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Title: Decay of Solutions to the Maxwell Equations on Schwarzschild-de Sitter Spacetimes,
Abstract: In this work, we consider solutions of the Maxwell equations on the
Schwarzschild-de Sitter family of black hole spacetimes. We prove that, in the
static region bounded by black hole and cosmological horizons, solutions of the
Maxwell equations decay to stationary Coulomb solutions at a super-polynomial
rate, with decay measured according to ingoing and outgoing null coordinates.
Our method employs a differential transformation of Maxwell tensor components
to obtain higher-order quantities satisfying a Fackerell-Ipser equation, in the
style of Chandrasekhar and the more recent work of Pasqualotto. The analysis of
the Fackerell-Ipser equation is accomplished by means of the vector field
method, with decay estimates for the higher-order quantities leading to decay
estimates for components of the Maxwell tensor. | [
0,
0,
1,
0,
0,
0
] |
Title: Machine Learning pipeline for discovering neuroimaging-based biomarkers in neurology and psychiatry,
Abstract: We consider a problem of diagnostic pattern recognition/classification from
neuroimaging data. We propose a common data analysis pipeline for
neuroimaging-based diagnostic classification problems using various ML
algorithms and processing toolboxes for brain imaging. We illustrate the
pipeline application by discovering new biomarkers for diagnostics of epilepsy
and depression based on clinical and MRI/fMRI data for patients and healthy
volunteers. | [
0,
0,
0,
1,
0,
0
] |
Title: The Strong Small Index Property for Free Homogeneous Structures,
Abstract: We show that in algebraically locally finite countable homogeneous structures
with a free stationary independence relation the small index property implies
the strong small index property. We use this and the main result of [15] to
deduce that countable free homogeneous structures in a locally finite
relational language have the strong small index property. We also exhibit new
continuum sized classes of $\aleph_0$-categorical structures with the strong
small index property whose automorphism groups are pairwise non-isomorphic. | [
0,
0,
1,
0,
0,
0
] |
Title: The Unheralded Value of the Multiway Rendezvous: Illustration with the Production Cell Benchmark,
Abstract: The multiway rendezvous introduced in Theoretical CSP is a powerful paradigm
to achieve synchronization and communication among a group of (possibly more
than two) processes. We illustrate the advantages of this paradigm on the
production cell benchmark, a model of a real metal processing plant, for which
we propose a compositional software controller, which is written in LNT and
LOTOS, and makes intensive use of the multiway rendezvous. | [
1,
0,
0,
0,
0,
0
] |
Title: Sequential Multiple Testing,
Abstract: We study an online multiple testing problem where the hypotheses arrive
sequentially in a stream. The test statistics are independent and assumed to
have the same distribution under their respective null hypotheses. We
investigate two procedures LORD and LOND, proposed by (Javanmard and Montanari,
2015), which are proved to control the FDR in an online manner. In some
(static) model, we show that LORD is optimal in some asymptotic sense, in
particular as powerful as the (static) Benjamini-Hochberg procedure to first
asymptotic order. We also quantify the performance of LOND. Some numerical
experiments complement our theory. | [
0,
0,
1,
1,
0,
0
] |
Title: On the selection of polynomials for the DLP algorithm,
Abstract: In this paper we characterize the set of polynomials $f\in\mathbb F_q[X]$
satisfying the following property: there exists a positive integer $d$ such
that for any positive integer $\ell$ less or equal than the degree of $f$,
there exists $t_0$ in $\mathbb F_{q^d}$ such that the polynomial $f-t_0$ has an
irreducible factor of degree $\ell$ over $\mathbb F_{q^d}[X]$. This result is
then used to progress in the last step which is needed to remove the heuristic
from one of the quasi-polynomial time algorithms for discrete logarithm
problems (DLP) in small characteristic. Our characterization allows a
construction of polynomials satisfying the wanted property. | [
1,
0,
1,
0,
0,
0
] |
Title: Pixel-Level Statistical Analyses of Prescribed Fire Spread,
Abstract: Wildland fire dynamics is a complex turbulent dimensional process. Cellular
automata (CA) is an efficient tool to predict fire dynamics, but the main
parameters of the method are challenging to estimate. To overcome this
challenge, we compute statistical distributions of the key parameters of a CA
model using infrared images from controlled burns. Moreover, we apply this
analysis to different spatial scales and compare the experimental results to a
simple statistical model. By performing this analysis and making this
comparison, several capabilities and limitations of CA are revealed. | [
0,
1,
0,
0,
0,
0
] |
Title: Experiment Segmentation in Scientific Discourse as Clause-level Structured Prediction using Recurrent Neural Networks,
Abstract: We propose a deep learning model for identifying structure within experiment
narratives in scientific literature. We take a sequence labeling approach to
this problem, and label clauses within experiment narratives to identify the
different parts of the experiment. Our dataset consists of paragraphs taken
from open access PubMed papers labeled with rhetorical information as a result
of our pilot annotation. Our model is a Recurrent Neural Network (RNN) with
Long Short-Term Memory (LSTM) cells that labels clauses. The clause
representations are computed by combining word representations using a novel
attention mechanism that involves a separate RNN. We compare this model against
LSTMs where the input layer has simple or no attention and a feature rich CRF
model. Furthermore, we describe how our work could be useful for information
extraction from scientific literature. | [
1,
0,
0,
0,
0,
0
] |
Title: Information-Theoretic Analysis of Refractory Effects in the P300 Speller,
Abstract: The P300 speller is a brain-computer interface that enables people with
neuromuscular disorders to communicate based on eliciting event-related
potentials (ERP) in electroencephalography (EEG) measurements. One challenge to
reliable communication is the presence of refractory effects in the P300 ERP
that induces temporal dependence in the user's EEG responses. We propose a
model for the P300 speller as a communication channel with memory. By studying
the maximum information rate on this channel, we gain insight into the
fundamental constraints imposed by refractory effects. We construct codebooks
based on the optimal input distribution, and compare them to existing codebooks
in literature. | [
1,
0,
1,
0,
0,
0
] |
Title: Growth, Industrial Externality, Prospect Dynamics and Well-being on Markets,
Abstract: Functions or 'functionnings' enable to give a structure to any economic
activity whether they are used to describe a good or a service that is
exchanged on a market or they constitute the capability of an agent to provide
the labor market with specific work and skills. That structure encompasses the
basic law of supply and demand and the conditions of growth within a
transaction and of the inflation control. Functional requirements can be
followed from the design of a product to the delivery of a solution to a
customer needs with different levels of externalities while value is created
integrating organizational and technical constraints whereas a budget is
allocated to the various entities of the firm involved in the production.
Entering the market through that structure leads to designing basic equations
of its dynamics and to finding canonical solutions out of particular
equilibria. This approach enables to tackle behavioral foundations of Prospect
Theory within a generalization of its probability weighting function turned
into an operator which applies to Western, Educated, Industrialized, Rich, and
Democratic societies as well as to the poorest ones. The nature of reality and
well-being appears then as closely related to the relative satisfaction reached
on the market, as it can be conceived by an agent, according to business
cycles. This reality being the result of the complementary systems that govern
human mind as structured by rational psychologists. | [
0,
0,
0,
0,
0,
1
] |
Title: Structure Preserving Model Reduction of Parametric Hamiltonian Systems,
Abstract: While reduced-order models (ROMs) have been popular for efficiently solving
large systems of differential equations, the stability of reduced models over
long-time integration is of present challenges. We present a greedy approach
for ROM generation of parametric Hamiltonian systems that captures the
symplectic structure of Hamiltonian systems to ensure stability of the reduced
model. Through the greedy selection of basis vectors, two new vectors are added
at each iteration to the linear vector space to increase the accuracy of the
reduced basis. We use the error in the Hamiltonian due to model reduction as an
error indicator to search the parameter space and identify the next best basis
vectors. Under natural assumptions on the set of all solutions of the
Hamiltonian system under variation of the parameters, we show that the greedy
algorithm converges with exponential rate. Moreover, we demonstrate that
combining the greedy basis with the discrete empirical interpolation method
also preserves the symplectic structure. This enables the reduction of the
computational cost for nonlinear Hamiltonian systems. The efficiency, accuracy,
and stability of this model reduction technique is illustrated through
simulations of the parametric wave equation and the parametric Schrodinger
equation. | [
0,
0,
1,
0,
0,
0
] |
Title: A Composition Theorem for Randomized Query Complexity,
Abstract: Let the randomized query complexity of a relation for error probability
$\epsilon$ be denoted by $R_\epsilon(\cdot)$. We prove that for any relation $f
\subseteq \{0,1\}^n \times \mathcal{R}$ and Boolean function $g:\{0,1\}^m
\rightarrow \{0,1\}$, $R_{1/3}(f\circ g^n) = \Omega(R_{4/9}(f)\cdot
R_{1/2-1/n^4}(g))$, where $f \circ g^n$ is the relation obtained by composing
$f$ and $g$. We also show that $R_{1/3}\left(f \circ \left(g^\oplus_{O(\log
n)}\right)^n\right)=\Omega(\log n \cdot R_{4/9}(f) \cdot R_{1/3}(g))$, where
$g^\oplus_{O(\log n)}$ is the function obtained by composing the xor function
on $O(\log n)$ bits and $g^t$. | [
1,
0,
0,
0,
0,
0
] |
Title: PBW bases and marginally large tableaux in types B and C,
Abstract: We explicitly describe the isomorphism between two combinatorial realizations
of Kashiwara's infinity crystal in types B and C. The first realization is in
terms of marginally large tableaux and the other is in terms of Kostant
partitions coming from PBW bases. We also discuss a stack notation for Kostant
partitions which simplifies that realization. | [
0,
0,
1,
0,
0,
0
] |
Title: Ensemble Clustering for Graphs,
Abstract: We propose an ensemble clustering algorithm for graphs (ECG), which is based
on the Louvain algorithm and the concept of consensus clustering. We validate
our approach by replicating a recently published study comparing graph
clustering algorithms over artificial networks, showing that ECG outperforms
the leading algorithms from that study. We also illustrate how the ensemble
obtained with ECG can be used to quantify the presence of community structure
in the graph. | [
1,
0,
0,
1,
0,
0
] |
Title: Testing for Feature Relevance: The HARVEST Algorithm,
Abstract: Feature selection with high-dimensional data and a very small proportion of
relevant features poses a severe challenge to standard statistical methods. We
have developed a new approach (HARVEST) that is straightforward to apply,
albeit somewhat computer-intensive. This algorithm can be used to pre-screen a
large number of features to identify those that are potentially useful. The
basic idea is to evaluate each feature in the context of many random subsets of
other features. HARVEST is predicated on the assumption that an irrelevant
feature can add no real predictive value, regardless of which other features
are included in the subset. Motivated by this idea, we have derived a simple
statistical test for feature relevance. Empirical analyses and simulations
produced so far indicate that the HARVEST algorithm is highly effective in
predictive analytics, both in science and business. | [
0,
0,
0,
1,
0,
0
] |
Title: Energy Harvesting Enabled MIMO Relaying through PS,
Abstract: This paper considers a multiple-input multiple-output (MIMO) relay system
with an energy harvesting relay node. All nodes are equipped with multiple
antennas, and the relay node depends on the harvested energy from the received
signal to support information forwarding. In particular, the relay node deploys
power splitting based energy harvesting scheme. The capacity maximization
problem subject to power constraints at both the source and relay nodes is
considered for both fixed source covariance matrix and optimal source
covariance matrix cases. Instead of using existing software solvers, iterative
approaches using dual decomposition technique are developed based on the
structures of the optimal relay precoding and source covariance matrices.
Simulation results demonstrate the performance gain of the joint optimization
against the fixed source covariance matrix case. | [
1,
0,
0,
0,
0,
0
] |
Title: X-ray diagnostics of massive star winds,
Abstract: Observations with powerful X-ray telescopes, such as XMM-Newton and Chandra,
significantly advance our understanding of massive stars. Nearly all early-type
stars are X-ray sources. Studies of their X-ray emission provide important
diagnostics of stellar winds. High-resolution X-ray spectra of O-type stars are
well explained when stellar wind clumping is taking into account, providing
further support to a modern picture of stellar winds as non-stationary,
inhomogeneous outflows. X-ray variability is detected from such winds, on time
scales likely associated with stellar rotation. High-resolution X-ray
spectroscopy indicates that the winds of late O-type stars are predominantly in
a hot phase. Consequently, X-rays provide the best observational window to
study these winds. X-ray spectroscopy of evolved, Wolf-Rayet type, stars allows
to probe their powerful metal enhanced winds, while the mechanisms responsible
for the X-ray emission of these stars are not yet understood. | [
0,
1,
0,
0,
0,
0
] |
Title: Local asymptotic properties for Cox-Ingersoll-Ross process with discrete observations,
Abstract: In this paper, we consider a one-dimensional Cox-Ingersoll-Ross (CIR) process
whose drift coefficient depends on unknown parameters. Considering the process
discretely observed at high frequency, we prove the local asymptotic normality
property in the subcritical case, the local asymptotic quadraticity in the
critical case, and the local asymptotic mixed normality property in the
supercritical case. To obtain these results, we use the Malliavin calculus
techniques developed recently for CIR process together with the $L^p$-norm
estimation for positive and negative moments of the CIR process. In this study,
we require the same conditions of high frequency $\Delta_n\rightarrow 0$ and
infinite horizon $n\Delta_n\rightarrow\infty$ as in the case of ergodic
diffusions with globally Lipschitz coefficients studied earlier by Gobet
\cite{G02}. However, in the non-ergodic cases, additional assumptions on the
decreasing rate of $\Delta_n$ are required due to the fact that the square root
diffusion coefficient of the CIR process is not regular enough. Indeed, we
assume $\frac{n\Delta_n^{\frac{3}{2}}}{\log(n\Delta_n)}\to 0$ for the critical
case and $n\Delta_n^2\to 0$ for the supercritical case. | [
0,
0,
1,
1,
0,
0
] |
Title: Topological Dirac Nodal-net Fermions in AlB$_2$-type TiB$_2$ and ZrB$_2$,
Abstract: Based on first-principles calculations and effective model analysis, a Dirac
nodal-net semimetal state is recognized in AlB$_2$-type TiB$_2$ and ZrB$_2$
when spin-orbit coupling (SOC) is ignored. Taking TiB$_2$ as an example, there
are several topological excitations in this nodal-net structure including
triple point, nexus, and nodal link, which are protected by coexistence of
spatial-inversion symmetry and time reversal symmetry. This nodal-net state is
remarkably different from that of IrF$_4$, which requires sublattice chiral
symmetry. In addition, linearly and quadratically dispersed two-dimensional
surface Dirac points are identified as having emerged on the B-terminated and
Ti-terminated (001) surfaces of TiB$_2$ respectively, which are analogous to
those of monolayer and bilayer graphene. | [
0,
1,
0,
0,
0,
0
] |
Title: Galactic Pal-eontology: Abundance Analysis of the Disrupting Globular Cluster Palomar 5,
Abstract: We present a chemical abundance analysis of the tidally disrupted globular
cluster (GC) Palomar 5. By co-adding high-resolution spectra of 15 member stars
from the cluster's main body, taken at low signal-to-noise with the Keck/HIRES
spectrograph, we were able to measure integrated abundance ratios of 24 species
of 20 elements including all major nucleosynthetic channels (namely the light
element Na; $\alpha$-elements Mg, Si, Ca, Ti; Fe-peak and heavy elements Sc, V,
Cr, Mn, Co, Ni, Cu, Zn; and the neutron-capture elements Y, Zr, Ba, La, Nd, Sm,
Eu). The mean metallicity of $-1.56\pm0.02\pm0.06$ dex (statistical and
systematic errors) agrees well with the values from individual, low-resolution
measurements of individual stars, but it is lower than previous high-resolution
results of a small number of stars in the literature. Comparison with Galactic
halo stars and other disrupted and unperturbed GCs renders Pal~5 a typical
representative of the Milky Way halo population, as has been noted before,
emphasizing that the early chemical evolution of such clusters is decoupled
from their later dynamical history. We also performed a test as to the
detectability of light element variations in this co-added abundance analysis
technique and found that this approach is not sensitive even in the presence of
a broad range in sodium of $\sim$0.6 dex, a value typically found in the old
halo GCs. Thus, while methods of determining the global abundance patterns of
such objects are well suited to study their overall enrichment histories,
chemical distinctions of their multiple stellar populations is still best
obtained from measurements of individual stars. | [
0,
1,
0,
0,
0,
0
] |
Title: Deep Graphs,
Abstract: We propose an algorithm for deep learning on networks and graphs. It relies
on the notion that many graph algorithms, such as PageRank, Weisfeiler-Lehman,
or Message Passing can be expressed as iterative vertex updates. Unlike
previous methods which rely on the ingenuity of the designer, Deep Graphs are
adaptive to the estimation problem. Training and deployment are both efficient,
since the cost is $O(|E| + |V|)$, where $E$ and $V$ are the sets of edges and
vertices respectively. In short, we learn the recurrent update functions rather
than positing their specific functional form. This yields an algorithm that
achieves excellent accuracy on both graph labeling and regression tasks. | [
0,
0,
0,
1,
0,
0
] |
Title: On the optimal investment-consumption and life insurance selection problem with an external stochastic factor,
Abstract: In this paper, we study a stochastic optimal control problem with stochastic
volatility. We prove the sufficient and necessary maximum principle for the
proposed problem. Then we apply the results to solve an investment, consumption
and life insurance problem with stochastic volatility, that is, we consider a
wage earner investing in one risk-free asset and one risky asset described by a
jump-diffusion process and has to decide concerning consumption and life
insurance purchase. We assume that the life insurance for the wage earner is
bought from a market composed of $M>1$ life insurance companies offering
pairwise distinct life insurance contracts. The goal is to maximize the
expected utilities derived from the consumption, the legacy in the case of a
premature death and the investor's terminal wealth. | [
0,
0,
0,
0,
0,
1
] |
Title: An Introduction to Adjoints and Output Error Estimation in Computational Fluid Dynamics,
Abstract: In recent years, the use of adjoint vectors in Computational Fluid Dynamics
(CFD) has seen a dramatic rise. Their utility in numerous applications,
including design optimization, data assimilation, and mesh adaptation has
sparked the interest of both researchers and practitioners alike. In many of
these fields, the concept of an adjoint is explained differently, with various
notations and motivations employed. Further complicating matters is the
existence of two seemingly different types of adjoints -- "continuous" and
"discrete" -- as well as the more formal definition of adjoint operators
employed in linear algebra and functional analysis. These issues can make the
fundamental concept of an adjoint difficult to pin down. In these notes, we
hope to clarify some of the ideas surrounding adjoint vectors and to provide a
useful reference for both continuous and discrete adjoints alike. In
particular, we focus on the use of adjoints within the context of output-based
mesh adaptation, where the goal is to achieve accuracy in a particular quantity
(or "output") of interest by performing targeted adaptation of the
computational mesh. While this is our application of interest, the ideas
discussed here apply directly to design optimization, data assimilation, and
many other fields where adjoints are employed. | [
1,
1,
0,
0,
0,
0
] |
Title: Incremental Sharpe and other performance ratios,
Abstract: We present a new methodology of computing incremental contribution for
performance ratios for portfolio like Sharpe, Treynor, Calmar or Sterling
ratios. Using Euler's homogeneous function theorem, we are able to decompose
these performance ratios as a linear combination of individual modified
performance ratios. This allows understanding the drivers of these performance
ratios as well as deriving a condition for a new asset to provide incremental
performance for the portfolio. We provide various numerical examples of this
performance ratio decomposition. | [
0,
0,
0,
0,
0,
1
] |
Title: Saliency Detection by Forward and Backward Cues in Deep-CNNs,
Abstract: As prior knowledge of objects or object features helps us make relations for
similar objects on attentional tasks, pre-trained deep convolutional neural
networks (CNNs) can be used to detect salient objects on images regardless of
the object class is in the network knowledge or not. In this paper, we propose
a top-down saliency model using CNN, a weakly supervised CNN model trained for
1000 object labelling task from RGB images. The model detects attentive regions
based on their objectness scores predicted by selected features from CNNs. To
estimate the salient objects effectively, we combine both forward and backward
features, while demonstrating that partially-guided backpropagation will
provide sufficient information for selecting the features from forward run of
CNN model. Finally, these top-down cues are enhanced with a state-of-the-art
bottom-up model as complementing the overall saliency. As the proposed model is
an effective integration of forward and backward cues through objectness
without any supervision or regression to ground truth data, it gives promising
results compared to state-of-the-art models in two different datasets. | [
1,
0,
0,
0,
0,
0
] |
Title: Active sorting of orbital angular momentum states of light with cascaded tunable resonators,
Abstract: Light carrying orbital angular momentum (OAM) has been shown to be of use in
a disparate range of fields ranging from astronomy to optical trapping, and as
a promising new dimension for multiplexing signals in optical communications
and data storage. A challenge to many of these applications is a reliable and
dynamic method that sorts incident OAM states without altering them. Here we
report a wavelength-independent technique capable of dynamically filtering
individual OAM states based on the resonant transmission of a tunable optical
cavity. The cavity length is piezo-controlled to facilitate dynamic
reconfiguration, and the sorting process leaves both the transmitted and
reflected signals in their original states for subsequent processing. As a
result, we also show that a reconfigurable sorting network can be constructed
by cascading such optical resonators to handle multiple OAM states
simultaneously. This approach to sorting OAM states is amenable to integration
into optical communication networks and has implications in quantum optics,
astronomy, optical data storage and optical trapping. | [
0,
1,
0,
0,
0,
0
] |
Title: Learning the Kernel for Classification and Regression,
Abstract: We investigate a series of learning kernel problems with polynomial
combinations of base kernels, which will help us solve regression and
classification problems. We also perform some numerical experiments of
polynomial kernels with regression and classification tasks on different
datasets. | [
1,
0,
0,
0,
0,
0
] |
Title: Critical system involving fractional Laplacian,
Abstract: In this paper, we study the following critical system with fractional
Laplacian: \begin{equation*} \begin{cases}
(-\Delta)^{s}u=
\mu_{1}|u|^{2^{\ast}-2}u+\frac{\alpha\gamma}{2^{\ast}}|u|^{\alpha-2}u|v|^{\beta}
\ \ \ \text{in} \ \ \mathbb{R}^{n},
(-\Delta)^{s}v=
\mu_{2}|v|^{2^{\ast}-2}v+\frac{\beta\gamma}{2^{\ast}}|u|^{\alpha}|v|^{\beta-2}v\
\ \ \ \text{in} \ \ \mathbb{R}^{n}, u,v\in D_{s}(\mathbb{R}^{n}). \end{cases}
\end{equation*}
By using the Nehari\ manifold,\ under proper conditions, we establish the
existence and nonexistence of positive least energy solution of the system. | [
0,
0,
1,
0,
0,
0
] |
Title: Cooperative Estimation via Altruism,
Abstract: A novel approach, based on the notion of altruism, is presented to
cooperative estimation in a system comprising two information-sharing
estimators. The underlying assumption is that the system's global mission can
be accomplished even if only one of the estimators achieves satisfactory
performance. The notion of altruism motivates a new definition of cooperative
estimation optimality that generalizes the common definition of minimum mean
square error optimality. Fundamental equations are derived for two types of
altruistic cooperative estimation problems, corresponding to heterarchical and
hierarchical setups. Although these equations are hard to solve in the general
case, their solution in the Gaussian case is straightforward and only entails
the largest eigenvalue of the conditional covariance matrix and its
corresponding eigenvector. Moreover, in that case the performance improvement
of the two altruistic cooperative estimation techniques over the conventional
(egoistic) estimation approach is shown to depend on the problem's
dimensionality and statistical distribution. In particular, the performance
improvement grows with the dispersion of the spectrum of the conditional
covariance matrix, rendering the new estimation approach especially appealing
in ill-conditioned problems. The performance of the new approach is
demonstrated using a numerical simulation study. | [
1,
0,
1,
0,
0,
0
] |
Title: Multi-GPU maximum entropy image synthesis for radio astronomy,
Abstract: The maximum entropy method (MEM) is a well known deconvolution technique in
radio-interferometry. This method solves a non-linear optimization problem with
an entropy regularization term. Other heuristics such as CLEAN are faster but
highly user dependent. Nevertheless, MEM has the following advantages: it is
unsupervised, it has a statistical basis, it has a better resolution and better
image quality under certain conditions. This work presents a high performance
GPU version of non-gridding MEM, which is tested using real and simulated data.
We propose a single-GPU and a multi-GPU implementation for single and
multi-spectral data, respectively. We also make use of the Peer-to-Peer and
Unified Virtual Addressing features of newer GPUs which allows to exploit
transparently and efficiently multiple GPUs. Several ALMA data sets are used to
demonstrate the effectiveness in imaging and to evaluate GPU performance. The
results show that a speedup from 1000 to 5000 times faster than a sequential
version can be achieved, depending on data and image size. This allows to
reconstruct the HD142527 CO(6-5) short baseline data set in 2.1 minutes,
instead of 2.5 days that takes a sequential version on CPU. | [
1,
1,
0,
0,
0,
0
] |
Title: Green function for linearized Navier-Stokes around a boundary layer profile: away from critical layers,
Abstract: In this paper, we construct the Green function for the classical
Orr-Sommerfeld equations, which are the linearized Navier-Stokes equations
around a boundary layer profile. As an immediate application, we derive uniform
sharp bounds on the semigroup of the linearized Navier-Stokes problem around
unstable profiles in the vanishing viscosity limit. | [
0,
0,
1,
0,
0,
0
] |
Title: Explicit Time Integration of Transient Eddy Current Problems,
Abstract: For time integration of transient eddy current problems commonly implicit
time integration methods are used, where in every time step one or several
nonlinear systems of equations have to be linearized with the Newton-Raphson
method due to ferromagnetic materials involved. In this paper, a generalized
Schur-complement is applied to the magnetic vector potential formulation, which
converts a differential-algebraic equation system of index 1 into a system of
ordinary differential equations (ODE) with reduced stiffness. For the time
integration of this ODE system of equations, the explicit Euler method is
applied. The Courant-Friedrich-Levy (CFL) stability criterion of explicit time
integration methods may result in small time steps. Applying a pseudo-inverse
of the discrete curl-curl operator in nonconducting regions of the problem is
required in every time step. For the computation of the pseudo-inverse, the
preconditioned conjugate gradient (PCG) method is used. The cascaded Subspace
Extrapolation method (CSPE) is presented to produce suitable start vectors for
these PCG iterations. The resulting scheme is validated using the TEAM 10
benchmark problem. | [
1,
1,
1,
0,
0,
0
] |
Title: On the maximum principle for the Riesz transform,
Abstract: Let $\mu$ be a measure in $\mathbb R^d$ with compact support and continuous
density, and let $$ R^s\mu(x)=\int\frac{y-x}{|y-x|^{s+1}}\,d\mu(y),\ \
x,y\in\mathbb R^d,\ \ 0<s<d. $$ We consider the following conjecture: $$
\sup_{x\in\mathbb R^d}|R^s\mu(x)|\le
C\sup_{x\in\text{supp}\,\mu}|R^s\mu(x)|,\quad C=C(d,s). $$ This relation was
known for $d-1\le s<d$, and is still an open problem in the general case. We
prove the maximum principle for $0< s<1$, and also for $0<s<d$ in the case of
radial measure. Moreover, we show that this conjecture is incorrect for
non-positive measures. | [
0,
0,
1,
0,
0,
0
] |
Title: Optimal rates of estimation for multi-reference alignment,
Abstract: In this paper, we establish optimal rates of adaptive estimation of a vector
in the multi-reference alignment model, a problem with important applications
in fields such as signal processing, image processing, and computer vision,
among others. We describe how this model can be viewed as a multivariate
Gaussian mixture model under the constraint that the centers belong to the
orbit of a group. This enables us to derive matching upper and lower bounds
that feature an interesting dependence on the signal-to-noise ratio of the
model. Both upper and lower bounds are articulated around a tight local control
of Kullback-Leibler divergences that showcases the central role of moment
tensors in this problem. | [
0,
0,
1,
1,
0,
0
] |
Title: Overcomplete Frame Thresholding for Acoustic Scene Analysis,
Abstract: In this work, we derive a generic overcomplete frame thresholding scheme
based on risk minimization. Overcomplete frames being favored for analysis
tasks such as classification, regression or anomaly detection, we provide a way
to leverage those optimal representations in real-world applications through
the use of thresholding. We validate the method on a large scale bird activity
detection task via the scattering network architecture performed by means of
continuous wavelets, known for being an adequate dictionary in audio
environments. | [
1,
0,
0,
1,
0,
0
] |
Title: A Hybrid Approach for Trajectory Control Design,
Abstract: This work presents a methodology to design trajectory tracking feedback
control laws, which embed non-parametric statistical models, such as Gaussian
Processes (GPs). The aim is to minimize unmodeled dynamics such as undesired
slippages. The proposed approach has the benefit of avoiding complex
terramechanics analysis to directly estimate from data the robot dynamics on a
wide class of trajectories. Experiments in both real and simulated environments
prove that the proposed methodology is promising. | [
1,
0,
0,
0,
0,
0
] |
Title: Foresight: Rapid Data Exploration Through Guideposts,
Abstract: Current tools for exploratory data analysis (EDA) require users to manually
select data attributes, statistical computations and visual encodings. This can
be daunting for large-scale, complex data. We introduce Foresight, a
visualization recommender system that helps the user rapidly explore large
high-dimensional datasets through "guideposts." A guidepost is a visualization
corresponding to a pronounced instance of a statistical descriptor of the
underlying data, such as a strong linear correlation between two attributes,
high skewness or concentration about the mean of a single attribute, or a
strong clustering of values. For each descriptor, Foresight initially presents
visualizations of the "strongest" instances, based on an appropriate ranking
metric. Given these initial guideposts, the user can then look at "nearby"
guideposts by issuing "guidepost queries" containing constraints on metric
type, metric strength, data attributes, and data values. Thus, the user can
directly explore the network of guideposts, rather than the overwhelming space
of data attributes and visual encodings. Foresight also provides for each
descriptor a global visualization of ranking-metric values to both help orient
the user and ensure a thorough exploration process. Foresight facilitates
interactive exploration of large datasets using fast, approximate sketching to
compute ranking metrics. We also contribute insights on EDA practices of data
scientists, summarizing results from an interview study we conducted to inform
the design of Foresight. | [
1,
0,
0,
0,
0,
0
] |
Title: Transport by Lagrangian Vortices in the Eastern Pacific,
Abstract: Rotationally coherent Lagrangian vortices (RCLVs) are identified from
satellite-derived surface geostrophic velocities in the Eastern Pacific
(180$^\circ$-130$^\circ$ W) using the objective (frame-invariant) finite-time
Lagrangian-coherent-structure detection method of Haller et al. (2016) based on
the Lagrangian-averaged vorticity deviation. RCLVs are identified for 30, 90,
and 270 day intervals over the entire satellite dataset, beginning in 1993. In
contrast to structures identified using Eulerian eddy-tracking methods, the
RCLVs maintain material coherence over the specified time intervals, making
them suitable for material transport estimates. Statistics of RCLVs are
compared to statistics of eddies identified from sea-surface height (SSH) by
Chelton et al. 2011. RCLVs and SSH eddies are found to propagate westward at
similar speeds at each latitude, consistent with the Rossby wave dispersion
relation. However, RCLVs are uniformly smaller and shorter-lived than SSH
eddies. A coherent eddy diffusivity is derived to quantify the contribution of
RCLVs to meridional transport; it is found that RCLVs contribute less than 1%
to net meridional dispersion and diffusion in this sector, implying that eddy
transport of tracers is mostly due to incoherent motions, such as swirling and
filamentation outside of the eddy cores, rather than coherent meridional
translation of eddies themselves. These findings call into question prior
estimates of coherent eddy transport based on Eulerian eddy identification
methods. | [
0,
1,
0,
0,
0,
0
] |
Title: A Note on a Quantitative Form of the Solovay-Kitaev Theorem,
Abstract: The problem of finding good approximations of arbitrary 1-qubit gates is
identical to that of finding a dense group generated by a universal subset of
$SU(2)$ to approximate an arbitrary element of $SU(2)$. The Solovay-Kitaev
Theorem is a well-known theorem that guarantees the existence of a finite
sequence of 1-qubit quantum gates approximating an arbitrary unitary matrix in
$SU(2)$ within specified accuracy $\varepsilon > 0$. In this note we study a
quantitative description of this theorem in the following sense. We will work
with a universal gate set $T$, a subset of $SU(2)$ such that the group
generated by the elements of $T$ is dense in $SU(2)$. For $\varepsilon > 0$
small enough, we define $t_{\varepsilon}$ as the minimum reduced word length
such that every point of $SU(2)$ lies within a ball of radius $\varepsilon$
centered at the points in the dense subgroup generated by $T$. For a measure of
efficiency on T, which we denote $K(T)$, we prove the following theorem: Fix a
$\delta$ in $[0, \frac{2}{3}]$. Choose $f: (0, \infty) \rightarrow (1, \infty)$
satisfying $\lim_{\varepsilon\to
0+}\dfrac{\log(f(t_{\varepsilon}))}{t_{\varepsilon}}$ exists with value $0$.
Assume that the inequality $\varepsilon \leqslant f(t_{\varepsilon})\cdot
5^{\frac{-t_{\varepsilon}}{6-3\delta}}$ holds. Then $K(T) \leqslant 2-\delta$.
Our conjecture implies the following: Let $\nu(5^{t_{\varepsilon}})$ denote the
set of integer solutions of the quadratic form:
$x_1^2+x_2^2+x_3^2+x_4^2=5^{t_{\varepsilon}}$. Let $M\equiv
M_{S^3}(\mathcal{N})$ denote the covering radius of the points
$\mathcal{N}=\nu(5^{t_{\varepsilon}})\cup\nu(5^{t_{\varepsilon}-1})$ on the
sphere $S^{3}$ in $\mathbb{R}^{4}$. Then $M \sim f(\log
N)N^{\frac{-1}{6-3\delta}}$. Here $N\equiv
N(\varepsilon)=6\cdot5^{t_{\varepsilon}}-2$. | [
0,
0,
1,
0,
0,
0
] |
Title: Max K-armed bandit: On the ExtremeHunter algorithm and beyond,
Abstract: This paper is devoted to the study of the max K-armed bandit problem, which
consists in sequentially allocating resources in order to detect extreme
values. Our contribution is twofold. We first significantly refine the analysis
of the ExtremeHunter algorithm carried out in Carpentier and Valko (2014), and
next propose an alternative approach, showing that, remarkably, Extreme Bandits
can be reduced to a classical version of the bandit problem to a certain
extent. Beyond the formal analysis, these two approaches are compared through
numerical experiments. | [
1,
0,
0,
1,
0,
0
] |
Title: Initial-boundary value problems in a rectangle for two-dimensional Zakharov-Kuznetsov equation,
Abstract: Initial-boundary value problems in a bounded rectangle with different types
of boundary conditions for two-dimensional Zakharov-Kuznetsov equation are
considered. Results on global well-posedness in the classes of weak and regular
solution are established. As applications of the developed technique results on
boundary controllability and long-time decay of weak solutions are also
obtained. | [
0,
0,
1,
0,
0,
0
] |
Title: A generalized quantum Slepian-Wolf,
Abstract: In this work we consider a quantum generalization of the task considered by
Slepian and Wolf [1973] regarding distributed source compression. In our task
Alice, Bob, Charlie and Reference share a joint pure state. Alice and Bob wish
to send a part of their respective systems to Charlie without collaborating
with each other. We give achievability bounds for this task in the one-shot
setting and provide the asymptotic and i.i.d. analysis in the case when there
is no side information with Charlie.
Our result implies the result of Abeyesinghe, Devetak, Hayden and Winter
[2009] who studied a special case of this problem. As another special case
wherein Bob holds trivial registers, we recover the result of Devetak and Yard
[2008] regarding quantum state redistribution. | [
1,
0,
0,
0,
0,
0
] |
Title: The rational SPDE approach for Gaussian random fields with general smoothness,
Abstract: A popular approach for modeling and inference in spatial statistics is to
represent Gaussian random fields as solutions to stochastic partial
differential equations (SPDEs) of the form $L^{\beta}u = \mathcal{W}$, where
$\mathcal{W}$ is Gaussian white noise, $L$ is a second-order differential
operator, and $\beta>0$ is a parameter that determines the smoothness of $u$.
However, this approach has been limited to the case $2\beta\in\mathbb{N}$,
which excludes several important covariance models and makes it necessary to
keep $\beta$ fixed during inference.
We introduce a new method, the rational SPDE approach, which is applicable
for any $\beta>0$ and therefore remedies the mentioned limitation. The
presented scheme combines a finite element discretization in space with a
rational approximation of the function $x^{-\beta}$ to approximate $u$. For the
resulting approximation, an explicit rate of strong convergence to $u$ is
derived and we show that the method has the same computational benefits as in
the restricted case $2\beta\in\mathbb{N}$ when used for statistical inference
and prediction. Several numerical experiments are performed to illustrate the
accuracy of the method, and to show how it can be used for likelihood-based
inference for all model parameters including $\beta$. | [
0,
0,
0,
1,
0,
0
] |
Title: An Analog of the Neumann Problem for the $1$-Laplace Equation in the Metric Setting: Existence, Boundary Regularity, and Stability,
Abstract: We study an inhomogeneous Neumann boundary value problem for functions of
least gradient on bounded domains in metric spaces that are equipped with a
doubling measure and support a Poincaré inequality. We show that solutions
exist under certain regularity assumptions on the domain, but are generally
nonunique. We also show that solutions can be taken to be differences of two
characteristic functions, and that they are regular up to the boundary when the
boundary is of positive mean curvature. By regular up to the boundary we mean
that if the boundary data is $1$ in a neighborhood of a point on the boundary
of the domain, then the solution is $-1$ in the intersection of the domain with
a possibly smaller neighborhood of that point. Finally, we consider the
stability of solutions with respect to boundary data. | [
0,
0,
1,
0,
0,
0
] |
Title: Activating spin-forbidden transitions in molecules by the highly localized plasmonic field,
Abstract: Optical spectroscopy has been the primary tool to study the electronic
structure of molecules. However the strict spin selection rule has severely
limited its ability to access states of different spin multiplicities. Here we
propose a new strategy to activate spin-forbidden transitions in molecules by
introducing spatially highly inhomogeneous plasmonic field. The giant
enhancement of the magnetic field strength resulted from the curl of the
inhomogeneous vector potential makes the transition between states of different
spin multiplicities naturally feasible. The dramatic effect of the
inhomogeneity of the plasmonic field on the spin and symmetry selection rules
is well illustrated by first principles calculations of C60. Remarkably, the
intensity of singlet-triplet transitions can even be stronger than that of
singlet-singlet transitions when the plasmon spatial distribution is comparable
with the molecular size. This approach offers a powerful means to completely
map out all excited states of molecules and to actively control their
photochemical processes. The same concept can also be applied to study nano and
biological systems. | [
0,
1,
0,
0,
0,
0
] |
Title: Charge and pairing dynamics in the attractive Hubbard model: mode coupling and the validity of linear-response theory,
Abstract: Pump-probe experiments have turned out as a powerful tool in order to study
the dynamics of competing orders in a large variety of materials. The
corresponding analysis of the data often relies on standard linear-response
theory generalized to non-equilibrium situations. Here we examine the validity
of such an approach within the attractive Hubbard model for which the dynamics
of pairing and charge-density wave orders is computed using the time-dependent
Hartree-Fock approximation (TDHF). Our calculations reveal that the
`linear-response assumption' is justified for small to moderate non-equilibrium
situations (i.e., pump pulses) when the symmetry of the pump-induced state
differs from that of the external field. This is the case, when we consider the
pairing response in a charge-ordered state or the charge-order response in a
superconducting state. The situation is very different when the non-equilibrium
state and the external probe field have the same symmetry. In this case, we
observe significant changes of the response in magnitude but also due to mode
coupling when moving away from an equilibrium state, indicating the failure of
the linear-response assumption. | [
0,
1,
0,
0,
0,
0
] |
Title: Interpretation of Neural Networks is Fragile,
Abstract: In order for machine learning to be deployed and trusted in many
applications, it is crucial to be able to reliably explain why the machine
learning algorithm makes certain predictions. For example, if an algorithm
classifies a given pathology image to be a malignant tumor, then the doctor may
need to know which parts of the image led the algorithm to this classification.
How to interpret black-box predictors is thus an important and active area of
research. A fundamental question is: how much can we trust the interpretation
itself? In this paper, we show that interpretation of deep learning predictions
is extremely fragile in the following sense: two perceptively indistinguishable
inputs with the same predicted label can be assigned very different
interpretations. We systematically characterize the fragility of several
widely-used feature-importance interpretation methods (saliency maps, relevance
propagation, and DeepLIFT) on ImageNet and CIFAR-10. Our experiments show that
even small random perturbation can change the feature importance and new
systematic perturbations can lead to dramatically different interpretations
without changing the label. We extend these results to show that
interpretations based on exemplars (e.g. influence functions) are similarly
fragile. Our analysis of the geometry of the Hessian matrix gives insight on
why fragility could be a fundamental challenge to the current interpretation
approaches. | [
1,
0,
0,
1,
0,
0
] |
Title: Dipolar phonons and electronic screening in monolayer FeSe on SrTiO$_3$,
Abstract: Monolayer films of FeSe grown on SrTiO$_3$ substrates exhibit significantly
higher superconducting transition temperatures than those of bulk FeSe.
Interaction of electrons in the FeSe layer with dipolar SrTiO$_3$ phonons has
been suggested as the cause of the enhanced transition temperature. In this
paper we systematically study the coupling of SrTiO$_3$ longitudinal optical
phonons to the FeSe electron, including also electron-electron Coulomb
interactions at the random phase approximation level. We find that the
electron-phonon interaction between FeSe and SrTiO$_3$ substrate is almost
entirely screened by the electronic fluctuations in the FeSe monolayer, so that
the net electron-phonon interaction is very weak and unlikely to lead to
superconductivity. | [
0,
1,
0,
0,
0,
0
] |
Title: A Supervised Approach to Extractive Summarisation of Scientific Papers,
Abstract: Automatic summarisation is a popular approach to reduce a document to its
main arguments. Recent research in the area has focused on neural approaches to
summarisation, which can be very data-hungry. However, few large datasets exist
and none for the traditionally popular domain of scientific publications, which
opens up challenging research avenues centered on encoding large, complex
documents. In this paper, we introduce a new dataset for summarisation of
computer science publications by exploiting a large resource of author provided
summaries and show straightforward ways of extending it further. We develop
models on the dataset making use of both neural sentence encoding and
traditionally used summarisation features and show that models which encode
sentences as well as their local and global context perform best, significantly
outperforming well-established baseline methods. | [
1,
0,
0,
1,
0,
0
] |
Title: Channel Feedback Based on AoD-Adaptive Subspace Codebook in FDD Massive MIMO Systems,
Abstract: Channel feedback is essential in frequency division duplexing (FDD) massive
multiple-input multiple-output (MIMO) systems. Unfortunately, previous work on
multiuser MIMO has shown that the codebook size for channel feedback should
scale exponentially with the number of base station (BS) antennas, which is
greatly increased in massive MIMO systems. To reduce the codebook size and
feedback overhead, we propose an angle-of-departure (AoD)-adaptive subspace
codebook for channel feedback in FDD massive MIMO systems. Our key insight is
to leverage the observation that path AoDs vary more slowly than the path
gains. Within the angle coherence time, by utilizing the constant AoD
information, the proposed AoD-adaptive subspace codebook is able to quantize
the channel vector in a more accurate way. We also provide performance analysis
of the proposed codebook in the large-dimensional regime, where we prove that
to limit the capacity degradation within an acceptable level, the required
number of feedback bits only scales linearly with the number of resolvable
(path) AoDs, which is much smaller than the number of BS antennas. Moreover, we
compare quantized channel feedback using the proposed AoD-adaptive subspace
codebook with analog channel feedback. Extensive simulations that verify the
analytical results are provided. | [
1,
0,
0,
0,
0,
0
] |
Title: Convergence of the free Boltzmann quadrangulation with simple boundary to the Brownian disk,
Abstract: We prove that the free Boltzmann quadrangulation with simple boundary and
fixed perimeter, equipped with its graph metric, natural area measure, and the
path which traces its boundary converges in the scaling limit to the free
Boltzmann Brownian disk. The topology of convergence is the so-called
Gromov-Hausdorff-Prokhorov-uniform (GHPU) topology, the natural analog of the
Gromov-Hausdorff topology for curve-decorated metric measure spaces. From this
we deduce that a random quadrangulation of the sphere decorated by a $2l$-step
self-avoiding loop converges in law in the GHPU topology to the random
curve-decorated metric measure space obtained by gluing together two
independent Brownian disks along their boundaries. | [
0,
0,
1,
0,
0,
0
] |
Title: Canonical quantization of nonlinear sigma models with theta term, with applications to symmetry-protected topological phases,
Abstract: We canonically quantize $O(D+2)$ nonlinear sigma models (NLSMs) with theta
term on arbitrary smooth, closed, connected, oriented $D$-dimensional spatial
manifolds $\mathcal{M}$, with the goal of proving the suitability of these
models for describing symmetry-protected topological (SPT) phases of bosons in
$D$ spatial dimensions. We show that in the disordered phase of the NLSM, and
when the coefficient $\theta$ of the theta term is an integer multiple of
$2\pi$, the theory on $\mathcal{M}$ has a unique ground state and a finite
energy gap to all excitations. We also construct the ground state wave
functional of the NLSM in this parameter regime, and we show that it is
independent of the metric on $\mathcal{M}$ and given by the exponential of a
Wess-Zumino term for the NLSM field, in agreement with previous results on flat
space. Our results show that the NLSM in the disordered phase and at
$\theta=2\pi k$, $k\in\mathbb{Z}$, has a symmetry-preserving ground state but
no topological order (i.e., no topology-dependent ground state degeneracy),
making it an ideal model for describing SPT phases of bosons. Thus, our work
places previous results on SPT phases derived using NLSMs on solid theoretical
ground. To canonically quantize the NLSM on $\mathcal{M}$ we use Dirac's method
for the quantization of systems with second class constraints, suitably
modified to account for the curvature of space. In a series of four appendices
we provide the technical background needed to follow the discussion in the main
sections of the paper. | [
0,
1,
0,
0,
0,
0
] |
Title: A Grouping Genetic Algorithm for Joint Stratification and Sample Allocation Designs,
Abstract: Predicting the cheapest sample size for the optimal stratification in
multivariate survey design is a problem in cases where the population frame is
large. A solution exists that iteratively searches for the minimum sample size
necessary to meet accuracy constraints in partitions of atomic strata created
by the Cartesian product of auxiliary variables into larger strata. The optimal
stratification can be found by testing all possible partitions. However the
number of possible partitions grows exponentially with the number of initial
strata. There are alternative ways of modelling this problem, one of the most
natural is using Genetic Algorithms (GA). These evolutionary algorithms use
recombination, mutation and selection to search for optimal solutions. They
often converge on optimal or near-optimal solution more quickly than exact
methods. We propose a new GA approach to this problem using grouping genetic
operators instead of traditional operators. The results show a significant
improvement in solution quality for similar computational effort, corresponding
to large monetary savings. | [
0,
0,
0,
1,
0,
0
] |
Title: Murmur Detection Using Parallel Recurrent & Convolutional Neural Networks,
Abstract: In this article, we propose a novel technique for classification of the
Murmurs in heart sound. We introduce a novel deep neural network architecture
using parallel combination of the Recurrent Neural Network (RNN) based
Bidirectional Long Short-Term Memory (BiLSTM) & Convolutional Neural Network
(CNN) to learn visual and time-dependent characteristics of Murmur in PCG
waveform. Set of acoustic features are presented to our proposed deep neural
network to discriminate between Normal and Murmur class. The proposed method
was evaluated on a large dataset using 5-fold cross-validation, resulting in a
sensitivity and specificity of 96 +- 0.6 % , 100 +- 0 % respectively and F1
Score of 98 +- 0.3 %. | [
1,
0,
0,
1,
0,
0
] |
Title: Learning across scales - A multiscale method for Convolution Neural Networks,
Abstract: In this work we establish the relation between optimal control and training
deep Convolution Neural Networks (CNNs). We show that the forward propagation
in CNNs can be interpreted as a time-dependent nonlinear differential equation
and learning as controlling the parameters of the differential equation such
that the network approximates the data-label relation for given training data.
Using this continuous interpretation we derive two new methods to scale CNNs
with respect to two different dimensions. The first class of multiscale methods
connects low-resolution and high-resolution data through prolongation and
restriction of CNN parameters. We demonstrate that this enables classifying
high-resolution images using CNNs trained with low-resolution images and vice
versa and warm-starting the learning process. The second class of multiscale
methods connects shallow and deep networks and leads to new training strategies
that gradually increase the depths of the CNN while re-using parameters for
initializations. | [
1,
0,
0,
0,
0,
0
] |
Title: Irregular Oscillatory-Patterns in the Early-Time Region of Coherent Phonon Generation in Silicon,
Abstract: Coherent phonon (CP) generation in an undoped Si crystal is theoretically
investigated to shed light on unexplored quantum-mechanical effects in the
early-time region immediately after the irradiation of ultrashort laser pulse.
One examines time signals attributed to an induced charge density of an ionic
core, placing the focus on the effects of the Rabi frequency $\Omega_{0cv}$ on
the signals; this frequency corresponds to the peak electric-field of the
pulse. It is found that at specific $\Omega_{0cv}$'s where the energy of
plasmon caused by photoexcited carriers coincides with the longitudinal-optical
phonon energy, the energetically {\it resonant } interaction between these two
modes leads to striking anticrossings, revealing irregular oscillations with
anomalously enhanced amplitudes in the observed time signals. Also, the
oscillatory pattern is subject to the Rabi flopping of the excited carrier
density that is controlled by $\Omega_{0cv}$. These findings show that the
early-time region is enriched with quantum-mechanical effects inherent in the
CP generation, though experimental signals are more or less masked by the
so-called coherent artifact due to nonlinear optical effects. | [
0,
1,
0,
0,
0,
0
] |
Title: Energy-Efficient Hybrid Stochastic-Binary Neural Networks for Near-Sensor Computing,
Abstract: Recent advances in neural networks (NNs) exhibit unprecedented success at
transforming large, unstructured data streams into compact higher-level
semantic information for tasks such as handwriting recognition, image
classification, and speech recognition. Ideally, systems would employ
near-sensor computation to execute these tasks at sensor endpoints to maximize
data reduction and minimize data movement. However, near- sensor computing
presents its own set of challenges such as operating power constraints, energy
budgets, and communication bandwidth capacities. In this paper, we propose a
stochastic- binary hybrid design which splits the computation between the
stochastic and binary domains for near-sensor NN applications. In addition, our
design uses a new stochastic adder and multiplier that are significantly more
accurate than existing adders and multipliers. We also show that retraining the
binary portion of the NN computation can compensate for precision losses
introduced by shorter stochastic bit-streams, allowing faster run times at
minimal accuracy losses. Our evaluation shows that our hybrid stochastic-binary
design can achieve 9.8x energy efficiency savings, and application-level
accuracies within 0.05% compared to conventional all-binary designs. | [
1,
0,
0,
0,
0,
0
] |
Title: Strongly correlated double Dirac fermions,
Abstract: Double Dirac fermions have recently been identified as possible
quasiparticles hosted by three-dimensional crystals with particular
non-symmorphic point group symmetries. Applying a combined approach of
ab-initio methods and dynamical mean field theory, we investigate how
interactions and double Dirac band topology conspire to form the electronic
quantum state of Bi$_2$CuO$_4$. We derive a downfolded eight-band model of the
pristine material at low energies around the Fermi level. By tuning the model
parameters from the free band structure to the realistic strongly correlated
regime, we find a persistence of the double Dirac dispersion until its
constituting time reveral symmetry is broken due to the onset of magnetic
ordering at the Mott transition. We analyze pressure as a promising route to
realize a double-Dirac metal in Bi$_2$CuO$_4$. | [
0,
1,
0,
0,
0,
0
] |
Title: A Phase Variable Approach for Improved Volitional and Rhythmic Control of a Powered Knee-Ankle Prosthesis,
Abstract: Although there has been recent progress in control of multi-joint prosthetic
legs for periodic tasks such as walking, volitional control of these systems
for non-periodic maneuvers is still an open problem. In this paper, we develop
a new controller that is capable of both periodic walking and common volitional
leg motions based on a piecewise holonomic phase variable through a finite
state machine. The phase variable is constructed by measuring the thigh angle,
and the transitions in the finite state machine are formulated through sensing
foot contact along with attributes of a nominal reference gait trajectory. The
controller was implemented on a powered knee-ankle prosthesis and tested with a
transfemoral amputee subject, who successfully performed a wide range of
periodic and non-periodic tasks, including low- and high-speed walking, quick
start and stop, backward walking, walking over obstacles, and kicking a soccer
ball. Use of the powered leg resulted in significant reductions in amputee
compensations including vaulting and hip circumduction when compared to use of
the take-home passive leg. The proposed approach is expected to provide better
understanding of volitional motions and lead to more reliable control of
multi-joint prostheses for a wider range of tasks. | [
1,
0,
0,
0,
0,
0
] |
Title: An Observer for an Occluded Reaction-Diffusion System With Spatially Varying Parameters,
Abstract: Spatially dependent parameters of a two-component chaotic reaction-diffusion
PDE model describing ocean ecology are observed by sampling a single species.
We estimate model parameters and the other species in the system by
autosynchronization, where quantities of interest are evolved according to
misfit between model and observations, to only partially observed data. Our
motivating example comes from oceanic ecology as viewed by remote sensing data,
but where noisy occluded data are realized in the form of cloud cover. We
demonstrate a method to learn a large-scale coupled synchronizing system that
represents spatio-temporal dynamics and apply a network approach to analyze
manifold stability. | [
0,
0,
1,
0,
0,
0
] |
Title: Human-in-the-Loop SLAM,
Abstract: Building large-scale, globally consistent maps is a challenging problem, made
more difficult in environments with limited access, sparse features, or when
using data collected by novice users. For such scenarios, where
state-of-the-art mapping algorithms produce globally inconsistent maps, we
introduce a systematic approach to incorporating sparse human corrections,
which we term Human-in-the-Loop Simultaneous Localization and Mapping
(HitL-SLAM). Given an initial factor graph for pose graph SLAM, HitL-SLAM
accepts approximate, potentially erroneous, and rank-deficient human input,
infers the intended correction via expectation maximization (EM),
back-propagates the extracted corrections over the pose graph, and finally
jointly optimizes the factor graph including the human inputs as human
correction factor terms, to yield globally consistent large-scale maps. We thus
contribute an EM formulation for inferring potentially rank-deficient human
corrections to mapping, and human correction factor extensions to the factor
graphs for pose graph SLAM that result in a principled approach to joint
optimization of the pose graph while simultaneously accounting for multiple
forms of human correction. We present empirical results showing the
effectiveness of HitL-SLAM at generating globally accurate and consistent maps
even when given poor initial estimates of the map. | [
1,
0,
0,
0,
0,
0
] |
Title: On Least Squares Linear Regression Without Second Moment,
Abstract: If X and Y are real valued random variables such that the first moments of X,
Y, and XY exist and the conditional expectation of Y given X is an affine
function of X, then the intercept and slope of the conditional expectation
equal the intercept and slope of the least squares linear regression function,
even though Y may not have a finite second moment. As a consequence, the affine
in X form of the conditional expectation and zero covariance imply mean
independence. | [
0,
0,
1,
1,
0,
0
] |
Title: Reducing asynchrony to synchronized rounds,
Abstract: Synchronous computation models simplify the design and the verification of
fault-tolerant distributed systems. For efficiency reasons such systems are
designed and implemented using an asynchronous semantics. In this paper, we
bridge the gap between these two worlds. We introduce a (synchronous)
round-based computational model and we prove a reduction for a class of
asynchronous protocols to our new model. The reduction is based on properties
of the code that can be checked with sequential methods. We apply the reduction
to state machine replication systems, such as, Paxos, Zab, and Viewstamped
Replication. | [
1,
0,
0,
0,
0,
0
] |
Title: Numerical simulation of oxidation processes in a cross-flow around tube bundles,
Abstract: An oxidation process is simulated for a bundle of metal tubes in a
cross-flow. A fluid flow is governed by the incompressible Navier-Stokes
equations. To describe the transport of oxygen, the corresponding
convection-diffusion equation is applied. The key point of the model is related
to the description of oxidation processes taking into account the growth of a
thin oxide film in the quasi-stationary approximation. Mathematical modeling of
oxidant transport in a tube bundle is carried out in the 2D approximation. The
numerical algorithm employed in the work is based on the finite-element
discretization in space and the fully implicit discretization in time. The tube
rows of a bundle can be either in-line or staggered in the direction of the
fluid flow velocity. The growth of the oxide film on tube walls is predicted
for various bundle structures using the developed oxidation model. | [
1,
1,
0,
0,
0,
0
] |
Title: Design and optimization of a portable LQCD Monte Carlo code using OpenACC,
Abstract: The present panorama of HPC architectures is extremely heterogeneous, ranging
from traditional multi-core CPU processors, supporting a wide class of
applications but delivering moderate computing performance, to many-core GPUs,
exploiting aggressive data-parallelism and delivering higher performances for
streaming computing applications. In this scenario, code portability (and
performance portability) become necessary for easy maintainability of
applications; this is very relevant in scientific computing where code changes
are very frequent, making it tedious and prone to error to keep different code
versions aligned. In this work we present the design and optimization of a
state-of-the-art production-level LQCD Monte Carlo application, using the
directive-based OpenACC programming model. OpenACC abstracts parallel
programming to a descriptive level, relieving programmers from specifying how
codes should be mapped onto the target architecture. We describe the
implementation of a code fully written in OpenACC, and show that we are able to
target several different architectures, including state-of-the-art traditional
CPUs and GPUs, with the same code. We also measure performance, evaluating the
computing efficiency of our OpenACC code on several architectures, comparing
with GPU-specific implementations and showing that a good level of
performance-portability can be reached. | [
0,
1,
0,
0,
0,
0
] |
Title: NodeTrix Planarity Testing with Small Clusters,
Abstract: We study the NodeTrix planarity testing problem for flat clustered graphs
when the maximum size of each cluster is bounded by a constant $k$. We consider
both the case when the sides of the matrices to which the edges are incident
are fixed and the case when they can be arbitrarily chosen. We show that
NodeTrix planarity testing with fixed sides can be solved in
$O(k^{3k+\frac{3}{2}} n^3)$ time for every flat clustered graph that can be
reduced to a partial 2-tree by collapsing its clusters into single vertices. In
the general case, NodeTrix planarity testing with fixed sides can be solved in
$O(n^3)$ time for $k = 2$, but it is NP-complete for any $k \geq 3$. NodeTrix
planarity testing remains NP-complete also in the free side model when $k > 4$. | [
1,
0,
0,
0,
0,
0
] |
Title: Some estimates for $θ$-type Calderón-Zygmund operators and linear commutators on certain weighted amalgam spaces,
Abstract: In this paper, we first introduce some new kinds of weighted amalgam spaces.
Then we discuss the strong type and weak type estimates for a class of
Calderón--Zygmund type operators $T_\theta$ in these new weighted spaces.
Furthermore, the strong type estimate and endpoint estimate of linear
commutators $[b,T_{\theta}]$ formed by $b$ and $T_{\theta}$ are established.
Also we study related problems about two-weight, weak type inequalities for
$T_{\theta}$ and $[b,T_{\theta}]$ in the weighted amalgam spaces and give some
results. | [
0,
0,
1,
0,
0,
0
] |
Title: A Cluster Elastic Net for Multivariate Regression,
Abstract: We propose a method for estimating coefficients in multivariate regression
when there is a clustering structure to the response variables. The proposed
method includes a fusion penalty, to shrink the difference in fitted values
from responses in the same cluster, and an L1 penalty for simultaneous variable
selection and estimation. The method can be used when the grouping structure of
the response variables is known or unknown. When the clustering structure is
unknown the method will simultaneously estimate the clusters of the response
and the regression coefficients. Theoretical results are presented for the
penalized least squares case, including asymptotic results allowing for p >> n.
We extend our method to the setting where the responses are binomial variables.
We propose a coordinate descent algorithm for both the normal and binomial
likelihood, which can easily be extended to other generalized linear model
(GLM) settings. Simulations and data examples from business operations and
genomics are presented to show the merits of both the least squares and
binomial methods. | [
0,
0,
0,
1,
0,
0
] |
Title: Achieving robust and high-fidelity quantum control via spectral phase optimization,
Abstract: Achieving high-fidelity control of quantum systems is of fundamental
importance in physics, chemistry and quantum information sciences. However, the
successful implementation of a high-fidelity quantum control scheme also
requires robustness against control field fluctuations. Here, we demonstrate a
robust optimization method for control of quantum systems by optimizing the
spectral phase of an ultrafast laser pulse, which is accomplished in the
framework of frequency domain quantum optimal control theory. By incorporating
a filtering function of frequency into the optimization algorithm, our
numerical simulations in an abstract two-level quantum system as well as in a
three-level atomic rubidium show that the optimization procedure can be
enforced to search optimal solutions while achieving remarkable robustness
against the control field fluctuations, providing an efficient approach to
optimize the spectral phase of the ultrafast laser pulse to achieve a desired
final quantum state of the system. | [
0,
1,
0,
0,
0,
0
] |
Title: Help Me Find a Job: A Graph-based Approach for Job Recommendation at Scale,
Abstract: Online job boards are one of the central components of modern recruitment
industry. With millions of candidates browsing through job postings everyday,
the need for accurate, effective, meaningful, and transparent job
recommendations is apparent more than ever. While recommendation systems are
successfully advancing in variety of online domains by creating social and
commercial value, the job recommendation domain is less explored. Existing
systems are mostly focused on content analysis of resumes and job descriptions,
relying heavily on the accuracy and coverage of the semantic analysis and
modeling of the content in which case, they end up usually suffering from
rigidity and the lack of implicit semantic relations that are uncovered from
users' behavior and could be captured by Collaborative Filtering (CF) methods.
Few works which utilize CF do not address the scalability challenges of
real-world systems and the problem of cold-start. In this paper, we propose a
scalable item-based recommendation system for online job recommendations. Our
approach overcomes the major challenges of sparsity and scalability by
leveraging a directed graph of jobs connected by multi-edges representing
various behavioral and contextual similarity signals. The short lived nature of
the items (jobs) in the system and the rapid rate in which new users and jobs
enter the system make the cold-start a serious problem hindering CF methods. We
address this problem by harnessing the power of deep learning in addition to
user behavior to serve hybrid recommendations. Our technique has been leveraged
by CareerBuilder.com which is one of the largest job boards in the world to
generate high-quality recommendations for millions of users. | [
1,
0,
0,
0,
0,
0
] |
Title: Glitch Classification and Clustering for LIGO with Deep Transfer Learning,
Abstract: The detection of gravitational waves with LIGO and Virgo requires a detailed
understanding of the response of these instruments in the presence of
environmental and instrumental noise. Of particular interest is the study of
anomalous non-Gaussian noise transients known as glitches, since their high
occurrence rate in LIGO/Virgo data can obscure or even mimic true gravitational
wave signals. Therefore, successfully identifying and excising glitches is of
utmost importance to detect and characterize gravitational waves. In this
article, we present the first application of Deep Learning combined with
Transfer Learning for glitch classification, using real data from LIGO's first
discovery campaign labeled by Gravity Spy, showing that knowledge from
pre-trained models for real-world object recognition can be transferred for
classifying spectrograms of glitches. We demonstrate that this method enables
the optimal use of very deep convolutional neural networks for glitch
classification given small unbalanced training datasets, significantly reduces
the training time, and achieves state-of-the-art accuracy above 98.8%. Once
trained via transfer learning, we show that the networks can be truncated and
used as feature extractors for unsupervised clustering to automatically group
together new classes of glitches and anomalies. This novel capability is of
critical importance to identify and remove new types of glitches which will
occur as the LIGO/Virgo detectors gradually attain design sensitivity. | [
1,
1,
0,
1,
0,
0
] |
Title: Provable Smoothness Guarantees for Black-Box Variational Inference,
Abstract: Black-box variational inference tries to approximate a complex target
distribution though a gradient-based optimization of the parameters of a
simpler distribution. Provable convergence guarantees require structural
properties of the objective. This paper shows that for location-scale family
approximations, if the target is M-Lipschitz smooth, then so is the objective,
if the entropy is excluded. The key proof idea is to describe gradients in a
certain inner-product space, thus permitting use of Bessel's inequality. This
result gives insight into how to parameterize distributions, gives bounds the
location of the optimal parameters, and is a key ingredient for convergence
guarantees. | [
1,
0,
0,
1,
0,
0
] |
Title: Label Sanitization against Label Flipping Poisoning Attacks,
Abstract: Many machine learning systems rely on data collected in the wild from
untrusted sources, exposing the learning algorithms to data poisoning.
Attackers can inject malicious data in the training dataset to subvert the
learning process, compromising the performance of the algorithm producing
errors in a targeted or an indiscriminate way. Label flipping attacks are a
special case of data poisoning, where the attacker can control the labels
assigned to a fraction of the training points. Even if the capabilities of the
attacker are constrained, these attacks have been shown to be effective to
significantly degrade the performance of the system. In this paper we propose
an efficient algorithm to perform optimal label flipping poisoning attacks and
a mechanism to detect and relabel suspicious data points, mitigating the effect
of such poisoning attacks. | [
0,
0,
0,
1,
0,
0
] |
Title: DeepCCI: End-to-end Deep Learning for Chemical-Chemical Interaction Prediction,
Abstract: Chemical-chemical interaction (CCI) plays a key role in predicting candidate
drugs, toxicity, therapeutic effects, and biological functions. In various
types of chemical analyses, computational approaches are often required due to
the amount of data that needs to be handled. The recent remarkable growth and
outstanding performance of deep learning have attracted considerable research
attention. However,even in state-of-the-art drug analysis methods, deep
learning continues to be used only as a classifier, although deep learning is
capable of not only simple classification but also automated feature
extraction. In this paper, we propose the first end-to-end learning method for
CCI, named DeepCCI. Hidden features are derived from a simplified molecular
input line entry system (SMILES), which is a string notation representing the
chemical structure, instead of learning from crafted features. To discover
hidden representations for the SMILES strings, we use convolutional neural
networks (CNNs). To guarantee the commutative property for homogeneous
interaction, we apply model sharing and hidden representation merging
techniques. The performance of DeepCCI was compared with a plain deep
classifier and conventional machine learning methods. The proposed DeepCCI
showed the best performance in all seven evaluation metrics used. In addition,
the commutative property was experimentally validated. The automatically
extracted features through end-to-end SMILES learning alleviates the
significant efforts required for manual feature engineering. It is expected to
improve prediction performance, in drug analyses. | [
1,
0,
0,
0,
0,
0
] |
Title: On a family of Caldero-Chapoton algebras that have the Laurent phenomenon,
Abstract: We realize a family of generalized cluster algebras as Caldero-Chapoton
algebras of quivers with relations. Each member of this family arises from an
unpunctured polygon with one orbifold point of order 3, and is realized as a
Caldero-Chapoton algebra of a quiver with relations naturally associated to any
triangulation of the alluded polygon. The realization is done by defining for
every arc $j$ on the polygon with orbifold point a representation $M(j)$ of the
referred quiver with relations, and by proving that for every triangulation
$\tau$ and every arc $j\in\tau$, the product of the Caldero-Chapoton functions
of $M(j)$ and $M(j')$, where $j'$ is the arc that replaces $j$ when we flip $j$
in $\tau$, equals the corresponding exchange polynomial of Chekhov-Shapiro in
the generalized cluster algebra. Furthermore, we show that there is a bijection
between the set of generalized cluster variables and the isomorphism classes of
$E$-rigid indecomposable decorated representations of $\Lambda$. | [
0,
0,
1,
0,
0,
0
] |
Title: Ricean K-factor Estimation based on Channel Quality Indicator in OFDM Systems using Neural Network,
Abstract: Ricean channel model is widely used in wireless communications to
characterize the channels with a line-of-sight path. The Ricean K factor,
defined as the ratio of direct path and scattered paths, provides a good
indication of the link quality. Most existing works estimate K factor based on
either maximum-likelihood criterion or higher-order moments, and the existing
works are targeted at K-factor estimation at receiver side. In this work, a
novel approach is proposed. Cast as a classification problem, the estimation of
K factor by neural network provides high accuracy. Moreover, the proposed
K-factor estimation is done at transmitter side for transmit processing, thus
saving the limited feedback bandwidth. | [
0,
0,
0,
1,
0,
0
] |
Title: Augmented lagrangian two-stage algorithm for LP and SCQP,
Abstract: In this paper, we consider a framework of projected gradient iterations for
linear programming (LP) and an augmented lagrangian two-stage algorithm for
strongly convex quadratic programming (SCQP). Based on the framework of
projected gradient, LP problem is transformed to a finite number of SCQP
problems. Furthermore, we give an estimate of the number of the SCQP problems.
We use augmented lagrangian method (ALM) to solve SCQP and each augmented
lagrangian subproblem is solved by a two-stage algorithm exactly, which ensures
the superlinear convergence of ALM for SCQP. The two-stage algorithm consists
of the accelerated proximal gradient algorithm as the first stage algorithm
which provides an approximate solution, and a simplified parametric active-set
method as the second stage algorithm which gives an exact solution. Moreover,
we improve the parametric active-set method by introducing a sorting technique
to update the cholesky factorization. Finally, the numerical experiments on
randomly generated and real-world test problems indicate that our algorithm is
effective, especially for random problems. | [
0,
0,
1,
0,
0,
0
] |
Title: Exact relations between homoclinic and periodic orbit actions in chaotic systems,
Abstract: Homoclinic and unstable periodic orbits in chaotic systems play central roles
in various semiclassical sum rules. The interferences between terms are
governed by the action functions and Maslov indices. In this article, we
identify geometric relations between homoclinic and unstable periodic orbits,
and derive exact formulae expressing the periodic orbit classical actions in
terms of corresponding homoclinic orbit actions plus certain phase space areas.
The exact relations provide a basis for approximations of the periodic orbit
actions as action differences between homoclinic orbits with well-estimated
errors. This make possible the explicit study of relations between periodic
orbits, which results in an analytic expression for the action differences
between long periodic orbits and their shadowing decomposed orbits in the cycle
expansion. | [
0,
1,
0,
0,
0,
0
] |
Title: A spectral approach to transit timing variations,
Abstract: The high planetary multiplicity revealed by Kepler implies that Transit Time
Variations (TTVs) are intrinsically common. The usual procedure for detecting
these TTVs is biased to long-period, deep transit planets whereas most
transiting planets have short periods and shallow transits. Here we introduce
the Spectral Approach to TTVs technique that allows expanding the TTVs catalog
towards lower TTV amplitude, shorter orbital period, and shallower transit
depth. In the Spectral Approach we assume that a sinusoidal TTV exists in the
data and then calculate the improvement to $\chi^2$ this model allows over that
of linear ephemeris model. This enables detection of TTVs even in cases where
the transits are too shallow so individual transits cannot be timed. The
Spectral Approach is more sensitive due to the reduced number of free
parameters in its model. Using the Spectral Approach, we: (a) detect 131 new
periodic TTVs in Kepler data (an increase of ~2/3 over a previous TTV catalog);
(b) Constrain the TTV periods of 34 long-period TTVs and reduce amplitude
errors of known TTVs; (c) Identify cases of multi-periodic TTVs, for which
absolute planetary mass determination may be possible. We further extend our
analysis by using perturbation theory assuming small TTV amplitude at the
detection stage, which greatly speeds up our detection (to a level of few
seconds per star). Our extended TTVs sample shows no deficit of short period or
low amplitude transits, in contrast to previous surveys in which the detection
schemes were significantly biased against such systems. | [
0,
1,
0,
0,
0,
0
] |
Title: Distinct dynamical behavior in random and all-to-all neuronal networks,
Abstract: Neuronal network dynamics depends on network structure. It is often assumed
that neurons are connected at random when their actual connectivity structure
is unknown. Such models are then often approximated by replacing the random
network by an all-to-all network, where every neuron is connected to all other
neurons. This mean-field approximation is a common approach in statistical
physics. In this paper we show that such approximation can be invalid. We solve
analytically a neuronal network model with binary-state neurons in both random
and all-to-all networks. We find strikingly different phase diagrams
corresponding to each network structure. Neuronal network dynamics is not only
different within certain parameter ranges, but it also undergoes different
bifurcations. Our results therefore suggest cautiousness when using mean-field
models based on all-to-all network topologies to represent random networks. | [
0,
0,
0,
0,
1,
0
] |
Title: A Note on Band-limited Minorants of an Euclidean Ball,
Abstract: We study the Beurling-Selberg problem of finding band-limited $L^1$-functions
that lie below the indicator function of an Euclidean ball. We compute the
critical radius of the support of the Fourier transform for which such
construction can have a positive integral. | [
0,
0,
1,
0,
0,
0
] |
Title: From Distance Correlation to Multiscale Graph Correlation,
Abstract: Understanding and developing a correlation measure that can detect general
dependencies is not only imperative to statistics and machine learning, but
also crucial to general scientific discovery in the big data age. In this
paper, we establish a new framework that generalizes distance correlation --- a
correlation measure that was recently proposed and shown to be universally
consistent for dependence testing against all joint distributions of finite
moments --- to the Multiscale Graph Correlation (MGC). By utilizing the
characteristic functions and incorporating the nearest neighbor machinery, we
formalize the population version of local distance correlations, define the
optimal scale in a given dependency, and name the optimal local correlation as
MGC. The new theoretical framework motivates a theoretically sound Sample MGC
and allows a number of desirable properties to be proved, including the
universal consistency, convergence and almost unbiasedness of the sample
version. The advantages of MGC are illustrated via a comprehensive set of
simulations with linear, nonlinear, univariate, multivariate, and noisy
dependencies, where it loses almost no power in monotone dependencies while
achieving better performance in general dependencies, compared to distance
correlation and other popular methods. | [
0,
0,
0,
1,
0,
0
] |
Title: Science and Facebook: the same popularity law!,
Abstract: The distribution of scientific citations for publications selected with
different rules (author, topic, institution, country, journal, etc.) collapse
on a single curve if one plots the citations relative to their mean value. We
find that the distribution of shares for the Facebook posts re-scale in the
same manner to the very same curve with scientific citations. This finding
suggests that citations are subjected to the same growth mechanism with
Facebook popularity measures, being influenced by a statistically similar
social environment and selection mechanism. In a simple master-equation
approach the exponential growth of the number of publications and a
preferential selection mechanism leads to a Tsallis-Pareto distribution
offering an excellent description for the observed statistics. Based on our
model and on the data derived from PubMed we predict that according to the
present trend the average citations per scientific publications exponentially
relaxes to about 4. | [
1,
1,
0,
0,
0,
0
] |
Title: Verifying Patterns of Dynamic Architectures using Model Checking,
Abstract: Architecture patterns capture architectural design experience and provide
abstract solutions to recurring architectural design problems. They consist of
a description of component types and restrict component connection and
activation. Therefore, they guarantee some desired properties for architectures
employing the pattern. Unfortunately, most documented patterns do not provide a
formal guarantee of whether their specification indeed leads to the desired
guarantee. Failure in doing so, however, might lead to wrong architectures,
i.e., architectures wrongly supposed to show certain desired properties. Since
architectures, in general, have a high impact on the quality of the resulting
system and architectural flaws are only difficult, if not to say impossible, to
repair, this may lead to badly reparable quality issues in the resulting
system. To address this problem, we propose an approach based on model checking
to verify pattern specifications w.r.t. their guarantees. In the following we
apply the approach to three well-known patterns for dynamic architectures: the
Singleton, the Model-View-Controller, and the Broker pattern. Thereby, we
discovered ambiguities and missing constraints for all three specifications.
Thus, we conclude that verifying patterns of dynamic architectures using model
checking is feasible and useful to discover ambiguities and flaws in pattern
specifications. | [
1,
0,
0,
0,
0,
0
] |
Title: Some integrals of hypergeometric functions,
Abstract: We consider a certain definite integral involving the product of two
classical hypergeometric functions having complicated arguments. We show in
this paper the surprising fact that this integral does not depend on the
parameters of the hypergeometric functions. | [
0,
0,
1,
0,
0,
0
] |
Title: Posterior contraction rates for support boundary recovery,
Abstract: Given a sample of a Poisson point process with intensity $\lambda_f(x,y) = n
\mathbf{1}(f(x) \leq y),$ we study recovery of the boundary function $f$ from a
nonparametric Bayes perspective. Because of the irregularity of this model, the
analysis is non-standard. We establish a general result for the posterior
contraction rate with respect to the $L^1$-norm based on entropy and one-sided
small probability bounds. From this, specific posterior contraction results are
derived for Gaussian process priors and priors based on random wavelet series. | [
0,
0,
1,
1,
0,
0
] |
Title: A Fast Image Simulation Algorithm for Scanning Transmission Electron Microscopy,
Abstract: Image simulation for scanning transmission electron microscopy at atomic
resolution for samples with realistic dimensions can require very large
computation times using existing simulation algorithms. We present a new
algorithm named PRISM that combines features of the two most commonly used
algorithms, the Bloch wave and multislice methods. PRISM uses a Fourier
interpolation factor $f$ that has typical values of 4-20 for atomic resolution
simulations. We show that in many cases PRISM can provide a speedup that scales
with $f^4$ compared to multislice simulations, with a negligible loss of
accuracy. We demonstrate the usefulness of this method with large-scale
scanning transmission electron microscopy image simulations of a crystalline
nanoparticle on an amorphous carbon substrate. | [
0,
1,
0,
0,
0,
0
] |
Title: X-ray Astronomical Point Sources Recognition Using Granular Binary-tree SVM,
Abstract: The study on point sources in astronomical images is of special importance,
since most energetic celestial objects in the Universe exhibit a point-like
appearance. An approach to recognize the point sources (PS) in the X-ray
astronomical images using our newly designed granular binary-tree support
vector machine (GBT-SVM) classifier is proposed. First, all potential point
sources are located by peak detection on the image. The image and spectral
features of these potential point sources are then extracted. Finally, a
classifier to recognize the true point sources is build through the extracted
features. Experiments and applications of our approach on real X-ray
astronomical images are demonstrated. comparisons between our approach and
other SVM-based classifiers are also carried out by evaluating the precision
and recall rates, which prove that our approach is better and achieves a higher
accuracy of around 89%. | [
1,
0,
0,
0,
0,
0
] |
Title: The Bag Semantics of Ontology-Based Data Access,
Abstract: Ontology-based data access (OBDA) is a popular approach for integrating and
querying multiple data sources by means of a shared ontology. The ontology is
linked to the sources using mappings, which assign views over the data to
ontology predicates. Motivated by the need for OBDA systems supporting
database-style aggregate queries, we propose a bag semantics for OBDA, where
duplicate tuples in the views defined by the mappings are retained, as is the
case in standard databases. We show that bag semantics makes conjunctive query
answering in OBDA coNP-hard in data complexity. To regain tractability, we
consider a rather general class of queries and show its rewritability to a
generalisation of the relational calculus to bags. | [
1,
0,
0,
0,
0,
0
] |
Title: Multilink Communities of Multiplex Networks,
Abstract: Multiplex networks describe a large number of complex social, biological and
transportation networks where a set of nodes is connected by links of different
nature and connotation. Here we uncover the rich community structure of
multiplex networks by associating a community to each multilink where the
multilinks characterize the connections existing between any two nodes of the
multiplex network. Our community detection method reveals the rich interplay
between the mesoscale structure of the multiplex networks and their
multiplexity. For instance some nodes can belong to many layers and few
communities while others can belong to few layers but many communities.
Moreover the multilink communities can be formed by a different number of
relevant layers. These results point out that mesoscopically there can be large
differences in the compressibility of multiplex networks. | [
1,
1,
0,
0,
0,
0
] |
Title: Deep generative models of genetic variation capture mutation effects,
Abstract: The functions of proteins and RNAs are determined by a myriad of interactions
between their constituent residues, but most quantitative models of how
molecular phenotype depends on genotype must approximate this by simple
additive effects. While recent models have relaxed this constraint to also
account for pairwise interactions, these approaches do not provide a tractable
path towards modeling higher-order dependencies. Here, we show how latent
variable models with nonlinear dependencies can be applied to capture
beyond-pairwise constraints in biomolecules. We present a new probabilistic
model for sequence families, DeepSequence, that can predict the effects of
mutations across a variety of deep mutational scanning experiments
significantly better than site independent or pairwise models that are based on
the same evolutionary data. The model, learned in an unsupervised manner solely
from sequence information, is grounded with biologically motivated priors,
reveals latent organization of sequence families, and can be used to
extrapolate to new parts of sequence space | [
0,
1,
0,
1,
0,
0
] |
Title: Consistent Rank Logits for Ordinal Regression with Convolutional Neural Networks,
Abstract: While extraordinary progress has been made towards developing neural network
architectures for classification tasks, commonly used loss functions such as
the multi-category cross entropy loss are inadequate for ranking and ordinal
regression problems. To address this issue, approaches have been developed that
transform ordinal target variables series of binary classification tasks,
resulting in robust ranking algorithms with good generalization performance.
However, to model ordinal information appropriately, ideally, a rank-monotonic
prediction function is required such that confidence scores are ordered and
consistent. We propose a new framework (Consistent Rank Logits, CORAL) with
theoretical guarantees for rank-monotonicity and consistent confidence scores.
Through parameter sharing, our framework benefits from low training complexity
and can easily be implemented to extend common convolutional neural network
classifiers for ordinal regression tasks. Furthermore, our empirical results
support the proposed theory and show a substantial improvement compared to the
current state-of-the-art ordinal regression method for age prediction from face
images. | [
1,
0,
0,
1,
0,
0
] |
Title: High-Pressure Synthesis and Characterization of $β$-GeSe - A Semiconductor with Six-Rings in an Uncommon Boat Conformation,
Abstract: Two-dimensional materials have significant potential for the development of
new devices. Here we report the electronic and structural properties of
$\beta$-GeSe, a previously unreported polymorph of GeSe, with a unique crystal
structure that displays strong two-dimensional structural features.
$\beta$-GeSe is made at high pressure and temperature and is stable under
ambient conditions. We compare it to its structural and electronic relatives
$\alpha$-GeSe and black phosphorus. The $\beta$ form of GeSe displays a boat
conformation for its Ge-Se six-ring, while the previously known $\alpha$ form,
and black phosphorus, display the more common chair conformation for their
six-rings. Electronic structure calculations indicate that $\beta$-GeSe is a
semiconductor, with an approximate bulk band gap of $\Delta~\approx$ 0.5 eV,
and, in its monolayer form, $\Delta~\approx$ 0.9 eV. These values fall between
those of $\alpha$-GeSe and black phosphorus, making $\beta$-GeSe a promising
candidate for future applications. The resistivity of our $\beta$-GeSe crystals
measured in-plane is on the order of $\rho \approx$ 1 $\Omega$cm, while being
essentially temperature independent. | [
0,
1,
0,
0,
0,
0
] |
Title: Machine Learning for Set-Identified Linear Models,
Abstract: Set-identified models often restrict the number of covariates leading to wide
identified sets in practice. This paper provides estimation and inference
methods for set-identified linear models with high-dimensional covariates where
the model selection is based on modern machine learning tools. I characterize
the boundary (i.e, support function) of the identified set using a
semiparametric moment condition. Combining Neyman-orthogonality and sample
splitting ideas, I construct a root-N consistent, the uniformly asymptotically
Gaussian estimator of the support function. I also prove the validity of the
Bayesian bootstrap procedure to conduct inference about the identified set. I
provide a general method to construct a Neyman-orthogonal moment condition for
the support function. I apply this result to estimate sharp nonparametric
bounds on the average treatment effect in Lee (2008)'s model of endogenous
selection and substantially tighten the bounds on this parameter in Angrist et
al. (2006)'s empirical setting. I also apply this result to estimate sharp
identified sets for two other parameters - a new parameter, called a partially
linear predictor, and the average partial derivative when the outcome variable
is recorded in intervals. | [
1,
0,
0,
1,
0,
0
] |
Title: An efficient algorithm to decide periodicity of b-recognisable sets using MSDF convention,
Abstract: Given an integer base $b>1$, a set of integers is represented in base $b$ by
a language over $\{0,1,...,b-1\}$. The set is said to be $b$-recognisable if
its representation is a regular language. It is known that eventually periodic
sets are $b$-recognisable in every base $b$, and Cobham's theorem implies the
converse: no other set is $b$-recognisable in every base $b$.
We are interested in deciding whether a $b$-recognisable set of integers
(given as a finite automaton) is eventually periodic. Honkala showed that this
problem decidable in 1986 and recent developments give efficient decision
algorithms. However, they only work when the integers are written with the
least significant digit first.
In this work, we consider the natural order of digits (Most Significant Digit
First) and give a quasi-linear algorithm to solve the problem in this case. | [
1,
0,
0,
0,
0,
0
] |
Title: An efficient distribution method for nonlinear two-phase flow in highly heterogeneous multidimensional stochastic porous media,
Abstract: In the context of stochastic two-phase flow in porous media, we introduce a
novel and efficient method to estimate the probability distribution of the
wetting saturation field under uncertain rock properties in highly
heterogeneous porous systems, where streamline patterns are dominated by
permeability heterogeneity, and for slow displacement processes (viscosity
ratio close to unity). Our method, referred to as the frozen streamline
distribution method (FROST), is based on a physical understanding of the
stochastic problem. Indeed, we identify key random fields that guide the
wetting saturation variability, namely fluid particle times of flight and
injection times. By comparing saturation statistics against full-physics Monte
Carlo simulations, we illustrate how this simple, yet accurate FROST method
performs under the preliminary approximation of frozen streamlines. Further, we
inspect the performance of an accelerated FROST variant that relies on a
simplification about injection time statistics. Finally, we introduce how
quantiles of saturation can be efficiently computed within the FROST framework,
hence leading to robust uncertainty assessment. | [
0,
1,
0,
0,
0,
0
] |
Title: Multi-Kernel LS-SVM Based Bio-Clinical Data Integration: Applications to Ovarian Cancer,
Abstract: The medical research facilitates to acquire a diverse type of data from the
same individual for particular cancer. Recent studies show that utilizing such
diverse data results in more accurate predictions. The major challenge faced is
how to utilize such diverse data sets in an effective way. In this paper, we
introduce a multiple kernel based pipeline for integrative analysis of
high-throughput molecular data (somatic mutation, copy number alteration, DNA
methylation and mRNA) and clinical data. We apply the pipeline on Ovarian
cancer data from TCGA. After multiple kernels have been generated from the
weighted sum of individual kernels, it is used to stratify patients and predict
clinical outcomes. We examine the survival time, vital status, and neoplasm
cancer status of each subtype to verify how well they cluster. We have also
examined the power of molecular and clinical data in predicting dichotomized
overall survival data and to classify the tumor grade for the cancer samples.
It was observed that the integration of various data types yields higher
log-rank statistics value. We were also able to predict clinical status with
higher accuracy as compared to using individual data types. | [
0,
0,
0,
1,
0,
0
] |
Title: The universal property of derived geometry,
Abstract: Derived geometry can be defined as the universal way to adjoin finite
homotopical limits to a given category of manifolds compatibly with products
and glueing. The point of this paper is to show that a construction closely
resembling existing approaches to derived geometry in fact produces a geometry
with this universal property.
I also investigate consequences of this definition in particular in the
differentiable setting, and compare the theory so obtained to D. Spivak's
axioms for derived C-infinity geometry. | [
0,
0,
1,
0,
0,
0
] |
Title: Rethinking Reprojection: Closing the Loop for Pose-aware ShapeReconstruction from a Single Image,
Abstract: An emerging problem in computer vision is the reconstruction of 3D shape and
pose of an object from a single image. Hitherto, the problem has been addressed
through the application of canonical deep learning methods to regress from the
image directly to the 3D shape and pose labels. These approaches, however, are
problematic from two perspectives. First, they are minimizing the error between
3D shapes and pose labels - with little thought about the nature of this label
error when reprojecting the shape back onto the image. Second, they rely on the
onerous and ill-posed task of hand labeling natural images with respect to 3D
shape and pose. In this paper we define the new task of pose-aware shape
reconstruction from a single image, and we advocate that cheaper 2D annotations
of objects silhouettes in natural images can be utilized. We design
architectures of pose-aware shape reconstruction which re-project the predicted
shape back on to the image using the predicted pose. Our evaluation on several
object categories demonstrates the superiority of our method for predicting
pose-aware 3D shapes from natural images. | [
1,
0,
0,
0,
0,
0
] |
Title: Rigid local systems and alternating groups,
Abstract: In earlier work, Katz exhibited some very simple one parameter families of
exponential sums which gave rigid local systems on the affine line in
characteristic p whose geometric (and usually, arithmetic) monodromy groups
were SL(2,q), and he exhibited other such very simple families giving SU(3,q).
[Here q is a power of the characteristic p with p odd]. In this paper, we
exhibit equally simple families whose geometric monodromy groups are the
alternating groups Alt(2q). $. We also determine their arithmetic monodromy
groups. By Raynaud's solution of the Abhyankar Conjecture, any finite simple
group whose order is divisible by p will occur as the geometric monodromy group
of some local system on the affine line in characteristic p; the interest here
is that it occurs in our particularly simple local systems.
In the earlier work of Katz, he used a theorem to Kubert to know that the
monodromy groups in question were finite, then work of Gross to determine which
finite groups they were. Here we do not have, at present, any direct way of
showing this finiteness. Rather, the situation is more complicated and more
interesting. Using some basic information about these local systems, a
fundamental dichotomy is proved:
The geometric monodromy group is either Alt(2q) or it is the special
orthogonal group SO(2q-1). An elementary polynomial identity is used to show
that the third moment is 1. This rules out the SO(2q-1) case. This roundabout
method establishes the theorem. It would be interesting to find a "direct"
proof that these local systems have integer (rather than rational) traces; this
integrality is in fact equivalent to their monodromy groups being finite, Even
if one had such a direct proof, it would still require serious group theory to
show that their geometric monodromy groups are the alternating groups. | [
0,
0,
1,
0,
0,
0
] |
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