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High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks | Advances in deep learning for natural images have prompted a surge of
interest in applying similar techniques to medical images. The majority of the
initial attempts focused on replacing the input of a deep convolutional neural
network with a medical image, which does not take into consideration the
fundamental differences between these two types of images. Specifically, fine
details are necessary for detection in medical images, unlike in natural images
where coarse structures matter most. This difference makes it inadequate to use
the existing network architectures developed for natural images, because they
work on heavily downscaled images to reduce the memory requirements. This hides
details necessary to make accurate predictions. Additionally, a single exam in
medical imaging often comes with a set of views which must be fused in order to
reach a correct conclusion. In our work, we propose to use a multi-view deep
convolutional neural network that handles a set of high-resolution medical
images. We evaluate it on large-scale mammography-based breast cancer screening
(BI-RADS prediction) using 886,000 images. We focus on investigating the impact
of the training set size and image size on the prediction accuracy. Our results
highlight that performance increases with the size of training set, and that
the best performance can only be achieved using the original resolution. In the
reader study, performed on a random subset of the test set, we confirmed the
efficacy of our model, which achieved performance comparable to a committee of
radiologists when presented with the same data.
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Adaptive posterior contraction rates for the horseshoe | We investigate the frequentist properties of Bayesian procedures for
estimation based on the horseshoe prior in the sparse multivariate normal means
model. Previous theoretical results assumed that the sparsity level, that is,
the number of signals, was known. We drop this assumption and characterize the
behavior of the maximum marginal likelihood estimator (MMLE) of a key parameter
of the horseshoe prior. We prove that the MMLE is an effective estimator of the
sparsity level, in the sense that it leads to (near) minimax optimal estimation
of the underlying mean vector generating the data. Besides this empirical Bayes
procedure, we consider the hierarchical Bayes method of putting a prior on the
unknown sparsity level as well. We show that both Bayesian techniques lead to
rate-adaptive optimal posterior contraction, which implies that the horseshoe
posterior is a good candidate for generating rate-adaptive credible sets.
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A Convex Optimization Approach to Dynamic Programming in Continuous State and Action Spaces | A convex optimization-based method is proposed to numerically solve dynamic
programs in continuous state and action spaces. This approach using a
discretization of the state space has the following salient features. First, by
introducing an auxiliary optimization variable that assigns the contribution of
each grid point, it does not require an interpolation in solving an associated
Bellman equation and constructing a control policy. Second, the proposed method
allows us to solve the Bellman equation with a desired level of precision via
convex programming in the case of linear systems and convex costs. We can also
construct a control policy of which performance converges to the optimum as the
grid resolution becomes finer in this case. Third, when a nonlinear
control-affine system is considered, the convex optimization approach provides
an approximate control policy with a provable suboptimality bound. Fourth, for
general cases, the proposed convex formulation of dynamic programming operators
can be simply modified as a nonconvex bi-level program, in which the inner
problem is a linear program, without losing convergence properties. From our
convex methods and analyses, we observe that convexity in dynamic programming
deserves attention as it can play a critical role in obtaining a tractable and
convergent numerical solution.
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Investigating Enactive Learning for Autonomous Intelligent Agents | The enactive approach to cognition is typically proposed as a viable
alternative to traditional cognitive science. Enactive cognition displaces the
explanatory focus from the internal representations of the agent to the direct
sensorimotor interaction with its environment. In this paper, we investigate
enactive learning through means of artificial agent simulations. We compare the
performances of the enactive agent to an agent operating on classical
reinforcement learning in foraging tasks within maze environments. The
characteristics of the agents are analysed in terms of the accessibility of the
environmental states, goals, and exploration/exploitation tradeoffs. We confirm
that the enactive agent can successfully interact with its environment and
learn to avoid unfavourable interactions using intrinsically defined goals. The
performance of the enactive agent is shown to be limited by the number of
affordable actions.
| 1 | 0 | 0 | 0 | 0 | 0 |
Computability of semicomputable manifolds in computable topological spaces | We study computable topological spaces and semicomputable and computable sets
in these spaces. In particular, we investigate conditions under which
semicomputable sets are computable. We prove that a semicomputable compact
manifold $M$ is computable if its boundary $\partial M$ is computable. We also
show how this result combined with certain construction which compactifies a
semicomputable set leads to the conclusion that some noncompact semicomputable
manifolds in computable metric spaces are computable.
| 1 | 0 | 1 | 0 | 0 | 0 |
Acoustic streaming and its suppression in inhomogeneous fluids | We present a theoretical and experimental study of boundary-driven acoustic
streaming in an inhomogeneous fluid with variations in density and
compressibility. In a homogeneous fluid this streaming results from dissipation
in the boundary layers (Rayleigh streaming). We show that in an inhomogeneous
fluid, an additional non-dissipative force density acts on the fluid to
stabilize particular inhomogeneity configurations, which markedly alters and
even suppresses the streaming flows. Our theoretical and numerical analysis of
the phenomenon is supported by ultrasound experiments performed with
inhomogeneous aqueous iodixanol solutions in a glass-silicon microchip.
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Predicting Pulsar Scintillation from Refractive Plasma Sheets | The dynamic and secondary spectra of many pulsars show evidence for
long-lived, aligned images of the pulsar that are stationary on a thin
scattering sheet. One explanation for this phenomenon considers the effects of
wave crests along sheets in the ionized interstellar medium, such as those due
to Alfvén waves propagating along current sheets. If these sheets are closely
aligned to our line-of-sight to the pulsar, high bending angles arise at the
wave crests and a selection effect causes alignment of images produced at
different crests, similar to grazing reflection off of a lake. Using geometric
optics, we develop a simple parameterized model of these corrugated sheets that
can be constrained with a single observation and that makes observable
predictions for variations in the scintillation of the pulsar over time and
frequency. This model reveals qualitative differences between lensing from
overdense and underdense corrugated sheets: Only if the sheet is overdense
compared to the surrounding interstellar medium can the lensed images be
brighter than the line-of-sight image to the pulsar, and the faint lensed
images are closer to the pulsar at higher frequencies if the sheet is
underdense, but at lower frequencies if the sheet is overdense.
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Disruptive Behavior Disorder (DBD) Rating Scale for Georgian Population | In the presented study Parent/Teacher Disruptive Behavior Disorder (DBD)
rating scale based on the Diagnostic and Statistical Manual of Mental Disorders
(DSM-IV-TR [APA, 2000]) which was developed by Pelham and his colleagues
(Pelham et al., 1992) was translated and adopted for assessment of childhood
behavioral abnormalities, especially ADHD, ODD and CD in Georgian children and
adolescents. The DBD rating scale was translated into Georgian language using
back translation technique by English language philologists and checked and
corrected by qualified psychologists and psychiatrist of Georgia. Children and
adolescents in the age range of 6 to 16 years (N 290; Mean Age 10.50, SD=2.88)
including 153 males (Mean Age 10.42, SD= 2.62) and 141 females (Mean Age 10.60,
SD=3.14) were recruited from different public schools of Tbilisi and the
Neurology Department of the Pediatric Clinic of the Tbilisi State Medical
University. Participants objectively were assessed via interviewing
parents/teachers and qualified psychologists in three different settings
including school, home and clinic. In terms of DBD total scores revealed
statistically significant differences between healthy controls (M=27.71,
SD=17.26) and children and adolescents with ADHD (M=61.51, SD= 22.79).
Statistically significant differences were found for inattentive subtype
between control (M=8.68, SD=5.68) and ADHD (M=18.15, SD=6.57) groups. In
general it was shown that children and adolescents with ADHD had high score on
DBD in comparison to typically developed persons. In the study also was
determined gender wise prevalence in children and adolescents with ADHD, ODD
and CD. The research revealed prevalence of males in comparison with females in
all investigated categories.
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A GPU Poisson-Fermi Solver for Ion Channel Simulations | The Poisson-Fermi model is an extension of the classical Poisson-Boltzmann
model to include the steric and correlation effects of ions and water treated
as nonuniform spheres in aqueous solutions. Poisson-Boltzmann electrostatic
calculations are essential but computationally very demanding for molecular
dynamics or continuum simulations of complex systems in molecular biophysics
and electrochemistry. The graphic processing unit (GPU) with enormous
arithmetic capability and streaming memory bandwidth is now a powerful engine
for scientific as well as industrial computing. We propose two parallel GPU
algorithms, one for linear solver and the other for nonlinear solver, for
solving the Poisson-Fermi equation approximated by the standard finite
difference method in 3D to study biological ion channels with crystallized
structures from the Protein Data Bank, for example. Numerical methods for both
linear and nonlinear solvers in the parallel algorithms are given in detail to
illustrate the salient features of the CUDA (compute unified device
architecture) software platform of GPU in implementation. It is shown that the
parallel algorithms on GPU over the sequential algorithms on CPU (central
processing unit) can achieve 22.8x and 16.9x speedups for the linear solver
time and total runtime, respectively.
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Quantum Monte Carlo with variable spins: fixed-phase and fixed-node approximations | We study several aspects of the recently introduced fixed-phase spin-orbit
diffusion Monte Carlo (FPSODMC) method, in particular, its relation to the
fixed-node method and its potential use as a general approach for electronic
structure calculations. We illustrate constructions of spinor-based wave
functions with the full space-spin symmetry without assigning up or down spin
labels to particular electrons, effectively "complexifying" even ordinary
real-valued wave functions. Interestingly, with proper choice of the simulation
parameters and spin variables, such fixed-phase calculations enable one to
reach also the fixed-node limit. The fixed-phase solution provides a
straightforward interpretation as the lowest bosonic state in a given effective
potential generated by the many-body approximate phase. In addition, the
divergences present at real wave function nodes are smoothed out to lower
dimensionality, decreasing thus the variation of sampled quantities and making
the sampling also more straightforward. We illustrate some of these properties
on calculations of selected first-row systems that recover the fixed-node
results with quantitatively similar levels of the corresponding biases. At the
same time, the fixed-phase approach opens new possibilities for more general
trial wave functions with further opportunities for increasing accuracy in
practical calculations.
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Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise | We introduce coroICA, confounding-robust independent component analysis, a
novel ICA algorithm which decomposes linearly mixed multivariate observations
into independent components that are corrupted (and rendered dependent) by
hidden group-wise stationary confounding. It extends the ordinary ICA model in
a theoretically sound and explicit way to incorporate group-wise (or
environment-wise) confounding. We show that our general noise model allows to
perform ICA in settings where other noisy ICA procedures fail. Additionally, it
can be used for applications with grouped data by adjusting for different
stationary noise within each group. We show that the noise model has a natural
relation to causality and explain how it can be applied in the context of
causal inference. In addition to our theoretical framework, we provide an
efficient estimation procedure and prove identifiability of the unmixing matrix
under mild assumptions. Finally, we illustrate the performance and robustness
of our method on simulated data, provide audible and visual examples, and
demonstrate the applicability to real-world scenarios by experiments on
publicly available Antarctic ice core data as well as two EEG data sets. We
provide a scikit-learn compatible pip-installable Python package coroICA as
well as R and Matlab implementations accompanied by a documentation at
this https URL.
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Diversification-Based Learning in Computing and Optimization | Diversification-Based Learning (DBL) derives from a collection of principles
and methods introduced in the field of metaheuristics that have broad
applications in computing and optimization. We show that the DBL framework goes
significantly beyond that of the more recent Opposition-based learning (OBL)
framework introduced in Tizhoosh (2005), which has become the focus of numerous
research initiatives in machine learning and metaheuristic optimization. We
unify and extend earlier proposals in metaheuristic search (Glover, 1997,
Glover and Laguna, 1997) to give a collection of approaches that are more
flexible and comprehensive than OBL for creating intensification and
diversification strategies in metaheuristic search. We also describe potential
applications of DBL to various subfields of machine learning and optimization.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Tutorial on Canonical Correlation Methods | Canonical correlation analysis is a family of multivariate statistical
methods for the analysis of paired sets of variables. Since its proposition,
canonical correlation analysis has for instance been extended to extract
relations between two sets of variables when the sample size is insufficient in
relation to the data dimensionality, when the relations have been considered to
be non-linear, and when the dimensionality is too large for human
interpretation. This tutorial explains the theory of canonical correlation
analysis including its regularised, kernel, and sparse variants. Additionally,
the deep and Bayesian CCA extensions are briefly reviewed. Together with the
numerical examples, this overview provides a coherent compendium on the
applicability of the variants of canonical correlation analysis. By bringing
together techniques for solving the optimisation problems, evaluating the
statistical significance and generalisability of the canonical correlation
model, and interpreting the relations, we hope that this article can serve as a
hands-on tool for applying canonical correlation methods in data analysis.
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Towards Neural Co-Processors for the Brain: Combining Decoding and Encoding in Brain-Computer Interfaces | The field of brain-computer interfaces is poised to advance from the
traditional goal of controlling prosthetic devices using brain signals to
combining neural decoding and encoding within a single neuroprosthetic device.
Such a device acts as a "co-processor" for the brain, with applications ranging
from inducing Hebbian plasticity for rehabilitation after brain injury to
reanimating paralyzed limbs and enhancing memory. We review recent progress in
simultaneous decoding and encoding for closed-loop control and plasticity
induction. To address the challenge of multi-channel decoding and encoding, we
introduce a unifying framework for developing brain co-processors based on
artificial neural networks and deep learning. These "neural co-processors" can
be used to jointly optimize cost functions with the nervous system to achieve
desired behaviors ranging from targeted neuro-rehabilitation to augmentation of
brain function.
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Enhanced conservation properties of Vlasov codes through coupling with conservative fluid models | Many phenomena in collisionless plasma physics require a kinetic description.
The evolution of the phase space density can be modeled by means of the Vlasov
equation, which has to be solved numerically in most of the relevant cases. One
of the problems that often arise in such simulations is the violation of
important physical conservation laws. Numerical diffusion in phase space
translates into unphysical heating, which can increase the overall energy
significantly, depending on the time scale and the plasma regime. In this
paper, a general and straightforward way of improving conservation properties
of Vlasov schemes is presented that can potentially be applied to a variety of
different codes. The basic idea is to use fluid models with good conservation
properties for correcting kinetic models. The higher moments that are missing
in the fluid models are provided by the kinetic codes, so that both kinetic and
fluid codes compensate the weaknesses of each other in a closed feedback loop.
| 0 | 1 | 0 | 0 | 0 | 0 |
A note on $p^λ$-convex set in a complete Riemannian manifold | In this paper we have generalized the notion of $\lambda$-radial contraction
in complete Riemannian manifold and developed the concept of $p^\lambda$-convex
function. We have also given a counter example proving the fact that in general
$\lambda$-radial contraction of a geodesic is not necessarily a geodesic. We
have also deduced some relations between geodesic convex sets and
$p^\lambda$-convex sets and showed that under certain conditions they are
equivalent.
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Bounded cohomology and virtually free hyperbolically embedded subgroups | Using a probabilistic argument we show that the second bounded cohomology of
an acylindrically hyperbolic group $G$ (e.g., a non-elementary hyperbolic or
relatively hyperbolic group, non-exceptional mapping class group, ${\rm
Out}(F_n)$, \dots) embeds via the natural restriction maps into the inverse
limit of the second bounded cohomologies of its virtually free subgroups, and
in fact even into the inverse limit of the second bounded cohomologies of its
hyperbolically embedded virtually free subgroups. This result is new and
non-trivial even in the case where $G$ is a (non-free) hyperbolic group. The
corresponding statement fails in general for the third bounded cohomology, even
for surface groups.
| 0 | 0 | 1 | 0 | 0 | 0 |
Transpiling Programmable Computable Functions to Answer Set Programs | Programming Computable Functions (PCF) is a simplified programming language
which provides the theoretical basis of modern functional programming
languages. Answer set programming (ASP) is a programming paradigm focused on
solving search problems. In this paper we provide a translation from PCF to
ASP. Using this translation it becomes possible to specify search problems
using PCF.
| 1 | 0 | 0 | 0 | 0 | 0 |
Scattering polarization of the $d$-states of ions and solar magnetic field: Effects of isotropic collisions | Analysis of solar magnetic fields using observations as well as theoretical
interpretations of the scattering polarization is commonly designated as a high
priority area of the solar research. The interpretation of the observed
polarization raises a serious theoretical challenge to the researchers involved
in this field. In fact, realistic interpretations need detailed investigations
of the depolarizing role of isotropic collisions with neutral hydrogen. The
goal of this paper is to determine new relationships which allow the
calculation of any collisional rates of the d-levels of ions by simply
determining the value of n^* and $E_p$ without the need of determining the
interaction potentials and treating the dynamics of collisions. The
determination of n^* and E_p is easy and based on atomic data usually available
online. Accurate collisional rates allow a reliable diagnostics of solar
magnetic fields. In this work we applied our collisional FORTRAN code to a
large number of cases involving complex and simple ions. After that, the
results are utilized and injected in a genetic programming code developed with
C-langugae in order to infer original relationships which will be of great help
to solar applications. We discussed the accurarcy of our collisional rates in
the cases of polarized complex atoms and atoms with hyperfine structure. The
relationships are expressed on the tensorial basis and we explain how to
include their contributions in the master equation giving the variation of the
density matrix elements. As a test, we compared the results obtained through
the general relationships provided in this work with the results obtained
directly by running our code of collisions. These comparisons show a percentage
of error of about 10% in the average value.
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Persistence Codebooks for Topological Data Analysis | Topological data analysis, such as persistent homology has shown beneficial
properties for machine learning in many tasks. Topological representations,
such as the persistence diagram (PD), however, have a complex structure
(multiset of intervals) which makes it difficult to combine with typical
machine learning workflows. We present novel compact fixed-size vectorial
representations of PDs based on clustering and bag of words encodings that cope
well with the inherent sparsity of PDs. Our novel representations outperform
state-of-the-art approaches from topological data analysis and are
computationally more efficient.
| 0 | 0 | 0 | 1 | 0 | 0 |
Multivariate Analysis for Computing Maxima in High Dimensions | We study the problem of computing the \textsc{Maxima} of a set of $n$
$d$-dimensional points. For dimensions 2 and 3, there are algorithms to solve
the problem with order-oblivious instance-optimal running time. However, in
higher dimensions there is still room for improvements. We present an algorithm
sensitive to the structural entropy of the input set, which improves the
running time, for large classes of instances, on the best solution for
\textsc{Maxima} to date for $d \ge 4$.
| 1 | 0 | 0 | 0 | 0 | 0 |
Generalized multi-Galileons, covariantized new terms, and the no-go theorem for non-singular cosmologies | It has been pointed out that non-singular cosmological solutions in
second-order scalar-tensor theories generically suffer from gradient
instabilities. We extend this no-go result to second-order gravitational
theories with an arbitrary number of interacting scalar fields. Our proof
follows directly from the action of generalized multi-Galileons, and thus is
different from and complementary to that based on the effective field theory
approach. Several new terms for generalized multi-Galileons on a flat
background were proposed recently. We find a covariant completion of them and
confirm that they do not participate in the no-go argument.
| 0 | 1 | 0 | 0 | 0 | 0 |
Tonic activation of extrasynaptic NMDA receptors decreases intrinsic excitability and promotes bistability in a model of neuronal activity | NMDA receptors (NMDA-R) typically contribute to excitatory synaptic
transmission in the central nervous system. While calcium influx through NMDA-R
plays a critical role in synaptic plasticity, indirect experimental evidence
also exists demonstrating actions of NMDAR-mediated calcium influx on neuronal
excitability through the activation of calcium-activated potassium channels.
But, so far, this mechanism has not been studied theoretically. Our theoretical
model provide a simple description of neuronal electrical activity including
the tonic activity of NMDA receptors and a cytosolic calcium compartment. We
show that calcium influx through NMDA-R can directly be coupled to activation
of calcium-activated potassium channels providing an overall inhibitory effect
on neuronal excitability. Furthermore, the presence of tonic NMDA-R activity
promotes bistability in electrical activity by dramatically increasing the
stimulus interval where both a stable steady state and repetitive firing can
exist. This results could provide an intrinsic mechanism for the constitution
of memory traces in neuronal circuits. They also shed light on the way by which
beta-amyloids can decrease neuronal activity when interfering with NMDA-R in
Alzheimer's disease.
| 0 | 0 | 0 | 0 | 1 | 0 |
Using Minimum Path Cover to Boost Dynamic Programming on DAGs: Co-Linear Chaining Extended | Aligning sequencing reads on graph representations of genomes is an important
ingredient of pan-genomics. Such approaches typically find a set of local
anchors that indicate plausible matches between substrings of a read to
subpaths of the graph. These anchor matches are then combined to form a
(semi-local) alignment of the complete read on a subpath. Co-linear chaining is
an algorithmically rigorous approach to combine the anchors. It is a well-known
approach for the case of two sequences as inputs. Here we extend the approach
so that one of the inputs can be a directed acyclic graph (DAGs), e.g. a
splicing graph in transcriptomics or a variant graph in pan-genomics.
This extension to DAGs turns out to have a tight connection to the minimum
path cover problem, asking for a minimum-cardinality set of paths that cover
all the nodes of a DAG. We study the case when the size $k$ of a minimum path
cover is small, which is often the case in practice. First, we propose an
algorithm for finding a minimum path cover of a DAG $(V,E)$ in $O(k|E|\log|V|)$
time, improving all known time-bounds when $k$ is small and the DAG is not too
dense. Second, we introduce a general technique for extending dynamic
programming (DP) algorithms from sequences to DAGs. This is enabled by our
minimum path cover algorithm, and works by mimicking the DP algorithm for
sequences on each path of the minimum path cover. This technique generally
produces algorithms that are slower than their counterparts on sequences only
by a factor $k$. Our technique can be applied, for example, to the classical
longest increasing subsequence and longest common subsequence problems,
extended to labeled DAGs. Finally, we apply this technique to the co-linear
chaining problem. We also implemented the new co-linear chaining approach.
Experiments on splicing graphs show that the new method is efficient also in
practice.
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Visual Integration of Data and Model Space in Ensemble Learning | Ensembles of classifier models typically deliver superior performance and can
outperform single classifier models given a dataset and classification task at
hand. However, the gain in performance comes together with the lack in
comprehensibility, posing a challenge to understand how each model affects the
classification outputs and where the errors come from. We propose a tight
visual integration of the data and the model space for exploring and combining
classifier models. We introduce a workflow that builds upon the visual
integration and enables the effective exploration of classification outputs and
models. We then present a use case in which we start with an ensemble
automatically selected by a standard ensemble selection algorithm, and show how
we can manipulate models and alternative combinations.
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Steady States of Rotating Stars and Galaxies | A rotating continuum of particles attracted to each other by gravity may be
modeled by the Euler-Poisson system. The existence of solutions is a very
classical problem. Here it is proven that a curve of solutions exists,
parametrized by the rotation speed, with a fixed mass independent of the speed.
The rotation is allowed to vary with the distance to the axis. A special case
is when the equation of state is $p=\rho^\gamma,\ 6/5<\gamma<2,\ \gamma\ne4/3$,
in contrast to previous variational methods which have required $4/3 < \gamma$.
The continuum of particles may alternatively be modeled microscopically by
the Vlasov-Poisson system. The kinetic density is a prescribed function. We
prove an analogous theorem asserting the existence of a curve of solutions with
constant mass. In this model the whole range $(6/5,2)$ is allowed, including
$\gamma=4/3$.
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Critical properties of the contact process with quenched dilution | We have studied the critical properties of the contact process on a square
lattice with quenched site dilution by Monte Carlo simulations. This was
achieved by generating in advance the percolating cluster, through the use of
an appropriate epidemic model, and then by the simulation of the contact
process on the top of the percolating cluster. The dynamic critical exponents
were calculated by assuming an activated scaling relation and the static
exponents by the usual power law behavior. Our results are in agreement with
the prediction that the quenched diluted contact process belongs to the
universality class of the random transverse-field Ising model. We have also
analyzed the model and determined the phase diagram by the use of a mean-field
theory that takes into account the correlation between neighboring sites.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Unified Strouhal-Reynolds Number Relationship for Laminar Vortex Streets Generated by Different Shaped Obstacles | A new Strouhal-Reynolds number relationship, $St=1/(A+B/Re)$, has been
recently proposed based on observations of laminar vortex shedding from
circular cylinders in a flowing soap film. Since the new $St$-$Re$ relation was
derived from a general physical consideration, it raises the possibility that
it may be applicable to vortex shedding from bodies other than circular ones.
The work presented herein provides experimental evidence that this is the case.
Our measurements also show that in the asymptotic limit
($Re\rightarrow\infty$), $St_{\infty}=1/A\simeq0.21$ is constant independent of
rod shapes, leaving $B$ the only parameter that is shape dependent.
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On Data-Dependent Random Features for Improved Generalization in Supervised Learning | The randomized-feature approach has been successfully employed in large-scale
kernel approximation and supervised learning. The distribution from which the
random features are drawn impacts the number of features required to
efficiently perform a learning task. Recently, it has been shown that employing
data-dependent randomization improves the performance in terms of the required
number of random features. In this paper, we are concerned with the
randomized-feature approach in supervised learning for good generalizability.
We propose the Energy-based Exploration of Random Features (EERF) algorithm
based on a data-dependent score function that explores the set of possible
features and exploits the promising regions. We prove that the proposed score
function with high probability recovers the spectrum of the best fit within the
model class. Our empirical results on several benchmark datasets further verify
that our method requires smaller number of random features to achieve a certain
generalization error compared to the state-of-the-art while introducing
negligible pre-processing overhead. EERF can be implemented in a few lines of
code and requires no additional tuning parameters.
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Anomalous electron spectrum and its relation to peak structure of electron scattering rate in cuprate superconductors | The recent discovery of a direct link between the sharp peak in the electron
quasiparticle scattering rate of cuprate superconductors and the well-known
peak-dip-hump structure in the electron quasiparticle excitation spectrum is
calling for an explanation. Within the framework of the kinetic-energy driven
superconducting mechanism, the complicated line-shape in the electron
quasiparticle excitation spectrum of cuprate superconductors is investigated.
It is shown that the interaction between electrons by the exchange of spin
excitations generates a notable peak structure in the electron quasiparticle
scattering rate around the antinodal and nodal regions. However, this peak
structure disappears at the hot spots, which leads to that the striking
peak-dip-hump structure is developed around the antinodal and nodal regions,
and vanishes at the hot spots. The theory also confirms that the sharp peak
observed in the electron quasiparticle scattering rate is directly responsible
for the remarkable peak-dip-hump structure in the electron quasiparticle
excitation spectrum of cuprate superconductors.
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New goodness-of-fit diagnostics for conditional discrete response models | This paper proposes new specification tests for conditional models with
discrete responses, which are key to apply efficient maximum likelihood
methods, to obtain consistent estimates of partial effects and to get
appropriate predictions of the probability of future events. In particular, we
test the static and dynamic ordered choice model specifications and can cover
infinite support distributions for e.g. count data. The traditional approach
for specification testing of discrete response models is based on probability
integral transforms of a jittered discrete data which leads to continuous
uniform iid series under the true conditional distribution. Then, standard
specification testing techniques for continuous variables could be applied to
the transformed series, but the extra randomness from jitters affects the power
properties of these methods. We investigate in this paper an alternative
transformation based only on original discrete data that avoids any
randomization. We analyze the asymptotic properties of goodness-of-fit tests
based on this new transformation and explore the properties in finite samples
of a bootstrap algorithm to approximate the critical values of test statistics
which are model and parameter dependent. We show analytically and in
simulations that our approach dominates the methods based on randomization in
terms of power. We apply the new tests to models of the monetary policy
conducted by the Federal Reserve.
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Tailoring Heterovalent Interface Formation with Light | Integrating different semiconductor materials into an epitaxial device
structure offers additional degrees of freedom to select for optimal material
properties in each layer. However, interface between materials with different
valences (i.e. III-V, II-VI and IV semiconductors) can be difficult to form
with high quality. Using ZnSe/GaAs as a model system, we explore the use of UV
illumination during heterovalent interface growth by molecular beam epitaxy as
a way to modify the interface properties. We find that UV illumination alters
the mixture of chemical bonds at the interface, permitting the formation of
Ga-Se bonds that help to passivate the underlying GaAs layer. Illumination also
helps to reduce defects in the ZnSe epilayer. These results suggest that
moderate UV illumination during growth may be used as a way to improve the
optical properties of both the GaAs and ZnSe layers on either side of the
interface.
| 0 | 1 | 0 | 0 | 0 | 0 |
Looking backward: From Euler to Riemann | We survey the main ideas in the early history of the subjects on which
Riemann worked and that led to some of his most important discoveries. The
subjects discussed include the theory of functions of a complex variable,
elliptic and Abelian integrals, the hypergeometric series, the zeta function,
topology, differential geometry, integration, and the notion of space. We shall
see that among Riemann's predecessors in all these fields, one name occupies a
prominent place, this is Leonhard Euler. The final version of this paper will
appear in the book \emph{From Riemann to differential geometry and relativity}
(L. Ji, A. Papadopoulos and S. Yamada, ed.) Berlin: Springer, 2017.
| 0 | 0 | 1 | 0 | 0 | 0 |
Algorithmic Performance-Accuracy Trade-off in 3D Vision Applications Using HyperMapper | In this paper we investigate an emerging application, 3D scene understanding,
likely to be significant in the mobile space in the near future. The goal of
this exploration is to reduce execution time while meeting our quality of
result objectives. In previous work we showed for the first time that it is
possible to map this application to power constrained embedded systems,
highlighting that decision choices made at the algorithmic design-level have
the most impact.
As the algorithmic design space is too large to be exhaustively evaluated, we
use a previously introduced multi-objective Random Forest Active Learning
prediction framework dubbed HyperMapper, to find good algorithmic designs. We
show that HyperMapper generalizes on a recent cutting edge 3D scene
understanding algorithm and on a modern GPU-based computer architecture.
HyperMapper is able to beat an expert human hand-tuning the algorithmic
parameters of the class of Computer Vision applications taken under
consideration in this paper automatically. In addition, we use crowd-sourcing
using a 3D scene understanding Android app to show that the Pareto front
obtained on an embedded system can be used to accelerate the same application
on all the 83 smart-phones and tablets crowd-sourced with speedups ranging from
2 to over 12.
| 1 | 0 | 0 | 0 | 0 | 0 |
A comparison theorem for MW-motivic cohomology | We prove that for a finitely generated field over an infinite perfect field
k, and for any integer n, the (n,n)-th MW-motivic cohomology group identifies
with the n-th Milnor-Witt K-theory group of that field
| 0 | 0 | 1 | 0 | 0 | 0 |
Wiener Filtering for Passive Linear Quantum Systems | This paper considers a version of the Wiener filtering problem for
equalization of passive quantum linear quantum systems. We demonstrate that
taking into consideration the quantum nature of the signals involved leads to
features typically not encountered in classical equalization problems. Most
significantly, finding a mean-square optimal quantum equalizing filter amounts
to solving a nonconvex constrained optimization problem. We discuss two
approaches to solving this problem, both involving a relaxation of the
constraint. In both cases, unlike classical equalization, there is a threshold
on the variance of the noise below which an improvement of the mean-square
error cannot be guaranteed.
| 1 | 0 | 0 | 0 | 0 | 0 |
Robust Navigation In GNSS Degraded Environment Using Graph Optimization | Robust navigation in urban environments has received a considerable amount of
both academic and commercial interest over recent years. This is primarily due
to large commercial organizations such as Google and Uber stepping into the
autonomous navigation market. Most of this research has shied away from Global
Navigation Satellite System (GNSS) based navigation. The aversion to utilizing
GNSS data is due to the degraded nature of the data in urban environment (e.g.,
multipath, poor satellite visibility). The degradation of the GNSS data in
urban environments makes it such that traditional (GNSS) positioning methods
(e.g., extended Kalman filter, particle filters) perform poorly. However,
recent advances in robust graph theoretic based sensor fusion methods,
primarily applied to Simultaneous Localization and Mapping (SLAM) based robotic
applications, can also be applied to GNSS data processing. This paper will
utilize one such method known as the factor graph in conjunction several robust
optimization techniques to evaluate their applicability to robust GNSS data
processing. The goals of this study are two-fold. First, for GNSS applications,
we will experimentally evaluate the effectiveness of robust optimization
techniques within a graph-theoretic estimation framework. Second, by releasing
the software developed and data sets used for this study, we will introduce a
new open-source front-end to the Georgia Tech Smoothing and Mapping (GTSAM)
library for the purpose of integrating GNSS pseudorange observations.
| 1 | 0 | 0 | 0 | 0 | 0 |
On reproduction of On the regularization of Wasserstein GANs | This report has several purposes. First, our report is written to investigate
the reproducibility of the submitted paper On the regularization of Wasserstein
GANs (2018). Second, among the experiments performed in the submitted paper,
five aspects were emphasized and reproduced: learning speed, stability,
robustness against hyperparameter, estimating the Wasserstein distance, and
various sampling method. Finally, we identify which parts of the contribution
can be reproduced, and at what cost in terms of resources. All source code for
reproduction is open to the public.
| 1 | 0 | 0 | 1 | 0 | 0 |
Density Estimation with Contaminated Data: Minimax Rates and Theory of Adaptation | This paper studies density estimation under pointwise loss in the setting of
contamination model. The goal is to estimate $f(x_0)$ at some
$x_0\in\mathbb{R}$ with i.i.d. observations, $$ X_1,\dots,X_n\sim
(1-\epsilon)f+\epsilon g, $$ where $g$ stands for a contamination distribution.
In the context of multiple testing, this can be interpreted as estimating the
null density at a point. We carefully study the effect of contamination on
estimation through the following model indices: contamination proportion
$\epsilon$, smoothness of target density $\beta_0$, smoothness of contamination
density $\beta_1$, and level of contamination $m$ at the point to be estimated,
i.e. $g(x_0)\leq m$. It is shown that the minimax rate with respect to the
squared error loss is of order $$
[n^{-\frac{2\beta_0}{2\beta_0+1}}]\vee[\epsilon^2(1\wedge
m)^2]\vee[n^{-\frac{2\beta_1}{2\beta_1+1}}\epsilon^{\frac{2}{2\beta_1+1}}], $$
which characterizes the exact influence of contamination on the difficulty of
the problem. We then establish the minimal cost of adaptation to contamination
proportion, to smoothness and to both of the numbers. It is shown that some
small price needs to be paid for adaptation in any of the three cases.
Variations of Lepski's method are considered to achieve optimal adaptation.
The problem is also studied when there is no smoothness assumption on the
contamination distribution. This setting that allows for an arbitrary
contamination distribution is recognized as Huber's $\epsilon$-contamination
model. The minimax rate is shown to be $$
[n^{-\frac{2\beta_0}{2\beta_0+1}}]\vee [\epsilon^{\frac{2\beta_0}{\beta_0+1}}].
$$ The adaptation theory is also different from the smooth contamination case.
While adaptation to either contamination proportion or smoothness only costs a
logarithmic factor, adaptation to both numbers is proved to be impossible.
| 0 | 0 | 1 | 1 | 0 | 0 |
A uniform bound on the Brauer groups of certain log K3 surfaces | Let U be the complement of a smooth anticanonical divisor in a del Pezzo
surface of degree at most 7 over a number field k. We show that there is an
effective uniform bound for the size of the Brauer group of U in terms of the
degree of k.
| 0 | 0 | 1 | 0 | 0 | 0 |
Composable Deep Reinforcement Learning for Robotic Manipulation | Model-free deep reinforcement learning has been shown to exhibit good
performance in domains ranging from video games to simulated robotic
manipulation and locomotion. However, model-free methods are known to perform
poorly when the interaction time with the environment is limited, as is the
case for most real-world robotic tasks. In this paper, we study how maximum
entropy policies trained using soft Q-learning can be applied to real-world
robotic manipulation. The application of this method to real-world manipulation
is facilitated by two important features of soft Q-learning. First, soft
Q-learning can learn multimodal exploration strategies by learning policies
represented by expressive energy-based models. Second, we show that policies
learned with soft Q-learning can be composed to create new policies, and that
the optimality of the resulting policy can be bounded in terms of the
divergence between the composed policies. This compositionality provides an
especially valuable tool for real-world manipulation, where constructing new
policies by composing existing skills can provide a large gain in efficiency
over training from scratch. Our experimental evaluation demonstrates that soft
Q-learning is substantially more sample efficient than prior model-free deep
reinforcement learning methods, and that compositionality can be performed for
both simulated and real-world tasks.
| 1 | 0 | 0 | 1 | 0 | 0 |
Stimulated Raman Scattering Imposes Fundamental Limits to the Duration and Bandwidth of Temporal Cavity Solitons | Temporal cavity solitons (CS) are optical pulses that can persist in passive
resonators, and they play a key role in the generation of coherent
microresonator frequency combs. In resonators made of amorphous materials, such
as fused silica, they can exhibit a spectral red-shift due to stimulated Raman
scattering. Here we show that this Raman-induced self-frequency-shift imposes a
fundamental limit on the duration and bandwidth of temporal CSs. Specifically,
we theoretically predict that stimulated Raman scattering introduces a
previously unidentified Hopf bifurcation that leads to destabilization of CSs
at large pump-cavity detunings, limiting the range of detunings over which they
can exist. We have confirmed our theoretical predictions by performing
extensive experiments in several different synchronously-driven fiber ring
resonators, obtaining results in excellent agreement with numerical
simulations. Our results could have significant implications for the future
design of Kerr frequency comb systems based on amorphous microresonators.
| 0 | 1 | 0 | 0 | 0 | 0 |
Investigation of Using VAE for i-Vector Speaker Verification | New system for i-vector speaker recognition based on variational autoencoder
(VAE) is investigated. VAE is a promising approach for developing accurate deep
nonlinear generative models of complex data. Experiments show that VAE provides
speaker embedding and can be effectively trained in an unsupervised manner. LLR
estimate for VAE is developed. Experiments on NIST SRE 2010 data demonstrate
its correctness. Additionally, we show that the performance of VAE-based system
in the i-vectors space is close to that of the diagonal PLDA. Several
interesting results are also observed in the experiments with $\beta$-VAE. In
particular, we found that for $\beta\ll 1$, VAE can be trained to capture the
features of complex input data distributions in an effective way, which is hard
to obtain in the standard VAE ($\beta=1$).
| 1 | 0 | 0 | 1 | 0 | 0 |
A Unifying Framework for Convergence Analysis of Approximate Newton Methods | Many machine learning models are reformulated as optimization problems. Thus,
it is important to solve a large-scale optimization problem in big data
applications. Recently, subsampled Newton methods have emerged to attract much
attention for optimization due to their efficiency at each iteration, rectified
a weakness in the ordinary Newton method of suffering a high cost in each
iteration while commanding a high convergence rate. Other efficient stochastic
second order methods are also proposed. However, the convergence properties of
these methods are still not well understood. There are also several important
gaps between the current convergence theory and the performance in real
applications. In this paper, we aim to fill these gaps. We propose a unifying
framework to analyze local convergence properties of second order methods.
Based on this framework, our theoretical analysis matches the performance in
real applications.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Newman property for BLD-mappings | We define a Newman property for BLD-mappings and study its connections to the
porosity of the branch set in the setting of generalized manifolds equipped
with complete path metrics.
| 0 | 0 | 1 | 0 | 0 | 0 |
Learning to Use Learners' Advice | In this paper, we study a variant of the framework of online learning using
expert advice with limited/bandit feedback. We consider each expert as a
learning entity, seeking to more accurately reflecting certain real-world
applications. In our setting, the feedback at any time $t$ is limited in a
sense that it is only available to the expert $i^t$ that has been selected by
the central algorithm (forecaster), \emph{i.e.}, only the expert $i^t$ receives
feedback from the environment and gets to learn at time $t$. We consider a
generic black-box approach whereby the forecaster does not control or know the
learning dynamics of the experts apart from knowing the following no-regret
learning property: the average regret of any expert $j$ vanishes at a rate of
at least $O(t_j^{\regretRate-1})$ with $t_j$ learning steps where $\regretRate
\in [0, 1]$ is a parameter.
In the spirit of competing against the best action in hindsight in
multi-armed bandits problem, our goal here is to be competitive w.r.t. the
cumulative losses the algorithm could receive by following the policy of always
selecting one expert. We prove the following hardness result: without any
coordination between the forecaster and the experts, it is impossible to design
a forecaster achieving no-regret guarantees. In order to circumvent this
hardness result, we consider a practical assumption allowing the forecaster to
"guide" the learning process of the experts by filtering/blocking some of the
feedbacks observed by them from the environment, \emph{i.e.}, not allowing the
selected expert $i^t$ to learn at time $t$ for some time steps. Then, we design
a novel no-regret learning algorithm \algo for this problem setting by
carefully guiding the feedbacks observed by experts. We prove that \algo
achieves the worst-case expected cumulative regret of $O(\Time^\frac{1}{2 -
\regretRate})$ after $\Time$ time steps.
| 1 | 0 | 0 | 0 | 0 | 0 |
Fast and Accurate Time Series Classification with WEASEL | Time series (TS) occur in many scientific and commercial applications,
ranging from earth surveillance to industry automation to the smart grids. An
important type of TS analysis is classification, which can, for instance,
improve energy load forecasting in smart grids by detecting the types of
electronic devices based on their energy consumption profiles recorded by
automatic sensors. Such sensor-driven applications are very often characterized
by (a) very long TS and (b) very large TS datasets needing classification.
However, current methods to time series classification (TSC) cannot cope with
such data volumes at acceptable accuracy; they are either scalable but offer
only inferior classification quality, or they achieve state-of-the-art
classification quality but cannot scale to large data volumes.
In this paper, we present WEASEL (Word ExtrAction for time SEries
cLassification), a novel TSC method which is both scalable and accurate. Like
other state-of-the-art TSC methods, WEASEL transforms time series into feature
vectors, using a sliding-window approach, which are then analyzed through a
machine learning classifier. The novelty of WEASEL lies in its specific method
for deriving features, resulting in a much smaller yet much more discriminative
feature set. On the popular UCR benchmark of 85 TS datasets, WEASEL is more
accurate than the best current non-ensemble algorithms at orders-of-magnitude
lower classification and training times, and it is almost as accurate as
ensemble classifiers, whose computational complexity makes them inapplicable
even for mid-size datasets. The outstanding robustness of WEASEL is also
confirmed by experiments on two real smart grid datasets, where it
out-of-the-box achieves almost the same accuracy as highly tuned,
domain-specific methods.
| 1 | 0 | 0 | 1 | 0 | 0 |
Deep Neural Network Architectures for Modulation Classification | In this work, we investigate the value of employing deep learning for the
task of wireless signal modulation recognition. Recently in [1], a framework
has been introduced by generating a dataset using GNU radio that mimics the
imperfections in a real wireless channel, and uses 10 different modulation
types. Further, a convolutional neural network (CNN) architecture was developed
and shown to deliver performance that exceeds that of expert-based approaches.
Here, we follow the framework of [1] and find deep neural network architectures
that deliver higher accuracy than the state of the art. We tested the
architecture of [1] and found it to achieve an accuracy of approximately 75% of
correctly recognizing the modulation type. We first tune the CNN architecture
of [1] and find a design with four convolutional layers and two dense layers
that gives an accuracy of approximately 83.8% at high SNR. We then develop
architectures based on the recently introduced ideas of Residual Networks
(ResNet [2]) and Densely Connected Networks (DenseNet [3]) to achieve high SNR
accuracies of approximately 83.5% and 86.6%, respectively. Finally, we
introduce a Convolutional Long Short-term Deep Neural Network (CLDNN [4]) to
achieve an accuracy of approximately 88.5% at high SNR.
| 1 | 0 | 0 | 1 | 0 | 0 |
Stack Overflow Considered Harmful? The Impact of Copy&Paste on Android Application Security | Online programming discussion platforms such as Stack Overflow serve as a
rich source of information for software developers. Available information
include vibrant discussions and oftentimes ready-to-use code snippets.
Anecdotes report that software developers copy and paste code snippets from
those information sources for convenience reasons. Such behavior results in a
constant flow of community-provided code snippets into production software. To
date, the impact of this behaviour on code security is unknown. We answer this
highly important question by quantifying the proliferation of security-related
code snippets from Stack Overflow in Android applications available on Google
Play. Access to the rich source of information available on Stack Overflow
including ready-to-use code snippets provides huge benefits for software
developers. However, when it comes to code security there are some caveats to
bear in mind: Due to the complex nature of code security, it is very difficult
to provide ready-to-use and secure solutions for every problem. Hence,
integrating a security-related code snippet from Stack Overflow into production
software requires caution and expertise. Unsurprisingly, we observed insecure
code snippets being copied into Android applications millions of users install
from Google Play every day. To quantitatively evaluate the extent of this
observation, we scanned Stack Overflow for code snippets and evaluated their
security score using a stochastic gradient descent classifier. In order to
identify code reuse in Android applications, we applied state-of-the-art static
analysis. Our results are alarming: 15.4% of the 1.3 million Android
applications we analyzed, contained security-related code snippets from Stack
Overflow. Out of these 97.9% contain at least one insecure code snippet.
| 1 | 0 | 0 | 0 | 0 | 0 |
United Nations Digital Blue Helmets as a Starting Point for Cyber Peacekeeping | Prior works, such as the Tallinn manual on the international law applicable
to cyber warfare, focus on the circumstances of cyber warfare. Many
organizations are considering how to conduct cyber warfare, but few have
discussed methods to reduce, or even prevent, cyber conflict. A recent series
of publications started developing the framework of Cyber Peacekeeping (CPK)
and its legal requirements. These works assessed the current state of
organizations such as ITU IMPACT, NATO CCDCOE and Shanghai Cooperation
Organization, and found that they did not satisfy requirements to effectively
host CPK activities. An assessment of organizations currently working in the
areas related to CPK found that the United Nations (UN) has mandates and
organizational structures that appear to somewhat overlap the needs of CPK.
However, the UN's current approach to Peacekeeping cannot be directly mapped to
cyberspace. In this research we analyze the development of traditional
Peacekeeping in the United Nations, and current initiatives in cyberspace.
Specifically, we will compare the proposed CPK framework with the recent
initiative of the United Nations named the 'Digital Blue Helmets' as well as
with other projects in the UN which helps to predict and mitigate conflicts.
Our goal is to find practical recommendations for the implementation of the CPK
framework in the United Nations, and to examine how responsibilities defined in
the CPK framework overlap with those of the 'Digital Blue Helmets' and the
Global Pulse program.
| 1 | 0 | 0 | 0 | 0 | 0 |
Motives of derived equivalent K3 surfaces | We observe that derived equivalent K3 surfaces have isomorphic Chow motives.
| 0 | 0 | 1 | 0 | 0 | 0 |
Robust Dual View Deep Agent | Motivated by recent advance of machine learning using Deep Reinforcement
Learning this paper proposes a modified architecture that produces more robust
agents and speeds up the training process. Our architecture is based on
Asynchronous Advantage Actor-Critic (A3C) algorithm where the total input
dimensionality is halved by dividing the input into two independent streams. We
use ViZDoom, 3D world software that is based on the classical first person
shooter video game, Doom, as a test case. The experiments show that in
comparison to single input agents, the proposed architecture succeeds to have
the same playing performance and shows more robust behavior, achieving
significant reduction in the number of training parameters of almost 30%.
| 0 | 0 | 0 | 1 | 0 | 0 |
Microplasma generation by slow microwave in an electromagnetically induced transparency-like metasurface | Microplasma generation using microwaves in an electromagnetically induced
transparency (EIT)-like metasurface composed of two types of radiatively
coupled cut-wire resonators with slightly different resonance frequencies is
investigated. Microplasma is generated in either of the gaps of the cut-wire
resonators as a result of strong enhancement of the local electric field
associated with resonance and slow microwave effect. The threshold microwave
power for plasma ignition is found to reach a minimum at the EIT-like
transmission peak frequency, where the group index is maximized. A pump-probe
measurement of the metasurface reveals that the transmission properties can be
significantly varied by varying the properties of the generated microplasma
near the EIT-like transmission peak frequency and the resonance frequency. The
electron density of the microplasma is roughly estimated to be of order
$1\times 10^{10}\,\mathrm{cm}^{-3}$ for a pump power of $15.8\,\mathrm{W}$ by
comparing the measured transmission spectrum for the probe wave with the
numerically calculated spectrum. In the calculation, we assumed that the plasma
is uniformly generated in the resonator gap, that the electron temperature is
$2\,\mathrm{eV}$, and that the elastic scattering cross section is $20 \times
10^{-16}\,\mathrm{cm}^2$.
| 0 | 1 | 0 | 0 | 0 | 0 |
K-theory of group Banach algebras and Banach property RD | We investigate Banach algebras of convolution operators on the $L^p$ spaces
of a locally compact group, and their K-theory. We show that for a discrete
group, the corresponding K-theory groups depend continuously on $p$ in an
inductive sense. Via a Banach version of property RD, we show that for a large
class of groups, the K-theory groups of the Banach algebras are independent of
$p$.
| 0 | 0 | 1 | 0 | 0 | 0 |
A Biologically Plausible Supervised Learning Method for Spiking Neural Networks Using the Symmetric STDP Rule | Spiking neural networks (SNNs) possess energy-efficient potential due to
event-based computation. However, supervised training of SNNs remains a
challenge as spike activities are non-differentiable. Previous SNNs training
methods can basically be categorized into two classes, backpropagation-like
training methods and plasticity-based learning methods. The former methods are
dependent on energy-inefficient real-valued computation and non-local
transmission, as also required in artificial neural networks (ANNs), while the
latter either be considered biologically implausible or exhibit poor
performance. Hence, biologically plausible (bio-plausible) high-performance
supervised learning (SL) methods for SNNs remain deficient. In this paper, we
proposed a novel bio-plausible SNN model for SL based on the symmetric
spike-timing dependent plasticity (sym-STDP) rule found in neuroscience. By
combining the sym-STDP rule with bio-plausible synaptic scaling and intrinsic
plasticity of the dynamic threshold, our SNN model implemented SL well and
achieved good performance in the benchmark recognition task (MNIST). To reveal
the underlying mechanism of our SL model, we visualized both layer-based
activities and synaptic weights using the t-distributed stochastic neighbor
embedding (t-SNE) method after training and found that they were well
clustered, thereby demonstrating excellent classification ability. As the
learning rules were bio-plausible and based purely on local spike events, our
model could be easily applied to neuromorphic hardware for online training and
may be helpful for understanding SL information processing at the synaptic
level in biological neural systems.
| 0 | 0 | 0 | 0 | 1 | 0 |
Learning from various labeling strategies for suicide-related messages on social media: An experimental study | Suicide is an important but often misunderstood problem, one that researchers
are now seeking to better understand through social media. Due in large part to
the fuzzy nature of what constitutes suicidal risks, most supervised approaches
for learning to automatically detect suicide-related activity in social media
require a great deal of human labor to train. However, humans themselves have
diverse or conflicting views on what constitutes suicidal thoughts. So how to
obtain reliable gold standard labels is fundamentally challenging and, we
hypothesize, depends largely on what is asked of the annotators and what slice
of the data they label. We conducted multiple rounds of data labeling and
collected annotations from crowdsourcing workers and domain experts. We
aggregated the resulting labels in various ways to train a series of supervised
models. Our preliminary evaluations show that using unanimously agreed labels
from multiple annotators is helpful to achieve robust machine models.
| 1 | 0 | 0 | 0 | 0 | 0 |
Modeling Information Flow Through Deep Neural Networks | This paper proposes a principled information theoretic analysis of
classification for deep neural network structures, e.g. convolutional neural
networks (CNN). The output of convolutional filters is modeled as a random
variable Y conditioned on the object class C and network filter bank F. The
conditional entropy (CENT) H(Y |C,F) is shown in theory and experiments to be a
highly compact and class-informative code, that can be computed from the filter
outputs throughout an existing CNN and used to obtain higher classification
results than the original CNN itself. Experiments demonstrate the effectiveness
of CENT feature analysis in two separate CNN classification contexts. 1) In the
classification of neurodegeneration due to Alzheimer's disease (AD) and natural
aging from 3D magnetic resonance image (MRI) volumes, 3 CENT features result in
an AUC=94.6% for whole-brain AD classification, the highest reported accuracy
on the public OASIS dataset used and 12% higher than the softmax output of the
original CNN trained for the task. 2) In the context of visual object
classification from 2D photographs, transfer learning based on a small set of
CENT features identified throughout an existing CNN leads to AUC values
comparable to the 1000-feature softmax output of the original network when
classifying previously unseen object categories. The general information
theoretical analysis explains various recent CNN design successes, e.g. densely
connected CNN architectures, and provides insights for future research
directions in deep learning.
| 1 | 0 | 0 | 1 | 0 | 0 |
Scalable Twin Neural Networks for Classification of Unbalanced Data | Twin Support Vector Machines (TWSVMs) have emerged an efficient alternative
to Support Vector Machines (SVM) for learning from imbalanced datasets. The
TWSVM learns two non-parallel classifying hyperplanes by solving a couple of
smaller sized problems. However, it is unsuitable for large datasets, as it
involves matrix operations. In this paper, we discuss a Twin Neural Network
(Twin NN) architecture for learning from large unbalanced datasets. The Twin NN
also learns an optimal feature map, allowing for better discrimination between
classes. We also present an extension of this network architecture for
multiclass datasets. Results presented in the paper demonstrate that the Twin
NN generalizes well and scales well on large unbalanced datasets.
| 1 | 0 | 0 | 0 | 0 | 0 |
Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition | Recurrent Neural Networks (RNNs) are powerful sequence modeling tools.
However, when dealing with high dimensional inputs, the training of RNNs
becomes computational expensive due to the large number of model parameters.
This hinders RNNs from solving many important computer vision tasks, such as
Action Recognition in Videos and Image Captioning. To overcome this problem, we
propose a compact and flexible structure, namely Block-Term tensor
decomposition, which greatly reduces the parameters of RNNs and improves their
training efficiency. Compared with alternative low-rank approximations, such as
tensor-train RNN (TT-RNN), our method, Block-Term RNN (BT-RNN), is not only
more concise (when using the same rank), but also able to attain a better
approximation to the original RNNs with much fewer parameters. On three
challenging tasks, including Action Recognition in Videos, Image Captioning and
Image Generation, BT-RNN outperforms TT-RNN and the standard RNN in terms of
both prediction accuracy and convergence rate. Specifically, BT-LSTM utilizes
17,388 times fewer parameters than the standard LSTM to achieve an accuracy
improvement over 15.6\% in the Action Recognition task on the UCF11 dataset.
| 1 | 0 | 0 | 1 | 0 | 0 |
Deep Learning applied to Road Traffic Speed forecasting | In this paper, we propose deep learning architectures (FNN, CNN and LSTM) to
forecast a regression model for time dependent data. These algorithm's are
designed to handle Floating Car Data (FCD) historic speeds to predict road
traffic data. For this we aggregate the speeds into the network inputs in an
innovative way. We compare the RMSE thus obtained with the results of a simpler
physical model, and show that the latter achieves better RMSE accuracy. We also
propose a new indicator, which evaluates the algorithms improvement when
compared to a benchmark prediction. We conclude by questioning the interest of
using deep learning methods for this specific regression task.
| 1 | 0 | 0 | 1 | 0 | 0 |
Wait For It: Identifying "On-Hold" Self-Admitted Technical Debt | Self-admitted technical debt refers to situations where a software developer
knows that their current implementation is not optimal and indicates this using
a source code comment. In this work, we hypothesize that it is possible to
develop automated techniques to understand a subset of these comments in more
detail, and to propose tool support that can help developers manage
self-admitted technical debt more effectively. Based on a qualitative study of
335 comments indicating self-admitted technical debt, we first identify one
particular class of debt amenable to automated management: "on-hold"
self-admitted technical debt, i.e., debt which contains a condition to indicate
that a developer is waiting for a certain event or an updated functionality
having been implemented elsewhere. We then design and evaluate an automated
classifier which can automatically identify these "on-hold" instances with a
precision of 0.81 as well as detect the specific conditions that developers are
waiting for. Our work presents a first step towards automated tool support that
is able to indicate when certain instances of self-admitted technical debt are
ready to be addressed.
| 1 | 0 | 0 | 0 | 0 | 0 |
Real time observation of granular rock analogue material deformation and failure using nonlinear laser interferometry | A better understanding and anticipation of natural processes such as
landsliding or seismic fault activity requires detailed theoretical and
experimental analysis of rock mechanics and geomaterial dynamics. These last
decades, considerable progress has been made towards understanding deformation
and fracture process in laboratory experiment on granular rock materials, as
the well-known shear banding experiment. One of the reasons for this progress
is the continuous improvement in the instrumental techniques of observation.
But the lack of real time methods does not allow the detection of indicators of
the upcoming fracture process and thus to anticipate the phenomenon. Here, we
have performed uniaxial compression experiments to analyse the response of a
granular rock material sample to different shocks. We use a novel
interferometric laser sensor based on the nonlinear self-mixing interferometry
technique to observe in real time the deformations of the sample and assess its
usefulness as a diagnostic tool for the analysis of geomaterial dynamics. Due
to the high spatial and temporal resolution of this approach, we observe both
vibrations processes in response to a dynamic loading and the onset of failure.
The latter is preceded by a continuous variation of vibration period of the
material. After several shocks, the material response is no longer reversible
and we detect a progressive accumulation of irreversible deformation leading to
the fracture process. We demonstrate that material failure is anticipated by
the critical slowing down of the surface vibrational motion, which may
therefore be envisioned as an early warning signal or predictor to the
macroscopic failure of the sample. The nonlinear self-mixing interferometry
technique is readily extensible to fault propagation measurements. As such, it
opens a new window of observation for the study of geomaterial deformation and
failure.
| 0 | 1 | 0 | 0 | 0 | 0 |
Virtual link and knot invariants from non-abelian Yang-Baxter 2-cocycle pairs | For a given $(X,S,\beta)$, where $S,\beta\colon X\times X\to X\times X$ are
set theoretical solutions of Yang-Baxter equation with a compatibility
condition, we define an invariant for virtual (or classical) knots/links using
non commutative 2-cocycles pairs $(f,g)$ that generalizes the one defined in
[FG2]. We also define, a group $U_{nc}^{fg}=U_{nc}^{fg}(X,S,\beta)$ and
functions $\pi_f, \pi_g\colon X\times X\to U_{nc}^{fg}(X)$ governing all
2-cocycles in $X$. We exhibit examples of computations achieved using GAP.
| 0 | 0 | 1 | 0 | 0 | 0 |
An extensible cluster-graph taxonomy for open set sound scene analysis | We present a new extensible and divisible taxonomy for open set sound scene
analysis. This new model allows complex scene analysis with tangible
descriptors and perception labels. Its novel structure is a cluster graph such
that each cluster (or subset) can stand alone for targeted analyses such as
office sound event detection, whilst maintaining integrity over the whole graph
(superset) of labels. The key design benefit is its extensibility as new labels
are needed during new data capture. Furthermore, datasets which use the same
taxonomy are easily augmented, saving future data collection effort. We balance
the details needed for complex scene analysis with avoiding 'the taxonomy of
everything' with our framework to ensure no duplicity in the superset of labels
and demonstrate this with DCASE challenge classifications.
| 1 | 0 | 0 | 0 | 0 | 0 |
CREATE: Multimodal Dataset for Unsupervised Learning, Generative Modeling and Prediction of Sensory Data from a Mobile Robot in Indoor Environments | The CREATE database is composed of 14 hours of multimodal recordings from a
mobile robotic platform based on the iRobot Create. The various sensors cover
vision, audition, motors and proprioception. The dataset has been designed in
the context of a mobile robot that can learn multimodal representations of its
environment, thanks to its ability to navigate the environment. This ability
can also be used to learn the dependencies and relationships between the
different modalities of the robot (e.g. vision, audition), as they reflect both
the external environment and the internal state of the robot. The provided
multimodal dataset is expected to have multiple usages, such as multimodal
unsupervised object learning, multimodal prediction and egomotion/causality
detection.
| 1 | 0 | 0 | 0 | 0 | 0 |
Deep learning enhanced mobile-phone microscopy | Mobile-phones have facilitated the creation of field-portable, cost-effective
imaging and sensing technologies that approach laboratory-grade instrument
performance. However, the optical imaging interfaces of mobile-phones are not
designed for microscopy and produce spatial and spectral distortions in imaging
microscopic specimens. Here, we report on the use of deep learning to correct
such distortions introduced by mobile-phone-based microscopes, facilitating the
production of high-resolution, denoised and colour-corrected images, matching
the performance of benchtop microscopes with high-end objective lenses, also
extending their limited depth-of-field. After training a convolutional neural
network, we successfully imaged various samples, including blood smears,
histopathology tissue sections, and parasites, where the recorded images were
highly compressed to ease storage and transmission for telemedicine
applications. This method is applicable to other low-cost, aberrated imaging
systems, and could offer alternatives for costly and bulky microscopes, while
also providing a framework for standardization of optical images for clinical
and biomedical applications.
| 1 | 1 | 0 | 0 | 0 | 0 |
On convergence of the sample correlation matrices in high-dimensional data | In this paper, we consider an estimation problem concerning the matrix of
correlation coefficients in context of high dimensional data settings. In
particular, we revisit some results in Li and Rolsalsky [Li, D. and Rolsalsky,
A. (2006). Some strong limit theorems for the largest entries of sample
correlation matrices, The Annals of Applied Probability, 16, 1, 423-447]. Four
of the main theorems of Li and Rolsalsky (2006) are established in their full
generalities and we simplify substantially some proofs of the quoted paper.
Further, we generalize a theorem which is useful in deriving the existence of
the pth moment as well as in studying the convergence rates in law of large
numbers.
| 0 | 0 | 1 | 1 | 0 | 0 |
Minimal Controllability of Conjunctive Boolean Networks is NP-Complete | Given a conjunctive Boolean network (CBN) with $n$ state-variables, we
consider the problem of finding a minimal set of state-variables to directly
affect with an input so that the resulting conjunctive Boolean control network
(CBCN) is controllable. We give a necessary and sufficient condition for
controllability of a CBCN; an $O(n^2)$-time algorithm for testing
controllability; and prove that nonetheless the minimal controllability problem
for CBNs is NP-hard.
| 1 | 0 | 1 | 0 | 0 | 0 |
Consensus report on 25 years of searches for damped Ly$α$ galaxies in emission: Confirming their metallicity-luminosity relation at $z \gtrsim 2$ | Starting from a summary of detection statistics of our recent X-shooter
campaign, we review the major surveys, both space and ground based, for
emission counterparts of high-redshift damped Ly$\alpha$ absorbers (DLAs)
carried out since the first detection 25 years ago. We show that the detection
rates of all surveys are precisely reproduced by a simple model in which the
metallicity and luminosity of the galaxy associated to the DLA follow a
relation of the form, ${\rm M_{UV}} = -5 \times \left(\,[{\rm M/H}] + 0.3\,
\right) - 20.8$, and the DLA cross-section follows a relation of the form
$\sigma_{DLA} \propto L^{0.8}$. Specifically, our spectroscopic campaign
consists of 11 DLAs preselected based on their equivalent width of SiII
$\lambda1526$ to have a metallicity higher than [Si/H] > -1. The targets have
been observed with the X-shooter spectrograph at the Very Large Telescope to
search for emission lines around the quasars. We observe a high detection rate
of 64% (7/11), significantly higher than the typical $\sim$10% for random,
HI-selected DLA samples. We use the aforementioned model, to simulate the
results of our survey together with a range of previous surveys: spectral
stacking, direct imaging (using the `double DLA' technique), long-slit
spectroscopy, and integral field spectroscopy. Based on our model results, we
are able to reconcile all results. Some tension is observed between model and
data when looking at predictions of Ly$\alpha$ emission for individual targets.
However, the object to object variations are most likely a result of the
significant scatter in the underlying scaling relations as well as
uncertainties in the amount of dust which affects the emission.
| 0 | 1 | 0 | 0 | 0 | 0 |
D-optimal designs for complex Ornstein-Uhlenbeck processes | Complex Ornstein-Uhlenbeck (OU) processes have various applications in
statistical modelling. They play role e.g. in the description of the motion of
a charged test particle in a constant magnetic field or in the study of
rotating waves in time-dependent reaction diffusion systems, whereas Kolmogorov
used such a process to model the so-called Chandler wobble, small deviation in
the Earth's axis of rotation. In these applications parameter estimation and
model fitting is based on discrete observations of the underlying stochastic
process, however, the accuracy of the results strongly depend on the
observation points.
This paper studies the properties of D-optimal designs for estimating the
parameters of a complex OU process with a trend. We show that in contrast with
the case of the classical real OU process, a D-optimal design exists not only
for the trend parameter, but also for joint estimation of the covariance
parameters, moreover, these optimal designs are equidistant.
| 0 | 0 | 1 | 1 | 0 | 0 |
On Stein's Identity and Near-Optimal Estimation in High-dimensional Index Models | We consider estimating the parametric components of semi-parametric multiple
index models in a high-dimensional and non-Gaussian setting. Such models form a
rich class of non-linear models with applications to signal processing, machine
learning and statistics. Our estimators leverage the score function based first
and second-order Stein's identities and do not require the covariates to
satisfy Gaussian or elliptical symmetry assumptions common in the literature.
Moreover, to handle score functions and responses that are heavy-tailed, our
estimators are constructed via carefully thresholding their empirical
counterparts. We show that our estimator achieves near-optimal statistical rate
of convergence in several settings. We supplement our theoretical results via
simulation experiments that confirm the theory.
| 0 | 0 | 1 | 1 | 0 | 0 |
Self-adjointness and spectral properties of Dirac operators with magnetic links | We define Dirac operators on $\mathbb{S}^3$ (and $\mathbb{R}^3$) with
magnetic fields supported on smooth, oriented links and prove self-adjointness
of certain (natural) extensions. We then analyze their spectral properties and
show, among other things, that these operators have discrete spectrum. Certain
examples, such as circles in $\mathbb{S}^3$, are investigated in detail and we
compute the dimension of the zero-energy eigenspace.
| 0 | 0 | 1 | 0 | 0 | 0 |
Latent tree models | Latent tree models are graphical models defined on trees, in which only a
subset of variables is observed. They were first discussed by Judea Pearl as
tree-decomposable distributions to generalise star-decomposable distributions
such as the latent class model. Latent tree models, or their submodels, are
widely used in: phylogenetic analysis, network tomography, computer vision,
causal modeling, and data clustering. They also contain other well-known
classes of models like hidden Markov models, Brownian motion tree model, the
Ising model on a tree, and many popular models used in phylogenetics. This
article offers a concise introduction to the theory of latent tree models. We
emphasise the role of tree metrics in the structural description of this model
class, in designing learning algorithms, and in understanding fundamental
limits of what and when can be learned.
| 0 | 0 | 1 | 1 | 0 | 0 |
Program Synthesis from Visual Specification | Program synthesis is the process of automatically translating a specification
into computer code. Traditional synthesis settings require a formal, precise
specification. Motivated by computer education applications where a student
learns to code simple turtle-style drawing programs, we study a novel synthesis
setting where only a noisy user-intention drawing is specified. This allows
students to sketch their intended output, optionally together with their own
incomplete program, to automatically produce a completed program. We formulate
this synthesis problem as search in the space of programs, with the score of a
state being the Hausdorff distance between the program output and the user
drawing. We compare several search algorithms on a corpus consisting of real
user drawings and the corresponding programs, and demonstrate that our
algorithms can synthesize programs optimally satisfying the specification.
| 1 | 0 | 0 | 0 | 0 | 0 |
Towards a Knowledge Graph based Speech Interface | Applications which use human speech as an input require a speech interface
with high recognition accuracy. The words or phrases in the recognised text are
annotated with a machine-understandable meaning and linked to knowledge graphs
for further processing by the target application. These semantic annotations of
recognised words can be represented as a subject-predicate-object triples which
collectively form a graph often referred to as a knowledge graph. This type of
knowledge representation facilitates to use speech interfaces with any spoken
input application, since the information is represented in logical, semantic
form, retrieving and storing can be followed using any web standard query
languages. In this work, we develop a methodology for linking speech input to
knowledge graphs and study the impact of recognition errors in the overall
process. We show that for a corpus with lower WER, the annotation and linking
of entities to the DBpedia knowledge graph is considerable. DBpedia Spotlight,
a tool to interlink text documents with the linked open data is used to link
the speech recognition output to the DBpedia knowledge graph. Such a
knowledge-based speech recognition interface is useful for applications such as
question answering or spoken dialog systems.
| 1 | 0 | 0 | 0 | 0 | 0 |
An asymptotic equipartition property for measures on model spaces | Let $G$ be a sofic group, and let $\Sigma = (\sigma_n)_{n\geq 1}$ be a sofic
approximation to it. For a probability-preserving $G$-system, a variant of the
sofic entropy relative to $\Sigma$ has recently been defined in terms of
sequences of measures on its model spaces that `converge' to the system in a
certain sense. Here we prove that, in order to study this notion, one may
restrict attention to those sequences that have the asymptotic equipartition
property. This may be seen as a relative of the Shannon--McMillan theorem in
the sofic setting.
We also give some first applications of this result, including a new formula
for the sofic entropy of a $(G\times H)$-system obtained by co-induction from a
$G$-system, where $H$ is any other infinite sofic group.
| 1 | 0 | 1 | 0 | 0 | 0 |
Dynamic Objects Segmentation for Visual Localization in Urban Environments | Visual localization and mapping is a crucial capability to address many
challenges in mobile robotics. It constitutes a robust, accurate and
cost-effective approach for local and global pose estimation within prior maps.
Yet, in highly dynamic environments, like crowded city streets, problems arise
as major parts of the image can be covered by dynamic objects. Consequently,
visual odometry pipelines often diverge and the localization systems
malfunction as detected features are not consistent with the precomputed 3D
model. In this work, we present an approach to automatically detect dynamic
object instances to improve the robustness of vision-based localization and
mapping in crowded environments. By training a convolutional neural network
model with a combination of synthetic and real-world data, dynamic object
instance masks are learned in a semi-supervised way. The real-world data can be
collected with a standard camera and requires minimal further post-processing.
Our experiments show that a wide range of dynamic objects can be reliably
detected using the presented method. Promising performance is demonstrated on
our own and also publicly available datasets, which also shows the
generalization capabilities of this approach.
| 1 | 0 | 0 | 0 | 0 | 0 |
Gradient Masking Causes CLEVER to Overestimate Adversarial Perturbation Size | A key problem in research on adversarial examples is that vulnerability to
adversarial examples is usually measured by running attack algorithms. Because
the attack algorithms are not optimal, the attack algorithms are prone to
overestimating the size of perturbation needed to fool the target model. In
other words, the attack-based methodology provides an upper-bound on the size
of a perturbation that will fool the model, but security guarantees require a
lower bound. CLEVER is a proposed scoring method to estimate a lower bound.
Unfortunately, an estimate of a bound is not a bound. In this report, we show
that gradient masking, a common problem that causes attack methodologies to
provide only a very loose upper bound, causes CLEVER to overestimate the size
of perturbation needed to fool the model. In other words, CLEVER does not
resolve the key problem with the attack-based methodology, because it fails to
provide a lower bound.
| 0 | 0 | 0 | 1 | 0 | 0 |
Gaussian process regression for forest attribute estimation from airborne laser scanning data | While the analysis of airborne laser scanning (ALS) data often provides
reliable estimates for certain forest stand attributes -- such as total volume
or basal area -- there is still room for improvement, especially in estimating
species-specific attributes. Moreover, while information on the estimate
uncertainty would be useful in various economic and environmental analyses on
forests, a computationally feasible framework for uncertainty quantifying in
ALS is still missing. In this article, the species-specific stand attribute
estimation and uncertainty quantification (UQ) is approached using Gaussian
process regression (GPR), which is a nonlinear and nonparametric machine
learning method. Multiple species-specific stand attributes are estimated
simultaneously: tree height, stem diameter, stem number, basal area, and stem
volume. The cross-validation results show that GPR yields on average an
improvement of 4.6\% in estimate RMSE over a state-of-the-art k-nearest
neighbors (kNN) implementation, negligible bias and well performing UQ
(credible intervals), while being computationally fast. The performance
advantage over kNN and the feasibility of credible intervals persists even when
smaller training sets are used.
| 0 | 0 | 0 | 1 | 0 | 0 |
Quantum capacitance of double-layer graphene | We study the ground-state properties of a double layer graphene system with
the Coulomb interlayer electron-electron interaction modeled within the random
phase approximation. We first obtain an expression of the quantum capacitance
of a two layer system. In addition, we calculate the many-body
exchange-correlation energy and quantum capacitance of the hybrid double layer
graphene system at zero-temperature. We show an enhancement of the majority
density layer thermodynamic density-of-states owing to an increasing interlayer
interaction between two layers near the Dirac point. The quantum capacitance
near the neutrality point behaves like square root of the total density,
$\alpha \sqrt{n}$, where the coefficient $\alpha$ decreases by increasing the
charge density imbalance between two layers. Furthermore, we show that the
quantum capacitance changes linearly by the gate voltage. Our results can be
verified by current experiments.
| 0 | 1 | 0 | 0 | 0 | 0 |
DeepTriangle: A Deep Learning Approach to Loss Reserving | We propose a novel approach for loss reserving based on deep neural networks.
The approach allows for jointly modeling of paid losses and claims outstanding,
and incorporation of heterogenous inputs. We validate the models on loss
reserving data across lines of business, and show that they attain or exceed
the predictive accuracy of existing stochastic methods. The models require
minimal feature engineering and expert input, and can be automated to produce
forecasts at a high frequency.
| 0 | 0 | 0 | 1 | 0 | 1 |
Distributed Time Synchronization for Networks with Random Delays and Measurement Noise | In this paper a new distributed asynchronous algorithm is proposed for time
synchronization in networks with random communication delays, measurement noise
and communication dropouts. Three different types of the drift correction
algorithm are introduced, based on different kinds of local time increments.
Under nonrestrictive conditions concerning network properties, it is proved
that all the algorithm types provide convergence in the mean square sense and
with probability one (w.p.1) of the corrected drifts of all the nodes to the
same value (consensus). An estimate of the convergence rate of these algorithms
is derived. For offset correction, a new algorithm is proposed containing a
compensation parameter coping with the influence of random delays and special
terms taking care of the influence of both linearly increasing time and drift
correction. It is proved that the corrected offsets of all the nodes converge
in the mean square sense and w.p.1. An efficient offset correction algorithm
based on consensus on local compensation parameters is also proposed. It is
shown that the overall time synchronization algorithm can also be implemented
as a flooding algorithm with one reference node. It is proved that it is
possible to achieve bounded error between local corrected clocks in the mean
square sense and w.p.1. Simulation results provide an additional practical
insight into the algorithm properties and show its advantage over the existing
methods.
| 1 | 0 | 0 | 0 | 0 | 0 |
Using High-Rising Cities to Visualize Performance in Real-Time | For developers concerned with a performance drop or improvement in their
software, a profiler allows a developer to quickly search and identify
bottlenecks and leaks that consume much execution time. Non real-time profilers
analyze the history of already executed stack traces, while a real-time
profiler outputs the results concurrently with the execution of software, so
users can know the results instantaneously. However, a real-time profiler risks
providing overly large and complex outputs, which is difficult for developers
to quickly analyze. In this paper, we visualize the performance data from a
real-time profiler. We visualize program execution as a three-dimensional (3D)
city, representing the structure of the program as artifacts in a city (i.e.,
classes and packages expressed as buildings and districts) and their program
executions expressed as the fluctuating height of artifacts. Through two case
studies and using a prototype of our proposed visualization, we demonstrate how
our visualization can easily identify performance issues such as a memory leak
and compare performance changes between versions of a program. A demonstration
of the interactive features of our prototype is available at
this https URL.
| 1 | 0 | 0 | 0 | 0 | 0 |
Controller Synthesis for Discrete-time Hybrid Polynomial Systems via Occupation Measures | We present a novel controller synthesis approach for discrete-time hybrid
polynomial systems, a class of systems that can model a wide variety of
interactions between robots and their environment. The approach is rooted in
recently developed techniques that use occupation measures to formulate the
controller synthesis problem as an infinite-dimensional linear program. The
relaxation of the linear program as a finite-dimensional semidefinite program
can be solved to generate a control law. The approach has several advantages
including that the formulation is convex, that the formulation and the
extracted controllers are simple, and that the computational complexity is
polynomial in the state and control input dimensions. We illustrate our
approach on some robotics examples.
| 1 | 0 | 0 | 0 | 0 | 0 |
Fast Stochastic Variance Reduced Gradient Method with Momentum Acceleration for Machine Learning | Recently, research on accelerated stochastic gradient descent methods (e.g.,
SVRG) has made exciting progress (e.g., linear convergence for strongly convex
problems). However, the best-known methods (e.g., Katyusha) requires at least
two auxiliary variables and two momentum parameters. In this paper, we propose
a fast stochastic variance reduction gradient (FSVRG) method, in which we
design a novel update rule with the Nesterov's momentum and incorporate the
technique of growing epoch size. FSVRG has only one auxiliary variable and one
momentum weight, and thus it is much simpler and has much lower per-iteration
complexity. We prove that FSVRG achieves linear convergence for strongly convex
problems and the optimal $\mathcal{O}(1/T^2)$ convergence rate for non-strongly
convex problems, where $T$ is the number of outer-iterations. We also extend
FSVRG to directly solve the problems with non-smooth component functions, such
as SVM. Finally, we empirically study the performance of FSVRG for solving
various machine learning problems such as logistic regression, ridge
regression, Lasso and SVM. Our results show that FSVRG outperforms the
state-of-the-art stochastic methods, including Katyusha.
| 1 | 0 | 1 | 1 | 0 | 0 |
A vertex-weighted-Least-Squares gradient reconstruction | Gradient reconstruction is a key process for the spatial accuracy and
robustness of finite volume method, especially in industrial aerodynamic
applications in which grid quality affects reconstruction methods
significantly. A novel gradient reconstruction method for cell-centered finite
volume scheme is introduced. This method is composed of two successive steps.
First, a vertex-based weighted-least-squares procedure is implemented to
calculate vertex gradients, and then the cell-centered gradients are calculated
by an arithmetic averaging procedure. By using these two procedures, extended
stencils are implemented in the calculations, and the accuracy of gradient
reconstruction is improved by the weighting procedure. In the given test cases,
the proposed method is showing improvement on both the accuracy and
convergence. Furthermore, the method could be extended to the calculation of
viscous fluxes.
| 0 | 1 | 0 | 0 | 0 | 0 |
The reverse mathematics of theorems of Jordan and Lebesgue | The Jordan decomposition theorem states that every function $f \colon [0,1]
\to \mathbb{R}$ of bounded variation can be written as the difference of two
non-decreasing functions. Combining this fact with a result of Lebesgue, every
function of bounded variation is differentiable almost everywhere in the sense
of Lebesgue measure. We analyze the strength of these theorems in the setting
of reverse mathematics. Over $\mathsf{RCA}_0$, a stronger version of Jordan's
result where all functions are continuous is equivalent to $\mathsf{ACA}_0$,
while the version stated is equivalent to $\mathsf{WKL}_0$. The result that
every function on $[0,1]$ of bounded variation is almost everywhere
differentiable is equivalent to $\mathsf{WWKL}_0$. To state this equivalence in
a meaningful way, we develop a theory of Martin-Löf randomness over
$\mathsf{RCA}_0$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Semiflat Orbifold Projections | We compute the semiflat positive cone $K_0^{+SF}(A_\theta^\sigma)$ of the
$K_0$-group of the irrational rotation orbifold $A_\theta^\sigma$ under the
noncommutative Fourier transform $\sigma$ and show that it is determined by
classes of positive trace and the vanishing of two topological invariants. The
semiflat orbifold projections are 3-dimensional and come in three basic
topological genera: $(2,0,0)$, $(1,1,2)$, $(0,0,2)$. (A projection is called
semiflat when it has the form $h + \sigma(h)$ where $h$ is a flip-invariant
projection such that $h\sigma(h)=0$.) Among other things, we also show that
every number in $(0,1) \cap (2\mathbb Z + 2\mathbb Z\theta)$ is the trace of a
semiflat projection in $A_\theta$. The noncommutative Fourier transform is the
order 4 automorphism $\sigma: V \to U \to V^{-1}$ (and the flip is $\sigma^2$:
$U \to U^{-1},\ V \to V^{-1}$), where $U,V$ are the canonical unitary
generators of the rotation algebra $A_\theta$ satisfying $VU = e^{2\pi i\theta}
UV$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Magnetization process of the S = 1/2 two-leg organic spin-ladder compound BIP-BNO | We have measured the magnetization of the organic compound BIP-BNO
(3,5'-bis(N-tert-butylaminoxyl)-3',5-dibromobiphenyl) up to 76 T where the
magnetization is saturated. The S = 1/2 antiferromagnetic Heisenberg two-leg
spin-ladder model accounts for the obtained experimental data regarding the
magnetization curve, which is clarified using the quantum Monte Carlo method.
The exchange constants on the rung and the side rail of the ladder are
estimated to be J(rung)/kB = 65.7 K and J(leg)/kB = 14.1 K, respectively,
deeply in the strong coupling region: J(rung)/J(leg) > 1.
| 0 | 1 | 0 | 0 | 0 | 0 |
Deep Over-sampling Framework for Classifying Imbalanced Data | Class imbalance is a challenging issue in practical classification problems
for deep learning models as well as traditional models. Traditionally
successful countermeasures such as synthetic over-sampling have had limited
success with complex, structured data handled by deep learning models. In this
paper, we propose Deep Over-sampling (DOS), a framework for extending the
synthetic over-sampling method to exploit the deep feature space acquired by a
convolutional neural network (CNN). Its key feature is an explicit, supervised
representation learning, for which the training data presents each raw input
sample with a synthetic embedding target in the deep feature space, which is
sampled from the linear subspace of in-class neighbors. We implement an
iterative process of training the CNN and updating the targets, which induces
smaller in-class variance among the embeddings, to increase the discriminative
power of the deep representation. We present an empirical study using public
benchmarks, which shows that the DOS framework not only counteracts class
imbalance better than the existing method, but also improves the performance of
the CNN in the standard, balanced settings.
| 1 | 0 | 0 | 1 | 0 | 0 |
Failsafe Mechanism Design of Multicopters Based on Supervisory Control Theory | In order to handle undesirable failures of a multicopter which occur in
either the pre-flight process or the in-flight process, a failsafe mechanism
design method based on supervisory control theory is proposed for the
semi-autonomous control mode. Failsafe mechanism is a control logic that guides
what subsequent actions the multicopter should take, by taking account of
real-time information from guidance, attitude control, diagnosis, and other
low-level subsystems. In order to design a failsafe mechanism for multicopters,
safety issues of multicopters are introduced. Then, user requirements including
functional requirements and safety requirements are textually described, where
function requirements determine a general multicopter plant, and safety
requirements cover the failsafe measures dealing with the presented safety
issues. In order to model the user requirements by discrete-event systems,
several multicopter modes and events are defined. On this basis, the
multicopter plant and control specifications are modeled by automata. Then, a
supervisor is synthesized by monolithic supervisory control theory. In
addition, we present three examples to demonstrate the potential blocking
phenomenon due to inappropriate design of control specifications. Also, we
discuss the meaning of correctness and the properties of the obtained
supervisor. This makes the failsafe mechanism convincingly correct and
effective. Finally, based on the obtained supervisory controller generated by
TCT software, an implementation method suitable for multicopters is presented,
in which the supervisory controller is transformed into decision-making codes.
| 1 | 0 | 0 | 0 | 0 | 0 |
Inverse Design of Single- and Multi-Rotor Horizontal Axis Wind Turbine Blades using Computational Fluid Dynamics | A method for inverse design of horizontal axis wind turbines (HAWTs) is
presented in this paper. The direct solver for aerodynamic analysis solves the
Reynolds Averaged Navier Stokes (RANS) equations, where the effect of the
turbine rotor is modeled as momentum sources using the actuator disk model
(ADM); this approach is referred to as RANS/ADM. The inverse problem is posed
as follows: for a given selection of airfoils, the objective is to find the
blade geometry (described as blade twist and chord distributions) which
realizes the desired turbine aerodynamic performance at the design point; the
desired performance is prescribed as angle of attack ($\alpha$) and axial
induction factor ($a$) distributions along the blade. An iterative approach is
used. An initial estimate of blade geometry is used with the direct solver
(RANS/ADM) to obtain $\alpha$ and $a$. The differences between the calculated
and desired values of $\alpha$ and $a$ are computed and a new estimate for the
blade geometry (chord and twist) is obtained via nonlinear least squares
regression using the Trust-Region-Reflective (TRF) method. This procedure is
continued until the difference between the calculated and the desired values is
within acceptable tolerance. The method is demonstrated for conventional,
single-rotor HAWTs and then extended to multi-rotor, specifically dual-rotor
wind turbines. The TRF method is also compared with the multi-dimensional
Newton iteration method and found to provide better convergence when
constraints are imposed in blade design, although faster convergence is
obtained with the Newton method for unconstrained optimization.
| 0 | 1 | 0 | 0 | 0 | 0 |
The Double Galaxy Cluster Abell 2465 III. X-ray and Weak-lensing Observations | We report Chandra X-ray observations and optical weak-lensing measurements
from Subaru/Suprime-Cam images of the double galaxy cluster Abell 2465
(z=0.245). The X-ray brightness data are fit to a beta-model to obtain the
radial gas density profiles of the northeast (NE) and southwest (SW)
sub-components, which are seen to differ in structure. We determine core radii,
central temperatures, the gas masses within $r_{500c}$, and the total masses
for the broader NE and sharper SW components assuming hydrostatic equilibrium.
The central entropy of the NE clump is about two times higher than the SW.
Along with its structural properties, this suggests that it has undergone
merging on its own. The weak-lensing analysis gives virial masses for each
substructure, which compare well with earlier dynamical results. The derived
outer mass contours of the SW sub-component from weak lensing are more
irregular and extended than those of the NE. Although there is a weak
enhancement and small offsets between X-ray gas and mass centers from weak
lensing, the lack of large amounts of gas between the two sub-clusters
indicates that Abell 2465 is in a pre-merger state. A dynamical model that is
consistent with the observed cluster data, based on the FLASH program and the
radial infall model, is constructed, where the subclusters currently separated
by ~1.2Mpc are approaching each other at ~2000km/s and will meet in ~0.4Gyr.
| 0 | 1 | 0 | 0 | 0 | 0 |
Superdensity Operators for Spacetime Quantum Mechanics | We introduce superdensity operators as a tool for analyzing quantum
information in spacetime. Superdensity operators encode spacetime correlation
functions in an operator framework, and support a natural generalization of
Hilbert space techniques and Dirac's transformation theory as traditionally
applied to standard density operators. Superdensity operators can be measured
experimentally, but accessing their full content requires novel procedures. We
demonstrate these statements on several examples. The superdensity formalism
suggests useful definitions of spacetime entropies and spacetime quantum
channels. For example, we show that the von Neumann entropy of a superdensity
operator is related to a quantum generalization of the Kolmogorov-Sinai
entropy, and compute this for a many-body system. We also suggest experimental
protocols for measuring spacetime entropies.
| 0 | 1 | 0 | 0 | 0 | 0 |
Monte Carlo study of magnetic nanoparticles adsorbed on halloysite $Al_2Si_2O_5(OH)_4$ nanotubes | We study properties of magnetic nanoparticles adsorbed on the halloysite
surface. For that a distinct magnetic Hamiltonian with random distribution of
spins on a cylindrical surface was solved by using a nonequilibrium Monte Carlo
method. The parameters for our simulations: anisotropy constant, nanoparticle
size distribution, saturated magnetization and geometrical parameters of the
halloysite template were taken from recent experiments. We calculate the
hysteresis loops and temperature dependence of the zero field cooling (ZFC)
susceptibility, which maximum determines the blocking temperature. It is shown
that the dipole-dipole interaction between nanoparticles moderately increases
the blocking temperature and weakly increases the coercive force. The obtained
hysteresis loops (e.g., the value of the coercive force) for Ni nanoparticles
are in reasonable agreement with the experimental data. We also discuss the
sensitivity of the hysteresis loops and ZFC susceptibilities to the change of
anisotropy and dipole-dipole interaction, as well as the 3d-shell occupation of
the metallic nanoparticles; in particular we predict larger coercive force for
Fe, than for Ni nanoparticles.
| 0 | 1 | 0 | 0 | 0 | 0 |
Inference in high-dimensional linear regression models | We introduce an asymptotically unbiased estimator for the full
high-dimensional parameter vector in linear regression models where the number
of variables exceeds the number of available observations. The estimator is
accompanied by a closed-form expression for the covariance matrix of the
estimates that is free of tuning parameters. This enables the construction of
confidence intervals that are valid uniformly over the parameter vector.
Estimates are obtained by using a scaled Moore-Penrose pseudoinverse as an
approximate inverse of the singular empirical covariance matrix of the
regressors. The approximation induces a bias, which is then corrected for using
the lasso. Regularization of the pseudoinverse is shown to yield narrower
confidence intervals under a suitable choice of the regularization parameter.
The methods are illustrated in Monte Carlo experiments and in an empirical
example where gross domestic product is explained by a large number of
macroeconomic and financial indicators.
| 0 | 0 | 1 | 1 | 0 | 0 |
Counterfactual Learning for Machine Translation: Degeneracies and Solutions | Counterfactual learning is a natural scenario to improve web-based machine
translation services by offline learning from feedback logged during user
interactions. In order to avoid the risk of showing inferior translations to
users, in such scenarios mostly exploration-free deterministic logging policies
are in place. We analyze possible degeneracies of inverse and reweighted
propensity scoring estimators, in stochastic and deterministic settings, and
relate them to recently proposed techniques for counterfactual learning under
deterministic logging.
| 1 | 0 | 0 | 1 | 0 | 0 |
Conversion of Mersenne Twister to double-precision floating-point numbers | The 32-bit Mersenne Twister generator MT19937 is a widely used random number
generator. To generate numbers with more than 32 bits in bit length, and
particularly when converting into 53-bit double-precision floating-point
numbers in $[0,1)$ in the IEEE 754 format, the typical implementation
concatenates two successive 32-bit integers and divides them by a power of $2$.
In this case, the 32-bit MT19937 is optimized in terms of its equidistribution
properties (the so-called dimension of equidistribution with $v$-bit accuracy)
under the assumption that one will mainly be using 32-bit output values, and
hence the concatenation sometimes degrades the dimension of equidistribution
compared with the simple use of 32-bit outputs. In this paper, we analyze such
phenomena by investigating hidden $\mathbb{F}_2$-linear relations among the
bits of high-dimensional outputs. Accordingly, we report that MT19937 with a
specific lag set fails several statistical tests, such as the overlapping
collision test, matrix rank test, and Hamming independence test.
| 1 | 0 | 0 | 1 | 0 | 0 |
BaHaMAS: A Bash Handler to Monitor and Administrate Simulations | Numerical QCD is often extremely resource demanding and it is not rare to run
hundreds of simulations at the same time. Each of these can last for days or
even months and it typically requires a job-script file as well as an input
file with the physical parameters for the application to be run. Moreover, some
monitoring operations (i.e. copying, moving, deleting or modifying files,
resume crashed jobs, etc.) are often required to guarantee that the final
statistics is correctly accumulated. Proceeding manually in handling
simulations is probably the most error-prone way and it is deadly uncomfortable
and inefficient! BaHaMAS was developed and successfully used in the last years
as a tool to automatically monitor and administrate simulations.
| 0 | 1 | 0 | 0 | 0 | 0 |
Computational Study of Halide Perovskite-Derived A$_2$BX$_6$ Inorganic Compounds: Chemical Trends in Electronic Structure and Structural Stability | The electronic structure and energetic stability of A$_2$BX$_6$ halide
compounds with the cubic and tetragonal variants of the perovskite-derived
K$_2$PtCl$_6$ prototype structure are investigated computationally within the
frameworks of density-functional-theory (DFT) and hybrid (HSE06) functionals.
The HSE06 calculations are undertaken for seven known A$_2$BX$_6$ compounds
with A = K, Rb and Cs, and B = Sn, Pd, Pt, Te, and X = I. Trends in band gaps
and energetic stability are identified, which are explored further employing
DFT calculations over a larger range of chemistries, characterized by A = K,
Rb, Cs, B = Si, Ge, Sn, Pb, Ni, Pd, Pt, Se and Te and X = Cl, Br, I. For the
systems investigated in this work, the band gap increases from iodide to
bromide to chloride. Further, variations in the A site cation influences the
band gap as well as the preferred degree of tetragonal distortion. Smaller A
site cations such as K and Rb favor tetragonal structural distortions,
resulting in a slightly larger band gap. For variations in the B site in the
(Ni, Pd, Pt) group and the (Se, Te) group, the band gap increases with
increasing cation size. However, no observed chemical trend with respect to
cation size for band gap was found for the (Si, Sn, Ge, Pb) group. The findings
in this work provide guidelines for the design of halide A$_2$BX$_6$ compounds
for potential photovoltaic applications.
| 0 | 1 | 0 | 0 | 0 | 0 |
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