title
stringlengths 7
239
| abstract
stringlengths 7
2.76k
| cs
int64 0
1
| phy
int64 0
1
| math
int64 0
1
| stat
int64 0
1
| quantitative biology
int64 0
1
| quantitative finance
int64 0
1
|
|---|---|---|---|---|---|---|---|
A two-dimensional hexagonal sheet of TiO$_2$ | We report on the ab initio discovery of a novel putative ground state for
quasi two-dimensional TiO$_2$ through a structural search using the minima
hopping method with an artificial neural network potential. The structure is
based on a honeycomb lattice and is energetically lower than the experimentally
reported lepidocrocite sheet by 7~meV/atom, and merely 13~meV/atom higher in
energy than the ground state rutile bulk structure. According to our
calculations, the hexagonal sheet is stable against mechanical stress, it is
chemically inert and can be deposited on various substrates without disrupting
the structure. Its properties differ significantly from all known TiO$_2$ bulk
phases with a large gap of 5.05~eV that can be tuned through strain
engineering.
| 0 | 1 | 0 | 0 | 0 | 0 |
Fast Markov Chain Monte Carlo Algorithms via Lie Groups | From basic considerations of the Lie group that preserves a target
probability measure, we derive the Barker, Metropolis, and ensemble Markov
chain Monte Carlo (MCMC) algorithms, as well as two new MCMC algorithms. The
convergence properties of these new algorithms successively improve on the
state of the art. We illustrate the new algorithms with explicit numerical
computations, and we empirically demonstrate the improved convergence on a spin
glass.
| 0 | 0 | 1 | 1 | 0 | 0 |
On the (parameterized) complexity of recognizing well-covered (r,l)-graphs | An $(r, \ell)$-partition of a graph $G$ is a partition of its vertex set into
$r$ independent sets and $\ell$ cliques. A graph is $(r, \ell)$ if it admits an
$(r, \ell)$-partition. A graph is well-covered if every maximal independent set
is also maximum. A graph is $(r,\ell)$-well-covered if it is both $(r,\ell)$
and well-covered. In this paper we consider two different decision problems. In
the $(r,\ell)$-Well-Covered Graph problem ($(r,\ell)$WCG for short), we are
given a graph $G$, and the question is whether $G$ is an
$(r,\ell)$-well-covered graph. In the Well-Covered $(r,\ell)$-Graph problem
(WC$(r,\ell)$G for short), we are given an $(r,\ell)$-graph $G$ together with
an $(r,\ell)$-partition of $V(G)$ into $r$ independent sets and $\ell$ cliques,
and the question is whether $G$ is well-covered. We classify most of these
problems into P, coNP-complete, NP-complete, NP-hard, or coNP-hard. Only the
cases WC$(r,0)$G for $r\geq 3$ remain open. In addition, we consider the
parameterized complexity of these problems for several choices of parameters,
such as the size $\alpha$ of a maximum independent set of the input graph, its
neighborhood diversity, its clique-width, or the number $\ell$ of cliques in an
$(r, \ell)$-partition. In particular, we show that the parameterized problem of
deciding whether a general graph is well-covered parameterized by $\alpha$ can
be reduced to the WC$(0,\ell)$G problem parameterized by $\ell$. In addition,
we prove that both problems are coW[2]-hard but can be solved in XP-time.
| 1 | 0 | 0 | 0 | 0 | 0 |
Comments on avalanche flow models based on the concept of random kinetic energy | In a series of papers, Bartelt and co-workers developed novel snow-avalanche
models in which \emph{random kinetic energy} $R_K$ (a.k.a.\ granular
temperature) is a key concept. The earliest models were for a single, constant
density layer, using a Voellmy model but with $R_K$-dependent friction
parameters. This was then extended to variable density, and finally a
suspension layer (powder-snow cloud) was added. The physical basis and
mathematical formulation of these models is critically reviewed here, with the
following main findings: (i) Key assumptions in the original RKE model differ
substantially from established results on dense granular flows; in particular,
the effective friction coefficient decreases to zero with velocity in the RKE
model. (ii) In the variable-density model, non-canonical interpretation of the
energy balance leads to a third-order evolution equation for the flow depth or
density, whereas the stated assumptions imply a first-order equation. (iii) The
model for the suspension layer neglects gravity and disregards well established
theoretical and experimental results on particulate gravity currents. Some
options for improving these aspects are discussed.
| 0 | 1 | 0 | 0 | 0 | 0 |
Continuous Implicit Authentication for Mobile Devices based on Adaptive Neuro-Fuzzy Inference System | As mobile devices have become indispensable in modern life, mobile security
is becoming much more important. Traditional password or PIN-like
point-of-entry security measures score low on usability and are vulnerable to
brute force and other types of attacks. In order to improve mobile security, an
adaptive neuro-fuzzy inference system(ANFIS)-based implicit authentication
system is proposed in this paper to provide authentication in a continuous and
transparent manner.To illustrate the applicability and capability of ANFIS in
our implicit authentication system, experiments were conducted on behavioural
data collected for up to 12 weeks from different Android users. The ability of
the ANFIS-based system to detect an adversary is also tested with scenarios
involving an attacker with varying levels of knowledge. The results demonstrate
that ANFIS is a feasible and efficient approach for implicit authentication
with an average of 95% user recognition rate. Moreover, the use of ANFIS-based
system for implicit authentication significantly reduces manual tuning and
configuration tasks due to its selflearning capability.
| 1 | 0 | 0 | 0 | 0 | 0 |
Ultracold heteronuclear three-body systems: How diabaticity limits the universality of recombination into shallow dimers | The mass-imbalanced three-body recombination process that forms a shallow
dimer is shown to possess a rich Efimov-Stückelberg landscape, with
corresponding spectra that differ fundamentally from the homonuclear case. A
semi-analytical treatment of the three-body recombination predicts an unusual
spectra with intertwined resonance peaks and minima, and yields in-depth
insight into the behavior of the corresponding Efimov spectra. In particular,
the patterns of the Efimov-Stückelberg landscape are shown to depend
inherently on the degree of diabaticity of the three-body collisions, which
strongly affects the universality of the heteronuclear Efimov states.
| 0 | 1 | 0 | 0 | 0 | 0 |
Variational Inference for Data-Efficient Model Learning in POMDPs | Partially observable Markov decision processes (POMDPs) are a powerful
abstraction for tasks that require decision making under uncertainty, and
capture a wide range of real world tasks. Today, effective planning approaches
exist that generate effective strategies given black-box models of a POMDP
task. Yet, an open question is how to acquire accurate models for complex
domains. In this paper we propose DELIP, an approach to model learning for
POMDPs that utilizes amortized structured variational inference. We empirically
show that our model leads to effective control strategies when coupled with
state-of-the-art planners. Intuitively, model-based approaches should be
particularly beneficial in environments with changing reward structures, or
where rewards are initially unknown. Our experiments confirm that DELIP is
particularly effective in this setting.
| 0 | 0 | 0 | 1 | 0 | 0 |
The ANAIS-112 experiment at the Canfranc Underground Laboratory | The ANAIS experiment aims at the confirmation of the DAMA/LIBRA signal at the
Canfranc Underground Laboratory (LSC). Several 12.5 kg NaI(Tl) modules produced
by Alpha Spectra Inc. have been operated there during the last years in various
set-ups; an outstanding light collection at the level of 15 photoelectrons per
keV, which allows triggering at 1 keV of visible energy, has been measured for
all of them and a complete characterization of their background has been
achieved. In the first months of 2017, the full ANAIS-112 set-up consisting of
nine Alpha Spectra detectors with a total mass of 112.5 kg was commissioned at
LSC and the first dark matter run started in August, 2017. Here, the latest
results on the detectors performance and measured background from the
commissioning run will be presented and the sensitivity prospects of the
ANAIS-112 experiment will be discussed.
| 0 | 1 | 0 | 0 | 0 | 0 |
A non-ellipticity result, or the impossible taming of the logarithmic strain measure | The logarithmic strain measures $\lVert\log U\rVert^2$, where $\log U$ is the
principal matrix logarithm of the stretch tensor $U=\sqrt{F^TF}$ corresponding
to the deformation gradient $F$ and $\lVert\,.\,\rVert$ denotes the Frobenius
matrix norm, arises naturally via the geodesic distance of $F$ to the special
orthogonal group $\operatorname{SO}(n)$. This purely geometric characterization
of this strain measure suggests that a viable constitutive law of nonlinear
elasticity may be derived from an elastic energy potential which depends solely
on this intrinsic property of the deformation, i.e. that an energy function
$W\colon\operatorname{GL^+}(n)\to\mathbb{R}$ of the form \begin{equation}
W(F)=\Psi(\lVert\log U\rVert^2) \tag{1} \end{equation} with a suitable
function $\Psi\colon[0,\infty)\to\mathbb{R}$ should be used to describe finite
elastic deformations.
However, while such energy functions enjoy a number of favorable properties,
we show that it is not possible to find a strictly monotone function $\Psi$
such that $W$ of the form (1) is Legendre-Hadamard elliptic.
Similarly, we consider the related isochoric strain measure
$\lVert\operatorname{dev}_n\log U\rVert^2$, where $\operatorname{dev}_n \log U$
is the deviatoric part of $\log U$. Although a polyconvex energy function in
terms of this strain measure has recently been constructed in the planar case
$n=2$, we show that for $n\geq3$, no strictly monotone function
$\Psi\colon[0,\infty)\to\mathbb{R}$ exists such that $F\mapsto
\Psi(\lVert\operatorname{dev}_n\log U\rVert^2)$ is polyconvex or even rank-one
convex. Moreover, a volumetric-isochorically decoupled energy of the form
$F\mapsto \Psi(\lVert\operatorname{dev}_n\log U\rVert^2) +
W_{\mathrm{vol}}(\det F)$ cannot be rank-one convex for any function
$W_{\mathrm{vol}}\colon(0,\infty)\to\mathbb{R}$ if $\Psi$ is strictly monotone.
| 0 | 0 | 1 | 0 | 0 | 0 |
Interpretable Neural Networks for Predicting Mortality Risk using Multi-modal Electronic Health Records | We present an interpretable neural network for predicting an important
clinical outcome (1-year mortality) from multi-modal Electronic Health Record
(EHR) data. Our approach builds on prior multi-modal machine learning models by
now enabling visualization of how individual factors contribute to the overall
outcome risk, assuming other factors remain constant, which was previously
impossible.
We demonstrate the value of this approach using a large multi-modal clinical
dataset including both EHR data and 31,278 echocardiographic videos of the
heart from 26,793 patients. We generated separate models for (i) clinical data
only (CD) (e.g. age, sex, diagnoses and laboratory values), (ii) numeric
variables derived from the videos, which we call echocardiography-derived
measures (EDM), and (iii) CD+EDM+raw videos (pixel data). The interpretable
multi-modal model maintained performance compared to non-interpretable models
(Random Forest, XGBoost), and also performed significantly better than a model
using a single modality (average AUC=0.82). Clinically relevant insights and
multi-modal variable importance rankings were also facilitated by the new
model, which have previously been impossible.
| 1 | 0 | 0 | 1 | 0 | 0 |
Automatic Temperature Setpoint Tuning of a Thermoforming Machine using Fuzzy Terminal Iterative Learning Control | This paper presents a new way to design a Fuzzy Terminal Iterative Learning
Control (TILC) to control the heater temperature setpoints of a thermoforming
machine. This fuzzy TILC is based on the inverse of a fuzzy model of this
machine, and is built from experimental (or simulation) data with kriging
interpolation. The Fuzzy Inference System usually used for a fuzzy model is the
zero order Takagi Sugeno Kwan system (constant consequents). In this paper, the
1st order Takagi Sugeno Kwan system is used, with the fuzzy model rules
expressed using matrices. This makes the inversion of the fuzzy model much
easier than the inversion of the fuzzy model based on the TSK of order 0. Based
on simulation results, the proposed fuzzy TILC seems able to give a very good
initial guess as to the heater temperature setpoints, making it possible to
have almost no wastage of plastic sheets. Simulation results show the
effectiveness of the fuzzy TILC compared to a crisp TILC, even though the fuzzy
controller is based on a fuzzy model built from noisy data.
| 1 | 0 | 0 | 0 | 0 | 0 |
A New Class of Integrals Involving Extended Hypergeometric Function | Our purpose in this present paper is to investigate generalized integration
formulas containing the extended generalized hypergeometric function and
obtained results are expressed in terms of extended hypergeometric function.
Certain special cases of the main results presented here are also pointed out
for the extended Gauss' hypergeometric and confluent hypergeometric functions.
| 0 | 0 | 1 | 0 | 0 | 0 |
The Special Theory of Relativity as Applied to the Born-Oppenheimer-Huang Approach | In two recent publications ( Int. J. Quant. Chem. 114, 1645 (2014) and Molec.
Phys. 114, 227 (2016)) it was shown that the Born -Hwang (BH) treatment of a
molecular system perturbed by an external field yields a set of decoupled
vectorial Wave Equations, just like in Electromagnetism. This finding led us to
declare on the existence of a new type of Fields, which were termed Molecular
Fields. The fact that such fields exist implies that at the vicinity of conical
intersections exist a mechanism that transforms a passing-by electric beam into
a field which differs from the original electric field. This situation is
reminiscent of what is encountered in astronomy where Black Holes formed by
massive stars may affect the nature of a near-by beam of light. Thus if the
NonAdiabatic-Coupling-Terms (NACT) with their singular points may affect the
nature of such a beam (see the above two publications) then it would be
interesting to know to what extend NACTs (and consequently also the BH
equation) will be affected by the special theory of relativity as introduced by
Dirac. Indeed while applying the Dirac approach we derived the relativistic
affected NACTs as well as the corresponding BH equation.
| 0 | 1 | 0 | 0 | 0 | 0 |
An efficient algorithm for finding all possible input nodes for controlling complex networks | Understanding structural controllability of a complex network requires to
identify a Minimum Input nodes Set (MIS) of the network. It has been suggested
that finding an MIS is equivalent to computing a maximum matching of the
network, where the unmatched nodes constitute an MIS. However, maximum matching
of a network is often not unique, and finding all MISs may provide deep
insights to the controllability of the network. Finding all possible input
nodes, which form the union of all MISs, is computationally challenging for
large networks. Here we present an efficient enumerative algorithm for the
problem. The main idea is to modify a maximum matching algorithm to make it
efficient for finding all possible input nodes by computing only one MIS. We
rigorously proved the correctness of the new algorithm and evaluated its
performance on synthetic and large real networks. The experimental results
showed that the new algorithm ran several orders of magnitude faster than the
existing method on large real networks.
| 1 | 1 | 0 | 0 | 0 | 0 |
Transformation thermal convection: Cloaking, concentrating, and camouflage | Heat can generally transfer via thermal conduction, thermal radiation, and
thermal convection. All the existing theories of transformation thermotics and
optics can treat thermal conduction and thermal radiation, respectively.
Unfortunately, thermal convection has never been touched in transformation
theories due to the lack of a suitable theory, thus limiting applications
associated with heat transfer through fluids (liquid or gas). Here, we develop,
for the first time, a general theory of transformation thermal convection by
considering the convection-diffusion equation, the Navier-Stokes equation, and
the Darcy law. By introducing porous media, we get a set of coupled equations
keeping their forms under coordinate transformation. As model applications, the
theory helps to show the effects of cloaking, concentrating, and camouflage.
Our finite element simulations confirm the theoretical findings. This work
offers a general transformation theory for thermal convection, thus revealing
some novel behaviors of thermal convection; it not only provides new hints on
how to control heat transfer by combining thermal conduction, thermal
radiation, and thermal convection, but also benefits the study of mass
diffusion and other related fields that contain a set of equations and need to
transform velocities at the same time.
| 0 | 1 | 0 | 0 | 0 | 0 |
Tunable terahertz reflection of graphene via ionic liquid gating | We report a highly efficient tunable THz reflector in graphene. By applying a
small gate voltage (up to 3 V), the reflectance of graphene is modulated from a
minimum of 0.79% to a maximum of 33.4% using graphene/ionic liquid structures
at room temperature, and the reflection tuning is uniform within a wide
spectral range (0.1 - 1.5 THz). Our observation is explained by the Drude
model, which describes the THz wave-induced intraband transition in graphene.
This tunable reflectance of graphene may contribute to broadband THz mirrors,
deformable THz mirrors, variable THz beam splitters and other optical
components.
| 0 | 1 | 0 | 0 | 0 | 0 |
Second-Order Kernel Online Convex Optimization with Adaptive Sketching | Kernel online convex optimization (KOCO) is a framework combining the
expressiveness of non-parametric kernel models with the regret guarantees of
online learning. First-order KOCO methods such as functional gradient descent
require only $\mathcal{O}(t)$ time and space per iteration, and, when the only
information on the losses is their convexity, achieve a minimax optimal
$\mathcal{O}(\sqrt{T})$ regret. Nonetheless, many common losses in kernel
problems, such as squared loss, logistic loss, and squared hinge loss posses
stronger curvature that can be exploited. In this case, second-order KOCO
methods achieve $\mathcal{O}(\log(\text{Det}(\boldsymbol{K})))$ regret, which
we show scales as $\mathcal{O}(d_{\text{eff}}\log T)$, where $d_{\text{eff}}$
is the effective dimension of the problem and is usually much smaller than
$\mathcal{O}(\sqrt{T})$. The main drawback of second-order methods is their
much higher $\mathcal{O}(t^2)$ space and time complexity. In this paper, we
introduce kernel online Newton step (KONS), a new second-order KOCO method that
also achieves $\mathcal{O}(d_{\text{eff}}\log T)$ regret. To address the
computational complexity of second-order methods, we introduce a new matrix
sketching algorithm for the kernel matrix $\boldsymbol{K}_t$, and show that for
a chosen parameter $\gamma \leq 1$ our Sketched-KONS reduces the space and time
complexity by a factor of $\gamma^2$ to $\mathcal{O}(t^2\gamma^2)$ space and
time per iteration, while incurring only $1/\gamma$ times more regret.
| 1 | 0 | 0 | 1 | 0 | 0 |
On Corecursive Algebras for Functors Preserving Coproducts | For an endofunctor $H$ on a hyper-extensive category preserving countable
coproducts we describe the free corecursive algebra on $Y$ as the coproduct of
the final coalgebra for $H$ and the free $H$-algebra on $Y$. As a consequence,
we derive that $H$ is a cia functor, i.e., its corecursive algebras are
precisely the cias (completely iterative algebras). Also all functors $H(-) +
Y$ are then cia functors. For finitary set functors we prove that, conversely,
if $H$ is a cia functor, then it has the form $H = W \times (-) + Y$ for some
sets $W$ and $Y$.
| 1 | 0 | 0 | 0 | 0 | 0 |
Semisimple characters for inner froms I: GL_n(D) | The article is about the representation theory of an inner form~$G$ of a
general linear group over a non-archimedean local field. We introduce
semisimple characters for~$G$ whose intertwining classes describe conjecturally
via Local Langlands correspondence the behavior on wild inertia. These
characters also play a potential role to understand the classification of
irreducible smooth representations of inner forms of classical groups. We prove
the intertwining formula for semisimple characters and an intertwining implies
conjugacy like theorem. Further we show that endo-parameters for~$G$, i.e.
invariants consisting of simple endo-classes and a numerical part, classify the
intertwining classes of semisimple characters for~$G$. They should be the
counter part for restrictions of Langlands-parameters to wild inertia under
Local Langlands correspondence.
| 0 | 0 | 1 | 0 | 0 | 0 |
Quantum Memristors in Quantum Photonics | We propose a method to build quantum memristors in quantum photonic
platforms. We firstly design an effective beam splitter, which is tunable in
real-time, by means of a Mach-Zehnder-type array with two equal 50:50 beam
splitters and a tunable retarder, which allows us to control its reflectivity.
Then, we show that this tunable beam splitter, when equipped with weak
measurements and classical feedback, behaves as a quantum memristor. Indeed, in
order to prove its quantumness, we show how to codify quantum information in
the coherent beams. Moreover, we estimate the memory capability of the quantum
memristor. Finally, we show the feasibility of the proposed setup in integrated
quantum photonics.
| 1 | 0 | 0 | 1 | 0 | 0 |
Spin dynamics and magnetic-field-induced polarization of excitons in ultrathin GaAs/AlAs quantum wells with indirect band gap and type-II band alignment | The exciton spin dynamics are investigated both experimentally and
theoretically in two-monolayer-thick GaAs/AlAs quantum wells with an indirect
band gap and a type-II band alignment. The magnetic-field-induced circular
polarization of photoluminescence, $P_c$, is studied as function of the
magnetic field strength and direction as well as sample temperature. The
observed nonmonotonic behaviour of these functions is provided by the interplay
of bright and dark exciton states contributing to the emission. To interpret
the experiment, we have developed a kinetic master equation model which
accounts for the dynamics of the spin states in this exciton quartet, radiative
and nonradiative recombination processes, and redistribution of excitons
between these states as result of spin relaxation. The model offers
quantitative agreement with experiment and allows us to evaluate, for the
studied structure, the heavy-hole $g$ factor, $g_{hh}=+3.5$, and the spin
relaxation times of electron, $\tau_{se} = 33~\mu$s, and hole, $\tau_{sh} =
3~\mu$s, bound in the exciton.
| 0 | 1 | 0 | 0 | 0 | 0 |
Resonant inelastic x-ray scattering probes the electron-phonon coupling in the spin-liquid kappa-(BEDT-TTF)2Cu2(CN)3 | Resonant inelastic x-ray scattering at the N K edge reveals clearly resolved
harmonics of the anion plane vibrations in the kappa-(BEDT-TTF)2Cu2(CN)3
spin-liquid insulator. Tuning the incoming light energy at the K edge of two
distinct N sites permits to excite different sets of phonon modes. Cyanide CN
stretching mode is selected at the edge of the ordered N sites which are more
strongly connected to the BEDT-TTF molecules, while positionally disordered N
sites show multi-mode excitation. Combining measurements with calculations on
an anion plane cluster permits to estimate the sitedependent electron-phonon
coupling of the modes related to nitrogen excitation.
| 0 | 1 | 0 | 0 | 0 | 0 |
Thermodynamic and kinetic fragility of Freon113: the most fragile plastic crystal | We present a dynamic and thermodynamic study of the orientational glass
former Freon113 (CCl2F-CClF2) in order to analyze its kinetic and thermodynamic
fragilities. Freon113 displays internal molecular degrees of freedom which
promote a complex energy landscape. Experimental specific heat and its
microscopic origin, the vibrational density of states from inelastic neutron
scattering, together with the orientational dynamics obtained by means of
dielectric spectroscopy have revealed the highest fragility value, both
thermodynamic and kinetic, found for this orientational glass former. The
excess in both Debye-reduced specific heat and density of states (boson peak)
evidences the existence of glassy low-energy excitations. We demonstrate that
early proposed correlations between the boson peak and the Debye specific heat
value are elusive as revealed by the clear counterexample of the studied case.
| 0 | 1 | 0 | 0 | 0 | 0 |
Semi-supervised Feature Learning For Improving Writer Identification | Data augmentation is usually used by supervised learning approaches for
offline writer identification, but such approaches require extra training data
and potentially lead to overfitting errors. In this study, a semi-supervised
feature learning pipeline was proposed to improve the performance of writer
identification by training with extra unlabeled data and the original labeled
data simultaneously. Specifically, we proposed a weighted label smoothing
regularization (WLSR) method for data augmentation, which assigned the weighted
uniform label distribution to the extra unlabeled data. The WLSR method could
regularize the convolutional neural network (CNN) baseline to allow more
discriminative features to be learned to represent the properties of different
writing styles. The experimental results on well-known benchmark datasets
(ICDAR2013 and CVL) showed that our proposed semi-supervised feature learning
approach could significantly improve the baseline measurement and perform
competitively with existing writer identification approaches. Our findings
provide new insights into offline write identification.
| 0 | 0 | 0 | 1 | 0 | 0 |
Error Bounds for Piecewise Smooth and Switching Regression | The paper deals with regression problems, in which the nonsmooth target is
assumed to switch between different operating modes. Specifically, piecewise
smooth (PWS) regression considers target functions switching deterministically
via a partition of the input space, while switching regression considers
arbitrary switching laws. The paper derives generalization error bounds in
these two settings by following the approach based on Rademacher complexities.
For PWS regression, our derivation involves a chaining argument and a
decomposition of the covering numbers of PWS classes in terms of the ones of
their component functions and the capacity of the classifier partitioning the
input space. This yields error bounds with a radical dependency on the number
of modes. For switching regression, the decomposition can be performed directly
at the level of the Rademacher complexities, which yields bounds with a linear
dependency on the number of modes. By using once more chaining and a
decomposition at the level of covering numbers, we show how to recover a
radical dependency. Examples of applications are given in particular for PWS
and swichting regression with linear and kernel-based component functions.
| 1 | 0 | 0 | 1 | 0 | 0 |
Civil Asset Forfeiture: A Judicial Perspective | Civil Asset Forfeiture (CAF) is a longstanding and controversial legal
process viewed on the one hand as a powerful tool for combating drug crimes and
on the other hand as a violation of the rights of US citizens. Data used to
support both sides of the controversy to date has come from government sources
representing records of the events at the time of occurrence. Court dockets
represent litigation events initiated following the forfeiture, however, and
can thus provide a new perspective on the CAF legal process. This paper will
show new evidence supporting existing claims about the growth of the practice
and bias in its application based on the quantitative analysis of data derived
from these court cases.
| 1 | 0 | 0 | 0 | 0 | 0 |
Covariance-Insured Screening | Modern bio-technologies have produced a vast amount of high-throughput data
with the number of predictors far greater than the sample size. In order to
identify more novel biomarkers and understand biological mechanisms, it is
vital to detect signals weakly associated with outcomes among
ultrahigh-dimensional predictors. However, existing screening methods, which
typically ignore correlation information, are likely to miss these weak
signals. By incorporating the inter-feature dependence, we propose a
covariance-insured screening methodology to identify predictors that are
jointly informative but only marginally weakly associated with outcomes. The
validity of the method is examined via extensive simulations and real data
studies for selecting potential genetic factors related to the onset of cancer.
| 0 | 0 | 0 | 1 | 0 | 0 |
Amari Functors and Dynamics in Gauge Structures | We deal with finite dimensional differentiable manifolds. All items are
concerned with are differentiable as well. The class of differentiability is
$C^\infty$. A metric structure in a vector bundle $E$ is a constant rank
symmetric bilinear vector bundle homomorphism of $E\times E$ in the trivial
bundle line bundle. We address the question whether a given gauge structure in
$E$ is metric. That is the main concerns. We use generalized Amari functors of
the information geometry for introducing two index functions defined in the
moduli space of gauge structures in $E$. Beside we introduce a differential
equation whose analysis allows to link the new index functions just mentioned
with the main concerns. We sketch applications in the differential geometry
theory of statistics. Reader interested in a former forum on the question
whether a giving connection is metric are referred to appendix.
| 0 | 0 | 1 | 0 | 0 | 0 |
An Experimental Analysis of the Power Consumption of Convolutional Neural Networks for Keyword Spotting | Nearly all previous work on small-footprint keyword spotting with neural
networks quantify model footprint in terms of the number of parameters and
multiply operations for a feedforward inference pass. These values are,
however, proxy measures since empirical performance in actual deployments is
determined by many factors. In this paper, we study the power consumption of a
family of convolutional neural networks for keyword spotting on a Raspberry Pi.
We find that both proxies are good predictors of energy usage, although the
number of multiplies is more predictive than the number of model parameters. We
also confirm that models with the highest accuracies are, unsurprisingly, the
most power hungry.
| 1 | 0 | 0 | 0 | 0 | 0 |
Formal Geometric Quantization III, Functoriality in the spin-c setting | In this paper, we prove a functorial aspect of the formal geometric
quantization procedure of non-compact spin-c manifolds.
| 0 | 0 | 1 | 0 | 0 | 0 |
Infinitesimal perturbation analysis for risk measures based on the Smith max-stable random field | When using risk or dependence measures based on a given underlying model, it
is essential to be able to quantify the sensitivity or robustness of these
measures with respect to the model parameters. In this paper, we consider an
underlying model which is very popular in spatial extremes, the Smith
max-stable random field. We study the sensitivity properties of risk or
dependence measures based on the values of this field at a finite number of
locations. Max-stable fields play a key role, e.g., in the modelling of natural
disasters. As their multivariate density is generally not available for more
than three locations, the Likelihood Ratio Method cannot be used to estimate
the derivatives of the risk measures with respect to the model parameters.
Thus, we focus on a pathwise method, the Infinitesimal Perturbation Analysis
(IPA). We provide a convenient and tractable sufficient condition for
performing IPA, which is intricate to obtain because of the very structure of
max-stable fields involving pointwise maxima over an infinite number of random
functions. IPA enables the consistent estimation of the considered measures'
derivatives with respect to the parameters characterizing the spatial
dependence. We carry out a simulation study which shows that the approach
performs well in various configurations.
| 0 | 0 | 0 | 0 | 0 | 1 |
Modeling and Analysis of Two-Way Relay Non-Orthogonal Multiple Access Systems | A two-way relay non-orthogonal multiple access (TWR-NOMA) system is
investigated, where two groups of NOMA users exchange messages with the aid of
one half-duplex (HD) decode-and-forward (DF) relay. Since the
signal-plus-interference-to-noise ratios (SINRs) of NOMA signals mainly depend
on effective successive interference cancellation (SIC) schemes, imperfect SIC
(ipSIC) and perfect SIC (pSIC) are taken into account. In order to characterize
the performance of TWR-NOMA systems, we first derive closed-form expressions
for both exact and asymptotic outage probabilities of NOMA users' signals with
ipSIC/pSIC. Based on the derived results, the diversity order and throughput of
the system are examined. Then we study the ergodic rates of users' signals by
providing the asymptotic analysis in high SNR regimes. Lastly, numerical
simulations are provided to verify the analytical results and show that: 1)
TWR-NOMA is superior to TWR-OMA in terms of outage probability in low SNR
regimes; 2) Due to the impact of interference signal (IS) at the relay, error
floors and throughput ceilings exist in outage probabilities and ergodic rates
for TWR-NOMA, respectively; and 3) In delay-limited transmission mode, TWR-NOMA
with ipSIC and pSIC have almost the same energy efficiency. However, in
delay-tolerant transmission mode, TWR-NOMA with pSIC is capable of achieving
larger energy efficiency compared to TWR-NOMA with ipSIC.
| 1 | 0 | 0 | 0 | 0 | 0 |
Totally positive matrices and dilogarithm identities | We show that two involutions on the variety $N_n^+$ of upper triangular
totally positive matrices are related, on the one hand, to the tetrahedron
equation and, on the other hand, to the action of the symmetric group $S_3$ on
some subvariety of $N_n^+$ and on the set of certain functions on $N_n^+$.
Using these involutions, we obtain a family of dilogarithm identities involving
minors of totally positive matrices. These identities admit a form manifestly
invariant under the action of the symmetric group $S_3$.
| 0 | 0 | 1 | 0 | 0 | 0 |
On the zeros of random harmonic polynomials: the Weyl model | Li and Wei (2009) studied the density of zeros of Gaussian harmonic
polynomials with independent Gaussian coefficients. They derived a formula for
the expected number of zeros of random harmonic polynomials as well as
asymptotics for the case that the polynomials are drawn from the Kostlan
ensemble. In this paper we extend their work to cover the case that the
polynomials are drawn from the Weyl ensemble by deriving asymptotics for this
class of harmonic polynomials.
| 0 | 0 | 1 | 0 | 0 | 0 |
Online deforestation detection | Deforestation detection using satellite images can make an important
contribution to forest management. Current approaches can be broadly divided
into those that compare two images taken at similar periods of the year and
those that monitor changes by using multiple images taken during the growing
season. The CMFDA algorithm described in Zhu et al. (2012) is an algorithm that
builds on the latter category by implementing a year-long, continuous,
time-series based approach to monitoring images. This algorithm was developed
for 30m resolution, 16-day frequency reflectance data from the Landsat
satellite. In this work we adapt the algorithm to 1km, 16-day frequency
reflectance data from the modis sensor aboard the Terra satellite. The CMFDA
algorithm is composed of two submodels which are fitted on a pixel-by-pixel
basis. The first estimates the amount of surface reflectance as a function of
the day of the year. The second estimates the occurrence of a deforestation
event by comparing the last few predicted and real reflectance values. For this
comparison, the reflectance observations for six different bands are first
combined into a forest index. Real and predicted values of the forest index are
then compared and high absolute differences for consecutive observation dates
are flagged as deforestation events. Our adapted algorithm also uses the two
model framework. However, since the modis 13A2 dataset used, includes
reflectance data for different spectral bands than those included in the
Landsat dataset, we cannot construct the forest index. Instead we propose two
contrasting approaches: a multivariate and an index approach similar to that of
CMFDA.
| 1 | 0 | 0 | 1 | 0 | 0 |
Analysis of Different Approaches of Parallel Block Processing for K-Means Clustering Algorithm | Distributed Computation has been a recent trend in engineering research.
Parallel Computation is widely used in different areas of Data Mining, Image
Processing, Simulating Models, Aerodynamics and so forth. One of the major
usage of Parallel Processing is widely implemented for clustering the satellite
images of size more than dimension of 1000x1000 in a legacy system. This paper
mainly focuses on the different approaches of parallel block processing such as
row-shaped, column-shaped and square-shaped. These approaches are applied for
classification problem. These approaches is applied to the K-Means clustering
algorithm as this is widely used for the detection of features for high
resolution orthoimagery satellite images. The different approaches are
analyzed, which lead to reduction in execution time and resulted the influence
of improvement in performance measurement compared to sequential K-Means
Clustering algorithm.
| 1 | 0 | 0 | 0 | 0 | 0 |
Preserving Order of Data When Validating Defect Prediction Models | [Context] The use of defect prediction models, such as classifiers, can
support testing resource allocations by using data of the previous releases of
the same project for predicting which software components are likely to be
defective. A validation technique, hereinafter technique defines a specific way
to split available data in training and test sets to measure a classifier
accuracy. Time-series techniques have the unique ability to preserve the
temporal order of data; i.e., preventing the testing set to have data
antecedent to the training set. [Aim] The aim of this paper is twofold: first
we check if there is a difference in the classifiers accuracy measured by
time-series versus non-time-series techniques. Afterward, we check for a
possible reason for this difference, i.e., if defect rates change across
releases of a project. [Method] Our method consists of measuring the accuracy,
i.e., AUC, of 10 classifiers on 13 open and two closed projects by using three
validation techniques, namely cross validation, bootstrap, and walk-forward,
where only the latter is a time-series technique. [Results] We find that the
AUC of the same classifier used on the same project and measured by 10-fold
varies compared to when measured by walk-forward in the range [-0.20, 0.22],
and it is statistically different in 45% of the cases. Similarly, the AUC
measured by bootstrap varies compared to when measured by walk-forward in the
range [-0.17, 0.43], and it is statistically different in 56% of the cases.
[Conclusions] We recommend choosing the technique to be used by carefully
considering the conclusions to draw, the property of the available datasets,
and the level of realism with the classifier usage scenario.
| 1 | 0 | 0 | 0 | 0 | 0 |
Graphettes: Constant-time determination of graphlet and orbit identity including (possibly disconnected) graphlets up to size 8 | Graphlets are small connected induced subgraphs of a larger graph $G$.
Graphlets are now commonly used to quantify local and global topology of
networks in the field. Methods exist to exhaustively enumerate all graphlets
(and their orbits) in large networks as efficiently as possible using orbit
counting equations. However, the number of graphlets in $G$ is exponential in
both the number of nodes and edges in $G$. Enumerating them all is already
unacceptably expensive on existing large networks, and the problem will only
get worse as networks continue to grow in size and density. Here we introduce
an efficient method designed to aid statistical sampling of graphlets up to
size $k=8$ from a large network. We define graphettes as the generalization of
graphlets allowing for disconnected graphlets. Given a particular (undirected)
graphette $g$, we introduce the idea of the canonical graphette $\mathcal K(g)$
as a representative member of the isomorphism group $Iso(g)$ of $g$. We compute
the mapping $\mathcal K$, in the form of a lookup table, from all
$2^{k(k-1)/2}$ undirected graphettes $g$ of size $k\le 8$ to their canonical
representatives $\mathcal K(g)$, as well as the permutation that transforms $g$
to $\mathcal K(g)$. We also compute all automorphism orbits for each canonical
graphette. Thus, given any $k\le 8$ nodes in a graph $G$, we can in constant
time infer which graphette it is, as well as which orbit each of the $k$ nodes
belongs to. Sampling a large number $N$ of such $k$-sets of nodes provides an
approximation of both the distribution of graphlets and orbits across $G$, and
the orbit degree vector at each node.
| 1 | 0 | 0 | 0 | 0 | 0 |
The toric sections: a simple introduction | We review, from a didactic point of view, the definition of a toric section
and the different shapes it can take. We'll then discuss some properties of
this curve, investigate its analogies and differences with the most renowned
conic section and show how to build its general quartic equation. A curious and
unexpected result was to find that, with some algebraic manipulation, a toric
section can also be obtained as the intersection of a cylinder with a cone.
Finally we'll show how it is possible to construct and represent toric sections
in the 3D Graphics view of Geogebra. In the article only elementary algebra is
used, and the requirements to follow it are just some notion of goniometry and
of tridimensional analytic geometry.
| 0 | 0 | 1 | 0 | 0 | 0 |
Quantum Field Theory, Quantum Geometry, and Quantum Algebras | We demonstrate how one can see quantization of geometry, and quantum
algebraic structure in supersymmetric gauge theory.
| 0 | 0 | 1 | 0 | 0 | 0 |
Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning | Embedded, continual learning for autonomous and adaptive behavior is a key
application of neuromorphic hardware. However, neuromorphic implementations of
embedded learning at large scales that are both flexible and efficient have
been hindered by a lack of a suitable algorithmic framework. As a result, the
most neuromorphic hardware is trained off-line on large clusters of dedicated
processors or GPUs and transferred post hoc to the device. We address this by
introducing the neural and synaptic array transceiver (NSAT), a neuromorphic
computational framework facilitating flexible and efficient embedded learning
by matching algorithmic requirements and neural and synaptic dynamics. NSAT
supports event-driven supervised, unsupervised and reinforcement learning
algorithms including deep learning. We demonstrate the NSAT in a wide range of
tasks, including the simulation of Mihalas-Niebur neuron, dynamic neural
fields, event-driven random back-propagation for event-based deep learning,
event-based contrastive divergence for unsupervised learning, and voltage-based
learning rules for sequence learning. We anticipate that this contribution will
establish the foundation for a new generation of devices enabling adaptive
mobile systems, wearable devices, and robots with data-driven autonomy.
| 1 | 0 | 0 | 0 | 0 | 0 |
Discrete Wavelet Transform Based Algorithm for Recognition of QRS Complexes | This paper proposes the application of Discrete Wavelet Transform (DWT) to
detect the QRS (ECG is characterized by a recurrent wave sequence of P, QRS and
T-wave) of an electrocardiogram (ECG) signal. Wavelet Transform provides
localization in both time and frequency. In preprocessing stage, DWT is used to
remove the baseline wander in the ECG signal. The performance of the algorithm
of QRS detection is evaluated against the standard MIT BIH (Massachusetts
Institute of Technology, Beth Israel Hospital) Arrhythmia database. The average
QRS complexes detection rate of 98.1 % is achieved.
| 1 | 0 | 0 | 0 | 0 | 0 |
Multipolar moments of weak lensing signal around clusters. Weighing filaments in harmonic space | Context. Upcoming weak lensing surveys such as Euclid will provide an
unprecedented opportunity to quantify the geometry and topology of the cosmic
web, in particular in the vicinity of lensing clusters. Aims. Understanding the
connectivity of the cosmic web with unbiased mass tracers, such as weak
lensing, is of prime importance to probe the underlying cosmology, seek
dynamical signatures of dark matter, and quantify environmental effects on
galaxy formation. Methods. Mock catalogues of galaxy clusters are extracted
from the N-body PLUS simulation. For each cluster, the aperture multipolar
moments of the convergence are calculated in two annuli (inside and outside the
virial radius). By stacking their modulus, a statistical estimator is built to
characterise the angular mass distribution around clusters. The moments are
compared to predictions from perturbation theory and spherical collapse.
Results. The main weakly chromatic excess of multipolar power on large scales
is understood as arising from the contraction of the primordial cosmic web
driven by the growing potential well of the cluster. Besides this boost, the
quadrupole prevails in the cluster (ellipsoidal) core, while at the outskirts,
harmonic distortions are spread on small angular modes, and trace the
non-linear sharpening of the filamentary structures. Predictions for the signal
amplitude as a function of the cluster-centric distance, mass, and redshift are
presented. The prospects of measuring this signal are estimated for current and
future lensing data sets. Conclusions. The Euclid mission should provide all
the necessary information for studying the cosmic evolution of the connectivity
of the cosmic web around lensing clusters using multipolar moments and probing
unique signatures of, for example, baryons and warm dark matter.
| 0 | 1 | 0 | 0 | 0 | 0 |
Charge and spin transport on graphene grain boundaries in a quantizing magnetic field | We study charge and spin transport along grain boundaries in single layer
graphene in the presence of a quantizing magnetic field. Transport states in a
grain boundary are produced by hybridization of Landau zero modes with
interfacial states. In selected energy regimes quantum Hall edge states can be
deflected either fully or partially into grain boundary states. The degree of
edge state deflection is studied in the nonlocal conductance and in the shot
noise. We also consider the possibility of grain boundaries as gate-switchable
spin filters, a functionality enabled by counterpropagating transport channels
laterally confined in the grain boundary.
| 0 | 1 | 0 | 0 | 0 | 0 |
Point Cloud Movement For Fully Lagrangian Meshfree Methods | In Lagrangian meshfree methods, the underlying spatial discretization,
referred to as a point cloud or a particle cloud, moves with the flow velocity.
In this paper, we consider different numerical methods of performing this
movement of points or particles. The movement is most commonly done by a first
order method, which assumes the velocity to be constant within a time step. We
show that this method is very inaccurate and that it introduces volume and mass
conservation errors. We further propose new methods for the same which
prescribe an additional ODE system that describes the characteristic velocity.
Movement is then performed along this characteristic velocity. The first new
way of moving points is an extension of mesh-based streamline tracing ideas to
meshfree methods. In the second way, the movement is done based on the
difference in approximated streamlines between two time levels, which
approximates the pathlines in unsteady flow. Numerical comparisons show these
method to be vastly superior to the conventionally used first order method.
| 0 | 1 | 1 | 0 | 0 | 0 |
A fast ILP-based Heuristic for the robust design of Body Wireless Sensor Networks | We consider the problem of optimally designing a body wireless sensor
network, while taking into account the uncertainty of data generation of
biosensors. Since the related min-max robustness Integer Linear Programming
(ILP) problem can be difficult to solve even for state-of-the-art commercial
optimization solvers, we propose an original heuristic for its solution. The
heuristic combines deterministic and probabilistic variable fixing strategies,
guided by the information coming from strengthened linear relaxations of the
ILP robust model, and includes a very large neighborhood search for reparation
and improvement of generated solutions, formulated as an ILP problem solved
exactly. Computational tests on realistic instances show that our heuristic
finds solutions of much higher quality than a state-of-the-art solver and than
an effective benchmark heuristic.
| 1 | 0 | 1 | 0 | 0 | 0 |
Strain broadening of the 1042-nm zero-phonon line of the NV- center in diamond: a promising spectroscopic tool for defect tomography | The negatively charged nitrogen-vacancy (NV-) center in diamond is a
promising candidate for many quantum applications. Here, we examine the
splitting and broadening of the center's infrared (IR) zero-phonon line (ZPL).
We develop a model for these effects that accounts for the strain induced by
photo-dependent microscopic distributions of defects. We apply this model to
interpret observed variations of the IR ZPL shape with temperature and
photoexcitation conditions. We identify an anomalous temperature dependent
broadening mechanism and that defects other than the substitutional nitrogen
center significantly contribute to strain broadening. The former conclusion
suggests the presence of a strong Jahn-Teller effect in the center's singlet
levels and the latter indicates that major sources of broadening are yet to be
identified. These conclusions have important implications for the understanding
of the center and the engineering of diamond quantum devices. Finally, we
propose that the IR ZPL can be used as a sensitive spectroscopic tool for
probing microscopic strain fields and performing defect tomography.
| 0 | 1 | 0 | 0 | 0 | 0 |
Learning Depthwise Separable Graph Convolution from Data Manifold | Convolution Neural Network (CNN) has gained tremendous success in computer
vision tasks with its outstanding ability to capture the local latent features.
Recently, there has been an increasing interest in extending convolution
operations to the non-Euclidean geometry. Although various types of convolution
operations have been proposed for graphs or manifolds, their connections with
traditional convolution over grid-structured data are not well-understood. In
this paper, we show that depthwise separable convolution can be successfully
generalized for the unification of both graph-based and grid-based convolution
methods. Based on this insight we propose a novel Depthwise Separable Graph
Convolution (DSGC) approach which is compatible with the tradition convolution
network and subsumes existing convolution methods as special cases. It is
equipped with the combined strengths in model expressiveness, compatibility
(relatively small number of parameters), modularity and computational
efficiency in training. Extensive experiments show the outstanding performance
of DSGC in comparison with strong baselines on multi-domain benchmark datasets.
| 1 | 0 | 0 | 1 | 0 | 0 |
Candidate Hα emission and absorption line sources in the Galactic Bulge Survey | We present a catalogue of candidate H{\alpha} emission and absorption line
sources and blue objects in the Galactic Bulge Survey (GBS) region. We use a
point source catalogue of the GBS fields (two strips of (l x b) = (6 x 1)
degrees centred at b = 1.5 above and below the Galactic centre), covering the
magnitude range 16 < r' < 22.5. We utilize (r'-i', r'-H{\alpha}) colour-colour
diagrams to select H{\alpha} emission and absorption line candidates, and also
identify blue objects (compared to field stars) using the r'-i' colour index.
We identify 1337 H{\alpha} emission line candidates and 336 H{\alpha}
absorption line candidates. These catalogues likely contain a plethora of
sources, ranging from active (binary) stars, early-type emission line objects,
cataclysmic variables (CVs) and low-mass X-ray binaries (LMXBs) to background
active galactic nuclei (AGN). The 389 blue objects we identify are likely
systems containing a compact object, such as CVs, planetary nebulae and LMXBs.
Hot subluminous dwarfs (sdO/B stars) are also expected to be found as blue
outliers. Crossmatching our outliers with the GBS X-ray catalogue yields
sixteen sources, including seven (magnetic) CVs and one qLMXB candidate among
the emission line candidates, and one background AGN for the absorption line
candidates. One of the blue outliers is a high state AM CVn system.
Spectroscopic observations combined with the multi-wavelength coverage of this
area, including X-ray, ultraviolet and (time-resolved) optical and infrared
observations, can be used to further constrain the nature of individual
sources.
| 0 | 1 | 0 | 0 | 0 | 0 |
Timely Updates over an Erasure Channel | Using an age of information (AoI) metric, we examine the transmission of
coded updates through a binary erasure channel to a monitor/receiver. We start
by deriving the average status update age of an infinite incremental redundancy
(IIR) system in which the transmission of a k-symbol update continuesuntil k
symbols are received. This system is then compared to a fixed redundancy (FR)
system in which each update is transmitted as an n symbol packet and the packet
is successfully received if and only if at least k symbols are received. If
fewer than k symbols are received, the update is discarded. Unlike the IIR
system, the FR system requires no feedback from the receiver. For a single
monitor system, we show that tuning the redundancy to the symbol erasure rate
enables the FR system to perform as well as the IIR system. As the number of
monitors is increased, the FR system outperforms the IIR system that guarantees
delivery of all updates to all monitors.
| 1 | 0 | 0 | 0 | 0 | 0 |
Stellar Absorption Line Analysis of Local Star-Forming Galaxies: The Relation Between Stellar Mass, Metallicity, Dust Attenuation and Star Formation Rate | We analyze the optical continuum of star-forming galaxies in SDSS by fitting
stacked spectra with stellar population synthesis models to investigate the
relation between stellar mass, stellar metallicity, dust attenuation and star
formation rate. We fit models calculated with star formation and chemical
evolution histories that are derived empirically from multi-epoch observations
of the stellar mass---star formation rate and the stellar mass---gas-phase
metallicity relations, respectively. We also fit linear combinations of single
burst models with a range of metallicities and ages. Star formation and
chemical evolution histories are unconstrained for these models. The stellar
mass---stellar metallicity relations obtained from the two methods agree with
the relation measured from individual supergiant stars in nearby galaxies.
These relations are also consistent with the relation obtained from emission
line analysis of gas-phase metallicity after accounting for systematic offsets
in the gas-phase-metallicity. We measure dust attenuation of the stellar
continuum and show that its dependence on stellar mass and star formation rate
is consistent with previously reported results derived from nebular emission
lines. However, stellar continuum attenuation is smaller than nebular emission
line attenuation. The continuum-to-nebular attenuation ratio depends on stellar
mass and is smaller in more massive galaxies. Our consistent analysis of
stellar continuum and nebular emission lines paves the way for a comprehensive
investigation of stellar metallicities of star-forming and quiescent galaxies.
| 0 | 1 | 0 | 0 | 0 | 0 |
Stratification as a general variance reduction method for Markov chain Monte Carlo | The Eigenvector Method for Umbrella Sampling (EMUS) belongs to a popular
class of methods in statistical mechanics which adapt the principle of
stratified survey sampling to the computation of free energies. By theoretical
analysis and numerical experiments, we demonstrate that EMUS is an efficient
general method for computing averages with respect to arbitrary target
distributions. We show that EMUS can be dramatically more efficient than direct
MCMC when the target distribution is multimodal or when the goal is to compute
tail probabilities.
| 0 | 1 | 0 | 1 | 0 | 0 |
Adaptive MCMC via Combining Local Samplers | Markov chain Monte Carlo (MCMC) methods are widely used in machine learning.
One of the major problems with MCMC is the question of how to design chains
that mix fast over the whole space; in particular, how to select the parameters
of an MCMC algorithm. Here we take a different approach and, similarly to
parallel MCMC methods, instead of trying to find a single chain to sample from
the whole distribution, we combine samples from several chains run in parallel,
each exploring only a few modes. The chains are prioritized based on the kernel
Stein discrepancy, which provides a good measure of performance locally. The
samples from the independent chains are combined using a novel technique for
estimating the probability of different regions of the sample space.
Experimental results demonstrate that the resulting algorithm may provide
significant speedups in different sampling problems. Most importantly, when
combined with the state-of-the-art NUTS algorithm as the base MCMC sampler, our
algorithm remained competitive with the basic version of NUTS on sampling from
unimodal distributions, while significantly outperformed state-of-the-art
competitors on synthetic multimodal problems as well as on a challenging sensor
localization task.
| 1 | 0 | 0 | 1 | 0 | 0 |
GAMBIT: The Global and Modular Beyond-the-Standard-Model Inference Tool | We describe the open-source global fitting package GAMBIT: the Global And
Modular Beyond-the-Standard-Model Inference Tool. GAMBIT combines extensive
calculations of observables and likelihoods in particle and astroparticle
physics with a hierarchical model database, advanced tools for automatically
building analyses of essentially any model, a flexible and powerful system for
interfacing to external codes, a suite of different statistical methods and
parameter scanning algorithms, and a host of other utilities designed to make
scans faster, safer and more easily-extendible than in the past. Here we give a
detailed description of the framework, its design and motivation, and the
current models and other specific components presently implemented in GAMBIT.
Accompanying papers deal with individual modules and present first GAMBIT
results. GAMBIT can be downloaded from gambit.hepforge.org.
| 0 | 1 | 0 | 0 | 0 | 0 |
Odd holes in bull-free graphs | The complexity of testing whether a graph contains an induced odd cycle of
length at least five is currently unknown. In this paper we show that this can
be done in polynomial time if the input graph has no induced subgraph
isomorphic to the bull (a triangle with two disjoint pendant edges).
| 1 | 0 | 0 | 0 | 0 | 0 |
Magnetic-Field-Induced Superconductivity in Ultrathin Pb Films with Magnetic Impurities | It is well known that external magnetic fields and magnetic moments of
impurities both suppress superconductivity. Here, we demonstrate that their
combined effect enhances the superconductivity of a few atomic layer thick Pb
films grown on a cleaved GaAs(110) surface. A Ce-doped film, where
superconductivity is totally suppressed at zero-field, actually turns
superconducting when an external magnetic field is applied parallel to the
conducting plane. For films with Mn adatoms, the screening of the magnetic
moment by conduction electrons, i.e., the Kondo singlet formation, becomes
important. We found that the degree of screening can be reduced by capping the
Pb film with a Au layer, and observed the positive magnetic field dependence of
the superconducting transition temperature.
| 0 | 1 | 0 | 0 | 0 | 0 |
Unimodal Category and the Monotonicity Conjecture | We completely characterize the unimodal category for functions $f:\mathbb
R\to[0,\infty)$ using a decomposition theorem obtained by generalizing the
sweeping algorithm of Baryshnikov and Ghrist. We also give a characterization
of the unimodal category for functions $f:S^1\to[0,\infty)$ and provide an
algorithm to compute the unimodal category of such a function in the case of
finitely many critical points.
We then turn to the monotonicity conjecture of Baryshnikov and Ghrist. We
show that this conjecture is true for functions on $\mathbb R$ and $S^1$ using
the above characterizations and that it is false on certain graphs and on the
Euclidean plane by providing explicit counterexamples. We also show that it
holds for functions on the Euclidean plane whose Morse-Smale graph is a tree
using a result of Hickok, Villatoro and Wang.
| 0 | 0 | 1 | 0 | 0 | 0 |
Holomorphic primary fields in free CFT4 and Calabi-Yau orbifolds | Counting formulae for general primary fields in free four dimensional
conformal field theories of scalars, vectors and matrices are derived. These
are specialised to count primaries which obey extremality conditions defined in
terms of the dimensions and left or right spins (i.e. in terms of relations
between the charges under the Cartan subgroup of $SO(4,2)$). The construction
of primary fields for scalar field theory is mapped to a problem of determining
multi-variable polynomials subject to a system of symmetry and differential
constraints. For the extremal primaries, we give a construction in terms of
holomorphic polynomial functions on permutation orbifolds, which are shown to
be Calabi-Yau spaces.
| 0 | 0 | 1 | 0 | 0 | 0 |
A Deep Generative Framework for Paraphrase Generation | Paraphrase generation is an important problem in NLP, especially in question
answering, information retrieval, information extraction, conversation systems,
to name a few. In this paper, we address the problem of generating paraphrases
automatically. Our proposed method is based on a combination of deep generative
models (VAE) with sequence-to-sequence models (LSTM) to generate paraphrases,
given an input sentence. Traditional VAEs when combined with recurrent neural
networks can generate free text but they are not suitable for paraphrase
generation for a given sentence. We address this problem by conditioning the
both, encoder and decoder sides of VAE, on the original sentence, so that it
can generate the given sentence's paraphrases. Unlike most existing models, our
model is simple, modular and can generate multiple paraphrases, for a given
sentence. Quantitative evaluation of the proposed method on a benchmark
paraphrase dataset demonstrates its efficacy, and its performance improvement
over the state-of-the-art methods by a significant margin, whereas qualitative
human evaluation indicate that the generated paraphrases are well-formed,
grammatically correct, and are relevant to the input sentence. Furthermore, we
evaluate our method on a newly released question paraphrase dataset, and
establish a new baseline for future research.
| 1 | 0 | 0 | 0 | 0 | 0 |
Stiff-response-induced instability for chemotactic bacteria and flux-limited Keller-Segel equation | Collective motion of chemotactic bacteria as E. Coli relies, at the
individual level, on a continuous reorientation by runs and tumbles. It has
been established that the length of run is decided by a stiff response to a
temporal sensingof chemical cues along the pathway.We describe a novel
mechanism for pattern formation stemming from the stiffness of chemotactic
response relying on a kinetic chemotaxis model which includes a recently
discovered formalism for the bacterial chemotaxis. We prove instability both
for amicroscopic description in the space-velocity space and for the
macroscopic equation, a flux-limited Keller-Segel equation, which has attracted
much attention recently.A remarkable property is that the unstable frequencies
remain bounded, as it is the case in Turing instability. Numerical
illustrations based on a powerful Monte Carlo method show that the stationary
homogeneous state of population density isdestabilized and periodic patterns
are generated in realistic ranges of parameters. These theoretical developments
are in accordance with several biological observations.
| 0 | 1 | 1 | 0 | 0 | 0 |
The Pfaffian state in an electron gas with small Landau level gaps | Landau level mixing plays an important role in the Pfaffian (or
anti-Pfaffian) states. In ZnO the Landau level gap is essentially an order of
magnitude smaller than that in a GaAs quantum well. We introduce the screened
Coulomb interaction in a single Landau level to tackle that situation. Here we
study the overlap of the ground state and the Pfaffian (or anti-Pfaffian) state
at evendenominator fractional quantum Hall (FQH) states present in ZnO. The
overlap is strongly system size-dependent which suggests a newly proposed
particle-hole symmetry Pfaffian ground state in the extreme Landau level mixing
limit. When the ratio of Coulomb interaction to the Landau level gap \k{appa}
varies, we find a possible topological phase transition in the range 2 <
\k{appa} < 3, which was actually observed in an experiment. We then study how
the width of quantum well combined with screening influences the overlap.
| 0 | 1 | 0 | 0 | 0 | 0 |
Three-Dimensional Numerical Modeling of Shear Stimulation of Naturally Fractured Reservoirs | Shear dilation based hydraulic stimulations enable exploitation of geothermal
energy from reservoirs with inadequate initial permeability. While contributing
to enhancing the reservoir's permeability, hydraulic stimulation processes may
lead to undesired seismic activity. Here, we present a three dimensional
numerical model aiming to increase understanding of this mechanism and its
consequences. The fractured reservoir is modeled as a network of explicitly
represented large scale fractures immersed in a permeable rock matrix. The
numerical formulation is constructed by coupling three physical processes:
fluid flow, fracture deformation, and rock matrix deformation. For flow
simulations, the discrete fracture matrix model is used, which allows the fluid
transport from high permeable conductive fractures to the rock matrix and vice
versa. The mechanical behavior of the fractures is modeled using a hyperbolic
model with reversible and irreversible deformations. Linear elasticity is
assumed for the mechanical deformation and stress alteration of the rock
matrix. Fractures are modeled as lower dimensional surfaces embodied in the
domain, subjected to specific governing equations for their deformation along
the tangential and normal directions. Both the fluid flow and momentum balance
equations are approximated by finite volume discretizations. The new numerical
model is demonstrated considering a three dimensional fractured formation with
a network of 20 explicitly represented fractures. The effects of fluid exchange
between fractures and rock matrix on the permeability evolution and the
generated seismicity are examined for test cases resembling realistic reservoir
conditions.
| 0 | 1 | 0 | 0 | 0 | 0 |
Decentralized DC MicroGrid Monitoring and Optimization via Primary Control Perturbations | We treat the emerging power systems with direct current (DC) MicroGrids,
characterized with high penetration of power electronic converters. We rely on
the power electronics to propose a decentralized solution for autonomous
learning of and adaptation to the operating conditions of the DC Mirogrids; the
goal is to eliminate the need to rely on an external communication system for
such purpose. The solution works within the primary droop control loops and
uses only local bus voltage measurements. Each controller is able to estimate
(i) the generation capacities of power sources, (ii) the load demands, and
(iii) the conductances of the distribution lines. To define a well-conditioned
estimation problem, we employ decentralized strategy where the primary droop
controllers temporarily switch between operating points in a coordinated
manner, following amplitude-modulated training sequences. We study the use of
the estimator in a decentralized solution of the Optimal Economic Dispatch
problem. The evaluations confirm the usefulness of the proposed solution for
autonomous MicroGrid operation.
| 1 | 0 | 0 | 0 | 0 | 0 |
AI4AI: Quantitative Methods for Classifying Host Species from Avian Influenza DNA Sequence | Avian Influenza breakouts cause millions of dollars in damage each year
globally, especially in Asian countries such as China and South Korea. The
impact magnitude of a breakout directly correlates to time required to fully
understand the influenza virus, particularly the interspecies pathogenicity.
The procedure requires laboratory tests that require resources typically
lacking in a breakout emergency. In this study, we propose new quantitative
methods utilizing machine learning and deep learning to correctly classify host
species given raw DNA sequence data of the influenza virus, and provide
probabilities for each classification. The best deep learning models achieve
top-1 classification accuracy of 47%, and top-3 classification accuracy of 82%,
on a dataset of 11 host species classes.
| 0 | 0 | 0 | 1 | 1 | 0 |
Constructing tame supercuspidal representations | A new approach to Jiu-Kang Yu's construction of tame supercuspidal
representations of $p$-adic reductive groups is presented. Connections with the
theory of cuspidal Deligne-Lusztig representations of finite groups of Lie type
are also discussed.
| 0 | 0 | 1 | 0 | 0 | 0 |
Cohomologies on hypercomplex manifolds | We review some cohomological aspects of complex and hypercomplex manifolds
and underline the differences between both realms. Furthermore, we try to
highlight the similarities between compact complex surfaces on one hand and
compact hypercomplex manifolds of real dimension 8 with holonomy of the Obata
connection in SL(2,H) on the other hand.
| 0 | 0 | 1 | 0 | 0 | 0 |
Automorphism groups of quandles and related groups | In this paper we study different questions concerning automorphisms of
quandles. For a conjugation quandle $Q={\rm Conj}(G)$ of a group $G$ we
determine several subgroups of ${\rm Aut}(Q)$ and find necessary and sufficient
conditions when these subgroups coincide with the whole group ${\rm Aut}(Q)$.
In particular, we prove that ${\rm Aut}({\rm Conj}(G))={\rm Z}(G)\rtimes {\rm
Aut}(G)$ if and only if either ${\rm Z}(G)=1$ or $G$ is one of the groups
$\mathbb{Z}_2$, $\mathbb{Z}_2^2$ or $\mathbb{Z}_3$. For a big list of Takasaki
quandles $T(G)$ of an abelian group $G$ with $2$-torsion we prove that the
group of inner automorphisms ${\rm Inn}(T(G))$ is a Coxeter group. We study
automorphisms of certain extensions of quandles and determine some interesting
subgroups of the automorphism groups of these quandles. Also we classify finite
quandles $Q$ with $3\leq k$-transitive action of ${\rm Aut}(Q)$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Forward Amortized Inference for Likelihood-Free Variational Marginalization | In this paper, we introduce a new form of amortized variational inference by
using the forward KL divergence in a joint-contrastive variational loss. The
resulting forward amortized variational inference is a likelihood-free method
as its gradient can be sampled without bias and without requiring any
evaluation of either the model joint distribution or its derivatives. We prove
that our new variational loss is optimized by the exact posterior marginals in
the fully factorized mean-field approximation, a property that is not shared
with the more conventional reverse KL inference. Furthermore, we show that
forward amortized inference can be easily marginalized over large families of
latent variables in order to obtain a marginalized variational posterior. We
consider two examples of variational marginalization. In our first example we
train a Bayesian forecaster for predicting a simplified chaotic model of
atmospheric convection. In the second example we train an amortized variational
approximation of a Bayesian optimal classifier by marginalizing over the model
space. The result is a powerful meta-classification network that can solve
arbitrary classification problems without further training.
| 0 | 0 | 0 | 1 | 0 | 0 |
Beyond-CMOS Device Benchmarking for Boolean and Non-Boolean Logic Applications | The latest results of benchmarking research are presented for a variety of
beyond-CMOS charge- and spin-based devices. In addition to improving the
device-level models, several new device proposals and a few majorly modified
devices are investigated. Deep pipelining circuits are employed to boost the
throughput of low-power devices. Furthermore, the benchmarking methodology is
extended to interconnect-centric analyses and non-Boolean logic applications.
In contrast to Boolean circuits, non-Boolean circuits based on the cellular
neural network demonstrate that spintronic devices can potentially outperform
conventional CMOS devices.
| 1 | 0 | 0 | 0 | 0 | 0 |
Cosmology and Convention | I argue that some important elements of the current cosmological model are
"conventionalist" in the sense defined by Karl Popper. These elements include
dark matter and dark energy; both are auxiliary hypotheses that were invoked in
response to observations that falsified the standard model as it existed at the
time. The use of conventionalist stratagems in response to unexpected
observations implies that the field of cosmology is in a state of "degenerating
problemshift" in the language of Imre Lakatos. I show that the "concordance"
argument, often put forward by cosmologists in support of the current paradigm,
is weaker than the convergence arguments that were made in the past in support
of the atomic theory of matter or the quantization of energy.
| 0 | 1 | 0 | 0 | 0 | 0 |
Kernel-estimated Nonparametric Overlap-Based Syncytial Clustering | Standard clustering algorithms usually find regular-structured clusters such
as ellipsoidally- or spherically-dispersed groups, but are more challenged with
groups lacking formal structure or definition. Syncytial clustering is the name
that we introduce for methods that merge groups obtained from standard
clustering algorithms in order to reveal complex group structure in the data.
Here, we develop a distribution-free fully-automated syncytial clustering
algorithm that can be used with $k$-means and other algorithms. Our approach
computes the cumulative distribution function of the normed residuals from an
appropriately fit $k$-groups model and calculates the nonparametric overlap
between each pair of groups. Groups with high pairwise overlaps are merged as
long as the generalized overlap decreases. Our methodology is always a top
performer in identifying groups with regular and irregular structures in
several datasets. The approach is also used to identify the distinct kinds of
gamma ray bursts in the Burst and Transient Source Experiment 4Br catalog and
also the distinct kinds of activation in a functional Magnetic Resonance
Imaging study.
| 0 | 0 | 0 | 1 | 0 | 0 |
Goal-oriented Trajectories for Efficient Exploration | Exploration is a difficult challenge in reinforcement learning and even
recent state-of-the art curiosity-based methods rely on the simple
epsilon-greedy strategy to generate novelty. We argue that pure random walks do
not succeed to properly expand the exploration area in most environments and
propose to replace single random action choices by random goals selection
followed by several steps in their direction. This approach is compatible with
any curiosity-based exploration and off-policy reinforcement learning agents
and generates longer and safer trajectories than individual random actions. To
illustrate this, we present a task-independent agent that learns to reach
coordinates in screen frames and demonstrate its ability to explore with the
game Super Mario Bros. improving significantly the score of a baseline DQN
agent.
| 0 | 0 | 0 | 1 | 0 | 0 |
Adaptive Sampling Strategies for Stochastic Optimization | In this paper, we propose a stochastic optimization method that adaptively
controls the sample size used in the computation of gradient approximations.
Unlike other variance reduction techniques that either require additional
storage or the regular computation of full gradients, the proposed method
reduces variance by increasing the sample size as needed. The decision to
increase the sample size is governed by an inner product test that ensures that
search directions are descent directions with high probability. We show that
the inner product test improves upon the well known norm test, and can be used
as a basis for an algorithm that is globally convergent on nonconvex functions
and enjoys a global linear rate of convergence on strongly convex functions.
Numerical experiments on logistic regression problems illustrate the
performance of the algorithm.
| 0 | 0 | 0 | 1 | 0 | 0 |
Explicit estimates for the distribution of numbers free of large prime factors | There is a large literature on the asymptotic distribution of numbers free of
large prime factors, so-called $\textit{smooth}$ or $\textit{friable}$ numbers.
But there is very little known about this distribution that is numerically
explicit. In this paper we follow the general plan for the saddle point
argument of Hildebrand and Tenenbaum, giving explicit and fairly tight
intervals in which the true count lies. We give two numerical examples of our
method, and with the larger one, our interval is so tight we can exclude the
famous Dickman-de Bruijn asymptotic estimate as too small and the
Hildebrand-Tenenbaum main term as too large.
| 0 | 0 | 1 | 0 | 0 | 0 |
Completely integrally closed Prufer $v$-multiplication domains | We study the effects on $D$ of assuming that the power series ring $D[[X]]$
is a $v$-domain or a PVMD. We show that a PVMD $D$ is completely integrally
closed if and only if $\bigcap_{n=1}^{\infty }(I^{n})_{v}=(0)$ for every proper
$t$-invertible $t$-ideal $I$ of $D$. Using this, we show that if $D$ is an AGCD
domain, then $D[[X]]$ is integrally closed if and only if $D$ is a completely
integrally closed PVMD with torsion $t$-class group. We also determine several
classes of PVMDs for which being Archimedean is equivalent to being completely
integrally closed and give some new characterizations of integral domains
related to Krull domains.
| 0 | 0 | 1 | 0 | 0 | 0 |
Masked Autoregressive Flow for Density Estimation | Autoregressive models are among the best performing neural density
estimators. We describe an approach for increasing the flexibility of an
autoregressive model, based on modelling the random numbers that the model uses
internally when generating data. By constructing a stack of autoregressive
models, each modelling the random numbers of the next model in the stack, we
obtain a type of normalizing flow suitable for density estimation, which we
call Masked Autoregressive Flow. This type of flow is closely related to
Inverse Autoregressive Flow and is a generalization of Real NVP. Masked
Autoregressive Flow achieves state-of-the-art performance in a range of
general-purpose density estimation tasks.
| 1 | 0 | 0 | 1 | 0 | 0 |
Temperature-dependent optical properties of plasmonic titanium nitride thin films | Due to their exceptional plasmonic properties, noble metals such as gold and
silver have been the materials of choice for the demonstration of various
plasmonic and nanophotonic phenomena. However, noble metals' softness, lack of
tailorability and low melting point along with challenges in thin film
fabrication and device integration have prevented the realization of real-life
plasmonic devices.In the recent years, titanium nitride (TiN) has emerged as a
promising plasmonic material with good metallic and refractory (high
temperature stable) properties. The refractory nature of TiN could enable
practical plasmonic devices operating at elevated temperatures for energy
conversion and harsh-environment industries such as gas and oil. Here we report
on the temperature dependent dielectric functions of TiN thin films of varying
thicknesses in the technologically relevant visible and near-infrared
wavelength range from 330 nm to 2000 nm for temperatures up to 900 0C using
in-situ high temperature ellipsometry. Our findings show that the complex
dielectric function of TiN at elevated temperatures deviates from the optical
parameters at room temperature, indicating degradation in plasmonic properties
both in the real and imaginary parts of the dielectric constant. However, quite
strikingly, the relative changes of the optical properties of TiN are
significantly smaller compared to its noble metal counterparts. Using
simulations, we demonstrate that incorporating the temperature-induced
deviations into the numerical models leads to significant differences in the
optical responses of high temperature nanophotonic systems. These studies hold
the key for accurate modeling of high temperature TiN based optical elements
and nanophotonic systems for energy conversion, harsh-environment sensors and
heat-assisted applications.
| 0 | 1 | 0 | 0 | 0 | 0 |
Succinct Partial Sums and Fenwick Trees | We consider the well-studied partial sums problem in succint space where one
is to maintain an array of n k-bit integers subject to updates such that
partial sums queries can be efficiently answered. We present two succint
versions of the Fenwick Tree - which is known for its simplicity and
practicality. Our results hold in the encoding model where one is allowed to
reuse the space from the input data. Our main result is the first that only
requires nk + o(n) bits of space while still supporting sum/update in O(log_b
n) / O(b log_b n) time where 2 <= b <= log^O(1) n. The second result shows how
optimal time for sum/update can be achieved while only slightly increasing the
space usage to nk + o(nk) bits. Beyond Fenwick Trees, the results are primarily
based on bit-packing and sampling - making them very practical - and they also
allow for simple optimal parallelization.
| 1 | 0 | 0 | 0 | 0 | 0 |
Deep Morphing: Detecting bone structures in fluoroscopic X-ray images with prior knowledge | We propose approaches based on deep learning to localize objects in images
when only a small training dataset is available and the images have low
quality. That applies to many problems in medical image processing, and in
particular to the analysis of fluoroscopic (low-dose) X-ray images, where the
images have low contrast. We solve the problem by incorporating high-level
information about the objects, which could be a simple geometrical model, like
a circular outline, or a more complex statistical model. A simple geometrical
representation can sufficiently describe some objects and only requires minimal
labeling. Statistical shape models can be used to represent more complex
objects. We propose computationally efficient two-stage approaches, which we
call deep morphing, for both representations by fitting the representation to
the output of a deep segmentation network.
| 0 | 0 | 0 | 1 | 0 | 0 |
Persistence Flamelets: multiscale Persistent Homology for kernel density exploration | In recent years there has been noticeable interest in the study of the "shape
of data". Among the many ways a "shape" could be defined, topology is the most
general one, as it describes an object in terms of its connectivity structure:
connected components (topological features of dimension 0), cycles (features of
dimension 1) and so on. There is a growing number of techniques, generally
denoted as Topological Data Analysis, aimed at estimating topological
invariants of a fixed object; when we allow this object to change, however,
little has been done to investigate the evolution in its topology. In this work
we define the Persistence Flamelets, a multiscale version of one of the most
popular tool in TDA, the Persistence Landscape. We examine its theoretical
properties and we show how it could be used to gain insights on KDEs bandwidth
parameter.
| 0 | 0 | 1 | 1 | 0 | 0 |
Effective modeling of ground penetrating radar in fractured media using analytic solutions for propagation, thin-bed interaction and dipolar scattering | We propose a new approach to model ground penetrating radar signals that
propagate through a homogeneous and isotropic medium, and are scattered at thin
planar fractures of arbitrary dip, azimuth, thickness and material filling. We
use analytical expressions for the Maxwell equations in a homogeneous space to
describe the propagation of the signal in the rock matrix, and account for
frequency-dependent dispersion and attenuation through the empirical Jonscher
formulation. We discretize fractures into elements that are linearly polarized
by the incoming electric field that arrives from the source to each element,
locally, as a plane wave. To model the effective source wavelet we use a
generalized Gamma distribution to define the antenna dipole moment. We combine
microscopic and macroscopic Maxwell's equations to derive an analytic
expression for the response of each element, which describes the full electric
dipole radiation patterns along with effective reflection coefficients of thin
layers. Our results compare favorably with finite-difference time-domain
modeling in the case of constant electrical parameters of the rock-matrix and
fracture filling. Compared with traditional finite-difference time-domain
modeling, the proposed approach is faster and more flexible in terms of
fracture orientations. A comparison with published laboratory results suggests
that the modeling approach can reproduce the main characteristics of the
reflected wavelet.
| 0 | 1 | 0 | 0 | 0 | 0 |
Crystallites in Color Glass Beads of the 19th Century and Their Influence on Fatal Deterioration of Glass | Glass corrosion is a crucial problem in keeping and conservation of beadworks
in museums. All kinds of glass beads undergo deterioration but blue-green
lead-potassium glass beads of the 19th century are subjected to the destruction
to the greatest extent. Blue-green lead-potassium glass beads of the 19th
century obtained from exhibits kept in Russian museums were studied with the
purpose to determine the causes of the observed phenomenon. For the comparison,
yellow lead beads of the 19th century were also explored. Both kinds of beads
contain Sb but yellow ones are stable. Using scanning electron microscopy,
energy dispersive X-ray microspectrometry, electron backscatter diffraction,
transmission electron microscopy and X-ray powder analysis, we have registered
the presence of crystallites of orthorhombic KSbOSiO$_4$ and cubic
Pb$_2$Sb$_{1.5}$Fe$_{0.5}$O$_{6.5}$ in glass matrix of blue-green and yellow
beads, respectively. Both compounds form at rather high temperatures obviously
during glass melting and/or melt cooling. We suppose that the crystallites
generate internal tensile strain in glass during its cooling which causes
formation of multiple microcracks in inner domains of blue-green beads. We
suggest that the deterioration degree depends on quantity of the precipitates,
their sizes and their temperature coefficients of linear expansion. In
blue-green beads, the crystallites are distributed in their sizes from
$\sim\,$200 nm to several tens of $\mu$m and tend to gather in large colonies.
The sizes of crystallites in yellow beads are several hundreds of nm and their
clusters contain few crystallites. This explains the difference in corrosion of
these kinds of beads containing crystals of Sb compounds.
| 0 | 1 | 0 | 0 | 0 | 0 |
CO2 infrared emission as a diagnostic of planet-forming regions of disks | [Abridged] The infrared ro-vibrational emission lines from organic molecules
in the inner regions of protoplanetary disks are unique probes of the physical
and chemical structure of planet forming regions and the processes that shape
them. The non-LTE excitation effects of carbon dioxide (CO2) are studied in a
full disk model to evaluate: (i) what the emitting regions of the different CO2
ro-vibrational bands are; (ii) how the CO2 abundance can be best traced using
CO2 ro-vibrational lines using future JWST data and; (iii) what the excitation
and abundances tell us about the inner disk physics and chemistry. CO2 is a
major ice component and its abundance can potentially test models with
migrating icy pebbles across the iceline. A full non-LTE CO2 excitation model
has been built. The characteristics of the model are tested using non-LTE slab
models. Subsequently the CO2 line formation has been modelled using a
two-dimensional disk model representative of T-Tauri disks. The CO2 gas that
emits in the 15 $\mu$m and 4.5 $\mu$m regions of the spectrum is not in LTE and
arises in the upper layers of disks, pumped by infrared radiation. The v$_2$ 15
$\mu$m feature is dominated by optically thick emission for most of the models
that fit the observations and increases linearly with source luminosity. Its
narrowness compared with that of other molecules stems from a combination of
the low rotational excitation temperature (~250 K) and the inherently narrower
feature for CO2. The inferred CO2 abundances derived for observed disks are
more than two orders of magnitude lower than those in interstellar ices
(~10$^5$), similar to earlier LTE disk estimates. Line-to-continuum ratios are
low, of order a few %, thus high signal-to-noise (S/N > 300) observations are
needed for individual line detections. Prospects of accurate abundance
retreival with JWST-MIRI and JWST-NIRSpec are discussed.
| 0 | 1 | 0 | 0 | 0 | 0 |
Public Evidence from Secret Ballots | Elections seem simple---aren't they just counting? But they have a unique,
challenging combination of security and privacy requirements. The stakes are
high; the context is adversarial; the electorate needs to be convinced that the
results are correct; and the secrecy of the ballot must be ensured. And they
have practical constraints: time is of the essence, and voting systems need to
be affordable and maintainable, and usable by voters, election officials, and
pollworkers. It is thus not surprising that voting is a rich research area
spanning theory, applied cryptography, practical systems analysis, usable
security, and statistics. Election integrity involves two key concepts:
convincing evidence that outcomes are correct and privacy, which amounts to
convincing assurance that there is no evidence about how any given person
voted. These are obviously in tension. We examine how current systems walk this
tightrope.
| 1 | 0 | 0 | 0 | 0 | 0 |
Imprints of Zero-Age Velocity Dispersions and Dynamical Heating on the Age-Velocity dispersion Relation | Observations of stars in the the solar vicinity show a clear tendency for old
stars to have larger velocity dispersions. This relation is called the
age-velocity dispersion relation (AVR) and it is believed to provide insight
into the heating history of the Milky Way galaxy. Here, in order to investigate
the origin of the AVR, we performed smoothed particle hydrodynamic simulations
of the self-gravitating multiphase gas disks in the static disk-halo
potentials. Star formation from cold and dense gas is taken into account, and
we analyze the evolution of these star particles. We find that exponents of
simulated AVR and the ratio of the radial to vertical velocity dispersion are
close to the observed values. We also find that the simulated AVR is not a
simple consequence of dynamical heating. The evolution tracks of stars with
different epochs evolve gradually in the age-velocity dispersion plane as a
result of: (1) the decrease in velocity dispersion in star forming regions, and
(2) the decrease in the number of cold/dense/gas as scattering sources. These
results suggest that the AVR involves not only the heating history of a stellar
disk, but also the historical evolution of the ISM in a galaxy.
| 0 | 1 | 0 | 0 | 0 | 0 |
Spatial Random Sampling: A Structure-Preserving Data Sketching Tool | Random column sampling is not guaranteed to yield data sketches that preserve
the underlying structures of the data and may not sample sufficiently from
less-populated data clusters. Also, adaptive sampling can often provide
accurate low rank approximations, yet may fall short of producing descriptive
data sketches, especially when the cluster centers are linearly dependent.
Motivated by that, this paper introduces a novel randomized column sampling
tool dubbed Spatial Random Sampling (SRS), in which data points are sampled
based on their proximity to randomly sampled points on the unit sphere. The
most compelling feature of SRS is that the corresponding probability of
sampling from a given data cluster is proportional to the surface area the
cluster occupies on the unit sphere, independently from the size of the cluster
population. Although it is fully randomized, SRS is shown to provide
descriptive and balanced data representations. The proposed idea addresses a
pressing need in data science and holds potential to inspire many novel
approaches for analysis of big data.
| 1 | 0 | 0 | 1 | 0 | 0 |
Adiponitrile-LiTFSI solution as alkylcarbonate free electrolyte for LTO/NMC Li-ion batteries | Recently, dinitriles (NC(CH2)nCN) and especially adiponitrile (ADN, n=4) have
attracted the attention as secure electrolyte solvents due to their chemical
stability, high boiling points, high flash points and low vapor pressure. The
good solvating properties of ADN toward lithium salts and its high
electrochemical stability (~ 6V vs. Li/Li+) make it suitable for safer Li-ions
cells without performances loss. In this study, ADN is used as a single
electrolyte solvent with lithium bis(trimethylsulfonyl)imide (LiTFSI). This
electrolyte allows the use of aluminum collectors as almost no corrosion occurs
at voltages up to 4.2 V. Physico-chemical properties of ADN-LiTFSI electrolyte
such as salt dissolution, conductivity and viscosity were determined. The
cycling performances of batteries using Li4Ti5O12 (LTO) as anode and
LiNi1/3Co1/3Mn1/3O2 (NMC) as cathode were determined. The results indicate that
LTO/NMC batteries exhibit excellent rate capabilities with a columbic
efficiency close to 100%. As an example, cells were able to reach a capacity of
165 mAh.g-1 at 0.1C and a capacity retention of more than 98% after 200 cycles
at 0.5C. In addition, electrodes analyses by SEM, XPS and electrochemical
impedance spectroscopy after cycling confirming minimal surface changes of the
electrodes in the studied battery system
| 0 | 1 | 0 | 0 | 0 | 0 |
Sea of Lights: Practical Device-to-Device Security Bootstrapping in the Dark | Practical solutions to bootstrap security in today's information and
communication systems critically depend on centralized services for
authentication as well as key and trust management. This is particularly true
for mobile users. Identity providers such as Google or Facebook have active
user bases of two billion each, and the subscriber number of mobile operators
exceeds five billion unique users as of early 2018. If these centralized
services go completely `dark' due to natural or man made disasters, large scale
blackouts, or country-wide censorship, the users are left without practical
solutions to bootstrap security on their mobile devices. Existing distributed
solutions, for instance, the so-called web-of-trust are not sufficiently
lightweight. Furthermore, they support neither cross-application on mobile
devices nor strong protection of key material using hardware security modules.
We propose Sea of Lights(SoL), a practical lightweight scheme for bootstrapping
device-to-device security wirelessly, thus, enabling secure distributed
self-organized networks. It is tailored to operate `in the dark' and provides
strong protection of key material as well as an intuitive means to build a
lightweight web-of-trust. SoL is particularly well suited for local or urban
operation in scenarios such as the coordination of emergency response, where it
helps containing/limiting the spreading of misinformation. As a proof of
concept, we implement SoL in the Android platform and hence test its
feasibility on real mobile devices. We further evaluate its key performance
aspects using simulation.
| 1 | 0 | 0 | 0 | 0 | 0 |
On the correlation between a level of structure order and properties of composites. In Memory of Yu.L. Klimontovich | Proposed the computerized method for calculating the relative level of order
composites. Correlation between a level of structure order and properties of
solids is shown. Discussed the possibility of clarifying the terminology used
in describing the structure.
| 1 | 0 | 0 | 0 | 0 | 0 |
Schmidt's subspace theorem for moving hypersurface targets | It was discovered that there is a formal analogy between Nevanlinna theory
and Diophantine approximation. Via Vojta's dictionary, the Second Main Theorem
in Nevanlinna theory corresponds to Schmidt's Subspace Theorem in Diophantine
approximation. Recently, Cherry, Dethloff, and Tan (arXiv:1503.08801v2
[math.CV]) obtained a Second Main Theorem for moving hypersurfaces intersecting
projective varieites. In this paper, we shall give the counterpart of their
Second Main Theorem in Diophantine approximation.
| 0 | 0 | 1 | 0 | 0 | 0 |
HoNVis: Visualizing and Exploring Higher-Order Networks | Unlike the conventional first-order network (FoN), the higher-order network
(HoN) provides a more accurate description of transitions by creating
additional nodes to encode higher-order dependencies. However, there exists no
visualization and exploration tool for the HoN. For applications such as the
development of strategies to control species invasion through global shipping
which is known to exhibit higher-order dependencies, the existing FoN
visualization tools are limited. In this paper, we present HoNVis, a novel
visual analytics framework for exploring higher-order dependencies of the
global ocean shipping network. Our framework leverages coordinated multiple
views to reveal the network structure at three levels of detail (i.e., the
global, local, and individual port levels). Users can quickly identify ports of
interest at the global level and specify a port to investigate its higher-order
nodes at the individual port level. Investigating a larger-scale impact is
enabled through the exploration of HoN at the local level. Using the global
ocean shipping network data, we demonstrate the effectiveness of our approach
with a real-world use case conducted by domain experts specializing in species
invasion. Finally, we discuss the generalizability of this framework to other
real-world applications such as information diffusion in social networks and
epidemic spreading through air transportation.
| 1 | 1 | 0 | 0 | 0 | 0 |
Direct observation of coupled geochemical and geomechanical impacts on chalk microstructural evolution under elevated CO2 pressure. Part I | The dissolution of porous media in a geologic formation induced by the
injection of massive amounts of CO2 can undermine the mechanical stability of
the formation structure before carbon mineralization takes place. The
geomechanical impact of geologic carbon storage is therefore closely related to
the structural sustainability of the chosen reservoir as well as the
probability of buoyancy driven CO2 leakage through caprocks. Here we show, with
a combination of ex situ nanotomography and in situ microtomography, that the
presence of dissolved CO2 in water produces a homogeneous dissolution pattern
in natural chalk microstructure. This pattern stems from a greater apparent
solubility of chalk and therefore a greater reactive subvolume in a sample.
When a porous medium dissolves homogeneously in an imposed flow field, three
geomechanical effects were observed: material compaction, fracturing and grain
relocation. These phenomena demonstrated distinct feedbacks to the migration of
the dissolution front and severely complicated the infiltration instability
problem. We conclude that the presence of dissolved CO2 makes the dissolution
front less susceptible to spatial and temporal perturbations in the strongly
coupled geochemical and geomechanical processes.
| 0 | 1 | 0 | 0 | 0 | 0 |
Symplectic resolutions for Higgs moduli spaces | In this paper, we study the algebraic symplectic geometry of the singular
moduli spaces of Higgs bundles of degree $0$ and rank $n$ on a compact Riemann
surface $X$ of genus $g$. In particular, we prove that such moduli spaces are
symplectic singularities, in the sense of Beauville [Bea00], and admit a
projective symplectic resolution if and only if $g=1$ or $(g, n)=(2,2)$. These
results are an application of a recent paper by Bellamy and Schedler [BS16] via
the so-called Isosingularity Theorem.
| 0 | 0 | 1 | 0 | 0 | 0 |
Investigation of Defect Modes of Chiral Photonic Crystals | Some properties of defect modes of cholesteric liquid crystals (CLC) are
presented. It is shown that when the CLC layer is thin the density of states
and emission intensity are maximum for the defect mode, whereas when the CLC
layer is thick, these peaks are observed at the edges of the photonic band gap.
Similarly, when the gain is low, the density of states and emission intensity
are maximum for the defect mode, whereas at high gains these peaks are also
observed at the edges of the photonic band gap. The possibilities of
low-threshold lasing and obtaining high-Q microcavities have been investigated.
| 0 | 1 | 0 | 0 | 0 | 0 |
Crystal Growth of Cu6(Ge,Si)6O18.6H2O and Assignment of UV-VIS Spectra in Comparison to Dehydrated Dioptase and Selected Cu(II) Oxo-Compounds Including Cuprates | It is reported on growth of mm-sized single-crystals of the low-dimensional S
= 1/2 spin compound Cu6(Ge,Si)6O18.6H2O by a diffusion technique in aqueous
solution. A route to form Si-rich crystals down to possibly dioptase, the pure
silicate, is discussed. Further, the assignment of dd excitations from UV-VIS
spectra of the hexahydrate and the fully dehydrated compound is proposed in
comparison to dioptase and selected Cu(II) oxo-compounds using bond strength
considerations. Non-doped cuprates as layer compounds show higher excitation
energies than the title compound. However, when the antiferromagnetic
interaction energy as Jzln(2) is taken into account for cuprates, a single
linear relationship between the Dqe excitation energy and equatorial Cu(II)-O
bond strength is confirmed for all compounds. A linear representation is also
confirmed between 2A1g energies and a function of axial and equatorial Cu-O
bond distances, when auxiliary axial bonds are used for four-coordinated
compounds. The quotient Dt/Ds of experimental orbital energies deviating from
the general trend to smaller values indicates the existence of H2O respectively
Cl1- axial ligands in comparison to oxo-ligands, whereas larger Dt/Dqe values
indicate missing axial bonds. The quotient of the excitation energy 2A1g by
2x2Eg-2B2g allows to check for correctness of the assignment and to distinguish
between axial oxo-ligands and others like H2O or Cl1-. Some assignments
previously reported were corrected.
| 0 | 1 | 0 | 0 | 0 | 0 |
Optical reconfiguration and polarization control in semi-continuous gold films close to the percolation threshold | Controlling and confining light by exciting plasmons in resonant metallic
nanostructures is an essential aspect of many new emerging optical
technologies. Here we explore the possibility of controllably reconfiguring the
intrinsic optical properties of semi-continuous gold films, by inducing
permanent morphological changes with a femtosecond (fs)-pulsed laser above a
critical power. Optical transmission spectroscopy measurements show a
correlation between the spectra of the morphologically modified films and the
wavelength, polarization, and the intensity of the laser used for alteration.
In order to understand the modifications induced by the laser writing, we
explore the near-field properties of these films with electron energy-loss
spectroscopy (EELS). A comparison between our experimental data and full-wave
simulations on the exact film morphologies hints toward a restructuring of the
intrinsic plasmonic eigenmodes of the metallic film by photothermal effects. We
explain these optical changes with a simple model and demonstrate
experimentally that laser writing can be used to controllably modify the
optical properties of these semi-continuous films. These metal films offer an
easy-to-fabricate and scalable platform for technological applications such as
molecular sensing and ultra-dense data storage.
| 0 | 1 | 0 | 0 | 0 | 0 |
Some Repeated-Root Constacyclic Codes over Galois Rings | Codes over Galois rings have been studied extensively during the last three
decades. Negacyclic codes over $GR(2^a,m)$ of length $2^s$ have been
characterized: the ring $\mathcal{R}_2(a,m,-1)= \frac{GR(2^a,m)[x]}{\langle
x^{2^s}+1\rangle}$ is a chain ring. Furthermore, these results have been
generalized to $\lambda$-constacyclic codes for any unit $\lambda$ of the form
$4z-1$, $z\in GR(2^a, m)$. In this paper, we study more general cases and
investigate all cases where $\mathcal{R}_p(a,m,\gamma)=
\frac{GR(p^a,m)[x]}{\langle x^{p^s}-\gamma \rangle}$ is a chain ring. In
particular, necessary and sufficient conditions for the ring
$\mathcal{R}_p(a,m,\gamma)$ to be a chain ring are obtained. In addition, by
using this structure we investigate all $\gamma$-constacyclic codes over
$GR(p^a,m)$ when $\mathcal{R}_p(a,m,\gamma)$ is a chain ring. Necessary and
sufficient conditions for the existence of self-orthogonal and self-dual
$\gamma$-constacyclic codes are also provided. Among others, for any prime $p$,
the structure of $\mathcal{R}_p(a,m,\gamma)=\frac{GR(p^a,m)[x]}{\langle
x^{p^s}-\gamma\rangle}$ is used to establish the Hamming and homogeneous
distances of $\gamma$-constacyclic codes.
| 1 | 0 | 1 | 0 | 0 | 0 |
Statistical solutions and Onsager's conjecture | We prove a version of Onsager's conjecture on the conservation of energy for
the incompressible Euler equations in the context of statistical solutions, as
introduced recently by Fjordholm et al. As a byproduct, we also obtain a new
proof for the conservative direction of Onsager's conjecture for weak
solutions. Dedicated to Edriss S. Titi on the occasion of his 60th birthday.
| 0 | 1 | 1 | 0 | 0 | 0 |
The sum of log-normal variates in geometric Brownian motion | Geometric Brownian motion (GBM) is a key model for representing
self-reproducing entities. Self-reproduction may be considered the definition
of life [5], and the dynamics it induces are of interest to those concerned
with living systems from biology to economics. Trajectories of GBM are
distributed according to the well-known log-normal density, broadening with
time. However, in many applications, what's of interest is not a single
trajectory but the sum, or average, of several trajectories. The distribution
of these objects is more complicated. Here we show two different ways of
finding their typical trajectories. We make use of an intriguing connection to
spin glasses: the expected free energy of the random energy model is an average
of log-normal variates. We make the mapping to GBM explicit and find that the
free energy result gives qualitatively correct behavior for GBM trajectories.
We then also compute the typical sum of lognormal variates using Ito calculus.
This alternative route is in close quantitative agreement with numerical work.
| 0 | 0 | 0 | 0 | 0 | 1 |
Slow Spin Dynamics and Self-Sustained Clusters in Sparsely Connected Systems | To identify emerging microscopic structures in low temperature spin glasses,
we study self-sustained clusters (SSC) in spin models defined on sparse random
graphs. A message-passing algorithm is developed to determine the probability
of individual spins to belong to SSC. Results for specific instances, which
compare the predicted SSC associations with the dynamical properties of spins
obtained from numerical simulations, show that SSC association identifies
individual slow-evolving spins. This insight gives rise to a powerful approach
for predicting individual spin dynamics from a single snapshot of an
equilibrium spin configuration, namely from limited static information, which
can be used to devise generic prediction tools applicable to a wide range of
areas.
| 0 | 1 | 0 | 0 | 0 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.