title
stringlengths 7
239
| abstract
stringlengths 7
2.76k
| cs
int64 0
1
| phy
int64 0
1
| math
int64 0
1
| stat
int64 0
1
| quantitative biology
int64 0
1
| quantitative finance
int64 0
1
|
|---|---|---|---|---|---|---|---|
The path to high-energy electron-positron colliders: from Wideroe's betatron to Touschek's AdA and to LEP | We describe the road which led to the construction and exploitation of
electron positron colliders, hightlighting how the young physics student Bruno
Touschek met the Norwegian engineer Rolf Wideroe in Germany, during WWII, and
collaborated in building the 15 MeV betatron, a secret project directed by
Wideroe and financed by the Ministry of Aviation of the Reich. This is how
Bruno Touschek learnt the science of making particle accelerators and was
ready, many years later, to propose and build AdA, the first electron positron
collider, in Frascati, Italy, in 1960. We shall then see how AdA was brought
from Frascati to Orsay, in France. Taking advantage of the Orsay Linear
Accelerator as injector, the Franco-Italian team was able to prove that
collisions had taken place, opening the way to the use of particle colliders as
a mean to explore high energy physics.
| 0 | 1 | 0 | 0 | 0 | 0 |
Lazarsfeld-Mukai Reflexive Sheaves and their Stability | Consider an ample and globally generated line bundle $L$ on a smooth
projective variety $X$ of dimension $N\geq 2$ over $\mathbb{C}$. Let $D$ be a
smooth divisor in the complete linear system of $L$. We construct reflexive
sheaves on $X$ by an elementary transformation of a trivial bundle on $X$ along
certain globally generated torsion-free sheaves on $D$. The dual reflexive
sheaves are called the Lazarsfeld-Mukai reflexive sheaves. We prove the
$\mu_L$-(semi)stability of such reflexive sheaves under certain conditions.
| 0 | 0 | 1 | 0 | 0 | 0 |
Dimensionality reduction for acoustic vehicle classification with spectral embedding | We propose a method for recognizing moving vehicles, using data from roadside
audio sensors. This problem has applications ranging widely, from traffic
analysis to surveillance. We extract a frequency signature from the audio
signal using a short-time Fourier transform, and treat each time window as an
individual data point to be classified. By applying a spectral embedding, we
decrease the dimensionality of the data sufficiently for K-nearest neighbors to
provide accurate vehicle identification.
| 1 | 0 | 0 | 1 | 0 | 0 |
Efficient Estimation of Linear Functionals of Principal Components | We study principal component analysis (PCA) for mean zero i.i.d. Gaussian
observations $X_1,\dots, X_n$ in a separable Hilbert space $\mathbb{H}$ with
unknown covariance operator $\Sigma.$ The complexity of the problem is
characterized by its effective rank ${\bf r}(\Sigma):= \frac{{\rm
tr}(\Sigma)}{\|\Sigma\|},$ where ${\rm tr}(\Sigma)$ denotes the trace of
$\Sigma$ and $\|\Sigma\|$ denotes its operator norm. We develop a method of
bias reduction in the problem of estimation of linear functionals of
eigenvectors of $\Sigma.$ Under the assumption that ${\bf r}(\Sigma)=o(n),$ we
establish the asymptotic normality and asymptotic properties of the risk of the
resulting estimators and prove matching minimax lower bounds, showing their
semi-parametric optimality.
| 0 | 0 | 1 | 1 | 0 | 0 |
A stroll in the jungle of error bounds | The aim of this paper is to give a short overview on error bounds and to
provide the first bricks of a unified theory. Inspired by the works of [8, 15,
13, 16, 10], we show indeed the centrality of the Lojasiewicz gradient
inequality. For this, we review some necessary and sufficient conditions for
global/local error bounds, both in the convex and nonconvex case. We also
recall some results on quantitative error bounds which play a major role in
convergence rate analysis and complexity theory of many optimization methods.
| 0 | 0 | 1 | 0 | 0 | 0 |
Variational obstacle avoidance problem on Riemannian manifolds | We introduce variational obstacle avoidance problems on Riemannian manifolds
and derive necessary conditions for the existence of their normal extremals.
The problem consists of minimizing an energy functional depending on the
velocity and covariant acceleration, among a set of admissible curves, and also
depending on a navigation function used to avoid an obstacle on the workspace,
a Riemannian manifold.
We study two different scenarios, a general one on a Riemannian manifold and,
a sub-Riemannian problem. By introducing a left-invariant metric on a Lie
group, we also study the variational obstacle avoidance problem on a Lie group.
We apply the results to the obstacle avoidance problem of a planar rigid body
and an unicycle.
| 1 | 0 | 1 | 0 | 0 | 0 |
An Extension of Heron's Formula | This paper introduces an extension of Heron's formula to approximate area of
cyclic n-gons where the error never exceeds $\frac{\pi}{e}-1$
| 0 | 0 | 1 | 0 | 0 | 0 |
Learning Less-Overlapping Representations | In representation learning (RL), how to make the learned representations easy
to interpret and less overfitted to training data are two important but
challenging issues. To address these problems, we study a new type of
regulariza- tion approach that encourages the supports of weight vectors in RL
models to have small overlap, by simultaneously promoting near-orthogonality
among vectors and sparsity of each vector. We apply the proposed regularizer to
two models: neural networks (NNs) and sparse coding (SC), and develop an
efficient ADMM-based algorithm for regu- larized SC. Experiments on various
datasets demonstrate that weight vectors learned under our regularizer are more
interpretable and have better generalization performance.
| 1 | 0 | 0 | 1 | 0 | 0 |
LDPC Code Design for Distributed Storage: Balancing Repair Bandwidth, Reliability and Storage Overhead | Distributed storage systems suffer from significant repair traffic generated
due to frequent storage node failures. This paper shows that properly designed
low-density parity-check (LDPC) codes can substantially reduce the amount of
required block downloads for repair thanks to the sparse nature of their factor
graph representation. In particular, with a careful construction of the factor
graph, both low repair-bandwidth and high reliability can be achieved for a
given code rate. First, a formula for the average repair bandwidth of LDPC
codes is developed. This formula is then used to establish that the minimum
repair bandwidth can be achieved by forcing a regular check node degree in the
factor graph. Moreover, it is shown that given a fixed code rate, the variable
node degree should also be regular to yield minimum repair bandwidth, under
some reasonable minimum variable node degree constraint. It is also shown that
for a given repair-bandwidth requirement, LDPC codes can yield substantially
higher reliability than currently utilized Reed-Solomon (RS) codes. Our
reliability analysis is based on a formulation of the general equation for the
mean-time-to-data-loss (MTTDL) associated with LDPC codes. The formulation
reveals that the stopping number is closely related to the MTTDL. It is further
shown that LDPC codes can be designed such that a small loss of
repair-bandwidth optimality may be traded for a large improvement in
erasure-correction capability and thus the MTTDL.
| 1 | 0 | 0 | 0 | 0 | 0 |
Structural, magnetic, and electronic properties of GdTiO3 Mott insulator thin films grown by pulsed laser deposition | We report on the optimization process to synthesize epitaxial thin films of
GdTiO3 on SrLaGaO4 substrates by pulsed laser deposition. Optimized films are
free of impurity phases and are fully strained. They possess a magnetic Curie
temperature TC = 31.8 K with a saturation magnetization of 4.2 muB per formula
unit at 10 K. Transport measurements reveal an insulating response, as
expected. Optical spectroscopy indicates a band gap of 0.7 eV, comparable to
the bulk value. Our work adds ferrimagnetic orthotitanates to the palette of
perovskite materials for the design of emergent strongly correlated states at
oxide interfaces using a versatile growth technique such as pulsed laser
deposition.
| 0 | 1 | 0 | 0 | 0 | 0 |
GPU-Based High-Performance Imaging for Mingantu Spectral RadioHeliograph | As a dedicated solar radio interferometer, the MingantU SpEctral
RadioHeliograph (MUSER) generates massive observational data in the frequency
range of 400 MHz -- 15 GHz. High-performance imaging forms a significantly
important aspect of MUSER's massive data processing requirements. In this
study, we implement a practical high-performance imaging pipeline for MUSER
data processing. At first, the specifications of the MUSER are introduced and
its imaging requirements are analyzed. Referring to the most commonly used
radio astronomy software such as CASA and MIRIAD, we then implement a
high-performance imaging pipeline based on the Graphics Processing Unit (GPU)
technology with respect to the current operational status of the MUSER. A
series of critical algorithms and their pseudo codes, i.e., detection of the
solar disk and sky brightness, automatic centering of the solar disk and
estimation of the number of iterations for clean algorithms, are proposed in
detail. The preliminary experimental results indicate that the proposed imaging
approach significantly increases the processing performance of MUSER and
generates images with high-quality, which can meet the requirements of the
MUSER data processing.
| 0 | 1 | 0 | 0 | 0 | 0 |
Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning | We present Deep Voice 3, a fully-convolutional attention-based neural
text-to-speech (TTS) system. Deep Voice 3 matches state-of-the-art neural
speech synthesis systems in naturalness while training ten times faster. We
scale Deep Voice 3 to data set sizes unprecedented for TTS, training on more
than eight hundred hours of audio from over two thousand speakers. In addition,
we identify common error modes of attention-based speech synthesis networks,
demonstrate how to mitigate them, and compare several different waveform
synthesis methods. We also describe how to scale inference to ten million
queries per day on one single-GPU server.
| 1 | 0 | 0 | 0 | 0 | 0 |
Ubenwa: Cry-based Diagnosis of Birth Asphyxia | Every year, 3 million newborns die within the first month of life. Birth
asphyxia and other breathing-related conditions are a leading cause of
mortality during the neonatal phase. Current diagnostic methods are too
sophisticated in terms of equipment, required expertise, and general logistics.
Consequently, early detection of asphyxia in newborns is very difficult in many
parts of the world, especially in resource-poor settings. We are developing a
machine learning system, dubbed Ubenwa, which enables diagnosis of asphyxia
through automated analysis of the infant cry. Deployed via smartphone and
wearable technology, Ubenwa will drastically reduce the time, cost and skill
required to make accurate and potentially life-saving diagnoses.
| 1 | 0 | 0 | 1 | 0 | 0 |
Fluid-Structure Interaction with the Entropic Lattice Boltzmann Method | We propose a novel fluid-structure interaction (FSI) scheme using the
entropic multi-relaxation time lattice Boltzmann (KBC) model for the fluid
domain in combination with a nonlinear finite element solver for the structural
part. We show validity of the proposed scheme for various challenging set-ups
by comparison to literature data. Beyond validation, we extend the KBC model to
multiphase flows and couple it with FEM solver. Robustness and viability of the
entropic multi-relaxation time model for complex FSI applications is shown by
simulations of droplet impact on elastic superhydrophobic surfaces.
| 0 | 1 | 0 | 0 | 0 | 0 |
Two provably consistent divide and conquer clustering algorithms for large networks | In this article, we advance divide-and-conquer strategies for solving the
community detection problem in networks. We propose two algorithms which
perform clustering on a number of small subgraphs and finally patches the
results into a single clustering. The main advantage of these algorithms is
that they bring down significantly the computational cost of traditional
algorithms, including spectral clustering, semi-definite programs, modularity
based methods, likelihood based methods etc., without losing on accuracy and
even improving accuracy at times. These algorithms are also, by nature,
parallelizable. Thus, exploiting the facts that most traditional algorithms are
accurate and the corresponding optimization problems are much simpler in small
problems, our divide-and-conquer methods provide an omnibus recipe for scaling
traditional algorithms up to large networks. We prove consistency of these
algorithms under various subgraph selection procedures and perform extensive
simulations and real-data analysis to understand the advantages of the
divide-and-conquer approach in various settings.
| 0 | 0 | 1 | 1 | 0 | 0 |
Feature-based visual odometry prior for real-time semi-dense stereo SLAM | Robust and fast motion estimation and mapping is a key prerequisite for
autonomous operation of mobile robots. The goal of performing this task solely
on a stereo pair of video cameras is highly demanding and bears conflicting
objectives: on one hand, the motion has to be tracked fast and reliably, on the
other hand, high-level functions like navigation and obstacle avoidance depend
crucially on a complete and accurate environment representation. In this work,
we propose a two-layer approach for visual odometry and SLAM with stereo
cameras that runs in real-time and combines feature-based matching with
semi-dense direct image alignment. Our method initializes semi-dense depth
estimation, which is computationally expensive, from motion that is tracked by
a fast but robust keypoint-based method. Experiments on public benchmark and
proprietary datasets show that our approach is faster than state-of-the-art
methods without losing accuracy and yields comparable map building
capabilities. Moreover, our approach is shown to handle large inter-frame
motion and illumination changes much more robustly than its direct
counterparts.
| 1 | 0 | 0 | 0 | 0 | 0 |
Effective Description of Higher-Order Scalar-Tensor Theories | Most existing theories of dark energy and/or modified gravity, involving a
scalar degree of freedom, can be conveniently described within the framework of
the Effective Theory of Dark Energy, based on the unitary gauge where the
scalar field is uniform. We extend this effective approach by allowing the
Lagrangian in unitary gauge to depend on the time derivative of the lapse
function. Although this dependence generically signals the presence of an extra
scalar degree of freedom, theories that contain only one propagating scalar
degree of freedom, in addition to the usual tensor modes, can be constructed by
requiring the initial Lagrangian to be degenerate. Starting from a general
quadratic action, we derive the dispersion relations for the linear
perturbations around Minkowski and a cosmological background. Our analysis
directly applies to the recently introduced Degenerate Higher-Order
Scalar-Tensor (DHOST) theories. For these theories, we find that one cannot
recover a Poisson-like equation in the static linear regime except for the
subclass that includes the Horndeski and so-called "beyond Horndeski" theories.
We also discuss Lorentz-breaking models inspired by Horava gravity.
| 0 | 1 | 0 | 0 | 0 | 0 |
Compressive Sensing Approaches for Autonomous Object Detection in Video Sequences | Video analytics requires operating with large amounts of data. Compressive
sensing allows to reduce the number of measurements required to represent the
video using the prior knowledge of sparsity of the original signal, but it
imposes certain conditions on the design matrix. The Bayesian compressive
sensing approach relaxes the limitations of the conventional approach using the
probabilistic reasoning and allows to include different prior knowledge about
the signal structure. This paper presents two Bayesian compressive sensing
methods for autonomous object detection in a video sequence from a static
camera. Their performance is compared on the real datasets with the
non-Bayesian greedy algorithm. It is shown that the Bayesian methods can
provide the same accuracy as the greedy algorithm but much faster; or if the
computational time is not critical they can provide more accurate results.
| 1 | 0 | 0 | 1 | 0 | 0 |
Making Neural QA as Simple as Possible but not Simpler | Recent development of large-scale question answering (QA) datasets triggered
a substantial amount of research into end-to-end neural architectures for QA.
Increasingly complex systems have been conceived without comparison to simpler
neural baseline systems that would justify their complexity. In this work, we
propose a simple heuristic that guides the development of neural baseline
systems for the extractive QA task. We find that there are two ingredients
necessary for building a high-performing neural QA system: first, the awareness
of question words while processing the context and second, a composition
function that goes beyond simple bag-of-words modeling, such as recurrent
neural networks. Our results show that FastQA, a system that meets these two
requirements, can achieve very competitive performance compared with existing
models. We argue that this surprising finding puts results of previous systems
and the complexity of recent QA datasets into perspective.
| 1 | 0 | 0 | 0 | 0 | 0 |
Finite-dimensional Gaussian approximation with linear inequality constraints | Introducing inequality constraints in Gaussian process (GP) models can lead
to more realistic uncertainties in learning a great variety of real-world
problems. We consider the finite-dimensional Gaussian approach from Maatouk and
Bay (2017) which can satisfy inequality conditions everywhere (either
boundedness, monotonicity or convexity). Our contributions are threefold.
First, we extend their approach in order to deal with general sets of linear
inequalities. Second, we explore several Markov Chain Monte Carlo (MCMC)
techniques to approximate the posterior distribution. Third, we investigate
theoretical and numerical properties of the constrained likelihood for
covariance parameter estimation. According to experiments on both artificial
and real data, our full framework together with a Hamiltonian Monte Carlo-based
sampler provides efficient results on both data fitting and uncertainty
quantification.
| 1 | 0 | 0 | 1 | 0 | 0 |
A Generalization of Quasi-twisted Codes: Multi-twisted codes | Cyclic codes and their various generalizations, such as quasi-twisted (QT)
codes, have a special place in algebraic coding theory. Among other things,
many of the best-known or optimal codes have been obtained from these classes.
In this work we introduce a new generalization of QT codes that we call
multi-twisted (MT) codes and study some of their basic properties. Presenting
several methods of constructing codes in this class and obtaining bounds on the
minimum distances, we show that there exist codes with good parameters in this
class that cannot be obtained as QT or constacyclic codes. This suggests that
considering this larger class in computer searches is promising for
constructing codes with better parameters than currently best-known linear
codes. Working with this new class of codes motivated us to consider a problem
about binomials over finite fields and to discover a result that is interesting
in its own right.
| 1 | 0 | 1 | 0 | 0 | 0 |
On the Successive Cancellation Decoding of Polar Codes with Arbitrary Linear Binary Kernels | A method for efficiently successive cancellation (SC) decoding of polar codes
with high-dimensional linear binary kernels (HDLBK) is presented and analyzed.
We devise a $l$-expressions method which can obtain simplified recursive
formulas of SC decoder in likelihood ratio form for arbitrary linear binary
kernels to reduce the complexity of corresponding SC decoder. By considering
the bit-channel transition probabilities $W_{G}^{(\cdot)}(\cdot|0)$ and
$W_{G}^{(\cdot)}(\cdot|1)$ separately, a $W$-expressions method is proposed to
further reduce the complexity of HDLBK based SC decoder. For a $m\times m$
binary kernel, the complexity of straightforward SC decoder is $O(2^{m}N\log
N)$. With $W$-expressions, we reduce the complexity of straightforward SC
decoder to $O(m^{2}N\log N)$ when $m\leq 16$. Simulation results show that
$16\times16$ kernel polar codes offer significant advantages in terms of error
performances compared with $2\times2$ kernel polar codes under SC and list SC
decoders.
| 1 | 0 | 1 | 0 | 0 | 0 |
OpenML: An R Package to Connect to the Machine Learning Platform OpenML | OpenML is an online machine learning platform where researchers can easily
share data, machine learning tasks and experiments as well as organize them
online to work and collaborate more efficiently. In this paper, we present an R
package to interface with the OpenML platform and illustrate its usage in
combination with the machine learning R package mlr. We show how the OpenML
package allows R users to easily search, download and upload data sets and
machine learning tasks. Furthermore, we also show how to upload results of
experiments, share them with others and download results from other users.
Beyond ensuring reproducibility of results, the OpenML platform automates much
of the drudge work, speeds up research, facilitates collaboration and increases
the users' visibility online.
| 1 | 0 | 0 | 1 | 0 | 0 |
Discovering Latent Patterns of Urban Cultural Interactions in WeChat for Modern City Planning | Cultural activity is an inherent aspect of urban life and the success of a
modern city is largely determined by its capacity to offer generous cultural
entertainment to its citizens. To this end, the optimal allocation of cultural
establishments and related resources across urban regions becomes of vital
importance, as it can reduce financial costs in terms of planning and improve
quality of life in the city, more generally. In this paper, we make use of a
large longitudinal dataset of user location check-ins from the online social
network WeChat to develop a data-driven framework for cultural planning in the
city of Beijing. We exploit rich spatio-temporal representations on user
activity at cultural venues and use a novel extended version of the traditional
latent Dirichlet allocation model that incorporates temporal information to
identify latent patterns of urban cultural interactions. Using the
characteristic typologies of mobile user cultural activities emitted by the
model, we determine the levels of demand for different types of cultural
resources across urban areas. We then compare those with the corresponding
levels of supply as driven by the presence and spatial reach of cultural venues
in local areas to obtain high resolution maps that indicate urban regions with
lack of cultural resources, and thus give suggestions for further urban
cultural planning and investment optimisation.
| 1 | 0 | 0 | 1 | 0 | 0 |
Direct mapping of the temperature and velocity gradients in discs. Imaging the vertical CO snow line around IM Lupi | Accurate measurements of the physical structure of protoplanetary discs are
critical inputs for planet formation models. These constraints are
traditionally established via complex modelling of continuum and line
observations. Instead, we present an empirical framework to locate the CO
isotopologue emitting surfaces from high spectral and spatial resolution ALMA
observations. We apply this framework to the disc surrounding IM Lupi, where we
report the first direct, i.e. model independent, measurements of the radial and
vertical gradients of temperature and velocity in a protoplanetary disc. The
measured disc structure is consistent with an irradiated self-similar disc
structure, where the temperature increases and the velocity decreases towards
the disc surface. We also directly map the vertical CO snow line, which is
located at about one gas scale height at radii between 150 and 300 au, with a
CO freeze-out temperature of $21\pm2$ K. In the outer disc ($> 300$ au), where
the gas surface density transitions from a power law to an exponential taper,
the velocity rotation field becomes significantly sub-Keplerian, in agreement
with the expected steeper pressure gradient. The sub-Keplerian velocities
should result in a very efficient inward migration of large dust grains,
explaining the lack of millimetre continuum emission outside of 300 au. The
sub-Keplerian motions may also be the signature of the base of an externally
irradiated photo-evaporative wind. In the same outer region, the measured CO
temperature above the snow line decreases to $\approx$ 15 K because of the
reduced gas density, which can result in a lower CO freeze-out temperature,
photo-desorption, or deviations from local thermodynamic equilibrium.
| 0 | 1 | 0 | 0 | 0 | 0 |
Learning Rates for Kernel-Based Expectile Regression | Conditional expectiles are becoming an increasingly important tool in finance
as well as in other areas of applications. We analyse a support vector machine
type approach for estimating conditional expectiles and establish learning
rates that are minimax optimal modulo a logarithmic factor if Gaussian RBF
kernels are used and the desired expectile is smooth in a Besov sense. As a
special case, our learning rates improve the best known rates for kernel-based
least squares regression in this scenario. Key ingredients of our statistical
analysis are a general calibration inequality for the asymmetric least squares
loss, a corresponding variance bound as well as an improved entropy number
bound for Gaussian RBF kernels.
| 0 | 0 | 0 | 1 | 0 | 0 |
Critical role of electronic correlations in determining crystal structure of transition metal compounds | The choice that a solid system "makes" when adopting a crystal structure
(stable or metastable) is ultimately governed by the interactions between
electrons forming chemical bonds. By analyzing 6 prototypical binary
transition-metal compounds we demonstrate here that the orbitally-selective
strong $d$-electron correlations influence dramatically the behavior of the
energy as a function of the spatial arrangements of the atoms. Remarkably, we
find that the main qualitative features of this complex behavior can be traced
back to simple electrostatics, i.e., to the fact that the strong $d$-electron
correlations influence substantially the charge transfer mechanism, which, in
turn, controls the electrostatic interactions. This result advances our
understanding of the influence of strong correlations on the crystal structure,
opens a new avenue for extending structure prediction methodologies to strongly
correlated materials, and paves the way for predicting and studying
metastability and polymorphism in these systems.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Machine Learning Alternative to P-values | This paper presents an alternative approach to p-values in regression
settings. This approach, whose origins can be traced to machine learning, is
based on the leave-one-out bootstrap for prediction error. In machine learning
this is called the out-of-bag (OOB) error. To obtain the OOB error for a model,
one draws a bootstrap sample and fits the model to the in-sample data. The
out-of-sample prediction error for the model is obtained by calculating the
prediction error for the model using the out-of-sample data. Repeating and
averaging yields the OOB error, which represents a robust cross-validated
estimate of the accuracy of the underlying model. By a simple modification to
the bootstrap data involving "noising up" a variable, the OOB method yields a
variable importance (VIMP) index, which directly measures how much a specific
variable contributes to the prediction precision of a model. VIMP provides a
scientifically interpretable measure of the effect size of a variable, we call
the "predictive effect size", that holds whether the researcher's model is
correct or not, unlike the p-value whose calculation is based on the assumed
correctness of the model. We also discuss a marginal VIMP index, also easily
calculated, which measures the marginal effect of a variable, or what we call
"the discovery effect". The OOB procedure can be applied to both parametric and
nonparametric regression models and requires only that the researcher can
repeatedly fit their model to bootstrap and modified bootstrap data. We
illustrate this approach on a survival data set involving patients with
systolic heart failure and to a simulated survival data set where the model is
incorrectly specified to illustrate its robustness to model misspecification.
| 1 | 0 | 0 | 1 | 0 | 0 |
Image Registration Techniques: A Survey | Image Registration is the process of aligning two or more images of the same
scene with reference to a particular image. The images are captured from
various sensors at different times and at multiple view-points. Thus to get a
better picture of any change of a scene or object over a considerable period of
time image registration is important. Image registration finds application in
medical sciences, remote sensing and in computer vision. This paper presents a
detailed review of several approaches which are classified accordingly along
with their contributions and drawbacks. The main steps of an image registration
procedure are also discussed. Different performance measures are presented that
determine the registration quality and accuracy. The scope for the future
research are presented as well.
| 1 | 0 | 0 | 0 | 0 | 0 |
Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes | This work initiates a general study of learning and generalization without
the i.i.d. assumption, starting from first principles. While the standard
approach to statistical learning theory is based on assumptions chosen largely
for their convenience (e.g., i.i.d. or stationary ergodic), in this work we are
interested in developing a theory of learning based only on the most
fundamental and natural assumptions implicit in the requirements of the
learning problem itself. We specifically study universally consistent function
learning, where the objective is to obtain low long-run average loss for any
target function, when the data follow a given stochastic process. We are then
interested in the question of whether there exist learning rules guaranteed to
be universally consistent given only the assumption that universally consistent
learning is possible for the given data process. The reasoning that motivates
this criterion emanates from a kind of optimist's decision theory, and so we
refer to such learning rules as being optimistically universal. We study this
question in three natural learning settings: inductive, self-adaptive, and
online. Remarkably, as our strongest positive result, we find that
optimistically universal learning rules do indeed exist in the self-adaptive
learning setting. Establishing this fact requires us to develop new approaches
to the design of learning algorithms. Along the way, we also identify concise
characterizations of the family of processes under which universally consistent
learning is possible in the inductive and self-adaptive settings. We
additionally pose a number of enticing open problems, particularly for the
online learning setting.
| 1 | 0 | 1 | 1 | 0 | 0 |
Obstructions for three-coloring and list three-coloring $H$-free graphs | A graph is $H$-free if it has no induced subgraph isomorphic to $H$. We
characterize all graphs $H$ for which there are only finitely many minimal
non-three-colorable $H$-free graphs. Such a characterization was previously
known only in the case when $H$ is connected. This solves a problem posed by
Golovach et al. As a second result, we characterize all graphs $H$ for which
there are only finitely many $H$-free minimal obstructions for list
3-colorability.
| 1 | 0 | 0 | 0 | 0 | 0 |
Hybrid quantum-classical modeling of quantum dot devices | The design of electrically driven quantum dot devices for quantum optical
applications asks for modeling approaches combining classical device physics
with quantum mechanics. We connect the well-established fields of
semi-classical semiconductor transport theory and the theory of open quantum
systems to meet this requirement. By coupling the van Roosbroeck system with a
quantum master equation in Lindblad form, we introduce a new hybrid
quantum-classical modeling approach, which provides a comprehensive description
of quantum dot devices on multiple scales: It enables the calculation of
quantum optical figures of merit and the spatially resolved simulation of the
current flow in realistic semiconductor device geometries in a unified way. We
construct the interface between both theories in such a way, that the resulting
hybrid system obeys the fundamental axioms of (non-)equilibrium thermodynamics.
We show that our approach guarantees the conservation of charge, consistency
with the thermodynamic equilibrium and the second law of thermodynamics. The
feasibility of the approach is demonstrated by numerical simulations of an
electrically driven single-photon source based on a single quantum dot in the
stationary and transient operation regime.
| 0 | 1 | 0 | 0 | 0 | 0 |
Efficient exploration with Double Uncertain Value Networks | This paper studies directed exploration for reinforcement learning agents by
tracking uncertainty about the value of each available action. We identify two
sources of uncertainty that are relevant for exploration. The first originates
from limited data (parametric uncertainty), while the second originates from
the distribution of the returns (return uncertainty). We identify methods to
learn these distributions with deep neural networks, where we estimate
parametric uncertainty with Bayesian drop-out, while return uncertainty is
propagated through the Bellman equation as a Gaussian distribution. Then, we
identify that both can be jointly estimated in one network, which we call the
Double Uncertain Value Network. The policy is directly derived from the learned
distributions based on Thompson sampling. Experimental results show that both
types of uncertainty may vastly improve learning in domains with a strong
exploration challenge.
| 1 | 0 | 0 | 1 | 0 | 0 |
Auxiliary Variables for Multi-Dirichlet Priors | Bayesian models that mix multiple Dirichlet prior parameters, called
Multi-Dirichlet priors (MD) in this paper, are gaining popularity. Inferring
mixing weights and parameters of mixed prior distributions seems tricky, as
sums over Dirichlet parameters complicate the joint distribution of model
parameters.
This paper shows a novel auxiliary variable scheme which helps to simplify
the inference for models involving hierarchical MDs and MDPs. Using this
scheme, it is easy to derive fully collapsed inference schemes which allow for
an efficient inference.
| 0 | 0 | 0 | 1 | 0 | 0 |
Improving and Assessing Planet Sensitivity of the GPI Exoplanet Survey with a Forward Model Matched Filter | We present a new matched filter algorithm for direct detection of point
sources in the immediate vicinity of bright stars. The stellar Point Spread
Function (PSF) is first subtracted using a Karhunen-Loéve Image Processing
(KLIP) algorithm with Angular and Spectral Differential Imaging (ADI and SDI).
The KLIP-induced distortion of the astrophysical signal is included in the
matched filter template by computing a forward model of the PSF at every
position in the image. To optimize the performance of the algorithm, we conduct
extensive planet injection and recovery tests and tune the exoplanet spectra
template and KLIP reduction aggressiveness to maximize the Signal-to-Noise
Ratio (SNR) of the recovered planets. We show that only two spectral templates
are necessary to recover any young Jovian exoplanets with minimal SNR loss. We
also developed a complete pipeline for the automated detection of point source
candidates, the calculation of Receiver Operating Characteristics (ROC), false
positives based contrast curves, and completeness contours. We process in a
uniform manner more than 330 datasets from the Gemini Planet Imager Exoplanet
Survey (GPIES) and assess GPI typical sensitivity as a function of the star and
the hypothetical companion spectral type. This work allows for the first time a
comparison of different detection algorithms at a survey scale accounting for
both planet completeness and false positive rate. We show that the new forward
model matched filter allows the detection of $50\%$ fainter objects than a
conventional cross-correlation technique with a Gaussian PSF template for the
same false positive rate.
| 0 | 1 | 0 | 0 | 0 | 0 |
Numerical non-LTE 3D radiative transfer using a multigrid method | 3D non-LTE radiative transfer problems are computationally demanding, and
this sets limits on the size of the problems that can be solved. So far
Multilevel Accelerated Lambda Iteration (MALI) has been to the method of choice
to perform high-resolution computations in multidimensional problems. The
disadvantage of MALI is that its computing time scales as $\mathcal{O}(n^2)$,
with $n$ the number of grid points. When the grid gets finer, the computational
cost increases quadratically. We aim to develop a 3D non-LTE radiative transfer
code that is more efficient than MALI. We implement a non-linear multigrid,
fast approximation storage scheme, into the existing Multi3D radiative transfer
code. We verify our multigrid implementation by comparing with MALI
computations. We show that multigrid can be employed in realistic problems with
snapshots from 3D radiative-MHD simulations as input atmospheres. With
multigrid, we obtain a factor 3.3-4.5 speedup compared to MALI. With
full-multigrid the speed-up increases to a factor 6. The speedup is expected to
increase for input atmospheres with more grid points and finer grid spacing.
Solving 3D non-LTE radiative transfer problems using non-linear multigrid
methods can be applied to realistic atmospheres with a substantial speed-up.
| 0 | 1 | 0 | 0 | 0 | 0 |
Test Case Prioritization Techniques for Model-Based Testing: A Replicated Study | Recently, several Test Case Prioritization (TCP) techniques have been
proposed to order test cases for achieving a goal during test execution,
particularly, revealing faults sooner. In the Model-Based Testing (MBT)
context, such techniques are usually based on heuristics related to structural
elements of the model and derived test cases. In this sense, techniques'
performance may vary due to a number of factors. While empirical studies
comparing the performance of TCP techniques have already been presented in
literature, there is still little knowledge, particularly in the MBT context,
about which factors may influence the outcomes suggested by a TCP technique. In
a previous family of empirical studies focusing on labeled transition systems,
we identified that the model layout, i.e. amount of branches, joins, and loops
in the model, alone may have little influence on the performance of TCP
techniques investigated, whereas characteristics of test cases that actually
fail definitely influences their performance. However, we considered only
synthetic artifacts in the study, which reduced the ability of representing
properly the reality. In this paper, we present a replication of one of these
studies, now with a larger and more representative selection of techniques and
considering test suites from industrial applications as experimental objects.
Our objective is to find out whether the results remain while increasing the
validity in comparison to the original study. Results reinforce that there is
no best performer among the investigated techniques and characteristics of test
cases that fail represent an important factor, although adaptive random based
techniques are less affected by it.
| 1 | 0 | 0 | 0 | 0 | 0 |
Magnetic order and interactions in ferrimagnetic Mn3Si2Te6 | The magnetism in Mn$_3$Si$_2$Te$_6$ has been investigated using thermodynamic
measurements, first principles calculations, neutron diffraction and diffuse
neutron scattering on single crystals. These data confirm that
Mn$_3$Si$_2$Te$_6$ is a ferrimagnet below a Curie temperature of $T_C$
approximately 78K. The magnetism is anisotropic, with magnetization and neutron
diffraction demonstrating that the moments lie within the basal plane of the
trigonal structure. The saturation magnetization of approximately 1.6$\mu_B$/Mn
at 5K originates from the different multiplicities of the two
antiferromagnetically-aligned Mn sites. First principles calculations reveal
antiferromagnetic exchange for the three nearest Mn-Mn pairs, which leads to a
competition between the ferrimagnetic ground state and three other magnetic
configurations. The ferrimagnetic state results from the energy associated with
the third-nearest neighbor interaction, and thus long-range interactions are
essential for the observed behavior. Diffuse magnetic scattering is observed
around the 002 Bragg reflection at 120K, which indicates the presence of strong
spin correlations well above $T_C$. These are promoted by the competing ground
states that result in a relative suppression of $T_C$, and may be associated
with a small ferromagnetic component that produces anisotropic magnetism below
$\approx$330K.
| 0 | 1 | 0 | 0 | 0 | 0 |
On the Solution of Linear Programming Problems in the Age of Big Data | The Big Data phenomenon has spawned large-scale linear programming problems.
In many cases, these problems are non-stationary. In this paper, we describe a
new scalable algorithm called NSLP for solving high-dimensional, non-stationary
linear programming problems on modern cluster computing systems. The algorithm
consists of two phases: Quest and Targeting. The Quest phase calculates a
solution of the system of inequalities defining the constraint system of the
linear programming problem under the condition of dynamic changes in input
data. To this end, the apparatus of Fejer mappings is used. The Targeting phase
forms a special system of points having the shape of an n-dimensional
axisymmetric cross. The cross moves in the n-dimensional space in such a way
that the solution of the linear programming problem is located all the time in
an "-vicinity of the central point of the cross.
| 1 | 0 | 1 | 0 | 0 | 0 |
Highly accurate acoustic scattering: Isogeometric Analysis coupled with local high order Farfield Expansion ABC | This work is concerned with a unique combination of high order local
absorbing boundary conditions (ABC) with a general curvilinear Finite Element
Method (FEM) and its implementation in Isogeometric Analysis (IGA) for
time-harmonic acoustic waves. The ABC employed were recently devised by
Villamizar, Acosta and Dastrup [J. Comput. Phys. 333 (2017) 331] . They are
derived from exact Farfield Expansions representations of the outgoing waves in
the exterior of the regions enclosed by the artificial boundary. As a
consequence, the error due to the ABC on the artificial boundary can be reduced
conveniently such that the dominant error comes from the volume discretization
method used in the interior of the computational domain. Reciprocally, the
error in the interior can be made as small as the error at the artificial
boundary by appropriate implementation of {\it p-} and {\it h}- refinement. We
apply this novel method to cylindrical, spherical and arbitrary shape
scatterers including a prototype submarine. Our numerical results exhibits
spectral-like approximation and high order convergence rate. Additionally, they
show that the proposed method can reduce both the pollution and artificial
boundary errors to negligible levels even in very low- and high- frequency
regimes with rather coarse discretization densities in the IGA. As a result, we
have developed a highly accurate computational platform to numerically solve
time-harmonic acoustic wave scattering in two- and three-dimensions.
| 1 | 0 | 0 | 0 | 0 | 0 |
Deep Domain Adaptation Based Video Smoke Detection using Synthetic Smoke Images | In this paper, a deep domain adaptation based method for video smoke
detection is proposed to extract a powerful feature representation of smoke.
Due to the smoke image samples limited in scale and diversity for deep CNN
training, we systematically produced adequate synthetic smoke images with a
wide variation in the smoke shape, background and lighting conditions.
Considering that the appearance gap (dataset bias) between synthetic and real
smoke images degrades significantly the performance of the trained model on the
test set composed fully of real images, we build deep architectures based on
domain adaptation to confuse the distributions of features extracted from
synthetic and real smoke images. This approach expands the domain-invariant
feature space for smoke image samples. With their approximate feature
distribution off non-smoke images, the recognition rate of the trained model is
improved significantly compared to the model trained directly on mixed dataset
of synthetic and real images. Experimentally, several deep architectures with
different design choices are applied to the smoke detector. The ultimate
framework can get a satisfactory result on the test set. We believe that our
approach is a start in the direction of utilizing deep neural networks enhanced
with synthetic smoke images for video smoke detection.
| 1 | 0 | 0 | 0 | 0 | 0 |
NaCl crystal from salt solution with far below saturated concentration under ambient condition | Under ambient conditions, we directly observed NaCl crystals experimentally
in the rGO membranes soaked in the salt solution with concentration below and
far below the saturated concentration. Moreover, in most probability, the NaCl
crystals show stoichiometries behavior. We attribute this unexpected
crystallization to the cation-{\pi} interactions between the ions and the
aromatic rings of the rGO.
| 0 | 1 | 0 | 0 | 0 | 0 |
Plasmonic properties of refractory titanium nitride | The development of plasmonic and metamaterial devices requires the research
of high-performance materials, alternative to standard noble metals. Renewed as
refractory stable compound for durable coatings, titanium nitride has been
recently proposed as an efficient plasmonic material. Here, by using a first
principles approach, we investigate the plasmon dispersion relations of TiN
bulk and we predict the effect of pressure on its optoelectronic properties.
Our results explain the main features of TiN in the visible range and prove a
universal scaling law which relates its mechanical and plasmonic properties as
a function of pressure. Finally, we address the formation and stability of
surface-plasmon polaritons at different TiN/dielectric interfaces proposed by
recent experiments. The unusual combination of plasmonics and refractory
features paves the way for the realization of plasmonic devices able to work at
conditions not sustainable by usual noble metals.
| 0 | 1 | 0 | 0 | 0 | 0 |
The localization transition in SU(3) gauge theory | We study the Anderson-like localization transition in the spectrum of the
Dirac operator of quenched QCD. Above the deconfining transition we determine
the temperature dependence of the mobility edge separating localized and
delocalized eigenmodes in the spectrum. We show that the temperature where the
mobility edge vanishes and localized modes disappear from the spectrum,
coincides with the critical temperature of the deconfining transition. We also
identify topological charge related close to zero modes in the Dirac spectrum
and show that they account for only a small fraction of localized modes, a
fraction that is rapidly falling as the temperature increases.
| 0 | 1 | 0 | 0 | 0 | 0 |
Conformation Clustering of Long MD Protein Dynamics with an Adversarial Autoencoder | Recent developments in specialized computer hardware have greatly accelerated
atomic level Molecular Dynamics (MD) simulations. A single GPU-attached cluster
is capable of producing microsecond-length trajectories in reasonable amounts
of time. Multiple protein states and a large number of microstates associated
with folding and with the function of the protein can be observed as
conformations sampled in the trajectories. Clustering those conformations,
however, is needed for identifying protein states, evaluating transition rates
and understanding protein behavior. In this paper, we propose a novel
data-driven generative conformation clustering method based on the adversarial
autoencoder (AAE) and provide the associated software implementation Cong. The
method was tested using a 208 microseconds MD simulation of the fast-folding
peptide Trp-Cage (20 residues) obtained from the D.E. Shaw Research Group. The
proposed clustering algorithm identifies many of the salient features of the
folding process by grouping a large number of conformations that share common
features not easily identifiable in the trajectory.
| 0 | 0 | 0 | 0 | 1 | 0 |
Optimal Low-Rank Dynamic Mode Decomposition | Dynamic Mode Decomposition (DMD) has emerged as a powerful tool for analyzing
the dynamics of non-linear systems from experimental datasets. Recently,
several attempts have extended DMD to the context of low-rank approximations.
This extension is of particular interest for reduced-order modeling in various
applicative domains, e.g. for climate prediction, to study molecular dynamics
or micro-electromechanical devices. This low-rank extension takes the form of a
non-convex optimization problem. To the best of our knowledge, only sub-optimal
algorithms have been proposed in the literature to compute the solution of this
problem. In this paper, we prove that there exists a closed-form optimal
solution to this problem and design an effective algorithm to compute it based
on Singular Value Decomposition (SVD). A toy-example illustrates the gain in
performance of the proposed algorithm compared to state-of-the-art techniques.
| 0 | 0 | 0 | 1 | 0 | 0 |
Improving Community Detection by Mining Social Interactions | Social relationships can be divided into different classes based on the
regularity with which they occur and the similarity among them. Thus, rare and
somewhat similar relationships are random and cause noise in a social network,
thus hiding the actual structure of the network and preventing an accurate
analysis of it. In this context, in this paper we propose a process to handle
social network data that exploits temporal features to improve the detection of
communities by existing algorithms. By removing random interactions, we observe
that social networks converge to a topology with more purely social
relationships and more modular communities.
| 1 | 0 | 0 | 0 | 0 | 0 |
The set of quantum correlations is not closed | We construct a linear system non-local game which can be played perfectly
using a limit of finite-dimensional quantum strategies, but which cannot be
played perfectly on any finite-dimensional Hilbert space, or even with any
tensor-product strategy. In particular, this shows that the set of
(tensor-product) quantum correlations is not closed. The constructed non-local
game provides another counterexample to the "middle" Tsirelson problem, with a
shorter proof than our previous paper (though at the loss of the universal
embedding theorem). We also show that it is undecidable to determine if a
linear system game can be played perfectly with a finite-dimensional strategy,
or a limit of finite-dimensional quantum strategies.
| 0 | 0 | 1 | 0 | 0 | 0 |
Correction to the article: Floer homology and splicing knot complements | This note corrects the mistakes in the splicing formulas of the paper "Floer
homology and splicing knot complements". The mistakes are the result of the
incorrect assumption that for a knot $K$ inside a homology sphere $Y$, the
involution on the knot Floer homology of $K$ which corresponds to moving the
basepoints by one full twist around $K$ is trivial. The correction implicitly
involves considering the contribution from this (possibly non-trivial)
involution in a number of places.
| 0 | 0 | 1 | 0 | 0 | 0 |
Tree tribes and lower bounds for switching lemmas | We show tight upper and lower bounds for switching lemmas obtained by the
action of random $p$-restrictions on boolean functions that can be expressed as
decision trees in which every vertex is at a distance of at most $t$ from some
leaf, also called $t$-clipped decision trees. More specifically, we show the
following:
$\bullet$ If a boolean function $f$ can be expressed as a $t$-clipped
decision tree, then under the action of a random $p$-restriction $\rho$, the
probability that the smallest depth decision tree for $f|_{\rho}$ has depth
greater than $d$ is upper bounded by $(4p2^{t})^{d}$.
$\bullet$ For every $t$, there exists a function $g_{t}$ that can be
expressed as a $t$-clipped decision tree, such that under the action of a
random $p$-restriction $\rho$, the probability that the smallest depth decision
tree for $g_{t}|_{\rho}$ has depth greater than $d$ is lower bounded by
$(c_{0}p2^{t})^{d}$, for $0\leq p\leq c_{p}2^{-t}$ and $0\leq d\leq
c_{d}\frac{\log n}{2^{t}\log t}$, where $c_{0},c_{p},c_{d}$ are universal
constants.
| 1 | 0 | 0 | 0 | 0 | 0 |
Gyrokinetic ion and drift kinetic electron model for electromagnetic simulation in the toroidal geometry | The kinetic effects of electrons are important to long wavelength
magnetohydrodynamic(MHD)instabilities and short wavelength drift-Alfvenic
instabilities responsible for turbulence transport in magnetized plasmas, since
the non-adiabatic electron can interact with, modify and drive the low
frequency instabilities. A novel conservative split weight scheme is proposed
for the electromagnetic simulation with drift kinetic electrons in tokamak
plasmas, which shows great computational advantages that there is no numerical
constrain of electron skin depth on the perpendicular grid size without
sacrificing any physics. Both kinetic Alfven wave and collision-less tearing
mode are verified by using this model, which has already been implemented into
the gyrokinetic toroidal code(GTC). This model will be used for the micro
tearing mode and neoclassical tearing mode simulation based on the first
principle in the future.
| 0 | 1 | 0 | 0 | 0 | 0 |
Transferring Agent Behaviors from Videos via Motion GANs | A major bottleneck for developing general reinforcement learning agents is
determining rewards that will yield desirable behaviors under various
circumstances. We introduce a general mechanism for automatically specifying
meaningful behaviors from raw pixels. In particular, we train a generative
adversarial network to produce short sub-goals represented through motion
templates. We demonstrate that this approach generates visually meaningful
behaviors in unknown environments with novel agents and describe how these
motions can be used to train reinforcement learning agents.
| 1 | 0 | 0 | 1 | 0 | 0 |
Learning to Segment and Represent Motion Primitives from Driving Data for Motion Planning Applications | Developing an intelligent vehicle which can perform human-like actions
requires the ability to learn basic driving skills from a large amount of
naturalistic driving data. The algorithms will become efficient if we could
decompose the complex driving tasks into motion primitives which represent the
elementary compositions of driving skills. Therefore, the purpose of this paper
is to segment unlabeled trajectory data into a library of motion primitives. By
applying a probabilistic inference based on an iterative
Expectation-Maximization algorithm, our method segments the collected
trajectories while learning a set of motion primitives represented by the
dynamic movement primitives. The proposed method utilizes the mutual
dependencies between the segmentation and representation of motion primitives
and the driving-specific based initial segmentation. By utilizing this mutual
dependency and the initial condition, this paper presents how we can enhance
the performance of both the segmentation and the motion primitive library
establishment. We also evaluate the applicability of the primitive
representation method to imitation learning and motion planning algorithms. The
model is trained and validated by using the driving data collected from the
Beijing Institute of Technology intelligent vehicle platform. The results show
that the proposed approach can find the proper segmentation and establish the
motion primitive library simultaneously.
| 1 | 0 | 0 | 0 | 0 | 0 |
NeuralPower: Predict and Deploy Energy-Efficient Convolutional Neural Networks | "How much energy is consumed for an inference made by a convolutional neural
network (CNN)?" With the increased popularity of CNNs deployed on the
wide-spectrum of platforms (from mobile devices to workstations), the answer to
this question has drawn significant attention. From lengthening battery life of
mobile devices to reducing the energy bill of a datacenter, it is important to
understand the energy efficiency of CNNs during serving for making an
inference, before actually training the model. In this work, we propose
NeuralPower: a layer-wise predictive framework based on sparse polynomial
regression, for predicting the serving energy consumption of a CNN deployed on
any GPU platform. Given the architecture of a CNN, NeuralPower provides an
accurate prediction and breakdown for power and runtime across all layers in
the whole network, helping machine learners quickly identify the power,
runtime, or energy bottlenecks. We also propose the "energy-precision ratio"
(EPR) metric to guide machine learners in selecting an energy-efficient CNN
architecture that better trades off the energy consumption and prediction
accuracy. The experimental results show that the prediction accuracy of the
proposed NeuralPower outperforms the best published model to date, yielding an
improvement in accuracy of up to 68.5%. We also assess the accuracy of
predictions at the network level, by predicting the runtime, power, and energy
of state-of-the-art CNN architectures, achieving an average accuracy of 88.24%
in runtime, 88.34% in power, and 97.21% in energy. We comprehensively
corroborate the effectiveness of NeuralPower as a powerful framework for
machine learners by testing it on different GPU platforms and Deep Learning
software tools.
| 1 | 0 | 0 | 1 | 0 | 0 |
The Minor Fall, the Major Lift: Inferring Emotional Valence of Musical Chords through Lyrics | We investigate the association between musical chords and lyrics by analyzing
a large dataset of user-contributed guitar tablatures. Motivated by the idea
that the emotional content of chords is reflected in the words used in
corresponding lyrics, we analyze associations between lyrics and chord
categories. We also examine the usage patterns of chords and lyrics in
different musical genres, historical eras, and geographical regions. Our
overall results confirms a previously known association between Major chords
and positive valence. We also report a wide variation in this association
across regions, genres, and eras. Our results suggest possible existence of
different emotional associations for other types of chords.
| 1 | 0 | 0 | 0 | 0 | 0 |
Effective computation of $\mathrm{SO}(3)$ and $\mathrm{O}(3)$ linear representations symmetry classes | We propose a general algorithm to compute all the symmetry classes of any
$\mathrm{SO}(3)$ or $\mathrm{O}(3)$ linear representation. This method relies
on the introduction of a binary operator between sets of conjugacy classes of
closed subgroups, called the clips. We compute explicit tables for this
operation which allows to solve definitively the problem.
| 0 | 0 | 1 | 0 | 0 | 0 |
Subextensions for co-induced modules | Using cohomological methods, we prove a criterion for the embedding of a
group extension with abelian kernel into the split extension of a co-induced
module. This generalises some earlier similar results. We also prove an
assertion about the conjugacy of complements in split extensions of co-induced
modules. Both results follow from a relation between homomorphisms of certain
cohomology groups.
| 0 | 0 | 1 | 0 | 0 | 0 |
Specht Polytopes and Specht Matroids | The generators of the classical Specht module satisfy intricate relations. We
introduce the Specht matroid, which keeps track of these relations, and the
Specht polytope, which also keeps track of convexity relations. We establish
basic facts about the Specht polytope, for example, that the symmetric group
acts transitively on its vertices and irreducibly on its ambient real vector
space. A similar construction builds a matroid and polytope for a tensor
product of Specht modules, giving "Kronecker matroids" and "Kronecker
polytopes" instead of the usual Kronecker coefficients. We dub this process of
upgrading numbers to matroids and polytopes "matroidification," giving two more
examples. In the course of describing these objects, we also give an elementary
account of the construction of Specht modules different from the standard one.
Finally, we provide code to compute with Specht matroids and their Chow rings.
| 0 | 0 | 1 | 0 | 0 | 0 |
Existence theorems for the Cauchy problem of 2D nonhomogeneous incompressible non-resistive MHD equations with vacuum | In this paper, we investigate the Cauchy problem of the nonhomogeneous
incompressible non-resistive MHD on $\mathbb{R}^2$ with vacuum as far field
density and prove that the 2D Cauchy problem has a unique local strong solution
provided that the initial density and magnetic field decay not too slow at
infinity. Furthermore, if the initial data satisfy some additional regularity
and compatibility conditions, the strong solution becomes a classical one.
| 0 | 0 | 1 | 0 | 0 | 0 |
Mass Conservative and Energy Stable Finite Difference Methods for the Quasi-incompressible Navier-Stokes-Cahn-Hilliard system: Primitive Variable and Projection-Type Schemes | In this paper we describe two fully mass conservative, energy stable, finite
difference methods on a staggered grid for the quasi-incompressible
Navier-Stokes-Cahn-Hilliard (q-NSCH) system governing a binary incompressible
fluid flow with variable density and viscosity. Both methods, namely the
primitive method (finite difference method in the primitive variable
formulation) and the projection method (finite difference method in a
projection-type formulation), are so designed that the mass of the binary fluid
is preserved, and the energy of the system equations is always non-increasing
in time at the fully discrete level. We also present an efficient, practical
nonlinear multigrid method - comprised of a standard FAS method for the
Cahn-Hilliard equation, and a method based on the Vanka-type smoothing strategy
for the Navier-Stokes equation - for solving these equations. We test the
scheme in the context of Capillary Waves, rising droplets and Rayleigh-Taylor
instability. Quantitative comparisons are made with existing analytical
solutions or previous numerical results that validate the accuracy of our
numerical schemes. Moreover, in all cases, mass of the single component and the
binary fluid was conserved up to 10 to -8 and energy decreases in time.
| 0 | 0 | 1 | 0 | 0 | 0 |
Density-equalizing maps for simply-connected open surfaces | In this paper, we are concerned with the problem of creating flattening maps
of simply-connected open surfaces in $\mathbb{R}^3$. Using a natural principle
of density diffusion in physics, we propose an effective algorithm for
computing density-equalizing flattening maps with any prescribed density
distribution. By varying the initial density distribution, a large variety of
mappings with different properties can be achieved. For instance,
area-preserving parameterizations of simply-connected open surfaces can be
easily computed. Experimental results are presented to demonstrate the
effectiveness of our proposed method. Applications to data visualization and
surface remeshing are explored.
| 1 | 0 | 1 | 0 | 0 | 0 |
Measuring the radius and mass of Planet Nine | Batygin and Brown (2016) have suggested the existence of a new Solar System
planet supposed to be responsible for the perturbation of eccentric orbits of
small outer bodies. The main challenge is now to detect and characterize this
putative body. Here we investigate the principles of the determination of its
physical parameters, mainly its mass and radius. For that purpose we
concentrate on two methods, stellar occultations and gravitational microlensing
effects (amplification, deflection and time delay). We estimate the main
characteristics of a possible occultation or gravitational effects: flux
variation of a background star, duration and probability of occurence. We
investigate also additional benefits of direct imaging and of an occultation.
| 0 | 1 | 0 | 0 | 0 | 0 |
Efficient and principled score estimation with Nyström kernel exponential families | We propose a fast method with statistical guarantees for learning an
exponential family density model where the natural parameter is in a
reproducing kernel Hilbert space, and may be infinite-dimensional. The model is
learned by fitting the derivative of the log density, the score, thus avoiding
the need to compute a normalization constant. Our approach improves the
computational efficiency of an earlier solution by using a low-rank,
Nyström-like solution. The new solution retains the consistency and
convergence rates of the full-rank solution (exactly in Fisher distance, and
nearly in other distances), with guarantees on the degree of cost and storage
reduction. We evaluate the method in experiments on density estimation and in
the construction of an adaptive Hamiltonian Monte Carlo sampler. Compared to an
existing score learning approach using a denoising autoencoder, our estimator
is empirically more data-efficient when estimating the score, runs faster, and
has fewer parameters (which can be tuned in a principled and interpretable
way), in addition to providing statistical guarantees.
| 1 | 0 | 0 | 1 | 0 | 0 |
Small presentations of model categories and Vopěnka's principle | We prove existence results for small presentations of model categories
generalizing a theorem of D. Dugger from combinatorial model categories to more
general model categories. Some of these results are shown under the assumption
of Vopěnka's principle. Our main theorem applies in particular to
cofibrantly generated model categories where the domains of the generating
cofibrations satisfy a slightly stronger smallness condition. As a consequence,
assuming Vopěnka's principle, such a cofibrantly generated model category
is Quillen equivalent to a combinatorial model category. Moreover, if there are
generating sets which consist of presentable objects, then the same conclusion
holds without the assumption of Vopěnka's principle. We also correct a
mistake from previous work that made similar claims.
| 0 | 0 | 1 | 0 | 0 | 0 |
Automatic Trimap Generation for Image Matting | Image matting is a longstanding problem in computational photography.
Although, it has been studied for more than two decades, yet there is a
challenge of developing an automatic matting algorithm which does not require
any human efforts. Most of the state-of-the-art matting algorithms require
human intervention in the form of trimap or scribbles to generate the alpha
matte form the input image. In this paper, we present a simple and efficient
approach to automatically generate the trimap from the input image and make the
whole matting process free from human-in-the-loop. We use learning based
matting method to generate the matte from the automatically generated trimap.
Experimental results demonstrate that our method produces good quality trimap
which results into accurate matte estimation. We validate our results by
replacing the automatically generated trimap by manually created trimap while
using the same image matting algorithm.
| 1 | 0 | 0 | 0 | 0 | 0 |
X-ray spectral properties of seven heavily obscured Seyfert 2 galaxies | We present the combined Chandra and Swift-BAT spectral analysis of seven
Seyfert 2 galaxies selected from the Swift-BAT 100-month catalog. We selected
nearby (z<=0.03) sources lacking of a ROSAT counterpart and never previously
observed with Chandra in the 0.3-10 keV energy range, and targeted these
objects with 10 ks Chandra ACIS-S observations. The X-ray spectral fitting over
the 0.3-150 keV energy range allows us to determine that all the objects are
significantly obscured, having NH>=1E23 cm^(-2) at a >99% confidence level.
Moreover, one to three sources are candidate Compton thick Active Galactic
Nuclei (CT-AGN), i.e., have NH>=1E24 cm^(-2). We also test the recent "spectral
curvature" method developed by Koss et al. (2016) to find candidate CT-AGN,
finding a good agreement between our results and their predictions. Since the
selection criteria we adopted have been effective in detecting highly obscured
AGN, further observations of these and other Seyfert 2 galaxies selected from
the Swift-BAT 100-month catalog will allow us to create a statistically
significant sample of highly obscured AGN, therefore better understanding the
physics of the obscuration processes.
| 0 | 1 | 0 | 0 | 0 | 0 |
Towards the LISA Backlink: Experiment design for comparing optical phase reference distribution systems | LISA is a proposed space-based laser interferometer detecting gravitational
waves by measuring distances between free-floating test masses housed in three
satellites in a triangular constellation with laser links in-between. Each
satellite contains two optical benches that are articulated by moving optical
subassemblies for compensating the breathing angle in the constellation. The
phase reference distribution system, also known as backlink, forms an optical
bi-directional path between the intra-satellite benches.
In this work we discuss phase reference implementations with a target
non-reciprocity of at most $2\pi\,\mathrm{\mu rad/\sqrt{Hz}}$, equivalent to
$1\,\mathrm{pm/\sqrt{Hz}}$ for a wavelength of $1064\,\mathrm{nm}$ in the
frequency band from $0.1\,\mathrm{mHz}$ to $1\,\mathrm{Hz}$. One phase
reference uses a steered free beam connection, the other one a fiber together
with additional laser frequencies. The noise characteristics of these
implementations will be compared in a single interferometric set-up with a
previously successfully tested direct fiber connection. We show the design of
this interferometer created by optical simulations including ghost beam
analysis, component alignment and noise estimation. First experimental results
of a free beam laser link between two optical set-ups that are co-rotating by
$\pm 1^\circ$ are presented. This experiment demonstrates sufficient thermal
stability during rotation of less than $10^{-4}\,\mathrm{K/\sqrt{Hz}}$ at
$1\,\mathrm{mHz}$ and operation of the free beam steering mirror control over
more than 1 week.
| 0 | 1 | 0 | 0 | 0 | 0 |
Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs | Generative Adversarial Networks (GANs) have shown remarkable success as a
framework for training models to produce realistic-looking data. In this work,
we propose a Recurrent GAN (RGAN) and Recurrent Conditional GAN (RCGAN) to
produce realistic real-valued multi-dimensional time series, with an emphasis
on their application to medical data. RGANs make use of recurrent neural
networks in the generator and the discriminator. In the case of RCGANs, both of
these RNNs are conditioned on auxiliary information. We demonstrate our models
in a set of toy datasets, where we show visually and quantitatively (using
sample likelihood and maximum mean discrepancy) that they can successfully
generate realistic time-series. We also describe novel evaluation methods for
GANs, where we generate a synthetic labelled training dataset, and evaluate on
a real test set the performance of a model trained on the synthetic data, and
vice-versa. We illustrate with these metrics that RCGANs can generate
time-series data useful for supervised training, with only minor degradation in
performance on real test data. This is demonstrated on digit classification
from 'serialised' MNIST and by training an early warning system on a medical
dataset of 17,000 patients from an intensive care unit. We further discuss and
analyse the privacy concerns that may arise when using RCGANs to generate
realistic synthetic medical time series data.
| 1 | 0 | 0 | 1 | 0 | 0 |
Embedded eigenvalues of generalized Schrödinger operators | We provide examples of operators $T(D)+V$ with decaying potentials that have
embedded eigenvalues. The decay of the potential depends on the curvature of
the Fermi surfaces of constant kinetic energy $T$. We make the connection to
counterexamples in Fourier restriction theory.
| 0 | 0 | 1 | 0 | 0 | 0 |
Multichannel Robot Speech Recognition Database: MChRSR | In real human robot interaction (HRI) scenarios, speech recognition
represents a major challenge due to robot noise, background noise and
time-varying acoustic channel. This document describes the procedure used to
obtain the Multichannel Robot Speech Recognition Database (MChRSR). It is
composed of 12 hours of multichannel evaluation data recorded in a real mobile
HRI scenario. This database was recorded with a PR2 robot performing different
translational and azimuthal movements. Accordingly, 16 evaluation sets were
obtained re-recording the clean set of the Aurora 4 database in different
movement conditions.
| 1 | 0 | 0 | 0 | 0 | 0 |
The Algorithmic Inflection of Russian and Generation of Grammatically Correct Text | We present a deterministic algorithm for Russian inflection. This algorithm
is implemented in a publicly available web-service www.passare.ru which
provides functions for inflection of single words, word matching and synthesis
of grammatically correct Russian text. The inflectional functions have been
tested against the annotated corpus of Russian language OpenCorpora.
| 1 | 0 | 0 | 0 | 0 | 0 |
Automatic Analysis of EEGs Using Big Data and Hybrid Deep Learning Architectures | Objective: A clinical decision support tool that automatically interprets
EEGs can reduce time to diagnosis and enhance real-time applications such as
ICU monitoring. Clinicians have indicated that a sensitivity of 95% with a
specificity below 5% was the minimum requirement for clinical acceptance. We
propose a highperformance classification system based on principles of big data
and machine learning. Methods: A hybrid machine learning system that uses
hidden Markov models (HMM) for sequential decoding and deep learning networks
for postprocessing is proposed. These algorithms were trained and evaluated
using the TUH EEG Corpus, which is the world's largest publicly available
database of clinical EEG data. Results: Our approach delivers a sensitivity
above 90% while maintaining a specificity below 5%. This system detects three
events of clinical interest: (1) spike and/or sharp waves, (2) periodic
lateralized epileptiform discharges, (3) generalized periodic epileptiform
discharges. It also detects three events used to model background noise: (1)
artifacts, (2) eye movement (3) background. Conclusions: A hybrid HMM/deep
learning system can deliver a low false alarm rate on EEG event detection,
making automated analysis a viable option for clinicians. Significance: The TUH
EEG Corpus enables application of highly data consumptive machine learning
algorithms to EEG analysis. Performance is approaching clinical acceptance for
real-time applications.
| 1 | 0 | 0 | 1 | 0 | 0 |
Implementing a Concept Network Model | The same concept can mean different things or be instantiated in different
forms depending on context, suggesting a degree of flexibility within the
conceptual system. We propose that a compositional network model can be used to
capture and predict this flexibility. We modeled individual concepts (e.g.,
BANANA, BOTTLE) as graph-theoretical networks, in which properties (e.g.,
YELLOW, SWEET) were represented as nodes and their associations as edges. In
this framework, networks capture the within-concept statistics that reflect how
properties correlate with each other across instances of a concept. We ran a
classification analysis using graph eigendecomposition to validate these
models, and find that these models can successfully discriminate between object
concepts. We then computed formal measures from these concept networks and
explored their relationship to conceptual structure. We find that diversity
coefficients and core-periphery structure can be interpreted as network-based
measures of conceptual flexibility and stability, respectively. These results
support the feasibility of a concept network framework and highlight its
ability to formally capture important characteristics of the conceptual system.
| 0 | 0 | 0 | 0 | 1 | 0 |
Two simple observations on representations of metaplectic groups | M. Hanzer and I. Matic have proved that the genuine unitary principal series
representations of the metaplectic groups are irreducible. A simple consequence
of that paper is a criterion for the irreducibility of the non-unitary
principal series representations of the metaplectic groups that we give in this
paper.
| 0 | 0 | 1 | 0 | 0 | 0 |
Spinors in Spacetime Algebra and Euclidean 4-Space | This article explores the geometric algebra of Minkowski spacetime, and its
relationship to the geometric algebra of Euclidean 4-space. Both of these
geometric algebras are algebraically isomorphic to the 2x2 matrix algebra over
Hamilton's famous quaternions, and provide a rich geometric framework for
various important topics in mathematics and physics, including stereographic
projection and spinors, and both spherical and hyperbolic geometry. In
addition, by identifying the time-like Minkowski unit vector with the extra
dimension of Euclidean 4-space, David Hestenes' Space-Time Algebra of Minkowski
spacetime is unified with William Baylis' Algebra of Physical Space.
| 0 | 0 | 1 | 0 | 0 | 0 |
Near-sphere lattices with constant nonlocal mean curvature | We are concerned with unbounded sets of $\mathbb{R}^N$ whose boundary has
constant nonlocal (or fractional) mean curvature, which we call CNMC sets. This
is the equation associated to critical points of the fractional perimeter
functional under a volume constraint. We construct CNMC sets which are the
countable union of a certain bounded domain and all its translations through a
periodic integer lattice of dimension $M\leq N$. Our CNMC sets form a $C^2$
branch emanating from the unit ball alone and where the parameter in the branch
is essentially the distance to the closest lattice point. Thus, the new
translated near-balls (or near-spheres) appear from infinity. We find their
exact asymptotic shape as the parameter tends to infinity.
| 0 | 0 | 1 | 0 | 0 | 0 |
Stability, convergence, and limit cycles in some human physiological processes | Mathematical models for physiological processes aid qualitative understanding
of the impact of various parameters on the underlying process. We analyse two
such models for human physiological processes: the Mackey-Glass and the Lasota
equations, which model the change in the concentration of blood cells in the
human body. We first study the local stability of these models, and derive
bounds on various model parameters and the feedback delay for the concentration
to equilibrate. We then deduce conditions for non-oscillatory convergence of
the solutions, which could ensure that the blood cell concentration does not
oscillate. Further, we define the convergence characteristics of the solutions
which govern the rate at which the concentration equilibrates when the system
is stable. Owing to the possibility that physiological parameters can seldom be
estimated precisely, we also derive bounds for robust stability\textemdash
which enable one to ensure that the blood cell concentration equilibrates
despite parametric uncertainty. We also highlight that when the necessary and
sufficient condition for local stability is violated, the system transits into
instability via a Hopf bifurcation, leading to limit cycles in the blood cell
concentration. We then outline a framework to characterise the type of the Hopf
bifurcation and determine the asymptotic orbital stability of limit cycles. The
analysis is complemented with numerical examples, stability charts and
bifurcation diagrams. The insights into the dynamical properties of the
mathematical models may serve to guide the study of dynamical diseases.
| 1 | 0 | 0 | 0 | 0 | 0 |
Poisson multi-Bernoulli mixture filter: direct derivation and implementation | We provide a derivation of the Poisson multi-Bernoulli mixture (PMBM) filter
for multi-target tracking with the standard point target measurements without
using probability generating functionals or functional derivatives. We also
establish the connection with the \delta-generalised labelled multi-Bernoulli
(\delta-GLMB) filter, showing that a \delta-GLMB density represents a
multi-Bernoulli mixture with labelled targets so it can be seen as a special
case of PMBM. In addition, we propose an implementation for linear/Gaussian
dynamic and measurement models and how to efficiently obtain typical estimators
in the literature from the PMBM. The PMBM filter is shown to outperform other
filters in the literature in a challenging scenario.
| 1 | 0 | 0 | 1 | 0 | 0 |
Surface magnetism in a chiral d-wave superconductor with hexagonal symmetry | Surface properties are examined in a chiral d-wave superconductor with
hexagonal symmetry, whose one-body Hamiltonian possesses the intrinsic
spin-orbit coupling identical to the one characterizing the topological nature
of the Kane-Mele honeycomb insulator. In the normal state spin-orbit coupling
gives rise to spontaneous surface spin currents, whereas in the superconducting
state there exist besides the spin currents also charge surface currents, due
to the chiral pairing symmetry. Interestingly, the combination of these two
currents results in a surface spin polarization, whose spatial dependence is
markedly different on the zigzag and armchair surfaces. We discuss various
potential candidate materials, such as SrPtAs, which may exhibit these surface
properties.
| 0 | 1 | 0 | 0 | 0 | 0 |
Penetrating a Social Network: The Follow-back Problem | Modern threats have emerged from the prevalence of social networks. Hostile
actors, such as extremist groups or foreign governments, utilize these networks
to run propaganda campaigns with different aims. For extremists, these
campaigns are designed for recruiting new members or inciting violence. For
foreign governments, the aim may be to create instability in rival nations.
Proper social network counter-measures are needed to combat these threats. Here
we present one important counter-measure: penetrating social networks. This
means making target users connect with or follow agents deployed in the social
network. Once such connections are established with the targets, the agents can
influence them by sharing content which counters the influence campaign. In
this work we study how to penetrate a social network, which we call the
follow-back problem. The goal here is to find a policy that maximizes the
number of targets that follow the agent.
We conduct an empirical study to understand what behavioral and network
features affect the probability of a target following an agent. We find that
the degree of the target and the size of the mutual neighborhood of the agent
and target in the network affect this probability. Based on our empirical
findings, we then propose a model for targets following an agent. Using this
model, we solve the follow-back problem exactly on directed acyclic graphs and
derive a closed form expression for the expected number of follows an agent
receives under the optimal policy. We then formulate the follow-back problem on
an arbitrary graph as an integer program. To evaluate our integer program based
policies, we conduct simulations on real social network topologies in Twitter.
We find that our polices result in more effective network penetration, with
significant increases in the expected number of targets that follow the agent.
| 1 | 0 | 0 | 1 | 0 | 0 |
Universality of density waves in p-doped La2CuO4 and n-doped Nd2CuO4+y | The contribution of $O^{2-}$ ions to antiferromagnetism in
$La_{2-x}Ae_xCuO_4$ ($Ae = Sr, Ba)$ is highly sensitive to doped holes. In
contrast, the contribution of $Cu^{2+}$ ions to antiferromagnetism in
$Nd_{2-x}Ce_xCuO_{4+y}$ is much less sensitive to doped electrons. The
difference causes the precarious and, respectively, robust antiferromagnetic
phase of these cuprates. The same sensitivities affect the doping dependence of
the incommensurability of density waves, $\delta (x)$. In the hole-doped
compounds this gives rise to a doping offset for magnetic and charge density
waves, $\delta_{m,c}^p(x) \propto \sqrt{x-x_{0p}^N}$. Here $x_{0p}^N$ is the
doping concentration where the Néel temperature vanishes, $T_N(x_{0p}^N) =
0$. No such doping offset occurs for density waves in the electron-doped
compound. Instead, excess oxygen (necessary for stability in crystal growth) of
concentration $y$ causes a different doping offset in the latter case,
$\delta_{m,c}^n(x) \propto \sqrt{x- 2y}$. The square-root formulas result from
the assumption of superlattice formation through partitioning of the $CuO_2$
plane by pairs of itinerant charge carriers. Agreement of observed
incommensurability $\delta(x)$ with the formulas is very good for the
hole-doped compounds and reasonable for the electron-doped compound. The
deviation in the latter case may be caused by residual excess oxygen.
| 0 | 1 | 0 | 0 | 0 | 0 |
The perceived assortativity of social networks: Methodological problems and solutions | Networks describe a range of social, biological and technical phenomena. An
important property of a network is its degree correlation or assortativity,
describing how nodes in the network associate based on their number of
connections. Social networks are typically thought to be distinct from other
networks in being assortative (possessing positive degree correlations);
well-connected individuals associate with other well-connected individuals, and
poorly-connected individuals associate with each other. We review the evidence
for this in the literature and find that, while social networks are more
assortative than non-social networks, only when they are built using
group-based methods do they tend to be positively assortative. Non-social
networks tend to be disassortative. We go on to show that connecting
individuals due to shared membership of a group, a commonly used method, biases
towards assortativity unless a large enough number of censuses of the network
are taken. We present a number of solutions to overcoming this bias by drawing
on advances in sociological and biological fields. Adoption of these methods
across all fields can greatly enhance our understanding of social networks and
networks in general.
| 1 | 0 | 0 | 1 | 0 | 0 |
Generalized Sheet Transition Conditions (GSTCs) for a Metascreen -- A Fishnet Metasurface | We used a multiple-scale homogenization method to derive generalized sheet
transition conditions (GSTCs) for electromagnetic fields at the surface of a
metascreen---a metasurface with a "fishnet" structure. These surfaces are
characterized by periodically-spaced arbitrary-shaped apertures in an otherwise
relatively impenetrable surface. The parameters in these GSTCs are interpreted
as effective surface susceptibilities and surface porosities, which are related
to the geometry of the apertures that constitute the metascreen. Finally, we
emphasize the subtle but important difference between the GSTCs required for
metascreens and those required for metafilms (a metasurface with a "cermet"
structure, i.e., an array of isolated (non-touching) scatterers).
| 0 | 1 | 0 | 0 | 0 | 0 |
$k^{τ,ε}$-anonymity: Towards Privacy-Preserving Publishing of Spatiotemporal Trajectory Data | Mobile network operators can track subscribers via passive or active
monitoring of device locations. The recorded trajectories offer an
unprecedented outlook on the activities of large user populations, which
enables developing new networking solutions and services, and scaling up
studies across research disciplines. Yet, the disclosure of individual
trajectories raises significant privacy concerns: thus, these data are often
protected by restrictive non-disclosure agreements that limit their
availability and impede potential usages. In this paper, we contribute to the
development of technical solutions to the problem of privacy-preserving
publishing of spatiotemporal trajectories of mobile subscribers. We propose an
algorithm that generalizes the data so that they satisfy
$k^{\tau,\epsilon}$-anonymity, an original privacy criterion that thwarts
attacks on trajectories. Evaluations with real-world datasets demonstrate that
our algorithm attains its objective while retaining a substantial level of
accuracy in the data. Our work is a step forward in the direction of open,
privacy-preserving datasets of spatiotemporal trajectories.
| 1 | 0 | 0 | 0 | 0 | 0 |
The Taipan Galaxy Survey: Scientific Goals and Observing Strategy | Taipan is a multi-object spectroscopic galaxy survey starting in 2017 that
will cover 2pi steradians over the southern sky, and obtain optical spectra for
about two million galaxies out to z<0.4. Taipan will use the newly-refurbished
1.2m UK Schmidt Telescope at Siding Spring Observatory with the new TAIPAN
instrument, which includes an innovative 'Starbugs' positioning system capable
of rapidly and simultaneously deploying up to 150 spectroscopic fibres (and up
to 300 with a proposed upgrade) over the 6-deg diameter focal plane, and a
purpose-built spectrograph operating from 370 to 870nm with resolving power
R>2000. The main scientific goals of Taipan are: (i) to measure the distance
scale of the Universe (primarily governed by the local expansion rate, H_0) to
1% precision, and the structure growth rate of structure to 5%; (ii) to make
the most extensive map yet constructed of the mass distribution and motions in
the local Universe, using peculiar velocities based on improved Fundamental
Plane distances, which will enable sensitive tests of gravitational physics;
and (iii) to deliver a legacy sample of low-redshift galaxies as a unique
laboratory for studying galaxy evolution as a function of mass and environment.
The final survey, which will be completed within 5 years, will consist of a
complete magnitude-limited sample (i<17) of about 1.2x10^6 galaxies,
supplemented by an extension to higher redshifts and fainter magnitudes
(i<18.1) of a luminous red galaxy sample of about 0.8x10^6 galaxies.
Observations and data processing will be carried out remotely and in a
fully-automated way, using a purpose-built automated 'virtual observer'
software and an automated data reduction pipeline. The Taipan survey is
deliberately designed to maximise its legacy value, by complementing and
enhancing current and planned surveys of the southern sky at wavelengths from
the optical to the radio.
| 0 | 1 | 0 | 0 | 0 | 0 |
The Externalities of Exploration and How Data Diversity Helps Exploitation | Online learning algorithms, widely used to power search and content
optimization on the web, must balance exploration and exploitation, potentially
sacrificing the experience of current users for information that will lead to
better decisions in the future. Recently, concerns have been raised about
whether the process of exploration could be viewed as unfair, placing too much
burden on certain individuals or groups. Motivated by these concerns, we
initiate the study of the externalities of exploration - the undesirable side
effects that the presence of one party may impose on another - under the linear
contextual bandits model. We introduce the notion of a group externality,
measuring the extent to which the presence of one population of users impacts
the rewards of another. We show that this impact can in some cases be negative,
and that, in a certain sense, no algorithm can avoid it. We then study
externalities at the individual level, interpreting the act of exploration as
an externality imposed on the current user of a system by future users. This
drives us to ask under what conditions inherent diversity in the data makes
explicit exploration unnecessary. We build on a recent line of work on the
smoothed analysis of the greedy algorithm that always chooses the action that
currently looks optimal, improving on prior results to show that a greedy
approach almost matches the best possible Bayesian regret rate of any other
algorithm on the same problem instance whenever the diversity conditions hold,
and that this regret is at most $\tilde{O}(T^{1/3})$. Returning to group-level
effects, we show that under the same conditions, negative group externalities
essentially vanish under the greedy algorithm. Together, our results uncover a
sharp contrast between the high externalities that exist in the worst case, and
the ability to remove all externalities if the data is sufficiently diverse.
| 0 | 0 | 0 | 1 | 0 | 0 |
Life-span of blowup solutions to semilinear wave equation with space-dependent critical damping | This paper is concerned with the blowup phenomena for initial value problem
of semilinear wave equation with critical space-dependent damping term
(DW:$V$). The main result of the present paper is to give a solution of the
problem and to provide a sharp estimate for lifespan for such a solution when
$\frac{N}{N-1}<p\leq p_S(N+V_0)$, where $p_S(N)$ is the Strauss exponent for
(DW:$0$). The main idea of the proof is due to the technique of test functions
for (DW:$0$) originated by Zhou--Han (2014, MR3169791). Moreover, we find a new
threshold value $V_0=\frac{(N-1)^2}{N+1}$ for the coefficient of critical and
singular damping $|x|^{-1}$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Invariant submanifolds of (LCS)n-Manifolds with respect to quarter symmetric metric connection | The object of the present paper is to study invariant submanifolds of
(LCS)n-manifolds with respect to quarter symmetric metric connection. It is
shown that the mean curvature of an invariant submanifold of (LCS)n-manifold
with respect to quarter symmetric metric connection and Levi-Civita connection
are equal. An example is constructed to illustrate the results of the paper. We
also obtain some equivalent conditions of such notion.
| 0 | 0 | 1 | 0 | 0 | 0 |
Iterated filtering methods for Markov process epidemic models | Dynamic epidemic models have proven valuable for public health decision
makers as they provide useful insights into the understanding and prevention of
infectious diseases. However, inference for these types of models can be
difficult because the disease spread is typically only partially observed e.g.
in form of reported incidences in given time periods. This chapter discusses
how to perform likelihood-based inference for partially observed Markov
epidemic models when it is relatively easy to generate samples from the Markov
transmission model while the likelihood function is intractable. The first part
of the chapter reviews the theoretical background of inference for partially
observed Markov processes (POMP) via iterated filtering. In the second part of
the chapter the performance of the method and associated practical difficulties
are illustrated on two examples. In the first example a simulated outbreak data
set consisting of the number of newly reported cases aggregated by week is
fitted to a POMP where the underlying disease transmission model is assumed to
be a simple Markovian SIR model. The second example illustrates possible model
extensions such as seasonal forcing and over-dispersion in both, the
transmission and observation model, which can be used, e.g., when analysing
routinely collected rotavirus surveillance data. Both examples are implemented
using the R-package pomp (King et al., 2016) and the code is made available
online.
| 0 | 0 | 1 | 1 | 0 | 0 |
An Algebraic-Combinatorial Proof Technique for the GM-MDS Conjecture | This paper considers the problem of designing maximum distance separable
(MDS) codes over small fields with constraints on the support of their
generator matrices. For any given $m\times n$ binary matrix $M$, the GM-MDS
conjecture, due to Dau et al., states that if $M$ satisfies the so-called MDS
condition, then for any field $\mathbb{F}$ of size $q\geq n+m-1$, there exists
an $[n,m]_q$ MDS code whose generator matrix $G$, with entries in $\mathbb{F}$,
fits $M$ (i.e., $M$ is the support matrix of $G$). Despite all the attempts by
the coding theory community, this conjecture remains still open in general. It
was shown, independently by Yan et al. and Dau et al., that the GM-MDS
conjecture holds if the following conjecture, referred to as the TM-MDS
conjecture, holds: if $M$ satisfies the MDS condition, then the determinant of
a transformation matrix $T$, such that $TV$ fits $M$, is not identically zero,
where $V$ is a Vandermonde matrix with distinct parameters. In this work, we
generalize the TM-MDS conjecture, and present an algebraic-combinatorial
approach based on polynomial-degree reduction for proving this conjecture. Our
proof technique's strength is based primarily on reducing inherent
combinatorics in the proof. We demonstrate the strength of our technique by
proving the TM-MDS conjecture for the cases where the number of rows ($m$) of
$M$ is upper bounded by $5$. For this class of special cases of $M$ where the
only additional constraint is on $m$, only cases with $m\leq 4$ were previously
proven theoretically, and the previously used proof techniques are not
applicable to cases with $m > 4$.
| 1 | 0 | 0 | 0 | 0 | 0 |
The Word Problem of $\mathbb{Z}^n$ Is a Multiple Context-Free Language | The \emph{word problem} of a group $G = \langle \Sigma \rangle$ can be
defined as the set of formal words in $\Sigma^*$ that represent the identity in
$G$. When viewed as formal languages, this gives a strong connection between
classes of groups and classes of formal languages. For example, Anisimov showed
that a group is finite if and only if its word problem is a regular language,
and Muller and Schupp showed that a group is virtually-free if and only if its
word problem is a context-free language. Above this, not much was known, until
Salvati showed recently that the word problem of $\mathbb{Z}^2$ is a multiple
context-free language, giving first such example. We generalize Salvati's
result to show that the word problem of $\mathbb{Z}^n$ is a multiple
context-free language for any $n$.
| 1 | 0 | 1 | 0 | 0 | 0 |
Rigorous proof of the Boltzmann-Gibbs distribution of money on connected graphs | Models in econophysics, i.e., the emerging field of statistical physics that
applies the main concepts of traditional physics to economics, typically
consist of large systems of economic agents who are characterized by the amount
of money they have. In the simplest model, at each time step, one agent gives
one dollar to another agent, with both agents being chosen independently and
uniformly at random from the system. Numerical simulations of this model
suggest that, at least when the number of agents and the average amount of
money per agent are large, the distribution of money converges to an
exponential distribution reminiscent of the Boltzmann-Gibbs distribution of
energy in physics. The main objective of this paper is to give a rigorous proof
of this result and show that the convergence to the exponential distribution is
universal in the sense that it holds more generally when the economic agents
are located on the vertices of a connected graph and interact locally with
their neighbors rather than globally with all the other agents. We also study a
closely related model where, at each time step, agents buy with a probability
proportional to the amount of money they have, and prove that in this case the
limiting distribution of money is Poissonian.
| 0 | 0 | 1 | 0 | 0 | 0 |
Stochastic population dynamics in spatially extended predator-prey systems | Spatially extended population dynamics models that incorporate intrinsic
noise serve as case studies for the role of fluctuations and correlations in
biological systems. Including spatial structure and stochastic noise in
predator-prey competition invalidates the deterministic Lotka-Volterra picture
of neutral population cycles. Stochastic models yield long-lived erratic
population oscillations stemming from a resonant amplification mechanism. In
spatially extended predator-prey systems, one observes noise-stabilized
activity and persistent correlations. Fluctuation-induced renormalizations of
the oscillation parameters can be analyzed perturbatively. The critical
dynamics and the non-equilibrium relaxation kinetics at the predator extinction
threshold are characterized by the directed percolation universality class.
Spatial or environmental variability results in more localized patches which
enhances both species densities. Affixing variable rates to individual
particles and allowing for trait inheritance subject to mutations induces fast
evolutionary dynamics for the rate distributions. Stochastic spatial variants
of cyclic competition with rock-paper-scissors interactions illustrate
connections between population dynamics and evolutionary game theory, and
demonstrate how space can help maintain diversity. In two dimensions,
three-species cyclic competition models of the May-Leonard type are
characterized by the emergence of spiral patterns whose properties are
elucidated by a mapping onto a complex Ginzburg-Landau equation. Extensions to
general food networks can be classified on the mean-field level, which provides
both a fundamental understanding of ensuing cooperativity and emergence of
alliances. Novel space-time patterns emerge as a result of the formation of
competing alliances, such as coarsening domains that each incorporate
rock-paper-scissors competition games.
| 0 | 1 | 0 | 0 | 0 | 0 |
Learning Policies for Markov Decision Processes from Data | We consider the problem of learning a policy for a Markov decision process
consistent with data captured on the state-actions pairs followed by the
policy. We assume that the policy belongs to a class of parameterized policies
which are defined using features associated with the state-action pairs. The
features are known a priori, however, only an unknown subset of them could be
relevant. The policy parameters that correspond to an observed target policy
are recovered using $\ell_1$-regularized logistic regression that best fits the
observed state-action samples. We establish bounds on the difference between
the average reward of the estimated and the original policy (regret) in terms
of the generalization error and the ergodic coefficient of the underlying
Markov chain. To that end, we combine sample complexity theory and sensitivity
analysis of the stationary distribution of Markov chains. Our analysis suggests
that to achieve regret within order $O(\sqrt{\epsilon})$, it suffices to use
training sample size on the order of $\Omega(\log n \cdot poly(1/\epsilon))$,
where $n$ is the number of the features. We demonstrate the effectiveness of
our method on a synthetic robot navigation example.
| 1 | 0 | 1 | 1 | 0 | 0 |
Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters | As autonomous vehicles become an every-day reality, high-accuracy pedestrian
detection is of paramount practical importance. Pedestrian detection is a
highly researched topic with mature methods, but most datasets focus on common
scenes of people engaged in typical walking poses on sidewalks. But performance
is most crucial for dangerous scenarios, such as children playing in the street
or people using bicycles/skateboards in unexpected ways. Such "in-the-tail"
data is notoriously hard to observe, making both training and testing
difficult. To analyze this problem, we have collected a novel annotated dataset
of dangerous scenarios called the Precarious Pedestrian dataset. Even given a
dedicated collection effort, it is relatively small by contemporary standards
(around 1000 images). To allow for large-scale data-driven learning, we explore
the use of synthetic data generated by a game engine. A significant challenge
is selected the right "priors" or parameters for synthesis: we would like
realistic data with poses and object configurations that mimic true Precarious
Pedestrians. Inspired by Generative Adversarial Networks (GANs), we generate a
massive amount of synthetic data and train a discriminative classifier to
select a realistic subset, which we deem the Adversarial Imposters. We
demonstrate that this simple pipeline allows one to synthesize realistic
training data by making use of rendering/animation engines within a GAN
framework. Interestingly, we also demonstrate that such data can be used to
rank algorithms, suggesting that Adversarial Imposters can also be used for
"in-the-tail" validation at test-time, a notoriously difficult challenge for
real-world deployment.
| 1 | 0 | 0 | 0 | 0 | 0 |
The sequence of open and closed prefixes of a Sturmian word | A finite word is closed if it contains a factor that occurs both as a prefix
and as a suffix but does not have internal occurrences, otherwise it is open.
We are interested in the {\it oc-sequence} of a word, which is the binary
sequence whose $n$-th element is $0$ if the prefix of length $n$ of the word is
open, or $1$ if it is closed. We exhibit results showing that this sequence is
deeply related to the combinatorial and periodic structure of a word. In the
case of Sturmian words, we show that these are uniquely determined (up to
renaming letters) by their oc-sequence. Moreover, we prove that the class of
finite Sturmian words is a maximal element with this property in the class of
binary factorial languages. We then discuss several aspects of Sturmian words
that can be expressed through this sequence. Finally, we provide a linear-time
algorithm that computes the oc-sequence of a finite word, and a linear-time
algorithm that reconstructs a finite Sturmian word from its oc-sequence.
| 1 | 0 | 1 | 0 | 0 | 0 |
Capital Regulation under Price Impacts and Dynamic Financial Contagion | We construct a continuous time model for price-mediated contagion
precipitated by a common exogenous stress to the trading book of all firms in
the financial system. In this setting, firms are constrained so as to satisfy a
risk-weight based capital ratio requirement. We use this model to find
analytical bounds on the risk-weights for an asset as a function of the market
liquidity. Under these appropriate risk-weights, we find existence and
uniqueness for the joint system of firm behavior and the asset price. We
further consider an analytical bound on the firm liquidations, which allows us
to construct exact formulas for stress testing the financial system with
deterministic or random stresses. Numerical case studies are provided to
demonstrate various implications of this model and analytical bounds.
| 0 | 0 | 0 | 0 | 0 | 1 |
On seaweed subalgebras and meander graphs in type D | In 2000, Dergachev and Kirillov introduced subalgebras of "seaweed type" in
$\mathfrak{gl}_n$ and computed their index using certain graphs, which we call
type-${\sf A}$ meander graphs. Then the subalgebras of seaweed type, or just
"seaweeds", have been defined by Panyushev (2001) for arbitrary reductive Lie
algebras. Recently, a meander graph approach to computing the index in types
${\sf B}$ and ${\sf C}$ has been developed by the authors. In this article, we
consider the most difficult and interesting case of type ${\sf D}$. Some new
phenomena occurring here are related to the fact that the Dynkin diagram has a
branching node.
| 0 | 0 | 1 | 0 | 0 | 0 |
Singular Riemannian flows and characteristic numbers | Let $M$ be an even-dimensional, oriented closed manifold. We show that the
restriction of a singular Riemannian flow on $M$ to a small tubular
neighborhood of each connected component of its singular stratum is
foliated-diffeomorphic to an isometric flow on the same neighborhood. We then
prove a formula that computes characteristic numbers of $M$ as the sum of
residues associated to the infinitesimal foliation at the components of the
singular stratum of the flow.
| 0 | 0 | 1 | 0 | 0 | 0 |
Transformation Models in High-Dimensions | Transformation models are a very important tool for applied statisticians and
econometricians. In many applications, the dependent variable is transformed so
that homogeneity or normal distribution of the error holds. In this paper, we
analyze transformation models in a high-dimensional setting, where the set of
potential covariates is large. We propose an estimator for the transformation
parameter and we show that it is asymptotically normally distributed using an
orthogonalized moment condition where the nuisance functions depend on the
target parameter. In a simulation study, we show that the proposed estimator
works well in small samples. A common practice in labor economics is to
transform wage with the log-function. In this study, we test if this
transformation holds in CPS data from the United States.
| 0 | 0 | 1 | 1 | 0 | 0 |
Subsets and Splits
No saved queries yet
Save your SQL queries to embed, download, and access them later. Queries will appear here once saved.