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Title: Estimating the reproductive number, total outbreak size, and reporting rates for Zika epidemics in South and Central America,
Abstract: As South and Central American countries prepare for increased birth defects
from Zika virus outbreaks and plan for mitigation strategies to minimize
ongoing and future outbreaks, understanding important characteristics of Zika
outbreaks and how they vary across regions is a challenging and important
problem. We developed a mathematical model for the 2015 Zika virus outbreak
dynamics in Colombia, El Salvador, and Suriname. We fit the model to publicly
available data provided by the Pan American Health Organization, using
Approximate Bayesian Computation to estimate parameter distributions and
provide uncertainty quantification. An important model input is the at-risk
susceptible population, which can vary with a number of factors including
climate, elevation, population density, and socio-economic status. We informed
this initial condition using the highest historically reported dengue incidence
modified by the probable dengue reporting rates in the chosen countries. The
model indicated that a country-level analysis was not appropriate for Colombia.
We then estimated the basic reproduction number, or the expected number of new
human infections arising from a single infected human, to range between 4 and 6
for El Salvador and Suriname with a median of 4.3 and 5.3, respectively. We
estimated the reporting rate to be around 16% in El Salvador and 18% in
Suriname with estimated total outbreak sizes of 73,395 and 21,647 people,
respectively. The uncertainty in parameter estimates highlights a need for
research and data collection that will better constrain parameter ranges. | [
0,
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] |
Title: A systematic study of the class imbalance problem in convolutional neural networks,
Abstract: In this study, we systematically investigate the impact of class imbalance on
classification performance of convolutional neural networks (CNNs) and compare
frequently used methods to address the issue. Class imbalance is a common
problem that has been comprehensively studied in classical machine learning,
yet very limited systematic research is available in the context of deep
learning. In our study, we use three benchmark datasets of increasing
complexity, MNIST, CIFAR-10 and ImageNet, to investigate the effects of
imbalance on classification and perform an extensive comparison of several
methods to address the issue: oversampling, undersampling, two-phase training,
and thresholding that compensates for prior class probabilities. Our main
evaluation metric is area under the receiver operating characteristic curve
(ROC AUC) adjusted to multi-class tasks since overall accuracy metric is
associated with notable difficulties in the context of imbalanced data. Based
on results from our experiments we conclude that (i) the effect of class
imbalance on classification performance is detrimental; (ii) the method of
addressing class imbalance that emerged as dominant in almost all analyzed
scenarios was oversampling; (iii) oversampling should be applied to the level
that completely eliminates the imbalance, whereas the optimal undersampling
ratio depends on the extent of imbalance; (iv) as opposed to some classical
machine learning models, oversampling does not cause overfitting of CNNs; (v)
thresholding should be applied to compensate for prior class probabilities when
overall number of properly classified cases is of interest. | [
1,
0,
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1,
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0
] |
Title: New simple lattices in products of trees and their projections,
Abstract: Let $\Gamma \leq \mathrm{Aut}(T_{d_1}) \times \mathrm{Aut}(T_{d_2})$ be a
group acting freely and transitively on the product of two regular trees of
degree $d_1$ and $d_2$. We develop an algorithm which computes the closure of
the projection of $\Gamma$ on $\mathrm{Aut}(T_{d_t})$ under the hypothesis that
$d_t \geq 6$ is even and that the local action of $\Gamma$ on $T_{d_t}$
contains $\mathrm{Alt}(d_t)$. We show that if $\Gamma$ is torsion-free and $d_1
= d_2 = 6$, exactly seven closed subgroups of $\mathrm{Aut}(T_6)$ arise in this
way. We also construct two new infinite families of virtually simple lattices
in $\mathrm{Aut}(T_{6}) \times \mathrm{Aut}(T_{4n})$ and in
$\mathrm{Aut}(T_{2n}) \times \mathrm{Aut}(T_{2n+1})$ respectively, for all $n
\geq 2$. In particular we provide an explicit presentation of a torsion-free
infinite simple group on $5$ generators and $10$ relations, that splits as an
amalgamated free product of two copies of $F_3$ over $F_{11}$. We include
information arising from computer-assisted exhaustive searches of lattices in
products of trees of small degrees. In an appendix by Pierre-Emmanuel Caprace,
some of our results are used to show that abstract and relative commensurator
groups of free groups are almost simple, providing partial answers to questions
of Lubotzky and Lubotzky-Mozes-Zimmer. | [
0,
0,
1,
0,
0,
0
] |
Title: The Memory Function Formalism: A Review,
Abstract: An introduction to the Zwanzig-Mori-Götze-Wölfle memory function
formalism (or generalized Drude formalism) is presented. This formalism is used
extensively in analyzing the experimentally obtained optical conductivity of
strongly correlated systems like cuprates and Iron based superconductors etc.
For a broader perspective both the generalised Langevin equation approach and
the projection operator approach for the memory function formalism are given.
The Götze-Wölfle perturbative expansion of memory function is presented
and its application to the computation of the dynamical conductivity of metals
is also reviewd. This review of the formalism contains all the mathematical
details for pedagogical purposes. | [
0,
1,
0,
0,
0,
0
] |
Title: RIPML: A Restricted Isometry Property based Approach to Multilabel Learning,
Abstract: The multilabel learning problem with large number of labels, features, and
data-points has generated a tremendous interest recently. A recurring theme of
these problems is that only a few labels are active in any given datapoint as
compared to the total number of labels. However, only a small number of
existing work take direct advantage of this inherent extreme sparsity in the
label space. By the virtue of Restricted Isometry Property (RIP), satisfied by
many random ensembles, we propose a novel procedure for multilabel learning
known as RIPML. During the training phase, in RIPML, labels are projected onto
a random low-dimensional subspace followed by solving a least-square problem in
this subspace. Inference is done by a k-nearest neighbor (kNN) based approach.
We demonstrate the effectiveness of RIPML by conducting extensive simulations
and comparing results with the state-of-the-art linear dimensionality reduction
based approaches. | [
1,
0,
0,
1,
0,
0
] |
Title: Erratum to: Medial axis and singularities,
Abstract: We correct one erroneous statement made in our recent paper "Medial axis and
singularities". | [
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0,
1,
0,
0,
0
] |
Title: AP-initiated Multi-User Transmissions in IEEE 802.11ax WLANs,
Abstract: Next-generation 802.11ax WLANs will make extensive use of multi-user
communications in both downlink (DL) and uplink (UL) directions to achieve high
and efficient spectrum utilization in scenarios with many user stations per
access point. It will become possible with the support of multi-user (MU)
multiple input, multiple output (MIMO) and orthogonal frequency division
multiple access (OFDMA) transmissions. In this paper, we first overview the
novel characteristics introduced by IEEE 802.11ax to implement AP-initiated
OFDMA and MU-MIMO transmissions in both downlink and uplink directions. Namely,
we describe the changes made at the physical layer and at the medium access
control layer to support OFDMA, the use of \emph{trigger frames} to schedule
uplink multi-user transmissions, and the new \emph{multi-user RTS/CTS
mechanism} to protect large multi-user transmissions from collisions. Then, in
order to study the achievable throughput of an 802.11ax network, we use both
mathematical analysis and simulations to numerically quantify the benefits of
MU transmissions and the impact of 802.11ax overheads on the WLAN saturation
throughput. Results show the advantages of MU transmissions in scenarios with
many user stations, also providing some novel insights on the conditions in
which 802.11ax WLANs are able to maximize their performance, such as the
existence of an optimal number of active user stations in terms of throughput,
or the need to provide strict prioritization to AP-initiated MU transmissions
to avoid collisions with user stations. | [
1,
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] |
Title: Evidence of new twinning modes in magnesium questioning the shear paradigm,
Abstract: Twinning is an important deformation mode of hexagonal close-packed metals.
The crystallographic theory is based on the 150-years old concept of simple
shear. The habit plane of the twin is the shear plane, it is invariant. Here we
present Electron BackScatter Diffraction observations and crystallographic
analysis of a millimeter size twin in a magnesium single crystal whose straight
habit plane, unambiguously determined both the parent crystal and in its twin,
is not an invariant plane. This experimental evidence demonstrates that
macroscopic deformation twinning can be obtained by a mechanism that is not a
simple shear. Beside, this unconventional twin is often co-formed with a new
conventional twin that exhibits the lowest shear magnitude ever reported in
metals. The existence of unconventional twinning introduces a shift of paradigm
and calls for the development of a new theory for the displacive
transformations | [
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] |
Title: Assessment of learning tomography using Mie theory,
Abstract: In Optical diffraction tomography, the multiply scattered field is a
nonlinear function of the refractive index of the object. The Rytov method is a
linear approximation of the forward model, and is commonly used to reconstruct
images. Recently, we introduced a reconstruction method based on the Beam
Propagation Method (BPM) that takes the nonlinearity into account. We refer to
this method as Learning Tomography (LT). In this paper, we carry out
simulations in order to assess the performance of LT over the linear iterative
method. Each algorithm has been rigorously assessed for spherical objects, with
synthetic data generated using the Mie theory. By varying the RI contrast and
the size of the objects, we show that the LT reconstruction is more accurate
and robust than the reconstruction based on the linear model. In addition, we
show that LT is able to correct distortion that is evident in Rytov
approximation due to limitations in phase unwrapping. More importantly, the
capacity of LT in handling multiple scattering problem are demonstrated by
simulations of multiple cylinders using the Mie theory and confirmed by
experimental results of two spheres. | [
0,
1,
0,
0,
0,
0
] |
Title: Single Molecule Studies Under Constant Force Using Model Based Robust Control Design,
Abstract: Optical tweezers have enabled important insights into intracellular transport
through the investigation of motor proteins, with their ability to manipulate
particles at the microscale, affording femto Newton force resolution. Its use
to realize a constant force clamp has enabled vital insights into the behavior
of motor proteins under different load conditions. However, the varying nature
of disturbances and the effect of thermal noise pose key challenges to force
regulation. Furthermore, often the main aim of many studies is to determine the
motion of the motor and the statistics related to the motion, which can be at
odds with the force regulation objective. In this article, we propose a mixed
objective H2-Hinfinity optimization framework using a model-based design, that
achieves the dual goals of force regulation and real time motion estimation
with quantifiable guarantees. Here, we minimize the Hinfinity norm for the
force regulation and error in step estimation while maintaining the H2 norm of
the noise on step estimate within user specified bounds. We demonstrate the
efficacy of the framework through extensive simulations and an experimental
implementation using an optical tweezer setup with live samples of the motor
protein kinesin; where regulation of forces below 1 pico Newton with errors
below 10 percent is obtained while simultaneously providing real time estimates
of motor motion. | [
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1,
1,
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0,
0
] |
Title: Affective Neural Response Generation,
Abstract: Existing neural conversational models process natural language primarily on a
lexico-syntactic level, thereby ignoring one of the most crucial components of
human-to-human dialogue: its affective content. We take a step in this
direction by proposing three novel ways to incorporate affective/emotional
aspects into long short term memory (LSTM) encoder-decoder neural conversation
models: (1) affective word embeddings, which are cognitively engineered, (2)
affect-based objective functions that augment the standard cross-entropy loss,
and (3) affectively diverse beam search for decoding. Experiments show that
these techniques improve the open-domain conversational prowess of
encoder-decoder networks by enabling them to produce emotionally rich responses
that are more interesting and natural. | [
1,
0,
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0,
0,
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] |
Title: Composite fermion basis for M-component Bose gases,
Abstract: The composite fermion (CF) formalism produces wave functions that are not
always linearly independent. This is especially so in the low angular momentum
regime in the lowest Landau level, where a subclass of CF states, known as
simple states, gives a good description of the low energy spectrum. For the
two-component Bose gas, explicit bases avoiding the large number of redundant
states have been found. We generalize one of these bases to the $M$-component
Bose gas and prove its validity. We also show that the numbers of linearly
independent simple states for different values of angular momentum are given by
coefficients of $q$-multinomials. | [
0,
1,
0,
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] |
Title: An FPT Algorithm Beating 2-Approximation for $k$-Cut,
Abstract: In the $k$-Cut problem, we are given an edge-weighted graph $G$ and an
integer $k$, and have to remove a set of edges with minimum total weight so
that $G$ has at least $k$ connected components. Prior work on this problem
gives, for all $h \in [2,k]$, a $(2-h/k)$-approximation algorithm for $k$-cut
that runs in time $n^{O(h)}$. Hence to get a $(2 - \varepsilon)$-approximation
algorithm for some absolute constant $\varepsilon$, the best runtime using
prior techniques is $n^{O(k\varepsilon)}$. Moreover, it was recently shown that
getting a $(2 - \varepsilon)$-approximation for general $k$ is NP-hard,
assuming the Small Set Expansion Hypothesis.
If we use the size of the cut as the parameter, an FPT algorithm to find the
exact $k$-Cut is known, but solving the $k$-Cut problem exactly is $W[1]$-hard
if we parameterize only by the natural parameter of $k$. An immediate question
is: \emph{can we approximate $k$-Cut better in FPT-time, using $k$ as the
parameter?}
We answer this question positively. We show that for some absolute constant
$\varepsilon > 0$, there exists a $(2 - \varepsilon)$-approximation algorithm
that runs in time $2^{O(k^6)} \cdot \widetilde{O} (n^4)$. This is the first FPT
algorithm that is parameterized only by $k$ and strictly improves the
$2$-approximation. | [
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0,
0,
0
] |
Title: Bias Correction For Paid Search In Media Mix Modeling,
Abstract: Evaluating the return on ad spend (ROAS), the causal effect of advertising on
sales, is critical to advertisers for understanding the performance of their
existing marketing strategy as well as how to improve and optimize it. Media
Mix Modeling (MMM) has been used as a convenient analytical tool to address the
problem using observational data. However it is well recognized that MMM
suffers from various fundamental challenges: data collection, model
specification and selection bias due to ad targeting, among others
\citep{chan2017,wolfe2016}.
In this paper, we study the challenge associated with measuring the impact of
search ads in MMM, namely the selection bias due to ad targeting. Using causal
diagrams of the search ad environment, we derive a statistically principled
method for bias correction based on the \textit{back-door} criterion
\citep{pearl2013causality}. We use case studies to show that the method
provides promising results by comparison with results from randomized
experiments. We also report a more complex case study where the advertiser had
spent on more than a dozen media channels but results from a randomized
experiment are not available. Both our theory and empirical studies suggest
that in some common, practical scenarios, one may be able to obtain an
approximately unbiased estimate of search ad ROAS. | [
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] |
Title: Neville's algorithm revisited,
Abstract: Neville's algorithm is known to provide an efficient and numerically stable
solution for polynomial interpolations. In this paper, an extension of this
algorithm is presented which includes the derivatives of the interpolating
polynomial. | [
1,
0,
0,
0,
0,
0
] |
Title: Forecasting and Granger Modelling with Non-linear Dynamical Dependencies,
Abstract: Traditional linear methods for forecasting multivariate time series are not
able to satisfactorily model the non-linear dependencies that may exist in
non-Gaussian series. We build on the theory of learning vector-valued functions
in the reproducing kernel Hilbert space and develop a method for learning
prediction functions that accommodate such non-linearities. The method not only
learns the predictive function but also the matrix-valued kernel underlying the
function search space directly from the data. Our approach is based on learning
multiple matrix-valued kernels, each of those composed of a set of input
kernels and a set of output kernels learned in the cone of positive
semi-definite matrices. In addition to superior predictive performance in the
presence of strong non-linearities, our method also recovers the hidden dynamic
relationships between the series and thus is a new alternative to existing
graphical Granger techniques. | [
1,
0,
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1,
0,
0
] |
Title: Multi-task Learning with Gradient Guided Policy Specialization,
Abstract: We present a method for efficient learning of control policies for multiple
related robotic motor skills. Our approach consists of two stages, joint
training and specialization training. During the joint training stage, a neural
network policy is trained with minimal information to disambiguate the motor
skills. This forces the policy to learn a common representation of the
different tasks. Then, during the specialization training stage we selectively
split the weights of the policy based on a per-weight metric that measures the
disagreement among the multiple tasks. By splitting part of the control policy,
it can be further trained to specialize to each task. To update the control
policy during learning, we use Trust Region Policy Optimization with
Generalized Advantage Function (TRPOGAE). We propose a modification to the
gradient update stage of TRPO to better accommodate multi-task learning
scenarios. We evaluate our approach on three continuous motor skill learning
problems in simulation: 1) a locomotion task where three single legged robots
with considerable difference in shape and size are trained to hop forward, 2) a
manipulation task where three robot manipulators with different sizes and joint
types are trained to reach different locations in 3D space, and 3) locomotion
of a two-legged robot, whose range of motion of one leg is constrained in
different ways. We compare our training method to three baselines. The first
baseline uses only joint training for the policy, the second trains independent
policies for each task, and the last randomly selects weights to split. We show
that our approach learns more efficiently than each of the baseline methods. | [
1,
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] |
Title: Mean square in the prime geodesic theorem,
Abstract: We prove upper bounds for the mean square of the remainder in the prime
geodesic theorem, for every cofinite Fuchsian group, which improve on average
on the best known pointwise bounds. The proof relies on the Selberg trace
formula. For the modular group we prove a refined upper bound by using the
Kuznetsov trace formula. | [
0,
0,
1,
0,
0,
0
] |
Title: An Application of Deep Neural Networks in the Analysis of Stellar Spectra,
Abstract: Spectroscopic surveys require fast and efficient analysis methods to maximize
their scientific impact. Here we apply a deep neural network architecture to
analyze both SDSS-III APOGEE DR13 and synthetic stellar spectra. When our
convolutional neural network model (StarNet) is trained on APOGEE spectra, we
show that the stellar parameters (temperature, gravity, and metallicity) are
determined with similar precision and accuracy as the APOGEE pipeline. StarNet
can also predict stellar parameters when trained on synthetic data, with
excellent precision and accuracy for both APOGEE data and synthetic data, over
a wide range of signal-to-noise ratios. In addition, the statistical
uncertainties in the stellar parameter determinations are comparable to the
differences between the APOGEE pipeline results and those determined
independently from optical spectra. We compare StarNet to other data-driven
methods; for example, StarNet and the Cannon 2 show similar behaviour when
trained with the same datasets, however StarNet performs poorly on small
training sets like those used by the original Cannon. The influence of the
spectral features on the stellar parameters is examined via partial derivatives
of the StarNet model results with respect to the input spectra. While StarNet
was developed using the APOGEE observed spectra and corresponding ASSET
synthetic data, we suggest that this technique is applicable to other
wavelength ranges and other spectral surveys. | [
0,
1,
0,
0,
0,
0
] |
Title: Analysis of Service-oriented Modeling Approaches for Viewpoint-specific Model-driven Development of Microservice Architecture,
Abstract: Microservice Architecture (MSA) is a novel service-based architectural style
for distributed software systems. Compared to Service-oriented Architecture
(SOA), MSA puts a stronger focus on self-containment of services. Each
microservice is responsible for realizing exactly one business or technological
capability that is distinct from other services' capabilities. Additionally, on
the implementation and operation level, microservices are self-contained in
that they are developed, tested, deployed and operated independently from each
other. Next to these characteristics that distinguish MSA from SOA, both
architectural styles rely on services as building blocks of distributed
software architecture and hence face similar challenges regarding, e.g.,
service identification, composition and provisioning. However, in contrast to
MSA, SOA may rely on an extensive body of knowledge to tackle these challenges.
Thus, due to both architectural styles being service-based, the question arises
to what degree MSA might draw on existing findings of SOA research and
practice. In this paper we address this question in the field of Model-driven
Development (MDD) for design and operation of service-based architectures.
Therefore, we present an analysis of existing MDD approaches to SOA, which
comprises the identification and semantic clustering of modeling concepts for
SOA design and operation. For each concept cluster, the analysis assesses its
applicability to MDD of MSA (MSA-MDD) and assigns it to a specific modeling
viewpoint. The goal of the presented analysis is to provide a conceptual
foundation for an MSA-MDD metamodel. | [
1,
0,
0,
0,
0,
0
] |
Title: A cyclic system with delay and its characteristic equation,
Abstract: A nonlinear cyclic system with delay and the overall negative feedback is
considered. The characteristic equation of the linearized system is studied in
detail. Sufficient conditions for the oscillation of all solutions and for the
existence of monotone solutions are derived in terms of roots of the
characteristic equation. | [
0,
0,
1,
0,
0,
0
] |
Title: Object Detection and Motion Planning for Automated Welding of Tubular Joints,
Abstract: Automatic welding of tubular TKY joints is an important and challenging task
for the marine and offshore industry. In this paper, a framework for tubular
joint detection and motion planning is proposed. The pose of the real tubular
joint is detected using RGB-D sensors, which is used to obtain a
real-to-virtual mapping for positioning the workpiece in a virtual environment.
For motion planning, a Bi-directional Transition based Rapidly exploring Random
Tree (BiTRRT) algorithm is used to generate trajectories for reaching the
desired goals. The complete framework is verified with experiments, and the
results show that the robot welding torch is able to transit without collision
to desired goals which are close to the tubular joint. | [
1,
0,
0,
0,
0,
0
] |
Title: Bayesian uncertainty quantification in linear models for diffusion MRI,
Abstract: Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue
microstructure. By fitting a model to the dMRI signal it is possible to derive
various quantitative features. Several of the most popular dMRI signal models
are expansions in an appropriately chosen basis, where the coefficients are
determined using some variation of least-squares. However, such approaches lack
any notion of uncertainty, which could be valuable in e.g. group analyses. In
this work, we use a probabilistic interpretation of linear least-squares
methods to recast popular dMRI models as Bayesian ones. This makes it possible
to quantify the uncertainty of any derived quantity. In particular, for
quantities that are affine functions of the coefficients, the posterior
distribution can be expressed in closed-form. We simulated measurements from
single- and double-tensor models where the correct values of several quantities
are known, to validate that the theoretically derived quantiles agree with
those observed empirically. We included results from residual bootstrap for
comparison and found good agreement. The validation employed several different
models: Diffusion Tensor Imaging (DTI), Mean Apparent Propagator MRI (MAP-MRI)
and Constrained Spherical Deconvolution (CSD). We also used in vivo data to
visualize maps of quantitative features and corresponding uncertainties, and to
show how our approach can be used in a group analysis to downweight subjects
with high uncertainty. In summary, we convert successful linear models for dMRI
signal estimation to probabilistic models, capable of accurate uncertainty
quantification. | [
0,
1,
0,
1,
0,
0
] |
Title: Analysis of Distributed ADMM Algorithm for Consensus Optimization in Presence of Error,
Abstract: ADMM is a popular algorithm for solving convex optimization problems.
Applying this algorithm to distributed consensus optimization problem results
in a fully distributed iterative solution which relies on processing at the
nodes and communication between neighbors. Local computations usually suffer
from different types of errors, due to e.g., observation or quantization noise,
which can degrade the performance of the algorithm. In this work, we focus on
analyzing the convergence behavior of distributed ADMM for consensus
optimization in presence of additive node error. We specifically show that (a
noisy) ADMM converges linearly under certain conditions and also examine the
associated convergence point. Numerical results are provided which demonstrate
the effectiveness of the presented analysis. | [
1,
0,
1,
0,
0,
0
] |
Title: Jet determination of smooth CR automorphisms and generalized stationary discs,
Abstract: We prove finite jet determination for (finitely) smooth CR diffeomorphisms of
(finitely) smooth Levi degenerate hypersurfaces in $\mathbb{C}^{n+1}$ by
constructing generalized stationary discs glued to such hypersurfaces. | [
0,
0,
1,
0,
0,
0
] |
Title: A Well-Tempered Landscape for Non-convex Robust Subspace Recovery,
Abstract: We present a mathematical analysis of a non-convex energy landscape for
robust subspace recovery. We prove that an underlying subspace is the only
stationary point and local minimizer in a specified neighborhood under
deterministic conditions on a dataset. If the deterministic condition is
satisfied, we further show that a geodesic gradient descent method over the
Grassmannian manifold can exactly recover the underlying subspace when the
method is properly initialized. Proper initialization by principal component
analysis is guaranteed with a similar deterministic condition. Under slightly
stronger assumptions, the gradient descent method with a special shrinking step
size scheme achieves linear convergence. The practicality of the deterministic
condition is demonstrated on some statistical models of data, and the method
achieves almost state-of-the-art recovery guarantees on the Haystack Model for
different regimes of sample size and ambient dimension. In particular, when the
ambient dimension is fixed and the sample size is large enough, we show that
our gradient method can exactly recover the underlying subspace for any fixed
fraction of outliers (less than 1). | [
1,
0,
1,
1,
0,
0
] |
Title: Intrinsic entropies of log-concave distributions,
Abstract: The entropy of a random variable is well-known to equal the exponential
growth rate of the volumes of its typical sets. In this paper, we show that for
any log-concave random variable $X$, the sequence of the $\lfloor n\theta
\rfloor^{\text{th}}$ intrinsic volumes of the typical sets of $X$ in dimensions
$n \geq 1$ grows exponentially with a well-defined rate. We denote this rate by
$h_X(\theta)$, and call it the $\theta^{\text{th}}$ intrinsic entropy of $X$.
We show that $h_X(\theta)$ is a continuous function of $\theta$ over the range
$[0,1]$, thereby providing a smooth interpolation between the values 0 and
$h(X)$ at the endpoints 0 and 1, respectively. | [
1,
0,
0,
0,
0,
0
] |
Title: Iteratively Linearized Reweighted Alternating Direction Method of Multipliers for a Class of Nonconvex Problems,
Abstract: In this paper, we consider solving a class of nonconvex and nonsmooth
problems frequently appearing in signal processing and machine learning
research. The traditional alternating direction method of multipliers
encounters troubles in both mathematics and computations in solving the
nonconvex and nonsmooth subproblem. In view of this, we propose a reweighted
alternating direction method of multipliers. In this algorithm, all subproblems
are convex and easy to solve. We also provide several guarantees for the
convergence and prove that the algorithm globally converges to a critical point
of an auxiliary function with the help of the Kurdyka-{\L}ojasiewicz property.
Several numerical results are presented to demonstrate the efficiency of the
proposed algorithm. | [
1,
0,
0,
1,
0,
0
] |
Title: Understanding Group Event Scheduling via the OutWithFriendz Mobile Application,
Abstract: The wide adoption of smartphones and mobile applications has brought
significant changes to not only how individuals behave in the real world, but
also how groups of users interact with each other when organizing group events.
Understanding how users make event decisions as a group and identifying the
contributing factors can offer important insights for social group studies and
more effective system and application design for group event scheduling.
In this work, we have designed a new mobile application called
OutWithFriendz, which enables users of our mobile app to organize group events,
invite friends, suggest and vote on event time and venue. We have deployed
OutWithFriendz at both Apple App Store and Google Play, and conducted a
large-scale user study spanning over 500 users and 300 group events. Our
analysis has revealed several important observations regarding group event
planning process including the importance of user mobility, individual
preferences, host preferences, and group voting process. | [
1,
0,
0,
0,
0,
0
] |
Title: Energy Level Alignment at Hybridized Organic-metal Interfaces: the Role of Many-electron Effects,
Abstract: Hybridized molecule/metal interfaces are ubiquitous in molecular and organic
devices. The energy level alignment (ELA) of frontier molecular levels relative
to the metal Fermi level (EF) is critical to the conductance and functionality
of these devices. However, a clear understanding of the ELA that includes
many-electron self-energy effects is lacking. Here, we investigate the
many-electron effects on the ELA using state-of-the-art, benchmark GW
calculations on prototypical chemisorbed molecules on Au(111), in eleven
different geometries. The GW ELA is in good agreement with photoemission for
monolayers of benzene-diamine on Au(111). We find that in addition to static
image charge screening, the frontier levels in most of these geometries are
renormalized by additional screening from substrate-mediated intermolecular
Coulomb interactions. For weakly chemisorbed systems, such as amines and
pyridines on Au, this additional level renormalization (~1.5 eV) comes solely
from static screened exchange energy, allowing us to suggest computationally
more tractable schemes to predict the ELA at such interfaces. However, for more
strongly chemisorbed thiolate layers, dynamical effects are present. Our ab
initio results constitute an important step towards the understanding and
manipulation of functional molecular/organic systems for both fundamental
studies and applications. | [
0,
1,
0,
0,
0,
0
] |
Title: Bandit Regret Scaling with the Effective Loss Range,
Abstract: We study how the regret guarantees of nonstochastic multi-armed bandits can
be improved, if the effective range of the losses in each round is small (e.g.
the maximal difference between two losses in a given round). Despite a recent
impossibility result, we show how this can be made possible under certain mild
additional assumptions, such as availability of rough estimates of the losses,
or advance knowledge of the loss of a single, possibly unspecified arm. Along
the way, we develop a novel technique which might be of independent interest,
to convert any multi-armed bandit algorithm with regret depending on the loss
range, to an algorithm with regret depending only on the effective range, while
avoiding predictably bad arms altogether. | [
1,
0,
0,
1,
0,
0
] |
Title: Resource Allocation for Containing Epidemics from Temporal Network Data,
Abstract: We study the problem of containing epidemic spreading processes in temporal
networks. We specifically focus on the problem of finding a resource allocation
to suppress epidemic infection, provided that an empirical time-series data of
connectivities between nodes is available. Although this problem is of
practical relevance, it has not been clear how an empirical time-series data
can inform our strategy of resource allocations, due to the computational
complexity of the problem. In this direction, we present a computationally
efficient framework for finding a resource allocation that satisfies a given
budget constraint and achieves a given control performance. The framework is
based on convex programming and, moreover, allows the performance measure to be
described by a wide class of functionals called posynomials with nonnegative
exponents. We illustrate our theoretical results using a data of temporal
interaction networks within a primary school. | [
1,
0,
0,
0,
0,
0
] |
Title: Learning for New Visual Environments with Limited Labels,
Abstract: In computer vision applications, such as domain adaptation (DA), few shot
learning (FSL) and zero-shot learning (ZSL), we encounter new objects and
environments, for which insufficient examples exist to allow for training
"models from scratch," and methods that adapt existing models, trained on the
presented training environment, to the new scenario are required. We propose a
novel visual attribute encoding method that encodes each image as a
low-dimensional probability vector composed of prototypical part-type
probabilities. The prototypes are learnt to be representative of all training
data. At test-time we utilize this encoding as an input to a classifier. At
test-time we freeze the encoder and only learn/adapt the classifier component
to limited annotated labels in FSL; new semantic attributes in ZSL. We conduct
extensive experiments on benchmark datasets. Our method outperforms
state-of-art methods trained for the specific contexts (ZSL, FSL, DA). | [
1,
0,
0,
0,
0,
0
] |
Title: Modulation of High-Energy Particles and the Heliospheric Current Sheet Tilts throughout 1976-2014,
Abstract: Cosmic ray intensities (CRIs) recorded by sixteen neutron monitors have been
used to study its dependence on the tilt angles (TA) of the heliospheric
current sheet (HCS) during period 1976-2014, which covers three solar activity
cycles 21, 22 and 23. The median primary rigidity covers the range 16-33 GV.
Our results have indicated that the CRIs are directly sensitive to, and
organized by, the interplanetary magnetic field (IMF) and its neutral sheet
inclinations. The observed differences in the sensitivity of cosmic ray
intensity to changes in the neutral sheet tilt angles before and after the
reversal of interplanetary magnetic field polarity have been studied. Much
stronger intensity-tilt angle correlation was found when the solar magnetic
field in the North Polar Region was directed inward than it was outward. The
rigidity dependence of sensitivities of cosmic rays differs according to the
IMF polarity, for the periods 1981-1988 and 2001-2008 (qA < 0) it was R-1.00
and R-1.48 respectively, while for the 1991-1998 epoch (qA > 0) it was R-1.35.
Hysteresis loops between TA and CRIs have been examined during three solar
activity cycles 21, 22 and 23. A consider differences in time lags during qA >
0 and qA < 0 polarity states of the heliosphere have been observed. We also
found that the cosmic ray intensity decreases at much faster rate with increase
of tilt angle during qA < 0 than qA > 0, indicating stronger response to the
tilt angle changes during qA < 0. Our results are discussed in the light of 3D
modulation models including the gradient, curvature drifts and the tilt of the
heliospheric current sheet. | [
0,
1,
0,
0,
0,
0
] |
Title: Detecting the impact of public transit on the transmission of epidemics,
Abstract: In many developing countries, public transit plays an important role in daily
life. However, few existing methods have considered the influence of public
transit in their models. In this work, we present a dual-perspective view of
the epidemic spreading process of the individual that involves both
contamination in places (such as work places and homes) and public transit
(such as buses and trains). In more detail, we consider a group of individuals
who travel to some places using public transit, and introduce public transit
into the epidemic spreading process. A novel modeling framework is proposed
considering place-based infections and the public-transit-based infections. In
the urban scenario, we investigate the public transit trip contribution rate
(PTTCR) in the epidemic spreading process of the individual, and assess the
impact of the public transit trip contribution rate by evaluating the volume of
infectious people. Scenarios for strategies such as public transit and school
closure were tested and analyzed. Our simulation results suggest that
individuals with a high public transit trip contribution rate will increase the
volume of infectious people when an infectious disease outbreak occurs by
affecting the social network through the public transit trip contribution rate. | [
1,
0,
0,
0,
0,
0
] |
Title: The Hamiltonian Dynamics of Magnetic Confinement in Toroidal Domains,
Abstract: We consider a class of magnetic fields defined over the interior of a
manifold $M$ which go to infinity at its boundary and whose direction near the
boundary of $M$ is controlled by a closed 1-form $\sigma_\infty \in
\Gamma(T^*\partial M)$. We are able to show that charged particles in the
interior of $M$ under the influence of such fields can only escape the manifold
through the zero locus of $\sigma_\infty$. In particular in the case where the
1-form is nowhere vanishing we conclude that the particles become confined to
its interior for all time. | [
0,
0,
1,
0,
0,
0
] |
Title: Multi-robot motion-formation distributed control with sensor self-calibration: experimental validation,
Abstract: In this paper, we present the design and implementation of a robust motion
formation distributed control algorithm for a team of mobile robots. The
primary task for the team is to form a geometric shape, which can be freely
translated and rotated at the same time. This approach makes the robots to
behave as a cohesive whole, which can be useful in tasks such as collaborative
transportation. The robustness of the algorithm relies on the fact that each
robot employs only local measurements from a laser sensor which does not need
to be off-line calibrated. Furthermore, robots do not need to exchange any
information with each other. Being free of sensor calibration and not requiring
a communication channel helps the scaling of the overall system to a large
number of robots. In addition, since the robots do not need any off-board
localization system, but require only relative positions with respect to their
neighbors, it can be aimed to have a full autonomous team that operates in
environments where such localization systems are not available. The
computational cost of the algorithm is inexpensive and the resources from a
standard microcontroller will suffice. This fact makes the usage of our
approach appealing as a support for other more demanding algorithms, e.g.,
processing images from onboard cameras. We validate the performance of the
algorithm with a team of four mobile robots equipped with low-cost commercially
available laser scanners. | [
1,
0,
0,
0,
0,
0
] |
Title: The maximum of the 1-measurement of a metric measure space,
Abstract: For a metric measure space, we treat the set of distributions of 1-Lipschitz
functions, which is called the 1-measurement. On the 1-measurement, we have a
partial order relation by the Lipschitz order introduced by Gromov. The aim of
this paper is to study the maximum and maximal elements of the 1-measurement
with respect to the Lipschitz order. We present a necessary condition of a
metric measure space for the existence of the maximum of the 1-measurement. We
also consider a metric measure space that has the maximum of its 1-measurement. | [
0,
0,
1,
0,
0,
0
] |
Title: Limits to Arbitrage in Markets with Stochastic Settlement Latency,
Abstract: Distributed ledger technologies rely on consensus protocols confronting
traders with random waiting times until the transfer of ownership is
accomplished. This time-consuming settlement process exposes arbitrageurs to
price risk and imposes limits to arbitrage. We derive theoretical arbitrage
boundaries under general assumptions and show that they increase with expected
latency, latency uncertainty, spot volatility, and risk aversion. Using
high-frequency data from the Bitcoin network, we estimate arbitrage boundaries
due to settlement latency of on average 124 basis points, covering 88 percent
of the observed cross-exchange price differences. Settlement through
decentralized systems thus induces non-trivial frictions affecting market
efficiency and price formation. | [
0,
0,
0,
0,
0,
1
] |
Title: Is One Hyperparameter Optimizer Enough?,
Abstract: Hyperparameter tuning is the black art of automatically finding a good
combination of control parameters for a data miner. While widely applied in
empirical Software Engineering, there has not been much discussion on which
hyperparameter tuner is best for software analytics. To address this gap in the
literature, this paper applied a range of hyperparameter optimizers (grid
search, random search, differential evolution, and Bayesian optimization) to
defect prediction problem. Surprisingly, no hyperparameter optimizer was
observed to be `best' and, for one of the two evaluation measures studied here
(F-measure), hyperparameter optimization, in 50\% cases, was no better than
using default configurations.
We conclude that hyperparameter optimization is more nuanced than previously
believed. While such optimization can certainly lead to large improvements in
the performance of classifiers used in software analytics, it remains to be
seen which specific optimizers should be applied to a new dataset. | [
1,
0,
0,
0,
0,
0
] |
Title: Deep Generalized Canonical Correlation Analysis,
Abstract: We present Deep Generalized Canonical Correlation Analysis (DGCCA) -- a
method for learning nonlinear transformations of arbitrarily many views of
data, such that the resulting transformations are maximally informative of each
other. While methods for nonlinear two-view representation learning (Deep CCA,
(Andrew et al., 2013)) and linear many-view representation learning
(Generalized CCA (Horst, 1961)) exist, DGCCA is the first CCA-style multiview
representation learning technique that combines the flexibility of nonlinear
(deep) representation learning with the statistical power of incorporating
information from many independent sources, or views. We present the DGCCA
formulation as well as an efficient stochastic optimization algorithm for
solving it. We learn DGCCA representations on two distinct datasets for three
downstream tasks: phonetic transcription from acoustic and articulatory
measurements, and recommending hashtags and friends on a dataset of Twitter
users. We find that DGCCA representations soundly beat existing methods at
phonetic transcription and hashtag recommendation, and in general perform no
worse than standard linear many-view techniques. | [
1,
0,
0,
1,
0,
0
] |
Title: Faithfulness of Probability Distributions and Graphs,
Abstract: A main question in graphical models and causal inference is whether, given a
probability distribution $P$ (which is usually an underlying distribution of
data), there is a graph (or graphs) to which $P$ is faithful. The main goal of
this paper is to provide a theoretical answer to this problem. We work with
general independence models, which contain probabilistic independence models as
a special case. We exploit a generalization of ordering, called preordering, of
the nodes of (mixed) graphs. This allows us to provide sufficient conditions
for a given independence model to be Markov to a graph with the minimum
possible number of edges, and more importantly, necessary and sufficient
conditions for a given probability distribution to be faithful to a graph. We
present our results for the general case of mixed graphs, but specialize the
definitions and results to the better-known subclasses of undirected
(concentration) and bidirected (covariance) graphs as well as directed acyclic
graphs. | [
0,
0,
1,
1,
0,
0
] |
Title: Low Resolution Face Recognition Using a Two-Branch Deep Convolutional Neural Network Architecture,
Abstract: We propose a novel couple mappings method for low resolution face recognition
using deep convolutional neural networks (DCNNs). The proposed architecture
consists of two branches of DCNNs to map the high and low resolution face
images into a common space with nonlinear transformations. The branch
corresponding to transformation of high resolution images consists of 14 layers
and the other branch which maps the low resolution face images to the common
space includes a 5-layer super-resolution network connected to a 14-layer
network. The distance between the features of corresponding high and low
resolution images are backpropagated to train the networks. Our proposed method
is evaluated on FERET data set and compared with state-of-the-art competing
methods. Our extensive experimental results show that the proposed method
significantly improves the recognition performance especially for very low
resolution probe face images (11.4% improvement in recognition accuracy).
Furthermore, it can reconstruct a high resolution image from its corresponding
low resolution probe image which is comparable with state-of-the-art
super-resolution methods in terms of visual quality. | [
1,
0,
0,
0,
0,
0
] |
Title: Some algebraic invariants of edge ideal of circulant graphs,
Abstract: Let $G$ be the circulant graph $C_n(S)$ with $S\subseteq\{ 1,\ldots,\left
\lfloor\frac{n}{2}\right \rfloor\}$ and let $I(G)$ be its edge ideal in the
ring $K[x_0,\ldots,x_{n-1}]$. Under the hypothesis that $n$ is prime we : 1)
compute the regularity index of $R/I(G)$; 2) compute the Castelnuovo-Mumford
regularity when $R/I(G)$ is Cohen-Macaulay; 3) prove that the circulant graphs
with $S=\{1,\ldots,s\}$ are sequentially $S_2$ . We end characterizing the
Cohen-Macaulay circulant graphs of Krull dimension $2$ and computing their
Cohen-Macaulay type and Castelnuovo-Mumford regularity. | [
0,
0,
1,
0,
0,
0
] |
Title: Efficient Pricing of Barrier Options on High Volatility Assets using Subset Simulation,
Abstract: Barrier options are one of the most widely traded exotic options on stock
exchanges. In this paper, we develop a new stochastic simulation method for
pricing barrier options and estimating the corresponding execution
probabilities. We show that the proposed method always outperforms the standard
Monte Carlo approach and becomes substantially more efficient when the
underlying asset has high volatility, while it performs better than multilevel
Monte Carlo for special cases of barrier options and underlying assets. These
theoretical findings are confirmed by numerous simulation results. | [
0,
0,
0,
1,
0,
1
] |
Title: Gaia and VLT astrometry of faint stars: Precision of Gaia DR1 positions and updated VLT parallaxes of ultracool dwarfs,
Abstract: We compared positions of the Gaia first data release (DR1) secondary data set
at its faint limit with CCD positions of stars in 20 fields observed with the
VLT/FORS2 camera. The FORS2 position uncertainties are smaller than one
milli-arcsecond (mas) and allowed us to perform an independent verification of
the DR1 astrometric precision. In the fields that we observed with FORS2, we
projected the Gaia DR1 positions into the CCD plane, performed a polynomial fit
between the two sets of matching stars, and carried out statistical analyses of
the residuals in positions. The residual RMS roughly matches the expectations
given by the Gaia DR1 uncertainties, where we identified three regimes in terms
of Gaia DR1 precision: for G = 17-20 stars we found that the formal DR1
position uncertainties of stars with DR1 precisions in the range of 0.5-5 mas
are underestimated by 63 +/- 5\%, whereas the DR1 uncertainties of stars in the
range 7-10 mas are overestimated by a factor of two. For the best-measured and
generally brighter G = 16-18 stars with DR1 positional uncertainties of <0.5
mas, we detected 0.44 +/- 0.13 mas excess noise in the residual RMS, whose
origin can be in both FORS2 and Gaia DR1. By adopting Gaia DR1 as the absolute
reference frame we refined the pixel scale determination of FORS2, leading to
minor updates to the parallaxes of 20 ultracool dwarfs that we published
previously. We also updated the FORS2 absolute parallax of the Luhman 16 binary
brown dwarf system to 501.42 +/- 0.11 mas | [
0,
1,
0,
0,
0,
0
] |
Title: Spectral Projector-Based Graph Fourier Transforms,
Abstract: The paper presents the graph Fourier transform (GFT) of a signal in terms of
its spectral decomposition over the Jordan subspaces of the graph adjacency
matrix $A$. This representation is unique and coordinate free, and it leads to
unambiguous definition of the spectral components ("harmonics") of a graph
signal. This is particularly meaningful when $A$ has repeated eigenvalues, and
it is very useful when $A$ is defective or not diagonalizable (as it may be the
case with directed graphs). Many real world large sparse graphs have defective
adjacency matrices. We present properties of the GFT and show it to satisfy a
generalized Parseval inequality and to admit a total variation ordering of the
spectral components. We express the GFT in terms of spectral projectors and
present an illustrative example for a real world large urban traffic dataset. | [
1,
0,
0,
0,
0,
0
] |
Title: Midgar: Detection of people through computer vision in the Internet of Things scenarios to improve the security in Smart Cities, Smart Towns, and Smart Homes,
Abstract: Could we use Computer Vision in the Internet of Things for using pictures as
sensors? This is the principal hypothesis that we want to resolve. Currently,
in order to create safety areas, cities, or homes, people use IP cameras.
Nevertheless, this system needs people who watch the camera images, watch the
recording after something occurred, or watch when the camera notifies them of
any movement. These are the disadvantages. Furthermore, there are many Smart
Cities and Smart Homes around the world. This is why we thought of using the
idea of the Internet of Things to add a way of automating the use of IP
cameras. In our case, we propose the analysis of pictures through Computer
Vision to detect people in the analysed pictures. With this analysis, we are
able to obtain if these pictures contain people and handle the pictures as if
they were sensors with two possible states. Notwithstanding, Computer Vision is
a very complicated field. This is why we needed a second hypothesis: Could we
work with Computer Vision in the Internet of Things with a good accuracy to
automate or semi-automate this kind of events? The demonstration of these
hypotheses required a testing over our Computer Vision module to check the
possibilities that we have to use this module in a possible real environment
with a good accuracy. Our proposal, as a possible solution, is the analysis of
entire sequence instead of isolated pictures for using pictures as sensors in
the Internet of Things. | [
1,
0,
0,
0,
0,
0
] |
Title: On the Performance of a Canonical Labeling for Matching Correlated Erdős-Rényi Graphs,
Abstract: Graph matching in two correlated random graphs refers to the task of
identifying the correspondence between vertex sets of the graphs. Recent
results have characterized the exact information-theoretic threshold for graph
matching in correlated Erdős-Rényi graphs. However, very little is known
about the existence of efficient algorithms to achieve graph matching without
seeds. In this work we identify a region in which a straightforward $O(n^2\log
n)$-time canonical labeling algorithm, initially introduced in the context of
graph isomorphism, succeeds in matching correlated Erdős-Rényi graphs.
The algorithm has two steps. In the first step, all vertices are labeled by
their degrees and a trivial minimum distance matching (i.e., simply sorting
vertices according to their degrees) matches a fixed number of highest degree
vertices in the two graphs. Having identified this subset of vertices, the
remaining vertices are matched using a matching algorithm for bipartite graphs. | [
0,
0,
0,
1,
0,
0
] |
Title: Semantic Evolutionary Concept Distances for Effective Information Retrieval in Query Expansion,
Abstract: In this work several semantic approaches to concept-based query expansion and
reranking schemes are studied and compared with different ontology-based
expansion methods in web document search and retrieval. In particular, we focus
on concept-based query expansion schemes, where, in order to effectively
increase the precision of web document retrieval and to decrease the users
browsing time, the main goal is to quickly provide users with the most suitable
query expansion. Two key tasks for query expansion in web document retrieval
are to find the expansion candidates, as the closest concepts in web document
domain, and to rank the expanded queries properly. The approach we propose aims
at improving the expansion phase for better web document retrieval and
precision. The basic idea is to measure the distance between candidate concepts
using the PMING distance, a collaborative semantic proximity measure, i.e. a
measure which can be computed by using statistical results from web search
engine. Experiments show that the proposed technique can provide users with
more satisfying expansion results and improve the quality of web document
retrieval. | [
1,
0,
1,
0,
0,
0
] |
Title: An example related to the slicing inequality for general measures,
Abstract: For $n\in \mathbb{N}$ let $S_n$ be the smallest number $S>0$ satisfying the
inequality $$ \int_K f \le S \cdot |K|^{\frac 1n} \cdot \max_{\xi\in S^{n-1}}
\int_{K\cap \xi^\bot} f $$ for all centrally-symmetric convex bodies $K$ in
$\mathbb{R}^n$ and all even, continuous probability densities $f$ on $K$. Here
$|K|$ is the volume of $K$. It was proved by the second-named author that
$S_n\le 2\sqrt{n}$, and in analogy with Bourgain's slicing problem, it was
asked whether $S_n$ is bounded from above by a universal constant. In this note
we construct an example showing that $S_n\ge c\sqrt{n}/\sqrt{\log \log n},$
where $c > 0$ is an absolute constant. Additionally, for any $0 < \alpha < 2$
we describe a related example that satisfies the so-called
$\psi_{\alpha}$-condition. | [
0,
0,
1,
0,
0,
0
] |
Title: Two-level schemes for the advection equation,
Abstract: The advection equation is the basis for mathematical models of continuum
mechanics. In the approximate solution of nonstationary problems it is
necessary to inherit main properties of the conservatism and monotonicity of
the solution. In this paper, the advection equation is written in the symmetric
form, where the advection operator is the half-sum of advection operators in
conservative (divergent) and non-conservative (characteristic) forms. The
advection operator is skew-symmetric. Standard finite element approximations in
space are used. The standart explicit two-level scheme for the advection
equation is absolutly unstable. New conditionally stable regularized schemes
are constructed, on the basis of the general theory of stability
(well-posedness) of operator-difference schemes, the stability conditions of
the explicit Lax-Wendroff scheme are established. Unconditionally stable and
conservative schemes are implicit schemes of the second (Crank-Nicolson scheme)
and fourth order. The conditionally stable implicit Lax-Wendroff scheme is
constructed. The accuracy of the investigated explicit and implicit two-level
schemes for an approximate solution of the advection equation is illustrated by
the numerical results of a model two-dimensional problem. | [
1,
0,
0,
0,
0,
0
] |
Title: Estimating functional time series by moving average model fitting,
Abstract: Functional time series have become an integral part of both functional data
and time series analysis. Important contributions to methodology, theory and
application for the prediction of future trajectories and the estimation of
functional time series parameters have been made in the recent past. This paper
continues this line of research by proposing a first principled approach to
estimate invertible functional time series by fitting functional moving average
processes. The idea is to estimate the coefficient operators in a functional
linear filter. To do this a functional Innovations Algorithm is utilized as a
starting point to estimate the corresponding moving average operators via
suitable projections into principal directions. In order to establish
consistency of the proposed estimators, asymptotic theory is developed for
increasing subspaces of these principal directions. For practical purposes,
several strategies to select the number of principal directions to include in
the estimation procedure as well as the choice of order of the functional
moving average process are discussed. Their empirical performance is evaluated
through simulations and an application to vehicle traffic data. | [
0,
0,
0,
1,
0,
0
] |
Title: Characterizing correlations and synchronization in collective dynamics,
Abstract: Synchronization, that occurs both for non-chaotic and chaotic systems, is a
striking phenomenon with many practical implications in natural phenomena.
However, even before synchronization, strong correlations occur in the
collective dynamics of complex systems. To characterize their nature is
essential for the understanding of phenomena in physical and social sciences.
The emergence of strong correlations before synchronization is illustrated in a
few piecewise linear models. They are shown to be associated to the behavior of
ergodic parameters which may be exactly computed in some models. The models are
also used as a testing ground to find general methods to characterize and
parametrize the correlated nature of collective dynamics. | [
0,
1,
0,
0,
0,
0
] |
Title: On the Optimality of Kernel-Embedding Based Goodness-of-Fit Tests,
Abstract: The reproducing kernel Hilbert space (RKHS) embedding of distributions offers
a general and flexible framework for testing problems in arbitrary domains and
has attracted considerable amount of attention in recent years. To gain
insights into their operating characteristics, we study here the statistical
performance of such approaches within a minimax framework. Focusing on the case
of goodness-of-fit tests, our analyses show that a vanilla version of the
kernel-embedding based test could be suboptimal, and suggest a simple remedy by
moderating the embedding. We prove that the moderated approach provides optimal
tests for a wide range of deviations from the null and can also be made
adaptive over a large collection of interpolation spaces. Numerical experiments
are presented to further demonstrate the merits of our approach. | [
0,
0,
1,
1,
0,
0
] |
Title: The influence of contrarians in the dynamics of opinion formation,
Abstract: In this work we consider the presence of contrarian agents in discrete
3-state kinetic exchange opinion models. The contrarians are individuals that
adopt the choice opposite to the prevailing choice of their contacts, whatever
this choice is. We consider binary as well as three-agent interactions, with
stochastic parameters, in a fully-connected population. Our numerical results
suggest that the presence of contrarians destroys the absorbing state of the
original model, changing the transition to the para-ferromagnetic type. In this
case, the consequence for the society is that the three opinions coexist in the
population, in both phases (ordered and disordered). Furthermore, the
order-disorder transition is suppressed for a sufficient large fraction of
contrarians. In some cases the transition is discontinuous, and it changes to
continuous before it is suppressed. Some of our results are complemented by
analytical calculations based on the master equation. | [
0,
1,
0,
0,
0,
0
] |
Title: Singular Spectrum and Recent Results on Hierarchical Operators,
Abstract: We use trace class scattering theory to exclude the possibility of absolutely
continuous spectrum in a large class of self-adjoint operators with an
underlying hierarchical structure and provide applications to certain random
hierarchical operators and matrices. We proceed to contrast the localizing
effect of the hierarchical structure in the deterministic setting with previous
results and conjectures in the random setting. Furthermore, we survey stronger
localization statements truly exploiting the disorder for the hierarchical
Anderson model and report recent results concerning the spectral statistics of
the ultrametric random matrix ensemble. | [
0,
1,
1,
0,
0,
0
] |
Title: Kinetic approach to relativistic dissipation,
Abstract: Despite a long record of intense efforts, the basic mechanisms by which
dissipation emerges from the microscopic dynamics of a relativistic fluid still
elude a complete understanding. In particular, no unique pathway from kinetic
theory to hydrodynamics has been identified as yet, with different approaches
leading to different values of the transport coefficients. In this Letter, we
approach the problem by matching data from lattice kinetic simulations with
analytical predictions. Our numerical results provide neat evidence in favour
of the Chapman-Enskog procedure, as suggested by recently theoretical analyses,
along with qualitative hints at the basic reasons why the Chapman-Enskog
expansion might be better suited than Grad's method to capture the emergence of
dissipative effects in relativistic fluids. | [
0,
1,
0,
0,
0,
0
] |
Title: DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding,
Abstract: Recurrent neural nets (RNN) and convolutional neural nets (CNN) are widely
used on NLP tasks to capture the long-term and local dependencies,
respectively. Attention mechanisms have recently attracted enormous interest
due to their highly parallelizable computation, significantly less training
time, and flexibility in modeling dependencies. We propose a novel attention
mechanism in which the attention between elements from input sequence(s) is
directional and multi-dimensional (i.e., feature-wise). A light-weight neural
net, "Directional Self-Attention Network (DiSAN)", is then proposed to learn
sentence embedding, based solely on the proposed attention without any RNN/CNN
structure. DiSAN is only composed of a directional self-attention with temporal
order encoded, followed by a multi-dimensional attention that compresses the
sequence into a vector representation. Despite its simple form, DiSAN
outperforms complicated RNN models on both prediction quality and time
efficiency. It achieves the best test accuracy among all sentence encoding
methods and improves the most recent best result by 1.02% on the Stanford
Natural Language Inference (SNLI) dataset, and shows state-of-the-art test
accuracy on the Stanford Sentiment Treebank (SST), Multi-Genre natural language
inference (MultiNLI), Sentences Involving Compositional Knowledge (SICK),
Customer Review, MPQA, TREC question-type classification and Subjectivity
(SUBJ) datasets. | [
1,
0,
0,
0,
0,
0
] |
Title: Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank,
Abstract: Recently low displacement rank (LDR) matrices, or so-called structured
matrices, have been proposed to compress large-scale neural networks. Empirical
results have shown that neural networks with weight matrices of LDR matrices,
referred as LDR neural networks, can achieve significant reduction in space and
computational complexity while retaining high accuracy. We formally study LDR
matrices in deep learning. First, we prove the universal approximation property
of LDR neural networks with a mild condition on the displacement operators. We
then show that the error bounds of LDR neural networks are as efficient as
general neural networks with both single-layer and multiple-layer structure.
Finally, we propose back-propagation based training algorithm for general LDR
neural networks. | [
1,
0,
0,
1,
0,
0
] |
Title: Maximum Number of Modes of Gaussian Mixtures,
Abstract: Gaussian mixture models are widely used in Statistics. A fundamental aspect
of these distributions is the study of the local maxima of the density, or
modes. In particular, it is not known how many modes a mixture of $k$ Gaussians
in $d$ dimensions can have. We give a brief account of this problem's history.
Then, we give improved lower bounds and the first upper bound on the maximum
number of modes, provided it is finite. | [
0,
0,
1,
1,
0,
0
] |
Title: Dynamic Clearing and Contagion in Financial Networks,
Abstract: In this paper we will consider a generalized extension of the Eisenberg-Noe
model of financial contagion to allow for time dynamics in both discrete and
continuous time. Derivation and interpretation of the financial implications
will be provided. Emphasis will be placed on the continuous-time framework and
its formulation as a differential equation driven by the operating cash flows.
Mathematical results on existence and uniqueness of firm wealths under the
discrete and continuous-time models will be provided. Finally, the financial
implications of time dynamics will be considered. The focus will be on how the
dynamic clearing solutions differ from those of the static Eisenberg-Noe model. | [
0,
0,
0,
0,
0,
1
] |
Title: A Theory of Solvability for Lossless Power Flow Equations -- Part I: Fixed-Point Power Flow,
Abstract: This two-part paper details a theory of solvability for the power flow
equations in lossless power networks. In Part I, we derive a new formulation of
the lossless power flow equations, which we term the fixed-point power flow.
The model is stated for both meshed and radial networks, and is parameterized
by several graph-theoretic matrices -- the power network stiffness matrices --
which quantify the internal coupling strength of the network. The model leads
immediately to an explicit approximation of the high-voltage power flow
solution. For standard test cases, we find that iterates of the fixed-point
power flow converge rapidly to the high-voltage power flow solution, with the
approximate solution yielding accurate predictions near base case loading. In
Part II, we leverage the fixed-point power flow to study power flow
solvability, and for radial networks we derive conditions guaranteeing the
existence and uniqueness of a high-voltage power flow solution. These
conditions (i) imply exponential convergence of the fixed-point power flow
iteration, and (ii) properly generalize the textbook two-bus system results. | [
0,
0,
1,
0,
0,
0
] |
Title: Narcissus: Deriving Correct-By-Construction Decoders and Encoders from Binary Formats,
Abstract: It is a neat result from functional programming that libraries of parser
combinators can support rapid construction of decoders for quite a range of
formats. With a little more work, the same combinator program can denote both a
decoder and an encoder. Unfortunately, the real world is full of gnarly
formats, as with the packet formats that make up the standard Internet protocol
stack. Most past parser-combinator approaches cannot handle these formats, and
the few exceptions require redundancy -- one part of the natural grammar needs
to be hand-translated into hints in multiple parts of a parser program. We show
how to recover very natural and nonredundant format specifications, covering
all popular network packet formats and generating both decoders and encoders
automatically. The catch is that we use the Coq proof assistant to derive both
kinds of artifacts using tactics, automatically, in a way that guarantees that
they form inverses of each other. We used our approach to reimplement packet
processing for a full Internet protocol stack, inserting our replacement into
the OCaml-based MirageOS unikernel, resulting in minimal performance
degradation. | [
1,
0,
0,
0,
0,
0
] |
Title: MATMPC - A MATLAB Based Toolbox for Real-time Nonlinear Model Predictive Control,
Abstract: In this paper we introduce MATMPC, an open source software built in MATLAB
for nonlinear model predictive control (NMPC). It is designed to facilitate
modelling, controller design and simulation for a wide class of NMPC
applications. MATMPC has a number of algorithmic modules, including automatic
differentiation, direct multiple shooting, condensing, linear quadratic program
(QP) solver and globalization. It also supports a unique Curvature-like Measure
of Nonlinearity (CMoN) MPC algorithm. MATMPC has been designed to provide
state-of-the-art performance while making the prototyping easy, also with
limited programming knowledge. This is achieved by writing each module directly
in MATLAB API for C. As a result, MATMPC modules can be compiled into MEX
functions with performance comparable to plain C/C++ solvers. MATMPC has been
successfully used in operating systems including WINDOWS, LINUX AND OS X.
Selected examples are shown to highlight the effectiveness of MATMPC. | [
1,
0,
0,
0,
0,
0
] |
Title: A Copula-based Imputation Model for Missing Data of Mixed Type in Multilevel Data Sets,
Abstract: We propose a copula based method to handle missing values in multivariate
data of mixed types in multilevel data sets. Building upon the extended rank
likelihood of \cite{hoff2007extending} and the multinomial probit model, our
model is a latent variable model which is able to capture the relationship
among variables of different types as well as accounting for the clustering
structure. We fit the model by approximating the posterior distribution of the
parameters and the missing values through a Gibbs sampling scheme. We use the
multiple imputation procedure to incorporate the uncertainty due to missing
values in the analysis of the data. Our proposed method is evaluated through
simulations to compare it with several conventional methods of handling missing
data. We also apply our method to a data set from a cluster randomized
controlled trial of a multidisciplinary intervention in acute stroke units. We
conclude that our proposed copula based imputation model for mixed type
variables achieves reasonably good imputation accuracy and recovery of
parameters in some models of interest, and that adding random effects enhances
performance when the clustering effect is strong. | [
0,
0,
0,
1,
0,
0
] |
Title: Factoring the Cycle Aging Cost of Batteries Participating in Electricity Markets,
Abstract: When participating in electricity markets, owners of battery energy storage
systems must bid in such a way that their revenues will at least cover their
true cost of operation. Since cycle aging of battery cells represents a
substantial part of this operating cost, the cost of battery degradation must
be factored in these bids. However, existing models of battery degradation
either do not fit market clearing software or do not reflect the actual battery
aging mechanism. In this paper we model battery cycle aging using a piecewise
linear cost function, an approach that provides a close approximation of the
cycle aging mechanism of electrochemical batteries and can be incorporated
easily into existing market dispatch programs. By defining the marginal aging
cost of each battery cycle, we can assess the actual operating profitability of
batteries. A case study demonstrates the effectiveness of the proposed model in
maximizing the operating profit of a battery energy storage system taking part
in the ISO New England energy and reserve markets. | [
0,
0,
1,
0,
0,
0
] |
Title: Hydrodynamic signatures of stationary Marangoni-driven surfactant transport,
Abstract: We experimentally study steady Marangoni-driven surfactant transport on the
interface of a deep water layer. Using hydrodynamic measurements, and without
using any knowledge of the surfactant physico-chemical properties, we show that
sodium dodecyl sulphate and Tergitol 15-S-9 introduced in low concentrations
result in a flow driven by adsorbed surfactant. At higher surfactant
concentration, the flow is dominated by the dissolved surfactant. Using
Camphoric acid, whose properties are {\it a priori} unknown, we demonstrate
this method's efficacy by showing its spreading is adsorption dominated. | [
0,
1,
0,
0,
0,
0
] |
Title: R-boundedness Approach to linear third differential equations in a UMD Space,
Abstract: The aim of this work is to study the existence of a periodic solutions of
third order differential equations $z'''(t) = Az(t) + f(t)$ with the periodic
condition $x(0) = x(2\pi), x'(0) = x'(2\pi)$ and $x''(0) = x''(2\pi)$. Our
approach is based on the R-boundedness and $L^{p}$-multiplier of linear
operators. | [
0,
0,
1,
0,
0,
0
] |
Title: Data-Efficient Multirobot, Multitask Transfer Learning for Trajectory Tracking,
Abstract: Transfer learning has the potential to reduce the burden of data collection
and to decrease the unavoidable risks of the training phase. In this letter, we
introduce a multirobot, multitask transfer learning framework that allows a
system to complete a task by learning from a few demonstrations of another task
executed on another system. We focus on the trajectory tracking problem where
each trajectory represents a different task, since many robotic tasks can be
described as a trajectory tracking problem. The proposed multirobot transfer
learning framework is based on a combined $\mathcal{L}_1$ adaptive control and
an iterative learning control approach. The key idea is that the adaptive
controller forces dynamically different systems to behave as a specified
reference model. The proposed multitask transfer learning framework uses
theoretical control results (e.g., the concept of vector relative degree) to
learn a map from desired trajectories to the inputs that make the system track
these trajectories with high accuracy. This map is used to calculate the inputs
for a new, unseen trajectory. Experimental results using two different
quadrotor platforms and six different trajectories show that, on average, the
proposed framework reduces the first-iteration tracking error by 74% when
information from tracking a different single trajectory on a different
quadrotor is utilized. | [
1,
0,
0,
0,
0,
0
] |
Title: Soft Weight-Sharing for Neural Network Compression,
Abstract: The success of deep learning in numerous application domains created the de-
sire to run and train them on mobile devices. This however, conflicts with
their computationally, memory and energy intense nature, leading to a growing
interest in compression. Recent work by Han et al. (2015a) propose a pipeline
that involves retraining, pruning and quantization of neural network weights,
obtaining state-of-the-art compression rates. In this paper, we show that
competitive compression rates can be achieved by using a version of soft
weight-sharing (Nowlan & Hinton, 1992). Our method achieves both quantization
and pruning in one simple (re-)training procedure. This point of view also
exposes the relation between compression and the minimum description length
(MDL) principle. | [
0,
0,
0,
1,
0,
0
] |
Title: On the optimal design of grid-based binary holograms for matter wave lithography,
Abstract: Grid based binary holography (GBH) is an attractive method for patterning
with light or matter waves. It is an approximate technique in which different
holographic masks can be used to produce similar patterns. Here we present an
optimal design method for GBH masks that allows for freely selecting the
fraction of open holes in the mask from below 10% to above 90%. Open-fraction
is an important design parameter when making masks for use in lithography
systems. The method also includes a rescaling feature that potentially enables
a better contrast of the generated patterns. Through simulations we investigate
the contrast and robustness of the patterns formed by masks generated by the
proposed optimal design method. It is demonstrated that high contrast patterns
are achievable for a wide range of open-fractions. We conclude that reaching a
desired open-fraction is a trade-off with the contrast of the pattern generated
by the mask. | [
0,
1,
0,
0,
0,
0
] |
Title: Alternating Iteratively Reweighted Minimization Algorithms for Low-Rank Matrix Factorization,
Abstract: Nowadays, the availability of large-scale data in disparate application
domains urges the deployment of sophisticated tools for extracting valuable
knowledge out of this huge bulk of information. In that vein, low-rank
representations (LRRs) which seek low-dimensional embeddings of data have
naturally appeared. In an effort to reduce computational complexity and improve
estimation performance, LRR has been viewed via a matrix factorization (MF)
perspective. Recently, low-rank MF (LRMF) approaches have been proposed for
tackling the inherent weakness of MF i.e., the unawareness of the dimension of
the low-dimensional space where data reside. Herein, inspired by the merits of
iterative reweighted schemes for rank minimization, we come up with a generic
low-rank promoting regularization function. Then, focusing on a specific
instance of it, we propose a regularizer that imposes column-sparsity jointly
on the two matrix factors that result from MF, thus promoting low-rankness on
the optimization problem. The problems of denoising, matrix completion and
non-negative matrix factorization (NMF) are redefined according to the new LRMF
formulation and solved via efficient Newton-type algorithms with proven
theoretical guarantees as to their convergence and rates of convergence to
stationary points. The effectiveness of the proposed algorithms is verified in
diverse simulated and real data experiments. | [
1,
0,
0,
0,
0,
0
] |
Title: The formation of the Milky Way halo and its dwarf satellites, a NLTE-1D abundance analysis. I. Homogeneous set of atmospheric parameters,
Abstract: We present a homogeneous set of accurate atmospheric parameters for a
complete sample of very and extremely metal-poor stars in the dwarf spheroidal
galaxies (dSphs) Sculptor, Ursa Minor, Sextans, Fornax, Boötes I, Ursa Major
II, and Leo IV. We also deliver a Milky Way (MW) comparison sample of giant
stars covering the -4 < [Fe/H] < -1.7 metallicity range. We show that, in the
[Fe/H] > -3.5 regime, the non-local thermodynamic equilibrium (NLTE)
calculations with non-spectroscopic effective temperature (Teff) and surface
gravity (log~g) based on the photometric methods and known distance provide
consistent abundances of the Fe I and Fe II lines. This justifies the Fe I/Fe
II ionisation equilibrium method to determine log g for the MW halo giants with
unknown distance. The atmospheric parameters of the dSphs and MW stars were
checked with independent methods. In the [Fe/H] > -3.5 regime, the Ti I/Ti II
ionisation equilibrium is fulfilled in the NLTE calculations. In the log~g -
Teff plane, all the stars sit on the giant branch of the evolutionary tracks
corresponding to [Fe/H] = -2 to -4, in line with their metallicities. For some
of the most metal-poor stars of our sample, we hardly achieve consistent NLTE
abundances from the two ionisation stages for both iron and titanium. We
suggest that this is a consequence of the uncertainty in the Teff-colour
relation at those metallicities. The results of these work provide the base for
a detailed abundance analysis presented in a companion paper. | [
0,
1,
0,
0,
0,
0
] |
Title: Converting topological insulators into topological metals within the tetradymite family,
Abstract: We report the electronic band structures and concomitant Fermi surfaces for a
family of exfoliable tetradymite compounds with the formula $T_2$$Ch_2$$Pn$,
obtained as a modification to the well-known topological insulator binaries
Bi$_2$(Se,Te)$_3$ by replacing one chalcogen ($Ch$) with a pnictogen ($Pn$) and
Bi with the tetravalent transition metals $T$ $=$ Ti, Zr, or Hf. This
imbalances the electron count and results in layered metals characterized by
relatively high carrier mobilities and bulk two-dimensional Fermi surfaces
whose topography is well-described by first principles calculations.
Intriguingly, slab electronic structure calculations predict Dirac-like surface
states. In contrast to Bi$_2$Se$_3$, where the surface Dirac bands are at the
$\Gamma-$point, for (Zr,Hf)$_2$Te$_2$(P,As) there are Dirac cones of strong
topological character around both the $\bar {\Gamma}$- and $\bar {M}$-points
which are above and below the Fermi energy, respectively. For Ti$_2$Te$_2$P the
surface state is predicted to exist only around the $\bar {M}$-point. In
agreement with these predictions, the surface states that are located below the
Fermi energy are observed by angle resolved photoemission spectroscopy
measurements, revealing that they coexist with the bulk metallic state. Thus,
this family of materials provides a foundation upon which to develop novel
phenomena that exploit both the bulk and surface states (e.g., topological
superconductivity). | [
0,
1,
0,
0,
0,
0
] |
Title: When Do Birds of a Feather Flock Together? k-Means, Proximity, and Conic Programming,
Abstract: Given a set of data, one central goal is to group them into clusters based on
some notion of similarity between the individual objects. One of the most
popular and widely-used approaches is k-means despite the computational
hardness to find its global minimum. We study and compare the properties of
different convex relaxations by relating them to corresponding proximity
conditions, an idea originally introduced by Kumar and Kannan. Using conic
duality theory, we present an improved proximity condition under which the
Peng-Wei relaxation of k-means recovers the underlying clusters exactly. Our
proximity condition improves upon Kumar and Kannan, and is comparable to that
of Awashti and Sheffet where proximity conditions are established for
projective k-means. In addition, we provide a necessary proximity condition for
the exactness of the Peng-Wei relaxation. For the special case of equal cluster
sizes, we establish a different and completely localized proximity condition
under which the Amini-Levina relaxation yields exact clustering, thereby having
addressed an open problem by Awasthi and Sheffet in the balanced case. Our
framework is not only deterministic and model-free but also comes with a clear
geometric meaning which allows for further analysis and generalization.
Moreover, it can be conveniently applied to analyzing various data generative
models such as the stochastic ball models and Gaussian mixture models. With
this method, we improve the current minimum separation bound for the stochastic
ball models and achieve the state-of-the-art results of learning Gaussian
mixture models. | [
0,
0,
1,
0,
0,
0
] |
Title: Differentially Private Bayesian Learning on Distributed Data,
Abstract: Many applications of machine learning, for example in health care, would
benefit from methods that can guarantee privacy of data subjects. Differential
privacy (DP) has become established as a standard for protecting learning
results. The standard DP algorithms require a single trusted party to have
access to the entire data, which is a clear weakness. We consider DP Bayesian
learning in a distributed setting, where each party only holds a single sample
or a few samples of the data. We propose a learning strategy based on a secure
multi-party sum function for aggregating summaries from data holders and the
Gaussian mechanism for DP. Our method builds on an asymptotically optimal and
practically efficient DP Bayesian inference with rapidly diminishing extra
cost. | [
1,
0,
0,
1,
0,
0
] |
Title: Metastability and avalanche dynamics in strongly-correlated gases with long-range interactions,
Abstract: We experimentally study the stability of a bosonic Mott-insulator against the
formation of a density wave induced by long-range interactions, and
characterize the intrinsic dynamics between these two states. The
Mott-insulator is created in a quantum degenerate gas of 87-Rubidium atoms,
trapped in a three-dimensional optical lattice. The gas is located inside and
globally coupled to an optical cavity. This causes interactions of global
range, mediated by photons dispersively scattered between a transverse lattice
and the cavity. The scattering comes with an atomic density modulation, which
is measured by the photon flux leaking from the cavity. We initialize the
system in a Mott-insulating state and then rapidly increase the global coupling
strength. We observe that the system falls into either of two distinct final
states. One is characterized by a low photon flux, signaling a Mott insulator,
and the other is characterized by a high photon flux, which we associate with a
density wave. Ramping the global coupling slowly, we observe a hysteresis loop
between the two states - a further signature of metastability. A comparison
with a theoretical model confirms that the metastability originates in the
competition between short- and global-range interactions. From the increasing
photon flux monitored during the switching process, we find that several
thousand atoms tunnel to a neighboring site on the time scale of the single
particle dynamics. We argue that a density modulation, initially forming in the
compressible surface of the trapped gas, triggers an avalanche tunneling
process in the Mott-insulating region. | [
0,
1,
0,
0,
0,
0
] |
Title: Skin Lesion Classification Using Deep Multi-scale Convolutional Neural Networks,
Abstract: We present a deep learning approach to the ISIC 2017 Skin Lesion
Classification Challenge using a multi-scale convolutional neural network. Our
approach utilizes an Inception-v3 network pre-trained on the ImageNet dataset,
which is fine-tuned for skin lesion classification using two different scales
of input images. | [
1,
0,
0,
0,
0,
0
] |
Title: Photographic dataset: playing cards,
Abstract: This is a photographic dataset collected for testing image processing
algorithms. The idea is to have images that can exploit the properties of total
variation, therefore a set of playing cards was distributed on the scene. The
dataset is made available at www.fips.fi/photographic_dataset2.php | [
1,
1,
0,
0,
0,
0
] |
Title: Dynamic constraints on activity and connectivity during the learning of value,
Abstract: Human learning is a complex process in which future behavior is altered via
the modulation of neural activity. Yet, the degree to which brain activity and
functional connectivity during learning is constrained across subjects, for
example by conserved anatomy and physiology or by the nature of the task,
remains unknown. Here, we measured brain activity and functional connectivity
in a longitudinal experiment in which healthy adult human participants learned
the values of novel objects over the course of four days. We assessed the
presence of constraints on activity and functional connectivity using an
inter-subject correlation approach. Constraints on activity and connectivity
were greater in magnitude than expected in a non-parametric permutation-based
null model, particularly in primary sensory and motor systems, as well as in
regions associated with the learning of value. Notably, inter-subject
connectivity in activity and connectivity displayed marked temporal variations,
with inter-subject correlations in activity exceeding those in connectivity
during early learning and \emph{visa versa} in later learning. Finally,
individual differences in performance accuracy tracked the degree to which a
subject's connectivity, but not activity, tracked subject-general patterns.
Taken together, our results support the notion that brain activity and
connectivity are constrained across subjects in early learning, with
constraints on activity, but not connectivity, decreasing in later learning. | [
0,
0,
0,
0,
1,
0
] |
Title: Smoothness-based Edge Detection using Low-SNR Camera for Robot Navigation,
Abstract: In the emerging advancement in the branch of autonomous robotics, the ability
of a robot to efficiently localize and construct maps of its surrounding is
crucial. This paper deals with utilizing thermal-infrared cameras, as opposed
to conventional cameras as the primary sensor to capture images of the robot's
surroundings. For localization, the images need to be further processed before
feeding them to a navigational system. The main motivation of this paper was to
develop an edge detection methodology capable of utilizing the low-SNR poor
output from such a thermal camera and effectively detect smooth edges of the
surrounding environment. The enhanced edge detector proposed in this paper
takes the raw image from the thermal sensor, denoises the images, applies Canny
edge detection followed by CSS method. The edges are ranked to remove any noise
and only edges of the highest rank are kept. Then, the broken edges are linked
by computing edge metrics and a smooth edge of the surrounding is displayed in
a binary image. Several comparisons are also made in the paper between the
proposed technique and the existing techniques. | [
1,
0,
0,
1,
0,
0
] |
Title: IDK Cascades: Fast Deep Learning by Learning not to Overthink,
Abstract: Advances in deep learning have led to substantial increases in prediction
accuracy but have been accompanied by increases in the cost of rendering
predictions. We conjecture that fora majority of real-world inputs, the recent
advances in deep learning have created models that effectively "overthink" on
simple inputs. In this paper, we revisit the classic question of building model
cascades that primarily leverage class asymmetry to reduce cost. We introduce
the "I Don't Know"(IDK) prediction cascades framework, a general framework to
systematically compose a set of pre-trained models to accelerate inference
without a loss in prediction accuracy. We propose two search based methods for
constructing cascades as well as a new cost-aware objective within this
framework. The proposed IDK cascade framework can be easily adopted in the
existing model serving systems without additional model re-training. We
evaluate the proposed techniques on a range of benchmarks to demonstrate the
effectiveness of the proposed framework. | [
1,
0,
0,
0,
0,
0
] |
Title: Long-Range Interactions for Hydrogen: 6P-1S and 6P-2S,
Abstract: The collisional shift of a transition constitutes an important systematic
effect in high-precision spectroscopy. Accurate values for van der
Waalsinteraction coefficients are required in order to evaluate the
distance-dependent frequency shift. We here consider the interaction of excited
hydrogen 6P atoms with metastable atoms (in the 2S state), in order to explore
the influence of quasi-degenerate 2P, and 6S states on the dipole-dipole
interaction. The motivation for the calculation is given by planned
high-precision measurements of the transition. Due to the presence of
quasi-degenerate levels, one can use the non-retarded approximation for the
interaction terms over wide distance ranges. | [
0,
1,
0,
0,
0,
0
] |
Title: Frustrated spin-1/2 molecular magnetism in the mixed-valence antiferromagnets Ba3MRu2O9 (M = In, Y, Lu),
Abstract: We have performed magnetic susceptibility, heat capacity, muon spin
relaxation, and neutron scattering measurements on three members of the family
Ba3MRu2O9, where M = In, Y and Lu. These systems consist of mixed-valence Ru
dimers on a triangular lattice with antiferromagnetic interdimer exchange.
Although previous work has argued that charge order within the dimers or
intradimer double exchange plays an important role in determining the magnetic
properties, our results suggest that the dimers are better described as
molecular units due to significant orbital hybridization, resulting in one
spin-1/2 moment distributed equally over the two Ru sites. These molecular
building blocks form a frustrated, quasi-two-dimensional triangular lattice.
Our zero and longitudinal field muSR results indicate that the molecular
moments develop a collective, static magnetic ground state, with oscillations
of the zero field muon spin polarization indicative of long-range magnetic
order in the Lu sample. The static magnetism is much more disordered in the Y
and In samples, but they do not appear to be conventional spin glasses. | [
0,
1,
0,
0,
0,
0
] |
Title: Data-Driven Model Predictive Control of Autonomous Mobility-on-Demand Systems,
Abstract: The goal of this paper is to present an end-to-end, data-driven framework to
control Autonomous Mobility-on-Demand systems (AMoD, i.e. fleets of
self-driving vehicles). We first model the AMoD system using a time-expanded
network, and present a formulation that computes the optimal rebalancing
strategy (i.e., preemptive repositioning) and the minimum feasible fleet size
for a given travel demand. Then, we adapt this formulation to devise a Model
Predictive Control (MPC) algorithm that leverages short-term demand forecasts
based on historical data to compute rebalancing strategies. We test the
end-to-end performance of this controller with a state-of-the-art LSTM neural
network to predict customer demand and real customer data from DiDi Chuxing: we
show that this approach scales very well for large systems (indeed, the
computational complexity of the MPC algorithm does not depend on the number of
customers and of vehicles in the system) and outperforms state-of-the-art
rebalancing strategies by reducing the mean customer wait time by up to to
89.6%. | [
1,
0,
0,
1,
0,
0
] |
Title: Quantum models with energy-dependent potentials solvable in terms of exceptional orthogonal polynomials,
Abstract: We construct energy-dependent potentials for which the Schroedinger equations
admit solu- tions in terms of exceptional orthogonal polynomials. Our method of
construction is based on certain point transformations, applied to the
equations of exceptional Hermite, Jacobi and Laguerre polynomials. We present
several examples of boundary-value problems with energy-dependent potentials
that admit a discrete spectrum and the corresponding normalizable solutions in
closed form. | [
0,
0,
1,
0,
0,
0
] |
Title: Discovery of the most metal-poor damped Lyman-alpha system,
Abstract: We report the discovery and analysis of the most metal-poor damped
Lyman-alpha (DLA) system currently known, based on observations made with the
Keck HIRES spectrograph. The metal paucity of this system has only permitted
the determination of three element abundances: [C/H] = -3.43 +/- 0.06, [O/H] =
-3.05 +/- 0.05, and [Si/H] = -3.21 +/- 0.05, as well as an upper limit on the
abundance of iron: [Fe/H] < -2.81. This DLA is among the most carbon-poor
environment currently known with detectable metals. By comparing the abundance
pattern of this DLA to detailed models of metal-free nucleosynthesis, we find
that the chemistry of the gas is consistent with the yields of a 20.5 M_sun
metal-free star that ended its life as a core-collapse supernova; the
abundances we measure are inconsistent with the yields of pair-instability
supernovae. Such a tight constraint on the mass of the progenitor Population
III star is afforded by the well-determined C/O ratio, which we show depends
almost monotonically on the progenitor mass when the kinetic energy of the
supernova explosion is E_exp > 1.5x10^51 erg. We find that the DLA presented
here has just crossed the critical 'transition discriminant' threshold,
rendering the DLA gas now suitable for low mass star formation. We also discuss
the chemistry of this system in the context of recent models that suggest some
of the most metal-poor DLAs are the precursors of the 'first galaxies', and are
the antecedents of the ultra-faint dwarf galaxies. | [
0,
1,
0,
0,
0,
0
] |
Title: A Concurrent Perspective on Smart Contracts,
Abstract: In this paper, we explore remarkable similarities between multi-transactional
behaviors of smart contracts in cryptocurrencies such as Ethereum and classical
problems of shared-memory concurrency. We examine two real-world examples from
the Ethereum blockchain and analyzing how they are vulnerable to bugs that are
closely reminiscent to those that often occur in traditional concurrent
programs. We then elaborate on the relation between observable contract
behaviors and well-studied concurrency topics, such as atomicity, interference,
synchronization, and resource ownership. The described
contracts-as-concurrent-objects analogy provides deeper understanding of
potential threats for smart contracts, indicate better engineering practices,
and enable applications of existing state-of-the-art formal verification
techniques. | [
1,
0,
0,
0,
0,
0
] |
Title: Noise Models in the Nonlinear Spectral Domain for Optical Fibre Communications,
Abstract: Existing works on building a soliton transmission system only encode
information using the imaginary part of the eigenvalue, which fails to make
full use of the signal degree-of-freedoms. Motivated by this observation, we
make the first step of encoding information using (discrete) spectral
amplitudes by proposing analytical noise models for the spectral amplitudes of
$N$-solitons ($N\geq 1$). To our best knowledge, this is the first work in
building an analytical noise model for spectral amplitudes, which leads to many
interesting information theoretic questions, such as channel capacity analysis,
and has a potential of increasing the transmission rate. The noise statistics
of the spectral amplitude of a soliton are also obtained without the Gaussian
approximation. | [
1,
0,
1,
0,
0,
0
] |
Title: Shape analysis on Lie groups and homogeneous spaces,
Abstract: In this paper we are concerned with the approach to shape analysis based on
the so called Square Root Velocity Transform (SRVT). We propose a
generalisation of the SRVT from Euclidean spaces to shape spaces of curves on
Lie groups and on homogeneous manifolds. The main idea behind our approach is
to exploit the geometry of the natural Lie group actions on these spaces. | [
0,
0,
1,
0,
0,
0
] |
Title: Towards the ab initio based theory of the phase transformations in iron and steel,
Abstract: Despite of the appearance of numerous new materials, the iron based alloys
and steels continue to play an essential role in modern technology. The
properties of a steel are determined by its structural state (ferrite,
cementite, pearlite, bainite, martensite, and their combination) that is formed
under thermal treatment as a result of the shear lattice reconstruction "gamma"
(fcc) -> "alpha" (bcc) and carbon diffusion redistribution. We present a review
on a recent progress in the development of a quantitative theory of the phase
transformations and microstructure formation in steel that is based on an ab
initio parameterization of the Ginzburg-Landau free energy functional. The
results of computer modeling describe the regular change of transformation
scenario under cooling from ferritic (nucleation and diffusion-controlled
growth of the "alpha" phase to martensitic (the shear lattice instability
"gamma" -> "alpha"). It has been shown that the increase in short-range
magnetic order with decreasing the temperature plays a key role in the change
of transformation scenarios. Phase-field modeling in the framework of a
discussed approach demonstrates the typical transformation patterns. | [
0,
1,
0,
0,
0,
0
] |
Title: Functional renormalization-group approach to the Pokrovsky-Talapov model via modified massive Thirring fermion model,
Abstract: A possibility of the topological Kosterlitz-Thouless~(KT) transition in the
Pokrovsky-Talapov~(PT) model is investigated by using the functional
renormalization-group (RG) approach by Wetterich. Our main finding is that the
nonzero misfit parameter of the model, which can be related with the linear
gradient term (Dzyaloshinsky-Moriya interaction), makes such a transition
impossible, what contradicts the previous consideration of this problem by
non-perturbative RG methods. To support the conclusion the initial PT model is
reformulated in terms of the 2D theory of relativistic fermions using an
analogy between the 2D sine-Gordon and the massive Thirring models. In the new
formalism the misfit parameter corresponds to an effective gauge field that
enables to include it in the RG procedure on an equal footing with the other
parameters of the theory. The Wetterich equation is applied to obtain flow
equations for the parameters of the new fermionic action. We demonstrate that
these equations reproduce the KT type of behavior if the misfit parameter is
zero. However, any small nonzero value of the quantity rules out a possibility
of the KT transition. To confirm the finding we develop a description of the
problem in terms of the 2D Coulomb gas model. Within the approach the breakdown
of the KT scenario gains a transparent meaning, the misfit gives rise to an
effective in-plane electric field that prevents a formation of bound
vortex-antivortex pairs. | [
0,
1,
0,
0,
0,
0
] |
Title: Micromagnetic Simulations for Coercivity Improvement through Nano-Structuring of Rare-Earth Free L1$_0$-FeNi Magnets,
Abstract: In this work we investigate the potential of tetragonal L1$_0$ ordered FeNi
as candidate phase for rare earth free permanent magnets taking into account
anisotropy values from recently synthesized, partially ordered FeNi thin films.
In particular, we estimate the maximum energy product ($BH$)$_\mathrm{max}$ of
L1$_0$-FeNi nanostructures using micromagnetic simulations. The maximum energy
product is limited due to the small coercive field of partially ordered
L1$_0$-FeNi. Nano-structured magnets consisting of 128 equi-axed, platelet-like
and columnar-shaped grains show a theoretical maximum energy product of 228
kJ/m$^3$, 208 kJ/m$^3$, 252 kJ/m$^3$, respectively. | [
0,
1,
0,
0,
0,
0
] |
Title: Influence of Resampling on Accuracy of Imbalanced Classification,
Abstract: In many real-world binary classification tasks (e.g. detection of certain
objects from images), an available dataset is imbalanced, i.e., it has much
less representatives of a one class (a minor class), than of another.
Generally, accurate prediction of the minor class is crucial but it's hard to
achieve since there is not much information about the minor class. One approach
to deal with this problem is to preliminarily resample the dataset, i.e., add
new elements to the dataset or remove existing ones. Resampling can be done in
various ways which raises the problem of choosing the most appropriate one. In
this paper we experimentally investigate impact of resampling on classification
accuracy, compare resampling methods and highlight key points and difficulties
of resampling. | [
1,
0,
0,
1,
0,
0
] |
Title: On smile properties of volatility derivatives and exotic products: understanding the VIX skew,
Abstract: We develop a method to study the implied volatility for exotic options and
volatility derivatives with European payoffs such as VIX options. Our approach,
based on Malliavin calculus techniques, allows us to describe the properties of
the at-the-money implied volatility (ATMI) in terms of the Malliavin
derivatives of the underlying process. More precisely, we study the short-time
behaviour of the ATMI level and skew. As an application, we describe the
short-term behavior of the ATMI of VIX and realized variance options in terms
of the Hurst parameter of the model, and most importantly we describe the class
of volatility processes that generate a positive skew for the VIX implied
volatility. In addition, we find that our ATMI asymptotic formulae perform very
well even for large maturities. Several numerical examples are provided to
support our theoretical results. | [
0,
0,
0,
0,
0,
1
] |
Title: Risk-Sensitive Optimal Control of Queues,
Abstract: We consider the problem of designing risk-sensitive optimal control policies
for scheduling packet transmissions in a stochastic wireless network. A single
client is connected to an access point (AP) through a wireless channel. Packet
transmission incurs a cost $C$, while packet delivery yields a reward of $R$
units. The client maintains a finite buffer of size $B$, and a penalty of $L$
units is imposed upon packet loss which occurs due to finite queueing buffer.
We show that the risk-sensitive optimal control policy for such a simple
set-up is of threshold type, i.e., it is optimal to carry out packet
transmissions only when $Q(t)$, i.e., the queue length at time $t$ exceeds a
certain threshold $\tau$. It is also shown that the value of threshold $\tau$
increases upon increasing the cost per unit packet transmission $C$.
Furthermore, it is also shown that a threshold policy with threshold equal to
$\tau$ is optimal for a set of problems in which cost $C$ lies within an
interval $[C_l,C_u]$. Equations that need to be solved in order to obtain
$C_l,C_u$ are also provided. | [
1,
0,
0,
0,
0,
0
] |
Title: SPECULOOS exoplanet search and its prototype on TRAPPIST,
Abstract: One of the most significant goals of modern science is establishing whether
life exists around other suns. The most direct path towards its achievement is
the detection and atmospheric characterization of terrestrial exoplanets with
potentially habitable surface conditions. The nearest ultracool dwarfs (UCDs),
i.e. very-low-mass stars and brown dwarfs with effective temperatures lower
than 2700 K, represent a unique opportunity to reach this goal within the next
decade. The potential of the transit method for detecting potentially habitable
Earth-sized planets around these objects is drastically increased compared to
Earth-Sun analogs. Furthermore, only a terrestrial planet transiting a nearby
UCD would be amenable for a thorough atmospheric characterization, including
the search for possible biosignatures, with near-future facilities such as the
James Webb Space Telescope. In this chapter, we first describe the physical
properties of UCDs as well as the unique potential they offer for the detection
of potentially habitable Earth-sized planets suitable for atmospheric
characterization. Then, we present the SPECULOOS ground-based transit survey,
that will search for Earth-sized planets transiting the nearest UCDs, as well
as its prototype survey on the TRAPPIST telescopes. We conclude by discussing
the prospects offered by the recent detection by this prototype survey of a
system of seven temperate Earth-sized planets transiting a nearby UCD,
TRAPPIST-1. | [
0,
1,
0,
0,
0,
0
] |
Title: Small-dimensional representations of algebraic groups of type $A_l$,
Abstract: For $G$ an algebraic group of type $A_l$ over an algebraically closed field
of characteristic $p$, we determine all irreducible rational representations of
$G$ in defining characteristic with dimensions $\le (l+1)^s$ for $s = 3, 4$,
provided that $l > 18$, $l > 35$ respectively. We also give explicit
descriptions of the corresponding modules for $s = 3$. | [
0,
0,
1,
0,
0,
0
] |
Title: Generalized Biplots for Multidimensional Scaled Projections,
Abstract: Dimension reduction and visualization is a staple of data analytics. Methods
such as Principal Component Analysis (PCA) and Multidimensional Scaling (MDS)
provide low dimensional (LD) projections of high dimensional (HD) data while
preserving an HD relationship between observations. Traditional biplots assign
meaning to the LD space of a PCA projection by displaying LD axes for the
attributes. These axes, however, are specific to the linear projection used in
PCA. MDS projections, which allow for arbitrary stress and dissimilarity
functions, require special care when labeling the LD space. We propose an
iterative scheme to plot an LD axis for each attribute based on the
user-specified stress and dissimilarity metrics. We discuss the details of our
general biplot methodology, its relationship with PCA-derived biplots, and
provide examples using real data. | [
0,
0,
0,
1,
0,
0
] |
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