title:
Subjectivity Classification using Machine Learning Techniques for Mining
  Feature-Opinion Pairs from Web Opinion Sources

abstract:
  Due to flourish of the Web 2.0, web opinion sources are rapidly emerging
containing precious information useful for both customers and manufactures.
Recently, feature based opinion mining techniques are gaining momentum in which
customer reviews are processed automatically for mining product features and
user opinions expressed over them. However, customer reviews may contain both
opinionated and factual sentences. Distillations of factual contents improve
mining performance by preventing noisy and irrelevant extraction. In this
paper, combination of both supervised machine learning and rule-based
approaches are proposed for mining feasible feature-opinion pairs from
subjective review sentences. In the first phase of the proposed approach, a
supervised machine learning technique is applied for classifying subjective and
objective sentences from customer reviews. In the next phase, a rule based
method is implemented which applies linguistic and semantic analysis of texts
to mine feasible feature-opinion pairs from subjective sentences retained after
the first phase. The effectiveness of the proposed methods is established
through experimentation over customer reviews on different electronic products.


title:
Investigation of doublet-bands in $^{124,126,130,132}$Cs odd-odd nuclei
  using triaxial projected shell model approach

abstract:
  Doublet bands observed in $^{124,126,130,132}$Cs isotopes are studied using
the recently developed multi-quasiparticle microscopic triaxial projected shell
model (TPSM) approach. It is shown that TPSM results for energies and
transition probabilities are in good agreement with known energies and the
recently measured extensive data on transition probabilities for the bands in
$^{126}$Cs. In particular, it is demonstrated that characteristics transition
probabilities expected for the doublet bands to originate from the chiral
symmetry breaking are well reproduced in the present work. The calculated
energies for $^{124,130,132}$Cs are also shown to be in reasonable agreement
with the available experimental data. Furthermore, a complete set of the
calculated transition probabilities is provided for the doublet bands in
$^{124,130,132}$Cs isotopes.


title: Soft {\beta}-Open Sets And Their Applications

abstract:
  First of all, we focused soft {\beta}-open sets and soft {\beta}-closed sets
over the soft topological space and investigated some properties of them.
Secondly, we defined the concepts soft {\beta}-continuity, soft
{\beta}-irresolute and soft {\beta}-homeomorphism on soft topological spaces.
We also obtained some characterizations of these mappings. Finally, we observed
that the collection S{\beta}r-h(X,{\tau},E) was a soft group.


title:
An Unsupervised Approach for Automatic Activity Recognition based on
  Hidden Markov Model Regression

abstract:
  Using supervised machine learning approaches to recognize human activities
from on-body wearable accelerometers generally requires a large amount of
labelled data. When ground truth information is not available, too expensive,
time consuming or difficult to collect, one has to rely on unsupervised
approaches. This paper presents a new unsupervised approach for human activity
recognition from raw acceleration data measured using inertial wearable
sensors. The proposed method is based upon joint segmentation of
multidimensional time series using a Hidden Markov Model (HMM) in a multiple
regression context. The model is learned in an unsupervised framework using the
Expectation-Maximization (EM) algorithm where no activity labels are needed.
The proposed method takes into account the sequential appearance of the data.
It is therefore adapted for the temporal acceleration data to accurately detect
the activities. It allows both segmentation and classification of the human
activities. Experimental results are provided to demonstrate the efficiency of
the proposed approach with respect to standard supervised and unsupervised
classification approaches


title:
Model-based functional mixture discriminant analysis with hidden process
  regression for curve classification

abstract:
  In this paper, we study the modeling and the classification of functional
data presenting regime changes over time. We propose a new model-based
functional mixture discriminant analysis approach based on a specific hidden
process regression model that governs the regime changes over time. Our
approach is particularly adapted to handle the problem of complex-shaped
classes of curves, where each class is potentially composed of several
sub-classes, and to deal with the regime changes within each homogeneous
sub-class. The proposed model explicitly integrates the heterogeneity of each
class of curves via a mixture model formulation, and the regime changes within
each sub-class through a hidden logistic process. Each class of complex-shaped
curves is modeled by a finite number of homogeneous clusters, each of them
being decomposed into several regimes. The model parameters of each class are
learned by maximizing the observed-data log-likelihood by using a dedicated
expectation-maximization (EM) algorithm. Comparisons are performed with
alternative curve classification approaches, including functional linear
discriminant analysis and functional mixture discriminant analysis with
polynomial regression mixtures and spline regression mixtures. Results obtained
on simulated data and real data show that the proposed approach outperforms the
alternative approaches in terms of discrimination, and significantly improves
the curves approximation.


title:
Model-based clustering and segmentation of time series with changes in
  regime

abstract:
  Mixture model-based clustering, usually applied to multidimensional data, has
become a popular approach in many data analysis problems, both for its good
statistical properties and for the simplicity of implementation of the
Expectation-Maximization (EM) algorithm. Within the context of a railway
application, this paper introduces a novel mixture model for dealing with time
series that are subject to changes in regime. The proposed approach consists in
modeling each cluster by a regression model in which the polynomial
coefficients vary according to a discrete hidden process. In particular, this
approach makes use of logistic functions to model the (smooth or abrupt)
transitions between regimes. The model parameters are estimated by the maximum
likelihood method solved by an Expectation-Maximization algorithm. The proposed
approach can also be regarded as a clustering approach which operates by
finding groups of time series having common changes in regime. In addition to
providing a time series partition, it therefore provides a time series
segmentation. The problem of selecting the optimal numbers of clusters and
segments is solved by means of the Bayesian Information Criterion (BIC). The
proposed approach is shown to be efficient using a variety of simulated time
series and real-world time series of electrical power consumption from rail
switching operations.


title:
A hidden process regression model for functional data description.
  Application to curve discrimination

abstract:
  A new approach for functional data description is proposed in this paper. It
consists of a regression model with a discrete hidden logistic process which is
adapted for modeling curves with abrupt or smooth regime changes. The model
parameters are estimated in a maximum likelihood framework through a dedicated
Expectation Maximization (EM) algorithm. From the proposed generative model, a
curve discrimination rule is derived using the Maximum A Posteriori rule. The
proposed model is evaluated using simulated curves and real world curves
acquired during railway switch operations, by performing comparisons with the
piecewise regression approach in terms of curve modeling and classification.


title: Time series modeling by a regression approach based on a latent process

abstract:
  Time series are used in many domains including finance, engineering,
economics and bioinformatics generally to represent the change of a measurement
over time. Modeling techniques may then be used to give a synthetic
representation of such data. A new approach for time series modeling is
proposed in this paper. It consists of a regression model incorporating a
discrete hidden logistic process allowing for activating smoothly or abruptly
different polynomial regression models. The model parameters are estimated by
the maximum likelihood method performed by a dedicated Expectation Maximization
(EM) algorithm. The M step of the EM algorithm uses a multi-class Iterative
Reweighted Least-Squares (IRLS) algorithm to estimate the hidden process
parameters. To evaluate the proposed approach, an experimental study on
simulated data and real world data was performed using two alternative
approaches: a heteroskedastic piecewise regression model using a global
optimization algorithm based on dynamic programming, and a Hidden Markov
Regression Model whose parameters are estimated by the Baum-Welch algorithm.
Finally, in the context of the remote monitoring of components of the French
railway infrastructure, and more particularly the switch mechanism, the
proposed approach has been applied to modeling and classifying time series
representing the condition measurements acquired during switch operations.


title: Heavy-traffic asymptotic formulas for the multiclass M${}^X$/G/1 queue

abstract:
  This paper studies the heavy-traffic asymptotics for the multiclass FIFO
M${}^X$/G/1 queue. We first derive the probability generating function of the
joint queue length distribution. Using the probability generating function, we
then present heavy-traffic asymptotic formulas for the joint queue length
distribution and its joint moments (i.e., the joint queue length moments).
These formulas are proved under weaker conditions on the service time
distributions, compared to the ones reported in the literature. This fact leads
us to conjectures that some of the conditions made in the literature are
relaxed.


title: Multidimensional potential Burgers turbulence

abstract:
  We consider the multidimensional generalised stochastic Burgers equation in
the space-periodic setting:
  $ \partial \mathbf{u}/\partial t+$ $(\nabla f(\mathbf{u}) \cdot \nabla)$
$\mathbf{u} -\nu \Delta \mathbf{u}=$ $\nabla \eta,\quad t \geq 0,\ \mathbf{x}
\in \mathbb{T}^d=(\mathbb{R}/\mathbb{Z})^d,$ under the assumption that
$\mathbf{u}$ is a gradient. Here $f$ is strongly convex and satisfies a growth
condition, $\nu$ is small and positive, while $\eta$ is a random forcing term,
smooth in space and white in time. For solutions $\mathbf{u}$ of this equation,
we study Sobolev norms of $\mathbf{u}$ averaged in time and in ensemble: each
of these norms behaves as a given negative power of $\nu$. These results yield
sharp upper and lower bounds for natural analogues of quantities characterising
the hydrodynamical turbulence, namely the averages of the increments and of the
energy spectrum. These quantities behave as a power of the norm of the relevant
parameter, which is respectively the separation $\mathbf{l}$ in the physical
space and the wavenumber $\mathbf{k}$ in the Fourier space. Our bounds do not
depend on the initial condition and hold uniformly in $\nu$. We generalise the
results obtained for the one-dimensional case in \cite{BorW}, confirming the
physical predictions in \cite{BK07,GMN10}. Note that the form of the estimates
does not depend on the dimension: the powers of $\nu, |\mathbf{k}|, \mathbf{l}$
are the same in the one- and the multi-dimensional setting.


title: Spontaneous Emission of Charged Bosons from Supercritical Point Charges

abstract:
  We study the spontaneous emission of charged bosons from supercritical
Coulomb potentials and charged black holes. We find the exact emission rate
from the Bogoliubov transformation by applying the tunneling boundary condition
on the Jost functions at the asymptotic boundaries. The emission rate for
charged bosons in the supercritical Coulomb potential increases as the charge
$Z\alpha > 1/2$ of the superatom and the energy of the bosons increase but is
suppressed for large angular momenta. We discuss physical implications of the
emission of charged bosons from superatoms and charged black holes.


title: On hypergraph cliques and polynomial programming

abstract:
  Motzkin and Straus established a close connection between the maximum clique
problem and a solution (namely graph-Lagrangians) to the maximum value of a
class of homogeneous quadratic multilinear functions over the standard simplex
of the Euclidean space in 1965. This connection provides a new proof of
Tur\'an's theorem. Recently, an extension of Motzkin-Straus theorem was proved
for non-uniform hypergraphs whose edges contain 1 or 2 vertices in \cite{PPTZ}.
It is interesting if similar results hold for other non-uniform hypergraphs. In
this paper, we give some connection between polynomial programming and the
clique of non-uniform hypergraphs whose edges contain 1, or 2, and more
vertices. Specifically, we obtain some Motzkin-Straus type results in terms of
the graph-Lagrangian of non-uniform hypergraphs whose edges contain 1, or 2,
and more vertices.


title:
Piecewise regression mixture for simultaneous functional data clustering
  and optimal segmentation

abstract:
  This paper introduces a novel mixture model-based approach for simultaneous
clustering and optimal segmentation of functional data which are curves
presenting regime changes. The proposed model consists in a finite mixture of
piecewise polynomial regression models. Each piecewise polynomial regression
model is associated with a cluster, and within each cluster, each piecewise
polynomial component is associated with a regime (i.e., a segment). We derive
two approaches for learning the model parameters. The former is an estimation
approach and consists in maximizing the observed-data likelihood via a
dedicated expectation-maximization (EM) algorithm. A fuzzy partition of the
curves in K clusters is then obtained at convergence by maximizing the
posterior cluster probabilities. The latter however is a classification
approach and optimizes a specific classification likelihood criterion through a
dedicated classification expectation-maximization (CEM) algorithm. The optimal
curve segmentation is performed by using dynamic programming. In the
classification approach, both the curve clustering and the optimal segmentation
are performed simultaneously as the CEM learning proceeds. We show that the
classification approach is the probabilistic version that generalizes the
deterministic K-means-like algorithm proposed in H\'ebrail et al. (2010). The
proposed approach is evaluated using simulated curves and real-world curves.
Comparisons with alternatives including regression mixture models and the
K-means like algorithm for piecewise regression demonstrate the effectiveness
of the proposed approach.


title:
Observed relationship between CO2-1 and dust emission during post-AGB
  phase

abstract:
  A CO\,2-1 line survey is performed toward a sample of 58 high Galactic
latitude post-AGB (pAGB) stars. To complement the observations, a compilation
of literature CO\,2-1 line data of known pAGB stars is done. After combining
the datasets, CO\,2-1 line data are available for 133 pAGB stars (about 34 per
cent of known pAGB stars) among which 44 are detections. The CO line strengths
are compared with infrared dust emission for these pAGB stars by defining a
ratio between the integrated CO\,2-1 line flux and {\it IRAS} 25\,mu flux
density (CO-IR ratio). The relationship between the CO-IR ratio and the {\it
IRAS} color C23 (defined with the 25 and 60\,mu flux densities) is called here
the CO-IR diagram. The pAGB objects are found to be located between AGB stars
and planetary nebulae (PNe), and segregate into three distinctive groups (I, II
and III) on the CO-IR diagram. By analyzing their various properties such as
chemical types, spectral types, binarity, circumstellar envelope expansion
velocities, and pAGB sub-types on the CO-IR diagram, it is argued that the
group-I objects are mainly intermediate mass C-rich pAGB stars in early pAGB
stage (almost all of the considered carbon rich `21\,mu' stars belong to this
group); the group-II objects are massive or intermediate mass pAGB stars which
already follow the profound trend of PNe; and the group-III objects are mainly
low mass binary pAGB stars with very weak CO\,2-1 line emission (almost all of
the considered RV\,Tau variables belong to this group). The CO-IR diagram is
proven to be a powerful tool to investigate the co-evolution of circumstellar
gas and dust during the short pAGB stage of stellar evolution.


title:
B\"acklund-Darboux Transformations and Discretizations of Super KdV
  Equation

abstract:
  For a generalized super KdV equation, three Darboux transformations and the
corresponding B\"acklund transformations are constructed. The compatibility of
these Darboux transformations leads to three discrete systems and their Lax
representations. The reduction of one of the B\"acklund-Darboux transformations
and the corresponding discrete system are considered for Kupershmidt's super
KdV equation. When all the odd variables vanish, a nonlinear superposition
formula is obtained for Levi's B\"acklund transformation for the KdV equation.


title:
A note on symmetric properties of the multiple q-Euler zeta functions
  and higher-order q-Euler polynomials

abstract:
  Recently, the higher-order q-Euler polynomials and multiple q-Euler zeta
functions are introduced by T. Kim ([8, 9]). In this paper, we investigate some
symmetric properties of the multiple q-Euler zeta function and derive various
identities concerning the higher-order q-Euler polynomials from the symmetric
properties of the multiple q-Euler zeta functions.


title:
Mod\`ele \`a processus latent et algorithme EM pour la r\'egression non
  lin\'eaire

abstract:
  A non linear regression approach which consists of a specific regression
model incorporating a latent process, allowing various polynomial regression
models to be activated preferentially and smoothly, is introduced in this
paper. The model parameters are estimated by maximum likelihood performed via a
dedicated expecation-maximization (EM) algorithm. An experimental study using
simulated and real data sets reveals good performances of the proposed
approach.


title:
Dynamical self-averaging for a lattice Schr\"odinger equation with weak
  random potential

abstract:
  We study the kinetic, weak coupling limit of the dynamics governed by a
discrete random Schr\"odinger operator on $\mathbb{Z}^3$. For sequences of
$\ell^2\left(\mathbb{Z}^3\right)$-bounded initial states and convergent initial
Wigner transform, we prove that the scaled Wigner transform converges to the
solution of a linear Boltzmann equation in $L^r\left(\mathbb{P}\right)$for all
$r>0$, thus considerably strengthening a previous result by Chen. The key
ingredients for the proof are a finer classification of graphs in the expansion
of the perturbed dynamics as well as a novel resolvent estimate for the
unperturbed Schr\"odinger operator. Under some additional assumption on the
sequence of initial states we even prove almost sure convergence.


title:
Induced representations and traces for chains of affine and cyclotomic
  Hecke algebras

abstract:
  Properties of relative traces and symmetrizing forms on chains of cyclotomic
and affine Hecke algebras are studied. The study relies on a use of bases of
these algebras which generalize a normal form for elements of the complex
reflection groups $G(m,1,n)$, $m=1,2,\dots,\infty$, constructed by a recursive
use of the Coxeter--Todd algorithm. Formulas for inducing, from representations
of an algebra in the chain, representations of the next member of the chain are
presented.


title:
Decompositions of Betti diagrams of powers of monomial ideals: A
  stability conjecture

abstract:
  We state a conjecture on the stability of Betti diagrams of powers of
monomial ideals.


title:
Peierls distortion, magnetism, and high hardness of manganese
  tetraboride

abstract:
  We report crystal structure, electronic structure, and magnetism of manganese
tetraboride, MnB4, synthesized under high-pressure high-temperature conditions.
In contrast to superconducting FeB4 and metallic CrB4, which are both
orthorhombic, MnB4 features a monoclinic crystal structure. Its lower symmetry
originates from a Peierls distortion of the Mn chains. This distortion nearly
opens the gap at the Fermi level, but despite the strong dimerization and the
proximity of MnB4 to the insulating state, we find indications for a sizable
paramagnetic effective moment of about 1.7 muB/f.u., ferromagnetic spin
correlations and, even more surprisingly, a prominent electronic contribution
to the specific heat. However, no magnetic order has been observed in standard
thermodynamic measurements down to 2 K. Altogether, this renders MnB4 a
structurally simple but microscopically enigmatic material; we argue that its
properties may be influenced by electronic correlations.


title:
Control of light scattering by nanoparticles with optically-induced
  magnetic responses

abstract:
  Conventional approaches to control and shape the scattering patterns of light
generated by different nanostructures are mostly based on engineering of their
electric response due to the fact that most metallic nanostructures support
electric resonances in the optical frequency range. Recently, fuelled by the
fast development in the fields of metamaterials and plasmonics, artificial
optically-induced magnetic responses have been demonstrated for various
nanostructures. This kind of response can be employed to provide an extra
degree of freedom for the efficient control and shaping of the scattering
patterns of nanoparticles and nanoantennas. Here we review the recent progress
in this research direction of nanoparticle scattering shaping and control
through the interference of both electric and optically-induced magnetic
responses. We discuss the magnetic resonances supported by various structures
in different spectral regimes, and then summarize the original results on the
scattering shaping involving both electric and magnetic responses, based on the
interference of both spectrally separated (with different resonant wavelengths)
and overlapped dipoles (with the same resonant wavelength), and also other
higher-order modes. Finally, we discuss the scattering control utilizing Fano
resonances associated with the magnetic responses.


title:
Structural and electronic origin of the magnetic structures in hexagonal
  LuFeO$_3$

abstract:
  Using combined theoretical and experimental approaches, we studied the
structural and electronic origin of the magnetic structure in hexagonal
LuFeO$_3$. Besides showing the strong exchange coupling that is consistent with
the high magnetic ordering temperature, the previously observed spin
reorientation transition is explained by the theoretically calculated magnetic
phase diagram. The structural origin of this spin reorientation that is
responsible for the appearance of spontaneous magnetization, is identified by
theory and verified by x-ray diffraction and absorption experiments.


title:
Electron spin relaxation due to D'yakonov-Perel' and Elliot-Yafet
  mechanisms in monolayer MoS$_2$: Role of intravalley and intervalley
  processes

abstract:
  We investigate the in-plane spin relaxation of electrons due to the
D'yakonov-Perel' and Elliot-Yafet mechanisms including the intra- and
inter-valley processes in monolayer MoS$_2$. We construct the effective
Hamiltonian for the conduction band using the L\"{o}wdin partition method from
the anisotropic two-band Hamiltonian with the intrinsic spin-orbit coupling of
the conduction band included. The spin-orbit coupling of the conduction band
induces the intra- and inter-valley D'yakonov-Perel' spin relaxation. In
addition, the Elliot-Yafet spin relaxation also takes place due to the
interband spin mixing. We find that the D'yakonov-Perel' mechanism dominates
the in-plane spin relaxation. In the framework of this mechanism, the
intravalley process is shown to play a more important role at low temperature
whereas the intervalley one becomes more important at high temperature. At the
temperature in between, the leading process of the in-plane spin relaxation
changes from the intervalley to intravalley one as the electron density
increases. Moreover, we find that the intravalley process is dominated by the
electron-electron Coulomb scattering even with high impurity density since the
dominant term in the spin-orbit coupling is isotropic, which does not lead to
the spin relaxation together with the electron-impurity scattering. This is
very different from the previous studies in semiconductors and graphene.


title:
Metastable and spin-polarized states in electron systems with localized
  electron-electron interaction

abstract:
  We study the formation of spontaneous spin polarization in inhomogeneous
electron systems with pair interaction localized in a small region that is not
separated by a barrier from surrounding gas of non-interacting electrons. Such
a system is interesting as a minimal model of a quantum point contact, in which
the electron-electron interaction is strong in a small constriction coupled to
electron reservoirs without barriers. Based on the analysis of the grand
potential within the self-consistent field approximation, we find that the
formation of the polarized state strongly differs from the Bloch or Stoner
transition in homogeneous interacting systems. The main difference is that a
metastable state appears in the critical point in addition to the globally
stable state, so that when the interaction parameter exceeds a critical value,
two states coexist. One state has spin polarization and the other is
unpolarized. Another feature is that the spin polarization increases
continuously with the interaction parameter and has a square-root singularity
in the critical point. We study the critical conditions and the grand
potentials of the polarized and unpolarized states for one-dimensional and
two-dimensional models in the case of extremely small size of the interaction
region.


title:
High Performance Semiconducting Enriched Carbon Nanotube Thin Film
  Transistors Using Metallic Carbon Nanotube Electrode

abstract:
  High-performance solution-processed short-channel carbon nanotube (CNT) thin
film transistors (TFTs) are fabricated using densely aligned arrays of metallic
CNTs (m-CNTs) as source and drain electrodes, and aligned arrays of
semiconducting enriched CNTs (s-CNTs) as channel material. The electrical
transport measurements at room temperature show that the m-CNT contacted s-CNT
array devices with a 2 um channel length perform superiorly to those of control
Pd contacted s-CNT devices. The m-CNT contacted devices exhibit a maximum
(average) on-conductance of 36.5 uS (19.2 uS), transconductance of 2.6 uS (1.2
uS), mobility of 51 cm2/Vs (25 cm2/Vs), and current on-off ratio of 1.1x10E6
(2.5x10E5). These values are almost an order of magnitude higher than that of
control Pd contacted devices with the same channel length and s-CNT linear
density. The low temperature charge transport measurements suggest that these
improved performances are due to the lower charge injection barrier of
m-CNT/s-CNT array devices compared to Pd/s-CNT array devices. We attribute the
lower injection barrier to unique geometry of our devices. In addition to using
semiconducting enriched CNT, our results suggest that using metallic CNT as an
electrode can significantly enhance the performance of CNT TFTs.


title: Quantum strong subadditivity condition for systems without subsystems

abstract:
  The strong subadditivity condition for the density matrix of a quantum
system, which does not contain subsystems, is derived using the qudit-portrait
method. An example of the qudit state in the seven-dimensional Hilbert space
corresponding to spin j = 3 is presented in detail. New entropic inequalities
in the form of subadditivity condition and strong subadditivity condition for
spin tomograms determining the qudit states are obtained and given on examples
of j = 2 and 3.


title: Disorder-assisted quantum transport in suboptimal decoherence regimes

abstract:
  We investigate quantum transport in binary tree structures and in hypercubes
for the disordered Frenkel-exciton Hamiltonian under pure dephasing noise. We
compute the energy transport efficiency as a function of disorder and dephasing
rates. We demonstrate that dephasing improves transport efficiency not only in
the disordered case, but also in the ordered one. The maximal transport
efficiency is obtained when the dephasing timescale matches the hopping
timescale, which represent new examples of the Goldilocks principle at the
quantum scale. Remarkably, we find that in weak dephasing regimes, away from
optimal levels of environmental fluctuations, the average effect of increasing
disorder is to improve the transport efficiency until an optimal value for
disorder is reached. Our results suggest that rational design of the site
energies statistical distributions could lead to better performances in
transport systems at nanoscale when their natural environments are far from the
optimal dephasing regime.


title:
Critical probabilities and convergence time of Percolation Probabilistic
  Cellular Automata

abstract:
  This paper considers a class of probabilistic cellular automata undergoing a
phase transition with an absorbing state. Denoting by ${\mathcal{U}}(x)$ the
neighbourhood of site $x$, the transition probability is $T(\eta_x = 1 |
\eta_{{\mathcal{U}}(x)}) = 0$ if $\eta_{{\mathcal{U}}(x)}= \mathbf{0}$ or $p$
otherwise, $\forall x \in \mathbb{Z}$. For any $\mathcal{U}$ there exists a
non-trivial critical probability $p_c({\mathcal{U}})$ that separates a phase
with an absorbing state from a fluctuating phase. This paper studies how the
neighbourhood affects the value of $p_c({\mathcal{U}})$ and provides lower
bounds for $p_c({\mathcal{U}})$. Furthermore, by using dynamic renormalization
techniques, we prove that the expected convergence time of the processes on a
finite space with periodic boundaries grows exponentially (resp.
logarithmically) with the system size if $p > p_c$ (resp. $p<p_c$). This
provides a partial answer to an open problem in Toom et al. (1990, 1994).


title: Dipolar Dark Matter and Cosmology

abstract:
  The phenomenology of the modified Newtonian dynamics (MOND) can be recovered
from a mechanism of "gravitational polarization" of some dipolar medium playing
the role of dark matter. We review a relativistic model of dipolar dark matter
(DDM) within standard general relativity to describe, at some effective level,
a fluid polarizable in a gravitational field. At first order in cosmological
perturbation theory, this model is equivalent to the concordance cosmological
scenario, or Lambda-cold dark matter (CDM) model. At second order, however, the
internal energy of DDM modifies the curvature perturbation generated by CDM.
This correction, which depends quadratically on the dipole, induces a new type
of non-Gaussianity in the bispectrum of the curvature perturbation with respect
to standard CDM. Recent observations by the Planck satellite impose stringent
constraints on the primordial value of the dipole field.


title:
Oscillatory decay of the survival probability of activated diffusion
  across a limit cycle

abstract:
  Activated escape of a Brownian particle from the domain of attraction of a
stable focus over a limit cycle exhibits non-Kramers behavior: it is
non-Poissonian. When the attractor is moved closer to the boundary oscillations
can be discerned in the survival probability. We show that these oscillations
are due to complex-valued higher order eigenvalues of the Fokker-Planck
operator, which we compute explicitly in the limit of small noise. We also show
that in this limit the period of the oscillations is the winding number of the
activated stochastic process. These peak probability oscillations are not
related to stochastic resonance and should be detectable in planar dynamical
systems with the topology described here.

categories: math-ph math.AP math.MP math.PR physics.class-ph


title: Asteroseismology observations and space missions

abstract: Contribution to a book entitled "Extraterrestrial seismology".


title:
A regression model with a hidden logistic process for signal
  parametrization

abstract:
  A new approach for signal parametrization, which consists of a specific
regression model incorporating a discrete hidden logistic process, is proposed.
The model parameters are estimated by the maximum likelihood method performed
by a dedicated Expectation Maximization (EM) algorithm. The parameters of the
hidden logistic process, in the inner loop of the EM algorithm, are estimated
using a multi-class Iterative Reweighted Least-Squares (IRLS) algorithm. An
experimental study using simulated and real data reveals good performances of
the proposed approach.


title:
Towards Using Unlabeled Data in a Sparse-coding Framework for Human
  Activity Recognition

abstract:
  We propose a sparse-coding framework for activity recognition in ubiquitous
and mobile computing that alleviates two fundamental problems of current
supervised learning approaches. (i) It automatically derives a compact, sparse
and meaningful feature representation of sensor data that does not rely on
prior expert knowledge and generalizes extremely well across domain boundaries.
(ii) It exploits unlabeled sample data for bootstrapping effective activity
recognizers, i.e., substantially reduces the amount of ground truth annotation
required for model estimation. Such unlabeled data is trivial to obtain, e.g.,
through contemporary smartphones carried by users as they go about their
everyday activities.
  Based on the self-taught learning paradigm we automatically derive an
over-complete set of basis vectors from unlabeled data that captures inherent
patterns present within activity data. Through projecting raw sensor data onto
the feature space defined by such over-complete sets of basis vectors effective
feature extraction is pursued. Given these learned feature representations,
classification backends are then trained using small amounts of labeled
training data.
  We study the new approach in detail using two datasets which differ in terms
of the recognition tasks and sensor modalities. Primarily we focus on
transportation mode analysis task, a popular task in mobile-phone based
sensing. The sparse-coding framework significantly outperforms the
state-of-the-art in supervised learning approaches. Furthermore, we demonstrate
the great practical potential of the new approach by successfully evaluating
its generalization capabilities across both domain and sensor modalities by
considering the popular Opportunity dataset. Our feature learning approach
outperforms state-of-the-art approaches to analyzing activities in daily
living.


title:
A New Approach to Constraint Weight Learning for Variable Ordering in
  CSPs

abstract:
  A Constraint Satisfaction Problem (CSP) is a framework used for modeling and
solving constrained problems. Tree-search algorithms like backtracking try to
construct a solution to a CSP by selecting the variables of the problem one
after another. The order in which these algorithm select the variables
potentially have significant impact on the search performance. Various
heuristics have been proposed for choosing good variable ordering. Many
powerful variable ordering heuristics weigh the constraints first and then
utilize the weights for selecting good order of the variables. Constraint
weighting are basically employed to identify global bottlenecks in a CSP.
  In this paper, we propose a new approach for learning weights for the
constraints using competitive coevolutionary Genetic Algorithm (GA). Weights
learned by the coevolutionary GA later help to make better choices for the
first few variables in a search. In the competitive coevolutionary GA,
constraints and candidate solutions for a CSP evolve together through an
inverse fitness interaction process. We have conducted experiments on several
random, quasi-random and patterned instances to measure the efficiency of the
proposed approach. The results and analysis show that the proposed approach is
good at learning weights to distinguish the hard constraints for quasi-random
instances and forced satisfiable random instances generated with the Model RB.
For other type of instances, RNDI still seems to be the best approach as our
experiments show.


title: Magnetic activity of F stars observed by Kepler

abstract:
  The study of stellar activity is important because it can provide new
constraints for dynamo models, when combined with surface rotation rates and
the depth of the convection zone. We know that the dynamo mechanism, which is
believed to be the main process to rule the magnetic cycle of solar-like stars
at least, results from the interaction between (differential) rotation,
convection, and magnetic field. The Kepler mission has been collecting data for
a large number of stars during 4 years allowing us to investigate magnetic
stellar cycles. We investigated the Kepler light curves to look for magnetic
activity or even hints of magnetic activity cycles. Based on the photometric
data we also looked for new magnetic indexes to characterise the magnetic
activity of the stars. We selected a sample of 22 solar-like F stars that have
a rotation period smaller than 12 days. We performed a time-frequency analysis
using the Morlet wavelet yielding a magnetic proxy. We computed the magnetic
index S_ph as the standard deviation of the whole time series and the index
<S_ph> that is the mean of standard deviations measured in subseries of length
five times the rotation period of the star. We defined new indicators to take
into account the fact that complete magnetic cycles are not observed for all
the stars, such as the contrast between high and low activity. We also inferred
the Rossby number of the stars and studied their stellar background. This
analysis shows different types of behaviours in the 22 F stars. Two stars show
behaviours very similar to magnetic activity cycles. Five stars show long-lived
spots or active regions suggesting the existence of active longitudes. Two
stars of our sample seem to have a decreasing or increasing trend in the
temporal variation of the magnetic proxies. Finally the last group of stars
show magnetic activity (with presence of spots) but no sign of cycle.


title:
Multiplicity of Positive Solutions of P-Laplacian Systems With
  Sign-Changing Weight Functions

abstract:
  In this paper, we study the multiplicity of positive solutions for the
p-Laplacian systems with sign-changing weight functions. Using the
decomposition of the Nehari manifold, we prove that an elliptic system has at
least two positive solutions.


title:
Spin structure of Graphene/Pt interface for spin current formation and
  induced magnetization in deposited (Ni-Fe)-nanodots

abstract:
  Spin electronic structure of graphene pi-states and Pt 5d-states for the
Graphene/Pt interface has been investigated. Here, we report a large induced
spin-orbit splitting (~70-100 meV) of graphene pi-states with formation of
non-degenerated Dirac-cone spin states at the K-point of the BZ crossed with
spin-polarized Pt 5d-states at Fermi level that opens up a possibility for
creation of new spintronics devices. We propose to use this spin structure for
formation of spin current with spin locked perpendicular to the momentum for
induced remagnetization of the (Ni-Fe)-nanodots arranged atop the interface.
Theoretical estimations of the spin current created at the Graphene/Pt
interface and the induced intrinsic effective magnetic field leading to the
in-plane remagnetization of the NiFe-nanodots due to spin-orbit torque effect
are presented. By micromagnetic modeling based on experimentally observed
spin-orbit splitting we demonstarte that the induced intrinsic magnetic field
might be effectively used for magnetization swithching of the deposited
(Ni-Fe)-nanodots.


title:
Nano-spectroscopic imaging of intermolecular structure, coupling and
  dynamics

abstract:
  Molecular self-assembly, the function of biomembranes, and the performance of
organic solar cells rely on molecular interactions on the nanoscale. The
understanding and design of such intrinsic or engineered heterogeneous
functional soft matter has long been impeded by a lack of spectroscopic tools
with sufficient nanometer spatial resolution, attomolar sensitivity, and
intermolecular spectroscopic specificity. We implement vibrational
scattering-scanning near-field optical microscopy ($s$-SNOM) in a
multi-spectral modality with unprecedented spectral precision to investigate
the structure-function relationship in nano-phase separated block-copolymers.
We use a vibrational resonance as a sensitive reporter of the local chemical
environment and resolve, with few nanometer spatial resolution and 0.2
cm$^{-1}$ spectral precision, spectral Stark shifts and line broadening
correlated with molecular-scale morphologies. By creating images of
solvatochromic vibrational shifts we discriminate local variations in electric
fields between the core and interface regions of the nano-domains with
quantitative agreement to dielectric continuum models. This new
nano-chemometric ability to directly resolve nanoscale morphology and
associated intermolecular interactions can form a basis for the systematic
control of functionality in multicomponent soft matter systems.


title:
A regression model with a hidden logistic process for feature extraction
  from time series

abstract:
  A new approach for feature extraction from time series is proposed in this
paper. This approach consists of a specific regression model incorporating a
discrete hidden logistic process. The model parameters are estimated by the
maximum likelihood method performed by a dedicated Expectation Maximization
(EM) algorithm. The parameters of the hidden logistic process, in the inner
loop of the EM algorithm, are estimated using a multi-class Iterative
Reweighted Least-Squares (IRLS) algorithm. A piecewise regression algorithm and
its iterative variant have also been considered for comparisons. An
experimental study using simulated and real data reveals good performances of
the proposed approach.


title:
Numerical Search for Periodic Solutions in the Vicinity of the
  Figure-Eight Orbit: Slaloming around Singularities on the Shape Sphere

abstract:
  We present the results of a numerical search for periodic orbits with zero
angular momentum in the Newtonian planar three-body problem with equal masses
focused on a narrow search window bracketing the figure-eight initial
conditions. We found eleven solutions that can be described as some power of
the "figure-eight" solution in the sense of the topological classification
method. One of these solutions, with the seventh power of the "figure-eight",
is a choreography. We show numerical evidence of its stability.


title:
Supervised learning of a regression model based on latent process.
  Application to the estimation of fuel cell life time

abstract:
  This paper describes a pattern recognition approach aiming to estimate fuel
cell duration time from electrochemical impedance spectroscopy measurements. It
consists in first extracting features from both real and imaginary parts of the
impedance spectrum. A parametric model is considered in the case of the real
part, whereas regression model with latent variables is used in the latter
case. Then, a linear regression model using different subsets of extracted
features is used fo r the estimation of fuel cell time duration. The
performances of the proposed approach are evaluated on experimental data set to
show its feasibility. This could lead to interesting perspectives for
predictive maintenance policy of fuel cell.


title: Planar lattices do not recover from forest fires

abstract:
  Self-destructive percolation with parameters $p,\delta$ is obtained by taking
a site percolation configuration with parameter $p$, closing all sites
belonging to infinite clusters, then opening every closed site with probability
$\delta$, independently of the rest. Call $\theta(p,\delta)$ the probability
that the origin is in an infinite cluster in the configuration thus obtained.
For two-dimensional lattices, we show the existence of $\delta>0$ such that,
for any $p>p_c$, $\theta(p,\delta)=0$. This proves the conjecture of van den
Berg and Brouwer [Random Structures Algorithms 24 (2004) 480-501], who
introduced the model. Our results combined with those of van den Berg and
Brouwer [Random Structures Algorithms 24 (2004) 480-501] imply the nonexistence
of the infinite parameter forest-fire model. The methods herein apply to site
and bond percolation on any two-dimensional planar lattice with sufficient
symmetry.


title: Breakdown of the scale invariance in the vicinity of Tonks-Girardeau gas

abstract:
  In this article, we consider the monopole excitations of the harmonically
trapped Bose gas in the vicinity of the Tonks-Girardeau limit. Using
Girardeau's Fermi-Bose duality and subsequently an effective fermion-fermion
odd-wave interaction, we obtain the dominant correction to the
scale-invariance-protected value of the excitation frequency, for
microscopically small excitation amplitudes. We produce a series of diffusion
Monte Carlo results that confirm our analytic prediction for three particles.
And less expectedly, our result stands in excellent agreement with the result
of a hydrodynamic simulation of the microscopically large but macroscopically
small excitations.


title:
A Convex Formulation for Mixed Regression with Two Components: Minimax
  Optimal Rates

abstract:
  We consider the mixed regression problem with two components, under
adversarial and stochastic noise. We give a convex optimization formulation
that provably recovers the true solution, and provide upper bounds on the
recovery errors for both arbitrary noise and stochastic noise settings. We also
give matching minimax lower bounds (up to log factors), showing that under
certain assumptions, our algorithm is information-theoretically optimal. Our
results represent the first tractable algorithm guaranteeing successful
recovery with tight bounds on recovery errors and sample complexity.


title:
Functional Mixture Discriminant Analysis with hidden process regression
  for curve classification

abstract:
  We present a new mixture model-based discriminant analysis approach for
functional data using a specific hidden process regression model. The approach
allows for fitting flexible curve-models to each class of complex-shaped curves
presenting regime changes. The model parameters are learned by maximizing the
observed-data log-likelihood for each class by using a dedicated
expectation-maximization (EM) algorithm. Comparisons on simulated data with
alternative approaches show that the proposed approach provides better results.


title:
On angular momentum balance for particle systems with periodic boundary
  conditions

abstract:
  The well-known issue with the absence of conservation of angular momentum in
classical particle systems with periodic boundary conditions is addressed. It
is shown that conventional theory based on Noether's theorem fails to explain
the simplest possible example, notably jumps of angular momentum in the case of
single particle moving in a periodic cell. It is suggested to consider the
periodic cell as an open system, exchanging mass, momentum, angular momentum,
and energy with surrounding cells. Then the behavior of the cell is described
by balance laws rather than conservation laws. It is shown using the law of
angular momentum balance that the variation of the angular momentum in systems
with periodic boundary conditions is a consequence of (i) the non-zero flux of
angular momentum through the boundaries and (ii) torque acting on the cell due
to the interactions between particles in the cell with images in the
neighboring cells. Two simple examples demonstrating both phenomena are
presented.


title: On orthogonal $p$-adic wavelet bases

abstract:
  A variety of different orthogonal wavelet bases has been found in L_2(R) for
the last three decades. It appeared that similar constructions also exist for
functions defined on some other algebraic structures, such as the Cantor and
Vilenkin groups and local fields of positive characteristic. In the present
paper we show that the situation is quite different for the field of $p$-adic
numbers. Namely, it is proved that any orthogonal wavelet basis consisting of
band-limited (periodic) functions is a modification of Haar basis. This is a
little bit unexpected because from the wavelet theory point of view, the
additive group of $p$-adic numbers looks very similar to the Vilenkin group
where analogs of the Daubechies wavelets (and even band-limited ones) do exist.
We note that all $p$-adic wavelet bases and frames appeared in the literature
consist of Schwartz-Bruhat functions (i.e., band-limited and compactly
supported ones).


title:
Contagion spreading on complex networks with local deterministic
  dynamics

abstract:
  Typically, contagion strength is modeled by a transmission rate $\lambda$,
whereby all nodes in a network are treated uniformly in a mean-field
approximation. However, local agents react differently to the same contagion
based on their local characteristics. Following our recent work [EPL
\textbf{99}, 58002 (2012)], we investigate contagion spreading models with
local dynamics on complex networks. We therefore quantify contagions by their
quality, $0 \leq \alpha \leq 1$, and follow their spreading as their
transmission condition (fitness) is evaluated by local agents. We choose
various deterministic local rules. Initial spreading with exponential
quality-dependent time scales is followed by a stationary state with a
prevalence depending on the quality of the contagion. We also observe various
interesting phenomena, for example, high prevalence without the participation
of the hubs. This is in sharp contrast with the general belief that hubs play a
central role in a typical spreading process. We further study the role of
network topology in various models and find that as long as small-world effect
exists, the underlying topology does not contribute to the final stationary
state but only affects the initial spreading velocity.


title:
Classification automatique de donn\'ees temporelles en classes
  ordonn\'ees

abstract:
  This paper proposes a method of segmenting temporal data into ordered
classes. It is based on mixture models and a discrete latent process, which
enables to successively activates the classes. The classification can be
performed by maximizing the likelihood via the EM algorithm or by
simultaneously optimizing the model parameters and the partition by the CEM
algorithm. These two algorithms can be seen as alternatives to Fisher's
algorithm, which improve its computing time.


title:
Statistical analysis of crystallization database links protein
  physico-chemical features with crystallization mechanisms

abstract:
  X-ray crystallography is the predominant method for obtaining atomic-scale
information about biological macromolecules. Despite the success of the
technique, obtaining well diffracting crystals still critically limits going
from protein to structure. In practice, the crystallization process proceeds
through knowledge-informed empiricism. Better physico-chemical understanding
remains elusive because of the large number of variables involved, hence little
guidance is available to systematically identify solution conditions that
promote crystallization. To help determine relationships between macromolecular
properties and their crystallization propensity, we have trained statistical
models on samples for 182 proteins supplied by the Northeast Structural
Genomics consortium. Gaussian processes, which capture trends beyond the reach
of linear statistical models, distinguish between two main physico-chemical
mechanisms driving crystallization. One is characterized by low levels of side
chain entropy and has been extensively reported in the literature. The other
identifies specific electrostatic interactions not previously described in the
crystallization context. Because evidence for two distinct mechanisms can be
gleaned both from crystal contacts and from solution conditions leading to
successful crystallization, the model offers future avenues for optimizing
crystallization screens based on partial structural information. The
availability of crystallization data coupled with structural outcomes analyzed
through state-of-the-art statistical models may thus guide macromolecular
crystallization toward a more rational basis.


title:
Theoretical Bitcoin Attacks with less than Half of the Computational
  Power (draft)

abstract:
  A widespread security claim of the Bitcoin system, presented in the original
Bitcoin white-paper, states that the security of the system is guaranteed as
long as there is no attacker in possession of half or more of the total
computational power used to maintain the system. This claim, however, is proved
based on theoretically flawed assumptions.
  In the paper we analyze two kinds of attacks based on two theoretical flaws:
the Block Discarding Attack and the Difficulty Raising Attack. We argue that
the current theoretical limit of attacker's fraction of total computational
power essential for the security of the system is in a sense not $\frac{1}{2}$
but a bit less than $\frac{1}{4}$, and outline proposals for protocol change
that can raise this limit to be as close to $\frac{1}{2}$ as we want.
  The basic idea of the Block Discarding Attack has been noted as early as
2010, and lately was independently though-of and analyzed by both author of
this paper and authors of a most recently pre-print published paper. We thus
focus on the major differences of our analysis, and try to explain the
unfortunate surprising coincidence. To the best of our knowledge, the second
attack is presented here for the first time.


title:
On the Parameterized Complexity of Computing Balanced Partitions in
  Graphs

abstract:
  A balanced partition is a clustering of a graph into a given number of
equal-sized parts. For instance, the Bisection problem asks to remove at most k
edges in order to partition the vertices into two equal-sized parts. We prove
that Bisection is FPT for the distance to constant cliquewidth if we are given
the deletion set. This implies FPT algorithms for some well-studied parameters
such as cluster vertex deletion number and feedback vertex set. However, we
show that Bisection does not admit polynomial-size kernels for these
parameters.
  For the Vertex Bisection problem, vertices need to be removed in order to
obtain two equal-sized parts. We show that this problem is FPT for the number
of removed vertices k if the solution cuts the graph into a constant number c
of connected components. The latter condition is unavoidable, since we also
prove that Vertex Bisection is W[1]-hard w.r.t. (k,c).
  Our algorithms for finding bisections can easily be adapted to finding
partitions into d equal-sized parts, which entails additional running time
factors of n^{O(d)}. We show that a substantial speed-up is unlikely since the
corresponding task is W[1]-hard w.r.t. d, even on forests of maximum degree
two. We can, however, show that it is FPT for the vertex cover number.


title:
Ambipolar Transport and Magneto-resistance Crossover in a Mott
  Insulator, Sr$_{2}$IrO$_{4}$

abstract:
  Electric field effect (EFE) controlled magnetoelectric transport in thin
films of undoped and La-doped Sr$_{2}$IrO$_{4}$ (SIO) were investigated under
the action of ionic liquid gating. Despite large carrier density modulation,
the temperature dependent resistance measurements exhibit insulating behavior
in chemically and EFE doped samples with the band filling up to 10\%. The
ambipolar transport across the Mott gap is demonstrated by EFE tuning of the
activation energy. Further, we observe a crossover from a negative
magnetoresistance (MR) at high temperatures to positive MR at low temperatures.
The crossover temperature was around $\sim$80-90 K, irrespective of the
filling. This temperature and magnetic field dependent crossover is
qualitatively associated with a change in the conduction mechanism from Mott to
Coulomb gap mediated variable range hopping (VRH). This explains the origin of
robust insulating ground state of SIO in electrical transport studies and
highlights the importance of disorder and Coulombic interaction on electrical
properties of SIO.


title: Topology Optimization Using Polytopes

abstract:
  Meshing complex engineering domains is a challenging task. Arbitrary
polyhedral meshes can provide the much needed flexibility in automated
discretization of such domains. The geometric property of the polyhedral meshes
such as the unstructured nature and the facial connectivity between elements
makes them specially attractive for topology optimization applications.
Numerical anomalies in designs such as the single node connections and
checkerboard pattern, which are difficult to manufacture physically, are
naturally alleviated with polyhedrons. Special interpolants such as Wachspress,
mean value coordinates, maximum entropy shape functions are available to handle
arbitrary shaped elements. But the finite elements approaches based on these
shape functions face some challenges such as accurate and efficient computation
of the shape functions and their derivatives for the numerical evaluation of
the weak form integrals. In the current work, we solve the governing
three-dimensional elasticity state equation using a Virtual Element Method
(VEM) approach. The main characteristic difference between VEM and standard
finite element methods (FEM) is that in VEM the canonical basis functions are
not constructed explicitly. Rather the stiffness matrix is computed directly
utilizing a projection map which extracts the linear component of the
deformation. Such a construction guarantees the satisfaction of the patch test
(used by engineers as an indicator of optimal convergence of numerical
solutions under mesh refinement). Finally, the computations reduce to the
evaluation of matrices which contain purely geometric surface facet quantities.
The present work focuses on the first-order VEM in which the degrees of freedom
associated with the vertices. Utilizing polyhedral elements for topology
optimization, we show that the mesh bias in the member orientation is
alleviated.


title: Phase Structure of Higher Spin Black Holes

abstract:
  We revisit the study of the phase structure of higher spin black holes
carried out in arXiv$:1210.0284$ using the "canonical formalism". In particular
we study the low as well as high temperature regimes. We show that the
Hawking-Page transition takes place in the low temperature regime. The
thermodynamically favoured phase changes from conical surplus to black holes
and then again to conical surplus as we increase temperature. We then show that
in the high temperature regime the diagonal embedding gives the appropriate
description. We also give a map between the parameters of the theory near the
IR and UV fixed points. This makes the "good" solutions near one end map to the
"bad" solutions near the other end and vice versa.


title:
Mixture model-based functional discriminant analysis for curve
  classification

abstract:
  Statistical approaches for Functional Data Analysis concern the paradigm for
which the individuals are functions or curves rather than finite dimensional
vectors. In this paper, we particularly focus on the modeling and the
classification of functional data which are temporal curves presenting regime
changes over time. More specifically, we propose a new mixture model-based
discriminant analysis approach for functional data using a specific hidden
process regression model. Our approach is particularly adapted to both handle
the problem of complex-shaped classes of curves, where each class is composed
of several sub-classes, and to deal with the regime changes within each
homogeneous sub-class. The model explicitly integrates the heterogeneity of
each class of curves via a mixture model formulation, and the regime changes
within each sub-class through a hidden logistic process. The approach allows
therefore for fitting flexible curve-models to each class of complex-shaped
curves presenting regime changes through an unsupervised learning scheme, to
automatically summarize it into a finite number of homogeneous clusters, each
of them is decomposed into several regimes. The model parameters are learned by
maximizing the observed-data log-likelihood for each class by using a dedicated
expectation-maximization (EM) algorithm. Comparisons on simulated data and real
data with alternative approaches, including functional linear discriminant
analysis and functional mixture discriminant analysis with polynomial
regression mixtures and spline regression mixtures, show that the proposed
approach provides better results regarding the discrimination results and
significantly improves the curves approximation.


title: Diophantine geometry and non-abelian reciprocity laws I

abstract:
  We use non-abelian fundamental groups to define a sequence of higher
reciprocity maps on the adelic points of a variety over a number field
satisfying certain conditions in Galois cohomology. The non-abelian reciprocity
law then states that the global points are contained in the kernel of all the
reciprocity maps.


title: Consequences of an Eccentric Orbit for Fomalhaut b

abstract:
  Fomalhaut b is currently the least massive, directly imaged exoplanet
candidate. New observation epochs reveal this object to be moving on a highly
eccentric orbit (Kalas et al. 2013), which sets important new constraints. I
consider scenarios where Fomalhaut b is the only object interacting with the
debris disk, and ones involving an additional unseen planet. I also investigate
the possibility that Fomalhaut b is merely a transient dust cloud in light of
the revised eccentric orbit. I argue that the scenario best able to match the
observational constraints is a super-Earth Fomalhaut b surrounded by a vast
cloud of dust that is generated by a population of irregular satellites, with
an undetected $\sim$ Saturn-mass planet orbiting interior to the disk and
driving the secular dynamics. Testable predictions are summarized that could
differentiate between this scenario and other possibilities.


title: Dynamic Lattice Supersymmetry in gl(n|m) Spin Chains

abstract:
  Supersymmetry operators that change a spin chain's length have appeared in
numerous contexts, ranging from the AdS/CFT correspondence to statistical
mechanics models. In this article, we present, via an analysis of the Bethe
equations, all homogeneous, rational and trigonometric, integrable gl(n|m) spin
chains for which length-changing supersymmetry can be present. Furthermore, we
write down the supercharges explicitly for the simplest new models, namely the
sl(n|1) spin chains with the (n-1)-fold antisymmetric tensor product of the
fundamental representation at each site and check their compatibility with
integrability.

categories: math-ph cond-mat.stat-mech hep-th math.MP nlin.SI


title: Robust EM algorithm for model-based curve clustering

abstract:
  Model-based clustering approaches concern the paradigm of exploratory data
analysis relying on the finite mixture model to automatically find a latent
structure governing observed data. They are one of the most popular and
successful approaches in cluster analysis. The mixture density estimation is
generally performed by maximizing the observed-data log-likelihood by using the
expectation-maximization (EM) algorithm. However, it is well-known that the EM
algorithm initialization is crucial. In addition, the standard EM algorithm
requires the number of clusters to be known a priori. Some solutions have been
provided in [31, 12] for model-based clustering with Gaussian mixture models
for multivariate data. In this paper we focus on model-based curve clustering
approaches, when the data are curves rather than vectorial data, based on
regression mixtures. We propose a new robust EM algorithm for clustering
curves. We extend the model-based clustering approach presented in [31] for
Gaussian mixture models, to the case of curve clustering by regression
mixtures, including polynomial regression mixtures as well as spline or
B-spline regressions mixtures. Our approach both handles the problem of
initialization and the one of choosing the optimal number of clusters as the EM
learning proceeds, rather than in a two-fold scheme. This is achieved by
optimizing a penalized log-likelihood criterion. A simulation study confirms
the potential benefit of the proposed algorithm in terms of robustness
regarding initialization and funding the actual number of clusters.


title:
Spontaneous emission of electric and magnetic dipoles in the vicinity of
  thin and thick metal

abstract:
  Strong modification of spontaneous emission of Eu3+ ions placed in close
vicinity to thin and thick gold and silver films was clearly demonstrated in a
microscope setup separately for electric and magnetic dipole transitions. We
have shown that the magnetic transition was very sensitive to the thickness of
the gold substrate and behaved distinctly different from the electric
transition. The observations were described theoretically based on the dyadic
Green's function approach for layered media and explained through modified
image models for the near and far-field emissions. We established that there
exists a "near-field event horizon", which demarcates the distance from the
metal at which the dipole emission is taken up exclusively in the near field.


title:
Model-based clustering with Hidden Markov Model regression for time
  series with regime changes

abstract:
  This paper introduces a novel model-based clustering approach for clustering
time series which present changes in regime. It consists of a mixture of
polynomial regressions governed by hidden Markov chains. The underlying hidden
process for each cluster activates successively several polynomial regimes
during time. The parameter estimation is performed by the maximum likelihood
method through a dedicated Expectation-Maximization (EM) algorithm. The
proposed approach is evaluated using simulated time series and real-world time
series issued from a railway diagnosis application. Comparisons with existing
approaches for time series clustering, including the stand EM for Gaussian
mixtures, $K$-means clustering, the standard mixture of regression models and
mixture of Hidden Markov Models, demonstrate the effectiveness of the proposed
approach.


title: Hierarchical Economic Agents and their Interactions

abstract:
  We present a new type of spin market model, populated by hierarchical agents,
represented as configurations of sites and arcs in an evolving network. We
describe two analytic techniques for investigating the asymptotic behavior of
this model: one based on the spectral theory of Markov chains and another
exploiting contingent submartingales to construct a deterministic cellular
automaton that approximates the stochastic dynamics. Our study of this system
documents a phase transition between a sub-critical and a super-critical regime
based on the values of a coupling constant that modulates the tradeoff between
local majority and global minority forces. In conclusion, we offer a
speculative socioeconomic interpretation of the resulting distributional
properties of the system.


title: Critical Ising model and spanning trees partition functions

abstract:
  We prove that the squared partition function of the two-dimensional critical
Ising model defined on a finite, isoradial graph $G=(V,E)$, is equal to
$2^{|V|}$ times the partition function of spanning trees of the graph
$\bar{G}$, where $\bar{G}$ is the graph $G$ extended along the boundary; edges
of $G$ are assigned Kenyon's [Ken02] critical weights, and boundary edges of
$\bar{G}$ have specific weights. The proof is an explicit construction,
providing a new relation on the level of configurations between two classical,
critical models of statistical mechanics.


title: The phenomenological MSSM in view of the 125 GeV Higgs data

abstract:
  The parameter space of the phenomenological MSSM (pMSSM) is explored by means
of Markov Chain Monte Charlo (MCMC) methods, taking into account the latest LHC
results on the Higgs signal at 125 GeV in addition to relevant low-energy
observables and LEP constraints. We use a Bayesian approach to derive posterior
densities for the parameters and observables of interests. We find in
particular that the Higgs measurements have a significant impact on the
parameters mu and tan beta due to radiative corrections to the bottom Yukawa
coupling. We show moreover the impact of the most recent dark matter
measurements on the probability distributions, and we discuss prospects for the
next run of the LHC at 13-14 TeV.


title: Reevaluation of the parton distribution of strange quarks in the nucleon

abstract:
  An earlier extraction from the HERMES experiment of the polarization-averaged
parton distribution of strange quarks in the nucleon has been reevaluated using
final data on the multiplicities of charged kaons in semi-inclusive
deep-inelastic scattering obtained with a kinematically more comprehensive
method of correcting for experimental effects. General features of the
distribution are confirmed, but the rise at low x is less pronounced than
previously reported.


title:
A thin diffuse component of the Galactic Ridge X-ray emission and
  heating of the interstellar medium contributed by the radiation of Galactic
  X-ray binaries

abstract:
  We predict a thin (scale height $\sim$ 80 pc) diffuse component of the
Galactic Ridge X-ray emission (GRXE) arising from the scattering of the
radiation of bright X-ray binaries (XBs) by the interstellar medium (ISM). The
morphology of this scattered component is expected to trace the clumpy
molecular and HI clouds. We calculate this contribution to the GRXE from known
Galactic XBs assuming that they are all persistent. The known XBs sample is
incomplete, however, because it is flux limited and spans the lifetime of X-ray
astronomy ($\sim 50$ years). We therefore also use a simulated sample of
sources, to estimate the diffuse emission we should expect in an optimistic
case assuming that the X-ray luminosity of our Galaxy is on average similar to
that of other galaxies. In the calculations we also take into account the
enhancement of the total scattering cross-section due to coherence effects in
the elastic scattering from multi-electron atoms and molecules. This scattered
emission can be distinguished from the contribution of low X-ray luminosity
stars by the presence of narrow fluorescent K-$\alpha$ lines of Fe, Si, and
other abundant elements present in the ISM and by directly resolving the
contribution of low X-ray luminosity stars. We find that within $1^\circ$
latitude of the Galactic plane the scattered emission contributes on average
$10-30\%$ of the GRXE flux in the case of known sources and over $50\%$ in the
case of simulated sources. In the latter case, the scattered component is found
to even dominate the stellar emission in certain parts of the Galactic plane.
X-rays with energies $\gtrsim 1$ keV from XBs should also penetrate deep inside
the HI and molecular clouds, where they are absorbed and heat the ISM. We find
that this heating rate dominates the heating by cosmic rays (assuming a solar
neighborhood energy density) in a considerable part of the Galaxy. [abridged]


title: Escape through a time-dependent hole in the doubling map

abstract:
  We investigate the escape dynamics of the doubling map with a time-periodic
hole. We use Ulam's method to calculate the escape rate as a function of the
control parameters. We consider two cases, oscillating or breathing holes,
where the sides of the hole are moving in or out of phase respectively. We find
out that the escape rate is well described by the overlap of the hole with its
images, for holes centred at periodic orbits.


title:
A network model of correlated growth of tissue stiffening in pulmonary
  fibrosis

abstract:
  During the progression of pulmonary fibrosis, initially isolated regions of
high stiffness form and grow in the lung tissue due to collagen deposition by
fibroblast cells. We have previously shown that ongoing collagen deposition may
not lead to significant increases in the bulk modulus of the lung until these
local remodeled regions have become sufficiently numerous and extensive to
percolate in a continuous path across the entire tissue [Bates {\it et al.}
2007 {\it Am. J. Respir. Crit. Care Med.} {\bf 176} 617]. In the present study,
we investigate whether spatial correlations influence the bulk modulus in a
two-dimensional elastic network model of lung tissue. Random collagen
deposition at a single site is modeled by increasing the elastic constant of
the spring at that site by a factor of 100.
  By contrast, correlated collagen deposition is represented by stiffening the
springs encountered along a random walk starting from some initial spring, the
rationale being that excess collagen deposition is more likely in the vicinity
of an already stiff region. A combination of random and correlated deposition
is modeled by performing random walks of length $N$ from randomly selected
initial sites, the balance between the two processes being determined by $N$.
We found that the dependence of bulk modulus, $B(N,c)$, on both $N$ and the
fraction of stiff springs, $c$, can be described by a strikingly simple set of
empirical equations.
  Our results suggest that the spatial correlation of collagen deposition in
the fibrotic lung has a strong effect on the rate of lung function decline and
on the mechanical environment in which the cells responsible for remodeling
find themselves.


title: Radiative p15N Capture At Astrophysical Energy

abstract:
  The possibility of description of the experimental data for the astrophysical
S-factor of the radiative proton capture on 15N at the energies from 50 to 1500
keV was considered in the framework of the modified potential cluster model
with the classification of the orbital states according to Young tableaux. It
was shown that, on the basis of the M1 and the E1 transitions from different
states of the p15N scattering to the ground state of 16O in the p15N channel,
it is quite succeed to explain general behavior of the S-factor in the
considered energy range in the presence of two resonances.


title:
Electron-Hole Asymmetric Integer and Fractional Quantum Hall Effect in
  Bilayer Graphene

abstract:
  The nature of fractional quantum Hall (FQH) states is determined by the
interplay between the Coulomb interaction and the symmetries of the system. The
unique combination of spin, valley, and orbital degeneracies in bilayer
graphene is predicted to produce novel and tunable FQH ground states. Here we
present local electronic compressibility measurements of the FQH effect in the
lowest Landau level of bilayer graphene. We observe incompressible FQH states
at filling factors v = 2p + 2/3 with hints of additional states appearing at v
= 2p + 3/5, where p = -2,-1, 0, and 1. This sequence of states breaks
particle-hole symmetry and instead obeys a v --> v + 2 symmetry, which
highlights the importance of the orbital degeneracy for many-body states in
bilayer graphene.


title:
A Topologically-informed Hyperstreamline Seeding Method for Alignment
  Tensor Fields

abstract:
  A topologically-informed method is presented for seeding of hyperstreamlines
for visualization of alignment tensor fields. The method is inspired by and
applied to visualization of nematic liquid crystal (LC) reorientation dynamics
simulations. The method distributes hyperstreamlines along domain boundaries
and edges of a nearest-neighbor graph whose vertices are degenerate regions of
the alignment tensor field, which correspond to orientational defects in a
nematic LC domain. This is accomplished without iteration while conforming to a
user-specified spacing between hyperstreamlines and avoids possible failure
modes associated with hyperstreamline integration in the vicinity of
degeneracies of alignment (orientational defects). It is shown that the
presented seeding method enables automated hyperstreamline-based visualization
of a broad range of alignment tensor fields which enhances the ability of
researchers to interpret these fields and provides an alternative to using
glyph-based techniques.


title: Shape-constrained Estimation of Value Functions

abstract:
  We present a fully nonparametric method to estimate the value function, via
simulation, in the context of expected infinite-horizon discounted rewards for
Markov chains. Estimating such value functions plays an important role in
approximate dynamic programming and applied probability in general. We
incorporate "soft information" into the estimation algorithm, such as knowledge
of convexity, monotonicity, or Lipchitz constants. In the presence of such
information, a nonparametric estimator for the value function can be computed
that is provably consistent as the simulated time horizon tends to infinity. As
an application, we implement our method on price tolling agreement contracts in
energy markets.


title: Deriving Latent Social Impulses to Determine Longevous Videos

abstract:
  Online video websites receive huge amount of videos daily from users all
around the world. How to provide valuable recommendations to viewers is an
important task for both video websites and related third parties, such as
search engines. Previous work conducted numerous analysis on the view counts of
videos, which measure a video's value in terms of popularity. However, the
long-lasting value of an online video, namely longevity, is hidden behind the
history that a video accumulates its "popularity" through time. Generally
speaking, a longevous video tends to constantly draw society's attention. With
focus on one of the leading video websites, Youtube, this paper proposes a
scoring mechanism quantifying a video's longevity. Evaluating a video's
longevity can not only improve a video recommender system, but also help us to
discover videos having greater advertising value, as well as adjust a video
website's strategy of storing videos to shorten its responding time. In order
to accurately quantify longevity, we introduce the concept of latent social
impulses and how to use them measure a video's longevity. In order to derive
latent social impulses, we view the video website as a digital signal filter
and formulate the task as a convex minimization problem. The proposed longevity
computation is based on the derived social impulses. Unfortunately, the
required information to derive social impulses are not always public, which
makes a third party unable to directly evaluate every video's longevity. To
solve this problem, we formulate a semi-supervised learning task by using part
of videos having known longevity scores to predict the unknown longevity
scores. We propose a Gaussian Random Markov model with Loopy Belief Propagation
to solve this problem. The conducted experiments on Youtube demonstrate that
the proposed method significantly improves the prediction results comparing to
baselines.


title: Variations of Kurepa's left factorial hypothesis

abstract:
  Kurepa's hypothesis asserts that for each integer $n\ge 2$ the greatest
common divisor of $!n:=\sum_{k=0}^{n-1}k!$ and $n!$ is $2$. Motivated by an
equivalent formulation of this hypothesis involving derangement numbers, here
we give a formulation of Kurepa's hypothesis in terms of divisibility of any
Kurepa's determinant $K_p$ of order $p-4$
  by a prime $p\ge 7$. In the previous version of this article we have proposed
the strong Kurepa's hypothesis involving a general Kurepa's determinant $K_n$
with any integer $n\ge 7$. We prove the ``even part'' of this hypothesis which
can be considered as a generalization of Kurepa's hypothesis. However, by using
a congruence for $K_n$ involving the derangement number $S_{n-1}$ with an odd
integer $n\ge 9$, we find that the integer $11563=31\times 373$ is a
counterexample to the ``odd composite part'' of strong Kurepa's hypothesis.
  We also present some remarks, divisibility properties and computational
results closely related to the questions on Kurepa's hypothesis involving
derangement numbers and Bell numbers.


title:
Analysis of diffusion and trapping efficiency for random walks on
  non-fractal scale-free trees

abstract:
  We study discrete random walks on the NFSFT and provide new methods to
calculate the analytic solutions of the MFPT for any pair of nodes, the MTT for
any target node and MDT for any source node. Further more, using the MTT and
the MDT as the measures of trapping efficiency and diffusion efficiency
respectively, we compare the trapping efficiency and diffusion efficiency for
any two nodes of NFSFT and find the best (or worst) trapping sites and the best
(or worst) diffusion sites. Our results show that: the two hubs of NFSFT is the
best trapping site, but it is also the worst diffusion site, the nodes which
are the farthest nodes from the two hubs are the worst trapping sites, but they
are also the best diffusion sites. Comparing the maximum and minimum of MTT and
MDT, we found that the ratio between the maximum and minimum of MTT grows
logarithmically with network order, but the ratio between the maximum and
minimum of MTT is almost equal to $1$. These results implie that the trap's
position has great effect on the trapping efficiency, but the position of
source node almost has no effect on diffusion efficiency. We also conducted
numerical simulation to test the results we have derived, the results we
derived are consistent with those obtained by numerical simulation.


title:
A Primal Dual Active Set Algorithm with Continuation for Compressed
  Sensing

abstract:
  The success of compressed sensing relies essentially on the ability to
efficiently find an approximately sparse solution to an under-determined linear
system. In this paper, we developed an efficient algorithm for the sparsity
promoting $\ell_1$-regularized least squares problem by coupling the primal
dual active set strategy with a continuation technique (on the regularization
parameter). In the active set strategy, we first determine the active set from
primal and dual variables, and then update the primal and dual variables by
solving a low-dimensional least square problem on the active set, which makes
the algorithm very efficient. The continuation technique globalizes the
convergence of the algorithm, with provable global convergence under restricted
isometry property (RIP). Further, we adopt two alternative methods, i.e., a
modified discrepancy principle and a Bayesian information criterion, to choose
the regularization parameter. Numerical experiments indicate that our algorithm
is very competitive with state-of-the-art algorithms in terms of accuracy and
efficiency.


title:
Cosmology in ghost-free bigravity theory with twin matter fluids: The
  origin of "dark matter"

abstract:
  We study dynamics of Friedmann-Lemaitre-Robertson-Walker (FLRW) spacetime
based on the ghost-free bigravity theory. Assuming the coupling parameters
guaranteeing the existence of de Sitter space as well as Minkowski spacetime,
we find two stable attractors for spacetime with "twin" dust matter fields: One
is de Sitter accelerating universe and the other is matter dominated universe.
Although a considerable number of initial data leads to de Sitter universe, we
also find matter dominated universe or spacetime with a future singularity for
some initial data. The cosmic no-hair conjecture does not exactly hold, but the
accelerating expansion can be found naturally. The $\Lambda$-CDM model is
obtained as an attractor. We also show that the dark matter component in the
Friedmann equation, which originates from another twin matter, can be about 5
times larger than the baryonic matter, by choosing the appropriate coupling
constants.


title:
Interference of high-order harmonics generated from molecules at
  different alignment angles

abstract:
  We theoretically investigate the interference effect of high-order harmonics
generated from molecules at different alignment angles. It is shown that the
interference of the harmonic emissions from molecules aligned at different
angles can significantly modulate the spectra and result in the anomalous
harmonic cutoffs observed in a recent experiment [ Nature Phys. 7, 822 (2011)
]. The shift of the spectral minimum position with decreasing the degree of
alignment is also explained by the interference effect of the harmonic
emissions.


title: Approximating Quadratic 0-1 Programming via SOCP

abstract:
  We consider the problem of approximating Quadratic O-1 Integer Programs with
bounded number of constraints and non-negative constraint matrix entries, which
we term as PIQP.
  We describe and analyze a randomized algorithm based on a program with
hyperbolic constraints (a Second-Order Cone Programming -SOCP- formulation)
that achieves an approximation ratio of $O(a_{max} \frac{n}{\beta(n)})$, where
$a_{max}$ is the maximum size of an entry in the constraint matrix and
$\beta(n) \leq \min_i{W_i} $, where $W_i$ are the constant terms that define
the constraint inequalities. We note that by appropriately choosing $\beta(n)$
the randomized algorithm, when combined with other algorithms that achieve good
approximations for smaller values of $ W_i$, allows better algorithms for the
complete range of $W_i$. This, together with a greedy algorithm, provides a
$O^*(a_{max} n^{1/2} )$ factor approximation, where $O^*$ hides logarithmic
terms. Our solution is achieved by a randomization of the optimal solution to
the relaxed version of the hyperbolic program. We show that this solution
provides the approximation bounds using concentration bounds provided by
Chernoff-Hoeffding and Kim-Vu.


title:
Type Ic core-collapse supernova explosions evolved from very massive
  stars

abstract:
  We investigate the possibility of a super-luminous Type Ic core-collapse
supernovae producing a large amount of 56Ni. Very massive stars with a
main-sequence mass larger than 100 Msun and a metallicity 0.001 < Z < 0.004 are
expected to explode as super-luminous Type Ic supernovae. Stars with ~ 110 -
150 Msun and Z < 0.001 would explode as Type Ic pulsational pair-instability
supernovae if the whole H and He layers has been lost by the mass loss during
pulsational pair-instability. We evaluate the total ejecta mass and the yields
of 56Ni, O, and Si in core-collapse supernovae evolved from very massive stars.
We adopt 43.1 and 61.1 Msun WO stars with Z=0.004 as supernova progenitors
expected to explode as Type Ic core-collapse supernovae. These progenitors have
masses of 110 and 250 Msun at the zero-age main sequence. Spherical explosions
with an explosion energy larger than 2e52 erg produce more than 3.5 Msun 56Ni,
enough to reproduce the light curve of SN 2007bi. Asphericity of the explosion
affects the total ejecta mass as well as the yields of 56Ni, O, and Si.
Aspherical explosions of the 110 and 250 Msun models reproduce the 56Ni yield
of SN 2007bi. These explosions will also show large velocity dispersion. An
aspherical core-collapse supernova evolved from a very massive star is a
possibility of the explosion of SN 2007bi.


title: Lowerable vector fields for a finitely L-determined multigerm

abstract:
  We show that the module of lowerable vector fields for a finitely
L-determined multigerm is finitely generated in a constructive way.


title:
Imprints of the molecular-orbital geometry on the high-harmonic
  ellipticity

abstract:
  The influence of the orbital symmetry on the ellipticity of the high-order
harmonics is investigated. It is found that the ellipticity maps have distinct
shapes for the molecular orbital with different symmetry. Our analysis shows
that the feature of the harmonic ellipticity map is essentially determined by
the nodal structure of the nonsymmetric orbital. The results indicate that the
molecular-orbital geometry is imprinted on the ellipticity of the high-order
harmonics, which invites the use of ellipticity measurements as a probe of the
orbital structure for polar molecules.


title:
Tomographic imaging of asymmetric molecular orbitals with a two-color
  multicycle laser field

abstract:
  We theoretically demonstrate a scheme for tomographic reconstruction of
asymmetric molecular orbitals based on high-order harmonic generation with a
two-color multicycle laser field. It is shown that by adjusting the relative
phase of the two fields, the returning electrons can be forced to recollide
from one direction for all the orientations of molecules. Thus the
reconstruction of the asymmetric orbitals can be carried out with multicycle
laser field. This releases the stringent requirement of a single-cycle pulse
with a stabilized and controllable carrier-envelop phase for the tomographic
imaging of asymmetric molecular orbitals.


title:
Poisson Reduction of Controlled Hamiltonian System by Controllability
  Distribution

abstract:
  In this paper, we first study the Poisson reductions of controlled
Hamiltonian (CH) system and symmetric CH system by controllability
distributions. These reductions are the extension of Poisson reductions by
distribution for Poisson manifolds to that for phase spaces of CH systems with
external force and control. We give Poisson reducible conditions of CH system
by controllability distribution, and prove that the Poisson reducible property
for CH systems leaves invariant under the CH-equivalence. Moreover, we study
the Poisson reduction of symmetric CH system by G-invariant controllability
distribution. Next, we consider the singular Poisson reduction and
SPR-CH-equivalence for CH system with symmetry, and prove the singular Poisson
reduction theorem of CH system. We also study the relationship between Poisson
reduction for singular Poisson reducible CH systems by G-invariant
controllability distribution and that for associated reduced CH system by
reduced controllability distribution. At last, some examples are given to state
the theoretical results.


title: A hyperplane inequality for measures of unconditional convex bodies

abstract:
  We prove an inequality that extends to arbitrary measures the hyperplane
inequality for volume of unconditional convex bodies originally observed by
Bourgain.


title: Ehrhart polynomials with negative coefficients

abstract:
  It is shown that, for each $d \geq 4$, there exists an integral convex
polytope $\mathcal{P}$ of dimension $d$ such that each of the coefficients of
$n, n^{2}, \ldots, n^{d-2}$ of its Ehrhart polynomial $i(\mathcal{P},n)$ is
negative.


title:
Nash Equilibrium Computation in Subnetwork Zero-Sum Games with Switching
  Communications

abstract:
  In this paper, we investigate a distributed Nash equilibrium computation
problem for a time-varying multi-agent network consisting of two subnetworks,
where the two subnetworks share the same objective function. We first propose a
subgradient-based distributed algorithm with heterogeneous stepsizes to compute
a Nash equilibrium of a zero-sum game. We then prove that the proposed
algorithm can achieve a Nash equilibrium under uniformly jointly strongly
connected (UJSC) weight-balanced digraphs with homogenous stepsizes. Moreover,
we demonstrate that for weighted-unbalanced graphs a Nash equilibrium may not
be achieved with homogenous stepsizes unless certain conditions on the
objective function hold. We show that there always exist heterogeneous
stepsizes for the proposed algorithm to guarantee that a Nash equilibrium can
be achieved for UJSC digraphs. Finally, in two standard weight-unbalanced
cases, we verify the convergence to a Nash equilibrium by adaptively updating
the stepsizes along with the arc weights in the proposed algorithm.


title:
Symmetry-adapted formulation of the combined G-particle-hole Hypervirial
  equation and Hermitian Operator method

abstract:
  High accuracy energies of low-lying excited states, in molecular systems,
have been determined by means of a procedure which combines the G-particle-hole
Hypervirial (GHV) equation method [Alcoba et al. Int. J. Quantum Chem. 109:3178
(2009)] and the Hermitian Operator (HO) one [Bouten et al. Nucl. Phys. A
202:127 (1973)]. This paper reports a suitable strategy to introduce the point
group symmetry within the framework of the combined GHV-HO method, what leads
to an improvement of the computational efficiency. The resulting
symmetry-adapted formulation has been applied to illustrate the computer
timings and the hardware requirements in selected chemical systems of several
geometries.


title: The Standard Model with New Order Parameters at Finite Temperature

abstract:
  We consider the finite temperature effective potential of the standard model
at the one-loop level in four dimensions by taking account of two kinds of
order parameters, the Higgs vacuum expectation value and the zero modes of
gauge fields for the Euclidean time direction. We study the vacuum structure of
the model, focusing on the existence of new phase, where the zero modes, that
is, the new order parameters develop nontrivial vacuum expectation values
except for the center of the gauge group. We find that under certain conditions
there appears no new phase at finite temperature.


title: Highest weight categories and Macdonald polynomials

abstract:
  The aim of this paper is to introduce the categorical setup which helps us to
relate the theory of Macdonald polynomials and the theory of Weyl modules for
current Lie algebras discovered by V.\,Chari and collaborators.
  We identify Macdonald pairing with the homological pairing on the ring of
characters of the Lie algebra of currents $\mathbf{g}\otimes\mathbb{C}[x,\xi]$.
We use this description in order to define complexes of modules whose Euler
characteristic of characters coincide with Macdonald polynomials. We generalize
this result to the case of graded Lie algebras with anti-involution. We show
that whenever the BGG reciprocity holds for the corresponding category of
modules then these complexes collapse to the modules concentrated in
homological degree $0$. The latter modules generalizes the notion of Weyl
modules for current Lie algebras and the notion of Verma modules in the BGG
category $\mathcal{O}$. We give different criterions of BGG reciprocity and
apply them to the Lie algebra of currents $\mathbf{g}\otimes\mathbb{C}[x]$ with
$\mathbf{g}$ semisimple.


title:
Collective phase dynamics of globally coupled oscillators: Noise-induced
  anti-phase synchronization

abstract:
  We formulate a theory for the collective phase description of globally
coupled noisy limit-cycle oscillators exhibiting macroscopic rhythms.
Collective phase equations describing such macroscopic rhythms are derived by
means of a two-step phase reduction. The collective phase sensitivity and
collective phase coupling functions, which quantitatively characterize the
macroscopic rhythms, are illustrated using three representative models of
limit-cycle oscillators. As an important result of the theory, we demonstrate
noise-induced anti-phase synchronization between macroscopic rhythms by direct
numerical simulations of the three models.


title:
Structural investigations in BaFe2-xRuxAs2 as a function of Ru and
  temperature

abstract:
  We present Synchrotron XRD measurements on powdered single crystal samples of
BaFe2-xRuxAs2 samples, as a function of Ru content at room temperature. The
Rietveld refinements reveal that the a-axis increases with Ru substitution,
while the c-axis decreases. The variation of positional co-ordinates of As
(zAs), the Fe-As bond length and the As-Fe-As bond angles have been determined
from the Rietveld refinements. In the sample with x=0.1, temperature dependent
XRD measurements were carried out. The results indicate that while the
orthorhombicity shows the characteristic increase with decrease in temperature,
the As-Fe-As bond angles, Fe-As bond length and positional co-ordinate of the
As show definite anomalies close to the structural transition. First principle
ab-initio simulations are performed in order to understand the experimentally
observed anomalies in structural parameters. The experimental observations are
discussed in the context of the simulation results.


title:
Development of Display Ads Retrieval System to Match Publisher's
  Contents

abstract:
  The technological transformation and automation of digital content delivery
has revolutionized the media industry. Advertising landscape is gradually
shifting its traditional media forms to the emergent of Internet advertising.
In this paper, the types of internet advertising to be discussed on are
contextual and sponsored search ads. These types of advertising have the
central challenge of finding the best match between a given context and a
suitable advertisement, through a principled method. Furthermore, there are
four main players that exist in the Internet advertising ecosystem: users,
advertisers, ad exchange and publishers. Hence, to find ways to counter the
central challenge, the paper addresses two objectives: how to successfully make
the best contextual ads selections to match to a web page content to ensure
that there is a valuable connection between the web page and the contextual
ads. All methods, discussions, conclusion and future recommendations are
presented as per sections. Hence, in order to prove the working mechanism of
matching contextual ads and web pages, web pages together with the ads matching
system are developed as a prototype.


title: Empirical Study of the GARCH model with Rational Errors

abstract:
  We use the GARCH model with a fat-tailed error distribution described by a
rational function and apply it for the stock price data on the Tokyo Stock
Exchange. To determine the model parameters we perform the Bayesian inference
to the model. The Bayesian inference is implemented by the Metropolis-Hastings
algorithm with an adaptive multi-dimensional Student's t-proposal density. In
order to compare the model with the GARCH model with the standard normal errors
we calculate information criterions: AIC and DIC, and find that both criterions
favor the GARCH model with a rational error distribution. We also calculate the
accuracy of the volatility by using the realized volatility and find that a
good accuracy is obtained for the GARCH model with a rational error
distribution. Thus we conclude that the GARCH model with a rational error
distribution is superior to the GARCH model with the normal errors and it can
be used as an alternative GARCH model to those with other fat-tailed
distributions.