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Hierarchical internal representation of spectral features in deep convolutional networks trained for EEG decoding | Recently, there is increasing interest and research on the interpretability
of machine learning models, for example how they transform and internally
represent EEG signals in Brain-Computer Interface (BCI) applications. This can
help to understand the limits of the model and how it may be improved, in
addition to possibly provide insight about the data itself. Schirrmeister et
al. (2017) have recently reported promising results for EEG decoding with deep
convolutional neural networks (ConvNets) trained in an end-to-end manner and,
with a causal visualization approach, showed that they learn to use spectral
amplitude changes in the input. In this study, we investigate how ConvNets
represent spectral features through the sequence of intermediate stages of the
network. We show higher sensitivity to EEG phase features at earlier stages and
higher sensitivity to EEG amplitude features at later stages. Intriguingly, we
observed a specialization of individual stages of the network to the classical
EEG frequency bands alpha, beta, and high gamma. Furthermore, we find first
evidence that particularly in the last convolutional layer, the network learns
to detect more complex oscillatory patterns beyond spectral phase and
amplitude, reminiscent of the representation of complex visual features in
later layers of ConvNets in computer vision tasks. Our findings thus provide
insights into how ConvNets hierarchically represent spectral EEG features in
their intermediate layers and suggest that ConvNets can exploit and might help
to better understand the compositional structure of EEG time series.
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Global weak solutions in a three-dimensional Keller-Segel-Navier-Stokes system with nonlinear diffusion | The coupled quasilinear Keller-Segel-Navier-Stokes system is considered under
Neumann boundary conditions for $n$ and $c$ and no-slip boundary conditions for
$u$ in three-dimensional bounded domains $\Omega\subseteq \mathbb{R}^3$ with
smooth boundary, where $m>0,\kappa\in \mathbb{R}$ are given constants, $\phi\in
W^{1,\infty}(\Omega)$. If $ m> 2$, then for all reasonably regular initial
data, a corresponding initial-boundary value problem for $(KSNF)$ possesses a
globally defined weak solution.
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On Consistency of Compressive Spectral Clustering | Spectral clustering is one of the most popular methods for community
detection in graphs. A key step in spectral clustering algorithms is the eigen
decomposition of the $n{\times}n$ graph Laplacian matrix to extract its $k$
leading eigenvectors, where $k$ is the desired number of clusters among $n$
objects. This is prohibitively complex to implement for very large datasets.
However, it has recently been shown that it is possible to bypass the eigen
decomposition by computing an approximate spectral embedding through graph
filtering of random signals. In this paper, we analyze the working of spectral
clustering performed via graph filtering on the stochastic block model.
Specifically, we characterize the effects of sparsity, dimensionality and
filter approximation error on the consistency of the algorithm in recovering
planted clusters.
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Large-scale validation of an automatic EEG arousal detection algorithm using different heterogeneous databases | $\textbf{Objective}$: To assess the validity of an automatic EEG arousal
detection algorithm using large patient samples and different heterogeneous
databases
$\textbf{Methods}$: Automatic scorings were confronted with results from
human expert scorers on a total of 2768 full-night PSG recordings obtained from
two different databases. Of them, 472 recordings were obtained during clinical
routine at our sleep center and were subdivided into two subgroups of 220
(HMC-S) and 252 (HMC-M) recordings each, attending to the procedure followed by
the clinical expert during the visual review (semi-automatic or purely manual,
respectively). In addition, 2296 recordings from the public SHHS-2 database
were evaluated against the respective manual expert scorings.
$\textbf{Results}$: Event-by-event epoch-based validation resulted in an
overall Cohen kappa agreement K = 0.600 (HMC-S), 0.559 (HMC-M), and 0.573
(SHHS-2). Estimated inter-scorer variability on the datasets was, respectively,
K = 0.594, 0.561 and 0.543. Analyses of the corresponding Arousal Index scores
showed associated automatic-human repeatability indices ranging in 0.693-0.771
(HMC-S), 0.646-0.791 (HMC-M), and 0.759-0.791 (SHHS-2).
$\textbf{Conclusions}$: Large-scale validation of our automatic EEG arousal
detector on different databases has shown robust performance and good
generalization results comparable to the expected levels of human agreement.
Special emphasis has been put on allowing reproducibility of the results and
implementation of our method has been made accessible online as open source
code
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Story of the Developments in Statistical Physics of Fracture, Breakdown \& Earthquake: A Personal Account | We review the developments of the statistical physics of fracture and
earthquake over the last four decades. We argue that major progress has been
made in this field and that the key concepts should now become integral part of
the (under-) graduate level text books in condensed matter physics. For arguing
in favor of this, we compare the development (citations) with the same for some
other related topics in condensed matter, for which Nobel prizes have already
been awarded.
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LocalNysation: A bottom up approach to efficient localized kernel regression | We consider a localized approach in the well-established setting of
reproducing kernel learning under random design. The input space $X$ is
partitioned into local disjoint subsets $X_j$ ($j=1,...,m$) equipped with a
local reproducing kernel $K_j$. It is then straightforward to define local KRR
estimates. Our first main contribution is in showing that minimax optimal rates
of convergence are preserved if the number $m$ of partitions grows sufficiently
slowly with the sample size, under locally different degrees on smoothness
assumptions on the regression function. As a byproduct, we show that low
smoothness on exceptional sets of small probability does not contribute,
leading to a faster rate of convergence. Our second contribution lies in
showing that the partitioning approach for KRR can be efficiently combined with
local Nyström subsampling, improving computational cost twofold. If the
number of locally subsampled inputs grows sufficiently fast with the sample
size, minimax optimal rates of convergence are maintained.
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Evolutionary Acyclic Graph Partitioning | Directed graphs are widely used to model data flow and execution dependencies
in streaming applications. This enables the utilization of graph partitioning
algorithms for the problem of parallelizing computation for multiprocessor
architectures. However due to resource restrictions, an acyclicity constraint
on the partition is necessary when mapping streaming applications to an
embedded multiprocessor. Here, we contribute a multi-level algorithm for the
acyclic graph partitioning problem. Based on this, we engineer an evolutionary
algorithm to further reduce communication cost, as well as to improve load
balancing and the scheduling makespan on embedded multiprocessor architectures.
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Mechanisms of near-surface structural evolution in nanocrystalline materials during sliding contact | The wear-driven structural evolution of nanocrystalline Cu was simulated with
molecular dynamics under constant normal loads, followed by a quantitative
analysis. While the microstructure far away from the sliding contact remains
unchanged, grain growth accompanied by partial dislocations and twin formation
was observed near the contact surface, with more rapid coarsening promoted by
higher applied normal loads. The structural evolution continues with increasing
number of sliding cycles and eventually saturates to a stable distinct layer of
coarsened grains, separated from the finer matrix by a steep gradient in grain
size. The coarsening process is balanced by the rate of material removal when
the normal load is high enough. The observed structural evolution leads to an
increase in hardness and decrease in friction coefficient, which also saturate
after a number of sliding cycles. This work provides important mechanistic
understanding of nanocrystalline wear, while also introducing a methodology for
atomistic simulations of cyclic wear damage under constant applied normal
loads.
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Generalized Hölder's inequality on Morrey spaces | The aim of this paper is to present necessary and sufficient conditions for
generalized Hölder's inequality on generalized Morrey spaces. We also
obtain similar results on weak Morrey spaces and on generalized weak Morrey
spaces. The necessary and sufficient conditions for the generalized
Hölder's inequality on these spaces are obtained through estimates for
characteristic functions of balls in $\mathbb{R}^d$.
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Endomorphism Algebras of Abelian varieties with special reference to Superelliptic Jacobians | This is (mostly) a survey article. We use an information about Galois
properties of points of small order on an abelian variety in order to describe
its endomorphism algebra over an algebraic closure of the ground field. We
discuss in detail applications to jacobians of cyclic covers of the projective
line.
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Ranks of rational points of the Jacobian varieties of hyperelliptic curves | In this paper, we obtain bounds for the Mordell-Weil ranks over cyclotomic
extensions of a wide range of abelian varieties defined over a number field $F$
whose primes above $p$ are totally ramified over $F/\mathbb{Q}$. We assume that
the abelian varieties may have good non-ordinary reduction at those primes. Our
work is a generalization of \cite{Kim}, in which the second author generalized
Perrin-Riou's Iwasawa theory for elliptic curves over $\mathbb{Q}$ with
supersingular reduction (\cite{Perrin-Riou}) to elliptic curves defined over
the above-mentioned number field $F$. On top of non-ordinary reduction and the
ramification of the field $F$, we deal with the additional difficulty that the
dimensions of the abelian varieties can be any number bigger than 1 which
causes a variety of issues. As a result, we obtain bounds for the ranks over
cyclotomic extensions $\mathbb{Q}(\mu_{p^{\max(M,N)+n}})$ of the Jacobian
varieties of {\it ramified} hyperelliptic curves $y^{2p^M}=x^{3p^N}+ax^{p^N}+b$
among others.
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A Generalized Function defined by the Euler first kind integral and its connection with the Dirac delta function | We have shown that in some region where the Euler integral of the first kind
diverges, the Euler formula defines a generalized function. The connected of
this generalized function with the Dirac delta function is found.
| 0 | 0 | 1 | 0 | 0 | 0 |
Optical emission of graphene and electron-hole pair production induced by a strong THz field | We report on the first experimental observation of graphene optical emission
induced by the intense THz pulse. P-doped CVD graphene with the initial Fermi
energy of about 200 meV was used, optical photons was detected in the
wavelength range of 340-600 nm. Emission started when THz field amplitude
exceeded 100 kV/cm. For THz fields from 200 to 300 kV/cm the temperature of
optical radiation was constant, while the number of emitted photons increased
several dozen times. This fact clearly indicates multiplication of
electron-hole pairs induced by an external field itself and not due to electron
heating. The experimental data are in a good agreement with the theory of
Landau-Zener interband transitions. It is shown theoretically that Landau-Zener
transitions are possible even in the case of heavily doped graphene because the
strong THz field removes quasiparticles from the region of interband
transitions during several femtoseconds, which cancels the Pauli blocking
effect.
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Occlusion-Aware Risk Assessment for Autonomous Driving in Urban Environments | Navigating safely in urban environments remains a challenging problem for
autonomous vehicles. Occlusion and limited sensor range can pose significant
challenges to safely navigate among pedestrians and other vehicles in the
environment. Enabling vehicles to quantify the risk posed by unseen regions
allows them to anticipate future possibilities, resulting in increased safety
and ride comfort. This paper proposes an algorithm that takes advantage of the
known road layouts to forecast, quantify, and aggregate risk associated with
occlusions and limited sensor range. This allows us to make predictions of risk
induced by unobserved vehicles even in heavily occluded urban environments. The
risk can then be used either by a low-level planning algorithm to generate
better trajectories, or by a high-level one to plan a better route. The
proposed algorithm is evaluated on intersection layouts from real-world map
data with up to five other vehicles in the scene, and verified to reduce
collision rates by 4.8x comparing to a baseline method while improving driving
comfort.
| 1 | 0 | 0 | 0 | 0 | 0 |
Quantum oscillations and Dirac-Landau levels in Weyl superconductors | When magnetic field is applied to metals and semimetals quantum oscillations
appear as individual Landau levels cross the Fermi level. Quantum oscillations
generally do not occur in superconductors (SC) because magnetic field is either
expelled from the sample interior or, if strong enough, drives the material
into the normal state. In addition, elementary excitations of a superconductor
-- Bogoliubov quasiparticles -- do not carry a well defined electric charge and
therefore do not couple in a simple way to the applied magnetic field. We
predict here that in Weyl superconductors certain types of elastic strain have
the ability to induce chiral pseudo-magnetic field which can reorganize the
electronic states into Dirac-Landau levels with linear band crossings at low
energy. The resulting quantum oscillations in the quasiparticle density of
states and thermal conductivity can be experimentally observed under a bending
deformation of a thin film Weyl SC and provide new insights into this
fascinating family of materials.
| 0 | 1 | 0 | 0 | 0 | 0 |
Parallel Implementation of Efficient Search Schemes for the Inference of Cancer Progression Models | The emergence and development of cancer is a consequence of the accumulation
over time of genomic mutations involving a specific set of genes, which
provides the cancer clones with a functional selective advantage. In this work,
we model the order of accumulation of such mutations during the progression,
which eventually leads to the disease, by means of probabilistic graphic
models, i.e., Bayesian Networks (BNs). We investigate how to perform the task
of learning the structure of such BNs, according to experimental evidence,
adopting a global optimization meta-heuristics. In particular, in this work we
rely on Genetic Algorithms, and to strongly reduce the execution time of the
inference -- which can also involve multiple repetitions to collect
statistically significant assessments of the data -- we distribute the
calculations using both multi-threading and a multi-node architecture. The
results show that our approach is characterized by good accuracy and
specificity; we also demonstrate its feasibility, thanks to a 84x reduction of
the overall execution time with respect to a traditional sequential
implementation.
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A note on the paper "Contraction mappings in $b$-metric spaces" by Czerwik | In this paper we correct an inaccuracy that appears in the proof of Theorem
1. in Czerwik's article "Contraction mappings in $b$-metric spaces.", Acta
Math. Inform. Univ. Ostraviensis, 1:5--11, 1993.
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Floquet Analysis of Kuznetsov--Ma breathers: A Path Towards Spectral Stability of Rogue Waves | In the present work, we aim at taking a step towards the spectral stability
analysis of Peregrine solitons, i.e., wave structures that are used to emulate
extreme wave events. Given the space-time localized nature of Peregrine
solitons, this is a priori a non-trivial task. Our main tool in this effort
will be the study of the spectral stability of the periodic generalization of
the Peregrine soliton in the evolution variable, namely the Kuznetsov--Ma
breather. Given the periodic structure of the latter, we compute the
corresponding Floquet multipliers, and examine them in the limit where the
period of the orbit tends to infinity. This way, we extrapolate towards the
stability of the limiting structure, namely the Peregrine soliton. We find that
multiple unstable modes of the background are enhanced, yet no additional
unstable eigenmodes arise as the Peregrine limit is approached. We explore the
instability evolution also in direct numerical simulations.
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Playing Music in Just Intonation - A Dynamically Adapting Tuning Scheme | We investigate a dynamically adapting tuning scheme for microtonal tuning of
musical instruments, allowing the performer to play music in just intonation in
any key. Unlike other methods, which are based on a procedural analysis of the
chordal structure, the tuning scheme continually solves a system of linear
equations without making explicit decisions. In complex situations, where not
all intervals of a chord can be tuned according to just frequency ratios, the
method automatically yields a tempered compromise. We outline the
implementation of the algorithm in an open-source software project that we have
provided in order to demonstrate the feasibility of the tuning method.
| 0 | 1 | 0 | 0 | 0 | 0 |
Photon propagation through linearly active dimers | We provide an analytic propagator for non-Hermitian dimers showing linear
gain or losses in the quantum regime. In particular, we focus on experimentally
feasible realizations of the $\mathcal{PT}$-symmetric dimer and provide their
mean photon number and second order two-point correlation. We study the
propagation of vacuum, single photon spatially-separable, and two-photon
spatially-entangled states. We show that each configuration produces a
particular signature that might signal their possible uses as photon switches,
semi-classical intensity-tunable sources, or spatially entangled sources to
mention a few possible applications.
| 0 | 1 | 0 | 0 | 0 | 0 |
End-to-End Learning of Geometry and Context for Deep Stereo Regression | We propose a novel deep learning architecture for regressing disparity from a
rectified pair of stereo images. We leverage knowledge of the problem's
geometry to form a cost volume using deep feature representations. We learn to
incorporate contextual information using 3-D convolutions over this volume.
Disparity values are regressed from the cost volume using a proposed
differentiable soft argmin operation, which allows us to train our method
end-to-end to sub-pixel accuracy without any additional post-processing or
regularization. We evaluate our method on the Scene Flow and KITTI datasets and
on KITTI we set a new state-of-the-art benchmark, while being significantly
faster than competing approaches.
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BLADYG: A Graph Processing Framework for Large Dynamic Graphs | Recently, distributed processing of large dynamic graphs has become very
popular, especially in certain domains such as social network analysis, Web
graph analysis and spatial network analysis. In this context, many
distributed/parallel graph processing systems have been proposed, such as
Pregel, GraphLab, and Trinity. These systems can be divided into two
categories: (1) vertex-centric and (2) block-centric approaches. In
vertex-centric approaches, each vertex corresponds to a process, and message
are exchanged among vertices. In block-centric approaches, the unit of
computation is a block, a connected subgraph of the graph, and message
exchanges occur among blocks. In this paper, we are considering the issues of
scale and dynamism in the case of block-centric approaches. We present bladyg,
a block-centric framework that addresses the issue of dynamism in large-scale
graphs. We present an implementation of BLADYG on top of akka framework. We
experimentally evaluate the performance of the proposed framework.
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Variational Inference via Transformations on Distributions | Variational inference methods often focus on the problem of efficient model
optimization, with little emphasis on the choice of the approximating
posterior. In this paper, we review and implement the various methods that
enable us to develop a rich family of approximating posteriors. We show that
one particular method employing transformations on distributions results in
developing very rich and complex posterior approximation. We analyze its
performance on the MNIST dataset by implementing with a Variational Autoencoder
and demonstrate its effectiveness in learning better posterior distributions.
| 1 | 0 | 0 | 1 | 0 | 0 |
VLSI Computational Architectures for the Arithmetic Cosine Transform | The discrete cosine transform (DCT) is a widely-used and important signal
processing tool employed in a plethora of applications. Typical fast algorithms
for nearly-exact computation of DCT require floating point arithmetic, are
multiplier intensive, and accumulate round-off errors. Recently proposed fast
algorithm arithmetic cosine transform (ACT) calculates the DCT exactly using
only additions and integer constant multiplications, with very low area
complexity, for null mean input sequences. The ACT can also be computed
non-exactly for any input sequence, with low area complexity and low power
consumption, utilizing the novel architecture described. However, as a
trade-off, the ACT algorithm requires 10 non-uniformly sampled data points to
calculate the 8-point DCT. This requirement can easily be satisfied for
applications dealing with spatial signals such as image sensors and biomedical
sensor arrays, by placing sensor elements in a non-uniform grid. In this work,
a hardware architecture for the computation of the null mean ACT is proposed,
followed by a novel architectures that extend the ACT for non-null mean
signals. All circuits are physically implemented and tested using the Xilinx
XC6VLX240T FPGA device and synthesized for 45 nm TSMC standard-cell library for
performance assessment.
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Simulated Tornado Optimization | We propose a swarm-based optimization algorithm inspired by air currents of a
tornado. Two main air currents - spiral and updraft - are mimicked. Spiral
motion is designed for exploration of new search areas and updraft movements is
deployed for exploitation of a promising candidate solution. Assignment of just
one search direction to each particle at each iteration, leads to low
computational complexity of the proposed algorithm respect to the conventional
algorithms. Regardless of the step size parameters, the only parameter of the
proposed algorithm, called tornado diameter, can be efficiently adjusted by
randomization. Numerical results over six different benchmark cost functions
indicate comparable and, in some cases, better performance of the proposed
algorithm respect to some other metaheuristics.
| 1 | 0 | 1 | 0 | 0 | 0 |
On a backward problem for multidimensional Ginzburg-Landau equation with random data | In this paper, we consider a backward in time problem for Ginzburg-Landau
equation in multidimensional domain associated with some random data. The
problem is ill-posed in the sense of Hadamard. To regularize the instable
solution, we develop a new regularized method combined with statistical
approach to solve this problem. We prove a upper bound, on the rate of
convergence of the mean integrated squared error in $L^2 $ norm and $H^1$ norm.
| 0 | 0 | 1 | 0 | 0 | 0 |
Electronic and atomic kinetics in solids irradiated with free-electron lasers or swift-heavy ions | In this brief review we discuss the transient processes in solids under
irradiation with femtosecond X-ray free-electron-laser (FEL) pulses and
swift-heavy ions (SHI). Both kinds of irradiation produce highly excited
electrons in a target on extremely short timescales. Transfer of the excess
electronic energy into the lattice may lead to observable target modifications
such as phase transitions and damage formation. Transient kinetics of material
excitation and relaxation under FEL or SHI irradiation are comparatively
discussed. The same origin for the electronic and atomic relaxation in both
cases is demonstrated. Differences in these kinetics introduced by the
geometrical effects ({\mu}m-size of a laser spot vs nm-size of an ion track)
and initial irradiation (photoabsorption vs an ion impact) are analyzed. The
basic mechanisms of electron transport and electron-lattice coupling are
addressed. Appropriate models and their limitations are presented.
Possibilities of thermal and nonthermal melting of materials under FEL and SHI
irradiation are discussed.
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Network analysis of the COSMOS galaxy field | The galaxy data provided by COSMOS survey for 1 by 1 degree field of sky are
analysed by methods of complex networks. Three galaxy samples (slices) with
redshifts ranging within intervals 0.88-0.91, 0.91-0.94 and 0.94-0.97 are
studied as two-dimensional projections for the spatial distributions of
galaxies. We construct networks and calculate network measures for each sample,
in order to analyse the network similarity of different samples, distinguish
various topological environments, and find associations between galaxy
properties (colour index and stellar mass) and their topological environments.
Results indicate a high level of similarity between geometry and topology for
different galaxy samples and no clear evidence of evolutionary trends in
network measures. The distribution of local clustering coefficient C manifests
three modes which allow for discrimination between stand-alone singlets and
dumbbells (0 <= C <= 0.1), intermediately (0 < C < 0.9) and clique (0.9 <= C <=
1) like galaxies. Analysing astrophysical properties of galaxies (colour index
and stellar masses), we show that distributions are similar in all slices,
however weak evolutionary trends can also be seen across redshift slices. To
specify different topological environments we have extracted selections of
galaxies from each sample according to different modes of C distribution. We
have found statistically significant associations between evolutionary
parameters of galaxies and selections of C: the distribution of stellar mass
for galaxies with interim C differ from the corresponding distributions for
stand-alone and clique galaxies, and this difference holds for all redshift
slices. The colour index realises somewhat different behaviour.
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A Functional Taxonomy of Music Generation Systems | Digital advances have transformed the face of automatic music generation
since its beginnings at the dawn of computing. Despite the many breakthroughs,
issues such as the musical tasks targeted by different machines and the degree
to which they succeed remain open questions. We present a functional taxonomy
for music generation systems with reference to existing systems. The taxonomy
organizes systems according to the purposes for which they were designed. It
also reveals the inter-relatedness amongst the systems. This design-centered
approach contrasts with predominant methods-based surveys and facilitates the
identification of grand challenges to set the stage for new breakthroughs.
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Covariant representations for singular actions on C*-algebras | Singular actions on C*-algebras are automorphic group actions on C*-algebras,
where the group need not be locally compact, or the action need not be strongly
continuous. We study the covariant representation theory of such actions. In
the usual case of strongly continuous actions of locally compact groups on
C*-algebras, this is done via crossed products, but this approach is not
available for singular C*-actions (this was our path in a previous paper). The
literature regarding covariant representations for singular actions is already
large and scattered, and in need of some consolidation. We collect in this
survey a range of results in this field, mostly known. We improve some proofs
and elucidate some interconnections. These include existence theorems by
Borchers and Halpern, Arveson spectra, the Borchers-Arveson theorem, standard
representations and Stinespring dilations as well as ground states, KMS states
and ergodic states and the spatial structure of their GNS representations.
| 0 | 0 | 1 | 0 | 0 | 0 |
Control Synthesis for Permutation-Symmetric High-Dimensional Systems With Counting Constraints | General purpose correct-by-construction synthesis methods are limited to
systems with low dimensionality or simple specifications. In this work we
consider highly symmetrical counting problems and exploit the symmetry to
synthesize provably correct controllers for systems with tens of thousands of
states. The key ingredients of the solution are an aggregate abstraction
procedure for mildly heterogeneous systems and a formulation of counting
constraints as linear inequalities.
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Commutativity and Commutative Pairs of Some Differential Equations | In this study, explicit differential equations representing commutative pairs
of some well-known second-order linear time-varying systems have been derived.
The commutativity of these systems are investigated by considering 30
second-order linear differential equations with variable coefficients. It is
shown that the system modeled by each one of these equations has a commutative
pair with (or without) some conditions or not. There appear special cases such
that both, only one or neither of the original system and its commutative pair
has explicit analytic solution. Some benefits of commutativity have already
been mentioned in the literature but a new application for in cryptology for
obscuring transmitted signals in telecommunication is illustrated in this
paper.
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Astrophysical signatures of leptonium | More than 10^43 positrons annihilate every second in the centre of our Galaxy
yet, despite four decades of observations, their origin is still unknown. Many
candidates have been proposed, such as supernovae and low mass X-ray binaries.
However, these models are difficult to reconcile with the distribution of
positrons, which are highly concentrated in the Galactic bulge, and therefore
require specific propagation of the positrons through the interstellar medium.
Alternative sources include dark matter decay, or the supermassive black hole,
both of which would have a naturally high bulge-to-disc ratio.
The chief difficulty in reconciling models with the observations is the
intrinsically poor angular resolution of gamma-ray observations, which cannot
resolve point sources. Essentially all of the positrons annihilate via the
formation of positronium. This gives rise to the possibility of observing
recombination lines of positronium emitted before the atom annihilates. These
emission lines would be in the UV and the NIR, giving an increase in angular
resolution of a factor of 10^4 compared to gamma ray observations, and allowing
the discrimination between point sources and truly diffuse emission.
Analogously to the formation of positronium, it is possible to form atoms of
true muonium and true tauonium. Since muons and tauons are intrinsically
unstable, the formation of such leptonium atoms will be localised to their
places of origin. Thus observations of true muonium or true tauonium can
provide another way to distinguish between truly diffuse sources such as dark
matter decay, and an unresolved distribution of point sources.
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Enhancing Stratified Graph Sampling Algorithms based on Approximate Degree Distribution | Sampling technique has become one of the recent research focuses in the
graph-related fields. Most of the existing graph sampling algorithms tend to
sample the high degree or low degree nodes in the complex networks because of
the characteristic of scale-free. Scale-free means that degrees of different
nodes are subject to a power law distribution. So, there is a significant
difference in the degrees between the overall sampling nodes. In this paper, we
propose an idea of approximate degree distribution and devise a stratified
strategy using it in the complex networks. We also develop two graph sampling
algorithms combining the node selection method with the stratified strategy.
The experimental results show that our sampling algorithms preserve several
properties of different graphs and behave more accurately than other
algorithms. Further, we prove the proposed algorithms are superior to the
off-the-shelf algorithms in terms of the unbiasedness of the degrees and more
efficient than state-of-the-art FFS and ES-i algorithms.
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Testing for Balance in Social Networks | Friendship and antipathy exist in concert with one another in real social
networks. Despite the role they play in social interactions, antagonistic ties
are poorly understood and infrequently measured. One important theory of
negative ties that has received relatively little empirical evaluation is
balance theory, the codification of the adage `the enemy of my enemy is my
friend' and similar sayings. Unbalanced triangles are those with an odd number
of negative ties, and the theory posits that such triangles are rare. To test
for balance, previous works have utilized a permutation test on the edge signs.
The flaw in this method, however, is that it assumes that negative and positive
edges are interchangeable. In reality, they could not be more different. Here,
we propose a novel test of balance that accounts for this discrepancy and show
that our test is more accurate at detecting balance. Along the way, we prove
asymptotic normality of the test statistic under our null model, which is of
independent interest. Our case study is a novel dataset of signed networks we
collected from 32 isolated, rural villages in Honduras. Contrary to previous
results, we find that there is only marginal evidence for balance in social tie
formation in this setting.
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Empirical Recurrence Rates for Seismic Signals on Planetary Surfaces | We review the recurrence intervals as a function of ground motion amplitude
at several terrestrial locations, and make the first interplanetary comparison
with measurements on the Moon, Mars, Venus and Titan. This empirical approach
gives an intuitive guide to the relative seismicity of these locations, without
invoking interior models and specific sources: for example a Venera-14
observation of possible ground motion indicates a microseismic environment
mid-way between noisy and quiet terrestrial locations; quiet terrestrial
regions see a peak velocity amplitude in mm/s roughly equal to 0.4*N(-0.7),
where N is the number of events observed per year. The Apollo data show signals
for a given recurrence rate are typically about 10,000 times smaller in
amplitude than a quiet site on Earth, while Viking data masked for low-wind
periods appears comparable with a quiet terrestrial site. Recurrence rate plots
from in-situ measurements provide a convenient guide to expectations for
seismic instrumentation on future planetary missions : while small geophones
can discriminate terrestrial activity rates, observations with guidance
accelerometers are typically too insensitive to provide meaningful constraints
unless operated for long periods.
| 0 | 1 | 0 | 0 | 0 | 0 |
Landau Damping of Beam Instabilities by Electron Lenses | Modern and future particle accelerators employ increasingly higher intensity
and brighter beams of charged particles and become operationally limited by
coherent beam instabilities. Usual methods to control the instabilities, such
as octupole magnets, beam feedback dampers and use of chromatic effects, become
less effective and insufficient. We show that, in contrast, Lorentz forces of a
low-energy, a magnetically stabilized electron beam, or "electron lens", easily
introduces transverse nonlinear focusing sufficient for Landau damping of
transverse beam instabilities in accelerators. It is also important that,
unlike other nonlinear elements, the electron lens provides the frequency
spread mainly at the beam core, thus allowing much higher frequency spread
without lifetime degradation. For the parameters of the Future Circular
Collider, a single conventional electron lens a few meters long would provide
stabilization superior to tens of thousands of superconducting octupole
magnets.
| 0 | 1 | 0 | 0 | 0 | 0 |
On Algebraic Characterization of SSC of the Jahangir's Graph $\mathcal{J}_{n,m}$ | In this paper, some algebraic and combinatorial characterizations of the
spanning simplicial complex $\Delta_s(\mathcal{J}_{n,m})$ of the Jahangir's
graph $\mathcal{J}_{n,m}$ are explored. We show that
$\Delta_s(\mathcal{J}_{n,m})$ is pure, present the formula for $f$-vectors
associated to it and hence deduce a recipe for computing the Hilbert series of
the Face ring $k[\Delta_s(\mathcal{J}_{n,m})]$. Finaly, we show that the face
ring of $\Delta_s(\mathcal{J}_{n,m})$ is Cohen-Macaulay and give some open
scopes of the current work.
| 0 | 0 | 1 | 0 | 0 | 0 |
Accelerated Gossip via Stochastic Heavy Ball Method | In this paper we show how the stochastic heavy ball method (SHB) -- a popular
method for solving stochastic convex and non-convex optimization problems
--operates as a randomized gossip algorithm. In particular, we focus on two
special cases of SHB: the Randomized Kaczmarz method with momentum and its
block variant. Building upon a recent framework for the design and analysis of
randomized gossip algorithms, [Loizou Richtarik, 2016] we interpret the
distributed nature of the proposed methods. We present novel protocols for
solving the average consensus problem where in each step all nodes of the
network update their values but only a subset of them exchange their private
values. Numerical experiments on popular wireless sensor networks showing the
benefits of our protocols are also presented.
| 1 | 0 | 0 | 0 | 0 | 0 |
Attention-Based Models for Text-Dependent Speaker Verification | Attention-based models have recently shown great performance on a range of
tasks, such as speech recognition, machine translation, and image captioning
due to their ability to summarize relevant information that expands through the
entire length of an input sequence. In this paper, we analyze the usage of
attention mechanisms to the problem of sequence summarization in our end-to-end
text-dependent speaker recognition system. We explore different topologies and
their variants of the attention layer, and compare different pooling methods on
the attention weights. Ultimately, we show that attention-based models can
improves the Equal Error Rate (EER) of our speaker verification system by
relatively 14% compared to our non-attention LSTM baseline model.
| 1 | 0 | 0 | 1 | 0 | 0 |
Escaping the Curse of Dimensionality in Similarity Learning: Efficient Frank-Wolfe Algorithm and Generalization Bounds | Similarity and metric learning provides a principled approach to construct a
task-specific similarity from weakly supervised data. However, these methods
are subject to the curse of dimensionality: as the number of features grows
large, poor generalization is to be expected and training becomes intractable
due to high computational and memory costs. In this paper, we propose a
similarity learning method that can efficiently deal with high-dimensional
sparse data. This is achieved through a parameterization of similarity
functions by convex combinations of sparse rank-one matrices, together with the
use of a greedy approximate Frank-Wolfe algorithm which provides an efficient
way to control the number of active features. We show that the convergence rate
of the algorithm, as well as its time and memory complexity, are independent of
the data dimension. We further provide a theoretical justification of our
modeling choices through an analysis of the generalization error, which depends
logarithmically on the sparsity of the solution rather than on the number of
features. Our experiments on datasets with up to one million features
demonstrate the ability of our approach to generalize well despite the high
dimensionality as well as its superiority compared to several competing
methods.
| 1 | 0 | 0 | 1 | 0 | 0 |
Automata-Guided Hierarchical Reinforcement Learning for Skill Composition | Skills learned through (deep) reinforcement learning often generalizes poorly
across domains and re-training is necessary when presented with a new task. We
present a framework that combines techniques in \textit{formal methods} with
\textit{reinforcement learning} (RL). The methods we provide allows for
convenient specification of tasks with logical expressions, learns hierarchical
policies (meta-controller and low-level controllers) with well-defined
intrinsic rewards, and construct new skills from existing ones with little to
no additional exploration. We evaluate the proposed methods in a simple grid
world simulation as well as a more complicated kitchen environment in AI2Thor
| 1 | 0 | 0 | 0 | 0 | 0 |
Emergent transport in a many-body open system driven by interacting quantum baths | We analyze an open many-body system that is strongly coupled at its
boundaries to interacting quantum baths. We show that the two-body interactions
inside the baths induce emergent phenomena in the spin transport. The system
and baths are modeled as independent spin chains resulting in a global
non-homogeneous XXZ model. The evolution of the system-bath state is simulated
using matrix-product-states methods. We present two phase transitions induced
by bath interactions. For weak bath interactions we observe ballistic and
insulating phases. However, for strong bath interactions a diffusive phase
emerges with a distinct power-law decay of the time-dependent spin current
$Q\propto t^{-\alpha}$. Furthermore, we investigate long-lasting current
oscillations arising from the non-Markovian dynamics in the homogeneous case,
and find a sharp change in their frequency scaling coinciding with the triple
point of the phase diagram.
| 0 | 1 | 0 | 0 | 0 | 0 |
Experimental Investigation of Optimum Beam Size for FSO Uplink | In this paper, the effect of transmitter beam size on the performance of free
space optical (FSO) communication has been determined experimentally.
Irradiance profile for varying turbulence strength is obtained using optical
turbulence generating (OTG) chamber inside laboratory environment. Based on the
results, an optimum beam size is investigated using the semi-analytical method.
Moreover, the combined effects of atmospheric scintillation and beam wander
induced pointing errors are considered in order to determine the optimum beam
size that minimizes the bit error rate (BER) of the system for a fixed
transmitter power and link length. The results show that the optimum beam size
increases with the increase in zenith angle but has negligible effect with the
increase in fade threshold level at low turbulence levels and has a marginal
effect at high turbulence levels. Finally, the obtained outcome is useful for
FSO system design and BER performance analysis.
| 1 | 1 | 0 | 0 | 0 | 0 |
The role of surface water in the geometry of Mars' valley networks and its climatic implications | Mars' surface bears the imprint of valley networks formed billions of years
ago and their relicts can still be observed today. However, whether these
networks were formed by groundwater sapping, ice melt, or fluvial runoff has
been continuously debated. These different scenarios have profoundly different
implications for Mars' climatic history, and thus for its habitability in the
distant past. Recent studies on Earth revealed that channel networks in arid
landscapes with more surface runoff branch at narrower angles, while in humid
environments with more groundwater flow, branching angles are much wider. We
find that valley networks on Mars generally tend to branch at narrow angles
similar to those found in arid landscapes on Earth. This result supports the
inference that Mars once had an active hydrologic cycle and that Mars' valley
networks were formed primarily by overland flow erosion with groundwater
seepage playing only a minor role.
| 0 | 1 | 0 | 0 | 0 | 0 |
An All-in-One Network for Dehazing and Beyond | This paper proposes an image dehazing model built with a convolutional neural
network (CNN), called All-in-One Dehazing Network (AOD-Net). It is designed
based on a re-formulated atmospheric scattering model. Instead of estimating
the transmission matrix and the atmospheric light separately as most previous
models did, AOD-Net directly generates the clean image through a light-weight
CNN. Such a novel end-to-end design makes it easy to embed AOD-Net into other
deep models, e.g., Faster R-CNN, for improving high-level task performance on
hazy images. Experimental results on both synthesized and natural hazy image
datasets demonstrate our superior performance than the state-of-the-art in
terms of PSNR, SSIM and the subjective visual quality. Furthermore, when
concatenating AOD-Net with Faster R-CNN and training the joint pipeline from
end to end, we witness a large improvement of the object detection performance
on hazy images.
| 1 | 0 | 0 | 0 | 0 | 0 |
GdPtPb: A non collinear antiferromagnet with distorted Kagomé lattice | In the spirit of searching for Gd-based, frustrated, rare earth magnets, we
have found antiferomagnetism (AF) in GdPtPb which crystallizes in the
ZrNiAl-type structure that has a distorted Kagomé lattice of Gd-triangles.
Single crystals were grown and investigated using structural, magnetic,
transport and thermodynamic measurements. GdPtPb orders antiferromagnetically
at 15.5 K arguably with a planar, non-collinear structure. The high temperature
magnetic susceptibility data reveal an "anti-frustration" behavior having a
frustration parameter, $|f|$ = $|\Theta|$/ $T_N$ = 0.25, which can be explained
by mean field theory (MFT) within a two sub-lattice model. Study of the
magnetic phase diagram down to $T$ = 1.8 K reveals a change of magnetic
structure through a metamagnetic transition at around 20 kOe and the
disappearance of the AF ordering near 140 kOe. In total, our work indicates
that, GdPtPb can serve as an example of a planar, non collinear, AF with a
distorted Kagomé magnetic sub-lattice.
| 0 | 1 | 0 | 0 | 0 | 0 |
Belitskii's canonical forms of linear dynamical systems | In the note, all indecomposable canonical forms of linear systems with
dimension less than or equal to $4$ are determined based on Belitskii's
algorithm. As an application, an effective way to calculate dimensions of
equivalence classes of linear systems is given by using Belitskii's canonical
forms.
| 0 | 0 | 1 | 0 | 0 | 0 |
The Geometry of Nodal Sets and Outlier Detection | Let $(M,g)$ be a compact manifold and let $-\Delta \phi_k = \lambda_k \phi_k$
be the sequence of Laplacian eigenfunctions. We present a curious new
phenomenon which, so far, we only managed to understand in a few highly
specialized cases: the family of functions $f_N:M \rightarrow \mathbb{R}_{\geq
0}$ $$ f_N(x) = \sum_{k \leq N}{ \frac{1}{\sqrt{\lambda_k}}
\frac{|\phi_k(x)|}{\|\phi_k\|_{L^{\infty}(M)}}}$$ seems strangely suited for
the detection of anomalous points on the manifold. It may be heuristically
interpreted as the sum over distances to the nearest nodal line and potentially
hints at a new phenomenon in spectral geometry. We give rigorous statements on
the unit square $[0,1]^2$ (where minima localize in $\mathbb{Q}^2$) and on
Paley graphs (where $f_N$ recovers the geometry of quadratic residues of the
underlying finite field $\mathbb{F}_p$). Numerical examples show that the
phenomenon seems to arise on fairly generic manifolds.
| 0 | 0 | 1 | 1 | 0 | 0 |
Multimodal Clustering for Community Detection | Multimodal clustering is an unsupervised technique for mining interesting
patterns in $n$-adic binary relations or $n$-mode networks. Among different
types of such generalized patterns one can find biclusters and formal concepts
(maximal bicliques) for 2-mode case, triclusters and triconcepts for 3-mode
case, closed $n$-sets for $n$-mode case, etc. Object-attribute biclustering
(OA-biclustering) for mining large binary datatables (formal contexts or 2-mode
networks) arose by the end of the last decade due to intractability of
computation problems related to formal concepts; this type of patterns was
proposed as a meaningful and scalable approximation of formal concepts. In this
paper, our aim is to present recent advance in OA-biclustering and its
extensions to mining multi-mode communities in SNA setting. We also discuss
connection between clustering coefficients known in SNA community for 1-mode
and 2-mode networks and OA-bicluster density, the main quality measure of an
OA-bicluster. Our experiments with 2-, 3-, and 4-mode large real-world networks
show that this type of patterns is suitable for community detection in
multi-mode cases within reasonable time even though the number of corresponding
$n$-cliques is still unknown due to computation difficulties. An interpretation
of OA-biclusters for 1-mode networks is provided as well.
| 1 | 0 | 0 | 1 | 0 | 0 |
Identification of a space varying coefficient of a linear viscoelastic string of Maxwell-Boltzman type | In this paper we solve the problem of the identification of a coefficient
which appears in the model of a distributed system with persistent memory
encountered in linear viscoelasticity (and in diffusion processes with memory).
The additional data used in the identification are subsumed in the input output
map from the deformation to the traction on the boundary. We extend a dynamical
approach to identification introduced by Belishev in the case of purely elastic
(memoryless) bodies and based on a special equation due to Blagoveshchenskii.
So, in particular, we extend Blagoveshchenskii equation to our class of systems
with persistent memory.
| 1 | 0 | 1 | 0 | 0 | 0 |
Efficient four-wave mixing at the nanofocus of integrated organic gap plasmon waveguides on silicon | Nonlinear optics, especially frequency mixing, underpins modern optical
technology and scientific exploration in quantum optics, materials and life
sciences, and optical communications. Since nonlinear effects are weak,
efficient frequency mixing must accumulate over large interaction lengths
restricting the integration of nonlinear photonics with electronics and
establishing limitations on mixing processes due to the requirement of phase
matching. In this work we report efficient four-wave mixing over micron-scale
interaction lengths at telecoms wavelengths. We use an integrated plasmonic gap
waveguide on silicon that strongly confines light within a nonlinear organic
polymer in the gap. Our approach is so effective because the gap waveguide
intensifies light by efficiently nanofocusing it to a mode cross-section of a
few tens of nanometres, generating a nonlinear response so strong that
efficient four-wave mixing accumulates in just a micron. This is significant as
our technique opens up nonlinear optics to a regime where phase matching and
dispersion considerations are relaxed, giving rise to the possibility of
compact, broadband, and efficient frequency mixing on a platform that can be
integrated with silicon photonics.
| 0 | 1 | 0 | 0 | 0 | 0 |
Weakly nonergodic dynamics in the Gross--Pitaevskii lattice | The microcanonical Gross--Pitaevskii (aka semiclassical Bose-Hubbard) lattice
model dynamics is characterized by a pair of energy and norm densities. The
grand canonical Gibbs distribution fails to describe a part of the density
space, due to the boundedness of its kinetic energy spectrum. We define
Poincare equilibrium manifolds and compute the statistics of microcanonical
excursion times off them. The tails of the distribution functions quantify the
proximity of the many-body dynamics to a weakly-nonergodic phase, which occurs
when the average excursion time is infinite. We find that a crossover to
weakly-nonergodic dynamics takes place inside the nonGibbs phase, being
unnoticed by the largest Lyapunov exponent. In the ergodic part of the
non-Gibbs phase, the Gibbs distribution should be replaced by an unknown
modified one. We relate our findings to the corresponding integrable limit,
close to which the actions are interacting through a short range coupling
network.
| 0 | 1 | 0 | 0 | 0 | 0 |
Does agricultural subsidies foster Italian southern farms? A Spatial Quantile Regression Approach | During the last decades, public policies become a central pillar in
supporting and stabilising agricultural sector. In 1962, EU policy-makers
developed the so-called Common Agricultural Policy (CAP) to ensure
competitiveness and a common market organisation for agricultural products,
while 2003 reform decouple the CAP from the production to focus only on income
stabilization and the sustainability of agricultural sector. Notwithstanding
farmers are highly dependent to public support, literature on the role played
by the CAP in fostering agricultural performances is still scarce and
fragmented. Actual CAP policies increases performance differentials between
Northern Central EU countries and peripheral regions. This paper aims to
evaluate the effectiveness of CAP in stimulate performances by focusing on
Italian lagged Regions. Moreover, agricultural sector is deeply rooted in
place-based production processes. In this sense, economic analysis which omit
the presence of spatial dependence produce biased estimates of the
performances. Therefore, this paper, using data on subsidies and economic
results of farms from the RICA dataset which is part of the Farm Accountancy
Data Network (FADN), proposes a spatial Augmented Cobb-Douglas Production
Function to evaluate the effects of subsidies on farm's performances. The major
innovation in this paper is the implementation of a micro-founded quantile
version of a spatial lag model to examine how the impact of the subsidies may
vary across the conditional distribution of agricultural performances. Results
show an increasing shape which switch from negative to positive at the median
and becomes statistical significant for higher quantiles. Additionally, spatial
autocorrelation parameter is positive and significant across all the
conditional distribution, suggesting the presence of significant spatial
spillovers in agricultural performances.
| 0 | 0 | 0 | 1 | 0 | 0 |
Proton fire hose instabilities in the expanding solar wind | Using two-dimensional hybrid expanding box simulations we study the
competition between the continuously driven parallel proton temperature
anisotropy and fire hose instabilities in collisionless homogeneous plasmas.
For quasi radial ambient magnetic field the expansion drives
$T_{\mathrm{p}\|}>T_{\mathrm{p}\perp}$ and the system becomes eventually
unstable with respect to the dominant parallel fire hose instability. This
instability is generally unable to counteract the induced anisotropization and
the system typically becomes unstable with respect to the oblique fire hose
instability later on. The oblique instability efficiently reduces the
anisotropy and the system rapidly stabilizes while a significant part of the
generated electromagnetic fluctuations is damped to protons. As long as the
magnetic field is in the quasi radial direction, this evolution repeats itself
and the electromagnetic fluctuations accumulate. For sufficiently oblique
magnetic field the expansion drives $T_{\mathrm{p}\perp}>T_{\mathrm{p}\|}$ and
brings the system to the stable region with respect to the fire hose
instabilities.
| 0 | 1 | 0 | 0 | 0 | 0 |
Minimax Optimal Rates of Estimation in Functional ANOVA Models with Derivatives | We establish minimax optimal rates of convergence for nonparametric
estimation in functional ANOVA models when data from first-order partial
derivatives are available. Our results reveal that partial derivatives can
improve convergence rates for function estimation with deterministic or random
designs. In particular, for full $d$-interaction models, the optimal rates with
first-order partial derivatives on $p$ covariates are identical to those for
$(d-p)$-interaction models without partial derivatives. For additive models,
the rates by using all first-order partial derivatives are root-$n$ to achieve
the "parametric rate". We also investigate the minimax optimal rates for
first-order partial derivative estimations when derivative data are available.
Those rates coincide with the optimal rate for estimating the first-order
derivative of a univariate function.
| 0 | 0 | 1 | 1 | 0 | 0 |
Phase Retrieval via Randomized Kaczmarz: Theoretical Guarantees | We consider the problem of phase retrieval, i.e. that of solving systems of
quadratic equations. A simple variant of the randomized Kaczmarz method was
recently proposed for phase retrieval, and it was shown numerically to have a
computational edge over state-of-the-art Wirtinger flow methods. In this paper,
we provide the first theoretical guarantee for the convergence of the
randomized Kaczmarz method for phase retrieval. We show that it is sufficient
to have as many Gaussian measurements as the dimension, up to a constant
factor. Along the way, we introduce a sufficient condition on measurement sets
for which the randomized Kaczmarz method is guaranteed to work. We show that
Gaussian sampling vectors satisfy this property with high probability; this is
proved using a chaining argument coupled with bounds on VC dimension and metric
entropy.
| 1 | 0 | 1 | 1 | 0 | 0 |
Beyond perturbation 1: de Rham spaces | It is shown that if one uses the notion of infinity nilpotent elements due to
Moerdijk and Reyes, instead of the usual definition of nilpotents to define
reduced $C^\infty$-schemes, the resulting de Rham spaces are given as quotients
by actions of germs of diagonals, instead of the formal neighbourhoods of the
diagonals.
| 0 | 0 | 1 | 0 | 0 | 0 |
Semantic Web Prefetching Using Semantic Relatedness between Web pages | Internet as become the way of life in the fast growing digital life.Even with
the increase in the internet speed, higher latency time is still a challenge.
To reduce latency, caching and pre fetching techniques can be used. However,
caching fails for dynamic websites which keeps on changing rapidly. Another
technique is web prefetching, which prefetches the web pages that the user is
likely to request for in the future. Semantic web prefetching makes use of
keywords and descriptive texts like anchor text, titles, text surrounding
anchor text of the present web pages for predicting users future requests.
Semantic information is embedded within the web pages during their designing
for the purpose of reflecting the relationship between the web pages. The
client can fetch this information from the server. However, this technique
involves load on web designers for adding external tags and on server for
providing this information along with the desired page, which is not desirable.
This paper is an effort to find the semantic relation between web pages using
the keywords provided by the user and the anchor texts of the hyperlinks on the
present web page.It provides algorithms for sequential and similar semantic
relations. These algorithms will be implemented on the client side which will
not cause overhead on designers and load on server for semantic information.
| 1 | 0 | 0 | 0 | 0 | 0 |
Learning Certifiably Optimal Rule Lists for Categorical Data | We present the design and implementation of a custom discrete optimization
technique for building rule lists over a categorical feature space. Our
algorithm produces rule lists with optimal training performance, according to
the regularized empirical risk, with a certificate of optimality. By leveraging
algorithmic bounds, efficient data structures, and computational reuse, we
achieve several orders of magnitude speedup in time and a massive reduction of
memory consumption. We demonstrate that our approach produces optimal rule
lists on practical problems in seconds. Our results indicate that it is
possible to construct optimal sparse rule lists that are approximately as
accurate as the COMPAS proprietary risk prediction tool on data from Broward
County, Florida, but that are completely interpretable. This framework is a
novel alternative to CART and other decision tree methods for interpretable
modeling.
| 0 | 0 | 0 | 1 | 0 | 0 |
Cold-Start Reinforcement Learning with Softmax Policy Gradient | Policy-gradient approaches to reinforcement learning have two common and
undesirable overhead procedures, namely warm-start training and sample variance
reduction. In this paper, we describe a reinforcement learning method based on
a softmax value function that requires neither of these procedures. Our method
combines the advantages of policy-gradient methods with the efficiency and
simplicity of maximum-likelihood approaches. We apply this new cold-start
reinforcement learning method in training sequence generation models for
structured output prediction problems. Empirical evidence validates this method
on automatic summarization and image captioning tasks.
| 1 | 0 | 0 | 0 | 0 | 0 |
Implicit Cooperative Positioning in Vehicular Networks | Absolute positioning of vehicles is based on Global Navigation Satellite
Systems (GNSS) combined with on-board sensors and high-resolution maps. In
Cooperative Intelligent Transportation Systems (C-ITS), the positioning
performance can be augmented by means of vehicular networks that enable
vehicles to share location-related information. This paper presents an Implicit
Cooperative Positioning (ICP) algorithm that exploits the Vehicle-to-Vehicle
(V2V) connectivity in an innovative manner, avoiding the use of explicit V2V
measurements such as ranging. In the ICP approach, vehicles jointly localize
non-cooperative physical features (such as people, traffic lights or inactive
cars) in the surrounding areas, and use them as common noisy reference points
to refine their location estimates. Information on sensed features are fused
through V2V links by a consensus procedure, nested within a message passing
algorithm, to enhance the vehicle localization accuracy. As positioning does
not rely on explicit ranging information between vehicles, the proposed ICP
method is amenable to implementation with off-the-shelf vehicular communication
hardware. The localization algorithm is validated in different traffic
scenarios, including a crossroad area with heterogeneous conditions in terms of
feature density and V2V connectivity, as well as a real urban area by using
Simulation of Urban MObility (SUMO) for traffic data generation. Performance
results show that the proposed ICP method can significantly improve the vehicle
location accuracy compared to the stand-alone GNSS, especially in harsh
environments, such as in urban canyons, where the GNSS signal is highly
degraded or denied.
| 1 | 0 | 0 | 1 | 0 | 0 |
A Generalized Accelerated Composite Gradient Method: Uniting Nesterov's Fast Gradient Method and FISTA | We demonstrate that the augmented estimate sequence framework unites the most
popular primal first-order schemes for large-scale problems: the Fast Gradient
Method (FGM) and the Fast Iterative Shrinkage Thresholding Algorithm (FISTA).
We further showcase the flexibility of the augmented estimate sequence by
deriving a Generalized Accelerated Composite Gradient Method endowed with
monotonicity alongside a versatile line-search procedure. The new method
surpasses both FGM and FISTA in terms of robustness and usability. In
particular, it is guaranteed to converge without requiring any quantitative
prior information on the problem. Additional information, if available, leads
to an improvement in performance at least on par with the state-of-the-art. We
support our findings with simulation results.
| 0 | 0 | 1 | 0 | 0 | 0 |
Piezoresponse of ferroelectric films in ferroionic states: time and voltage dynamics | The interplay between electrochemical surface charges and bulk
ferroelectricity in thin films gives rise to a continuum of coupled ferro-ionic
states. These states are exquisitely sensitive to chemical and electric
conditions at the surfaces, applied voltage, and oxygen pressure. Using the
analytical approach combining the Ginzburg-Landau-Devonshire description of the
ferroelectricity with Langmuir adsorption isotherm for the ions at the film
surface, we have studied the temperature-, time- and field- dependent
polarization changes and electromechanical response of the ferro-ionic states.
The responses are found to be inseparable in thermodynamic equilibrium and at
low frequencies of applied voltage. The states become separable in high
frequency dynamic mode due to the several orders of magnitude difference in the
relaxation times of ferroelectric polarization and surface ions charge density.
These studies provide an insight into dynamic behavior of nanoscale
ferroelectrics with open surface exposed to different kinds of
electrochemically active gaseous surrounding.
| 0 | 1 | 0 | 0 | 0 | 0 |
Penalty Alternating Direction Methods for Mixed-Integer Optimization: A New View on Feasibility Pumps | Feasibility pumps are highly effective primal heuristics for mixed-integer
linear and nonlinear optimization. However, despite their success in practice
there are only few works considering their theoretical properties. We show that
feasibility pumps can be seen as alternating direction methods applied to
special reformulations of the original problem, inheriting the convergence
theory of these methods. Moreover, we propose a novel penalty framework that
encompasses this alternating direction method, which allows us to refrain from
random perturbations that are applied in standard versions of feasibility pumps
in case of failure. We present a convergence theory for the new penalty based
alternating direction method and compare the new variant of the feasibility
pump with existing versions in an extensive numerical study for mixed-integer
linear and nonlinear problems.
| 0 | 0 | 1 | 0 | 0 | 0 |
Lorentz covariant and gauge invariant description of orbital and spin angular momentum and the non-symmetric energy momentum tensor | Starting from covariant expressions, a gauge independent separation of
orbital and spin angular momentum for electrodynamics is presented. This
results from the non-symmetric canonical energy momentum tensor of the
electromagnetic field. The origin of the difficulty is discussed and a
covariant gauge invariant spin vector is derived. The paradox concerning the
spin angular momentum of a plane wave finds a natural solution.
| 0 | 1 | 0 | 0 | 0 | 0 |
Dirichlet's theorem and Jacobsthal's function | If $a$ and $d$ are relatively prime, we refer to the set of integers
congruent to $a$ mod $d$ as an `eligible' arithmetic progression. A theorem of
Dirichlet says that every eligible arithmetic progression contains infinitely
many primes; the theorem follows from the assertion that every eligible
arithmetic progression contains at least one prime. The Jacobsthal function
$g(n)$ is defined as the smallest positive integer such that every sequence of
$g(n)$ consecutive integers contains an integer relatively prime to $n$. In
this paper, we show by a combinatorial argument that every eligible arithmetic
progression with $d\le76$ contains at least one prime, and we show that certain
plausible bounds on the Jacobsthal function of primorials would imply that
every eligible arithmetic progression contains at least one prime. That is,
certain plausible bounds on the Jacobsthal function would lead to an elementary
proof of Dirichlet's theorem.
| 0 | 0 | 1 | 0 | 0 | 0 |
Asymptotic control theory for a closed string | We develop an asymptotical control theory for one of the simplest distributed
oscillating systems, namely, for a closed string under a bounded load applied
to a single distinguished point. We find exact classes of string states that
admit complete damping and an asymptotically exact value of the required time.
By using approximate reachable sets instead of exact ones, we design a
dry-friction like feedback control, which turns out to be asymptotically
optimal. We prove the existence of motion under the control using a rather
explicit solution of a nonlinear wave equation. Remarkably, the solution is
determined via purely algebraic operations. The main result is a proof of
asymptotic optimality of the control thus constructed.
| 0 | 0 | 1 | 0 | 0 | 0 |
Large Scale Replication Projects in Contemporary Psychological Research | Replication is complicated in psychological research because studies of a
given psychological phenomenon can never be direct or exact replications of one
another, and thus effect sizes vary from one study of the phenomenon to the
next--an issue of clear importance for replication. Current large scale
replication projects represent an important step forward for assessing
replicability, but provide only limited information because they have thus far
been designed in a manner such that heterogeneity either cannot be assessed or
is intended to be eliminated. Consequently, the non-trivial degree of
heterogeneity found in these projects represents a lower bound on
heterogeneity. We recommend enriching large scale replication projects going
forward by em- bracing heterogeneity. We argue this is key for assessing
replicability: if effect sizes are sufficiently heterogeneous--even if the sign
of the effect is consistent--the phenomenon in question does not seem
particularly replicable and the theory underlying it seems poorly constructed
and in need of enrichment. Uncovering why and revising theory in light of it
will lead to improved theory that explains heterogeneity and in- creases
replicability. Given this, large scale replication projects can play an
important role not only in assessing replicability but also in advancing
theory.
| 0 | 0 | 0 | 1 | 0 | 0 |
Intrinsic pinning by naturally occurring correlated defects in FeSe$_\text{1-x}$Te$_\text{x}$ superconductors | We study the angular dependence of the dissipation in the superconducting
state of FeSe and Fe(Se$_\text{1-x}$Te$_\text{x}$) through electrical transport
measurements, using crystalline intergrown materials. We reveal the key role of
the inclusions of the non superconducting magnetic phase
Fe$_\text{1-y}$(Se$_\text{1-x}$Te$_\text{x}$), growing into the
Fe(Se$_\text{1-x}$Te$_\text{x}$) pure $\beta$-phase, in the development of a
correlated defect structure. The matching of both atomic structures defines the
growth habit of the crystalline material as well as the correlated planar
defects orientation.
| 0 | 1 | 0 | 0 | 0 | 0 |
Models for the Displacement Calculus | The displacement calculus $\mathbf{D}$ is a conservative extension of the
Lambek calculus $\mathbf{L1}$ (with empty antecedents allowed in sequents).
$\mathbf{L1}$ can be said to be the logic of concatenation, while $\mathbf{D}$
can be said to be the logic of concatenation and intercalation. In many senses,
it can be claimed that $\mathbf{D}$ mimics $\mathbf{L1}$ in that the proof
theory, generative capacity and complexity of the former calculus are natural
extensions of the latter calculus. In this paper, we strengthen this claim. We
present the appropriate classes of models for $\mathbf{D}$ and prove some
completeness results; strikingly, we see that these results and proofs are
natural extensions of the corresponding ones for $\mathbf{L1}$.
| 1 | 0 | 0 | 0 | 0 | 0 |
Labeled homology of higher-dimensional automata | We construct labeling homomorphisms on the cubical homology of
higher-dimensional automata and show that they are natural with respect to
cubical dimaps and compatible with the tensor product of HDAs. We also indicate
two possible applications of labeled homology in concurrency theory.
| 1 | 0 | 1 | 0 | 0 | 0 |
Magnetic Actuation and Feedback Cooling of a Cavity Optomechanical Torque Sensor | We demonstrate the integration of a mesoscopic ferromagnetic needle with a
cavity optomechanical torsional resonator, and its use for quantitative
determination of the needle's magnetic properties, as well as amplification and
cooling of the resonator motion. With this system we measure torques as small
as 32 zNm, corresponding to sensing an external magnetic field of 0.12 A/m (150
nT). Furthermore, we are able to extract the magnetization (1710 kA/m) of the
magnetic sample, not known a priori, demonstrating this system's potential for
studies of nanomagnetism. Finally, we show that we can magnetically drive the
torsional resonator into regenerative oscillations, and dampen its mechanical
mode temperature from room temperature to 11.6 K, without sacrificing torque
sensitivity.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Maximum Matching Algorithm for Basis Selection in Spectral Learning | We present a solution to scale spectral algorithms for learning sequence
functions. We are interested in the case where these functions are sparse (that
is, for most sequences they return 0). Spectral algorithms reduce the learning
problem to the task of computing an SVD decomposition over a special type of
matrix called the Hankel matrix. This matrix is designed to capture the
relevant statistics of the training sequences. What is crucial is that to
capture long range dependencies we must consider very large Hankel matrices.
Thus the computation of the SVD becomes a critical bottleneck. Our solution
finds a subset of rows and columns of the Hankel that realizes a compact and
informative Hankel submatrix. The novelty lies in the way that this subset is
selected: we exploit a maximal bipartite matching combinatorial algorithm to
look for a sub-block with full structural rank, and show how computation of
this sub-block can be further improved by exploiting the specific structure of
Hankel matrices.
| 1 | 0 | 0 | 1 | 0 | 0 |
Bäcklund Transformations for the Boussinesq Equation and Merging Solitons | The Bäcklund transformation (BT) for the "good" Boussinesq equation and its
superposition principles are presented and applied. Unlike many other standard
integrable equations, the Boussinesq equation does not have a strictly
algebraic superposition principle for 2 BTs, but it does for 3. We present
associated lattice systems. Applying the BT to the trivial solution generates
standard solitons but also what we call "merging solitons" --- solutions in
which two solitary waves (with related speeds) merge into a single one. We use
the superposition principles to generate a variety of interesting solutions,
including superpositions of a merging soliton with $1$ or $2$ regular solitons,
and solutions that develop a singularity in finite time which then disappears
at some later finite time. We prove a Wronskian formula for the solutions
obtained by applying a general sequence of BTs on the trivial solution.
Finally, we show how to obtain the standard conserved quantities of the
Boussinesq equation from the BT, and how the hierarchy of local symmetries
follows in a simple manner from the superposition principle for 3 BTs.
| 0 | 1 | 1 | 0 | 0 | 0 |
Towards Evolutional Compression | Compressing convolutional neural networks (CNNs) is essential for
transferring the success of CNNs to a wide variety of applications to mobile
devices. In contrast to directly recognizing subtle weights or filters as
redundant in a given CNN, this paper presents an evolutionary method to
automatically eliminate redundant convolution filters. We represent each
compressed network as a binary individual of specific fitness. Then, the
population is upgraded at each evolutionary iteration using genetic operations.
As a result, an extremely compact CNN is generated using the fittest
individual. In this approach, either large or small convolution filters can be
redundant, and filters in the compressed network are more distinct. In
addition, since the number of filters in each convolutional layer is reduced,
the number of filter channels and the size of feature maps are also decreased,
naturally improving both the compression and speed-up ratios. Experiments on
benchmark deep CNN models suggest the superiority of the proposed algorithm
over the state-of-the-art compression methods.
| 1 | 0 | 0 | 1 | 0 | 0 |
Spin inversion in fluorinated graphene n-p junction | We consider a dilute fluorinated graphene nanoribbon as a spin-active
element. The fluorine adatoms introduce a local spin-orbit Rashba interaction
that induces spin-precession for electron passing by. In the absence of the
external magnetic field the transport is dominated by multiple scattering by
adatoms which cancels the spin precession effects, since the direction of the
spin precession depends on the electron momentum. Accumulation of the spin
precession effects is possible provided that the Fermi level electron passes
many times near the same adatom with the same momentum. In order to arrange for
these conditions a circular n-p junction can be introduced to the ribbon by
e.g. potential of the tip of an atomic force microscope. In the quantum Hall
conditions the electron current gets confined along the junction. The electron
spin interaction with the local Rashba field changes with the lifetime of the
quasi-bound states that is controlled with the coupling of the junction to the
edge of the ribbon. We demonstrate that the spin-flip probability can be
increased in this manner by as much as three orders of magnitude.
| 0 | 1 | 0 | 0 | 0 | 0 |
Brunn-Minkowski inequalities in product metric measure spaces | Given one metric measure space $X$ satisfying a linear Brunn-Minkowski
inequality, and a second one $Y$ satisfying a Brunn-Minkowski inequality with
exponent $p\ge -1$, we prove that the product $X\times Y$ with the standard
product distance and measure satisfies a Brunn-Minkowski inequality of order
$1/(1+p^{-1})$ under mild conditions on the measures and the assumption that
the distances are strictly intrinsic. The same result holds when we consider
restricted classes of sets. We also prove that a linear Brunn-Minkowski
inequality is obtained in $X\times Y$ when $Y$ satisfies a Prékopa-Leindler
inequality.
In particular, we show that the classical Brunn-Minkowski inequality holds
for any pair of weakly unconditional sets in $\mathbb{R}^n$ (i.e., those
containing the projection of every point in the set onto every coordinate
subspace) when we consider the standard distance and the product measure of $n$
one-dimensional real measures with positively decreasing densities. This yields
an improvement of the class of sets satisfying the Gaussian Brunn-Minkowski
inequality.
Furthermore, associated isoperimetric inequalities as well as recently
obtained Brunn-Minkowski's inequalities are derived from our results.
| 0 | 0 | 1 | 0 | 0 | 0 |
Deep Learning with Low Precision by Half-wave Gaussian Quantization | The problem of quantizing the activations of a deep neural network is
considered. An examination of the popular binary quantization approach shows
that this consists of approximating a classical non-linearity, the hyperbolic
tangent, by two functions: a piecewise constant sign function, which is used in
feedforward network computations, and a piecewise linear hard tanh function,
used in the backpropagation step during network learning. The problem of
approximating the ReLU non-linearity, widely used in the recent deep learning
literature, is then considered. An half-wave Gaussian quantizer (HWGQ) is
proposed for forward approximation and shown to have efficient implementation,
by exploiting the statistics of of network activations and batch normalization
operations commonly used in the literature. To overcome the problem of gradient
mismatch, due to the use of different forward and backward approximations,
several piece-wise backward approximators are then investigated. The
implementation of the resulting quantized network, denoted as HWGQ-Net, is
shown to achieve much closer performance to full precision networks, such as
AlexNet, ResNet, GoogLeNet and VGG-Net, than previously available low-precision
networks, with 1-bit binary weights and 2-bit quantized activations.
| 1 | 0 | 0 | 0 | 0 | 0 |
Degenerate cyclotomic Hecke algebras and higher level Heisenberg categorification | We associate a monoidal category $\mathcal{H}^\lambda$ to each dominant
integral weight $\lambda$ of $\widehat{\mathfrak{sl}}_p$ or
$\mathfrak{sl}_\infty$. These categories, defined in terms of planar diagrams,
act naturally on categories of modules for the degenerate cyclotomic Hecke
algebras associated to $\lambda$. We show that, in the $\mathfrak{sl}_\infty$
case, the level $d$ Heisenberg algebra embeds into the Grothendieck ring of
$\mathcal{H}^\lambda$, where $d$ is the level of $\lambda$. The categories
$\mathcal{H}^\lambda$ can be viewed as a graphical calculus describing
induction and restriction functors between categories of modules for degenerate
cyclotomic Hecke algebras, together with their natural transformations. As an
application of this tool, we prove a new result concerning centralizers for
degenerate cyclotomic Hecke algebras.
| 0 | 0 | 1 | 0 | 0 | 0 |
Intense automorphisms of finite groups | Let $G$ be a group. An automorphism of $G$ is called intense if it sends each
subgroup of $G$ to a conjugate; the collection of such automorphisms is denoted
by $\mathrm{Int}(G)$. In the special case in which $p$ is a prime number and
$G$ is a finite $p$-group, one can show that $\mathrm{Int}(G)$ is the
semidirect product of a normal $p$-Sylow and a cyclic subgroup of order
dividing $p-1$. In this thesis we classify the finite $p$-groups whose groups
of intense automorphisms are not themselves $p$-groups. It emerges from our
investigation that the structure of such groups is almost completely determined
by their nilpotency class: for $p>3$, they share a quotient, growing with their
class, with a uniquely determined infinite $2$-generated pro-$p$ group.
| 0 | 0 | 1 | 0 | 0 | 0 |
The spectra of harmonic layer potential operators on domains with rotationally symmetric conical points | We study the adjoint of the double layer potential associated with the
Laplacian (the adjoint of the Neumann-Poincaré operator), as a map on the
boundary surface $\Gamma$ of a domain in $\mathbb{R}^3$ with conical points.
The spectrum of this operator directly reflects the well-posedness of related
transmission problems across $\Gamma$. In particular, if the domain is
understood as an inclusion with complex permittivity $\epsilon$, embedded in a
background medium with unit permittivity, then the polarizability tensor of the
domain is well-defined when $(\epsilon+1)/(\epsilon-1)$ belongs to the
resolvent set in energy norm. We study surfaces $\Gamma$ that have a finite
number of conical points featuring rotational symmetry. On the energy space, we
show that the essential spectrum consists of an interval. On $L^2(\Gamma)$,
i.e. for square-integrable boundary data, we show that the essential spectrum
consists of a countable union of curves, outside of which the Fredholm index
can be computed as a winding number with respect to the essential spectrum. We
provide explicit formulas, depending on the opening angles of the conical
points. We reinforce our study with very precise numerical experiments,
computing the energy space spectrum and the spectral measures of the
polarizability tensor in two different examples. Our results indicate that the
densities of the spectral measures may approach zero extremely rapidly in the
continuous part of the energy space spectrum.
| 0 | 0 | 1 | 0 | 0 | 0 |
Experimental Determination of the Structural Coefficient of Restitution of a Bouncing Asteroid Lander | The structural coefficient of restitution describes the kinetic energy
dissipation upon low-velocity (~0.1 m/s) impact of a small asteroid lander,
MASCOT, against a hard, ideally elastic plane surface. It is a crucial
worst-case input for mission analysis for landing MACOT on a 1km asteroid in
2018. We conducted pendulum tests and describe their analysis and the results.
| 0 | 1 | 0 | 0 | 0 | 0 |
Onset of a modulational instability in trapped dipolar Bose-Einstein condensates | We explore the phase diagram of a finite-sized dysprosium dipolar
Bose-Einstein condensate in a cylindrical harmonic trap. We monitor the final
state after the scattering length is lowered from the repulsive BEC regime to
the quantum droplet regime. Either an adiabatic transformation between a BEC
and a quantum droplet is obtained or, above a critical trap aspect ratio
$\lambda_{\rm c}=1.87(14)$, a modulational instability results in the formation
of multiple droplets. This is in full agreement with the predicted structure of
the phase diagram with a crossover region below $\lambda_{\rm c}$ and a
multistable region above. Our results provide the missing piece connecting the
previously explored regimes resulting in a single or multiple dipolar quantum
droplets.
| 0 | 1 | 0 | 0 | 0 | 0 |
Visual Detection of Structural Changes in Time-Varying Graphs Using Persistent Homology | Topological data analysis is an emerging area in exploratory data analysis
and data mining. Its main tool, persistent homology, has become a popular
technique to study the structure of complex, high-dimensional data. In this
paper, we propose a novel method using persistent homology to quantify
structural changes in time-varying graphs. Specifically, we transform each
instance of the time-varying graph into metric spaces, extract topological
features using persistent homology, and compare those features over time. We
provide a visualization that assists in time-varying graph exploration and
helps to identify patterns of behavior within the data. To validate our
approach, we conduct several case studies on real world data sets and show how
our method can find cyclic patterns, deviations from those patterns, and
one-time events in time-varying graphs. We also examine whether
persistence-based similarity measure as a graph metric satisfies a set of
well-established, desirable properties for graph metrics.
| 1 | 0 | 0 | 0 | 0 | 0 |
Controller Synthesis for Discrete-Time Polynomial Systems via Occupation Measures | In this paper, we design nonlinear state feedback controllers for
discrete-time polynomial dynamical systems via the occupation measure approach.
We propose the discrete-time controlled Liouville equation, and use it to
formulate the controller synthesis problem as an infinite-dimensional linear
programming problem on measures, which is then relaxed as finite-dimensional
semidefinite programming problems on moments of measures and their duals on
sums-of-squares polynomials. Nonlinear controllers can be extracted from the
solutions to the relaxed problems. The advantage of the occupation measure
approach is that we solve convex problems instead of generally non-convex
problems, and the computational complexity is polynomial in the state and input
dimensions, and hence the approach is more scalable. In addition, we show that
the approach can be applied to over-approximating the backward reachable set of
discrete-time autonomous polynomial systems and the controllable set of
discrete-time polynomial systems under known state feedback control laws. We
illustrate our approach on several dynamical systems.
| 1 | 0 | 0 | 0 | 0 | 0 |
Lose The Views: Limited Angle CT Reconstruction via Implicit Sinogram Completion | Computed Tomography (CT) reconstruction is a fundamental component to a wide
variety of applications ranging from security, to healthcare. The classical
techniques require measuring projections, called sinograms, from a full
180$^\circ$ view of the object. This is impractical in a limited angle
scenario, when the viewing angle is less than 180$^\circ$, which can occur due
to different factors including restrictions on scanning time, limited
flexibility of scanner rotation, etc. The sinograms obtained as a result, cause
existing techniques to produce highly artifact-laden reconstructions. In this
paper, we propose to address this problem through implicit sinogram completion,
on a challenging real world dataset containing scans of common checked-in
luggage. We propose a system, consisting of 1D and 2D convolutional neural
networks, that operates on a limited angle sinogram to directly produce the
best estimate of a reconstruction. Next, we use the x-ray transform on this
reconstruction to obtain a "completed" sinogram, as if it came from a full
180$^\circ$ measurement. We feed this to standard analytical and iterative
reconstruction techniques to obtain the final reconstruction. We show with
extensive experimentation that this combined strategy outperforms many
competitive baselines. We also propose a measure of confidence for the
reconstruction that enables a practitioner to gauge the reliability of a
prediction made by our network. We show that this measure is a strong indicator
of quality as measured by the PSNR, while not requiring ground truth at test
time. Finally, using a segmentation experiment, we show that our reconstruction
preserves the 3D structure of objects effectively.
| 0 | 0 | 0 | 1 | 0 | 0 |
A three-dimensional symmetry result for a phase transition equation in the genuinely nonlocal regime | We consider bounded solutions of the nonlocal Allen-Cahn equation $$
(-\Delta)^s u=u-u^3\qquad{\mbox{ in }}{\mathbb{R}}^3,$$ under the monotonicity
condition $\partial_{x_3}u>0$ and in the genuinely nonlocal regime in
which~$s\in\left(0,\frac12\right)$. Under the limit assumptions $$
\lim_{x_n\to-\infty} u(x',x_n)=-1\quad{\mbox{ and }}\quad \lim_{x_n\to+\infty}
u(x',x_n)=1,$$ it has been recently shown that~$u$ is necessarily $1$D, i.e. it
depends only on one Euclidean variable. The goal of this paper is to obtain a
similar result without assuming such limit conditions. This type of results can
be seen as nonlocal counterparts of the celebrated conjecture formulated by
Ennio De Giorgi.
| 0 | 0 | 1 | 0 | 0 | 0 |
The growth of bismuth on Bi$_2$Se$_3$ and the stability of the first bilayer | Bi(0001) films with thicknesses up to several bilayers (BLs) are grown on
Se-terminated Bi$_2$Se$_3$(0001) surfaces, and low energy electron diffraction
(LEED), low energy ion scattering (LEIS) and atomic force microscopy (AFM) are
used to investigate the surface composition, topography and atomic structure.
For a single deposited Bi BL, the lattice constant matches that of the
substrate and the Bi atoms adjacent to the uppermost Se atoms are located at
fcc-like sites. When a 2nd Bi bilayer is deposited, it is incommensurate with
the substrate. As the thickness of the deposited Bi film increases further, the
lattice parameter evolves to that of bulk Bi(0001). After annealing a multiple
BL film at 120°C, the first commensurate Bi BL remains intact, but the
additional BLs aggregate to form thicker islands of Bi. These results show that
a single Bi BL on Bi$_2$Se$_3$ is a particularly stable structure. After
annealing to 490°C, all of the excess Bi desorbs and the Se-terminated
Bi$_2$Se$_3$ surface is restored.
| 0 | 1 | 0 | 0 | 0 | 0 |
Introduction to Tensor Decompositions and their Applications in Machine Learning | Tensors are multidimensional arrays of numerical values and therefore
generalize matrices to multiple dimensions. While tensors first emerged in the
psychometrics community in the $20^{\text{th}}$ century, they have since then
spread to numerous other disciplines, including machine learning. Tensors and
their decompositions are especially beneficial in unsupervised learning
settings, but are gaining popularity in other sub-disciplines like temporal and
multi-relational data analysis, too.
The scope of this paper is to give a broad overview of tensors, their
decompositions, and how they are used in machine learning. As part of this, we
are going to introduce basic tensor concepts, discuss why tensors can be
considered more rigid than matrices with respect to the uniqueness of their
decomposition, explain the most important factorization algorithms and their
properties, provide concrete examples of tensor decomposition applications in
machine learning, conduct a case study on tensor-based estimation of mixture
models, talk about the current state of research, and provide references to
available software libraries.
| 1 | 0 | 0 | 1 | 0 | 0 |
Sampling-based vs. Design-based Uncertainty in Regression Analysis | Consider a researcher estimating the parameters of a regression function
based on data for all 50 states in the United States or on data for all visits
to a website. What is the interpretation of the estimated parameters and the
standard errors? In practice, researchers typically assume that the sample is
randomly drawn from a large population of interest and report standard errors
that are designed to capture sampling variation. This is common practice, even
in applications where it is difficult to articulate what that population of
interest is, and how it differs from the sample. In this article, we explore an
alternative approach to inference, which is partly design-based. In a
design-based setting, the values of some of the regressors can be manipulated,
perhaps through a policy intervention. Design-based uncertainty emanates from
lack of knowledge about the values that the regression outcome would have taken
under alternative interventions. We derive standard errors that account for
design-based uncertainty instead of, or in addition to, sampling-based
uncertainty. We show that our standard errors in general are smaller than the
infinite-population sampling-based standard errors and provide conditions under
which they coincide.
| 0 | 0 | 1 | 1 | 0 | 0 |
Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction | Missing data and noisy observations pose significant challenges for reliably
predicting events from irregularly sampled multivariate time series
(longitudinal) data. Imputation methods, which are typically used for
completing the data prior to event prediction, lack a principled mechanism to
account for the uncertainty due to missingness. Alternatively, state-of-the-art
joint modeling techniques can be used for jointly modeling the longitudinal and
event data and compute event probabilities conditioned on the longitudinal
observations. These approaches, however, make strong parametric assumptions and
do not easily scale to multivariate signals with many observations. Our
proposed approach consists of several key innovations. First, we develop a
flexible and scalable joint model based upon sparse multiple-output Gaussian
processes. Unlike state-of-the-art joint models, the proposed model can explain
highly challenging structure including non-Gaussian noise while scaling to
large data. Second, we derive an optimal policy for predicting events using the
distribution of the event occurrence estimated by the joint model. The derived
policy trades-off the cost of a delayed detection versus incorrect assessments
and abstains from making decisions when the estimated event probability does
not satisfy the derived confidence criteria. Experiments on a large dataset
show that the proposed framework significantly outperforms state-of-the-art
techniques in event prediction.
| 1 | 0 | 0 | 1 | 0 | 0 |
Concurrent Coding: A Reason to Think Differently About Encoding Against Noise, Burst Errors and Jamming | Concurrent coding is an unconventional encoding technique that simultaneously
provides protection against noise, burst errors and interference. This
simple-to-understand concept is investigated by distinguishing 2 types of code,
open and closed, with the majority of the investigation concentrating on closed
codes. Concurrent coding is shown to possess an inherent method of
synchronisation thus requiring no additional synchronisation signals to be
added. This enables an isolated codeword transmission to be synchronised and
decoded in the presence of noise and burst errors. Comparisons are made with
the spread spectrum technique CDMA. With a like-for-like comparison concurrent
coding performs comparably against random noise effects, performs better
against burst errors and is far superior in terms of transmitted energy
efficiency
| 1 | 0 | 0 | 0 | 0 | 0 |
Traveling dark-bright solitons in a reduced spin-orbit coupled system: application to Bose-Einstein condensates | In the present work, we explore the potential of spin-orbit (SO) coupled
Bose-Einstein condensates to support multi-component solitonic states in the
form of dark-bright (DB) solitons. In the case where Raman linear coupling
between components is absent, we use a multiscale expansion method to reduce
the model to the integrable Mel'nikov system. The soliton solutions of the
latter allow us to reconstruct approximate traveling DB solitons for the
reduced SO coupled system. For small values of the formal perturbation
parameter, the resulting waveforms propagate undistorted, while for large
values thereof, they shed some dispersive radiation, and subsequently distill
into a robust propagating structure. After quantifying the relevant radiation
effect, we also study the dynamics of DB solitons in a parabolic trap,
exploring how their oscillation frequency varies as a function of the bright
component mass and the Raman laser wavenumber.
| 0 | 1 | 0 | 0 | 0 | 0 |
On methods to determine bounds on the Q-factor for a given directivity | This paper revisit and extend the interesting case of bounds on the Q-factor
for a given directivity for a small antenna of arbitrary shape. A higher
directivity in a small antenna is closely connected with a narrow impedance
bandwidth. The relation between bandwidth and a desired directivity is still
not fully understood, not even for small antennas. Initial investigations in
this direction has related the radius of a circumscribing sphere to the
directivity, and bounds on the Q-factor has also been derived for a partial
directivity in a given direction. In this paper we derive lower bounds on the
Q-factor for a total desired directivity for an arbitrarily shaped antenna in a
given direction as a convex problem using semi-definite relaxation techniques
(SDR). We also show that the relaxed solution is also a solution of the
original problem of determining the lower Q-factor bound for a total desired
directivity.
SDR can also be used to relax a class of other interesting non-convex
constraints in antenna optimization such as tuning, losses, front-to-back
ratio. We compare two different new methods to determine the lowest Q-factor
for arbitrary shaped antennas for a given total directivity. We also compare
our results with full EM-simulations of a parasitic element antenna with high
directivity.
| 0 | 1 | 1 | 0 | 0 | 0 |
Nano-optical imaging of monolayer MoSe2 using tip-enhanced photoluminescence | Band gap tuning in two-dimensional transitional metal dichalcogenides (TMDs)
is crucial in fabricating new optoelectronic devices. High resolution
photoluminescence (PL) microscopy is needed for accurate band gap
characterization. We performed tip-enhanced photoluminescence (TEPL)
measurements of monolayer MoSe2 with nanoscale spatial resolution, providing an
improved characterization of the band gap correlated with the topography
compared with the conventional far field spectroscopy. We also observed PL
shifts at the edges and investigated the spatial dependence of the TEPL
enhancement factors.
| 0 | 1 | 0 | 0 | 0 | 0 |
Electron paramagnetic resonance and photochromism of $\mathrm{N}_{3}\mathrm{V}^{0}$ in diamond | The defect in diamond formed by a vacancy surrounded by three
nearest-neighbor nitrogen atoms and one carbon atom,
$\mathrm{N}_{3}\mathrm{V}$, is found in $\approx98\%$ of natural diamonds.
Despite $\mathrm{N}_{3}\mathrm{V}^{0}$ being the earliest electron paramagnetic
resonance spectrum observed in diamond, to date no satisfactory simulation of
the spectrum for an arbitrary magnetic field direction has been produced due to
its complexity. In this work, $\mathrm{N}_{3}\mathrm{V}^{0}$ is identified in
$^{15}\mathrm{N}$-doped synthetic diamond following irradiation and annealing.
The $\mathrm{^{15}N}_{3}\mathrm{V}^{0}$ spin Hamiltonian parameters are revised
and used to refine the parameters for $\mathrm{^{14}N}_{3}\mathrm{V}^{0}$,
enabling the latter to be accurately simulated and fitted for an arbitrary
magnetic field direction. Study of $\mathrm{^{15}N}_{3}\mathrm{V}^{0}$ under
excitation with green light indicates charge transfer between
$\mathrm{N}_{3}\mathrm{V}$ and $\mathrm{N_s}$. It is argued that this charge
transfer is facilitated by direct ionization of $\mathrm{N}_{3}\mathrm{V}^{-}$,
an as-yet unobserved charge state of $\mathrm{N}_{3}\mathrm{V}$.
| 0 | 1 | 0 | 0 | 0 | 0 |
On central leaves of Hodge-type Shimura varieties with parahoric level structure | Kisin and Pappas constructed integral models of Hodge-type Shimura varieties
with parahoric level structure at $p>2$, such that the formal neighbourhood of
a mod~$p$ point can be interpreted as a deformation space of $p$-divisible
group with some Tate cycles (generalising Faltings' construction). In this
paper, we study the central leaf and the closed Newton stratum in the formal
neighbourhoods of mod~$p$ points of Kisin-Pappas integral models with parahoric
level structure; namely, we obtain the dimension of central leaves and the
almost product structure of Newton strata. In the case of hyperspecial level
strucure (i.e., in the good reduction case), our main results were already
obtained by Hamacher, and the result of this paper holds for ramified groups as
well.
| 0 | 0 | 1 | 0 | 0 | 0 |
The sequential loss of allelic diversity | This paper gives a new flavor of what Peter Jagers and his co-authors call
`the path to extinction'. In a neutral population with constant size $N$, we
assume that each individual at time $0$ carries a distinct type, or allele. We
consider the joint dynamics of these $N$ alleles, for example the dynamics of
their respective frequencies and more plainly the nonincreasing process
counting the number of alleles remaining by time $t$. We call this process the
extinction process. We show that in the Moran model, the extinction process is
distributed as the process counting (in backward time) the number of common
ancestors to the whole population, also known as the block counting process of
the $N$-Kingman coalescent. Stimulated by this result, we investigate: (1)
whether it extends to an identity between the frequencies of blocks in the
Kingman coalescent and the frequencies of alleles in the extinction process,
both evaluated at jump times; (2) whether it extends to the general case of
$\Lambda$-Fleming-Viot processes.
| 0 | 0 | 0 | 0 | 1 | 0 |
Online classification of imagined speech using functional near-infrared spectroscopy signals | Most brain-computer interfaces (BCIs) based on functional near-infrared
spectroscopy (fNIRS) require that users perform mental tasks such as motor
imagery, mental arithmetic, or music imagery to convey a message or to answer
simple yes or no questions. These cognitive tasks usually have no direct
association with the communicative intent, which makes them difficult for users
to perform. In this paper, a 3-class intuitive BCI is presented which enables
users to directly answer yes or no questions by covertly rehearsing the word
'yes' or 'no' for 15 s. The BCI also admits an equivalent duration of
unconstrained rest which constitutes the third discernable task. Twelve
participants each completed one offline block and six online blocks over the
course of 2 sessions. The mean value of the change in oxygenated hemoglobin
concentration during a trial was calculated for each channel and used to train
a regularized linear discriminant analysis (RLDA) classifier. By the final
online block, 9 out of 12 participants were performing above chance (p<0.001),
with a 3-class accuracy of 83.8+9.4%. Even when considering all participants,
the average online 3-class accuracy over the last 3 blocks was 64.1+20.6%, with
only 3 participants scoring below chance (p<0.001). For most participants,
channels in the left temporal and temporoparietal cortex provided the most
discriminative information. To our knowledge, this is the first report of an
online fNIRS 3-class imagined speech BCI. Our findings suggest that imagined
speech can be used as a reliable activation task for selected users for the
development of more intuitive BCIs for communication.
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