  The Quadruple Aim is a framework which prioritizes four aims, or dimensions
of performance, for innovating in the healthcare domain, respectively: 1)
enhancing the individual experience of care; 2) improving the work life of
health care clinicians and staff; 3) improving the health of populations; and
4) reducing the per capita cost of care. In this contribution, recent
literature providing examples of design research in the eHealth domain is
reviewed to answer the research question: in which measure has design research
contributed to each of the four aims of eHealth innovation in the past five
years?. The results of the review are presented and employed to draw three main
observations: 1) design researchers in eHealth seem to be largely focused on
improving experiences of care, either patients' or health professionals; 2)
design researchers' contribution on reducing per capita costs of care appears
to be less pronounced, which is outlined as a point for improvement; and 3) in
a considerable amount of reviewed contributions, design researchers appear to
be contributing to multiple aims at once. In this sub-group of reviewed
contributions, several disciplinary areas and types of stakeholders interact
and integrate through design research activities. The latter observation leads
to a reflection on the strategic role of design research in the contexts of the
convergence revolution and of the non-communicable disease crisis. Implications
of this reflection for design researchers are recognized in the opportunity and
timeliness to develop eHealth-specific ways to orchestrate design integration.
A direction for further research in this sense is identified in the use of
sensory and self-monitored data as a boundary object for eHealth innovation.
The prospective value of this direction is finally exemplified through the case
of blood pressure.


  It is shown that the existence of an $\omega$-compatible Einstein metric on a
compact symplectic manifold $(M,\omega)$ imposes certain restrictions on the
symplectic Chern numbers. Examples of symplectic manifolds which do not satisfy
these restrictions are given. The results offer partial support to a conjecture
of Goldberg.


  Let $\mathbb{F}_q$ be a finite field of order $q$. In this paper, we study
the distribution of rectangles in a given set in $\mathbb{F}_q^2$. More
precisely, for any $0<\delta\le 1$, we prove that there exists an integer
$q_0=q_0(\delta)$ with the following property: if $q\ge q_0$ and $A$ is a
multiplicative subgroup of $\mathbb{F}^*_q$ with $|A|\ge q^{2/3}$, then any set
$S\subset \mathbb{F}_q^2$ with $|S|\ge \delta q^2$ contains at least $\gg
\frac{|S|^4|A|^2}{q^5}$ rectangles with side-lengths in $A$. We also consider
the case of rectangles with one fixed side-length and the other in a
multiplicative subgroup $A$.


  Recent results obtained by various authors on the properties of HgMn stars
are reviewed. Substantial progress has been achieved in the study of abundances
and isotopic anomalies. The results about the magnetic fields and membership in
multiple systems suggest further directions of investigations to be followed in
view of answering the question of the development of abundance peculiarities in
HgMn stars.


  In this paper, we are concerned with the inverse electromagnetic scattering
problem of recovering a complex scatterer by the corresponding electric
far-field data. The complex scatterer consists of an inhomogeneous medium and a
possibly embedded perfectly electric conducting (PEC) obstacle. The far-field
data are collected corresponding to incident plane waves with a fixed incident
direction and a fixed polarisation, but frequencies from an open interval. It
is shown that the embedded obstacle can be uniquely recovered by the
aforementioned far-field data, independent of the surrounding medium.
Furthermore, if the surrounding medium is piecewise homogeneous, then the
medium can be recovered as well. Those unique recovery results are new to the
literature. Our argument is based on low-frequency expansions of the
electromagnetic fields and certain harmonic analysis techniques.


  By the von Neumann inequality for homogeneous polynomials there exists a
positive constant $C_{k,q}(n)$ such that for every $k$-homogeneous polynomial
$p$ in $n$ variables and every $n$-tuple of commuting operators $(T_1, \dots,
T_n)$ with $\sum_{i=1}^{n} \Vert T_{i} \Vert^{q} \leq 1$ we have \[ \|p(T_1,
\dots, T_n)\|_{\mathcal L(\mathcal H)} \leq C_{k,q}(n) \; \sup\{ |p(z_1, \dots,
z_n)| : \textstyle \sum_{i=1}^{n} \vert z_{i} \vert^{q} \leq 1 \}\,. \] For
fixed $k$ and $q$, we study the asymptotic growth of the smallest constant
$C_{k,q}(n)$ as $n$ (the number of variables/operators) tends to infinity. For
$q = \infty$, we obtain the correct asymptotic behavior of this constant
(answering a question posed by Dixon in the seventies). For $2 \leq q < \infty$
we improve some lower bounds given by Mantero and Tonge, and prove the
asymptotic behavior up to a logarithmic factor. To achieve this we provide
estimates of the norm of homogeneous unimodular Steiner polynomials, i.e.
polynomials such that the multi-indices corresponding to the nonzero
coefficients form partial Steiner systems.


  We study the residuated basic logic ($\mathsf{RBL}$) of residuated basic
algebra in which the basic implication of Visser's basic propositional logic
($\mathsf{BPL}$) is interpreted as the right residual of a non-associative
binary operator $\cdot$ (product). We develop an algebraic system
$\mathsf{S_{RBL}}$ of residuated basic algebra by which we show that
$\mathsf{RBL}$ is a conservative extension of $\mathsf{BPL}$. We present the
sequent formalization $\mathsf{L_{RBL}}$ of $\mathsf{S_{RBL}}$ which is an
extension of distributive full non-associative Lambek calculus
($\mathsf{DFNL}$), and show that the cut elimination and subformula property
hold for it.


  The origin of the pseudogap (PG) phase and superconducting behaviors in
high-Tc superconductors is proposed, based on the picture of Euclidean Q-balls
formation, that carry Cooper/local-pair condensates inside their volumes.
Unlike the baryonic Q-balls in supersymmetric standard model, the Euclidean
Q-balls describe spin-/charge densities, that oscillate in Matsubara time, and
are found in the 'nested' repulsive Hubbard model of high-Tc superconductors.
Euclidean Q-balls arise due to global invariance of the effective theory under
the phase rotation of the Fourier amplitudes of spin-/charge fluctuations,
leading to conservation of the 'Noether charge' Q in Matsubara time. Due to
local minimum of their potential energy at finite amplitude of the density
fluctuations, the Q-balls provide greater binding energy of fermions into
local/Cooper pairs than an exchange with infinitesimal lattice/charge/spin
quasiparticles in the usual Frohlich mechanism. We show that below some
temperature T* the Q-balls arise with a finite density of superconducting
condensate inside them. The Q-balls expand their sizes to infinity at
superconducting transition temperature Tc. Fermionic spectral gap inside the
Q-balls arises in the vicinity of the 'nested' regions of the bare Fermi
surface. Solutions are found analytically from the Eliashberg like equations
with the 'nesting' wave vectors connecting different 'hot spots' on the bare
Fermi surface. The experimental dependences of Uemura (1989) of Tc on
superconducting density n_s, as well as of Li et al. (2010) of diamagnetic
moment on magnetic field above Tc in cuprates follow naturally from the
proposed theory. The 'breathing modes' of the Q-balls in Matsubara time, as
well as sharp maximum in specific heat temperature dependence in the vicinity
of the 1-st order phase transition into Q-balls gas phase are also predicted.


  We present an extension of the canonical coupled mode theory of
electromagnetic waves to the case of pulses and spatio-temporal perturbations
in complex media. Unlike previous attempts to derive such a model, our approach
involves no approximation, and does not impose any restriction on the
spatio-temporal profile. Moreover, the effect of modal dispersion on mode
evolution and on the coupling to other modes is fully taken into account. Thus,
our approach can yield any required accuracy by retaining as many terms in the
expansion as needed. It also avoids various artifacts of previous derivations
by introducing the correct form of the solution. We then provide what is, to
the best of our knowledge, the first ever validation of the coupled mode
equations with exact numerical simulations, and demonstrate the wide range of
possibilities enabled by spatio-temporal perturbations of pulses, including
pulse shortening or broadening or more complex shaping. Our formulation is
valid across the electromagnetic spectrum, and can be applied directly also to
other wave systems.


  The study of collective animal behaviour must progress through a comparison
between the theoretical predictions of numerical models and data coming from
empirical observations. To this aim it is important to develop methods of
three-dimensional (3D) analysis that are at the same time informative about the
structure of the group and suitable to empirical data. In fact, empirical data
are considerably noisier than numerical data, and they are subject to several
constraints. We review here the tools of analysis used by the STARFLAG project
to characterise the 3D structure of large flocks of starlings in the field. We
show how to avoid the most common pitfalls i the quantitative analysis of 3D
animal groups, with particular attention to the problem of the bias introduced
by the border of the group. By means of practical examples, we demonstrate that
neglecting border effects gives rise to artefacts when studying the 3D
structure of a group. Moreover, we show that mathematical rigour is essential
to distinguish important biological properties from trivial geometric features
of animal groups.


  Two inequalities concerning the symmetry of the zeta-function and the
Ramanujan $\tau$-function are improved through the use of some elementary
considerations.


  This is the first of two papers devoted to showing how the rich algebraic
formalism of Eliashberg-Givental-Hofer's symplectic field theory (SFT) can be
used to define higher algebraic structures on the symplectic cohomology of open
symplectic manifolds. Using the SFT of Hamiltonian mapping tori we show how to
define a homotopy extension of the well-known Lie bracket on symplectic
cohomology. Apart from discussing applications to the existence of closed Reeb
orbits, we outline how the $L_{\infty}$-structure is conjecturally related via
mirror symmetry to the extended deformation theory of complex structures.


  We present a robust test for change-points in time series which is based on
the two-sample Hodges-Lehmann estimator. We develop new limit theory for a
class of statistics based on the two-sample U-quantile processes, in the case
of short range dependent observations. Using this theory we can derive the
asymptotic distribution of our test statistic under the null hypothesis. We
study the finite sample properties of our test via a simulation study and
compare the test with the classical CUSUM test and a test based on the
Wilcoxon-Mann-Whitney statistic.


  We use variable-temperature x-ray thermal diffuse scattering and inelastic
scattering to investigate the lattice dynamics in single crystals of
multiferroic CaMn7O12 which undergo a series of orbital and magnetic
transitions at low temperatures. Upon approaching the charge and orbital
ordering temperature To=250 K from above, we observe intense diffuse scattering
features and a pronounced optical phonon softening centered around the
superstructure reflections of the incommensurately modulated structure. The
phonon anomaly appears well above To and continuously increases upon cooling,
following a canonical power-law temperature dependence that confirms the
transition at To to be of second order and related to a soft-phonon lattice
instability. Microscopic mechanisms for the incommensurate charge and orbital
ordering based on competing interactions and on momentum dependent
electron-phonon coupling could both account for the observed extended momentum
width of the phonon softening. Our results highlight the importance of the
lattice interactions in the physics of this magnetically induced ferroelectric
system.


  Constrained Hamiltonian description of the classical limit is utilized in
order to derive consistent dynamical equations for hybrid quantum-classical
systems. Starting with a compound quantum system in the Hamiltonian formulation
conditions for classical behavior are imposed on one of its subsystems and the
corresponding hybrid dynamical equations are derived. The presented formalism
suggests that the hybrid systems have properties that are not exhausted by
those of quantum and classical systems.


  Single-file diffusion is a ubiquitous physical process exploited by living
and synthetic systems to exchange molecules with their environment. It is
paramount quantifying the escape time needed for single files of particles to
exit from constraining synthetic channels and biological pores. This quantity
depends on complex cooperative effects, whose predominance can only be
established through a strict comparison between theory and experiments. By
using colloidal particles, optical manipulation, microfluidics, digital
microscopy and theoretical analysis we uncover the self-similar character of
the escape process and provide closed-formula evaluations of the escape time.
We find that the escape time scales inversely with the diffusion coefficient of
the last particle to leave the channel. Importantly, we find that at the
investigated {\bf microscale}, bias forces as tiny as $10^{-15}\;{\rm N}$
determine the magnitude of the escape time by drastically reducing
interparticle collisions. Our findings provide crucial guidelines to optimize
the design of micro- and nano-devices for a variety of applications including
drug delivery, particle filtering and transport in geometrical constrictions.


  A concept of martingale-fair index of return, consistent with Arbitrage Free
Pricing Theory, is introduced. An explicit formula for the average rate of
return of a group of investment/pension funds in a discrete time stochastic
model is derived and several properties of this index are shown. In particular,
it is proven to be martingale-fair, i.e. be a martingale provided the prices of
assets on the financial market form a vector martingale. The problem of merger
of the funds is treated in detail.


  The causal structure of the flat brane universe of RSII type is
re-investigated to clarify the boundary conditions for stochastic gravitational
waves. In terms of the Gaussian normal coordinate of the brane, a singularity
of the equation for gravitational waves appears in the bulk. We show that this
singularity corresponds to the ``seam singularity'' which is a singular
subspace on the brane universe. Based upon the causal structure, we discuss the
boundary conditions for gravitational waves in the bulk. Introducing a null
coordinate, we propose a numerical procedure to solve gravitational waves with
appropriate boundary conditions and show some examples of our numerical
results. This procedure could be also applied in scalar type perturbations. The
problem in the choice of the initial condition for gravitational waves is
briefly discussed.


  This paper studies some temporal dependence properties and addresses the
issue of parametric estimation for a class of state-dependent autoregressive
models for nonlinear time series in which we assume a stochastic autoregressive
coefficient depending on the first lagged value of the process itself. We call
such a model state-dependent first-order autoregressive process, (SDAR). We
introduce some assumptions under which this class of models is strictly
stationary and uniformly ergodic and we establish consistency and asymptotic
normality of the quasi-maximum likelihood estimator of the parameters. In order
to capture the potentiality of the model, we present an empirical application
to nonlinear time series provided by the weekly realized volatility extracted
from returns of some European financial indices. The comparison of forecasting
accuracy is made considering an alternative approach provided by a two-regime
SETAR model


  M star spectra, at wavelengths beyond 1.35 $\mu$m, are dominated by water
vapour yet terrestrial water vapour makes it notoriously difficult to make
accurate measurement from ground-based observations. We have used the short
wavelength spectrometer on the Infrared Space Observatory at four wavelength
settings to cover the 2.5--3.0 $\mu$m region for a range of M stars. The
observations show a good match with previous ground-based observations and with
synthetic spectra based on the Partridge & Schwenke (1997) line list though not
with the SCAN (Jorgensen et al. 2001) line list. We used a least-squared
minimisation technique to systematically find best fit parameters for the
sample of stars. The temperatures that we find indicate a relatively hot
temperature scale for M dwarfs. We consider that this could be a consequence of
problems with the Partridge & Schwenke linelist which leads to synthetic
spectra predicting water bands which are too strong for a given temperature.
Such problems need to be solved in the next generation of water vapour line
lists which will extend the calculation of water vapour to higher energy levels
with the good convergence necessary for reliable modelling of hot water vapour.
Then water bands can assume their natural role as the primary tool for the
spectroscopic analysis of M stars.


  The notion of the capacity of a polynomial was introduced by Gurvits around
2005, originally to give drastically simplified proofs of the Van der Waerden
lower bound for permanents of doubly stochastic matrices and Schrijver's
inequality for perfect matchings of regular bipartite graphs. Since this
seminal work, the notion of capacity has been utilized to bound various
combinatorial quantities and to give polynomial-time algorithms to approximate
such quantities (e.g., the number of bases of a matroid). These types of
results are often proven by giving bounds on how much a particular differential
operator can change the capacity of a given polynomial. In this paper, we unify
the theory surrounding such capacity preserving operators by giving tight
capacity preservation bounds for all nondegenerate real stability preservers.
We then use this theory to give a new proof of a recent result of Csikv\'ari,
which settled Friedland's lower matching conjecture.


  Inelastic neutron scattering measurements on the low energy spin waves in
CaFe2As2 show that the magnetic exchange interactions in the Fe layers are
exceptionally large and similar to the cuprates. However, the exchange between
layers is ~10% of the coupling in the layers and the magnetism is more
appropriately categorized as anisotropic three-dimensional, in contrast to the
two-dimensional cuprates. Band structure calculations of the spin dynamics and
magnetic exchange interactions are in good agreement with the experimental
data.


  We study the interface representation of the contact process (CP) at its
directed-percolation critical point, where the scaling properties of the
interface can be related to those of the original particle model.
Interestingly, such a behavior happens to be intrinsically anomalous and more
complex than that described by the standard Family-Vicsek dynamic scaling
Ansatz of surface kinetic roughening. We expand on a previous numerical study
by Dickman and Mu\~noz [Phys. Rev. E 62, 7632 (2000)] to fully characterize the
kinetic roughening universality class for interface dimensions $d=1, 2$, and 3.
Beyond obtaining scaling exponent values, we characterize the interface
fluctuations via their probability density function (PDF) and covariance, seen
to display universal properties which are qualitatively similar to those
recently assessed for the Kardar-Parisi-Zhang (KPZ) and other important
universality classes of kinetic roughening. Quantitatively, while for $d=1$ the
interface covariance seems to be well described by the KPZ, Airy$_1$
covariance, no such agreement occurs in terms of the fluctuation PDF nor the
scaling exponents.


  Dynamics of ethylene autoignition and Deflagration-to-Detonation Transition
(DDT) in a one-dimensional shock tube are numerically investigated using a
skeletal chemistry including 10 species and 10 reactions. Different combustion
modes are investigated through considering various premixed gas equivalence
ratios (0.2 to 2.0) and incident shock wave Mach numbers (1.8 to 3.2). Four
ignition and DDT modes are observed from the studied cases, i.e., no ignition,
deflagration combustion, detonation after reflected shock and deflagration
behind the incident shock. For detonation development behind the reflected
shock, three autoignition hot spots are formed. The first one occurs at the
wall surface after the re-compression of the reflected shock and contact
surface, which further develops to a reaction shock because of the explosion in
the explosion regime. The other two are off the wall, respectively caused by
the reflected shock rarefaction wave interaction and reaction induction in the
compressed mixture. The last hot spot develops to a reaction wave and couples
with the reflected shock after a DDT process, which eventually leads to
detonation combustion. For deflagration development behind the reflected shock,
the wave interactions, wall surface autoignition hot spot as well as its
induction of reaction shock are qualitatively similar to the mode of detonation
after incident shock reflection, before the reflected shock rarefaction wave
collision point. However, only one hot spot is induced after the collision,
which also develops to a reaction wave but cannot catch up with the reflected
shock. For deflagration behind the incident shock, deflagration combustion is
induced by the incident shock compression whereas detonation occurs after the
shock reflection.


  We study the minimum spanning tree problem on the complete graph $K_n$ where
an edge $e$ has a weight $W_e$ and a cost $C_e$, each of which is an
independent copy of the random variable $U^\gamma$ where $\gamma\leq 1$ and $U$
is the uniform $[0,1]$ random variable. There is also a constraint that the
spanning tree $T$ must satisfy $C(T)\leq c_0$. We establish, for a range of
values for $c_0,\gamma$, the asymptotic value of the optimum weight via the
consideration of a dual problem.


  In this paper we formulate a kind of new geometric measure of quantum
correlations. This new measure is in terms of the quantum Tsallis relative
entropy and can be viewed as a one-parameter extension quantum discordlike
measure that satisfies all requirements of a good measure of quantum
correlations. It is of an elegant analytic expression and contains several
existing good quantum correlation measures as special cases.


  The local galaxy distribution offers an interesting constraint to
cosmological models of structure formation. The galaxies are distributed in a
long, filamentary structure, presumably the result of large amplitude
gravitational instability, yet the local velocity field is cold. In particular,
there are no blueshifted galaxies within 5h^-1 Mpc except those within the
Local Group radius of 1h^-1 Mpc. Using numerical simulations we demonstrate
that such a situation is extremely rare for an observer in Omega=1 CDM models,
but is not uncommon in mixed dark matter (MDM) models that include massive
neutrinos.


  Experiments at CERN and Fermilab have been competing each other to improve
the measurement of a CP violation parameter, epsilon'/epsilon. Fermilab
KTeV-E832 recently announced their final result, Re(epsilon'/epsilon) = [19.2
+- 2.1] x 10^{-4}. The new world average shows the existence of direct CP
violation in the decay process itself with 12 standard deviations.


  We propose a general purpose approach to detect landmarks with improved
temporal consistency, and personalization. Most sparse landmark detection
methods rely on laborious, manually labelled landmarks, where inconsistency in
annotations over a temporal volume leads to sub-optimal landmark learning.
Further, high-quality landmarks with personalization is often hard to achieve.
We pose landmark detection as an image translation problem. We capture two sets
of unpaired marked (with paint) and unmarked videos. We then use a generative
adversarial network and cyclic consistency to predict deformations of landmark
templates that simulate markers on unmarked images until these images are
indistinguishable from ground-truth marked images. Our novel method does not
rely on manually labelled priors, is temporally consistent, and image class
agnostic -- face, and hand landmarks detection examples are shown.


  We report the results of three-dimensional numerical simulations of
convection-driven dynamos in relatively thin rotating spherical shells that
show a transition from an strong non-oscillatory dipolar magnetic field to a
weaker regularly oscillating dipolar field. The transition is induced primarily
by the effects a stress-free boundary condition. The variation of the inner to
outer radius ratio is found to have a less important effect.


  Recently, low-dimensional models of neuronal activity have been exactly
derived for large networks of deterministic, Quadratic Integrate-and-Fire (QIF)
neurons. Such firing rate models (FRM) describe the emergence of fast
collective oscillations (>30~Hz) via the frequency-locking of a subset of
neurons to the global oscillation frequency. However, the suitability of such
models to describe realistic neuronal states is seriously challenged by fact
that during episodes of fast collective oscillations, neuronal discharges are
often very irregular and have low firing rates compared to the global
oscillation frequency. Here we extend the theory to derive exact FRM for QIF
neurons to include noise, and show that networks of stochastic neurons
displaying irregular discharges at low firing rates during episodes of fast
oscillations, are governed by exactly the same evolution equations as
deterministic networks. Our results reconcile two traditionally confronted
views on neuronal synchronization, and upgrade the applicability of exact FRM
to describe a broad range of biologically realistic neuronal states.


  In classical sparse representation based classification and weighted SRC
algorithms, the test samples are sparely represented by all training samples.
They emphasize the sparsity of the coding coefficients but without considering
the local structure of the input data. To overcome the shortcoming, aiming at
the difficult problem of plant leaf recognition on the large-scale database, a
two-stage local similarity based classification learning method is proposed by
combining local mean-based classification method and local WSRC. In the first
stage, LMC is applied to coarsely classifying the test sample. nearest
neighbors of the test sample, as a neighbor subset, is selected from each
training class, then the local geometric center of each class is calculated. S
candidate neighbor subsets of the test sample are determined with the first
smallest distances between the test sample and each local geometric center. In
the second stage, LWSRC is proposed to approximately represent the test sample
through a linear weighted sum of all samples of the candidate neighbor subsets.
The rationale of the proposed method is as follows: the first stage aims to
eliminate the training samples that are far from the test sample and assume
that these samples have no effects on the ultimate classification decision,
then select the candidate neighbor subsets of the test sample. Thus the
classification problem becomes simple with fewer subsets; the second stage pays
more attention to those training samples of the candidate neighbor subsets in
weighted representing the test sample. This is helpful to accurately represent
the test sample. Experimental results on the leaf image database demonstrate
that the proposed method not only has a high accuracy and low time cost, but
also can be clearly interpreted.


  Developing secure distributed systems is difficult, and even harder when
advanced cryptography must be used to achieve security goals. Following prior
work, we advocate using secure program partitioning to synthesize cryptographic
applications: instead of implementing a system of communicating processes, the
programmer implements a centralized, sequential program, which is automatically
compiled into a secure distributed version that uses cryptography.
  While this approach is promising, formal results for the security of such
compilers are limited in scope. In particular, no security proof yet
simultaneously addresses subtleties essential for robust, efficient
applications: multiple cryptographic mechanisms, malicious corruption, and
asynchronous communication.
  In this work, we develop a compiler security proof that handles these
subtleties. Our proof relies on a novel unification of simulation-based
security, information-flow control, choreographic programming, and
sequentialization techniques for concurrent programs. While our proof targets
hybrid protocols, which abstract cryptographic mechanisms as idealized
functionalities, our approach offers a clear path toward leveraging Universal
Composability to obtain end-to-end, modular security results with fully
instantiated cryptographic mechanisms.
  Finally, following prior observations about simulation-based security, we
prove that our result guarantees robust hyperproperty preservation, an
important criterion for compiler correctness that preserves all source-level
security properties in target programs.


  The safety concern for unmanned systems, namely the concern for the potential
casualty caused by system abnormalities, has been a bottleneck for their
development, especially in populated areas. Evidently, the collision between
the unmanned system and the obstacles, including both moving and static
objects, accounts for a great proportion of the system abnormalities. The route
planning and corresponding controller are established in order to avoid the
collision, whereas, in the presence of uncertainties, it is possible that the
unmanned system would deviate from the predetermined route and collide with the
obstacles. Therefore, for the safety of unmanned systems, collision probability
estimation and further safety decision are very important. To estimate the
collision probability, the Monte Carlo method could be applied, however, it is
generally rather slow. This paper introduces a fast collision probability
estimation method based on finite-dimensional distribution, whose main idea is
to filter out the sampling points needed and generate the states directly by
samples of finite-dimensional distribution, reducing the estimation time
significantly. Besides, further techniques including the probabilistic
equidistance sampling and dimension reduction, also serve to reduce the
estimation time. The simulation shows that the proposed method reduces over 99%
of the estimation time.


  Feature selection is one of the most challenging issues in machine learning,
especially while working with high dimensional data. In this paper, we address
the problem of feature selection and propose a new approach called Evolving
Fast and Slow. This new approach is based on using two parallel genetic
algorithms having high and low mutation rates, respectively. Evolving Fast and
Slow requires a new parallel architecture combining an automatic system that
evolves fast and an effortful system that evolves slow. With this architecture,
exploration and exploitation can be done simultaneously and in unison. Evolving
fast, with high mutation rate, can be useful to explore new unknown places in
the search space with long jumps; and Evolving Slow, with low mutation rate,
can be useful to exploit previously known places in the search space with short
movements. Our experiments show that Evolving Fast and Slow achieves very good
results in terms of both accuracy and feature elimination.


  Kinetic traps are a notorious problem in equilibrium statistical mechanics,
where temperature quenches ultimately fail to bring the system to low energy
configurations. Using multifarious self-assembly as a model system, we
introduce a mechanism to escape kinetic traps by utilizing non-reciprocal
interactions between components. Introducing non-equilibrium effects offered by
broken action-reaction symmetry in the system, we can push the trajectory of
the system out of arrested dynamics. The dynamics of the model is studied using
tools from the physics of interfaces and defects. Our proposal can find
applications in self-assembly, glassy systems and systems with arrested
dynamics.


  In "Singularities on Normal Varieties", de Fernex and Hacon started the study
of singularities on non-Q-Gorenstein varieties using pullbacks of Weil
divisors. In "Log Terminal Singularities", the author of this paper and
Urbinati introduce a new class of singularities, called log terminal+, or
simply lt+, which they prove is rather well behaved. In this paper we will
continue the study of lt+ singularities, and we will show that they satisfy a
Bertini type result, inversion of adjunction and small deformation invariance,
and that they are naturally related to rational singularities. Finally, we will
provide a list of example (all of them with lt+ singularities) of the
pathologies that can occur in the study of non-Q-Gorenstein singularities.


  This work deals with a mathematical analysis of sodium's transport in a
tubular architecture of a kidney nephron. The nephron is modelled by two
counter-current tubules. Ionic exchange occurs at the interface between the
tubules and the epithelium and between the epithelium and the surrounding
environment (interstitium). From a mathematical point of view, this model
consists of a 5x5 semi-linear hyperbolic system. In the literature similar
models neglect the epithelial layers. In this paper, we show rigorously that
such models may be obtained by assuming that the permeabilities between lumen
and epithelium are large. Indeed we show that when these grow, solutions of the
5x5 system converge in a certain way to solutions of a reduced 3x3 system where
no epithelial layer is present. The problem is dened on a bounded spacial
domain with initial and boundary data. Establishing BV compactness forces to
introduce initial layers and to handle carefully the presence of lateral
boundaries.


  Effective progress monitoring is crucial for the successful delivery of the
construction project within the stipulated time and budget. Construction
projects are often monitored irregularly through time-consuming physical site
visits by multiple project stakeholders. Remote monitoring using robotic
cyber-physical systems (CPS) can make the process more efficient and safer.
This article presents a conceptual framework for robotic CPS for automated
reality capture and visualization for remote progress monitoring in
construction. The CPS integrates quadruped robot, Building Information
Modelling (BIM), and 360{\deg} reality capturing to autonomously capture, and
visualize up-to-date site information. Additionally, the study explores the
factors affecting acceptance of the proposed robotic CPS through
semi-structured interviews with seventeen progress monitoring experts. The
findings will guide construction management teams in adopting CPS in
construction and drive further research in the human-centered development of
CPS for construction.


  We characterize helix surfaces in the Berger sphere, that is surfaces which
form a constant angle with the Hopf vector field. In particular, we show that,
locally, a helix surface is determined by a suitable 1-parameter family of
isometries of the Berger sphere and by a geodesic of a 2-torus in the
3-dimensional sphere.


  We discuss a new way to develop the exactly solvable model of the QCD
critical endpoint by matching the deconfinement phase transition line for the
system of quark-gluon bags with the line of their vanishing surface tension
coefficient. In contrast to all previous findings in such models the deconfined
phase is defined not by an essential singularity of the isobaric partition
function, but by its simple pole. As a result we find out that the first order
deconfinement phase transition which is defined by a discontinuity of the first
derivative of system pressure is generated by a discontinuity of the derivative
of surface tension coefficient.


  In this study, the thermochemical state during turbulent flame-wall
interaction of a stoichiometric methane-air flame is investigated using a fully
resolved simulation with detailed chemistry. The turbulent side-wall quenching
flame shows both head-on quenching and side-wall quenching-like behavior that
significantly affects the CO formation in the near-wall region. The detailed
insights from the simulation are used to evaluate a recently proposed flame
(tip) vortex interaction mechanism identified from experiments on turbulent
side-wall quenching. It describes the entrainment of burnt gases into the fresh
gas mixture near the flame's quenching point. The flame behavior and
thermochemical states observed in the simulation are similar to the phenomena
observed in the experiments. A novel chemistry manifold is presented that
accounts for both the effects of flame dilution due to exhaust gas
recirculation in the flame vortex interaction area and enthalpy losses to the
wall. The manifold is validated in an a-priori analysis using the simulation
results as a reference. The incorporation of exhaust gas recirculation effects
in the manifold leads to a significantly increased prediction accuracy in the
near-wall regions of flame-vortex interactions.


  Modern vehicles rely on a fleet of electronic control units (ECUs) connected
through controller area network (CAN) buses for critical vehicular control.
With the expansion of advanced connectivity features in automobiles and the
elevated risks of internal system exposure, the CAN bus is increasingly prone
to intrusions and injection attacks. As ordinary injection attacks disrupt the
typical timing properties of the CAN data stream, rule-based intrusion
detection systems (IDS) can easily detect them. However, advanced attackers can
inject false data to the signal/semantic level, while looking innocuous by the
pattern/frequency of the CAN messages. The rule-based IDS, as well as the
anomaly-based IDS, are built merely on the sequence of CAN messages IDs or just
the binary payload data and are less effective in detecting such attacks.
Therefore, to detect such intelligent attacks, we propose CANShield, a deep
learning-based signal-level intrusion detection framework for the CAN bus.
CANShield consists of three modules: a data preprocessing module that handles
the high-dimensional CAN data stream at the signal level and parses them into
time series suitable for a deep learning model; a data analyzer module
consisting of multiple deep autoencoder (AE) networks, each analyzing the
time-series data from a different temporal scale and granularity, and finally
an attack detection module that uses an ensemble method to make the final
decision. Evaluation results on two high-fidelity signal-based CAN attack
datasets show the high accuracy and responsiveness of CANShield in detecting
advanced intrusion attacks.


  Solar activity has been recorded as auroras or sunspots in various historical
sources. These records are of much importance for investigating both long-term
solar activities and extremely intense solar flares. In previous studies, they
were recorded as "vapor," "cloud," or "light," especially in oriental
historical sources; however, the terminology was not discussed adequately and
is still quite vague. In this paper, we suggest the possibility of "unusual
rainbow" and "white rainbow" as candidates of historical auroras in oriental
historical sources and examine if it is probable. This discovery will help us
to make more comprehensive historical auroral catalogues and require us to add
these terms to auroral candidates in oriental historical sources.


  The name "big bang" introduced by Fred Hoyle in 1949 is one of the most
successful scientific neologisms ever. How did the name originate and how was
it received by physicists and astronomers in the period leading up to the hot
big bang consensus model in the late 1960s? How did it reflect the meanings of
the big bang, a concept that predates the name by nearly two decades? The paper
gives a detailed account of names and concepts associated with finite-age
cosmological models from the 1920s to the 1970s. It turns out that Hoyle's
celebrated name has a richer and more surprising history than commonly assumed
and also that the literature on modern cosmology and its history includes many
common mistakes and errors. By following the story of "big bang" a new
dimension is added to the historical understanding of the emergence of modern
cosmology.


  This paper considers the epistemic justification for a simplicity preference
in inductive inference that may be obtained from the machine learning framework
of statistical learning theory. Uniting elements from both earlier arguments
suggesting and rejecting such a justification, the paper spells out a qualified
means-ends and model-relative justificatory argument, built on statistical
learning theory's central mathematical learning guarantee for the method of
empirical risk minimization.


  By the example of phase transition (alpha - beta) in p-dichlorobenzene it is
shown, that for phase transition presence of vacancies is necessary. In an
ideal crystal, such transition is improbable, that is confirmed with
calculations and their consent with experimental information on spectrums of
the lattice oscillations.


  This paper presents a mixed methods study on how deaf, hard of hearing and
hearing viewers perceive live TV caption quality with captioned video stimuli
designed to mirror TV captioning experiences. To assess caption quality, we
used four commonly-used quality metrics focusing on accuracy: word error rate,
weighted word error rate, automated caption evaluation (ACE), and its successor
ACE2. We calculated the correlation between the four quality metrics and viewer
ratings for subjective quality and found that the correlation was weak,
revealing that other factors besides accuracy affect user ratings.
Additionally, even high-quality captions are perceived to have problems,
despite controlling for confounding factors. Qualitative analysis of viewer
comments revealed three major factors affecting their experience: Errors within
captions, difficulty in following captions, and caption appearance. The
findings raise questions as to how objective caption quality metrics can be
reconciled with the user experience across a diverse spectrum of viewers.


  We develop a new finite difference method for the wave equation in second
order form. The finite difference operators satisfy a summation-by-parts (SBP)
property. With boundary conditions and material interface conditions imposed
weakly by the simultaneous-approximation-term (SAT) method, we derive energy
estimates for the semi-discretization. In addition, error estimates are derived
by the normal mode analysis. The proposed method is termed as energy-based
because of its similarity with the energy-based discontinuous Galerkin method.
When imposing the Dirichlet boundary condition and material interface
conditions, the traditional SBP-SAT discretization uses a penalty term with a
mesh-dependent parameter, which is not needed in our method. Furthermore,
numerical dissipation can be added to the discretization through the boundary
and interface conditions. We present numerical experiments that verify
convergence and robustness of the proposed method.


  In terms of linearized Gross-Pitaevskii equation we have studied the process
of sound emission arises from a supersonic particle motion in a Bose-condensed
gas. By analogy with the method used for description of Vavilov-Cherenkov
phenomenon, we have found a friction work created by the particle generated
condensate polarization. For comparison we have found radiation intensity of
excitations. Both methods gives the same result.


  One of the few ways to measure the properties of Dark Energy is to extend the
Hubble daigram (HD) to higher redshifts with Gamma-Ray Bursts (GRBs). GRBs have
at least five properties (their spectral lag, variability, spectral peak photon
energy, time of the jet break, and the minimum rise time) which have
correlations to the luminosity of varying quality. In this paper, I construct a
GRB HD with 69 GRBs over a redshift range of 0.17 to >6, with half the bursts
having a redshift larger than 1.7. This paper uses over 3.6 times as many GRBs
and 12.7 times as many luminosity indicators as any previous GRB HD work. For
the gravitational lensing and Malmquist biases, I find that the biases are
small, with an average of 0.03 mag and an RMS scatter of 0.14 mag in the
distance modulus. The GRB HD is well-behaved and nicely delineates the shape of
the HD. The reduced chi-square for the fit to the concordance model is 1.05 and
the RMS scatter about the concordance model is 0.65 mag. This accuracy is just
a factor of 2.0 times that gotten for the same measure from all the big
supernova surveys. I fit the GRB HD to a variety of models, including where the
Dark Energy has its equation of state parameter varying as w(z)=w_0 + w_a
z/(1+z). I find that the concordance model is consistent with the data. That
is, the Dark Energy can be described well as a Cosmological Constant that does
not change with time. (abridged)


  Einstein believed that Mach's principle should play a major role in finding a
meaningful spacetime geometry, though it was discovered later that his field
equations gave some solutions which were not Machian. It is shown, in this
essay, that the kinematical $\Lambda$ models, which are invoked to solve the
cosmological constant problem, are in fact consistent with Mach's ideas. One
particular model in this category is described which results from the
microstructure of spacetime and seems to explain the current observations
successfully and also has some benefits over the conventional models. This
forces one to think whether the Mach's ideas and the cosmological constant are
interrelated in some way.


  Distilling knowledge from convolutional neural networks (CNNs) is a
double-edged sword for vision transformers (ViTs). It boosts the performance
since the image-friendly local-inductive bias of CNN helps ViT learn faster and
better, but leading to two problems: (1) Network designs of CNN and ViT are
completely different, which leads to different semantic levels of intermediate
features, making spatial-wise knowledge transfer methods (e.g., feature
mimicking) inefficient. (2) Distilling knowledge from CNN limits the network
convergence in the later training period since ViT's capability of integrating
global information is suppressed by CNN's local-inductive-bias supervision. To
this end, we present Cumulative Spatial Knowledge Distillation (CSKD). CSKD
distills spatial-wise knowledge to all patch tokens of ViT from the
corresponding spatial responses of CNN, without introducing intermediate
features. Furthermore, CSKD exploits a Cumulative Knowledge Fusion (CKF)
module, which introduces the global response of CNN and increasingly emphasizes
its importance during the training. Applying CKF leverages CNN's local
inductive bias in the early training period and gives full play to ViT's global
capability in the later one. Extensive experiments and analysis on ImageNet-1k
and downstream datasets demonstrate the superiority of our CSKD. Code will be
publicly available.


  We study in detail the sensitivity of neutrino oscillation probabilities to
the fundamental neutrino parameters and their possible determination through
experiments. The first part of the paper is devoted to the broad theme of
isolating regions in the neutrino (and anti-neutrino) energy and propagation
length that are sensitive to the oscillation parameters. Such a study is
relevant to neutrinos both from the Earth's atmosphere or from a neutrino
factory. For completeness we discuss the sensitivity, however small, to the
parameters involved in a three-generation framework, and to the Earth matter
density profile. We then study processes relevant to atmospheric neutrinos
which are sensitive to and allow precision measurements of the mixing angle
theta_23 and mass-squared difference delta_32 apart from the mixing angle
theta_13. Crucial to this analysis is charge identification; detectors having
this capability can isolate these matter effects. In particular, we address the
issue of using matter effects to determine whether the mixing angle theta_23 is
maximal, and, if not, to explore how well its octant can be determined. When
realistic detector resolutions are included, we find that deviations of about
15% (20%) from a maximal value of sin^2 theta_23=1/2 can be measured at 95%
(99%) CL provided theta_13 is non-zero, sin^2 theta_13 >= 0.015, and the
neutrino mass ordering is normal, with fairly large exposures of 1000
kton-years.


  The Berry phase in a composite system with only one subsystem being driven
has been studied in this Letter. We choose two spin-$\frac 1 2 $ systems with
spin-spin couplings as the composite system, one of the subsystems is driven by
a time-dependent magnetic field. We show how the Berry phases depend on the
coupling between the two subsystems, and what is the relation between these
Berry phases of the whole system and those of the subsystems.


  We show that the Hamiltonians of the open relativistic Toda system are
elements of the generic basis of a cluster algebra, and in particular are
cluster characters of nonrigid representations of a quiver with potential.
Using cluster coordinates defined via spectral networks, we identify the phase
space of this system with the wild character variety related to the periodic
nonrelativistic Toda system by the wild nonabelian Hodge correspondence. We
show that this identification takes the relativistic Toda Hamiltonians to
traces of holonomies around a simple closed curve. In particular, this provides
nontrivial examples of cluster coordinates on $SL_n$-character varieties for $n
> 2$ where canonical functions associated to simple closed curves can be
computed in terms of quivers with potential, extending known results in the
$SL_2$ case.


  An overview is given in section 1, of uncertain building blocks of
present-day cosmologies. Thereafter, these edited lecture notes deal with the
following four special problems: (1) They advertise Wiltshire's result --
making `dark energy' obsolete -- that accelerated cosmic expansion may be an
artefact, due to an incorrect evaluation of the cosmic timescale in a Universe
whose bulk matter is inhomogeneously distributed. (2) They cast doubt on
Hawking's prediction of black-hole evaporation. (3) They point at various
inconsistencies of the black-hole paradigm, in favour of nuclear-burning
central engines of AGN. (4) They re-interpret (a best case of) `anomalous
redshifts' as non-cosmological, kinematic redshifts in strong jet sources.


  Fine control of the chiral light-matter interaction at the nanoscale, by
exploiting designed metamaterial architecture, represents a cutting-edge craft
in the field of biosensing, quantum and classic nanophotonics. Recently,
artificially engineered 3D nanohelices have demonstrated programmable wide
chiroptical properties by tuning materials and architecture, but fundamental
diffractive aspects that are to the origin of chiral resonances still remain
elusive. Here, we proposed a novel concept of three-dimensional chiral
MetaCrystal, where the chiroptical properties are finely tuned by in-plane and
out-of-plane diffractive coupling. Different chiral dipolar modes can be
excited along the helix arms, generating far field optical resonances and
radiation pattern with in-plane side lobes and suggesting that a combination of
efficient dipole excitation and diffractive coupling matching controls the
collective oscillations among the neighbor helices in the chiral MetaCrystal.
This concept enables the tailorability of chiral properties in a broad spectral
range for a plethora of forefront applications, since the proposed compact
chiral MetaCrystal can be suitable for integration with quantum emitters and
can open perspectives in novel schemes of enantiomeric detection.


  The reheating dynamics after the inflation induced by $R^2$-corrected $f(R)$
model is considered. To avoid the complexity of solving the fourth order
equations, we analyze the inflationary and reheating dynamics in the Einstein
frame and its analytical solutions are derived. We also perform numerical
calculation including the backreaction from the particle creation and compare
the results with the analytical solutions. Based on the results, observational
constraints on the model are discussed.


  We present Cobalt, a novel atomic broadcast algorithm that works in networks
with non-uniform trust and no global agreement on participants, and is
probabilistically guaranteed to make forward progress even in the presence of
maximal faults and arbitrary asynchrony. The exact properties that Cobalt
satisfies makes it particularly applicable to designing an efficient
decentralized "voting network" that allows a public, open-entry group of nodes
to agree on changes to some shared set of rules in a fair and consistent manner
while tolerating some trusted nodes and arbitrarily many untrusted nodes
behaving maliciously. We also define a new set of properties which must be
satisfied by any safe decentralized governance algorithm, and all of which
Cobalt satisfies.


  Given an image with multiple people, our goal is to directly regress the pose
and shape of all the people as well as their relative depth. Inferring the
depth of a person in an image, however, is fundamentally ambiguous without
knowing their height. This is particularly problematic when the scene contains
people of very different sizes, e.g. from infants to adults. To solve this, we
need several things. First, we develop a novel method to infer the poses and
depth of multiple people in a single image. While previous work that estimates
multiple people does so by reasoning in the image plane, our method, called
BEV, adds an additional imaginary Bird's-Eye-View representation to explicitly
reason about depth. BEV reasons simultaneously about body centers in the image
and in depth and, by combing these, estimates 3D body position. Unlike prior
work, BEV is a single-shot method that is end-to-end differentiable. Second,
height varies with age, making it impossible to resolve depth without also
estimating the age of people in the image. To do so, we exploit a 3D body model
space that lets BEV infer shapes from infants to adults. Third, to train BEV,
we need a new dataset. Specifically, we create a "Relative Human" (RH) dataset
that includes age labels and relative depth relationships between the people in
the images. Extensive experiments on RH and AGORA demonstrate the effectiveness
of the model and training scheme. BEV outperforms existing methods on depth
reasoning, child shape estimation, and robustness to occlusion. The code and
dataset are released for research purposes.


  We present a new approach to obtaining photometric redshifts using a kernel
learning technique called Support Vector Machines (SVMs). Unlike traditional
spectral energy distribution fitting, this technique requires a large and
representative training set. When one is available, however, it is likely to
produce results that are comparable to the best obtained using template fitting
and artificial neural networks. Additional photometric parameters such as
morphology, size and surface brightness can be easily incorporated. The
technique is demonstrated using samples of galaxies from the Sloan Digital Sky
Survey Data Release 2 and the hybrid galaxy formation code GalICS. The RMS
error in redshift estimation is $<0.03$ for both samples. The strengths and
limitations of the technique are assessed.


  The mirror curves enable us to study B-model topological strings on
non-compact toric Calabi--Yau threefolds. One of the method to obtain the
mirror curves is to calculate the partition function of the topological string
with single brane. In this paper, we discuss two types of geometries; one is
the chain of $N$ $\mathbb{P}^1$'s which we call `$N$-chain geometry,' the other
is the chain of $N$ $\mathbb{P}^1$'s with a compactification which we call
`periodic $N$-chain geometry.' We calculate the partition functions of the open
topological strings on these geometries, and obtain the mirror curves and their
quantization. Through the computation, we find some difference equations of
(elliptic) hypergeometric functions. We also find a relation between the
periodic chain and $\infty$-chain geometries, which implies a possible
connection between 5d and 6d gauge theories in the large $N$ limit.


  The main part of this book, Elements of Linear Accelerators, outlines in Part
1 a framework for non-relativistic linear accelerator focusing and accelerating
channel design, simulation, optimization and analysis where space charge is an
important factor. Part 1 is the most important part of the book; grasping the
framework is essential to fully understand and appreciate the elements within
it, and the myriad application details of the following Parts. The treatment
concentrates on all linacs, large or small, intended for high-intensity, very
low beam loss, factory-type application. The Radio-Frequency-Quadrupole (RFQ)
is especially developed as a representative and the most complicated linac form
(from dc to bunched and accelerated beam), extending to practical design of
long, high energy linacs, including space charge resonances and beam halo
formation, and some challenges for future work. Also a practical method is
presented for designing Alternating-Phase- Focused (APF) linacs with long
sequences and high energy gain. Full open-source software is available. The
following part, Calm in the Resonances and Other Tales, contains eyewitness
accounts of nearly 60 years of participation in accelerator technology.
  (September 2023) The LINACS codes are released at no cost and, as always,
with fully open-source coding. (p.2 & Ch 19.10)


  We propose an experimental mathematics approach leading to the
computer-driven discovery of various structural properties of general counting
functions coming from enumeration of walks.


  In the paper we present a proof of the local criterion for crystalline
structures which generalizes the local criterion for regular systems. A Delone
set is called a crystal if it is invariant with respect to a crystallgraphic
group. So-called locally antipodal Delone sets, i.e. such sets in which all
2R-clusters are centrally symmetrical, are considered. It turns out that the
local antipodal sets have crystalline structure. Moreover, if in a locally
antipodal set all 2R-clusters are the same the set is a regular system, i.e. a
Delone set whose symmetry group operates transitively on the set.


  A method is proposed to handle the sign problem in the simulation of systems
having indefinite or complex-valued measures. In general, this new approach,
which is based on renormalisation blocking, is shown to yield statistical
errors smaller than the crude Monte Carlo method using absolute values of the
original measures. The improved method is applied to the 2D Ising model with
temperature generalised to take on complex values. It is also adapted to
implement Monte Carlo Renormalisation Group calculations of the magnetic and
thermal critical exponents.


  A recently formulated statistical mechanics method is used to study the phase
transition occurring in a generalisation of the Traveling Salesman Problem
(TSP) known as the centred TSP. The method shows that the problem has clear
signs of a crossover, but is only able to access (unscaled) finite temperatures
above the transition point. The solution of the problem using this method
displays a curious duality.


  Simplifying the geometry of a CAD model using defeaturing techniques enables
more efficient discretisation and subsequent simulation for engineering
analysis problems. Understanding the effect this simplification has on the
solution helps to decide whether the simplification is suitable for a specific
simulation problem. It can also help to understand the functional effect of a
geometry feature. The effect of the simplification is quantified by a
user-defined quantity of interest which is assumed to be (approximately) linear
in the solution. A bound on the difference between the quantity of interest of
the original and simplified solutions based on the energy norm is derived. The
approach is presented in the context of electrostatics problems, but can be
applied in general to a range of elliptic partial differential equations.
Numerical results on the efficiency of the bound are provided for
electrostatics problems with simplifications involving changes inside the
problem domain as well as changes to the boundaries.


  In this paper, a modified Gerchberg Saxton algorithm for generating improved
robust binary hologram is presented.


  We study the absorption of plane waves by Kerr black holes. We calculate the
absorption cross section: the area of the black hole shadow at a finite
wavelength. We present a unified picture of the absorption of all massless
bosonic fields, focussing on the on-axis incidence case. We investigate the
spin-helicity effect, arising from a coupling between dragging of frames and
the helicity of a polarized wave. We introduce and calibrate an extended sinc
approximation which provides new quantitative data on the spin-helicity effect
in strong-field gravity.


  We study the structure of newly born neutron stars (protoneutron stars)
within the finite temperature Brueckner-Bethe-Goldstone theoretical approach
including also hyperons. We find that for purely nucleonic stars both finite
temperature and neutrino trapping reduce the value of the maximum mass. For
hyperonic stars the effect is reversed, because neutrino trapping shifts the
appearance of hyperons to larger baryon density and stiffens considerably the
equation of state.


  Starting from the Fierz transform of the two-flavour 't Hooft interaction (a
four-fermion Lagrangian with antisymmetric Lorentz tensor interaction terms
augmented by an NJL type Lorentz scalar inetraction responsible for dynamical
symmetry breaking and quark mass generation), we show that: (1) antisymmetric
tensor Nambu-Goldstone bosons appear provided that the scalar and tensor
couplings stand in the proportion of two to one, which ratio appears naturally
in the Fierz transform of the two-flavour 't Hooft interaction; (2) non-Abelian
vector gauge bosons coupled to this system acquire a non-zero mass.
Axial-vector fields do not mix with antisymmetric tensor fields, so there is no
mass shift there.


  We present an inductive method for constructing the basic spin
representations of the double covers of the symmetric groups over fields of any
characteristic.


  The two dimensional system of electrons in a high magnetic field offers an
opportunity to investigate a phase transition from a quantum liquid into a
Wigner solid. Recent experiments have revealed an incipient composite fermion
liquid in a parameter range where theory and many experiments had previously
suggested the Wigner crystal phase, thus calling into question our current
understanding. This Letter shows how very small quantitative corrections (< 1%)
in the energy due to the weak interaction between composite fermions can cause
a fundamental change in the nature of the ground state, thus providing insight
into the puzzling experimental results.


  We show that Dranishnikov's asymptotic property C is preserved by direct
products and the free product of discrete metric spaces. In particular, if $G$
and $H$ are groups with asymptotic property C, then both $G \times H$ and $G *
H$ have asymptotic property C. We also prove that a group~$G$ has asymptotic
property C if $1\to K\to G\to H\to 1$ is exact, if $\operatorname{asdim}
K<\infty$, and if $H$ has asymptotic property C. The groups are assumed to have
left-invariant proper metrics and need not be finitely generated. These results
settle questions of Dydak and Virk, of Bell and Moran, and an open problem in
topology from the Lviv Topological Seminar.


  We have completed the one-loop renormalisation of the Next-to-Minimal
Supersymmetric Standard Model (NMSSM) allowing for and comparing between
different renormalisation schemes. A special attention is paid to on-shell
schemes. We study a variety of these schemes based on alternative choices of
the physical input parameters. In this paper we present our approach to the
renormalisation of the NMSSM and report on our results for the
neutralino-chargino and sfermion sectors. We will borrow some results from our
study of the Higgs sector whose full discussion is left for a separate
publication. We have implemented the set up for all the sectors of the NMSSM
within \sloops, a code for the automatic computation of one-loop corrections
initially developed for the standard model and the MSSM. Among the many
applications that allows the code, we present here the one-loop corrections to
neutralino masses and to partial widths of neutralinos and charginos into final
states with one gauge boson. One-loop electroweak and QCD corrections to the
partial widths of third generation sfermions into a fermion and a chargino or a
neutralino are also computed.


  Topological flat band (TFB) has been proposed theoretically in various
lattice models, to exhibit a rich spectrum of intriguing physical behaviors.
However, the experimental demonstration of flat band (FB) properties has been
severely hindered by the lack of materials realization. Here, by screening
materials from a first-principles materials database, we identify a group of 2D
materials with TFBs near the Fermi level, covering some simple line-graph and
generalized line-graph FB lattice models. These include the Kagome sublattice
of O in TiO2 yielding a spin-unpolarized TFB, and that of V in ferromagnetic
V3F8 yielding a spin-polarized TFB. Monolayer Nb3TeCl7 and its counterparts
from element substitution are found to be breathing-Kagome-lattice crystals.
The family of monolayer III2VI3 compounds exhibit a TFB representing the
coloring-triangle lattice model. ReF3, MnF3 and MnBr3 are all predicted to be
diatomic-Kagome-lattice crystals, with TFB transitions induced by atomic
substitution. Finally, HgF2, CdF2 and ZnF2 are discovered to host dual TFBs in
the diamond-octagon lattice. Our findings pave the way to further experimental
exploration of eluding FB materials and properties.


  We describe new autoequivalences of derived categories of coherent sheaves
arising from what we call $\mathbb P^n$-objects of the category. Standard
examples arise from holomorphic symplectic manifolds. Under mirror symmetry
these autoequivalences should be mirror to Seidel's Dehn twists about
lagrangian $\mathbb P^n$ submanifolds.
  We give various connections to spherical objects and spherical twists, and
include a simple description of Atiyah and Kodaira-Spencer classes in an
appendix.


  We develop a scheme of Gor'kov Green's functions to treat impurity effects on
Caroli-de Gennes-Matricon (CdGM) mode in superconductors (SCs) by improving the
Kopnin-Kravtsov scheme with respect to the coherence factors and applicability
to various SCs. We can study the impurity effects keeping the discreteness of
the energy spectrum in contrast to the quasiclassical theory. We can thus apply
this scheme to the SCs with the small quasiclassical parameter
$k_{\mathrm{F}}\xi_{0}$ (which is the product of the Fermi wavenumber
$k_{\mathrm{F}}$ and the coherence length $\xi_{0}$ in pure SC at zero
temperature) and/or superclean regime $\Delta_{\mathrm{mini}} \tau \gg1$
($\Delta_{\mathrm{mini}}$ and $\tau$ denote, respectively, the level spacing of
the CdGM mode called minigap and the relaxation time for CdGM mode and we take
$\hbar =1$). We investigate the impurity effects as a white noise for a vortex
in an s-wave SC and two types of vortices in a chiral p-wave SC, for various
values of the quasiclassical parameters and impurity strengths (from moderately
clean regime to superclean regime), and confirm the validity of this scheme.


  The challenge that this paper addresses is how to efficiently minimize the
cost and manual labour for automatically generating object oriented geometric
Digital Twins (gDTs) of industrial facilities, so that the benefits provide
even more value compared to the initial investment to generate these models.
Our previous work achieved the current state-of-the-art class segmentation
performance (75% average accuracy per point and average AUC 90% in the CLOI
dataset classes) as presented in (Agapaki and Brilakis 2020) and directly
produces labelled point clusters of the most important to model objects (CLOI
classes) from laser scanned industrial data. CLOI stands for C-shapes,
L-shapes, O-shapes, I-shapes and their combinations. However, the problem of
automated segmentation of individual instances that can then be used to fit
geometric shapes remains unsolved. We argue that the use of instance
segmentation algorithms has the theoretical potential to provide the output
needed for the generation of gDTs. We solve instance segmentation in this paper
through (a) using a CLOI-Instance graph connectivity algorithm that segments
the point clusters of an object class into instances and (b) boundary
segmentation of points that improves step (a). Our method was tested on the
CLOI benchmark dataset (Agapaki et al. 2019) and segmented instances with
76.25% average precision and 70% average recall per point among all classes.
This proved that it is the first to automatically segment industrial point
cloud shapes with no prior knowledge other than the class point label and is
the bedrock for efficient gDT generation in cluttered industrial point clouds.


  For positive integers $d,m,n\geq 1$ with $(m,n)\not= (1,1)$ and $\Bbb K=\Bbb
R$ or $\Bbb C$, let $Q^{d,m}_{n}(\Bbb K)$ denote the space of $m$-tuples
$(f_1(z),\cdots ,f_m(z))\in \Bbb K [z]^m$ of $\Bbb K$-coefficients monic
polynomials of the same degree $d$ such that polynomials $\{f_k(z)\}_{k=1}^m$
have no common {\it real} root of multiplicity $\geq n$ (but may have complex
common root of any multiplicity). %% These spaces can be regarded as one of
generalizations of the spaces defined and studied by Arnold and Vassiliev, and
they may be also considered as the real analogues of the spaces studied by B.
Farb and J. Wolfson. In this paper, we shall determine their homotopy types
explicitly and generalize some previously obtained results.


  We present an assessment of the performance of WISE and the AllWISE data
release in a section of the Galactic Plane. We lay out an approach to
increasing the reliability of point source photometry extracted from the
AllWISE catalog in Galactic Plane regions using parameters provided in the
catalog. We use the resulting catalog to construct a new, revised young star
detection and classification scheme combining WISE and 2MASS near and
mid-infrared colors and magnitudes and test it in a section of the Outer Milky
Way. The clustering properties of the candidate Class I and II stars using a
nearest neighbor density calculation and the two-point correlation function
suggest that the majority of stars do form in massive star forming regions, and
any isolated mode of star formation is at most a small fraction of the total
star forming output of the Galaxy. We also show that the isolated component may
be very small and could represent the tail end of a single mechanism of star
formation in line with models of molecular cloud collapse with supersonic
turbulence and not a separate mode all to itself.


  This paper has been removed by arXiv administrators because it plagiarizes
hep-th/0311050, gr-qc/0306101, gr-qc/9601044, gr-qc/0511106, gr-qc/0303034, and
gr-qc/0212018.


  (Abridged) We consider the expansion of an initially self-gravitating,
static, singular, isothermal cloud core. For t>0, the gas is ionized and heated
to a higher uniform temperature by the formation of a luminous, but massless,
star in its center. The approximation that the mass and gravity of the central
star is negligible for the subsequent motion of the HII region holds for
distances much greater than about 100 AU and for the massive cloud cores that
give rise to high-mass stars. If the initial ionization and heating is
approximated to occur instantaneously at t=0, then the subsequent flow (for r
>> 100 AU) caused by the resulting imbalance between self-gravity and thermal
pressure is self-similar. Because of the steep density profile, pressure
gradients produce a shock front that travels into the cloud, accelerating the
gas to supersonic velocities in what has been called the ``champagne phase.''
We then study the self-similar solutions of the expansion of HII regions
embedded in molecular clouds characterized by more general power-law density
distributions with exponent 3/2 < n < 3. In these cases, the shock velocity is
an increasing function of the exponent n, and diverges as n tends to 3. We show
that this happens because the model includes an origin, where the pressure
driving the shock diverges because the enclosed heated mass is infinite. Our
results imply that the continued photoevaporation of massive reservoirs of
neutral gas (e.g., surrounding disks and/or globules) nearby to the embedded
ionizing source is required in order to maintain over a significant timescale
the emission measure observed in champagne flows.


  Nations with non-competitive higher education systems and with high levels of
corruption, are more exposed to phenomena of discrimination and favoritism in
faculty recruitment. Italy is a case in point, as shown by empirical studies,
judicial reports and media attention. Governments have intervened repeatedly to
reduce the problem, with scarce success. The 2010 reforms to the university
recruitment system provided that access to the ranks of associate and full
professor would now be possible only through an initial "scientific
habilitation" to be awarded by sectorial committees of national experts. The
objective of this work is to analyze the relationship of the recent
habilitation procedure outcomes to the actual scientific merit of the various
candidates, as well as to other variables that are explicative of possible
practices of favoritism and discrimination. The analyses identify the presence
of potential cases of discrimination and favoritism.


  Learning based approaches have not yet achieved their full potential in
optical flow estimation, where their performance still trails heuristic
approaches. In this paper, we present a CNN based patch matching approach for
optical flow estimation. An important contribution of our approach is a novel
thresholded loss for Siamese networks. We demonstrate that our loss performs
clearly better than existing losses. It also allows to speed up training by a
factor of 2 in our tests. Furthermore, we present a novel way for calculating
CNN based features for different image scales, which performs better than
existing methods. We also discuss new ways of evaluating the robustness of
trained features for the application of patch matching for optical flow. An
interesting discovery in our paper is that low-pass filtering of feature maps
can increase the robustness of features created by CNNs. We proved the
competitive performance of our approach by submitting it to the KITTI 2012,
KITTI 2015 and MPI-Sintel evaluation portals where we obtained state-of-the-art
results on all three datasets.


  The proliferation of IoT devices which can be more easily compromised than
desktop computers has led to an increase in the occurrence of IoT based botnet
attacks. In order to mitigate this new threat there is a need to develop new
methods for detecting attacks launched from compromised IoT devices and
differentiate between hour and millisecond long IoTbased attacks. In this paper
we propose and empirically evaluate a novel network based anomaly detection
method which extracts behavior snapshots of the network and uses deep
autoencoders to detect anomalous network traffic emanating from compromised IoT
devices. To evaluate our method, we infected nine commercial IoT devices in our
lab with two of the most widely known IoT based botnets, Mirai and BASHLITE.
Our evaluation results demonstrated our proposed method's ability to accurately
and instantly detect the attacks as they were being launched from the
compromised IoT devices which were part of a botnet.


  This paper addresses the problem of exponentially increasing
sub-packetization with the number of users in a centralized coded caching
system by introducing a new coded caching scheme inspired by the symmetric
neighboring consecutive side information index coding problem. The scheme has a
placement policy where the number of sub-packets required grows only linearly
with the number of users, with no restriction on file size, and a delivery
policy which is instantaneously decodable. Further, an application of the new
delivery scheme in a multi-access coded caching set-up is studied and a few
results in that direction are presented. In particular, in the multi-access
set-up, for cases where optimality rate-memory trade-off characterizations are
available, it is shown that the new delivery scheme achieves optimal or
near-optimal rates.


  The plasmonic investigation involves activating a multilayer consisting of
silver, platinum, silica, and silicon. Hybrid waveguide and surface plasmon
polariton modes define the device plasmonic activity. The presence of
micrometric features on the multilayer surface facilitates the formation of the
waveguide mode. Moreover, they produce a transmitted signal that is associated
with localized surface plasmon resonance. A red dye adsorbed onto a multilayer
surface causes a strong coupling between excitons and polaritons. This coupling
increases the radiation force, and the dye-induced enhancement of radiation
force supports the use of passive chirality spectroscopy to measure the optical
forces acting on enantiomers. In conclusion, when a Kretschmann scheme is
combined with the de-polarization, a built-in asymmetry results in a different
optical flux of spectrum photons, resulting in distinct, enantioselective, and
solely polarization-dependent spectral contrast, and the enantioselectivity is
demonstrated for the D and L penicillamine.


  In this work we performed a detailed analysis on the calculation of 43
critical experiments from 6 experimental series all describing plutonium
nitrate in aqueous solution contained in metal spheres. The underlying
experimental data is taken from the handbook of the International Criticality
Safety Benchmark Evaluation Project (ICSBEP) Working Group. We present our
modeling assumptions which were derived from the interpretation of the
experimental data and discuss the resulting sensitivity analysis. Although the
experiments share some components, the derived correlation coefficients are for
many cases statistically not significant. Comparing our findings for the
correlation coefficients with available data from the DICE Database we find an
agreement for the correlation coefficients due to nuclear data. We also compare
our results for the correlation coefficients due to experimental uncertainty.
Our findings indicate that for the reliable Determination of correlation
coefficients a detailed study of the underlying experimental data, the modeling
approach and assumptions, and the resulting sensitivity analysis seems to be
inevitable.


  An essential requirement for a real-world Knowledge Base Question Answering
(KBQA) system is the ability to detect answerability of questions when
generating logical forms. However, state-of-the-art KBQA models assume all
questions to be answerable. Recent research has found that such models, when
superficially adapted to detect answerability, struggle to satisfactorily
identify the different categories of unanswerable questions, and simultaneously
preserve good performance for answerable questions. Towards addressing this
issue, we propose RetinaQA, a new KBQA model that unifies two key ideas in a
single KBQA architecture: (a) discrimination over candidate logical forms,
rather than generating these, for handling schema-related unanswerability, and
(b) sketch-filling-based construction of candidate logical forms for handling
data-related unaswerability. Our results show that RetinaQA significantly
outperforms adaptations of state-of-the-art KBQA models in handling both
answerable and unanswerable questions and demonstrates robustness across all
categories of unanswerability. Notably, RetinaQA also sets a new
state-of-the-art for answerable KBQA, surpassing existing models.


  We provide a higher dimensional extension of the gravitational decoupling
method. This extended method allows to obtain new analytic and well behaved
solutions that could be associated to higher dimensional stellar distributions.
Furthermore, we find a new five dimensional anisotropic and well behaved
analytical solution.


  Recovery from transient failures is one of the prime issues in the context of
distributed systems. These systems demand to have transparent yet efficient
techniques to achieve the same. Checkpoint is defined as a designated place in
a program where normal processing of a system is interrupted to preserve the
status information. Checkpointing is a process of saving status information.
Mobile computing systems often suffer from high failure rates that are
transient and independent in nature. To add reliability and high availability
to such distributed systems, checkpoint based rollback recovery is one of the
widely used techniques for applications such as scientific computing, database,
telecommunication applications and mission critical applications. This paper
surveys the algorithms which have been reported in the literature for
checkpointing in Mobile Computing Systems.


  UNet and its latest extensions like TransUNet have been the leading medical
image segmentation methods in recent years. However, these networks cannot be
effectively adopted for rapid image segmentation in point-of-care applications
as they are parameter-heavy, computationally complex and slow to use. To this
end, we propose UNeXt which is a Convolutional multilayer perceptron (MLP)
based network for image segmentation. We design UNeXt in an effective way with
an early convolutional stage and a MLP stage in the latent stage. We propose a
tokenized MLP block where we efficiently tokenize and project the convolutional
features and use MLPs to model the representation. To further boost the
performance, we propose shifting the channels of the inputs while feeding in to
MLPs so as to focus on learning local dependencies. Using tokenized MLPs in
latent space reduces the number of parameters and computational complexity
while being able to result in a better representation to help segmentation. The
network also consists of skip connections between various levels of encoder and
decoder. We test UNeXt on multiple medical image segmentation datasets and show
that we reduce the number of parameters by 72x, decrease the computational
complexity by 68x, and improve the inference speed by 10x while also obtaining
better segmentation performance over the state-of-the-art medical image
segmentation architectures. Code is available at
https://github.com/jeya-maria-jose/UNeXt-pytorch


  Online advertising is currently the greatest source of revenue for many
Internet giants. The increased number of specialized websites and modern
profiling techniques, have all contributed to an explosion of the income of ad
brokers from online advertising. The single biggest threat to this growth, is
however, click-fraud. Trained botnets and even individuals are hired by
click-fraud specialists in order to maximize the revenue of certain users from
the ads they publish on their websites, or to launch an attack between
competing businesses.
  In this note we wish to raise the awareness of the networking research
community on potential research areas within this emerging field. As an example
strategy, we present Bluff ads; a class of ads that join forces in order to
increase the effort level for click-fraud spammers. Bluff ads are either
targeted ads, with irrelevant display text, or highly relevant display text,
with irrelevant targeting information. They act as a litmus test for the
legitimacy of the individual clicking on the ads. Together with standard
threshold-based methods, fake ads help to decrease click-fraud levels.


  Trajectory data combines the complexities of time series, spatial data, and
(sometimes irrational) movement behavior. As data availability and computing
power have increased, so has the popularity of deep learning from trajectory
data. This review paper provides the first comprehensive overview of deep
learning approaches for trajectory data. We have identified eight specific
mobility use cases which we analyze with regards to the deep learning models
and the training data used. Besides a comprehensive quantitative review of the
literature since 2018, the main contribution of our work is the data-centric
analysis of recent work in this field, placing it along the mobility data
continuum which ranges from detailed dense trajectories of individual movers
(quasi-continuous tracking data), to sparse trajectories (such as check-in
data), and aggregated trajectories (crowd information).


  We present a general and versatile technique of population transfer based on
{\it parallel adiabatic passage} by femtosecond shaped pulses. Their amplitude
and phase are specifically designed to optimize the adiabatic passage
corresponding to parallel eigenvalues at all times. We show that this technique
allows the robust adiabatic population transfer in a Raman system with the
total pulse area as low as 3 $\pi$, corresponding to a fluence of one order of
magnitude below the conventional stimulated Raman adiabatic passage process.
This process of short duration, typically pico- and subpicosecond, is easily
implementable with the modern pulse shaper technology and opens the possibility
of ultrafast robust population transfer with interesting applications in
quantum information processing.


  Managerial decision making is likely to be a dominant determinant of
performance of teams in team sports. Here we use Japanese and German football
data to investigate correlates between temporal patterns of formation changes
across matches and match results. We found that individual teams and managers
both showed win-stay lose-shift behavior, a type of reinforcement learning. In
other words, they tended to stick to the current formation after a win and
switch to a different formation after a loss. In addition, formation changes
did not statistically improve the results of succeeding matches.The results
indicate that a swift implementation of a new formation in the win-stay
lose-shift manner may not be a successful managerial rule of thumb.


  We report our study of the real-time charge counting statistics measured by a
quantum point contact (QPC) coupled to a single quantum dot (QD) under
different back-action strength. By tuning the QD-QPC coupling or QPC bias, we
controlled the QPC back-action which drives the QD electrons out of thermal
equilibrium. The random telegraph signal (RTS) statistics showed strong and
tunable non-thermal-equilibrium saturation effect, which can be quantitatively
characterized as a back-action induced tunneling out rate. We found that the
QD-QPC coupling and QPC bias voltage played different roles on the back-action
strength and cut-off energy.


  Long-exposure spectro-polarimetry in the near-infrared is a preferred method
to measure the magnetic field and other physical properties of solar
prominences. In the past, it has been very difficult to observe prominences in
this way with sufficient spatial resolution to fully understand their dynamical
properties. Solar prominences contain highly transient structures, visible only
at small spatial scales; hence they must be observed at sub-arcsecond
resolution, with a high temporal cadence. An adaptive optics (AO) system
capable of directly locking-on to prominence structure away from the solar limb
has the potential to allow for diffraction-limited spectro-polarimetry of solar
prominences. In this paper, the performance of the off-limb AO system and its
expected performance, at the desired science wavelength {\CaII} 8542A, are
shown.


  For rotation powered pulsars, the rate of change of the spin period is
expected to be positive. On the other hand, for a clean binary system (where
gravity is the only force acting on the pulsar) the rate of change of the
orbital period is expected to be negative. There are, however, some pulsars
with the measurements of the rate of change of the spin period as negative
quantities. Likewise, there are some pulsars with positive measurements of the
rate of change of the orbital period. We investigate these cases by eliminating
the external dynamical effects. We also look at those cases where at first, the
measured values possess the correct sign, but on subtracting the dynamical
effects, the sign changes. We use `GalDynPsr' package to perform these tasks.
Moreover, we investigate possible reasons for such anomalies.


  As a continuation of the paper [20] on standard $f$-divergences, we make a
systematic study of maximal $f$-divergences in general von Neumann algebras.
For maximal $f$-divergences, apart from their definition based on Haagerup's
$L^1$-space, we present the general integral expression and the variational
expression in terms of reverse tests. From these definition and expressions we
prove important properties of maximal $f$-divergences, for instance, the
monotonicity inequality, the joint convexity, the lower semicontinuity, and the
martingale convergence. The inequality between the standard and the maximal
$f$-divergences is also given.


  We discuss the design and construction of a novel target array of nonmagnetic
test masses used in a neutron polarimetry measurement made in search for new
possible exotic spin dependent neutron-atom interactions of Nature at
sub\,-$mm$ length scales. This target was designed to accept and efficiently
transmit a transversely polarized slow neutron beam through a series of long
open parallel slots bounded by flat rectangular plates. These openings
possessed equal atom density gradients across the slots from the flat test
masses with dimensions optimized to achieve maximum sensitivity to an exotic
spin-dependent interaction from vector boson exchanges with ranges in the
millimeter to micron regime. The parallel slots were oriented differently in
four quadrants that can be rotated about the neutron beam axis using a Geneva
drive. The spin rotation signals from the 4 quadrants were measured using a
segmented neutron ion chamber in parallel to suppress possible systematic
errors from stray magnetic fields in the target region. Design constraints and
systematic effects are discussed.


  We estimate the cross sections for the production of resonances, pion pairs
and a central cluster of hadrons in peripheral heavy-ion collisions through
two-photon and double-pomeron exchange, at energies that will be available at
RHIC and LHC. The effect of the impact parameter in the diffractive reactions
is introduced, and imposing the condition for realistic peripheral collisions
we verify that in the case of very heavy ions the pomeron-pomeron contribution
is indeed smaller than the electromagnetic one. However, they give a
non-negligible background in the collision of light ions. This diffractive
background will be more important at RHIC than at LHC.


  While there are many examples in which robots provide social assistance, a
lack of theory on how the robots should decide how to assist impedes progress
in realizing these technologies. To address this deficiency, we propose a pair
of computational models to guide a robot as it provides social assistance. The
model of social autonomy helps a robot select an appropriate assistance that
will help with the task at hand while also maintaining the autonomy of the
person being assisted. The model of social alliance describes how a to
determine whether the robot and the person being assisted are cooperatively
working towards the same goal. Each of these models are rooted in social
reasoning between people, and we describe here our ongoing work to adapt this
social reasoning to human-robot interactions.


  In this work, we present an approach for the efficient treatment of
parametrized geometries in the context of POD-Galerkin reduced order methods
based on Finite Volume full order approximations. On the contrary to what is
normally done in the framework of finite element reduced order methods,
different geometries are not mapped to a common reference domain: the method
relies on basis functions defined on an average deformed configuration and
makes use of the Discrete Empirical Interpolation Method (D-EIM) to handle
together non-affinity of the parametrization and non-linearities. In the first
numerical example, different mesh motion strategies, based on a Laplacian
smoothing technique and on a Radial Basis Function approach, are analyzed and
compared on a heat transfer problem. Particular attention is devoted to the
role of the non-orthogonal correction. In the second numerical example the
methodology is tested on a geometrically parametrized incompressible
Navier--Stokes problem. In this case, the reduced order model is constructed
following the same segregated approach used at the full order level


  In this paper, we investigate an unmanned aerial vehicle (UAV)-assisted
integrated communication and localization network in emergency scenarios where
a single UAV is deployed as both an airborne base station (BS) and anchor node
to assist ground BSs in communication and localization services. We formulate
an optimization problem to maximize the sum communication rate of all users
under localization accuracy constraints by jointly optimizing the 3D position
of the UAV, and communication bandwidth and power allocation of the UAV and
ground BSs. To address the intractable localization accuracy constraints, we
introduce a new performance metric and geometrically characterize the UAV
feasible deployment region in which the localization accuracy constraints are
satisfied. Accordingly, we combine Gibbs sampling (GS) and block coordinate
descent (BCD) techniques to tackle the non-convex joint optimization problem.
Numerical results show that the proposed method attains almost identical rate
performance as the meta-heuristic benchmark method while reducing the CPU time
by 89.3%.


  In this paper, we discuss extending the sub-system embedding sub-algebra
coupled cluster (SESCC) formalism and the double unitary coupled cluster (DUCC)
Ansatz to the time domain. An important part of the analysis is associated with
proving the exactness of the DUCC Ansatz based on the general many-body form of
anti-Hermitian cluster operators defining external and internal excitations.
Using these formalisms, it is possible to calculate the energy of the entire
system as an eigenvalue of downfolded/effective Hamiltonian in the active
space, that is identifiable with the sub-system of the composite system. It can
also be shown that downfolded Hamiltonians integrate out Fermionic degrees of
freedom that do not correspond to the physics encapsulated by the active space.
In this paper, we extend these results to the time-dependent Schroedinger
equation, showing that a similar construct is possible to partition a system
into a sub-system that varies slowly in time and a remaining sub-system that
corresponds to fast oscillations. This time-dependent formalism allows coupled
cluster quantum dynamics to be extended to larger systems and for the
formulation of novel quantum algorithms based on the quantum Lanczos approach,
which has recently been considered in the literature.


  Backflow, or retropropagation, is a counterintuitive phenomenon whereby for a
forward-propagating wave the energy locally propagates backward. In this study,
energy backflow has been examined in connection with (a) (2+1)-dimensional
unidirectional scalar and vector-valued monochromatic waves; (b) a (2+1)D
scalar spatiotemporal wavepacket constructed by using an appropriate temporal
frequency spectrum; (c) a scalar closed-form analytical unidirectional version
of the Focus Wave Mode -- a localized pulse propagating luminally and without
spread. Furthermore, an extended class of (2+1)D and (3+1)D finite-energy
unidirectional spatiotemporally localized wave packets has been derived.


  Large-scale radial transport of solids appears to be a fundamental
consequence of protoplanetary disk evolution based on the presence of high
temperature minerals in comets and the outer regions of protoplanetary disks
around other stars. Further, inward transport of solids from the outer regions
of the solar nebula has been postulated to be the manner in which short-lived
radionuclides were introduced to the terrestrial planet region and the cause of
the variations in oxygen isotope ratios seen in primitive materials. Here, both
outward and inward transport of solids are investigated in the context of a
two-dimensional, viscously evolving protoplanetary disk. The dynamics of solids
are investigated to determine how they depend on particle size and the
particular stage of protoplanetary disk evolution, corresponding to different
rates of mass transport. It is found that the outward flows that arise around
the disk midplane of a protoplanetary disk aid in the outward transport of
solids up to the size of CAIs and can increase the crystallinity fraction of
silicate dust at 10 AU around a solar mass star to as much as $\sim$40% in the
case of rapidly evolving disks, decreasing as the accretion rate onto the star
slows. High velocity, inward flows along the disk surface aid in the rapid
transport of solids from the outer disk to the inner disk, particularly for
small dust. Despite the diffusion that occurs throughout the disk, the
large-scale, meridonal flows associated with mass transport prevent complete
homogenization of the disk, allowing compositional gradients to develop that
vary in intensity for a timescale of one million years.