  Event cameras are bio-inspired sensors capable of providing a continuous
stream of events with low latency and high dynamic range. As a single event
only carries limited information about the brightness change at a particular
pixel, events are commonly accumulated into spatio-temporal windows for further
processing. However, the optimal window length varies depending on the scene,
camera motion, the task being performed, and other factors. In this research,
we develop a novel ensemble-based scheme for combining temporal windows of
varying lengths that are processed in parallel. For applications where the
increased computational requirements of this approach are not practical, we
also introduce a new "approximate" ensemble scheme that achieves significant
computational efficiencies without unduly compromising the original performance
gains provided by the ensemble approach. We demonstrate our ensemble scheme on
the visual place recognition (VPR) task, introducing a new Brisbane-Event-VPR
dataset with annotated recordings captured using a DAVIS346 color event camera.
We show that our proposed ensemble scheme significantly outperforms all the
single-window baselines and conventional model-based ensembles, irrespective of
the image reconstruction and feature extraction methods used in the VPR
pipeline, and evaluate which ensemble combination technique performs best.
These results demonstrate the significant benefits of ensemble schemes for
event camera processing in the VPR domain and may have relevance to other
related processes, including feature tracking, visual-inertial odometry, and
steering prediction in driving.


  A new generalization of Grassmannians in supergeometry, called
$\nu-$Grassmannians, are constructed by gluing $\nu-$domains. By a
$\nu-$domain, we mean a superdomain with an odd involution say $\nu$ on its
structure sheaf, as morphism of modules. Then we show that $\nu-$Grassmannians
are homogeneous superspaces. In addition, in the last section, a supergroup
associated to the odd involution $\nu$ is introduced.


  Minimizing the weight in topology optimization of frame structures under
free-vibration eigenvalue constraints constitutes a challenging nonconvex
polynomial optimization problem with strong singularities in the feasible set.
Here, we adopt a nonlinear semidefinite programming formulation, which consists
of a minimization of a linear function over a basic semi-algebraic feasible
set, and provide its bilevel reformulation. This bilevel program maintains a
special structure: The lower level is univariate and quasiconvex, and the upper
level is enumerative. After deriving the sufficient and necessary conditions
for the solvability of the lower-level problem, we provide a way to construct
feasible points to the original semidefinite program, and using such a feasible
point, we show that the conditions for convergence of the Lasserre hierarchy
are met. Moreover, we show how to construct lower and upper bounds for each
level of the Lasserre hierarchy. Using these bounds, we develop a simple
sufficient condition of global {\epsilon}-optimality. Finally, we prove that
the optimality gap {\epsilon} converges to zero in the limit if the set of
global minimizers is convex. We demonstrate these results with three
representative problems, for which the hierarchy indeed converges in a finite
number of steps.


  The lineshape of crystal-field transitions in single crystals of Mn12ac
molecular magnets is determined by the magnetic history. The absorption lines
are symmetric and Gaussian for the non-magnetized state obtained by zero-field
cooling (zfc). In the magnetized state which is reached when the sample is
cooled in a magnetic field (fc), however, they are asymmetric even in the
absence of an external magnetic field. These observations are quantitatively
explained by inhomogeneous symmetrical (Gaussian) broadening of the
crystal-field transitions combined with a contribution of off-diagonal
components of the magnetic susceptibility to the effective magnetic
permeability.


  By exploiting the property that the RBM log-likelihood function is the
difference of convex functions, we formulate a stochastic variant of the
difference of convex functions (DC) programming to minimize the negative
log-likelihood. Interestingly, the traditional contrastive divergence algorithm
is a special case of the above formulation and the hyperparameters of the two
algorithms can be chosen such that the amount of computation per mini-batch is
identical. We show that for a given computational budget the proposed algorithm
almost always reaches a higher log-likelihood more rapidly, compared to the
standard contrastive divergence algorithm. Further, we modify this algorithm to
use the centered gradients and show that it is more efficient and effective
compared to the standard centered gradient algorithm on benchmark datasets.


  This is the second paper in a series to study regular representations for
vertex operator algebras. In this paper, given a module $W$ for a vertex
operator algebra $V$, we construct, out of the dual space $W^{*}$, a family of
canonical (weak) $V\otimes V$-modules called ${\cal{D}}_{Q(z)}(W)$ parametrized
by a nonzero complex number $z$. We prove that for $V$-modules $W,W_{1}$ and
$W_{2}$, a $Q(z)$-intertwining map of type ${W'\choose W_{1}W_{2}}$ in the
sense of Huang and Lepowsky exactly amounts to a $V\otimes V$-homomorphism from
$W_{1}\otimes W_{2}$ to ${\cal{D}}_{Q(z)}(W)$ and that a $Q(z)$-tensor product
of $V$-modules $W_{1}$ and $W_{2}$ in the sense of Huang and Lepowsky amounts
to a universal from $W_{1}\otimes W_{2}$ to the functor ${\cal{F}}_{Q(z)}$,
where ${\cal{F}}_{Q(z)}$ is a functor from the category of $V$-modules to the
category of weak $V\otimes V$-modules defined by
${\cal{F}}_{Q(z)}(W)={\cal{D}}_{Q(z)}(W')$ for a $V$-module $W$. Furthermore,
Huang-Lepowsky's $P(z)$ and $Q(z)$-tensor functors for the category of
$V$-modules are extended to functors $T_{P(z)}$ and $T_{Q(z)}$ from the
category of $V\otimes V$-modules to the category of $V$-modules. It is proved
that functors ${\cal{F}}_{P(z)}$ and ${\cal{F}}_{Q(z)}$ are right adjoints of
$T_{P(z)}$ and $T_{Q(z)}$, respectively.


  Although Generative Adversarial Networks (GANs) have shown remarkable success
in various tasks, they still face challenges in generating high quality images.
In this paper, we propose Stacked Generative Adversarial Networks (StackGAN)
aiming at generating high-resolution photo-realistic images. First, we propose
a two-stage generative adversarial network architecture, StackGAN-v1, for
text-to-image synthesis. The Stage-I GAN sketches the primitive shape and
colors of the object based on given text description, yielding low-resolution
images. The Stage-II GAN takes Stage-I results and text descriptions as inputs,
and generates high-resolution images with photo-realistic details. Second, an
advanced multi-stage generative adversarial network architecture, StackGAN-v2,
is proposed for both conditional and unconditional generative tasks. Our
StackGAN-v2 consists of multiple generators and discriminators in a tree-like
structure; images at multiple scales corresponding to the same scene are
generated from different branches of the tree. StackGAN-v2 shows more stable
training behavior than StackGAN-v1 by jointly approximating multiple
distributions. Extensive experiments demonstrate that the proposed stacked
generative adversarial networks significantly outperform other state-of-the-art
methods in generating photo-realistic images.


  Video text spotting refers to localizing, recognizing, and tracking textual
elements such as captions, logos, license plates, signs, and other forms of
text within consecutive video frames. However, current datasets available for
this task rely on quadrilateral ground truth annotations, which may result in
including excessive background content and inaccurate text boundaries.
Furthermore, methods trained on these datasets often produce prediction results
in the form of quadrilateral boxes, which limits their ability to handle
complex scenarios such as dense or curved text. To address these issues, we
propose a scalable mask annotation pipeline called SAMText for video text
spotting. SAMText leverages the SAM model to generate mask annotations for
scene text images or video frames at scale. Using SAMText, we have created a
large-scale dataset, SAMText-9M, that contains over 2,400 video clips sourced
from existing datasets and over 9 million mask annotations. We have also
conducted a thorough statistical analysis of the generated masks and their
quality, identifying several research topics that could be further explored
based on this dataset. The code and dataset will be released at
\url{https://github.com/ViTAE-Transformer/SAMText}.


  This paper aims at predicting the next maxima values of the state variables
of the seasonal SEIR epidemic model and their in-between time intervals.
Lorenz's method of analogues is applied on the attractor formed by the maxima
of the corresponding state variables. It is found that both quantities are
characterized by a high degree of predictability in the case of the chaotic
regime of the parameter space.


  We introduce \emph{$p_n$-random $q_n$-proportion Bulgarian solitaire}
($0<p_n,q_n\le 1$), played on $n$ cards distributed in piles. In each pile, a
number of cards equal to the proportion $q_n$ of the pile size rounded upward
to the nearest integer are candidates to be picked. Each candidate card is
picked with probability $p_n$, independently of other candidate cards. This
generalizes Popov's random Bulgarian solitaire, in which there is a single
candidate card in each pile. Popov showed that a triangular limit shape is
obtained for a fixed $p$ as $n$ tends to infinity. Here we let both $p_n$ and
$q_n$ vary with $n$. We show that under the conditions $q_n^2 p_n n/{\log
n}\rightarrow \infty$ and $p_n q_n \rightarrow 0$ as $n\to\infty$, the
$p_n$-random $q_n$-proportion Bulgarian solitaire has an exponential limit
shape.


  Scalarization in vector optimization is essentially based on the minimization
of Gerstewitz functionals. In this paper, the minimizer sets of Gerstewitz
functionals are investigated. Conditions are given under which such a set is
nonempty and compact. Interdependencies between solutions of problems with
different parameters or with different feasible point sets are shown.
Consequences for the parameter control in scalarization methods are derived. It
is pointed out that the minimization of Gerstewitz functionals is equivalent to
an optimization problem which generalizes the scalarization by Pascoletti and
Serafini.


  We present a technique for automatic determination of flare-ribbon separation
and the energy released during the course of two-ribbon flares. We have used
chromospheric H$\alpha$ filtergrams and photospheric line-of-sight magnetograms
to analyse flare-ribbon separation and magnetic-field structures, respectively.
Flare-ribbons were first enhanced and then extracted by the technique of
"region growing", i.e., a morphological operator to help resolve the
flare-ribbons. Separation of flare-ribbons was then estimated from magnetic
polarity reversal line using an automatic technique implemented into
Interactive Data Language (IDL\tm) platform. Finally, the rate of flare-energy
release was calculated using photospheric magnetic-field data and the
corresponding separation of the chromospheric H$\alpha$ flare-ribbons. This
method could be applied to measure the motion of any feature of interest (e.g.,
intensity, magnetic, Doppler) from a given point of reference.


  The capacity of a graph is defined as the rate of exponential grow of
independent sets in the strong powers of the graph. In strong power, an edge
connects two sequences if at each position letters are equal or adjacent. We
consider a variation of the problem where edges in the power graphs are removed
among sequences which differ in more than a fraction $\delta$ of coordinates.
For odd cycles, we derive an upper bound on the corresponding rate which
combines Lov\'asz' bound on the capacity with Delsarte's linear programming
bounds on the minimum distance of codes in Hamming spaces. For the pentagon,
this shows that for $\delta \ge {1-{1\over\sqrt{5}}}$ the Lov\'asz rate is the
best possible, while we prove by a Gilbert-Varshamov-type bound that a higher
rate is achievable for $\delta < {2\over 5}$.
  Communication interpretation of this question is the problem of sending
quinary symbols subject to $\pm 1\mod 5$ disturbance. The maximal communication
rate subject to the zero undetected-error equals capacity of a pentagon. The
question addressed here is how much this rate can be increased if only a
fraction $\delta$ of symbols is allowed to be disturbed


  Observations of the HBL blazar 1RXS J023832.6-311658 were made in 2013, 2015
and 2016 with the High Energy Stereoscopic System (H.E.S.S.). An excess of very
high energy (VHE: > 100 GeV) gamma rays is clearly observed. The spectral
energy distribution including the VHE spectrum will be presented. This object
has a hard spectrum at TeV energies, and has a redshift z=0.23. These
characteristics could be suitable for extragalactic magnetic fields for which
the perspectives with H.E.S.S. will be discussed.


  Common research tasks ask students to identify a correct answer and justify
their answer choice. We propose expanding the array of research tasks to access
different knowledge that students might have. By asking students to discuss
answers they may not have chosen naturally, we can investigate students'
abilities to explain something that is already established or to disprove an
incorrect response. The results of these research tasks also provide us with
information about how students' responses vary across the different tasks. We
discuss three underused question types, their possible benefits and some
preliminary results from an electric circuits pretest utilizing these new
question types. We find that the answer students most commonly choose as
correct is the same choice most commonly eliminated as incorrect. Also,
students given the correct answer can provide valuable reasoning to explain it,
but they do not spontaneously identify it as the correct answer.


  Steering resources, central for quantum advantages in one-sided
device-independent quantum information tasks, can be enhanced via local
filters. Recently, reversible steering conversion under local filters has been
fully characterised. Here, we solve the problem in the irreversible scenario,
which leads to a complete understanding of stochastic steering distillation.
This result also provides an operational interpretation of the max-relative
entropy as the optimal filter success probability. We further show that all
steering measures can be used to quantify measurement incompatibility in
certain stochastic steering distillation scenarios. Finally, for a broad class
of steering robustness measures, we show that their maximally achievable values
in stochastic steering distillation are always upper bounded by different types
of incompatibility robustness measures. Hence, measurement incompatibility sets
the fundamental limitations for stochastic steering distillation.


  The interaction of surfaces in relative motion in wet environments is
dominated by lubrication forces, which play a pivotal role in the dynamics of
microscopic systems. Here, we develop motile vesicles that exploit lubrication
forces to roll on substrates. The activity of the vesicle comes from the
confined rotational flow generated by a driven rotating particle encapsulated
within the vesicle by droplet-microfluidics. Lubrication forces driving vesicle
rolling are controlled by membrane mechanics and its tribological properties.
This provides the design principles for motile vesicles that exploit frictional
forces to efficiently navigate through complex environments.


  Radio observations of ultracool dwarfs, objects comprising brown dwarfs and
the very lowest mass stars, have mainly focused on analyzing their light-curve
and spectral energy distributions providing valuable insights into their
magnetic fields. However, spatially-resolved studies of such magnetospheres
have been elusive so far. Radio interferometric observations of the brown dwarf
LSR J1835+3259 reveal an extended magnetosphere with a morphology compatible
with the presence of a radiation belt, similar to that of Jupiter and Earth,
consisting of energetic particles confined via magnetic mirroring. Our finding
suggests that radio emitting ultracool dwarfs may behave as scaled up versions
of Jupiter, validating the connection between dipole-ordered magnetic fields
and the presence of belt-like morphologies and aurorae beyond our Solar System.


  Vision problems ranging from image clustering to motion segmentation to
semi-supervised learning can naturally be framed as subspace segmentation
problems, in which one aims to recover multiple low-dimensional subspaces from
noisy and corrupted input data. Low-Rank Representation (LRR), a convex
formulation of the subspace segmentation problem, is provably and empirically
accurate on small problems but does not scale to the massive sizes of modern
vision datasets. Moreover, past work aimed at scaling up low-rank matrix
factorization is not applicable to LRR given its non-decomposable constraints.
In this work, we propose a novel divide-and-conquer algorithm for large-scale
subspace segmentation that can cope with LRR's non-decomposable constraints and
maintains LRR's strong recovery guarantees. This has immediate implications for
the scalability of subspace segmentation, which we demonstrate on a benchmark
face recognition dataset and in simulations. We then introduce novel
applications of LRR-based subspace segmentation to large-scale semi-supervised
learning for multimedia event detection, concept detection, and image tagging.
In each case, we obtain state-of-the-art results and order-of-magnitude speed
ups.


  In the present paper, motivated by point interaction, we propose a new and
explicit approach to inverse Sturm-Liouville eigenvalue problems under
Dirichlet boundary. More precisely, when a given Sturm-Liouville eigenvalue
problem with the unknown integrable potential interacts with $\delta$-function
potentials, we obtain a family of perturbation problems, called point
interaction models in quantum mechanics. Then, only depending on the first
eigenvalues of these perturbed problems, we define and study the first
eigenvalue function, by which the desired potential can be expressed explicitly
and uniquely. As by-products, using the analytic function theoretic tools, we
also generalize several fundamental theorems of classical Sturm-Liouville
problems to measure differential equations.


  The integrated optical circuit is a promising architecture for the
realization of complex quantum optical states and information networks. One
element that is required for many of these applications is a high-efficiency
photon detector capable of photon-number discrimination. We present an
integrated photonic system in the telecom band at 1550 nm based on UV-written
silica-on-silicon waveguides and modified transition-edge sensors capable of
number resolution and over 40% efficiency. Exploiting the mode transmission
failure of these devices, we multiplex three detectors in series to demonstrate
a combined 79% +/- 2% detection efficiency with a single pass, and 88% +/- 3%
at the operating wavelength of an on-chip terminal reflection grating.
Furthermore, our optical measurements clearly demonstrate no significant
unexplained loss in this system due to scattering or reflections. This
waveguide and detector design therefore allows the placement of
number-resolving single-photon detectors of predictable efficiency at arbitrary
locations within a photonic circuit - a capability that offers great potential
for many quantum optical applications.


  The inner parts of an accretion disk around a spinning black hole are forced
to align with the spin of the hole by the Bardeen-Petterson effect. Assuming
that any jet produced by such a system is aligned with the angular momentum of
either the hole or the inner disk, this can, in principle provide a mechanism
for producing steady jets in AGN whose direction is independent of the angular
momentum of the accreted material. However, the torque which aligns the inner
disk with the hole, also, by Newton's third law, tends to align the spin of the
hole with the outer accretion disk. In this letter, we calculate this alignment
timescale for a black hole powering an AGN, and show that it is relatively
short. This timescale is typically much less than the derived ages for jets in
radio loud AGN, and implies that the jet directions are not in general
controlled by the spin of the black hole. We speculate that the jet directions
are most likely controlled either by the angular momentum of the accreted
material or by the gravitational potential of the host galaxy.


  The reasoning is a slight extension of that employed in my article [1] and
its further development [2]. A formal solution to the title problem is
presented for a general periodic laminate and its application for the
construction of "effective constitutive relations" is developed. Its
implementation in detail will be reported separately.


  We propose a method for unsupervised reconstruction of a
temporally-consistent sequence of surfaces from a sequence of time-evolving
point clouds. It yields dense and semantically meaningful correspondences
between frames. We represent the reconstructed surfaces as atlases computed by
a neural network, which enables us to establish correspondences between frames.
The key to making these correspondences semantically meaningful is to guarantee
that the metric tensors computed at corresponding points are as similar as
possible. We have devised an optimization strategy that makes our method robust
to noise and global motions, without a priori correspondences or pre-alignment
steps. As a result, our approach outperforms state-of-the-art ones on several
challenging datasets. The code is available at
https://github.com/bednarikjan/temporally_coherent_surface_reconstruction.


  In this article, we extend several algebraic graph analysis methods to
bipartite networks. In various areas of science, engineering and commerce, many
types of information can be represented as networks, and thus the discipline of
network analysis plays an important role in these domains. A powerful and
widespread class of network analysis methods is based on algebraic graph
theory, i.e., representing graphs as square adjacency matrices. However, many
networks are of a very specific form that clashes with that representation:
They are bipartite. That is, they consist of two node types, with each edge
connecting a node of one type with a node of the other type. Examples of
bipartite networks (also called \emph{two-mode networks}) are persons and the
social groups they belong to, musical artists and the musical genres they play,
and text documents and the words they contain. In fact, any type of feature
that can be represented by a categorical variable can be interpreted as a
bipartite network. Although bipartite networks are widespread, most literature
in the area of network analysis focuses on unipartite networks, i.e., those
networks with only a single type of node. The purpose of this article is to
extend a selection of important algebraic network analysis methods to bipartite
networks, showing that many methods from algebraic graph theory can be applied
to bipartite networks with only minor modifications. We show methods for
clustering, visualization and link prediction. Additionally, we introduce new
algebraic methods for measuring the bipartivity in near-bipartite graphs.


  Recent research in adversarial machine learning has focused on visual
perception in Autonomous Driving (AD) and has shown that printed adversarial
patches can attack object detectors. However, it is important to note that AD
visual perception encompasses more than just object detection; it also includes
Multiple Object Tracking (MOT). MOT enhances the robustness by compensating for
object detection errors and requiring consistent object detection results
across multiple frames before influencing tracking results and driving
decisions. Thus, MOT makes attacks on object detection alone less effective. To
attack such robust AD visual perception, a digital hijacking attack has been
proposed to cause dangerous driving scenarios. However, this attack has limited
effectiveness.
  In this paper, we introduce a novel physical-world adversarial patch attack,
ControlLoc, designed to exploit hijacking vulnerabilities in entire AD visual
perception. ControlLoc utilizes a two-stage process: initially identifying the
optimal location for the adversarial patch, and subsequently generating the
patch that can modify the perceived location and shape of objects with the
optimal location. Extensive evaluations demonstrate the superior performance of
ControlLoc, achieving an impressive average attack success rate of around 98.1%
across various AD visual perceptions and datasets, which is four times greater
effectiveness than the existing hijacking attack. The effectiveness of
ControlLoc is further validated in physical-world conditions, including real
vehicle tests under different conditions such as outdoor light conditions with
an average attack success rate of 77.5%. AD system-level impact assessments are
also included, such as vehicle collision, using industry-grade AD systems and
production-grade AD simulators with an average vehicle collision rate and
unnecessary emergency stop rate of 81.3%.


  The globular cluster AL~3 is old and located in the inner bulge. Three
individual stars were observed with the Phoenix spectrograph at the Gemini
South telescope. The wavelength region contains prominent lines of CN, OH, and
CO, allowing the derivation of C, N, and O abundances of cool stars. We aim to
derive C, N, O abundances of three stars in the bulge globular cluster AL3, and
additionally in stars of NGC 6558 and HP1. The spectra of AL3 allows us to
derive the cluster's radial velocity. For AL3, we applied a new code to analyse
its colour-magnitude diagram. Synthetic spectra were computed and compared to
observed spectra for the three clusters. We present a detailed identification
of lines in the spectral region centred at 15555 A, covering the wavelength
range 15525-15590 A. C, N, and O abundances are tentatively derived for the
sample stars.


  Application of quantum principles to living cells requires a new
approximation of the full quantum mechanical description of intracellular
dynamics. We discuss what principal elements any such good approximation should
contain. As one such element, the notion of "Catalytic force" Cf is introduced.
Cf is the effect of the molecular target of catalysis on the catalytic
microenvironment that adjusts the microenvironment towards a state that
facilitates the catalytic act. This phenomenon is experimentally testable and
has an intriguing implication for biological organization and evolution, as it
amounts to "optimization without natural selection of replicators". Unlike the
statistical-mechanical approaches to self-organization, the Cf principle does
not encounter the problem of "tradeoff between stability and complexity" at the
level of individual cell. Physically, the Cf is considered as a harmonic-like
force of reaction, which keeps the state of the cell close to the ground state,
defined here as a state where enzymatic acts work most efficiently. Ground
state is subject to unitary evolution, and serves as a starting point in a
general strategy of quantum description of intracellular processes, termed here
"Euclidean approach". The next step of this strategy is transition from the
description of ground state to that one of growing state, and we suggest how it
can be accomplished using arguments from the fluctuation-dissipation theorem.
Finally, given that the most reliable and informative observable of an
individual cell is the sequence of its genome, we propose that the
non-classical correlations between individual molecular events at the single
cell level could be easiest to detect using high throughput DNA sequencing.


  An approach for generating the entangled photonic states |F1,F2>+|F2,F1> from
two arbitrary states |F1> and |F2> is proposed. The protocol is implemented by
the conditionally induced beam-splitter coupling which leads to the selective
swapping between two photonic modes. Such coupling occurs in a quantum system
prepared in the superposition of two ground states with only one of them being
involved in the swapping. All the entangled states in the category, such as
entangled pairs of coherent states or Fock states (N00N states), can be
efficiently produced in the same way by this method.


  A double sequence $\{x_{k,l}\}$ is quasi-Cauchy if given an $\epsilon > 0$
there exists an $N \in {\bf N}$ such that $$\max_{r,s= 1\mbox{ and/or} 0} \left
\{|x_{k,l} - x_{k+r,l+s}|< \epsilon\right \} .$$ We study continuity type
properties of factorable double functions defined on a double subset $A\times
A$ of ${\bf R}^{2}$ into $\textbf{R}$, and obtain interesting results related
to uniform continuity, sequential continuity, continuity, and a newly
introduced type of continuity of factorable double functions defined on a
double subset $A\times A$ of ${\bf R}^{2}$ into $\textbf{R}$.


  In with-profit life insurance, the prudent valuation of future insurance
liabilities leads to systematic surplus that mainly belongs to the
policyholders and is redistributed as bonus. For a fair and lawful
redistribution of surplus the insurer needs to decompose the total portfolio
surplus with respect to the contributions of individual policies and with
respect to different risk sources. For this task, actuaries have a number of
heuristic decomposition formulas, but an overarching decomposition principle is
still missing. This paper fills that gap by introducing a so-called ISU
decomposition principle that bases on infinitesimal sequential updates of the
insurer's valuation basis. It is shown that the existing heuristic
decomposition formulas can be replicated as ISU decompositions. Furthermore,
alternative decomposition principles and their relation to the ISU
decomposition principle are discussed. The generality of the ISU concept makes
it a useful tool also beyond classical surplus decompositions in life
insurance.


  Traditional cryptography is suffering a huge threat from the development of
quantum computing. While many currently used public-key cryptosystems would be
broken by Shor's algorithm, the effect of quantum computing on symmetric ones
is still unclear. The security of symmetric ciphers relies heavily on the
development of cryptanalytic tools. Thus, in order to accurately evaluate the
security of symmetric primitives in the post-quantum world, it is significant
to improve classical cryptanalytic methods using quantum algorithms. In this
paper, we focus on two variants of differential cryptanalysis: truncated
differential cryptanalysis and impossible differential cryptanalysis. Based on
the fact that Bernstein-Vazirani algorithm can be used to find the linear
structures of Boolean functions, we propose two quantum algorithms that can be
used to find high-probability truncated differentials and impossible
differentials of block ciphers, respectively. We rigorously prove the validity
of the algorithms and analyze their complexity. Our algorithms treat all rounds
of the reduced cipher as a whole and only concerns the input and output
differences at its both ends, instead of specific differential characteristics.
Therefore, to a certain extent, they alleviate the weakness of conventional
differential cryptanalysis, namely the difficulties in finding differential
characteristics as the number of rounds increases.


  Two-sample multiple testing problems of sparse spatial data are frequently
arising in a variety of scientific applications. In this article, we develop a
novel neighborhood-assisted and posterior-adjusted (NAPA) approach to
incorporate both the spatial smoothness and sparsity type side information to
improve the power of the test while controlling the false discovery of multiple
testing. We translate the side information into a set of weights to adjust the
$p$-values, where the spatial pattern is encoded by the ordering of the
locations, and the sparsity structure is encoded by a set of auxiliary
covariates. We establish the theoretical properties of the proposed test,
including the guaranteed power improvement over some state-of-the-art
alternative tests, and the asymptotic false discovery control. We demonstrate
the efficacy of the test through intensive simulations and two neuroimaging
applications.


  During grain growth in multiphase system, a convincing understanding on how
the evolution kinetics of an individual phase-grains relates to the growth rate
of entire microstructure is yet to be achieved. Therefore, in this work,
comfortably comprehensible statistical tools are employed to realise the
influence of the evolving phase-grains on the overall grain-growth kinetics of
multiphase systems, both duplex and triplex, with varying phase-fractions. A
dataset capable of rendering statistically significant, and accessible,
information is built by modelling, and analysing, grain growth in twenty
different polycrystalline systems, comprising of fourteen triplex and five
duplex microstructures with varying phase-fractions, and one homogeneous
single-phase system, in multiphase-field framework. Statistical analyses of
these wide-range of microstructures unravel that, during isotropic
grain-growth, the overall evolution kinetics of the duplex and triplex systems
are principally governed by the growth-rate of the major-phase grains,
irrespective of the phase-fractions. Furthermore, through multiple non-linear
regression, it is realised that with increase in the volume-fraction of the
major-phase grains, the overall isotropic grain-growth rate exhibited by duplex
and triplex microstructures increase in accordance with a definite exponential
relation. Additionally, the present investigation unravels that, while the
evolution of individual phase-grains during grain growth in triplex
microstructures exhibit a level of interdependency, the least interaction is
observed between the grains of minor-phases with relatively low
volume-fraction.


  While the Kes 73/1E 1841-045 system had been confirmed as an extended GeV
source, whether its morphology depends on the photon energy or not deserves our
further investigation. Adopting data collected by Fermi Large Area Telescope
(LAT) again, we look into the extensions of this source in three energy bands
individually: 0.3-1 GeV, 1-3 GeV and 3-200 GeV. We find that the 0.3-1 GeV
morphology is point-like and is quite different from those in the other two
bands, although we cannot robustly reject a unified morphology for the whole
LAT band.


  There are three fundamental physical processes that gives rise to the
morphology of a surface: deposition, surface diffusion and desorption. The
characteristics of the interfaces generated by the combination of deposition
and surface diffusion has been well studied during the past decades . In
particular for growth models, particles are added to the surface and then are
allowed to relax by different mechanisms. Many of this models have been shown
to lead to the formation of self-affine surfaces, characterized by scaling
exponents. From a theoretical point of view, the studies dedicated to the
self-affine interfaces generated by growth models can be considered to follow
two main branches. The studies about the properties of discrete models and the
studies about continuous models. The first ones where dedicated mainly to the
study of the properties of computational models in which the growth proceeds on
an initially empty lattice representing a d-dimensional substrate. At each time
step, the height of the lattice sites is increased by units (usually one unit)
representing the incoming particles. Different models only differs on the
relaxation mechanisms proposed to capture specific experimental
characteristics. Then, the models are classified according to the values of the
scaling exponents in several universality classes.


  We study low temperature electron transport in p-wave
superconductor-insulator-normal metal junctions. In diffusive metals the p-wave
component of the order parameter decays exponentially at distances larger than
the mean free path $l$. At the superconductor-normal metal boundary, due to
spin-orbit interaction, there is a triplet to singlet conversion of the
superconducting order parameter. The singlet component survives at distances
much larger than $l$ from the boundary. It is this component that controls the
low temperature resistance of the junctions. As a result, the resistance of the
system strongly depends on the angle between the insulating boundary and the
${\bf d}$-vector characterizing the spin structure of the triplet
superconducting order parameter. We also analyze the spatial dependence of the
electric potential in the presence of the current, and show that the electric
field is suppressed in the insulating boundary as well as in the normal metal
at distances of order of the coherence length away from the boundary. This is
very different from the case of the normal metal-insulator-normal metal
junctions, where the voltage drop takes place predominantly at the insulator.


  Neural networks are commonly used in safety-critical real-world applications.
Unfortunately, the predicted output is often highly sensitive to small, and
possibly imperceptible, changes to the input data. Proving that either no such
adversarial examples exist, or providing a concrete instance, is therefore
crucial to ensure safe applications. As enumerating and testing all potential
adversarial examples is computationally infeasible, verification techniques
have been developed to provide mathematically sound proofs of their absence
using overestimations of the network activations. We propose an improved
technique for computing tight upper and lower bounds of these node values,
based on increased flexibility gained by computing both bounds independently of
each other. Furthermore, we gain an additional improvement by re-implementing
part of the original state-of-the-art software "Neurify", leading to a faster
analysis. Combined, these adaptations reduce the necessary runtime by up to
94%, and allow a successful search for networks and inputs that were previously
too complex. We provide proofs for tight upper and lower bounds on max-pooling
layers in convolutional networks. To ensure widespread usability, we open
source our implementation "Debona", featuring both the implementation specific
enhancements as well as the refined boundary computation for faster and more
exact~results.


  We present a study of the trade-off between depth and resolution using a
large number of U-band imaging observations in the GOODS-North field
(Giavalisco et al. 2004) from the Large Binocular Camera (LBC) on the Large
Binocular Telescope (LBT). Having acquired over 30 hours of data (315 images
with 5-6 mins exposures), we generated multiple image mosaics, starting with
the best atmospheric seeing images (FWHM $\lesssim$0.8"), which constitute
$\sim$10% of the total data set. For subsequent mosaics, we added in data with
larger seeing values until the final, deepest mosaic included all images with
FWHM $\lesssim$1.8" ($\sim$94% of the total data set). From the mosaics, we
made object catalogs to compare the optimal-resolution, yet shallower image to
the lower-resolution but deeper image. We show that the number counts for both
images are $\sim$90% complete to $U_{AB}$ $\lesssim26$. Fainter than
$U_{AB}$$\sim$ 27, the object counts from the optimal-resolution image start to
drop-off dramatically (90% between $U_{AB}$ = 27 and 28 mag), while the deepest
image with better surface-brightness sensitivity ($\mu^{AB}_{U}$$\lesssim$ 32
mag arcsec$^{-2}$) show a more gradual drop (10% between $U_{AB}$ $\simeq$ 27
and 28 mag). For the brightest galaxies within the GOODS-N field, structure and
clumpy features within the galaxies are more prominent in the
optimal-resolution image compared to the deeper mosaics. Finally, we find - for
220 brighter galaxies with $U_{AB}$$\lesssim$ 24 mag - only marginal
differences in total flux between the optimal-resolution and lower-resolution
light-profiles to $\mu^{AB}_{U}$$\lesssim$ 32 mag arcsec$^{-2}$. In only 10% of
the cases are the total-flux differences larger than 0.5 mag. This helps
constrain how much flux can be missed from galaxy outskirts, which is important
for studies of the Extragalactic Background Light.


  A class of harmonic solutions to the steady Euler equations for
incompressible fluids is presented in two dimensions in circular, elliptic and
bipolar coordinates. Since the velocity field is solenoidal in this case, it
can be written as the curl of a vector potential, which will then satisfy
Poisson's equation with vorticity as a source term. In regions with zero
vorticity, Poisson's equation reduces to Laplace's equation, and this allows
for the construction of harmonic potentials inside and outside circles and
ellipses, depending on the coordinate system. The vector potential is normal to
the coordinate plane, and is proportional to the scalar harmonic functions on
the plane, thereby guaranteeing that the velocity field is also harmonic and is
located on the coordinate plane. The components of the velocity field normal to
either a circle or an ellipse are continuous, but the tangential components are
discontinuous, so that, in effect, a vortex sheet is introduced at these
boundaries. This discontinuity is a measure of the vorticity, normal to the
plane and distributed harmonically along the perimeter of the respective
circles or ellipses. An analytic expression for the streamlines is obtained
which makes visualisation of vortices of various geometries and harmonicities
possible. This approach also permits a reformulation of the notion of
multipolarity of vortices in the traditional sense of a multipolar expansion of
the Green's function for Poisson's equation. As an example of the applicability
of this formulation to known vortical structures, Rankine vortices of different
geometries are expressed in terms of harmonic functions.


  The concept of entanglement, in which coherent quantum states become
inextricably correlated, has evolved from one of the most startling and
controversial outcomes of quantum mechanics to the enabling principle of
emerging technologies such as quantum computation and quantum sensors. The use
of entangled particles in measurement permits the transcendence of the standard
quantum limit in sensitivity, which scales as N^1/2 for N particles, to the
Heisenberg limit, which scales as N. This approach has been applied to optical
interferometry using entangled photons and spin pairs for the measurement of
magnetic fields and improvements on atomic clocks. Here, we demonstrate
experimentally an 9.4-fold increase in sensitivity to an external magnetic
field of a 10-spin entangled state, compared with an isolated spin, using
nuclear spins in a highly symmetric molecule. This approach scales in a
favourable way compared to systems where qubit loss is prevalent, and paves the
way for enhanced precision in magnetic field sensing


  In principle, the baryon asymmetry of the Universe can be generated at the
electroweak phase transition but the experimental lower limit on the Higgs mass
seems to rule out a Standard Model scenario. However, it has been shown
recently that in the Minimal Supersymmetric Standard Model, the electroweak
phase transition can be a strong enough first order one for baryogenesis if the
mass of one top squark is close to or smaller than the top mass.


  We study quiver gauge theories on the round and squashed seven-spheres, and
orbifolds thereof. They arise by imposing $G$-equivariance on the homogeneous
space $G/H=\mathrm{SU}(4)/\mathrm{SU}(3)$ endowed with its Sasaki-Einstein
structure, and $G/H=\mathrm{Sp}(2)/\mathrm{Sp}(1)$ as a 3-Sasakian manifold. In
both cases we describe the equivariance conditions and the resulting quivers.
We further study the moduli spaces of instantons on the metric cones over these
spaces by using the known description for Hermitian Yang-Mills instantons on
Calabi-Yau cones. It is shown that the moduli space of instantons on the
hyper-Kahler cone can be described as the intersection of three Hermitian
Yang-Mills moduli spaces. We also study moduli spaces of translationally
invariant instantons on the metric cone $\mathbb{R}^8/\mathbb{Z}_k$ over
$S^7/\mathbb{Z}_k$.


  In the presence of crystalline symmetries, topological phases of matter
acquire a host of invariants leading to non-trivial quantized responses. Here
we study a particular invariant, the discrete shift $\mathscr{S}$, for the
square lattice Hofstadter model of free fermions. $\mathscr{S}$ is associated
with a $\mathbb{Z}_M$ classification in the presence of $M$-fold rotational
symmetry and charge conservation. $\mathscr{S}$ gives quantized contributions
to (i) the fractional charge bound to a lattice disclination, and (ii) the
angular momentum of the ground state with an additional, symmetrically inserted
magnetic flux. $\mathscr{S}$ forms its own `Hofstadter butterfly', which we
numerically compute, refining the usual phase diagram of the Hofstadter model.
We propose an empirical formula for $\mathscr{S}$ in terms of density and flux
per plaquette for the Hofstadter bands, and we derive a number of general
constraints. We show that bands with the same Chern number may have different
values of $\mathscr{S}$, although odd and even Chern number bands always have
half-integer and integer values of $\mathscr{S}$ respectively.


  A new approach to Poisson approximation is proposed. The basic idea is very
simple and based on properties of the Charlier polynomials and the Parseval
identity. Such an approach quickly leads to new effective bounds for several
Poisson approximation problems. A selected survey on diverse Poisson
approximation results is also given.


  We examine the ghost contribution to the one-loop integrands in open string
field theory using the Moyal representation of the star product. We primarily
focus on the open string tadpole integrand, which is an intrinsically off-shell
quantity. Due to the closed string tachyon, the full amplitude is badly
divergent from the closed string degeneration region $t\to0^+$ of the Schwinger
parameter. We obtain expansions for the finite factors from the squeezed state
matrix ${\bf R}(t)$ characterizing the ghost part of the tadpole in Siegel
gauge. The analytic structure of the integrands, as a function of the Schwinger
parameter, captures the correct linear order behaviour near both the closed and
open string degeneration limits. Using a geometric series for the matrix
inverse, we obtain an approximation for the even parity matrix elements. We
employ an expansion based on results from the oscillator basis to construct
Pad\'e approximants to further analyse hints of non-analyticity near this
limit. We also briefly discuss the evaluation of ghost integrands for the four
string diagrams contributing to the one-loop $2$-point function in open string
field theory.


  Many moons have been detected around planets in our Solar System, but none
has been detected unambiguously around any of the confirmed extrasolar planets.
We test the feasibility of a supervised convolutional neural network to
classify photometric transit light curves of planet-host stars and identify
exomoon transits, while avoiding false positives caused by stellar variability
or instrumental noise. Convolutional neural networks are known to have
contributed to improving the accuracy of classification tasks. The network
optimization is typically performed without studying the effect of noise on the
training process. Here we design and optimize a 1D convolutional neural network
to classify photometric transit light curves. We regularize the network by the
total variation loss in order to remove unwanted variations in the data
features. Using numerical experiments, we demonstrate the benefits of our
network, which produces results comparable to or better than the standard
network solutions. Most importantly, our network clearly outperforms a
classical method used in exoplanet science to identify moon-like signals. Thus
the proposed network is a promising approach for analyzing real transit light
curves in the future.


  Change detection, or anomaly detection, from street-view images acquired by
an autonomous robot at multiple different times, is a major problem in robotic
mapping and autonomous driving. Formulation as an image comparison task, which
operates on a given pair of query and reference images is common to many
existing approaches to this problem. Unfortunately, providing relevant
reference images is not straightforward. In this paper, we propose a novel
formulation for change detection, termed compressive change retrieval, which
can operate on a query image and similar reference images retrieved from the
web. Compared to previous formulations, there are two sources of difficulty.
First, the retrieved reference images may frequently contain non-relevant
reference images, because even state-of-the-art place-recognition techniques
suffer from retrieval noise. Second, image comparison needs to be conducted in
a compressed domain to minimize the storage cost of large collections of
street-view images. To address the above issues, we also present a practical
change detection algorithm that uses compressed bag-of-words (BoW) image
representation as a scalable solution. The results of experiments conducted on
a practical change detection task, "moving object detection (MOD)," using the
publicly available Malaga dataset validate the effectiveness of the proposed
approach.


  We study memoryless interference channels with gradual data arrival in the
absence of feedback. The information bits arrive at the transmitters according
to independent and asynchronous~(Tx-Tx asynchrony) Bernoulli processes with
average data rate $\lambda$. Each information source turns off after generating
a number of $n$ bits. In a scenario where the transmitters are unaware of the
amount of Tx-Tx asynchrony, we say $\epsilon$ is an \textit{achievable outage
level} in the asymptote of large~$n$ if (i) the average transmission rate at
each transmitter is $\lambda$ and (ii) the probability that the bit-error-rate
at each receiver does not eventually vanish is not larger than~$\epsilon$.
Denoting the infimum of all achievable outage levels by $\epsilon(\lambda)$,
the contribution of this paper is an upper bound (achievability result) on
$\epsilon(\lambda)$. The proposed method of communication is a simple block
transmission scheme where a transmitter sends a random point-to-point codeword
upon availability of enough bits in its buffer. Both receivers that treat
interference as noise or decode interference are addressed.


  Spin-spin correlations are calculated in frustrated hierarchical Ising models
that exhibit chaotic renormalization-group behavior. The spin-spin
correlations, as a function of distance, behave chaotically. The far
correlations, but not the near correlations, are sensitive to small changes in
temperature or frustration, with temperature changes having a larger effect. On
the other hand, the calculated free energy, internal energy, and entropy are
smooth functions of temperature. The recursion-matrix calculation of
thermodynamic densities in a chaotic band is demonstrated. The leading Lyapunov
exponents are calculated as a function of frustration.


  We study basic properties of the generalized ideal transforms $D_I(M, N)$ and
the set of associated primes of the modules $R^iD_I(M,N).$


  Tools for the spectroscopic determination of fundamental stellar parameters
should not only comprise customized solutions for one particular survey or
instrument, but, in order to enable cross-survey comparability, they should
also be capable of dealing with spectra from a variety of spectrographs,
resolutions, and wavelength coverages. To meet these ambitious specifications,
we developed ATHOS (A Tool for HOmogenizing Stellar parameters), a
fundamentally new analysis tool that adopts computationally inexpensive
analytical relations tying flux ratios (FRs) of designated wavelength regions
in optical spectra to the stellar parameters effective temperature
($T_\mathrm{eff}$), iron abundance ([Fe/H]), and surface gravity ($\log{g}$).
Our $T_\mathrm{eff}$ estimator is based on FRs from nine pairs of wavelength
ranges around the Balmer lines H$\beta$ and H$\alpha$, while for [Fe/H] and
$\log{g}$ we provide 31 and 11 FRs between 4800 {\AA} and 6500 \AA. The
analytical relations employing these FRs were trained on 124 real spectra of a
benchmark sample that covers a large parameter space of $T_{\rm eff}\approx$
4000 to 6500 K, [Fe/H] $\approx$ -4.5 to 0.3 dex, and $\log{g}\approx$ 1 to 5
dex, which also reflects ATHOS' range of applicability. The method's accuracies
are merely bounded by finite uncertainties in the training sample parameters,
while its internal precisions can be better by up to 70%. We tested ATHOS on
six independent large surveys, amongst which are the Gaia-ESO and the
SDSS/SEGUE surveys. The exceptionally low execution time (<30 ms/spectrum)
together with a comparison to the literature parameters showed that ATHOS can
successfully achieve its main objectives, i.e. fast stellar parameterization
with cross-survey validity, high accuracy, and high precision. These are key to
homogenize the output from future surveys, such as 4MOST or WEAVE. [abridged]


  We study various models of independent particles hopping between energy
`traps' with a density of energy barriers $\rho(E)$, on a $d$ dimensional
lattice or on a fully connected lattice. If $\rho(E)$ decays exponentially, a
true dynamical phase transition between a high temperature `liquid' phase and a
low temperature `aging' phase occurs. More generally, however, one expects that
for a large class of $\rho(E)$, `interrupted' aging effects appear at low
enough temperatures, with an ergodic time growing faster than exponentially.
The relaxation functions exhibit a characteristic shoulder, which can be fitted
as stretched exponentials. A simple way of introducing interactions between the
particles leads to a modified model with an effective diffusion constant in
energy space, which we discuss in detail.


  Aspect-based sentiment analysis(ABSA) is a textual analysis methodology that
defines the polarity of opinions on certain aspects related to specific
targets. The majority of research on ABSA is in English, with a small amount of
work available in Arabic. Most previous Arabic research has relied on deep
learning models that depend primarily on context-independent word embeddings
(e.g.word2vec), where each word has a fixed representation independent of its
context. This article explores the modeling capabilities of contextual
embeddings from pre-trained language models, such as BERT, and making use of
sentence pair input on Arabic aspect sentiment polarity classification task. In
particular, we develop a simple but effective BERT-based neural baseline to
handle this task. Our BERT architecture with a simple linear classification
layer surpassed the state-of-the-art works, according to the experimental
results on three different Arabic datasets. Achieving an accuracy of 89.51% on
the Arabic hotel reviews dataset, 73% on the Human annotated book reviews
dataset, and 85.73% on the Arabic news dataset.


  We present a fully-differential calculation of the NNLO QCD corrections to
the t-channel mechanism for producing single top quarks at the LHC. We work in
the structure function approximation, computing QCD corrections to the light-
and heavy-quark lines separately and neglecting the dynamical cross-talk
between the two. The neglected contribution, which appears at NNLO for the
first time, is color-suppressed and is expected to be sub-dominant. Within this
approximation, we find that, for the total cross section, NNLO QCD corrections
are in the few percent range and, therefore, are comparable to NLO QCD
corrections. We also find that the scale independence of the theoretical
prediction for single-top production improves significantly once NNLO QCD
corrections are included. Furthermore, we show how these results change if a
cut on the transverse momentum of the top quark is applied and derive the NNLO
QCD prediction for the ratio of single top and single anti-top production cross
sections at the 8 TeV LHC.


  In this paper I introduce a criterion for the Riemann hypothesis, and then
using that I prove $\sum_{k=1}^\infty \mu(k)/k^s$ converges for $\Re(s) >
\frac{1}{2}$. I use a step function $\nu(x) = 2\{x/2\} - \{x\}$ for the
Dirichlet eta function ($\{x\}$ is the fractional part of $x$), which was at
the core of my investigations, and hence derive the stated result subsequently.


  We prove the existence of a positive solution to a semipositone $N$-Laplacian
problem with a critical Trudinger-Moser nonlinearity. The proof is based on
obtaining uniform $C^{1,\alpha}$ a priori estimates via a compactness argument.
Our result is new even in the semilinear case $N = 2$, and our arguments can
easily be adapted to obtain positive solutions of more general semipositone
problems with critical Trudinger-Moser nonlinearities.


  Summary: NHANES, the National Health and Nutrition Examination Survey, is a
program of studies led by the Centers for Disease Control and Prevention (CDC)
designed to assess the health and nutritional status of adults and children in
the United States (U.S.). NHANES data is frequently used by biostatisticians
and clinical scientists to study health trends across the U.S., but every
analysis requires extensive data management and cleaning before use and this
repetitive data engineering collectively costs valuable research time and
decreases the reproducibility of analyses. Here, we introduce NHANES-GCP, a
Cloud Development Kit for Terraform (CDKTF) Infrastructure-as-Code (IaC) and
Data Build Tool (dbt) resources built on the Google Cloud Platform (GCP) that
automates the data engineering and management aspects of working with NHANES
data. With current GCP pricing, NHANES-GCP costs less than $2 to run and less
than $15/yr of ongoing costs for hosting the NHANES data, all while providing
researchers with clean data tables that can readily be integrated for
large-scale analyses. We provide examples of leveraging BigQuery ML to carry
out the process of selecting data, integrating data, training machine learning
and statistical models, and generating results all from a single SQL-like
query. NHANES-GCP is designed to enhance the reproducibility of analyses and
create a well-engineered NHANES data resource for statistics, machine learning,
and fine-tuning Large Language Models (LLMs).
  Availability and implementation" NHANES-GCP is available at
https://github.com/In-Vivo-Group/NHANES-GCP


  Single image reflection removal problem aims to divide a
reflection-contaminated image into a transmission image and a reflection image.
It is a canonical blind source separation problem and is highly ill-posed. In
this paper, we present a novel deep architecture called deep unfolded single
image reflection removal network (DURRNet) which makes an attempt to combine
the best features from model-based and learning-based paradigms and therefore
leads to a more interpretable deep architecture. Specifically, we first propose
a model-based optimization with transform-based exclusion prior and then design
an iterative algorithm with simple closed-form solutions for solving each
sub-problems. With the deep unrolling technique, we build the DURRNet with
ProxNets to model natural image priors and ProxInvNets which are constructed
with invertible networks to impose the exclusion prior. Comprehensive
experimental results on commonly used datasets demonstrate that the proposed
DURRNet achieves state-of-the-art results both visually and quantitatively.


  As a novel privacy-preserving paradigm aimed at reducing client computational
costs and achieving data utility, split learning has garnered extensive
attention and proliferated widespread applications across various fields,
including smart health and smart transportation, among others. While recent
studies have primarily concentrated on addressing privacy leakage concerns in
split learning, such as inference attacks and data reconstruction, the
exploration of security issues (e.g., backdoor attacks) within the framework of
split learning has been comparatively limited. Nonetheless, the security
vulnerability within the context of split learning is highly posing a threat
and can give rise to grave security implications, such as the illegal
impersonation in the face recognition model. Therefore, in this paper, we
propose a stealthy backdoor attack strategy (namely SBAT) tailored to the
without-label-sharing split learning architecture, which unveils the inherent
security vulnerability of split learning. We posit the existence of a potential
attacker on the server side aiming to introduce a backdoor into the training
model, while exploring two scenarios: one with known client network
architecture and the other with unknown architecture. Diverging from
traditional backdoor attack methods that manipulate the training data and
labels, we constructively conduct the backdoor attack by injecting the trigger
embedding into the server network. Specifically, our SBAT achieves a higher
level of attack stealthiness by refraining from modifying any intermediate
parameters (e.g., gradients) during training and instead executing all
malicious operations post-training.


  Entanglement distribution between trapped-atom quantum memories, viz. single
atoms in optical cavities, is addressed. In most scenarios, the rate of
entanglement distribution depends on the efficiency with which the state of
traveling single photons can be transferred to trapped atoms. This loading
efficiency is analytically studied for two-level, $V$-level, $\Lambda$-level,
and double-$\Lambda$-level atomic configurations by means of a system-reservoir
approach. An off-resonant non-adiabatic approach to loading $\Lambda$-level
trapped-atom memories is proposed, and the ensuing trade-offs between the
atom-light coupling rate and input photon bandwidth for achieving a high
loading probability are identified. The non-adiabatic approach allows a broad
class of optical sources to be used, and in some cases it provides a higher
system throughput than what can be achieved by adiabatic loading mechanisms.
The analysis is extended to the case of two double-$\Lambda$ trapped-atom
memories illuminated by a polarization-entangled biphoton.


  We present Mean Box Pooling, a novel visual representation that pools over
CNN representations of a large number, highly overlapping object proposals. We
show that such representation together with nCCA, a successful multimodal
embedding technique, achieves state-of-the-art performance on the Visual
Madlibs task. Moreover, inspired by the nCCA's objective function, we extend
classical CNN+LSTM approach to train the network by directly maximizing the
similarity between the internal representation of the deep learning
architecture and candidate answers. Again, such approach achieves a significant
improvement over the prior work that also uses CNN+LSTM approach on Visual
Madlibs.


  Modern statistical estimation is often performed in a distributed setting
where each sample belongs to a single user who shares their data with a central
server. Users are typically concerned with preserving the privacy of their
samples, and also with minimizing the amount of data they must transmit to the
server. We give improved private and communication-efficient algorithms for
estimating several popular measures of the entropy of a distribution. All of
our algorithms have constant communication cost and satisfy local differential
privacy. For a joint distribution over many variables whose conditional
independence is given by a tree, we describe algorithms for estimating Shannon
entropy that require a number of samples that is linear in the number of
variables, compared to the quadratic sample complexity of prior work. We also
describe an algorithm for estimating Gini entropy whose sample complexity has
no dependence on the support size of the distribution and can be implemented
using a single round of concurrent communication between the users and the
server. In contrast, the previously best-known algorithm has high communication
cost and requires the server to facilitate interaction between the users.
Finally, we describe an algorithm for estimating collision entropy that
generalizes the best known algorithm to the private and communication-efficient
setting.


  We propose a Markov chain Monte Carlo (MCMC) scheme to perform state
inference in non-linear non-Gaussian state-space models. Current
state-of-the-art methods to address this problem rely on particle MCMC
techniques and its variants, such as the iterated conditional Sequential Monte
Carlo (cSMC) scheme, which uses a Sequential Monte Carlo (SMC) type proposal
within MCMC. A deficiency of standard SMC proposals is that they only use
observations up to time $t$ to propose states at time $t$ when an entire
observation sequence is available. More sophisticated SMC based on lookahead
techniques could be used but they can be difficult to put in practice. We
propose here replica cSMC where we build SMC proposals for one replica using
information from the entire observation sequence by conditioning on the states
of the other replicas. This approach is easily parallelizable and we
demonstrate its excellent empirical performance when compared to the standard
iterated cSMC scheme at fixed computational complexity.


  A series of spectra of the RV Tauri star R Scuti taken as it rose from a deep
minimum to a bright maximum during 2013 October and November reveals major
changes taking place in the photosphere and outer atmosphere of the star. This
may be the first such series of spectra and demonstrates the capability of
amateur spectroscopy for studying these complex stars.


  The Weisfeiler-Lehman (WL) test is a classical procedure for graph
isomorphism testing. The WL test has also been widely used both for designing
graph kernels and for analyzing graph neural networks. In this paper, we
propose the Weisfeiler-Lehman (WL) distance, a notion of distance between
labeled measure Markov chains (LMMCs), of which labeled graphs are special
cases. The WL distance is polynomial time computable and is also compatible
with the WL test in the sense that the former is positive if and only if the WL
test can distinguish the two involved graphs. The WL distance captures and
compares subtle structures of the underlying LMMCs and, as a consequence of
this, it is more discriminating than the distance between graphs used for
defining the state-of-the-art Wasserstein Weisfeiler-Lehman graph kernel.
Inspired by the structure of the WL distance we identify a neural network
architecture on LMMCs which turns out to be universal w.r.t. continuous
functions defined on the space of all LMMCs (which includes all graphs) endowed
with the WL distance. Finally, the WL distance turns out to be stable w.r.t. a
natural variant of the Gromov-Wasserstein (GW) distance for comparing metric
Markov chains that we identify. Hence, the WL distance can also be construed as
a polynomial time lower bound for the GW distance which is in general NP-hard
to compute.


  Signal inpainting is the task of restoring degraded or missing samples in a
signal. In this paper we address signal inpainting when Fourier magnitudes are
observed. We propose a mathematical formulation of the problem that highlights
its connection with phase retrieval, and we introduce two methods for solving
it. First, we derive an alternating minimization scheme, which shares
similarities with the Gerchberg-Saxton algorithm, a classical phase retrieval
method. Second, we propose a convex relaxation of the problem, which is
inspired by recent approaches that reformulate phase retrieval into a
semidefinite program. We assess the potential of these methods for the task of
inpainting gaps in speech signals. Our methods exhibit both a high probability
of recovering the original signals and robustness to magnitude noise.


  We study families of fermionic field states in non-inertial frames which show
no entanglement survival in the infinite acceleration limit. We generalise some
recent results where some particular examples of such states where found. We
analyse the abundance and characteristics of the states showing this behaviour
and discuss its relation with the statistics of the field. We also consider the
phenomenon beyond the single mode approximation.


  We propose a diagnostic for finite temperature topological order using
`topological entanglement negativity', the long-range component of a
mixed-state entanglement measure. As a demonstration, we study the toric code
model in $d$ spatial dimension for $d$=2,3,4, and find that when topological
order survives thermal fluctuations, it possesses a non-zero topological
entanglement negativity, whose value is equal to the topological entanglement
entropy at zero temperature. Furthermore, we show that the Gibbs state of 2D
and 3D toric code at any non-zero temperature, and that of 4D toric code above
a certain critical temperature, can be expressed as a convex combination of
short-range entangled pure states, consistent with the absence of topological
order.


  We consider the physical implications of various choices of the
three-momentum basis in the kappa-deformed Poincare algebra. In particular, we
find that the energy dependence of the velocity of a kappa-particle leads to
unexpected features in kappa-deformed kinematics. We also discuss the notion of
kappa-deformed dynamics, and as a tool example we investigate the motion of a
kappa-deformed particle under the action of a constant force.


  Phase-amplitude coupling (PAC) describes the phenomenon where the power of a
high-frequency oscillation evolves with the phase of a low-frequency one. We
propose a model that explains the emergence of PAC in two commonly-accepted
architectures in the brain, namely, a high-frequency neural oscillation driven
by an external low-frequency input and two interacting local oscillations with
distinct, locally-generated frequencies. We further propose an interconnection
structure for brain regions and demonstrate that low-frequency phase synchrony
can integrate high-frequency activities regulated by local PAC and control the
direction of information flow across distant regions.


  Inspired by the works in linkage theory of ideals, we define the concept of
linkage of ideals over a module. Several known theorems in linkage theory are
improved or recovered by new approaches. Specially, we make some extensions and
generalizations of the basic result of Peskine and Szpiro \cite[prop 1.3]{PS},
namely if $R$ is a Gorenstain local ring, $\mathfrak{a} \neq 0$ (an ideal of
$R$) and $\mathfrak{b} := 0:_R \mathfrak{a}$ then $\frac{R}{\mathfrak{a}}$ is
Cohen-Macaulay if and only if $\frac{R}{\mathfrak{a}}$ is unmixed and
$\frac{R}{\mathfrak{b}}$ is Cohen-Macaulay.


  This paper is dedicated to the study of weight complexes (defined on
triangulated categories endowed with weight structures) and their applications.
We introduce pure (co)homological functors that "ignore all non-zero weights";
these have a nice description in terms of weight complexes. For the weight
structure $w^G$ generated by the orbit category in the $G$-equivariant stable
homotopy category $SH(G)$ the corresponding pure cohomological functors into
abelian groups are the Bredon cohomology associated to Mackey functors ones;
pure functors related to motivic weight structures are also quite useful.
  Our results also give some (more) new weight structures. Moreover, we prove
that certain exact functors are conservative and "detect weights".


  We investigate the stability against small deformations of strings dangling
into AdS_5-Schwarzschild from a moving heavy quark-anti-quark pair. We
speculate that emission of massive string states may be an important part of
the evolution of certain unstable configurations.


  This paper presents a nonlinear model predictive control strategy for
stochastic systems with general (state and input dependent) disturbances
subject to chance constraints. Our approach uses an online computed stochastic
tube to ensure stability, constraint satisfaction and recursive feasibility in
the presence of stochastic uncertainties. The shape of the tube and the
constraint backoff is based on an offline computed incremental Lyapunov
function.


  We analyze the geometry of the Ext-quiver of a coalgebra $C$ in order to
study the behavior of simple and injective $C$-comodules under the action of
the functors associated to a localizing subcategory of the category of
$C$-comodules.


  This work concerns minimizing the achievable distortion of a Gaussian source
over a two-hop block fading channel under mean square-error criterion. It is
assumed that there is not a direct link between transmission ends and the
communication is carried out through the use of a relay, employing the Decode
and Forward (DF) strategy. Moreover, for both of transmitter-to-relay and
relay-to-destination links the channel statistics are merely available at the
corresponding receivers, while the channel gains are not available at the
affiliated transmitters. It is assumed that a Gaussian source is hierarchically
encoded through the use of successive refinement source coding approach and
sent to the relay using a multi-layer channel code. Similarly, the relay
transmits the retrieved information to the destination through the use of a
multi-layer code. Accordingly, in a Rayleigh block fading environment, the
optimal power allocation across code layers is derived. Numerical results
demonstrate that the achievable distortion of the proposed multi-layer approach
outperforms that of single-layer code. Moreover, the resulting distortion of DF
strategy is better than Amplify and Forward~(AF) strategy for channel mismatch
factors greater than one, while the resulting distortion has a marginal
degradation for channel mismatch factors lower than one.


  Forthcoming large-scale spectroscopic surveys will soon provide data on
thousands of galaxy clusters. It is important that the systematics of the
various mass estimation techniques are well understood and calibrated. We
compare three different dynamical mass estimators using the C-EAGLE galaxy
clusters, a set of high resolution simulations with resolved galaxies a median
total mass, $M_{200c} = 10^{14.7} \, \mathrm{M_\odot}$. We quantify the bias
and scatter of the Jeans, virial, and caustic mass estimators using all
galaxies with a stellar mass $M_*> 10^9 \, \mathrm{M_\odot}$, both in the ideal
3D case and in the more realistic projected case. On average we find our mass
estimates are unbiased, though relative to the true mass within $r_{200c}$ the
scatter is large with a range of $0.09$ - $0.15$ dex. We see a slight increase
in the scatter when projecting the clusters. Selecting galaxies using the same
criteria, we find no significant difference in the mass bias or scatter when
comparing results from hydrodynamical and dark matter only simulations.
However, selecting galaxies by stellar mass reduces the bias compared to
selecting by total mass. Comparing X-ray derived hydrostatic and dynamical
masses, the former are ${\sim} 30$ per cent lower. We find a slight dependence
between substructure, measured using two different metrics, and mass bias. In
conclusion, we find that dynamical mass estimators, when averaged together, are
unbiased with a scatter of $0.11 \pm 0.02$ dex when including interloper
galaxies and with no prior knowledge of $r_{200c}$.


  Machine learning techniques used in computer-aided medical image analysis
usually suffer from the domain shift problem caused by different distributions
between source/reference data and target data. As a promising solution, domain
adaptation has attracted considerable attention in recent years. The aim of
this paper is to survey the recent advances of domain adaptation methods in
medical image analysis. We first present the motivation of introducing domain
adaptation techniques to tackle domain heterogeneity issues for medical image
analysis. Then we provide a review of recent domain adaptation models in
various medical image analysis tasks. We categorize the existing methods into
shallow and deep models, and each of them is further divided into supervised,
semi-supervised and unsupervised methods. We also provide a brief summary of
the benchmark medical image datasets that support current domain adaptation
research. This survey will enable researchers to gain a better understanding of
the current status, challenges.


  We present a calculation of the branching ratio of orthopositronium decay
into an invisible mode, which is done in the context of Mirror World models,
where ordinary positronium can disappear from our world due to oscillation into
its mirror twin. In this revision we clarify some formulas and approximations
used previously, correct them at some places, add new effects relevant for a
feasible experiment and finally perform a combined analysis. We include into
consideration various effects due to external magnetic and electric fields,
collisions with cavity walls and scattering off gas atoms in the cavity.
Oscillations of the Rydberg positroniums are also considered. To perform a
numerical estimates in a realistic case we wrote computer code, which can be
adopted in any experimental setup. Its work is illustrated with an example of a
planned positronium experiment within the AEgIS project.


  Our proposed hyper kurtosis based histogram equalized DIC images enhances the
contrast by preserving the brightness. The evolution and development of
precancerous activity among tissues are studied through S-transform (ST). The
significant variations of amplitude spectra can be observed due to increased
medium roughness from normal tissue were observed in time-frequency domain. The
randomness and inhomogeneity of the tissue structures among human normal and
different grades of DIC tissues is recognized by ST based timefrequency
analysis. This study offers a simpler and better way to recognize the
substantial changes among different stages of DIC tissues, which are reflected
by spatial information containing within the inhomogeneity structures of
different types of tissue.


  The ensemble of antagonistic matrices is introduced and studied. In
antagonistic matrices the entries $\mathcal A_{i,j}$ and $\mathcal A_{j,i}$ are
real and have opposite signs, or are both zero, and the diagonal is zero. This
generalization of antisymmetric matrices is suggested by the linearized
dynamics of competitive species in ecology.


  This paper considers the problem of interference control through the use of
second-order statistics in massive MIMO multi-cell networks. We consider both
the cases of co-located massive arrays and large-scale distributed antenna
settings. We are interested in characterizing the low-rankness of users'
channel covariance matrices, as such a property can be exploited towards
improved channel estimation (so-called pilot decontamination) as well as
interference rejection via spatial filtering. In previous work, it was shown
that massive MIMO channel covariance matrices exhibit a useful finite rank
property that can be modeled via the angular spread of multipath at a MIMO
uniform linear array. This paper extends this result to more general settings
including certain non-uniform arrays, and more surprisingly, to two dimensional
distributed large scale arrays. In particular our model exhibits the dependence
of the signal subspace's richness on the scattering radius around the user
terminal, through a closed form expression. The applications of the
low-rankness covariance property to channel estimation's denoising and
low-complexity interference filtering are highlighted.


  Automated driving is now possible in diverse road and traffic conditions.
However, there are still situations that automated vehicles cannot handle
safely and efficiently. In this case, a Transition of Control (ToC) is
necessary so that the driver takes control of the driving. Executing a ToC
requires the driver to get full situation awareness of the driving environment.
If the driver fails to get back the control in a limited time, a Minimum Risk
Maneuver (MRM) is executed to bring the vehicle into a safe state (e.g.,
decelerating to full stop). The execution of ToCs requires some time and can
cause traffic disruption and safety risks that increase if several vehicles
execute ToCs/MRMs at similar times and in the same area. This study proposes to
use novel C-ITS traffic management measures where the infrastructure exploits
V2X communications to assist Connected and Automated Vehicles (CAVs) in the
execution of ToCs. The infrastructure can suggest a spatial distribution of
ToCs, and inform vehicles of the locations where they could execute a safe stop
in case of MRM. This paper reports the first field operational tests that
validate the feasibility and quantify the benefits of the proposed
infrastructure-assisted ToC and MRM management. The paper also presents the CAV
and roadside infrastructure prototypes implemented and used in the trials. The
conducted field trials demonstrate that infrastructure-assisted traffic
management solutions can reduce safety risks and traffic disruptions.


  In this paper we study canonical $\gamma$-structures, a class of RNA
pseudoknot structures that plays a key role in the context of polynomial time
folding of RNA pseudoknot structures. A $\gamma$-structure is composed by
specific building blocks, that have topological genus less than or equal to
$\gamma$, where composition means concatenation and nesting of such blocks. Our
main result is the derivation of the generating function of $\gamma$-structures
via symbolic enumeration using so called irreducible shadows. We furthermore
recursively compute the generating polynomials of irreducible shadows of genus
$\le \gamma$. $\gamma$-structures are constructed via $\gamma$-matchings. For
$1\le \gamma \le 10$, we compute Puiseux-expansions at the unique, dominant
singularities, allowing us to derive simple asymptotic formulas for the number
of $\gamma$-structures.


  Multiple Sclerosis (MS) is a chronic disease developed in human brain and
spinal cord, which can cause permanent damage or deterioration of the nerves.
The severity of MS disease is monitored by the Expanded Disability Status Scale
(EDSS), composed of several functional sub-scores. Early and accurate
classification of MS disease severity is critical for slowing down or
preventing disease progression via applying early therapeutic intervention
strategies. Recent advances in deep learning and the wide use of Electronic
Health Records (EHR) creates opportunities to apply data-driven and predictive
modeling tools for this goal. Previous studies focusing on using single-modal
machine learning and deep learning algorithms were limited in terms of
prediction accuracy due to the data insufficiency or model simplicity. In this
paper, we proposed an idea of using patients' multimodal longitudinal and
longitudinal EHR data to predict multiple sclerosis disease severity at the
hospital visit. This work has two important contributions. First, we describe a
pilot effort to leverage structured EHR data, neuroimaging data and clinical
notes to build a multi-modal deep learning framework to predict patient's MS
disease severity. The proposed pipeline demonstrates up to 25% increase in
terms of the area under the Area Under the Receiver Operating Characteristic
curve (AUROC) compared to models using single-modal data. Second, the study
also provides insights regarding the amount useful signal embedded in each data
modality with respect to MS disease prediction, which may improve data
collection processes.


  Machine learning techniques are increasingly used to predict material
behavior in scientific applications and offer a significant advantage over
conventional numerical methods. In this work, an Artificial Neural Network
(ANN) model is used in a finite element formulation to define the flow law of a
metallic material as a function of plastic strain, plastic strain rate and
temperature. First, we present the general structure of the neural network, its
operation and focus on the ability of the network to deduce, without prior
learning, the derivatives of the flow law with respect to the model inputs. In
order to validate the robustness and accuracy of the proposed model, we compare
and analyze the performance of several network architectures with respect to
the analytical formulation of a Johnson-Cook behavior law for a 42CrMo4 steel.
In a second part, after having selected an Artificial Neural Network
architecture with $2$ hidden layers, we present the implementation of this
model in the Abaqus Explicit computational code in the form of a VUHARD
subroutine. The predictive capability of the proposed model is then
demonstrated during the numerical simulation of two test cases: the necking of
a circular bar and a Taylor impact test. The results obtained show a very high
capability of the ANN to replace the analytical formulation of a Johnson-Cook
behavior law in a finite element code, while remaining competitive in terms of
numerical simulation time compared to a classical approach.


  The local descriptors have gained wide range of attention due to their
enhanced discriminative abilities. It has been proved that the consideration of
multi-scale local neighborhood improves the performance of the descriptor,
though at the cost of increased dimension. This paper proposes a novel method
to construct a local descriptor using multi-scale neighborhood by finding the
local directional order among the intensity values at different scales in a
particular direction. Local directional order is the multi-radius relationship
factor in a particular direction. The proposed local directional order pattern
(LDOP) for a particular pixel is computed by finding the relationship between
the center pixel and local directional order indexes. It is required to
transform the center value into the range of neighboring orders. Finally, the
histogram of LDOP is computed over whole image to construct the descriptor. In
contrast to the state-of-the-art descriptors, the dimension of the proposed
descriptor does not depend upon the number of neighbors involved to compute the
order; it only depends upon the number of directions. The introduced descriptor
is evaluated over the image retrieval framework and compared with the
state-of-the-art descriptors over challenging face databases such as PaSC, LFW,
PubFig, FERET, AR, AT&T, and ExtendedYale. The experimental results confirm the
superiority and robustness of the LDOP descriptor.


  We study a quantum harmonic oscillator linearly coupled through the position
operator $\hat{q}$ to a first bath and through the momentum operator $\hat{p}$
to a second bath yielding an Ohmic-Drude dissipation. We analyse the
oscillator's fluctuations as a function of the ratio between the strength of
the two couplings, focusing in particular on the situation in which the two
dissipative interactions are comparable. Analytic formulas are derived in the
relevant regimes corresponding to the low temperature limit and when the Drude
high frequency cutoff is much larger than all other frequencies. At low
temperature, each bath operates to suppress the oscillator's ground state
quantum fluctuations ${\langle \Delta \hat{q}^2 \rangle}_0$ or ${\langle \Delta
\hat{p}^2 \rangle}_0$ appearing in the corresponding interaction. When one of
the two dissipative interactions dominates over the other, the fluctuations for
the coupling operator are squeezed. When the two interactions are comparable,
the two baths enter in competition as the two conjugate operators do not
commute yielding quantum frustration. In this regime, remarkably, the
fluctuations of both two quadratures can be enhanced by increasing the
dissipative coupling.


  In this work, we consider $d$-{\sc Hyperedge Estimation} and $d$-{\sc
Hyperedge Sample} problem in a hypergraph
$\mathcal{H}(U(\mathcal{H}),\mathcal{F}(\mathcal{H}))$ in the query complexity
framework, where $U(\mathcal{H})$ denotes the set of vertices and
$\mathcal{F}(\mathcal{H})$ denotes the set of hyperedges. The oracle access to
the hypergraph is called {\sc Colorful Independence Oracle} ({\sc CID}), which
takes $d$ (non-empty) pairwise disjoint subsets of vertices $A_1,\ldots,A_d
\subseteq U(\mathcal{H})$ as input, and answers whether there exists a
hyperedge in $\mathcal{H}$ having (exactly) one vertex in each $A_i, i \in
\{1,2,\ldots,d\}$. The problem of $d$-{\sc Hyperedge Estimation} and $d$-{\sc
Hyperedge Sample} with {\sc CID} oracle access is important in its own right as
a combinatorial problem. Also, Dell {\it{et al.}}~[SODA '20] established that
{\em decision} vs {\em counting} complexities of a number of combinatorial
optimization problems can be abstracted out as $d$-{\sc Hyperedge Estimation}
problems with a {\sc CID} oracle access.
  The main technical contribution of the paper is an algorithm that estimates
$m= \lvert {\mathcal{F}(\mathcal{H})}\rvert$ with $\widehat{m}$ such that { $$
  \frac{1}{C_{d}\log^{d-1} n} \;\leq\; \frac{\widehat{m}}{m} \;\leq\; C_{d}
\log ^{d-1} n . $$ by using at most $C_{d}\log ^{d+2} n$ many {\sc CID}
queries, where $n$ denotes the number of vertices in the hypergraph
$\mathcal{H}$ and $C_{d}$ is a constant that depends only on $d$}. Our result
coupled with the framework of Dell {\it{et al.}}~[SODA '21] implies improved
bounds for a number of fundamental problems.


  Nonlinear fluctuating hydrodynamics (NFHD) is a powerful framework for
understanding transport, but checking its validity with molecular dynamics is
still challenging. Here, we overcome this challenge by developing an effective
scheme for detecting hydrodynamic modes that takes into account the role of
pressure fluctuations. We show that the predictions given by NFHD on the
relaxation processes of hydrodynamic modes are valid only when the pressure of
the system is zero and the pressure fluctuations are weak. For nonvanishing
pressure, two other regimes arise as the hydrodynamic modes can respond to
small and large pressure fluctuations and relax in two additionally distinct
manners. In contrast to the previous finding of two classes, our results
suggest that there are at least three universality classes of transport in
anharmonic chains.


  The dynamics of a vesicle suspension in a shear flow between parallel plates
has been investigated under microgravity conditions, where vesicles are only
submitted to hydrodynamic effects such as lift forces due to the presence of
walls and drag forces. The temporal evolution of the spatial distribution of
the vesicles has been recorded thanks to digital holographic microscopy, during
parabolic flights and under normal gravity conditions. The collected data
demonstrates that vesicles are pushed away from the walls with a lift velocity
proportional to $\dot{\gamma} R^3/z^2$ where $\dot{\gamma}$ is the shear rate,
$R$ the vesicle radius and $z$ its distance from the wall. This scaling as well
as the dependence of the lift velocity upon vesicle aspect ratio are consistent
with theoretical predictions by Olla [J. Phys. II France {\bf 7}, 1533--1540
(1997)].


  The aim of this work is to develop methods for studying the determinants of
marriage incidence using marriage histories collected under two different types
of retrospective cross-sectional study designs. These designs are: sampling of
ever married women before the cross-section, a prevalent cohort, and sampling
of women irrespective of marital status, a general cross-sectional cohort.
While retrospective histories from a prevalent cohort do not identify incidence
rates without parametric modelling assumptions, the rates can be identified
when combined with data from a general cohort. Moreover, education, a strong
endogenous covariate, and marriage processes are correlated. Hence, they need
to be modelled jointly in order to estimate the marriage incidence. For this
purpose, we specify a multi-state model and propose a likelihood-based
estimation method. We outline the assumptions under which a likelihood
expression involving only marriage incidence parameters can be derived. This is
of particular interest when either retrospective education histories are not
available or related parameters are not of interest. Our simulation results
confirm the gain in efficiency by combining data from the two designs, while
demonstrating how the parameter estimates are affected by violations of the
assumptions used in deriving the simplified likelihood expressions. Two Indian
National Family Health Surveys are used as motivation for the methodological
development and to demonstrate the application of the methods.


  We consider inverse boundary value problems for polyharmonic operators and in
particular, the problem of recovering the coefficients of terms up to order
one. The main interest of our result is that it further relaxes the regularity
required to establish uniqueness. The proof relies on an averaging technique
introduced by Haberman and Tataru for the study of an inverse boundary value
problem for a second order operator.


  In many complex systems, elements interact via time-varying network
topologies. Recent research shows that temporal correlations in the
chronological ordering of interactions crucially influence network properties
and dynamical processes. How these correlations affect our ability to control
systems with time-varying interactions remains unclear. In this work, we use
higher-order network models to extend the framework of structural
controllability to temporal networks, where the chronological ordering of
interactions gives rise to time-respecting paths with non-Markovian
characteristics. We study six empirical data sets and show that non-Markovian
characteristics of real systems can both increase or decrease the minimum time
needed to control the whole system. With both empirical data and synthetic
models, we further show that spectral properties of generalisations of graph
Laplacians to higher-order networks can be used to analytically capture the
effect of temporal correlations on controllability. Our work highlights that
(i) correlations in the chronological ordering of interactions are an important
source of complexity that significantly influences the controllability of
temporal networks, and (ii) higher-order network models are a powerful tool to
understand the temporal-topological characteristics of empirical systems.


  In this paper, we study the spin and energy dynamic correlations of the one
dimensional spin 1/2 Heisenberg model, using mostly exact diagonalization
numerical techniques. In particular, observing that the uniform spin and energy
currents decay to finite values at long times, we argue for the absence of spin
and energy diffusion in the easy plane anisotropic Heisenberg model.


  We investigate the electromagnetic (EM) counterpart of gravitational waves
(GWs) emitted by a supermassive black hole binary (SMBHB) through the viscous
dissipation of the GW energy in an accretion disk and stars surrounding the
SMBHB. We account for the suppression of the heating rate if the forcing period
is shorter than the turnover time of the largest turbulent eddies. We find that
the viscous heating luminosity in 0.1 solar mass stars can be significantly
higher than their intrinsic luminosity, but still too low to be detected for
extragalactic sources. The relative brightening is small for accretion disks.


  Let Phi be the KZ associator and Psi_N be its analogue for N-th roots of 1.
We prove a hexagon relation for Psi_4. Similarly to the Broadhurst (for Psi_2)
and Okuda (for Psi_4) duality relations, it relies on the "supplementary"
(i.e., non-dihedral) symmetries of C^* - mu_4(C) (i.e., the octahedron group
S_4). We also derive relations between Phi and Psi_2, which are analogues of
equations, found by Nakamura and Schneps, satisfied by the image of the Galois
group of Q in the Grothendieck-Teichm"uller group.


  Numerical simulations of soft-core frictionless disks in two dimensions are
carried out to study behavior of a simple liquid as a function of thermal
temperature $T$, packing fraction $\phi$, and uniform applied shear strain rate
$\dot\gamma$. Inferring the hard-core limit from our soft-core results, we find
that it depends on the two parameters $\phi$ and $T/\dot\gamma$.
$T/\dot\gamma\to 0$ defines the athermal limit in which a shear driven jamming
transition occurs at a well defined $\phi_J$. $T/\dot\gamma\to\infty$ defines
the thermalized limit where an equilibrium glass transition may take place at a
$\phi_G$. This conclusion argues that athermal jamming and equilibrium glassy
behavior are not controlled by the same critical point. Preliminary results
suggest $\phi_G <\phi_J$.


  This Letter reports results from a haloscope search for dark matter axions
with masses between 2.66 and 2.81 $\mu$eV. The search excludes the range of
axion-photon couplings predicted by plausible models of the invisible axion.
This unprecedented sensitivity is achieved by operating a large-volume
haloscope at sub-kelvin temperatures, thereby reducing thermal noise as well as
the excess noise from the ultra-low-noise SQUID amplifier used for the signal
power readout. Ongoing searches will provide nearly definitive tests of the
invisible axion model over a wide range of axion masses.


  We study the relation between entropy and Action Complexity (AC) for various
examples of cosmological singularities in General Relativity. The complexity is
defined with respect to the causal domain of dependence of the singular set,
and the entropy is evaluated on the boundary of the same causal domain. We find
that, contrary to the situation for black hole singularities, the complexity
growth near the singularity is controlled by the dynamics of the entropy $S$,
with a characteristic linear relation. This formula is found to apply to
singularities with vanishing entropy as well as those with diverging entropy.
In obtaining these results it is crucial to take into account the AC expansion
counterterm, whose associated length scale must be chosen sufficiently large in
order to ensure the expected monotonicity properties of the complexity.