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Title: CollaGAN : Collaborative GAN for Missing Image Data Imputation,
Abstract: In many applications requiring multiple inputs to obtain a desired output, if
any of the input data is missing, it often introduces large amounts of bias.
Although many techniques have been developed for imputing missing data, the
image imputation is still difficult due to complicated nature of natural
images. To address this problem, here we proposed a novel framework for missing
image data imputation, called Collaborative Generative Adversarial Network
(CollaGAN). CollaGAN converts an image imputation problem to a multi-domain
images-to-image translation task so that a single generator and discriminator
network can successfully estimate the missing data using the remaining clean
data set. We demonstrate that CollaGAN produces the images with a higher visual
quality compared to the existing competing approaches in various image
imputation tasks. | [
1,
0,
0,
1,
0,
0
] |
Title: Existence of closed geodesics through a regular point on translation surfaces,
Abstract: We show that on any translation surface, if a regular point is contained in a
simple closed geodesic, then it is contained in infinitely many simple closed
geodesics, whose directions are dense in the unit circle. Moreover, the set of
points that are not contained in any simple closed geodesic is finite. We also
construct explicit examples showing that such points exist. For a surface in
any hyperelliptic component, we show that this finite exceptional set is
actually empty. The proofs of our results use Apisa's classifications of
periodic points and of $\GL(2,\R)$ orbit closures in hyperelliptic components,
as well as a recent result of Eskin-Filip-Wright. | [
0,
0,
1,
0,
0,
0
] |
Title: Mathematical and numerical validation of the simplified spherical harmonics approach for time-dependent anisotropic-scattering transport problems in homogeneous media,
Abstract: In this work, we extend the solid harmonics derivation, which was used by
Ackroyd et al to derive the steady-state SP$_N$ equations, to transient
problems. The derivation expands the angular flux in ordinary surface harmonics
but uses harmonic polynomials to generate additional surface spherical harmonic
terms to be used in Galerkin projection. The derivation shows the equivalence
between the SP$_N$ and the P$_N$ approximation. Also, we use the line source
problem and McClarren's "box" problem to demonstrate such equivalence
numerically. Both problems were initially proposed for isotropic scattering,
but here we add higher-order scattering moments to them. Results show that the
difference between the SP$_N$ and P$_N$ scalar flux solution is at the roundoff
level. | [
0,
1,
0,
0,
0,
0
] |
Title: L1-norm Principal-Component Analysis of Complex Data,
Abstract: L1-norm Principal-Component Analysis (L1-PCA) of real-valued data has
attracted significant research interest over the past decade. However, L1-PCA
of complex-valued data remains to date unexplored despite the many possible
applications (e.g., in communication systems). In this work, we establish
theoretical and algorithmic foundations of L1-PCA of complex-valued data
matrices. Specifically, we first show that, in contrast to the real-valued case
for which an optimal polynomial-cost algorithm was recently reported by
Markopoulos et al., complex L1-PCA is formally NP-hard in the number of data
points. Then, casting complex L1-PCA as a unimodular optimization problem, we
present the first two suboptimal algorithms in the literature for its solution.
Our experimental studies illustrate the sturdy resistance of complex L1-PCA
against faulty measurements/outliers in the processed data. | [
1,
0,
0,
0,
0,
0
] |
Title: DyNet: The Dynamic Neural Network Toolkit,
Abstract: We describe DyNet, a toolkit for implementing neural network models based on
dynamic declaration of network structure. In the static declaration strategy
that is used in toolkits like Theano, CNTK, and TensorFlow, the user first
defines a computation graph (a symbolic representation of the computation), and
then examples are fed into an engine that executes this computation and
computes its derivatives. In DyNet's dynamic declaration strategy, computation
graph construction is mostly transparent, being implicitly constructed by
executing procedural code that computes the network outputs, and the user is
free to use different network structures for each input. Dynamic declaration
thus facilitates the implementation of more complicated network architectures,
and DyNet is specifically designed to allow users to implement their models in
a way that is idiomatic in their preferred programming language (C++ or
Python). One challenge with dynamic declaration is that because the symbolic
computation graph is defined anew for every training example, its construction
must have low overhead. To achieve this, DyNet has an optimized C++ backend and
lightweight graph representation. Experiments show that DyNet's speeds are
faster than or comparable with static declaration toolkits, and significantly
faster than Chainer, another dynamic declaration toolkit. DyNet is released
open-source under the Apache 2.0 license and available at
this http URL. | [
1,
0,
0,
1,
0,
0
] |
Title: Polyteam Semantics,
Abstract: Team semantics is the mathematical framework of modern logics of dependence
and independence in which formulae are interpreted by sets of assignments
(teams) instead of single assignments as in first-order logic. In order to
deepen the fruitful interplay between team semantics and database dependency
theory, we define "Polyteam Semantics" in which formulae are evaluated over a
family of teams. We begin by defining a novel polyteam variant of dependence
atoms and give a finite axiomatisation for the associated implication problem.
We also characterise the expressive power of poly-dependence logic by
properties of polyteams that are downward closed and definable in existential
second-order logic (ESO). The analogous result is shown to hold for
poly-independence logic and all ESO-definable properties. | [
1,
0,
0,
0,
0,
0
] |
Title: Buckling in Armored Droplets,
Abstract: The issue of the buckling mechanism in droplets stabilized by solid particles
(armored droplets) is tackled at a mesoscopic level using dissipative particle
dynamics simulations. We consider spherical water droplet in a decane solvent
coated with nanoparticle monolayers of two different types: Janus and
homogeneous. The chosen particles yield comparable initial three-phase contact
angles, chosen to maximize the adsorption energy at the interface. We study the
interplay between the evolution of droplet shape, layering of the particles,
and their distribution at the interface when the volume of the droplets is
reduced. We show that Janus particles affect strongly the shape of the droplet
with the formation of a crater-like depression. This evolution is actively
controlled by a close-packed particle monolayer at the curved interface. On the
contrary, homogeneous particles follow passively the volume reduction of the
droplet, whose shape does not deviate too much from spherical, even when a
nanoparticle monolayer/bilayer transition is detected at the interface. We
discuss how these buckled armored droplets might be of relevance in various
applications including potential drug delivery systems and biomimetic design of
functional surfaces. | [
0,
1,
0,
0,
0,
0
] |
Title: On non-Abelian Lie Bracket of Generalized Covariant Hamilton Systems,
Abstract: This is a theoretical paper, which is a continuation of [arXiv:1710.10597],
it considers the non-abelian Lie algebra $\mathcal{G}$ of Lie groups for
$\left[ {{X}_{i}},{{X}_{j}} \right]=c_{ij}^{k}{{X}_{k}}\in \mathcal{G}$ on the
foundation of the GCHS, where $c_{ij}^{k}\in {{C}^{\infty }}\left( U,R \right)$
are the structure constants. The GPWB [arXiv:1710.10597] is nonlinear bracket
applying to the non-Euclidean space, the second order (2,0) form antisymmetric
curvature tensor ${{F}_{ij}}=c_{ij}^{k}{{D}_{k}}$, and Qsu quantity
${{q}_{i}}=w_{i}^{k}{{D}_{k}}$ are accordingly obtained by using the
non-abelian Lie bracket. The GCHS $\left\{ H,f \right\}\in {{C}^{\infty
}}\left( M,\mathbb{R} \right)$ holds for the non-symplectic vector field
$X_{H}^{M}\in \mathcal{G}$ and $f\in {{C}^{\infty }}\left( M,\mathbb{R}
\right)$ that implies the covariant evolution equation consists of two parts,
NGHS and W dynamics along with the second order invariant operator
$\frac{{\mathcal{D}^{2}}}{d{{t}^{2}}}=\frac{{{d}^{2}}}{d{{t}^{2}}}+2w\frac{d}{dt}+\beta$. | [
0,
0,
1,
0,
0,
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] |
Title: The Topology of Statistical Verifiability,
Abstract: Topological models of empirical and formal inquiry are increasingly
prevalent. They have emerged in such diverse fields as domain theory [1, 16],
formal learning theory [18], epistemology and philosophy of science [10, 15, 8,
9, 2], statistics [6, 7] and modal logic [17, 4]. In those applications, open
sets are typically interpreted as hypotheses deductively verifiable by true
propositional information that rules out relevant possibilities. However, in
statistical data analysis, one routinely receives random samples logically
compatible with every statistical hypothesis. We bridge the gap between
propositional and statistical data by solving for the unique topology on
probability measures in which the open sets are exactly the statistically
verifiable hypotheses. Furthermore, we extend that result to a topological
characterization of learnability in the limit from statistical data. | [
1,
0,
0,
0,
0,
0
] |
Title: Phase I results with the Large Angle Beamstrahlung Monitor (LABM) with SuperKEKB beams,
Abstract: We report on the SuperKEKB Phase I operations of the Large Angle
Beamstrhalung Monitor (LABM). The detector is described and its performance
characterized using the synchrotron radiation backgrounds from the last Beam
Line magnets. The backgrounds are also used to determine the expected position
of the Interaction Point (IP), and the expected background rates during Phase
II. | [
0,
1,
0,
0,
0,
0
] |
Title: Network flow of mobile agents enhances the evolution of cooperation,
Abstract: We study the effect of contingent movement on the persistence of cooperation
on complex networks with empty nodes. Each agent plays Prisoner's Dilemma game
with its neighbors and then it either updates the strategy depending on the
payoff difference with neighbors or it moves to another empty node if not
satisfied with its own payoff. If no neighboring node is empty, each agent
stays at the same site. By extensive evolutionary simulations, we show that the
medium density of agents enhances cooperation where the network flow of mobile
agents is also medium. Moreover, if the movements of agents are more frequent
than the strategy updating, cooperation is further promoted. In scale-free
networks, the optimal density for cooperation is lower than other networks
because agents get stuck at hubs. Our study suggests that keeping a smooth
network flow is significant for the persistence of cooperation in ever-changing
societies. | [
0,
0,
0,
0,
1,
0
] |
Title: Neural Face Editing with Intrinsic Image Disentangling,
Abstract: Traditional face editing methods often require a number of sophisticated and
task specific algorithms to be applied one after the other --- a process that
is tedious, fragile, and computationally intensive. In this paper, we propose
an end-to-end generative adversarial network that infers a face-specific
disentangled representation of intrinsic face properties, including shape (i.e.
normals), albedo, and lighting, and an alpha matte. We show that this network
can be trained on "in-the-wild" images by incorporating an in-network
physically-based image formation module and appropriate loss functions. Our
disentangling latent representation allows for semantically relevant edits,
where one aspect of facial appearance can be manipulated while keeping
orthogonal properties fixed, and we demonstrate its use for a number of facial
editing applications. | [
1,
0,
0,
0,
0,
0
] |
Title: Dynamic transport in a quantum wire driven by spin-orbit interaction,
Abstract: We consider a gated one-dimensional (1D) quantum wire disturbed in a
contactless manner by an alternating electric field produced by a tip of a
scanning probe microscope. In this schematic 1D electrons are driven not by a
pulling electric field but rather by a non-stationary spin-orbit interaction
(SOI) created by the tip. We show that a charge current appears in the wire in
the presence of the Rashba SOI produced by the gate net charge and image
charges of 1D electrons induced on the gate (iSOI). The iSOI contributes to the
charge susceptibility by breaking the spin-charge separation between the
charge- and spin collective excitations, generated by the probe. The velocity
of the excitations is strongly renormalized by SOI, which opens a way to
fine-tune the charge and spin response of 1D electrons by changing the gate
potential. One of the modes softens upon increasing the gate potential to
enhance the current response as well as the power dissipated in the system. | [
0,
1,
0,
0,
0,
0
] |
Title: A Nonlinear Kernel Support Matrix Machine for Matrix Learning,
Abstract: In many problems of supervised tensor learning (STL), real world data such as
face images or MRI scans are naturally represented as matrices, which are also
called as second order tensors. Most existing classifiers based on tensor
representation, such as support tensor machine (STM) need to solve iteratively
which occupy much time and may suffer from local minima. In this paper, we
present a kernel support matrix machine (KSMM) to perform supervised learning
when data are represented as matrices. KSMM is a general framework for the
construction of matrix-based hyperplane to exploit structural information. We
analyze a unifying optimization problem for which we propose an asymptotically
convergent algorithm. Theoretical analysis for the generalization bounds is
derived based on Rademacher complexity with respect to a probability
distribution. We demonstrate the merits of the proposed method by exhaustive
experiments on both simulation study and a number of real-word datasets from a
variety of application domains. | [
1,
0,
0,
1,
0,
0
] |
Title: Performance Evaluation of Channel Decoding With Deep Neural Networks,
Abstract: With the demand of high data rate and low latency in fifth generation (5G),
deep neural network decoder (NND) has become a promising candidate due to its
capability of one-shot decoding and parallel computing. In this paper, three
types of NND, i.e., multi-layer perceptron (MLP), convolution neural network
(CNN) and recurrent neural network (RNN), are proposed with the same parameter
magnitude. The performance of these deep neural networks are evaluated through
extensive simulation. Numerical results show that RNN has the best decoding
performance, yet at the price of the highest computational overhead. Moreover,
we find there exists a saturation length for each type of neural network, which
is caused by their restricted learning abilities. | [
1,
0,
0,
0,
0,
0
] |
Title: On the Tropical Discs Counting on Elliptic K3 Surfaces with General Singular Fibres,
Abstract: Using Lagrangian Floer theory, we study the tropical geometry of K3 surfaces
with general singular fibres. In particular, we give the local models for the
type $I_n$, $II$, $III$ and $IV$ singular fibres in the Kodaira's
classification and generalize the correspondence theorem between open
Gromov-Witten invariants/tropical discs counting to these cases. | [
0,
0,
1,
0,
0,
0
] |
Title: Optimal Caching and Scheduling for Cache-enabled D2D Communications,
Abstract: To maximize offloading gain of cache-enabled device-to-device (D2D)
communications, content placement and delivery should be jointly designed. In
this letter, we jointly optimize caching and scheduling policies to maximize
successful offloading probability, defined as the probability that a user can
obtain desired file in local cache or via D2D link with data rate larger than a
given threshold. We obtain the optimal scheduling factor for a random
scheduling policy that can control interference in a distributed manner, and a
low complexity solution to compute caching distribution. We show that the
offloading gain can be remarkably improved by the joint optimization. | [
1,
0,
0,
0,
0,
0
] |
Title: Phytoplankton Hotspot Prediction With an Unsupervised Spatial Community Model,
Abstract: Many interesting natural phenomena are sparsely distributed and discrete.
Locating the hotspots of such sparsely distributed phenomena is often difficult
because their density gradient is likely to be very noisy. We present a novel
approach to this search problem, where we model the co-occurrence relations
between a robot's observations with a Bayesian nonparametric topic model. This
approach makes it possible to produce a robust estimate of the spatial
distribution of the target, even in the absence of direct target observations.
We apply the proposed approach to the problem of finding the spatial locations
of the hotspots of a specific phytoplankton taxon in the ocean. We use
classified image data from Imaging FlowCytobot (IFCB), which automatically
measures individual microscopic cells and colonies of cells. Given these
individual taxon-specific observations, we learn a phytoplankton community
model that characterizes the co-occurrence relations between taxa. We present
experiments with simulated robot missions drawn from real observation data
collected during a research cruise traversing the US Atlantic coast. Our
results show that the proposed approach outperforms nearest neighbor and
k-means based methods for predicting the spatial distribution of hotspots from
in-situ observations. | [
1,
0,
0,
1,
0,
0
] |
Title: First constraints on fuzzy dark matter from Lyman-$α$ forest data and hydrodynamical simulations,
Abstract: We present constraints on the masses of extremely light bosons dubbed fuzzy
dark matter from Lyman-$\alpha$ forest data. Extremely light bosons with a De
Broglie wavelength of $\sim 1$ kpc have been suggested as dark matter
candidates that may resolve some of the current small scale problems of the
cold dark matter model. For the first time we use hydrodynamical simulations to
model the Lyman-$\alpha$ flux power spectrum in these models and compare with
the observed flux power spectrum from two different data sets: the XQ-100 and
HIRES/MIKE quasar spectra samples. After marginalization over nuisance and
physical parameters and with conservative assumptions for the thermal history
of the IGM that allow for jumps in the temperature of up to $5000\rm\,K$,
XQ-100 provides a lower limit of 7.1$\times 10^{-22}$ eV, HIRES/MIKE returns a
stronger limit of 14.3$\times 10^{-22}$ eV, while the combination of both data
sets results in a limit of 20 $\times 10^{-22}$ eV (2$\sigma$ C.L.). The limits
for the analysis of the combined data sets increases to 37.5$\times 10^{-22}$
eV (2$\sigma$ C.L.) when a smoother thermal history is assumed where the
temperature of the IGM evolves as a power-law in redshift. Light boson masses
in the range $1-10 \times10^{-22}$ eV are ruled out at high significance by our
analysis, casting strong doubts that FDM helps solve the "small scale crisis"
of the cold dark matter models. | [
0,
1,
0,
0,
0,
0
] |
Title: On functionals involving the torsional rigidity related to some classes of nonlinear operators,
Abstract: In this paper we study optimal estimates for two functionals involving the
anisotropic $p$-torsional rigidity $T_p(\Omega)$, $1<p<+\infty$. More
precisely, we study $\Phi(\Omega)=\frac{T_p(\Omega)}{|\Omega|M(\Omega)}$ and
$\Psi(\Omega)=\frac{T_p(\Omega)}{|\Omega|[R_{F}(\Omega)]^{\frac{p}{p-1}}}$,
where $M(\Omega)$ is the maximum of the torsion function $u_{\Omega}$ and
$R_F(\Omega)$ is the anisotropic inradius of $\Omega$. | [
0,
0,
1,
0,
0,
0
] |
Title: Pressure-induced magnetic collapse and metallization of $\mathrm{TlF}{\mathrm{e}}_{1.6}\mathrm{S}{\mathrm{e}}_{2}$,
Abstract: The crystal structure, magnetic ordering, and electrical resistivity of
TlFe1.6Se2 were studied at high pressures. Below ~7 GPa, TlFe1.6Se2 is an
antiferromagnetically ordered semiconductor with a ThCr2Si2-type structure. The
insulator-to-metal transformation observed at a pressure of ~ 7 GPa is
accompanied by a loss of magnetic ordering and an isostructural phase
transition. In the pressure range ~ 7.5 - 11 GPa a remarkable downturn in
resistivity, which resembles a superconducting transition, is observed below 15
K. We discuss this feature as the possible onset of superconductivity
originating from a phase separation in a small fraction of the sample in the
vicinity of the magnetic transition. | [
0,
1,
0,
0,
0,
0
] |
Title: Real-Time Adaptive Image Compression,
Abstract: We present a machine learning-based approach to lossy image compression which
outperforms all existing codecs, while running in real-time.
Our algorithm typically produces files 2.5 times smaller than JPEG and JPEG
2000, 2 times smaller than WebP, and 1.7 times smaller than BPG on datasets of
generic images across all quality levels. At the same time, our codec is
designed to be lightweight and deployable: for example, it can encode or decode
the Kodak dataset in around 10ms per image on GPU.
Our architecture is an autoencoder featuring pyramidal analysis, an adaptive
coding module, and regularization of the expected codelength. We also
supplement our approach with adversarial training specialized towards use in a
compression setting: this enables us to produce visually pleasing
reconstructions for very low bitrates. | [
1,
0,
0,
1,
0,
0
] |
Title: Probabilistic Graphical Modeling approach to dynamic PET direct parametric map estimation and image reconstruction,
Abstract: In the context of dynamic emission tomography, the conventional processing
pipeline consists of independent image reconstruction of single time frames,
followed by the application of a suitable kinetic model to time activity curves
(TACs) at the voxel or region-of-interest level. The relatively new field of 4D
PET direct reconstruction, by contrast, seeks to move beyond this scheme and
incorporate information from multiple time frames within the reconstruction
task. Existing 4D direct models are based on a deterministic description of
voxels' TACs, captured by the chosen kinetic model, considering the photon
counting process the only source of uncertainty. In this work, we introduce a
new probabilistic modeling strategy based on the key assumption that activity
time course would be subject to uncertainty even if the parameters of the
underlying dynamic process were known. This leads to a hierarchical Bayesian
model, which we formulate using the formalism of Probabilistic Graphical
Modeling (PGM). The inference of the joint probability density function arising
from PGM is addressed using a new gradient-based iterative algorithm, which
presents several advantages compared to existing direct methods: it is flexible
to an arbitrary choice of linear and nonlinear kinetic model; it enables the
inclusion of arbitrary (sub)differentiable priors for parametric maps; it is
simpler to implement and suitable to integration in computing frameworks for
machine learning. Computer simulations and an application to real patient scan
showed how the proposed approach allows us to weight the importance of the
kinetic model, providing a bridge between indirect and deterministic direct
methods. | [
0,
0,
0,
1,
0,
0
] |
Title: On some mellin transforms for the Riemann zeta function in the critical strip,
Abstract: We offer two new Mellin transform evaluations for the Riemann zeta function
in the region $0<\Re(s)<1.$ Some discussion is offered in the way of evaluating
some further Fourier integrals involving the Riemann xi function. | [
0,
0,
1,
0,
0,
0
] |
Title: The existence and global exponential stability of almost periodic solutions for neutral type CNNs on time scales,
Abstract: In this paper, a class of neutral type competitive neural networks with mixed
time-varying delays and leakage delays on time scales is proposed. Based on the
exponential dichotomy of linear dynamic equations on time scales, Banach's
fixed point theorem and the theory of calculus on time scales, some sufficient
conditions that are independent of the backwards graininess function of the
time scale are obtained for the existence and global exponential stability of
almost periodic solutions for this class of neural networks. The obtained
results are completely new and indicate that both the continuous time and the
discrete time cases of the networks share the same dynamical behavior. Finally,
an examples is given to show the effectiveness of the obtained results. | [
0,
0,
1,
0,
0,
0
] |
Title: Modelling the evaporation of nanoparticle suspensions from heterogeneous surfaces,
Abstract: We present a Monte Carlo (MC) grid-based model for the drying of drops of a
nanoparticle suspension upon a heterogeneous surface. The model consists of a
generalised lattice-gas in which the interaction parameters in the Hamiltonian
can be varied to model different properties of the materials involved. We show
how to choose correctly the interactions, to minimise the effects of the
underlying grid so that hemispherical droplets form. We also include the
effects of surface roughness to examine the effects of contact-line pinning on
the dynamics. When there is a `lid' above the system, which prevents
evaporation, equilibrium drops form on the surface, which we use to determine
the contact angle and how it varies as the parameters of the model are changed.
This enables us to relate the interaction parameters to the materials used in
applications. The model has also been applied to drying on heterogeneous
surfaces, in particular to the case where the suspension is deposited on a
surface consisting of a pair of hydrophilic conducting metal surfaces that are
either side of a band of hydrophobic insulating polymer. This situation occurs
when using inkjet printing to manufacture electrical connections between the
metallic parts of the surface. The process is not always without problems,
since the liquid can dewet from the hydrophobic part of the surface, breaking
the bridge before the drying process is complete. The MC model reproduces the
observed dewetting, allowing the parameters to be varied so that the conditions
for the best connection can be established. We show that if the hydrophobic
portion of the surface is located at a step below the height of the
neighbouring metal, the chance of dewetting of the liquid during the drying
process is significantly reduced. | [
0,
1,
0,
0,
0,
0
] |
Title: Image retargeting via Beltrami representation,
Abstract: Image retargeting aims to resize an image to one with a prescribed aspect
ratio. Simple scaling inevitably introduces unnatural geometric distortions on
the important content of the image. In this paper, we propose a simple and yet
effective method to resize an image, which preserves the geometry of the
important content, using the Beltrami representation. Our algorithm allows
users to interactively label content regions as well as line structures. Image
resizing can then be achieved by warping the image by an orientation-preserving
bijective warping map with controlled distortion. The warping map is
represented by its Beltrami representation, which captures the local geometric
distortion of the map. By carefully prescribing the values of the Beltrami
representation, images with different complexity can be effectively resized.
Our method does not require solving any optimization problems and tuning
parameters throughout the process. This results in a simple and efficient
algorithm to solve the image retargeting problem. Extensive experiments have
been carried out, which demonstrate the efficacy of our proposed method. | [
1,
0,
0,
0,
0,
0
] |
Title: Coupling Load-Following Control with OPF,
Abstract: In this paper, the optimal power flow (OPF) problem is augmented to account
for the costs associated with the load-following control of a power network.
Load-following control costs are expressed through the linear quadratic
regulator (LQR). The power network is described by a set of nonlinear
differential algebraic equations (DAEs). By linearizing the DAEs around a known
equilibrium, a linearized OPF that accounts for steady-state operational
constraints is formulated first. This linearized OPF is then augmented by a set
of linear matrix inequalities that are algebraically equivalent to the
implementation of an LQR controller. The resulting formulation, termed LQR-OPF,
is a semidefinite program which furnishes optimal steady-state setpoints and an
optimal feedback law to steer the system to the new steady state with minimum
load-following control costs. Numerical tests demonstrate that the setpoints
computed by LQR-OPF result in lower overall costs and frequency deviations
compared to the setpoints of a scheme where OPF and load-following control are
considered separately. | [
1,
0,
1,
0,
0,
0
] |
Title: On relation between discrete Frenet frames and the bi-Hamiltonian structure of the discrete nonlinear Schrödinger equation,
Abstract: The discrete Frenet equation entails a local framing of a discrete, piecewise
linear polygonal chain in terms of its bond and torsion angles. In particular,
the tangent vector of a segment is akin the classical O(3) spin variable. Thus
there is a relation to the lattice Heisenberg model, that can be used to model
physical properties of the chain. On the other hand, the Heisenberg model is
closely related to the discrete nonlinear Schrödinger (DNLS) equation. Here
we apply these interrelations to develop a perspective on discrete chains
dynamics: We employ the properties of a discrete chain in terms of a spinorial
representation of the discrete Frenet equation, to introduce a bi-hamiltonian
structure for the discrete nonlinear Schrödinger equation (DNLSE), which we
then use to produce integrable chain dynamics. | [
0,
1,
1,
0,
0,
0
] |
Title: A stronger version of a question proposed by K. Mahler,
Abstract: In 1902, P. Stäckel proved the existence of a transcendental function
$f(z)$, analytic in a neighbourhood of the origin, and with the property that
both $f(z)$ and its inverse function assume, in this neighbourhood, algebraic
values at all algebraic points. Based on this result, in 1976, K. Mahler raised
the question of the existence of such functions which are analytic in
$\mathbb{C}$. Recently, the authors answered positively this question. In this
paper, we prove a much stronger version of this result by considering other
subsets of $\mathbb{C}$. | [
0,
0,
1,
0,
0,
0
] |
Title: Distinguishing differential susceptibility, diathesis-stress and vantage sensitivity: beyond the single gene and environment model,
Abstract: Currently, two main approaches exist to distinguish differential
susceptibility from diathesis-stress and vantage sensitivity in genotype x
environment interaction (GxE) research: Regions of significance (RoS) and
competitive-confirmatory approaches. Each is limited by their
single-gene/single-environment foci given that most phenotypes are the product
of multiple interacting genetic and environmental factors. We thus addressed
these two concerns in a recently developed R package (LEGIT) for constructing
GxE interaction models with latent genetic and environmental scores using
alternating optimization. Herein we test, by means of computer simulation,
diverse GxE models in the context of both single and multiple genes and
environments. Results indicate that the RoS and competitive-confirmatory
approaches were highly accurate when the sample size was large, whereas the
latter performed better in small samples and for small effect sizes. The
confirmatory approach generally had good accuracy (a) when effect size was
moderate and N >= 500 and (b) when effect size was large and N >= 250, whereas
RoS performed poorly. Computational tools to determine the type of GxE of
multiple genes and environments are provided as extensions in our LEGIT R
package. | [
0,
0,
0,
1,
0,
0
] |
Title: A weak type estimate for rough singular integrals,
Abstract: We obtain a weak type $(1,1)$ estimate for a maximal operator associated with
the classical rough homogeneous singular integrals $T_{\Omega}$. In particular,
this provides a different approach to a sparse domination for $T_{\Omega}$
obtained recently by Conde-Alonso, Culiuc, Di Plinio and Ou. | [
0,
0,
1,
0,
0,
0
] |
Title: A unified theory for exact stochastic modelling of univariate and multivariate processes with continuous, mixed type, or discrete marginal distributions and any correlation structure,
Abstract: Hydroclimatic processes are characterized by heterogeneous spatiotemporal
correlation structures and marginal distributions that can be continuous,
mixed-type, discrete or even binary. Simulating exactly such processes can
greatly improve hydrological analysis and design. Yet this challenging task is
accomplished often by ad hoc and approximate methodologies that are devised for
specific variables and purposes. In this study, a single framework is proposed
allowing the exact simulation of processes with any marginal and any
correlation structure. We unify, extent, and improve of a general-purpose
modelling strategy based on the assumption that any process can emerge by
transforming a parent Gaussian process with a specific correlation structure. A
novel mathematical representation of the parent-Gaussian scheme provides a
consistent and fully general description that supersedes previous specific
parameterizations, resulting in a simple, fast and efficient simulation
procedure for every spatiotemporal process. In particular, introducing a simple
but flexible procedure we obtain a parametric expression of the correlation
transformation function, allowing to assess the correlation structure of the
parent-Gaussian process that yields the prescribed correlation of the target
process after marginal back transformation. The same framework is also
applicable for cyclostationary and multivariate modelling. The simulation of a
variety of hydroclimatic variables with very different correlation structures
and marginals, such as precipitation, stream flow, wind speed, humidity,
extreme events per year, etc., as well as a multivariate application,
highlights the flexibility, advantages, and complete generality of the proposed
methodology. | [
0,
0,
1,
1,
0,
0
] |
Title: The Guiding Influence of Stanley Mandelstam, from S-Matrix Theory to String Theory,
Abstract: The guiding influence of some of Stanley Mandelstam's key contributions to
the development of theoretical high energy physics is discussed, from the
motivation for the study of the analytic properties of the scattering matrix
through to dual resonance models and their evolution into string theory. | [
0,
1,
0,
0,
0,
0
] |
Title: Investigating prescriptions for artificial resistivity in smoothed particle magnetohydrodynamics,
Abstract: In numerical simulations, artificial terms are applied to the evolution
equations for stability. To prove their validity, these terms are thoroughly
tested in test problems where the results are well known. However, they are
seldom tested in production-quality simulations at high resolution where they
interact with a plethora of physical and numerical algorithms. We test three
artificial resistivities in both the Orszag-Tang vortex and in a star formation
simulation. From the Orszag-Tang vortex, the Price et. al. (2017) artificial
resistivity is the least dissipative thus captures the density and magnetic
features; in the star formation algorithm, each artificial resistivity
algorithm interacts differently with the sink particle to produce various
results, including gas bubbles, dense discs, and migrating sink particles. The
star formation simulations suggest that it is important to rely upon physical
resistivity rather than artificial resistivity for convergence. | [
0,
1,
0,
0,
0,
0
] |
Title: A Planning and Control Framework for Humanoid Systems: Robust, Optimal, and Real-time Performance,
Abstract: Humanoid robots are increasingly demanded to operate in interactive and
human-surrounded environments while achieving sophisticated locomotion and
manipulation tasks. To accomplish these tasks, roboticists unremittingly seek
for advanced methods that generate whole-body coordination behaviors and
meanwhile fulfill various planning and control objectives. Undoubtedly, these
goals pose fundamental challenges to the robotics and control community. To
take an incremental step towards reducing the performance gap between
theoretical foundations and real implementations, we present a planning and
control framework for the humanoid, especially legged robots, for achieving
high performance and generating agile motions. A particular concentration is on
the robust, optimal and real-time performance. This framework constitutes three
hierarchical layers: First, we present a robust optimal phase-space planning
framework for dynamic legged locomotion over rough terrain. This framework is a
hybrid motion planner incorporating a series of pivotal components. Second, we
take a step toward formally synthesizing high-level reactive planners for
whole-body locomotion in constrained environments. We formulate a two-player
temporal logic game between the contact planner and its possibly-adversarial
environment. Third, we propose a distributed control architecture for the
latency-prone humanoid robotic systems. A central experimental phenomenon is
observed that the stability of high impedance distributed controllers is highly
sensitive to damping feedback delay but much less to stiffness feedback delay.
We pursue a detailed analysis of the distributed controllers where damping
feedback effort is executed in proximity to the control plant, and stiffness
feedback effort is implemented in a latency-prone centralized control process. | [
1,
0,
1,
0,
0,
0
] |
Title: Attention-based Wav2Text with Feature Transfer Learning,
Abstract: Conventional automatic speech recognition (ASR) typically performs
multi-level pattern recognition tasks that map the acoustic speech waveform
into a hierarchy of speech units. But, it is widely known that information loss
in the earlier stage can propagate through the later stages. After the
resurgence of deep learning, interest has emerged in the possibility of
developing a purely end-to-end ASR system from the raw waveform to the
transcription without any predefined alignments and hand-engineered models.
However, the successful attempts in end-to-end architecture still used
spectral-based features, while the successful attempts in using raw waveform
were still based on the hybrid deep neural network - Hidden Markov model
(DNN-HMM) framework. In this paper, we construct the first end-to-end
attention-based encoder-decoder model to process directly from raw speech
waveform to the text transcription. We called the model as "Attention-based
Wav2Text". To assist the training process of the end-to-end model, we propose
to utilize a feature transfer learning. Experimental results also reveal that
the proposed Attention-based Wav2Text model directly with raw waveform could
achieve a better result in comparison with the attentional encoder-decoder
model trained on standard front-end filterbank features. | [
1,
0,
0,
0,
0,
0
] |
Title: Quintessential Inflation with $α$-attractors,
Abstract: A novel approach to quintessential inflation model building is studied,
within the framework of $\alpha$-attractors, motivated by supergravity
theories. Inflationary observables are in excellent agreement with the latest
CMB observations, while quintessence explains the dark energy observations
without any fine-tuning. The model is kept intentionally minimal, avoiding the
introduction of many degrees of freedom, couplings and mass scales. In stark
contrast to $\Lambda$CDM, for natural values of the parameters, the model
attains transient accelerated expansion, which avoids the future horizon
problem, while it maintains the field displacement mildly sub-Planckian such
that the flatness of the quintessential tail is not lifted by radiative
corrections and violations of the equivalence principle (fifth force) are under
control. In particular, the required value of the cosmological constant is near
the eletroweak scale. Attention is paid to the reheating of the Universe, which
avoids gravitino overproduction and respects nucleosynthesis constraints.
Kination is treated in a model independent way. A spike in gravitational waves,
due to kination, is found not to disturb nucleosynthesis as well. | [
0,
1,
0,
0,
0,
0
] |
Title: Enhancing Blood Glucose Prediction with Meal Absorption and Physical Exercise Information,
Abstract: Objective: Numerous glucose prediction algorithm have been proposed to
empower type 1 diabetes (T1D) management. Most of these algorithms only account
for input such as glucose, insulin and carbohydrate, which limits their
performance. Here, we present a novel glucose prediction algorithm which, in
addition to standard inputs, accounts for meal absorption and physical exercise
information to enhance prediction accuracy. Methods: a compartmental model of
glucose-insulin dynamics combined with a deconvolution technique for state
estimation is employed for glucose prediction. In silico data corresponding
from the 10 adult subjects of UVa-Padova simulator, and clinical data from 10
adults with T1D were used. Finally, a comparison against a validated glucose
prediction algorithm based on a latent variable with exogenous input (LVX)
model is provided. Results: For a prediction horizon of 60 minutes, accounting
for meal absorption and physical exercise improved glucose forecasting
accuracy. In particular, root mean square error (mg/dL) went from 26.68 to
23.89, p<0.001 (in silico data); and from 37.02 to 35.96, p<0.001 (clinical
data - only meal information). Such improvement in accuracy was translated into
significant improvements on hypoglycaemia and hyperglycaemia prediction.
Finally, the performance of the proposed algorithm is statistically superior to
that of the LVX algorithm (26.68 vs. 32.80, p<0.001 (in silico data); 37.02 vs.
49.17, p<0.01 (clinical data). Conclusion: Taking into account meal absorption
and physical exercise information improves glucose prediction accuracy. | [
1,
0,
0,
0,
1,
0
] |
Title: The fraction of cool-core clusters in X-ray vs. SZ samples using Chandra observations,
Abstract: We derive and compare the fractions of cool-core clusters in the {\em Planck}
Early Sunyaev-Zel'dovich sample of 164 clusters with $z \leq 0.35$ and in a
flux-limited X-ray sample of 100 clusters with $z \leq 0.30$, using {\em
Chandra} observations. We use four metrics to identify cool-core clusters: 1)
the concentration parameter: the ratio of the integrated emissivity profile
within 0.15 $r_{500}$ to that within $r_{500}$, and 2) the ratio of the
integrated emissivity profile within 40 kpc to that within 400 kpc, 3) the
cuspiness of the gas density profile: the negative of the logarithmic
derivative of the gas density with respect to the radius, measured at 0.04
$r_{500}$, and 4) the central gas density, measured at 0.01 $r_{500}$. We find
that the sample of X-ray selected clusters, as characterized by each of these
metrics, contains a significantly larger fraction of cool-core clusters
compared to the sample of SZ selected clusters (44$\pm$7\% vs. 28$\pm$4\% using
the concentration parameter in the 0.15--1.0 $r_{500}$ range, 61$\pm$8\% vs.
36$\pm$5\% using the concentration parameter in the 40--400 kpc range,
64$\pm$8\% vs. 38$\pm$5\% using the cuspiness, and 53$\pm$7\% vs. 39$\pm$5\%
using the central gas density). Qualitatively, cool-core clusters are more
X-ray luminous at fixed mass. Hence, our X-ray flux-limited sample, compared to
the approximately mass-limited SZ sample, is over-represented with cool-core
clusters. We describe a simple quantitative model that uses the excess
luminosity of cool-core clusters compared to non-cool-core clusters at fixed
mass to successfully predict the observed fraction of cool-core clusters in
X-ray selected samples. | [
0,
1,
0,
0,
0,
0
] |
Title: Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers,
Abstract: Online Multi-Object Tracking (MOT) from videos is a challenging computer
vision task which has been extensively studied for decades. Most of the
existing MOT algorithms are based on the Tracking-by-Detection (TBD) paradigm
combined with popular machine learning approaches which largely reduce the
human effort to tune algorithm parameters. However, the commonly used
supervised learning approaches require the labeled data (e.g., bounding boxes),
which is expensive for videos. Also, the TBD framework is usually suboptimal
since it is not end-to-end, i.e., it considers the task as detection and
tracking, but not jointly. To achieve both label-free and end-to-end learning
of MOT, we propose a Tracking-by-Animation framework, where a differentiable
neural model first tracks objects from input frames and then animates these
objects into reconstructed frames. Learning is then driven by the
reconstruction error through backpropagation. We further propose a
Reprioritized Attentive Tracking to improve the robustness of data association.
Experiments conducted on both synthetic and real video datasets show the
potential of the proposed model. | [
0,
0,
0,
1,
0,
0
] |
Title: Tunable coupling-induced resonance splitting in self-coupled Silicon ring cavity with robust spectral characteristics,
Abstract: We propose and demonstrate a self-coupled microring resonator for resonance
splitting by mutual mode coupling of cavity mode and counter-propagating mode
in Silicon-on-Insulator platform The resonator is constructed with a
self-coupling region that can excite counter-propagating mode. We
experimentally study the effect of self-coupling on the resonance splitting,
resonance extinction, and quality-factor evolution and stability. Based on the
coupling, we achieve 72% of FSR splitting for a cavity with FSR 2.1 nm with <
5% variation in the cavity quality factor. The self-coupled resonance splitting
shows highly robust spectral characteristic that can be exploited for sensing
and optical signal processing. | [
0,
1,
0,
0,
0,
0
] |
Title: 2D reductions of the equation $u_{yy} = u_{tx} + u_yu_{xx} - u_xu_{xy}$ and their nonlocal symmetries,
Abstract: We consider the 3D equation $u_{yy} = u_{tx} + u_yu_{xx} - u_xu_{xy}$ and its
2D reductions: (1) $u_{yy} = (u_y+y)u_{xx}-u_xu_{xy}-2$ (which is equivalent to
the Gibbons-Tsarev equation) and (2) $u_{yy} = (u_y+2x)u_{xx} + (y-u_x)u_{xy}
-u_x$. Using reduction of the known Lax pair for the 3D equation, we describe
nonlocal symmetries of~(1) and~(2) and show that the Lie algebras of these
symmetries are isomorphic to the Witt algebra. | [
0,
1,
0,
0,
0,
0
] |
Title: An improved Belief Propagation algorithm finds many Bethe states in the random field Ising model on random graphs,
Abstract: We first present an empirical study of the Belief Propagation (BP) algorithm,
when run on the random field Ising model defined on random regular graphs in
the zero temperature limit. We introduce the notion of maximal solutions for
the BP equations and we use them to fix a fraction of spins in their ground
state configuration. At the phase transition point the fraction of
unconstrained spins percolates and their number diverges with the system size.
This in turn makes the associated optimization problem highly non trivial in
the critical region. Using the bounds on the BP messages provided by the
maximal solutions we design a new and very easy to implement BP scheme which is
able to output a large number of stable fixed points. On one side this new
algorithm is able to provide the minimum energy configuration with high
probability in a competitive time. On the other side we found that the number
of fixed points of the BP algorithm grows with the system size in the critical
region. This unexpected feature poses new relevant questions on the physics of
this class of models. | [
1,
1,
0,
0,
0,
0
] |
Title: Large-scale diversity estimation through surname origin inference,
Abstract: The study of surnames as both linguistic and geographical markers of the past
has proven valuable in several research fields spanning from biology and
genetics to demography and social mobility. This article builds upon the
existing literature to conceive and develop a surname origin classifier based
on a data-driven typology. This enables us to explore a methodology to describe
large-scale estimates of the relative diversity of social groups, especially
when such data is scarcely available. We subsequently analyze the
representativeness of surname origins for 15 socio-professional groups in
France. | [
0,
0,
0,
1,
0,
0
] |
Title: Attentive Convolutional Neural Network based Speech Emotion Recognition: A Study on the Impact of Input Features, Signal Length, and Acted Speech,
Abstract: Speech emotion recognition is an important and challenging task in the realm
of human-computer interaction. Prior work proposed a variety of models and
feature sets for training a system. In this work, we conduct extensive
experiments using an attentive convolutional neural network with multi-view
learning objective function. We compare system performance using different
lengths of the input signal, different types of acoustic features and different
types of emotion speech (improvised/scripted). Our experimental results on the
Interactive Emotional Motion Capture (IEMOCAP) database reveal that the
recognition performance strongly depends on the type of speech data independent
of the choice of input features. Furthermore, we achieved state-of-the-art
results on the improvised speech data of IEMOCAP. | [
1,
0,
0,
0,
0,
0
] |
Title: Galaxies as High-Resolution Telescopes,
Abstract: Recent observations show a population of active galaxies with milliarcseconds
offsets between optical and radio emission. Such offsets can be an indication
of extreme phenomena associated with supermassive black holes including
relativistic jets, binary supermassive black holes, or even recoiling
supermassive black holes. However, the multi-wavelength structure of active
galaxies at a few milliarcseconds cannot be fathomed with direct observations.
We propose using strong gravitational lensing to elucidate the multi-wavelength
structure of sources. When sources are located close to the caustic of lensing
galaxy, even small offset in the position of the sources results in a drastic
difference in the position and magnification of mirage images. We show that the
angular offset in the position of the sources can be amplified more than 50
times in the observed position of mirage images. We find that at least 8% of
the observed gravitationally lensed quasars will be in the caustic
configuration. The synergy between SKA and Euclid will provide an ideal set of
observations for thousands of gravitationally lensed sources in the caustic
configuration, which will allow us to elucidate the multi-wavelength structure
for a large ensemble of sources, and study the physical origin of radio
emissions, their connection to supermassive black holes, and their cosmic
evolution. | [
0,
1,
0,
0,
0,
0
] |
Title: Continuous CM-regularity of semihomogeneous vector bundles,
Abstract: We show that if $X$ is an abelian variety of dimension $g \geq 1$ and
${\mathcal E}$ is an M-regular coherent sheaf on $X$, the Castelnuovo-Mumford
regularity of ${\mathcal E}$ with respect to an ample and globally generated
line bundle ${\mathcal O}(1)$ on $X$ is at most $g$, and that equality is
obtained when ${\mathcal E}^{\vee}(1)$ is continuously globally generated. As
an application, we give a numerical characterization of ample semihomogeneous
vector bundles for which this bound is attained. | [
0,
0,
1,
0,
0,
0
] |
Title: Obtaining the Current-Flux Relations of the Saturated PMSM by Signal Injection,
Abstract: This paper proposes a method based on signal injection to obtain the
saturated current-flux relations of a PMSM from locked-rotor experiments. With
respect to the classical method based on time integration, it has the main
advantage of being completely independent of the stator resistance; moreover,
it is less sensitive to voltage biases due to the power inverter, as the
injected signal may be fairly large. | [
1,
0,
0,
0,
0,
0
] |
Title: Approximation properties of (p,q)-Meyer-Konig-Zeller Durrmeyer operators,
Abstract: In this paper, we introduce Durrmeyer type modification of Meyer-Konig-Zeller
operators based on (p,q)-integers. Rate of convergence of these operators are
explored with the help of Korovkin type theorems. We establish some direct
results for proposed operators. We also obtain statistical approximation
properties of operators. In last section, we show rate of convergence of
(p,q)-Meyer-Konig-Zeller Durrmeyer operators for some functions by means of
Matlab programming. | [
0,
0,
1,
0,
0,
0
] |
Title: Deep Graph Infomax,
Abstract: We present Deep Graph Infomax (DGI), a general approach for learning node
representations within graph-structured data in an unsupervised manner. DGI
relies on maximizing mutual information between patch representations and
corresponding high-level summaries of graphs---both derived using established
graph convolutional network architectures. The learnt patch representations
summarize subgraphs centered around nodes of interest, and can thus be reused
for downstream node-wise learning tasks. In contrast to most prior approaches
to unsupervised learning with GCNs, DGI does not rely on random walk
objectives, and is readily applicable to both transductive and inductive
learning setups. We demonstrate competitive performance on a variety of node
classification benchmarks, which at times even exceeds the performance of
supervised learning. | [
1,
0,
0,
1,
0,
0
] |
Title: Does a growing static length scale control the glass transition?,
Abstract: Several theories of the glass transition propose that the structural
relaxation time {\tau}{\alpha} is controlled by a growing static length scale
{\xi} that is determined by the free energy landscape but not by the local
dynamical rules governing its exploration. We argue, based on recent
simulations using particle-radius-swap dynamics, that only a modest factor in
the increase in {\tau}{\alpha} on approach to the glass transition may stem
from the growth of a static length, with a vastly larger contribution
attributable instead to a slowdown of local dynamics. This reinforces arguments
that we base on the observed strong coupling of particle diffusion and density
fluctuations in real glasses | [
0,
1,
0,
0,
0,
0
] |
Title: A Faster Implementation of Online Run-Length Burrows-Wheeler Transform,
Abstract: Run-length encoding Burrows-Wheeler Transformed strings, resulting in
Run-Length BWT (RLBWT), is a powerful tool for processing highly repetitive
strings. We propose a new algorithm for online RLBWT working in run-compressed
space, which runs in $O(n\lg r)$ time and $O(r\lg n)$ bits of space, where $n$
is the length of input string $S$ received so far and $r$ is the number of runs
in the BWT of the reversed $S$. We improve the state-of-the-art algorithm for
online RLBWT in terms of empirical construction time. Adopting the dynamic list
for maintaining a total order, we can replace rank queries in a dynamic wavelet
tree on a run-length compressed string by the direct comparison of labels in a
dynamic list. The empirical result for various benchmarks show the efficiency
of our algorithm, especially for highly repetitive strings. | [
1,
0,
0,
0,
0,
0
] |
Title: Age-at-harvest models as monitoring and harvest management tools for Wisconsin carnivores,
Abstract: Quantifying and estimating wildlife population sizes is a foundation of
wildlife management. However, many carnivore species are cryptic, leading to
innate difficulties in estimating their populations. We evaluated the potential
for using more rigorous statistical models to estimate the populations of black
bears (Ursus americanus) in Wisconisin, and their applicability to other
furbearers such as bobcats (Lynx rufus). To estimate black bear populations, we
developed an AAH state-space model in a Bayesian framework based on Norton
(2015) that can account for variation in harvest and population demographics
over time. Our state-space model created an accurate estimate of the black bear
population in Wisconsin based on age-at-harvest data and improves on previous
models by using little demographic data, no auxiliary data, and not being
sensitive to initial population size. The increased accuracy of the AAH
state-space models should allow for better management by being able to set
accurate quotas to ensure a sustainable harvest for the black bear population
in each zone. We also evaluated the demography and annual harvest data of
bobcats in Wisconsin to determine trends in harvest, method, and hunter
participation in Wisconsin. Each trait of harvested bobcats that we tested
(mass, male:female sex ratio, and age) changed over time, and because these
were interrelated, and we inferred that harvest selection for larger size
biased harvests in favor of a) larger, b) older, and c) male bobcats by hound
hunters. We also found an increase in the proportion of bobcats that were
harvested by hound hunting compared to trapping, and that hound hunters were
more likely to create taxidermy mounts than trappers. We also found that
decreasing available bobcat tags and increasing success have occurred over
time, and correlate with substantially increasing both hunter populations and
hunter interest. | [
0,
0,
0,
0,
1,
0
] |
Title: Development of Si-CMOS hybrid detectors towards electron tracking based Compton imaging in semiconductor detectors,
Abstract: Electron tracking based Compton imaging is a key technique to improve the
sensitivity of Compton cameras by measuring the initial direction of recoiled
electrons. To realize this technique in semiconductor Compton cameras, we
propose a new detector concept, Si-CMOS hybrid detector. It is a Si detector
bump-bonded to a CMOS readout integrated circuit to obtain electron trajectory
images. To acquire the energy and the event timing, signals from N-side are
also read out in this concept. By using an ASIC for the N-side readout, the
timing resolution of few us is achieved. In this paper, we present the results
of two prototypes with 20 um pitch pixels. The images of the recoiled electron
trajectories are obtained with them successfully. The energy resolutions (FWHM)
are 4.1 keV (CMOS) and 1.4 keV (N-side) at 59.5 keV. In addition, we confirmed
that the initial direction of the electron is determined using the
reconstruction algorithm based on the graph theory approach. These results show
that Si-CMOS hybrid detectors can be used for electron tracking based Compton
imaging. | [
0,
1,
0,
0,
0,
0
] |
Title: Sharp constant of an anisotropic Gagliardo-Nirenberg-type inequality and applications,
Abstract: In this paper we establish the best constant of an anisotropic
Gagliardo-Nirenberg-type inequality related to the
Benjamin-Ono-Zakharov-Kuznetsov equation. As an application of our results, we
prove the uniform bound of solutions for such a equation in the energy space. | [
0,
0,
1,
0,
0,
0
] |
Title: Treatment Effect Quantification for Time-to-event Endpoints -- Estimands, Analysis Strategies, and beyond,
Abstract: A draft addendum to ICH E9 has been released for public consultation in
August 2017. The addendum focuses on two topics particularly relevant for
randomized confirmatory clinical trials: estimands and sensitivity analyses.
The need to amend ICH E9 grew out of the realization of a lack of alignment
between the objectives of a clinical trial stated in the protocol and the
accompanying quantification of the "treatment effect" reported in a regulatory
submission. We embed time-to-event endpoints in the estimand framework, and
discuss how the four estimand attributes described in the addendum apply to
time-to-event endpoints. We point out that if the proportional hazards
assumption is not met, the estimand targeted by the most prevalent methods used
to analyze time-to-event endpoints, logrank test and Cox regression, depends on
the censoring distribution. We discuss for a large randomized clinical trial
how the analyses for the primary and secondary endpoints as well as the
sensitivity analyses actually performed in the trial can be seen in the context
of the addendum. To the best of our knowledge, this is the first attempt to do
so for a trial with a time-to-event endpoint. Questions that remain open with
the addendum for time-to-event endpoints and beyond are formulated, and
recommendations for planning of future trials are given. We hope that this will
provide a contribution to developing a common framework based on the final
version of the addendum that can be applied to design, protocols, statistical
analysis plans, and clinical study reports in the future. | [
0,
0,
0,
1,
0,
0
] |
Title: The concentration-mass relation of clusters of galaxies from the OmegaWINGS survey,
Abstract: The relation between a cosmological halo concentration and its mass (cMr) is
a powerful tool to constrain cosmological models of halo formation and
evolution. On the scale of galaxy clusters the cMr has so far been determined
mostly with X-ray and gravitational lensing data. The use of independent
techniques is helpful in assessing possible systematics. Here we provide one of
the few determinations of the cMr by the dynamical analysis of the
projected-phase-space distribution of cluster members. Based on the WINGS and
OmegaWINGS data sets, we used the Jeans analysis with the MAMPOSSt technique to
determine masses and concentrations for 49 nearby clusters, each of which has
~60 spectroscopic members or more within the virial region, after removal of
substructures. Our cMr is in statistical agreement with theoretical predictions
based on LambdaCDM cosmological simulations. Our cMr is different from most
previous observational determinations because of its flatter slope and lower
normalization. It is however in agreement with two recent cMr obtained using
the lensing technique on the CLASH and LoCuSS cluster data sets. In the future
we will extend our analysis to galaxy systems of lower mass and at higher
redshifts. | [
0,
1,
0,
0,
0,
0
] |
Title: On the Joint Distribution Of $\mathrm{Sel}_ϕ(E/\mathbb{Q})$ and $\mathrm{Sel}_{\hatϕ}(E^\prime/\mathbb{Q})$ in Quadratic Twist Families,
Abstract: If $E$ is an elliptic curve with a point of order two, then work of Klagsbrun
and Lemke Oliver shows that the distribution of
$\dim_{\mathbb{F}_2}\mathrm{Sel}_\phi(E^d/\mathbb{Q}) - \dim_{\mathbb{F}_2}
\mathrm{Sel}_{\hat\phi}(E^{\prime d}/\mathbb{Q})$ within the quadratic twist
family tends to the discrete normal distribution $\mathcal{N}(0,\frac{1}{2}
\log \log X)$ as $X \rightarrow \infty$.
We consider the distribution of $\mathrm{dim}_{\mathbb{F}_2}
\mathrm{Sel}_\phi(E^d/\mathbb{Q})$ within such a quadratic twist family when
$\dim_{\mathbb{F}_2} \mathrm{Sel}_\phi(E^d/\mathbb{Q}) - \dim_{\mathbb{F}_2}
\mathrm{Sel}_{\hat\phi}(E^{\prime d}/\mathbb{Q})$ has a fixed value $u$.
Specifically, we show that for every $r$, the limiting probability that
$\dim_{\mathbb{F}_2} \mathrm{Sel}_\phi(E^d/\mathbb{Q}) = r$ is given by an
explicit constant $\alpha_{r,u}$. The constants $\alpha_{r,u}$ are closely
related to the $u$-probabilities introduced in Cohen and Lenstra's work on the
distribution of class groups, and thus provide a connection between the
distribution of Selmer groups of elliptic curves and random abelian groups.
Our analysis of this problem has two steps. The first step uses algebraic and
combinatorial methods to directly relate the ranks of the Selmer groups in
question to the dimensions of the kernels of random $\mathbb{F}_2$-matrices.
This proves that the density of twists with a given $\phi$-Selmer rank $r$ is
given by $\alpha_{r,u}$ for an unusual notion of density. The second step of
the analysis utilizes techniques from analytic number theory to show that this
result implies the correct asymptotics in terms of the natural notion of
density. | [
0,
0,
1,
0,
0,
0
] |
Title: Deformation estimation of an elastic object by partial observation using a neural network,
Abstract: Deformation estimation of elastic object assuming an internal organ is
important for the computer navigation of surgery. The aim of this study is to
estimate the deformation of an entire three-dimensional elastic object using
displacement information of very few observation points. A learning approach
with a neural network was introduced to estimate the entire deformation of an
object. We applied our method to two elastic objects; a rectangular
parallelepiped model, and a human liver model reconstructed from computed
tomography data. The average estimation error for the human liver model was
0.041 mm when the object was deformed up to 66.4 mm, from only around 3 %
observations. These results indicate that the deformation of an entire elastic
object can be estimated with an acceptable level of error from limited
observations by applying a trained neural network to a new deformation. | [
1,
0,
0,
1,
0,
0
] |
Title: Comparative analysis of two discretizations of Ricci curvature for complex networks,
Abstract: We have performed an empirical comparison of two distinct notions of discrete
Ricci curvature for graphs or networks, namely, the Forman-Ricci curvature and
Ollivier-Ricci curvature. Importantly, these two discretizations of the Ricci
curvature were developed based on different properties of the classical smooth
notion, and thus, the two notions shed light on different aspects of network
structure and behavior. Nevertheless, our extensive computational analysis in a
wide range of both model and real-world networks shows that the two
discretizations of Ricci curvature are highly correlated in many networks.
Moreover, we show that if one considers the augmented Forman-Ricci curvature
which also accounts for the two-dimensional simplicial complexes arising in
graphs, the observed correlation between the two discretizations is even
higher, especially, in real networks. Besides the potential theoretical
implications of these observations, the close relationship between the two
discretizations has practical implications whereby Forman-Ricci curvature can
be employed in place of Ollivier-Ricci curvature for faster computation in
larger real-world networks whenever coarse analysis suffices. | [
1,
0,
1,
0,
0,
0
] |
Title: Noise-gating to clean astrophysical image data,
Abstract: I present a family of algorithms to reduce noise in astrophysical im- ages
and image sequences, preserving more information from the original data than is
retained by conventional techniques. The family uses locally adaptive filters
("noise gates") in the Fourier domain, to separate coherent image structure
from background noise based on the statistics of local neighborhoods in the
image. Processing of solar data limited by simple shot noise or by additive
noise reveals image structure not easily visible in the originals, preserves
photometry of observable features, and reduces shot noise by a factor of 10 or
more with little to no apparent loss of resolution, revealing faint features
that were either not directly discernible or not sufficiently strongly detected
for quantitative analysis. The method works best on image sequences containing
related subjects, for example movies of solar evolution, but is also applicable
to single images provided that there are enough pixels. The adaptive filter
uses the statistical properties of noise and of local neighborhoods in the
data, to discriminate between coherent features and incoherent noise without
reference to the specific shape or evolution of the those features. The
technique can potentially be modified in a straightforward way to exploit
additional a priori knowledge about the functional form of the noise. | [
0,
1,
0,
0,
0,
0
] |
Title: Deep Belief Networks Based Feature Generation and Regression for Predicting Wind Power,
Abstract: Wind energy forecasting helps to manage power production, and hence, reduces
energy cost. Deep Neural Networks (DNN) mimics hierarchical learning in the
human brain and thus possesses hierarchical, distributed, and multi-task
learning capabilities. Based on aforementioned characteristics, we report Deep
Belief Network (DBN) based forecast engine for wind power prediction because of
its good generalization and unsupervised pre-training attributes. The proposed
DBN-WP forecast engine, which exhibits stochastic feature generation
capabilities and is composed of multiple Restricted Boltzmann Machines,
generates suitable features for wind power prediction using atmospheric
properties as input. DBN-WP, due to its unsupervised pre-training of RBM layers
and generalization capabilities, is able to learn the fluctuations in the
meteorological properties and thus is able to perform effective mapping of the
wind power. In the deep network, a regression layer is appended at the end to
predict sort-term wind power. It is experimentally shown that the deep learning
and unsupervised pre-training capabilities of DBN based model has comparable
and in some cases better results than hybrid and complex learning techniques
proposed for wind power prediction. The proposed prediction system based on
DBN, achieves mean values of RMSE, MAE and SDE as 0.124, 0.083 and 0.122,
respectively. Statistical analysis of several independent executions of the
proposed DBN-WP wind power prediction system demonstrates the stability of the
system. The proposed DBN-WP architecture is easy to implement and offers
generalization as regards the change in location of the wind farm is concerned. | [
0,
0,
0,
1,
0,
0
] |
Title: Control Interpretations for First-Order Optimization Methods,
Abstract: First-order iterative optimization methods play a fundamental role in large
scale optimization and machine learning. This paper presents control
interpretations for such optimization methods. First, we give loop-shaping
interpretations for several existing optimization methods and show that they
are composed of basic control elements such as PID and lag compensators. Next,
we apply the small gain theorem to draw a connection between the convergence
rate analysis of optimization methods and the input-output gain computations of
certain complementary sensitivity functions. These connections suggest that
standard classical control synthesis tools may be brought to bear on the design
of optimization algorithms. | [
1,
0,
1,
0,
0,
0
] |
Title: Variational Inference of Disentangled Latent Concepts from Unlabeled Observations,
Abstract: Disentangled representations, where the higher level data generative factors
are reflected in disjoint latent dimensions, offer several benefits such as
ease of deriving invariant representations, transferability to other tasks,
interpretability, etc. We consider the problem of unsupervised learning of
disentangled representations from large pool of unlabeled observations, and
propose a variational inference based approach to infer disentangled latent
factors. We introduce a regularizer on the expectation of the approximate
posterior over observed data that encourages the disentanglement. We also
propose a new disentanglement metric which is better aligned with the
qualitative disentanglement observed in the decoder's output. We empirically
observe significant improvement over existing methods in terms of both
disentanglement and data likelihood (reconstruction quality). | [
1,
0,
0,
1,
0,
0
] |
Title: Counting Multiplicities in a Hypersurface over a Number Field,
Abstract: We fix a counting function of multiplicities of algebraic points in a
projective hypersurface over a number field, and take the sum over all
algebraic points of bounded height and fixed degree. An upper bound for the sum
with respect to this counting function will be given in terms of the degree of
the hypersurface, the dimension of the singular locus, the upper bounds of
height, and the degree of the field of definition. | [
0,
0,
1,
0,
0,
0
] |
Title: Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis,
Abstract: We consider the task of fine-grained sentiment analysis from the perspective
of multiple instance learning (MIL). Our neural model is trained on document
sentiment labels, and learns to predict the sentiment of text segments, i.e.
sentences or elementary discourse units (EDUs), without segment-level
supervision. We introduce an attention-based polarity scoring method for
identifying positive and negative text snippets and a new dataset which we call
SPOT (as shorthand for Segment-level POlariTy annotations) for evaluating
MIL-style sentiment models like ours. Experimental results demonstrate superior
performance against multiple baselines, whereas a judgement elicitation study
shows that EDU-level opinion extraction produces more informative summaries
than sentence-based alternatives. | [
1,
0,
0,
0,
0,
0
] |
Title: Converging Shock Flows for a Mie-Grüneisen Equation of State,
Abstract: Previous work has shown that the one-dimensional (1D) inviscid compressible
flow (Euler) equations admit a wide variety of scale-invariant solutions
(including the famous Noh, Sedov, and Guderley shock solutions) when the
included equation of state (EOS) closure model assumes a certain
scale-invariant form. However, this scale-invariant EOS class does not include
even simple models used for shock compression of crystalline solids, including
many broadly applicable representations of Mie-Grüneisen EOS. Intuitively,
this incompatibility naturally arises from the presence of multiple dimensional
scales in the Mie-Grüneisen EOS, which are otherwise absent from
scale-invariant models that feature only dimensionless parameters (such as the
adiabatic index in the ideal gas EOS). The current work extends previous
efforts intended to rectify this inconsistency, by using a scale-invariant EOS
model to approximate a Mie- Grüneisen EOS form. To this end, the adiabatic
bulk modulus for the Mie-Grüneisen EOS is constructed, and its key features
are used to motivate the selection of a scale-invariant approximation form. The
remaining surrogate model parameters are selected through enforcement of the
Rankine-Hugoniot jump conditions for an infinitely strong shock in a
Mie-Grüneisen material. Finally, the approximate EOS is used in conjunction
with the 1D inviscid Euler equations to calculate a semi-analytical,
Guderley-like imploding shock solution in a metal sphere, and to determine if
and when the solution may be valid for the underlying Mie-Grüneisen EOS. | [
0,
1,
0,
0,
0,
0
] |
Title: What Propels Celebrity Follower Counts? Language Use or Social Connectivity,
Abstract: Follower count is a factor that quantifies the popularity of celebrities. It
is a reflection of their power, prestige and overall social reach. In this
paper we investigate whether the social connectivity or the language choice is
more correlated to the future follower count of a celebrity. We collect data
about tweets, retweets and mentions of 471 Indian celebrities with verified
Twitter accounts. We build two novel networks to approximate social
connectivity of the celebrities. We study various structural properties of
these two networks and observe their correlations with future follower counts.
In parallel, we analyze the linguistic structure of the tweets (LIWC features,
syntax and sentiment features and style and readability features) and observe
the correlations of each of these with the future follower count of a
celebrity. As a final step we use there features to classify a celebrity in a
specific bucket of future follower count (HIGH, MID or LOW). We observe that
the network features alone achieve an accuracy of 0.52 while the linguistic
features alone achieve an accuracy of 0.69 grossly outperforming the network
features. The network and linguistic features in conjunction produce an
accuracy of 0.76. We also discuss some final insights that we obtain from
further data analysis celebrities with larger follower counts post tweets that
have (i) more words from friend and family LIWC categories, (ii) more positive
sentiment laden words, (iii) have better language constructs and are (iv) more
readable. | [
1,
0,
0,
0,
0,
0
] |
Title: GPUQT: An efficient linear-scaling quantum transport code fully implemented on graphics processing units,
Abstract: We present GPUQT, a quantum transport code fully implemented on graphics
processing units. Using this code, one can obtain intrinsic electronic
transport properties of large systems described by a real-space tight-binding
Hamiltonian together with one or more types of disorder. The DC Kubo
conductivity is represented as a time integral of the velocity auto-correlation
or a time derivative of the mean square displacement. Linear scaling (with
respect to the total number of orbitals in the system) computation time and
memory usage are achieved by using various numerical techniques, including
sparse matrix-vector multiplication, random phase approximation of trace,
Chebyshev expansion of quantum evolution operator, and kernel polynomial method
for quantum resolution operator. We describe the inputs and outputs of GPUQT
and give two examples to demonstrate its usage, paying attention to the
interpretations of the results. | [
0,
1,
0,
0,
0,
0
] |
Title: Inkjet printing-based volumetric display projecting multiple full-colour 2D patterns,
Abstract: In this study, a method to construct a full-colour volumetric display is
presented using a commercially available inkjet printer. Photoreactive
luminescence materials are minutely and automatically printed as the volume
elements, and volumetric displays are constructed with high resolution using
easy-to-fabricate means that exploit inkjet printing technologies. The results
experimentally demonstrate the first prototype of an inkjet printing-based
volumetric display composed of multiple layers of transparent films that yield
a full-colour three-dimensional (3D) image. Moreover, we propose a design
algorithm with 3D structures that provide multiple different 2D full-colour
patterns when viewed from different directions and experimentally demonstrates
prototypes. It is considered that these types of 3D volumetric structures and
their fabrication methods based on widely deployed existing printing
technologies can be utilised as novel information display devices and systems,
including digital signage, media art, entertainment and security. | [
1,
0,
0,
0,
0,
0
] |
Title: Support Estimation via Regularized and Weighted Chebyshev Approximations,
Abstract: We introduce a new framework for estimating the support size of an unknown
distribution which improves upon known approximation-based techniques. Our main
contributions include describing a rigorous new weighted Chebyshev polynomial
approximation method and introducing regularization terms into the problem
formulation that provably improve the performance of state-of-the-art
approximation-based approaches. In particular, we present both theoretical and
computer simulation results that illustrate the utility and performance
improvements of our method. The theoretical analysis relies on jointly
optimizing the bias and variance components of the risk, and combining new
weighted minmax polynomial approximation techniques with discretized
semi-infinite programming solvers. Such a setting allows for casting the
estimation problem as a linear program (LP) with a small number of variables
and constraints that may be solved as efficiently as the original Chebyshev
approximation-based problem. The described approach also applies to the support
coverage and entropy estimation problems. Our newly developed technique is
tested on synthetic data and used to estimate the number of bacterial species
in the human gut. On synthetic datasets, we observed up to five-fold
improvements in the value of the worst-case risk. For the bioinformatics
application, metagenomic data from the NIH Human Gut and the American Gut
Microbiome was combined and processed to obtain lists of bacterial taxonomies.
These were subsequently used to compute the bacterial species histograms and
estimate the number of bacterial species in the human gut to roughly 2350, with
the species being represented by trillions of cells. | [
1,
0,
0,
1,
0,
0
] |
Title: Two-photon superbunching of pseudothermal light in a Hanbury Brown-Twiss interferometer,
Abstract: Two-photon superbunching of pseudothermal light is observed with single-mode
continuous-wave laser light in a linear optical system. By adding more
two-photon paths via three rotating ground glasses,g(2)(0) = 7.10 is
experimentally observed. The second-order temporal coherence function of
superbunching pseudothermal light is theoretically and experimentally studied
in detail. It is predicted that the degree of coherence of light can be
increased dramatically by adding more multi-photon paths. For instance, the
degree of the second- and third-order coherence of the superbunching
pseudothermal light with five rotating ground glasses can reach 32 and 7776,
respectively. The results are helpful to understand the physics of
superbunching and to improve the visibility of thermal light ghost imaging. | [
0,
1,
0,
0,
0,
0
] |
Title: Extensions of the Benson-Solomon fusion systems,
Abstract: The Benson-Solomon systems comprise the only known family of simple saturated
fusion systems at the prime two that do not arise as the fusion system of any
finite group. We determine the automorphism groups and the possible almost
simple extensions of these systems and of their centric linking systems. | [
0,
0,
1,
0,
0,
0
] |
Title: Run-Wise Simulations for Imaging Atmospheric Cherenkov Telescope Arrays,
Abstract: We present a new paradigm for the simulation of arrays of Imaging Atmospheric
Cherenkov Telescopes (IACTs) which overcomes limitations of current approaches.
Up to now, all major IACT experiments rely on the same Monte-Carlo simulation
strategy, using predefined observation and instrument settings. Simulations
with varying parameters are generated to provide better estimates of the
Instrument Response Functions (IRFs) of different observations. However, a
large fraction of the simulation configuration remains preserved, leading to
complete negligence of all related influences. Additionally, the simulation
scheme relies on interpolations between different array configurations, which
are never fully reproducing the actual configuration for a given observation.
Interpolations are usually performed on zenith angles, off-axis angles, array
multiplicity, and the optical response of the instrument. With the advent of
hybrid systems consisting of a large number of IACTs with different sizes,
types, and camera configurations, the complexity of the interpolation and the
size of the phase space becomes increasingly prohibitive. Going beyond the
existing approaches, we introduce a new simulation and analysis concept which
takes into account the actual observation conditions as well as individual
telescope configurations of each observation run of a given data set. These
run-wise simulations (RWS) thus exhibit considerably reduced systematic
uncertainties compared to the existing approach, and are also more
computationally efficient and simple. The RWS framework has been implemented in
the H.E.S.S. software and tested, and is already being exploited in science
analysis. | [
0,
1,
0,
0,
0,
0
] |
Title: Multi-party Poisoning through Generalized $p$-Tampering,
Abstract: In a poisoning attack against a learning algorithm, an adversary tampers with
a fraction of the training data $T$ with the goal of increasing the
classification error of the constructed hypothesis/model over the final test
distribution. In the distributed setting, $T$ might be gathered gradually from
$m$ data providers $P_1,\dots,P_m$ who generate and submit their shares of $T$
in an online way.
In this work, we initiate a formal study of $(k,p)$-poisoning attacks in
which an adversary controls $k\in[n]$ of the parties, and even for each
corrupted party $P_i$, the adversary submits some poisoned data $T'_i$ on
behalf of $P_i$ that is still "$(1-p)$-close" to the correct data $T_i$ (e.g.,
$1-p$ fraction of $T'_i$ is still honestly generated). For $k=m$, this model
becomes the traditional notion of poisoning, and for $p=1$ it coincides with
the standard notion of corruption in multi-party computation.
We prove that if there is an initial constant error for the generated
hypothesis $h$, there is always a $(k,p)$-poisoning attacker who can decrease
the confidence of $h$ (to have a small error), or alternatively increase the
error of $h$, by $\Omega(p \cdot k/m)$. Our attacks can be implemented in
polynomial time given samples from the correct data, and they use no wrong
labels if the original distributions are not noisy.
At a technical level, we prove a general lemma about biasing bounded
functions $f(x_1,\dots,x_n)\in[0,1]$ through an attack model in which each
block $x_i$ might be controlled by an adversary with marginal probability $p$
in an online way. When the probabilities are independent, this coincides with
the model of $p$-tampering attacks, thus we call our model generalized
$p$-tampering. We prove the power of such attacks by incorporating ideas from
the context of coin-flipping attacks into the $p$-tampering model and
generalize the results in both of these areas. | [
0,
0,
0,
1,
0,
0
] |
Title: Bernoulli Correlations and Cut Polytopes,
Abstract: Given $n$ symmetric Bernoulli variables, what can be said about their
correlation matrix viewed as a vector? We show that the set of those vectors
$R(\mathcal{B}_n)$ is a polytope and identify its vertices. Those extreme
points correspond to correlation vectors associated to the discrete uniform
distributions on diagonals of the cube $[0,1]^n$. We also show that the
polytope is affinely isomorphic to a well-known cut polytope ${\rm CUT}(n)$
which is defined as a convex hull of the cut vectors in a complete graph with
vertex set $\{1,\ldots,n\}$. The isomorphism is obtained explicitly as
$R(\mathcal{B}_n)= {\mathbf{1}}-2~{\rm CUT}(n)$. As a corollary of this work,
it is straightforward using linear programming to determine if a particular
correlation matrix is realizable or not. Furthermore, a sampling method for
multivariate symmetric Bernoullis with given correlation is obtained. In some
cases the method can also be used for general, not exclusively Bernoulli,
marginals. | [
0,
0,
1,
1,
0,
0
] |
Title: Tensor products of NCDL-C*-algebras and the C*-algebra of the Heisenberg motion groups,
Abstract: We show that the tensor product $A\otimes B$ over $\mathbb{C}$ of two $C^*
$-algebras satisfying the \textit{NCDL} conditions has again the same property.
We use this result to describe the $C^* $-algebra of the Heisenberg motion
groups $G_n = \mathbb{T}^n \ltimes \mathbb{H}_n$ as algebra of operator fields
defined over the spectrum of $G_n $. | [
0,
0,
1,
0,
0,
0
] |
Title: ViP-CNN: Visual Phrase Guided Convolutional Neural Network,
Abstract: As the intermediate level task connecting image captioning and object
detection, visual relationship detection started to catch researchers'
attention because of its descriptive power and clear structure. It detects the
objects and captures their pair-wise interactions with a
subject-predicate-object triplet, e.g. person-ride-horse. In this paper, each
visual relationship is considered as a phrase with three components. We
formulate the visual relationship detection as three inter-connected
recognition problems and propose a Visual Phrase guided Convolutional Neural
Network (ViP-CNN) to address them simultaneously. In ViP-CNN, we present a
Phrase-guided Message Passing Structure (PMPS) to establish the connection
among relationship components and help the model consider the three problems
jointly. Corresponding non-maximum suppression method and model training
strategy are also proposed. Experimental results show that our ViP-CNN
outperforms the state-of-art method both in speed and accuracy. We further
pretrain ViP-CNN on our cleansed Visual Genome Relationship dataset, which is
found to perform better than the pretraining on the ImageNet for this task. | [
1,
0,
0,
0,
0,
0
] |
Title: Robust clustering of languages across Wikipedia growth,
Abstract: Wikipedia is the largest existing knowledge repository that is growing on a
genuine crowdsourcing support. While the English Wikipedia is the most
extensive and the most researched one with over five million articles,
comparatively little is known about the behavior and growth of the remaining
283 smaller Wikipedias, the smallest of which, Afar, has only one article. Here
we use a subset of this data, consisting of 14962 different articles, each of
which exists in 26 different languages, from Arabic to Ukrainian. We study the
growth of Wikipedias in these languages over a time span of 15 years. We show
that, while an average article follows a random path from one language to
another, there exist six well-defined clusters of Wikipedias that share common
growth patterns. The make-up of these clusters is remarkably robust against the
method used for their determination, as we verify via four different clustering
methods. Interestingly, the identified Wikipedia clusters have little
correlation with language families and groups. Rather, the growth of Wikipedia
across different languages is governed by different factors, ranging from
similarities in culture to information literacy. | [
1,
0,
0,
1,
0,
0
] |
Title: Attractive Heaviside-Maxwellian (Vector) Gravity from Special Relativity and Quantum Field Theory,
Abstract: Adopting two independent approaches (a) Lorentz-invariance of physical laws
and (b) local phase invariance of quantum field theory applied to the Dirac
Lagrangian for massive electrically neutral Dirac particles, we rediscovered
the fundamental field equations of Heaviside Gravity (HG) of 1893 and
Maxwellian Gravity (MG), which look different from each other due to a sign
difference in some terms of their respective field equations. However, they are
shown to represent two mathematical representations of a single physical theory
of vector gravity that we name here as Heaviside-Maxwellian Gravity (HMG), in
which the speed of gravitational waves in vacuum is uniquely found to be equal
to the speed of light in vacuum. We also corrected a sign error in Heaviside's
speculative gravitational analogue of the Lorentz force law. This spin-1 HMG is
shown to produce attractive force between like masses under static condition,
contrary to the prevalent view of field theorists. Galileo's law of
universality of free fall is a consequence of HMG, without any initial
assumption of the equality of gravitational mass with velocity-dependent mass.
We also note a new set of Lorentz-Maxwell's equations having the same physical
effects as the standard set - a byproduct of our present study. | [
0,
1,
0,
0,
0,
0
] |
Title: GeoSeq2Seq: Information Geometric Sequence-to-Sequence Networks,
Abstract: The Fisher information metric is an important foundation of information
geometry, wherein it allows us to approximate the local geometry of a
probability distribution. Recurrent neural networks such as the
Sequence-to-Sequence (Seq2Seq) networks that have lately been used to yield
state-of-the-art performance on speech translation or image captioning have so
far ignored the geometry of the latent embedding, that they iteratively learn.
We propose the information geometric Seq2Seq (GeoSeq2Seq) network which
abridges the gap between deep recurrent neural networks and information
geometry. Specifically, the latent embedding offered by a recurrent network is
encoded as a Fisher kernel of a parametric Gaussian Mixture Model, a formalism
common in computer vision. We utilise such a network to predict the shortest
routes between two nodes of a graph by learning the adjacency matrix using the
GeoSeq2Seq formalism; our results show that for such a problem the
probabilistic representation of the latent embedding supersedes the
non-probabilistic embedding by 10-15\%. | [
1,
0,
0,
1,
0,
0
] |
Title: Comparing Neural and Attractiveness-based Visual Features for Artwork Recommendation,
Abstract: Advances in image processing and computer vision in the latest years have
brought about the use of visual features in artwork recommendation. Recent
works have shown that visual features obtained from pre-trained deep neural
networks (DNNs) perform very well for recommending digital art. Other recent
works have shown that explicit visual features (EVF) based on attractiveness
can perform well in preference prediction tasks, but no previous work has
compared DNN features versus specific attractiveness-based visual features
(e.g. brightness, texture) in terms of recommendation performance. In this
work, we study and compare the performance of DNN and EVF features for the
purpose of physical artwork recommendation using transactional data from
UGallery, an online store of physical paintings. In addition, we perform an
exploratory analysis to understand if DNN embedded features have some relation
with certain EVF. Our results show that DNN features outperform EVF, that
certain EVF features are more suited for physical artwork recommendation and,
finally, we show evidence that certain neurons in the DNN might be partially
encoding visual features such as brightness, providing an opportunity for
explaining recommendations based on visual neural models. | [
1,
0,
0,
0,
0,
0
] |
Title: The First Comparison Between Swarm-C Accelerometer-Derived Thermospheric Densities and Physical and Empirical Model Estimates,
Abstract: The first systematic comparison between Swarm-C accelerometer-derived
thermospheric density and both empirical and physics-based model results using
multiple model performance metrics is presented. This comparison is performed
at the satellite's high temporal 10-s resolution, which provides a meaningful
evaluation of the models' fidelity for orbit prediction and other space weather
forecasting applications. The comparison against the physical model is
influenced by the specification of the lower atmospheric forcing, the
high-latitude ionospheric plasma convection, and solar activity. Some insights
into the model response to thermosphere-driving mechanisms are obtained through
a machine learning exercise. The results of this analysis show that the
short-timescale variations observed by Swarm-C during periods of high solar and
geomagnetic activity were better captured by the physics-based model than the
empirical models. It is concluded that Swarm-C data agree well with the
climatologies inherent within the models and are, therefore, a useful data set
for further model validation and scientific research. | [
0,
1,
0,
0,
0,
0
] |
Title: Gaussian Parsimonious Clustering Models with Covariates,
Abstract: We consider model-based clustering methods for continuous, correlated data
that account for external information available in the presence of mixed-type
fixed covariates by proposing the MoEClust suite of models. These allow
covariates influence the component weights and/or component densities by
modelling the parameters of the mixture as functions of the covariates. A
familiar range of constrained eigen-decomposition parameterisations of the
component covariance matrices are also accommodated. This paper thus addresses
the equivalent aims of including covariates in Gaussian Parsimonious Clustering
Models and incorporating parsimonious covariance structures into the Gaussian
mixture of experts framework. The MoEClust models demonstrate significant
improvement from both perspectives in applications to univariate and
multivariate data sets. | [
0,
0,
0,
1,
0,
0
] |
Title: A Deep Learning Approach for Population Estimation from Satellite Imagery,
Abstract: Knowing where people live is a fundamental component of many decision making
processes such as urban development, infectious disease containment, evacuation
planning, risk management, conservation planning, and more. While bottom-up,
survey driven censuses can provide a comprehensive view into the population
landscape of a country, they are expensive to realize, are infrequently
performed, and only provide population counts over broad areas. Population
disaggregation techniques and population projection methods individually
address these shortcomings, but also have shortcomings of their own. To jointly
answer the questions of "where do people live" and "how many people live
there," we propose a deep learning model for creating high-resolution
population estimations from satellite imagery. Specifically, we train
convolutional neural networks to predict population in the USA at a
$0.01^{\circ} \times 0.01^{\circ}$ resolution grid from 1-year composite
Landsat imagery. We validate these models in two ways: quantitatively, by
comparing our model's grid cell estimates aggregated at a county-level to
several US Census county-level population projections, and qualitatively, by
directly interpreting the model's predictions in terms of the satellite image
inputs. We find that aggregating our model's estimates gives comparable results
to the Census county-level population projections and that the predictions made
by our model can be directly interpreted, which give it advantages over
traditional population disaggregation methods. In general, our model is an
example of how machine learning techniques can be an effective tool for
extracting information from inherently unstructured, remotely sensed data to
provide effective solutions to social problems. | [
1,
0,
0,
0,
0,
0
] |
Title: Trends in the Diffusion of Misinformation on Social Media,
Abstract: We measure trends in the diffusion of misinformation on Facebook and Twitter
between January 2015 and July 2018. We focus on stories from 570 sites that
have been identified as producers of false stories. Interactions with these
sites on both Facebook and Twitter rose steadily through the end of 2016.
Interactions then fell sharply on Facebook while they continued to rise on
Twitter, with the ratio of Facebook engagements to Twitter shares falling by
approximately 60 percent. We see no similar pattern for other news, business,
or culture sites, where interactions have been relatively stable over time and
have followed similar trends on the two platforms both before and after the
election. | [
1,
0,
0,
0,
0,
1
] |
Title: SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications,
Abstract: We describe the SemEval task of extracting keyphrases and relations between
them from scientific documents, which is crucial for understanding which
publications describe which processes, tasks and materials. Although this was a
new task, we had a total of 26 submissions across 3 evaluation scenarios. We
expect the task and the findings reported in this paper to be relevant for
researchers working on understanding scientific content, as well as the broader
knowledge base population and information extraction communities. | [
1,
0,
0,
1,
0,
0
] |
Title: The nilpotent variety of $W(1;n)_{p}$ is irreducible,
Abstract: In the late 1980s, Premet conjectured that the nilpotent variety of any
finite dimensional restricted Lie algebra over an algebraically closed field of
characteristic $p>0$ is irreducible. This conjecture remains open, but it is
known to hold for a large class of simple restricted Lie algebras, e.g. for Lie
algebras of connected reductive algebraic groups, and for Cartan series $W, S$
and $H$. In this paper, with the assumption that $p>3$, we confirm this
conjecture for the minimal $p$-envelope $W(1;n)_p$ of the Zassenhaus algebra
$W(1;n)$ for all $n\geq 2$. | [
0,
0,
1,
0,
0,
0
] |
Title: A New Pseudo-color Technique Based on Intensity Information Protection for Passive Sensor Imagery,
Abstract: Remote sensing image processing is so important in geo-sciences. Images which
are obtained by different types of sensors might initially be unrecognizable.
To make an acceptable visual perception in the images, some pre-processing
steps (for removing noises and etc) are preformed which they affect the
analysis of images. There are different types of processing according to the
types of remote sensing images. The method that we are going to introduce in
this paper is to use virtual colors to colorize the gray-scale images of
satellite sensors. This approach helps us to have a better analysis on a sample
single-band image which has been taken by Landsat-8 (OLI) sensor (as a
multi-band sensor with natural color bands, its images' natural color can be
compared to synthetic color by our approach). A good feature of this method is
the original image reversibility in order to keep the suitable resolution of
output images. | [
1,
0,
0,
0,
0,
0
] |
Title: Tunneling anisotropic magnetoresistance driven by magnetic phase transition,
Abstract: The independent control of two magnetic electrodes and spin-coherent
transport in magnetic tunnel junctions are strictly required for tunneling
magnetoresistance, while junctions with only one ferromagnetic electrode
exhibit tunneling anisotropic magnetoresistance dependent on the anisotropic
density of states with no room temperature performance so far. Here we report
an alternative approach to obtaining tunneling anisotropic magnetoresistance in
alfa-FeRh-based junctions driven by the magnetic phase transition of alfa-FeRh
and resultantly large variation of the density of states in the vicinity of MgO
tunneling barrier, referred to as phase transition tunneling anisotropic
magnetoresistance. The junctions with only one alfa-FeRh magnetic electrode
show a magnetoresistance ratio up to 20% at room temperature. Both the polarity
and magnitude of the phase transition tunneling anisotropic magnetoresistance
can be modulated by interfacial engineering at the alfa-FeRh/MgO interface.
Besides the fundamental significance, our finding might add a different
dimension to magnetic random access memory and antiferromagnet spintronics. | [
0,
1,
0,
0,
0,
0
] |
Title: Finding Efficient Swimming Strategies in a Three Dimensional Chaotic Flow by Reinforcement Learning,
Abstract: We apply a reinforcement learning algorithm to show how smart particles can
learn approximately optimal strategies to navigate in complex flows. In this
paper we consider microswimmers in a paradigmatic three-dimensional case given
by a stationary superposition of two Arnold-Beltrami-Childress flows with
chaotic advection along streamlines. In such a flow, we study the evolution of
point-like particles which can decide in which direction to swim, while keeping
the velocity amplitude constant. We show that it is sufficient to endow the
swimmers with a very restricted set of actions (six fixed swimming directions
in our case) to have enough freedom to find efficient strategies to move upward
and escape local fluid traps. The key ingredient is the
learning-from-experience structure of the algorithm, which assigns positive or
negative rewards depending on whether the taken action is, or is not,
profitable for the predetermined goal in the long term horizon. This is another
example supporting the efficiency of the reinforcement learning approach to
learn how to accomplish difficult tasks in complex fluid environments. | [
1,
1,
0,
0,
0,
0
] |
Title: Prospects of dynamical determination of General Relativity parameter beta and solar quadrupole moment J2 with asteroid radar astronomy,
Abstract: We evaluated the prospects of quantifying the parameterized post-Newtonian
parameter beta and solar quadrupole moment J2 with observations of near-Earth
asteroids with large orbital precession rates (9 to 27 arcsec century$^{-1}$).
We considered existing optical and radar astrometry, as well as radar
astrometry that can realistically be obtained with the Arecibo planetary radar
in the next five years. Our sensitivity calculations relied on a traditional
covariance analysis and Monte Carlo simulations. We found that independent
estimates of beta and J2 can be obtained with precisions of $6\times10^{-4}$
and $3\times10^{-8}$, respectively. Because we assumed rather conservative
observational uncertainties, as is the usual practice when reporting radar
astrometry, it is likely that the actual precision will be closer to
$2\times10^{-4}$ and $10^{-8}$, respectively. A purely dynamical determination
of solar oblateness with asteroid radar astronomy may therefore rival the
helioseismology determination. | [
0,
1,
0,
0,
0,
0
] |
Title: Distributed methods for synchronization of orthogonal matrices over graphs,
Abstract: This paper addresses the problem of synchronizing orthogonal matrices over
directed graphs. For synchronized transformations (or matrices), composite
transformations over loops equal the identity. We formulate the synchronization
problem as a least-squares optimization problem with nonlinear constraints. The
synchronization problem appears as one of the key components in applications
ranging from 3D-localization to image registration. The main contributions of
this work can be summarized as the introduction of two novel algorithms; one
for symmetric graphs and one for graphs that are possibly asymmetric. Under
general conditions, the former has guaranteed convergence to the solution of a
spectral relaxation to the synchronization problem. The latter is stable for
small step sizes when the graph is quasi-strongly connected. The proposed
methods are verified in numerical simulations. | [
1,
0,
1,
0,
0,
0
] |
Title: Stochastic Model of SIR Epidemic Modelling,
Abstract: Threshold theorem is probably the most important development of mathematical
epidemic modelling. Unfortunately, some models may not behave according to the
threshold. In this paper, we will focus on the final outcome of SIR model with
demography. The behaviour of the model approached by deteministic and
stochastic models will be introduced, mainly using simulations. Furthermore, we
will also investigate the dynamic of susceptibles in population in absence of
infective. We have successfully showed that both deterministic and stochastic
models performed similar results when $R_0 \leq 1$. That is, the disease-free
stage in the epidemic. But when $R_0 > 1$, the deterministic and stochastic
approaches had different interpretations. | [
0,
0,
0,
1,
1,
0
] |
Title: Parent Oriented Teacher Selection Causes Language Diversity,
Abstract: An evolutionary model for emergence of diversity in language is developed. We
investigated the effects of two real life observations, namely, people prefer
people that they communicate with well, and people interact with people that
are physically close to each other. Clearly these groups are relatively small
compared to the entire population. We restrict selection of the teachers from
such small groups, called imitation sets, around parents. Then the child learns
language from a teacher selected within the imitation set of her parent. As a
result, there are subcommunities with their own languages developed. Within
subcommunity comprehension is found to be high. The number of languages is
related to the relative size of imitation set by a power law. | [
1,
0,
0,
0,
0,
0
] |
Title: A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach,
Abstract: We consider solving convex-concave saddle point problems. We focus on two
variants of gradient decent-ascent algorithms, Extra-gradient (EG) and
Optimistic Gradient (OGDA) methods, and show that they admit a unified analysis
as approximations of the classical proximal point method for solving
saddle-point problems. This viewpoint enables us to generalize EG (in terms of
extrapolation steps) and OGDA (in terms of parameters) and obtain new
convergence rate results for these algorithms for the bilinear case as well as
the strongly convex-concave case. | [
1,
0,
0,
1,
0,
0
] |
Title: Poisson-Nernst-Planck equations with steric effects - non-convexity and multiple stationary solutions,
Abstract: We study the existence and stability of stationary solutions of
Poisson-Nernst- Planck equations with steric effects (PNP-steric equations)
with two counter-charged species. These equations describe steady current
through open ionic channels quite well. The current levels in open ionic
channels are known to switch between `open' or `closed' states in a spontaneous
stochastic process called gating, suggesting that their governing equations
should give rise to multiple stationary solutions that enable such multi-stable
behavior. We show that within a range of parameters, steric effects give rise
to multiple stationary solutions that are smooth. These solutions, however, are
all unstable under PNP-steric dynamics. Following these findings, we introduce
a novel PNP-Cahn-Hilliard model, and show that it admits multiple stationary
solutions that are smooth and stable. The various branches of stationary
solutions and their stability are mapped utilizing bifurcation analysis and
numerical continuation methods. | [
0,
1,
1,
0,
0,
0
] |
Title: Fitting ReLUs via SGD and Quantized SGD,
Abstract: In this paper we focus on the problem of finding the optimal weights of the
shallowest of neural networks consisting of a single Rectified Linear Unit
(ReLU). These functions are of the form $\mathbf{x}\rightarrow
\max(0,\langle\mathbf{w},\mathbf{x}\rangle)$ with $\mathbf{w}\in\mathbb{R}^d$
denoting the weight vector. We focus on a planted model where the inputs are
chosen i.i.d. from a Gaussian distribution and the labels are generated
according to a planted weight vector. We first show that mini-batch stochastic
gradient descent when suitably initialized, converges at a geometric rate to
the planted model with a number of samples that is optimal up to numerical
constants. Next we focus on a parallel implementation where in each iteration
the mini-batch gradient is calculated in a distributed manner across multiple
processors and then broadcast to a master or all other processors. To reduce
the communication cost in this setting we utilize a Quanitzed Stochastic
Gradient Scheme (QSGD) where the partial gradients are quantized. Perhaps
unexpectedly, we show that QSGD maintains the fast convergence of SGD to a
globally optimal model while significantly reducing the communication cost. We
further corroborate our numerical findings via various experiments including
distributed implementations over Amazon EC2. | [
1,
0,
0,
1,
0,
0
] |
Title: The meet operation in the imbalance lattice of maximal instantaneous codes: alternative proof of existence,
Abstract: An alternative proof is given of the existence of greatest lower bounds in
the imbalance order of binary maximal instantaneous codes of a given size.
These codes are viewed as maximal antichains of a given size in the infinite
binary tree of 0-1 words. The proof proposed makes use of a single balancing
operation instead of expansion and contraction as in the original proof of the
existence of glb. | [
0,
0,
1,
0,
0,
0
] |
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