  Due to the rapid advancements in recent years, medical image analysis is
largely dominated by deep learning (DL). However, building powerful and robust
DL models requires training with large multi-party datasets. While multiple
stakeholders have provided publicly available datasets, the ways in which these
data are labeled vary widely. For Instance, an institution might provide a
dataset of chest radiographs containing labels denoting the presence of
pneumonia, while another institution might have a focus on determining the
presence of metastases in the lung. Training a single AI model utilizing all
these data is not feasible with conventional federated learning (FL). This
prompts us to propose an extension to the widespread FL process, namely
flexible federated learning (FFL) for collaborative training on such data.
Using 695,000 chest radiographs from five institutions from across the globe -
each with differing labels - we demonstrate that having heterogeneously labeled
datasets, FFL-based training leads to significant performance increase compared
to conventional FL training, where only the uniformly annotated images are
utilized. We believe that our proposed algorithm could accelerate the process
of bringing collaborative training methods from research and simulation phase
to the real-world applications in healthcare.


  The answer to this question is `yes it can!' as we will see in this
manuscript. More, precisely after a discussion of M. F. Pusey, J. Barrett and
T. Rudolph (PBR) result (arXiv:1111.3328) we will show that contrarily to the
PBR claim the epistemic approach is in general not disproved by their `no-go'
theorem.


  Adiabatic following has been an widely-employed technique for achieving
near-complete population transfer in a `two-level' quantum mechanical system.
The theoretical basis, however, could be generalized to a broad class of
systems exhibiting SU(2) symmetry. In the present work, we present an analogy
of population transfer dynamics of two level atomic system with that of light
propagation in a classical `one-dimensional' photonic crystal, commonly known
as distributed-Bragg-reflector (DBR). This formalism facilitates in adapting
the idea of adiabatic following, more precisely the rapid adiabatic passage
(RAP) which is usually encountered in a broad class of quantum-mechanical
systems. We present a chirped DBR configuration in which the adiabatic
constraints are satisfied by virtue of optimally chirping the DBR. The
reflection spectrum of the configuration exhibit broadening of photonic bandgap
(PBG) in addition to a varying degree of suppression of sharp reflection peaks
in the transmission band. The intermodal coupling between counter-propagating
modes as well as their phase-mismatch, for the DBR configuration, exhibits a
longitudinal variation which is usually observed in `Allen-Eberly' scheme of
adiabatic population transfer in two-level atomic systems.


  Ge with a quasi-direct band gap can be realized by strain engineering,
alloying with Sn, or ultrahigh n-type doping. In this work, we use all three
approaches together to fabricate direct-band-gap Ge-Sn alloys. The heavily
doped n-type Ge-Sn is realized with CMOS-compatible nonequilibrium material
processing. P is used to form highly doped n-type Ge-Sn layers and to modify
the lattice parameter of P-doped Ge-Sn alloys. The strain engineering in
heavily-P-doped Ge-Sn films is confirmed by x-ray diffraction and micro Raman
spectroscopy. The change of the band gap in P-doped Ge-Sn alloy as a function
of P concentration is theoretically predicted by density functional theory and
experimentally verified by near-infrared spectroscopic ellipsometry. According
to the shift of the absorption edge, it is shown that for an electron
concentration greater than 1x10^20 cm-3 the band-gap renormalization is
partially compensated by the Burstein-Moss effect. These results indicate that
Ge-based materials have high potential for use in near-infrared optoelectronic
devices, fully compatible with CMOS technology.


  The polar magneto-optical Kerr effect (MOKE) for low-symmetric ferromagnetic
crystals is investigated theoretically based on first-principle calculations of
optical conductivities and a transfer matrix approach for the electrodynamics
part of the problem. Exact average magneto-optical properties of polycrystals
are described, taking into account realistic models for the distribution of
domain orientations. It is shown that for low-symmetric ferromagnetic single
crystals the MOKE is determined by an interplay of crystallographic
birefringence and magnetic effects. Calculations for single and bi-crystal of
hcp 11-20 Co and for a polycrystal of CrO_2 are performed, with results being
in good agreement with experimental data.


  We consider an asymmetric control of platoons of identical vehicles with
nearest-neighbor interaction. Recent results show that if the vehicle uses
different asymmetries for position and velocity errors, the platoon has a short
transient and low overshoots. In this paper we investigate the properties of
vehicles with friction. To achieve consensus, an integral part is added to the
controller, making the vehicle a third-order system. We show that the
parameters can be chosen so that the platoon behaves as a wave equation with
different wave velocities. Simulations suggest that our system has a better
performance than other nearest-neighbor scenarios. Moreover, an
optimization-based procedure is used to find the controller properties.


  For the one-dimensional XXX model under the periodic boundary conditions, we
discuss two types of eigenvectors, regular eigenvectors which have
finite-valued rapidities satisfying the Bethe ansatz equations, and non-regular
eigenvectors which are descendants of some regular eigenvectors under the
action of the SU(2) spin-lowering operator. It was pointed out by many authors
that the non-regular eigenvectors should correspond to the Bethe ansatz
wavefunctions which have multiple infinite rapidities. However, it has not been
explicitly shown whether such a delicate limiting procedure should be possible.
In this paper, we discuss it explicitly in the level of wavefunctions: we prove
that any non-regular eigenvector of the XXX model is derived from the Bethe
ansatz wavefunctions through some limit of infinite rapidities. We formulate
the regularization also in terms of the algebraic Bethe ansatz method. As an
application of infinite rapidity, we discuss the period of the spectral flow
under the twisted periodic boundary conditions.


  The paper is devoted to differential geometric invariants determining a
Frenet curve in up to a direct similarity These invariants can be presented by
the Euclidean curvatures in terms of an arc lengths of the spherical
indicatrices. Then, these invariants expressed by focal curvatures of the
curve. And then, we give the relationship between curvatures of evolute curve
and shape curvatures. Morever, these invariants is given the geometric
interpretation.


  Unlabelled Necklaces are an equivalence class of cyclic words under both the
rotation (cyclic shift) and the relabelling operations. The relabelling of a
word is a bijective mapping from the alphabet to itself. The main result of the
paper is the first polynomial-time algorithm for ranking unlabelled necklaces
of a binary alphabet. The time-complexity of the algorithm is $O(n^6 \log^2
n)$, where $n$ is the length of the considered necklaces. The key part of the
algorithm is to compute the rank of any word with respect to the set of
unlabelled necklaces by finding three other ranks: the rank over all necklaces,
the rank over symmetric unlabelled necklaces, and the rank over necklaces with
an enclosing labelling. The last two concepts are introduced in this paper.


  It is expected that all astrophysical black holes in equilibrium are well
described by the Kerr solution. Moreover, any black hole far away from
equilibrium, such as one initially formed in a compact binary merger or by the
collapse of a massive star, will eventually reach a final equilibrium Kerr
state. At sufficiently late times in this process of reaching equilibrium, we
expect that the black hole is modeled as a perturbation around the final state.
The emitted gravitational waves will then be damped sinusoids with frequencies
and damping times given by the quasinormal mode spectrum of the final Kerr
black hole. An observational test of this scenario, often referred to as black
hole spectroscopy, is one of the major goals of gravitational wave astronomy.
It was recently suggested that the quasinormal mode description including the
higher overtones might hold even right after the remnant black hole is first
formed. At these times, the black hole is expected to be highly dynamical and
nonlinear effects are likely to be important. In this paper we investigate this
remarkable scenario in terms of the horizon dynamics. Working with high
accuracy simulations of a simple configuration, namely the head-on collision of
two nonspinning black holes with unequal masses, we study the dynamics of the
final common horizon in terms of its shear and its multipole moments. We show
that they are indeed well described by a superposition of ringdown modes as
long as a sufficiently large number of higher overtones are included. This
description holds even for the highly dynamical final black hole shortly after
its formation. We discuss the implications and caveats of this result for black
hole spectroscopy and for our understanding of the approach to equilibrium.


  This paper presents a solution to the landing gear system case study using
Event-B and Rodin. We study the whole system (both the digital part and the
controlled part). We use feature augmentation to build an abstract model of the
whole system and structural refinement to detail more specifically the digital
part. The required safety properties are formalised and proved. We propose a
specific approach to deal with a family of reachability properties. The
experimentations conducted during the study are supported by the Rodin tools.
We show that the presented solution is systematic and it can be applied to
similar case studies.


  We consider discrete one-dimensional Schr\"odinger operators on the whole
line and establish a criterion for continuity of spectral measures with respect
to $\log$-Hausdorff measures. We apply this result to operators with Sturmian
potentials and thereby prove logarithmic quantum dynamical lower bounds for all
coupling constants and almost all rotation numbers, uniformly in the phase.


  Even though the brain operates in pure darkness, within the skull, it can
infer the most likely causes of its sensory input. An approach to modelling
this inference is to assume that the brain has a generative model of the world,
which it can invert to infer the hidden causes behind its sensory stimuli, that
is, perception. This assumption raises key questions: how to formulate the
problem of designing brain-inspired generative models, how to invert them for
the tasks of inference and learning, what is the appropriate loss function to
be optimised, and, most importantly, what are the different choices of mean
field approximation (MFA) and their implications for variational inference
(VI).


  We provide a simple solution to the $\mu$ and strong CP problems in the
context of gauge mediated supersymmetry breaking. The generic appearance of R
symmetry in dynamical supersymmetry breaking is used to implement Peccei-Quinn
symmetry. Acceptable $\mu$ and $B$ terms as well as the large symmetry breaking
scale are induced in the presence of nonrenormalizable interactions.
Cosmological consequences of this scheme turn out to yield constraints on the
PQ symmetry breaking scale and the number of the messenger/heavy quarks.
Complexity in introducing non-R Peccei-Quinn symmetry is contrasted with the
case of R symmetry.


  In this paper we examine the pair production through the Breit-Wheeler
process $\gamma~\gamma \to e^+ e^-$ in a thermal background. We compute the
thermal contribution to the Breit-Wheeler differential cross section within the
thermofield dynamics formalism. We evaluate in details the cross section for
this process, which possess a surprisingly simple expression valid for any
temperature $\beta$, from which we discuss some physically relevant aspects. We
also consider the high temperature regime of the cross section in order to have
a better understanding about its thermal behavior.


  When deploying deep neural networks on robots or other physical systems, the
learned model should reliably quantify predictive uncertainty. A reliable
uncertainty allows downstream modules to reason about the safety of its
actions. In this work, we address metrics for evaluating such an uncertainty.
Specifically, we focus on regression tasks, and investigate Area Under
Sparsification Error (AUSE), Calibration Error, Spearman's Rank Correlation,
and Negative Log-Likelihood (NLL). Using synthetic regression datasets, we look
into how those metrics behave under four typical types of uncertainty, their
stability regarding the size of the test set, and reveal their strengths and
weaknesses. Our results indicate that Calibration Error is the most stable and
interpretable metric, but AUSE and NLL also have their respective use cases. We
discourage the usage of Spearman's Rank Correlation for evaluating
uncertainties and recommend replacing it with AUSE.


  A new knowledge-based and machine learning hybrid modeling approach, called
conditional Gaussian neural stochastic differential equation (CGNSDE), is
developed to facilitate modeling complex dynamical systems and implementing
analytic formulae of the associated data assimilation (DA). In contrast to the
standard neural network predictive models, the CGNSDE is designed to
effectively tackle both forward prediction tasks and inverse state estimation
problems. The CGNSDE starts by exploiting a systematic causal inference via
information theory to build a simple knowledge-based nonlinear model that
nevertheless captures as much explainable physics as possible. Then, neural
networks are supplemented to the knowledge-based model in a specific way, which
not only characterizes the remaining features that are challenging to model
with simple forms but also advances the use of analytic formulae to efficiently
compute the nonlinear DA solution. These analytic formulae are used as an
additional computationally affordable loss to train the neural networks that
directly improve the DA accuracy. This DA loss function promotes the CGNSDE to
capture the interactions between state variables and thus advances its modeling
skills. With the DA loss, the CGNSDE is more capable of estimating extreme
events and quantifying the associated uncertainty. Furthermore, crucial
physical properties in many complex systems, such as the translate-invariant
local dependence of state variables, can significantly simplify the neural
network structures and facilitate the CGNSDE to be applied to high-dimensional
systems. Numerical experiments based on chaotic systems with intermittency and
strong non-Gaussian features indicate that the CGNSDE outperforms
knowledge-based regression models, and the DA loss further enhances the
modeling skills of the CGNSDE.


  The bosonic representation of the half string ghost in the full string basis
is examined in full. The proof that the comma 3- vertex (matter and ghost) in
the bosonic representation satisfy the Ward-like identities is established thus
completing the proof of the Bose Fermi equivalence in the half string theory.


  The optical responses of semiconducting transition metal dichalcogenides are
dominated by excitons. Being able to strongly interact with light and other
materials excitations, excitons in semiconductors are prototypes for
investigating many-particle and strong-field physics, including
exciton-exciton, exciton-photon, and exciton-phonon interactions. Strong
exciton-photon interactions, in particular, can lead to the emergence of
exciton-polariton hybrid quasiparticles with peculiar characteristics, and a
tendency toward macroscopic and spontaneous coherence. Normally, far-field and
near-field optical spectroscopy techniques are used to investigate
exciton-photon interactions. Here, we demonstrate that the radiation generated
by moving electrons in transition metal dichalcogenides, namely Cherenkov
radiation, can strongly interact with excitons. We investigate the coherence
properties and spectral signatures of exciton-photon interactions in TMDC bulk
crystals, using cathodoluminescence spectroscopy. Our findings lay the ground
for cathodoluminescence spectroscopy and in particular electron-beam techniques
as probes of exciton-polariton spontaneous coherence in semiconductors, beyond
the well-known plasmonic investigations.


  A quasi-equilibrium (QE) computational scheme was recently developed in
general relativity to calculate the complete gravitational wavetrain emitted
during the inspiral phase of compact binaries. The QE method exploits the fact
that the the gravitational radiation inspiral timescale is much longer than the
orbital period everywhere outside the ISCO. Here we demonstrate the validity
and advantages of the QE scheme by solving a model problem in relativistic
scalar gravitation theory. By adopting scalar gravitation, we are able to
numerically track without approximation the damping of a simple, quasi-periodic
radiating system (an oscillating spherical matter shell) to final equilibrium,
and then use the exact numerical results to calibrate the QE approximation
method. In particular, we calculate the emitted gravitational wavetrain three
different ways: by integrating the exact coupled dynamical field and matter
equations, by using the scalar-wave monopole approximation formula
(corresponding to the quadrupole formula in general relativity), and by
adopting the QE scheme. We find that the monopole formula works well for weak
field cases, but fails when the fields become even moderately strong. By
contrast, the QE scheme remains quite reliable for moderately strong fields,
and begins to breakdown only for ultra-strong fields. The QE scheme thus
provides a promising technique to construct the complete wavetrain from binary
inspiral outside the ISCO, where the gravitational fields are strong, but where
the computational resources required to follow the system for more than a few
orbits by direct numerical integration of the exact equations are prohibitive.


  Survival analysis is playing a major role in manufacturing sector by
analyzing occurrence of any unwanted event based on the input data. Predictive
maintenance, which is a part of survival analysis, helps to find any device
failure based on the current incoming data from different sensor or any
equipment. Deep learning techniques were used to automate the predictive
maintenance problem to some extent, but they are not very helpful in predicting
the device failure for the input data which the algorithm had not learned.
Since neural network predicts the output based on previous learned input
features, it cannot perform well when there is more variation in input
features. Performance of the model is degraded with the occurrence of changes
in input data and finally the algorithm fails in predicting the device failure.
This problem can be solved by our proposed method where the algorithm can
predict the device failure more precisely than the existing deep learning
algorithms. The proposed solution involves implementation of Deep Reinforcement
Learning algorithm called Double Deep Q Network (DDQN) for classifying the
device failure based on the input features. The algorithm is capable of
learning different variation of the input feature and is robust in predicting
whether the device will fail or not based on the input data. The proposed DDQN
model is trained with limited or lesser amount of input data. The trained model
predicted larger amount of test data efficiently and performed well compared to
other deep learning and machine learning models.


  Coronae exist in most astrophysical objects: stars, accretion disks, and
individual galaxies and clusters of galaxies. Coronae in these varied systems
have some common properties: 1) hydrostatic equilibrium in background gravity
is a good assumption; 2) they are optically thin, i.e., photons escape as soon
as they are born; 3) they have cooling times shorter than their ages, and thus
require heating for sustenance. Generally the coronal heating mechanisms are
quite complex but the structure of the corona is tightly constrained by the
interplay of cooling, {\em global} heating, and background gravity. We briefly
summarize the results from our studies of the intracluster medium (the cluster
corona) and draw inferences which should apply to most astrophysical coronae.


  As an application of the general theory on extrinsic geometry, we investigate
extrinsic geometry in frag varieties and systems of linear PDE's for a class of
special interest associated with the adjoint representation of
$\mathfrak{sl}(3)$. We carry out a complete local classification of the
homogeneous structures in this class. As a result, we find 7 kinds of new
systems of linear PDE's of second order on a 3-dimensional contact manifold
each of which has a solution space of dimension 8. Among them there are
included a system of PDE's called contact Cayley's surface and one which has
$\mathfrak{sl}(2)$ symmetry.


  Graphyne is a planar two-dimensional carbon allotrope formed by atoms in sp,
sp2, and sp3 hybridized states. Topologically graphyne nanotubes (GNTs) can be
considered as cylindrically rolled up graphyne sheets, similarly as carbon
nanotubes (CNTs) can be considered rolled up graphene sheets. Due to the
presence of single, double, and triple bonds, GNTs exhibit porous sidewalls
that can be exploited in many diverse applications. In this work, we
investigated the mechanical behavior of GNTs under torsional strains through
reactive molecular dynamics simulations. Our results show that GNTs are more
flexible than CNTs and exhibit 'superplasticity', with fracture angles that are
up to 35 times higher than the ones reported to CNTs. This GNT 'superplastic'
behavior can be explained in terms of irreversible reconstruction processes
(mainly associated with the triple bonds) that occur during torsional strains.


  One way to define the Matching Cut problem is: Given a graph $G$, is there an
edge-cut $M$ of $G$ such that $M$ is an independent set in the line graph of
$G$? We propose the more general Conflict-Free Cut problem: Together with the
graph $G$, we are given a so-called conflict graph $\hat{G}$ on the edges of
$G$, and we ask for an edge-cutset $M$ of $G$ that is independent in $\hat{G}$.
Since conflict-free settings are popular generalizations of classical
optimization problems and Conflict-Free Cut was not considered in the
literature so far, we start the study of the problem. We show that the problem
is $\textsf{NP}$-complete even when the maximum degree of $G$ is 5 and
$\hat{G}$ is 1-regular. The same reduction implies an exponential lower bound
on the solvability based on the Exponential Time Hypothesis. We also give
parameterized complexity results: We show that the problem is fixed-parameter
tractable with the vertex cover number of $G$ as a parameter, and we show
$\textsf{W[1]}$-hardness even when $G$ has a feedback vertex set of size one,
and the clique cover number of $\hat{G}$ is the parameter. Since the clique
cover number of $\hat{G}$ is an upper bound on the independence number of
$\hat{G}$ and thus the solution size, this implies $\textsf{W[1]}$-hardness
when parameterized by the cut size. We list polynomial-time solvable cases and
interesting open problems. At last, we draw a connection to a symmetric variant
of SAT.


  We study a class of dynamically constructed point processes in which at every
step a new point (particle) is added to the current configuration with a
distribution depending on the local structure around a uniformly chosen
particle. This class covers, in particular, generalised Polya urn scheme,
Dubbins--Freedman random measures and cooperative sequential adsorption models
studied previously. Specifically, we address models where the distribution of a
newly added particle is determined by the distance to the closest particle from
the chosen one. We address boundedness of the processes and convergence
properties of the corresponding sample measure. We show that in general the
limiting measure is random when exists and that this is the case for a wide
class of almost surely bounded processes.


  In this paper, we study a newly developed shearlet system on bounded domains
which yields frames for $H^s(\Omega)$ for some $s\in \mathbb{N}$, $\Omega
\subset \mathbb{R}^2$. We will derive approximation rates with respect to
$H^s(\Omega)$ norms for functions whose derivatives admit smooth jumps along
curves and demonstrate superior rates to those provided by pure wavelet
systems. These improved approximation rates demonstrate the potential of the
novel shearlet system for the discretization of partial differential equations.
Therefore, we implement an adaptive shearlet-based algorithm for the solution
of an elliptic PDE and analyze its computational complexity and convergence
properties.


  Massive black holes in galactic nuclei vary their mass M and spin vector J
due to accretion. In this study we relax, for the first time, the assumption
that accretion can be either chaotic, i.e. when the accretion episodes are
randomly and isotropically oriented, or coherent, i.e. when they occur all in a
preferred plane. Instead, we consider different degrees of anisotropy in the
fueling, never confining to accretion events on a fixed direction. We follow
the black hole growth evolving contemporarily mass, spin modulus a and spin
direction. We discover the occurrence of two regimes. An early phase (M <~ 10
million solar masses) in which rapid alignment of the black hole spin direction
to the disk angular momentum in each single episode leads to erratic changes in
the black hole spin orientation and at the same time to large spins (a ~ 0.8).
A second phase starts when the black hole mass increases above >~ 10 million
solar masses and the accretion disks carry less mass and angular momentum
relatively to the hole. In the absence of a preferential direction the black
holes tend to spin-down in this phase. However, when a modest degree of
anisotropy in the fueling process (still far from being coherent) is present,
the black hole spin can increase up to a ~ 1 for very massive black holes (M >~
100 million solar masses), and its direction is stable over the many accretion
cycles. We discuss the implications that our results have in the realm of the
observations of black hole spin and jet orientations.


  The primary goal of this paper is to describe the diffraction production
using the model that takes into account the Pomeron interaction, and satisfies
both $t$ and $s$ channel unitarity. We hope that these features will allow us
to describe the diffraction production in a more convenient way than in CGC
motivated models, that do not satisfy these unitarity constraints.
Unfortunately, we show that both approaches are only able to describe half of
the cross section for the single diffraction production, leaving the second
half to be estimate of the large mass production in the Pomeron approach.The
impact parameter dependance of the scattering amplitudes show that soft
interactions at high energies measured at the LHC, have a much richer structure
than presumed. We discuss the $t$-dependence of the elastic cross section in
wide range of $|t|=0 \div 1 \,GeV^2 $. We show that in the kinemati region of
the minimum, we cannot use approximate formulae to calculate the real part of
the amplitude. The exact calculation in our model, shows that the real part is
rather small, and it is necessary to include the Odderon contribution in order
to describe the experimental data.


  We consider the quantum mechanics of a spinless charged particle on a
2-dimensional sphere. When threaded with a magnetic monopole field, this is the
well-known Haldane sphere that furnishes a translationally-invariant,
incompressible quantum fluid state of a gas of electrons confined to the
sphere. This letter presents the results of a novel variant of the Haldane
solution where the monopole field is replaced by that of a dipole. We argue
that this system is relevant to the physics on the surface of compact
astrophysical objects like neutron stars.


  We propose and implement a novel, robust, and non-parametric test of
statistical isotropy of the expansion of the universe, and apply it to around
one thousand type Ia supernovae from the Pantheon sample. We calculate the
angular clustering of supernova magnitude residuals and compare it to the noise
expected under the isotropic assumption. We also test for systematic effects
and demonstrate that their effects are negligible or are already accounted for
in our procedure. We express our constraints as an upper limit on the rms
spatial variation in the Hubble parameter at late times. For the sky smoothed
with a Gaussian with fwhm=60 deg, less than 1% rms spatial variation in the
Hubble parameter is allowed at 99.7% confidence.


  The ability of a researcher to re-identify (re-ID) an individual animal upon
re-encounter is fundamental for addressing a broad range of questions in the
study of ecosystem function, community and population dynamics, and behavioural
ecology. In this review, we describe a brief history of camera traps for re-ID,
present a collection of computer vision feature engineering methodologies
previously used for animal re-ID, provide an introduction to the underlying
mechanisms of deep learning relevant to animal re-ID, highlight the success of
deep learning methods for human re-ID, describe the few ecological studies
currently utilizing deep learning for camera trap analyses, and our predictions
for near future methodologies based on the rapid development of deep learning
methods. By utilizing novel deep learning methods for object detection and
similarity comparisons, ecologists can extract animals from an image/video data
and train deep learning classifiers to re-ID animal individuals beyond the
capabilities of a human observer. This methodology will allow ecologists with
camera/video trap data to re-identify individuals that exit and re-enter the
camera frame. Our expectation is that this is just the beginning of a major
trend that could stand to revolutionize the analysis of camera trap data and,
ultimately, our approach to animal ecology.


  Neural Machine Translation (NMT) models from English to SPARQL are a
promising development for SPARQL query generation. However, current
architectures are unable to integrate the knowledge base (KB) schema and handle
questions on knowledge resources, classes, and properties unseen during
training, rendering them unusable outside the scope of topics covered in the
training set. Inspired by the performance gains in natural language processing
tasks, we propose to integrate a copy mechanism for neural SPARQL query
generation as a way to tackle this issue. We illustrate our proposal by adding
a copy layer and a dynamic knowledge base vocabulary to two Seq2Seq
architectures (CNNs and Transformers). This layer makes the models copy KB
elements directly from the questions, instead of generating them. We evaluate
our approach on state-of-the-art datasets, including datasets referencing
unknown KB elements and measure the accuracy of the copy-augmented
architectures. Our results show a considerable increase in performance on all
datasets compared to non-copy architectures.


  Poisson algebraic structures on current manifolds (of maps from a finite
dimensional Riemannian manifold into a 2-dimensional manifold) are investigated
in terms of symplectic geometry. It is shown that there is a one to one
correspondence between such current manifolds and Poisson current algebras with
three generators. A geometric meaning is given to q-deformations of current
algebras. The geometric quantization of current algebras and quantum current
algebraic maps is also studied.


  This work is a contribution to the understanding of the electrical
resistivity in strontium ferromolybdate (SFMO) ceramics. It demonstrates that
an appropriate thermal treatment leads to the formation of dielectric SrMoO4
shells at the surface of SFMO nanograins. In samples without SrMoO4 shells, the
sign of the temperature coefficient of resistance changes with increasing
temperature from negative at very low temperature to positive at higher
temperatures. Samples exhibiting a negative temperature coefficient contain
SrMoO4 shells and demonstrate a behavior of the resistivity that can be
described in terms of the fluctuation-induced tunneling model, and near room
temperature the conductivity mechanism converts to a variable-range hopping
one. The results of this work serve as a starting point for the understanding
of the low-field magnetoresistance which is very promising for spintronic
device application.


  In response to the growing demand for 3D object detection in applications
such as autonomous driving, robotics, and augmented reality, this work focuses
on the evaluation of semi-supervised learning approaches for point cloud data.
The point cloud representation provides reliable and consistent observations
regardless of lighting conditions, thanks to advances in LiDAR sensors. Data
annotation is of paramount importance in the context of LiDAR applications, and
automating 3D data annotation with semi-supervised methods is a pivotal
challenge that promises to reduce the associated workload and facilitate the
emergence of cost-effective LiDAR solutions. Nevertheless, the task of
semi-supervised learning in the context of unordered point cloud data remains
formidable due to the inherent sparsity and incomplete shapes that hinder the
generation of accurate pseudo-labels. In this study, we consider these
challenges by posing the question: "To what extent does unlabelled data
contribute to the enhancement of model performance?" We show that improvements
from previous semi-supervised methods may not be as profound as previously
thought. Our results suggest that simple grid search hyperparameter tuning
applied to a supervised model can lead to state-of-the-art performance on the
ONCE dataset, while the contribution of unlabelled data appears to be
comparatively less exceptional.


  Detecting redshifted 21cm emission from neutral hydrogen in the early
Universe promises to give direct constraints on the epoch of reionization
(EoR). It will, though, be very challenging to extract the cosmological signal
(CS) from foregrounds and noise which are orders of magnitude larger.
Fortunately, the signal has some characteristics which differentiate it from
the foregrounds and noise, and we suggest that using the correct statistics may
tease out signatures of reionization. We generate mock datacubes simulating the
output of the Low Frequency Array (LOFAR) EoR experiment. These cubes combine
realistic models for Galactic and extragalactic foregrounds and the noise with
three different simulations of the CS. We fit out the foregrounds, which are
smooth in the frequency direction, to produce residual images in each frequency
band. We denoise these images and study the skewness of the one-point
distribution in the images as a function of frequency. We find that, under
sufficiently optimistic assumptions, we can recover the main features of the
redshift evolution of the skewness in the 21cm signal. We argue that some of
these features - such as a dip at the onset of reionization, followed by a rise
towards its later stages - may be generic, and give us a promising route to a
statistical detection of reionization.


  The main topic of this note is a discussion of applicability conditions of
the Nehari manifold method depending on the value of parameters of equations.
As the main tool, we apply the nonlinear generalized Rayleigh quotient method.


  In the following few pages an account is given of a theme, which I began in
1966 and followed to the present.


  We present the results of an outflow and infall survey towards a distance
limited sample of 31 massive star forming regions drawn from the RMS survey.
The presence of young, active outflows is identified from SiO (8-7) emission
and the infall dynamics are explored using HCO$^+$/H$^{13}$CO$^+$ (4-3)
emission. We investigate if the infall and outflow parameters vary with source
properties, exploring whether regions hosting potentially young active outflows
show similarities or differences with regions harbouring more evolved, possibly
momentum driven, "fossil" outflows. SiO emission is detected towards
approximately 46% of the sources. When considering sources with and without an
SiO detection (i.e. potentially active and fossil outflows respectively), only
the $^{12}$CO outflow velocity shows a significant difference between samples,
indicating SiO is more prevalent towards sources with higher outflow
velocities. Furthermore, we find the SiO luminosity increases as a function of
the Herschel 70$\mu$m to WISE 22$\mu$m flux ratio, suggesting the production of
SiO is prevalent in younger, more embedded regions. Similarly, we find
tentative evidence that sources with an SiO detection have a smaller bolometric
luminosity-to-mass ratio, indicating SiO (8-7) emission is associated with
potentially younger regions. We do not find a prevalence towards sources
displaying signatures of infall in our sample. However, the higher energy
HCO$^+$ transitions may not be the best suited tracer of infall at this spatial
resolution in these regions.


  We searched the Northern Hemisphere Fields of the GALEX Time-Domain Survey
(TDS) for galaxies with UV variability indicative of active galactic nuclei
(AGNs). We identified 48 high-probability candidate AGNs from a parent sample
of 1819 galaxies in the NASA Sloan Atlas (NSA) catalog. We further
characterized these systems using optical spectroscopic diagnostics, WISE IR
color selection criteria, and spectral energy distribution (SED) modeling. Of
the 48 candidates, eight were identified as AGNs from optical emission lines,
two were identified by their IR colors, and 28 were identified through spectral
energy decomposition. Observational biases of each selection method are
discussed in connecting these AGNs subsamples to another. By selecting AGNs
based on UV variability, we also identified six low-mass AGNs candidates, all
of which would have been missed by spectroscopic selection.


  Language grounding is an active field aiming at enriching textual
representations with visual information. Generally, textual and visual elements
are embedded in the same representation space, which implicitly assumes a
one-to-one correspondence between modalities. This hypothesis does not hold
when representing words, and becomes problematic when used to learn sentence
representations --- the focus of this paper --- as a visual scene can be
described by a wide variety of sentences. To overcome this limitation, we
propose to transfer visual information to textual representations by learning
an intermediate representation space: the grounded space. We further propose
two new complementary objectives ensuring that (1) sentences associated with
the same visual content are close in the grounded space and (2) similarities
between related elements are preserved across modalities. We show that this
model outperforms the previous state-of-the-art on classification and semantic
relatedness tasks.


  We present a fresh perspective on shot noise corrupted images and noise
removal. By viewing image formation as the sequential accumulation of photons
on a detector grid, we show that a network trained to predict where the next
photon could arrive is in fact solving the minimum mean square error (MMSE)
denoising task. This new perspective allows us to make three contributions: We
present a new strategy for self-supervised denoising, We present a new method
for sampling from the posterior of possible solutions by iteratively sampling
and adding small numbers of photons to the image. We derive a full generative
model by starting this process from an empty canvas. We call this approach
generative accumulation of photons (GAP). We evaluate our method quantitatively
and qualitatively on 4 new fluorescence microscopy datasets, which will be made
available to the community. We find that it outperforms supervised,
self-supervised and unsupervised baselines or performs on-par.


  We compute light and strange quark masses for quenched overlap fermions at
two values of the gauge coupling. The renormalisation is done
non-perturbatively. We test the predictions of quenched chiral perturbation
theory for the quark mass dependence of the hadron spectrum and see evidence
for the existence of chiral logs.


  The hydrogen-deficient carbon-rich [WCL] type central star HD167362 and its
oxygen-rich planetary nebula (PN) SwSt~1 are investigated. The nebular
chemistry might indicate a recent origin for the carbon-rich stellar spectrum.
Its stellar and nebular properties might therefore provide further
understanding of the origin of the [WCL] central star class. The UV-IR stellar
spectra are modelled with state of the codes and show ~40kK central star with a
wind and a C/O~3, indicative of efficient third dredge-up. The synthetic
stellar flux distribution is used to model the high density, compact PN, which
has a solar C/O ratio, is still enshrouded by 1200K and 230K dust shells and,
reported here for the first time, in molecular hydrogen. Although it appears
that the change in C/O ratio has been recent, the published spectroscopy since
1895 has been re-examined and no clear spectral change is seen. If an event
occurred that has turned it into a hydrogen-deficient central star, it did not
happen in the last 100 years.


  Honeypots are more and more used to collect data on malicious activities on
the Internet and to better understand the strategies and techniques used by
attackers to compromise target systems. Analysis and modeling methodologies are
needed to support the characterization of attack processes based on the data
collected from the honeypots. This paper presents some empirical analyses based
on the data collected from the Leurr{\'e}.com honeypot platforms deployed on
the Internet and presents some preliminary modeling studies aimed at fulfilling
such objectives.


  We propose a new one-bit feedback scheme with scheduling decision based on
the maximum expected weighted rate. We show the concavity of the $2$-user case
and provide the optimal solution which achieves the maximum weighted rate of
the users. For the general asymmetric M-user case, we provide a heuristic
method to achieve the maximum expected weighted rate. We show that the sum rate
of our proposed scheme is very close to the sum rate of the full channel state
information case, which is the upper bound performance.


  Through several studies, it has been highlighted that mobility patterns in
mobile networks are driven by human behaviors. This effect has been
particularly observed in intermittently connected networks like DTN (Delay
Tolerant Networks). Given that common social intentions generate similar human
behavior, it is relevant to exploit this knowledge in the network protocols
design, e.g. to identify the closeness degree between two nodes. In this paper,
we propose a temporal link prediction technique for DTN which quantifies the
behavior similarity between each pair of nodes and makes use of it to predict
future links. We attest that the tensor-based technique is effective for
temporal link prediction applied to the intermittently connected networks. The
validity of this method is proved when the prediction is made in a distributed
way (i.e. with local information) and its performance is compared to well-known
link prediction metrics proposed in the literature.


  We have carried out a detailed analysis of the interesting and important very
young planetary nebula (PN) Hen3-1357 (Stingray Nebula) based on a unique
dataset of optical to far-IR spectra and photometric images. We calculated the
abundances of nine elements using collisionally excited lines (CELs) and
recombination lines (RLs). The RL C/O ratio indicates that this PN is O-rich,
which is also supported by the detection of the broad 9/18 um bands from
amorphous silicate grain. The observed elemental abundances can be explained by
asymptotic giant branch (AGB) nucleosynthesis models for initially 1-1.5 Msun
stars with Z = 0.008. The Ne overabundance might be due to the enhancement of
22Ne isotope in the He-rich intershell. By using the spectrum of the central
star synthesized by Tlusty as the ionization/heating source of the PN, we
constructed the self-consistent photoionization model with Cloudy to the
observed quantities, and we derived the gas and dust masses, dust-to-gas mass
ratio, and core-mass of the central star. About 80 % of the total dust mass is
from warm-cold dust component beyond ionization front. Comparison with other
Galactic PNe indicates that Hen3-1357 is an ordinary amorphous silicate rich
and O-rich gas PN. Among other studied PNe, IC4846 shows many similarities in
properties of the PN to Hen3-1357, although their post-AGB evolution is quite
different from each other. Further monitoring observations and comparisons with
other PNe such as IC4846 are necessary to understand the evolution of
Hen3-1357.


  Traditional network diagnosis methods of Client-Terminal Device (CTD)
problems tend to be laborintensive, time consuming, and contribute to increased
customer dissatisfaction. In this paper, we propose an automated solution for
rapidly diagnose the root causes of network performance issues in CTD. Based on
a new intelligent inference technique, we create the Intelligent Automated
Client Diagnostic (IACD) system, which only relies on collection of
Transmission Control Protocol (TCP) packet traces. Using soft-margin Support
Vector Machine (SVM) classifiers, the system (i) distinguishes link problems
from client problems and (ii) identifies characteristics unique to the specific
fault to report the root cause. The modular design of the system enables
support for new access link and fault types. Experimental evaluation
demonstrated the capability of the IACD system to distinguish between faulty
and healthy links and to diagnose the client faults with 98% accuracy. The
system can perform fault diagnosis independent of the user's specific TCP
implementation, enabling diagnosis of diverse range of client devices


  We present 2D-spectroscopic observations from Centro Astron\'omico Hispano
Alem\'an (CAHA) 3.5 m telescope and the millimetre observation from NOrthern
Extended Millimeter Array (NOEMA) of the nearby S0 galaxy PGC 26218, which
shows central star-formation activity and post-starburst outside in the disk.
We estimate the star formation rate (SFR = $0.28\pm0.01$ $M_{\odot} \rm
yr^{-1}$) and molecular gas mass ($\rm log\ $$M_{\rm H_{2}}=7.60\pm0.15\
M_{\odot}$) of PGC 26218 based on the extinction-corrected H$\alpha$ emission
line and the CO-$\rm H_{2}$ conversion factor ($\alpha_{\rm CO}$) of the Milky
Way, respectively. We find that PGC 26218 follows the star forming main
sequence (SFMS) and the Kennicutt-Schmidt law. Comparing the kinematics of
CO($J$=1-0), stars and H$\alpha$, we find that the rotational axis of
CO($J$=1-0) is 45$^{\circ}$ different from that of H$\alpha$. In addition, the
profile of the CO($J$=1-0) emission line shows asymmetry and has an inflow
component of $\sim$ 46 $\rm km\ s^{-1}$. With the decomposition of the optical
image, we confirm that PGC 26218 shows multiple nuclear structures. The
projected offset between the most luminous optical center and the center of
CO($J$=1-0) is $5.2\arcsec$ ($\sim$ 0.6 kpc) and the latter overlaps with one
of the optical cores. These results support that PGC 26218 may have experienced
a gas-rich minor merger, extending its star formation and locating it in the
SFMS.


  Maps $f,g\colon I\to I$ are called strongly commuting if $f\circ
g^{-1}=g^{-1}\circ f$. We show that strongly commuting, piecewise monotone maps
$f,g$ can be decomposed into a finite number of invariant intervals (or period
2 intervals) on which $f,g$ are either both open maps, or at least one of them
is monotone. As a consequence, we show that strongly commuting piecewise
monotone interval maps have a common fixed point. Results of the paper also
have implications in understanding dynamical properties of certain maps on
inverse limit spaces.


  Let $\mathfrak{g}$ be a Leibniz algebra and $E$ a vector space containing
$\mathfrak{g}$ as a subspace. All Leibniz algebra structures on $E$ containing
$\mathfrak{g}$ as a subalgebra are explicitly described and classified by two
non-abelian cohomological type objects: ${\mathcal H}{\mathcal
L}^{2}_{\mathfrak{g}} \, (V, \, \mathfrak{g})$ provides the classification up
to an isomorphism that stabilizes $\mathfrak{g}$ and ${\mathcal H}{\mathcal
L}^{2} \, (V, \, \mathfrak{g})$ will classify all such structures from the view
point of the extension problem - here $V$ is a complement of $\mathfrak{g}$ in
$E$. A general product, called the unified product, is introduced as a tool for
our approach. The crossed (resp. bicrossed) products between two Leibniz
algebras are introduced as special cases of the unified product: the first one
is responsible for the extension problem while the bicrossed product is
responsible for the factorization problem. The description and the
classification of all complements of a given extension $\mathfrak{g} \subseteq
\mathfrak{E} $ of Leibniz algebras are given as a converse of the factorization
problem. They are classified by another cohomological object denoted by
${\mathcal H}{\mathcal A}^{2}(\mathfrak{h}, \mathfrak{g} \, | \,
(\triangleright, \triangleleft, \leftharpoonup, \rightharpoonup))$, where
$(\triangleright, \triangleleft, \leftharpoonup, \rightharpoonup)$ is the
canonical matched pair associated to a given complement $\mathfrak{h}$. Several
examples are worked out in details.


  Ultraluminous X-ray sources (ULXs), first observed ~30 years ago, have been
argued as extreme stellar mass black hole binaries or a new class intermediate
mass black hole. In order to settle this debate, scientists have utilised a
wide range of telescopes, exploiting large sections of the electro-magnetic
spectrum. Here we review some of the insight gained from these observational
studies, collating an overview of our current position in ULX research.


  We reinterpret a result of T. Mochizuki about the Fourier transform of Stokes
data of irregular connections on the Riemann sphere in the language of Stokes
local systems due to P. Boalch. We thus obtain a clean topological description
of the Stokes matrices of the Fourier transform from infinity to infinity in a
large number of cases of one level. In particular, this induces explicit
isomorphisms between wild character varieties, in a much larger range of
examples than those for which such isomorphisms have previously been written
down. We conjecture that these isomorphisms are compatible with the
quasi-Hamiltonian structure of the wild character varieties.


  In this work, we present a regular black hole solution, in the context of
scale-dependent General Relativity, satisfying the weak energy condition. The
source of this solution is an anisotropic effective energy-momentum tensor
which appears when the scale dependence of the theory is turned-on. In this
sense, the solution can be considered as a semiclassical extension of the
Schwarzschild one.


  A planar Tangle is a smooth simple closed curve piecewise defined by
quadrants of circles with constant curvature. We can enumerate Tangles by
counting their dual graphs, which consist of a certain family of polysticks.
The number of Tangles with a given length or area grows exponentially, and we
show the existence of their growth constants by comparing Tangles to two
families of polyominoes.


  We survey most of the different types of approximation algorithms which
minimize the number of output vertices. We present their main qualities and
their inherent drawbacks.


  We construct a distinct class of nonlinear displaced Kerr state by
application of the displacement operator upon a state which is prepared by
sending the well-known photon-added coherent state through a normal Kerr
medium. A sketch for the experimental set-up for preparing the state is
suggested. We evaluate some statistical properties such as the photon number
distribution, Mandel's $Q$ parameter, Husimi-$Q$ and Wigner functions, and
quadrature squeezing, for the nonlinear displaced Kerr state, and then analyze
the nonclassicality in terms of these standard parameters. We reduce the
infinite-level problem to a truncated discrete two-level system by using low
Kerr parameter approximation and then convert the generated nonclassicality
into bipartite entanglement between the two modes of an output state of a
linear optical device.


  The algorithm of Edelsbrunner for surface reconstruction by ``wrapping'' a
set of points in R^3 is described.


  Recent years have witnessed an increasing interest in end-to-end learned
video compression. Most previous works explore temporal redundancy by detecting
and compressing a motion map to warp the reference frame towards the target
frame. Yet, it failed to adequately take advantage of the historical priors in
the sequential reference frames. In this paper, we propose an Advanced Learned
Video Compression (ALVC) approach with the in-loop frame prediction module,
which is able to effectively predict the target frame from the previously
compressed frames, without consuming any bit-rate. The predicted frame can
serve as a better reference than the previously compressed frame, and therefore
it benefits the compression performance. The proposed in-loop prediction module
is a part of the end-to-end video compression and is jointly optimized in the
whole framework. We propose the recurrent and the bi-directional in-loop
prediction modules for compressing P-frames and B-frames, respectively. The
experiments show the state-of-the-art performance of our ALVC approach in
learned video compression. We also outperform the default hierarchical B mode
of x265 in terms of PSNR and beat the slowest mode of the SSIM-tuned x265 on
MS-SSIM. The project page: https://github.com/RenYang-home/ALVC.


  Background: Animal trade plays an important role for the spread of infectious
diseases in livestock populations. As a case study, we consider pig trade in
Germany, where trade actors (agricultural premises) form a complex network. The
central question is how infectious diseases can potentially spread within the
system of trade contacts. We address this question by analyzing the underlying
network of animal movements.
  Methodology/Findings: The considered pig trade dataset spans several years
and is analyzed with respect to its potential to spread infectious diseases.
Focusing on measurements of network-topological properties, we avoid the usage
of external parameters, since these properties are independent of specific
pathogens. They are on the contrary of great importance for understanding any
general spreading process on this particular network. We analyze the system
using different network models, which include varying amounts of information:
(i) static network, (ii) network as a time series of uncorrelated snapshots,
(iii) temporal network, where causality is explicitly taken into account.
  Findings: Our approach provides a general framework for a
topological-temporal characterization of livestock trade networks. We find that
a static network view captures many relevant aspects of the trade system, and
premises can be classified into two clearly defined risk classes. Moreover, our
results allow for an efficient allocation strategy for intervention measures
using centrality measures. Data on trade volume does barely alter the results
and is therefore of secondary importance. Although a static network description
yields useful results, the temporal resolution of data plays an outstanding
role for an in-depth understanding of spreading processes. This applies in
particular for an accurate calculation of the maximum outbreak size.


  We study the evolution of black hole entropy and temperature in collapse
scenarios, finding three generic lessons. First, entropy evolution is
extensive. Second, at large times, entropy and temperature ring with twice the
frequency of the lowest quasinormal mode. Third, the entropy oscillations
saturate black hole area theorems in general relativity. The first two features
are characteristic of entanglement dynamics in `democratic' models. Solely
based on general relativity and Bekenstein-Hawking entropy formula, our results
point to democratic models as microscopic theories of black holes. The third
feature can be taken as a prediction for democratic models coming from black
hole physics.


  We present a general phase matching condition for the quantum search
algorithm with arbitrary unitary transformation and arbitrary phase rotations.
We show by an explicit expression that the phase matching condition depends
both on the unitary transformation U and the initial state. Assuming that the
initial amplitude distribution is an arbitrary superposition sin\theta_0 |1> +
cos\theta_0 e^{i\delta} |2> with |1> = {1 / sin\beta} \sum_k |\tau_k>
<\tau_k|U|0> and |2> = {1 / cos\beta} \sum_{i \ne \tau}|i> <i|U|0>, where
|\tau_k> is a marked state and \sin\beta = \sqrt{\sum_k|U_{\tau_k 0}|^2} is
determined by the matrix elements of unitary transformation U between |\tau_k>
and the |0> state, then the general phase matching condition is tan{\theta / 2}
[cos 2\beta + tan\theta_0 cos\delta sin 2\beta]= tan{\phi / 2} [1-tan\theta_0
sin\delta sin 2\beta tan{\theta / 2}], where \theta and \phi are the phase
rotation angles for |0> and |\tau_k>, respectively. This generalizes previous
conclusions in which the dependence of phase matching condition on $U$ and the
initial state has been disguised. We show that several phase conditions
previously discussed in the literature are special cases of this general one,
which clarifies the question of which condition should be regarded as exact.


  The atmospheric structure of chemically peculiar stars deviates from that of
normal stars with similar fundamental parameters due to unusual chemistry,
abundance inhomogeneities and the presence of strong magnetic field. These
effects are not considered in the standard model atmospheres, possibly leading
to large errors in the stellar parameter determination and abundance analysis.
To tackle this problem we used the state-of-the-art opacity sampling model
atmosphere code LLmodels to calculate comprehensive grid of new generation
model atmospheres for magnetic CP stars. This grid covers the whole parameter
space occupied by SrCrEu and Si-peculiar stars, taking into account
characteristic temperature dependence of the chemical abundances. Here we
present the first results of our model atmosphere calculations.


  Open Information Extraction (OIE) methods extract a large number of OIE
triples (noun phrase, relation phrase, noun phrase) from text, which compose
large Open Knowledge Bases (OKBs). However, noun phrases (NPs) and relation
phrases (RPs) in OKBs are not canonicalized and often appear in different
paraphrased textual variants, which leads to redundant and ambiguous facts. To
address this problem, there are two related tasks: OKB canonicalization (i.e.,
convert NPs and RPs to canonicalized form) and OKB linking (i.e., link NPs and
RPs with their corresponding entities and relations in a curated Knowledge Base
(e.g., DBPedia). These two tasks are tightly coupled, and one task can benefit
significantly from the other. However, they have been studied in isolation so
far. In this paper, we explore the task of joint OKB canonicalization and
linking for the first time, and propose a novel framework JOCL based on factor
graph model to make them reinforce each other. JOCL is flexible enough to
combine different signals from both tasks, and able to extend to fit any new
signals. A thorough experimental study over two large scale OIE triple data
sets shows that our framework outperforms all the baseline methods for the task
of OKB canonicalization (OKB linking) in terms of average F1 (accuracy).


  Quantum secure direct communication (QSDC) is a quantum communication
paradigm that transmits confidential messages directly using quantum states.
Measurement-device-independent (MDI) QSDC protocols can eliminate the security
loopholes associated with measurement devices. To enhance the practicality and
performance of MDI-QSDC protocols, we propose a one-photon-interference MDI
QSDC (OPI-QSDC) protocol which transcends the need for quantum memory, ideal
single-photon sources, or entangled light sources. The security of our OPI-QSDC
protocol has also been analyzed using quantum wiretap channel theory.
Furthermore, our protocol could double the distance of usual
prepare-and-measure protocols, since quantum states sending from adjacent nodes
are connected with single-photon interference, which demonstrates its potential
to extend the communication distance for point-to-point QSDC.


  The study of frequency synchronization configurations in Kuramoto models is a
ubiquitous mathematical problem that has found applications in many seemingly
independent fields. In this paper, we focus on networks whose underlying graph
are cycle graphs. Based on the recent result on the upper bound of the
frequency synchronization configurations in this context, we propose a toric
deformation homotopy method for locating all frequency synchronization
configurations with complexity that is linear in this upper bound. Loosely
based on the polyhedral homotopy method, this homotopy induces a deformation of
the set of the synchronization configurations into a series of toric varieties,
yet our method has the distinct advantage of avoiding the costly step of
computing mixed cells. We also explore the important consequences of this
homotopy method in the context of direct acyclic decomposition of Kuramoto
networks and tropical stable intersection points for Kuramoto equations.


  Given a set of snapshots from a temporal network we develop, analyze, and
experimentally validate a so-called network interpolation scheme. Our method
allows us to build a plausible, albeit random, sequence of graphs that
transition between any two given graphs. Importantly, our model is well
characterized by a Markov chain, and we leverage this representation to
analytically estimate the hitting time (to a predefined distance to the target
graph) and long term behavior of our model. These observations also serve to
provide interpretation and justification for a rate parameter in our model.
Lastly, through a mix of synthetic and real-world data experiments we
demonstrate that our model builds reasonable graph trajectories between
snapshots, as measured through various graph statistics. In these experiments,
we find that our interpolation scheme compares favorably to common network
growth models, such as preferential attachment and triadic closure.


  Identifying the effects of causes and causes of effects is vital in virtually
every scientific field. Often, however, the needed probabilities may not be
fully identifiable from the data sources available. This paper shows how
partial identifiability is still possible for several probabilities of
causation. We term this epsilon-identifiability and demonstrate its usefulness
in cases where the behavior of certain subpopulations can be restricted to
within some narrow bounds. In particular, we show how unidentifiable causal
effects and counterfactual probabilities can be narrowly bounded when such
allowances are made. Often those allowances are easily measured and reasonably
assumed. Finally, epsilon-identifiability is applied to the unit selection
problem.


  We explore the design of a space mission, Project Lyman, which has the goal
of quantifying the ionization history of the universe from the present epoch to
a redshift of z ~ 3. Observations from WMAP and SDSS show that before a
redshift of z >~ 6 the first collapsed objects, possibly dwarf galaxies,
emitted Lyman continuum (LyC) radiation shortward of 912 A, reionizing most of
the universe. How LyC escapes from galactic environments, whether it induces
positive or negative feedback on the local and global collapse of structures,
and the role played by clumping, molecules, metallicity and dust are major
unanswered theoretical questions, requiring observational constraint. Numerous
intervening Lyman limit systems, which frustrate the detection of LyC from high
z objects, thin below z ~ 3 where there are a few objects with apparently very
high fesc. At low z there are only controversial detections and a handful of
upper limits. A wide-field multi-object spectroscopic survey with moderate
spectral and spatial resolution can quantify fesc within diverse spatially
resolved galactic environments over redshifts with significant evolution in
galaxy assemblage and quasar activity. It can also calibrate LyC escape against
Ly-alpha escape, providing an essential tool to JWST for probing the beginnings
of reionization. We present calculations showing the evolution of the
characteristic apparent magnitude of star-forming galaxy luminosity functions
at 900 A, as a function of redshift and assumed escape fraction to determine
the required aperture for detecting LyC. We review our efforts to build a
pathfinding dual order multi-object spectro/telescope with a (0.5deg)^2
field-of-view, using a GSFC microshutter array, and crossed delay-line
micro-channel plate detector.


  We use the POTENT reconstruction of the mass density field in the nearby
universe to estimate the amplitude of the density fluctuation power spectrum
for various cosmological models. We find sigma_8\Omega_m^{0.6}=
1.3^{+0.4}_{-0.3}, almost independently of the power spectrum. This value
agrees well with the COBE normalization for the standard CDM model, while some
alternative models predict an excessive amplitude compared with COBE. Flat low
\Omega_m models and tilted models with spectral index n<0.8 are particularly
discordant.


  In this paper, we prove that the $L^p(\mathbb{R}^d)$ norm of the maximal
truncated Riesz transform in terms of the $L^p(\mathbb{R}^d)$ norm of Riesz
transform is dimension-free for any $2\leq p<\infty$, using integration by
parts formula for radial Fourier multipliers. Moreover, we show that
$$\|R_j^*f\|_{L^p}\leq
\left({2+\frac{1}{\sqrt{2}}}\right)^{\frac{2}{p}}\|R_jf\|_{L^p},\ \ \mbox{for}\
\ p\geq2,\ \ d\geq2.$$ As by products of our calculations, we infer the $L^p$
norm contractivity of the truncated Riesz transforms $R^t_j$ in terms of $R_j$,
and their accurate $L^p$ norms. More precisely, we prove:
$$\|R^t_jf\|_{L^p}\leq\|R_jf\|_{L^p}$$ and $$\|R^t_j\|_{L^p}=\|R_j\|_{L^p},$$
for all $1<p<+\infty,$ $j\in \{1,\dots,d\}$ and $t>0.$


  Charged impurities in semiconductor quantum dots comprise one of the main
obstacles to achieving scalable fabrication and manipulation of singlet-triplet
spin qubits. We theoretically show that using dots that contain several
electrons each can help to overcome this problem through the screening of the
rough and noisy impurity potential by the excess electrons. We demonstrate how
the desired screening properties turn on as the number of electrons is
increased, and we characterize the properties of a double quantum dot
singlet-triplet qubit for small odd numbers of electrons per dot. We show that
the sensitivity of the multi-electron qubit to charge noise may be an order of
magnitude smaller than that of the two-electron qubit.


  Self-supervised learning on large-scale multi-modal datasets allows learning
semantically meaningful embeddings in a joint multi-modal representation space
without relying on human annotations. These joint embeddings enable zero-shot
cross-modal tasks like retrieval and classification. However, these methods
often struggle to generalize well on out-of-domain data as they ignore the
semantic structure present in modality-specific embeddings. In this context, we
propose a novel Semantic-Structure-Preserving Consistency approach to improve
generalizability by preserving the modality-specific relationships in the joint
embedding space. To capture modality-specific semantic relationships between
samples, we propose to learn multiple anchors and represent the multifaceted
relationship between samples with respect to their relationship with these
anchors. To assign multiple anchors to each sample, we propose a novel
Multi-Assignment Sinkhorn-Knopp algorithm. Our experimentation demonstrates
that our proposed approach learns semantically meaningful anchors in a
self-supervised manner. Furthermore, our evaluation on MSR-VTT and YouCook2
datasets demonstrates that our proposed multi-anchor assignment based solution
achieves state-of-the-art performance and generalizes to both inand
out-of-domain datasets. Code: https://github.com/Swetha5/Multi_Sinkhorn_Knopp


  I study the large-N reduction a la Eguchi--Kawai in the Kazakov--Migdal
lattice gauge model. I show that both quenching and twisting prescriptions lead
to the coordinate-independent master field. I discuss properties of loop
averages in reduced as well as unreduced models and demonstrate those coincide
in the large mass expansion. I derive loop equations for the Kazakov--Migdal
model at large N and show they are reduced for the quadratic potential to a
closed set of two equations. I find an exact strong coupling solution of these
equations for any D and extend the result to a more general interacting
potential.


  We challenge the conventional view that the majority of MgII/Lyman limit
absorbers are extended halos of galaxies comprised of ``clouds'' with
near-unity covering factor. The gaseous disks of spiral galaxies are known to
extend to large radii and likely contribute a significant cross-section for
absorption. We perform a Monte-Carlo survey of QSO fields in which the model
galaxies have MgII absorbing ``clouds'' in a spherical halo or in a randomly
oriented disk. For both geometries, models that recover the observed properties
of MgII absorbers have only a 70-80% covering factor. Therefore, regardless of
absorber geometry, a survey of randomly selected QSO fields should yield a
non-negligible number of non-absorbing galaxies at small impact parameters.
Very few have been observed (Steidel (1995)). However, selection effects are
important, and once the observational procedures are applied to our model
fields we find that this result is expected. Since both spherical halo and disk
models can be made consistent with survey results, we present tests for
discerning the geometric distribution of absorbing gas from Hubble Space
Telescope images and high resolution spectra.


  This Note presents the resolution of a differential system on the plane that
translates a geometrical problem about isotropic deformations of area and
length. The system stems from a probability study on deformed random fields
[J.Fournier '17], which are the composition of a random field with invariance
properties defined on the plane with a deterministic diffeomorphism. The
explicit resolution of the differential system allows to prove that a weak
notion of isotropy of the deformed field, linked to its excursion sets, in fact
coincides with the strong notion of isotropy. The present Note first introduces
the probability framework that gave rise to the geometrical issue and then
proposes its resolution.


  Even as we understand for long that the world is quantal and buried in it is
classical dynamics which is chaotic, finding eigenfunctions analytically from
the the Schroedinger equation has turned out to be a near-impossibility. Here,
we discover a class of chaotic quantum systems for which we obtain some
analytically exact eigenfunctions in closed form. This paves way to an exact
classical and quantum mechanical treatment of chaotic systems. Furthermore, we
bring out connections, underlying the discovery, between different areas of
physics and mathematics related to universality in fluctuations observed in a
wide variety of complex quantum systems.


  We show that the isotropic to nematic liquid crystalline phase transition of
F-actin can be either continuous or discontinuous, depending critically on the
filament length. For F-actin with average filament length > 3 um, we confirm
that the transition is continuous in both filament alignment and local
concentration. In contrast, for filament length < 2 um the F-actin solution
undergoes a first order transition. Tactoidal droplets of co-existing isotropic
and nematic domains were observed. Phenomena of nucleation-and-growth and
spinodal decomposition both occur, depending sensitively on the exact
concentration and average filament length of F-actin. In the late stage, the
tactoidal droplets continually grow and occasionally coalesce to form larger
granules.


The possibility of X(1835) as a 0^{-+} glueball is investigated.


  In this paper, we study spinor Frenet equations in three dimensional Lie
Groups with a bi-invariant metric. Also, we obtain spinor Frenet equations for
special cases of three dimensional Lie groups.


  We present an analytic formula for the $O(\alpha_s)$ corrections to the decay
$t\to H^+b$ for nonzero $m_H$ and $m_b$. The Equivalence Theorem is used to
relate these corrections to those for the process $t\to W^+b$. Contrary to
previously published results we find that the effect of the $b$ quark mass is
negligible for a wide range of $m_t$, $m_H$ and $\tan \beta$.


  We present analysis of the shape and radial mass distribution of the E4
galaxy NGC 3923 using archival X-ray data from the ROSAT PSPC and HRI. The
X-ray isophotes are significantly elongated with ellipticity e_x=0.15
(0.09-0.21) (90% confidence) for semi-major axis a\sim 10h^{-1}_70 kpc and have
position angles aligned with the optical isophotes within the estimated
uncertainties. Applying the Geometric Test for dark matter, which is
independent of the gas temperature profile, we find that the ellipticities of
the PSPC isophotes exceed those predicted if M propto L at a marginal
significance level of 85% (80%) for oblate (prolate) symmetry. Detailed
hydrostatic models of an isothermal gas yield ellipticities for the gravitating
matter, e_mass=0.35-0.66 (90% confidence), which exceed the intensity weighted
ellipticity of the R-band optical light, <e_R> = 0.30 (e_R^max=0.39).
  We conclude that mass density profiles with rho\sim r^{-2} are favored over
steeper profiles if the gas is essentially isothermal (which is suggested by
the PSPC spectrum) and the surface brightness in the central regions (r<~15")
is not modified substantially by a multi-phase cooling flow, magnetic fields,
or discrete sources. We argue that these effects are unlikely to be important
for NGC 3923. (The derived e_{mass} range is very insensitive to these issues.)
Our spatial analysis also indicates that the allowed contribution to the ROSAT
emission from a population of discrete sources with Sigma_x propto Sigma_R is
significantly less than that indicated by the hard spectral component measured
by ASCA.


  We study asymptotic normality of the randomized periodogram estimator of
quadratic variation in the mixed Brownian--fractional Brownian model. In the
semimartingale case, that is, where the Hurst parameter $H$ of the fractional
part satisfies $H\in(3/4,1)$, the central limit theorem holds. In the
nonsemimartingale case, that is, where $H\in(1/2,3/4]$, the convergence toward
the normal distribution with a nonzero mean still holds if $H=3/4$, whereas for
the other values, that is, $H\in(1/2,3/4)$, the central convergence does not
take place. We also provide Berry--Esseen estimates for the estimator.


  Liquid democracy is a form of transitive delegative democracy that has
received a flurry of scholarly attention from the computer science community in
recent years. In its simplest form, every agent starts with one vote and may
have other votes assigned to them via delegation from other agents. They can
choose to delegate all votes assigned to them to another agent or vote directly
with all votes assigned to them. However, many proposed realizations of liquid
democracy allow for agents to express their delegation/voting preferences in
more complex ways (e.g., a ranked list of potential delegates) and employ a
centralized delegation mechanism to compute the final vote tally. In doing so,
centralized delegation mechanisms can make decisions that affect the outcome of
a vote and where/whether agents are able to delegate their votes. Much of the
analysis thus far has focused on the ability of these mechanisms to make a
correct choice. We extend this analysis by introducing and formalizing other
important properties of a centralized delegation mechanism in liquid democracy
with respect to crucial features such as accountability, transparency,
explainability, fairness, and user agency. In addition, we evaluate existing
methods in terms of these properties, show how some prior work can be augmented
to achieve desirable properties, prove impossibility results for achieving
certain sets of properties simultaneously, and highlight directions for future
work.


  By taking full advantage of the dynamic property imposed by the detailed
balance condition, we derive a new refined unified fluctuation theorem (FT) for
general stochastic thermodynamic systems. This FT involves the joint
probability distribution functions of the final phase space point and a
thermodynamic variable. Jarzynski equality, Crooks fluctuation theorem, and the
FTs of heat as well as the trajectory entropy production can be regarded as
special cases of this refined unified FT, and all of them are generalized to
arbitrary initial distributions. We also find that the refined unified FT can
easily reproduce the FTs for processes with the feedback control, due to its
unconventional structure that separates the thermodynamic variable from the
choices of initial distributions. Our result is heuristic for further
understanding of the relations and distinctions between all kinds of FTs, and
might be valuable for studying thermodynamic processes with information
exchange.


  Current pre-trained language models have enabled remarkable improvements in
downstream tasks, but it remains difficult to distinguish effects of
statistical correlation from more systematic logical reasoning grounded on the
understanding of real world. We tease these factors apart by leveraging
counterfactual conditionals, which force language models to predict unusual
consequences based on hypothetical propositions. We introduce a set of tests
from psycholinguistic experiments, as well as larger-scale controlled datasets,
to probe counterfactual predictions from five pre-trained language models. We
find that models are consistently able to override real-world knowledge in
counterfactual scenarios, and that this effect is more robust in case of
stronger baseline world knowledge -- however, we also find that for most models
this effect appears largely to be driven by simple lexical cues. When we
mitigate effects of both world knowledge and lexical cues to test knowledge of
linguistic nuances of counterfactuals, we find that only GPT-3 shows
sensitivity to these nuances, though this sensitivity is also non-trivially
impacted by lexical associative factors.


  Single-wall carbon nanotube (SWNT) nanofibrils were assembled onto a variety
of conductive scanning probes including atomic force microscope (AFM) tips and
scanning tunnelling microscope (STM) needles using positive dielectrophoresis
(DEP). The magnitude of the applied electric field was varied in the range of
1-20 V to investigate its effect on the dimensions of the assembled SWNT
nanofibrils. Both length and diameter grew asymptotically as voltage increased
from 5 to 18 V. Below 4 V, stable attachment of SWNT nanofibrils could not be
achieved due to the relatively weak DEP force versus Brownian motion. At
voltages of 20 V and higher, low quality nanofibrils resulted from
incorporating large amounts of impurities. For intermediate voltages, optimal
nanofibrils were achieved, though pivotal to this assembly is the wetting
behaviour upon tip immersion in the SWNT suspension drop. This process was
monitored in situ to correlate wetting angle and probe geometry (cone angles
and tip height), revealing that probes with narrow cone angles and long shanks
are optimal. It is proposed that this results from less wetting of the probe
apex, and therefore reduces capillary forces and especially force transients
during the nanofibril drawing process. Relatively rigid probes (force constant
>= 2 N/m) exhibited no perceivable cantilever bending upon wetting and
de-wetting, resulting in the most stable process control.


  Based on Makino's solutions with radially symmetry, we extend the
corresponding ones with elliptic symmetry for the compressible Euler and
Navier-Stokes equations in R^{N} (N\geq2). By the separation method, we reduce
the Euler and Navier-Stokes equations into 1+N differential functional
equations. In detail, the velocity is constructed by the novel Emden dynamical
system:
  {<K1.1/>|
  <K1.1 ilk="MATRIX" > a_{i}(t)=({\xi}/(a_{i}(t)({\Pi}a_{k}(t))^{{\gamma}-1})),
for i=1,2,....,N a_{i}(0)=a_{i0}>0, a_{i}(0)=a_{i1} </K1.1> with arbitrary
constants {\xi}, a_{i0} and a_{i1}. Some blowup phenomena or global existences
of the solutions obtained could be shown.


  A high-degree of polarization of gamma-ray burst (GRB) prompt emission has
been confirmed in recent years. In this paper, we apply jitter radiation to
study the polarization feature of GRB prompt emission. In our framework,
relativistic electrons are accelerated by turbulent acceleration. Random and
small-scale magnetic fields are generated by turbulence. We further determine
that the polarization property of GRB prompt emission is governed by the
configuration of the random and small-scale magnetic fields. A two-dimensional
compressed slab, which contains stochastic magnetic fields, is applied in our
model. If the jitter condition is satisfied, the electron deflection angle in
the magnetic field is very small and the electron trajectory can be treated as
a straight line. A high-degree of polarization can be achieved when the angle
between the line of sight and the slab plane is small. Moreover, micro-emitters
with mini-jet structure are considered to be within a bulk GRB jet. The jet
"off-axis" effect is intensely sensitive to the observed polarization degree.
We discuss the depolarization effect on GRB prompt emission and afterglow. We
also speculate that the rapid variability of GRB prompt polarization may be
correlated with the stochastic variability of the turbulent dynamo or the
magnetic reconnection of plasmas.


  We study the structure of asymptotic symmetries in N=1+1 supersymmetric
extension of three-dimensional gravity with torsion. Using a natural
generalization of the bosonic anti-de Sitter asymptotic conditions, we show
that the asymptotic Poisson bracket algebra of the canonical generators has the
form of two independent super-Virasoro algebras with different central charges.


  The mass asymmetry in the fission of U-236 at low excitation energy is
clarified by the analysis of the trajectories obtained by solving the Langevin
equations for the shape degrees of freedom. It is demonstrated that the
position of the peaks in the mass distribution of fission fragments is
determined mainly by the saddle point configuration originating from the shell
correction energy. The width of the peaks, on the other hand, results from the
shape fluctuations close to the scission point caused by the random force in
the Langevin equation. We have found out that the fluctuations between
elongated and compact shapes are essential for the fission process. According
to our results the fission does not occur with continuous stretching in the
prolate direction, similarly to that observed in starch syrup, but is
accompanied by the fluctuations between elongated and compact shapes. This
picture presents a new viewpoint of fission dynamics and the splitting
mechanism.


  Most prominent research today addresses compliance with data protection laws
through consumer-centric and public-regulatory approaches. We shift this
perspective with the Privatech project to focus on corporations and law firms
as agents of compliance. To comply with data protection laws, data processors
must implement accountability measures to assess and document compliance in
relation to both privacy documents and privacy practices. In this paper, we
survey, on the one hand, current research on GDPR automation, and on the other
hand, the operational challenges corporations face to comply with GDPR, and
that may benefit from new forms of automation. We attempt to bridge the gap. We
provide a roadmap for compliance assessment and generation by identifying
compliance issues, breaking them down into tasks that can be addressed through
machine learning and automation, and providing notes about related developments
in the Privatech project.


  In this paper, we study the problem of approximating the minimum cut in a
distributed message-passing model, the CONGEST model. The minimum cut problem
has been well-studied in the context of centralized algorithms. However, there
were no known non-trivial algorithms in the distributed model until the recent
work of Ghaffari and Kuhn. They gave algorithms for finding cuts of size
$O(\epsilon^{-1}\lambda)$ and $(2+\epsilon)\lambda$ in
$O(D)+\tilde{O}(n^{1/2+\epsilon})$ rounds and $\tilde{O}(D+\sqrt{n})$ rounds
respectively, where $\lambda$ is the size of the minimum cut. This matches the
lower bound they provided up to a polylogarithmic factor. Yet, no scheme that
achieves $(1+\epsilon)$-approximation ratio is known. We give a distributed
algorithm that finds a cut of size $(1+\epsilon)\lambda$ in
$\tilde{O}(D+\sqrt{n})$ time, which is optimal up to polylogarithmic factors.


  We study spin-resolved resonant tunneling in a system of two quantum dots
sandwiched between doped quantum wells. In the coherent (Dicke) regime, i.e.,
when quantum dot separation is smaller than the Fermi wavelength in a
two-dimensional electron gas in quantum wells, application of an in-plane
magnetic field leads to a pronounced spin-resolved structure of the conductance
peak lineshape even for very small Zeeman splitting of the quantum dots'
resonant levels. In the presence of electron-gas spin-orbit coupling, this
spin-resolved structure gets washed out due to Fermi surface deformation in the
momentum space. We also show that Aharonov-Bohm flux penetrating the area
enclosed by electron tunneling pathways completely destroys conductance spin
structure.


  We investigate the parameterized complexity of finding diverse sets of
solutions to three fundamental combinatorial problems, two from the theory of
matroids and the third from graph theory. The input to the Weighted Diverse
Bases problem consists of a matroid $M$, a weight function
$\omega:E(M)\to\mathbb{N}$, and integers $k\geq 1, d\geq 0$. The task is to
decide if there is a collection of $k$ bases $B_{1}, \dotsc, B_{k}$ of $M$ such
that the weight of the symmetric difference of any pair of these bases is at
least $d$. This is a diverse variant of the classical matroid base packing
problem. The input to the Weighted Diverse Common Independent Sets problem
consists of two matroids $M_{1},M_{2}$ defined on the same ground set $E$, a
weight function $\omega:E\to\mathbb{N}$, and integers $k\geq 1, d\geq 0$. The
task is to decide if there is a collection of $k$ common independent sets
$I_{1}, \dotsc, I_{k}$ of $M_{1}$ and $M_{2}$ such that the weight of the
symmetric difference of any pair of these sets is at least $d$. This is
motivated by the classical weighted matroid intersection problem. The input to
the Diverse Perfect Matchings problem consists of a graph $G$ and integers
$k\geq 1, d\geq 0$. The task is to decide if $G$ contains $k$ perfect matchings
$M_{1},\dotsc,M_{k}$ such that the symmetric difference of any two of these
matchings is at least $d$.
  We show that Weighted Diverse Bases and Weighted Diverse Common Independent
Sets are both NP-hard, and derive fixed-parameter tractable (FPT) algorithms
for all three problems with $(k,d)$ as the parameter.


  The scattering properties of randomly layered optical media with ${\cal
PT}$-symmetric index of refraction are studied using the transfer-matrix
method. We find that the transmitance decays exponentially as a function of the
system size, with an enhanced rate
$\xi_{\gamma}(W)^{-1}=\xi_0(W)^{-1}+\xi_{\gamma} (0)^{-1}$, where $\xi_0(W)$ is
the localization length of the equivalent passive random medium and
$\xi_{\gamma}(0)$ is the attenuation/amplification length of the corresponding
perfect system with a ${\cal PT}$-symmetric refraction index profile. While
transmitance processes are reciprocal to left and right incident waves, the
reflectance is enhanced from one side and is inversely suppressed from the
other, thus allowing such ${\cal PT}$-symmetric random media to act as
unidirectional coherent absorbers.


  Gold nanorod has generated great research interest due to its tunable
longitudinal plasmon resonance. However, little progress has been made in the
understanding of the effect. A major reason is that, except for metallic
spheres and ellipsoids, the interaction between light and nanoparticles is
generally insoluble. In this paper, a new scheme has been proposed to study the
plasmon resonance of gold nanorod, in which the nanorod is modeled as an LC
circuit with an inductance and a capacitance. The obtained resonance wavelength
is dependent on not only aspect ratio but also rod radius, suggesting the
importance of self-inductance and the breakdown of linear scaling. Moreover,
the cross sections for light scattering and absorption have been deduced
analytically, giving rise to a Lorentzian line-shape for the extinction
spectrum. The result provides us with new insight into the phenomenon.


  A model of electric energization of the Solar Wind and Corona is developed,
including electro-magnetic (EM) particle effects precluded by traditional
magneto-hydro (MHD) assumptions. Using standard 1-D radial Solar models for
particle density and temperature, the Core gravito-electric field is
calculated; and the range of possible Photospheric photoelectric fields is
estimated. The extant DC field apparently arises from about 460.Coulombs of
charge displacement, mainly caused by the immense Solar energy flux pushing
electrons outward. Energetically, this electric field can accelerate surface
protons out of the 2.keV gravity well and up to the 4. keV energies observed in
the Fast Solar Wind. The electrical energy is released in pervasive, persistent
"proton lightning jets", which are proton beams, charge-neutralized by
co-propagating electrons. The jets are formed by pinched "avalanche breakdown"
of the weakly ionized Photosphere, probably initiated on the down-welling edges
of Solar surface granulations. These energetic jets will glow as discrete
filamentary surface spicules, and will be observed in reflect solar light as
the diffuse K-Corona. Significantly, the total charge displacement and observed
Fast Wind Speed (FWS) are quantitatively predicted by a novel "virial limit":
the (positive) electrical potential energy at r=0 is limited in magnitude to
the 10.keV gravitational well at r=0.


  In fair division of indivisible goods, using sequences of sincere choices (or
picking sequences) is a natural way to allocate the objects. The idea is as
follows: at each stage, a designated agent picks one object among those that
remain. Another intuitive way to obtain an allocation is to give objects to
agents in the first place, and to let agents exchange them as long as such
"deals" are beneficial. This paper investigates these notions, when agents have
additive preferences over objects, and unveils surprising connections between
them, and with other efficiency and fairness notions. In particular, we show
that an allocation is sequenceable iff it is optimal for a certain type of
deals, namely cycle deals involving a single object. Furthermore, any
Pareto-optimal allocation is sequenceable, but not the converse. Regarding
fairness, we show that an allocation can be envy-free and non-sequenceable, but
that every competitive equilibrium with equal incomes is sequenceable. To
complete the picture, we show how some domain restrictions may affect the
relations between these notions. Finally, we experimentally explore the links
between the scales of efficiency and fairness.


  Gaits engineered for snake-like robots to rotate in-place instrumentally fill
a gap in the set of locomotive gaits that have traditionally prioritized
translation. This paper designs a Turn-in-Place gait and demonstrates the
ability of a shape-centric modeling framework to capture the gait's locomotive
properties. Shape modeling for turning involves a time-varying continuous body
curve described by a standing wave. Presumed viscous robot-ground frictional
interactions lead to body dynamics conditioned on the time-varying shape model.
The dynamic equations describing the Turn-in-Place gait are validated by an
articulated snake-like robot using a physics-based simulator and a physical
robot. The results affirm the shape-centric modeling framework's capacity to
model a variety of snake-like robot gaits with fundamentally different
body-ground contact patterns. As an applied demonstration, example locomotion
scenarios partner the shape-centric Turn-in-Place gait with a Rectilinear gait
for maneuvering through constrained environments based on a multi-modal
locomotive planning strategy. Unified shape-centric modeling facilitates
trajectory planning and tracking for a snake-like robot to successfully
negotiate non-trivial obstacle configurations.


  We discuss a class of evolution equations equivalent to the simplest
Universal Field Equation, the so--called Bateman equation, and show that all of
them possess (at least) biHamiltonian structure. The first few conserved
charges are calculated.


  We report the effect of Si doping on the magnetization behavior of CeFe2. It
is found that Si stabilizes the dynamic antiferromagnetic state of CeFe2.
Multi-step magnetization behavior, unusual relaxation effect, thermal and
magnetic history dependence, which are signatures of martensitic scenario, are
found to be present in this system. We also show that one can induce the
magnetization steps with the help of appropriate measurement protocol. Detailed
magnetization relaxation studies have been carried out to understand the
dynamics of magnetic phase transition.


  Multi-photon emissions constitute a fundamental source of noise in quantum
repeaters and other quantum communication protocols when probabilistic photon
sources are employed. In this paper, it is shown that by alternating the Bell
state measurement (BSM) basis in concatenated entanglement swapping links one
can automatically identify and discard many errors from stimulated multi-photon
emissions. The proposed protocol is shown to completely eliminate the dominant
quadratic growth of multi-photon errors with the length of the repeater chain.
Furthermore, it is shown that the protocol can be employed in
satellite-assisted entanglement distribution links to enable links which are
more robust in the presence of imbalanced channel losses. The analysis
introduces a convenient calculus based on Clifford algebra for modeling
concatenated entanglement swapping links with multi-photon emissions. In
particular, we present a compact expression for the fidelity of the Bell state
produced by a repeater chain of arbitrary length including noise from
double-pair emissions.


  We describe a general correspondence between injective (resp. projective)
recollements of triangulated categories and injective (resp. projective)
cotorsion pairs. This provides a model category description of these
recollement situations. Our applications focus on displaying several
recollements that glue together various full subcategories of K(R), the
homotopy category of chain complexes of modules over a general ring R. When R
is (left) Noetherian ring, these recollements involve complexes built from the
Gorenstein injective modules. When R is a (left) coherent ring for which all
flat modules have finite projective dimension we obtain the duals. These
results extend to a general ring R by replacing the Gorenstein modules with the
Gorenstein AC-modules introduced recently in the work of Bravo-Gillespie-Hovey.
We also see that in any abelian category with enough injectives, the Gorenstein
injective objects enjoy a maximality property in that they contain every other
class making up the right half of an injective cotorsion pair.


  We present a neurosymbolic framework for the lifelong learning of algorithmic
tasks that mix perception and procedural reasoning. Reusing high-level concepts
across domains and learning complex procedures are key challenges in lifelong
learning. We show that a program synthesis approach that combines gradient
descent with combinatorial search over programs can be a more effective
response to these challenges than purely neural methods. Our framework, called
HOUDINI, represents neural networks as strongly typed, differentiable
functional programs that use symbolic higher-order combinators to compose a
library of neural functions. Our learning algorithm consists of: (1) a symbolic
program synthesizer that performs a type-directed search over parameterized
programs, and decides on the library functions to reuse, and the architectures
to combine them, while learning a sequence of tasks; and (2) a neural module
that trains these programs using stochastic gradient descent. We evaluate
HOUDINI on three benchmarks that combine perception with the algorithmic tasks
of counting, summing, and shortest-path computation. Our experiments show that
HOUDINI transfers high-level concepts more effectively than traditional
transfer learning and progressive neural networks, and that the typed
representation of networks significantly accelerates the search.