  A limiting factor towards the wide routine use of wearables devices for
continuous healthcare monitoring is their cumbersome and obtrusive nature. This
is particularly true for electroencephalography (EEG) recordings, which require
the placement of multiple electrodes in contact with the scalp. In this work,
we propose to identify the optimal wearable EEG electrode set-up, in terms of
minimal number of electrodes, comfortable location and performance, for
EEG-based event detection and monitoring. By relying on the demonstrated power
of autoencoder (AE) networks to learn latent representations from
high-dimensional data, our proposed strategy trains an AE architecture in a
one-class classification setup with different electrode set-ups as input data.
The resulting models are assessed using the F-score and the best set-up is
chosen according to the established optimal criteria. Using alpha wave
detection as use case, we demonstrate that the proposed method allows to detect
an alpha state from an optimal set-up consisting of electrodes in the forehead
and behind the ear, with an average F-score of 0.78. Our results suggest that a
learning-based approach can be used to enable the design and implementation of
optimized wearable devices for real-life healthcare monitoring.


  We study spectral stability estimates of elliptic operators in divergence
form $-\textrm{div} [A(w) \nabla g(w)]$ with the Neumann boundary condition in
non-Lipschitz domains $\Omega \subset \mathbb C$. The suggested method is based
on connections of planar quasiconformal mappings with Sobolev spaces and its
applications to the Poincar\'e inequalities.


  We present a detailed description of the generalized geometric cluster
algorithm for the efficient simulation of continuum fluids. The connection with
well-known cluster algorithms for lattice spin models is discussed, and an
explicit full cluster decomposition is derived for a particle configuration in
a fluid. We investigate a number of basic properties of the geometric cluster
algorithm, including the dependence of the cluster-size distribution on density
and temperature. Practical aspects of its implementation and possible
extensions are discussed. The capabilities and efficiency of our approach are
illustrated by means of two example studies.


  The objective of this paper is to use transfer functions to comprehend the
formation of band gaps in locally resonant acoustic metamaterials. Identifying
a recursive approach for any number of serially arranged locally resonant mass
in mass cells, a closed form expression for the transfer function is derived.
Analysis of the end-to-end transfer function helps identify the fundamental
mechanism for the band gap formation in a finite metamaterial. This mechanism
includes (a) repeated complex conjugate zeros located at the natural frequency
of the individual local resonators, (b) the presence of two poles which flank
the band gap, and (c) the absence of poles in the band-gap. Analysis of the
finite cell dynamics are compared to the Bloch-wave analysis of infinitely long
metamaterials to confirm the theoretical limits of the band gap estimated by
the transfer function modeling. The analysis also explains how the band gap
evolves as the number of cells in the metamaterial chain increases and
highlights how the response varies depending on the chosen sensing location
along the length of the metamaterial. The proposed transfer function approach
to compute and evaluate band gaps in locally resonant structures provides a
framework for the exploitation of control techniques to modify and tune band
gaps in finite metamaterial realizations.


  The fundamental solution and the heat semigroup of the Vladimirov-Taibleson
operator on constant-order noncommutative Vilenkin groups are obtained,
together with some estimates on the associated heat kernel. We also show the
existence of a fundamental solution for the "Vladimirov Laplacian" on the
$p$-adic Heisenberg group and the $p$-adic Engel group, and discuss possible
extensions of our results to more general homogeneous operators on graded
$p$-adic Lie groups.


  An introduction is given to projects investigating galaxy evolution
quantitatively by spectroscopic observations of very distant galaxies that have
weak apparent brightnesses and small sizes as it is feasible with 10m-class
telescopes like SALT. Such methods encompass scaling relations like the
Tully-Fisher and Fundamental Plane relations that can be utilized to determine
the luminosity evolution and mass assembly of galaxies. The stellar populations
can be analyzed with respect to age, metallicity, and chemical enrichment by
measureing absorption line strengths. Possible effects on galaxy evolution of
the environment in rich clusters of galaxies compared to the field are also
addressed. For each method, recent applications are presented like the
evolution of the TFR determined with 77 field spirals up to z=1, a study of the
internal kinematics of distant cluster spirals and a comparison of the stellar
populations of ellipticals in the field and in rich clusters at z=0.4.


  Most recent approaches for action recognition from video leverage deep
architectures to encode the video clip into a fixed length representation
vector that is then used for classification. For this to be successful, the
network must be capable of suppressing irrelevant scene background and extract
the representation from the most discriminative part of the video. Our
contribution builds on the observation that spatio-temporal patterns
characterizing actions in videos are highly correlated with objects and their
location in the video. We propose Top-down Attention Action VLAD (TA-VLAD), a
deep recurrent architecture with built-in spatial attention that performs
temporally aggregated VLAD encoding for action recognition from videos. We
adopt a top-down approach of attention, by using class specific activation maps
obtained from a deep CNN pre-trained for image classification, to weight
appearance features before encoding them into a fixed-length video descriptor
using Gated Recurrent Units. Our method achieves state of the art recognition
accuracy on HMDB51 and UCF101 benchmarks.


  A novel approach to entanglement, based on the Gelfand-Naimark-Segal (GNS)
construction, is introduced. It considers states as well as algebras of
observables on an equal footing. The conventional approach to the emergence of
mixed from pure ones based on taking partial traces is replaced by the more
general notion of the restriction of a state to a subalgebra. For bipartite
systems of nonidentical particles, this approach reproduces the standard
results. But it also very naturally overcomes the limitations of the usual
treatment of systems of identical particles. This GNS approach seems very
general and can be applied for example to systems obeying para- and braid-
statistics including anyons.


  Recent investigations have revealed that ion transport from aqueous
electrolytes to ion-selective surfaces is subject to electroconvective
instability that stems from coupling of hydrodynamics with electrostatic
forces. Electroconvection is shown to enhance ion mixing and the net rate of
transport. However, systems subject to electroconvection inherently involve
fluid density variation set by salinity gradient in the bulk fluid. In this
study we thoroughly examine the interplay of gravitational convection and
chaotic electroconvection. Our results reveal that buoyant forces can
significantly influence the transport rates, otherwise set by
electroconvection, when the Rayleigh number $Ra$ of the system exceeds a value
$Ra \sim 1000$. We show that buoyancy forces can significantly alter the flow
patterns in these systems. When the buoyancy acts in the stabilizing direction,
it limits the extent of penetration of electroconvection, but without
eliminating it. When the buoyancy destabilizes the flow, it alters the
electroconvective patterns by introducing upward and downward fingers of
respectively light and heavy fluid.


  In this exposition, we get examples of what is called a "linear
hyperdoctrine", based on categories of comodules indexed by coalgebras. This
structures can model first order linear logic.


  It has been argued in the literature that if a universe is expanding with an
accelerating rate indefinitely, it presents a challenge to string theories due
to the existence of event horizons. We study the fate of a currently
accelerating universe. We show that the universe will continue to accelerate
indefinitely if the parameter $\omega = p/\rho$ of the equation of state is a
constant, no matter how many different types of energy (matter, radiation,
quintessence, cosmological constant and etc) are contained in the universe.
This type of universe will always exhibit an event horizon indicating that such
a universe may not be derived from string theories. We also comment on some
related issues.


  A labeling of a digraph $D$ with $m$ arcs is a bijection from the set of arcs
of $D$ to $\{1, \ldots, m\}$. A labeling of $D$ is antimagic if no two vertices
in $D$ have the same vertex-sum, where the vertex-sum of a vertex $u\in V(D)$
for a labeling is the sum of labels of all arcs entering $u$ minus the sum of
labels of all arcs leaving $u$. Motivated by the conjecture of Hartsfield and
Ringel from 1990 on antimagic labelings of graphs, Hefetz, M\"utze, and
Schwartz [On antimagic directed graphs, J Graph Theory 64 (2010) 219--232]
initiated the study of antimagic labelings of digraphs, and conjectured that
every connected graph admits an antimagic orientation, where an orientation $D$
of a graph $G$ is antimagic if $D$ has an antimagic labeling. It remained
unknown whether every disjoint union of cycles admits an antimagic orientation.
In this paper, we first answer this question in the positive by proving that
every $2$-regular graph has an antimagic orientation. We then show that for any
integer $d\ge2$, every connected, $2d$-regular graph has an antimagic
orientation. Our technique is new.


  The water hydration shell has decisive impact on the structural and
functional properties of RNA. Changes of RNA structure upon melting and in
biochemical processes are accompanied by a change of hydration patterns, a
process which is barely characterized. To discern hydration geometries around
the backbone phosphate groups of an RNA double helix at the molecular level, we
combine two-dimensional infrared spectroscopy of phosphate vibrations with
theoretical simulations. There are three distinct coexisting hydration motifs
of the RNA A-helix, an ordered chain-like arrangement of water molecules with
links between neighboring phosphate groups, separate local hydration shells of
up to six water molecules, and hydrated phosphate/counterion contact pairs. RNA
disordering upon melting is connected with a transition from predominant
ordered water structures to local hydration shells around phosphate units.
Structural fluctuations are dominated by librational water motions occurring on
a 300 fs time scale, without exchange between hydration motifs.


  Multivariate stochastic volatility models with skew distributions are
proposed. Exploiting Cholesky stochastic volatility modeling, univariate
stochastic volatility processes with leverage effect and generalized hyperbolic
skew t-distributions are embedded to multivariate analysis with time-varying
correlations. Bayesian prior works allow this approach to provide parsimonious
skew structure and to easily scale up for high-dimensional problem. Analyses of
daily stock returns are illustrated. Empirical results show that the
time-varying correlations and the sparse skew structure contribute to improved
prediction performance and VaR forecasts.


  The understanding of disordered quantum systems is still far from being
complete, despite many decades of research on a variety of physical systems. In
this review we discuss how Bose-Einstein condensates of ultracold atoms in
disordered potentials have opened a new window for studying fundamental
phenomena related to disorder. In particular, we point our attention to recent
experimental studies on Anderson localization and on the interplay of disorder
and weak interactions. These realize a very promising starting point for a
deeper understanding of the complex behaviour of interacting, disordered
systems.


  Lattice QCD calculations of radiative transitions between hadrons have in the
past been limited to processes of hadrons stable under the strong interaction.
Recently developed methods for $1\to2$ transition matrix elements in a finite
volume now enable the determination of radiative decay rates of strongly
unstable particles. Our lattice QCD study focuses on the process $\pi \pi \to
\pi \gamma^{*}$, where the $\rho$ meson is present as an enhancement in the
cross-section. We use $2+1$ flavors of clover fermions at a pion mass of
approximately $320$ MeV and a lattice size of approximately $3.6$ fm. The
required $2$-point and $3$-point correlation functions are constructed from a
set of forward, sequential and stochastic light quark propagators. In addition
to determining the $\rho$ meson resonance parameters via the L\"uscher method,
the scattering phase shift is used in conjunction with the $1\to2$ transition
matrix element formalism of Brice\~no, Hansen and Walker-Loud to compute the
$\pi\pi\to\pi\gamma^{*}$ amplitude at several values of the momentum transfer
and $\pi\pi$ invariant mass.


  A radial basis function (RBF) method based on matrix-valued kernels is
presented and analyzed for computing two types of vector decompositions on
bounded domains: one where the normal component of the divergence-free part of
the field is specified on the boundary, and one where the tangential component
of the curl-free part of the field specified. These two decompositions can then
be combined to obtain a full Helmholtz-Hodge decomposition of the field, i.e.
the sum of divergence-free, curl-free, and harmonic fields. All decompositions
are computed from samples of the field at (possibly scattered) nodes over the
domain, and all boundary conditions are imposed on the vector fields, not their
potentials, distinguishing this technique from many current methods.
Sobolev-type error estimates for the various decompositions are provided and
demonstrated with numerical examples.


  Irradiation with 2.5 MeV electrons at doses up to 5.2 $\times$10$^{19}$
electrons/cm$^2$ was used to introduce point-like defects in single crystals of
Ba$_{1-x}$K$_x$Fe$_2$As$_2$ with $x=$ 0.19 ($T_c=$ 14 K), $x=$ 0.26 ($T_c=$ 32
K) and 0.34 ($T_c=$ 39 K) to study the superconducting gap structure by probing
the effect of non-magnetic scattering on electrical resistivity, $\rho(T)$, and
London penetration depth, $ \lambda (T)$. For all compositions, the irradiation
suppressed the superconducting transition temperature, $T_c$ and increased
resistivity. The low - temperature behavior of $\lambda (T)$ is best described
by the power - law function, $\Delta \lambda (T) = A(T/T_c)^n$. While
substantial suppression of $T_c$ supports $s_{\pm}$ pairing mechanism, in
samples close to the optimal doping, $x=$ 0.26 and 0.34, the exponent $n$
remained high ($n \geq 3$) indicating robust full superconducting gaps. For the
$x=$ 0.19 composition, exhibiting coexistence of superconductivity and long -
range magnetism, the suppression of $T_c$ was much more rapid and the exponent
$n$ decreased toward dirty limit of $n$ = 2. In this sample, the irradiation
also suppressed the temperature of structural/magnetic transition, $T_{sm}$,
from 103 K to 98 K consistent with the itinerant nature of the magnetic order.
Our results suggest that underdoped compositions, especially in the coexisting
regime are most susceptible to non-magnetic scattering and imply that in
multi-band Ba$_{1-x}$K$_x$Fe$_2$As$_2$ superconductors, the ratio of the
inter-band to intra-band pairing strength, and associated gap anisotropy,
increases upon the departure from the optimal doping.


  We investigate the color-magnitude diagram (CMD) of cluster galaxies in the
hierarchical $\Lambda$-CDM cosmological scenario using both single stellar
populations and simple galaxy models. First, we analyze the effect of bursts
and mergers and companion chemical pollution and rejuvenation of the stellar
content on the integrated light emitted by galaxies. The dispersion of the
galaxy magnitudes and colors on the $M_V-(B-V)$ plane is mainly due to mixing
of ages and metallicities of the stellar populations, with mergers weighting
more than bursts of similar mass fractions. The analysis is made using the
Monte-Carlo technique applied to ideal model galaxies reduced to single stellar
populations with galaxy-size mass to evaluate mass, age and metallicity of each
object. We show that separately determining the contributions by bursts and
mergers leads to a better understanding of observed properties of CMD of
cluster galaxies. Then we repeat the analysis using suitable chemo-photometric
models of galaxies whose mass is derived from the cosmological predictions of
the galaxy content of typical clusters. Using the halo mass function and the
Monte-Carlo technique, we derive the formation redshift of each galaxy and its
photometric history. These are used to simulate the CMD of the cluster
galaxies. The main conclusion is that most massive galaxies have acquired the
red color they show today in very early epochs and remained the same ever
since. The simulations nicely reproduce the Red Sequence, the Green Valley and
the Blue Cloud, the three main regions of the CMD in which galaxies crowd.


  The infiltration of molten silicon into carbon preforms is a widespread
technique employed in the industry in order to enhance the thermal and
mechanical properties of the final ceramic products. A proper understanding of
this phenomenon is quite challenging since it stems from the reciprocal action
and reaction between fluid flow, the transition to wetting, mass transport,
precipitation, surface growth as well as heat transfer. As a result, the
exhaustive modeling of such problem is an involved task. Lattice Boltzmann
simulations in 2D for capillary infiltration are carried out in the isothermal
regime taking into account surface reaction and subsequent surface growth.
Precisely, for a single capillary in the linear Washburn regime, special
attention is paid to the retardation for the infiltration process induced by
the thickening of the surface behind the contact line of the invading front.
Interestingly, it turns out that the process of surface growth leading to pore
closure marginally depends on the infiltration velocity. We conclude that
porous matrices with straight and wide pathways represent the optimal case for
impregnation. Our analysis includes also a comparison between the radii
characterizing the infiltration process (i.e., minimum, hydraulic, average and
effective radii).


  This paper proves that it is possible to build a Lagrangian for quantum
electrodynamics which makes it explicit that the photon mass is eventually set
to zero in the physical part on observational ground. Gauge independence is
achieved upon considering the joint effect of gauge-averaging term and ghost
fields. It remains possible to obtain a counterterm Lagrangian where the only
non-gauge-invariant term is proportional to the squared divergence of the
potential, while the photon propagator in momentum space falls off like 1 over
(k-squared) at large k, which indeed agrees with perturbative
renormalizability. The resulting radiative corrections to the Coulomb potential
in QED are also shown to be gauge-independent. The experience acquired with
quantum electrodynamics is used to investigate properties and problems of the
extension of such ideas to non-Abelian gauge theories.


  We present an efficient stochastic algorithm (RSG+) for canonical correlation
analysis (CCA) using a reparametrization of the projection matrices. We show
how this reparametrization (into structured matrices), simple in hindsight,
directly presents an opportunity to repurpose/adjust mature techniques for
numerical optimization on Riemannian manifolds. Our developments nicely
complement existing methods for this problem which either require $O(d^3)$ time
complexity per iteration with $O(\frac{1}{\sqrt{t}})$ convergence rate (where
$d$ is the dimensionality) or only extract the top $1$ component with
$O(\frac{1}{t})$ convergence rate. In contrast, our algorithm offers a strict
improvement for this classical problem: it achieves $O(d^2k)$ runtime
complexity per iteration for extracting the top $k$ canonical components with
$O(\frac{1}{t})$ convergence rate. While the paper primarily focuses on the
formulation and technical analysis of its properties, our experiments show that
the empirical behavior on common datasets is quite promising. We also explore a
potential application in training fair models where the label of protected
attribute is missing or otherwise unavailable.


  In this submission, I discuss my research on values, norms and practices of
subcultures formed as an "alternative" to the dominant way of life. In
particular, I explore how the Internet of Things (IoT) or intelligent agents
relates to alternative forms of interaction or be understood and reconstructed
through alternative concepts or frameworks. For the past three years I have
been conducting fieldwork on communities pursuing alternative lifestyles. This
work considers how those alternative lifestyles may contribute to an
understanding of objects, spaces in future smart home. Through my fieldwork and
research through design, I hope to offer an alternative vision to living with
IoT and envision future domesticity in a unique and even groundbreaking way.


  Let $f$ be a polynomial system consisting of $n$ polynomials $f_1,\cdots,
f_n$ in $n$ variables $x_1,\cdots, x_n$, with coefficients in $\mathbb{Q}$ and
let $\langle f\rangle$ be the ideal generated by $f$. Such a polynomial system,
which has as many equations as variables is called a square system. It may be
zero-dimensional, i.e the system of equations $f = 0$ has finitely many complex
solutions, or equivalently the dimension of the quotient algebra $A =
\mathbb{Q}[x]/\langle f\rangle$ is finite. In this case, the companion matrices
of $f$ are defined as the matrices of the endomorphisms of $A$, called
multiplication maps, $x_j : \left\vert \begin{array}{c} h \mapsto x_jh
\end{array} \right.$, written in some basis of $A$. We present a practical and
efficient method to compute the companion matrices of $f$ in the case when the
system is zero-dimensional. When it is not zero-dimensional, then the method
works as well and still produces matrices having properties similar to the
zero-dimensional case. The whole method consists in matrix calculations. An
experiment illustrates the method's effectiveness.


  After the initial stage of fast expanding of the hot Universe comes epoch of
temperature fall and subsequent formation of dense clouds of hydrogen. Due to
process of accretion some of this clouds became compact objects. Initially
accretion is spherical, but then take the shape of the disk, and this 2D disk
collect matter more efficient than 3D sphere. Accretion disks provide the
mechanism of redistribution of angular moment and extraction of potential
energy,leads to occurrence of compact objects from gas clouds. To fall moving
along a spiral trajectory on the central gravitating body, a particle rotation
around must reduce angular moment and energy. Outward of angular moment and
movement of gas particles in the opposite direction (to the central body)
happens due to of viscose friction. We can think that the disc is composed of
multiple adjacent rings friction each other, each inner ring has a large
angular velocity to the adjacent outer ring and thus friction of the drive ring
decreased the rate of internal. The occurrence of intense dissipation in
accretion flows is among the long-standing unsolved problems in astrophysics.
Revealing how the magnetized turbulence creates shear stress tensor is of
primary importance to understand the heating mechanism and the transport of
angular momentum in accretion disks. The transport of angular momentum at
greatly enhanced rates is important for the main problem of cosmogony, that is
understanding the dynamics of creation of compact astrophysical objects.
Without a theory explaining the enhanced energy dissipation in accretion flows
of turbulent magnetized plasma we would have no clear picture of how our solar
system has been created, why the angular momentum of the Sun is only 2% of the
angular momentum of solar system, while carrying 99% of the solar system's
mass.


  The influence of ligands on the low frequency vibration of different
thicknesses cadmium selenide colloidal nanoplatelets is investigated using
resonant low frequency Raman scattering. The strong vibration frequency shifts
induced by ligand modifications as well as the sharp spectral linewidths make
low frequency Raman scattering a tool of choice to follow ligand exchange as
well as the nano-mechanical properties of the NPLs, as evidenced by a
carboxylate to thiolate exchange study. Apart from their molecular weight, the
nature of the ligands, such as the sulfur to metal bond of thiols, induces a
modification of the NPLs as a whole, increasing the thickness by one monolayer.
Moreover, as the weight of the ligands increases, the discrepancy between the
massload model and the experimental measurements increase. These effects are
all the more important when the number of layers is small and can only be
explained by a modification of the longitudinal sound velocity. This
modification takes its origin in a change of lattice structure of the NPLs,
that reflects on its elastic properties. These nanobalances are finally used to
characterize ligands affinity with the surface using binary thiols mixtures,
illustrating the potential of low frequency Raman scattering to finely
characterize nanocrystals surfaces.


  A new distributed MPC algorithm for the regulation of dynamically coupled
subsystems is presented in this paper. The current control action is computed
via two robust controllers working in a nested fashion. The inner controller
builds a nominal reference trajectory from a decentralized perspective. The
outer controller uses this information to take into account the effects of the
coupling and generate a distributed control action. The tube-based approach to
robustness is employed. A supplementary constraint is included in the outer
optimization problem to provide recursive feasibility of the overall controller


  For a complementary split-ring resonators (CSRRs) etched on one screen of two
infinite and perfectly conducting plates under cylindrical plane wave
illumination, the transmission coefficients analysis were performed through
finite-difference time-domain simulations. For the single slot ring CSRR, two
transmission dips are observed for the direction parallel to aperture at
resonance whereas two enhanced transmission for the direction normal to the
aperture. For double slot ring CSRR, the transmission coefficients appear more
anisotropic. The results provided considerable insight into the electromagnetic
response of CSRR and would be very helpful for developing new electronic
devices such as filters.


  This article proposes the design of H-infinity-based robust current
controller for single-phase grid-feeding voltage source inverter with an LCL
filter. The main objective of the proposed controller is to have good reference
tracking, disturbance rejection and sufficient LCL resonance damping under a
large range of variations of grid impedance. Based on the aforementioned
performance requirements, frequency dependent weighting functions are designed.
Subsequently, the sub-optimal control problem is formulated and solved to
determine the stabilizing controller. Computational footprint of the controller
is addressed for ease-of-implementability on low-cost controller boards.
Finally controller hardware-in-the-loop simulations on OPAL-RT are performed in
the validation stage to obtain performance guarantees of the controller. The
proposed controller exhibits fast response during transients and superior
reference tracking, disturbance rejection at steady-state when compared with
proportional- and resonant-based current controllers.


  Sparse representation learning has recently gained a great success in signal
and image processing, thanks to recent advances in dictionary learning. To this
end, the $\ell_0$-norm is often used to control the sparsity level.
Nevertheless, optimization problems based on the $\ell_0$-norm are non-convex
and NP-hard. For these reasons, relaxation techniques have been attracting much
attention of researchers, by priorly targeting approximation solutions (e.g.
$\ell_1$-norm, pursuit strategies). On the contrary, this paper considers the
exact $\ell_0$-norm optimization problem and proves that it can be solved
effectively, despite of its complexity. The proposed method reformulates the
problem as a Mixed-Integer Quadratic Program (MIQP) and gets the global optimal
solution by applying existing optimization software. Because the main
difficulty of this approach is its computational time, two techniques are
introduced that improve the computational speed. Finally, our method is applied
to image denoising which shows its feasibility and relevance compared to the
state-of-the-art.


  In many applications data are measured or defined on a spherical manifold;
spherical harmonic transforms are then required to access the frequency content
of the data. We derive algorithms to perform forward and inverse spin spherical
harmonic transforms for functions of arbitrary spin number. These algorithms
involve recasting the spin transform on the two-sphere S^2 as a Fourier
transform on the two-torus T^2. Fast Fourier transforms are then used to
compute Fourier coefficients, which are related to spherical harmonic
coefficients through a linear transform. By recasting the problem as a Fourier
transform on the torus we appeal to the usual Shannon sampling theorem to
develop spherical harmonic transforms that are theoretically exact for
band-limited functions, thereby providing an alternative sampling theorem on
the sphere. The computational complexity of our forward and inverse spin
spherical harmonic transforms scale as O(L^3) for any arbitrary spin number,
where L is the harmonic band-limit of the spin function on the sphere.
Numerical experiments are performed and unfortunately the forward transform is
found to be unstable for band-limits above L~32. The instability is due to the
poorly conditioned linear system relating Fourier and spherical harmonic
coefficients. The inverse transform is expected to be stable, although it is
not possible to verify this hypothesis.


  Model predictive control (MPC) may provide local motion planning for mobile
robotic platforms. The challenging aspect is the analytic representation of
collision cost for the case when both the obstacle map and robot footprint are
arbitrary. We propose a Neural Potential Field: a neural network model that
returns a differentiable collision cost based on robot pose, obstacle map, and
robot footprint. The differentiability of our model allows its usage within the
MPC solver. It is computationally hard to solve problems with a very high
number of parameters. Therefore, our architecture includes neural image
encoders, which transform obstacle maps and robot footprints into embeddings,
which reduce problem dimensionality by two orders of magnitude. The reference
data for network training are generated based on algorithmic calculation of a
signed distance function. Comparative experiments showed that the proposed
approach is comparable with existing local planners: it provides trajectories
with outperforming smoothness, comparable path length, and safe distance from
obstacles. Experiment on Husky UGV mobile robot showed that our approach allows
real-time and safe local planning. The code for our approach is presented at
https://github.com/cog-isa/NPField together with demo video.


  Actigraphic measurements are an important part of research in different
disciplines, yet the procedure of determining activity values is unexpectedly
not standardized in the literature. Although the measured raw acceleration
signal can be diversely processed, and then the activity values can be
calculated by different activity calculation methods, the documentations of
them are generally incomplete or vary by manufacturer. These numerous activity
metrics may require different types of preprocessing of the acceleration
signal. For example, digital filtering of the acceleration signals can have
various parameters; moreover, both the filter and the activity metrics can also
be applied per axis or on the magnitudes of the acceleration vector. Level
crossing-based activity metrics also depend on unclearly determined threshold
level values. Due to the serious inconsistency of determining activity values,
we created a detailed and comprehensive comparison of the different available
activity calculation procedures because, up to the present, it was lacking in
the literature. We assessed the different methods by analysing the triaxial
acceleration signals measured during a 10-day movement of 42 subjects. We
calculated 148 different activity signals for each subject's movement using the
combinations of various types of preprocessing and 7 different activity metrics
applied on both axial and magnitude data. We determined the linear relationship
between the metrics by correlation analysis, while we also examined the effects
of the preprocessing steps. Moreover, we established that the standard
deviation of the data series can be used as an appropriate, adaptive and
generalized threshold level for the level intersection-based metrics. On the
basis of these results, our work also serves as a general guide on how to
proceed if one wants to determine activity from the raw acceleration data.


  We summarise the results of RoboCup 2D Soccer Simulation League in 2016
(Leipzig), including the main competition and the evaluation round. The
evaluation round held in Leipzig confirmed the strength of RoboCup-2015
champion (WrightEagle, i.e. WE2015) in the League, with only eventual finalists
of 2016 competition capable of defeating WE2015. An extended, post-Leipzig,
round-robin tournament which included the top 8 teams of 2016, as well as
WE2015, with over 1000 games played for each pair, placed WE2015 third behind
the champion team (Gliders2016) and the runner-up (HELIOS2016). This
establishes WE2015 as a stable benchmark for the 2D Simulation League. We then
contrast two ranking methods and suggest two options for future evaluation
challenges. The first one, "The Champions Simulation League", is proposed to
include 6 previous champions, directly competing against each other in a
round-robin tournament, with the view to systematically trace the advancements
in the League. The second proposal, "The Global Challenge", is aimed to
increase the realism of the environmental conditions during the simulated
games, by simulating specific features of different participating countries.


  The partially observed major minor LQG and nonlinear mean field game (PO MM
LQG MFG) systems where it is assumed the major agent's state is partially
observed by each minor agent, and the major agent completely observes its own
state have been analysed in the literature. In this paper, PO MM LQG MFG
problems with general information patterns are studied where (i) the major
agent has partial observations of its own state, and (ii) each minor agent has
partial observations of its own state and the major agent's state. The
assumption of partial observations by all agents leads to a new situation
involving the recursive estimation by each minor agent of the major agent's
estimate of its own state. For a general case of indefinite LQG MFG systems,
the existence of $\epsilon$-Nash equilibria together with the individual
agents' control laws yielding the equilibria are established via the Separation
Principle.


  Measurement Device Independent Quantum Private Query (MDI QPQ) with qutrits
is presented. We compare the database security and client's privacy in MDI QPQ
for qubits with qutrits. For some instances, we observe that qutrit will
provide better security for database than qubit. However, when it comes to the
question of client's privacy we have to take additional measures in case of
qutrit. Hence we conclude that though in case of Quantum Key Distribution (QKD)
higher dimension provides better security but in case of QPQ this is not
obvious.


  We prove that the number of "hole elements" $H(K)$ in the infrastructure of a
hyperelliptic function field $K$ of genus $g$ with finite constant field $\F_q$
with $n + 1$ places at infinity, of whom $n' + 1$ are of degree one, satisfies
  $|\frac{H(K)}{\abs{\Pic^0(K)}} - \frac{n'}{q}| = O(16^g n q^{-3/2}).$ We
obtain an explicit formula for the number of holes using only information on
the infinite places and the coefficients of the $L$-polynomial of the
hyperelliptic function field. This proves a special case of a conjecture by E.
Landquist and the author on the number of holes of an infrastructure of a
global function field.
  Moreover, we investigate the size of a hole in case $n = n'$, and show that
asymptotically for $n \to \infty$, the size of a hole next to a reduced divisor
$D$ behaves like the function $\frac{n^{g - \deg D}}{(g - \deg D)!}$.


  We study (coalitional) exchange stability, which Alcalde [Economic Design,
1995] introduced as an alternative solution concept for matching markets
involving property rights, such as assigning persons to two-bed rooms. Here, a
matching of a given Stable Marriage or Stable Roommates instance is called
coalitional exchange-stable if it does not admit any exchange-blocking
coalition, that is, a subset S of agents in which everyone prefers the partner
of some other agent in S. The matching is exchange-stable if it does not admit
any exchange-blocking pair, that is, an exchange-blocking coalition of size
two.
  We investigate the computational and parameterized complexity of the
Coalitional Exchange-Stable Marriage (resp. Coalitional Exchange Roommates)
problem, which is to decide whether a Stable Marriage (resp. Stable Roommates)
instance admits a coalitional exchange-stable matching. Our findings resolve an
open question and confirm the conjecture of Cechl\'arov\'a and Manlove
[Discrete Applied Mathematics, 2005] that Coalitional Exchange-Stable Marriage
is NP-hard even for complete preferences without ties. We also study
bounded-length preference lists and a local-search variant of deciding whether
a given matching can reach an exchange-stable one after at most k swaps, where
a swap is defined as exchanging the partners of the two agents in an
exchange-blocking pair.


  In this article, we study a certain Galois property of subextensions of
$k(A_{\mathrm{tors}})$, the minimal field of definition of all torsion points
of an abelian variety $A$ defined over a number field $k$. Concretely, we show
that each subfield of $k(A_{\mathrm{tors}})$ which is Galois over $k$ (of
possibly infinite degree) and whose Galois group has finite exponent is
contained in an abelian extension of some finite extension of $k$. As an
immediate corollary of this result and a theorem of Bombieri and Zannier, we
deduce that each such field has the Northcott property, i.e. does not contain
any infinite set of algebraic numbers of bounded height.


  A detailed solution of an initial value problem of a vertically localized
initial perturbation in rotating magnetized vertically stratified disk is
presented. The appropriate linearized MHD equations are solved by employing the
WKB approximation and the results are verified numerically. The
eigenfrequencies as well as eigenfunctions are explicitly obtained. It is
demonstrated that the initial perturbation remains confined within the disk. It
is further shown that thin enough disks are stable but as their thickness grows
increasing number of unstable modes participate in the solution of the initial
value problem. However it is demonstrated that due to the localization of the
initial perturbation the growth time of the instability is significantly longer
than the calculated inverse growth rate of the individual unstable
eigenfunctions.


  We study breathers and solitons on different backgrounds in optical fiber
system, which is governed by generalized coupled Hirota equations with four
wave mixing effect. On plane wave background, a transformation between
different types of solitons is discovered. Then, on periodic wave background,
we find breather-like nonlinear localized waves of which formation mechanism
are related to the energy conversion between two components. The energy
conversion results from four wave mixing. Furthermore, we prove that this
energy conversion is controlled by amplitude and period of backgrounds.
Finally, solitons on periodic wave background are also exhibited. These results
would enrich our knowledge of nonlinear localized waves' excitation in coupled
system with four wave mixing effect.


  The widespread adoption of whole slide imaging has increased the demand for
effective and efficient gigapixel image analysis. Deep learning is at the
forefront of computer vision, showcasing significant improvements over previous
methodologies on visual understanding. However, whole slide images have
billions of pixels and suffer from high morphological heterogeneity as well as
from different types of artefacts. Collectively, these impede the conventional
use of deep learning. For the clinical translation of deep learning solutions
to become a reality, these challenges need to be addressed. In this paper, we
review work on the interdisciplinary attempt of training deep neural networks
using whole slide images, and highlight the different ideas underlying these
methodologies.


  Markov random fields (MRFs) are difficult to evaluate as generative models
because computing the test log-probabilities requires the intractable partition
function. Annealed importance sampling (AIS) is widely used to estimate MRF
partition functions, and often yields quite accurate results. However, AIS is
prone to overestimate the log-likelihood with little indication that anything
is wrong. We present the Reverse AIS Estimator (RAISE), a stochastic lower
bound on the log-likelihood of an approximation to the original MRF model.
RAISE requires only the same MCMC transition operators as standard AIS.
Experimental results indicate that RAISE agrees closely with AIS
log-probability estimates for RBMs, DBMs, and DBNs, but typically errs on the
side of underestimating, rather than overestimating, the log-likelihood.


  It is shown how the two experimentally measurable properties of the mixing
matrix $V$, the asymmetry Delta(V)=|V_{12}|^2- |V_{21}|^2 of V with respect to
the main diagonal and the Jarlskog invariant
J(V)=Im(V_{11}V_{12}^*V_{21}^*V_{22}), can be exploited to obtain constraints
on possible structures of mass matrices in the quark sector. Specific mass
matrices are examined in detail as an illustration.


  In this paper, we use deep learning to estimate living conditions in India.
We use both census and surveys to train the models. Our procedure achieves
comparable results to those found in the literature, but for a wide range of
outcomes.


  We describe the role of algebraic extensions in the theory of commutative,
unital normed algebras, with special attention to uniform algebras. We shall
also compare these constructions and show how they are related to each other.


  Cooperative behavior, the costly provision of benefits to others, is common
across all domains of life. This review article discusses cooperative behavior
in the microbial world, mediated by the exchange of extracellular products
called public goods. We focus on model species for which the production of a
public good and the related growth disadvantage for the producing cells are
well described. To unveil the biological and ecological factors promoting the
emergence and stability of cooperative traits we take an interdisciplinary
perspective and review insights gained from both mathematical models and
well-controlled experimental model systems. Ecologically, we include crucial
aspects of the microbial life cycle into our analysis and particularly consider
population structures where an ensemble of local communities (sub populations)
continuously emerge, grow, and disappear again. Biologically, we explicitly
consider the synthesis and regulation of public good production. The discussion
of the theoretical approaches includes general evolutionary concepts,
population dynamics, and evolutionary game theory. As a specific but generic
biological example we consider populations of Pseudomonas putida and its
regulation and utilization of pyoverdines, iron scavenging molecules. The
review closes with an overview on cooperation in spatially extended systems and
also provides a critical assessment of the insights gained from the
experimental and theoretical studies discussed. Current challenges and
important new research opportunities are discussed, including the biochemical
regulation of public goods, more realistic ecological scenarios resembling
native environments, cell to cell signalling, and multi-species communities.


  A module is called automorphism-invariant if it is invariant under any
automorphism of its injective hull. Dickson and Fuller have shown that if $R$
is a finite-dimensional algebra over a field $\mathbb F$ with more than two
elements then an indecomposable automorphism-invariant right $R$-module must be
quasi-injective. In this note, we extend and simplify the proof of this result
by showing that any automorphism-invariant module over an algebra over a field
with more than two elements is quasi-injective. Our proof is based on the study
of the additive unit structure of endomorphism rings.


  Critical comments on the entropic lattice Boltzmann equation (ELBE), by
Li-Shi Luo, Wei Liao, Xingwang Chen, Yan Peng and Wei Zhang, Physical Review E
83, 056710 (2011), are based on simulations which make use of a model that,
despite being called ELBE by the authors, is in fact fully equivalent to the
standard lattice Bhatnagar-Gross-Krook equation. As a result, the conclusion of
Luo et al on ELBE is circular, hence devoid of scientific bearing.


  We re-analyse recent Cepheid data to estimate the Hubble parameter $H_0$ by
using Bayesian hyper-parameters (HPs). We consider the two data sets from Riess
et al 2011 and 2016 (labelled R11 and R16, with R11 containing less than half
the data of R16) and include the available anchor distances (megamaser system
NGC4258, detached eclipsing binary distances to LMC and M31, and MW Cepheids
with parallaxes), use a weak metallicity prior and no period cut for Cepheids.
We find that part of the R11 data is down-weighted by the HPs but that R16 is
mostly consistent with expectations for a Gaussian distribution, meaning that
there is no need to down-weight the R16 data set. For R16, we find a value of
$H_0 = 73.75 \pm 2.11 \, \mathrm{km} \, \mathrm{s}^{-1} \, \mathrm{Mpc}^{-1}$
if we use HPs for all data points (including Cepheid stars, supernovae type Ia,
and the available anchor distances), which is about 2.6 $\sigma$ larger than
the Planck 2015 value of $H_0 = 67.81 \pm 0.92 \,\mathrm{km}\, \mathrm{s}^{-1}
\, \mathrm{Mpc}^{-1}$ and about 3.1 $\sigma$ larger than the updated Planck
2016 value $66.93 \pm 0.62 \,\mathrm{km}\, \mathrm{s}^{-1} \,
\mathrm{Mpc}^{-1}$. We test the effect of different assumptions, and find that
the choice of anchor distances affects the final value significantly. If we
exclude the Milky Way from the anchors, then the value of $H_0$ decreases. We
find however no evident reason to exclude the MW data. The HP method used here
avoids subjective rejection criteria for outliers and offers a way to test
datasets for unknown systematics.


  We propose and demonstrate a new read-out technique for a superconducting
qubit by dispersively coupling it to a Josephson parametric oscillator. We
employ a tunable quarter-wavelength superconducting resonator and modulate its
resonant frequency at twice its value with an amplitude surpassing the
threshold for parametric instability. We map the qubit states onto two distinct
states of classical parametric oscillation: one oscillating state, with
$185\pm15$ photons in the resonator, and one with zero oscillation amplitude.
This high contrast obviates a following quantum-limited amplifier. We
demonstrate proof-of-principle, single-shot readout performance, and present an
error budget indicating that this method can surpass the fidelity threshold
required for quantum computing.


  Data load balancing is a challenging task in the P2P systems.
  Distributed hash table (DHT) abstraction, heterogeneous nodes, and non
uniform distribution of objects are the reasons to cause load imbalance in
structured P2P overlay networks.
  Previous works solved the load balancing problem by assuming the homogeneous
capabilities of nodes, unawareness of the link latency during transferring
load, and imposing logical structures to collect and reassign load.
  We propose a distributed load balancing algorithm with the topology awareness
feature by using the concept of virtual servers.
  In our approach, each node collects neighborhood load information from
physically close nodes and reassigns virtual servers to overlay nodes based
upon the topology of underlying network.
  Consequently, our approach converges data load balancing quickly and it also
reduces the load transfer cost between nodes.
  Moreover, our approach increases the quality of load balancing among close
nodes of overlay and it also introduces a new tradeoff between the quality of
load balancing and load transfer cost among all overlay nodes.
  Our simulations show that our approach reduces the load transfer cost and it
saves network bandwidth respectively.
  Finally, we show that the in-degree imbalance of nodes, as a consequence of
topology awareness, cannot lead to a remarkable problem in topology aware
overlays.


  This paper proposes a design of low complexity, reconfigurable and narrow
transition band (NTB) filter bank (FB). In our proposed Modified Frequency
Response Masking (ModFRM) architecture, the modal filter and complementary
filter in conventional FRM approach are replaced by a power complementary and
linear phase FB. Additionally, a new masking strategy is proposed by which an
M-channel FB can be designed by alternately masking even channels and odd
channels. By using this masking strategy, it is only necessary to alternately
mask even channels and odd channels by the two masking filters while modulating
them over the multiple spectra replicas appearing in [0,2$\pi$] to generate
uniform ModFRM FB. Also, reconfigurability of filter bank can be achieved by
adjusting the interpolation values appropriately, to obtain more masking
responses. To reduce the overall implementation complexity, masking filters are
optimized using the interpolated FIR (IFIR) technique. The results indicate
that the proposed method requires substantially less multipliers in comparison
to the reconfigurable FB existing in literature. Finally, non-uniform FB are
generated from the uniform FB by combining nearby channels. The proposed
non-uniform ModFRM FB is used for the extraction of different communication
standards in the software defined radio (SDR) channelizer. When more channels
are to be extracted, the proposed scheme was able to achieve a reduced hardware
complexity in comparison to other filter bank based SDR channelizers.


  This paper deals with the two-species chemotaxis system with Lotka-Volterra
competitive kinetics, \begin{align*} \begin{cases} u_t = d_1 \Delta u - \chi_1
\nabla \cdot (u \nabla w) + \mu_1 u (1 - u - a_1 v), & x\in\Omega,\ t>0,\\ v_t
= d_2 \Delta v - \chi_2 \nabla \cdot (v \nabla w) + \mu_2 v (1 - a_2 u - v), &
x\in\Omega,\ t>0,\\ 0 = d_3 \Delta w + \alpha u + \beta v - \gamma w, &
x\in\Omega,\ t>0, \end{cases} \end{align*} under homogeneous Neumann boundary
conditions and suitable initial conditions, where $\Omega \subset \mathbb{R}^n$
$(n \in \mathbb{N})$ is a bounded domain with smooth boundary, $d_1, d_2, d_3,
\chi_1, \chi_2, \mu_1, \mu_2 > 0$, $a_1, a_2 \ge 0$ and $\alpha, \beta, \gamma
> 0$. Under largeness conditions on $\chi_1$ and $\chi_2$, we show that for
suitably regular initial data, any thresholds of the population density can be
surpassed, which extends the previous results to the non-symmetric case. The
paper contains a well-posedness result for the hyperbolic-elliptic limit system
with $d_1=d_2=0$.


  The emerging interest in van der Waals heterostructures as new materials for
opto-electronics and photonics poses questions about their stability and
structure-property relations. In the framework of density-functional and
many-body perturbation theory, we investigate the structural, electronic, and
optical properties of periodic heterostructures formed by graphene and
hexagonal boron nitride (h-BN). To understand how the constituents affect each
other depending on the layer stacking, we examine 12 commensurate arrangements.
We find that interaction with graphene improves the stability of bulk h-BN also
in those configurations that are predicted to be energetically metastable. In
return, the interaction with h-BN can open a band gap of a few hundred meV in
graphene. Its actual size can be tuned by the arrangement of the layers. In the
semiconducting configurations, the character and spatial distribution of
optical excitations are affected by the specific stacking, that determines the
electronic states involved in the transitions. Remarkably, six out of the 12
explored heterostructures remain semi-metallic.


  We present a photometric and kinematical analysis of two and poorly studied
open clusters; Koposov 12 (FSR 802) and Koposov 43 (FSR 848) by using
cross-matched data from PPMXL and Gaia DR2 catalog. We use astrometric
parameters to identify 285 and 310 cluster members for Koposov 12 and Koposov
43, respectively. Using the extracted member candidates and isochrone fitting
to near-infrared (J, H, Ks) and Gaia DR2 bands (G, GBP, GRP), and Color
Magnitude Diagrams (CMDs), we have estimated ages: log (age/yr) = 9.00 +/- 0.20
and 9.50 +/- 0.20, and distances d = 1850 +/- 43 pc and 2500 +/- 50 pc for
Koposov 12 and Koposov 43, respectively, assuming Solar metallicity (Z=0.019).
The estimated masses of the cluster derived using initial mass function and
synthetic CMD are 364 +/- 19 M_sun and 352 +/- 19 M_sun. We have also computed
their velocity ellipsoid parameters based on (3x3) matrix elements (mu_ij).


  We study a quantum spin-1/2 chain that is dual to the canonical problem of
non-equilibrium Kawasaki dynamics of a classical Ising chain coupled to a
thermal bath. The Hamiltonian is obtained for the general disordered case with
non-uniform Ising couplings. The quantum spin chain (dubbed Ising-Kawasaki) is
stoquastic, and depends on the Ising couplings normalized by the bath's
temperature. We give its exact ground states. Proceeding with uniform
couplings, we study the one- and two-magnon excitations. Solutions for the
latter are derived via a Bethe Ansatz scheme. In the antiferromagnetic regime,
the two-magnon branch states show intricate behavior, especially regarding
their hybridization with the continuum. We find that that the gapless chain
hosts multiple dynamics at low energy as seen through the presence of multiple
dynamical critical exponents. Finally, we analyze the full energy level spacing
distribution as a function of the Ising coupling. We conclude that the system
is non-integrable for generic parameters, or equivalently, that the
corresponding non-equilibrium classical dynamics are ergodic.


  We use a combination of first-principles density functional calculations and
spin-dynamics simulations to explain the unusual diffuse inelastic neutron
scattering in the hexagonal multiferroic yttrium manganite, YMnO$_3$. Using
symmetry considerations, we construct a model spin Hamiltonian with parameters
derived from our density functional calculations and show that it captures the
measured behavior. We then show that the observed directionality in the
structured diffuse scattering in momentum space is a hallmark of the triangular
geometry, and that its persistence across a wide range of temperatures, both
above and below the N\'eel temperature, T$_\text{N}$, is a result of the strong
magnetic frustration. We predict that this diffuse scattering exists in a
yet-to-be-observed modulated continuum of energies, that its associated spin
excitations have distinct in-plane and out-of-plane character and that the
frustration influences the magnetism below the N\'eel temperature. Finally, we
show that visualizing the magnetic order in terms of composite trimer
magnetoelectric monopoles and toroidal moments, rather than individual spins,
provides insight into the real space fluctuations, revealing clusters of
emerging order in the paramagnetic state, as well as collective short-range
excitations in the ordered antiferromagnetic phase. Our understanding of this
directional diffuse scattering in such a wide temperature range, both below and
above T$_\text{N}$, provides new insight into the magnetic phase transitions in
frustrated systems.


  In this literature, we carefully investigate the structure of single- and
multi-frequency imaging functions, that are usually employed in inverse
scattering problems. Based on patterns of the singular vectors of the
Multi-Static Response (MSR) matrix, we establish a relationship between imaging
functions and the Bessel function. This relationship indicates certain
properties of imaging functions and the reason behind enhancement in the
imaging performance by multiple frequencies. Several numerical simulations with
a large amount of noisy data are performed in order to support our
investigation.


  A duality invariant first order action is constructed on the loop group of a
Drinfeld double. It gives at the same time the description of both of the pair
of $\sigma$-models related by Poisson-Lie T-duality. Remarkably, the action
contains a WZW-term on the Drinfeld double not only for conformally invariant
$\si$-models. The resulting actions of the models from the dual pair differ
just by a total derivative corresponding to an ambiguity in specifying a
two-form whose exterior derivative is the WZW three-form. This total derivative
is nothing but the Semenov-Tian-Shansky symplectic form on the Drinfeld double
and it gives directly a generating function of the canonical transformation
relating the $\si$-models from the dual pair.


  The temporal variability of the telluric sodium layer is investigated by
analyzing 28 nights of data obtained with the Colorado State University LIDAR
experiment. The mean height power spectrum of the sodium layer was found to be
well fit by a power law over the observed range of frequencies, 10 microhertz
to 4 millhertz. The best fitting power law was found to be 10^\beta \nu^\alpha,
with \alpha = -1.79 +/- 0.02 and \beta = 1.12 +/- 0.40. Applications to
wavefront sensing require knowledge of the behavior of the sodium layer at kHz
frequencies. Direct measurements at these frequencies do not exist.
Extrapolation from low-frequency behavior to high frequencies suggests that
this variability may be a significant source of error for laser-guide-star
adaptive optics on large-aperture telescopes.


  We present results on the dynamics of split-ring dimers having both gain and
loss in one dimensional nonlinear parity-time- (PT-)symmetric magnetic
metamaterials. For the longwave (continuum) limit approximation and in the
weakly nonlinear limit, we show analytic results on the existence of gap
soliton solutions and on symmetry breaking phenomenon at a critical value of
the gain/loss term.


  The ATLAS detector at the Large Hadron Collider is used to search for
high-mass resonances decaying to dielectron or dimuon final states. Results are
presented from an analysis of proton-proton (pp) collisions at a center-of-mass
energy of 8 TeV corresponding to an integrated luminosity of 20.3 fb-1 in the
dielectron channel and 20.5 fb-1 in the dimuon channel. A narrow resonance with
Standard Model Z couplings to fermions is excluded at 95% confidence level for
masses less than 2.79 TeV in the dielectron channel, 2.53 TeV in the dimuon
channel, and 2.90 TeV in the two channels combined. Limits on other model
interpretations are also presented, including a grand-unification model based
on the E6 gauge group, Z* bosons, Minimal Z' Models, a spin-2 graviton
excitation from Randall-Sundrum models, quantum black holes and a Minimal
Walking Technicolor model with a composite Higgs boson.


  The availability of empirical data that capture the structure and behavior of
complex networked systems has been greatly increased in recent years, however a
versatile computational toolbox for unveiling a complex system's nodal and
interaction dynamics from data remains elusive. Here we develop a two-phase
approach for autonomous inference of complex network dynamics, and its
effectiveness is demonstrated by the tests of inferring neuronal, genetic,
social, and coupled oscillators dynamics on various synthetic and real
networks. Importantly, the approach is robust to incompleteness and noises,
including low resolution, observational and dynamical noises, missing and
spurious links, and dynamical heterogeneity. We apply the two-phase approach to
inferring the early spreading dynamics of H1N1 flu upon the worldwide airline
network, and the inferred dynamical equation can also capture the spread of
SARS and COVID-19 diseases. These findings together offer an avenue to discover
the hidden microscopic mechanisms of a broad array of real networked systems.


  In this partly historical and partly research oriented note, we display a
page of an unpublished mathematical diary of Helmut Wielandt's for 1951. There
he gives a new proof of a theorem due to H. S. A. Potter on the matrix equation
$AB = \omega BA$, which is related to the $q$-binomial theorem, and asks some
further questions, which we answer. We also describe results by M. P. Drazin
and others on this equation.


  In the future-included real action theory whose path runs over not only past
but also future, we demonstrate a theorem, which states that the normalized
matrix element of a Hermitian operator $\hat{\cal O}$ defined in terms of the
future state at the final time $T_B$ and the fixed past state at the initial
time $T_A$ becomes real for the future state selected such that the absolute
value of the transition amplitude from the past state to the future state is
maximized. This is a special version of our previously proposed theorem for the
future-included complex action theory. We find that though the maximization
principle leads to the reality of the normalized matrix element in the
future-included real action theory, it does not specify the future and past
states so much as in the case of the future-included complex action theory. In
addition, we argue that the normalized matrix element seems to be more natural
than the usual expectation value. Thus we speculate that the functional
integral formalism of quantum theory could be most elegant in the
future-included complex action theory.


  We consider the cooperative spontaneous emission of a system of two identical
atoms, interacting with the electromagnetic field in the vacuum state and in
the presence of an oscillating mirror. We assume that the two atoms, one in the
ground state and the other in the excited state, are prepared in a correlated
(symmetric or antisymmetric) {\em Bell}-type state. We also suppose that the
perfectly reflecting plate oscillates adiabatically, with the field modes
satisfying the boundary conditions at the mirror surface at any given instant,
so that the time-dependence of the interaction Hamiltonian is entirely enclosed
in the instantaneous atoms-wall distance. Using time-dependent perturbation
theory, we investigate the spectrum of the radiation emitted by the two-atom
system, showing how the oscillation of the boundary modifies the features of
the emitted spectrum, which exhibits two lateral peaks not present in the case
of a static boundary. We also evaluate the transition rate to the collective
ground state of the two-atom system in both cases of the superradiant
(symmetric) and subradiant (antisymmetric) state. We show that it is modulated
in time, and that the presence of the oscillating mirror can enhance or inhibit
the decay rate compared to the case of atoms in vacuum space or near a static
boundary. Our results thus suggest that a dynamical (i.e. time-modulated)
environment can give new possibilities to control and manipulate radiative
processes of atoms or molecules nearby, such as the cooperative decay, and
strongly indicate a similar possibility for other radiative processes, for
example the resonance interaction and the energy transfer between atoms or
molecules.


  A collective, macroscopic signature to detect radiation friction in
laser-plasma experiments is proposed. In the interaction of superintense
circularly polarized laser pulses with high density targets, the effective
dissipation due to radiative losses allows the absorption of electromagnetic
angular momentum, which in turn leads to the generation of a quasistatic axial
magnetic field. This peculiar "inverse Faraday effect" is investigated by
analytical modeling and three-dimensional simulations, showing that
multi-gigagauss magnetic fields may be generated at laser intensities
$>10^{23}~\mbox{W cm}^{-2}$.


  We present high-dispersion long-slit IR spectra of the double-shell bipolar
planetary nebula M 2-9 in the emission lines [Fe II] 16435 and H2 v=1--0 S(1)
21218. H2 spectra reveal for the first time the kinematic structure of the
outer shell in M 2-9. Kinematics of the inner shell, traced by [Fe II],
resemble those of optical lines like [N II]. [Fe II] and H2 shells have
expansion speeds roughly proportional to distance from the star (``Hubble''
flows) and share the same dynamical age of 1200--2000 yr, depending on the
distance to M 2-9. Thus, the inner ionized lobes and outer molecular lobes, as
well as the molecular torus and ``outer loops'' measured by other observers,
were all formed around the same time. Consequently, their nested structure
likely arises from an excitation gradient rather than independent ejections. H2
and [Fe II] emission is distributed more uniformly than [N II], and IR lines
are not dominated by the moving ionization pattern like visual-wavelength
lines. We suggest that this is because IR lines of [Fe II] and H2 are excited
by relatively isotropic far-UV radiation (Balmer continuum), whereas optical
lines respond to a directed rotating beam of Lyman continuum. Finally, we
highlight intriguing similarities between M 2-9 and the Homunculus of eta
Carinae, despite the different central engines powering the two nebulae.


  We consider a status information updating system where a fusion center
collects the status information from a large number of sources and each of them
has its own age of information (AoI) constraints. A novel grouping-based
scheduler is proposed to solve this complex large-scale problem by dividing the
sources into different scheduling groups. The problem is then transformed into
deriving the optimal grouping scheme. A two-step grouping algorithm (TGA) is
proposed: 1) Given AoI constraints, we first identify the sources with harmonic
AoI constraints, then design a fast grouping method and an optimal scheduler
for these sources. Under harmonic AoI constraints, each constraint is divisible
by the smallest one and the sum of reciprocals of the constraints with the same
value is divisible by the reciprocal of the smallest one. 2) For the other
sources without such a special property, we pack the sources which can be
scheduled together with minimum update rates into the same group. Simulations
show the channel usage of the proposed TGA is significantly reduced as compared
to a recent work and is 0.42% larger than a derived lower bound when the number
of sources is large.


  Path planning is typically considered in Artificial Intelligence as a graph
searching problem and R* is state-of-the-art algorithm tailored to solve it.
The algorithm decomposes given path finding task into the series of subtasks
each of which can be easily (in computational sense) solved by well-known
methods (such as A*). Parameterized random choice is used to perform the
decomposition and as a result R* performance largely depends on the choice of
its input parameters. In our work we formulate a range of assumptions
concerning possible upper and lower bounds of R* parameters, their
interdependency and their influence on R* performance. Then we evaluate these
assumptions by running a large number of experiments. As a result we formulate
a set of heuristic rules which can be used to initialize the values of R*
parameters in a way that leads to algorithm's best performance.


  The widespread adoption of mobile devices, such as smartphones and tablets,
has made touchscreens a common interface for musical performance. New mobile
musical instruments have been designed that embrace collaborative creation and
that explore the affordances of mobile devices, as well as their constraints.
While these have been investigated from design and user experience
perspectives, there is little examination of the performers' musical outputs.
In this work, we introduce a constrained touchscreen performance app, MicroJam,
designed to enable collaboration between performers, and engage in a novel
data-driven analysis of more than 1600 performances using the app. MicroJam
constrains performances to five seconds, and emphasises frequent and casual
music making through a social media-inspired interface. Performers collaborate
by replying to performances, adding new musical layers that are played back at
the same time. Our analysis shows that users tend to focus on the centre and
diagonals of the touchscreen area, and tend to swirl or swipe rather than tap.
We also observe that while long swipes dominate the visual appearance of
performances, the majority of interactions are short with limited expressive
possibilities. Our findings are summarised into a set of design recommendations
for MicroJam and other touchscreen apps for social musical interaction.


  Ranking algorithms are fundamental to various online platforms across
e-commerce sites to content streaming services. Our research addresses the
challenge of adaptively ranking items from a candidate pool for heterogeneous
users, a key component in personalizing user experience. We develop a user
response model that considers diverse user preferences and the varying effects
of item positions, aiming to optimize overall user satisfaction with the ranked
list. We frame this problem within a contextual bandits framework, with each
ranked list as an action. Our approach incorporates an upper confidence bound
to adjust predicted user satisfaction scores and selects the ranking action
that maximizes these adjusted scores, efficiently solved via maximum weight
imperfect matching. We demonstrate that our algorithm achieves a cumulative
regret bound of $O(d\sqrt{NKT})$ for ranking $K$ out of $N$ items in a
$d$-dimensional context space over $T$ rounds, under the assumption that user
responses follow a generalized linear model. This regret alleviates dependence
on the ambient action space, whose cardinality grows exponentially with $N$ and
$K$ (thus rendering direct application of existing adaptive learning algorithms
-- such as UCB or Thompson sampling -- infeasible). Experiments conducted on
both simulated and real-world datasets demonstrate our algorithm outperforms
the baseline.


  We investigate the effects of low-lying fermion eigenmodes on the QCD
partition function in the $\epsilon$-regime. The fermion determinant is
approximated by a truncated product of low-lying eigenvalues of the
overlap-Dirac operator. With two flavors of dynamical quarks, we observe that
the lattice results for the lowest eigenvalue distribution, eigenvalue sum
rules and partition function reproduce the analytic predictions made by
Leutwyler and Smilga, which strongly depend on the topological charge of the
background gauge configuration. The value of chiral condensate extracted from
these measurements are consistent with each other. For one dynamical quark
flavor, on the other hand, we find an apparent disagreement among different
determinations of the chiral condensate, which may suggest the failure of the
$\epsilon$-expansion in the absence of massless Nambu-Goldstone boson.


  Matrix factorization techniques compute low-rank product approximations of
high dimensional data matrices and as a result, are often employed in
recommender systems and collaborative filtering applications. However, many
algorithms for this task utilize an exact least-squares solver whose
computation is time consuming and memory-expensive. In this paper we discuss
and test a block Kaczmarz solver that replaces the least-squares subroutine in
the common alternating scheme for matrix factorization. This variant trades a
small increase in factorization error for significantly faster algorithmic
performance. In doing so we find block sizes that produce a solution comparable
to that of the least-squares solver for only a fraction of the runtime and
working memory requirement.


  The spread of hatred that was formerly limited to verbal communications has
rapidly moved over the Internet. Social media and community forums that allow
people to discuss and express their opinions are becoming platforms for the
spreading of hate messages. Many countries have developed laws to avoid online
hate speech. They hold the companies that run the social media responsible for
their failure to eliminate hate speech. But as online content continues to
grow, so does the spread of hate speech However, manual analysis of hate speech
on online platforms is infeasible due to the huge amount of data as it is
expensive and time consuming. Thus, it is important to automatically process
the online user contents to detect and remove hate speech from online media.
Many recent approaches suffer from interpretability problem which means that it
can be difficult to understand why the systems make the decisions they do.
Through this work, some solutions for the problem of automatic detection of
hate messages were proposed using Support Vector Machine (SVM) and Na\"ive
Bayes algorithms. This achieved near state-of-the-art performance while being
simpler and producing more easily interpretable decisions than other methods.
Empirical evaluation of this technique has resulted in a classification
accuracy of approximately 99% and 50% for SVM and NB respectively over the test
set.
  Keywords: classification; hate speech; feature extraction, algorithm,
supervised learning


  We reveal how symmetry protected nodal points in topological semimetals may
be promoted to pairs of generically stable exceptional points (EPs) by
symmetry-breaking fluctuations at the onset of long-range order. This novel
route to non-Hermitian (NH) topology is exemplified by a magnetic NH Weyl phase
spontaneously emerging at the surface of a strongly correlated
three-dimensional topological insulator when entering the ferromagnetic regime
from a high temperature paramagnetic phase. Here, electronic excitations with
opposite spin acquire significantly different life-times, thus giving rise to
an anti-Hermitian structure in spin that is incompatible with the chiral spin
texture of the nodal surface states, and hence facilitates the spontaneous
formation of EPs. We present numerical evidence of this phenomenon by solving a
microscopic multi-band Hubbard model non-perturbatively in the framework of
dynamical mean-field theory.


  Anisotropic fluids appear in a diverse array of systems, from liquid-crystal
displays to bacterial swarms, and are characterized by orientational order.
Large colloidal particles immersed in such environments disturb the medium's
orientational order, however, resulting in a stored elastic energy within the
bulk. As a consequence, multiple immersed bodies interact at equilibrium
through fluid-mediated forces and torques, which depend on the bodies'
positions, orientations, and shapes. We provide the equilibrium configuration
of a model nematic liquid crystal with multiple immersed bodies or inclusions
in two-dimensions, as well as the associated body forces, torques, and surface
tractions. A complex variables approach is taken which leans on previous work
by Crowdy (2020) for describing solutions with multiply-connected domains. Free
periods of a complex director field, which correspond to topological defect
positioning and net topological charge, are determined numerically to minimize
a global stored elastic energy, including a contribution of a weak (finite)
anchoring strength on the body surfaces. Finally, a general, analytical
description of two-body far-field interactions is provided, along with examples
using two cylindrical inclusions of arbitrary position and size, and two
triangles of arbitrary position and orientation.


  We analyze several properties of the lattice solitons in the incommensurate
phase of spin-Peierls systems using exact diagonalization and quantum Monte
Carlo. These systems are modelled by an antiferromagnetic Heisenberg chain with
nearest and next-nearest neighbor interactions coupled to the lattice in the
adiabatic approximation. Several relations among features of the solitons and
magnetic properties of the system have been determined and compared with
analytical predictions. We have studied in particular the relation between the
soliton width and the spin-Peierls gap. Although this relation has the form
predicted by bosonized field theories, we have found some important
quantitative differences which could be relevant to describe experimental
studies of spin-Peierls materials.


  We establish an analogue of Pontryagin duality for modules over compact
discrete valuation rings $R$. Namely, we define the dual of a topological $R$
module to be its continuous $R$-module homomorphisms into $K/R$, the quotient
module of the fraction field by its ring of integers. It is established that
for locally compact $R$-modules the double dual map is an isomorphism and
homeomorphism. Additionally, given a non-topological $R$-module a canonical
topology is constructed, uniquely defined so that the double dual map will be
injective and continuous. Finally, the functor assigning a module to itself
equipped with canonical topology is shown to be fully faithful, allowing one to
recontextualize the topological statements in purely algebraic forms.


  In this paper, we describe the systems submitted by our IITP-AINLPML team in
the shared task of SocialNLP 2020, EmotionGIF 2020, on predicting the
category(ies) of a GIF response for a given unlabelled tweet. For the round 1
phase of the task, we propose an attention-based Bi-directional GRU network
trained on both the tweet (text) and their replies (text wherever available)
and the given category(ies) for its GIF response. In the round 2 phase, we
build several deep neural-based classifiers for the task and report the final
predictions through a majority voting based ensemble technique. Our proposed
models attain the best Mean Recall (MR) scores of 52.92% and 53.80% in round 1
and round 2, respectively.


  The skyrmion-substrate interaction is an important issue to consider when
studying particle-like skyrmions in nanostructures, which may strongly affect
the skyrmion dynamics and lead to complex dynamic phenomena. Here, we report
the current-driven dynamic behaviors of particle-like skyrmions interacting
with the funnel obstacle in a ferromagnetic layer. We computationally
demonstrate that a funnel obstacle with enhanced perpendicular magnetic
anisotropy can be used to modify the dynamics of skyrmions due to the repulsive
skyrmion-funnel interaction, which could generate several nontrivial effects,
including the compression, clogging, annihilation, guided motion, and capturing
of skyrmions. It is also found that the funnel obstacle can separate an
incoming flow of skyrmions into two asymmetric flows. Moreover, we show that it
is possible to capture skyrmions by the funnel obstacle in both static and
dynamic ways. Our results are useful for understanding the skyrmion dynamics
interacting with the substrate and could provide guidelines for the control of
skyrmion flows in a transmission channel.


  We discuss thermalization of a test particle schematized as a harmonic
oscillator and coupled to a Boltzmann heat bath of finite size and with a
finite bandwidth for the frequencies of its particles. We find that complete
thermalization only occurs when the test particle frequency is within a certain
range of the bath particle frequencies, and for a certain range of mass ratios
between the test particle and the bath particles. These results have
implications for the study of classical and quantum behaviour of high-frequency
nanomechanical resonators.


  Most organizations use large and complex spreadsheets that are embedded in
their mission-critical processes and are used for decision-making purposes.
Identification of the various types of errors that can be present in these
spreadsheets is, therefore, an important control that organizations can use to
govern their spreadsheets. In this paper, we propose a taxonomy for
categorizing qualitative errors in spreadsheet models that offers a framework
for evaluating the readiness of a spreadsheet model before it is released for
use by others in the organization. The classification was developed based on
types of qualitative errors identified in the literature and errors committed
by end-users in developing a spreadsheet model for Panko's (1996) "Wall
problem". Closer inspection of the errors reveals four logical groupings of the
errors creating four categories of qualitative errors. The usability and
limitations of the proposed taxonomy and areas for future extension are
discussed.


  In his extension of W. Siegel's ideas on string quantization, N. Berkovits
made several observations which deserve further study and development. Indeed,
interesting accounts of this work have already appeared in the mathematical
literature and in a different guise due to Avramov. In this paper we bridge
between these three approaches, by providing a complex that is useful in the
calculation of some homologies.


  Navigation process is studied on a variant of the Watts-Strogatz small world
network model embedded on a square lattice. With probability $p$, each vertex
sends out a long range link, and the probability of the other end of this link
falling on a vertex at lattice distance $r$ away decays as $ r^{-\alpha}$.
Vertices on the network have knowledge of only their nearest neighbors. In a
navigation process, messages are forwarded to a designated target. For $\alpha
<3$ and $\alpha \neq 2$, a scaling relation is found between the average actual
path length and $pL$, where $L$ is the average length of the additional long
range links. Given $pL>1$, dynamic small world effect is observed, and the
behavior of the scaling function at large enough $pL$ is obtained. At $\alpha
=2$ and 3, this kind of scaling breaks down, and different functions of the
average actual path length are obtained. For $\alpha >3$, the average actual
path length is nearly linear with network size.


  We investigate a phenomenon of creation of excitations in the homogenous
Bose-Einstein condensate due to an impurity moving with a constant velocity. A
simple model is considered to take into account dynamical effects due to
motions of the impurity. Based on this model, we show that there can be a
finite amount of excitations created even if velocity of the impurity is below
Landau's critical velocity. We also show that the total number of excitations
scales differently for large time across the speed of sound. Thus, our result
dictates the critical behavior across Landau's one and validates Landau's
institution to the problem. We discuss how Landau's critical velocity emerges
and its validity within our model.


  Proximity induced quantum coherence of electrons in multi-terminal
voltage-driven hybrid normal-superconducting nanostructures may result in a
non-trivial interplay between topology-dependent Josephson and Aharonov-Bohm
effects. We elucidate a trade-off between stimulation of the voltage-dependent
Josephson current due to non-equilibrium effects and quantum dephasing of
quasiparticles causing reduction of both Josephson and Aharonov-Bohm currents.
We also predict phase-shifted quantum coherent oscillations of the induced
electrostatic potential as a function of the externally applied magnetic flux.
Our results may be employed for engineering superconducting nanocircuits with
controlled quantum properties.


  We search for three-neutron resonances which were predicted from pion double
charge exchange experiments on He-3. All partial waves up to J=5/2 are
nonresonant except the J=3/2^+ one, where we find a state at E=14 MeV energy
with 13 MeV width. The parameters of the mirror state in the three-proton
system are E=15 MeV and Gamma=14 MeV. The possible existence of an excited
state in the triton, which was predicted from a H(He-6,alpha) experiment, is
also discussed.


  We prove $t^{- \frac 14}-$decay for the solutions of the 1-dim Schrodinger
equation with a one-gap periodic potential as $t \to +\infty $. Generically,
one has $t^{- \frac 13}$-decay and this decay is optimal. Our approach is to
analyze the stationary phase in the Schr\"odinger evolution as an integral
operator.


  Koopman operator theory is shown to be directly related to the
renormalization group. This observation allows us, with no assumption of
translational invariance, to compute the critical exponents $\eta$ and
$\delta$, as well as ratios of critical exponents, of classical spin systems
from single observables alone. This broadens the types of problems that the
renormalization group framework can be applied to and establish universality
classes of. In addition, this connection may allow for a new, data-driven way
in which to find the renormalization group fixed point(s), and their relevant
and irrelevant directions.


  We consider cosmological models where dark energy is described by a dynamical
field equipped with the Chameleon screening mechanism, which serves to hide its
effects in local dense regions and to conform to Solar System observations. In
these models, there is no universal gravitational coupling and here we study
the effective couplings that determine the force between massive objects,
$G_N$, and the propagation of gravitational waves, $G_{gw}$. In particular, we
revisit the Chameleon screening mechanism without neglecting the time
dependence of the galactic environment where local regions are embedded in, and
analyze the induced time evolution on $G_N$ and $G_{gw}$, which can be tested
with Lunar Laser Ranging and direct gravitational waves observations. We
explicitly show how and why these two couplings generically differ. We also
find that due to the particular way the Chameleon screening mechanism works,
their time evolutions are highly suppressed in the weak-field non-relativistic
approximation.


  Solar activity, ranging from the background solar wind to energetic coronal
mass ejections (CMEs), is the main driver of the conditions in the
interplanetary space and in the terrestrial space environment, known as space
weather. A better understanding of the Sun-Earth connection carries enormous
potential to mitigate negative space weather effects with economic and social
benefits. Effective space weather forecasting relies on data and models. In
this paper, we discuss some of the most used space weather models, and propose
suitable locations for data gathering with space weather purposes. We report on
the application of \textit{Representer analysis (RA)} and \textit{Domain of
Influence (DOI) analysis} to three models simulating different stages of the
Sun-Earth connection: the OpenGGCM and Tsyganenko models, focusing on solar
wind - magnetosphere interaction, and the PLUTO model, used to simulate CME
propagation in interplanetary space. Our analysis is promising for space
weather purposes for several reasons. First, we obtain quantitative information
about the most useful locations of observation points, such as solar wind
monitors. For example, we find that the absolute values of the DOI are
extremely low in the magnetospheric plasma sheet. Since knowledge of that
particular sub-system is crucial for space weather, enhanced monitoring of the
region would be most beneficial. Second, we are able to better characterize the
models. Although the current analysis focuses on spatial rather than temporal
correlations, we find that time-independent models are less useful for Data
Assimilation activities than time-dependent models. Third, we take the first
steps towards the ambitious goal of identifying the most relevant heliospheric
parameters for modelling CME propagation in the heliosphere, their arrival
time, and their geoeffectiveness at Earth.


  I compute explicitly the regulator map on $K_4(X)$ for an arbitrary curve $X$
over a number field. Using this and Beilinson's theorem about regulators for
modular curves ([B2]) I prove a formula expressing the value of the
$L$-function $L(E,s)$ of a modular elliptic curve $E$ over $\Bbb Q$ at $s=3$ by
the double Eisenstein-Kronecker series.


  This paper generalises Mori's famous theorem about "Projective manifolds with
ample tangent bundles" to normal projective varieties in the following way:
  A normal projective variety over $\mathbb{C}$ with ample tangent sheaf is
isomorphic to the complex projective space.


  The contribution from hadronic vacuum polarisation effects is responsible for
a large fraction of the theoretical uncertainty in the running of the QED
coupling. The current level of uncertainty has become a limitation for
electroweak precision tests. We use lattice QCD simulations with two flavours
of O$(a)$ improved Wilson fermions to determine the Adler function in a broad
range of the momentum transfer $Q^2$. The running of the QED coupling,
including valence contributions from $u$, $d$, $s$ and $c$ quarks, is compared
to phenomenological results at intermediate $Q^2$ values. In the large $Q^2$
regime, the lattice determination of the Adler function is fitted to
perturbation theory in order to examine the feasibility of a determination of
the strong coupling constant.


  In a wired network, a packet can be transmitted to a specified destination
only, no broadcasting required. But in ad hoc wireless network a packet
transmitted by a node can reach all neighbors due to broadcasting. This
broadcasting introduces unnecessary retransmissions of same message. Therefore,
the total number of transmissions (forward nodes) is generally used as the cost
criterion for broadcasting. The problem of finding the minimum number of
forward nodes is NP-complete. In this paper, the goal is to reduce the number
of forward nodes which will reduce redundant transmission as a result. Thus
some of approximation approaches are analyzed, especially dominant pruning and
total dominant pruning which use 2-hop neighborhood information and a new
approach: Probability based algorithm is proposed with a view to minimizing
number of forward nodes. Simulation results of applying this algorithm shows
performance improvements with compared to dominant pruning and total dominant
pruning algorithms.


  We propose a human-centred explanation method for rule-based automated
decision-making systems in the legal domain. Firstly, we establish a conceptual
framework for developing explanation methods, representing its key internal
components (content, communication and adaptation) and external dependencies
(decision-making system, human recipient and domain). Secondly, we propose an
explanation method that uses a graph database to enable question-driven
explanations and multimedia display. This way, we can tailor the explanation to
the user. Finally, we show how our conceptual framework is applicable to a
real-world scenario at the Dutch Tax and Customs Administration and implement
our explanation method for this scenario.


  We consider a correction to energy levels in a pionic atom induced by the
Uehling potential, i.e., by a free electron vacuum-polarization loop. The
calculation is performed for circular states (l=n-1). The result is obtained in
a closed analytic form as a function of $ Z\alpha$ and the pion-to-electron
mass ratio. Certain asymptotics of the result are also presented.


  A short review of our recent work on amplitude parametrizations of three-body
hadronic weak $B$ and $D$ decays is presented. The final states are here
composed of three light mesons, namely the various charge $\pi\pi\pi$,
$K\pi\pi$ and $KK\bar K$ states. These parametrizations are derived from
previous calculations based on a quasi-two-body factorization approach where
the two-body hadronic final state interactions are fully taken into account in
terms of unitary $S$- and $P$-wave $\pi\pi$, $\pi K$ and $K \bar K$ form
factors. They are an alternative to the isobar-model description and can be
useful in the interpretation of CP asymmetries.


  The integration of large language models (LLMs) with social robots has
emerged as a promising avenue for enhancing human-robot interactions at a time
when news reports generated by artificial intelligence (AI) are gaining in
credibility. This integration is expected to intensify and become a more
productive resource for journalism, media, communication, and education. In
this paper a novel system is proposed that integrates AI's generative
pretrained transformer (GPT) model with the Pepper robot, with the aim of
improving the robot's natural language understanding and response generation
capabilities for enhanced social interactions. By leveraging GPT's powerful
language processing capabilities, this system offers a comprehensive pipeline
that incorporates voice input recording, speech-to-text transcription, context
analysis, and text-to-speech synthesis action generation. The Pepper robot is
enabled to comprehend user queries, generate informative responses with general
knowledge, maintain contextually relevant conversations, and act as a more
domain-oriented news reporter. It is also linked with a news resource and
powered with a Google search capability. To evaluate the performance of the
framework, experiments were conducted involving a set of diverse questions. The
robot's responses were assessed on the basis of eight criteria, including
relevance, context, and fluency. Despite some identified limitations, this
system contributes to the field of journalism and human-robot interaction by
showcasing the potential of integrating LLMs with social robots. The proposed
framework opens up opportunities for improving the conversational capabilities
of robots, enabling interactions that are smoother, more engaging, and more
context aware.


  In this paper we found a new model for compact star with anisotropic matter
distribution considering the new version of Chaplygin fluid equation of state
of Errehymy and Daoud (2021). We specify the particular form of the metric
potential proposed for Thirukanesh and Ragel (2012) and generalized for Malaver
(2014) in order to integrate the Einstein`s field equations. The obtained model
satisfies all physical properties expected in a realistic star. The radial
pressure, energy density, metric coefficients, anisotropy and mass are well
defined and are regular in the stellar interior. The results of this research
can be useful in the development and description of new models of compact
structures.


  Coherent perfect absorption (CPA), also known as time-reversed laser, is a
wave phenomenon resulting from the reciprocity of destructive interference of
transmitted and reflected waves. In this work we consider quasi one-dimensional
lattice networks which posses at least one flat band, and show that CPA and
lasing can be induced in both linear and nonlinear regimes of this lattice by
fine-tuning non-Hermitian defects (dissipative terms localized within one
unit-cell). We show that local dissipations that yield CPA simultaneously yield
novel dissipative compact solutions of the lattice, whose growth or decay in
time can be fine-tuned via the dissipation parameter. The scheme used to
numerically visualize the theoretical findings offers a novel platform for the
experimental implementation of these phenomena in optical devices.


  Interest in spiking neural networks (SNNs) has been growing steadily,
promising an energy-efficient alternative to formal neural networks (FNNs),
commonly known as artificial neural networks (ANNs). Despite increasing
interest, especially for Edge applications, these event-driven neural networks
suffered from their difficulty to be trained compared to FNNs. To alleviate
this problem, a number of innovative methods have been developed to provide
performance more or less equivalent to that of FNNs. However, the spiking
activity of a network during inference is usually not considered. While SNNs
may usually have performance comparable to that of FNNs, it is often at the
cost of an increase of the network's activity, thus limiting the benefit of
using them as a more energy-efficient solution.
  In this paper, we propose to leverage Knowledge Distillation (KD) for SNNs
training with surrogate gradient descent in order to optimize the trade-off
between performance and spiking activity. Then, after understanding why KD led
to an increase in sparsity, we also explored Activations regularization and
proposed a novel method with Logits Regularization. These approaches, validated
on several datasets, clearly show a reduction in network spiking activity
(-26.73% on GSC and -14.32% on CIFAR-10) while preserving accuracy.