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Emission line galaxies behind the planetary nebula IC 5148: Potential for a serendipity survey with archival data | During the start of a survey program using FORS2 long slit spectroscopy on
planetary nebulae (PN) and their haloes, we serendipitously discovered six
background emission line galaxies (ELG) with redshifts of z = 0.2057, 0.3137,
0.37281, 0.4939, 0.7424 and 0.8668. Thus they clearly do not belong to a common
cluster structure. We derived the major physical properties of the targets.
Since the used long slit covers a sky area of only 570 arcsec^2, we discuss
further potential of serendipitous discoveries in archival data, beside the
deep systematic work of the ongoing and upcoming big surveys. We conclude that
archival data provide a decent potential for extending the overall data on ELGs
without any selection bias.
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An example related to the slicing inequality for general measures | For $n\in \mathbb{N}$ let $S_n$ be the smallest number $S>0$ satisfying the
inequality $$ \int_K f \le S \cdot |K|^{\frac 1n} \cdot \max_{\xi\in S^{n-1}}
\int_{K\cap \xi^\bot} f $$ for all centrally-symmetric convex bodies $K$ in
$\mathbb{R}^n$ and all even, continuous probability densities $f$ on $K$. Here
$|K|$ is the volume of $K$. It was proved by the second-named author that
$S_n\le 2\sqrt{n}$, and in analogy with Bourgain's slicing problem, it was
asked whether $S_n$ is bounded from above by a universal constant. In this note
we construct an example showing that $S_n\ge c\sqrt{n}/\sqrt{\log \log n},$
where $c > 0$ is an absolute constant. Additionally, for any $0 < \alpha < 2$
we describe a related example that satisfies the so-called
$\psi_{\alpha}$-condition.
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Two-level schemes for the advection equation | The advection equation is the basis for mathematical models of continuum
mechanics. In the approximate solution of nonstationary problems it is
necessary to inherit main properties of the conservatism and monotonicity of
the solution. In this paper, the advection equation is written in the symmetric
form, where the advection operator is the half-sum of advection operators in
conservative (divergent) and non-conservative (characteristic) forms. The
advection operator is skew-symmetric. Standard finite element approximations in
space are used. The standart explicit two-level scheme for the advection
equation is absolutly unstable. New conditionally stable regularized schemes
are constructed, on the basis of the general theory of stability
(well-posedness) of operator-difference schemes, the stability conditions of
the explicit Lax-Wendroff scheme are established. Unconditionally stable and
conservative schemes are implicit schemes of the second (Crank-Nicolson scheme)
and fourth order. The conditionally stable implicit Lax-Wendroff scheme is
constructed. The accuracy of the investigated explicit and implicit two-level
schemes for an approximate solution of the advection equation is illustrated by
the numerical results of a model two-dimensional problem.
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Estimating functional time series by moving average model fitting | Functional time series have become an integral part of both functional data
and time series analysis. Important contributions to methodology, theory and
application for the prediction of future trajectories and the estimation of
functional time series parameters have been made in the recent past. This paper
continues this line of research by proposing a first principled approach to
estimate invertible functional time series by fitting functional moving average
processes. The idea is to estimate the coefficient operators in a functional
linear filter. To do this a functional Innovations Algorithm is utilized as a
starting point to estimate the corresponding moving average operators via
suitable projections into principal directions. In order to establish
consistency of the proposed estimators, asymptotic theory is developed for
increasing subspaces of these principal directions. For practical purposes,
several strategies to select the number of principal directions to include in
the estimation procedure as well as the choice of order of the functional
moving average process are discussed. Their empirical performance is evaluated
through simulations and an application to vehicle traffic data.
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Benford's law: a 'sleeping beauty' sleeping in the dirty pages of logarithmic tables | Benford's law is an empirical observation, first reported by Simon Newcomb in
1881 and then independently by Frank Benford in 1938: the first significant
digits of numbers in large data are often distributed according to a
logarithmically decreasing function. Being contrary to intuition, the law was
forgotten as a mere curious observation. However, in the last two decades,
relevant literature has grown exponentially, - an evolution typical of
"Sleeping Beauties" (SBs) publications that go unnoticed (sleep) for a long
time and then suddenly become center of attention (are awakened). Thus, in the
present study, we show that Newcomb (1881) and Benford (1938) papers are
clearly SBs. The former was in deep sleep for 110 years whereas the latter was
in deep sleep for a comparatively lesser period of 31 years up to 1968, and in
a state of less deep sleep for another 27 years up to 1995. Both SBs were
awakened in the year 1995 by Hill (1995a). In so doing, we show that the waking
prince (Hill, 1995a) is more often quoted than the SB whom he kissed, - in this
Benford's law case, wondering whether this is a general effect, - to be
usefully studied.
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Characterizing correlations and synchronization in collective dynamics | Synchronization, that occurs both for non-chaotic and chaotic systems, is a
striking phenomenon with many practical implications in natural phenomena.
However, even before synchronization, strong correlations occur in the
collective dynamics of complex systems. To characterize their nature is
essential for the understanding of phenomena in physical and social sciences.
The emergence of strong correlations before synchronization is illustrated in a
few piecewise linear models. They are shown to be associated to the behavior of
ergodic parameters which may be exactly computed in some models. The models are
also used as a testing ground to find general methods to characterize and
parametrize the correlated nature of collective dynamics.
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Soft modes and strain redistribution in continuous models of amorphous plasticity: the Eshelby paradigm, and beyond? | The deformation of disordered solids relies on swift and localised
rearrangements of particles. The inspection of soft vibrational modes can help
predict the locations of these rearrangements, while the strain that they
actually redistribute mediates collective effects. Here, we study soft modes
and strain redistribution in a two-dimensional continuous mesoscopic model
based on a Ginzburg-Landau free energy for perfect solids, supplemented with a
plastic disorder potential that accounts for shear softening and
rearrangements. Regardless of the disorder strength, our numerical simulations
show soft modes that are always sharply peaked at the softest point of the
material (unlike what happens for the depinning of an elastic interface).
Contrary to widespread views, the deformation halo around this peak does not
always have a quadrupolar (Eshelby-like) shape. Instead, for finite and
narrowly-distributed disorder, it looks like a fracture, with a strain field
that concentrates along some easy directions. These findings are rationalised
with analytical calculations in the case where the plastic disorder is confined
to a point-like `impurity'. In this case, we unveil a continuous family of
elastic propagators, which are identical for the soft modes and for the
equilibrium configurations. This family interpolates between the standard
quadrupolar propagator and the fracture-like one as the anisotropy of the
elastic medium is increased. Therefore, we expect to see a fracture-like
propagator when extended regions on the brink of failure have already softened
along the shear direction and thus rendered the material anisotropic, but not
failed yet. We speculate that this might be the case in carefully aged glasses
just before macroscopic failure.
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On the Optimality of Kernel-Embedding Based Goodness-of-Fit Tests | The reproducing kernel Hilbert space (RKHS) embedding of distributions offers
a general and flexible framework for testing problems in arbitrary domains and
has attracted considerable amount of attention in recent years. To gain
insights into their operating characteristics, we study here the statistical
performance of such approaches within a minimax framework. Focusing on the case
of goodness-of-fit tests, our analyses show that a vanilla version of the
kernel-embedding based test could be suboptimal, and suggest a simple remedy by
moderating the embedding. We prove that the moderated approach provides optimal
tests for a wide range of deviations from the null and can also be made
adaptive over a large collection of interpolation spaces. Numerical experiments
are presented to further demonstrate the merits of our approach.
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The influence of contrarians in the dynamics of opinion formation | In this work we consider the presence of contrarian agents in discrete
3-state kinetic exchange opinion models. The contrarians are individuals that
adopt the choice opposite to the prevailing choice of their contacts, whatever
this choice is. We consider binary as well as three-agent interactions, with
stochastic parameters, in a fully-connected population. Our numerical results
suggest that the presence of contrarians destroys the absorbing state of the
original model, changing the transition to the para-ferromagnetic type. In this
case, the consequence for the society is that the three opinions coexist in the
population, in both phases (ordered and disordered). Furthermore, the
order-disorder transition is suppressed for a sufficient large fraction of
contrarians. In some cases the transition is discontinuous, and it changes to
continuous before it is suppressed. Some of our results are complemented by
analytical calculations based on the master equation.
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Risk-neutral valuation under differential funding costs, defaults and collateralization | We develop a unified valuation theory that incorporates credit risk
(defaults), collateralization and funding costs, by expanding the replication
approach to a generality that has not yet been studied previously and reaching
valuation when replication is not assumed. This unifying theoretical framework
clarifies the relationship between the two valuation approaches: the adjusted
cash flows approach pioneered for example by Brigo, Pallavicini and co-authors
([12, 13, 34]) and the classic replication approach illustrated for example by
Bielecki and Rutkowski and co-authors ([3, 8]). In particular, results of this
work cover most previous papers where the authors studied specific replication
models.
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Singular Spectrum and Recent Results on Hierarchical Operators | We use trace class scattering theory to exclude the possibility of absolutely
continuous spectrum in a large class of self-adjoint operators with an
underlying hierarchical structure and provide applications to certain random
hierarchical operators and matrices. We proceed to contrast the localizing
effect of the hierarchical structure in the deterministic setting with previous
results and conjectures in the random setting. Furthermore, we survey stronger
localization statements truly exploiting the disorder for the hierarchical
Anderson model and report recent results concerning the spectral statistics of
the ultrametric random matrix ensemble.
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Weyl states and Fermi arcs in parabolic bands | Weyl fermions are shown to exist inside a parabolic band, where the kinetic
energy of carriers is given by the non-relativistic Schroedinger equation.
There are Fermi arcs as a direct consequence of the folding of a ring shaped
Fermi surface inside the first Brillouin zone. Our results stem from the
decomposition of the kinetic energy into the sum of the square of the Weyl
state, the coupling to the local magnetic field and the Rashba interaction. The
Weyl fermions break the time and reflection symmetries present in the kinetic
energy, thus allowing for the onset of a weak three-dimensional magnetic field
around the layer. This field brings topological stability to the current
carrying states through a Chern number. In the special limit that the Weyl
state becomes gapless this magnetic interaction is shown to be purely
attractive, thus suggesting the onset of a superconducting condensate of zero
helicity states.
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Flexible Deep Neural Network Processing | The recent success of Deep Neural Networks (DNNs) has drastically improved
the state of the art for many application domains. While achieving high
accuracy performance, deploying state-of-the-art DNNs is a challenge since they
typically require billions of expensive arithmetic computations. In addition,
DNNs are typically deployed in ensemble to boost accuracy performance, which
further exacerbates the system requirements. This computational overhead is an
issue for many platforms, e.g. data centers and embedded systems, with tight
latency and energy budgets. In this article, we introduce flexible DNNs
ensemble processing technique, which achieves large reduction in average
inference latency while incurring small to negligible accuracy drop. Our
technique is flexible in that it allows for dynamic adaptation between quality
of results (QoR) and execution runtime. We demonstrate the effectiveness of the
technique on AlexNet and ResNet-50 using the ImageNet dataset. This technique
can also easily handle other types of networks.
| 0 | 0 | 0 | 1 | 0 | 0 |
Kinetic approach to relativistic dissipation | Despite a long record of intense efforts, the basic mechanisms by which
dissipation emerges from the microscopic dynamics of a relativistic fluid still
elude a complete understanding. In particular, no unique pathway from kinetic
theory to hydrodynamics has been identified as yet, with different approaches
leading to different values of the transport coefficients. In this Letter, we
approach the problem by matching data from lattice kinetic simulations with
analytical predictions. Our numerical results provide neat evidence in favour
of the Chapman-Enskog procedure, as suggested by recently theoretical analyses,
along with qualitative hints at the basic reasons why the Chapman-Enskog
expansion might be better suited than Grad's method to capture the emergence of
dissipative effects in relativistic fluids.
| 0 | 1 | 0 | 0 | 0 | 0 |
Cross-label Suppression: A Discriminative and Fast Dictionary Learning with Group Regularization | This paper addresses image classification through learning a compact and
discriminative dictionary efficiently. Given a structured dictionary with each
atom (columns in the dictionary matrix) related to some label, we propose
cross-label suppression constraint to enlarge the difference among
representations for different classes. Meanwhile, we introduce group
regularization to enforce representations to preserve label properties of
original samples, meaning the representations for the same class are encouraged
to be similar. Upon the cross-label suppression, we don't resort to
frequently-used $\ell_0$-norm or $\ell_1$-norm for coding, and obtain
computational efficiency without losing the discriminative power for
categorization. Moreover, two simple classification schemes are also developed
to take full advantage of the learnt dictionary. Extensive experiments on six
data sets including face recognition, object categorization, scene
classification, texture recognition and sport action categorization are
conducted, and the results show that the proposed approach can outperform lots
of recently presented dictionary algorithms on both recognition accuracy and
computational efficiency.
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DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding | Recurrent neural nets (RNN) and convolutional neural nets (CNN) are widely
used on NLP tasks to capture the long-term and local dependencies,
respectively. Attention mechanisms have recently attracted enormous interest
due to their highly parallelizable computation, significantly less training
time, and flexibility in modeling dependencies. We propose a novel attention
mechanism in which the attention between elements from input sequence(s) is
directional and multi-dimensional (i.e., feature-wise). A light-weight neural
net, "Directional Self-Attention Network (DiSAN)", is then proposed to learn
sentence embedding, based solely on the proposed attention without any RNN/CNN
structure. DiSAN is only composed of a directional self-attention with temporal
order encoded, followed by a multi-dimensional attention that compresses the
sequence into a vector representation. Despite its simple form, DiSAN
outperforms complicated RNN models on both prediction quality and time
efficiency. It achieves the best test accuracy among all sentence encoding
methods and improves the most recent best result by 1.02% on the Stanford
Natural Language Inference (SNLI) dataset, and shows state-of-the-art test
accuracy on the Stanford Sentiment Treebank (SST), Multi-Genre natural language
inference (MultiNLI), Sentences Involving Compositional Knowledge (SICK),
Customer Review, MPQA, TREC question-type classification and Subjectivity
(SUBJ) datasets.
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Eigenvalues of compactly perturbed operators via entropy numbers | We derive new estimates for the number of discrete eigenvalues of compactly
perturbed operators on Banach spaces, assuming that the perturbing operator is
an element of a weak entropy number ideal. Our results improve upon earlier
results by the author and by Demuth et al. The main tool in our proofs is an
inequality of Carl. In particular, in contrast to all previous results we do
not rely on tools from complex analysis.
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Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank | Recently low displacement rank (LDR) matrices, or so-called structured
matrices, have been proposed to compress large-scale neural networks. Empirical
results have shown that neural networks with weight matrices of LDR matrices,
referred as LDR neural networks, can achieve significant reduction in space and
computational complexity while retaining high accuracy. We formally study LDR
matrices in deep learning. First, we prove the universal approximation property
of LDR neural networks with a mild condition on the displacement operators. We
then show that the error bounds of LDR neural networks are as efficient as
general neural networks with both single-layer and multiple-layer structure.
Finally, we propose back-propagation based training algorithm for general LDR
neural networks.
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A data driven trimming procedure for robust classification | Classification rules can be severely affected by the presence of disturbing
observations in the training sample. Looking for an optimal classifier with
such data may lead to unnecessarily complex rules. So, simpler effective
classification rules could be achieved if we relax the goal of fitting a good
rule for the whole training sample but only consider a fraction of the data. In
this paper we introduce a new method based on trimming to produce
classification rules with guaranteed performance on a significant fraction of
the data. In particular, we provide an automatic way of determining the right
trimming proportion and obtain in this setting oracle bounds for the
classification error on the new data set.
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The Salesman's Improved Tours for Fundamental Classes | Finding the exact integrality gap $\alpha$ for the LP relaxation of the
metric Travelling Salesman Problem (TSP) has been an open problem for over
thirty years, with little progress made. It is known that $4/3 \leq \alpha \leq
3/2$, and a famous conjecture states $\alpha = 4/3$. For this problem,
essentially two "fundamental" classes of instances have been proposed. This
fundamental property means that in order to show that the integrality gap is at
most $\rho$ for all instances of metric TSP, it is sufficient to show it only
for the instances in the fundamental class. However, despite the importance and
the simplicity of such classes, no apparent effort has been deployed for
improving the integrality gap bounds for them. In this paper we take a natural
first step in this endeavour, and consider the $1/2$-integer points of one such
class. We successfully improve the upper bound for the integrality gap from
$3/2$ to $10/7$ for a superclass of these points, as well as prove a lower
bound of $4/3$ for the superclass. Our methods involve innovative applications
of tools from combinatorial optimization which have the potential to be more
broadly applied.
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Maximum Number of Modes of Gaussian Mixtures | Gaussian mixture models are widely used in Statistics. A fundamental aspect
of these distributions is the study of the local maxima of the density, or
modes. In particular, it is not known how many modes a mixture of $k$ Gaussians
in $d$ dimensions can have. We give a brief account of this problem's history.
Then, we give improved lower bounds and the first upper bound on the maximum
number of modes, provided it is finite.
| 0 | 0 | 1 | 1 | 0 | 0 |
Dynamic Clearing and Contagion in Financial Networks | In this paper we will consider a generalized extension of the Eisenberg-Noe
model of financial contagion to allow for time dynamics in both discrete and
continuous time. Derivation and interpretation of the financial implications
will be provided. Emphasis will be placed on the continuous-time framework and
its formulation as a differential equation driven by the operating cash flows.
Mathematical results on existence and uniqueness of firm wealths under the
discrete and continuous-time models will be provided. Finally, the financial
implications of time dynamics will be considered. The focus will be on how the
dynamic clearing solutions differ from those of the static Eisenberg-Noe model.
| 0 | 0 | 0 | 0 | 0 | 1 |
On the Semantics of Intensionality and Intensional Recursion | Intensionality is a phenomenon that occurs in logic and computation. In the
most general sense, a function is intensional if it operates at a level finer
than (extensional) equality. This is a familiar setting for computer
scientists, who often study different programs or processes that are
interchangeable, i.e. extensionally equal, even though they are not implemented
in the same way, so intensionally distinct. Concomitant with intensionality is
the phenomenon of intensional recursion, which refers to the ability of a
program to have access to its own code. In computability theory, intensional
recursion is enabled by Kleene's Second Recursion Theorem. This thesis is
concerned with the crafting of a logical toolkit through which these phenomena
can be studied. Our main contribution is a framework in which mathematical and
computational constructions can be considered either extensionally, i.e. as
abstract values, or intensionally, i.e. as fine-grained descriptions of their
construction. Once this is achieved, it may be used to analyse intensional
recursion.
| 1 | 0 | 1 | 0 | 0 | 0 |
A Theory of Solvability for Lossless Power Flow Equations -- Part I: Fixed-Point Power Flow | This two-part paper details a theory of solvability for the power flow
equations in lossless power networks. In Part I, we derive a new formulation of
the lossless power flow equations, which we term the fixed-point power flow.
The model is stated for both meshed and radial networks, and is parameterized
by several graph-theoretic matrices -- the power network stiffness matrices --
which quantify the internal coupling strength of the network. The model leads
immediately to an explicit approximation of the high-voltage power flow
solution. For standard test cases, we find that iterates of the fixed-point
power flow converge rapidly to the high-voltage power flow solution, with the
approximate solution yielding accurate predictions near base case loading. In
Part II, we leverage the fixed-point power flow to study power flow
solvability, and for radial networks we derive conditions guaranteeing the
existence and uniqueness of a high-voltage power flow solution. These
conditions (i) imply exponential convergence of the fixed-point power flow
iteration, and (ii) properly generalize the textbook two-bus system results.
| 0 | 0 | 1 | 0 | 0 | 0 |
Narcissus: Deriving Correct-By-Construction Decoders and Encoders from Binary Formats | It is a neat result from functional programming that libraries of parser
combinators can support rapid construction of decoders for quite a range of
formats. With a little more work, the same combinator program can denote both a
decoder and an encoder. Unfortunately, the real world is full of gnarly
formats, as with the packet formats that make up the standard Internet protocol
stack. Most past parser-combinator approaches cannot handle these formats, and
the few exceptions require redundancy -- one part of the natural grammar needs
to be hand-translated into hints in multiple parts of a parser program. We show
how to recover very natural and nonredundant format specifications, covering
all popular network packet formats and generating both decoders and encoders
automatically. The catch is that we use the Coq proof assistant to derive both
kinds of artifacts using tactics, automatically, in a way that guarantees that
they form inverses of each other. We used our approach to reimplement packet
processing for a full Internet protocol stack, inserting our replacement into
the OCaml-based MirageOS unikernel, resulting in minimal performance
degradation.
| 1 | 0 | 0 | 0 | 0 | 0 |
Discrete and Continuous Green Energy on Compact Manifolds | In this article we study the role of the Green function for the Laplacian in
a compact Riemannian manifold as a tool for obtaining well-distributed points.
In particular, we prove that a sequence of minimizers for the Green energy is
asymptotically uniformly distributed. We pay special attention to the case of
locally harmonic manifolds.
| 0 | 0 | 1 | 0 | 0 | 0 |
High Capacity, Secure (n, n/8) Multi Secret Image Sharing Scheme with Security Key | The rising need of secret image sharing with high security has led to much
advancement in lucrative exchange of important images which contain vital and
confidential information. Multi secret image sharing system (MSIS) is an
efficient and robust method for transmitting one or more secret images
securely. In recent research, n secret images are encrypted into n or n+ 1
shared images and stored in different database servers. The decoder has to
receive all n or n+1 encrypted images to reproduce the secret image. One can
recover partial secret information from n-1 or fewer shared images, which poses
risk for the confidential information encrypted. In this proposed paper we
developed a novel algorithm to increase the sharing capacity by using (n, n/8)
multi-secret sharing scheme with increased security by generating a unique
security key. A unrevealed comparison image is used to produce shares which
makes the secret image invulnerable to the hackers
| 1 | 0 | 0 | 0 | 0 | 0 |
MATMPC - A MATLAB Based Toolbox for Real-time Nonlinear Model Predictive Control | In this paper we introduce MATMPC, an open source software built in MATLAB
for nonlinear model predictive control (NMPC). It is designed to facilitate
modelling, controller design and simulation for a wide class of NMPC
applications. MATMPC has a number of algorithmic modules, including automatic
differentiation, direct multiple shooting, condensing, linear quadratic program
(QP) solver and globalization. It also supports a unique Curvature-like Measure
of Nonlinearity (CMoN) MPC algorithm. MATMPC has been designed to provide
state-of-the-art performance while making the prototyping easy, also with
limited programming knowledge. This is achieved by writing each module directly
in MATLAB API for C. As a result, MATMPC modules can be compiled into MEX
functions with performance comparable to plain C/C++ solvers. MATMPC has been
successfully used in operating systems including WINDOWS, LINUX AND OS X.
Selected examples are shown to highlight the effectiveness of MATMPC.
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A Copula-based Imputation Model for Missing Data of Mixed Type in Multilevel Data Sets | We propose a copula based method to handle missing values in multivariate
data of mixed types in multilevel data sets. Building upon the extended rank
likelihood of \cite{hoff2007extending} and the multinomial probit model, our
model is a latent variable model which is able to capture the relationship
among variables of different types as well as accounting for the clustering
structure. We fit the model by approximating the posterior distribution of the
parameters and the missing values through a Gibbs sampling scheme. We use the
multiple imputation procedure to incorporate the uncertainty due to missing
values in the analysis of the data. Our proposed method is evaluated through
simulations to compare it with several conventional methods of handling missing
data. We also apply our method to a data set from a cluster randomized
controlled trial of a multidisciplinary intervention in acute stroke units. We
conclude that our proposed copula based imputation model for mixed type
variables achieves reasonably good imputation accuracy and recovery of
parameters in some models of interest, and that adding random effects enhances
performance when the clustering effect is strong.
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Rovibrational optical cooling of a molecular beam | Cooling the rotation and the vibration of molecules by broadband light
sources was possible for trapped molecular ions or ultracold molecules. Because
of a low power spectral density, the cooling timescale has never fell below
than a few milliseconds. Here we report on rotational and vibrational cooling
of a supersonic beam of barium monofluoride molecules in less than 440 $\mu$s.
Vibrational cooling was optimized by enhancing the spectral power density of a
semiconductor light source at the underlying molecular transitions allowing us
to transfer all the populations of $v''=1-3$ into the vibrational ground state
($v''=0$). Rotational cooling, that requires an efficient vibrational pumping,
was then achieved. According to a Boltzmann fit, the rotation temperature was
reduced by almost a factor of 10. In this fashion, the population of the lowest
rotational levels increased by more than one order of magnitude.
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When Work Matters: Transforming Classical Network Structures to Graph CNN | Numerous pattern recognition applications can be formed as learning from
graph-structured data, including social network, protein-interaction network,
the world wide web data, knowledge graph, etc. While convolutional neural
network (CNN) facilitates great advances in gridded image/video understanding
tasks, very limited attention has been devoted to transform these successful
network structures (including Inception net, Residual net, Dense net, etc.) to
establish convolutional networks on graph, due to its irregularity and
complexity geometric topologies (unordered vertices, unfixed number of adjacent
edges/vertices). In this paper, we aim to give a comprehensive analysis of when
work matters by transforming different classical network structures to graph
CNN, particularly in the basic graph recognition problem. Specifically, we
firstly review the general graph CNN methods, especially in its spectral
filtering operation on the irregular graph data. We then introduce the basic
structures of ResNet, Inception and DenseNet into graph CNN and construct these
network structures on graph, named as G_ResNet, G_Inception, G_DenseNet. In
particular, it seeks to help graph CNNs by shedding light on how these
classical network structures work and providing guidelines for choosing
appropriate graph network frameworks. Finally, we comprehensively evaluate the
performance of these different network structures on several public graph
datasets (including social networks and bioinformatic datasets), and
demonstrate how different network structures work on graph CNN in the graph
recognition task.
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Factoring the Cycle Aging Cost of Batteries Participating in Electricity Markets | When participating in electricity markets, owners of battery energy storage
systems must bid in such a way that their revenues will at least cover their
true cost of operation. Since cycle aging of battery cells represents a
substantial part of this operating cost, the cost of battery degradation must
be factored in these bids. However, existing models of battery degradation
either do not fit market clearing software or do not reflect the actual battery
aging mechanism. In this paper we model battery cycle aging using a piecewise
linear cost function, an approach that provides a close approximation of the
cycle aging mechanism of electrochemical batteries and can be incorporated
easily into existing market dispatch programs. By defining the marginal aging
cost of each battery cycle, we can assess the actual operating profitability of
batteries. A case study demonstrates the effectiveness of the proposed model in
maximizing the operating profit of a battery energy storage system taking part
in the ISO New England energy and reserve markets.
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Asynchronous Parallel Bayesian Optimisation via Thompson Sampling | We design and analyse variations of the classical Thompson sampling (TS)
procedure for Bayesian optimisation (BO) in settings where function evaluations
are expensive, but can be performed in parallel. Our theoretical analysis shows
that a direct application of the sequential Thompson sampling algorithm in
either synchronous or asynchronous parallel settings yields a surprisingly
powerful result: making $n$ evaluations distributed among $M$ workers is
essentially equivalent to performing $n$ evaluations in sequence. Further, by
modeling the time taken to complete a function evaluation, we show that, under
a time constraint, asynchronously parallel TS achieves asymptotically lower
regret than both the synchronous and sequential versions. These results are
complemented by an experimental analysis, showing that asynchronous TS
outperforms a suite of existing parallel BO algorithms in simulations and in a
hyper-parameter tuning application in convolutional neural networks. In
addition to these, the proposed procedure is conceptually and computationally
much simpler than existing work for parallel BO.
| 1 | 0 | 0 | 1 | 0 | 0 |
Hydrodynamic signatures of stationary Marangoni-driven surfactant transport | We experimentally study steady Marangoni-driven surfactant transport on the
interface of a deep water layer. Using hydrodynamic measurements, and without
using any knowledge of the surfactant physico-chemical properties, we show that
sodium dodecyl sulphate and Tergitol 15-S-9 introduced in low concentrations
result in a flow driven by adsorbed surfactant. At higher surfactant
concentration, the flow is dominated by the dissolved surfactant. Using
Camphoric acid, whose properties are {\it a priori} unknown, we demonstrate
this method's efficacy by showing its spreading is adsorption dominated.
| 0 | 1 | 0 | 0 | 0 | 0 |
Exponential growth of homotopy groups of suspended finite complexes | We study the asymptotic behavior of the homotopy groups of simply connected
finite $p$-local complexes, and define a space to be locally hyperbolic if its
homotopy groups have exponential growth. Under some certain conditions related
to the functorial decomposition of loop suspension, we prove that the suspended
finite complexes are locally hyperbolic if suitable but accessible information
of the homotopy groups is known. In particular, we prove that Moore spaces are
locally hyperbolic, and other candidates are also given.
| 0 | 0 | 1 | 0 | 0 | 0 |
Approximation Algorithms for Rectangle Packing Problems (PhD Thesis) | In rectangle packing problems we are given the task of placing axis-aligned
rectangles in a given plane region, so that they do not overlap with each
other. In Maximum Weight Independent Set of Rectangles (MWISR), their position
is given and we can only select which rectangles to choose, while trying to
maximize their total weight. In Strip Packing (SP), we have to pack all the
given rectangles in a rectangular region of fixed width, while minimizing its
height. In 2-Dimensional Geometric Knapsack (2DGK), the target region is a
square of a given size, and our goal is to select and pack a subset of the
given rectangles of maximum weight. We study a generalization of MWISR and use
it to improve the approximation for a resource allocation problem called
bagUFP. We revisit some classical results on SP and 2DGK, by proposing a
framework based on smaller containers that are packed with simpler rules; while
variations of this scheme are indeed a standard technique in this area, we
abstract away some of the problem-specific differences, obtaining simpler
algorithms that work for different problems. We obtain improved approximations
for SP in pseudo-polynomial time, and for a variant of 2DGK where one can to
rotate the rectangles by 90°. For the latter, we propose the first
algorithms with approximation factor better than 2. For the main variant of
2DGK (without rotations), a container-based approach seems to face a natural
barrier of 2 in the approximation factor. Thus, we consider a generalized kind
of packing that combines container packings with another packing problem that
we call L-packing problem, where we have to pack rectangles in an L-shaped
region of the plane. By finding a (1 + {\epsilon})-approximation for this
problem and exploiting the combinatorial structure of 2DGK, we obtain the first
algorithms that break the barrier of 2 for the approximation factor of this
problem.
| 1 | 0 | 0 | 0 | 0 | 0 |
R-boundedness Approach to linear third differential equations in a UMD Space | The aim of this work is to study the existence of a periodic solutions of
third order differential equations $z'''(t) = Az(t) + f(t)$ with the periodic
condition $x(0) = x(2\pi), x'(0) = x'(2\pi)$ and $x''(0) = x''(2\pi)$. Our
approach is based on the R-boundedness and $L^{p}$-multiplier of linear
operators.
| 0 | 0 | 1 | 0 | 0 | 0 |
Entanglement and quantum transport in integrable systems | Understanding the entanglement structure of out-of-equilibrium many-body
systems is a challenging yet revealing task. Here we investigate the
entanglement dynamics after a quench from a piecewise homogeneous initial state
in integrable systems. This is the prototypical setup for studying quantum
transport, and it consists in the sudden junction of two macroscopically
different and homogeneous states. By exploiting the recently developed
integrable hydrodynamic approach and the quasiparticle picture for the
entanglement dynamics, we conjecture a formula for the entanglement production
rate after joining two semi-infinite reservoirs, as well as the steady-state
entanglement entropy of a finite subregion. We show that both quantities are
determined by the quasiparticles created in the Non Equilibrium steady State
(NESS) appearing at large times at the interface between the two reservoirs.
Specifically, the steady-state entropy coincides with the thermodynamic entropy
of the NESS, whereas the entropy production rate reflects its spreading into
the bulk of the two reservoirs. Our results are numerically corroborated using
time-dependent Density Matrix Renormalization Group (tDMRG) simulations in the
paradigmatic XXZ spin-1/2 chain.
| 0 | 1 | 0 | 0 | 0 | 0 |
Data-Efficient Multirobot, Multitask Transfer Learning for Trajectory Tracking | Transfer learning has the potential to reduce the burden of data collection
and to decrease the unavoidable risks of the training phase. In this letter, we
introduce a multirobot, multitask transfer learning framework that allows a
system to complete a task by learning from a few demonstrations of another task
executed on another system. We focus on the trajectory tracking problem where
each trajectory represents a different task, since many robotic tasks can be
described as a trajectory tracking problem. The proposed multirobot transfer
learning framework is based on a combined $\mathcal{L}_1$ adaptive control and
an iterative learning control approach. The key idea is that the adaptive
controller forces dynamically different systems to behave as a specified
reference model. The proposed multitask transfer learning framework uses
theoretical control results (e.g., the concept of vector relative degree) to
learn a map from desired trajectories to the inputs that make the system track
these trajectories with high accuracy. This map is used to calculate the inputs
for a new, unseen trajectory. Experimental results using two different
quadrotor platforms and six different trajectories show that, on average, the
proposed framework reduces the first-iteration tracking error by 74% when
information from tracking a different single trajectory on a different
quadrotor is utilized.
| 1 | 0 | 0 | 0 | 0 | 0 |
Exponential Bounds for the Erdős-Ginzburg-Ziv Constant | The Erdős-Ginzburg-Ziv constant of an abelian group $G$, denoted
$\mathfrak{s}(G)$, is the smallest $k\in\mathbb{N}$ such that any sequence of
elements of $G$ of length $k$ contains a zero-sum subsequence of length
$\exp(G)$. In this paper, we use the partition rank, which generalizes the
slice rank, to prove that for any odd prime $p$, \[
\mathfrak{s}\left(\mathbb{F}_{p}^{n}\right)\leq(p-1)2^{p}\left(J(p)\cdot
p\right)^{n} \] where $0.8414<J(p)<0.91837$ is the constant appearing in
Ellenberg and Gijswijt's bound on arithmetic progression-free subsets of
$\mathbb{F}_{p}^{n}$. For large $n$, and $p>3$, this is the first exponential
improvement to the trivial bound. We also provide a near optimal result
conditional on the conjecture that $\left(\mathbb{Z}/k\mathbb{Z}\right)^{n}$
satisfies property $D$, showing that in this case \[
\mathfrak{s}\left(\left(\mathbb{Z}/k\mathbb{Z}\right)^{n}\right)\leq(k-1)4^{n}+k.
\]
| 0 | 0 | 1 | 0 | 0 | 0 |
Soft Weight-Sharing for Neural Network Compression | The success of deep learning in numerous application domains created the de-
sire to run and train them on mobile devices. This however, conflicts with
their computationally, memory and energy intense nature, leading to a growing
interest in compression. Recent work by Han et al. (2015a) propose a pipeline
that involves retraining, pruning and quantization of neural network weights,
obtaining state-of-the-art compression rates. In this paper, we show that
competitive compression rates can be achieved by using a version of soft
weight-sharing (Nowlan & Hinton, 1992). Our method achieves both quantization
and pruning in one simple (re-)training procedure. This point of view also
exposes the relation between compression and the minimum description length
(MDL) principle.
| 0 | 0 | 0 | 1 | 0 | 0 |
Neural Discourse Structure for Text Categorization | We show that discourse structure, as defined by Rhetorical Structure Theory
and provided by an existing discourse parser, benefits text categorization. Our
approach uses a recursive neural network and a newly proposed attention
mechanism to compute a representation of the text that focuses on salient
content, from the perspective of both RST and the task. Experiments consider
variants of the approach and illustrate its strengths and weaknesses.
| 1 | 0 | 0 | 0 | 0 | 0 |
On the optimal design of grid-based binary holograms for matter wave lithography | Grid based binary holography (GBH) is an attractive method for patterning
with light or matter waves. It is an approximate technique in which different
holographic masks can be used to produce similar patterns. Here we present an
optimal design method for GBH masks that allows for freely selecting the
fraction of open holes in the mask from below 10% to above 90%. Open-fraction
is an important design parameter when making masks for use in lithography
systems. The method also includes a rescaling feature that potentially enables
a better contrast of the generated patterns. Through simulations we investigate
the contrast and robustness of the patterns formed by masks generated by the
proposed optimal design method. It is demonstrated that high contrast patterns
are achievable for a wide range of open-fractions. We conclude that reaching a
desired open-fraction is a trade-off with the contrast of the pattern generated
by the mask.
| 0 | 1 | 0 | 0 | 0 | 0 |
Thermal physics of the inner coma: ALMA studies of the methanol distribution and excitation in comet C/2012 K1 (PanSTARRS) | We present spatially and spectrally-resolved observations of CH$_3$OH
emission from comet C/2012 K1 (PanSTARRS) using The Atacama Large
Millimeter/submillimeter Array (ALMA) on 2014 June 28-29. Two-dimensional maps
of the line-of-sight average rotational temperature ($T_{rot}$) were derived,
covering spatial scales $0.3''-1.8''$ (corresponding to sky-projected distances
$\rho\sim500$-2500 km). The CH$_3$OH column density distributions are
consistent with isotropic, uniform outflow from the nucleus, with no evidence
for extended sources of CH$_3$OH in the coma. The $T_{rot}(\rho)$ radial
profiles show a significant drop within a few thousand kilometers of the
nucleus, falling from about 60 K to 20 K between $\rho=0$ and 2500 km on June
28, whereas on June 29, $T_{rot}$ fell from about 120 K to 40 K between $\rho=$
0 km and 1000 km. The observed $T_{rot}$ behavior is interpreted primarily as a
result of variations in the coma kinetic temperature due to adiabatic cooling
of the outflowing gas, as well as radiative cooling of the CH$_3$OH rotational
levels. Our excitation model shows that radiative cooling is more important for
the $J=7-6$ transitions (at 338 GHz) than for the $K=3-2$ transitions (at 252
GHz), resulting in a strongly sub-thermal distribution of levels in the $J=7-6$
band at $\rho\gtrsim1000$ km. For both bands, the observed temperature drop
with distance is less steep than predicted by standard coma theoretical models,
which suggests the presence of a significant source of heating in addition to
the photolytic heat sources usually considered.
| 0 | 1 | 0 | 0 | 0 | 0 |
Alternating Iteratively Reweighted Minimization Algorithms for Low-Rank Matrix Factorization | Nowadays, the availability of large-scale data in disparate application
domains urges the deployment of sophisticated tools for extracting valuable
knowledge out of this huge bulk of information. In that vein, low-rank
representations (LRRs) which seek low-dimensional embeddings of data have
naturally appeared. In an effort to reduce computational complexity and improve
estimation performance, LRR has been viewed via a matrix factorization (MF)
perspective. Recently, low-rank MF (LRMF) approaches have been proposed for
tackling the inherent weakness of MF i.e., the unawareness of the dimension of
the low-dimensional space where data reside. Herein, inspired by the merits of
iterative reweighted schemes for rank minimization, we come up with a generic
low-rank promoting regularization function. Then, focusing on a specific
instance of it, we propose a regularizer that imposes column-sparsity jointly
on the two matrix factors that result from MF, thus promoting low-rankness on
the optimization problem. The problems of denoising, matrix completion and
non-negative matrix factorization (NMF) are redefined according to the new LRMF
formulation and solved via efficient Newton-type algorithms with proven
theoretical guarantees as to their convergence and rates of convergence to
stationary points. The effectiveness of the proposed algorithms is verified in
diverse simulated and real data experiments.
| 1 | 0 | 0 | 0 | 0 | 0 |
The formation of the Milky Way halo and its dwarf satellites, a NLTE-1D abundance analysis. I. Homogeneous set of atmospheric parameters | We present a homogeneous set of accurate atmospheric parameters for a
complete sample of very and extremely metal-poor stars in the dwarf spheroidal
galaxies (dSphs) Sculptor, Ursa Minor, Sextans, Fornax, Boötes I, Ursa Major
II, and Leo IV. We also deliver a Milky Way (MW) comparison sample of giant
stars covering the -4 < [Fe/H] < -1.7 metallicity range. We show that, in the
[Fe/H] > -3.5 regime, the non-local thermodynamic equilibrium (NLTE)
calculations with non-spectroscopic effective temperature (Teff) and surface
gravity (log~g) based on the photometric methods and known distance provide
consistent abundances of the Fe I and Fe II lines. This justifies the Fe I/Fe
II ionisation equilibrium method to determine log g for the MW halo giants with
unknown distance. The atmospheric parameters of the dSphs and MW stars were
checked with independent methods. In the [Fe/H] > -3.5 regime, the Ti I/Ti II
ionisation equilibrium is fulfilled in the NLTE calculations. In the log~g -
Teff plane, all the stars sit on the giant branch of the evolutionary tracks
corresponding to [Fe/H] = -2 to -4, in line with their metallicities. For some
of the most metal-poor stars of our sample, we hardly achieve consistent NLTE
abundances from the two ionisation stages for both iron and titanium. We
suggest that this is a consequence of the uncertainty in the Teff-colour
relation at those metallicities. The results of these work provide the base for
a detailed abundance analysis presented in a companion paper.
| 0 | 1 | 0 | 0 | 0 | 0 |
Fixed-Gain Augmented-State Tracking-Filters | A procedure for the design of fixed-gain tracking filters, using an
augmented-state observer with signal and interference subspaces, is proposed.
The signal subspace incorporates an integrating Newtonian model and a
second-order maneuver model that is matched to a sustained constant-g turn; the
deterministic interference model creates a Nyquist null for smoother track
estimates. The selected models provide a simple means of shaping and analyzing
the (transient and steady-state) response of tracking-filters of elevated
order.
| 1 | 0 | 0 | 0 | 0 | 0 |
The Detectability of Radio Auroral Emission from Proxima B | Magnetically active stars possess stellar winds whose interaction with
planetary magnetic fields produces radio auroral emission. We examine the
detectability of radio auroral emission from Proxima b, the closest known
exosolar planet orbiting our nearest neighboring star, Proxima Centauri. Using
the Radiometric Bode's Law, we estimate the radio flux produced by the
interaction of Proxima Centauri's stellar wind and Proxima b's magnetosphere
for different planetary magnetic field strengths. For plausible planetary
masses, Proxima b produces 6-83 mJy of auroral radio flux at frequencies of
0.3-0.8 MHz for planetary magnetic field strengths of 1-3 B$_{\oplus}$.
According to recent MHD models that vary the orbital parameters of the system,
this emission is expected to be highly variable. This variability is due to
large fluctuations in the size of Proxima b's magnetosphere as it crosses the
equatorial streamer regions of the dense stellar wind and high dynamic
pressure. Using the MHD model of Garraffo et al. 2016 for the variation of the
magnetosphere radius during the orbit, we estimate that the observed radio flux
can vary nearly by an order of magnitude over the 11.2 day period of Proxima b.
The detailed amplitude variation depends on the stellar wind, orbital, and
planetary magnetic field parameters. We discuss observing strategies for
proposed future space-based observatories to reach frequencies below the
ionospheric cut off ($\sim 10$ MHz) as would be required to detect the signal
we investigate.
| 0 | 1 | 0 | 0 | 0 | 0 |
Converting topological insulators into topological metals within the tetradymite family | We report the electronic band structures and concomitant Fermi surfaces for a
family of exfoliable tetradymite compounds with the formula $T_2$$Ch_2$$Pn$,
obtained as a modification to the well-known topological insulator binaries
Bi$_2$(Se,Te)$_3$ by replacing one chalcogen ($Ch$) with a pnictogen ($Pn$) and
Bi with the tetravalent transition metals $T$ $=$ Ti, Zr, or Hf. This
imbalances the electron count and results in layered metals characterized by
relatively high carrier mobilities and bulk two-dimensional Fermi surfaces
whose topography is well-described by first principles calculations.
Intriguingly, slab electronic structure calculations predict Dirac-like surface
states. In contrast to Bi$_2$Se$_3$, where the surface Dirac bands are at the
$\Gamma-$point, for (Zr,Hf)$_2$Te$_2$(P,As) there are Dirac cones of strong
topological character around both the $\bar {\Gamma}$- and $\bar {M}$-points
which are above and below the Fermi energy, respectively. For Ti$_2$Te$_2$P the
surface state is predicted to exist only around the $\bar {M}$-point. In
agreement with these predictions, the surface states that are located below the
Fermi energy are observed by angle resolved photoemission spectroscopy
measurements, revealing that they coexist with the bulk metallic state. Thus,
this family of materials provides a foundation upon which to develop novel
phenomena that exploit both the bulk and surface states (e.g., topological
superconductivity).
| 0 | 1 | 0 | 0 | 0 | 0 |
When Do Birds of a Feather Flock Together? k-Means, Proximity, and Conic Programming | Given a set of data, one central goal is to group them into clusters based on
some notion of similarity between the individual objects. One of the most
popular and widely-used approaches is k-means despite the computational
hardness to find its global minimum. We study and compare the properties of
different convex relaxations by relating them to corresponding proximity
conditions, an idea originally introduced by Kumar and Kannan. Using conic
duality theory, we present an improved proximity condition under which the
Peng-Wei relaxation of k-means recovers the underlying clusters exactly. Our
proximity condition improves upon Kumar and Kannan, and is comparable to that
of Awashti and Sheffet where proximity conditions are established for
projective k-means. In addition, we provide a necessary proximity condition for
the exactness of the Peng-Wei relaxation. For the special case of equal cluster
sizes, we establish a different and completely localized proximity condition
under which the Amini-Levina relaxation yields exact clustering, thereby having
addressed an open problem by Awasthi and Sheffet in the balanced case. Our
framework is not only deterministic and model-free but also comes with a clear
geometric meaning which allows for further analysis and generalization.
Moreover, it can be conveniently applied to analyzing various data generative
models such as the stochastic ball models and Gaussian mixture models. With
this method, we improve the current minimum separation bound for the stochastic
ball models and achieve the state-of-the-art results of learning Gaussian
mixture models.
| 0 | 0 | 1 | 0 | 0 | 0 |
Collective Sedimentation of Squirmers under Gravity | Active particles, which interact hydrodynamically, display a remarkable
variety of emergent collective phenomena. We use squirmers to model spherical
microswimmers and explore the collective behavior of thousands of them under
the influence of strong gravity using the method of multi-particle collision
dynamics for simulating fluid flow. The sedimentation profile depends on the
ratio of swimming to sedimentation velocity as well as on the squirmer type. It
shows close packed squirmer layers at the bottom and a highly dynamic region
with exponential density dependence towards the top. The mean vertical
orientation of the squirmers strongly depends on height. For swimming
velocities larger than the sedimentation velocity, squirmers show strong
convection in the exponential region. We quantify the strength of convection
and the extent of convection cells by the vertical current density and its
current dipole, which are large for neutral squirmers as well as for weak
pushers and pullers.
| 0 | 1 | 0 | 0 | 0 | 0 |
Differentially Private Bayesian Learning on Distributed Data | Many applications of machine learning, for example in health care, would
benefit from methods that can guarantee privacy of data subjects. Differential
privacy (DP) has become established as a standard for protecting learning
results. The standard DP algorithms require a single trusted party to have
access to the entire data, which is a clear weakness. We consider DP Bayesian
learning in a distributed setting, where each party only holds a single sample
or a few samples of the data. We propose a learning strategy based on a secure
multi-party sum function for aggregating summaries from data holders and the
Gaussian mechanism for DP. Our method builds on an asymptotically optimal and
practically efficient DP Bayesian inference with rapidly diminishing extra
cost.
| 1 | 0 | 0 | 1 | 0 | 0 |
Metastability and avalanche dynamics in strongly-correlated gases with long-range interactions | We experimentally study the stability of a bosonic Mott-insulator against the
formation of a density wave induced by long-range interactions, and
characterize the intrinsic dynamics between these two states. The
Mott-insulator is created in a quantum degenerate gas of 87-Rubidium atoms,
trapped in a three-dimensional optical lattice. The gas is located inside and
globally coupled to an optical cavity. This causes interactions of global
range, mediated by photons dispersively scattered between a transverse lattice
and the cavity. The scattering comes with an atomic density modulation, which
is measured by the photon flux leaking from the cavity. We initialize the
system in a Mott-insulating state and then rapidly increase the global coupling
strength. We observe that the system falls into either of two distinct final
states. One is characterized by a low photon flux, signaling a Mott insulator,
and the other is characterized by a high photon flux, which we associate with a
density wave. Ramping the global coupling slowly, we observe a hysteresis loop
between the two states - a further signature of metastability. A comparison
with a theoretical model confirms that the metastability originates in the
competition between short- and global-range interactions. From the increasing
photon flux monitored during the switching process, we find that several
thousand atoms tunnel to a neighboring site on the time scale of the single
particle dynamics. We argue that a density modulation, initially forming in the
compressible surface of the trapped gas, triggers an avalanche tunneling
process in the Mott-insulating region.
| 0 | 1 | 0 | 0 | 0 | 0 |
Optical nanoscopy via quantum control | We present a scheme for nanoscopic imaging of a quantum mechanical two-level
system using an optical probe in the far-field. Existing super-resolution
schemes require more than two-levels and depend on an incoherent response to
the lasers. Here, quantum control of the two states proceeds via rapid
adiabatic passage. We implement this scheme on an array of semiconductor
self-assembled quantum dots. Each quantum dot results in a bright spot in the
image with extents down to 30 nm ({\lambda}/31). Rapid adiabatic passage is
established as a versatile tool in the super-resolution toolbox.
| 0 | 1 | 0 | 0 | 0 | 0 |
Skin Lesion Classification Using Deep Multi-scale Convolutional Neural Networks | We present a deep learning approach to the ISIC 2017 Skin Lesion
Classification Challenge using a multi-scale convolutional neural network. Our
approach utilizes an Inception-v3 network pre-trained on the ImageNet dataset,
which is fine-tuned for skin lesion classification using two different scales
of input images.
| 1 | 0 | 0 | 0 | 0 | 0 |
Confidence Intervals and Hypothesis Testing for the Permutation Entropy with an application to Epilepsy | In nonlinear dynamics, and to a lesser extent in other fields, a widely used
measure of complexity is the Permutation Entropy. But there is still no known
method to determine the accuracy of this measure. There has been little
research on the statistical properties of this quantity that characterize time
series. The literature describes some resampling methods of quantities used in
nonlinear dynamics - as the largest Lyapunov exponent - but all of these seems
to fail. In this contribution we propose a parametric bootstrap methodology
using a symbolic representation of the time series in order to obtain the
distribution of the Permutation Entropy estimator. We perform several time
series simulations given by well known stochastic processes: the 1=f? noise
family, and show in each case that the proposed accuracy measure is as
efficient as the one obtained by the frequentist approach of repeating the
experiment. The complexity of brain electrical activity, measured by the
Permutation Entropy, has been extensively used in epilepsy research for
detection in dynamical changes in electroencephalogram (EEG) signal with no
consideration of the variability of this complexity measure. An application of
the parametric bootstrap methodology is used to compare normal and pre-ictal
EEG signals.
| 0 | 1 | 0 | 1 | 0 | 0 |
Feature overwriting as a finite mixture process: Evidence from comprehension data | The ungrammatical sentence "The key to the cabinets are on the table" is
known to lead to an illusion of grammaticality. As discussed in the
meta-analysis by Jaeger et al., 2017, faster reading times are observed at the
verb are in the agreement-attraction sentence above compared to the equally
ungrammatical sentence "The key to the cabinet are on the table". One
explanation for this facilitation effect is the feature percolation account:
the plural feature on cabinets percolates up to the head noun key, leading to
the illusion. An alternative account is in terms of cue-based retrieval (Lewis
& Vasishth, 2005), which assumes that the non-subject noun cabinets is
misretrieved due to a partial feature-match when a dependency completion
process at the auxiliary initiates a memory access for a subject with plural
marking. We present evidence for yet another explanation for the observed
facilitation. Because the second sentence has two nouns with identical number,
it is possible that these are, in some proportion of trials, more difficult to
keep distinct, leading to slower reading times at the verb in the first
sentence above; this is the feature overwriting account of Nairne, 1990. We
show that the feature overwriting proposal can be implemented as a finite
mixture process. We reanalysed ten published data-sets, fitting hierarchical
Bayesian mixture models to these data assuming a two-mixture distribution. We
show that in nine out of the ten studies, a mixture distribution corresponding
to feature overwriting furnishes a superior fit over both the feature
percolation and the cue-based retrieval accounts.
| 1 | 0 | 0 | 1 | 0 | 0 |
Photographic dataset: playing cards | This is a photographic dataset collected for testing image processing
algorithms. The idea is to have images that can exploit the properties of total
variation, therefore a set of playing cards was distributed on the scene. The
dataset is made available at www.fips.fi/photographic_dataset2.php
| 1 | 1 | 0 | 0 | 0 | 0 |
Dynamic constraints on activity and connectivity during the learning of value | Human learning is a complex process in which future behavior is altered via
the modulation of neural activity. Yet, the degree to which brain activity and
functional connectivity during learning is constrained across subjects, for
example by conserved anatomy and physiology or by the nature of the task,
remains unknown. Here, we measured brain activity and functional connectivity
in a longitudinal experiment in which healthy adult human participants learned
the values of novel objects over the course of four days. We assessed the
presence of constraints on activity and functional connectivity using an
inter-subject correlation approach. Constraints on activity and connectivity
were greater in magnitude than expected in a non-parametric permutation-based
null model, particularly in primary sensory and motor systems, as well as in
regions associated with the learning of value. Notably, inter-subject
connectivity in activity and connectivity displayed marked temporal variations,
with inter-subject correlations in activity exceeding those in connectivity
during early learning and \emph{visa versa} in later learning. Finally,
individual differences in performance accuracy tracked the degree to which a
subject's connectivity, but not activity, tracked subject-general patterns.
Taken together, our results support the notion that brain activity and
connectivity are constrained across subjects in early learning, with
constraints on activity, but not connectivity, decreasing in later learning.
| 0 | 0 | 0 | 0 | 1 | 0 |
A note on Weyl groups and crystallographic root lattices | We follow the dual approach to Coxeter systems and show for Weyl groups a
criterium which decides whether a set of reflections is generating the group
depending on the root and the coroot lattice. Further we study special
generating sets involving a parabolic subgroup and show that they are very
tame.
| 0 | 0 | 1 | 0 | 0 | 0 |
Smoothness-based Edge Detection using Low-SNR Camera for Robot Navigation | In the emerging advancement in the branch of autonomous robotics, the ability
of a robot to efficiently localize and construct maps of its surrounding is
crucial. This paper deals with utilizing thermal-infrared cameras, as opposed
to conventional cameras as the primary sensor to capture images of the robot's
surroundings. For localization, the images need to be further processed before
feeding them to a navigational system. The main motivation of this paper was to
develop an edge detection methodology capable of utilizing the low-SNR poor
output from such a thermal camera and effectively detect smooth edges of the
surrounding environment. The enhanced edge detector proposed in this paper
takes the raw image from the thermal sensor, denoises the images, applies Canny
edge detection followed by CSS method. The edges are ranked to remove any noise
and only edges of the highest rank are kept. Then, the broken edges are linked
by computing edge metrics and a smooth edge of the surrounding is displayed in
a binary image. Several comparisons are also made in the paper between the
proposed technique and the existing techniques.
| 1 | 0 | 0 | 1 | 0 | 0 |
Distributed resource allocation through utility design - Part I: optimizing the performance certificates via the price of anarchy | Game theory has emerged as a novel approach for the coordination of
multiagent systems. A fundamental component of this approach is the design of a
local utility function for each agent so that their selfish maximization
achieves the global objective. In this paper we propose a novel framework to
characterize and optimize the worst case performance (price of anarchy) of any
resulting equilibrium as a function of the chosen utilities, thus providing a
performance certificate for a large class of algorithms. More specifically, we
consider a class of resource allocation problems, where each agent selects a
subset of the resources with the goal of maximizing a welfare function. First,
we show that any smoothness argument is inconclusive for the design problems
considered. Motivated by this, we introduce a new approach providing a tight
expression for the price of anarchy (PoA) as a function of the chosen utility
functions. Leveraging this result, we show how to design the utilities so as to
maximize the PoA through a tractable linear program. In Part II we specialize
the results to submodular and supermodular welfare functions, discuss
complexity issues and provide two applications.
| 1 | 0 | 0 | 0 | 0 | 0 |
IDK Cascades: Fast Deep Learning by Learning not to Overthink | Advances in deep learning have led to substantial increases in prediction
accuracy but have been accompanied by increases in the cost of rendering
predictions. We conjecture that fora majority of real-world inputs, the recent
advances in deep learning have created models that effectively "overthink" on
simple inputs. In this paper, we revisit the classic question of building model
cascades that primarily leverage class asymmetry to reduce cost. We introduce
the "I Don't Know"(IDK) prediction cascades framework, a general framework to
systematically compose a set of pre-trained models to accelerate inference
without a loss in prediction accuracy. We propose two search based methods for
constructing cascades as well as a new cost-aware objective within this
framework. The proposed IDK cascade framework can be easily adopted in the
existing model serving systems without additional model re-training. We
evaluate the proposed techniques on a range of benchmarks to demonstrate the
effectiveness of the proposed framework.
| 1 | 0 | 0 | 0 | 0 | 0 |
Learning a Unified Control Policy for Safe Falling | Being able to fall safely is a necessary motor skill for humanoids performing
highly dynamic tasks, such as running and jumping. We propose a new method to
learn a policy that minimizes the maximal impulse during the fall. The
optimization solves for both a discrete contact planning problem and a
continuous optimal control problem. Once trained, the policy can compute the
optimal next contacting body part (e.g. left foot, right foot, or hands),
contact location and timing, and the required joint actuation. We represent the
policy as a mixture of actor-critic neural network, which consists of n control
policies and the corresponding value functions. Each pair of actor-critic is
associated with one of the n possible contacting body parts. During execution,
the policy corresponding to the highest value function will be executed while
the associated body part will be the next contact with the ground. With this
mixture of actor-critic architecture, the discrete contact sequence planning is
solved through the selection of the best critics while the continuous control
problem is solved by the optimization of actors. We show that our policy can
achieve comparable, sometimes even higher, rewards than a recursive search of
the action space using dynamic programming, while enjoying 50 to 400 times of
speed gain during online execution.
| 1 | 0 | 0 | 0 | 0 | 0 |
Long-Range Interactions for Hydrogen: 6P-1S and 6P-2S | The collisional shift of a transition constitutes an important systematic
effect in high-precision spectroscopy. Accurate values for van der
Waalsinteraction coefficients are required in order to evaluate the
distance-dependent frequency shift. We here consider the interaction of excited
hydrogen 6P atoms with metastable atoms (in the 2S state), in order to explore
the influence of quasi-degenerate 2P, and 6S states on the dipole-dipole
interaction. The motivation for the calculation is given by planned
high-precision measurements of the transition. Due to the presence of
quasi-degenerate levels, one can use the non-retarded approximation for the
interaction terms over wide distance ranges.
| 0 | 1 | 0 | 0 | 0 | 0 |
Generating Music Medleys via Playing Music Puzzle Games | Generating music medleys is about finding an optimal permutation of a given
set of music clips. Toward this goal, we propose a self-supervised learning
task, called the music puzzle game, to train neural network models to learn the
sequential patterns in music. In essence, such a game requires machines to
correctly sort a few multisecond music fragments. In the training stage, we
learn the model by sampling multiple non-overlapping fragment pairs from the
same songs and seeking to predict whether a given pair is consecutive and is in
the correct chronological order. For testing, we design a number of puzzle
games with different difficulty levels, the most difficult one being music
medley, which requiring sorting fragments from different songs. On the basis of
state-of-the-art Siamese convolutional network, we propose an improved
architecture that learns to embed frame-level similarity scores computed from
the input fragment pairs to a common space, where fragment pairs in the correct
order can be more easily identified. Our result shows that the resulting model,
dubbed as the similarity embedding network (SEN), performs better than
competing models across different games, including music jigsaw puzzle, music
sequencing, and music medley. Example results can be found at our project
website, this https URL.
| 1 | 0 | 0 | 1 | 0 | 0 |
Collisional Dynamics of Solitons in the Coupled PT symmetric Nonlocal nonlinear Schrodinger equations | We investigate the focusing coupled PT-symmetric nonlocal nonlinear
Schrodinger equation employing Darboux transformation approach. We find a
family of exact solutions including pairs of Bright-Bright, Dark-Dark and
Bright-Dark solitons in addition to solitary waves. We show that one can
convert bright bound state onto a dark bound state in a two-soliton solution by
selectively fine tuning the amplitude dependent parameter. We also show that
the energy in each mode remains conserved unlike the celebrated Manakov model.
We also characterize the behaviour of the soliton solutions in detail. We
emphasize that the above phenomenon occurs due to the nonlocality of the model.
| 0 | 1 | 0 | 0 | 0 | 0 |
Frustrated spin-1/2 molecular magnetism in the mixed-valence antiferromagnets Ba3MRu2O9 (M = In, Y, Lu) | We have performed magnetic susceptibility, heat capacity, muon spin
relaxation, and neutron scattering measurements on three members of the family
Ba3MRu2O9, where M = In, Y and Lu. These systems consist of mixed-valence Ru
dimers on a triangular lattice with antiferromagnetic interdimer exchange.
Although previous work has argued that charge order within the dimers or
intradimer double exchange plays an important role in determining the magnetic
properties, our results suggest that the dimers are better described as
molecular units due to significant orbital hybridization, resulting in one
spin-1/2 moment distributed equally over the two Ru sites. These molecular
building blocks form a frustrated, quasi-two-dimensional triangular lattice.
Our zero and longitudinal field muSR results indicate that the molecular
moments develop a collective, static magnetic ground state, with oscillations
of the zero field muon spin polarization indicative of long-range magnetic
order in the Lu sample. The static magnetism is much more disordered in the Y
and In samples, but they do not appear to be conventional spin glasses.
| 0 | 1 | 0 | 0 | 0 | 0 |
Data-Driven Model Predictive Control of Autonomous Mobility-on-Demand Systems | The goal of this paper is to present an end-to-end, data-driven framework to
control Autonomous Mobility-on-Demand systems (AMoD, i.e. fleets of
self-driving vehicles). We first model the AMoD system using a time-expanded
network, and present a formulation that computes the optimal rebalancing
strategy (i.e., preemptive repositioning) and the minimum feasible fleet size
for a given travel demand. Then, we adapt this formulation to devise a Model
Predictive Control (MPC) algorithm that leverages short-term demand forecasts
based on historical data to compute rebalancing strategies. We test the
end-to-end performance of this controller with a state-of-the-art LSTM neural
network to predict customer demand and real customer data from DiDi Chuxing: we
show that this approach scales very well for large systems (indeed, the
computational complexity of the MPC algorithm does not depend on the number of
customers and of vehicles in the system) and outperforms state-of-the-art
rebalancing strategies by reducing the mean customer wait time by up to to
89.6%.
| 1 | 0 | 0 | 1 | 0 | 0 |
Predictions of planet detections with near infrared radial velocities in the up-coming SPIRou Legacy Survey-Planet Search | The SPIRou near infrared spectro-polarimeter is destined to begin science
operations at the Canada-France-Hawaii Telescope in mid-2018. One of the
instrument's primary science goals is to discover the closest exoplanets to the
Solar System by conducting a 3-5 year long radial velocity survey of nearby M
dwarfs at an expected precision of $\sim 1$ m s$^{-1}$; the SPIRou Legacy
Survey-Planet Search (SLS-PS). In this study we conduct a detailed Monte-Carlo
simulation of the SLS-PS using our current understanding of the occurrence rate
of M dwarf planetary systems and physical models of stellar activity. From
simultaneous modelling of planetary signals and activity, we predict the
population of planets detected in the SLS-PS. With our fiducial survey strategy
and expected instrument performance over a nominal survey length of $\sim 3$
years, we expect SPIRou to detect $85.3^{+29.3}_{-12.4}$ planets including
$20.0^{+16.8}_{-7.2}$ habitable zone planets and $8.1^{+7.6}_{-3.2}$ Earth-like
planets from a sample of 100 M1-M8.5 dwarfs out to 11 pc. By studying
mid-to-late M dwarfs previously inaccessible to existing optical velocimeters,
SPIRou will put meaningful constraints on the occurrence rate of planets around
those stars including the value of $\eta_{\oplus}$ at an expected level of
precision of $\lesssim 45$%. We also predict a subset of $46.7^{+16.0}_{-6.0}$
planets may be accessible with dedicated high-contrast imagers on the next
generation of ELTs including $4.9^{+4.7}_{-2.0}$ potentially imagable
Earth-like planets. Lastly, we compare the results of our fiducial survey
strategy to other foreseeable survey versions to quantify which strategy is
optimized to reach the SLS-PS science goals. The results of our simulations are
made available to the community on github.
| 0 | 1 | 0 | 0 | 0 | 0 |
Quantum models with energy-dependent potentials solvable in terms of exceptional orthogonal polynomials | We construct energy-dependent potentials for which the Schroedinger equations
admit solu- tions in terms of exceptional orthogonal polynomials. Our method of
construction is based on certain point transformations, applied to the
equations of exceptional Hermite, Jacobi and Laguerre polynomials. We present
several examples of boundary-value problems with energy-dependent potentials
that admit a discrete spectrum and the corresponding normalizable solutions in
closed form.
| 0 | 0 | 1 | 0 | 0 | 0 |
Constructions and classifications of projective Poisson varieties | This paper is intended both an introduction to the algebraic geometry of
holomorphic Poisson brackets, and as a survey of results on the classification
of projective Poisson manifolds that have been obtained in the past twenty
years. It is based on the lecture series delivered by the author at the Poisson
2016 Summer School in Geneva. The paper begins with a detailed treatment of
Poisson surfaces, including adjunction, ruled surfaces and blowups, and leading
to a statement of the full birational classification. We then describe several
constructions of Poisson threefolds, outlining the classification in the
regular case, and the case of rank-one Fano threefolds (such as projective
space). Following a brief introduction to the notion of Poisson subspaces, we
discuss Bondal's conjecture on the dimensions of degeneracy loci on Poisson
Fano manifolds. We close with a discussion of log symplectic manifolds with
simple normal crossings degeneracy divisor, including a new proof of the
classification in the case of rank-one Fano manifolds.
| 0 | 0 | 1 | 0 | 0 | 0 |
Generalised Reichenbachian Common Cause Systems | The principle of the common cause claims that if an improbable coincidence
has occurred, there must exist a common cause. This is generally taken to mean
that positive correlations between non-causally related events should disappear
when conditioning on the action of some underlying common cause. The extended
interpretation of the principle, by contrast, urges that common causes should
be called for in order to explain positive deviations between the estimated
correlation of two events and the expected value of their correlation. The aim
of this paper is to provide the extended reading of the principle with a
general probabilistic model, capturing the simultaneous action of a system of
multiple common causes. To this end, two distinct models are elaborated, and
the necessary and sufficient conditions for their existence are determined.
| 1 | 0 | 0 | 1 | 0 | 0 |
Discovery of the most metal-poor damped Lyman-alpha system | We report the discovery and analysis of the most metal-poor damped
Lyman-alpha (DLA) system currently known, based on observations made with the
Keck HIRES spectrograph. The metal paucity of this system has only permitted
the determination of three element abundances: [C/H] = -3.43 +/- 0.06, [O/H] =
-3.05 +/- 0.05, and [Si/H] = -3.21 +/- 0.05, as well as an upper limit on the
abundance of iron: [Fe/H] < -2.81. This DLA is among the most carbon-poor
environment currently known with detectable metals. By comparing the abundance
pattern of this DLA to detailed models of metal-free nucleosynthesis, we find
that the chemistry of the gas is consistent with the yields of a 20.5 M_sun
metal-free star that ended its life as a core-collapse supernova; the
abundances we measure are inconsistent with the yields of pair-instability
supernovae. Such a tight constraint on the mass of the progenitor Population
III star is afforded by the well-determined C/O ratio, which we show depends
almost monotonically on the progenitor mass when the kinetic energy of the
supernova explosion is E_exp > 1.5x10^51 erg. We find that the DLA presented
here has just crossed the critical 'transition discriminant' threshold,
rendering the DLA gas now suitable for low mass star formation. We also discuss
the chemistry of this system in the context of recent models that suggest some
of the most metal-poor DLAs are the precursors of the 'first galaxies', and are
the antecedents of the ultra-faint dwarf galaxies.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Concurrent Perspective on Smart Contracts | In this paper, we explore remarkable similarities between multi-transactional
behaviors of smart contracts in cryptocurrencies such as Ethereum and classical
problems of shared-memory concurrency. We examine two real-world examples from
the Ethereum blockchain and analyzing how they are vulnerable to bugs that are
closely reminiscent to those that often occur in traditional concurrent
programs. We then elaborate on the relation between observable contract
behaviors and well-studied concurrency topics, such as atomicity, interference,
synchronization, and resource ownership. The described
contracts-as-concurrent-objects analogy provides deeper understanding of
potential threats for smart contracts, indicate better engineering practices,
and enable applications of existing state-of-the-art formal verification
techniques.
| 1 | 0 | 0 | 0 | 0 | 0 |
Noise Models in the Nonlinear Spectral Domain for Optical Fibre Communications | Existing works on building a soliton transmission system only encode
information using the imaginary part of the eigenvalue, which fails to make
full use of the signal degree-of-freedoms. Motivated by this observation, we
make the first step of encoding information using (discrete) spectral
amplitudes by proposing analytical noise models for the spectral amplitudes of
$N$-solitons ($N\geq 1$). To our best knowledge, this is the first work in
building an analytical noise model for spectral amplitudes, which leads to many
interesting information theoretic questions, such as channel capacity analysis,
and has a potential of increasing the transmission rate. The noise statistics
of the spectral amplitude of a soliton are also obtained without the Gaussian
approximation.
| 1 | 0 | 1 | 0 | 0 | 0 |
Shape analysis on Lie groups and homogeneous spaces | In this paper we are concerned with the approach to shape analysis based on
the so called Square Root Velocity Transform (SRVT). We propose a
generalisation of the SRVT from Euclidean spaces to shape spaces of curves on
Lie groups and on homogeneous manifolds. The main idea behind our approach is
to exploit the geometry of the natural Lie group actions on these spaces.
| 0 | 0 | 1 | 0 | 0 | 0 |
Markov Models for Health Economic Evaluations: The R Package heemod | Health economic evaluation studies are widely used in public health to assess
health strategies in terms of their cost-effectiveness and inform public
policies. We developed an R package for Markov models implementing most of the
modelling and reporting features described in reference textbooks and
guidelines: deterministic and probabilistic sensitivity analysis, heterogeneity
analysis, time dependency on state-time and model-time (semi-Markov and
non-homogeneous Markov models), etc. In this paper we illustrate the features
of heemod by building and analysing an example Markov model. We then explain
the design and the underlying implementation of the package.
| 0 | 0 | 0 | 1 | 0 | 0 |
Amorphous Alloys, Degradation Performance of Azo Dyes: Review | Today freshwater is more important than ever before and it is contaminated
from textile industry. Removal of dyes from effluent of textile using amorphous
alloys has been studied extensively by many researchers. In this review article
it is presented up to date development on the azo dye degradation performance
of amorphous alloys, a new class of catalytic materials. Numerous amorphous
alloys have been developed for increasing higher degradation efficiency in
comparison to conventional ones for the removal of azo dyes in wastewater. One
of the objectives of this review article is to organize the scattered available
information on various aspects on a wide range of potentially effective in the
removal of dyes by using amorphous alloys. This study comprises the affective
removal factors of azo dye such as solution pH, initial dye concentration, and
adsorbent dosage. It was concluded that Fe, Mg, Co, Al and Mn-based amorphous
alloys with wide availability have appreciable for removing several types of
azo dyes from wastewater. Concerning amorphous alloys for future research, some
suggestions are proposed and conclusions have been drawn.
| 0 | 1 | 0 | 0 | 0 | 0 |
Towards the ab initio based theory of the phase transformations in iron and steel | Despite of the appearance of numerous new materials, the iron based alloys
and steels continue to play an essential role in modern technology. The
properties of a steel are determined by its structural state (ferrite,
cementite, pearlite, bainite, martensite, and their combination) that is formed
under thermal treatment as a result of the shear lattice reconstruction "gamma"
(fcc) -> "alpha" (bcc) and carbon diffusion redistribution. We present a review
on a recent progress in the development of a quantitative theory of the phase
transformations and microstructure formation in steel that is based on an ab
initio parameterization of the Ginzburg-Landau free energy functional. The
results of computer modeling describe the regular change of transformation
scenario under cooling from ferritic (nucleation and diffusion-controlled
growth of the "alpha" phase to martensitic (the shear lattice instability
"gamma" -> "alpha"). It has been shown that the increase in short-range
magnetic order with decreasing the temperature plays a key role in the change
of transformation scenarios. Phase-field modeling in the framework of a
discussed approach demonstrates the typical transformation patterns.
| 0 | 1 | 0 | 0 | 0 | 0 |
Solitons in a modified discrete nonlinear Schroedinger equation | We study the bulk and surface nonlinear modes of the modified one-dimensional
discrete nonlinear Schroedinger (mDNLS) equation. A linear and a modulational
stability analysis of the lowest-order modes is carried out. While for the
fundamental bulk mode there is no power threshold, the fundamental surface mode
needs a minimum power level to exist. Examination of the time evolution of
discrete solitons in the limit of strongly localized modes, suggests ways to
manage the Peierls- Nabarro barrier, facilitating in this way a degree of
steering. The long-time propagation of an initially localized excitation shows
that, at long evolution times, nonlinear effects become negligible and as a
result, the propagation becomes ballistic. The similarity of all these results
to the ones obtained for the DNLS equation, points out to the robustness of the
discrete soliton phenomenology.
| 0 | 1 | 0 | 0 | 0 | 0 |
Functional renormalization-group approach to the Pokrovsky-Talapov model via modified massive Thirring fermion model | A possibility of the topological Kosterlitz-Thouless~(KT) transition in the
Pokrovsky-Talapov~(PT) model is investigated by using the functional
renormalization-group (RG) approach by Wetterich. Our main finding is that the
nonzero misfit parameter of the model, which can be related with the linear
gradient term (Dzyaloshinsky-Moriya interaction), makes such a transition
impossible, what contradicts the previous consideration of this problem by
non-perturbative RG methods. To support the conclusion the initial PT model is
reformulated in terms of the 2D theory of relativistic fermions using an
analogy between the 2D sine-Gordon and the massive Thirring models. In the new
formalism the misfit parameter corresponds to an effective gauge field that
enables to include it in the RG procedure on an equal footing with the other
parameters of the theory. The Wetterich equation is applied to obtain flow
equations for the parameters of the new fermionic action. We demonstrate that
these equations reproduce the KT type of behavior if the misfit parameter is
zero. However, any small nonzero value of the quantity rules out a possibility
of the KT transition. To confirm the finding we develop a description of the
problem in terms of the 2D Coulomb gas model. Within the approach the breakdown
of the KT scenario gains a transparent meaning, the misfit gives rise to an
effective in-plane electric field that prevents a formation of bound
vortex-antivortex pairs.
| 0 | 1 | 0 | 0 | 0 | 0 |
Reentrant Phase Coherence in Superconducting Nanowire Composites | The short coherence lengths characteristic of low-dimensional superconductors
are associated with usefully high critical fields or temperatures.
Unfortunately, such materials are often sensitive to disorder and suffer from
phase fluctuations in the superconducting order parameter which diverge with
temperature $T$, magnetic field $H$ or current $I$. We propose an approach to
overcome synthesis and fluctuation problems: building superconductors from
inhomogeneous composites of nanofilaments. Macroscopic crystals of
quasi-one-dimensional Na$_{2-\delta}$Mo$_6$Se$_6$ featuring Na vacancy disorder
($\delta\approx$~0.2) are shown to behave as percolative networks of
superconducting nanowires. Long range order is established via transverse
coupling between individual one-dimensional filaments, yet phase coherence
remains unstable to fluctuations and localization in the zero-($T$,$H$,$I$)
limit. However, a region of reentrant phase coherence develops upon raising
($T$,$H$,$I$). We attribute this phenomenon to an enhancement of the transverse
coupling due to electron delocalization. Our observations of reentrant phase
coherence coincide with a peak in the Josephson energy $E_J$ at non-zero
($T$,$H$,$I$), which we estimate using a simple analytical model for a
disordered anisotropic superconductor. Na$_{2-\delta}$Mo$_6$Se$_6$ is therefore
a blueprint for a future generation of nanofilamentary superconductors with
inbuilt resilience to phase fluctuations at elevated ($T$,$H$,$I$).
| 0 | 1 | 0 | 0 | 0 | 0 |
Micromagnetic Simulations for Coercivity Improvement through Nano-Structuring of Rare-Earth Free L1$_0$-FeNi Magnets | In this work we investigate the potential of tetragonal L1$_0$ ordered FeNi
as candidate phase for rare earth free permanent magnets taking into account
anisotropy values from recently synthesized, partially ordered FeNi thin films.
In particular, we estimate the maximum energy product ($BH$)$_\mathrm{max}$ of
L1$_0$-FeNi nanostructures using micromagnetic simulations. The maximum energy
product is limited due to the small coercive field of partially ordered
L1$_0$-FeNi. Nano-structured magnets consisting of 128 equi-axed, platelet-like
and columnar-shaped grains show a theoretical maximum energy product of 228
kJ/m$^3$, 208 kJ/m$^3$, 252 kJ/m$^3$, respectively.
| 0 | 1 | 0 | 0 | 0 | 0 |
Semi-parametric Dynamic Asymmetric Laplace Models for Tail Risk Forecasting, Incorporating Realized Measures | The joint Value at Risk (VaR) and expected shortfall (ES) quantile regression
model of Taylor (2017) is extended via incorporating a realized measure, to
drive the tail risk dynamics, as a potentially more efficient driver than daily
returns. Both a maximum likelihood and an adaptive Bayesian Markov Chain Monte
Carlo method are employed for estimation, whose properties are assessed and
compared via a simulation study; results favour the Bayesian approach, which is
subsequently employed in a forecasting study of seven market indices and two
individual assets. The proposed models are compared to a range of parametric,
non-parametric and semi-parametric models, including GARCH, Realized-GARCH and
the joint VaR and ES quantile regression models in Taylor (2017). The
comparison is in terms of accuracy of one-day-ahead Value-at-Risk and Expected
Shortfall forecasts, over a long forecast sample period that includes the
global financial crisis in 2007-2008. The results favor the proposed models
incorporating a realized measure, especially when employing the sub-sampled
Realized Variance and the sub-sampled Realized Range.
| 0 | 0 | 0 | 0 | 0 | 1 |
Mobility tensor of a sphere moving on a super-hydrophobic wall: application to particle separation | The paper addresses the hydrodynamic behavior of a sphere close to a
micro-patterned superhydrophobic surface described in terms of alternated
no-slip and perfect-slip stripes. Physically, the perfect-slip stripes model
the parallel grooves where a large gas cushion forms between fluid and solid
wall, giving rise to slippage at the gas-liquid interface. The potential of the
boundary element method (BEM) in dealing with mixed no-slip/perfect-slip
boundary conditions is exploited to systematically calculate the mobility
tensor for different particle-to-wall relative positions and for different
particle radii. The particle hydrodynamics is characterized by a non trivial
mobility field which presents a distinct near wall behavior where the wall
patterning directly affects the particle motion. In the far field, the effects
of the wall pattern can be accurately represented via an effective description
in terms of a homogeneous wall with a suitably defined apparent slippage. The
trajectory of the sphere under the action of an external force is also
described in some detail. A resonant regime is found when the frequency of the
transversal component of the force matches a characteristic crossing frequency
imposed by the wall pattern. It is found that, under resonance, the particle
undergoes a mean transversal drift. Since the resonance condition depends on
the particle radius the effect can in principle be used to conceive devices for
particle sorting based on superhydrophobic surfaces.
| 0 | 1 | 0 | 0 | 0 | 0 |
Influence of Resampling on Accuracy of Imbalanced Classification | In many real-world binary classification tasks (e.g. detection of certain
objects from images), an available dataset is imbalanced, i.e., it has much
less representatives of a one class (a minor class), than of another.
Generally, accurate prediction of the minor class is crucial but it's hard to
achieve since there is not much information about the minor class. One approach
to deal with this problem is to preliminarily resample the dataset, i.e., add
new elements to the dataset or remove existing ones. Resampling can be done in
various ways which raises the problem of choosing the most appropriate one. In
this paper we experimentally investigate impact of resampling on classification
accuracy, compare resampling methods and highlight key points and difficulties
of resampling.
| 1 | 0 | 0 | 1 | 0 | 0 |
Literature Survey on Interplay of Topics, Information Diffusion and Connections on Social Networks | Researchers have attempted to model information diffusion and topic trends
and lifecycle on online social networks. They have investigated the role of
content, social connections and communities, familiarity and behavioral
similarity in this context. The current article presents a survey of
representative models that perform topic analysis, capture information
diffusion, and explore the properties of social connections in the context of
online social networks. The article concludes with a set of outlines of open
problems and possible directions of future research interest. This article is
intended for researchers to identify the current literature, and explore
possibilities to improve the art.
| 1 | 0 | 0 | 0 | 0 | 0 |
Extended B-Spline Collocation Method For KdV-Burgers Equation | The extended form of the classical polynomial cubic B-spline function is used
to set up a collocation method for some initial boundary value problems derived
for the Korteweg-de Vries-Burgers equation. Having nonexistence of third order
derivatives of the cubic B-splines forces us to reduce the order of the term
uxxx to give a coupled system of equations. The space discretization of this
system is accomplished by the collocation method following the time
discretization with Crank-Nicolson method. Two initial boundary value problems,
one having analytical solution and the other is set up with a non analytical
initial condition, have been simulated by the proposed method.
| 0 | 0 | 1 | 0 | 0 | 0 |
On smile properties of volatility derivatives and exotic products: understanding the VIX skew | We develop a method to study the implied volatility for exotic options and
volatility derivatives with European payoffs such as VIX options. Our approach,
based on Malliavin calculus techniques, allows us to describe the properties of
the at-the-money implied volatility (ATMI) in terms of the Malliavin
derivatives of the underlying process. More precisely, we study the short-time
behaviour of the ATMI level and skew. As an application, we describe the
short-term behavior of the ATMI of VIX and realized variance options in terms
of the Hurst parameter of the model, and most importantly we describe the class
of volatility processes that generate a positive skew for the VIX implied
volatility. In addition, we find that our ATMI asymptotic formulae perform very
well even for large maturities. Several numerical examples are provided to
support our theoretical results.
| 0 | 0 | 0 | 0 | 0 | 1 |
Risk-Sensitive Optimal Control of Queues | We consider the problem of designing risk-sensitive optimal control policies
for scheduling packet transmissions in a stochastic wireless network. A single
client is connected to an access point (AP) through a wireless channel. Packet
transmission incurs a cost $C$, while packet delivery yields a reward of $R$
units. The client maintains a finite buffer of size $B$, and a penalty of $L$
units is imposed upon packet loss which occurs due to finite queueing buffer.
We show that the risk-sensitive optimal control policy for such a simple
set-up is of threshold type, i.e., it is optimal to carry out packet
transmissions only when $Q(t)$, i.e., the queue length at time $t$ exceeds a
certain threshold $\tau$. It is also shown that the value of threshold $\tau$
increases upon increasing the cost per unit packet transmission $C$.
Furthermore, it is also shown that a threshold policy with threshold equal to
$\tau$ is optimal for a set of problems in which cost $C$ lies within an
interval $[C_l,C_u]$. Equations that need to be solved in order to obtain
$C_l,C_u$ are also provided.
| 1 | 0 | 0 | 0 | 0 | 0 |
SPECULOOS exoplanet search and its prototype on TRAPPIST | One of the most significant goals of modern science is establishing whether
life exists around other suns. The most direct path towards its achievement is
the detection and atmospheric characterization of terrestrial exoplanets with
potentially habitable surface conditions. The nearest ultracool dwarfs (UCDs),
i.e. very-low-mass stars and brown dwarfs with effective temperatures lower
than 2700 K, represent a unique opportunity to reach this goal within the next
decade. The potential of the transit method for detecting potentially habitable
Earth-sized planets around these objects is drastically increased compared to
Earth-Sun analogs. Furthermore, only a terrestrial planet transiting a nearby
UCD would be amenable for a thorough atmospheric characterization, including
the search for possible biosignatures, with near-future facilities such as the
James Webb Space Telescope. In this chapter, we first describe the physical
properties of UCDs as well as the unique potential they offer for the detection
of potentially habitable Earth-sized planets suitable for atmospheric
characterization. Then, we present the SPECULOOS ground-based transit survey,
that will search for Earth-sized planets transiting the nearest UCDs, as well
as its prototype survey on the TRAPPIST telescopes. We conclude by discussing
the prospects offered by the recent detection by this prototype survey of a
system of seven temperate Earth-sized planets transiting a nearby UCD,
TRAPPIST-1.
| 0 | 1 | 0 | 0 | 0 | 0 |
Small-dimensional representations of algebraic groups of type $A_l$ | For $G$ an algebraic group of type $A_l$ over an algebraically closed field
of characteristic $p$, we determine all irreducible rational representations of
$G$ in defining characteristic with dimensions $\le (l+1)^s$ for $s = 3, 4$,
provided that $l > 18$, $l > 35$ respectively. We also give explicit
descriptions of the corresponding modules for $s = 3$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Detection of methylisocyanate (CH3NCO) in a solar-type protostar | We report the detection of the prebiotic molecule CH3NCO in a solar-type
protostar, IRAS16293-2422 B. A significant abundance of this species on the
surface of the comet 67P/Churyumov-Gerasimenko has been proposed, and it has
recently been detected in hot cores around high-mass protostars. We observed
IRAS16293-2422 B with ALMA in the 90 GHz to 265 GHz range, and detected 8
unblended transitions of CH3NCO. From our Local Thermodynamic Equilibrium
analysis we derived an excitation temperature of 110+-19 K and a column density
of (4.0+-0.3)x10^15 cm^-2 , which results in an abundance of
<=(1.4+-0.1)x10^-10 with respect to molecular hydrogen. This implies a
CH3NCO/HNCO and CH3NCO/NH2CHO column density ratios of ~0.08. Our modelling of
the chemistry of CH3NCO suggests that both ice surface and gas phase formation
reactions of this molecule are needed to explain the observations.
| 0 | 1 | 0 | 0 | 0 | 0 |
A prismatic classifying space | A qualgebra $G$ is a set having two binary operations that satisfy
compatibility conditions which are modeled upon a group under conjugation and
multiplication. We develop a homology theory for qualgebras and describe a
classifying space for it. This space is constructed from $G$-colored prisms
(products of simplices) and simultaneously generalizes (and includes)
simplicial classifying spaces for groups and cubical classifying spaces for
quandles. Degenerate cells of several types are added to the regular prismatic
cells; by duality, these correspond to "non-rigid" Reidemeister moves and their
higher dimensional analogues. Coupled with $G$-coloring techniques, our
homology theory yields invariants of knotted trivalent graphs in $\mathbb{R}^3$
and knotted foams in $\mathbb{R}^4$. We re-interpret these invariants as
homotopy classes of maps from $S^2$ or $S^3$ to the classifying space of $G$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Generalized Biplots for Multidimensional Scaled Projections | Dimension reduction and visualization is a staple of data analytics. Methods
such as Principal Component Analysis (PCA) and Multidimensional Scaling (MDS)
provide low dimensional (LD) projections of high dimensional (HD) data while
preserving an HD relationship between observations. Traditional biplots assign
meaning to the LD space of a PCA projection by displaying LD axes for the
attributes. These axes, however, are specific to the linear projection used in
PCA. MDS projections, which allow for arbitrary stress and dissimilarity
functions, require special care when labeling the LD space. We propose an
iterative scheme to plot an LD axis for each attribute based on the
user-specified stress and dissimilarity metrics. We discuss the details of our
general biplot methodology, its relationship with PCA-derived biplots, and
provide examples using real data.
| 0 | 0 | 0 | 1 | 0 | 0 |
Semi-supervised Conditional GANs | We introduce a new model for building conditional generative models in a
semi-supervised setting to conditionally generate data given attributes by
adapting the GAN framework. The proposed semi-supervised GAN (SS-GAN) model
uses a pair of stacked discriminators to learn the marginal distribution of the
data, and the conditional distribution of the attributes given the data
respectively. In the semi-supervised setting, the marginal distribution (which
is often harder to learn) is learned from the labeled + unlabeled data, and the
conditional distribution is learned purely from the labeled data. Our
experimental results demonstrate that this model performs significantly better
compared to existing semi-supervised conditional GAN models.
| 1 | 0 | 0 | 1 | 0 | 0 |
Implicit Quantile Networks for Distributional Reinforcement Learning | In this work, we build on recent advances in distributional reinforcement
learning to give a generally applicable, flexible, and state-of-the-art
distributional variant of DQN. We achieve this by using quantile regression to
approximate the full quantile function for the state-action return
distribution. By reparameterizing a distribution over the sample space, this
yields an implicitly defined return distribution and gives rise to a large
class of risk-sensitive policies. We demonstrate improved performance on the 57
Atari 2600 games in the ALE, and use our algorithm's implicitly defined
distributions to study the effects of risk-sensitive policies in Atari games.
| 0 | 0 | 0 | 1 | 0 | 0 |
Looping and Clustering model for the organization of protein-DNA complexes on the bacterial genome | The bacterial genome is organized in a structure called the nucleoid by a
variety of associated proteins. These proteins can form complexes on DNA that
play a central role in various biological processes, including chromosome
segregation. A prominent example is the large ParB-DNA complex, which forms an
essential component of the segregation machinery in many bacteria. ChIP-Seq
experiments show that ParB proteins localize around centromere-like parS sites
on the DNA to which ParB binds specifically, and spreads from there over large
sections of the chromosome. Recent theoretical and experimental studies suggest
that DNA-bound ParB proteins can interact with each other to condense into a
coherent 3D complex on the DNA. However, the structural organization of this
protein-DNA complex remains unclear, and a predictive quantitative theory for
the distribution of ParB proteins on DNA is lacking. Here, we propose the
Looping and Clustering (LC) model, which employs a statistical physics approach
to describe protein-DNA complexes. The LC model accounts for the extrusion of
DNA loops from a cluster of interacting DNA-bound proteins. Conceptually, the
structure of the protein-DNA complex is determined by a competition between
attractive protein interactions and the configurational and loop entropy of
this protein-DNA cluster. Indeed, we show that the protein interaction strength
determines the "tightness" of the loopy protein-DNA complex. With this approach
we consider the genomic organization of such a protein-DNA cluster around a
single high-affinity binding site. Thus, our model provides a theoretical
framework to quantitatively compute the binding profiles of ParB-like proteins
around a cognate (parS) binding site.
| 0 | 1 | 0 | 0 | 0 | 0 |
Simultaneous-equation Estimation without Instrumental Variables | For a single equation in a system of linear equations, estimation by
instrumental variables is the standard approach. In practice, however, it is
often difficult to find valid instruments. This paper proposes a maximum
likelihood method that does not require instrumental variables; it is
illustrated by simulation and with a real data set.
| 0 | 0 | 1 | 1 | 0 | 0 |
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