  Motivated by recent experimental studies, we derive and analyze a
twodimensional model for the contraction patterns observed in protoplasmic
droplets of Physarum polycephalum. The model couples a model of an active
poroelastic two-phase medium with equations describing the spatiotemporal
dynamics of the intracellular free calcium concentration. The poroelastic
medium is assumed to consist of an active viscoelastic solid representing the
cytoskeleton and a viscous fluid describing the cytosol. The model equations
for the poroelastic medium are obtained from continuum force-balance equations
that include the relevant mechanical fields and an incompressibility relation
for the two-phase medium. The reaction-diffusion equations for the calcium
dynamics in the protoplasm of Physarum are extended by advective transport due
to the flow of the cytosol generated by mechanical stresses. Moreover, we
assume that the active tension in the solid cytoskeleton is regulated by the
calcium concentration in the fluid phase at the same location, which introduces
a chemomechanical feedback. A linear stability analysis of the homogeneous
state without deformation and cytosolic flows exhibits an oscillatory Turing
instability for a large enough mechanochemical coupling strength. Numerical
simulations of the model equations reproduce a large variety of wave patterns,
including traveling and standing waves, turbulent patterns, rotating spirals
and antiphase oscillations in line with experimental observations of
contraction patterns in the protoplasmic droplets.


  Scanning ion conductance microscopy (SICM) can image the surface topography
of specimens in ionic solutions without mechanical probe--sample contact. This
unique capability is advantageous for imaging fragile biological samples but
its highest possible imaging rate is far lower than the level desired in
biological studies. Here, we present the development of high-speed SICM. The
fast imaging capability is attained by a fast Z-scanner with active vibration
control and pipette probes with enhanced ion conductance. By the former, the
delay of probe Z-positioning is minimized to sub-10 us, while its maximum
stroke is secured at 6 um. The enhanced ion conductance lowers a noise floor in
ion current detection, increasing the detection bandwidth up to 100 kHz. Thus,
temporal resolution 100-fold higher than that of conventional systems is
achieved, together with spatial resolution around 20 nm.


  In this letter we apply the methods of our previous paper hep-th/0108045 to
noncommutative fermions. We show that the fermions form a spin-1/2
representation of the Lorentz algebra. The covariant splitting of the conformal
transformations into a field-dependent part and a \theta-part implies the
Seiberg-Witten differential equations for the fermions.


  A novel scheme is proposed for realizing quantum entanglement, quantum
information transfer and a set of universal quantum gates with
superconducting-quantum-interference-device (SQUID) qubits in cavity QED. In
the scheme, the two logical states of a qubit are the two lowest levels of the
SQUID. An intermediate level of the SQUID is utilized to facilitate coherent
control and manipulation of quantum states of the qubits. The method presented
here does not create finite intermediate-level population or cavity-photon
population during the operations. Thus, decoherence due to spontaneous decay
from the intermediate levels is minimized and the requirement on the quality
factor of the cavity is greatly loosened.


  Chirality is inherent to a broad range of systems, including in solid-state
and wave physics. The precession (chiral motion) of electron spins in magnetic
materials, forming spin waves, has various properties and many applications in
magnetism and spintronics. We show that an optical analog of spin waves can be
generated in arrays of plasmonic nanohelices. Such optical waves arise from the
interaction between chiral helix eigenmodes carrying spin angular momentum. We
demonstrate that these optical spin waves are reflected at the interface
between successive domains of enantiomeric nanohelices, forming a heterochiral
lattice, regardless of the wave propagation direction within the lattice.
Optical spin waves may be applied in techniques involving photon spin, ranging
from data processing and storage to quantum optics.


  We quickly approach a "pervasive future" where pervasive computing is the
norm. In this scenario, humans are surrounded by a multitude of heterogeneous
devices that assist them in almost every aspect of their daily routines. The
realization of this future demands strong authentication guarantees to ensure
that these devices are not abused and that their users are not endangered.
However, providing authentication for these systems is a challenging task due
to the high heterogeneity of pervasive computing applications. This
heterogeneity makes it unfeasible to propose a single authentication solution
for all of the pervasive computing applications. In this paper, we review
several pervasive application scenarios and promising authentication methods
for each. To do this, we first identify the key characteristics of each
pervasive application scenario. Then, we review the strengths and weaknesses of
prominent authentication methods from the literature. Finally, we identify
which authentication methods are well suited for each application scenario
based on the identified characteristics. Our goal is to provide promising
directions to be explored for authentication in each of these scenarios.


  A massive black hole resides in the center of most, perhaps all galaxies. The
one in the center of our home galaxy, the Milky Way, provides a uniquely
accessible laboratory for studying in detail the connections and interactions
between a massive black hole and the stellar system in which it grows; for
investigating the effects of extreme density, velocity and tidal fields on
stars; and for using stars to probe the central dark mass and probe
post-Newtonian gravity in the weak- and strong-field limits. Recent results,
open questions and future prospects are reviewed in the wider context of the
theoretical framework and physical processes that underlie them.
  Contents: [1] Introduction (1.1) Astrophysical context (1.2) Science
questions (1.3) Scope and connections to related topics [2] Observational
overview: Stars in the Galactic center (2.1) The central 100 parsecs (2.2) The
central parsec [3] Stellar dynamics at extreme densities (3.1) Physical
processes and scales (3.2) The stellar cusp in the Galactic center (3.3) Mass
segregation (3.4) Stellar Collisions [4] Probing the dark mass with stellar
dynamics (4.1) Weighing and pinpointing the dark mass (4.2) Constraints on
non-BH dark mass alternatives (4.3) Limits on MBH binarity (4.4) High-velocity
runaway stars [5] Probing post-Newtonian gravity near the MBH (5.1)
Relativistic orbital effects (5.2) Gravitational lensing [6] Strong star-MBH
interactions (6.1) Tidal disruption (6.2) Dissipative interactions with the MBH
[7] The riddle of the young stars (7.1) The difficulties of forming or
importing stars near a MBH (7.2) Proposed solutions (7.3) Feeding the MBH with
stellar winds [8] Outlook (8.1) Progress report (8.2) Future directions


  In this paper the determination of material properties such as Sieverts'
constant (solubility) and diffusivity (transport rate) via so-called gas
release experiments is discussed. In order to simulate the time-dependent
hydrogen fluxes and concentration profiles efficiently, we make use of an
analytical method, namely we provide an analytical solution for the
corresponding diffusion equations on a cylindrical specimen and a cylindrical
container for three boundary conditions. These conditions occur in three phases
-- loading phase, evacuation phase and gas release phase. In the loading phase
the specimen is charged with hydrogen assuring a constant partial pressure of
hydrogen. Then the gas will be quickly removed by a vacuum pump in the second
phase, and finally in the third time interval, the hydrogen is released from
the specimen to the gaseous phase, where the pressure increase will be measured
by an equipment which is attached to the cylindrical container. The
investigated diffusion equation in each phase is a simple homogeneous equation,
but due to the complex time-dependent boundary conditions which include the
Sieverts' constant and the pressure, we transform the homogeneous equations to
the non-homogeneous ones with a zero Dirichlet boundary condition. Compared
with the time consuming numerical methods our analytical approach has an
advantage that the flux of desorbed hydrogen can be explicitly given and
therefore can be evaluated efficiently. Our analytical solution also assures
that the time-dependent boundary conditions are exactly satisfied and
furthermore that the interaction between specimen and container is correctly
taken into account.


  Lower limbs exoskeletons provide assistance during standing, squatting, and
walking. Gait dynamics, in particular, implies a change in the configuration of
the device in terms of contact points, actuation, and system dynamics in
general. In order to provide a comfortable experience and maximize performance,
the exoskeleton should be controlled smoothly and in a transparent way, which
means respectively, minimizing the interaction forces with the user and jerky
behavior due to transitions between different configurations. A previous study
showed that a smooth control of the exoskeleton can be achieved using a gait
phase segmentation based on joint kinematics. Such a segmentation system can be
implemented as linear regression and should be personalized for the user after
a calibration procedure. In this work, a nonlinear segmentation function based
on neural networks is implemented and compared with linear regression. An
on-line implementation is then proposed and tested with a subject.


  We study the behavior of the fermion propagator in an external time dependent
potential in 0+1 dimension. We show that, when the potential has upto quadratic
terms in time, the propagator can be expressed in terms of generalized Airy
functions (or standard Airy functions depending on the exact time dependence).
We study various properties of these new generalized functions which reduce to
the standard Airy functions in a particular limit.


  Suppose that $F$ is a free group and $k$ is a natural number. We show that
the fully compressed membership problem for $k$-generated subgroups of $F$ is
solvable in polynomial time. In order to do this, we adapt the theory of
Stallings' foldings to handle edges with compressed labels. This partially
answers a question of Markus Lohrey.


  Given a nonlinear matrix-valued function $F(\lambda)$ and approximate
eigenpairs $(\lambda_i, v_i)$, we discuss how to determine the smallest
perturbation $\delta F$ such that $[F + \delta F](\lambda_i) v_i = 0$; we call
the distance between the $F$ and $F + \delta F$ the backward error for this set
of approximate eigenpairs. We focus on the case where $F(\lambda)$ is given as
a linear combination of scalar functions multiplying matrix coefficients $F_i$,
and the perturbation is done on the matrix coefficients. We provide inexpensive
upper bounds, and a way to accurately compute the backward error by means of
direct computations or through Riemannian optimization. We also discuss how the
backward error can be determined when the $F_i$ have particular structures
(such as symmetry, sparsity, or low-rank), and the perturbations are required
to preserve them. For special cases (such as for symmetric coefficients),
explicit and inexpensive formulas to compute the $\delta F_i$ are also given.


  In this paper, we study a class of discrete Morse functions, coming from
Discrete Morse Theory, that are equivalent to a class of simplicial stacks,
coming from Mathematical Morphology. We show that, as in Discrete Morse Theory,
we can see the gradient vector field of a simplicial stack (seen as a discrete
Morse function) as the only relevant information we should consider. Last, but
not the least, we also show that the Minimum Spanning Forest of the dual graph
of a simplicial stack is induced by the gradient vector field of the initial
function. This result allows computing a watershed-cut from a gradient vector
field.


  Many academic studies in the field of software testing rely on mutation
testing to use as their comparison criteria. However, recent studies have shown
that redundant mutants have a significant effect on the accuracy of their
results. One solution to this problem is to use mutant subsumption to detect
redundant mutants. Therefore, in order to facilitate research in this field, a
mutation testing tool that is capable of detecting redundant mutants is needed.
In this paper, we describe how we improved our tool, LittleDarwin, to fulfill
this requirement.


  We obtain game-theoretic characterizations for meagerness and rareness of
filters on a countable set.


  We present a sample of 115 very low optical surface brightness, highly
extended, HI-rich galaxies carefully selected from the ALFALFA survey that have
similar optical absolute magnitudes, surface brightnesses, and radii to
recently discovered "ultra-diffuse" galaxies (UDGs). However, these systems are
bluer and have more irregular morphologies than other UDGs, are isolated, and
contain significant reservoirs of HI. We find that while these sources have
normal star formation rates for HI selected galaxies of similar stellar mass,
they have very low star formation efficiencies. We further present deep optical
and HI synthesis follow up imaging of three of these HI-bearing ultra-diffuse
sources. We measure HI diameters extending to ~40 kpc, but note that while all
three sources have large HI diameters for their stellar mass, they are
consistent with the HI mass - HI radius relation. We further analyze the HI
velocity widths and rotation velocities for the unresolved and resolved sources
respectively, and find that the sources appear to inhabit halos of dwarf
galaxies. We estimate spin parameters, and suggest that these sources may exist
in high spin parameter halos, and as such may be potential HI-rich progenitors
to the ultra-diffuse galaxies observed in cluster environments.


  Some well-known Lorentzian concepts are transferred into the more general
setting of cone structures, which provide both the causality of the spacetime
and the notion of cone geodesics without making use of any metric. Lightlike
hypersurfaces are defined within this framework, showing that they admit a
unique folitation by cone geodesics. This property becomes crucial after
proving that, in globally hyperbolic spacetimes, achronal boundaries are
lightlike hypersurfaces under some restrictions, allowing one to easily obtain
some time-minimization properties of cone geodesics among causal curves
departing from a hypersurface of the spacetime.


  Quantum wells made of simple polyvalent metals represent a novel family of
doped 2D Mott-Hubbard insulators. As scanning tunneling microscopy experiments
show, these systems exhibit an anisotropic form of metal-insulator transition.
Their elementary excitations possess coherent wave-like properties along the
normal axis, and show an incoherent behavior in-plane. The development of such
an anisotropic coherence is most likely related to Coulomb interaction between
localized and delocalized thin film electronic states - 2D Kondo screening.


  The DDoS attack is a serious threat to Internet of Things (IoT). As a new
class of DDoS attack, Link-flooding attack (LFA) disrupts connectivity between
legitimate IoT devices and target servers by flooding only a small number of
links. In this paper, we propose an active LFA mitigation mechanism, called
Linkbait, that is a proactive and preventive defense to throttle LFA for IoT.
We propose a link obfuscation algorithm in Linkbait that selectively reroutes
probing flows to hide target links from adversaries and mislead them to
identify bait links as target links. To block attack traffic and further reduce
the impact in IoT, we propose a compromised IoT devices detection algorithm
that extracts unique traffic patterns of LFA for IoT and leverages support
vector machine (SVM) to identify attack traffic. We evaluate the performance of
Linkbait by using both real-world experiments and large-scale simulations. The
experimental results demonstrate the effectiveness of Linkbait.


  The globular cluster HP~1 is projected at only 3.33 degrees from the Galactic
center. Together with its distance, this makes it one of the most central
globular clusters in the Milky Way. It has a blue horizontal branch (BHB) and a
metallicity of [Fe/H]~-1.0. This means that it probably is one of the oldest
objects in the Galaxy. Abundance ratios can reveal the nucleosynthesis pattern
of the first stars as well as the early chemical enrichment and early formation
of stellar populations. High-resolution spectra obtained for six stars were
analyzed to derive the abundances of the light elements C, N, O, Na, and Al,
the alpha-elements Mg, Si, Ca, and Ti, and the heavy elements Sr, Y , Zr, Ba,
La, and Eu.} High-resolution spectra of six red giants that are confirmed
members of the bulge globular cluster HP~1 were obtained with the 8m VLT
UT2-Kueyen telescope with the UVES spectrograph in FLAMES-UVES configuration.
The spectroscopic parameter derivation was based on the excitation and
ionization equilibrium of FeI and FeII. We confirm a mean metallicity of [Fe/H]
= -1.06~0.10, by adding the two stars that were previously analyzed in HP~1.
The alpha-elements O and Mg are enhanced by about +0.3<[O,Mg/Fe]<+0.5 dex, Si
is moderately enhanced with +0.15<[Si/Fe]<+0.35dex, while Ca and Ti show lower
values of -0.04<[Ca,Ti/Fe]<+0.28dex. The r-element Eu is also enhanced with
[Eu/Fe]~+0.4, which together with O and Mg is indicative of early enrichment by
type II supernovae. Na and Al are low, but it is unclear if Na-O are
anticorrelated. The heavy elements are moderately enhanced, with
-0.20<[La/Fe]<+0.43dex and 0.0<[Ba/Fe]<+0.75~dex, which is compatible with
r-process formation. The spread in Y, Zr, Ba, and La abundances, on the other
hand, appears to be compatible with the spinstar scenario or other additional
mechanisms such as the weak r-process.


  We investigate Landau-Ginzburg string theory with the singular superpotential
X^{-1} on arbitrary Riemann surfaces. This theory, which is a topological
version of the c=1 string at the self-dual radius, is solved using results from
intersection theory and from the analysis of matter Landau-Ginzburg systems,
and consistency requirements. Higher-genus amplitudes decompose as a sum of
contributions from the bulk and the boundary of moduli space. These amplitudes
generate the W-infinity algebra.


  We analyze the uncertainties induced by different definitions of the momentum
fraction $z$ in the application of gluon to heavy quarkonium fragmentation
function. We numerically calculate the initial $g \to J / \psi$ fragmentation
functions by using the non-covariant definitions of $z$ with finite gluon
momentum and find that these fragmentation functions have strong dependence on
the gluon momentum $\vec{k}$. As $| \vec{k} | \to \infty$, these fragmentation
functions approach to the fragmentation function in the light-cone definition.
Our numerical results show that large uncertainties remains while the
non-covariant definitions of $z$ are employed in the application of the
fragmentation functions. We present for the first time the polarized gluon to
$J/\psi$ fragmentation functions, which are fitted by the scheme exploited in
this work.


  We provide a survey of the current state of the study of diagonals of
operators, especially selfadjoint operators. In addition, we provide a few new
results made possible by recent work of M\"uller-Tomilov and Kaftal-Loreaux.
This is an expansion of the second author's lecture part II at OT27.


  We study distributions of differences of unscaled Riemann zeta zeros,
$\gamma-\gamma^{'}$, at large. We show, that independently of the location of
the zeros, i.e., even for zeros as high as $10^{23}$, their differences have
similar statistical properties. The distributions of differences are skewed
usually towards the nearest zeta zero. We show, however, that this is not
always the case, but depends upon the distance and number of nearby zeros on
each side of the corresponding distribution. The skewness, however, always
decreases when zeta zero is crossed from left to right, i.e., in increasing
direction. Furthermore, we show that the variance of distributions has local
maximum or, at least, a turning point at every zeta zero, i.e., local minimum
of the second derivative of the variance. In addition, it seems that the higher
the zeros the more compactly the distributions of the differences are located
in the skewness-kurtosis -plane. Furthermore, we show that distributions can be
fitted with Johnson probability density function, despite the value of skewness
or kurtosis of the distribution.


  Composite joints with high-performance close to the parent materials of
aluminum matrix composites were fabricated by a new joining technology assisted
by ultrasonic vibration. We found both the microstructure and the mechanical
performance were systematically dependent on the volume fraction and the
distribution of reinforcement particles in the bond region. This study can be
generalized to the bonding of other ceramic-reinforced metal matrix composites.


  Group field theories are particular quantum field theories defined on D
copies of a group which reproduce spin foam amplitudes on a space-time of
dimension D. In these lecture notes, we present the general construction of
group field theories, merging ideas from tensor models and loop quantum
gravity. This lecture is organized as follows. In the first section, we present
basic aspects of quantum field theory and matrix models. The second section is
devoted to general aspects of tensor models and group field theory and in the
last section we examine properties of the group field formulation of BF theory
and the EPRL model. We conclude with a few possible research topics, like the
construction of a continuum limit based on the double scaling limit or the
relation to loop quantum gravity through Schwinger-Dyson equations


  We study the effect of separate chemical and kinetic freeze-outs to the ideal
hydrodynamical flow in Au+Au collisions at RHIC (sqrt(s_NN) = 200 GeV energy).
Unlike in earlier studies we explore how these effects can be counteracted by
changes in the initial state of the hydrodynamical evolution. We conclude that
the reproduction of pion, proton and antiproton yields necessitates a chemical
freeze-out temperature of T = 150 MeV instead of T = 160 - 170 MeV motivated by
thermal models. Unlike previously reported, this lower temperature makes it
possible to reproduce the p_T-spectra of hadrons if one assumes very small
initial time, tau_0 = 0.2 fm/c. However, the p_T-differential elliptic flow,
v_2(p_T) remains badly reproduced. This points to the need to include
dissipative effects (viscosity) or some other refinement to the model.


  We consider the minimizers of $L^{2}$-critical inhomogeneous variational
problems with a spatially decaying nonlinear term in an open bounded domain
$\Omega$ of $\mathbb{R}^{N}$ which contains $0$. We prove that there is a
threshold $a^{*}>0$ such that minimizers exist for $0<a<a^{*}$ and the
minimizer does not exist for any $a>a^{*}$. In contrast to the homogeneous
case, we show that both the existence and nonexistence of minimizers may occur
at the threshold $a^*$ depending on the value of $V(0)$, where $V(x)$ denotes
the trapping potential. Moreover, under some suitable assumptions on $V(x)$,
based on a detailed analysis on the concentration behavior of minimizers as
$a\nearrow a^*$, we prove local uniqueness of minimizers when $a$ is close
enough to $a^*$.


  We tackle the problem of building adaptive estimation procedures for
ill-posed inverse problems. For general regularization methods depending on
tuning parameters, we construct a penalized method that selects the optimal
smoothing sequence without prior knowledge of the regularity of the function to
be estimated. We provide for such estimators oracle inequalities and optimal
rates of convergence. This penalized approach is applied to Tikhonov
regularization and to regularization by projection.


  Using the Atacama Large Millimeter/submillimeter Array, we have made the
first high spatial and spectral resolution observations of the molecular gas
and dust in the prototypical blue compact dwarf galaxy II Zw 40. The CO(2-1)
and CO(3-2) emission is clumpy and distributed throughout the central
star-forming region. Only one of eight molecular clouds has associated star
formation. The continuum spectral energy distribution is dominated by free-free
and synchrotron: at 870$\mu m$, only 50% of the emission is from dust. We
derive a CO-to-H$_2$ conversion factor by several methods including a new
method that uses simple photodissocation models and resolved CO line intensity
measurements to derive a relationship that uniquely predicts $\alpha_{CO}$ for
a given metallicity. We find that the CO-to-H$_2$ conversion factor is 4 to 35
times that of the Milky Way (18.1 to 150.5 M$_\odot$ / (K km/s pc$^2$)). The
star formation efficiency of the molecular gas at least 10 times higher than
that found in normal spiral galaxies, which is likely due to the
burst-dominated star formation history of II Zw 40 rather than an intrinsically
higher efficiency. The molecular clouds within II Zw 40 resemble those in other
strongly interacting systems like the Antennae: overall they have high
size-linewidth coefficients and molecular gas surface densities. These
properties appear to be due to the high molecular gas surface densities
produced in this merging system rather than to increased external pressure.
Overall, these results paint a picture of II Zw 40 as a complex, rapidly
evolving system whose molecular gas properties are dominated by the large-scale
gas shocks from its on-going merger.


  The empirical Bayes (EB) method based on parametric statistical models such
as the negative binomial (NB) has been widely used for ranking sites in road
network safety screening process. This paper is the continuation of the authors
previous research, where a novel non-parametric EB method for modelling crash
frequency data data based on Conditional Generative Adversarial Networks (CGAN)
was proposed and evaluated over several simulated crash data sets. Unlike
parametric approaches, there is no need for a pre-specified underlying
relationship between dependent and independent variables in the proposed
CGAN-EB and they are able to model any types of distributions. The proposed
methodology is now applied to a real-world data set collected for road segments
from 2012 to 2017 in Washington State. The performance of CGAN-EB in terms of
model fit, predictive performance and network screening outcomes is compared
with the conventional approach (NB-EB) as a benchmark. The results indicate
that the proposed CGAN-EB approach outperforms NB-EB in terms of prediction
power and hotspot identification tests.


  This paper presents a theoretical description of both the valley Zeeman
effect (g-factors) and Landau levels in two-dimensional H-phase transition
metal dichalcogenides (TMDs) using the Luttinger-Kohn approximation with
spin-orbit coupling. At the valley extrema in TMDs, energy bands split into
Landau levels with a Zeeman shift in the presence of a uniform out-of-plane
external magnetic field. The Landau level indices are symmetric in the $K$ and
$K'$ valleys. We develop a numerical approach to compute the single band
g-factors from first principles without the need for a sum over unoccupied
bands. Many-body effects are included perturbatively within the GW
approximation. Non-local exchange and correlation self-energy effects in the GW
calculations increase the magnitude of single band g-factors compared to those
obtained from density functional theory. Our first principles results give
spin- and valley-split Landau levels, in agreement with recent optical
experiments. The exciton g-factors deduced in this work are also in good
agreement with experiment for the bright and dark excitons in monolayer
WSe$_2$, as well as the lowest-energy bright excitons in MoSe$_2$-WSe$_2$
heterobilayers with different twist angles.


  Subgraph queries also known as subgraph isomorphism search is a fundamental
problem in querying graph-like structured data. It consists to enumerate the
subgraphs of a data graph that match a query graph. This problem arises in many
real-world applications related to query processing or pattern recognition such
as computer vision, social network analysis, bioinformatic and big data
analytic. Subgraph isomorphism search knows a lot of investigations and
solutions mainly because of its importance and use but also because of its
NP-completeness. Existing solutions use filtering mechanisms and optimise the
order within witch the query vertices are matched on the data vertices to
obtain acceptable processing times. However, existing approaches are iterative
and generate several intermediate results. They also require that the data
graph is loaded in main memory and consequently are not adapted to large graphs
that do not fit into memory or are accessed by streams. To tackle this problem,
we propose a new approach based on concepts widely different from existing
works. Our approach distills the semantic and topological information that
surround a vertex into a simple integer. This simple vertex encoding that can
be computed and updated incrementally reduces considerably intermediate results
and avoid to load the entire data graph into main memory. We evaluate our
approach on several real-word datasets. The experimental results show that our
approach is efficient and scalable.


  In the LHC searches for gluinos it is usually assumed that they decay
predominantly into the lightest neutralino plus jets. In this work we perform a
proof-of-concept collider analysis of a novel supersymmetric signal in which
gluinos decay mostly into jets and the bino-like neutralino ($\tilde\chi_3^0$),
which in turn decays into the lightest Higgsino-like neutralino
($\tilde\chi_1^0$), considered the dark matter candidate, together with the
SM-like Higgs boson ($h$). This new physics signal then consists of an LHC
final state made up by four light jets, four $b$-jets, and a large amount of
missing transverse energy. We identify $t \bar t$, $V$+jets ($V$= $W$, $Z$),
and $t \bar t + X$ ($X$ = $W$, $Z$, $\gamma^*$, $h$) productions as the most
problematic backgrounds, and develop a search strategy for the high luminosity
phase of the LHC, reaching signal significances at the evidence level for a
luminosity of 1000 fb$^{-1}$. The prospects for a luminosity of 3000 fb$^{-1}$
are even more promising, with discovery-level significances.


  I suggest to consider a proton as a body in a state of free precession. Such
approach allows to define a proton as periodic system with two rotary degrees
of freedom with corresponding frequency ratio and resonances. In result on a
power scale the points corresponding to a birth of leptons are designated. The
interrelation between masses of leptons is established through a fine structure
constant. The given approach is distributed to a nucleus. Other representation
is given on the nature of X-rays which connect with a charge of a nucleus and
its mass.


  Consistently checking the statistical significance of experimental results is
the first mandatory step towards reproducible science. This paper presents a
hitchhiker's guide to rigorous comparisons of reinforcement learning
algorithms. After introducing the concepts of statistical testing, we review
the relevant statistical tests and compare them empirically in terms of false
positive rate and statistical power as a function of the sample size (number of
seeds) and effect size. We further investigate the robustness of these tests to
violations of the most common hypotheses (normal distributions, same
distributions, equal variances). Beside simulations, we compare empirical
distributions obtained by running Soft-Actor Critic and Twin-Delayed Deep
Deterministic Policy Gradient on Half-Cheetah. We conclude by providing
guidelines and code to perform rigorous comparisons of RL algorithm
performances.


  Dynamic vision sensor (DVS) event camera output is affected by noise,
particularly in dim lighting conditions. A theory explaining how photon and
electron noise affect DVS output events has so far not been developed.
Moreover, there is no clear understanding of how DVS parameters and operating
conditions affect noise. There is an apparent paradox between the real noise
data observed from the DVS output and the reported noise measurements of the
logarithmic photoreceptor. While measurements of the logarithmic photoreceptor
predict that the photoreceptor is approximately a first-order system with RMS
noise voltage independent of the photocurrent, DVS output shows higher noise
event rates at low light intensity. This paper unravels this paradox by showing
how the DVS photoreceptor is a second-order system, and the assumption that it
is first-order is generally not reasonable. As we show, at higher
photocurrents, the photoreceptor amplifier dominates the frequency response,
causing a drop in RMS noise voltage and noise event rate. We bring light to the
noise performance of the DVS photoreceptor by presenting a theoretical
explanation supported by both transistor-level simulation results and chip
measurements.


  We predict several new bound states and resonances for B-pion, B-Kaon, and
B-D S-wave elastic scattering, within the same model which was recently
employed to explain the D*sJ(2317)+ meson. In the charm-nonstrange sector we
find a (virtual ?) bound state close to the B-pion threshold, and moreover two
nearby resonances of comparable width in the range 5.9 to 6.1 GeV. We obtain
bound states B*s0(5570) in b-sbar and B*c0(6490) in bbar-c, as well as
resonances at higher energies. No JP=0+ states are found at 5.6 GeV (in
bbar-u/d), at 5.7 GeV (in b-sbar), or at 6.1 GeV (in bbar-c), where they are
predicted from pure confinement.


  Optical implementation of artificial neural networks has been attracting
great attention due to its potential in parallel computation at speed of light.
Although all-optical deep neural networks (AODNNs) with a few neurons have been
experimentally demonstrated with acceptable errors recently, the feasibility of
large scale AODNNs remains unknown because error might accumulate inevitably
with increasing number of neurons and connections. Here, we demonstrate a
scalable AODNN with programmable linear operations and tunable nonlinear
activation functions. We verify its scalability by measuring and analyzing
errors propagating from a single neuron to the entire network. The feasibility
of AODNNs is further confirmed by recognizing handwritten digits and fashions
respectively.


  Based on the time-dependent density-functional theory, we have derived a
rigorous formula for the stopping power of an {\it interacting} electron gas
for ions in the limit of low projectile velocities. If dynamical correlation
between electrons is not taken into account, this formula recovers the
corresponding stopping power of {\it noninteracting} electrons in an effective
Kohn-Sham potential. The correlation effect, specifically the excitonic one in
electron-hole pair excitations, however, is found to considerably enhance the
stopping power for intermediately charged ions, bringing our theory into good
agreement with experiment.


  There is significant correlation between main development region sea surface
temperature and the number of hurricanes that form in the Atlantic basin. The
correlation between the same sea surface temperatures and the number of
\emph{landfalling} hurricanes is much lower, however. Why is this? Do we need
to consider complex physical hypotheses, or is there a simple statistical
explanation?


  Active galactic nuclei (AGN) are one of the most luminous objects in the
Universe, emitting powerful continuum and line emission across all wavelength
bands. They represent an important link in the investigations of the galaxy
evolution and cosmology. The resolving of the AGN inner structure is still a
difficult task with current instruments, therefore the spectroscopy and
spectropolarimetry are crucial tools to investigate these objects and their
components, such as the properties of the supermassive black hole, the broad
line region, and the dusty torus. In this review, we present the results of the
project "Astrophysical spectroscopy of extragalactic objects", from the
observations, data processing and analysis, to the modelling of different
regions in AGN.


  Filters that act by adsorbing contaminant onto their pore walls will
experience a decrease in porosity over time, and may eventually block. As
adsorption will generally be larger towards the entrance of a filter, where the
concentration of contaminant particles is higher, these effects can also result
in a spatially varying porosity. We investigate this dynamic process using an
extension of homogenization theory that accounts for a macroscale variation in
microstructure. We formulate and homogenize the coupled problems of flow
through a filter with a near-periodic time-dependent microstructure, solute
transport due to advection, diffusion, and filter adsorption, and filter
structure evolution due to the adsorption of contaminant. We use the
homogenized equations to investigate how the contaminant removal and filter
lifespan depend on the initial porosity distribution for a unidirectional flow.
We confirm a conjecture made in Dalwadi et al. (2015) that filters with an
initially negative porosity gradient have a longer lifespan and remove more
contaminant than filters with an initially constant porosity, or worse, an
initially positive porosity gradient. Additionally, we determine which initial
porosity distributions result in a filter that will block everywhere at once by
exploiting an asymptotic reduction of the homogenized equations. We show that
these filters remove more contaminant than other filters with the same initial
average porosity, but that filters which block everywhere at once are limited
by how large their initial average porosity can be.


  We present some of the latest results from our numerical investigations of
N=4 supersymmetric Yang--Mills theory formulated on a space-time lattice. Based
on a construction that exactly preserves a single supersymmetry at non-zero
lattice spacing, we recently developed an improved lattice action that is now
being employed in large-scale calculations. Here we update our studies of the
static potential using this new action, also applying tree-level lattice
perturbation theory to improve the analysis of the potential itself.
Considering relatively weak couplings, we obtain results for the Coulomb
coefficient that are consistent with continuum perturbation theory.


  Measurements of the electroweak production of a $W$ boson in association with
two jets at high dijet invariant mass are performed using $\sqrt{s} = 7$ and
$8$ TeV proton-proton collision data produced by the Large Hadron Collider,
corresponding respectively to 4.7 and 20.2 fb$^{-1}$ of integrated luminosity
collected by the ATLAS detector. The measurements are sensitive to the
production of a $W$ boson via a triple-gauge-boson vertex and include both the
fiducial and differential cross sections of the electroweak process.


  The one-dimensional XXZ model is studied in the presence of disorder in the
Heisenberg Exchange Integral. Recent predictions obtained from renormalization
group calculations are investigated numerically using a Lanczos algorithm on
chains of up to 18 sites. It is found that in the presence of strong
X-Y-symmetric random exchange couplings, a ``random singlet'' phase with
quasi-long-range order in the spin-spin correlations persists. As the planar
anisotropy is varied, the full zero-temperature phase diagram is obtained and
compared with predictions of Doty and Fisher [Phys. Rev. B {\bf 45 }, 2167
(1992)].


  Deep neural networks have been demonstrated to achieve phenomenal success in
many domains, and yet their inner mechanisms are not well understood. In this
paper, we investigate the curvature of image manifolds, i.e., the manifold
deviation from being flat in its principal directions. We find that
state-of-the-art trained convolutional neural networks for image classification
have a characteristic curvature profile along layers: an initial steep
increase, followed by a long phase of a plateau, and followed by another
increase. In contrast, this behavior does not appear in untrained networks in
which the curvature flattens. We also show that the curvature gap between the
last two layers has a strong correlation with the generalization capability of
the network. Moreover, we find that the intrinsic dimension of latent codes is
not necessarily indicative of curvature. Finally, we observe that common
regularization methods such as mixup yield flatter representations when
compared to other methods. Our experiments show consistent results over a
variety of deep learning architectures and multiple data sets. Our code is
publicly available at https://github.com/azencot-group/CRLM


  This paper proposes an approach to automatically categorize the social
interactions of a user wearing a photo-camera 2fpm, by relying solely on what
the camera is seeing. The problem is challenging due to the overwhelming
complexity of social life and the extreme intra-class variability of social
interactions captured under unconstrained conditions. We adopt the
formalization proposed in Bugental's social theory, that groups human relations
into five social domains with related categories. Our method is a new deep
learning architecture that exploits the hierarchical structure of the label
space and relies on a set of social attributes estimated at frame level to
provide a semantic representation of social interactions. Experimental results
on the new EgoSocialRelation dataset demonstrate the effectiveness of our
proposal.


  Relation triple extraction (RTE) is an essential task in information
extraction and knowledge graph construction. Despite recent advancements,
existing methods still exhibit certain limitations. They just employ
generalized pre-trained models and do not consider the specificity of RTE
tasks. Moreover, existing tagging-based approaches typically decompose the RTE
task into two subtasks, initially identifying subjects and subsequently
identifying objects and relations. They solely focus on extracting relational
triples from subject to object, neglecting that once the extraction of a
subject fails, it fails in extracting all triples associated with that subject.
To address these issues, we propose BitCoin, an innovative Bidirectional
tagging and supervised Contrastive learning based joint relational triple
extraction framework. Specifically, we design a supervised contrastive learning
method that considers multiple positives per anchor rather than restricting it
to just one positive. Furthermore, a penalty term is introduced to prevent
excessive similarity between the subject and object. Our framework implements
taggers in two directions, enabling triples extraction from subject to object
and object to subject. Experimental results show that BitCoin achieves
state-of-the-art results on the benchmark datasets and significantly improves
the F1 score on Normal, SEO, EPO, and multiple relation extraction tasks.


  The statistical analysis of massive and complex data sets will require the
development of algorithms that depend on distributed computing and
collaborative inference. Inspired by this, we propose a collaborative framework
that aims to estimate the unknown mean $\theta$ of a random variable $X$. In
the model we present, a certain number of calculation units, distributed across
a communication network represented by a graph, participate in the estimation
of $\theta$ by sequentially receiving independent data from $X$ while
exchanging messages via a stochastic matrix $A$ defined over the graph. We give
precise conditions on the matrix $A$ under which the statistical precision of
the individual units is comparable to that of a (gold standard) virtual
centralized estimate, even though each unit does not have access to all of the
data. We show in particular the fundamental role played by both the non-trivial
eigenvalues of $A$ and the Ramanujan class of expander graphs, which provide
remarkable performance for moderate algorithmic cost.


  The reproducibility of academic research has long been a persistent issue,
contradicting one of the fundamental principles of science. What is even more
concerning is the increasing number of false claims found in academic
manuscripts recently, casting doubt on the validity of reported results. In
this paper, we utilize an adaptive version of Benford's law, a statistical
phenomenon that describes the distribution of leading digits in naturally
occurring datasets, to identify potential manipulation of results in research
manuscripts, solely using the aggregated data presented in those manuscripts.
Our methodology applies the principles of Benford's law to commonly employed
analyses in academic manuscripts, thus, reducing the need for the raw data
itself. To validate our approach, we employed 100 open-source datasets and
successfully predicted 79% of them accurately using our rules. Additionally, we
analyzed 100 manuscripts published in the last two years across ten prominent
economic journals, with ten manuscripts randomly sampled from each journal. Our
analysis predicted a 3% occurrence of result manipulation with a 96% confidence
level. Our findings uncover disturbing inconsistencies in recent studies and
offer a semi-automatic method for their detection.


  When using boundary integral equation methods, we represent solutions of a
linear partial differential equation as layer potentials. It is well-known that
the approximation of layer potentials using quadrature rules suffer from poor
resolution when evaluated closed to (but not on) the boundary. To address this
challenge, we provide modified representations of the problem's solution.
Similar to Gauss's law used to modify Laplace's double-layer potential, we use
modified representations of Laplace's single-layer potential and Helmholtz
layer potentials that avoid the close evaluation problem. Some techniques have
been developed in the context of the representation formula or using
interpolation techniques. We provide alternative modified representations of
the layer potentials directly (or when only one density is at stake). Several
numerical examples illustrate the efficiency of the technique in two and three
dimensions.


  We view sorites in terms of stimuli acting upon a system and evoking this
system's responses. Supervenience of responses on stimuli implies that they
either lack tolerance (i.e., they change in every vicinity of some of the
stimuli), or stimuli are not always connectable by finite chains of stimuli in
which successive members are `very similar'. If supervenience does not hold,
the properties of tolerance and connectedness cannot be formulated and
therefore soritical sequences cannot be constructed. We hypothesize that
supervenience in empirical systems (such as people answering questions) is
fundamentally probabilistic. The supervenience of probabilities of responses on
stimuli is stable, in the sense that `higher-order' probability distributions
can always be reduced to `ordinary' ones. In making rules about which stimuli
ought to correspond to which responses, the main characterization of choices in
soritical situations is their arbitrariness. We argue that arbitrariness poses
no problems for classical logic.


  We study the critical energy and magnetization profiles for the Ising quantum
chain with a marginal extended surface perturbation of the form A/y, y being
the distance from the surface (Hilhorst-van Leeuwen model). For weak local
couplings, A<A_c, the model displays a continuous surface transition with
A-dependent exponents, whereas, for A>A_c, there is surface order at the bulk
critical point. If conformal invariance is assumed to hold with such marginal
perturbations, it predicts conformal profiles with the same scaling form as for
the unperturbed quantum chain, with marginal surface exponents replacing the
unperturbed ones. The results of direct analytical and numerical calculations
of the profiles confirm the validity of the conformal expressions in the
regimes of second- and first-order surface transitions.


  We introduce the notions of deformation Higgs bundle and Riemann-Finsler
metric on the moduli space of polarized varieties. We also use the Higgs-de
Rham flow in the p-adic setting. These are the key novelties in our program.


  We will study metric measure spaces $(X,d,m)$ beyond the scope of spaces with
synthetic lower Ricci bounds. In particular, we introduce distribution-valued
lower Ricci bounds BE$_1(\kappa,\infty)$
  $\bullet$ for which we prove the equivalence with sharp gradient estimates,
  $\bullet$ the class of which will be preserved under time changes with
arbitrary $\psi\in{\mathrm Lip}_b(X)$, and
  $\bullet$ which are satisfied for the Neumann Laplacian on arbitrary
semi-convex subsets $Y\subset X$.
  In the latter case, the distribution-valued Ricci bound will be given by the
signed measure $\kappa= k\, m_Y + \ell\,\sigma_{\partial Y}$ where $k$ denotes
a variable synthetic lower bound for the Ricci curvature of $X$ and $\ell$
denotes a lower bound for the "curvature of the boundary" of $Y$, defined in
purely metric terms.
  We also present a new localization argument which allows us to pass on the
RCD property to arbitrary open subsets of RCD spaces. And we introduce new
synthetic notions for boundary curvature, second fundamental form, and boundary
measure for subsets of RCD spaces.


  The case study analyzed in the report involves the behavioral specification
and verification of a three-stage pipeline consisting of mutually concurrent
modules which also compete for a shared resource. The system components are
specified in terms of Concurrent State Machines (CSM) and the verification
technique used is the temporal model checking in the COSMA environment.


  We analyze a few illustrative examples of scenarios in which relativistic
symmetries are deformed by Planck-scale effects in particle-type-dependent
manner. The novel mathematical structures required by such scenarios are the
mixing coproducts, which govern the (deformed) law of conservation of energy
and momentum when particles with different relativistic properties interact. We
also comment on the relevance of these findings for recent proposals concerning
the possibility that neutrinos might have relativistic properties which are
different from those of photons and/or the possibility that composite particles
might have relativistic properties which are different from those of
fundamental ones.


  For a graph G, the spectral radius \r{ho}(G) of G is the largest eigenvalue
of its adjacency matrix. In this paper, we seek the relationship between
\r{ho}(G) and the walks of the subgraphs of G. Especially, if G contains a
complete multi-partite graph as a spanning subgraph, we give a formula for
\r{ho}(G) by using an infinite series on walks of the subgraphs of G. These
results are useful for the current popular spectral extremal problem.


  QCD is not supersymmetrical in the traditional sense -- the QCD Lagrangian is
based on quark and gluonic fields, not squarks nor gluinos. However, its
hadronic eigensolutions conform to a representation of superconformal algebra,
reflecting the underlying conformal symmetry of chiral QCD and its Pauli matrix
representation. The eigensolutions of superconformal algebra provide a unified
Regge spectroscopy of meson, baryon, and tetraquarks in the same 4-plet
representation with a universal Regge slope. The pion $q \bar q$ eigenstate has
zero mass for $m_q=0.$ The superconformal relations also can be extended to
heavy-light quark mesons and baryons. The combined approach of light-front
holography and superconformal algebra also provides insight into the origin of
the QCD mass scale and color confinement. A key observation is the remarkable
dAFF principle which shows how a mass scale can appear in the Hamiltonian and
the equations of motion while retaining the conformal symmetry of the action.
When one applies the dAFF procedure to chiral QCD, a mass scale $\kappa$
appears which determines universal Regge slopes, hadron masses in the absence
of the Higgs coupling, and the mass parameter underlying the form of the
nonperturbative QCD running coupling: $\alpha_s(Q^2) \propto \exp{-{Q^2/4
\kappa^2}}$, in agreement with the effective charge determined from
measurements of the Bjorken sum rule. The mass scale $\kappa$ underlying hadron
masses can be connected to the parameter $\Lambda_{\overline {MS}}$ in the QCD
running coupling by matching its predicted nonperturbative form to the
perturbative QCD regime. One also obtains predictions for spacelike and
timelike hadronic form factors, structure functions, distribution amplitudes,
and transverse momentum distributions.


  We discuss the recent results obtained in the context of PDF determination
with the inclusion of logarithmic resummations and their phenomenological
applications.


  The effect of the signs in the tunneling matrix elements on the transmission
zeros and the transmission phase in transport through a quantum dot is studied.
The existence of the transmission zeros is determined by both the relative
signs and the strength of the tunneling matrix elements for two neighboring
energy levels of a dot. The experimentally observed oscillating behavior of the
transmission phase over several Coulomb peaks can be explained by the uniform
distribution of the relative signs. Based on the simple model of a quantum dot,
we present a possible scenario which can give the uniform signs over several
conductance peaks. We suggest that the location of the transmission zeros can
be identified by inspecting the Fano interference pattern in the linear
response conductance of the Aharonov-Bohm (AB) interferometer with an embedded
quantum as a function of the number of electrons in a dot and the AB flux.


  Hypersound amplification/absorption of acoustic phonons in Graphene with
degenerate energy dispersion $\varepsilon(p)$ near the Fermi level was
theoretically studied. For $k_{\beta}T << 1$ and $ql >> 1$, the dependence of
the absorption coefficient $\Gamma/\Gamma_0$ on ${V_D\over V_s}$ was studied
where the results satisfied the Cerenkov effect. That is when ${V_D\over V_s} >
1$, an amplification was obtained but for ${V_D\over V_s} < 1$, an absorption
was obtained which could lead to Acoustoelectric Effect (AE) in Graphene. A
linear dependence of the $\Gamma/\Gamma_0$ on $\omega_q$ was observed where the
result obtianed qualitatively agreed with an experimentally observed
acoustoelectric current in Graphene via the Weinrich relation. It is
interesting to note from this study that, frequencies above $10THz$ can be
attained for $V_D = 1.1ms^{-1}$. This study permit the use of Graphene as
hypersound phonon laser (SASER).


  The curse of dimensionality in the realm of association rules is twofold.
Firstly, we have the well known exponential increase in computational
complexity with increasing item set size. Secondly, there is a \emph{related
curse} concerned with the distribution of (spare) data itself in high
dimension. The former problem is often coped with by projection, i.e., feature
selection, whereas the best known strategy for the latter is avoidance. This
work summarizes the first attempt to provide a computationally feasible method
for measuring the extent of dimension curse present in a data set with respect
to a particular class machine of learning procedures. This recent development
enables the application of various other methods from geometric analysis to be
investigated and applied in machine learning procedures in the presence of high
dimension.


  In this paper, a novel distributed control strategy addressing a (feasible)
psycho-social-physical welfare problem in islanded Direct Current (DC) smart
grids is proposed. Firstly, we formulate a (convex) optimization problem that
allows prosumers to share current with each other, taking into account the
technical and physical aspects and constraints of the grid (e.g., stability,
safety), as well as psycho-social factors (i.e., prosumers' personal values).
Secondly, we design a controller whose (unforced) dynamics represent the
continuous time primal-dual dynamics of the considered optimization problem.
Thirdly, a passive interconnection between the physical grid and the controller
is presented. Global asymptotic convergence of the closed-loop system to the
desired steady-state is proved and simulations based on collected data on
psycho-social aspects illustrate and confirm the theoretical results.


  An arithmetic method of proving the irrationality of smooth projective
3-folds is described, using reduction modulo $p$. It is illustrated by an
application to a cubic threefold, for which the hypothesis that its
intermediate Jacobian is isomorphic to the Jacobian of a curve is contradicted
by reducing modulo 3 and counting points over appropriate extensions of
$\mathbb F_3$. As a spin-off, it is shown that the 5-dimensional Prym varieties
arising as intermediate Jacobians of certain cubic 3-folds have the maximal
number of points over $\mathbb F_q$ which attains Perret's and Weil's upper
bounds.


  Ensuring the safety of reinforcement learning (RL) algorithms is crucial to
unlock their potential for many real-world tasks. However, vanilla RL and most
safe RL approaches do not guarantee safety. In recent years, several methods
have been proposed to provide hard safety guarantees for RL, which is essential
for applications where unsafe actions could have disastrous consequences.
Nevertheless, there is no comprehensive comparison of these provably safe RL
methods. Therefore, we introduce a categorization of existing provably safe RL
methods, present the conceptual foundations for both continuous and discrete
action spaces, and empirically benchmark existing methods. We categorize the
methods based on how they adapt the action: action replacement, action
projection, and action masking. Our experiments on an inverted pendulum and a
quadrotor stabilization task indicate that action replacement is the
best-performing approach for these applications despite its comparatively
simple realization. Furthermore, adding a reward penalty, every time the safety
verification is engaged, improved training performance in our experiments.
Finally, we provide practical guidance on selecting provably safe RL approaches
depending on the safety specification, RL algorithm, and type of action space.


  This paper studies the stability and convergence properties of a class of
multi-agent concurrent learning (CL) algorithms with momentum and restart. Such
algorithms can be integrated as part of the estimation pipelines of
data-enabled multi-agent control systems to enhance transient performance while
maintaining stability guarantees. However, characterizing restarting policies
that yield stable behaviors in decentralized CL systems, especially when the
network topology of the communication graph is directed, has remained an open
problem. In this paper, we provide an answer to this problem by synergistically
leveraging tools from graph theory and hybrid dynamical systems theory.
Specifically, we show that under a cooperative richness condition on the
overall multi-agent system's data, and by employing coordinated periodic
restart with a frequency that is tempered by the level of asymmetry of the
communication graph, the resulting decentralized dynamics exhibit robust
asymptotic stability properties, characterized in terms of input-to-state
stability bounds, and also achieve a desirable transient performance. To
demonstrate the practical implications of the theoretical findings, three
applications are also presented: cooperative parameter estimation over networks
with private data sets, cooperative model-reference adaptive control, and
cooperative data-enabled feedback optimization of nonlinear plants.


  Optimal reinsurance when Value at Risk and expected surplus is balanced
through their ratio is studied, and it is demonstrated how results for
risk-adjusted surplus can be utilized. Simplifications for large portfolios are
derived, and this large-portfolio study suggests a new condition on the
reinsurance pricing regime which is crucial for the results obtained. One or
two-layer contracts now become optimal for both risk-adjusted surplus and the
risk over expected surplus ratio, but there is no second layer when portfolios
are large or when reinsurance prices are below some threshold. Simple
approximations of the optimum portfolio are considered, and their degree of
degradation compared to the optimum is studied which leads to theoretical
degradation rates as the number of policies grows. The theory is supported by
numerical experiments which suggest that the shape of the claim severity
distributions may not be of primary importance when designing an optimal
reinsurance program. It is argued that the approach can be applied to
Conditional Value at Risk as well.


  We introduce a novel single-shot object detector to ease the imbalance of
foreground-background class by suppressing the easy negatives while increasing
the positives. To achieve this, we propose an Anchor Promotion Module (APM)
which predicts the probability of each anchor as positive and adjusts their
initial locations and shapes to promote both the quality and quantity of
positive anchors. In addition, we design an efficient Feature Alignment Module
(FAM) to extract aligned features for fitting the promoted anchors with the
help of both the location and shape transformation information from the APM. We
assemble the two proposed modules to the backbone of VGG-16 and ResNet-101
network with an encoder-decoder architecture. Extensive experiments on MS COCO
well demonstrate our model performs competitively with alternative methods
(40.0\% mAP on \textit{test-dev} set) and runs faster (28.6 \textit{fps}).


  The goal of the Sloan Digital Sky Survey is ``to map in detail one-quarter of
the entire sky, determining the positions and absolute brightnesses of more
than 100 million celestial objects''. The survey will be performed by taking
``snapshots'' through a large telescope. Each snapshot can capture up to 600
objects from a small circle of the sky. This paper describes the design and
implementation of the algorithm that is being used to determine the snapshots
so as to minimize their number. The problem is NP-hard in general; the
algorithm described is a heuristic, based on Lagriangian-relaxation and
min-cost network flow. It gets within 5-15% of a naive lower bound, whereas
using a ``uniform'' cover only gets within 25-35%.


  Despite its putative robustness, the realization of and control over
topological quantum matter is an ongoing grand challenge. Looking forward,
robust characterization protocols are needed to first certify topological
substrates before they are utilized in quantum algorithms. We contribute to
this grand challenge by providing a series of experimentally accessible near-
and medium-term protocols assessing the fidelity of logical processes. To do so
we examine logical operators and anyonic quasiparticle excitations in twisted
$\mathbb{Z}_{N=2,4}$ gauge theories. Extending the finite twist, a promising
route to Ising computing in its own right, to a non-contractible twist fuses
prior logical operators together and results in a twisted qubit code. The code
is notable for a doubled and tripled code distance for logical $Y$ and $X$
errors respectively. Next, we review the deconfinement properties of a
$\mathbb{Z}_4$ double semion condensation and provide an error correction
algorithm. Based on this understanding we then present a $\mathbb{Z}_4$
topological quasiparticle reflectometry and scattering protocol. The protocol
infers the topological properties of the system and serves as a high-level
metric for the performance and lifetime of the interfaced topological codes.
Our logical and scattering protocols are suitable for near-term devices where
many physical qubits encode few logical qubits. The topological lifetime of a
particle within a condensate conjugacy class, previously considered in
fabricated and hetero-structured condensed-matter experiments, serves as a
unifying performance metric across synthetic, qubit-based, and naturally
occurring topological order.


  Subject to intricate environmental variables, the precise classification of
jamming signals holds paramount significance in the effective implementation of
anti-jamming strategies within communication systems. In light of this
imperative, we propose an innovative fusion algorithm based on conditional
generative adversarial network (CGAN) and convolutional neural network (CNN),
which aims to deal with the difficulty in applying deep learning (DL)
algorithms due to the instantaneous nature of jamming signals in practical
communication systems. Compared with previous methods, our algorithm embeds
jamming category labels to constrain the range of generated signals in the
frequency domain by using the CGAN model, which simultaneously captures
potential label information while learning the distribution of signal data thus
achieves an 8% improvement in accuracy even when working with a few-sample
dataset. Real-world satellite communication scenarios are simulated by adopting
hardware platform, and we validate our algorithm by using the resulting
time-domain waveform data. The experimental results indicate that our algorithm
still performs extremely well, which demonstrates significant potential for
practical application in real-world communication scenarios.


  We study the Nernst effect due to vortex motion in two-dimensional granular
superconductors using simulations with Langevin or resistively shunted
Josephson-junction dynamics. In particular, we show that the geometric
frustration of both regular and irregular granular materials can lead to
thermally driven transport of vortices from colder to hotter regions, resulting
in a sign reversal of the Nernst signal. We discuss the underlying mechanisms
of this anomalous behavior in terms of heat transport by mobile vacancies in an
otherwise pinned vortex lattice.


  The effects of a wind on the emerging spectrum from an
inefficiently-radiating accretion flow in a global magnetic field are examined,
based on the analytic solution obtained recently by one of the present authors.
The results exhibit the steepening of the negative slope appearing in the
intermediate frequency range of bremsstrahlung spectrum and the decrease in the
luminosity ratio of thermal synchrotron to bremsstrahlung, in accordance with
the increasing wind strength. Both effects are due to a suppressed mass
accretion rate in the inner disk, caused by a mass loss in terms of wind.
  In order to demonstrate the reliability of this model, Sagittarius A^* (Sgr
A^*) and the nucleus of M 31, both of which have been resolved in an X-ray band
by Chandra, are taken up as the best candidates for the broadband spectral
fittings. Although the observed X-ray data are reproduced for these objects by
both of the inverse-Compton and the bremsstrahlung fittings, some evidence of
preference for the latter are recognized. The wind effects are clearly seen in
the latter fitting case, in which we can conclude that a widely extending
accretion disk is present in each nucleus, with no or only weak wind in Sgr A^*
and with a considerably strong wind in the nuclear region of M 31. Especially
in Sgr A^*, the inferred mass accretion rates are much smaller than the Bondi
rate whose estimate has become reliable due to Chandra. This fact strongly
suggests that the accretion in this object does not proceed like Bondi's
prediction, though its extent almost reaches the Bondi radius.


  Monte Carlo calculations using the codes PENELOPE and GEANT4 have been
performed to characterize the dosimetric parameters of the new 20 mm long
catheter based $^{32}$P beta source manufactured by Guidant Corporation. The
dose distribution along the transverse axis and the two dimensional dose rate
table have been calculated. Also, the dose rate at the reference point, the
radial dose function and the anisotropy function were evaluated according to
the adapted TG-60 formalism for cylindrical sources. PENELOPE and GEANT4 codes
were first verified against previous results corresponding to the old 27 mm
Guidant $^{32}$P beta source. The dose rate at the reference point for the
unsheathed 27 mm source in water was calculated to be $0.215 \pm 0.001$ cGy
s$^{-1}$ mCi$^{-1}$, for PENELOPE, and $0.2312 \pm 0.0008$ cGy s$^{-1}$
mCi$^{-1}$, for GEANT4. For the unsheathed 20 mm source these values were
$0.2908 \pm 0.0009$ cGy s$^{-1}$ mCi$^{-1}$ and $0.311 \pm 0.001$ cGy s$^{-1}$
mCi$^{-1}$, respectively. Also, a comparison with the limited data available on
this new source is shown. We found non negligible differences between the
results obtained with PENELOPE and GEANT4.


  Measuring treatment efficacy in mixture of subgroups from a randomized
clinical trial is a fundamental problem in personalized medicine development,
in deciding whether to treat the entire patient population or to target a
subgroup. We show that some commonly used efficacy measures are not suitable
for a mixture population. We also show that, while it is important to adjust
for imbalance in the data using least squares means (LSmeans) (not marginal
means) estimation, the current practice of applying LSmeans to directly
estimate the efficacy in a mixture population for any type of outcome is
inappropriate. Proposing a new principle called {\em subgroup mixable
estimation}, we establish the logical relationship among parameters that
represent efficacy and develop a general inference procedure to confidently
infer efficacy in subgroups and their mixtures. Using oncology studies with
time-to-event outcomes as an example, we show that Hazard Ratio is not suitable
for measuring efficacy in a mixture population, and provide alternative
efficacy measures with a valid inference procedure.


  Working in doubling metric spaces, we examine the connections between
different dimensions, Whitney covers, and geometrical properties of tubular
neighborhoods. In the Euclidean space, we relate these concepts to the behavior
of the surface area of the boundaries of parallel sets. In particular, we give
characterizations for the Minkowski and the spherical dimensions by means of
the Whitney ball count.


  Recently the theory of widths of Kolmogorov-Gelfand has received a great deal
of interest due to its close relationship with the newly born area of
Compressed Sensing. It has been realized that widths reflect properly the
sparsity of the data in Signal Processing. However fundamental problems of the
theory of widths in multidimensional Theory of Functions remain untouched, as
well as analogous problems in the theory of multidimensional Signal Analysis.
In the present paper we provide a multidimensional generalization of the
original result of Kolmogorov by introducing a new hierarchy of
infinite-dimensional spaces based on solutions of higher order elliptic
equation.


  We investigate a method of construction of Calabi--Yau manifolds, that is, by
smoothing normal crossing varieties. We develop some theories for calculating
the Picard groups of the Calabi--Yau manifolds obtained in this method. Some
applications are included, such as construction of new examples of Calabi--Yau
3-folds with Picard number one with some interesting properties.


  Due to the availability of huge amounts of data and processing abilities,
current artificial intelligence (AI) systems are effective in solving complex
tasks. However, despite the success of AI in different areas, the problem of
designing AI systems that can truly mimic human cognitive capabilities such as
artificial general intelligence, remains largely open. Consequently, many
emerging cross-device AI applications will require a transition from
traditional centralized learning systems towards large-scale distributed AI
systems that can collaboratively perform multiple complex learning tasks. In
this paper, we propose a novel design philosophy called democratized learning
(Dem-AI) whose goal is to build large-scale distributed learning systems that
rely on the self-organization of distributed learning agents that are
well-connected, but limited in learning capabilities. Correspondingly, inspired
by the societal groups of humans, the specialized groups of learning agents in
the proposed Dem-AI system are self-organized in a hierarchical structure to
collectively perform learning tasks more efficiently. As such, the Dem-AI
learning system can evolve and regulate itself based on the underlying duality
of two processes which we call specialized and generalized processes. In this
regard, we present a reference design as a guideline to realize future Dem-AI
systems, inspired by various interdisciplinary fields. Accordingly, we
introduce four underlying mechanisms in the design such as plasticity-stability
transition mechanism, self-organizing hierarchical structuring, specialized
learning, and generalization. Finally, we establish possible extensions and new
challenges for the existing learning approaches to provide better scalable,
flexible, and more powerful learning systems with the new setting of Dem-AI.


  On-chip optical filters are fundamental components in optical signal
processing. While rare-earth ion-doped crystals offer ultra-narrow optical
filtering via spectral hole burning, their applications have primarily been
limited to those using bulk crystals, restricting their utility. In this work,
we demonstrate cavity-enhanced spectral filtering based on rare-earth ions in
an integrated nonlinear optical platform. We incorporate rare-earth ions into
high quality-factor ring resonators patterned in thin-film lithium niobate. By
spectral hole burning at 4K in a critically coupled resonance mode, we achieve
bandpass filters ranging from 7 MHz linewidth, with 13.0 dB of extinction, to
24 MHz linewidth, with 20.4 dB of extinction. By reducing the temperature to
100 mK to eliminate phonon broadening, we achieve an even narrower linewidth of
681 kHz, which is comparable to the narrowest filter linewidth demonstrated in
an integrated photonic device, while only requiring a small device footprint.
Moreover, the cavity enables reconfigurable filtering by varying the cavity
coupling rate. For instance, as opposed to the bandpass filter, we demonstrate
a bandstop filter utilizing an under-coupled ring resonator. Such versatile
integrated spectral filters with high extinction ratio and narrow linewidth
could serve as fundamental components for optical signal processing and optical
memories on-a-chip.


  In this paper the concept and the first successful tests towards constructing
a superconductive micro-undulator with a period length of 3.8 mm are described.
The structures were built with a commercially available NbTi wire. A full 100
periods prototype is now under construction.


  Analog network coding (ANC) is a throughput increasing technique for the
two-way relay channel (TWRC) whereby two end nodes transmit simultaneously to a
relay at the same time and band, followed by the relay broadcasting the
received sum of signals to the end nodes. Coherent reception under ANC is
challenging due to requiring oscillator synchronization for all nodes, a
problem further exacerbated by Doppler shift. This work develops a noncoherent
M-ary frequency-shift keyed (FSK) demodulator implementing ANC. The demodulator
produces soft outputs suitable for use with capacity-approaching channel codes
and supports information feedback from the channel decoder. A unique aspect of
the formulation is the presence of an infinite summation in the received symbol
probability density function. Detection and channel decoding succeed when the
truncated summation contains a sufficient number of terms. Bit error rate
performance is investigated by Monte Carlo simulation, considering modulation
orders two, four and eight, channel coded and uncoded operation, and with and
without information feedback from decoder to demodulator. The channel code
considered for simulation is the LDPC code defined by the DVB-S2 standard. To
our knowledge this work is the first to develop a noncoherent soft-output
demodulator for ANC.


  Nanoporous anodized alumina oxide have been used as templates for obtaining
nanomaterials such as nanowires, which exhibit interesting electronic, magnetic
and optical properties. This article presents how the regularity of the spatial
distribution of pores in such templates, which affects several of the physical
properties of the obtained nanomaterials, has been objectively quantified. The
method uses adaptive thresholding, wave propagation image analysis methods, as
well as an hexagonality measurement which was found to be particularly suitable
because of its locality and invariance to translation, rotation and scaling. A
comparison between commercial and laboratory-made samples is presented in order
to test the method, resulting higher hexagonality for the latter type of
templates.


  In a previous paper, Yakubovich and the author of this article proved that
certain shifted combinations of completed Dirichlet series have infinitely many
zeros on the critical line. Here we provide some lower bounds for the number of
critical zeros of a subclass of shifted combinations.


  New and existing X-ray, UBVJHKsW(1-4), and spectroscopic observations were
analyzed to constrain fundamental parameters for M25, NGC 7790, and dust along
their sight-lines. The star clusters are of particular importance given they
host the classical Cepheids U Sgr, CF Cas, and the visual binary Cepheids CEa
and CEb Cas. Precise results from the multiband analysis, in tandem with a
comprehensive determination of the Cepheids' period evolution (dP/dt) from ~140
years of observations, helped resolve concerns raised regarding the clusters
and their key Cepheid constituents. Specifically, distances derived for members
of M25 and NGC 7790 are 630+-25 pc and 3.40+-0.15 kpc, respectively.


  Buoyancy effects in unstably stratified mixing layers express themselves
through gravity currents of heavy fluid which propagate in an ambient lighter
fluid. These currents are encountered in numerous geophysical flows, industrial
safety and environmental protection issues. During transition to turbulence a
strong distortion of the separating interface between regions containing
`heavy' or `light' fluid arises. The complexity of this interface will be used
to monitor the progress of the mixing. We concentrate on the enhancement of
surface-area and `surface-wrinkling' of the separating interface as a result of
gravity-effects. We also show that this process can be simulated quite
accurately using large-eddy simulation with dynamic subgrid modeling. However,
the subgrid resolution, defined as the ratio between filter-width Delta and
grid-spacing h, should be sufficiently high to avoid contamination due to
spatial discretization error effects.


  Magnetic-shape-memory materials (e.g. specific NiMnGa alloys) react with a
large change of shape to the presence of an external magnetic field. As an
alternative for the difficult to manifacture single crystal of these alloys we
study composite materials in which small magnetic-shape-memory particles are
embedded in a polymer matrix. The macroscopic properties of the composite
depend strongly on the geometry of the microstructure and on the
characteristics of the particles and the polymer.
  We present a variational model based on micromagnetism and elasticity, and
derive via homogenization an effective macroscopic model under the assumption
that the microstructure is periodic. We then study numerically the resulting
cell problem, and discuss the effect of the microstructure on the macroscopic
material behavior. Our results may be used to optimize the shape of the
particles and the microstructure.


  We provide microscopic diagrammatic derivations of the Molecular Coherent
Potential Approximation (MCA) and Dynamical Cluster Approximation (DCA) and
show that both are Phi-derivable. The MCA (DCA) maps the lattice onto a
self-consistently embedded cluster with open (periodic) boundary conditions,
and therefore violates (preserves) the translational symmetry of the original
lattice. As a consequence of the boundary conditions, the MCA (DCA) converges
slowly (quickly) with corrections O(1/Lc) (O(1/Lc^2)), where Lc is the linear
size of the cluster. However, local quantities, when measured in the center of
the MCA cluster, converge more quickly than the DCA result. These results are
demonstrated numerically for the one-dimensional symmetric Falicov-Kimball
model.


  This paper introduces a new deep learning approach to approximately solve the
Covering Salesman Problem (CSP). In this approach, given the city locations of
a CSP as input, a deep neural network model is designed to directly output the
solution. It is trained using the deep reinforcement learning without
supervision. Specifically, in the model, we apply the Multi-head Attention to
capture the structural patterns, and design a dynamic embedding to handle the
dynamic patterns of the problem. Once the model is trained, it can generalize
to various types of CSP tasks (different sizes and topologies) with no need of
re-training. Through controlled experiments, the proposed approach shows
desirable time complexity: it runs more than 20 times faster than the
traditional heuristic solvers with a tiny gap of optimality. Moreover, it
significantly outperforms the current state-of-the-art deep learning approaches
for combinatorial optimization in the aspect of both training and inference. In
comparison with traditional solvers, this approach is highly desirable for most
of the challenging tasks in practice that are usually large-scale and require
quick decisions.


  The number of users using wireless Local Area Network is increasing
exponentially and their behavior is changing day after day. Nowadays, users of
wireless LAN are using huge amount of bandwidth because of the explosive growth
of some services and applications such as video sharing. This situation imposes
massive pressure on the wireless LAN performance especially in term of fairness
among wireless stations. The limited resources are not distributed fairly in
saturated conditions. The most important resource is the access point buffer
space. This importance is a result of access point being the bottleneck between
two different types of networks. These two types are wired network with
relatively huge bandwidth and wireless network with much smaller bandwidth.
Also the unfairness problem is keep getting worse because of the greedy nature
Transmission Control Protocol (TCP). In this paper, we conduct a comprehensive
study on wireless LAN dynamics and proposed a new mathematical model that
describes the performance and effects of its behavior. We validate the proposed
model by using the simulation technique. The proposed model was able to produce
very good approximation in most of the cases. It also gave us a great insight
into the effective variables in the wireless LAN behavior and what are the
dimensions of the unfairness problem.


  We develop a projection imaging technique to study ultracold plasma dynamics.
We image the charged particle spatial distributions by extraction with a
high-voltage pulse onto a position-sensitive detector. Measuring the 2D width
of the ion image at later times (the ion image size in the first 20 $\mu$s is
dominated by the Coulomb explosion of the dense ion cloud), we extract the
plasma expansion velocity. These velocities at different initial electron
temperatures match earlier results obtained by measuring the plasma oscillation
frequency. The electron image size slowly decreases during the plasma lifetime
because of the strong Coulomb force of the ion cloud on the electrons, electron
loss and Coulomb explosion effects.


  Molecular dynamics calculations of the fluctuation of bond vibration revealed
the shell-resolved kinetics of surface melting of closed-shelled cluster
containing 147atoms with Lennard-Jones type interaction. It is found that the
surface melting is imitated by the migrating of the vertex atoms and the
melting process can be divided into three major stages, i.e., vertex migrating,
surface melting, and general melting. Although the melting process of the LJ147
cluster could be divided into discrete stages of shell-by-shell surface
melting, in general, there is still a continuous process of melting from the
surface to the core interior.


  We give explicit formulas for Whittaker functions for the class one principal
series representations of the orthogonal groups $ SO_{2n+1}(\R) $ of odd
degree. Our formulas are similar to the recursive formulas for Whittaker
functions on $SL_n(\R)$ given by Stade and the author \cite{ISt}. Some parts of
our results are announced in \cite{I3}.


  The nematic susceptibility of Ba$_{1-x}$Na$_{x}$Fe$_{2}$As$_{2}$ single
crystals is studied by measuring the Young's modulus using a
three-point-bending setup in a capacitance dilatometer over a wide doping
range. Particular emphasis is placed on the behavior within the double-Q
antiferromagnetic $C_{4}$ re-entrant phase. Here, we surprisingly still observe
a sizable nematic susceptibility in spite of the well-developed magnetic order,
consistent with recent theoretical calculations. Outside the $C_{4}$ re-entrant
region, the behavior is very similar to that of K-doped BaFe$_{2}$As$_{2}$. A
significant coupling of superconductivity to the shear modulus is observed and
is discussed in terms of strong competition between the superconducting and
magnetic phases.


  The hyperoctahedral group is the Weyl group of type B and is associated with
a two-parameter family of differential-difference operators T_i, i=1,..,N (the
dimension of the underlying Euclidean space). These operators are analogous to
partial derivative operators. This paper finds all the polynomials in N
variables which are annihilated by the sum of the squares (T_1)^2+(T_2)^2 and
by all T_i for i>2 (harmonic). They are given explicitly in terms of a novel
basis of polynomials, defined by generating functions. The harmonic polynomials
can be used to find wave functions for the quantum many-body spin Calogero
model.


  We explore whether stellar tidal streams can provide information on the
secular, cosmological evolution of the Milky Way's gravitational potential and
on the presence of subhalos. We carry out long-term (~t_hubble) N-body
simulations of disrupting satellite galaxies in a semi-analytic Galaxy
potential where the dark matter halo and the subhalos evolve according to a
LCDM cosmogony. All simulations are constrained to end up with the same
position and velocity at present. Our simulations account for: (i) the secular
evolution of the host halo's mass, size and shape, (ii) the presence of
subhalos and (iii) dynamical friction. We find that tidal stream particles
respond adiabatically to the Galaxy growth so that, at present, the energy and
angular momentum distribution is exclusively determined by the present Galaxy
potential. In other words, all present-day observables can only constrain the
present mass distribution of the Galaxy independent of its past evolution. We
also show that, if the full phase-space distribution of a tidal stream is
available, we can accurately determine (i) the present Galaxy's shape and (ii)
the amount of mass loss from the stream's progenitor, even if this evolution
spanned a cosmologically significant epoch.


  We show that it is possible to ``store'' quantum states of single-photon
fields by mapping them onto {\it collective} meta-stable states of an optically
dense, coherently driven medium inside an optical resonator. An adiabatic
technique is suggested which allows to transfer non-classical correlations from
traveling-wave single-photon wave-packets into atomic states and vise versa
with nearly 100% efficiency. In contrast to previous approaches involving
single atoms, the present technique does not require the strong coupling regime
corresponding to high-Q micro-cavities. Instead, intracavity
Electromagnetically Induced Transparency is used to achieve a strong coupling
between the cavity mode and the atoms.


  The manual examination of X-ray images for fractures is a time-consuming
process that is prone to human error. In this work, we introduce a robust yet
simple training loop for the classification of fractures, which significantly
outperforms existing methods. Our method achieves superior performance in less
than ten epochs and utilizes the latest dataset to deliver the best-performing
model for this task. We emphasize the importance of training deep learning
models responsibly and efficiently, as well as the critical role of selecting
high-quality datasets.


  The nonadiabatic dynamics of a many-body system driven through a quantum
critical point can be controlled using counterdiabatic driving, where the
formation of excitations is suppressed by assisting the dynamics with auxiliary
multiple-body nonlocal interactions. We propose an alternative scheme which
circumvents practical challenges to realize shortcuts to adiabaticity in
mesoscopic systems by tailoring the functional form of the auxiliary
counterdiabatic interactions. A driving scheme resorting in few-body
short-range interactions is shown to generate an effectively adiabatic
dynamics.


  Vortex-induced vibration (VIV) manifests its destructiveness on marine risers
in deep-water oil and gas development. Many types of riser fairings are
employed in the practical applications of ocean engineering due to their
effectiveness in suppressing VIV. However, few reports have been found on the
optimal design of the fairing profile. In this study, we conduct shape
optimization by integrating computational fluid dynamics and genetic algorithm
at a low Reynolds number. The fairing profile is parameterized by B\'ezier
curves with control points adopted as design variables, and lift coefficient is
selected as the objective function to obtain optimal fairing. The optimal
solution shows that the water-drop-shaped fairing presents a high performance
in the condition of two degrees of freedom (DOFs), whereas the caudal
fin-like-shaped fairing has good stability in 3-DOF condition. Optimization
results indicate that the water-drop-shaped fairing suppresses amplitude up to
99% and reduces drag coefficient by almost 51% at Ur=5.0 for the 2-DOF
condition. For the 3-DOF condition, the formation of two symmetric vorticities
with opposite directions generated at the bilateral of the optimal fairing
leads to fairing stability in flow.


  In this article, we consider the path-planning problem of a cooperative
homogeneous robotic system with rigid formation. An optimal controller is
designed for each agent in such rigid systems based on Pontryagin's minimum
principle theory. We found that the optimal control for each agent is
equivalent to the optimal control for the Center of Mass (CoM). This
equivalence is then proved by using some analytical mechanics. Three examples
are finally simulated to illustrate our theoretical results. One application
could be utilizing this equivalence to simplify the original multi-agent
optimal control problem.


  We exploit the key idea that nonlinear system identification is equivalent to
linear identification of the socalled Koopman operator. Instead of considering
nonlinear system identification in the state space, we obtain a novel linear
identification technique by recasting the problem in the infinite-dimensional
space of observables. This technique can be described in two main steps. In the
first step, similar to the socalled Extended Dynamic Mode Decomposition
algorithm, the data are lifted to the infinite-dimensional space and used for
linear identification of the Koopman operator. In the second step, the obtained
Koopman operator is "projected back" to the finite-dimensional state space, and
identified to the nonlinear vector field through a linear least squares
problem. The proposed technique is efficient to recover (polynomial) vector
fields of different classes of systems, including unstable, chaotic, and open
systems. In addition, it is robust to noise, well-suited to model low sampling
rate datasets, and able to infer network topology and dynamics.


  Distributional semantics, the quantitative study of meaning variation and
change through corpus collocations, is currently one of the most productive
research areas in computational linguistics. The wider availability of big data
and of reproducible algorithms for analysis has boosted its application to
living languages in recent years. But can we use distributional semantics to
study a language with such a limited corpus as ancient Greek? And can this
approach tell us something about such vexed questions in classical studies as
the language and composition of the Homeric poems? Our paper will compare the
semantic flexibility of formulae involving transitive verbs in archaic Greek
epic to similar verb phrases in a non-formulaic corpus, in order to detect
unique patterns of variation in formulae. To address this, we present AGVaLex,
a computational valency lexicon for ancient Greek automatically extracted from
the Ancient Greek Dependency Treebank. The lexicon contains quantitative
corpus-driven morphological, syntactic and lexical information about verbs and
their arguments, such as objects, subjects, and prepositional phrases, and has
a wide range of applications for the study of the language of ancient Greek
authors.


  Novel bitwise retransmission schemes are devised which retransmit only the
bits received with small reliability. The retransmissions are used to
accumulate the reliabilities of individual bits. Unlike the conventional
automatic repeat request (ARQ) schemes, the proposed scheme does not require a
checksum for the error detection. The bits to be retransmitted are reported as
a combination number, or two synchronized random number generators (RNGs) at
the transmitter and receiver are used to greatly compress the feedback message.
The bitwise retransmission decisions and/or combining can be performed after
the demodulation or after the channel decoding at the receiver. The bit-error
rate (BER) expressions are derived for the case of one and two retransmissions,
and verified by computer simulations. Assuming three specific retransmission
strategies, the scheme parameters are optimized to minimize the overall BER.
For the same number of retransmissions and packet length, the proposed schemes
always outperform the frequently used stop-and-wait ARQ. The impact of feedback
errors is also considered. Finally, practical designs of the bitwise
retransmissions for data fusion from sensor nodes in Zigbee, Wifi and Bluetooth
networks are presented.


  We present a detection of the unnormalized skewness <T^3> induced by the
thermal Sunyaev-Zel'dovich (tSZ) effect in filtered Atacama Cosmology Telescope
(ACT) 148 GHz cosmic microwave background temperature maps. Contamination due
to infrared and radio sources is minimized by template subtraction of resolved
sources and by constructing a mask using outlying values in the 218 GHz
(tSZ-null) ACT maps. We measure <T^3>= -31 +- 6 \mu K^3 (measurement error
only) or +- 14 \mu K^3 (including cosmic variance error) in the filtered ACT
data, a 5-sigma detection. We show that the skewness is a sensitive probe of
sigma_8, and use analytic calculations and tSZ simulations to obtain
cosmological constraints from this measurement. From this signal alone we infer
a value of sigma_8= 0.79 +0.03 -0.03 (68 % C.L.) +0.06 -0.06 (95 % C.L.). Our
results demonstrate that measurements of non-Gaussianity can be a useful method
for characterizing the tSZ effect and extracting the underlying cosmological
information.


  The Galactic O-Star Spectroscopic Survey (GOSSS) is a project that is
observing all known Galactic O stars with B < 13 (~2000 objects) in the
blue-violet part of the spectrum with R~2500. It also includes two companion
surveys (a spectroscopic one at R~1500 and a high resolution imaging one). It
is based on v2.0 of the Galactic O star catalog (v1, Ma\'iz Apell\'aniz et al.
2004; v2, Sota et al. 2008). We have completed the first part of the main
project. Here we present results on the first 400 objects of the sample.


  The strong-property-fluctuation theory (SPFT) provides a general framework
for estimating the constitutive parameters of a homogenized composite material
(HCM). We developed the elastodynamic SPFT for orthotropic HCMs, in order to
undertake numerical studies. A specific choice of two-point covariance function
- which characterizes the distributional statistics of the generally
ellipsoidal particles that constitute the component materials - was
implemented. Representative numerical examples revealed that the lowest-order
SPFT estimate of the HCM stiffness tensor is qualitatively similar to the
estimate provided by the Mori-Tanaka mean-field formalism, but the differences
between the two estimates vary as the orthotropic nature of the HCM is
accentuated. The second-order SPFT provides a correction to the lowest-order
estimate of the HCM stiffness tensor and density. The correction, indicating
effective dissipation due to scattering loss, increases as the HCM becomes less
orthotropic but decreases as the correlation length becomes smaller.


  A wealth of epidemiological data suggests an association between
mortality/morbidity from pulmonary and cardiovascular adverse events and air
pollution, but uncertainty remains as to the extent implied by those
associations although the abundance of the data. In this paper we describe an
SSA (Singular Spectrum Analysis) based approach in order to decompose the
time-series of particulate matter concentration into a set of exposure
variables, each one representing a different timescale. We implement our
methodology to investigate both acute and long-term effects of $PM_{10}$
exposure on morbidity from respiratory causes within the urban area of Bari,
Italy.