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MRA - Proof of Concept of a Multilingual Report Annotator Web Application | MRA (Multilingual Report Annotator) is a web application that translates
Radiology text and annotates it with RadLex terms. Its goal is to explore the
solution of translating non-English Radiology reports as a way to solve the
problem of most of the Text Mining tools being developed for English. In this
brief paper we explain the language barrier problem and shortly describe the
application. MRA can be found at this https URL .
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Contagions in Social Networks: Effects of Monophilic Contagion, Friendship Paradox and Reactive Networks | We consider SIS contagion processes over networks where, a classical
assumption is that individuals' decisions to adopt a contagion are based on
their immediate neighbors. However, recent literature shows that some
attributes are more correlated between two-hop neighbors, a concept referred to
as monophily. This motivates us to explore monophilic contagion, the case where
a contagion (e.g. a product, disease) is adopted by considering two-hop
neighbors instead of immediate neighbors (e.g. you ask your friend about the
new iPhone and she recommends you the opinion of one of her friends). We show
that the phenomenon called friendship paradox makes it easier for the
monophilic contagion to spread widely. We also consider the case where the
underlying network stochastically evolves in response to the state of the
contagion (e.g. depending on the severity of a flu virus, people restrict their
interactions with others to avoid getting infected) and show that the dynamics
of such a process can be approximated by a differential equation whose
trajectory satisfies an algebraic constraint restricting it to a manifold. Our
results shed light on how graph theoretic consequences affect contagions and,
provide simple deterministic models to approximate the collective dynamics of
contagions over stochastic graph processes.
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Derivation of a multilayer approach to model suspended sediment transport: application to hyperpycnal and hypopycnal plumes | We propose a multi-layer approach to simulate hyperpycnal and hypopycnal
plumes in flows with free surface. The model allows to compute the vertical
profile of the horizontal and the vertical components of the velocity of the
fluid flow. The model can describe as well the vertical profile of the sediment
concentration and the velocity components of each one of the sediment species
that form the turbidity current. To do so, it takes into account the settling
velocity of the particles and their interaction with the fluid. This allows to
better describe the phenomena than a single layer approach. It is in better
agreement with the physics of the problem and gives promising results. The
numerical simulation is carried out by rewriting the multi-layer approach in a
compact formulation, which corresponds to a system with non-conservative
products, and using path-conservative numerical scheme. Numerical results are
presented in order to show the potential of the model.
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Continued fractions and conformal mappings for domains with angle points | Here we construct the conformal mappings with the help of continuous
fractions approximations. These approximations converge to the algebraic roots
$\sqrt[N]{z}$ for $N \in \mathbb{N}$ and $z$ from the right half-plane of the
complex plane. We estimate both the convergence rate and the compact set of
convergence. Also we give the examples that illustrate the introduced technique
of a conformal mapping construction.
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Stochastic Activation Pruning for Robust Adversarial Defense | Neural networks are known to be vulnerable to adversarial examples. Carefully
chosen perturbations to real images, while imperceptible to humans, induce
misclassification and threaten the reliability of deep learning systems in the
wild. To guard against adversarial examples, we take inspiration from game
theory and cast the problem as a minimax zero-sum game between the adversary
and the model. In general, for such games, the optimal strategy for both
players requires a stochastic policy, also known as a mixed strategy. In this
light, we propose Stochastic Activation Pruning (SAP), a mixed strategy for
adversarial defense. SAP prunes a random subset of activations (preferentially
pruning those with smaller magnitude) and scales up the survivors to
compensate. We can apply SAP to pretrained networks, including adversarially
trained models, without fine-tuning, providing robustness against adversarial
examples. Experiments demonstrate that SAP confers robustness against attacks,
increasing accuracy and preserving calibration.
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Canonical bases of modules over one dimensional k-algebras | Let K be a field and denote by K[t], the polynomial ring with coefficients in
K. Set A = K[f1,. .. , fs], with f1,. .. , fs $\in$ K[t]. We give a procedure
to calculate the monoid of degrees of the K algebra M = F1A + $\times$ $\times$
$\times$ + FrA with F1,. .. , Fr $\in$ K[t]. We show some applications to the
problem of the classification of plane polynomial curves (that is, plane
algebraic curves parametrized by polynomials) with respect to some oh their
invariants, using the module of K{ä}hler differentials.
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Detecting Adversarial Samples from Artifacts | Deep neural networks (DNNs) are powerful nonlinear architectures that are
known to be robust to random perturbations of the input. However, these models
are vulnerable to adversarial perturbations--small input changes crafted
explicitly to fool the model. In this paper, we ask whether a DNN can
distinguish adversarial samples from their normal and noisy counterparts. We
investigate model confidence on adversarial samples by looking at Bayesian
uncertainty estimates, available in dropout neural networks, and by performing
density estimation in the subspace of deep features learned by the model. The
result is a method for implicit adversarial detection that is oblivious to the
attack algorithm. We evaluate this method on a variety of standard datasets
including MNIST and CIFAR-10 and show that it generalizes well across different
architectures and attacks. Our findings report that 85-93% ROC-AUC can be
achieved on a number of standard classification tasks with a negative class
that consists of both normal and noisy samples.
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Bots sustain and inflate striking opposition in online social systems | Societies are complex systems which tend to polarize into sub-groups of
individuals with dramatically opposite perspectives. This phenomenon is
reflected -- and often amplified -- in online social networks where, however,
humans are no more the only players, and co-exist alongside with social bots,
i.e. software-controlled accounts. Analyzing large-scale social data collected
during the Catalan referendum for independence on October 1 2017, consisting of
nearly 4 millions Twitter posts generated by almost 1 million users, we
identify the two polarized groups of Independentists and Constitutionalists and
quantify the structural and emotional roles played by social bots. We show that
bots act from peripheral areas of the social system to target influential
humans of both groups, mostly bombarding Independentists with negative and
violent contents, sustaining and inflating instability in this online society.
These results quantify the potential dangerous influence of political bots
during voting processes.
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Approximation of full-boundary data from partial-boundary electrode measurements | Measurements on a subset of the boundary are common in electrical impedance
tomography, especially any electrode model can be interpreted as a partial
boundary problem. The information obtained is different to full-boundary
measurements as modeled by the ideal continuum model. In this study we discuss
an approach to approximate full-boundary data from partial-boundary
measurements that is based on the knowledge of the involved projections. The
approximate full-boundary data can then be obtained as the solution of a
suitable optimization problem on the coefficients of the Neumann-to-Dirichlet
map. By this procedure we are able to improve the reconstruction quality of
continuum model based algorithms, in particular we present the effectiveness
with a D-bar method. Reconstructions are presented for noisy simulated and real
measurement data.
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A Random Block-Coordinate Douglas-Rachford Splitting Method with Low Computational Complexity for Binary Logistic Regression | In this paper, we propose a new optimization algorithm for sparse logistic
regression based on a stochastic version of the Douglas-Rachford splitting
method. Our algorithm sweeps the training set by randomly selecting a
mini-batch of data at each iteration, and it allows us to update the variables
in a block coordinate manner. Our approach leverages the proximity operator of
the logistic loss, which is expressed with the generalized Lambert W function.
Experiments carried out on standard datasets demonstrate the efficiency of our
approach w.r.t. stochastic gradient-like methods.
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Corrective Re-gridding Techniques for Non-Uniform Sampling in Time Domain Terahertz Spectroscopy | Time domain terahertz spectroscopy typically uses mechanical delay stages
that inherently suffer from non-uniform sampling positions. We review,
simulate, and experimentally test the ability of corrective cubic spline and
Shannon re-gridding algorithms to mitigate the inherent sampling position
noise. We present simulations and experimental results that show re-gridding
algorithms can increase the signal to noise ratio within the frequency range of
100 GHz to 2 THz. We also predict that re-gridding corrections will become
increasingly important to both spectroscopy and imaging as THz technology
continues to improve and higher frequencies become experimentally accessible.
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Optimistic Robust Optimization With Applications To Machine Learning | Robust Optimization has traditionally taken a pessimistic, or worst-case
viewpoint of uncertainty which is motivated by a desire to find sets of optimal
policies that maintain feasibility under a variety of operating conditions. In
this paper, we explore an optimistic, or best-case view of uncertainty and show
that it can be a fruitful approach. We show that these techniques can be used
to address a wide variety of problems. First, we apply our methods in the
context of robust linear programming, providing a method for reducing
conservatism in intuitive ways that encode economically realistic modeling
assumptions. Second, we look at problems in machine learning and find that this
approach is strongly connected to the existing literature. Specifically, we
provide a new interpretation for popular sparsity inducing non-convex
regularization schemes. Additionally, we show that successful approaches for
dealing with outliers and noise can be interpreted as optimistic robust
optimization problems. Although many of the problems resulting from our
approach are non-convex, we find that DCA or DCA-like optimization approaches
can be intuitive and efficient.
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High Dimensional Time Series Generators | Multidimensional time series are sequences of real valued vectors. They occur
in different areas, for example handwritten characters, GPS tracking, and
gestures of modern virtual reality motion controllers. Within these areas, a
common task is to search for similar time series. Dynamic Time Warping (DTW) is
a common distance function to compare two time series. The Edit Distance with
Real Penalty (ERP) and the Dog Keeper Distance (DK) are two more distance
functions on time series. Their behaviour has been analyzed on 1-dimensional
time series. However, it is not easy to evaluate their behaviour in relation to
growing dimensionality. For this reason we propose two new data synthesizers
generating multidimensional time series. The first synthesizer extends the well
known cylinder-bell-funnel (CBF) dataset to multidimensional time series. Here,
each time series has an arbitrary type (cylinder, bell, or funnel) in each
dimension, thus for $d$-dimensional time series there are $3^{d}$ different
classes. The second synthesizer (RAM) creates time series with ideas adapted
from Brownian motions which is a common model of movement in physics. Finally,
we evaluate the applicability of a 1-nearest neighbor classifier using DTW on
datasets generated by our synthesizers.
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An isoperimetric inequality for Laplace eigenvalues on the sphere | We show that for any positive integer k, the k-th nonzero eigenvalue of the
Laplace-Beltrami operator on the two-dimensional sphere endowed with a
Riemannian metric of unit area, is maximized in the limit by a sequence of
metrics converging to a union of k touching identical round spheres. This
proves a conjecture posed by the second author in 2002 and yields a sharp
isoperimetric inequality for all nonzero eigenvalues of the Laplacian on a
sphere. Earlier, the result was known only for k=1 (J.Hersch, 1970), k=2
(N.Nadirashvili, 2002; R.Petrides, 2014) and k=3 (N.Nadirashvili and Y.Sire,
2017). In particular, we argue that for any k>=2, the supremum of the k-th
nonzero eigenvalue on a sphere of unit area is not attained in the class of
Riemannin metrics which are smooth outsitde a finite set of conical
singularities. The proof uses certain properties of harmonic maps between
spheres, the key new ingredient being a bound on the harmonic degree of a
harmonic map into a sphere obtained by N. Ejiri.
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Numerical solutions of an unsteady 2-D incompressible flow with heat and mass transfer at low, moderate, and high Reynolds numbers | In this paper, we have proposed a modified Marker-And-Cell (MAC) method to
investigate the problem of an unsteady 2-D incompressible flow with heat and
mass transfer at low, moderate, and high Reynolds numbers with no-slip and slip
boundary conditions. We have used this method to solve the governing equations
along with the boundary conditions and thereby to compute the flow variables,
viz. $u$-velocity, $v$-velocity, $P$, $T$, and $C$. We have used the staggered
grid approach of this method to discretize the governing equations of the
problem. A modified MAC algorithm was proposed and used to compute the
numerical solutions of the flow variables for Reynolds numbers $Re = 10$, 500,
and 50,000 in consonance with low, moderate, and high Reynolds numbers. We have
also used appropriate Prandtl $(Pr)$ and Schmidt $(Sc)$ numbers in consistence
with relevancy of the physical problem considered. We have executed this
modified MAC algorithm with the aid of a computer program developed and run in
C compiler. We have also computed numerical solutions of local Nusselt $(Nu)$
and Sherwood $(Sh)$ numbers along the horizontal line through the geometric
center at low, moderate, and high Reynolds numbers for fixed $Pr = 6.62$ and
$Sc = 340$ for two grid systems at time $t = 0.0001s$. Our numerical solutions
for u and v velocities along the vertical and horizontal line through the
geometric center of the square cavity for $Re = 100$ has been compared with
benchmark solutions available in the literature and it has been found that they
are in good agreement. The present numerical results indicate that, as we move
along the horizontal line through the geometric center of the domain, we
observed that, the heat and mass transfer decreases up to the geometric center.
It, then, increases symmetrically.
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A Dynamic Programming Solution to Bounded Dejittering Problems | We propose a dynamic programming solution to image dejittering problems with
bounded displacements and obtain efficient algorithms for the removal of line
jitter, line pixel jitter, and pixel jitter.
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Delayed pull-in transitions in overdamped MEMS devices | We consider the dynamics of overdamped MEMS devices undergoing the pull-in
instability. Numerous previous experiments and numerical simulations have shown
a significant increase in the pull-in time under DC voltages close to the
pull-in voltage. Here the transient dynamics slow down as the device passes
through a meta-stable or bottleneck phase, but this slowing down is not well
understood quantitatively. Using a lumped parallel-plate model, we perform a
detailed analysis of the pull-in dynamics in this regime. We show that the
bottleneck phenomenon is a type of critical slowing down arising from the
pull-in transition. This allows us to show that the pull-in time obeys an
inverse square-root scaling law as the transition is approached; moreover we
determine an analytical expression for this pull-in time. We then compare our
prediction to a wide range of pull-in time data reported in the literature,
showing that the observed slowing down is well captured by our scaling law,
which appears to be generic for overdamped pull-in under DC loads. This
realization provides a useful design rule with which to tune dynamic response
in applications, including state-of-the-art accelerometers and pressure sensors
that use pull-in time as a sensing mechanism. We also propose a method to
estimate the pull-in voltage based only on data of the pull-in times.
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How ConvNets model Non-linear Transformations | In this paper, we theoretically address three fundamental problems involving
deep convolutional networks regarding invariance, depth and hierarchy. We
introduce the paradigm of Transformation Networks (TN) which are a direct
generalization of Convolutional Networks (ConvNets). Theoretically, we show
that TNs (and thereby ConvNets) are can be invariant to non-linear
transformations of the input despite pooling over mere local translations. Our
analysis provides clear insights into the increase in invariance with depth in
these networks. Deeper networks are able to model much richer classes of
transformations. We also find that a hierarchical architecture allows the
network to generate invariance much more efficiently than a non-hierarchical
network. Our results provide useful insight into these three fundamental
problems in deep learning using ConvNets.
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Volcano transition in a solvable model of oscillator glass | In 1992 a puzzling transition was discovered in simulations of randomly
coupled limit-cycle oscillators. This so-called volcano transition has resisted
analysis ever since. It was originally conjectured to mark the emergence of an
oscillator glass, but here we show it need not. We introduce and solve a
simpler model with a qualitatively identical volcano transition and find,
unexpectedly, that its supercritical state is not glassy. We discuss the
implications for the original model and suggest experimental systems in which a
volcano transition and oscillator glass may appear.
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Recent advances and open questions on the susy structure of the chiral de Rham Complex | We review different constructions of the supersymmetry subalgebras of the
chiral de Rham complex on special holonomy manifolds. We describe the
difference between the holomorphic-anti-holomorphic sectors based on a local
free ghost system vs the decomposition in left-right sectors from a local
Boson-Fermion system. We describe the topological twist in the case of $G_2$
and $Spin_7$ manifolds. We describe the construction of these algebras as
quantum Hamiltonian reduction of Lie superalgebras at the minimal or
superprincipal nilpotent.
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Toda maps, cocycles, and canonical systems | I present a discussion of the hierarchy of Toda flows that gives center stage
to the associated cocycles and the maps they induce on the $m$ functions. In
the second part, these ideas are then applied to canonical systems; an
important feature of this discussion will be my proposal that the role of the
shift on Jacobi matrices should now be taken over by the more general class of
twisted shifts.
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The Gibbs paradox, the Landauer principle and the irreversibility associated with tilted observers | It is well known that, in the context of General Relativity, some spacetimes,
when described by a congruence of comoving observers, may consist in a
distribution of a perfect (non-dissipative) fluid, whereas the same spacetime
as seen by a "tilted"' (Lorentz-boosted) congruence of observers, may exhibit
the presence of dissipative processes. As we shall see, the appearence of
entropy producing processes are related to the tight dependence of entropy on
the specific congruence of observers. This fact is well illustrated by the
Gibbs paradox. The appearance of such dissipative processes, as required by the
Landauer principle, are necessary, in order to erase the different amount of
information stored by comoving observers, with respect to tilted ones.
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PlumX As a Potential Tool to Assess the Macroscopic Multidimensional Impact of Books | The main purpose of this macro-study is to shed light on the broad impact of
books. For this purpose, the impact of a very large collection of books has
been analyzed by using PlumX, an analytical tool providing a great number of
different metrics provided by various tools.
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Action Robust Reinforcement Learning and Applications in Continuous Control | A policy is said to be robust if it maximizes the reward while considering a
bad, or even adversarial, model. In this work we formalize two new criteria of
robustness to action uncertainty. Specifically, we consider two scenarios in
which the agent attempts to perform an action $\mathbf{a}$, and (i) with
probability $\alpha$, an alternative adversarial action $\bar{\mathbf{a}}$ is
taken, or (ii) an adversary adds a perturbation to the selected action in the
case of continuous action space. We show that our criteria are related to
common forms of uncertainty in robotics domains, such as the occurrence of
abrupt forces, and suggest algorithms in the tabular case. Building on the
suggested algorithms, we generalize our approach to deep reinforcement learning
(DRL) and provide extensive experiments in the various MuJoCo domains. Our
experiments show that not only does our approach produce robust policies, but
it also improves the performance in the absence of perturbations. This
generalization indicates that action-robustness can be thought of as implicit
regularization in RL problems.
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A characterization of finite vector bundles on Gauduchon astheno-Kahler manifolds | A vector bundle E on a projective variety X is called finite if it satisfies
a nontrivial polynomial equation with integral coefficients. A theorem of Nori
implies that E is finite if and only if the pullback of E to some finite etale
Galois covering of X is trivial. We prove the same statement when X is a
compact complex manifold admitting a Gauduchon astheno-Kahler metric.
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APSYNSIM: An Interactive Tool To Learn Interferometry | The APerture SYNthesis SIMulator is a simple interactive tool to help the
students visualize and understand the basics of the Aperture Synthesis
technique, applied to astronomical interferometers. The users can load many
different interferometers and source models (and also create their own), change
the observing parameters (e.g., source coordinates, observing wavelength,
antenna location, integration time, etc.), and even deconvolve the
interferometric images and corrupt the data with gain errors (amplitude and
phase). The program is fully interactive and all the figures are updated in
real time. APSYNSIM has already been used in several interferometry schools and
has got very positive feedback from the students.
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The dimensionless dissipation rate and the Kolmogorov (1941) hypothesis of local stationarity in freely decaying isotropic turbulence | An expression for the dimensionless dissipation rate was derived from the
Karman-Howarth equation by asymptotic expansion of the second- and third- order
structure functions in powers of the inverse Reynolds number. The implications
of the time-derivative term for the assumption of local stationarity (or local
equilibrium) which underpins the derivation of the Kolmogorov `4/5' law for the
third-order structure function were studied. It was concluded that neglect of
the time-derivative cannot be justified by reason of restriction to certain
scales (the inertial range) nor to large Reynolds numbers. In principle,
therefore, the hypothesis cannot be correct, although it may be a good
approximation. It follows, at least in principle, that the quantitative aspects
of the hypothesis of local stationarity could be tested by a comparison of the
asymptotic dimensionless dissipation rate for free decay with that for the
stationary case. But in practice this is complicated by the absence of an
agreed evolution time for making the measurements during the decay. However, we
can assess the quantitative error involved in using the hypothesis by comparing
the exact asymptotic value of the dimensionless dissipation in free decay
calculated on the assumption of local stationarity to the experimentally
determined value (e.g. by means of direct numerical simulation), as this
relationship holds for all measuring times. Should the assumption of local
stationarity lead to significant error, then the `4/5' law needs to be
corrected. Despite this, scale invariance in wavenumber space appears to hold
in the formal limit of infinite Reynolds numbers, which implies that the `-5/3'
energy spectrum does not require correction in this limit.
| 0 | 1 | 0 | 0 | 0 | 0 |
Consistent estimation of the spectrum of trace class data augmentation algorithms | Markov chain Monte Carlo is widely used in a variety of scientific
applications to generate approximate samples from intractable distributions. A
thorough understanding of the convergence and mixing properties of these Markov
chains can be obtained by studying the spectrum of the associated Markov
operator. While several methods to bound/estimate the second largest eigenvalue
are available in the literature, very few general techniques for consistent
estimation of the entire spectrum have been proposed. Existing methods for this
purpose require the Markov transition density to be available in closed form,
which is often not true in practice, especially in modern statistical
applications. In this paper, we propose a novel method to consistently estimate
the entire spectrum of a general class of Markov chains arising from a popular
and widely used statistical approach known as Data Augmentation. The transition
densities of these Markov chains can often only be expressed as intractable
integrals. We illustrate the applicability of our method using real and
simulated data.
| 0 | 0 | 0 | 1 | 0 | 0 |
Balancing Selection Pressures, Multiple Objectives, and Neural Modularity to Coevolve Cooperative Agent Behavior | Previous research using evolutionary computation in Multi-Agent Systems
indicates that assigning fitness based on team vs.\ individual behavior has a
strong impact on the ability of evolved teams of artificial agents to exhibit
teamwork in challenging tasks. However, such research only made use of
single-objective evolution. In contrast, when a multiobjective evolutionary
algorithm is used, populations can be subject to individual-level objectives,
team-level objectives, or combinations of the two. This paper explores the
performance of cooperatively coevolved teams of agents controlled by artificial
neural networks subject to these types of objectives. Specifically, predator
agents are evolved to capture scripted prey agents in a torus-shaped grid
world. Because of the tension between individual and team behaviors, multiple
modes of behavior can be useful, and thus the effect of modular neural networks
is also explored. Results demonstrate that fitness rewarding individual
behavior is superior to fitness rewarding team behavior, despite being applied
to a cooperative task. However, the use of networks with multiple modules
allows predators to discover intelligent behavior, regardless of which type of
objectives are used.
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High-throughput nanofluidic device for one-dimensional confined detection of single fluorophores | Ensemble averaging experiments may conceal many fundamental molecular
interactions. To overcome that, a high-throughput detection of single molecules
or colloidal nanodots is crucial for biomedical, nanoelectronic, and
solid-state applications. One-dimensional (1D) discrete flow of nanoscale
objects is an efficient approach in this direction. The development of simple
and cost-effective nanofluidic devices is a critical step to realise 1D flow.
This letter presents a nanofabrication technique using
shadow-angle-electron-beam-deposition for a high-throughput preparation of
parallel nanofluidic channels. These were used to flow and detect DNA,
carbon-nanodots, and organic fluorophores. The 1D molecular mass transport was
performed using electro-osmotic flow. The 1D flow behaviour was identified and
analysed using two-focus fluorescence correlation spectroscopy (2fFCS). A range
of flow velocities of single molecules was achieved. The transitions of single
molecules or nanodots through the two foci were quantitatively analysed using
confocal scanning imaging, correlative photon detection, and burst size
distribution analysis. The results suggest an efficient nanofabrication
technique is developed to prepare nanofluidic devices. This first demonstration
of high-throughput nanochannel fabrication process and using 2fFCS-based single
molecule flow detection should have a potential impact on ultra-sensitive
biomedical diagnostics and studying biomolecular interactions as well as
nanomaterials.
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Coupled Graphs and Tensor Factorization for Recommender Systems and Community Detection | Joint analysis of data from multiple information repositories facilitates
uncovering the underlying structure in heterogeneous datasets. Single and
coupled matrix-tensor factorization (CMTF) has been widely used in this context
for imputation-based recommendation from ratings, social network, and other
user-item data. When this side information is in the form of item-item
correlation matrices or graphs, existing CMTF algorithms may fall short.
Alleviating current limitations, we introduce a novel model coined coupled
graph-tensor factorization (CGTF) that judiciously accounts for graph-related
side information. The CGTF model has the potential to overcome practical
challenges, such as missing slabs from the tensor and/or missing rows/columns
from the correlation matrices. A novel alternating direction method of
multipliers (ADMM) is also developed that recovers the nonnegative factors of
CGTF. Our algorithm enjoys closed-form updates that result in reduced
computational complexity and allow for convergence claims. A novel direction is
further explored by employing the interpretable factors to detect graph
communities having the tensor as side information. The resulting community
detection approach is successful even when some links in the graphs are
missing. Results with real data sets corroborate the merits of the proposed
methods relative to state-of-the-art competing factorization techniques in
providing recommendations and detecting communities.
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A computer algebra system for R: Macaulay2 and the m2r package | Algebraic methods have a long history in statistics. The most prominent
manifestation of modern algebra in statistics can be seen in the field of
algebraic statistics, which brings tools from commutative algebra and algebraic
geometry to bear on statistical problems. Now over two decades old, algebraic
statistics has applications in a wide range of theoretical and applied
statistical domains. Nevertheless, algebraic statistical methods are still not
mainstream, mostly due to a lack of easy off-the-shelf implementations. In this
article we debut m2r, an R package that connects R to Macaulay2 through a
persistent back-end socket connection running locally or on a cloud server.
Topics range from basic use of m2r to applications and design philosophy.
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Transversal magnetoresistance and Shubnikov-de Haas oscillations in Weyl semimetals | We explore theoretically the magnetoresistance of Weyl semimetals in
transversal magnetic fields away from charge neutrality. The analysis within
the self-consistent Born approximation is done for the two different models of
disorder: (i) short-range impurties and (ii) charged (Coulomb) impurities. For
these models of disorder, we calculate the conductivity away from charge
neutrality point as well as the Hall conductivity, and analyze the transversal
magnetoresistance (TMR) and Shubnikov-de Haas oscillations for both types of
disorder. We further consider a model with Weyl nodes shifted in energy with
respect to each other (as found in various materials) with the chemical
potential corresponding to the total charge neutrality. In the experimentally
most relevant case of Coulomb impurities, we find in this model a large TMR in
a broad range of quantizing magnetic fields. More specifically, in the
ultra-quantum limit, where only the zeroth Landau level is effective, the TMR
is linear in magnetic field. In the regime of moderate (but still quantizing)
magnetic fields, where the higher Landau levels are relevant, the rapidly
growing TMR is supplemented by strong Shubnikov-de Haas oscillations,
consistent with experimental observations.
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In Search of Lost (Mixing) Time: Adaptive Markov chain Monte Carlo schemes for Bayesian variable selection with very large p | The availability of data sets with large numbers of variables is rapidly
increasing. The effective application of Bayesian variable selection methods
for regression with these data sets has proved difficult since available Markov
chain Monte Carlo methods do not perform well in typical problem sizes of
interest. The current paper proposes new adaptive Markov chain Monte Carlo
algorithms to address this shortcoming. The adaptive design of these algorithms
exploits the observation that in large $p$ small $n$ settings, the majority of
the $p$ variables will be approximately uncorrelated a posteriori. The
algorithms adaptively build suitable non-local proposals that result in moves
with squared jumping distance significantly larger than standard methods. Their
performance is studied empirically in high-dimension problems (with both
simulated and actual data) and speedups of up to 4 orders of magnitude are
observed. The proposed algorithms are easily implementable on multi-core
architectures and are well suited for parallel tempering or sequential Monte
Carlo implementations.
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Network growth models: A behavioural basis for attachment proportional to fitness | Several growth models have been proposed in the literature for scale-free
complex networks, with a range of fitness-based attachment models gaining
prominence recently. However, the processes by which such fitness-based
attachment behaviour can arise are less well understood, making it difficult to
compare the relative merits of such models. This paper analyses an evolutionary
mechanism that would give rise to a fitness-based attachment process. In
particular, it is proven by analytical and numerical methods that in
homogeneous networks, the minimisation of maximum exposure to node unfitness
leads to attachment probabilities that are proportional to node fitness. This
result is then extended to heterogeneous networks, with supply chain networks
being used as an example.
| 1 | 1 | 0 | 0 | 0 | 0 |
On Convergence Property of Implicit Self-paced Objective | Self-paced learning (SPL) is a new methodology that simulates the learning
principle of humans/animals to start learning easier aspects of a learning
task, and then gradually take more complex examples into training. This
new-coming learning regime has been empirically substantiated to be effective
in various computer vision and pattern recognition tasks. Recently, it has been
proved that the SPL regime has a close relationship to a implicit self-paced
objective function. While this implicit objective could provide helpful
interpretations to the effectiveness, especially the robustness, insights under
the SPL paradigms, there are still no theoretical results strictly proved to
verify such relationship. To this issue, in this paper, we provide some
convergence results on this implicit objective of SPL. Specifically, we prove
that the learning process of SPL always converges to critical points of this
implicit objective under some mild conditions. This result verifies the
intrinsic relationship between SPL and this implicit objective, and makes the
previous robustness analysis on SPL complete and theoretically rational.
| 1 | 0 | 0 | 0 | 0 | 0 |
Graphene quantum dots prevent alpha-synucleinopathy in Parkinson's disease | While the emerging evidence indicates that the pathogenesis of Parkinson's
disease (PD) is strongly correlated to the accumulation of alpha-synuclein
({\alpha}-syn) aggregates, there has been no clinical success in
anti-aggregation agents for the disease to date. Here we show that graphene
quantum dots (GQDs) exhibit anti-amyloid activity via direct interaction with
{\alpha}-syn. Employing biophysical, biochemical, and cell-based assays as well
as molecular dynamics (MD) simulation, we find that GQDs have notable potency
in not only inhibiting fibrillization of {\alpha}-syn but also disaggregating
mature fibrils in a time-dependent manner. Remarkably, GQDs rescue neuronal
death and synaptic loss, reduce Lewy body (LB)/Lewy neurite (LN) formation,
ameliorate mitochondrial dysfunctions, and prevent neuron-to-neuron
transmission of {\alpha}-syn pathology induced by {\alpha}-syn preformed
fibrils (PFFs) in neurons. In addition, in vivo administration of GQDs protects
against {\alpha}-syn PFFs-induced loss of dopamine neurons, LB/LN pathology,
and behavioural deficits through the penetration of the blood-brain barrier
(BBB). The finding that GQDs function as an anti-aggregation agent provides a
promising novel therapeutic target for the treatment of PD and related
{\alpha}-synucleinopathies.
| 0 | 1 | 0 | 0 | 0 | 0 |
Liu-Nagel phase diagrams in infinite dimension | We study Harmonic Soft Spheres as a model of thermal structural glasses in
the limit of infinite dimensions. We show that cooling, compressing and
shearing a glass lead to a Gardner transition and, hence, to a marginally
stable amorphous solid as found for Hard Spheres systems. A general outcome of
our results is that a reduced stability of the glass favors the appearance of
the Gardner transition. Therefore using strong perturbations, e.g. shear and
compression, on standard glasses or using weak perturbations on weakly stable
glasses, e.g. the ones prepared close to the jamming point, are the generic
ways to induce a Gardner transition. The formalism that we discuss allows to
study general perturbations, including strain deformations that are important
to study soft glassy rheology at the mean field level.
| 0 | 1 | 0 | 0 | 0 | 0 |
Networks of planar Hamiltonian systems | We introduce diffusively coupled networks where the dynamical system at each
vertex is planar Hamiltonian. The problems we address are synchronisation and
an analogue of diffusion-driven Turing instability for time-dependent
homogeneous states. As a consequence of the underlying Hamiltonian structure
there exist unusual behaviours compared with networks of coupled limit cycle
oscillators or activator-inhibitor systems.
| 0 | 1 | 1 | 0 | 0 | 0 |
Robust Tracking Using Region Proposal Networks | Recent advances in visual tracking showed that deep Convolutional Neural
Networks (CNN) trained for image classification can be strong feature
extractors for discriminative trackers. However, due to the drastic difference
between image classification and tracking, extra treatments such as model
ensemble and feature engineering must be carried out to bridge the two domains.
Such procedures are either time consuming or hard to generalize well across
datasets. In this paper we discovered that the internal structure of Region
Proposal Network (RPN)'s top layer feature can be utilized for robust visual
tracking. We showed that such property has to be unleashed by a novel loss
function which simultaneously considers classification accuracy and bounding
box quality. Without ensemble and any extra treatment on feature maps, our
proposed method achieved state-of-the-art results on several large scale
benchmarks including OTB50, OTB100 and VOT2016. We will make our code publicly
available.
| 1 | 0 | 0 | 0 | 0 | 0 |
Recovering sparse graphs | We construct a fixed parameter algorithm parameterized by d and k that takes
as an input a graph G' obtained from a d-degenerate graph G by complementing on
at most k arbitrary subsets of the vertex set of G and outputs a graph H such
that G and H agree on all but f(d,k) vertices.
Our work is motivated by the first order model checking in graph classes that
are first order interpretable in classes of sparse graphs. We derive as a
corollary that if G_0 is a graph class with bounded expansion, then the first
order model checking is fixed parameter tractable in the class of all graphs
that can obtained from a graph G from G_0 by complementing on at most k
arbitrary subsets of the vertex set of G; this implies an earlier result that
the first order model checking is fixed parameter tractable in graph classes
interpretable in classes of graphs with bounded maximum degree.
| 1 | 0 | 0 | 0 | 0 | 0 |
Remark on arithmetic topology | We formalize the arithmetic topology, i.e. a relationship between knots and
primes. Namely, using the notion of a cluster C*-algebra we construct a functor
from the category of 3-dimensional manifolds M to a category of algebraic
number fields K, such that the prime ideals (ideals, resp.) in the ring of
integers of K correspond to knots (links, resp.) in M. It is proved that the
functor realizes all axioms of the arithmetic topology conjectured in the
1960's by Manin, Mazur and Mumford.
| 0 | 0 | 1 | 0 | 0 | 0 |
Stable Architectures for Deep Neural Networks | Deep neural networks have become invaluable tools for supervised machine
learning, e.g., classification of text or images. While often offering superior
results over traditional techniques and successfully expressing complicated
patterns in data, deep architectures are known to be challenging to design and
train such that they generalize well to new data. Important issues with deep
architectures are numerical instabilities in derivative-based learning
algorithms commonly called exploding or vanishing gradients. In this paper we
propose new forward propagation techniques inspired by systems of Ordinary
Differential Equations (ODE) that overcome this challenge and lead to
well-posed learning problems for arbitrarily deep networks.
The backbone of our approach is our interpretation of deep learning as a
parameter estimation problem of nonlinear dynamical systems. Given this
formulation, we analyze stability and well-posedness of deep learning and use
this new understanding to develop new network architectures. We relate the
exploding and vanishing gradient phenomenon to the stability of the discrete
ODE and present several strategies for stabilizing deep learning for very deep
networks. While our new architectures restrict the solution space, several
numerical experiments show their competitiveness with state-of-the-art
networks.
| 1 | 0 | 1 | 0 | 0 | 0 |
Geometry and Arithmetic of Crystallographic Sphere Packings | We introduce the notion of a "crystallographic sphere packing," defined to be
one whose limit set is that of a geometrically finite hyperbolic reflection
group in one higher dimension. We exhibit for the first time an infinite family
of conformally-inequivalent such with all radii being reciprocals of integers.
We then prove a result in the opposite direction: the "superintegral" ones
exist only in finitely many "commensurability classes," all in dimensions below
30.
| 0 | 0 | 1 | 0 | 0 | 0 |
On the global convergence of the Jacobi method for symmetric matrices of order 4 under parallel strategies | The paper analyzes special cyclic Jacobi methods for symmetric matrices of
order $4$. Only those cyclic pivot strategies that enable full parallelization
of the method are considered. These strategies, unlike the serial pivot
strategies, can force the method to be very slow or very fast within one cycle,
depending on the underlying matrix. Hence, for the global convergence proof one
has to consider two or three adjacent cycles. It is proved that for any
symmetric matrix $A$ of order~$4$ the inequality
$S(A^{[2]})\leq(1-10^{-5})S(A)$ holds, where $A^{[2]}$ results from $A$ by
applying two cycles of a particular parallel method. Here $S(A)$ stands for the
Frobenius norm of the strictly upper-triangular part of $A$. The result holds
for two special parallel strategies and implies the global convergence of the
method under all possible fully parallel strategies. It is also proved that for
every $\epsilon>0$ and $n\geq4$ there exist a symmetric matrix $A(\epsilon)$ of
order $n$ and a cyclic strategy, such that upon completion of the first cycle
of the appropriate Jacobi method the inequality $S(A^{[1]})>
(1-\epsilon)S(A(\epsilon))$ holds.
| 0 | 0 | 1 | 0 | 0 | 0 |
Counting Motifs with Graph Sampling | Applied researchers often construct a network from a random sample of nodes
in order to infer properties of the parent network. Two of the most widely used
sampling schemes are subgraph sampling, where we sample each vertex
independently with probability $p$ and observe the subgraph induced by the
sampled vertices, and neighborhood sampling, where we additionally observe the
edges between the sampled vertices and their neighbors.
In this paper, we study the problem of estimating the number of motifs as
induced subgraphs under both models from a statistical perspective. We show
that: for any connected $h$ on $k$ vertices, to estimate $s=\mathsf{s}(h,G)$,
the number of copies of $h$ in the parent graph $G$ of maximum degree $d$, with
a multiplicative error of $\epsilon$, (a) For subgraph sampling, the optimal
sampling ratio $p$ is $\Theta_{k}(\max\{ (s\epsilon^2)^{-\frac{1}{k}}, \;
\frac{d^{k-1}}{s\epsilon^{2}} \})$, achieved by Horvitz-Thompson type of
estimators. (b) For neighborhood sampling, we propose a family of estimators,
encompassing and outperforming the Horvitz-Thompson estimator and achieving the
sampling ratio $O_{k}(\min\{ (\frac{d}{s\epsilon^2})^{\frac{1}{k-1}}, \;
\sqrt{\frac{d^{k-2}}{s\epsilon^2}}\})$. This is shown to be optimal for all
motifs with at most $4$ vertices and cliques of all sizes.
The matching minimax lower bounds are established using certain algebraic
properties of subgraph counts. These results quantify how much more informative
neighborhood sampling is than subgraph sampling, as empirically verified by
experiments on both synthetic and real-world data. We also address the issue of
adaptation to the unknown maximum degree, and study specific problems for
parent graphs with additional structures, e.g., trees or planar graphs.
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Optimizing noise level for perturbing geo-location data | With the tremendous increase in the number of smart phones, app stores have
been overwhelmed with applications requiring geo-location access in order to
provide their users better services through personalization. Revealing a user's
location to these third party apps, no matter at what frequency, is a severe
privacy breach which can have unpleasant social consequences. In order to
prevent inference attacks derived from geo-location data, a number of location
obfuscation techniques have been proposed in the literature. However, none of
them provides any objective measure of privacy guarantee. Some work has been
done to define differential privacy for geo-location data in the form of
geo-indistinguishability with l privacy guarantee. These techniques do not
utilize any prior background information about the Points of Interest (PoIs) of
a user and apply Laplacian noise to perturb all the location coordinates.
Intuitively, the utility of such a mechanism can be improved if the noise
distribution is derived after considering some prior information about PoIs.
In this paper, we apply the standard definition of differential privacy on
geo-location data. We use first principles to model various privacy and utility
constraints, prior background information available about the PoIs
(distribution of PoI locations in a 1D plane) and the granularity of the input
required by different types of apps, in order to produce a more accurate and a
utility maximizing differentially private algorithm for geo-location data at
the OS level. We investigate this for a particular category of apps and for
some specific scenarios. This will also help us to verify that whether
Laplacian noise is still the optimal perturbation when we have such prior
information.
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Hochschild Cohomology and Deformation Quantization of Affine Toric Varieties | For an affine toric variety $\mathrm{Spec}(A)$, we give a convex geometric
description of the Hodge decomposition of its Hochschild cohomology. Under
certain assumptions we compute the dimensions of the Hodge summands
$T^1_{(i)}(A)$, generalizing the existing results about the Andre-Quillen
cohomology group $T^1_{(1)}(A)$. We prove that every Poisson structure on a
possibly singular affine toric variety can be quantized in the sense of
deformation quantization.
| 0 | 0 | 1 | 0 | 0 | 0 |
Adversarial Examples for Semantic Image Segmentation | Machine learning methods in general and Deep Neural Networks in particular
have shown to be vulnerable to adversarial perturbations. So far this
phenomenon has mainly been studied in the context of whole-image
classification. In this contribution, we analyse how adversarial perturbations
can affect the task of semantic segmentation. We show how existing adversarial
attackers can be transferred to this task and that it is possible to create
imperceptible adversarial perturbations that lead a deep network to misclassify
almost all pixels of a chosen class while leaving network prediction nearly
unchanged outside this class.
| 1 | 0 | 0 | 1 | 0 | 0 |
Graphical virtual links and a polynomial of signed cyclic graphs | For a signed cyclic graph G, we can construct a unique virtual link L by
taking the medial construction and convert 4-valent vertices of the medial
graph to crossings according to the signs. If a virtual link can occur in this
way then we say that the virtual link is graphical. In the article we shall
prove that a virtual link L is graphical if and only if it is checkerboard
colorable. On the other hand, we introduce a polynomial F[G] for signed cyclic
graphs, which is defined via a deletion-marking recursion. We shall establish
the relationship between F[G] of a signed cyclic graph G and the bracket
polynomial of one of the virtual link diagrams associated with G. Finally we
give a spanning subgraph expansion for F[G].
| 0 | 0 | 1 | 0 | 0 | 0 |
Exploring the Single-Particle Mobility Edge in a One-Dimensional Quasiperiodic Optical Lattice | A single-particle mobility edge (SPME) marks a critical energy separating
extended from localized states in a quantum system. In one-dimensional systems
with uncorrelated disorder, a SPME cannot exist, since all single-particle
states localize for arbitrarily weak disorder strengths. However, if
correlations are present in the disorder potential, the localization transition
can occur at a finite disorder strength and SPMEs become possible. In this
work, we find experimental evidence for the existence of such a SPME in a
one-dimensional quasi-periodic optical lattice. Specifically, we find a regime
where extended and localized single-particle states coexist, in good agreement
with theoretical simulations, which predict a SPME in this regime.
| 0 | 1 | 0 | 0 | 0 | 0 |
Integral field observations of the blue compact galaxy Haro14. Star formation and feedback in dwarf galaxies | (Abridged) Low-luminosity, gas-rich blue compact galaxies (BCG) are ideal
laboratories to investigate many aspects of the star formation in galaxies. We
study the morphology, stellar content, kinematics, and the nebular excitation
and ionization mechanism in the BCG Haro 14 by means of integral field
observations with VIMOS in the VLT. From these data we build maps in continuum
and in the brighter emission lines, produce line-ratio maps, and obtain the
velocity and velocity dispersion fields. We also generate the integrated
spectrum of the major HII regions and young stellar clusters identified in the
maps to determine reliable physical parameters and oxygen abundances. We find
as follows: i) the current star formation in Haro 14 is spatially extended with
the major HII regions placed along a linear structure, elongated in the
north-south direction, and in a horseshoe-like curvilinear feature that extends
about 760 pc eastward; the continuum emission is more concentrated and peaks
close to the galaxy center; ii) two different episodes of star formation are
present: the recent starburst, with ages $\leq$ 6 Myrs and the intermediate-age
clusters, with ages between 10 and 30 Myrs; these stellar components rest on a
several Gyr old underlying host galaxy; iii) the H$\alpha$/H$\beta$ pattern is
inhomogeneous, with excess color values varying from E(B-V)=0.04 up to
E(B-V)=1.09; iv) shocks play a significant role in the galaxy; and v) the
velocity field displays a complicated pattern with regions of material moving
toward us in the east and north galaxy areas. The morphology of Haro 14, its
irregular velocity field, and the presence of shocks speak in favor of a
scenario of triggered star formation. Ages of the knots are consistent with the
ongoing burst being triggered by the collective action of stellar winds and
supernovae originated in the central clusters.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Framework for Automated Cellular Network Tuning with Reinforcement Learning | Tuning cellular network performance against always occurring wireless
impairments can dramatically improve reliability to end users. In this paper,
we formulate cellular network performance tuning as a reinforcement learning
(RL) problem and provide a solution to improve the signal to
interference-plus-noise ratio (SINR) for indoor and outdoor environments. By
leveraging the ability of Q-learning to estimate future SINR improvement
rewards, we propose two algorithms: (1) voice over LTE (VoLTE) downlink closed
loop power control (PC) and (2) self-organizing network (SON) fault management.
The VoLTE PC algorithm uses RL to adjust the indoor base station transmit power
so that the effective SINR meets the target SINR. The SON fault management
algorithm uses RL to improve the performance of an outdoor cluster by resolving
faults in the network through configuration management. Both algorithms exploit
measurements from the connected users, wireless impairments, and relevant
configuration parameters to solve a non-convex SINR optimization problem using
RL. Simulation results show that our proposed RL based algorithms outperform
the industry standards today in realistic cellular communication environments.
| 0 | 0 | 0 | 1 | 0 | 0 |
A Survey of Augmented Reality Navigation | Navigation has been a popular area of research in both academia and industry.
Combined with maps, and different localization technologies, navigation systems
have become robust and more usable. By combining navigation with augmented
reality, it can be improved further to become realistic and user friendly. This
paper surveys existing researches carried out in this area, describes existing
techniques for building augmented reality navigation systems, and the problems
faced.
| 1 | 0 | 0 | 0 | 0 | 0 |
Strong Landau-quantization effects in high-magnetic-field superconductivity of a two-dimensional multiple-band metal near the Lifshitz transition | We investigate the onset of superconductivity in magnetic field for a clean
two-dimensional multiple-band superconductor in the vicinity of the Lifshitz
transition when one of the bands is very shallow. Due to small number of
carriers in this band, the quasiclassical Werthamer-Helfand approximation
breaks down and Landau quantization has to be taken into account. We found that
the transition temperature TC2(H) has giant oscillations and is resonantly
enhanced at the magnetic fields corresponding to full occupancy of the Landau
levels in the shallow band. This enhancement is especially pronounced for the
lowest Landau level. As a consequence, the reentrant superconducting regions in
the temperature-field phase diagram emerge at low temperatures near the
magnetic fields at which the chemical potential matches the Landau levels. The
specific behavior depends on the relative strength of the intraband and
interband pairing interactions and the reentrance is most pronounced in the
purely interband coupling scenario. The reentrant behavior is suppressed by the
Zeeman spin splitting in the shallow band, the separated regions disappear
already for very small spin-splitting factors. On the other hand, the
reentrance is restored in the resonance cases when the spin-splitting energy
exactly matches the separation between the Landau levels. The predicted
behavior may realize in the gate-tuned FeSe monolayer.
| 0 | 1 | 0 | 0 | 0 | 0 |
On boundary extension of mappings in metric spaces in terms of prime ends | We study the boundary behavior of the so-called ring $Q$-mappings obtained as
a natural generalization of mappings with bounded distortion. We establish a
series of conditions imposed on a function $Q(x)$ for the continuous extension
of given mappings with respect to prime ends in domains with regular boundaries
in metric spaces.
| 0 | 0 | 1 | 0 | 0 | 0 |
Temporally Evolving Community Detection and Prediction in Content-Centric Networks | In this work, we consider the problem of combining link, content and temporal
analysis for community detection and prediction in evolving networks. Such
temporal and content-rich networks occur in many real-life settings, such as
bibliographic networks and question answering forums. Most of the work in the
literature (that uses both content and structure) deals with static snapshots
of networks, and they do not reflect the dynamic changes occurring over
multiple snapshots. Incorporating dynamic changes in the communities into the
analysis can also provide useful insights about the changes in the network such
as the migration of authors across communities. In this work, we propose
Chimera, a shared factorization model that can simultaneously account for graph
links, content, and temporal analysis. This approach works by extracting the
latent semantic structure of the network in multidimensional form, but in a way
that takes into account the temporal continuity of these embeddings. Such an
approach simplifies temporal analysis of the underlying network by using the
embedding as a surrogate. A consequence of this simplification is that it is
also possible to use this temporal sequence of embeddings to predict future
communities. We present experimental results illustrating the effectiveness of
the approach.
| 1 | 0 | 0 | 1 | 0 | 0 |
A Robust Utility Learning Framework via Inverse Optimization | In many smart infrastructure applications flexibility in achieving
sustainability goals can be gained by engaging end-users. However, these users
often have heterogeneous preferences that are unknown to the decision-maker
tasked with improving operational efficiency. Modeling user interaction as a
continuous game between non-cooperative players, we propose a robust parametric
utility learning framework that employs constrained feasible generalized least
squares estimation with heteroskedastic inference. To improve forecasting
performance, we extend the robust utility learning scheme by employing
bootstrapping with bagging, bumping, and gradient boosting ensemble methods.
Moreover, we estimate the noise covariance which provides approximated
correlations between players which we leverage to develop a novel correlated
utility learning framework. We apply the proposed methods both to a toy example
arising from Bertrand-Nash competition between two firms as well as to data
from a social game experiment designed to encourage energy efficient behavior
amongst smart building occupants. Using occupant voting data for shared
resources such as lighting, we simulate the game defined by the estimated
utility functions to demonstrate the performance of the proposed methods.
| 1 | 0 | 1 | 0 | 0 | 0 |
Evaluating stochastic seeding strategies in networks | When trying to maximize the adoption of a behavior in a population connected
by a social network, it is common to strategize about where in the network to
seed the behavior, often with an element of randomness. Selecting seeds
uniformly at random is a basic but compelling strategy in that it distributes
seeds broadly throughout the network. A more sophisticated stochastic strategy,
one-hop targeting, is to select random network neighbors of random individuals;
this exploits a version of the friendship paradox, whereby the friend of a
random individual is expected to have more friends than a random individual,
with the hope that seeding a behavior at more connected individuals leads to
more adoption. Many seeding strategies have been proposed, but empirical
evaluations have demanded large field experiments designed specifically for
this purpose and have yielded relatively imprecise comparisons of strategies.
Here we show how stochastic seeding strategies can be evaluated more
efficiently in such experiments, how they can be evaluated "off-policy" using
existing data arising from experiments designed for other purposes, and how to
design more efficient experiments. In particular, we consider contrasts between
stochastic seeding strategies and analyze nonparametric estimators adapted from
policy evaluation and importance sampling. We use simulations on real networks
to show that the proposed estimators and designs can dramatically increase
precision while yielding valid inference. We then apply our proposed estimators
to two field experiments, one that assigned households to an intensive
marketing intervention and one that assigned students to an anti-bullying
intervention.
| 1 | 0 | 0 | 0 | 0 | 0 |
Flux noise in a superconducting transmission line | We study a superconducting transmission line (TL) formed by distributed LC
oscillators and excited by external magnetic fluxes which are aroused from
random magnetization (A) placed in substrate or (B) distributed at interfaces
of a two-wire TL. Low-frequency dynamics of a random magnetic field is
described based on the diffusion Langevin equation with a short-range source
caused by (a) random amplitude or (b) gradient of magnetization. For a TL
modeled as a two-port network with open and shorted ends, the effective
magnetic flux at the open end has non-local dependency on noise distribution
along the TL. The flux-flux correlation function is evaluated and analyzed for
the regimes (Aa), (Ab). (Ba), and (Bb). Essential frequency dispersion takes
place around the inverse diffusion time of random flux along the TL. Typically,
noise effect increases with size faster than the area of TL. The flux-flux
correlator can be verified both via the population relaxation rate of the
qubit, which is formed by the Josephson junction shunted by the TL with flux
noises, and via random voltage at the open end of the TL.
| 0 | 1 | 0 | 0 | 0 | 0 |
Relativistic Newtonian Dynamics for Objects and Particles | Relativistic Newtonian Dynamics (RND) was introduced in a series of recent
papers by the author, in partial cooperation with J. M. Steiner. RND was
capable of describing non-classical behavior of motion under a central
attracting force. RND incorporates the influence of potential energy on
spacetime in Newtonian dynamics, treating gravity as a force in flat spacetime.
It was shown that this dynamics predicts accurately gravitational time
dilation, the anomalous precession of Mercury and the periastron advance of any
binary.
In this paper the model is further refined and extended to describe also the
motion of both objects with non-zero mass and massless particles, under a
conservative attracting force. It is shown that for any conservative force a
properly defined energy is conserved on the trajectories and if this force is
central, the angular momentum is also preserved. An RND equation of motion is
derived for motion under a conservative force. As an application, it is shown
that RND predicts accurately also the Shapiro time delay - the fourth test of
GR.
| 0 | 1 | 0 | 0 | 0 | 0 |
Spatio-Temporal Structured Sparse Regression with Hierarchical Gaussian Process Priors | This paper introduces a new sparse spatio-temporal structured Gaussian
process regression framework for online and offline Bayesian inference. This is
the first framework that gives a time-evolving representation of the
interdependencies between the components of the sparse signal of interest. A
hierarchical Gaussian process describes such structure and the
interdependencies are represented via the covariance matrices of the prior
distributions. The inference is based on the expectation propagation method and
the theoretical derivation of the posterior distribution is provided in the
paper. The inference framework is thoroughly evaluated over synthetic, real
video and electroencephalography (EEG) data where the spatio-temporal evolving
patterns need to be reconstructed with high accuracy. It is shown that it
achieves 15% improvement of the F-measure compared with the alternating
direction method of multipliers, spatio-temporal sparse Bayesian learning
method and one-level Gaussian process model. Additionally, the required memory
for the proposed algorithm is less than in the one-level Gaussian process
model. This structured sparse regression framework is of broad applicability to
source localisation and object detection problems with sparse signals.
| 0 | 0 | 0 | 1 | 0 | 0 |
Answer Set Solving with Bounded Treewidth Revisited | Parameterized algorithms are a way to solve hard problems more efficiently,
given that a specific parameter of the input is small. In this paper, we apply
this idea to the field of answer set programming (ASP). To this end, we propose
two kinds of graph representations of programs to exploit their treewidth as a
parameter. Treewidth roughly measures to which extent the internal structure of
a program resembles a tree. Our main contribution is the design of
parameterized dynamic programming algorithms, which run in linear time if the
treewidth and weights of the given program are bounded. Compared to previous
work, our algorithms handle the full syntax of ASP. Finally, we report on an
empirical evaluation that shows good runtime behaviour for benchmark instances
of low treewidth, especially for counting answer sets.
| 1 | 0 | 0 | 0 | 0 | 0 |
Community structure: A comparative evaluation of community detection methods | Discovering community structure in complex networks is a mature field since a
tremendous number of community detection methods have been introduced in the
literature. Nevertheless, it is still very challenging for practioners to
determine which method would be suitable to get insights into the structural
information of the networks they study. Many recent efforts have been devoted
to investigating various quality scores of the community structure, but the
problem of distinguishing between different types of communities is still open.
In this paper, we propose a comparative, extensive and empirical study to
investigate what types of communities many state-of-the-art and well-known
community detection methods are producing. Specifically, we provide
comprehensive analyses on computation time, community size distribution, a
comparative evaluation of methods according to their optimisation schemes as
well as a comparison of their partioning strategy through validation metrics.
We process our analyses on a very large corpus of hundreds of networks from
five different network categories and propose ways to classify community
detection methods, helping a potential user to navigate the complex landscape
of community detection.
| 1 | 0 | 0 | 0 | 0 | 0 |
Classification of isoparametric submanifolds admitting a reflective focal submanifold in symmetric spaces of non-compact type | In this paper, we assume that all isoparametric submanifolds have flat
section. The main purpose of this paper is to prove that, if a full irreducible
complete isoparametric submanifold of codimension greater than one in a
symmetric space of non-compact type admits a reflective focal submanifold and
if it of real analytic, then it is a principal orbit of a Hermann type action
on the symmetric space. A hyperpolar action on a symmetric space of non-compact
type admits a reflective singular orbit if and only if it is a Hermann type
action. Hence is not extra the assumption that the isoparametric submanifold
admits a reflective focal submanifold. Also, we prove that, if a full
irreducible complete isoparametric submanifold of codimension greater than one
in a symmetric space of non-compact type satisfies some additional conditions,
then it is a principal orbit of the isotropy action of the symmetric space,
where we need not impose that the submanifold is of real analytic. We use the
building theory in the proof.
| 0 | 0 | 1 | 0 | 0 | 0 |
Training Group Orthogonal Neural Networks with Privileged Information | Learning rich and diverse representations is critical for the performance of
deep convolutional neural networks (CNNs). In this paper, we consider how to
use privileged information to promote inherent diversity of a single CNN model
such that the model can learn better representations and offer stronger
generalization ability. To this end, we propose a novel group orthogonal
convolutional neural network (GoCNN) that learns untangled representations
within each layer by exploiting provided privileged information and enhances
representation diversity effectively. We take image classification as an
example where image segmentation annotations are used as privileged information
during the training process. Experiments on two benchmark datasets -- ImageNet
and PASCAL VOC -- clearly demonstrate the strong generalization ability of our
proposed GoCNN model. On the ImageNet dataset, GoCNN improves the performance
of state-of-the-art ResNet-152 model by absolute value of 1.2% while only uses
privileged information of 10% of the training images, confirming effectiveness
of GoCNN on utilizing available privileged knowledge to train better CNNs.
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Antiunitary representations and modular theory | Antiunitary representations of Lie groups take values in the group of unitary
and antiunitary operators on a Hilbert space H. In quantum physics, antiunitary
operators implement time inversion or a PCT symmetry, and in the modular theory
of operator algebras they arise as modular conjugations from cyclic separating
vectors of von Neumann algebras. We survey some of the key concepts at the
borderline between the theory of local observables (Quantum Field Theory (QFT)
in the sense of Araki--Haag--Kastler) and modular theory of operator algebras
from the perspective of antiunitary group representations. Here a central point
is to encode modular objects in standard subspaces V in H which in turn are in
one-to-one correspondence with antiunitary representations of the
multiplicative group R^x. Half-sided modular inclusions and modular
intersections of standard subspaces correspond to antiunitary representations
of Aff(R), and these provide the basic building blocks for a general theory
started in the 90s with the ground breaking work of Borchers and Wiesbrock and
developed in various directions in the QFT context. The emphasis of these notes
lies on the translation between configurations of standard subspaces as they
arise in the context of modular localization developed by Brunetti, Guido and
Longo, and the more classical context of von Neumann algebras with cyclic
separating vectors. Our main point is that configurations of standard subspaces
can be studied from the perspective of antiunitary Lie group representations
and the geometry of the corresponding spaces, which are often fiber bundles
over ordered symmetric spaces. We expect this perspective to provide new and
systematic insight into the much richer configurations of nets of local
observables in QFT.
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ARTENOLIS: Automated Reproducibility and Testing Environment for Licensed Software | Motivation:
Automatically testing changes to code is an essential feature of continuous
integration. For open-source code, without licensed dependencies, a variety of
continuous integration services exist. The COnstraint-Based Reconstruction and
Analysis (COBRA) Toolbox is a suite of open-source code for computational
modelling with dependencies on licensed software. A novel automated framework
of continuous integration in a semi-licensed environment is required for the
development of the COBRA Toolbox and related tools of the COBRA community.
Results:
ARTENOLIS is a general-purpose infrastructure software application that
implements continuous integration for open-source software with licensed
dependencies. It uses a master-slave framework, tests code on multiple
operating systems, and multiple versions of licensed software dependencies.
ARTENOLIS ensures the stability, integrity, and cross-platform compatibility of
code in the COBRA Toolbox and related tools.
Availability and Implementation:
The continuous integration server, core of the reproducibility and testing
infrastructure, can be freely accessed under artenolis.lcsb.uni.lu. The
continuous integration framework code is located in the /.ci directory and at
the root of the repository freely available under
github.com/opencobra/cobratoolbox.
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Droplet states in quantum XXZ spin systems on general graphs | We study XXZ spin systems on general graphs. In particular, we describe the
formation of droplet states near the bottom of the spectrum in the Ising phase
of the model, where the Z-term dominates the XX-term. As key tools we use
particle number conservation of XXZ systems and symmetric products of graphs
with their associated adjacency matrices and Laplacians. Of particular interest
to us are strips and multi-dimensional Euclidean lattices, for which we discuss
the existence of spectral gaps above the droplet regime. We also prove a
Combes-Thomas bound which shows that the eigenstates in the droplet regime are
exponentially small perturbations of strict (classical) droplets.
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Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning | There is growing interest in estimating and analyzing heterogeneous treatment
effects in experimental and observational studies. We describe a number of
meta-algorithms that can take advantage of any supervised learning or
regression method in machine learning and statistics to estimate the
Conditional Average Treatment Effect (CATE) function. Meta-algorithms build on
base algorithms---such as Random Forests (RF), Bayesian Additive Regression
Trees (BART) or neural networks---to estimate the CATE, a function that the
base algorithms are not designed to estimate directly. We introduce a new
meta-algorithm, the X-learner, that is provably efficient when the number of
units in one treatment group is much larger than in the other, and can exploit
structural properties of the CATE function. For example, if the CATE function
is linear and the response functions in treatment and control are Lipschitz
continuous, the X-learner can still achieve the parametric rate under
regularity conditions. We then introduce versions of the X-learner that use RF
and BART as base learners. In extensive simulation studies, the X-learner
performs favorably, although none of the meta-learners is uniformly the best.
In two persuasion field experiments from political science, we demonstrate how
our new X-learner can be used to target treatment regimes and to shed light on
underlying mechanisms. A software package is provided that implements our
methods.
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Counting triangles formula for the first Chern class of a circle bundle | We consider the problem of the combinatorial computation of the first Chern
class of a circle bundle. N.Mnev found such a formula in terms of canonical
shellings. It represents certain invariant of a triangulation computed by
analyzing cyclic word in 3-character alphabet associated to the bundle. This
curvature is a kind of discretization of Konstevich's curvature differential
2-form.
We find a new expression of Mnev's curvature by counting triangles in a
cyclic word. Our formula is different from that of Mnev. In particular, it is
cyclically invariant by its very form. We present also some sample computations
of this invariant and also provide a small Mathematica code for the computation
of this invariant.
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Probing homogeneity with standard candles | We show that standard candles can provide some valuable information about the
density contrast, which could be particularly important at redshifts where
other observations are not available. We use an inversion method to reconstruct
the local radial density profile from luminosity distance observations assuming
background cosmological parameters obtained from large scale observations.
Using type Ia Supernovae% (SNe) , Cepheids and the cosmological parameters from
the Planck mission we reconstruct the radial density profiles along two
different directions of the sky. We compare these profiles to other density
maps obtained from luminosity density, in particular Keenan et al. 2013 and the
2M++ galaxy catalogue. The method independently confirms the existence of
inhomogeneities, could be particularly useful to correctly normalize density
maps from galaxy surveys with respect to the average density of the Universe,
and could clarify the apparent discrepancy between local and large scale
estimations of the Hubble constant. When better observational supernovae data
will be available, the accuracy of the reconstructed density profiles will
improve and will allow to further investigate the existence of structures whose
size is beyond the reach of galaxy surveys.
| 0 | 1 | 0 | 0 | 0 | 0 |
Fabrication tolerant chalcogenide mid-infrared multimode interference coupler design with application for Bracewell nulling interferometry | Understanding exoplanet formation and finding potentially habitable
exoplanets is vital to an enhanced understanding of the universe. The use of
nulling interferometry to strongly attenuate the central starlight provides the
opportunity to see objects closer to the star than ever before. Given that
exoplanets are usually warm, the 4 microns Mid-Infrared region is advantageous
for such observations. The key performance parameters for a nulling
interferometer are the extinction ratio it can attain and how well that is
maintained across the operational bandwidth. Both parameters depend on the
design and fabrication accuracy of the subcomponents and their wavelength
dependence. Via detailed simulation it is shown in this paper that a planar
chalcogenide photonic chip, consisting of three highly fabrication tolerant
multimode interference couplers, can exceed an extinction ratio of 60 dB in
double nulling operation and up to 40 dB for a single nulling operation across
a wavelength window of 3.9 to 4.2 microns. This provides a beam combiner with
sufficient performance, in theory, to image exoplanets.
| 0 | 1 | 0 | 0 | 0 | 0 |
Accurate Bayesian Data Classification without Hyperparameter Cross-validation | We extend the standard Bayesian multivariate Gaussian generative data
classifier by considering a generalization of the conjugate, normal-Wishart
prior distribution and by deriving the hyperparameters analytically via
evidence maximization. The behaviour of the optimal hyperparameters is explored
in the high-dimensional data regime. The classification accuracy of the
resulting generalized model is competitive with state-of-the art Bayesian
discriminant analysis methods, but without the usual computational burden of
cross-validation.
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Offline Biases in Online Platforms: a Study of Diversity and Homophily in Airbnb | How diverse are sharing economy platforms? Are they fair marketplaces, where
all participants operate on a level playing field, or are they large-scale
online aggregators of offline human biases? Often portrayed as easy-to-access
digital spaces whose participants receive equal opportunities, such platforms
have recently come under fire due to reports of discriminatory behaviours among
their users, and have been associated with gentrification phenomena that
exacerbate preexisting inequalities along racial lines. In this paper, we focus
on the Airbnb sharing economy platform, and analyse the diversity of its user
base across five large cities. We find it to be predominantly young, female,
and white. Notably, we find this to be true even in cities with a diverse
racial composition. We then introduce a method based on the statistical
analysis of networks to quantify behaviours of homophily, heterophily and
avoidance between Airbnb hosts and guests. Depending on cities and property
types, we do find signals of such behaviours relating both to race and gender.
We use these findings to provide platform design recommendations, aimed at
exposing and possibly reducing the biases we detect, in support of a more
inclusive growth of sharing economy platforms.
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Parameter Estimation for Thurstone Choice Models | We consider the estimation accuracy of individual strength parameters of a
Thurstone choice model when each input observation consists of a choice of one
item from a set of two or more items (so called top-1 lists). This model
accommodates the well-known choice models such as the Luce choice model for
comparison sets of two or more items and the Bradley-Terry model for pair
comparisons.
We provide a tight characterization of the mean squared error of the maximum
likelihood parameter estimator. We also provide similar characterizations for
parameter estimators defined by a rank-breaking method, which amounts to
deducing one or more pair comparisons from a comparison of two or more items,
assuming independence of these pair comparisons, and maximizing a likelihood
function derived under these assumptions. We also consider a related binary
classification problem where each individual parameter takes value from a set
of two possible values and the goal is to correctly classify all items within a
prescribed classification error.
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Quantitative modeling and analysis of bifurcation-induced bursting | Modeling and parameter estimation for neuronal dynamics are often challenging
because many parameters can range over orders of magnitude and are difficult to
measure experimentally. Moreover, selecting a suitable model complexity
requires a sufficient understanding of the model's potential use, such as
highlighting essential mechanisms underlying qualitative behavior or precisely
quantifying realistic dynamics. We present a novel approach that can guide
model development and tuning to achieve desired qualitative and quantitative
solution properties. Our approach relies on the presence of disparate time
scales and employs techniques of separating the dynamics of fast and slow
variables, which are well known in the analysis of qualitative solution
features. We build on these methods to show how it is also possible to obtain
quantitative solution features by imposing designed dynamics for the slow
variables in the form of specified two-dimensional paths in a
bifurcation-parameter landscape.
| 0 | 1 | 1 | 0 | 0 | 0 |
Uniqueness and radial symmetry of minimizers for a nonlocal variational problem | In this paper we prove the uniqueness and radial symmetry of minimizers for
variational problems that model several phenomena. The uniqueness is a
consequence of the convexity of the functional. The main technique is Fourier
transform of tempered distributions.
| 0 | 0 | 1 | 0 | 0 | 0 |
Improved Accounting for Differentially Private Learning | We consider the problem of differential privacy accounting, i.e. estimation
of privacy loss bounds, in machine learning in a broad sense. We propose two
versions of a generic privacy accountant suitable for a wide range of learning
algorithms. Both versions are derived in a simple and principled way using
well-known tools from probability theory, such as concentration inequalities.
We demonstrate that our privacy accountant is able to achieve state-of-the-art
estimates of DP guarantees and can be applied to new areas like variational
inference. Moreover, we show that the latter enjoys differential privacy at
minor cost.
| 1 | 0 | 0 | 1 | 0 | 0 |
Scattertext: a Browser-Based Tool for Visualizing how Corpora Differ | Scattertext is an open source tool for visualizing linguistic variation
between document categories in a language-independent way. The tool presents a
scatterplot, where each axis corresponds to the rank-frequency a term occurs in
a category of documents. Through a tie-breaking strategy, the tool is able to
display thousands of visible term-representing points and find space to legibly
label hundreds of them. Scattertext also lends itself to a query-based
visualization of how the use of terms with similar embeddings differs between
document categories, as well as a visualization for comparing the importance
scores of bag-of-words features to univariate metrics.
| 1 | 0 | 0 | 0 | 0 | 0 |
Tight contact structures on Seifert surface complements | We consider complements of standard Seifert surfaces of special alternating
links. On these handlebodies, we use Honda's method to enumerate those tight
contact structures whose dividing sets are isotopic to the link, and find their
number to be the leading coefficient of the Alexander polynomial. The Euler
classes of the contact structures are identified with hypertrees in a certain
hypergraph. Using earlier work, this establishes a connection between contact
topology and the Homfly polynomial. We also show that the contact invariants of
our tight contact structures form a basis for sutured Floer homology. Finally,
we relate our methods and results to Kauffman's formal knot theory.
| 0 | 0 | 1 | 0 | 0 | 0 |
Modular invariant representations of the $\mathcal{N}=2$ superconformal algebra | We compute the modular transformation formula of the characters for a certain
family of (finitely or uncountably many) simple modules over the simple
$\mathcal{N}=2$ vertex operator superalgebra of central charge
$c_{p,p'}=3\left(1-\frac{2p'}{p}\right),$ where $(p,p')$ is a pair of coprime
positive integers such that $p\geq2$. When $p'=1$, the formula coincides with
that of the $\mathcal{N}=2$ unitary minimal series found by F. Ravanini and
S.-K. Yang. In addition, we study the properties of the corresponding "modular
$S$-matrix", which is no longer a matrix if $p'\geq2$.
| 0 | 0 | 1 | 0 | 0 | 0 |
The CODALEMA/EXTASIS experiment: Contributions to the 35th International Cosmic Ray Conference (ICRC 2017) | Contributions of the CODALEMA/EXTASIS experiment to the 35th International
Cosmic Ray Conference, 12-20 July 2017, Busan, South Korea.
| 0 | 1 | 0 | 0 | 0 | 0 |
Distributed Kernel K-Means for Large Scale Clustering | Clustering samples according to an effective metric and/or vector space
representation is a challenging unsupervised learning task with a wide spectrum
of applications. Among several clustering algorithms, k-means and its
kernelized version have still a wide audience because of their conceptual
simplicity and efficacy. However, the systematic application of the kernelized
version of k-means is hampered by its inherent square scaling in memory with
the number of samples. In this contribution, we devise an approximate strategy
to minimize the kernel k-means cost function in which the trade-off between
accuracy and velocity is automatically ruled by the available system memory.
Moreover, we define an ad-hoc parallelization scheme well suited for hybrid
cpu-gpu state-of-the-art parallel architectures. We proved the effectiveness
both of the approximation scheme and of the parallelization method on standard
UCI datasets and on molecular dynamics (MD) data in the realm of computational
chemistry. In this applicative domain, clustering can play a key role for both
quantitively estimating kinetics rates via Markov State Models or to give
qualitatively a human compatible summarization of the underlying chemical
phenomenon under study. For these reasons, we selected it as a valuable
real-world application scenario.
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The Origin of Solar Filament Plasma Inferred from in situ Observations of Elemental Abundances | Solar filaments/prominences are one of the most common features in the
corona, which may lead to energetic coronal mass ejections (CMEs) and flares
when they erupt. Filaments are about one hundred times cooler and denser than
the coronal material, and physical understanding of their material origin
remains controversial. Two types of scenarios have been proposed: one argues
that the filament plasma is brought into the corona from photosphere or
chromosphere through a siphon or evaporation/injection process, while the other
suggests that the material condenses from the surrounding coronal plasma due to
thermal instability. The elemental abundance analysis is a reasonable clue to
constrain the models, as the siphon or evaporation/injection model would
predict that the filament material abundances are close to the photospheric or
chromospheric ones, while the condensation model should have coronal
abundances. In this letter, we analyze the elemental abundances of a magnetic
cloud that contains the ejected filament material. The corresponding filament
eruption occurred on 1998 April 29, accompanying an M6.8 class soft X-ray flare
located at the heliographic coordinates S18E20 (NOAA 08210) and a fast halo CME
with the linear velocity of 1374 km s$^{-1}$ near the Sun. We find that the
abundance ratios of elements with low and high First Ionization Potential such
as Fe/O, Mg/O, and Si/O are 0.150, 0.050, and 0.070, respectively, approaching
their corresponding photospheric values 0.065, 0.081, and 0.066, which does not
support the coronal origin of the filament plasma.
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Distributed Stochastic Optimization via Adaptive SGD | Stochastic convex optimization algorithms are the most popular way to train
machine learning models on large-scale data. Scaling up the training process of
these models is crucial, but the most popular algorithm, Stochastic Gradient
Descent (SGD), is a serial method that is surprisingly hard to parallelize. In
this paper, we propose an efficient distributed stochastic optimization method
by combining adaptivity with variance reduction techniques. Our analysis yields
a linear speedup in the number of machines, constant memory footprint, and only
a logarithmic number of communication rounds. Critically, our approach is a
black-box reduction that parallelizes any serial online learning algorithm,
streamlining prior analysis and allowing us to leverage the significant
progress that has been made in designing adaptive algorithms. In particular, we
achieve optimal convergence rates without any prior knowledge of smoothness
parameters, yielding a more robust algorithm that reduces the need for
hyperparameter tuning. We implement our algorithm in the Spark distributed
framework and exhibit dramatic performance gains on large-scale logistic
regression problems.
| 0 | 0 | 0 | 1 | 0 | 0 |
Bilipschitz Equivalence of Trees and Hyperbolic Fillings | We combine conditions found in [Wh] with results from [MPR] to show that
quasi-isometries between uniformly discrete bounded geometry spaces that
satisfy linear isoperimetric inequalities are within bounded distance to
bilipschitz equivalences. We apply this result to regularly branching trees and
hyperbolic fillings of metric spaces.
| 0 | 0 | 1 | 0 | 0 | 0 |
On the contribution of thermal excitation to the total 630.0 nm emissions in the northern cusp ionosphere | Direct impact excitation by precipitating electrons is believed to be the
main source of 630.0 nm emissions in the cusp ionosphere. However, this paper
investigates a different source, 630.0 emissions caused by thermally excited
atomic oxygen O$(^{1}$D) when high electron temperature prevail in the cusp. On
22 January 2012 and 14 January 2013, the European Incoherent Scatter Scientific
Association (EISCAT) radar on Svalbard measured electron temperature
enhancements exceeding 3000 K near magnetic noon in the cusp ionosphere over
Svalbard. The electron temperature enhancements corresponded to electron
density enhancements exceeding $10^{11}$m$^{-3}$ accompanied by intense 630.0
nm emissions in a field of view common to both the EISCAT Svalbard radar and a
meridian scanning photometer. This offered an excellent opportunity to
investigate the role of thermally excited O$(^{1}$D) 630.0 nm emissions in the
cusp ionosphere. The thermal component was derived from the EISCAT Radar
measurements and compared with optical data. For both events the calculated
thermal component had a correlation coefficient greater than 0.8 to the total
observed 630.0 nm intensity which contains both thermal and particle impact
components. Despite fairly constant solar wind, the calculated thermal
component intensity fluctuated possibly due to dayside transients in the
aurora.
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Dose finding for new vaccines: the role for immunostimulation/immunodynamic modelling | Current methods to optimize vaccine dose are purely empirically based,
whereas in the drug development field, dosing determinations use far more
advanced quantitative methodology to accelerate decision-making. Applying these
established methods in the field of vaccine development may reduce the
currently large clinical trial sample sizes, long time frames, high costs, and
ultimately have a better potential to save lives. We propose the field of
immunostimulation/immunodynamic (IS/ID) modelling, which aims to translate
mathematical frameworks used for drug dosing towards optimizing vaccine dose
decision-making. Analogous to PK/PD modelling, IS/ID modelling approaches apply
mathematical models to describe the underlying mechanisms by which the immune
response is stimulated by vaccination (IS) and the resulting measured immune
response dynamics (ID). To move IS/ID modelling forward, existing datasets and
further data on vaccine allometry and dose-dependent dynamics need to be
generated and collate, requiring a collaborative environment with input from
academia, industry, regulators, governmental and non-governmental agencies to
share modelling expertise, and connect modellers to vaccine data.
| 0 | 0 | 0 | 0 | 1 | 0 |
On Learning Mixtures of Well-Separated Gaussians | We consider the problem of efficiently learning mixtures of a large number of
spherical Gaussians, when the components of the mixture are well separated. In
the most basic form of this problem, we are given samples from a uniform
mixture of $k$ standard spherical Gaussians, and the goal is to estimate the
means up to accuracy $\delta$ using $poly(k,d, 1/\delta)$ samples.
In this work, we study the following question: what is the minimum separation
needed between the means for solving this task? The best known algorithm due to
Vempala and Wang [JCSS 2004] requires a separation of roughly
$\min\{k,d\}^{1/4}$. On the other hand, Moitra and Valiant [FOCS 2010] showed
that with separation $o(1)$, exponentially many samples are required. We
address the significant gap between these two bounds, by showing the following
results.
1. We show that with separation $o(\sqrt{\log k})$, super-polynomially many
samples are required. In fact, this holds even when the $k$ means of the
Gaussians are picked at random in $d=O(\log k)$ dimensions.
2. We show that with separation $\Omega(\sqrt{\log k})$, $poly(k,d,1/\delta)$
samples suffice. Note that the bound on the separation is independent of
$\delta$. This result is based on a new and efficient "accuracy boosting"
algorithm that takes as input coarse estimates of the true means and in time
$poly(k,d, 1/\delta)$ outputs estimates of the means up to arbitrary accuracy
$\delta$ assuming the separation between the means is $\Omega(\min\{\sqrt{\log
k},\sqrt{d}\})$ (independently of $\delta$).
We also present a computationally efficient algorithm in $d=O(1)$ dimensions
with only $\Omega(\sqrt{d})$ separation. These results together essentially
characterize the optimal order of separation between components that is needed
to learn a mixture of $k$ spherical Gaussians with polynomial samples.
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Sylvester's Problem and Mock Heegner Points | We prove that if $p \equiv 4,7 \pmod{9}$ is prime and $3$ is not a cube
modulo $p$, then both of the equations $x^3+y^3=p$ and $x^3+y^3=p^2$ have a
solution with $x,y \in \mathbb{Q}$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Community Detection in the Network of German Princes in 1225: a Case Study | Many social networks exhibit some underlying community structure. In
particular, in the context of historical research, clustering of different
groups into warring or friendly factions can lead to a better understanding of
how conflicts may arise, and whether they could be avoided or not. In this work
we study the crisis that started in 1225 when the Emperor of the Holy Roman
Empire, Frederick II and his son Henry VII got into a conflict which almost led
to the rupture and dissolution of the Empire. We use a spin-glass-based
community detection algorithm to see how good this method is in detecting this
rift and compare the results with an analysis performed by one of the authors
(Gramsch) using standard social balance theory applied to History.
| 1 | 1 | 0 | 0 | 0 | 0 |
Coupon Advertising in Online Social Systems: Algorithms and Sampling Techniques | Online social systems have become important platforms for viral marketing
where the advertising of products is carried out with the communication of
users. After adopting the product, the seed buyers may spread the information
to their friends via online messages e.g. posts and tweets. In another issue,
electronic coupon system is one of the relevant promotion vehicles that help
manufacturers and retailers attract more potential customers. By offering
coupons to seed buyers, there is a chance to convince the influential users who
are, however, at first not very interested in the product. In this paper, we
propose a coupon based online influence model and consider the problem that how
to maximize the profit by selecting appropriate seed buyers. The considered
problem herein is markedly different from other influence related problems as
its objective function is not monotone. We provide an algorithmic analysis and
give several algorithms designed with different sampling techniques. In
particular, we propose the RA-T and RA-S algorithms which are not only provably
effective but also scalable on large datasets. The proposed theoretical results
are evaluated by extensive experiments done on large-scale real-world social
networks. The analysis of this paper also provides an algorithmic framework for
non-monotone submodular maximization problems in social networks.
| 1 | 0 | 0 | 0 | 0 | 0 |
Statistical study of auroral omega bands | The presence of very few statistical studies on auroral omega bands motivated
us to test-use a semi-automatic method for identifying large-scale undulations
of the diffuse aurora boundary and to investigate their occurrence. Five
identical all-sky cameras with overlapping fields of view provided data for 438
auroral omega-like structures over Fennoscandian Lapland from 1996 to 2007. The
results from this set of omega band events agree remarkably well with previous
observations of omega band occurrence in magnetic local time (MLT), lifetime,
location between the region 1 and 2 field-aligned currents, as well as current
density estimates. The average peak emission height of omega forms corresponds
to the estimated precipitation energies of a few keV, which experienced no
significant change during the events. Analysis of both local and global
magnetic indices demonstrates that omega bands are observed during substorm
expansion and recovery phases that are more intense than average substorm
expansion and recovery phases in the same region. The omega occurrence with
respect to the substorm expansion and recovery phases is in a very good
agreement with an earlier observed distribution of fast earthward flows in the
plasma sheet during expansion and recovery phases. These findings support the
theory that omegas are produced by fast earthward flows and auroral streamers,
despite the rarity of good conjugate observations.
| 0 | 1 | 0 | 0 | 0 | 0 |
Efficient Lightweight Encryption Algorithm for Smart Video Applications | The future generation networks: Internet of things (IoT), in combination with
the advanced computer vision techniques poses new challenges for securing
videos for end-users. The visual devices generally have constrained resources
in respects to their low computation power, small memory with limited power
supply. Therefore, to facilitate the video security in smart environment,
lightweight security schemes are required instead of inefficient existing
traditional cryptography algorithms. This research paper provides the solution
to overcome such problems. A novel lightweight cipher algorithm is proposed
here which targets multimedia in IoT with an in-house name EXPer i.e. Extended
permutation with eXclusive OR (XOR). EXPer is a symmetric stream cipher that
consists of simple XOR and left shift operations with three keys of 128 bits.
The proposed cipher algorithm has been tested on various sample videos.
Comparison of proposed algorithm has been made with the traditional cipher
algorithms XOR and Advanced Encryption Standard (AES). Visual results confirm
that EXPer provides security level equivalent to the AES algorithm with less
computational cost than AES. Therefore, it can easily be perceived that the
EXPer is a better replacement of AES for securing real-time video applications
in IoT.
| 1 | 0 | 0 | 0 | 0 | 0 |
Inter-site pair superconductivity: origins and recent validation experiments | The challenge of understanding high-temperature superconductivity has led to
a plethora of ideas, but 30 years after its discovery in cuprates, very few
have achieved convincing experimental validation. While Hubbard and t-J models
were given a lot of attention, a number of recent experiments appear to give
decisive support to the model of real-space inter-site pairing and percolative
superconductivity in cuprates. Systematic measurements of the doping dependence
of the superfluid density show a linear dependence on superfluid density -
rather than doping - over the entire phase diagram, in accordance with the
model's predictions. The doping-dependence of the anomalous lattice dynamics of
in-plane Cu-O mode vibrations observed by inelastic neutron scattering, gives
remarkable reciprocal space signature of the inter-site pairing interaction
whose doping dependence closely follows the predicted pair density.
Symmetry-specific time-domain spectroscopy shows carrier localization, polaron
formation, pairing and superconductivity to be distinct processes occurring on
distinct timescales throughout the entire superconducting phase diagram. The
three diverse experimental results confirm non-trivial predictions made more
than a decade ago by the inter-site pairing model in the cuprates, remarkably
also confirming some of the fundamental notions mentioned in the seminal paper
on the discovery of high-temperature superconductivity in cuprates.
| 0 | 1 | 0 | 0 | 0 | 0 |
Non-standard FDTD implementation of the Schrödinger equation | In this work, we apply the Cole's non-standard form of the FDTD to solve the
time dependent Schrödinger equation. We deduce the equations for the
non-standard FDTD considering an electronic wave function in the presence of
potentials which can be higher or lower in comparison with the energy of the
electron. The non-standard term is found to be almost the same, except for a
sine functions which is transformed to a hyperbolic sine function,as the
argument is imaginary when the potential has higher energy than the electron.
Perfectly Matched Layers using this methodology are also presented.
| 0 | 1 | 0 | 0 | 0 | 0 |
The magnetic and electronic properties of Oxyselenides - influence of transition metal ions and lanthanides | Magnetic oxyselenides have been the topic of research for several decades
being first of interest in the context of photoconductivity and
thermoelectricity owing to their intrinsic semiconducting properties and
ability to tune the energy gap through metal ion substitution. More recently,
interest in the oxyselenides has experienced a resurgence owing to the possible
relation to strongly correlated phenomena given the fact that many oxyslenides
share a similar structure to unconventional superconducting pnictides and
chalcogenides. The two dimensional nature of many oxyselenide systems also
draws an analogy to cuprate physics where a strong interplay between
unconventional electronic phases and localised magnetism has been studied for
several decades. It is therefore timely to review the physics of the
oxyselenides in the context of the broader field of strongly correlated
magnetism and electronic phenomena. Here we review the current status and
progress in this area of research with the focus on the influence of
lanthanides and transition metal ions on the intertwined magnetic and
electronic properties of oxyselenides. The emphasis of the review is on the
magnetic properties and comparisons are made with iron based pnictide and
chalcogenide systems.
| 0 | 1 | 0 | 0 | 0 | 0 |
Localizing the Object Contact through Matching Tactile Features with Visual Map | This paper presents a novel framework for integration of vision and tactile
sensing by localizing tactile readings in a visual object map. Intuitively,
there are some correspondences, e.g., prominent features, between visual and
tactile object identification. To apply it in robotics, we propose to localize
tactile readings in visual images by sharing same sets of feature descriptors
through two sensing modalities. It is then treated as a probabilistic
estimation problem solved in a framework of recursive Bayesian filtering.
Feature-based measurement model and Gaussian based motion model are thus built.
In our tests, a tactile array sensor is utilized to generate tactile images
during interaction with objects and the results have proven the feasibility of
our proposed framework.
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Scattering Cross Section in a Cylindrical anisotropic layered metamaterial | To design a uniaxial anisotropic metamaterial a layered cylindrical
metamaterial is introduced for TE polarization. Unlike to the previous work,
which the layers were in radial direction, here the layers are in azimuthal
direction. Scattering efficiency for this metamaterial in different frequency
is analyzed with solving Maxwell's wave equation. It is observed that in some
frequencies when the effective permittivity of the structure goes to zero the
scattering efficiency would be negligible. This result approves the previous
predictions. It is also found out that the scattering cancellation depends on
the relative permittivity of the environmental medium for the cylinder. The
finite element simulations are also confirmed the results.
| 0 | 1 | 0 | 0 | 0 | 0 |
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