchet distribution from both frequentist and Bayesian points of view. First
we briefly describe different frequentist approaches, namely, maximum
likelihood, method of moments, percentile estimators, L-moments, ordinary and
weighted least squares, maximum product of spacings, maximum goodness-of-fit
estimators and compare them with respect to mean relative estimates, mean
squared errors and the 95\% coverage probability of the asymptotic confidence
intervals using extensive numerical simulations. Next, we consider the Bayesian
inference approach using reference priors. The Metropolis-Hasting algorithm is
used to draw Markov Chain Monte Carlo samples, and they have in turn been used
to compute the Bayes estimates and also to construct the corresponding credible
intervals. Five real data sets related to the minimum flow of water on
Piracicaba river in Brazil are used to illustrate the applicability of the
discussed procedures.
| 0 | 0 | 0 | 1 | 0 | 0 |
Prediction of Sea Surface Temperature using Long Short-Term Memory | This letter adopts long short-term memory(LSTM) to predict sea surface
temperature(SST), which is the first attempt, to our knowledge, to use
recurrent neural network to solve the problem of SST prediction, and to make
one week and one month daily prediction. We formulate the SST prediction
problem as a time series regression problem. LSTM is a special kind of
recurrent neural network, which introduces gate mechanism into vanilla RNN to
prevent the vanished or exploding gradient problem. It has strong ability to
model the temporal relationship of time series data and can handle the
long-term dependency problem well. The proposed network architecture is
composed of two kinds of layers: LSTM layer and full-connected dense layer.
LSTM layer is utilized to model the time series relationship. Full-connected
layer is utilized to map the output of LSTM layer to a final prediction. We
explore the optimal setting of this architecture by experiments and report the
accuracy of coastal seas of China to confirm the effectiveness of the proposed
method. In addition, we also show its online updated characteristics.
| 1 | 0 | 0 | 0 | 0 | 0 |
An Operational Framework for Specifying Memory Models using Instantaneous Instruction Execution | There has been great progress recently in formally specifying the memory
model of microprocessors like ARM and POWER. These specifications are, however,
too complicated for reasoning about program behaviors, verifying compilers
etc., because they involve microarchitectural details like the reorder buffer
(ROB), partial and speculative execution, instruction replay on speculation
failure, etc. In this paper we present a new Instantaneous Instruction
Execution (I2E) framework which allows us to specify weak memory models in the
same style as SC and TSO. Each instruction in I2E is executed instantaneously
and in-order such that the state of the processor is always correct. The effect
of instruction reordering is captured by the way data is moved between the
processors and the memory non-deterministically, using three conceptual
devices: invalidation buffers, timestamps and dynamic store buffers. We prove
that I2E models capture the behaviors of modern microarchitectures and
cache-coherent memory systems accurately, thus eliminating the need to think
about microarchitectural details.
| 1 | 0 | 0 | 0 | 0 | 0 |
Efficient Algorithms for Moral Lineage Tracing | Lineage tracing, the joint segmentation and tracking of living cells as they
move and divide in a sequence of light microscopy images, is a challenging
task. Jug et al. have proposed a mathematical abstraction of this task, the
moral lineage tracing problem (MLTP), whose feasible solutions define both a
segmentation of every image and a lineage forest of cells. Their branch-and-cut
algorithm, however, is prone to many cuts and slow convergence for large
instances. To address this problem, we make three contributions: (i) we devise
the first efficient primal feasible local search algorithms for the MLTP, (ii)
we improve the branch-and-cut algorithm by separating tighter cutting planes
and by incorporating our primal algorithms, (iii) we show in experiments that
our algorithms find accurate solutions on the problem instances of Jug et al.
and scale to larger instances, leveraging moral lineage tracing to practical
significance.
| 1 | 0 | 0 | 0 | 0 | 0 |
Interpolating between $k$-Median and $k$-Center: Approximation Algorithms for Ordered $k$-Median | We consider a generalization of $k$-median and $k$-center, called the {\em
ordered $k$-median} problem. In this problem, we are given a metric space
$(\mathcal{D},\{c_{ij}\})$ with $n=|\mathcal{D}|$ points, and a non-increasing
weight vector $w\in\mathbb{R}_+^n$, and the goal is to open $k$ centers and
assign each point each point $j\in\mathcal{D}$ to a center so as to minimize
$w_1\cdot\text{(largest assignment cost)}+w_2\cdot\text{(second-largest
assignment cost)}+\ldots+w_n\cdot\text{($n$-th largest assignment cost)}$. We
give an $(18+\epsilon)$-approximation algorithm for this problem. Our
algorithms utilize Lagrangian relaxation and the primal-dual schema, combined
with an enumeration procedure of Aouad and Segev. For the special case of
$\{0,1\}$-weights, which models the problem of minimizing the $\ell$ largest
assignment costs that is interesting in and of by itself, we provide a novel
reduction to the (standard) $k$-median problem showing that LP-relative
guarantees for $k$-median translate to guarantees for the ordered $k$-median
problem; this yields a nice and clean $(8.5+\epsilon)$-approximation algorithm
for $\{0,1\}$ weights.
| 1 | 0 | 0 | 0 | 0 | 0 |
Statistical Challenges in Modeling Big Brain Signals | Brain signal data are inherently big: massive in amount, complex in
structure, and high in dimensions. These characteristics impose great
challenges for statistical inference and learning. Here we review several key
challenges, discuss possible solutions, and highlight future research
directions.
| 0 | 0 | 0 | 1 | 0 | 0 |
Injective and Automorphism-Invariant Non-Singular Modules | Every automorphism-invariant right non-singular $A$-module is injective if
and only if the factor ring of the ring $A$ with respect to its right Goldie
radical is a right strongly semiprime ring.
| 0 | 0 | 1 | 0 | 0 | 0 |
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