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Duality and Universal Transport in a Mixed-Dimension Electrodynamics | We consider a theory of a two-component Dirac fermion localized on a (2+1)
dimensional brane coupled to a (3+1) dimensional bulk. Using the fermionic
particle-vortex duality, we show that the theory has a strong-weak duality that
maps the coupling $e$ to $\tilde e=(8\pi)/e$. We explore the theory at
$e^2=8\pi$ where it is self-dual. The electrical conductivity of the theory is
a constant independent of frequency. When the system is at finite density and
magnetic field at filling factor $\nu=\frac12$, the longitudinal and Hall
conductivity satisfies a semicircle law, and the ratio of the longitudinal and
Hall thermal electric coefficients is completely determined by the Hall angle.
The thermal Hall conductivity is directly related to the thermal electric
coefficients.
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Beyond similarity assessment: Selecting the optimal model for sequence alignment via the Factorized Asymptotic Bayesian algorithm | Pair Hidden Markov Models (PHMMs) are probabilistic models used for pairwise
sequence alignment, a quintessential problem in bioinformatics. PHMMs include
three types of hidden states: match, insertion and deletion. Most previous
studies have used one or two hidden states for each PHMM state type. However,
few studies have examined the number of states suitable for representing
sequence data or improving alignment accuracy.We developed a novel method to
select superior models (including the number of hidden states) for PHMM. Our
method selects models with the highest posterior probability using Factorized
Information Criteria (FIC), which is widely utilised in model selection for
probabilistic models with hidden variables. Our simulations indicated this
method has excellent model selection capabilities with slightly improved
alignment accuracy. We applied our method to DNA datasets from 5 and 28
species, ultimately selecting more complex models than those used in previous
studies.
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Experimental evidence for Glycolaldehyde and Ethylene Glycol formation by surface hydrogenation of CO molecules under dense molecular cloud conditions | This study focuses on the formation of two molecules of astrobiological
importance - glycolaldehyde (HC(O)CH2OH) and ethylene glycol (H2C(OH)CH2OH) -
by surface hydrogenation of CO molecules. Our experiments aim at simulating the
CO freeze-out stage in interstellar dark cloud regions, well before thermal and
energetic processing become dominant. It is shown that along with the formation
of H2CO and CH3OH - two well established products of CO hydrogenation - also
molecules with more than one carbon atom form. The key step in this process is
believed to be the recombination of two HCO radicals followed by the formation
of a C-C bond. The experimentally established reaction pathways are implemented
into a continuous-time random-walk Monte Carlo model, previously used to model
the formation of CH3OH on astrochemical time-scales, to study their impact on
the solid-state abundances in dense interstellar clouds of glycolaldehyde and
ethylene glycol.
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New Methods of Enhancing Prediction Accuracy in Linear Models with Missing Data | In this paper, prediction for linear systems with missing information is
investigated. New methods are introduced to improve the Mean Squared Error
(MSE) on the test set in comparison to state-of-the-art methods, through
appropriate tuning of Bias-Variance trade-off. First, the use of proposed Soft
Weighted Prediction (SWP) algorithm and its efficacy are depicted and compared
to previous works for non-missing scenarios. The algorithm is then modified and
optimized for missing scenarios. It is shown that controlled over-fitting by
suggested algorithms will improve prediction accuracy in various cases.
Simulation results approve our heuristics in enhancing the prediction accuracy.
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Revisiting Imidazolium Based Ionic Liquids: Effect of the Conformation Bias of the [NTf$_{2}$] Anion Studied By Molecular Dynamics Simulations | We study ionic liquids composed 1-alkyl-3-methylimidazolium cations and
bis(trifluoromethyl-sulfonyl)imide anions ([C$_n$MIm][NTf$_2$]) with varying
chain-length $n\!=\!2, 4, 6, 8$ by using molecular dynamics simulations. We
show that a reparametrization of the dihedral potentials as well as charges of
the [NTf$_2$] anion leads to an improvment of the force field model introduced
by Köddermann {\em et al.} [ChemPhysChem, \textbf{8}, 2464 (2007)] (KPL-force
field). A crucial advantage of the new parameter set is that the minimum energy
conformations of the anion ({\em trans} and {\em gauche}), as deduced from {\em
ab initio} calculations and {\sc Raman} experiments, are now both well
represented by our model. In addition, the results for [C$_n$MIm][NTf$_2$] show
that this modification leads to an even better agreement between experiment and
molecular dynamics simulation as demonstrated for densities, diffusion
coefficients, vaporization enthalpies, reorientational correlation times, and
viscosities. Even though we focused on a better representation of the anion
conformation, also the alkyl chain-length dependence of the cation behaves
closer to the experiment. We strongly encourage to use the new NGKPL force
field for the [NTf$_2$] anion instead of the earlier KPL parameter set for
computer simulations aiming to describe the thermodynamics, dynamics and also
structure of imidazolium based ionic liquids.
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Tick: a Python library for statistical learning, with a particular emphasis on time-dependent modelling | Tick is a statistical learning library for Python~3, with a particular
emphasis on time-dependent models, such as point processes, and tools for
generalized linear models and survival analysis. The core of the library is an
optimization module providing model computational classes, solvers and proximal
operators for regularization. tick relies on a C++ implementation and
state-of-the-art optimization algorithms to provide very fast computations in a
single node multi-core setting. Source code and documentation can be downloaded
from this https URL
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An energy method for rough partial differential equations | We present a well-posedness and stability result for a class of nondegenerate
linear parabolic equations driven by rough paths. More precisely, we introduce
a notion of weak solution that satisfies an intrinsic formulation of the
equation in a suitable Sobolev space of negative order. Weak solutions are then
shown to satisfy the corresponding en- ergy estimates which are deduced
directly from the equation. Existence is obtained by showing compactness of a
suitable sequence of approximate solutions whereas unique- ness relies on a
doubling of variables argument and a careful analysis of the passage to the
diagonal. Our result is optimal in the sense that the assumptions on the
deterministic part of the equation as well as the initial condition are the
same as in the classical PDEs theory.
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Sparse Inverse Covariance Estimation for Chordal Structures | In this paper, we consider the Graphical Lasso (GL), a popular optimization
problem for learning the sparse representations of high-dimensional datasets,
which is well-known to be computationally expensive for large-scale problems.
Recently, we have shown that the sparsity pattern of the optimal solution of GL
is equivalent to the one obtained from simply thresholding the sample
covariance matrix, for sparse graphs under different conditions. We have also
derived a closed-form solution that is optimal when the thresholded sample
covariance matrix has an acyclic structure. As a major generalization of the
previous result, in this paper we derive a closed-form solution for the GL for
graphs with chordal structures. We show that the GL and thresholding
equivalence conditions can significantly be simplified and are expected to hold
for high-dimensional problems if the thresholded sample covariance matrix has a
chordal structure. We then show that the GL and thresholding equivalence is
enough to reduce the GL to a maximum determinant matrix completion problem and
drive a recursive closed-form solution for the GL when the thresholded sample
covariance matrix has a chordal structure. For large-scale problems with up to
450 million variables, the proposed method can solve the GL problem in less
than 2 minutes, while the state-of-the-art methods converge in more than 2
hours.
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Orthogonal free quantum group factors are strongly 1-bounded | We prove that the orthogonal free quantum group factors
$\mathcal{L}(\mathbb{F}O_N)$ are strongly $1$-bounded in the sense of Jung. In
particular, they are not isomorphic to free group factors. This result is
obtained by establishing a spectral regularity result for the edge reversing
operator on the quantum Cayley tree associated to $\mathbb{F}O_N$, and
combining this result with a recent free entropy dimension rank theorem of Jung
and Shlyakhtenko.
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Enhanced spin ordering temperature in ultrathin FeTe films grown on a topological insulator | We studied the temperature dependence of the diagonal double-stripe spin
order in one and two unit cell thick layers of FeTe grown on the topological
insulator Bi_2Te_3 via spin-polarized scanning tunneling microscopy. The spin
order persists up to temperatures which are higher than the transition
temperature reported for bulk Fe_1+yTe with lowest possible excess Fe content
y. The enhanced spin order stability is assigned to a strongly decreased y with
respect to the lowest values achievable in bulk crystal growth, and effects due
to the interface between the FeTe and the topological insulator. The result is
relevant for understanding the recent observation of a coexistence of
superconducting correlations and spin order in this system.
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High Order Hierarchical Divergence-free Constrained Transport $H(div)$ Finite Element Method for Magnetic Induction Equation | In this paper, we will use the interior functions of an hierarchical basis
for high order $BDM_p$ elements to enforce the divergence-free condition of a
magnetic field $B$ approximated by the H(div) $BDM_p$ basis. The resulting
constrained finite element method can be used to solve magnetic induction
equation in MHD equations. The proposed procedure is based on the fact that the
scalar $(p-1)$-th order polynomial space on each element can be decomposed as
an orthogonal sum of the subspace defined by the divergence of the interior
functions of the $p$-th order $BDM_p$ basis and the constant function.
Therefore, the interior functions can be used to remove element-wise all higher
order terms except the constant in the divergence error of the finite element
solution of $B$-field. The constant terms from each element can be then easily
corrected using a first order H(div) basis globally. Numerical results for a
3-D magnetic induction equation show the effectiveness of the proposed method
in enforcing divergence-free condition of the magnetic field.
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REMOTEGATE: Incentive-Compatible Remote Configuration of Security Gateways | Imagine that a malicious hacker is trying to attack a server over the
Internet and the server wants to block the attack packets as close to their
point of origin as possible. However, the security gateway ahead of the source
of attack is untrusted. How can the server block the attack packets through
this gateway? In this paper, we introduce REMOTEGATE, a trustworthy mechanism
for allowing any party (server) on the Internet to configure a security gateway
owned by a second party, at a certain agreed upon reward that the former pays
to the latter for its service. We take an interactive incentive-compatible
approach, for the case when both the server and the gateway are rational, to
devise a protocol that will allow the server to help the security gateway
generate and deploy a policy rule that filters the attack packets before they
reach the server. The server will reward the gateway only when the latter can
successfully verify that it has generated and deployed the correct rule for the
issue. This mechanism will enable an Internet-scale approach to improving
security and privacy, backed by digital payment incentives.
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Distributed Event-Triggered Control for Global Consensus of Multi-Agent Systems with Input Saturation | We consider the global consensus problem for multi-agent systems with input
saturation over digraphs. Under a mild connectivity condition that the
underlying digraph has a directed spanning tree, we use Lyapunov methods to
show that the widely used distributed consensus protocol, which solves the
consensus problem for the case without input saturation constraints, also
solves the global consensus problem for the case with input saturation
constraints. In order to reduce the overall need of communication and system
updates, we then propose a distributed event-triggered control law. Global
consensus is still realized and Zeno behavior is excluded. Numerical
simulations are provided to illustrate the effectiveness of the theoretical
results.
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Autocommuting probability of a finite group relative to its subgroups | Let $H \subseteq K$ be two subgroups of a finite group $G$ and Aut$(K)$ the
automorphism group of $K$. The autocommuting probability of $G$ relative to its
subgroups $H$ and $K$, denoted by ${\rm Pr}(H, {\rm Aut}(K))$, is the
probability that the autocommutator of a randomly chosen pair of elements, one
from $H$ and the other from Aut$(K)$, is equal to the identity element of $G$.
In this paper, we study ${\rm Pr}(H, {\rm Aut}(K))$ through a generalization.
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Total variation regularization with variable Lebesgue prior | This work proposes the variable exponent Lebesgue modular as a replacement
for the 1-norm in total variation (TV) regularization. It allows the exponent
to vary with spatial location and thus enables users to locally select whether
to preserve edges or smooth intensity variations. In contrast to earlier work
using TV-like methods with variable exponents, the exponent function is here
computed offline as a fixed parameter of the final optimization problem,
resulting in a convex goal functional. The obtained formulas for the convex
conjugate and the proximal operators are simple in structure and can be
evaluated very efficiently, an important property for practical usability.
Numerical results with variable $L^p$ TV prior in denoising and tomography
problems on synthetic data compare favorably to total generalized variation
(TGV) and TV.
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Radio observations confirm young stellar populations in local analogues to $z\sim5$ Lyman break galaxies | We present radio observations at 1.5 GHz of 32 local objects selected to
reproduce the physical properties of $z\sim5$ star-forming galaxies. We also
report non-detections of five such sources in the sub-millimetre. We find a
radio-derived star formation rate which is typically half that derived from
H$\alpha$ emission for the same objects. These observations support previous
indications that we are observing galaxies with a young dominant stellar
population, which has not yet established a strong supernova-driven synchrotron
continuum. We stress caution when applying star formation rate calibrations to
stellar populations younger than 100 Myr. We calibrate the conversions for
younger galaxies, which are dominated by a thermal radio emission component. We
improve the size constraints for these sources, compared to previous unresolved
ground-based optical observations. Their physical size limits indicate very
high star formation rate surface densities, several orders of magnitude higher
than the local galaxy population. In typical nearby galaxies, this would imply
the presence of galaxy-wide winds. Given the young stellar populations, it is
unclear whether a mechanism exists in our sources that can deposit sufficient
kinetic energy into the interstellar medium to drive such outflows.
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Sparse Deep Neural Network Exact Solutions | Deep neural networks (DNNs) have emerged as key enablers of machine learning.
Applying larger DNNs to more diverse applications is an important challenge.
The computations performed during DNN training and inference are dominated by
operations on the weight matrices describing the DNN. As DNNs incorporate more
layers and more neurons per layers, these weight matrices may be required to be
sparse because of memory limitations. Sparse DNNs are one possible approach,
but the underlying theory is in the early stages of development and presents a
number of challenges, including determining the accuracy of inference and
selecting nonzero weights for training. Associative array algebra has been
developed by the big data community to combine and extend database, matrix, and
graph/network concepts for use in large, sparse data problems. Applying this
mathematics to DNNs simplifies the formulation of DNN mathematics and reveals
that DNNs are linear over oscillating semirings. This work uses associative
array DNNs to construct exact solutions and corresponding perturbation models
to the rectified linear unit (ReLU) DNN equations that can be used to construct
test vectors for sparse DNN implementations over various precisions. These
solutions can be used for DNN verification, theoretical explorations of DNN
properties, and a starting point for the challenge of sparse training.
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Variation formulas for an extended Gompf invariant | In 1998, R. Gompf defined a homotopy invariant $\theta_G$ of oriented 2-plane
fields in 3-manifolds. This invariant is defined for oriented 2-plane fields
$\xi$ in a closed oriented 3-manifold $M$ when the first Chern class $c_1(\xi)$
is a torsion element of $H^2(M;\mathbb{Z})$. In this article, we define an
extension of the Gompf invariant for all compact oriented 3-manifolds with
boundary and we study its iterated variations under Lagrangian-preserving
surgeries. It follows that the extended Gompf invariant is a degree two
invariant with respect to a suitable finite type invariant theory.
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A SAT+CAS Approach to Finding Good Matrices: New Examples and Counterexamples | We enumerate all circulant good matrices with odd orders divisible by 3 up to
order 70. As a consequence of this we find a previously overlooked set of good
matrices of order 27 and a new set of good matrices of order 57. We also find
that circulant good matrices do not exist in the orders 51, 63, and 69, thereby
finding three new counterexamples to the conjecture that such matrices exist in
all odd orders. Additionally, we prove a new relationship between the entries
of good matrices and exploit this relationship in our enumeration algorithm.
Our method applies the SAT+CAS paradigm of combining computer algebra
functionality with modern SAT solvers to efficiently search large spaces which
are specified by both algebraic and logical constraints.
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An Exploration of Mimic Architectures for Residual Network Based Spectral Mapping | Spectral mapping uses a deep neural network (DNN) to map directly from noisy
speech to clean speech. Our previous study found that the performance of
spectral mapping improves greatly when using helpful cues from an acoustic
model trained on clean speech. The mapper network learns to mimic the input
favored by the spectral classifier and cleans the features accordingly. In this
study, we explore two new innovations: we replace a DNN-based spectral mapper
with a residual network that is more attuned to the goal of predicting clean
speech. We also examine how integrating long term context in the mimic
criterion (via wide-residual biLSTM networks) affects the performance of
spectral mapping compared to DNNs. Our goal is to derive a model that can be
used as a preprocessor for any recognition system; the features derived from
our model are passed through the standard Kaldi ASR pipeline and achieve a WER
of 9.3%, which is the lowest recorded word error rate for CHiME-2 dataset using
only feature adaptation.
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Deep Neural Networks to Enable Real-time Multimessenger Astrophysics | Gravitational wave astronomy has set in motion a scientific revolution. To
further enhance the science reach of this emergent field, there is a pressing
need to increase the depth and speed of the gravitational wave algorithms that
have enabled these groundbreaking discoveries. To contribute to this effort, we
introduce Deep Filtering, a new highly scalable method for end-to-end
time-series signal processing, based on a system of two deep convolutional
neural networks, which we designed for classification and regression to rapidly
detect and estimate parameters of signals in highly noisy time-series data
streams. We demonstrate a novel training scheme with gradually increasing noise
levels, and a transfer learning procedure between the two networks. We showcase
the application of this method for the detection and parameter estimation of
gravitational waves from binary black hole mergers. Our results indicate that
Deep Filtering significantly outperforms conventional machine learning
techniques, achieves similar performance compared to matched-filtering while
being several orders of magnitude faster thus allowing real-time processing of
raw big data with minimal resources. More importantly, Deep Filtering extends
the range of gravitational wave signals that can be detected with ground-based
gravitational wave detectors. This framework leverages recent advances in
artificial intelligence algorithms and emerging hardware architectures, such as
deep-learning-optimized GPUs, to facilitate real-time searches of gravitational
wave sources and their electromagnetic and astro-particle counterparts.
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Contribution of cellular automata to the understanding of corrosion phenomena | We present a stochastic CA modelling approach of corrosion based on spatially
separated electrochemical half-reactions, diffusion, acido-basic neutralization
in solution and passive properties of the oxide layers. Starting from different
initial conditions, a single framework allows one to describe generalised
corrosion, localised corrosion, reactive and passive surfaces, including
occluded corrosion phenomena as well. Spontaneous spatial separation of anodic
and cathodic zones is associated with bare metal and passivated metal on the
surface. This separation is also related to local acidification of the
solution. This spontaneous change is associated with a much faster corrosion
rate. Material morphology is closely related to corrosion kinetics, which can
be used for technological applications.
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Involutive bordered Floer homology | We give a bordered extension of involutive HF-hat and use it to give an
algorithm to compute involutive HF-hat for general 3-manifolds. We also explain
how the mapping class group action on HF-hat can be computed using bordered
Floer homology. As applications, we prove that involutive HF-hat satisfies a
surgery exact triangle and compute HFI-hat of the branched double covers of all
10-crossing knots.
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Orbital Evolution, Activity, and Mass Loss of Comet C/1995 O1 (Hale-Bopp). I. Close Encounter with Jupiter in Third Millennium BCE and Effects of Outgassing on the Comet's Motion and Physical Properties | This comprehensive study of comet C/1995 O1 focuses first on investigating
its orbital motion over a period of 17.6 yr (1993-2010). The comet is suggested
to have approached Jupiter to 0.005 AU on -2251 November 7, in general
conformity with Marsden's (1999) proposal of a Jovian encounter nearly 4300 yr
ago. The variations of sizable nongravitational effects with heliocentric
distance correlate with the evolution of outgassing, asymmetric relative to
perihelion. The future orbital period will shorten to ~1000 yr because of
orbital-cascade resonance effects. We find that the sublimation curves of
parent molecules are fitted with the type of a law used for the
nongravitational acceleration, determine their orbit-integrated mass loss, and
conclude that the share of water ice was at most 57%, and possibly less than
50%, of the total outgassed mass. Even though organic parent molecules (many
still unidentified) had very low abundances relative to water individually,
their high molar mass and sheer number made them, summarily, important
potential mass contributors to the total production of gas. The mass loss of
dust per orbit exceeded that of water ice by a factor of ~12, a dust loading
high enough to imply a major role for heavy organic molecules of low volatility
in accelerating the minuscule dust particles in the expanding halos to terminal
velocities as high as 0.7 km s^{-1}. In Part II, the comet's nucleus will be
modeled as a compact cluster of massive fragments to conform to the integrated
nongravitational effect.
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A Note on Property Testing Sum of Squares and Multivariate Polynomial Interpolation | In this paper, we investigate property testing whether or not a degree d
multivariate poly- nomial is a sum of squares or is far from a sum of squares.
We show that if we require that the property tester always accepts YES
instances and uses random samples, $n^{\Omega(d)}$ samples are required, which
is not much fewer than it would take to completely determine the polynomial. To
prove this lower bound, we show that with high probability, multivariate
polynomial in- terpolation matches arbitrary values on random points and the
resulting polynomial has small norm. We then consider a particular polynomial
which is non-negative yet not a sum of squares and use pseudo-expectation
values to prove it is far from being a sum of squares.
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Closed-form mathematical expressions for the exponentiated Cauchy-Rayleigh distribution | The Cauchy-Rayleigh (CR) distribution has been successfully used to describe
asymmetric and heavy-tail events from radar imagery. Employing such model to
describe lifetime data may then seem attractive, but some drawbacks arise: its
probability density function does not cover non-modal behavior as well as the
CR hazard rate function (hrf) assumes only one form. To outperform this
difficulty, we introduce an extended CR model, called exponentiated
Cauchy-Rayleigh (ECR) distribution. This model has two parameters and hrf with
decreasing, decreasing-increasing-decreasing and upside-down bathtub forms. In
this paper, several closed-form mathematical expressions for the ECR model are
proposed: median, mode, probability weighted, log-, incomplete and order
statistic moments and Fisher information matrix. We propose three estimation
procedures for the ECR parameters: maximum likelihood (ML), bias corrected ML
and percentile-based methods. A simulation study is done to assess the
performance of estimators. An application to survival time of heart problem
patients illustrates the usefulness of the ECR model. Results point out that
the ECR distribution may outperform classical lifetime models, such as the
gamma, Birnbaun-Saunders, Weibull and log-normal laws, before heavy-tail data.
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HTEM data improve 3D modelling of aquifers in Paris Basin, France | In Paris Basin, we evaluate how HTEM data complement the usual borehole,
geological and deep seismic data used for modelling aquifer geometries. With
these traditional data, depths between ca. 50 to 300m are often relatively
ill-constrained, as most boreholes lie within the first tens of meters of the
underground and petroleum seismic is blind shallower than ca. 300m. We have
fully reprocessed and re-inverted 540km of flight lines of a SkyTEM survey of
2009, acquired on a 40x12km zone with 400m line spacing. The resistivity model
is first "calibrated" with respect to ca. 50 boreholes available on the study
area. Overall, the correlation between EM resistivity models and the
hydrogeological horizons clearly shows that the geological units in which the
aquifers are developed almost systematically correspond to relative increase of
resistivity, whatever the "background" resistivity environment and the
lithology of the aquifer. In 3D Geomodeller software, this allows interpreting
11 aquifer/aquitar layers along the flight lines and then jointly interpolating
them in 3D along with the borehole data. The resulting model displays 3D
aquifer geometries consistent with the SIGES "reference" regional
hydrogeological model and improves it in between the boreholes and on the
50-300m depth range.
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Implementing GraphQL as a Query Language for Deductive Databases in SWI-Prolog Using DCGs, Quasi Quotations, and Dicts | The methods to access large relational databases in a distributed system are
well established: the relational query language SQL often serves as a language
for data access and manipulation, and in addition public interfaces are exposed
using communication protocols like REST. Similarly to REST, GraphQL is the
query protocol of an application layer developed by Facebook. It provides a
unified interface between the client and the server for data fetching and
manipulation. Using GraphQL's type system, it is possible to specify data
handling of various sources and to combine, e.g., relational with NoSQL
databases. In contrast to REST, GraphQL provides a single API endpoint and
supports flexible queries over linked data.
GraphQL can also be used as an interface for deductive databases. In this
paper, we give an introduction of GraphQL and a comparison to REST. Using
language features recently added to SWI-Prolog 7, we have developed the Prolog
library GraphQL.pl, which implements the GraphQL type system and query syntax
as a domain-specific language with the help of definite clause grammars (DCG),
quasi quotations, and dicts. Using our library, the type system created for a
deductive database can be validated, while the query system provides a unified
interface for data access and introspection.
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Social Network based Short-Term Stock Trading System | This paper proposes a novel adaptive algorithm for the automated short-term
trading of financial instrument. The algorithm adopts a semantic sentiment
analysis technique to inspect the Twitter posts and to use them to predict the
behaviour of the stock market. Indeed, the algorithm is specifically developed
to take advantage of both the sentiment and the past values of a certain
financial instrument in order to choose the best investment decision. This
allows the algorithm to ensure the maximization of the obtainable profits by
trading on the stock market. We have conducted an investment simulation and
compared the performance of our proposed with a well-known benchmark (DJTATO
index) and the optimal results, in which an investor knows in advance the
future price of a product. The result shows that our approach outperforms the
benchmark and achieves the performance score close to the optimal result.
| 1 | 0 | 0 | 0 | 0 | 1 |
New Determinant Expressions of the Multi-indexed Orthogonal Polynomials in Discrete Quantum Mechanics | The multi-indexed orthogonal polynomials (the Meixner, little $q$-Jacobi
(Laguerre), ($q$-)Racah, Wilson, Askey-Wilson types) satisfying second order
difference equations were constructed in discrete quantum mechanics. They are
polynomials in the sinusoidal coordinates $\eta(x)$ ($x$ is the coordinate of
quantum system) and expressed in terms of the Casorati determinants whose
matrix elements are functions of $x$ at various points. By using shape
invariance properties, we derive various equivalent determinant expressions,
especially those whose matrix elements are functions of the same point $x$.
Except for the ($q$-)Racah case, they can be expressed in terms of $\eta$ only,
without explicit $x$-dependence.
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Rethinking generalization requires revisiting old ideas: statistical mechanics approaches and complex learning behavior | We describe an approach to understand the peculiar and counterintuitive
generalization properties of deep neural networks. The approach involves going
beyond worst-case theoretical capacity control frameworks that have been
popular in machine learning in recent years to revisit old ideas in the
statistical mechanics of neural networks. Within this approach, we present a
prototypical Very Simple Deep Learning (VSDL) model, whose behavior is
controlled by two control parameters, one describing an effective amount of
data, or load, on the network (that decreases when noise is added to the
input), and one with an effective temperature interpretation (that increases
when algorithms are early stopped). Using this model, we describe how a very
simple application of ideas from the statistical mechanics theory of
generalization provides a strong qualitative description of recently-observed
empirical results regarding the inability of deep neural networks not to
overfit training data, discontinuous learning and sharp transitions in the
generalization properties of learning algorithms, etc.
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Towards an Understanding of the Effects of Augmented Reality Games on Disaster Management | Location-based augmented reality games have entered the mainstream with the
nearly overnight success of Niantic's Pokémon Go. Unlike traditional video
games, the fact that players of such games carry out actions in the external,
physical world to accomplish in-game objectives means that the large-scale
adoption of such games motivate people, en masse, to do things and go places
they would not have otherwise done in unprecedented ways. The social
implications of such mass-mobilisation of individual players are, in general,
difficult to anticipate or characterise, even for the short-term. In this work,
we focus on disaster relief, and the short- and long-term implications that a
proliferation of AR games like Pokémon Go, may have in disaster-prone regions
of the world. We take a distributed cognition approach and focus on one natural
disaster-prone region of New Zealand, the city of Wellington.
| 1 | 0 | 0 | 0 | 0 | 0 |
Integrating a Global Induction Mechanism into a Sequent Calculus | Most interesting proofs in mathematics contain an inductive argument which
requires an extension of the LK-calculus to formalize. The most commonly used
calculi for induction contain a separate rule or axiom which reduces the valid
proof theoretic properties of the calculus. To the best of our knowledge, there
are no such calculi which allow cut-elimination to a normal form with the
subformula property, i.e. every formula occurring in the proof is a subformula
of the end sequent. Proof schemata are a variant of LK-proofs able to simulate
induction by linking proofs together. There exists a schematic normal form
which has comparable proof theoretic behaviour to normal forms with the
subformula property. However, a calculus for the construction of proof schemata
does not exist. In this paper, we introduce a calculus for proof schemata and
prove soundness and completeness with respect to a fragment of the inductive
arguments formalizable in Peano arithmetic.
| 1 | 0 | 0 | 0 | 0 | 0 |
An analytic resolution to the competition between Lyman-Werner radiation and metal winds in direct collapse black hole hosts | A near pristine atomic cooling halo close to a star forming galaxy offers a
natural pathway for forming massive direct collapse black hole (DCBH) seeds
which could be the progenitors of the $z>6$ redshift quasars. The close
proximity of the haloes enables a sufficient Lyman-Werner flux to effectively
dissociate H$_2$ in the core of the atomic cooling halo. A mild background may
also be required to delay star formation in the atomic cooling halo, often
attributed to distant background galaxies. In this letter we investigate the
impact of metal enrichment from both the background galaxies and the close star
forming galaxy under extremely unfavourable conditions such as instantaneous
metal mixing. We find that within the time window of DCBH formation, the level
of enrichment never exceeds the critical threshold (Z$_{cr} \sim 1 \times
10^{-5} \ \rm Z_{\odot})$, and attains a maximum metallicity of Z $\sim 2
\times 10^{-6} \ \rm Z_{\odot}$. As the system evolves, the metallicity
eventually exceeds the critical threshold, long after the DCBH has formed.
| 0 | 1 | 0 | 0 | 0 | 0 |
Driving Interactive Graph Exploration Using 0-Dimensional Persistent Homology Features | Graphs are commonly used to encode relationships among entities, yet, their
abstractness makes them incredibly difficult to analyze. Node-link diagrams are
a popular method for drawing graphs. Classical techniques for the node-link
diagrams include various layout methods that rely on derived information to
position points, which often lack interactive exploration functionalities; and
force-directed layouts, which ignore global structures of the graph. This paper
addresses the graph drawing challenge by leveraging topological features of a
graph as derived information for interactive graph drawing. We first discuss
extracting topological features from a graph using persistent homology. We then
introduce an interactive persistence barcodes to study the substructures of a
force-directed graph layout; in particular, we add contracting and repulsing
forces guided by the 0-dimensional persistent homology features. Finally, we
demonstrate the utility of our approach across three datasets.
| 1 | 0 | 0 | 0 | 0 | 0 |
Identification of Conduit Countries and Community Structures in the Withholding Tax Networks | Due to economic globalization, each country's economic law, including tax
laws and tax treaties, has been forced to work as a single network. However,
each jurisdiction (country or region) has not made its economic law under the
assumption that its law functions as an element of one network, so it has
brought unexpected results. We thought that the results are exactly
international tax avoidance. To contribute to the solution of international tax
avoidance, we tried to investigate which part of the network is vulnerable.
Specifically, focusing on treaty shopping, which is one of international tax
avoidance methods, we attempt to identified which jurisdiction are likely to be
used for treaty shopping from tax liabilities and the relationship between
jurisdictions which are likely to be used for treaty shopping and others. For
that purpose, based on withholding tax rates imposed on dividends, interest,
and royalties by jurisdictions, we produced weighted multiple directed graphs,
computed the centralities and detected the communities. As a result, we
clarified the jurisdictions that are likely to be used for treaty shopping and
pointed out that there are community structures. The results of this study
suggested that fewer jurisdictions need to introduce more regulations for
prevention of treaty abuse worldwide.
| 0 | 0 | 0 | 0 | 0 | 1 |
A comprehensive study of batch construction strategies for recurrent neural networks in MXNet | In this work we compare different batch construction methods for mini-batch
training of recurrent neural networks. While popular implementations like
TensorFlow and MXNet suggest a bucketing approach to improve the
parallelization capabilities of the recurrent training process, we propose a
simple ordering strategy that arranges the training sequences in a stochastic
alternatingly sorted way. We compare our method to sequence bucketing as well
as various other batch construction strategies on the CHiME-4 noisy speech
recognition corpus. The experiments show that our alternated sorting approach
is able to compete both in training time and recognition performance while
being conceptually simpler to implement.
| 1 | 0 | 0 | 1 | 0 | 0 |
On a class of shift-invariant subspaces of the Drury-Arveson space | In the Drury-Arveson space, we consider the subspace of functions whose
Taylor coefficients are supported in the complement of a set
$Y\subset\mathbb{N}^d$ with the property that $Y+e_j\subset Y$ for all
$j=1,\dots,d$. This is an easy example of shift-invariant subspace, which can
be considered as a RKHS in is own right, with a kernel that can be explicitely
calculated. Moreover, every such a space can be seen as an intersection of
kernels of Hankel operators, whose symbols can be explicity calcuated as well.
Finally, this is the right space on which Drury's inequality can be optimally
adapted to a sub-family of the commuting and contractive operators originally
considered by Drury.
| 0 | 0 | 1 | 0 | 0 | 0 |
Airborne gamma-ray spectroscopy for modeling cosmic radiation and effective dose in the lower atmosphere | In this paper we present the results of a $\sim$5 hour airborne gamma-ray
survey carried out over the Tyrrhenian sea in which the height range (77-3066)
m has been investigated. Gamma-ray spectroscopy measurements have been
performed by using the AGRS_16L detector, a module of four 4L NaI(Tl) crystals.
The experimental setup was mounted on the Radgyro, a prototype aircraft
designed for multisensorial acquisitions in the field of proximal remote
sensing. By acquiring high-statistics spectra over the sea (i.e. in the absence
of signals having geological origin) and by spanning a wide spectrum of
altitudes it has been possible to split the measured count rate into a constant
aircraft component and a cosmic component exponentially increasing with
increasing height. The monitoring of the count rate having pure cosmic origin
in the >3 MeV energy region allowed to infer the background count rates in the
$^{40}$K, $^{214}$Bi and $^{208}$Tl photopeaks, which need to be subtracted in
processing airborne gamma-ray data in order to estimate the potassium, uranium
and thorium abundances in the ground. Moreover, a calibration procedure has
been carried out by implementing the CARI-6P and EXPACS dosimetry tools,
according to which the annual cosmic effective dose to human population has
been linearly related to the measured cosmic count rates.
| 0 | 1 | 0 | 0 | 0 | 0 |
Search for axions in streaming dark matter | A new search strategy for the detection of the elusive dark matter (DM) axion
is proposed. The idea is based on streaming DM axions, whose flux might get
temporally enormously enhanced due to gravitational lensing. This can happen if
the Sun or some planet (including the Moon) is found along the direction of a
DM stream propagating towards the Earth location. The experimental requirements
to the axion haloscope are a wide-band performance combined with a fast axion
rest mass scanning mode, which are feasible. Once both conditions have been
implemented in a haloscope, the axion search can continue parasitically almost
as before. Interestingly, some new DM axion detectors are operating wide-band
by default. In order not to miss the actually unpredictable timing of a
potential short duration signal, a network of co-ordinated axion antennae is
required, preferentially distributed world-wide. The reasoning presented here
for the axions applies to some degree also to any other DM candidates like the
WIMPs.
| 0 | 1 | 0 | 0 | 0 | 0 |
Faster integer and polynomial multiplication using cyclotomic coefficient rings | We present an algorithm that computes the product of two n-bit integers in
O(n log n (4\sqrt 2)^{log^* n}) bit operations. Previously, the best known
bound was O(n log n 6^{log^* n}). We also prove that for a fixed prime p,
polynomials in F_p[X] of degree n may be multiplied in O(n log n 4^{log^* n})
bit operations; the previous best bound was O(n log n 8^{log^* n}).
| 1 | 0 | 0 | 0 | 0 | 0 |
Reward Maximization Under Uncertainty: Leveraging Side-Observations on Networks | We study the stochastic multi-armed bandit (MAB) problem in the presence of
side-observations across actions that occur as a result of an underlying
network structure. In our model, a bipartite graph captures the relationship
between actions and a common set of unknowns such that choosing an action
reveals observations for the unknowns that it is connected to. This models a
common scenario in online social networks where users respond to their friends'
activity, thus providing side information about each other's preferences. Our
contributions are as follows: 1) We derive an asymptotic lower bound (with
respect to time) as a function of the bi-partite network structure on the
regret of any uniformly good policy that achieves the maximum long-term average
reward. 2) We propose two policies - a randomized policy; and a policy based on
the well-known upper confidence bound (UCB) policies - both of which explore
each action at a rate that is a function of its network position. We show,
under mild assumptions, that these policies achieve the asymptotic lower bound
on the regret up to a multiplicative factor, independent of the network
structure. Finally, we use numerical examples on a real-world social network
and a routing example network to demonstrate the benefits obtained by our
policies over other existing policies.
| 1 | 0 | 0 | 1 | 0 | 0 |
Verifying Safety of Functional Programs with Rosette/Unbound | The goal of unbounded program verification is to discover an inductive
invariant that safely over-approximates all possible program behaviors.
Functional languages featuring higher order and recursive functions become more
popular due to the domain-specific needs of big data analytics, web, and
security. We present Rosette/Unbound, the first program verifier for Racket
exploiting the automated constrained Horn solver on its backend. One of the key
features of Rosette/Unbound is the ability to synchronize recursive
computations over the same inputs allowing to verify programs that iterate over
unbounded data streams multiple times. Rosette/Unbound is successfully
evaluated on a set of non-trivial recursive and higher order functional
programs.
| 1 | 0 | 0 | 0 | 0 | 0 |
Extended Formulations for Polytopes of Regular Matroids | We present a simple proof of the fact that the base (and independence)
polytope of a rank $n$ regular matroid over $m$ elements has an extension
complexity $O(mn)$.
| 1 | 0 | 1 | 0 | 0 | 0 |
Multiscale Change-point Segmentation: Beyond Step Functions | Modern multiscale type segmentation methods are known to detect multiple
change-points with high statistical accuracy, while allowing for fast
computation. Underpinning theory has been developed mainly for models that
assume the signal as a piecewise constant function. In this paper this will be
extended to certain function classes beyond such step functions in a
nonparametric regression setting, revealing certain multiscale segmentation
methods as robust to deviation from such piecewise constant functions. Our main
finding is the adaptation over such function classes for a universal
thresholding, which includes bounded variation functions, and (piecewise)
Hölder functions of smoothness order $ 0 < \alpha \le1$ as special cases.
From this we derive statistical guarantees on feature detection in terms of
jumps and modes. Another key finding is that these multiscale segmentation
methods perform nearly (up to a log-factor) as well as the oracle piecewise
constant segmentation estimator (with known jump locations), and the best
piecewise constant approximants of the (unknown) true signal. Theoretical
findings are examined by various numerical simulations.
| 0 | 0 | 1 | 1 | 0 | 0 |
Data Motif-based Proxy Benchmarks for Big Data and AI Workloads | For the architecture community, reasonable simulation time is a strong
requirement in addition to performance data accuracy. However, emerging big
data and AI workloads are too huge at binary size level and prohibitively
expensive to run on cycle-accurate simulators. The concept of data motif, which
is identified as a class of units of computation performed on initial or
intermediate data, is the first step towards building proxy benchmark to mimic
the real-world big data and AI workloads. However, there is no practical way to
construct a proxy benchmark based on the data motifs to help simulation-based
research. In this paper, we embark on a study to bridge the gap between data
motif and a practical proxy benchmark. We propose a data motif-based proxy
benchmark generating methodology by means of machine learning method, which
combine data motifs with different weights to mimic the big data and AI
workloads. Furthermore, we implement various data motifs using light-weight
stacks and apply the methodology to five real-world workloads to construct a
suite of proxy benchmarks, considering the data types, patterns, and
distributions. The evaluation results show that our proxy benchmarks shorten
the execution time by 100s times on real systems while maintaining the average
system and micro-architecture performance data accuracy above 90%, even
changing the input data sets or cluster configurations. Moreover, the generated
proxy benchmarks reflect consistent performance trends across different
architectures. To facilitate the community, we will release the proxy
benchmarks on the project homepage this http URL.
| 1 | 0 | 0 | 0 | 0 | 0 |
The neighborhood lattice for encoding partial correlations in a Hilbert space | Neighborhood regression has been a successful approach in graphical and
structural equation modeling, with applications to learning undirected and
directed graphical models. We extend these ideas by defining and studying an
algebraic structure called the neighborhood lattice based on a generalized
notion of neighborhood regression. We show that this algebraic structure has
the potential to provide an economic encoding of all conditional independence
statements in a Gaussian distribution (or conditional uncorrelatedness in
general), even in the cases where no graphical model exists that could
"perfectly" encode all such statements. We study the computational complexity
of computing these structures and show that under a sparsity assumption, they
can be computed in polynomial time, even in the absence of the assumption of
perfectness to a graph. On the other hand, assuming perfectness, we show how
these neighborhood lattices may be "graphically" computed using the separation
properties of the so-called partial correlation graph. We also draw connections
with directed acyclic graphical models and Bayesian networks. We derive these
results using an abstract generalization of partial uncorrelatedness, called
partial orthogonality, which allows us to use algebraic properties of
projection operators on Hilbert spaces to significantly simplify and extend
existing ideas and arguments. Consequently, our results apply to a wide range
of random objects and data structures, such as random vectors, data matrices,
and functions.
| 1 | 0 | 1 | 1 | 0 | 0 |
The 2-adic complexity of a class of binary sequences with almost optimal autocorrelation | Pseudo-random sequences with good statistical property, such as low
autocorrelation, high linear complexity and large 2-adic complexity, have been
applied in stream cipher. In general, it is difficult to give both the linear
complexity and 2-adic complexity of a periodic binary sequence. Cai and Ding
\cite{Cai Ying} gave a class of sequences with almost optimal autocorrelation
by constructing almost difference sets. Wang \cite{Wang Qi} proved that one
type of those sequences by Cai and Ding has large linear complexity. Sun et al.
\cite{Sun Yuhua} showed that another type of sequences by Cai and Ding has also
large linear complexity. Additionally, Sun et al. also generalized the
construction by Cai and Ding using $d$-form function with difference-balanced
property. In this paper, we first give the detailed autocorrelation
distribution of the sequences was generalized from Cai and Ding \cite{Cai Ying}
by Sun et al. \cite{Sun Yuhua}. Then, inspired by the method of Hu \cite{Hu
Honggang}, we analyse their 2-adic complexity and give a lower bound on the
2-adic complexity of these sequences. Our result show that the 2-adic
complexity of these sequences is at least $N-\mathrm{log}_2\sqrt{N+1}$ and that
it reach $N-1$ in many cases, which are large enough to resist the rational
approximation algorithm (RAA) for feedback with carry shift registers (FCSRs).
| 1 | 0 | 1 | 0 | 0 | 0 |
Nesterov's Smoothing Technique and Minimizing Differences of Convex Functions for Hierarchical Clustering | A bilevel hierarchical clustering model is commonly used in designing optimal
multicast networks. In this paper, we consider two different formulations of
the bilevel hierarchical clustering problem, a discrete optimization problem
which can be shown to be NP-hard. Our approach is to reformulate the problem as
a continuous optimization problem by making some relaxations on the
discreteness conditions. Then Nesterov's smoothing technique and a numerical
algorithm for minimizing differences of convex functions called the DCA are
applied to cope with the nonsmoothness and nonconvexity of the problem.
Numerical examples are provided to illustrate our method.
| 0 | 0 | 1 | 0 | 0 | 0 |
Minimal solutions to generalized Lambda-semiflows and gradient flows in metric spaces | Generalized Lambda-semiflows are an abstraction of semiflows with
non-periodic solutions, for which there may be more than one solution
corresponding to given initial data. A select class of solutions to generalized
Lambda-semiflows is introduced. It is proved that such minimal solutions are
unique corresponding to given ranges and generate all other solutions by time
reparametrization. Special qualities of minimal solutions are shown. The
concept of minimal solutions is applied to gradient flows in metric spaces and
generalized semiflows. Generalized semiflows have been introduced by Ball.
| 0 | 0 | 1 | 0 | 0 | 0 |
Newton-Type Methods for Non-Convex Optimization Under Inexact Hessian Information | We consider variants of trust-region and cubic regularization methods for
non-convex optimization, in which the Hessian matrix is approximated. Under
mild conditions on the inexact Hessian, and using approximate solution of the
corresponding sub-problems, we provide iteration complexity to achieve $
\epsilon $-approximate second-order optimality which have shown to be tight.
Our Hessian approximation conditions constitute a major relaxation over the
existing ones in the literature. Consequently, we are able to show that such
mild conditions allow for the construction of the approximate Hessian through
various random sampling methods. In this light, we consider the canonical
problem of finite-sum minimization, provide appropriate uniform and non-uniform
sub-sampling strategies to construct such Hessian approximations, and obtain
optimal iteration complexity for the corresponding sub-sampled trust-region and
cubic regularization methods.
| 1 | 0 | 0 | 1 | 0 | 0 |
$G 1$-smooth splines on quad meshes with 4-split macro-patch elements | We analyze the space of differentiable functions on a quad-mesh $\cM$, which
are composed of 4-split spline macro-patch elements on each quadrangular face.
We describe explicit transition maps across shared edges, that satisfy
conditions which ensure that the space of differentiable functions is ample on
a quad-mesh of arbitrary topology. These transition maps define a finite
dimensional vector space of $G^{1}$ spline functions of bi-degree $\le (k,k)$
on each quadrangular face of $\cM$. We determine the dimension of this space of
$G^{1}$ spline functions for $k$ big enough and provide explicit constructions
of basis functions attached respectively to vertices, edges and faces. This
construction requires the analysis of the module of syzygies of univariate
b-spline functions with b-spline function coefficients. New results on their
generators and dimensions are provided. Examples of bases of $G^{1}$ splines of
small degree for simple topological surfaces are detailed and illustrated by
parametric surface constructions.
| 0 | 0 | 1 | 0 | 0 | 0 |
BanglaLekha-Isolated: A Comprehensive Bangla Handwritten Character Dataset | Bangla handwriting recognition is becoming a very important issue nowadays.
It is potentially a very important task specially for Bangla speaking
population of Bangladesh and West Bengal. By keeping that in our mind we are
introducing a comprehensive Bangla handwritten character dataset named
BanglaLekha-Isolated. This dataset contains Bangla handwritten numerals, basic
characters and compound characters. This dataset was collected from multiple
geographical location within Bangladesh and includes sample collected from a
variety of aged groups. This dataset can also be used for other classification
problems i.e: gender, age, district. This is the largest dataset on Bangla
handwritten characters yet.
| 1 | 0 | 0 | 0 | 0 | 0 |
Estimates for maximal functions associated to hypersurfaces in $\Bbb R^3$ with height $h<2:$ Part I | In this article, we continue the study of the problem of $L^p$-boundedness of
the maximal operator $M$ associated to averages along isotropic dilates of a
given, smooth hypersurface $S$ of finite type in 3-dimensional Euclidean space.
An essentially complete answer to this problem had been given about seven years
ago by the last named two authors in joint work with M. Kempe for the case
where the height h of the given surface is at least two. In the present
article, we turn to the case $h<2.$ More precisely, in this Part I, we study
the case where $h<2,$ assuming that $S$ is contained in a sufficiently small
neighborhood of a given point $x^0\in S$ at which both principal curvatures of
$S$ vanish. Under these assumptions and a natural transversality assumption, we
show that, as in the case where $h\ge 2,$ the critical Lebesgue exponent for
the boundedness of $M$ remains to be $p_c=h,$ even though the proof of this
result turns out to require new methods, some of which are inspired by the more
recent work by the last named two authors on Fourier restriction to S. Results
on the case where $h<2$ and exactly one principal curvature of $S$ does not
vanish at $x^0$ will appear elsewhere.
| 0 | 0 | 1 | 0 | 0 | 0 |
Using Inertial Sensors for Position and Orientation Estimation | In recent years, MEMS inertial sensors (3D accelerometers and 3D gyroscopes)
have become widely available due to their small size and low cost. Inertial
sensor measurements are obtained at high sampling rates and can be integrated
to obtain position and orientation information. These estimates are accurate on
a short time scale, but suffer from integration drift over longer time scales.
To overcome this issue, inertial sensors are typically combined with additional
sensors and models. In this tutorial we focus on the signal processing aspects
of position and orientation estimation using inertial sensors. We discuss
different modeling choices and a selected number of important algorithms. The
algorithms include optimization-based smoothing and filtering as well as
computationally cheaper extended Kalman filter and complementary filter
implementations. The quality of their estimates is illustrated using both
experimental and simulated data.
| 1 | 0 | 0 | 0 | 0 | 0 |
Heisenberg equation for a nonrelativistic particle on a hypersurface: from the centripetal force to a curvature induced force | In classical mechanics, a nonrelativistic particle constrained on an $N-1$
curved hypersurface embedded in $N$ flat space experiences the centripetal
force only. In quantum mechanics, the situation is totally different for the
presence of the geometric potential. We demonstrate that the motion of the
quantum particle is "driven" by not only the the centripetal force, but also a
curvature induced force proportional to the Laplacian of the mean curvature,
which is fundamental in the interface physics, causing curvature driven
interface evolution.
| 0 | 1 | 0 | 0 | 0 | 0 |
On constraining projections of future climate using observations and simulations from multiple climate models | A new Bayesian framework is presented that can constrain projections of
future climate using historical observations by exploiting robust estimates of
emergent relationships between multiple climate models. We argue that emergent
relationships can be interpreted as constraints on model inadequacy, but that
projections may be biased if we do not account for internal variability in
climate model projections. We extend the previously proposed coexchangeable
framework to account for natural variability in the Earth system and internal
variability simulated by climate models. A detailed theoretical comparison with
previous multi-model projection frameworks is provided.
The proposed framework is applied to projecting surface temperature in the
Arctic at the end of the 21st century. A subset of available climate models are
selected in order to satisfy the assumptions of the framework. All available
initial condition runs from each model are utilized in order maximize the
utility of the data. Projected temperatures in some regions are more than 2C
lower when constrained by historical observations. The uncertainty about the
climate response is reduced by up to 30% where strong constraints exist.
| 0 | 0 | 0 | 1 | 0 | 0 |
Higher order molecular organisation as a source of biological function | Molecular interactions have widely been modelled as networks. The local
wiring patterns around molecules in molecular networks are linked with their
biological functions. However, networks model only pairwise interactions
between molecules and cannot explicitly and directly capture the higher order
molecular organisation, such as protein complexes and pathways. Hence, we ask
if hypergraphs (hypernetworks), that directly capture entire complexes and
pathways along with protein-protein interactions (PPIs), carry additional
functional information beyond what can be uncovered from networks of pairwise
molecular interactions. The mathematical formalism of a hypergraph has long
been known, but not often used in studying molecular networks due to the lack
of sophisticated algorithms for mining the underlying biological information
hidden in the wiring patterns of molecular systems modelled as hypernetworks.
We propose a new, multi-scale, protein interaction hypernetwork model that
utilizes hypergraphs to capture different scales of protein organization,
including PPIs, protein complexes and pathways. In analogy to graphlets, we
introduce hypergraphlets, small, connected, non-isomorphic, induced
sub-hypergraphs of a hypergraph, to quantify the local wiring patterns of these
multi-scale molecular hypergraphs and to mine them for new biological
information. We apply them to model the multi-scale protein networks of baker
yeast and human and show that the higher order molecular organisation captured
by these hypergraphs is strongly related to the underlying biology.
Importantly, we demonstrate that our new models and data mining tools reveal
different, but complementary biological information compared to classical PPI
networks. We apply our hypergraphlets to successfully predict biological
functions of uncharacterised proteins.
| 0 | 0 | 0 | 0 | 1 | 0 |
The Massive CO White Dwarf in the Symbiotic Recurrent Nova RS Ophiuchi | If accreting white dwarfs (WD) in binary systems are to produce type Ia
supernovae (SNIa), they must grow to nearly the Chandrasekhar mass and ignite
carbon burning. Proving conclusively that a WD has grown substantially since
its birth is a challenging task. Slow accretion of hydrogen inevitably leads to
the erosion, rather than the growth of WDs. Rapid hydrogen accretion does lead
to growth of a helium layer, due to both decreased degeneracy and the
inhibition of mixing of the accreted hydrogen with the underlying WD. However,
until recently, simulations of helium-accreting WDs all claimed to show the
explosive ejection of a helium envelope once it exceeded $\sim 10^{-1}\, \rm
M_{\odot}$. Because CO WDs cannot be born with masses in excess of $\sim 1.1\,
\rm M_{\odot}$, any such object, in excess of $\sim 1.2\, \rm M_{\odot}$, must
have grown substantially. We demonstrate that the WD in the symbiotic nova RS
Oph is in the mass range 1.2-1.4\,M$_{\odot}$. We compare UV spectra of RS Oph
with those of novae with ONe WDs, and with novae erupting on CO WDs. The RS Oph
WD is clearly made of CO, demonstrating that it has grown substantially since
birth. It is a prime candidate to eventually produce an SNIa.
| 0 | 1 | 0 | 0 | 0 | 0 |
Semi-equivelar maps on the torus are Archimedean | If the face-cycles at all the vertices in a map on a surface are of same type
then the map is called semi-equivelar. There are eleven types of Archimedean
tilings on the plane. All the Archimedean tilings are semi-equivelar maps. If a
map $X$ on the torus is a quotient of an Archimedean tiling on the plane then
the map $X$ is semi-equivelar. We show that each semi-equivelar map on the
torus is a quotient of an Archimedean tiling on the plane.
Vertex-transitive maps are semi-equivelar maps. We know that four types of
semi-equivelar maps on the torus are always vertex-transitive and there are
examples of other seven types of semi-equivelar maps which are not
vertex-transitive. We show that the number of ${\rm Aut}(Y)$-orbits of vertices
for any semi-equivelar map $Y$ on the torus is at most six. In fact, the number
of orbits is at most three except one type of semi-equivelar maps. Our bounds
on the number of orbits are sharp.
| 0 | 0 | 1 | 0 | 0 | 0 |
Dynamics of Porous Dust Aggregates and Gravitational Instability of Their Disk | We consider the dynamics of porous icy dust aggregates in a turbulent gas
disk and investigate the stability of the disk. We evaluate the random velocity
of porous dust aggregates by considering their self-gravity, collisions,
aerodynamic drag, turbulent stirring and scattering due to gas. We extend our
previous work by introducing the anisotropic velocity dispersion and the
relaxation time of the random velocity. We find the minimum mass solar nebular
model to be gravitationally unstable if the turbulent viscosity parameter
$\alpha$ is less than about $4 \times 10^{-3}$. The upper limit of $\alpha$ for
the onset of gravitational instability is derived as a function of the disk
parameters. We discuss the implications of the gravitational instability for
planetesimal formation.
| 0 | 1 | 0 | 0 | 0 | 0 |
Localization landscape theory of disorder in semiconductors II: Urbach tails of disordered quantum well layers | Urbach tails in semiconductors are often associated to effects of
compositional disorder. The Urbach tail observed in InGaN alloy quantum wells
of solar cells and LEDs by biased photocurrent spectroscopy is shown to be
characteristic of the ternary alloy disorder. The broadening of the absorption
edge observed for quantum wells emitting from violet to green (indium content
ranging from 0 to 28\%) corresponds to a typical Urbach energy of 20~meV. A 3D
absorption model is developed based on a recent theory of disorder-induced
localization which provides the effective potential seen by the localized
carriers without having to resort to the solution of the Schrödinger equation
in a disordered potential. This model incorporating compositional disorder
accounts well for the experimental broadening of the Urbach tail of the
absorption edge. For energies below the Urbach tail of the InGaN quantum wells,
type-II well-to-barrier transitions are observed and modeled. This contribution
to the below bandgap absorption is particularly efficient in near-UV emitting
quantum wells. When reverse biasing the device, the well-to-barrier below
bandgap absorption exhibits a red shift, while the Urbach tail corresponding to
the absorption within the quantum wells is blue shifted, due to the partial
compensation of the internal piezoelectric fields by the external bias. The
good agreement between the measured Urbach tail and its modeling by the new
localization theory demonstrates the applicability of the latter to
compositional disorder effects in nitride semiconductors.
| 0 | 1 | 0 | 0 | 0 | 0 |
Global teleconnectivity structures of the El Niño-Southern Oscillation and large volcanic eruptions -- An evolving network perspective | Recent work has provided ample evidence that global climate dynamics at
time-scales between multiple weeks and several years can be severely affected
by the episodic occurrence of both, internal (climatic) and external
(non-climatic) perturbations. Here, we aim to improve our understanding on how
regional to local disruptions of the "normal" state of the global surface air
temperature field affect the corresponding global teleconnectivity structure.
Specifically, we present an approach to quantify teleconnectivity based on
different characteristics of functional climate network analysis. Subsequently,
we apply this framework to study the impacts of different phases of the El
Niño-Southern Oscillation (ENSO) as well as the three largest volcanic
eruptions since the mid 20th century on the dominating spatiotemporal
co-variability patterns of daily surface air temperatures. Our results confirm
the existence of global effects of ENSO which result in episodic breakdowns of
the hierarchical organization of the global temperature field. This is
associated with the emergence of strong teleconnections. At more regional
scales, similar effects are found after major volcanic eruptions. Taken
together, the resulting time-dependent patterns of network connectivity allow a
tracing of the spatial extents of the dominating effects of both types of
climate disruptions. We discuss possible links between these observations and
general aspects of atmospheric circulation.
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Unbiased Shrinkage Estimation | Shrinkage estimation usually reduces variance at the cost of bias. But when
we care only about some parameters of a model, I show that we can reduce
variance without incurring bias if we have additional information about the
distribution of covariates. In a linear regression model with homoscedastic
Normal noise, I consider shrinkage estimation of the nuisance parameters
associated with control variables. For at least three control variables and
exogenous treatment, I establish that the standard least-squares estimator is
dominated with respect to squared-error loss in the treatment effect even among
unbiased estimators and even when the target parameter is low-dimensional. I
construct the dominating estimator by a variant of James-Stein shrinkage in a
high-dimensional Normal-means problem. It can be interpreted as an invariant
generalized Bayes estimator with an uninformative (improper) Jeffreys prior in
the target parameter.
| 0 | 0 | 1 | 1 | 0 | 0 |
Characterizing The Influence of Continuous Integration. Empirical Results from 250+ Open Source and Proprietary Projects | Continuous integration (CI) tools integrate code changes by automatically
compiling, building, and executing test cases upon submission of code changes.
Use of CI tools is getting increasingly popular, yet how proprietary projects
reap the benefits of CI remains unknown. To investigate the influence of CI on
software development, we analyze 150 open source software (OSS) projects, and
123 proprietary projects. For OSS projects, we observe the expected benefits
after CI adoption, e.g., improvements in bug and issue resolution. However, for
the proprietary projects, we cannot make similar observations. Our findings
indicate that only adoption of CI might not be enough to the improve software
development process. CI can be effective for software development if
practitioners use CI's feedback mechanism efficiently, by applying the practice
of making frequent commits. For our set of proprietary projects we observe
practitioners commit less frequently, and hence not use CI effectively for
obtaining feedback on the submitted code changes. Based on our findings we
recommend industry practitioners to adopt the best practices of CI to reap the
benefits of CI tools for example, making frequent commits.
| 1 | 0 | 0 | 0 | 0 | 0 |
Why Abeta42 Is Much More Toxic Than Abeta40 | Amyloid precursor with 770 amino acids dimerizes and aggregates, as do its c
terminal 99 amino acids and amyloid 40,42 amino acids fragments. The titled
question has been discussed extensively, and here it is addressed further using
thermodynamic scaling theory to analyze mutational trends in structural factors
and kinetics. Special attention is given to Family Alzheimer's Disease
mutations outside amyloid 42. The scaling analysis is connected to extensive
docking simulations which included membranes, thereby confirming their results
and extending them to Amyloid precursor.
| 0 | 0 | 0 | 0 | 1 | 0 |
A Polynomial Time Algorithm for Spatio-Temporal Security Games | An ever-important issue is protecting infrastructure and other valuable
targets from a range of threats from vandalism to theft to piracy to terrorism.
The "defender" can rarely afford the needed resources for a 100% protection.
Thus, the key question is, how to provide the best protection using the limited
available resources. We study a practically important class of security games
that is played out in space and time, with targets and "patrols" moving on a
real line. A central open question here is whether the Nash equilibrium (i.e.,
the minimax strategy of the defender) can be computed in polynomial time. We
resolve this question in the affirmative. Our algorithm runs in time polynomial
in the input size, and only polylogarithmic in the number of possible patrol
locations (M). Further, we provide a continuous extension in which patrol
locations can take arbitrary real values. Prior work obtained polynomial-time
algorithms only under a substantial assumption, e.g., a constant number of
rounds. Further, all these algorithms have running times polynomial in M, which
can be very large.
| 1 | 0 | 0 | 0 | 0 | 0 |
TIDBD: Adapting Temporal-difference Step-sizes Through Stochastic Meta-descent | In this paper, we introduce a method for adapting the step-sizes of temporal
difference (TD) learning. The performance of TD methods often depends on well
chosen step-sizes, yet few algorithms have been developed for setting the
step-size automatically for TD learning. An important limitation of current
methods is that they adapt a single step-size shared by all the weights of the
learning system. A vector step-size enables greater optimization by specifying
parameters on a per-feature basis. Furthermore, adapting parameters at
different rates has the added benefit of being a simple form of representation
learning. We generalize Incremental Delta Bar Delta (IDBD)---a vectorized
adaptive step-size method for supervised learning---to TD learning, which we
name TIDBD. We demonstrate that TIDBD is able to find appropriate step-sizes in
both stationary and non-stationary prediction tasks, outperforming ordinary TD
methods and TD methods with scalar step-size adaptation; we demonstrate that it
can differentiate between features which are relevant and irrelevant for a
given task, performing representation learning; and we show on a real-world
robot prediction task that TIDBD is able to outperform ordinary TD methods and
TD methods augmented with AlphaBound and RMSprop.
| 0 | 0 | 0 | 1 | 0 | 0 |
Enhanced clustering tendency of Cu-impurities with a number of oxygen vacancies in heavy carbon-loaded TiO2 - the bulk and surface morphologies | The over threshold carbon-loadings (~50 at.%) of initial TiO2-hosts and
posterior Cu-sensitization (~7 at.%) was made using pulsed ion-implantation
technique in sequential mode with 1 hour vacuum-idle cycle between sequential
stages of embedding. The final Cx-TiO2:Cu samples were qualified using XPS
wide-scan elemental analysis, core-levels and valence band mappings. The
results obtained were discussed on the theoretic background employing
DFT-calculations. The combined XPS and DFT analysis allows to establish and
prove the final formula of the synthesized samples as Cx-TiO2:[Cu+][Cu2+] for
the bulk and Cx-TiO2:[Cu+][Cu0] for thin-films. It was demonstrated the in the
mode of heavy carbon-loadings the remaining majority of neutral C-C bonds
(sp3-type) is dominating and only a lack of embedded carbon is fabricating the
O-C=O clusters. No valence base-band width altering was established after
sequential carbon-copper modification of the atomic structure of initial
TiO2-hosts except the dominating majority of Cu 3s states after
Cu-sensitization. The crucial role of neutral carbon low-dimensional impurities
as the precursors for the new phases growth was shown for Cu-sensitized Cx-TiO2
intermediate-state hosts.
| 0 | 1 | 0 | 0 | 0 | 0 |
On Controllable Abundance Of Saturated-input Linear Discrete Systems | Several theorems on the volume computing of the polyhedron spanned by a
n-dimensional vector set with the finite-interval parameters are presented and
proved firstly, and then are used in the analysis of the controllable regions
of the linear discrete time-invariant systems with saturated inputs. A new
concept and continuous measure on the control ability, control efficiency of
the input variables, and the diversity of the control laws, named as the
controllable abundance, is proposed based on the volume computing of the
regions and is applied to the actuator placing and configuring problems, the
optimizing problems of dynamics and kinematics of the controlled plants, etc..
The numerical experiments show the effectiveness of the new concept and methods
for investigating and optimizing the control ability and efficiency.
| 1 | 0 | 1 | 0 | 0 | 0 |
Localization and dynamics of sulfur-oxidizing microbes in natural sediment | Organic material in anoxic sediment represents a globally significant carbon
reservoir that acts to stabilize Earth's atmospheric composition. The dynamics
by which microbes organize to consume this material remain poorly understood.
Here we observe the collective dynamics of a microbial community, collected
from a salt marsh, as it comes to steady state in a two-dimensional ecosystem,
covered by flowing water and under constant illumination. Microbes form a very
thin front at the oxic-anoxic interface that moves towards the surface with
constant velocity and comes to rest at a fixed depth. Fronts are stable to all
perturbations while in the sediment, but develop bioconvective plumes in water.
We observe the transient formation of parallel fronts. We model these dynamics
to understand how they arise from the coupling between metabolism, aerotaxis,
and diffusion. These results identify the typical timescale for the oxygen flux
and penetration depth to reach steady state.
| 0 | 1 | 0 | 0 | 0 | 0 |
Probabilistic Surfel Fusion for Dense LiDAR Mapping | With the recent development of high-end LiDARs, more and more systems are
able to continuously map the environment while moving and producing spatially
redundant information. However, none of the previous approaches were able to
effectively exploit this redundancy in a dense LiDAR mapping problem. In this
paper, we present a new approach for dense LiDAR mapping using probabilistic
surfel fusion. The proposed system is capable of reconstructing a high-quality
dense surface element (surfel) map from spatially redundant multiple views.
This is achieved by a proposed probabilistic surfel fusion along with a
geometry considered data association. The proposed surfel data association
method considers surface resolution as well as high measurement uncertainty
along its beam direction which enables the mapping system to be able to control
surface resolution without introducing spatial digitization. The proposed
fusion method successfully suppresses the map noise level by considering
measurement noise caused by laser beam incident angle and depth distance in a
Bayesian filtering framework. Experimental results with simulated and real data
for the dense surfel mapping prove the ability of the proposed method to
accurately find the canonical form of the environment without further
post-processing.
| 1 | 0 | 0 | 0 | 0 | 0 |
Quantum Paramagnet and Frustrated Quantum Criticality in a Spin-One Diamond Lattice Antiferromagnet | Motivated by the proposal of topological quantum paramagnet in the diamond
lattice antiferromagnet NiRh$_2$O$_4$, we propose a minimal model to describe
the magnetic interaction and properties of the diamond material with the
spin-one local moments. Our model includes the first and second neighbor
Heisenberg interactions as well as a local single-ion spin anisotropy that is
allowed by the spin-one nature of the local moment and the tetragonal symmetry
of the system. We point out that there exists a quantum phase transition from a
trivial quantum paramagnet when the single-ion spin anisotropy is dominant to
the magnetic ordered states when the exchange is dominant. Due to the
frustrated spin interaction, the magnetic excitation in the quantum
paramagnetic state supports extensively degenerate band minima in the spectra.
As the system approaches the transition, extensively degenerate bosonic modes
become critical at the criticality, giving rise to unusual magnetic properties.
Our phase diagram and experimental predictions for different phases provide a
guildeline for the identification of the ground state for NiRh$_2$O$_4$.
Although our results are fundamentally different from the proposal of
topological quantum paramagnet, it represents interesting possibilities for
spin-one diamond lattice antiferromagnets.
| 0 | 1 | 0 | 0 | 0 | 0 |
Characterizations of minimal dominating sets and the well-dominated property in lexicographic product graphs | A graph is said to be well-dominated if all its minimal dominating sets are
of the same size. The class of well-dominated graphs forms a subclass of the
well studied class of well-covered graphs. While the recognition problem for
the class of well-covered graphs is known to be co-NP-complete, the recognition
complexity of well-dominated graphs is open.
In this paper we introduce the notion of an irreducible dominating set, a
variant of dominating set generalizing both minimal dominating sets and minimal
total dominating sets. Based on this notion, we characterize the family of
minimal dominating sets in a lexicographic product of two graphs and derive a
characterization of the well-dominated lexicographic product graphs. As a side
result motivated by this study, we give a polynomially testable
characterization of well-dominated graphs with domination number two, and show,
more generally, that well-dominated graphs can be recognized in polynomial time
in any class of graphs with bounded domination number. Our results include a
characterization of dominating sets in lexicographic product graphs, which
generalizes the expression for the domination number of such graphs following
from works of Zhang et al. (2011) and of Šumenjak et al. (2012).
| 1 | 0 | 1 | 0 | 0 | 0 |
To the Acceleration of Charged Particles with Travelling Laser Focus | We describe here the latest results of calculations with FlexPDE code of
wake-fields induced by the bunch in micro-structures. These structures,
illuminated by swept laser bust, serve for acceleration of charged particles.
The basis of the scheme is a fast sweeping device for the laser bunch. After
sweeping, the laser bunch has a slope ~45o with respect to the direction of
propagation. So the every cell of the microstructure becomes excited locally
only for the moment when the particles are there. Self-consistent parameters of
collider based on this idea allow consideration this type of collider as a
candidate for the near-future accelerator era.
| 0 | 1 | 0 | 0 | 0 | 0 |
Affiliation networks with an increasing degree sequence | Affiliation network is one kind of two-mode social network with two different
sets of nodes (namely, a set of actors and a set of social events) and edges
representing the affiliation of the actors with the social events. Although a
number of statistical models are proposed to analyze affiliation networks, the
asymptotic behaviors of the estimator are still unknown or have not been
properly explored. In this paper, we study an affiliation model with the degree
sequence as the exclusively natural sufficient statistic in the exponential
family distributions. We establish the uniform consistency and asymptotic
normality of the maximum likelihood estimator when the numbers of actors and
events both go to infinity. Simulation studies and a real data example
demonstrate our theoretical results.
| 0 | 0 | 1 | 1 | 0 | 0 |
Coarse Grained Parallel Selection | We analyze the running time of the Saukas-Song algorithm for selection on a
coarse grained multicomputer without expressing the running time in terms of
communication rounds. This shows that while in the best case the Saukas-Song
algorithm runs in asymptotically optimal time, in general it does not. We
propose other algorithms for coarse grained selection that have optimal
expected running time.
| 1 | 0 | 0 | 0 | 0 | 0 |
An IoT Analytics Embodied Agent Model based on Context-Aware Machine Learning | Agent-based Internet of Things (IoT) applications have recently emerged as
applications that can involve sensors, wireless devices, machines and software
that can exchange data and be accessed remotely. Such applications have been
proposed in several domains including health care, smart cities and
agriculture. However, despite their increased adoption, deploying these
applications in specific settings has been very challenging because of the
complex static and dynamic variability of the physical devices such as sensors
and actuators, the software application behavior and the environment in which
the application is embedded. In this paper, we propose a modeling approach for
IoT analytics based on learning embodied agents (i.e. situated agents). The
approach involves: (i) a variability model of IoT embodied agents; (ii)
feedback evaluative machine learning; and (iii) reconfiguration of a group of
agents in accordance with environmental context. The proposed approach advances
the state of the art in that it facilitates the development of Agent-based IoT
applications by explicitly capturing their complex and dynamic variabilities
and supporting their self-configuration based on an context-aware and machine
learning-based approach.
| 1 | 0 | 0 | 0 | 0 | 0 |
Aggregation of Classifiers: A Justifiable Information Granularity Approach | In this study, we introduce a new approach to combine multi-classifiers in an
ensemble system. Instead of using numeric membership values encountered in
fixed combining rules, we construct interval membership values associated with
each class prediction at the level of meta-data of observation by using
concepts of information granules. In the proposed method, uncertainty
(diversity) of findings produced by the base classifiers is quantified by
interval-based information granules. The discriminative decision model is
generated by considering both the bounds and the length of the obtained
intervals. We select ten and then fifteen learning algorithms to build a
heterogeneous ensemble system and then conducted the experiment on a number of
UCI datasets. The experimental results demonstrate that the proposed approach
performs better than the benchmark algorithms including six fixed combining
methods, one trainable combining method, AdaBoost, Bagging, and Random
Subspace.
| 1 | 0 | 0 | 1 | 0 | 0 |
FRET-based nanocommunication with luciferase and channelrhodopsin molecules for in-body medical systems | The paper is concerned with an in-body system gathering data for medical
purposes. It is focused on communication between the following two components
of the system: liposomes gathering the data inside human veins and a detector
collecting the data from liposomes. Foerster Resonance Energy Transfer (FRET)
is considered as a mechanism for communication between the system components.
The usage of bioluminescent molecules as an energy source for generating FRET
signals is suggested and the performance evaluation of this approach is given.
FRET transmission may be initiated without an aid of an external laser, which
is crucial in case of communication taking place inside of human body. It is
also shown how to solve the problem of FRET signals recording. The usage of
channelrhodopsin molecules, able to receive FRET signals and convert them into
voltage, is proposed. The communication system is modelled with molecular
structures and spectral characteristics of the proposed molecules and further
validated by using Monte Carlo computer simulations, calculating the data
throughput and the bit error rate.
| 0 | 0 | 0 | 0 | 1 | 0 |
FLASH: Randomized Algorithms Accelerated over CPU-GPU for Ultra-High Dimensional Similarity Search | We present FLASH (\textbf{F}ast \textbf{L}SH \textbf{A}lgorithm for
\textbf{S}imilarity search accelerated with \textbf{H}PC), a similarity search
system for ultra-high dimensional datasets on a single machine, that does not
require similarity computations and is tailored for high-performance computing
platforms. By leveraging a LSH style randomized indexing procedure and
combining it with several principled techniques, such as reservoir sampling,
recent advances in one-pass minwise hashing, and count based estimations, we
reduce the computational and parallelization costs of similarity search, while
retaining sound theoretical guarantees.
We evaluate FLASH on several real, high-dimensional datasets from different
domains, including text, malicious URL, click-through prediction, social
networks, etc. Our experiments shed new light on the difficulties associated
with datasets having several million dimensions. Current state-of-the-art
implementations either fail on the presented scale or are orders of magnitude
slower than FLASH. FLASH is capable of computing an approximate k-NN graph,
from scratch, over the full webspam dataset (1.3 billion nonzeros) in less than
10 seconds. Computing a full k-NN graph in less than 10 seconds on the webspam
dataset, using brute-force ($n^2D$), will require at least 20 teraflops. We
provide CPU and GPU implementations of FLASH for replicability of our results.
| 1 | 0 | 0 | 0 | 0 | 0 |
Neural Sequence Model Training via $α$-divergence Minimization | We propose a new neural sequence model training method in which the objective
function is defined by $\alpha$-divergence. We demonstrate that the objective
function generalizes the maximum-likelihood (ML)-based and reinforcement
learning (RL)-based objective functions as special cases (i.e., ML corresponds
to $\alpha \to 0$ and RL to $\alpha \to1$). We also show that the gradient of
the objective function can be considered a mixture of ML- and RL-based
objective gradients. The experimental results of a machine translation task
show that minimizing the objective function with $\alpha > 0$ outperforms
$\alpha \to 0$, which corresponds to ML-based methods.
| 1 | 0 | 0 | 1 | 0 | 0 |
Output Range Analysis for Deep Neural Networks | Deep neural networks (NN) are extensively used for machine learning tasks
such as image classification, perception and control of autonomous systems.
Increasingly, these deep NNs are also been deployed in high-assurance
applications. Thus, there is a pressing need for developing techniques to
verify neural networks to check whether certain user-expected properties are
satisfied. In this paper, we study a specific verification problem of computing
a guaranteed range for the output of a deep neural network given a set of
inputs represented as a convex polyhedron. Range estimation is a key primitive
for verifying deep NNs. We present an efficient range estimation algorithm that
uses a combination of local search and linear programming problems to
efficiently find the maximum and minimum values taken by the outputs of the NN
over the given input set. In contrast to recently proposed "monolithic"
optimization approaches, we use local gradient descent to repeatedly find and
eliminate local minima of the function. The final global optimum is certified
using a mixed integer programming instance. We implement our approach and
compare it with Reluplex, a recently proposed solver for deep neural networks.
We demonstrate the effectiveness of the proposed approach for verification of
NNs used in automated control as well as those used in classification.
| 1 | 0 | 0 | 1 | 0 | 0 |
Projection Theorems of Divergences and Likelihood Maximization Methods | Projection theorems of divergences enable us to find reverse projection of a
divergence on a specific statistical model as a forward projection of the
divergence on a different but rather "simpler" statistical model, which, in
turn, results in solving a system of linear equations. Reverse projection of
divergences are closely related to various estimation methods such as the
maximum likelihood estimation or its variants in robust statistics. We consider
projection theorems of three parametric families of divergences that are widely
used in robust statistics, namely the Rényi divergences (or the Cressie-Reed
power divergences), density power divergences, and the relative
$\alpha$-entropy (or the logarithmic density power divergences). We explore
these projection theorems from the usual likelihood maximization approach and
from the principle of sufficiency. In particular, we show the equivalence of
solving the estimation problems by the projection theorems of the respective
divergences and by directly solving the corresponding estimating equations. We
also derive the projection theorem for the density power divergences.
| 0 | 0 | 1 | 1 | 0 | 0 |
Optimal Timing in Dynamic and Robust Attacker Engagement During Advanced Persistent Threats | Advanced persistent threats (APTs) are stealthy attacks which make use of
social engineering and deception to give adversaries insider access to
networked systems. Against APTs, active defense technologies aim to create and
exploit information asymmetry for defenders. In this paper, we study a scenario
in which a powerful defender uses honeynets for active defense in order to
observe an attacker who has penetrated the network. Rather than immediately
eject the attacker, the defender may elect to gather information. We introduce
an undiscounted, infinite-horizon Markov decision process on a continuous state
space in order to model the defender's problem. We find a threshold of
information that the defender should gather about the attacker before ejecting
him. Then we study the robustness of this policy using a Stackelberg game.
Finally, we simulate the policy for a conceptual network. Our results provide a
quantitative foundation for studying optimal timing for attacker engagement in
network defense.
| 1 | 0 | 0 | 0 | 0 | 0 |
The Mismeasure of Mergers: Revised Limits on Self-interacting Dark Matter in Merging Galaxy Clusters | In an influential recent paper, Harvey et al (2015) derive an upper limit to
the self-interaction cross section of dark matter ($\sigma_{\rm DM} < 0.47$
cm$^2$/g at 95\% confidence) by averaging the dark matter-galaxy offsets in a
sample of merging galaxy clusters. Using much more comprehensive data on the
same clusters, we identify several substantial errors in their offset
measurements. Correcting these errors relaxes the upper limit on $\sigma_{\rm
DM}$ to $\lesssim 2$ cm$^2$/g, following the Harvey et al prescription for
relating offsets to cross sections in a simple solid body scattering model.
Furthermore, many clusters in the sample violate the assumptions behind this
prescription, so even this revised upper limit should be used with caution.
Although this particular sample does not tightly constrain self-interacting
dark matter models when analyzed this way, we discuss how merger ensembles may
be used more effectively in the future. We conclude that errors inherent in
using single-band imaging to identify mass and light peaks do not necessarily
average out in a sample of this size, particularly when a handful of
substructures constitute a majority of the weight in the ensemble.
| 0 | 1 | 0 | 0 | 0 | 0 |
International crop trade networks: The impact of shocks and cascades | Analyzing available FAO data from 176 countries over 21 years, we observe an
increase of complexity in the international trade of maize, rice, soy, and
wheat. A larger number of countries play a role as producers or intermediaries,
either for trade or food processing. In consequence, we find that the trade
networks become more prone to failure cascades caused by exogenous shocks. In
our model, countries compensate for demand deficits by imposing export
restrictions. To capture these, we construct higher-order trade dependency
networks for the different crops and years. These networks reveal hidden
dependencies between countries and allow to discuss policy implications.
| 0 | 0 | 0 | 0 | 0 | 1 |
Winning on the Merits: The Joint Effects of Content and Style on Debate Outcomes | Debate and deliberation play essential roles in politics and government, but
most models presume that debates are won mainly via superior style or agenda
control. Ideally, however, debates would be won on the merits, as a function of
which side has the stronger arguments. We propose a predictive model of debate
that estimates the effects of linguistic features and the latent persuasive
strengths of different topics, as well as the interactions between the two.
Using a dataset of 118 Oxford-style debates, our model's combination of content
(as latent topics) and style (as linguistic features) allows us to predict
audience-adjudicated winners with 74% accuracy, significantly outperforming
linguistic features alone (66%). Our model finds that winning sides employ
stronger arguments, and allows us to identify the linguistic features
associated with strong or weak arguments.
| 1 | 0 | 0 | 0 | 0 | 0 |
Gene regulatory networks: a primer in biological processes and statistical modelling | Modelling gene regulatory networks not only requires a thorough understanding
of the biological system depicted but also the ability to accurately represent
this system from a mathematical perspective. Throughout this chapter, we aim to
familiarise the reader with the biological processes and molecular factors at
play in the process of gene expression regulation.We first describe the
different interactions controlling each step of the expression process, from
transcription to mRNA and protein decay. In the second section, we provide
statistical tools to accurately represent this biological complexity in the
form of mathematical models. Amongst other considerations, we discuss the
topological properties of biological networks, the application of deterministic
and stochastic frameworks and the quantitative modelling of regulation. We
particularly focus on the use of such models for the simulation of expression
data that can serve as a benchmark for the testing of network inference
algorithms.
| 0 | 0 | 0 | 1 | 1 | 0 |
Mathematical Knowledge and the Role of an Observer: Ontological and epistemological aspects | As David Berlinski writes (1997), the existence and nature of mathematics is
a more compelling and far deeper problem than any of the problems raised by
mathematics itself. Here we analyze the essence of mathematics making the main
emphasis on mathematics as an advanced system of knowledge. This knowledge
consists of structures and represents structures, existence of which depends on
observers in a nonstandard way. Structural nature of mathematics explains its
reasonable effectiveness.
| 0 | 0 | 1 | 0 | 0 | 0 |
Persuasive Technology For Human Development: Review and Case Study | Technology is an extremely potent tool that can be leveraged for human
development and social good. Owing to the great importance of environment and
human psychology in driving human behavior, and the ubiquity of technology in
modern life, there is a need to leverage the insights and capabilities of both
fields together for nudging people towards a behavior that is optimal in some
sense (personal or social). In this regard, the field of persuasive technology,
which proposes to infuse technology with appropriate design and incentives
using insights from psychology, behavioral economics, and human-computer
interaction holds a lot of promise. Whilst persuasive technology is already
being developed and is at play in many commercial applications, it can have the
great social impact in the field of Information and Communication Technology
for Development (ICTD) which uses Information and Communication Technology
(ICT) for human developmental ends such as education and health. In this paper
we will explore what persuasive technology is and how it can be used for the
ends of human development. To develop the ideas in a concrete setting, we
present a case study outlining how persuasive technology can be used for human
development in Pakistan, a developing South Asian country, that suffers from
many of the problems that plague typical developing country.
| 1 | 0 | 0 | 0 | 0 | 0 |
Variable Prioritization in Nonlinear Black Box Methods: A Genetic Association Case Study | The central aim in this paper is to address variable selection questions in
nonlinear and nonparametric regression. Motivated by statistical genetics,
where nonlinear interactions are of particular interest, we introduce a novel
and interpretable way to summarize the relative importance of predictor
variables. Methodologically, we develop the "RelATive cEntrality" (RATE)
measure to prioritize candidate genetic variants that are not just marginally
important, but whose associations also stem from significant covarying
relationships with other variants in the data. We illustrate RATE through
Bayesian Gaussian process regression, but the methodological innovations apply
to other "black box" methods. It is known that nonlinear models often exhibit
greater predictive accuracy than linear models, particularly for phenotypes
generated by complex genetic architectures. With detailed simulations and two
real data association mapping studies, we show that applying RATE enables an
explanation for this improved performance.
| 0 | 0 | 0 | 1 | 1 | 0 |
Activit{é} motrice des truies en groupes dans les diff{é}rents syst{è}mes de logement | Assessment of the motor activity of group-housed sows in commercial farms.
The objective of this study was to specify the level of motor activity of
pregnant sows housed in groups in different housing systems. Eleven commercial
farms were selected for this study. Four housing systems were represented:
small groups of five to seven sows (SG), free access stalls (FS) with exercise
area, electronic sow feeder with a stable group (ESFsta) or a dynamic group
(ESFdyn). Ten sows in mid-gestation were observed in each farm. The
observations of motor activity were made for 6 hours at the first meal or at
the start of the feeding sequence, two consecutive days and at regular
intervals of 4 minutes. The results show that the motor activity of
group-housed sows depends on the housing system. The activity is higher with
the ESFdyn system (standing: 55.7%), sows are less active in the SG system
(standing: 26.5%), and FS system is intermediate. The distance traveled by sows
in ESF system is linked to a larger area available. Thus, sows travel an
average of 362 m $\pm$ 167 m in the ESFdyn system with an average available
surface of 446 m${}^2$ whereas sows in small groups travel 50 m $\pm$ 15 m for
15 m${}^2$ available.
| 0 | 0 | 0 | 0 | 1 | 0 |
Linking High-Energy Cosmic Particles by Black-Hole Jets Embedded in Large-Scale Structures | The origin of ultrahigh-energy cosmic rays (UHECRs) is a half-century old
enigma (Linsley 1963). The mystery has been deepened by an intriguing
coincidence: over ten orders of magnitude in energy, the energy generation
rates of UHECRs, PeV neutrinos, and isotropic sub-TeV gamma rays are
comparable, which hints at a grand-unified picture (Murase and Waxman 2016).
Here we report that powerful black hole jets in aggregates of galaxies can
supply the common origin of all of these phenomena. Once accelerated by a jet,
low-energy cosmic rays confined in the radio lobe are adiabatically cooled;
higher-energy cosmic rays leaving the source interact with the magnetized
cluster environment and produce neutrinos and gamma rays; the highest-energy
particles escape from the host cluster and contribute to the observed cosmic
rays above 100 PeV. The model is consistent with the spectrum, composition, and
isotropy of the observed UHECRs, and also explains the IceCube neutrinos and
the non-blazar component of the Fermi gamma-ray background, assuming a
reasonable energy output from black hole jets in clusters.
| 0 | 1 | 0 | 0 | 0 | 0 |
Quantifying and suppressing ranking bias in a large citation network | It is widely recognized that citation counts for papers from different fields
cannot be directly compared because different scientific fields adopt different
citation practices. Citation counts are also strongly biased by paper age since
older papers had more time to attract citations. Various procedures aim at
suppressing these biases and give rise to new normalized indicators, such as
the relative citation count. We use a large citation dataset from Microsoft
Academic Graph and a new statistical framework based on the Mahalanobis
distance to show that the rankings by well known indicators, including the
relative citation count and Google's PageRank score, are significantly biased
by paper field and age. We propose a general normalization procedure motivated
by the $z$-score which produces much less biased rankings when applied to
citation count and PageRank score.
| 1 | 1 | 0 | 1 | 0 | 0 |
Posterior Concentration for Bayesian Regression Trees and Forests | Since their inception in the 1980's, regression trees have been one of the
more widely used non-parametric prediction methods. Tree-structured methods
yield a histogram reconstruction of the regression surface, where the bins
correspond to terminal nodes of recursive partitioning. Trees are powerful, yet
susceptible to over-fitting. Strategies against overfitting have traditionally
relied on pruning greedily grown trees. The Bayesian framework offers an
alternative remedy against overfitting through priors. Roughly speaking, a good
prior charges smaller trees where overfitting does not occur. While the
consistency of random histograms, trees and their ensembles has been studied
quite extensively, the theoretical understanding of the Bayesian counterparts
has been missing. In this paper, we take a step towards understanding why/when
do Bayesian trees and their ensembles not overfit. To address this question, we
study the speed at which the posterior concentrates around the true smooth
regression function. We propose a spike-and-tree variant of the popular
Bayesian CART prior and establish new theoretical results showing that
regression trees (and their ensembles) (a) are capable of recovering smooth
regression surfaces, achieving optimal rates up to a log factor, (b) can adapt
to the unknown level of smoothness and (c) can perform effective dimension
reduction when p>n. These results provide a piece of missing theoretical
evidence explaining why Bayesian trees (and additive variants thereof) have
worked so well in practice.
| 0 | 0 | 1 | 1 | 0 | 0 |
Predicting Oral Disintegrating Tablet Formulations by Neural Network Techniques | Oral Disintegrating Tablets (ODTs) is a novel dosage form that can be
dissolved on the tongue within 3min or less especially for geriatric and
pediatric patients. Current ODT formulation studies usually rely on the
personal experience of pharmaceutical experts and trial-and-error in the
laboratory, which is inefficient and time-consuming. The aim of current
research was to establish the prediction model of ODT formulations with direct
compression process by Artificial Neural Network (ANN) and Deep Neural Network
(DNN) techniques. 145 formulation data were extracted from Web of Science. All
data sets were divided into three parts: training set (105 data), validation
set (20) and testing set (20). ANN and DNN were compared for the prediction of
the disintegrating time. The accuracy of the ANN model has reached 85.60%,
80.00% and 75.00% on the training set, validation set and testing set
respectively, whereas that of the DNN model was 85.60%, 85.00% and 80.00%,
respectively. Compared with the ANN, DNN showed the better prediction for ODT
formulations. It is the first time that deep neural network with the improved
dataset selection algorithm is applied to formulation prediction on small data.
The proposed predictive approach could evaluate the critical parameters about
quality control of formulation, and guide research and process development. The
implementation of this prediction model could effectively reduce drug product
development timeline and material usage, and proactively facilitate the
development of a robust drug product.
| 0 | 0 | 0 | 1 | 0 | 0 |
HNCcorr: A Novel Combinatorial Approach for Cell Identification in Calcium-Imaging Movies | Calcium imaging has emerged as a workhorse method in neuroscience to
investigate patterns of neuronal activity. Instrumentation to acquire calcium
imaging movies has rapidly progressed and has become standard across labs.
Still, algorithms to automatically detect and extract activity signals from
calcium imaging movies are highly variable from~lab~to~lab and more advanced
algorithms are continuously being developed. Here we present HNCcorr, a novel
algorithm for cell identification in calcium imaging movies based on
combinatorial optimization. The algorithm identifies cells by finding distinct
groups of highly similar pixels in correlation space, where a pixel is
represented by the vector of correlations to a set of other pixels. The HNCcorr
algorithm achieves the best known results for the cell identification benchmark
of Neurofinder, and guarantees an optimal solution to the underlying
deterministic optimization model resulting in a transparent mapping from input
data to outcome.
| 0 | 0 | 1 | 0 | 0 | 0 |
Intense cross-tail field-aligned currents in the plasma sheet at lunar distances | Field-aligned currents in the Earth's magnetotail are traditionally
associated with transient plasma flows and strong plasma pressure gradients in
the near-Earth side. In this paper we demonstrate a new field-aligned current
system present at the lunar orbit tail. Using magnetotail current sheet
observations by two ARTEMIS probes at $\sim60 R_E$, we analyze statistically
the current sheet structure and current density distribution closest to the
neutral sheet. For about half of our 130 current sheet crossings, the
equatorial magnetic field component across-the tail (along the main, cross-tail
current) contributes significantly to the vertical pressure balance. This
magnetic field component peaks at the equator, near the cross-tail current
maximum. For those cases, a significant part of the tail current, having an
intensity in the range 1-10nA/m$^2$, flows along the magnetic field lines (it
is both field-aligned and cross-tail). We suggest that this current system
develops in order to compensate the thermal pressure by particles that on its
own is insufficient to fend off the lobe magnetic pressure.
| 0 | 1 | 0 | 0 | 0 | 0 |
First non-icosahedral boron allotrope synthesized at high pressure and high temperature | Theoretical predictions of pressure-induced phase transformations often
become long-standing enigmas because of limitations of contemporary available
experimental possibilities. Hitherto the existence of a non-icosahedral boron
allotrope has been one of them. Here we report on the first non-icosahedral
boron allotrope, which we denoted as {\zeta}-B, with the orthorhombic
{\alpha}-Ga-type structure (space group Cmce) synthesized in a diamond anvil
cell at extreme high-pressure high-temperature conditions (115 GPa and 2100 K).
The structure of {\zeta}-B was solved using single-crystal synchrotron X-ray
diffraction and its compressional behavior was studied in the range of very
high pressures (115 GPa to 135 GPa). Experimental validation of theoretical
predictions reveals the degree of our up-to-date comprehension of condensed
matter and promotes further development of the solid state physics and
chemistry.
| 0 | 1 | 0 | 0 | 0 | 0 |
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