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Title: Oxygen Partial Pressure during Pulsed Laser Deposition: Deterministic Role on Thermodynamic Stability of Atomic Termination Sequence at SrRuO3/BaTiO3 Interface,
Abstract: With recent trends on miniaturizing oxide-based devices, the need for
atomic-scale control of surface/interface structures by pulsed laser deposition
(PLD) has increased. In particular, realizing uniform atomic termination at the
surface/interface is highly desirable. However, a lack of understanding on the
surface formation mechanism in PLD has limited a deliberate control of
surface/interface atomic stacking sequences. Here, taking the prototypical
SrRuO3/BaTiO3/SrRuO3 (SRO/BTO/SRO) heterostructure as a model system, we
investigated the formation of different interfacial termination sequences
(BaO-RuO2 or TiO2-SrO) with oxygen partial pressure (PO2) during PLD. We found
that a uniform SrO-TiO2 termination sequence at the SRO/BTO interface can be
achieved by lowering the PO2 to 5 mTorr, regardless of the total background gas
pressure (Ptotal), growth mode, or growth rate. Our results indicate that the
thermodynamic stability of the BTO surface at the low-energy kinetics stage of
PLD can play an important role in surface/interface termination formation. This
work paves the way for realizing termination engineering in functional oxide
heterostructures. | [
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0,
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] |
Title: A Central Limit Theorem for Wasserstein type distances between two different laws,
Abstract: This article is dedicated to the estimation of Wasserstein distances and
Wasserstein costs between two distinct continuous distributions $F$ and $G$ on
$\mathbb R$. The estimator is based on the order statistics of (possibly
dependent) samples of $F$ resp. $G$. We prove the consistency and the
asymptotic normality of our estimators. \begin{it}Keywords:\end{it} Central
Limit Theorems- Generelized Wasserstein distances- Empirical processes- Strong
approximation- Dependent samples. | [
0,
0,
1,
1,
0,
0
] |
Title: Contaminated speech training methods for robust DNN-HMM distant speech recognition,
Abstract: Despite the significant progress made in the last years, state-of-the-art
speech recognition technologies provide a satisfactory performance only in the
close-talking condition. Robustness of distant speech recognition in adverse
acoustic conditions, on the other hand, remains a crucial open issue for future
applications of human-machine interaction. To this end, several advances in
speech enhancement, acoustic scene analysis as well as acoustic modeling, have
recently contributed to improve the state-of-the-art in the field. One of the
most effective approaches to derive a robust acoustic modeling is based on
using contaminated speech, which proved helpful in reducing the acoustic
mismatch between training and testing conditions.
In this paper, we revise this classical approach in the context of modern
DNN-HMM systems, and propose the adoption of three methods, namely, asymmetric
context windowing, close-talk based supervision, and close-talk based
pre-training. The experimental results, obtained using both real and simulated
data, show a significant advantage in using these three methods, overall
providing a 15% error rate reduction compared to the baseline systems. The same
trend in performance is confirmed either using a high-quality training set of
small size, and a large one. | [
1,
0,
0,
0,
0,
0
] |
Title: Speaker Diarization with LSTM,
Abstract: For many years, i-vector based audio embedding techniques were the dominant
approach for speaker verification and speaker diarization applications.
However, mirroring the rise of deep learning in various domains, neural network
based audio embeddings, also known as d-vectors, have consistently demonstrated
superior speaker verification performance. In this paper, we build on the
success of d-vector based speaker verification systems to develop a new
d-vector based approach to speaker diarization. Specifically, we combine
LSTM-based d-vector audio embeddings with recent work in non-parametric
clustering to obtain a state-of-the-art speaker diarization system. Our system
is evaluated on three standard public datasets, suggesting that d-vector based
diarization systems offer significant advantages over traditional i-vector
based systems. We achieved a 12.0% diarization error rate on NIST SRE 2000
CALLHOME, while our model is trained with out-of-domain data from voice search
logs. | [
1,
0,
0,
1,
0,
0
] |
Title: Almost h-conformal semi-invariant submersions from almost quaternionic Hermitian manifolds,
Abstract: As a generalization of Riemannian submersions, horizontally conformal
submersions, semi-invariant submersions, h-semi-invariant submersions, almost
h-semi-invariant submersions, conformal semi-invariant submersions, we
introduce h-conformal semi-invariant submersions and almost h-conformal
semi-invariant submersions from almost quaternionic Hermitian manifolds onto
Riemannian manifolds.
We study their properties: the geometry of foliations, the conditions for
total manifolds to be locally product manifolds, the conditions for such maps
to be totally geodesic, etc. Finally, we give some examples of such maps. | [
0,
0,
1,
0,
0,
0
] |
Title: Use of Source Code Similarity Metrics in Software Defect Prediction,
Abstract: In recent years, defect prediction has received a great deal of attention in
the empirical software engineering world. Predicting software defects before
the maintenance phase is very important not only to decrease the maintenance
costs but also increase the overall quality of a software product. There are
different types of product, process, and developer based software metrics
proposed so far to measure the defectiveness of a software system. This paper
suggests to use a novel set of software metrics which are based on the
similarities detected among the source code files in a software project. To
find source code similarities among different files of a software system,
plagiarism and clone detection techniques are used. Two simple similarity
metrics are calculated for each file, considering its overall similarity to the
defective and non defective files in the project. Using these similarity
metrics, we predict whether a specific file is defective or not. Our
experiments on 10 open source data sets show that depending on the amount of
detected similarity, proposed metrics could achieve significantly better
performance compared to the existing static code metrics in terms of the area
under the curve (AUC). | [
1,
0,
0,
0,
0,
0
] |
Title: Estimating the spectral gap of a trace-class Markov operator,
Abstract: The utility of a Markov chain Monte Carlo algorithm is, in large part,
determined by the size of the spectral gap of the corresponding Markov
operator. However, calculating (and even approximating) the spectral gaps of
practical Monte Carlo Markov chains in statistics has proven to be an extremely
difficult and often insurmountable task, especially when these chains move on
continuous state spaces. In this paper, a method for accurate estimation of the
spectral gap is developed for general state space Markov chains whose operators
are non-negative and trace-class. The method is based on the fact that the
second largest eigenvalue (and hence the spectral gap) of such operators can be
bounded above and below by simple functions of the power sums of the
eigenvalues. These power sums often have nice integral representations. A
classical Monte Carlo method is proposed to estimate these integrals, and a
simple sufficient condition for finite variance is provided. This leads to
asymptotically valid confidence intervals for the second largest eigenvalue
(and the spectral gap) of the Markov operator. The efficiency of the method is
studied. For illustration, the method is applied to Albert and Chib's (1993)
data augmentation (DA) algorithm for Bayesian probit regression, and also to a
DA algorithm for Bayesian linear regression with non-Gaussian errors (Liu,
1996). | [
0,
0,
1,
1,
0,
0
] |
Title: Tidal synchronization of an anelastic multi-layered body: Titan's synchronous rotation,
Abstract: This paper presents one analytical tidal theory for a viscoelastic
multi-layered body with an arbitrary number of homogeneous layers. Starting
with the static equilibrium figure, modified to include tide and differential
rotation, and using the Newtonian creep approach, we find the dynamical
equilibrium figure of the deformed body, which allows us to calculate the tidal
potential and the forces acting on the tide generating body, as well as the
rotation and orbital elements variations. In the particular case of the
two-layer model, we study the tidal synchronization when the gravitational
coupling and the friction in the interface between the layers is added. For
high relaxation factors (low viscosity), the stationary solution of each layer
is synchronous with the orbital mean motion (n) when the orbit is circular, but
the spin rates increase if the orbital eccentricity increases. For low
relaxation factors (high viscosity), as in planetary satellites, if friction
remains low, each layer can be trapped in different spin-orbit resonances with
frequencies n/2,n,3n/2,... . We apply the theory to Titan. The main results
are: i) the rotational constraint does not allow us confirm or reject the
existence of a subsurface ocean in Titan; and ii) the crust-atmosphere exchange
of angular momentum can be neglected. Using the rotation estimate based on
Cassini's observation, we limit the possible value of the shell relaxation
factor, when a subsurface ocean is assumed, to 10^-9 Hz, which correspond to a
shell's viscosity 10^18 Pa s, depending on the ocean's thickness and viscosity
values. In the case in which the ocean does not exist, the maximum shell
relaxation factor is one order of magnitude smaller and the corresponding
minimum shell's viscosity is one order higher. | [
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1,
0,
0,
0,
0
] |
Title: On Lie algebras responsible for zero-curvature representations of multicomponent (1+1)-dimensional evolution PDEs,
Abstract: Zero-curvature representations (ZCRs) are one of the main tools in the theory
of integrable $(1+1)$-dimensional PDEs. According to the preprint
arXiv:1212.2199, for any given $(1+1)$-dimensional evolution PDE one can define
a sequence of Lie algebras $F^p$, $p=0,1,2,3,\dots$, such that representations
of these algebras classify all ZCRs of the PDE up to local gauge equivalence.
ZCRs depending on derivatives of arbitrary finite order are allowed.
Furthermore, these algebras provide necessary conditions for existence of
Backlund transformations between two given PDEs. The algebras $F^p$ are defined
in arXiv:1212.2199 in terms of generators and relations.
In the present paper, we describe some methods to study the structure of the
algebras $F^p$ for multicomponent $(1+1)$-dimensional evolution PDEs. Using
these methods, we compute the explicit structure (up to non-essential nilpotent
ideals) of the Lie algebras $F^p$ for the Landau-Lifshitz, nonlinear
Schrodinger equations, and for the $n$-component Landau-Lifshitz system of
Golubchik and Sokolov for any $n>3$. In particular, this means that for the
$n$-component Landau-Lifshitz system we classify all ZCRs (depending on
derivatives of arbitrary finite order), up to local gauge equivalence and up to
killing nilpotent ideals in the corresponding Lie algebras.
The presented methods to classify ZCRs can be applied also to other
$(1+1)$-dimensional evolution PDEs. Furthermore, the obtained results can be
used for proving non-existence of Backlund transformations between some PDEs,
which will be described in forthcoming publications. | [
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1,
1,
0,
0,
0
] |
Title: Scalable Surface Reconstruction from Point Clouds with Extreme Scale and Density Diversity,
Abstract: In this paper we present a scalable approach for robustly computing a 3D
surface mesh from multi-scale multi-view stereo point clouds that can handle
extreme jumps of point density (in our experiments three orders of magnitude).
The backbone of our approach is a combination of octree data partitioning,
local Delaunay tetrahedralization and graph cut optimization. Graph cut
optimization is used twice, once to extract surface hypotheses from local
Delaunay tetrahedralizations and once to merge overlapping surface hypotheses
even when the local tetrahedralizations do not share the same topology.This
formulation allows us to obtain a constant memory consumption per sub-problem
while at the same time retaining the density independent interpolation
properties of the Delaunay-based optimization. On multiple public datasets, we
demonstrate that our approach is highly competitive with the state-of-the-art
in terms of accuracy, completeness and outlier resilience. Further, we
demonstrate the multi-scale potential of our approach by processing a newly
recorded dataset with 2 billion points and a point density variation of more
than four orders of magnitude - requiring less than 9GB of RAM per process. | [
1,
0,
0,
0,
0,
0
] |
Title: Arrangements of pseudocircles on surfaces,
Abstract: A pseudocircle is a simple closed curve on some surface. Arrangements of
pseudocircles were introduced by Grünbaum, who defined them as collections of
pseudocircles that pairwise intersect in exactly two points, at which they
cross. There are several variations on this notion in the literature, one of
which requires that no three pseudocircles have a point in common. Working
under this definition, Ortner proved that an arrangement of pseudocircles is
embeddable into the sphere if and only if all of its subarrangements of size at
most $4$ are embeddable into the sphere. Ortner asked if an analogous result
held for embeddability into a compact orientable surface $\Sigma_g$ of genus
$g>0$. In this paper we answer this question, under an even more general
definition of an arrangement, in which the pseudocircles in the collection are
not required to intersect each other, or that the intersections are crossings:
it suffices to have one pseudocircle that intersects all other pseudocircles in
the collection. We show that under this more general notion, an arrangement of
pseudocircles is embeddable into $\Sigma_g$ if and only if all of its
subarrangements of size at most $4g+5$ are embeddable into $\Sigma_g$, and that
this can be improved to $4g+4$ under the concept of an arrangement used by
Ortner. Our framework also allows us to generalize this result to arrangements
of other objects, such as arcs. | [
1,
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] |
Title: Spectral Method and Regularized MLE Are Both Optimal for Top-$K$ Ranking,
Abstract: This paper is concerned with the problem of top-$K$ ranking from pairwise
comparisons. Given a collection of $n$ items and a few pairwise comparisons
across them, one wishes to identify the set of $K$ items that receive the
highest ranks. To tackle this problem, we adopt the logistic parametric model
--- the Bradley-Terry-Luce model, where each item is assigned a latent
preference score, and where the outcome of each pairwise comparison depends
solely on the relative scores of the two items involved. Recent works have made
significant progress towards characterizing the performance (e.g. the mean
square error for estimating the scores) of several classical methods, including
the spectral method and the maximum likelihood estimator (MLE). However, where
they stand regarding top-$K$ ranking remains unsettled.
We demonstrate that under a natural random sampling model, the spectral
method alone, or the regularized MLE alone, is minimax optimal in terms of the
sample complexity --- the number of paired comparisons needed to ensure exact
top-$K$ identification, for the fixed dynamic range regime. This is
accomplished via optimal control of the entrywise error of the score estimates.
We complement our theoretical studies by numerical experiments, confirming that
both methods yield low entrywise errors for estimating the underlying scores.
Our theory is established via a novel leave-one-out trick, which proves
effective for analyzing both iterative and non-iterative procedures. Along the
way, we derive an elementary eigenvector perturbation bound for probability
transition matrices, which parallels the Davis-Kahan $\sin\Theta$ theorem for
symmetric matrices. This also allows us to close the gap between the $\ell_2$
error upper bound for the spectral method and the minimax lower limit. | [
1,
0,
1,
1,
0,
0
] |
Title: One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay,
Abstract: Recently, model-free reinforcement learning algorithms have been shown to
solve challenging problems by learning from extensive interaction with the
environment. A significant issue with transferring this success to the robotics
domain is that interaction with the real world is costly, but training on
limited experience is prone to overfitting. We present a method for learning to
navigate, to a fixed goal and in a known environment, on a mobile robot. The
robot leverages an interactive world model built from a single traversal of the
environment, a pre-trained visual feature encoder, and stochastic environmental
augmentation, to demonstrate successful zero-shot transfer under real-world
environmental variations without fine-tuning. | [
1,
0,
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] |
Title: Model and Integrate Medical Resource Availability into Verifiably Correct Executable Medical Guidelines - Technical Report,
Abstract: Improving effectiveness and safety of patient care is an ultimate objective
for medical cyber-physical systems. A recent study shows that the patients'
death rate can be reduced by computerizing medical guidelines. Most existing
medical guideline models are validated and/or verified based on the assumption
that all necessary medical resources needed for a patient care are always
available. However, the reality is that some medical resources, such as special
medical equipment or medical specialists, can be temporarily unavailable for an
individual patient. In such cases, safety properties validated and/or verified
in existing medical guideline models without considering medical resource
availability may not hold any more. The paper argues that considering medical
resource availability is essential in building verifiably correct executable
medical guidelines. We present an approach to explicitly and separately model
medical resource availability and automatically integrate resource availability
models into an existing statechart-based computerized medical guideline model.
This approach requires minimal change in existing medical guideline models to
take into consideration of medical resource availability in validating and
verifying medical guideline models. A simplified stroke scenario is used as a
case study to investigate the effectiveness and validity of our approach. | [
1,
0,
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0,
0,
0
] |
Title: On Calabi-Yau compactifications of toric Landau-Ginzburg models for Fano complete intersections,
Abstract: Toric Landau--Ginzburg models of Givental's type for Fano complete
intersections are known to have Calabi--Yau compactifications. We give an
alternative proof of this fact. As an output of our proof we get a description
of fibers over infinity for compactified toric Landau--Ginzburg models. | [
0,
0,
1,
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0,
0
] |
Title: Self-Learning Monte Carlo Method: Continuous-Time Algorithm,
Abstract: The recently-introduced self-learning Monte Carlo method is a general-purpose
numerical method that speeds up Monte Carlo simulations by training an
effective model to propose uncorrelated configurations in the Markov chain. We
implement this method in the framework of continuous time Monte Carlo method
with auxiliary field in quantum impurity models. We introduce and train a
diagram generating function (DGF) to model the probability distribution of
auxiliary field configurations in continuous imaginary time, at all orders of
diagrammatic expansion. By using DGF to propose global moves in configuration
space, we show that the self-learning continuous-time Monte Carlo method can
significantly reduce the computational complexity of the simulation. | [
0,
1,
0,
0,
0,
0
] |
Title: Non-Linear Least-Squares Optimization of Rational Filters for the Solution of Interior Eigenvalue Problems,
Abstract: Rational filter functions can be used to improve convergence of contour-based
eigensolvers, a popular family of algorithms for the solution of the interior
eigenvalue problem. We present a framework for the optimization of rational
filters based on a non-convex weighted Least-Squares scheme. When used in
combination with the FEAST library, our filters out-perform existing ones on a
large and representative set of benchmark problems. This work provides a
detailed description of: (1) a set up of the optimization process that exploits
symmetries of the filter function for Hermitian eigenproblems, (2) a
formulation of the gradient descent and Levenberg-Marquardt algorithms that
exploits the symmetries, (3) a method to select the starting position for the
optimization algorithms that reliably produces effective filters, (4) a
constrained optimization scheme that produces filter functions with specific
properties that may be beneficial to the performance of the eigensolver that
employs them. | [
1,
0,
0,
0,
0,
0
] |
Title: Discontinuous classical ground state magnetic response as an even-odd effect in higher order rotationally invariant exchange interactions,
Abstract: The classical ground state magnetic response of the Heisenberg model when
rotationally invariant exchange interactions of integer order q>1 are added is
found to be discontinuous, even though the interactions lack magnetic
anisotropy. This holds even in the case of bipartite lattices which are not
frustrated, as well as for the frustrated triangular lattice. The total number
of discontinuities is associated with even-odd effects as it depends on the
parity of q via the relative strength of the bilinear and higher order exchange
interactions, and increases with q. These results demonstrate that the precise
form of the microscopic interactions is important for the ground state
magnetization response. | [
0,
1,
0,
0,
0,
0
] |
Title: A Lagrangian scheme for the solution of nonlinear diffusion equations using moving simplex meshes,
Abstract: A Lagrangian numerical scheme for solving nonlinear degenerate Fokker-Planck
equations in space dimensions $d\ge2$ is presented. It applies to a large class
of nonlinear diffusion equations, whose dynamics are driven by internal
energies and given external potentials, e.g. the porous medium equation and the
fast diffusion equation. The key ingredient in our approach is the gradient
flow structure of the dynamics. For discretization of the Lagrangian map, we
use a finite subspace of linear maps in space and a variational form of the
implicit Euler method in time. Thanks to that time discretisation, the fully
discrete solution inherits energy estimates from the original gradient flow,
and these lead to weak compactness of the trajectories in the continuous limit.
Consistency is analyzed in the planar situation, $d=2$. A variety of numerical
experiments for the porous medium equation indicates that the scheme is
well-adapted to track the growth of the solution's support. | [
0,
0,
1,
0,
0,
0
] |
Title: Optimal Control for Multi-Mode Systems with Discrete Costs,
Abstract: This paper studies optimal time-bounded control in multi-mode systems with
discrete costs. Multi-mode systems are an important subclass of linear hybrid
systems, in which there are no guards on transitions and all invariants are
global. Each state has a continuous cost attached to it, which is linear in the
sojourn time, while a discrete cost is attached to each transition taken. We
show that an optimal control for this model can be computed in NEXPTIME and
approximated in PSPACE. We also show that the one-dimensional case is simpler:
although the problem is NP-complete (and in LOGSPACE for an infinite time
horizon), we develop an FPTAS for finding an approximate solution. | [
1,
0,
0,
0,
0,
0
] |
Title: Generating global network structures by triad types,
Abstract: This paper addresses the question of whether it is possible to generate
networks with a given global structure (defined by selected blockmodels, i.e.,
cohesive, core-periphery, hierarchical and transitivity), considering only
different types of triads. Two methods are used to generate networks: (i) the
method of relocating links; and (ii) the Monte Carlo Multi Chain algorithm
implemented in the "ergm" package implemented in R. Although all types of
triads can generate networks with the selected blockmodel types, the selection
of only a subset of triads improves the generated networks' blockmodel
structure. However, in the case of a hierarchical blockmodel without complete
blocks on the diagonal, additional local structures are needed to achieve the
desired global structure of generated networks. This shows that blockmodels can
emerge based on only local processes that do not take attributes into account. | [
0,
0,
1,
1,
0,
0
] |
Title: Adaptive quadrature by expansion for layer potential evaluation in two dimensions,
Abstract: When solving partial differential equations using boundary integral equation
methods, accurate evaluation of singular and nearly singular integrals in layer
potentials is crucial. A recent scheme for this is quadrature by expansion
(QBX), which solves the problem by locally approximating the potential using a
local expansion centered at some distance from the source boundary. In this
paper we introduce an extension of the QBX scheme in 2D denoted AQBX - adaptive
quadrature by expansion - which combines QBX with an algorithm for automated
selection of parameters, based on a target error tolerance. A key component in
this algorithm is the ability to accurately estimate the numerical errors in
the coefficients of the expansion. Combining previous results for flat panels
with a procedure for taking the panel shape into account, we derive such error
estimates for arbitrarily shaped boundaries in 2D that are discretized using
panel-based Gauss-Legendre quadrature. Applying our scheme to numerical
solutions of Dirichlet problems for the Laplace and Helmholtz equations, and
also for solving these equations, we find that the scheme is able to satisfy a
given target tolerance to within an order of magnitude, making it useful for
practical applications. This represents a significant simplification over the
original QBX algorithm, in which choosing a good set of parameters can be hard. | [
0,
0,
1,
0,
0,
0
] |
Title: Fixed-Parameter Tractable Sampling for RNA Design with Multiple Target Structures,
Abstract: The design of multi-stable RNA molecules has important applications in
biology, medicine, and biotechnology. Synthetic design approaches profit
strongly from effective in-silico methods, which can tremendously impact their
cost and feasibility. We revisit a central ingredient of most in-silico design
methods: the sampling of sequences for the design of multi-target structures,
possibly including pseudoknots. For this task, we present the efficient, tree
decomposition-based algorithm. Our fixed parameter tractable approach is
underpinned by establishing the P-hardness of uniform sampling. Modeling the
problem as a constraint network, our program supports generic
Boltzmann-weighted sampling for arbitrary additive RNA energy models; this
enables the generation of RNA sequences meeting specific goals like expected
free energies or \GCb-content. Finally, we empirically study general properties
of the approach and generate biologically relevant multi-target
Boltzmann-weighted designs for a common design benchmark. Generating seed
sequences with our program, we demonstrate significant improvements over the
previously best multi-target sampling strategy (uniform sampling).Our software
is freely available at: this https URL . | [
0,
0,
0,
0,
1,
0
] |
Title: On the relevance of generalized disclinations in defect mechanics,
Abstract: The utility of the notion of generalized disclinations in materials science
is discussed within the physical context of modeling interfacial and bulk line
defects like defected grain and phase boundaries, dislocations and
disclinations. The Burgers vector of a disclination dipole in linear elasticity
is derived, clearly demonstrating the equivalence of its stress field to that
of an edge dislocation. We also prove that the inverse deformation/displacement
jump of a defect line is independent of the cut-surface when its g.disclination
strength vanishes. An explicit formula for the displacement jump of a single
localized composite defect line in terms of given g.disclination and
dislocation strengths is deduced based on the Weingarten theorem for
g.disclination theory (Weingarten-gd theorem) at finite deformation. The
Burgers vector of a g.disclination dipole at finite deformation is also
derived. | [
0,
1,
0,
0,
0,
0
] |
Title: Characterizing Feshbach resonances in ultracold scattering calculations,
Abstract: We describe procedures for converging on and characterizing zero-energy
Feshbach resonances that appear in scattering lengths as a function of an
external field. The elastic procedure is appropriate for purely elastic
scattering, where the scattering length is real and displays a true pole. The
regularized scattering length (RSL) procedure is appropriate when there is weak
background inelasticity, so that the scattering length is complex and displays
an oscillation rather than a pole, but the resonant scattering length $a_{\rm
res}$ is close to real. The fully complex procedure is appropriate when there
is substantial background inelasticity and the real and complex parts of
$a_{\rm res}$ are required. We demonstrate these procedures for scattering of
ultracold $^{85}$Rb in various initial states. All of them can converge on and
provide full characterization of resonances, from initial guesses many
thousands of widths away, using scattering calculations at only about 10 values
of the external field. | [
0,
1,
0,
0,
0,
0
] |
Title: Speeding-up Object Detection Training for Robotics with FALKON,
Abstract: Latest deep learning methods for object detection provide remarkable
performance, but have limits when used in robotic applications. One of the most
relevant issues is the long training time, which is due to the large size and
imbalance of the associated training sets, characterized by few positive and a
large number of negative examples (i.e. background). Proposed approaches are
based on end-to-end learning by back-propagation [22] or kernel methods trained
with Hard Negatives Mining on top of deep features [8]. These solutions are
effective, but prohibitively slow for on-line applications. In this paper we
propose a novel pipeline for object detection that overcomes this problem and
provides comparable performance, with a 60x training speedup. Our pipeline
combines (i) the Region Proposal Network and the deep feature extractor from
[22] to efficiently select candidate RoIs and encode them into powerful
representations, with (ii) the FALKON [23] algorithm, a novel kernel-based
method that allows fast training on large scale problems (millions of points).
We address the size and imbalance of training data by exploiting the stochastic
subsampling intrinsic into the method and a novel, fast, bootstrapping
approach. We assess the effectiveness of the approach on a standard Computer
Vision dataset (PASCAL VOC 2007 [5]) and demonstrate its applicability to a
real robotic scenario with the iCubWorld Transformations [18] dataset. | [
1,
0,
0,
0,
0,
0
] |
Title: Statics and dynamics of a self-bound dipolar matter-wave droplet,
Abstract: We study the statics and dynamics of a stable, mobile, self-bound
three-dimensional dipolar matter-wave droplet created in the presence of a tiny
repulsive three-body interaction. In frontal collision with an impact parameter
and in angular collision at large velocities {along all directions} two
droplets behave like quantum solitons. Such collision is found to be quasi
elastic and the droplets emerge undeformed after collision without any change
of velocity. However, in a collision at small velocities the axisymmeric
dipolar interaction plays a significant role and the collision dynamics is
sensitive to the direction of motion. For an encounter along the $z$ direction
at small velocities, two droplets, polarized along the $z$ direction, coalesce
to form a larger droplet $-$ a droplet molecule. For an encounter along the $x$
direction at small velocities, the same droplets stay apart and never meet each
other due to the dipolar repulsion. The present study is based on an analytic
variational approximation and a numerical solution of the mean-field
Gross-Pitaevskii equation using the parameters of $^{52}$Cr atoms. | [
0,
1,
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0,
0
] |
Title: Finite Blaschke products with prescribed critical points, Stieltjes polynomials, and moment problems,
Abstract: The determination of a finite Blaschke product from its critical points is a
well-known problem with interrelations to other topics. Though existence and
uniqueness of solutions are established for long, we present several new
aspects which have not yet been explored to their full extent. In particular,
we show that the following three problems are equivalent: (i) determining a
finite Blaschke product from its critical points, (ii) finding the equilibrium
position of moveable point charges interacting with a special configuration of
fixed charges, (iii) solving a moment problem for the canonical representation
of power moments on the real axis. These equivalences are not only of
theoretical interest, but also open up new perspectives for the design of
algorithms. For instance, the second problem is closely linked to the
determination of certain Stieltjes and Van Vleck polynomials for a second order
ODE and allows the description of solutions as global minimizers of an energy
functional. | [
0,
0,
1,
0,
0,
0
] |
Title: The Stochastic Processes Generation in OpenModelica,
Abstract: Background: Component-based modeling language Modelica (OpenModelica is open
source implementation) is used for the numerical simulation of complex
processes of different nature represented by ODE system. However, in
OpenModelica standard library there is no routines for pseudo-random numbers
generation, which makes it impossible to use for stochastic modeling processes.
Purpose: The goal of this article is a brief overview of a number of algorithms
for generation a sequence of uniformly distributed pseudo random numbers and
quality assessment of the sequence given by them, as well as the ways to
implement some of these algorithms in OpenModelica system. Methods: All the
algorithms are implemented in C language, and the results of their work tested
using open source package DieHarder. For those algorithms that do not use bit
operations, we describe there realisation using OpwnModelica. The other
algorithms can be called in OpenModelica as C functions Results: We have
implemented and tested about nine algorithms. DieHarder testing revealed the
highest quality pseudo-random number generators. Also we have reviewed
libraries Noise and AdvancedNoise, who claim to be adding to the Modelica
Standard Library. Conclusions: In OpenModelica system can be implemented
generators of uniformly distributed pseudo-random numbers, which is the first
step towards to make OpenModelica suitable for simulation of stochastic
processes. | [
1,
1,
0,
0,
0,
0
] |
Title: MIT SuperCloud Portal Workspace: Enabling HPC Web Application Deployment,
Abstract: The MIT SuperCloud Portal Workspace enables the secure exposure of web
services running on high performance computing (HPC) systems. The portal allows
users to run any web application as an HPC job and access it from their
workstation while providing authentication, encryption, and access control at
the system level to prevent unintended access. This capability permits users to
seamlessly utilize existing and emerging tools that present their user
interface as a website on an HPC system creating a portal workspace.
Performance measurements indicate that the MIT SuperCloud Portal Workspace
incurs marginal overhead when compared to a direct connection of the same
service. | [
1,
0,
0,
0,
0,
0
] |
Title: Estimator of Prediction Error Based on Approximate Message Passing for Penalized Linear Regression,
Abstract: We propose an estimator of prediction error using an approximate message
passing (AMP) algorithm that can be applied to a broad range of sparse
penalties. Following Stein's lemma, the estimator of the generalized degrees of
freedom, which is a key quantity for the construction of the estimator of the
prediction error, is calculated at the AMP fixed point. The resulting form of
the AMP-based estimator does not depend on the penalty function, and its value
can be further improved by considering the correlation between predictors. The
proposed estimator is asymptotically unbiased when the components of the
predictors and response variables are independently generated according to a
Gaussian distribution. We examine the behaviour of the estimator for real data
under nonconvex sparse penalties, where Akaike's information criterion does not
correspond to an unbiased estimator of the prediction error. The model selected
by the proposed estimator is close to that which minimizes the true prediction
error. | [
0,
0,
0,
1,
0,
0
] |
Title: Faster Multiplication for Long Binary Polynomials,
Abstract: We set new speed records for multiplying long polynomials over finite fields
of characteristic two. Our multiplication algorithm is based on an additive FFT
(Fast Fourier Transform) by Lin, Chung, and Huang in 2014 comparing to
previously best results based on multiplicative FFTs. Both methods have similar
complexity for arithmetic operations on underlying finite field; however, our
implementation shows that the additive FFT has less overhead. For further
optimization, we employ a tower field construction because the multipliers in
the additive FFT naturally fall into small subfields, which leads to speed-ups
using table-lookup instructions in modern CPUs. Benchmarks show that our method
saves about $40 \%$ computing time when multiplying polynomials of $2^{28}$ and
$2^{29}$ bits comparing to previous multiplicative FFT implementations. | [
1,
0,
1,
0,
0,
0
] |
Title: Effect of Heterogeneity in Models of El-Niño Southern Oscillations,
Abstract: The emergence of oscillations in models of the El-Niño effect is of utmost
relevance. Here we investigate a coupled nonlinear delay differential system
modeling theEl-Niño/ Southern Oscillation (ENSO) phenomenon, which arises
through the strong coupling of the ocean-atmosphere system. In particular, we
study the temporal patterns of the sea surface temperature anomaly of the two
sub-regions. For identical sub-regions we typically observe a co-existence of
amplitude and oscillator death behavior for low delays, and heterogeneous
oscillations for high delays, when inter-region coupling is weak. For moderate
inter-region coupling strengths one obtains homogeneous oscillations for
sufficiently large delays and amplitude death for small delays. When the
inter-region coupling strength is large, oscillations are suppressed
altogether, implying that strongly coupled sub-regions do not exhibit ENSO-like
oscillations. Further we observe that larger strengths of self-delay coupling
favours oscillations, while oscillations die out when the delayed coupling is
weak. This indicates again that delayed feedback, incorporating oceanic wave
transit effects, is the principal cause of oscillatory behaviour. So the effect
of trapped ocean waves propagating in a basin with closed boundaries is crucial
for the emergence of ENSO. Further, we show how non-uniformity in delays, and
difference in the strengths of the self-delay coupling of the sub-regions,
affect the rise of oscillations. Interestingly we find that larger delays and
self-delay coupling strengths lead to oscillations, while strong inter-region
coupling kills oscillatory behaviour. Thus, we find that coupling sub-regions
has a very significant effect on the emergence of oscillations, and strong
coupling typically suppresses oscillations, while weak coupling of
non-identical sub-regions can induce oscillations, thereby favouring ENSO. | [
0,
1,
0,
0,
0,
0
] |
Title: The relationships between PM2.5 and meteorological factors in China: Seasonal and regional variations,
Abstract: The interactions between PM2.5 and meteorological factors play a crucial role
in air pollution analysis. However, previous studies that have researched the
relationships between PM2.5 concentration and meteorological conditions have
been mainly confined to a certain city or district, and the correlation over
the whole of China remains unclear. Whether or not spatial and seasonal
variations exit deserves further research. In this study, the relationships
between PM2.5 concentration and meteorological factors were investigated in 74
major cities in China for a continuous period of 22 months from February 2013
to November 2014, at season, year, city, and regional scales, and the spatial
and seasonal variations were analyzed. The meteorological factors were relative
humidity (RH), temperature (TEM), wind speed (WS), and surface pressure (PS).
We found that spatial and seasonal variations of their relationships with PM2.5
do exist. Spatially, RH is positively correlated with PM2.5 concentration in
North China and Urumqi, but the relationship turns to negative in other areas
of China. WS is negatively correlated with PM2.5 everywhere expect for Hainan
Island. PS has a strong positive relationship with PM2.5 concentration in
Northeast China and Mid-south China, and in other areas the correlation is
weak. Seasonally, the positive correlation between PM2.5 concentration and RH
is stronger in winter and spring. TEM has a negative relationship with PM2.5 in
autumn and the opposite in winter. PS is more positively correlated with PM2.5
in autumn than in other seasons. Our study investigated the relationships
between PM2.5 and meteorological factors in terms of spatial and seasonal
variations, and the conclusions about the relationships between PM2.5 and
meteorological factors are more comprehensive and precise than before. | [
0,
1,
0,
0,
0,
0
] |
Title: Rank-two Milnor idempotents for the multipullback quantum complex projective plane,
Abstract: The $K_0$-group of the C*-algebra of multipullback quantum complex projective
plane is known to be $\mathbb{Z}^3$, with one generator given by the C*-algebra
itself, one given by the section module of the noncommutative (dual)
tautological line bundle, and one given by the Milnor module associated to a
generator of the $K_1$-group of the C*-algebra of Calow-Matthes quantum
3-sphere. Herein we prove that these Milnor modules are isomorphic either to
the section module of a noncommutative vector bundle associated to the
$SU_q(2)$-prolongation of the Heegaard quantum 5-sphere $S^5_H$ viewed as a
$U(1)$-quantum principal bundle, or to a complement of this module in the
rank-four free module. Finally, we demonstrate that one of the above Milnor
modules always splits into the direct sum of the rank-one free module and a
rank-one non-free projective module that is \emph{not} associated with $S^5_H$. | [
0,
0,
1,
0,
0,
0
] |
Title: Lost Relatives of the Gumbel Trick,
Abstract: The Gumbel trick is a method to sample from a discrete probability
distribution, or to estimate its normalizing partition function. The method
relies on repeatedly applying a random perturbation to the distribution in a
particular way, each time solving for the most likely configuration. We derive
an entire family of related methods, of which the Gumbel trick is one member,
and show that the new methods have superior properties in several settings with
minimal additional computational cost. In particular, for the Gumbel trick to
yield computational benefits for discrete graphical models, Gumbel
perturbations on all configurations are typically replaced with so-called
low-rank perturbations. We show how a subfamily of our new methods adapts to
this setting, proving new upper and lower bounds on the log partition function
and deriving a family of sequential samplers for the Gibbs distribution.
Finally, we balance the discussion by showing how the simpler analytical form
of the Gumbel trick enables additional theoretical results. | [
1,
0,
0,
1,
0,
0
] |
Title: Bar formation in the Milky Way type galaxies,
Abstract: Many barred galaxies, possibly including the Milky Way, have cusps in the
centres. There is a widespread belief, however, that usual bar instability
taking place in bulgeless galaxy models is impossible for the cuspy models,
because of the presence of the inner Lindblad resonance for any pattern speed.
At the same time there are numerical evidences that the bar instability can
form a bar. We analyse this discrepancy, by accurate and diverse N-body
simulations and using the calculation of normal modes. We show that bar
formation in cuspy galaxies can be explained by taking into account the disc
thickness. The exponential growth time is moderate for typical current disc
masses (about 250 Myr), but considerably increases (factor 2 or more) upon
substitution of the live halo and bulge with a rigid halo/bulge potential;
meanwhile pattern speeds remain almost the same. Normal mode analysis with
different disc mass favours a young bar hypothesis, according to which the bar
instability saturated only recently. | [
0,
1,
0,
0,
0,
0
] |
Title: TLR: Transfer Latent Representation for Unsupervised Domain Adaptation,
Abstract: Domain adaptation refers to the process of learning prediction models in a
target domain by making use of data from a source domain. Many classic methods
solve the domain adaptation problem by establishing a common latent space,
which may cause the loss of many important properties across both domains. In
this manuscript, we develop a novel method, transfer latent representation
(TLR), to learn a better latent space. Specifically, we design an objective
function based on a simple linear autoencoder to derive the latent
representations of both domains. The encoder in the autoencoder aims to project
the data of both domains into a robust latent space. Besides, the decoder
imposes an additional constraint to reconstruct the original data, which can
preserve the common properties of both domains and reduce the noise that causes
domain shift. Experiments on cross-domain tasks demonstrate the advantages of
TLR over competing methods. | [
0,
0,
0,
1,
0,
0
] |
Title: On the MISO Channel with Feedback: Can Infinitely Massive Antennas Achieve Infinite Capacity?,
Abstract: We consider communication over a multiple-input single-output (MISO) block
fading channel in the presence of an independent noiseless feedback link. We
assume that the transmitter and receiver have no prior knowledge of the channel
state realizations, but the transmitter and receiver can acquire the channel
state information (CSIT/CSIR) via downlink training and feedback. For this
channel, we show that increasing the number of transmit antennas to infinity
will not achieve an infinite capacity, for a finite channel coherence length
and a finite input constraint on the second or fourth moment. This insight
follows from our new capacity bounds that hold for any linear and nonlinear
coding strategies, and any channel training schemes. In addition to the channel
capacity bounds, we also provide a characterization on the beamforming gain
that is also known as array gain or power gain, at the regime with a large
number of antennas. | [
1,
0,
0,
0,
0,
0
] |
Title: Structural Analysis and Optimal Design of Distributed System Throttlers,
Abstract: In this paper, we investigate the performance analysis and synthesis of
distributed system throttlers (DST). A throttler is a mechanism that limits the
flow rate of incoming metrics, e.g., byte per second, network bandwidth usage,
capacity, traffic, etc. This can be used to protect a service's backend/clients
from getting overloaded, or to reduce the effects of uncertainties in demand
for shared services. We study performance deterioration of DSTs subject to
demand uncertainty. We then consider network synthesis problems that aim to
improve the performance of noisy DSTs via communication link modifications as
well as server update cycle modifications. | [
1,
0,
1,
0,
0,
0
] |
Title: Sensivity of the Hermite rank,
Abstract: The Hermite rank appears in limit theorems involving long memory. We show
that an Hermite rank higher than one is unstable when the data is slightly
perturbed by transformations such as shift and scaling. We carry out a "near
higher order rank analysis" to illustrate how the limit theorems are affected
by a shift perturbation that is decreasing in size. As a byproduct of our
analysis, we also prove the coincidence of the Hermite rank and the power rank
in the Gaussian context. The paper is a technical companion of
\citet{bai:taqqu:2017:instability} which discusses the instability of the
Hermite rank in the statistical context. (Older title "Some properties of the
Hermite rank">) | [
0,
0,
1,
1,
0,
0
] |
Title: Link Mining for Kernel-based Compound-Protein Interaction Predictions Using a Chemogenomics Approach,
Abstract: Virtual screening (VS) is widely used during computational drug discovery to
reduce costs. Chemogenomics-based virtual screening (CGBVS) can be used to
predict new compound-protein interactions (CPIs) from known CPI network data
using several methods, including machine learning and data mining. Although
CGBVS facilitates highly efficient and accurate CPI prediction, it has poor
performance for prediction of new compounds for which CPIs are unknown. The
pairwise kernel method (PKM) is a state-of-the-art CGBVS method and shows high
accuracy for prediction of new compounds. In this study, on the basis of link
mining, we improved the PKM by combining link indicator kernel (LIK) and
chemical similarity and evaluated the accuracy of these methods. The proposed
method obtained an average area under the precision-recall curve (AUPR) value
of 0.562, which was higher than that achieved by the conventional Gaussian
interaction profile (GIP) method (0.425), and the calculation time was only
increased by a few percent. | [
1,
0,
0,
1,
0,
0
] |
Title: A novel approach to fractional calculus: utilizing fractional integrals and derivatives of the Dirac delta function,
Abstract: While the definition of a fractional integral may be codified by Riemann and
Liouville, an agreed-upon fractional derivative has eluded discovery for many
years. This is likely a result of integral definitions including numerous
constants of integration in their results. An elimination of constants of
integration opens the door to an operator that reconciles all known fractional
derivatives and shows surprising results in areas unobserved before, including
the appearance of the Riemann Zeta Function and fractional Laplace and Fourier
Transforms. A new class of functions, known as Zero Functions and closely
related to the Dirac Delta Function, are necessary for one to perform
elementary operations of functions without using constants. The operator also
allows for a generalization of the Volterra integral equation, and provides a
method of solving for Riemann's "complimentary" function introduced during his
research on fractional derivatives. | [
0,
0,
1,
0,
0,
0
] |
Title: An exploration to visualize finite element data with a DSL,
Abstract: The scientific community use PDEs to model a range of problems. The people in
this domain are interested in visualizing their results, but existing
mechanisms for visualization can not handle the full richness of computations
in the domain. We did an exploration to see how Diderot, a domain specific
language for scientific visualization and image analysis, could be used to
solve this problem.
We demonstrate our first and modest approach of visualizing FE data with
Diderot and provide examples. Using Diderot, we do a simple sampling and a
volume rendering of a FE field. These examples showcase Diderot's ability to
provide a visualization result for Firedrake. This paper describes the
extension of the Diderot language to include FE data. | [
1,
0,
0,
0,
0,
0
] |
Title: Abelian varieties isogenous to a power of an elliptic curve over a Galois extension,
Abstract: Given an elliptic curve $E/k$ and a Galois extension $k'/k$, we construct an
exact functor from torsion-free modules over the endomorphism ring ${\rm
End}(E_{k'})$ with a semilinear ${\rm Gal}(k'/k)$ action to abelian varieties
over $k$ that are $k'$-isogenous to a power of $E$. As an application, we show
that every elliptic curve with complex multiplication geometrically is
isogenous over the ground field to one with complex multiplication by a maximal
order. | [
0,
0,
1,
0,
0,
0
] |
Title: Radiative nonrecoil nuclear finite size corrections of order $α(Z α)^5$ to the Lamb shift in light muonic atoms,
Abstract: On the basis of quasipotential method in quantum electrodynamics we calculate
nuclear finite size radiative corrections of order $\alpha(Z \alpha)^5$ to the
Lamb shift in muonic hydrogen and helium. To construct the interaction
potential of particles, which gives the necessary contributions to the energy
spectrum, we use the method of projection operators to states with a definite
spin. Separate analytic expressions for the contributions of the muon
self-energy, the muon vertex operator and the amplitude with spanning photon
are obtained. We present also numerical results for these contributions using
modern experimental data on the electromagnetic form factors of light nuclei. | [
0,
1,
0,
0,
0,
0
] |
Title: Wind Riemannian spaceforms and Randers metrics of constant flag curvature,
Abstract: Recently, wind Riemannian structures (WRS) have been introduced as a
generalization of Randers and Kropina metrics. They are constructed from the
natural data for Zermelo navigation problem, namely, a Riemannian metric $g_R$
and a vector field $W$ (the wind), where, now, the restriction of mild wind
$g_R(W,W)<1$ is dropped.
Here, the models of WRS spaceforms of constant flag curvature are determined.
Indeed, the celebrated classification of Randers metrics of constant flag
curvature by Bao, Robles and Shen, extended to the Kropina case in the works by
Yoshikawa, Okubo and Sabau, can be used to obtain the local classification. For
the global one, a suitable result on completeness for WRS yields the complete
simply connected models. In particular, any of the local models in the Randers
classification does admit an extension to a unique model of wind Riemannian
structure, even if it cannot be extended as a complete Finslerian manifold.
Thus, WRS's emerge as the natural framework for the analysis of Randers
spaceforms and, prospectively, wind Finslerian structures would become
important for other global problems too. For the sake of completeness, a brief
overview about WRS (including a useful link with the conformal geometry of a
class of relativistic spacetimes) is also provided. | [
0,
0,
1,
0,
0,
0
] |
Title: Developing a Purely Visual Based Obstacle Detection using Inverse Perspective Mapping,
Abstract: Our solution is implemented in and for the frame of Duckietown. The goal of
Duckietown is to provide a relatively simple platform to explore, tackle and
solve many problems linked to autonomous driving. "Duckietown" is simple in the
basics, but an infinitely expandable environment. From controlling single
driving Duckiebots until complete fleet management, every scenario is possible
and can be put into practice. So far, none of the existing modules was capable
of reliably detecting obstacles and reacting to them in real time. We faced the
general problem of detecting obstacles given images from a monocular RGB camera
mounted at the front of our Duckiebot and reacting to them properly without
crashing or erroneously stopping the Duckiebot. Both, the detection as well as
the reaction have to be implemented and have to run on a Raspberry Pi in real
time. Due to the strong hardware limitations, we decided to not use any
learning algorithms for the obstacle detection part. As it later transpired, a
working "hard coded" software needs thorough analysis and understanding of the
given problem. In layman's terms, we simply seek to make Duckietown a safer
place. | [
1,
0,
0,
0,
0,
0
] |
Title: Dynamic Optimization of Neural Network Structures Using Probabilistic Modeling,
Abstract: Deep neural networks (DNNs) are powerful machine learning models and have
succeeded in various artificial intelligence tasks. Although various
architectures and modules for the DNNs have been proposed, selecting and
designing the appropriate network structure for a target problem is a
challenging task. In this paper, we propose a method to simultaneously optimize
the network structure and weight parameters during neural network training. We
consider a probability distribution that generates network structures, and
optimize the parameters of the distribution instead of directly optimizing the
network structure. The proposed method can apply to the various network
structure optimization problems under the same framework. We apply the proposed
method to several structure optimization problems such as selection of layers,
selection of unit types, and selection of connections using the MNIST,
CIFAR-10, and CIFAR-100 datasets. The experimental results show that the
proposed method can find the appropriate and competitive network structures. | [
0,
0,
0,
1,
0,
0
] |
Title: Novel Phases of Semi-Conducting Silicon Nitride Bilayer: A First-Principle Study,
Abstract: In this paper, we have predicted the stabilities of several two-dimensional
phases of silicon nitride, which we name as \alpha-phase, \beta-phase, and
\gamma-phase, respectively. Both \alpha- and \beta-phases has formula
Si$_{2}$N$_{2}$, and are consisted of two similar layer of buckled SiN sheet.
Similarly, \gamma-phase is consisted of two puckered SiN sheets. For these
phases, the two layers are connected with Si-Si covalent bonds. Transformation
between \alpha- and \beta-phases is difficult because of the high energy
barrier. Phonon spectra of both \alpha- and \beta-phase suggest their
thermodynamic stabilities, because no phonon mode with imaginary frequency is
present. By Contrast, \gamma-phase is unstable because phonon modes with
imaginary frequencies are found along \Gamma-Y path in the Brilliouin zone.
Both \alpha- and \beta-phase are semiconductor with narrow fundamental indirect
band gap of 1.7eV and 1.9eV, respectively. As expected, only s and p orbitals
in the outermost shells contribute the band structures. The p$_{z}$ orbitals
have greater contribution near the Fermi level. These materials can easily
exfoliate to form 2D structures, and may have potential electronic
applications. | [
0,
1,
0,
0,
0,
0
] |
Title: Schwarz-Christoffel: piliero en rivero (a pillar on a river),
Abstract: La transformoj de Schwarz-Christoffel mapas, konforme, la superan kompleksan
duon-ebenon al regiono limigita per rektaj segmentoj. Cxi tie ni priskribas
kiel konvene kunigi mapon de la suba duon-ebeno al mapo de la supera
duon-ebeno. Ni emfazas la bezonon de klara difino de angulo de kompleksa
nombro, por tiu kunigo. Ni diskutas kelkajn ekzemplojn kaj donas interesan
aplikon pri movado de fluido.
-------
Schwarz-Christoffel transformations map, conformally, the complex upper half
plane into a region bounded by right segments. Here we describe how to couple
conveniently a map of the lower half plane to the map of the upper half plane.
We emphasize the need of a clear definition of angle of a complex, to that
coupling. We discuss some examples and give an interesting application for
motion of fluid. | [
0,
0,
1,
0,
0,
0
] |
Title: Towards Audio to Scene Image Synthesis using Generative Adversarial Network,
Abstract: Humans can imagine a scene from a sound. We want machines to do so by using
conditional generative adversarial networks (GANs). By applying the techniques
including spectral norm, projection discriminator and auxiliary classifier,
compared with naive conditional GAN, the model can generate images with better
quality in terms of both subjective and objective evaluations. Almost
three-fourth of people agree that our model have the ability to generate images
related to sounds. By inputting different volumes of the same sound, our model
output different scales of changes based on the volumes, showing that our model
truly knows the relationship between sounds and images to some extent. | [
1,
0,
0,
0,
0,
0
] |
Title: Data-Mining Research in Education,
Abstract: As an interdisciplinary discipline, data mining (DM) is popular in education
area especially when examining students' learning performances. It focuses on
analyzing educational related data to develop models for improving learners'
learning experiences and enhancing institutional effectiveness. Therefore, DM
does help education institutions provide high-quality education for its
learners. Applying data mining in education also known as educational data
mining (EDM), which enables to better understand how students learn and
identify how improve educational outcomes. Present paper is designed to justify
the capabilities of data mining approaches in the filed of education. The
latest trends on EDM research are introduced in this review. Several specific
algorithms, methods, applications and gaps in the current literature and future
insights are discussed here. | [
1,
0,
0,
1,
0,
0
] |
Title: Online Learning Rate Adaptation with Hypergradient Descent,
Abstract: We introduce a general method for improving the convergence rate of
gradient-based optimizers that is easy to implement and works well in practice.
We demonstrate the effectiveness of the method in a range of optimization
problems by applying it to stochastic gradient descent, stochastic gradient
descent with Nesterov momentum, and Adam, showing that it significantly reduces
the need for the manual tuning of the initial learning rate for these commonly
used algorithms. Our method works by dynamically updating the learning rate
during optimization using the gradient with respect to the learning rate of the
update rule itself. Computing this "hypergradient" needs little additional
computation, requires only one extra copy of the original gradient to be stored
in memory, and relies upon nothing more than what is provided by reverse-mode
automatic differentiation. | [
1,
0,
0,
1,
0,
0
] |
Title: The Parameterized Complexity of Positional Games,
Abstract: We study the parameterized complexity of several positional games. Our main
result is that Short Generalized Hex is W[1]-complete parameterized by the
number of moves. This solves an open problem from Downey and Fellows'
influential list of open problems from 1999. Previously, the problem was
thought of as a natural candidate for AW[*]-completeness. Our main tool is a
new fragment of first-order logic where universally quantified variables only
occur in inequalities. We show that model-checking on arbitrary relational
structures for a formula in this fragment is W[1]-complete when parameterized
by formula size. We also consider a general framework where a positional game
is represented as a hypergraph and two players alternately pick vertices. In a
Maker-Maker game, the first player to have picked all the vertices of some
hyperedge wins the game. In a Maker-Breaker game, the first player wins if she
picks all the vertices of some hyperedge, and the second player wins otherwise.
In an Enforcer-Avoider game, the first player wins if the second player picks
all the vertices of some hyperedge, and the second player wins otherwise. Short
Maker-Maker is AW[*]-complete, whereas Short Maker-Breaker is W[1]-complete and
Short Enforcer-Avoider co-W[1]-complete parameterized by the number of moves.
This suggests a rough parameterized complexity categorization into positional
games that are complete for the first level of the W-hierarchy when the winning
configurations only depend on which vertices one player has been able to pick,
but AW[*]-completeness when the winning condition depends on which vertices
both players have picked. However, some positional games where the board and
the winning configurations are highly structured are fixed-parameter tractable.
We give another example of such a game, Short k-Connect, which is
fixed-parameter tractable when parameterized by the number of moves. | [
1,
0,
0,
0,
0,
0
] |
Title: Lunar laser ranging in infrfared at hte Grasse laser station,
Abstract: For many years, lunar laser ranging (LLR) observations using a green
wavelength have suffered an inhomogeneity problem both temporally and
spatially. This paper reports on the implementation of a new infrared detection
at the Grasse LLR station and describes how infrared telemetry improves this
situation. Our first results show that infrared detection permits us to densify
the observations and allows measurements during the new and the full Moon
periods. The link budget improvement leads to homogeneous telemetric
measurements on each lunar retro-reflector. Finally, a surprising result is
obtained on the Lunokhod 2 array which attains the same efficiency as Lunokhod
1 with an infrared laser link, although those two targets exhibit a
differential efficiency of six with a green laser link. | [
0,
1,
0,
0,
0,
0
] |
Title: Cocycles of nilpotent quotients of free groups,
Abstract: We focus on the cohomology of the $k$-th nilpotent quotient of the free
group, $F/F_k$. This paper describes all the group 2-, 3-cocycles in terms of
Massey products, and gives expressions for some of the 3-cocycles. We also give
simple proofs of some of the results on Milnor invariants and the
Johnson-Morita homomorphisms. | [
0,
0,
1,
0,
0,
0
] |
Title: A rigourous demonstration of the validity of Boltzmann's scenario for the spatial homogenization of a freely expanding gas and the equilibration of the Kac ring,
Abstract: Boltzmann provided a scenario to explain why individual macroscopic systems
composed of a large number $N$ of microscopic constituents are inevitably
(i.e., with overwhelming probability) observed to approach a unique macroscopic
state of thermodynamic equilibrium, and why after having done so, they are then
observed to remain in that state, apparently forever. We provide here rigourous
new results that mathematically prove the basic features of Boltzmann's
scenario for two classical models: a simple boundary-free model for the spatial
homogenization of a non-interacting gas of point particles, and the well-known
Kac ring model. Our results, based on concentration inequalities that go back
to Hoeffding, and which focus on the typical behavior of individual macroscopic
systems, improve upon previous results by providing estimates, exponential in
$N$, of probabilities and time scales involved. | [
0,
1,
1,
0,
0,
0
] |
Title: A Nonlinear Dimensionality Reduction Framework Using Smooth Geodesics,
Abstract: Existing dimensionality reduction methods are adept at revealing hidden
underlying manifolds arising from high-dimensional data and thereby producing a
low-dimensional representation. However, the smoothness of the manifolds
produced by classic techniques over sparse and noisy data is not guaranteed. In
fact, the embedding generated using such data may distort the geometry of the
manifold and thereby produce an unfaithful embedding. Herein, we propose a
framework for nonlinear dimensionality reduction that generates a manifold in
terms of smooth geodesics that is designed to treat problems in which manifold
measurements are either sparse or corrupted by noise. Our method generates a
network structure for given high-dimensional data using a nearest neighbors
search and then produces piecewise linear shortest paths that are defined as
geodesics. Then, we fit points in each geodesic by a smoothing spline to
emphasize the smoothness. The robustness of this approach for sparse and noisy
datasets is demonstrated by the implementation of the method on synthetic and
real-world datasets. | [
1,
0,
0,
1,
0,
0
] |
Title: Associated Graded Rings and Connected Sums,
Abstract: In 2012, Ananthnarayan, Avramov and Moore gave a new construction of
Gorenstein rings from two Gorenstein local rings, called their connected sum.
In this article, we investigate conditions on the associated graded ring of a
Gorenstein Artin local ring Q, which force it to be a connected sum over its
residue field. In particular, we recover some results regarding short, and
stretched, Gorenstein Artin rings. Finally, using these decompositions, we
obtain results about the rationality of the Poincare series of Q. | [
0,
0,
1,
0,
0,
0
] |
Title: La leggenda del quanto centenario,
Abstract: Around year 2000 the centenary of Planck's thermal radiation formula awakened
interest in the origins of quantum theory, traditionally traced back to the
Planck's conference on 14 December 1900 at the Berlin Academy of Sciences. A
lot of more accurate historical reconstructions, conducted under the stimulus
of that recurrence, placed the birth date of quantum theory in March 1905 when
Einstein advanced his light quantum hypothesis. Both interpretations are yet
controversial, but science historians agree on one point: the emergence of
quantum theory from a presumed "crisis" of classical physics is a myth with
scarce adherence to the historical truth. This article, written in Italian
language, was originally presented in connection with the celebration of the
World Year of Phyics 2005 with the aim of bringing these scholarly theses to a
wider audience.
---
Tradizionalmente la nascita della teoria quantistica viene fatta risalire al
14 dicembre 1900, quando Planck presentò all'Accademia delle Scienze di
Berlino la dimostrazione della formula della radiazione termica. Numerose
ricostruzioni storiche più accurate, effettuate nel periodo intorno al 2000
sotto lo stimolo dell'interesse per il centenario di quell'avvenimento,
collocano invece la nascita della teoria quantistica nel marzo del 1905, quando
Einstein avanzò l'ipotesi dei quanti di luce. Entrambe le interpretazioni
sono tuttora controverse, ma gli storici della scienza concordano su un punto:
l'emergere della teoria quantistica da una presunta "crisi" della fisica
classica è un mito con scarsa aderenza alla verità storica. Con questo
articolo in italiano, presentato originariamente in occasione delle
celebrazioni per il World Year of Phyics 2005, si è inteso portare a un più
largo pubblico queste tesi già ben note agli specialisti. | [
0,
1,
0,
0,
0,
0
] |
Title: Estimation of quantile oriented sensitivity indices,
Abstract: The paper concerns quantile oriented sensitivity analysis. We rewrite the
corresponding indices using the Conditional Tail Expectation risk measure.
Then, we use this new expression to built estimators. | [
0,
0,
1,
1,
0,
0
] |
Title: Electromagnetically Induced Transparency (EIT) Amplitude Noise Spectroscopy,
Abstract: Intensity noise cross-correlation of the polarization eigenstates of light
emerging from an atomic vapor cell in the Hanle configuration allows one to
perform high resolution spectroscopy with free- running semiconductor lasers.
Such an approach has shown promise as an inexpensive, simpler approach to
magnetometry and timekeeping, and as a probe of dynamics of atomic coherence in
warm vapor cells. We report that varying the post-cell polarization state basis
yields intensity noise spectra which more completely probe the prepared atomic
state. We advance and test the hypothesis that the observed intensity noise can
be explained in terms of an underlying stochastic process in lightfield
amplitudes themselves. Understanding this stochastic process in the light field
amplitudes themselves provides a new test of the simple atomic quantum optics
model of EIT noise. | [
0,
1,
0,
0,
0,
0
] |
Title: Tunable $φ$-Josephson junction with a quantum anomalous Hall insulator,
Abstract: We theoretically study the Josephson current in a superconductor/quantum
anomalous Hall insulator/superconductor junction by using the lattice Green
function technique. When an in-plane external Zeeman field is applied to the
quantum anomalous Hall insulator, the Josephson current $J$ flows without a
phase difference across the junction $\theta$. The phase shift $\varphi$
appealing in the current-phase relationship $J\propto \sin(\theta-\varphi$) is
proportional to the amplitude of Zeeman fields and depends on the direction of
Zeeman fields. A phenomenological analysis of the Andreev reflection processes
explains the physical origin of $\varphi$. A quantum anomalous Hall insulator
breaks time-reversal symmetry and mirror reflection symmetry simultaneously.
However it preserves magnetic mirror reflection symmetry. Such characteristic
symmetry property enable us to have a tunable $\varphi$-junction with a quantum
Hall insulator. | [
0,
1,
0,
0,
0,
0
] |
Title: What kind of content are you prone to tweet? Multi-topic Preference Model for Tweeters,
Abstract: According to tastes, a person could show preference for a given category of
content to a greater or lesser extent. However, quantifying people's amount of
interest in a certain topic is a challenging task, especially considering the
massive digital information they are exposed to. For example, in the context of
Twitter, aligned with his/her preferences a user may tweet and retweet more
about technology than sports and do not share any music-related content. The
problem we address in this paper is the identification of users' implicit topic
preferences by analyzing the content categories they tend to post on Twitter.
Our proposal is significant given that modeling their multi-topic profile may
be useful to find patterns or association between preferences for categories,
discover trending topics and cluster similar users to generate better group
recommendations of content. In the present work, we propose a method based on
the Mixed Gaussian Model to extract the multidimensional preference
representation for 399 Ecuadorian tweeters concerning twenty-two different
topics (or dimensions) which became known by manually categorizing 68.186
tweets. Our experiment findings indicate that the proposed approach is
effective at detecting the topic interests of users. | [
1,
0,
0,
0,
0,
0
] |
Title: The Godunov Method for a 2-Phase Model,
Abstract: We consider the Godunov numerical method to the phase-transition traffic
model, proposed in [6], by Colombo, Marcellini, and Rascle. Numerical tests are
shown to prove the validity of the method. Moreover we highlight the
differences between such model and the one proposed in [1], by Blandin, Work,
Goatin, Piccoli, and Bayen. | [
0,
0,
1,
0,
0,
0
] |
Title: Safe Active Feature Selection for Sparse Learning,
Abstract: We present safe active incremental feature selection~(SAIF) to scale up the
computation of LASSO solutions. SAIF does not require a solution from a heavier
penalty parameter as in sequential screening or updating the full model for
each iteration as in dynamic screening. Different from these existing screening
methods, SAIF starts from a small number of features and incrementally recruits
active features and updates the significantly reduced model. Hence, it is much
more computationally efficient and scalable with the number of features. More
critically, SAIF has the safe guarantee as it has the convergence guarantee to
the optimal solution to the original full LASSO problem. Such an incremental
procedure and theoretical convergence guarantee can be extended to fused LASSO
problems. Compared with state-of-the-art screening methods as well as working
set and homotopy methods, which may not always guarantee the optimal solution,
SAIF can achieve superior or comparable efficiency and high scalability with
the safe guarantee when facing extremely high dimensional data sets.
Experiments with both synthetic and real-world data sets show that SAIF can be
up to 50 times faster than dynamic screening, and hundreds of times faster than
computing LASSO or fused LASSO solutions without screening. | [
0,
0,
0,
1,
0,
0
] |
Title: Cobwebs from the Past and Present: Extracting Large Social Networks using Internet Archive Data,
Abstract: Social graph construction from various sources has been of interest to
researchers due to its application potential and the broad range of technical
challenges involved. The World Wide Web provides a huge amount of continuously
updated data and information on a wide range of topics created by a variety of
content providers, and makes the study of extracted people networks and their
temporal evolution valuable for social as well as computer scientists. In this
paper we present SocGraph - an extraction and exploration system for social
relations from the content of around 2 billion web pages collected by the
Internet Archive over the 17 years time period between 1996 and 2013. We
describe methods for constructing large social graphs from extracted relations
and introduce an interface to study their temporal evolution. | [
1,
1,
0,
0,
0,
0
] |
Title: Number-conserving interacting fermion models with exact topological superconducting ground states,
Abstract: We present a method to construct number-conserving Hamiltonians whose ground
states exactly reproduce an arbitrarily chosen BCS-type mean-field state. Such
parent Hamiltonians can be constructed not only for the usual $s$-wave BCS
state, but also for more exotic states of this form, including the ground
states of Kitaev wires and 2D topological superconductors. This method leads to
infinite families of locally-interacting fermion models with exact topological
superconducting ground states. After explaining the general technique, we apply
this method to construct two specific classes of models. The first one is a
one-dimensional double wire lattice model with Majorana-like degenerate ground
states. The second one is a two-dimensional $p_x+ip_y$ superconducting model,
where we also obtain analytic expressions for topologically degenerate ground
states in the presence of vortices. Our models may provide a deeper conceptual
understanding of how Majorana zero modes could emerge in condensed matter
systems, as well as inspire novel routes to realize them in experiment. | [
0,
1,
0,
0,
0,
0
] |
Title: JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction,
Abstract: We present a new parallel corpus, JHU FLuency-Extended GUG corpus (JFLEG) for
developing and evaluating grammatical error correction (GEC). Unlike other
corpora, it represents a broad range of language proficiency levels and uses
holistic fluency edits to not only correct grammatical errors but also make the
original text more native sounding. We describe the types of corrections made
and benchmark four leading GEC systems on this corpus, identifying specific
areas in which they do well and how they can improve. JFLEG fulfills the need
for a new gold standard to properly assess the current state of GEC. | [
1,
0,
0,
0,
0,
0
] |
Title: From Pragmatic to Systematic Software Process Improvement: An Evaluated Approach,
Abstract: Software processes improvement (SPI) is a challenging task, as many different
stakeholders, project settings, and contexts and goals need to be considered.
SPI projects are often operated in a complex and volatile environment and,
thus, require a sound management that is resource-intensive requiring many
stakeholders to contribute to the process assessment, analysis, design,
realisation, and deployment. Although there exist many valuable SPI approaches,
none address the needs of both process engineers and project managers. This
article presents an Artefact-based Software Process Improvement & Management
approach (ArSPI) that closes this gap. ArSPI was developed and tested across
several SPI projects in large organisations in Germany and Eastern Europe. The
approach further encompasses a template for initiating, performing, and
managing SPI projects by defining a set of 5 key artefacts and 24 support
artefacts. We present ArSPI and discus results of its validation indicating
ArSPI to be a helpful instrument to set up and steer SPI projects. | [
1,
0,
0,
0,
0,
0
] |
Title: Discrete Cycloids from Convex Symmetric Polygons,
Abstract: Cycloids, hipocycloids and epicycloids have an often forgotten common
property: they are homothetic to their evolutes. But what if use convex
symmetric polygons as unit balls, can we define evolutes and cycloids which are
genuinely discrete? Indeed, we can! We define discrete cycloids as eigenvectors
of a discrete double evolute transform which can be seen as a linear operator
on a vector space we call curvature radius space. We are also able to classify
such cycloids according to the eigenvalues of that transform, and show that the
number of cusps of each cycloid is well determined by the ordering of those
eigenvalues. As an elegant application, we easily establish a version of the
four-vertex theorem for closed convex polygons. The whole theory is developed
using only linear algebra, and concrete examples are given. | [
0,
0,
1,
0,
0,
0
] |
Title: Gaussian Graphical Models: An Algebraic and Geometric Perspective,
Abstract: Gaussian graphical models are used throughout the natural sciences, social
sciences, and economics to model the statistical relationships between
variables of interest in the form of a graph. We here provide a pedagogic
introduction to Gaussian graphical models and review recent results on maximum
likelihood estimation for such models. Throughout, we highlight the rich
algebraic and geometric properties of Gaussian graphical models and explain how
these properties relate to convex optimization and ultimately result in
insights on the existence of the maximum likelihood estimator (MLE) and
algorithms for computing the MLE. | [
0,
0,
1,
1,
0,
0
] |
Title: THAP: A Matlab Toolkit for Learning with Hawkes Processes,
Abstract: As a powerful tool of asynchronous event sequence analysis, point processes
have been studied for a long time and achieved numerous successes in different
fields. Among various point process models, Hawkes process and its variants
attract many researchers in statistics and computer science these years because
they capture the self- and mutually-triggering patterns between different
events in complicated sequences explicitly and quantitatively and are broadly
applicable to many practical problems. In this paper, we describe an
open-source toolkit implementing many learning algorithms and analysis tools
for Hawkes process model and its variants. Our toolkit systematically
summarizes recent state-of-the-art algorithms as well as most classic
algorithms of Hawkes processes, which is beneficial for both academical
education and research. Source code can be downloaded from
this https URL. | [
1,
0,
0,
1,
0,
0
] |
Title: Studies of the Response of the SiD Silicon-Tungsten ECal,
Abstract: Studies of the response of the SiD silicon-tungsten electromagnetic
calorimeter (ECal) are presented. Layers of highly granular (13 mm^2 pixels)
silicon detectors embedded in thin gaps (~ 1 mm) between tungsten alloy plates
give the SiD ECal the ability to separate electromagnetic showers in a crowded
environment. A nine-layer prototype has been built and tested in a 12.1 GeV
electron beam at the SLAC National Accelerator Laboratory. This data was
simulated with a Geant4 model. Particular attention was given to the separation
of nearby incident electrons, which demonstrated a high (98.5%) separation
efficiency for two electrons at least 1 cm from each other. The beam test study
will be compared to a full SiD detector simulation with a realistic geometry,
where the ECal calibration constants must first be established. This work is
continuing, as the geometry requires that the calibration constants depend upon
energy, angle, and absorber depth. The derivation of these constants is being
developed from first principles. | [
0,
1,
0,
0,
0,
0
] |
Title: Multiple Hypothesis Tracking Algorithm for Multi-Target Multi-Camera Tracking with Disjoint Views,
Abstract: In this study, a multiple hypothesis tracking (MHT) algorithm for
multi-target multi-camera tracking (MCT) with disjoint views is proposed. Our
method forms track-hypothesis trees, and each branch of them represents a
multi-camera track of a target that may move within a camera as well as move
across cameras. Furthermore, multi-target tracking within a camera is performed
simultaneously with the tree formation by manipulating a status of each track
hypothesis. Each status represents three different stages of a multi-camera
track: tracking, searching, and end-of-track. The tracking status means targets
are tracked by a single camera tracker. In the searching status, the
disappeared targets are examined if they reappear in other cameras. The
end-of-track status does the target exited the camera network due to its
lengthy invisibility. These three status assists MHT to form the
track-hypothesis trees for multi-camera tracking. Furthermore, they present a
gating technique for eliminating of unlikely observation-to-track association.
In the experiments, they evaluate the proposed method using two datasets,
DukeMTMC and NLPR-MCT, which demonstrates that the proposed method outperforms
the state-of-the-art method in terms of improvement of the accuracy. In
addition, they show that the proposed method can operate in real-time and
online. | [
1,
0,
0,
0,
0,
0
] |
Title: Quantized Laplacian growth, III: On conformal field theories of Laplacian growth,
Abstract: A one-parametric stochastic dynamics of the interface in the quantized
Laplacian growth with zero surface tension is introduced. The quantization
procedure regularizes the growth by preventing the formation of cusps at the
interface, and makes the interface dynamics chaotic. In a long time asymptotic,
by coupling a conformal field theory to the stochastic growth process we
introduce a set of observables (the martingales), whose expectation values are
constant in time. The martingales are connected to degenerate representations
of the Virasoro algebra, and can be written in terms of conformal correlation
functions. A direct link between Laplacian growth and the conformal Liouville
field theory with the central charge $c\geq25$ is proposed. | [
0,
1,
0,
0,
0,
0
] |
Title: Supervised Saliency Map Driven Segmentation of the Lesions in Dermoscopic Images,
Abstract: Lesion segmentation is the first step in most automatic melanoma recognition
systems. Deficiencies and difficulties in dermoscopic images such as color
inconstancy, hair occlusion, dark corners and color charts make lesion
segmentation an intricate task. In order to detect the lesion in the presence
of these problems, we propose a supervised saliency detection method tailored
for dermoscopic images based on the discriminative regional feature integration
(DRFI). DRFI method incorporates multi-level segmentation, regional contrast,
property, background descriptors, and a random forest regressor to create
saliency scores for each region in the image. In our improved saliency
detection method, mDRFI, we have added some new features to regional property
descriptors. Also, in order to achieve more robust regional background
descriptors, a thresholding algorithm is proposed to obtain a new
pseudo-background region. Findings reveal that mDRFI is superior to DRFI in
detecting the lesion as the salient object in dermoscopic images. The proposed
overall lesion segmentation framework uses detected saliency map to construct
an initial mask of the lesion through thresholding and post-processing
operations. The initial mask is then evolving in a level set framework to fit
better on the lesion's boundaries. The results of evaluation tests on three
public datasets show that our proposed segmentation method outperforms the
other conventional state-of-the-art segmentation algorithms and its performance
is comparable with most recent approaches that are based on deep convolutional
neural networks. | [
1,
0,
0,
0,
0,
0
] |
Title: The Mean and Median Criterion for Automatic Kernel Bandwidth Selection for Support Vector Data Description,
Abstract: Support vector data description (SVDD) is a popular technique for detecting
anomalies. The SVDD classifier partitions the whole space into an inlier
region, which consists of the region near the training data, and an outlier
region, which consists of points away from the training data. The computation
of the SVDD classifier requires a kernel function, and the Gaussian kernel is a
common choice for the kernel function. The Gaussian kernel has a bandwidth
parameter, whose value is important for good results. A small bandwidth leads
to overfitting, and the resulting SVDD classifier overestimates the number of
anomalies. A large bandwidth leads to underfitting, and the classifier fails to
detect many anomalies. In this paper we present a new automatic, unsupervised
method for selecting the Gaussian kernel bandwidth. The selected value can be
computed quickly, and it is competitive with existing bandwidth selection
methods. | [
1,
0,
0,
1,
0,
0
] |
Title: Monte Carlo modified profile likelihood in models for clustered data,
Abstract: The main focus of the analysts who deal with clustered data is usually not on
the clustering variables, and hence the group-specific parameters are treated
as nuisance. If a fixed effects formulation is preferred and the total number
of clusters is large relative to the single-group sizes, classical frequentist
techniques relying on the profile likelihood are often misleading. The use of
alternative tools, such as modifications to the profile likelihood or
integrated likelihoods, for making accurate inference on a parameter of
interest can be complicated by the presence of nonstandard modelling and/or
sampling assumptions. We show here how to employ Monte Carlo simulation in
order to approximate the modified profile likelihood in some of these
unconventional frameworks. The proposed solution is widely applicable and is
shown to retain the usual properties of the modified profile likelihood. The
approach is examined in two instances particularly relevant in applications,
i.e. missing-data models and survival models with unspecified censoring
distribution. The effectiveness of the proposed solution is validated via
simulation studies and two clinical trial applications. | [
0,
0,
0,
1,
0,
0
] |
Title: An Annotated Corpus of Relational Strategies in Customer Service,
Abstract: We create and release the first publicly available commercial customer
service corpus with annotated relational segments. Human-computer data from
three live customer service Intelligent Virtual Agents (IVAs) in the domains of
travel and telecommunications were collected, and reviewers marked all text
that was deemed unnecessary to the determination of user intention. After
merging the selections of multiple reviewers to create highlighted texts, a
second round of annotation was done to determine the classes of language
present in the highlighted sections such as the presence of Greetings,
Backstory, Justification, Gratitude, Rants, or Emotions. This resulting corpus
is a valuable resource for improving the quality and relational abilities of
IVAs. As well as discussing the corpus itself, we compare the usage of such
language in human-human interactions on TripAdvisor forums. We show that
removal of this language from task-based inputs has a positive effect on IVA
understanding by both an increase in confidence and improvement in responses,
demonstrating the need for automated methods of its discovery. | [
1,
0,
0,
0,
0,
0
] |
Title: Taming Wild High Dimensional Text Data with a Fuzzy Lash,
Abstract: The bag of words (BOW) represents a corpus in a matrix whose elements are the
frequency of words. However, each row in the matrix is a very high-dimensional
sparse vector. Dimension reduction (DR) is a popular method to address sparsity
and high-dimensionality issues. Among different strategies to develop DR
method, Unsupervised Feature Transformation (UFT) is a popular strategy to map
all words on a new basis to represent BOW. The recent increase of text data and
its challenges imply that DR area still needs new perspectives. Although a wide
range of methods based on the UFT strategy has been developed, the fuzzy
approach has not been considered for DR based on this strategy. This research
investigates the application of fuzzy clustering as a DR method based on the
UFT strategy to collapse BOW matrix to provide a lower-dimensional
representation of documents instead of the words in a corpus. The quantitative
evaluation shows that fuzzy clustering produces superior performance and
features to Principal Components Analysis (PCA) and Singular Value
Decomposition (SVD), two popular DR methods based on the UFT strategy. | [
1,
0,
0,
1,
0,
0
] |
Title: Spatial structure of shock formation,
Abstract: The formation of a singularity in a compressible gas, as described by the
Euler equation, is characterized by the steepening, and eventual overturning of
a wave. Using a self-similar description in two space dimensions, we show that
the spatial structure of this process, which starts at a point, is equivalent
to the formation of a caustic, i.e. to a cusp catastrophe. The lines along
which the profile has infinite slope correspond to the caustic lines, from
which we construct the position of the shock. By solving the similarity
equation, we obtain a complete local description of wave steepening and of the
spreading of the shock from a point. | [
0,
1,
1,
0,
0,
0
] |
Title: How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks),
Abstract: This paper investigates how far a very deep neural network is from attaining
close to saturating performance on existing 2D and 3D face alignment datasets.
To this end, we make the following 5 contributions: (a) we construct, for the
first time, a very strong baseline by combining a state-of-the-art architecture
for landmark localization with a state-of-the-art residual block, train it on a
very large yet synthetically expanded 2D facial landmark dataset and finally
evaluate it on all other 2D facial landmark datasets. (b) We create a guided by
2D landmarks network which converts 2D landmark annotations to 3D and unifies
all existing datasets, leading to the creation of LS3D-W, the largest and most
challenging 3D facial landmark dataset to date ~230,000 images. (c) Following
that, we train a neural network for 3D face alignment and evaluate it on the
newly introduced LS3D-W. (d) We further look into the effect of all
"traditional" factors affecting face alignment performance like large pose,
initialization and resolution, and introduce a "new" one, namely the size of
the network. (e) We show that both 2D and 3D face alignment networks achieve
performance of remarkable accuracy which is probably close to saturating the
datasets used. Training and testing code as well as the dataset can be
downloaded from this https URL | [
1,
0,
0,
0,
0,
0
] |
Title: Inhabitants of interesting subsets of the Bousfield lattice,
Abstract: The set of Bousfield classes has some important subsets such as the
distributive lattice $\mathbf{DL}$ of all classes $\langle E\rangle$ which are
smash idempotent and the complete Boolean algebra $\mathbf{cBA}$ of closed
classes. We provide examples of spectra that are in $\mathbf{DL}$, but not in
$\mathbf{cBA}$; in particular, for every prime $p$, the Bousfield class of the
Eilenberg-MacLane spectrum $\langle
H\mathbb{F}_p\rangle\in\mathbf{DL}{\setminus}\mathbf{cBA}$. | [
0,
0,
1,
0,
0,
0
] |
Title: A Framework for Implementing Machine Learning on Omics Data,
Abstract: The potential benefits of applying machine learning methods to -omics data
are becoming increasingly apparent, especially in clinical settings. However,
the unique characteristics of these data are not always well suited to machine
learning techniques. These data are often generated across different
technologies in different labs, and frequently with high dimensionality. In
this paper we present a framework for combining -omics data sets, and for
handling high dimensional data, making -omics research more accessible to
machine learning applications. We demonstrate the success of this framework
through integration and analysis of multi-analyte data for a set of 3,533
breast cancers. We then use this data-set to predict breast cancer patient
survival for individuals at risk of an impending event, with higher accuracy
and lower variance than methods trained on individual data-sets. We hope that
our pipelines for data-set generation and transformation will open up -omics
data to machine learning researchers. We have made these freely available for
noncommercial use at www.ccg.ai. | [
0,
0,
0,
0,
1,
0
] |
Title: Actively Calibrated Line Mountable Capacitive Voltage Transducer For Power Systems Applications,
Abstract: A class of Actively Calibrated Line Mounted Capacitive Voltage Transducers
(LMCVT) are introduced as a viable line mountable instrumentation option for
deploying large numbers of voltage transducers onto the medium and high voltage
systems. Active Calibration is shown to reduce the error of line mounted
voltage measurements by an order of magnitude from previously published
techniques. The instrument physics and sensing method is presented and the
performance is evaluated in a laboratory setting. Finally, a roadmap to a fully
deployable prototype is shown. | [
0,
1,
0,
0,
0,
0
] |
Title: Identifying Condition-Action Statements in Medical Guidelines Using Domain-Independent Features,
Abstract: This paper advances the state of the art in text understanding of medical
guidelines by releasing two new annotated clinical guidelines datasets, and
establishing baselines for using machine learning to extract condition-action
pairs. In contrast to prior work that relies on manually created rules, we
report experiment with several supervised machine learning techniques to
classify sentences as to whether they express conditions and actions. We show
the limitations and possible extensions of this work on text mining of medical
guidelines. | [
1,
0,
0,
0,
0,
0
] |
Title: Dealing with the Dimensionality Curse in Dynamic Pricing Competition: Using Frequent Repricing to Compensate Imperfect Market Anticipations,
Abstract: Most sales applications are characterized by competition and limited demand
information. For successful pricing strategies, frequent price adjustments as
well as anticipation of market dynamics are crucial. Both effects are
challenging as competitive markets are complex and computations of optimized
pricing adjustments can be time-consuming. We analyze stochastic dynamic
pricing models under oligopoly competition for the sale of perishable goods. To
circumvent the curse of dimensionality, we propose a heuristic approach to
efficiently compute price adjustments. To demonstrate our strategy's
applicability even if the number of competitors is large and their strategies
are unknown, we consider different competitive settings in which competitors
frequently and strategically adjust their prices. For all settings, we verify
that our heuristic strategy yields promising results. We compare the
performance of our heuristic against upper bounds, which are obtained by
optimal strategies that take advantage of perfect price anticipations. We find
that price adjustment frequencies can have a larger impact on expected profits
than price anticipations. Finally, our approach has been applied on Amazon for
the sale of used books. We have used a seller's historical market data to
calibrate our model. Sales results show that our data-driven strategy
outperforms the rule-based strategy of an experienced seller by a profit
increase of more than 20%. | [
0,
0,
0,
0,
0,
1
] |
Title: Interleaved Group Convolutions for Deep Neural Networks,
Abstract: In this paper, we present a simple and modularized neural network
architecture, named interleaved group convolutional neural networks (IGCNets).
The main point lies in a novel building block, a pair of two successive
interleaved group convolutions: primary group convolution and secondary group
convolution. The two group convolutions are complementary: (i) the convolution
on each partition in primary group convolution is a spatial convolution, while
on each partition in secondary group convolution, the convolution is a
point-wise convolution; (ii) the channels in the same secondary partition come
from different primary partitions. We discuss one representative advantage:
Wider than a regular convolution with the number of parameters and the
computation complexity preserved. We also show that regular convolutions, group
convolution with summation fusion, and the Xception block are special cases of
interleaved group convolutions. Empirical results over standard benchmarks,
CIFAR-$10$, CIFAR-$100$, SVHN and ImageNet demonstrate that our networks are
more efficient in using parameters and computation complexity with similar or
higher accuracy. | [
1,
0,
0,
0,
0,
0
] |
Title: Lower Bounding Diffusion Constant by the Curvature of Drude Weight,
Abstract: We establish a general connection between ballistic and diffusive transport
in systems where the ballistic contribution in canonical ensemble vanishes. A
lower bound on the Green-Kubo diffusion constant is derived in terms of the
curvature of the ideal transport coefficient, the Drude weight, with respect to
the filling parameter. As an application, we explicitly determine the lower
bound on the high temperature diffusion constant in the anisotropic spin 1/2
Heisenberg chain for anisotropy parameters $\Delta \geq 1$, thus settling the
question whether the transport is sub-diffusive or not. Addi- tionally, the
lower bound is shown to saturate the diffusion constant for a certain classical
integrable model. | [
0,
1,
0,
0,
0,
0
] |
Title: Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task,
Abstract: End-to-end control for robot manipulation and grasping is emerging as an
attractive alternative to traditional pipelined approaches. However, end-to-end
methods tend to either be slow to train, exhibit little or no generalisability,
or lack the ability to accomplish long-horizon or multi-stage tasks. In this
paper, we show how two simple techniques can lead to end-to-end (image to
velocity) execution of a multi-stage task, which is analogous to a simple
tidying routine, without having seen a single real image. This involves
locating, reaching for, and grasping a cube, then locating a basket and
dropping the cube inside. To achieve this, robot trajectories are computed in a
simulator, to collect a series of control velocities which accomplish the task.
Then, a CNN is trained to map observed images to velocities, using domain
randomisation to enable generalisation to real world images. Results show that
we are able to successfully accomplish the task in the real world with the
ability to generalise to novel environments, including those with dynamic
lighting conditions, distractor objects, and moving objects, including the
basket itself. We believe our approach to be simple, highly scalable, and
capable of learning long-horizon tasks that have until now not been shown with
the state-of-the-art in end-to-end robot control. | [
1,
0,
0,
0,
0,
0
] |
Title: Robust Implicit Backpropagation,
Abstract: Arguably the biggest challenge in applying neural networks is tuning the
hyperparameters, in particular the learning rate. The sensitivity to the
learning rate is due to the reliance on backpropagation to train the network.
In this paper we present the first application of Implicit Stochastic Gradient
Descent (ISGD) to train neural networks, a method known in convex optimization
to be unconditionally stable and robust to the learning rate. Our key
contribution is a novel layer-wise approximation of ISGD which makes its
updates tractable for neural networks. Experiments show that our method is more
robust to high learning rates and generally outperforms standard
backpropagation on a variety of tasks. | [
0,
0,
0,
1,
0,
0
] |
Title: Experimental and Theoretical Study of Magnetohydrodynamic Ship Models,
Abstract: Magnetohydrodynamic (MHD) ships represent a clear demonstration of the
Lorentz force in fluids, which explains the number of students practicals or
exercises described on the web. However, the related literature is rather
specific and no complete comparison between theory and typical small scale
experiments is currently available. This work provides, in a self-consistent
framework, a detailed presentation of the relevant theoretical equations for
small MHD ships and experimental measurements for future benchmarks.
Theoretical results of the literature are adapted to these simple
battery/magnets powered ships moving on salt water. Comparison between theory
and experiments are performed to validate each theoretical step such as the
Tafel and the Kohlrausch laws, or the predicted ship speed. A successful
agreement is obtained without any adjustable parameter. Finally, based on these
results, an optimal design is then deduced from the theory. Therefore this work
provides a solid theoretical and experimental ground for small scale MHD ships,
by presenting in detail several approximations and how they affect the boat
efficiency. Moreover, the theory is general enough to be adapted to other
contexts, such as large scale ships or industrial flow measurement techniques. | [
0,
1,
0,
0,
0,
0
] |
Title: On the validity of the formal Edgeworth expansion for posterior densities,
Abstract: We consider a fundamental open problem in parametric Bayesian theory, namely
the validity of the formal Edgeworth expansion of the posterior density. While
the study of valid asymptotic expansions for posterior distributions
constitutes a rich literature, the validity of the formal Edgeworth expansion
has not been rigorously established. Several authors have claimed connections
of various posterior expansions with the classical Edgeworth expansion, or have
simply assumed its validity. Our main result settles this open problem. We also
prove a lemma concerning the order of posterior cumulants which is of
independent interest in Bayesian parametric theory. The most relevant
literature is synthesized and compared to the newly-derived Edgeworth
expansions. Numerical investigations illustrate that our expansion has the
behavior expected of an Edgeworth expansion, and that it has better performance
than the other existing expansion which was previously claimed to be of
Edgeworth-type. | [
0,
0,
1,
1,
0,
0
] |
Title: Resonance fluorescence in the resolvent operator formalism,
Abstract: The Mollow spectrum for the light scattered by a driven two-level atom is
derived in the resolvent operator formalism. The derivation is based on the
construction of a master equation from the resolvent operator of the atom-field
system. We show that the natural linewidth of the excited atomic level remains
essentially unmodified, to a very good level of approximation, even in the
strong-field regime, where Rabi flopping becomes relevant inside the
self-energy loop that yields the linewidth. This ensures that the obtained
master equation and the spectrum derived matches that of Mollow. | [
0,
1,
0,
0,
0,
0
] |
Title: On the the Berge Conjecture for tunnel number one knots,
Abstract: In this paper we use an approach based on dynamics to prove that if $K\subset
S^3$ is a tunnel number one knot which admits a Dehn filling resulting in a
lens space $L$ then $K$ is either a Berge knot, or $K\subset S^3$ is
$(1,1)$-knot. | [
0,
0,
1,
0,
0,
0
] |
Title: Exact diagonalization of cubic lattice models in commensurate Abelian magnetic fluxes and translational invariant non-Abelian potentials,
Abstract: We present a general analytical formalism to determine the energy spectrum of
a quantum particle in a cubic lattice subject to translationally invariant
commensurate magnetic fluxes and in the presence of a general space-independent
non-Abelian gauge potential. We first review and analyze the case of purely
Abelian potentials, showing also that the so-called Hasegawa gauge yields a
decomposition of the Hamiltonian into sub-matrices having minimal dimension.
Explicit expressions for such matrices are derived, also for general
anisotropic fluxes. Later on, we show that the introduction of a translational
invariant non-Abelian coupling for multi-component spinors does not affect the
dimension of the minimal Hamiltonian blocks, nor the dimension of the magnetic
Brillouin zone. General formulas are presented for the U(2) case and explicit
examples are investigated involving $\pi$ and $2\pi/3$ magnetic fluxes.
Finally, we numerically study the effect of random flux perturbations. | [
0,
1,
0,
0,
0,
0
] |
Title: Latent Gaussian Mixture Models for Nationwide Kidney Transplant Center Evaluation,
Abstract: Five year post-transplant survival rate is an important indicator on quality
of care delivered by kidney transplant centers in the United States. To provide
a fair assessment of each transplant center, an effect that represents the
center-specific care quality, along with patient level risk factors, is often
included in the risk adjustment model. In the past, the center effects have
been modeled as either fixed effects or Gaussian random effects, with various
pros and cons. Our numerical analyses reveal that the distributional
assumptions do impact the prediction of center effects especially when the
effect is extreme. To bridge the gap between these two approaches, we propose
to model the transplant center effect as a latent random variable with a finite
Gaussian mixture distribution. Such latent Gaussian mixture models provide a
convenient framework to study the heterogeneity among the transplant centers.
To overcome the weak identifiability issues, we propose to estimate the latent
Gaussian mixture model using a penalized likelihood approach, and develop
sequential locally restricted likelihood ratio tests to determine the number of
components in the Gaussian mixture distribution. The fitted mixture model
provides a convenient means of controlling the false discovery rate when
screening for underperforming or outperforming transplant centers. The
performance of the methods is verified by simulations and by the analysis of
the motivating data example. | [
0,
0,
0,
1,
0,
0
] |
Title: Poisson brackets with prescribed family of functions in involution,
Abstract: It is well known that functions in involution with respect to Poisson
brackets have a privileged role in the theory of completely integrable systems.
Finding functionally independent functions in involution with a given function
$h$ on a Poisson manifold is a fundamental problem of this theory and is very
useful for the explicit integration of the equations of motion defined by $h$.
In this paper, we present our results on the study of the inverse, so to speak,
problem. By developing a technique analogous to that presented in P. Damianou
and F. Petalidou, Poisson brackets with prescribed Casimirs, Canad. J. Math.,
2012, vol. 64, 991-1018, for the establishment of Poisson brackets with
prescribed Casimir invariants, we construct an algorithm which yields Poisson
brackets having a given family of functions in involution. Our approach allows
us to deal with bi-Hamiltonian structures constructively and therefore allows
us to also deal with the completely integrable systems that arise in such a
framework. | [
0,
0,
1,
0,
0,
0
] |
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