  Understanding the evolution of paper and author citations is of paramount
importance for the design of research policies and evaluation criteria that can
promote and accelerate scientific discoveries. Recently many studies on the
evolution of science have been conducted in the context of the emergent science
of science field. While many studies have probed the link problem in citation
networks, only a few works have analyzed the temporal nature of link prediction
in author citation networks. In this study we compared the performance of 10
well-known local network similarity measurements to predict future links in
author citations networks. Differently from traditional link prediction
methods, the temporal nature of the predict links is relevant for our approach.
Our analysis revealed interesting results. The Jaccard coefficient was found to
be among the most relevant measurements. The preferential attachment
measurement, conversely, displayed the worst performance. We also found that
the extension of local measurements to their weighted version do not
significantly improved the performance of predicting citations. Finally, we
also found that a neural network approach summarizing the information from all
10 considered similarity measurements was not able to provide the highest
prediction performance.


  In this paper, a stage structured predator-prey model with general nonlinear
type of functional response is established and analyzed. The state-dependent
time delay (hereafter SDTD) is the time taken from predator's birth to its
maturity, formatted as a monotonical (ly) increasing, continuous(ly)
differentiable and bounded function on the number of mature predator. The model
is quite different from many previous models with SDTD, in the sense that the
derivative of delay on the time is involved in the model. First, we have shown
that for a large class of commonly used types of functional responses,
including Holling types I, II and III, Beddington-DeAngelis-type (hereafter
BD-type), etc, the predator coexists with the prey permanently if and only if
the predator's net reproduction number is larger than one unit; Secondly, we
have discussed the local stability of the equilibria of the model; Finally, for
the special case of BD-type functional response, we claim that if the system is
permanent, that is, the derivative of SDTD on the state is small enough and the
predator interference is large enough, then the coexistence equilibrium is
globally asymptotically stable.


  The covariantly constant spacetime torsion is one of the fields which may
break Lorentz and CPT symmetry. We review the previous works on the dynamical
torsion in the framework of effective quantum field theory (QFT). It turns out
that the existence of propagating torsion is strongly restricted by the QFT
principles. In particular, the torsion mass must be much greater than the
masses of all fermionic particles. In this situation, the main chance to
observe torsion is due to some symmetry breaking which may, in principle,
produce almost constant background torsion field.


  Unitary and analytic ten-resonance model of the nucleon electromagnetic
(e.m.) structure with canonical normalizations and asymptotics is constructed
on a four-sheeted Riemann surface. It describes well all existing experimental
space-like and time-like data on the nucleon e.m. form factors (ff's),
including also FENICE (Frascati) results on the neutron, for the first time.
This is achieved without any external constraints on the isovector spectral
functions following from the piN-scattering data and pion e.m. ff behaviour
through the unitarity condition. Just opposite, the model itself predicts a
pronounced effect of the two-pion continuum on the isovector spectral functions
revealing the strong enhancement of the left wing of the rho(770)-resonance
close to two-pion threshold. The existence of the fourth excited state of the
rho(770) meson with parameters mass and width equal to 2506 MeV and 700 MeV,
respectively. The isoscalar spectral function behaviours are predicted by the
presented model.


  Classical parking functions are defined as the parking preferences for $n$
cars driving (from west to east) down a one-way street containing parking
spaces labeled from $1$ to $n$ (from west to east). Cars drive down the street
toward their preferred spot and park there if the spot is available. Otherwise,
the car continues driving down the street and takes the first available parking
space, if such a space exists. If all cars can park using this parking rule, we
call the $n$-tuple containing the cars' parking preferences a parking function.
  In this paper, we introduce a generalization of the parking rule allowing
cars whose preferred space is taken to first proceed up to $k$ spaces west of
their preferred spot to park before proceeding east if all of those $k$ spaces
are occupied. We call parking preferences which allow all cars to park under
this new parking rule $k$-Naples parking functions of length $n$. This
generalization gives a natural interpolation between classical parking
functions, the case when $k=0$, and all $n$-tuples of positive integers $1$ to
$n$, the case when $k\geq n-1$. Our main result provides a recursive formula
for counting $k$-Naples parking functions of length $n$. We also give a
characterization for the $k=1$ case by introducing a new function that maps
$1$-Naples parking functions to classical parking functions, i.e. $0$-Naples
parking functions. Lastly, we present a bijection between $k$-Naples parking
functions of length $n$ whose entries are in weakly decreasing order and a
family of signature Dyck paths.


  How can we plan efficiently in a large and complex environment when the time
budget is limited? Given the original simulator of the environment, which may
be computationally very demanding, we propose to learn online an approximate
but much faster simulator that improves over time. To plan reliably and
efficiently while the approximate simulator is learning, we develop a method
that adaptively decides which simulator to use for every simulation, based on a
statistic that measures the accuracy of the approximate simulator. This allows
us to use the approximate simulator to replace the original simulator for
faster simulations when it is accurate enough under the current context, thus
trading off simulation speed and accuracy. Experimental results in two large
domains show that when integrated with POMCP, our approach allows to plan with
improving efficiency over time.


  We investigate the thermodynamics of a one-dimensional Hubbard model with
bond-charge interaction X using the transfer matrix renormalization group
method (TMRG). Numerical results for various quantities like spin and charge
susceptibilities, particle densities, specific heat and thermal correlation
lengths are presented and discussed. We compare our data also to results for
the exactly solvable case X/t=1 as well as to bosonisation results for weak
coupling X/t << 1, which shows excellent agreement. We confirm the existence of
a Tomonaga-Luttinger and a Luther-Emery liquid phase, in agreement with
previous studies at zero temperature. Thermal singlet-pair correlation lengths
are shown to dominate density and spin correlations for finite temperatures in
certain parameter regimes.


  We prove conformal versions of the local decomposition theorems of de Rham
and Hiepko of a Riemannian manifold as a Riemannian or a warped product of
Riemannian manifolds. Namely, we give necessary and sufficient conditions for a
Riemannian manifold to be locally conformal to either a Riemannian or a warped
product. We also obtain other related de Rham-type decomposition theorems. As
an application, we study Riemannian manifolds that admit a Codazzi tensor with
two distinct eigenvalues everywhere.


  We theoretically show that the initial coherence plays a crucial role in
enhancing the speed of excitation energy transfer (EET) in Fenna-Matthews-Olson
(FMO) complex. We choose a simplistic eight-level model considering all the
bacateriochlorophyll-a sites in a monomer of FMO complex. We make a comparative
numerical study of the EET, in terms of non-Markovian evolution of an initial
coherent superposition state and a mixed state. A femto-second coherent laser
pulse is suitably chosen to create the initial coherent superposition state.
Such an initial state relaxes much faster than a mixed state thereby speeding
up the EET. In this analysis, we have taken into account the relative
orientation of the transition dipole moments of the bacateriochlorophyll-a
sites and their relative excitation energies. Our results reveal that for 2D
electronic spectroscopy experiments, the existing two-pathway model of energy
transfer in FMO complex may not be suitable in our understanding of EET.


  Social influence prediction has permeated many domains, including marketing,
behavior prediction, recommendation systems, and more. However, traditional
methods of predicting social influence not only require domain expertise,they
also rely on extracting user features, which can be very tedious. Additionally,
graph convolutional networks (GCNs), which deals with graph data in
non-Euclidean space, are not directly applicable to Euclidean space. To
overcome these problems, we extended DeepInf such that it can predict the
social influence of COVID-19 via the transition probability of the page rank
domain. Furthermore, our implementation gives rise to a deep learning-based
personalized propagation algorithm, called DeepPP. The resulting algorithm
combines the personalized propagation of a neural prediction model with the
approximate personalized propagation of a neural prediction model from page
rank analysis. Four social networks from different domains as well as two
COVID-19 datasets were used to demonstrate the efficiency and effectiveness of
the proposed algorithm. Compared to other baseline methods, DeepPP provides
more accurate social influence predictions. Further, experiments demonstrate
that DeepPP can be applied to real-world prediction data for COVID-19.


  A class of homogeneous isotropic space-time models including pseudo-Euclidean
space as a special case is considered. Such a model is chosen, where the
particle motion is described in the most adequate way. It means that the world
tubes of all free particles (both classical and quantal) are characteristic
geometrical structures of the space-time. The world function $\sigma $ of this
model has the form $\sigma =\sigma_E+\sigma_{0}$, $\sigma_E>\sigma_{0}$, where
\sigma_E is the world function of the pseudo-Euclidean space, \sigma_0 is a
constant responsible for quantum effects. It is proportional to Planck's
constant \hbar .


  The variability of the Vela pulsar (PSR B0833-45) corresponds to well-defined
field statistics that vary with pulsar phase, ranging from Gaussian intensity
statistics off-pulse to approximately power-law statistics in a transition
region and then lognormal statistics on-pulse, excluding giant micropulses.
These data are analyzed here in terms of two superposed wave populations, using
a new calculation for the amplitude statistics of two vectorially-combined
transverse fields. Detailed analyses show that the approximately power-law and
lognormal distributions observed are fitted well at essentially all on-pulse
phases by Gaussian-lognormal and double-lognormal combinations, respectively.
These good fits, plus the smooth but significant variations in fit parameters
across the source, provide strong evidence that the approximately power-law
statistics observed in the transition region are not intrinsic. Instead, the
data are consistent with normal pulsar emission having lognormal statistics at
all phases. This is consistent with generation in an inhomogeneous source
obeying stochastic growth theory (SGT) and with the emission mechanism being
purely linear (either direct or indirect). A nonlinear mechanism is viable only
if it produces lognormal statistics when suitably ensemble-averaged. Variations
in the SGT fit parameters with phase imply that the radiation is relatively
more variable near the pulse edges than near the center, as found in earlier
work. In contrast, Vela's giant micropulses come from a very restricted phase
range and have power-law statistics with indices ($6.7 \pm 0.6$) not
inconsistent with nonlinear wave collapse. These results imply that normal
pulses have a different source and generation mechanism than giant micropulses,
as suggested previously on other grounds.


  Suppose that $n \ge 2$, and we wish to plant $k$ different types of trees in
the squares of an $n \times n$ square grid. We can have as many of each type as
we want. The only rule is that every pair of types must occur in an adjacent
pair of squares somewhere in the grid. The question is: given $n$, what is the
largest that $k$ can be? Denote this number by $\Gamma(n)$, and call this the
*complete coloring number* of the $n \times n$ grid. A little thought shows
that $\Gamma(n) \le 2n-1$. The main question we are interested in is whether
$\Gamma(n) = 2n-1$ for every $n \ge 2$.


  In a zero-trust fabless paradigm, designers are increasingly concerned about
hardware-based attacks on the semiconductor supply chain. Logic locking is a
design-for-trust method that adds extra key-controlled gates in the circuits to
prevent hardware intellectual property theft and overproduction. While
attackers have traditionally relied on an oracle to attack logic-locked
circuits, machine learning attacks have shown the ability to retrieve the
secret key even without access to an oracle. In this paper, we first examine
the limitations of state-of-the-art machine learning attacks and argue that the
use of key hamming distance as the sole model-guiding structural metric is not
always useful. Then, we develop, train, and test a corruptibility-aware graph
neural network-based oracle-less attack on logic locking that takes into
consideration both the structure and the behavior of the circuits. Our model is
explainable in the sense that we analyze what the machine learning model has
interpreted in the training process and how it can perform a successful attack.
Chip designers may find this information beneficial in securing their designs
while avoiding incremental fixes.


  Collaborative learning enables two or more participants, each with their own
training dataset, to collaboratively learn a joint model. It is desirable that
the collaboration should not cause the disclosure of either the raw datasets of
each individual owner or the local model parameters trained on them. This
privacy-preservation requirement has been approached through differential
privacy mechanisms, homomorphic encryption (HE) and secure multiparty
computation (MPC), but existing attempts may either introduce the loss of model
accuracy or imply significant computational and/or communicational overhead. In
this work, we address this problem with the lightweight additive secret sharing
technique. We propose PrivColl, a framework for protecting local data and local
models while ensuring the correctness of training processes. PrivColl employs
secret sharing technique for securely evaluating addition operations in a
multiparty computation environment, and achieves practicability by employing
only the homomorphic addition operations. We formally prove that it guarantees
privacy preservation even though the majority (n-2 out of n) of participants
are corrupted. With experiments on real-world datasets, we further demonstrate
that PrivColl retains high efficiency. It achieves a speedup of more than 45X
over the state-of-the-art MPC/HE based schemes for training linear/logistic
regression, and 216X faster for training neural network.


  We perform a measurement of the Hubble constant, $H_0$, using the latest
baryonic acoustic oscillations (BAO) measurements from galaxy surveys of 6dFGS,
SDSS DR7 Main Galaxy Sample, BOSS DR12 sample, and eBOSS DR14 quasar sample, in
the framework of a flat $\Lambda$CDM model. Based on the Kullback-Leibler (KL)
divergence, we examine the consistency of $H_0$ values derived from various
data sets. We find that our measurement is consistent with that derived from
Planck and with the local measurement of $H_0$ using the Cepheids and type Ia
supernovae. We perform forecasts on $H_0$ from future BAO measurements, and
find that the uncertainty of $H_0$ determined by future BAO data alone,
including complete eBOSS, DESI and Euclid-like, is comparable with that from
local measurements.


  Current-based deep level transient spectroscopy was used to study trap states
in poly(3- hexylthiophene-2,5-diyl) (P3HT), [6,6]-phenyl-C61 butyric acid
methyl ester (PCBM) and P3HT:PCBM blend. The obtained spectra showed traps of
87 meV activation energy in pure P3HT and 21 meV for PCBM. The blend shows a
complex emission rate spectrum consisting of several different emission rate
bands in the range of (0.1-30) s^-1, yielding activation energies between about
30 meV and 160 meV.


  We study the equation $-\Delta u+h(x)|u|^{q-1}u=0$, $q>1$, in
$R^N_+=R^{N-1}\ti R_+$ where $h\in C(\bar{R^N_+})$, $h\geq 0$. Let $(x_1,...,
x_N)$ be a coordinate system such that $R^N_+=[x_N>0]$ and denote a point $x\in
\RN$ by $(x',x_N)$. Assume that $h(x', x_N)>0$ when $x'\neq 0$ but
$h(x',x_N)\to 0$ as $|x'|\to 0$. For this class of equations we obtain sharp
necessary and sufficient conditions in order that singularities on the boundary
do not propagate in the interior.


  Players in pricing games with complex structures are increasingly adopting
artificial intelligence (AI) aided learning algorithms to make pricing
decisions for maximizing profits. This is raising concern for the antitrust
agencies as the practice of using AI may promote tacit algorithmic collusion
among otherwise independent players. Recent studies of games in canonical forms
have shown contrasting claims ranging from none to a high level of tacit
collusion among AI-guided players. In this paper, we examine the concern for
tacit collusion by considering a practical game where EV charging hubs compete
by dynamically varying their prices. Such a game is likely to be commonplace in
the near future as EV adoption grows in all sectors of transportation. The hubs
source power from the day-ahead (DA) and real-time (RT) electricity markets as
well as from in-house battery storage systems. Their goal is to maximize
profits via pricing and efficiently managing the cost of power usage. To aid
our examination, we develop a two-step data-driven methodology. The first step
obtains the DA commitment by solving a stochastic model. The second step
generates the pricing strategies by solving a competitive Markov decision
process model using a multi-agent deep reinforcement learning (MADRL)
framework. We evaluate the resulting pricing strategies using an index for the
level of tacit algorithmic collusion. An index value of zero indicates no
collusion (perfect competition) and one indicates full collusion (monopolistic
behavior). Results from our numerical case study yield collusion index values
between 0.14 and 0.45, suggesting a low to moderate level of collusion.


  In this paper, we first give a convenient formula for bi-Laplacian on a
sphere and the complete description of its eigenvalues, buckling eigenvalues,
and their corresponding eigenfunctions. We then show that the radial (or
rotationally symmetric) solutions for biharmonic equation on the model space $(
\mathbb{R}^+\times S^{m-1}, dr^2 + \sigma^2(r)\, g^{S^{m-1}})$ can be given by
an integral formula. We also prove that the model space always admits proper
biharmonic functions as the products of any eigenfunctions of the factor sphere
with certain radial functions. Many explicit examples of proper biharmonic
functions on space forms are given. Finally, we give a complete classification
of proper biharmonic functions with positive Laplacian on the punctured
Euclidean space.


  The optical response of the two-band superconductor MgB$_2$ has been studied
in the 0.7-4 THz range on films with very low impurity level. The effect of the
high-energy $\sigma$-gap is observed in the ratio $R_S/R_N$ between the normal
and superconducting state reflectance, while in a neutron irradiated film with
a slightly higher impurity level mainly the effect of the $\pi$-gap is evident
as reported in previous experiments. At terahertz frequencies, the
electrodynamic of MgB$_2$ can be well described by the two-band parallel
conductivity model and is dominated by the $\pi$-bands when the impurity level
is only slightly higher than that of an ultra-clean sample.


  We investigate the possibility that present-day galactic haloes contain a
population of massive black holes (MBHs) that form by hierarchical merging of
the black hole remnants of the first stars. Some of the MBHs may be large
enough or close enough to the centre of the galactic host that they merge
within a Hubble time. We estimate to what extent this process could contribute
to the mass of the super-massive black holes (SMBHs) observed in galactic
centres today. Many MBHs will not reach the centre of the main halo, however,
but continue to orbit within satellite subhaloes. Using a semi-analytical
approach that explicitly accounts for dynamical friction, tidal disruption and
encounters with the galactic disk, we follow the dynamics of the satellites and
their MBHs and determine the abundance and distribution of MBHs in present-day
haloes of various masses. Considering two different accretion scenarios we also
compute the bolometric luminosity function for the MBHs.


  In this paper, we study the higher Hochschild functor and its relationship
with factorization algebras and topological chiral homology. To this end, we
emphasize that the higher Hochschild complex is a $(\infty,1)$-functor from the
category $\hsset \times \hcdga$ to the category $\hcdga$ (where $\hsset$ and
$\hcdga$ are the $(\infty,1)$-categories of simplicial sets and commutative
differential graded algebras) and give an axiomatic characterization of this
functor. From the axioms we deduce several properties and computational tools
for this functor. We study the relationship between the higher Hochschild
functor and factorization algebras by showing that, in good cases, the
Hochschild functor determines a constant commutative factorization algebra.
Conversely, every constant commutative factorization algebra is naturally
equivalent to a Hochschild chain factorization algebra. Similarly, we study the
relationship between the above concepts and topological chiral homology. In
particular, we show that on their common domains of definition, the higher
Hochschild functor is naturally equivalent to topological chiral homology.
Finally, we prove that topological chiral homology determines a locally
constant factorization algebra and, further, that this functor induces an
equivalence between locally constant factorization algebras on a manifold and
(local system of) $E_n$-algebras. We also deduce that Hochschild chains and
topological chiral homology satisfies an exponential law, i.e., a Fubini type
Theorem to compute them on products of manifolds.


  Let $n$ be a positive integer. We provide an explicit geometrically motivated
$1$-Lipschitz map from the space of persistence diagrams on $n$ points
(equipped with the Bottleneck distance) into Hilbert space. Such maps are a
crucial step in topological data analysis, allowing the use of statistic (and
thus data analysis) on collections of persistence diagrams. The main advantage
of our maps as compared to most of the other such transformations is that they
are coarse and uniform embeddings with explicit distortion functions. This
allows us to control the amount of geometric information lost through their
application. Furthermore, we provide an explicit $1$-Lipschitz map from the
space of persistence diagrams on $n$ points on a bounded domain into a
Euclidean space with an explicit distortion function. The mentioned maps are
fairly simple, with each component depending depending only on the bottleneck
distance to the corresponding landmark persistence diagram. Due to geometric
motivation from classical dimension theory, our methods are best described as
quantitative dimension theory. We discuss the advantages and disadvantages of
our approach. We conclude with differently flavoured embedding of the space of
persistence diagrams on $n$ points on a bounded domain into
$\mathbb{R}^{n(n+1)}$.


  The half-filled Landau level is widely believed to be described by the
Halperin-Lee-Read theory of the composite Fermi liquid (CFL). In this paper, we
develop a theory for the particle-hole conjugate of the CFL, the Anti-CFL,
which we argue to be a distinct phase of matter as compared with the CFL. The
Anti-CFL provides a possible explanation of a recent experiment [Kamburov et.
al., Phys. Rev. Lett. 113, 196801 (2014)] demonstrating that the density of
composite fermions in GaAs quantum wells corresponds to the electron density
when the filling fraction $\nu < 1/2$ and to the hole density when $\nu > 1/2$.
We introduce a local field theory for the CFL and Anti-CFL in the presence of a
boundary, which we use to study CFL - Insulator - CFL junctions, and the
interface between the Anti-CFL and CFL. We show that the CFL - Anti-CFL
interface allows partially fused boundary phases in which "composite electrons"
can directly tunnel into "composite holes," providing a non-trivial example of
transmutation between topologically distinct quasiparticles. We discuss several
observable consequences of the Anti-CFL, including a predicted resistivity jump
at a first order transition between uniform CFL and Anti-CFL phases. We also
present a theory of a continuous quantum phase transition between the CFL and
Anti-CFL. We conclude that particle-hole symmetry requires a modified view of
the half-filled Landau level, in the presence of strong electron-electron
interactions and weak disorder, as a critical point between the CFL and the
Anti-CFL.


  It is argued that the heavy-quark limit of QCD requires a certain combination
of hyperfine mass splittings in heavy-quark hybrid-meson multiplets to be
unusually small. This observation will assist in the exploration of the
heavy-quark hybrid spectrum at facilities such as PANDA. Alternatively, a large
measured value for this mass splitting indicates that at least one member of
the multiplet must contain significant light-quark degrees of freedom.


  Our purpose in this paper is to study when a planar differential system
polynomial in one variable linearizes in the sense that it has an inverse
integrating factor which can be constructed by means of the solutions of linear
differential equations. We give several families of differential systems which
illustrate how the integrability of the system passes through the solutions of
a linear differential equation. At the end of the work, we describe some
families of differential systems which are Darboux integrable and whose inverse
integrating factor is constructed using the solutions of a second--order linear
differential equation defining a family of orthogonal polynomials.


  Pauli-Fierz approach to description of a massless spin-2 particle is
investigated in the framework of 30-component first order relativistic wave
equation theory on a curved space-time background. It is shown that additional
gauge symmetry of massless equations established by Pauli-Fierz can be extended
only to curved space-time regions where Ricci tensor vanishes. In all such
space-time models the generally covariant S=2 massless wave equation exhibits
gauge symmetry property, otherwise it is not so.


  Last-mile delivery systems commonly propose the use of autonomous robotic
vehicles to increase scalability and efficiency. The economic inefficiency of
collecting accurate prior maps for navigation motivates the use of planning
algorithms that operate in unmapped environments. However, these algorithms
typically waste time exploring regions that are unlikely to contain the
delivery destination. Context is key information about structured environments
that could guide exploration toward the unknown goal location, but the abstract
idea is difficult to quantify for use in a planning algorithm. Some approaches
specifically consider contextual relationships between objects, but would
perform poorly in object-sparse environments like outdoors. Recent deep
learning-based approaches consider context too generally, making
training/transferability difficult. Therefore, this work proposes a novel
formulation of utilizing context for planning as an image-to-image translation
problem, which is shown to extract terrain context from semantic gridmaps, into
a metric that an exploration-based planner can use. The proposed framework has
the benefit of training on a static dataset instead of requiring a
time-consuming simulator. Across 42 test houses with layouts from satellite
images, the trained algorithm enables a robot to reach its goal 189\% faster
than with a context-unaware planner, and within 63\% of the optimal path
computed with a prior map. The proposed algorithm is also implemented on a
vehicle with a forward-facing camera in a high-fidelity, Unreal simulation of
neighborhood houses.


  For conventional imaging, shaking of the imaging system or the target leads
to the degradation of imaging resolution. In this work, the influence of the
target's shaking to fourier-transform ghost diffraction (FGD) is investigated.
The analytical results, which are backed up by numerical simulation and
experiments, demonstrate that the quiver of target has no effect on the
resolution of FGD, thus the target's imaging with high spatial resolution can
be always achieved by phase-retrieval method from the FGD patterns. This
approach can be applied in high-precision imaging systems, to overcome the
influence of the system's shaking to imaging resolution.


  Determining possible failure scenarios is a critical step in the evaluation
of autonomous vehicle systems. Real-world vehicle testing is commonly employed
for autonomous vehicle validation, but the costs and time requirements are
high. Consequently, simulation-driven methods such as Adaptive Stress Testing
(AST) have been proposed to aid in validation. AST formulates the problem of
finding the most likely failure scenarios as a Markov decision process, which
can be solved using reinforcement learning. In practice, AST tends to find
scenarios where failure is unavoidable and tends to repeatedly discover the
same types of failures of a system. This work addresses these issues by
encoding domain relevant information into the search procedure. With this
modification, the AST method discovers a larger and more expressive subset of
the failure space when compared to the original AST formulation. We show that
our approach is able to identify useful failure scenarios of an autonomous
vehicle policy.


  The embedded sensors in widely used smartphones and other wearable devices
make the data of human activities more accessible. However, recognizing
different human activities from the wearable sensor data remains a challenging
research problem in ubiquitous computing. One of the reasons is that the
majority of the acquired data has no labels. In this paper, we present an
unsupervised approach, which is based on the nature of human activity, to
project the human activities into an embedding space in which similar
activities will be located closely together. Using this, subsequent clustering
algorithms can benefit from the embeddings, forming behavior clusters that
represent the distinct activities performed by a person. Results of experiments
on three labeled benchmark datasets demonstrate the effectiveness of the
framework and show that our approach can help the clustering algorithm achieve
improved performance in identifying and categorizing the underlying human
activities compared to unsupervised techniques applied directly to the original
data set.


  A new form of governing equations is derived from Hamilton's principle of
least action for a constrained Lagrangian, depending on conserved quantities
and their derivatives with respect to the time-space. This form yields
conservation laws both for non-dispersive case (Lagrangian depends only on
conserved quantities) and dispersive case (Lagrangian depends also on their
derivatives). For non-dispersive case the set of conservation laws allows to
rewrite the governing equations in the symmetric form of
Godunov-Friedrichs-Lax. The linear stability of equilibrium states for
potential motions is also studied. In particular, the dispersion relation is
obtained in terms of Hermitian matrices both for non-dispersive and dispersive
case. Some new results are extended to the two-fluid non-dispersive case.


  The stochastic proximal gradient method is a powerful generalization of the
widely used stochastic gradient descent (SGD) method and has found numerous
applications in Machine Learning. However, it is notoriously known that this
method fails to converge in non-convex settings where the stochastic noise is
significant (i.e. when only small or bounded batch sizes are used). In this
paper, we focus on the stochastic proximal gradient method with Polyak
momentum. We prove this method attains an optimal convergence rate for
non-convex composite optimization problems, regardless of batch size.
Additionally, we rigorously analyze the variance reduction effect of the Polyak
momentum in the composite optimization setting and we show the method also
converges when the proximal step can only be solved inexactly. Finally, we
provide numerical experiments to validate our theoretical results.


  The N=1 supergravity coupled to chiral and Yang-Mills superfields is regarded
as the ultraviolet theory of the supersymmetric Standard Model. The gravitino
acquires a mass from the hidden sector and participate in mediating
supersymmetry breaking to the visible sector. We consider the quantum effects
of the gravitino and evaluate its contribution to the gaugino masses at one
loop level, which turns out to be irrespective of the SM gauge couplings. Due
to the universality of gravitino, its contribution to the gaugino masses can
significantly modify the gaugino mass relations of other supersymmetry breaking
mechanisms depending on the gravitino mass. For a given gaugino mass the
gravitino mass cannot be arbitrarily large even when other supersymmetry
breaking mechanisms are dominant.


  A Monte-Carlo calculation of the atmospheric neutrino fluxes [1,2] has been
extended to take account of the three-dimensional (3D) nature of the problem,
including the bending of secondary particles in the geomagnetic field. Emphasis
has been placed on minimizing the approximations when introducing the 3D
considerations. In this paper, we describe the techniques used and quantify the
effects of the small approximations which remain. We compare 3D and 1D
calculations using the same physics input in order to evaluate the conditions
under which the 3D calculation is required and when the considerably simpler 1D
calculation is adequate. We find that the 1D and 3D results are essentially
identical for neutrino energy greater than 5 GeV except for small effects in
the azimuthal distributions due to bending of the secondary muon by the
geomagnetic field during their propagation in the atmosphere.


  Studies on the social behaviors of bats show that they have the ability to
eavesdrop on the signals emitted by conspecifics in their vicinity. They can
fuse this ``passive" data with actively collected data from their own signals
to get more information about their environment, allowing them to fly and hunt
more efficiently and to avoid or cause jamming when competing for prey.
Acoustic sensors are capable of similar feats but are generally used in only an
active or passive capacity at one time. Is there a benefit to using both active
and passive sensing simultaneously in the same array? In this work we define a
family of models for active, passive, and fused sensing systems to measure
range and bearing data from an environment defined by point-based landmarks.
These measurements are used to solve the problem of simultaneous localization
and mapping (SLAM) with extended Kalman filter (EKF) and FastSLAM 2.0
approaches. Our results show agreement with previous findings. Specifically,
when active sensing is limited to a narrow angular range, fused sensing can
perform just as accurately if not better, while also allowing the sensor to
perceive more of the surrounding environment.


  We present detailed multi-wavelength observations of GRB 161219B at
$z=0.1475$, spanning the radio to X-ray regimes, and the first ALMA light curve
of a GRB afterglow. The cm- and mm-band observations before $8.5$ d require
emission in excess of that produced by the afterglow forward shock (FS). These
data are consistent with radiation from a refreshed reverse shock (RS) produced
by the injection of energy into the FS, signatures of which are also present in
the X-ray and optical light curves. We infer a constant-density circumburst
environment with an extremely low density, $n_0\approx 3\times10^{-4}$
cm$^{-3}$ and show that this is a characteristic of all strong RS detections to
date. The VLA observations exhibit unexpected rapid variability on $\sim$
minute timescales, indicative of strong interstellar scintillation. The X-ray,
ALMA, and VLA observations together constrain the jet break time, $t_{\rm
jet}\approx32$ day, yielding a wide jet opening angle of $\theta_{\rm
jet}\approx13^{\circ}$, implying beaming corrected $\gamma$-ray and kinetic
energies of $E_{\gamma}\approx4.9\times10^{48}$ erg and $E_{\rm
K}\approx1.3\times10^{50}$ erg, respectively. Comparing the RS and FS emission,
we show that the ejecta are only weakly magnetized, with relative
magnetization, $R_{\rm B}\approx1$, compared to the FS. These direct,
multi-frequency measurements of a refreshed RS spanning the optical to radio
bands highlight the impact of radio and millimeter data in probing the
production and nature of GRB jets.


  We investigate the eigenstate thermalization properties of the spin-1/2 $XXZ$
model in two-dimensional rectangular lattices of size $L_1\times L_2$ under
periodic boundary conditions. Exploiting the symmetry property, we can perform
an exact diagonalization study of the energy eigenvalues up to system size
$4\times 7$ and of the energy eigenstates up to $4\times 6$. Numerical analysis
of the Hamiltonian eigenvalue spectrum and matrix elements of an observable in
the Hamiltonian eigenstate basis supports that the two-dimensional $XXZ$ model
follows the eigenstate thermalization hypothesis. When the spin interaction is
isotropic the $XXZ$ model Hamiltonian conserves the total spin and has SU(2)
symmetry. We show that the eigenstate thermalization hypothesis is still valid
within each subspace where the total spin is a good quantum number.


  Using a personal computer and a smartphone, iMecaProf is a software that
provides a complete teaching environment for practicals associated to a
Classical Mechanics course. iMecaProf proposes a visual, real time and
interactive representation of data transmitted by a smartphone using the
formalism of Classical Mechanics. Using smartphones is more than using a set of
sensors. iMecaProf shows students that important concepts of physics they here
learn, are necessary to control daily life smartphone operations. This is
practical introduction to mechanical microsensors that are nowadays a key
technology in advanced trajectory control. First version of iMecaProf can be
freely downloaded. It will be tested this academic year in Universit\'e Joseph
Fourier (Grenoble, France)


  A relaxed factorization is used to obtain many of the properties obeyed by
the confluent hypergeometric functions. Their implications on the analytical
solutions of some interesting physical problems are also studied. It is quite
remarkable that, although these properties appear frequently in solving the
Schroedinger equation, it has been not clear the role they play in describing
the physical systems. The main objective of this communication is precisely to
throw some light on the subject.


  The Scotogenic Model is one of the most minimal models to account for both
neutrino masses and dark matter (DM). In this model, neutrino masses are
generated at the one-loop level, and in principle, both the lightest fermion
singlet and the lightest neutral component of the scalar doublet can be viable
DM candidates. However, the correct DM relic abundance can only be obtained in
somewhat small regions of the parameter space, as there are strong constraints
stemming from lepton flavour violation, neutrino masses, electroweak precision
tests and direct detection. For the case of scalar DM, a sufficiently large
lepton-number-violating coupling is required, whereas for fermionic DM,
coannihilations are typically necessary. In this work, we study how the new
scalar singlet modifies the phenomenology of the Scotogenic Model, particularly
in the case of scalar DM. We find that the new singlet modifies both the
phenomenology of neutrino masses and scalar DM, and opens up a large portion of
the parameter space of the original model.


  Using as a flavor symmetry a finite nonabelian dicyclic $Q_{2N}$ group, we
show how to derive quark mass matrices with two arrangements of symmetric
texture zeros which are phenomenologically viable. Three other such acceptable
textures in the recent literature are unattainable in this approach and hence
disfavored. We assume massive vector-like fermions and Higgs singlets
transforming as judiciously-chosen $Q_{2N}$ doublets and use the tree-level
mass generation mechanism of Froggatt and Nielsen.


  In recent years, the interest in leveraging quantum effects for enhancing
machine learning tasks has significantly increased. Many algorithms speeding up
supervised and unsupervised learning were established. The first framework in
which ways to exploit quantum resources specifically for the broader context of
reinforcement learning were found is projective simulation. Projective
simulation presents an agent-based reinforcement learning approach designed in
a manner which may support quantum walk-based speed-ups. Although classical
variants of projective simulation have been benchmarked against common
reinforcement learning algorithms, very few formal theoretical analyses have
been provided for its performance in standard learning scenarios. In this
paper, we provide a detailed formal discussion of the properties of this model.
Specifically, we prove that one version of the projective simulation model,
understood as a reinforcement learning approach, converges to optimal behavior
in a large class of Markov decision processes. This proof shows that a
physically-inspired approach to reinforcement learning can guarantee to
converge.


  This paper describes the solution of Hello World transformations in MOLA
transformation language. Transformations implementing the task are relatively
straightforward and easily inferable from the task specification. The required
additional steps related to model import and export are also described.


  Large Language models (LLMs) have demonstrated impressive in-context learning
(ICL) capabilities, where a LLM makes predictions for a given test input
together with a few input-output pairs (demonstrations). Nevertheless, the
inclusion of demonstrations leads to a quadratic increase in the computational
overhead of the self-attention mechanism. Existing solutions attempt to distill
lengthy demonstrations into compact vectors. However, they often require
task-specific retraining or compromise LLM's in-context learning performance.
To mitigate these challenges, we present Meta dEmonstratioN Distillation
(MEND), where a language model learns to distill any lengthy demonstrations
into vectors without retraining for a new downstream task. We exploit the
knowledge distillation to enhance alignment between MEND and LLM, achieving
both efficiency and effectiveness simultaneously. MEND is endowed with the
meta-knowledge of distilling demonstrations through a two-stage training
process, which includes meta-distillation pretraining and fine-tuning.
Comprehensive evaluations across seven diverse ICL task partitions using
decoder-only (GPT-2) and encoder-decoder (T5) attest to MEND's prowess. It not
only matches but often outperforms the Vanilla ICL as well as other
state-of-the-art distillation models, while significantly reducing the
computational demands. This innovation promises enhanced scalability and
efficiency for the practical deployment of large language models


  We consider a weak scale supersymmetric seesaw model where the Higgsino is
the next-to-lightest supersymmetric particle and the right-handed sneutrino is
the dark matter candidate. It is shown that, in this model, inverse decays,
which had been previously neglected, may suppress the sneutrino relic density
by several orders of magnitude. After including such processes and numerically
solving the appropriate Boltzmann equation, we study the dependence of the
relic density on the mu parameter, the sneutrino mass, and the neutrino Yukawa
coupling. We find that, even though much smaller than in earlier calculations,
the sneutrino relic density is still larger than the observed dark matter
density.


  We constrain the stellar population properties of a sample of 52 massive
galaxies, with stellar mass log Ms>10.5, over the redshift range 0.5<z<2 by use
of observer-frame optical and near-infrared slitless spectra from HST's ACS and
WFC3 grisms. The deep exposures (~100 ks) allow us to target individual spectra
of massive galaxies to F160W=22.5AB. Our spectral fitting approach uses a set
of six base models adapted to the redshift and spectral resolution of each
observation, and fits the weights of the base models, including potential dust
attenuation, via an MCMC method. Our sample comprises a mixed distribution of
quiescent (19) and star-forming galaxies (33). We quantify the width of the age
distribution (Dt) that is found to dominate the variance of the retrieved
parameters according to Principal Component Analysis. The population parameters
follow the expected trend towards older ages with increasing mass, and Dt
appears to weakly anti-correlate with stellar mass, suggesting a more efficient
star formation at the massive end. As expected, the redshift dependence of the
relative stellar age (measured in units of the age of the Universe at the
source) in the quiescent sample rejects the hypothesis of a single burst (aka
monolithic collapse). Radial colour gradients within each galaxy are also
explored, finding a wider scatter in the star-forming subsample, but no
conclusive trend with respect to the population parameters.


  While large-scale pre-trained text-to-image models can synthesize diverse and
high-quality human-centric images, an intractable problem is how to preserve
the face identity for conditioned face images. Existing methods either require
time-consuming optimization for each face-identity or learning an efficient
encoder at the cost of harming the editability of models. In this work, we
present an optimization-free method for each face identity, meanwhile keeping
the editability for text-to-image models. Specifically, we propose a novel
face-identity encoder to learn an accurate representation of human faces, which
applies multi-scale face features followed by a multi-embedding projector to
directly generate the pseudo words in the text embedding space. Besides, we
propose self-augmented editability learning to enhance the editability of
models, which is achieved by constructing paired generated face and edited face
images using celebrity names, aiming at transferring mature ability of
off-the-shelf text-to-image models in celebrity faces to unseen faces.
Extensive experiments show that our methods can generate identity-preserved
images under different scenes at a much faster speed.


  Quark distribution and spectator functions are estimated in a diquark
spectator model. The representation of the functions in terms of non-local
operators together with the rather simple model allow estimates for the yet
experimentally undetermined functions, like the polarized fragmentation
functions or higher twist functions.


  Rapid progress has been made in the field of reading comprehension and
question answering, where several systems have achieved human parity in some
simplified settings. However, the performance of these models degrades
significantly when they are applied to more realistic scenarios, such as
answers involve various types, multiple text strings are correct answers, or
discrete reasoning abilities are required. In this paper, we introduce the
Multi-Type Multi-Span Network (MTMSN), a neural reading comprehension model
that combines a multi-type answer predictor designed to support various answer
types (e.g., span, count, negation, and arithmetic expression) with a
multi-span extraction method for dynamically producing one or multiple text
spans. In addition, an arithmetic expression reranking mechanism is proposed to
rank expression candidates for further confirming the prediction. Experiments
show that our model achieves 79.9 F1 on the DROP hidden test set, creating new
state-of-the-art results. Source
code\footnote{\url{https://github.com/huminghao16/MTMSN}} is released to
facilitate future work.


  We present VoxelTrack for multi-person 3D pose estimation and tracking from a
few cameras which are separated by wide baselines. It employs a multi-branch
network to jointly estimate 3D poses and re-identification (Re-ID) features for
all people in the environment. In contrast to previous efforts which require to
establish cross-view correspondence based on noisy 2D pose estimates, it
directly estimates and tracks 3D poses from a 3D voxel-based representation
constructed from multi-view images. We first discretize the 3D space by regular
voxels and compute a feature vector for each voxel by averaging the body joint
heatmaps that are inversely projected from all views. We estimate 3D poses from
the voxel representation by predicting whether each voxel contains a particular
body joint. Similarly, a Re-ID feature is computed for each voxel which is used
to track the estimated 3D poses over time. The main advantage of the approach
is that it avoids making any hard decisions based on individual images. The
approach can robustly estimate and track 3D poses even when people are severely
occluded in some cameras. It outperforms the state-of-the-art methods by a
large margin on three public datasets including Shelf, Campus and CMU Panoptic.


  This paper is concerned with constructing an optimal controller in the
coherent quantum Linear Quadratic Gaussian problem. A coherent quantum
controller is itself a quantum system and is required to be physically
realizable. The use of coherent control avoids the need for classical
measurements, which inherently entail the loss of quantum information. Physical
realizability corresponds to the equivalence of the controller to an open
quantum harmonic oscillator and relates its state-space matrices to the
Hamiltonian, coupling and scattering operators of the oscillator. The
Hamiltonian parameterization of the controller is combined with Frechet
differentiation of the LQG cost with respect to the state-space matrices to
obtain equations for the optimal controller. A quasi-separation principle for
the gain matrices of the quantum controller is established, and a Newton-like
iterative scheme for numerical solution of the equations is outlined.


  Bacteria can spontaneously develop collective motions by aligning their
motions in dense systems. Here, we show that bacteria can also respond
collectively to an alternating electrical field and form dynamic clusters
oscillating at the same frequency of the field. As the dynamic clusters go
beyond a critical size, they split into smaller ones spontaneously. The
critical size for splitting depends on the frequency of electric field and the
concentration of bacteria. We show that instead of their biological activity,
the physical properties of bacteria as charged particles are responsible for
the formation of dynamic clusters. Electroconvective flows across the system
play the key role in stabilizing the clusters. However, to form clusters,
collective hydrodynamic cooperation between bacteria is important such that no
aggregation occurs in dilute suspensions. The findings in this study illustrate
that bio-systems can respond collectively to an external field, promising an
effective way to control and modulate the behavior of organisms. Moreover, the
controlled aggregation and condensation of bacteria offer a robust approach to
improve the local concentration of bacteria for early and rapid detection,
which has wide applications in clinics.


  Using 3D numerical hydrodynamical simulations we show that a type Ia
supernova (SN Ia) explosion inside a planetary nebula (PN) can explain the
observed shape of the G1.9+0.3 supernova remnant (SNR) and its X-ray
morphology. The SNR G1.9+0.3 morphology can be generally described as a sphere
with two small and incomplete lobes protruding on opposite sides of the SNR,
termed "ears", a structure resembling many elliptical PNe. Observations show
the synchrotron X-ray emission to be much stronger inside the two ears than in
the rest of the SNR. We numerically show that a spherical SN Ia explosion into
a circumstellar matter (CSM) with the structure of an elliptical PN with ears
and clumps embedded in the ears can explain the X-ray properties of SNR
G1.9+0.3. While the ejecta has already collided with the PN shell in most of
the SNR and its forward shock has been slowed down, the ejecta is still
advancing inside the ears. The fast forward shock inside the ears explains the
stronger X-ray emission there. SN Ia inside PNe (SNIPs) seem to comprise a
non-negligible fraction of resolved SN Ia remnants.


  A fundamental fact about bounded-degree graph expanders is that three notions
of expansion -- vertex expansion, edge expansion, and spectral expansion -- are
all equivalent. In this paper, we study to what extent such a statement is true
for linear-algebraic notions of expansion.
  There are two well-studied notions of linear-algebraic expansion, namely
dimension expansion (defined in analogy to graph vertex expansion) and quantum
expansion (defined in analogy to graph spectral expansion). Lubotzky and
Zelmanov proved that the latter implies the former. We prove that the converse
is false: there are dimension expanders which are not quantum expanders.
  Moreover, this asymmetry is explained by the fact that there are two distinct
linear-algebraic analogues of graph edge expansion. The first of these is
quantum edge expansion, which was introduced by Hastings, and which he proved
to be equivalent to quantum expansion. We introduce a new notion, termed
dimension edge expansion, which we prove is equivalent to dimension expansion
and which is implied by quantum edge expansion. Thus, the separation above is
implied by a finer one: dimension edge expansion is strictly weaker than
quantum edge expansion. This new notion also leads to a new, more modular proof
of the Lubotzky--Zelmanov result that quantum expanders are dimension
expanders.


  We provide relations of the results obtained in the articles
\cite{ThuyCidinha} and \cite{VuiThang}. Moreover, we provides some examples to
illustrate these relations, using the software {\it Maple} to complete the
complicate calculations of the examples. We give some discussions on these
relations.


  A typical product or place often has hundreds of reviews, and summarization
of these texts is an important and challenging problem. Recent progress on
abstractive summarization in domains such as news has been driven by supervised
systems trained on hundreds of thousands of news articles paired with
human-written summaries. However for opinion texts, such large scale datasets
are rarely available. Unsupervised methods, self-training, and few-shot
learning approaches bridge that gap. In this work, we present a novel
self-training approach, OpineSum, for abstractive opinion summarization. The
summaries in this approach are built using a novel application of textual
entailment and capture the consensus of opinions across the various reviews for
an item. This method can be used to obtain silver-standard summaries on a large
scale and train both unsupervised and few-shot abstractive summarization
systems. OpineSum achieves state-of-the-art performance in both settings.


  Quasiparticle poisoning has remained one of the main challenges in the
implementation of Majorana-based quantum computing. It inevitably occurs when
the system hosting Majorana qubits is not completely isolated from its
surrounding, thus considerably limiting its computational time. We propose the
use of periodic driving to generate multiple MFs at each end of a single
quantum wire, which naturally provides the necessary resources to implement
active quantum error corrections with minimal space overhead. In particular, we
present a stabilizer code protocol that can specifically detect and correct any
single quasiparticle poisoning event. Such a protocol is implementable via
existing proximitized semiconducting nanowire proposals, where all of its
stabilizer operators can be measured from an appropriate Majorana parity
dependent four-terminal conductance.


  The \emph{choice number} of a graph $G$, denoted $\ch(G)$, is the minimum
integer $k$ such that for any assignment of lists of size $k$ to the vertices
of $G$, there is a proper colouring of $G$ such that every vertex is mapped to
a colour in its list. For general graphs, the choice number is not bounded
above by a function of the chromatic number.
  In this thesis, we prove a conjecture of Ohba which asserts that
$\ch(G)=\chi(G)$ whenever $|V(G)|\leq 2\chi(G)+1$. We also prove a
strengthening of Ohba's Conjecture which is best possible for graphs on at most
$3\chi(G)$ vertices, and pose several conjectures related to our work.


  A recently developed three-dimensional version of the quasistatic code LCODE
has a novel feature that enables high-accuracy simulations of the long-term
evolution of waves in plasma wakefield accelerators. Equations of plasma
particle motion are modified to suppress clustering and numerical heating of
macroparticles, which otherwise occur because the Debye length is not resolved
by the numerical grid. The previously observed effects of premature wake
chaotization and wavebreaking disappear with the modified equations.


  If the halo of the lensing galaxy 0957+561 is made of massive compact objects
(MACHOs), they must affect the lightcurves of the quasar images Q0957+561 A and
B differently. We search for this microlensing effect in the double quasar by
comparing monitoring data for the two images A and B - obtained with the 3.5m
Apache Point Observatory from 1995 to 1998 - with intensive numerical
simulations. This way we test whether the halo of the lensing galaxy can be
made of MACHOs of various masses. We can exclude a halo entirely made out of
MACHOs with masses between 10e(-6) Msun and 10e(-2) Msun for quasar sizes of
less than 3*10e(14)cm (h = 0.6), hereby extending previous limits upwards by
one order of magnitude.


  We study a family of nonlinear damped wave equations indexed by a parameter
$\epsilon >0$ and forced by a space-time white noise on the two dimensional
torus, with polynomial and sine nonlinearities. We show that as $\epsilon \to
0$, the solutions to these equations converge to the solution of the
corresponding two dimensional stochastic quantization equation. In the sine
nonlinearity case, the convergence is proven over arbitrary large times, while
in the polynomial case, we prove that this approximation result holds over
arbitrary large times when the parameter $\epsilon$ goes to zero even with a
lack of suitable global well-posedness theory for the corresponding wave
equations.


  Dry eye disease (DED) affects approximately half of the United States
population. DED is characterized by dryness on the corena surface due to a
variety of causes. This study fills the spatiotemporal gaps in DED epidemiology
by using Google Trends as a novel epidemiological tool for geographically
mapping DED in relation to environmental risk factors. We utilized Google
Trends to extract DED-related queries estimating user intent from 2004-2019 in
the United States. We incorporated national climate data to generate heat maps
comparing geographic, temporal, and environmental relationships of DED.
Multi-variable regression models were constructed to generate quadratic
forecasts predicting DED and control searches. Our results illustrated the
upward trend, seasonal pattern, environmental influence, and spatial
relationship of DED search volume across US geography. Localized patches of DED
interest were visualized along the coastline. There was no significant
difference in DED queries across US census regions. Regression model 1
predicted DED searches over time (R^2=0.97) with significant predictors being
control queries (p=0.0024), time (p=0.001), and seasonality (Winter p=0.0028;
Spring p<0.001; Summer p=0.018). Regression model 2 predicted DED queries per
state (R^2=0.49) with significant predictors being temperature (p=0.0003) and
coastal zone (p=0.025). Importantly, temperature, coastal status, and
seasonality were stronger risk factors of DED searches than humidity, sunshine,
pollution, or region as clinical literature may suggest. Our work paves the way
for future exploration of geographic information systems for locating DED and
other diseases via online search query metrics.


  A graph is $d$-rigid if for any generic realisation of the graph in
$\mathbb{R}^d$ (equivalently, the $d$-dimensional sphere $\mathbb{S}^d$), there
are only finitely many non-congruent realisations in the same space with the
same edge lengths. By extending this definition to complex realisations in a
natural way, we define $c_d(G)$ to be the number of equivalent $d$-dimensional
complex realisations of a $d$-rigid graph $G$ for a given generic realisation,
and $c^*_d(G)$ to be the number of equivalent $d$-dimensional complex spherical
realisations of $G$ for a given generic spherical realisation. Somewhat
surprisingly, these two realisation numbers are not always equal. Recently
developed algorithms for computing realisation numbers determined that the
inequality $c_2(G) \leq c_2^*(G)$ holds for any minimally 2-rigid graph $G$
with 12 vertices or less. In this paper we confirm that, for any dimension $d$,
the inequality $c_d(G) \leq c_d^*(G)$ holds for every $d$-rigid graph $G$. This
result is obtained via new techniques involving coning, the graph operation
that adds an extra vertex adjacent to all original vertices of the graph.


  For a long time, there have been huge discrepancies between different models
and experiments concerning the liquid-liquid phase transition (LLPT) in dense
hydrogen. In this work, we present the results of extensive calculations of the
LLPT in dense hydrogen using the most expensive first-principle path-integral
molecular dynamics simulations available. The nonlocal density functional rVV10
and hybrid functional PBE0 are used to improve the description of the
electronic structure of hydrogen. Of all the density functional theory
calculations available, we report the most consistent results through quantum
Monte Carlo simulations and coupled electron-ion Monte Carlo simulations of the
LLPT in dense hydrogen. The critical point of the first-order LLPT is estimated
above 2000 K according to the equation of state. Moreover, the metallization
pressure obtained from the jump of dc electrical conductivity almost coincides
with the plateau of equation of state.


  Topological representations are rapidly becoming a popular way to capture and
encode higher-order interactions in complex systems. They have found
applications in disciplines as different as cancer genomics, brain function,
and computational social science, in representing both descriptive features of
data and inference models. While intense research has focused on the
connectivity and homological features of topological representations,
surprisingly scarce attention has been given to the investigation of the
community structures of simplicial complexes. To this end, we adopt recent
advances in symbolic embeddings to compute and visualize the community
structures of simplicial complexes. We first investigate the stability
properties of embedding obtained for synthetic simplicial complexes to the
presence of higher order interactions. We then focus on complexes arising from
social and brain functional data and show how higher order interactions can be
leveraged to improve clustering detection and assess the effect of higher order
interaction on individual nodes. We conclude delineating limitations and
directions for extension of this work.


  We show that quantification of the performance of quantum-enhanced
measurement schemes based on the concept of quantum Fisher information yields
asymptotically equivalent results as the rigorous Bayesian approach, provided
generic uncorrelated noise is present in the setup. At the same time, we show
that for the problem of decoherence-free phase estimation this equivalence
breaks down and the achievable estimation uncertainty calculated within the
Bayesian approach is by a $\pi$ factor larger than that predicted by the QFI
even in the large prior knowledge (small parameter fluctuation) regime, where
QFI is conventionally regarded as a reliable figure of merit. We conjecture
that the analogous discrepancy is present in arbitrary decoherence-free unitary
parameter estimation scheme and propose a general formula for the
asymptotically achievable precision limit. We also discuss protocols utilizing
states with indefinite number of particles and show that within the Bayesian
approach it is legitimate to replace the number of particles with the mean
number of particles in the formulas for the asymptotic precision, which as a
consequence provides another argument that proposals based on the properties of
the QFI of indefinite particle number states leading to sub-Heisenberg
precisions are not practically feasible.


  In this work we concentrate on the evolution of the cluster population of the
interacting galaxy M51 (NGC 5194), namely the timescale of cluster disruption
and possible variations in the cluster formation rate. We present a method to
compare observed age vs. mass number density diagrams with predicted
populations including various physical input parameters like the cluster
initial mass function, cluster disruption, cluster formation rate and star
bursts. If we assume that the cluster formation rate increases at the moments
of the encounters with NGC 5195, we find an increase in the cluster formation
rate of a factor of 3, combined with a disruption timescale which is slightly
higher then when assuming a constant formation rate (t_4 = 200 Myr vs. 100
Myr). The measured cluster disruption time is a factor of 5 shorter than
expected on theoretical grounds. This implies that the disk of M51 is not a
preferred location for survival of young globular clusters, since even clusters
with masses of the order of 10^6 M_sun will be destroyed within a few Gyr.


  US voters shared large volumes of polarizing political news and information
in the form of links to content from Russian, WikiLeaks and junk news sources.
Was this low quality political information distributed evenly around the
country, or concentrated in swing states and particular parts of the country?
In this data memo we apply a tested dictionary of sources about political news
and information being shared over Twitter over a ten day period around the 2016
Presidential Election. Using self-reported location information, we place a
third of users by state and create a simple index for the distribution of
polarizing content around the country. We find that (1) nationally, Twitter
users got more misinformation, polarizing and conspiratorial content than
professionally produced news. (2) Users in some states, however, shared more
polarizing political news and information than users in other states. (3)
Average levels of misinformation were higher in swing states than in
uncontested states, even when weighted for the relative size of the user
population in each state. We conclude with some observations about the impact
of strategically disseminated polarizing information on public life.


  Software security can be ensured by specifying and verifying security
properties of software using formal methods with strong theoretical bases. In
particular, programs can be modeled in the framework of lambda-calculi, and
interesting properties can be expressed formally by contextual equivalence
(a.k.a. observational equivalence). Furthermore, imperative features, which
exist in most real-life software, can be nicely expressed in the so-called
computational lambda-calculus. Contextual equivalence is difficult to prove
directly, but we can often use logical relations as a tool to establish it in
lambda-calculi. We have already defined logical relations for the computational
lambda-calculus in previous work. We devote this paper to the study of their
completeness w.r.t. contextual equivalence in the computational
lambda-calculus.


  We show how the birth of perspective painting in the Italian Renaissance led
to a new way of interpreting space that resulted in the creation of projective
geometry. Unlike other works on this subject, we explicitly show how the craft
of the painters implied the introduction of new points and lines (points and
lines at infinity) and their projective coordinates to complete the Euclidean
space to what is now called projective space. We demonstrate this idea by
looking at original paintings from the Renaissance, and by carrying out the
explicit analytic calculations that underpin those masterpieces.


  Occluded person re-identification is a challenging task as the appearance
varies substantially with various obstacles, especially in the crowd scenario.
To address this issue, we propose a Pose-guided Visible Part Matching (PVPM)
method that jointly learns the discriminative features with pose-guided
attention and self-mines the part visibility in an end-to-end framework.
Specifically, the proposed PVPM includes two key components: 1) pose-guided
attention (PGA) method for part feature pooling that exploits more
discriminative local features; 2) pose-guided visibility predictor (PVP) that
estimates whether a part suffers the occlusion or not. As there are no ground
truth training annotations for the occluded part, we turn to utilize the
characteristic of part correspondence in positive pairs and self-mining the
correspondence scores via graph matching. The generated correspondence scores
are then utilized as pseudo-labels for visibility predictor (PVP). Experimental
results on three reported occluded benchmarks show that the proposed method
achieves competitive performance to state-of-the-art methods. The source codes
are available at https://github.com/hh23333/PVPM


  M82 is the nearest starburst galaxy. It contains two large systems of super
star clusters, one being spawned today in the active starforming core, and one
produced by an earlier starburst event which coincided with the last orbital
passage of its neighbor, M81. The proximity of M82 makes it uniquely valuable
for a wide range of studies of massive young clusters and their environments.


  Image compression has been the subject of extensive research for several
decades, resulting in the development of well-known standards such as JPEG,
JPEG2000, and H.264/AVC. However, recent advancements in deep learning have led
to the emergence of learned image compression methods that offer significant
improvements in coding efficiency compared to traditional codecs. These learned
compression techniques have shown noticeable gains and even outperformed
traditional schemes


  We introduce a definition of admissibility for subintervals in interval
exchange transformations. Using this notion, we prove a property of the natural
codings of interval exchange transformations, namely that any derived set of a
regular interval exchange set is a regular interval exchange set with the same
number of intervals. Derivation is taken here with respect to return words. We
characterize the admissible intervals using a branching version of the Rauzy
induction. We also study the case of regular interval exchange transformations
defined over a quadratic field and show that the set of factors of such a
transformation is primitive morphic. The proof uses an extension of a result of
Boshernitzan and Carroll.


  In this paper, an efficient ensemble domain decomposition algorithm is
proposed for fast solving the fully-mixed random Stokes-Darcy model with the
physically realistic Beavers-Joseph (BJ) interface conditions. We utilize the
Monte Carlo method for the coupled model with random inputs to derive some
deterministic Stokes-Darcy numerical models and use the idea of the ensemble to
realize the fast computation of multiple problems. One remarkable feature of
the algorithm is that multiple linear systems share a common coefficient matrix
in each deterministic numerical model, which significantly reduces the
computational cost and achieves comparable accuracy with the traditional
methods. Moreover, by domain decomposition, we can decouple the Stokes-Darcy
system into two smaller sub-physics problems naturally. Both mesh-dependent and
mesh-independent convergence rates of the algorithm are rigorously derived by
choosing suitable Robin parameters. Optimized Robin parameters are derived and
analyzed to accelerate the convergence of the proposed algorithm. Especially,
for small hydraulic conductivity in practice, the almost optimal geometric
convergence can be obtained by finite element discretization. Finally, two
groups of numerical experiments are conducted to validate and illustrate the
exclusive features of the proposed algorithm.


  Flux Lines Lattice (FLL) states have been studied using transport
measurements and Small Angle Neutron Scattering in low T$_c$ materials. In
Pb-In, the bulk dislocations in the FLL do not influence the transport
properties. In Fe doped NbSe$_{2}$, transport properties can differ after a
Field Cooling (FC) or a Zero Field Cooling (ZFC) procedure, as previously
reported. The ZFC FLL is found ordered with narrow Bragg Peaks and is linked to
a linear V(I) curve and to a superficial critical current. The FC FLL pattern
exhibits two Bragg peaks and the corresponding V(I) curve shows a S-shape. This
can be explained by the coexistence of two ordered FLL slightly tilted from the
applied field direction by different superficial currents. These currents are
wiped out when the transport current is increased.


  In three-dimensional ideal magnetohydrodynamics, closed flux surfaces cannot
maintain both rational rotational-transform and pressure gradients, as these
features together produce unphysical, infinite currents. A proposed set of
equilibria nullifies these currents by flattening the pressure on sufficiently
wide intervals around each rational surface. Such rational surfaces exist at
every scale, which characterizes the pressure profile as self-similar and thus
fractal. The pressure profile is approximated numerically by considering a
finite number of rational regions and analyzed mathematically by classifying
the irrational numbers that support gradients into subsets. Applying these
results to a given rotational-transform profile in cylindrical geometry, we
find magnetic field and current density profiles compatible with the fractal
pressure.


  In this paper, we consider the higher-order convergence rates for the 2D
stationary and non-stationary Navier-Stokes Equations over highly oscillating
periodic bumpy John domains with $C^{2}$ regularity in some neighborhood of the
boundary point (0,0). For the stationary case and any $\gamma\in (0,1/2)$,
using the variational equation satisfied by the solution and the correctors for
the bumpy John domains obtained by Higaki, Prange and Zhuge
\cite{higaki2021large,MR4619004} after correcting the values on the
inflow/outflow boundaries $(\{0\}\cup\{1\})\times(0,1)$, we can obtain an
$O(\varepsilon^{2-\gamma})$ approximation in $L^2$ for the velocity and an
$O(\varepsilon^{2-\gamma})$ convergence rates in $L^1$ approximated by the so
called Navier's wall laws, which generalized the results obtained by J\"{a}ger
and Mikeli\'{c} \cite{MR1813101}. Moreover, for the non-stationary case, using
the energy method, we can obtain an $O(\varepsilon^{2-\gamma}+\exp(-Ct))$
convergence rate for the velocity in $L_x^1$.


  Due to global warming and its detrimental effect, every country is
responsible to join the global effort to reduce carbon emissions. In order to
improve the mitigation plan of climate change, accurate es-timates of carbon
emissions, population, and electricity consumption are critical. Carbon
footprint is significantly linked to the socioeconomic development of the
country which can be reflected in the city's infrastructure and urbanization.
We may be able to estimate the carbon footprint, population growth, and
electricity consumption of a city by observing the nighttime light reflecting
its urbanization. This is more challenging in oil-producing countries where
urbanization can be more complicated. In this study, we are therefore
investigating the possibility of correlating the remotely sensed NPP-VIIRS
Nighttime light (NTL) estimation with the aforementioned socioeconomic
indicators. Daily NPP-VIIRS NTL were obtained for the period between 2012 to
2021 for the United Arab Emirates (UAE) which is one of the top oil producing
countries. The socioeconomic indicators of the UAE, including the population,
electricity consumption, and carbon dioxide emissions, have been obtained for
the same period. The analysis of the correlation between the NTLs and the
population indicates that there is a high correlation of more than 0.9. There
is also a very good correlation of 0.7 between NTLs and carbon emissions and
electricity consumption. However, these correlations differ from one city to
another. For example, Dubai has shown the highest correlation between
population and NTLs (R2 > 0.8). However, the correlation was the lowest in
Al-Ain, a rural city (R2 < 0.4) with maximum electricity consumption of 1.1E04
GWh. These results demonstrate that NTLs can be considered as a promising proxy
for carbon footprint and urbanization in oil-producing regions.


  We aim at constraining the angular momentum evolution of low mass stars by
measuring their rotation rates when they begin to evolve freely towards the
ZAMS, i.e. after the disk accretion phase has stopped. We conducted a
multi-site photometric monitoring of the young open cluster h Persei that has
an age of ~13 Myr. The observations were done in the I-band using 4 different
telescopes and the variability study is sensitive to periods from less than 0.2
day to 20 days. Rotation periods are derived for 586 candidate cluster members
over the mass range 0.4<=M/Msun<=1.4. The rotation period distribution
indicates a sligthly higher fraction of fast rotators for the lower mass
objects, although the lower and upper envelopes of the rotation period
distribution, located respectively at ~0.2-0.3d and ~10d, are remarkably flat
over the whole mass range. We combine this period distribution with previous
results obtained in younger and older clusters to model the angular momentum
evolution of low mass stars during the PMS. The h Per cluster provides the
first statistically robust estimate of the rotational period distribution of
solar-type and lower mass stars at the end of the PMS accretion phase (>10
Myr). The results are consistent with models that assume significant
core-envelope decoupling during the angular momentum evolution to the ZAMS.


  Spectral sub-bands do not portray the same perceptual relevance. In audio
coding, it is therefore desirable to have independent control over each of the
constituent bands so that bitrate assignment and signal reconstruction can be
achieved efficiently. In this work, we present a novel neural audio coding
network that natively supports a multi-band coding paradigm. Our model extends
the idea of compressed skip connections in the U-Net-based codec, allowing for
independent control over both core and high band-specific reconstructions and
bit allocation. Our system reconstructs the full-band signal mainly from the
condensed core-band code, therefore exploiting and showcasing its bandwidth
extension capabilities to its fullest. Meanwhile, the low-bitrate high-band
code helps the high-band reconstruction similarly to MPEG audio codecs'
spectral bandwidth replication. MUSHRA tests show that the proposed model not
only improves the quality of the core band by explicitly assigning more bits to
it but retains a good quality in the high-band as well.


  We describe a setup for optical quality assurance of silicon microstrip
sensors. Pattern recognition algorithms were developed to analyze microscopic
scans of the sensors for defects. It is shown that the software has a
recognition and classification rate of $>$~90\% for defects like scratches,
shorts, broken metal lines etc. We have demonstrated that advanced image
processing based on neural network techniques is able to further improve the
recognition and defect classification rate.


  Usually, interpretation of redshift in static spacetimes (for example, near
black holes) is opposed to that in cosmology. In this methodological note we
show that both explanations are unified in a natural picture. This is achieved
if considering the static spacetime one (i) makes a transition to a synchronous
frame, (ii) returns to the original frame by means of local Lorentz boost. To
reach our goal, we consider a rather general class of spherically symmetric
spacetimes. In doing so, we construct frames that generalize the well-known
Lemaitre and Painlev\'{e}--Gullstand ones and elucidate the relation between
them. This helps to understanding in an unifying approach, how gravitation
reveals itself in different branches of general relativity. This can be useful
for general relativity university courses.


  We consider initial boundary value problem for uniformly 2-parabolic
differential operator of second order in cylinder domain in ${\mathbb R}^n $
with non-coercive boundary conditions. In this case there is a loss of
smoothness of the solution in Sobolev type spaces compared with the coercive
situation. Using by Faedo-Galerkin method we prove that problem has unique
solution in special Bochner space.


  We present an overview of pulsating stars in close binaries, focusing on the
question what role the dupliticity plays in triggering and/or modifying stellar
oscillations and on how it can help us to interpret the oscillatory behaviour
of (one of) the components. We give examples of characteristic types of
oscillations observed in binaries: forced oscillations and free oscillations in
both, short- and long-period binaries. The importance of studies of
oscillations in eclipsing binaries is also pointed out. A list of line-profile
and rapid light variables in close binaries with their basic properties is
provided. No obvious relations among the orbital eccentricity, orbital
frequency, rotational frequency and intrinsic frequencies of oscillations were
found. The value and future prospects of asteroseismic studies of binary stars
are briefly outlined while the complexity of the problem and its possible
complications are also discussed.


  Graph-based text representation focuses on how text documents are represented
as graphs for exploiting dependency information between tokens and documents
within a corpus. Despite the increasing interest in graph representation
learning, there is limited research in exploring new ways for graph-based text
representation, which is important in downstream natural language processing
tasks. In this paper, we first propose a new heterogeneous word-character text
graph that combines word and character n-gram nodes together with document
nodes, allowing us to better learn dependencies among these entities.
Additionally, we propose two new graph-based neural models, WCTextGCN and
WCTextGAT, for modeling our proposed text graph. Extensive experiments in text
classification and automatic text summarization benchmarks demonstrate that our
proposed models consistently outperform competitive baselines and
state-of-the-art graph-based models.


  We present an analytic and numerical analysis of the Gledzer-Ohkitani-Yamada
(GOY) cascade model for turbulence. We concentrate on the dynamic correlations,
and demonstrate both numerically and analytically, using resummed perturbation
theory, that the correlations do not follow a dynamic scaling ansatz. The basic
reason for this is the existence of a second quadratic invariant, in addition
to energy. This implies the breakdown of the Kolmogorov type scaling law, in a
manner different from the conventional mechanisms proposed for Navier-Stokes
intermittency. By modifying the model equation so as to eliminate the spurious
invariant, we recover to good accuracy, both dynamic scaling and the Kolmogorov
exponents. We conclude that intermittency in the GOY model may be attributed to
the effects of the spurious invariant which does not exist in the 3-dimensional
Navier-Stokes flow.


  Let H(t,x) be the Hopf-Cole solution at time t of the Kardar-Parisi-Zhang
(KPZ) equation starting with narrow wedge initial condition, i.e. the logarithm
of the solution of the multiplicative stochastic heat equation starting from a
Dirac delta. Also let H^{eq}(t,x) be the solution at time t of the KPZ equation
with the same noise, but with initial condition given by a standard two-sided
Brownian motion, so that H^{eq}(t,x)-H^{eq}(0,x) is itself distributed as a
standard two-sided Brownian motion. We provide a simple proof of the following
fact: for fixed t, H(t,x)-(H(t,x)-H^{eq}(t,0)) is locally of finite variation.
Using the same ideas we also show that if the KPZ equation is started with a
two-sided Brownian motion plus a Lipschitz function then the solution stays in
this class for all time.


  A self-consistent string field theory with interaction is formulated. The
symmetry algebra of this theory includes, in the low-energy limit, local
space-time symmetries, and the Brans-Dicke equation describes a class of
low-energy solutions. (Talk given at the conference Journees Relativistes'93
held on April 5-7 1993, To be published in the International Journal of Modern
Physics D.)


  The very first results of the application of the Tremaine-Weinberg (hereafter
TW) method on Halpha velocity fields are presented to find pattern speeds of
galaxies. The technique is used for a sample of four barred galaxies(NGC4321,
NGC3359, NGC6946 and NGC2903) and M51. For two of these, our results are very
similar to those obtained using the same technique but based on CO
observations. For the three other galaxies, a comparison is provided between
this observational method and numerical ones.


  We study the motion of neutral test particles along circular orbits in the
Reissner-Nordstr\"om spacetime. We use the method of the effective potential
with the constants of motion associated to the underlying Killing symmetries. A
comparison between the black hole and naked singularity cases is performed. In
particular we find that in the naked singularity case for $r<Q^2/M$ no circular
orbits can exist, this radius plays a fundamental role in the physics of the
naked singularity. For $r>Q^2/M$ and $1<Q/M<\sqrt{9/8}$ there are two stability
regions together with a region where all the circular orbits are instable and a
zone in which all trajectory are possible but not circular orbits, for
$\sqrt{9/8}<Q/M<\sqrt{5}/2$ one stability region and a region where all
circular orbits are instable appear, finally for $Q/M>\sqrt{5}/2$ there are all
stable circular orbits for $r>Q^2/M$.


  Urban air mobility is the new mode of transportation aiming to provide a fast
and secure way of travel by utilizing the low-altitude airspace. This goal
cannot be achieved without the implementation of new flight regulations which
can assure safe and efficient allocation of flight paths to a large number of
vertical takeoff/landing aerial vehicles. Such rules should also allow
estimating the effective capacity of the low-altitude airspace for planning
purposes. Path planning is a vital subject in urban air mobility which could
enable a large number of UAVs to fly simultaneously in the airspace without
facing the risk of collision. Since urban air mobility is a novel concept,
authorities are still working on the redaction of new flight rules applicable
to urban air mobility. In this study, an autonomous UAV path planning framework
is proposed using a deep reinforcement learning approach and a deep
deterministic policy gradient algorithm. The objective is to employ a
self-trained UAV to reach its destination in the shortest possible time in any
arbitrary environment by adjusting its acceleration. It should avoid collisions
with any dynamic or static obstacles and avoid entering prior permission zones
existing on its path. The reward function is the determinant factor in the
training process. Thus, two different reward function compositions are compared
and the chosen composition is deployed to train the UAV by coding the RL
algorithm in python. Finally, numerical simulations investigated the success
rate of UAVs in different scenarios providing an estimate of the effective
airspace capacity.


  Abundant data is the key to successful machine learning. However, supervised
learning requires annotated data that are often hard to obtain. In a
classification task with limited resources, Active Learning (AL) promises to
guide annotators to examples that bring the most value for a classifier. AL can
be successfully combined with self-training, i.e., extending a training set
with the unlabelled examples for which a classifier is the most certain. We
report our experiences on using AL in a systematic manner to train an SVM
classifier for Stack Overflow posts discussing performance of software
components. We show that the training examples deemed as the most valuable to
the classifier are also the most difficult for humans to annotate. Despite
carefully evolved annotation criteria, we report low inter-rater agreement, but
we also propose mitigation strategies. Finally, based on one annotator's work,
we show that self-training can improve the classification accuracy. We conclude
the paper by discussing implication for future text miners aspiring to use AL
and self-training.


  In quantum systems theory one of the fundamental problems boils down to:
given an initial state, which final states can be reached by the dynamic system
in question. Here we consider infinite dimensional open quantum dynamical
systems following a unital Kossakowski-Lindblad master equation extended by
controls. More precisely, their time evolution shall be governed by an
inevitable potentially unbounded Hamiltonian drift term $H_0$, finitely many
bounded control Hamiltonians $H_j$ allowing for (at least) piecewise constant
control amplitudes $u_j(t)\in{\mathbb R}$ plus a bang-bang (i.e. on-off)
switchable noise term $\mathbf{\Gamma}_V$ in Kossakowski-Lindblad form.
Generalizing standard majorization results from finite to infinite dimensions,
we show that such bilinear quantum control systems allow to approximately reach
any target state majorized by the initial one, as up to now only has been known
in finite dimensional analogues.---The proof of the result is currently limited
to the control Hamiltonians $ H_j$ being bounded and noise terms
$\mathbf{\Gamma}_V$ with compact normal $V$.


  We investigate the possibility to obtain higly multipartite-entangled states
as nondegenerate eigenstates of Hamiltonians that involve only short-range and
few-body interactions. We study small-size systems (with a number of qubits
ranging from three to five) and search for Hamiltonians with a Maximally
Multipartite Entangled State (MMES) as a nondegenerate eigenstate. We then find
conditions, including bounds on the number of coupled qubits, to build a
Hamiltonian with a Greenberger-Horne-Zeilinger (GHZ) state as a nondegenerate
eigenstate. We finally comment on possible applications.


  We apply the fermionic description of CFT obtained in our previous work to
the computation of the one-point functions of the descendant fields in the
sine-Gordon model.


  There is a lot of current astrophysical evidence and interest in intermediate
mass black holes, ranging from a few hundred to several thousand solar masses.
The active galaxy M82 and the globular cluster in M31, for example, are known
to host such objects. Here we discuss several aspects of intermediate mass
black holes such as their expected luminosity, spectral nature of radiation,
associated jets, etc. We also discuss possible scenarios for their formation
including the effects of dynamical friction, gravitational radiation, etc. We
also consider their formation in the early universe and also discuss the
possibility of supermassive black holes forming from mergers of several
intermediate mass black holes and compare the relevant time scales involved
with other scenarios.


  A concrete strategy is presented for generating strong topological insulators
in $d+d'$ dimensions which have quantized physics in $d$ dimensions. Here, $d$
counts the physical and $d'$ the virtual dimensions. It consists of seeking
$d$-dimensional representations of operator algebras which are usually defined
in $d+d'$ dimensions where topological elements display strong topological
invariants. The invariants are shown, however, to be fully determined by the
physical dimensions, in the sense that their measurement can be done at fixed
virtual coordinates. We solve the bulk-boundary correspondence and show that
the boundary invariants are also fully determined by the physical coordinates.
We analyze the virtual Chern insulator in $(1+1)$-dimensions realized in
Ref.~\cite{KrausPRL2012hh} and predict quantized forces at the edges. We
generate a novel topological system in $(3+1)$-dimensions, which is predicted
to have quantized magneto-electric response.


  We decribe and announce some results (joint with G. Besson, L. Bessieres, M.
Boileau and J.Porti) about the geometry and topology of 3-manifolds. Most of
the article is primarily intended as an introduction for nonexperts to
geometrization of 3-manifolds, Ricci flow with surgery, and the simplicial
volume approach to collapsing theorems. In the last section, Ricci flow with
surgery on open 3-manifolds and obstructions to positive scalar curvature are
discussed.


  Spectral estimation is a fundamental task in signal processing. Recent
algorithms in quantum phase estimation are concerned with the large noise,
large frequency regime of the spectral estimation problem. The recent work in
Ding-Epperly-Lin-Zhang shows that the ESPRIT algorithm exhibits
superconvergence behavior for the spike locations in terms of the maximum
frequency. This note provides a perturbative analysis to explain this behavior.
It also extends the discussion to the case where the noise grows with the
sampling frequency.


  We study the transverse expansion in central Pb+Pb collisions at the CERN
SPS. Strong collective motion of hadrons can be created. This flow is mainly
due to meson baryon rescattering. It allows to study the angular distribution
of intermediate mass meson baryon interactions.


  This is a slightly revised version (with references added in) of a survey
article which appeared in the Spring 2005 edition of the MSRI newsletter, the
Emissary. The article describes some of the themes from the Fall 2004 MSRI
program on Hyperplane Arrangements and Applications.


  The advent of quantum algorithms has initiated a discourse on the potential
for quantum speedups for optimization problems. However, several factors still
hinder a practical realization of the potential benefits. These include the
lack of advanced, error-free quantum hardware, the absence of accessible
software stacks for seamless integration and interaction, and the lack of
methods that allow us to leverage the theoretical advantages to real-world use
cases. This paper works towards the creation of an accessible hybrid software
stack for solving optimization problems, aiming to create a fundamental
platform that can utilize quantum technologies to enhance the solving process.
We introduce a novel approach that we call Hybrid Meta-Solving, which combines
classical and quantum optimization techniques to create customizable and
extensible hybrid solvers. We decompose mathematical problems into multiple
sub-problems that can be solved by classical or quantum solvers, and propose
techniques to semi-automatically build the best solver for a given problem.
Implemented in our ProvideQ toolbox prototype, Meta-Solving provides
interactive workflows for accessing quantum computing capabilities. Our
evaluation demonstrates the applicability of Meta-Solving in industrial use
cases. It shows that we can reuse state-of-the-art classical algorithms and
extend them with quantum computing techniques. Our approach is designed to be
at least as efficient as state-of-the-art classical techniques, while having
the potential to outperform them if future advances in the quantum domain are
made.


  This paper presents a novel framework called HST for semi-supervised video
object segmentation (VOS). HST extracts image and video features using the
latest Swin Transformer and Video Swin Transformer to inherit their inductive
bias for the spatiotemporal locality, which is essential for temporally
coherent VOS. To take full advantage of the image and video features, HST casts
image and video features as a query and memory, respectively. By applying
efficient memory read operations at multiple scales, HST produces hierarchical
features for the precise reconstruction of object masks. HST shows
effectiveness and robustness in handling challenging scenarios with occluded
and fast-moving objects under cluttered backgrounds. In particular, HST-B
outperforms the state-of-the-art competitors on multiple popular benchmarks,
i.e., YouTube-VOS (85.0%), DAVIS 2017 (85.9%), and DAVIS 2016 (94.0%).