  Conversational agents (CAs) are gaining traction in both industry and
academia, especially with the advent of generative AI and large language
models. As these agents are used more broadly by members of the general public
and take on a number of critical use cases and social roles, it becomes
important to consider the values embedded in these systems. This consideration
includes answering questions such as 'whose values get embedded in these
agents?' and 'how do those values manifest in the agents being designed?'
Accordingly, the aim of this paper is to present the Value-Sensitive
Conversational Agent (VSCA) Framework for enabling the collaborative design
(co-design) of value-sensitive CAs with relevant stakeholders. Firstly,
requirements for co-designing value-sensitive CAs which were identified in
previous works are summarised here. Secondly, the practical framework is
presented and discussed, including its operationalisation into a design
toolkit. The framework facilitates the co-design of three artefacts that elicit
stakeholder values and have a technical utility to CA teams to guide CA
implementation, enabling the creation of value-embodied CA prototypes. Finally,
an evaluation protocol for the framework is proposed where the effects of the
framework and toolkit are explored in a design workshop setting to evaluate
both the process followed and the outcomes produced.


  A novel recipe for exactly solving in finite terms a class of special
differential Riccati equations is reported. Our procedure is entirely based on
a successful resolution strategy quite recently applied to quantum dynamical
time-dependent SU(2) problems. The general integral of exemplary differential
Riccati equations, not previously considered in the specialized literature, is
explicitly determined to illustrate both mathematical usefulness and easiness
of applicability of our proposed treatment. The possibility of exploiting the
general integral of a given differential Riccati equation to solve an SU(2)
quantum dynamical problem, is succinctly pointed out.


  By a $z$-coloring of a graph $G$ we mean any proper vertex coloring
consisting of the color classes $C_1, \ldots, C_k$ such that $(i)$ for any two
colors $i$ and $j$ with $1 \leq i < j \leq k$, any vertex of color $j$ is
adjacent to a vertex of color $i$, $(ii)$ there exists a set $\{u_1, \ldots,
u_k\}$ of vertices of $G$ such that $u_j \in C_j$ for any $j \in \{1, \ldots,
k\}$ and $u_k$ is adjacent to $u_j$ for each $1 \leq j \leq k$ with $j \not=k$,
and $(iii)$ for each $i$ and $j$ with $i \not= j$, the vertex $u_j$ has a
neighbor in $C_i$. Denote by $z(G)$ the maximum number of colors used in any
$z$-coloring of $G$. Denote the Grundy and {\rm b}-chromatic number of $G$ by
$\Gamma(G)$ and ${\rm b}(G)$, respectively. The $z$-coloring is an improvement
over both the Grundy and b-coloring of graphs. We prove that $z(G)$ is much
better than $\min\{\Gamma(G), {\rm b}(G)\}$ for infinitely many graphs $G$ by
obtaining an infinite sequence $\{G_n\}_{n=3}^{\infty}$ of graphs such that
$z(G_n)=n$ but $\Gamma(G_n)={\rm b}(G_n)=2n-1$ for each $n\geq 3$. We show that
acyclic graphs are $z$-monotonic and $z$-continuous. Then it is proved that to
decide whether $z(G)=\Delta(G)+1$ is $NP$-complete even for bipartite graphs
$G$. We finally prove that to recognize graphs $G$ satisfying $z(G)=\chi(G)$ is
$coNP$-complete, improving a previous result for the Grundy number.


  Currency trading (Forex) is the largest world market in terms of volume. We
analyze trading and tweeting about the EUR-USD currency pair over a period of
three years. First, a large number of tweets were manually labeled, and a
Twitter stance classification model is constructed. The model then classifies
all the tweets by the trading stance signal: buy, hold, or sell (EUR vs. USD).
The Twitter stance is compared to the actual currency rates by applying the
event study methodology, well-known in financial economics. It turns out that
there are large differences in Twitter stance distribution and potential
trading returns between the four groups of Twitter users: trading robots,
spammers, trading companies, and individual traders. Additionally, we observe
attempts of reputation manipulation by post festum removal of tweets with poor
predictions, and deleting/reposting of identical tweets to increase the
visibility without tainting one's Twitter timeline.


  Oscillations are omnipresent in neural population signals, like multi-unit
recordings, EEG/MEG, and the local field potential. They have been linked to
the population firing rate of neurons, with individual neurons firing in a
close-to-irregular fashion at low rates. Using a combination of mean-field and
linear response theory we predict the spectra generated in a layered
microcircuit model of V1, composed of leaky integrate-and-fire neurons and
based on connectivity compiled from anatomical and electrophysiological
studies. The model exhibits low- and high-gamma oscillations visible in all
populations. Since locally generated frequencies are imposed onto other
populations, the origin of the oscillations cannot be deduced from the spectra.
We develop an universally applicable systematic approach that identifies the
anatomical circuits underlying the generation of oscillations in a given
network. Based on a theoretical reduction of the dynamics, we derive a
sensitivity measure resulting in a frequency-dependent connectivity map that
reveals connections crucial for the peak amplitude and frequency of the
observed oscillations and identifies the minimal circuit generating a given
frequency. The low-gamma peak turns out to be generated in a sub-circuit
located in layer 2/3 and 4, while the high-gamma peak emerges from the
inter-neurons in layer 4. Connections within and onto layer 5 are found to
regulate slow rate fluctuations. We further demonstrate how small perturbations
of the crucial connections have significant impact on the population spectra,
while the impairment of other connections leaves the dynamics on the population
level unaltered. The study uncovers connections where mechanisms controlling
the spectra of the cortical microcircuit are most effective.


  In atomic systems, clock states feature a zero projection of the total
angular momentum and thus a low sensitivity to magnetic fields. This makes them
widely used for metrological applications like atomic fountains or gravimeters.
Here, we show that a mixture of two such non-magnetic states still display
magnetic dipole-dipole interactions. Using high resolution spectroscopy of a
planar gas of $^{87}$Rb atoms with a controlled in-plane shape, we explore the
effective isotropic and extensive character of these interactions and
demonstrate their tunability. Our measurements set strong constraints on the
relative values of the s-wave scattering lengths $a_{ij}$ involving the two
clock states.


  Context. In time-distance helioseismology, wave travel times are measured
from the two-point cross-covariance function of solar oscillations and are used
to image the solar convection zone in three dimensions. There is, however, also
information in the amplitude of the cross-covariance function, for example
about seismic wave attenuation. Aims. Here we develop a convenient procedure to
measure the amplitude of the cross-covariance function of solar oscillations.
Methods. In this procedure, the amplitude of the cross-covariance function is
linearly related to the cross-covariance function and can be measured even for
high levels of noise. Results. As an example application, we measure the
amplitude perturbations of the seismic waves that propagate through the sunspot
in active region NOAA 9787. We can recover the amplitude variations due to the
scattering and attenuation of the waves by the sunspot and associated
finite-wavelength effects. Conclusions. The proposed definition of
cross-covariance amplitude is robust to noise, can be used to relate measured
amplitudes to 3D perturbations in the solar interior under the Born
approximation, and will provide independent information from the travel times.


  Antireflection with broadband and wide angle properties is important for a
wide range of applications on photovoltaic cells and display. The SiOx shell
layer provides a natural antireflection from air to the Si core absorption
layer. In this work, we have demonstrated the random core-shell silicon
nanowires with both broadband (from 400nm to 900nm) and wide angle (from normal
incidence to 60\degree) antireflection characteristics within AM1.5 solar
spectrum. The graded index structure from the randomly oriented core-shell
(Air/SiOx/Si) nanowires may provide a potential avenue to realize a broadband
and wide angle antireflection layer.


  For practical reasons, many forecasts of case, hospitalization and death
counts in the context of the current COVID-19 pandemic are issued in the form
of central predictive intervals at various levels. This is also the case for
the forecasts collected in the COVID-19 Forecast Hub
(https://covid19forecasthub.org/). Forecast evaluation metrics like the
logarithmic score, which has been applied in several infectious disease
forecasting challenges, are then not available as they require full predictive
distributions. This article provides an overview of how established methods for
the evaluation of quantile and interval forecasts can be applied to epidemic
forecasts in this format. Specifically, we discuss the computation and
interpretation of the weighted interval score, which is a proper score that
approximates the continuous ranked probability score. It can be interpreted as
a generalization of the absolute error to probabilistic forecasts and allows
for a decomposition into a measure of sharpness and penalties for over- and
underprediction.


  We develop innovative algorithms for solving the strong-constraint
formulation of four-dimensional variational data assimilation in large-scale
applications. We present a space-time decomposition approach that employs
domain decomposition along both the spatial and temporal directions in the
overlapping case and involves partitioning of both the solution and the
operators. Starting from the global functional defined on the entire domain, we
obtain a type of regularized local functionals on the set of subdomains
providing the order reduction of both the predictive and the data assimilation
models. We analyze the algorithm convergence and its performance in terms of
reduction of time complexity and algorithmic scalability. The numerical
experiments are carried out on the shallow water equation on the sphere
according to the setup available at the Ocean Synthesis/Reanalysis Directory
provided by Hamburg University.


  Galilei-Newton spacetime $\mathbb{G}$ with its Galilei group can be
understood as a `degeneration' as $c \rightarrow \infty$ of Minkowski spacetime
$\mathbb{M}$ with its Poincar\'e group. $\mathbb{G}$ does not have a spacetime
metric and its Galilei symmetry transformations do not include energy; but
Bargmann-Galilei spacetime $B\mathbb{G}$, a 5-dimensional extension that
preserves Galilei physics, remedies these infelicities. Here an analogous
Bargmann-Minkowski spacetime $B\mathbb{M}$ is described. While not necessary
for Poincar\'e physics, it may illuminate a path towards a more extensive
`Galilei general relativity' than is presently known, which would be a useful
-- and conceptually and mathematically sound -- approximation in astrophysical
scenarios such as core-collapse supernovae.


  A partial-wave analysis of all antiproton-proton scattering data below 925
MeV/c antiproton laboratory momentum is presented. The method used is adapted
from the Nijmegen phase-shift analyses of pp and np scattering data. The
Nijmegen 1993 antiproton-proton database, consisting of 3646 antiproton-proton
scattering data, is presented and discussed. The best fit to this database
results in chi^2_min/Ndata = 1.043. The pseudovector coupling constant of the
charged pion to nucleons is determined to be (f_c)^2 = 0.0732(11) at the pion
pole, where the error is statistical.


  In the dynamics of driven impurity models, there is a fundamental asymmetry
between the processes of emission and absorption of environment excitations:
most of the emitted excitations are rapidly and irreversibly scattered away,
and only a small amount of them is reabsorbed back. We propose to use a
stochastic simulation of the irreversible quantum emission processes in
real-time dynamics, while taking into account the reabsorbed virtual
excitations by the bath discretization. The resulting method delivers a fast
convergence with respect to the number of bath sites, on a wide time interval,
without the sign problem.


  We analyze the steady motion of a viscous incompressible fluid in a
three-dimensional channel containing an obstacle through the Navier-Stokes
equations with mixed boundary conditions: the inflow is given by a fairly
general datum and the flow is assumed to satisfy a constant traction boundary
condition on the outlet, together with the standard no-slip assumption on the
obstacle and on the remaining walls of the domain. Explicit bounds on the
inflow velocity guaranteeing existence and uniqueness of such steady motion are
provided after estimating some Sobolev embedding constants and constructing a
suitable solenoidal extension of the inlet velocity through the Bogovskii
formula. A quantitative analysis of the forces exterted by the fluid over the
obstacle constitutes the main application of our results: by deriving a volume
integral formula for the drag and lift, explicit upper bounds on these forces
are given in terms of the geometrical constraints of the domain.


  What mechanisms causes GAN's entanglement? Although developing disentangled
GAN has attracted sufficient attention, it is unclear how entanglement is
originated by GAN transformation. We in this research propose a
difference-in-difference (DID) counterfactual framework to design experiments
for analyzing the entanglement mechanism in on of the Progressive-growing GAN
(PG-GAN). Our experiment clarify the mechanisms how pixel normalization causes
PG-GAN entanglement during a input-unit-ablation transformation. We discover
that pixel normalization causes object entanglement by in-painting the area
occupied by ablated objects. We also discover the unit-object relation
determines whether and how pixel normalization causes objects entanglement. Our
DID framework theoretically guarantees that the mechanisms that we discover is
solid, explainable and comprehensively.


  We present a review of recent QCD related results from the Fermilab Tevatron
fixed target and collider experiments. Topics include jet and boson production,
W boson and top quark mass measurements, and studies of CP violation.


  We have investigated the dynamics of superfluid phase slippage in an array of
apertures. The magnitude of the dissipative phase slips shows that they occur
simultaneously in all the apertures when the temperature is around 10 mK below
the superfluid transition, and subsequently lose their simultaneity as the
temperature is lowered. We find that when periodic synchronous phase slippage
occurs, the synchronicity exists from the very first phase slip, and therefore
is not due to mode locking of interacting oscillators. When the system is
allowed to relax freely from a given initial energy, the total number of phase
slips that occur and the energy left in the system after the last phase slip
depends reproducibly on the initial energy. We find the energy remaining after
the final phase slip is a periodic function of the initial system energy. This
dependence directly reveals the discrete and dissipative nature of the phase
slips and is a powerful diagnostic for investigation of synchronicity in the
array. When the array slips synchronously, this periodic energy function is a
sharp sawtooth. As the temperature is lowered and the degree of synchronicity
drops, the peak of this sawtooth becomes rounded, suggesting a broadening of
the time interval over which the array slips. The underlying mechanism for the
higher temperature synchronous behavior and the following loss of synchronicity
at lower temperatures is not yet understood. We discuss the implications of our
measurements and pose several questions that need to be resolved by a theory
explaining the synchronous behavior in this quantum system. An understanding of
the array phase slip process is essential to the optimization of superfluid
`dc-SQUID' gyroscopes and interferometers.


  Molecular spiders are synthetic catalytic DNA-based nanoscale walkers. We
study the mean first passage time for abstract models of spiders moving on a
finite two-dimensional lattice with various boundary conditions, and compare it
with the mean first passage time of spiders moving on a one-dimensional track.
We evaluate by how much the slowdown on newly visited sites, owing to
catalysis, can improve the mean first passage time of spiders and show that in
one dimension, when both ends of the track are an absorbing boundary, the
performance gain is lower than in two dimensions, when the absorbing boundary
is a circle; this persists even when the absorbing boundary is a single site.


  The ability to control the speed and polarisation of light pulses will allow
for faster data flow in optical networks of the future. Optical delay and
switching have been achieved using slow-light techniques in various media,
including atomic vapour. Most of these vapour schemes utilise resonant
narrowband techniques for optical switching, but suffer the drawback of having
a limited frequency range or high loss. In contrast, the Faraday effect in a
Doppler-broadened slow-light medium allows polarisation switching over tens of
GHz with high transmission. This large frequency range opens up the possibility
of switching telecommunication bandwidth pulses and probing of dynamics on a
nanosecond timescale. Here we demonstrate the slow-light Faraday effect for
light detuned far from resonance. We show that the polarisation dependent group
index can split a linearly polarised nanosecond pulse into left and right
circularly polarised components. The group index also enhances the spectral
sensitivity of the polarisation rotation, and large rotations of up to 15 pi
rad are observed for continuous-wave light. Finally, we demonstrate dynamic
broadband pulse switching, by rotating a linearly polarised nanosecond pulse
from vertical to horizontal with no distortion and transmission close to unity.


  The p53 protein is well-known for its tumour suppressor function. The
p53-MDM2 negative feedback loop constitutes the core module of a network of
regulatory interactions activated under cellular stress. In normal cells, the
level of p53 proteins is kept low by MDM2, i.e. MDM2 negatively regulates the
activity of p53. In the case of DNA damage,the p53-mediated pathways are
activated leading to cell cycle arrest and repair of the DNA. If repair is not
possible due to excessive damage, the p53-mediated apoptotic pathway is
activated bringing about cell death. In this paper, we give an overview of our
studies on the p53-MDM2 module and the associated pathways from a systems
biology perspective. We discuss a number of key predictions, related to some
specific aspects of cell cycle arrest and cell death, which could be tested in
experiments.


  We explore possible effects of vacuum energy on the evolution of black holes.
If the universe contains a cosmological constant, and if black holes can absorb
energy from the vacuum, then black hole evaporation could be greatly
suppressed. For the magnitude of the cosmological constant suggested by current
observations, black holes larger than $\sim 4 \times 10^{24}$ g would accrete
energy rather than evaporate. In this scenario, all stellar and supermassive
black holes would grow with time until they reach a maximum mass scale of $\sim
6 \times 10^{55}$ g, comparable to the mass contained within the present day
cosmological horizon.


  By using the action of certain Galois groups on complex irreducible
characters and conjugacy classes, we define the Galois characters and Galois
classes. We will introduce a set of Galois characters, called Galois
irreducible characters, and we show that each Galois character is a
non-negative linear combination of the Galois irreducible characters. It is
shown that whenever the complex characters of the groups of a tower produce a
positive self-dual Hopf algebra (PSH), Galois characters of the groups of the
tower also produce a PSH. Then we will classify the Galois characters and
Galois classes of the general linear groups over finite fields. In the end, we
will precisely indicate the isomorphism between the PSH of Galois characters
and a certain tensor product of positive self-dual Hopf algebras.


  This paper is concerned with the constrained approximate null controllability
of heat equation coupled by a real matrix $P$, where the controls are impulsive
and periodically acted into the system through a series of real matrices
$\{Q_k\}_{k=1}^\hbar$. The conclusions are given in two cases. In the case that
the controls act globally into the system, we prove that the system is global
constrained approximate null controllable under a spectral condition of $P$
together with a rank condition of $P$ and $\{Q_k\}_{k=1}^\hbar$; While in the
case that the controls act locally into the system, we prove the global
constrained approximate null controllability under a stronger condition for $P$
and the same rank condition as the above case. Moreover, we prove that the
above mentioned spectral condition of $P$ is necessary for global constrained
approximate null controllability of the control problem considered in this
paper.


  Laser cooling of single atoms in optical tweezers is a prerequisite for
neutral atom quantum computing and simulation. Resolved sideband cooling
comprises a well-established method for efficient motional ground-state
preparation, but typically requires careful cancellation of light shifts in
so-called magic traps. Here, we study a novel laser cooling scheme which
overcomes such constraints, and applies when the ground-state of a narrow
cooling transition is trapped stronger than the excited state. We demonstrate
our scheme, which exploits sequential addressing of red sideband transitions
via frequency chirping of the cooling light, at the example of $^{88}$Sr atoms,
and report ground-state populations compatible with recent experiments in magic
tweezers. The scheme also induces light-assisted collisions, which are key to
the assembly of large atom arrays. Our work enriches the toolbox for
tweezer-based quantum technology, also enabling applications for
tweezer-trapped molecules and ions that are incompatible with resolved sideband
cooling conditions.


  We present combined measurements of the spatially-resolved optical spectrum
and the total excited-atom number in an ultracold gas of three-level atoms
under electromagnetically induced transparency conditions involving high-lying
Rydberg states. The observed optical transmission of a weak probe laser at the
center of the coupling region exhibits a double peaked spectrum as a function
of detuning, whilst the Rydberg atom number shows a comparatively narrow single
resonance. By imaging the transmitted light onto a charge-coupled-device
camera, we record hundreds of spectra in parallel, which are used to map out
the spatial profile of Rabi frequencies of the coupling laser. Using all the
information available we can reconstruct the full one-body density matrix of
the three-level system, which provides the optical susceptibility and the
Rydberg density as a function of spatial position. These results help elucidate
the connection between three-level interference phenomena, including the
interplay of matter and light degrees of freedom and will facilitate new
studies of many-body effects in optically driven Rydberg gases.


  Coherent spin states in semiconductor quantum dots offer promise as
electrically controllable quantum bits (qubits) with scalable fabrication. For
few-electron quantum dots made from gallium arsenide (GaAs), fluctuating
nuclear spins in the host lattice are the dominant source of spin decoherence.
We report a method of preparing the nuclear spin environment that suppresses
the relevant component of nuclear spin fluctuations below its equilibrium value
by a factor of ~ 70, extending the inhomogeneous dephasing time for the
two-electron spin state beyond 1 microsecond. The nuclear state can be readily
prepared by electrical gate manipulation and persists for > 10 seconds.


  Over the centuries, humans have developed and acquired a number of ways to
communicate. But hardly any of them can be as natural and instinctive as facial
expressions. On the other hand, neural networks have taken the world by storm.
And no surprises, that the area of Computer Vision and the problem of facial
expressions recognitions hasn't remained untouched. Although a wide range of
techniques have been applied, achieving extremely high accuracies and preparing
highly robust FER systems still remains a challenge due to heterogeneous
details in human faces. In this paper, we will be deep diving into implementing
a system for recognition of facial expressions (FER) by leveraging neural
networks, and more specifically, Convolutional Neural Networks (CNNs). We adopt
the fundamental concepts of deep learning and computer vision with various
architectures, fine-tune it's hyperparameters and experiment with various
optimization methods and demonstrate a state-of-the-art single-network-accuracy
of 70.10% on the FER2013 dataset without using any additional training data.


  Our aim is to calculate the evolution of Algol binaries within the framework
of the osculating orbital theory, which considers the perturbing forces acting
on the orbit of each star arising from mass exchange via Roche lobe overflow
(RLOF). The scheme is compared to results calculated from a `classical'
prescription. Using our stellar binary evolution code BINSTAR, we calculate the
orbital evolution of Algol binaries undergoing case A and case B mass transfer,
by applying the osculating scheme. The velocities of the ejected and accreted
material are evaluated by solving the restricted three-body equations of
motion, within the ballistic approximation. This allows us to determine the
change of linear momentum of each star, and the gravitational force applied by
the mass transfer stream. Torques applied on the stellar spins by tides and
mass transfer are also considered. Using the osculating formalism gives shorter
post-mass transfer orbital periods typically by a factor of 4 compared to the
classical scheme, owing to the gravitational force applied onto the stars by
the mass transfer stream. Additionally, during the rapid phase of mass
exchange, the donor star is spun down on a timescale shorter than the tidal
synchronization timescale, leading to sub-synchronous rotation. Consequently,
between 15 and 20 per cent of the material leaving the inner-Lagrangian point
is accreted back onto the donor (so-called `self-accretion'), further enhancing
orbital shrinkage. Self-accretion, and the sink of orbital angular momentum
which mass transfer provides, may potentially lead to more contact binaries.
Even though Algols are mainly considered, the osculating prescription is
applicable to all types of interacting binaries, including those with eccentric
orbits.


  We demonstrate the decoy-state quantum key distribution over 200 km with
photon polarization through optical fiber, by using super-conducting single
photon detector with a repetition rate of 320 Mega Hz and a dark count rate of
lower than 1 Hz. Since we have used the polarization coding, the
synchronization pulses can be run in a low frequency. The final key rate is
14.1 Hz. The experiment lasts for 3089 seconds with 43555 total final bits.


  We study one-parameter families of S-unit equations of the form
f(t)u+g(t)v=h(t), where f, g, and h are univariate polynomials over a number
field, t is an S-integer, and u and v are S-units. For many possible choices of
f, g, and h, we are able to determine all but finitely many solutions to the
corresponding one-parameter family of S-unit equations. The results are
obtained as consequences of some recent results on integral points on surfaces.


  Iterative algorithms aimed at solving some problems are discussed. For
certain problems, such as finding a common point in the intersection of a
finite number of convex sets, there often exist iterative algorithms that
impose very little demand on computer resources. For other problems, such as
finding that point in the intersection at which the value of a given function
is optimal, algorithms tend to need more computer memory and longer execution
time. A methodology is presented whose aim is to produce automatically for an
iterative algorithm of the first kind a "superiorized version" of it that
retains its computational efficiency but nevertheless goes a long way towards
solving an optimization problem. This is possible to do if the original
algorithm is "perturbation resilient," which is shown to be the case for
various projection algorithms for solving the consistent convex feasibility
problem. The superiorized versions of such algorithms use perturbations that
drive the process in the direction of the optimizer of the given function.
After presenting these intuitive ideas in a precise mathematical form, they are
illustrated in image reconstruction from projections for two different
projection algorithms superiorized for the function whose value is the total
variation of the image.


  Using data from the MOSFIRE Deep Evolution Field (MOSDEF) survey, we present
a census of AGN-driven ionized outflows in a sample of 159 AGNs at $1.4 \le z
\le 3.8$. The sample spans AGN bolometric luminosities of $10^{44-47}
\mathrm{~erg~s}^{-1}$ and includes both quiescent and star-forming galaxies
extending across three orders of magnitude in stellar mass. We identify and
characterize outflows from the \hbeta, [OIII], \halpha ~and [NII] emission line
spectra. We detect outflows in $17\%$ of the AGNs, seven times more often than
in a mass-matched sample of inactive galaxies in MOSDEF. The outflows are fast
and galaxy-wide, with velocities of $\sim 400-3500 ~\mathrm{km~s}^{-1}$ and
spatial extents of $0.3-11.0$ kpc. The incidence of outflows among AGNs is
independent of the stellar mass of the host galaxy, with outflows detected in
both star-forming and quiescent galaxies. This suggests that outflows exist
across different phases in galaxy evolution. We investigate relations between
outflow kinematic, spatial, and energetic properties and both AGN and host
galaxy properties. Our results show that AGN-driven outflows are widespread in
galaxies along the star-forming main sequence. The mass-loading factors of the
outflows are typically $0.1-1$ and increase with AGN luminosity, capable of
exceeding unity at $L_\mathrm{AGN} \gtrsim 10^{46.3} \mathrm{~erg~s}^{-1}$. In
these more luminous sources the ionized outflow alone is likely sufficient to
regulate star formation, and when combined with outflowing neutral and
molecular gas may be able to quench star formation in their host galaxies.


  When reporting the results of clinical studies, some researchers may choose
the five-number summary (including the sample median, the first and third
quartiles, and the minimum and maximum values) rather than the sample mean and
standard deviation, particularly for skewed data. For these studies, when
included in a meta-analysis, it is often desired to convert the five-number
summary back to the sample mean and standard deviation. For this purpose,
several methods have been proposed in the recent literature and they are
increasingly used nowadays. In this paper, we propose to further advance the
literature by developing a smoothly weighted estimator for the sample standard
deviation that fully utilizes the sample size information. For ease of
implementation, we also derive an approximation formula for the optimal weight,
as well as a shortcut formula for the sample standard deviation. Numerical
results show that our new estimator provides a more accurate estimate for
normal data and also performs favorably for non-normal data. Together with the
optimal sample mean estimator in Luo et al., our new methods have dramatically
improved the existing methods for data transformation, and they are capable to
serve as "rules of thumb" in meta-analysis for studies reported with the
five-number summary. Finally for practical use, an Excel spreadsheet and an
online calculator are also provided for implementing our optimal estimators.


  The optimum off-line energy management scheme for multi-user multi-relay
networks employing energy harvesting and wireless energy transfer is studied.
Specifically, the users are capable of harvesting and transferring energy to
each other over consecutive transmissions, though they have no fixed energy
supplies. Meanwhile, network coding for the users' messages is conducted at the
relays to enable cooperative transmission with source nodes in independent but
not necessarily identically distributed (i.n.i.d.) Nakagami-$m$ fading
channels. Therefore, a simultaneous two level cooperation, i.e.,
information-level and energy-level cooperation is conducted. The problem of
energy efficiency (EE) maximization under constraints of the energy causality
and a predefined outage probability threshold is formulated and shown to be
non-convex. By exploiting fractional and geometric programming, a convex
form-based iterative algorithm is developed to solve the problem efficiently.
Close-to-optimal power allocation and energy cooperation policies across
consecutive transmissions are found. Moreover, the effects of relay locations
and wireless energy transmission efficiency are investigated and the
performance comparison with the current state of solutions demonstrates that
the proposed policies can manage the harvested energy more efficiently.


  In this paper, we study three connection games among the most widely played:
Havannah, Twixt, and Slither. We show that determining the outcome of an
arbitrary input position is PSPACE-complete in all three cases. Our reductions
are based on the popular graph problem Generalized Geography and on Hex itself.
We also consider the complexity of generalizations of Hex parameterized by the
length of the solution and establish that while Short Generalized Hex is
W[1]-hard, Short Hex is FPT. Finally, we prove that the ultra-weak solution to
the empty starting position in hex cannot be fully adapted to any of these
three games.


  Nonreciprocal transport refers to charge transfer processes that are
sensitive to the bias polarity. Until recently, nonreciprocal transport was
studied only in dissipative systems, where the nonreciprocal quantity is the
resistance. Recent experiments have, however, demonstrated nonreciprocal
supercurrent leading to the observation of a supercurrent diode effect in
Rashba superconductors, opening the vision of dissipationless electronics. Here
we report on a supercurrent diode effect in NbSe$_2$ constrictions obtained by
patterning NbSe$_2$ flakes with both even and odd layer number. The observed
rectification is driven by valley-Zeeman spin-orbit interaction. We demonstrate
a rectification efficiency as large as 60%, considerably larger than the
efficiency of devices based on Rashba superconductors. In agreement with recent
theory for superconducting transition metal dichalcogenides, we show that the
effect is driven by an out-of-plane magnetic field component. Remarkably, we
find that the effect becomes field-asymmetric in the presence of an additional
in-plane field component transverse to the current direction. Supercurrent
diodes offer a further degree of freedom in designing superconducting quantum
electronics with the high degree of integrability offered by van der Waals
materials.


  We have performed polarized electronic Raman scattering on URu2Si2 single
crystals at low temperature down to 8 K in the hidden order state and under
magnetic field up to 10 T. The hidden order state is characterized by a sharp
excitation at 1.7 meV and a gap in the electronic continuum below 6.8 meV. Both
Raman signatures are of pure A2g symmetry. By comparing the behavior of the
Raman sharp excitation and the neutron resonance at Q0=(0,0,1), we provide new
evidence, constrained by selection rules of the two probes, that the hidden
order state breaks the translational symmetry along the c axis such that Gamma
and Z points fold on top of each other. The observation of these distinct Raman
features with a peculiar A2g symmetry as a signature of the hidden order phase
places strong constraints on current theories of the hidden order in URu2Si2.


  We report the temperature dependence of the upper critical magnetic field in
the quasi-onedimensional molecular organic superconductor (DMET)2I3, for
magnetic field applied along the intrachain, interchain, and interplane
directions. The upper critical field tends to saturation at low temperature for
field in all directions and does not exceed the Pauli paramagnetic limit.
Superconductivity in (DMET)2I3 thus appears to be conventional spin singlet, in
contrast to the status of the isostructural Bechgaard salts. We also discuss a
magnetic field-induced dimensional crossover effect in the normal metallic
state which had previously appeared to be associated with superconductivity.


  In this paper, we propose energy-conserving Eulerian solvers for the
two-species Vlasov-Amp\`{e}re (VA) system and apply the methods to simulate
current-driven ion-acoustic instability. The algorithm is generalized from our
previous work for the single-species VA system and Vlasov-Maxwell (VM) system.
The main feature of the schemes is their ability to preserve the total particle
number and total energy on the fully discrete level regardless of mesh size.
Those are desired properties of numerical schemes especially for long time
simulations with under-resolved mesh. The conservation is realized by explicit
and implicit energy-conserving temporal discretizations, and the discontinuous
Galerkin (DG) spatial discretizations. We benchmarked our algorithms on a test
example to check the one-species limit, and the current-driven ion-acoustic
instability. To simulate the current-driven ion-acoustic instability, a slight
modification for the implicit method is necessary to fully decouple the split
equations. This is achieved by a Gauss-Seidel type iteration technique.
Numerical results verified the conservation and performance of our methods.


  Impacts of micrometeoroids on the surfaces of Nix and Hydra can produced dust
particles and form a ring around Pluto. However, dissipative forces, such as
the solar radiation pressure, can lead the particles into collisions in a very
short period of time. In this work we investigate the orbital evolution of
escaping ejecta under the effects of the radiation pressure force combined with
the gravitational effects of Pluto,Charon, Nix and Hydra. The mass production
rate from the surfaces of Nix and Hydra was obtained from analytical models. By
comparing the lifetime of the survived particles, derived from our numerical
simulations, and the mass of a putative ring mainly formed by the particles
released from the surfaces of Nix and Hydra we could estimate the ring normal
optical depth. The released particles, encompassing the orbits of Nix and
Hydra, temporarily form a 16000 km wide ring. Collisions with the massive
bodies, mainly due to the effects of the radiation pressure force, remove about
50% of the $1\mu$m particles in 1 year. A tenuous ring with a normal optical
depth of $6 \times 10^{-11}$ can be maintained by the dust particles released
from the surfaces of Nix and Hydra.


  In causal mediation studies that decompose an average treatment effect into a
natural indirect effect (NIE) and a natural direct effect (NDE), examples of
post-treatment confounding are abundant. Past research has generally considered
it infeasible to adjust for a post-treatment confounder of the mediator-outcome
relationship due to incomplete information: it is observed under the actual
treatment condition while missing under the counterfactual treatment condition.
This study proposes a new sensitivity analysis strategy for handling
post-treatment confounding and incorporates it into weighting-based causal
mediation analysis without making extra identification assumptions. Under the
sequential ignorability of the treatment assignment and of the mediator, we
obtain the conditional distribution of the post-treatment confounder under the
counterfactual treatment as a function of not just pretreatment covariates but
also its counterpart under the actual treatment. The sensitivity analysis then
generates a bound for the NIE and that for the NDE over a plausible range of
the conditional correlation between the post-treatment confounder under the
actual and that under the counterfactual conditions. Implemented through either
imputation or integration, the strategy is suitable for binary as well as
continuous measures of post-treatment confounders. Simulation results
demonstrate major strengths and potential limitations of this new solution. A
re-analysis of the National Evaluation of Welfare-to-Work Strategies (NEWWS)
Riverside data reveals that the initial analytic results are sensitive to
omitted post-treatment confounding.


  The closed topological vertex is the simplest ``off-strip'' case of
non-compact toric Calabi-Yau threefolds with acyclic web diagrams. By the
diagrammatic method of topological vertex, open string amplitudes of
topological string theory therein can be obtained by gluing a single
topological vertex to an ``on-strip'' subdiagram of the tree-like web diagram.
If non-trivial partitions are assigned to just two parallel external lines of
the web diagram, the amplitudes can be calculated with the aid of techniques
borrowed from the melting crystal models. These amplitudes are thereby
expressed as matrix elements, modified by simple prefactors, of an operator
product on the Fock space of 2D charged free fermions. This fermionic
expression can be used to derive $q$-difference equations for generating
functions of special subsets of the amplitudes. These $q$-difference equations
may be interpreted as the defining equation of a quantum mirror curve.


  A hydrodynamic description is used to study the normal modes of a vortex in a
zero-temperature Bose-Einstein condensate. In the Thomas-Fermi (TF) limit, the
circulating superfluid velocity far from the vortex core provides a small
perturbation that splits the originally degenerate normal modes of a
vortex-free condensate. The relative frequency shifts are small in all cases
considered (they vanish for the lowest dipole mode with |m|=1), suggesting that
the vortex is stable. The Bogoliubov equations serve to verify the existence of
helical waves, similar to those of a vortex line in an unbounded weakly
interacting Bose gas. In the large-condensate (small-core) limit, the
condensate wave function reduces to that of a straight vortex in an unbounded
condensate; the corresponding Bogoliubov equations have no bound-state
solutions that are uniform along the symmetry axis and decay exponentially far
from the vortex core.


  We perform a one-loop study of the small-$z_3^2$ behavior of the Ioffe-time
distribution (ITD) ${\cal M} (\nu, z_3^2)$, the basic function that may be
converted into parton pseudo- and quasi-distributions. We calculate the
corrections at the operator level, so that our results may be later used for
pseudo-distribution amplitudes and generalized parton pseudo-distributions. We
separate two sources of the $z_3^2$-dependence at small $z_3^2$. One is related
to the ultraviolet (UV) singularities generated by the gauge link, and another
to short-distance logarithms generating perturbative evolution of parton
densities. Our calculation explicitly shows that, for a finite UV cut-off, the
UV-singular terms vanish when $z_3^2=0$. The UV divergences are absent in the
ratio ${\cal M} (\nu, z_3^2)/{\cal M} (0, z_3^2)$ ("reduced" ITD). Still, it
has a non-trivial short-distance behavior due to $\ln z_3^2 \Lambda^2$ terms
generating perturbative evolution of the parton densities. We give an explicit
expression, up to constant terms, for the reduced ITD at one loop. It may be
used in extraction of PDFs from the lattice QCD simulations. We also use our
results to get new insights concerning the structure of parton
quasi-distributions at one-loop level.


  We introduce a notion of rough paths on embedded submanifolds and demonstrate
that this class of rough paths is natural. On the way we develop a notion of
rough integration and an efficient and intrinsic theory of rough differential
equations (RDEs) on manifolds. The theory of RDEs is then used to construct
parallel translation along manifold valued rough paths. Finally, this framework
is used to show there is a one to one correspondence between rough paths on a
d-dimensional manifold and rough paths on d-dimensional Euclidean space. This
last result is a rough path analogue of Cartan's development map and its
stochastic version which was developed by Eeels and Elworthy and Malliavin.


  We consider a procedure to reduce simply generated trees by iteratively
removing all leaves. In the context of this reduction, we study the number of
vertices that are deleted after applying this procedure a fixed number of times
by using an additive tree parameter model combined with a recursive
characterization.
  Our results include asymptotic formulas for mean and variance of this
quantity as well as a central limit theorem.


  We use the recent supernova data set from the ESSENCE collaboration combined
with data from the Supernova Legacy Survey and nearby supernovae to test the
DGP brane world model and its generalisations. Combination of this data with a
flatness prior and the position of the peak of the CMB disfavours the DGP model
slightly. Inclusion of the baryon acoustic peak from the Sloan Digital Sky
Survey increase the tension of the DGP model with the data, although it is not
clear how self consistent this procedure would be without a re-analysis of the
survey data in the framework of the DGP cosmology. Generalisations of the DGP
model are tested and constraints on relevant parameters obtained.


  There is a growing interest in the possibility that dark matter could be
formed of weakly interacting particles with a mass in the 100 GeV - 2 TeV
range, and supersymmetric particles are favorite candidates. If they constitute
the dark halo of our Galaxy, their mutual annihilations produce energetic gamma
rays that could be detected using existing atmospheric \u{C}erenkov techniques.


  We give a review of results on the operator-norm convergence of the Trotter
product formula on Hilbert and Banach spaces, which is focused on the problem
of its convergence rates. Some recent results concerning evolution semigroups
are presented in details.


  In software engineering (SE) tasks, the naming approach is so important that
it attracts many scholars from all over the world to study how to improve the
quality of method names. To accurately recommend method names, we employ a
novel framework to handle this problem. In our expeirments, nearly 8 million
Java methods are collected from open source organizations as our evaluation
dataset. In the first-phase recommendation, we introduce a fast and simple
classifier based on the fast text neural network for reccomending potential
method category. In the second-phase recomendation, we employ both two Long
Short Term Memory Networks to reccomend consitent method names from each
classification. Evaluation results prove that the proposed approach
significantly outperforms state-of-the-art approach.


  The string quantum kernel is normally written as a functional sum over the
string coordinates and the world--sheet metrics. As an alternative to this
quantum field--inspired approach, we study the closed bosonic string
propagation amplitude in the functional space of loop configurations. This
functional theory is based entirely on the Jacobi variational formulation of
quantum mechanics, {\it without the use of a lattice approximation}. The
corresponding Feynman path integral is weighed by a string action which is a
{\it reparametrization invariant} version of the Schild action. We show that
this path integral formulation is equivalent to a functional ``Schrodinger''
equation defined in loop--space. Finally, for a free string, we show that the
path integral and the functional wave equation are {\it exactly } solvable.


  This paper develops a general approach, rooted in statistical learning
theory, to learning an approximately revenue-maximizing auction from data. We
introduce $t$-level auctions to interpolate between simple auctions, such as
welfare maximization with reserve prices, and optimal auctions, thereby
balancing the competing demands of expressivity and simplicity. We prove that
such auctions have small representation error, in the sense that for every
product distribution $F$ over bidders' valuations, there exists a $t$-level
auction with small $t$ and expected revenue close to optimal. We show that the
set of $t$-level auctions has modest pseudo-dimension (for polynomial $t$) and
therefore leads to small learning error. One consequence of our results is
that, in arbitrary single-parameter settings, one can learn a mechanism with
expected revenue arbitrarily close to optimal from a polynomial number of
samples.


  This paper presents an algorithm to automatically design two-level fat-tree
networks, such as ones widely used in large-scale data centres and cluster
supercomputers. The two levels may each use a different type of switches from
design database to achieve an optimal network structure. Links between layers
can run in bundles to simplify cabling. Several sample network designs are
examined and their technical and economic characteristics are discussed.
  The characteristic feature of our approach is that real life equipment prices
and values of technical characteristics are used. This allows to select an
optimal combination of hardware to build the network (including semi-populated
configurations of modular switches) and accurately estimate the cost of this
network. We also show how technical characteristics of the network can be
derived from its per-port metrics and suggest heuristics for equipment
placement.
  The algorithm is useful as a part of a bigger design procedure that selects
optimal hardware of cluster supercomputer as a whole. Therefore the article is
focused on the use of fat-trees for high-performance computing, although the
results are valid for any type of data centres.


  The novelty of new human coronavirus COVID-19/SARS-CoV-2 and the lack of
effective drugs and vaccines gave rise to a wide variety of strategies employed
to fight this worldwide pandemic. Many of these strategies rely on the
repositioning of existing drugs that could shorten the time and reduce the cost
compared to de novo drug discovery. In this study, we presented a new
network-based algorithm for drug repositioning, called SAveRUNNER (Searching
off-lAbel dRUg aNd NEtwoRk), which predicts drug-disease associations by
quantifying the interplay between the drug targets and the disease-specific
proteins in the human interactome via a novel network-based similarity measure
that prioritizes associations between drugs and diseases locating in the same
network neighborhoods. Specifically, we applied SAveRUNNER on a panel of 14
selected diseases with a consolidated knowledge about their disease-causing
genes and that have been found to be related to COVID-19 for genetic
similarity, comorbidity, or for their association to drugs tentatively
repurposed to treat COVID-19. Focusing specifically on SARS subnetwork, we
identified 282 repurposable drugs, including some the most rumored off-label
drugs for COVID-19 treatments, as well as a new combination therapy of 5 drugs,
actually used in clinical practice. Furthermore, to maximize the efficiency of
putative downstream validation experiments, we prioritized 24 potential
anti-SARS-CoV repurposable drugs based on their network-based similarity
values. These top-ranked drugs include ACE-inhibitors, monoclonal antibodies,
and thrombin inhibitors. Finally, our findings were in-silico validated by
performing a gene set enrichment analysis, which confirmed that most of the
network-predicted repurposable drugs may have a potential treatment effect
against human coronavirus infections.


  Starting from the famous Pauli problem on the possibility to associate
quantum states with probabilities, the formulation of quantum mechanics in
which quantum states are described by fair probability distributions
(tomograms, i.e. tomographic probabilities) is reviewed in a pedagogical style.
The relation between the quantum state description and the classical state
description is elucidated. The difference of those sets of tomograms is
described by inequalities equivalent to a complete set of uncertainty relations
for the quantum domain and to nonnegativity of probability density on phase
space in the classical domain. Intersection of such sets is studied. The
mathematical mechanism which allows to construct different kinds of tomographic
probabilities like symplectic tomograms, spin tomograms, photon number
tomograms, etc., is clarified and a connection with abstract Hilbert space
properties is established. Superposition rule and uncertainty relations in
terms of probabilities as well as quantum basic equation like quantum evolution
and energy spectra equations are given in explicit form. A method to check
experimentally uncertainty relations is suggested using optical tomograms.
Entanglement phenomena and the connection with semigroups acting on simplexes
are studied in detail for spin states in the case of two qubits. The
star-product formalism is associated with the tomographic probability
formulation of quantum mechanics.


  We give a slope equality for fibered surfaces whose general fiber is a smooth
plane curve. As a corollary, we prove a "strong" Durfee-type inequality for
isolated hypersurface surface singularities, which implies Durfee's strong
conjecture for such singularities with non-negative topological Euler number of
the exceptional set of the minimal resolution.


  We have constructed noncommutative phi^4 field theory on kappa-Minkowski
spacetime. Quantum properties via 2-point functions were analized, and effect
of birefringence for the massive scalar field has been found.


  In \cite{Kramer11} Kramer proves for a large class of semisimple Lie groups
that they admit just one locally compact $\sigma$-compact Hausdorff topology
compatible with the group operations. We present two different methods of
generalising this to the group of rational points of an absolutely quasi-simple
algebraic group over a non-archimedean local field (the second method only
achieves this on the additional hypothesis that the group is isotropic). The
first method of argument involves demonstrating that, given any topological
group $G$ which is totally disconnected, locally compact, $\sigma$-compact,
locally topologically finitely generated, and has the property that no compact
open subgroup has an infinite abelian continuous quotient, the group $G$ is
topologically rigid in the previously described sense. Then the desired
conclusion for the group of rational points of an absolutely quasi-simple
algebraic group over a non-archimedean local field may be inferred as a special
case. The other method of argument involves proving that any group of
automorphisms of a regular locally finite building, which is closed in the
compact-open topology and acts Weyl transitively on the building, has the
topological rigidity property in question. This again yields the desired result
in the case that the group is isotropic.


  To understand the photophysics of molecular aggregates, exciton model of J-
and H-aggregate has been extensively utilized. However, it lacks consideration
of crystal symmetry. Although discrete molecules may lack symmetry, their
aggregates can exhibit a high degree of symmetry. Herein, we utilized group
theory to study the optical properties of centrosymmetric molecular aggregates,
showing that their optical selection rules (transition dipole moment and
spin-orbit coupling) are determined by the symmetry of singlet and triplet
excited states and the intermolecular orbital overlap. Symmetry-forbidden
electronic transitions are closely related to ultralong organic
phosphorescence. Our model's scope is broad, as over 50% of organic crystals
belong to centrosymmetric space groups according to Cambridge Structural
Database.


  We look at the periodic behaviour of the Earth's glacial cycles and the
transitions between different periodic states when either external parameters
(such as $\omega$) or internal parameters (such as $d$) are varied. We model
this using the PP04 model of climate change. This is a forced discontinuous
Filippov (non-smooth) dynamical system. When periodically forced this has
co-existing periodic orbits. We find that the transitions in this system are
mainly due to grazing events, leading to grazing bifurcations. An analysis of
the grazing bifurcations is given and the impact of these on the domains of
attraction and regions of existence of the periodic orbits is determined under
various changes in the parameters of the system. Grazing transitions arise for
general variations in the parameters (both internal and external) of the PP04
model. We find that the grazing transitions between the period orbits resemble
those of the Mid-Pleistocene-Transition.


  We study induced gravity dark energy models coupled with a simple monomial
potential $\propto \sigma^n$ and a positive exponent $n$. These simple
potentials lead to viable dark energy models with a weak dependence on the
exponent, which characterizes the accelerated expansion of the cosmological
model in the asymptotic attractor, when ordinary matter becomes negligible. We
use recent cosmological data to constrain the coupling $\gamma$ to the Ricci
curvature, under the assumptions that the scalar field starts at rest deep in
the radiation era and that the gravitational constant in the Einstein equations
is compatible with the one measured in a Cavendish-like experiment. By using
$Planck$ 2015 data only, we obtain the 95 % CL bound $\gamma < 0.0017$ for
$n=4$, which is further tightened to $\gamma < 0.00075$ by adding Baryonic
Acoustic Oscillations (BAO) data. This latter bound improves by $\sim 30$ % the
limit obtained with the $Planck$ 2013 data and the same compilation of BAO
data. We discuss the dependence of the $\gamma$ and $\dot G_N/G_N (z=0)$ on
$n$.


  Post-hoc explanation methods are an important tool for increasing model
transparency for users. Unfortunately, the currently used methods for
attributing token importance often yield diverging patterns. In this work, we
study potential sources of disagreement across methods from a linguistic
perspective. We find that different methods systematically select different
classes of words and that methods that agree most with other methods and with
humans display similar linguistic preferences. Token-level differences between
methods are smoothed out if we compare them on the syntactic span level. We
also find higher agreement across methods by estimating the most important
spans dynamically instead of relying on a fixed subset of size $k$. We
systematically investigate the interaction between $k$ and spans and propose an
improved configuration for selecting important tokens.


  Tree-based machine learning techniques, such as Decision Trees and Random
Forests, are top performers in several domains as they do well with limited
training datasets and offer improved interpretability compared to Deep Neural
Networks (DNN). However, while easier to train, they are difficult to optimize
for fast inference without accuracy loss in von Neumann architectures due to
non-uniform memory access patterns. Recently, we proposed a novel analog, or
multi-bit, content addressable memory(CAM) for fast look-up table operations.
Here, we propose a design utilizing this as a computational primitive for rapid
tree-based inference. Large random forest models are mapped to arrays of analog
CAMs coupled to traditional analog random access memory (RAM), and the unique
features of the analog CAM enable compression and high performance. An
optimized architecture is compared with previously proposed tree-based model
accelerators, showing improvements in energy to decision by orders of magnitude
for common image classification tasks. The results demonstrate the potential
for non-volatile analog CAM hardware in accelerating large tree-based machine
learning models.


  We extend the scaling behaviour observed in the inclusive charged hadron
transverse momentum ($p_{T}$) distributions to the $p_{T}$ spectra of pions,
kaons and protons produced in proton-proton ($pp$) collisions with center of
mass energies ($\sqrt{s}$ ) at 0.9, 2.76 and 7 TeV. This scaling behaviour
arises when a linear transformation, $p_{T} \rightarrow p_{T}/K$, is applied on
the pion, kaon or proton $p_{T}$ spectra. The scaling parameter $K$ depends on
$\sqrt{s}$ and is determined by a new method, the quality factor method, which
does not rely on the shape of the scaling function. We argue that the pions,
kaons and protons originate from different distributions of clusters which are
formed by strings overlapping, and the scaling behaviours of these identified
particles $p_{T}$ spectra could be understood with the colour string
percolation model in a quantitative way simultaneously.


  We introduce a novel construction which allows us to identify the elements of
the skeletons of a CW-complex $P(m)$ and the monomials in $m$ variables. From
this, we infer that there is a bijection between finite CW-subcomplexes of
$P(m)$, which are quotients of finite simplicial complexes, and some bigraded
standard Artinian Gorenstein algebras, generalizing previous constructions in
\cite{F:S}, \cite{CGIM} and \cite{G:Z}.
  We apply this to a generalization of Nagata idealization for level algebras.
These algebras are standard graded Artinian algebras whose Macaulay dual
generator is given explicitly as a bigraded polynomial of bidegree $(1,d)$. We
consider the algebra associated to polynomials of the same type of bidegree
$(d_1,d_2)$.


  Although learning from data is effective and has achieved significant
milestones, it has many challenges and limitations. Learning from data starts
from observations and then proceeds to broader generalizations. This framework
is controversial in science, yet it has achieved remarkable engineering
successes. This paper reflects on some epistemological issues and some of the
limitations of the knowledge discovered in data. The document discusses the
common perception that getting more data is the key to achieving better machine
learning models from theoretical and practical perspectives. The paper sheds
some light on the shortcomings of using generic mathematical theories to
describe the process. It further highlights the need for theories specialized
in learning from data. While more data leverages the performance of machine
learning models in general, the relation in practice is shown to be logarithmic
at its best; After a specific limit, more data stabilize or degrade the machine
learning models. Recent work in reinforcement learning showed that the trend is
shifting away from data-oriented approaches and relying more on algorithms. The
paper concludes that learning from data is hindered by many limitations. Hence
an approach that has an intensional orientation is needed.


  We prove that a Stein manifold of dimension $d$ admits a proper holomorphic
embedding into any Stein manifold of dimension at least $2d+1$ satisfying the
holomorphic density property. This generalizes classical theorems of Remmert,
Bishop and Narasimhan pertaining to embeddings into complex Euclidean spaces,
as well as several other recent results.


  In this work we study numerically the influence of a homogeneous electrical
field on the fluid and heat transfer phenomena at macroscale and mesoscale
during unidirectional solidification of a binary metal alloy. The numerical
results showed that a pulse electric discharging applied perpendicularly to the
solidification front leads to a much stronger Joule heating of the liquid phase
in comparison to the solid phase. It was found that on the mesoscopic scale the
electric current density is not homogeneous due to the complex shape of the
dendrite and the difference between electrical conductivities of the solid and
liquid phases. This inhomogeneity of the electrical current density in the
mushy zone leads to the increase of the Joule heating of the dendrite in
comparison to the interdendritic liquid and induces a pinch force
(electromagnetic Lorentz force). The main features of the resulting convection
in the interdendritic liquid are discussed.


  In this talk we consider amplitudes for processes involving a Higgs boson,
either scalar or pseudoscalar, plus four light partons. These amplitudes are
relevant to the production of a Higgs boson plus two jets in hadronic
collisions. They are also relevant to calculating the transverse momentum
spectrum for Higgs bosons at next-to-leading order in the strong coupling. We
work in the limit that the top quark is much heavier than the Higgs bosons and
use effective Lagrangians for the interactions of gluons with the Higgs bosons.
We present the amplitudes involving a Higgs boson and: 1) four gluons, 2) two
quarks and two gluons; and 3) four quarks. We show that the pseudoscalar
amplitudes are nearly identical to those for the scalar case, the only
differences being the overall size and the relative signs between terms.


  GHZ2/GLASS-z12 has been recently observed by JWST with both NIRSpec and MIRI
spectrographs, making it the most distant galaxy ($z_{spec}=12.34$) with
complete spectroscopic coverage from rest-frame UV to optical. It is identified
as a strong CIV$_{1549}$ emitter with many other detected emission lines (NIV],
HeII, OIII], NIII], CIII], [OII], [NeIII], [OIII], and H$\alpha$), including a
remarkable OIII$_{1333}$ Bowen fluorescence line. We analyze in this paper the
joint NIRSpec+MIRI spectral data set. Combining six optical diagnostics (namely
R2, R3, R23, O32, Ne3O2, and Ne3O2Hd), we find extreme ionization conditions,
with O32 $=1.39 \pm 0.19$ and Ne3O2 $=0.37 \pm 0.18$ in stark excess compared
to typical values in the ISM at lower redshifts. These line properties are
compatible either with an AGN or with a compact, very dense star-forming
environment ($\Sigma_{\rm SFR}$ $\sim 10^2$-$10^3$ Msun/yr/kpc$^2$), with a
high ionization parameter ($\log_{10}$(U) $=-1.75 \pm 0.16$), a high ionizing
photon production efficiency $\log(\xi_{\rm ion}) = 25.7_{-0.1}^{+0.2}$, and a
low, although not pristine, metal content ranging between $5\%$ and $11\%$
Z$_\odot$, indicating a rapid enrichment of the ISM in the last few Myrs. These
properties also suggest that a substantial amount of ionizing photons ($\sim
10\%$) are leaking outside. The general lessons learned from GHZ2 are the
following: (i) the UV to optical combined nebular indicators are broadly in
agreement with UV-only or optical-only indicators. (ii) UV+optical diagnostics
fail to discriminate between an AGN and star-formation in a low metallicity,
high density, and extreme ionization environment. (iii) comparing the nebular
line ratios with local analogs may be approaching its limits at $z \gtrsim 10$,
as this approach is potentially challenged by the unique conditions of star
formation experienced by galaxies at these extreme redshifts.


  We give a complete proof of the expression of capacities of a measure in
terms of its Fourier transform.


  Iris recognition is used in many applications around the world, with
enrollment sizes as large as over one billion persons in India's Aadhaar
program. Large enrollment sizes can require special optimizations in order to
achieve fast database searches. One such optimization that has been used in
some operational scenarios is 1:First search. In this approach, instead of
scanning the entire database, the search is terminated when the first
sufficiently good match is found. This saves time, but ignores potentially
better matches that may exist in the unexamined portion of the enrollments. At
least one prominent and successful border-crossing program used this approach
for nearly a decade, in order to allow users a fast "token-free" search. Our
work investigates the search accuracy of 1:First and compares it to the
traditional 1:N search. Several different scenarios are considered trying to
emulate real environments as best as possible: a range of enrollment sizes,
closed- and open-set configurations, two iris matchers, and different
permutations of the galleries. Results confirm the expected accuracy
degradation using 1:First search, and also allow us to identify acceptable
working parameters where significant search time reduction is achieved, while
maintaining accuracy similar to 1:N search.


  We present the results of a synthetic spectral analysis of the far
ultraviolet archival IUE, HST and FUSE observations of the fast old nova V603
Aql, obtained some 90 years after its 1918 nova outburst. Our analysis utilizes
the new Hubble FGS parallax distance for this nearly face-on old nova, a high
white dwarf mass and a low reddening. Our analysis includes non-truncated
optically thick accretion disks since V603 Aql is neither a polar nor an
intermediate polar. Our synthetic spectral modeling of the FUSE and HST spectra
analyzed separately indicate a mass transfer rate of 1.5-2.2xe-9 solar mass per
year for the FUSE and HST spectra respectively, assuming a WD mass of 1.2Msun.
The mass accretion rate also depends on the assumed WD mass, and increases by a
factor of two for a WD mass of 0.8Msun. Combining the FUSE and HST spectra
together lead to the same results. Potential implications are discussed.


  We produce congruences modulo a prime $p>3$ for sums $\sum_k\binom{3k}{k}x^k$
over ranges $0\le k<q$ and $0\le k<q/3$, where $q$ is a power of $p$. Here $x$
equals either $c^2/(1-c)^3$, or $4s^2/\bigl(27(s^2-1)\bigr)$, where $c$ and $s$
are indeterminates. In the former case we deal more generally with shifted
binomial coefficients $\binom{3k+e}{k}$. Our method derives such congruences
directly from closed forms for the corresponding series.


  We provide an efficient algorithm for computing the nucleolus for an instance
of a weighted cooperative matching game. This resolves a long-standing open
question posed in [Faigle, Kern, Fekete, Hochst\"{a}ttler, Mathematical
Programming, 1998].


  We examine the spectra and infrared colors of the cool methane-dominated
atmospheres at Teff < 1400 K expected for young gas giant planets. We couple
these spectral calculations to an updated version of the Marley et al. (2007)
giant planet thermal evolution models that include formation by core
accretion-gas capture. These relatively cool "young Jupiters" can be 1-6
magnitudes fainter than predicted by standard cooling tracks that include a
traditional initial condition, which may provide a diagnostic of formation. If
correct, this would make true Jupiter-like planets much more difficult to
detect at young ages than previously thought. Since Jupiter and Saturn are of
distinctly super-solar composition, we examine emitted spectra for model
planets at both solar metallicity and a metallicity of 5 times solar. These
metal-enhanced young Jupiters have lower pressure photospheres than field brown
dwarfs of the same effective temperatures arising from both lower surface
gravities and enhanced atmospheric opacity. We highlight several diagnostics
for enhanced metallicity. A stronger CO absorption band at 4.5 $\mu$m for the
warmest objects is predicted. At all temperatures, enhanced flux in $K$ band is
expected due to reduced collisional induced absorption by H$_2$. This leads to
correspondingly redder near infrared colors, which are redder than solar
metallicity models with the same surface gravity by up to 0.7 in $J-K$ and 1.5
in $H-K$. Molecular absorption band depths increase as well, most significantly
for the coolest objects. We also qualitatively assess the changes to emitted
spectra due to nonequilibrium chemistry.


  Complete two-loop calculation of a dimensionally regularized Wilson loop with
light-like segments is performed in the light-like axial gauge with the
Mandelstam-Leibbrandt prescription for the gluon propagator. We find an
expression which {\it exactly} coincides with the one previously obtained for
the same Wilson loop in covariant Feynman gauge. The renormalization of Wilson
loop is performed in the $\MS-$scheme using a general procedure tailored to the
light-like axial gauge. We find that the renormalized Wilson loop obeys a
renormalization group equation with the same anomalous dimensions as in
covariant gauges. Physical implications of our result for investigation of
infrared asymptotics of perturbative QCD are pointed out.


  We show that cosmic-ray observations set a model-independent upper bound to
the flux of high-energy, > 10^14 eV, neutrinos produced by photo-meson (or p-p)
interactions in sources of size not much larger than the proton photo-meson (or
pp) mean-free-path. The bound applies, in particular, to neutrino production by
either AGN jets or GRBs. This upper limit is two orders of magnitude below the
flux predicted in some popular AGN jet models, but is consistent with our
predictions from GRB models. We discuss the implications of these results for
future km^2 high-energy neutrino detectors.


  The Dynamic Mode Decomposition (DMD)---a popular method for performing
data-driven Koopman spectral analysis---has gained increased adoption as a
technique for extracting dynamically meaningful spatio-temporal descriptions of
fluid flows from snapshot measurements. Often times, DMD descriptions can be
used for predictive purposes as well, which enables informed decision-making
based on DMD model-forecasts. Despite its widespread use and utility, DMD
regularly fails to yield accurate dynamical descriptions when the measured
snapshot data are imprecise due to, e.g., sensor noise. Here, we express DMD as
a two-stage algorithm in order to isolate a source of systematic error. We show
that DMD's first stage, a subspace projection step, systematically introduces
bias errors by processing snapshots asymmetrically. To remove this systematic
error, we propose utilizing an augmented snapshot matrix in a subspace
projection step, as in problems of total least-squares, in order to account for
the error present in all snapshots. The resulting unbiased and noise-aware
total DMD (TDMD) formulation reduces to standard DMD in the absence of snapshot
errors, while the two-stage perspective generalizes the de-biasing framework to
other related methods as well. TDMD's performance is demonstrated in numerical
and experimental fluids examples.


  With the recent advancements in Artificial Intelligence (AI), various
organizations and individuals are debating about the progress of AI as a
blessing or a curse for the future of the society. This paper conducts an
investigation on how the public perceives the progress of AI by utilizing the
data shared on Twitter. Specifically, this paper performs a comparative
analysis on the understanding of users belonging to two categories -- general
AI-Tweeters (AIT) and expert AI-Tweeters (EAIT) who share posts about AI on
Twitter. Our analysis revealed that users from both the categories express
distinct emotions and interests towards AI. Users from both the categories
regard AI as positive and are optimistic about the progress of AI but the
experts are more negative than the general AI-Tweeters. Expert AI-Tweeters
share relatively large percentage of tweets about their personal news compared
to technical aspects of AI. However, the effects of automation on the future
are of primary concern to AIT than to EAIT. When the expert category is
sub-categorized, the emotion analysis revealed that students and industry
professionals have more insights in their tweets about AI than academicians.


  The goal of confidence-set learning in the binary classification setting is
to construct two sets, each with a specific probability guarantee to cover a
class. An observation outside the overlap of the two sets is deemed to be from
one of the two classes, while the overlap is an ambiguity region which could
belong to either class. Instead of plug-in approaches, we propose a support
vector classifier to construct confidence sets in a flexible manner.
Theoretically, we show that the proposed learner can control the non-coverage
rates and minimize the ambiguity with high probability. Efficient algorithms
are developed and numerical studies illustrate the effectiveness of the
proposed method.


  We have used polarized neutron reflectometry to study the structural and
magnetic properties of the individual layers in a series of
(Al,Be,Ga)As/(Ga,Mn)As/GaAs/(Ga,Mn)As multilayer samples. Structurally, we
observe that the samples are virtually identical except for the GaAs spacer
thickness (which varies from 3-12 nm), and confirm that the spacers contain
little or no Mn. Magnetically, we observe that for the sample with the thickest
spacer layer, modulation doping by the(Al,Be,Ga)As results in (Ga,Mn)As layers
with very different temperature dependent magnetizations. However, as the
spacer layer thickness is reduced, the temperature dependent magnetizations of
the top an bottom (Ga,Mn)As layers become progressively more similar - a trend
we find to be independent of the crystallographic direction along which spins
are magnetized. These results definitively show that (Ga,Mn)As layers can
couple across a non-magnetic spacer, and that such coupling depends on spacer
thickness.


  With $G = \mathbb{Z}/p$, $p$ prime, we calculate the ordinary $G$-cohomology
(with Burnside ring coefficients) of $\mathbb{C}P_G^\infty = B_G U(1)$, the
complex projective space, a model for the classifying space for $G$-equivariant
complex line bundles. The $RO(G)$-graded ordinary cohomology was calculated by
Gaunce Lewis, but here we extend to a larger grading in order to capture a more
natural set of generators, including the Euler class of the canonical bundle,
as well as a significantly simpler set of relations.


  At the beginning of the twentieth century while Henri Poincar\'e (1854-1912)
was already deeply involved in the developments of wireless telegraphy, he was
invited, in 1908, to give a series of lectures at the \'Ecole Sup\'erieure des
Postes et T\'el\'egraphes (today Sup'T\'elecom). In the last part of his
presentation he established that the necessary condition for the existence of a
stable regime of maintained oscillations in a device of radio engineering
completely analogous to the triode: the singing arc, is the presence in the
phase plane of stable limit cycle. The aim of this work is to prove that the
correspondence highlighted by Andronov between the periodic solution of a
non-linear second order differential equation and Poincar\'e's concept of limit
cycle has been carried out by Poincar\'e himself, twenty years before in these
forgotten conferences of 1908.


  IceCube has performed several all-sky searches for point-like neutrino
sources using track-like events, including a recent time-integrated analysis
using 10 years of IceCube data. This paper accompanies the public data release
of these neutrino candidates detected by IceCube between April 6, 2008 and July
8, 2018. The selection includes through-going tracks, primarily due to muon
neutrino candidates, that reach the detector from all directions, as well as
neutrino track events that start within the instrumented volume. An updated
selection and reconstruction for data taken after April 2012 slightly improves
the sensitivity of the sample. While more than 80% of the sample overlaps
between the old and new versions, differing events can lead to changes relative
to the previous 7 year event selection. An a posteriori estimate of the
significance of the 2014-2015 TXS flare is reported with an explanation of
observed discrepancies with previous results. This public data release, which
includes 10 years of data and binned detector response functions for muon
neutrino signal events, shows improved sensitivity in generic time-integrated
point source analyses and should be preferred over previous releases.


  Cellular automata (CA) play an important role in the development of
computationally efficient microscopic traffic models and recently have gained
considerable importance as a mean of optimising traffic control strategies.
However, real-time application of the available CA models in traffic control
systems is a difficult task due to their discrete and stochastic nature. This
paper introduces a novel method for simulation of signalised traffic streams,
which combines CA and fuzzy numbers. The introduced traffic simulation
algorithm eliminates main drawbacks of the CA approach, i.e. necessity of
multiple Monte Carlo simulations and calibration issues. Computational cost of
traffic simulation for the proposed algorithm is considerably lower than the
cost of simulation based on stochastic CA. Thus, the simulation results can be
obtained in a much shorter time. Experiments confirmed that the simulation
results for the introduced algorithm are consistent with that observed for
stochastic CA. The proposed simulation algorithm is suitable for real-time
applications in traffic control systems.


  Heart Rate Variability (HRV) plays an important role for reporting several
cardiological and non-cardiological diseases. Also, the HRV has a prognostic
value and is therefore quite important in modelling the cardiac risk. The
nature of the HRV is chaotic, stochastic and it remains highly controversial.
Because the HRV has utmost importance, it needs a sensitive tool to analyze the
variability. In previous work, Rosenstein and Wolf had used the Lyapunov
exponent as a quantitative measure for HRV detection sensitivity. However, the
two methods diverge in determining the HRV sensitivity. This paper introduces a
modification to both the Rosenstein and Wolf methods to overcome their
drawbacks. The introduced Mazhar-Eslam algorithm increases the sensitivity to
HRV detection with better accuracy.


  Recent estimations of the Hubble parameter $H_0$ based on gravitational waves
(GW) observations can be used to shed some light on the discrepancy between the
value of the Hubble parameter $H_0^P$ obtained from large scale observations
such as the Planck mission, and the small scale value $H_0^R$, obtained from
low redshift supernovae (SNe).
  In order to investigate the origin of this discrepancy we perform a combined
analysis of the luminosity distance of SNe and GW sources, using different
methods, finding that the impact of the GW data is very limited, due to the
small number of data points, and their large errors. We analyze separately data
from the Pantheon and the Union 2.1 catalogues, finding that a model with
$H_0^P$ and a small local void can fit the data as well as a homogeneous model
with $H_0^R$, resolving the apparent $H_0$ tension. We find that there is a
significant difference between the size and depth of the inhomogeneity obtained
using the two datasets, which could be due to the different sky coverage of the
two catalogues. For Pantheon we obtain evidence of a local inhomogeneity with a
density contrast $\delta_v=-0.155 \pm 0.026$, extending up to a redshift of
$z_v = 0.056 \pm 0.0002$, while for Union 2.1 we obtain $\delta_v=-0.461 \pm
0.032$ and $z_v= 0.081\pm 0.008$. We also perform some analysis using redshift
shell averaged data, and obtain approximately the same results, hinting to the
fact that the effects of the monopole component of the local inhomogeneity are
the dominant ones


  Turbulence is a key ingredient for the evolution of the intracluster medium,
whose properties can be predicted with high resolution numerical simulations.
We present initial results on the generation of solenoidal and compressive
turbulence in the intracluster medium during the formation of a small-size
cluster using highly resolved, non-radiative cosmological simulations, with a
refined monitoring in time. In this first of a series of papers, we closely
look at one simulated cluster whose formation was distinguished by a merger
around $z \sim 0.3$. We separate laminar gas motions, turbulence and shocks
with dedicated filtering strategies and distinguish the solenoidal and
compressive components of the gas flows using Hodge-Helmholtz decomposition.
Solenoidal turbulence dominates the dissipation of turbulent motions ($\sim
95\%$) in the central cluster volume at all epochs. The dissipation via
compressive modes is found to be more important ($\sim 30 \%$ of the total)
only at large radii ($\geq 0.5 ~r_{\rm vir}$) and close to merger events. We
show that enstrophy (vorticity squared) is good proxy of solenoidal turbulence.
All terms ruling the evolution of enstrophy (i.e. baroclinic, compressive,
stretching and advective terms) are found to be significant, but in amounts
that vary with time and location. Two important trends for the growth of
enstrophy in our simulation are identified: first, enstrophy is continuously
accreted into the cluster from the outside, and most of that accreted enstrophy
is generated near the outer accretion shocks by baroclinic and compressive
processes. Second, in the cluster interior vortex stretching is dominant,
although the other terms also contribute substantially.


  Motivated by the Landau-Ginzburg model, we study the Witten deformation on a
noncompact manifold with bounded geometry, together with some tameness
condition on the growth of the Morse function $f$ near infinity. We prove that
the cohomology of the Witten deformation $d_{Tf}$ acting on the complex of
smooth $L^2$ forms is isomorphic to the cohomology of Thom-Smale complex of $f$
as well as the relative cohomology of a certain pair $(M, U)$ for sufficiently
large $T$. We establish an Agmon estimate for eigenforms of the Witten
Laplacian which plays an essential role in identifying these cohomologies via
Witten's instanton complex, defined in terms of eigenspaces of the Witten
Laplacian for small eigenvalues. As an application we obtain the strong Morse
inequalities in this setting.


  This paper proposes a cooperative mechanism for mitigating the performance
degradation due to non-independent-and-identically-distributed (non-IID) data
in collaborative machine learning (ML), namely federated learning (FL), which
trains an ML model using the rich data and computational resources of mobile
clients without gathering their data to central systems. The data of mobile
clients is typically non-IID owing to diversity among mobile clients' interests
and usage, and FL with non-IID data could degrade the model performance.
Therefore, to mitigate the degradation induced by non-IID data, we assume that
a limited number (e.g., less than 1%) of clients allow their data to be
uploaded to a server, and we propose a hybrid learning mechanism referred to as
Hybrid-FL, wherein the server updates the model using the data gathered from
the clients and aggregates the model with the models trained by clients. The
Hybrid-FL solves both client- and data-selection problems via heuristic
algorithms, which try to select the optimal sets of clients who train models
with their own data, clients who upload their data to the server, and data
uploaded to the server. The algorithms increase the number of clients
participating in FL and make more data gather in the server IID, thereby
improving the prediction accuracy of the aggregated model. Evaluations, which
consist of network simulations and ML experiments, demonstrate that the
proposed scheme achieves a 13.5% higher classification accuracy than those of
the previously proposed schemes for the non-IID case.


  We review the observations and the basic laws describing the essential
aspects of collective motion -- being one of the most common and spectacular
manifestation of coordinated behavior. Our aim is to provide a balanced
discussion of the various facets of this highly multidisciplinary field,
including experiments, mathematical methods and models for simulations, so that
readers with a variety of background could get both the basics and a broader,
more detailed picture of the field. The observations we report on include
systems consisting of units ranging from macromolecules through metallic rods
and robots to groups of animals and people. Some emphasis is put on models that
are simple and realistic enough to reproduce the numerous related observations
and are useful for developing concepts for a better understanding of the
complexity of systems consisting of many simultaneously moving entities. As
such, these models allow the establishing of a few fundamental principles of
flocking. In particular, it is demonstrated, that in spite of considerable
differences, a number of deep analogies exist between equilibrium statistical
physics systems and those made of self-propelled (in most cases living) units.
In both cases only a few well defined macroscopic/collective states occur and
the transitions between these states follow a similar scenario, involving
discontinuity and algebraic divergences.


  Being able to forecast the popularity of new garment designs is very
important in an industry as fast paced as fashion, both in terms of
profitability and reducing the problem of unsold inventory. Here, we attempt to
address this task in order to provide informative forecasts to fashion
designers within a virtual reality designer application that will allow them to
fine tune their creations based on current consumer preferences within an
interactive and immersive environment. To achieve this we have to deal with the
following central challenges: (1) the proposed method should not hinder the
creative process and thus it has to rely only on the garment's visual
characteristics, (2) the new garment lacks historical data from which to
extrapolate their future popularity and (3) fashion trends in general are
highly dynamical. To this end, we develop a computer vision pipeline fine tuned
on fashion imagery in order to extract relevant visual features along with the
category and attributes of the garment. We propose a hierarchical label sharing
(HLS) pipeline for automatically capturing hierarchical relations among fashion
categories and attributes. Moreover, we propose MuQAR, a Multimodal
Quasi-AutoRegressive neural network that forecasts the popularity of new
garments by combining their visual features and categorical features while an
autoregressive neural network is modelling the popularity time series of the
garment's category and attributes. Both the proposed HLS and MuQAR prove
capable of surpassing the current state-of-the-art in key benchmark datasets,
DeepFashion for image classification and VISUELLE for new garment sales
forecasting.


  We show that a class of topological field theories are quantum duals of the
harmonic oscillator. This is demonstrated by establishing a correspondence
between the creation and annihilation operators and non-local gauge invariant
observables of the topological field theory. The example is used to discuss
some issues concerning background independence and the relation of vacuum
energy to the problem of time in quantum gravity.


  We study the influence of atomic interactions on quantum simulations in
momentum-space lattices (MSLs), where driven transitions between discrete
momentum states mimic transport between sites of a synthetic lattice. Low
energy atomic collisions, which are short ranged in real space, relate to
nearly infinite-ranged interactions in momentum space. However, the added
exchange energy between atoms in distinguishable momentum states leads to an
effectively attractive, finite-ranged interaction in momentum space. In this
work, we observe the onset of self-trapping driven by such interactions in a
momentum-space double well, paving the way for more complex many-body studies
in tailored MSLs. We consider the types of phenomena that may result from these
interactions, including the formation of chiral solitons in topological zigzag
lattices.


  We examine the effect of the transverse breathing mode on the longitudinal
sound propagation in a Bose-Einstein condensate in a one-dimensional optical
lattice. In particular, we discuss how the coupling with the transverse
breathing mode influences the sound velocity in an optical lattice. Using a
variational approach we calculate the dispersion relations for the longitudinal
sound mode and the transverse breathing mode analytically and find that the
shift in the sound velocity from the uncoupled result can be large enough to be
experimentally relevant. We also find that the effective mass of the transverse
breathing mode is affected considerably by the coupling to longitudinal sound.


  We present a data-driven algorithm for generating gaits of virtual characters
with varying dominance traits. Our formulation utilizes a user study to
establish a data-driven dominance mapping between gaits and dominance labels.
We use our dominance mapping to generate walking gaits for virtual characters
that exhibit a variety of dominance traits while interacting with the user.
Furthermore, we extract gait features based on known criteria in visual
perception and psychology literature that can be used to identify the dominance
levels of any walking gait. We validate our mapping and the perceived dominance
traits by a second user study in an immersive virtual environment. Our gait
dominance classification algorithm can classify the dominance traits of gaits
with ~73% accuracy. We also present an application of our approach that
simulates interpersonal relationships between virtual characters. To the best
of our knowledge, ours is the first practical approach to classifying gait
dominance and generate dominance traits in virtual characters.


  We present a new method to compute wavelength- and phase-dependent limb
darkening corrections for classical Cepheids. These corrections are derived
from hydrodynamic simulations and radiative transfer modeling with a full set
of atomic and molecular opacities. Comparison with hydrostatic models having
the same stellar parameters show a larger limb darkening for most phases in our
models, and temporal variations related to the hydrodynamics of the stellar
pulsation. We assess the implications of our results with respect to the
geometric Baade-Wesselink method, which uses interferometric measurements of
Cepheid angular diameters to determine their distances and radii. The relevance
of the hydrodynamic effects predicted by our model on the limb darkening of
pulsating Cepheids is finally discussed in terms of the predicted capabilities
of the VLTI.


  In this paper, we propose applying Non-Orthogonal Multiple Access (NOMA)
technology in a multiuser beam steering OWC system. We study the performance of
the NOMA-based multiuser beam steering system in terms of the achievable rate
and Bit Error Rate (BER). We investigate the impact of the power allocation
factor of NOMA and the number of users in the room. The results show that the
power allocation factor is a vital parameter in NOMA-based transmission that
affects the performance of the network in terms of data rate and BER.


  Some models within the framework of Gauss-Bonnet gravities are considered in
the presence of a non-minimally coupled scalar field. By imposing a particular
constraint on the scalar field coupling, an extension of the called
Einstein-Gauss-Bonnet gravity that keeps the correct speed of propagation for
gravitational waves, is considered. The cosmological evolution for this viable
class of models is studied and compared with observational data (BAO, CMB, Sne
Ia,..), where we obtain the corresponding bounds for these theories and show
that such theories fit well the data and provide a well-behaved cosmological
evolution in comparison to the standard model of cosmology. Some statistical
parameters show that the goodness of the fits are slightly better than those
for $\Lambda$CDM model.


  Nb$_{3}$Sn is a superconductor of great relevance for perspective RF
applications. We present for the first time surface impedance $Z_s$
measurements at 15 GHz and low RF field amplitude on Nb$_{3}$Sn in high
magnetic fields up to 12 T, with the aim of increasing the knowledge of
Nb$_{3}$Sn behavior in such conditions. $Z_s$ is a fundamental material
parameter that directly gives useful information about the dissipative and
reactive phenomena when the superconductor is subjected to high-frequency
excitations. Therefore, we present an analysis of the measured $Z_s$ with the
aim of extracting interesting data about pinning in Nb$_{3}$Sn at high
frequencies. From $Z_s$ we extract the vortex motion complex resistivity to
obtain the $r$-parameter and the depinning frequency $\nu_p$ in high magnetic
fields. The comparison of the results with the literature shows that the
measured $\nu_p$ on bulk Nb$_{3}$Sn is several times greater than that of pure
Nb. This demonstrates how Nb$_{3}$Sn can be a good candidate for RF
technological applications, also in high magnetic fields.


  It is well known that a minimum error quantum measurement for arbitrary
binary optical coherent states can be realized by a receiver that comprises
interfering with a coherent reference light, photon counting, and feedback
control. We show that, for ternary optical coherent states, a minimum error
measurement cannot always be realized by such a receiver. The problem of
finding an upper bound on the maximum success probability of such a receiver
can be formulated as a convex programming. We derive its dual problem and
numerically find the upper bound. At least for ternary phase-shift keyed
coherent states, this bound does not reach that of a minimum error measurement.


  SmB6 is a strongly correlated material that has been attributed as a
topological insulator and a Kondo insulator. Recent studies have found the
topological surface states and low temperature insulating character to be
profoundly robust against magnetic and non-magnetic impurities. Here, we use
angle resolved photoemission spectroscopy to chart the evolution of
topologically-linked electronic structure features versus magnetic doping and
temperature in Sm$_{1-x}$M$_x$B$_6$ (M=Eu, Ce). Topological coherence phenomena
are observed out to unprecedented ~30% Eu and 50% Ce concentrations that
represent extreme nominal hole and electron doping, respectively. Theoretical
analysis reveals that a recent re-designation of the topologically inverted
band symmetries provides a natural route to reconciling the persistence of
topological surface state emergence even as the insulating gap is lost through
decoherence.