  We prove a duality relation and an integration by parts formula for
fractional operators with a general analytical kernel. Based on these basic
results, we are able to prove a new Gronwall's inequality and continuity and
differentiability of solutions of control differential equations. This allow us
to obtain a weak version of Pontryagin's maximum principle. Moreover, our
approach also allow us to consider mixed problems with both integer and
fractional order operators and derive necessary optimality conditions for
isoperimetric variational problems and other problems of the calculus of
variations.


  Image Edge detection (ED) is a base task in computer vision. While the
performance of the ED algorithm has been improved greatly by introducing
CNN-based models, current models still suffer from unsatisfactory precision
rates especially when only a low error toleration distance is allowed.
Therefore, model architecture for more precise predictions still needs an
investigation. On the other hand, the unavoidable noise training data provided
by humans would lead to unsatisfactory model predictions even when inputs are
edge maps themselves, which also needs improvement. In this paper, more precise
ED models are presented with cascaded skipping density blocks (CSDB). Our
models obtain state-of-the-art(SOTA) predictions in several datasets,
especially in average precision rate (AP), which is confirmed by extensive
experiments. Moreover, our models do not include down-sample operations,
demonstrating those widely believed operations are not necessary. Also, a novel
modification on data augmentation for training is employed, which allows
noiseless data to be employed in model training and thus improves the
performance of models predicting on edge maps themselves.


  We study the beyond-mean-field corrections to the energy of a dipolar Bose
gas confined to two dimensions by a box potential with dipoles oriented in
plane. At a critical strength of the dipolar interaction the system becomes
unstable on the mean field level. We find that the ground state of the gas is
strongly influenced by the corrections, leading to formation of a self-bound
droplet, in analogy to the free space case. Properties of the droplet state can
be found by minimizing the extended Gross-Pitaevskii energy functional. In the
limit of strong confinement we show analytically that the correction can be
interpreted as an effective three-body repulsion which stabilizes the gas at
finite density.


  The present report summarizes an exploratory study which we carried out in
the context of the COST Action IS1310 "Reassembling the Republic of Letters,
1500-1800", and which is relevant to the activities of Working Group 3 "Texts
and Topics" and Working Group 2 "People and Networks". In this study we
investigated the use of Natural Language Processing (NLP) and Network Text
Analysis on a small sample of seventeenth-century letters selected from Hartlib
Papers, whose records are in one of the catalogues of Early Modern Letters
Online (EMLO) and whose online edition is available on the website of the
Humanities Research Institute at the University of Sheffield
(http://www.hrionline.ac.uk/hartlib/). We outline the NLP pipeline used to
automatically process the texts into a network representation, in order to
identify the texts' "narrative centrality", i.e. the most central entities in
the texts, and the relations between them.


  Due to the characteristics of Information and Communications Technology (ICT)
products, the critical information of ICT devices is often summarized in big
tabular data shared across supply chains. Therefore, it is critical to
automatically interpret tabular structures with the surging amount of
electronic assets. To transform the tabular data in electronic documents into a
machine-interpretable format and provide layout and semantic information for
information extraction and interpretation, we define a Table Structure
Recognition (TSR) task and a Table Cell Type Classification (CTC) task. We use
a graph to represent complex table structures for the TSR task. Meanwhile,
table cells are categorized into three groups based on their functional roles
for the CTC task, namely Header, Attribute, and Data. Subsequently, we propose
a multi-task model to solve the defined two tasks simultaneously by using the
text modal and image modal features. Our experimental results show that our
proposed method can outperform state-of-the-art methods on ICDAR2013 and UNLV
datasets.


  We survey recent results in hermitian integral geometry, i.e. integral
geometry on complex vector spaces and complex space forms. We study valuations
and curvature measures on complex space forms and describe how the global and
local kinematic formulas on such spaces were recently obtained. While the local
and global kinematic formulas in the Euclidean case are formally identical, the
local formulas in the hermitian case contain strictly more information than the
global ones. Even if one is only interested in the flat hermitian case, i.e.
$\mathbb C^n$, it is necessary to study the family of all complex space forms,
indexed by the holomorphic curvature $4\lambda$, and the dependence of the
formulas on the parameter $\lambda$. We will also describe Wannerer's recent
proof of local additive kinematic formulas for unitarily invariant area
measures.


  We study the weak link between current-carrying superconductors, both
conventional and d-wave. The state of the system is controlled by two
parameters: the order parameter phase difference $\phi$ and the superfluid
velocity $v_{s}$, which parameterizes the parallel to the boundary transport
supercurrent which is injected externally. The low-temperature current-phase
relations are derived. We consider two models of weak links: a constriction
between two conventional superconductors and a plane boundary between two
differently orientated d-wave superconductors. We show that for some relation
between $\phi $ and $v_{s}$ quasiparticles create the current along the
boundary which flows in the direction opposite to the transport supercurrent.


  We derived the influence of dark matter (DM) decays and annihilations on
structure formation. The energy deposited by DM decays and annihilations into
metal free halos both increases the gas temperature and enhances the formation
of molecules. Within the primordial halos the temperature increase generally
dominates over the molecular cooling, slightly delaying the collapse. In fact,
the critical mass for collapse is generally higher than in the unperturbed
case, when we consider the energy input from DM. In presence of DM decays
and/or annihilations the fraction of baryons inside collapsed metal free halos
should be slightly less (~0.4) than the expected cosmological value.


  We study the properties of the Luttinger-Ward functional (LWF) in a
simplified Hubbard-type model without time or spatial dimensions, but with $N$
identical replicas located on a single site. The simplicity of this $(0+0)d$
model permits an exact solution for all $N$ and for both bosonic and fermionic
statistics. We show that fermionic statistics are directly linked to the fact
that multiple values of the noninteracting Green function $G_0$ map to the same
value of the interacting Green function $G$, i.e. the mapping $G_0 \mapsto G$
is non-injective. This implies that with fermionic statistics the $(0+0)d$
model has a multiply-valued LWF. The number of LWF values in the fermionic
model increases proportionally to the number of replicas $N$, while in the
bosonic model the LWF has a single value regardless of $N$. We also discuss the
formal connection between the $(0+0)d$ model and the $(0+1)d$ model which was
used in previous studies of LWF multivaluedness.


  A $C^0$-Finsler structure is a continuous function $F:TM \rightarrow
[0,\infty)$ defined on the tangent bundle of a differentiable manifold $M$ such
that its restriction to each tangent space is an asymmetric norm. We use the
convolution of $F$ with the standard mollifier in order to construct a
mollifier smoothing of $F$, which is a one parameter family of Finsler
structures $F_\varepsilon$ (of class $\mathit{C}^\infty$ on $TM\backslash 0$)
that converges uniformly to $F$ on compact subsets of $TM$. We prove that when
$F$ is a Finsler structure, then the Chern connection, the Cartan connection,
the Hashiguchi connection, the Berwald connection and the flag curvature of
$F_\varepsilon$ converges uniformly on compact subsets to the corresponding
objects of $F$. As an application of this mollifier smoothing, we study
examples of two-dimensional piecewise smooth Riemannian manifolds with nonzero
total curvature on a line segment. We also indicate how to extend this study to
the correspondent piecewise smooth Finsler manifolds.


  It is shown that the Lie algebra of the automorphic, meromorphic sl(2, C)
-valued functions on a torus is a geometric realization of a certain
infinite-dimensional finitely generated Lie algebra. In the trigonometric
limit, when the modular parameter of the torus goes to zero, the former Lie
algebra goes over into the sl(2,C) -valued loop algebra, while the latter one -
into the Lie algebra (sl(2)^)'/(centre) .


  We consider the non-equilibrium dynamics of the East model, a linear chain of
0-1 spins evolving under a simple Glauber dynamics in the presence of a kinetic
constraint which forbids flips of those spins whose left neighbor is 1. We
focus on the glassy effects caused by the kinetic constraint as $q\downarrow
0$, where $q$ is the equilibrium density of the 0's. In the physical literature
this limit is equivalent to the zero temperature limit. We first prove that,
for any given $L=O(1/q)$, the divergence as $q\downarrow 0$ of three basic
characteristic time scales of the East process of length $L$ is the same. Then
we examine the problem of dynamic heterogeneity, i.e. non-trivial
spatio-temporal fluctuations of the local relaxation to equilibrium, one of the
central aspects of glassy dynamics. For any mesoscopic length scale
$L=O(q^{-\gamma})$, $\gamma<1$, we show that the characteristic time scale of
two East processes of length $L$ and $\lambda L$ respectively are indeed
separated by a factor $q^{-a}$, $a=a(\gamma)>0$, provided that $\lambda \geq 2$
is large enough (independent of $q$, $\lambda=2$ for $\gamma<1/2$). In
particular, the evolution of mesoscopic domains, i.e. maximal blocks of the
form $111..10$, occurs on a time scale which depends sharply on the size of the
domain, a clear signature of dynamic heterogeneity. A key result for this part
is a very precise computation of the relaxation time of the chain as a function
of $(q,L)$, well beyond the current knowledge, which uses induction on length
scales on one hand and a novel algorithmic lower bound on the other. Finally we
show that no form of time scale separation occurs for $\gamma=1$, i.e. at the
equilibrium scale $L=1/q$, contrary to what was assumed in the physical
literature based on numerical simulations.


  We present an AI-based decoding agent for quantum error correction of
depolarizing noise on the toric code. The agent is trained using deep
reinforcement learning (DRL), where an artificial neural network encodes the
state-action Q-values of error-correcting $X$, $Y$, and $Z$ Pauli operations,
occurring with probabilities $p_x$, $p_y$, and $p_z$, respectively. By learning
to take advantage of the correlations between bit-flip and phase-flip errors,
the decoder outperforms the minimum-weight-perfect-matching (MWPM) algorithm,
achieving higher success rate and higher error threshold for depolarizing noise
($p_z = p_x = p_y$), for code distances $d\leq 9$. The decoder trained on
depolarizing noise also has close to optimal performance for uncorrelated noise
and provides functional but sub-optimal decoding for biased noise ($p_z \neq
p_x = p_y$). We argue that the DRL-type decoder provides a promising framework
for future practical error correction of topological codes, striking a balance
between on-the-fly calculations, in the form of forward evaluation of a deep
Q-network, and pre-training and information storage. The complete code, as well
as ready-to-use decoders (pre-trained networks), can be found in the repository
https://github.com/mats-granath/toric-RL-decoder.


  This study examines whether income distribution in Thailand has a property of
scale invariance or self-similarity across years. By using the data on income
shares by quintile and by decile of Thailand from 1988 to 2021, the results
from 306-pairwise Kolmogorov-Smirnov tests indicate that income distribution in
Thailand is statistically scale-invariant or self-similar across years with
p-values ranging between 0.988 and 1.000. Based on these empirical findings,
this study would like to propose that, in order to change income distribution
in Thailand whose pattern had persisted for over three decades, the change
itself cannot be gradual but has to be like a phase transition of substance in
physics.


  Given $n$ i.i.d. samples drawn from an unknown distribution $P$, when is it
possible to produce a larger set of $n+m$ samples which cannot be distinguished
from $n+m$ i.i.d. samples drawn from $P$? (Axelrod et al. 2019) formalized this
question as the sample amplification problem, and gave optimal amplification
procedures for discrete distributions and Gaussian location models. However,
these procedures and associated lower bounds are tailored to the specific
distribution classes, and a general statistical understanding of sample
amplification is still largely missing. In this work, we place the sample
amplification problem on a firm statistical foundation by deriving generally
applicable amplification procedures, lower bound techniques and connections to
existing statistical notions. Our techniques apply to a large class of
distributions including the exponential family, and establish a rigorous
connection between sample amplification and distribution learning.


  This paper describes a new research paradigm for studying human-AI
collaboration, named "human-AI mutual learning", defined as the process where
humans and AI agents preserve, exchange, and improve knowledge during human-AI
collaboration. We describe relevant methodologies, motivations, domain
examples, benefits, challenges, and future research agenda under this paradigm.


  When two terms occur together in a document, the probability of a close
relationship between them and the document itself is greater if they are in
nearby positions. However, ranking functions including term proximity (TP)
require larger indexes than traditional document-level indexing, which slows
down query processing. Previous studies also show that this technique is not
effective for all types of queries. Here we propose a document ranking model
which decides for which queries it would be beneficial to use a proximity-based
ranking, based on a collection of features of the query. We use a machine
learning approach in determining whether utilizing TP will be beneficial.
Experiments show that the proposed model returns improved rankings while also
reducing the overhead incurred as a result of using TP statistics.


  The main obstacle for practical quantum technology is the noise, which can
induce the decoherence and destroy the potential quantum advantages. The
fluctuation of a field, which induces the dephasing of the system, is one of
the most common noises and widely regarded as detrimental to quantum
technologies. Here we show, contrary to the conventional belief, the
fluctuation can be used to improve the precision limits in quantum metrology
for the estimation of various parameters. Specifically, we show that for the
estimation of the direction and rotating frequency of a field, the achieved
precisions at the presence of the fluctuation can even surpass the highest
precision achievable under the unitary dynamics which have been widely taken as
the ultimate limit. We provide explicit protocols, which employs the adaptive
quantum error correction, to achieve the higher precision limits with the
fluctuating fields. Our study provides a completely new perspective on the role
of the noises in quantum metrology. It also opens the door for higher
precisions beyond the limit that has been believed to be ultimate.


  We revisit the issue on signatures of pre-inflationary background anisotropy
by considering the quantization of a massless and minimally coupled scalar
field in an axially symmetric Kasner background, mimicking cosmological
perturbations. We show that the power spectrum of the scalar field fluctuation
has a negligible difference from the standard inflation in the non-planar
directions, but it has a sharp peak around the symmetry plane. For the
non-planar high-momentum modes, we use the WKB approximation for the first
period and the asymptotic approximation based on the de Sitter solution for the
next period. At the boundary, two mode functions have the same accuracy with
error of $O({H_{i}}/k)$. We calculate the approximation up to the order of
$({H_{i}}/k)^6$ and show that the power spectrum of the scalar field fails to
get corrections until we execute the approximation up to $6^{\rm th}$ order.


  The colored Jones function of a knot is a sequence of Laurent polynomials. It
was shown by TTQ. Le and the author that such sequences are $q$-holonomic, that
is, they satisfy linear $q$-difference equations with coefficients Laurent
polynomials in $q$ and $q^n$. We show from first principles that $q$-holonomic
sequences give rise to modules over a $q$-Weyl ring. Frohman-Gelca-LoFaro have
identified the latter ring with the ring of even functions of the quantum
torus, and with the Kauffman bracket skein module of the torus. Via this
identification, we study relations among the orthogonal, peripheral and
recursion ideal of the colored Jones function, introduced by the above
mentioned authors. In the second part of the paper, we convert the linear
$q$-difference equations of the colored Jones function in terms of a hierarchy
of linear ordinary differential equations for its loop expansion. This
conversion is a version of the WKB method, and may shed some information on the
problem of asymptotics of the colored Jones function of a knot.Final version.
To appear in the Journal of Knot Theory and its Ramifications.


  For a remote sensing optical payload to achieve a Ground Sampling Distance of
~ 10-30 m, a critical problem is platform-induced motion blur. While forward
motion compensation can reduce this transit speed, it comes at the expense of a
more challenging satellite attitude control system and induces a variable
observation/illumination angle. This relative motion can be frozen out by
simply reading the sensor system at a frame rate that matches the ground
resolution element's pixel crossing time. To achieve high resolution using this
Time-Delay Integration (TDI)-like approach requires high speed and hence near
"zero" readout noise detector arrays to avoid swamping the observed signal.
This requires associated control electronics for fast frame readout and direct
interface with smart- Artificial Intelligence (AI) onboard processing. With
this technique, the platform freezes out its movement concerning the ground,
reducing the demands placed on the attitude control systems, which can
otherwise be difficult to implement on a small satellite platform. Here we
report the Australian National University's OzFuel mission which applies this
technical solution to deliver high ground resolution via high frame rate
imaging. OzFuel is built around the Leonardo SAPHIRA Mercury Cadmium Telluride
linear mode electron avalanche photodiode (LMeAPD) detector and the in-house
developed Rosella electronics control system. The mission will deliver an
integrated sensor system in a suite of Short-Wave Infrared (SWIR) passbands
dedicated to monitoring the flammability of Eucalypt trees. The OzFuel mission
concept focuses on the application of SWIR remote sensing data to deliver a
strategic evaluation of fuel loads and moisture content in the bushfire-prone
Australian environment.


  The notion of $n$th order convexity in the sense of Hopf and Popoviciu is
defined via the nonnegativity of the $(n+1)$st order divided differences of a
given real-valued function. In view of the well-known recursive formula for
divided differences, the nonnegativity of $(n+1)$st order divided differences
is equivalent to the $(n-k-1)$st order convexity of the $k$th order divided
differences which provides a characterization of $n$th order convexity.
  The aim of this paper is to apply the notion of higher-order divided
differences in the context of convexity with respect to Chebyshev systems
introduced by Karlin in 1968. Using a determinant identity of Sylvester, we
then establish a formula for the generalized divided differences which enables
us to obtain a new characterization of convexity with respect to Chebyshev
systems. Our result generalizes that of W\k{a}sowicz which was obtained in
2006. As an application, we derive a necessary condition for functions which
can be written as the difference of two functions convex with respect to a
given Chebyshev system.


  We estimate B(K -> pi nu nubar) in the context of the Standard Model by
fitting for lambda_t = Vtd x V*ts of the `kaon unitarity triangle' relation. We
fit data from epsilon_K, the CP-violating parameter describing K-mixing, and
a_{psi K}, the CP-violating asymmetry in B -> J/psi K decays. Our estimate is
independent of the CKM matrix element Vcb and of the ratio of Bs to Bd mixing
frequencies. The measured value of B(K+ -> pi+ nu nubar) can be compared both
to this estimate and to predictions made from the ratio of B mixing
frequencies.


  Acceleration of bulk motion of plasma due to magneto centrifugal mechanism is
investigated for different shapes of the field lines of 3-D magnetic field. It
is shown that this mechanism can be efficient provided that the field line is
twisted into direction of rotation. According to the last results of the
numerical modelling a fraction of the field lines with such geometrical shape
apparently exist in the pulsar magnetosphere close to the last closed field
line. In this case the Lorentz factor $\gamma$ increases with the radius $r$
along these field lines as $\gamma\sim (1-(r\Omega/c)^2)^{-1}$, where $\Omega$
- angular velocity of the pulsar rotation. The magneto centrifugal mechanism
provides acceleration of the electrons to the energy which is sufficient to
explain the observed VHE pulsed $\gamma$-rays from Crab pulsar.


  We study existence and regularity of the density for the solution $u(t,x)$
(with fixed $t > 0$ and $x \in D$) of the heat equation in a bounded domain $D
\subset \mathbb R^d$ driven by a stochastic inhomogeneous Neumann boundary
condition with stochastic term. The stochastic perturbation is given by a
fractional Brownian motion process. Under suitable regularity assumptions on
the coefficients, by means of tools from the Malliavin calculus, we prove that
the law of the solution has a smooth density with respect to the Lebesgue
measure in $\mathbb R$.


  We provide several novel solutions of the coupled Ablowitz-Musslimani (AM)
version of the nonlocal nonlinear Schr\"odinger (NLS) equation and the coupled
nonlocal modified Korteweg-de Vries (mKdV) equations. In each case we compare
and contrast the corresponding solutions of the relevant coupled local
equations. Further, we provide new solutions of the coupled local NLS and the
coupled local mKdV equations which are not the solutions of the corresponding
nonlocal equations.


  The abundance of deuterated molecules in a star-forming region is sensitive
to the environment in which they are formed. Deuteration fractions therefore
provide a powerful tool for studying the physical and chemical evolution of a
star-forming system. While local low-mass star-forming regions show very high
deuteration ratios, much lower fractions are observed towards Orion and the
Galactic Centre. We derive methanol deuteration fractions at a number of
locations towards the high-mass star-forming region NGC 6334I, located at a
mean distance of 1.3 kpc, and discuss how these can shed light on the
conditions prevailing during its formation. We use high sensitivity, high
spatial and spectral resolution observations obtained with ALMA to study
transitions of the less abundant, optically thin, methanol-isotopologues:
(13)CH3OH, CH3(18)OH, CH2DOH and CH3OD, detected towards NGC 6334I. Assuming
LTE and excitation temperatures of 120-330 K, we derive column densities for
each of the species and use these to infer CH2DOH/CH3OH and CH3OD/CH3OH
fractions. Interestingly, the column densities of CH3OD are consistently higher
than those of CH2DOH throughout the region. All regions studied in this work
show CH2DOH/CH3OH as well as CH2DOH/CH3OD ratios that are considerably lower
than those derived towards low-mass star-forming regions and slightly lower
than those derived for the high-mass star-forming regions in Orion and the
Galactic Centre. The low ratios indicate a grain surface temperature during
formation ~30 K, for which the efficiency of the formation of deuterated
species is significantly reduced.


  We consider a formal discretisation of Euclidean quantum gravity defined by a
statistical model of random $3$-regular graphs and making using of the Ollivier
curvature, a coarse analogue of the Ricci curvature. Numerical analysis shows
that the Hausdorff and spectral dimensions of the model approach $1$ in the
joint classical-thermodynamic limit and we argue that the scaling limit of the
model is the circle of radius $r$, $S^1_r$. Given mild kinematic constraints,
these claims can be proven with full mathematical rigour: speaking precisely,
it may be shown that for $3$-regular graphs of girth at least $4$, any sequence
of action minimising configurations converges in the sense of Gromov-Hausdorff
to $S^1_r$. We also present strong evidence for the existence of a second-order
phase transition through an analysis of finite size effects. This --
essentially solvable -- toy model of emergent one-dimensional geometry is meant
as a controllable paradigm for the nonperturbative definition of random flat
surfaces.


  This paper is part of a study of the frictional dynamics of a confined solid
lubricant film - modelled as a one-dimensional chain of interacting particles
confined between two ideally incommensurate substrates, one of which is driven
relative to the other through an attached spring moving at constant velocity.
This model system is characterized by three inherent length scales; depending
on the precise choice of incommensurability among them it displays a strikingly
different tribological behavior. Contrary to two length-scale systems such as
the standard Frenkel-Kontorova (FK) model, for large chain stiffness one finds
that here the most favorable (lowest friction) sliding regime is achieved by
chain-substrate incommensurabilities belonging to the class of non-quadratic
irrational numbers (e.g., the spiral mean). The well-known golden mean
(quadratic) incommensurability which slides best in the standard FK model shows
instead higher kinetic-friction values. The underlying reason lies in the
pinning properties of the lattice of solitons formed by the chain with the
substrate having the closest periodicity, with the other slider.


  Peptide vaccines are growing in significance for fighting diverse diseases.
Machine learning has improved the identification of peptides that can trigger
immune responses, and the main challenge of peptide vaccine design now lies in
selecting an effective subset of peptides due to the allelic diversity among
individuals. Previous works mainly formulated this task as a constrained
optimization problem, aiming to maximize the expected number of peptide-Major
Histocompatibility Complex (peptide-MHC) bindings across a broad range of
populations by selecting a subset of diverse peptides with limited size; and
employed a greedy algorithm, whose performance, however, may be limited due to
the greedy nature. In this paper, we propose a new framework PVD-EMO based on
Evolutionary Multi-objective Optimization, which reformulates Peptide Vaccine
Design as a bi-objective optimization problem that maximizes the expected
number of peptide-MHC bindings and minimizes the number of selected peptides
simultaneously, and employs a Multi-Objective Evolutionary Algorithm (MOEA) to
solve it. We also incorporate warm-start and repair strategies into MOEAs to
improve efficiency and performance. We prove that the warm-start strategy
ensures that PVD-EMO maintains the same worst-case approximation guarantee as
the previous greedy algorithm, and meanwhile, the EMO framework can help avoid
local optima. Experiments on a peptide vaccine design for COVID-19, caused by
the SARS-CoV-2 virus, demonstrate the superiority of PVD-EMO.


  In many situations when people are assigned to coalitions, the utility of
each person depends on the friends in her coalition. Additionally, in many
situations, the size of each coalition should be bounded. This paper studies
such coalition formation scenarios in both weighted and unweighted settings.
Since finding a partition that maximizes the utilitarian social welfare is
computationally hard, we provide a polynomial-time approximation algorithm. We
also investigate the existence and the complexity of finding stable partitions.
Namely, we show that the Contractual Strict Core (CSC) is never empty, but the
Strict Core (SC) of some games is empty. Finding partitions that are in the CSC
is computationally easy, but even deciding whether an SC of a given game exists
is NP-hard. The analysis of the core is more involved. In the unweighted
setting, we show that when the coalition size is bounded by 3 the core is never
empty, and we present a polynomial time algorithm for finding a member of the
core. However, for the weighted setting, the core may be empty, and we prove
that deciding whether there exists a core is NP-hard.


  We report on the observation of the coherent enhancement of the return
probability ("enhanced return to the origin" , ERO) in a periodically kicked
cold-atom gas. By submitting an atomic wave packet to a pulsed, periodically
shifted laser standing wave, we induce an oscillation of ERO in time and
explain it in terms of a periodic, reversible dephasing in the
weak-localization interference sequences responsible for ERO. Monitoring the
temporal decay of ERO, we exploit its quantum coherent nature to quantify the
decoherence rate of the atomic system.


  Silicon nanowires (SiNWs) attached to a wafer substrate are converted to
inversely tapered silicon nanocones (SiNCs). After excitation with visible
light, individual SiNCs show a 200-fold enhanced integral band-to-band
luminescence as compared to a straight SiNW reference. Furthermore, the reverse
taper is responsible for multifold emission peaks in addition to the relatively
broad nearinfrared (NIR) luminescence spectrum. A thorough numerical mode
analysis reveals that unlike a SiNW the inverted SiNC sustains a multitude of
leaky whispering gallery modes. The modes are unique to this geometry and they
are characterized by a relatively high quality factor ($Q = 1300$) and a low
mode volume ($0.2 < ({\lambda}/n_{eff})^3 < 4$). In addition they show a
vertical out coupling of the optically excited NIR luminescence with a
numerical aperture as low as 0.22. Estimated Purcell factors $F_p \propto
Q/V_m$ of these modes can explain the enhanced luminescence in individual
emission peaks as compared to the SiNW reference. Investigating the relation
between the SiNC geometry and the mode formation leads to simple design rules
that permit to control the number and wavelength of the hosted modes and
therefore the luminescent emission peaks.


  Most ultra hot Jupiters (UHJs) show evidence of temperature inversions, in
which temperature increases with altitude over a range of pressures.
Temperature inversions can occur when there is a species that absorbs the
stellar irradiation at a relatively high level of the atmospheres. However, the
species responsible for this absorption remains unidentified. In particular,
the UHJ KELT-20b is known to have a temperature inversion. Using high
resolution emission spectroscopy from LBT/PEPSI we investigate the atomic and
molecular opacity sources that may cause the inversion in KELT-20b, as well as
explore its atmospheric chemistry. We confirm the presence of Fe I with a
significance of 17$\sigma$. We also report a tentative $4.3\sigma$ detection of
Ni I. A nominally $4.5\sigma$ detection of Mg I emission in the PEPSI blue arm
is likely in fact due to aliasing between the Mg I cross-correlation template
and the Fe I lines present in the spectrum. We cannot reproduce a recent
detection of Cr I, while we do not have the wavelength coverage to robustly
test past detections of Fe II and Si I. Together with non-detections of
molecular species like TiO, this suggests that Fe I is likely to be the
dominant optical opacity source in the dayside atmosphere of KELT-20b and may
be responsible for the temperature inversion. We explore ways to reconcile the
differences between our results and those in literature and point to future
paths to understand atmospheric variability.


  In this paper we study the decomposition of the direct image of
$\pi_+(\Oc_{X})$ the polynomial ring $\Oc_X$ as a $\D$-module, under the map
$\pi: \spec \Oc_{X} \to \spec \Oc_{X}^{G(r,n)}$, where $\Oc_{X}^{G(r,n)}$ is
the ring of invariant polynomial under the action of the wreath product
$G(r,p):= \ZZ / r \ZZ \wr \Sc_n $. We first describe the generators of the
simple components of $\pi_+(\Oc_X)$ and give their multiplicities. Using an
equivalence of categories and the higher Specht polynomials, we describe a
$\D$-module decomposition of the polynomial ring localized at the discriminant
of $\pi$. Furthermore, we study the action invariants, differential operators,
on the higher Specht polynomials.


  This paper deals with evaluating constant terms of a special class of
rational functions, the Elliott-rational functions. The constant term of such a
function can be read off immediately from its partial fraction decomposition.
We combine the theory of iterated Laurent series and a new algorithm for
partial fraction decompositions to obtain a fast algorithm for MacMahon's Omega
calculus, which (partially) avoids the "run-time explosion" problem when
eliminating several variables. We discuss the efficiency of our algorithm by
investigating problems studied by Andrews and his coauthors; our running time
is much less than that of their Omega package.


  Persistent plasma upflows were observed with Hinode's EUV Imaging
Spectrometer (EIS) at the edges of active region (AR) 10978 as it crossed the
solar disk. We analyze the evolution of the photospheric magnetic and velocity
fields of the AR, model its coronal magnetic field, and compute the location of
magnetic null-points and quasi-sepratrix layers (QSLs) searching for the origin
of EIS upflows. Magnetic reconnection at the computed null points cannot
explain all of the observed EIS upflow regions. However, EIS upflows and QSLs
are found to evolve in parallel, both temporarily and spatially. Sections of
two sets of QSLs, called outer and inner, are found associated to EIS upflow
streams having different characteristics. The reconnection process in the outer
QSLs is forced by a large-scale photospheric flow pattern which is present in
the AR for several days. We propose a scenario in which upflows are observed
provided a large enough asymmetry in plasma pressure exists between the
pre-reconnection loops and for as long as a photospheric forcing is at work. A
similar mechanism operates in the inner QSLs, in this case, it is forced by the
emergence and evolution of the bipoles between the two main AR polarities. Our
findings provide strong support to the results from previous individual case
studies investigating the role of magnetic reconnection at QSLs as the origin
of the upflowing plasma. Furthermore, we propose that persistent reconnection
along QSLs does not only drive the EIS upflows, but it is also responsible for
a continuous metric radio noise-storm observed in AR 10978 along its disk
transit by the Nan\c{c}ay Radio Heliograph.


  We have fabricated planar amorphous Indium Oxide superconducting resonators
($T_c\sim2.8$ K) that are sensitive to frequency-selective radiation in the
range of 7 to 10 GHz. Those values lay far below twice the superconducting gap
that worths about 200 GHz. The photons detection consists in a shift of the
fundamental resonance frequency. We show that the detected frequency can be
adjusted by modulating the total length of the superconducting resonator. We
attribute those observations to the excitation of higher-order resonance modes.
The coupling between the fundamental lumped and the higher order distributed
resonance is due to the kinetic inductance non-linearity with current. These
devices, that we have called Sub-gap Kinetic Inductance Detectors (SKIDs), are
to be distinguished from the standard Kinetic Inductance Detectors (KIDs) in
which quasi-particles are generated when incident light breaks down Cooper
pairs.


  A semiclassical approach to the low-temperature real time dynamics of generic
one-dimensional, gapped models in the sine-Gordon model universality class is
developed. Asymptotically exact universal results for correlation functions are
obtained in the temperature regime T << Delta, where Delta is the energy gap.


  Heusler alloys are promising for several applications, including magnetic
refrigeration, due to high magnetocaloric and magnetovolume effects. One way to
optimize this potential is by increasing the magnetovolume effect. Using
density functional theory with the Korringa-Kohn-Rostoker method, we calculate
the effective exchange interaction energies and corresponding mean field Curie
temperature as a function of the volume (hydrostatic pressure) in several L2 1
-type Co 2 YZ Heusler alloys. Different qualitative trends and signs of the
pressure derivatives of the Curie temperature and moments are found among these
compounds, discussed and compared with previous calculations and experiments.


  In this paper we construct an $\mathcal{A}_\infty$-category associated to a
Legendrian submanifold of jet spaces. Objects of the category are augmentations
of the Chekanov algebra $\mathcal{A}(\Lambda)$ and the homology of the morphism
spaces forms a new set of invariants of Legendrian submanifolds called the
bilinearised Legendrian contact homology. Those are constructed as a
generalisation of linearised Legendrian contact homology using two
augmentations instead of one. Considering similar constructions with more
augmentations leads to the higher order compositions map in the category and
generalises the idea of [6] where an $\mathcal{A}_\infty$-algebra was
constructed from one augmentation. This category allows us to define a notion
of equivalence of augmentations when the coefficient ring is a field regardless
of its characteristic. We use simple examples to show that bilinearised
cohomology groups are efficient to distinguish those equivalences classes. We
also generalise the duality exact sequence from [12] in our contest, and
interpret geometrically the bilinearised homology in terms of the Floer
homology of Lagrangian fillings (following [8]).


  We experimentally demonstrate how coherent population trapping (CPT) for
donor-bound electron spins in GaAs results in autonomous feedback that prepares
stabilized states for the spin polarization of nuclei around the electrons. CPT
was realized by excitation with two lasers to a bound-exciton state.
Transmission studies of the spectral CPT feature on an ensemble of electrons
directly reveal the statistical distribution of prepared nuclear spin states.
Tuning the laser driving from blue to red detuned drives a transition from one
to two stable states. Our results have importance for ongoing research on
schemes for dynamic nuclear spin polarization, the central spin problem and
control of spin coherence.


  Based on Peskun's theorem it is shown that optimal transition matrices in
Markov chain Monte Carlo should have zero diagonal elements except for the
diagonal element corresponding to the largest weight. We will compare the
statistical efficiency of this sampler to existing algorithms, such as
heat-bath updating and the Metropolis algorithm. We provide numerical results
for the Potts model as an application in classical physics. As an application
in quantum physics we consider the spin 3/2 XY model and the Bose-Hubbard model
which have been simulated by the directed loop algorithm in the stochastic
series expansion framework.


  We theoretically investigate the possibility of creating phonon-mediated
spin-spin interactions between neutral atoms trapped in optical tweezers. By
laser coupling the atoms to Rydberg states, collective modes of motion appear.
We show that these can be used to mediate effective spin-spin interactions or
quantum logic gates between the atoms in analogy to schemes employed in trapped
ions. In particular, we employ Rydberg dressing in a novel scheme to induce the
needed interaction, and we show that it is possible to replicate the working of
the M{\o}lmer-S{\o}rensen entanglement scheme. The M{\o}lmer-S{\o}rensen gate
is widely used in emerging quantum computers using trapped ion qubits and
currently features some of the highest fidelities of any quantum gate under
consideration. We find arbitrarily high fidelity for the coherent time
evolution of the two-atom state even at non-zero temperature.


  This paper presents Flash, an optimized private inference (PI) hybrid
protocol utilizing both homomorphic encryption (HE) and secure two-party
computation (2PC), which can reduce the end-to-end PI latency for deep CNN
models less than 1 minute with CPU. To this end, first, Flash proposes a
low-latency convolution algorithm built upon a fast slot rotation operation and
a novel data encoding scheme, which results in 4-94x performance gain over the
state-of-the-art. Second, to minimize the communication cost introduced by the
standard nonlinear activation function ReLU, Flash replaces the entire ReLUs
with the polynomial $x^2+x$ and trains deep CNN models with the new activation
function. The trained models improve the inference accuracy for CIFAR-10/100
and TinyImageNet by 16% on average (up to 40% for ResNet-32) compared to prior
art. Last, Flash proposes an efficient 2PC-based $x^2+x$ evaluation protocol
that does not require any offline communication and that reduces the total
communication cost to process the activation layer by 84-196x over the
state-of-the-art. As a result, the end-to-end PI latency of Flash implemented
on CPU is 0.02 minute for CIFAR-100 and 0.57 minute for TinyImageNet
classification, while the total data communication is 0.07GB for CIFAR-100 and
0.22GB for TinyImageNet. Flash improves the state-of-the-art PI by 16-45x in
latency and 84-196x in communication cost. Moreover, even for ImageNet, Flash
can deliver the latency less than 1 minute on CPU with the total communication
less than 1GB.


  We propose a novel foundational framework for why-not explanations, that is,
explanations for why a tuple is missing from a query result. Our why-not
explanations leverage concepts from an ontology to provide high-level and
meaningful reasons for why a tuple is missing from the result of a query. A key
algorithmic problem in our framework is that of computing a most-general
explanation for a why-not question, relative to an ontology, which can either
be provided by the user, or it may be automatically derived from the data
and/or schema. We study the complexity of this problem and associated problems,
and present concrete algorithms for computing why-not explanations. In the case
where an external ontology is provided, we first show that the problem of
deciding the existence of an explanation to a why-not question is NP-complete
in general. However, the problem is solvable in polynomial time for queries of
bounded arity, provided that the ontology is specified in a suitable language,
such as a member of the DL-Lite family of description logics, which allows for
efficient concept subsumption checking. Furthermore, we show that a
most-general explanation can be computed in polynomial time in this case. In
addition, we propose a method for deriving a suitable (virtual) ontology from a
database and/or a data workspace schema, and we present an algorithm for
computing a most-general explanation to a why-not question, relative to such
ontologies. This algorithm runs in polynomial-time in the case when concepts
are defined in a selection-free language, or if the underlying schema is fixed.
Finally, we also study the problem of computing short most-general
explanations, and we briefly discuss alternative definitions of what it means
to be an explanation, and to be most general.


  Resonant interactions associated with the emergence of a bound state
constitute one of the cornerstones of modern many-body physics, ranging from
Kondo physics, BEC-BCS crossover, to tunable interactions at Feshbach
resonances in ultracold atoms or 2D semiconductors. Here we present a Feshbach
perspective on the origin of strong pairing in Fermi-Hubbard type models. We
perform a theoretical analysis of interactions between charge carriers in doped
Mott insulators, modeled by a near-resonant two-channel scattering problem, and
find strong evidence for Feshbach-type interactions in the $d_{x^2-y^2}$
channel that can support strong pairing, consistent with the established
phenomenology of cuprates. Existing experimental and numerical results on
hole-doped cuprates lead us to conjecture the existence of a light, long-lived,
low-energy excited state of two holes with bipolaron character in these
systems, which enables near-resonant interactions and can thus provide a
microscopic foundation for theories of high-temperature superconductivity
involving strong attraction, as assumed e.g. in BEC-BCS crossover scenarios. To
put our theory to a direct test we suggest to use coincidence angle-resolved
photoemission spectroscopy (cARPES), pair-tunneling measurements or less direct
pump-probe experiments. The emergent Feshbach resonance we propose could also
underlie superconductivity in other doped antiferromagnetic Mott insulators, as
recently proposed for bilayer nickelates, highlighting its potential as a
unifying strong-coupling pairing mechanism rooted in quantum magnetism.


  We perform a post-processing radiative feedback analysis on a 3D ab initio
cosmological simulation of an atomic cooling halo under the direct collapse
black hole (DCBH) scenario. We maintain the spatial resolution of the
simulation by incorporating native ray-tracing on unstructured mesh data,
including Monte Carlo Lyman-alpha (Ly{\alpha}) radiative transfer. DCBHs are
born in gas-rich, metal-poor environments with the possibility of Compton-thick
conditions, $N_H \gtrsim 10^{24} {\rm cm}^{-2}$. Therefore, the surrounding gas
is capable of experiencing the full impact of the bottled-up radiation
pressure. In particular, we find that multiple scattering of Ly{\alpha} photons
provides an important source of mechanical feedback after the gas in the
sub-parsec region becomes partially ionized, avoiding the bottleneck of
destruction via the two-photon emission mechanism. We provide detailed
discussion of the simulation environment, expansion of the ionization front,
emission and escape of Ly{\alpha} radiation, and Compton scattering. A sink
particle prescription allows us to extract approximate limits on the
post-formation evolution of the radiative feedback. Fully coupled Ly{\alpha}
radiation hydrodynamics will be crucial to consider in future DCBH simulations.


  The efficient energy storage devices are crucial to meet the soaring global
energy demand for sustainable future. Recently, the sodium-ion batteries (SIBs)
have emerged as one of the excellent cost effective solution due to the uniform
geographical distribution and abundance of sodium. Here, we use hard carbon
(HC) as an anode and Na$_3$V$_2$(PO$_4$)$_3$/C (NVP/C) as a cathode to
fabricate a HC$||$NVP/C full cell battery and understand its electrochemical
performance and diffusion kinetics. These materials are characterized through
the analysis of x-ray diffraction and Raman spectroscopy to confirm their
single phase and structure. The full cell demonstrates a high operating voltage
of $\sim$3.3 V, with minimal polarization of 0.05 V, attributed to the lower
working voltage of the HC. Interestingly, for the full cell battery we find the
specific capacity of around 70 mAh/g at 0.1 C and even around 35 mAh/g at high
current rate of 5 C along with high rate capability up to 55 cycles. The
diffusion kinetics of the full cell battery is investigated through detailed
analysis of CV curves at various scan rates, and the diffusion coefficient is
found to be 5--8$\times$10$^{-11}$ cm$^2$/s for the anodic as well as cathodic
peaks.


  We propose a novel binary polarization shift keying modulation scheme for a
line-of-sight environment by exploiting the polarization control ability of the
reconfigurable intelligent surface (RIS). The RIS encodes the information data
in terms of the polarization states of either the reflected wave from the RIS
or the composite wireless channel between an RF source and receiver. In the
first case, polarization mismatch correction becomes essential at the receiver.
In the second case, the RIS pre-codes the reflected wave to compensate for the
polarization mismatch which allows non-coherent demodulation at the receiver.


  We describe three recent results from D0 related to the top quark: a
preliminary measurement of the t-tbar spin correlation in top quark pair
production, a search for top quark decays into charged Higgs bosons, and an
improved cross section analysis in the t-tbar -> e mu channel using neural
networks.


  We show how modern methods can be applied to quantum gravity at low energy.
We test how quantum corrections challenge the classical framework behind the
Equivalence Principle, for instance through introduction of non-locality from
quantum physics, embodied in the Uncertainty Principle. When the energy is
small we now have the tools to address this conflict explicitly. Despite the
violation of some classical concepts, the EP continues to provide the core of
the quantum gravity framework through the symmetry - general coordinate
invariance - that is used to organize the effective field theory.


  Some studies have claimed the existence of a stellar upper-mass limit of 150
Msun. A factor that is often overlooked concerns the issue that there might be
a significant difference between the present-day and the initial mass of the
most massive stars - as a result of mass loss. The upper-mass limit may be
substantially higher, possibly exceeding 200 Msun. The issue of the upper
mass-limit will however remain uncertain as long as there is only limited
quantitative knowledge of mass loss in close proximity to the Eddington (=
Gamma) limit. For this reason, we present mass-loss predictions from Monte
Carlo radiative transfer models for very massive stars up to 300 Msun. Using
our new dynamical approach, we find an upturn or "kink" in the mass-loss versus
Gamma dependence, at the point where our model winds become optically thick.
These are the first mass-loss predictions where the transition from optically
thin O-star winds to optically thick Wolf-Rayet winds has been resolved.


  In this work, we study position-based cryptography in the quantum setting.
The aim is to use the geographical position of a party as its only credential.
On the negative side, we show that if adversaries are allowed to share an
arbitrarily large entangled quantum state, no secure position-verification is
possible at all. We show a distributed protocol for computing any unitary
operation on a state shared between the different users, using local operations
and one round of classical communication. Using this surprising result, we
break any position-verification scheme of a very general form. On the positive
side, we show that if adversaries do not share any entangled quantum state but
can compute arbitrary quantum operations, secure position-verification is
achievable. Jointly, these results suggest the interesting question whether
secure position-verification is possible in case of a bounded amount of
entanglement. Our positive result can be interpreted as resolving this question
in the simplest case, where the bound is set to zero.
  In models where secure positioning is achievable, it has a number of
interesting applications. For example, it enables secure communication over an
insecure channel without having any pre-shared key, with the guarantee that
only a party at a specific location can learn the content of the conversation.
More generally, we show that in settings where secure position-verification is
achievable, other position-based cryptographic schemes are possible as well,
such as secure position-based authentication and position-based key agreement.


  We present a coupled cluster and linear response theory to compute properties
of many-electron systems at non-zero temperatures. For this purpose, we make
use of the thermofield dynamics, which allows for a compact wavefunction
representation of the thermal density matrix, and extend our recently developed
framework [J. Chem. Phys. 150, 154109 (2019)] to parameterize the so-called
thermal state using an exponential ansatz with cluster operators that create
thermal quasiparticle excitations on a mean-field reference. As benchmark
examples, we apply this method to both model (one-dimensional Hubbard and
Pairing) as well as ab-initio (atomic Beryllium and molecular Hydrogen)
systems, while comparing with exact results.


  We present a simulation study of the prospects for the mass measurement of
TeV-scale light-flavored right-handed squarks at a 3 TeV e+e- collider based on
CLIC technology. In the considered model, these particles decay into their
standard-model counterparts and the lightest neutralino, resulting in a
signature of two jets plus missing energy. The analysis is based on full GEANT4
simulations of the CLIC_ILD detector concept, including Standard Model physics
backgrounds and beam-induced hadronic backgrounds from two-photon processes.
The analysis serves as a generic benchmark for the reconstruction of highly
energetic jets in events with substantial missing energy. Several jet finding
algorithms were evaluated, with the longitudinally invariant kt algorithm
showing a high degree of robustness towards beam-induced background while
preserving the features typically found in algorithms developed for e+e-
collisions. The presented study of the reconstruction of light-flavored squarks
shows that for TeV-scale squark masses, sub-percent accuracy on the mass
measurement can be achieved at CLIC.


  We determine scattering phase shifts for S,P,D, and F partial wave channels
in two-nucleon systems using lattice QCD methods. We use a generalization of
Luscher's finite volume method to determine infinite volume phase shifts from a
set of finite volume ground- and excited-state energy levels on two volumes,
V=(3.4 fm)^3 and V=(4.5 fm)^3. The calculations are performed in the
SU(3)-flavor limit, corresponding to a pion mass of approximately 800 MeV. From
the energy dependence of the phase shifts we are able to extract scattering
parameters corresponding to an effective range expansion.


  The existing theory of hard exclusive QCD processes is based on two
assumptions: (i) $factorization$ into a $hard\,block$ times light front
distribution amplitudes (DA's); (ii) use of perturbative gluon exchanges within
the hard block. However, unlike DIS and jet physics, the characteristic
momentum transfer $Q$ involved in the factorized block is not large enough for
this theory to be phenomenologically successful. In this work, we revisit the
latter assumption (ii), by explicitly calculating the $instanton-induced$
contributions to the hard block, and show that they contribute substantially to
the vector, scalar and gravitational form factors of the pseudoscalar, scalar
and vector mesons, over a wide range of momentum transfer.


  With the rapid demand of data and computational resources in deep learning
systems, a growing number of algorithms to utilize collaborative machine
learning techniques, for example, federated learning, to train a shared deep
model across multiple participants. It could effectively take advantage of the
resources of each participant and obtain a more powerful learning system.
However, integrity and privacy threats in such systems have greatly obstructed
the applications of collaborative learning. And a large amount of works have
been proposed to maintain the model integrity and mitigate the privacy leakage
of training data during the training phase for different collaborative learning
systems. Compared with existing surveys that mainly focus on one specific
collaborative learning system, this survey aims to provide a systematic and
comprehensive review of security and privacy researches in collaborative
learning. Our survey first provides the system overview of collaborative
learning, followed by a brief introduction of integrity and privacy threats. In
an organized way, we then detail the existing integrity and privacy attacks as
well as their defenses. We also list some open problems in this area and
opensource the related papers on GitHub:
https://github.com/csl-cqu/awesome-secure-collebrative-learning-papers.


  We present a survey of the two-dimensional and tensorial structure of the
lifting doctrine in constructive domain theory, i.e. in the theory of
directed-complete partial orders (dcpos) over an arbitrary elementary topos. We
establish the universal property of lifting of dcpos as the Sierpi\'nski cone,
from which we deduce (1) that lifting forms a Kock-Z\"oberlein doctrine, (2)
that lifting algebras, pointed dcpos, and inductive partial orders form
canonically equivalent locally posetal 2-categories, and (3) that the category
of lifting algebras is cocomplete, with connected colimits created by the
forgetful functor to dcpos. Finally we deduce the symmetric monoidal closure of
the Eilenberg-Moore resolution of the lifting 2-monad by means of smash
products; these are shown to classify both bilinear maps and strict maps, which
we prove to coincide in the constructive setting. We provide several concrete
computations of the smash product as dcpo coequalisers and lifting algebra
coequalisers, and compare these with the more abstract results of Seal.
Although all these results are well-known classically, the existing proofs do
not apply in a constructive setting; indeed, the classical analysis of the
Eilenberg-Moore category of the lifting monad relies on the fact that all
lifting algebras are free, a condition that is not known to hold
constructively.


  A plausible dark matter candidate is an ultralight bosonic particle referred
to as fuzzy dark matter. The equivalent mass-energy of the fuzzy dark matter
boson is $\sim 10^{-22}$eV and has a corresponding de Broglie wavelength of
kiloparsec scale, thus exhibiting wave behaviour in scales comparable to a
galactic core, which could not appear in conventional cold dark matter models.
The presence of fuzzy dark matter in galactic clusters will impact the motion
of their members through dynamical friction. In this work, we present
simulations of the dynamical friction on satellites traversing an initially
uniform fuzzy dark matter halo. We focus on the satellites whose shapes are
beyond spherical symmetry described by ellipsoidal and logarithmic potentials.
We find that the wakes created on the fuzzy dark matter halo due to the passage
of such satellites are qualitatively different from those generated by
spherically symmetric ones. Furthermore, we quantify the dynamical friction
coefficient for such systems, finding that the same satellite may experience a
drag differing by a factor of $5$ depending on its ellipticity and the
direction of motion. Finally, we find that the dynamical friction time-scale is
close to Hubble time, assuming a satellite of $10^{11}$M$_{\odot}$ traversing
at $10^{3}$km/s a FDM halo whose mean density is $\sim
10^6$M$_{\odot}$kpc$^{-3}$.


  The nuclear-spin-relaxation rate 1/T_1 has been measured in a bilayer
electron system at and around total Landau level filling factor nu =1. The
measured 1/T_1, which probes electron spin fluctuations, is found to increase
gradually from the quantum Hall (QH) state at low fields through a phase
transition to the compressible state at high fields. Furthermore, 1/T_1 in the
QH state shows a small but noticeable increase away from nu =1. These results
demonstrate that, as opposed to common assumption, the electron spin degree of
freedom is completely frozen neither in the QH nor compressible states.


  Search engines play a vital role in day to day life on internet. People use
search engines to find content on internet. Cloud computing is the computing
concept in which data is stored and accessed with the help of a third party
server called as cloud. Data is not stored locally on our machines and the
softwares and information are provided to user if user demands for it. Search
queries are the most important part in searching data on internet. A search
query consists of one or more than one keywords. A search query is searched
from the database for exact match, and the traditional searchable schemes do
not tolerate minor typos and format inconsistencies, which happen quite
frequently. This drawback makes the existing techniques unsuitable and they
offer very low efficiency. In this paper, we will for the first time formulate
the problem of effective fuzzy search by introducing tree search methodologies.
We will explore the benefits of B trees in search mechanism and use them to
have an efficient keyword search. We have taken into consideration the security
analysis strictly so as to get a secure and privacy-preserving system.


  Astronomy is one of the most data-intensive of the sciences. Data technology
is accelerating the quality and effectiveness of its research, and the rate of
astronomical discovery is higher than ever. As a result, many view astronomy as
being in a 'Golden Age', and projects such as the Virtual Observatory are
amongst the most ambitious data projects in any field of science. But these
powerful tools will be impotent unless the data on which they operate are of
matching quality. Astronomy, like other fields of science, therefore needs to
establish and agree on a set of guiding principles for the management of
astronomical data. To focus this process, we are constructing a 'data
manifesto', which proposes guidelines to maximise the rate and
cost-effectiveness of scientific discovery.


  Equivariant neural networks have shown improved performance, expressiveness
and sample complexity on symmetrical domains. But for some specific symmetries,
representations, and choice of coordinates, the most common point-wise
activations, such as ReLU, are not equivariant, hence they cannot be employed
in the design of equivariant neural networks. The theorem we present in this
paper describes all possible combinations of finite-dimensional
representations, choice of coordinates and point-wise activations to obtain an
exactly equivariant layer, generalizing and strengthening existing
characterizations. Notable cases of practical relevance are discussed as
corollaries. Indeed, we prove that rotation-equivariant networks can only be
invariant, as it happens for any network which is equivariant with respect to
connected compact groups. Then, we discuss implications of our findings when
applied to important instances of exactly equivariant networks. First, we
completely characterize permutation equivariant networks such as Invariant
Graph Networks with point-wise nonlinearities and their geometric counterparts,
highlighting a plethora of models whose expressive power and performance are
still unknown. Second, we show that feature spaces of disentangled steerable
convolutional neural networks are trivial representations.


  Abridged: In one widely discussed model for the formation of nuclear star
clusters (NSCs), massive globular clusters spiral into the center of a galaxy
and merge to form the nucleus. It is now known that at least some NSCs coexist
with supermassive black holes (SBHs); this is the case, for instance, in the
Milky Way (MW). In this paper, we investigate how the presence of a SMBH at the
center of the MW impacts the merger hypothesis for the formation of its NSC.
Starting from a model consisting of a low-density nuclear stellar disk and the
SMBH, we use N-body simulations to follow the successive inspiral and merger of
globular clusters. The clusters are started on circular orbits of radius 20 pc,
and their initial masses and radii are set up in such a way as to be consistent
with the galactic tidal field at that radius. The total accumulated mass by ~10
clusters is about 1.5x10^7 Solar masses. Each cluster is disrupted by the SMBH
at a distance of roughly one parsec. The density profile that results after the
final inspiral event is characterized by a core of roughly this radius, and an
envelope with density that falls off as 1/r^2. These properties are similar to
those of the MW NSC, with the exception of the core size, which in the MW is a
little smaller. But by continuing the evolution of the model after the final
inspiral event, we find that the core shrinks substantially via gravitational
encounters in a time (when scaled to the MW) of 10 Gyr as the stellar
distribution evolves toward a Bahcall-Wolf cusp. We also show that the
luminosity function of the MW NSC is consistent with the hypothesis that a
large fraction of the mass comes from (~10Gyr) old stars, brought in by
globular clusters. We conclude that a model in which a large fraction of the
mass of the MW NSC arose from infalling globular clusters is consistent with
existing observational constraints.


  We use the concept of entropy power to derive a new one-parameter class of
information-theoretic uncertainty relations for pairs of conjugate observables
in an infinite-dimensional Hilbert space. This class constitutes an infinite
tower of higher-order statistics uncertainty relations, which allows one in
principle to determine the shape of the underlying information-distribution
function by measuring the relevant entropy powers. We illustrate the capability
of the new class by discussing two examples: superpositions of vacuum and
squeezed states and the Cauchy-type heavy-tailed wave function.


  Differential evolution (DE) algorithm with a small population size is called
Micro-DE (MDE). A small population size decreases the computational complexity
but also reduces the exploration ability of DE by limiting the population
diversity. In this paper, we propose the idea of combining ensemble mutation
scheme selection and opposition-based learning concepts to enhance the
diversity of population in MDE at mutation and selection stages. The proposed
algorithm enhances the diversity of population by generating a random mutation
scale factor per individual and per dimension, randomly assigning a mutation
scheme to each individual in each generation, and diversifying individuals
selection using opposition-based learning. This approach is easy to implement
and does not require the setting of mutation scheme selection and mutation
scale factor. Experimental results are conducted for a variety of objective
functions with low and high dimensionality on the CEC Black- Box Optimization
Benchmarking 2015 (CEC-BBOB 2015). The results show superior performance of the
proposed algorithm compared to the other micro-DE algorithms.


  We point out a non-trivial connection between the model proposed by Horava
and Keeler as a candidate for noncritical M-theory and the Gross-Neveu model
with fermionic fields obeying periodic boundary conditions in 2+1 dimensions.
Specifically, the vacuum energy of the former is identified with the large-N
free-energy of the latter up to an overall constant. This identification
involves an appropriate analytic continuation of the subtraction point in
noncritical M-theory, which is related to the volume of the Liouville
dimension. We show how the world-sheet cosmological constant may be obtained
from the Gross-Neveu model. At its critical point, which is given in terms of
the golden mean, the values of the vacuum energy and of the cosmological
constant are 4/5 and 2/5 of the corresponding values at infinite string
coupling constant.


  We consider a class of singular ordinary differential equations describing
analytic systems of arbitrary finite dimension, subject to a quasi-periodic
forcing term and in the presence of dissipation. We study the existence of
response solutions, i.e. quasi-periodic solutions with the same frequency
vector as the forcing term, in the case of large dissipation. We assume the
system to be conservative in the absence dissipation, so that the forcing term
is --- up to the sign --- the gradient of a potential energy, and both the mass
and damping matrices to be symmetric and positive definite. Further, we assume
a non-degeneracy condition on the forcing term, essentially that the
time-average of the potential energy has a strict local minimum. On the
contrary, no condition is assumed on the forcing frequency; in particular we do
not require any Diophantine condition. We prove that, under the assumptions
above, a response solution always exist provided the dissipation is strong
enough. This extends results previously available in literature in the
one-dimensional case.


  We report on a complete calculation of electroweak production of top quark
pairs in $e^+e^-$ annihilation at next-to-next-to-leading order in Quantum
Chromodynamics. Our setup is fully differential and can be used to calculate
any infrared-safe observable. Especially we calculated the
next-to-next-to-leading order corrections to top-quark forward-backward
asymmetry and found sizable effects. Our results show a large reduction of the
theoretical uncertainties in predictions of the forward-backward asymmetry, and
allow a precision determination of the top quark electroweak couplings at
future $e^+e^-$ colliders.


  We show that $\varsigma$, the radial location of the minimum in the
differential radial mass profile $M^\prime(r)$ of a galaxy cluster, can probe
the theory of gravity. We derived $M^\prime(r)$ of the dark matter halos of
galaxy clusters from N-body cosmological simulations that implement two
different theories of gravity: standard gravity in the $\Lambda$CDM model, and
$f(R)$. We extracted 49169 dark matter halos in 11 redshift bins in the range
$0\leq z\leq 1$ and in three different mass bins in the range
$0.9<M_{200c}/10^{14}h^{-1}$M$_\odot<11$. We investigated the correlation of
$\varsigma$ with the redshift and the mass accretion rate (MAR) of the halos.
We show that $\varsigma$ decreases from $\sim 3R_{200c}$ to $\sim 2R_{200c}$
when $z$ increases from 0 to $1$ in the $\Lambda$CDM model. At $z\sim 0.1$,
$\varsigma$ decreases from $2.8R_{200c}$ to $\sim 2.5R_{200c}$ when the MAR
increases from $\sim 10^4h^{-1}$M$_\odot$~yr$^{-1}$ to $\sim 2\times
10^5h^{-1}$M$_\odot$~yr$^{-1}$. In the $f(R)$ model, $\varsigma$ is $\sim 15$%
larger than in $\Lambda$CDM. The median test shows that for samples of $\gtrsim
400$ dark matter halos at $z\leq 0.8$, $\varsigma$ is able to distinguish
between the two theories of gravity with a $p$-value $\lesssim 10^{-5}$.
Upcoming advanced spectroscopic and photometric programs will allow a robust
estimation of the mass profile of enormous samples of clusters up to large
clustercentric distances. These samples will allow us to statistically exploit
$\varsigma$ as probe of the theory of gravity, which complements other
large-scale probes.


  We present numerical results of a model calculation for the 3He bilayer
system, which captures the interplay between fast and slow dynamics of the
different layers and incorporates an independent scale for the three body ring
exchange. By means of Cluster-DMFT+CTQMC, we find remarkable similarities with
the experiments: a suppression of the coherence temperature upon approaching
the solidification point of the first layer accompanied by the onset of strong
ferromagnetic correlations within the first layer. Based on the consistent
results for different cluster sizes we conjecture a first-order transition
cutting short the Kondo breakdown, which allows us to interpret the
experimental observation of an intervening phase preempting the quantum
critical point in terms of a phase separation.


  In this paper, we focus on a new practical task, document-scale text content
manipulation, which is the opposite of text style transfer and aims to preserve
text styles while altering the content. In detail, the input is a set of
structured records and a reference text for describing another recordset. The
output is a summary that accurately describes the partial content in the source
recordset with the same writing style of the reference. The task is
unsupervised due to lack of parallel data, and is challenging to select
suitable records and style words from bi-aspect inputs respectively and
generate a high-fidelity long document. To tackle those problems, we first
build a dataset based on a basketball game report corpus as our testbed, and
present an unsupervised neural model with interactive attention mechanism,
which is used for learning the semantic relationship between records and
reference texts to achieve better content transfer and better style
preservation. In addition, we also explore the effectiveness of the
back-translation in our task for constructing some pseudo-training pairs.
Empirical results show superiority of our approaches over competitive methods,
and the models also yield a new state-of-the-art result on a sentence-level
dataset.


  A theoretical realization of an all-optical quantum ratchet is proposed in a
medium composed of an array of coupled waveguides. By coupling light into two
adjacent waveguides, and calculating the expectation value for the position
space operator, we demonstrate the quantum ratchet like behavior of this
optical system.


  We prove that the continuous cohomology of $\text{Isom}^+(\mathbb{H}^n)$ can
be measurably realized on the boundary of hyperbolic space. This implies in
particular that for $\text{Isom}^+(\mathbb{H}^n)$ the comparison map from
continuous bounded cohomology to continuous cohomology is injective in degree
$3$. We furthermore prove a stability result for the continuous bounded
cohomology of $\text{Isom}(\mathbb{H}^n)$ and
$\text{Isom}(\mathbb{H}_{\mathbb{C}}^n)$.


  We present coordinated coronal observations of the August 21, 2017 total
solar eclipse with the Airborne Infrared Spectrometer (AIR-Spec) and the
Extreme-ultraviolet Imaging Spectrometer (EIS). These instruments provide an
unprecedented view of the solar corona in two disparate wavelength regimes, the
near to mid infrared (IR) and the extreme ultraviolet (EUV), opening new
pathways for characterizing the complex coronal plasma environment. During
totality, AIR-Spec sampled coronal IR spectra near the equatorial west limb,
detecting strong sources of Mg VIII, S XI, Si IX, and Si X in two passbands
encompassing 1.4 - 4 $\mu$m. We apply emission measure (EM) loci analysis to
these IR emission lines to test their capacity as coronal temperature
diagnostics. The density-sensitive Fe XII 186.9 \r{A}/192.4 \r{A} line pair
supplies spatially resolved, line-of-sight electron densities, supporting the
EM loci analysis. From this, we find EM loci intersections at temperatures of
$10^{6.13}$ K at 30 arcsec from the limb and $10^{6.21}$ K at 100 arcsec.
Applying the same EM loci analysis to 27 EIS emission lines associated with
seven ion species (Fe X-XIV, S X, and Si X) confirms these results, displaying
strong evidence of isothermal plasma throughout the region. However, the IR EM
loci analysis suffers from moderate uncertainties. The likely sources include:
poor signal, infrared contamination from a prominence, and photoexcitation by
continuum radiation. Regardless, we demonstrate that EUV spectral data are
valuable constraints to coronal infrared emission models, and will be powerful
supplements for future IR solar observatories, particularly DKIST.


  It is always possible to decide, with one-sided error, whether two quantum
states are the same under a specific unitary transformation. However we show
here that it is {\em impossible} to do so if the transformation is anti-linear
and non-singular. This result implies that unitary and anti-unitary operations
exist on an unequal footing in quantum information theory.


  In this paper we address a classification problem where two sources of labels
with different levels of fidelity are available. Our approach is to combine
data from both sources by applying a co-kriging schema on latent functions,
which allows the model to account item-dependent labeling discrepancy. We
provide an extension of Laplace inference for Gaussian process classification,
that takes into account multi-fidelity data. We evaluate the proposed method on
real and synthetic datasets and show that it is more resistant to different
levels of discrepancy between sources than other approaches for data fusion.
Our method can provide accuracy/cost trade-off for a number of practical tasks
such as crowd-sourced data annotation and feasibility regions construction in
engineering design.


  The current study detects different morphologies related to prostate
pathology using deep learning models; these models were evaluated on 2,121
hematoxylin and eosin (H&E) stain histology images captured using bright field
microscopy, which spanned a variety of image qualities, origins (whole slide,
tissue micro array, whole mount, Internet), scanning machines, timestamps, H&E
staining protocols, and institutions. For case usage, these models were applied
for the annotation tasks in clinician-oriented pathology reports for
prostatectomy specimens. The true positive rate (TPR) for slides with prostate
cancer was 99.7% by a false positive rate of 0.785%. The F1-scores of Gleason
patterns reported in pathology reports ranged from 0.795 to 1.0 at the case
level. TPR was 93.6% for the cribriform morphology and 72.6% for the ductal
morphology. The correlation between the ground truth and the prediction for the
relative tumor volume was 0.987 n. Our models cover the major components of
prostate pathology and successfully accomplish the annotation tasks.


  We discuss the construction of a free scalar quantum field theory on
$\kappa$-Minkowski noncommutative spacetime. We do so in terms of
$\kappa$-Poincar\'e-invariant $N$-point functions, i.e. multilocal functions
which respect the deformed symmetries of the spacetime. As shown in a previous
paper by some of us, this is only possible for a lightlike version of the
commutation relations, which allow the construction of a covariant algebra of
$N$ points that generalizes the $\kappa$-Minkowski commutation relations. We
solve the main shortcoming of our previous approach, which prevented the
development of a fully covariant quantum field theory: the emergence of a
non-Lorentz-invariant boundary of momentum space. To solve this issue, we
propose to ``extend" momentum space by introducing a class of new Fourier modes
and we prove that this approach leads to a consistent definition of the
Pauli-Jordan function, which turns out to be undeformed with respect to the
commutative case. We finally address the quantization of our scalar field and
obtain a deformed, $\kappa$-Poincar\'e-invariant, version of the bosonic
oscillator algebra.


  Within the magnetar scenario, the "twisted magnetosphere" model appears very
promising in explaining the persistent X-ray emission from the Soft Gamma
Repeaters and the Anomalous X-ray Pulsars (SGRs and AXPs). In the first two
papers of the series, we have presented a 3D Monte Carlo code for solving
radiation transport as soft, thermal photons emitted by the star surface are
resonantly upscattered by the magnetospheric particles. A spectral model
archive has been generated and implemented in XSPEC. Here we report on the
systematic application of our spectral model to different XMM-Newton and
Integral observations of SGRs and AXPs. We find that the synthetic spectra
provide a very good fit to the data for the nearly all the source (and source
states) we have analyzed.


  Deep Convolutional Neural Networks have proven effective in solving the task
of semantic segmentation. However, their efficiency heavily relies on the
pixel-level annotations that are expensive to get and often require domain
expertise, especially in medical imaging. Weakly supervised semantic
segmentation helps to overcome these issues and also provides explainable deep
learning models. In this paper, we propose a novel approach to the semantic
segmentation of medical chest X-ray images with only image-level class labels
as supervision. We improve the disease localization accuracy by combining three
approaches as consecutive steps. First, we generate pseudo segmentation labels
of abnormal regions in the training images through a supervised classification
model enhanced with a regularization procedure. The obtained activation maps
are then post-processed and propagated into a second classification
model-Inter-pixel Relation Network, which improves the boundaries between
different object classes. Finally, the resulting pseudo-labels are used to
train a proposed fully supervised segmentation model. We analyze the robustness
of the presented method and test its performance on two distinct datasets:
PASCAL VOC 2012 and SIIM-ACR Pneumothorax. We achieve significant results in
the segmentation on both datasets using only image-level annotations. We show
that this approach is applicable to chest X-rays for detecting an anomalous
volume of air in the pleural space between the lung and the chest wall. Our
code has been made publicly available.


  I apply the algebraic classification of self-adjoint endomorphisms of ${\bf
R}^{2,2}$ provided by their Jordan canonical form to the Ricci curvature tensor
of four-dimensional neutral manifolds and relate this classification to an
algebraic classification of the Ricci curvature spinor. These results parallel
similar results well known in four-dimensional Lorentzian geometry. The
classification is summarized in Table 2 at the end of the paper.


  We revisit Akbulut and King's first example of a compact semialgebraic set
which satisfies Sullivan's local Euler characteristic condition, but which is
not homeomorphic to an algebraic set. A nontrivial obstruction is computed
using the link operator on the ring of constructible functions.


  A widespread view is that Artificial Intelligence cannot be creative. We
tested this assumption by comparing human-generated ideas with those generated
by six Generative Artificial Intelligence (GAI) chatbots: $alpa.\!ai$,
$Copy.\!ai$, ChatGPT (versions 3 and 4), $Studio.\!ai$, and YouChat. Humans and
a specifically trained AI independently assessed the quality and quantity of
ideas. We found no qualitative difference between AI and human-generated
creativity, although there are differences in how ideas are generated.
Interestingly, 9.4 percent of humans were more creative than the most creative
GAI, GPT-4. Our findings suggest that GAIs are valuable assistants in the
creative process. Continued research and development of GAI in creative tasks
is crucial to fully understand this technology's potential benefits and
drawbacks in shaping the future of creativity. Finally, we discuss the question
of whether GAIs are capable of being truly creative.


  We report about influence of external pressure on electrical resistivity of
EuB5.99C0.01, the compound believed to be intrinsically inhomogeneous due to
fluctuation of carbon content. Our results show that the low-temperature
resistivity maximum shifts to lower temperature with applied pressure, opposite
to the behavior reported for stoichiometric EuB6. The origin of such
qualitative difference we associate with the increasing volume fraction of the
phase that is not compatible with ferromagnetic ordering (originating in
regions with relatively higher carbon concentratio) with enhancing pressure.
Our results support a recent proposition that carbon-rich regions strongly
influence magnetotransport properties of carbon-doped EuB6, such as they play a
role of spacers, which prevent percolation of ferromagnetic phase.


  Artificial intelligence has started to transform histopathology impacting
clinical diagnostics and biomedical research. However, while many computational
pathology approaches have been proposed, most current AI models are limited
with respect to generalization, application variety, and handling rare
diseases. Recent efforts introduced self-supervised foundation models to
address these challenges, yet existing approaches do not leverage pathologist
knowledge by design. In this study, we present a novel approach to designing
foundation models for computational pathology, incorporating pathologist
expertise, semi-automated data curation, and a diverse dataset from over 15
laboratories, including 58 tissue types, and encompassing 129 different
histochemical and immunohistochemical staining modalities. We demonstrate that
our model "RudolfV" surpasses existing state-of-the-art foundation models
across different benchmarks focused on tumor microenvironment profiling,
biomarker evaluation, and reference case search while exhibiting favorable
robustness properties. Our study shows how domain-specific knowledge can
increase the efficiency and performance of pathology foundation models and
enable novel application areas.


  We have analyzed the $B_{1g}$ and $B_{2g}$ Raman spectra of electron-doped
cuprate superconductors Nd$_{2-x}$Ce$_{x}$CuO$_4$ and Pr$_{2-x}$Ce$_{x}$CuO$_4$
using a weakly coupled two-band model. One of these two bands is centered
around $(\pm \pi/2, \pm \pi/2)$ and couples more strongly with the $B_{2g}$
mode, while the other is centered around $(\pm \pi, 0)$ and $(0, \pm \pi)$ and
couples more strongly with the $B_{1g}$ mode. This model explains in a natural
way why the $B_{2g}$ Raman peak occurs at a higher frequency than the $B_{1g}$
one at optimal doping, and how these two peaks change with doping in agreement
with experiments. Our result suggests that the superconducting pairing in
electron-doped high-$T_c$ cuprates has the $d_{x^2-y^2}$-wave symmetry and
results from the same mechanism as for hole doped materials.


  In this Chapter we provide a review of the main results obtained in the
modeling of graphene kinks and antikinks, which are elementary topological
excitations of buckled graphene membranes. We introduce the classification of
kinks, as well as discuss kink-antikink scattering, and radiation-kink
interaction. We also report some new findings including i) the evidence that
the kinetic energy of graphene kinks is described by a relativistic expression,
and ii) demonstration of damped dynamics of kinks in membranes compressed in
the longitudinal direction. Special attention is paid to highlight the
similarities and differences between the graphene kinks and kinks in the
classical scalar $\phi^4$ theory. The unique properties of graphene kinks
discussed in this Chapter may find applications in nanoscale motion.


  We propose a statistical modeling technique, called the Hierarchical
Association Rule Model (HARM), that predicts a patient's possible future
medical conditions given the patient's current and past history of reported
conditions. The core of our technique is a Bayesian hierarchical model for
selecting predictive association rules (such as "condition 1 and condition 2
$\rightarrow$ condition 3") from a large set of candidate rules. Because this
method "borrows strength" using the conditions of many similar patients, it is
able to provide predictions specialized to any given patient, even when little
information about the patient's history of conditions is available.


  AI systems cannot exist without data. Now that AI models (data science and
AI) have matured and are readily available to apply in practice, most
organizations struggle with the data infrastructure to do so. There is a
growing need for data engineers that know how to prepare data for AI systems or
that can setup enterprise-wide data architectures for analytical projects. But
until now, the data engineering part of AI engineering has not been getting
much attention, in favor of discussing the modeling part. In this paper we aim
to change this by perform a mapping study on data engineering for AI systems,
i.e., AI data engineering. We found 25 relevant papers between January 2019 and
June 2023, explaining AI data engineering activities. We identify which life
cycle phases are covered, which technical solutions or architectures are
proposed and which lessons learned are presented. We end by an overall
discussion of the papers with implications for practitioners and researchers.
This paper creates an overview of the body of knowledge on data engineering for
AI. This overview is useful for practitioners to identify solutions and best
practices as well as for researchers to identify gaps.


  Transit network simulation models are often used for performance and
retrospective analysis of urban rail systems, taking advantage of the
availability of extensive automated fare collection (AFC) and automated vehicle
location (AVL) data. Important inputs to such models, in addition to
origin-destination flows, include passenger path choices and train capacity.
Train capacity, which has often been overlooked in the literature, is an
important input that exhibits a lot of variabilities. The paper proposes a
simulation-based optimization (SBO) framework to simultaneously calibrate path
choices and train capacity for urban rail systems using AFC and AVL data. The
calibration is formulated as an optimization problem with a black-box objective
function. Seven algorithms from four branches of SBO solving methods are
evaluated. The algorithms are evaluated using an experimental design that
includes five scenarios, representing different degrees of path choice
randomness and crowding sensitivity. Data from the Hong Kong Mass Transit
Railway (MTR) system is used as a case study. The data is used to generate
synthetic observations used as "ground truth". The results show that the
response surface methods (particularly Constrained Optimization using Response
Surfaces) have consistently good performance under all scenarios. The proposed
approach drives large-scale simulation applications for monitoring and
planning.


  We propose a message passing algorithm, based on variational Bayesian
inference, for low-rank tensor completion with automatic rank determination in
the canonical polyadic format when additional side information (SI) is given.
The SI comes in the form of low-dimensional subspaces the contain the fiber
spans of the tensor (columns, rows, tubes, etc.). We validate the
regularization properties induced by SI with extensive numerical experiments on
synthetic and real-world data and present the results about tensor recovery and
rank determination. The results show that the number of samples required for
successful completion is significantly reduced in the presence of SI. We also
discuss the origin of a bump in the phase transition curves that exists when
the dimensionality of SI is comparable with that of the tensor.


  Quantum computing is a new form of computing that is based on the principles
of quantum mechanics. It has the potential to revolutionize many fields,
including the humanities and social sciences. The idea behind quantum
humanities is to explore the potential of quantum computing to answer new
questions in these fields, as well as to consider the potential societal
impacts of this technology. This paper proposes a research program for quantum
humanities, which includes the application of quantum algorithms to humanities
and social science research, the reflection on the methods and techniques of
quantum computing, and the evaluation of its potential societal implications.
This research program aims to define the field of quantum humanities and to
establish it as a meaningful part of the humanities and social sciences.


  We prove that if $G$ is a graph and $f(v) \leq 1/(d(v) + 1/2)$ for each $v\in
V(G)$, then either $G$ has an independent set of size at least $\sum_{v\in
V(G)}f(v)$ or $G$ contains a clique $K$ such that $\sum_{v\in K}f(v) > 1$. This
result implies that for any $\sigma \leq 1/2$, if $G$ is a graph and every
clique $K\subseteq V(G)$ has at most $(1 - \sigma)(|K| - \sigma)$ simplicial
vertices, then $\alpha(G) \geq \sum_{v\in V(G)} 1 / (d(v) + 1 - \sigma)$.
Letting $\sigma = 0$ implies the famous Caro-Wei Theorem, and letting $\sigma =
1/2$ implies that if fewer than half of the vertices in each clique of $G$ are
simplicial, then $\alpha(G) \geq \sum_{v\in V(G)}1/(d(v) + 1/2)$, which is
tight for the 5-cycle. When applied to the complement of a graph, this result
implies the following new Tur\' an stability result. If $G$ is a $K_{r +
1}$-free graph with more than $(1 - 1/r)n^2/2 - n/4$ edges, then $G$ contains
an independent set $I$ such that at least half of the vertices in $I$ are
complete to $G - I$. Applying this stability result iteratively provides a new
proof of the stability version of Tur\' an's Theorem in which $K_{r + 1}$-free
graphs with close to the extremal number of edges are $r$-partite.


  This article presents a new semi-automatic method for charge and mass
identification in two-dimensional matrices. The proposed algorithm is based on
the matrix's properties and uses as little information as possible on the
global form of the identification lines, making it applicable to a large
variety of matrices, including Particular attention has been paid to the
implementation in a suitable graphical environment, so that only two
mouse-clicks are required from the user to calculate all initialization
parameters. Example applications to recent data from both INDRA and FAZIA
telescopes are presented.


  Cu2Ta4O12 (CTaO) thin films were successfully deposited on Si(100) substrates
by pulsed-laser deposition technique. The crystalline structure and the surface
morphology of the CTaO thin films were strongly affected by substrate
temperature, oxygen pressure and target - substrate distance. In general during
deposition of CTaO the formation of a Ta2O5 phase appeared, on which CTaO grew
with different orientations. We report on the experimental set-up, details for
film deposition and the film properties determined by SEM, EDX and XRD.


  The goal of this work is to constrain the strength and structure of the
magnetic field in the nearby cluster of galaxies A2255. At radio wavelengths
A2255 is characterized by the presence of a polarized radio halo at the cluster
center, a relic source at the cluster periphery, and several embedded radio
galaxies. The polarized radio emission from all these sources is modified by
Faraday rotation as it traverses the magnetized intra-cluster medium. The
distribution of Faraday rotation can be used to probe the magnetic field
strength and topology in the cluster. For this purpose, we performed Very Large
Array observations at 3.6 and 6 cm of four polarized radio galaxies embedded in
A2255, obtaining detailed rotation measure images for three of them. We
analyzed these data together with the very deep radio halo image recently
obtained by us. We simulated random 3-dimensional magnetic field models
characterized by different power spectra and produced synthetic rotation
measure and radio halo images. By comparing the simulations with the data we
are able to determine the strength and the power spectrum of the intra-cluster
magnetic field fluctuations which best reproduce the observations. The data
require a steepening of the power spectrum spectral index from n=2, at the
cluster center, up to n=4, at the cluster periphery and the presence of
filamentary structures on large scales. The average magnetic field strength at
the cluster center is 2.5 muG. The field strength declines from the cluster
center outward with an average magnetic field strength calculated over 1 Mpc^3
of about 1.2 muG.


  CP violation has so far been observed in one system only, namely in the
decays of neutral kaons, and it can still be described by a single real
quantity corresponding to a superweak scenario. In these lectures I describe
why limitations on CP invariance are a particularly fundamental phenomenon and
what experimental information is available. The KM ansatz constitutes the
minimal implementation of CP violation: without requiring unknown degrees of
freedom it can reproduce the known CP phenomenology. It unequivocally predicts
large or even huge CP asymmetries of various kinds in the decays of beauty
hadrons. New theoretical technologies will enable us in the foreseeable future
to express at least some of these predictions in a quantitatively reliable
fashion. There is tremendous potential for discovering New Physics in beauty
transitions. Continuing efforts in strange decays and further dedicated
searches for electric dipole moments and for CP asymmetries in charm decays are
likewise essential for discovering crucial elements that still are missing in
the puzzle that is Nature's Grand Design.


  Quantum spin Hall (QSH) state of matter has a charge excitation bulk bandgap
and a pair of gapless spin-filtered edge-states, which can support
backscattering-free transport. Bilayer phosphorene possesses a large tunable
bandgap and high carrier mobilities, and therefore has the widely potential
applications in nanoelectronics and optics. Here, we demonstrate an
strain-induced electronic topological phase transition from a normal to QSH
state in bilayer phosphorene accompanying by a band inversion that changes
$\mathbbm{Z}_{2}$ from 0 to 1, which is highly dependent on the interlayer
stacking. When the bottom layer is shifted by 1/2 unit cell along axial
direction with respect to the top layer, the topological bandgap reaches up to
92.5 meV, which is sufficiently large to realize the QSH effect at room
temperature. Its optical absorption spectrum becomes broadened, and even
extends to the far-infra-red region leading to a wider range of brightness,
which is highly desirable in optic devices.